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Our datasets cover real indoor scenes to support smart devices, robotics, and home intelligence.",[27,29,31,33,35,37,40,43,46,49,52,55,58,61,64,67,70],{"name":28,"value":7},"Living Room",{"name":30,"value":11},"Bedroom",{"name":32,"value":14},"Kitchen",{"name":34,"value":17},"Dining Area",{"name":36,"value":20},"Bathroom",{"name":38,"value":39},"Entrance \u002F Foyer","6",{"name":41,"value":42},"Corridor \u002F Hallway","7",{"name":44,"value":45},"Staircase","8",{"name":47,"value":48},"Home Office","9",{"name":50,"value":51},"Hobby \u002F Game Room","10",{"name":53,"value":54},"Worship Space","11",{"name":56,"value":57},"Garage","12",{"name":59,"value":60},"Utility \u002F Laundry Room","13",{"name":62,"value":63},"Pantry","14",{"name":65,"value":66},"Basement","15",{"name":68,"value":69},"Garden","16",{"name":71,"value":72},"Balcony","17",{"name":74,"value":11,"add_value":75,"describe":76,"children":77},"Office & Educational","workshop","AI is reshaping work and learning. Our datasets capture offices and classrooms to power automation and next-generation productivity tools.",[78,81,84,87,90,93,96,99,102,105,108,111,114,117,120,123,126,129,132],{"name":79,"value":80},"Open Office","18",{"name":82,"value":83},"Private Office","19",{"name":85,"value":86},"Co-working Space","20",{"name":88,"value":89},"Reception \u002F Lobby","21",{"name":91,"value":92},"Conference Room","22",{"name":94,"value":95},"Huddle Room \u002F Booth","23",{"name":97,"value":98},"Lecture Hall \u002F Auditorium","24",{"name":100,"value":101},"Classroom","25",{"name":103,"value":104},"Science Laboratory","26",{"name":106,"value":107},"Library","27",{"name":109,"value":110},"Design Studio","28",{"name":112,"value":113},"Pantry \u002F Breakroom","29",{"name":115,"value":116},"Cafeteria \u002F Canteen","30",{"name":118,"value":119},"Restroom (Commercial)","31",{"name":121,"value":122},"Gymnasium \u002F Sports Hall","32",{"name":124,"value":125},"Server Room","33",{"name":127,"value":128},"Mailroom \u002F Logistics","34",{"name":130,"value":131},"Janitorial Closet","35",{"name":133,"value":134},"Corridor \u002F Elevators","36",{"name":136,"value":14,"add_value":137,"describe":138,"children":139},"Healthcare & Medical","love-and-help","AI is advancing modern healthcare. Our datasets include clinics and hospitals to enable clinical insights and medical automation.",[140,143,146,149,152,155,158,161,164,167,170,173,176,179,182,185,188,191,194],{"name":141,"value":142},"Operating Theatre \u002F OR","37",{"name":144,"value":145},"Consultation Room","38",{"name":147,"value":148},"Treatment Room","39",{"name":150,"value":151},"Dental Clinic","40",{"name":153,"value":154},"Inpatient Ward","41",{"name":156,"value":157},"Nurse Station","42",{"name":159,"value":160},"ICU","43",{"name":162,"value":163},"NICU (Neonatal ICU)","44",{"name":165,"value":166},"Emergency Room (ER)","45",{"name":168,"value":169},"Hospital Corridor","46",{"name":171,"value":172},"Waiting Area","47",{"name":174,"value":175},"CT\u002FMRI","48",{"name":177,"value":178},"Medical Laboratory","49",{"name":180,"value":181},"Phlebotomy Room","50",{"name":183,"value":184},"Rehabilitation Gym","51",{"name":186,"value":187},"Assisted Living Room","52",{"name":189,"value":190},"Pharmacy","53",{"name":192,"value":193},"Sterile Processing Dept","54",{"name":195,"value":196},"Medical Storage","55",{"name":198,"value":17,"add_value":199,"describe":200,"children":201},"Business & Public Service","business","AI is elevating commercial and public services. Our datasets span retail, dining, and service centers to improve intelligent operations.",[202,205,208,211,214,217,220,223,226,229],{"name":203,"value":204},"Shopping Mall Atrium","86",{"name":206,"value":207},"Supermarket Shelf","87",{"name":209,"value":210},"Self-Checkout Counter","88",{"name":212,"value":213},"Restaurant Dining Hall","89",{"name":215,"value":216},"Commercial Kitchen","90",{"name":218,"value":219},"Hotel Lobby","91",{"name":221,"value":222},"Subway Platform","92",{"name":224,"value":225},"Security Checkpoint","93",{"name":227,"value":228},"Underground