{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:NB63KASHXTWKBUU2S5Z2WK7EC3","short_pith_number":"pith:NB63KASH","schema_version":"1.0","canonical_sha256":"687db50247bceca0d29a9773ab2be416c847ee9996a92369fc6663ff7c7d65ff","source":{"kind":"arxiv","id":"2605.16159","version":1},"attestation_state":"computed","paper":{"title":"Restoring CFAR Validity for Single-Channel IoT Sensor Streams: A Monte Carlo Comparison of Five Detectors under Cortex-M0+ Constraints","license":"http://creativecommons.org/licenses/by/4.0/","headline":"The Temporal Spectral Noise-Floor Adaptation detector achieves high detection rate, 100% precision, and low bandwidth where classical CFAR methods fail in IoT sensor streams.","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Lars Thomsen, Sergii Makovetskyi","submitted_at":"2026-05-15T16:39:30Z","abstract_excerpt":"Real-time event detection in IoT mesh sensor networks must balance sensitivity against false-positive load on a constrained mesh radio. We present a Monte Carlo comparison of the Temporal Spectral Noise-Floor Adaptation (TSNFA) detector against four classical comparators drawn from the radar Constant False Alarm Rate (CFAR) family and from sequential change detection: the Lipski FFT energy detector, Cell-Averaging CFAR (CA-CFAR), Ordered-Statistic CFAR (OS-CFAR), and state-machine Cumulative Sum (CUSUM). All five detectors are implemented to fit a Cortex-M0+ class envelope, process a 1-D 100 H"},"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":true,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.16159","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NI","submitted_at":"2026-05-15T16:39:30Z","cross_cats_sorted":[],"title_canon_sha256":"e18aecf125fefd4b4e5c3bf6d5af5031cc948021a61df26cb48d6ae407f1bffd","abstract_canon_sha256":"00b82ebdc7b5b86cd6e4b4584d05912b75687149607c3165f5d7134a207035a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:01:55.482767Z","signature_b64":"jHQDfUlg2jAOiOB89QPpadlIB81drd0EVeiSb4FoO9BoLZhJIa2KvdrhdDuz7crRwtf3FVJ5Anh7snM7XnJcAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"687db50247bceca0d29a9773ab2be416c847ee9996a92369fc6663ff7c7d65ff","last_reissued_at":"2026-05-20T00:01:55.481903Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:01:55.481903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Restoring CFAR Validity for Single-Channel IoT Sensor Streams: A Monte Carlo Comparison of Five Detectors under Cortex-M0+ Constraints","license":"http://creativecommons.org/licenses/by/4.0/","headline":"The Temporal Spectral Noise-Floor Adaptation detector achieves high detection rate, 100% precision, and low bandwidth where classical CFAR methods fail in IoT sensor streams.","cross_cats":[],"primary_cat":"cs.NI","authors_text":"Lars Thomsen, Sergii Makovetskyi","submitted_at":"2026-05-15T16:39:30Z","abstract_excerpt":"Real-time event detection in IoT mesh sensor networks must balance sensitivity against false-positive load on a constrained mesh radio. We present a Monte Carlo comparison of the Temporal Spectral Noise-Floor Adaptation (TSNFA) detector against four classical comparators drawn from the radar Constant False Alarm Rate (CFAR) family and from sequential change detection: the Lipski FFT energy detector, Cell-Averaging CFAR (CA-CFAR), Ordered-Statistic CFAR (OS-CFAR), and state-machine Cumulative Sum (CUSUM). All five detectors are implemented to fit a Cortex-M0+ class envelope, process a 1-D 100 H"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"TSNFA is the only algorithm tested that simultaneously achieves high detection rate, high precision, and low per-node bandwidth.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The factorial Monte Carlo configurations (10/50 nodes, 12/18 dB SNR, 24-hour runs repeated five times) sufficiently represent the statistical properties of real single-channel IoT sensor streams under Cortex-M0+ constraints.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Monte Carlo evaluation shows TSNFA detector achieves 99.97-100% detection rate, 100% precision, and zero false-positive clusters per node while classical CFAR and CUSUM variants each fail on at least one performance dimension under Cortex-M0+ constraints.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"The Temporal Spectral Noise-Floor Adaptation detector achieves high detection rate, 100% precision, and low bandwidth where classical CFAR methods fail in IoT sensor streams.