{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3789d586",
   "metadata": {},
   "source": [
    "# Physics twin walkthrough\n",
    "\n",
    "The **physics twin** (`services/ocp-physics-digitalTwin/`) 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,\n",
    "using the 3GPP TR 38.901 *Urban Macro* (UMa) propagation model and the TR 38.901 §7.3 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 how data goes in, how the twin fits its parameters, and how a\n",
    "configuration change becomes a KPI change.\n",
    "\n",
    "**Pipeline this notebook walks through** (run the cell below to draw it):\n"
   ]
  },
  {
   "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(read by the caller)\", \"#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 × 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, `scipy.optimize`** | The \"training\" step. `differential_evolution` is a derivative-free, population-based global optimiser; the service also runs a bounded **Nelder-Mead** 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, 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()`, a synthetic hexagonal estate generator with exactly the columns the engine needs; `default_params()`, the ~12 model parameters set to public 3GPP defaults, each reasoned in a comment; `generic_build_kwargs()`, grid-specific overrides for `Estate.build`. | Section 2 (data), Section 4 (ground truth for the calibration demo) |\n",
    "| `app.physics.evaluator` | `Estate`, 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`, the TR 38.901 Table 7.4.1-1 / 7.4.2-1 formulas; `rng_for`, deterministic random draws. | Called inside `Estate.build`; plotted directly in Section 3 |\n",
    "| `app.physics.antenna` | `gain_db`, the TR 38.901 §7.3 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`, flat-earth lat/lon → metres; `cell_point_geometry`, distances and angles from one cell to every point. | Called inside `Estate.build` |\n",
    "| `app.physics.calibrate` | `BOUNDS`/`NAMES`, the parameter vector and its search box; `loss()`, the quantile-matching objective; `acceptance_gate()`, 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 | exists only once someone has run a local calibration |\n",
    "\n",
    "**This notebook runs entirely on the generic rung.** It needs no input data: Section 2\n",
    "generates a fictional estate. For the calibration path, see the [service README](../README.md).\n",
    "\n",
    "**Setup:** `pip install -r requirements.txt -r notebooks/requirements-notebook.txt` from the\n",
    "`services/ocp-physics-digitalTwin/` 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, no HTTP layer, no server. `sys.path` gets the\n",
    "service 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`, the no-data estate\n",
    "  generator and its public-default parameter set.\n",
    "- `Estate`, the model class: geometry cache + evaluation.\n",
    "- `loss, NAMES, BOUNDS, acceptance_gate`, 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`, imported only so Section 3 can *plot* the\n",
    "  propagation and antenna equations; `Estate` calls them internally regardless.\n",
    "- `differential_evolution`, 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·log10(15 kHz) + 7 dB noise figure)\n"
     ]
    }
   ],
   "source": [
    "import sys, pathlib\n",
    "sys.path.insert(0, str(pathlib.Path.cwd().parent))  # the service directory, 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·log10(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, 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° = North, clockwise | horizontal antenna pattern $\\phi$ |\n",
    "| `cell_el_deg` (from `config.csv`) | electrical downtilt, degrees, positive = down | vertical antenna pattern $\\theta$, **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 targets describe the NR leg); all cells interfere |\n",
    "\n",
    "Topology (static) 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 calibration mode the twin loads a `topology.csv` + `config.csv` pair that you supply\n",
    "locally (`/v1/load-local` or `/v1/upload`).\n",
    "\n",
    "This notebook uses the **same CSV shape**, generated by the no-data generator in\n",
    "`app/generic.py` and saved under an `example_` name so it is unmistakably fictional.\n",
    "`generic_estate()` places sites on a hexagonal grid with inter-site distance 500 m (the\n",
    "TR 38.913 UMa assumption), three 120° sectors per site at azimuths 0°/120°/240°, and one cell per\n",
    "band per sector. Grid positions are converted to latitude/longitude by inverting the flat-earth\n",
    "projection, around $(0°, 0°)$, 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` — 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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       "      <th>cell_id</th>\n",
       "      <th>site_id</th>\n",
       "      <th>cell_lat</th>\n",
       "      <th>cell_lon</th>\n",
       "      <th>cell_az_deg</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>g-s1-b-n3</td>\n",
       "      <td>g-s1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>30.0</td>\n",
       "      <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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    "config.head()"
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  },
  {
   "cell_type": "markdown",
   "id": "59abd6a6",
   "metadata": {},
   "source": [
    "3 sites × 3 sectors × 2 bands = 18 cells, ids like `g-s1-a-n3` (site 1, sector a, band n3, `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° electrical tilt, 30 m height, 65° beamwidth."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aeb7a7d",
   "metadata": {},
   "source": [
    "## 3. Build the Estate, 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 → 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",
    "Accurate to about a metre over an extent of a few kilometres.\n",
    "\n",
    "### 3.2 Where the users are (the traffic-proxy grid)\n",
    "\n",
    "The engine does not evaluate on a uniform rectangle, 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 grid, $r_{\\max} = \\text{ISD}/\\sqrt{3}$,\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",
    "$$\\phi = \\operatorname{atan2}(x_n-x_c,\\ y_n-y_c) - \\alpha_c \\ (\\text{wrapped to } \\pm180°), \\qquad\n",
    "\\theta = \\arctan\\frac{h_{BS}-h_{UT}}{d_{2D}}$$\n",
    "\n",
    "$\\phi$ is the horizontal offset from boresight, $\\theta$ the *depression* angle (positive\n",
    "downward, 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, 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 §7.3, `antenna.gain_db`)\n",
    "\n",
    "With vertical/horizontal 3 dB beamwidths $\\theta_{3dB} = 6.5°$, $\\phi_{3dB}$ (65° 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**, 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 $0°, 60°, \\ldots, 300°$\n",
    "(for the hex grid $d_{\\text{wrap}} = \\sqrt{n}\\cdot\\text{ISD}$, an exact tiling). Tier cells\n",
    "*interfere* but never *serve*, and tilt overrides never touch them, because the surroundings are\n",
    "not ours to tune. Their path loss, gain and shadowing are cached like everything else.\n",
    "\n",
    "`generic_build_kwargs()` supplies the two grid-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 §3.3 to 3.7; the\n",
    "`ext_*` arrays are the $(6C \\times N)$ wrap-around tier. `grp` is the band group\n",
    "(0 = below 1 GHz, 1 = 1 to 3 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 §3.5 and §3.7 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": {
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",
      "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 (§3.5)\"); 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 (§3.4)\")\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}°\")\n",
    "ax3.axhline(peak_gain_dbi(6.5, 65.0), ls=\":\", color=\"#888\", lw=1)\n",
    "ax3.set_xlabel(\"depression angle θ (deg, + = down)\"); ax3.set_ylabel(\"gain at boresight azimuth (dBi)\")\n",
    "ax3.set_title(\"§7.3 vertical pattern vs tilt (§3.7)\"); ax3.legend(frameon=False, fontsize=8)\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79cce765",
   "metadata": {},
   "source": [
    "Left: NLOS loss runs ~15 to 20 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, a point at depression angle 5° (roughly\n",
    "330 m out from a 30 m mast) sits near peak gain at 6° tilt but ~14 dB down at 14° tilt.\n",
    "\n",
    "### Estate map\n",
    "\n",
    "A top-down view of the generated hex grid: 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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QQ9j6IVQD/x4erNBM6L8t6tqs7dEua2jIs+FNivaFTR9L/NBuWC+gGUBojhhIWh+yrjkAwn5gvTMlRxjPOKz993//d11aBckV9lQ9JFBR63hRe4PPWpOF9EG6/te//rXe7jB1gLd3SNgUEyF9wIB5Gw4f0LI0paQPYQZsNn0Y/BjoAOyIMGgffvjhOhtGx8LOBLZEiB8FFRMmKjoVkephNAlDRwwquNunbeRZcdxxx6lN41e/+pWaVCgnjJ9PO+20xN9Btw8RPWIwwf4F5cfCgtMkwgf88Y9/zP1biIjhIg5bFJykYE/z1ltvKYcYjkmHAYs6gETjO/yeY0P5tmfSPfLUsyhAfI9xg8UVKjGo7LD4s1F2mgTIp11tiNFOvs9GtgicohFrKgkwekZ2BwCbCsrI8xHSJI4RhXAtIDzY6GCrArUSFlScSKHiPPfccxOfA+kRVMSIdYcFC2XFIoc+QNw5IPZYidlWWOyvv/56tZkgDATHUWOYqbP+8pe/qLRakAJh7sB2B2QKIW9M4/uirs3SHu2yhoY8G22F9j3kkEPU2IX0CJIzCCPwuc12KwuS1oesaw6AfsNBAeRWz3SFNoOTCmLcYX7D+QZmAiAcOMzptoJFrONF7Q0+a00ocADAYQ9qZ9gwo61g8oA1ARkysiKkD1jTgqxJSaFnnHWACy81EHg8TpAuoLKIy4fBjomFhQcqXR04meNUBhauB8nEKQEdDLE2Jij+BzuG4StUd2DSWOQhMdQNjqHew/1AIvWJDMkGVDrY6Fi8j4CP+B+TCSdBnIwxCOAEgI7TgToggCsGNTrWBAY5jFrB7HHPkEUk7bcgNFgcoIrAiRAneVsqHlyD0zzKijKirL7t6XOPvPUsAugvlBn9i1MT6uZr4J/Wrkl9nredfPoUwacxJ9IWJJA2V9on2EUivpQOlBnPhpoDCzPGOoymQ+Y9NsyxY8cqAoX7IyCriaQ2sLWtq71jtRXaKEnloq8NOt5++21VD7QVTFOS7AiLujbLetDqa2jIs/UywKYOJBtjF2pE02vbLKOrnQB4wuM+UG/6rA++fWcDHEDQJjZnK5hnQIACwDkSz8H9EYwYDpMhbZ2lvnnumTQufNcan+cwEB8PbQUBAUg47ofDMJ7Nav4sbeDbB5AsnnzyyUrSD3IbhKAAL4KFwLF4rrrqKmkdQaWAOG6IlTZq1KhGF6XykLZqzzW03dbvp556SsWTs8V4NDF8+HDVNvfee28pZRP49QFSLyIuJuJDZkFT2fQJBAJ/4PQJFYBNIiKQthK0HyDJhpTJ9DSFaYfunAWTDajyofkwQxEJioFvH0DKCDMapIXLgsrY9AkEgriAutKWW1ggbSVoX+ge34xPPvmknpcXpi3wXIbaEDmGQ+PACbLBtw/wmW9M40ra9DU7fPT/AoFAIKjeGirrN3Www4QHPpxDYB8P+7giA70LGtMHQvoEAoFAIBAI2gBi0ycQCAQCgUDQBhDSJxAIBAKBQNAGEEeOjEDGBuS+QyT2pGjxAoFAIBAIqo1arabiSC6xxBK5smtUHUL6MgKEDxHbBQKBQCAQtAZuuumm8IDHTQQhfRkBCR8PkKTo3gK7lBTu6YsvvnhLn6jKhLSptGmzQMaqtGkVMXv2bCXI4b29VSGkLyNYpQvChzyBgrBFH8mq0W5C+uJA2jQ+pE2LgbSrtGmV0anFzbVEzCIQCAQCgUDQBhDSJxAIBAKBQNAGaFrSh9yBZv5AE1999VXqfXyuEQgEAoFAIGh2NB3pGzlyJG211VbK2HLJJZekE044QblZ6/j1r39N3/72t6lXr160wgor0PDhwxe6j881AoFAIBAIBK2CpiJ977zzDm222WY0dOhQ+vjjj1V+OhC2N954o37NJZdcQhdddJHK5QhvnEMPPZR22203GjNmTNA1AoFAIBAIBK2EpiJ9v/zlL2nIkCF0wQUXKAndIossQv/v//0/laibATJ3xBFH0CabbELdunWjU089lf7rv/6LLr/88qBrBAKBQCAQCFoJTUP6YL8HFexee+2l3iPkh4np06fTe++9R5tuummHzyEdfP75572vEQgEAoFAIGg1NE2cvmnTpilVLBwvVlttNRo9erSS9h188MF07rnnUvfu3Wnq1Knq2n79+nX4LWz/mND5XGMDSKZONFEWjjmFP4E/0F5IeSPtFg/SpvEhbVoMpF2lTauIBW2yjzcN6QNJYNUsJH5Q6cKpY/vtt1dq3vPOO8/ZeV9//fVCARd9rtEBYnnmmWfW33fp0oW23HJLlVnCJnUUuIG2/+KLL1SfSnDmOJA2jQ9p02Ig7SptWkXM/l9BTqujaUgfcuHB/m7//fev2/DBoQOSvnvvvVeRvgEDBqjPP/roow6/hXSPv/O5xoZTTjmFTjrppA4D5MADD1SpxCQjR/iiD4ItadjiQdo0PqRNi0GztivswHfffXfacccdqWqoSpvefPPN9OKLL9KFF15IzYbu3btTO6BpZlzXrl1po402WkiqBnUvdxZCsED1++ijj3aYDCNGjFBOG77X2IBn9OnTp/7Xu3dv9TkmmPyFtwEWKGm3uGNH2jT+XJQ2bb12xVp/0EEHqfUeNuIQGqT95qGHHqInnniCdthhh/pn77//Pp1++um07rrrKk2Q7Xf//ve/laBi4403pp122okuvvhimjdvXodrEIXiJz/5idrfEEXiwQcfXOg+PtfobXrPPffQ2muvrepnXvfCCy+o71B//XoIU2x1wLX43qdtUTYmfo3q3zx/7YCm89696aab6LbbbqPJkyfTXXfdRdddd50KucKAJy4+u/HGG5XDBrx7QRSPOeaYoGsEAoFA0FrA/nHppZfSLrvsoqJAgIztvffedMUVVyT+7ne/+x0dddRRSvgAvPzyy8q8Z9FFF1UkDgTQxJNPPklbb701DR48WEm+oJX6/e9/T8cee2z9mpkzZyryNXHiRDrnnHMUqUPZEE4s5BoTM2bMoLfffluZQr355psdvkNdUd7XX3+9w/Wvvvqq9V6vvfaa+t4H0HqB5J5//vle1wsagFqT4e67766tt956te985zu1ddddt3bFFVfUFixY0OGaa665prb66qvXllxyydrWW29de+WVVxa6j881SZg5c2Ztm222Ua+CMMyfP782bdo09SqIA2nT+JA2ba52/ctf/lI75JBDarfffntt9913r2266aa1008/vfbVV1/Vr9H/Z+y999617bbbznnfcePGwaC8NmbMmPpnX375Zb38G2+8ce3oo49e6He/+MUvaiussEKHz84555zaUkstVX9/0UUX1Xr37l2bNWtW/bODDz64NnTo0KBrzDa96qqraj179qwdeuihtRNOOKF+3eeff64+P/bYY9UrA9d36dLFWn/UHd8z1lxzzYX+hg0bVv/+6aefrnXt2rX2ySef1JoJM9tkT28amz7Grrvuqv6ScNhhh6m/vNcIBAKBoDkAFSgkeVOmTKGf/exnyu4aWpy5c+cqSZ3NbgshvF555RXac889nfd9/PHHVbQHSOwYkPClYcMNN1Tq3HfffZdWXHFFpU2CuheSOgbMjLbYYgtabLHF6p/tvPPOdP311ysnQdjo+VzjAva4H/7wh8rmHTbxaJ/VV1+dVlllFcqKv/71r/X/wQkPP/zwDlmxhg0bplTNkHT+4Ac/yPwcQTFoOtInEAgEgnJx2B+G08efd0x3mQewo/axoerbpwdd+7MdvO8LYnTHHXfUnetAuEBSmPQxEKcVKstx48bR0UcfTWeddZbznrhm4MCBFAoQHqiS11lnHeUkCOdBqHtvuOGG+jVQs+rJBQB2KMR3IHQ+17gAtfASSyyh7Bb32GMPuvrqq1V9Ea3CFgt3rbXWSq2Xfs2vfvUrGj9+fIdwZyDWcLyE6ZSgehDSJxAIBIJEgPBN+ywe6SsKyNikR1P47ne/u1CkBuCyyy5TtnJPPfWUCsW15pprdrAN1wEJHaRkoXjppZfoxBNPVDZ88PidMGEC/fznP1cElMN/wR7QlD7ye3zne00SIO275pprlLQRdn6wYYQ9uwmEIdOleAxEybAB5BoOLA888IBKh2qWT0KZVRNC+gQCgcADUGVhk0Vc0KSYnq0ISNxiIkTSFwJ2tLDFeNUBFSerYD/77DPl3Ocifd/5zneUGjgUZ599tpLQwfkCgNMIxg1CfR133HFKAoc/00mC3+M7fk27JglwIIFE7owzzlDevBx5wgYfSR8AJxB2TNluu+0W+h7thdSmgupBSJ9AIBCkkDp8B1Xap59+qsI+fe9732sr4heiYvUhfB9//DH17du3EmEyoIqEbZwLIG5QYXLf+wJkcumll14o8xPUqJAygrDB/u2+++7rcM2zzz6rbAiXWWYZ9d7nmiSAtCLUDKJdQLKZFyCcCM0CdTEkmSbGjBmjgu9vsMEGuZ8liI/GzziBQCBoIEDooIpCSIx33nlHkTtTOgQyiE0fBut49VGrCaoH2Njptmaw14OqF44RLqy//vq01FJL0cMPPxz0LIR0QVgVqFQ5puwll1xCgwYNqpNBqF5RBlarYuxdeeWVyjmCDxU+16QB1yMkC6SNeQBbQDi9gLy6wtygneAoAlW7oHoQ0icQCNoWLMH7z3/+o6Q58Pi0kTpI/yDl6dGjh3rFe/0e8BC1qRF9np/1t4JwgIjAmxWOGcstt5wiJ3B2uOqqqxJVxvACvvbaa+ufoc+gCsUfyNTtt9+u/se9Gb/