Parking","94",{"name":230,"value":231},"Bank Teller Zone","95",{"name":233,"value":20,"add_value":234,"describe":235,"children":236},"Industrial & Manufacturing","robot-arm","AI is modernizing industry. Our datasets capture factories and logistics spaces to support robotics and intelligent manufacturing.",[237,240,243,246,249,252,255,258,261,264],{"name":238,"value":239},"Final Assembly Line","96",{"name":241,"value":242},"Quality Control Station","97",{"name":244,"value":245},"Robotic Welding Cell","98",{"name":247,"value":248},"CNC Machining Center","99",{"name":250,"value":251},"Cleanroom","100",{"name":253,"value":254},"Automated Warehouse","101",{"name":256,"value":257},"Loading Dock","102",{"name":259,"value":260},"Sorting Center","103",{"name":262,"value":263},"Chemical Plant","104",{"name":265,"value":266},"Power Substation","105",{"name":268,"value":39,"add_value":269,"describe":270,"children":271},"Transportation & Driving","car","AI is revolutionizing mobility. 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座舱，通过语音、手势、面部识别等实现人机交互，如语音控制导航、音乐播放。\n驾驶员状态监测：识别疲劳、分心、抽烟、打电话等行为，通过大模型判断危险等级，触发相应提醒或干预机制。\n智能驾驶：AI 用于环境感知、路径规划与决策控制，使车辆能识别道路、行人、障碍物等，实现自适应巡航、自动泊车、车道保持等功能，提升驾驶安全性与便利性。",{"id":492,"value":492,"name":136,"intro":493,"add_value":137,"describe":493},4,"医学影像诊断：分析 CT、MRI 等影像，辅助医生检测疾病、识别病灶，如 AI 系统可快速发现肺部结节。\n医患对话助手：通过自然语言处理理解患者问题，提供初步健康建议或就诊指导，缓解医生接诊压力。\n智能健康管理：根据用户健康数据，提供个性化健康建议、疾病预警，如智能手环结合 AI 分析用户运动、睡眠、心率等数据。",{"id":495,"value":495,"name":496,"intro":497,"add_value":498,"describe":497},6,"Smart Devices","多模态交互：支持语音、手势、眼神等多种交互方式，如 AI 眼镜可通过语音指令实现翻译、搜索功能。\n智能感知与处理：使设备能感知环境、理解用户需求，如智能音箱根据环境噪音自动调节音量。\n个性化服务：根据用户使用习惯和偏好，提供个性化功能与服务，如智能手表为用户定制运动健身计划。","microscope",{"id":500,"value":500,"name":501,"intro":502,"add_value":503,"describe":502},8,"Law & 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E-commerce","智能客服：通过自然语言对话理解用户需求，提供产品推荐、订单查询、退换货支持等服务，提升客户满意度。\n个性化营销推荐：基于用户浏览、购买、偏好等行为数据，生成个性化推荐内容与优惠策略，提高转化率和客单价。\n商品内容生成：自动撰写商品标题、卖点描述、使用说明等内容，适用于海量SKU的内容生产与多语言发布。","buy",{"id":520,"value":520,"name":521,"intro":522,"add_value":523,"describe":8},10,"Security & Protection",null,"safe-shield-line",{"id":525,"value":525,"name":526,"intro":522,"add_value":527,"describe":8},11,"Environment & Climate","weather",{"id":529,"value":529,"name":530,"intro":522,"add_value":531,"describe":8},12,"Energy & Resource","energy-leaf",{"id":533,"value":533,"name":534,"intro":522,"add_value":535,"describe":8},13,"Express & Logistics","speed-outline",{"id":537,"value":537,"name":538,"intro":522,"add_value":539,"describe":8},15,"Sports","baseball-outline",{"id":541,"value":541,"name":542,"intro":522,"add_value":543,"describe":8},16,"General Data","daily-booth",{"id":545,"value":545,"name":546,"intro":522,"add_value":547,"describe":8},17,"Content & 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Culture","lang",{"id":564,"unique_code":565,"title":566,"icon":8,"intro":567,"cate":483,"detail":568,"create_time":569,"update_time":570,"price":571,"view_count":560,"download_count":572,"unit":572,"amount":573,"is_create_order":572,"enterprise_id":488,"seo_keywords":574,"seo_description":575,"data_format":576,"file_size":577,"apply_scene":578,"training_task":579,"version":580,"dataset_type_id":492,"dataset_scene_id":553,"sample_file":581,"banner":8,"unit_name":8,"cate_name":449,"icon_url":8,"banner_url":8,"sample_file_url":8,"data_dict":582,"qa_list":618,"sample_metadatas":635,"text_sample_content":672,"enterprise_name":673,"enterprise_logo":674,"data_distribution":675},1140,"5698d1eafdbdfa60be624f62d13cdffb","Private 5G Network Fault Diagnosis Reasoning Q&A Dataset","Strengthen network operations diagnostic reasoning with private 5G fault Q&A that includes evidence analysis and cause elimination.","\u003Cp>5000This dataset focuses on operational fault scenarios in private 5G networks and covers fault symptoms, related configuration and observational information, diagnostic questions, evidence analysis, excluded causes, and fault judgments. Samples use a structured question-and-answer format to show how to connect symptoms with operational clues, rule out inconsistent hypotheses, and reach evidence-based diagnostic conclusions. It is suitable for reasoning SFT, fault localization, root-cause analysis, and training or evaluating private network operations models.