{"as_of":"2026-08-06T16:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d09b6ef76eb405e353ae8a22c3962e5f50323e25957e6eb2b9b9972468af21b4","coverage":[{"denominator":20,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":20,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T18:32:01.702556Z","state":"measured"},{"denominator":21,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":21,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-12T01:58:56.905017Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-12T02:01:15.219598Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"cited_work":{"arxiv_id":"2508.14507","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2508.14507","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"DeepTelecom: A digital-twin deep learning dataset for channel and MIMO applications","venue":null,"work_id":"02f4e2de-3bdc-4607-b68f-17cc19180810","year":2025},"citing_paper":{"arxiv_id":"2605.08772","last_updated":"2026-05-09T07:58:20Z","snapshot_observed_at":"2026-08-03T04:20:41.446890Z","submitted_at":"2026-05-09T07:58:20Z","title":"Fidelity Where it Matters: Site-Specific Nonuniform Refinement for Wireless Digital Twins","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-12T01:58:56.905017Z"},"links":{"cited_paper":"/paper/2508.14507","citing_paper":"/paper/2605.08772"},"observation_digest":"sha256:1453ad06bcaeb9a0c0e3e4c3e44bbcf3754a1ca4a83096edf20c3fb356186f4b","observation_id":"5d9a1b8f-7bad-46b2-8f8d-425032039714","resolution":{"observed_at":"2026-05-12T02:01:15.221566Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2508.14507/citation-record","integrity":"/paper/2508.14507/integrity","json":"/paper/2508.14507/citation-record.json","paper":"/paper/2508.14507"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2503.04184","last_updated":"2025-03-06T07:53:24Z","snapshot_observed_at":"2026-07-06T20:47:41.890652Z","submitted_at":"2025-03-06T07:53:24Z","title":"Large-Scale AI in Telecom: Charting the Roadmap for Innovation, Scalability, and Enhanced Digital Experiences","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.04184","snapshot_observed_at":"2026-08-05T18:32:00.176272Z","title":"Large- scale AI in telecom: Charting the roadmap for innovation, scalability, and enhanced digital experiences,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:00.176272Z"},"links":{"cited_paper":"/paper/2503.04184","citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:43e4f9cd58e1f1f08a0a132e71d6a137146a9439f9d1ac6c0efb6097d405a879","observation_id":"a190d4ac-d119-460e-bbc5-c26495750251","resolution":{"observed_at":"2026-08-05T18:32:00.176272Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.14653","last_updated":"2026-05-09T07:25:09Z","snapshot_observed_at":"2026-07-06T21:12:12.799570Z","submitted_at":"2025-04-20T15:25:58Z","title":"Wireless large AI model: shaping the AI-empowered future of 6G and beyond","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.14653","snapshot_observed_at":"2026-08-05T18:32:00.228906Z","title":"Wireless large AI model: Shaping the AI-native future of 6G and beyond,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:00.228906Z"},"links":{"cited_paper":"/paper/2504.14653","citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:4d2a13a258b24f75e4950432afe0ae76640f16589156a9088ef9860e80da5fd0","observation_id":"e90e19c5-ac29-4c54-bae6-a4bc2b2e73e1","resolution":{"observed_at":"2026-08-05T18:32:00.228906Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:32:02.167039Z","title":"A survey of efficient ray-tracing techniques for mobile radio propagation analysis,","venue":null,"work_id":"d6674ab0-2e7b-4b72-ae75-7e24a751bf55","year":2017},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:00.286634Z"},"links":{"citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:5b3883348c5983eaf1a9460da2c0e13db8ef61a089b8fa1c9c403c192a367d97","observation_id":"719d956d-0d75-4a79-8216-1a7c44f95e94","resolution":{"observed_at":"2026-08-05T18:32:02.170418Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2212.02159","last_updated":"2022-12-05T10:59:05Z","snapshot_observed_at":"2026-08-05T14:00:51.908144Z","submitted_at":"2022-12-05T10:59:05Z","title":"WAIR-D: Wireless AI Research Dataset","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.02159","snapshot_observed_at":"2026-08-05T18:32:00.363220Z","title":"W AIR-D: Wireless AI research dataset,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:00.363220Z"},"links":{"cited_paper":"/paper/2212.02159","citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:6cf7e7d05a7c5c762fbfda9e3013f67df6513c0768e50be7136f5d405112d9fa","observation_id":"d4e33d26-8b25-456f-a6c0-09a0e37a2f74","resolution":{"observed_at":"2026-08-05T18:32:00.363220Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10839","last_updated":"2024-09-30T11:52:24Z","snapshot_observed_at":"2026-07-06T19:33:16.949210Z","submitted_at":"2024-09-30T11:52:24Z","title":"BUPTCMCC-6G-DataAI+: