{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3789d586",
   "metadata": {},
   "source": [
    "<p><img src=\"assets/telecogeco-lockup.svg\" alt=\"telecogeco\" height=\"40\"></p>\n",
    "\n",
    "**telecogeco** by <img src=\"assets/ugeco-wordmark.svg\" alt=\"ugeco\" height=\"14\"> &nbsp;\u00b7&nbsp; Copyright \u00a9 2026 UGECO. All rights reserved. &nbsp;\u00b7&nbsp; telecogeco source code is released under Apache-2.0; brand assets are trademarks of UGECO.\n",
    "\n",
    "# telecogecoTwin \u2014 Physics Twin walkthrough\n",
    "\n",
    "`telecogecoTwin`'s **physics twin** (`submodules/physics-twin/`) is a standalone RF simulator.\n",
    "Given a cell topology (where each antenna is, which way it points, how far it is tilted down, what\n",
    "frequency it transmits on) and a dozen global radio parameters, it predicts the distribution of\n",
    "signal strength (RSRP) and signal quality (SINR) that users across an estate would experience \u2014\n",
    "using the 3GPP TR 38.901 *Urban Macro* (UMa) propagation model and the TR 38.901 \u00a77.3 sector\n",
    "antenna pattern. It then answers *what-if* questions: what happens to those distributions if a\n",
    "cell is tilted, or switched off?\n",
    "\n",
    "This notebook builds and runs the engine's **actual code** cell by cell. Nothing is mocked or\n",
    "simplified: the functions imported from `app.*` are the same ones the service's HTTP handlers\n",
    "call. Each section first explains the mathematics and the library machinery involved, then runs\n",
    "it, so a reader can see \u2014 not just be told \u2014 how data goes in, how the twin learns its\n",
    "parameters, and how a configuration change becomes a KPI change.\n",
    "\n",
    "**Pipeline this notebook walks through** (run the cell below to draw it):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0899c739",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x290 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import FancyBboxPatch\n",
    "\n",
    "def flow_diagram(nodes, loop=None, title=None, width=14):\n",
    "    # nodes: list of (label, colour); loop: (from_idx, to_idx, label) drawn as a dashed return arrow\n",
    "    n = len(nodes)\n",
    "    fig, ax = plt.subplots(figsize=(width, 2.9 if loop else 2.2))\n",
    "    ax.set_xlim(0, n); ax.set_ylim(-0.45 if loop else 0.1, 1.0); ax.axis(\"off\")\n",
    "    bw, bh, y = 0.82, 0.62, 0.55\n",
    "    for i, (label, colour) in enumerate(nodes):\n",
    "        x = i + (1 - bw) / 2\n",
    "        ax.add_patch(FancyBboxPatch((x, y - bh / 2), bw, bh, boxstyle=\"round,pad=0.02,rounding_size=0.06\",\n",
    "                                    fc=colour, ec=\"none\"))\n",
    "        ax.text(i + 0.5, y, label, ha=\"center\", va=\"center\", fontsize=9, color=\"white\", linespacing=1.4)\n",
    "        if i < n - 1:\n",
    "            ax.annotate(\"\", xy=(x + bw + 0.09, y), xytext=(x + bw, y),\n",
    "                        arrowprops=dict(arrowstyle=\"-|>\", color=\"#444\", lw=1.5))\n",
    "    if loop:\n",
    "        a, b, label = loop\n",
    "        ya = y - bh / 2\n",
    "        off = 0.2 if a == b else 0.0\n",
    "        ax.annotate(\"\", xy=(b + 0.5 - off, ya - 0.02), xytext=(a + 0.5 + off, ya - 0.02),\n",
    "                    arrowprops=dict(arrowstyle=\"-|>\", color=\"#444\", lw=1.3, ls=\"--\",\n",
    "                                    connectionstyle=\"arc3,rad=-1.5\" if a == b else \"arc3,rad=-0.45\"))\n",
    "        ax.text((a + b) / 2 + 0.5, ya - (0.52 if a == b else 0.30), label,\n",
    "                ha=\"center\", va=\"top\", fontsize=8.5, color=\"#444\")\n",
    "    if title:\n",
    "        ax.set_title(title, fontsize=11, loc=\"left\")\n",
    "    plt.tight_layout(); plt.show()\n",
    "\n",
    "flow_diagram([\n",
    "    (\"topology.csv\\n+ config.csv\", \"#1a1a2e\"),\n",
    "    (\"Estate.build()\\ngeometry + propagation\\nprecompute\", \"#3a6ea5\"),\n",
    "    (\"calibrate.fit()\\nfit ~12 global params to\\nRSRP/SINR quantiles\", \"#3a6ea5\"),\n",
    "    (\"Estate.evaluate()\\nRSRP/SINR\\n+ guardrail_kpis\", \"#2f8f7f\"),\n",
    "    (\"guardrail_kpis\\n(platform decision hub)\", \"#c2703d\"),\n",
    "], loop=(3, 3, \"chain a new tilt / on-off config, re-evaluate\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5637b4d",
   "metadata": {},
   "source": [
    "## The libraries, and why each one is here\n",
    "\n",
    "| Library | Role in the twin |\n",
    "|---|---|\n",
    "| **NumPy** | The engine is *vectorised*: every quantity is a `(cells \u00d7 points)` matrix, so \"path loss from every cell to every grid point\" is one array, and an evaluation is a handful of matrix operations rather than a loop over cells and users. `np.log10`, `np.where`, `np.argmax`, `np.percentile` do most of the physics. |\n",
    "| **pandas** | Tabular I/O and bookkeeping: reading the CSV pair, merging topology with config on `cell_id`, holding the per-cell parameter table (`estate.cells`), and presenting results as DataFrames. |\n",
    "| **SciPy \u2014 `scipy.optimize`** | The \"training\" step. `differential_evolution` is a derivative-free, population-based global optimiser; the service also runs a bounded **Nelder\u2013Mead** polish (`minimize(method=\"Nelder-Mead\")`). Derivative-free matters: the loss goes through an `argmax` (serving-cell choice) and `np.percentile`, both piecewise-constant in the parameters, so gradients are zero almost everywhere and gradient methods cannot be used. |\n",
    "| **Matplotlib** | Plots only \u2014 the estate map, the path-loss / antenna curves, the tilt sweep and the on/off comparison. |\n",
    "| **hashlib (`blake2s`)** | Inside `app.physics.propagation.rng_for`: every random draw is seeded from a hash of a name such as `\"cell|g-s1-a-n3\"`, so identical inputs give identical bytes on any machine. The twin is deterministic by construction. |\n",
    "\n",
    "## What is imported from `app.*`\n",
    "\n",
    "| Module | What it contains | Where this notebook uses it |\n",
    "|---|---|---|\n",
    "| `app.generic` | `generic_estate()` \u2014 a synthetic hexagonal estate generator with exactly the columns the engine needs; `default_params()` \u2014 the ~12 model parameters set to public 3GPP defaults, each reasoned in a comment; `generic_build_kwargs()` \u2014 lattice-specific overrides for `Estate.build`. | Section 2 (data), Section 4 (ground truth for the calibration demo) |\n",
    "| `app.physics.evaluator` | `Estate` \u2014 the model. `Estate.build()` projects coordinates, samples a grid of user positions, and precomputes path loss, LOS state, shadow fading and antenna gain for every (cell, point). `Estate.evaluate()` turns a parameter vector into RSRP/SINR distributions and KPIs. | Sections 3, 4, 5 |\n",
    "| `app.physics.propagation` | `uma_los`, `uma_nlos`, `p_los_uma` \u2014 the TR 38.901 Table 7.4.1-1 / 7.4.2-1 formulas; `rng_for` \u2014 deterministic random draws. | Called inside `Estate.build`; plotted directly in Section 3 |\n",
    "| `app.physics.antenna` | `gain_db` \u2014 the TR 38.901 \u00a77.3 element pattern with explicit tilt and azimuth. | Called inside `Estate.build` and on every `tilt_override`; plotted in Section 3 |\n",
    "| `app.physics.geometry` | `to_enu` \u2014 flat-earth lat/lon \u2192 metres; `cell_point_geometry` \u2014 distances and angles from one cell to every point. | Called inside `Estate.build` |\n",
    "| `app.physics.calibrate` | `BOUNDS`/`NAMES` \u2014 the parameter vector and its search box; `loss()` \u2014 the quantile-matching objective; `acceptance_gate()` \u2014 the hard/soft pass criteria a fitted model must clear. | Section 4 |\n",
    "\n",
    "**Two rungs, one engine.** Every number the twin produces carries a *rung* label saying where its\n",
    "parameters came from:\n",
    "\n",
    "| Rung | Parameters | What the numbers mean |\n",
    "|---|---|---|\n",
    "| `simulation, generic 3GPP defaults (uncalibrated)` | public TR 38.901 / TR 38.913 defaults | illustrative; relative comparisons only |\n",
    "| `simulation, calibrated to supplied measurement data` | fitted to a locally supplied measurement report | the twin's product rung \u2014 exists only once someone has run a local calibration |\n",
    "\n",
    "**This notebook runs entirely on the generic rung.** It never touches, needs, or ships any\n",
    "operator's network data \u2014 see Section 2 for why and how. For the calibrated path, see the\n",
    "[service README](../README.md#run-with-locally-supplied-data-calibrated-mode).\n",
    "\n",
    "**Setup:** `pip install -r requirements.txt -r notebooks/requirements-notebook.txt` from the\n",
    "`physics-twin/` directory, then run all cells top to bottom (~30 s total, mostly the calibration\n",
    "cell)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "799afea4",
   "metadata": {},
   "source": [
    "## 1. Setup\n",
    "\n",
    "The engine's modules are imported directly \u2014 no HTTP layer, no server. `sys.path` gets the\n",
    "`physics-twin/` directory (the parent of `notebooks/`) so `app.*` resolves the same way it does\n",
    "when the service runs with `PYTHONPATH=$(pwd)`.\n",
    "\n",
    "Line by line:\n",
    "\n",
    "- `generic_estate, generic_build_kwargs, default_params, band_label` \u2014 the no-data estate\n",
    "  generator and its public-default parameter set.\n",
    "- `Estate` \u2014 the model class: geometry cache + evaluation.\n",
    "- `loss, NAMES, BOUNDS, acceptance_gate` \u2014 the calibration objective, the parameter vector\n",
    "  layout, its search box, and the pass/fail criteria.\n",
    "- `uma_los, uma_nlos, p_los_uma` and `gain_db` \u2014 imported only so Section 3 can *plot* the\n",
    "  propagation and antenna equations; `Estate` calls them internally regardless.\n",
    "- `differential_evolution` \u2014 SciPy's global optimiser, used in Section 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "46948afb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "engine modules loaded ok\n",
      "thermal noise per resource element: -125.2 dBm  (= -174 + 10\u00b7log10(15 kHz) + 7 dB noise figure)\n"
     ]
    }
   ],
   "source": [
    "import sys, pathlib\n",
    "sys.path.insert(0, str(pathlib.Path.cwd().parent))  # repo root of physics-twin/, so `app.*` imports resolve\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import differential_evolution\n",
    "\n",
    "from app.generic import generic_estate, generic_build_kwargs, default_params, band_label\n",
    "from app.physics.evaluator import Estate, NOISE_DBM_RE, OUTAGE_SINR_DB, COVERAGE_RSRP_DBM\n",
    "from app.physics.calibrate import loss, NAMES, BOUNDS, acceptance_gate\n",
    "from app.physics.propagation import uma_los, uma_nlos, p_los_uma, SIGMA_LOS, SIGMA_NLOS\n",
    "from app.physics.antenna import gain_db, peak_gain_dbi\n",
    "\n",
    "INK, COBALT, COPPER, TEAL = \"#1a1a2e\", \"#3a6ea5\", \"#c2703d\", \"#2f8f7f\"\n",
    "plt.rcParams.update({\"figure.facecolor\": \"white\", \"axes.facecolor\": \"white\",\n",
    "                      \"font.size\": 10, \"axes.edgecolor\": \"#444\", \"axes.grid\": True,\n",
    "                      \"grid.alpha\": 0.25})\n",
    "print(\"engine modules loaded ok\")\n",
    "print(f\"thermal noise per resource element: {NOISE_DBM_RE:.1f} dBm  \"\n",
    "      f\"(= -174 + 10\u00b7log10(15 kHz) + 7 dB noise figure)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b214fac0",
   "metadata": {},
   "source": [
    "## 2. Load the estate data\n",
    "\n",
    "### What the twin needs to know about each cell\n",
    "\n",
    "The whole model is driven by one table with a row per cell. `Estate.build()` merges the two CSVs\n",
    "on `cell_id` and refuses to proceed if any of the required columns has a null \u2014 a defaulted tilt\n",
    "or azimuth would produce a confident model of an antenna that does not exist.\n",
    "\n",
    "| Column | Meaning | Used for |\n",
    "|---|---|---|\n",
    "| `cell_id` | unique identifier | joins, overrides, serving-share reporting |\n",
    "| `cell_lat`, `cell_lon` | antenna position (degrees) | projected to east/north metres (Section 3) |\n",
    "| `cell_az_deg` | sector azimuth, 0\u00b0 = North, clockwise | horizontal antenna pattern $\\phi$ |\n",
    "| `cell_el_deg` (from `config.csv`) | electrical downtilt, degrees, positive = down | vertical antenna pattern $\\theta$ \u2014 **the control knob for tilt optimisation** |\n",
    "| `hTx` | antenna height above ground, metres | path loss and depression angle |\n",
    "| `cell_carrier_freq_mhz` | downlink carrier frequency | path loss ($20\\log_{10} f_c$ term) and the band group that picks $P_0$ |\n",
    "| `beam_width_deg` | horizontal 3 dB beamwidth | horizontal pattern width $\\phi_{3dB}$ |\n",
    "| `technology` | `NR` / `LTE` | only NR cells may *serve* in calibration (the measurement report covers the NR leg); all cells interfere |\n",
    "\n",
    "Topology (static, from planning) and config (tunable, `cell_el_deg`) are separate files because\n",
    "the *config* is what a what-if changes; the topology never does.\n",
    "\n",
    "### Where this notebook's data comes from\n",
    "\n",
    "In calibrated mode the twin loads a `topology.csv` + `config.csv` pair exported from an\n",
    "operator's planning tool (the shape `submodules/physics-twin/var/topology.csv` /\n",
    "`var/config.csv` take once someone has run `/v1/load-local`). That export is confidential and\n",
    "this repository never ships it or any file derived from it.\n",
    "\n",
    "What this notebook ships instead: the **same CSV shape**, generated from `app/generic.py`'s\n",
    "public, no-data generator, saved under an `example_` name so it is unmistakably not a real export.\n",
    "`generic_estate()` places sites on a hexagonal lattice with inter-site distance 500 m (the\n",
    "TR 38.913 UMa assumption), three 120\u00b0 sectors per site at azimuths 0\u00b0/120\u00b0/240\u00b0, and one cell per\n",
    "band per sector. Lattice positions are converted to latitude/longitude by inverting the flat-earth\n",
    "projection, around $(0\u00b0, 0\u00b0)$ \u2014 a point in the open ocean, so the geometry can never be mistaken\n",
    "for a real deployment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a020dd1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wrote notebooks/data/example_topology.csv and notebooks/data/example_config.csv\n"
     ]
    }
   ],
   "source": [
    "topology, config = generic_estate(n_sites=3, isd_m=500.0, bands_mhz=(1800.0, 3500.0))\n",
    "\n",
    "data_dir = pathlib.Path.cwd() / \"data\"\n",
    "data_dir.mkdir(exist_ok=True)\n",
    "topology.to_csv(data_dir / \"example_topology.csv\", index=False)\n",
    "config.to_csv(data_dir / \"example_config.csv\", index=False)\n",
    "print(\"wrote notebooks/data/example_topology.csv and notebooks/data/example_config.csv\")\n",
    "\n",
    "# Load back exactly as app.main._rebuild() does on `/v1/load-local` \u2014 CSV in, DataFrame out.\n",
    "topology = pd.read_csv(data_dir / \"example_topology.csv\")\n",
    "config = pd.read_csv(data_dir / \"example_config.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "57b27a3c",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>g-s1-b-n3</td>\n",
       "      <td>g-s1</td>\n",
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       "      <td>65.0</td>\n",
       "      <td>NR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>g-s1-c-n3</td>\n",
       "      <td>g-s1</td>\n",
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       "      <td>65.0</td>\n",
       "      <td>NR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      cell_id site_id  cell_lat  cell_lon  cell_az_deg  cell_carrier_freq_mhz  \\\n",
       "0   g-s1-a-n3    g-s1       0.0       0.0          0.0                 1800.0   \n",
       "1  g-s1-a-n78    g-s1       0.0       0.0          0.0                 3500.0   \n",
       "2   g-s1-b-n3    g-s1       0.0       0.0        120.0                 1800.0   \n",
       "3  g-s1-b-n78    g-s1       0.0       0.0        120.0                 3500.0   \n",
       "4   g-s1-c-n3    g-s1       0.0       0.0        240.0                 1800.0   \n",
       "\n",
       "    hTx  beam_width_deg technology  \n",
       "0  30.0            65.0         NR  \n",
       "1  30.0            65.0         NR  \n",
       "2  30.0            65.0         NR  \n",
       "3  30.0            65.0         NR  \n",
       "4  30.0            65.0         NR  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topology.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "acee21ec",
   "metadata": {},
   "outputs": [
    {
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       "      cell_id  cell_el_deg\n",
       "0   g-s1-a-n3          6.0\n",
       "1  g-s1-a-n78          6.0\n",
       "2   g-s1-b-n3          6.0\n",
       "3  g-s1-b-n78          6.0\n",
       "4   g-s1-c-n3          6.0"
      ]
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "config.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59abd6a6",
   "metadata": {},
   "source": [
    "3 sites \u00d7 3 sectors \u00d7 2 bands = 18 cells, ids like `g-s1-a-n3` (site 1, sector a, band n3 \u2014\n",
    "`band_label()` maps the carrier frequency to a 3GPP band name using the TS 38.101-1 downlink\n",
    "ranges). Every cell starts at 6\u00b0 electrical tilt, 30 m height, 65\u00b0 beamwidth."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aeb7a7d",
   "metadata": {},
   "source": [
    "## 3. Build the Estate \u2014 geometry + propagation precompute\n",
    "\n",
    "`Estate.build()` is the expensive step, and it is expensive on purpose: everything that does\n",
    "**not** depend on the fitted parameters is computed once and cached, so that a calibration\n",
    "search (thousands of evaluations) or an interactive what-if costs only matrix arithmetic. Here is\n",
    "what it computes, in order.\n",
    "\n",
    "### 3.1 Coordinates \u2192 metres (`geometry.to_enu`)\n",
    "\n",
    "Latitude/longitude are projected onto a local flat East-North-Up frame centred on the estate's\n",
    "mean position $(\\varphi_0, \\lambda_0)$, with $R_E = 6\\,371\\,000$ m:\n",
    "\n",
    "$$x = (\\lambda - \\lambda_0)\\,R_E \\cos\\varphi_0, \\qquad y = (\\varphi - \\varphi_0)\\,R_E$$\n",
    "\n",
    "Exact to under a metre at the ~1.5 km extent of an estate.\n",
    "\n",
    "### 3.2 Where the users are (the traffic-proxy grid)\n",
    "\n",
    "The engine does not evaluate on a uniform rectangle \u2014 that would put mass at tower bases and in\n",
    "far corners no user occupies. It samples $N$ points (here 2000) uniformly *by area* in annuli\n",
    "around each site, from 100 m out to $r_{\\max}$ (for the hex lattice, $r_{\\max} = \\text{ISD}/\\sqrt{3}$,\n",
    "the cell circumradius). Uniform-by-area means $r = \\sqrt{U(100^2, r_{\\max}^2)}$, not $U(100, r_{\\max})$.\n",
    "\n",
    "### 3.3 Per-(cell, point) geometry (`geometry.cell_point_geometry`)\n",
    "\n",
    "For cell $c$ at $(x_c, y_c)$ with height $h_{BS}$ and azimuth $\\alpha_c$, and point $n$ at\n",
    "$(x_n, y_n)$ with user height $h_{UT} = 1.5$ m:\n",
    "\n",
    "$$d_{2D} = \\max\\!\\big(\\sqrt{(x_n-x_c)^2 + (y_n-y_c)^2},\\ 10\\big), \\qquad\n",
    "d_{3D} = \\sqrt{d_{2D}^2 + (h_{BS}-h_{UT})^2}$$\n",
    "\n",
    "$$\\phi = \\operatorname{atan2}(x_n-x_c,\\ y_n-y_c) - \\alpha_c \\ (\\text{wrapped to } \\pm180\u00b0), \\qquad\n",
    "\\theta = \\arctan\\frac{h_{BS}-h_{UT}}{d_{2D}}$$\n",
    "\n",
    "$\\phi$ is the horizontal offset from boresight, $\\theta$ the *depression* angle (positive\n",
    "downward \u2014 the same sense as downtilt, so the two can be compared directly). The 10 m floor is the\n",
    "TR 38.901 validity limit.\n",
    "\n",
    "### 3.4 Line-of-sight probability (TR 38.901 Table 7.4.2-1, UMa, $h_{UT} \\le 13$ m)\n",
    "\n",
    "$$P_{LOS}(d_{2D}) = \\begin{cases} 1 & d_{2D} \\le 18 \\text{ m} \\\\[4pt]\n",
    "\\dfrac{18}{d_{2D}} + e^{-d_{2D}/63}\\Big(1 - \\dfrac{18}{d_{2D}}\\Big) & \\text{otherwise}\\end{cases}$$\n",
    "\n",
    "Each (cell, point) pair draws a Bernoulli LOS/NLOS state from this probability. The draw is\n",
    "seeded by `blake2s(\"cell|<cell_id>\")`, so the same cell always sees the same environment.\n",
    "\n",
    "### 3.5 Path loss (TR 38.901 Table 7.4.1-1, UMa), $f_c$ in GHz\n",
    "\n",
    "Breakpoint distance: $d_{BP} = 4\\,(h_{BS}-1)(h_{UT}-1)\\,f_c \\cdot 10^9 / c$.\n",
    "\n",
    "$$PL_{LOS} = \\begin{cases} 28 + 22\\log_{10} d_{3D} + 20\\log_{10} f_c & d_{2D} \\le d_{BP} \\\\[4pt]\n",
    "28 + 40\\log_{10} d_{3D} + 20\\log_{10} f_c - 9\\log_{10}\\!\\big(d_{BP}^2 + (h_{BS}-h_{UT})^2\\big) & d_{2D} > d_{BP}\\end{cases}$$\n",
    "\n",
    "$$PL_{NLOS} = \\max\\!\\Big(PL_{LOS},\\ 13.54 + 39.08\\log_{10} d_{3D} + 20\\log_{10} f_c - 0.6\\,(h_{UT}-1.5)\\Big)$$\n",
    "\n",
    "Two things to notice: the slope steepens from 22 to 40 dB/decade past the breakpoint (ground\n",
    "reflection), and NLOS is ~39 dB/decade throughout \u2014 cell edge is dominated by NLOS points.\n",
    "\n",
    "### 3.6 Shadow fading\n",
    "\n",
    "Large-scale fading is log-normal: a per-(cell, point) draw $z \\sim \\mathcal{N}(0,1)$, scaled by\n",
    "$\\sigma_{LOS} = 4$ dB or $\\sigma_{NLOS} = 6$ dB (Table 7.4.1-1). The cache stores the *unit*\n",
    "field $z\\,\\sigma/\\sigma_{NLOS}$; at evaluation time it is multiplied by\n",
    "$(\\sigma_{NLOS} + \\sigma_{\\text{extra}})$, so the fitted parameter `sigma_extra` can widen the\n",
    "spread without recomputing anything.\n",
    "\n",
    "### 3.7 Antenna gain (TR 38.901 \u00a77.3, `antenna.gain_db`)\n",
    "\n",
    "With vertical/horizontal 3 dB beamwidths $\\theta_{3dB} = 6.5\u00b0$, $\\phi_{3dB}$ (65\u00b0 here), tilt\n",
    "$\\tau$, and side-lobe limits $SLA_V = A_{\\max} = 30$ dB:\n",
    "\n",
    "$$A_V(\\theta) = -\\min\\!\\Big(12\\Big(\\frac{\\theta-\\tau}{\\theta_{3dB}}\\Big)^2,\\ SLA_V\\Big), \\qquad\n",
    "A_H(\\phi) = -\\min\\!\\Big(12\\Big(\\frac{\\phi}{\\phi_{3dB}}\\Big)^2,\\ A_{\\max}\\Big)$$\n",
    "\n",
    "$$G(\\theta,\\phi) = G_{\\max} - \\min\\!\\big(-(A_V + A_H),\\ A_{\\max}\\big), \\qquad\n",
    "G_{\\max} = 10\\log_{10}\\frac{41000}{\\theta_{3dB}\\,\\phi_{3dB}}$$\n",
    "\n",
    "The vertical lobe is a parabola in $(\\theta - \\tau)$: **changing tilt slides the lobe up or\n",
    "down the depression axis** \u2014 that is the entire mechanism by which a tilt change alters\n",
    "coverage. The gain is cached at the baseline tilt; a `tilt_override` recomputes just the affected\n",
    "cell's row using the cached $\\theta, \\phi$.\n",
    "\n",
    "### 3.8 The wrap-around tier\n",
    "\n",
    "A handful of modelled sites would sit in an unrealistically quiet RF environment. The engine adds\n",
    "six translated copies of the whole estate at offsets $d_{\\text{wrap}}$ along $0\u00b0, 60\u00b0, \\ldots, 300\u00b0$\n",
