{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9cb9a9e8",
   "metadata": {},
   "source": [
    "# Unidirectional waveguide grating antenna"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5e68cb5",
   "metadata": {},
   "source": [
    "There has been a growing interest in using silicon photonic phased arrays for applications in light detection and ranging (LIDAR) and free-space communication. For these technologies to be effectively implemented, it is crucial to have large optical apertures. These apertures are necessary to maintain a small diffraction angle and ensure a large area for receiving signals. In the context of one-dimensional arrays, there has been extensive exploration of long waveguide grating antennas (WGAs). WGAs are created by introducing periodic perturbations in an integrated waveguide and have been a popular choice for achieving large apertures, as their length can be extended to several millimeters.\n",
    "\n",
    "In this notebook, we demonstrate a unidirectional silicon nitride WGA with a dual-layer design based on the work `Manan Raval, Christopher V. Poulton, and Michael R. Watts, \"Unidirectional waveguide grating antennas with uniform emission for optical phased arrays,\" Opt. Lett. 42, 2563-2566 (2017)` [DOI: 10.1364/OL.42.002563](https://doi.org/10.1364/OL.42.002563).\n",
    "\n",
    "<img src=\"img/wga_schematic.png\" width=\"400\" alt=\"Schematic of the waveguide grating antenna\">\n",
    "\n",
    "For more integrated photonic examples such as the [8-Channel mode and polarization de-multiplexer](https://www.flexcompute.com/tidy3d/examples/notebooks/8ChannelDemultiplexer/), the [broadband bi-level taper polarization rotator-splitter](https://www.flexcompute.com/tidy3d/examples/notebooks/BilevelPSR/), and the [broadband directional coupler](https://www.flexcompute.com/tidy3d/examples/notebooks/BroadbandDirectionalCoupler/), please visit our [examples page](https://www.flexcompute.com/tidy3d/examples/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad580b1c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:34:46.677166Z",
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     "shell.execute_reply": "2025-05-15T10:34:48.047419Z"
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   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import tidy3d as td\n",
    "import tidy3d.web as web\n",
    "from tidy3d.plugins.mode import ModeSolver\n",
    "from tidy3d.plugins.mode.web import run as run_mode_solver"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6b5be88",
   "metadata": {},
   "source": [
    "## Design Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c7d9b0c",
   "metadata": {},
   "source": [
    "In the first section of the notebook, we will design and optimize the WGA to ensure maximum unidirectional emission. This includes performing mode solving to calculate the effective indices of different waveguide sections and parameter sweeping the offset length between the two waveguides.\n",
    "\n",
    "The WGA is designed to work at 1550 nm. Since FDTD simulation typically requires launching a broadband light source, we defined the central frequency of the pulse to be `td.C_0/(1.55 μm)`, where `td.C_0` is the speed of light in μm/s, and the frequency width to be 10% of the central frequency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "afbb70a2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:34:48.049662Z",
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     "shell.execute_reply": "2025-05-15T10:34:48.051100Z"
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   "outputs": [],
   "source": [
    "lda0 = 1.55  # central wavelength\n",
    "freq0 = td.C_0 / lda0  # central frequency\n",
    "fwidth = freq0 / 10  # width of the source frequency range"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22606231",
   "metadata": {},
   "source": [
    "The waveguide is made of silicon nitride and the cladding is made of silicon oxide. Both materials can be imported from the [material library](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/material_library.html#) directly. Note that the refractive index of the materials might not be identical to what is represented in the [reference](https://doi.org/10.1364/OL.42.002563). Therefore, the simulation results are not quantitatively comparable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7b4afb8e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:34:48.052824Z",
     "iopub.status.busy": "2025-05-15T10:34:48.052681Z",
     "iopub.status.idle": "2025-05-15T10:34:48.054278Z",
     "shell.execute_reply": "2025-05-15T10:34:48.054040Z"
    }
   },
   "outputs": [],
   "source": [
    "SiN = td.material_library[\"Si3N4\"][\"Luke2015PMLStable\"]\n",
    "\n",
    "SiO2 = td.material_library[\"SiO2\"][\"Palik_Lossless\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d14eb836",
   "metadata": {},
   "source": [
    "Define geometric parameters including nitride layer thickness, gap size between the layers, and so on. In this particular case, we only model 10 periods of the gratings, similar to what is demonstrated in the [reference](https://doi.org/10.1364/OL.42.002563). Since Tidy3D can handle much larger simulations, a much longer WGA can be simulated in principle. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f3d13220",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-05-15T10:34:48.056764Z"
    }
   },
   "outputs": [],
   "source": [
    "t = 0.2  # silicon nitride layer thickness\n",
    "w = 1  # waveguide width of the full width regions\n",
    "gap = 0.1  # gap size between the layers\n",
    "L_f = 0.5  # length of the full width regions\n",
    "N = 10  # number of periods"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18eb589e",
   "metadata": {},
   "source": [
    "### Mode Solving to Determine the Effective Indices"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb5f7a16",
   "metadata": {},
   "source": [
    "The WGA is engineered to emit vertically at a specific design wavelength of 1550 nm. Consequently, the dimensions of the four waveguide segments must be selected to ensure that the total optical path length for one cycle equals exactly one wavelength. That is, \n",
    "\n",
    "$$\\lambda_0 = (L_f-L_0)n_{full} + (L_p(p)-L_0)n_s(p) + 2L_0n_a(p),$$\n",
    "\n",
    "where $L_f$ is the length of the full width regions, $L_0$ is the offset in the direction of propagation between the two waveguides, $L_p$ is the length of the perturbed regions, $p$ is the perturbation value, $n_{full}$, $n_s$ and $n_a$ are the effective indices of different cross sections as indicated below as well as in Fig.2(a) of the [reference](https://doi.org/10.1364/OL.42.002563). \n",
    "\n",
    "<img src=\"img/wga_schematic_2.png\" width=\"400\" alt=\"Schematic of the waveguide grating antenna\">\n",
    "\n",
    "These effective indices can be calculated from mode solving. To do so, we define a function `cal_n_eff` that takes two arguments `p1` and `p2`, which represent the perturbations of the top waveguide and button waveguide, perform the mode solving, and return the effective index. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1bd71b32",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-05-15T10:34:48.060664Z"
    }
   },
   "outputs": [],
   "source": [
    "def cal_n_eff(p1, p2):\n",
    "    # define the top waveguide\n",
    "    top_layer = td.Structure(\n",
    "        geometry=td.Box(center=(0, 0, gap / 2 + t / 2), size=(td.inf, w - 2 * p1, t)), medium=SiN\n",
    "    )\n",
    "\n",
    "    # define the bottom waveguide\n",
    "    bottom_layer = td.Structure(\n",
    "        geometry=td.Box(center=(0, 0, -gap / 2 - t / 2), size=(td.inf, w - 2 * p2, t)), medium=SiN\n",
    "    )\n",
    "\n",
    "    # define a simulation for mode solving\n",
    "    sim = td.Simulation(\n",
    "        size=(1, 3 * w, 10 * t),\n",
    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=50, wavelength=lda0),\n",
    "        structures=[top_layer, bottom_layer],\n",
    "        run_time=1e-12,\n",
    "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    "        medium=SiO2,\n",
    "    )\n",
    "\n",
    "    # define a mode solver\n",
    "    mode_spec = td.ModeSpec(num_modes=1, target_neff=2)\n",
    "    mode_solver = ModeSolver(\n",
    "        simulation=sim,\n",
    "        plane=td.Box(size=(0, td.inf, td.inf)),\n",
    "        mode_spec=mode_spec,\n",
    "        freqs=[freq0],\n",
    "    )\n",
    "\n",
    "    # solver for the effective index\n",
    "    mode_data = run_mode_solver(mode_solver, verbose=False)\n",
    "    n_eff = mode_data.n_eff.values[0][0]\n",
    "\n",
    "    return n_eff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3057547",
   "metadata": {},
   "source": [
    "$n_{full}$ is calculated by `p1=p2=0`. $n_a$ is calculated by `p1=p, p2=0`. And $n_s$ is calculated by `p1=p2=p`. Here we set $p$ to be 100 nm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4040ea25",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:34:48.062219Z",
     "iopub.status.busy": "2025-05-15T10:34:48.062090Z",
     "iopub.status.idle": "2025-05-15T10:35:39.440745Z",
     "shell.execute_reply": "2025-05-15T10:35:39.440253Z"
    }
   },
   "outputs": [],
   "source": [
    "p = 0.1  # width perturbation\n",
    "\n",
    "# calculate the effective indices\n",
    "n_full = cal_n_eff(0, 0)\n",
    "n_a = cal_n_eff(p, 0)\n",
    "n_s = cal_n_eff(p, p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec396285",
   "metadata": {},
   "source": [
    "### Parameter Sweep to Determine the Offset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f485a81",
   "metadata": {},
   "source": [
    "A unidirectional emitter can be achieved by utilizing two scattering elements spaced a quarter wavelength apart. This arrangement generates constructive interference in one vertical direction while causing destructive interference in the opposite direction. In this example, the constant thicknesses of the silicon nitride layers and the separation gap hinder the achievement of full constructive interference for upward emission. Therefore, to find the optimal $L_0$, one needs to perform a parameter sweep to find the $L_0$ value such that the downward emission is minimized.\n",
    "\n",
    "To do so, we define a helper function `make_sim` that takes a given $L_0$ and returns a simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "82534b36",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "def make_sim(L_0):\n",
    "    # calculate L_p\n",
    "    L_p = (lda0 + L_0 * (n_full + n_s - 2 * n_a) - L_f * n_full) / n_s\n",
    "\n",
    "    period = L_p + L_f  # periodic the grating\n",
    "\n",
    "    # define the grating structures\n",
    "    structures = []\n",
    "    for i in range(N):\n",
    "        structures.append(\n",
    "            td.Structure(\n",
    "                geometry=td.Box(\n",
    "                    center=(L_p / 2 + i * period, 0, -gap / 2 - t / 2), size=(L_p, w - 2 * p, t)\n",
    "                ),\n",
    "                medium=SiN,\n",
    "            )\n",
    "        )\n",
    "\n",
    "        structures.append(\n",
    "            td.Structure(\n",
    "                geometry=td.Box(\n",
    "                    center=(L_p / 2 + L_0 + i * period, 0, gap / 2 + t / 2),\n",
    "                    size=(L_p, w - 2 * p, t),\n",
    "                ),\n",
    "                medium=SiN,\n",
    "            )\n",
    "        )\n",
    "\n",
    "        structures.append(\n",
    "            td.Structure(\n",
    "                geometry=td.Box(\n",
    "                    center=(L_p + L_f / 2 + i * period, 0, -gap / 2 - t / 2), size=(L_f, w, t)\n",
    "                ),\n",
    "                medium=SiN,\n",
    "            )\n",
    "        )\n",
    "\n",
    "        structures.append(\n",
    "            td.Structure(\n",
    "                geometry=td.Box(\n",
    "                    center=(L_p + L_f / 2 + L_0 + i * period, 0, gap / 2 + t / 2), size=(L_f, w, t)\n",
    "                ),\n",
    "                medium=SiN,\n",
    "            )\n",
    "        )\n",
    "\n",
    "    # create the input/output straight waveguide structures\n",
    "    inf_eff = 1e3  # effective infinity\n",
    "\n",
    "    structures.append(\n",
    "        td.Structure(\n",
    "            geometry=td.Box.from_bounds(\n",
    "                rmin=(-inf_eff, -w / 2, -gap / 2 - t), rmax=(0, w / 2, -gap / 2)\n",
    "            ),\n",
    "            medium=SiN,\n",
    "        )\n",
    "    )\n",
    "\n",
    "    structures.append(\n",
    "        td.Structure(\n",
    "            geometry=td.Box.from_bounds(\n",
    "                rmin=(-inf_eff, -w / 2, gap / 2), rmax=(L_0, w / 2, gap / 2 + t)\n",
    "            ),\n",
    "            medium=SiN,\n",
    "        )\n",
    "    )\n",
    "\n",
    "    structures.append(\n",
    "        td.Structure(\n",
    "            geometry=td.Box.from_bounds(\n",
    "                rmin=(L_p + L_f / 2 + (N - 1) * period, -w / 2, -gap / 2 - t),\n",
    "                rmax=(inf_eff, w / 2, -gap / 2),\n",
    "            ),\n",
    "            medium=SiN,\n",
    "        )\n",
    "    )\n",
    "\n",
    "    structures.append(\n",
    "        td.Structure(\n",
    "            geometry=td.Box.from_bounds(\n",
    "                rmin=(L_p + L_f / 2 + L_0 + (N - 1) * period, -w / 2, gap / 2),\n",
    "                rmax=(inf_eff, w / 2, gap / 2 + t),\n",
    "            ),\n",
    "            medium=SiN,\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # define a mode source to launch the fundamental mode\n",
    "    mode_spec = td.ModeSpec(num_modes=1, target_neff=2)\n",
    "    mode_source = td.ModeSource(\n",
    "        center=(-lda0, 0, 0),\n",
    "        size=(0, 3 * w, 10 * t),\n",
    "        source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
    "        direction=\"+\",\n",
    "        mode_spec=mode_spec,\n",
    "        mode_index=0,\n",
    "    )\n",
    "\n",
    "    # define a flux monitor to measure downward radiation\n",
    "    flux_down = td.FluxMonitor(\n",
    "        center=(0, 0, -8 * t), size=(td.inf, td.inf, 0), freqs=[freq0], name=\"flux\"\n",
    "    )\n",
    "\n",
    "    run_time = 5e-13  # simulation run time\n",
    "\n",
    "    # define simulation\n",
    "    sim = td.Simulation(\n",
    "        center=(period * (N + 1) // 2, 0, 0),\n",
    "        size=(2 * N * period, 4 * w, 20 * t),\n",
    "        grid_spec=td.GridSpec.auto(min_steps_per_wvl=30, wavelength=lda0),\n",
    "        structures=structures,\n",
    "        sources=[mode_source],\n",
    "        monitors=[flux_down],\n",
    "        run_time=run_time,\n",