4xS+URAw2cbCng8QNYwy2cEzWkCgAMWOPP/54JT1efvnllZOJnvLT55o0gKTBbjEvIMV77LHHlJ0kyDC3AZxVGCCneixCQbXQCXFbGl2IZsSsWbPUhL777rslDVuTq3daAdKm2YDNG9I9kD1IYuAEgA0Skhgsjfo4tal/86h921VlXNRYRcgWqE1Bihho2w8++EARJx0gLehzOHroBN4FXLvyyivTP/7xD0V20HcIWmwCoVxWWmmlDp9BOsxetlAl24CxB2keVLZZrjHbFP9PmjSpbrtoU9EiwQF/j+tRRhsxBKnFfMC9IRFnyaUOjFv8FplCoPKFzZ/NvrLKmNUme3pz9YpAIBBEBEvwsIljk0oiIdjYTC9OXe0LwHjd19Mzz28FC8PmOIC+tdnhQeoWAoSCeeaZZ+rjA2PB1+kB0mFI+pIAsghSmfcaBgga/lxgJxKf6/V6wis3qd6rrrqqSnPXbISvnSA9IxAIKu0RW6TXLO4HCRs2QKiuIE0BEYM0xScAL5NGwFT7FvlbQfnIEquv3QDJqaDaENInEAgaAh/1ZkwVqIs84n9IcqB+gxcnjNVBAPE8PQuC7fdMGiFlCiGlfC9skqG/FQgEgqwQ0icQCBoCH/VmLBVoGnnUJX6jR49W9j34DT8r6fc2ta/t+UwYgZi2fO0cP1AgEIRBSJ9AIGgIfNSbsVSgJnmETRcIkim1g/0V4qh9/vnnisghS0Fe8mkSRjw7li1fuzqDCASCbBDSJxAICkWSWjVNNZpVfZpEHr/1rW8pT08EzzWJElS7MELv06ePegUBzEo+ud54NQlnLFs+cQYRCAQhENInEAgKA0JJIMwEQmnAZs6mVk2TcvlckwadPIKEjRo1yippAwFDOXE9yCF7IdrIp28GD9wHf0zy8KysRNZ8pjiDCASCEAjpEwgE0WDariEYLWzkONRFI8OSMHlEGV2SNp3coczTpk2rB07WyWeaWtWUwCEoMLI66CQvtB1cz4whCRUIBO0BIX0CgSAKbLZryMEJ8gSJX69evSoRliSNKLEEDWnZoAZGAGcEp00idSaZNSVw+C4vIXM9M4YkVCAQtAeE9AkEgiiwOUtAVQr07t2bll122VS1aFneqmlEieuCDAR4hZQuidSZZJaJZVIQ4JB64Fr86WriKhBoQUfceOONapznzXHbynjkkUeUo9Tuu+/e6KK0JYT0CQSCKLBJt2x2cHm8TcvyVuW6IFhzEqlLU6si3ZetrCH1MO0DoSp2SQ4lfEsy7rzzTnrhhRcW+hyZJvRctrBBRY5cmCegf7bddluVpzcJMGM44YQTOqQpA7m599576b333qMBAwao3Lx6Jgx8fuWVVy50L6Qy0w8M6Nd77rlHpURDmsA999xzoVRsuAYpxJACzXWNjhdffFHVcZlllqFjjjmmw3cYb5deemmHdsH1uP8555yz0L2QZ/hHP/oRrbvuupQGpMkbNmwYbbDBBqpNBOVCEp8KBIJMwCYD1adu8wbygtyjTGJYosYERZcG4hXvQ5D3975AeRE4GWpdvLo8i1n6p7eDT1nN7/B72z3Ma+FxzO3qIofIJYxXSau+MBCSh1Oz8d/111+vpE+MCRMm0AorrEBXXHGFMksYOXKkSj12+eWXJ46Z3/zmN7TvvvuqbC7AE088oUjQo48+qrzAb7vtNkWwnn766fpvYEJw3nnnKW9xvUwcKoj7FVKxY489VsWPBFFDurN33323wzWHHHIIHX/88c5rTCB38B/+8AdFMKdOndrhOxDRCy+8kC644IIO16OsNuBzWy5iGyAJBYnGswXlQyR9AoEgGC5JVZraNK+3aVHeqq5sG/p72zVJEruksvqGkAmpc7uHbwFxQyaVXXbZhf75z38qJ5zNN9+8g6r1+9//vvpjQJJ36qmn0rnnnlv/7K9//atqf5A2bmtI537/+9/Tj3/8Y+uz0e633norPfnkk/XPcFh4+eWXlS0rY5tttqHzzz9/IfXvz3/+c2e+2rvuuovuu+8+JUEEGQUR3WKLLei///u/lfSPr3nwwQfprbfeUpJg2zU2INXgRhttRDfccAOdfPLJ6jMQVLQB2hH3zAJI/kyAyLKUcL/99qODDz5YkUUxUygXIukTCATByCNxg9oKG1MW1axNmpgXSRIylmZiE7Vdk9QOSWXVvwM5A+FztaVvnZkcsjSr3TbTp556is444wzaeuutVUieDz74gLbaaiv6+9//7vzNddddp+xN99prr/pnffv2VcSHYzQCGAP43IXHH39ckZq11167/tngwYM7ED4A98TzTPzxj3+k3/72t0p9qj+XVdIgZiB8AByjDjroIPrXv/6lzA+Y9EGqmHSNC4cffjhdc8019ffDhw9XdUE7ZoUpTcU9oS5mbLrppmrMP/fcc5mfIcgGkfQJBIJgZA1WbErFQsCSNpdEJCtcEjI8D2qvyZMnq80bdl68gfI1Pg4dLmmbTwgZn/vo1/iGb2lV2z/kTob6FIQLAMG65JJL6IADDljoWhB5SLT233//DjmWjzrqKEUaISXccMMNlQoWUsO//e1vzudCwga1pW1swiYOBOell15SUl1IDHUMHDhQPQN9cdxxxyniOmLEiDrJhIp2tdVWW8guDv2HGJgrr7yysifEZ0nXuLDbbrvRT37yE3r22WdVfa+++mo69NBD62GWzDazSfFM6NeAeJ5++ulKvc2Agxekp2+88YYigILyIKRPIBAEI0t8uBipzLCpc9YMW7DnLHARN5QXRI+leiAQeBZfw8QJarw8cfJikjXfPMChzjAjLziS5n7xcUCtEkugyMM3pCL5ud1696WhJ1/lfWc4WzDhA1ZffXVls2fDQw89pMjWkUce2eFzOF+A9HGfg/BDTYtrXc4cIHU2CR4Aoof6os9w3ylTpihvcABkDNJjlgiC8K2xxhpK5cw2hLDRg82feU/+jl/N55vXuABnDRDfa6+9lpZbbjmlGr/ooosUWTPB498XqC/sHH/1q18pcqkDdcIYFJQLIX0CgSATQuPDMbkC6cAmFyKxY8KIDQybMjYMPD+G3ZqLdOF/lBMkEwQT5A7/MymM6UVcFFlztSXIM9oS9/RpQxC+uZ9No6qjZ8+eHd5jjKHPbIBKE16kQ4cO7fD5L3/5S2VjCU9ZdqiA7Rns0CZNmqTU5yYwPkD8bIBHLwPkClI0dnowQ/pgXO+xxx6KeDEwBs17M1lisohXzIuka9JUvJC4wZsWEk44nNiA8WaT9J1yyikLfYYy77rrrspp47TTTrN+n6QyFxQDIX0CgaAUtR97xOL+CNoMmytf4qITRhAUSIkgyTBVoVnLbyNd+AyhLyBBgTTE9NYt22kilqMGEyGQBNTLh3xD4hYPYZK+IjB9+nQVSuXiiy+2qmphH6d70CK8CIgySJ+pRgWgfoUaFeMiqU822WQT+sc//pFYNtj06XZ4kAay5JGB9+g7SOYA2HvqoWJs1yRhzTXXpBVXXFHZFSbZQPoC/QuSDEcRdozRMWPGDPUHqaagXAjpEwgEpcXAA9mAhAmbGp7jS1z0YMdQj3388cdRyu/yyMVn2PTNkDOMRuS8jfVMVo9DqsQEMK0PQlSsPoQA/Qcpj81urAzAWxXtB9WjCZAsOIXoBO6xxx5TEj6XBAzSMdTl+eefr9uowUZu/fXXr9cR4+r+++/vQHRwzXrrrVcnmLAdhBfwTjvtVL8G8fZ+8IMfKPs3qKsxNuGAgs+4fJAOQn2Ka0DgbNekAc4kzzzzzEJq2CyA5A9x/fBnSl8BeEbDpg91F5QLIX0CgSBIkpRHGpiHuHBIGEio8Hyoh/DKZQyVhJlBj1m9Cwkkf6Yb+JtlKTvnbaxn4recKSVUzd4qgGp37733XshWju3qQNwQmw8hVhDWBfZ/l112mXO84j5Q3YJMMun797//TUcffbSSGkLiBqLDMfQYIFmwKQQ5BBCaBVI7PYQMCCAcUeBNC3UpVMNQP+sSQ1wD4gdVquuaNKDcMZwqIPGEswrK+5e//MUasgWZS6BSbsex12hIiwsEgkRCppM8II80MAtx0Z/vG/vOh1AySZw9e7ZS3UH6BKIH1TMkkaxKTqpL2XHwYjyT1eyQ8IWq2asMEDQOWcKA1AtOBDpQZ5AoZMewAW0D5wrEqAPhA2mDB7BLyseA3RpsBEEaYRuH2HsglgjngjEGqRvKqBMdxMYDWYMkEZJF2PtBBWwCzii4BnaGkCqC2JmOGwiJArKHjByua3RAynbWWWc5v4dKW/8e10P9awNIKkvtMId00spgiSc8jRHPMC3YtaAYdKpJ2PZMwIkNYnDEVbKJrwXVVu+0GmK1qR4WhdWALPkCkYJ6FbZCkKZB3QWpRJHExxXmxSVpTFLXmtfzvWHfBSIAaQzqBOkX5jdL+qCGCm3TqodEAcEAsSmrH9tl/sNOEAeastWWzdSmIHw4VG233XZUJcxqkz1dJH0CgaAOzkLBRAuLnx6fDqSvTFs2l8o2Lfadj52fbieoZ8RgKRjUUZAAhqKs/MB50AibxHYApHmCZGy22WbSRA2EkD6BQOAkWiAwenw6EKoybdmyBoFmKVuanR/KDwkfcuzqkjlcA+lJFjQqHVqIdLERNokCgaDxENInEAgSiRaHWWmELRvbn0HFCjWkjzeuLmXDb31IY5Y6uUhWI6RotnpzTEFXmzXCJlEgEDQWQvoEAkGiFAj46KOPClNXJkmo8J1uU5j2bJuULa9Ei/PvmnaCaWrjWFK0JJtEm0QTn4PwQS1fVfWyQCBoDIT0CQSChVBWIOI0+7dQVSkcT2CHyPlsdXVt1vKBdLK9H5cvKV8vE7EYbeRqnySJJurPHsh62QQCgUBIn0DQBjDDroR4lhaprkwjdSHPZoIGCRfsEEGE8kq4IDFD+UwCZStXEQ4crvZJkmiaHtfipCEQCBhC+gSCFocZhBgwJVeNMvpPI3Uhz2YiBIKG63wyTaQBBArl4vbi8tnKlSQRzRrCxdU+ts91iWYjnDSqHqZGIBAI6RMIWh66VAh5PXlzBkLSoBWhIvQhdfxsm21dkRJJto0bOHAgLbXUUgs912wTPT8wVKwchDePBNDVPmntVraTRjOEqREIBETVjuIoEAhyg8kIvF8RvJU9YctW/YEYzJkzR/3pMeFd+W1tpAIBhfFqiynPRAiBhvOSDlYVT5w4kSZNmpQovWIyCkClDNUyVMz4PRNsJt141T2hfeBqH592Kwt561hlcAYJ9CeA8Yucssi0gfq+9NJLakwKwoA5gjZspbHSDBD1rkDQht64MdRwIeo8XAsChbRWwLLLLqti4/k+P832L7YDBT8PGzxeIelz5SHWJVwI9MzBrPEZt3kzBULOoqatQh0xvqZNm6ZSocXCXnvtpXLoDhkyhH7605+qwMJrr722MpPo37+/yiF7xBFHqLRuP/vZz6I9tx0wZswY2nLLLVWf9evXz+s3cFB6+eWXVao6ydubDUL6BIIWgksFaqr78hKjUHUeSMSMGTPo888/V++RMspFpEJJRRGqRX4eyFsSiTHJqC1jSVE2kUXY0GVtyyoEe77pppvo9ttvV5K3GBg7dizddtttNG7cOHVIAf70pz+pdnnzzTfr162zzjr19IACfyDdIXIEhxwQkAISRBGZcnieCcIgpE8gaBG4wosUgdBQKljYoVbGSR2AmjlksU8iFbEzYDCZgi0f7rPkkks629Eko66MJXlt7EyCV5QNHZ6BDRV5SPGMkLaMGeA6FFOmTKH33ntPjS+oDAFI5yCpRX3WWmstZRqAPMubbrqpijv59ttvq+tgf7niiivWnZwAzKN//etf6v933323LqF+4YUXVAYXfgYAKd/gwYMXKhPmIZ4JlT/GUhJwfy7nhAkTaOrUqep/vT31a/S6cDviM+TdXX311ZWJAePVV19Vn6+xxhr1z5B2ENdvsMEG9Pzzz9Nqq62m5icD94NKe9VVV11ICod6jRw5Ukk9cXhDuy+//PJqTpvAIe8///kP9enTh1ZeeeUOfYyxdcYZZ6j81uZ9Ma5x30GDBtXLhQMYpHzAU089pUgj5uYqq6yisuegn7AG4D36SOBATZAJM2fOrG2zzTbqVRCG+fPn16ZNm6ZeBXGAtpw8eXLt1VdfrT333HO11157rTZnzpzCmnfBggW1CRMmqOfgFe99fvPVV1+pP5/riy4L2se8Vr/X+PHja1OnTk0dp657FVk/PA/vY/c16vrSSy/V/vWvf6nXvHP0iy++qE2aNEm9mvP/66+/Du43F+6///7aoEGDar169aptvvnm6g+f/frXv64NGTKktvHGG9dWWWWV2lZbbaWuf+ihh+rXrb322rUePXrUzj777Pr9brvtttpaa60Fw9HapptuWr/2e9/7Xu3b3/52/T3+evbsWTv//PPrv0U9fv7zn6t7rrbaarXvfve7tf333782b948Z/m5nBtssEFtpZVWUr8ZMGBA7ZVXXlnoGrMuo0ePVu/79++vnofy/PnPf67/7u67765169at9vLLL6v3GCtDhw6t7bPPPur9euutp8qr49FHH6116dJFrSkm/v3vf6t2OeSQQ2r/9V//VVt11VVriy66aO2aa67pcN2f/vSn2mKLLabKxGUbO3Zs/fuRI0eq+2As6Pc98sgjVTujjN27d69dddVV6vspU6aovsI1aAO0/ZlnnlkbN26cahf8Zp111qkttdRStVtvvbUWipltsqcL6cuIdhkgRUBIXzFtCoICohJjE/VBUWQny31DfpNEEnUyBQKNTS+U+PiWxfc6G8HLQnR9gHuj3k8//bR69SWTtrq8/fbbtSuvvLJ2xRVXqFe81+f/l19+GZW4nnvuubVhw4Z1+AxECSThuuuuS/zt66+/rsjS888/X/+MSQjKyfif//mf2tZbb93ht2uuuWYH0vfHP/6x1qdPn9qLL75Y/+yGG25I3Cu4nH/4wx/qbbTffvsp4slt6qrLZpttVttxxx0VucbvQHhA2N544436NUcddZQiRrNmzaqdfPLJtaWXXrr2ySefqO/QNyBvOikFSd1pp52sZeV22WWXXWpz585Vn1199dWKoL3//vvqPcZO586dFeEEcNDbdttt60Q1ifQdc8wx9Tl36aWXKiKP3wNoU1zDZQdwPerP7fTZZ5+p9g7FzDbZ08V7VyBoEXCe2hjeq77Pi+096uOlm7csSZ6muqczXkONxX3LH1JPs0y6nWDsvsa9F198caU6w6uPCt5WFzizwAGC64VXvMfnSfUqApgThxxyiLXcsNuDqhD2plDR4v+8uOKKK+joo49Wtn66Chhq5CRAPXr88cer/6GOPfPMM5Vq9vXXX3fWBeWHGvbXv/51XRUM5xOocv/2t7/Vr/vjH/+oxsiOO+6o7BJvuOGGulnCvvvuq/qF1dlQs9555510+OGHJ5b3tNNOq/fZYYcdRgMGDKBbb71Vvf/rX/9K6667Lu26667qPdStUOWOGDFCOdwk4Re/+IWqP7DLLruoskHl7QLCUCG0EnvQQ5WM9hbYITZ9AkETIc0Gquz4bLHrhdeiUr75OIXotoNdunRRNlQh8LUvDLFDTIrVF7ttsjhk2OoCWy6TyOI9yBVIXtZnZYHNyeKNN96gPffcU9mkwUkDdmUgrLANzAsQMdjIudrq6aefrr+HMxNsDwHYr+mHDJBQvIdt25prrmmtC74DcA/98ILDAH8HoH7nnXeeImEgc7CbY4Dg77333nTttdcqknXzzTerz3beeefEesIOkoG+W2GFFerPxCvXSy8TfwfPfRdgp8fgsTJ79uxEkoi+RFvCyWOHHXagAw88kBZddNHE8rcrhPQJBE2CVg2Aa2YMYYP6oqQ/voGNYRweCt/QJaEhTsok86HPstUF0hZ2ONEBBwV8xsG2IdEpul4sNdJx3HHHqbAfV155Zf17ODX4SpaTAIkepGU2gLxA4sXYaaed6L//+7/V/+zkxACJhgRL91I168JzBb/VSQ7e6+QJ0rDf/e53yqHk7rvvprPOOktJ5hgIOwMiiP4B+QNpShuTeIZeNv09ymXWh9/rDjMxALIMj22EgHnsscfo4osvpmuuuYaee+65qM9pFYh6VyBoErRqAFy9XtgsQcaKVlEXFdjYV+1alHq2URJa06wAkiJ4lupSyeWWW06RD3ih4o+DV8cCpFms4ksDnr/RRhvVSRQCcL/22mtRyrHFFlvQHXfc0eEzeBGj7iA88PzlPyZ8AEgLpIQMqFuhFoU3rgvwiIWnLqtmAUhZoUpfb7316p+dffbZqs6vvPKKuh9UxHrbg/Ci/yA1Q+BpqGvT8OCDD9b/hzc0PG+h0gXWX399pXbWJXQPPPCAqr8pAQwBe/rq/cxhoOBBfOSRR9Kll16qPJJdxLvdIZI+gaBJUIUAuEXAFvakWUlQiKQs7bqq57JNkzyDRIAMYlMGMYGqHOpdhN6ABCsp6HUWgMwgjhsCJkOilUQuttlmGyX5QrnQxueccw7Fwm9/+1vaeOONaffdd1e2ZajnX/7yFyWFAhl2AQRvt912U7ZyaLNTTjlF2fglBS5G+WHPd/LJJ6sgx1C5wmYP9T/00EPVNZB4oUz33Xefkv7B1g/q4osuuohOPPHE+r2g9j3hhBMUYUOoljScfvrpqi9xSIPqGCpttuGD5PCSSy5Rksxjjz1W2fGhXmhnJm5ZAFU86oyyb7vttiouJggtJMtbb721knbiuahDbIliq0AkfQJBxaGn+fKRDvH1aVIU3+vyIu05rSL1itm+WR1a0p4ds899JM8gOVAjYqNG3yKGGjZuEJzYBxfYc8FhAQGa4QQBqRaeNXTo0IWuBTHaf//96brrrqO77rqLfvWrXylyosfbA2lA8GBdpQppJdvXuYIzQ/oGqRfUjlAfQ+p0/fXXJxI+AJk+QJ7++c9/1st07rnn1r931QWED84jzz77LF111VWqzJD0sQodn4Ggbb/99up6lBXqT0jqQMIZ++yzj3r1kfIBw4cPV+MT98IzcT+eu+hfOMVsuOGGqg1gxwjnDnZUsQVntrU3vsNn3HYgjPfcc4+SWoLsoa9BYtEHkK5CNY3rdcmnoCM6wYXX+EzgAQQvxakM9hFpXlmCjoCtFAyo4a1ms7cRpEtTTCkQtyk8LqGqSrP7871vO9sh5hmneesNYoYNFYQKxuwgxL4SsaS+jdkXWe8HNSdsxyB1grNMs0o6YwJ2fo888khm7+EYayoIFKSDWD8gOXMBZYTqHmOzlZwlZrXJni7qXYGgwrB5RmITNDfbpOv5xK9voL73zbvZxs6W0SzIW+88qnzXs4voC6jX8Beiksd1aUSumQ8LzQZ4LEMyCfXrMccck0j4BM0PIX0CQYVh2/xtmzeHerBdb9tAfe+blxS0mh2ir/QptN7mffOEM3E9O2Zf2MZUTNhyGvuQxWaFS3VbBqAGv/DCC5X9Hbx602BTwwqaB6LezYh2EQUXAVHvxs+7is9YvYNr2AaQJTw2VWFoPtc0wuP6vkpqupCymOM0VPrk+6wsUq1G90Ue9bPP/DfD+ABl5JRuVsiamh+z2mRPF0mfQFBBmJuzvqHapECmaS5CKDCJgAelHj8L12KTgAelGezXJV3yIYSu76sSMDqvyjBUEmrW20W4fO/Lv4dUF+FOkurBUjHzebH6omgJrj4WUW945babiYBAUASE9AkEFYMPOUnavG0kAveAiuzDDz9UEhoQPpAHOH74ELQ0YtIM6rhG2tmBZMPjEAFqzTb3ua8+JiCFQGoqhMvA50sssYTyagwh6XlRRjYNHouoSyuZCAgEjYSQPoGgYiiCnLCdGFRkUGMgDhhnTfC5fxox0b+HNBHksmrquLzSqaxEB6Rl3LhxNHr06Lo6U29zn/vqYwL3QwgUAOQdQX1NIhnbPtMmpSxLgltWujaBoB0gpE8gqBiKIid8X2zgiKMFSZ/v/X1Sl1VdHReDPGQhOiBLkMyB8EHiB8Jmtnnafc0xAZU92heEDxI/EDy9nYt22ohhyxiCqpgICATNDiF9AkHFUBQ50e8Lwmfa9GW5p22jB2yEIy8ZiEEmYpKHEE9eSOIABJmFp2Zo+W1jAipd3JfJmE7szOvZ+SJL24VIDSXUikBQbQjpEwgqiKIkG/p9Y5IfUxJkczTJY2NWNTIRUp5Y6kkfhx7b9T5lTSKwIVLDPGrlKnl5CwStCiF9AoEgF1wbfZKjR6jKF79H7lbYI4IcNFplXJQnbxGHg7SyppHCENKaVa1cNVIvELQqhPQJBAIvuIiKz0af18aM1dFwQGF7xKyIEcMOz4cXLXuWhtSnbIKT1vY+BNZX8pxVqtmumVsEgrIhpE8gEKQiLQ5f2kafV8XJIWbgccwEMAspCMlLm3QPxMmDcwacMuBUUWS8v7xSwbS21x18YHOYh1Dz80L7ptUytwgEVYWQPoFAkJuo+Gz0eVSc7AyB65JIQdo9Q/LSusgPXwuvWTwjlICGEJxYUsGk/sF3IK6oF+IIgtCWrV6VsCwCQTkQ0icQCEqXxISSGR9S4HPPkLy0ZpaTtHsUQXDKUnuCuMJe0hb+JSbQpvAitrWthGURCIqHkD6BoAHImju1UYgticlCZtJIga9tmq0ets9dpC9PW+j96kOqylJ7lvEcJuVwyIEUFbl3qz7uBYJWg5A+gaBk5Mlj20jElMQUQTJ87+mqR0j9srRFln4tS+1ZxnN0Uo77432XLl0qP+4FglaCkD6BoGSE5rFtRU9GM1B0rPAlVU7XlbVfy0x3VuRzdIcR9HkWL2KBQJAPQvoEgpLhkkixaovTo5nftxpAMlC3mNKdKtiF5Qlt02wIUccyKV9yySWVw0ieINAxyiMQtCOE9AkEJcNlP6aTH3hTVlViFRN5pTuhm3xWUmCmmXPF+YOTwocffkifffZZptA2zYSs6mr0bxFhfUQ9LBCkQ0ifQNAA6Cmy2JuxHVVbeaQ7oZt8VlKwYMECmjhxopJOcVlNUsf3njFjRj2AdNbQNkWgiLzHRahj87SPqIcFgnQI6RMIGgSdhHzrW99Sf62k+vNBFukOE5BQopyFFOAZ48ePpzFjxlDnzp1VaBO8gqjr9+B7z5kzR32Oz0LTkBWlliwq73HV1NVVK49AUEUI6RMIGgSThAwZMoSWWmqpllD9FSXdyUOUs5ACkDxk3gDRg8QPv4PNJUv6bHH+0IdMBn1JbJFeqz5kN4l0un5fNXV11cojEFQRQvoEggYBGxNIC4gFXtNIghip5yPKWUgBCB5nAkGKskGDBtXDjZj3+M53vqP+fMleWWrJNLJrsyfFmOT6Jf3eNFPwiTtZJKrgyCMQVBlC+gSCBiONIKQ5CLQTTAISSrBCSQGuHzhwIPXr14969OihJH6AmU7ON2+vT51iE6M0squTTiZnyM6hj7Ok34fGnUR7xoQchgSCNiB9d999N11++eX0wx/+kI4++ugO340cOVJ999FHH9Hqq69OJ510kjqth14jEBQJbK4gcSB0eMV7m9oNG+b06dOVI4HLQUC/Pkni0uwoW4WH9pw0aVIi2c4rqSujTklkVyedkGZinCEdm6nKdf0+NO4kQrbEgnjsCgRh+ObY2mSAYfWxxx5Lr732Gr399tsdvnvuuedoww03VAvZnnvuSY8//jhtvPHGNHv27KBrBIKiwZstJEguCQ9vmOw4ANLnuhYb4AcffEDvvPOOIoqwQXPlOW1mJIX9yJP71QaoOZmw4BX9Yd7Hpx9j1ympHqF1ZNK50kor0bLLLqsOvyF1Sat/jPZxQSeUev8IBIIWkfRhEd53333p7LPPposuumih70899VT6/ve/T3/+85/V+5133lmpE6655hpFFH2vEQiKBKuk0uLxuRwEAGzssDnDnEA6KyYokNKYajrTTqtZkUeVp+d+7d27tyI4rK51gQNl644bNulS3vhyobEGXerUrJIvXZIXWpc0SaX5fcxDiHjsCgQtLuk77bTT1AZ2yCGHLPQdTntPPvkk7bbbbvXPYCC/zTbb0PDhw72vEQiKBG/MkMhBMsdpyGyboS6FWXrppevqXfweUm6YKeAV9wHxY4kKSA28TjHeQXIgHWcJYNKmq0uJXP8X1SZp99fbLa0eNqCN0RYw6Xj33Xdp3LhxqfdA+2O9QfszgbJJl7JKH7PUKUm6FUPylaUu+m9sfckOIa5xnhX6/GhXO1eBoGUlfQ8//DDddNNN9Oqrr1q/x6I5f/78hQypsWg/9thj3tfYgPhbHIMLYFUwVGj4E/gD7YWFv5XbLUl6gw0R5AMbM6tgdYmcbeMCMdTJF34PUodAwH369KmrGQcMGKA8SHE9bNGw8ffs2VNdxxJA2FS57AFBHjkcCgAJl/6/WcYYRvT6c5PaQG83Wz3SygJSDJu1yZMnKwkfbNcwp122ajxOOVcs/4/7oE3wP17xPutYTquTDUnPj1m2LGAS+/HHH1Pfvn07BK/Wx9Ziiy0WtVx6/7RjOrZ2WFOLxoI2abumIX04nR988MF0ww03qMXEBrZ7gqRDBxYY/s7nGhvOPfdcOvPMM+vvsZhuueWWasHWyaDAb3Jhw8UilaZea0agXlOnTlWkDCQDG7npzYhNCp+h/nDSwNgDKcOGn2bzxL/HGAThwyve4yCCzRbti/cY07gfvudDCz635T0FsEnCQxjXgYSyVAv/45k4LOllTKunWWaol7ne+ue455QpU1LbQG83sx6+ZYEEFMQYJAvPMNtCLyf+d41TbltchzUgK/Q6oZ/Qf7b0bmbbJT0/VtmyAH2IQNY8FlGnRRddVJWfxxbanp3m0ua/a9wkXYtnTps2zWtctgpafU0tA7PbxKa/aUjf7bffrjaf888/X/0B7733njo5QjXyz3/+s277hMVGB1Ij8SLjc40Np5xyivLw1QfIgQceqH4DSYogbIHCIoy2a8UFChsfpEm8CYFosGqLN3QcXPBel8hhbPpuUPrveaPDBo9xaZPILbHEEqmSD5Yi6pI+VhFiA8XvIEXkMtrqmSZB1Muleyaj/Pg9/vr37+8cF1xvsx4hZcF3gKnCNMsJcljGOEWdUH4cbEFWzDYy2w6omhSLJWt84MB7EDwQcYS7gc20PrZATFHvpHblumNcg7wl2WDq7YRrWY2cNBZaCa2+ppaB7v9rOtPqaBrSt8suu9DgwYM7fDZ69GhaY401VMgWtr3BAgM7p5122ql+Hd4PGzZM/e9zjWtA6IMCmxSACSaTLBws5WrFtsM44YC+HEtO37ih8kK9eQzBVi9LJg7+PTZZLPqQxIHwQVqGV9xT3+z4+iToZQFAwCC5gWQc9cJ3fB+znnhvi+HG9mVmuUAKJkyYoFTP2JjxOQ52IG9J9lm2eviWxewHkzjq7QeCW9Y4RZ3QDmgr9COcHlAHs0z4HOSwqOwdWWCqbkHOQORxUOCwROhbHlt8QElrVyaJII6QEuJaDo5tu5bbCcB4wvU8FqpIlGOjldfUMtC5TdqtaUgfFgz86cDEXmaZZWiHHXaof3bQQQcpL9wf//jHitw9+OCDitBdfPHFQdcIBFlheiumxTGLlUVA9zRluy785QleDImMSWBd9XQF7cUGD4kL1HxJ4WZAEnAdrodkEs/2LbtPrLu0fmikJyhnZ2FzEZAcrHdmmYAis3fEzJKiBxPnPkFZfW2n8BtI7aD6ZxtMWyxLvlZvJ91bHSgyzZ1A0ExoGtLni7POOoveeOMNWmGFFdQf/v/tb39Lm2yySdA1AkEe6OSpLDLB0m4QAWy4o0aNyr3J+YTjSAvaC+kLCN/yyy/fgcjhd5AKwdSCiSWux2YN6SI+Cyl7GnlO64ciQ4ukAc/Gc9EWIH16sG69TFx2lA2ECFKzkGDcaQ4OZro0H+mYK0sKSGtoKBr9evxhfEDyCbMFjAfX3LGNUz20UdWIskDQKDQ16bvssssWcuqAfd1DDz1E//nPf5QaZJVVVlFqmtBrBIJYyJtxAZIRbFh6GrCkZ+EPpCHWJpdVEmmSAVNyx8RAVydD8g6zDah5XWXP6pnp0w96XcsOao3nQsLJEiluE7P9QexRf6iDEcmA8wOnEeSQdGmmx3bSvV3tGjJudKmwnuMY9q4Yx9DquLy69XL4SAHLlOAKBFVDU5O+jTbayPkdiBz+kuBzjUAQA3qcshCyAsIH0wMY+MO+bq211lKSj6R7JG1yZYayYMkjiAwIaxrJAnAd6gdCA1ssEJqYabdiqdIbeTiAJBSkGNIvDtnDksKkuoWkS8Mz2FnHdm3edmWnIba91GMoQp2Lz+D8wZJivEeZsvRd3kOXQNBKaGrSJxA0C7KSFWzAIHx4hUH72LFj1SaYdA/XJld2ntI0xwkbODwHiAx7JiflcW01VZ0PeWJSj/ZlYuwjwTIPA/idntWF7wOYkr4Y0jE+cEBajbEMhx2WUNrs97ic/JqnDFUm+wJBmRDSJxCUgKxkBZIvSPhA/GDKAOkOx4V0qT7Zxsv8zqcMMSWBWeqM5+qOI648ru2sqtNJPRM2n/4yf8fhUJjwod31tIBArLGgHzig5mdSp0soWaWL71AW055RJHQCQX4I6RMISoCLrKSRLEg9hg4dqogTnCH0mH4m4WHJmssOK40wxZYEZiFoPo4jQgQ6Sq50G8Q0ksa/Y+cGqImTVMSxpGP6AQAmCyB+KK8+Ljgki1kHkdAJBPEgpE8gKAE2suJLsrAZwvkI18PhCH+23KiQ2LANlCs0TBJhiq06zUrQ0lRxoqpbGKGEnUPEsPqcCXpRklP9AIDncn5o0zEpT9+2W+o1gSALhPQJBCXB3NBCSJZtU0+K0+fawJM21SJUp0LQygEkd8gqlKT6dwEe0xwwOxZZsoVf4QMAp5uDXZ+Pd7Dv8yQWn0CQDiF9AkGDEEKyfAgie8vq2T1C464lSRJDIZKXcoB2RlxGqGkBPQSOC5wpg0O/cMq5WOWxETA9OHOaVDpUetfqDj4CQSwI6RMIGoQQ9acvQTTjzPnGXfORJLab5OWhm/9Ks0f+i1bY+79p9XXXo6qCCRw8edH3GE9pbV2kQ4wPAfORSoeMIXHwEQj8IKRPIGggfNWfWezj9M0Xse9YapIWo832fSiaXfLy5ewvqfbSP6gvfUmj7riYVl/371RV2DJipKFIhxgfAmaTSpuSvZAxlBSmSOz8BIL/g5A+gaBJEGofZxrPAy7JSmxJSbNLXp6673ZahOZRZ6Q7mzON3n3zDVpxtdUbVp4k8lKUw0xWcHnSshyZzzcleyCFIWMo7X7NKG0WCGJDSJ9A0KIwyQCgEweTSNi8i7NKSZo5tAqkfJ+PHE59ab5635W+ptfvv5FWXO13DSmPD3mposMMsmuEEC6bZC/PGGp2abNAUASSE3kKBIJKgIMuh+aDZTKgG9Lr4WLeeecd9Yr3ad+HQr9fzDqVIeXrPvcz6lxbQAuokyJ93ae9raR9jYBOXvDKKvoqI0uZWTqMcC4s2UsbQ6H3EwjaHSLpEwgqDpvayzcLg+t+yOyBbAwu78lYUhKXtLCqqjeW8vWpfUULOnVGQUFfqeuCOQ2T9jWjqryIwNyhaGZps0BQFIT0CQQVh07AQJZA+GbOnJmJLDHZ4vRbyPLha+MXqu5NInZVVb29+Ohw6j73c+pC85U9Xyeq0QJkhaC59PW0t+mD8ePpu8suW2qZYqvey0BV7AyrqPYWCBoJIX0CQcWhEzBk5kBu0rT4Zi4w2cLvQfiWX355lRIrzTkACJXMJRG7qkqv+vRbkj7u0p1m1jpTj/mzlGp3DnWnuV0WpXmL9KYePXs2pFxMXkD2EIAZcfliBTbWEZNMCuESCKoHIX0CQROAgyZjM0Z+XVf+3TSYZMtG+GybNudqDZHMJRG7qqre1tpwY1pt3fWUFPTe3/+CFv10HHVaZVva7oAjaJGu3ahL1y4NKxtLTpF5AwGVEZcvppQ0j8q96pJHgUDwDYT0CQQVg5lFw9yITQkcCBlSW/kgK9kqwkarqpKgrl0XUX+dkBe2Uyfq3HURWnTRHo0uVl1yCkkfxgj6HKF40LbIcgFCDqcFM59t6P1DiD074xQlecwDIaICwcIQ0icQVAimtAXSPdtGzKo+PeMGpHY+EpgsZKsqNlrtDCbe6FNktGByB8L36quv0rRp06h///40dOjQTMTPjOuI57BXtw08/qZPn65MDmJLHvOgqo5CAkGjISFbBILIyBOKxAx1AbjCTpjXQiVpliNv2BUdecJnNEOolqqDiTfsMDljBaRrIFwgfBgH/Jrn/kOGDFHvR40alThuePyhLwGQPt3pp5F93IxhbgSCMiCSPoEgIlw5bH3tnWwptVwSNlMyA+mPjip6yIoEJh/Q/5Doot8//vhj1e+9e/dWEj6W9OGAkOf++AOZTBs3+vhDOjUeo7HVvWlqWtv3VXUUEggaDSF9AkFEmEQL6tmQzAQuNapt09WvhX0XwrDoqOLGV0Ui2qxgKRpUuVDp5rXpCx03Lg9vl6MJkzNf+1MfNa3r+6o6CgkEjYaQPoEgIswNEwglOWl2cKZkA9fCrst2n7SNr2xj9yoS0apjzJgxdPvtt6vDw6qrrkp77rmnkqKxehevGAMI5xMDIYRJH6twMAHh43BC+FxX96bZn2Y5JCR9L/akAsHCEJs+gSAieMNcaaWV1CtvfLFSQYXa6SXZ4cW2+cvSPiKBScbf/vY32m233RSxWXrppemyyy6jTTfdVPVpkenFQu03MXag0oWED+rdZZZZRhFU7mObjZ2PzV9aKjVJtSYQhEEkfQJBgYF0sbkhbVosNRM2SnhLsvF8HvVoo1StrS6BAYEGWYODxTbbbEOTJk1SgbD33ntv6/WQ0kKS99xzzykp2D777FN3pgDB23///esq0cMPP5yWXHJJevvtt2mnnXYqTUKbJhHGeISUDypd9C1s/Fi9a0p4+/Tpo+wRYYO4+OKLJ5J/n7A/osYVCPwhkj6BoKBNcuLEifTmm28qEpC2Oft4O7I0BWQCqjSO0ZYVIiWJDwTOHjZsmOp32HOeeeaZ9D//8z/07LPPOn9z1lln0SmnnKKIDZxxQPJA6oBBgwYtZAM3f/58JfmK4UntMxbTJMLmuFxiiSUWIvW6hBftMmvWLG/P2jSpYyyvcoGgHSCSPoGgAGDTHD9+vFJ3YTPEhq5LPrJ4tLINF+6DP9wzz0YnUpL4uOCCC2i99dajm2++Wb3/yU9+QoMHD078zfPPP0/HHnssnXDCCer9ySefTLNnz7Ze+7Of/YxWWWUV2nrrrQsovX0s+tjV+YxLJmcgrb169VIhhsSuUyAoF0L6BIIGw1fNagvnkgdlOXG0U2aE1157jXbcccf6e5CgjTfeuP7+F7/4hVLPA9tvv71yyjj++OPpmGOOoXfffZc22WQT2m677ahfv34L3Ru/ffjhh+nxxx8vzAHGNhbNcQdpJA413J+h4xK/gYoaoWbQPq0+JgSCKkFIn0BQALDxLbvsssp2qW/fvokbYZpHq06aYtkv+UgXs5A18zftFpcPJIbtLRlQeTIQWgWSXwDODsAOO+ygAiG/+OKL9Nhjj9Gaa66pbAJZmoc2BDEcPnw4Pfnkk/XYj0XANhZ1iTAIn577mfszdFyKSlYgaAyE9AkEGZBGiPAZvC1h0O6zEcLOCX+mbZKNNMVwgkiTLoaSNVcO1naLywdJHZwyoIZFPSdPnkwjRoygww47TH1vc+a4//776fvf/76SCOLvrbfeonvvvVeRPjh5HHHEEYoQgvCh/YqEi8AxSUMf2/qzGZ1z2kkCLRAwhPQJBIHwJUQ+G6HtXvpmVBRpSpMuhjw3KQdru8XlO+644+juu++mNdZYg9Zaay16+eWXFflPCkj8wgsv0Iknnkhrr722Isxw+njwwQfVd+eeey5dd911KmzLaaedVv/NXnvtpdTARZCfpHHbKv3ZbhJogYAhpE8gCERMIpaWwQPhXorYZNNUciGbe1IOVttzWlnCAju1p59+mp544gmaOXMm/fGPf1RhVqDiT/LehTQPIVsQ2uXWW29VntkApH0YEyYgQW4E+Ynt/NOosdBuEmiBgCGkTyAIRExph08GD92eKuYGaZPo+NoP6tfZcrDqamr9Oe0gYRk5ciRtu+229WwasNODE0YSIA3En4kNNthA/VWJ/MRS5WIs2OwDy0CrSCwFglAI6RMIAsHSDpsEJuu99PylNkN6vBZNlnztB23X2QiiTYrTDhKWs88+W8Whg3Tv0UcfpUMPPZQ222wzqiIaSX7yjgUfKaHrGglXJGhXCOkTCDICathPPvlEqfTgqZs10b0pObERqDLIku8zXNf5OIK0g4Tlvvvuo2eeeUZJsRCcebXVViv8mVnVpI0kP3nGgq/3edI1zeh8IhDkhZA+gSADsMGC8IH4TZkyRW0wyy23XJSwJ7bNqAyy5PsMn+t0Yog2QJaGxRZbrC0kLKiTHpuvaOjkBraApno9DTHJjz7egTQP96xjweeA0g5SZYEgFEL6BIIMwCYFCR8IHyR8MNrHJpMn7EkSyiBLvs/wuY6JIdoAmRdg26bnWW2GzXdBrUajPvqSVlrVnRqvCmBygyweOIggNiRINpyA0PZl5ufVySegh+/J6uGe9eDRDlJlgSAUQvoEggzAZgWVLjY6ED4QmjxhT3yfmeX3IdJG32ekXcfEEOQDhO+rr75SbdFM0pYX3plCtzz+ER28+CjahaoLJjcgeAgEjT+QP7zH2CzLSUIf73g2nsce3bH73efg0Q5SZYEgFEL6BIKMgIQPKl0XodKlXcg1Cu/bstFIb1k8BypdEGJ+frNIW9Cnw18ap/6/79EX6UxNZVllxyIOjt2zZ08VMxFkGyiDbOuSNVPSZ2s7DujtowrOekBpFqmyQFAWhPQJBDmQtKngO6jYsJlhA4Zhf9khShpt19Ss0pY77rifPp35Tfq0jz+bSXfe+QDtvfduVFWgXREfEWFf0OdmurQyCKuZrg3lcNkXcrgWkEJfVbBAIMiPbO6GAoHAC1BzIXwHqzch2cAfNr0ywNKXHj16NEzS1mx5VkFWLrjgcuLSotwXXPAX9XnVwW0NKTTI00orrVS6dBdjDIQO+YTh6GST7umHEdggzpgxQ/2Pz5qhnQWCZoVI+gSCnEiymTNVXmZu2qI340ZI2po94wakfJMnf9ihPpMmfVh5aV9VVJsu6TLyCI8fP54+//xzJQnEfEAZfVTBAoEgDoT0CQQ54BMLjEkXroX0o2xVa5mbf7Nn3ABhOf/8y+rp4hh4/4c/XEa7775TJUlJlYi2zWsW5Rs3bhyNHj26TvQGDx5MAwYMyGzTJxAIwiHqXYEgklTDpZpi0oW/Rqtaq9AeVcUXn39Opx93PH344dSF1O94P3nyR3TOL39NX39drTox0X7nnXfUa1mmAy7wQUdXLWMcwJMYamdI/ODgw3MC3zebCYBA0KwQSZ9AkAMhscCa1akhBM0YGw0k7qn776FpT99B9z8yKvHa2+8aTistMpVW/sHhtOb6G1IV0GhnHR/pMsYBvLgBeBb36dOnYeO/SlJRgaBsCOkTCHIglMi1egiJZiO2b7z0Ar1z37XUfdYUennsF/TJ7PmJ1+P7V99+jxab8zsa/cSqtNE+P6YBSy9NVVSnNorY2J6tj4suXbqoOIJlPLfVzA8EgrwQ0icQ5ESViJwe+8y2mZVBBqrUHi5Mfv99evbWK2mRD1+n7kQ0fwHR/W/PXsiWzwSa7P63ZtL6y/aiblPfohcvOYG6rLQVbbn3IdSzV69S6+Ai2kCjiE0SqeJxAfVumc+1pU+ERz1+UwWpqEBQJsSmTyAoiYjh1fY/NsEYYVw49pnLtivN9ksvW1IdspatzFA1Lnw5axY9cN1l9MLFJyjCB8zpszRNGLgtffLFN3mCk4CvP5k5h/7Ta12au8i3qFNtPi14+2Ea/puj6d8P3EPzv06WFBYF3SbOx66yqP5olE2n73PhNYwwSvAURuYSSB0FgnaCSPoEggbkI+X/8Tk2IWxGnJsWyCKNw31wP1cWhiTbryRJia8UxSVFrIJKDcT62QcfoA+fvJUWmfeFOu3OXaQPLbXFPrTOFtvQJpv8IFXKx8B19wx/ih599HZ66u5baM7rD9IiX8+kTx67lu588SFabbcjaOW1hlJV7SqL7I9G2XT6PhdzBEQP9UVswAkTJtCgQYNExStoGwjpEwgakI+U/8fniFvGhu1IpYWAtlk2ZBBH/MYV7yxpY0wihD6OAklEotGOBqPeeJ1ev/NK6v7F+4QaL+jUlbqtuh1tu9dB1GOxHjRixFP1uHxmU4MDLvzZN3H7XnzxNfr+gUfStCk7079vuZK6TnqFus/8gEb//Qx6+7G1adN9jqL+Sy1FVbOrLLI/GmXT6ftcfNe7d28VLxPkD5ly0B6i4hW0C4T0CQQNyEeqS/qQPosJG3+WZUPmtG9LLbWUdeNL2hiTCKGPFCWJSDRK+jP9o4/oyZuvpK4fvEzd6RsJ3ryl1qKN9j6Slvrud+vXrbfeULrggjNo7tx5C5G7WbNmU8+eiy3Ult26LaJ+B4DY7X7ir+k/I1+ht+65mrrPnKQI4NMXHkvdV9+ettzrAFp00R5UJpLsKovuj0bZdPrm4mXJHggfpOvN4GEuEMSCkD6BoGQje1aBAhy0Wd+w8mzIaRsfp8lyeVdC0phFipJEJMqW/sz56it67I6b6MvX/kVdF8z95rOeA2iVHxxOqw5bZ6Hre/XqSfvs80OrShgpwvr27aviy6VhlaFr05DVL6Fnht9HU5+6Tal8571+P93/zlP03a32o/W22tbrPlnBdnpAUsy7qnlYl+1pjD4A8ZOwLYJ2hJA+gaBgmERMt6OzqXKL3JDT7LlcqmUfMplU7tjSn88/+4zmfvUV9dNIKkjaS4+PoImP/J26zf2EYKL/ddee1HejPWjTHXelLl2LN9rHMzbdeTeatcU2NOKW62jBqMeo29xPaerwy+iOZ/9JQ390NC2/yiodfjNp4gRa8r+WokVytA/6deLEiSrNGbDsssvS0ksvnUj8QvqjKGLWKHvPZvAwFwiKgJA+gaBBcKlEi9yQ8truVWEj/eyTj+nh3x9PXb/+klY/7GwavPLK9N47b9PLd1xF3T8ZSyhBjbpQ5yGb07Z7H0q9+/ShsoHwLbsccSxNmvgDevaWK1R4l+6fjaf/XHsqvbnM+rTZfkdR3yWWoMfvvZM+f+J6mrPEivSjX5yXWRKIvoNjAuxDAUgooeaP0R9FErMy7D1thFUCNAvaFUL6BIIGoRG2bnlt90JRxOb6yHUX02JzZlCn2gJ6+R+X0RtLfJc6jX+OutM38d/m9luZ1t/rKPrecstRozFw6WVoj5//ll57/ll69/5rqfuXU6nzhOfoifNepe6rb01fvTGCei2YTZ1mvENP3HM7bfnDvTI9B+27xBJLKDs1ACrpWOOpSGJW9BywEVag0d7kAkGjIKRPICgAPmSnEbZVSc+MXZ4iJEQvPP4odZv0Ci1a+5I60wJa9NM3afZnY2l+50VozqL9afkdD6ahG21KVQNStsGe8Ml77qBPX7ibus7/kjq/cjt9m+apenRaUKNPn7mNpqy7UQcnE1+gXaHORd8BMfPYFknMip4DNsIKVC1tnUBQFiQ4s0CQEa4At2lBkHVkTTTvCvjsg6Rnxkx8HztQ7+effkrvD7+WeiyY9Q1RqtWoc20BdavNoe7DdqcfnvGXShI+Rteui9BWP9qHtjnlcpr7X2tRd5pLXWrzoYumRWrzqMfXn9NTf7soc8YK9Bk8wfEXkzwxMVtppZUSiXvWgM8xx5yLsPbo0aNOWG2fCQTtApH0CQQZ0Mi4dLaAz/gfYV+g1qsKTAkRypeUIi4ND13xO/r2nA+pW20eLUAgZepEuMvcTotSl/nzFKlqBvT59repNncWzaHu1IO+olonok41osVqX1Knaa/TiDtupm323J+qhDR7zRCpLkvBs2QDCTUXcEkSq+S9LBCUCSF9AkEGuIgdS96YjBUhSbAFfEZKKU7BVZXUUvqGC8KHFHFZVb1P3ncXLfHhc9Sl9o0UDCRpfifI+zpTl9rXNPvNEUR0BDUD3n/vPeo+YzR1pvk0r1NXJbXE/8Cita9o3jPX0ozNt6Elllw4fE5V4XvQ4VSBCI6MA0CSh3EscwEbYRXvXUG7QkifQBDJzsmUwA0ZMqQQtZUt4DNL+rIQzCI9GXlzxQafR/r51awvVDYNkKQ5nRalrzstQgs6L0Lzu3SnWtfutNjy61Oz4Dvf/S7N/c5qNPezSdT56znUZf4c6lybT10XQPY3hyDDnDPnm3h7zQJfuz8mhzik4DXEw7jRmV0EglaAkD6BIANsaiOT2GBDK0J1ZAv4jKDK8NzMYhtYhCejSSTzOgNst89BNGaNYSqWXe/F+6pUWnni2jUSICp7/PycDp999dWXNPOzz+mT6dOoZ+8+NOB/vUxdqFrIkZA0aOh/5IcOHQeNyuwiELQShPQJBBlhqojK3JRsAZ+zbP5J0pOsxMJFJPPaUS2/yqrUqkCaNvzpwaazEvWiCaHr/r5p0JAqENctueSSQeWrWiYRgaAZIaRPIIiErJuSvokCsTZsn6C0LqKaRwLYiKDT7YQ0oo5+++STT6hXr14q3VjM1G8xJMM87pJ+l4VYVk36KRBUEUL6BIIcMDeaLOmtTE/czz77LLeqNSQorY2ohtpP6e0garhiYdp0svMQO/KA8CGd3pQpU9RnIH7NlkHDRSxdxM6XjAoxFLQ7hPQJBA2Uetg8cWEb6Luh8iYGaY4eBiMkKK2NqIYQN1s7iBquODBRhx0nvGBHjRpVb3f0EyR8IHwYE7DzxFjIorK3XYv/QTTnz5+vXkNMGPh+ad7lSZ7xecIkIf4hchOjTRZffHHJxCFoSwjpEwgaKPWweeKypC9tQ9VVeSgLSCOHwXCRNl8iF6KqdrWDqHKLA0uVMVbMdmfJHpObLCp7H7tB1+/SJHEY54sttpizbq6xi7E9ffp066Eo7ZCC548bN45Gjx5dV3eL96+gHSGkTyDICJ+NJjQVG+AriWGyNWvWLLX5I8OAHgYjb1BaX1W1qHMbA1e7g9SA+JnjKOSQ4roWn2Os6a+mPWGaJA7XJY0r24EDv4H0EvdA/fr3769CFCX9xqzPzJkz1W8h8VPe3+L9K2hDCOkTCDIiaaMJkarYPHFDNn3eRCHp0zf/soLSildlY5CWRzmPyt51bdI9kkilKdHWCZurbnr5Id2bMGGCkl5CPYznIMizPq+SxjaeD6knAPX3sssuK84egraEkD6BIAdcG00ZBu/6pg8JxrRp04LDYMQsi6hzqdLt7pKgubxkbYQyiWgmEUL9dyBtMEnIWl+UF3H+ML9855UcTASCbyCkTyAoAGWrPH3CYDQS4jVZPZKYJo12Ecqkz01TBT3XMv8O6tVQ4HeQzs2YMUP9nudXyLySg4lAIKRPICgEZUgWQozjG4misn4I8qEIaTQTq9h9jt/CQQn2qlANw5ShyoccgaCqiBe1UyBoQ2BzgzTD5s3IG2BRG5O+aeMVG2HWshYJs5x4LygOvv3M0jI4AMWWRhfR5zyfYMpQ5LwSCFoZot4VCJpUghViHN/Isop3b3kIdSAqShotfS4QVBNC+gSCjCjDWcMnj+kSSyxB3bt3V2WpYlnFiL48hPZzUXZu0ucCQTUhpE8gaFJpBqQ6CFuRJ+BtWRAj+nLQ6H7WkSUloeTOFQiKRdORPqQdevHFF5Uqa9iwYSpEhQnYNv373/9W+SdXX311WnXVVTNdIxBUWZqRN+BtozZcn2cJAajWmIzdH2x3CAk1h44RZx+BoHg0DenDonDYYYfRo48+SmuttRbNnj2bnn32WfrDH/5AxxxzTP26jz/+mLbddlv1utpqq9ETTzyhfnfRRRcFXSMQlCHByrOZ5g14q5ehyA1XryOgexwjf6zp8CIEoFpS1dj9gftNnTqVJk+eXM+B22hTCYGgXdBUpG/zzTenq666qr65XXPNNXT00UfTDjvsoNIOAaeeeqpaON544w0Vef3555+nDTfckL7//e/T9ttv732NQFDGmM6zmcYKeGtKDGEjCFVxLImOXkeQPPyPQxvK+9577ylvTMRgQ0gODr4rBKB4KazvfbP0R9K9OSUarsF3LJWsilpaIGhlNE3IFmwMhxxySAdpxi677ELz58+nt99+W73HInLLLbcoqR3IHLD++uurv5tuusn7GoGgDMQIaxEjLAxvuIsuuqgyexgzZowiaq6QH74hQfA9kzuuI4BnocyYu0ir9fnnnyupO9e/yFAizQYmze+8805inxR539D+SLs3fo+1V78fH2BWWmklr8NPo8IPCQTNjqaR9NkwfPhwRQahogWwwCAJuGmfh+9feeUV72tsmDNnjvpjYDMDEB0+S4T5dgbaC4t1u7cbpHNQcaIt8Ir3Wdskb5sOHDhQqdrGjh1bl+jAXtaU6OjOI9iw4T1s26D5OhA+EEnYbrEKGs/q37+/ss9FPlUAz9brj2vwfBAC3KsRm3sVximIDZNmlMXWJ6FgMg6ijXRmPvcN6Y+0MuMz9D+IH8aFfj+MD/29TWLoOwbbCVUYq82OBW3Sdk1L+iCNOPHEE+m4445TaiEAZA5gNQGjb9++9e98rrHh3HPPpTPPPLP+HhvUlltuqRY3nQwK/CYXJDxYpEDa2xlQo2JDxGaXVT0bq03xW2yuGM8oD+5nU82BrOEaEAaU3Sb50a/DNVDrQpKo1xGb/oorrqj+xz3y1L9Vxymejb5AP7j6JIs9He4DSSvWMdt9cR3IOj87Zpm5XfXDc1JZoQrGWOG80r5jsJ1QhbHa7JidMBZbCU1J+iZOnEjbbLMNbbHFFnT++efXP4e6AJg1a1aH6zEZ+Dufa2w45ZRT6KSTTuowQA488EAloejZs2ekmrXPAoXFG20nC1S12hSHnyQ7L1arsZSFN+K06wYMGNB00pi8bRrLFi+pT0KfgT6BAwWAdWvw4MEL2W/GkKQlldm3XbmsTEB79+5dT/HmMwbbCbKm5kf37t2pHdB0pA/qWZC9oUOHKts83cYPEj8sMuPHj+/wG7zH4uZ7jWtA6IMCJ2QAi5YQl3BgkZa2q2ab8th2gXOgphEN3+tasU1N4pTX49XWJ1megTUMZAvX4TeQoJm/AaGC1gNSNLyiD7OolJPGkU+7mmXl8C6tMrZiQ9bUfOjcJhLSpqolFjgQvjXXXJP+8Y9/LCTSx8KEUCy33npr/TPE4RsxYgTtvPPO3tcIBGWf0iF5bhabEl/nET10SKsZ3ac5EpSRbzjLM3wcJhrtSMNtC7jKWnRea4GgVdE0kj6cOrfaaitl37HjjjvSjTfeWP9u4403phVWWEH9f95556n3e+21F22wwQZ03XXXqbh+Bx98cP16n2sEgjIAojdy5EiaNm2aMm6HBLuME2dZwY+zhKUpomwx7+lTpzJCkGR9Rlocv0YGHbe1rcTrEwjakPTBpmOjjTZS/z/99NMdvltuueXqpA9euK+99hpde+21NGrUKBXH74gjjuiwIPpcIxCUAUhpQPj4FbaibJhe1GZbZvDj0BhvRZQt9j0hhZoxY0bdgctWpzKIU5HPaFTavJDxYhJ5yeIiELQQ6YPtyV//+levaxHo9ayzzsp9jUBQNKBCg4QPhK9fv340ffp0FbfOJCcxN7Qygx+HSqPMsnHGjjz1jllf9AM8R9FHANuV5SVOVSUsZZfLd7yYRB7OJjFtKAWCVkXTkD6BoBUBVS5UuiAkMHx/9913FyInWSVVrg27zOwHodIoM7UcCBacCfJs5K76ZiE0uB7lgVMB+gb1apQksoz0ebHub6biy5sjGq86kUcWGcniIhCkQ0ifQFAB4ofwGdjIbOQkaxos14btQ8RiSnhCJF562VAGmF/kldDZ6puV0JgEMkbeZZPA+NbTd1y4Ahyn9W8sCanZ1gj0nDdHNA4E+AN0pxN+L6Y6AoEdQvoEgooAmx3UVJBaYBPjzTiLZC5tw04iYmXZ/LmIB5fNRYLTfm+DWd+shEYnkAgXlZUYJxEYX8LiMy5sfQn49G9eibCL1CKuXhaYfTZkyJAOYVsa5XwiEDQThPQJBBWBK+5alg0tz4Zdhs2fD7FMqndeYpqnfdjGMM/z0wiMbzmS2sclRQR8+jcPkXKRWk47yH9wivG9N0i2LhE3Q7Y0yvlEIGgmCOkTCCqCJLKVxSkAUsMsG3YZNn++xNJV77zENK9kKO/zbWriLNIpW/v4SBF9+zdt3LmkrTZSizaCjSbsVmG/itiUNqelpAMRQnYhM4fk2xUIskFIn0BQEcQgW3kyQegbeNGqsrx1jdFWeSRDeZ9fpDoyTYqIfoZXNP7yBDhOkrbaSC07waBc+C1C3iAUlw9p5johXiuegd+JVE8gCIeQPoGgYPjansUgAlklUEUFxU2y28tT10YHEDYlqUCIqrJIdWSSFNFl41eEZNrsHy4XygBJH8JwgQRCZaun0/SpkzhqCATZIKRPICgQobZneYlA1s3RtYH7EFbXNWl1z1vXRthw5XGMKAtJhDimvSbuDdXx/Pnz1atvOBY4cnzxxRcqry4y0uB/SKfNdjPHlThqCAT5IaRPICgQZQZCBnw3R3NDtZFFH8KadE3ZdS8DtjoBrno2KuiyixCbcRDZoSKLCYD+me0627jgcrGKFjZ6UNniOrPdbL9v9vEjEDQaQvoEggLRCLWUj/G9bUM1ySLUlWmkLYnYtaJKzlUnV/DnKkkAAe5n2PPBqQJxEENyIuv1wT2gnmVbPbxy3yeNC/bahZTPFaqmFQ8MAkEVIKRPICgQrsDAjUy55dpQTbLoQ9qSrmkFlRwTFGTgSAqfY/ssBnEpYqxwPdipwrdsZn2gptVDqOh9z6pfSPN01S8TxzFjxig7PqTDhKOJ6VCSVyIpEAjsENInEBQMnUy5bMKqmN/Uh7SlXdPMKjn01dSpU2ny5MnK/sxUUeqwfRYjuHFRksIsZTOJ2EcffZQaQsX8DON8xowZKlwL8PHHH9OAAQOcMRqzSCQFAoEbQvoEghJhSkuwqWHz5JhqutStKOlPiATOh7Q1G7HzbT9cA1LDEqZQW71GxwJMQpay2VLkuUKosMoXUlJd9Ytn9e3bVxFpfAcnEJfnbohEstHSc4GgWSCkTyAoEaaEBcDGPnv2bPrkk0+UFKRfv35OiUYs6Q87bxS1UVZ1Ew5pP5QdYUVAaLLa6jUyFmAaspTNN0Weq+z4PQ46CNDMpBqvUJ/HTDVXpTEnELQE6cMJD2J3Pn0tuuiiMcslELQkTAkLgI0KEg8Eq2XnCf7eJE6+0p800lXkRlnlTTgkNA1eYbcG9SXb9CXdIzaqahPpE6cwrey4FnsISB8cOvT7mn2QZhMrTh8CQUGkDylz/vrXv9Itt9xCL774Yj2aOsTz6623Hu2777500EEHUZ8+fUJuKxC0FUwJi267BFUWiBLmlI04mflHzdAZ2Exx77SsHEVulGVuwqESRVNyhPYE2dbbXm8vPcSI6x4c3iaLZDPtd1VTnYfEKXSVHZ/36NFDjQ/sIVD1Dho0yDlm02xiW9FLXCBoOOm79NJL6Ve/+hUts8wytPPOO9Mpp5yiNioANkkvvPACXXPNNfTrX/+azj77bPrJT35SWKEFglYCNjVIkpZeeuk6AdCJEza6JZZYQm2UrvyjutMBvkPAW0hSXNIs340yC5mxEau0bBVZnpNFoqhLjlAutCdU6jjQsorRRlKTAgUDWSSbVZaIxopTaAO+69+/vxqf+B/jGb/1kcC6DhRVlIgKBE1N+v75z3/SQw89RMOGDbN+v8suuyiy9/LLLytyKKRP0E6IYcOmSzSYOEH1hc1x9OjRSoIOMocND+pg3RZKdzoAYIuG+yUFWk7bKLOSEhux4nuAqEK6oz8z63OyShS5nVmVDkkfgM9sBNiVz5if5RPPMGb5G2lLGRKn0FUuAGnYFltsMfWK3+BAY4Zowdg32z3JVrAK4XAEgpYhfQ888IDXdSCFvtcKBK2AIsKw4DcgSCB8U6ZMUZsjACkeExWoJCEdZKcMqH3xOZdBJ1cuYuKT5D4LKTGJFUsscU+E69DJU9bn5FXr6b9faqmlFvKc5vKmkbqs5YitlixDchgSp9BVLkj5MAZA9GALjt917tx5oRAtGM8sBcRYhtcvPosh1WtGKatAEAPivSsQZASTAry6wrDk2VCw0eGeIHyQeoDwDRw4sB7CAvcHWdEJiM0OKkZctqykRL8HpI+m2pmlkK7MDElIcxbwsZdz/Z6leyAgINJJ5cvicGFzhshLOsp0MPGJU+gqF0gfe0Uj/iH/zgzRgjbCdezVDhU82hn2f3nrJc4fgnZFZtKHxRCqXExIEwcccEDecgkE0RFTnaNLCkAIdFIAxNh8mTBxQntsdvxskCdISTjGGUvQ8B3HRTO9f1mSUqb3aJKqV3dWQf1smRl87u/yXvaR5Lh+z6QAhA/tudJKKymCHcPhoigpU1UdGsxyoZ3YKxr/J+WAxnUI4AzCB5tVhDbCdZgLedqsqm0lEFSS9N100010+OGHq0ln89QV0ieoGmJvtKakAISFSQEQa0Nh2yZI+1gVCrKEeYdXDoprxpSzef8CoRLImN6jJok02xDtF0vFlleSw6QAUkkmKlUpWx6SXpSXcUi5ONg1/rfZSppOMpAMYtzi+SDhIIB6nt9WCocjEFSS9MFz94ILLqBjjjlGJougKRB7o7VJL/SNI8aGAluyCRMmqE0OUjzcj50zdCcNW0w51BdSeJb+ZfGyLIpsm84qRUhbXPf2JS9sU8lSqZikoMh6J5H0rAefGAcmJnloe7ZP9c0BjcMA1Lu6XWuMNqtaOByBoLKkb9q0aXTwwQcL4RM0DXwTuIeQgpg5Z32CKU+aNEmROJcqVI8px1JADkWC90wUi1Jp2eqQRLaLlLbY7h1KXpiohNjomV7Jtj5tlJQp68EnxoHJNIeA564v+eUQLxjDIHzLLrus+jwtDFCZ4L7meVeVcgkEUUjfxhtvTE899RRtv/32WX4uEJSOtATuWLShOoK6CcQKBuY+pCCGpMBFRnBvbHCwaYI6F56M7ARhU4VycGaQPGw8NjVwUWTDVYe0jb0oaUsoAS0rYHEjpUyN9DI2407y4cRnPNpUvo3wvHWReO5/SNZ53vmsHwJB05A+BGrebrvtaI899qDBgwcvNLB//OMfxyqfQBANpncgwA4G48aNU6pUfAcpBH9XxsacpOZCSBYQPNMJwhZLjoMzY8OBly+kIgDeZ4lnFmLHlVQH28bOBBXIai+XtgmHEtBGBixOqk8shEoY9fLkPSyYknZ2QPIdjzHiIuZBkpSY+x+HRUjWcdDCd2WtHwJB4aTv1ltvVRskUrLxRNYhpE/QCPhsmrZsESB8kPxBkgbAWxZOEWV59CWREX2zS9p4zeDMkDjAwxf10DN3+CJUFepbB773xIkTafz48eo9pJkcbzBG+UIJaKMCFvvWJyZ8SZbLFpPJemj76W0P2zxb1Icqe97axhTbKGINQTnQNmxKIR7BgpYifX/6058U4YNdn0BQBYSE6DA9SEGWsFDjPaQQSDUIIlKE1MU3qTwDnrvYaBDINo10sfeuHg8Pv2HVbkgZERoDG7OZys2FEEKF+nMYDgBG+ma8wTQkqWrTCKgZyiYvXHUPIZhVixvnIjl5iCkTTozpPEiSHhclKbUdFvW24HiLYtMnaEnSB0kIVLsCQVEIXcBDNk1d2oH7Q/0JgCxBLYrvXMnf08qZVO4kYmqTwGBzHDlypHKc6tevn/oDUQIxNbNH6N67Zvl9JSEsxYHNI34LsohYgEn3MOvrm0kD2RWY9CGvsE8ZfXMHJxHQoiRqtrqHqNJjSq9ikB9bedKkXS4HhiLImE16XKSklL25OQc26upab0SlK2g50rfuuuvSo48+Sj/4wQ/il0jQ9siygGfdNG0EwddmyCwnNoUkshgqzcF1IHx4hb0egM2GMxSABHIuW47jh/txSqvQDBGoC2c+gJoKm9vyyy+vbBxj5+aFOpdt33xs+kJzB7sIV9UkaoxYqudY5MdWHhA7pEHDeGO7PJcDA8Bk0JwTRaDofjXzL2PeSXBnQduQPmwE++67Lx1yyCHqf3NROeGEE2KVT9CGyLKA59k0TYLgSyDNckIKkDdHqy4VAelCqApd0ocNFl7GIKa82UI9zWEwXHVKg56BAsBvsYG7CF+M3LwglqHlM6VMzWAP5osYHr2uPgmRSNvKw2QStpiQQGNsuBwY9DSEnD8XZgK4B+YIpMexYVO/xgzpYmtXCe4saBvS9+STT6o0OE888YT6MyGkT5A31lVWqV1ZmQ1sGw1IWlp4kpDE9Lh26NChHWz6YPsG9Su8jLGhsu0eh8FwGdqnbfJ6XfAMU33caAKVZlMVImVs5c3a1iehEmkbMHamT5+u/nDQgDQZ48TmwADo4VnYixy/GzNmjLpGP6DEgN6vNuli3n62tWsjwu4IBA0hfW+++WbuBwsEOmwbUyM3Zp8F3UYg0ghF0n1dUhqQOwY2V6hGTdUZJH1Q8eI9CKEZgzCNIGUhQ2USKJsDThYpY9FhURoNH3OFNIl0EunhUCsgfSbRYps+QCdImMt4FggfDih4dhFkiedWmnlGljFQpcNCq49hQUVIH4vvfYBNhxPQCwRl2+SUuSiaJC7P6d9XcmYL4wLCB7s/tKHpcevbtlnKXqa0w3TACZUylhUWpdGbc5q5QppE2nVPaHd4fQdxNFOm6c80CRIke1AJ8wGFyWNZTigxxkAVJHtlj2FBG5M+pH2C2vawww5TdkY2QO103XXX0cUXX0xTpkyJWU5BiyOWqtC1KDbD6TirtI3DYLB6jSV93IZVtmMrs63KdOJo5OZsjvUsEmnbfSDdW2655bzTFJqHobQ4fUU4oWBO6OWtqiOPL5q9/IImIn2PPfYYnXzyyfSrX/2K1l9/fRo2bJgy2MVEBdl78cUX1R8ydeBagSAEsdQnRcQXKxNp0oQk8sphJWBr5RsHMPQZMRDr/qajQWhg7iLJb97NOWsbuYhTqETa9z4h0A8oRRMaPMs272MeLhtxiHTZbEKlzYHZBYIopG+llVaiBx54gP7zn//QLbfcQk8//bSaULzRbLvttnTNNdfQyiuv7HtLgSC6+sQ3vlgjT8exN3SfNvRtW2zIyJTB5hwIUg0JT1bYvEZjE/DQwNw4rBaNPOQipI3M9o011pPuE4vwMFlhe8CsDlyhdUiK4ehK61f0GPaFeYADOGwO2g/xLwWCqI4cq6yyCp111lmhPxMISoFNqlUl9WaeDaOMWGRISffuu++q0C1wGsFnUOll2dRsdS2iDqH35HAieTbsNOKTR3LtWx9b+8Ya66776M+0BQnPEvdOj/EX04HLVQfbASjJLKSMMRwCLr+eOQdlYdIP9XkzmLMImsh7VyBoJlTJ8y7PhlE0eeWUdJDsQQKDeGp4j89jSYuKqEPIPWNs2CGSxSztljVOpE2SBeTNlav/lp/JZAPp9ODUEUqeOaOFGeMvJoEKmfdmW0IazKSvjDEcCh6DHKsTcxXlsIXoqbI5i6B8COkTNBwxT6VF2CLFhO+GEZqj1xdJKiz8cdw1vELSB+lLbGkRNlT8ZZEQ2WBrF1c9Y2zYRUt6fPs5TZKVd/O3zRl+JogGpMH4w/1D20BX5eox/mITKN95r7clJJgcCxP/cyQKPT5fWrzNoqVsPAbhqQ/CB2k859putCRSUG0I6RM0FLFPpbEM6ItKnO6zoYfm6PU15PZRYWGDg10uypa3/i77oyJScplOHbbn8EaclziXIenxIStpYylkLvgSFd0ukolRljbQnY5sc61s9aTelnj2qFGj6u2GyBXsHKVLTn1V7kWU3xyDCInD2XSqIIkUVBdC+gTREUKcYp9KYxjQ23KJxiZ+Sdkz0tpE3xBDDLld9zU/xwYHFW+s7CZ8H1fQ3NgbvE1VZ9rx5alblcwFksihb2qyUDs9fK4HCc/aBnrZzfHdCPWkPi/1duO2SCuXbmNnxsosoqz6GNQPfFUan4LqQUifICpCiVPsU2kMA/oi7YwYSRtIUpuYvwOhYYKjG3Lb4LpvWZIBV7iJ2HEVzecAebJ32CSoVTEX8J0LGBPwykbaPnaY4AOZaac3Y8YMlec5jWwV1QaNVk+m2TO6PJpNG7uscylE6qo/3/WdQBCF9CF8C8K2wJDXxOWXX571toImRyhxKuJU6lrwfPPPmrlEiyBBSRsIq75gIM85d12/A+njMqO8SWV1tXVIH6S1YVocwbQUYTHiKtrUylmyd8C7FCpMlBFSrSpKTHzCjODvvffeo9GjRysJLn+HOYr2wFjD6/z585WKkPskq5NLkRk1inheqD2jrVymjd3yyy+vVK5ZvJqLknKKR68gM+k77bTT6Pe//z1tuumm6tQoEOQhTmWcSkPzzxZl08dIk+a5EsbjOqjgUDa8srfmkksuqaQ4gKnCMxf70Dh+urreLBc/j8tmKzersQE8IylFmC55yiPpMesTGp+Py8COClB5V01y4htmBPVmr2zEYQQZwXtdBWmz00tSB7uIZqyMGq5Dj0/9i4K5PuhtYLOxCz0UFRkfUTx6BblI39VXX00PPvggbbnllll+LmhhlEmcQpAl/2yRm3ySdM2nrPr1eplBunjTZlJmc2rQSViaZI9/36tXL0UsmSwwSYDaEMBGbZIJ1G3ixIn1axDsWZea2dqhKHVzSHw+LgPq0mhjeNdm72ujiX7iwzn6EH0wadKkelvoXr9oF3ausBF4Dt7NamK9HWOpZZMOPY1WA/P4tJHNNGk52g5xMDFHXCYvrrGfl7Q1WmUuaHLSh40cqdgEAhvKIk4hqKJHm0u6lqZGAqkDacOrHkOPY5/ppAvQF3tIBLHhczYdk4QlZXjAdyAN+JzLB/MOqPH5Oficv2epHWzE+Bpcb0rNzHaIrfLPamCPtsLzOWZbI5DF9tP83JaFIs2r2hVjEaSF1cRmO4aqZTGGoQ3I6nXcqDntKl+atBxk2dV2IfaEuBekoCHq4yquf4ImIn1Q6z788MO06667xi+RQBCA0FATzeDRlqTe0tXnIGCQyDBYlW6G0dDjj4HwIRwF7Lmw4YKQMQnzyfCgOwAA8BZmQgfjf3wPYsXlxnWoB6uecb2pys5qI+iDLAb2+m/QpmWkbctq+xlio8kk3FTzu+wqbWp3XU3cu3fvDu3oO8fQvlOnTqXJkydbJV6+BKVRczoLgULbYQ5w22HuJtXLZU+ItsM4HjNmTFBkgWZa/wQVIX0XXXRR/f/BgwfTfvvtR4cccogyWDUH0AknnBC3lAKBBT4qDx97tqohSb3FhJDJAOoPSZ3+HccU498wgYQHJwgfXkFmsGnjc958XFkJzNRYehvi2SxRdNn06dfoRMOlQuR+hVSQ08GBLGax2cpiYK+3Q5pHdChCSWwawfC10UyTGOp2ojZpIBN4U01ss4tNm2NMHlEmXG9KvNIISiPnND8bcyLkMKC3Headre2SwG2C+QrCh/Ec6mzTLOufoCKkD3Z8OgYNGkRPPPGE+jMhpE9QBnzi2TVTOiI9REhSvXANyBIkbJAe6Go622bPxAobOhOInj17qo1LV6+5shIktR0+wz10iRFUqfAExabI9+dr9D5BGUwbQLZJg5QNz0cdURYbOfCBr4G97Tc+HtFFH1JiSWhcc0UP7J1G4ELLkpQVBaQRJNMlKXMRlEbOaTOeIZA2P7ISWdc9MH5BHk17zDSVuUAQTPrefPPN+v884GzAdwJB0eC0YVh88QoCoas6m8142dxQzNRPWWG2AWcXYCcbc7NxZSXgzSot5A3KDcIGCR1ImxnqxLRNgtRDtwHUSQG+R39BHQZ1sdm/PshCmvg37BGt/yaPOjrrISWGhMYmMeTnsc0lyIJpJ2prG5+yJDl84BVti74PJSiNnNP6szF3UG52iPIpRwwi6xrPaSpzgSCXTR8GlSvlU9J3AkEM6Isk4gCCIEBiBImWb5DjmIgR/8pFzmz3xMYB9RDUn1B7ssTGBptRP5AUQNuWlQDXwAsXBAEqJlvcOpbG4Rqbo4mtPLqNoE4KoEnAK98D0kOzf337Igtp4t+YhC8tI0PS90n2mABLOGFviWvyEhoXoef3LJnl9F14Vox5gucmOXy42rfqDgmmJBzImoYuD5G1jec0lblAUEhGDrabEQgaceI2bVzKMF6OpW6ykbOk0z5Il04KXaQvabNPC6AN9Sz+8Bl+A8mNrlJmla0OXAuHDpv6yVYeF3DdwIEDlURo7Nixqq9BJlllbEPRqr+0zTnte90eE21okliWvrLULYtkM60tXAQcYykt/Zov0hw+8qCRDgm2sRsjMHQMIqurzFn7YVPZp0ECOLc+glaV3/3ud9b/AUzul156iVZbbbV4pRMIcp64izZejqVuCt3MQurl2uxdAbRthCFmPXRpYlpwYVZ1s0TKpjIuS/WXFEONg1enbd7YlEG2bYb4LHUFCWcCmLX8vrEe9aDVeo7ZPGTGx+EjD/LO6Tz1M58dKz91XiLLKnO0N9S8MM0IPfg0mw20oATSd/vtt1v/BzBYMbmvvfbajEURNAPKOgmGpPMCyiiTDdicYU/I6tA8Eg2bE0ZRKaaSAmi7CIOpUvathwu+wYWXW2459VyXyrgs1Z9tczY3StPTOaSMTJZMG8csCGkLPWg1yu8TGLmq0jgfm0sXsWmklCvG4ZTtPzFHshx8mskGWlAS6YMkD9hhhx1o+PDhOR4raEaUdRL0TZkWcuIuYkFnz1iosqDCwoYZ895FtrXefraYYJCuwQNXD+HBKmUzBZWr/GnXuIiJzeM2SWWcdBiwpRHLA3PcZbHHchGiELKU1r6+9zLLD3vNGGnAipaw22Aj4L4HmmaScplpDW12o0DowSHmAVZQXWQyGnF57gpaD/pCzwumGZIj9F5pMc9inziLWtD1GHC4Xx51nAks6tOnTw/yDowN004QdcQ48Ak74tPeLmJik0SmSdD0e5a5iWfZZG2ESJ9nPgcY3/ZNupfuAc/lh5e0S4VdRVJkW5/YMxzjBgcyvbyu/nLFqGyE1C8JqJctrWFeKWuRB1hBC5C+++67T00OLBCC1oXt5JwWksPnXrgHpDcuxFbTFaW2MG0LsxpPm8A90LacxYIdNsoCp3rTX7m9fNoySZri61nrSzAbraqKocoMJVQxDgR61hHYga244or18Cm6nV8VVX+6DaWuisb6pMeh1PNE604qrmwl/FvYVDIBqlr4E9RbT33IGXV0p58sUtYiD7CCFiB9W2yxBd1///205557xi+RoDKw5XvEZpAUksP3Xnpg2DxqLh8UYetlRuYHSctiPG0Dky20USNyv6bZnaW1ZVJMuBACl4dolBnaI68qM6SesQ4EHB4G9nxTpkyp207a7PzMdHwxDzihSAvurUuIdUKot5Gtv3Syi9+l5chtFFAPPfUhZ9TJGyatkaFwBE1A+lZaaSU68MAD6YEHHqBVVllloQkhGTlaA7Z8j3iPhSY0PpW5aUDFi8XVFdGeJT0x7PBik0iTwGCjyGo8bQPb1GFTBinGBmyTqOqSs5hwtZdOdNO8c11hYmAagLHEoWBc/WtTPdrq6bIzS8pfXDW4SLKtXnwggFQOf2jjEBUe3xN/UOOB8IHcgDzxAc522INknklRrAOO69CXVO604N46ocsSlBt/RYWbiQFbWkN8hrJyNp+s95XcvO2BTKTvwQcfVDks4djBzh06hPS1BnjjhLoDNiQ4UWPDSQoc7LOogPAhrACrE8pIoRbTsNxm/xPzlOwT5NhsH8S0iwmbJ7Gp6jdJiUlUzDAxumkAyAZ7Gtq8KHVTAIw3W/y4NE/MvF6oMWEjcfpnpgNKUp5cW7Btn+eb94Q9GD5nNabpSKMf9nQ1Z4wDDvdPWhoz27hLC+6dZ84XHW4mBlAe3Zaa2xJkHOuFr8mN7b5VkWgKKkb69JRsgtaFnqYJJ0kE3k4LHOyzqOBeHNPMJTGskg2RCdvGG/uUnBbk2GwfxOgqAkxM8JpkJA8kkXQmsrBHAukD+eN7m3Z/kAbiez4Q4IBha9OkMVLU+MniBe6Ke2h+xuVjqait7FklMq72gErXZmeJZ0DCB8KnxxSMpQbE+Ek69CWV26x/zDBHzSjxYhtPzCu0FeZLVdZKQYtn5BC0FvQsDFBzrLPOOsqOJsZCyFJEl8SwKBuTGKFbXBtDzIU2bfMpwwbHFiRZN5LnjChsc5hGsvCeQ4LYgmrrzgW4Lx8yXHXLa3uYpz3MYNJJY8pGXABXe5llh2QcAZ2hpoba0ccrV1f72wJH472efs0mJYNKF1Iv/eARixSlHfqS+tFV/1jagWaSeOk2npyNI8tYl0wc7YPMpA8n9iuvvJLefvttNWBg23fUUUcpI1NB66GI8AVJi2sRJ+6YKuMyNoaQ9slryO3jJcpqfWzY3I7sxY2ypJEss8yATpZYwsXpHGFCAuLh6qOkMVLE+LGRNx8PY1vOXbZDs7WXaQrx6quv0rRp06h///40dOjQuoOBD1EHmFjBBACkG22La8ywHzbi5wqpY45LSO85okNS+XwPfWllsCHEa7wINII4mTae7HUdEp+yquF4BBUifS+++CJtt912agEbNmyYGiCXXnop/f73v1f2fuuuu278kgpKB2dhgHqXyXyRHnvmohmbWFVZZZwFevskkb6sKknTS1SXCOmqWmw6+N4nMLLZp65csByUOa28SVKf2BuwTerkSzRsOXdtoVHMekHCB8KH+/MrJK0u6OVBLE1dPc8qeazbkKbiOjwH/ehSCfrMQRC+kSNHphJTM06n7/z2lWza0uBVKaB8EeAxiT5Am+sHsjK85AVtQvpOOukkOuCAA+iPf/xjPT4QFpQTTzyRTj75ZHryySdjl1PQYE+xmCFJGrVotmNYgqztmuYlqqtqTbVb1mfGks7pamKYJeDg4iN9ylI+3/A0tpy7uNYMjWLWGZIzECkmVGmxUU0veQD3h3QP7YF+ZU9PvKK/dCeOLNAJqYuY4lnjxo2r24HqcTrzEPS0NHhJ9pEx0SjixAcKlspPmDChPs58y9GO62I7I7Ok7+677+4QEBL///rXv1YDUNA6YIlbzJAkWRbNIm3xmgFZ6591M0rzEk1qyzwbYAzpLm+A+IOXMNoODgs279/QNjWlq7YQNjaiESIlNJ8H0xls4vCiTyOvNrU/CCeIEcgAyBjII4diwXuXo4wv0ogpngVVMse+4zidMQ57aW1YFqFpJHHiAwXGHUg1xgmbD/iUo5nXRUFJpA/qASymWDR04DN8J2gtlLGgJT2j2WzxYiNP/UNiwIVuBK62LCOHp6sOrJbGIYVJjh6DzlcayDHkXGpzM6PFoEGD6uVwOR/4SAl9Um6FqLxZYgu1Mu6H9hgwYEA9ZiLe5+0ftNtaa62lnoG2NIkpq7U59h3bNcaQkKW1YSihyXq4aiRx4jbAwYBD63AYG1/7vmZcFwUlkr4f/ehHtM8++9BFF11E6623nvrs+eefV/H58J2gtVDGglaU5KgV4AqWm0VlCvgSyCwbQRk5PJNIMJMcqDOh3sT3NvWlnpFi8uTJapOE4whLopjQgZzo9qx6kGD+/aRJk9T3LE30dYBIm1d4BuxpOfuCzfYujaTgc4wXts1EaB+2/YJEiHMb++T7TQqkjTZwjSk99h3GBIgrro1xoExrwxAS53O4Srpfo4gTq3jxbO5fm5kFHHlADH2dbQStiUyk74ILLqCf/OQnypmDT8IYeLDzu/DCC2OXUVAgfBfFohe0pHIULWmseriCtGC5adD7rmgbp5g5PJOyUviEO4FES8+5qgPXgYCA8KFNxo4dqwgQJHb6/fE7vMcmadqOQWL13nvv1dWn+IyD5saYLyZhA/kMlYDrdrkAlwnjx1cFmPactEOZTeXs+i7L/Ety5gmRkPuYmFTVy5Ulx65+gYQVEnAcHHy8wJtlbRSUqN7929/+Rr/73e9UyBYMBqRmwyIraB6U6dmWtHCklaNISWNRbWDWOc/imRQsN0vGgRgE2lUf1/1D65/UL0l18B0r+BzqUhA2ED54lIJccRmZZIMIuuzvIDmB+hW/8+1TM4Ze2ri3pdwKlYCbGRyAkPkUw27OtIV0fRcToRqCtHo0m8ZBrw8k3zC/wtrh4wVedZIraFBwZpA8IXrNizIWMVc2gtByVGVj8IFNrQKvOpCDEAmdDlewXN/y6IQrL4FO2gxs98+yeST1i01lrdst+Y4VSDqg0gXh04ME8/2hKmNC5yIEUOkmhTRKInkI1+Iz7k3CFoPAc518iHgWu7kqSIhC2ydtbjSbl6teHz7U+HqBNyPJFRRM+h544AF6+umnlbjYxOWXX571toISUcYiZls4dK/vssrhQhHPNm3w8IpwFaxOybp4ZiFsTLgwT0EaBw8enNuex0edpxMK381DJwo+RCNPeBj9PuZ41O+fRGaBpJBGZtlMkpc3Z3MeAh/Sbj7PMSV5jZYQuTyr05B0YGiks0ZW6PWBSjckgLbv2lgFgi8omPSddtppKhDzpptuWjfQbVbop/B2QxmLmMt7tMxypC1K2Hzxl5ZT2Hdx0+vMKcvYcxF2ZHnGW6jEE+UF4YMHKKRh+B/tDOeArBuyj9epaf+WtnnYiILPmMgrjWDHD/01adO3SeJcIY3MspkkL0bO5qwS8NB2C3lOo/MeF0k6G+WskQY9CLcLWIPSVLqh63IVCL6gBNJ39dVXq8wbW265JTUrzj77bOV9jMG64oor0iWXXELbbLMNtRuKXsRcqp+yypG0KPmonvXQHfDUZDWgr2QEUiR4s+Iz2MLCjqzMRRFtDgkf6gDSCVUkyoRsDVk35LTNwLbpp20eZso3LpdZNnPjzyuNSPq9z0aadA/zcxfJY4llSOqsvChSul7EvUPIRSupJdO8pjkTCdYYSJsxhnzC+vgibV1upbZuF2QifTB+Xn/99alZgZRx559/Pt17772qHn/4wx9ol112oTfffFOpvwRxEYvQZVEjJC1KPt56HCcNhAnPhEG07dqkOpcRJyypHBjTCC/Cnnv9+vWrS/qybshJfWrb9JOut6V885UG5pVG8O/NdGgcesZnIzVJvs1+Uv/cRmSzSEuyOMek2XZWNQh6CLloNts7V7v7HlhZm4CUiHjvSqlXBJqxrdsdmUgf1LoPP/ww7brrrtSMgITv8MMPpy222EK9P/3005X0EraIIIOC6iHrxojNFhI2W6BgH289jpOG30M1i8U0i9G8zyJclKoERtzIh832PLhnkTY4oZt+Wso3/TpbpguWDuYhDGY6NP6N70bKUkdb/7k+Dylf3rHiur6okCSxJfch5KIRtncxU8lxu/seWHltwlgtm3g1o51juyMT6YPkYL/99qNDDjlEeb+ZHY0gzVUFNnGEvdhss806fL755pvTc88917BytQqKMupNWgCTsjNAWoNTMIgfvDFDvfX0OGnLLLNMffMvYnErUlVi2vOkOVK4JGa+fRuy6dvUoD7XgdD7ZKzIEoqDf4MQF74bqav/ikgVFjpWfK6vsqouaa7axmWZtndFpJJj22fOn5x2YOV1KW9KvVaycxREJH3Dhw9XQUyfeOIJ9WeiyqQPJ3oAai4deI+sIi7gFIU/BlIYAVD74a+doduW6JH5XdkY0F74TUi7QVqFBZAXQrzn+4DY2Z4JCRDUmugrvEKaBBJnlgvlxn3YdkvfRHAtk0Ume/q1MeGqow+ytKkZTsTVjnxt0vd5gbA2aGfe7Fztq1+H/sUhDv0KwCaQHXJC7m9rd/yPcFS4nnPVpvW5q/98+tW3/mnPynN9nvEXgixjNWmuFjkufcDrDEvd0I8hJMhsdxzQcJhBnfB+hRVWqNt96uOCx4xu0wdbXXGmyIYFbbKPZyJ9sH1rtQ5mmy0Xzj33XDrzzDM7TFQ4smCy62Sw3YBFaOrUqSrtFuzdsPBhEYSEBAuVTWqBtuZcoCHhQ+CQgHtikUO7AyAtWOzQB+YzObguXmGHqudgTTL45/qwdJDHBXK5Fg1bHX0Q2qZmPeGF72rHtHZuFFAHlANtBkA1HBIoOa3d0aYYz3hva1MeV/gNP9PVf1n7NbTMea8vopyx5r8NWcelre+ygtcZ3Aev5hj0eZbe7oilp9cJ4zqpTtwGWJ/wmna9wA4W5LQ6cgVnbkZwMGlseDrwHqJxF0455RQ66aSTOgyQAw88UG2WIW7wrQYQPKSy4lMopCIgEjh164TJRrDRdnkXffZ65JO++UwEzcX3uuetq1xmfbBQc4iVqsehCm1T1BNSWdQRdcPvWM1ka6O0dm4U0L/sfBVb7Z7UplWQMDVTvDRdG8A2mOi7oue/6zcx+w73w8HJNgazPCu0TrgeBBGED6rhqszNZkN3RxD0VkPbkT5MolVXXZUeffRR2mOPPeqL+4gRI+iII45w/o6NzHVJH4BFq52TV6NN9DyeWNRAJNI2HnwXq+30VFW4n/lcOC/AHs9nQzTrg0VcX7SrrDpxtalJBlhFhH6CVAISWtQbdYNnr8vZA+3MnrVVagOf7AKx2xSbMg4RkMTgtUyPSRtMcuEap40ghlw2SA4x5rB2sr0s2jVvmULGJZ4F6S3nh3b1XUhMwKR2Z/LGWgLfcRI61/BczGEQPt6bBGHo3Cb7eNuRPuAXv/iFInjw3t1www1VoGksBMcccwy1G/IuuDYD65ibn2/5dO9LG/H0LZcZZoPVe9ggGmXcHtMzEG2DTQr2b5DIQpLJ4US4Ddl43IxJKAbb8UJVxCZfPk4YjQqky2UD8YEnvD7msNHmLVOodzyTTxx2fIKLh8YE1DUDqCeehXqDlNmyv+Spk3591Q5jgmqiLUnfAQccoNSzsNHDRrf66qvTI4880pJ5hNOCe8bYBIoiA77lM8MX4D02mDybCIfZSNsgms0zENIVvIcEQs/nC/B1MAbndmxERoVmQJ5QFUWQLx8S2ijvXC4b6g3iw5I+JkdllYmfhQMc5jMiT2D8m22fJyYgSJ2Zeg+f9enTp04AxdNV0Ei0JekDjjrqKPXXykjbXKocogFlZ69blA+LJRZQm92FvvDCtgYqy7ySOd8Nomi4+siXQJmbElSh/B7qI719+HNT0ld2RoVmQdbDThHzzoeENiqQrik9x4GDnR3KLJP5LNd8Di0TVKqwT8T9sE6Zqfd0cxFxsGitw18zom1JXzugiPhgZUBXw6AOIF5sqGyLw2ZuKrqNDW8yoQuM7wZRNGx9ZCNQIZkXXORA/xwoU/3IxusxHTKqtrnozgxFzLs0EtrIQLp62fT+LbNMvs/yvQ79acaJxO98Uu81I2LMp1Y8/DUbhPS1MNJIXYwFN0sqKM5l6yNlw30hvcMJGpInDrmStKm4CGDIAtPIDTKtHGwcrhMothVyGZbrbeYiB+bnRWZU0Mk44AqyHDK+zLiDVdpcbLaVrrFVJFmtol1mmXbAIba9addxxh6sS3gm/of03EzrV8U2Dx1vschalbVL7QIhfS0MH+KSZ0EKXQh0CR42fag9bJ6+OkHIomrkOtnIUahxdNL1ZUmSzHKg7RAmCM/XpX9VXlRRBxAd2BRCXa4TU2yQnO4OQI5g9lz0HV/mWMQ9s7YD92tML0jffmk2SUgVpalltR/qDLXulClT1FqDKBB8+DLT+jWqbdL6J4vddNp8SnpmVbVL7QQhfS2OIk+ZeVNBQbIDuz1zsTHJKhYRPZSIL0zyyLEEY6kOy9pcbJkzYBOF9kBUfj0MS1UXVV0CCcIKz2G2uwRB09PdYSMNNfLHtThMwIEHz8I9s7SD3q8YM2hjbOiwJc3Tv779kjUPbxnEyxb6p2oEtaxDD7cF7o9DCp6HdsEhFqjCwcunf3zby3f8pj2zKhqUdoaQvjZG3s0ilGDoXnx4HjZ+zmZiLjZMVl3qSp+y8wIDAgB7wFGjRkXbnMrcXFg6CjU3SB7+RzBxSBZQf6RErPqiaksQz8btaDeOtaiT8qTxZfa/GRoDn2dpB72caF/8D0kOpNJ5xo1vv4TOqbKIl+05VZQql3HoMQ8GOLCYmoi0MmQ1W8jjuW/rH9/28h2/Ps+surq71SGkrwVRlo1G0kLAKawgKeGgl3w9vN2wOeP7NLWtuYiwrQxInBlHzlVG/OHamJtTWZsLJKGQJIDkgXwAkJShfdGukI6xc0DZi2rIZmS2ly2WIspsjknb+LKNXU5zZYbG8I3fxvXQy4l2RkosAM/OO258+iWUtMcmXq4+dcWjq5pUuYxDj9kWQ4YMWSiQsukUpTuThay9Wddp1mqweYyrf0wnuKT57DN+Qw5qgsZASF+LoQgbjSTYFgIQkpEjR6oNE2nZhg4d2oH44Xq8x8afFHUedcG92H4NCxjIHkgQS3R8yp5lc0pboIreXHQJHzYMxM6DjRmko9hkmPBBAqXb9JWF0M3I1l5mn7nGpM91uGdSaAxXf9rqweVEG0MFjd9hDPoG1s2LENIek3gl9antOVWVKhd96DHbwuZtrmsq8khIs6r7dUkk1oskj3h8HmI/mwTXmKiiKUC7Qkhfi8FUo0H1YAs3UuQpHc8G4dNfbfmJkxZnMxwCCCKkfO+++25dJYzf+pQ9dHPyXaBCN5eQk67uwQxyu9xyy6n/QWzwHipdW2o19o4uehPOshmltZfvmAwlIEn9mUQ0ceDAoQXkGmQbZgZZN6uipByxiJceF9MW49L1nGZV1eXpD7MtANec85GQJoWVyrJOm8/EwbpMiXHIgU6HSALLgZC+FoNuNwcV15gxY6z2SEWe0qHSxWbJkr4suVGxSLBED4B6FsTPFljYp+whm1MRtkqhJ11fdaj5DBATH7V33rr4qI5CF3FfVVMoAUnqT/we9UDb4tWsBySs7HSCe2QZC0VLOfISL9/0ZK7nxN6si978Y/aHy+bY94CSFlYqyzqdhSgWrapPu79IAsuDkL4mhG1R1D/DIgEJHwhf0maVZ7NIWpihFoNK17TpCwHuCy9OJn2oT1mBTouwSwklkj7qUBOcDUD3imXVTay28lUdZV3EfVVNoSpQlBUEzkbs+H42cCDlPNlJqujw4Jt9Jk/IjyxzpYzNP29/6GVMywCUdkCB1gJ5sDkweYx1OgtRLFpVn3b/qs+RVoKQviaDuenyJDJPi5xTlT+LeXJj1Stiq4GM2bJkgOjZVLq+wP3YoxPQiUXRi0ERdilZTtKhi71OUNj+MbbUz1d1lGcRL2oDsNk94lloI47piNBAujkEXtNsT8uSohQlATPLpxO+rCE/ss6VIjf/WBlRTBMaED+bLaneXzapPMYcnLM4VBGPsRjIcqAvWlWfdP8qOgW1KoT0NRl4wWH7GxAvbPB63DNeKIs6uWGxgq0dpHBYsPAMW07cWIsE6ly2vUdWu5SYdoWh9dUJCn6PEDWxN888dnexn+ELJnb6q67edZlDxNoMY0hRipSAucqXJ+RH1rlS1OYfkhEltIw204s0CSi+g4QP6yfaBRJWPuCWYZNbNRQtaRT8H4T0NRl4wYGqCqoBLBAgfHrcM14ofTerqhrQmrZGnMWjUZ5feTekkP7Iu8EXtXn6Ls55FvHYG0BSW/CzbOYQMb118xLHJIlajLlrK5/PGHL1VdbxV9TmH9tRIasnuv4dq3RB+DD+YnnQFhmMu8h9olmdgpoNQvqaDHqsu0mTJinCByJkO236wIdgmJMdExP5UeFoAbu7ojN+IMsCpIqIwYbnN8reo6zTaNYNynTkyCPNiLE451nEY9qbpvUb3tvMIcoOg5MEG4kqw0HEbDfbxm/rq7ykP/b8jn0ISitj0vP073SHtCrZtdkko1nzmAuqBSF9TQoE62UJHyYkbOiyLBBpC41rY4G9XR47Jx/oqjeoj9kWp5H2HmWcRrNuUKYjRzsaQ7vGa1q/uQhOVWArX97c0r7P5Xtmic1YlfFXtvow6Xl5paNlaGbMfQGSyCIlzYLyIKSvCaF722GicfYBho/HnS0DgW2hcZHCIhd00xOZVW1ZJJmxy1OWPWGWDSrU07RVFmy9HnntLqtCUnzKV7bxe5UkUY2WHvs+j8ekjfhlkY6WFdrEHFuIwlC2pFlQDIT0NSHSQookTcSkDAS2habsjcVWPl4cG7HBNGphy+p95+tpmqVeVSSJNjVU1vFaxfpVSXrVrh6WeVKhIcoBm8HYohyEzvuyiLdtbDVC0iyIDyF9TYikxT5tUfBNdeXzrHaQJlStPGlI2jTySMRs5KpRklcdtnpkGa+2jb0ZCGKZ0sl29bD0MYGxjQU9ygH+YkQ5KJN4m2OL33O4mbyhbwSNgZC+JoVrsU9bFLIsGmVuLEWGbMiySbeKdCOvRMyMTYb3cLBptFrH1j9ZxqttY3d57yI927hx4+pOVO2k1qq6CrzRmhX9MFQUEHQdf77ZiGIiZugbQWMgpK/F4OOpWOXTehHly6OirXp7lSUR0ze+tCwEZSK0f1zk3+Uda/s9pDejR4+uZ5qpuvRXUMwYM/MVc6xHHAaYECHKAQexLyKVY7NIPQXVgZC+FoSPp2KVN6nY5curoq16e8WQiKUt1kn5Qlmq0agF37d/ksi/r/cu6gfCC8IHiR8IsE2qI5tfa8EcY7Z8xbbDUMwoB1UwNcljTy6oBoT0tTnaYXOqkor2+RGP0Gf3nUeffWtF2vOMSyshEfNdrHWbHlPF1AwLPsY5NmmopbkO7GHpE9YFwLVQ6QIIl7TMMstY82DHzEdbdTS6TnPnzqF/nboPdV0wlzY67W+0eN8lGpKvGB6u5mEo5oGxCutYHnvyRo8TwTcQ0tckiDFhbBHWq75Rx6h3lVS0Hzx8PS1Bc2nxz96miWPH0tKDB5f2bNcGFCJBcKmYTEJVRZUnh/2BUT3Khryn8Kx05Y+2IUniyfPH1p4YdzB+j5EPuUqbZxXWkCfvuZ2+Nf9j9f8TN11Fu/30F4U/05WvOHSdCenLqqxjWezJqzBOBN9ASF8TIMaEsd2jCuqCJMRcKKqgon3r5Zdosa+mUedajTp3qtGL99xAS590RsPbGH/I3+wjQXCNGZ1QcSDtRsPcUDmVH2d2YWeM0PzRPJZcISvMzQ/PxDiGbRe3j359WXOiCLLY6DXk66/n0aevDKf+rIof/yJ98fnn1LtPn0Kf6yJgIetMlr6MtY7FHgt8P5djR6PHieD/8I0lsqDS0CcMXvE+xj14c+LAm1XzTI1R7yrhP8Nvpq70tfq/S20BLTL5VSXtaxR40xk1apR6P2TIkNSNxzVmdELFBLCR4Lq988476hXvWTUL2yuQ3LybnastmBCgPaFC5nGMXNkANrus8y3rnLC1Rww0eg156p/30aJzPiH0JP66LZhNT911SynPZgLmGkcc2sTV1o1a32KMBb1u+v0g+U6K91rVvaad0PjjuKAUWw6XIT9OZlBvYTLGUhvHQhVsWGJK+Rb9eDQtQl/T/E6dqHONaJEFcxoq7TNP3zA4z6piYkKF9zbVTtnqSJdkQVfNojx58kenqds++ugjVQYQTJak6rlWk9oixMs4T3vkRSNVjpDyTX/uXupTm0OgLTXqRN1qc+mLtx6lLz7fp3BpX14pXhnrm20c5R0LZt1wsEm7X1VU0wIhfU2BGBPG5Z2YN4l2LNWzK1VRqywUkPL1XvCNpKdTjajWiahbbQ7N+19pX5m2fXk3HZuKKSmsRSNseVx108tuelZmTbXlYycJqZ+vF2eol3Ge9oiBRplOfCPlm0GL0DxF+ABI0rt9PVNJ+75/8FHUKPgSq7wx93jMdunSxXsc5R0LZt1Qfp/7VcHERiCkr2kQY8KY94hx+o99ajRJQSssFNM++pC6fzyGOtMCmk+d1caETWoBdaYuC76m1x8fTksP/n+llys2qbb1VaNseXzqZoasiRkDzdxYQzZ1qM2mT5+uXqEmx6aq2xtmmROtdIBiTHvtCepV+5q+pq7Ujeaqz+bRItS5Np9mvfsMETWO9KURqxjjTQ8Szs4kPnMPz4Z0e8kll1TvQw86trFd5NiqkuNSK0DUu22MotTGIchCCpptEVh8iSXo6++tQ599Mom6fPkJ9Zk3XW1OM/ssS9S1O62x3qYNK1vRpDrL+IjVvyF1C8nIoZcTxAwwSV1WkoV7wsMXDh8I/IuNHO99vYuT0AoHKB3/tfZW9OGL84jmz6PFZ76nPvukx/eIFulB315lo4aWLS1Ekh7UOethWQ8Sjvfm721zD0Rx5MiRNG3aNOrXr5/6w1gLkcK76pZlbKXNdfH6jQ8hfS2E0M2yKLVxkaSgGReBrl0Xod1POF39/+jtN9Os526hOT2WpD3PvJxaHaHjo0rq4CQDd3w3ceJEtfECyLxgErMkkuWaq/gMIV14HuAV7/F5KxG2GNh0p12JdtqVPv/sM3r87IPUZ+scfAoNGjKEqgBb/9uCOmc9LIOsAfPnz1fOSeYhxTb3QDZB+HC4mTp1av33ocQzxgHCZ66L1298COlrEWTdLItQGxdJCpohHpxgYVKTR+LWKHUwkz6W6JnG8ByCBYBDCGz28mYGYfKJa0AIOKl9GU5MzSZBb0bYgjpzjL8QYFyArLGqFmMPhM6EOffwTFyPsdu/f/8Okr6yHeV85norOfNVBUL6WgQxbOsateCHkIIqxoMTxJPWFbXI+4xvl2QGzk5mQGXcB17viPEHYCON4Umrk08e62XMyWaUoDcjXEGdQ9dfPUQSCCTCLmE9xDh0Ac+YNGmSGmuDBw9Wf1ANx1j3s+wfPnO9FW1RGw3ZMZsISRMrz2bZiAWfpSdYqEKeZYsHJ5K+4vooy4aQ5wBSxCKfZ3xjfLFkxqwL/udUbiGOGmlzVSefRY5tvX9FjVYOXBLl0PHJIZIg7cPBAzEg0YcuT15AjxcJsoff4tqi1LQh+bxDD2OC7BDS1yTw8XLNuln6LvixpIG4D+xJJk+erBauUEmQKx6coDWkdWmLfOg4zENoWL3Kkj629ctzSKqC9MKsA+J1pnmbiuo3DmJEUeAxhEMHHH3we449WbYU3ZV2MCSft6A8COlrEvgsDFknkM9iEFMaiLogzADuiXs0WhIkqL60jpFVKmKmRTNt9GzkhusCQqTH2HOlXwtBlrkak3jZ+jfJ21RUv8Uhaf1N6nO8h6YEDkQgf5D4JY2LoualrfwiOa4uhPQ1CYoOrpq2GMScxHgGvM2gOtPr4rupyekwP/bff396+eWX1f+rrbYa3X777aVK67Iij1SE7eRcAclNcjNw4EBrXRoRhiYv8TKfb6uDq8/EecoPWM+uvPJKuuuuuxQBW3fddemUU06hAQMGZFp/ffs8xLygiHlpK784YFQXQvqaBEVLuNIWg5gbHf5HYNDevXvXbfpEmlAuzj33XOXtd/nll9NTTz3VFBJVzvPJKc2yZBHRpXS4F5wx2JjeJDcYo1UJQ6OTXbPcaXA9X68D4JJ+ivOUH377298qsnfaaacpW7nf/e53tPXWW9Nbb72lbOhC199mkpaZ5fedI2I2UD6SR6KgUgg1GI8BPQAtJvFKK63ktWmlJfU269Ko5OPNAtg/HnzwwUp6cNBBB9HNN99MW2yxReJvrrjiCnXN+uuvryQOekgHqITQlwjZUKXxljae4KXIac2ykCc+vCB0BSQzY8aMqY9Pndyww1CMtokxtpPKnfX5+kadNFdtzlOtAAQqPu+882ijjTaiHXfcke655x7acMMN6YUXXnD+BtLxPfbYg9Zee23ae++96c0336x/d+qpp9L5559Pm2++OW2yySZ04YUXqjZF1ows4D5HXvRmtF9OmyNpe4SgGAjpEzhhTkqgqI0OCxokOPqr4Btgk91yyy1Vf/z5z3+mbbfdln7605+qjd+F4cOHq03opJNOossuu0xJhn7/+983bZPq4wlOFRiDuhoMBxMzrp5tE2EJBOKjQRoDqR6ke7i/D7lJuneWzdv3fnq5UTZ4E/sSyDTykDZX2XkKJhl4bZW5ecYZZ9DVV19Np59+ujoUgbCB8Nni3QHoo+9///vqsHTttdfSPvvsQyeffHL9e/OQgDkI1S6bCWTtcxxwYLOXZexVGagL4gWivc1x12p1rRJEvdskaIQYnCclXPxD1QtZbTqqIFWqGh588EFFTLBBof0huXvvvffoqquucv4GwYLhgIBNCm0/bNiwppbQuMaTzQvVZbPHwHsQIISs0OM94vMkz/A8AdDz2Gzp94FKF2Xk3/jMq6TnY01hD2W879mzp5KAsZNV0u+bHZdccgndcMMNao4A119/vSLVaVkwfvjDH9Jaa62l/nbZZZcO10CV+6Mf/UjNV7TVAw88oKSzecbQRx99VM/ggb4KjXhQJehjjtMNAuwoxdeI41BxENLXBGhUHD3XpPRB6EbB6adANCXtVEeMHTuWVlxxxQ6Ee4011qj/DwkeJA8gMVDbPvroo7TnnnvS888/rzYmOGpstdVWSi3ciGDWrgNLyEHGNZ5MuydINH3soFzxHvVnmFKGvB7NMWy2shIw8/k2pxWUB5+BNJtp5VrNeQqHItQdc4OBYMUg1QC0G7vttlv9u5tuukmpdG+88UY66qijVJttvPHGak7pRBH3uPvuuxVJu+666xQpfOWVV1T2iyx9ztdDIo21GOM1KeKBLXtMVaCPOUiNYf+IAxfqgfrY5nSo/aogHUL6mgCxDHptm2xaDlDbpPRFyEaRNWxBOwB2d5wnk6G/P/TQQ2nnnXdW/cU2emirP/3pT+r/d999l84880y68847ldSwTCQFbjU/B9KCuaalaWIVZlr4Ifzhe1Oqpz/DJH0hoV98kPV+eQgYzyW8mmQZpIIzjEDC75tWrhnBJiSYRyC4AMJIcXsMGjRIkTfGMssso14hxWNJHr4HEYRdHwgyAKke1L8A7APRrw8//DDtt99+QRoQmxSWJdJJ49qWPcZ230aspSaZA/Hj+Wfzjueywo4X49PMbS3IBiF9TYAY7u96om94zWKhwwRKywHKzyx68U9SQSGxPTahdp348ACEdAHOG/vuuy9Nnz5dqaYYkCKgbSC94DRM//jHP9T/+C2khNiALrjggsocWMzPYbMENVYM1WmSJEwnm9j4YS8VEu7CFfoFauXQlGm+oWRiway77gUNsozxwpJ9jKdWsd2zAfacO+20k/Jiv/XWW9UYwMGIiT4IFpM3xpQpU+ivf/0rHX/88UrFusMOO6g+R5tiXYL37pFHHlmX6uGQBRK96qqrBklqbSYLPmn5OHsM5hT+x5xCPVz3zTvG0ghkWpgg15zhNsJ4fOmll1Qb4g9toNdHkA1C+poAMWxqOBwFNlYsXpiQmHQ+OUDLOhXaJBiQfIwfP74uhWjHiY/F+29/+xsdfvjhynAcalx4Gz7++OPO30ACAWcPqHnZTgsOHYzf/OY39Pe//12RaUg4sMGhbZPuGfPAYn4OxFKdJknCTLIJaVbI2HaFfsHmhXYM3Uxt9ysqPIdZdxBePeg0iAueC1TFa7tI4OAEqR2k45DQQVqOueIiuyDC6GM4Z4D04fB1yCGHqAMVADXveuutp8YC1LE4XMPsYs011wyS1CZpdpJ+B1IIIs+p1mCeox+SY+ZnB5IIpE+YIFOqrhNEJomm9qkdxmXRENLXJMhrU4MJhEUIhA8xo7B4Ab45QAWNxe6770677rqrWsix6WDD4vAlNmADgvcgk2Vs7nqsMEgkEHpCRxFBhl2HB1ucuKKCj2eVmrN9FDZQkAK9vvp9QBTQxrb8vEWUKyts0ntTwtJOByocemH3ChUv1kaQIdjhYa7YgPY655xz6KyzzlJSWcRxhISUsddee6k/hFdCO4IkZplLeZzgcEDE4R5jlm2jOUMGq4ZD72sjcXhOEoF0EUzbnuIiiLgOGikcTHFohW1zMzuxVAVC+toEmCSYQJhgIHyYPKbhehUnEk98Vl22KwmFITmcMWBwP2HCBGWvd8QRR6T+Dobf+DOBRRt/IcjjvWrrN/PzMsairwSb64qwONgsYeNlOjaYqllstiCAWZxlypCst6oXbla8/vrrNG3aNGUCAenccccdR6ussgott9xyqaphtvGzIS0DR9pcytNPmE/szMR2ojZVcVpAbhM2c4wkG+yQIOpJBBFzDvWBXTK+T3JiEfhBSF+FEdvoFpIeLGh86uN7V3kC8cTX1VDtCGwkCNUC1S42qgMPPJBOPPHEUstQdIaAsiTLPs9BXXHQ4JhtNscG/T5snwSJHwhgFmlEGfUX6f3/AevKz3/+c+VkAXUspOOwha3CXHL1k8+ewAc603YWv8XBmT3TQ2xIbVLitDBEvjazaZJNzD1oLHBvnbAKskFIX5uFaWFbiWaKgyQbFanMGpDwTZo0SRmKN4Kol6GCrMrBCddjgwTxwwEpzbGBbfo4aLJII6oPjGGYQKC/ME6g/ajSXDLHbtqeYPPeNT1h4RiB3+CQwuMVSBuvLuljWhgiH5tZ271NL3NIJDHHcOgV5IOQvgoCAx0SBqiL8tgJxZbYtHvolFhYUKvR6CmfKzuVtJycJszo/mWi2dSDeQ5OXFfMC5AB06avmQixHv4j1MO4qebU1K9o1QULgn/L/VYmTPMAWygtc+ymrdvsvWvuGfgtDi2wAeaQPEwMzZBFaWVO2yfy2CPqTh26tBChXWAnibUS5WeHDkE2COmrGPTQKpjE2GxibSJm7KeYp0yBP557bybd8MIEGnzbvbT33v8XALYZUGWpqzlmkzZJnwMM1xVG+VmkFXnLX8R60uwZHZLn1AzqMewJ+unKK1MzIEnrYhu7aYSK13UmdHrsSQQ3BvFj0of/8azYh4BYkSb0uq+wwgrKUxpSd0j6GhFgvpUgrVcx8IDHaQ2ED3YmMaKRu2I/pcUy8z1l5kW7SBG/nj+f7n/zU/X/BRf8hXbffadKSYXKRNY+dwUZN8esa5P0UZPh/jDY5/dZgiaH1K+oQ1VoRodmxLx5X9fn1LXX30ZH/7/Dm2ZOudZV29hNI1SstrXZP7tC8hQxBnzumzQ3zLpDwoe9EFI/PXuOIBuE9FUM5oCPlX7GdXJ0ZeMIPWXmQTtJEZ9+8U36ZPY3dimTJn1Id975QNNJ+xrZ567fuTZPn9RtpgRQVy0hJEdaloMY9SvqUKXbdKVldGhW3Hvvg/U59dHU6U01p1zralKoozTbO9f3SSF5yjx0p80NrjtHF0CZknJiC8IgpK9iKMpuylxcTFf+pGwcPqfMPAtJ0VLEqgD1vHv40/X3aJN2lfZl7fMQyYhrE0w6wJjejpAy6HZS2IhYLRdzTBd1qDJtx1jF2ypSddTj0sv+2lRzylwXXeuqbewWaQJQ9KHblQLQNjdwDZzWoI4G4dOzkrTK2G0UhPRVEEWI3c3FJWlTynrKzLqQpG14raL6veOO+2nGJ9+kufq/ha09pX1ZSU6oZMSGpGv1+0PSB8kI20nhPYJj+0j9QutXpJOMPm+bzXPfZ05NmfJR08yppEDEvr/leJAgQj72po3M7e6TAtC23o8bN45Gjx5dd3TjedFKY7dRENJXcWQhPC6bp6Q8iObEy0s8QxaSpA2vVVS/aI8LLri87hTTTJKJIpCV5IRKRrIEjOb7w6YPGyzbSaHf4AGZd0z7lqmIw04rSdWbcU7Zghz7SI75txiPOHggugPWRcRdha1eHiTZvsaySU1LAWjWE+FkQPgQ4QDZUtIEFQJ/COmrMEIJDxucm9IIwCcPYkzY1MlsDB+yCbfKRL/80qtp8uQPF/q86pKJIpH1YFG02ovvjw1Hf49nhErvso7Vog47VQ4tE4q/33Bb080pU5LsKznm3yJ8CTxYORA4Bw3P49Fq2wti2aS6IkYkBWxmGz4A9UVGJh9BhcAPQvoqjBDCw5MUru1YRODtxCdJLBC2mH/6ZuabjscXpsQE4npO/2bGmgvx5Gq2if7xtGn02I1X0hWX3pF43bm/vaiSkolmQFnS4KIPSmUcdsqsQ1H4+ut59Njdd9Afzr0g8brfnPUH2nnn7ahnz8WoKtDbP0RyzL9FOkAA4x3vOWi4LunMWi6f3Lku2NbpkIgRWfJ1N+PYrQKE9FUYIYSHRf8gfQjPgOtxApwyZYqKbwSphS3mX5EbJp/OTPsMJCr3fb7PRK+izd+8uXPp8btupZkv308vjp5W9y50Ydr0T+iUIw6jE8/+DQ1c2p3Xs0pIs+GJFRA4rX/LlAb7SO9ijMciDztF2AyXhddfeJ5G3X8tvfzGaPp09teJ13786Rf060P3pr1/ciIN22yL4EDoRSGr5FhPpQkCBfDhPS/pK8ImFYKErPMyyfyiWcduVSCkr8IIOdmw6B8kD0a+iMeFU+Arr7xSj881bNgw9V2SrUXsDRP3hxqC7TNQRtNbEmQVRJVzK5rPT5roVbT5e+nfj9P4B/9G3b6agVQBdN+bn3n9bvgzb9FGfzqBuq2yNW259yHUs1cvqgpCgnXHDAjs079ZCFJRBwWM8fHjx9e9Drm8oc8TqUZHTH7/fXrmliuo20dvUNcFtXpcvjQMHzmJNr73Ihr/zD9pnT2PokErrEitYNfqCr1SZtnMMW2u0z4OeiCGQFp+XkE8COmrGNImUproH6+84ZiTrBHqU90+Awa5sM/QwZIgEFOOIxaCKtn8TRw7ll74xxXUbcYoQglq1Jmem/Ud+nT2BK/fQxr4wrjPaKNOD9Pw3zxLS266F220/c7Upes3QYIbhdBg3TEDAvv0b+jmWdRBIbbXYSOkGlWTmn85axaNuPV6mvf2o9St9o1k76kPu6dKzhm47vnxM2mjzqPp9Sv+h15fbiPacr8jqU8DUq+V2ccx+jHvYTvNQW/ixInqgARgX4BDShXGXKtDSF+FkHczwkYD4seTnScT1LtIHp/mrRh7oeeFx7TlYAN5gCVBIAZZoq1Xwebv888+o8dvuZZqo5+kbvRN3eb0XYGG/vBw+s1BJ3irX3DdA6Pm0LqDFqdFvp5Jnzx2Ld354kO02m5H0MprDaVGITRYd96AwPqG5du/IZtnUQeFZvM6rHKqRbTfM8MfoI/+fSstMu8LAoWeu0gf6r/JnvTkGX8OmlPDxyygtVbqS4vN/ZjovafokXNfpm+tuyttvtse1LVrc9vQsrQM7cXtYbOli51uzXdMu+Ylyjxt2jRlf45rsEfBHKkK86LVIaSvQoixOZiTDKcnl2u86zex4zIlbSAsCcR3WUhbI9VgMCj/93130yfP3Uld588mPHlu97607HYH0jqbb0UjRjxV9y70KRba7ONPZ1LnDQ+mr98fSV0nvULdZ35Ao/9+Br392Nq06T5HUf+llqKyYSNeSe2eJyCwbdwkSQuySDNiHRRMu0VWYzeD12EjUi36YtQbr9Prd15J3b94n9BSCzp1pW6rbkfb7nUQPfvcyzR58kdBc2rajE+o7xa/pk6ffKBsbLvO/5JmPXcL3fXaCFpxp0NpzQ02omaELi3D//369VOHe4xFPcA4+pXtvKvgBY4ywWuZzX5gcoRyV2FetAOE9FUIvhMpZLPjTafsKO5JLvw45fGpNAZpa4Qa7PXnn6NRD1xH3Wd/qCbR/M7dqefQnWiHH+1bL8t66w2lCy44g+bOned9327dFqFtd9iaevX6Af1n5Cv01j1XU/eZkxQBfPrCY6n7GtvTlnseQIsu2oPKQpI3XVKcuiwBgV3jxhaiJatUyhWiImSOuOwWbR6KVbTPs9nSNpqcTv/oI3ry5iup6wcvU3f6Zn2Yt9RatNHeR9JS/+u4kHVObbTRetSr15b08TY70pM3X0Wdxz9H3b+cShNuP49GP7kqbbjP0U3jQKX3ISRkMKFAH2KO4DMOKM6HD5ArM3KDCUgKMeeQdtDH4SXLmDYzcmAfwO8glKiCLXa7QEhfheBrPBsau6/