\u003C\u002Fp>","2026-10-12 11:01:06","2026-10-19 17:18:38","0.00",0,5000,"private 5G fault diagnosis, 5G operations dataset, network fault Q&A, fault reasoning data, reasoning SFT dataset, root cause analysis, telecom operations training, 5G network troubleshooting","Private 5G fault diagnosis Q&A covering symptoms, configuration observations, evidence analysis, and cause elimination for reasoning SFT and fault localization.","JSON","0.5GB","private 5G network maintenance, network fault diagnosis, telecom operations troubleshooting","fault diagnosis, evidence-based reasoning, root cause analysis","V1.0","rivo\u002Fsample-packages\u002F9b5f50fa-eeeb-449c-aae6-c2d12936b91b\u002F20261012.zip",[583,588,593,598,603,608,613],{"id":572,"name":584,"field":585,"intro":586,"type":587},"File Name","file_name","File name","string",{"id":589,"name":590,"field":591,"intro":592,"type":587},8291,"Fault Judgment and Rationale","fault_judgment","Provides the most evidence-based fault judgment and explains the reasoning using key supporting evidence.",{"id":594,"name":595,"field":596,"intro":597,"type":587},8292,"Ruled-Out Causes","excluded_causes","Lists candidate causes that are inconsistent with the available clues and explains the basis for ruling them out.",{"id":599,"name":600,"field":601,"intro":602,"type":587},8293,"Evidence Analysis","evidence_analysis","Analyzes relationships among symptoms, configuration, and observed clues, and explains which clues support or weaken relevant fault hypotheses.",{"id":604,"name":605,"field":606,"intro":607,"type":587},8294,"Diagnostic Question","diagnostic_question","States the question to be answered, such as fault localization, cause analysis, or a troubleshooting decision.",{"id":609,"name":610,"field":611,"intro":612,"type":587},8295,"Symptom Description","symptom_description","Records the observed fault symptoms in private network operations, including the impact scope and occurrence time when available.",{"id":614,"name":615,"field":616,"intro":617,"type":587},8296,"Configuration and Observations","configuration_observations","Records relevant private network configuration parameters, equipment status, performance metrics, alarms, and other observed clues.",[619,623,627,631],{"id":620,"question":621,"answer":622},6010,"What does the dataset contain?","Samples are organized around private 5G fault diagnosis and include symptom descriptions, configuration and observational information, diagnostic questions, evidence analysis, excluded causes, and fault judgments with supporting evidence.",{"id":624,"question":625,"answer":626},6011,"How are diagnostic reasoning steps represented?","Structured fields present the fault clues and analysis, explain which details support or weaken candidate causes, and record the reasons for ruling out alternatives and the final judgment. This supports reasoning training and diagnostic evaluation.",{"id":628,"question":629,"answer":630},6012,"How can I access and use the data?","Use the download or delivery method listed on the product page and parse samples according to their field definitions. Potential applications include private 5G operations Q&A, fault localization, root-cause analysis, and reasoning SFT.",{"id":632,"question":633,"answer":634},6013,"What are the licensing and usage terms?","The product page or purchase agreement defines the license scope, permitted applications, and redistribution restrictions. Before using the data for model training, evaluation, or commercial deployment, confirm that your license covers the intended use.",[636,650,661],{"id":637,"relate_table":638,"relate_id":639,"file_size":572,"file_name":640,"path":8,"duration":572,"cate":483,"file_format":641,"audio_rate":8,"audio_channel":522,"audio_bit_depth":522,"quality":8,"annotation":642,"cate_name":643,"fault_judgment":644,"excluded_causes":645,"evidence_analysis":646,"diagnostic_question":647,"symptom_description":648,"configuration_observations":649},14821,"union_cubo",124333113,"样例_3.json","json","{\"fault_judgment\": \"The most likely fault is an MTU mismatch on the N3 underlay path combined with filtering of ICMP Packet Too Big messages, creating a path-MTU black hole for larger packets. The differing interface MTUs and trace-confirmed drops support this conclusion. Align the path MTU or provide a suitable end-to-end MTU, and allow the required ICMP feedback; then verify delivery of large packets across the affected path.