    "        boundary_spec=td.BoundarySpec.all_sides(boundary=td.PML()),\n",
    "        medium=SiO2,\n",
    "        symmetry=(0, -1, 0),\n",
    "    )\n",
    "\n",
    "    return sim"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce77c5e5",
   "metadata": {},
   "source": [
    "To ensure the simulation setup is correct, we create a test simulation and visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99e29f7a",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-05-15T10:35:39.529966Z"
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   },
   "outputs": [
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       "\n",
       "    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"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\" ></div>\n",
       "    <script>\n",
       "        \n",
       "        /**\n",
       "        * Simulation Viewer Injector\n",
       "        *\n",
       "        * Monitors the document for elements being added in the form:\n",
       "        *\n",
       "        *    <div class=\"simulation-viewer\" data-width=\"800\" data-height=\"800\" data-simulation=\"{...}\" />\n",
       "        *\n",
       "        * This script will then inject an iframe to the viewer application, and pass it the simulation data\n",
       "        * via the postMessage API on request. The script may be safely included multiple times, with only the\n",
       "        * configuration of the first started script (e.g. viewer URL) applying.\n",
       "        *\n",
       "        */\n",
       "        (function() {\n",
       "            const TARGET_CLASS = \"simulation-viewer\";\n",
       "            const ACTIVE_CLASS = \"simulation-viewer-active\";\n",
       "            const VIEWER_URL = \"https://tidy3d.simulation.cloud/simulation-viewer\";\n",
       "\n",
       "            class SimulationViewerInjector {\n",
       "                constructor() {\n",
       "                    for (var node of document.getElementsByClassName(TARGET_CLASS)) {\n",
       "                        this.injectViewer(node);\n",
       "                    }\n",
       "\n",
       "                    // Monitor for newly added nodes to the DOM\n",
       "                    this.observer = new MutationObserver(this.onMutations.bind(this));\n",
       "                    this.observer.observe(document.body, {childList: true, subtree: true});\n",
       "                }\n",
       "\n",
       "                onMutations(mutations) {\n",
       "                    for (var mutation of mutations) {\n",
       "                        if (mutation.type === 'childList') {\n",
       "                            /**\n",
       "                            * Have found that adding the element does not reliably trigger the mutation observer.\n",
       "                            * It may be the case that setting content with innerHTML does not trigger.\n",
       "                            *\n",
       "                            * It seems to be sufficient to re-scan the document for un-activated viewers\n",
       "                            * whenever an event occurs, as Jupyter triggers multiple events on cell evaluation.\n",
       "                            */\n",
       "                            var viewers = document.getElementsByClassName(TARGET_CLASS);\n",
       "                            for (var node of viewers) {\n",
       "                                this.injectViewer(node);\n",
       "                            }\n",
       "                        }\n",
       "                    }\n",
       "                }\n",
       "\n",
       "                injectViewer(node) {\n",
       "                    // (re-)check that this is a valid simulation container and has not already been injected\n",
       "                    if (node.classList.contains(TARGET_CLASS) && !node.classList.contains(ACTIVE_CLASS)) {\n",
       "                        // Mark node as injected, to prevent re-runs\n",
       "                        node.classList.add(ACTIVE_CLASS);\n",
       "\n",
       "                        var uuid;\n",
       "                        if (window.crypto && window.crypto.randomUUID) {\n",
       "                            uuid = window.crypto.randomUUID();\n",
       "                        } else {\n",
       "                            uuid = \"\" + Math.random();\n",
       "                        }\n",
       "\n",
       "                        var frame = document.createElement(\"iframe\");\n",
       "                        frame.width = node.dataset.width || 800;\n",
       "                        frame.height = node.dataset.height || 800;\n",
       "                        frame.style.cssText = `width:${frame.width}px;height:${frame.height}px;max-width:none;border:0;display:block`\n",
       "                        frame.src = VIEWER_URL + \"?uuid=\" + uuid;\n",
       "\n",
       "                        var postMessageToViewer;\n",
       "                        postMessageToViewer = event => {\n",
       "                            if(event.data.type === 'viewer' && event.data.uuid===uuid){\n",
       "                                frame.contentWindow.postMessage({ type: 'jupyter', uuid, value: node.dataset.simulation, fileType: 'hdf5'}, '*');\n",
       "\n",
       "                                // Run once only\n",
       "                                window.removeEventListener('message', postMessageToViewer);\n",
       "                            }\n",
       "                        };\n",
       "                        window.addEventListener(\n",
       "                            'message',\n",
       "                            postMessageToViewer,\n",
       "                            false\n",
       "                        );\n",
       "\n",
       "                        node.appendChild(frame);\n",
       "                    }\n",
       "                }\n",
       "            }\n",
       "\n",
       "            if (!window.simulationViewerInjector) {\n",
       "                window.simulationViewerInjector = new SimulationViewerInjector();\n",
       "            }\n",
       "        })();\n",
       "    \n",
       "    </script>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim = make_sim(0.25)\n",
    "sim.plot_3d()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e4a55a0",
   "metadata": {},
   "source": [
    "We perform a parameter sweep for $L_0$ from 100 nm to 250 nm to determine the optimal offset length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c613b54",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:35:39.532528Z",
     "iopub.status.busy": "2025-05-15T10:35:39.532396Z",
     "iopub.status.idle": "2025-05-15T10:36:28.537189Z",
     "shell.execute_reply": "2025-05-15T10:36:28.536156Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b6ad788d47a43c989f1a4fc75d94f54",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:35:45 CEST </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">16</span> tasks.                     \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:35:45 CEST\u001b[0m\u001b[2;36m \u001b[0mStarted working on Batch containing \u001b[1;36m16\u001b[0m tasks.                     \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:36:02 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.227</span> for the whole batch.               \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:36:02 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m1.227\u001b[0m for the whole batch.               \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Use <span style=\"color: #008000; text-decoration-color: #008000\">'Batch.real_cost()'</span> to get the billed FlexCredit cost after   \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>the Batch has completed.                                          \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mUse \u001b[32m'Batch.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed FlexCredit cost after   \n",
       "\u001b[2;36m              \u001b[0mthe Batch has completed.                                          \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5756d9113acf4e96bdca8d5355ff59a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:36:13 CEST </span>Batch complete.                                                   \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:36:13 CEST\u001b[0m\u001b[2;36m \u001b[0mBatch complete.                                                   \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b111c9199e854176b58e5b93667ab891",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "L_0_list = np.linspace(0.1, 0.25, 16)  # range of the parameter sweep\n",
    "\n",
    "# create simulations\n",
    "sims = {f\"L_0={L_0:.2f}\": make_sim(L_0) for L_0 in L_0_list}\n",
    "\n",
    "# create a batch and run it\n",
    "batch = web.Batch(simulations=sims, verbose=True)\n",
    "batch_results = batch.run(path_dir=\"data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16530715",
   "metadata": {},
   "source": [
    "Extract the downward radiation flux from the sweep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5bb9132e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:36:28.798346Z",
     "iopub.status.busy": "2025-05-15T10:36:28.798204Z",
     "iopub.status.idle": "2025-05-15T10:36:33.162520Z",
     "shell.execute_reply": "2025-05-15T10:36:33.162012Z"
    }
   },
   "outputs": [],
   "source": [
    "downward_radiation = []\n",
    "\n",
    "for L_0 in L_0_list:\n",
    "    sim_data = batch_results[f\"L_0={L_0:.2f}\"]\n",
    "    downward_radiation.append(np.abs(sim_data[\"flux\"].flux))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9c08725",
   "metadata": {},
   "source": [
    "Plot the downward radiation flux as a function of $L_0$. Then determine the optimal $L_0$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7a72a4a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:36:33.164162Z",
     "iopub.status.busy": "2025-05-15T10:36:33.164041Z",
     "iopub.status.idle": "2025-05-15T10:36:33.210450Z",
     "shell.execute_reply": "2025-05-15T10:36:33.210107Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The optimal offset is 170.0 nm\n"
     ]
    }
   ],
   "source": [
    "plt.plot(L_0_list, downward_radiation, linewidth=3, c=\"red\")\n",
    "plt.xlabel(\"Offset (μm)\")\n",
    "plt.ylabel(\"Downward radiation (%)\")\n",
    "plt.show()\n",
    "\n",
    "# find the offset that results in minimal downward radiation\n",
    "i = np.argmin(downward_radiation)\n",
    "L_0_opt = L_0_list[i]\n",
    "print(f\"The optimal offset is {L_0_opt * 1e3} nm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "609cb8a2",
   "metadata": {},
   "source": [
    "## Radiation Pattern of the Optimal Design"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb104a88",
   "metadata": {},
   "source": [
    "To simulate the optimal design, we also define two [FieldProjectionAngleMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FieldProjectionAngleMonitor.html) objects to compute the far-field radiation pattern upward and downward. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "dcaf9545",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:36:33.211670Z",
     "iopub.status.busy": "2025-05-15T10:36:33.211562Z",
     "iopub.status.idle": "2025-05-15T10:36:40.179730Z",
     "shell.execute_reply": "2025-05-15T10:36:40.179263Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:36:33 CEST </span>Created task <span style=\"color: #008000; text-decoration-color: #008000\">'optimized wga'</span> with task_id                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><span style=\"color: #008000; text-decoration-color: #008000\">'fdve-a3953bd2-7d28-457f-8936-d45738c2f392'</span> and task_type <span style=\"color: #008000; text-decoration-color: #008000\">'FDTD'</span>. \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:36:33 CEST\u001b[0m\u001b[2;36m \u001b[0mCreated task \u001b[32m'optimized wga'\u001b[0m with task_id                         \n",
       "\u001b[2;36m              \u001b[0m\u001b[32m'fdve-a3953bd2-7d28-457f-8936-d45738c2f392'\u001b[0m and task_type \u001b[32m'FDTD'\u001b[0m. \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>View task using web UI at                                         \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3953bd2-7d28-457f-8936-d45738c2f392\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3953bd2-7d</span></a>\n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span><a href=\"https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3953bd2-7d28-457f-8936-d45738c2f392\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">28-457f-8936-d45738c2f392'</span></a>.                                       \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mView task using web UI at                                         \n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=872314;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3953bd2-7d28-457f-8936-d45738c2f392\u001b\\\u001b[32m'https://tidy3d.simulation.cloud/workbench?\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=922922;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3953bd2-7d28-457f-8936-d45738c2f392\u001b\\\u001b[32mtaskId\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=872314;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3953bd2-7d28-457f-8936-d45738c2f392\u001b\\\u001b[32m=\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=237251;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3953bd2-7d28-457f-8936-d45738c2f392\u001b\\\u001b[32mfdve\u001b[0m\u001b]8;;\u001b\\\u001b]8;id=872314;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3953bd2-7d28-457f-8936-d45738c2f392\u001b\\\u001b[32m-a3953bd2-7d\u001b[0m\u001b]8;;\u001b\\\n",
       "\u001b[2;36m              \u001b[0m\u001b]8;id=872314;https://tidy3d.simulation.cloud/workbench?taskId=fdve-a3953bd2-7d28-457f-8936-d45738c2f392\u001b\\\u001b[32m28-457f-8936-d45738c2f392'\u001b[0m\u001b]8;;\u001b\\.                                       \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>Task folder: <a href=\"https://tidy3d.simulation.cloud/folders/folder-bfe11006-2cae-4abd-bc02-04b6047e01be\" target=\"_blank\"><span style=\"color: #008000; text-decoration-color: #008000\">'default'</span></a>.                                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m             \u001b[0m\u001b[2;36m \u001b[0mTask folder: \u001b]8;id=553885;https://tidy3d.simulation.cloud/folders/folder-bfe11006-2cae-4abd-bc02-04b6047e01be\u001b\\\u001b[32m'default'\u001b[0m\u001b]8;;\u001b\\.                                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a5eb541ad55541c59808573e238e09b1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:36:35 CEST </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.081</span>. Minimum cost depends on task      \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>execution details. Use <span style=\"color: #008000; text-decoration-color: #008000\">'web.real_cost(task_id)'</span> to get the billed \n",
