[{"data":1,"prerenderedAt":539},["ShallowReactive",2],{"method-gslivo2025":3},{"method":4,"reference":57,"equipment":82,"figures":139,"results":140},{"id":5,"label":6,"shortName":7,"title":8,"year":9,"era":10,"cluster":11,"scope":12,"keyIdeaZh":13,"keyIdeaEn":14,"fulltextStatus":15,"publicationStatus":16,"recommendation":17,"constructionRelevance":18,"validationEnvironment":19,"strengths":23,"limitations":26,"sensors":31,"platform":35,"estimator":37,"association":38,"timeModel":39,"deskew":40,"loopClosure":41,"globalOptimization":42,"mapRepresentation":43,"prior":44,"outputGeometry":45,"compute":46,"codeUrl":47,"codeLicense":48,"relatedVersions":49},"gslivo2025","Hong et al., 2025","GS-LIVO","GS-LIVO: Real-Time LiDAR, Inertial, and Visual Multisensor Fused Odometry With Gaussian Mapping",2025,"recent","C07","odometry_with_local_mapping","GS-LIVO 以三維高斯（3D Gaussians）取代傳統彩色點雲與稀疏區塊地圖：全域高斯地圖以空間雜湊索引的八元樹管理，只將視野內的高斯放入 GPU 上的滑動視窗即時最佳化，以控制顯示記憶體用量。高斯由光達點與影像聯合初始化，里程計沿用 FAST-LIVO2 的序列更新 IESKF，但視覺殘差改為渲染影像與實際影像的光度誤差。作者宣稱這是首個可在 Jetson Orin NX 嵌入式平台即時運作並線上更新地圖的高斯式 SLAM。","Replaces colored point clouds with a hash-octree Gaussian map optimized in a GPU sliding window and uses rendered-image photometric residuals inside a FAST-LIVO2-derived sequential IESKF.","full_text_reviewed","peer_reviewed_published","main_body","not_reported（資料為 FAST-LIVO2 校園序列、MARS-LVIG 空拍與小型室內動作捕捉場地）",[20,21,22],"public_benchmark","controlled_experiment","independent_reference",[24,25],"Outdoor trajectory RMSE of 0.58 m (HKisland03), 0.63 m (HKairport01) and, in the T-RO version, 0.75 m (Radcliffe01), versus 1.71, 1.22 and 1.85 m for R3LIVE and 4.12, 5.21 and 6.15 m for LVI-SAM","the FAST-LIVO baseline was better outdoors (0.51, 0.56, 0.63 m). The 0.042 m outdoor RMSE in the Sec. III-B2 text contradicts the table, whereas the quoted R3LIVE 1.465 m and LVI-SAM 4.665 m equal the means of their MARS-LVIG rows, so 0.042 m should not be cited | Sliding window keeps map update time below 100 ms and PSNR around 25 dB while limiting GPU memory (Sec. III-C) | Runs on Jetson Orin NX (Sec. III-D)",[27,28,29,30],"Localization slightly less precise than the FAST-LIVO baseline, which the tables label 'FAST-LIVO [7]' (IROS 2022) while the text cites [8] (FAST-LIVO2), so the baseline identity is ambiguous","per-frame time 48.5 to 94.8 ms versus 8.75 to 49.8 ms for that baseline (Sec. III-B2","T-RO Table II) | Level-of-detail adaptation and merging of homogeneous Gaussians left for future work (arXiv Sec. III-E) | T-RO conclusion: the system still struggles with indoor-outdoor transitions","size-adaptive voxels proposed (T-RO Sec. IV) | Mapping quality evaluated by rendering metrics (PSNR), not metric geometry accuracy (inference from Sec. III) | GS-LIVO memory for Playground01 and Playground02 is swapped between the rendering table (1.5, 1.2 GB) and the GS-SLAM table (1.2, 1.5 GB), and the rendering-table 'Dur.\u002Fs' values equal the per-frame ms values (T-RO Tables I and III)",[32,33,34],"3D LiDAR","IMU","camera",[36],"aerial robotic vehicles (MARS-LVIG) | mobile chassis carrying the sensor suite and Jetson Orin NX | carrying mode of the FAST-LIVO2 sequences, the Playground sequences and (T-RO) Oxford Spires not stated in this paper","iterated error-state Kalman filter with sequential updates, modified from FAST-LIVO2","LiDAR update with planar features of a size-adaptive voxel map (FAST-LIVO2 and VoxelMap-type LIO); visual update