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{"embedding_dim": 1024, "data": [{"__id__": "chunk-c5b36c7bc30ea3197cf3de1efee747b5", "__created_at__": 1751722343, "content": "第十四届蓝桥杯大赛青少组省赛准考证\n\n比赛信息：\n\n注意事项\n\n一、参赛选手须在赛前按照《线上比赛操作指南》中的要求进行准备。（指南请自行下载）。\n\n二、比赛系统自下载准考证当日起开始公测，请参赛选手务必在赛前使用准考证上的账号密码提前登录系统进行练习测试。\n\n三、准考证可下载后，因设备原因或未测试环境造成不能参赛的情况，由参赛选手自行承担。\n\n四、电脑配置要求：必须有前置摄像头，麦克风，请尽量使用 2017 年以后购买的电脑（不包含微软 Surface 平板电脑），win10或以上版本，MAC OS10.9 或以上版本。\n\n五、准备一台带摄像头的移动设备（手机等）用于监考，安装“微信”软件（微信版本 8.0.30或以上版本）。\n\n六、提前在电脑下载谷歌浏览器最新版本（浏览器务必从官网下载 https://www.Google.cn/chrome/）。\n\n七、比赛当天晚于开始时间 30分钟登录的将被取消比赛资格。\n\n八、比赛采用多重防作弊措施，比赛过程中包括但不限于以下作弊行为：\n\n1.选手冒名顶替参赛； \n2.授权他人远程登录； \n3.使用分屏； \n4.不开“微考试助手”； \n5.离开“微考试助手”摄像头 5 分钟以上； \n6.故意遮挡摄像头或者关闭摄像头 5 分钟以上； \n7.故意关闭麦克风及摄像头行为； \n8.比赛环境中出现非参赛选手及任何与题目有关的声音； \n9.监控画面未按照比赛要求设置；\n\n10.比赛过程中出现缩小比赛平台并打开其他文件、资料、网页等；有上述行为的参赛选手，组委会有权降级或者取消参赛选手成绩。九、比赛时间不中断，允许准备草稿纸、笔以便答题时演算使用。\n\n十、选手做编程题的方式：\n\n1.Scratch 程序可以在网页直接编写（Scratch 3.0 版）。2.Python 和 $\\mathrm { C } { + } { + }$ 程序既可以在网页编写，也可以在本地 IDE编程环境编写，编写之后拷贝到网页“代码编写区域”。（参加比赛的考生应在电脑上自行安装对应编程科目的编程 IDE：Python3.7 以上， ${ \\mathrm { C } } { + } { + } 5 . 1 . 1$ 以上）3.EV3 和 Arduino 程序使用本地编程软件编写。将程序源文件保存到桌面，然后在比赛系统中上传。（EV3 编程软件采用乐高 Mindstorms 图形化编程环境，Arduino 统一使用 Mixly 0.997 及以上版本软件或 Arduino IDE1.8.5 版）4.EV3 器材为 45544 或 9898 核心套装。十一、《线上比赛操作指南》请扫描下方二维码查看。\n\n《线上比赛操作指南》\n\n十二、交卷后将无法继续作答，请确认不再继续答题后点击交卷。 \n十三、登录比赛系统前关闭电脑上所有软件和弹窗，以免造成卡顿和闪退情况。 \n十四、比赛中系统后台全程监控。 \n十五、比赛咨询电话 400-055-9099。 \n十六、上述内容解释权归组委会所有。", "full_doc_id": "doc-c5b36c7bc30ea3197cf3de1efee747b5", "file_path": "黄琬乔2023蓝桥杯省赛准考证.pdf"}, {"__id__": "chunk-6193ee83fcb4e9c4677af0eb360fcf1d", "__created_at__": 1751722431, "content": "Table Analysis:\nImage Path: images/4463c70fb5f0b6083da95d04f4d0404245d5bbbc47b6d92d91e9f8a001041608.jpg\nCaption: None\nStructure: <html><body><table><tr><td>姓名</td><td>黄琬乔</td><td>准考证号</td><td>2305C10579</td><td rowspan=\"3\"></td></tr><tr><td>证件类型</td><td>身份证号</td><td>证件号码</td><td>220105201212060427</td></tr><tr><td>比赛名称</td><td>第十四届蓝桥杯大赛 青少组C++初级组</td><td>比赛时间</td><td>2023年05月14日 10:00~11:30</td></tr><tr><td>比赛网址</td><td colspan=\"3\">http://kao.lanqiaoqingshao.cn</td><td></td></tr><tr><td>账号/密码</td><td colspan=\"3\">登录名：Ianqiao 登录密码：542t96</td><td>组织委员会</td></tr></table></body></html>\nFootnotes: None\n\nAnalysis: The table is structured in a clear, multi-row format with merged cells to logically group related information. It contains the following