       "<span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">              </span>FlexCredit cost after a simulation run.                           \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:36:35 CEST\u001b[0m\u001b[2;36m \u001b[0mMaximum FlexCredit cost: \u001b[1;36m0.081\u001b[0m. Minimum cost depends on task      \n",
       "\u001b[2;36m              \u001b[0mexecution details. Use \u001b[32m'web.real_cost\u001b[0m\u001b[32m(\u001b[0m\u001b[32mtask_id\u001b[0m\u001b[32m)\u001b[0m\u001b[32m'\u001b[0m to get the billed \n",
       "\u001b[2;36m              \u001b[0mFlexCredit cost after a simulation run.                           \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:36:36 CEST </span>status = success                                                  \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:36:36 CEST\u001b[0m\u001b[2;36m \u001b[0mstatus = success                                                  \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5477623a0ff44369974a7986b9f2d2f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7fbfbf; text-decoration-color: #7fbfbf\">12:36:40 CEST </span>loading simulation from simulation_data.hdf5                      \n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[2;36m12:36:40 CEST\u001b[0m\u001b[2;36m \u001b[0mloading simulation from simulation_data.hdf5                      \n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim_opt = make_sim(L_0_opt)  # create simulation for the optimal design\n",
    "\n",
    "# define field projection angle ranges\n",
    "theta_array = np.linspace(0, np.deg2rad(60), 100)  # polar angle\n",
    "phi_array = np.linspace(0, 2 * np.pi, 200)  # azimuthal angle\n",
    "\n",
    "# define the top field projection monitor\n",
    "monitor_top = td.FieldProjectionAngleMonitor(\n",
    "    name=\"top\",\n",
    "    center=(0, 0, 8 * t),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    freqs=[freq0],\n",
    "    theta=theta_array,\n",
    "    phi=phi_array,\n",
    ")\n",
    "\n",
    "# define the bottom field projection monitor\n",
    "monitor_bottom = td.FieldProjectionAngleMonitor(\n",
    "    name=\"bottom\",\n",
    "    normal_dir=\"-\",\n",
    "    center=(0, 0, -8 * t),\n",
    "    size=(td.inf, td.inf, 0),\n",
    "    freqs=[freq0],\n",
    "    theta=np.pi - theta_array,\n",
    "    phi=phi_array,\n",
    ")\n",
    "\n",
    "# add the field projection monitors to the simulation\n",
    "sim_opt = sim_opt.copy(update={\"monitors\": [monitor_top, monitor_bottom]})\n",
    "\n",
    "# run the simulation\n",
    "sim_data_opt = web.run(simulation=sim_opt, task_name=\"optimized wga\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f037e81",
   "metadata": {},
   "source": [
    "Plot the normalized upward radiation pattern. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "28b08cf9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:36:40.198733Z",
     "iopub.status.busy": "2025-05-15T10:36:40.198675Z",
     "iopub.status.idle": "2025-05-15T10:36:41.065844Z",
     "shell.execute_reply": "2025-05-15T10:36:41.065481Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "P = sim_data_opt[\"top\"].power.squeeze(drop=True)  # extract upward radiated power\n",
    "P_max = np.max(P)\n",
    "P_norm = P / P_max\n",
    "\n",
    "# plot the radiation pattern in polar coordinates\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, polar=True)\n",
    "c = ax.pcolor(\n",
    "    phi_array, np.rad2deg(theta_array), P_norm, shading=\"auto\", cmap=\"hot\", vmin=0, vmax=1\n",
    ")\n",
    "cb = fig.colorbar(c, ax=ax)\n",
    "cb.set_label(\"Intensity\")\n",
    "_ = plt.setp(ax.get_yticklabels(), color=\"white\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c56cfcb1",
   "metadata": {},
   "source": [
    "Plot the normalized downward radiation pattern on the same color scale. In comparison, we can see that the downward radiation is nearly invisible since it is orders of magnitude weaker than the upward radiation as desired. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9e55075b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-05-15T10:36:41.067208Z",
     "iopub.status.busy": "2025-05-15T10:36:41.067124Z",
     "iopub.status.idle": "2025-05-15T10:36:42.010866Z",
     "shell.execute_reply": "2025-05-15T10:36:42.010467Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hVKlSQpsokgf4TyDcVoJAqFvoBIK4xCCSKzx8+JDZs2dz/Ngx7GxtWbhoEZFhYRw/eJAXz54JbZ5IPic+Pp6rFy5w9cIFyleqxO/9+qFraMjmTZvYuXMnP/74IzNmzKBKlSpCmyqSJ8jb3RzzKqJAEFEq169fZ+bMmbi6ulKpQgVWrlqFj5cX29at452Xl9DmiRRAXjx9younT7ErVYquXbtSvGRJNm/aRPXq1WnevDmzZ8+mfv36QpspIpLvkAhtgEjB4Pbt29StW5emTZsSGx3N+nXraNKoES7z5rF2+XJRHIjkOt5v37Jm6VJWLlxIs8aN2bhxI5Hh4TRr2pT69epx9+5doU0UEYxEJWyFD1EgiCjE8+fPad++PY0bN0ZLQ4NNf/5J5QoVmOPszNb16wkMCBDaRJFCRoCfH1vWrWOuszPVKldm/YYNSCQSGjZowA8//MCLFy+ENlFE5aRkMci7Fc4sBlEgiMiFj48Pv/76K1WrVsXP15d1a9dSsXx5po4dy57t2wtdYSORvEfw58/s3raN6ePGUbViRdauXcs7Ly+qVK7Mb7/9hq+vr9AmiojkaUSBIJIjPn/+zNixY7G3t+fGtWu4LF9Oi6ZNmT99Oru3biUiPFxoE0VE0hEeFsZfW7awcOZM2rZuzfLly7nu6oq9vT3jx48nJCREaBNFch1xiUEexCBFkWwRHx/PypUrmT1rFqYmJsydO5fYqCjWu7jg8+6d0OaJiHyXT4GBbFi5kpJ2dvTu3RstHR3WrV3L2jVrmDN3LqNHj0ZLS0toM0VyBTGLQR5EgSDyXc6dO8fAgQOJCAtj+IgRFDU15Z+//uLV8+dCmyYikmPee3uzbN48ylesyJjRo/kYFMTCBQvYsH49W7ZupUWLFkKbKKJ0RIEgD+ISg0iWvH//no4dO9K+fXuqV63KwkWLuHP1KrOdnUVxIJLvefHsGbMmT8btxg0WLVxI5cqVad+uHT/++CMfPnwQ2jwREcERBYLIV8TFxTF//nzKlinD61evWL9+PSaGhjiPGsW9O3eENk9ERKm43bqF8+jRFDU2ZvWaNTx/+hR7e3sWL15MvFjps4AgxiDIg7jEIJKOc+fOMXDAAMLDw5kwYQLqamosnjlTTFcUKdDEx8dz4O+/cb18mb4DBxKXkMCihQvZsGEDW7ZsoWXLlkKbKKIQYrMmeRA9CCLAl+yErl270r59e2rUqMHChQu5eu4cy+bNE8WBSKEh0N+fJXPmcOPSJRYtXEjVKlXo0KEDP//8M8Fi6q5IIUMUCCIcPnwYBwcH3N3cWLt2LcZ6ejiPHo2Hm5vQpomICMK9O3dwHj0aMyMjVq1axd07d7C1teXYsWNCmyYiF+ISgzyISwyFmM+fPzNkyBCOHz9Oj+7dqVypEivmz8dXDNBSCbq6uqhraKCurv7lp0SCRF0ddXV19PX1AbCytiYqOhqZVIpUJkOamIhUKkWamCh2ecxl4uPjOfj339y+fp1RI0fy4OFDuv38M51+/JENGzZgZmYmtIki2UbMYpAHsd1zIeXo0aP06tULE2Njpk2fztXz5zl55AgymUxo0/I1ampqGBoZYWJqiqmZGSampv9tZmaYJv80NjH5Kuc+5cIvlcmQSaXoGxgQGxODRCJJFRJpiY+PJzQk5L8tOJjQkBBCkn+mbBHh4Yhfc8WQSCT80KULjVu0YMH8+URFR7Njxw46duwotGki3+C/ds9/YWSkp8A40Rgb9yl07Z5FgVDICA4OZsiQIRw7epTu3btTtUoVNq1Zg6+Pj9Cm5Rs0NDQoYWuLvYMDJe3sMCtSJPXib2RigoaGBlFRUYQGBxPyjQt3eFgYCfHxSKXSr4SZrq4uW/75hwE9e6bzFEgkEtTV1dHU0sLI2DhVfKQTI2mEiL6+PomJiYSFhqbaEfz5M++9vfHy9MTn3TsSEwvn3ZE82JQsyaARI/C4f58DBw/SpUsX1q9fj6mpqdCmiWTCfwJhmxIEQn9RIIgUXC5dusTPP/+Mgb4+k52duXbxIicOHxa9Bt9AU1OTEnZ22Ds4pG42JUsSGxODl6cn7729+fzpU6oQCEm++CcomB6XlUDIKVpaWhinERCmpqYUKVYMW3t77B0c0NbWxuf9e7w9PfFK3nzevSMhIUEh+wsy6urqdOzShYbNmzNn9mziExI4ePAgTZs2Fdo0kQz8JxA2KUEgDCp0AkGMQSgEJCYmMnPmTBYvWkTXLl2oU6cOqxYt4sP790KblqfQ1NLCNlkM2CWLgeIlShATHY3327d4vXnD0QMH8PL05NPHj0Kbmy3i4+P5FBjIp8DATJ8vZmGRKnyc6tWjW+/e6Ojq4uvjkyoYvN684f27dwqLnoKCVCrlyIEDuN+9y4Tx47l58yatW7dm8uTJzJw586ulIBGR/IroQfgGrq6uLF26FHd3d/z9/Tl8+DA//vhj6vOzZs1i7969+Pj4oKWlhaOjI/Pnz6dOnTqp+9jZ2fEuQ6+ChQsXMnny5NS/N2/ezLx58zAzM2Pjxo3pXq8ovr6+dOnShZcvXjBlyhTevHjBvt27kYpuZbS0tKhcrRo1nJwoXbYsxUuUICoyMvXC6P32LV6engQJIAaU5UGQh6Lm5uk8JvYODujp6/Ph/XvevHrF/Xv3ePrwoVhEiC/LTT369MGudGkWLlxIpcqVOXjwINbW1iqZf9GiRTg7OzNq1ChWrlwJQNOmTbl69Wq6/QYPHszGjRtT/z527Bjjx49HTU2N5cuX88MPP6jEXlXznwdhA0ZGugqME4Ox8VDRgyDyH1FRUVSrVo3+/fvTpUuXr54vW7Ysa9eupVSpUsTExLBixQpat27NmzdvKFasWOp+c+bMYeDAgal/Gxoapv7+/v17lixZwt69e/H19aVfv348e/ZMKfafOnWKnj16YGNjw7Lly9mxcSMP3N2VMnZ+xcTUlBpOTtR0cqJy1aoEf/6Mh5sbh/75By9PTz4HBQltouAEffxI0MePuN26lfpY0WLFsHNwoHzFivTu3x8zMzMeP3yIx9273L93j7DQUOEMFpDExER2b9tGTScnli9fztIlSyhTpgz//vsvbdq0ydW53dzc+PPPP6latepXzw0cOJA5c+ak/q2n9597PS4ujmHDhrF9+3aSkpLo378/rVu3LuCNqqQoVuyocBZKEgXCN2jXrh3t2rXL8vlffvkl3d8uLi5s3bqVR48epWv4YmhoiKWlZaZjhIeHY2JiQtWqVbG0tFTK3WJCQgKTJ09mzZo19OzenYoVKjBn8mSCP39WeOz8SEk7O2rWrk3NWrWwc3DgzcuXeLi58feOHfj7+gptXr4g6NMngj594t7t2+zeto3iNjbUcHKiSYsW9B86FC9PT+67ueF+926h7O7p4ebGOy8vho8bx6MnT/hfx46MHDWKBQsWoKmpqfT5IiMj6dWrV6r3MSN6enpZnnPi4uJQV1enevXqwBcvSFxcXAEXCGKaozyIAkFJxMfHs2nTJoyNjalWrVq65xYtWsTcuXMpWbIkv/zyC2PGjEFD48tbX7lyZapWrYqxsTFaWlps3rxZITt8fHzo2LEjnm/esHjRIh66uzN/+vRCFYiooaFBhcqVqZnsKTAwNOTR/fucP32aB+7uRISHC21ivsf3wwd8P3zgxOHDGBkbU93RkZpOTnTs0oWIiAg83NzwuHuX50+fFprlrM9BQcybNo2uPXuycOFC5s6dy6WLFzl67Bg2NjZKnWvYsGF06NCBli1bZioQ9uzZw+7du7G0tKRjx45Mnz491YtgZGREv379sLKyQk1NjXnz5qXzaoqIpCAKBAU5ceIEPXr0IDo6GisrK86fP0/RokVTnx85ciQ1a9bEzMyMmzdv4uzsjL+/Py4uLqn7bN26lSVLlqCnp4eurvzrZDdv3qRD+/aUsLHBZcUKtqxdy9NHjxQ6vvyCjo4OterWpaaTE1Vr1CA6OhqPu3fZumEDz588EaPyc5HwsDBcL13C9dIlNDU1qVilCjWcnBg8YgS6eno8vH8fj7t3cb9zh9jYWKHNzVWkUin7d+/m+ZMnLF26lJUrV1K1alVOnTpF3bp1lTLH3r178fDwwC2LSqe//PILtra2WFtb8+jRIyZNmsTLly/5999/U/eZOXMmo0ePRiKRFBJxIHoQ5EEMUswmampqXwUpwpc4BX9/f4KCgti8eTOXLl3izp07mJubZzrOtm3bGDx4MJGRkWhrayvNvm3btjFkyBA6/vADdZycWLVkSaFYF3YoU4ZmrVtTr2FDAvz9uXf7dqq7Nz8jZJCiMrG1t8exdm1q1a2LuaUlt69d49K5c7x980Zo03IdE1NTRk2cyI2bNzl95gybNm+mb9++Co3p4+NDrVq1OH/+fGrsQdOmTalevXpqkGJGLl26RIsWLXjz5g0ODg4KzZ/f+C9IcTFGRjoKjBOLsfGkQhekKAqEbJKVQMhImTJl6N+/P87Ozpk+//TpUypXrsyLFy8oV66cwnYlJiYyYsQItm/fzsiRI0mIiWH7xo0FuviNnr4+DZo0oXnr1hQzN+f61atcPncu34uCtBQUgZAWOwcHmrdqRf3GjfkYGMjlc+e44epKdFSU0KblGpqamvQfOhSJpiZr1q5lyJAhLF++XO5UyCNHjtC5c+d0r5dKpaipqSGRSFLjC9ISFRWFgYEBZ86cyfXAybyGKBAUQ1xiUDIymYy4uLgsn3/w4AESiSRLD0NOCAkJ4cdOnXj48CFLlizh6vnznD1xQuFx8yoOZcrQqn176tSvj7eXF6ePHePOjRvffL9F8g7enp5s8/Rkz44d1G3QgOatW9Pzt9+4c+MG50+f5u3r10KbqHQSEhL4c/Vq2v3vfyxatIhZM2fy6OFD/j18GBMTkxyP16JFCx4/fpzusX79+lG+fHkmTZqUqfB48OABAFZWVvIcQgFBXGKQB1EgfIPIyEjepHGFenl58eDBA8zMzChSpAjz58/nf//7H1ZWVgQFBbFu3Tp8fX35+eefAbh16xZ37tyhWbNmGBoacuvWLcaMGUPv3r0VLs36/PlzWrdujbqaGsuWLWPr+vU8Tj4RFCTU1dWpXb8+bX/4geIlSuB66RLTJ0wQizzlY+JiY7l68SJXL16khK0tzVu3ZsqcOXx4944zJ07gdusWUmnBSis7fewYvj4+LFu+nLmzZ1OhfHmuXL2aYy+ioaEhlStXTveYvr4+RYoUoXLlynh6evL333/Tvn17ihQpwqNHjxgzZgyNGzfONB2y8CAKBHkQBcI3uHfvHs2aNUv9e+zYsQD07duXjRs38uLFC3bu3ElQUBBFihTBycmJa9euUalSJQC0tbXZu3cvs2bNIi4uDnt7e8aMGZM6jrxcuHCBTv/7HzVr1qR7t24snDmTQH9/hcbMaxgaGdG8dWtatmtHfHw8506cwPXSpQLjbhf5gs+7d+zcvJn9e/bQpEULuvXuTa/ffuP8mTNcOnuWyIgIoU1UGo/u32fxzJlMdHZmz+7d1KxZkxMnTqQ7xyiKlpYWFy5cYOXKlURFRVGiRAm6du3KtGnTlDaHSOFBjEHIZ+zdu5fevXvzS8+elLKzY52LCzHR0UKbpTQsLC35308/Ub9RI14+f87ZEyd44O5e6LoRFsQYhOygJpFQw9GRNh07UrZcOW5cvcrRQ4eyLBWdH9HT12f4uHG8evOG/QcOsGfPnlSvo4hy+S8GYboSYhDmijEIInmXVatWMWHCBEYMH440Pp7lCxaQVEDqGxibmNC5WzeatGjBrevXxWWEQkqSTPalhoKbGyVsbWnfqRNLVq/m8oULHNm/n/CwMKFNVJjoqCiWzZvH73/8wdDBg+ndqxcfP35k2LBhQptWgBGXGORBIrQBIt8nKSmJiRMnMnnSJKZMmUJwYCDbNmwoEOJAV0+Pn3/5BZcNGzAxNWXquHFsWrNGFAci+Lx7x5+rVzNt/HiKFC3Kio0b6dqjh0K1QvIKMpmMzWvXEh4SwuTJkxk9ahRTp04tdJ4ykbyN6EHI4yQmJtKvXz8O//sv8xcswPXCBS6cPi20WQqjqalJy3bt6PTTT/i8f8/CmTN58+qV0GaJ5EF8fXxYsXAhZcqVo0efPrRs146jBw9y4fTpfJ/Oe2DPHlp36MDCRYuYOXMmfn5+bN68ObXSqoiykKKYF6BgBc1mF/FTmIeJjo6mc+fO3Lh+naVLl3Jg927upmmgkx9Rk0ho1LQpXXv0ICoqig0rV/LQw0Nos0TyAa9fvmTu1KlUd3Sk+6+/0rZjRw7+/Tc3XF3ztTft3MmThIeGsmTJEqZOmUJgYCCHDh0qEJ6SvEMioEgb7vwtROVFFAh5lLCwMFo0b84HHx9WrFzJ5tWrefbkidBmKURNJye69e6NtrY2+3bv5ta1a6JLVSTHPHB35+H9+9Rv1IiffvmFHzp3Zt+uXdy/d09o0+Tm9o0bREREsGzZMqZOmUKTJk24cOFCoQqIy11EgSAPokDIg4SGhtK4cWNCg4OZO28eKxcuzNdVAstVrEiPX3/FwsqKw/v3c+ncuULTwEckd0iSybhx9Sp3btygRZs2DBoxAj9fX/b99RevXrwQ2jy5eProESsXL2be/PnMmD6dxo0bc/XqVYyNjYU2TaSQIqY55jGCg4Np3KgRkRERTJkyhcVz5uTbGgdWxYvT67ffKF+pEiePHOH0sWMFvlmPsiisaY7yoqOjQ/sff6R9p048e/yYf3bswN/PT2iz5MKqeHEmzpjB3DlzMDEz4+rVqwoXVius/Jfm+AdGRvL3vgkPj8PYeH2hS3MUsxjyEJ8/f6aWoyMR4eE4T5nCotmz86U4UJNI+KFzZ+YtX87Hjx8ZM2QIh/fvF8WBSK4RGxvLv3v3MnbIEIKDgpjn4kL7Tp1Qk+S/U5y/ry+LZs1i2rRphIeF0aBBA4KDg4U2K5+TEqQo71Y4gxTz37engBIcHEzDBg0oUrQo8+bPZ9HMmXwMCBDarBxjVbw4sxYupEnLliyaOZO/Nm8mIjxcaLNECgnhYWHs2LSJxbNn06JNG2bMn4+ltbXQZuWYQH9/Fs2ezbx589DV1aVRw4aEhIQIbZZIIUMUCHmAlJiD2NhYBg8cSFJSEs1atxbarByhJpHQ4ccfmbd8Oa9evGDKmDG8fvlSaLNECimvnj/HefRoPF+/Zr6LC+3+9798501o0aYNUqmUoUOGEB4WRt06dQgrAIWihEER74GiRZbyL2KQosCEhYXRpEkTIsPDmTJ1KvNnzEBDXZ2p8+YBsH/3boEt/D5W1tYMGjkSQ0NDFs+alW+DxEQKFvHx8ezetg2327cZNHw4TnXrsmntWgLyQWxCz759qd+4MXOmTIGkJKbPmsW8uXNp2qQJV11dC9U6uHJIRLH74cIpEPKXpC5gxMbG0rRJE4I/f2bK1KksnjWLT4GB+Pv5MX/aNBo3b0633r2FNjNL1CQS2nfqxDwXF968fMmUMWNEcSCS53j57BnOo0fz1tOT+cuX07ZjxzztTUgRB/OmTSPQ35/AgACWzJnD9Bkz+PTxI23bthVbnIuohLz7LSngSKVSunbtiu+HD8ycOZOlc+YQmCbmIK+LBEtra2bMn0+LNm1YPHs2e7ZvJz4+XmizREQyJT4+nt1bt7J07lxatW/PtHnzsLC0FNqsr8goDlII8PNj6dy5zJo9m1cvX9KtW7cC1xI7dxGXGORBFAgCkJSUxMCBA7lx7RrzFyxg1eLFmaZk5UWRoCaR0O5//2O+iwuer1/jPHo0r54/F9osEZFs8eLZM6aMHo23pycLVqygzQ8/oKamJrRZQNbiIAW/Dx9Ys3QpCxYu5MrlywwaNEgsNJZtpErYCh9iDIIAzJgxg31797Jk6VK2rl+Pl6dnlvumiIS8EJNgaW3NoOHDMTYxYcmcObx89kwwW0RE5CUuLo5dW7fidusWg0aOpHa9emxasyadB0/VfE8cpOD5+jU7Nm5k3vz5TJo0CVtbW2bMmKFCS0UKE2KhJBWzYcMGxowZw9y5czl3/Di3rl3L1uusrK2ZOm8erpcuCSISaterx6CRI7ly4QL7d+0SlxPkQE9fHxNTU0zNzDAxNU3ddHV1kairo66ujkQiQV1dHS0tLWo4OXHv9m3i4+ORyWRIpVJkUikxMTGEhoQQGhJCSHBw6u/RUVFCH2K+Q1tbm+6//krjFi3YuGoV927fVrkN2RUHaWnYtCnN2rZl5owZrFm7loEDB+aylfmT/wol/YyRkaYC4yRgbHyg0BVKEj0IKuTQoUOMGD6cKVOmcNvVNdviAITzJKipqdGle3fa/e9/rHdxwcPNTSXz5kfMLS2xK1UKC0vL9CLAzAxTU1O0tLWJi4tLf2EPDiYkJASZVIpUJkOamIhUKkVdXZ0aTk54vnqFVCZDXUMDdYkEibo6enp6lCpTBtPksU1MTdFOM3Zo8tgpcwQGBODl6cmnwECh36I8R1xcHH9t2cLTx48ZOmoUJ0uW5PD+/SqbXx5xAHD9yhWMjI0ZN24cw4YNw9zcnE6dOuWipfmdRECRpaTCGYMgehBUhKurK61atWLQwIFEh4WxT84LvCo9Cdo6OgwZNQpbe3uWz5+Pr49Prs6Xn7CwtMTewQH70qWxc3DAvlQptLW18Xn/Hn9f30xFQGhICDHR0dkaP6ellnX19L7yTqT8blW8OCVKliQ2Nhbvt2/x8vRM3fJjMa7cooStLWOnTOHtmzdsWr061zMF5BUHGcfQ1NVl67ZtXLx4kYYNGyrZyvzNfx6EjkrwIBwvdB4EUSCogLdv31K9enXat2uHlbk5m9euVWg8VYiEoubmjHV2JjIigtVLlxIZEZEr8+QHLKysvoiB5M3OwQEtLS3ee3vj7emJV/JF98O7dyQqqQmVsnsxaGhoYGNrm+44StraEhcf/+UY0mz5sby3sjAwNGTUpEno6enhsmABn4OCcmUeZYgD+OLhGzxyJO8+fODkqVM8f/4cW1tbJVqavxEFgmKIAiGXiYiIoGaNGpiamPBjp04snj1bKelJuSkSylesyKhJk7h1/Tp7tm0rdOlUmpqaVKxaFUcnJ2rUqoWBkRE+3t7pLqIffHxytSOlKpo1qWtoUKJkyXTCp6SdHeFhYdx3c8PDzY1njx+TkJCQK/PnVdTV1fl1wABq16vHysWLlZ6loyxxkIK6hgbOs2Zx4OBBIqOicPfwwMDAQAmW5n/+EwjtlCAQTosCQUR5yGQyOnTogIe7O7NnzWLW5MlKvRPPDZHQrHVrevfvz+5t27h87pxSxswPGBkbU93RkZq1a1OlevXUi6T73bu8ePZM5e2phermqKGhQflKlahZuzY1nZwwNDTk8YMHeLi5cf/evULVV6NF27b88ttv/LVlC1cvXFDKmMoWBykYGhkxc9Ei5syeTc1atTh27BiSPFwMSlX8JxBaKUEgnBcFgojycHZ2Zt3atbi4uLBs3jw+vH+v9DmUJRLU1dXp3b8/dRs2ZNXixbwoBCmMxUuUoKaTEzVr18ahTBm83rzBI/nO2efdO0FtyyvtnkvY2uJYuzY1nJywd3DA89Ur3O/excPNDb8PHwSzS1VUqFyZURMncuPqVfZs345MJpN7rNwSBymUsLVl7NSpjB0zhtHJmVKFHVEgKIYoEHKJffv20adPHxYsWMDRfftyNfpfUZFgYGjIyAkTMDA0xGXhQoI+fswFK/MGJWxtady8OY516mBiasqTBw9wd3Pjwb17hIWGCm1eKnlFIKTFxNSUGrVqUcPJiSrVqhEcHIz7nTtcvXixQAewFjM3Z9zUqYSGhLBm2TKiIiNzPEZui4MUatWtS4fOnXF2dmbvvn389NNPuTZXfuA/gdAcIyP5k/bCwxMxNr4kCgQRxXF3d6d+/foMGTSITwEBHDt4MNfnlFck2JQsybgpU/B++5aNq1cTFxubi1YKg7aODnUbNqR5q1aUsLPj7s2b3LlxgyePHpGQR+s55EWBkBYtLS0qVatG3QYNqF2vHt5eXlw+d447N24UyD4BOjo6DBk9mhK2trjMn49vDrwnqhIHKXTu1g3jokXZsnUrt2/fpnr16rk+Z17lP4HQWAkCwbXQCQRxkUrJBAYG0q