minimizes the photometric loss between the image rendered from Gaussians in the current FoV at the LiDAR-updated pose and the captured image, with Jacobians derived as in MonoGS and chained to the IMU pose inside the IESKF","discrete poses; emulated PPS hardware synchronization","Not described; the LiDAR-inertial update is taken from FAST-LIVO2 and size-adaptive voxel LIO ([57], [59] in T-RO) without re-describing motion compensation","none reported","none","Planar 3D Gaussians initialized from LiDAR leaf voxels (normal from LiDAR, color by bilinear sampling) in a global hash-indexed octree in CPU RAM; Gaussians in the current FoV kept in a contiguous CPU buffer mirrored in GPU memory and optimized with Adam; root voxel 0.03 or 0.06 m indoors and 1.0 or 0.5 m outdoors with 2 subdivision levels; window of 100,000 Gaussians (desktop) or 20,000 (Orin NX)","offline camera intrinsic and LiDAR-camera extrinsic calibration","Gaussian map with photorealistic rendering; 2D occupancy grid derived for navigation (Sec. III-D); point-cloud export not_reported","GPU required; desktop i9-13900KF, 128 GB RAM, RTX-4090: 48.5 to 94.8 ms per frame; map updates over 10 Hz indoors and about 3 Hz outdoors (Sec. I-B); Jetson Orin NX 16 GB: 15.3 ms optimization, 18.9 ms map maintenance, 48.3 ms total at 256x216 images and 20,000 Gaussians","https:\u002F\u002Fgithub.com\u002FHKUST-Aerial-Robotics\u002FGS-LIVO","GPL-2.0 (LICENSE file in src\u002Fgs-livo; root README is a demo page without a license statement; package.xml carries template metadata 'BSD' with Ji Zhang as author, an inconsistency inherited from a template)",[50,54],{"relation":51,"title":52,"doi_or_url":53},"preprint","GS-LIVO arXiv v1 (2025-01-15)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2501.08672",{"relation":55,"title":56,"doi_or_url":47},"code_release","HKUST-Aerial-Robotics\u002FGS-LIVO",{"id":5,"kind":58,"shortName":7,"title":8,"authors":59,"year":9,"venue":67,"venueType":68,"publisher":69,"volumeIssuePages":70,"doi":71,"arxivId":72,"url":53,"firstPublicDate":73,"publicationStatus":16,"metadataStatus":74,"fulltextStatus":15,"era":10,"classicReason":75,"codeUrl":47,"cluster":11,"topics":76,"mdpi":78,"verification":79,"label":6,"fulltextRoute":80,"versionRead":81,"addedByCensus":78},"method",[60,61,62,63,64,65,66],"Sheng Hong","Chunran Zheng","Yishu Shen","Changze Li","Fu Zhang","Tong Qin","Shaojie Shen","IEEE Transactions on Robotics","journal","IEEE","41: 4253-4268","10.1109\u002Ftro.2025.3582809","2501.08672","2025-01-15","metadata_verified","not_applicable",[11,77],"C09",false,"corrected","NTU institutional (Chrome)","IEEE T-RO version of record (Xplore document 11049044, HTML full text; Tables I to III read as images) and arXiv 2501.08672v1 HTML (2025-01-15); both read in full",[83,90,94,98,102,107,113,117,123,129,133],{"category":84,"model":85,"canonical":85,"role":86,"dataset":87,"specs":88,"locator":89},"lidar","LiDAR (model not stated)","method input",null,"not_reported","Sec. II; Fig. 8(e) arXiv, Fig. 12(e) T-RO",{"category":91,"model":92,"canonical":92,"role":86,"dataset":87,"specs":88,"locator":93},"imu","IMU (model not stated)","Sec. II",{"category":34,"model":95,"canonical":95,"role":86,"dataset":87,"specs":96,"locator":97},"camera (model not stated; pinhole projection model)","intrinsics calibrated with a checkerboard","Sec. II; Sec. III-A",{"category":99,"model":100,"canonical":100,"role":86,"dataset":87,"specs":101,"locator":93},"other","emulated