{"as_of":"2026-08-09T07:43:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:6a46f7dd6e9b582b01986bd0db6a7911e58d9f46d7fd07d46cb9ad762176a330","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T11:56:02.545058Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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":"2026-08-05T19:13:00.027112Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-05T19:13:02.169334Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"cited_work":{"arxiv_id":"2507.22339","doi":null,"metadata_source":"pith","pith_arxiv_id":"2507.22339","snapshot_observed_at":"2026-08-05T19:13:02.169334Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","venue":"cs.DC","work_id":"6182009b-4ecc-4694-a672-89a07a3f978f","year":2025},"citing_paper":{"arxiv_id":"2608.03436","last_updated":"2026-08-04T10:31:58Z","snapshot_observed_at":"2026-08-09T07:02:33.613792Z","submitted_at":"2026-08-04T10:31:58Z","title":"FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellations","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T19:13:00.027112Z"},"links":{"cited_paper":"/paper/2507.22339","citing_paper":"/paper/2608.03436"},"observation_digest":"sha256:b0394f87e56c701696e82ad81ef61f896a8cd5b8246cb38b5c987fb19b6b8ba1","observation_id":"3641e118-894a-492e-bb49-f385f70d0f59","resolution":{"observed_at":"2026-08-05T19:13:02.232357Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2507.22339/citation-record","integrity":"/paper/2507.22339/integrity","json":"/paper/2507.22339/citation-record.json","paper":"/paper/2507.22339"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:56:02.422789Z","title":"Revolutionizing future connectivity: A contem- porary survey on AI-empowered satellite-based non-terrestrial networks in 6G,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.422789Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:20f1a5f79c20263e9871dfb1bb4bb4a961792fa64946f5dfe0c8a8057bcd5fd1","observation_id":"49db320b-4248-453a-9584-924dc9ecbe9d","resolution":{"observed_at":"2026-08-06T11:56:02.422789Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:56:02.426403Z","title":"A survey of next-generation computing technologies in space-air-ground integrated networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.426403Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:44ed7ce577b1d197df519c78e59cb6046424181380f3043e159dcdd97a759d7b","observation_id":"9c24069d-e56c-49d7-9c88-15554b432859","resolution":{"observed_at":"2026-08-06T11:56:02.426403Z","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":"2026-08-06T11:56:02.998355Z","title":"Energy-efficient computation peer offloading in satellite edge computing networks,","venue":null,"work_id":"211cbbff-d689-4b0a-94db-8cd814035380","year":2024},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.429985Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:4f7175aaee2cb357da6c4f368a9908e21063e563670a0598fc492247dfdbaece","observation_id":"3bf42af0-0e04-4fdb-9ad5-ee597197f8cb","resolution":{"observed_at":"2026-08-06T11:56:03.001532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.989403Z","title":"Satellite edge intelligence: DRL-based resource management for task inference in LEO-based satellite-ground collaborative networks,","venue":null,"work_id":"1729e14a-dcd5-496c-a40e-e391ba71ac9b","year":2025},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.433096Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:f47388561637f3fd833aa9655113440123851f1186da567166c185c9268fcc61","observation_id":"832f6f59-0b04-404f-9d5a-bad10480c5d0","resolution":{"observed_at":"2026-08-06T11:56:02.992675Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.979845Z","title":"Satellite internet of things for smart agriculture applications: A case study of computer vision,","venue":null,"work_id":"a87bd9c1-a047-4f78-9c9e-bb9cca95659b","year":2023},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.436081Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:30f0a8bd4b66f7b0b18e36c138092c58a9b7879a4420e3fcdc94c9306a64c36e","observation_id":"6fefd745-d19c-4270-b34c-e0587a014af0","resolution":{"observed_at":"2026-08-06T11:56:02.983383Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.970399Z","title":"Semi-supervised federated learning for assessing building damage from satellite imagery,","venue":null,"work_id":"731169de-4fde-4df4-9108-6ff185f3b3d0","year":2024},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.439312Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:6c3183d3f88e68ca719d30a1888cfbb5accf38c50519623cf9478f674af3fd32","observation_id":"15df7e9d-f339-4324-b1ae-29a3dfa270bc","resolution":{"observed_at":"2026-08-06T11:56:02.973579Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.960765Z","title":"APT- SAT: An adaptive DNN partitioning and task offloading framework within collaborative satellite computing