IRO4uqUCSCz82G2ySON1xvZtFrK8blH9jSt2J5i+9Hm2275G0RP/+Ha7t1asn7bPPDzM/a5Wha9OQ1S+hZ4bfR1Ofuk2pfOe9dj/d//ZT9N2t9qP1ttq2NI/EPH0UIkHyJR4+DkBFqjVD7RarNsZttrSNIqdzvvqKHrvjJvrytX9R1wXfGPbP6TmAVvnB4bTqsHWizqm+SyxBu/30FzT27bfpldsvp+6fjaduU9+ily45kV4ZsiVttc+hlXKgSgL6CGsokz6MQVMKj35F7mge27b5BMI3cuRIpW7t378/DR061Jv4ZcnQhPiEIKP4DOWZM2dOsFmPIDuE9FUMaRMpVAVTdCL3pIwekHrAgxinR9OFH+9RNsTwawbYDMrn9FmG1tz9SFpxtdULey6cODbdeTeatcU2NOKW62jBqMeo29xPaerwy+iOZ/9JQ390NC2/yipUZYRIkHyJR1YHoFhqTa4TNkyMYTwfm1mzqKhctrRlklO03UuPj6CJj/ydus39hLASfN21J/XdaA/adMddC3VgGrzyyjTol3+kF0Y8TB+MuEnNqdo7j9Dw3zxHS26yJ220wy4Nd6BKA/oK5jsYr2hL2JPapPBp8wnjHoRPf4XKNSbMObbiit94UXPs1maZN60AIX1NhlAVTFEqm7TNGZurfsLEtfpGiZNknijyReCTGR/Ta0+NoPW33Yl6LNYj0aB84Jb70frbbF+apA3Sh12OOJYmTfwBPQtJ49S3lJTiP9eeSm8usz5tus8RHYycXnv+WerUqTOtsd761GiESpB8iEdWB6AktSbeQwLhMy75UMObGcYJDjhFeMIW4VGb15bWF2iX5x95kJYatDwtu8IK9c/fHzuWHnv4Vur+6dj/9XTvQp2HbE7b7XMY9erdm8oA5u4G22xPX22yGT12299pzusPfuNA9fh1dOdLD9Gqux6hpO2MKR98QGNee5k23H7HKA4gsTxscehBO2NMu65Jmhs4lEPCx5I+vI9dL3MfQpkRb7BKnuLtgmrtuoLUScRZNvDnE9vIteHGdOm3ZfRwSU+wUeJaBJGeNGlSZdz04ZTxyMW/pEVnTqJ/jXubdj/hdBr12mv0+t0wKP/gfw3KF6Fuq26rDMqZFJYN2B3t8fPfKlL37v3XKrukzhOeoyfPf5W6rroNbbf3QTT27bdowj9+p67v0uX0hVRkRY3LpPGUFv4hdCxmJS0utSYTONg/4bDC6QuTyor7wPsQvwFAJgcMGFCfEzHMKnSTCBBSeOfHOGiUpcp9+Obrae4rd9GUrr2o76l/odqC+fTYTVdRp/ee/cZurxPR3P4r0/p7HU3f+99sE2UD9rHfP/BImjZlZ/r3LVdR10kvKzvaMTeeSW8/NpQ22/do+tYSS9DTf/kVdf9qOj0wcTTteszPcj0zbXyUGToH4wkq3RCbviSnJvOwz6HHbONNVLrlQ0hfxWHaQgBmXt00mBtuTDs/171cEkZslDiRsqq3Km76j9x6A/X44n3qUZtNs99/gW4752Tq/snYDgblm+xzFH3HSCHXKKy5/oaKzD15zx306Qt3U9evv6Ta6/fTvaOeoU61r6nXgq+oE9Xo7bsuo8GrXhrd8SNtXLrGU6wwIVlJi0tCyOMSBu8oC9tDuerMoTAg3WPSp3sg+tgc+mzsfJ+PPvpIBV7X43H6IOkZRatyJ4wZQ1+OvI96LZhFnefNp+EX/ZIWmTWNusz/xqlgTo/+tPyOB9PQjTalKgCe8buf+Ct6+9WR9ObdcKD6gBaZPFI5UM3r813q8eVUtT7MGvNvevOlzWm1ddbN/KwkkwJXgG8dsUkhe9FmgT4vcA+obG2OI1Wza21XCOmrOPTFAYm2ebLnsc+LaefnupdrU2YyiEWhKuErxo8eTXNG3k89a19SF5pPvRd8Tj2mv0qzO/WkOb0HWg3KqwComLb60T70+dY70AhIT8Y+TT2//JAWo9nUqQbJZCfqMXsqPXzDlbTLkcfnepa5yWQZl7HDhGTZRFwSQpfjkQ5bWdmmCtAl72k2hyEhjTjTDjZmEAGUw6fejYy7B8n5Czf+kXrNn01daT4tsmAmLfbpmzSrUy+a17UnLbL69rTr3vtTt27FZpbIAsTEXHG1i+mZh+6nqU/+g7rN/Zz6fPwadaktoFonou4LvqR3cJha5bLMEn/XodgV4Dvt0N5I6PMCZUN8yiQJfNUCgLcbhPRVHPriYEpUshKmmHZ+2MzYE8u8l21TZlUaPkcO3kZPemxOL954EX1r/he0SO1rWtCJqEutRp07LaCvl16Xdv/pqZU36EZ2gR/8+GR66ZkN6OM7zqFOHA6nRrQYzaQv3hlB77y2Oa205lqZ7m/bZLKMy9Agz0WADyMwj/AJ4+ITrxCkztzI0mwOQ4LbpmXacaGRcfdG3H4zLfbZeFqs9qWaU5A6qziWXXvSlv9zGc2v1SodGFk5UO24K83abGv652+Ppd5ffq7qsIA60aK1r2j+lx/Sw3+/gn5w1AmZ7u86FLsCfKf1ayPto815kRQM2vcgIsSwOAjpqzjMxQGIYYsXw56HnTWwGYH4cRJw077P9vyqnPIeuPoSWvyzd6hbbZ5S5HaqdVISsq+pC9U+HEVz53xFPbrG9WQrCu+//jwt2qnL/26vyjpeSSf6LPiU3r7xHFpmheupx2KLBd/XRR5CxyW+A0GEZJA9XYu0OXXl+ASgLrXZHSWRIhdhdJHiJJvDELJrZtoJsXtsRNy90f95i+Y/fxP1qs1W77+ZU99IrRaZ/yWNfesNWnaV1agZMHn8eOo+52NaQJ2p1qlWP1D1XDCLvnr7n/Tyv4fSsE03z3Rv23jjcWMG+DavSZNKJyE2obLNYdc88jmIVCkzTCtCSF8TwJxEWWyDku6ZdRHgCQxVLav8bJtpVTFr5izqNep+6lqbrwgfNqavqes3Czx1om7zvqAxb79Fq6+zHjUD5k96W0kj5lB39dql03zqVMNfjRafN5X+deUfaPcTfhV8X3OTgVSBiX3SuHRBTxfF465Im1Mzxyc2mjwSMHOM+5Bimz2dz8HL1UY+aFTcvdf/dg4tQd+0RU0doKDg/eYw0qm2QB1OmoX0jRn5LHWmBTSXutXnVFf6Ws2p7jSHJt19YWbSl7XPfKTSLhRFqHxNLXwOIlXJDNOqENLX5Mg7ifP83pzAQOzJWqSYf5FuXWlml2/RovNn0exFvk1f9+hL1LU7deq2GHXu3oMW6zegoTlvQ/Hdrfahj956kebPmU21eV8Rzf2SOs2dST2+mk6L1ObSt5dZOdN99U3G5Z3nA/QjVMC8qLPzhHmf2DanM2bMULZ1ADIY4H5ZJWAhKmofyWHS9zE26EYYz9f6DqJ5U6bSl1160pwe/YgWWYw6deuh/rou1pvW+f4e1CxYd7sf0LNfzaZZX3xCtblfqj+aN5u6fTmDFpv/Bc3p/Y12IyZ8+ixrvzaaUPmQ2kZJqNsFQvqaHHkncV5DelPFF2uyskoOORpRvpjhKhgwIt/1D3dRq2DFNYfSBltuXUjsQN5k9ADboeNFX8yT0kPZ1MBZgd+aHrYcOkgPHO57wHDZ9ZnzIM3EoRk26KzY4+fnJH4PWzWQ72ZAv+98J7cTVJXQDISqURLqdkFTkb7Zs2fTDTfcQM8884ySOGyyySZ00EEHLZTV4fXXX6crrrhCqWFWX311Ov744+sDPeSadpjEeX/P9nm8WcayFYSEgyU0SNMTO1yFIBvyjJckiaHLyy8v8EzTwxYwpZWAj0eka0PSY1bGUp9laevYtpAyf1oLjSZUvvNDwrsUh3LSCUQAToerrroqvfrqq7T11lvTuuuuS2eeeSbtsssu6jvGCy+8QOuvv776DN89+OCDihzyadn3mmabxCuttFJm9U+e3/Mkfuedd9Qr4BM02nYfSEd4o8GiALKHPoIaUA9XkaVcMQhEu0DviyLGG8YH+tN1H10NzK95wB62+DPDzeAV722fpdXBVvek+yS1q+s5IW0dc8zL/GldJI3fLOM0BCHzTNDmkj4M0Oeff16F+WCsvfbairy99NJLtN563xjbn3LKKbTddtvRX/7yF/V+1113Vaqca665hn760596X9NMyHsqyvP7GCooW9BblnDgfpz5IESy1KyqsUbD5yQe6xSuS8d0dWjRKijX/WN4REKC6Yq/ljUItW9blxF/U9DaKNpzthnUy62OpiJ9OuEDWGXDRtqwD3riiScUeWNgYEEyOHz4cEXofK4RlDuJbRuMrgbEIuRrp5a2+Qqqtdm7NpkiVVCu+8fyiMShxRZ/reh2jbmhyubcnih6nDZavSxoItJnw4UXXqgMsyHtAxCaAcbfHC+OgUH22GOPeV9jA1SN+NPtCwGoH3X1cjMgtq3OwIEDFSFn6UjaZon2wjXcbrDJhME+PsMr3uN/Dg3Cql68cs5hV710W63QcjUzzDbNCltfFDm+0b9IM8bR/NFf3L/o/yL7zXZ//TO9TfHnyk3qqoN+77LaNeaYL2r+xBqrrYY863Kzzf+i53YWLGiT8dhQ0nfRRRfRI488knjNVVddpfKzmoBDx5///Ge6/fbbVdRygAOwLmYEoMV7/s7nGhvOPfdcZUPIwGTYcsst1WKvk8FGA5OIswG4Ip1PnTpVRX2HB2UjsmJgcsE+D2XhDRTtj40S5Uab6uXluuAVv3OVFwsmvH3RH5Do4n7tIuGztWlWuPoiZMyljcMs/VskbOXlNsXrmDFjlIkBNsIVVlihQxv71iG0XWPUBfDph2Ydq60C17rsO48aOf9bBbP/V5DT6mgo6QNpWn755ROv4RRPOm677TY6/PDDFSHcbbfd6p+zasMMBwAvUDOeXNI1NsAO8KSTTuowQA488EAVPT1rourYMCVdkGaaCwWI7eTJk+uLCRaYmBkyfE6rWKDwHdrOZ4GCNNfnBMy2YVz/KqR5KwuhbVrkmAPSxmGW/i173nCbYgNkL3K8QtpnznmuA6dcq0JdzPR4af3Q6mO1yrCtywCiS/j0X8w2ta3h7eDJ3b179fJAtxzpW3PNNdVfCO644w464IAD6PLLL6dDDz10IXUEYm+99tprtNNOO9U/h8fv0KFDva+xgb3/GBwmBhOsKgsXFg4sEJBy4RUSUlMVijpweihsCjhd8qKS12jX3DyT7ofPQ9rODMvjAp7Zr18/qxqu1RHapkWNOSBtHGbt30bMG2x0/fv3p+nTp6tXED60tZ7ajdu90emj9LrAjIXL6dsPZW3wjRirVYZtXYZgAocM9jpP678YbWpbw4GsAdmbCZ3bZCw2VS3vuusu2m+//ZTX7WGHHbbQ9xiIkL5dffXVSnIHPPzww/TKK6+oeH6+1zQr2PgahMdlyK2HgYAxLTaDWO7zRbrj+4QR4AVr7Nix6rVK9iKtCtuY8xmHVYKrvKxyGz16tDpIIKwTDoaYQ7ANfu6559Qf/tdDDTUyHIVeFxxuIYEM6QcJ1dIY2NZlNhsC0StrHuUNZxSKIsPDCJrckQMnnr333ludgu688071xzjuuONUCBbg7LPPVlK8IUOG0IorrkgjR46ks846izbbbLP69T7XNCN8PaP0UBkxPVyL8vjzDSMgYSbKh68nbDPWAeMJNlYYf/hswIABShqATYqlMAAOj5DCVMHj1QyAjTqwB6ZPP8gcahxs6zLGVUj/lRXOKPa6jn29zHq2M5qG9OG0qhM9HSuv/H85RWELMWLECJVxA/YQCOiMxVqHzzXNipC4XiHu8z4qn6Lc8X03oipsuq0M1xiwjblYsfzKsiWylRfPxFoBGyt9POEVEjQmfX369Kl7/lWB7LKNrk+GERMyhxqPRoY1KfMQx+s67OPhMILDEyTqrao+rgqahvRhsO28887e16+xxhpRrqkCzHARMTdCV4DcrAE7Y232WTYiiQHVvEFbzWexU0QjbYk4NiiiA7BdlZ7aDeGDUD78TZo0iZZddln1eRWCGGeV2MU+CAqyoYh1NM+zi1zXYXsKVTY74Ukg8GLRNKSvXQHCB/XztGnTlBH5WmutpTaYmBth2obuu4EU5fUVshE1crFsZZSl9tPHIhwmoF6FU0KRz9SfzWMV0B01bFlJcACDtA+hMgCofH2dJYpGVomd73wt8xDQTBAiHL6u4/CEUFuhWZcE2SCkr+LAJgvCx6/YYPTNFxMmLeRK2kKEzQ2eibzJmZurzwZi2wQA22dZDHeFzDV2YypL7aeTS5QTUjaUschnspSbNx4OdYJyQNoIVa4NKA+cJZj04bqqbFhZpN4hRE5s//K1n+D/ximk6JCQi9S4HAjpqzig0oWEjyV92AR588XmpJ+QbItM2kKE73EP3rjYID10A7FtAoBJUGFDCfsNhOiAvdSiiy4qC2NBcEles+aALdrOiCP0M+ni2GRFxr3j9oA9EYfHwPPwLPaehM0Rx7PUwWpeHutFGKH7EPQQW8skhBA5sf3zb7880r9WkxzGGquC7BDSV3FAhYQwEbpNH2++mECjRo1KXKTTFiK8gjRyHEJ8b1tc0ialj9cXwIa7s2bNUpsqiKyciOPDRe7ySGiKXJhNTz541jOJ4mcWsQFye+jhMSC9A3A4wThFCCBI8WzjlCUVRcCHoPsc6nzbLITIif2sX/vlkf5VWXKYZS5WuT7tBCF9TQAQPT0DQEjIlbSFCBusLlnJuqn7eH0BeBZvsD6Gu3kkHe0MF7mrqoTGLC8kziZ5sW0Yee1I9fbQw2MAIHpvvfWWsilshIG52SY2U44kEh+6ydrCvfiEfhIs3H7cbrzGJR2yXOO1qir0rOQttD6yrhcDIX1NDJ/Tts9CBKkKq3XzkKY0ry+248NnkFqmBY2NIeloV2LoIndVldCAZOBgwwcZc0zYNgxXWJKsRMdsD+QghTmFGbKlEX2om3LoMc2SSHwW0uAK91KVcVJ1mGtg2iEraf1qlgOaLxkNqY9IBYuDkL4mh89pO20hKisgJhYLbFrYRGHTN3jwYKutVMji4nMNPKDHjRunPEGR6qgdNrEkMlM1CQ1nUkH/gGTZcozaNgwfO9I80gRXyJZYdQ6Je8mmHHpMM5C/QYMGOfs5K2moqoSp2T1UQ9u6qge0rOMqpD4yBouDkL42RJIap0iJGC8WeAaeC0lK0jN8FhfzGtxXjzeIZ40fP16l0uLciq2+iel92Az15AUealT0GQ4FNomxuWHkzR7gI01ggmwbp2b8TF9kiXvJElC0DdoJjl1IEYfvQfxs/ZyVNIRu6s0uRS+j/HBgyyLNq9oBLS8Z9a1PVaWcrQAhfW0KmxoHEhZbMNxYiyIvFpCewFs4zQYraXHRf6MTWLP8uAbPwqaMIKDYoHFdq8KWML1sCVWRwbf1DcPHjjSmNEGvO/7X42fC2cqX+KU919bGusQIc3bMmDHqeRjbuNZVbrPNYmfWSSKwzUAGy1AjxpDmVa0t08hb3vJWVcrZCmjd3U8QvBjBa5E9FjFpXXZTWb0DbZITXnTxXKjRkNVA3zxti4ttocY1pq0iq1RYuojvoR4DKWpVFW+RapGiNsgYkgMzo0zRNkZw8oCkDeMJ36G9dWerrM9NamO8Qs283HLL1QkfzBXM37vmY6inr08bJkUHaAa7wDLUiHmlec3SlrHLW0UpZytASF8bw1yMEDMP6iOOV8aq31jegTbg/iB8UH9MmTJF3RObWoiEhr0aUV6X4fvyyy+vQm+keWIyeQCaMfl3kWqRIjfIPAt8GbEHzbpDuofygnjhFXPHF0nP9WljED6odENjMBYxl13jrUybrDxSpTLUiHmlVs1m39Zs5W03COlrY5iLEec7RQJ5vHJQ3JjegSZwP0j4QPiwmcGYP0ldxb9xBaiGitoVwxBSEd7UbIs7fjNx4kRlAwhwLtUYxC8ktEieTaxItUhV7Wx8Qpu4wNeltbdZd4xVSMYxTjF+YToA56S8JDerqttnPhYxl13jrayxkvfgWZYaMelQkzbfzfWOg5hX9UBa1XVC8A2E9LU59MUIkxPECJ/xZE1aFGNMbtwP5AqLGHvXpt3H5tVo2/BND2WfrCLInwpJJwAPyRi5VH1T1LH9pO8mZuaK1duniJN1Ve1sfEKbuGxTfdvbrDsAFS/IX8yNLU8b+6gRi5jLLrtBPoAVNVbwHPZmzpOfOet8CclTnFXlbstRi/Wuymreqq4Tgm8gpK9JUYRhr2uyuhbFWJMbGydUur71MclOmtRPv2eaBBEbOZM+SHLybOZ61hPf0CJpeZBdm8XAgQOpDCRJxhplbG47BDAZAIlHP7qclEIkXOY8KGpjy0pCfOajywYy1lwOPbRwGYCQsaPbAkMjAfV6mVKlkPigISp3l5Sa+whrWzOoTcUer7oQ0teEKNKwN3SyxprcrvuYRMJW95C0dGlliJVLNSnriSu0iJkHGXZjLk9jc7OAR3QehEgtbGOv0bEQbaFNkPkFfygriF/sDCUuJyP9QFI2CfaZj64+9J2DSfAl0exlzhJZICmHuOs5kPCB8MFmNy0EVEzw83G4wFgDWbOl4wtRuaflUs8zVss4kFXNw1hgh5C+JkS7GMraNidX3V1p6bJ4F8fIpWqW08x6gnJho8Afk0sQFEimcA3+x+Lv8jSOaTcTcohwZcaoSixEUxXGGyhnfzHby6a21aVPeYh+KJGp0vrB88YWBimpHr7jEkSJSRvsIfl5rvKkPadMwsfPRx9D0oi5irFms/8NUbmnHVqzSGNZmmpqQNheO0ub5TGTEDQeQvqaEM1iKJv35OciGEnEztzAG7UQ2WwKkxZIlvJBtYxNEORJz01sqlTNDYBT3GXpg5BDhK399ViIkPghy0ojxyQTd2zCet3TMpTkVZnr7ZiFyMREUr+nrR96OyAMDaS3vjZzvsSEPe1tkj6fsZNE1l1qVl9Jto/3Pj7Hs3FI4wOazQEtVOXO4aUwh2xS/hDNCvcjbJM5IgM+A+FDn2ZZE13krl0EEa0AIX1NiBj2N/pJPs+pL+n+eQmXbXOyLfa2mH2AT7LztDoU4UXrSh+GjQMLM67Hoo+NFnVCH6Wp42ykz7cPQg4RtnqxAxDAsRarcMo3N8i0DRPjBRskNvEsKnNTVRdKZEKQxzkgbf3QxyfuhT7FNT718J0z+A4SJ5Z+83ND5pqLrNviD/ra302YMIHee++9elicJO99PJvNBkI9rZPahA9ReeOJcj/yeEYZsK7g3lhbUF+U3yUltfWli9w1iyBCIKSvaZHHls40ggapCLXDSlvcY5z8XJuTXvckYpfXBiYvaXX1katcfMpHn6A+nIeWVWGhbenbB6GHCBuZanZvPUgoscmyE49ORkynBxdsB5IibJzyxOPTy+orzfVVB4bOGbMMWdeztPr6zgP0MQgfJO6oK0JXJXnvxxz3utMXguOnxRP1gd6PqAeXE+Oc135kdrGt/a6IA/jcZp/cCmtAu0AkfW0IXgSxuGCTw+LG6gqfBcZncY918ksjt0kxrPIsREUHInalD8PJGwsxFn1IG9JiJSYhVIKXp355f99owvfuu++qDR8HoH79+qm+YdtLdjgok8gkIU88vqzjM+YhIzbS6uv63uYkBscMNq/AupjWdmnOPL52d2lOX0WuMzZyafMq5vzBKB9slE31dzOvAe0EIX1tCF4Esdhw5o2QBcZXkhD7FGy7T1oMq6wLUdHqClu58BlULWYQ6axt2eqn7xjZU3APeB3/5z//UeMZDh+DBw+uqw11h4OyiIxPsF5svCgbXm0kJm+/Z5k3jVLxpY1z2/eugyvUn2hXED5IOE3pVxqZC5V22mIN6k5fgI+UOcY6k9SXgL7mo3xcHvHabS4I6WsBZPFQxYKG0x7CHeBkyynXfO7hu7jHOPn5Bi+NHcOqTMJk9h+T2Fix21rx9B0rewraHeMGcwC/xWYPBw4mBpgXIFaQBuoEqyiEkIY0dZyv1D6WCrqRh4y0cW5+7zq4wo4Pdp1YG3Uvft9+CZF2umIN6sHEiwzNxX2FTDIgnjjwsPe9zVTBJS0Vr93mgpC+JkeWSccqK/6NK3CtCy5iUgR8F9EipAxlECaX7QyrUiT8gXtcsFciAC/K0OwpbArAAbnxPzZ9W8gel3d0VrjIls94Z6IKCRB7jQKhqtUiNuzYc6YoKZLLSUyP01n0OpQWazCEQOJQguvwysQtDXgODjUjR46kadOmqbigQ4cO7UD80oKRi9du80FIX5Mjy6Qzf4NTLU6bsPHDIusrJSuDmCQtojYJmbkoVV314PLkDelTVnPGJiZVBvoT45YDWYO4ZXXUgQRvgw02qG9y+jiBBIaJlSssRyiSyJYPaUhyBEr6nYkqbdiu2G+Q5rLkLVYe7DSppG1dC9Fu+Eo7zXuaXrS+zwTRe/XVV2nq1Kmq7GuvvbY38UPfg/DprwjT46qbOT587SUF1YGQviZHFgmX+RucMrG5cSwnVxaIWBuG7qmWhiQyZ9s4zRygVVc95N3AdRUR+g3kpx1gSmVCbfrM8atLCXl8Qu2lx5OLJUFOmjs+pCHJQD9EtRq6driIWd7N3TVPcZCB+h7rEsg96hYjcHoSiXH1TQiZ85V2ZrFFtIEJG0KzpBE3E1DpQsLHkj68D0GIvaSgGhDS1+RIWhhcC7L5G47XB3smjtsXGhHfdzNk1TKcLrCo+5zeQxbn0GsajbwbuF5HXMdkpR2A+mYlAUkSCl0CCOmLHk8uxuaVNnd8SIPLQD807aDvOHOZIcTY3Ks0T7H+gTBxoGS9b4ow9wi1RUwibpD0hRI3SASh0mUnJl8JYVIZq9SfgoUhpK8FwOqHtBy1JvHT49nBkwuf+RK4rEbbesBQvIbaYaV5MJrXVDVgqEnIs27guic2NqxG17FZ1Dqu8WsGJmYpT8xNK3TuFNmmvnXLY4bg441sm6e4Fxx0YK8JCXYZ9rU4kCJbBcfIbPQY9ul7ELW11lqLpkyZotbTUOKG69MkgyFjsMrrrkBIX0vARvBCTltZCZxrw/BJAcXZJvIuCD4OJ1ULWRJT/cF1RPYIqMAaWUefzAhVIoRpNkqQfLOzR+yy+5KtqqjKfM0QbLHvbBJCn3nKKvwsUtYs/aU7VuA3PhqPIhHS9yBuWSV1MctR5XVX8A1E0tcC8M1Rm4RY0gwfCSNO0HgWiErWBcHmwWja8/GiXzXVQmz1h80BoWr1ykpeyiaKumc6JCcoM8YYPncFaC6yjFVRlfmYIQBJh09O+wUbYtv9XdliQuubdaxVTUJVlb7PUo4iVOGCOBDS1wJwhR9oRJw5WyR3Vj/rJ/i8ZUrz6q2CdCRNNY3YcGXEfisLSX2SZePIEug2lHzZfoNXJnkwQ4B6Edfhf9O7veixVqWxYnOU0tvOlhJRNz/gtF/sqVoUQsdazKDWMVEGCfWZM7ZyVE1qL/CHkL4WQJJ6JJb0zje5O6R4vEBgk4LDhm8KqxAkkdqiT8h5yYX+WSvAZ9PMsoFlDXQLeyzYgqWpunxCp0DVB3tXjgloereHlpGdpnxy2Zq/LQpZx7PZdkmHTzPtV5FSIJ1oIsNGUjSCrEGty0DRB3d9zqCdEKPSNmfMcgBVPlQLkiGkr0WQleClLfhpkgzbpscLBH6LtGhFkS9XnYs8IWcNhq3/BtLP2LHffMse+3Tuu2lm2cDMfsTm7UpJhXph80KcMqhlUa7lllvOy/MZ2QhAwtAv7A2smyHg2Zyj2vRu9x1rZvYF3MeW6N5WxiLHSlZJpYvsug6fetovHAZ9wkJlBfcdygg7VzhnxMig0QgUqSY15wyeBeJnaye9HDaJbpXaTJAMIX1tDJ8FP0tyd14gOOyB/l0ZCCEYoUQoazBsPfg1yEXZ7VKUGjLUYShrSBGQhKSsMWhDSPiweUFaAQ/MNILEqlP0DVS3kErrIYTYDAH3cHm3+441bieMASaQuDbN45XjBBY1VrKSHhfZTbLN09N+oc2LBIg12poli74ZNJIOFq0G1BESPp4zIMg8Z3yc8fLEdxQ0DkL62hg+C75PTDHXptdILy4fgpGFCIWojhgsHWL1YCzbIZ0YcEYOzs6RV+Xtu1CXZfyeVn6UESpdlBuEDyTNx3kJ94HNHkifS5KWNo59xpo+blhF7GovZFgYN25cvR6QWjHxjb15ptnGup6XZW5zO6F+RcN3XNoOFiEmAjHQKFKkS/ZA+HjO+DjjmepeF1Guun11O0JIXxvDZ2H0WdyTNr0qe3Fl9UrzVR0xWJ1nqgfztAunqJo+fXo9Rh/+WJLkslELyfLhs1AXSezNQMn4Syo/NmiodNM2UNOzGzHgQPySHCXyjmOTXLhs+kCIYPs2duzYepBtbtsiNk9X//mMgSrP7ZBxyfUAcQk1EYhtE102KcKcAfFD3UPWRV2bE2r+U9Ux0y4Q0tfGsC2MLm/GKk7UvCdkbL6Q1tmi78dQHTFCgl/71olTVOH5IAqIxA+SAIkVyudK7wXVctrzQhfq2ONDT9Onl2PIkCH1oMBZy2JzPOLPi4ZeNpdKFxI+ED70IySCGJ82r/iYm6etzWyhVszcsHnBHtFAESGHQsdlFhOBNNjGm074Y6SzjCFd53zDOPhgjUg7YOUx/xE0FkL62hz6wph0aquaXUbeEzJH34e0DhsrYgb6wmchM9vLR+qQpU7cfwjMyiSBJX22cunJ5LEB2ezkGrlQp0n3kuz6fGBuUiAzjXCqcYU7AtEAeUdfDh48uK5+K7tPbKFWfBxPQvt5woQJ6j1UqT4pGV33irE2ZTERSINJnvEeB8Ykj2efOplrxcCBA3OXEQ5NkHTCUx3zAoesNDKex/xH0BgI6ROkntpiqSB4IYuRGzav5EP/PV5BlLDQ+RKtpIXM1V4+Dh++ddJTVGFzwukcGxVUlYjp5vJwNQlP3uTysTdls4zYeDgjQwxpl7lJgSxXgeAyCUdfAmYIjbI3T1eolVgSRhBJDoMDYByHpmRk+1U9LJQpSQuFr4lAlvGGPsUhE22Z5PEc6mCH6zFuWFodSoK5jFg3IHnlNRH9kXctrLKmqF0hpE+QemqLsdmaEhxXcNayHAj499ggsNBxLmDfuiUtZLE9Il3P11NUod0gQUgqVwjhibVQhx4YzDLqkoYY0i7bJtUoSURSuKOsDiOxJV96qJWYpBimFSCUOKgAOKyE3JvHFWxaQaQgGWWpJO6Z53Aac+yjH3RHHF1S7fJ4Tstso3t1c51hEsBmJJMnTw7WFnAqxzfffFO1KbKm+IbVEVLXXBDSJ6jDtQHG2GzNk6nLpqksBwLdxk2XFMTY1LK2V2idTNV8FQlPFvvAor3BzU2qUZuWbZzkLUvsLCZFjRG+Lw4tQJoakaV6fC2PK/6MTRtskrRGwNUPPm1pjgv2zjdJI8gkS2Ixv3QpXeiBE2WBlBPPtsWjDKl3lcyABAtDSJ+gA2ybToyFX1/IXMFZy3YgwO+xWUBiFnOh8mkv1+JYNAHJSniyLuZZCHBSmZK+q/KG43KQyjOv8jrh+BLEosYkzz9fT3U4LgEwa0BZeVyBOHIb2iRpWZFnPLn6wact9XGhEz0bqYW0np3RUM485gohDmc2NNoTWeAHIX2C6GTEtcHxyRQLPRYGE41yIChiU0sjJ2UtjnlTxvnaGLlQlvq0yhtOUrqrrGOPiRDs4qAaZXu4kDnULOE0UE6b/Z9tXMUaa3nHU961jMeFnv2CY4MyKWNCCHU2vI7xHaRzrE4ObYO8c7VZxlO7Q0ifwGl3p59O8y6W7C2bZNOXddGJQWzy3Cv0OUUujnpwZiBvyjj8Jm95y7BFKyPESIx0V7C3gsH88ssvnyvwL4fsgVkCzBNAhPr166f6S5cSJbVnFi/02CF5fIBnoz8h5dLt/1xaiRhzKcaYD13LbG1t9pHuqMJlhOSPPY4xFvJ4Wedpv0Z6/Qv8IaRP4HTdx0bi69Gqb0YwBGZbG14sfWz6bHZqPoF2YxCbrBKtLIFXbYtjjM2ViTXbJ8JeMUvKOPM3RZW3CMlKUSFG8gBlgxQGhA92Vxx4OUbgX0C35eI+xjN9gitn8ULPA842wmuET5gWfI/rOEajeRAtgpjGchryXctcbW3rI1ve5549e9K0adPqz22ElK0sqb4gH4T0CawerTg9gvxBGuG7gGDhwkmTT+TsWarfG/BJuO4rdcyaC9f2myypyrJEozdtdsywE1k3V/Qbn/yBLDl+Xc4FZtql2GQglmSlqBAjeYCyQaULCR8TvryBfzlkD9SeIFLcb6Ee90mSndgSacwXSCdHjx6t3sND1DdMi8v+ryi1fkwCExqCxRZYPS2fMdZpjHmsAY2UsjXKKUrgDyF9gg4AUUB2h7feeivYdZ8D3GJxxh8WI5vRuk/CdVvAUFZf6Qtm0oncdbp2/Sb0dJ8nGj1L/LAZsL0Sb2pZN1cO48Dk0RYDLOtmpy/mup1RnvLqSGt7H2lOkSFG8gKbMlS6TPjyBv5l6RdIky2tWwxJVWx1HacuRFuAAHO2kbz31Mci1i+eW3nJnw+B8RmXMXKcp5URxB8hV2Dbh3VEpGwCF4T0CRY6jUJVYMsVmwbbwqUn4tYXqNCAoUw0zAXTRlJsQVt1sphEbEJIUtpCnXY/3gz0VFR5Nld2lmEJKz8vlJClbXZF2IIltVWZoXyKBAf+1fOc5oHeT0V43MduS/YORX+iLSCpjHFPXYPgKzGPZU7hMy595otPW/uE12Fbv6qNfUF1IKRPoGDa3IHwYWHOGmfOjCkVqnYx4+hB2ocTrC2bh2k74yM9S1KZ+JIkn4U66X567l8OO5E3B2kZ6pWstmA+m5at7CBJ6E8mx2XnAo4NPRVekTaHMdohZlvyuIFEiiV+eUmYPhZxj1GjRqVKoGOphENU6D7kOamtfcps2vRWwZ5VUD0I6ROkeomFLsJmqIGsKkC240FZsFEiETpUY0OHDnV6PsaWnvmUMWuCdD33Ly/QzXJKD7UF83EsSLIT5XAdup2o6/oqtKGrHO0e1oLHTVZJbtI9cR8fFWmePtD7NUQlm5c8+5TZtOmNYYNZhbkkiAshfQKFJC+xLIhpD4RFDJ7EeIWHGhY+qKDTnqsHbY2xeOVZBM2NjT1rUScme2VJgIqGre+zbrS6nSiu1+1EqxqrL6kcEtZiYcQiwr4Stax9YOvXrOrvrPlxk8ps2vTmWXOrMpcE8SGkT1CoKieGPRCizMO5BIQPr3jv+1wgxuKVdxG0GZzrizjQKhIgW99n3WjN3yW1SVWkaC5JJ2/yjbA5bLTUxpRy64hJhH3WsKxrk2t8ZUlVFrqW+JqS2Gx6s6Aqc0kQH0L6BIXBtLVzLfppgCoXKl0sQCB8pmrX3NCK8DTNuwjayItJTmN6SpYRZDpk88260Yb8ripSNLMckMCYm3yZG2ijpTbm8wcOHJhoC1wGOc1ywI01vrKuJb6ENsbY0u2Nq+QFL8gPIX2Chi/6PgDRs6l00za0WAt13vu4yIu+QFclhVTa/fLae4bC93dV8dw1y9FoqUnVng9HDhMsDQ4Zt2VLL2ONL9uhQI9y0GiwQwjbG2O+V6FcgjgQ0ifIhSxxqmyLflEbWqyFOlZIhaK8TvXUVmzQ7VIvhraB6dmN+8Ohpoq2PlXx3NXL0WgJZLM8n9PVzZo1S42zpJh7SQebIslgLI/oWFEOinQ2Yntj35BdguaAkD5BZsSMU5UVvjGwsia1d6mNq6RC45M5vFwhMcCz09SLIWXjNsZzoF6HJ61v+BRB4yWQsZ+POJsuUwuf57ty7nJMUIwvjCl46+N/25h1HfYarcpuRJSDrDnGXWGV8IeYh4CodlsPQvoEmZE1TpVvovVGbqihm0cjVWh6mBq8wpA7pnqRDcR5Q0bAbGRq8d0QfCUvjXY2aGUJZKzng/CNHDmy7lSVFD7J9XzX/Mf44qDwuCc89jEeQrLdcO5vlkpnlXKXNRaLPBAnrWFpYZVA+oYMGZI7bqigehDSJ8iMrHGqbIt+nkW2iA01lCg1UoXGz4Y6hp8dql5Ma39W6YJYgvAhpRhSnqX1lS95bhYJTaPRaGKM+cBhk/TwSTHKxRk78HuWNLnCj9gOeygDpIP4A/njYO6h46rMsVikFDhpDfMJq4TDo8zB1oOQPkHDF6wqbvihJK6RKjyWxGFBh72k+ey0svm0v9kePoQvhDw32tmgGVCFeWILnxSrXOY4BUJsZNkmEJJoSArxh4NQ6LiKORZ9c0YXMdaT1jDbmtBo209BORDSJ8i1eMVYsKq44WchcUWq8HzzbmbZWHza34c42srnu5HIhtMc88QWPimmXZo5TvX/0+YAPu/bt289e8sSSyyhymgbf0n3co3FUGlmo0l62pw127rRtqeCciCkTxB98Soi2nw72mGVtXn4bnKu9kgqn+9GIhtO9n4qG2b4pDLK5TMH8H7ppZdWYw1gezSbGjjpXll+UyZJR1lAtPU6xlrDqrLmCYqDkD5BKkIWL9/E4BxepGobfqNtpmzAAj9jxgxvj9ksgZTzbHI8PmbPnq1Uawi1AXsq/f5Zg89WsT8ahRjzpKj2RJ/jryjDf581iOtmlsGmBvaRbIf+xkQRZBh1nDhxIo0fP169X3bZZRXR9fmdzCMBIKRPkIqQxSttcTTDi2DBSguHUhYarY5xlQltxSorTrGUdH2WuF95NjmUB4b3sKcCMUV5uV9brT8ajaR5kraxF9GetnuGlCnWGhRStyxkLMtvijjMoi1xAOT1AB7OWBPg9eyCzCOBDiF9giiLFy/unPTbtTjawovkIXsxT7B51DFFnaRxT3gwQnKGsqBMWUPHhJTRtskl2XXiOdiMQORRXpa42OBbjirYsDULWAIEEgC7NhvpLqI908ZbLJKZtgaF1M3mMJKWESMrgctD0m3AtbBVRLYMAH2dFgYrLRd0ux+k2g1C+gRRFi8zTZdrcbSFF3HdM21Rin2CzaqOKfIkbZYpq0optIz6JsdpoiDB4xAa5u9Rrn79+tXvn9SvRUpk2hXoH6j8IAHCH/pNV7Hr7Yk+gE1eknTIF0l9FJtkJq1BWbztQ4M5x9RGuCSkPp6+NrvFJNJntk3eYO2C5oaQPkFumIs7p09ykbik8CJ8rc+iVMSmkuU0H7scJuENKZPr+ixlZI9g9AUC3kK6wEQia7q7PBKZdt2YYkmR9SDb6EuYAeTZ8LlcrkNemaS9KnM3z5r50UcfeZNPk9CHtI1I0NsbQvoEuaEv7rDtMiVCgE7iBg4cGIUYFLGpZDnNxyyHi/Dm9cDLWkbuC/YWxGbj+r1PObNKZNoVvgcgtBGM+qFih/rP5WWNOQXCx/HrspKctHKlEcIiYI4VH7JclLNF6HOBIsmn3jYiQW9vCOkT5IZ+ksSCN2rUqA6Ll7mgQcLnQkjux9DTfVF2dzElUjrhRXmxgfsGQi6ijPoGAftL3oyS7DolNEtjUh1C7ceOPjZJO0ganG0g6bOl0QuZH0m5b9NMAapElpPmRUh78LXIADJhwgRFrJFdxPe5QNWlooLWgJA+QRToNjK2xctnQdMXat/cj76SoKI92GJJpHS7K2zOY8aM6bB5JCVQ18PgxCqj7wbRKNuoVkeIVIbV8bbxwSQNEj5bGr3Q+eFy9ME92LsUtmOc/7bs/g41I7CFf/FtD+QjZntKOKjBmQbkL+S5eF7RYW9czxa0F4T0CaLCRRL0z1zEpMjcj2mx5KrWfpDwgfDpaji2rzM3IlcYnFiSTp8NQuyEGi+VSSIqJkkzpcehHp62cnFmDoxZ5GnGZyCYMRxGQpFXhek7ntHm48aNo9GjR9cJIAgfXnv37u313LSwNwJBTAjpE0SHjSSYp9qy7Wuw8RQRSy4mdFKGTRkSPt0b1rUR+YTBaVRWD0F+uEi3SeJdDlU+DkFZPDzNcvE9IKGGdzDe4z54X7ZUKa8K03c8o81BcJGlBIdJHNY4bBVsLH2eC7IMRym2m5XQRIIiIaRPUBnEtjUxiQ42Qd9YcmXDRsp8E6L7hMFJssHKa+eYxWhfMgTEHy9pDlVJEtsYHp58D8wz/dmNOgDkUWH6rkX4DoczoFevXor8od1A/HzmEwdf57h7acHXBYK8ENInqBRi2prYJB8+seSKQhLRcW2yrrAo2FT0e+lhcGzBZvE/iACkEXjVbbDySP+y3EMyBOSHa7wkOVT5kLa8Hp4cTgRSdJ/DRJXJv89aZHNiCzlU6sHX8ZcWfL0Z2k1QbQjpE7QcXNlB9E2x7MUyjeikbbL6Iu+y7ePfpIXSiGmHl+UeYv+XH67xkuZQVYQDjytLS9o4yJoyMOn5ZZAh/RmA/n/e9G5ZndIEgpYnfVgsHn/8cVp55ZVp2LBhHb6DNOOZZ55RwS5XX3115QVqwucaQfPBJztIzLRIvkgjOmmhI0w1tXkvNpZPsvtj6QO/xrDDy3IPsf/LjzRSltdUooyMOHnJv22u5yWRIc/ksFK6Cj20zbP0k63dGuEsI2hONOVIgWHwHnvsQa+++ir9+Mc/7kD6YKi//fbbKzuJVVddlZ566ik6+uij6Q9/+EPQNYLGIisBy7qRVMHRwSUdsampbeEykp5j+zyGDWWWe0icsDhIk6ZlNZUoKyNO0pzwmf/m8+FEUXR2Df2ZEBxwOfXnZQmLlLfdkkI1CQRNT/p++ctf0oorrqgM123fIV7SW2+9pVzmIc3bZJNNFMnbdtttva8RNA55CFiIFEnfWIpQOeZJp5ZUJ5uamhd913OSPs9bz6zx/6riRCPIlxEHYw9ODKHSJteY9J3/5rzo0aNH4R7kprMMULbDiq3dhPQJWpb0PfTQQ3T77bfTyJEjabPNNuvwHQb+zTffTKeeeqoic8BGG21E6623Ht14442K0PlcI2gs8hCwrMGEoRqKuWFkSaeWZB9lq5OeAYGDwfL1rqwNQrQEMQ5OPFaRUhGvPrl89awVSWMyJAuJOS9i2ez6zkUub9k2wjKXBW1B+mB/d+ihh9I//vEP6tOnz0Lf8ya72mqrdfgcNnuvvPKK9zU2IAYa/hgI8gsgCCf+BP5Ae2FRdbUbNgWcojkdGwc7DQFnA3CdgEGUoObndGfwesUGhldWl+Q5Pdvun0b4sGniN5CaIMYXwj/o3/NY0yUibMOEdkJsP24n8e4rfpy2MpLmgj7uEI8PcepY6+Ia50ljNe/8N+c63uP6LGSMD1HYa1iChwOheQ9+pu35jUA7j9VYWNAmbddQ0vfss8/S2LFjE6/Zbbfd1CaIAX3ggQfS4YcfThtvvLH1WkxSgOMmMfr27Vv/zucaG84991w688wz6++xEG255Zb1YL+CsMkFyQD6VCc2OrApYPPAgoo2jg08m2Np4RXliXlSD70/NqgpU6aouuK3OFRwRhK8nzp1qtpcMRewsXIwXtilYvyBXPKYxne262PUCfa0vjHI2mGctiPMccfZPZLGuWusFjH/XfPF93cw/Zk1a5Z6PsgsXotW3eadWzJW82P2/wpyWh0NJX1vvPEGPfnkk4nXQN2KiQuV7AsvvED77bcf/f3vf1ffYTF455131Pv9999f2XQAmOw6sBDxdz7X2HDKKafQSSed1GGAgIRi8cJpV+APllah7Rq5mYLo28I9cBaBvCob8/5JwLOx0WBM62mcsOFA8jB58uT6xsCfs1RCl57gmbjGdn0W6G0yadKkDurwVid+VRmnVYM+7jAWIBW0zRcztIltrIa0q6/0GsRy4sSJqv9Cxj/PMwDjHes64nrGOjT5hq7JMrdkrOZH9wqm5Ww50nfUUUepPx/079+fdt55Z3rkkUc6ELUJEybQ8OHDFRnkqPT4TAfeL7fccup/n2ts4OCZDLZLwaIlG0I4sKhVoe24H3X1KjYWlAvegHlTten2S2nA+MNzMa5BNDDeONgt3uN/bAr8OYDyQSKI56Ds+H3S9Vk3Ixy8UC5IPiARt6V6a0VUZZxWDTzumICZThy2GHy2serbrr4x/XQpHzBgwADv8a/PG/yObQmLPtxwMOe8c0vGaj50bpM53jQ2fZD4mU4Wa621Fm2xxRZ00UUX1Sft1ltvrWz+oAYGpk2bRiNGjKA//elP3tcI2g9sPI7NApsGNiaQHCz8ZZ0AsegMGjSoLlnTpY6uFGds0K3b8iVdHwLdoJ49NJlISqqo1keSZC3NkSApw0wW26kk5w7TCz9LhguuUwxHkBB7WrYFZE9gmVuCotE0pM8X5513nrL523fffWnDDTeka6+99v+3dyewUVR/AMdfS+UuRMphoUCwHCUgHhzigXKokUNBSOQQjSZIBIRAIhGUQ01pxCARERSwIkZBpJwaCSHchwgolwiICHIfrSBHQTneP7/3/8/8d9vp7mwPyvZ9P8mynbdvp8PrzOxv32kGbbz44osR5YFdAheLlw8LefZzEy/sARPOyhoy4EhqQ5x+PlIDEW7qGqc2JOd6q4U1ilMCycJo9kbJmjbJ6xoo7Am489pfuFH4N3sqIQloDx065NbWhyu3wImeZYGAm1GzCLtFddDXuXNnsyJHoGbNmplJm9PT09WOHTtMICdNyIEXsp88iE5+VxLw6ssnHxgy8bH0NcrMzDT9efJa8qwoJ3R2ajWkj590KpeR6rLfcFPXSEAm73NGUhZ0rkGvmg+uETv4nTYlr2sg0lqzcNdtXvvzOs6iXGox3BfAgwcPqv3797tNhaGuwZzH7gzcAopSVAd948aN80xPTk5WaWlpId/rJw+ii58gLNzSTU4namnedUbI5lwNw6mNy9n8KX0AnZGMBRE46a3UOjprCIerLXHyFeZksQR6dvJbUxcqOPR77vj98uS1v7xWmimKLyd5HacTCMqzdA+RgM8ZiBXqGmQ5QhSHqA76gEhrJ8It3STbEjQ5natlH4Ez8Mu0E4HzdznBmdSy/f77776aYUWokcKBtRqRjiSWkYY3qwM6Si6/NXV+A5ecI3m9rkmZEUGWNpMvVn770ea3H15etf2RLPvm/M7AJlqnLJy5NkMdT2FOJg34RdCHEsPPB1C4pZtybgcueSYfDPv27cvVlCSBogR80qwqN/9wzapOjUGoPnuBtRV+a0tkAIpMOeHsCygIPzVmfgKXnDVkMsVLIHmfBEzOnKfyxSqSUfORrnQTrrbf77JvXss3Sr+8wFHNBT12oLAR9KHE8PMB5GfpplBLnnk1JUmTrgRazodGuGbV/PbZC7dPaVqSYyzovoBIRDqSV2qjc75fztesrCy3hl3eUxjnr1eTbLja/kiWffNaF5saO9zKCPpgZe1EYJ68tp1JaJ0bfF4BYaTNNPntsxdun9KkJDWHzr5Yig3RUgMv15sMnPL7xSmUnFO4eDXJhqrdD/W7ve4VNNEimhD0AR5CjUrMqxbAb81EQfrshdqn1KBI53GnP1RRjCwG8nNuBgZGXmvUFuYceaGmcImkdj+S/x+16ogWBH1AAaasyK9I++xFsk95dpa8KqrjB/JzbgqvoC9nnvzyO4VLuNp+oKQi6ANK4HQK0X78QH7czClcgGhE0Ad4iPa+OtF+/EB+cN4DoRH0AXmI9hqCaD9+ID8474G8/XetGAAAAJRoBH0AAAAWIOgDAACwAEEfAACABQj6AAAALEDQBwAAYAGCPgAAAAsQ9AEAAFiAoA8AAMACBH0AAAAWIOgDAACwAEEfAACABQj6AAAALEDQBwAAYAGCPgAAAAsQ9AEAAFggrrgPIFpprc1zdnZ2cR9K1Llx44YptzJlyqjYWL53UKa3Js5TyjVacK4WXPb/Psudz/aSiqAvny5fvmye+/TpU5h/DwAAUIyf7RUrViyx5R+jS3pYW4TfrLKyslS5cuVUTExMcR9OVLlw4YJKSkpSR48eVfHx8cV9OCUCZUqZRgvOVcr0VqS1NgFfQkJCiW6BoqYvn+SkqFatWuH+NSxx/fp18yhfvryqUKFCcR9OiUCZUqbRgnOVMr1VVSzBNXyOkhvOAgAAwEXQBwAAYAGCPtx0Mmp37Nix5hmU6a2K85RyjRacq/CLgRwAAAAWoKYPAADAAgR9AAAAFiDoAwAAsABBH26aTZs2qa1bt3q+9vfff5vXjhw5kuf7/eSxxcWLF9W2bdvUiRMnQk4gvmvXLvOQn/Obx1aHDx8259v58+eL+1CiwvHjx9WOHTvM5Mt5kbKUMpWyLUge2xw4cECtX7/e81x0ruGdO3eaORC9+MkDS8iKHEBRmzZtmo6NjdV169bN9dqHH36oy5Urpxs3bmyeu3fvrq9cuRJxHhscPnxY9+7dW1euXFnfc8895rldu3b62LFjQfm2b9+u69WrpxMTE3XNmjXNz5IWaR4bZWdn665du+ry5cvrlJQUc75NmTKluA/rlrVs2TJ97733mvOoWbNmptyGDx+eK5+UoZSllKnkkTKWso40j22OHDmiq1WrJitn6XXr1gW9tnPnTp2cnGzKvlatWub++vPPP0ecB/Yg6EOR++WXX3RSUpLu169frqBv06ZNOiYmRi9ZssRsS/AiAcjIkSMjymOLNWvW6Dlz5ujr16+b7XPnzumWLVvqjh07unmuXr2qGzRooJ977jl948YN8+jVq5dJu3btmu88tnrttdd0nTp19MmTJ832vHnzzPm3devW4j60W9LUqVP1tm3b3O3NmzfrsmXL6vT0dDdty5YtpgwXLFhgtk+cOGHuCYHBoZ88tpFrsU2bNvr111/PFfTJPUCC4549e5rrV/Tt21ffeeed5vr2mwd2IehDkZJv6U2bNtUZGRl67NixuYK+/v37mxqrQKNGjdI1atSIKI/NPvjgAx0fH+9ur1y50nxA7N27NyjwlrRVq1b5zmMj+WBMSEjQqampQelSwzxo0KBiO65o8/DDD+uXXnrJ3R44cKC5DwR66623dNWqVd1gxE8e24wePVp369ZN79+/P1fQt3btWpMm161j3759Jm358uW+88Au9OlDkRo6dKhq2bKl6tGjh+fr0i+tefPmQWmtWrVSp06dcvur+cljsy1btqj69eu721JesqZxo0aN3LQmTZqYtY7lNb95bCT9RbOysnKdb3IO21wukbh06ZLas2dPrnPS6xrOzMxUR48e9Z3HJmvWrFEzZ85UM2bM8HxdyksmZZbr1tGwYUNVqVKloOs8XB7YJa64DwDR46+//lK//vpryDz16tVTtWrVMj9nZGSoFStWqO3bt4fcZ0JCQlCasy2vJSYm+soTzTZu3BhyEEV8fLy6++67PV9bsmSJmj17tpo/f76b5lVeQtLkNb95bOT8373Ot82bNxfTUUWXQYMGqbi4ONW/f383Ldw1XLt2bV95bCFfPPr27avS09NV1apV1blz53Ll4TpHfhD0wbfdu3erkSNHhszz6quvql69eplRZv369VOjR492gz4ZjffPP/+YUWiNGzc2N/TbbrtNXblyJWgfly9fNs+lS5c2z37yRDMpIymXvKSkpKhPP/00V/ratWtV79691TvvvKOeeeYZN92rvJwyC1WmOfPYSMpFeJ1vNpeLX3J/WLhwoVq+fLkJVhxc55EZNmyYuUdKzbvcL48dO2bSZQSu3DflNa5z5AdBH3xr06aNuQH5IUFM06ZNzQeAPJyg7+zZs2rEiBEqNTVVtW3bVtWtW9e9oTlkOzY2ViUlJZltP3mimdSGRkr+Dp07d1bDhw9Xo0aNCnpNyktqCiRwKVu2rBu0SNnXqVPHdx4byf89JibG83yzuVz8kPNwypQpatmyZaZZNlBe17CUtVOD5yePLSpXrqz++OMPc68M/BLy8ccfmy4IaWlpprzkes3OzjbBoXPfles68DoPlweWKe5OhbCH10COCRMmmGlHLl265KbJdCwyYi2SPDZZv369rlixoh4zZkye07rI9Djz589307755huTJq/5zWOr1q1bm2lxHOfPn9cVKlTQkyZNKtbjutUHHMhgog0bNoQcbHTx4kU37dlnn9UPPPBARHls5TWQQ2YxKFWqlJ47d66bJiOfZQT0wYMHfeeBXQj6UKxBn3ygyvQBTzzxhF68eLGZmiAuLs5MTRJJHlvI1BjywdipUyfzARD4cKZxEYMHD9bVq1fXs2bNMg+Z52vIkCFB+/KTx0YrVqww59cbb7yhFy1apNu3b2+msgkMRvB/48ePN0GEPAeej4EjRqXspAw7dOhgylSmW5IyllHkkeSxlVfQJ4YNG2ZGN8+cOVN/8cUXZkYDGQUdaR7YIwm9nLcAAAX0SURBVEb+Ke7aRtjhs88+U0uXLlXz5s0LSj958qR69913TX+VGjVqmI7gDz30UMR5bLBgwQI1ceJEz9ekH1W5cuXMzzIw5JNPPlHfffed2e7SpYt65ZVXTJO4w08eW61bt05NnTpVnT592gyikWa26tWrF/dh3ZIGDx7sORK0devWasKECe62lKVcw7Jqh5TlwIEDTZeRQH7y2EiauXv27Gmad++6666ga3j69OlmQJd8lEuXjwEDBqhSpUpFlAf2IOgDAACwAF/pAQAALEDQBwAAYAGCPgAAAAsQ9AEAAFiAoA8AAMACBH0AAAAWIOgDAACwAEEfABSATCTsNTlxJPbs2aN+/PFH/g4AihRBHwDkIIvdf/vtt2HL5dKlS+qpp55S165dK1AZli5d2qyIcubMGf4WAIoMQR8A5LBy5UqzvFg4kydPVo0bN1YtW7YsUBkmJyerxx9/3CxBBgBFJa7I9gwARejPP/9U27dvVwkJCeq+++5T5cuXD1qrefXq1eZnWY+4UaNGKiUlJdc+srKy1E8//aTi4uJUixYtVKVKlcw6p1u2bDG1eF9//bXJJ+vvSnAXSNYxlfV5x48f76bt37/fPCSA27lzp9mX7LdmzZrq6tWrauPGjWa/si5tlSpVgvb3/PPPqz59+qhx48apsmXLFnp5AQBBH4CoIsHW0KFD1ZdffmmCp8zMTHXixAk1f/58t8bt1KlTatGiReZnCbI2bNigOnbsaN4TExNj0uX1F154QTVv3tw0rx44cEBNmzZNVa5c2fTRy87OdvchAWXOoE/68h05ckS1a9fOTVu2bJlKTU1VVatWVTVq1FAXL140wd9HH32kJk6caIK/s2fPqsOHD5tjatCggfveRx55xORfv369euyxx25KWQKwjAaAKDJ9+nSdnJysMzMz3bTU1FSdkpKS53vOnDmjExMTdUZGhpvWokULnZaW5m5nZWXp1atXm59nzJih69atG/I40tPTdZUqVYLSJk+erOW2unDhQjetS5cuJm3p0qVuWtu2bfWAAQNy7bNBgwZBxwQAhYmaPgBRZebMmapZs2Zq1apVptZPHhUrVlR79+41NX6JiYkmnzSnStPt8ePH1b///qtq166tNm/erHr06OE2+/7222/qypUrpjlVmlsfffRR38chNYy33357rvTq1aurbt26udtSGym1fU8++WRQmjQh5yT7k/0CQFEg6AMQVQ4dOmSCuIyMjKD0nj17mnSxa9cu1alTJ7c/X4UKFUyT7+nTp4MGYbz88suqWrVq6sEHH1RPP/206tevnypTpoyv45BAU5qOc8oZCMr+vNIk2MxJ9hcfH+/r9wNApAj6AEQVGWzRvn179d577+WZ58033zR5Zs2a5aZJTZvUCjpkcIbU/Ent4IoVK9Tbb79t+tnNnj3b13E0bNjQTLEigZoElQV148YN09dPglQAKApM2QIgqkjw9tVXX6kLFy4EpctI2cDRu4HBk2xLQOeVX5qD+/btq4YMGaI2bdrk1uJ51cQFktpBGQDivKegpAlYAsjAgSEAUJio6QMQVcaMGWNq5mSkrjTPyshaWc1CJlReu3atySN96t5//33TV0+mY5GmXHkO1L17d9WkSRN1//33q8uXL5vRtb179zavyRQwUosnI3Hr16/vOWWL/F6ZYmXOnDmqQ4cOBf5/zZ0710zQfMcddxR4XwDghaAPQFSRARfSLCvTr0iwJ4GdTHHSq1cvN8+IESNUUlKSWrNmjek/N2nSJHXw4EEVG/v/xg2ZGkVqDH/44QdTYyd5unbt6jbdfv/992rJkiVq9+7dnlO2OL+nVatWKi0tzQzgkPd17tw5KI/MDxg4iEM0bdo0aFtq+D7//HO1ePHiQisnAMgpRobw5koFAPgiEzTL/HuBI3YjJSORpZl45MiRlDqAIkPQBwAAYAEGcgAAAFiAoA8AAMACBH0AAAAWIOgDAACwAEEfAACABQj6AAAALEDQBwAAYAGCPgAAAAsQ9AEAAFiAoA8AAMACBH0AAACq5PsPw/y9f+H7g08AAAAASUVORK5CYII=",
      "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 — 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, 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**, the vector the\n",
    "calibration fits, 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",
    "- $P_0[g]$, conducted power per resource element for band group $g$ (`p0_low`, `p0_mid`, `p0_hi`).\n",
    "  The §7.3 peak gain is inside $G$, so $P_0 + G_{\\max}$ is the boresight EIRP per RE.\n",