\", \"excluded_causes\": \"A general radio failure is inconsistent with normal radio measurements and the successful delivery of small packets. Session-establishment failure is inconsistent with PDU sessions remaining established. General congestion is weakened by low transport utilization and by the repeatable packet-size-dependent drops on the affected path.\", \"evidence_analysis\": \"The size-dependent failure is consistent with a path MTU problem: small packets pass, while larger packets with fragmentation disallowed exceed the 1,400-byte aggregation-segment MTU once encapsulated on the N3 path. Filtering ICMP Packet Too Big messages prevents the sender from learning the usable path MTU, which can leave oversized traffic retransmitting without successful delivery. Stable PDU sessions, normal radio counters, and low transport utilization do not support a session outage, radio impairment, or general congestion as the primary explanation.\", \"diagnostic_question\": \"What is the most likely reason large transfers stall while small transactions succeed, and which alternative causes are weakened by the observations?\", \"symptom_description\": \"Users report that small transactions complete normally, but large file transfers over the private network stall and retry. The issue is limited to traffic traversing one data-network path.\", \"configuration_observations\": \"PDU sessions remain established during the failures. Radio measurements and retransmission counters are within their recorded normal ranges, and transport utilization is low. A packet trace shows that 1,200-byte inner packets pass, while larger IPv4 packets with the Don't Fragment flag set are dropped on the affected N3 underlay path. The gNB and UPF interfaces use an MTU of 1,500 bytes; an aggregation segment on that path is configured for an MTU of 1,400 bytes. The trace shows the encapsulated N3 packets exceeding that segment's MTU, and ICMP Packet Too Big messages are filtered on the path.\"}","文本","The most likely fault is an MTU mismatch on the N3 underlay path combined with filtering of ICMP Packet Too Big messages, creating a path-MTU black hole for larger packets. The differing interface MTUs and trace-confirmed drops support this conclusion. Align the path MTU or provide a suitable end-to-end MTU, and allow the required ICMP feedback; then verify delivery of large packets across the affected path.","A general radio failure is inconsistent with normal radio measurements and the successful delivery of small packets. Session-establishment failure is inconsistent with PDU sessions remaining established. General congestion is weakened by low transport utilization and by the repeatable packet-size-dependent drops on the affected path.","The size-dependent failure is consistent with a path MTU problem: small packets pass, while larger packets with fragmentation disallowed exceed the 1,400-byte aggregation-segment MTU once encapsulated on the N3 path. Filtering ICMP Packet Too Big messages prevents the sender from learning the usable path MTU, which can leave oversized traffic retransmitting without successful delivery. Stable PDU sessions, normal radio counters, and low transport utilization do not support a session outage, radio impairment, or general congestion as the primary explanation.","What is the most likely reason large transfers stall while small transactions succeed, and which alternative causes are weakened by the observations?","Users report that small transactions complete normally, but large file transfers over the private network stall and retry. The issue is limited to traffic traversing one data-network path.","PDU sessions remain established during the failures. Radio measurements and retransmission counters are within