5tW5o3a4autrZKxAHIV5a5uqMjsxYt4trly6xeurTAiQNbe3v6DxnCum3baNOhA9evXGF4//5sXLWK+/fu5VlxkB+Ij4/nvpsbG1auZFj//tx0daXd//7H2m3b6Dd4MCXt7IQ2UanExsayavFibrq6MmvxYqrWqJGt16laHAAcOXAAI319mjRqRJs2bfj06ZNK5hX5mnXr1mFnZ4eOjg516tTh7t2739x/5cqVlCtXDl1dXUqUKMGYMWOIFfC8LHoQlIhMJqNpkyb4fvjA8OHDmTtlisorDmbXk1C7fn0GjxzJn6tXc/fmTRVamLtIJBIc69Sh7Q8/YOfgwC1XVy6dO8fbN2+ENi1H5HUPQlY4lClDs9atqd+4MZ6vX3P2+HHc3dxIUmDtPq9Rt2FDBg4fzoaVK79ZeVEIcZCCtrY20xcsYO3atZS0teXCxYuFMmjxPw9CfSV4EG7myIOQssy8ceNG6tSpw8qVKzlw4AAvX77E3Nz8q/3//vtv+vfvz7Zt26hfvz6vXr3it99+o0ePHri4uMhtuyKIAkGJzJs3jyWLF+Pi4sK8adMEW8v/nkho0KQJ/YcMYfWyZTx0dxfAQuWjp69P05Ytad2hA2rAuVOnuHz+fL4tP5xfBUIK+gYGNGvVilbt2yOTyTh/6hRXLlzIt/+PjNRwcmL42LFsWb8+04qoQoqDFIpZWDB13jzGjB7N1GnTmDx5siB2CMl/AqG2EgTC3RwJhDp16uDk5MTa5Lo3MpmMEiVKMGLEiEz/F8OHD+f58+dcvHgx9bFx48Zx584drl+/LrftilD4JGUuce3aNebMmcOkyZPZvW2boIF+31puaNKyJf2GDMFl4cICIQ509fT4+ZdfWLNlC461a7Nn+3bGDBnCySNHCszFKD8SFRnJicOHGTN4MP/s3EmtOnVYvWULP/3yC7q6ukKbpzD33dxYuXgxA/74g8bNm6d7Li+IA4BPgYH8vX07EydOZNrUqdwsQJ5CoQgPD0+3ZRVvEx8fj7u7Oy1btkx9TCKR0LJlS27dupXpa+rXr4+7u3vqMsTbt285deoU7du3V/6BZBOxF4MSCAoKokuXLnRo3x7fd+8EafiSkcx6N7Rq147uv/7Ksrlz830ao6amJq3at+d/Xbvi8+4dC2fO5M2rV0KbJZIBmUzG3Zs3uXvzJmXKlaP7r7/Ssm1bjhw4wMUzZ/J1EabHDx6wfP58xk6diqamJhfPns0z4iCFuzdvUqlKFdq3b0/nzp15/vw5ZmZmQpslAIoWe/vy+hIlSqR7dObMmcyaNeurvYOCgpBKpVhYWKR73MLCghcvXmQ6wy+//EJQUBANGzYkKSmJxMREhgwZwpQpUxS0XX5EgaAgSUlJdO3aFT0dHRo3asQcZ2ehTUolrUhwKFMG+9KlWTRrVr6+kEokEho1a0bXnj2JCA9n/YoVPLp/X2izRLLB65cvmTdtGtUcHeneuzftOnbk0N69XLtyJd/GKDx78oQls2czYfp06jRogFXx4nlGHKSwe/t2Zi1axKOHD+nduzcnT55ETU2RxkX5kURAkdX0LwLBx8cn3RKDtra2Ymal4cqVKyxYsID169dTp04d3rx5w6hRo5g7dy7Tp09X2jw5QRQICrJixQrc3d1ZuWIFC2fOzHN3RP5+frhevEjHrl1xvXQpX4sDxzp16N67Nxqamvyzcye3r19HDKHJfzx0d+eRhwf1GjXi519+oX2nTuzfvTvfdgp99eIFHm5u1G/cmKMHD+YpcQCQEB/P2uXLmTpzJqNHj2bNmjWMHDlSaLNUjHIEgpGRUbZiEIoWLYq6ujqBGTqoBgYGYmlpmelrpk+fzq+//sqAAQMAqFKlClFRUQwaNIipU6cKEmQqxiAowN27d5k8eTLjx41j365dee7EAF/KxbZs3541S5dSrWbNbKdA5iXKV6zIzEWL+H3oUM6fOsWE4cO5de2aKA7yMUlJSdx0dWX88OFcOneOAcOGMWPBAspWqCC0aTmmZ9++VKxShdVLl9KmQweatW4ttElf4e/ry6G//2bMmDGMHz8e9wIQf5SX0dLSwtHRMV3AoUwm4+LFi9SrVy/T10RHR38lAtTV1QEEO9eJHgQ5iY6OpmvXrjRv1ozQz58zjWQWmiYtWtCzTx8Wz57N65cv8Xn3Ll1MQl6nhK0t3X/9lfIVK3Li8GFOHz9e4Go1FHakiYmcO3kS14sXad+pExNnzOD5kyfs27UrV0qTK5uMMQfhYWFMmD6dhPh4rl+5IrR56bh+5QqVqlalSePGdO3ShecvXhSIgNHsoRwPQk4YO3Ysffv2pVatWtSuXZuVK1cSFRVFv379AOjTpw/Fixdn4cKFAHTs2BEXFxdq1KiRusQwffp0OnbsmCoUVI3oQZCTiRMnEh8Xx/86duSvzZuFNucr6jduzK8DBrBs/nxev3wJyFdMSQjUNTTo2rMns5csIcDPj9GDB3PkwAFRHBRgYmNj+XffPsYMHkxgQABzli6lS/fugp0Ys0NmAYkvnz3DZcEC+g0eTN0GDQS28Gt2bNpE165diYqKwjkPxUvlPolK2HJG9+7dWbZsGTNmzKB69eo8ePCAM2fOpAYuvn//Hv80Xudp06Yxbtw4pk2bRsWKFfn9999p06YNf/75p9xHrShiHQQ5uHbtGk2bNmXZsmXs2rSJV1lEpQqFU716DBk1ipWLFvH4wYOvns/NVtGKYmtvz+Dk9dE/V6/mnZeXwBYJQ36vg6AodqVKMXjkSGQyGX+uXs17b2+hTUrH97IVqtaowahJk1i/YgXud+4IYGHWlK9UiV/69WOyszNXrlyhfv36QpuUa/xXB8EBIyP5xWZ4uBRjY0+x1LLIt4mKiqJnz560b9eOF48f5zlxULlaNYaMGsXaZcsyFQeQNz0J6hoadO3Rg5mLFuF+5w7TJ0wotOJABLzfvmXa+PHcv3ePWYsX07lbtzzjTchOKuOj+/dZ7+LCH2PGULFKFRVb+G1ePH3KmxcvaNmiBT179CgkAlSKYt4DRdMk8yeiQMghEydOJCE+nnZt23Jgzx6hzUmHhZUVIydMYPvGjdy/d++b++YlkWBrb8/cpUtxrFOH2ZMnc2jvXqSJOXfpiRQspImJHPz7b+ZMmUKdBg2YvWQJJWxtBbUpJ3UO3O/eZeemTYycMAHzLCLXhWLfrl107NiRmOhoJkyYILQ5KkCqhK3wIQqEHODq6srmzZtxnjKFLevWqbzPwrfQ1dNj3JQpXD5/PtvBUUKLBHUNDbqkeA3u3hW9BiKZ4u3pybRx43jo7s7sJUv4USBvgjxFkFwvXeLa5cuMmzIlTwUExsXFsWXdOiY7O7Np0yZu3LghtEkieRAxBiGbREVFUbZsWRxr1sSqWDH2bN8utEmpqEkkjJsyBYmaGkvnz89x0RkhYhJK2tkxZNQo1NTU+HP1arzfvlXJvPmFwh6DkBX2Dg4MHjmSxMRE/ly9Gp9371QyryIVEiUSyZfMhoQEVixcmKfSc3v//js+vr48fvKEFy9foqenJ7RJSuW/GAQrjIzkvx8OD5dhbOwvxiCIZM7EiROJjYmhTZs27M9jSwvde/fGwtKStS4uclWkU6UnQU1Njf/99BOzFi/Gw82NaePHi+IgAxKgsNW5yy5eyd6ER/fvM3vJEn7o3DnX51S0fLJMJmPNsmVYFy/OT7/8kgsWys/+Xbv44YcfiI2NLeBZDarPYigIiAIhGzx48IBNmzYxbfp0tqxdS0IeWlpo0KQJzVu3ZvmCBQo1J1KFSNDW0WHkxIk0a9mSOVOmcPDvv8VYA5Eck5iYyP7du5k3dSot27VjxIQJSi15mxZl9VaIjopi+YIFtGrfnnqNGinRQsWIj49n87p1TJo8mfXr1/P48WOhTRLJQ4gC4TvIZDJ+79+fOk5OeL95k1pTIC9QqkwZ+g8Zwpplywjw81N4vNwUCUXNzZm5cCH6+vpMnzABb09PpY6f35Gk2USyx9s3b5g+fjzGxsbMWLiQIkWLKnV8ZTde8vf1Zd3y5Qz44w/sHByUYKFyePX8OR+8valVsya9evXKU0sgykP0IMiDeD76Dn/99RcvX77kt/792bdrl9DmpGJiaspYZ2cO/P13lumM8pAbIqFcxYrMXbqUl8+esXj2bCIjIpQyrohIRHg4C2fNwvPVK+YuW6a0Us251ZXxoYcHh/buZZyzM8YmJkobV1H27drFgIED8Xzzhj15bAlVOYhpjvIgCoRvEBoayuhRo+jdqxfHDhzIMxc2TS0txjo78+j+fc4cP6708ZUpEpq1asXEGTPYv2cPOzdvRiotnF80kdxDmpjIto0bObR3L5NmzqRJy5YKjZfbLZtPHT3K08ePGTN5MhoaeaPafXhYGCf+/ZeePXsycuRIwsLChDZJyYgeBHkQBcI3cHZ2Rkdbm7Jly3L5wgWhzUllwB9/IJXJ2LZhQ67NoahIUFdXp8/AgXTr3Zulc+Zw+dy5XLBSROQ/Lp45w/J58+jZpw+//v67XN3vclscpLB1/XrUJBJ+/+OPXJsjp1w4e5bKlSqho6XFtGnThDZHJA8gCoQsePDgAVu2bGGSszM7Nm3KM/3qf+jcmQqVK7Ny0SIScznAT16RYGBoyMSZMylfsSLTJ0zgxbNnuWhl/iUl5kBHS4sixYphYWWFtY0NNra2lLSzA8CmRAlsbGywsrKiaLFiaGlpCWlynufZkydMnzCBSlWrMnHGDPQNDLL9WlWJAyA15bFytWq0+9//cnWu7JIkk7Fz0ybGTZjAxo0bC1jAouhBkAexDkImyGQynGrVQkdHB6caNdiyfr3QJgFQ3dGREePHM3faNJUG+eWkTkLxEiUYN3Uq77y82LhqVaFvsKRvYECJkiUxLVIEUzMzTExN022mZmbo6esjk0pJTExEKpMhk0qRyWQYGhkRFRmJRF0ddYkEDQ0NJOrqREVFERocTGhISOoWkvx3yOfPvH/3TqGMloKAjo4OQ8eMwaZkSVzmz8f3w4dv7q9KcZCWUqVLM3XuXFYtWcKj+/dVNu+3GDRiBDdv3yYmNpYHDx+ippZ/k27/q4MgwchI/uMID0/C2FhW6OogiAIhE3bv3s2QwYNZtWoVU8eOzROxByampixevZqdmzdz09VV5fNnRyRUqFyZsc7OnD5+nMP79hXQaOisMTA0xN7BIXWzc3DA3MKCz0FBfA4KIjQ4mJCUi3pwMCHBwYQn/x0ZEZHu/dLR1WXzP/8wsGdPYpMLJSWpqWFgaJgqLExMTTFJ/pnyd5GiRSlStCgfAwLw8vRMt0VFRgr11giCmpoaXXv2pE2HDiyfPz9LT5ZQ4iCFBk2a8OvvvzNxxAjC88Dav5GxMXOXL2fkiBFs3baNHj16CG2S3IgCQTFEgZCBuLg4bG1tadOqFeHBwZw/dUpokwAYO2UKsTExrF+xQjAbviUSqlSvzqhJk/hr82ZcL10SyELVUtLOjqo1auBQpkyqGAhMvjB7J1+Uvd++/abAzGqNLzOBkN1FrhShYleqVKpYMbe05GNgIN6enni+fs1DDw+VVSEUmiYtW/Lr77+zYuFCnj56lO45ocVBCsPHjUNDQ4OVixcLZkNa2vzwAzqGhly5cgUvb+98u7SVKhBCQZHreng4GJtQ6ARC3gihzUNs2LCByIgI6tarx+TktsNC07BpUxzKlGGSwPakxCRMnTcPIFUk1HByYvjYsWxZv55b164JaWKuoqGhQYXKlanh5ISjkxMGhoY8fviQNy9fcuHMGbw8PfOEaz8yIoLHDx6kS3/VNzBIFQxlypenc/fuRISH43H3Lh5ubjx/+rTAFq26euECiQkJjHV2ZvWyZTx0dwfyjjgA2LFpE0vWrKFeo0Z54jt08cwZFq1ezcmTJ9m8eTPDhg0T2iTFkJF9hZ3V6wshogchDeHh4ZSwsaFv3748f/gwT3xRTUxNWbJmDX+uXo373btCmwOk9yR4eXoyZNQoNqxcyb3bt4U2TekYGBpS3dGRmk5OVK1Rg5joaNzd3PC4e5fnT56QkJCg0Pi54UHIDpqamlSsWpWatWpRs3ZtdHR0eHT/Ph5ubjxwdy+QyxG169dn8MiRbFixgjLly+cZcZBCrbp1GfDHH0waOZKw0FChzflyY1KhAnv+/psPHz5gkIOAz7xCqgchWAkeBDPRg1CoWbZsGTo6Otjb2bF93TqhzQHg9z/+4P69e3lGHMB/noRZS5ago6PDioULeZB8V1YQ0NDQwKlePZq2bEmFypV57+2Nx927HD14sMB0m0xISOChuzsP3d3Z/uef2Dk4UNPJiXb/+x+DR47k2ePHXDl/nnt37uR6toyquHvzJokJCYyaNImYmBhmTpyYZ8QBwL3bt6lTvz79hgxh5aJFQpvDDVdX2nfqhARYvnw5M2fOFNok+VG0Y3MhLd8iCoRkPn78yJIlS5g0cSL7d+/OEwF2DZs2xd7BQfClhcwoXrIkWpqaxMTEULZChQIhEKxtbGjWqhWNmjUjMiKCy+fP8+fq1QR//iy0abmOd3LcxL9792JWtCj1GzXi51696DtoENevXOHSuXP4+/oKbabClKtYkZjoaLS0tbEpUSJPCQSAnZs3s2TNGuo3bixIMHJakmQy9u/ezdA//mDx4sUMGzaMokouZ60yRIEgF6JASGbWrFlYmJujramp1NLF8mJiakqfAQPYuGpVnnP31qhVi6GjR7N62TICfH2/iknIT6ipqVGtZk3aduxIuYoVcbt1i1VLlvD8yZNcmzOvFx8JDgrixOHDnDxyhAqVKtGsdWsWrFjBi6dPOXPiBI88PPKEgM4pKTEHMydNwqZkSf4YO5bVS5bw0MNDaNNSiYyIYNuGDQwcPpxnjx8TGhIiqD0P3N3p2KULRc3MmDNnDqtXrxbUHhHVIsYgAG/fvqVMmTIsXryYvzZt4u3r10KbxPipU4mKimLDypVCm5KOytWqMWbyZDauXo3brVtAzuok5BU0tbRo2rIlbTp0QEdXlwunT3Pp3DmVpJllRyBkFoOQghDxUkbGxrRo04YWbdsSExPDuRMnuHLhgsIxGKois4DEug0aMHD4cJYvWMCzPFYUaNjYsWhra+OycKHQplC6XDl+6dePadOm8frNG2xtbYU2KdukxiD4KSEGwbrwxSDk9ZsZlTB16lTKli5NyKdPeUIcNGrWDDsHB/7askVoU9JRvmJFRk+ezNYNG1LFAaimVbSykEgkNGnZkuXr19O0ZUv+3bePUYMGceTAAZXloOfHgOjwsDAO79//5b3av59mrVuzbP16GjdvjpocJY1VSVbZCrdv3GD7n38ydsoUpTV5UhY7N2/GoWxZGjRpIrQpvHn5koiwMOzt7PJvCWapErZCSKH3IHh7e1O6dGlWrFjBmiVL8PtOxbXcxtTMjMWrV7Nh5Uru37snqC1psbCyYu7Spfy9YwdXsuhLkdc9CbXq1KFb796oa2hwYPdu7ty8KZirPLNLqnqa3zXzmAchI2pqatRt2JCfe/UiIT6e/bt356lA2hSyk8rYok0bfu7VixkTJ/IxIEDFFmaNY506DBo+nEkjRwq+1GBTsiRDx4xh/IQJvH37lhIlSghqT3ZJ9SC8U4IHwVb0IBQ6Fi1ahF3JkgT4+gouDuBL1oL73bt5Shzo6ukxbsoULp8/n6U4gLzrSShfqRKzFi2i35AhnD15konDh3P7xo18uY6eV0hKSuLWtWtMGD6cC6dP8/sffzBz4ULKVawotGmpZLfOwcWzZ7l+5QrjpkxBV1dXhRZ+G/c7d3jo4UH/oUOFNoUP79/z+eNHrC0tWZxHijmJ5D6FWiAEBgaybds2Bg4axLGDB4U2h8bNm2Nrb8+urVuFNiUVNYmEYWPHEvTxI3t37fru/nlJJNja2zNx+nTGTZnC/Xv3GDtkCBfPnMkXLafVv79LnkCamMj506cZM2QIDz08mDBtGuOnTqWEwOvUOS2C9PeOHYQEBzN0zJg81Xvgry1bcChThoZNmwptCscOHWLIH3+wefNmPn36JLQ5OUOGYssLecFtJwCFWiC4uLhQxMyMhLg4vFTY/CgzTExN6f3772xdvz5PVONLoXvv3lhYWrLWxSXbHS2FFglaWlr06tePmYsW4fvhA2OGDOHowYPExcWp3JbCQlxsLEcOHGD04MH4+/kxe8kSevbti6YAJXrlqZAok8lYvXQpxW1s+OmXX3LZwuyTktXQZ8AAjIyNBbXlzatXIJVSrEgRVuax4OnvIsYgyEWhFQhhYWGsWbOGgQMH5gnvQedu3Xj66FGeqifQoEkTmrduzfIFC3IsWoQSCWXKlWPBihWUKVeOqWPGsGf79jzRbOtbqJO1x0D9O8/nNSIjItizfTvTxo2jfMWKLHBxoXTZsiqbX5HyydFRUSxfsIBW7dtTr1GjXLIw57jfvcvzp0/5sVs3oU3h6MGD9P3tN1auXEl4eLjQ5ojkMoVWIKxfvx4jAwOMDQ15los579nBwsqKxs2bsz8bLnxVUapMGfoPGcKaZcsI8POTawxVigTNZK/B5NmzuXTuHLOnTMFfTrtFFMfvwwdmOztz9eJFnOfM+eJN0NTM1TmV0VvB78MH1rm4MOCPP7BzcFCyhfKzf/dumrVsSTELC0HtePLwIUXMzNDX1WXjxo2C2pIjZErYCiGFUiDExMSwePFiev7yC8cOHRLaHLr16sWNq1fzzAXNxNSUsc7OHPj7b4WLRqlCJKR4DcqWL8+0ceM4dfRotpdDVI0Exb90yhhDFchkMk4cPsyM8eMpX6kS83PRm6DMxksP3d35d98+xjk7Y2xiohwDFcTXx4db16/zcx5Y/jj+77/06NmTuXPmEBsbK7Q52UNcYpCL/HCeUTo7duxAoqaGQ6lSeLi5CWqLnYMDNZycOLR3r6B2pKCppcVYZ2ce3b/PmePHlTJmbokETS0tfvntNybPns3VCxeY5excIMoBZ0Z+Pj/5fvjA7MmTuXb5MlPmzqVHnz5K9SbkRlfGk0eO8OzJE8ZMnoyGRt4oOHvon39wqlsXW3t7Qe24d/s25cqWRUNDg507dwpqi0juUugEQlJSEosWLqRjx46cPHJE8FS3Hr/+yrmTJwkJDhbUjhQG/PEHUpmMbRs2KHVcZYuEoubmzF68mHIVKzJ9/HhOHD6cZ70GIl+8Ccf//Zfp48dTsUoVZi1eTBEl1PXPzZbNW9atQ00iyRNphgCfg4K4cOYM3QXODkpKSuLUsWN0aN+exYsWCX4OzRaiB0EuCp1AuHDhAv7+/jRu3Fjwds6VqlalVOnSeWKZA74UjKlYuTIrFy3KlQ5+yhIJ5StWZO7Spbx89ow5zs55on6FMpBk2NKSnwIVv4Wvjw+znZ3xfPWKucuWKVTBMDfFAXzpeLli4UKq1qhBs1atlD6+PBw9eJAy5ctToXJlQe246epK8xYt8A8I4PLly4Laki3EGAS5KHQCYdmyZdSuXZvrly8L3sa2R58+HDt0KE+kNRYzN6fnb7+xcfXqXO1Fr6hIaNa6NRNmzODAnj3s3Lw5X9Q0SEva84w8F/2CIBKkiYls27iRQ3v3MmnmTJq0bJnjMXJbHKQQGhLCn6tX06tfP4oWK5Zr82SXyIgIThw+TI9ffxXUjoT4eG5cvUr1atVYvmyZoLaI5B6FSiB4e3tz8eJFfu3Th4vnzglqS+369TExNeXcyZOC2gFfyuYOHD6cG1eu8PTRo1yfTx6RoK6uTt+BA+nWqxdL58zhksD/P2XyraDD/BKQKA8Xz5xh+bx59OzTh19//x1JNns6qEocpPD4wQNuX7/OgGHDcn2u7HDm+HGKFCtGrTp1BLXj4tmz9Onbl9NnzvDu3TtBbfku4hKDXBTUc0+mrF+/nhI2Nrz38iI4KEgwO9TV1enWqxf/7t1LfHy8YHak0KJtW8wtLflHhQFHOREJBoaGTJo580u8wYQJvHj2TEVW5m0Kgnh49uQJ0ydMoFLVqkycMQN9A4Nv7q9qcZDCnu3bsSpenOatW6tszqyIi4vj8P79dOvdO0tRNWXmTCKTktJtHs+fpz6vra2Ny9q1vAsKIiAigj0HD2Jubp4jO4I+fsTPx4eSNjb8+eefCh1TrpOEYssL+SDMIjfI7+eXbBMfH8+mTZvo1q0bF06fFtSWJi1akJSUxNWLFwW1A6CYhQU9+vRh05o1Kk9Zyo5IKF6iBHOXLiU6KorZzs4EffyoUhvzGgXxRuZTYCCzJk0iLi6OOUuXYm1jk+l+QokD+JIavWXdOn757TeK5vBCmhtcOX8edXV1GjVvnuU+z548oZSlZerWqmHD1OcWr1hBu44d6fPzz7Rt0gRLa2v2/Ptvju24eOYMXX/6iY0bNuTt1t+iB0EuCo1AOHLkCHGxsdjZ2gra+11LS4suPXqwf88eZAJH3aupqTFoxAiuX7ki2HvyLZFg7+DAjAULuOHqyqolS4jLLznXOaSQxj+lIzY2lpWLFnH7+nVmLFiAXalS6Z4XUhyk8PjBA25dv87APLDUIJVKObBnDz/16JFlOevExEQ+Bgambp8/fwbAyMiIPr//jvPYsVy9fJkHHh4M7dePeg0a4JTDZYvHDx5QtmxZEhITOXbsmMLHJZK3KDQCYcWKFTRt2pTL588LmpbTtmNHgoOCcLt1SzAbUmjZrh1FixVT6dJCZmQmEkqXK4fznDkc3r+fg3//nT9SqXJIZjclWZVWTllOyPhcQRIXSUlJHNizh+P//suUOXNwKFMGyBviIIW/t2/H0sqKFm3aCGoHwJ2bNwkLC6N1+/aZPu9QpgyvfX157OnJ1t27sUlu0VzD0REtLS0up+nM+urlS96/e0ftevUyHSurC0VSUhJXzp+nbp06ebs/g+hBkItCIRDevn3L3bt3+ennn3G9dEkwO/QNDPihS5dsdUXMbcwtLenx669sXrMmT9yZpxUJQ0ePZvLMmezbtUtpxZryAgUhZkAVnDxyhIP//MPkWbP4Y+zYPCMO4MtSw+Z16+jZt6/gSw1JSUns27WL/3Xtip6+frrn3O7cYchvv/Fj27aMHjoUW3t7zl27hoGBAeaWlsTFxREWFpbuNR8DA7GwtEz9O6uU24xcvXiRbt27c/PmzbwbrCimOcpFoThf7dmzBxtra7xevxa0cU/Ltm3x9vQUdIkDkpcWhg/n2uXLgvehSIu/nx//7NhBgyZNePHsGRfPnBHapFwjxRPwvRNwQal/kFPOnTzJm1evqN+wIbu3bcsT4iCFJw8fcvPaNQYOHy54a+jHDx7w4f37r4Inz585w+GDB3n6+DEXz52ja/v2GJuY0CUbDZ9yKmTDw8Lw8famWNGi/P333zk7AJE8TYEXCElJSWzevJnWbdpw/epVwexQV1enZbt2nM4Dd8St2renSNGi/PPXX0Kbko7SZcvSb8gQDuzZg12pUoK0is5NCulNiFz07NsXm5IlOfjPPwz44w9KJS835BX+2bEDS0tLmueBpYbTx47Rqn37b6aJhoWF8ebVK0qVLs3HgAC0tbUxztA+2tzCgk8BAVmO8a2LxY2rV2nXrh2bN2/Om8uB4hKDXBR4geDh4YGfnx/1GzTgoYCtlGvXr098fDwP7t0TzAb40oipe+/ebF67Nk8sLaRgV6oUE2fMYN+uXRw9eFCQVtFCktFTkF2vQUFctkgbc3DkwAEO/vMPk2bMELwHQVpiYmLYtHYtPfv2Fbyhk7ubGzKZjFp162a5j76+PvYODgT6+3Pf3Z34+HiatmiR+nyZsmUpaWvLXTljozzc3GjSpAkfPnzgkQpqqeQYUSDIRd7oQpKL7Ny5k1J2djy8d0/QNJy2P/zAuZMnBVfXXXr04KGHR55aWrCwsmLyrFkc3r+f88kpqCkxCVPnzQO+tLstKKhraFDUzAwTU1NMzcwwNjXF1NQUY1NTdHV1UVdXT41MHzh2LAnx8UTHxBAWEkJISAihwcEEh4QQGhJCyOfP+a6a5LfILCDx7IkTaGpqMnnWLGZOmsTHb9zlqpKnjx7x5OFDOnfrxo5NmwSzI0km4/ypU7T94Qfu3rwJwPylSzl9/Djv373DytqaqbNnI5NKOfDPP4SHh/PX1q0sdHEhJDiY8PBwlq1Zw+2bN7lz545cgjMhPp5H9+9jV7Ikf/31F8uXL1fuQYoIQoEWCImJiezetYuBgwZx6dQpwexwKFOG4iVK4Cpw3QNLa2saNWuG8+jRgtqRFl09PcZNmYLrpUuczpAmVRBEgoaGBja2ttg7OKRuJWxtkUgkhIeGfrnoBwcTHhJCcFAQcVFRSGUy1CUSKtWsyeunT5FJpWjr62NsaoqNnR0myWLCyMQEmUyGj7c3Xm/f4uXpiZenJz7v3iEVuIy4PHwrW+HE4cOYmJoybsoUZk2aRExMjEBWpmf/7t3MX76cU8eOCSpcrly4QNcePbBzcMDb05PiNjZs/+cfzIoUIejTJ25dv06zunUJSi4QN2nMGGQyGbsPHUJbW5uLZ88y+o8/vjnH95bIbly9SsdOndi5cydLlixBXT0PRc8oGmhYSNcH1ZKEvqXNRc6dO0fnzp1ZtXIlYwYPFuzufdjYsYSHhbFr61ZB5k9hxIQJREVGKr1To7yoSSSMc3ZGIpGwdP78LLsxWllbM3XePFwvXcoXIqG4jQ01a9emppMTpUqXJi4uDq+3b/H29MT7zRu8PD0J/viRJJks9W4tYya7jq4uK//5h9E9exIbE5Pq4Uz5KePL+2dmbo69gwN2pUtjlyxAtLW08Hz9Gg83Nzzc3PJFM6vspDJKJBImzphBfHw8KxYuFNwbl8LA4cPR0tJinYuLoHb8NmgQOrq6bFy1SqFxsvIgfO8aqSaRsPLPPxk6dChnzp6l+TeKOKmK8PBwjI2NCbsARvrf3z/LcaLAuOWXWA4jIyPlGZjHKdAehO3bt1OxfHluXbsm2MnExNQUp3r1mDRihCDzp2Dv4EB1R0fG5ZHWtQDdevXCwsqKmZMmfbNVc173JEgkEspXrPhFFNSujampKU8ePcL10iU2rFqVemep7A6NSTIZHwMCCAgI4NaNG6mPW1haUrFqVRydnOjaowfBnz+nioWXz54JXqArI9mtcyCTyVi9dClzly7lp19+4cCePSq0MmsO7d3LsnXrsLW3552X13f3HztpEnMWLWLdypVMGjMG+FL6eOHy5XTt0SP1jn7MH3/wMQeVQ8+eOMGClSv5Z+dOuRuufWt5QcK3RUKSTMadGzeoUrky27dvzxMCQUQxClp8UyoxMTEcPnyYrj//zE1XV8HsaNqqFU8fPSJQ4HXTHn36cO7ECUJDQgS1I4X6jRvTok0bli9YkK1ulspqFa1Mipmb8/Mvv7Bq82aGjRuHjo4Ou7duZfCvv7J8/nwunz+fY7dzdto9p5yks4o8CAwI4PK5cyybP58hffrw944d6OrpMWL8eFZt2kTXnj3zRGdCyHkRpOioKJYvWECr9u2p16iRCiz8PsFBQZw/dYru2eiwWLNWLfoPHszjhw/TPa6M0sf+fn68fPaMJmmCD7OLsoJdb129SteffuLQoUMqL93+TcQ6CHJRYAXCxYsXUQMM9fXxEah4h5pEQtOWLbkscOfBytWqYe/gwPHDhwW1I4VSpUvz+9ChrFm2jAA/v2y/Lq+IhMrVqjF+6lSWrVtH8ZIl2bJ+PcN//50t69fj4eb23QZcaWsgZFw6kIdvndzj4uJwv3uXLevWMax/f7Zu2ICtnR3L169n7JQpVKpaVYGZFUPeCol+Hz6wzsWFAX/8gZ2DQy5amH2O//svpcuVo2Llylnuo6+vz9Y9exg+cGA6oa7M0seXzp6laatWOarPoMyLwDsvL0xNTFCXSLhy5YoSR1YQMYtBLgqsQDh06BAVK1TA4+5dwWyoUq0aGhoa3BcwtVFNTY0effpw7NChbN2p5zYmpqaMcXbmwN9/8/jBgxy/XkiR4FSvHotWrWLY2LG88/Ji9ODBrFy0iIfu7t9cIlGEjBf/jMIiJyTJZDxwd8dl4ULGDBmCz7t3jBg/nkWrVqm8dbCi5ZMfurvz7759jHN2FjzNECAyIoIThw/TvU+fLPdxWbeOsydPciVDsLI8pY+zwt3NDR0dHSpWqZKzA1Ai993ccChVin/laP6Ua4gCQS4KpECQyWQcPXqU1m3b4u7mJpgdzVq3xvXSJUHT0GrXr4+xsTFnT54UzIYUJBIJoyZN4vGDBwqVUFa1SKhYuTKzlyyh78CBnD99mpEDBnDg778JCQ7O0TiKSoi0nyJNFIthCP78mQN79jBiwAAunjlDvyFDmLVoEeUrVVLQyu+jrN4KJ48c4dmTJ4ycOBG1bxQJUhVnjx+nSNGimdYj+Kl7d6rXrMlMZ+evnstu6ePsIE1M5Nrly7nSljo7n18ZX2oitG7ThsP//ptnAklF5EP4b1Uu4O7uTnRUFGVKl8bz9WtBbDAyNqZmrVpcSXNXoGrU1dXp1qsXh/buJeE7bm9V0L5TJwwMDNiuhN7xqhAJtvb2TJwxgzHOzrjfvcu4oUO5eOaM0utppF1ySPtYTpBXfCTEx3P+9GnGDh3KA3d3xk2ZwoTp0ylpZyfniN9G2Y2Xtq5fj7GxMe06dlSCdYoRFxfH4X376N67d7qqhsVtbFiyahX9e/UiLi4u1+24fP48jnXqYJjNaHtl+77evHhBhQoVCAkN5YEcXsJcQYxBkIsCKRCOHj2KTfHiPH7wINdcv9+jcfPmvHj2TNDc6KYtWyKVSgVtUJVCcRsbOnfvzp+rVytNrOSWSNDV1aX/0KHMXLQIn3fvGDNkCMcOHlTayV2Z/iRljRUXG8uRAwcYM2QIfh8+MHvxYvoNHoyOjo6SZsidrozx8fFsWrOGrj17YmVtrZQxFeHKhQuoqanROE0Efw1HR8wtLLjh4UFoQgKhCQk0atqUoSNHEpqQwKfAwCxLH8sT3Bzg58frFy9o1KyZwseTluxeLNRkMp4/eoRtiRIcPXpUqTbIjbjEIBcFUiDs3LmTxk2bcl/I5YVWrQQNTlSTSPihSxf+3btX8LQ2iUTC4JEjuXD6NG9evVLq2MoWCZWqVmXRqlVYWFoyccQI/tm5M9cafMnI3FOQseRyindBFY2bIiMi2LN9OxNHjsTaxoZFq1crZT07N1s2v3rxgktnzzJo5EjBlxqkUin/7ttHx65dU225cvEitStXpn716qmbu5sb+/bsoX716njcu6f00seXz5+nWatW391PWdkLabNvpMADNzcaNmzIvr17lTC6iFAUOIHw7t07fH19adSo0VepRKrCzsEBYxMT7t25I8j8AI5OTkjU1OQ+wSiTDj/+iI6uLgdzqdObMkSCjo4O/YcOZczkyRw7dIiFM2cSlIMc9Owg75cts5uXjJJP2T0ZPgUGsmDGDE4ePszYKVP4bfBgtOX0JuSmOEjhwJ49GBoa5omlhjs3bqClqUkNR0cAIiMjefb0abotOiqK4M+fefb0abrSx42bNqV6zZps2L6d2zdv4ibnOcTt1i1MixT55lJRbp78H9+/T7PmzXn16hUf8kKhLtGDIBcFTiCcOHECC3Nz3r19K1gzoppOTjz08CBRwHK3bTp25Pzp04J7D4qXKMGP3bp9WVrIxV4YioiEshUqsGj16i89IUaP5uLZs7lk5RcytnnO6bkn7evTehSU/Z9OSkri/OnTOI8eTXEbGxatWkWZcuVyNIYqxAF8WWr4c/XqL0sNxYsrZUx5RZdUKuX8mTO0yYFYmTRmDGdOnGD3oUOcdXXlY0AAv3TpIsfsX0hISODxgwfUdHKSe4yM5OTzFRsTw4d37zAvVowTJ04ozQa5SUKx+INCGmtZ4ATCgQMHqFmzpqDLCzWdnPAQcH5be3tKlS7N5fPnBbMB/ltaOHfypEqCReURCc1atWLSzJmcOnqURbngNcgpKRf8vPTFTPEmnD1xgsmzZ2e7EI+qxEEKr1++5OKZMwxRcKkhozCQZ6RLZ89Stlw5StjaZvp8u2bNUqsowpcAx7HDh1OySBEsDAz4pWtXPgYGyjHzf3jcvasUgZCdGL2Mz6csM1SvVo3du3YpbIOIMOSl85DCJCQkcOf2bZo3b84jgaJnzYoWpaSdHQ8EbC3dukMHbly5QlRkpGA2APzQuTPa2tr8q8J1yOyKBHV1dfoMHEi33r1ZOmeOyjttZrZEkJ34AqE8nUlJSZw5fpzl8+bRs29fev/+e7pI/YyoWhykcODvv9HT16dDp05yvV5ZJ8TIiAhuuLrSpkMHJY2Ycx64u2Pn4ICJqWmmz+e2b/HJgwc0bdECj/v3he84Ki4xyEWBEggeHh7Al8pknxRU3/JSo1YtXj5/LtjF2cDQkPqNGnFWYLeeqZkZnX7+mc1r16q8zfb3RIKBoSGTZs6kfMWKTJ8wgRfPnqnEruyekNMKhbTBiRm/rEIsHj178oTpEyZQuWpVJs6Ygb6BwVf7CCUO4Eva5ua1a+ncrVuWF8YBQ4Zw++FD/MLC8AsL4+LNm7Rq2zb1eW1tbZavXYt3UBB+ERHsOngQC3PzHNty9sQJGjRpgoGhodzHowgR4eG8efmSGrVqKTROTi8SmslbsJ8f5kWLIpNKhU93FNMc5aJACYQrV65QtEgRXqrohJ8Zjk5Ogi5vNG7enDevX+MrcGBQlx49eHDvntKzFrJLViLBwtKSOUuXEh0VxWxnZ0GWFKQZfs9JZkJWmQyqPH99Cgxk1qRJxMfFMWfJEopZWKQ+J6Q4SOHVixc8evCAH7t1y/R53w8fmDF5Mo0cHWlcqxauly6x7+hRKlSsiAxYuGIFbZP7IrRv0gQrOfoiAPi8e8dbT08aNGmi4BHJj4ebGzVr1/7qcWUHtaaMlfGz+frZMyzNzbl8+bISZxNRFQVKIJw6dYoaNWvy/MkTQebX0dGhYtWquAtY3lno9Er40p65YZMm7Be4215GkWBtY8P0BQtwv3OHVUuWCBbEmhXZ9WJmVkVC2Sf87xEbG8uKRYu47+7OjPnzsbK2zhPiIIX9u3fTpHnzTCsRnj5xgnOnT+P55g1vXr9m9rRpREZGUrtuXUyS+yJMGTsW1zR9Eeo2aEAdOcpRXz53LlvphrmFh5sblatWRUsrfUPx3BSUaZuJvXzyhMpVqnD61KlcnDEbiEsMclFgBEJCQgJ37tyhSdOmPBNIIFSuXp2PAQGCnRzLVayIkbExbgKnNnbr3RvXS5cEv0jAfyKhaatWzFu2jCsXLrBn+/Y8UQJW3roGWqRfcsjY5VFVJCUlsXvrVq5ducI8FxcaNmuWJ8QBgL+vLzeuXuXnXr2+uZ9EIuGn7t3R19fnzq1bVE/ui5C2Aurr5L4ItXLYFwHg7s2bmBUtSukcZn8oC78PHwj+/JnK1aoJMv+rJ09o3KQJt27fFjSrSxQI8lFgBIK7uztqgJGhoWDxB461awvamKlZq1Zcv3JF5Wv+aSlVpgxVa9bk8P79gtnwFWpqSNTUkMlk3wysy22+NbM62RcMee18pa6uTpJMhiQHHQRVwaG9e6nh5JRpx8dKlSsTEBFBcFwcKzdupGfnzrx4/hwLJfZFgC/plzeuXBHci5DZMkN2UUR4Bvr5UaxoURLi44WNQxBjEOSiwAiEK1euYGlhIVj8gZpEQnVHR8GWF/T09alTv77gqY09fv2VM8ePp2tnKyTFLCyYMmcOF8+eZfr48YK3igbFOjKmHSMFKcKdv1KWFaaOG8fVixeZOncuReUI6MsNQoKDOX/qFN0z+X+/evmS+tWr07ROHbZs2MCmnTspX6FClu+jvNJHAlw9f556DRuiq6cn5yiK4eHmRo1atb5qAa3Mz0zaZYW0f2sCns+eYWNllbfaP4tkiwIjEE6dOkXVqlUFiz8oU7YsampqvH75UpD5a9erx/t37/jw/r0g8wNUqV4dW3t7Th45IpgNadHV02PCtGm43brFgT17BG0VDVmfkLMjFjI+n/FvIb7IGWMO9v71Fx537zJ+6lSl9nBQhOP//otDmTJflYtOSEjgracnDzw8mDVlCo8fPuSPUaP4FBCQaV+EYjnoi5C27DDAe29vfD98wCmTLo+q4NXz52hoaFCqdOl0j+fGZ0YzzabFl8/8q7wQhyAuMchFgRAIMpmM27dv07BxY148fSqIDTWTlxeEag5Vs3Zt7t2+LcjcAGpqanT/9VeOHjxIdFSUYHak2iORMGzMGII+fmTXtm2pjwspEtJWTszvHsusAhL/2rKF0JAQ/hgz5qs7ViGIiozk+L//0qNPn2/uJ5FI0NbW5oG7O/Hx8emKQZXOYV+EzP637rdvK7WqYU6QSqU89PCgZu3aX4mX7JDVvinjpBUFmfH6yRMaNmnCnTt3hIv9kaGYOJDzC7tu3Trs7OzQ0dGhTp063P2Ohzk0NJRhw4ZhZWWFtrY2ZcuW5ZSAwqpACITXr18jk8kwL1ZMru5nykDI6olaWlpUqVYNDwGzJ6rVrImJqSnnT58WzIa0dOvVCwtra9a6uHwl2oT2JKSQEnOQnXNPZk2bhF5WyCwgUSaTsWbZMoqXKMFPPXsKZGF6zp44QdFixahSvToAsxYsoEGjRpS0taVS5crMWrCARk2bsn/PntS+CAtcXGikpL4I8MXNX7VGDTQ1s7qM5i4pVRVzc1khKz75+2NtaUlsbCxv375VogV5m3379jF27FhmzpyJh4cH1apVo02bNnzMIrU6Pj6eVq1a4e3tzcGDB3n58iWbN2+muJJKh8tDgRAI7u7umJqY4PPunSDzW1hZYW5pyeP79wWZv1K1agQHBwta+6Btx45cOHNGaa2cFaF+48a0aNMGl/nzs/Rm5BWRkBOyWlbI7Q6PaclOKmNUZCTLFyygVYcO1G3QQIXWZU58fDwXz5yhbXJvhGLm5mz66y/uv3zJiYsXcXRyolObNlxOzlxwHjOGs8l9EU67uhKoYF8EgA/v3hEeHq6Uzpjy8PD+fayLF6dYLsaHZCUYkpKSCPDxwczEBHehKswKEKTo4uLCwIED6devHxUrVmTjxo3o6emxLY1HMy3btm0jODiYI0eO0KBBA+zs7GjSpAnVBMpAgQIiEO7du4eVpSVenp6CzF+1Rg1ePH1KrEB59UIXZypuY0O5ihW5lMtNjrKDrb09vw8dypply/D38/vmvqoWCWnPMTlp1KRKAfAtclLnwO/DB9a5uDBw+PBvdhRUFRfOnKFilSpYWVszbMAAKtnbU0RHB3sLCzq2asXVNGmNcXFxjBs+HNsiRbAyMKB3164E5SAzKquT6n0l9UaQh5joaF4+f07VmjWz3EfZ7cTTBiy+9/TEvGhR4QSCkmIQwsPD021xcXGZThcfH4+7uzstW7ZMfUwikdCyZUtuZbFUdezYMerVq8ewYcOwsLCgcuXKLFiwQNAy1QVCIFy/fp2KFSsKJhBKlS4tWHCimpoaNZycBF1eaPPDD9y6do3wDKlhqkZDQ4Oho0dz8sgRHmczpUoIT0LKSVier31mNzKqOH3IUwTpobs7p44dY8ioUahraOSabdlZUw8LDeXuzZu0zqQ3gqpOgspqniQvb169+ipQEbInDOR9j1JiE/w8PalYqRLXr12Tc6S8QYkSJTA2Nk7dFi5cmOl+QUFBSKVSLNJUGQWwsLAgIItl8Ldv33Lw4EGkUimnTp1i+vTpLF++nHnz5in9OLJLvhcIMpmMhw8e4OjkJJhAsHdwwFuouUuXRlNTk5fPnwsyv66eHg2aNhW89wNA527dSExM5OjBgzl6nRAiQZG14IylmnMbRSokHjlwAIBOP/2U5T7jJk/m6t27+IeH4xUYyD+HD1OmbNl0+2hra+Oydi3vgoIIiIhgz8GDmJub56jr4tkTJ2jcvPlXGRbZ/V8oerJ89uwZOrq62JUqpeBI8uHl6Yl9JjUhFCHlvUsRaVrJW8agRR9PT2o6OfHo0SNhAhWVtMTg4+NDWFhY6ubs7Kw8E2UyzM3N2bRpE46OjnTv3p2pU6eyceNGpc2RU/K9QPD09CQhMRHzYsUEKZCkpaVFcRsbwcSJo5MTD9zdBXND1W/cGN/373nn5SXI/CnYOzjQrlMn/ly9Wq73QpUiITPrcupVSNlP2W7hjChaPlmamMifq1fzQ+fO2Nrbp3su5aLSsEkTNq1bR/O6denYqhWampocPXcOvTR1AxavWEG75P4IbZs0wTKL/gjf8ia8ffMGfz8/6jVqlOPjyAlZCQ5pYiKP7t9XqGiRInh5elK8RImvAiXlPXOkvNfZCZgN8venuLU10TExeHt7yzmjAihpicHIyCjdpq2tnel0RYsWRV1dncAM16TAwEAssyi4ZWVlRdmyZVFX/+8bXaFCBQICAogXKLYr3wsEd3d3ipiZCZb/X9LenoiICII/fxZk/pq1awuWPQHJvR8ELs6koaHB4JEjOXbokEKBqqoUCWkDDPPql1BZvRXeeXlx8vBhBo8cmbrUkPaYu7Zrx56dO3n+7BlPHj1iyG+/UdLWlhqOjsCXk3Kf33/HeexYrqbpj1CvQQMcs+iPkNV7evn8+a+qGmbn/c9unNq3BIoEcBdwmSHo40dioqMp8Z2YkIyCMycxeimXsYz7JyUl8fHDB4oIGaioQrS0tHB0dOTixYupj8lkMi5evEi9LEp2N2jQgDdv3iBLk3X16tUrrKysvuqloSry6rkp27i7u2NpYSHo8oJQcxc1N8faxoZHAmVP2Dk4YGltzS2B1xV/TF5aOH7okMJj5aZIyCrrICVFW0dXl6KWlliWKEHxZDe0hY0NRSwt0U52i2d2t5cbviNlN146krzsk3GpIbOLj1FykaKQ4GAAaiT3R7icJpDwVXJ/hLo57I9w09WV4iVLfuXNyEjG/5GyTpQPPTwoYWuLWZEiShoxZ3i/ffvNZYbMagIpcuzSND99kgMV7wlRjl6AQkljx45l8+bN7Ny5k+fPnzN06FCioqLo168fAH369Em3RDF06FCCg4MZNWoUr1694uTJkyxYsIBhw4bJe9QKk3uRQyrizp07lC1bVjAXt5ACoaaTEy+ePhWsMFGT5s25fe2aYNkbAGZFitC+UyfmTJmitGWWFJEwNTk4aP/u3UoZNwUpX2I37EuVwt7BATsHB0o6OGBRvDiJCQkkJiSk9tP4Y/ZsNDQ1kair88nfn3eenrz39MTL0xPvt2+Jio5Wqm2QOy2bpYmJbFm/nqlz53Lp7FlCQ0JS77bTCgQ1NTUWr1zJzevXeZZc9Mw8i/4InwIDMc9hf4SY6GhuX79OkxYt+GvLlkz3UWTJRsa3L6hRkZG8evGCGk5OXDxzRoGZ5MPrzRts08RAqDJD5sPbt5QrVw43IQKqFe2nIMdru3fvzqdPn5gxYwYBAQFUr16dM2fOpAYuvn//Pl1vmBIlSnD27FnGjBlD1apVKV68OKNGjWLSpEkKGK4Y+V4gvH71ikb9+3Nk715B5rd3cBAsg0DI4kwAterU4c81awSbH6BL9+543L2r9CDR3BAJBsbGNGzcmAbNmmFXqhTBQUG88/TEx9OTu1ev4vvmDRFhYagD2rq6LP7nH2b+/jvRMTEYmphQPFlIOFSsSPOOHTErWhRvT09uXrnC9atXCQsPV9jG3GzZ/Pb1ax55eNCle3e2pQm8SisSVqxbR8XKlWnVsOF3x/teqFtG8ZHC3Zs36T9kSJYCITeRAPfd3KgpgECQAO/evqVj166pj0mRXyR8TwylOMVT9gn29aW2kxN///OPnDMqQIqbTpHXy8Hw4cMZPnx4ps9l1puiXr163BawIm5G8rVAiIuLIzAwkFIODgQI0GJWU0uL4iVKCOJBUFNTo3TZsuzZvl3lcwPYlSqFrp4ezwUqbQ1gVbw4DZo0YfKoUbkyvrJEgkOZMrT/8Udq1a7N65cvuXDiBA/d3YlKFgNpKyRmRURoKE/d3Xns7p7q8TQyNqayoyMNmzenW58+3Ltzh5NHj/L29Wu57MxNcZDC/t27WbByJaeOHeNjhjoVy9esoe0PP9CmcWP8fH1TH/+Ypj9CihdBAphbWPBRjsqpzx4/xsDQkBK2tvhmErOSctFUJ/01JSvBkfb57M7/488/Z9dcpeLl6YlNyZJoaGhk2n454zFnl5SwR3WyLrkc5O9PqTJl8PP3JyEhQbCqkiLZJ1/HIHh6en4JekpKIk4AN7etnR1RkZF8DgpS+dyWVlaoq6vj6+Oj8rnhi/fi4f37SAXs8d6td2+uXrqUq+W1FYlJKFu+PNMXLMB5zhyCg4KYMGIEC6ZN4+qlS0TksGZEZift8LAwrl+6xPxp05g0YgQhwcFMnTOH6fPnU7pcuRyNrwpxAF/ez+tXrtCtV690j7usWUPHzp3p0Lw57zJEud9P7o/QNE1/BIcc9kdIS0JCAo8fPMAxk2wCRbJCsnuT6fP+PVra2jleHlEGnwIDiY2NpYStrVyvl2TYMnuvsmo2HxsVhY6mJmpqanipeklYbPcsF/laILx69QojQ0O57iKUgZDxB/alS/Pe2ztdxKsqqVm7tqDVGx3KlKFqjRoc2b8/1+fKqUjQ0tKid//+TJo1i8f37zNywAD2bN+e5edUnju2jCfmjwEB7N62jREDBvD44UOcZ8/ml99+QzMb0c+qEgcp/Lt3L9Vr1UoNlnNZt45uvXvT/5dfiIiIwNzCAnMLi9R6BSn9ERa6uNA4TX+EO9noj5DVCc4j2c0vxLdHmpiIz7t3Sq9JkF28377FLgdzy/seZfa5/hwQgLGhIa9evZJzVAWMUXGQYkEg3wsEY2Pj75bUzS3shBQIAs5tVqQIJe3seCBgulKnn3/m3MmThIaEqGS+7IqEsuXLs2DFChzKlmXa2LEcOXAgNYg0qy9bSh552ojvFDI7Oae4gTN7LjoqiiP79zNt3DjKVajAghUrvulNULU4AAgLDubCqVP8L9nNPuCPPzAxMeHM1au8DQhI3bp27576mkljxnAmuT/CWVdXPgYE0EuB/gj3793DzsEBE1PTdI9nvA7kRhCfhNwpWvQ9Uj4v3p6e2JcuLZe3RB6xkLZHQ5CfH4a6uqoXCIWEvn374urqqrTx8rVAePr0KVZWVgQIJBCEvEgLKU5q1KrFq+fPiYyIEGR+C0tLqtaoofLqjd8SCVpaWvTq149Js2Zx6dw55kyZ8pVwzcnJNe2+GS9aKX9/68vr7+vLLGdnrpw/n6U3QQhxAF+O7dzJk1RzdKSYuTlGamoYZLLt2bkz9TVxcXGMHT6ckkWKYGFgwC9duxKQzcJomb1PEeHheL56RY1atXJku7JOmKoWCGnrM3h5emLn4KDym2J1IMjXl+I2Njx/9ky1kxcSD0JYWBgtW7akTJkyLFiwAN80sTzykK8FwuPHjyllby+IB0FTU5PiJUoIUmJZTU0N+1KlhEuvFLg4U+sOHXC7dUtl3oO0ZCYSDAwNmTpvHmXLl2fa2LGcOnr0qxbTkPWXTfqN5+D7d3lZPZ8kk3HyyBGmjx9PuYoVmTpnDnr6+sD3xUGDRo3Yf+wYr319iUxK4odOnb7aZ9rs2bzx8+NTdDTHz5/HIZM6/1kRHBSEx927tMykN0J2UbTAkfvdu9RIU7Qot6tSpsXL01Owksvenp6UKFlSKf0xMl43U0osp7yXaf+GLx4E+1KlePzokcJz54hCEoNw5MgRfH19GTp0KPv27cPOzo527dpx8ODB1NTpnJCvBcLLly8pX6ECAQqqJHkoYWdHTHQ0QZ8+qXxuCysr1DU0BAlQ1NbRoVLVqoIJBC0tLRo1b865kycFmR/Si4Q+AwYwbd48gj9/Zu7Uqd8Uq4qeY9K+Pu2F7Hs3N34fPjBv6lTCwsKYPn8+vw0a9F3PgZ6+Pk8ePmRsFkVaxkycyJCRIxk1ZAhN69QhKiqKI2fPZll6NrNjOXfyJI2bN0dTUzPHJyJlnLg83NyoUq1aapW6772P2blOZPd//OHdO3R0dCiWoZmPKvgYEEBcXBw2JUtm+nx2RVLaDJzspksG+flRpkIF3gh0c1MYKFasGGPHjuXhw4fcuXOH0qVL8+uvv2Jtbc2YMWN4nYMsp3wrEGJiYoiOjsamRAk+fvyo8vmL29jgI1B5Z3sHB957ewvSf6FchQqEfP4s2LJO7fr1CQ4KEqx7Zgr+fn6sXrKElu3akZSUxJqlSzNNG/seKSfWnIqHtP/57JyYExISWLV4MRKJhOZt2rBy8eJvLiucP3OGOdOnc/zIkUyfHzZ6NEvmzePksWM8ffyYQX36YGVtTccff/yuLSnu7lfPnhEWGopTDqshgnJu6Pw+fCAsLIyyFSp8c7/c8CokJibi8/69YIGKH96/z1IgZEV2umZ+7x71c2AgJUqW5HNwsGr7CxSSJYa0+Pv7c/78ec6fP4+6ujrt27fn8ePHVKxYkRUrVmRrjHwrEPz9/VFTUyNJJhMk1c7UzIwQgfovCJo94eCAp5x59sogL/R+gC932P2GDOHBvXsYGhnx0y+/KGVceUopZ/fc1f3XX9HT1+ehhwf9hwxJXW7IKXb29lhaWaUrfRweHs69O3eonY2LfdqL+9ULF2jaurVcdmSH753gvF6/xs7BQSktj7NzAU27ryrjEDIKqtDgYEwzBGiC8kotZ/WZTIyPR1MiQQ2ybHucKxQSgZCQkMChQ4f44YcfsLW15cCBA4wePRo/Pz927tzJhQsX2L9/P3PmzMnWePlaIOjp6RGew3xyZWFiZibIGjgI3F5aQHFibGJCmfLlBe/9IJFIGDd1Kp8CA1m1ZIlCvRvkLUyTU9LGHKxctIiQz58Z6+ycrtRrdrFIzt//mCFI8GNgYOpz2eWmqytlK1TAyNhYkJPRWwGyCVLwVtHcmQmXsJCQrzI4voc8XpvMPArR4eFoa2riL0Bxu4KOlZUVAwcOxNbWlrt373Lv3j2GDBmCkZFR6j7NmjXDxMQkW+Pla4Ggq6Mj2EXa1NQ0tZmMKlFTU8NOwABFIQVC9Vq18Hz9WjBRmMIPnTtjaGjImuXLkUqlChVTUrU4CPT3J0kqZe3SpRibmNA+k+BDVRIWEoK3pydVHR0FiQP7XkR/Tm4ec2q/EKmOKQQHB2NiZqaSuVIESkqwYkRICEaGhqoVCEkoFqD4vbreeYQVK1bg5+fHunXrqF69eqb7mJiYZLtQVb4WCHq6uoIJBBNTU0HmLmZhgaaWFh8ECFA0MDSkqLk53m/fqnxuAEcnJ8H6XqRgU7IkP3brxsbVq0lIs4aqjC6QudFLJqtshfj4eDavWUPn7t2xtrHJ0VwplSvNMwTYmVtYyFXVMqU3QU5Q1onL++1bzC0t0TcwyPT53Mxq8Hn37ksHz2LFcnGWzAmVw4OQkZQLv1bylra6YtoMhoxEhISgr6urWoFQSJYYLl++nGm2QlRUFP3798/xePlaIBgYGAhyFw/CCYSiRYsS/PmzIHEX9sk9L4ToHqmppUXl6tUFTa+USCQMGjGCsydOZNrvIKciIeNFPeXLmPZ8lN3zUmZf5MzEQcp+Mr60TL5w+jSDR47M0VKDt5cXAf7+NGvRInU8Q0NDatWpk63Sxxlnuu/mRtUaNXKtNn9WRyYBoiMi+BQYKEjKYUJCAqHBwRQRQiAEB2OcA4GQcZlCnsDaFMKDgzFUtQehkKQ57ty5k5iYmK8ej4mJ4a+//srxePlWILx7946ixYoJ50EwMyNUAHEi1LzwpbyzULEPlatWJTQkRLDeEwA/dOmCtrY2/36jc6gyPAnfIjPBkNnJOivPQcp+KV/8g//8g56e3ldLDfr6+lSpVo0q1aoBYGtvT5Vq1bApUQKAdStXMmHaNNp17EjlypXZ9Ndf+Pv5ZZn18C3ee3sTER5OxSpVsv0aZZyvUysL5qD0sLJPmKEhIZkGC+Y24Vl4EOTxmOQ0FyEiJIQiZmaCZYEVRMLDwwkLCyMpKYmIiAjCw8NTt5CQEE6dOoW5uXmOx8233RzfvXuHlaUlQQKk2+np66OlpSWI90IozwUkZzAIVCK1Rq1agnoPrG1s+PHnn5k3bdp3C458qwvkt1rkfq99bmakraqYcsH7VhEkSYbfE+Lj+XP1apznzMH9zp3UOg41a9XidJp2tIuT06J279jBkH79WLFkCfr6+qzetAljExNuXb9O57ZtiYuL+67NmR3nfTc3atSqxUMPj2weufLw8vSkVA6KPOWElAtuVp6gkJCQXI8FyPh+q/PFg6Cjq4uOjg6xKm50FxEcTFFz86+acuUqii4T5PElBhMTE9TU1FBTU6Ns2bJfPa+mpsbs2bNzPG6+FQj+fn441qjBmydPVD63qakpsTExKv9iQXJ6pVAeBAcHLpw+LcjcpcuVU0ljpqz4X9euXL9yJdutlLMSCWmXETIWO/rW3VtWd8wZL0DylE9+8+oVt65d44cuXdi8di0A165exUBNLcvXSIAFM2eyYObMb9qXXV49f067bNRQyA2837yhacuWCo8jT7Gn0OBghWMB5CE6KoqE+HhMzMyUVtMkuwI3IiQEh6pVuXvvnlLmzRYFXCBcvnyZpKQkmjdvzqFDhzBLIzq1tLSwtbXF2to6x+PmW4EQEhqKhZWVIBHtQqY4mpia8l6VyjsZTS0tipmb4/PunernTi5rLWRzqroNGzJ51Kgcve5bnoSMYkCRYDhFxEGK5+HU0aPMd3HhwJ492fpsZ7wYpPVgyIOXpyclbG1R19BQenzN9+z68P495paWaGhoyFXsShFCQ0KwlOPEray5lSkQUpDyJTgx5fOR8bMdFRZGMUtLojNZKxeRjyZNmgDg5eVFyZIlUfuGuM8J+TYGISY6GkNDw0wDMnIbU4EFghBzm5iakpiYSER4uMrnLmFrS2xMDJ8EqJgJ0KpdOx56eMh1Is1uTEJ2blB+++MP3L28eB8Tw7nbt9NF/vdQsPGS34cPPHn4kFbt2uX4tTkhq2JCHwMCSIiPT41xUCVhoaHIZDKMspkbLg9ZFWIKCQnBVEXphimkfNZCQ0JyFKiYGWnLLadkMHyPuJgYjIyNVSsQCnCQ4qNHj5Al934JCwvj8ePHPHr0KNMtp+RLgSCTyYiNi/uyfiaAQDARqAZCytxCCARTU1PCQkNJSlJ9QrCQtRckEgmNmjdXaGklrUj4OQuRkHJizeo89L9u3Zjl4sLS2bNpWbMmTx4+5MDZsxQtVowefftST05xkHa+C2fO0Lh5c9S+k9GQk4qB35ovLUlJSXi/fStIXQCZTEZ4aGiWF2p5vTvZeZ0y0g3lJbeXN7KK1ImLiUmNfVDZ+aQApzlWr16doKCg1N9r1KhB9erVv9pq1KiR47Hz5RJDdHQ0ANpaWqqt552MkIGCQnkvTExNhcueEFAgVKlRA5lMxhMFu89lXG74N81yA3w7BkEGDB47lj2bN7N3xw4Axg0ZQusOHVi8bh1hEREsUELL5sf376MmkVC5alUeP3iQ5X7yBFNmh5TKglfSlHD+Fsq0Qxl303LNm8sX6W+9P/JUU1QG8XFx6OrqIpXJiEu+0RORHy8vL4olp8pmtwBSdsmXAiEyMhJAaessOcXUzEyQC5a2tja6enrCZE8IuKxiW6oUJw4fFmTu+o0acePKlUzbN+cUfz8/FkybxpQMIuF7AYqamppUdXRkzcKFqY8lJSXh7eVFrdq1aV2vXrbFwTezKGQybly9Sv3Gjb8pELJCGXEIbX74IdtzKUraMcJykE2g6HGmJTQkBANDQzS1tNIV3lIFoSEhFBdgSSdJJkM9+dwdGRmpGoEgQzEvQB5eYrC1tc30d2WQL5cYIiIiUFdXV3lAUQqCxgEkJBAZEaHyuYUqLQ1QpGhRPmWo+68qypQvzzMlZcpIgIBkkdCoeXO6Ji83ZBQHGc9FZkWLoqGhkfoeSIGf+/bFwMiIkM+f8fP3z9b5K7OlgYx/P3vyhNLlymXncJRO0MePmBUtKsjcQmUThIeHI5VKs10bX5nktFiSvGR2XU6SSlHjy7lcJRTgGIS07Ny5k5MnT6b+PXHiRExMTKhfvz7v5Agwz7cCQVNTU5D4AwB9AwPVfbDTIGj2hEBzq6urY2xiIsjcevr6WFhaKs1blHKeSSsSuvTuneMbm259+1K3cWPuXL+eWpNBWV9krzdvsLSyQldXV0kjZp/QkJAv+dxyNJBSlOBsLjEoeq3IKAaTZDLCQkMFESeREREYGBrm+HVpgxIzey7tltVz8bGxaGtrC3IeLcgsWLAg9bt769Yt1q5dy5IlSyhatChjxozJ8Xj5UiBERkZCUpIgdQjgy0Ur8TvFcnIDYxMTQkNDVT4vCOc1MTYxQZZ8ElU1dqVK8enjR6V6bFK+cAF+fiyaNo2GzZvz03cqLgYHBZGYmEgxCwu69u1LncaNWTxtGvr6+nwMCEg9EX/vy5ydC1t4WBihwcHYfqf0cG70jQgLCUGirp6u85yqyMkSg7IJVeHcaS/c0sREubp5guI31PGxsWhpaKQuF+c6BThIMS0+Pj6UTi76deTIEX766ScGDRrEwoULuSZHF9wcfzpcXV3p2LEj1tbWqKmpcSRDadXIyEiGDx+OjY0Nurq6VKxYkY0bN6bbJzY2lmHDhlGkSBEMDAzo2rUrgRlcyMeOHaNs2bKUK1eOEydOpHsuIiICXV1dwTwIEnX11LQSVaKlpUWcQKJIyODIiGQ3rKrJzeBIdb6IhCXTplG/eXM6ZyESZHyp2f/I3Z0+w4dTp3Fjlk2bxqeAAJq0aIHbrVtIUezONuNJQKgugwkJCUSEhwtyNy1kNkF8XFyu9aH4FlKpFHX13GxHlTVxMTFoamrK7UFYt24ddnZ26OjoUKdOHe6maeL28uVLGjRogI2NDUuWLPnyYCFZYjAwMODz588AnDt3jlatWgGgo6MjV0mAHAuEqKgoqlWrxrp16zJ9fuzYsZw5c4bdu3fz/PlzRo8ezfDhwzl27FjqPmPGjOH48eMcOHCAq1ev4ufnR5cuXVKfj4uLY9iwYaxfv561a9cydOjQdNkKMTExaKirC5LBAKChri7IBUtdIGECAi6rCFxaWpm9JzIWjkmJSUgRCZ3SiISM/+X77u40btqUVw8fYmxszLING9DT12fP9u3pxlYG30s3VCTN8Xvkxp18tqr7hYdn2dExtxHqQi2TyVDXUF2cetozZmJ8PJrq6qkZaTlh3759jB07lpkzZ+Lh4UG1atVo06YNH5PrpAwfPpzevXtz9OhRTp069d/khcCD0KpVKwYMGMCAAQN49eoV7du3B+Dp06fY2dnleLwcf8/btWvHvHnz6Ny5c6bP37x5k759+9K0aVPs7OwYNGgQ1apVS1V4YWFhbN26FRcXF5o3b46joyPbt2/n5s2b3L59G/giENTV1VNzNzU0NNLVeJfJZKipqQl2sZSoqyMTSCAIIUxS5hYiKNRYQIFQ0s4ObyWmDaXciGQ833zy82PZtGnUyyASUujSty8aOjosmTmToWPHcunBA6pUr85PbdvmSvEor7dvsbW3l+u1igoHoe7kpYmJgt1Ny2QyJALMLZVK5V5iUBSZVEq8nHUQXFxcGDhwIP369Uv1UOvp6bFt2zbgS/EpR0dHqlatiqWlpbJNz9OsW7eOevXq8enTJw4dOkSRIkUAcHd3p2fPnjkeT+nysX79+hw7doz+/ftjbW3NlStXePXqFSuSm724u7uTkJBAyzS1z8uXL0/JkiW5desWdevWxcjIiH79+mFlZYWamhrz5s3DME0wjUwmAzU1paSeyYNQF2p1dXVB2jzDl4JBQogiLS2tbDUAyg30DQyIyMVS3mlTDgP9/FgxbRpjklMgzx46BEDHX37BsX59XKZNw9/fnxULFqSKi5x++rN7KYgID0dPTy+HoyuHuLg4tLLhbld2LQapTCaYQJBKpagLcKEWUhQlJSUhkUhyfB6Nj4/H