    "- $\\text{indoor}_n = (u_n < p_{in})$, 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 §7.4.3) 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 measurement 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, 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 guardrail KPIs 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 $\\boldsymbol\\theta$, 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, 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-Mead 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 to 28 |\n",
    "| `sigma_extra` | extra shadowing σ on top of the 4/6 dB spec values, dB | 0 to 6 |\n",
    "| `p_in` | fraction of users indoors | 0.30 to 0.995 |\n",
    "| `ltw_low`, `ltw_hi` | outdoor-to-indoor wall loss, dB, low/mid vs C-band | 5 to 20, 10 to 30 |\n",
    "| `load` | average PRB load of interfering cells (fraction) | 0.02 to 0.60 |\n",
    "| `agg` | pseudo-device session size (measurement operator) | 1 to 24 |\n",
    "| `bias_mid`, `bias_hi` | NSA band-priority serving bias, dB | 0 to 20 |\n",
    "| `w_tier` | visibility of the wrap-around tier | 0 to 1 |\n",
    "\n",
    "The $P_0$ bounds *are* the physical window: with wider bounds a search can satisfy every\n",
    "quantile target by pushing one $P_0$ to an unphysical EIRP. Constraining power forces the\n",
    "optimiser to explain the targets 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 any calibration is held to, *does the search find a model\n",
    "that reproduces the targets and passes the gate?*, 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",
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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 — 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, and only these, 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: 0.0525   generations run: 60   loss evaluations: 7320\n",
      "CPU times: user 19.2 s, sys: 257 ms, total: 19.5 s\n",
      "Wall time: 19.7 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>0.08</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>0.07</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsrp_p95_within_3db</th>\n",
       "      <td>0.07</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sinr_p50_within_2db</th>\n",
       "      <td>0.01</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       0.08  True   True\n",
       "sinr_p05_within_1p5db      0.0  True   True\n",
       "eirp_sanity_window         NaN  True   True\n",
       "rsrp_p05_within_3db       0.07  True  False\n",
       "rsrp_p95_within_3db       0.07  True  False\n",
       "sinr_p50_within_2db       0.01  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>16.468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p0_mid</th>\n",
       "      <td>12.2</td>\n",
       "      <td>10.377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p0_hi</th>\n",
       "      <td>14.8</td>\n",
       "      <td>15.512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sigma_extra</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>p_in</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ltw_low</th>\n",
       "      <td>12.0</td>\n",
       "      <td>9.975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ltw_hi</th>\n",
       "      <td>27.0</td>\n",
       "      <td>29.652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>load</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>agg</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bias_mid</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4.687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bias_hi</th>\n",
       "      <td>0.0</td>\n",
       "      <td>6.231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>w_tier</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             true (3GPP defaults)  fitted\n",
       "p0_low                       12.2  16.468\n",
       "p0_mid                       12.2  10.377\n",
       "p0_hi                        14.8  15.512\n",
       "sigma_extra                   0.0   0.230\n",
       "p_in                          0.8   0.802\n",
       "ltw_low                      12.0   9.975\n",
       "ltw_hi                       27.0  29.652\n",
       "load                          0.5   0.493\n",
       "agg                           1.0   1.167\n",
       "bias_mid                      0.0   4.687\n",
       "bias_hi                       0.0   6.231\n",
       "w_tier                        1.0   0.994"
      ]
     },
     "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, 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 §4.5: 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, 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, 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, inference, and chaining a cell config\n",
    "\n",
    "### 5.1 What an evaluation returns\n",
    "\n",
    "`Estate.evaluate(params, ...)` runs the forward model of §4.1 once and summarises the result as\n",
    "`guardrail_kpis`, the vocabulary a caller such as the energy-saving rApp reads:\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` | $P(\\mathrm{SINR} < -6 \\text{ dB})$, points that cannot sustain a link |\n",
    "| `coverage_rate` | $P(\\mathrm{RSRP} > -110 \\text{ dBm})$, points inside the coverage footprint |\n",
    "| `serving_share` | fraction of points attached to each cell, 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: τ'}`, recomputes only that cell's gain row,\n",
    "  $G_{c,n} = G(\\theta_{c,n}, \\phi_{c,n};\\ \\tau')$, from the cached angles (§3.7). 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, ...}`, 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 §4.1. 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, 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>-102.466032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsrp_p50</th>\n",
       "      <td>-86.419380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsrp_p95</th>\n",
       "      <td>-61.212970</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>4.487097</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>0.148500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coverage_rate</th>\n",
       "      <td>0.995500</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        -102.466032\n",
       "rsrp_p50        -86.419380\n",
       "rsrp_p95        -61.212970\n",
       "sinr_p5         -15.000000\n",
       "sinr_p50          4.487097\n",
       "sinr_p95         23.000000\n",
       "outage_rate       0.148500\n",
       "coverage_rate     0.995500\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 §4.1\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>-102.498897</td>\n",
       "      <td>-86.467502</td>\n",
       "      <td>-61.274714</td>\n",
       "      <td>-15.015070</td>\n",
       "      <td>4.381411</td>\n",
       "      <td>27.433967</td>\n",
       "      <td>0.1490</td>\n",
       "      <td>0.9955</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>-102.466032</td>\n",
       "      <td>-86.419380</td>\n",
       "      <td>-61.212970</td>\n",
       "      <td>-15.015070</td>\n",
       "      <td>4.487097</td>\n",
       "      <td>27.955000</td>\n",
       "      <td>0.1485</td>\n",
       "      <td>0.9955</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>-102.370230</td>\n",
       "      <td>-86.401641</td>\n",
       "      <td>-61.076828</td>\n",
       "      <td>-15.015070</td>\n",
       "      <td>4.567577</td>\n",
       "      <td>28.114346</td>\n",
       "      <td>0.1475</td>\n",
       "      <td>0.9955</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>-102.439657</td>\n",
       "      <td>-86.439211</td>\n",
       "      <td>-61.087484</td>\n",
       "      <td>-15.044738</td>\n",
       "      <td>4.452512</td>\n",
       "      <td>27.955000</td>\n",
       "      <td>0.1490</td>\n",
       "      <td>0.9955</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18</td>\n",
       "      <td>-102.466032</td>\n",
       "      <td>-86.486309</td>\n",
       "      <td>-61.193694</td>\n",
       "      <td>-15.106126</td>\n",
       "      <td>4.392688</td>\n",
       "      <td>27.132619</td>\n",
       "      <td>0.1495</td>\n",
       "      <td>0.9955</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>22</td>\n",
       "      <td>-102.498897</td>\n",
       "      <td>-86.543277</td>\n",
       "      <td>-61.274714</td>\n",
       "      <td>-15.106126</td>\n",
       "      <td>4.304045</td>\n",
       "      <td>27.132619</td>\n",
       "      <td>0.1510</td>\n",
       "      <td>0.9955</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>0.0030</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 -102.498897 -86.467502 -61.274714 -15.015070  4.381411  27.433967   \n",
       "1         6 -102.466032 -86.419380 -61.212970 -15.015070  4.487097  27.955000   \n",
       "2        10 -102.370230 -86.401641 -61.076828 -15.015070  4.567577  28.114346   \n",
       "3        14 -102.439657 -86.439211 -61.087484 -15.044738  4.452512  27.955000   \n",
       "4        18 -102.466032 -86.486309 -61.193694 -15.106126  4.392688  27.132619   \n",
       "5        22 -102.498897 -86.543277 -61.274714 -15.106126  4.304045  27.132619   \n",
       "\n",
       "   outage_rate  coverage_rate  samples  own_share  \n",
       "0       0.1490         0.9955   2000.0     0.0060  \n",
       "1       0.1485         0.9955   2000.0     0.0190  \n",
       "2       0.1475         0.9955   2000.0     0.0225  \n",
       "3       0.1490         0.9955   2000.0     0.0165  \n",
       "4       0.1495         0.9955   2000.0     0.0085  \n",
       "5       0.1510         0.9955   2000.0     0.0030  "
      ]
     },
     "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 §3.7 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 to 10° from a 30 m mast. At 2° the lobe\n",
    "points *over* that area, gain at the users is down the side of the parabola and the cell barely\n",
    "serves anyone. At 22° it points at the mast base, and again the served area is off-peak. In\n",
    "between there is an optimum (here around 6 to 10°) 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, 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`, 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>-102.466032</td>\n",
       "      <td>-103.857801</td>\n",
       "      <td>-1.391769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsrp_p50</th>\n",
       "      <td>-86.419380</td>\n",
       "      <td>-87.137580</td>\n",
       "      <td>-0.718200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rsrp_p95</th>\n",
       "      <td>-61.212970</td>\n",
       "      <td>-61.890487</td>\n",
       "      <td>-0.677516</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>4.487097</td>\n",
       "      <td>4.372259</td>\n",
       "      <td>-0.114838</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>0.148500</td>\n",
       "      <td>0.161500</td>\n",
       "      <td>0.013000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>coverage_rate</th>\n",
       "      <td>0.995500</td>\n",
       "      <td>0.992500</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             -102.466032       -103.857801 -1.391769\n",
       "rsrp_p50             -86.419380        -87.137580 -0.718200\n",
       "rsrp_p95             -61.212970        -61.890487 -0.677516\n",
       "sinr_p5              -15.000000        -15.000000  0.000000\n",
       "sinr_p50               4.487097          4.372259 -0.114838\n",
       "sinr_p95              23.000000         23.000000  0.000000\n",
       "outage_rate            0.148500          0.161500  0.013000\n",
       "coverage_rate          0.995500          0.992500 -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 §4.1 `argmax` to the next-best\n",
    "eligible cell, the co-sited 3.5 GHz cell on the same sector, the neighbouring sectors, or the\n",
    "adjacent site on the same band, 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, 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 guardrail check reads exactly this\n",
    "`delta` column: if `sinr_p5` or `coverage_rate` had fallen past its floor, the rApp 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§3: ENU, grid, P_LOS,\\nPL, SF, G — cached\", COBALT),\n",
    "    (\"differential_evolution\\n§4: min L(θ)\\nover 10 quantiles\", COBALT),\n",
    "    (\"fitted_params\", INK),\n",
    "    (\"Estate.evaluate(fitted_params,\\ntilt_override / off_cells)\\n§4.1 forward model\", TEAL),\n",
    "    (\"guardrail_kpis\", COPPER),\n",
    "], loop=(5, 4, \"chain another config\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b147cb64",
   "metadata": {},
   "source": [
    "- **Section 2** generated a fictional example estate in the CSV shape the service loads.\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 §7.3 antenna pattern, and a\n",
    "  wrap-around tier, all as $(C \\times N)$ NumPy matrices, all deterministic.\n",
    "- **Section 4** ran the actual calibration search, 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, 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, `Estate.evaluate()`, 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",
    "  caller uses.\n",
    "\n",
    "Every number in this notebook carries the **generic** rung, `simulation, generic 3GPP defaults\n",
    "(uncalibrated)`: public defaults, not fitted to anything. For the calibration path, see the\n",
    "[service README](../README.md)."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}