their recorded normal ranges, and transport utilization is low. A packet trace shows that 1,200-byte inner packets pass, while larger IPv4 packets with the Don't Fragment flag set are dropped on the affected N3 underlay path. The gNB and UPF interfaces use an MTU of 1,500 bytes; an aggregation segment on that path is configured for an MTU of 1,400 bytes. The trace shows the encapsulated N3 packets exceeding that segment's MTU, and ICMP Packet Too Big messages are filtered on the path.",{"id":651,"relate_table":638,"relate_id":652,"file_size":572,"file_name":653,"path":8,"duration":572,"cate":483,"file_format":641,"audio_rate":8,"audio_channel":522,"audio_bit_depth":522,"quality":8,"annotation":654,"cate_name":643,"fault_judgment":655,"excluded_causes":656,"evidence_analysis":657,"diagnostic_question":658,"symptom_description":659,"configuration_observations":660},14820,693039491,"样例_2.json","{\"fault_judgment\": \"The most likely fault is precedence shadowing in the UPF policy table: the broad UDP rule captures the control application's packets before the dedicated rule can match them. The unchanged specific-rule counter and use of default 5QI 9 support this diagnosis. Adjust the rule ordering or narrow the broad match, then confirm that the application's packets use the intended QoS flow.\", \"excluded_causes\": \"Radio degradation is not supported by the recorded normal retransmission rates. General transport congestion is not supported by the recorded normal transport utilization and would be less consistent with the application-specific impact. The evidence does not indicate a complete application or session outage; the observed issue is degraded response time.\", \"evidence_analysis\": \"The application's packets match the broad UDP rule, which is evaluated before the more specific control-application rule. The counters confirm that the intended rule is not handling those packets, and the resulting flow uses the default 5QI 9 rather than the intended 5QI 7. This connects the policy ordering to the observed QoS treatment. Normal radio retransmission rates and transport utilization provide no comparable indication of a radio or transport-wide impairment.\", \"diagnostic_question\": \"Which fault best explains the increased response time, and what evidence weighs against radio degradation or general transport congestion?\", \"symptom_description\": \"A low-latency control application has experienced increased response times since a policy update. Other application traffic on the same private network has not shown a comparable change.\", \"configuration_observations\": \"The affected application uses UDP and is intended to receive a dedicated QoS flow with 5QI 7. The UPF policy table contains a broad rule matching all UDP traffic at precedence 10 and a more specific rule for the control application's traffic at precedence 20. The broad rule is evaluated first. During the observed period, its packet counter increases for the control application's packets, while the specific rule counter remains unchanged; the resulting flow uses the default 5QI 9. Radio retransmission rates and transport utilization are within their recorded normal ranges.\"}","The most likely fault is precedence shadowing in the UPF policy table: the broad UDP rule captures the control application's packets before the dedicated rule can match them. The unchanged specific-rule counter and use of default 5QI 9 support this diagnosis. Adjust the rule ordering or narrow the broad match, then confirm that the application's packets use the intended QoS flow.","Radio degradation is not supported by the recorded normal retransmission rates. General transport congestion is not supported by the recorded normal transport utilization and would be less consistent with the application-specific impact. The evidence does not indicate a complete application or session outage; the observed issue is degraded response time.","The application's packets match the broad UDP rule, which is evaluated before the more specific control-application rule. The counters confirm that the intended rule is not handling those packets, and the resulting flow uses