3d0dZ2fn1MckEgktW7bkVnKr8Tlz5tCyZUtiYmJo06bNl50KeC+GFExMTFib3E8lLfI0aoJcEAhr1qxh0KBB2NjYoKGhgUQiYfPmzTRu3BiAgIAAtLS0vkrrsbCwICAgIPXvmTNnMnr0aCQSSTpxAGk8CAJU9YPki6UA4kSiro60EIoiIYQJfOk/8b3OjfKinsnPtCKhSq1aADg2aIDL1Kl89PdHnf9qJkhRbj5+WhLi49HU0sqFkb+Pqt3eqfNKpQrfxcsrWoS6UEsF8lzAF4Egjxc4KCgIqVSKhYVFusctLCx48eIFAO3bt+fTp0+Eh4ejra2NsbGx4nEE+SQGASA0NJS7d+/y8ePHdO+vmpoav/76a47GyhWBcPv2bY4dO4atrS2urq4MGzYMa2vrdF6D7GBsbJzp4zKZjMSEBNQlEkHSsdQ1NNDU1FT53Do6OqipqQlzzOrqaGlpqXxubW1twY5ZS1MTiZI/YymlvVJOy5oZ/g4LCcHt6lVaJnc2jIuJITw0FJ1kG1KkUkYvghT4nlz+VlmxtK8tWqwYhkZGWR7398qTZUe2ZzWGU716WBUvztVsVFPMSZm0zGxK+3rL4sUxMDRMfZ8zkvJ+5+Q9zurSm1buJgGOdepgWqQI19O011YWGd+jtDYVt7HB2MTkq2P+nhxPG0OT8XOc8WfGuJuUn+WdnPgUHs6pU6fo3r37d2bMOdra2hQrVoxwAXrHCMnx48fp1asXkZGRGBkZpSsmKI9AUEtSoBi2mpoahw8f5sfkk1lMTAzGxsYcPnyYDh06pO43YMAAPnz4wJkzZ7h06RItWrT40gc9jRfB1taW0aNHZytXc8+ePcydO5fFixfLa7qIiIiIiID8+OOPLF26lPHjx2f7NfHx8ejp6XHw4MHU6w5A3759CQ0N5ejRo+n2Dw8Px9jYmLCfwEiBRJHwBDA++CWGTog03OxStmxZ2rdvz4IFC5SyTKhUD0JCQgIJCQlfBb6kjb53dHREU1OTixcv0rVrV+BLWsr79++pV69etuaRSCSEhYZy382NdS4uyjyEbLHizz9ZsXChytsuN2/ThvKVKrFegGNetXkzS+bMwdfHR6XztmrXDoeyZdm4apVK5wVYsWkTLgsW4KPk/7Ma/xWbyXhn1bFnTxwbNmTfli0MnjiR8NBQ7ly5wsl9+4DMPQjy3N1mJO1r7UqVYsSECYwbOjTH42THjm+N8cfYsbx4+pSLZ8/KPUZ2bUr7+hJ2doydMoXxgwZl+vrc9CCMGD+exw8eZLsHRU74lgfBtlQp/hg/nkl//JGlfZmhDA9C9wkTKGZsTMmSJb8zW3q0tLRwdHTk4sWLqQJBJpNx8eJFhg8fnvULU9bk5CWfxCD4+voycuRIpcUQ5VggREZG8ubNm9S/vby8ePDgAWZmZpQsWZImTZowYcIEdHV1sbW15erVq/z111+4JF/UjI2N+f333xk7dixmZmYYGRkxYsQI6tWrR926dbNlQ0oMgCwpSZB2z4mJiSQkJqp87tjYWNRAkGOWSqUkJCSo/pjj4lBTUxPkmBPi4kCJn7G05yd1vj55/tSnT2rMQXhyYai1c+bwx7RpJCYmpuvdAOmXF9L+nZ35M5L2tUlJScTHx2d53MooyPStMWJiY7P1nufkfJ+ZTWlfn5iQgDQxMcvaKtl5jzPakx2BIANQUyM2m8ecU75lkzQxkUSp9KtjlkcgZHx/ZGn2SfualJ9JSUlyx16MHTuWvn37UqtWLWrXrs3KlSuJioqiX79+OR6roNGmTRvu3btHqe8UOssuORYI9+7do1mzZql/jx07Fvji4tmxYwd79+7F2dmZXr16ERwcjK2tLfPnz2fIkCGpr1mxYgUSiYSuXbsSFxdHmzZtWL9+fbZtkEgkX6JgBWrWJJPJBIk6VkYglbwIlasdGRGBvhzlYJXBx8BALK2teZUc/KQsMnsXf+rThzpNmuAybRof/f3RS14X/ujvnxq4mAQcztAFMjewKl5csN4XBoaGRGWj3oayv33q6uokCphCLEQAsFDp2vBleVpLR0euNMvu3bvz6dMnZsyYQUBAANWrV+fMmTNfBS6mo5AEKXbo0IEJEybw7NkzqlSp8lUBrv/97385Gi/HAqFp06bfzF21tLRke3LxlqzQ0dFh3bp1WRZb+h4aGhrIZDI0BKg+BsJdLIWsfJYQH4+2AG1Zhaxwl1JR0PXSJaWMlxLlnpKJIOOLK7ZrBnGQkYwpkId3784yWDE7838PRSpIKnoezY2eH9mxSUtbW+XdFFMQqjKroGnTKedwOTNWhg8f/u0lhYwUkiWGgQMHAl9SPTOipqaWYyGaL3sxGBgYEBMdjba2tiDzSxMTBStsIu8XSlFCQ0MxFqLjXEgIpgLVyPfy9MQuF0sOS4DOmYiDzL6UgcnFlNJWXMytL69dqVKCtDOHL5UzQwQq6R0mUEEuoSqzqjptOu0ZU0tHhwSpFAOBqlcWVGQyWZabPJ+xfCkQDA0NUZNIskxJym1kAhVViYyMFKwcrFB38iHBwWhra6MrQOEeb09PbO3tlVZtLrOYg3pNmrAsC89BRlJEQsPkLpAysl7nzglpLxHq6uqUsLP7pkBQ1Fub1btpYGiIpqamYD0/hKrYaWBoKEgLdyFrjGjr6hKfkPBVjZtco5D0YkiLMpoZ5kuBYGBgQEJCAjoCuLzhS6qNEN4LoXrWp8wtxJ18THQ0cXFxghy3n58fSTIZ1jY2Sh/75yzEwfe+kCkioUHz5nRNU5Y5O1/k7OxjU7Ik0sREAr8jWHLjxGFiavrl/y1AQzJTMzOVeBAyuxyrUpykLSiopaUl97KKov9/LR0d4hMSVOdBKCS9GKRSKXPnzqV48eIYGBjw9u1bAKZPn87WrVtzPF6+FAiGhobEJyQIsiYOX+6ms9M7PjfmNTQyEqTzW4iAsQBCLTMkyWS88/KiVHL7VEVJuQn5qU8f6jdpwuLk8sk5xc/Pj6VpPAlpx1YUewcHvN6+latGvqLkRvxBdjE1NSU0OPi7+ym7UZW2tjZ6+vrCeE3MzORqo/6t7qHZvY5qamuTkJioOg9CIREI8+fPZ8eOHSxZsgStNNVQK1euzJYtW3I8Xr4VCEC6KlGqRCh3e3h4ODKpVBhxEhyssp71GfH78AGbHOZLK4uHHh7Url9faeN179OHBk2asCjZcyDvEkGAnx+Lp02jUfPm/JxFq+jskPEkX6dBAx7fvy/3eIpgU6IEfh8+CDJ3TpqCKdPbbGJqSmJCgiBLDMbZFEU5JeM1VT2Tx1PeQ5UJhELCX3/9xaZNm+jVq1e6ZfBq1aqllqLOCflSIKS4paQymSDdyEJDQjAV4CKdJJMRGhoqiDgR6pjhv2wCIbh+5QpVqlVTynveM9lzMH/aND4lew5SMhqy4luf7gA/PxYki4Tu2RAJ37uwmRUpQsUqVbh2+fJ3x5Jn/O9hp0D2hDyktdckly6W38PEzIzQ0FBhPDYCxV1oaGoSn9zjRF9fXzWTJqFY/IEwbX9yjK+vL6Uz8XjKZDK5+srkS4GgpaWFRCIhJiZGkEDF0FzoWZ+juQUKFhTqmL0FFAifg4J4/vQpjdLU/pCHHsniYMG0aQT5+6denFIaL4F8X8a0IqHbd0RCxvEzXtAbNWvG00ePCPnOhTK3Thr2pUtnWyAoO2ZMqOUNIYMjc/uYs1oI1dbVJSYmBm0tLdUFexeSJYaKFSty7dq1rx4/ePAgNWrUyPF4wuTMKQE9PT2io6PR0dUlOipKpXOHCBksKNCdfGhICPr6+l86HKo4X9zr7VuK29igra0tSOvnC6dP03fgQE4dOyZX3niP5GWFBdOmEZhhWUEKZLdvYloxkfaxAD8/5k+bxtTkOgn75SimpKGhQav27dm6YcN391Wk1XJWr9PR0cHS2hrv5KAqVaKjo4OOrm6WQYq5eW0wMTX9riBThIwVDeE/j5VQqZ1aOjpERUUJ0oCtoDNjxgz69u2Lr68vMpmMf//9l5cvX/LXX39x4sSJHI+XLz0I8MU1FRwcrDoXVRoEzSYQyHsRGRFBXFwc5ubmKp87OCiIiIgIStrZqXxuAHc3N+Lj46nboEGOX5siDualCUhMe8HJ7KKfFZntl/KYf7JIaPwNT0LGu+60X/76jRsTEx3Ng3v3smVLxrGye0efVXCbbalShIWGClIkqaiFBbExMURFRip1bmmG3zP7/5kKGJiZWzEI3/MJ6OjrE/r5MwaqPHcXEg9Cp06dOH78OBcuXEBfX58ZM2bw/Plzjh8/TqtWrXI8Xr4VCObm5vj7+Qm2Hm9sbCxM/INA4iQpKYn3Xl65WjjoW3h5elKqTBlB5k6SyTh17BidfvopR4Wq0oqDtGmDGT0IynKyfk8kZPVp1dTU5H9du3Lq6NFsr4Ur+5Ofk+UFZWPv4MB7Ly+FxpD3/RBqiUFdQwMjY2OFBULaoMO0jcNkGR5Le301NDMjwM8PI1UGKBaiOgiNGjXi/PnzfPz4kejoaK5fv07r1q3lGivfCoQSNjYEBAQIIxBCQ5Goq2NkbKzyuUOCgwtlsODTR4+oKscamrK4cv488fHxdOnRI1v7ZyYOMvuypW18kxU5qXGQHU9CCinnvK49exIdFcXVixezMVP618pDZsdTtWZNnj56pMCoWc/1vffPzsEBbwHEiQzVBEdmdvzGJibIZDLCw8JydZ7MMDQ1JTAggLLlyiltbpEvlCpVis+fP3/1eGhoqFwNnPKtQLCzt+fTp0+C5MdLExMJDwsTxNUvZICkkALBw82NilWqCFb7QiqV8ufq1bTt2PG7dRGy8hxkOm4Wj+fUq5D2gp2VSMjsol66bFlad+jAn6tXy90PQNGbKx1dXSpUqoSHm1u2X6OME1fKGHalSvFWQYEg702mkKWlI8LClNIDIrsxNCkYmZnxOShItUuGhWSJwdvbO9OSynFxcfj6+uZ4vHwbpFi8eHE+BwXhJNBdZUqwoLcQ8wooEPoMHIiamprK07IC/PwI+vSJKtWqce/OHZXOnYLPu3ccO3iQwSNHMm3cuEzThrIrDtK2yoXsBf7lJF7B/zuBizK+LC0MGjGCw/v24StQ/QGAqjVqEBgQwMeAAJXPrfb/9s47LIqri8Pv7tI7iKIICiJWRATBEnsltsQYo8aSGEvsKBbsXbHEHlusMcZuLNHYIxq7gti7YMdGFVSkfH/o8gGCAluGct/nmQfYnbn37LI785tzzj1HLqeko2OWPQjZvZx+7v9laWWl9eWViby/SGcU2tDGddDU0pLIqCiKFy+uhdk+kM+7Oe7cuTPl93379mGeyrudmJjIoUOHcMiBIMuzAqFYsWK8efNG2mRBCeZ+8ugRJqammJmbq9U9mBUePXiAQi6nqK0tT3KgRlUl6MwZqnh6SiYQAP7+6y+qVq/ON+3asTHdRTc7ngNIm3+Qvk+DOviUSJAD337/Pa9fv2b39u1qnDX7VPH0JPDMGY2N/6lzu+2Hi9TjR48yFGiavGBaWllhZGRE2OPHGpwlY7Kb+5CRgM2pF8fU0pLY2FiKFSuWwxEE6fn666+B98UDf/jhhzTP6erq4uDgwKxZs7I9bp4NMRQrVozXEgsESXoTvH5N2JMnkrj6k5KSuB8aKmmYoUrVqsgkSA5Vogw1NG3ZkoqurimPZ1ccgHrFQGZkFm5wqVyZxs2a8ZuKoYWcHJn6GLlcTmUPD4I0KBA+ZUdJJyfuhYSQLEG7ZQcnJx4/eiTJ0l2LHHguUr9DqRMT4z9sn3oHUz9namlJ7Js32hUI+TzEoOzYWKJECZ49e5ami+Pbt2+5ceMGLVq0yPa4eVYg2NraEvf6tWTudilrIUhZOEjKPIRb168jl8spU66cJPMreXDvHisXL2bQ8OE4OTvnSByA9s45qUXCt506UbpsWQYMH87yhQslDS0AlK1QgeSkJG7fupWt49R1OS/1IUFRHWItuydTRy1XjkyNZQ5qIGTl9aVetfAu1WN8+FsZ3oh59QpbW9tsza8SqQ3LyZbLQwxKQkJCsLa2Vtt4eTrEEB8fL0kbYHjvQbCzt5dk7pA7dyhTvrxkc6taVTCnJCUlceLoUeo1asSNq1clsUHJsYAAjIyNGT1lCq/j4pgwYsRncw7SoyCtFyGji5S6l0CO9ffny1atWLtyJSeOHlXT6Nkj9XtRt1EjThw9iiwpSZJqtg5OTgQcOJDp86n7CKiTJN4LhMsXLqh55IzngrTvu4WlJfeyWJRK3ddGhb4+CUlJ2vUgJAGqtO7JIwIB4NChQxw6dCjFk5CalStXZmusPOtBKFSoEDo6OkTHxGAiQcOPF8+eUaRoUa3PC9Lexd++cYNSzs7o6mU3d1k9HD5wgGpffIGRBAWy0mNVqBAJ796hq6eHmZnZJ/fN7PyiTc+lmYUFurq6JCQkUEiNdxk5xdjEBM+aNTm8f7/G5vjUeV1fXx9HJydu37jxyf9D6rX96kSbHoT0J3prGxuePXumsfkyE7YmFhZERkWhp6uLhYWFxuYvqEyYMIEmTZpw6NAhXrx4QURERJotu+RZD4JcLqeQlRW3bt6kqK0tt2/c0Or8oSEh2NrZSVJ6OPTuXawLF8bUzIyY6Gitzv3o4UOiIiJwcXXlfBar7qmTB/fucT8khNr16rFv926tz69EGVYYPWQILpUr4zd+PAtnz+Z8Jkv1MlPiik88lxnZWc2gxMPLiz6DBrF25UquX7miUllmVVEmvNWuX5+Q27dz1MFRHXc2LpUr8/LFi5QkQVWLVmV0p54ZFpaWmFtYcE/FAk05Qd/AgGLFi2foQcjJZys1n2tEb21ry+3r1ylhb6/dbryqKvFcnoOgZMmSJaxevZrOnTurZbw860EAcHNz4+bNmxTTZizrA+EvXhD76hUlJSj/+zoujiePHknmRQg8exZ3Ly9J5gbY/88/NG7WTLJ23+lzDg7t3cvyX3+lz8CB9OjXL8OwV1YTuDIj9Rc1O+cqI2NjevbvTy8fH5bOn8/hAweyVUxJU8jkcho3b86BDyJPCg+uu5dXGpGrzTwERycnnjx6xNs3b9Qwa/Yo6ehIdA7KWqdPSk39OcyqP7GwrS23b92inLZDpPk8SVFJfHw8NdXYnj5PCwTXypV5cO8eRSUQCCCtq1/Kuc8rVxNIdIE+feIEBoaGVHZ31/rcmSUknjp+nOE+PhSytmb6/PlUcnNLc1x2vmg58SpkRGV3d6bPm4eFpSV+Pj6cOXky5TmpRUKVqlXR0dHh3KlTOTpe1WXtMpmMKlWrZmt5ZVaqMmYVKRMU1d1WO31iojIZMSOsbW25HxpKpVQrgATqo3v37qxbt05t4+XZEANA2bJlWfvHH5J4EOD9RVrK3gRSZfNfv3IFvQ/x27u3b2t9/sSEBPbv3s3XbdsSHBiotXk/t1rh5YsXTBs/nvpNmuAzbBinjh3jz9WreR0Xl6n7WenWzs7d6+fcwEbGxnTs2hWvGjVYu2oVRw4ezHC/zxVT0hRyoNW337J/924SExNzfKHPagnqjMZ3cnZGoVBw89q1lMeU72n6XhmpUYenI4n3F+mrly6pYbSszwnv3w9HFVZuZEcgpX7vlIXBrG1tCY+Koqy2yywXkCTFN2/e8Ntvv3Hw4EFcXV3R1U0b9Jk9e3a2xsvTHoQyZcoQExNTID0IUi51TExM5EJgIFU8PSWZH2Dfrl0UtrHBo1o1rcyXnaWMh/fvZ7iPD9ZFijB78WK+ad8eUzOzNF825fkmvbdAFTe3mbk5bdq3Z/bixVgVKoSfj0+m4kCJFJ6EqjVqYGVtnRJeUOedeVZx9/J6Ly7TZXlrozYFSONBSCkt/WFubXnOU/9vrW1tiYqJoUyZMlqYORUFJMRw8eJF3NzckMvlXL58mfPnz6fZskue9iCUKVOGV7GxWFhZSVL+N/TOHexKlJAmUTEkBOsiRTAxNeVVTIxW54b3RYtafvMNW9ev1/rc8L62+LZNm2jXqRPnz55VS035zMhJnYMXz58zbfx4XCpXpvnXX9OydWtOHjvG8UOHuHn1aqaehKyQ+lwlk8koW6ECtRs2pPoXX3Dt8mUW/PJLthofadOTIJfLadupE9s2bCA+1Xcmu/+9rAqKzMZ19/Rk26ZN2Zz102TVJnMLi2wtM1Qn+vr62BYvrtHmVMp71vSfZ5lcjrGFBa/i4rQvEAoIhw8fVut4eVogFClSBENDQ168eIFloUKEv3ih1flfPH9OXGwsJUqW5E42C72oSlxsLGGPH1O6TBmtutmVXAgKoteHmPtLLb/vSg7v38+XrVpRu379bHUizA45LYKk5PKFC1y+cAH7kiVp0Lgx/YcP53VcHBcDA3lw5w4P79zh6YMHyD4hcFKHFORyOcXs7XFwcqKEkxOuHh4YGhlx/MgRxgwdysP793P0OrUlEuo2aoQMOKqh/1dWKGxjQzFbWy5mcEf1KaGmLgnq5OzMk0ePeCNBgmIJR0eio6OJyKSKYkY3yqnDNJ/qGfI5gWthbU1YWBgmxsYUKlQoS/aqjXweYvjmm28+u49MJmPr1q3ZGjdPCwSZTEbJkiW5fv06xWxttS4Q4P95CNoWCACXgoNx8/CQRCDExcZy4+pVPKpVY79Eyw0TExPZ/OeffP/jj5z47z+1e3FUFQepeXTvHn8sX8761aup4uFBeRcXajVqhH2PHsjlch6FhvLgzh2iXrwg4d27FG9YrS+/RAaYW1tj7+SEnYMDSUlJ3Lt7l9A7d9iwahVBgYG8S0hQ+fVqWiTo6enRpn171ixfzrvExJQLTU7LNec0LOHh5cW1K1d4HRf3yTGy61XOyjLHJMCtalUuBQdnc3T1kL61dU5CKun/XwreJyZ+bqzCtrbcuHqVUo6OOZhVRVS9wOdygZC6OZM6ydMCAaBSpUrcvH4duxIlNNJP/nNImYcQeOYM3fv2ZfVvv0ky/8ljx2jQpIlkAgHg9PHjtGjdmuZffcX2zZvVNq46xUFqkhMSCD59muDTp1EAOnI5RYsVw8HJCXsnJ4qVLImunl5KW+tSZcvy5s0boiIjObx7N/dv3+bJkyckJCenZI+rE02KhBatWxP+8iVnTpxQ25ifIrNzep0GDVI+s6oIjYz43FjK1RNL5s1T46xZQ1m9MVRNoY30rzW9PFd+NpUN2ouWLMntmzdxqVRJLfML/s+qVas0Mm6eFwjVq1dn3ty5VM2Ci0UThNy5w9dt20oy97XLlzE2MaGko6MkBVdO/fcfnX76SbL5AZKTk1m5eDGjJk/m3OnTOXaxp0YT4iCjC5ECkCUl8fTRI148esS5o0dT7sL0DQ2Zvn49a+fOJe7165Ry8NrIl9KESCjh4ECLb75h4ogRACp5D1ShVOnSFClalJPHjmXrOHXZ6VCqFAYGBlyXqFS4g5MTgam6oWa0ciM9qr52pXfB1smJQ+fO0bpdOxVHzAGJoFIt71zuQdAUeXoVA4CHhwcvw8MlrUdgV6LER8tJtEFCQgKXzp/HXaLVBK9fv+bUsWPUb9JEkvmV3L19m327dvHzgAEoFKrloWvKc6D8oqXu+6K841LFYk1l3atzdYNCoeDnAQPYvX37R3evqpyAclJ8qn6TJpw4ejSlQFFmLZ41JcLcPT25EBREohpCQtlFT0+P4nZ2n1w9oa7PU0bty+1KleJ5eDgeHh5qmiUbJKlhywELFy7EwcEBAwMDqlWrxpks1t3YsGEDMpkspY2zVOR5gVClShViY2MxMjZGX19f6/O/ePaM13Fx2JcsqfW54X2YQcrlhof37+eLOnUkee9T89eGDejq6tJSBU+SpsQBZHyOUUCaMIG2lthlFXWJhK++/RaZTKbWEFBO0DcwoEbt2ml6P6j7xvBzosXdy4ugTMpxa5oSjo68ion5KFfrc5+77CxDzayior6hIXJ9feLevMEtXRExrSDBMseNGzfi6+vLuHHjCAoKonLlyjRt2vSzPTBCQ0MZMmQItWvXzv6kaibPCwQzMzPsihfn0qVLlJQi+YX3vRGk8mAEBwbiUKqUZG2vb9+8yfNnz6hZt64k8ytJSEhg6fz5tPr22xyJNU2KAyXKYjGqfuk+VchH3agqEko6OtLim29YOn9+mrtmVSshwsfvY1K6n+mpXb8+YY8ff3QHnd1S1zmlkLU1diVKSJJUDOqvvZD6ffvc+2NXqhSXL1ygZIkSGOeCRmvaYPbs2fTo0YOuXbtSoUIFlixZgpGR0Sc7KiYmJtKxY0cmTJhAqVKltGhtxuR5gQDgVa0a58+fly7McPu2ZHO/ionh1o0bVKlaVZL54X3RoqbNm0s2v5KQO3fYs3MnvQcOzJZHQxviID059RZI4WXIqUjQNzCg98CB7N62LdMcFVVEQkbHZjaeTCajafPm7P3774+e09ZJ0N3Tk5vXrhEXG6ulGdOiqeJMyrBZEh8nKiqxd3LiwvnzVKteXe3zZwk1eRCio6PTbG/fvs1wuvj4eAIDA2nUqFHKY3K5nEaNGnEyVcnz9EycOJEiRYrQrVs3lV6uusgXAqFmzZqE3L0rWdnjG9euUVHC2uJBZ85I2jzpxNGjmFtYSPoeKPlr40biYmP5ecCALO2vLXGQUQ5CTsdIjbYEQ3ZFgkwmo7ePD9FRURoNLWQ1RFzJzQ0jY2NOZTM5UZ24e3pmq/eDuqno6sqNVKWls0N2PrPx/L8ng7Ivg52TE7dv36ZGjRo5ml9l1JSDYG9vj7m5ecrm7++f4XQvXrwgMTERGxubNI/b2NgQFhaW4THHjh1jxYoVLFu2TKWXqk7yhUBwd3eXNFHxyqVLmFtaUtzeXpL5g86epaKrq2R5AO/evePQvn14t2wpyfypSUxIYN6MGZRydubr77775L5SeA5Sk5WLe25Kns6OSGjdrh0lHR2ZP3MmiYnS16n1btGCQ3v3kpBBcmBW3uPs/B8y2tfAwIDylSpl2g5c05RwcMDYxCTD/g/qriisSPcT3guEiJgY3CVosKZOHjx4QFRUVMo24sOqHFWJiYmhc+fOLFu2DGtra7WMqQ7yjUB4FRuLiampJBfJd/HxXAoOxkOiu/gnjx7x8vlzXKRI/vnAwT17qOjqKlkeSGpioqOZNWUKLVq3pmomvRqkEgfKk2Z2T8jpjyPV39okKyLBq0YNmrVqxaypUyUpA54eBycnylasyMG9eyWzwaVKFZ6FhfE0k7tHTePu5cWl8+dJ0sDqCeVnM7OLib6BAToGBsS9fk2VKlXUPn+WSL1GOCfbB9VnZmaWZsvsemNtbY1CoeDp06dpHn/69ClFixb9aP87d+4QGhpKy5Yt0dHRQUdHhzVr1rBz5050dHS4I1Hnz3whEMzNzSlcuDBB58/j5OwsiQ1BZ89KttwwN8wfGRHBwT17aCdB6+CMeHDvHkvmzaOXj89HSYtSiAPlXWX6C3pWFsemO0dlOra2+JRIKOnoSM8BA1g0Z45aalKog/adO7N/926io6IyfP5zJ0F19ImQOrzgXrWq2ldPZPXi4Vi2LOcDAylZogQmJiZqtSHLaHmZo56eHh4eHhxKVVI8KSmJQ4cOZRhmKVeuHJcuXSI4ODhla9WqFfXr1yc4OBh7ibzT+UIgALRo0YKTx49T3sVFkvnPnz1LKWdnzDRU8vJznDl5Es/q1dHVy2yhkebZuXUrpcuVk+x/kJ5zp06xa/t2ho0dS5EPqj03hBXSd3XM7gUofWhCii9xRiLBplgxho4Zw84tWyRbypeeiq6ulCpdmr//+ivHY6j6/urr6+NRrRpnT51ScaScYWFpiYOTE8GBgTkua53Z40m8zzP4FGVcXDh14gRNmjbNwex5F19fX5YtW8bvv//OtWvX6N27N7GxsXTt2hWALl26pIQoDAwMcHFxSbNZWFhgamqKi4sLehKd1/ONQGjSpAkhoaGSXZyio6K4e+uWZKsJbt+4QWREBNVq1pRkfni/omLXtm2079JFMhvSs33TJk4fP86oiRPp+vPPkomD1EmK6fmcFyG31UdQklok/NizJ6MmTeL4kSPszGZDGE0hk8lo36ULO7du/eTKgazcJGbnRJl+HK8vvuDF8+fclaBfC0AVT09u3bhBTHQ0oJ4lpulRJiQqBUNKczHA2cWFW7dupcno1zoS1EFo164dv/zyC2PHjsXNzY3g4GD27t2bkrh4//59nkhwk5Id8o1AqFu3LuERERSzs5PsLjro3DlJVxP8u38/9Rs3lmx+gH1//00ha+tMY/9SsHblSl69ekX9Jk1YOHu2JJ6D1CTxcS2DnCaISS0enjx+zOI5c2jQtClRkZGs//13iS36P541amBhack+NfQKye4FNbXoqNe4MYcPHFDZhpzi7ump8eTIjC4k8YBMXx+rokWJjoujrpS1UiQQCAD9+vXj3r17vH37ltOnT1Mt1XkxICCA1atXZ3rs6tWr2b59e84mVhP5RiAUK1YMu+LFCTp3DmeJeo0HnTmDS+XKkgmUYwEBlHJ2pljx4pLMD/D27Vu2bdrEd506IZfnjo9X+y5dMDM35+SxY/QeOBCbDJKENI267tjSCwKp1wcUtbXlZx8fThw9iqWVlcplmdWFQqHgu44d+WvDBrV3+cwOxe3tcShVimMBAZLMr6enh4urq2T5D07lynHuzBkcHRwoUqSIJDYIck7uOIOriSZNm3Ls2DHJwgwP798nOjKSihJ1K3sVE8O5U6ck9yIEHDiAQqGgdoMGktoBaXMOFs+Zw+njxxkzdSqlJEpmVZL+piQ7noCMEhal+CKXLlOGMVOmcPzIEZbMm6e23g3qoG7DhiQnJ3MkVZLY59DEe1i/SRPOnDwpWXEkl8qVefniBU8ePcrxGKq8L2VdXDh5/Lj0+QcS9WLI6+QrgdCoUSPu3b9PBQnbiQadPStpmOHw/v3Url9fkuZRShITE9n855982769pEmTGSUkrlu9mt3btzNq0iS+kMDlqRQCSWRetz67SHH+ql2/PiMmTmTn1q1s/OMPQL0NnlRBT0+Pb9q3Z9Off5KUlPV3Rl05CEp09fSoVbdumt4P2sbdy4vz58599LgmTvzplzsqgLKVKhF6/760+QcgWYghr5OvBEK9evUIj4jAxtZWsqzPoLNnqVK1KjKZTJL5r16+TEx0NNVr1ZJkfiWnT5wgIjyc1p8pVqQpPrVaYc/OncybPp0fevakfZcuyCQIhcjJ+LyT0d9Kj0Fm5yltWi+Ty/n+xx/p9NNPzPH3Z9+uXWmezw0ioU2HDrx49oyznyhpm11yIsBq1qlDZESEZK2dZTIZVapWzTC8oA1BqW9ggFWRIkRERlKnTh0tzPgJhEDIEflKIBQtWhR7OzsCz53DuVw5SWy4fvUqBgYGOEjYaGPfrl00bdFCsvkBkpOTWbZwId4tW1KqdGmtzp2VpYwXz59n3NCheFSrxuCRIzE0MtKoTfJMflfHeNrCyNiYoaNG4ebhwdhhw7h84UKG+0kpEkqXKUOjL79k2a+/anXejPBu0eIjAaVNSpUujY6ODjczKa+cVZGQ1UqTyuuoctlj6Q/5B0VtbET+QR4lXwkEgGbNm3MkIAA3KXqO877U74WgIEnDDMcCAihiY0PZChUkswHeFyvasXkzPw8YoLWQR3bqHDx5/Jhxw4Yhl8mYMns25SR6v9J7BzIriiTlioXyLi5MmTWLpORkxvn5Zem91bZI0NXT4+cBA9i2cSOPHj5U27g5uduu4OKCpZUVx48cUZsd2cXdy+t97YNMwiyaPPknARWrVuXokSO0adtWgzNlkWRUyz9I1r7JuYF8JxC++eYb7oaEUKUAVzV8+/Ythw8cwFtiLwLA33/9RXx8PN+0a6fxuXJSBCkuNpaZkyezf/duho4ZQ+du3TRarlt58VeesnN60VeKCk2jb2DADz16MGTUKPb8/TezpkzhdVxclo7Vtkj4tkMH4uLi+GfHjhyPoS7Xe9OWLfl3/37iJVxB4e7pqbWCVRldSFw9PLgbGkrr1q21YsOnEBGGnJHvBELdunWJf/eOqKgobO3sJLEhODAQO3t7bIoVk2R+gAP//EMVT0+sCxeWzAZ4X1506fz5NG3ZUqMrB1SpkJicnMzev/9mpK8vjk5OTJ07V+3el5xeeDJa1qithMRyFSsybe5cSjg4MGLQIPbv3k1ycvZupbQlEpzLlqXRl1+ydP78bCUmZkRqAZeTkQrb2FDZ3Z2De/aoZIcqFLO1pVjx4lw8f17lseSptqxiV7IkL16+BKB27doq2yCQhnwnEPT09PD29mbPP/9IdhcfFxtL4Jkz1JMwc/fF8+cEnjlDi1yg3h/ev8/2zZvppaFQg7rKJz998oRJo0dz4J9/GDZmDD/27Kmx0tnKJMWc3F+m/9KqWzCYmZvT9eefGTp6NHv//pvJo0fzTIUmQ5oWCbp6evT8EFp4LHFoAaDlN99w9uRJwj9cIKWgXuPGnD15MsveHnXj5unJnt27aertLemKKiXCg5Az8p1AAGjbti2Xr1yR1M3/7/791G3YEIWOjmQ2bF2/nroNG0pSGCg9u/76izdv3tCmQwe1jqvu3grJSUkp3gSrQoWYvWQJ37Rvj4GBgRqs/Zj0a20yWsXwqRUM6sTQ0JBvv/+eOUuWYG5pychBg9iXA69BRmhSJHzXsSNxr16xW4XQgrooZmtL7Xr12Lphg2Q26OjoUKdBg88ur1Tn8tj0YbMqnp5cvX6dtrkh/wBRBiGn5EuB0KxZMyIiI7G0tsbUzEwSG65cvMjbN28kFSmPHz7k+NGjfPv995LZoEQZamjSvLna3PeabLz09MkTZvv7M2PCBCq6ujJ7yRKaNm+OjoqCL6OQQerMb1XGyik6Ojp4t2zJnKVLKVehAv7jxzN32jS1tybWhEgoV7EiDZo2Zcn8+SSrGFpQB207duS/w4cJe/xYMhs8qlUjLjaWq5cvq3XcrK5mMLGwwMjMjIjISLy9vdVqg0C75