{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Tailoring directional scattering in silicon nanodisks\n",
        "\n",
        "The manipulation of light at the nanoscale is essential for advancing\n",
        "modern photonic technologies. High-index all-dielectric nanoparticles\n",
        "have emerged as a low-loss alternative to plasmonic structures, offering\n",
        "strong electric and magnetic resonances without the significant\n",
        "absorption losses typical of metals. A key advantage of these dielectric\n",
        "systems is the ability to exploit the interference between optically\n",
        "induced electric and magnetic modes, enabling precise control over light\n",
        "scattering directionality. In particular, silicon nanodisks can be\n",
        "engineered to achieve spectral overlap between electric and magnetic\n",
        "dipole resonances by adjusting their aspect ratio. This overlap leads to\n",
        "suppressed backscattering and enhanced forward scattering.\n",
        "\n",
        "In this notebook, we reproduce the key results presented in\n",
        "`Isabelle Staude, Andrey E. Miroshnichenko, Manuel Decker, Nche T. Fofang, Sheng Liu, Edward Gonzales, Jason Dominguez, Ting Shan Luk, Dragomir N. Neshev, Igal Brener, and Yuri Kivshar, \"Tailoring Directional Scattering through Magnetic and Electric Resonances in Subwavelength Silicon Nanodisks\", ACS NANO, (2013).`\n",
        "[DOI:\n",
        "https://doi.org/10.1021/nn402736f](https://doi.org/10.1021/nn402736f).\n",
        "\n",
        "We begin by performing a multipole decomposition of a single silicon\n",
        "nanodisk to identify its electric and magnetic resonances. By varying\n",
        "the aspect ratio of the structure, we highlight the potential for\n",
        "spectral overlap between these contributions to the extinction\n",
        "cross-section. Next, we simulate a periodic array of nanodisks with\n",
        "different aspect ratios and demonstrate that backscattering is strongly\n",
        "suppressed when the electric and magnetic dipole resonances are\n",
        "spectrally aligned.\n",
        "\n",
        "<img src=\"img/cylinder.png\" width=\"400\" alt=\"Schematic of the experiment\">"
      ],
      "id": "22ccc80c-f7b1-4757-b258-085df7c65de1"
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tidy3d as td\n",
        "import tidy3d.web as web"
      ],
      "id": "e939974f"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Multipole Decomposition\n",
        "\n",
        "First, we will sweep the disk aspect ratio and analyze the position of\n",
        "the electric and magnetic dipole resonance. To carry out the multipole\n",
        "decomposition, we will follow the methods described in\n",
        "[this](https://www.flexcompute.com/tidy3d/examples/notebooks/MultipoleExpansion/)\n",
        "example notebook.\n",
        "\n",
        "From the example above we will copy the function to carry out the\n",
        "multipole decomposition, and create a function to carry out the\n",
        "decomposition for a given `SimulationData` object."
      ],
      "id": "95b8a3da-0b3d-467d-afb1-16b3c543366d"
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "# function for carrying out multipole decomposition\n",
        "def ME(Ex, Ey, Ez, x, y, z, eps_xx, eps_yy, eps_zz, freqs, eps_medium):\n",
        "    EModulus = 2 / (td.C_0 * td.EPSILON_0 * eps_medium)\n",
        "\n",
        "    # first we create the 4-dimensional (x,y,z,f) datasets\n",
        "    X, Y, Z, Freqs = np.meshgrid(x, y, z, freqs, indexing=\"ij\")\n",
        "\n",
        "    # defining the angular frequencies\n",
        "    omega = 2 * np.pi * freqs\n",
        "\n",
        "    # import libraries to carry out integration and Bessel functions\n",
        "    from scipy.integrate import trapezoid as trapz\n",
        "    from scipy.special import spherical_jn as jn\n",
        "\n",
        "    # function for calculating the current density\n",
        "    J = lambda Ei, epsilon: 1j * (2 * np.pi * Freqs) * td.EPSILON_0 * (epsilon - eps_medium) * Ei\n",
        "\n",
        "    # function for calculating volume integral\n",
        "    integrate = lambda Data: trapz(trapz(trapz(Data, x=x, axis=0), x=y, axis=0), x=z, axis=0)\n",
        "\n",
        "    # defining the current densities\n",
        "    Jx = J(Ex, eps_xx)\n",
        "    Jy = J(Ey, eps_yy)\n",
        "    Jz = J(Ez, eps_zz)\n",
        "\n",
        "    # r vector\n",
        "    r = np.sqrt(X**2 + Y**2 + Z**2)\n",
        "\n",
        "    # wavevector\n",
        "    k = omega / td.C_0\n",
        "\n",
        "    # dot product k.r\n",
        "    kr = k * r\n",
        "\n",
        "    # dot product r.J\n",
        "    rj = X * Jx + Y * Jy + Z * Jz\n",
        "\n",
        "    # function for calculating dipole moments\n",
        "    P = lambda Ji, Xi: (\n",
        "        (1j / omega)\n",
        "        * (\n",
        "            integrate(Ji * jn(0, kr))\n",
        "            + ((k**2) / 2) * integrate((3 * rj * Xi - Ji * r**2) * jn(2, kr) / kr**2)\n",
        "        )\n",
        "    )\n",
        "\n",
        "    # dipole moments\n",
        "    Px = P(Jx, X)\n",
        "    Py = P(Jy, Y)\n",
        "    Pz = P(Jz, Z)\n",
        "\n",
        "    Ed = np.abs(Px) ** 2 + np.abs(Py) ** 2 + np.abs(Pz) ** 2\n",
        "\n",
        "    # cross product for calculating magnetic moments\n",
        "    rXJ_x = Y * Jz - Z * Jy\n",
        "    rXJ_y = Z * Jx - X * Jz\n",
        "    rXJ_z = X * Jy - Y * Jx\n",
        "\n",
        "    # dipole magnetic moments\n",
        "    Mx = (3 / 2) * integrate(rXJ_x * jn(1, kr) / kr)\n",
        "    My = (3 / 2) * integrate(rXJ_y * jn(1, kr) / kr)\n",
        "    Mz = (3 / 2) * integrate(rXJ_z * jn(1, kr) / kr)\n",
        "\n",
        "    Md = np.abs(Mx) ** 2 + np.abs(My) ** 2 + np.abs(Mz) ** 2\n",
        "\n",
        "    # auxiliary lists for calculating quadrupoles\n",
        "    coords = [X, Y, Z]\n",
        "    crossProd = [rXJ_x, rXJ_y, rXJ_z]\n",
        "    Js = [Jx, Jy, Jz]\n",
        "\n",
        "    # electric quadrupole\n",
        "    Eq = np.zeros(Px.shape)\n",
        "    for alpha in range(3):\n",
        "        for beta in range(3):\n",
        "            ra = coords[alpha]\n",
        "            rb = coords[beta]\n",
        "            ja = Js[alpha]\n",
        "            jb = Js[beta]\n",
        "\n",
        "            delta = 0 if alpha != beta else 1\n",
        "\n",
        "            integrand1 = (3 * (ra * jb + rb * ja) - 2 * rj * delta) * (jn(1, kr) / kr)\n",
        "            integrand2 = ((5 * ra * rb * rj) - (ra * jb + rb * ja) * r**2 - (r**2 * rj * delta)) * (\n",
        "                jn(3, kr) / kr**3\n",
        "            )\n",
        "\n",
        "            Eq_ab = (3j / omega) * (integrate(integrand1) + (2 * k**2) * integrate(integrand2))\n",
        "\n",
        "            Eq += (1 / 120) * np.abs(k * Eq_ab) ** 2\n",
        "\n",
        "    # magnetic quadrupole\n",
        "    Mq = np.zeros(Px.shape)\n",
        "    for alpha in range(3):\n",
        "        for beta in range(3):\n",
        "            ra = coords[alpha]\n",
        "            rb = coords[beta]\n",
        "\n",
        "            rxja = crossProd[alpha]\n",
        "            rxjb = crossProd[beta]\n",
        "\n",
        "            Mq_ab = 15 * integrate((ra * rxjb + rb * rxja) * jn(2, kr) / kr**2)\n",
        "            Mq += (1 / 120) * (np.abs(k * Mq_ab) ** 2) / td.C_0**2\n",
        "\n",
        "    # constants\n",
        "    k = (2 * np.pi * freqs) / td.C_0\n",
        "    const = (k**4) / (6 * np.pi * ((eps_medium * td.EPSILON_0) ** 2) * EModulus)\n",
        "\n",
        "    return Ed * const, Md * const / (td.C_0 / np.sqrt(eps_medium)) ** 2, Eq * const, Mq * const"
      ],
      "id": "cd5185a4"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "We will also define an auxiliary function for calling the ME function\n",
        "for a given SimulationData object."
      ],
      "id": "50c88439-c466-43d5-91d8-d41fe74cd15e"
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "def run_me(sim_data):\n",
        "    freqs = sim_data[\"fieldMon\"].Ex.f.values\n",
        "    eps_medium = sim_data.simulation.medium.permittivity\n",
        "\n",
        "    # electric field and permittivity data\n",
        "    E = sim_data[\"fieldMon\"]\n",
        "    Eps = sim_data[\"epsMon\"]\n",
        "\n",
        "    eps_xx = Eps.eps_xx\n",
        "    eps_yy = Eps.eps_yy\n",
        "    eps_zz = Eps.eps_zz\n",
        "\n",
        "    Ex = E.Ex\n",
        "    Ey = E.Ey\n",
        "    Ez = E.Ez\n",
        "\n",
        "    # spatial components\n",
        "    x, y, z = eps_xx.x, eps_xx.y, eps_xx.z\n",
        "\n",
        "    # calling the function.\n",
        "\n",
        "    Ed, Md, Eq, Mq = ME(\n",
        "        Ex.values,\n",
        "        Ey.values,\n",
        "        Ez.values,\n",
        "        x.values,\n",
        "        y.values,\n",
        "        z.values,\n",
        "        eps_xx.values,\n",
        "        eps_yy.values,\n",
        "        eps_zz.values,\n",
        "        freqs,\n",
        "        eps_medium,\n",
        "    )\n",
        "\n",
        "    return Ed, Md, Eq, Mq"
      ],
      "id": "f70c8687"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Simulation Setup\n",
        "\n",
        "Next we will define a function for creating the `Simulation` object. The\n",
        "model consists in a single unit cell of a Si cylinder, with the needed\n",
        "monitors for calculating the multipole expansion;"
      ],
      "id": "e52b45ae-efed-4d81-b075-e576bca54948"
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "# global variables\n",
        "height = 0.22\n",
        "\n",
        "index_disk = 3.5\n",
        "index_medium = 1.5\n",
        "\n",
        "disk_medium = td.Medium(permittivity=index_disk**2)\n",
        "background_medium = td.Medium(permittivity=index_medium**2)\n",
        "\n",
        "run_time = 5e-12"
      ],
      "id": "6eaed734"
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [],
      "source": [
        "def get_me_sim(radius):\n",
        "    # defining source and monitor frequency\n",
        "    wl2 = 1.7\n",
        "    wl1 = 0.8\n",
        "\n",
        "    freq2 = td.C_0 / wl1\n",
        "    freq1 = td.C_0 / wl2\n",
        "\n",
        "    freq0 = freq1 + (freq2 - freq1) / 2\n",
        "    fwidth = (freq2 - freq1) / 2\n",
        "    wl = td.C_0 / freq0\n",
        "\n",
        "    freqs = np.linspace(freq0 - fwidth, freq0 + fwidth, 101)\n",
        "\n",
        "    # defining the cylinder structure\n",
        "    geometry = td.Cylinder(radius=radius, length=height)\n",
        "    disk = td.Structure(geometry=geometry, medium=disk_medium)\n",
        "\n",
        "    # monitor and simulation size\n",
        "    lx = 2 * radius\n",
        "    ly = 2 * radius\n",
        "    lz = height\n",
        "\n",
        "    Lx = wl2 + lx\n",
        "    Ly = Lx\n",
        "    Lz = wl2 + lz\n",
        "\n",
        "    # adding PMLs\n",
        "    boundary_spec = td.BoundarySpec(x=td.Boundary.pml(), y=td.Boundary.pml(), z=td.Boundary.pml())\n",
        "\n",
        "    # TFSF source\n",
        "    source_TFSF = td.TFSF(\n",
        "        center=(geometry.center[0], geometry.center[1], -wl / 16),\n",
        "        size=(\n",
        "            lx * 1.3,\n",
        "            ly * 1.3,\n",
        "            lz + wl / 8 + 0.1,\n",
        "        ),\n",
        "        source_time=td.GaussianPulse(freq0=freq0, fwidth=fwidth),\n",
        "        direction=\"+\",\n",
        "        injection_axis=2,\n",
        "    )\n",
        "\n",
        "    # since the structure is smaller than the target wavelength,\n",
        "    # a finer mesh is required to ensure accurate results\n",
        "    structure_override = td.MeshOverrideStructure(\n",
        "        geometry=td.Box(center=source_TFSF.center, size=source_TFSF.size),\n",
        "        dl=(0.02,) * 3,\n",
        "    )\n",
        "\n",
        "    mesh_override = [structure_override]\n",
        "\n",
        "    grid_spec = td.GridSpec.auto(min_steps_per_wvl=15, override_structures=mesh_override)\n",
        "\n",
        "    # field monitor for calculating the fields\n",
        "    field_monitor = td.FieldMonitor(\n",
        "        center=geometry.center,\n",
        "        fields=[\"Ex\", \"Ey\", \"Ez\"],\n",
        "        size=(lx * 1.2, ly * 1.2, lz * 1.2),\n",
        "        freqs=freqs,\n",
        "        name=\"fieldMon\",\n",
        "        colocate=False,\n",
        "    )\n",
        "\n",
        "    # permittivity monitor\n",
        "    permittivity_monitor = td.PermittivityMonitor(\n",
        "        center=field_monitor.center,\n",
        "        size=field_monitor.size,\n",
        "        freqs=field_monitor.freqs,\n",
        "        name=\"epsMon\",\n",
        "    )\n",
        "\n",
        "    sim_me = td.Simulation(\n",
        "        size=(Lx, Ly, Lz),\n",
        "        boundary_spec=boundary_spec,\n",
        "        grid_spec=grid_spec,\n",
        "        sources=[source_TFSF],\n",
        "        monitors=[field_monitor, permittivity_monitor],\n",
        "        structures=[disk],\n",
        "        run_time=run_time,\n",
        "        medium=background_medium,\n",
        "    )\n",
        "\n",
        "    return sim_me"
      ],
      "id": "8e165fbe"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we plot the model to make sure everything is correct."
      ],
      "id": "abaf2c5a-02f0-42d5-9620-2f4a0e03d3f5"
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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              "        \n",
              "        /**\n",
              "        * Simulation Viewer Injector\n",
              "        *\n",
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              "        *\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",
              "    "
            ]
          }
        }
      ],
      "source": [
        "sim = get_me_sim(0.5)\n",
        "sim.plot_3d()\n",
        "\n",
        "plt.show()"
      ],
      "id": "c027c468"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Tuning the Electric and Magnetic Components\n",
        "\n",
        "Next, we perform a parameter sweep over the disk diameter to vary the\n",
        "aspect ratio of the structure, aiming to identify the condition where\n",
        "the electric and magnetic dipole resonances spectrally overlap."
      ],
      "id": "5883b427-b0c4-4c74-a55d-9d87a1b8e086"
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "r_list = np.linspace(0.2, 0.6, 10) / 2\n",
        "sims_me = {str(r): get_me_sim(r) for r in r_list}\n",
        "batch_me = web.Batch(simulations=sims_me, folder_name=\"cylinders_me\")"
      ],
      "id": "81538aa4"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Run the batch."
      ],
      "id": "bb8194a4-3729-4f12-9f64-079a8b47df08"
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"e03184fd987f438583a684bf965a4b48\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">03:56:20 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">10</span> tasks.                      \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">03:56:28 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.338</span> for the whole batch.                \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>completion.                                                        \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"98daf7d05f8b45eea9a06cd91036b855\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">03:56:33 UTC </span>Batch complete.                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        }
      ],
      "source": [
        "batch_data_me = batch_me.run()"
      ],
      "id": "2df0e501"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Carry out multipole decomposition for each simulation."
      ],
      "id": "18f75030-67db-428b-8260-e4fb5ff39cbf"
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [],
      "source": [
        "MDs = []\n",
        "EDs = []\n",
        "\n",
        "for r in r_list:\n",
        "    sim_data = batch_data_me[str(r)]\n",
        "    Ed, Md, Eq, Mq = run_me(sim_data)\n",
        "\n",
        "    EDs.append(Ed)\n",
        "    MDs.append(Md)"
      ],
      "id": "3f887086"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now, we analyze the position of the resonance peak of the electric and\n",
        "magnetic dipole."
      ],
      "id": "1d0139d8-b26f-4a79-bf24-74afe994001f"
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [],
      "source": [
        "freq_ed = 1e15\n",
        "freq_md = 1e15\n",
        "\n",
        "max_ed = []\n",
        "max_md = []\n",
        "\n",
        "freqs = sim.monitors[0].freqs\n",
        "\n",
        "for i in range(len(MDs)):\n",
        "    # electric and magnetic dipole contributions\n",
        "    Ed = np.array(EDs[i])\n",
        "    Md = np.array(MDs[i])\n",
        "\n",
        "    # ignore the data to the lower frequencies of the last resonance to make sure we will follow the same resonance\n",
        "    Ed[freqs > freq_ed] = 0\n",
        "    Md[freqs > freq_md] = 0\n",
        "\n",
        "    idx_ed = np.argmax(Ed)\n",
        "    idx_md = np.argmax(Md)\n",
        "\n",
        "    freq_ed = freqs[idx_ed]\n",
        "    freq_md = freqs[idx_md]\n",
        "\n",
        "    max_ed.append(td.C_0 / freqs[idx_ed])\n",
        "    max_md.append(td.C_0 / freqs[idx_md])"
      ],
      "id": "ad3e4885"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "As we can see, the electric and magnetic components are at the same\n",
        "frequency for a disk diameter of around 0.29 µm. The results match very\n",
        "well the ones presented on figure 1 of the reference\n",
        "[paper](https://doi.org/10.1021/nn402736f)."
      ],
      "id": "9a2c92db-e6dd-45fa-8a58-90f36ab6f2bb"
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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Ll9ejjz6q06dP59rLnj17NGTIENWoUUPe3t4KCAhQiRIl1LZtW82ZMyfLY7p0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\n6bfffpOU8n1VoEAB29VYs+t9ldkkPhfnzp1b999/f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          }
        }
      ],
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(2 * r_list, max_ed, label=\"electric dipole\", color=\"blue\")\n",
        "ax.plot(2 * r_list, max_md, label=\"magnetic dipole\", color=\"red\")\n",
        "ax.set_xlabel(\"wavelength (µm)\")\n",
        "ax.set_ylabel(\"disk diameter\")\n",
        "ax.set_ylim(0.85, 1.6)\n",
        "ax.legend()\n",
        "\n",
        "plt.show()"
      ],
      "id": "a541952f"
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "image/png": 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ICkZReA3FRRH/a0BERDyaYcAzz1j72raFW26xJx53atkSunWDZctS+959F+bM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\nAMUNqry8PNS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          }
        }
      ],
      "source": [
        "# electric and magnetic contributions to the diameter in which the resonances are close in frequency\n",
        "idx = np.argmin(abs(r_list - 0.29 / 2))\n",
        "fig, ax = plt.subplots()\n",
        "ax.plot(td.C_0 / freqs, EDs[idx], label=\"electric dipole\", color=\"blue\")\n",
        "ax.plot(td.C_0 / freqs, np.array(MDs[idx]), label=\"magnetic dipole\", color=\"red\")\n",
        "ax.set_xlabel(\"wavelength (µm)\")\n",
        "ax.set_ylabel(\"Scattering cross-section (a.u.)\")\n",
        "ax.legend()\n",
        "\n",
        "plt.show()"
      ],
      "id": "b1e04f5f"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Backscattering suppression\n",
        "\n",
        "Now, we can simulate a periodic array of disks, and analyze the spectral\n",
        "transmittance and reflectance. We will define another auxiliary function\n",
        "to return the simulation object, now with a\n",
        "[FluxMonitor](https://docs.flexcompute.com/projects/tidy3d/en/latest/api/_autosummary/tidy3d.FluxMonitor.html)\n",
        "and [Periodic Boundary\n",
        "conditions](https://docs.flexcompute.com/projects/tidy3d/en/v2.7.6/faq/docs/faq/how-do-i-set-the-periodic-boundary-condition.html)."
      ],
      "id": "764eda80-6fbc-4682-af36-12c11445a725"
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [],
      "source": [
        "def get_sim(diameter, min_steps_per_wvl=15):\n",
        "    # defining frequencies\n",
        "    wl2 = 1.65\n",
        "    wl1 = 1.25\n",
        "\n",
        "    freq2 = td.C_0 / wl1\n",
        "    freq1 = td.C_0 / wl2\n",
        "\n",
        "    freq0 = freq1 + (freq2 - freq1) / 2\n",
        "    fwidth = (freq2 - freq1) / 2\n",
        "    wl = td.C_0 / freq0\n",
        "\n",
        "    freqs = np.linspace(freq0 - fwidth, freq0 + fwidth, 101)\n",
        "\n",
        "    source_time = td.GaussianPulse(freq0=freq0, fwidth=fwidth)\n",
        "\n",
        "    radius = diameter / 2\n",
        "    lattice_constant = 0.8\n",
        "\n",
        "    disk = td.Structure(geometry=td.Cylinder(radius=radius, length=height), medium=disk_medium)\n",
        "\n",
        "    sim_size = (lattice_constant, lattice_constant, height + 3 * wl)\n",
        "\n",
        "    source = td.PlaneWave(\n",
        "        source_time=source_time,\n",
        "        pol_angle=0,\n",
        "        angle_theta=0,\n",
        "        center=(0, 0, -sim_size[2] / 2 + 1),\n",
        "        direction=\"+\",\n",
        "        size=(td.inf, td.inf, 0),\n",
        "    )\n",
        "\n",
        "    # flux monitor\n",
        "    flux_mon = td.FluxMonitor(\n",
        "        name=\"flux_mon\",\n",
        "        size=(td.inf, td.inf, 0),\n",
        "        center=(0, 0, sim_size[2] / 2 - 1),\n",
        "        freqs=freqs,\n",
        "    )\n",
        "\n",
        "    # field monitor\n",
        "    field_mon = td.FieldMonitor(name=\"field_mon\", size=(0, 0, 0), center=(0, 0, 0), freqs=freqs)\n",
        "\n",
        "    # grid spec\n",
        "    grid_spec = td.GridSpec.auto(min_steps_per_wvl=min_steps_per_wvl)\n",
        "\n",
        "    boundary_spec = td.BoundarySpec(\n",
        "        x=td.Boundary.periodic(), y=td.Boundary.periodic(), z=td.Boundary.pml()\n",
        "    )\n",
        "\n",
        "    sim = td.Simulation(\n",
        "        size=sim_size,\n",
        "        center=(0, 0, 0),\n",
        "        grid_spec=grid_spec,\n",
        "        structures=[disk],\n",
        "        sources=[source],\n",
        "        monitors=[flux_mon, field_mon],\n",
        "        run_time=run_time,\n",
        "        boundary_spec=boundary_spec,\n",
        "        medium=background_medium,\n",
        "    )\n",
        "\n",
        "    return sim"
      ],
      "id": "9094df00"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Sweep over multiple disk diameters"
      ],
      "id": "47ed5474-8483-419a-bb16-cb2f29b4d00f"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
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        {
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          "metadata": {},
          "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>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">04:00:31 UTC </span>Started working on Batch containing <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">9</span> tasks.                       \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">04:00:39 UTC </span>Maximum FlexCredit cost: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.225</span> for the whole batch.                \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>completion.                                                        \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
            "text/html": [
              "<script type=\"application/vnd.jupyter.widget-view+json\">\n",
              "{\"model_id\":\"8b764a1bda56451a8dd9af7fea8abec4\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n",
              "</script>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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\">04:00:43 UTC </span>Batch complete.                                                    \n",
              "</pre>"
            ]
          }
        },
        {
          "output_type": "display_data",
          "metadata": {},
          "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>"
            ]
          }
        }
      ],
      "source": [
        "diameters = np.linspace(0.4, 0.6, 9)\n",
        "\n",
        "batch = web.Batch(simulations={str(i): get_sim(i) for i in diameters})\n",
        "batch_data = batch.run()"
      ],
      "id": "fa0a971f"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Now we analyze the transmitted flux over all simulations, and also the\n",
        "points of maximum for the electric and magnetic fields."