key columns and data points:\n\n1. **Personal Information**: \n - 姓名 (Name): 黄琬乔\n - 证件类型 (ID Type): 身份证号 (ID Card)\n - 证件号码 (ID Number): 220105201212060427 (reveals birthdate: 2012-12-06, indicating the participant is ~10 years old)\n\n2. **Exam Details**:\n - 准考证号 (Admission Number): 2305C10579 (prefix '2305' likely denotes May 2023 exam date)\n - 比赛名称 (Competition): 第十四届蓝桥杯大赛 青少组C++初级组 (14th Lanqiao Cup Youth C++ Beginner Group)\n - 比赛时间 (Time): 2023年05月14日 10:00~11:30 (90-minute duration)\n\n3. **Access Credentials**:\n - 比赛网址 (URL): http://kao.lanqiaoqingshao.cn\n - 账号/密码 (Credentials): Login 'Ianqiao', Password '542t96'\n\nKey patterns:\n- The alphanumeric admission number follows a clear structure (year-month-code-ID).\n- The competition's technical requirements (C++ 5.1.1+, IDE needed) directly relate to the 'C++初级组' designation.\n- The birthdate embedded in the ID confirms eligibility for the youth group (青少组).\n\nThe table operationalizes the surrounding content by:\n1) Providing the exact credentials needed for system testing (per Point #2 in注意事项)\n2) Specifying the C++ track details that correlate with IDE requirements in Point #10\n3) Including the precise competition URL mentioned throughout the guidelines", "full_doc_id": "chunk-6193ee83fcb4e9c4677af0eb360fcf1d", "file_path": "黄琬乔2023蓝桥杯省赛准考证.pdf"}, {"__id__": "chunk-d106a3f9cff8e605ebc0173f231fb0ef", "__created_at__": 1751722941, "content": "[Image not found: - https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/202311240947996.png] \n\n■\\$q_1,q_2,...,q_n\\$■■■■■■■ \n\n■■■■■■■\\$n\\$■■■■■■■■■■■■■■■■\\$q_1\\$■■■■■■■■■■■■■■\\$q_2\\$■■■■...,■■■\\$q_ n\\$■■■■■■\\$q_n\\$■■■■ \n\n■■■ $\\$ 123$ ■■■\\$a_1,a_2,a_3,...,a_m\\$■ \n\n■■■■■■■■ \\$s,t(0≤s■t≤m)\\$,■■ \\$m\\$ ■■ \\$a_{s+1}+a_{s+2}+…+a_t\\$■ \n\n■■\\$m\\$■■ \n\n\\$\\$\\large a_1,a_1+a_2,a_1+a_2+a_3,……,a_1+a_2+a_3+...+a_m\\$\\$ \n\n\\$\\$ \n\\large \\left\\{\\begin{matrix} \na_1+a_2+...+a_s=b\\*m+r & \\\\ a_1+a_2+...+a_t=c\\*m+r & \\end{matrix}\\right. \n\n\\$\\$ \n\n■■■■■■\\$a_{s+1}+a_{s+2}+……+a_t=(c-b)\\*m\\$■■■ \\$a_{s+1}+a_{s+2}+……+a_t\\$ ■■■\\$m\\$■■,■■■ \n\n■■■■■\\$m=7\\$■■ \n■■■: \\$a[]=\\{2,4,6,3,5,5,6\\}\\$ \n■■■: \\$s[]=\\{2,6,12,15,20,25,31\\}\\$ \n■■■■■\\$7\\$■■■■■■■ \n\\$r[]=\\{2,6,5,1,6,4,3\\}\\$■■■■■■\\$6\\$■■■(■■■\\$2\\$■\\$5\\$)■■■■■■■■ \n■\\$2+1=3\\$■■■■\\$5\\$■■■■\\$a[3]+a[4]+a[5]=6+3+5=14\\$ ■■\\$7\\$■■■ \n\n2.■■■■■■■■■■■ ■■ ■\\$1\\$■■■■■■ $\\$ 923,456$ $= 1 3 9 2 . 