    "(for the hex lattice $d_{\\text{wrap}} = \\sqrt{n}\\cdot\\text{ISD}$, an exact tiling). Tier cells\n",
    "*interfere* but never *serve*, and tilt overrides never touch them \u2014 the surrounding city is not\n",
    "ours to tune. Their path loss, gain and shadowing are cached like everything else.\n",
    "\n",
    "`generic_build_kwargs()` supplies the two lattice-specific values above ($d_{\\text{wrap}}$ and\n",
    "$r_{\\max}$) as additive overrides; the calibrated path derives them from the supplied geometry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "51340b24",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18 cells, 2000 traffic-proxy points, 18 eligible to serve\n",
      "cached fields (shape): {'pl': (18, 2000), 'sf_unit': (18, 2000), 'theta': (18, 2000), 'phi': (18, 2000), 'gain0': (18, 2000), 'ext_pl': (108, 2000), 'ext_gain': (108, 2000), 'ext_sf': (108, 2000)}\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell_id</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>az_deg</th>\n",
       "      <th>tilt_deg</th>\n",
       "      <th>freq_mhz</th>\n",
       "      <th>grp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>g-s1-a-n3</td>\n",
       "      <td>-250.0</td>\n",
       "      <td>-144.337567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>g-s1-a-n78</td>\n",
       "      <td>-250.0</td>\n",
       "      <td>-144.337567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>g-s1-b-n3</td>\n",
       "      <td>-250.0</td>\n",
       "      <td>-144.337567</td>\n",
       "      <td>120.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>g-s1-b-n78</td>\n",
       "      <td>-250.0</td>\n",
       "      <td>-144.337567</td>\n",
       "      <td>120.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>g-s1-c-n3</td>\n",
       "      <td>-250.0</td>\n",
       "      <td>-144.337567</td>\n",
       "      <td>240.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      cell_id      x           y  az_deg  tilt_deg  freq_mhz  grp\n",
       "0   g-s1-a-n3 -250.0 -144.337567     0.0       6.0    1800.0    1\n",
       "1  g-s1-a-n78 -250.0 -144.337567     0.0       6.0    3500.0    2\n",
       "2   g-s1-b-n3 -250.0 -144.337567   120.0       6.0    1800.0    1\n",
       "3  g-s1-b-n78 -250.0 -144.337567   120.0       6.0    3500.0    2\n",
       "4   g-s1-c-n3 -250.0 -144.337567   240.0       6.0    1800.0    1"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "build_kwargs = generic_build_kwargs(n_sites=3, isd_m=500.0)\n",
    "estate = Estate.build(topology, config, n_points=2000, **build_kwargs)\n",
    "serving_mask = estate.nr_serving_mask()\n",
    "\n",
    "C, N = len(estate.cells), len(estate.grid_x)\n",
    "print(f\"{C} cells, {N} traffic-proxy points, {int(serving_mask.sum())} eligible to serve\")\n",
    "print(\"cached fields (shape):\", {k: v.shape for k, v in estate.fields.items() if v.ndim == 2})\n",
    "estate.cells[[\"cell_id\", \"x\", \"y\", \"az_deg\", \"tilt_deg\", \"freq_mhz\", \"grp\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "450aabec",
   "metadata": {},
   "source": [
    "`pl`, `sf_unit`, `theta`, `phi`, `gain0` are the $(C \\times N)$ matrices from \u00a73.3\u20133.7; the\n",
    "`ext_*` arrays are the $(6C \\times N)$ wrap-around tier. `grp` is the band group\n",
    "(0 = below 1 GHz, 1 = 1\u20133 GHz, 2 = above 3 GHz) that selects which $P_0$ a cell uses.\n",
    "\n",
    "### 3.9 The equations, plotted\n",
    "\n",
    "Before building on them, here are \u00a73.5 and \u00a73.7 evaluated directly from the imported functions,\n",
    "for this estate's two carriers and a 30 m antenna."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ec558915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xT2Wsz2XKsRcH7YjYhmFfkv9btBch2rMx2Kbh71mwTmdAE9tUHPimFsK2lWG7yxD2SdmnZB3OPAeEx11SKHJT/C7sf5vXeWl98u0aS4f6CrZpj/Dqq6+q0H5Oj9yyZYuuU6dOJZ6ez5B6fkd7MFQ+PDz8hikOd955p5qCqa3n4eFh0vS9X375Ra3/7bff5vt8/vz56vOWLVvmfUb7gf79+9+wjaNHj6p1+R1jVKQ9AunYsaNuypQp6nVcXJzOxcVF9/XXX6vpEQXPf2pq6g3b3759u1rvm2++Mamc7u7uukuXLt1gI/HUU08VuR9O4ed6mzdvLvLYNPg5j+X06dN5n9G+gp9//PHHJZ5mymlYQUFBupiYmHzb4/Tee+65J+8zX1/fIqfL7t+/v9S2E5x62qFDhxs+p8UFr5m3335b7b927dq6TZs23bBeREREvv+PkJAQ3bJly0za94gRI3TvvPOO+j9ZsGCBrlevXmob06dPN7r+oEGDdM2aNSvxMQqCYBsUN5X/ww8/VMtXrFhR6DZiY2PVOrfddlveZ7yf8jNOYx81apTuvffe04WFhZlUppLUM5wWyM+ef/75fNvgPY6fz549O9/nt99+u2o3GNYp2r2U0+80Lly4oHNzc1NlL2ndqZ1T3uc1yyTD6Ya//fZbvnqcj6LqLU6TLNj2KsweYcaMGTpXV9d8UxqjoqKUJU9x0y1p2cP9HD58ON/nPO/cJtt3bGs4Ojoqa4KcnJwbtkErBO18su5+4IEHbpjmWBj8bbp3726SbcWSJUvUcpZNEATbZOTIkbp27drpZs2ape4x7NMUvP8YQjsDWq507ty5RPsxpV9pjLvvvlv1GQxtFNgGZ5+BFi4a3F6rVq3yWfbRCoj3M0MbB61uYD+goDVDUX2hglPgeb647vLly29YV7NY4/YzMjLyLWP/kOfv3nvvzfe5KdPxtf5krVq1dImJiXmf//jjj+rzefPmFVlX8jdg3cu6tSz2COVxrlnnFqy3ea5q1KihbAWL4sSJE0b7oLS48/Lyyjv2J554Qk3zL6kFR1HHbqwtUFZ7hPHjx+uqVq1qtO3H9hvbbjVr1lSvaZtnDP7vav9b3NZHH32kKw1sM3FfxtoWZOrUqapdKORH7BGEUsGswxwp4uiTNgr4/vvv55suUBwcmeIIFaNFGUVKDKdUEEbCMAKW2+XIJBNecHoEE4wVl5maUygZCcTpd4zgZOQLR+lefPFFFUlkuC++NrRg0NDC9guWy1LwPPFYGOGiWToUHG3UMBxZpik/R8iYEIsRQYbT/4uCI5oc5dRgxA+zhNNU3th+GCXFUWAt+Yyp+yEcwTVMNsIRR0bWMIq3pNMuOBLMKTmcYmG4PVofGJad54Im7Bw1NYYWScvILY4mlwSe74KR5xz9/eSTT9RoLEd6OZLMKVIsV8HprCw7/z9ogcERTY6oc9TdFDgKP336dDWCytF5Rhxw1J3m7hwBLwjLqU21EgSh8sHoHGIs6l9DW8bIHg1GMvGexvsYp+uzvmW0B6MvGOlaXvWMBiNTDOE6rAcZdWIIo0bYR2VCNEMYdcXoTQ1O6+R9kvd4RpCUpu5kFKyhZRLLyDaGsfKXF4x65gwQtgMMo8IYjcz2UVFo1kUF6ydGfnGqJSOV2L7itE1GWxmLFmKkLBMEMaKL9T3bJKZYKjAimlFSjNoyBa2MUj8Jgm3C2Qe0PmNkIe8pjOZjxB+j+wpr369btw6RkZEltkYwpV9ZWFQlo0kNp5fz3spoQC4jsbGxagYbozVZX/KexAfvp2xfc0ZcwTqPka+sn0oL75Vt2rQx2s/TZnFw+1rUMsvLcvJezL5ySfpgxuoYw/YA++E1a9YstP/H/g3PB2dpsO7l71xayvNcM4rasE7kuWIbo7i+JbUHRikbWimyjcDrgnZR2rGzXcBj53VnzRjrk2p1OfujbDNxJhHbbpw5Zdi20GB7ir8/9R62nYqz3zMGI80524eRyoUl3WM5+X9b0n63vSOirVAq2EnRBCXNv0br8JgKBVUKdaxkOQ2GU9n53rCC5bRD7oOh9PwHZwXOqe+sOAqbdmAouNJXlFM2OM2eU+JZCbFDQkGMN3IN3nw1OwVDKEJqyyuSwqZUal6qvHHynN18882FdrJ5Hnmsms8fRT/Nd644P1aNRo0aGa3IDL39WLnyt+B0Cp4n7oMdeGLqfggrAGM37oLegMVBcZ7Qj7AgFBNY8WsVDX2NOK2T54iVOKfCGFbkPI6nn35aTT3i+WNjgd5Mph5XQU9FCgBsTGkiMYUKegtyCm7BabxsWPD/gb8xp5Nxv5z+y6nJpbme+H/KfRvzPGI5JUunIFRetHpEE29NFXbZ6KatARvhvLdyKjunHLLDVdBPviz1DKEQqnnVGd7v6TNfsB7Upglq9UFx+2LnIDo6ulR1Z8Ftsm3BNoop/vGlpWnTpujUqVPeFGLC1xRQKTCXpn7icXMqKD9n55cD3OzM0UKi4IAhO7McbOSgINuCnBZb0IuxIKx/KK4zUzXLXpIySv0kCLaJ1qfT2r28N1OUJfPnzzf6Hd7LeA/SBNPy7Fcag9PnGaRhKNDxNe9zmucn/XV5P2J7nPWB4YOBTITCryFaX6i00MNds2YoCvqeMzCF/V72eVkm9n9L0gcrrl7jPZh1i2G9xmnyWoAM6z3uV7OYKMu+y/Ncs81QsP4wtW/J64/T9DWBmP0n7tfwuqQtA68Rtnu4L9aJZcnHYk6M+fzzf0P736QAzeA2Cu/GfH85qMvjZL+Y9hcchOGgfUkwxfZE6n3jiGgrlPpmrglKjN6jIETPGXrNlhaO4tHkXEsKwsgNRnHQb8hwNIYRLbxpcF9FmcsTehFRlOODhum8MXE0jp1LrSIm7GAxQrMg2mfsFJYHxUXuaqNKBY25DctJXz2OcvE8aSPJxqBfKqNkOFLJmzCjYti5YoVumMysrHD7X375pfJqZRQw96NVWCXZT2Gj0YUlkymvslOkpQcTf2Mm4uE1YxidxXPNZGv0DeLvxk4n1zEWsWoIz3PBRoGWEI0NES2JDIVubq+442Qlyt/fsJNeEjRPZorsBWE5NW8tQRAqH5rIyXtdYWjL6P9d2D2P/rKMxGDHjd5vBUXTskABtbDIjPKkourOssJBaM6iYF3Ezj2jY4uLsiWaR3zB+omdb85SoY+uNmDNaC1S1HFzgJG/O+v/osQRet8y6o4eh+z4aw9tQICvC0bWaGWU+kkQbBOKifTRZB+BdYLmn81AjcJEJLaP2ccszFuztP3KouoWzvjgfjm4RJGOYp2hOKfdAzmbhPWBsUfBAbOKCPjhDFTeuzlTkX1m9r9YFvoHm7O+YqAWB+4oDnNwj9HU3C+9i0lZ9l2e57osfUv+/lxP8+dle4DiPkV+DV7LrDc5w1Hz16dGwTw81oSxPqlhojhGwmtCKT1lizs/vN74f13SPimj4BlUZTjjqSAsJ3MHVHTAnLUjiciEcoERGRTuZs6cWeoRJq2xr43OaUmYjEXwMmKRN3VTont5A6IopsEOJb9rmEGRo6m80RZMdsGp89ry8oCjhLwRseNiDH7O5UV1UCjUslNFAZAWEIXBqQ2sNCg6arAjVpJMj5yCYmxqg5bIizdWjphztI2RSUV9r6JGzLTkOMbOMTOR8tzS0F6DQihHSvngCCorK3bYDQ3qaVjPB69vmrnTHJ8RArNnzy4yEopTm4w1sBgxS+N5/paDBg1SooApU2n5+5V29FqLIDaW8IBZ4DkFSxCEygmnqrJOYYOa9bmxjg5FN8LB2uKg2EdBkQOfBZMxlrSeKQpum7NvKPoZRtvyXq8tN2VfrHe1e2NJ605u0zDLNKNSedxF1c+mUlS9yUhmRrzQOortJw5omxKZxrpJu++zXtOgaEG7C0ZOU1x58803Vd1Ocbi4JGDcPzt5/B0K62gxKottN9afxq4tPiiasBwaLCOFesNBdkEQbAf+/7JfyPsVB/M4g5L3dg40MSqxIBS/eB8pqTWCKf3KouC9kxGr7NPQ8o/3M8P7KaN2Ce+zhv3HklKSvhCFMQYdFQXrK5aNg2aG29YiUktLwbqS54MRsFqyLCYKZd3Jc8Y6QsOYTUBhx1zY5+V1rssKo3c5C5NR17xueY5ZPxW0U+TAJS0T+KC+wP4kE3kyQKewmS9l7ROX9Pus9ymw8n/BMIk2E4/xt2WZKbwzsI3CO4OZTPn/MjZLuTCoqfAa0pKYFQbr/aKSqlVWJNJWKBfY2WP0BH3hOOJUFNoUxIJwhFAb4dFGr7hdNuINI2rZIaJlAm9Ahp0DdtLYKSjuBsObKIU6Tp8xHI2lAPzFF1/kfcYbETsw9NbTohTLCjvCFOlY/oJl5Xt+zuVFeSCxrKyMOWWxqOyr3EbBkTLehEtiY8EbuKFvEKdA8qarCZpaOQvux5hfnSaUlkQ0Lg38bSmysyFhuC82fCiOah1pnoeCDTlec4y41SohivgFffrYyWUjtLiKit6JFLUL+iZxChEbAByxpdjAUU52jDkqTmjdYMzHhwIwt6dFPhF2gHndG0aJM5K24G/M9ehbxOvFUFwgPAdsPDOSVxCEyglFS0a1cLCLom1BGE3DhjwtYjTPck6lpwdaQVhfs+PL+6QpU/WLq2eKgvdz3u8KTtGj3QzbEwW3Qe9wQ58/RmHR0sGw3i1p3cl2A++xGoxYYb1hSvmLg/VmYXUmByC5Dw4EsjPG6B9TIlIZ4cK6wNjMKNZH7IyzzaPlKOCAvEbBKamE5WP9xO+wDjVs02jiOaFow/ZcwYf2O/I121uG0HaDg+6GnUxBEGwL3hdYJ/A+vXDhQjXYxGdjwgwHDlkfFZavg21W3lcM2++m9iuLguIgp/mzfc4HxTrDKfc8Bs50pBhnbGZmYWUoS1+I1n4HDx7Mu08aotVRxvphrD8L5skoKRxEM7RLojjM4y6q/8fX8+bNM/mY+Tsb+7y8znV5QOGes1h4vXKWbsGBUc0jXoPtHk3YLqqfWNixm0pJ+9Tsk/L3YZ1a0HaKvxktrSio0s+WAyZapDDbMsYidNlOY1vBsE9anBbD/21S1Cxhwjaa9ElvRCJthXKDvqYU6ygO0YO2MBjFyGkn7GBwegxFJjb4d+/eraYlap08VgjsRDK6kZ1EjuSx08RKmNMB2VExhJU/R3ENPTs5vZEiHKdzUoDjTZciGjughlE57CjwRjVjxgzVKWEZKPpxuh73V3CKKEeCCW9wbDhoEZeMVuRIW1EweoXHw0YEvYE54sz9sOPHxgWXFwU7L/ReLQ5GQ3377bdqfR4/K3CKhNrUSFPgeWAEFhOrsPLh78vvM8kVYfRN7969lfcNO630aKUwylGygmhTISgIsPPGEVSeK8Oo1/KCNgdsWLCSYlQrxXp2ug3PHRsj9B9ix5S/G8VUnh9eh1qEFSsxjq7y2mCkDysvnlNem2xMFQVtPVgZcpv8nQvCbTKJH69Z/vacXqSNbrPxyIYBBybYAGDnmtc7rxVDL2cKHbzuWblqU5J4bfJ65HGxwcn/L1aUFK15bTF5oCEsHyty2jYIgmDfsA40NhuG9xVaHDF5CBMlsr7gPY4Do4y65P2H9xrWixqsh9mx5YATk1fw3sL6k5Gf7Gg++eSTJomIxdUzRcE6hANRrFdYj/JezjqIQiz3b5jcktAfkMIzbW4YLcPBT8KBs9LWnRSpefxsb1D05jZ5PJwqWVZYb3LftKFiW4b3dENhk+0iTVx9/fXXTdom7ZcoUnO7xiJeGOXEaCnaXrEzx9klrOeJ5tvHMrBjzc4ZB7dpPWXoB6mVjdHWWqee9ZkW5VsQHpdhhC1hm4LfZwSQIAi2Dfs3vMfzfs37CQW5ggIY26u0J2PdY5h3xBAKmJMnT1b3Hc1H29R+ZVGwT8IZcOy/MniCEYgF4TRy3tsZvMGIRN4rmTCNdQTrQ9Z7xVGSvtC0adOUWMr+AqOS+V0eG9v5nO3H+o71FSNAKXKz38H+F5ex7jI1ebExKGDzWHmueYysl3keedyE93LWr+ynsy/C/qAWXFLYMbPeZf3LPhSPne0LlpN1B/tY3CfraD7K41yXB6zXeYx8sHwFI38585W/CdtBrBtpCcX+JoOHiooWLerYTYHb53lke406BNszLIPhwKkhPJdsw7De14KEDOH/JYPuWMdzEJjXP69JXkMckOX/KgdQ+RnFWv7/sY3EQLjitBhC/YbHSv2jYLvMEIrKPJ/SJzWCThAK8Mgjj7CFbXTZuXPn1LI5c+YYXT5p0iSdo6Oj7vTp04Vu/6+//tLdfPPNuuDgYJ2zs7PO29tb16NHD92iRYt0ubm5N6z//fff6zp37qzz8/PTubu767p06aL7+eefb1iP5erTp0++z9555x1d06ZNdW5ubjp/f3/dLbfcotu/f7/RcqWlpemeffZZXY0aNXSurq66Tp066dauXXvDeiwn92XsMXHiRJ0phIWF6e666y5dUFCQzsnJST2PGTNGfV4QHlOLFi2K3N6GDRvU/n/66ae8z+Li4nSTJ0/WBQYG6ry8vHSDBw/WHT9+XFe3bt1iy2n4O7///vu62rVrq3PSq1cv3cGDB/Ote+nSJd2oUaPU7+Pr66u74447dFeuXFHff/nll/Ot+/rrr+tq1aqlc3BwUMu5H8LXvO4KUpKy8ncx5J9//lHXFa8ZHx8f3YgRI3THjh3LW56RkaGbNm2ark2bNuoa9PT0VK8/++yzvHXOnj2ru/fee3UNGjRQ11BAQICuX79+atumwOttwIABRa4zb948Vf6YmBj1Pjo6Wjd16lR13bJMLi4uukaNGumefPJJtczYsRueoz179qhj5Xnmd/nb9+zZU/fjjz8a3T+vQy4XBMF+Kare4uPixYtqvZycHLUu7528b/K+x/rn1Vdf1SUnJ+fbZmJiorp/sW4JCQnJq8+7deum+/LLL43W56WtZ3iP4/3QGElJSbqnnnoqr03B+yW3WXD/Wj3z3XffqXW4r3bt2qn60xBT607tnG7atEnds9nG4Prjxo3Lu58b1uOG7RNj9Rbry4JtL+63d+/eqh4z1sZgPcb9su5lG8ZUli9frqtSpYouPDy80HUSEhLUPvnbaHzyySeqvuC5YdulWrVqqr7ZvHnzDd/n8ZrSzSis/l+zZo1adurUKZOPSxAE64f3a9YVBZk/f776n1+5cmWh39Xuu4b3zpL2Kwvj77//VtvmvVGrEwty5swZ3T333KP6itwX29rct2G/VCvj7t27jW6jsL6QsT4P65JHH300r03PupbrXLt2TS3n8b355pvqu1qdtmrVKrUOPzPEWL+ssP7k0qVLdTNmzFD9U9Y/w4cP1124cCHfuuxTDRw4UNV7rBPuv/9+VXcX/H2ys7N1jz32mKoveG4N64Vt27bpOnTooI6tYPnKeq4L6z8bOzdFwWuJ+7jvvvtuWMayDBo0SJ0nHkOdOnV0DzzwgC4iIqLY7RZ27MbaAsauDbaz6tevr3QXrl+wLVOQxx9/XNewYcMi1/ntt9/Utvbu3ZvXxnjiiSd0rVu3Vm1C/g4sy5QpU/Ku2+K0GEI9hcs++uijIvf/3HPPqXNYkv/bykIV/jEm5gqCUHlhxBIjXxixytFFoXQw+R2n+HC6iJaJlaPm9Efk1A+OYDIKlyOzxqYAmRtOb+bvzMgCGdUUBKEikXqm7HD2ByNwGalVcFZQUTDqhVE+jCLSInQZxcToJkabMSKHtghcxsg3w8QrFQUjbxmdZ2xqsCAItgPvJZx1wRmGbHdy+jXfc9q5YF0wQpJRl0y+pc3iEOwDzjRmhDTrdM4O0maU0ptYi7595plnVDQu+6S0qKxIONOKM0o568twVqmgR+wRBEEQzATtDzgNlfYRmi8gp31wupbm08zkNyXNvllecLoTpx6JYCsIgmB70A+Y/n6GiWBMgdMqaY3A6cq05tGmItOqRxNxac3DusoSgi0TAa1atarYHAmCIFg/9A5/8MEHVZImQmGGU/gFQag4aDFBy0DaWGqiLYOIKNRquVTYFmB/taIFW0LLBdqF8F4h3IhE2gqCcAMSAWVe6K/M6Ft2nBntVFi2bUEQBHtF6pnSw0Qz9NenwMqINcPkamWBogrrJvoS0neuoAe6IAhCaWDCJuZsYHuXvpdFJVIWLIdE2lY+mPeFA6X0kef/Jv2JBetDIm0FQRAqGFaITOAjCIIgCCXl888/V8nhmIxES0JZHjC6lgOJgiAI5QktV0qSCFkQhIqBAym0LhGsG4m0FQRBEARBEARBEARBEARBsCIcLF0AQRAEQRAEQRAEQRAEQRAE4T9EtBUEQRAEQRAEQRAEQRAEQbAixNP2euIFGqTT06NKlSqWLo4gCIJgAegWREN++q7R21EoGqk7BUEQBKk7S47Un4IgCILOxPpTRNvrGS3Hjh1r6WIIgiAIVsCSJUtQrVo1SxfD6pG6UxAEQdCQutN0pP4UBEEQTK0/RbS9njVPO1keHh6WLo5gJyPocXFx8Pf3l6gDwa6xp2s9NTVVdaK0OkEo/7rT1q8XWy6/LZfd1stvy2W39fLbctltpfxSd1qu72kL14clkPNiHDkvhSPnxjhyXsx7XkytP0W0BfKmpbDS9PT0tHRxBDv5R87IyFDXk9zgBHvGHq91W5yquHnzZsyZMwd79+5FREQEVqxYgZEjRxb5nY0bN+Lpp5/G0aNHUbt2bcycOROTJk0ya91p69eLLZfflstu6+W35bLbevltuey2Vn5brDttve9pS9dHRSLnxThyXgpHzo1x5LxUzHkprv6UMy8IgiAINk5KSgratGmDTz/91KT1z507h+HDh6Nfv344cOAAnnzySdx33334888/zV5WQRAEQRAEQRAEoXgk0lYQBEEQbJyhQ4eqh6nMnz8f9erVw/vvv6/eN2vWDFu3bsUHH3yAwYMHm7GkgiAIgiAIgiAIgtWLtsVN5+Q0za+//jrfd9iZXLt2bd772NhYPPbYY/j9999VaPLo0aMxb948eHl5VeixCIIgCIKtsH37dgwcOPCG+pURt4XBaUB8GPowaVOE+DAFrsdMqaaub23Ycvltuey2Xn5bLrutl9+Wy24r5bfmsgmCIAiCreNkDdM57733Xtx2221G1xkyZAgWLVqU997V1TXf8nHjxinB9++//0ZWVhYmT56MqVOnKmN3QRAEQRBu5OrVq6hevXq+z/g+MTERaWlpRg3x33rrLbz66qt57x0dHZW9Ao34DcXc4jr3SUlJSoSwRW8sWy6/LZfd1stvy2W39fLbctltpfzaAJ4gCIIgCHYm2poynZMibY0aNYwuCwsLU1G3u3fvRseOHdVnH3/8MYYNG4b33nsPwcHBZim3IAiCIFQ2ZsyYoRKXGXbUJ0yYoDKnliQRGc32bTULrS2X35bLbuvlt+Wy23r5bbnstlL+ggE1giAIgiBUIk9bZrcOCgpSjZX+/ftj9uzZqFq1at70Tj8/vzzBlnC6Jxs1O3fuxKhRo8w2xVMQbH06myCUB/Z0rdvDMZgKB0MjIyPzfcb3Pj4+RqNstY65YeeckbaEdW5JxAQKECX9jjVhy+W35bLbevltuey2Xn5bLrstlN9ay1VaOKtk+fLlOH78uKoPu3fvjnfeeQdNmjTJWyc9PR3PPPMMfvjhB9WnpL3QZ599dsMMFkEQBEGwa9GW1gi0TWCylDNnzuCFF15QkbkUa9lZ5PROCrqGODk5ISAgQC0rjPKY4ikItj6dTRDKA3u61ivTFM9u3bph9erV+T6jzRA/FwRBEITKyqZNm/DII4+gU6dOyM7OVv3PQYMG4dixY3mzSp566in88ccf+Omnn+Dr64tHH31U9Vn//fdfSxdfEARBsDOsWrQdM2ZM3utWrVqhdevWaNCggYq+HTBggEWneFqK+vXrq9Hftm3b5vs8ISEBzz33HP755x+4uLjA29sbL7/8srKKIPT7ffbZZ7F+/XolbPP9lClTVKOjIAsXLlTJ3Gg/wURxTzzxRN6yU6dO4cEHH1QCN0eZuf13331XiTUUb5jEZs2aNSoqgN9jo4cUtawgFNHpdbxv3768z+hrfN9992Hu3Lnqu4sXL8Zvv/2mktdp8Lrg72r4PUthC9PZBKE8sKdr3ZaneCYnJ+P06dN578+dO4cDBw6oQcw6deqoeu/y5cv45ptv1HLexz/55BNMnz5d+cqzbvjxxx9VJ1QQBEEQKiuGCa8J+xwMEmLi7N69e6s+14IFC1T+FM4C1fopzZo1w44dO9C1a1cLlVwQBEGwR6xatDUmWAYGBqqOKUVbTu+MiorKtw5HRGNjYwv1wS3PKZ6WomA5GeF28803o127djh58qQSZdlZHz58uGpo3HTTTcrrlwnbDh48qJZTcGX0srHj5cgyO++MSNamZGlQGKbtxOOPP662wXX/+usvJd5+9913SuhlGdigYXnYmGnRokWRy4zBMu7fvx8dOnRQ73kctMEwnCJWsGzaa2v5Da19OpsglBf2cq3bcvn37NmjZoxoaAOTEydOVPdP3v/Dw8PzlnMGCwVaDtxxkC4kJARfffWVmuIpCIIgCIIe9lsIB0EJxVsGv9CST6Np06ZqgJSzQUW0FQRBECqtaHvp0iXExMSgZs2a6j2nccbHx6vKUxP3GC3EyK8uXbqU674pjKZn5sCcuLk4KvGjpKxbtw4XLlzAhg0blNhJGIn74osv4vXXX1eiLc8dR4m15W5uboUKpoxyLUzAYPm0xgszjLPRov0ey5Ytw/33369EcDZs7rrrLixdulT5EBe1zBiTJ09WEb/8XSn0cj+FldcYFB8YRaZx+PBhdW306dPH5G0IgiDYCn379lX1VGFQuDX2HQ6OWRKWOTkzAymZGajh7WvRsgiCIAiCIdpMwR49eqBly5bqM1rwcVYj86oYQj/bwuz5zJVPxZ7yCpQnpTkvXD8tIxuODlXg7OQIB4eS98lLSkpKqtoPg8kqInCgoq+X3OxM5GZnwdHFDVUc9EFy1or8LxlHzot5z4up33ey1umcfNB3dvTo0SpqllGhnMbZsGHDvEggTkOh7y3FwPnz5ythj55CtFUIDg4u17JSsB04/UeYk3/evRPuriX/SWgHQHGTDQhDKGpPmzZNveY54rmisNuzZ08VqXzHHXfkRRmbyocffogRI0bg888/VxYJL730koqaJYziqlu3bt66oaGhappQccuMQV+o9957T0XzUryliMvRa0N4LIY2EbyevLy81GtaKfBB3njjDdWQYoNLEAT7IuXqeYT/tRiNxzyvGoWCbRGVkoTBX82FY5Uq2PP4rFINXAqCIAiCOaCV25EjR7B169YybaewfCpnz55FrVq11GfsVzk7O6u+DGeOMjCJyUHZv2OgDIVeLRk3l3EbtPVjME10dLQSkbV1U1JS1OxUwuUUBGmdR4GAM1L5mkIh+1nsP3G7rH8TExPVd7hfChIMlmJ5GOxD0Zk5DNhH5/b4mtujpy+5du2aKg+Tt2VmZqptGa6bk5OTJ3Rzux4eHvnW5TIGF7E87NPTdouwvNw/1+fnPB5tXR4nv6+ty3PIc8Dt8hzxvHAZzyvPH49Xi5jm8vjUbOw9l4TdJyIRGxeHmLQqyNY5wMcFqF/dFc3rh6B/m1pw1qWr823sHBo73zyXPIcF1716NQrr12/Bxo07cPLkGeTkZKt1U1LS0aRJA3Tp0gaDB/dH06aNij3fxs4hj5Xl5DnkNaSdb+0c8sFzxPPC7Ro7h1yXZeVvqV2Hhueb5dLOoXa+tXXjYq/BMfoM0k7tQsKlM0hNSYZntv6aygoIhWfN+qjeqgfc6rZEfHxCode3Kdesdr6NXbPaugXPIdflOsauWZaBCXl5nfJYTblmizvfJblmDc83y2fsmi3NPcLwHBqua+o9gtvkzHZ+zu3Zyz3C04TzzWXcBo+PZeN3tXPI7fJ4tJwuxs63KfcIHospOFh6OicFP03043ROvp41a5Y66EOHDuGWW25B48aNlf8qhcktW7bkszb4/vvv1ZQUipCcok9B8osvvrDgUVknjFKl8P3pp58q8ZR+tzy3JYWZUe+++25cuXJFRffy/DN5TXnDf1CK8zT454P7LAgbOxT5tQejawvy7bff4pdffsHPP/+cF2UsCILto8vNxeXNP+PA3Ptwbf96XFi70NJFEkqBj6teaM/R6ZCalWnp4giCIAiCgoFAq1atUkEitBDSYDARRQB20g2h4FOYPR995dlR1x6cAUm4bS1YafPmzSp3CF9TQGDuDu6H7+lJT69dbd1t27Yp2zm+poDw+++/K1GA71kOlltbd9euXapPzdcUEbhdTSyhsMD3FDT4noFAfPA1P+MyrsP3/A7fcxt8z21y29p+uE/um69ZFq5LIYXvWVaWWVuXx8Jj4mseI9flMfM9zwHPhbYu+5nsc/I1xRCuS/ieAV+cSamty/PJ/q4muvC8UJjh+4sXLypLP77OdnDDzyv/xIKf1+Gb9adw/mosuvhdgZ+zvh0S6JyEmllnsGTTGUz9eAt++v1v7N2vP4cUhlgG7XxTAF25cmVeGahv0I6QrynKcN3LlyPwv/99i2eemYW0tHjs2XMAcXEJ6NmzPXr37qDKePToCXh5OeG11+bg8cdfwokTp9R3KS5xW5w1unPnzrz90OKKkd18TVGo4PlmUjzD881rTjvfPC9a8naebybf09blDN7z58+r1xSkuF0+8z0/53JtXX6P3/f380P80X+x8vc/cOqPhUg8ugWXs91xNKBz3v/AsSq1EHYxChd/eB2nFjyntkuRTEsgz+PRtsvj5PHyNY+f62piIIUyvtfW5YxrLeiP4pvhNcv/Ub7n78D3/F34+2jf5e+mCdj8PXls/H35/ujRoyrATFuXyXupf/A1hT1ul/97fH/ixAmlUWnr8jrj9aautexstS6FPb7n9Wn4f8/rl9exds1yXV7nfM/rntd/We8RvC4Mr9mS3iN4XuzxHuHg4KDWNXaP4IO/KX9b7T7LdTVRl7Zzhv83vFZ4zZTmHmEKVXRFzaesJPCEjhw5Er/++muhicisxR6BEaosp2GEKZOPTZo0SV2UvJgNBVZaEPCCKwhvjrQ14CiGdpMoCLfJ/XBakAZHGmhXoEUyM5KXFzGjWemhy4RuWgI5RkZzGS0QilpWEJ4DjmTwn4RevYwQpvhqWB5O9eV54MMwERmX8cZN+A/6wAMPqH9qzcKhotBGWLQbgiDYK5a41jPio3Fy6VuIP7lHvfdv2hmNxjwPV1/9CKY56wKhbOer4PXCurXzJ7ORlZODNfc+iZo++aebWhu2fG+35bLbevltuey2Xn5bLrutlN/e6k7WS4899phKdsy+RaNGjfItp+harVo11cfijFDCPguDiEz1tNXOGQNOtJmIFKPYN2InniIC31MUYbAS1+eDVneE/TeKbhRHuB6jwHiNaJFxFLg4y5Dw+uG1w8gzXk+MmuNriiUU+3g8XFfrfxHul+eBAguFMAbTUKygCMYyaJFlmrhEuC7LQ8GCEXfcFs8TA7G4Dx6TFgXIMvBa4UNbl1FoPCYKPxRptKg0Ch7cP/ug/JzHw+2wz8vjZLm4H21dngOuy33wGLguzysj2ngO911Iwfs/74FjThpydFXQtF4w+rcNQV0/RzSoGwxnFxeER1zD4dOXsfl4PPafjoKnYxacnR3xwK1dMaxzqDrn2jnk+WaZNcG+4Pleu/YfvP76XISHR8DV1QXt2rXAwIF90bNnFwQE+IJd/8zMHBw4cAR//rkO69ZtRUpKGjw83HD//ePw1FMPqWM1dr55nIbnUDvfLA8jDg3PN38XPvg9wnULO988Lv6W3AavtcLOt4o0TI1HxMqPEHfmEFKdvODj7oKaHQbArUEH6DwDUTOkNrLTU3Ap7ABSzh1C0oG/kJWeglQnb9Ro2Bwtxs5AloP+9zHlmi14vnncvHa5vOA1q13fhV2z1EP4/8bzwu1yZjCT3vNYC16zxs53Ydesdr6NXbNcl983vGYLnm9NJOQ54TEYnu+S3iN4Lnh/0KLOS3qP4DYpZvI+yGvLXu4RPj4+Rs83z412Dos639wuRXyeFx6rsfNtyj2C2+EM8+LqTwk9NBHeDEpjXVARMKFX7dq1VUIZ2hdoicgopNJagFC45EWliZccleIFWtCPyZRkcBz5YLZx/hNztOKZZ55Ry2i38OWXX6pn/uPRx5YjKsUtKwz6Es+cOVN58pYUjs6xjBy1q2jBVhAE88LGX+K5Q3BwdkW9Wx9Bze63yrR6G4W/m7erG2JTU5CYkQ65WwuCIAiWtkRYsmRJXsSY5lOrCRN85gxQzhDVIqUo8tKWrqRJyDTRoeBr9uUMo3Y18UJDEzYoiLDzz+9qor4mzmkYBudwHcPt8nj40NCmEGv1s+G6FB8M3xvz9NWgqGG4rjY9WkMTRYytWzDyTBNbCAUUw3X5+/BRcF2eFy2SVDsvbu4e+HjlEazaeVa9bxlaE4+ObIdW9f7bvkazejXV486bgLDwGHy0fB8OnYvGO8t24eDZKDw3pgtcnByLPN8UtObN+xLvvfeZel+/fl3MmvUMBg7sbbTNGhpaGyNHDsX58xfx+uvvY+3aDZg3bwH27j2Czz9/FwEBpp/vgudQO988L1q0onZeijrfXLeo8+0UdwHHFr6EnPRkOLm4o9WQ8ajZYxQcXf6bFU1cnF1Rv3N/oHN/5IyciovrluDS+qVIPr4D++feh+aTXkf1us1vOIfGrtmC57uoa7bg9V3wmi14ffP/SLOOLHjNluZ8G7tmS3K+KUJq1o+lvUcQHlNR57C4ewT3q12z9nKPKI/zbXiOy3K+TcE6VUihSGgbYBhRy3DsNWvWqKhXCrO8WHlx0udX8//lyBGjUDnSwOW8ILVQ/YIwipViKUc0qPrTW5bTKGhd8fXXX6vpQsw2zpEMWixo0bOMpN29e7cqA/+J2Zhp1apVscuK4oknnijVOZo7d64SlceNG5f3GUezO3bsWKrtCYJgWZjIwMFJf9/zrBGKxmNfhGdwA3gE1bF00YRysEhQom16mqWLIgiCIFRymLdDS9ZpyKJFi9SsP/LBBx+oPhQjbRkFxv4WZzgK1gkTjM1ctAU7wiJUorF7h7TChJuaw9EEwaRZnar49PGBWLI+DF/8cRBrd59HTGI63prSu9CALkbazZjxBr7//hf1fsKEO/Dyy8/kExALg+LtggUfYsWK1Zg27VVs3boTd9wxBUuX/g9BQWWbUVaeRO37ByeXvAldTja86zZHk/EvwT1Q789cFI6uHggddh+qtRuAsMUvIS0qHIc+exIt7nsLfo30ieUFQciP2CPY4bQewfLYwnQ2QbCVaz3uxB6cWvYOmt7zMnxC9dmbzYHUBRVvj0Du+eErHLp6CXNvvgv9GzaDNWPL93ZbLrutl9+Wy27r5bflsttK+aXutNw5s4XrwxIYnpfsXB2m/W8j9pyMhKuzI2ZP7onuLYoXF42x+8RVzPhqM9Iys9G6fjV88FA/uLnkF24prVBsXbp0hfpNZs+egYkT7yzV/o4fP4Vx4x7C1avRqFevDpYvX1Qm4ba8rpfoAxtx/JtXmGACgW37ocnYF9Tst9LMnAtbPAvxJ3ajipMLWkx5U1meWQL5XzKOnBfznhdT6wI584IgCIJVkpOZgTPL5+HI/KeREReJ8D+/tnSRBDPg7aZPRpaYIZG2giAIgiCUDzm5uXj1m21KsGVU7EeP9C+1YEs6NamBDx/pD293Zxw6G622zX0Y8uabH+YJtp999k6pBVvStGkjLF++GLVrB+PcuXBMnPgYUlJSYUniTuzGie9eU4Jt9S7D0XTCy6USbImTm6eKsA1o2QO67EwVeZt8+VS5l1kQbB0RbQVBEASrI/niCeVzdWWLfmpZzR4j0WzSa5YulmAGfFz10wWTMtItXRRBEARBEOyEr9YcxsaDF+Hs6IC3p/RGSyP+tSWlZWgg3r6/D1ycHLD58CV8/Ov+vGXLlv2Kzz5brF7PmTMLI0YMKvP+6tYNUdYIAQH+OHToGB56aLqK8rMEadEXEbb4ZWWJENiuPxrd+SyqlDH60sHJBc0mvgbfRu2Rk5GGo19MR0Z8VLmVWRDsARFtBUEQBKuBDcHwv7/FgQ8fRFrkBTh7B6DF1HfR8Pan4Xhd3BPsCx8t0jZdRFtBEARBEMrO9rBIfPdPmHr9wtiu6Njkv8RAZaVtgyDMmtBdvf5p0wms238BBw4cUT625JlnHsKYMaPKbX+0Rvj6649Uoqd167bg448XoKLJTk/F0QUvqKRjtCprMvZFVHHQJ+0qK8xZ0Xzy6/CoUQ+ZiTE4/s2rqj8gCIIeEW0FQRAEq+Ha4a24sPpL6HJzULV1H3SYvhgBzUqWjVmwzUhbsUcQBEEQBKGsXIlJxryVR9TrMX2bYlDH0HLfR7+2dTBhYHP1evY3m3Hf/c8iIyMTgwb1xZNPTi33/bVv3xpvvvmCev3ee5/h3393oSI5s/xDFUzh4huIZpNfz0sOXF44uXuj+ZQ34ejmicRzh3F+9Vflun1BsGVEtBUEQRCshsA2fVCt/UA0HvuiskNw9vKzdJEEMyORtoIgCIIglAf0mH1zyU6kZeagVb1APHRLW7Pt675hrdGuYRCu7PoTEVciEFI7GPPmzTZbwqa77rpVPWiP8MQTM5GYmISK4NrBjYjavRao4oCm97wCF5+qZtmPe2AtNLrrOfX60voliD/9n/WEIFRmRLQVBEEQLEZmUixOLZuD7LRk9b5KlSpoOmEWqncarF4L9o+3RNoKgiAIglAOLNt4AgfPRsPNxREzx3aFk6P55A5uu3etHCSfP6ze3zzhfvj4eMOcvPHGDISG1kZERCRee+19VEg7/cf31OvaA8bBt35rs+6vWtu+qNF1hHp96od3lM+tIFR2RLQVBEEQLELM4S3Y9+4kXN3xO87+9omliyNYCB9XfaRtkkTaCoIgCIJQBluEL1cfUq+nDGqC4EAvs+4vOTkF77yhFzR9GnfBujOZuBCZYNZ9uru7Y+7c11Rgw9KlK7Bp0zaz7u/sb58iOzURnrUaoc7gSagI6t36MFz9gpAecwXn//iiQvYpCNaMiLY2RmhoKJo2bYrs7P/MuTt27IiNGzeq16+88gqefPJJo99dvHgxWrVqhWbNmqFhw4aYMWMGMjMz85YvX74cHTp0QNu2bdU++vfvbzQ75fnz59G3b1/4+vqqdQ3h+k8//TSaN2+O1q1bo1+/fjh9+nTe8lWrVqltN2rUCLfddhsSExNNWmbIpEmTVEW1f/9/UyaSkpLg5eWVrzxcJz4+/obzd+DAgULOriAIFZXM4OQPb+PYwheRlRwPj5r1Uav3HZYulmAhfNwk0lYQBEEQhNKj0+nwwc97kJmVg/YNg3BT21pm3ye9Za9ejULduiEYdNtoZOXk4oNf9qqymJMuXdrj3nvvVq9ffPEt5aVrDuJO7EH03r+VLUKjO6eVu49tYTi5eaLRXdPV6ytbVyD58qkK2a8gWCsi2togGRkZWLCgZFkjv/jiC7zzzjtYuXIlwsLCcOjQIZw8eRJTpkxRyyMiIjB16lQl3FLUPH78ON577z2j05N9fHwwe/ZsLFmy5IZl3P6///6LgwcPqn0MGDAAL7ygN01PTk5W+/v1119x6tQpBAcH4/XXXy92mTEoLi9cuDDv/bJly5QYLQiCdZNw9hD2z5mMyJ2rObKCkH53o93TX8AzuIGliyZYONI2MUMibQVBEARBKDlbj1zGtmNXlGXB07d3NLvF1rFjJ7Fggb4v/MYbL2Da3d3h4uSA3SeuYuPBizA306Y9gmrVquLcuXB8+eW35b793OwsnPnlA/U6uOcoeNdpiorEv2lnBLbtB+hyceaXeWYXwgXBmhHRtoSkZWQX+sjIyjF93cz/ImVLCqNpKWimpqaa/J3XXnsN77//PurVq6fee3h4KCH3l19+wZkzZxAZGQlHR0cEBATkfad9+/ZGKzyu07NnT3h6et6wjOtTVE5PT1c3V0bLhoSEqGVr1qxBu3btVDQtefjhh7F06dJilxmDkbiMzOW+yKJFi3DvvfeafD6ioqJUVK72CAwMxOTJk03+viAIJSdq3z849MljSI+NgKt/DbR+ZB7q3fIQHJxcLF00wRoibdPTpFEuCIIgCEKJyMrOwSe/7lOv7+7XFHWr+5h1f2yr0E+WM0yHD78J/fr1QEigN8YOaK6Wf7xiX5n6+qbg7e2FmTOfUq8//PAL5XFbnkRs+w1p0Rfh7OWPusPugyWof8vDcHB2ReK5Q4je949FyiAI1oCTpQtgawyc/mOhy7o1D8Z7D/TNe3/zzF+QnplfyNVgpslPHhtYqjK0adNG2Q588MEHePHFF00SKC9fvoxu3brl+7xq1arKimDfvn0YPXq0EmLr1q2LPn36oHv37hg7dixq1SrZ1JIRI0Zgw4YNqFGjBry9vdX3N23apJaFh4er7RtaFTDCl1YPRS1zcrrxMqXofNNNN6nIXJ4PVp7GIm179eqlxGiNK1euqOegoKA8m4SjR49i+PDheOaZZ0p0rIIglAz/xh1V44+j5w1ue0JNfxIE7+uRttm5uUjPzoK7s4j4giAIgiCYxq/bTuPStWQEeLvhnkEtzL6/jRu3YcuWHXB2dsKLL/5nSzhhYHOs2XkWkfGp+GXrKYztb95ZoKNH34xvv/0Ze/YcwAcf/A/vvjurXLablZqE8D8Xq9d1h06xWHvd1b86ag8cjwtrFuD8H18isE0fCfQQKiUSaWujMNJ23rx5iImJKZftOTg4qKjbbdu2YciQIcrioEWLFvn8aE1hz549OHLkiBKJKZDSHuHBBx+EOWBkLW0i+CgsSnbLli1KnNUetF0whGW89dZbldVCy5YtzVJOQais6HJzEXP0vwQJzl5+aD9tEZqMfUEEWyEPD2cXOF6f1cFoW0EQBEEQBFNITsvE4rVH1OspQ1vBw9W8vqs5OTmYPXuuej158t3Kz1bDzcUJU4a1Vq+/+fsoklLN4zVrOMN15ky9aPzDD7/i9Onz5bLdS/98p5KPedSohxpdhsGS1Op7F1x8A5ERdxUR21ZatCyCYCkk0raE/PPunYUuc3DIbyWwavbowtcto80OI1EZCUtv2eJgVCkjXrdv345hw/678VLwpX8sbRA0aE/AxwMPPKDEW3rUMrGYqXzzzTcqgZmfn596P3HiRAwaNEi9rlOnDv7+++98Cc1q1qypImmLWlYYXbt2VaIrPXqPHTuGvXv3oiQwednNN9+s7CZYZkEQyo/0uEicXPImEk7vR5MJsxDUXj+zwMXb39JFE6wMdjpokRCXlqp8bat7+1q6SIIgCIIg2