      ],
      "id": "f0fe43f3-fe3a-4274-ad5e-b23e036196bc"
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [],
      "source": [
        "T = []\n",
        "E_max = []\n",
        "H_max = []\n",
        "\n",
        "for d in diameters:\n",
        "    sim_data = batch_data[str(d)]\n",
        "    flux = sim_data[\"flux_mon\"].flux\n",
        "\n",
        "    # extract E field components\n",
        "    Ex = sim_data[\"field_mon\"].Ex\n",
        "    Ey = sim_data[\"field_mon\"].Ey\n",
        "    Ez = sim_data[\"field_mon\"].Ez\n",
        "\n",
        "    # extract H field components\n",
        "    Hx = sim_data[\"field_mon\"].Hx\n",
        "    Hy = sim_data[\"field_mon\"].Hy\n",
        "    Hz = sim_data[\"field_mon\"].Hz\n",
        "\n",
        "    # calculate |E| and |H| (magnitude)\n",
        "    E = np.sqrt(np.abs(Ex) ** 2 + np.abs(Ey) ** 2 + np.abs(Ez) ** 2)\n",
        "    H = np.sqrt(np.abs(Hx) ** 2 + np.abs(Hy) ** 2 + np.abs(Hz) ** 2)\n",
        "\n",
        "    idx_e = E.argmax()\n",
        "    E_max.append(float(td.C_0 / E.f[idx_e]))\n",
        "\n",
        "    idx_m = H.argmax()\n",
        "    H_max.append(float(td.C_0 / H.f[idx_m]))\n",
        "\n",
        "    flux = flux.assign_coords(wl=td.C_0 / flux.coords[\"f\"])\n",
        "    flux = flux.swap_dims({\"f\": \"wl\"})\n",
        "    flux = flux.drop_vars(\"f\")\n",
        "\n",
        "    T.append(flux)\n",
        "\n",
        "T = np.array(T)"
      ],
      "id": "adeb5dbe"
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "By visualizing the transmitted and reflected flux, we observe a\n",
        "suppression of backscattering when the magnetic and electric field\n",
        "resonances overlap. The results closely match figure 4 of the reference\n",
        "[paper](https://doi.org/10.1021/nn402736f)."
      ],
      "id": "421d10b1-d5cd-4eb7-8e50-c7b7308d0e43"
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
          "data": {
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mmRQZAAAAAHdXvWtX3fjdd/KuVMmqff9XX2nt\nmDHKzcoyKTLAPRlSoIqPj1fXrl11+n/7dvr7+6tp06YKCwuTp6dnkccGBwcbESIAAADKqHbtAC1f\n3l0tWoRZte/efU7du6/Q0aM8cwoAAACAOeoNG6brvvhCFl9fq/Zd772nDQ88IOXmmhQZ4L4MKVBN\nnTpVp0+flp+fn95++23dfvvt8vPzM2JquzIyMrR9+3Zt3LhRx48flyS1bdtWgwYNcsj53333XcXH\nxxc7rnXr1ho6dGix406ePKkVK1bo6NGj8vX1VePGjdWjRw/5XvKGCgAAYIYrrgjVsmXdVbu29cPT\nNm8+rV69Vur06XSTIoOjZMuibBm3bUa2+AMBAAAAHCNq4kS1e+steVyyWGL7K69o69NPmxQVHMXo\nXCVvTvIVRzCkQLVhwwZJ0lNPPaV77rnHiCntmjJlir755htt27ZN6enWfygZO3aswwpUH374oXbu\n3FnsuJEjRxZZoLpw4YIef/xxvf/++zbP6woLC9Pbb7+t0aNHlzleAACAy9WlS3V9++0NCg31sWr/\n5ZcT6t//RyUlsS83AAAAABN4eKjtv/+tKx591Kbr90ce0c433jAhKAD5DClQ5f5veWS3bt2MmK5I\n3377rTZs2CBvb2+1adNGbdq00UcffeS0+a666ipFR0fb7W/ZsqXdvuzsbA0ZMkTLli2TJHXo0EFd\nunTR+fPntWDBAh07dky33367EhMTNXHiRIfHDgAAUJzhw+vp88+vk6+vxao9JuaQRo5co/T0bDtH\nAgAAAIDzePr46NqZM9Xg1lut2nMyM7X2zjt14MsvTYoMQD5DClRNmjTRzz//rJSUFCOmK9Kjjz6q\nKVOm6KqrrpKvr6/S0tKcWqC68sor9fRlLhOdOnVqQXHqxRdf1DPPPFPQ9/rrr6t///76+eefNWnS\nJHXv3l1RUVEOiRkAAKAkHnqoud56q51N+7vv7tJDD21UTg5bHlQkWbIoy8BtM7LYMgMAAACXyTs4\nWN1iY1Xzxhut2jOTk/XzkCH6Z+VKkyKDMxidq+TNSb7iCIYUqMaOHasPP/xQCxcuVM+ePY2Y0q6B\nAweaOn9Jpaen69VXX5UkderUyao4JUmBgYH68ssv1aBBA6Wnp+vFF1/UnDlzzAjVLXlbih/jtgx5\nV0GFwqP07Assfog7OqR6Zofgwg4bMouHh/Tf//bQ5Mm2xanHHlup//73V0PiKCkfhZsdgsvxENsu\nAhWbt9kBuC5j/3aFioB8pXBBZgfguurpkNkhuCRjMpU8QTVqaOSyZareqpVVe8qJE/qqd2+d2LbN\nwGiKFy6f4ge5mUx+YbsNz+KHlN0111yjRx55RB9//LFiY2ONmLLcW7lypc6cOSNJuu+++wodU7Nm\nTQ0ePFiS9N133+nChQuGxQcAANyTj49FX30VrcmTO1q1Z2Zma9SoWJcrTgEAAABwH5WbNdPY9ett\nilOn9+zRjI4dXa44Bbg7w9Y6/Pe//1XlypU1atQo9erVS8OHD1eTJk0UEBBQ5HH16tWTn5+fQVG6\njh9++KHg9Y2XLEW92E033aS5c+cqNTVV69at00033WREeAAAwA0FB/tqwYIRuuGG+lbtycnpio6e\nrx9+OGBSZDBCtizKNub+tv/Nl2PYXAAAACj/Ijt31i3ffSf/cOsdFOLWr9ec/v114X+LAVDxGJ2r\n5M1JvuIIhm7G1aNHDy1btkwLFizQggULSnTM77//rrZt2zo5Muc5c+aMZs+erUOHDsnDw0PVqlVT\nhw4d1Lx58yKP27lzpySpatWqqlatmt1xV1xxRcHrv/76iwIVAABwipo1K2nZspG68krrzyXHjyer\nT5/Z2rbthEmRAQAAAHB3zQYP1pDZs+V1yUKH3QsXKuaWW5TFzlOASzKsQDV16lRNmjRJOTnuVVlc\nvHixFi9ebNN+9dVX64033lDXrl0LPe7QoUOSpMjIyCLPf3F//jEllZCQYLcvMTGxVOcCAAAVV1RU\nZS1fPkp16oRYte/efVq9en2pw4fPmRQZjJRj8F2JOew7DwAAgBK45r771Pudd+Thaf1ZddP06Vp6\n//3Kzc42KTIYxehcJW9O8hVHMKRAtWHDBj300EOSpFq1amnkyJG68sorFRYWJk/Pon9wmjRpYkCE\nzhEUFKSOHTuqXr16ql69uhISErRmzRpt375dW7Zs0Y033qiPP/5Yd955p82xycnJBecoSqVKlWyO\nKanwcB4YDgAAinbttXX03Xc3KyzM36r911/j1L//HJ09y52IAAAAAMxx46uv6tonn7Rp/+npp7Xm\nlVdMiAhAaRhSoPrwww8lSddcc41++umnYosuFcHUqVN17bXXytfX16Zv8eLFuu2225SYmKh77rlH\n7dq1s9qqT5Iu/G/ZqY+PT5HzXHz+CyxVBQAADhQdHaWvvoqWn5/1R8Zvv92tW2+N0YULWSZFBgAA\nAMCdeXp7a8Ann6jV6NFW7TlZWVp0113aNmuWOYEBKBVDClT79u2TJD3++ONuUZySpBtvvNFuX79+\n/TRnzhz17t1bWVlZev311/Xll19ajfH391dSUpIyMjKKnCc9Pd3qmNI4e/as3b7ExEQ1aNCgVOcD\nAAAVx/33t9PUqb3k6Wm9bcEHH2zS/fcvVU5OrkmRwSxGP3g4my0zAAAAUAifoCANj4lRwx49rNoz\nUlL09bBh2rd8uUmRwSxG5yp5c5KvOIIhBarQ0FBJUo0aNYyYrlzo1auXWrdurW3btmnFihU2/cHB\nwYqPjy92276L+y/e7q8kwsLCSjTO+39fAMrIsKf+lTNcF/tCih/ijvaoqdkhuLD4Mp/Bw0N69dUb\n9cQT19r0PfXUT3r11TVlnsNoqSrdTTzuIEusfkNFQsYClBmfye3j7aVwoWYH4Lqaao/ZIbiksmcq\neQKrVdPIpUtV4+qrrdrPnzyp2X376p9Nmxw0k3H8lWp2CC6HfMV9GFJWvO666yRJe/fuNWK6cqN1\n69aSpNOnT9tsz1evXj1JUlxcXJHnuLi/fv36Do0PAAC4F29vT3322SCb4lRWVo7GjPm2XBanAAAA\nAFQMEU2aaOz69TbFqTN792pGx47lsjgFuDtDClTjxo1T7dq19fbbb1ttSYf/5+FhvSSwRYsWkqRT\np07pxIkTdo/7448/bI4BAAAorUqVfLRkya267bZWVu0pKRnq12+2Pvtsu0mRwVXkbZth7BcAAAAg\nSbU7dNCdv/6qsEtu0D+2caM+7dxZCQcOmBQZXIEZuQr5imMYUqAKCwvT0qVLlZiYqD59+ugAbxiS\npC1btkiSqlSpIj8/P6u+7t27F7xeuXKl3XPk9wUGBqpTp05OiBIAAFR01asHafXqMerevaFVe3x8\nirp2naUVK/abFBkAAAAAd9d0wACN/uknBUREWLX/vWSJPuvWTamnTpkUGYCyMmSX4ddee02HDx9W\np06dNG/ePDVu3Fht27ZV06ZNFRAQUOSx//rXv1SnTh0jwjTUkiVLClY/9erVy6b/pptuUpUqVXTq\n1Cm99957GjVqlM0qq7i4OC1cuFCSNGjQIJsiFwAAQHGaNo3Q8uWjVK9eqFX73r1n1LPnlzp4MNGU\nuAAAAACgzd13q8/778vTYr1aZcsnn2jx+PHKzc42KTLAOOfOndOBAwd04cIF1apVS3Xr1nXaXBkZ\nGTp06JBOnjypiIgI1atXT/7+znuusyEFqpiYGG3evNmqbePGjdq4cWOxx44bN87lClRTp07VqVOn\n5O/vr6eeesqmf+XKlapdu7aioqIKPf7bb7/VmDFjJEleXl564oknbMb4+PjomWee0cSJE7VhwwY9\n88wzevnllwv6k5KSdOuttyojI0NeXl569tlnHfPNAQAAt9GxY20tWnSLIiKsbxjasOGo+vWbo9On\neVgv/l+WPJVl4DYWRj0WOTMzU8uXL9evv/5akIS1adNGAwYMcHgilpubq/Xr1+unn37SP//8I09P\nT9WrV0+dO3dWx44dHToXAABAedftxRd1/TPP2LSvev55rX7hBRMigqsyOlfJm9P5Dhw4oMmTJ2vJ\nkiXKzMwsaG/WrJmeeeYZ3XrrrQ6b6+DBg3ruuee0cOFCJSUlFbRbLBbdeOONuuuuuzR06FCHzZfP\nkAJVvXr1lJKSclnHOjop3Lx5sxYsWFDw39kXVdk3b96sp59+uuC/fX199Uwhb4IffPCB9uzZo5CQ\nkEILVDNmzNC8efPUsGFDRUVFqXr16oqIiNDZs2e1du1a7dq1S1JecWrWrFlq3rx5obHed999+uGH\nH/Tdd9/plVde0ZIlS9S1a1edP39eCxcu1MmTJyVJ06ZNU5MmTS7vggAAALc0cGBTzZkzRP7+3lbt\nixf/rREjvlFqaqadI4GKY8OGDRo5cqT277fdxrJ69eqaOXNmobsdXI5Vq1bp3nvvLcgFLlWvXj39\n8ssvioyMdMh8AAAA5ZWnl5f6ffihrrrzTqv2nOxsLRk/Xls++cSkyADj/P7777rhhhsK6irNmzdX\ncHCw/vzzT+3evVsjR47Uli1bNGXKlDLPNXfuXN155526cOGCJKlOnTqqU6eOMjIytGfPHn3//feK\nj48vvwWqb775xohpSmTr1q165ZVXCu3btm2btm3bVvDfgYGBhRaoitOzZ0/t3r1bf/zxR6HJriTd\ncMMN+ve//622bdvaPY+np6fmz5+vp59+Wu+8845NfFWrVtU777yjESNGlDpGAADgvu65p43ee6+P\nLBbrx5F+/PEWTZiwWNnZuSZFBhhn+/bt6t69u5KTkxUSEqJx48apWbNmOnTokD755BOdOHFCAwYM\n0LJly3TjjTeWaa4vv/xSY8aMUXZ2tsLDwzVs2DC1bNlS/v7+OnLkiH7++WetWbNG586do0AFAADc\nmndgoIZ9/bUa9+5t1Z6Zmqqvhw/X3iVLTIoMME5ycrIGDhyolJQU1ahRQwsWLFD79u0l5W33N27c\nOH3zzTd64403dM0115SpPrBo0SKNGjVK2dnZ6tixo6ZNm6arr766oD8nJ0c///yzNmzYUObvqzAe\nubm5bvUXiG3btmnx4sUlGuvj46PHHnvMpn369Ok6ffq0/Pz89Mgjj9g9/vjx49q2bZuOHz+u06dP\ny9/fX7Vr11b79u1Vs2bNUsWdkJCgH374QXFxcfL19VWTJk3UrVs3eXk5p8aYkJCg8PBwSdI2SSFO\nmaX8qsUFscu7qtkRuDDX2q3UdTQ2OwAXNsrsAFxT285rzQ7BZW32+KPYMS+91E5PP93Gpv25537X\niy9uckZYpqub283sEFxOdsI5HQ3vIEk6e/aswsLCCh138WfCdWfrKSTMuG0zziVkq3P4IUlFx3g5\ncnNz1bZtW23ZskXh4eFav3691Y4Ex48fV6dOnXTo0CFFRkbq77//vuznva5fv17XXnutcnJy1KNH\nD82ZM6fgml7s8OHDCgsLU3Bw8GV/X+7k4p9NMpbC1DI7ANfl5V38GHdUz+wAXFjhT2/AOLMDcF1r\nB9i/Gd2d/eGxudgx/lWrqs+SJap6yQ39F06d0tJ+/XSyBI+LKY+65TrveULl1bmEbHUIPyrJfi5g\nZq6SH6Oz8pUXXnhBzz//vCTpp59+Urdu1jltenq6Wrdurd27d6t+/fr6+++/L6tOcPbsWTVv3lzx\n8fHq0KGDfvrpJ6c+b6owhqyguhw7duyQj4+Pw7eua926tVq3bl2mc4wfP75E42rUqKEaNWqUaa58\nYWFhGjZsmEPOBQAA3I+Xl6c+/riLxoxpZtWelZWj8eN/0YwZhW87BlREixcv1pYtWyRJL7/8sk3O\nUaNGDU2dOlUDBw5UXFycZs2aVeIc4GI5OTkaN26ccnJy1LBhQ3377bd2Ez5nPugYAADA1YU0aqS+\ny5crpGFDq/akAwe0uFcvndu716TIAOPNmjVLktSuXTub4pSU92iihx9+WPfcc48OHjyo1atXX9au\nD9OmTVN8fLwk6aOPPjK8OCVJnsUPMcfo0aN1/fXXKyvLqMcjAwAAVEyBgV5atKi3TXHq/PlMDRy4\njOIU3M7XX38tKS+xGz16dKFj+vXrV3CzWf740lqxYoX++usvSXl3QZqR8AEAALi6qtdco8G//mpT\nnDq5aZNiO3akOAW3snPnTh06dEhSXk5iz8V9Sy5z68sZM2ZIkq677jq1bNnyss5RVoasoFq2bJnO\nnDlT4vHHjh3Tzp07lZGRof3796tp06ZOjA4AAKDiqlrVX0uW9FHbttZ7sJ46dUF9+y7V77+fNCky\nlDc5sihbxm2bkePEc69atUqSdM011ygwMLDQMZ6enrr++us1b948rV27VllZWaXeNmPOnDmSJD8/\nP0VHR5cpZgAAgIqoTp8+6jF/vrwv+Ux2ZPlyrRg6VFnnz5sUGcoTo3OVvDmd448//n/r/jZtbLfn\nz1ezZk3VqFFDx48ftzqmpPbv368jR45Ikrp3717QfvjwYcXFxcnf31+NGjVSSIhzt9I2pED1zDPP\naPPm4vcZvZSPj48qV67shIgAAAAqvkaNQrRiRT81aGD9TJv9+8+pV68l2rfvnEmRAeZJTk5WXFyc\nJKl58+ZFjo2KynvwSEZGhvbt26dmzZoVOf5S69evl5SXWPr7++v06dP6+uuvC27Gq1Wrlrp166br\nr7/+Mr4TAACA8i1q7FhdP326PC+5CWj3rFlafdddymFnLbihffv2FbwubhvwOnXq6Pjx49p7GasM\n87c8l6SrrrpKy5cv1xNPPKHt27cXtFssFnXt2lWvvPKK2rdvX+o5SsJln0HVqFEjvfTSS4qIiDA7\nFAAAgHKnXbuqWry4j6pUsd5SbNOmk+rbd6lOnrxgUmRA6SUmJhbZX5oHEh89erTgda1atYocW7t2\n7YLXcXFxpSpQ5Re1JKlp06aaMWOGHnroIaWkpFiNe/7559W2bVvNnj1bjRs3LvH5AQAAyrO2zz6r\na154waZ988sva+Mzz5gQEXD5HJmvXHyu4o7L7z93rvQ3n+Y/e0rKe0bvhx9+KEmqV6+eIiMjdezY\nMR04cEA//vijfvnlF82aNUu33nprqecpjiEFqlWrVhX7LKn09HTt379fs2bN0ieffKLQ0FD16NHD\niPAAAAAqlL5962r+/O4KCPC2al+27IiGDVuh8+e5ExGll23wthnZF71u0KBBkWNzc3NLfN7k5OSC\n1/a29yus/9LCUnESEhIKXq9bt06ffvqpfH19dffdd6tt27ZKT0/XihUrtHjxYm3atEmdOnXS5s2b\nVadOnVLNAwAAUJ54WCy6/oMP1Pyuu6zac7Kztfb++7Vz+nSTIkN5ZnSukjfn/3NkvnL+om0t/fz8\nihyb/4zb0uYqknVR68MPP1R4eLhmz56tnj17FrT/9NNPuvnmm3Xq1CmNHTtWbdq0cfjjmAwpUAUF\nBZVoXLVq1dSpUyfVqVNHzzzzjB566CF9/vnnTo4OAACg4hg3LkrTp18vi8XTqn3mzN26++7Vyspy\n5pN9ANeXnp5e8Nrb27uIkXlbjhd2XElcuPD/qxT37NmjSpUq6ZdfflHr1q0L2u+//37NmDFD48aN\n0+nTp/XAAw9o4cKFpZoHAACgvPAKCFD3uXNVr39/q/asCxe08pZbdIjPQYBVjlLcop/MzExJ1nlL\nSV36fN3PPvvMqjglSTfccIO++uor9ejRQ2lpafrPf/6jGTNmlHquIuNw6NkcZNKkSXr55Zc1b948\nvf/++yUucAGAS3PJd1wX4Gt2AC7Muc+hLLe2xrc2OwSX9dxzlfX887Z3M7300t969tm9kmoaH5QL\nOPx36Z4b5BbOJRQ/xoUcOHBAoaGhDjnXxauiiis6paWlFXpcSQQEBFj99zPPPGNVnMo3duxYxcTE\naNmyZfruu+907NixYrceBACHK7pe797IVwoXanYArqt1+lazQ3BJKZUrq93ixQq75Dk2GWfOaGP/\n/spav1617Rxb0TU7ddjsEFxOQqLZEZSOI/OVi2shqampRY7N77+c+snFxzRo0ED9+vUrdFz37t0V\nFRWlXbt2admyZaWepzgu+efSgIAANWzYUH/99ZeOHDlS7MOLAQAA3JlF0gfBPhp7SXEqOztX9933\npz78kIQHZZcli7IM3Dbj4nsFQ0NDS7Vve1GCg4MLXhe3V/vF/RcfV9p5JGno0KF2xw4dOrQg2Vu/\nfn2RYwEAAMobD9VX53VLFdSkiVV76qFD2tCrl1L27DEpMlQURucqeXP+P0fmKxffrHbs2DHVq1fP\n7thjx45JkmrWLP3NqBc/b/eqq64qcmzr1q21a9cuHT9+XBcuXCjYWtARPIsfYo786p8jv1kAAICK\nJkDSglBfjfW3vvX5woVsRUf/TnEKuETdunULtrM4cuRIkWMv7m/YsGGp5vHz87NK+urWrWt37MVJ\n54kTJ0o1DwAAgCvz9Lha/j7rbIpT57Zu1dqOHSlOAZeIiooqeL2niH8fGRkZOnTokCRd1gKfFi1a\nFLwu6bOuJOtdJhzBJQtUZ8+e1ZEjR1S9enUeEgwAAGBHZQ/px3A/9fW1XhR/5kyGbrhhvb77Lt6k\nyFAR5T142MvAL+fcAent7V3wYN8tW7YUOTa/PzQ01KrYVFJXXnllweui9o/P3zteurz94wEAAFyR\nxaOn/Lx/lodHNav2UytX6tcuXZTOjTlwEONzFeflK+3atZOvb97+sr/88ovdcRs2bCjYsvy6664r\n9TwNGzYsWK0VFxdX5Nj8fl9fX4dtZZjP5QpU2dnZmjRpknJycvTcc8/JYjF2aR4AAEB50MDiobXh\n/mrnbf1Z6eDBVHXqtFa//Va+njEEGKl3796SpL/++stuMpacnKx169ZZjS+tPn36FLz+888/7Y67\nuK9BgwaXNRcAAIAr8fK8Xb7eC+XhYf1snKNffqkNffsqKznZpMgA11apUiX16NFDkhQbG6ukpKRC