pulse-per-second (PPS) synchronization","temporal alignment of LiDAR, IMU and camera",{"category":103,"model":104,"canonical":104,"role":86,"dataset":87,"specs":105,"locator":106},"platform","mobile chassis","carries the sensor suite and Jetson Orin NX","Fig. 8(e) arXiv; Fig. 12(e) T-RO",{"category":108,"model":109,"canonical":109,"role":110,"dataset":87,"specs":111,"locator":112},"compute","NVIDIA Jetson Orin NX","compute for runtime","8-core CPU, 1024 CUDA cores, 16 GB LPDDR5","Abstract footnote; Sec. III-D",{"category":108,"model":114,"canonical":114,"role":110,"dataset":87,"specs":115,"locator":116},"desktop with Intel i9-13900KF CPU and NVIDIA RTX-4090 GPU","128 GB RAM","Sec. III",{"category":99,"model":118,"canonical":118,"role":119,"dataset":120,"specs":121,"locator":122},"motion capture system (MoCap)","reference or ground truth","proprietary Playground sequences","ground truth for small indoor sequences; tracker-odometry alignment calibrated","Sec. III-A",{"category":124,"model":125,"canonical":125,"role":119,"dataset":126,"specs":127,"locator":128},"gnss","D-RTK (DJI Differential Real-Time Kinematic GNSS system)","MARS-LVIG","precise ground truth for odometry","Sec. III-A (T-RO wording)",{"category":103,"model":130,"canonical":130,"role":131,"dataset":126,"specs":132,"locator":122},"aerial robotic vehicles","dataset sensor","MARS-LVIG data collection over mountains and seas",{"category":134,"model":135,"canonical":135,"role":119,"dataset":136,"specs":137,"locator":138},"tls_scanner","TLS (model not stated in this paper)","Oxford Spires (Radcliffe01; T-RO only)","LiDAR-TLS map registration giving 1 to 2 cm ground-truth trajectories","T-RO Sec. III-A",[],{"totalRows":141,"groupCount":142,"groups":143,"others":533},36,5,[144,280,379,498],{"slug":145,"group":146,"sourceId":5,"sourceLabel":6,"table":147,"selfRows":148,"metrics":149,"seqs":160,"entrants":177,"cells":189,"outcomes":270,"locators":272,"hardware":275,"wordings":277,"notes":278},"gslivo2025-table-iii","gslivo2025:Table III","Table III",15,[150,154,157],{"label":151,"unit":152,"statistic":153,"alignment":88},"RMSE\u002Fm","m","RMSE",{"label":155,"unit":156,"statistic":88,"alignment":75},"Dur.\u002Fms","ms",{"label":158,"unit":159,"statistic":88,"alignment":75},"Mem.\u002FGB","GB",[161,165,167,170,173],{"dataset":162,"sequence":163,"environment":164},"proprietary (MoCap)","Playground01","small indoor UAV playground",{"dataset":162,"sequence":166,"environment":164},"Playground02",{"dataset":126,"sequence":168,"environment":169},"HKisland03","aerial, island and sea",{"dataset":126,"sequence":171,"environment":172},"HKairport01","aerial, airport area",{"dataset":174,"sequence":175,"environment":176},"Oxford Spires","Radcliffe01","outdoor (Oxford Spires dataset)",[178,182,185,187],{"name":179,"methodId":180,"linkable":181,"proposed":78,"self":78},"SplaTAM","splatam2024",true,{"name":183,"methodId":184,"linkable":181,"proposed":78,"self":78},"MonoGS*","monogs2024",{"name":186,"methodId":184,"linkable":181,"proposed":78,"self":78},"MonoGS",{"name":188,"methodId":5,"linkable":181,"proposed":181,"self":181},"GS-LIVO (Ours)",[190,194,197,200,202,204,206,208,210,212,214,216,218,220,222,223,225,227,229,232,234,236,238,240,242,243,244,245,247,249,251,252,253,254,256,258,260,262,263,264,266,268],[191,191,191,192,193,191,193,193,191],0,0.28,-1,[191,195,191,196,193,191,191,193,191],1,612.8,[191,198,191,199,193,191,191,193,191],2,12.5,[191,191,195,201,193,191,193,193,191],0.23,[191,195,195,203,193,191,191,193,191],831.6,[191,198,195,205,193,191,191,193,191],21,[195,191,191,207,193,191,193,193,191],0.09,[195,195,191,209,193,191,191,193,191],841.5,[195,198,191,211,193,191,191,193,191],19.6,[195,191,195,213,193,191,193,193,191],0.11,[195,195,195,215,193,191,191,193,191],851.2,[195,198,195,217,193,191,191,193,191],17.2,[198,191,191,219,193,191,193,193,191],0.18,[198,195,191,221,193,191,191,193,191],541.5,[198,198,191,205,193,191,191,193,191],[198,191,195,224,193,191,193,193,191],0.17,[198,195,195,226,193,191,191,193,191],451.2,[198,198,195,228,193,191,191,193,191],18.7,[230,191,191,231,193,191,193,193,191],3,0.006,[230,195,191,233,193,191,191,193,191],48.5,[230,198,191,235,193,191,191,193,191],1.2,[230,191,195,237,193,191,193,193,191],0.005,[230,195,195,239,193,191,191,193,191],63.4,[230,198,195,241,193,191,191,193,191],1.5,[191,191,198,87,191,195,193,193,191],[195,191,198,87,191,195,193,193,191],[198,191,198,87,191,195,193,193,191],[230,191,198,246,193,195,193,193,191],0.58,[230,195,198,248,193,195,191,193,191],82.8,[230,198,198,250,193,195,191,193,191],8,[191,191,230,87,191,195,193,193,191],[195,191,230,87,191,195,193,193,191],[198,191,230,87,191,195,193,193,191],[230,191,230,255,193,195,193,193,191],0.63,[230,195,230,257,193,195,191,193,191],93.2,[230,198,230,259,193,195,191,193,191],9.5,[191,191,261,87,191,195,193,193,191],4,[195,191,261,87,191,195,193,193,191],[198,191,261,87,191,195,193,193,191],[230,191,261,265,193,195,193,193,191],0.75,[230,195,261,267,193,195,191,193,191],94.8,[230,198,261,269,193,195,191,193,191],9.7,[271],"failed (x for RMSE, duration and memory)",[273,274],"T-RO Table III; arXiv v1 Table IV","T-RO Table III",[276],"desktop, Intel i9-13900KF CPU, 128 GB RAM, NVIDIA RTX-4090 GPU",[],[279],"Gaussian-based SLAM comparison (T-RO Table III; arXiv v1 Table IV without Radcliffe01); MonoGS* uses LiDAR-projected depth, MonoGS is monocular; x = failed on all outdoor sequences (one row per failed method and sequence); Dur.\u002Fms column header carries an upward arrow in the table",{"slug":281,"group":282,"sourceId":5,"sourceLabel":6,"table":283,"selfRows":284,"metrics":285,"seqs":288,"entrants":294,"cells":304,"outcomes":371,"locators":372,"hardware":375,"wordings":376,"notes":377},"gslivo2025-table-ii","gslivo2025:Table II","Table II",10,[286,287],{"label":151,"unit":152,"statistic":153,"alignment":88},{"label":155,"unit":156,"statistic":88,"alignment":75},[289,290,291,292,293],{"dataset":126,"sequence":168,"environment":169},{"dataset":126,"sequence":171,"environment":172},{"dataset":174,"sequence":175,"environment":176},{"dataset":162,"sequence":163,"environment":164},{"dataset":162,"sequence":166,"environment":164},[295,297,300,303],{"name":296,"methodId":87,"linkable":78,"proposed":78,"self":78},"FAST-LIVO [7]",{"name":298,"methodId":299,"linkable":181,"proposed":78,"self":78},"R3LIVE","r3live2022",{"name":301,"methodId":302,"linkable":181,"proposed":78,"self":78},"LVI-SAM","lvisam2021",{"name":188,"methodId":5,"linkable":181,"proposed":181,"self":181},[305,307,309,311,313,314,316,317,319,320,322,324,326,328,330,332,334,336,338,339,341,343,345,347,349,351,353,355,357,359,361,362,363,364,365,366,367,368,369,370],[191,191,191,306,193,191,193,193,191],0.51,[191,195,191,308,193,191,191,193,191],38.9,[191,191,195,310,193,191,193,193,191],