environments,","venue":null,"work_id":"a552e9f3-59f4-45bd-813a-48861580513d","year":2025},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.442532Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:4eea46630518b3a5392244cb16d54dc92c5eb161356fc383265078c73c87225c","observation_id":"b3d3786a-7744-4643-a489-7d9f4990fdb3","resolution":{"observed_at":"2026-08-06T11:56:02.964399Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.951694Z","title":"Adaptive configuration for heterogeneous participants in decentralized federated learning,","venue":null,"work_id":"92dddba9-949d-4cf5-ab23-7a6bdf314a97","year":2023},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.445463Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:491e43929a44fe6bdf334debc31f415bf8ea3956e07a2b54bf08dd4bbe7a5536","observation_id":"876b6d40-12c6-46d7-959d-25a6a52cc137","resolution":{"observed_at":"2026-08-06T11:56:02.955015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.942628Z","title":"Resource management for MEC assisted multi-layer federated learning framework,","venue":null,"work_id":"af29b169-c25d-49b2-8dd2-95efdacb31dc","year":2024},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.448405Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:cb18017469015576e77b5e49e4404ede5d1ae2015e2b524c9e47996b909e5dc2","observation_id":"db4ac9c2-8c30-4251-a425-2584e3af45fc","resolution":{"observed_at":"2026-08-06T11:56:02.945990Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.933556Z","title":"Communication-efficient satellite-ground federated learning through progressive weight quantization,","venue":null,"work_id":"15c1ff22-1730-434b-9ebe-ff3c695ece44","year":2024},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.451357Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:14da5e6a57f8385ee4241c24a1013f020d7bef413475603f9c3b9942627824b6","observation_id":"027408bb-21d4-42e8-8361-6ad0b8d9ee42","resolution":{"observed_at":"2026-08-06T11:56:02.936915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:56:02.455565Z","title":"FedSN: A fed- erated learning framework over heterogeneous LEO satellite networks,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.455565Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:336df173eb06bf93c97ef607acd97f70962144415107f5e29ce6c0d46b55b151","observation_id":"656d267e-a47a-4c35-a154-8b6d7f812186","resolution":{"observed_at":"2026-08-06T11:56:02.455565Z","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":"2026-08-06T11:56:02.919567Z","title":"ALANINE: A novel decentralized personalized federated learning for heterogeneous leo satellite constellation,","venue":null,"work_id":"9ba6c2a7-c975-4bda-860d-0c550ce097d7","year":2025},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.458925Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:777f2e4dbf2fdc151b2e3582d24f9795ef870f8592e4b3307952da9f27214a2d","observation_id":"8e435ce6-591f-4ff8-bc3d-81e55c09ff48","resolution":{"observed_at":"2026-08-06T11:56:02.922760Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.911069Z","title":"Federated learning on non-IID data silos: An experimental study,","venue":null,"work_id":"8f09e04e-9c01-4079-b55e-742dfe5bed4f","year":2022},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.461932Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:2b5693385e56d2d705b9c26c16cc4e9fbfa9a116442940471916a2193f1e01b0","observation_id":"d4f3f153-177c-462e-9c10-18cb3f95674a","resolution":{"observed_at":"2026-08-06T11:56:02.914229Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.902509Z","title":"Self-supervised spatio-temporal representation learning of satellite image time series,","venue":null,"work_id":"c011674c-f0c2-41e5-92b1-bc1562269cd2","year":2024},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.464676Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:a85682a38668a42cf4f4124623a06b80af9a3fb9edb3234d58beae5c7867ac59","observation_id":"3d69a386-81a9-4b73-a31f-bed81e0cf523","resolution":{"observed_at":"2026-08-06T11:56:02.905680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.893398Z","title":"Energy-efficient federated learning for earth observation in LEO satellite systems,","venue":null,"work_id":"f5a7ae54-0b7c-467e-8636-be0d82dd962d","year":2024},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.467345Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:9a53a7dc9cab96e6319785f44cd6a7048ef08e05be07a11646d8c0c3c7214bbb","observation_id":"4ce81dc0-807c-430f-94b6-f97727c87b16","resolution":{"observed_at":"2026-08-06T11:56:02.896727Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.884762Z","title":"Connection- density-aware satellite-ground federated learning via asynchronous dy- namic aggregation,","venue":null,"work_id":"7666c4c6-afac-4fe1-8377-7eb35267f452","year":2024},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.470032Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:f961b9f1eaa1af2cf608b1a5f6fbf0026ad44916aaee885967bf2b9dda94f3cd","observation_id":"50c51235-6a2d-4681-842b-298617830545","resolution":{"observed_at":"2026-08-06T11:56:02.887916Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.875107Z","title":"Communication-efficient federated learning for