\nx82cOVOSZLFYNGjQoFLP4+HhoSFDhkjKK3adOnWq0HEJCQkFeVGnTp3k4eFR6rmKYsgzqObPn6+T\nJ08WOSY3N1dHjhzRihUr9Oeff6pdu3bKysrStGnTCh0/YsQIValSxRnhAgAAuLQ2Xp5aHOanqp7W\nHwy3bj2nPn026MSJdJMiA8qHUaNG6Y033lBubq7efPNNvfXWWzZjPvzwQ50/f75g/OUYNmyYHnvs\nMaWmpmr69On6+OOPbcZkZmbq008/lSSFhISoc+fOlzUXAACAq/C2PCkfr5dt2ve+/rp2/+tfUm6u\nCVEB5ceTTz6pRYsWKTk5WRMnTtTMmTOtCkM//fSTPvvsM0nSmDFjVKNGDZtzHDhwQAcOHJCU94yp\niIgImzGPPvqoPv74Y124cEHjx4/X/PnzrRYM5eTk6L777it4HNO9997r0O9TMqhA9Z///EebN28u\n1TEbN27Uxo0b7fZ36NCBAhUAAHA7vXwsmhfiq6BLilM/pGcrusuvSk62v40YUBZ522YYt7tBtpz3\nh4tWrVppxIgRmjt3rt555x1dddVVGj16dEH/0qVL9eyzz0rK2y7j4pVQ+eLj4zV58mRJUufOnTVh\nwgSbMVWrVtWjjz6qF154QTNmzFCLFi304IMPFiSXKSkpGj9+vHbt2iVJeuyxx3gGLwAAKMc85eP1\njrwt1p+LcnNztGPigzpkZyECUFZG5yp5czovX+nYsaMeeOABvfvuu/rss8909OhRjRkzRpUqVdLa\ntWs1bdo05eTkqF69enr11VcLPcfnn3+uF154QZK0bNky9erVy2ZM7dq19eabb2rChAmKjY1Vx44d\nNW7cOEVGRurYsWOaMWOGfvvtN0nS8OHDNXToUId/r4YUqAAAAFB2t/t56aNgH3ldsqT+ywtZGpeU\nrnSKU0CJTZ8+XTt27NCOHTt0++236z//+Y+aNWumQ4cOFdxcV7t2bc2ZM6fQ45OTk/XVV18V/Hdh\nBSpJeuaZZ7RlyxYtWrRIDz/8sKZOnao2bdooPT1d69atU0JC3nacQ4YM0RNPPOHg7xIAAMAofvL1\n+kpelkFWrbm5aUrPuk2Hpn1jTlhAOfXWW2/Jy8tLb7/9tn788Uf9+OOPVv1XXXWV5s2bp6pVq5Zp\nnvHjx0uSHnnkEf3+++/6/fffrfo9PT01YcKEQnedcARDClSbNm0yYho4iZckb7ODcDHelHbt49rY\nxyP1CudndgAuzHb1NSTlrAo0OwRTPNVcermlbftrf0n/+tNLeW/AZftgWmEtNzsAF5RqdgDmCwkJ\n0bp16/TII4/oiy++0M6dO7Vz505JkpeXl6Kjo/XOO++oWrVqxZypaBaLRbGxsZoyZYreeOMNHTp0\nSIcOHSror1Gjhh5//HFNnDjR4fu5uw+L+BB6KTI4u7g0heOfkH0sbC1cZbMDcF2B63LMDsF4XuFS\nq0VSSCfr9swEefwxQH7n1pKpFOXH4oe4nRSzAzCfxWLRm2++qXHjxmnu3LnavXu3Lly4oNq1a6tn\nz57q16+fvLzs/wJv0KCBbrzxRklS5cpFv2mPHz9egwcP1rx587Rp0yadPn1awcHBatmypYYNG6Ym\nTZo49Hu7GB9BAAAAXJjFQ5p2tTS+kXV7Tq40cYv03j5z4oL7yTF424wcJ26ZkS84OFgfffSRpkyZ\noi1btujUqVMKDw9Xq1atik3iqlevri+++EJSXvJXFC8vLz3xxBN65JFHtGXLFh09elSenp6qV6+e\nrrzySnl6ejrsewIAADCUX12p1XIpsJl1e9oRaXtv6fxf5sQFt2J0rpI3pzHPUmvevLlefPHFUh83\nevRoq23Mi1OtWjVNnDix1POUFQUqAAAAF+VvkeZ0lAbWsm5Py5ZuXS8tOGZOXEBFExwcrK5du5bq\nmKCgII0aNapUx3h5ealdu3Zq165dqY4DAABwSUGtpVZLJd8a1u0pf0jbeksZ/5gSFoDygwIVAACA\nC4rwkRZdJ3W8ZBFHQobUf4207rQ5cQEAAACAwm6SWsZIXsHW7Qk/S38MkrKTTAkLQPni0AJVbm6u\nU/dNd/b5AQAAXEG9QGn59VLTS3K9I+elXr9Iu8j1YIJsg7fNyDZoywwAAACUUrWRUtRMyfOSB/vF\nz5X+ul3KzTAnLrgto3OVvDnJVxzBoZudDx48WB988IEyMhz7JrRx40b16tVLW7Zsceh5AQAAXE3r\nUOnXG22LU9sTpY4/UpwCAAAAYKI6j0otvrQtTh15Q9p5K8UpAKXi0ALV0aNHde+996px48aaOnWq\nTp48ednnysrK0tKlS9W/f3+1b99eK1asUG4uVUkAAFBx3VRN+uUGqYa/dftP8dL1P0n/XDAnLgAA\nAADuzlNqPFVq9B/brr0PS/sekVhRAqCUHLrF3/Tp0/Xwww9r7dq1euihh/TII4+oZ8+eGjx4sDp0\n6KCoqCh5etqviZ08eVIbN27UDz/8oDlz5hQUuCIiIvTss8+qdevWjgwXAADAZYyqK33aTvK+5KPS\n7MPSHRuljBxz4gLyZcmiLAO3zcjiDxwAAACuwdNXav6lVHWodXtOuvTXaOnkfHPiAv7H6Fwlb07y\nFUdwaIGqbdu2WrNmjb755hs9/fTT2rNnj5YsWaIlS5ZIkoKDg9WsWTNFREQoPDxcvr6+Onv2rM6e\nPasjR47o0KFDVucLDAzU+PHj9fTTTys0NNSRoQJwFoe+q1QwXJvC+ZodgOtKqupjdgiuabXZATje\n4z2k1zvYtk/5QXpsgVTyReTBxQ9xR3PNDsAFZZkdAACYhM/kheO62Ee+Uiif6uw7bVeM2QE4mF+Y\nNPxbqer11u1p56T5g6TDq0p0GjKVIsSaHYALYqdIt+GUjyBDhw7VkCFD9MMPP+iDDz7QokWLlJWV\npaSkJG3cuLHY41u0aKHx48dr9OjRCg7m7QsAAFRMnh7S1GHS/V2t23NypEkx0tSfTQkLAAAAAKTg\nSOnW5VKV5tbtSUelOb2lkzvMiQtAheG0e2Q8PDzUvXt3de/eXYmJiVqzZo1++eUXbd68WfHx8Tp1\n6pQyMjJUuXJlValSRY0bN1aXLl3UpUsXNWrUyFlhAQAAuAQ/b+nLMdKQq6zb0zOl2z6Tvt5iSliA\nXdmyKNvAW+yz2TIDAADAPFVbSrcsk4JrWbef2inN7pVXpAJchNG5St6c5CuOYMj/tdDQUPXv31/9\n+/c3YjoAAACXFhYgLRwvXXfJPTmJqdKgD6XVe82JCwAAAABUt2vetn5+Idbth3+R5g+U0hJNCApA\nRcQuwwAAAAaKDJOW3y81r2HdfjRB6jVN2nncnLgAAAAAQC1GSAM/lyyXPBP5r6+lb2+TstPNiQtA\nhUSBCgAAwCBX1pKW3ifVCrVu3/GP1Pu9vCIV4KpyZFG2LAbOl2PYXAAAAJDU/mGpx5u27Rvfkb5/\nWMrl8xlck9G5St6c/HtwBApUAAAABujWRPr2HinY37p99V5p0HQp8YI5cQEAAABwdx5S9ylSh0m2\nXT88Kq2fYnxIANwCBSoAAAAnu7mt9NloyeeST17zN0ujP5PSs8yJCwAAAICbs/jkbenXYoR1e3aG\n9N0d0o7Z5sQFwC1QoAIuh7ErRssXro19vOMWjuti1xlLhNkhuKbfzA6gdCbfJk2507b97dnSpDek\n3FxHzpbpyJNVHOvjzY7ABSWW+ohsg7fNyGbLDADOwGfPwpHH2edtdgCuKSL8jNkhuK5NZgdQCoEh\n0pPfSi26WrenJkmvR0vbf3TYVGQq9sV/bXYEriexlOONzlXy5iRfcQQ+mgEAADiBh4f0xiTp4ZG2\nfY+8Jb3xhfExAQAAAIAkKaKW9OwyqV5L6/azx6UXe0sHt5sTFwC3QoEKAADAwXx9pM9elEb0sG7P\nyJTGPCfNWW5OXAAAAACgyObS88ulypHW7Ud3Sy/0kk4eNicuAG6HAhUAAIADhQRJ374pdW1r3Z6U\nIg1+RPppozlxAWWVJYuyDNw2I4stMwAAAByv+XXSUwuloDDr9l3rpFcGSMlnzYkLKAOjc5W8OclX\nHIECFQAAgIPUriYte1e6opF1+z+npN73S3/sNScuAAAAAFCnodKkLyVvX+v23xZIb9wqZaSZExcA\nt0WBCgAAwAFaNJSWT8srUl1s10Gp1/3SkePmxAU4St6Dh41LH3joMAAAgAP1e0Aa+7bk6WndvvQ9\n6eOJUg6fvVB+GZ2r5M3JvxlHoEAFAABQRtdfLS18SwqtZN2+dqs04GEpIcmcuAAAAAC4OQ8PafTr\nUvRjtn2fPynFvG58TADwPxSoUCwv8YMCOISxW+GWH4FmB+C64lWt+EHuaJvZAVgbNkz64j3J95Jd\nMmJjpZGjpDRDd8k4aeRk5QgP/rJ13uwAAAfy/t8XgMvGPyH7yFcKVU3xZofgunaYHcBFvL2lf8+U\nBo60bs/MlP41Tor93LBQyFTsI1uxRbbiPgyvO5w+fVrr1q3Tvn37lJSUpOzs7CLH33vvvapZs6ZB\n0QEAAJTcxInSW2/a7pLx3vt5feySgYokb9sM4+62MHIuAACACieokvR+rNT5Juv28ynSfUOkNd+b\nExfgBEbnKvlzouwMK1AlJiZq8uTJ+uKLL5SZmVni4wYNGkSBCgAAuBSLRfr3v6XJk2z7nngyrw8A\nAAAATFGjtvTRIql5a+v20/HS2D7Sji2mhAUAlzKkQJWZmakePXro999/L2jz9vZWSEiILJaiK43e\n3qwzBwAArqNmTWn2V1KXLtbtmZnS2HHSF1+YExcAAAAAqFtf6b+fSWER1u0H/5bu6CXFHTQnLgAo\nhCEFqlmzZhUUp26++WY98sgjat26dbHFKQAAAFfSs6f0xedSlSrW7cnJ0pCh0sqV5sQFGCHH4G0z\nctgyAwAAoOS8vKRHX5PGPWLbt/U36a5+UsIZ4+MCDGB0rpI/J8rOkALVsmXLJEkjRozQnDlzjJgS\nAADAYSwW6aWXpCefsO375x+pX39p61bj4wIAAAAA1awjvTNPuqqDbd/KhdJDt0hpF4yPCwCKYUiB\nKj4+XpI0ZswYI6YDAABwmNq1pTmzpWuvte37/nvpttHSyZPGxwUAAAAAummA9J9ZUkiYdXtWlvTG\nv6SPp0i5uaaEBgDFMaRAVeV/++BUqlTJiOkAAAAcok8f6fPPpIhLtm/PzpaefU567TVyPbiPLFmU\nZeA2FkbOBQAAUO54e0uP/0e64yHbvuNx0oM3S5t/NTwswAxG5yr5c6LsDClQ3XjjjVq4cKF27Nih\nzp07GzEl4FyG/Mspp7g29vmZHYCL8jU7ANf1j2qYHYJryj3q9Cm8vKRXXgnRY4/Z3lxz7Fi2brnl\njNasyXB6HKV3yOwAXNQxswNwQWzxAsBN8bekwpGr2OdvdgCuqYb+MTsEl3XsgPPnsNSrp/DP58mn\nXTubvguLFilxzBjlnD3r/EBK4ZDZAbgwshVbZCvuw9OISe644w41btxYb7/9ti5c4McLAAC4rshI\ni1avrlJocWrZsjS1bh3vosUplAdXXVVdL73UTUFBPmaHAgAAgHLIb/BgVd261aY4lZuZqXOTJ+vs\ngAEuV5xC+eDl76+mAweq7YQJZocCN2LIWoegoCAtXbpUffr0Ua9evTRjxgw1atTIiKkBAABKrH9/\nP82aFa7wcOt7eLKycvXUU0n673+T2dIPpeLhIXXsGKno6ChFRzdT/fp5zwb488+Tmj9/p8nRlU62\nvJRt4FLpbOUYNhcAAIDL8/FRyJQpCnrgAZuurEOHdPbmm5W5YYMJgaE886lUSU369lWz6Gg17tNH\nPoGBSk9O1tZPP1V2errZ4ZWY0blK3pzkK47g0P9rr732mg4fPmy3v127dpo7d66aNm2qNm3aqFmz\nZgoICCjynP/6179Up04dR4YJAABgxdtbev31EE2aZLtqKi4uSzfffFa//sqqKZSMl5enunSpq+jo\nKA0e3Ew1atj+XEVHNyt3BSoAAACYw9KggcLnzZNP27Y2fRe+/VYJd9yh3MRE4wNDueQfEaGmAwYo\nKjpaDbp3l5ev9bMXfCtVUoObbtLeJUtMihDuxKEFqpiYGG3evLlEY3///Xf9/vvvxY4bN24cBSoA\nAOA09epZNHduhNq3t91ybfHiC7r99gSdPcudUSiar69F3bs3VHR0Mw0Y0FQREUXfhNW3bxP5+Xkp\nLS3LoAgBAABQHvkNHaqwTz6RZ0iIVXtuRobOPfqozr/zjkmRoTwJqlFDUYMHq1l0tOp16SJPr6LL\nAlFDhlCggiEcWqCqV6+eUlJSHHlK+fvzNEoAAOAcgwb5aebMcIWGWm/pl5mZqyefPKc330xhSz/Y\nFRjorT59Gis6Okp9+zZWpUq+xR5z4UKmli/fp9jY3cotZz9c2fJUtiyGzgcAAOC2fH0V8uabCrr3\nXpuurIMHdXb4cGVu2mRCYCgvQuvXV9TgwYoaMkSRnTqV6JhzcXHaHRurHfPmOTk6xzI6V8mfE2Xn\n0ALVN99848jTAQAAOIWPj/Sf/4TowQdtt147fDhvS7/ffmNLP9gKDfVT//5NNGRIlHr2bCQ/v+I/\nTiclpWvx4r8VG7tLy5btU2pqpgGRAgAAoLyyNGqk8Pnz5XPVVTZ9F2JilDB2rHLPnTMhMri6ylFR\nioqOVtSQIapRyM9PYc7s3atdMTHaFRurf0qw4xngSMY+OQwAAMBk9etbNH9+hNq2td3Sb+HCC7rj\njrNKSChfK1vgXNWqBWrgwGYaMiRK3brVk7d38XfmnT6dqoUL9yg2dpd++OGAMjKyDYgUAAAA5Z3/\niBEK/fhjeVayvpkuNz1d5yZP1vn33jMpMriqGldfrajoaDWLjlaVqKgSHRP/xx8FRamTO3Y4OULA\nPgpUKJa3RfL2MDsKF8O/HPu4NvYZu9K4/PAzOwDXdVw1zQ7BRZ287COHDAnVjBl1FRJi/Q8yIyNH\njz/+j95++/LP7RqOmR2Ai2pU6iPq1AnU4MG1NWRIHXXuXEWensV/GPrnn1TFxsYpNjZOv/xyUtnZ\n+YXO+qWe3/nOl/qIbFkM3uKPX5woKe//fQElwI9K4XjLtY98pVA1ddzsEFxW/GUe5+Hnp9pvv63w\ne+6x6Uvbt08HR4zQhS1byhacichU7CtttuLh6anKHTsqMjpakdHRCqpXr0THnd6wQXExMYpbsEDJ\n+/ZJkoL/9+VqSputGJ2r5M+JsjPkT8nPPvusDhw4oJdeekn165csQb+cYwAAAArj6+uhN96orfvu\nq2LTd/BgukaMOKjff081ITK4kiZNKik6uo6GDIlU27YRJTrmwIFkxcTkFaU2bDjNM8sAAABQar5N\nmqj+/PkKaNXKpi9h3jwdvvtu5SQlmRAZXIWHl5eqde2aV5QaNEj+NWoUe0xOdrZOrVmjuNhYxS1Y\noNSjRw2IFCgdQwpUS5cu1ebNm/XQQw+VuNh0OccAAABcqlEjX82bV19XXx1g0xcbm6g77zysc+fY\nfs1dtWoVpujoSEVHR+qKK0JLdMzOnYmKjY1TTEyctm9PcG6AAAAAqNDCR45U5PTpsgQFWbXnpKXp\n6EMP6fSHH5oUGczm6eurGj16KDI6WrUHDJBveHixx2RnZCj+xx91JDZWRxcuVPqpUwZEClw+l9+M\ny8ODveUAAMDlGTEiTB99VEfBwbZb+j3yyDG9+y4f1t2Nh4fUrl2Ehgypo+joSDVsWKn4gyRt2nSm\nYPu+PXvc8+7VHIO3zchhywwAAFCBefj7K/Ldd1V57FibvrS//9bB4cN1Yft2EyKDmbyCglSzd2/V\nGTJENfv0kXel4vOVrNRU/bN8ueJiY3Vs8WJlnjtnQKSuxehcJX9OlJ3LFqhSUlIkSf7+/iZHAgAA\nyhs/Pw+99VZtjR9vu6Xf/v15W/pt3syWfu7CYvHQdddV1ZAhkRo8OFK1atmuprtUTk6u1q07pdjY\nOC1YEKfDh0v/zCYAAACgMH5RUao/f778r7jCpu/s7Nk6cs89yvnf30ZR8fmEhalW//6qM2SIavTo\nIYtf8Q+/y0xK0tFFixQXG6t/li9Xdir5LconlyxQ7d+/X/v+96C26tWrmxwNAAAoT5o08dX8+fXV\nqpVtEeLrrxM0btxhJSXlmBAZjOTjY9GNN9bXkCHNNXBglCpXLj7Jy8rK0U8/xSs2Nk4LF8bpxIk0\nAyIFAACAOwkfPVqR778vS2CgVXvOhQuKmzhRZz75xKTIYKTAatXUbNAgRQ0ZovrdusnTq/g/06ed\nPq2jCxcqLiZGJ378UTkZGQZECjiXUwpUzz77rA4cOFDw3wcPHixoDy9ir8ycnBydOXNGv/76q7Kz\ns9WiRYsixwMAAFzs1lvD9OGHdRQUZL3UPj09Rw8/fFQffHDapMhghIAAb/Xq1UjR0c3Ur18ThYQU\nX5RKS8vW998fV0zMES1adEwJCSR59mTJoiwDt7Ewci4AAABn8wwIUOR77ylizBibvrTdu/O29Pvz\nT+MDg2FC6tZV1ODBahYdrTqdO8vD07PYY1KPHVPcggWKi4nRyTVrlJvN85MLY3Sukj8nys4pBaql\nS5dq8+bNNu3Lli0r8TksFotefvllR4YFwAi8N9vnkmtWXYCv2QG4rn9U0+wQXNQFmxZ/f09NnVpf\nd91lu/J6794LGj58j7Ztc4ct2tzvjSYkxFf9+jVWdHRT9erVUAEB3sUek5IiLVkixcRIy5ZZlJJS\nW1Jt5wfrUhIk3WN2EABgPPf7VVkyxf/6dF/F3+/ilmrqH7NDcFm22YoU0KKFmsyfr4DmzW36Tn7x\nhfZPmKCc8xU7X3HXt9/wJk3UbMgQNYuOVo22bUt20OED0uIYaUmMArZsVNPcXDWVpAhnRupaEnKk\ne7i/1C045b2h+SVvtrt27VJqaqqioqIUEGB/z3+LxaKQkBC1bNlSo0ePVqtWrZwRHgAAqECaNfPX\n/PlN1bJloE3f3LmndPfd+5WczF1mFUmVKgEaOLCJoqOb6cYb68nHp/i7I86elb77ToqNlVaulNLY\nva/UsmVRtoF/WshWlmFzAQAAOEvVO+9Ug3ffleWSv4lmp6bqwP336+TMmSZFBmep1qqVmg0ZoqbR\n0arSokXJDtq9U1oSIy2JlXZud26AFZDRuUrenOQrjuCU/2uff/651X+3bdtWmzdv1ueff662Ja0U\nAwAAFOO226rogw8aKjDQukCRlpajBx88oI8+ijcpMjharVqVFB3dVNHRzXTddZGyWIrfDuPEiRQt\nWLBHsbFttGqVlEX+AAAAAIN4Bgaq4fTpqjpqlE1f6l9/ac/w4UrdudOEyOBwHh6q1b59QVEqrEGD\nEh12fNMm7Y6JUbdfFkj79jg5SMA1GVJWHDFihDp06KCqVasaMR0AAKjgAgI8NW1aA91xRzWbvj17\nLmj48N36449UEyKDIzVsGKbo6KYaMqSZ2revVaJjDh8+p5iY3YqN3a31648pJydXUhvnBgoAAABc\nJKBlSzX7+mv5N21q0xc/a5YO3HefclLJV8ozD4tFda6/Xs2io9V08GBVqlV8vpKbk6O4deu0OyZG\nexYsUNKRI5KkbvzJHG7MkALVo48+asQ0AADADTRv7q/585upRQvbbYO//PKkJkw4oJQUtvQrr664\nooqio5tpyJCmuvJK2wJkYXbvPq2YmD2Kjd2tLVtOODlC95W3bYZxD5s0ci4AAABHqXbXXao/daos\n/v5W7dnnz2v/vffq1CU7T6H8sPj4qP5NN6lpdLSaDByogMqViz0mOzNTh3/+WXtiY7Xn2291Pp5d\nPpzB6Fwlf06Unbs+nw4AAJRDV+sqPfV7KwUEWH8QvHAhW/fff0CffnrSpMhQFtdcU/N/2/c1VZMm\nJXvy79atJxQbu0cxMbu1axdPzwUAAIC5fOWr/hqoRh+1suk7v2OH9gwfrgu7dpkQGcrCOyBADXv3\nVtPoaDXu10++wcHFHpOVlqYDK1Zod2ys9i5apLSEBAMiBconQwpUCxcu1KlTp0p1jMViUXBwsGrU\nqKFWrVopMND2wecAAMA9+MhHA9RPV6m1dMnCqV27UjVs2B7t3MkWGeWFp6eHrr02suCZUpGRxSd5\nkvTrr0cVG7tbsbF7dPBgonODBAAAAEqoumpohG5WhGxX1MR/8okOTJyonAsXTIgMl8M3JOT/2Lvv\n+Kaq/4/jryTdAyhllL03CAgyFBAQAVEUXHxFQUCW4yfgYLhQEQQBQRFlKcutCKICylaWDNl7g+wC\nLXQ3SX9/VNCSpIM2N2n7fj4efdjmnHvPh2tHzv3c8zlUue8+qj/0EBXbt8f3htVwziRevcqhX35h\n/w8/cHjxYpJiYgyIVCT3MyRBNWLECLZs2XLTx1ssFtq2bcsrr7xCs2bNcjAyERER8XbFKcb/6EIx\nijq0zZ59nmefPUxsrN0DkUlW+PqaadWqPA89VJ1OnapSrFjGDx/ZbHZWrTrODz/sZ8GCA5w+fdWA\nSMUVu8FlM+wqmSEiIiK5wG00oj0d8MU3zeu2mBgO9+/PhS++8FBkkhVBRYtS9YEHqP7QQ5S/6y4s\nvr4ZHhN/6RIHfvyR/T/8wJGlS7ElJhoQqThj9Fzl2piSfbmixJ/NZmPx4sX8+uuvjBs3jkGDBnk6\npHzF4gM+Jk9H4WVyxU+Oh+jauBbg6QC8lK6LS+fI3P47eVlzavIYzfG74ZdLXJydZ545wezZlwB/\nzwTnlW71dABpBAaaadu2CA89FEHHjsUoVCjjSV5iop2lSyP54YdzLFx4josXk/9pqXLzgfS8+UPz\nrETgS08HIZJTLOhN6A00f3NN3yrO6T25a7o2ThVH++j4EUgrulOZ2xza4nfs4Mijj5K4fz8Zr73J\nH7xrppIqoHRpinfuTMRDDxHWrBkmS8YJh8SzZzk3fz5n583j0urVpFithAKOhR2zoHd2Ds6j4oEJ\nng5CjGDIW7N169aRmJjIiBEjmDhxIg8//DCPP/44t9xyC2FhYSQkJHDkyBGWLl3Khx9+SNmyZfn0\n008pXbo0J06cYOnSpUyYMIEzZ87w4osv0qBBA1q0aJEjsdlsNq5eTX0a19/fn8BMLNnMDqvVSsx/\nlngWKFAAs9nstO/Vq1ex2TLe5N3Pz4+gIMeN4kVERHIrf3zpTksaU9WhbffueB555Ch79yZ4IDLJ\nSIECPnToUJSHHorgnnuKEByc8dvN2FgrixdHMm/eWRYtusCVK1YDIhURERERuTlFKUc7+lGQYg5t\nF6ZO5e+BA0lJ0HzFGwVVqUJE584Uf+ghCjVqlKlj4o4d49wPP3B23jyiNmwAuyp4iOQUQxJUvr6+\n9OrVi2+//ZYff/yRe+65J017SEgIRYoUoVGjRvTv35927drRpk0b1q9fT7169ahXrx69evWiRYsW\n7Nmzh1GjRt10gurUqVOsX7+eP//8k40bN7JlyxZiY2MBeOqpp5gxY0a2/73pefrpp9OMcfToUcqX\nL++0b9OmTdm9e3eG53z88cf5/PPPcypEERERjypNOP1pTwSFHNr+YA/tbkskPj7F+MDEpeLF/Xjg\ngeJ07lyc1q3D8fNz/vDNf0VFJfPTT+eZN+8sv/0WSXy8JnnezooFq4FlLIwcS0RERCSz6tCKO3gU\nyw0l/ZJIYCWzKdq/v4ciE1cK3HorxTt3pnjnzoTWqpWpY2L27ePsvHmcmzePK1u3ujlCyS6j5yrX\nxpTsMyRBtWTJEr744gsGDBjgkJy6UXh4OHPmzKFWrVq8+OKLLFiw4PrrkydPplWrVqxevZrExET8\n/bNe0qdLly6sXbv2+teuVi+5w7Jly24qAebr65vuCqng4Iz3cBAREckN7qQW/6MZvje8RUkgmc9Z\nxQYOEB/f1EPRyX9VrBhE586pSammTQthNmdcT+r8+UQWLDjHDz+cY8WKiyQnK9EoIiIiIrmDH4G0\npgeVaODQdoET/MoUojnvZOdcMZrJYiGsWbPUpFSnTgSWK5ep46L/+otzP/zAuR9+IGbvXjdHKSJg\nUILq66+/BqBDhw6Z6l+zZk3Kly/PTz/9RGxs7PUEzJ133klYWBiXL1/m9OnTVKhQIcuxlC5dmoce\neohGjRrRqFEjateuTdGi7v/TERMTQ58+fYDUf9+ePXsyfWzXrl2ZNWuWmyITERHxvAB8eZJW3OZk\nn6G/iWQKv3KWKOMDkzTq1g29npS65ZYCmTrm5Ml4fvghNSm1Zs0lVcMQERERkVynGOVpSz8KOkk/\n7WQFa/kWGypT7Ulmf3+K3H03xTt3ptj99+NXpEimjru8di1nf/iBc/PnE3/0qJujFJEbGZKgOvrP\nD3dW9kkKCgrCbrdz4sQJatSoAYDJZKJUqVJcvnw5zT5OWXEtWXZNgkH1YIcMGcKxY8e47777qFWr\nVpYSVCIiInlZWYrQn/YUo6BD22p28zV/kEzGezJKzjOboWnTsOtJqYoVM/debv/+GObPT01KbdoU\n7eYoxSg2fLAZM324Pp6IiIiIp91CG27nYSw3vDdJIp4VzOIwWzwUmfgUKEDRe++leOfOFL3nHnxC\nQjI8xm61cmnVKs7Nn8+5+fNJPHPGgEjF3Yyeq1wbU7LPkKsYEBAAwNatW2nWrFmG/a9cucLhw4fT\nHHtNdHTqTY5ixRw3IfRWv//+O5988gkhISFMnjyZjz/+2NMhiYiIeIVW1OFR7sD3htrNCSQxh1Vs\n5KCHIsu//PzMtG5dmM6di/PAA8UpXjxzJZU3b45m/vxzzJ9/jr17b+5BIhERERERb+FPMK3pQUXq\nO7Sd5zi/MoUrXPBAZPmbX/HiFH/gAYp37kx469aY/fwyPMYWF8eFX3/l3Pz5XPj5Z5IvXzYgUhHJ\nDEMSVA0aNGDp0qWMHTuWxx57jCIZLLF8/fXXSUxMpFixYpQtW/b66zabjdOnT+Pr65trElTx8fE8\n9dRTpKSkMGLEiDT/ntzC1wK+GW8rkb9oDzzX9PCAa/q+cU7b6Ll0ntzxt+5mBOPLczTkDko7tB0h\nivdYz2liwOk1KO/u8HKvwFtv6rCQEOjQDjrfn/rfApmo3mezwe9rYP5CWPATnPy7IFAQqAqBNxWG\n+wz0dABe6ArwpaeDEMkpvv98yHV63+maro1zui6uZbwgI18qxnlPh+BWhalGE14m2Ml85CA/sYOZ\nBGAlwMmx5XVfxKlbHau5Z16ZitCmM9zVGeo1TS31kJHoy7DqJ1g2H8vaX4lIiCcCIOKfD2/S39MB\neKEoYIKngxAjGPIrs3fv3kyYMIGTJ0/StGlTxo8fT4cOHfDxSTv8wYMHeeutt/jiiy8A6N+/PxbL\nv++SNm/ejM1mo3HjxphMuSNj8uqrr3Lo0CEaNmzI//3f/2XrXImJiZjNZnx9NfkSEZHcqzJhDKYJ\nEU5m+4s5zAy2kYw2KnK3okXh/ntTk1JtWoF/JhZKJSTAb8tTk1I/LYKLF90fp3gPG2ZsBt7BtJGJ\nGw8iIiIiOawqnahDd8wOJf1i2MwkTrHeQ5HlM9Xr/puUqnZL5o45dwqWL4Dl82HTarBqX7D8wui5\nyrUxJfsMSVBVqlSJDz/8kP79+3Po0CEeeOABQkNDqVmzJmFhYSQkJHD06FGOHz9+/ZgWLVowbNiw\nNOf5/PPPAejatasRYWfb+vXr+eCDD7BYLEybNi1Nsi0rfv31V0qWLMmZf2qihoWF0aRJE7p168aj\njz560+cVERExWkcq04O6+N7wRi6OZCaxmbX87aHI8odyZVMTUp3vhzuaQmbeQkRHw8+LU5NSS5ZC\nbKz74xQRERERMZofodzGAErSyKHtEgdYz1jiOOeByPIJsxnq356akGrTGUpXyNxxR/fDsvmpSamd\nmyAlxb1xikiOMmzRad++fSlWrBjPPvssp0+f5urVq/z5558O/SwWC08//TRjxoxx2H9q/PjxjB07\nFv/MPOLrYYmJifTq1Qu73c5LL71E/fqO9Woz6+zZswAEBgYSHx/P5cuXWbx4MYsXL2bSpEnMmzeP\nEiVKZPm8l9OptxoVFXWz4YqIiDgIxpfnuY2mlHJoO8RlxrKeMyjz4Q61a/2blKpfN3PHnDkLP/6c\nmpRauRqSk90bo4iIiIiIJ4VTnSa8TBBFHdoO8CM7mE0KWo2T43z9oOldqUmpVvdDkeKZO27X5tSE\n1LL5cHive2MUEbcytCpqp06d6NChAwsXLmTFihUcOHCA6Oho/P39KVWqFI0bN+bRRx+ldGnH/SgA\n/DKx6Z23GD58OPv27aN8+fK89dZbN3WO+vXr07dvX1q2bEmFChUIDQ0lISGBNWvWMGrUKFauXMn6\n9eu555572LBhg0NCLyOFCxe+qbhERESyoiqFeZkmFHey4djPHOQzdmBVSb8cYzJBk0b/JqUqV8rc\ncYcOpyak5i+EDRv14KE4smExuMSfqgSIiIiIu5moRmdq0w3zDe89kohhEx9wGscH7CUbgkKgRYfU\nVVItOkBIJjfA3fx7alJq+QI4c9LtYUruYvRc5dqYkn2Gb9vn5+fHww8/zMMPP2z00IbZsmUL48aN\nA+Djjz8mKCjops4zd+5ch9cCAgJo06YNd911F0899RQzZ85k+/btTJ48mRdffDFbcYuIiOS0B6jC\nk9yCzw0l/WJJZhKbWMcpD0WWt/j6QqtWvnR+BB64D0pkctPfrdv/TUrt2u3eGEVEREREvIkfBWjE\nQErQ0KHtIvvZwHvEccEDkeU9pqJF4aH7U5NSTduAXyaqYyUmwNrfUpNSK3+CKG2AK5IXGZ6gyuuS\nk5Pp2bMnNpuN//3vf9xzzz1uGcdkMvHhhx+yYMECLl++zOeff57lBNWlS5dctkVFRVGxYsXshiki\nIvlUCL4MoBGNKenQdpBLvMcGzqmkX7YEB0P79r507uzHvff6UqhQxhu02u2wZl1qQmrBT3DseIaH\niIiIiIjkOUWoSRNeJpBwh7b9zGcnc1XSL5vM5crh26kzfg90xuf2OzK3Ae7VaFj9c2rpvjVLIE5z\nRpG8TgmqHPbFF1+wc+dOQkJCePPNN53u5ZSYmHj98ytXrhAVFYXZbKZAgUwsaf2PkJAQWrZsyfz5\n89mxYwd2ux2zOeObU9eEhYVlaTwREZHMqEZhBtOUojiuIF7IAWaxUyX9blJ4uImOHVOTUnff7Utg\noCnDYxITYdnK1KTUwl/ggh4ClZtkN7hshl0lM0RERCTHmajOw9SmK6Yb3mskcoVNTOQMmz0UW+5n\nqV0b3wc649epMz716mfuoAtnYMWPqUmpjSu1Aa7cFKPnKtfGlOwzPEEVGRnJ2rVrOXToEFeuXMFm\ns6Xb/5lnnqFkScenr71VTEzM9f9Wr149w/5166buVh4eHk5kZGSWxytaNHXzRrvdTkxMTJaTXHKT\nlNp1TdfGNV0b5/T33KU4Jwkeb2YCHqEivajmUNLvKsmMZTvrOAdkbc9EByXy1x6KZUpBp/bQuT00\nbww+mfhdcjURfjkI8/fB4kNwNQmIAJ5yd7TeqeUtSzwdgtdJvnyVtZ4OQkTcx9fTAXgxXRvndF1c\n0zzOqSDiPB1ClvlRiHoMpaiTkn6X2MVWRpLAhWzPwgo7nj7vMpmgdhO4s3PqR+nKmTsu+hAcmw9H\n58O5DUAKNAQn/2vyhSVlWno6BK9zNSQZNGPJFwz7MxsVFcWLL77I3LlzSc5CJrxTp065KkHl7+9P\nwYIF0+2TkJBwfRVVaGgoZrM5w2NcOXUqde8OHx8fQkNDb+ocIiIi2VUAX4ZQj8YUc2jby2XeYSvn\niPdAZLlTjSrQ+Z7Uj4Z1M3fM+Uj48URqUmr5UUhK/xkgEREREZF8ozB1qc8wAiji0HaIrznATFLQ\nG+hM8fGFBq1SE1LNH4AiJTJ3XOTW1ITUsflwaZd7YxSRXMOQBFVycjJt27Zl06ZN11/z9fWlYMGC\nWDKoP+rrm7se4+nTpw99+vRJt8/QoUMZM2YMADt27KB8+fI3NVZkZCSrVq0CoGHDhphMGZf5ERER\nyWm1CeNV6lOUQIe27zjCp+zDSooHIss9TCa4rd6/SalqlTJ33NETMH9x6se6zWDv69YwJZ+zYsFq\n4LJXI8cSERGRvMpMZbpSlW4OJf2SiGYbY7jARg/FlosEBkOT9qlJqdvvhdBCGR9jt8P2NfD7fFi9\nANodc3OQkp8ZPVe5NqZknyEJqlmzZl1PTv3vf//jpZdeol69ehkmp7zV1atXsdlsmEymm175lJGU\nlJR0E06JiYn07NmT2NjUzQJ79erlljhERERcMQH/oxI9qIrlhpJ+V0jiPbazgfOeCS4X8PGBO5um\nJqQeaAulM7lgfMcemL8kNSm1fbd7YxQRERERya38CaMewyjCrQ5tl9j5T0m/rG+3kW8UDIdmHVOT\nUo3agn8mSrUnJcKmZbB6PqxZCJe1Aa6IpM+QBNXixYsB6NKlC1999ZURQ7qUlJREXNy/dXKvldq7\n1hYVFXX9a1cJqNtuu439+/dTsGDBNP1zUq9evTh//jwdO3akRo0aREREEB4ezqVLl1izZg3jx49n\nz549ALRu3VoJKhERMVQh/BhCXW5zUtJvN5cZyV+cJ8EDkXm3wEBod2dqUuq+NlA4LONj7HZYvyU1\nIbVgCRw+5vYwRZyy4YPNwI04jBxLRERE8pZw6lOPYQSQdv/aFOwc5isOMJsU7B6KzosVLwMtOqUm\npeq1gMwsLoi9AusWpSal1i+GuKtuD1PkRkbPVa6NKdlnyFU8d+4cAD169DBiuHTNmTPHZQm+uXPn\nMnfu3OtfBwcHExMTY1RoaSQmJrJo0SIWLVqUbr/HH3+cKVOm5NrVaCIikvvcQmFeoT5FcHyC7msO\nM5P92FTS77qwQqnJqM73QLuWEORYCdFBUhKsWJualPrxVzinBw9FRERERDLBTBWeoApPYLqhykMi\nl9nGaCLZ4qHYvFT5GqkJqTs7Q42GmTvm0nn448fUpNTm5ZCc5N4YRSTPMiRBVbRoUQBCQ0ONGC5d\n/v7+mS7LFxIS4vT1AgUKULBgwZsu7xcYGHj9WLPZ7LTPzJkz6dWrF0uWLGHr1q2cOXOGyMhIAgMD\nKV26NE2aNKFbt27Uq1fvpmIQERHJKjPwGJXpTlUspC1DG00SY9jGRpRJASgZAZ3apyalWjZNLeeX\nkZhYWLwitXzfouUQfcX9cYqIiIiI5BX+FKY+rxBOPYe2i2xjK++SyEXjA/M2JhPUuO3fpFS5apk7\n7vTR1ITU6vmwc11qqQcRkWwyJEF111138eOPP7Jr1y7uuOMOI4Z0qVu3bnTr1i1b59i4MXubJw4f\nPpzhw4en28ff3582bdrQpk2bbI2VE3x8wMf1dlj5k1ZwuqbFfK75ezoAL5WJMtbieYXwYxj1aEBR\nh7ZdXOIdthJpVEm//xkzTFZVLQqd66R+NC6XuWMiY2Dhbpi/E5YdhIRkUn9XdLi5GMJfO3VzB+Zx\n7fjV0yF4nXgSWJvFY2yYsRn4h96G8we5RBz5oDfoN9DlcE3Xxjm9J3dN1yZXKMKt1GMY/qStoZ2C\nnYN8zkE+B6NK+nU2ZpgsMftAmTuhSmeo0glCS2XuuPM74OD81I/z21Nfq/jPRxad6hee9YPyiV9p\n5+kQvE4C8ZCFGYvRc5VrY0r2GfLWrGfPnkyaNImJEyfSvXt3AgMzUdtGREREvEI9whlGPcKdzM6/\n5BCzOIA9n5b0a1D636RUzYjMHXPicmpCav5OWHMUbHrwUERERETkppgwU4UnqcxjDiX9ErjENkZz\nkb88FJ2H+QRChXapSalK90Fg4YyPSbHDqfX/JqWijrg/ThHJ1wxJUIWEhLBo0SI6dOhA+/bt+fTT\nT6lcubIRQ4uIiEg2PEYlelIN8w0l/aJIZDTb2EykhyLznIIB8ORt8PTtUL145o7ZffbfpNRff7s3\nPhERERGR/MCPgtzKG4RT16Etkr/YxrskctkDkXlYkVpQ72mo3R38MrHdii0Jjq9ITUgd+hFiz7k/\nRhGRfxiSoHr33Xc5fvw4jRo14uuvv6ZatWo0aNCA6tWrExQUlO6xr7zyCmXLljUiTBEREfmHP2Ze\noi6tKOnQtp2LjGIrF0n0QGSeU78UPH0HPH4rBPll3H/D8X+TUge1NZfkATYsBpf4U91gERERcS6U\nijTkbYJIW8YgBRsHmMshvsSwkn7ewOwLVR+E+s9AmRYZ90+KgSOLU5NSh3+BJG2AK7mb0XOVa2NK\n9hmSoJo3bx5btmxJ89qmTZvYtGlThsf27t1bCSoREREDFSOAt2hIFQqmed1OCl9yiDkczDcl/fx9\n4NF6qaulmpZPv2+yDVYdSk1I/bgbTkcbEaGIiIiISP4SQXPqMhgf0m4hksBFtjKKS2z3UGQeEFoG\n6vaFun0gOIPyDnGRcGhhalLq+DKwGrSHsIhIOgxJUJUvX56YmJibOlb7VYmIiBinFmG8SQPC8E/z\n+hWSeIet/JVPSvpVKAz9b4dejaBIiOt+yTb4eQ/8sCP1v1HxxsUoIiIiIpK/mKhCN6rS3aHlItv4\ni3dIIsr4sAxngvJtUldLVeoI5nRWccScgX3fpial/l4DKTbjwhQRyQRDElTff/+9EcOIiIhINrSn\nDAOoje8Nmwsf5SpvsIkz5O3si9kE99SAZ+6A9tXAbHbd98RlmLoePv0Tzl01LkYRT7JhwaoSfyIi\nIuIBFgKoxxAiaO7QdowF7OETUsjjyZeAMKjTM3V/qbDK6fc9sRK2fgwHF4Ddakh4Ip5k9Fzl2piS\nfYYkqERERMR7mTHxNDXoTAWHtrWcZTTbiM/Dk72iIakrpfrfDuULp9/3133w8Vr4ZS/Y8lFJexER\nERERTwkkgoa8TQEqpnndjpVdTOIkv3goMoNENExdLVX9f+CbTqWpxGjYNRu2TYGLe42LT0QkG5Sg\nkgz5WsA3nafI8yUlyF3TbxXXdG0kiwoZUJ4iBD9eoBm3UMKh7Xt28g078IcbCv55lnlIbI6cp6mv\nmacDfXk4wIK/yeSy3yV7CrPirUyNT+ZQWArcB9wH3vinsav5S0+H4JUa86enQ/A6V9GTtJKX+P7z\nIdfpfadrujbOaY4rWRRFIUPGKUYtbudFAiiQ5vUEovmdcVxgLxgUS2bFDsiJmUIgPuYu+Fj6YzHf\nlm5Pm307VtsnWPkSasdCbfDG2cqXdPV0CF7rTxp7OgSvY0WlSvILvTUTERHJp0pTkKHcSQShaV5P\nxMpHrGc9JzwUmfsEm+DxAB/6B/pQ1zf9OzGbkm1MibPyTYI1jxc3FMkcGz7YDJw+GDmWiIiIeJ8q\ntKUhvTDf8J7gEkf5nTHE5sH9cU2myvia++Fj6YHJ5Lq8Q0pKIjb79yTbPsGest7ACEW8k9FzlWtj\nSvYZfhUPHTrEzJkzWbduHefOnSMuLo6ffvqJOnXqXO+zZs0aIiMjad68OeHh4UaHKCIikuc1pBQD\nuIPAG544v0As77Gao1z2UGTuUcNion+QL90DfChgdr1aKj4lha8TrEyJs7LZqhp+IiIiIiJGM+ND\nQ3pRhbYObcdZz3o+wkaiByJzFwsW8334WPrjY3b8N/+XPeUYVttUkm0zgQvGhCci4kaGJahSUlIY\nPnw4I0eOxG5Pe8MnMTHtH5XvvvuODz/8kEmTJvHcc88ZFaKIiEi+8CC1+B91MZM2UbOX84zjD6JJ\n8FBkOcsH6ORvoX+QL6380l8tddBqZ0p8MrPjrVxOMSY+ERERERFJy58CNOdFilPLoW07X7OL7z0Q\nlXuYKI6P5Sl8LH0xm8q47JeSYsdm/xWr/RNs9sWAHqQTkbzDsATV66+/zsiRIwEoW7YsrVu3ZvHi\nxZw7d86hb4cOHfjwww/57bfflKASERHJIX5YeJYm3EF5h7ZlHGIGm7DmgclOKbOJ3oE+9An0oYTF\nde11W0oKPyfa+CTeyrIkG8pLiaTPhhmbgZuU2Lxw7wQRERFxn0KU406GEEKxNK8nE886JvE3Gz0U\nWc4ym5rja3kai/lBTCbXeyimpESSbPsMq20aKRw1MEKR3Mfoucq1MSX7DElQHTx4kNGjRwPw2muv\nMXz4cHx8fGjYsKHTBFWLFi0AWL16tRHhiYiI5HnhBDGYFlQibelcG3ZmsoUlHPBQZDnnLj8z/QN9\nud/fgo/JdRm/c7YUZsQnMz3eykm70lIiIiIiIp5Whsbczv/hQ0Ca169yjt8ZQ1Su3x83FB/z4/ha\nnsZsrp1uT5t9PVbbFKz27yBPlTIUEXFkSILq888/x2az0bFjR0aMGHH9dZOLm0eBgYEULFiQ6Oho\nzp49S0REhBFhioiI5EnVKMrLNKcQgWlev0oi4/mDXTg+LJJbFDJB90Af+gf6Us0n/aeXfk+yMSUu\nmR8SbSQbFJ+IiIiIiKTHRB0e4RYedWg5y07W8D6JXPVAXDnDZKqNr6U/PuYnMJlCXfZLSYnDav8S\nq20K9pStBkYoIuJZhiSoNm/eDED37t0zfUxERATR0dFERkYqQSXex7DimLmQro1rujbO5Y3tjtyi\nJnuyfY5bacB9tMbnhm/Ac5zjS+Zi5zI1sz2KsUoWP01pitGc+jSgBn64LouRQBKb2M0atnHGL5Ii\nftDXwFiN1oFFng7BK9Vhp6dD8DpRN1HU0obF4BJ/xpboEMlT9OPjmq6Nc67fTonmK07tyYFZhB9+\nPMyD1HJyrg38yS8sxo7rvZm81Vn/MlSlLvVpTmkqpdv3EufYyhp2mzaSaIkHy+3A7cYEarBFdPB0\nCF5rZ1wdT4fgdVLiorLU3+i5yrUxJfsMuV166dIlAEqXLp3mdVcrqADMZtVwFBERuVlmzLTjHpo6\nmdzsZS/z+JYkkjwQ2c3zwYda1KYBd1Cekun2Pc0F1rCNTewmUeulRERERES8ShiFeIKuRJD2oXQb\nNn7iZzaxxUOR3byCFKQRDbmN+gRTwGU/OzYOspNtrOFEHii1LiKSHYYkqAoUSP2lHBUVlan+NpuN\nkydPAhAeHp5BbxEREfmvQAJ5lP9RicoObatYyUqWk3ITqyc8JYzC3EYjbqUBQQS57GfFxnYO8Adb\nOcIpAyMUEREREZHMqkB5HqMLwQSneT2WWL7ka45x3EORZZ0JE5WoRBNuoxrVMOP6gfsYotnOOnaw\njhiiDYxSRMR7GZKgqlatGr/99hurV6+mffv2GfZftmwZMTExFC9enBIlShgQoYiISN5QlGJ05QnC\nSfuARxJJzGceu9nlociyxoSJqlSjEY2pQtV0+17iCuvYznp2cpVYgyIUyX/sBpfNsKtkhoiISJ7T\niNu4jw5Ybvg7f4YzfM6XROWSxE0ggdxKfRpzm8Pc60bHOcA21nCIHdixGxShSP5i9Fzl2piSfYYk\nqDp27MikSZOYMmUKzzzzDGXKpNaPdVbiLyYmhpdeegmA+++/34jwRERE8oRqVOdhHsUf/zSvRxHF\nl3zOWc54KLLMCyaYW2lAQxoRRli6ffdylDVsYzeHseeiFWEiIiIiIvmNBQv3cg+NaeTQtpNdzGM+\nybmgNHcpStKYRtxCHXzT2bwtkXh2sZHtrOUiZw2MUEQkdzEkQXX33XfTpEkTNmzYwJ133snHH39M\nu3bt0vRJSUlhxYoVDBw4kF27duHv78/LL79sRHgiIiK5XgvupDVtHEpKHOcYX/MlsV6+sqgsZbmN\nxtSiNj7pvD2JI4717GYd27hAlHEBioiIiIjITQkiiK78jwqUd2hbxnJWstr4oLLAF19uoTaNaUQp\nSqXb9wxn2MFq9rKZ5Fy256+IiCcYkqAC+Oqrr2jSpAlHjx7lnnvuITw8nPj4eACeeuopTp8+TWRk\n5PX+kydPpkqVKkaFJyIikiv54ksnHqQOtzi0bWEzP7MQGzYPRJYxP/y4hXo0ohERpF/S92/+ZiMb\n2MVOIgkxKEIR+S8rFswGlrGwqmSGiIhIrhdBcZ6gq0N1hEQS+Z4f2MNeD0WWsXDCacRtNKA+gQS6\n7GfFyk528SebOMlJCulBOhHDGT1XuTamZJ9hCary5cuzadMmunfvzqpVq7h48eL1th07dlz/vEiR\nIkyePJlHH33UqNAkIxZIZ4/H/Mmwn5xcSL+bJau8e2GPR7ViZbrt/hSjDqMJpXqa1+1YOcSHXOF7\nWrgzwJsURAVK0ZkI7sHnho2R/8tGIudZyinmc5W9hAHNgRCuGhZrblMzcY+nQ/BKwSdV6/9Gltyx\nvYOI3CzXVadE10ayKsbTAXinlbTKsE9LitKbGgTecKPgNPEMZidHiAAi3BThzbFg4g7CeZBSNKJw\nun3PEM98TvMzZ4giGagMVOaqHqhzas+5mp4OwWvZj7meF+dbV3SDMb8w9DZ7mTJlWLlyJevXr2fh\nwoVs376dixcv4u/vT5kyZWjVqhVdunQhNDTUyLBERERynQLUpjbv4n/DhrzJRLOb17jMFg9F5pwJ\nH4rQglI8SBi3pts3jpOcZj5n+AWrklEiXsOGDzYDpw9GjiUiIiI5xwT0ojxPUcGhbQuXeY3dRHvZ\nflOF8eN+SvAAJSlOgMt+dlLYwCV+4BQbuIgegxLxDkbPVa6NKdnnkavYtGlTmjZt6omhRUREcr0I\n7qUagzHf8AhwDEfYyRASOOWhyBz5U5SSPEAJ7sefIi77pWAjkjWc4gcusxlIMS5IERERERHJEYFY\neI3qtKKYQ9s8/mYih7B50Xv9ehTiQUrSkqL4pFM+KIokfuYMCzjNaRIMjFBEJG9Tmk9ERCSXMGGh\nEv9HGRzL4F7gD/byFjbiPBDZjUyE0YBSPEg4zTCn83YjkYucYSGn+ZFEzhsYo4iIiIiI5KQIAhhD\nHarcUOLOip3xHOBHzngosrSCsNCeCB6kFBXTKTkOsJNo5nOKFVwgSeulRERynBJUIiIiuYAPodRi\nBIVp5NB2jFkcZTqeXnXkQygRdKAUnQmibLp9o9jKKX7gAqtJwWpQhCKSHTbM2AzcbNKmTVBFRERy\njXoUYiS1CMMvzeuXSeJVdrENz2+AWYlgOlOK9hQnKJ1bovHY+I1zzOcUB7QJmUiuYPRc5dqYkn1K\nUImIiHi5IMpTh/cIonSa120ksI+RnGe5hyJLFUI1SvEgxbkbSzr12q3EcpYlnGY+sRwxMEIRERER\nEXGXzpRkEFUcSuQdJIYh7OSsB0vi+WKiJUXpTCnqUSjdvseJ5QdOsZhzxOghOhERQ+RogmrIkCEc\nPnw4J0/Je++9R8WKFXP0nCIiIrlFOHdQkzfxuaH0RAJn2clQYjjgkbjM+FGMuyjFgxSgVrp9YzjM\nKX7gHL96SQlCERERERHJLh9MDKQKD1LKoW0l5xnBXhI8VBYvAn8eoBT3U8JhVdd/WbHzB5H8wCm2\nEGVcgCIiAuRwgmr58uVs2bIlJ0/J0KFDlaDyNB/QisUbGLtiNHdxvXhCxLmTng7Ae7W6dCeEjQTT\nDb+EE9YQcO4hbrN7YM8mn4oQ2h9Ce4El3HW/lCSI/R6ufExI4lqqAdVyKoYrOXWiPEgVSJzT94yj\nq1k/xIbF4BJ/esMlctNUK8U1zVckq455OgDvdHBHK76rAC1DHdveOA3vnC1GCsUMjckEtC0AzxSB\newuCxeS67+kkmHYRpkeaOZ1cDHIy1qicO1WeormKa1GeDsALxWatu9FzlWtjSvbl6NvWBg0aEBIS\nknHHLAgNdfKXTkREJC/zCYS7PoXCjzm2XZ0Bkc8CSQYGZIbADlDgGQi6J/2u1hNwZQpc/RQ8kUAT\nERERERG3qhMBC6tBef+0r8fYoNsxWGDwdlOFLdAzHPoXgcoZJKGXX4FPIuHHKFTET0TEC+Rogmrq\n1Kk5eToREZH8J6Q03LcAijVI+3qKFS4NgisfGReLJQJCn4LQPuBTLv2+cUvg6scQ9wt4qIyHiIiI\niIi4V+daMOdhCLkhOXU0Ee4/DLsM3G6qaTD0KwJdwiAgnco/0TaYdRGmRMI+z22HJSIiTmjhv4iI\niLeIaAr3/gDBEWlft12C849AwgoDgjBBwF1QoB8EPQAmX9ddbZcg5rPUFVPWnN2DUkS8jw0LVpX4\nExERyZdMJnijNbx5l2Pbqqvw8BG4aHN/HAXM8Hhh6F8UbglMv++2OJh8Ab66DLF6hk4kTzN6rnJt\nTMk+JahERES8Qc2e0OoTsNzwKGLSbjh3P1iPuHd8cxEI7QmhfcG3cvp9E/6Eq59A7DeQokcQRURE\nRETysmA/mP0wPFTbsW3yBRh40v3l8hoEpa6W6hoGwencE060w7eX4eNI2JDFPWxERMR4SlCJiIh4\nkskCzcZB/YGObbEL4cITkHLVfeMHtIDQ/hD8IJj8Xfezx0Psl3DlE0ja4r54RERERETEa5QrBD92\ng7ol0r6enALPnYRpke4bO9gMj4WlJqYaBqff92hiagm/zy5CpDaXEhHJNQxPUB06dIiZM2eybt06\nzp07R1xcHD/99BN16tS53mfNmjVERkbSvHlzwsPDjQ5RRETEGP5hcM83UPZux7aN70DRN4CUnB/X\nHAYh3SG0H/jVSL9v0h64OgVi5oI9KudjEZFcw4YPZgOnDzY9SyciIuJRLSrA912h6A3JoQux8NAp\n+CPGPePWCUxNSnUrDAXSWS1lS4GfomFqJPx2RTvhiuRnRs9Vro0p2WfYVUxJSWH48OGMHDkSuz3t\nn4zExMQ0X3/33Xd8+OGHTJo0ieeee86oEEVERIxTuAbctxAK3VBOLzkOlvWEg99Cxxwe079JalIq\nuAuY0ynYnpIIsd+n7i2VuCaHgxAREREREW/XrxFM6gi+NySItp+BB+bC8TY5O16ACR79Z7XU7SHp\n9/07CaZHwqcX4VRyzsYhIiLGMixB9frrrzNy5EgAypYtS+vWrVm8eDHnzp1z6NuhQwc+/PBDfvvt\nNyWovIEPYPZ0EF5GCXLXtD+gayoz4NxeTwfgAbXvhYe+hMACaV+/dBKmPAAnt6Z+nRM10/1CofYT\nUK8fFK+bft9LB2HrVNg5C+Iv5sDgbpJOJcJ8r6CnA5BcQ3syiORtmq+4pvmKc7rJ79ouTwdgLB8L\nfNAHnung2Pb9WujxAcQmAH/nzHjVikG/O+DJRlA4nTJ+djss2QtT18Evu8HmzculNF9xLszTAUiu\nEu/pAMQohrxtPXjwIKNHjwbgtddeY/jw4fj4+NCwYUOnCaoWLVoAsHr1aiPCExERMU7bIfDAKDDf\nkPk/vBamPghXz+fMOBG3pialanUFv3QeQbQlw4EFsHUKHF+JW0oKikieYMOC2cA7uzbdRRYRETFU\nkQLw3RBoWcexbfiXMOIbSMmB6YKfD3S+BfrfAS2rpN/37BX4dD1MXw/HL2V/bBHJm4yeq1wbU7LP\nkATV559/js1mo2PHjowYMeL66yaTyWn/wMBAChYsSHR0NGfPniUiIsKIMEVERNzHNxC6zYDbujq2\nrZ0BXz8L1qRsjhEENR+D+v2gxG3p9406BtumwY7PINbxYREREREREck/6pSHha9C+eJpX4+Jh+4T\nYf767I9RsQj0vR16NYGiGZTxW7YfpqyFhTsh2Zb9sUVExDsZkqDavHkzAN27d8/0MREREURHRxMZ\nGakElYiI5G6FSkH/BVCuYdrXbVb4fhCs+ih75y9aOzUpVasbBKRT481ug0M/p66WOvobpHhzXQwR\nERERETFC56YwZyCE3LBN7dFz8MBI2Hns5s/tY4aOtaF/M2hbPf2+kTEw80+Ytg4OXbj5MUVEJPcw\nJEF16VLqGtzSpUuned3VCioA842lj0RERHKjik2h7w9Q8IaHLWIvwfRHYP+KmzuvTwBUfyQ1MVX6\njvT7Xvkbts+A7Z/C1RwqFi8i+Y4Ns8El/jQfEBERcSeTCV7vAm85KfKwaic8PBouXr25c5cNg95N\nUz9KZLBP6u+HUldL/bAdErV3s4jcBKPnKtfGlOwzJEFVoEDqJvBRUVGZ6m+z2Th58iQA4eHh7gpL\nRETEvZr2hMc+Ad8bdsk9vRs+uR8ij2T9nIWrQf2+UKcHBBZ23S/FDkd+TV0tdegXSFFdDBERERER\nSRUcALMGwMNOnnWb/AsMnAHWLE4hzCa4p2bq3lIdajpuu/tfl+NgzkaYug72ns3aOCIikncYkqCq\nVq0av/32G6tXr6Z9+/YZ9l+2bBkxMTEUL16cEiVKGBChiIhIDjJb4MGxcNcgx7YdC2HmE5CQhUcR\nLX5QtXPqaqlyrdLvG3sudaXUtukQfSxLYYuIiIiISN5Xrhj8+CrUrZD29WQr/N80mLoka+crUQCe\nagp9mkLZdJ6hA9hwDKasgW+3Qnxy1sYREZG8x5AEVceOHZk0aRJTpkzhmWeeoUyZMoDzEn8xMTG8\n9NJLANx///1GhCcZsfzzIf8y5CdH8pxETwfgpTZ7OoAcViAMRn4NTdo6tn02Eqa8DikpmTvXhYrw\nQF+4tycULpZ+383LYf4U+P1HsObxmV4VTwfgxcp4OgAvFeDpALxQfNYPsWHBZGiJP70BlSxwXT0+\nf9KPj2SV5iqurfd0ADmrxe3w/WgoWiTt6xci4eGn4Pd1mTuPaQvc3QL6dYP724JPOvdJrsbA5/Ng\n6uewfffNx55rZLDXVr5V3tMBeDHNVxxl8e+S0XOVa2NK9hlym/3uu++mSZMmbNiwgTvvvJOPP/6Y\ndu3apemTkpLCihUrGDhwILt27cLf35+XX37ZiPBERERyRvnqMH4hlL0hg5IQB2/3gqXfZHwOiw+0\n6Aid+0HTdun3jYqERbNgwTQ4efCmwxYRERERkbyv35Mw6V3w9U37+vZd8EB3OH4y43MULQI9H4O+\n3aFS+fT7/rUTpsyBrxZATOzNRi0iInmZYetAvvrqK5o0acLRo0e55557CA8PJz4+9dHNp556itOn\nTxMZGXm9/+TJk6lSRY9Ii4hILtHsXhjxJYQUSPv6uZPw4gOwf2v6xxcvA537wP1PQdGS6ffd+jss\nmAqr5kGSHncVERERERHXfHzgg5HwTC/Htu9/gh7/B7EZJJBa3pGa4HrwXvDzc90vLj41ITVlDmze\nnq2wRUQkHzAsQVW+fHk2bdpE9+7dWbVqFRcvXrzetmPHjuufFylShMmTJ/Poo48aFZqIiEj2PDkE\nnhnluAvw9rUw+EG4dN75cWYz3H4PPNgPbu8AlnSWh1+NgkWz4cdpcHRPjoUuIpJZNnwwGVjn2Kaa\nyiIiItkWXhi+/yw1wXSj4WNgxHjXFcjDCsGTXVITU9UzeIZ81z6YMje1lF/0lWyHLSKSJUbPVa6N\nKdln6FUsU6YMK1euZP369SxcuJDt27dz8eJF/P39KVOmDK1ataJLly6EhoYaGZaIiMjN8Q+A1z6F\n9l0d2378FMY8A8lJjm1FSsD9vaBTHyhRLv0xdm1IXS217BtIvIlNY0REREREJF+qUxN+nAMVbphy\nxMZCt2dh/i/Oj7u9EfTrDo8+AAHp7I2TkADf/ZyamFq3KefiFhGR/MMjab6mTZvStGlTTwwtIiKS\nM4qVgrELoGbDtK9brTDxBfhmUtrXTSZo1CZ1tVSLB9LfRTj2Kiz+HBZOhYOqiyEi3sHojYe16bCI\niMjN63wvzJkMIcFpXz92InW/qR27075eIBS6PZqamKpTM/1z7z8EUz+H2d/Bpcs5G7eIyM0weq5y\nbUzJPq1DExERyao6TWDsfAiPSPt69CV45VHYuPzf18KKQsee0LkvlK6U/nn3b4V5U+DXLyEuRn+l\nRUREREQkS0wmeP1FeGuIY9uqtfDIUxD5764bNKgL/XvAY50hONjxmGuSkmD+IpgyK/U8+OZw4CIi\nki8Zcuvrueeeo27dujz66KMULFjQiCElJ1n++ZB/6aaxa7o2rkV7OgDvdGyzpyPImpAePQj/ZAom\nf/80ryft3s35Bx7AevgwAAF33klIv34EP/QQpnR2EbbHxRH71VdcnTqVpE1p62IU0M+TU4U9HYA3\n099r5/T201GCpwMQyUEWwOTpILyMbhy7pmvjXJSnA/Bikcc8HUGmBQebmDWrCA8/7Jhp+vjjKwwY\ncAmrNbXfY48F079/KA0a+Ds507+OHElm2rQYZs68yvnz9v+0FMjh6PMSzVic0lzFtUKeDsALOdkt\nQfImQ259bdiwgcmTJ/P888/zwAMP0L17d9q1a4clvc3gRUREvInFQtjYsRQcNMihKW7hQi488QQm\nHx8KDBxIaL9++Favnu7pknbt4urUqcTOnYs9WtlLEfF+NswGl/gzGzaWiIhIbleunA8//liMunXT\nPhyXnJzC//3fJaZOvcott/jSr18oTzwRQoECrv/OWq0pLFwYx9SpV1m6NIGUFHdHLyKSPUbPVa6N\naQSr1crKlSvZt28f8fHxlCpVirvvvptixYq5bcz4+HjGjRuHzWYDoE2bNjRr1swtYxmSoKpYsSJb\ntmwhISGBb775hm+++YaIiAi6du3Kk08+yS233GJEGCIiIjfFHBZG0a+/JrBtW4e2qJEjiV+0iPBJ\nkwju0gVTOrsIpyQkEPvdd1ydOpXEtWvdGbKIiGTSwYMHmTFjBuvWreP8+fOEh4fToEEDnnrqKerV\nq+eWMe12Ox07dmT37tQNQKpVq8avv/7qlrFERCTva97cn3nzilG0aNqbsxcu2Oja9TwlS/qwbl0E\nTZu6nqsAnDxpZfr0q3z6aQynT9vcGbKIiGTCDz/8wLPPPsvZs2fTvG6xWOjbty9jx44lOL36rDfp\ntdde4/3337/+dUBAQO5OUH377bfs2bOHOXPm8MUXX/D3339z9uxZ3n//fd5//33q1q1L9+7defzx\nxylevLgRIYmIiGSKb/XqFFu4EN8qVdK8bo+PJ/arrwjq2JFCr76a7jmSDxzg6pQpxMyejf3SJXeG\nKyIiWTB16lQGDBhAYmJimtfXr1/PJ598wuuvv87w4cNzfNwPP/yQRYsWXf86JCQkx8cQEZH8oW/f\nED76KBxf37S1TvftS2LdukS+/bYYYWGuVxXY7SksWhTP1KlXWbw4HpvyUiIiXmH27Nn06NEDgKJF\ni9K5c2cKFCjAmjVr2LBhA5988gn79+9nyZIl+PrmXA3jDRs2MHHiRMxmM3a7PeMDssmwuhk1a9Zk\n9OjRHD9+nKVLl9KtW7fr2b3t27fz4osvUrp0ae69916+/fZbEhJUGF9ERDwrsEMHSvz5p2NyKiYG\ngNBevfBzsQo4JTmZ2G++4Wzr1pyqVo0rEyYoOSUiuZoNi+Ef7jRv3jyefvppEhMTqV+/PvPnz2fv\n3r0sXryYFi1aYLPZePPNN5k0aVKOjnvkyBFeffVVfH19CQsLy9Fzi4hI/uHjA5MnF2bq1CIOyakL\nF2xUr+5Hr16hLpNTZ85YeeedKCpU+JuOHc/z889KTolI7uWJuYo75yvHjx+nX79+ADRu3JgDBw4w\ndepUxo4dy/r16xkzZgwAK1as4L333suxcRMTE+nVqxd2u53+/fvn2HnTY3hhd7PZTJs2bZgzZw5n\nz55l1qxZtG7dGrPZjNVqZdGiRXTp0oWIiAj69u3LxYsXjQ5RRESEAoMHU+ynnzAXcNz81xwSgjkw\n0OlxyUePcnnoUE6WLs2F//2PhJUr3R2qiIhkUVxcHM8//zwpKSnUqFGDP/74g06dOlG9enXat2/P\nsmXLaNGiBQCvvPIK586dy5FxU1JSeOqpp4iLi+Pll1+mZMmSOXJeERHJX8LDzfz2W3GeecZxrgI4\nlPr7r99+i+ehh85TtuzfvP56FCdOKCslIuJtRo4cSWJiIhaLhS+++IJChQqlaR88eDB33XUXAO+9\n9x4x/zxInV1vvfUWe/fupXnz5vTu3TtHzpkRj+48HBISwpNPPsny5cs5fvw4o0aNokaNGgBER0cz\nffp0jh496skQRUQknzEFBFDk888pPGYMJnPm/kymWK3Ezp/P2XbtOFWpEtFjxmA/f97NkYqIyM36\n6quvOH36NABjxoxxqNvu6+vLhAkTAIiJiWHKlCk5Mu4nn3zCqlWrqFy5Mq+//nqOnFNERPKXOnV8\n2bSpBK1aOX9gzpkLF2yMGRNN5cp/067dOX74IQ6r1Y1BiojITbNarcybNw+Ae+65h0qVKjnt99xz\nzwFw5cqVNOXDb9Zff/3F2LFj8fPzY+rUqZhMpowPygGG7EGVGaVLl2bYsGHcddddvPDCC6zV5vHe\nwwfcXGEl99H1kJtx2tMBeCdv+m0fWKoUd/74IyENGmSqf+zJkxyaPp3Dn35K/Omc/x9cXpNGp+7Q\nz5JrxTwdgOQaiRl3uVFqCQvj3gS5s2TG/PnzAShUqBAdOnRw2ufWW2+levXq7Nu3j/nz52d7L6rj\nx48zdOhQAKZMmUJAQPob1UsW+ALGzJ9zD6+Z6Uuu8benA/Bm3jNj6dy5DHPnNiU4OHN7jaxadY6p\nUw/yww8nSUrK6X1Eyufw+fKQM3d4OgLvFOHpACRXSc5ad6PnKv+OmfO2bNnCpX+2iGjTpo3Lfq1a\ntbq+T9TSpUt59NFHb3rM5ORkevbsidVq5Y033qBGjRps27btps+XFV7xtvX48eN88cUXzJkzh/37\n96dpMypTJyIi+Vu5Ll1o8umn+NzwFP2NUux2Ti9ezMEpUzi9eDEpKtQuIpLrbNiwAYAmTZpgsbie\nWDZr1ox9+/axc+dO4uLiCAoKuukx+/Tpw9WrV+nevfv1chwiIiKZ4eNjYtaspjz+eIUM+16+nMis\nWUeYNu0Q+/ZdMSA6ERHJSbt27br+eZ06dVz2K1iwIGXLluXYsWPs3r07W2OOHDmSHTt2UK1aNV55\n5ZVsnSurPJagunr1Kt9//z1z5sxh9erVpKSkXG8rXLgw//vf/+jevTsNMvkUu4iISFZZAgIo16UL\ndYYPJ6RC+pO9+DNnOPzppxyaMYPY48cNilBERHLa+fPnr+9zW6VKlXT7XiunYbfb2b9/P/Xr17+p\nMT/99FOWLl1KkSJFGD9+/E2dQ0RE8p9y5YIZOLA6/ftXISAg/Sf11627wJQpB/nuuxMkJOghOhER\nI0VFRaXbHhYWlulzHTt27PrnZcqUSbdvmTJlOHbsWJpjsmrHjh2MGjUKgKlTp+Lv73/T57oZhiao\nbDYbS5cuZc6cOSxYsID4+Pjrbb6+vtxzzz10796djh074ufnZ2RoIiKSjxSqU4fKffpQoXt3/AoW\nTLfvmaVLOThlCn8vXEiKCrWLSD5mwwcjpw+2/4yVkxO+c+fOXf88IiL9WjMlSpRwelxWnDp1ihdf\nfBGA8ePHU6RIkZs6j4iI5A8+PiY6dixN376Vadu2BGaz68pCV64kM3fuUaZOPcjOnVHGBSki4mWM\nnqv8O2aqihUrptv3v4tzMnL16tXrn4eGhqbb91r7f4/JCqvVSq9evUhOTqZXr17ceeedN3We7DDk\n/9rOnTuZNWsWX375JWfPnk3Tduutt9K9e3e6du1K0aJFjQhHRETyIZ/gYMp16ULlPn0o0qRJun2t\ncXEcmDyZg1OnEnP4sEERioiIKzk54YuJibn+eWBg+hvM/7f9v8dlRf/+/YmOjuauu+6ie/fuN3UO\nERHJ+ypVCqF378r06FGRiIj0/z7t23eFceP28PXXx4mN1UN0IiJ5yX8X9WS0iOfaaqf/HpMVY8eO\nZcuWLRQrVoyxY8fe1Dmyy5AEVc+ePdmyZcv1r0uUKMHjjz/Ok08+Se3atY0IQURE8qnCt95K5T59\nKN+1K74FCqTb15aUxIHJk9k2dCj2pCSDIhQRESPZ7f9uEm82m9Pt+9/9qWw3sefg3Llz+fnnnwkI\nCGDKlClZPl5ERPI2Pz8znTuXoU+fytx1V/qregHOnImjb9+N/PzzKQOiExGRzDpy5AiFChXKkXP9\n9yG5pAzuTSUmJjock1l79+7lrbfeAmDChAkULlw4y+fICYatewsMDOSBBx7gySef5O677053M2IR\nEZHs8AkNpULXrlTu25fCt96aqWPOLl/O2scfJ+EmSziJiOR1NiyAce/hbf8ZKycnfMHBwdc/T0hI\nSLfvf59EDAkJydI4586dY+DAgQC88cYbVK5cOUvHi4hI3lWtWgH69KnMk09WoEiRgAz7JyfbeeON\n7YwZs4csLBoWEck3jJ6r/DtmqkKFCmWp7Hh6/lvW7+rVq+mWJb9W2i+jUoA3stvt9OrVi8TERNq2\nbUvXrl1vLtgcYEiC6p133uH222+nQAZProuX8sHon2/vl/H7x/xL1QVcO+jpALzTsRw8V8nGjbm1\nb19qdOmC339uQKYn8epVlg4YwPaZM3MwkpyR9edf8of4i56OwHsF6to4Z2wp8twhly0SzckJ33+f\nDLx4Mf0fmgsXLjg9LjNeeeUVLl26RO3atXnppZeyFqRkjQ/geouW/MnYva1zl2RPB+Cl9nk6AG92\nLEfOEhDgw8MPV6dPn/q0aFE208f99ddZunX7kT17InMkjpyj2YprN1dqK8+L1PeMS7rv6igf318s\nX7789c9PnjxJlSpVXPY9efKkwzGZcenSJTZs2ACkVrt78803Hfr8d6umZcuWXX+4r3v37hmWYM8K\nQ6br7du3N2IYERHJhwIKFaJ2t27U79OHYnXqZOnY46tX81OPHkQfO+ae4ERExOuUKVOGoKAg4uLi\nOHr0aLp9jx8/fv3zatWqZWmcU6dSyy/9/fffLieVp0+fBuDAgQPXJ5UPP/ww48aNy9JYIiLivWrX\nLkqfPvXp1q02YWGZv0FvtdoZNWot77yzhuRke8YHiIhInvDfLZF27txJ69atnfaLjo7mxIkTANSq\nVeumx5s9e3aGfZYvX87y5csBaNasWe5LUImIiOS0Ms2aUb9vX6o//DC+Way1a01IYOUrr7Bx4kRU\nI0NEJHNsmDG2xF/6+0PdLJPJRN26dVm/fj1//vlnun2vtZctW/ama7JHRUURFRWVbp/k5OTrybDI\nSG97Ql5ERLIqKMiXLl1q0rdvfZo0KZXl4/fvv0j37gvZuPG0G6ITEcl7jJ6r/DtmzmvQoAFhYWFc\nvnyZZcuWMWDAAKf9Vq5ceX1/3bvvvjtLYwQFBTF8+PB0+5w9e5apU6cCcNddd9GsWTOAHE1OgRJU\nIiKSiwSGh3PLk09Sr3dvitSokW5fu9WK2cfxz9yZLVtY2L07kXv2uCtMERHxcvfffz/r16/n5MmT\nbNmyhQYNGjj0OXXqFJs2bbreP6tmz56dZg8rZ9q1a8eBAweoWrUqv/76K5D1va5ERMR71K8fQd++\n9enatRYFCqRfa9NuT8FsdqxP+uGHmxg6dAXx8fm4vpWISD7m4+PDww8/zPTp01m8eDGHDx+mUqVK\nDv0++ugjAAoUKECHDh2yNEZQUJDTsn7/tW3btusJqjZt2jB06NAsjZFZhieo9u7dy7x589i6dSuR\nkZHExcWRks7T63PnzqVGBjchRUQkDzOZKN+qFfX69KH6gw9i8fNz2dVutRJ19ChhlSo5JKfsVitr\nR41izYgR2K2a7ImI5Gfdu3fn7bffJj4+njfffJOffvrJoc+IESOw2WyYTCZ69+6d5TGKFy+eYR9f\nX9/r/81q3XgREfEOoaF+dO1amz596tGgQYl0+166FE9CgpWSJUMdklMnT16hZ8+fWL78mBujFRGR\n3ODVV19lzpw5JCYm8vjjj7NkyRIKFSp0vf299967XnJv8ODBTh9yW7VqFatWrQLgiSeeoHLlykaE\nnmWGJaji4uJ45plnMlXT8L9iY2PdFJGIiHiz4OLFuaVHD+r17k3hDP6IRh09ysGff6ZCmzZOV1Zd\n3L+fhd26cfqfJ+FFRCTrrFhIMbTEn/vGKlmyJIMHD+att97i559/pl+/fowaNYrw8HBiYmJ49913\nrz8t2KtXL+rWretwjuPHj3PnnXcC0LlzZyZMmOC2eEVExPs0alSSvn3r87//1SQ42PVDdABLlx7h\n2LFounatReHCjuXJ58zZwfPP/0Z0dKK7whURydOMnquAe+cr5cqVY8qUKfTs2ZM///yTatWq0blz\nZ0JDQ1mzZg0bNmwAoHXr1gwePNjpOVatWsVbb70FQJMmTZSg6tGjB9999x2QWqewYcOGLF++nIsX\nL15fgrZhwwYuXbrErbfeSp1/NrovUqSIW+I5d+4cGzduZOPGjZw5cwaA5s2b8+STT7plvGuWL1/O\nV199df3rMWPGEB4enuFxe/bs4ZdffuHvv//G39+fKlWq0LlzZ7ddHxERTzCZzVS4+27q9+lDlfvv\nx/LPk+XO2JKS2L9gAds++4wSDRrQYvhwp6urNn7wASuHDcOaQZklERHJX9544w0OHDjAV199xbRp\n05gxYwZFixbl4sWLWP9ZaXvXXXcxefJkp8f/d9+oCxcuGBa3iIh4TqFCATzxROpqqVtuSX+l7Jkz\nMcycuZ1ffjnE8OHN6dOnvkOfCxdi6ddvMfPn73dXyCIikkv16NGDAgUK8Oyzz6bZDwrAYrHQp08f\nxo0bd70qQ25lSIJq3bp115NTo0ePZvDgwZhMJho2bMjFixd56623aNiwIQkJCYwePZrRo0fz1FNP\n8cwzz+R4LMOGDeOrr766Ppm8kTsTVFFRUXTr1u16QgzgtddeSzdBdfnyZfr163f9+v3Xc889x4gR\nI1xmSXOMH9qt7EbGJuRzF5unA/Bep/Z6OgLvZAVCS5Wifs+e1O/dm0LlyqXb/+KBA/w1fTrbZs8m\noGBBOs2ZQ5mmTR36RZ88yY89enB0xQo3Re5+lzwdgJe6osXVLgXq2jgX7ekAvFCypwPwPLPZzJdf\nfkn79u356KOP2Lx5M+fOnQOgdu3a9O3bl2effRaz2T2bH0sO8gM37VGde+Xu+xTupfmKc/F/ezoC\nL2alWbOy9OlzK488UpPAQNc/YHZ7CkuWHGLatC388stBHn20Fj///ChhYY6rphYu3E+fPj9x/nxu\nfQOn2YprVzwdgHeKcfw5kH+kv2Vd/qS/1wA8+OCD3H///axYsYJ9+/YRHx9P6dKladOmTYYlxVu2\nbHn985tZPRUREcHw4cMBaNasWZaPzyxD0g7z5s0DUi/KkCFDXPYLCAjgzTffJC4ujueee47y5ctn\neYOvjPzxxx8cP36cYsWKcdttt3HrrbcyYsSIHB3DlYEDB3LmzBl8fX1JTs74rkBCQgL33nsv69ev\nB6BTp07ceeedxMbG8vXXX7Nr1y6GDBnC1atXDfs3iIjkGIuFgHvu4X99+1KlQwfMFteZX2tiInu+\n/56/pk/n+OrVADR8+mnuHjsWv+Bgh/7bZs9myYABJEbrrrSISE6x44ORTy3ZDRqre/fudO/eneTk\nZCIjIwkLCyMgICDD48qXL8/Ro0cBnNZ8z4zffvuNpKQk/NLZX1FERDwjPNxM9+5B9OnzDDVqFE23\n78mT0Xz66VY++2wrJ09eITw8kC+/fJBHHqnl0PfKlUQGDFjCrFnb3BS5iEj+Y/Rc5d8x3c/Hx4e2\nbdvStm3bLB3XsmXLNEmqrIqIiODNN9+86eMzy5CruHv3bgC6devmtN1ut6f5etiwYbz//vu89dZb\nOZ6gGj16NKVLl76+CXFCQoIhyZ0lS5Ywe/ZsihUrxgMPPMD06dMzPGbMmDHXk1NTp06lb9++19sG\nDx5Mly5dmD9/PiNHjqRTp040aNDAbfGLiOQUS7lyBD31FMG9emEpVYr0ipye372bv6ZPZ8fcucRf\nSn1CL7RUKe7/9FMqt2vn0D/2wgV+7tePffPnuyl6ERHJq3x9fSlRIv3N7f/Lx8fn+pziZpUsWTJb\nx4uISM4ymaBlS3/69AnmwQcD8fc3uexrtdr5+ecDTJ/+F0uWHMJuTwGgQ4cqzJjRkRIlQh2OWbXq\nGD16LOD4cT1IJyIiAgYlqK5cSV3aWqlSpTSvX3tSMC4uLs3rYWFhVK5cmY0bNxIVFUWhQoVyLBZ3\nLkdz5cqVK9eTSxMmTGDHjh0ZHhMbG8v48eMBaNu2bZrkFKROoGfMmMHSpUuJiYnh7bff5scff8z5\n4EVEcoKPDwH3309wnz74t22LKZ1ySclxcez+9lu2TJvG3/8k6a+p/dhjdJg8mcCwMIfj9i9cyE99\n+hB7/nyOhy8iIqmbABu58bBdNZVFRMQgxYub6dEjmN69g6lcOf1bZUePXmbGjK3MnLmVM2dirr8e\nEuLH+++3o0+fWx2OSUiwMmzYcj74YAMpKTkevohIvmf0XAU0X8kphiSoChcuDEBSUlKa1wsUKADA\n6dOnHY5J+ecv9qlTp3I0QeUJL7/8MidPnqRt27Z07do1UwmqxYsXc/XqVQD69evntE/hwoV5+OGH\nmTVrFkuWLOHKlSvXr6mIiDewVKpEcO/eBPXogSUiIt2+Z7dtS10t9cUXDqX5AsPDuffjj6n16KMO\nxyVeucKSAQPYNmtWToYuIiIiIiJ5mMkEd9/tT9++wdx/fyC+vq5XSyUn21iwYB/Tp//FsmVHHJJM\nzZuXZdasTlSs6Pgg3ZYtp+nWbT5790bm9D9BREQk1zMkQXWtdMWNiagqVarw66+/snHjRrp27Xr9\n9cuXL3Ps2DGAXF+PfcWKFUyfPp3AwECmTJmS6eNWrVoFgMlkSrdWZOvWrZk1axZJSUmsW7eO9u3b\nZzNiEZFs8vMjsHNngvr0IeCuu9Ltao+JYdtXX7Fl2jROb97stE+VDh3oOGMGoU7KLh1btYoFPXoQ\nffx4joQuIiIiIiJ5W8mSZnr1Cuapp4IpXz7922IHDyYzffoqZs/ezvnzsQ7t/v4WRoxozYsvNsVs\nTpvgslrtjBz5B++88ztWq93hWBERETEoQXXrranLm/fu3Zvm9ZYtW/LRRx8xe/Zsnn76aapVq4bN\nZuOll14iKSmJ0NBQKlasaESIbhEbG0vv3r1JSUnhzTffpEKFCpk+9tq+XREREddXoDlTo0aN65/v\n2bNHCSoR8RifatUI6tOHoCefxFKkSLp9kzZtInb6dOK/+oqfYmKc9vELCaHt+PE0uKHEKYA1IYHl\nw4ax4YMPUI0MERFjqMSfiIjkVhYL3HNPAH36BHPvvQFYLK5XSyUmpjBvXjzTp8eyalUisM5pv3r1\nIpg7tzO1axdzaNu3L5Lu3eezaZNjxSAREcl5KvGXexmSoOrQoQMACxYsYPTo0ddfv++++yhbtiwn\nTpzglltuoV69epw4cYKzZ88C0KtXLyyW3Ps/eujQoRw9epS6devywgsvZOnYEydOAFC6dOl0+/23\n/doxmXX58mWXbVFRUf9+4YNB3ym5iK6Ha44Plck/1ng6ADewBARQ+uGHqdS3L0WbN0+3b1J0NMc/\n/5wj06cTtX379dcDnfQt3awZHWbPppCThxTObtnCL926cXHvXqfH5iXJng7AS13xdABerLh+BzsX\n7OkAvJDV0wGI5CBfwPX2lvmT5iuuXfV0AN5qracDcIuyZYN46qmK9OpVgdKlg9Ltu2dPNNOnH2Hu\n3GNcvPjfLSrSzjosFhNDhjTmzTfvwNfX8Z7VBx9sZtiw34mPtzocm7dotuKaZixOXS3u6Qi8V4in\nA/BCWniabxjytrVs2bIMGzaMS5cuERUVdX1PKX9/f7766is6dOhAdHQ0GzduvH7M7bffzjvvvGNE\neG7xxx9/MHnyZMxmM9OmTcPHJ2uX+tr+U8HB6d9RCQn59zfYtWMyK72VWSIi6SlYuzYV+/ShfLdu\n+IU51ln/r8h16zg8bRonv/sOW1xcun0t/v40HzGC2158EZM57Z0mu9XK+pEjWf/OO9iturMqIiIi\nIiKOfHxMdOxYkj59KtGuXYRD6b3/io+38u23J5k27Qjr1mW8R1SVKmHMmXMvTZqUdGg7ceIKPXsu\nYsWKrD08LCIikp8Z9lzVqFGjnL5+++23s3fvXmbNmsWePXvw9/fnzjvv5LHHHstyUsdbxMfH89RT\nT5GSksJzzz1Ho0aNsnyOxMREAHx9fdPt9989uq4dIyLiDj7BwZTp0oVKffoQ3qRJun0TL13i+Jw5\nHJkxg+h/SpZmpFi9etw7dy5Fa9d2aLu4bx+/dOvGWRf7VImIiPvZ7GZS7AaW+LNrSYyIiGRexYrB\n9O5dkZ49KxARkf7Kpe3bo5g27TBffHGc6OiMVwKZTPD00/UZO7YlQUGO92lmzdrJgAHLuXIlycnR\nIiLibkbPVUDzlZziFRmgEiVKMGzYME+HkWNef/11Dh48SOnSpRk5cuRNnSMoKIgrV65kmHRKSEhI\nc0xWXLp0yWVbVFRUrt7/S0RyTlj9+lTs25dyXbviW6BAun3Pr1rFkenTOTlvHvZMJs1NFguNhwzh\njjffxOIkKb954kR+HzYM639+34mIiIiIiPj5menUqRR9+1birrvSLx8WE5PM11+fZNq0w2za5Pp+\nyI1KlQrhs8/uoW1bx33FL1yIo2/fX1mw4GCWYxcREREvSVDlJZs2bWLChAkAfPTRR4SGht7UeQoU\nKMDZs2e5ciX9urXR0dFpjsmKsAzKcolI/uUTGkq5rl2p2KcPhRs0SLdvwoULHJs1iyMzZnD1wIEs\njRNSpQqPz5lDSScrsq6cOMGiHj04sXJlls4pIiIiIiJ5W7VqofTpU4nu3ctRtGhAun03b77EtGmH\n+frrE1y9mrVS4V27lmPy5AcoVMhxjAULDtKv36+cP59+GXMRERFxzZAE1cSJEylbtiwtW7bM8/se\n/fnnn9jtdkJCQvjpp5/46aefHPps2rTp+udDhw4lJCSE0NDQ64ktgEqVKnHgwAFOnEi/dvHJkyfT\nHCMikh3hjRtTsU8fyv7vf/hksAfe2aVLOTJ9Oqd+/BF7UhZLWZhMVH76aeqOHYuPk9WfO2fNYvmA\nASRlkKQXERHj2KwW7FbjymakGDiWiIh4v4AACw8/XJo+fSrSokWxdPteuZLM558fZ/r0w2zbFpXl\nscLD/ZgypSEPP1zGybkTef755cyevSvL5xUREfcweq4Cmq/kFEMSVJ9//jlbtmzBbDZTt25dWrdu\nTatWrWjRosVNrzDydjExMXz66acZ9vvmm28ACA8PT5Ogql27NosXL+bSpUucOHGCsmXLOj1+69at\naY4REckq30KFKP/EE1Ts04dCt9ySbt/4M2c4OnMmRz79lNgjR25qvMBSpWj02WdEtG3r0BZ7/jy/\n9evHwQULburcIiIiIiKSt9SuXZA+fSrSrVt5wsL80u27bl0k06cf4dtvTxAXZ7up8e69twQzZtzm\ndB+rlStP0KPHIk6c0IN0IiIiOcGQBFVERAQAdrudrVu3snXrVsaPH4+Pjw8NGzakdevWtG7dmjvu\nuIOAgPSXZnu71q1bM3369HT7/PDDDyxevBiA0aNHEx4e7vDvbt++PWPHjgXg119/pU+fPk7P9euv\nvwJQqFAhmjgpkZUjLP98yL9UHNO1054OwHsd8nQANyjTrBn1+vShxiOP4BvoehPhFLudw0uWsG36\ndA7+/DN2a9bKYvxXra5daTt5MgGFCjm0HV2wgNV9+xJ/4QJ589GFrIv3dABeStclHbGeDsBL6bo4\nuvlf5SLexwfQHtVpab7i2kVPB+CtvGu2EhTkS5cuNenTpx5Nm5ZOt++lS/HMnbuT6dO3sXv3hZse\nMzTUj/ffv5veves5tCUkWBk69E8+/HAHKSmAZizoXXl6dG2c0vtP12I8HYAXsns6ADGKIW9bf/75\nZ06ePMny5ctZsWIFK1eu5O+//8ZqtbJhwwY2bNjAqFGj8Pf3p2nTptdXWDVu3BhfX18jQswxNWvW\npGbNmun2OXTo0PUEVZcuXShfvrxDnzvvvJMyZcpw8uRJJk2aRM+ePfHxSfu/a//+/SxatAiARx99\nNNddKxExnl9oKPV696Z+nz4UqVEj3b5XTp5k+2efse2zz7iSQbnRjASGh3PPJ59Q45FHHNqSrlxh\nzfPPs3/27GyNISIi7mWz+mCyGnfXO8XAsURExDvUrFmE555ryOOP16ZAAf90+65efZzp07cxb94+\nEhKyd+e7efMyzJ59PxUqFHJo27z5PN27r2Dv3svZGkNERNzH6LkKaL6SUwy7imXKlKFHjx706NED\ngAMHDrBixQpWrFjBqlWruHDhAomJiaxatYpVq1YBEBwczMaNGzNM+Bjt9ddf58yZMwQFBfHhhx+6\nZQyLxcKIESPo0aMHO3fu5Nlnn2Xy5MnXk1Rnzpzh0UcfxWazERgYyGuvveaWOEQkjzCZqNujBy1H\njSLkn1WtztitVg798gtbp03j8JIlpNiz/8hK5Xvv5d4ZM5yOe2zFCv7o2ZOYbCbAREREREQk9ypS\nJIi3325B3771sVhcL4m8cCGW2bN3MmPGNvbvz/5yOH9/C++805IXXmiM2WxK02a12nnnnTWMHLkX\nq1WP8ouIiLiDx9J8VatWpWrVqvTv35+UlBR27tzJihUr+PLLL9m0aRMAsbGxxMXF5ei4a9asYdas\nWde/ttlsadp69+59/euAgAA++ugjh3N899137N+/n4IFC7otQQXw5JNPsnr1ambOnMm0adNYtGgR\nzZo1IzY2lmXLlhEfH4/JZGLOnDmUKeO4caeICEDp22+n7QcfUKJhQ5d9oo4eZduMGWyfOZOYM2dy\nZFy/kBDavP8+9Z2UKE2Oj2flsGFs+vBDCqfWyBARERERkXzGx8fMM8804M03mxMW5rrs+LJlR5k+\nfSsLFhwgKenm9pa6Uf36Ecydez+1ahV1aNu7N5Lu3ReyefMZoHCOjCciIiKOPL4O7fLly6xatep6\n+b+9e/e6dbx9+/bx6aefOm3bv38/+/fvv/51cHCw0wSVkWbMmEGtWrUYNWoUf//9N19//fX1tpo1\na/LRRx/RqlUrD0YoIt4qtHRpWo8ZQ+2uXZ2225KTObBgAVunT+fosmWQg4miMs2bc//s2RSqUMGh\n7czmzfzYrRsX9+3LsfFERMT9bFYLJqtxG5OmGDiWiIgYr127ikyYcDc1ahRx2n72bAwzZ25nxoxt\nHDkSlWPjWiwmhg69neHDm+Pr6/i3ZsKEP3nllVXZLhsoIiLGMXquApqv5BTDE1SxsbH88ccf1xNS\n27Ztw/6fElI+Pj40bNiQ1q1b07p1a+rWrZuj4zdv3pzp06dnqq+rPZ3eeecdoqKi8PdPvx6yKw89\n9BCVK1cGoEgR52/ErjGbzbz44os8//zzbNiwgZMnT+Lv70/VqlWpU6fOTY0vInmbT2AgTV56iduH\nDsU3KMihPTk+nj/Hj2fzpEnEnj+fo2Nb/P1p+c47NH7hBUzmtKU57FYra0aMYO2oUditmuyJiIiI\niORHVaoU5v3323DffVWcth88eIlXXlnJggUHcry0XtWqhZkz534aNy7l0Hb8eDQ9e/7EypXHc3RM\nERERcc2QBNWff/7JokWLWL58ORs3biQ5Ofl6m8lkol69etcTUi1atCA0NNRtsVSrVo1q1apl6xwP\nP/xwto6/7bbbuO2227J0jK+vL82bN8/WuCKS99V45BHuGjuWguXKOW3f8+23rBg8mOjjOT/piqhf\nn45z5lCsdm2Htsi9e1nYrRtntmzJ8XFFRERERMT7FSjgz+uvN+P552/Dz8/xqfMrVxJ5++0/mDRp\nc46V8bvGZIJnnmnIe++1JijI8WHkmTO3M3DgUq5cSczRcUVERCR9hiSonn32Wbb856Zk9erVryek\nWrVqReHCqufr1fwB54vJRByd9nQA+VPxevVo+8EHlG3Rwmn7uW3b+G3AAE78/nuOj22yWLh96FCa\nDx+OxcnK0z8nTGDVK69gTUhwenyBHI8o74j3dABeStclHbqn4pyui6ObuO9ns5mNLfFnM2fcSQTA\nD1CFFcmsk54OIP8xm0306HELo0a1pHjxEId2uz2Fzz7bzquvruL8+dgcH7906VA+++w+7r67okPb\n+fOx9O27iB9/PJDOGTRjcU7vyl3TtZEscn67JH/L4i4URs9VQPOVnGJ4ib/777+fxx9/nFatWlG0\nqONGlCIiknlBRYvS8p13qNe7t0NJPYDYCxdY/eqrbPv0U1LsOVseA6Bw1arcP3s2pZo0cWiLPn6c\nn3r04PiqVTk+roiIiIiIeL877ijNBx+0pUGDEk7b16w5yYABv/HXX2fdMv4TT9Rm0qR2FCoU4NC2\nYMF++vZdxIULcW4ZW0RERDJmSIKqVq1a7Nixg+TkZBYuXMjChQsxmUzUqVOHu+6663ppvwIF9FSK\niEhmmH19afjcczQfPpyAggUd2m3JyWyeNIk/3n6bxOjonA/AZKLhM8/Q+r33nO5ztX3mTJYOHEji\nlSs5P7aIiIiIiHi1MmUK8N57rfnf/2o5bT958govv7ycb77Z45bxixQJYsqUe3jooeoObdHRCTz/\n/G/MmbPTLWOLiIhI5hmSoJo9ezYff/wxf/zxBytWrGDFihVs3bqVHTt2sGPHDiZMmIDFYuG22267\nXvrvjjvuICDA8QkXEZH8rtI993D3hAmEu9hP79CiRSx74QUu7t/vlvFDS5fmvs8+o+Lddzu0xZ4/\nz6K+fTnw449uGVtERDzHmmzBlGxgiT8DxxIRkZwRGOjD4MFNGTy4qdO9nuLjkxkzZj3vvbee+Hir\nW2Lo2LEK06d3cFpOcMWKY/Ts+RMnTuhBOhGRvMTouQpovpJTDCvxFxwcTPv27Wnfvj0AUVFRrFq1\n6nrCavfu3WzYsIENGzYwatQoAgICaNq0KbNmzaJs2bJGhSki4rXCq1WjzfvvU7lDB6ftF/fvZ+mg\nQRxevNhtMdR+/HHaffQRAYUKObTtnz+fRf36EXfhgtvGFxERERER79SlS03ee681Zcs6VngA+Prr\n3QwevIKTJ92THAoN9WPChLt56ql6Dm3x8ckMHbqSSZM2kZLFfU1ERETEfQzfg+qaQoUK0alTJzp1\n6gTA+fPnWb58ORMnTmTjxo0kJCSwcuVKzp8/rwSViORr/gUL0vyNN2j4f/+HxdfxKcSE6Gj+eOst\nNn/0EfbkZLfEEFSkCO0/+YQaDz/sdPzfnn+enXPmuGVsERERERHxXrfeGsEHH7SlWbMyTtv/+uss\nAwb8xpo1J90WQ4sWZZk9uyPlyxdyaNu06TTduy9k376LbhtfREREbo7HElTX7N+/nxUrVrB8+XJW\nrVrFxYt6wyAiAmAym6n31FPcOXIkwUWLOrSn2O1smzGDVa+95tZVS1Xuu48O06cTEhHh0HZ0+XJ+\n7tmTKyfdN9kUERHvkGLzIcVm4PTByLFERCTLihULZuTIlvTqVRez2eTQfv58LK+8soqZM7djt7tn\n2ZK/v4WRI1syaFBjhxisVjsjRqxh1Ki1WK12t4wvIiLewfC5Cmi+kkMMv4onTpy4npBasWIFp0+f\ndugTGBhI06ZNad26NZUrVzY6RLmRP+C4aEPEqUtHPR2B97olC33DW7Sg1gcfULBePaftF3//nV0D\nBnBl2zbc9VvSJzSUmu+/T7nevR3abPHx7B0yhKMffUT5bNbIiMvW0Xmbe6ry5366Lq7FJ3g6Au8U\nmOjpCLyQzdMBiOSgQEBbAEhmWS95OgIvlfnZiq+vieefr8Drr1elYEHHmwXJyXY++OAoI0Yc4MoV\nO1AnB+P81623FmTOnPrUqhXq0LZ371W6ddvKli3RQO1sjqQZi3N6V+6aro1z8Z4OwHslBHo6Au+j\ncqz5hiEJqkWLFvHjjz+yfPlyDh8+7NDu6+tLo0aNaN26Na1bt6Zp06b4+/sbEZqIiFcJLFuWmmPH\nUvLRR522xx0/zp6XX+bMd9+5NY7wFi2oN2sWQRUqOLRFbdrE1m7diNm/360xiIiIl7FZwGpgFsCm\njIOIiLe5995ivP9+LapWDXHa/vPP53jxxd0cOBDrthgsFhPDhlXmjTeq4utrdmh///3DvPrqPhIS\ntGpKRCTfMHqucm1MyTZDElRvvPEGW7Zsuf612Wymfv361xNSzZs3Jzg42IhQRES8kiUoiMpDhlDp\n5ZexBDo+OWOLi+Pg6NEcGTcOW7z7njoy+/tTfeRIKg4ahMmcdrJnT07mwIgRHHr3XVKseiJMRERE\nRCS/qF49hAkTatG+fTGn7fv2xTBo0G6WLDnv1jiqVg1mzpz6NG4c5tB2/HgcPXpsY9UqbR0hIiKS\nWxiSoDKbzdSuXft6Qqply5YULFjQiKFFRLxeqcceo8Z77xFYurTT9r+/+IK9Q4eS8Pffbo2jYP36\n1J87l9BatRzaru7Zw9bu3Yn+z8MGIiIiIiKStxUq5Mvw4VV57rny+Pg4rlaKikrmzTf3M3nyMaxW\n99VjMpng2WfL8957NQkMdHxi/bPPTjBw4G6uXtWDdCIiIrmJIQmqFStWEBLifPm3iEh+VbBBA2p/\n8AGF77jDaXvU5s3sGjCAy+vWuTUOk8VC5WHDqPrGG5h909aQT7HbOTJxIvtefRV7gja3ERHJ16wG\nl80wukSHiIhcZzZDnz7leOed6hQp4ufQbrOlMH36cV5/fT+RkUlujaV06QBmzqxHmzZFHdrOnUuk\nb9/tLFx4zq0xiIiIlzN6rnJtTMk2QxJUSk6JiPzLv3hxqo8aRdlevZy2J5w9y75hwzg5ezakuHdX\nyOCqVak/Zw5hjRs7tMUdP862J5/k4urVbo1BRERERES8R8uW4UycWIu6dZ1Xvlm1KpIBA3azY8cV\nt8fyxBOl+eij2hQs6OvQ9sMPZ+jXb4fbE2QiIiLiPoYkqEREBMx+flQYMICqr7+OT2ioQ7s9KYkj\nEyZwcORIrFevujcYk4nyzz5LzTFjsAQFOTSf+Owzdg8c6P44RERERETEK5QvH8jYsTV5+OGSTtuP\nHYvjpZf2MG/eGbfHUqSIH1On3sKDD5ZwaIuOTub//m8Xc+e6twS6iIiIuJ8SVJIxC/pOkUz7SyW/\nnSrSsSP3THgfc6XKTtuTf15I4tAXKXb4EMUAAtwXi6l0aQKnzsSndRuHNvu5cyQ824dCv/zEHW6O\n45rFqhwoWZTs6QC8WHyipyPwToH6PePIfhPHWE2pH0YxcizJ3TRfkSz5y9MBeJ3gYAtDh7blpZf8\nCAhw/N0bG5vCu+8mMn68lYSEqkBVt8bTsaMP06cHULy4455Xy5db6dkzgZMnKwGV3BrHvxYbNI7k\nHZqxOBfv6QC8V0qgpyPI/Yyeq1wbU7JNb+NFRNwouGZNqk6YQHjbtk7bbXv3kPDyQGzLl7o/GIsF\n3x5PEfDOGEyFCjk0J//4AwnP9SMlMtL9sYiIiIiIiEeZTNC1awRjxlSmVCl/p30+/zyJoUMTOXXK\nvaXHAYoXNzFmjD9PPum451V8fAqDBycweXKyu6ugi4iIiIGUoBIRcQOfsDAqvvkmpZ95BrOP46/a\nlMuXSRjxBsnTp4DV/cvOLK3uImDM+1jq3OIYS3Q0CYOeI/mrz90eh4iIiIiIeN5ttxXggw+q0rRp\nIaftmzbZGDAggfXrbW6Pxd8fBg3y45VX/AkNdXwafeNGG927x7N//80s/xURERFvpgSViEgOMlks\nlOrbl4ojRuAXHu7QnmKzkTx9ConvDCfl4kW3x2OuXAX/d8fhe9/9TtutK5YR378XKSdPuj0WERHJ\n5az/fBg5noiI5KiICD/efbcyPXo432fqzBk7Q4cmMneuMSuVHnnEh/feC6B8ecdyfsnJKbz9diLv\nvpuEzf15MhERyc2MnqtcG1OyTQkqEZEcEta6NdUmTiSkTh2n7dZVK0h4aQD23bvcH0yhQvi/8gZ+\n/Z/D5Ovr0JwSG0vC60NJnjIZ1cgQEREREcnb/P3NDBxYhldfrUBoqOOtoMTEFN5/P4lRoxKJiXF/\nPA0amJk4MYBmzZzfltq1y8aTT8bz119aNSUiIpKXKUElIpJNgRUqUGXcOIo9+KDT9rgjRzj44otU\nWLLA/cH4+ODbux/+r72F2ckKLoCkz2eTOPwVUk6fdn88IiIiIiLiUQ88UJTx46tQqVKQ0/b588/z\n0kuBHDni/gfXSpY0MWqU832mAC5etDN8eCJTpyYbUQldREREPEwJKsmYP+D8vWP+leDpALzXDk8H\nYCDfkBAaDRtGgxdfxMffcVPhpJgY/hw5kr8mTMCWmMgLDd0cUNN2MPB9qFjTefu2NTBhEH57NuNX\nGCjs5ngyybrZ0xFIbqN7Fa4l6+I4l+jpALzQzTyQbsPYH0CVc5LMCkAz2xtpvpKO/DNjqVUrnIkT\nW9KmTTmn7bt2RTJw4CqWLz8BRV6AIu6LJTAQXnoGhvwfBAc7ticnw0efwojxZi5HBUKhQPcFk1WR\neoMlWaXvGeeSPR2A5GVGz1WujSnZprfxIiJZZTJR44knaD56NCElnddu3z17NmuGDSP2zBn3x1Oh\nBgwcD7ff47z99DGYNBiWfef+WERERERExKMKFw7grbdu5+mnb8Ficdzb6dKlBF5/fS1Tp+7AZnPv\nqimTCR57EEa/DmVKOe+zcAm8/CYcOOzWUERERMQLKUElIpIFJRo3puUHH1CicWOn7Wf+/JOVzz/P\n2Y0b3R9MwXDo+yY82B98nPw6j70Ks0bBlxMgScsHRERERETyMovFRP/+dXn77dspXDjAod1qtfPJ\nJ9t58831XLrk/mV2jRvAxHegiYtKEjv3wAtvwLLVbg9FREREvJQSVCIimRBSsiTN3n2Xmt27O22P\nOX2aP4YMYe8XX0CKm2u3+/jCI89C7zegQJhju90OCz+DKa/BxXPujUVERPIPK8aWzVB1HBGRTLvr\nrrJMnNiS2rWd1+lbtuw4AweuYvfui26PpUyp1BVTXR9y3n4hEl57Fz79AmwqjyQiIjnB6LnKtTEl\n25SgEhFJh8XfnwYvvECjV17BLyTEod2akMCW8ePZ+O67JMfGuj+g5vfBgPFQrqrz9i2r4P1BcGCb\n+2MRERERERGPqlSpEOPGtaBTp8pO2w8fjuKFF1azcKH76+cFB8Pg5+DlZ1P3nLpRUhJMnAojJ8CV\nq24PR0RERHIBJahERFyo8uCDtBg3joIVKjhtPzhvHr+//DLRR4+6P5jKdWDQ+9CojfP2vw/DBy/B\nqgXuj0VERERERDwqJMSXV19tzKBBt+Lv73hr5+rVJN55508mTvyLpCT3LlMymaB7Fxj1KpSMcN7n\nh59h8Ntw2ICpk4iIiOQeSlCJiNygSJ06tPrgA8q0auW0/cKOHawaOJCTK1e6P5iwotB/BDzQGywW\nx/aYaPh0BHwzCZKT3B+PiIjkX8n/fBg5noiIpGEyQffuNXn33WaUKOFY4QFg5sxdvPLKWs6edX+F\nh2ZNYMIIaFjPefvWnTDoNVi9zu2hiIhIfmb0XOXamJJtSlBJxvz/+ZB/ub9sd66Vmys1BIaH03rE\nCG7t2xezk2RQXGQkK19/nS3Tp5NyM8XSy2ahr48f3Ps8PPQaBBd0bLfZYNl0+PoNuHIBSmQ9HG+R\nvNnTEUhuo/eArqkEtnPJujAOkt28XaKIofwBX08H4WUiPR2AN8u9M5YmTUrz4Yftue22Uk7b168/\nyfPPL2Hz5tNZP3n5LHYvCe/9HzziosDD2Yvw6scw6+fULXKzen6vEql3n5JV+p5xTtfFNV0bR7om\n+YUSVCKS75l9fGj49NO0fOstAsPCHNrtViubJk9m1VtvkXD5svsDatwZuo+FiErO27cvhVkvwIld\n7o9FREREREQ8qlSpUEaPbsMTT9zitP3UqSsMHryML7/c6fZYQoPhlZ4w6DHw93NsT0iE97+Ed2dB\nTJzbwxEREZFcTgkqEcnXKrVtS7sJEyhas6bT9sO//caSgQOJ3LvX/cFUqAc9JkDtls7bT+2H2S/C\nll/cH4uIiMiNbP98GDmeiEg+FhDgw4svNmXYsGYEBztmgxISrIwdu44xY9YQG+veJ83NZujZEUY+\nDcXDnff5ZikMmQTHz7g1FBEREUdGz1WujSnZpgSViORLhStXpu3771OtY0en7RcPHuS3F17gwM8/\nuz+YQhHQ9R1o1TN15nejmMvw7Vvw68dg1RJnEREREZG87qGHajBuXFvKly/ktP2773YzePAyjh2L\ncnssLRvAhBegXlXn7Zv2wKD3Ye12t4ciIiIieYwSVCKSr/gXKECL116j8YABWPwcn0JMvHKF30eM\n4M8PP8SWlOTeYPwC4L5B8OAwCAx1bLdZ4ddP4Js3IeaSe2MRERERERGPq1u3OBMntqdly/JO27dv\nP8uAAUtYvfq422OpVBrGPg+dWzlvP3Uehk2GzxdDivY2FBERkZugBJWI5Asms5l6PXrQetQoQooX\nd2hPsdvZNnMmy199ldhz59wf0O2PQrcxUKy88/a/FsHsl+BvA0oLioiIZIYNsBo8nohIPlGkSBDv\nvNOa3r3rY7E4VlWIjIzj1VdXMGPGX9jt7s0GFQyB156C57uAn69je3wCvDcX3psDcQluDUVERCRz\njJ6rXBtTsk0JKhHJ88rccQftP/iAkg0aOG0/sXYtSwYM4MyWLe4PplJD6DkBajRz3n5yT+o+U1uX\nuD8WERERERHxKF9fM88+24jhw++kUKEAh/bkZBsffbSJt99eTVSUe7NBFgv06QRv94OiYc77fLEE\nhn0EJw14pk9ERETyPiWoJGP+/3zIv1RtzaVbPR3AfwSULk3N996j1GOPOW2PP3mSPYMHc/rrrykB\nlHBnLCVLwqB3oVl35x1iLsK8N2DVtNTSfsFuDMbLGP2Ai+R++p5xLc7TAXip+ERPR+B94m/m4Xsr\nxv4A6oddMisAcLLKI1+L9HQA3sx7Zizt2hVl4sTaVK8e4rR9yZLzDBq0m337YoCabo3l7lbw/tNQ\nu4Lz9g17YOBk+PNagQcnFcrzLv1BkqzS94xz8Z4OwIvp2jjK4jUxeq5ybUzJNiWoRCTP8S1UiAoD\nB1L55ZexBAU5tNvi4zk0ZgyHx47FFufe27mWwEAqvvQSlYcMgWAnWSdrMiybBAtGQFyUW2MRERER\nERHPq1u3AO+8U5377nMsPQ5w4EAMgwbtZtGi826PpVq1EMaNq8l99zlvP3EOhk6Hr1a4PRQRERHJ\nh5SgEpE8I6BUKSoOGkS5vn3xCXX+SN+pr79m75AhxJ844d5gTCZKPfYYNUaPJrBMGed9/loIX78E\nZw+6NxYREREREfG4O+8MZ+jQyrRvX8xpe3R0Mm+/fYBJk46SnOzefabCwnwZPrwqzzxTHl9fxz2v\nYuNh9Fcw/jutRBYRERH3UYJKRHK9kGrVqPTyy5Tu1g2zn5/TPtFbt7Lr+ee5tGaN2+MJa9KEWhMm\nENakifMOJ3bAVy/A7uVuj0VERCTHqMSfiEiWmUzwwAMRDB1amcaNnW/sZLen8OmnJ3jttX2cP5/k\n1nh8fEz071+Ot96qRuHCzudOs5bAq5/BaZWKFBGR3EIl/nItJahEJNcqdNttVB46lIhOnTCZHZ/6\nA0g8f559r77Kic8+A7vdrfEEli1LjdGjXe55xZXz8P1rsPpTSHFvLCIiIiIi4jm+viYef7w0Q4ZU\ndrnHFMAff1xkwIDdbN0a7faY7rmnGOPH16RGDefVJv7YAYM+hi0H3B6KiIiICKAElYjkQkXvvpvK\nQ4dSpHVrl30Szp7lyIQJHJ8yBeuVK26NxxIcTOWhQ6n04otYAgMd2m2JiRydOJHKR0ZBvHtjERER\nERERzwkJsdCnTzleeKEipUs7zg2u+eOPi4wZc4hffnH/PlM1a4Ywfnwtl6UFjx6N4+XZQcz73e2h\niIiIiKShBJWI5Aomi4USDz1E5SFDKHjrrS77xR46xOGxYzk5ezb2RDcXSzeZKN29OzVGjSKgZEmn\nXc7Mm8eewYOJO3KEyt3cG46IiIhbqcSfiIhLRYr48fzzFXj22fIuS+cB/PTTWcaMOczatZfcHlN4\nuB9vvVWVfv3K4ePjWHHi6lUrI0ceZOLEIyTefq/b4xEREXEblfjLtZSgkoz5oO+UG53wdADeq2OV\nHD6hnz882AOeegnKVXbdb/dfMG00wb/O4xa7nVvK5nAcN2rYHF6ZALUbuI5n1CBKbPqdEhYgp69L\nHpLs6QBE8hC9P3bOqgvjwJbi6QhEcpAFzVdudMbTAXgxn445erpy5eDFQfBUTwgKct7HaoUvv4b3\nxsHu3RFAhFu/Z3194bln4Y1XoVAhx3a7HT6dCa8P9+HcuRpADfcFkydoxiKSM/Sm3DVdG0c2Twcg\nBtHbeBHxTiEFoOvT8ORAKBrhut/65TBtDKxdakxcpcvDy+/BPY84bz9/Bia8CvNnu33PKxERERER\n8YzatWHwS/BYF/BxcWclLg5mfAbvT4Tjx42J6/6OMG4MVHHxgNzKVTDoJdi+3Zh4RERERNKjBJWI\neJeiEalJqcf6Q2hB533sdlg6H6aPgR2bjIkrOBSefgV6DEpd1XWjxAT4bDxMGw2xMcbEJCIiYiQb\nxj7cqYcmRcQL3XEHDH0Z7kunIt6lSzBpMnz0MURGGhNXnTowYRzc5WKb3kOH4KUh8ONCY+IREREx\nlNFzlWtjSrYpQSUi3qFsJej9cmo5P2cJIICkJPhxDswYC0cPGBOX2QwP94KB70CR4s77/PI1jBsK\npwx6LFJERERERAxjMsG9HWDoYLjjdtf9/v4bxk+E6TMgNtaY2IoVgxFvpZYYtFgc26Oj4e13UpNl\nSUnGxCQiIiKSWUpQiYhn1boV+gyBdg85n1EBxFyFb6bCrAlw7rRxsTVplbrPVPW6ztt3bIRRg+Cv\ndcbFJCIiIiIihvDxgf91gSEvpZb0c2XvXhgzDr78CpIN2q7Izw8G/B+89goUKODYbrPB1Okw/C3j\nVnGJiIiIZJUSVCLiGU1bpyammrV13efieZj9AXz5MVyJMiw0ylWGIWOhTSfn7edOpa6YWvgFpGiX\neRERySesGFs2Q3tFi4iHBAXBU73gxYFQrpzrfhv+hNHvwcKfjJ0WPNgZxo6GihWdt/+2FF54GXbv\nNi4mERERjzJ6rnJtTMk2JagkY36Ai4pr+dVBAxfx5DZV3kin0WSG6p2g+VAodZvrfpePwrpxsHUm\nJMdDOvXdc1RAQbjzdWj0f+Dj59ieFAdr34O1YyEoDv6XxfPvypEo8xz9PZesMujB5FxJ18a5ZP2i\ncaDvFclT/Emds8h/HPR0AN6rRxWXTYWD4dlW8HxrKBLq+hSLd8LoJfD7AaAw8GSOR+lU/bIw4VG4\ns5rz9v1n4YVvYdFO4LZ/PjJrW/bjy7v0RkKySu+0nNN1cU3XxpGuSX6hBJWIuJ/FD+o+AXcMhiIu\nZlMAZ3fAmtGw+1uwG7jToNkCDfpAq7chuKjzPts/h2XD4MrfxsUlIiIiIiJuVzoMXrgb+raAYBcP\nZ9rs8M0meO9X2H7S2PgiCsLITtDj9tQtcm90KRbe+gk+XgVWbdguIiIiuYgSVCLiPv6h0KAvNB0E\nBUq57ndsNawZAwcXGxfbNZXuhnbvQ3EXReVProfFA+HURkPDEhER8TrJGPsgox6aFBE3q1ECBreH\nxxuBr4u7IwnJ8NkaGPcbHDV4L6cA39TE2bB7ICTAsd1qS01KvfVTapJKREQk3zJ6rnJtTMk2JahE\nJOcFF4UmA+C2ZyAwzHW/fT+mJqZOrjcutmuKVIO246Dafc7bo07A0sGw6xtj4xIREREREbdqXBGG\ntodO9V33iYqDySvhw+Vw/qpxsV3T5TYY8xCUC3fe/ssOeOk72HfW2LhEREREcpISVCKSY3zKl4d7\nX4L6vcA30HknWzLs+CJ1H6cLewyND0hNmLUcnpo8s/g6tifGpJYZXDcerAnGxyciIiIiIm7Rvn0Q\nQ16ClulUHT8dBROWwtTf4aoHpgO3lYcJXeCOys7bd59O3Wfqt92GhiUiIiLiFkpQiUi2+d1yC4WH\nDCHk0UfBx8WvlaRY2DId1r8P0QYXbQcw+8BtT0PLNyGosPM+W2fC8lfh6hlDQxMREckV7ICRe5vY\nDRxLRPIsiwUeeSSEIUMKU6+eiw2mgAPn4L0lMHcDJFkNDPAfpcLg3c7Qranz9sir8MZCmPZ76n5Y\nIiIi8h9Gz1WujSnZpgSViNy0gObNKTx0KMEdOrjuFHcR/vwQ/vwI4i8ZF9x/VekA7cZD0erO24/9\nDksGwZm/jI1LRERERETcIiDARM+eBXjppTAqVnRSOeEfm4/B6MUwfyvYU4yL75ogP3i5HQxuB0FO\n8mfJVvhwBbzzS2rZQREREZG8RAkqyZgP+k65wUZPB+BJJhOlO3ak5tChFG3q4vE+gMQTcGo8nJsB\nfnHQ3LgQrwusCRXeh7B2ztsTjsCxwXBxHtQh9cOd/nTz+UVExCkPPAjv9XRNJE/x/edD/iP/zlgK\nFvTlmWeqMnBgdYoVC3DZb+kFGH0QVkQCpf75MJAJ6FoaRteA0i6qoy84Ay/vgUMJwF1uDmitm88v\nIiLpSPZ0AF5I1yS/UNpBRDLF7OtL+a5dqTl4MAVr1nTdMW43/D0GIr+CFA/d/vItCmXehIh+YLI4\ntluvwN8j4fQHkJJoeHgiIiK5khVjM1vKoolIFpQoEcigQdXp378KoaHOM5Z2ewrfnzEx5hD8FW1w\ngP9xR2F4vxY0CnPevj0aBu2GlZHGxiUiIpJrGT1XuTamZJsSVCKSLp/gYCr17k2NF18kuEwZl/0u\nrFvH7tGjaTnkZ8ADtTEAzIFQchCUHgqWUMf2FHvqiq4Tr0PyeePjExERERGRHFW1aigvv1yTbt0q\n4O/v5OE0IDHRxuzZRxg3bi8Hx91vcIT/qhIMY2pC5xLO288lwmt74bMT2tZCRERE8gclqETEKf/w\ncKo+9xzV/u//8A8Pd9nv1C+/sHv0aC6sWZP6whCDAkzDDMW6Q9kR4F/aeZeoFXB0EMTtMDY0ERGR\nvEIrqETEizRsWJghQ2rx4INlMJtNTvtcuZLMlCkHmThxH2fOxBsc4b+K+sHwatCvHPiYHdsTbTDh\nCIw6CFf1u09ERCTrtIIq11KCSkTSCC5bluovvEDl3r3xCQ522sdutXL866/Z8957RO3caXCENyjU\nFsqPheBbnLfHH4RjL8GlhcbGJSIiIiIiOa5NmwiGDKlJmzYuliEB587FM3Hifj755ADR0Z7bwyLQ\nAoMqwpDKUMDFPmnfn4bBe+BonLGxiYiIiHgDJahEBICCtWpRc/Bgyj/2GGZf57Mna3w8hz/9lL3j\nxxN77JixAd4o6JbUxFRYW+ftyRfh5Ntw9hNI0caKIiIiIiK5ldls4sEHyzB0aE0aNHBd3eHw4auM\nHbuX2bOPkJBgMzDCtMxAtzLwTnUoHei8z4ZL8NIeWHvJ0NBEREREvIoSVCL5XJGmTak1dCil73dd\niz3x8mUOfPQR+ydNIvHCBQOjc8KvFJR9J7Wkn8lJfQx7Apz+AP5+F2we3PlYREQkr1GJPxExmL+/\nme7dK/LyyzWoUqWAy37btl1i9Og9fP/9CWw2D+2H+4+7i8LYmlC3oPP2w7EwbC98d9rYuERERPI0\nlfjLtZSgErkJhzwdQA6o0qEDdwwdSrnmzV32uXLqFBvef58t06aRFBOTuRNXzaEAb2QKhaChEDgI\nTC4eQ0yYC7Gvgf8JqOSmOLJD+TKntL5NskrfM67p/bFz+p5xpO8Vkbwud89YChTwp1+/Bgwa1IQS\nJUJd9lu58iijR6/lt98OZ/7k1XMgQCfq+MHYotDOeZV0LtpgxEX4JBqSCgCu822eEeXpALyZ3klI\nVul7xjm9A3VN18aR51ZCi7GUoBLJR0wWC7W7dOGOIUMofouLPZuAyP37WTtmDDu/+AJbUpKBETrj\nAwH9IHg4mIs675K0HGJfButWY0MTEREREZEcU7x4MAMGNOGZZxpSsGCAy37z5+9lzJi1/PnnKQOj\nc66UD4wIhycLgNnk2J5ghw+jYNQliLYbHp6IiIiIV1OCSiQf8AkMpH6vXtz+0ksUKl/eZb9TGzey\nZvRo9v/4Iyl2L5g9+XWG4NHg42JZlnUXxA6GpMXGxiUiIpIf2TD24U49NCmSb1SqFMZLL91Ojx71\nCAhwfpsiOdnG3Lk7GDt2Hfv2RRocoaNQMwwJg0FhEOSk8jjA51fg1Ug4oQfjRURE3Mvoucq1MSXb\nlKASycMCwsJo9OyzNHr+eYKLulh9BBz69VfWjhnDsZUrDYwuHT6NIWQc+DZz3m47A3GvQ8Is9NdA\nRERERCR3qlcvgiFD7uCRR2pisTjP8sTEJDFt2hYmTNjA339fMThCRz5A34IwPByKubijsiIOXr4A\nfyUaGpqIiIhIrqMElUgeFFqqFE0HDaJBv374hYQ47WO32djz/fesHTOGs1u9pDSeuRKEvAv+jzhv\nT4mBuLEQNx6INTQ0ERERERHJGS1blmfo0Dto166yyz6RkXF88MGfTJ68kcuXEwyMzrVOITC6CFTz\nc96+OxEGR8IiTVVEREREMkUJKpE8JLxaNe4YPJhbnngCi5/zWZM1MZFtM2eybtw4Lh/OwmbC7mQq\nDEGvQ+AzYHISd4oNEmZA7HBIOWd8fCIiIpJaMsPIshkqiSWSp5hM0KlTdYYMuYPGjUu77Hf8eBTj\nxq3ns8+2EheXbGCErjUKgHFFoHmQ8/azVnj9IsyMVn0HERERjzB6rnJtTMk2JagkY574AfdypTwd\nwA2KNmpE3SFDKNepEyaz89IYSdHR7P3kE3Z98AHxZ88SBLiYX2VLbAEXBdid8sfX8n/4WoZhMhVy\n2sNq+/MN7IIAAJkcSURBVJkk21BSzHshFCAr5/cuwRe9YF8vL6RfLyI5xztu43kfXRdHuiaSpyQD\nJk8H4W28Z8bi62vmiScqM3jwLVSvXshlv507LzFmzA6++eYwVmsKUMwt8ZgLZn55U0WLiZEhfjzq\nYl+s2JQUxsUmMz4uObW+Q8HcO1uxXwj2dAheTDMWkZyhd6Cu6do40jXJL5SgEsnFijVtSsN33qFk\n69Yu+8SdPcuuCRPYO2UKyVc8X7M9lQmLuSt+PiMwm8o57WGzbybJOhh7ymqDYxMRERERkeyyWEw8\n/XQNhgypS+nSrpMff/xxltGjt7No0UkDo0tfYRO8GuzHM0E++Jkcs5+2lBQ+i7fyZmwyZ+0pHohQ\nREREJG/I9wmqy5cvc+5casmwQoUKERERkeNjpKSkcPHiRaKjoylYsCBFihTJ1HFHjx4l8f/Zu+/w\nqIq2j+O/TSEJCYTeS+ggCChFpApIE1CaiiI2QMQK2OCRFxR9bKigiFKtCGJXkGahCEgTpHeEQAgh\nENJ79rx/YPYh7m7KZluS7+e6crFk5szcu+fs5szeZ+ak5X1X1dDQUFWvXr2wYaII8S9bVu1fe03N\nxo2zWyfu+HHtmzFDxz75RFn5OI7cxcfUXaX8XpevTxub5WbjlNIzpyjL/IUkBnsAAHgNlvgDkE/X\nX19RCxZ00fXX2x/7Ll8ertdf36PNm71nCe8ASY+V9td/gv1Vzsf2tLyf0jI1KSFdB7MYqwAA4DVY\n4q/IKnEJqr1792rjxo3atm2btm/frmPHjskwrpxYjho1SgsXLix0H1lZWfrpp5/0yy+/6LffftOx\nY8eUnp5uKa9UqZL69u2rp556Sq1bt7bbzsCBA3XgwIE8+xsxYoQWL15c6LhRNIQNHqwb33tPwTVq\n2Cy/uGuX9rz2mk59840Ms/csKWcyXaNSvq/Lz/cWm+WGEav0rP8qM2uOJO9JqAEAAADIn9Kl/TR9\nehuNH99cvr7Wi91lZpq1ZMkJvfHGXh04cNkDEdpmknRXoK9eCimlMBtxS9KfGVl6NiFd6zO8Z4wF\nAABQ1JW4BNUjjzyizZs3W/5ftmxZxTt52bPLly/rtttus/zfx8fHMsMpKipKFy9e1OLFi7V06VK9\n9dZbevLJJ3Ntr2zZsrnOkKphJ1GB4qV0jRrq+N57Chs82GZ5xK+/au9rrynil1/cHFnuTKomf78X\n5efzgEwmX6tyw0hXZtb7Ss/6r6QY9wcIAAAAoND69KmluXM7KSysjFVZcnKmFi48orfe2qfw8EQP\nRGffTf4+eqNMKbXxtx6rSNLpLLOmJKZraWoW6zsAAAA4WYlLULVq1UqtW7dW+/bt1b59e9WtW1el\nS5d2ah9+fn66/fbb1b9/f3Xq1ElhYWHy87vyUqelpemLL77Qf/7zH507d04TJkxQ69at1a1bN7vt\nDR48WB9//LFTY0QRYjKp2cMPq91rr6lU2bJWxXFHj2rT2LGKXL/e/bHlKlj+vk/L3/cpmUy215zP\nzPpS6ZnPy9BJN8cGAAAKLEPuvVcx90UGioTKlQM1c2YHjRjR0Gb5kiXHNXHiNkVFpbg5stw18zXp\n9TKl1D/A9tcisWZDryRl6L3kDNZ3AADA27l7rJLdJwqtxCWo5syZk+P/qampTu+jXLly+vLLL22W\nBQQE6L777lOrVq103XXXyTAMzZ07N9cEFUquctdcoy4LFqhqx45WZeaMDO15/XX99fLLXnWPKclX\nfj4Pyt/vBfmYbN/TLcv8u9Izn5XZ2O7m2AAAAAA4y333NdJbb92gihUDrcpOn07QuHGbtWrVWQ9E\nZl81H5NeCPbXg0F+8jVZ32cq3TD0QXKmXk5KVwxTpgAAAFyqxCWovEXr1q0VFhamU6dO6fjx454O\nJ3fJkrI8HYR3GV3FxR0EBEjjn5cee04qVcq6/M+t8nlqjK47tF/Xhbo4loLo1V9m/zfk43ONzWKz\n+YiyMp6TOesH+Uiyvbp7MRbn6QAAFHfco9U2XhdrvCYoVpLFFaxWRru09QYNpHnzpJ49rcuysqR3\n3pGmTi2jpKS+Lo2jIIKDpaeflp6uaCjExzoxJUlfpmRpcnymTmZJJe7rEv4wAHA5Pmjs47WxxmtS\nUpSwMy7vkpJyZYkD7iGFHG7sKr05X2rYxLosIV7672Tpk7mS2YtuztuqjTRthtSpu82kk2FcUGbG\nCzJnLhB/YAAAKKLMcu9FS150qgPgCj+/K0meqVOloCDr8t27pTFjpD//dH9s9vj6Sg8+KL34onTl\n1s7WyalNaWY9HZ+pbRlMmQIAoEhy91glu08UGgkqD/nyyy8VFRUlSbrjjjvyrJ+enq7z58/LZDKp\nUqVKCrI1GkDRFlruSpJnhJ2rHVf/IE16VIqMcGtYuapdV5r8X2noCJvFhpGirMy3lJXxhqQE98YG\nAAAAwGnat5cWLJBatrQuS06Wpk2TZs2SMr3oerRbbpHeeENq3tx2+dFMs56Lz9L3qXzDBAAA4Akk\nqFwsKSlJZ86ckSQlJyfr7Nmz+vbbb7VkyRJJ0kMPPaQRI2x/uZ/t+++/1xdffKG0f+4z5OPjo1at\nWmnkyJEaO3asSpcu7donAde77Q7p5XekKjbu2XT+nPSfx6WfvnV/XPaElpOe/I80+okryxH+i2GY\nZc76RJkZ/ycZXpRQAwAAAFAgISHSf/8rPfaY5GNjuYS1a6Vx46STJ90fmz3XXy/NmCH16GG7PDrL\n0AsJmZqfbGZ9BwAAAA8iQeViv//+u/r162f1+w4dOuiFF15Qnz598mwjLi5OpUuXVr169XT58mXF\nxsZq9+7d2r17t+bPn68ff/xRjRo1KnBsly9ftlsWGxtb4PbggJq1pdffl3oNsF3+yVzp5UlSvJfc\nvKhUKemBR6QJ/yeVr2CzijlrtTLTn5Vh7HNzcAAAwKUy5d6VevnWGPC4AQOk99+Xate2Lrt4UZow\nQVq82P1x2VOnzpVk2j332C5PSZHezszU64lZSmA1PwAAig93j1Wy+0Sh2bpdDJwoJCRETZo0UZMm\nTVS3bl35+/tLkrZt26ZXXnlFBw8etLttr169tGzZMkVFRSkpKUknT57U5cuXtW/fPt15552SpMOH\nD6tv375KSCj48mkVKlSw+1O/fn3HnjDyx8dHGvOE9PtB28mpo4ekgZ2lZ8d5T3LqtjukTYek6TNt\nJ6cO7JHu6K2MtH4kpwAAAIAirFo1adkyafly28mpTz+Vmjb1nuRUaKj0+uvSkSO2k1Nms/TRR1Kj\nRtKUBJJTAAAA3oIZVC7WuXNnHT582PL/1NRUff/993r66ae1ceNGdezYUWvXrlX79u2ttp05c6bN\nNlu0aKEvvvhCtWvX1ptvvqmTJ09q5syZmjp1qmueRLSkUq5push6qhDbVm4p9V0oVW9nXZaZJm19\nRdr2mtQlXepSiH6cpVZn6aY3pRo32C5POCv9PkU68JnU1qwAL8mneaP4eE9H4J0yPB2AF/P3dABe\niouUUFB8zljjfYRiJUqMbP9tgmObmUzS6BukNwZK5Wzc9vjERenhr6Vfjkq6t1AROoW/r/RIJ+n/\nekkVg23XWXtEema5tPecpDskxVovUQ5JYrBiH2cStjFasY8zLRQUnzPWeB+VFJzGu1lgYKCGDx+u\nG264Qa1atVJcXJzGjh2r3bt3F7itl19+WZ9++qkuXLigZcuWFThBFRMTY7csNjaWWVTO5hcodZwm\ntX9a8rHx1jvzu7TmISnmsHWZJ1RoLHV7XWo0yHZ5WvyVRNrOWVJmijsjAwAAnsASf0Cx1qSKNP92\nqWsD67LMLOmtDdKLa6QUL/kO7fZW0qv9pQaVbJfvPXclMbX2iHvjAgAAHsASf0UWCSoPqVevnu6/\n/37Nnj1bf/31l44fP66GDRsWqI2AgAB169ZNX331lQ4fPqysrCz5+vrme/vy5csXNGw4qm5Pqfdc\nqbyNfZwaK214VtqzUJIXrDVRurLU6QWp1UO2E2lZGdKeedKW6VJytNvDAwAAAOA8pXyl53pKz98s\nBdg4/d8RLo35Utpzzv2x2dKpnvTmrVKHurbLI+KkKSulT3dKZi8YXgEAAMA+ElQe1Lx5c8vjM2fO\nFDhBJUnlypWTJJnNZiUnJ6tMmTLOCg/OEFRR6v6W1OI+2+WHv5J+fUJKOu/euGzxC5LaTpA6TJJK\n2TmOjn4rbZgkXT7m3tgAAIDnMYMKKHY6hkkL7pCuqWZdlpgmTVklzf7dOxI9jSpLrw+QBl9ruzwh\nVXrtN2nmBu+Z5QUAANyEGVRFFgkqD0pMTLQ8djSxdPr0aUlXZlORnPIy14yQesy8MiPp3xLOSj8/\nIh1f7v64/s3kIzW/V+ryklSmlu0657ZK656WIja7NzYAAAAATlc2UHqtvzSuk+3ylYekcV9L4Zfd\nG5ctlUOkab2lsTdKfjYWDMnMkub9Ib24VopOtC4HAACA9yJB5UGrV6+WJJUqVUpNmjQp8PZnz57V\nhg0bJEkdOnRwamwohNB6Uu8PpHp9rMsMs7RrjvT781J6gvtj+7ew3tJNM6QqLW2XXz4hbZwkHfna\nvXEBAAAAcInB10rvDZFqhFqXRSVIT34nLfvL7WFZCfKXJnSTnutxJaFmy3f7pEkrpKOsPA4AAFAk\nkaBywN9//620tDT5+vqqUaNGVuXJyckqXbp0rm28//77+uWXXyRJw4YNs5r9lFcbsbGxGj58uNLS\n0iRJY8eOLejTgLOZfKV2E6ROL0r+NvZd9D5p9Rgpcpv7Y/u3yi2vJKbq9bZdnnJJ2vKS9NcHUla6\ne2MDAADeKVOSO5fNYskMwKlqhkqzh9hfIm/RNumZ5dLlZPfG9W8+JmlkW+nlflKtcrbrbDstPf2j\ntOlvt4YGAAC8lbvHKtl9otBKXIIqLi5OkZGRlv+np6fnKDt8+LDl/z4+PmrcuLFVG/369dORI0cU\nGhqq2NhYq/L7779fp06d0sCBA9WkSRNVq1ZNFSpUUFJSkg4cOKAlS5bo119/lSTVqVNHM2bMsGpj\n1KhROnr0qAYOHKhmzZqpWrVqqlixomJiYrRp0ya9//77ioiIkHQlwTV8+HCHX5M8hUuysZRCSZb5\n2b9+4XO9fIMWyuR7nVVdw0iVOW26jIA3pds8vBi6qaZ8Al6Wyf9emUw+VsWGkSoj/V2ZM16R2sVJ\n7QrehR+DRLsSUj0dAYDijltu2Ma4wRqvCYqVvyWZPB2El/n+f+9yk6RxISa9Ws5HZX2sX6ijGYbG\nxpi1vrohPeTGGG3oFWjSjHI+alXK9g49mWlocqxZX5oM6TYHOzle4r4Gyad4TwcAoNhjtGIfZ+fW\neE1KihJ3ZvbVV19pzJgxNsu+/vprff31/5YyCw4OznGfqPwqXbq0duzYoR07duRar1evXlq4cKFq\n1KhhVRYcHKxdu3Zp165ddrf39fXV+PHj9corr8hkYkTmGaXlEzBdplLjZTJZZ/HMmb/JnDpWMh/3\nQGxXKyOfgEkylZogkynIZg1z+mKZ056XjHA3xwYAAADAFZr7S/Mr+KpjgPV4McMw9Hq8oZfjzErz\nQGxXu9ZfmlHOR32CrC+ik6SYLEMvxZv1foIh1ncAAAAoPkpcgqpcuXL5vt9TcHCwzd/Xr19fklS2\nbFmb5R9//LGeeeYZrVmzRrt27VJkZKSio6MVEBCgqlWr6vrrr9egQYN0/fXX2+174cKFGj9+vFav\nXq3du3crMjJSFy9eVFBQkGrVqqUOHTrorrvuUq1atfL1XOB8Jr++8gn8QCafMKsyw4iROfVpGRkf\nuT+wHPxk8h8rn4BpMvlUtlnjShLtGclsPxkKAACgrH9+3NkfAIcESHo+1EfPlTWplI2LGbemGRoT\nk6X9Hr6Yvaav9FKoj+4LNsnHRpxphqF3Ewy9EmdWrOGBAAEAQNHg7rFKdp8otBKXoBo2bJiGDRtW\nqDZWrlyZZ53mzZurefPmheqnRYsWatGiRaHagAtUqCyfoFny8b/bZrE5Y4nMqRMk44KbA8vJ5DdI\nPgGvy+RrvUylJBlZB2ROe1ZGZt7HMwAAAICioWsnaX51XzXxt074xJuvLJE3N9GQ2QOxZStjkp4r\n66MJZUwqbWPZQUn6PMms52PNOs2XPwAAAMVWiUtQAYVy2/3S02/Jx7+CVZFhPi1z6jgZmavcH9fV\nfG+Qb8CbMvl1tllsmM/LnPZ//8zuYrQHAAAAFAfly0tvTJdG3yfZuinXD8lmPXrZrAgPDgH8JD0U\nYtK0UB9V8bWdmFqXatYzsWb9yVp+AAAAxR4JKiA/6jSUps6T2vewKjKMLBnp78icNlVSkvtjy2Zq\nIJ/AV+Xjf7vNYsNIkpE+Q+a0N+XROAEAQNGUKffeq5j7IgP5dscQ6d03pKpVrMvOZRp6/LJZ36Z4\ndo28QUEmvVbOx+bMLkk6mGHo2ctm/ZTKWn4AAKCA3D1Wye4ThUaCCnk6d1BK9nQQnuLnp5BnnlHZ\nqVNlCgy0Kk7UEZ0wvazEgENSQFMPBCj5KVS1NUrVdLt85G9VbihLUfpB4aZ5ygi45PI4O4X/6dL2\ni7J4TwcAoNjj/Ng2D99ixSvxmqBYSYxQSb0Aq04dX82ZU04DBgTZLP9G5zXb75QSK2epjZtjy9Zc\nIRqvMF2nUJvlF5Wu+QrXD/5Ryqoil8f55y+dXNxDUZXg6QAAFHuMVuzj7Nwar0lJQYIKsMO/fXuV\nX7hQ/tdea1WWpVSd0Tyd0xIZHlomz1/lVU23q4bukp/K2KwTo991SrOVopNujg4AAACAq/j4SI8/\nHqKXXy6rkBAfq/KTStZ/dVx7PJh0aK4Q3aea6qFKNstTlKXPFKHFOqdklh4HAAAokUhQAf9iCglR\n2f/+V8GPPSaTj/VgL3XtWu3vPUdpivBAdFKQ6qmmRqiy+slHATbrJOqQTmmW4sRsJgAA4CRZcu+F\nr3xfDdjUqpW/Fiwor3btSlmVpaUZeuWVeK198YAy5P6l8nwk3aSKGqEaaqWyNutkydCPitI8ndFF\ncaMpAADgBO4eq2T3iUIjQQVcJXDgQIXOmSO/2rWtyrIuXlTchAlKWbxYaYb7F8gI1Q2qqbtVXvaX\npEhVpMI1R9FaI3lgQAoAAADANYKCTJo2rayeeipEfn7W93H6/fc0PfTQZR0+nKk2L7p3LFBavhqo\nKrpLNVRL1kujZ9ukGL2rUzqpFDdGBwAAAG9FggqQ5FOtmsq9+66Cbr/dZnnyJ58o7qmnZL50ya1x\nmeSvyuqjGhqhYDWyWy9TCTqrD3VOy2RwFSIAAABQrPTsGaB588qrQQPrIXxsrFnPPhunhQuTZLj5\nGrWqKqU7VV2DVU1lcvl64bASNUuntFNxbowOAAAA3o4EFUo2k0mlR49W6BtvyKdcOavizBMnFPvw\nw0r75Re3huWnUFXTMFXX7SplZ812SUrVOZ3TUl3QD8pSshsjBAAAJU6Grqzf5c7+gBKuYkUfvfVW\nqO67L9hm+VdfJeuJJ2J1/rzZrXE1VbDuUU3drEryk/Vsrmx/6LIW65y2KdZ9wQEAgJLH3WOV7D5R\naCSokKdfJNkeDhVtoU2aqPP8+SrftatVmTkzU/vefFO7pk9XVor18hMVVMUlMYWophpqkOqop/xy\nWRrjkg7pmL5VpP6QIbOkkH9+vMA5TwfgveI9HYCXcvcSwUWJv6cD8FKcA6Kg+JyxxmuC4uVXFccR\ny4gRDTVz5g2qXDnIquzs2SQ98shmLV8ebnPbKqrg9HhMkm5QdQ1WQ12rynbrZShLv+mMvtdxnf7n\nDNg1oycHnfV0AN6K0Yp9/NW0jdGKfYxYUFB8zljjNSkpSFChxPEpVUqtJk1S6//8R74BAVbl0Tt2\n6PcxYxSzZ4/bYqqklmqkwaquG+zWMZSlCG3RcX2nGB12W2wAAAAA3KdevTL64INO6tOnllWZ2Wxo\nzpyDev75nUpIcM8XoAHy1c2qq0FqqJq5XBQXpzSt0En9pJOKVZpbYgMAAEDRRoIKJUrVTp2uzJq6\n5hqrsozERO2cMkUHZ8+WYXb9Ehkm+amWuqiRBqucGtqtl6FkndZaHdcPSlaUy+MCAACwKeufH3f2\nB5Qgvr4mTZjQQi++2EalS1sP1ffti9GYMb9r27Zot8RTQYEaqAa6RfVURqXs1jujBH2nY/pN4UqX\ne5caBAAAkOT+sUp2nyg0ElQoEUqFhqrdq6+q2bhxNsvPrFypzePGKTHc9hIZzuSvENVTPzXQQAXl\ncn+pZEXrhH7QKa1RhpJcHhcAAAAAz2jTppIWLOis666zHh+kpmZq+vTdevPNfcrIcH0CqL5CNUgN\n1U215Z/LzRz+0gV9p+PaqfMyXB4VAAAAiiMSVCj2woYM0Y2zZyu4Rg2rspSoKP3xxBM6+eWXLo8j\nWNXVULeprnrnen+pyzqqY/pWEdosg1Q8AAAAUGwFB/tp+vQ2evLJ5vL1tU4G/fbbOY0du0nHj7v2\n/kAmSW1VTYPVUK1zuWNUhsza8M/9pU4qzqUxAQAAoPgjQYViq3TNmur43nsKGzTIZvmRhQu1/dln\nlXb5skvjqKjm/9xfqoNMdq5ANGRWpLbqmL7TJR1waTwAAAAOyZTk6+b+gGKsb99a+uCDTgoLK2NV\nFhOTqqee2qaPPz7m0hhKyUc9VEeD1FB1VNZuvQSla6X+1nKdUIxSXRoTAABAgbl7rJLdJwqNBBWK\nHZOPj5o+/LDavfqqSpW1HmTFHT2qTQ89pMgNG1wXg3xVU53UUINVQU3s1stUqk7rZx3X90pSpMvi\nAQAAAOAdqlQJ0syZN+juu23fh3bJkuOaMGGbLlxIcVkM5RSg/qqvAaqvUAXYrRehRH2v4/pFp5XG\n6g4AAABwMhJUyFOkpCBPB5FPlZs318AFC1T7xhutyrIyMrT59de18eWXlZWWVqh+Qu28dfxUWvXV\nW400UKVV2e72KbqkY1qhk1qjDCVati4Wznk6AO/luq8YAOCKDE8H4KWKynkMAEcVnRHL/fe31ltv\n9VaFCtbxnj4dq3HjftKqVccL3Y+fQm3+vo7K6jY11k2qI/9cLjPer2j9oKPaoXPKvutVMRmtSGc8\nHYC3YrQCwNUYrdhXNM5jAFcoNueYKNl8AwLUdcoUdXruOfn6+1uVn/njDy0fM0bRB1yzfF5pVVUj\nDVA99ZK/Stutd1kndVTf64w2yWAeKAAAKEqy5N5lLNw8WSMjI0MXL15UhQoVFBBgf0ZJYWRmZio6\nOlpBQUEKDQ2VyWRyST/wPg0bVtC8eQPUo0c9q7KsLLPeeWebpk5dp6Qk13x511pVNUiNdb2q2a2T\nJbN+1xn9qGM6Ltcugw4AAOBU7h6rZPfpBvv27dMXX3yhw4cPKyUlRTVr1lTfvn116623yt/G9+AF\nYTabtWfPHm3YsEEnTpzQ+fPnFRcXp/Lly6tVq1YaNGiQrrnmGic9E9tIUKHIq9utmwbOn6+KjRtb\nlaXFx+vXyZO1c+5cGWazja0Lp4KaqLEGqZY6yJTLFYjntF1H9YOitc/pMQAAAMBxn332md577z3t\n2LFDhmFIklq0aKGHHnpIjz76qHx8bN9DND+2bt2q3377TevXr9dff/2lixcvWvooX768evTooSef\nfFJdunRxynOB9/H399HTT3fU1KndFBhoPfzevTtSY8Ys159/On+5b3/5qJvq6DY1Vl07M6okKVHp\nWquTWqHjusgsGgAAAK+QlZWliRMnavbs2ZYxRLaFCxeqVatWWrZsmZo0sX97mdxs3bpV/fr1U2xs\nrM3yL7/8Us8//7zuuusuffDBBwoNtX8+WRgkqFBkBZYvr15vvKHrR4+2WX74hx+08tFHlRAR4dR+\nTb6+qqmOaqzbVEnN7NbLUppO6Tcd049KkHNjAAAAcLsMSe6c8OPiVWDMZrNGjhypJUuWSJJ8fHxU\npUo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fx9v39S0x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\nAAAAAElFTkSuQmCC\n"
          }
        }
      ],
      "source": [
        "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
        "\n",
        "freqs = E.f\n",
        "\n",
        "fig, ax = plt.subplots(ncols=2, figsize=(10, 5))\n",
        "pm = ax[0].pcolormesh(diameters, td.C_0 / freqs, T.T, shading=\"auto\", cmap=\"jet\")\n",
        "pm2 = ax[1].pcolormesh(diameters, td.C_0 / freqs, 1 - T.T, shading=\"auto\", cmap=\"jet\")\n",
        "\n",
        "for axes, p in zip(ax, [pm, pm2]):\n",
        "    divider = make_axes_locatable(axes)\n",
        "    cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n",
        "    plt.colorbar(p, cax=cax)\n",
        "\n",
        "\n",
        "for a in ax:\n",
        "    x_lim = a.get_xlim()\n",
        "\n",
        "    a.plot(diameters, E_max, color=\"white\")\n",
        "    a.plot(diameters, H_max, color=\"white\")\n",
        "\n",
        "    a.set_xlim(x_lim)\n",
        "    a.set_xlabel(\"disk diameter (µm)\")\n",
        "\n",
        "ax[0].set_ylabel(\"wavelength (µm)\")\n",
        "ax[1].tick_params(left=False, labelleft=False)\n",
        "\n",
        "plt.show()"
      ],
      "id": "fff90c7a"
    }
  ],
  "nbformat": 4,
  "nbformat_minor": 5,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  }
}