the default 5QI 9 rather than the intended 5QI 7. This connects the policy ordering to the observed QoS treatment. Normal radio retransmission rates and transport utilization provide no comparable indication of a radio or transport-wide impairment.","Which fault best explains the increased response time, and what evidence weighs against radio degradation or general transport congestion?","A low-latency control application has experienced increased response times since a policy update. Other application traffic on the same private network has not shown a comparable change.","The affected application uses UDP and is intended to receive a dedicated QoS flow with 5QI 7. The UPF policy table contains a broad rule matching all UDP traffic at precedence 10 and a more specific rule for the control application's traffic at precedence 20. The broad rule is evaluated first. During the observed period, its packet counter increases for the control application's packets, while the specific rule counter remains unchanged; the resulting flow uses the default 5QI 9. Radio retransmission rates and transport utilization are within their recorded normal ranges.",{"id":662,"relate_table":638,"relate_id":663,"file_size":572,"file_name":664,"path":8,"duration":572,"cate":483,"file_format":641,"audio_rate":8,"audio_channel":522,"audio_bit_depth":522,"quality":8,"annotation":665,"cate_name":643,"fault_judgment":666,"excluded_causes":667,"evidence_analysis":668,"diagnostic_question":669,"symptom_description":670,"configuration_observations":671},14819,228585512,"样例_1.json","{\"fault_judgment\": \"The most likely fault is a mismatch between the DNN requested by the devices, plant-edge, and the DNN configured on the SMF, plant_edge. The exact name mismatch and the SMF's no-matching-configuration log directly account for the affected sessions failing while the other DNN remains available. Align the device subscription\u002Frequest and the SMF DNN configuration, then verify session establishment.\", \"excluded_causes\": \"A general radio-access outage is inconsistent with successful registration and working office-data sessions. An authentication failure is inconsistent with the completed authentication procedure. A broad SMF outage is also less consistent with the successful office-data sessions and the explicit no-matching-DNN log entry.\", \"evidence_analysis\": \"Successful registration and authentication show that the devices can reach the access and registration procedures. The failure occurs when the requested DNN is resolved for session establishment. The UE requests plant-edge, while the SMF configuration contains plant_edge; the SMF log independently reports no matching DNN configuration. Normal office-data sessions show that session establishment is not failing for every DNN.\", \"diagnostic_question\": \"What is the most likely cause of the plant-edge session failures, and which alternatives are inconsistent with the observations?\", \"symptom_description\": \"Devices at the production site register successfully, but attempts to establish a data session for the plant-edge service fail. Sessions using the office-data service remain available.\", \"configuration_observations\": \"Authentication and registration complete successfully for the affected devices. Their session requests specify the DNN plant-edge. The SMF has a DNN profile named plant_edge but no profile named plant-edge, and its session logs report that no matching DNN configuration was found. The office-data DNN is configured and its sessions are established normally.