0.56,[191,195,195,312,193,191,191,193,191],44.5,[191,191,198,255,193,195,193,193,191],[191,195,198,315,193,195,191,193,191],49.8,[191,191,230,237,193,191,193,193,191],[191,195,230,318,193,191,191,193,191],10.5,[191,191,261,237,193,191,193,193,191],[191,195,261,321,193,191,191,193,191],8.75,[195,191,191,323,193,191,193,193,191],1.71,[195,195,191,325,193,191,191,193,191],283.3,[195,191,195,327,193,191,193,193,191],1.22,[195,195,195,329,193,191,191,193,191],266.8,[195,191,198,331,193,195,193,193,191],1.85,[195,195,198,333,193,195,191,193,191],292.4,[195,191,230,335,193,191,193,193,191],0.014,[195,195,230,337,193,191,191,193,191],60.6,[195,191,261,335,193,191,193,193,191],[195,195,261,340,193,191,191,193,191],66.6,[198,191,191,342,193,191,193,193,191],4.12,[198,195,191,344,193,191,191,193,191],73.5,[198,191,195,346,193,191,193,193,191],5.21,[198,195,195,348,193,191,191,193,191],84.8,[198,191,198,350,193,195,193,193,191],6.15,[198,195,198,352,193,195,191,193,191],83.6,[198,191,230,354,193,191,193,193,191],0.023,[198,195,230,356,193,191,191,193,191],96.6,[198,191,261,358,193,191,193,193,191],0.021,[198,195,261,360,193,191,191,193,191],86.6,[230,191,191,246,193,191,193,193,191],[230,195,191,248,193,191,191,193,191],[230,191,195,255,193,191,193,193,191],[230,195,195,257,193,191,191,193,191],[230,191,198,265,193,195,193,193,191],[230,195,198,267,193,195,191,193,191],[230,191,230,231,193,191,193,193,191],[230,195,230,233,193,191,191,193,191],[230,191,261,237,193,191,193,193,191],[230,195,261,239,193,191,191,193,191],[],[373,374],"T-RO Table II; arXiv v1 Table III","T-RO Table II",[276],[],[378],"LIV-based SLAM comparison (T-RO Table II; arXiv v1 Table III without Radcliffe01); image 640x480; octree 0.06 m (indoor) or 0.5 m (outdoor), 2 layers; window 100,000 Gaussians; ground truth D-RTK (MARS-LVIG), MoCap (Playground), TLS-registered trajectories (Oxford Spires Radcliffe01); baseline labelled 'FAST-LIVO [7]' but the text attributes it to [8] (FAST-LIVO2), so method_id left null",{"slug":380,"group":381,"sourceId":5,"sourceLabel":6,"table":382,"selfRows":383,"metrics":384,"seqs":388,"entrants":399,"cells":412,"outcomes":490,"locators":492,"hardware":494,"wordings":495,"notes":496},"gslivo2025-table-i","gslivo2025:Table I","Table I",6,[385],{"label":386,"unit":387,"statistic":88,"alignment":75},"PSNR (dB), higher is better","dB",[389,393,395,396,397,398],{"dataset":390,"sequence":391,"environment":392},"FAST-LIVO2 dataset","HKU01","large-scale university campus",{"dataset":390,"sequence":394,"environment":392},"CBD03",{"dataset":162,"sequence":163,"environment":164},{"dataset":162,"sequence":166,"environment":164},{"dataset":126,"sequence":168,"environment":169},{"dataset":126,"sequence":171,"environment":172},[400,403,405,406,407,409,411],{"name":401,"methodId":402,"linkable":181,"proposed":78,"self":78},"3D-GS","kerbl2023_3dgs",{"name":404,"methodId":87,"linkable":78,"proposed":78,"self":78},"M2Mapping",{"name":179,"methodId":180,"linkable":181,"proposed":78,"self":78},{"name":186,"methodId":184,"linkable":181,"proposed":78,"self":78},{"name":408,"methodId":87,"linkable":78,"proposed":78,"self":78},"S3GS",{"name":410,"methodId":87,"linkable":78,"proposed":78,"self":78},"LetsGo",{"name":188,"methodId":5,"linkable":181,"proposed":181,"self":181},[413,415,417,419,421,422,424,426,428,430,432,434,436,438,440,442,444,446,448,450,452,454,456,458,460,462,464,466,468,470,472,474,475,477,479