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"413f28cbfe847ff51db828877b1b2a73bbc50597cbb7a1868ae77afd64d20632"},"source":{"id":"2605.16159","kind":"arxiv","version":1},"verdict":{"id":"1961b5f0-cc7e-43a6-928e-762604f37674","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T18:21:50.803775Z","strongest_claim":"TSNFA is the only algorithm tested that simultaneously achieves high detection rate, high precision, and low per-node bandwidth.","one_line_summary":"Monte Carlo evaluation shows TSNFA detector achieves 99.97-100% detection rate, 100% precision, and zero false-positive clusters per node while classical CFAR and CUSUM variants each fail on at least one performance dimension under Cortex-M0+ constraints.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The factorial Monte Carlo configurations (10/50 nodes, 12/18 dB SNR, 24-hour runs repeated five times) sufficiently represent the statistical properties of real single-channel IoT sensor streams under Cortex-M0+ constraints.","pith_extraction_headline":"The Temporal Spectral Noise-Floor Adaptation detector achieves high detection rate, 100% precision, and low bandwidth where classical CFAR methods fail in IoT sensor streams."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.16159/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T18:31:18.748723Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T18:31:12.057142Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"cited_work_retraction","ran_at":"2026-05-19T18:22:01.996121Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T17:33:31.037876Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"external_links","ran_at":"2026-05-19T17:31:48.221298Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T16:41:55.438532Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"686321dc00c5c7b11c194ccf8799f93075d6ac94e5e1160e1c8ef27db9e45116"},"references":{"count":23,"sample":[{"doi":"","year":2000,"title":"with the Tartakovsky linear-quadratic instantaneous log- likelihood ratio [6] as the per-sample increment. A third class, TinyML autoencoder anomaly detection [7], is excluded from the present compari","work_id":"fd4cd6ec-152f-4307-a91b-719e9c9e0988","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"X[k] <- FFT(x) for k in K = {1,...,6}","work_id":"da57c301-5087-488e-8401-51fde0af554d","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"|X_k| <- sqrt(Re(X_k)^2 + Im(X_k)^2)","work_id":"640ea23f-2b22-4217-90b7-adc9d512d745","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"for each bin k in K do:","work_id":"da5610e9-186a-4772-b859-868988cc66a4","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"insert |X_k| into B_d,k","work_id":"3c1bbc40-e47f-4724-94f9-8232457dd037","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":23,"snapshot_sha256":"8caddcf08f052ebc443e05530faa2a2623dcfc44359763f8e57c5d0d0e42c6a9","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":"2605.16159","created_at":"2026-05-20T00:01:55.482038+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.16159v1","created_at":"2026-05-20T00:01:55.482038+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16159","created_at":"2026-05-20T00:01:55.482038+00:00"},{"alias_kind":"pith_short_12","alias_value":"NB63KASHXTWK","created_at":"2026-05-20T00:01:55.482038+00:00"},{"alias_kind":"pith_short_16","alias_value":"NB63KASHXTWKBUU2","created_at":"2026-05-20T00:01:55.482038+00:00"},{"alias_kind":"pith_short_8","alias_value":"NB63KASH","created_at":"2026-05-20T00:01:55.482038+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.23117","citing_title":"Combined Radar and Magnetometer Sensor Network with LoRa-Mediated Awareness for Wildlife-Vehicle Collision Prevention: A Monte Carlo Analysis","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NB63KASHXTWKBUU2S5Z2WK7EC3","json":"https://pith.science/pith/NB63KASHXTWKBUU2S5Z2WK7EC3.json","graph_json":"https://pith.science/api/pith-number/NB63KASHXTWKBUU2S5Z2WK7EC3/graph.json","events_json":"https://pith.science/api/pith-number/NB63KASHXTWKBUU2S5Z2WK7EC3/events.json","paper":"https://pith.science/paper/NB63KASH"},"agent_actions":{"view_html":"https://pith.science/pith/NB63KASHXTWKBUU2S5Z2WK7EC3","download_json":"https://pith.science/pith/NB63KASHXTWKBUU2S5Z2WK7EC3.json","view_paper":"https://pith.science/paper/NB63KASH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.16159&json=true","fetch_graph":"https://pith.science/api/pith-number/NB63KASHXTWKBUU2S5Z2WK7EC3/graph.json","fetch_events":"https://pith.science/api/pith-number/NB63KASHXTWKBUU2S5Z2WK7EC3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NB63KASHXTWKBUU2S5Z2WK7EC3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NB63KASHXTWKBUU2S5Z2WK7EC3/action/storage_attestation","attest_author":"https://pith.science/pith/NB63KASHXTWKBUU2S5Z2WK7EC3/action/author_attestation","sign_citation":"https://pith.science/pith/NB63KASHXTWKBUU2S5Z2WK7EC3/action/citation_signature","submit_replication":"https://pith.science/pith/NB63KASHXTWKBUU2S5Z2WK7EC3/action/replication_record"}},"created_at":"2026-05-20T00:01:55.482038+00:00","updated_at":"2026-05-20T00:01:55.482038+00:00"}