AA/bjqB+JQMZYlwc1fz50dYteovHD9+Gr6+3nj88ftvWD6kUyiWrg/DuasJ+G7dMTw0In/S7vKmU6d2GDSoL/76ayPeeedjfPnl+2XaXmZSHK5sXa5e17v5QVRxtKxc5OjihjqDJuH0T+/h4t/foHrnYXBy87BomQShopFI2xLi7upU6MPV2dH0dV3KfgOcOXMmvvvuu7wp/0VBGwVO/z937px6Tz9cJh4bNWoUGjRooCJjGV2rERcXp9blspJQv359rF+/HpmZ+pFF+s5qEawUgWnFwCRn5LPPPsOYMWOKXVYUjDZmwjRaMZQE2i7cfvvt6jF+/PgSfVcQhMKhVUnU3r+w793JSrB1cHGDLjvLrD5mf+05L16odmKRIJG2giAIgiCYQkp6lhJtyb1DWqkkZOaEHrbz5n2pXt9//3j4+984yOzo4IAHR7RRr3/ZfBIJKfr8K+bk+ecfUwPgq1f/oxKklYXLG39AblYGvOo0hX/zrrAGqncZBvdqIchKjseVLb9YujiCUOFIpK0Nw+RZjz/+OGbNyu9fQ7uAn3/+Oe89I2X5cHZ2Vp6znNZBUZU+trRZ0ERMJiujUEu/WL5nlCytAwpCwbdx48YqCVhCQoJKNDZhwgS89dZbeOSRR1TkK31muT96286fP199j8LqV199hZEjR6rtU8z9+uuvi11WFLRfKA0UqP/55x+VgO3HH/U+xbfccos6B4IglI6slESc/vl9XDuwQb33rtscTca9CPdqtc2yv/NXE/Dat9tw4lIccnU6DOmkT7Qo2B4+rteTkWWkW7oogiAIgiDYAMu3nFQWBHWCfNCvrXnamoasWbMeJ06cUUnA7r13bKHr9WhRC41D/HHyUhx+3nwCU4bqLRPMRZMmDTFixCCsXPknPv10IT799O1SbUeJolt/Va/rDppsNCG5JXBwdFLRtie+n40rm39CrT53qAhcQagsiGhrY9A2wJCXXnpJPTQ41Z8PY9x3333qYQwmAfvzzz9NKgNF3UuXLhld5urqii+/1I9AGoPCKB8lXWbI4sV6Y/SC9O3bNy+5GDEWeWd4/iheC4JQPsSf3o9TS95AZsI1tq5Qd/Ak1B4wzizTqnJzdfhl60l8tvIAMrNy4O3hAvdymL0gWA4fN33jO0lEW0EQBEEQiiEtIxs/bNDP0Jw4qIWKcDUn7Fd++OEX6vWUKWPh6+tT6LoUO++5qQVmLtqKnzafxJh+zeDpZl6v3UcfnaJEWz6effZh1KtXp8TbuLzpR+RmpsErpLHVRNlqVGvXH+fXfIWM2KuI3PkHgnsVbkMpCPaG2CMIgiAIZSc3Rwm2jKpt+8RnqDNootl8sPadisSHv+xVgm3npjXw7fPD0KeN+SMshAqItBV7BEEQBEEQiuG3baeUl22tQC8MbF/X7Pv7++9NOHbsBDw9PXDffcVb6/VuHaIigBkJ/Ou/p8xevhYtmmDAgF7KwuGzzxaV+PtZKQl51gOqDW8lUbYa7FOE9NdHN1/a8ANyc7ItXSRBqDBEtBUEQRBKRXZact5rv8Yd0WzSa2j37AJ412lm1v12bFIDI7o1wNO3d8TcB/uhmq8kJLB1vK9H2oo9giAIgiAIRZGZnYMl68PU6wkDW5jdy5Z8/PEC9Tx58hijXrYFYeTv+IHN1WtGBGdkml9kfOwx/Yzan35aiStXIkv03YhtvyEnIw2ewQ0R0LInrJEanYfC2TsAGXGRiN77XwJzQbB3RLQVBEEQSoQuJxvhf32N3bPHID0mIu/zwDZ9zeIxlZiaifd+3I24pP8EvefHdMHoXo2tLhJAKFukbVKGRNoKgiAIglA4G/aHIyYxHdV83TGkU6jZ97d//2Hs23cILi7OJkXZagzuGIrq/h6ITUrH6l36ZODmpFOntujWrSOysrLxxRffmPy93OwsRPyr97IN6TfGatvWDs6uys+WXFy/BLrcXEsXSRDsX7TdvHmzSowVHBysbg6//qq/WRjjwQcfVOt8+OGH+T6PjY3FuHHj4OPjAz8/P0yZMgXJyf9FfwmCIAjlR1r0JRz8+DFcWLMA2amJiN5n3pHuPSeuYuI7q7Hi31OY8+Nus+5LsLynbWK6RNoKgiAIglC4t+yPm06o16N6Noazk6PZ97lgwRL1fOutQ1GtWlWTv8cI4Lv6NlWvf9ly0mi+lfLmoYcmqedly35DSkqqSd+5dnCjsjhjFGtg236wZmr2GAlHN0+kRV5A/Mk9li6OINi/aJuSkoI2bdrg008/LXK9FStWYMeOHUrcLQgF26NHj+Lvv//GqlWrlBA8depUM5ZaEASh8sGGJqdO7XvvXiRdOApHNy80GT8TIQMnmGV/nEY2b/lePPHZekTFp6J2NW+MG2Be2wXBCjxtJdJWEARBEIRCOHr+Go5fjIWLkwNu6d7A7Pu7ejUKv//+V14CspIyrHN9lSz33NUE7D8dBXPTr18PhIbWRmJiEpYv/8Ok71zZ/LN6Du45Cg5O5k2YVlac3DxRvfNQ9frKluWWLo4g2L9oO3ToUMyePRujRo0qdJ3Lly/jsccew/fffw9n5/w3kbCwMKxduxZfffUVunTpgp49e+Ljjz/GDz/8gCtXrlTAEQiCINg/mYkxOPrlczj90/vIzUyHb8N2aD99EYI6DDLLFKqTl2Jx7/t/5kVSjOzREIumDUWL0MBy35dgXZG2CZKITBAEQRCEQvhp80n1fFOHUPh7lb8lV0G+/fYnZGdno3PndmjVquTBA94eLhh83cLh5+tlNycODg6YOPEu9frrr5cVG92beP4oksLDUMXRGTW63QJboGYPvXYUG7Yd6TGi+Qj2j1V72jL74YQJEzBt2jS0aNHihuXbt29XlggdO3bM+2zgwIHqZrVz507YI6GhoThw4MANnyckJOCBBx5A/fr10bRpU3Tq1AmrV6/OW56VlYXHH39cnUdGNzdv3hxz5841uo8XX3wRrVq1Qtu2bdWDIrjGpEmTUKtWrbxl/G00UlNTcffdd6Nhw4Zo3Lgxfv75Z5OWGXL+/HklAt166635Pn/55ZfzWWi88sorePLJJ/Ots3jxYowcOdKEsygIQkmI+Pc3xIXtQBUnF9S/9VG0eugDuPlXN8u+th29jPvn/oXzVxMQ4O2GOVP7YNqdneHu6mSW/QlWFmkroq0gCIIgCEaIjk/FhgPh6vXtvZuYfX/p6RlKtCVTpowr9XZG92ysnrccvoSrsSkwN3feeSvc3d0QFnYKO3fuK3Ldy5v1xxfU4Sa4ePvDFvAIqq0SIOP6LEBBsHesuhf8zjvvwMnJSYmNxrh69SqCgoLyfcb1AwIC1LLCyMjIUA9DQVETifmwdgqWkyNow4YNUyLq8ePH1TmgsEu/4IULF+Kmm25SXsCMWt6/f79anp6ejjNnzhg93meeeQavv/66es3vUOjt378/AgMD1b6effZZPPHEE/nKQ+bMmQMXFxecPHkS586dQ7du3dCnTx9UrVq1yGUFj83X11etFxERgerVq6vPli5dqoRk7dhZDj4My699bg2/oTWVRRDKSq0BY5EWcxm1+t0Nz5r1wTF7zfy/vK/1VvUCVWKJxiH+ePaOTvDzcq2w/yP5f7Ucfu4e6lkibQVBEARBMAbzG+Tk6tC2QZBqJ5qbVav+QkxMHIKDa2DIkNJ7vdYP9kP7RtWx71Qkftt2Gg/c3AbmxM/PB7fdNhzff/8LFi36AV27djC6XmZSLGIOblKvg3uPhi1BKwd62l7duRp1Bt8LRxdXSxdJECqfaLt3717MmzcP+/btK/fpt2+99RZeffXVvPeOjo7o168f4uLi8om51ghFBUbVMgGbxqZNm5QQ+tNPPyExMVF9VqdOHSWsMiK1Q4cOOH36tBJDteWkZs2a+bZjiPY5RVvuMyYmRkUw8/zQi9jY9yisUhzmMu6Lwux3332noqWLWmZIfHy8EoBomfG///0Pjz76KDZs2KAig6Ojo1WSOW4jLS1NCc+G5WC5GFHMz9577z3lcUxycnKUmM1rqXbt2qgIeM6SkpLUsfC8CYItkRp+DLF7VqPWyKdQxUGf4CFwyEPg3TGjwP9+Wa91fm/v6Wvo0DAw717/9qSO8PVwQW4m7zXmj0goOIAnVDy+bu55om2uLhcOVeS+KQiCIAiCnuycXPyx86x6fVuvRhWyz6VLV6jn8eNvV0FPZWF0r8ZKtF257TQmD2kJFzMnUJs0aYwSbdesWYeIiEjUrHnjDLmo3X9Cl5sD77ot4FWrYs5peRHQojtc/asjIy4S1w6sz/O5FQR7xGpF2y1btiAqKkqJjxoU3xgFSvGP0+hr1Kih1jGEnjMU7bisMGbMmIGnn346X0ed4qG/vz88PT2NfkdFkmWaN6u1g4tbsQI1RRGKnowm1qAgS4uIgsc8YMAAJU5zXYqfjMbt1asXevTooSJn77jjDiVYG4PewJ999hkuXbqEL774Ak2a6KeguLq6YsGCBfjxxx+VAMqIXEb4agIvo2G1stEGgb8F3xe1zBCKyjwHDz74oCrvrFmzlJUCrR8Yee3l5aW+4+7ujkWLFuWzweD22rVrp5a/+eab6kH4XYrEtIWoKChk8Th4TYloK9gKudmZCF+7CJc3/qCmHKU3aotafe4027Uem5SOOct24d+jV/D07R0wqoe+wVjgtlBh8P4mWAY/N32kba5Oh+SMDPhcF3EFQRCEygXbFQxIYV/wwoULqp9WrVo11canDV5FBWAI1sWu4xG4lpAGX09X9GoVYvb9nT17ATt27FVt2zvvLLvXa8+WtdRMsuiENPx75DL6tf1P4zAHzZs3Rpcu7ZU9wk8//Y7HH7/vBm3j6g59gFONrsNhazCopGb3W3H+jy+URYKItoI9Y7WiLUVUVsyGDB48WH0+efJk9Z5CHCMzGZXLaFKyfv16VdkzMVlRHXPDzrkmXPKmXJjokJORhh0vmPdm0P3tP+Fw3devKAqWk4IJHwXLrr3nMwVT2iFs3boV27ZtU2Iuk7v98YfxrJKM0uXj4MGDGD9+PIYMGaKsDCiEMkKX21yxYgWGDx+OU6dOKTG1YNkKlquoZQXLTLE+JCRE+fIyQpaRuhRttW3wu+PGjVMCvqGnLT1vDbfJRHcXL15UUbcVLZ5qxyeirWALpEScxYnvZiPlymn1vnqXYajZbYRJ129prnX6er39w07EJ2fA2dEBdCaw9P+KpfdfmXFxcoK7szPSsrIQn54qoq0gCEIlg7Po3n//fXz++ecqEINBIcHBwSpQgwEqbOPff//9GDRokArq6Nq1q6WLLFQgq3boo2wHdQw1e5QqWbZMn0elb98eRqNUS4qTowOGdKqHb/85piKGzS3akjFjRinRdtmy3/DYY1PyLUs8dxhp0Rfh4OKOwLb9YYtQqD2/ZgGSLhxDauQFeFSva+kiCYJZsGgPlVPd6b2qJdbiFH++Dg8PVwJhy5Yt8z2cnZ1VNKkW9dmsWTMlJrIC37VrF/79918VUTpmzBhVyVcW2rdvr4RrWgMUTNTGZRr0lGWE7cyZM9UINgXRwuwRNBidysRjGzduVO/5WhM2aGHg4+ODEydO5AmtHBHXYDS0Fild1LLCoDjPB3/P0ogp33zzDZYvX64idcs6pUUQ7BV6017a8AP2v3+/EmydPH3R7N430HjM83ByMz7zoCykpGfhraU78fxXm5Vg26CmH756ZjDu6GP+hBKCbUTbJqSJr60gCEJlg7PwDh06hC+//FLNvGM/5pdfflF2auyzsH/IABTOGmTfgOsJlYO4pHRsPXJJvR7RtYHZ98eZu4xOJWPGlF+S62Fd6qvnnWERiE4wvyXX8OED4eHhjvPnw7F79/58yyJ36gO3qrXtB6fr7S9bw8WnKgKa6gP1InevtXRxBMFsWFTJ2rNnj/KS1dAsCyZOnKiiJk2B0aIUamkFQGFv9OjR+Oijj8xiXcBIWHPCfZQGCrGcKvTUU0+pyFMtEdkbb7yhEpGRzZs3o1GjRipKllDkpY2An5/fDds7duyY8pAlbBwxeZn2nnYJjIAlO3bsUF63DRs2VO9ptzB//nw18k0BnkIvLRaKW1YYI0eOVOIuI31Lyj///KOsG3jcWhSwIAg3cvbXj3Flyy/qdUDz7mg0ZjpcvM3jT3DswjW8/PU2XIlJBp1gxvRrhvuHtYars/kjJgTb8LWNSEpQkbaCIAhC5eKvv/5SATlFUbduXWVzx6TIFHGFysGfe86pBGTN6gSgQfCNfdfyZv36rYiMjEbVqv646aY+5bbdOkE+aF2vGg6di8afu89j/EB9/9pceHp6YMSIQSrS9scfV6JjR72lYXZ6CqIPbLBZawRDqncegthj2xC150+EDrsvLxeHINgTFhVt+/btq/xUTIUCXkEoPC5ZsgTmhtN/HU2wLqgIaBPBqGMNiqdr1qzBtGnTlDDLiFpvb28lknJdwobNk08+qZJ3cTmFzN9++81oBOv06dOVsMp9UAD+5JNP8hpRkyZNQmRkpLKU4HQlJj+jxy7h/u+99140aNBALef3AgMDi11WGLSweO6550p1jihY0wNr6ND/LC04Sl+ZIrAFwRRq9hiJ6P3rUHfY/ajR9eZyT/xoCG/3kXEpqO7vgZfGd0O7hmWfbibYD77u1yNt0yXSVhAEobJRnGBrCPso7FMI9g+1gt+vWyMM71Ixv/kPP+itEUaPvhkuLv/1ucuDoV3qKdGWFgnjBjQza7ub3HXXSCXarlz5J155ZZr67Nr+9SpXj3tQHXiHtoQtE9Cih5olmJlwDfEn98K/aWdLF0kQyh2ZM25jGBOuNYqaJsRoVVMjVun/WlQEa2EwiduyZctKvMyQ0NBQ5VNsDM2igbzyyis3LKegzAfZsEE/eigIQn6yUhIQf3IPqrUboN7T/6nTSz/CsZSR/qbYIXi66Ru8LUIDMXtyT7RvVB1e7i5m2Z9gu/hd97GNT5NIW0EQhMoGrRFoh8eAEr4uitatW1dYuQTLEhYeg/NXE+Di7IibOpjfszQmJhbr1m0pd2sEjQHt6uLDX/YiPCoRR89fQ8t61WBOOnduh9DQ2jh//iJWr/4H/fv3UFGppEaX4WYXjc2Ng5MzgtoPVLMGI3etEdFWsEtEtBUEQagkxIbtxKkf3kZmUhxcfIPgW7+V+twcgm1urg4/bz6BhWsP47MnbkL9mvrpbL1bS9ZnwTi+mqetRNoKgiBUOph47OrVqwgKClKvKSYZzsjU3vM5JyfHomUVKo6/9upzovRpFVIhA/6///638rRt1aoZmjTRWwCWJwxk6Ne2NtbuPq+ibc0t2vL/5Y47bsGcOZ8qi4ReHRoh6fwRLkC19vmTvtsq1TsNUaLttcNbkJ2WBCd3b0sXSRDKFUmVLQiCYOfkZKbj9M9zcfSLachMjIF7tRA4uriabX+0QHjq8/WYt2IfktKysGrHGbPtS7Af/NyvR9qKp60gCEKlg9Zs1apVy3t99uxZ9aw9tPd8FioHObm5WL9fL9oOrIAoW/Lbb2vU88iR/1nslTdDO+kTkm04eBFZ2eYfgLjjjhHqeceOvbi8Ux9l69ugLVz9zCsYVxSeIY3hUbM+dNmZiN4vs20F+0MibQVBEOyYpAvHcOL7N5AWfVG9D+41GqE3P2g20favPefx/s+7kZyWBTcXRzx6a3uM7FH+kQqC/UbaJkqkrSAIQqWDScaMvRYqL/tPRSEmMR3eHi7o0lSfTNucXL4cgV279qvo1FtuGWK2/bRrFIQAbzfEJqVj94mr6N6iFsxJrVo1lU0Cjy3mwAawtWUvUbaEv1dQh5twftX/EH1gPWp2v8XSRRKEckVEW0EQBDvl0vqlOPfHF0BuDlx8A9H47hnwb9LJLPtKTMnAB8v34Z99+oiI5nWrqmRjzJQrCCXytBXRVhAEoVITExODqlWrqtcXL15UeTvS0tJwyy23oFevXpYunlBB/H29TdmvTW04OzmafX+//bZWPXft2gHBweZLluvo4ID+7erg580nsW7/BbOLtuSWWwbjyvGD8MiKRxVHJwS27gN7olrbfkq0TTh9AJlJsXDxDrB0kQSh3BB7BEEQBDvF0d1LCbaB7fqj/bTFZhNsyZrd55Vg6+hQBVOGtsLnT9wkgq1QInyui7YJkohMEAShUnL48GGVlJi+tk2bNsWBAwfQqVMnfPDBB/jiiy/Qr18//Prrr2Ytw+bNmzFixAgEBwerCL6C+2PSY35u+BgyxHxRmZWVzOwcbDoYrl4P7BBaIfv89Ve9NcKtt5r/9xzYXh9NvvnQJWRkZpt9f8OH34QedfWewK51W8PZ077a6G5Vg+Fdpxmgy8W1g/8lLxcEe0BEW0EQBDuBCToy4qPz3tfoejNaPfwBmt3zitkbZ7f3boShneth/pM34d4hreDkKNWLUDL8JBGZIAhCpWb69Olo1aqVEk779u2Lm2++GcOHD0dCQgLi4uLwwAMP4O233zZrGVJSUtCmTRt8+umnha5DkTYiIiLvsXTpUrOWqTKyMyxC5UWo6uOOtg3M77166tRZHD16Ak5OTkrgNDct6gaiur8HUjOysT0swuz7q1atKvo38lKvT6Ton+0NBqmQ6P3rLV0UQShXpFctCIJgB2QmXMPRL6fj4LyHkJ2WrD5j9Idfow5m2d/xi7F4YcEWZGTl5E31mjmuG5rXDTTL/gT7x1cSkQmCIFRqdu/ejTfeeAM9evTAe++9hytXruDhhx+Gg4ODejz22GM4fvy4WcswdOhQzJ49G6NGjSp0HVdXV9SoUSPv4e/vb9YyVUY0u60B7eqoNmZFRdn26dMNAQF+Zt+fg0MV9G+nj7Zdd/1YzUlSeBh8nbKQnqXDkg0nYI9Ua9NXPSeeO5wviEUQbB0RbW0MThnidKHs7P+mUXTs2BEbN+qnAbzyyit48sknjX538eLFavS6WbNmaNiwIWbMmIHMzMy85cuXL0eHDh3Qtm1btY/+/fsjNzf3hu1s375drcNHixYt1Kh3RkaGWsZyuLu75y3ngx5UGgsWLECjRo3QoEED3H///cjKyjJpmSEceXdxcUFUVFTeZ8wky8bcyJEj1fvz58/Dz+/GCpciVnx8fDFnWRBsC04D2jtnMuLCdiIzOV4lHzMX2Tm5WPznEUyd+yc2HbqIb/8+arZ9CZUz0jYtKwuZBnWcIAiCUDmIjY1VIijx8vKCp6dnPkGUr5OSkmBp2N+hhUOTJk3w0EMPKQ/eomA/KTExMe+hHQP7WWV9cJZVeWzHmh5p6ZnYeuSSOkf0fq2I8/LHH3/neb9W1HEOaFtb7fPfo5eRnJZh1n1dO7hJ7Wv/1WwcPHJSRRZb+ncu74ezbzV4h7bk1EOVkMzc10xlech5yTXreTEFSURmg7Dip8BJsdRU6ANFP6jVq1ejXr16SE1NxYQJEzBlyhR8++23amrP1KlTsXfv3ryMrfv27VMiZ0E4ZYgj4c7OzupCGz16ND777DM89dRTajkbMPSgKsi5c+fw0ksvqe1Wr14dt956qyrXI488UuQyY7Ru3VqV+5lnnlHvFy5cqARnQahMMKL2zPJ5iNrzp3rvWasRmox/CZ41zOP9dSk6Ca9/tx1Hzl9T7/u1rY07+jRBTkaKWfYnlBxO55wzZw6uXr2q7tUff/wxOnfuXOj6H374IT7//HOEh4cjMDAQt99+O9566y24ubmhovFydYVDlSrI1elUtG2Ql335rQmCIAjFU7DvYawvYklojXDbbbep/tSZM2fwwgsvqOhcBrU4OhpPlsV69dVXX817z/Xoz0vLBy3wpTSwH0YBmOIBg1fshZ0nopCemYNAHzfU8NKL+eY8L+fOhePUqXPKGqFDh1Yl3l9pCfTQoWaAByJiU/HXzpPo3bKmWfbD8xB1QB/gFePGpGensGzZr3jwwXtgb3g07oqk80cQsfsvuLUcgMr+v1RW5LyY97xQkzMFEW1LSE5G4V57VTh1x9nVpHVRxQGOLv+tWxIYTfviiy8q0dXDQx+ZVByvvfaaEkHZwCD8Ht/Xrl1bbY8XHRsQAQH/ZVps37690W0Z7pORuoykNaVB9fPPP6usr9oI+oMPPog333xTCbNFLTPGxIkTVSZZirb8p1m2bJmaPrVpk34UsTgoKjORgGGkLrf18ssvm/R9QbA08acP4OSSN5ERd1XdT2oPGIc6gyfBwcm53PfFCun37Wfw0Yp9SMvMhqebM565vSMGdQxVy2JFtLUKeB98+umnMX/+fHTp0kUJsoMHD8aJEydURFBBlixZgueff14NenXv3h0nT57MS7Ayd+7cCi+/QxUH+Lq5Iy4tVfnaimgrCIJQ+WA9RPsBkp6ervoEjLglZRE4y4sxY8bkveYMRgaScJYgo28HDDAuEnF2I+tnDS14hpHD2rGVBvaBWGdzO/YkqOw7d1I9921TB1WrVjX7eVmyZIV67tmzM+rWrYOKhBYJ368Lw/5z8RjZu4VZ9pEScRZZcRGo4uSCRr2GARvmYcuWnXjhBeOzc20Zr25DEfn3AqRfOQXPKtlw9b+x/VuZ/pfKipwX854Xra4rDhFtS8i25wcXusy/WVe0nPpu3vsds25Fbma60XV9G7RF60c/KlUZGD3F0VlGzlK8LQ7aCFy+fBndunXL9zkrQdoRMLqV0bI9e/ZUUbZ9+vRRHfixY8eiVi2Oxt0I7QcYDcsRZiYIoGCqwc8o+FIEnjx5ct4yRnJpUbya1QM/K26ZMSg2U+DduXOnGqWmRURBPykK0bRnMAY/16KBN2zYoOwY7rvvviLPoyBYExFbVyjBltlSG499Eb71W5ltXwvWHMaiP4+o1+0aBinv2hoB+k4GRVvBOqDQynsZ77uE4u0ff/yhRFmKswXZtm2b8g3kvV677959993qvmopNNE2Pk18bQVBECobDMowZPz48Tesc8891hUdWL9+fTVT5fTp04WKtuyYG3bOtYhczau3LFA4KI/tWAu04dp29Ip63adN7VIfV0nOy9q1G9TzsGEDK/w89m1TW4m2TEaWnauDi5PxaO2yEHt4i3r2b9IJvXv3hsPsj3HkyHFcuXIVISHBsCfc/KrBJ7Sl8rWNC9uG4J63Vdr/pfJCzov5zoup3xXR1kZ5/fXX1ZRXjj6XB7xgfvnlF2Xuz2jVNWvWqEQAe/bsUf63BWHn/uDBg0hOTlYNKvrhcuSZYu2lS5fg6+urnocNG6YaMnfeeSfKm3vvvVfZRFC0pbUDhWlDvL29b7BpKBgRfOTIESVwrFq1qlCBWhCsBQqk2jXc8I6n4epfDXUG3wun616g5uLmrg3w67+nMX5gM9zZp6lKniBYF5z1QHsbRvMY3tcHDhyopmwag4Nz3333HXbt2qXqE844oIUOo38sha+6lmNUpK0gCIJQuVi0aBFsDfZ36Glbs6Z5prZXNg6eiUJiaib8PF3Run41s+/v8uUIHDx4VLWvBw3SJ7KqSJrWropAX3dcS0jD3pOR6Na8/EXUmMOb1XPVVr3gFOCHTp3aYufOffjzzw2YMmUc7I2qrXoq0Tbm8NYSibaCYK2IaFtCur+t944szB7BkK6v/Vb4hqqUbaSCoimjo5jdtDg4LZaCJDvuFFE12MA4depUPhsEJiDjg3659GxauXJlvuk8BWGSAIq133//vXr28flvOmtISIiK2tqyZYsSbevUqaOicA2jdfkZKWpZYTDp2HPPPadGrjmy/c0336AkMCMtt8EGYsuWLUv0XUGoSHS5Obi88Uc1vanx2BdUw9LZ0xf1b33ULPtLSc/Cv0cuK/sDwqjan2bdAndXqTKslWvXriEnJ0d5ghvC94Vl2mYdwu9xlgUHBJjgkgOB9OczBqelGk5N1XyYSmKkb2jcX1ikLYlLSzF5mxVJceW3Zmy57LZeflsuu62X35bLbivlt+aylQYGpDBqVoN5NxgEQgs5PuhNyxmKnPHHvsv06dNVgAvtiISys/HQRfXcs2UtODmaP7Jv7dr16rlz53aoVq3kVgxlhYEQvVqGYMW/p7Dl8KVyF23Toi8h5coZwMER/i26Iyk9G4MH91OiLSOM7VK0bdkT51Z+joTT+5GdlgQnd29LF0kQyoT0wEuIo6u7WdYtDTNnzkSzZs1UQrDioI0CPVu5vpaIjNGpo0aNUj5MjFKlUMqpsoTRq2ykcFlB2JChlQH3y+iuFStWKD8nwoRmFAkY4UV7AkawMtkZ0SwY6KHLdTh1V/OFKmpZYTBRDi0i6LFb0rB0lo22Dmx40WpCEKyV9NgInFjyJhLPHFTvgzoNgX/jDmaNcJj9/XZciUmBj6cLujbTNx5FsLU/6L9H73AmkqQHLu/tTzzxhJrJwcSQ5kikUpxxv/v1Ac2I2JgKSwRSWRIy2HLZbb38tlx2Wy+/LZfdVspvaiKV4mByL1PhDD9zwVmGhn0DLXiF1g1M3Hno0CF8/fXXiI+PR3BwMAYNGqTqTVO9CYXCyc3VYcuhS3nWCBXBmjV60XbIkP6wFL1a/yfaPntHp3Kd0RZz3RrBr2E7OHv4sGOhRNvXXntfCbexsfEICPCDPeFerTY8qociNfI8Yo/tQFCHmyxdJEEoE9ILt2FoO/D4449j1qxZ+T6nZQATexk2NvigyDpixAgVjUWxlUIpGxmEEVZMVkahliIo37NxQt/agqxfvx4fffSR6rBzPUa5ah18WiywQcPsm1x2xx135Pkr0vOJHX5NGO7bt6+K6C1uWXk18Ao29hh9xizrfBBGmJWX3YQglEum191rcWb5PORkpMLBxR0NRj0Gv0bGEwSWlazsHHy15jC+X3cMtKmtGeAJD9fyT2ommK8+4D05MjIy3+d8ryV4LAjv27RC0Py8mVAlJSVFDehxoK+gQFAeiVSKM+4P8tV3HDKrIF9iTGvBlhMy2HLZbb38tlx2Wy+/LZfdVspfXmIlrdUM20AMCuFnzFtBaAFEobS0bX9TYR+kKL/+P/8sfNalUDaOX4xBdEKaChTo0Nh426U8iYmJVcIlGTrUcqJt+4ZBKslvbFI6jl24hpb1ys8W4tqh69YIrXvnfVanTi00b94Ex46dwLp1W3DHHSNgb9AKgqJtzJGtItoKNo+ItjYGo2ELdroNI6IYqcqHMdgxLyzZFiNnTW2EsEPPhzEeffRR9SgMJsnho6TLCkaHFZZtlg/NPoINu4JojTAK0gWTHQiCtZCVHI9TP72HmOsNLRrqNx73ItwDzeO7fPZKPF79bhtOX9b/zwzvUh9P3NZBNSAF28DFxQUdOnTAunXrlO2L1tnn+8LuyRRdC4oAWnIUYx3W8kqkUpRxf4CHl3qOT0+zWoHClhMy2HLZbb38tlx2Wy+/LZfdFspfXuUy9LOl/Rmt1TjzTqtrGHTC5MaGVmyCfbH5epQtLQJcncs/IVdB/vlni2ortWzZFLVrWy63ibOTI7o3D8bf+y5g06FL5SbaZibFIunC0TzLAEOGDOmnRNs//1xvn6Jtyx64+M+3iAvbgdzsTDg4uVi6SIJQaqyz9hcEQaikUCw78uV0JdhWcXBE6PCpaP3Yx2YTbH/bdhpT3l+rBFsmfXjz3l54YWxXEWxtEEbBfvnll2raZlhYGB566CEVOavNdmDGbcNEZZx5wZkRP/zwg5pl8ffff6tBQH6udZIrGn8PjzxPW0EQBKFysnDhQjz77LP56iK+Zj3HZYJ9su3oFfVMj9eK4J9/9MERN93UB5aGFglk86GLRUZ6l4S4sJ3q2SukMVx9A/Mto0UC2bBhG9LS7C/5q1ftpnDxDURORhriT+mjqQXBVpFIW0EQBCuLqKl38wM4s/wjNBn7ArxqNzHr/rzdXZCZnatG+J+/uwuq+pjXi1swH3fddReio6OVZc7Vq1fRtm1brF27Ni85WXh4eL6IKPqi83rjM33Nq1WrpgTbN954w2LH4O+ut1mISxXRVhAEobJCizXamDVpkr8NxM/sLfGZoOdqbArORMTDoUoVdGlW0+z7y8zMwubN29XrAQN6wdIwhwQTr126loyL0UmoE1T2iPLYY/rj82/e7YZlLVo0QUhIMC5duoKtW3dZhXBdnjBBPKOLI/79FTGHtyKgWVdLF0kQSo2ItoIgCBYm8cIxZMRFoVrbvuq9X6MOaD9toYq0LW84en8tIQ3V/PQRjf3b1YGPR390aFxdCXiCbVOURU1Baxl6j7/88svqYS0EaKJtWvkkthEEQRBsD84QYSLjM2fOoHPnzuqznTt34u23386bPSLYF9uP6aNsW4RWha+n+ZO67dq1D8nJKQgMDECbNi1gaTjDrW2DathzMlKdi7KKtrnZWYg7sVu9DjAi2rLNT7H666+XYcOGf+1OtCUBLbor0ZYWCez/SD9HsFVEtBUEQbAQuTnZuPjXNwj/51vlteQV0ijPBsEcgm1iSgbm/LQb+05F4tvnhiHgelRtxybmT/YgCKYg9giCIAjCe++9p5Jovv/++4iIiFCf1axZE9OmTcMzzzxj6eIJZmB7mF607da8YrxlmYCL9OvX02r8ounlq4m2d/VtWqZtJZ47hJz0FDh7+cG7tvFt9evXQ4m269dvsUtR07dBOzg4uyAjPgqpV8/Bs2Z9SxdJEEqFddyhBEEQKhmpUeE4+NHDCP9rMdVbZZjv5GG+5Bo7w65gwjursX5/OJJSM7H/TJTZ9iUIZbVHSM/ORlpWpqWLIwiCIFgAimjTp09X1j1MLMwHX/MzS3muC+YjIysHe09eVa+7twiukH2uX7/VaqwRDEVbcuB0FNIyssvHGqFZV2UVYIwePTrB1dUFFy9ewZkz+ZOd2wOOLq5KuDX09xUEW8Siou3mzZuVf15wcLAa2fn111/zLX/llVfQtGlTeHp6wt/fHwMHDlRTYwyJjY3FuHHjVCZRPz8/NZUmOTm5go9EEATBNDiSfWXrCux/bwqSw4/Dyd0LTSa8jKYTZsHZw7vc95eemY25P+/B0/M3KlsETrf635ODMKBd3XLflyCUFQ9nF7hc75DHiq+tIAhCpYd9PD4E+2X/6UikZ+agmq87Ggb7mX1/Fy5cwunT55RNVJ8+N1oHWAq20YOreiIrJxd7T+lF7NISe2xHodYIGh4eHujatUO+yGN7g6I1iQ3Tnw9BsEUsKtoyq3WbNm3w6aefGl3euHFjfPLJJzh8+DC2bt2K0NBQDBo0SCVa0aBge/ToUZX1etWqVUoInjp1agUehSAIgmnocnNw9MvncOaXD5CblQG/xh3RfvpiBLUfYJb9hYXHYPKctfhly0n1fnSvxlg0bQia1a1qlv0JQlnhAG5eMjLxtRUEQag0DBkyBDt2FC+sJCUl4Z133im0/yjYHtuOatYI+kAuc7Nu3Wb13KlTW/j4lH/ARGnhsXe9Hm2refyWhrToS0iLCldWa/5NOhW5bv/+vfJFHtsbAc26qOfEs4eQnS7BAIJtYlFP26FDh6pHYYwdOzbf+7lz52LBggU4dOgQBgwYgLCwMJUZe/fu3ejYsaNa5+OPP8awYcOUFxIjeAVBEKwFNp48gxsg4fQ+hN78EIJ7jip0ylJ5sHLbaYRHJSLQ1x0vjO2KLk3Nn41XEMqKv7sHIpMTESu+toIgCJWGO+64A6NHj4avr6+aicm+Hftybm5uiIuLw7Fjx1QQz+rVqzF8+HDMmTPH0kUWymkG2vZjl9Xr7i0q1s924MDesDa6NQvG8i2nlGhbWp9ZzRrBp35rNaOvKPr374mXX34XO3fuVYnZvLz0A+f2gnu1ELgF1kL6tcuIP7kXga2t7zcXBLtJRJaZmYkvvvhCVeSMziXbt29XlgiaYEtooUAfJNoojBo1yoIlFgRBALLTktXDLUCf7KvukHtRvfNQeATVMcv+DBt4j45sD1dnR9w7pBV8KiATryCUBwEe1yNtxR5BEASh0kCLu/Hjx+Onn37CsmXLVL8vISFBLWO7pnnz5hg8eLAK1mnWrJmliyuUEwwuuBKTAmdHB3RoXN3s+0tLS8P27Xuszs9Wo32j6nBxckBkXCrOXU1A/Zolt4uIO7FLPQdctwYoivr16yI0tDbOn7+If//dhcGD+8He4Hm4suUX5Wsroq1gi1i9aEvLgzFjxiA1NVVlDaUNQmBgoFp29epVBAUF5Vuf3jQBAQFqWWFkZGSohwa3TXJzc9VDEMoKryOKZ3I9VW4STu/HyaVvwcU7AK0e+wQOjk6AgyPcAkPK/drg9bZy+xnsPnEVr0/qoTo47i6OeHxUe7XcXNeiPV3r9nAM9oDYIwiCIFROXF1dlXDLB6FoS5GtatWqcHZ2tnTxBDOw67i+z96mQTV4uJr/N965cx8yMjIRHFwDDRvWg7Xh5uKkhNsdYRHqUVLRNjc7EwlnDqrXfsVYIxhG2y5cuFRFINujaOt/XbSlr21po5cFwZJYvWjbr18/HDhwANeuXcOXX36JO++8U0XRFhRrS8Jbb72FV199Ne89s5ByP5x6YyjmCkJZxB96brFiYOS3ULlggyl64xLE7vydcip0VRwQdeE0XPxKf98qirjkDHz8+1HsPX1NvV/1bxh6NNdH9pobe7rWtQE8wfL2CCRO7BEEQRAqNZxhyYdgv+w+EaGeOzWpGAuvTZv01gFMQGat4h19bZVoe+wKxvYvWVR54rkjyM1Mh7N3ADxr1jfpO/366UXbDRv+tUtR07dBWzg4uyAzIRqpEWeVVZ0g2BJWL9p6enqiYcOG6tG1a1c0atRI+drOmDEDNWrUQFRUVL71s7OzERsbq5YVBr/79NNP5+uoT5gwAf7+/mp/glAeQpZKqOPvb/NCllAyUq6cxsnv30Dq1XPqffWuI1Dvlofg6KoXosqbTYcuYs6Pu5GQkqmmUz1wcxsM794YDg4V0+Cyp2udET6C5ZFIW0EQBEGwf7JzcrHvtL4v36lJxQQbbNmiT3bXq1fx1gGWovP1c3H4XDQyMrPh6mK6ZBN/Um/94N+4o8l5M7p16wAXF2dcuXIVZ89eQIMGobAnHF1c4dugHeKO70Tcid0i2go2h9WLtsYEAi0atlu3boiPj8fevXvRoUMH9dn69evVOl266DMFFtYxN+ycM9KWUHCwddFBsB4oZMk1VXnQ5ebg0vqluLB2IXQ52XD28kejMc+haovuZtlfSnoWPvxlL1bvOqveN6rlj1kTupXK+6qs2Mu1buvltxf8PSTSVhAEQRDsnSPnryEtIxt+nq6qHWtuIiOjERZ2SrVbe/UqXCuwNHWCfBDk54Go+FQcPBuNziVIJBx3fLd69mvyX86f4nB3d0fHjm2xbdtubNmy0+5EW+18ULRlMrKQfmMsXRxBKBEW7aEmJycr6wM+yLlz59Tr8PBwpKSk4IUXXsCOHTtw4cIFJczee++9uHz5ssouSmhCP2TIENx///3YtWsX/v33Xzz66KPKA5fZRgVBECoKTie6dnizEmyrtuyJ9s99bTbBlsxctEUJtpzBNH5gc3z59CCLCLaCUN4EaJG2YlchCIIgCHYL8zCQjk1qVMgMMS3KtlWrZggIML9IXFooKndsrI+23XOy8Dw9BclKjkfy5ZN5kbYlQYs83rJFbx9hb2jnI+HsQeRmZ1m6OIJgO6Ltnj170K5dO/UgtCzg61mzZqno1+PHj2P06NFo3LgxRowYgZiYGGzZsgUtWrTI28b333+Ppk2bYsCAARg2bBh69uypso0KgiBUhFBLkZYwyViTcTPRaMzzaHbvG3DxMq+Aev+w1qhdzRufPjYQD41oC2cn/YwBQbAXe4RYibQVBEEQhErgZ1sx1gibN+tF2969rdcaQaNjk+rqec91YdsU4k/tZecEHjXrwcVXn7jdVLTI423b9ii7SXvDo0Y9NQuSfr9JF45aujiCYDv2CH379lWiR2EsX7682G0EBARgyZIl5VwyQRCEoslMisWpZe/Co3oo6o14UH3mEVRHPczBmSvxOH0lDoM76jPdNq8biO9mDIeTo0zpF+wLsUcQBEEQSGZmpspfQus7Q+rUMU9bS6g4ElMzEXYhtsJEW2oOWqRt797dYO10uh5pe/JyHOKT0+Hn5Vbsd+jXSvybdCrx/lq3bg5fX28kJCTh0KFjaN++NewJ+vv6Ne6A6H3/IO7kXpWcTBBsBentC4IglJBrhzZj7zsTEXt0G65s+UUJuOYiN1eHpevDMOW9tXhryU6cvRKft0wEW8GeI23TsrKQLlPYBEEQKh2nTp1Cr169lNdm3bp1Ua9ePfUIDQ1Vz4Lts+9UJHJ1OuXfWt3f/InAjx8/jaioa3B3d1P+rdZOgI87GtT0Y+As9p6KNEmUjj9xPQlZk84l3h9nOffoof8efW3tEYq2hsnaBMFWsLlEZIIgCJYiOy0ZZ1Z8hKjda9V7z+CGaDLuRbh4B5hlf1djUzD7++3Yfz2zbo8WwfDz+i+JoiDYI14urnBycEB2bi7iUlNQ00e8mgVBECoTkyZNgpOTE1atWoWaNWsqj0/Bvqh4awS9V2vXrh3g6uoCW4AWCWci4pVFwoB2dYtcNy36IjLio1DF0Rk+9UsXJduzZ1esXr1ORSQ/8cT9sDf8rvvaJoUfV306J3cvSxdJEExCRFtBEAQTiD+9HyeXvImMuEjOsUFI/7tRd8hkODiVf8OPo+V/7jmPuT/vQUp6FtxdnPD4be0xomsD6bgIdg+vcUbbRqckIS4tVURbQRCESgYTUzMJNfOWCPaJ5tVaUaLt1q278iXcsgU6NamJZRtPqIRt7BsU1QeIP7VPPfvUawlHl+KtFIrytd2z5wBSU1Phcd2uyl5w868O92ohSIu+hIQzB1TiaEGwBWRurSAIQjFwNPbYgheUYOtWNRitH/0Y9W5+wGyC7avfbsPr321Xgm3L0EAsfm4obunWUARbodIQ4HE9GVmq+NoKgiBUNpo3b45r165ZuhiCmYiKT8Wla8lwqFIF7RoGmX1/TKy1a5de1NQsAGyBtg2ClBVaRGwKLl9LLnLdhFP71bNvQ32C99JQr14dhIQEIysrGzt36rdnr9G28Sf3WroogmAyItoKgiAUA6fP1L/1EdToOgLtnl0I3/qtzLYvCrP0sHJ0qIKpw9vg08cHIiTQ22z7EwRrJOC6r22sJCMTBEGoFCQmJuY93nnnHUyfPh0bN25ETExMvmV8CLbN/userY1D/OHlbn6rgsOHw5CcnAI/Px80b94YtoK7q5MK3tA8gIsK+Eg4oxdZ/cog2rIPokXbaknb7NXXVkvaJgi2gNgjCIIgFECXk41LG36AV51m8L9eudfoerN6mIP0zGzEJKajVqDeW2nsgGbo0bIW6teUaeFC5aSqp/5/ISal6MgSQRAEwT7w8/PLN6OIQtSAAQPyraNNEc/JybFACYXyYt/1XA3tG1WvkP1t27Y7z8/WwcG2YtYYiXzgTBT2nY7ELd0bGl0n9ep5ZCXHw8HZFd51m5Vpf927d8LSpSuwY4d9RqL6NmhHdRppUeHITIyBi09VSxdJEIpFRFtBEAQD6HN0YskbSDp/FC5+1dDx+W/h6Go+T6djF67hte+2w9HBAQufGQxXFyf1WgRboTIT6HFdtE0V0VYQBKEysGHDBksXQaggtKjRirBGINu371HP3bp1gq3RrlF1LPrzCA6cjirU1zbhtOZn26rM1m1du3bMi05OTEyCj499zfZz9vSBZ80GSLlyGglnDqJau/6WLpIgFIuItoIgCNejN65uX4mzv32G3Mw0OLp5InTY/XBwcTfL/rJzcvHNX0ex+K8jyMnVoZqvO67EpqBeDV+z7E8QbImqnnp7hGsi2gqCIFQK+vTpk/c6PDwctWvXvkGgYlvt4sWLFiidUF5cjU3BlZhkZQPWpoH5RdusrCzs3LkvL4rU1mhZtyqcHR0QnZCmfG1DqnkbTZZcVj9bjeDg6ggNrY3z5y9i9+4DGDCgF+wN3wZtrou2B0S0FWwC25ofIAiCYAYyE67h6JfP4fRP7yvBlo2e9tMWoXqnIWZJ/hUelYiH5v2NBWsPK8F2YPu6+Ob54SLYCsJ1qmqRtmKPIAiCUOmoV68eoqOjb/g8NjZWLRNsl/2n9VG2TWoHwNPN2ez7O3QoDKmpafD390PTpsbtBawZzsBrHqqfwk+LhILocnORcPqAeu3XsG257JM2EoYRyvaGJm5r500QrB0RbQVBqNRkxEdh75zJiAvbgSpOLqh/66No9dAHcAuoUe77YoTIiq2nMOndNTh2IQbe7s545Z7ueHViD/h4mD8RgyDYnGibKonIBEEQKhuFTQNPTk6Gm5ubRcoklA/7r/vZVpQ1guZn262b7fnZarRrqPf+pUVCQVIiziI7NVHNDGQujvKgWze9RcKOHXYq2tZvrZ5TI88jMynO0sURhGIRewRBECo1Lr7V4NewPdKuXUKTcTPhWdN8ERw6HbDxYDgysnLQsXF1vDiuG4L8zOeXKwg2n4hM7BEEQRAqDU8//bR6pmD70ksvwcPjvzYSk4/t3LkTbduWTzShYFk/24pKQrZ9++58QqQt0r5hEBb/qRe8Cw5oJGjWCPVbwcGxfKQd7VwxSjkpKRne3vo2mb3g7OUHj5r1kRpxVu9r27avpYskCEUioq0gCJUOej951qinKm02fBqNma6M+x2czDNNKyc3VyUXc3CoghfGdsWWw5dwW8/G6r0gCIUnIktIT0NWTjacy6kjIgiCIFgv+/frBSgKU4cPH4aLy3+zkPi6TZs2ePbZZy1YQqEsRMQkIyI2RfnZtqpXzez7y8zMwq5d+23Wz1ajRWggnBwdEBWfissxyQgJ9L5RtC0HP1uNWrVqok6dWggPv6x8bfv37wl7w7dB2+ui7QERbQWrR3pBgiBUGnIyM3D+jy9wZfNPqNq6D5pNek2Jtk5u+qRH5U1yWiY++GUP3FycMO3Ozuqz6v6euL13E7PsTxDsBR83Nzg5OCA7NxexqSmo7i1+z4IgCPbOhg0b1PPkyZMxb948+Pj4WLpIQjmy/4x+en/TOhXlZ3sUaWnpCAjwR+PGDWCrsB/RvG5VHDobrSwSNNFW+dmePZQnQpYnjLalaEuLBHsUben/G7F1uRJtBcHasU1jF0EQhBKSfPEE9s+9Twm2xNnTF7rcHLNO/7rnndVYu/s8ft9+Bpeik8y2L0GwNxyqOCDAQz+Yck0sEgRBECoVixYtEsHWDjmo+dk2qBhrBC3KtkuXdjbrZ6uheQBridxIalS43s/W2RVetcs3IKRrV71FwrZt9ulr63Nd5Ga0bVZyvKWLIwhFIpG2giDYNbqcbFxc9z3C/1ysRFpn7wA0HvMcApp3M8v+MrNz8MWqg/hh43HlYRtc1QuzJnRDSLX/pjIJgmCaRUJUcpIkIxMEQahk9O/fv8jl69evr7CyCOXHwXPR6rl1ffNbIxBO7SedOpWfdYAlk5F9/ddR7D/1XzKyxOtRtt51m5ebn60GE7eRQ4eOISUlFZ6e9pWDw4W+ttVDVTIyRisHtLS/aGLBfhDRVhAEuyUjLhJhX7+CpAtH1XtaIjS64xnlZWsOTl2Ow2vfbsPZiAT1fkS3BnhsZPsKmQImCPZGwHVf25gUibQVBEGoTNC71pCsrCwcOHAAR44cwcSJEy1WLqH0xCWn42KUftZZq3qBZt9fbm4u9uw5qF537mz7om3L0EDlBRwZn4qrsSmoEeCJxHOH1TKfeq3KfX+1a9dCSEgwLl26gr17D6F3766wN3wbttWLtmcOiGgrWDUi2gqCYLc4unkiM/Gaem5w25MI6jgoX8bV8o6wfWb+RsQkpsHPyxXPj+mCXq1CzLIvQagMBHrqRVuxRxAEQahcfPDBB0Y/f+WVV5CcLHWCLXL4rD7KNrSGL3w8Xc2+v/PnLyI+PgFubm5o2bIpbB13Vyc0DvFHWHgsDp+L1ou2mp9t/dZm2WenTm2VaLt79367FG196rdBxL+/IvGsXvwWBGvFts1dBEEQCkBfImYdJk7uXirZWPvpi1G902CzCbbExckRT43uoITa754fLoKtIJQRzdM2VkRbQRAEAcD48eOxcOFCs+5j8+bNGDFiBIKDg1W78ddff823nG3MWbNmoWbNmnB3d8fAgQNx6tQps5bJHjh07pp6blNB1gj79x9Rz+3atYSzs33MeNNsJZiQLCM+GumxEUAVB3iHtjDL/jRbCc1mwt7wrddSPSdfOY2cjFRLF0cQCkVEW0EQ7IboAxuw563xuLpjVd5n3nWawc2//BMesNG+cvtpbDl8Ke+zfm3r4K0pveDv7Vbu+xOEyuhpS66JPYIgCIIAYPv27Spy0pykpKQoe4ZPP/3U6PJ3330XH330EebPn4+dO3fC09MTgwcPRnp6ulnLZescOhtVYdYIZP/+w3ZjjaDRqt5/oq1mjeAZ3ABObvpB7vJGO3d79x5EdnY27A1X/+rqgdwcJIUft3RxBKFQxB5BEASbJzstCad/+RDRe/9W76P2/oUaXW82W2RtbGIa3l62C/8euQw/T1e0rBcIfy99J8Kc0byCUJmoet0eIUYibQVBECoVt9122w0D5REREdizZw9eeukls+576NCh6mEMluPDDz/EzJkzceutt6rPvvnmG1SvXl1F5I4ZM8asZbNVMjKzceJinHrdun5QhezzwIGjdpOErGCk7ZmIeMScijSbn61GkyYN4OPjjcTEJISFnUKrVs1gb/D8RcdFKhHcq2qopYsjCEaRSFtBEGyauJN7sffdSXrBtooDat90D1o9ONds4ummgxcx4e3VSrB1dnTA+IHN4ethfm8uQahsVNUSkaWmWLoogiAIQgXi6+ub7xEQEIC+ffti9erVePnlly1WrnPnzuHq1avKEsGwrF26dFFRwIJx6MOanZOLqj7uCK5qnqhQQyIjo3Hx4hXVF+jQwTx+r5aA569WoBfoAhd9Sm9Z4FvffKKtg4MDOnbUJwXctWsf7BGf6xYJSef0dhqCYDeRtuHh4bhw4QJSU1NRrVo1tGjRAq6uIloIglBx5GRm4Pyq+biy5Rf13i2wFpqMmwkfM/k6Jadl4sPle7Fm1zn1vmEtP8wa3x0Ngv3Msj9BqOxo9ggxYo8gCIJQqVi0aBGsEQq2hJG1hvC9tswYGRkZ6qHBPjTJzc1Vj9LC7zL6tyzbqAgOnInMs0ZgebXcE+Zi16796rlZs0bw8vK0+vNTEngOo6/FQnftPBie4lW3hcnHV5rrpUOHNli/fit27TqAyZPvhr3B80eSLhxF9Zxsu7pWygNbucfY6nkx9fsmi7bnz5/H559/jh9++AGXLl3Kd7N1cXFBr169MHXqVIwePVqNygiCIJiT1IgzuLJ1hXpds8dI1BvxEBxd3c2yr6TUTEyaswZXY1PgUKUKxg5ohilDW6nkY4IgmDcRWXJmBtKyMuHu7GLpIgmCIAgVTHJy8g0dWx8fH9gSb731Fl599dW8946OjujXrx/i4uLyibklheclKSlJ9cutuf+972SEem5Y3QOxsbFm39+//+5Uzy1bNqmQ/VUkDYI8cBqRqAIdnH2DkJLrhBQTj7E010vTpg3U886dexETE2N3NnA6Vz84uLirRGSx58OAKi2s+n+porGVe4ytnhdtAK9cRNvHH38cX3/9tTJZnz17Njp37qwyajJjJm+ER44cwZYtW1QmTVZIHB3t1KmTSdk558yZg7179yqfohUrVmDkyJFqWVZWlvIL4jSYs2fPqqknnIry9ttvq31rcP+PPfYYfv/9d3XCKBrPmzcPXl76CB1BEOwT77rNUW/EA/CoUQ8Bzbqad18eLujcpAb2nIzES+O75XlKCYJgPrxd3eDm5IT07GzlaxviG2DpIgmCIAgVZEPw6KOPYuPGjfkSfLGDTNEoJyfHIuWqUaOGeo6MjETNmjXzPuf7tm3bFvq9GTNm4Omnn87XUZ8wYQL8/f1VIrOyCAc8H9yOtQoqubk6nLicoF53aVFXWV2Ym8OH9UmlevbsWiH7q0i6tnLEidX6qG6f+q1LdHyluV569eoKJycnREfHIDU1A7Vr/6fD2As+oS0Rf3I3HGIvIqBDL6v9X7IEtnCPseXzYqpbgUmiLSsTCqdVq1a9YVlQUBD69++vHvQYWrt2LS5evGiSaKtl57z33ntvMJxnZbZv3z5lNs91OBL5xBNP4JZbblEm9Brjxo1Tgu/ff/+thN7JkyeriN8lS5aYdAIEQbANUqMu4vRP76Hh7U/Do3pd9VlIP/NN0zl5KQ5+Xq4I8vNQ7x8b1V55SHm6OZttn4Ig/AcbQ9U8vXExIQ5RyUki2gqCIFQSxo8frwTahQsXKusBa4nuq1evnhJu161blyfSJiYmYufOnXjooYeK7Jgbds4ZaUvY2S+rEMJzUx7bMRfnr8YjOS0L7i5OaFw7wOzlTElJxdGjJ9TrLl3aW+15KS31avihoWMkkAuk+zco8fGV9HqhDsQEZPv3H8aePQdQt24I7A2f+q2UaJt2+YRV/y9ZCmu/x9jyeTH1u06mTukwlSFDhpRLdk5G1lKINeSTTz5RUb701K1Tpw7CwsKUSLx792507NhRrfPxxx9j2LBheO+99/JF5AqCYJuojMH//opzKz9DblYGziyfh1YPzTXb/nJyc/H9ujAsWHMY7RoGYe6D/eDgUAUeriLWCkJFU81LL9pGJydZuiiCIAhCBXHw4EE1E7NJkyYWsWM4ffp0vqjfAwcOqIhG9j+ffPJJNfO0UaNGSsRlgBH7nNpsUSE/B89Gq+fmdavCydH8og/FRUZi16gRhOBgfWS0PaHLzUGITu8RfDa3OtpXwD47d26nzuvu3QcwevTNsDd86umTuaVeDLN0UQSh/BKRGZKZmakeFWFHkJCQoBRtPz994h9m6eRrTbAltFCgYs0Rz1GjRlWoGbwgaIhpd/mQkRCN0z+8q0Y/iW+j9mh453SzndfL15Iw+/udOHL+mnrPqIC0jCy4u5b5Vmm32NO1bg/HYG8Eeep9C6NSRLQVBEGoLHDGJmduWkK05YxO+s1qaLYGEydOxOLFizF9+nQ1W5QzO+Pj49GzZ08VROTm5lbhZbUFDp/Ti7YVZS22e7c+CVm7di1hj6RcPgUnXRbSdC7Yf80Zt1fAPjt1aov//e8bFWlrj/jUbcaQR2QnXkNGXBTcq9qf2C/YNiVSIuhVS8uCrl27KlsC+vPMnTsX2dnZyh6BScqMWSiUB/Qzeu6553D33Xfnmc8zSyftGQyh5wpHQovK4GkuM3hB0BDT7rKTeGwrItZ8gdz0ZFRxckFQ/wnw7zgUKToHkw33TYW/01/7L2PhXyeQnpUDdxdHTB3SFP1aByMtJRFpKeW6O7vCnq51U83ghYqNtCXRyYmWLoogCIJQQXz11Vd48MEHcfnyZbRs2RLOzvlnO7Vu3dps++7bt2++hNsFYQDRa6+9ph5C8Rw6W7Gi7a5detG2bdsWsEcSzx1RzxdQA8fC4ypkn+3b6//fTpw4g+TkFHh5ld6H2RpxdPWAZ3BDpFw6iaQLR0W0FWxXtH3jjTfUo0ePHsovduvWrfj1119VhcWO+kcffaQSh33++eflXkh61d55552qAi2P7ZvLDF4QNMS0u2xcO7QJl1foLRA8Qxqj8dgX83xsy5uElAy8sWQnth+7ot7TEuGFu7ugRoDcCyrbtW6qGbxQcQRdF20l0lYQBKHyEB0djTNnzqhcJRpsa1g6EZlQMq4lpCEiNgW0JG4RGlghbdL9+/WiZps29inaJoXrp/BfRHVExaciOj4V1a7n3zAX1atXQ61aNXH5cgQOHjyKHj06w97wrtNcL9qGhyGo/QBLF0cQSifacjrIggULVKQrp4106dIFP/74I0aPHq2WcxSUI6LmEmwvXLiA9evX50XZEhrBR0VF5VufUb+xsbF52T0r2gxeEDTEtLv0VGvVCxGhLeHbuAPqDJoIB0fz2RO4uzrj8rVkuDg54IGb2+DOPk2Vh61Q+a51Wy+/PcJEZEQ8bQVBECoPTFLdrl07LF261KoSkQkl49gFvd1YvRq+FZLI98yZ80hKSlZWFY0a1Yc9knThmHrOrlofuAYcvRCDvmYWbUm7dq2UaEtvW7sUbes2w9Vtv+adX0GwJkxWQpj8i549hB6ytCGgUGs4TSUiIsIsgu2pU6ewYcOGG6wXunXrpryEaFTfoUMH9RmFXY6yUVQWBME2yE5PxeWNy1B74Dg4OLmgiqMTWj/6kXo2BynpWXBzcYSjgwPcXJzw6sQecHKogvrBer9sQRCsS7SVSFtBEITKA4N1Vq5ciYYNG1q6KEIZOHYhRj03r2v+KFuyb99h9dyqVTM4OemDsuyJzOR4pMfoZwZWrd8SuHZZCeN929Q2+77bt2+FVav+yjvH9oZXnWbqmdG2uTnZZg0YEoSS4lASAdUwOtXFxSWfvxBF3JJOVWF2Tmbj5MMwOycFYu7v9ttvV1G933//vdo2fWr5YOIz0qxZMwwZMgT3338/du3ahX///RePPvooxowZo7J4CoJg/SScOYj9cyYj/M9FuLBmQd7n5hJs9568iglv/YFlG0/kfdY4xF8EW0GwQoK8fPIibYvyGBQEQRDsB+ZKOXjwoKWLIZSTaNuirnly3hSEUaD2nIRMiwJ1D6qDpg1C1Ouj5/XnuCJEW+0c22N7zD0wBA5unsjNzkTqlTOWLo4g5KNEqsixY8fyEnzxn/X48eNKeCXXrumnP5RXds5XXnlFjbCStm3b5vseo25pEk8o6FKoHTBggJraSrsG+usKgmDd5GZl4Pzqr3B504+8ocDVvzr8m5ovQj4jMxvzVx3Ej5v0Yu0fO8/izj5N4OQoU+IFwdoTkaVnZyEpIx0+bu6WLpIgCIJgZkaMGIGnnnoKhw8fRqtWrW5IRHbLLbdYrGyCaeTk5iIsXC8oNqtw0VYvMNqraOtdtzmqXvcIPn4xBtk5uWbvz7RsyehlJ0RFXcOVK1eVx609UcXBAe7BjZBy9gASLxyDV+0mli6SIJROtKUwajiycvPNN99gDF+e2TlNGcUJCAhQidEEQbAdki+ewInv30Bq5Hn1vnqXYag/8jE4uZkn+deJi7F47bvtOH81Qb2/tXtDPDqynQi2gt3x6aefYs6cOWqAtU2bNvj444/RuXPh3mO0GHrxxRexfPly5Qdft25dfPjhhxg2bBisATcnZ/i4uiExIx3RKUki2gqCIFQCtDwpTHhdEElEZhtciExEaka2siOjp625SUtLQ1jYqUoRaUvRtkaQj/IJpuXb2YgENWvQnLi7u6F588Y4dOgY9u49ZHeiLXEPbqxEW3Wee46ydHEEoeSiLa0LBEEQykrU3r9wcslb0OXmwNk7AI3unIaqLXuYZV8cef5+3TEsWHMYObk6BHi7YcbdXdC9RS2z7E8QLMmyZcvUjJX58+crX3eKr4MHD8aJEycQFBR0w/q0GrrpppvUsp9//hm1atVSPoJ+fn5WF21L0TYqOQkNqt54HIIgCIJ9wfwkgn1YIzStXbVCgiQOHw5TYn5QUCCCg2sgLi4O9oQuNxdJ4cfVa586zVTS5GZ1qmLPyavK19bcoq0WwUzRlhHNt9wyGPaGe61G6jkpPMzSRRGE0om2jL4RBEEoKz6hLeHg7KKsEBre/jScvcwnEF2+loRFa48owZYm/dPu7AQ/Lzez7U8QLMncuXOVx/vkyZPVe4q3f/zxBxYuXIjnn3/+hvX5OaNrt23bljf1NDQ0FNZGkKcPzsREq0hbQRAEQRBsKQlZxVgjaAmyKCyWdPavLZAWfRE56cmqD+UR3EB91iJUL9rS13ZkD73gaE54br/+elmeDYW94RasP4dpUeHISk2Cs4feoksQbEK0PXTokMkbbN26dVnKIwiCncGR4YQzB+DXqL1671Y1GO2nLYJrQE2zN6rqVvdVNghe7i4Y3DHULhtxgqBFze7duxczZszI+4w+7wMHDsT27duNfoe+8d26dcMjjzyC3377DdWqVcPYsWPx3HPPwdHR0ep8bZmMTBAEQbBPmJNk6tSpcHNzKzY/yeOPP15h5RJsQ7Tdv/9IpfCz9QppAofryZpb1NX72jLStiKTkTGqmUnjC3pN2zpOHj6qn5oecwXJ4WHwb1q4vZggWJ1oy0RgpvrWiseQIAga6bFXcXLpW0g4vR8tH5wL/yYd1eesEM3BtYQ0zPlxFyYNbqmmDJHbe4uRvGD/MBko69/q1avn+5zvmTTUGGfPnsX69esxbtw4rF69GqdPn8bDDz+sGuIvv/zyDetnZGSoh0ZqamreNFZTp7JyPbYlSjL1NdDDSz1HJidafMpsacpvLdhy2W29/LZcdlsvvy2X3VbKX15l++CDD1R9RNGWrwuDfVERba2b9MxsnI2IV69bXE+YVXFJyOzUz/b6lH3vus3yPmsequ/rnI9MRFJqJrw9XMxahvr168LPzwfx8YnKP7h16+awN7zqNFeiLZORiWgr2JRoa+hnu3//fjz77LOYNm2aitAhjOJ5//338e6775qvpIIg2AzsYETuWoOzKz5CTkYqHFzckJUca9Z9rt8frgTbxNRMRMWnYuGzQySyVhCK6WjTz/aLL75QkbUdOnTA5cuXVSIzY6LtW2+9hVdffTXvPb/Tr18/5RtnKOYWt8+kpCR1j2AksCl4VtFH/V6Ji1F2DpakNOW3Fmy57LZeflsuu62X35bLbivl1wbwyophf1Nyqdg2xy/GKmuyQF93BPl5mH1/UVHXcPlyhGr3t2nTAvZI4vmj6tm7zn9Cqb+XG4KreuFKTDLCwmPQual5k4Px/LZt2xIbN25TdhT2KNoyydu1/f/kRTYLgs2ItoZ+tnfccYeasmKYWZqWCLVr18ZLL72EkSNHmqekgiDYBJlJsTi17F3EHt2W52HbeOwLcK8WYpb9cWR57s978Nfe8+p9kxB/vDShuwi2QqUiMDBQiaiRkZH5Puf7GjVqGP1OzZo11dQ2QyuEZs2a4erVq8puwcUlf8QGrReY6Mywoz5hwgT4+/vD09PTZAGC/5v8jqkCRGiQPno4PjMdAQEBsCSlKb+1YMtlt/Xy23LZbb38tlx2Wym/q6truW9z8+bNaNq06Q1JNLOzs5UPe+/evct9n4ItWyPoo2wbN64Pb28vq45MLw05melIiTirXnuH5helW9StqkRbnnNzi7akffvW10XbQ5g06S7YG1okMyObTZllLghWlYhM4/Dhw6hXr94Nn/OzY8dkREIQKjMxR7bi5A/vIDslAVUcnVF36L0I6TcGVRzM44+558RVvLFkh4qsdahSBRNuao7Jg1vC2cl6/DgFoSKgwMpI2XXr1uUNnrLTwvePPvqo0e/06NEDS5YsUetpYsDJkyeVmFtQsNU65oadc03s5XdLIiawAVyS79Tw8VXPkclJViFalLT81oQtl93Wy2/LZbf18tty2W2h/OYoV9++fZW9z4oVK9C1a9e8z2NiYtQMD7Hjs240j9Xm163KKjIJmT2SfOkkkJsDZ+8AuPrlH8ig/cTf+y7g6PmK8bXVzrG9JiPzDG6g+rDsy9ImwT2wlqWLJAgocS3LKBxOkWQUjgZf8zMuEwSh8sKRYFZyrPDaPf0Fag8YZzbB9sDpKDzx2Xol2IYEeuHzJ2/C1OFtRLAVKi2Mgv3yyy/x9ddfIywsDA899BBSUlIwefJktfyee+7Jl6iMy2k38MQTTyix9o8//sCbb76pEpNZEzW89KLttZQkZElHXRAEoVIwZswYDBgwAIsXL873OaPfBBuJtK1wP1v7FG21qfo+dZvfEPmpRTPznFfE/wbtEcjZsxcQF5cAe8PByQVeIY3Ua7FIEGw20nb+/PkYMWIEQkJClC0COXTokLqB/P777+YooyAIVkxWahKcPfTZ3au1G8DWNALb9IWDk3kzirauXw0dG9dA7WreeOTWdnB3LfHtTLAjslIS4ezpg8rMXXfdhejoaMyaNUtZHDCJ6Nq1a/OSk4WHh+eLiKKt0Z9//omnnnpK1ee1atVSAu5zzz0Ha8LfwwMujo7IzMlBVHIiavn6W7pIgiAIghlhv5KDjL169VIDjuxrMn+KtkywXmIS0xAZlwr+TE1rm9/SiFHXBw8etXPRVktCdqOHbKMQfzg7OiA+JQNXYlJQK1CfvNVcBAT4oV69Ojh3LhwHDhxBv349YG9412mmBFs+gjrcZOniCELJRdvOnTurjNPff/99XkZqdhTHjh1rsqedIAi2T05GGs79/jmuHdqMDtMXw9nLTzWkzVW5Zefk4pctJzGiWwN4uDrDwaEK3nugj0TWVnIY3X1u5WfqOmw/bRFcvCu3oEcrhMLsEDZu3HjDZ0woumPHDlgzDlUcUN3LBxcT4hApoq0gCILdo0UM3nbbbcqC79Zbb1U2fPPmzbN00QQTo2zr1fCFp5t5AzjI6dPnkJycAnd3NzRp0gD2CP1VDf1WDXFxckSDYD+V/O3ExVizi7ZatC1FW4rldinaUhzf8kveeRcES1Oq0DSKs1OnTi3/0giCYBMknjuME0veRPq1y+p97LHtqN55qNn2Fx6ViNe/264aghciEzH9rs7qcxFsKzfJl0/h+LevIS3ygnofF7bDrNehYDlqePsq0TYiyf6m4gmCIAiF065dO+zatUv5tdMuQbCVJGQVY43AaE/SunVzODnZ36y7rOR4ZMRdVa+9QpoYXadJ7QAl2h6/GIP+7eqYvUw81ytWrMbhw/ZpH+Bdp6l6TrlyGrqcbFRxtL/rSrBDT9uSROEwm/TRo/opCoIg2Be52Zk49/t8HPz4MSXYuvhVQ8sH55pNKGOkxfItJzHp3TWqEejl7ow2DaqZZV+C7aDLzcWlDT/gwAcPKME209kHy9xug0uzfpYummAmqntfT0Ymoq0gCILdM3HiRLi7u+e9r1GjBjZt2qRE2zp1zC9KCaWHwiFpVsf81gjk0KFjeUKi3SYhA+BeLQRO7sajaJteP9eMtK0ItHN98KB9irZuVWvB0c0TuVmZSL0eGCIIVi/aTpgwAYMHD8ZPP/2kkpoYg1NWXnjhBTRo0AB79+4t73IKgmBhki+fxv65U3Fp/RKqZgjqOFjZIvg36WiW/UUnpOKZ+Rvx/s97kJGVgw6NquOb54ZhcMd6ZtmfYBtkJFzDkf89qywROPp91qUR3s4Yhf2pQdgedsXSxRPMRM3roq1E2gqCINg/ixYtgre3Pl+Chqurq0q0ee7cOYuVSyg+2OLExbi86M+K4PDh43Yt2iZdPF5klC3RvIMp2lZEMrKWLZsqS7yIiEhcu6YX6e2JKg4O8AppnO/8C4IlMSnWm4Ls559/jpkzZyrv2saNGyM4OBhubm6Ii4tT3rbJyckYNWoU/vrrL7RqZZ8m4IJQmbmy5RekRpxV3rUN73gWga17m21f+09HYsaCLUhKzYSLsyMeurkNbu/dRPnYCpUX+taeWvYuslMTkevogpU53bEjvQkCfT3w5rhu6NSkhqWLKJiJ6t76JHORSYmWLoogCIJQQXAGJ5NoZmZm5vtcS4YtWBdMQJaQkgFHhyqoX9PP7PvLzs7G0aMn7Fq0Tb6oPz6v2noR0Rj0D2YysqS0LFyOSUZIYP4Bj/LGy8sT9evXxZkz53HoUBj69+8Je4OibcLp/frz32W4pYsjVHJMirR1dnbG448/jhMnTmD79u24//770bJlS5Vpum/fvvjf//6HK1euYOnSpSLYCoIdYThaW//WR1Cj2y1oP/1rswq2pE6QDxyqVFGj9IueHYI7+zYVwbaSJ707tWwOwhbNVIJtrGtNzM26DTtym6J369oqAlsE28oRaXtVIm0FQRDsnujoaNx8880q2rZFixbK19bwIVgn9FUlFGxdnR0rJAlZeno6PD09lIhoz/YIRUXaMsdHw1p+FWqR0KZN83z2FPaGV+0m+c6/YN288sor6l6gMWvWLHz//fd5y5588kn1+sCBA/jhhx8K3Q6XtW3bVmmdfLz//vv5dJGHH35Y1UELFy5ERVJiV+WOHTuqhyAI9u0ZemXrciScOYhmk15TU2Doo9TozmfNts+Tl2LROEQ/vaeqjzs+eXQA6lT3gZOjSWNLgp2SdPEETjDZWPRF6FAF2x3b44+09nB2ccFzt3XAiK4N1PVpq4SFhakGwpYtW3DhwgUVVVStWjXVIKAt0ejRo9WU0MqO5mkroq0gCIL9ww52fHw8du7cqQKEVqxYgcjISMyePTtfJ1qwLjTBsKKsERjlqU3Xd3BwsNMkZJHqtTZdvzB4zsPCY3E8PBYD2plfwG7VqjmWL19tv6LtdZFckpHZBq+++qqqN+gEQF577TWj61G0/fXXXzFmzBijy2vXro21a9cqH/WEhAR06NBBPVgP/f3338prnYkx+/Xrh5tuugkBARVzr5OrTxCEfKTHXMHJpW8j4cwB9T726L+o2tJ8015S0rPw0Yp9WLXjDGZP7ol+bfUJJuoHm39alWC96HJzVLKxC6u/Uq8znH3xdUZvnM2tpRqmr9zTXUVk2yr79u3D9OnTsXXrVvTo0QNdunRRFkNsDMTGxuLIkSN48cUX8dhjj6n12BCpzOJtDS/9b52YkY7UzAx4uFTecyEIgmDvrF+/Hr/99psKFKIYV7duXdVB9vHxwVtvvYXhw2W6sjXCAAxDj1Vzc/jwsTwB0R7RojzdAmsVmoRMQxPKKzoZmb2Ktu6BteDo6oGcjFSVjMwzuIGli2SVMPo0PTPHrPtwc3EsMkDnwQcfVM+9evWCo6Ojsmtl34kRs1qELYmKilIRuBRjuaxr166YP39+vm2xT6bh6+uLpk2b4vz583nHyvqIZanogCERbQVByLsRXd22EmdXfobczDQ4uLij3i0PIaDFfzcvc3jXvvH9DkTEpoD3vnNXE9DPbHsTbAVGFZxY8qbykiKnXRrj+/TuSK/ihvEDmuO+oa3UVDBbhhG006ZNw88//ww/v8IHKGhJNG/ePBVZxGSflRUvVzd4ubgiOTMDV5MTUT+gmqWLJAiCIJgJJr4OCgpSr/39/ZVdAnOq0IaPg56CdfYjNHuEJrX9KzTStnXrZrDX2WaGU/WLomntqur5xCV9MjJzi0qGyciio2NQrZp+//aWjIxBTPwdRLQ1DgXbgdN/NOs+/nn3Tri7Fi5bUnilXStnLRbVp2KdwghcRtryYUpeL/bDNGGXA4fLly9Hp06d8NBDD8HT0xMVhYi2giAokezkD+8g/uQe9d6nQRs0HjMD7oHB5tlfVg6+WHUQyzYdB21zawZ44sVxXdGuYXWz7E+wHaIPbMTpn+YgOzUJuY6uWJHTHbvTGyPIzxPvju+G9o3s4xo5efKk8osvjm7duqlHVlYWKjs1fXxx6loUriYmiGgrCIJgxzRp0kTlUgkNDUWbNm1Uh5yv2XmuWbOmpYsnGCEqPhXxyfokZA2CzS/a5uTk4OjR4/adhOySXrT1NkG0rV/TFy5ODkhmMrJryQipZv5kZA0ahCpfYUbbDhjQC/YGxXKKtup36DLM0sURKpBLly7h1ltvVXVOSEiI+oxRtqyLSG5urpoZWVGIaCsIlRyOxh5b/BKSw4/DwdkFocOnIrjX7WqE0Rxw2s5r327D+Uh9FvgR3RrgsZHt4elWvIAl2C/Z6ak4u+IjRO5ard7HuAZjYVpvxMAP/drWxvQ7O8PH036mxJsi2JZlfXukutd10TZZfG0FQRDsmSeeeAIRERHq9csvv4whQ4aopDIuLi5YvHixpYsnGEGbll+vpm+FJSFLS0uHh4e7/SYhu1h8EjIN5gBpEOyn97W9GGt20VaLcLZ30ZYkX494FoxbFzAS1tz7qEiuXLmCgQMHYubMmbjjjjtgDZSLaEuj+KJCkQVBsF44taXBqMdxbuXnaDTmOXgE6T1lzUVMYpoSbAO83fD8mC7o0bKWWfcnWD9JF47h+HevI/3aZZVs7F/HDlid1g6uLq544faOGNa5nk0nGysMTrmJiYlRGbI1vvnmG9VB5dTQkSNH4uOPP67UXraG1JRkZIIgCJWC8ePH571mEhgm6jx+/Djq1KmDwMBAi5ZNME6eNcL1pMIVmYSMPpb2RlZKAjLirpqUhMzQIoGiLQX0ge0rLhnZ4cP638LekGRkxcP+WVHWBRWFt7e38qotTpOkLzrXKwwOFg4YMADPPfccJk6cCGuhxKF077zzDpYtW5b3/s4770TVqlVRq1YtHDx4sLzLJwiCGSJro/b+jYhtv+V95hPaEq0f+8Rsgm1GZnbe6+4tauG5MZ3x7fPDRLCt5DDBWPjf3+DAR48owTbdxQ9f5I7AqqyOaFo3CIunD8XwLvXtUrAl9FU6evRo3vvDhw9jypQpanT3+eefx++//64Srgh6alwXba8kimgrCIJQmfDw8ED79u1FsLWBSFstIZa5sfskZNejO01JQqahnXtNQDc3bdq0UM8HD/7XlrXHZGS5WZkqGZlgvTzzzDPKc5YJxphwrDAoyGZkZKB169Z5CcwMYaKy8PBwlU+E2+Jj0aJFsDnRlr4OtWvXVq///vtv9VizZg2GDh2qkqqUhM2bN2PEiBEIDg5WnfKChsA0+h00aJAShbn8wAF9NntD0tPT8cgjj6h1vLy8VHKXyMjIkh6WIFQKMpPiELb4JZz47nWc/fVjpEVfyltmDmEsN1eHZRuP447XVyqvK41bujWEn5dbue9PsB3SY6/i0KdP4MLqr4DcHJx0aYK300fhQpVamDioBT5/4qYKmdplSVinsfGg8cMPP6BLly748ssv8fTTT+Ojjz7Cjz+a19zflgj20Y+eX0mMs3RRBEEQBEEwCAg5cTGugkVb+05ClnzpujWCCX62Gtq5P3kpVvXBzI2WjOzq1SiVjMze0JKRGSaFE6wTzlJkvhD2rZhwjDY6Tz75pFr2yiuv4MMPP1SvfX19sW3bNhw6dCgvwZgh7INxtiO3oz0mT54MmxNtr169mifarlq1SkXaUlidPn06du/eXaJt8YTQXP7TTz8tdHnPnj1VdG9hPPXUUyoa6aeffsKmTZuUB8Vtt91WwqMSBPvn2sGN2PvOPYg5tBlVHBxRe+AEuAbUMNv+ImKS8fin6/DRin2ISUzHb9tOm21fgm0RtW8d9s25F4lnDyHX0Q0/oT8WpveFr78/Pn5sAKYOb6O8ueyduLg4VK/+X2I11mEcANVgdtKLFy9aqHTWRy1fTbSNt3RRBEEQBEG4TnRCGuKS01USskbBfhWShOzIEftOQqaJhN4m+NkaT0aWBHPj6emhkpER+traI161G+dLCicIlqDEBhT+/v6qE0nhdu3atZg9e3beCBtvoCWBnVPDDmpBJkyYoJ7Pnz9vdDn9KBYsWIAlS5agf//+6jOGLzdr1gw7duxA165dS1QeQbBH6Il0Zvk8RO/7R733DG6AxmNfgFetRmbZH+8Fq3acxUcr9iI1I1uZhz96a3uM7NHQLPsTbIfs9BSc+eVDRO35U72/5loLC9P6IBY+ynvr2Ts6wdvDBZUFCrbnzp1T9WlmZib27duHV199NW95UlKSJCAzoJaPPht1ZFIisnKy4SzeYoIgCIJgNdYIodV94epi/rr57NkLSE1NU0nINNHQ3tBEQk00NAUGPDSs5Y9jF2Jw4lIsagf5wNzYfTKy66K5lhROECxBie+qjGIdO3YsGjVqpBKoaKLr/v370bBhxYoye/fuRVZWlvL/02jatKkyqWeCFxFthcpOblYG9r9/HzLiIgFG1w4YizqDJsHBydlsScbe+WEn/j16Rb1vXa8aXhzfFSGB9j3NXSiexPNHcOLb15EeGwFdlSrYXKUT/kxrCzdXF8y6oxMGdQy1W+/awhg2bJjyruVsEtoD0bOvV6//GrycutOgQQOLltGaCPDwhJuTM9KzsxCRlIA6flUtXSRBEATBDNBTkAOaBdsFDAxg8BD7ekLl9bPVojpbtGhiv0nIYkuWhEyDieAo2h4PZzIy8wvarVu3UMnI7DfSVktGdkqSkQkWo8RX3QcffIDQ0FBVYb777rvKR1bLtPbwww+jIqFVg4uLyw1Z4hi9xGWFQfNhPjRSU/Vem7m5ueohCGWF1xEblha/nhydUb3LcETvX4dGY2bAu07TvPKZgx83HVeCrbOjA6YMbYUx/ZrA0cHB8udBsNi1zgbOxX++w8V/vuHKSHPxx+L03riAmmgZWhUvjeuG4EAvtQ0+LElFX6evv/66Ggjt06ePqkvpv8Q6TWPhwoXKfkjQw847fW3PxkbjckK8iLaCIAh2Sr169VTfkt6EhsTGxqplJZ3dKVSUaKufEWNuDh0Ks2trBM3PVp+ErGSBL5pwfupyxfj/a7+BvYq27oEhKhlZTkaqSkbGGauCYPWiLadqPvvss0a9ZW0FZuM2nILKEbp+/fopf0FDMVcQyiL+cGozRSgHh4r15kw+ewBOHr5wq1FPvfdoNxR12gxClpOLauyak1s6BuPclVjc2bM+Qqt7IyFevCcr87WeGR+JK799iLTrU7zCnJvih/RuyKriirt7NcAdverB0SHT7NelqWgDeBUFs2AzISetfijaFowWoVe7NjAq/Odrq0RbSUYmCIJgt7BNYWz2TXJyMtzcJJGttcGp+BWbhEwvELZqZd+ibUmjbEmjEL1wfupyfKH/R+VJ8+b6MjIZWWxsHAICKka4r8hkZJ4hjZB45qD6XUS0FWxCtP36669VR3P48OHqPROQffHFF2jevDmWLl2KunXroqKoUaOG8gGMj4/PF20bGRmplhXGjBkzVGZuw446/XPp1+vp6Wn2cguVQ8hiJclrqqJE2+z0VJz//XNE7vgdHjXqoc1T/4ODk3n9QXcej8DqnWcxa0I3FVFL3r6/n1n3KdjGtR619y+c/+VDNTKd4+SGn7N6YH9GI9QM+D97ZwHexpW14c+SLVtmZoYkDjpxGBpoGmzK3TZtkzbdMjNtk8Ju+d82ZdzSFtJ226YQbpqkYWZwzMzMsiX9z7nyKHbiJAbh+LzPMx4NSDoaXWvmfnPud9yw+IZxGBLjD1vD2dnZKu9LlUzpPJaWZijWR1ZDdE7z9bVM58cefW0LqvmGEMMwjNyQ+md0XbF48WJhGyRB2bU7d+5EUlKSFSNkTqesulEUHFY4OCA+1Mci151HjxqSAYYMMYwglBv1+YbrwZ7UH4kJ9hIF4arrm0WBuEDvU/9D5sDDwx1RUeHIzs7D0aMnMWnSGLO+nzVwD40Xom19Qbq1Q2H6KN0WbV988UW8//774jH5xr777rvCMuH3338X2bY//fQTLEVycrLI/F2/fj2uvPJKsS4lJUX4II0bN+6cHfP2nXMpu4kEB0tnRTLyhS44LdWmqlL34eSyl43+R94JI+AAw/ubg4bmFrz7y34s32q4qBiREIzLJ5qnsBljX229tbEWaf97w1j4rsQ5HJ81XoBKeGLWqGg8dNUouLnYZnEta/z+U6HNu+++G2vWrDHaQ9DxnDVrFt555x1hR8R0zLQlONOWYRhGflCNFILOh4cPH+5gGUSPhw0b1umIT0vz7LPPdhi1SfTv3x8nTpxAX0Mahh8Z6AG1s/n9PnNzC1BXVw9nZxXi4w2jCuVGXYGhf+UW1v16Qc5OSkQFeSKjsBqpeZVmF20lb2ESbY8dS5GlaOvWJp7X5adaOxTmHL/JVCdEGomxZMkS8Zt8/fXXi22UHLN06VIcOHBA/E5fe+21Z3spbNq0SZxnKLGTzkX/+c9/hLbY2NiIRYsWITU1Vfz+W7J+Vrd/WcnLVio4RoVTSCy97bbbMGHCBEyZMqVbr0VDXKTMIoKqaNOBpOwiMpinIbMkwBYUFBgFWYKyaGmi7KS///3v4q4sPcfT0xP33nuvOKhchIzpC4js2t8/QOHW5WLZ2TcY/a59Qoi25uJQRin+9fV25JfVieWrLuiH2aPledHEdI/qjENI+epfaK4sgt5BgY0Oo7CucRhc1c549upRuCiZBcjTz6d0rqKbj+Rvm5iYKNYfO3ZM3Bylc9nu3bsRHh5u7VBtLtOWPG0ZhmEYebFhwwYxp47xm2++Kfp2tsqgQYPwxx+GG9SEo2PfLFCU1ibaxodZZlj80aMGYbxfvzhZHnOtpgmNJbnGDM+ekBDmYxBt8ysxYXAYzM3Agf2xcuV6Ywa03HBvE8/rC9IsYjnBdJ/nnnsODzzwgFG0ff755zvdj7RG0jDPJtqS7njjjTdi1apVol9G1qkk1hL//e9/heb5ySefYPr06SJp1VJ0+5eO/PXKy8uFqLp27VrjMBY6QNIH6ip79uwRXrIS0mvRgaKCLL/++qs4aUtIB/eZZ54RijlBWb6UHUXiMR3UmTNn4r333uvux2IYu6O5shgH375XCGRE8LhLEHPJXXB0Mc8dVU2rFp+sPIRv/jwOSggM8nbFU9eNxcj+Z7ciYfoGVGwse9V/kLf+a0CvQ4PKF581TUYugjA0NgDPLBiPYF+2njkdOo/RXWDKsm3v0XfZZZeJkSuUbUv70MUBY4AKkRGcacswDCNfPvvsM9g6JBiey46vr2Xaxod2LAxuLo4dO2nM7pQjDYUZ4lrayd0bTp5+PRZt1+zJslgxMum7oExbOeIaHA0HhRKtDbXQVJXA2SfI2iHZDCRid1cD7C5qtfqcQvkdd9wh5pMmTRIj6EmjJAtXstIhIVeipKREZOBSLRHaRokzH3zwQYfXIh3xuuuuMybStB+hL9VvsYZo323R9qKLLsItt9yC4cOH4+TJk5gzZ45Yf/To0W4P46TM3HNVC7/pppvEdC6oo0sWDTQxTF9C5RUAF99gwAHod+3j8E5INuv7vfjNDqzbmy0eU2btA1ckw11tXs9cxvZpLMtH1pfPoqnAMGToqNNAfNc0FlqFM26bPQQ3TB9o9DtmOrJ69Wp89913nRZVoQsUyr491/CdvizaVjY2oEHTDFeVdXyIGYZhGPNRX1+Pl19+WVjgUUebfEzbk5GRAWtDQ2RDQ0PFOZxGxlCha0pq6gxKLGpf7FoqfEqf6/TP1h3oudSX7s1r9Ja0/CqjaGuJOI4cMWTaJiYmnPX9bOG49JTaNj9bt9B48RnOpZWcjbhQLzEn0bb9MTDXcaHvQrxfaiYaG5uEdYW9cc5jo3CEOihKCOq1ealw8gpAX+F8baahoRH9+483awwpKdvg6qo+63YSWj/88ENhayDVuZL+d6T4aaK6XJQM88svv+Dnn38W+53+uUjTpBpdlElbVlaGiRMnit92qntFVgukTX766adC/DXF/1JXn99t0ZbE0aeffloM6/zxxx/h52e4A7R3717Mnz+/+5EyDNNlKlP2wDN6IJTOrqKaZf8FS0RmLS2bmxsuHIiD6aV48MpkXDA0wuzvx9g2dKIq2b0aaT8tha65EVpHNb5vmYiDzXEI83cX2bWDom2v2JgtQRcD57rZGRsbK2yCmFN4uqjh4eyC2uYmFNRUId6fsx0YhmHkBiUIUQecCkWHhITY3HDkMWPGiFGhNFqmsLBQDM2lLK8jR47Aw8PjjP2p09/eA5eywWi0aWVlZQcxtycd/traWmMGmKVpbtEit7RGPPZzhUWuWSR7hIiI0LO+n7WPS2+oyDgi5grf8B4fT/+2biFZ2eUVlsC1zWvYXMfFxUUFT08P1NTUYs+e/UYR154437Fx8o8ACjNQln4YCJFnAbyeHBcSbc1NRUUFmprOLtpK0O+pJILS7yrd/KPnUiZwU1OTeEzrWlpazvq/RfuSTQ/pnCTU3nfffcIrV/r9ljJz6X1qamp6/b8k3cAzuWhL6jUVRzmd083YGYYxHVTcKeOXd1G8cyVCJl6B+CsNqf7OXuYTxbKLq3E4swwXj40zelV9v3genBwNhfuYvktLQy3Svv8/lB00eM8VOoXj8+bJqIYH5o6Jxf1XJNtssTFbgjqi5F97Ns9a6vzx0MszCfP0xonSIuSzaMswDCNLyE9wxYoVwj/QFpk9e7bx8dChQ4WIS9lZ33//vai3cjpPPvmk0QZQ6qiTIO3j4yOEgZ5CwgEJ2vQ61hAnj2WXQ6cHfNydERcZbHZxvaqqBgUFxeLx6NHJ8PI6UyC3hePSG/LK88TcL3aQqNnTE+hpAV5qlFY3oqLRAeEhvmY/LoMHD8C2bbuRl1eECRPsrxjZ+Y5NY/RAVB/eBF1lfo+/F3vkfMfFx0cvMmHNiVrt0qXfFopRyrQlSwP6baXvikYvkohLj2kd1RI523cYFxeHUaNGicQZYuHChXjllVfO2N9U/0uS9cL56JF7N1Vfoypqx48fNxqx33zzzaIwGMMwpqXi2Hakfv9/0FSXUll54aljThN0rU6HHzal4MMVh6DV6oRYOyDC8EPFgi1TlbYfKV//C5qqUugdlPjTYRT+aB4KN7Uz/nnNGEwb3vnQQOZMyLuWqpPS8M+AgI5DrWg46OOPPy72YToS7u0rRNvcKs5CZhiGkSPUEbYnYYSEgn79+nUosN2e9r6IUqYtQZ393opn1B8wxev0BCp2RVBfQfpM5iQlJc2YZevj42Wzx6Wn6HU6NBSmi8ce4f16FTv52pJom15YjaT4ILMfF/K1JdH2+PGTdnXM23OuY+Me3k/M6wvS7fbz9ZTztRl3d+vXLfHw8BAZwdJ5g2KW4m7/mH6rKUP2bJ+FLBCo/0XZuPSbTXVHhg0b1un+pvhf6upzu/0OVDyMFGgqAEZpxTS9/vrrYt2+fft6EivDMGfJZkz55kUc/fhxIdi6+Idh6N1vIe7ye80m2OaV1uKet9fj7eX7oWnRYmS/YPh6nOm3yfQ9dK0tyPz9Qxx+7wEh2Nar/PCu9lKsa03CoCg/fP7oLBZsuwkV1aThOnT+vOuuu/DWW2+JatlkqB8fHy+G6JBnEtORSC/DBVluNYu2DMMwcoQ83en819Who9amrq4O6enpYgRNX0IqdEUCoSU4etRQ6GrgQHkWIWsqL4C2uREOjiq4BvbOii4h3PCdWKoYmfSdSIXi5IZ7qGHkaVNZPlqb7ON3qS/x8MMPi9pbVGCMEl/OxoUXXiiybmmEhFTArD3jx4/HJZdcIup3DRkyRFjZvfDCC7A23c60pYrW9EE+/vhjUTWTaG1tFd5DVJ3tr7/+MkecDNOnqMk6iuOfPQ1NTbnIrg2bfDWiZt8Cpco8AqpOp8dPW07i/d8OoEmjhdrZEfddNgLzxsXZnI8YY3kaSnKR8tU/UZdr8BE77DQYPzSNgVahwu2zh2BmUhACfKx/l9UeM4l27tyJp556CsuWLROjWAi6C0yVS1988UW7yjSyFJE+hmOSU8miLcMwjFygTnL7a07KWg0KChLe7zSctT3WThSiUTLz5s0TlggFBQXiJixlmva1+i7GImRhhiHJ5ubYsRRjVqccqS9oK0IWHAMHZY8GRBuRhPTUPMuIttJ3QsK6OUeEWgsnd29RBJwSqSjb1it2iLVDYtpBv8E0SZDnuAQVH5MgZ4Bt285t5/Doo4+KyZZw7EmmbXvBVryIoyMee+wxjBw50tTxMUyfxMUnCLpWDdQBEeg3/wl4xpjvxEAn1if+8xe2HskXyyMSgvDU/DEI8XM323sy9gG1jeKdK5D+81vQaZrQ6uiKZS0TcaQ5FhEBHnhm4Xj0D/fhYlm9FG7ff/99Ufm0tLRUrCOrBLrYpTvFJNySqMucItLbUAA1p6rc2qEwDMMwJsKe7IDy8vKEQFteXi7O2VRhfMeOHWdYHckZSvhILbBWpq1hqLrcqMtvE23D4nv9WtJ3klFYhVatDo5K8w7pT0iIhZOToyhGlpdXgIiIMMgN97B4VAjRNo1FW8a2RVtPT0/k5ORgwICOVfNyc3M7rZbJMEzXqM05AY9Iw/+Vyssfg2//N1yDY6BUdc2guqeQODS6fzD2nizCnfOScMVE8lCS191Rpvu01Fcj9fvXUH7IMHqiwDkKnzdOQg3cRQb2fZePgKuzk7FKJ9P7/8PAwMAO66gi9eLFi1m0PY1Ib0OmbWFtNVq0rXDqZTYKwzAMY33aZ0nZOjQ6pq9TUFGHxuZWqBwViAz0NPv7kcfkyZPp8s60bRNtSRzsLaF+7mLkJH1HOSU1iA0xbza0SuWEfv3ihLBOkxxFWxLTqdaM9D0xjKXo9i2Xa665RlTF/O6774RQSxOduMgeoa8NCWEYU9BSV4UT/30eB964DeVHthjXk4BrLsG2pKoBJ/NOZUeSUPv1kxfjqgv6s2DLoPLkXux7bZEQbKnY2FrFOLzdOAtw9cULN0/CE9eOEYItw1gDP1d3uDqpoNPrkV9tGJrJMAzDMIzlrRFigr3MnsUp3i8tCxpNCzw83GUpCHawRwjtvWhL/bn4UO8O35W5kbuvrfS91BWkWjsUpo/R7fSU//u//xMZQQsXLhRetgT5DN155514+eWXzREjw8iW0gMbkf7jG2ipqwQcFGgsyTX7cPfVuzOx9Ke98HR1xhePzxbiG53Yg33Zk7SvQ5Yc2Sv/g7yNy6ixoE7lj8+aJiMfARjZLwhPXz8OAd6u1g6T6ePQNUiEty9SSouERUK0r7+1Q2IYhmFMbB3UmScmrXNxcRHFOm+66SYsWrTIKvExli9CJvnZJiYmyM4vlWipr0FzlaGAkltb0aveQt/N4cwy8V3NGBkNc0MZ0D/8QDYWhhoYckPKgG4ozIBe29pr32GG6SrdbmkqlUpUt37ppZdElUyCKl+7unJHnmG6ChUYS//pTZQd3CiWyQaBvGs9IhPN9p7lNY149btd2NLmXUuepLUNGs6YZAQNxdki47s+33D3+IDTEPzYNBp6pTPuvngYrp0ygLOwGZuySCDRNruK/ZQZhmHkxpIlS0TF7tmzZ2P06NFi3a5du7B69WrcfffdyMzMFAlDlEB06623WjvcPklam2gbb2E/W9laI7Rl2br4hcJRbZq6IsZiZG3flSWLkckRF78wKFRq6DSNaCzLg2uQ+YVwhiF6fHuARNohQ9iAmWF6kl2b9sNraG2oBRRKRFx4HSJn3AiFo8ps7/nHvmy8/r89qK5vFkOYbp41BNdfmGiR4UyMbUPZ10XbfkXGL+9A19KMVkc3fKOZiGPNMYgK8sSzC8ejX7jBQ5QxLQ899NA5t0uFyZgzoUxbgouRMQzDyI8tW7bgX//6F+64444O6z/88EOsXbsWP/74I4YOHYq33nqLRds+k2l7ssMQfLkh+aSaKsv2dNGWrvfNjfTd5OYWoLq6Bl5e5vc6tiQOCgXcQmNRm3UUdfnpLNoytiXaXnHFFV1+wZ9++qk38TCM7FE6OQvB1i0sAf2ufRzu4eargNrcosW/vtqOPw/kiOV+4T5iiHtcm8cR07fR1FUh9btXUHFkq1jOV0Xj86ZJqIUbLpsQj3svGwEXFQ/9MRf79+8/7z4XXHCBRWKxNyK9/cQ8lzNtGYZhZMeaNWvwyiuvnLH+wgsvxMMPPywez5kzB0888YQVomNqGjQormwQjy3RpyDBUe6ZtnVtI92of2gqYkO8oHBwQFVdM8pqGuHn4QJz4u3tibCwEOTnF+L48VSMHZsMueEeliBEWzEyccSF1g6H6SN0qTfu5eVl/kgYRqbodVo0FOfALSRGLPsOGoeBN78A34HjzO6FQxVdm1u1UCoccOOMQbhxxmDOrmUElSd2IeWbF9FSWwG9whGr9WPwV9NgeLm54OX5YzBpSLi1Q5Q9GzZssHYIdkuUMdOWRVuGYRi54evri99++w0PPvhgh/W0jrYR9fX18PDwsFKEfRvJGiHE1w0eruYbKShRXFyKiopKKBQK9OtnukxUW6K+wGA76W7CTFtnlSMiAj2QXVyD9IIq+PUPhrkhUZ1EW/K1laNoKxUjk+wsGNvg2WefFTfxyPNcstjp378/rr/+erGtqqoKS5cuxYEDB3DixAlce+21nb7O7t27cf/994v9ZsyYgeXLl5+xT2NjI5KTk8Xv0aFDh4w3lsi6Z/v27bj33ntx8803m/TzdUkx+uyzz0z6pgzTV2goykLa968Jv9CRT3wJlZehYI7fkElmvftNzqN0EUVG/Y9fMxql1Y0YEMFD3BkIC4TM3z9CwV8/iOVaVQA+bZqMQvhj9IBg/OO6cfD3Uls7TIbpUqZtUW01NK2tUDlyRjjDMIxcWLx4sfCspZubkqctdaZXrlyJDz74QCyvW7cOkydPtnKkfRNr+dnGx0dDrTZvtqg10Glb0VCcJR67tRW7MhVxId5G0Xa0BUTbAQMSsHbtRqSkGERouSF9P5LIztgGzz33HB544AGjaPv88893uh+JsSTEnk20DQkJEeIujYZctWpVp/s8/vjjGD9+PHbs2GFcR+cjtVotvNenTp2Ka665Bm5upivyzr0chjEDutYWlG7+HuVb/yeqSyqdXVFfmGEUbc3FtqP5eHnZLozsF4QlC8aLdX6eajExTH1hJlK+et54obHPcRh+bhoJKJ1x3yVJuPqC/lxszEK8/PLLuO+++7pUxHPnzp0oKyvD3LlzLRKbPeDr6gY3lQr1Gg1yqysQ5xdo7ZAYhmEYE0E+tQMHDsQ777xjtN6jrKlNmzaJzjIh2SQwlietoErM4y1kt3bihME6IDHRfJZy1qSxJMfYX3T2Ma2wGhvqhT8PABmF1bAEAwYYMoXJHkGOSD62VFS8pb4aTm59e0Q6ZZjqNE1mfQ+FykUkop0Nyft80qRJUCqVwvf8scceQ1JSkhByJUpKSkQGbnV1tdg2duxY401AifDwcDEdO3as0/f6448/kJ+fL7Jq24u2dBwo85biPFesPYVFW4YxMbXZx3By2StoKMoUy74DxyP+6ofg7G0+UaG+qQVv/bwPv+8wiHHHcypQ16iBu9r8Q5YY24dOJIVbfkLmb+9D16JBi5M7vm6ehBOaKEQHe+G5heMtli3BGKCLgaioKFx99dWYN28eRo4ciYCAALGNqmHTdirE8tVXX6GgoABffvmltUO2KeiCKNrHH0eLC5BZUcaiLcMwjMyYMGGCmBjbI6PQINrGWVi0pSxOuSZVEK4hMSYXfCjTtv13Zm6k7yglJQ06nU4IWXLC0cUVLr4haKooFN+bd3wS+jIk2G57YqZZ32P8y2ugdD57AhoJr1SkcvPmzfD2PvtvUmBgoMjApUzbzmwPzgdZLJAYvHr1ahw5cqTDtosuukjcYBw1apQQdE2ZZUuwaMswJhTGsn77AHkbvyMjWyhdPRF3xf0IHDHdLHdcJHYeL8Ary3ahuKoB9DZ/m9wft88dJnyMGEZTW4GT376MyuOGu4G5qlh80TQRdXDFlZP64e5LkritWAESYQ8ePCiyiK677jrU1NSIu8POzs5oaDAU9xg+fDhuueUW3HTTTcbhPswpYnwD2kTbUmuHwjAMw/QSOg96enoaH58LaT/G8uh0emQWGbI2Y9sEQXNz4oQhKWXAANNaB9gKDYUZYu4WEmvy15a+o6yiarRqdSZ//TPeLzYKTk6OqK9vEN62ERFhkBuuobFCtG0oTO/zom1f4p577sFTTz0lxN/ToZsTJBybC+6pM4yJIGFWp20Rgm1A8gx4X3AdAsKjzSbYUibt25Rdu9Nwog/1c8c/rh+LpDjOOGMMVBzdjpPLXkJLXZUoNrZSPxabmwbBx12N164bg/GD5HchZU8MGzYMH3/8sTjJk5F9dna2MLf39/cXw3ZozpydWF/D8cmoKLN2KAzDMEwv8fHxQWFhoegQU7ZUZ9fPlCBB67VarVViZICCijo0abSi2HGYv7vZ36+lpQVpaRmyFm3riwyfzzXY9KIt9Q9dVErxnRWU18HdzOqPk5MT4uNjcfz4SWGRIEfR1i04FhVHthozpPsyZF1AmbDmfg9bgEZA0vTII4+gqakJFRUVSExMREqKwXPbnLBoyzC9oKW+BlpNI1x8gsRy9Jxb4Js4Fl79Rop/ZHOi0wPbjxeK7NqrLjBk16qd+V+aAbSaZmT+9h4Kt/wslqtVQaLYWDF8MTYxBP+4bix82efY6tCwsddeew2//vorNBoNLrzwQjzzzDPCyJ7pWqYtwZm2DMMw9s+ff/4JX19D0VwqQMbYJplt3qhkr+WoNP/Q96ysXGg0LXBzc0V4eCjkSENBW6ZtqOlFW6pVERPsJazz0guqMSzStMO2O4PEdRJtT5xIw4wZUyA3XNsyoqUM6b4M3UQ7l3WBpfDw8BBeteeyR5BGadB+PSEry1AsUDpfUW0SSrqxBD1SeNavXy8mMvOlTmd7Pv30U1PFxjA2C93pL933BzKWvwN1YASG3v0WHBQKYSDvM2D0Gf8XpvSudXV2FD+Qnq4qLL5hnLjTPYyza5k26vLTkPLf541VaHc5DsevTclQOKrwwKXDcdWkfma162C6zgsvvIBnn30W06dPF0Ltm2++Kc6rfB7tXqZtVmUZdHodFA7y8k1jGIbpS0yePLnTx4xtkd5WhCw2xDIFmKSCVv36xcnOH5XQNjeIofaEW3CMWd6DfG1JtCVfW0uJtpKvrRxxCzF8T/VFmcbsf8a6PPzww8JXlgo8UyGys0EJMv/3f/+HoUOHiqKWpxcio6xZ2oes6mj0IxUlI0uEu+66C9ak26Ltc889Jwx8qWhKSEgIN1Kmz9FYVoC0//0bVSm7xbKjm6fwDXX2Mu9Q5i1H8vDqd7tx57xhmD3acIdvVH/TVhhl7Be9Tof8v35A1u8fQa9tgcbJA/9tvgCpmghR3feZBeMRa6GCEUzXfW3fe+893H777caKpHPnzsUnn3wiy46JqQnz8oGTUomm1lYU1lSLZYZhGMb+oUIv7u7umDhxolh+9913hZ3QwIEDxWOyUmCsg1TQylJ+tpLwl5go0yJkRYYkCycPXzi5m+eYxhiLkVGGYZjFipFJBeTkhjogAg5KR2ib6tFcVWIccctYj2eeeUZMEp9//rnxMSXISHh5eWHbtm1nfZ3+/fsjLy/vvO83ZcoUbNy4ETYr2pIaTQdhwYIF5omIYWwUnbYV+Ru/Q86az6FraYaDowqRFy1A+LTroHB0Mtv7Vtc3Y+mPe7F2r+Gk/vPWVMwaZfrqooz90lxdhpPfvIiqk3vEcqYqHl81TUA91LhmSn/cfnESnJ2U1g6TOY2cnBzMmTPHuEwZt/R/XVBQIO7sMufGUaFEpLcv0stLkVlRxqItwzCMTHj00UfxyiuviMeHDx/GQw89JDKpyDaBHn/22WfWDrHPYhD+LC/a9u8vTz9bcxYhk4hrS9rIaCsgZ24kgT0tLUtYW6hU5usnWwPq99NI24bCTGFtwaItY3OiLfnuUSoxw/QlmiuLceTjx40nVq+EEYi/6mG4BkaY9X3/OpSL177fjYraJigcHDB/2gD8fdYQFmwZQWtjLUr2rkP26s/QWl8NncIJv+nGY3vTAPh5uuKf14/FmAEh1g6TOQutra1wcXE5o4ADFd1guu5rK0TbylJMjJFnFg7DMExfIzMzU2TVEj/++CPmzZuHF198Efv27etws5OxLC2tWuSU1FjYHkHeom29JUTbtu8qv6wWzS3mL+IXGhoMDw931NbWIT09S5ZZ0m4hcUK0pSJyvoPGWTscRuZ0e/zlLbfcgm+++cYkb/7XX3+Jk3BoaKgQoZYvX95hO3mELFmyRNgwkN8fZSGlpnZMs6diT9dff70wFSbj4b///e+oq6szSXwMI+Hk6SfaqKObF/pd9xSG3PmGWQXbqromPPvFVjz5n81CsI0O8sQHD1yEuy4ZDmcVFxvry9DvYnX6QaR8/QJ2PnMF0n9cKgTbSudgvNF6BbbrEjFpSAS+fHw2C7Z28F3edNNNuOKKK4wTVSO94447OqzrDjRsNDo6WojBY8aMwa5du7r0vGXLlonfuMsuuwz26GubUVFm7VAYhmEYE6FSqYSnoGQdNGPGDPGYCpXV1BhEQ8by5JTUQqvTw13thEBvV7O/H3lKZmfniseJifIUbUn4I1zbfFLNARUf9nZ3hl4P5JSaXyeh60lJZJerr61rm/+wJLozjDnpkvpDw1AkqMDSRx99JE6gZOBLWUHtef3117v85vX19Rg2bBhuvvnmTjumr776Kt566y188cUXiImJweLFizFz5kwcO3bMmJ1Egm1hYSHWrVsnspMWLVqE2267zWTCMtN3xZSKo9vgM2CUKOCkUDpiwI3PwcnV02x+Q+3JKq7BH/uzoVQ44LppiVg0awgPb+/jkG9yye41KNrxOxpLDRewhN47HGvrYvFXY384qVR4/PJkzBsXx9nYdsCNN954xrobbrihx6/33XffifM12RiRYLt06VJxziRT/cDAwHNWQ33kkUcwadIk2BvRPgbRNrOi1NqhMAzDMCaCvGzpfDZhwgRx85HOb8TJkyfZPsgWipAFe1vkOvPkyQzRJ/Pz84G/vx/kiCUybSU7i32pxcguqcMYwCLFyPbsOSAKyV122WzIDen7kkbhMozVRdv9+/d3WE5KShLzI0eO9OrNZ8+eLabOoB9o6nA+/fTTuPTSS41FW4KCgkRG7rXXXovjx48Lo/rdu3eLwmjE22+/LYbNUFU4yuBlmO7SVFGE9B/fQMWx7YicdTOiZt4k1rsGRpr1fVu1OjgqDcnvSXGBuPuS4UiKD0RipDwvUpjzo9dpUZmyWwi1FUe2imVCoVLDd9hU/FEfh2VHNHRPG/0jfEWxsaggT2uHzXQRU3vy0U3TW2+9Vdy8JEi8XbFiBT799FM88cQTnT5Hq9WKm59UZHTz5s2oqjJ0yOyFWN8AMc8oL+UKvgzDMDLhnXfeEdW6//e//+H9999HWJiheNKqVaswa9Ysa4eHvl6ELMZC1ggnTqR1KGwlNzS1lWipq6TUVLgGRZv1vcgigURbS2TaSqKtnIuRuYW2ibbFOaLuDSV4MYy56FLrItN3a3gZFRUVCUuE9tXeKHto+/btQrSlOVkiSIItQftT1e2dO3fi8ssv7/S1m5ubxSQhDb+hLGKamL6JXtuKgi0/IWf1p9BpmkRVSKInbYKeQwJCV55L+/15IBfv/3YAS++civAAD7GeCkj19P0Z+79xULJ7FYp3rYKmqsS43j0yEUFj5qLcLwnPfn8AeWV1QqSibOy/zxoMJ0elxdtLd9q6rWPPn4H85vfu3Ysnn3zSuI7OhXROpHPl2Xj++edFFi5ZC5Foey5Mce40dXuJ9vaD0sEBNc1NKK6tRqC7eW9a2HN7t+fY7T1+e47d3uO359jtJX5zxBYZGYnff//9jPVvvPGGyd+L6TpSISupsJW5kQQ/SQCUGw1FhixNF79QKJ3VZn2v2LbvjDJtLYEktEvCu9xw9g4S35m2uRGNpXlwCzav6M70bbp9S4CsDN588014eBiEpfZWB/fee6/I6DEFJNgSlFnbHlqWttH89CGfjo6Owu9I2qczXnrpJZFVJKFUKjF16lRUVlZ26JAyfYfG/FQUrvoAzcUGXyF1xECEzLkDzv7hwje5JxewtbW14kKbhJOzUVXXjA9WHcf2EwZh7rNVB3D3xYN68UkYe0WvbUHtyd2oOvAH6jMO0hqxXql2h+fgyfBOmg4n/0j8uDUT336/FTq9Hv6eLnjg0sEYEu2L2hrLVITtaVu3ByQR0h4pKysTWbOdnTNPnDjR6XO2bNmC//znPzhw4ECX3sMU505ztJdwTx9kV1dgX2Y6RodFwZzYc3u359jtPX57jt3e47fn2O0lfnOdO9PT08WIFJpT35P6fJRpS4LuoEF8rWwNMiR7BAtl2kp+qHLNtK1v87M1tzWCZI9AZJfUwhJI31leXoEoSEaFyeSEg0IB1+BY1GYfFRYJLNoyNiXakr/syy+/fIZoS0bhZF9gKtHWnFAmUnufXrrYWLBgAXx8fODm5mbV2BjLU7jtF2T9tJRSXoVAFn3xHQgaPUf8GPfmIpsyIKlNdXaRTRff6/Zl482f9qGmQSO8a2+8aBBumJ4osiWZvkNDcTaKd65A6d61aKk7NTTdK2GEyKr1GzwRCidnFFXU47mvd+BghsG7c1pSBB65ehQ8XFVWjP78bd2ecHZ2Rl+BRAA673388cfw9zf4wlri3GmO9tI/MESItkXNDeKmrTmx5/Zuz7Hbe/z2HLu9x2/PsdtL/OY4d27atElY6JGnLRWufuGFF4Roe/DgQXGzkWwTGMtS39SCwor6DgKgpURbqaiV3JD8UKWiVuYkJtggtFfWaUTSEBUnMyc+Pl4IDg5AUVEpUlLSMXLkMMgNt5AYIdqSL3HA8GnWDoeRMV0WbalSJwlNNFFnTyoERlB2z8qVK89Z6KS7BAcHi3lxcTFCQk5VQKdlyVOX9ikpOTV0mGhtbRWZkdLzz3Zx0f4Cg7KFCLoYstULIsZ8+A4YjSxHJ/gPm4qYS+6EysM0nX66yO6sTZH49tr3u7DjeKFYTgjzwT+uHyvmTN+AhtKUHdwovGprMg8b16u8/BE0ejaCRs+F2v+UJ/e6vVl47fvd4oJZ7eyIh68aiVmjYmzGv/Nsbd3esOf4SXilcxmdI9tDy52dDylziQqQzZs374whrjRihYqXxcXFmeXcaer20i8gCGtTjyK1vMQi36E9t3d7jt3e47fn2O09fnuO3R7iN0dc5MP+r3/9S9wobJ8oNG3aNOF3y1ierDZrBD9PNbzczH+Tu7KyWgh+RP/+Ha9H5IKlipCJ93BxQoivmxDeM4uqzC7aSmI7fYdkcyFH0daVi5ExtibakncsXTTQ1K9fvzO20/r2wyZ7S0xMjOhorl+/3ijSknBMXrV33nmnWB43bpwomkI+fsnJyWLdn3/+KTqe5H3LMJ1Rl3cSVal7ET51vlhW+4dh5FPfwtnbUNDG3KzenSkEWyelAjfNHIwbpg80FiBj5Avd8KK2R0Jt6b4/oG0yZCtAoYTvwLEIHnMxfBPHGL2UibpGDV7/3x6s2ZMllgdH+2PJgvEI85fXECOm96hUKnEepHPmZZddJtbRuZCW77nnnjP2HzBgAA4fPnXDgKDCn3RTloahRkREwF7o52+whEgr7yhYMwzDMPYJnZ+++eabM9ZTghDZATGWJ73QOtYIERGhcHeX30hYvU6HhqLMDuKfuSEvYhJt0wuqkdzvVFKcOS0SNm3abvwu5ZhpS9S3fY8MY3XRloqRkehAdzh//PHHDkMQqbMYFRWF0NBTmWFdoa6uDmlpaR2Kj5G3Hr02+RU98MAD4i5rQkKCEHEXL14s3kPqkCYmJooKolQtm6pkt7S0iM4pFSnrbiyM/GltrEXWyv+gcOtyOlPCM2YIPKMHi23mFmy1Oh2UbZkI11+YiILyOlE8KrptqAojX6jdlexdh6IdK1Cff6qCqot/GILHzEXgqFlw9jpzePrB9BI8/9V2kZmtcHDATTMH4cYZg1ngZ84KZSTdeOONojjn6NGjsXTpUuE3v2jRIrF94cKFogI3edPSaJnBgw2/f+1vzhKnr7d14ttE28yKMrRoW+HEFXwZhmHsGjofFRYWiv5fe/bv3y/OY4zlySistqhoe6oImTz9bJsri8TIOwelE9QB4RZ5T7JI2HIkHxltAry5kb6748dP9X/khGuIIQO8qbxAfJfmLibH9F263LOZPHmyUVilDBxTDIXZs2ePKGIiIXnlUafz888/x2OPPSY6nLfddpvIqJ04cSJWr17dwZrh66+/FkLthRdeKGK68sor8dZbb/U6NkY+0M2Gkj1rkfnb+2ipNRQV8x8+TVR9NDetWh2+/eM4thzJw/v3XyQEN/Ksfeq6sWZ/b8a6ba4m46AQassOboCuRSPWOziq4D/0AgSPvRhecUmd+iZTm/lszRF8ufaoKDYW6ucmsmuHxFgmE5yxX6655hqUlpZiyZIlohgnjVKhc6ZUnCwnJ8dmh9f2hhAPL7irnFGnaRbCbb+As9sjMQzDMLYPJeA8/vjj+OGHH8RoTho5snXrVjzyyCPiBiRjeTLbhL44C/nZnjghbz9bqQiZa1AkFBa62SwJ7plFNRZ5v8TEhA7fpdxQuXvDyd0HLXWVImvaI2qgtUNiZEq3fyEoo1YqQEIdQI3GIEZIDB06tMuvNWXKFCFunA06ST///PNiOhuUldvZ8BmGkbyC0n58AzXpB8WyOjAScVc+CJ9+BjsNc5JWWIP3/7MLaW2VVv/cn4MZI7mypJzR1JSjePdqUVissTTPuJ6GPQWPnYfA5Ivg5OZ51ufnldbiuf9uw7HscrFMvrUPXTVS+FAxTFegm5id2SEQGzduPOdz6WapPULXCvH+gThQkCt8bVm0ZRiGsW9efPFF3H333SJRiGqnDBw4UMyvu+46YeXDWB4aUm+NImSS8Cc3jNYIFihCJiGN8MwqrhYajLlrY8THR4v3qKysQnl5Bfz8zFss1hqQHzHZLpJFAou2jM2ItpTFQ0MtV61a1el2OqEyjC2ga23BkQ8fgaa6DAqVCyIvWoiwKddA4WheAaxZ04pPVh3Csg0pIlOSzPrvv3wELko23PBg5IVe24qKE7tQvON3lB/bDugMv4E0RCZgxHSRVeseMeCcF0Z04bRyZwbe+HEvGjWtcFc74dG/jcb0EdxmGKYrJPgHGUTb0mJggLWjYRiGYXoKXRPRaBEaOUkjR8jfliz1hg8fLizzGMtTWdckJrqUtYS1G7UBSbTt10+eRcgairIsVoRMIiLAAwoHqpnRgvKaJvh7mXc4v1qtRmRkGLKz83DyZAbGjZOfaOsaHC1E24bibGuHwsiYbou25DNLVgVUEIwyZX/++WdRnZq8Z//973+bJ0qG6YapO11RiEq7jk6InnMryo9sQezl98HFx/x2CPtSi/HKsp3IK6sTyxcOj8SDV46Ej8cpSw9GHjSW5aN450oU714lbgxIkE9y0NiLEZA0BUpn1/O+Tk19M177fjf+PJAjlpPiArH4hnEI9pVf0QWGMXcxspTSImuHwjAMw/RSsIuPj8fRo0eFSGtPhTHlSlaRIcs2xNcNamfzD+UvKSlDdXWtsHSKi5PnKMWGYoNoqw6yXIKGs5MSwT6uKKhoEN+puUVbIiEhtp1oOxJywzUouoMIzzDmoNu/un/++Sd++eUXUeiEfkjJLuGiiy6Cp6enKG4yd+5cswTKMOejNuc40n96C2GTr0LA8AvFOiryFDR6tsUuMj9dfVgItgFeatw2qz9mjR0gSw/JvoqupRllh/5C0Y7fUZ2237jeyd0bgSNnInjsXOPJuyvsTS3GP/+7DaXVjVAqHHDrnKG47sJEY9E6hmG6RmKgofjo8ZICiwz5YxiGYcwDXTeTWFteXs6ZtTZCVpsHKhWysgQk8BHR0RFwdlZBjklGUmZmd/oNpiDC302ItpnF1RjZ3/x2Uv36xeKPP/5Camo65IhrcFQHEZ5hbEK0pcJggYGB4rGPj4+wS+jXrx+GDBmCffv2mSNGhjmvj2jWio9QvMtg2ZGzph7+SdNEp90SHXetTidENnqvx64ZjR82peDWOUOgaTRk2zL2T11+mhBqS/euRav0vTo4wKf/aCHU+g6a0C3bjZZWLT5eeQjf/HkcZOtNw5WeWTgeiZF+5vsQDCNjEvwD4ahQoKqpEYW11Qj1tIznHsMwDGN6Xn75ZTz66KN4//33MXjwYGuH0+fJbMu0jQqyjGgrCXwk+MmR5soikQjioHSC2i/Eou8dEeCOnSdLjdnT5iYhIa6DEC83JNG9uaII2uZGYY/HMFYXbfv374+UlBRER0dj2LBh+PDDD8XjDz74ACEhlv3RYfo25Flb8Nf/kLP2C2ibG8Q6ynaMvvh2i4i1pdUNePOnvfD3csUDVxgKm0UGeuLhq0eJKrcVjWYPgTEjJM6W7lsvxNq6vBTjemefYASNmSMyuHtiuZFdXI3nvtyGlLxKsXzJuDjcd3myRYabMYxccXZ0QqxvAE6WFeNESSGLtgzDMHbMwoULRdFr6muqVCrhjdmeiooKq8XWF5EEPkv42RKpqYYiXfHx8hRtG4oMWbbqwAg4KB0tnmnbPnva3EjCe2qqPEVbGm1JU0tdFRpKcuAR0d/aITEypNu/Evfffz8KCwvF42eeeQazZs3C119/LU6o9lp5mrE/qtIOIO37V9FYmieW3SMHIO7y++EZPcjs763T6bF8ayo++P0g6pta4KhU4LppiQj0Pr9/KWPb0LDqmoxDQqgtO7hR3AUn6E6435CJoqiYd0IyHHpgX0CvvXxrGt5evg/NLVpRoO7xa0Zj8jD2amMYUzAwKFSItsdKCjAtPtHa4TAMwzA9ZOnSpdYOgWlHVrFkj+BpkfeTBD65ZtpKQ+mpiJWliQxwF3NLZdrGx8cYfYorK6vh42MZ4d/S2bbVdQeEry2LtoxNiLY33HCD8XFycjKys7Nx4sQJREZGwt/f39TxMcxZ0AvB1snDFzEX3y4ybHsipHWX9IIqvPLdThzNKhfLA6P8hCUCC7b2jaa2AsW7VqN45wo0luYa17sGxwj7g8DkGeIuak+hirsvfbsTW4/ki+VR/YPxj+vHIsCL2w3DmIrEwBAsP7ofx4sNN5YZhmEY++TGG2+0dghMGzUNGpTXNFrUHuHkSckewTC0XrairYX9bIkwfzdyeENVfbPon/i4m7dYtru7G0JDg1FQUIS0tAyMGjUccoPE9+r0A2gsMWRQM4yp6VU+PmWO0XCVESNGmC4ihjmLqFabcwJ+g8aLZe/44eh33T9E9qOji2GYhzlp0rTis9VH8O2G49Dq9HB1dsTtFw/D5RMTuGiUnaLXtqLyxG4U7fwdFUe3Qa/TivXkRUSF7ILGXgyPyMReW23sOF6AF77egYraJjgpFbhjXhL+Nrk/FAoulMQwpmRgWzEyyrTlYmQMwzD2jVarxfLly3H8+HGxPGjQIFxyySVQKpWwBd5991289tprKCoqEjYOb7/9NkaPHg25QbZeBCWouLl0vX5DTykvr0B5eaU4h8fHW17UtASUkWkt0dbZSYkQXzcUlNeLbFufePOKtlLGNIm25GsrS9E2KKrD98owNiHa/uc//8Ebb7yB1NRUsUyVPR944AHccsstpo6P6eNoNU3I3/gd8v78RnTCRz71DZy9DBndQaNmWiyOhqYW/LItVQi2k4dG4MErkxHA2bV2SWNZAYp3rRCZtZrqUuN6j+hBwv4gIGkqlM69/27JAuH93w6IwnSSD9izC8cjIcyn16/NMMyZJAQEQenggMrGBpTU1SDIQ35D8BiGYfoCaWlpmDNnDvLz80U9FeKll15CREQEVqxYgbg462Zgfvfdd3jooYdETZcxY8YIO4eZM2eKui9SwW65IHmfRlvMGsHgZxseHnKGl7EcoP5sQ3G21ewRiOggrzbRtgbD47tfn6O7JCTEYuPGbfItRtb2PUoZ1AxjddF2yZIleP3113Hvvfdi3LhxYt327dvx4IMPIicnB88//7zJg2T6HnqdDiV71yJr5cfQVJUafWupOJQk2pqb2gYNPFxV4rGvpxqP/G20uDs5aUi4Rd6fMR3kTVt2eLPwqq1O3Wdc7+jmJcT/oDEXw82EF05ko/Hsl1uRUWjITrhyUj/cfUkSnFVcbIxhzIWLoxPi/ALbfG0LWbRlGIaxU+677z4hzO7YsQO+vr5iXXl5ubDpo20k3FoT6gvfeuutWLRokVgm8ZZi+vTTT/HEE09AlkXILGSNIPnZJiTI0xqBEkaogLaDQgm1f5hVYiABftuxAmRayNdWsrlITTXYXsgNKWOaEoOoz6lwcrZ2SIzM6LaC8P777+Pjjz/G/PnzjetoqMrQoUOFkMuiLdNbqlL3IeOXd1Gfb8jkdvYJRvTFtyEgaZpFfGup0NjvO9Lx3q/78fQN4zBxsEGknT7CMPSBsR/qC9KFUEs3AFobag0rHRzg038UgsbMhd/gCVA4GoR5U7Wd//2VIjJsNa064RP11HVjMH6QdS7KGKavFiM7UpSPqXEDrB0OwzAM0wM2bdrUQbAl/Pz88PLLL2PChAlWjU2j0WDv3r148sknjesUCgWmT58uEpk6o7m5WUwSDQ0NYq7T6cTUU+i5lLnZm9c4H5KwFxXoYdb3Od3PNiEhpsfvZ4nj0lPqCg2ZxC4B4YBCadEYpeMSFWjIms4sqrLI+0s2F5Rpa4vfSW/bjNLNG45qD7Q21qK+OAduofK54WDL/0tyOC5dfX63RduWlhaMHDnyjPVUlKy1tbW7L8cwZ3jXHvnwUei1LVC6uCFi+gKEXXClxe5Y0YXJq9/twqEMQ3bv7zsyjKItYx+0NtWjdN8fKNq5AnU5J4zrnX2CEDR6DoJGz4aLb7DJ37esuhEvfrMDO08YiiCNHxiKp64bCx8P83tFMQxjYEhwuChGdqjwVEFBhmEYxr5wdnZGbW3bzfZ21NXVQaUy3c32nlBWVib8doOCOg4rp2Uqzt0ZZO3w3HPPGZfJl3fq1KmorKzsIOb2pMNPx4nEAxKOzUFGYaWY+7o6oKKiAubm2DGDrVhoaFCP388Sx6WnVGQeE3NH7xCLHM/OjouPWi+WMwurLBKDv7/BGq6wsBg5ObmiOJmt0ds24+QXhta8EyhJPwovF/lY4dny/5Icjot0A8/kou2CBQtEti0NC2nPRx99hOuvv767L8cwaKmvgZOb4Y6fysMX4VOvQWtTA6Jm3gQnd2+LxNCsacWXfxzDV38cQ6tWB7XKEbfMGYqrLuhnkfdnegf9YNZkHkbxzhUoPbABOk2TWO+gdITf4EkIHjsX3v2SxVAkc7D5cB5e/nanqMSqclLi3kuHiyJ1XAiJYSzLsBDDTbYjxfnQ6nRcKJJhGMYOufjii3HbbbeJOipSca+dO3fijjvuECM87Q3KyiUP3PYddepT+/j4wM3NrVfCAV1r0uuYQ1BpaG5BabXhmnpIQji83MyfRJOZabjpmpQ0uEOmtS0dl95QUWtIDPKKSOjx5+sp0nEJCHIHsBuVdRo4OrvB08zfK33OwEB/lJSUoby8CpGREbA1ettmKsLj0Zh3Aor6Mot/r+bElv+X5HBc6AalWQuRrV27FmPHjjWeRMnPduHChR1OSKcLuwzTHk1dFfLWf4XCrcsx9O634BE1UKyPnnubRePYm1osBLeC8jqxPGFQKB66ahSCfW3vLiBzZmZ2ye41Iqu2sSSng7eQKCo2cgZUZhT+mzSteHv5PizfmiaW48O88ezCCYgJZi9NhrEGMb4BcFOpUK/RIK28BP0DTJ9VzzAMw5iXt956CzfeeKOon+Lk5CTW0YhOEmzffPNNq8bm7+8vMmWLi4s7rKfl4ODgs3bM23fO6fkEdfZ7K4SQcGCK1+mM3FJD38jXwwU+HuYvClZdXYPi4lKjD2pvPpM5j0tvaGwrQuYWEmOV2Oi4uLs6I8jHFcWVDcgprcNQC3y39H2SaJuWlonk5GGwRXrTZtwkX9vibJtrc73FVv+X5HBcuvrcbou2R44cwYgRI8Tj9PR048mLJtomwRlmzNmgYmL5G79H/qbvoG1uFOsoO1ISbS0NZdmSYBvgpcb9VyRjyrAIbr82jF6nReWJXUKorTiyVSwTCpUaAcOniaxaj6hBZv8OU3Ir8OyX25BTYqiqO3/qANx28TCoHM2TzcswzPmhzNrBweHYmZOBw4V5LNoyDMPYId7e3vjll1+QmpqK48ePi2u6xMRExMfHWzs0Yc9AtoDr16/HZZddZsy6ouV77rkHcixCFhVkGBFpblJTDX6vwcGB8PT0gBxHBjYUZ3UoXmUtKMGERFv6jofGBpj9/fr1i8WWLTuFr60ccW0raC2J8gxjSrot2m7YsMGkATB9B62mCQWbf0Len9+gtcEgdLmH90PUnFvhM8Aw9MkStLRqkV5YjQERhqELVCTqqfljMCUpEm4uhrv5jO3RVF6Aop0rUbx7FTRVhrvwBAm0JNT6J02Do4ur2eOgYmPfbjiOj1YcElYa/l5qPH39OIzqz+IQw9gCQ9tE24OFubhq6Jke/AzDMIx9kJCQYBRqbSmhgkaWUiYw1Xkh+4alS5eivr4eixYtgpzIKjb01yw1giwtLcMo8MmRltoKQ2FkBwXUAda1CIgO8sKO44XGQnPmJiHB8J3KVrQNMhQsbyzNhU7bCoWyRwPaGaZTuDUxFruzeOjd+4yFodSBkYiecwv8hk626EXYnpQi/Pt/e1BR24hvn7oYvp6G4SBzx8qnyqOc0LU0o+zwFuFVW3Vyj3G9o5sXgkbORNCYuWJ4kaUorqzHv77egX2phiFxFwwNxxPXjrGIxxfDMF1jaJuv7eGiPGuHwjAMw/QQsuN74403RLatJOA+8MADuOWWW6wdGq655hqUlpZiyZIlKCoqQlJSElavXn1GcTK5ZNpGW0i0lQQ9SeCTG1KWrYtfCJQq6/YdpO80q9gyoi3ZIxCpqYaR2nJD5R0IpbNajCJuKsuzeiY1Iy9YtGXMhjRsnYo/kTAbPGYucmurEDVrEQJHzjBbUajOKK1qwFvL9+HP/TlGb6bc0lqjaMvYFvUF6Sja8TtK9q4zZmXDwQHe/UaKduQ3ZCIUjpatHkxt59Xvd6G2QQMXlRIPXDESF4+NtanMD4ZhgCHBBtE2q7IcVY0N8FabPwOfYRiGMR0khlJtlHvvvVf42hLbt2/Hgw8+KOqoPP/889YOUVghyM0OwdqibWqqzEXboqwOQ+mtSUywwfIiq6itn2VmpOzp3NwCUYjP1VVe12bUHyShtjbnuPieWbRlTAmLtozJ0et0KDv0F3JWf4rwC69H0KiZYj1lRQaNnm1RsY2Gr/+wKQX/WX0Yjc2tUDg44IpJCbhl9lB4uFpW9GPOTWtTPUr3rRdZtXTCk3D2DkTQ6DkIGjMbLr4hFo+rvqkFS3/ci5W7DBeSZKvxzMLxiAy0jL8XwzDdg0TaGB9/ZFaW4UBBDqbEDbB2SAzDMEw3eP/99/Hxxx9j/vz5xnVUhGzo0KFCyLUF0VbuNLdoUVBeLx5HW8jTVsq0lbIy5UZDm9+pLQh6khBfUtUg+jrmtgj09fWBn58PyssrkZaWhaFDrVPLxpyQGC9EW/a1ZUwMi7aMSTNrqaBY7tovjcM/Cjb/aMiqpep6FvZ2Ie/aW19fi9T8SrE8KNoPj1w9Cv3CDV62jG3YZtRkHhFCbemBP6HTNIn1DkpH+A6agOCxF8On/0iLZmW350hWGZ77cpsoVEcJtQumD8LfZw+Bo5KrZzKMLTMiLEqItnvzs1m0ZRiGsTNaWlqEX+zpUAGw1tZWq8TU18gtqYFOrxdJLjRC0dzU1zcgL6+gb2Ta2oBo665WiSLcpdWNwtd2cLS/2d+TxPjt2/cgJSVdnqJt2/cqfc8MYypYtGV6jV7bipJ965G77kthvk0oXdwRdsFVCJvyN6sNH3dyVCIpPlDcQbxzXhLmjomFQsFD2W0BTU05SvasRdHOFWgsMVhWEOqgKGF/EDhyJlQePlaLjzK0//vHUXy2+gi0Oj2CfFyx5Ibxoj0xDGP7JIdH4ccje7E3jy+cGYZh7I0FCxaIbFuySGjPRx99hOuvv95qcfUlJK/TmCAvi/Tl0tMN52vKxvT19YasM21twB6BiAryFKJtTkmNRUTb+PgYIdqmpWVCjkiibX2RPD8fYz1YtGV6zcllr6Bkzxrx2NHVA2GT/4bQSVfCUe1u8WE83208gQmDwhAXajjZ3zpnKBbNHMyFomwAqqRZeXwHineuRPmx7bRCrFeoXBCQNA3BY+fCI3qw1T1iKav2n//djkOZpWJ5+ogokaHNdhoMYz8khxsunE+UFqG2uQkezubPEmIYhmF6zkMPPWR8TNeCn3zyCdauXYuxY8eKdTt37hR+tgsXLrRilH2HzDav0+g271Nzc/JkuqytEVrqq9FSZxj96RoYAVsgMsgTe04WI7vYMr62JNoSGRnyvKFOyUcEFSKjEcjWGinKyA+bF21ra2uxePFi/PzzzygpKcHw4cPx5ptvYtSoUcbh1c8884zwPaqqqsKECRPEnVmqMMqYB21zg/ghclR7iGXyqa04vgPhU69FyITL4ehiWWNxagObD+fh7eX7heC2O6UIb909TVzwmdufh+naXWUSaov3rEFLbYVxvUf0IJFV6580zeJt5mys2Z2J//thNxqaW+Hq7CjE2hkjo60uJDMM0z2C3D0R4eWD3OpK4Ws7KaaftUNiGIZhzsH+/fvPsEIg0tMNYp6/v7+Yjh49apX4+hrZbZm2UUGWKkKW2UHYkxvSyEKVdwCUzrbR75Hqc+RYTLQ13FCXa6ati28QHBxV0LVo0FxZDBe/UGuHxMgEmxdtb7nlFhw5cgT//e9/ERoaiq+++grTp0/HsWPHEBYWhldffRVvvfUWvvjiC8TExAiBd+bMmWK7iwtn1pgSTV0VCv76Hwq3/ozgsfMQM+8Osd4rfjhGL/kBSpXljzd58Lz5014h1BL+XmpcPFaePkj2RGtTA8oObkDxjhWoyTpiXO/k7oPAUTMRPGaOTfg5SdQ2aPDvH3Zj3T7DsKUhMf5YsmA8Qv0smy3OMIxps21JtN2bl82iLcMwjI2zYcMGa4fAtEPKvqQh9JZAyr6Uq2jbUGKwEHQNNGRj2gJRkmhbYhnRNi7O0PfLzMwR3tSOjjYvRXULyqxVB4ShoTBTfN8s2jKmwqb/UxobG/Hjjz/il19+wQUXXCDWPfvss/jtt99ENu0///lPLF26FE8//TQuvfRSsf3LL79EUFAQli9fjmuvvdbKn0AeNJYVIH/jMhTvWinuHBFVqftEhitlINJkacGWRLb/rD6MnzafFJ6jTkoF5k9LxIKLBsLVmbNrrVdU7LDIqqWCdDpNo2GDQgnfxDEIGjMXvgPHWbwg3fk4kF6C5/+7DcWVDVAqHLBo1mBRcIyLjTGMfZMcFoXlR/djb748h+ExDMMwjDnQ6nTIK63tkI1pbqTsS0nYkxuNJYbEELWNWCO0/27zyupEPQ9z933CwkLg4uKMpqZm5OYWICYmEnJDHRApRFvxfSeOsXY4jEywLfXkNOgOjFarPSNjVq1WY8uWLcjMzERRUZHIvJXw8vLCmDFjsH37dhZte0ltznHkb/xeCHDQ68Q694gBCJ92HfyHTrLqkPG1e7Pww6YU8XjSkHDcc9lwhPsb7BoYy6KpLhPWByTWSoXoCHVABILGzEEQFRXzMr+5fXehi5NPVx/Gf9cdE9VxKav2mYXjLWLEzzCM+RnZ5mt7rLiAfW0ZhmEYpotQIoOmVQeVowLBvuYfyk/9/ays3A5D6OVGozHT1naEykBvV7iolGjSaIXFoLkFeoVCgdjYaBw7liJEejmKtq5BkShvl1nNMLIXbT08PDBu3DiRUZuYmCgyaL/99lshyMbHxwvBlqD17aFlaVtnNDc3i0mioaFBzHU6nZgYA8V71qJ0/3rx2LvfKIRNmy+sEEis1VNmpQWPFWVxVtU3w8fd0OmeNzYWe08W4dLx8RjVP1iss6XvjmKhmG0pJpMXFTu2HcW7VqHyxA76wMaiYv7Dpgqxtn1RMVs7DrmltfjnV9txPMfgsTt7dAweuHwEXF2cbC5WW0dObV0On4E5RYinN6J8/JBdWY49eVmYGjfA2iExDMMwjM0jWSOEB3hAqTD/yDPKutRoWkQWJmVjypGGNk9btQ2JtgqFgxBqT+ZVCosES2RVx8VFCdE2PT0LF100GXLMtG3vYcwwshdtCfKyvfnmm4V/rVKpxIgRIzB//nzs3bu3x6/50ksv4bnnnjMu0+tOnToVlZWVHcTcvkRrfTWq9q+DW8wQqMP6i3Wug6fBq6ocvqMvhktwLEjOoGNkaTKLavH5+pMoqmzAO3dOEFYIxEOXDhTziopTxa1sSfyhInokZtFdRbnQXJaHqoPrUX14I7T1hgIFhDq8P7yHTYdH4ngondVotVJbOR/0ffxxIB+frElBU4sWbi6OuHvuQEwYGIymhlo0Ge7fMH20rUs38Bj5MDYyVoi227PTWbRlGIZhmC4geZxa2hqBMi/t/VrybMkuTWX5NpdpS0iiLQn1Eweb//0kz2ISbeUIZdpKhbgZps+ItnFxcdi0aRPq6+tRU1ODkJAQXHPNNYiNjUVwsCHDsri4WKyXoOWkpKSzvuaTTz6Jhx56qENHfcGCBfDx8YGbmxv6EvWFGaK4WOm+ddC3tkBXeQHChowzbPT1RXD8IKvFVlbdiE9WHcLKXZnQ6yHE2sIaHZLi/O1CyKIsU2pT9n7x0dpUj7IDG1CyaxVqs09V7HXy8EFg8kwEjp4N1yDbMdU/G8WV9fj3j3ux/ViBWB4eH4h/XDcGQT5963/e1MiprTs7O1s7BMbEjIuMw3cHd2N7jqH6OMMwDGP7/PXXXxg/fvwZhYrIOm/btm3GWieMPIqQSQJeXJw8i5A1lRdAr9OKEYkqrwDYEsZiZG3fubmRPIsloV5uSJnULbUVaG2sg6Oai1ozfUC0lSAxlSbK3luzZg1effVVxMTECOF2/fr1RpGWhN2dO3fizjvvPGfHvH3nnDJtCRIc7F106ApaTRPKDm5E0fbfROEoCffw/vAfOtnqx6CxuRXf/HkM3/x5XHjsENOGR+KOi5MQ5m8/P3wkZNlrmxJFxTIOieJzhqJiTaeKig0ci+DRc+Bjg0XFOvscBzNK8fuOdPy5PwfNLVoh/t8yZyjmTxtgkSFffQF7buvtsff4mc59bR0VCuRWVSCvugLhXr7WDolhGIY5DzQCsrCwEIGBgR3WV1dXi23kgcrIJ9P2lGhr+0kgPUEaKk/1Phxs7Fozsk2YzykxFJ4zN3LPtHV0cYPK0w+amnLxvXtEGUYGM0xvsG3FBRACLQkv/fv3R1paGh599FEMGDAAixYtEkLBAw88gH/9619ISEgQIu7ixYsRGhqKyy67zNqh2yyH338QtVltGZMKJfyHXIDQC66CZ8wpD1JrUVHTiJteW43ymkaxTEWh7r1sOAbH2NZdSbnSXF2Gkt2rhVjbWJrX4a5h8Ji5CBw5Q5yIbB1qP6t3Z+K3HenIbXcRMjQ2AI9fMxrRwV5WjY9hGMvg7uyCoSHh2Jefgx3ZGbhqKIu2DMMwtg71/Trrk5SXl/e5UZHWQBLwIgMtU+RZEvAkQU9uNBTn2KQ1QnthPrtNqDc3sbEGYb68vBKVldXw8ZFfn4z6zSTaUjEyFm2ZPiHa0h1VsjPIy8uDr68vrrzySrzwwgtwcnIS2x977DFhnXDbbbehqqoKEydOxOrVq+HiwlWiCW1zo8iq9U+aCqXKcEwCkqaipaYCwePmIWjULKi8bMduwNdTjYQwbzg7KXHXJUmYMizC6kKy3NG1tqCCiortXIGK4zupwpxYr1CpETB8qhBr2xcVs1VatTrsPF4ohNptR/Oh1VG5PECtcsSFI6JE8bpB0f42/zkYhjG9RQKJtluz03DV0JHWDodhGIY5C1dccYWY07XaTTfd1GFkJGXXHjp0SNgmMOajvqnFmDxj+Uxbw9B5udFYantFyCQkYb66vhlVdU3wbiv6bS7c3FwREhKEwsJi8b2PHDkMcoO+5+q0/VyMjOk7ou3f/vY3MZ0NOqk///zzYmJO3Z2uyzmO4t2rUbJ3HbRN9WJ90OjZ7IRBdgAAaZdJREFUYh4y4TKETrrKJoZnHEgvwedrjmDxDePg56kW6/5x3Vi4u6qgcjTYVjDmob4wE8W7VqBkz1q01FUZ13vGDEHQmLkISJoCpbMrbJ28slqs2JGBlbsyhA+yxKBoP8wbGy+sNdxcDDd5GIbpe0yITsC72zeIYmTNrS1wduTfA4ZhGFvEy8vL2Jfx8PCAWm3oGxAqlQpjx47FrbfeasUI5Y/kbern6QJ3tcrs71ddXYPS0nJ5i7bFtivauqgcEeTjiuLKBuFlbG7RVsqoNoi2mbIUbV0DI8S8gUVbpq+ItkzXoeHsJNJSUbH2Q9td/MPg0M57VOFo/hPw+TiRW4GPVxzEjuOFYvm/fxzDA1ckG7NtGfPQ0lCLsv1/CvuD2pzjxvVOHr4IGjVTiLW2OHTndJo1rdh4KBe/bU/H/rQS43pvN2fMHBWNi8fGITbE26oxMgxjGyQGhiDAzQOl9bXYnZuFiTEJ1g6JYRiG6YTPPvtMzKOjo/HII4+wFYIVkIbJWzrLNjg4AO7u8vy+G0pzxdw1yDb7WFRwjkRbssUYFtfRR9ocxMdHY/PmHTIuRmawgOBMW8ZUsGgrEzS1Fdjz0vV0a1osK5yc4TdkkhDhvOOH20RWLZFVVI2PVx7CxoOGk5dS4YB54+Jw/bREa4cmW6haadXJvSjatRLlh7dA36oxbFAo4TdwnGgjPoljbL6oGHEyr0LYH6zbk4XaxhaxjtwORvcPEe1o4uAwOHGGNsMwp43ImRLbHz8c3oNNGSks2jIMw9g4zzzzjLVD6LNYvghZtpjHxsozy5ZGM7bWV4vHav9w2CJRgZ7YdaLIYr62Uka19N3LNdOWkuioH+6g4L4p0ztsX6VhOvWpLT+yWZhbR8/+u1in8vCFd0KyEGcDRlwkBFtHF9sZ2k7DnF77frfIjNSJ4gLAzJHRuHnWUIT5u1s7PFlCQzKKd61CyZ410FSXGde7hsQgaPQcBCZfJNqNrVPboMG6vVn4fUc6UvIqjeuDfd0wd0ws5oyOFY8ZhmHOxmRJtM1MwVP6uextzTAMY+P873//w/fff4+cnBxoNG0JB23s27fPanHJHUuLtlK2pWyLkJUYEpWcfYKgdLbN0aTSd51TbBCXzU1cnOG7lmumLX3XCicVdC0aNFUUQe0fZu2QGDuHRVs7obWxThSJqjiyBeVHt0GnaQQcFAideLlReBt826sdbBBsCeogq5yUQrC9YGg4bp09FLGhPHzdHO2klOwPdq9CbdZR43pHV08EjJgufI3dw/vZvGBBIj/ZHpBQu+FgLjQtWrHeSanApKHhmDc2DiP7BUOhsO3PwTCMbTAqIhpqJyeU1NXiRGkhEgNDrR0SwzAMcxbeeust/OMf/xDFyH755RcsWrQI6enp2L17N+6++25rhydryNe0fYEqc5ORIfMiZCWGbFJ1gCH70lbtEQiyR7AE0nednZ2HlpYWY4F5uUCZtS7+4WgozBB+xizaMr3FNhU+xkjF8R3I3/SDqECo17Z28KmlTEkSbiVsSbCtqW/G1+uPY0pSBBIj/cS6Gy8ahJnJ0UiMMiwzJrQ/SN0nsmrLD/8l7uoJHBTwTRyDwFGz4Td4vE14GZ+P0uoGrNqVKcTa/LI64/rYEC/hU0vZ2ZYwyGcYRl5Q8bFxkXH4M/0E/kw7waItwzCMDfPee+/ho48+wvz58/H555/jscceQ2xsLJYsWYKKigprhydbtDod8koNwl1UkKEonOUybeUq2ubabBEyicg20bagvA6aVq3Zi4GHhATC1VWNhoZGZGfny/K7dw2MEqJtQ2kOfDHO2uEwdo7tqHyMyC6kf24nTz+o3A1ZqDSsvSplt/HH3m/wRPgNvQAekYk2mS1ZWdeEZRtO4MfNJ9HY3IqT+RV4485pYpuPh4uYGNPQWJqL4t2rUbJ7DZqrThXjcg2KFhm1wv7Ayx+2TqtWh+3HCoRX7Y5jBdDqDL7MamdHXDQiSnjVkvBvi+2dYRj74cKEgUK0XZt6FHeNm8q/KQzDMDYKWSKMHz9ePFar1aitNQiJCxYswNixY/HOO+9YOUJ5UlTRAE2rDipHBYJ9zW+zp9VqkZWVK+tM24a2TFtbLUJG+HuqRb+L+u6UNBMTbF7BXqFQiO/78OHjItNajqKtWvK1LeZiZEzvYdHWylD2bHXmYVEgiqwPmioKEXvZvQibfLXY7jtoAmLm3QnfwRONpta2SHlNI7798zh+3pqKJo1hKHtCmA+umtRfiNHcOTYNrU31KDuwAcW7VqMm85BxvaPa/ZT9QcQAuzjeNPxq5c4MrNyVgYraJuP6oTEBuHhsLKYOj4Srs7yGyzAMYz2oGJlKqUR2ZTlOlhWjf0CwtUNiGIZhOiE4OFhk1EZFRSEyMhI7duzAsGHDkJmZKfoVjHn9bMP8PaC0QBHr3NwCaDQtcHFxRlhYCGSdaRtgu6It9RupGNmJ3ArkFNeYXbQlJNGWMq1nzJgCuSGJ9JRpyzC9hUVbK9Da1ICiHb+hOnUfqtMPQtvcYNxGptUtbRUmCZWHD8KnzYct8991R/HpmiNG39EBEb5YNGswJgwKswvx0NbR63SoTt8vhNqyQ5ug07QJnA4K+AwYhSBhfzABCidn2DoNzS34c38OVuzIwKHMUuN6H3cXzBodg3ljYy02HIthmL6Fm8oZE6MTDNm2J4+yaMswDGOjTJs2Db/++iuGDx8u/GwffPBBUZhsz549uOKKK6wdnuxFW8nj1FLWCDExUSL7Um7oWlvQWF5g85m20ndOom12WxswN1J2rVyLkUkiPWfaMqaARVtLWB4UZ6O1vhpeccOM5tRZKz6GvtXgPero5gXfgeOE9YFP/1E2W1nybHi4qoRgOyjaD4tmDsHYxBAWa01AY1kBSnavFhYIzZVFxvVkk2GwP5gBZ+8A2MP/wOHMMqzYkY71+3PQqDF4MyscHERbmTs2DhMHh8FRKb+LNYZhbIsZ/QYZLRLuGT+Nz1UMwzA2CPnZ6nQ68ZgKj/n5+WHbtm245JJLcPvtt1s7vD5QhMwyom16uryLkDWRYKvTQqFSQ+Vl23026TuX2oC5iYuLEfP0dIN9hNyQPIxb6irR0lALJ1fLFPZj5AmLtmYQqOgHmgpDUSZtVdp+tNRWwC00DiMe/Uzso1Q5I3zK34RY650wAm4hcXCwk7uLx3PK8fX6YxiXGCrENmLOmFiE+3sguV8Qd4BNkIVdfmgTinatRE36QeN6pYs7AoZPQ9CYOTbrZ9yZZcbq3VRULMN4556ICPDA3DGxIrM2wMv8flkMwzASF8T0g4ujI3KrKnCspACDgriiL8MwjK1BWZftMy+vvfZaMTHyyrSVRFs5epoSDSWGLEuyOLT1vpv0nbfvs5kTSaiXa6ato4urqC1D9YkaS3LgFD3I2iExdgyLtiYk87cPULp/PZorizusJ8sDJ3cf6LStUCgNhzx67m2wJyF6V0oRvv7jGPamGj5bRmG1EGvpBEQVJkf252GmPUWv06Lq5F4U71mD8kN/QdfSbNjg4ADvfiMRNHqOyMImsd9eior9viNdzKWiYi4qJaYlRWHu2FgMiw2w+QsXhmHkiavKGZNj+2PNyaP4/fhBFm0ZhmEYpg1JsIsMtExWoNwzbUmsa591acuEBxi+87xSQ9E/cxMbazgmVVXVqKiogq+voQi7nKDvXRJtPVm0ZXoBi7YmRFNTLgRbB6UjPCIHiixar4QR8IweCIWjCvYGCXAbD+bg6/XHcTKvUqxTKhwwfUQUrr9wIAtvvaS+IF0ItaV714m2I6EOiDDYH4wk+4NA2APZxdUio5Yya9sXFRsc7S+E2guHR8HNhYuKMQxjfS4ZmCRE21UnDuOhSTPg1HYzlWEYhmH6KvVNLSivabKKPUJ8vGGovNxoLJWKkNluMXEJGjVL1DRoUF3fDC838yYLqdVqhIQEobCwGJmZOfIUbQMixMjrxtI8a4fC2DncUzEhoZOuRGDyRfCMGWJ3vrSd8fKynVi1K9OYKTlvbByunZqIYF83a4dmt9DdtpJ9f6Bkz1rUF6QZ15NVhrA/GDkT7nZif0AXd1RUjLJqj2SVnVFUjCwQLFF9lGEYpjuMjYxDgJsHSutrsTkzFdPiE60dEsMwDMNYlZw2L1NfDxe4q82fbFRTU4vSUkPSSkyM7Wei9oTGEoNYpw60fdFW7eyIQG9XlFQ1ILekBl4x5vfgjY2NEqJtRkY2kpOHQm6oA8I7iPcM01NYtDUhHpEDYM9kFFTB080Z/l4GwXnmyBjsOFaIKyYm4MoL+pn9jptc0WqaUH54M0r2rEFlyh7yQxDrHZRO8B00Tgi1PoljoXB0sgurjEMZpVixM0MItlJRMcrAHjswFBePicX4QVxUjGGsxbvvvovXXnsNRUVFGDZsGN5++22MHj26030//vhjfPnllzhy5IhYTk5OxosvvnjW/eWCUqHA3AFD8fnerfj12AEWbRmGYZg+T27bsPgIC1kjUHYlERjoDw8Pd8iRxrI20dbfIN7Zg0WCEG1LazHYAqItifVbt+5CZqZMi5G1ZVhzpi3TW1i07eNodTpsO1qAHzalCL/a66Yl4u5Lh4ttI/sF4afnLhWetUz30Ot0qM86jPK120VhMW1zo3GbR/QgBI2cBf+kqXBys8zwo95SVt1WVGxnOnJLTnkd0YXdxWPiMGtUjFHsZxjGOnz33Xd46KGH8MEHH2DMmDFYunQpZs6ciZSUFAQGnmm1snHjRsyfPx/jx4+Hi4sLXnnlFcyYMQNHjx5FWJi8vV4vGZQkRNvNmSdRUleDQHf7+C1mGIZhGHOQW2rItI1oGyZvKdFWrlm2rU31ohh5+4xLW4eKRe9LLTYK+JbItCUo01aOGDNty/JE4pM9jKRlbBMWbfsolbVNWLkrA79sS0N+WZ1Yp3BwQG2jxriPVGSM6ToNxVnC+qB4z1poqkqM6118QxA4ciYCR15kF75GkqfxtqP5wv5gx/FCY1ExtcoR04ZHCvuDoVxUjGFshtdffx233norFi1aJJZJvF2xYgU+/fRTPPHEE2fs//XXX3dY/uSTT/Djjz9i/fr1WLhwIeRMrG8AhodGYn9BDn4+sg+3j51i7ZAYhmGYNoqLi/HII4+I81FJSYkQPNqj1WqtFpv8M20tcxNTyq6Uq2grZVdSMXJHtbvdiLaEpURb6buXBHy5Qf1/KJTQaZqERaKzt/mzlxl5wqJtH+TV73aJ4e0kyhEeaifMGxePKyf1Y7/aHqCpq0LpvvXC/qAu94RxvcLZFQHDL0TQqJnC59hexM3Momqs2JGO1buzUFl3qqjYkBh/XDw2DlOTIrmoGMPYGBqNBnv37sWTTz5pXKdQKDB9+nRs3769S6/R0NCAlpYW+Pr6drq9ublZTO33J3Q6nZi6Au1Hne+u7m9Orh4yUoi2/zu8FzclT4CTUmlX8XcXe47d3uO359jtPX57jt1e4jdHbDfddBNycnKwePFihISE2M01tD2T1ybU0RB5SyBlV8pdtLWXLNv21hjtR1Wak9jYaGNbkGMmKlkfuvgGo6ksX2TbsmjL9BQWbftIVq23u7Pxh1DlpBSC7cAoP8wbF4eLRkQL83Gm62ibG1B+ZCtK9/2ByhO7oNcZ7vg7KJTwSRyDgOQZQMgA+AcGC+HEHoqKrd+XLewPjmYZigJIxQhmtxUViwriomIMY6uUlZWJzKOgoKAO62n5xIlTN5POxeOPP47Q0FAh9HbGSy+9hOeee864rFQqMXXqVFRWVnYQc8/Xua+trRUX59b+bUzyDYK3i1oUJFt5eB8mRcbZVfzdxZ5jt/f47Tl2e4/fnmO3l/ilG3imZMuWLdi8eTOSkpJM/trMmVD7kkRbKdvS3EjZldIQebkhFZ+ylxGW7b97aguWEFEjI8PE71pDQyOKi0sRHHymlZe9Q37GQrQtyYV3vMGCkmG6Cyt1Mvaq3XOyGL9uS8Pmw3lYetc0jEgwdOavnTIAc0bHol+4j7XDtCt0rS1CoCWhtvzoVjHUQcI9YoCwPwgYcSFU7t7iIruiwuBjZKvQyfhgeqkQajccyEGTRmssKjaOioqNjRNzLirGMPLn5ZdfxrJly4TPLfnbdgZl8ZJnbvuO+oIFC+Dj4wM3t66N0qDfRuoE0HNsQYC4YnAyPt2zBb+nH8OlSaPsLv7uYM+x23v89hy7vcdvz7HbS/zOzqYvVBwREXGGJQJjPqrrm1Hb2CIeh/mbfyg/fbenMm3lKdo22WGmbaifu7BLpELTZTWNCPByNev7qVROQrjNysoVIr4sRduAcFSe2GksSscwPYFFWxlBJ8DU/Cqs3ZuFdXuzRPEoCSoyJom2ZIHANghdLyhWnXEIpfvWoezgRrQ2nBou4uIfhsARFwmh1jXIfi44qCroql2ZWLkzHXltfsZEZKAnLh4bK4qK+XlyUTGGsSf8/f1F5iv5ALaHloODg8/53P/7v/8Tou0ff/yBoUOHnrNj3r5zTu9HkJDQHTGBBIjuPsdcXDNsFL7ctw1787NxvLQQg4LC7Cr+7mLPsdt7/PYcu73Hb8+x20P85oiLCmmSF/uHH36I6GjDEGpbgeLJzs4+YyRKZ97x9oLkYRrk7QoXlfnlgcrKKlRXG94zOtp+RM3uIIl0Lv728/mcHJVCIygorxPZtuYWbSV7DBJtScQfN24k5IYk2ksiPsP0BBZtZWSBcO8764UfqQR51c4cFYNLxsUjLtTbqvHZm/hdn5+Kkn1/oHT/emiqSo3bnDx8hU9tYPJ0kV1rL947zZpWbD6SJ7yMd6cUQUpeIFuMC4dHCbF2cLS/3XwehmE6olKpkJycLIq2XHbZZcYMLVq+5557zvq8V199FS+88ALWrFmDkSPld7F8PoI8vDCr/2D8fvwQvtizDa/OvdraITEMw/R5rrnmGjGaIy4uDq6urnBy6lhLwdqj2Z5//nlR+FPCw8MylgLmFm0t52drsEYICQmCWq2WtT2Ca6D92CMQkYEeQrQlX9vh8R0tt8wB2WNs2LBVtsXIJHuMhrb2wDA9gUVbO6WmvhnphVXGH1PyrG1p1ULlqMD4QWGYOTIaYweGQuV4/sIqzCnDeBJpS/auQ2PJqROH0sUd/sMuQMCIi+AdnyR8a+1FfD6WXS6E2vX7s1HXNuyJSIoLxJzRMZg6PBKuzlxUjGHkAFkX3HjjjUJ8HT16tMhUqq+vx6JFi8T2hQsXIiwsTGQEEa+88gqWLFmCb775RmQOFRUVifXu7u5i6ivcmDxBiLZ/pB1DXnUFwr06L8TGMAzDWAY6f9kyJNKebxSLPWHpImRy97Ntqa82js6kkZn2hGgDxwuNQr65kQrRSXYZss20LSsQNXDsRUdgbAsWbe2IukYNdhwvwB/7crD9WAFcVEr8+s8r4OykFBmS/1w0ESG+7vBwVVk7VLtBU1OO0v1/Cp/a2pzjxvUOjir4DRqPgBHT4Zs4Bgon0/t1mYvS6gas2Z2FlbsykF1cY1wf5OOK2aNjRWGxcH/7zghgGKbzzKTS0lIhxJIASwVcVq9ebSxORpW42w9jff/996HRaHDVVVd1eJ1nnnkGzz77LPoKCf5BmBAVj63Zafhs91Ysnj7P2iExDMP0aegGpC1DlkL//Oc/ERkZieuuuw4PPvggHB3tt1uda/EiZNkdBDu5ZtmqvAOgVHVeJ8BWkdqApURbSbiX2oTccPYJgoPSCXptC5qrSuDiG2LtkBg7xKbPLlQJmzqOX331leiAUlXrm266CU8//bRxGDdlE1IH8+OPP0ZVVRUmTJggOqIJCQmQAyTAbT6Uh78O52F/WglatTrjtmBfT5RWNRjvivYL5+ygrt79LD+8WYi1Van7yLjWsMFBAe9+yQgcMR1+QybBUW0/mWbNLVpsPZKPFTvTsetEEXRt/gck6E8ZFiEKz5GnsULB9gcMI2fICuFsdghUZKw9WVlZForK9rll9CQh2v5ybD9uHjURYV5cqJNhGMaS1NTUwNPT0/j4XEj7WYP77rsPI0aMgK+vL7Zt2yaKdBYWFuL1118/63Oam5vFJEHWD5KNEU09hZ5LfeHevAaRW2I43uH+7r1+ra6Qnm4Q6KKjI8zyfqY6Lj2locQg2qr9w60WQ0+Pi1SIjtqEJWKPijLYB5CvbUtLi7FegqUxX5txgIt/KBqLs1FfnAOVt/ktJ+T0vyT346Lr4vNtWrSloZskwH7xxRcYNGgQ9uzZI4Z5enl5iROm5Mf31ltviX1iYmKwePFizJw5E8eOHTtrBWxbRnz5ej2UbdlQK3Zk4OOVh4zbo4I8ccGQcMwYGY3YEPap7Sot9TUoP7IZZQc2oPLkXkCnNW7ziBokMmoDhk+FysPXrtrKidwKrNyZgXX7slHboDFuGxoTgDljYjFteCTcXNj+gGEY5lwMD4vC2MhY7MjJwCe7/sIzF11q7ZAYhmH6FD4+PkL8DAwMhLe3d6d1Fujal9ZTYo8poSJi1O88F8ePH8eAAQOEFZEEFe8kT/nbb79dWA+1L9bZHtr23HPPGZdJmJo6dSoqKys7iLk96fDX1taK49LTgnD0XPIvJTxUOov4BaelZYh5QICvWd7PFMelN1TkpIq5g4e/1f2Xu3tcPJ0M/1t5ZbUoLSuH0swJP2q1SnhWazQtOHYsBWFh1rEdMWebUXoGAsXZKM8+CX1AHOwJa/8vyf24NLTdwLNr0ZbuXl566aWYO3euWCbPvW+//Ra7du0Sy3SQyPOIMm9pP+LLL78UQ0GXL1+Oa6+9FvYAZc8eyijF5sN52HIkD7dfnITpIwxDBUigJUuESUPCMXFwuBBtma7R0lCLiiNbUHrgT1Sl7BE+MhJuYQkISJoK/6SpUNuZ11B5TSPW7MkSYm37wnOB3q6YNSpGeNVGBHI7YRiG6Q53jp0qRNtfjx3ATSMnIsrHz9ohMQzD9Bn+/PNPkb1KbNiwwaLv/fDDD4vRnOciNja20/VjxoxBa2urGL3Sv3//TvehbNz2Yi911BcsWCCEajc3t14JByRi0+v0VDgoq25EU4sWCgcHJMaGwsnM9VCESJxbIB4PHTrI+J2bElMcl95QUlcu5t7h8Wb5fOY8Ll7e3nBSKtCi1aFV4YwAX/OPPI2ODkdqaiYqKqoxZMhAWANztpnqsFjUpe6GoqHCptqDPfwvyf24OJ/lRp9dibbjx4/HRx99hJMnT6Jfv344ePAgtmzZYhx+kpmZKWwTpk+fbnwOZeHSyXP79u1nFW3NNUSlOyer/LI67Esrwb7UYuxOKUJNuyzJrUfyMC3JMFQgOtgT79136vNxavq5aW2sRcXRbSKjturkHui1rcZtriFx8E+aAv9hU4yVHAl7GJZDRea2Hi3Aql2Z2HmiEFqdwf5A5aTEBUPC2uwPAo0Z2txOGEshp2EzcvgMTM8ZFhqBSTEJ2JyZijc2r8XSS+ZbOySGYZg+w+TJkzt9bAkCAgLE1BMOHDggOu2UIXyujnn7zrk0BJye11shhISD3rxOQXm9mAf7usFZZf7RecXFpaivbxDxRkdHmk0I6u1x6Q1NZXli7hoYYXNC1/mOC60ni4Ss4hrkldUjzN/8iUCxsdFCtM3OzoVCMQHWwlxtxrVNd6B2YWvtwdb/l+R+XBRdfK5Ni7Y0VIU8jWgoCp3caCjMCy+8gOuvv15slypdS0VWJGhZ2mbJISrn8xwlf1Giul6Dha939Bb0UDthVEIARvcPwPBYP5saSmHraBvrUJe2BzXHt6E+40AHodY5MAqeiePhkTgezn6GjNpGmsx8fE01XCmjqBbrDxbgryOFqG1sMW7rH+6FC4eFYcLAILi32R9UV1WZLH6G6YvDZro6RIWRLw9OmoFtWWnYmJGCnTkZGBPZeWYVwzAMY5nzMhXRpKKZ7SFbAmtASUE7d+4U/UYPDw+xTEXIbrjhBpFxZY/klNZYuAhZjpiHh4dAZQGR2NLQ9bAk2rZPErInaMSmEG1LazFmgPkLZ0kF6TIy5FmMTB1oaAeNpYZ2wTDdxaZF2++//x5ff/01vvnmG+FpS3cyH3jgAVGQrDdVRc01RKU9FbVNonAYZdLSFOjjijfvmia2UVZ8v3AfqFWOIjuSCkQNjvaHo9K+BQ9L0lxdhoqjW1FxeDOq0/Z3sD5wDY6G37CpIqPWNchgM2FPKfOVtU1Yuzcbq3ZnIr3glBDr76XGzJHRmD0qhm0yGJtBTsNmujpEhZEvsb4B+Nuw0fj2wE68tmk1ll1/OxwV1imKwTAM01cpLS0VdUxWrVrV6XZTe9p25zph2bJlolA2JfpQPRUSbdv3K+0NEuYIqbC1ucnMNAhzsbHW6aOZm5aacmibG0WBaxe/UNgjUluQCtSZG6ktSIK+3KCCdERTRRF02lYolDYtwTE2iE23mEcffVRk20o2B0OGDEF2drbIlCXRNjjYYFRdXFyMkJBTd4FoOSkpyeJDVLYfKxD+s3tPFnfwGiXKa5pAI9olYfaTh2cah7IzXaOxNBdlhzaj/PBm1GYf7bDNNTgG/kMnC49at5AY2FvKPGVibzuaL4TaHccKTtkfOCqEnzEVFRvVP5jbDGOTyGXYjL3Hz5iGO8ZOxsoTh5BWXoKfDu/D34aNsnZIDMMwfQpK0qmqqhJZrVOmTMHPP/8s+nf/+te/8O9//9tqcY0YMQI7duyAnMhtE20tnWkrZVfKjca2LFsXnyAoHO0zkzhSEm3b2oa5kXumrcrLHwqVC3SaJjSVFwrbDIaRjWhLGbCnd6JJYJV8B+nuJgm369evN4q0ZKdAJ/g777zT4vH+768U7DheaFyOD/NGckIQkhOCMSwuoEMmLYtvXRteUp+firJDfwmhtqEos8N2j6hB8BsyCf5DJ9nl8BP6fFSAjoqKrd+fjbp29gcDo/yET+2FI6Lg6aqyapwMwzB9CS8XV9w5bipe3rAS72xbj2nxA+DvZpnOLMMwDGMoSvbLL79g5MiRoi8YFRWFiy66CJ6eniJ5RypSzZgu0zYi0DLnOUmYk61o2zYEXhoSb9eZthYSbaVMWypQp9G0yM42g5JbKNu2viBNJKGxaMvISrSdN2+e8LCNjIwU9gj79+8XRchuvvlm4z8A3Ymlu64JCQlCxF28eLGwT7jsssssHi8JbKF+7sLuYHh8ILzdXSweg71DNgc1mYeNGbXNlae8iR0USnjFDxdCrd/giXD27lnBAFu4OFq9O1OItQXldcb1gd6uwv5g1qgYRAd7WTVGhmGYvsxVQ5Lxy9H9OF5SiH+t/x1vzLtWXHMwDMMw5qe+vt5Y2Ivsl8gugYpS06jLffv2WTs82aDT6ZFXVmdhewQp01ae9ggkyhEubUPi7RFJwC+qqBfFsJ0czWsTFRQUAFdXNRoaGpGTk4/4+GjIDXWAQbRtYl9bRm6i7dtvvy1E2LvuugslJSVCjL399tuxZMkS4z6PPfaYOLHfdtttYhjNxIkTsXr1ari4WF4wpcxImpjuQb4/Val7UX5kq/Cpbak75eOqcHKGz4DR8BtyAXwHjYeTq31mO9XUN2P9/hwh1h7JKjOuVzs7YuqwSMwaHYPhcYFQKFgUYBiGsTbkY/vcRZfium8/EkXJVqUcxpwB1il8wzAM09fo378/UlJSEB0djWHDhuHDDz8Ujz/44IMOlnhM7yipaoCmRQulwgHBPqap63IuaLRsVlaurD1tjZm2djgKVMLfUy1q7zRqWlFQXm/2Wip0U5wyr48eTRGex/IUbSM62GcwjGxEW6rKuXTpUjGd65/8+eefFxNjPzSVF6Di2HZUHNuBKiok1nqqKqyj2h2+gybAb+gF8Ok/CkqVfWYst2h12Hw4D2v2ZAu/WlomFA4OGD0gGDNHxuCCoeFwUdn0vyHDMEyfpF9AMG4bMxnvbd+AVzauwuiIGPiqzd+pZRiG6evcf//9KCw0WM4988wzmDVrlihOrVKp8Pnnn1s7PNlZI4T5e1ikIHZhYTGamprh6OiI8PAQmYu29ptpS/oKZV6n5leKNmKJAtiUeU2irVx9baX20FhiuGnBMN2B1SLGIui1rajJOmIQao9uR0NxVoftzj7B8B04VmTUesUn2W1VRfKpPZZdLjJq1+3NQm07n1ryOJ41MgYXJUfD30tt1TgZhmGY87No5ERsSD8hbBKeXvMz3r7kOmuHxDAMI3tuuOEG4+Pk5GRRiPrEiRPCMs/f39+qscmzCJm7Rd4vI8NgjRAZGSaEW7mh1+nQVJ5v96ItER7gLkTb/DLLFiOTMrFlK9pypi3TA+T3a8nYDGRzUHlilxBqK0/sRGvjKf9WKJTwjBkM38Rx8B04Dq7B0XbtF1hYXic8alfvyURuyamTm5+nC2aMjMGskdGID/OxaowMwzBM93BSKvHPmZfjhm8/xo6cDHy06y9c049tEhiGYSyJq6srRowYYe0wZEduaY2F/WyzZW2NoKkuha5FI+qwuPgEwZ4J8zO0Ccnz2NzExBjsA7Kz5SnaSh7HzVUl0LU0CwtIhukqLNoyJkPX2oLa7KOoTNmDqpN7UJtzgm45Grc7unrCN3EsfAaOEz619upP296nduPBXCHWHkgvMa53dlIK24MJ/f0wJTkeTjK8k8wwDNNXiPcLxOILL8Y/1vyMT3b9hRh3L8zy9bV2WAzDMLLloYce6nQ9JXhQ3ZL4+Hhceuml8OXfYpPYI0QEmH/4e/siZNHRhqxKudFYZsiydfELgYOdjhqVCGvLvrZUpm1UVISsM22d3L2hdHaFtrkBTRWFcA2Sn28vYz7s+9eEsboVQENRFqpO7hZCbXX6Qeg0jR32cQuNg+/A8cL6wCNqoLjzaM80aVqx5Ui+sD7YcbwQrW0+tZQkPCI+SBQUmzw0AmqVEhUVFVAqzO8PxTAMw5iXuYnDcKAgFz8c3oOXtqzFwPAoRPvyEF2GYRhzsH//fuzbtw9arVYUJSNOnjwJpVKJAQMG4L333sPDDz+MLVu2YODAgdYO127Jb8uiDPO3jD1CdnZeh6xKOdZsIVz8wmDvkM9x+zZibqKjDW0iL68QLS0tcHJygpwQN5z8w1Cfn4rGsgIWbZluwaIt0y001WWoPLlXZNLSpKkpP+Mukne/kWLy6T8Szt6BsHdImN2dUiSE2r8O5YlKmu19ai8aEY2LkqMQ1K7qKlVHZRiGYeTDo5Nn4VhJAY4WF+CeX77Gl9f8Hb6ulunoMgzD9CWkLNrPPvsMnp6GLNDq6mrccsstmDhxIm699VZcd911ePDBB7FmzRprh2uX6HR6FJRLoq1lRj9KQ9+lrErZZtr6h8LeCW8T8qmNUF/Y3IXqgoICRBZ9U1OTEG4lj1s5oW4TbZvY15bpJizaMueEUvgpg1ayPGgoyuywXeGkgmfsMCHQevcbBbeQWDjIILuULmSOZJUJofbP/Tmoqm82bgv1cxPFxKaPiEJsiLdV42QYhmEsg8rREW9cfC0WLvsYedWVuPeXb/DJVTdB7aSydmgMwzCy4rXXXsO6deuMgi3h5eWFZ599FjNmzMD999+PJUuWiMdMzyitboCmVQelwgFBPq4WGaEpZdpGRdl3ka6z0VRmyLRVyyDTNsDLFSpHhWgjJVUNCPVzN3smanR0OE6cSBMWCXIUbV38DGI+ZdoyTHdg0ZbpABULq8k8jOr0A6hOP4S63BPQ67SndnBwgHtYP3gLkXYkvGIGy8pIO72gCmv3ZuGPfdkoqqg3rvdxd8GFIyKFWDsoys+ui6YxDMMwPcPfzR0vTbsED677SWTcPvL793h93jVwdpTXMD6GYRhrQlm1JSUlZ1gflJaWoqbGUDzL29sbGo3GShHaP9Kw9xBfN7NnURIlJWVobGyCQqFAeLj9Z6J2RmNbBiUNg7d3FAoHIdRmFdcI72Nzi7ZSBjaJtnItRqYOMNysaGrLyGaYrsKibR+npb4a1RmHUJ12ADUZB1GXn9aheBjh7BsMn36j4N0vGd4JI4QFgpwoLK/Dun3ZIqs2o7DauN7V2RGTh0UIoTY5IcgiFzQMwzCMbRPh5YM35l2LO3/+L7Zmp+GBX5fhjUuuhQsLtwzDMCazR7j55pvx73//G6NGjRLrdu/ejUceeQSXXXaZWN61axf69etn5Ujl4GdrGWsEqcBUWFgwVCr5nS8pk9joaSsD0ZYIC/AQoq2lfW0zM+Up2kqZtk3lLNoy3YNF2z6EXqdDQ3EWarKOojbriJg3lhiqeLaHTjReccPgFTsMXvFJcPENgdwor2nExoO5Qqg9nFlmXO+kVGDcoFDMSI7G+IGhcFbxvwjDMAzTkWEhEXj70utx36/fYHtOOu775Ru8ecl8tkpgGIYxAR9++KHwq7322mvR2mqoJeHo6Igbb7wRb7zxhlimgmSffPKJlSO1X/LLai1ahEwSbaOj5TfsnWitr4a2yTBKUy59Z8nXVmor5kbyOpZtpm2bmN9UXgi9thUOStYZmK7BLUXmVge12cdQ0ybQ1mYfh7bpzDtl6qAoeMUlGYVaZ+8AyJGKmkZsOpSHPw/k4EBaCXR6vVhPTgeUSUsZtZOHRsDDlTvdDMMwzLkZFRGD9y67AXcv/xq7cjPx9/99LoTbADfLZC0xDMPIFXd3d3z88cdCoM3IyBDrYmNjxXqJpKQkK0Zo/+Qbi5C5W7gIWbisi5CpvAKgVMnDOlDKws6zUKZtTExEB4FfblDbcHBUQd+qQXNViTHzlmHOB4u2MioYVpeXirrcFNTmpYh5Y2kujdXosJ9C5QKPyER4Rg+GR/QgeEYNlJ3dQXsq65qw6WCuKCa2v51QSwyM8sOFw6OEVy2ZrTMMwzBMdxgeFoUPrliA+3/9FseKC7Bg2cd465Lr0C8g2NqhMQzD2D0k0g4dOtTaYcgSS9sjSEXIpCHwckMa8i5lU8oBa2Xa5uTkQafTCf9jOUHF2l38QtBYnC1EfhZtma7Coq29CrT5aUKYlURaYXNwmkBL0I+BEGfFNBhuIbGyT8WvqmvCX4fysF4ItcXQ6k4dl8RIX0wbHoWpwyIQYgFDdYZhGEbeDA2JwJfX3IJ7f/kaWZXluPH7/2DxhfMwZwALDQzDMIxt+q8a7REs1B+Sf6at5GcrHyFOEvRJ4Nfp9KI4mVnfLyxY2KA0N2tQWFgiluWG2i9MiLZN1F76Wzsaxl6Qt3pn5+h1WmFoXl+YgfrCTDSIeQYaS/POKBZGqLwD4BHeH+4RbVN4f6g8fNAXqK5vFkLtnweysfdkR6F2QAQJtZGYmhRpkcqXDMMwTN8iwttXCLePrvgeO3Mz8dTqn7A7LwuPT5nNBcoYhmEYm6KmQYO6xhbxONRC9ghScSkpm1JuNLXZI7j4ySfTNtjXDUqFA5pbtKIeTIC3eUemkmAbHh6KrKwcMclRtJWK1DWWGTLPGaYrsGhrI3c7NTXlRlG2oSgT9QUZomiYrqX5rJ4o7hH9hDDr0SbSqjx80ZcorWrAX4fz8NehXGF90F6o7R/uY8ioTYq0mFcTwzAM03fxdFHjvcsX4MOdm/Dxzk34+cg+7M/PxrMXXYqkUHkWXmEYhmHsDynL1s9TDRcLFF2urq5BVVW1vO0RyuRnj+CoVAjhljJtaTK3aCv52pJgS3YaEyaMhtxQt2ViU2Iew3QVFm0tbGtAWbJiKjPMm0rz0FCSg9aGmk6fo3BSwTUoBm6hsXANjhH2Bm6hcVB5+qEvkl1cI0RaEmuPZZd32BYf5i08aqclRSI8gAvBMAzDMJZFqVDgrnFTkRwWhX+s/knYJSz6/lNcN3ysWO8mk+IkDMMwjP372YYHWMoawZBVGBDgBzc3edYRaWwT4aRMSrlAyU/UXvLKapEUH2j295NEfRJu5YiLf3iHwnUM0xVYtDUxWk2TuNNGRcDai7M0tdRWnP2JDgqoA8KFKCvE2dBY8Zg8aR0USvTlLOSUvEoh1G46lIesIsNdWsLBARgc7Y8LhkbggqHhCLeQkT7DMAzDnIsxkbH4aeHd+L+/1uDXYwfw9f4dWJNyBHePn4ZLBiYJcZdhGIZhrFqEzM8yfaesLHlbI1BiltTPlzIp5QL1r3ehyNhmzI3URrKy5Gkf0D7TlnQOBxI0GOY8sGhrItJ+fAPlR7ZAU1V6zv0c3byEOKv2DzfMxRQBdWAUlJyBI9DqdDiUUYpNBw0ZtcWVDcZt5KuT3C8Ik4dGYNKQcDGsh2EYhmFs0S7h+RmXYWa/QXh54yrkVlXguT9+xbKDu/DAxIswNjKWL9YZhmEY64m2/pbNtJVrEbKm8kIxd3T1hKNaXklEUhuhTFtLIPdMW2efYEChhE7ThJaacqi8/K0dEmMHsGhrIlob64yCraOrhxBlXU4TZykd3slVXj/kpqJZ04o9J4tFRu2WI/moqj/l5euiUmJsYqjIph0/MAweriqrxsowDMMwXWVCdAJ+WhAjxNoPd2xCSmkR7vz5vxgSHIZbR1+ASTH9WLxlGIZhLIYkwFlKtJUEOLn62UpFpeTkZysR1jaSNb/UsqItCf1yzERVODrBxTsQTRWFwiKBRVumK7BoayIipl2H0IlXCHHWyc3L2uHYBSVVDdh2NF9MJNhSZUoJT1cVJg4OF0Lt6P7BcLaAST7DMAzDmAMnpSMWjBiPixOH4aOdf+Gnw3txuCgf9/36Lfr5BwnPW8rIVTvxTUmGYRhGnpm2chVtm8rk6WdLSHVi8svrLCKiRkSEifeoq6tHeXkF/P3lV8eH2gmJtmSR4BU3zNrhMHYAK2EmgoqDMee3PaDiYduOFgihNq2gqsP2QG9XYXkweWg4hsUFioqVDMMwDCMXfNRueHzKbNwyahL+u287vj+0GyfLivHsul/w77/W4NKBSbhySDJifAOsHSrDMAwjQ5o0rSivaeyQRWlu5O5pKxWVolo0ciPU103M6xpbUF3fDG93F7O+n4uLM0JCglBQUITMzFzZirY4uceYoc0w54NFW8as1DRosOsEibQF2HG8UPzYn15IbNzAMIwfFIr4UG/ZDYFgGIZhmNPxc3PHA5MuwqJRE/DT4X344fAeFNRU4av9O8Q0ICAYswcMwcx+gxHswaN3GIZhGNNQ0JZl6652EiMbzU1TUzOKikrE4+houXraFsjWHoFGu1JiFY2QpQxtc4u2UkY2ibbZ2bkYNSoJckNqJ1KGNsOcDxZtGZNn057Mq8TulCLsPF6Iw5ml0Or0xu0eaieMSQzF+IGhGJMYYpEffoZhGIaxRbxcXLFo1EQsTB6Pbdlp+OHQHmzNSsWJ0iIxvbF5HUaERWJybH/hfRvj4883NxmGYZgeQ8PciTA/d4ucT3Jz88Wwend3N/j6+kCOSBmTcrRHkGw0SLQlL+RB0f4WEW23bdttzNCWG1I7kTK0GcbuRdvo6GhkZ2efsf6uu+7Cu+++i6amJjz88MNYtmwZmpubMXPmTLz33nsICgqySrx9kYLyOiHS7qHpZJHIrm1PTLCXyKSlImKDY/zZ9oBhGIZh2qFUKIQoS1NlYz3+SD2G1SlHsDc/G/vyc8REAm6YpzcmxiRgXGQcRoRFwdNFbe3QGYZhGDsiXypC1uZVam4yMw1FyKKiwmV501HX2oLmSkMmsVqG9giSjcb+tBLklxoEf8sVI5OnaGvMtC1n0ZaRiWi7e/duaLWnClQdOXIEF110Ea6++mqx/OCDD2LFihX44Ycf4OXlhXvuuQdXXHEFtm7dasWo5Q2JsntPFhmE2pNFRjN7CTcXJ4xICMKo/sEiozbEzzIm9wzDMAwjB9/bq4eOElNRbTX+TDuOzZmp2JOfhfyaKnx3cLeYqOub4B+E5PAoJIdFY3hopLBdYBiGYZizkVd6KtPWskXIIiFHmiuLAL0OCpULnDzl579KhLcVrMsvNwj+5kbyPiZPWzkieR+3NtSipb4GTm6e1g6JsXFsXrQNCOhYjOPll19GXFwcJk+ejOrqavznP//BN998g2nTpontn332GRITE7Fjxw6MHTvWSlHLC02rFkcyy4RIuzulECm5ldDpT1keKBUOYqgEibQ0JUb6cTYtwzAMw/QS8rO9bvhYMTW2aLAzJwNbslKxJy8LWZXloogZTd8e2CX2D3L3RGJgCAYGhWJgYCgGBoXA15WFXIZhGAYdhDdLFSGTsiXl6md7qghZiCwzidu3FUnwt1ymrTwLdSlVLlB5+UNTXSaybVm0ZexetG2PRqPBV199hYceekj8KO7duxctLS2YPn26cZ8BAwYgMjIS27dvZ9G2h7S0anEitwIH0kpwIN0wNWlOZTsT0UGeQqAd2T8Yw+ODRHYtwzAMwzDmQe2kwpS4AWIiyuvr2uwTsrE3PwtpZSUorqsR08aMFOPzAt09EOcbiBhffwQ5u2JIRBTi/AOFny7DMAzTt5BGSJJPqSWQhDcpe1JuNLWJtmp/eYrS7dvK6aNrzS3aVlRUoqamFp6elrnBYOlsWxJtSfT3iEy0djiMjWNXou3y5ctRVVWFm266SSwXFRVBpVLB29u7w37kZ0vbzgZ539Ik0dDQIOY6nU5MfY3mFi2OZZcbBdqjWeViXXt83J0xsh+JtEFiTlUk29MXj9u5oONBpvt8XBi5I6e2LofPwPQdyAphRr9BYiIaNM2ieNmx4gIcKynA8eJCZFWWoaSuVkzbc9INT9xpmPm7uiPa1x8RXj4I9/JFuJcPwsRjH3i5qGWbMcQwDNNXadXqUFRRb1HRViomRZ62cqSxrKDDkHc5IrWVyromNDS3wNXZvMlaVLTOz88H5eWVyMnJw+DB8hM1SeSvyThkFP0ZRjaiLVkhzJ49G6GhvftRfOmll/Dcc88Zl5VKJaZOnYrKysoOYq5cqapvRkpeNU7kVYnpZH41WrWn7A4ID7UTBkX6YHCUD4ZE+yIqsF2FUV0TKiqarBO8HYk/tbW1QsxSKNgqgpEvcmrr0g08hrFHXFXOojgZTRL1mmZhn5BZUYr0shKkFBcir7YKRXU1KGuoExNZLZyOu8pZCLhU+CzIwxOBbp4I9PAU9guUuRvo7gkXRx5hwzAMY08UVzZAq9ND5ahAgJf5R1tQXZrc3PwO2ZNyQyom5dJWXEqOuKtV8HRVibo2heX1iAvtmDBnDiIjw4Vom5UlT9GW7DSIpvJCa4fC2AF2I9pmZ2fjjz/+wE8//WRcFxwcLCwTKPu2fbZtcXGx2HY2nnzySWGx0L6jvmDBAvj4+MDNzQ1yu6OaWVSNI1llIoOW5p0NbfD1cEFSfCCS4gKQFBuIqCBPKBScZdMbIYtEbmpT9i5kMUxfaevOzs7WDoFhTIqbylkUKKOJ/lcrKirg6+uLxtYWZFaUiUzcvOpKMeW3zUvra1GnaUZKaZGYzgZl40oCboCbB3zVbvBxdYOfqxt8aVIb5t5qVzgqlBb93AzDMMyZ5JcZ/GypSLQl+nkFBUVoaWmFSuWEkJAgyNnTVi3jTFsp27Ymp0K0IUuItiTy799/WGTayhFJ5G9sE/0ZRhaiLRUYCwwMxNy5c43rkpOT4eTkhPXr1+PKK68U61JSUpCTk4Nx48ads2PevnNOmbYECQ72LDpQplthRT1ScitwIqcCx3PKcSynHI3NrWfsGxPshcEx/hgc7Y9hsQEID/DgoZAmho6nvbcphulLbd3e42eY7oi5g4PDxHQ6Ta0tKKipEgIuzUvqatqmWoNnbm2N2Ke6qVFMqWUl530/bxe1EHB92oRdWvakyVktxF8v47KL8TFl8vJ1CcMwjBn8bP0sZY1gENwiIsKM/W05odfpjJmSLjL2tCVC/dxxPKcCBeWW8bWNjAzrYK8hNyQ7jaZyg70Gw9i9aEvZISTa3njjjXB0PBWyl5cX/v73v4usWcoc8fT0xL333isEW7kXISOBljyJqGAYTSltEw1bOB0qEjYwyg9D2kTagVH+8HBVWSVuhmEYhmFsFxJLY30DxHS264/a5qYOQi7ZLFQ01KOyoR4VjfXiMc2rGhug0+tR1dQoJqCsy3E4KZXwcm4Tc11c4K5yARky+Lp7wt3ZWVg4kPjs7uxifCyWad62nYVfhmGYMzNtwwIsU9gpO1vys5WnNYKmpgz6Vg0cFEq4+ARCzoT5e1i0GJnUZnJy8mUt2lIxMl1LMxROPNqPsXPRlmwRKHv25ptvPmPbG2+8IbKjKNOW/GhnzpyJ9957D3JC06pFdnENMgqqkF5YhdT8SqTkVqK6/kz/XUelQgxZGBDhKyYSaaOCPaHkDDKGYRiGYXoJiaAGIVWNeP9zD3fV6nSobmowirhi3lCPmuYmkaVbQ1Nzo/ExzUkQbtXp0KLVGn13e4rSweGUmOvsDLWjCmonJ6idVHBpm6sd2+ZO7ebt9utsPT1X4cDXVQwjN1544QWsWLECBw4cEMWuyYLvdKhPeuedd2LDhg1wd3cXSUVUL6V9YpHNZ9paqAiZJNpGR8u7CJmzbzAclLb//fcGqc3klBiEf3MjFa6T2pDccHLzgtLZFdrmBjRVFME16FQ9AoY5Hbv4dZkxY4bI7OgMFxcXvPvuu2Kyd3Q6vRhykFFoEGczCqrF49zSWmEa36lAG+KFAZF+6N8m0saGeMHJUX7DTxiGYRiGsS/ohrGvq7uYugpd7zW0aM4QdWubmlBaVQmdo1JsJ9/d+uZmUWxNPNZ0fEwZvlq9XgjENMHE/UzK4nVxdITK0RHO0mOlo1hPc2fHtse0XekIXUsLvNw9hOgrPce5bT8xKZ1OPW63jTKO6fVoThP7AzOM+aBaKVdffbUYtUkFsDsrrEVWfVQ7Zdu2bSgsLMTChQuFXd+LL74IW8fy9gjyzrRtKjPYP6hlXISsvbUiQbVyLEFCQowx07aurh7u7m6yuwFO2bb1BWnCF5lFW8buRVu50axpFSdNEmPzympFFm16QRWyiqvRpNF2+hwPtRNiQ70RF+JtyKSN9BMCrYoFWoZhGIZhZNSRkbJjQz1PFTtpX0jtfP7TJPyS764k7NZqmoSQ29jSgsYWzal566nlJml9qwYNmlOP2z+HXlOCHrdfthQKBwc4KQwCriToqtoeO5G4q1CKa0PDPqdtE/sbtrUXgts/lrZRYoDSQQFHpRJKOKCxvh4+TXXGbSQeOyrazWk/2l+haBOXDdvYnoKxJ5577jkx//zzzzvdvnbtWhw7dkyMAg0KCkJSUhL++c9/4vHHH8ezzz4rsnNtFfpdlPxIpaHulvK0paJScqQu76SYqwMjIXei20Tb8ppGMdrXy828w/n9/HwRHByIoqISHDuWgtGjR0BuuIXECtG2NvsY/AaNt3Y4jA3Doq0ZLQ0KJGG2tNY4p6mkugFnSRyGylEhfhRj28RZEmZJqPX3UvOFL8MwDMMwzHmg6yWDpYEKAW6mEyd0eh2aW1s7iLi03KxtRXNrCzStrWiiZVov1rWKdbR/dV0dFE6OYr1Ga9hP07aveNz2GuL1xOu0iHUabceb+ZRBLF5be2aRWVuELCqEsNuJ0Ks8TfQVgm/7fU57DgnW0vPInsLwfHrsIB6TaCzm4rFhnQIOaGpshKe7h0FYVjic2q9tfvrrnnrs0O492t7PgYputnsN4+t0/rp87S4vtm/fjiFDhgjBVoKs+cgu4ejRoxg+fHinzyMLP5okGhoajDejaOop9FwSY7vyGtQXbdS0gppkkI+6V+/bFUpKynDyZLp4HBcXbfb36+lx6Q2VKbvF3DMuyaKfzxrHRa1SIsTXTRQ9Ty+oRFKc+T18Bw8eIETbw4ePY+TIJFm0mfZ4xg1Dyd61qE7bb7PtxxrHxR7Qmei4dPX5LNqaiLV7snA4q9Qg0JbUorjSUHzjbLirnRDu74GIAA9EBHoIkZam8AB39p9lGIZhegRZBb322msoKirCsGHD8Pbbb2P06NFn3f+HH37A4sWLkZWVhYSEBLzyyiuYM2eORWNmGHuBhDtJDO4O3ckSPh3qFBg8flvRotMKEZf8fsWy1rBM4q5hnbZtn1PbWtu2i+fpOj5Peh3psXie2EeLVh09VyfmLTodmls0pIaLWMQ2mov9Ti13dt1LFhVaITKjT0LisaJNPJbEXhJzxdQmLis6XXdqPVWoVwkfZcM6EojbP0e8pljXtr39a7bte644/FzdsWjURGsfKruAzq3tBVtCWqZtZ4M8b6UsXkKpVGLq1KmorKzsIOZ2h9++/goVR7eK34iu3BxoatFiWrMWamclvntqFcxNVVUNrh3kiICAIDTs/B7Hd8Ji0DEhq4tilcpsN070dPOtLB9wUEDnFy1+420dOhfV1taK49PdcxER7ucqRNvPVh1EZID5LTZqHQzv8faH32L5+r3mfTM9+fBroSQLIgvda/NWNuE2f6Ay4wjeuOMa2Cpd/Y3pa+j1evgPnYyL58/v8WtIN/DOB4u2JmLDgRz8ddgwBERC7ewoRNlwEmZPm3u7OXPjZxiGYUzGd999h4ceeggffPABxowZg6VLl4oMoJSUFAQGnpkRQX588+fPF53Jiy++GN988w0uu+wy7Nu3D4MHD7bKZ2AYpiN0rShZF1iLrorOlIksCb2SmNvSTtzV6s8UetuLw0KcPosgTHOq/UCvQQXuDHN9u8ft54b1UjwNTU1wdHIUorJBXDbsJwnN4rHekPFofF3ptdq9rvR842u3PT57ioYkWrcp1jYqXEf5+MlatH3iiSfEDclzcfz4cQwYMMBsMTz55JPi/Ny+o75gwQL4+PjAza1nXp1VGUcw2q24ZwFpy2B2PIAR/WkIfR0qdv4Ka1BvgffwjBmMgBD7KLRGv3F0TqF21xPRdlBMIHanlmJfermYzE1Dg6uYl+bnikmOXD7XAwFuip7/LzNWZU/mEXFt1FOcnbtmM8KirYmYPCwCkYGeImtWEmd9PVxYmGUYhmEswuuvv45bb70VixYtEssk3lIV7E8//VR0Wk/nzTffxKxZs/Doo4+KZfLlW7duHd555x3xXIZhmO5AGZ1k86Wyoe5Fb7Kcu/webULv2cRgysaRRF/aRyqSpzOuM4jEJEqTBCytF2KxVofqmhq4ursJcVjfbl+aG5al99af2t72nnrQvG27vm17u/el9V4uBmFErjz88MO46aabzrlPbGxsl16LCpDt2rWrw7ri4mLjtnN1zNt3zinTlhDZzz1slyGDR2HP3jrRXrr6Gi7OjvByNa8XaXtcXdUYMjRR/DZYEmrnjU1NULuYty/uoFQicOQMs/22mAM6Hj1td9dMGQBHRwUamixjz6PX9cfuEBXqqirN/14AWltb4ejoaKlEW8H61lrEN5pfAO8N3fmN6UvodDpEjBnVq2PT1efazlWVnTNrlKHCIcMwDMNYGhoGuHfvXpHN0/5CYPr06cKDrzNoffvMH4Iyc5cvX272eBmGYeSCsCIQGpzSLkVnuRMQECAmUzBu3Di88MILKCkpMY5goZudnp6eGDhwICzJtbfeDt3fb+X20Qn8f2MePN2csWjmEMu+6eXJFnkbbjOdw8fl/MfFErBoyzAMwzB2TllZmRiC25nX3okTJ7rlzXc2Xz5TFFKx94IG9hy/Pcdu7/Hbc+z2Hr89x24v8dtybN0lJydHdMRpTufUAwcOiPXx8fFwd3fHjBkzhDhL1gavvvqqOF8+/fTTuPvuu7s8zJVhGIZhugOLtgzDMAzDnBdTFFLpbREMa2PP8dtz7PYevz3Hbu/x23Ps9hJ/Vwup2ANLlizBF198YVwePny4mG/YsAFTpkwR573ff/8dd955p8i6JT/aG2+8Ec8//7wVo2YYhmHkDIu2DMMwDGPn+Pv7i86k5K0nQctn89mj9d3Z3xSFVHpbBMPa2HP89hy7vcdvz7Hbe/z2HLu9xC+nDNPPP/9cTOciKioKK1eutFhMDMMwTN+GRVuGYRiGsXNUKhWSk5Oxfv16XHbZZcbOPi3fc889nT6HsoRo+wMPPGBcR958tN6chVR6UwTDFrDn+O05dnuP355jt/f47Tl2e4jfVuNiGIZhGDnAoi3DMAzDyADKgqVhmiNHjsTo0aOxdOlS1NfXY9GiRWL7woULERYWJmwOiPvvvx+TJ0/Gv//9b8ydOxfLli3Dnj178NFHH1n5kzAMwzAMwzAMwzAs2jIMwzCMDLjmmmtQWloqPPmoOEpSUhJWr15tLDZGhVXaZ0SNHz8e33zzjSii8tRTTyEhIQHLly/H4MGDrfgpGIZhGIZhGIZhGIJFW4ZhGIaRCWSFcDY7hI0bN56x7uqrrxYTwzAMwzAMwzAMY1uwCRHDMAzDMAzDMAzDMAzDMIwNwaItwzAMwzAMwzAMwzAMwzCMDcGiLcMwDMMwDMMwDMMwDMMwjA3BnrYA9Hq9mDc0NFg7FEYm6HQ60Z6cnZ07FP5hGLkhp7YunQOkcwJj+nOnvbcXe47fnmO39/jtOXZ7j9+eY7eX+Pncab2+pz20D2vAx6Vz+LicHT42ncPHxbzHpavnTxZtATQ2Nor5ddddZ+1QGIZhGBs4J7i7u1s7DJuHz50MwzCMBJ87uw6fPxmGYZiunj8d9HxbVCjl5eXlUKvVcHBw6PHrjB49Grt27TJpbLYWg6levzev093ndmf/ru57vv1qa2sRHh6OvLw8eHh4QI7Ivb2b8rXl3N7l1NbpdEgnTT8/P76bbKZzp723F3uO355jt/f47Tl2e4/fnmO3l/j53Gm9vqc9tA9rwMelc/i4nB0+Np3Dx8W8x6Wr50/OtCVjX4UCAQEBvX4dOuhubm4miclWYzDV6/fmdbr73O7s39V9z7efVqsVk6urq9XbhLmQe3s35WvLub3Lra1zlpB5z5323l7sOX57jt3e47fn2O09fnuO3Z7i53Ondfqe9tI+LA0fl87h43J2+Nh0Dh8X8x+Xrpw/+XaoCbn77rtlH4OpXr83r9Pd53Zn/67uawvftbWxhWNgzhhM+drc3hmGYRiGYRiGYRiG6Q5sj8AwZqCmpgZeXl6orq6Gp6entcNhGLPBbZ3pS+3FnuO359jtPX57jt3e47fn2OUQP2NeuH10Dh+XzuHjcnb42HQOHxfbOC6cacswZoAqCT7zzDNizjByhts605faiz3Hb8+x23v89hy7vcdvz7HLIX7GvHD76Bw+Lp3Dx+Xs8LHpHD4utnFcONOWYRiGYRiGYRiGYRiGYRjGhuBMW4ZhGIZhGIZhGIZhGIZhGBuCRVuGYRiGYRiGYRiGYRiGYRgbgkVbhmEYhmEYhmEYhmEYhmEYG4JFW4ZhGIZhGIZhGIZhGIZhGBuCRVuGsSC5ubmYMmUKBg4ciKFDh+KHH36wdkgMYzaqqqowcuRIJCUlYfDgwfj444+tHRJjZd59911ER0fDxcUFY8aMwa5du6wdEl566SWMGjUKHh4eCAwMxGWXXYaUlJQO+zQ1NeHuu++Gn58f3N3dceWVV6K4uLjDPjk5OZg7dy5cXV3F6zz66KNobW216Gd5+eWX4eDggAceeMBuYs/Pz8cNN9wg4lOr1RgyZAj27Nlj3E71cpcsWYKQkBCxffr06UhNTe3wGhUVFbj++uvh6ekJb29v/P3vf0ddXZ1Z49ZqtVi8eDFiYmJEXHFxcfjnP/8p4rXF2P/66y/MmzcPoaGhoo0sX768w3ZTxXro0CFMmjRJ/I9HRETg1VdfNWvsLS0tePzxx0W7cXNzE/ssXLgQBQUFNhH7+eI/nTvuuEPss3TpUpuJn7E9XnjhBYwfP178ZlN76AxbOCdZA1u8zrA0pvi9lyOmut6TG++//77QJej8QtO4ceOwatWqPn1MTHmNbRL0DMNYjIKCAv3+/fvF48LCQn1oaKi+rq7O2mExjFlobW3V19fXi8fUzqOjo/VlZWXWDouxEsuWLdOrVCr9p59+qj969Kj+1ltv1Xt7e+uLi4utGtfMmTP1n332mf7IkSP6AwcO6OfMmaOPjIzs8Nt8xx136CMiIvTr16/X79mzRz927Fj9+PHjO7T1wYMH66dPny5+41euXKn39/fXP/nkkxb7HLt27RL/Y0OHDtXff//9dhF7RUWFPioqSn/TTTfpd+7cqc/IyNCvWbNGn5aWZtzn5Zdf1nt5eemXL1+uP3jwoP6SSy7Rx8TE6BsbG437zJo1Sz9s2DD9jh079Js3b9bHx8fr58+fb9bYX3jhBb2fn5/+999/12dmZup/+OEHvbu7u/7NN9+0ydjpe/3HP/6h/+mnn0hV1v/8888dtpsi1urqan1QUJD++uuvF/9P3377rV6tVus//PBDs8VeVVUl2u53332nP3HihH779u360aNH65OTkzu8hrViP1/87aHtFCNdG77xxhs2Ez9jeyxZskT/+uuv6x966CHxf3s6tnBOsga2ep1haUzxey9HTHG9J0d+/fVX/YoVK/QnT57Up6Sk6J966im9k5OTOE599ZiY6hrbVLBoyzBWhP7xc3JyrB0Gw5id8vJyIc6UlpZaOxTGSpCQcvfddxuXtVqtECdeeuklvS1RUlIiOjmbNm0yikJ08UqinMTx48fFPiQQSR0khUKhLyoqMu7z/vvv6z09PfXNzc1mj7m2tlafkJCgX7dunX7y5MnGC0pbj/3xxx/XT5w48azbdTqdPjg4WP/aa68Z19FncnZ2FqIUcezYMfF5du/ebdxn1apVegcHB31+fr7ZYp87d67+5ptv7rDuiiuuEKKZrcd+eifeVLG+9957eh8fnw7thr7j/v37my32s3WuaL/s7Gybiv1c8efl5enDwsJEJ5nOle1FW1uKn7EtSIDqTLS19jnJWtjLdYYl6cnvfV+hJ9d7fQU6n3zyySd8TPS9u8Y2FWyPwDAmHuLW1WE5e/fuFcMraQgbw8i1vZNFwrBhwxAeHi6G5vn7+1vwEzC2gkajEb95NARPQqFQiOXt27fDlqiurhZzX19fMae4aQh2+9gHDBiAyMhIY+w0p+HZQUFBxn1mzpyJmpoaHD161Owx09AsGgbbPkZ7iP3XX38VFipXX321GKo4fPjwDjYqmZmZKCoq6hC/l5eX+K1pHz8ND6bXkaD9qX3t3LnTbLHT0OT169fj5MmTYvngwYPYsmULZs+ebfOxn46pYqV9LrjgAqhUqg5tiYafVlZWWvR/mM5Z0rBxW49dp9NhwYIF4hw5aNCgM7bbevyM7WHtc5I1sKfrDGvSld/7vkJPrvfkDmkTy5YtQ319vbBJ4GOCXl1jmwoWbRmmG9APGAlQJFR1xnfffYeHHnoIzzzzDPbt2yf2pYukkpKSM7zJyHPto48+slDkDGOd9k4dTRIz6CLxm2++6ZMeSAxQVlYmLgTbdyAJWqbOg61A4gl5VU2YMEH4MBMUHwkhp/sGto+d5p19NmmbOaGLa/r/I6+207H12DMyMoSXWkJCAtasWYM777wT9913H7744osO73+udkNzEnzb4+joKDph5oz/iSeewLXXXisu0J2cnITgTG2HfEdtPfbTMVWs1mxL7f3lyON2/vz5wpvPHmJ/5ZVXRDzU9jvD1uNnbI++2B7s5TrD2nTl974v0NPrPbly+PBh4cnq7OwsvNV//vlnUYOnLx8TU1xjmwpHk74aw8gcyqCRsmg64/XXX8ett96KRYsWieUPPvgAK1aswKeffio6eERzc7MwPqdlytRhGDm39/YnMBJ1N2/ejKuuusrssTNMT++mHzlyRGRM2ktxy/vvvx/r1q0T2e722Gmi7MEXX3xRLJPwSceffktuvPFG2DLff/89vv76a3EzirIjDxw4IDqANDLB1mOXK5Tx8re//U0U2aGbAfYAZeq8+eabolNI2cFM34Wum0jAPxfHjx8XN4oYhulb13vmpn///uI6hrKP//e//4nrmE2bNqEvk2tD19icacswFhyWQx2Jm266CdOmTRND4RhGzu2dsmpra2vFY7oIILsFuihg+h5ki6FUKs/ItKbl4OBg2AL33HMPfv/9d2zYsEHYeUhQfNTeyerjbLHTvLPPJm0zF/Q/SJntI0aMEJl3NNFF9ltvvSUe080SW42doMrVlMnRnsTERFH1vP37n6vd0Pz00SxUIZ1GtJgzfhrKLmXb0jBkOqc/+OCDxmwMW479dEwVqzXbkiTYZmdniw6WlGVr67HTjUyKjYZTSv/D9BkefvhhYT1k6/EzpoO+cxJlzzXFxsZ26bX6Ynuwh+sMW6Arv/dypzfXe3KFMkbj4+ORnJwsrmMo0YZuKPblY7LXBNfYpoJFW4ax4LCcrVu3iiHl5A2alJQkJhqOwDBybO/U8Zw0aZI48dP83nvvFeIG0/egi0G6ECQP0PZZlrRMnlnWhG6m0QU8DQX7888/ERMT02E7xU3D39vHTj6RJCxKsdOcfsvbCyuScHS6KGlKLrzwQvG+lB0hTZS5SkP0pce2GjtBwxIpnvaQR2xUVJR4TN8FXfi2j588GcnHs338dMFMF9cS9D1S+yKPPnPR0NAgblS1hwQDel9bj/10TBUr7UM350hAbd+W6Gadj4+P2QXb1NRU/PHHH/Dz8+uw3ZZjJ7H/0KFDHf6HKVubbgqQZYitx8+YjoCAAJFFe66pvWfxubDm77q1sOXrDFuiK7/3csUU13t9BfrfodHBffmYXGiCa2yTYdKyZgzTh6txUgVfWrdt27YO+z366KOiminD2DPc3pnesmzZMlGd+PPPPxfV0G+77Ta9t7d3h+rW1uDOO+8U1bc3btyoLywsNE4NDQ3Gfe644w59ZGSk/s8//9Tv2bNHP27cODFJtLa26gcPHqyfMWOG/sCBA/rVq1frAwIC9E8++aTFP0/7yra2HvuuXbv0jo6O+hdeeEGfmpqq//rrr/Wurq76r776yrjPyy+/LNrJL7/8oj906JD+0ksv1cfExOgbGxuN+8yaNUs/fPhw/c6dO/VbtmwRVX7nz59v1thvvPFGfVhYmP7333/XZ2Zm6n/66Se9v7+//rHHHrPJ2Kn68f79+8VEv92vv/66eJydnW2yWKmSclBQkH7BggX6I0eOiP95+j4//PBDs8Wu0Wj0l1xyiT48PFy03/b/w83NzVaP/Xzxd0ZUVJT+jTfe6LDOmvEztge1HWpDzz33nN7d3d3Yvqit2do5yZLY6nWGpTHF770cMcX1nhx54okn9Js2bRLXMtQeaNnBwUG/du3aPntMTHWNbSpYtGUYE4lY1DlQKpUd1hELFy4UHQqGsWe4vTOm4O233xYXNyqVSoj7O3bssIm23dn02WefGfehjsxdd92l9/HxEULI5ZdfLi7025OVlaWfPXu2Xq1WC/Hu4Ycf1re0tFj9gtLWY//tt9+EuEAd7QEDBug/+uijDtt1Op1+8eLFQpCifS688