LEO constellations integrated with HAPs using hybrid NOMA-OFDM,","venue":null,"work_id":"d8f34cdc-e41f-4e51-b8df-726743339a6f","year":2024},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.472668Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:60fe534cb24d36349afbd7d9524c829fd6df9990e75a75baccc09cbe3f31ebe6","observation_id":"24bcd589-2671-4be0-9ff0-e3586d52e62c","resolution":{"observed_at":"2026-08-06T11:56:02.878651Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.865759Z","title":"Energy-efficient resource manage- ment for federated learning in LEO satellite IoT,","venue":null,"work_id":"74f61d4a-d31d-4eac-af72-c3afaeca7c03","year":2024},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.475416Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:455ac7926b0e93e54c722ce0f320d599de0c9805ebdd9748adbd65f4b388a27b","observation_id":"dfdb79d5-1e4c-4efa-ad66-a9fea08d3404","resolution":{"observed_at":"2026-08-06T11:56:02.869322Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.856247Z","title":"Edge selection and clustering for federated learning in optical inter-LEO satellite constellation,","venue":null,"work_id":"7bb9c102-8522-4ac4-8910-864de5a3a23a","year":2023},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.478270Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:1ef8b06be1d8f78dfc4c3c37d128a240686f13b00fc3178494af4a103fe45673","observation_id":"0c36c86e-62ec-4c35-9f23-b27847892d22","resolution":{"observed_at":"2026-08-06T11:56:02.859472Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.847539Z","title":"A survey on satellite networks with federated learning to analyze data or manage resource,","venue":null,"work_id":"91887736-dd1c-4c16-b0e5-3248a2f91350","year":2025},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.480834Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:15dec572cd889babe559ccc518a75aed48b724a923d7bac89eff1ff8ab558264","observation_id":"54ce027d-7873-47fa-8592-368b0b536b25","resolution":{"observed_at":"2026-08-06T11:56:02.850716Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.838930Z","title":"Decomposition and meta-DRL based multi-objective optimization for asynchronous federated learning in 6G-satellite systems,","venue":null,"work_id":"55b2e0a4-a7c3-4835-9245-722818f75a20","year":2024},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.483553Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:cc9a9a8115e2a6e9c52203cb262fd279fc12a018ea43a507b7d5f3dafdbc1641","observation_id":"b6e83c3c-0a9b-4b72-8a23-4e3279bac35f","resolution":{"observed_at":"2026-08-06T11:56:02.842184Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.830363Z","title":"Cross-domain federated computation offloading for age of information minimization in satellite-airborne-terrestrial networks,","venue":null,"work_id":"998eba54-59ff-48d2-a9a2-c9b15d3a2101","year":2023},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.486325Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:103528bd3149a6d0b1d445ea1c1126ea84b3e83182fa2c3d1bf8fa279bac6cd9","observation_id":"0b823a0e-4fb0-4339-af0d-b2c26465fa53","resolution":{"observed_at":"2026-08-06T11:56:02.833486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.821261Z","title":"Exploitation maximization of unlabeled data for federated semi-supervised learning,","venue":null,"work_id":"740e0787-10c5-4989-99e1-9c7a5f84c81a","year":2025},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.488964Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:64ad4776ecd0664383b34f170fece0c44cb0936f9bf06cf017c6e3b2104ee835","observation_id":"ca0fc5dc-4134-4481-b666-3e0beda54973","resolution":{"observed_at":"2026-08-06T11:56:02.824393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.812762Z","title":"Federated semi- supervised learning with inter-client consistency & disjoint learning,","venue":null,"work_id":"093ef15f-5fe0-4b25-9a9e-4590722639d5","year":2021},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.491644Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:9d092112d9b862a7f04fb61f5c50381e28a0f0c69dee307377c8247f4ee59ba3","observation_id":"affda217-8a3a-49b5-accc-3487f7c22a94","resolution":{"observed_at":"2026-08-06T11:56:02.815909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.803536Z","title":"SemiFL: Semi-supervised federated learning for unlabeled clients with alternate training,","venue":null,"work_id":"f27ba2f0-a0cf-44b8-907d-53d8906868e7","year":2022},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.494243Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:6563a19a32ba566ea8be27f225ff1a4ca0fad31d5db7de2ea5a2e5fb0182c3f6","observation_id":"5bb26295-54eb-44ba-8794-414a26f35ac1","resolution":{"observed_at":"2026-08-06T11:56:02.806898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.794351Z","title":"Toward fast personalized semi-supervised federated learning in edge networks: Algorithm design and theoretical