{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UFUMWIZ74QBBMDW6ODLTYSVCKT","short_pith_number":"pith:UFUMWIZ7","schema_version":"1.0","canonical_sha256":"a168cb233fe402160ede70d73c4aa254c6d520a1942f646a9c44d8574fc809f3","source":{"kind":"arxiv","id":"2511.08851","version":5},"attestation_state":"computed","paper":{"title":"Measurement-Driven Early Warning of Reliability Breakdown in 5G NSA Railway Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"cs.NI","authors_text":"Da-Chih Lin, Hung-Yu Wei, Po-Heng Chou, Walid Saad, Yu Tsao","submitted_at":"2025-11-12T00:13:37Z","abstract_excerpt":"This paper presents a measurement-driven study of early warning for reliability breakdown events in 5G non-standalone (NSA) railway networks. Using 10~Hz metro-train measurement traces with serving- and neighbor-cell indicators, we benchmark six representative learning models, including CNN, LSTM, XGBoost, Anomaly Transformer, PatchTST, and TimesNet, under multiple observation windows and prediction horizons. Rather than proposing a new prediction architecture, this study develops a measurement-driven benchmark to quantify the feasibility and operating trade-offs of seconds-ahead reliability p"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2511.08851","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NI","submitted_at":"2025-11-12T00:13:37Z","cross_cats_sorted":["cs.LG","eess.SP"],"title_canon_sha256":"262df3a25806e42463466e3d85138f647d1489859efe743105db44d1a312f1a3","abstract_canon_sha256":"f4d8819872e3f70f06b899182353de98e04de09fbfaaa5570f7999feac012bc0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-02T02:04:10.840816Z","signature_b64":"7aVTyX8wtZsVltP/lBojNFd62phh+K6aZ+3wQw5PYF0NvpAiHxkViIXfOrcG5Hx44JZUdaoVSdIKNgDEj3sfAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a168cb233fe402160ede70d73c4aa254c6d520a1942f646a9c44d8574fc809f3","last_reissued_at":"2026-06-02T02:04:10.840335Z","signature_status":"signed_v1","first_computed_at":"2026-06-02T02:04:10.840335Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Measurement-Driven Early Warning of Reliability Breakdown in 5G NSA Railway Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP"],"primary_cat":"cs.NI","authors_text":"Da-Chih Lin, Hung-Yu Wei, Po-Heng Chou, Walid Saad, Yu Tsao","submitted_at":"2025-11-12T00:13:37Z","abstract_excerpt":"This paper presents a measurement-driven study of early warning for reliability breakdown events in 5G non-standalone (NSA) railway networks. Using 10~Hz metro-train measurement traces with serving- and neighbor-cell indicators, we benchmark six representative learning models, including CNN, LSTM, XGBoost, Anomaly Transformer, PatchTST, and TimesNet, under multiple observation windows and prediction horizons. Rather than proposing a new prediction architecture, this study develops a measurement-driven benchmark to quantify the feasibility and operating trade-offs of seconds-ahead reliability p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.08851","kind":"arxiv","version":5},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2511.08851/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2511.08851","created_at":"2026-06-02T02:04:10.840405+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.08851v5","created_at":"2026-06-02T02:04:10.840405+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.08851","created_at":"2026-06-02T02:04:10.840405+00:00"},{"alias_kind":"pith_short_12","alias_value":"UFUMWIZ74QBB","created_at":"2026-06-02T02:04:10.840405+00:00"},{"alias_kind":"pith_short_16","alias_value":"UFUMWIZ74QBBMDW6","created_at":"2026-06-02T02:04:10.840405+00:00"},{"alias_kind":"pith_short_8","alias_value":"UFUMWIZ7","created_at":"2026-06-02T02:04:10.840405+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UFUMWIZ74QBBMDW6ODLTYSVCKT","json":"https://pith.science/pith/UFUMWIZ74QBBMDW6ODLTYSVCKT.json","graph_json":"https://pith.science/api/pith-number/UFUMWIZ74QBBMDW6ODLTYSVCKT/graph.json","events_json":"https://pith.science/api/pith-number/UFUMWIZ74QBBMDW6ODLTYSVCKT/events.json","paper":"https://pith.science/paper/UFUMWIZ7"},"agent_actions":{"view_html":"https://pith.science/pith/UFUMWIZ74QBBMDW6ODLTYSVCKT","download_json":"https://pith.science/pith/UFUMWIZ74QBBMDW6ODLTYSVCKT.json","view_paper":"https://pith.science/paper/UFUMWIZ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.08851&json=true","fetch_graph":"https://pith.science/api/pith-number/UFUMWIZ74QBBMDW6ODLTYSVCKT/graph.json","fetch_events":"https://pith.science/api/pith-number/UFUMWIZ74QBBMDW6ODLTYSVCKT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UFUMWIZ74QBBMDW6ODLTYSVCKT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UFUMWIZ74QBBMDW6ODLTYSVCKT/action/storage_attestation","attest_author":"https://pith.science/pith/UFUMWIZ74QBBMDW6ODLTYSVCKT/action/author_attestation","sign_citation":"https://pith.science/pith/UFUMWIZ74QBBMDW6ODLTYSVCKT/action/citation_signature","submit_replication":"https://pith.science/pith/UFUMWIZ74QBBMDW6ODLTYSVCKT/action/replication_record"}},"created_at":"2026-06-02T02:04:10.840405+00:00","updated_at":"2026-06-02T02:04:10.840405+00:00"}