{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:FWD2MAKXCPYMAUUOFWZX6LAREF","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ae4cbfcb48805106db54059c6d7cdddb1d309c883713b503ae3a75059be91a2b","cross_cats_sorted":["eess.SP","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2022-02-10T15:50:46Z","title_canon_sha256":"6510d73320a8b37088552c68f60d9ef675fd9465923566716595691985cc1bf0"},"schema_version":"1.0","source":{"id":"2202.05107","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2202.05107","created_at":"2026-07-05T04:35:43Z"},{"alias_kind":"arxiv_version","alias_value":"2202.05107v1","created_at":"2026-07-05T04:35:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.05107","created_at":"2026-07-05T04:35:43Z"},{"alias_kind":"pith_short_12","alias_value":"FWD2MAKXCPYM","created_at":"2026-07-05T04:35:43Z"},{"alias_kind":"pith_short_16","alias_value":"FWD2MAKXCPYMAUUO","created_at":"2026-07-05T04:35:43Z"},{"alias_kind":"pith_short_8","alias_value":"FWD2MAKX","created_at":"2026-07-05T04:35:43Z"}],"graph_snapshots":[{"event_id":"sha256:60c4bfccd12f7c50db13d3fa7bcd1ac7cf9385a6774e396051576665495b030f","target":"graph","created_at":"2026-07-05T04:35:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2202.05107/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large bandwidth at mm-wave is crucial for 5G and beyond but the high path loss (PL) requires highly accurate PL prediction for network planning and optimization. Statistical models with slope-intercept fit fall short in capturing large variations seen in urban canyons, whereas ray-tracing, capable of characterizing site-specific features, faces challenges in describing foliage and street clutter and associated reflection/diffraction ray calculation. Machine learning (ML) is promising but faces three key challenges in PL prediction: 1) insufficient measurement data; 2) lack of extrapolation to ","authors_text":"Ankit Gupta, Dmitry Chizhik, Jinfeng Du, Mathini Sellathurai, Reinaldo A. Valenzuela","cross_cats":["eess.SP","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2022-02-10T15:50:46Z","title":"Machine Learning-based Urban Canyon Path Loss Prediction using 28 GHz Manhattan Measurements"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.05107","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8054dd16fafdfc43b43c292d43794582e3a5105e6e03d69117f5aff9cb9fbe61","target":"record","created_at":"2026-07-05T04:35:43Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ae4cbfcb48805106db54059c6d7cdddb1d309c883713b503ae3a75059be91a2b","cross_cats_sorted":["eess.SP","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2022-02-10T15:50:46Z","title_canon_sha256":"6510d73320a8b37088552c68f60d9ef675fd9465923566716595691985cc1bf0"},"schema_version":"1.0","source":{"id":"2202.05107","kind":"arxiv","version":1}},"canonical_sha256":"2d87a6015713f0c0528e2db37f2c11214a3f92a0c486dddf76d93499d70913c8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2d87a6015713f0c0528e2db37f2c11214a3f92a0c486dddf76d93499d70913c8","first_computed_at":"2026-07-05T04:35:43.572810Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:35:43.572810Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pLNATRYwFonFi3s80PfpbQF6O0PqKnGkWBV5ZfVxYDF9XKTPEj4xVvl5zqEZe4tQJ21252bfGz6KV0Ml6f8VBg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:35:43.573206Z","signed_message":"canonical_sha256_bytes"},"source_id":"2202.05107","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8054dd16fafdfc43b43c292d43794582e3a5105e6e03d69117f5aff9cb9fbe61","sha256:60c4bfccd12f7c50db13d3fa7bcd1ac7cf9385a6774e396051576665495b030f"],"state_sha256":"ffa52e7ad9a4fb1c85c87da1991713608ffe1a6522bef3760ae3ebc20f693e46"}