,481,483,485,486,488],[191,191,191,414,193,191,193,193,191],26.22,[195,191,191,416,193,191,193,193,191],25.06,[198,191,191,418,193,191,193,193,191],24.06,[230,191,191,420,193,191,193,193,191],23.51,[261,191,191,87,191,191,193,193,191],[142,191,191,423,193,191,193,193,191],24.51,[383,191,191,425,193,191,193,193,191],25.34,[191,191,195,427,193,191,193,193,191],29.54,[195,191,195,429,193,191,193,193,191],27.65,[198,191,195,431,193,191,193,193,191],26.85,[230,191,195,433,193,191,193,193,191],27.1,[261,191,195,435,193,191,193,193,191],24.92,[142,191,195,437,193,191,193,193,191],25.51,[383,191,195,439,193,191,193,193,191],27.52,[191,191,198,441,193,191,193,193,191],29.75,[195,191,198,443,193,191,193,193,191],27.32,[198,191,198,445,193,191,193,193,191],22.01,[230,191,198,447,193,191,193,193,191],21.15,[261,191,198,449,193,191,193,193,191],20.5,[142,191,198,451,193,191,193,193,191],25.2,[383,191,198,453,193,191,193,193,191],24.09,[191,191,230,455,193,191,193,193,191],26.54,[195,191,230,457,193,191,193,193,191],25.12,[198,191,230,459,193,191,193,193,191],24.45,[230,191,230,461,193,191,193,193,191],25.93,[261,191,230,463,193,191,193,193,191],22.24,[142,191,230,465,193,191,193,193,191],23.12,[383,191,230,467,193,191,193,193,191],25.52,[191,191,261,469,193,191,193,193,191],17.52,[198,191,261,471,193,191,193,193,191],12.6,[230,191,261,473,193,191,193,193,191],14.22,[261,191,261,87,191,191,193,193,191],[142,191,261,476,193,191,193,193,191],18.32,[383,191,261,478,193,191,193,193,191],15.32,[191,191,142,480,193,191,193,193,191],16.98,[198,191,142,482,193,191,193,193,191],12.39,[230,191,142,484,193,191,193,193,191],13.87,[261,191,142,87,191,191,193,193,191],[142,191,142,487,193,191,193,193,191],17.32,[383,191,142,489,193,191,193,193,191],15.18,[491],"failed (x)",[493],"T-RO Table I; arXiv v1 Table II",[],[],[497],"Rendering comparison (T-RO Table I; arXiv v1 Table II without M2Mapping); 15,000 iterations per method; indoor root voxel 0.03 m, outdoor 1.0 m, 2 levels; SplaTAM and MonoGS fed with LiDAR-projected depth; x = failed; Dur.\u002Fs and Mem.\u002FGB columns omitted for the row cap",{"slug":499,"group":500,"sourceId":5,"sourceLabel":6,"table":501,"selfRows":261,"metrics":502,"seqs":511,"entrants":514,"cells":516,"outcomes":525,"locators":526,"hardware":528,"wordings":530,"notes":531},"gslivo2025-text-sec-iii-d","gslivo2025:Text Sec.III-D","Text Sec.III-D",[503,505,507,509],{"label":504,"unit":156,"statistic":88,"alignment":75},"optimization time",{"label":506,"unit":156,"statistic":88,"alignment":75},"map maintenance time",{"label":508,"unit":156,"statistic":88,"alignment":75},"total pipeline time",{"label":510,"unit":387,"statistic":88,"alignment":75},"PSNR",[512],{"dataset":88,"sequence":513,"environment":104},"embedded platform run",[515],{"name":188,"methodId":5,"linkable":181,"proposed":181,"self":181},[517,519,521,523],[191,191,191,518,193,191,191,193,191],15.3,[191,195,191,520,193,191,191,193,191],18.9,[191,198,191,522,193,191,191,193,191],48.3,[191,230,191,524,193,191,191,193,191],23.52,[],[527],"Sec. III-D; Fig. 8 (arXiv) or Fig. 12 (T-RO)",[529],"NVIDIA Jetson Orin NX 16 GB",[],[532],"Embedded test on Jetson Orin NX 16 GB: root voxel 0.5 m, 2 layers, 256x216 images, window of 20,000 Gaussians",[534],{"group":535,"slug":536,"sourceLabel":6,"table":537,"selfRows":195,"datasets":538},"gslivo2025:Text Sec.III-B2","gslivo2025-text-sec-iii-b2","Text Sec.III-B2",[126],1790510661438]