\"}","The most likely fault is a mismatch between the DNN requested by the devices, plant-edge, and the DNN configured on the SMF, plant_edge. The exact name mismatch and the SMF's no-matching-configuration log directly account for the affected sessions failing while the other DNN remains available. Align the device subscription\u002Frequest and the SMF DNN configuration, then verify session establishment.","A general radio-access outage is inconsistent with successful registration and working office-data sessions. An authentication failure is inconsistent with the completed authentication procedure. A broad SMF outage is also less consistent with the successful office-data sessions and the explicit no-matching-DNN log entry.","Successful registration and authentication show that the devices can reach the access and registration procedures. The failure occurs when the requested DNN is resolved for session establishment. The UE requests plant-edge, while the SMF configuration contains plant_edge; the SMF log independently reports no matching DNN configuration. Normal office-data sessions show that session establishment is not failing for every DNN.","What is the most likely cause of the plant-edge session failures, and which alternatives are inconsistent with the observations?","Devices at the production site register successfully, but attempts to establish a data session for the plant-edge service fail. Sessions using the office-data service remain available.","Authentication and registration complete successfully for the affected devices. Their session requests specify the DNN plant-edge. The SMF has a DNN profile named plant_edge but no profile named plant-edge, and its session logs report that no matching DNN configuration was found. The office-data DNN is configured and its sessions are established normally.","{\"fault_judgment\":\"The most likely fault is an MTU mismatch on the N3 underlay path combined with filtering of ICMP Packet Too Big messages, creating a path-MTU black hole for larger packets. The differing interface MTUs and trace-confirmed drops support this conclusion. Align the path MTU or provide a suitable end-to-end MTU, and allow the required ICMP feedback; then verify delivery of large packets across the affected path.\",\"excluded_causes\":\"A general radio failure is inconsistent with normal radio measurements and the successful delivery of small packets. Session-establishment failure is inconsistent with PDU sessions remaining established. General congestion is weakened by low transport utilization and by the repeatable packet-size-dependent drops on the affected path.\",\"evidence_analysis\":\"The size-dependent failure is consistent with a path MTU problem: small packets pass, while larger packets with fragmentation disallowed exceed the 1,400-byte aggregation-segment MTU once encapsulated on the N3 path. Filtering ICMP Packet Too Big messages prevents the sender from learning the usable path MTU, which can leave oversized traffic retransmitting without successful delivery. Stable PDU sessions, normal radio counters, and low transport utilization do not support a session outage, radio impairment, or general congestion as the primary explanation.\",\"diagnostic_question\":\"What is the most likely reason large transfers stall while small transactions succeed, and which alternative causes are weakened by the observations?\",\"symptom_description\":\"Users report that small transactions complete normally, but large file transfers over the private network stall and retry. The issue is limited to traffic traversing one data-network path.\",\"configuration_observations\":\"PDU sessions remain established during the failures. Radio measurements and retransmission counters are within their recorded normal ranges, and transport utilization is low. A packet trace shows that 1,200-byte inner packets pass, while larger IPv4 packets with the Don't Fragment flag set are dropped on the affected N3 underlay path. The gNB and UPF interfaces use an MTU of 1,500 bytes; an aggregation segment on that path is configured for an MTU of 1,400 bytes. The trace shows the encapsulated N3 packets exceeding that segment's MTU, and ICMP Packet Too Big messages are filtered on the path.\"}","MOBIUSI INC","https:\u002F\u002Fstatic.mobiusi.com\u002Fmobiwusi\u002F20250829\u002F7bf4ac62b4533e2b809998a6f1c5491e.png?Expires=1792463130&OSSAccessKeyId=LTAI5tH8MQqhgeiAtrzCHrxM&Signature=v6Tr8jBrdm4s5wU1SnnyGsYg%2Bhw%3D&x-oss-process=image%2Fresize%2Cm_fill%2Cw_35%2Ch_35",[676,682],{"type":677,"title":678,"count_data":679},"bar","Length distribution",[680],{"name":681,"value":483},"1k lower",{"type":683,"title":684,"count_data":685},"pie","Language distribution",[686],{"name":687,"value":483},"English"]