1 > = 1 9 . 1 2 3 9 1 3 9 5 8 . 1 8 4 \\} < = 1 3 * 1 2 = 1 5 6 8 . 9 5 1 1 5 0 9 5 1 1 5 0 . 1 2 5 1 1 5 0 9 5 1 1 5 0 . 1 2 5 1 1 5 0 9 5 1 1 5 0 . 1 5 1 2 < 0 . 5 1 1 5 0 5 1 1 5 0 . 1 5 1 1 5 0 5 1 1 5 0 . 1 5 1 1 5 0 5 1 1 5 1 5 0 . 1 5 1 1 5 0 5 1 1 5 0 . 1 5 1 1 5 0 5 1 1 5 1 5 0 . 1 5 1 1 5 0 5 1 1 5 0 5 1 5 1 5 0 . 1 5 1 5 1 5 0 5 1 5 1 5 0 5 1 5 1 5 0 5 1 5 1 5 0 . 1 5 1 5 1 5 0 5 1 5 0 5 1 5 1 5 0 5 1 5 5 0 5 1 5 5 1 5 0 5 1 5 5 0 5 1 5 5 0 5 1 5 5 0 5 1 5 5 5 0 5 1 5 5 5 0 5 5 1 5 5 5 0 5 5 1 5 5 5 5 5 5 1 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5", "full_doc_id": "doc-58b2cc606eac773417192c69b060fa4d", "file_path": "鸽巢原理.md"}, {"__id__": "chunk-d3dff7590af11f7079d6cda6f619482b", "__created_at__": 1751722941, "content": "5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5$ \n\n$\\$ 1+11,23,456$ $\\$ 1+11<= a,1+11 < a, 2 +1 1 < a, 3 + 1 1 <\\ldots < a, \\{ 84\\} + 1 1 < = 156 + 1 1 \\ S$ $\\$ 123,456$ \n\n■■■■■■■■■ \n\n■■\\$168\\$■■■■■■■■■■■\\$1\\$■\\$167\\$■■■■■■■■ \n\n■■\\$a_1,a_2,a_3,...,,a_{84}\\$■■■■■■■■■■■ \n■\\$a_1+11,a_2+11,a_3+11,...,a_{84} $^ +$ 11\\$■■■■■ ■■■■■■■\\$x\\$■\\$y\\$,■■ \n\\$\\$\\large a_x=a_y+11\\$\\$ \n\n■■■■■■■■■\\$oier\\$■■\\$y+1,y+2,y+3,...,x\\$■■■■■■\\$11\\$■■■ \n\n■■■■■■■ $\\$ 23$ ■■■■■■ ■■ ■■\\$5\\$■■■■■■■■■■■■■■■■■■■■■■", "full_doc_id": "doc-58b2cc606eac773417192c69b060fa4d", "file_path": "鸽巢原理.md"}, {"__id__": "chunk-67ea8f43013fac749f9f17ae6b44ceb3", "__created_at__": 1751722941, "content": "+$ 11\\$■■■■■ ■■■■■■■\\$x\\$■\\$y\\$,■■ \n\\$\\$\\large a_x=a_y+11\\$\\$ \n\n■■■■■■■■■\\$oier\\$■■\\$y+1,y+2,y+3,...,x\\$■■■■■■\\$11\\$■■■ \n\n■■■■■■■ $\\$ 23$ ■■■■■■ ■■ ■■\\$5\\$■■■■■■■■■■■■■■■■■■■■■■■\\$1\\$. \n\n[Image not found: - https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/202311241310117.png] \n\n■■\\$n+1\\$■■■■ ■■■■■\\$2n\\$■■■■■ \n\n$\\$ 1,2,3,4,.. . ,2n \\$ 1$ \n\n[Image not found: - https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/202311241316080.png] \n\n■■■\\$1,2,...,200\\$■■■\\$101\\$■■■■ \n\n■■■■■\\$2^k\\*a\\$■■■■■\\$k> $\\mathbf { }$ 0\\&\\&a\\$■■■■ \n\n■■\\$1\\$■\\$200\\$■■■■■■■■\\$a\\$■\\$100\\$■■\\$1,3,5■...■199\\$■■■ ■■■■\\$101\\$■■■■ ■■■■■■■■\\$a\\$■■■■■■■■\\$2^r\\*a\\$■\\$2^s\\*a\\$■■■\\$r<s\\$,■■■■■■ \n\n■■■■\\$1,2■...■200\\$■■■■■\\$100\\$■■■■■■■ ■■■■■■■\\$100\\$■■■■\\$101,10 2■...