guarantee,","venue":null,"work_id":"061f5246-459f-42a7-a89d-35a8cfd482db","year":2024},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.497013Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:19e54477217278a989331d9bd44a3b6c33161fb54117b735a492b5ee6b80169c","observation_id":"0fbfa5ef-6c95-4632-ae42-fc32808bdab9","resolution":{"observed_at":"2026-08-06T11:56:02.797549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.785150Z","title":"Boosting semi-supervised federated learning by effectively exploiting server-side knowledge and client-side unconfident samples,","venue":null,"work_id":"93eb8577-62ac-4a5c-9307-bae9d88ba7c1","year":2025},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.499911Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:165fee73ae34f9dbb32687941f3ffb82548ff30fbe56f7351240aff5b4b6e38b","observation_id":"06f41e25-a8c8-4e36-bd9f-95c358e190af","resolution":{"observed_at":"2026-08-06T11:56:02.788699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.775464Z","title":"Hybrid-FL for wireless networks: Cooperative learning mechanism us- ing non-IID data,","venue":null,"work_id":"d8f18e09-7410-40f1-a795-59bedf6a4436","year":2020},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.502549Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:5e9f030c5a9c2d00e2433f3cd96d7742a0034029cefea3942addc3931c2e6bfa","observation_id":"235878b6-e9b1-4113-acbe-1a2a33e8133e","resolution":{"observed_at":"2026-08-06T11:56:02.778590Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.766398Z","title":"Gradient scheduling with global momentum for asynchronous federated learning in edge environment,","venue":null,"work_id":"5309bc2a-3864-4fc7-8dd0-d166a371fb7f","year":2022},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.505344Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:d8217a5d92d6a50a4db649dbaaaac7ac483c9045b6b856e1fb05c8188cb3ef2f","observation_id":"4290df84-a3bc-485c-8e5e-1449e9e71b66","resolution":{"observed_at":"2026-08-06T11:56:02.769561Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.757614Z","title":"A triple-step asynchronous fed- erated learning mechanism for client activation, interaction optimization, and aggregation enhancement,","venue":null,"work_id":"d069b0e2-afed-4107-b497-ea6cac4db7e9","year":2022},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.508213Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:94b7e82b279e01a735222619d915fa5f1c0f7cd36a82970ff206c81540a64269","observation_id":"8e0dedff-89e6-4995-b382-b2ca3497a96a","resolution":{"observed_at":"2026-08-06T11:56:02.760903Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.748482Z","title":"Towards efficient asynchronous federated learning in heterogeneous edge environments,","venue":null,"work_id":"72494a75-caeb-4f89-8a04-4c64347fbb69","year":2024},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.510824Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:d8e8b501af594245259208977e83a62ac1de59d6cc0938f5a44ac38c6133fd4c","observation_id":"21fed80f-ab27-4018-ba47-8c341ea36deb","resolution":{"observed_at":"2026-08-06T11:56:02.751739Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.739169Z","title":"FixMatch: Simplifying semi- supervised learning with consistency and confidence,","venue":null,"work_id":"20115ca1-000a-418e-ac9c-0569afccef4b","year":2020},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.513610Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:17f5f476b29b3ef3537e46910686d17b04dfb4e088528c24c827c9ad33614831","observation_id":"97b18c56-ff0b-4f25-9d1f-6bff9e049677","resolution":{"observed_at":"2026-08-06T11:56:02.742654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.729628Z","title":"RandAugment: Practical automated data augmentation with a reduced search space,","venue":null,"work_id":"3a3d5e43-4464-4b7c-b539-e9b06c044a4a","year":2020},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.516255Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:ed7055558cc9cb8ebef75712670ffc47ac1365347fa8bfb1cb8be2366dd5886a","observation_id":"9e65afa2-0229-4ea1-8a06-e9367134c532","resolution":{"observed_at":"2026-08-06T11:56:02.732999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.720705Z","title":"AC-SGD: Adaptively compressed sgd for communication-efficient distributed learning,","venue":null,"work_id":"131511f1-e472-4570-b972-09b34bec991f","year":2022},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.518977Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:109e83e9c0f90de023a4558466b8e5463135176dd7b2a4352d5a06e9503c0079","observation_id":"77989e50-df14-4a40-9431-b1592eea8fe4","resolution":{"observed_at":"2026-08-06T11:56:02.723766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.711947Z","title":"On the conver- gence of fedavg on non-iid data,","venue":null,"work_id":"de0710c7-e640-4fdc-95a3-ec6a58579e52","year":2020},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.521832Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:efa60f8cccb1b94d986fbb50b208330e868c8bf538f911d8c4227e11036b0739","observation_id":"7ff1beff-7564-4ee3-872d-da7d8f42d803","resolution":{"observed_at":"2026-08-06T11:56:02.715078Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.703069Z","title":"Deepsat: a learning framework for satellite imagery,","venue":null,"work_id":"aca5f3bb-c158-4631-bf1e-605db3e42edd","year":2015},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.524575Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:8efdfc65ef8e85acb8c7e05ea2ae8055ee5deba84a425dd4bc43c5e16fcff922","observation_id":"f260e221-fa30-4c59-afa6-a5bd5290f9dd","resolution":{"observed_at":"2026-08-06T11:56:02.706257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1605.07146","last_updated":"2017-06-14T06:06:48Z","snapshot_observed_at":"2026-08-08T14:57:17.868613Z","submitted_at":"2016-05-23T19:27:13Z","title":"Wide Residual Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1605.07146","snapshot_observed_at":"2026-08-06T11:56:02.527293Z","title":"Wide residual networks.,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.527293Z"},"links":{"cited_paper":"/paper/1605.07146","citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:044f9832fcff134ccc011e33259773f684b50f0c944bf580b0e993bfc2662e80","observation_id":"0cf45394-dd9d-4210-bd4f-535cf81e4e86","resolution":{"observed_at":"2026-08-06T11:56:02.527293Z","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":"2026-08-06T11:56:02.694427Z","title":"QSGD: Communication-efficient SGD via gradient quantization and encoding,","venue":null,"work_id":"f1f2b07c-153f-4cc2-ab4d-3e8c1bcf40ce","year":2017},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.530187Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:5316988cfe71775ffba928678f8438c485e53d2ad0fa0acb60735ad2a40ed66c","observation_id":"e67f61cf-4c96-4e1f-94ce-40fcb06f201a","resolution":{"observed_at":"2026-08-06T11:56:02.697520Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T11:56:02.532936Z","title":"Delay optimization for cooperative multi-tier computing in integrated satellite-terrestrial networks,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.532936Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:f18d49a1db09e94d1f068e80861eabf4db409a9b117a2ed617f8e0541b67f0cf","observation_id":"e0c50c37-fdf0-44d0-a9ca-ab7a134f596a","resolution":{"observed_at":"2026-08-06T11:56:02.532936Z","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":"2026-08-06T11:56:02.680054Z","title":"Satellite edge computing with collaborative computation offloading: An intelligent deep determin- istic policy gradient approach,","venue":null,"work_id":"7a65659f-a067-4c72-a8af-087317cf1410","year":2023},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.535800Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:7268b9f88daed5b1589b502cedbacc6c471eef039c25d7f6bddd661aa931b92a","observation_id":"4c29ed60-1811-43e7-9758-7504692a1d2c","resolution":{"observed_at":"2026-08-06T11:56:02.683224Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.670296Z","title":"Client-edge-cloud hier- archical federated learning,","venue":null,"work_id":"86a90810-693e-4578-9c48-8c7ae512408b","year":2020},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.538590Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:33470a4bf66376d87aac1ba9ff1288acd13ff3d150102c17a64bb9263b7c39ca","observation_id":"37369517-7c81-4a67-a997-c9fde2892ec2","resolution":{"observed_at":"2026-08-06T11:56:02.673603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.649024Z","title":"A distinctive feature of his research is its real- world impact and industry focus","venue":null,"work_id":"9f3f04d5-6f5e-44f2-ba79-2186c893b098","year":2006},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":2006,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.545058Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:34752fcbd05842abe98a6812289a8de2068183706d1b0f0c2dda01ea619036e8","observation_id":"06441021-fb56-4949-9992-d958c4d3a01b","resolution":{"observed_at":"2026-08-06T11:56:02.653819Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-06T11:56:02.659975Z","title":"His research interests encompass col- laborative learning/optimization, edge intelligence, graph learning, and the Internet of Things","venue":null,"work_id":"9962be7d-0713-4d8a-8087-1444c8a16b5d","year":null},"citing_paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-06T11:56:02.541511Z"},"links":{"citing_paper":"/paper/2507.22339"},"observation_digest":"sha256:9873040100c79b2555db8160667fe895d8331a7b9e66b4d3b332590ff7ed891c","observation_id":"b7d89aa1-ec8b-4922-a3eb-4164259a24a3","resolution":{"observed_at":"2026-08-06T11:56:02.663790Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2507.22339","last_updated":"2025-07-30T02:47:14Z","latest_version":1,"primary_category":"cs.DC","snapshot_observed_at":"2026-08-06T11:56:01.880978Z","submitted_at":"2025-07-30T02:47:14Z","title":"A Semi-Supervised Federated Learning Framework with Hierarchical Clustering Aggregation for Heterogeneous Satellite Networks"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":0,"verified_fuzzy":38},"total_outbound_references":43},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2507.22339."}