EJ9SkpKh33Ky8uFgEXihaenp37RokVG8cJc1NTUiONM7dnFxUUfGxur/8c//tFBKLSl2Dds2NBpOyfx2ZSxHjx4UD9x4kTxGiRqkzhgztipk3m2/2F6nrVjP1/8XRVtrRk/Y3tQ2zlfm7eVc5KlscXrDEtjit97OWKq6z25cfPNN4vzDv3P0M0dag+SYNtXj4kpr7FNgQP9MW3uLsP0DahgBA2voKJiEjRMbfTo0Xj77beNQwvIp4yGYpxemIlh7Alu7wzDMAzDMAzDMAxjORwt+F4MY/fU1dUhLS3NuJyZmSk8TXx9fYVY9dBDD4lqi+RxQmLW0qVLUV9fj0WLFlk1bobpCdzeGYZhGIZhGIZhGMY6cKYtw3SDjRs3YurUqWesJ+Hq888/F4/feecdvPbaa6IYExUaowqDliwowjCmgts7wzAMwzAMwzAMw1gHFm0ZhmEYhmEYhmEYhmEYhmFsCIW1A2AYhmEYhmEYhmEYhmEYhmFOwaItwzAMwzAMwzAMwzAMwzCMDcGiLcMwDMMwDMMwDMMwDMMwjA3Boi3DMAzDMAzDMAzDMAzDMIwNwaItwzAMwzAMwzAMwzA2xZQpU/DAAw/Antm4cSMcHBxQVVUFOfDss88iKSmp16/z/vvvIzIyEm5ubrjiiitQWlp63uekpKQgODgYtbW1Z93n888/h7e3d6/jkwO2cCzGjh2LH3/80aox2Dss2jIMwzAMwzCyxhod//Xr1yMxMRFarbbHr6HRaBAdHY09e/aYNDaGYRjGMowfPx6FhYXw8vKydig2w08//YRHH30Ub7/9tji/kQh71VVXnfd5Tz75JO699154eHhYJE6m9zz99NN44oknoNPprB2K3cKiLcMwDMMwDNNnaGlpweOPP44hQ4aIDJ/Q0FAsXLgQBQUFHfajzChpov0SEhJw0003Ye/evV16n8cee0x0VpRKZY9jValUeOSRR0S8DMMwjOmhm2PmhH7HKTuUziWMgRdeeAH33HMPLr30UnFz84svvsCWLVvEdDZycnLw+++/i/OwvWS50g3jvs7s2bOFKL9q1Sprh2K3sGjLMAzDMAzD9BkaGhqwb98+LF68WMwp44eGXF5yySVn7PvZZ5+JDKmjR4/i3XffRV1dHcaMGYMvv/zynO9BHc/09HRceeWVvY73+uuvF69HMTAMw8iV+vp6cQPN3d0dISEh+Pe//33GPs3NzeJGVlhYmLiZRr/HZD9w+nDw5cuXixttLi4umDlzJnJzc88Y3v/JJ58gJiZG7EOQfcEtt9yCgIAAeHp6Ytq0aTh48KDxefR46tSpIsuTticnJxtHQWRnZ2PevHnw8fERcQ0aNAgrV648qz0CDRenfZydncVoitM/K6178cUXcfPNN4v3IxuBjz766JzHb/Xq1Zg4caL4/H5+frj44ovFeUgiKytLxEHnPPocrq6uGDZsGLZv397hdT7++GNERESI7Zdffjlef/318w6xp2NJ4isdywEDBuC99947676VlZXi3Dt37lzjOrp5OnjwYPzxxx9nfd73338v4qXvvj30ndPxkeItLy8/47m//PILRowYIeKLjY3Fc889h9bWVuP2EydOiGNH2wcOHCjioGNF7cie6MqxIFuKuLg4cTOh//+3d+8hUlb/A8efTCW7I6GE4UY3IUgrumBZ/mFaqWCUtl4Ci9rMpKQiQ4MMjbwUlCXVgqm5ZSnBkhVdlCQt0ySMtdJySYK2i1KxCil0eb68z49nfmfGmXFm1Rq39wsG3bk8M3PmmefM+Tyf8zn9+iVNTU252/husd9knn766dAO7FuZc845J3zeIIB+ww03JE8++WT4zrLfTZkyJZwcz3Dievjw4clrr712BN9552bQVpIkSf+ZgT9TVFevXp3cfPPNYcBCvbWFCxeGDFoyeWIMVMmQYgA9bNiw5PXXXw9BVDKEGHiWwuBk6NChuWBAHChYvHhxGFTx+u6+++5QPmH+/PnheXr16hUykGIEAa688koHPJI6NabLf/jhhyHA9v7774dgJ8G9GMdegowcD1taWpIxY8Yk1113XbJjx468E3McRzm59vHHH4dg6dixY/O209raGgKnBDA///zzcB3b2rVrV8gIpD8gyDdkyJDk119/Dbdz7D/jjDOSzZs3h9uZ8t2tW7dwG4EqAsrr1q1Ltm7dmsybNy8c44vhsfQ/vCbuS9/ASUQCbjH6rksuuSTZsmVL6CsmT54cTjCW6/vuv//+EEimPE+XLl1C4K5wWvrDDz8cgnO87/POOy8ZN25cLoBJe911113J1KlTw+30Y4V9UqFXXnkleeSRR8L9tm3bFoLNvB+yZ4v59ttvc8G/GEH27LZi1q9fH9ojtmnTpuT2228P+wWvl2D0Y489dsDj+E3Ae/rqq6+SxsbG0NbZ+6IPJvBIoJPtERynjf5p/P5gnyl3oW1LqaQtmpubQzs88MADyRdffJFMmjQpue2225K1a9eG2wcPHhxOEmdlnfg+nnbaabkTI21tbeFEQJxBzGO5jn/5zGnbwn35sssuC5+DOiiVJEmSOonJkyenffv2TdesWZO2tLSkI0eOTE866aR06tSpJR+zevXq9Jhjjknb29tz1/Ezubm5+YD7btmyJdy2YsWKktvr379/Onfu3LzrZs6cmZ544onp6NGj0y+//DJdtWpV2r179/Taa69N77nnnnT79u3p4sWLw7Y3btyY99iHHnooHTx4cJUtIUlHh71794bj4cqVK3PX/fLLL2mPHj1yx+7vvvsuPfbYY9O2tra8xw4ZMiSdPn16+P+SJUsOOIZu27YtXLdp06bcsbhbt27prl27cvdZv359evLJJ6f79+/P2/bZZ5+dNjY2hv/TjyxdurTo67/gggvSRx99tOhta9euDc//22+/hb/Hjx+fDh06NO8+Dz74YHr++efn/q6rq0tvueWW3N9///132qtXr/T5559PK7V79+7wvFu3bg1/79y5M/y9aNGi3H3oi7iONkJ9fX06YsSIvO1MmDAhPeWUU3J/034DBgzIa6Ply5fnPWb27NnpwIEDi76udevWhec84YQT8i5du3ZNb7rpppLvh+ecNWtW3nXjxo1Lhw8fnncd7yF+vewfjz/+eN59mpqa0tNPPz38/5133gnP/eOPP+b9Jij1G6BS7IvV9Nt//PFHumPHjrIXvhOlVNIWV1xxRdrQ0JB3nzFjxuQexz7apUuXdPPmzWGf69mzZzpnzpz08ssvD7e//PLLaZ8+fXKPnThxYthX//zzz7zt8byxN954I2z3r7/+qrg99P/MtJWOMi6mIklScZQvePHFF8NUPTKkqFtL5kc8DbLQ/v37Q81Yso2Y8nowTP3MppqWwlRZpnsWIuOJTFumXzKVlkwYMqeYgkjWLxkv/JtlvWTYFtuUpM6ITD3GCpQ7yPTs2TMcDzNkpTIWITs0zj4kGzAuA9C1a9fk0ksvzTtmM2uCLNBMXV1dKIMQlz6g/2B6d7ztnTt35rZNFivlE6655ppk7ty5ec957733hqxGZkXMnDkzZAGXwuvgfjH+Jls4Hmv1798/93+mqDMbg0zgUng8/RjT/+nLGHehcAZJvF1moyDbLv0RWZGxwr8Ls3tpBzI843ajLeL2iZHRCrI3yQjNLsxmyW4rZt++fXmzV7K2jPcZDBw4MO9vPttZs2blvb6GhoZQ+oisbN4z5SBo30rec6WZsmQsk11aaaYs+y3Zx+UufCdKqaQtSu172XeD7wklKPhs+L5RQuHOO+8M2d58P/iukY0bo8xHXLuffapwP+3Ro0f4/UM2uqrXtQOPkVQjqBfDIifUTGI6CVM+sx8S8WAxLnxPZ8htHKBZfZN6TP/0YioEgSVJ+jcG/oX9KNNUSaylzlsl/i8JN79vrWRwCQbR8arXvXv3Dn0r01jj64oNeBhcStJ/FUEjjpeUFygck5QqRVAKdWcLt02wKa6Pm8nquVLGYPz48cnbb78dSigQnKVMAyUICOZSO5fbKO0wZ86cUN6AsVZHZaUXMvQ5haUOYpwIJBhNTVrGetyXOrGFC63F2836sXLbLYd2A89ZGDAsNW4kqAwCy3GJBE6gZrcVwzT9cmWJyr1GatjeeOONB9xWrJ/uKNo8K7UBSm9QgoPyEZlyQVeCvpzQLWfGjBnhcqQTxPgeUG+ZAC2vmeQtyiYQtKW0QrX7KSVG+M7xW0bVM2grdZLFVDgrRkdGnRoWUynMaGUxFWo+0SF+8803oV4PnSsZP9T5+acWU+FAz2IqnJWTJOnfkgVsyWD94IMPKsqyRZaRwgI21Q4uiw1uKh3wxFlhktSZsDASx0LqclLzGxxDGbNkmX0XXXRRyETlpNZVV11VclvMrGAclGVLkklJXVsCT6VQv/ann34K2Y5ZhmoxZPlyue+++0JWK+MrgrYgW5PsSi7Tp08PgcxiQVteB7VjY/zNdjuaIMOCU7xPnjNrG8Zw1eIEJzV7Y4V/xzjJSLCS5CHGeZWgTjtJQ2Sh8p6zwCq1ismILYXPn5q0hW3JPhPbuHHjAZ8tbVNYQzd+zyxU9/PPP4f3c7D3fLBM2Qw16glSlnregwV9iykX9K2kLbJ9b+LEibnr+DsOFvN9Iz7A+yF2kAVyX3311fB9jOvZVor6uXx+6hiDtlINY8oJRec5U0dmDpmqxRZTibGYCj9SOFuX/eiJF1NBtqAKB2yKlWernVazmAqraTIViP8zmCTw++yzz4azyqwyyoCTAHJcyD1eTGX27NmHrZ0kSap04B8HbJlOSikCpsRWilIGBHiZ2VLN4PJQOOCR1JmRKcsUexYj43hMwIsxRDwLgQAfgUHGHIw3OCbu3r07zOBjyv+IESPC/egDCJY+88wzIfDEWIcFJ8tNeed4zlRyFqRiYUie64cffgiZswRlSTbhtY0ePTqcsPv+++9DYC9LaqF03fXXXx8eR59Dv1IqSEwCC+UbGAvV19eHYCXjt+eee67D7ccYi3YjKYeMYcaBLJRWLdrt6quvDmM5xoec0CSruNzMErJYGRMyLiXIxxR4gua0AyUliuGzZYxLoJv2ZEYnyUSFU/djZDKT0UzgPgtu87w8hpJIo0aNSt57773k3XffzXsci6SNHDky/Cbg82OfomQC/SplHBjn8tuBcTGf/d69e8PrQbn3fbgVBn2rVUlbsA/z24fvDvv8m2++GeIMa9asyd2Hz582eOutt8LsXRCope3Yt7JAezUI0BN7UMdY01Y6yldRLdTe3h46mGwqTzmcJeagXBj4PdhKnSD7lk6czoAzb9QQ5McSP2J4zayaSodXeMbP1SMlSf/EwJ/BJoOyW2+9NW/gT8CWwQeDSqYtMgAkw4pL4TRSsrO4nmxc+koet3z58lBKoVw/y+CyI1lOpTjgkdTZPfHEEyFLlGAhAaVBgwYdUMaNzFaCtgQ+yZAkyErwNE5UoRQc5dgoZUAQi35hxYoVZZ+bsRPl5ghYUVucwNTYsWPDsT8rY0M2K8/NbQS+CNISsAT9yJQpU0KglsAl9ykVhCXzc+XKlSGJhfIFBBXJMKWv6ij6OLZH6Qi2yRiP9qwW7fXCCy+EoC2zOBnnsa1yZQQIpC5atCh8NtSR5wTp0qVLy85GIRBO4g/9Nc9Dv0yblEN7E9iMA4wE48kuXrBgQdgO4+Us4Br3xwQguY1gOY956qmnQikJ8NmSjES2L7fzfrKko/h9E7g8lM/oSKukLfi+cDuBXU5ENDY2hs8tzp7lBACfI7N7shr+fC9IyCqsZ1uJtra2ZMOGDeF7pQ6KFiWTdJStolpo37596cUXXxxWJY2VWv2S+3PbvHnzSr4OVpxctmxZ3nWsGnr88cene/bsyV3H6tdnnnlm3qqQ/fr1CytOxhYsWBDuJ0nSkeo/WXWbfqp3797p/PnzwwrOWd+ZraBd7MIq35n4+uOOOy6skM1KyZ999tlBXwP9NY/Zvn17yRW3wfZGjRqVd138WrFhw4b01FNPTX///fdDahdJ6uyWLFkSxi46fO6444500KBBaS1YuHBhOmzYsCP+PB999FHo+1tbW3PX9e3bN+xfqs60adPShoaGf/tlHNUsjyDVKBdTkSSpemRVNTU1hUuGzNu4/8r6v3IquU8p9NdMySVbiUwWkFXEJUY2UqHChXAox8DrdwEPSdKRRhYmJQNYOIpZlS+99NIhlW44nCZNmhRmwDBTNB6HHqrm5ubw2+Hcc89NWltbQ4k/so4pmwDWY6H8Q7l1YFQcpU5KlclQZQzaSp2Ai6lIklRbmF7JQJd+MD6hWQ1O3jJNkempkiQdaZ9++mmututZZ50VagNTMqAWUB4hXi/lcOG9UlKDWsCMfSnPQd3kDKUEWlpaDvvz/hdQykSHxqCtVKNcTEWSpKMXNW9nzJhxSNvo3r37ATXpJEnFUXO0luuOHg0OVlu2MyKD1ixa1SqDttJRvIpqtpgKi5NRYD1bTCWbmslgr3AxFVb0JPDLdE2Kri9btuygi6kwLeZwLqbCaqmSJEmSJEkqzqCtVMNY9ZOVLFlFlbo9TC9ob2/PW41x1apV4f8XXnhh3mPJuo1XgsxWbKQ+bZ8+fcKKrEx/YQXTciZMmJBMmzYt+frrr0vW063UJ598El4/gWZJkiRJkiQVdwyrkZW4TZICsn337NmTW0ylo+rr65MBAwYc8nRRSZIkSZKkzqxjqyJI+k+hLENdXd0Bi4pVw8VUJEmSJEmSKmOmrSRJkiRJkiTVEDNtJUmSJEmSJKmGGLSVJEmSJEmSpBpi0FaSJEmSJEmSaohBW0mSJEmSJEmqIQZtJUmSJEmSJKmGGLSVJEmSJEmSpBpi0FaSJEmSJEmSaohBW0mSJEmSJEmqIQZtJUmSJEmSJCmpHf8DyPgD4BBYUIMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1400x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d2d = np.linspace(20, 1500, 400)\n",
    "h_bs, h_ut = 30.0, 1.5\n",
    "d3d = np.hypot(d2d, h_bs - h_ut)\n",
    "\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 4))\n",
    "\n",
    "for fc_mhz, color in ((1800.0, COBALT), (3500.0, COPPER)):\n",
    "    fc = fc_mhz / 1000.0\n",
    "    ax1.plot(d2d, uma_los(d2d, d3d, fc, h_bs, h_ut), color=color, label=f\"LOS {int(fc_mhz)} MHz\")\n",
    "    ax1.plot(d2d, uma_nlos(d2d, d3d, fc, h_bs, h_ut), \"--\", color=color, label=f\"NLOS {int(fc_mhz)} MHz\")\n",
    "ax1.set_xscale(\"log\"); ax1.set_xlabel(\"d2D (m)\"); ax1.set_ylabel(\"path loss (dB)\")\n",
    "ax1.set_title(\"TR 38.901 UMa path loss (\u00a73.5)\"); ax1.legend(frameon=False, fontsize=8)\n",
    "\n",
    "ax2.plot(d2d, p_los_uma(d2d), color=TEAL)\n",
    "ax2.set_xlabel(\"d2D (m)\"); ax2.set_ylabel(\"P(LOS)\"); ax2.set_title(\"LOS probability (\u00a73.4)\")\n",
    "\n",
    "theta = np.linspace(-10, 40, 400)\n",
    "for tilt, color in ((2.0, COBALT), (6.0, INK), (14.0, COPPER)):\n",
    "    ax3.plot(theta, gain_db(theta, 0.0, tilt, 65.0), color=color, label=f\"tilt {tilt:g}\u00b0\")\n",
    "ax3.axhline(peak_gain_dbi(6.5, 65.0), ls=\":\", color=\"#888\", lw=1)\n",
    "ax3.set_xlabel(\"depression angle \u03b8 (deg, + = down)\"); ax3.set_ylabel(\"gain at boresight azimuth (dBi)\")\n",
    "ax3.set_title(\"\u00a77.3 vertical pattern vs tilt (\u00a73.7)\"); ax3.legend(frameon=False, fontsize=8)\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79cce765",
   "metadata": {},
   "source": [
    "Left: NLOS loss runs ~15\u201320 dB above LOS at cell edge, and 3.5 GHz costs $20\\log_{10}(3.5/1.8) \\approx 5.8$ dB\n",
    "more than 1.8 GHz everywhere. Middle: beyond ~200 m almost every point is NLOS. Right: the\n",
    "vertical lobe is a parabola centred on the tilt angle \u2014 a point at depression angle 5\u00b0 (roughly\n",
    "330 m out from a 30 m mast) sits near peak gain at 6\u00b0 tilt but ~14 dB down at 14\u00b0 tilt.\n",
    "\n",
    "### Estate map\n",
    "\n",
    "A top-down view of the generated lattice: site markers, one arrow per cell pointing along its\n",
    "sector azimuth, coloured by band. This is the geometry the cached fields were computed over."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "66cd375d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "band_color = {b: c for b, c in zip(sorted(estate.cells.freq_mhz.unique()), [COBALT, COPPER])}\n",
    "\n",
    "for _, c in estate.cells.iterrows():\n",
    "    az = np.radians(c.az_deg)\n",
    "    dx, dy = 90 * np.sin(az), 90 * np.cos(az)\n",
    "    ax.annotate(\"\", xy=(c.x + dx, c.y + dy), xytext=(c.x, c.y),\n",
    "                arrowprops=dict(arrowstyle=\"-|>\", color=band_color[c.freq_mhz], lw=1.6, alpha=0.85))\n",
    "\n",
    "site_xy = estate.cells.groupby(estate.cells.cell_id.str.extract(r\"(g-s\\d+)\")[0])[[\"x\", \"y\"]].first()\n",
    "ax.scatter(site_xy.x, site_xy.y, c=INK, s=80, zorder=5, marker=\"^\")\n",
    "for site_id, row in site_xy.iterrows():\n",
    "    ax.annotate(site_id, (row.x, row.y), textcoords=\"offset points\", xytext=(8, 8), fontsize=9)\n",
    "ax.scatter(estate.grid_x, estate.grid_y, s=2, c=\"#999\", alpha=0.35, zorder=1, label=\"traffic-proxy points\")\n",
    "\n",
    "handles = [plt.Line2D([0], [0], color=c, lw=2, label=f\"{band_label(b)} ({int(b)} MHz)\")\n",
    "           for b, c in band_color.items()]\n",
    "handles.append(plt.Line2D([0], [0], marker=\".\", color=\"#999\", lw=0, label=\"traffic-proxy points\"))\n",
    "ax.legend(handles=handles, loc=\"upper right\", frameon=False)\n",
    "ax.set_aspect(\"equal\"); ax.set_xlabel(\"east (m)\"); ax.set_ylabel(\"north (m)\")\n",
    "ax.set_title(\"Example estate \u2014 3 sites, 18 cells, 2000 sample points (generic rung)\")\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98440347",
   "metadata": {},
   "source": [
    "## 4. Core logic \u2014 calibration (\"training\")\n",
    "\n",
    "### 4.1 The forward model: from parameters to RSRP and SINR\n",
    "\n",
    "Everything cached in Section 3 is parameter-free. The **model parameters** \u2014 the vector the\n",
    "calibration fits \u2014 enter only in `Estate.evaluate()`. For cell $c$ and point $n$:\n",
    "\n",
    "$$\\mathrm{RSRP}_{c,n} = P_0[g_c] + G_{c,n} - PL_{c,n} - z_{c,n}\\,(\\sigma_{NLOS} + \\sigma_{\\text{extra}}) - \\mathbb{1}[\\text{indoor}_n]\\,\\big(L_{tw}[g_c] + 4\\,z^{O2I}_n\\big)$$\n",
    "\n",
    "- $P_0[g]$ \u2014 conducted power per resource element for band group $g$ (`p0_low`, `p0_mid`, `p0_hi`).\n",
    "  The \u00a77.3 peak gain is inside $G$, so $P_0 + G_{\\max}$ is the boresight EIRP per RE.\n",
    "- $\\text{indoor}_n = (u_n < p_{in})$ \u2014 a cached uniform draw compared with the fitted indoor\n",
    "  fraction `p_in`; indoor points pay an outdoor-to-indoor wall loss `ltw_low` / `ltw_hi`\n",
    "  (TR 38.901 \u00a77.4.3) plus a $\\pm 4$ dB per-building spread.\n",
    "\n",
    "**Serving cell.** Each point attaches to the cell with the highest *biased* RSRP among cells that\n",
    "are switched on and allowed to serve:\n",
    "\n",
    "$$s(n) = \\arg\\max_{c\\ \\in\\ \\text{eligible}} \\big(\\mathrm{RSRP}_{c,n} + b[g_c]\\big), \\qquad b = (0,\\ \\text{bias\\_mid},\\ \\text{bias\\_hi})$$\n",
    "\n",
    "The bias exists because NSA serving is a *policy*, not max-RSRP: a capacity-preferred band wins\n",
    "even when a low-band cell is slightly stronger. The *reported* RSRP is still the chosen cell's\n",
    "true, unbiased value.\n",
    "\n",
    "**SINR.** Interference comes from every other switched-on cell on the same carrier, scaled by the\n",
    "fitted average PRB load $\\ell$, plus the wrap-around tier scaled by its visibility $w_{\\text{tier}}$,\n",
    "plus thermal noise $N_0 = -174 + 10\\log_{10}(15\\,000) + 7 \\approx -125.2$ dBm per RE:\n",
    "\n",
    "$$\\mathrm{SINR}_n = \\frac{10^{\\mathrm{RSRP}_{s(n),n}/10}}\n",
    "{\\ell \\displaystyle\\sum_{\\substack{c \\ne s(n)\\\\ f_c = f_{s(n)}}} 10^{\\mathrm{RSRP}_{c,n}/10}\n",
    "\\;+\\; \\ell\\, w_{\\text{tier}} \\displaystyle\\sum_{\\substack{e \\in \\text{tier}\\\\ f_e = f_{s(n)}}} 10^{\\mathrm{RSRP}_{e,n}/10}\n",
    "\\;+\\; 10^{N_0/10}}$$\n",
    "\n",
    "**The measurement operator.** A real calibration report lists per-*device* session averages,\n",
    "not per-point samples, and averaging along a device's day compresses the spread. The parameter\n",
    "`agg` groups points into pseudo-devices of that size and averages in dB, then clamps SINR to the\n",
    "reporting tool's $[-15, 23]$ dB range. This is `aggregate=True`, the *device-mean* view the\n",
    "acceptance gate is judged on. `aggregate=False` skips both \u2014 the *point-sample* physical field,\n",
    "which Section 5 uses for the tilt sweep. On the generic rung `agg = 1`, so the two views differ\n",
    "only by the clamp.\n",
    "\n",
    "### 4.2 What \"training\" means here\n",
    "\n",
    "There are no labelled points. The engine's *structure* comes from the standard; calibration only\n",
    "anchors the ~12 global unknowns by matching **marginal distributions**: the 5th/25th/50th/75th/95th\n",
    "percentiles of RSRP and of SINR, ten numbers in total. The objective (`calibrate.loss`) is a\n",
    "weighted squared error on those quantiles:\n",
    "\n",
    "$$\\mathcal{L}(\\boldsymbol\\theta) = \\sum_{q} w^{R}_q \\big(Q^{\\text{pred}}_{R,q}(\\boldsymbol\\theta) - Q^{\\text{target}}_{R,q}\\big)^2\n",
    "\\;+\\; 2\\sum_{q} w^{S}_q \\big(Q^{\\text{pred}}_{S,q}(\\boldsymbol\\theta) - Q^{\\text{target}}_{S,q}\\big)^2$$\n",
    "\n",
    "with $q \\in \\{5, 25, 50, 75, 95\\}$, $w^R = (2,1,1,1,1)$, $w^S = (3,1,1,1,1)$. The extra weight on\n",
    "the 5th percentiles is declared, not hidden: the platform's guardrails read the cell-edge tail,\n",
    "so that is where the fit is asked to be most accurate.\n",
    "\n",
    "**Why differential evolution.** $\\mathcal{L}$ is evaluated through an `argmax` and `np.percentile`,\n",
    "so it is piecewise-constant with respect to $\\boldsymbol\\theta$ \u2014 flat almost everywhere, with\n",
    "jumps where a point changes serving cell or a quantile crosses a sample. Gradients are useless.\n",
    "`scipy.optimize.differential_evolution` needs none: it keeps a *population* of candidate vectors\n",
    "inside `BOUNDS` and, each generation, builds a trial for every member $x_i$ by mutation and\n",
    "crossover,\n",
    "\n",
    "$$v = x_a + F\\,(x_b - x_c), \\qquad u_j = \\begin{cases} v_j & \\text{with probability } CR \\\\ x_{i,j} & \\text{otherwise}\\end{cases}$$\n",
    "\n",
    "keeping $u$ if $\\mathcal{L}(u) < \\mathcal{L}(x_i)$. It is robust to the flat, multi-modal\n",
    "landscape at the price of many evaluations \u2014 which is exactly why Section 3 cached everything.\n",
    "With `popsize=10` and 12 parameters the population is 120 vectors; `maxiter=60` bounds the run at\n",
    "~7 200 loss evaluations. The service uses `maxiter=140, popsize=14` and then a bounded\n",
    "Nelder\u2013Mead polish (`calibrate.fit`); this notebook inlines the DE stage only, so the mechanics\n",
    "are visible and the cell runs in seconds.\n",
    "\n",
    "### 4.3 The parameter vector\n",
    "\n",
    "| Name | Meaning | Bounds |\n",
    "|---|---|---|\n",
    "| `p0_low`, `p0_mid`, `p0_hi` | conducted power per RE, dBm, per band group | 8 \u2013 28 |\n",
    "| `sigma_extra` | extra shadowing \u03c3 on top of the 4/6 dB spec values, dB | 0 \u2013 6 |\n",
    "| `p_in` | fraction of users indoors | 0.30 \u2013 0.995 |\n",
    "| `ltw_low`, `ltw_hi` | outdoor-to-indoor wall loss, dB, low/mid vs C-band | 5 \u2013 20, 10 \u2013 30 |\n",
    "| `load` | average PRB load of interfering cells (fraction) | 0.02 \u2013 0.60 |\n",
    "| `agg` | pseudo-device session size (measurement operator) | 1 \u2013 24 |\n",
    "| `bias_mid`, `bias_hi` | NSA band-priority serving bias, dB | 0 \u2013 20 |\n",
    "| `w_tier` | visibility of the wrap-around tier | 0 \u2013 1 |\n",
    "\n",
    "The $P_0$ bounds *are* the physical window: an earlier version of the search satisfied every\n",
    "quantile target by pushing one $P_0$ to an EIRP ~15 dB above any real radio. Constraining power\n",
    "forces the optimiser to explain the data with geometry and environment instead.\n",
    "\n",
    "### 4.4 The acceptance gate\n",
    "\n",
    "A fitted model is only allowed into a decision path if it clears the **hard** checks; **soft**\n",
    "checks are reported alongside, never hidden:\n",
    "\n",
    "| Check | Rule | Hard? |\n",
    "|---|---|---|\n",
    "| RSRP median | $\\lvert Q^{\\text{pred}}_{R,50} - Q^{\\text{target}}_{R,50}\\rvert \\le 2$ dB | yes |\n",
    "| SINR cell edge | $\\lvert Q^{\\text{pred}}_{S,5} - Q^{\\text{target}}_{S,5}\\rvert \\le 1.5$ dB | yes |\n",
    "| EIRP sanity | $25 \\le P_0[g] + 17 \\le 45$ dBm for every band group | yes |\n",
    "| RSRP tails | p5, p95 within 3 dB | no |\n",
    "| SINR p50, p95 | within 2 dB | no |\n",
    "\n",
    "### 4.5 This notebook's demo\n",
    "\n",
    "There is no measurement report here, so the targets are **generated by the engine itself**:\n",
    "evaluate a known parameter set (the public defaults) over the example estate, take its ten\n",
    "quantiles as if they were a supplied report, then fit *blind* to those targets. The question the\n",
    "cells below answer is the same one a real calibration is held to \u2014 *does the search find a model\n",
    "that reproduces the targets and passes the gate?* \u2014 not *does it recover the exact true values*.\n",
    "Those are different questions: ten quantile constraints cannot pin down twelve parameters, and\n",
    "several combinations (say, higher $P_0$ with more indoor loss) produce nearly the same marginals.\n",
    "That non-identifiability is precisely why the gate includes a physical-plausibility check and not\n",
    "only statistical ones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "684864ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p0_low</th>\n",
       "      <th>p0_mid</th>\n",
       "      <th>p0_hi</th>\n",
       "      <th>sigma_extra</th>\n",
       "      <th>p_in</th>\n",
       "      <th>ltw_low</th>\n",
       "      <th>ltw_hi</th>\n",
       "      <th>load</th>\n",
       "      <th>agg</th>\n",
       "      <th>bias_mid</th>\n",
       "      <th>bias_hi</th>\n",
       "      <th>w_tier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>true (3GPP defaults)</th>\n",
       "      <td>12.2</td>\n",
       "      <td>12.2</td>\n",
       "      <td>14.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>12.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      p0_low  p0_mid  p0_hi  sigma_extra  p_in  ltw_low  \\\n",
       "true (3GPP defaults)    12.2    12.2   14.8          0.0   0.8     12.0   \n",
       "\n",
       "                      ltw_hi  load  agg  bias_mid  bias_hi  w_tier  \n",
       "true (3GPP defaults)    27.0   0.5  1.0       0.0      0.0     1.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_params = default_params()  # the \"ground truth\" for this demo only \u2014 public 3GPP defaults\n",
    "pd.Series(true_params, name=\"true (3GPP defaults)\").to_frame().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1bfb99ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>p5</th>\n",
       "      <th>p25</th>\n",
       "      <th>p50</th>\n",
       "      <th>p75</th>\n",
       "      <th>p95</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rsrp</th>\n",
       "      <td>-102.54</td>\n",
       "      <td>-93.75</td>\n",
       "      <td>-86.34</td>\n",
       "      <td>-77.44</td>\n",
       "      <td>-61.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinr</th>\n",
       "      <td>-15.00</td>\n",
       "      <td>-1.57</td>\n",
       "      <td>4.50</td>\n",
       "      <td>12.87</td>\n",
       "      <td>23.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          p5    p25    p50    p75    p95\n",
       "rsrp -102.54 -93.75 -86.34 -77.44 -61.14\n",
       "sinr  -15.00  -1.57   4.50  12.87  23.00"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "QS = [5, 25, 50, 75, 95]\n",
    "ref = estate.evaluate(true_params, serving_mask=serving_mask)  # aggregate=True: device-mean, report-comparable\n",
    "targets = {\n",
    "    \"rsrp\": [round(float(v), 2) for v in np.percentile(ref[\"_arrays\"][\"rsrp\"], QS)],\n",
    "    \"sinr\": [round(float(v), 2) for v in np.percentile(ref[\"_arrays\"][\"sinr\"], QS)],\n",
    "}\n",
    "pd.DataFrame(targets, index=[f\"p{q}\" for q in QS]).T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ca14628",
   "metadata": {},
   "source": [
    "These ten numbers \u2014 and only these \u2014 are what the optimiser sees. `loss()` will call\n",
    "`estate.evaluate()` for each candidate vector, take the same ten percentiles of the result, and\n",
    "score the squared distance to this table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5a557f61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DE loss: 0.0735   generations run: 60   loss evaluations: 7320\n",
      "CPU times: user 18.3 s, sys: 146 ms, total: 18.4 s\n",
      "Wall time: 18.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "bounds = [BOUNDS[n] for n in NAMES]\n",
    "de = differential_evolution(loss, bounds, args=(estate, targets, serving_mask),\n",
    "                            seed=7, maxiter=60, popsize=10, tol=1e-4, polish=False)\n",
    "fitted_params = dict(zip(NAMES, [float(v) for v in de.x]))\n",
    "print(f\"DE loss: {de.fun:.4f}   generations run: {de.nit}   loss evaluations: {de.nfev}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a8b0d980",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hard_pass: True\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>delta_db</th>\n",
       "      <th>pass</th>\n",
       "      <th>hard</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rsrp_p50_within_2db</th>\n",