■119,200\\$■ \n\n■■\\$Ramsey\\$■■ \n\n■■■■■■■■\\$Frank\\$ \\$Plumpton\\$ \\$Ramsey\\$■■■■·■■■■·■■■■\\$1903−1930\\$■■■. \n\n[Image not found: - https://dsideal.obs.cn-north-1.myhuaweicloud.com/HuangHai/BlogImages/202311241334465.png] \n\n2.■■■■■■■ \\$Sum[i]\\$ ■ $\\$ 03$ ■■■■■■■ $\\$ 10$ Sum[i] $1 \\%$ $\\complement \\$ 8$ ■■■■■■■■■■■ \\$Sum[i] \\% C == Sum[j] $1 \\%$ C■j<i\\$■■■ \\$j +1\\$■■■ $\\$ 1$ ■■■■■■ $\\$ 03$ ■■■■ \n\nno sweets ■ \n\n#include using namespace std; const int $\\mathrm { ~ N ~ } = \\mathrm { ~ 1 } { \\tt e } 5 + \\mathrm { ~ 1 0 0 ~ }$ ; int sum[N], mod[N]; int main() { ios::sync_with_stdio(false); int c, n; while ( $\\therefore \\mathrm { i } \\mathrm { n } > > \\mathrm { c } > > \\mathrm { n } , \\mathrm { c } +$ n) { memset(mod, $^ { - 1 }$ , sizeof mod); memset(sum, 0, sizeof sum); $\\mod [ 0 ] = 0 ;$ ; int $\\mathrm { ~ { ~ s ~ } ~ } = \\mathrm { ~ 0 ~ }$ , $ { \\mathrm { ~ ~ t ~ } } = { \\mathrm { ~ ~ 0 ~ } }$ ; for (int $\\begin{array} { r l r } { \\dot { \\perp } } & { { } = } & { 1 } \\end{array}$ ; i $< = \\ n$ ; $\\dot { \\beth } + +$ ) { int $_ \\textrm { x }$ ; cin $> > \\mathrm { ~ ~ { ~ x ~ } ~ }$ ; $\\begin{array} { l c l } { \\textsf { s u m } [ \\dot { \\mathrm { ~ \\textbar { ~ } 1 ~ } } ] } & { = } & { ( \\textsf { s u m } [ \\dot { \\mathrm { ~ \\bar { ~ } 1 ~ } } } \\end{array}$ $- \\mathrm { ~ ~ \\underline { ~ } { ~ 1 ~ } ~ } ] + \\mathrm { ~ ~ x ~ } )$ % c; // ■■■ $\\textstyle { \\frac { 0 } { \\circ } } \\mathbf { C }$ if $( \\mathsf { s u m } [ \\mathrm { ~ i ~ } ] \\mathrm { ~ \\mathrel { = } ~ 0 ~ } )$ ) $\\begin{array}{c} \\{ \\begin{array} { r l } \\end{array} \\} \\begin{array} { r l } \\end{array} , \\begin{array} { r l } \\end{array} \\end{array}$ ; continue; } if $( { \\mathrm { m o d } } [ { \\tt s u m } [ \\tt { i } ] ] ] = = - 1 )$ mod[sum[i]] $=$ i; // ■■■■■■■■ else { // ■■■■■■■ $_ { \\textrm { { S } } } =$ mod[sum[i]]; // ■■■■ ${ \\begin{array} { l } { { \\ t } } \\end{array}", "full_doc_id": "doc-58b2cc606eac773417192c69b060fa4d", "file_path": "鸽巢原理.md"}, {"__id__": "chunk-3e54175328c36e5fbc0428b86db45829", "__created_at__": 1751722941, "content": "if $( { \\mathrm { m o d } } [ { \\tt s u m } [ \\tt { i } ] ] ] = = - 1 )$ mod[sum[i]] $=$ i; // ■■■■■■■■ else { // ■■■■■■■ $_ { \\textrm { { S } } } =$ mod[sum[i]]; // ■■■■ ${ \\begin{array} { l } { { \\ t } } \\end{array} } = { \\begin{array} { l } { { \\dot { \\ } } } } \\end{array} $ ; // ■■■■ } } for (int $\\begin{array} { r } { \\mathrm { ~ \\underline { ~ { ~ i ~ } ~ } ~ } = \\mathrm { ~ \\underline { ~ { ~ s ~ } ~ } ~ } + \\mathrm { ~ \\underline { ~ { ~ 1 ~ } ~ } } } \\end{array}$ ; i $< =$ t; $\\dot { 2 } + + 1$ ) cout << i << (i != t ? \" \" : \"\\n\"); } } \n\n\\$HDU\\$ \\$1205\\$ ■■■ \n\n■2■■\\$s>=n-1■\\$■■■■ \n\n■-■■■> $> =$ ■■■-1■■■■■■ \n\n■1■■■■■■■■■■■■■■\\$n<=1000000\\$■■■■\\$a[1000000]\\$■ \n\n■2■■\\$t,n\\$■■■■■■long long■■■\\$int\\$■■■■■\\$WA\\$■■ \n\n#include using namespace std; #define int long long const int $\\begin{array} { l } { \\displaystyle \\mathrm { ~ N ~ \\ = ~ \\ 1 0 0 0 0 1 0 ~ } } \\end{array}$ ; int a[N]; signed main() { int T; cin $> >$ T; while (T--) { int n, sum, mx; cin $> >$ n; sum $\\qquad = \\quad 0$ , $\\mathrm { ~ m x ~ } = ~ 0$ ; for (int $\\dot { ] } ~ = ~ 0$ ; $\\dot { \\mathrm { ~ \\scriptsize ~ J ~ } } < \\mathrm { ~ \\scriptsize ~ n ~ }$ ; $\\dot { ] } + +$ ) { cin $> >$ a[j]; sum $+ = \\texttt { a } [ \\dot { } ]$ ; mx = mx < a[j] ? a[j] : mx; } if ((sum - mx) $> =$ ( $\\mathrm { m } \\times \\mathrm { ~ - ~ } 1 )$ )) puts(\"Yes\"); else puts(\"No\"); } }", "full_doc_id": "doc-58b2cc606eac773417192c69b060fa4d", "file_path": "鸽巢原理.md"}, {"__id__": "chunk-303a6253ad5ce6cedc3106ad88ac03a7", "__created_at__": 1751723229, "content": "驿来特平台安全\n\n平台硬件\n\n目前，微信公众号管理功能模块上线了运维监控功能，可以实现域名、账户余额、数据库实例或MongoDb实例、Redis实例的CPU磁盘情况等情况的监控，同时，也可以提示到期时间等功能，对于未尽的监控事宜，可以考虑要求增加功能即可完成监控。经观察，上线一个月多的时间内，Mysql 数据库的磁盘由原来的 40GB，增加到 46GB，增长过快，按这个速度，一年后就是将近 100G，而100GB 的 Mysql 数据库在性能上是无法正常工作的，亟需解决主数据库磁盘增长过快的问题，不能等到问题发生时才关注。解决思路如下：\n\n1、 哪个表增长过快？ \n2、 是否可以考虑迁移到历史库？ \n这个工作虽然无法带来业务上的增长，但可以保证业务系统健康的运行，也是非常重要的工作，需要未雨绸缪。\n\n二、 防护措施\n\n1.网络安全层面：云防火墙，通过 ACL 访问控制策略，对于需要接入的平台允许放行，对于没有访问需求的 IP 进行阻断。实现精细的访问控制策略。通过云防火墙的内置入侵防御模块，对于网络攻击、病毒传播都有较好的防护效果。\n\n2.应用层面：Web 应用防火墙，通过 CNAME 的接入方式将域名接入WAF，可以隐藏您的网站源 IP，对于所有请求进行分析和识别，将具备攻击行为的请求进行拦截。利用区域封禁功能拦截境外不需要访问的 IP；扫描防护攻击将一些恶意扫描拦截；BOT 管理可以拦截爬虫、IDC 等机器行为。\n\n3.主机层面：云安全中心，使用云安全中心对主机进行防护、可以针对病毒、漏洞、入侵攻击、恶意行为、暴力破解等行为进行识别和防御。还支持主机防勒索和数据库防勒索功能。\n\n三、 维护制度\n\n1、 数据库只进行了备份，未按等保要求进行定期的恢复实验不能保证备份的东西我们就一定能恢复，需要制订制度由专人定期（比如每月 1 次）进行全量恢复实验，增量的采用 RedoLog 日志进行恢复实验，保证随时可以更新到出故障的最后一刻。实验完成后，需要实验人员签字留痕。 \n2、 购买阿里云提供的漏洞扫描服务对软件系统进行定期扫描一来是由于系统在不断的增加代码，带来更多风险，二来是 Java 或系统的缺陷会随时间的增长而更多的暴露出来，\n\n漏洞库也在不断的更新，建议每 3 个月增加一次扫描工作。3、 进行空难性故障恢复演练，梳理出异常情况下快速恢复服务的方法和路径。", "full_doc_id": "doc-303a6253ad5ce6cedc3106ad88ac03a7", "file_path": "驿来特平台安全.docx"}], "matrix": 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"}
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