EuBUKhQIaq4ubFvzx7JVjMkJycTcPCg5FUN/9q4EY9q1SRddqnk0YMHrFu9Gp9hwyhkba3SWNrqynjz+nUmjRzJsl9/pX6TJvyyaBHeLVtme1nk575oGfVmUNfYmWFkbMyXrVoxa9Ei6jRowOJ585g8ejS3b9zI4YifR50iwbpIEXyGDePPlStVqhSoLkqVLk1lDw/+2rhRUjvqS9z7wa1qVfbt2YOXlxeWlpaS2ZEaEWLIGflSIAC079CB48ePS7rc8MihQ1SsVAlrCdcAh794wYF//qFd586S2ZCag3v2cO7UKXxHjszxagEpWjafP3eOEYMGsf7336n2xRcsWLGCH3r0wL5kSa3MryDtCghVTlolHBz4sWdPFixfjleNGvy5ejWjfH25EBioLnM/iTpEgr6BAb4jRnD6+HH+lbBSYWradenC/l27iIyIkMyGwjY2lKtYkaP//ivJ/HLehxfOnj1L+/btJbEhI0SIIWfkW4HQqlUrnj1/jnO5chpdtvYpIiMiuBAURN2GDSWZX8nOrVtxKlNG0hLUqfl9+XJex8XRc8CAbB8rhThQkpyUxOnjx5kwfDhTx47FwNCQCTNmMGH6dBo2bYqlldVnx8joov6pUEFW3bqfw6pQIRp6ezNhxgzGT5+Onr4+k0ePZsKIEZw5cUIteQbZQRWRIJPJ6O3jQ+yrV/yxYoWGLMweLpUr4+DoyN/btklqR/3GjTl/7hzRUVFZ2j87F4Cs7GtgYECpMmV48vQpLVu2zMbogtyILFnbZwYtUsbZmerVqxN2/z4njh6VxAY3Dw+69enDwJ49SUyUzlHVqk0bPKpVY9ywYZLZkBpTMzMm/fILAQcOsH3z5iwdI6U4yAwjY2Nq1qlDjVq1cC5XjnshIQSdOUPQ2bPcCwlJ2S/9yVUB6Kb6PT0GhobMXb+egR06EPv6NfB/cZGU7vfMcChVCndPT9y9vCjh4MDNa9c4eewYJ44elSy7PT3FbG0ZNXkyR//9l01r12bpmG/at6d2vXqMGTqUVzExGrbw88hkMib98gsn//uP3RK259XR0WHesmUsmTePS8HBWTomu3eInxOjtevVw7JoUYKDg7maSQVHbRIdHY25uTn3AFWy0aKBkkBUVBRmEuW1SYF0KfZaoEfPnsydM4ce3bpJJhAuBAURHx+PV82anPzvP0lsANj79980ad4czxo11FqjPqfEREcza8oUxk2bxsP79zl3+vQn98+N4gDeL2k9uGcPB/fswcTUFDcPD9w9PWneujWxr15x9dIlQu/cIeTOHR6EhPD27Vu1zKsMOch5f9LWNzCgpKMjjk5OODo5UaFSJYyMjLhw/jx7du7kQlBQrriYpkfpSRg1eTLAZ0WCV40aNGvVivHDh+ea11OtZk3MzM3Z/88/ktpRvVYt4uLiMi2BnRGpL/ifEwtZ8VTVrFuX+QsWMHL06CzboA1ULSxWUEMM+VogdOjQgeHDh2NXogRm5uZZdrupk+TkZPbv3o13ixaSCoT4+Hj+2rCB7zp2JPD0aZWLyaiDB/fusWTePHr5+DBhxAge3LuX4X65VRyk51VMDMcCAjgWEICOjg7lXVwoU748lapUodW332JmZsbjR4+4d+cOYY8fEx0RQWREBDHh4USGh/M6OjpTV79MLsfMzAwLS0vMrKze/7S0pKitLQ5OTtja2REdFUXIBzHy24IFXLtyhcSEBC2/C9knqyKhpKMjPQcMYPGcOTy8f1+bJmaKQqGgbceObN2wgXcSVk0EaNqiBft37cpxuEh5Rshp3NncwgIbW1vCIyNzVf4BqJ5HIP3ZUhrydYgBoEb16hQqVAhd3lcXlAJDQ0MWrFiB/7hx3Ll1SxIb4P3JbPr8+ezevl3SLOf0tP7uO+o0bMiE4cM/SvDKK+IgKxSyssLByYlSTk4UtrHB0soKc0tLLCwtMTY1JTExkTevX5OUmEhSUhLmlpa8io5GJpdjYGiIQqHgVUwMkeHhRH4QF8+fPuXuB1EQER4u9UtUiU+FG6wKFWKcvz//7t/Pji1bJLLwYxp6e9O0eXOG+/hIKrqdy5Zl6Nix9O/Wjbdv3qg0VmYC4XOvzrtlS2LfvuVVXBz/SXgzlBpliOEOYKrCODGAEyLEkO/o0bMng3x8GDtmjGQC4fXr1xw5dIimLVqwaM4cSWwASExMZPO6dXT66SdOHD2qNne3qmzfvJnCNjaMnDiRyaNHp3h68pM4AIgIDyciPJyLH+rjK0/ECkBHVxdzS0sMDA3RlcsxNDbGd/JkFvr78youjjevXxMZEcG7d+8+cpXml7ubzDwJ5hYWjJg4kUsXLuQqcWBgYEDr775j9W+/Se6R827ZkiMHD2pMHGSFL+rWZfKUKcyZO1clGzSBqksVxTLHfEqbNm2Ie/MGhZ6epBUF9+/ejWeNGlhIvC74zIkTPAsLo12XLpLakRpla+h7ISGMmDABE1PTfCcOMiL1JSXh3TtePnvGo3v3eBASwv07dwB4GBrKo3v3ePnsGe/evfvo+PwiDpSkX91gYmrKiAkTuHvrFisWL5bavDR0+OEHnjx6xLlTpyS1w8raGo9q1di/e7dG5/nUxaJY8eIkATGvXuWK5kzpEXUQcka+Fwjm5uZ4e3uzZfNmvqhbVzI7noaFcfnCBRp9+aVkNsD7i/FvCxZQt0EDyru4SGpLapKTklg8dy5hT54wff58atWrl6/FAfz/y5f+BFSQT0jwf5FQt2FDps+fz6OHD1maSyolKqno6soX9erx26+/Sm0Kjb29CQ4M5PmzZ5LZ8EXdumzZtIkWLVpgaqqKM1+Qm8j3AgGge/fuXLx4kZp16khqx56dO2nk7Z3tKnzq5mlYGJv+/JOe/fqhr6EeAzkhKSmJZ2FhGBkbE/vqFbGvXkltktpI/UVTCoD86AFQF68+/P+NjI15FhYmuQs/NQYGBvTo14+Na9bw/OlTSW0xMjamobc3e//+W+WxcrqKQSaTUaNWLc4HB9OtWzeV7dAEolBSzigQAuHLD3ft9x88oFzFipLZcfXSJR7cu5crOizu372b8JcvaZ9LKizC+5yDmnXqMMrXl0cPHjB68mSNdVPUNjk9wRRET4KFpSWjJ0/mfmgoo319qV2/vkZbRWeX73/8kedPn3Jw716pTaFVmzbcvX2b61euqDxWTi+GFSpV4m5ICPoGBjRp0kRlOzSBCDHkjAIhEPT09Ojeowfr162jYdOmktqyYc0avFu2lDwXITk5maULFlCnQQMq5IJQQ+qcg8cPH/LrrFncCwlhzJQpWBUqJLV5aiH1iTcnjZbSn6jy45fXytqa0ZMnc/f2bRbOmcOjhw812io6u1R0daVm3br8tmCB1qtPpsfSyoomzZuz4Y8/1D52erHwKdHQsGlTNm/aRM+ePXNFa2eB+siP55gM6d27N/cePKBMhQqS3pXeuXWLi+fP8/V330lmg5JnYWFsXLuWHv37SxpqyCghMSkpiSXz53P96lUmzJhBqdKlJbNPk2QUekgvBAqKi9PJ2ZmJM2Zw5eLF9xfgD2EFTbaKzg6Ghob07NePDWvWSBrvV/JN+/acP3uW0A8JrZriU589cwsLHJ2duf/wIb1799aoHaogQgw5o8AIBGdnZ2rUqMGmDRuo06CBpLZsWruWug0aSLqqQsmBf/7hxfPndJBoVcOnViskJyWxYtEi/tmxg1GTJkmeQyI16mrvnBupVa8eIydN4u+tW1m1dOlHd+e5QSR0+PFHnoaFcSgXhBaK2dpSq25dNv35p6R21G3UiA3r11OzZk1K5YKOsZmRusFZTjYhEAoAgwYN4uSpU9Rv0gSZXLqX/uTRI44fOULbjh0ls0GJclVDrfr1td7MKatLGffs3Mm8GTP48eefade5s6T/O4F6kcnldPjhBzp368Ycf3/2fWKpnpQiwaVyZWrWqcOyX3+VPLQA8F2nThz9919JV/nI5XLqNWrE8ePHGThwoGR2ZAWRg5AzCtSZ9quvvkJPX5+rV6/i5uEhqS1/bdyIu5cXDk5OktoB8PzpUzb+8Qc9tLiqIbt1Di6eP8+4oUOpWr06g0eMwNDQUAtWap7PuTA/9Xhev6sxMjZm6KhRVKlalbHDhmWph4AUIsHQ0JAe/fqx/vffc0VooZSzM67u7mzbtElSO9w9Pbl44QJm5ua0atVKUlsEmqFACQRdXV18fHzYumULTZo1k9SW8JcvOfDPP7lmFcHBPXt48ewZHbt21fhcOS2C9OTxY8YNG4ZcoWDCjBm5IkSTHT71ZStQX0SgqK0tE6ZPJyk5mXF+ftn+HGhTJHT86SfCnjzh3337ND5XVmjfuTP7du36qCy5tmncvDl/bdvGwIED0dHJ3UV5RQ5Cziho5yV69uzJk6dPMbO0pJitraS27Ny6lVKlS1PR1VVSO+D/qxq8atSgbqNGGptH1QqJcbGxzJw8meDAQCbOnCm5JyinZJaImNEJKb+5Od09PZk4YwaBZ84wa+rUHLWe1pZIaNCkCR5eXizLBasWACq5uVHS0ZFd27ZJakdxe3uMjI15/uIFPXr0kNSWrCBCDDmjwAkEGxsb2rRpw6qVK/lSYrdY7KtX/P3XX7TLJV6EF8+eMX/GDLp0706ZcuXUPr66yicnJyWxbvVq1ixfTl9fX3r274+RsbEaLRVoAiNjY34eMIBeAwey+rff2LBmjUrVETUtEspVqEDHn35i3vTpvHj+XO3jZxeZTEb7Ll3YsWULcbGxktryZatWrFq5krZt21K4cGFJbRFojgInEABGjhzJlatXcXV3l7wewb5du7C0ssKrRg1J7VBy9fJl1v/+OwOHD6eQtbXaxtVEb4XjR47g5+ODpZUV0+fNo7K7u1rGzW3kh9ULbh4eTJ8/HzNzc4YPGMCJo0fVMq6mRIJ1kSL4+PmxduVKrl+9qrZxVaHaF19gamYmWdM5JVaFClGxcmWu3bjBiBEjJLUlqwgPQs4okALBzc2N2rVqsXrVKsm9CPHx8fy1cSNtO3VCnkuy8w/u2cO5U6fwHTkSfX19lcfTZOOl8BcvmD5hAls3bKDf4MH06NdP8lLWmVEQY5lGxsb07N+fvr6+bFm3jpmTJhH+8qVa51C3SNA3MMB3xAhOHTvG4f371WCh6ih0dGjbsSNb16//qGmXtvmyVStWLl9OjerVqaTllU85ReQg5IzccUWSgAkTJ3IuMJAadepI7p4+cvAggEZj/9nl9+XLeR0XR88BA1QaR1tdGQMOHmS4jw+FrK2ZPn8+ldzcNDaXlOSlk1Vld3emz5uHhYUFfj4+HDl0SGNzqUskyGQyevv48ComhrUrV6rRQtWo37gxiQkJ/Hf4sKR2mJiaUu2LLwg6f55p06dLaotA8xRYgVC7dm0quriwcf16mjRvLqktSUlJbF67ljbt20suVpQkJiQwb8YMnJyd+bpt2xyNoe2WzS9fvGDa+PFs27gRn2HD6NGvX67v5ZAfXZfmFhb07N+ffoMHs2X9emZMmkT4ixcan1cdIqF1u3aUdHRk/syZJCbmjv+OsYkJ37Rrx6a1ayVvWtW0eXPW/fknld3cqFmzpqS2ZAcRYsgZBVYgAEycOJH/jh2jQZMmanGlq8KZkye5HxJCp59+ktSO1MRERzNryhRafPMNVatVy9ax2hYHqTl84AB+AwZgYmrKnCVLaNOhQ66um/Cpk09eOjkZGhnx7fffM3vxYoyMjDTuNcgIVUSCV40aNGvVillTp/IqJkZDFmafLt27c/fWLc6dPi2pHQYGBtRt1IjjJ08yceJESW3JLsmoFl6Qfv2KNBRogdCsWTPs7Oz4Z/du6jVuLLU5LF+0iKrVq+eqpXsP7t1jybx59PLxwb5kySwdI6U4UPLyxQvm+Pszbfx4Kri4MHvJErxbtsz167XzIrq6unzZqhVzliyhbPnyTB07lrnTp2vFa5AROREJJR0d6TlgAIvmzOHh/fsatjDruHt6UqVqVVYsXiy1KTRo2pS/d+7EwcGBphI3vRNohwItEGQyGRMmTmTfgQN4t2yJQuKLR/jLl/y5ciXd+vTJNaEGgHOnTrF7+3Z8R47ExNT0k/vmBnGQmls3bjBp1CiWzJ9PnQYN+GXRImrVq5eryzXnFa+BTC5//54uXEjt+vVZNGcOU8aM4c6tW1Kbli2RYGZuju+IEezcupWgs2e1ZOHnMTYx4afevVmzfDkR4eGS2qKjo0PjZs3Ys3cvEydORCaTSWpPdhEhhpwhS84N1T8kJCEhgZIlStCoYUMinj3j31yQtTx0zBiio6JYOn++1Kakod/gwRQpWhT/ceMyLG6T28RBemQyGTXr1OHb77/n7Zs3bFy7lvMSXBCyIk0MDA1Ztn49PTp04M3r1ymP54YERZlMhrunJ207dkRfX5/N69Zx8r//ckUhofQUs7Vl1OTJHP33XzatXfvR80bGxoycOJHHDx+yaM4cCSzMnN4DB2JkbMysKVOkNoVGX36JiaUlR//7j5CQEBSKvLH4Njo6GnNzc/YAqtxyxQJfAlFRUZiZmanHuDxA7r2N0hI6OjpMnjKFTZs30+rbb9HT05PaJFYsWoRHtWq5KtQAsHjePKKjohg2ZsxHPRtyuziA99Uijx85wtC+ffl3/3669+nDjAULaOjtLXkOSl5A38CAxl9+yYwFC+jaqxeH9u1jSL9+nDh6NFeKA/i0J8HAwAC/sWMJf/ky14lxDy+v96GFRYukNgV9AwNatG7Njh07mDJlSp4RB6kRyxxzRoH3IAAkJibi7OyMW+XK6Mvl/P3XX1KblHJCG9a/v+RV01Kjq6vL4FGjUCgUzJw0ifj4+DwhDjJCR0eH6rVq4d2yJUVsbAg4eJBDe/fyNCxMY3NmVZGn9yBIeYKyKVaMRt7e1G3UiKdPnrBv1y5OHTtGQkKChFZlj/SeBH19fYaNHcvbt2+ZPXVqrnotJqamTJ8/n/W//86xgACpzeHrtm2Jef2aq9evc/369TwlEJQehN2o7kFoTsHzIAiB8IEdO3bQoX17fl24kBE+PsS+eiW1SQwZPZpXMTEsmTdPalPSoDy5xsfH8+DePWrUrp3nxEF6ylaoQJPmzfHw8uLGtWsc3r+fc6dOaeTCkZMQg7YFgq6uLp41alC/cWOcy5Uj8PRp9u3axc3r17VsifpQioRjAQE4li6NXCZLEbm5iT6DBmFgaMjsqVOlNgUTU1OmzJ5Nv/792bJlC80lXhKeXZQCYSeqC4RWCIFQYElOTsbD3R0LCwucSpZk/e+/S23S+xLC8+ezeN48SWLln8LAwICZCxdiYmrK6MGDefTggdQmqQUzc3Nq169P/caNMTE15czJkwSdOcOVS5d4p+YLyaeEghQCQVdPDxdXV9y9vPCsXp2Y6GgOHzjAf4cPExMdrQULNI99yZJMmjmT6Ohohvbrx9s3b6Q2KQ0e1arRs18//AYMkLxbI0DHrl25fusW4ZGRXLx4Mc8lJyoFwjZUFwitKXgCQaz5+oBMJmPuvHk0btSI7zt2ZN+uXWovCZtdIsLD+WPFCrr17o3ftWu5wquh5OvvvkMmk/HowQM6d+vG7KlTc92dWE6Ijopi9/bt7N6+nXIVKlC1Rg1+6NkTM3NzLl+4QNCZM5w/d47oqCipTVUL5hYWuFWtioenJy5ubkRFRBB45gxzpk3jRi7pQaAu9PX16dytGw/u38fSyoqvvv02w8RFqTAxNeWnXr1Ys3x5rhAHhaytqVq9OqvWriUgICDPiQOB6ggPQjoaNmxIZHg4tWvWZHkuSBACGDJqFLGxsSyeO1dqU4C0CYlREREMHj0aGfDL5Mm8yWV3ZOrCrkQJ3L28cK9alVLOzty9dYugc+c4f/YsD+/fz1GSnhQeBJlcjn2JElSpWhV3T08cS5fmzq1bBJ05Q9DZs/nGE5QeQ0NDho4Zw7uEBGZPmYJVoUKfXN0gBX19fdHX12e2v7/UpgDQs39/Ao4epYiNDXv37ZPanByh9CBsRXUPQhsKngdBCIR0XLx4kSpVqrBgwQLmTJ3Kk0ePpDYJC0tLZixYwJJ58yRfp51RQqKenh6+I0diYGDAjEmTclVSpSZQ3nW7e3pSyc2NpKQk7t29S8idOynbk8ePP9vKWNMCQSaXY2tri2Pp0jg4OeHo5ERJR0dkMhmXgoM5f/ZsvvKGZIaRsTF+48YRGxPDnOnTU0JFn1sCqU2qVqtG97598RswgKjISEltAShub0//oUMZ5OtLcHAwFStWlNqkHKEUCJsBVVq4xQFtEQJBALRr144L58/T7rvvcsUaZIBa9erRvksXhn9oJCMFn1qtoKury4Bhw7C0suKXyZNzhYtUGygUCmzt7HD8cAFWXoSTkpIIDQkh5M4d7oeE8PLFCyLDw4mIiEgRUOoSCEbGxlhaWWFhaUkha2tKOjr+XwzI5dz7YEfoB/Hy6MEDyWv6awtLKyuGjB7Ny+fPmT9z5kdJp7lBJJiYmjJjwQLWrlyptjbYqjJszBjWrluHZ7Vq/Pnnn1Kbk2OEQFANIRAy4P79+zg7O+Pn58fhvXtzTYKgj58fhoaGzJg4Uesn+KwsZVTo6NC9Tx9cKldmjr8/d2/f1qqNuQW5XE5xe3scnZxwKFUKewcHLC0tsbSywsDQkPi3b4mMiEizRUdG8u7dO5ISE0lMSkIul9OpWzfWr15NUmIiMoUCXV1dzCwssLS0xOKDILCwtERPT483r18T8UGAPAgNTfFkPH74sMCIgfQ4OTszaMQILgQFsXLx4kybL0kpEhQKBX7jxhETE8OCmTO1OndmeFSrRq0GDZgxcyYhISHY2dlJbVKOUQqEjaguENohBILgA5MmTWLu7NlMmTKFEYMGqT2DPScYGBgwfvp0Ll+8yNoVK7Q2b3brHHzZqhXfdujAisWLc80dUW7BwMAACysrrFJd4C2trDAzN0ehUKBQKJArFOjp6eHq7s75s2d5Fx9PQmIiiYmJREVGpoiKiPDw97+Hh+fb3I+cUqtePbr26sWmtWvZt2vXZ/eXSiR06dGDchUqMGHEiFyxokJPTw//uXMZPmIEfsOHM3z4cKlNUgmlQFiH6gLhe4RAEHzg7du3lC5dGk8PD0wMDPhr40apTQKgsI0Nk2bOZP2aNRw5eFDj8+W0CJJrlSr0GzKEQ3v3sunPPz8bjy/IZBRqUIYYunfowOtUpZYFn0Yml9O+c2fqN27Mgl9+4VJwcJaP1bZIqN+kCd917MiYoUN58eyZxufLCt9+/z0R0dFcuHiRm7du5YrKsqogBIJqFPhSy5mhr6/PsmXL2LlrF7UbNqSwjY3UJgHw/OlT5s+YQZfu3SlTrpxG51KlQuLF8+cZN3QoVatXZ/CIEbm63bIgf2BkbMzQUaOoUrUqY4YOzZY4ANVaRWeXchUq0Omnn5g3fXquEQc2RYtSo04d9u7fz/IVK/K8OEiNaNaUM4RA+ATe3t40bdqUObNn07lbN6nNSeHq5cus//13Bg4fTiFra43MoY7yyU8eP2bcsGHIFQomzJiBTdGiarZSIHhPUVtbJkyfTlJyMuP8/FT6zGpaJFgXLoyPnx9rV67kei6qNdGlRw/mzJqFt7c3jRo1ktoctSJ6MeQMIRA+w6JFi7hz9y4oFFSpWlVqc1I4uGcP506dwnfkSLU3GlJnb4W42FhmTp5McGAgE2fOpKKrq5qsFAjeU8nNjYkzZhB45gyzpk7NsNNodtCkSNA3MMB35EhOHTvG4VzQOVaJh5cX7xITuRMSwq+//iq1OWpHeBByhhAIn6FkyZKMHj2ahQsW0Omnn9DNRW6335cv53VcHD0HDFDbmJpovJSclMS61atZu3IlviNH0jSP1XPXNAX9LkUVvFu2ZKCfH78vW8aGNWvUluuiCZEgk8noNWAAr2JiWLtypVrGVAd6enp0+PFHFi5cyKRJk7C3t5faJEEuQQiELDBs2DCMTEzYs2cP33boILU5KSQmJDBvxgycnJ35um1blcfTdFfG/w4fxn/sWFq2aYPPsGGYmZurfQ5BwcDM3JyBfn60aN2aKWPHcvzIEbXPoW6R0Pq773AoVYr5M2dmuuRSCtp27MiuXbswMzfH19dXanM0gvAg5AwhELKAvr4+f/zxB7t276aSuzuly5aV2qQUYqKjmTVlCi2++Yaq1arleBxttWy+ffMmIwcNIjk5menz51Ptiy80NldeROlJEEuLMqdG7drMWLCAhIQEhvv4cPfWLY3NpS6R4FmjBs2++opZU6dKVugsI8qUL0+5SpXYf+AAa9euzVeJialJRrX8g4L6fRQCIYvUq1eP7j16MHXyZHr07ZurQg0P7t1jybx59PLxwb5kyWwfry1xoCQ6Kor5M2ey+rff6PrzzwwYOhTTArR0SJAzzMzN8fHzo3O3bqxcvJhfZ83SysVWVZFQwsGBnwcMYNGcOTy8f18DFuYMPT09evTti//UqfTs2ZPatWtLbZIglyEEQjb45ZdfUOjqcuDAgVwVagA4d+oUu7dvx3fkSExMTbN8nLbFQWpOHz/OsP79kclkzFiwAK+aNbU6vyDvUP2LL5ixYAFJSUn4DRjAmZMntTp/TkWCmbk5g0eOZOfWrZL3UUnPd506sWv37vffvxkzpDZHo4gQQ84QAiEbmJiYsG7dOv7Zu5eKbm65KtQAsH3zZkJu32bQ8OFZchVKKQ6UREdFMW/GDH5ftoyfevWi/5AhwpsgSMHM3ByfYcP4oWdPVi5ZwoKZM4mJjpbEluyKBD09PQb6+XHrxg12btmiBQuzTtkKFShbsSIHDh7kr23bMDJSpYxQ7kcsc8wZQiBkk7p169K9e3cmTZxIj379clWoITk5maXz54NMxqARI9DV1c1039wgDlJz6tgxhvXvj0KhYPr8+XjWqCG1SQKJqfbFF0yfP5/k5OT3XoMTJ6Q2KcsiQVdXF9+RI0lMSuK3BQu0aOHn0dPTo3ufPvj7+9OrVy++EHlAgkwQpZZzQGxsLI4ODlSvXp2i1tasW71aapPSYGhoyPDx44n6cHeemK6DXW4TB+mpUbs2P/TowZWLF1mzfHmuaH+rbQwNDVleQEstW1ha0qV7dypUqsSqpUs5ffy41CZ9xKfKMit0dBg0fDjGJiZMGz8+V/RYSE2nbt148OgRly5f5vqNG/nae6AstTwfUKWW62tgAKLUsiALGBsbs2XrVvbu20d5V1fK5bJe6a9fv2b6xIlYFSpE30GDkMv//2/O7eIA4OR//+E3YAAymYzZixfz7fffY5iPT2KC9xgZG9O2Y0dmLV5McnIyw/r3z5XiADL3JCgUCvoPHoyZuTkzJk7MdeKgQqVKlClfngOHDrF+w4Z8LQ5SI3IQcoYQCDmkTp069OrViymTJtGzf/9cFzePi41l2vjx2NrZ0cvH530TmzwgDpRERUYyf+ZM/MeNo2yFCsxZsoQvW7X6ZNhEkDfR1dWl2VdfMXvxYpzLlmXKmDEs+OUXoqOipDbtk6QXCTK5nN4DB1KkaFGmT5igckVHdWNmbk6Pfv0YP348ffv2FaEFwWcRIQYVePv2Le5VqmBkZESLZs34ZfJkctvbaWZuzpgpU0hISMDE1DRPiIOMqOzuTrvOnTE2Nmbrhg38FxCQrztEFoQQg1wup3b9+rTp0IGY6Gg2rFmT7QZLuQFluCEmOhq5XM7k0aMlS6TMDJlMxrCxY9m2fTuJSUmcPXcu39Y8SI0yxDAL1UMMg8l+iGHhwoXMnDmTsLAwKleuzIIFC/Dy8spw32XLlrFmzRouX74MgIeHB1OnTs10f20gPAgqoK+vz46dO7l48SLPXr7ky1atpDbpI6Kjorhy8SLF7e25feMGz54+ldqkHHEhKIhRvr5sXLuW1t99h/+cObh7ekptliCHVK1WjWnz5vFV27as//13Rg8enCfFAcDTsDDu3LpFcXt7Ll+8mOvEAUCL1q159OQJN27eZPuOHQVCHKRGihDDxo0b8fX1Zdy4cQQFBVG5cmWaNm3Ks0y6dwYEBNChQwcOHz7MyZMnsbe3p0mTJjx69CgHs6sHIRBUpHTp0vy+Zg0rVqygbuPGlC5TRmqT0tC+Sxc8qlVj4siRFLe3p8/AgSgUCqnNyhHJycmcOHqUIf36cWjfPrr37cs4f3/KlC8vtWmCLFKuYkXGT5tG11692P/PPwzt14+T//2X6zxvWUWhUNB38GCKFivG5FGjqFazpsZbRWeXMuXKUbNePVavXs3va9bg6OgotUlaJwnVxEFOfJWzZ8+mR48edO3alQoVKrBkyRKMjIxYmUkfjj///JM+ffrg5uZGuXLlWL58OUlJSRw6dCgHs6sHIRDUQPv27fn++++ZOH48vQYOxMjYWGqTgLQJibdv3GDy6NHYlShBvyFDUOjoSG1ejklMSODAP//g26sXF8+fZ9iYMQwZPZryLi5SmybIhAqVKjFszBgGjxxJcGAgvr17c3DPno9W2OQldHR0GDBs2HtxMHo0N69f13ir6OxiYmrKzz4+jB09mi4//MC3334rtUl5mujo6DTb27dvM9wvPj6ewMDANG2z5XI5jRo14mQWi3zFxcXx7t07rKys1GJ7ThACQU0sXLgQM3Nz/vjjD3r26ye1ORkmJEZHRTFlzBisCxdmkJ9fnk/4e/PmDds2bWJQr148CA3FZ9gwps6ZQ92GDfP8a8sP6OrpUa9RI/znzmXA0KGE3L3LoF692L55c67L7s8uunp6+I4YgaWlJVPHjk0JK2iyVXRO6Nm/P6tXrcLC0pL58+dLbY5kqKtQkr29Pebm5imbv79/hvO9ePGCxMREbGxs0jxuY2NDWFhYlmz28/PD1tY2jcjQNnn3NjKXYWhoyM6//6ZKlSpUq1aNpi1asG/XLkls+dRqhVcxMfiPG/f+bm7UKGZPnUp8fLwkdqqLmOhoNq5dy7ZNm/iibl2+bNWK9l268O++fRzat4/wly+lNrFAYWVtTSNvbxo0aUJEeDh7d+3ixNGjvMvjnzMl+vr6+I4ahZ6uLv7jx3+0WkEpEkZNngzwUZ0EbfFlq1ZExcQQfPEiFy5cwMDAQBI7cgOqLlVUHvvgwYM0SYr6+vqqmJUp06ZNY8OGDQQEBEj6fxMeBDVSvnx5li5dyoJff6VekyZUqFRJ6zZkZSmjcgmkjo4OoyZPxsLSUstWaob4+HgOHzjAcB8ffp01C3sHB+YsWcLgUaNw9/RMUw9CoF4UCgUe1aoxZPRo5ixeTHF7e+bPmMGIgQM5cvBgvhEHllZWjJo8GRkw7RNLGaX2JFRyc6N2w4Ys/e03li1bRplclhuVVzEzM0uzZSYQrK2tUSgUPE2XFP706VOKFi36yTl++eUXpk2bxv79+3F1dVWb7TlBLHPUAL1792bj+vXMmDmTqWPH8iyLLiVVyW6dA11dXX7q3RuXypWZ4+/P3du3tWCldrGytqZew4bUbdQIuVzO0UOHCDh4kOeZZBLnFvLKMsfCNjbUb9yYOg0akJCQQMDBgxw9dChfem2cnJ0ZNGIEF4KCWLVkCQlZyJ/4VMVFTWFTrBgjJ05k8ODBdO7ShQW5rNSzNlEuc5wAqHIf/gYYR/aWOVarVg0vL6+U9z8pKYkSJUrQr18/hg8fnuExM2bMYMqUKezbt4/q1aurYLF6EAJBA7x794569erx5NEjBg0axMQRIzR+klelCNKXrVrxbYcOrFi8mBNHj2rIQmmRyeW4urlRv3FjqlStyr3QUILOnuX82bPcCwmR2ryPyM0CoaSjIx5eXlTx9KREyZIEnTvHv/v2cfnixXxbm6JWvXp07dWLTWvXZjt0qE2RYGhkxDh/f6ZPn04pJycO/fsvOnk4IVlVlAJhLKoLhIlkTyBs3LiRH374gaVLl+Ll5cXcuXPZtGkT169fx8bGhi5dulC8ePGUPIbp06czduxY1q1bl6aIlYmJCSYmJipYn3MK7idHg+jq6rJz505cXV3Z+tdf9B40iDn+/hpbyqVqhcQ9O3fy6MED+g0Zgn3Jkmz68898d6JPTkriQlAQF4KCMDUzw83DA3dPT1q2bs2rV68IOnOGoLNnuXb5cpbuDAsSOjo6VKhUCXdPT9y9vDAyNuZiUBD7du0iODCQVzExUpuoMWRyOe07d6Z+48bMnTYtR7UatJWTIJPL6evry8ZNm0AuZ/uOHQVaHEhNu3bteP78OWPHjiUsLAw3Nzf27t2bkrh4//79NGHPxYsXEx8f/9FKk3HjxjF+/Hhtmp6C8CBokCtXrlC1alU6d+rEu7g4Nq9bp/Y51Fk+uZitLb6jRvH08WMWzp6d6+5cNYGuri7lXVxw9/LC3dMz5eIXdPaspBc/qT0IJqamVKlaFXdPTypVqUJsARRRRsbG9PP1xbpIEWZNnaqW75cmPQntOncmWaFg48aNnAsMpLyoD5LiQRiF6h6EKRS8Zk1CIGiYv//+m2/btGHc+PH8+88/nFJj8xlN9FYwMjam3+DBWBcurJaTYl6jpKNjyp1ySUdHbl2/zqXgYO7evk3InTtaq5KnbYFgZm6Oo5MTjqVLU8nNDeeyZQm9ezdXh2E0SVFbWwaPHMnTsLD3YllNfRU0JRJq1qlD3caNmTBxItu3b6dZs2ZqGzsvoxQII1BdIPgjBIJAA0yZMgX/qVOZNWsWC2fP5u6tWyqPqcnGSzK5nA5dulC3YUMW/PILly9cUOv4eQVLKyvcPT0pV7Eijk5OFCtenBfPnxNy5w6hd+4Q8mHTRFMhTQoEM3NzHEuXfi8IPmyFrK158ugRIXfucO3yZYLOniUyIkKt8+YVKrm50X/IEP7dv5+Na9eqPdymbpFQukwZeg8ahK+vL2PHjcPPz08NVuYPhEBQDSEQtEBycjJt27blSEAA/v7+TBs/nrDHj3M8nra6MtauX58ff/6ZTX/8wb7duzU2T17B0MgIh1KlUi6qDk5O2BYvTviLFyliIeTOHZ48ekRkRESmVdayNJcaBIK+vj4WlpYUK148jRiwsLIi7PHjNDbfCwnJdd0HpcC7ZUvaduzIysWLOX7kiMbmUZdIsLWzY9jYsQwZPJgGjRqxdetWZDKZGi3N2ygFgh+gSsWCt8B0hEAQaIg3b95Qv359Hj14wMhRo5g8alSO7tC03bK5dJkyDBw+nODAQFYvXVogYs/ZwdDQkJKpRIOjkxNFbGzQ0dXldVwcEeHhREZEpGwpf4eHE/HhsYwuzJ8SCIZGRlhYWmJpZYWFpWXa3z/8tLS0xNDIiHfv3vEsLCxFCIQqxUAByC/JDjo6OvzUuzeuVaow299fLV6+z6GqSLAqVIhRkyczadIkHBwdOXTokMYK9+RVlAJhCKoLhF8QAkGgQaKionBzc0NPR4fevXszdexY4mJjs3y8tsWBEqtChRg0YgTv4uNZOHs2L1+80NrceRUTU9M0F/D0F3TlhVx5Qk9MTEzZkhITSUpKwtTMjNhXr5ArFChSbfC+1XgaoZGJEMnPKwzUhXXhwvQdPBiFXM6cadOICA/X2tw5FQlGxsaMnjyZRYsWodDR4eSpUwXqwpVVlALBF9UFwmyEQBBomCdPnuDh4YFDiRK0atmS6RMm8O7du88eJ5U4UKKrp8cPPXpQrWZN/ly1ioCDB7VuQ37EyNgYAwODj0SAkZERY/39GTdsGHFxcWnEw+vXr0U4QE3Ub9KEjj/+yMljx1izfLkkFR+zKxJ09fQYPm4cf23bxqMnTwgMDPyo5r/gPUIgqIZYJKtlihUrxpEjR/CsWhVLKyv6+voyb+bMTyZCSS0OAN7Fx7N84ULOnjxJ9z598KpZk+WLFhEuvAkqERcbm6EXydDQEIBHDx6IcIAGKGRtTY9+/bC1s2PejBk5qm+gLrJTJ0Eul9N/8GACjh7lyrVrXLhwQYiDLKCuXgwFDVGcXgKcnZ059O+//Hv4MA8eP6brzz9num9uEAepuRAUhJ+PDxHh4UyfN4+6EnYaEwhyQv3GjZk2bx4vnj/Hb8AAScWBkqz2bujWpw93793jyNGj/Pfffzg5OWnRyryLuro5FjSEQJAIDw8Pdu7cyZ/r1qHQ06Nd584f7ZPbxIGSuNhYlv36K7/Ons23HTowbMwYrAoVktosgeCTFLK2xm/cOFq3a8eCX35h+cKFuSpU8zmR8P2PP5IEbNq8mV27dlGlShXtGykoUAiBICGNGzfmzz//ZOGiRRQqWpS233+f8lxuFQepuRAYiN+AAURFRTFt/nzqNGggtUkCQYbUa9SIafPmEf7yJX4DBnDx/HmpTcqQzERCu86dMbOyYvHSpaxbt44G4ruWLRLVsBVERA6CxLRr1463b9/So3t3fH19+aZdO/T09XO9OFASFxvL0vnzcfPwoHvfvnjVrMmKRYu0mgkuEGSGlbU13fv0wb5ECX6dNYsLQUFSm/RZ0uckJCclUahoUWbPmsXva9Z8VKtf8HlEDkLOEB6EXECXLl1YsnQps2bPxqlcORp6e+cJcZCa4MBAhvXvT0x0NNMXLBDeBIHk1G3UiOnz5hERHo6fj0+eEAdKlCKhSfPmlChdmrlz57Ji5Uo6duwotWmCAoTwIOQSunbtSnx8PAMGDGDYsGHUqluXrRs2SG1WtlB6E6pUrUq3Pn3wqlGDFYsXC2+CQKtYFSr03mvg4MCvs2dzITBQapNyRK169bh44QKz58xh0aJF/PDDD1KblGdJRrVEw4JaC0B4EHIRP//8MwsWLGD69OkUK1kyTU5CXuL8uXMM69+fV69e8cuiRbTt2BFDIyOpzRLkc4yMjWnXqRMzFy4kMjISvwED8qw4aNe5M4VtbZk1axaLFi2ie/fuUpuUpxE5CDlDCIRcRs+ePVm6dCmzZ8+msK0tHfLoXUNcbCxL5s1j6pgxlClXjjlLltDsq6/Q1dWV2jRBPkNXT4/mX3/NnCVLKFWmDFNGj+a3BQuyVaU0tyCTyejYtSsW1tbMnTePZcuX89NPP0ltlqCAIiop5lL++OMPunXrRs8ePdCVy1mxaBFJau4qp01cq1ShXefOmJiasnX9ev4LCFB7l7z8hLbbPedF5HI5tRs0oE379kRHRbFhzZo83XlUoVDQo18/4t6+ZfmKFaxatUrkHKiIspLij4CeCuPEA6sRlRQFuYTOnTtjZGTE9x060KZNGwaNGMGCmTOJl6AUrDq4eP48l4KDqV6rFm07dqT511+zae1aAs+ckdo0QR6kavXqfNexIwqFgnWrVnH6xAny8r2Ovr4+A4YN49r162zbsYONGzfSunVrqc3KN6ha7Kig3soID0IuJyAggObNmlHriy9o2LAhs6ZMIfbVK6nNUgmFjg4NGjemdbt2PA0LY+OaNVy/elVqs3IVwoOQMeVdXGjfuTPWRYrw18aNBBw4QGJi3o4Qm5iaMmTUKPbu38/JU6fYs2cPderUkdqsfIHSg9AR1T0IfyI8CIJcRr169Thx8iT169cnPDycMVOmMGPSpDzdAyExIYEDe/Zw9PBhvmzViiGjR3P96lU2/vEHD+7dk9o8QS6kpKMj7Tp1wrlcOXZt28bev//m7du3UpulMtaFCzN0zBiWr1jB/QcPOHXqFJUqVZLaLIEAEB6EPENISAh16tTByMCAwUOGMGvyZB49fCi1WWrB1MyMr779loZNm3Lm5Em2rFvH82fPpDZLUoQH4T2FbWxo+/33VK1enYN79rBz69Z808LarkQJfEeOZOaMGcQnJHDkyBEcHBykNitfofQgdEB1D8J6Cp4HQaxiyCM4OjoSGBiIrr4+Y8aMYciYMZQpX15qs9RCTHQ0a1euZGi/fiQlJTFz4UL6DR5M6bJlpTZNIBFlypWj/9ChzPz1V969e8fQvn1Zt3p1vhEH5SpUYMjo0YwfPx4jExMCAwOFONAgYpljzhAC4RP4+/vj6emJqakpRYoU4euvv+bGjRspz4eHh9O/f3/Kli2LoaEhJUqUYMCH3gSpkclkH20b0hVBmjBhAnZ2dtSqVYubN29maE+RIkU4efIk5cqVY7ifH9379aNuw4bqf+ES8eL5c5bOn8/Qfv3eV78bO5aJM2ZQs04dFDoiGpbf0dHRoVa9ekz65ReGjBlD+IsXDO3bl2W//srLPBxSS0/9xo3p2qcPw4YNo2y5chw/fhxra+sM9128eDGurq6YmZlhZmZGjRo12LNnT8rzv/32G/Xq1cPMzAyZTEZkZORHYzg4OHx0/pk2bVqafZYtW0bJkiWpUqUKp0+fVuvrFeRdxFn3Exw5coS+ffvi6elJQkICI0eOpEmTJly9ehVjY2MeP37M48eP+eWXX6hQoQL37t2jV69ePH78mC1btqQZa9WqVXh7e6f8bWFhkfL78ePH2b17Nzt27OD06dP069eP/fv3Z2iTqakpBw4epGfPngwZPJihQ4dSwsGBP1etytPLIFPz/OlT/ly1iq3r11O7QQO+adeO73/8kUN793Jo3z6i0wkwQd7GzNycRt7eNPT2Ji4ujv27dnH08GHevnkjtWlqRaFQ0PGnnyhUpAiDBg6kc5cuLFmy5JO1Qezs7Jg2bRrOzs4kJyfz+++/89VXX3H+/HkqVqxIXFwc3t7eeHt7M2LEiEzHmThxIj169Ej529TUNOX3+/fvM2PGDDZs2MCjR4/o2rUrV/NZ0nASqnkB8seZNfsIgfAJ9u7dm+bv1atXU6RIEQIDA6lTpw4uLi5s3bo15XknJyemTJlCp06dSEhIQCfVXa+FhQVFixbNcJ6IiAhsbW1xdXUlISGB1atXf9IuPT09Vq1ahbu7O0OGDKHdd98xbOxYFvzyS55f4ZCaN2/ecOCffzi4Zw+u7u54t2jBV23bEnj6NP/u38/VS5fy9NK2goxMJsOlcmXqN26Mu5cXVy9d4rcFC7h4/ny+/J+amJrSf+hQrly9yvLVq5k7bx59+vT57HEtW7ZM8/eUKVNYvHgxp06domLFigwcOBB4v9rpU5iammZ6/omOjsbCwgJXV1eKFi2aL3NexDLHnCEEQjZQhg6srKw+uY+ZmVkacQDQt29funfvTqlSpejVqxddu3ZFJpMB0LRpU3799VeMjIwwMTH5yPuQETKZjAEDBlChQgVatGiBa6VKjJs2jXnTpuWb5EUlycnJXAgM5EJgIDbFilG/cWP6+vry9s0bDh88yNFDh4iMiJDaTEEWsLSyom7DhtRt1Ag9PT2O/vsvfv378zQsTGrTNIZdiRL4+PmxauVKrt+8yb59+6hfv362x0lMTGTz5s3ExsZSo0aNbB07bdo0Jk2aRIkSJfj+++8ZNGhQyjnKxcUFV1dXzM3N0dPTY9myZdm2TZA/EasYskhSUhKtWrUiMjKSY8eOZbjPixcv8PDwoFOnTkyZMiXl8UmTJtGgQQOMjIzYv38/48aNY8aMGQwYMCDN8c+ePcPCwgI9vezl296+fZsmTZrw7u1bxo0fz++//cb5c+ey/yLzEAodHTw8PanfpAkVXFwIDgriyMGDXLpwgXd5tJhUavLTKgZdPT1c3dyo27Ahld3duXLxIv8eOMD5s2fzfA2Dz+Hu6UmXHj0YN24cRkZG7Nu/n1KlSmVrjEuXLlGjRg3evHmDiYkJ69ato1mzZmn2CQgIoH79+kRERKQJXwLMnj0bd3d3rKysOHHiBCNGjKBr167Mnj07zX4vX77EyMgIQ0PDHL3W3IhyFcNXgCpF3t8BOyh4qxiEQMgivXv3Zs+ePRw7dgw7O7uPno+OjqZx48ZYWVmxc+fOT8YVx44dy6pVq3jw4IHa7IuOjqZNmzacOX2a8ePHE3T6NNu3bCkQ5YytixShXqNG1KpXD1MzMy4FBxN05gzBgYF5Nl8hrwsEM3Nzqnh64u7pSSU3N6IiIzkWEMCRgwd58fy51OZpHJlcTuvvvqNy1aqMHjWKuvXqsXnz5jSx/6wSHx/P/fv3iYqKYsuWLSxfvpwjR45QoUKFlH0+JRDSs3LlSn7++WdevXqFvr5+tu3JSygFQgtUFwi7EAJBkAH9+vVjx44dHD16FEdHx4+ej4mJoWnTphgZGbFr1y4MDAw+Od7u3btp0aIFb968UesXNDExkeHDhzN//nw6d+qEo4MDi+fMKVDud/uSJXH/cGFyLF2aO7duEXTmDEFnz/JIjYJM0+RFgWBXogTuXl64V61KKWdn7t66RdDZswSdPcvD+/elNk9rWFha0tfXl1t37vDnunUMGjSIqVOnIperZ9FYo0aNcHJyYunSpSmPZUcgXLlyBRcXF65fv07ZfL6UWAgE1RA5CJ8gOTmZ/v37s23bNgICAjIUB9HR0TRt2hR9fX127tz5WXEAEBwcjKWlpdrVu0KhYObMmXzxxRe0b9+ecmXKMH76dJYvXJinm9hkhwf37vHg3j12bNmCuYUFblWr4uHpSet27YiMiHh/wTpzhhtXr+Z797amUSgUlKtYMUWQmVtacik4mMMHDjDb3z/Pem9UoZKbGz/17s38+fMJvXePLVu2fJRoqCpJSUkqVZEMDg5GLpdTpEgRNVqVuxFJijlDCIRP0LdvX9atW8eOHTswNTUl7EMilbm5OYaGhkRHR9OkSRPi4uJYu3Yt0dHRREdHA1C4cGEUCgV///03T58+pXr16hgYGHDgwAGmTp3KkCFDNGa3sl5D69atGTp0KGPGjKG8iwtb16/PN0shs0JUZCRHDh7kyMGD6OnpUdHVFXcvL/oMGoSevj4XAgMJOnuWC0FBebI1sBQYGRvj5uGBu6cnru7uxL99S9DZs/y+bBlXLl3KF/kfOUGhUPDt999T3tWVQYMGUa58ea5evYq9vb1K444YMYIvv/ySEiVKEBMTw7p16wgICGDfvn0AhIWFERYWxu3bt4H3+QqmpqaUKFECKysrTp48yenTp6lfvz6mpqacPHmSQYMG0alTJywtLVV+3XmFRFQr+lNQbyVEiOETKFcZpGfVqlX8+OOPKW69jAgJCcHBwYG9e/cyYsQIbt++TXJyMqVLl6Z379706NFDbS7HzHj37h0jR45k/rx5fPfdd1SqWJFfZ8/O030c1IFMJsPRyem9O9zTk+L29jy8f5+QO3dStgehobx7904yG3NDiEFXT48SJUviWLo0jk5OODo5pbxXSk9M6N27+XJZYnawLlyYPr6+BF+4wJatW/H19WXy5MkfrWTKCd26dePQoUM8efIEc3NzXF1d8fPzo3HjxgCMHz+eCRMmfHSc8hwVFBREnz59uH79Om/fvsXR0ZHOnTvj6+ub7/MP4P8hhsaoHmI4QMELMQiBUADYu3cv7du1w8bGhqFDh/LH8uWizXIqrKytcXJ2TrkIOjo5YWRsnCIaQj+IhnuhoVq7Q9a2QNDT06OEgwOOTk44fHgP7EqUIPbVqxTRFHr3Lndu3iT85UuN25NXqFqtGp26deOXmTMJe/aMTZs20aRJE6nNEnxACATVEAKhgPD48WPatGnD1StXGDJkCFHh4axZvly41jPBunDhlAulcjM2MeHRgwdpPA33Q0KI14Bo0KRA0NPTo6SjYxrPgK2dHa9iYlLEkHLLTyWO1YmJqSldunfH2MyM2bNn41KpElu3bs20GJFAGpQCoSGqxdMTgEMUPIEgchAKCLa2thw7dgx/f38mTpyIl6cnU2bPZs3y5Zw/e1Zq83IdL54/58Xz55w7dSrlsULW1il32G4eHrT+7jvMLSyIiY4mMiKCiPBwIiMiiPzwU/l3REQEkRERGvc+6OrpYWFpiaWlJRZWVu9/Kn+3snr/u6UlpmZmREVGpoiAc6dPE3LnToEPPWUVDy8vOnXvzqqVKzlz5gwTJ03Cz88PhUIhtWmCTEgEMg4YZ/34gojwIBRALl26RPt27Xj8+DGDhwwh4vlz/lixQngTcoCZuTlWhQqluQBbWFr+/28rK8wtLNDR0SH21av/C4bwcKKjonj37h2JiYkkJSaSmJT0/mdiIgq5nA4//sjGP/4gKSkJuUKBQi5//1OhQFdXFzNz8/8LASsrjI2NSUhIICoyksjw8JR5Ij8IFKVgCX/5skCuMFAVYxMTunTvjpmlJbNmzcLO3p6NGzdSsWJFqU0TZILSg1AP1T0IARQ8D4IQCAWUd+/eMWXKFKZOnUo1Ly86d+7MmmXLCA4MlNq0fIdMJsPUzOwj8WBmbo5CRwfFh4u+QqFALpejUCjQ09Ojiqcn506dIj4+nqSkJBI/iIfExEQSExKIior6SAC8iokp8EmDmsDd05PO3buzevVqzp47x9ixYxk+fLhaEhEFmkMpEOqgukA4ihAIggLGxYsXadeuHWFPnjBk6FDCnz3jz1WreBUTI7VpBZrcsIpB8D7XoHO3bphZWjJ92jRKOjqyceNGXFxcpDZNkAWUAuELVBcIxyl4AkGz6+wEuR5XV1cuXryIz8CBTJgwgVNnzzJ59mzqN2mCTMPLMAWC3IpMLqdh06ZMmT2b46dOMWnyZPxGjCA4OFiIA0GBQVwBBOjq6jJ+/HjOnj1LZGQkA318KO7oyPhp0yhVurTU5gkEWsXJ2ZmJM2ZQtEQJfHx8iI+PJzAwkDFjxnyyx4og95Kohq0gIgJoghQqV67MpcuX+f333xk4cCCmxsYMGTaMkFu32PjHHyLsIMjXmJqZ0a5zZ0qUKsU0f3/evnvHosWL6dy5c6ZF0wR5A1FqOWcID4IgDXK5nK5duxIaGkqLVq0YOnQogcHBTJ49mwYi7CDIh8jkchp6ezN51izOBgbi5+dHm7ZtCQ0NpUuXLkIcCAos4mwvyBBLS0uWLFnCmTNniIiIYKCPD7YODkyYPp2Krq5SmycQqAWXypXfhxPs7fHx8eFtfDznzp1j0aJFn+2KKMg7iBBDzhAhBsEnqVKlCpcuX2bNmjUM9PHB0MCAXn360PKbb9iwZg2hd+9KbaJAkG0cnZxo36ULCUlJTJkyhYSkJBFOyMcko1qYoKAu9RMCQfBZ5HI5P/74I61bt2batGn4T51KkcKFGejry7PHj9n85588/dDpUiDIzRS1taVtx45Y29iwYP58wp4+ZfDgwQwbNgxzc3OpzRNoCFU9AMKDIBB8BnNzc/z9/RkwYAAjR47Ez8+Pss7ODBkzhisXL7Jt40aiIiOlNlMg+AgLS0u+ad+e8pUqsWDePG7ducNPP/3ExIkTsbGxkdo8gSBXInIQBNmmWLFirFq1imvXruFctiwDfHw4FxTExJkz+a5TJ0wLUCERQe7GzNycdp07M37GDE6dOUO/fv0oX7Ei169fZ+nSpUIcFBBEDkLOEAJBkGNKly7Ntm3bOHXqFHoGBvTt25eQ+/eZPHs2nbt1o5C1tdQmCgoo1oUL06VHDybNmsWd0FD69u2LkYkJ586dY+vWrTg5OUltokCLJKlhK4gIgSBQGQ8PD44ePco/e/bwJj6e3r17E3jhAiMnT6Zn//4UK15cahMFBYTidnb8PGAAIyZN4sy5c/Tu3ZuExET27dtHQEAAVapUkdpEgSDPIHIQBGqjQYMGnDx5ktOnTzNu3Dj69etHuTJlGODnR9ijR+zcsoWQO3ekNlOQDynl7EyrNm0oUrQoS5csYd7ChdSrW5fjx4/j6ekptXkCiRFJijlDCASB2qlWrRp79+7lypUrTJo0iYEDB1LKwYEePXsS/+YN+3btIjgoiOSkguq4E6gDmVyOm4cH3i1aINfVZfmyZdx/8IC2bduy5a+/KF++vNQmCnIJQiDkDNHNUaBxQkNDmTZtGitXrqRI4cJ8++23lCtXjn/37SPg4EHiYmOlNjHXIbo5Zo6xiQl1GzakYdOmXL12jQ0bNvAyPJyePXvi5+dHyZIlpTZRkEtQdnMsDShUGCcRuE3B6+YoBIJAazx79ozFixczb948XsfF0bBhQ75p04ZL589zaN8+7t66JbWJuQYhED7GydmZht7euLi5sXXzZv47dgxdPT0GDhxIr169KFy4sNQmCnIZSoFQCtUFwl2EQBAINM67d+/Ytm0bM6ZP58LFizg7OfF9x44YGxlxeP9+Tv73X4G/KAqB8B5DIyNq1q5Ng6ZNiY6JYe0ff3A3NBQ3NzeGDRvG119/LTosCjJFKRAcUC0jPwkIRQgEgUCrXLx4kV9//ZU//vgDI0NDGjRoQLNmzbh57RrHjhzh4vnzJCYkSG2m1inIAkGho4Obuzs169alTPny7P77bw4HBPD6zRu6dOlC3759qVSpktRmCvIAQiCohhAIglzBq1ev2LBhA3PnzuX69evYFy/Ol82a4enpSeCZM5w4coSb169LbabWKGgCQSaT4VyuHLXq1sWtalXOnD3Lrr//JuzpU8qXL8/AgQNp3749xsbGUpsqyEMoBUIJVBcI9xECQSCQnJs3b/LHH3+wYsUKwsPDcSxZkjbffou9nR2njh3jxNGjPHr4UGozNUpBEQjF7e2pWacO1WvV4t69e+zYvp07d+9ibW1N9x496NixI87OzlKbKcijKAVCcVQXCI8QAkEgyDUkJydz+vRpVq9ezbp160hOSsKlYkVaf/MNpiYmBAcGEnjmDDevXSMpny2ZzK8CQaFQUKZ8edw9PalStSpR0dH8tXUrV65dQ6FQ0LFjR3744Qe8vLxEV0WByigFQlFUFwhhCIEgEORK3r17x/79+1m5ciX/7N6Njo4OjiVL0sTbGxcXF65cvEjQ2bNcPH+e13FxUpurMvlJIBgaGVG5ShXcvbyoUKkSly5d4sD+/dwNDSUxMZHmzZvTrVs3GjVqJBIOBWpFCATVEAJBkOd4+/YtAQEBbNu2jb+2biUyKorixYpRq3Zt6tevz+OHD7l4/jxXL1/mXkhInizIlJcFgkwup6SjIxVcXKjs7k4xOzv+PXiQ48eP8+DhQ6wKFaJNmzZ888031K1bFz09PalNFuRTlAKhCKoLhGcIgSAQ5CmSk5O5ePEiO3bsYOPGjVy7do3C1taUL1eOOnXrUsrJibu3bnHt8mWuXb7MvdDQPCEY8pJASC0Iyru4UKp0aW7dvs1/R45w/cYNXrx4QfkKFWjXrh1fffUVlSpVEuEDgVZQCoRCqC4QXiIEgkCQp3n8+DH//PMPe/bs4d9//+XVq1cULlSIMmXKUKduXZxKl+bOzZtcu3yZOzdvci8khLdv30pt9kfkZoGgb2BASUdHSpcpkyIIbt+5w9EjR7h58ybPX7zA0MCApt7eeHt707x5c4oVKya12YICiBAIqiEEgiDfkpyczM2bNwkICGDnzp2cOH78vWCwtqZMmTK4e3hQqVIl4mJjCblzJ2W7FxLC2zdvJLU9twgEfQMDHEqVwtHJKWUzNDbm0sWLBJ47x5UrV4iMjsbU1JQGDRrQtGlT6tWrh7Ozs/ASCCRHKRAsAVU+jclABEIgCAT5luTkZG7cuEFAQAAHDhzg6JEjvHj5EmNjYwpZWVHK0RG3KlWo5OrKm7g4Qu7c4fHDhzx5/Jiwx495+uSJ1rwN2hYI+vr62BQrRlFbW4rZ2mJrZ4ejkxP6hoZcuniR4PPnuXnrFtHR0cTGxWFXvDjVqlencePG1KtXjzJlyghBIMh1KAWCOaoLhCiEQBAIChTR0dGcP3+ewMBATpw4wZnTp3nw8CHGRkZYWVpiY2ODg4MD5cqXp5STE0mJiYQ9eULYo0c8efyYp2FhRLx8SUR4ONHR0WrLb1C3QJDJ5Zibm2NhaYlloULYFC1KMVtbihYvTtFixZDJ5dy9c4cb168TEhrKs6dPeRkRwevXr7G3s8OrWjVq1qyJu7s7VapUKVAnSUHeRWqBsHDhQmbOnElYWBiVK1dmwYIFeHl5Zbr/5s2bGTNmDKGhoTg7OzN9+nSaNWumguWqIQSCQJCOmJgYgoODCQ4O5tq1a1y8cIFbt2/z7NkzdHV1MTM1xdzcHNtixXBwdMS2eHHs7OwwMzMjKTGRyIiI/2/h4URGRvI6Lo43r1/z5s2bDH+mr+OQmUCQy+UYGBpiYGDw/ueH3w0NDdE3MMDQyAgLCwssrKywtLTEwsoKcwsLFAoFkVFRPHr0iEcPHxIaEsLDhw95FRtLdEwMCQkJFC5cGGdnZ1xdXSlfvjxubm64ublhamqq7X+BQKAWlALBBNUFwiuyJxA2btxIly5dWLJkCdWqVWPu3Lls3ryZGzduUKRIkY/2P3HiBHXq1MHf358WLVqwbt06pk+fTlBQEC4uLipYn3OEQBAIssibN2+4ffs2N2/e5ObNm1y5coVrV68SGhrKq9hY3r59i0KhwMjQEIVcjomJCYUKFcLS0hITU1OMjY0xNTXF1NQUM3NzzM3NMTQ0xNDQELlcDsnJyGQyknl/MjM2MSH2Qyvs5KQkkMlISkri9evXvH79mqioKKKjooiJiSEmJobYV6949eoVERERREVFERcXx+s3b4iNjSUpORkDfX0KFSpE0aJFKV+hAi4uLpQpU4YyZcrg5OSEgYGBpO+vQKBulALBCNUFQhzZEwjVqlXD09OTX3/9FYCkpCTs7e3p378/w4cP/2j/du3aERsby65du1Ieq169Om5ubixZskQF63OOjiSzCgR5EAMDA1xcXDJV8zExMTx58iRle/z4MU+ePOH+/ftERUZy7/59nj19SlJSErGxscR9uNBntwqkXC5P8WQYGxtjbGKC6QevhoWVFVW9vLC1taVYsWJpNuEJEBRUVL0LVh4fHR2d5nF9fX309fU/2j8+Pp7AwEBGjBiR8phcLqdRo0acPHkywzlOnjyJr69vmseaNm3K9u3bVbJdFYRAEAjUhNI7UKZMmWwd9/btW2JjY0lMTCQpKYmkpCQSExORyWTI5fKUTaFQYGxsnOEJSSAQfIyenh5FixYlLCxM5bFMTEywt7dP89i4ceMYP378R/u+ePGCxMREbGxs0jxuY2PD9UyazoWFhWW4vzpszylCIAgEEpPZXYhAIFANAwMDQkJCiI+PV3ms5A8hwNTk9++tEAgCgUAgyLcYGBhoPb/G2toahULB06dP0zz+9OlTihYtmuExRYsWzdb+2kCV4lICgUAgEAjSoaenh4eHB4cOHUp5LCkpiUOHDlGjRo0Mj6lRo0aa/QEOHDiQ6f7aQHgQBAKBQCBQM76+vvzwww9UrVoVLy8v5s6dS2xsLF27dgWgS5cuFC9eHH9/fwB8fHyoW7cus2bNonnz5mzYsIFz587x22+/SfYahEAQCAQCgUDNtGvXjufPnzN27FjCwsJwc3Nj7969KYmI9+/ff7+8+QM1a9Zk3bp1jB49mpEjR+Ls7Mz27dslq4EAog6CQCAQCASCDBA5CAKBQCAQCD5CCASBQCAQCAQfIQSCQCAQCASCjxACQSAQCAQCwUcIgSAQCAQCgeAjhEAQCAQCgUDwEUIgCAQawt/fH09PT0xNTSlSpAhff/01N27cSHk+NDQUmUyW4bZ58+aU/e7fv0/z5s0xMjKiSJEiDB06lISEhDRzTZgwATs7O2rVqsXNmze19hoFAkH+RQgEgUBDHDlyhL59+3Lq1CkOHDjAu3fvaNKkCbGxsQDY29unaQ/95MkTJkyYgImJCV9++SUAiYmJNG/enPj4eE6cOMHvv//O6tWrGTt2bMo8x48fZ/fu3ezYsYPvv/+efv36SfJ6BQJB/kIUShIItMTz588pUqQIR44coU6dOhnuU6VKFdzd3VmxYgUAe/bsoUWLFjx+/DilAtuSJUvw8/Pj+fPn6OnpsWvXLpYvX87mzZsJCgqif//+nDlzRmuvSyAQ5E+EB0Eg0BJRUVEAWFlZZfh8YGAgwcHBdOvWLeWxkydPUqlSpTR94ps2bUp0dDRXrlxJ+fvNmzcYGRnh7e2dUttdIBAIVEH0YhAItEBSUhIDBw7kiy++yLS2+ooVK0V3r5wAAAHxSURBVChfvjw1a9ZMeSwsLCyNOABS/g4LCwNAV1eXvXv38uzZMywsLNDT09PQqxAIBAUJIRAEAi3Qt29fLl++zLFjxzJ8/vXr16xbt44xY8bkeI4iRYrk+FiBQCBIjwgxCAQapl+/fuzatYvDhw9jZ2eX4T5btmwhLi6OLl26pHm8aNGiPH36NM1jyr+LFi2qGYMFAoEAIRAEAo2RnJxMv3792LZtG//++y+Ojo6Z7rtixQpatWpF4cKF0zxeo0YNLl26xLNnz1IeO3DgAGZmZlSoUEFjtgsEAoFYxSAQaIg+ffqwbt06duzYQdmyZVMeNzc3x9DQMOXv27dvU6ZMGf755x+8vb3TjJGYmIibmxu2trbMmDGDsLAwOnfuTPfu3Zk6darWXotAICh4CIEgEGgImUyW4eOrVq3ixx9/TPl75MiRrF27ltDQUOTyj5169+7do3fv3gQEBGBsbMwPP/zAtGnT0NERKUQCgUBzCIEgEAgEAoHgI0QOgkAgEAgEgo8QAkEgEAgEAsFHCIEgEAgEAoHgI4RAEAgEAoFA8BFCIAgEAoFAIPgIIRAEAoFAIBB8hBAIAoFAIBAIPkIIBIFAIBAIBB8hBIJAIBAIBIKPEAJBIBAIBALBRwiBIBAIBAKB4COEQBAIBAKBQPAR/wMf0y4DkqoW2gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "P = sim_data_opt[\"bottom\"].power.squeeze(drop=True)  # extract downward radiated power\n",
    "P_norm = P / P_max\n",
    "\n",
    "# plot the radiation pattern in polar coordinates\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, polar=True)\n",
    "c = ax.pcolor(\n",
    "    phi_array, np.rad2deg(theta_array), P_norm, shading=\"auto\", cmap=\"hot\", vmin=0, vmax=1\n",
    ")\n",
    "cb = fig.colorbar(c, ax=ax)\n",
    "cb.set_label(\"Intensity\")\n",
    "_ = plt.setp(ax.get_yticklabels(), color=\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba183ab6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "applications": [
   "Passive photonic integrated circuit components",
   "Optical scattering and far-field radiation"
  ],
  "description": "This notebook demonstrates how to simulate a waveguide grating antenna Tidy3D FDTD.",
  "feature_image": "./img/wga_schematic.png",
  "features": [
   "Far field projection"
  ],
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "WGA, waveguide, grating, antenna, lidar, integrated photonics, Tidy3D, FDTD",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  },
  "title": "Waveguide Grating Antenna Modeling in Tidy3D | Flexcompute",
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        {
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