       "      <td>0.06</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinr_p05_within_1p5db</th>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eirp_sanity_window</th>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsrp_p05_within_3db</th>\n",
       "      <td>0.09</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsrp_p95_within_3db</th>\n",
       "      <td>0.19</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinr_p50_within_2db</th>\n",
       "      <td>0.05</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinr_p95_within_2db</th>\n",
       "      <td>0.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      delta_db  pass   hard\n",
       "rsrp_p50_within_2db       0.06  True   True\n",
       "sinr_p05_within_1p5db      0.0  True   True\n",
       "eirp_sanity_window         NaN  True   True\n",
       "rsrp_p05_within_3db       0.09  True  False\n",
       "rsrp_p95_within_3db       0.19  True  False\n",
       "sinr_p50_within_2db       0.05  True  False\n",
       "sinr_p95_within_2db        0.0  True  False"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred = estate.evaluate(fitted_params, serving_mask=serving_mask)\n",
    "predicted = {\n",
    "    \"rsrp\": [round(float(v), 2) for v in np.percentile(pred[\"_arrays\"][\"rsrp\"], QS)],\n",
    "    \"sinr\": [round(float(v), 2) for v in np.percentile(pred[\"_arrays\"][\"sinr\"], QS)],\n",
    "}\n",
    "report = {\"params\": fitted_params,\n",
    "          \"quantiles\": {\"rsrp\": {\"predicted\": predicted[\"rsrp\"], \"target\": targets[\"rsrp\"]},\n",
    "                        \"sinr\": {\"predicted\": predicted[\"sinr\"], \"target\": targets[\"sinr\"]}}}\n",
    "gate = acceptance_gate(report)\n",
    "\n",
    "print(f\"hard_pass: {gate['hard_pass']}\\n\")\n",
    "pd.DataFrame(gate[\"checks\"]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a0643eeb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>true (3GPP defaults)</th>\n",
       "      <th>fitted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>p0_low</th>\n",
       "      <td>12.2</td>\n",
       "      <td>22.311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p0_mid</th>\n",
       "      <td>12.2</td>\n",
       "      <td>10.710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p0_hi</th>\n",
       "      <td>14.8</td>\n",
       "      <td>16.421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_extra</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_in</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ltw_low</th>\n",
       "      <td>12.0</td>\n",
       "      <td>10.582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ltw_hi</th>\n",
       "      <td>27.0</td>\n",
       "      <td>29.494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>load</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.530</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bias_mid</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bias_hi</th>\n",
       "      <td>0.0</td>\n",
       "      <td>11.411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_tier</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             true (3GPP defaults)  fitted\n",
       "p0_low                       12.2  22.311\n",
       "p0_mid                       12.2  10.710\n",
       "p0_hi                        14.8  16.421\n",
       "sigma_extra                   0.0   0.314\n",
       "p_in                          0.8   0.798\n",
       "ltw_low                      12.0  10.582\n",
       "ltw_hi                       27.0  29.494\n",
       "load                          0.5   0.530\n",
       "agg                           1.0   1.363\n",
       "bias_mid                      0.0  12.313\n",
       "bias_hi                       0.0  11.411\n",
       "w_tier                        1.0   0.903"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\"true (3GPP defaults)\": true_params, \"fitted\": fitted_params}).round(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1b73914",
   "metadata": {},
   "source": [
    "`hard_pass: True`: the fitted model clears the gate \u2014 median RSRP and cell-edge SINR within\n",
    "budget, implied EIRP inside the physical window. The parameter comparison shows the\n",
    "non-identifiability described in \u00a74.5: several fitted values differ from the \"true\" ones while\n",
    "the quantiles match, because those parameters trade off against each other in the marginals. On\n",
    "the calibrated rung, the same limitation is why the service also validates against a *held-out*\n",
    "device split \u2014 reproducing marginals you fit to is necessary, not sufficient.\n",
    "\n",
    "`fitted_params` is now \"the model\" the rest of this notebook does inference with \u2014 standing in\n",
    "for what `/v1/train` would persist as `var/model.json` in calibrated mode."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "670d8f78",
   "metadata": {},
   "source": [
    "## 5. Core logic \u2014 inference, and chaining a cell config\n",
    "\n",
    "### 5.1 What an evaluation returns\n",
    "\n",
    "`Estate.evaluate(params, ...)` runs the forward model of \u00a74.1 once and summarises the result as\n",
    "`guardrail_kpis` \u2014 the vocabulary a platform decision hub consumes:\n",
    "\n",
    "| KPI | Definition |\n",
    "|---|---|\n",
    "| `rsrp_p5`, `rsrp_p50`, `rsrp_p95` | percentiles of serving-cell RSRP over the sample (dBm) |\n",
    "| `sinr_p5`, `sinr_p50`, `sinr_p95` | percentiles of SINR (dB); `sinr_p5` is *cell-edge quality* |\n",
    "| `outage_rate` | $P(\\mathrm{SINR} < -6 \\text{ dB})$ \u2014 points that cannot sustain a link |\n",
    "| `coverage_rate` | $P(\\mathrm{RSRP} > -110 \\text{ dBm})$ \u2014 points inside the coverage footprint |\n",
    "| `serving_share` | fraction of points attached to each cell \u2014 where the traffic lands |\n",
    "\n",
    "### 5.2 How a config change enters the model\n",
    "\n",
    "Two overrides, both applied *without* touching the cache:\n",
    "\n",
    "- `tilt_override={cell_id: \u03c4'}` \u2014 recomputes only that cell's gain row,\n",
    "  $G_{c,n} = G(\\theta_{c,n}, \\phi_{c,n};\\ \\tau')$, from the cached angles (\u00a73.7). Every other row,\n",
    "  and all path loss and fading, is reused. Changing the tilt of one cell therefore costs one\n",
    "  vectorised pattern evaluation, not a rebuild.\n",
    "- `off_cells={cell_id, ...}` \u2014 sets the cell's *active* flag false. An inactive cell is removed\n",
    "  from the serving `argmax` **and** its term is dropped from the interference sum in \u00a74.1. Points\n",
    "  it was serving re-attach to the next-best eligible cell; points it was interfering with see a\n",
    "  cleaner channel.\n",
    "\n",
    "\"Chaining a cell config\" below means exactly that: call `evaluate()` again with a different\n",
    "override and compare \u2014 a tilt sweep (a sequence of configs on one cell) and an on/off switch (the\n",
    "same knob a rApp's cell-sleep action drives). Nothing is re-fit; the same `fitted_params` are used\n",
    "throughout, which is what makes the comparison fair: *same model, same points, same draws,\n",
    "different configuration*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "58f6c140",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>baseline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rsrp_p5</th>\n",
       "      <td>-102.632652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsrp_p50</th>\n",
       "      <td>-86.282409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsrp_p95</th>\n",
       "      <td>-60.948981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinr_p5</th>\n",
       "      <td>-15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinr_p50</th>\n",
       "      <td>4.445050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinr_p95</th>\n",
       "      <td>23.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outage_rate</th>\n",
       "      <td>0.148000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coverage_rate</th>\n",
       "      <td>0.994500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples</th>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  baseline\n",
       "rsrp_p5        -102.632652\n",
       "rsrp_p50        -86.282409\n",
       "rsrp_p95        -60.948981\n",
       "sinr_p5         -15.000000\n",
       "sinr_p50          4.445050\n",
       "sinr_p95         23.000000\n",
       "outage_rate       0.148000\n",
       "coverage_rate     0.994500\n",
       "samples        2000.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baseline = estate.evaluate(fitted_params, serving_mask=serving_mask)[\"guardrail_kpis\"]\n",
    "pd.Series(baseline, name=\"baseline\").to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "703d73d8",
   "metadata": {},
   "source": [
    "### 5a. Tilt sweep\n",
    "\n",
    "Re-evaluate the *same* estate and parameters while chaining a sequence of `tilt_override` configs\n",
    "on one cell. `aggregate=False` selects the point-sample view: the device-mean view of \u00a74.1\n",
    "averages several points into a pseudo-device and blunts a single cell's response, so an\n",
    "optimiser comparing tilts must read the unclamped physical field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1b4cc202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tilt_deg</th>\n",
       "      <th>rsrp_p5</th>\n",
       "      <th>rsrp_p50</th>\n",
       "      <th>rsrp_p95</th>\n",
       "      <th>sinr_p5</th>\n",
       "      <th>sinr_p50</th>\n",
       "      <th>sinr_p95</th>\n",
       "      <th>outage_rate</th>\n",
       "      <th>coverage_rate</th>\n",
       "      <th>samples</th>\n",
       "      <th>own_share</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>-102.705481</td>\n",
       "      <td>-86.364150</td>\n",
       "      <td>-61.054236</td>\n",
       "      <td>-15.123149</td>\n",
       "      <td>4.320483</td>\n",
       "      <td>26.901885</td>\n",
       "      <td>0.1495</td>\n",
       "      <td>0.9945</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>-102.632652</td>\n",
       "      <td>-86.282409</td>\n",
       "      <td>-60.948981</td>\n",
       "      <td>-15.048123</td>\n",
       "      <td>4.445050</td>\n",
       "      <td>27.709603</td>\n",
       "      <td>0.1480</td>\n",
       "      <td>0.9945</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>-102.484028</td>\n",
       "      <td>-86.282409</td>\n",
       "      <td>-60.728837</td>\n",
       "      <td>-15.048123</td>\n",
       "      <td>4.516939</td>\n",
       "      <td>27.947449</td>\n",
       "      <td>0.1475</td>\n",
       "      <td>0.9945</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>-102.632652</td>\n",
       "      <td>-86.332817</td>\n",
       "      <td>-60.748080</td>\n",
       "      <td>-15.123149</td>\n",
       "      <td>4.403563</td>\n",
       "      <td>27.676224</td>\n",
       "      <td>0.1490</td>\n",
       "      <td>0.9945</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>-102.632652</td>\n",
       "      <td>-86.380189</td>\n",
       "      <td>-60.834083</td>\n",
       "      <td>-15.214286</td>\n",
       "      <td>4.368803</td>\n",
       "      <td>26.877381</td>\n",
       "      <td>0.1495</td>\n",
       "      <td>0.9945</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22</td>\n",
       "      <td>-102.705481</td>\n",
       "      <td>-86.416654</td>\n",
       "      <td>-61.054236</td>\n",
       "      <td>-15.474926</td>\n",
       "      <td>4.197480</td>\n",
       "      <td>26.877381</td>\n",
       "      <td>0.1510</td>\n",
       "      <td>0.9945</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tilt_deg     rsrp_p5   rsrp_p50   rsrp_p95    sinr_p5  sinr_p50   sinr_p95  \\\n",
       "0         2 -102.705481 -86.364150 -61.054236 -15.123149  4.320483  26.901885   \n",
       "1         6 -102.632652 -86.282409 -60.948981 -15.048123  4.445050  27.709603   \n",
       "2        10 -102.484028 -86.282409 -60.728837 -15.048123  4.516939  27.947449   \n",
       "3        14 -102.632652 -86.332817 -60.748080 -15.123149  4.403563  27.676224   \n",
       "4        18 -102.632652 -86.380189 -60.834083 -15.214286  4.368803  26.877381   \n",
       "5        22 -102.705481 -86.416654 -61.054236 -15.474926  4.197480  26.877381   \n",
       "\n",
       "   outage_rate  coverage_rate  samples  own_share  \n",
       "0       0.1495         0.9945   2000.0     0.0055  \n",
       "1       0.1480         0.9945   2000.0     0.0170  \n",
       "2       0.1475         0.9945   2000.0     0.0215  \n",
       "3       0.1490         0.9945   2000.0     0.0145  \n",
       "4       0.1495         0.9945   2000.0     0.0080  \n",
       "5       0.1510         0.9945   2000.0     0.0020  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sweep_cell = \"g-s2-a-n78\"\n",
    "tilts = [2, 6, 10, 14, 18, 22]\n",
    "rows = []\n",
    "for t in tilts:\n",
    "    r = estate.evaluate(fitted_params, tilt_override={sweep_cell: t},\n",
    "                        serving_mask=serving_mask, aggregate=False)\n",
    "    rows.append({\"tilt_deg\": t, **r[\"guardrail_kpis\"],\n",
    "                 \"own_share\": r[\"serving_share\"].get(sweep_cell, 0.0)})\n",
    "sweep = pd.DataFrame(rows)\n",
    "sweep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "64e81610",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 650x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6.5, 4))\n",
    "ax.plot(sweep.tilt_deg, sweep.sinr_p5, \"o-\", color=COBALT, label=\"estate cell-edge SINR (p5)\")\n",
    "ax2 = ax.twinx()\n",
    "ax2.plot(sweep.tilt_deg, 100 * sweep.own_share, \"s--\", color=COPPER, label=f\"{sweep_cell} serving share\")\n",
    "ax.axvline(6, color=\"#bbb\", lw=1, ls=\":\")\n",
    "ax.set_xlabel(f\"{sweep_cell} electrical tilt (deg)\")\n",
    "ax.set_ylabel(\"SINR p5 (dB)\", color=COBALT); ax2.set_ylabel(\"share of points served (%)\", color=COPPER)\n",
    "ax.set_title(\"Chaining a tilt config: cell-edge quality vs footprint\")\n",
    "lines = ax.get_lines()[:1] + ax2.get_lines()\n",
    "ax.legend(lines, [l.get_label() for l in lines], loc=\"best\", frameon=False)\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e201097",
   "metadata": {},
   "source": [
    "Read this with \u00a73.7 in mind. The vertical lobe is a parabola centred on the tilt angle, and\n",
    "the served area sits at depression angles of roughly 3\u201310\u00b0 from a 30 m mast. At 2\u00b0 the lobe\n",
    "points *over* that area \u2014 gain at the users is down the side of the parabola and the cell barely\n",
    "serves anyone. At 22\u00b0 it points at the mast base, and again the served area is off-peak. In\n",
    "between there is an optimum (here around 6\u201310\u00b0) where both the cell's own footprint (`own_share`)\n",
    "and the estate's cell-edge SINR peak: the beam lands on the users and the energy that would have\n",
    "spilled into neighbouring cells' edges stays home. The estate-level `sinr_p5` moves by fractions\n",
    "of a dB for a *single* cell in an 18-cell estate \u2014 the right order of magnitude, and exactly the\n",
    "trade a tilt-optimisation rApp negotiates across many cells at once. The response is smooth and\n",
    "has a maximum, which is what makes tilt an optimisable knob at all.\n",
    "\n",
    "### 5b. Switching a cell off\n",
    "\n",
    "The same chaining pattern with `off_cells` instead of `tilt_override` \u2014 the knob an energy-saving\n",
    "rApp's guardrail check exercises before it lets a cell go to sleep: evaluate with the candidate\n",
    "cell off, compare against baseline, only actuate if the guardrails still hold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "348fc313",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>awake (baseline)</th>\n",
       "      <th>g-s2-b-n3 asleep</th>\n",
       "      <th>delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>rsrp_p5</th>\n",
       "      <td>-102.632652</td>\n",
       "      <td>-104.092191</td>\n",
       "      <td>-1.459540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsrp_p50</th>\n",
       "      <td>-86.282409</td>\n",
       "      <td>-87.078033</td>\n",
       "      <td>-0.795624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsrp_p95</th>\n",
       "      <td>-60.948981</td>\n",
       "      <td>-61.920761</td>\n",
       "      <td>-0.971780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinr_p5</th>\n",
       "      <td>-15.000000</td>\n",
       "      <td>-15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinr_p50</th>\n",
       "      <td>4.445050</td>\n",
       "      <td>4.351004</td>\n",
       "      <td>-0.094046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinr_p95</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>outage_rate</th>\n",
       "      <td>0.148000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>0.012000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coverage_rate</th>\n",
       "      <td>0.994500</td>\n",
       "      <td>0.991500</td>\n",
       "      <td>-0.003000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>samples</th>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               awake (baseline)  g-s2-b-n3 asleep     delta\n",
       "rsrp_p5             -102.632652       -104.092191 -1.459540\n",
       "rsrp_p50             -86.282409        -87.078033 -0.795624\n",
       "rsrp_p95             -60.948981        -61.920761 -0.971780\n",
       "sinr_p5              -15.000000        -15.000000  0.000000\n",
       "sinr_p50               4.445050          4.351004 -0.094046\n",
       "sinr_p95              23.000000         23.000000  0.000000\n",
       "outage_rate            0.148000          0.160000  0.012000\n",
       "coverage_rate          0.994500          0.991500 -0.003000\n",
       "samples             2000.000000       2000.000000  0.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sleep_cell = \"g-s2-b-n3\"\n",
    "awake = estate.evaluate(fitted_params, serving_mask=serving_mask)\n",
    "asleep = estate.evaluate(fitted_params, off_cells={sleep_cell}, serving_mask=serving_mask)\n",
    "\n",
    "compare = pd.DataFrame({\"awake (baseline)\": awake[\"guardrail_kpis\"],\n",
    "                        f\"{sleep_cell} asleep\": asleep[\"guardrail_kpis\"]})\n",
    "compare[\"delta\"] = compare.iloc[:, 1] - compare.iloc[:, 0]\n",
    "compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "645f659f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "share_before = pd.Series(awake[\"serving_share\"], name=\"awake\")\n",
    "share_after = pd.Series(asleep[\"serving_share\"], name=\"asleep\").reindex(share_before.index).fillna(0)\n",
    "shares = pd.concat([share_before, share_after], axis=1).sort_values(\"awake\", ascending=False)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "x = np.arange(len(shares))\n",
    "ax.bar(x - 0.2, shares[\"awake\"], width=0.4, color=COBALT, label=\"awake\")\n",
    "ax.bar(x + 0.2, shares[\"asleep\"], width=0.4, color=TEAL, label=\"asleep\")\n",
    "ax.set_xticks(x); ax.set_xticklabels(shares.index, rotation=60, ha=\"right\", fontsize=8)\n",
    "ax.set_ylabel(\"share of served traffic-proxy points\")\n",
    "ax.set_title(f\"Serving-share redistribution when {sleep_cell} goes to sleep\")\n",
    "ax.legend(frameon=False)\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd1ab4fa",
   "metadata": {},
   "source": [
    "The points `g-s2-b-n3` was serving re-attach via the \u00a74.1 `argmax` to the next-best\n",
    "eligible cell \u2014 the co-sited 3.5 GHz cell on the same sector, the neighbouring sectors, or the\n",
    "adjacent site on the same band \u2014 at a lower RSRP than before (hence `rsrp_p5` drops: those\n",
    "points are now further from their server). The sleeping cell also leaves the interference sum,\n",
    "so its 1800 MHz neighbours see a cleaner channel \u2014 which is why the SINR percentiles move far\n",
    "less than the RSRP ones. `outage_rate` ticks up by about a percentage point and `coverage_rate`\n",
    "slips fractionally. A real guardrail check reads exactly this\n",
    "`delta` column: if `sinr_p5` or `coverage_rate` had fallen past its floor, the platform would\n",
    "refuse the sleep action rather than actuate it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d3f5a69",
   "metadata": {},
   "source": [
    "## 6. Recap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1f56eafc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x290 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flow_diagram([\n",
    "    (\"example_topology.csv\\nexample_config.csv\", INK),\n",
    "    (\"Estate.build()\\n\u00a73: ENU, grid, P_LOS,\\nPL, SF, G \u2014 cached\", COBALT),\n",
    "    (\"differential_evolution\\n\u00a74: min L(\u03b8)\\nover 10 quantiles\", COBALT),\n",
    "    (\"fitted_params\", INK),\n",
    "    (\"Estate.evaluate(fitted_params,\\ntilt_override / off_cells)\\n\u00a74.1 forward model\", TEAL),\n",
    "    (\"guardrail_kpis\", COPPER),\n",
    "], loop=(5, 4, \"chain another config\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b147cb64",
   "metadata": {},
   "source": [
    "- **Section 2** loaded an example estate shaped exactly like the real workflow's input CSVs, with\n",
    "  none of the confidential content \u2014 the twin cannot tell the difference at the API boundary.\n",
    "- **Section 3** computed, once, everything the standard says about this geometry: TR 38.901 UMa\n",
    "  path loss and LOS probability, log-normal shadowing, the \u00a77.3 antenna pattern, and a\n",
    "  wrap-around tier \u2014 all as $(C \\times N)$ NumPy matrices, all deterministic.\n",
    "- **Section 4** ran the actual calibration search \u2014 SciPy's `differential_evolution` over the\n",
    "  same bounded 12-vector, the same quantile loss and the same acceptance gate\n",
    "  `app/physics/calibrate.py` uses \u2014 and it passed the hard gates, while also showing why matching\n",
    "  marginals does not pin down every parameter.\n",
    "- **Section 5** showed the one function \u2014 `Estate.evaluate()` \u2014 that answers every \"what if\"\n",
    "  question the twin is asked: change a tilt (one gain row recomputed), change which cells are on\n",
    "  (one active flag flipped), read the new `guardrail_kpis`. That is the entire inference surface a\n",
    "  platform decision hub calls.\n",
    "\n",
    "Every number in this notebook carries the **generic** rung \u2014 `simulation, generic 3GPP defaults\n",
    "(uncalibrated)` \u2014 public defaults, not fitted to any operator's data. For the calibrated path, see\n",
    "the [service README](../README.md#run-with-locally-supplied-data-calibrated-mode); its inputs and\n",
    "outputs stay local by design and are never in this repository."
   ]
  }
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