A generative channel dataset for 6G AI air interface research","version":1},"cited_work":{"arxiv_id":"2410.10839","doi":null,"metadata_source":"pith","pith_arxiv_id":"2410.10839","snapshot_observed_at":"2026-08-05T18:32:01.825333Z","title":"BUPTCMCC-6G-DataAI+: A generative channel dataset for 6G AI air interface research","venue":"eess.SP","work_id":"036d307c-3455-41e4-996f-af1b8f59557a","year":2024},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:00.443708Z"},"links":{"cited_paper":"/paper/2410.10839","citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:07726cc5147f843f3d057f6e324c62a490d33eef724dc11e35511b9d16279e06","observation_id":"1eaad936-4dcf-4ea7-a051-c562bc3a42ee","resolution":{"observed_at":"2026-08-05T18:32:01.917843Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:32:02.156230Z","title":"DataAI-6G: a system parameters configurable channel dataset for AI-6G research,","venue":null,"work_id":"5f3f74de-8ab7-4765-9fd0-5526955ab28b","year":2023},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:00.514956Z"},"links":{"citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:8c074fdad9a78e64baa3befe27e435370949af966b2ef0a7c89c416422bdbff4","observation_id":"e2388bf1-311b-4882-b7f3-78aa85ff7e47","resolution":{"observed_at":"2026-08-05T18:32:02.159771Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:32:02.145387Z","title":"Pervasive wireless channel modeling theory and applications to 6G GBSMs for all frequency bands and all scenarios,","venue":null,"work_id":"6f8987f9-7aea-4a9e-b67d-0bb3ddb43f01","year":2022},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:00.573496Z"},"links":{"citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:8cb0191b12f7ad176b55ea3d7d8ba21835222314b5f45a8e1e65a8497a9268a8","observation_id":"4872826e-7485-49bb-9012-665ffa77814c","resolution":{"observed_at":"2026-08-05T18:32:02.149182Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:32:02.135222Z","title":"A complete study of space- time-frequency statistical properties of the 6G pervasive channel model,","venue":null,"work_id":"3cd2737e-4941-429c-9eba-e97ec50a90af","year":2023},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:00.641678Z"},"links":{"citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:d675af7a4ed3f1043d37512d6f365de3546adfbaec4dfb5cc323f2a7381ce032","observation_id":"b054d9c5-1b9c-4d44-a598-85b4dae6cddf","resolution":{"observed_at":"2026-08-05T18:32:02.138411Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:32:02.123405Z","title":"M 3SC: A generic dataset for mixed multi-modal (MMM) sensing and communication integration,","venue":null,"work_id":"ab3ba2db-f7d8-4076-a77d-0e626d3452a7","year":2023},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:00.696880Z"},"links":{"citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:de50f68b8f79fb26e7e8a8e68a1338eb66ec6c0af46726a7bfa9a4cef6ef0045","observation_id":"e6287300-8724-4e7f-9119-4db765c9122d","resolution":{"observed_at":"2026-08-05T18:32:02.127679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.06435","last_updated":"2019-02-18T07:44:08Z","snapshot_observed_at":"2026-08-05T21:26:49.618130Z","submitted_at":"2019-02-18T07:44:08Z","title":"DeepMIMO: A Generic Deep Learning Dataset for Millimeter Wave and Massive MIMO Applications","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.06435","snapshot_observed_at":"2026-08-05T18:32:00.791815Z","title":"DeepMIMO: A generic deep learning dataset for mil- limeter wave and massive MIMO applications,","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:00.791815Z"},"links":{"cited_paper":"/paper/1902.06435","citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:76ef9d7f91f0edc494b1fec5d4d71e0d0fa8f322c5b750f95514c8eec968be70","observation_id":"38b585fb-5fb7-42d1-85ed-6e7df4f658d2","resolution":{"observed_at":"2026-08-05T18:32:00.791815Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:32:02.113089Z","title":"ViWi: A deep learning dataset framework for vision-aided wireless communications,","venue":null,"work_id":"6db2e7e7-c985-4c8d-ac7c-d55b65737079","year":2020},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:00.860144Z"},"links":{"citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:1ab73d17b822ddfc7a60067060dd9b68f5e08acab512b5b6373d35e98fdffd9d","observation_id":"3eac277d-3671-4b5e-8c72-907cb5b0c26a","resolution":{"observed_at":"2026-08-05T18:32:02.116574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:32:02.102666Z","title":"WiTh- Ray: A versatile ray-tracing simulator for smart wireless environments,","venue":null,"work_id":"96787b8b-a111-4e27-b380-a023a42eeed9","year":2023},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:00.952242Z"},"links":{"citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:ed311c5cb463d7153327c9f2c99217ce809c2efeb1e46133d98fef0ec1a04a7b","observation_id":"f571357b-c930-43dc-91bd-126ab1e04144","resolution":{"observed_at":"2026-08-05T18:32:02.106332Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:32:02.092128Z","title":"Sionna: An open-source library for next-generation physical layer research,","venue":null,"work_id":"3a792409-f029-4e9b-9e7d-a8e366f1a8da","year":2022},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:01.059293Z"},"links":{"citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:538b1bc72a9560b2706fb9c632fb439a48ffd9fe716a3eac443d0330c4ff5d39","observation_id":"4e90671b-0c30-479e-86f3-2c543e2b7b4b","resolution":{"observed_at":"2026-08-05T18:32:02.095569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:32:02.081836Z","title":"Integrated optical sensing, communication, and computation system for quadruped robots,","venue":null,"work_id":"ebd4b508-484e-45ab-817a-3ccea0e25ec4","year":2024},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:01.120755Z"},"links":{"citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:4da41414ef743e298755619e3b915859217726009e874876de242bb84990f441","observation_id":"565fa9a0-2ae2-4514-8d05-ca7c4f6aa7b8","resolution":{"observed_at":"2026-08-05T18:32:02.085463Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:32:02.070202Z","title":"Real-time 3D reconstruction in dynamic scenes using point-based fusion,","venue":null,"work_id":"c4ed4f50-6f2d-4838-825f-4a5d5fd7c6d0","year":2013},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:01.245709Z"},"links":{"citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:b8f9c1b3eff6a52b59e46e732c883b8aa8cdbc6793cd4741177b27057572edae","observation_id":"5e1c21a0-9e85-466f-ac7f-ae1b2fcd2bb8","resolution":{"observed_at":"2026-08-05T18:32:02.074109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:32:02.058605Z","title":"Stochastic learning-based robust beamforming design for RIS-aided millimeter-wave systems in the presence of random blockages,","venue":null,"work_id":"5e6a08bf-ec57-4bb5-b8ae-ac6b6e28c92e","year":2021},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:01.347018Z"},"links":{"citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:93a98baabc9e9e8718afcf59987214edbfafdc3cd493477959b1f5f6cd2046ca","observation_id":"3ca5c9e8-27df-480a-96c3-c8b1a034238a","resolution":{"observed_at":"2026-08-05T18:32:02.062177Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:32:02.047681Z","title":"Multi-sources information fusion learning for multi- points NLOS localization,","venue":null,"work_id":"5f82a32d-0662-4fcc-ab57-fa7c99a473c0","year":2024},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:01.438247Z"},"links":{"citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:2da4cdcff13876928e25b35605fa4ffc4c243e0720fa6b23e294c7535698af30","observation_id":"1fb65d8c-5997-4ada-982e-1b83ac5a4376","resolution":{"observed_at":"2026-08-05T18:32:02.051342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:32:02.035011Z","title":"Multi-sources fusion learning for multi-points NLOS localization in OFDM system,","venue":null,"work_id":"ff5a7309-1e7a-4fb5-8ce8-4df440f819df","year":2024},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:01.498589Z"},"links":{"citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:40fab77c380bf2f638193cc37c028194294ce9bb1d55c5f0b69366120428428a","observation_id":"af2926d3-2335-4ab4-80e8-561ca4fd74fc","resolution":{"observed_at":"2026-08-05T18:32:02.039286Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:32:02.022552Z","title":"Robust millimeter beamforming via self- supervised hybrid deep learning,","venue":null,"work_id":"694089d4-77f7-494d-9e31-bb8f62815906","year":2023},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:01.601353Z"},"links":{"citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:133fe30b620aa9e161e745a7a46dada6192f19f3cde093bc5754680a47c8036f","observation_id":"83db3c9b-55a9-4471-82b5-cda8c718385b","resolution":{"observed_at":"2026-08-05T18:32:02.026176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T18:32:02.011611Z","title":"Robust Deep Learning-Based Physical Layer Communications: Strategies and Approaches,","venue":null,"work_id":"aaaed5c9-0863-4576-b0ad-195aa1980c74","year":2025},"citing_paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T18:32:01.702556Z"},"links":{"citing_paper":"/paper/2508.14507"},"observation_digest":"sha256:0bd43ff95f994ae70eac19f245bc4ee9c09a9650c61edf397d9f9aed99bc0f9c","observation_id":"9fc6e8da-e915-4f6d-a981-eb2b58e73ee5","resolution":{"observed_at":"2026-08-05T18:32:02.015115Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.14507","last_updated":"2025-08-20T07:56:13Z","latest_version":1,"primary_category":"cs.IT","snapshot_observed_at":"2026-08-05T18:31:59.019160Z","submitted_at":"2025-08-20T07:56:13Z","title":"DeepTelecom: A Digital-Twin Deep Learning Dataset for Channel and MIMO Applications"},"reference_resolution":{"displayed":20,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":1,"verified_fuzzy":15},"total_outbound_references":20},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2508.14507."}