{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:J5UEMBVL52WZDAHSSTCTJIPD3U","short_pith_number":"pith:J5UEMBVL","schema_version":"1.0","canonical_sha256":"4f684606abeead9180f294c534a1e3dd39eebebb86542c3817b6ad3f419cc4b3","source":{"kind":"arxiv","id":"2403.11875","version":1},"attestation_state":"computed","paper":{"title":"Towards Real-Time Fast Unmanned Aerial Vehicle Detection Using Dynamic Vision Sensors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Jakub Mandula, Jonas K\\\"uhne, Luca Pascarella, Michele Magno","submitted_at":"2024-03-18T15:27:58Z","abstract_excerpt":"Unmanned Aerial Vehicles (UAVs) are gaining popularity in civil and military applications. However, uncontrolled access to restricted areas threatens privacy and security. Thus, prevention and detection of UAVs are pivotal to guarantee confidentiality and safety. Although active scanning, mainly based on radars, is one of the most accurate technologies, it can be expensive and less versatile than passive inspections, e.g., object recognition. Dynamic vision sensors (DVS) are bio-inspired event-based vision models that leverage timestamped pixel-level brightness changes in fast-moving scenes th"},"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":"2403.11875","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-18T15:27:58Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"01d13e940e677d7aaf0c0b26338f38995b7511eadc0c3ec09f362beaf1742c5a","abstract_canon_sha256":"4d62e337cb3564cf85e6190d91daf228ec03d9f64d96e428ab538b581352c4a8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:57:33.097938Z","signature_b64":"dIPPNYpBaM6ejjm0vXJGxzv+OdZeLzsiN553DBP0aFhIVQdsT8VN7SuqpS90RknL7aTuJRkqH0kCAsfmXAOvCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f684606abeead9180f294c534a1e3dd39eebebb86542c3817b6ad3f419cc4b3","last_reissued_at":"2026-07-05T07:57:33.097438Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:57:33.097438Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Real-Time Fast Unmanned Aerial Vehicle Detection Using Dynamic Vision Sensors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Jakub Mandula, Jonas K\\\"uhne, Luca Pascarella, Michele Magno","submitted_at":"2024-03-18T15:27:58Z","abstract_excerpt":"Unmanned Aerial Vehicles (UAVs) are gaining popularity in civil and military applications. However, uncontrolled access to restricted areas threatens privacy and security. Thus, prevention and detection of UAVs are pivotal to guarantee confidentiality and safety. Although active scanning, mainly based on radars, is one of the most accurate technologies, it can be expensive and less versatile than passive inspections, e.g., object recognition. Dynamic vision sensors (DVS) are bio-inspired event-based vision models that leverage timestamped pixel-level brightness changes in fast-moving scenes th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.11875","kind":"arxiv","version":1},"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/2403.11875/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":"2403.11875","created_at":"2026-07-05T07:57:33.097493+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.11875v1","created_at":"2026-07-05T07:57:33.097493+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.11875","created_at":"2026-07-05T07:57:33.097493+00:00"},{"alias_kind":"pith_short_12","alias_value":"J5UEMBVL52WZ","created_at":"2026-07-05T07:57:33.097493+00:00"},{"alias_kind":"pith_short_16","alias_value":"J5UEMBVL52WZDAHS","created_at":"2026-07-05T07:57:33.097493+00:00"},{"alias_kind":"pith_short_8","alias_value":"J5UEMBVL","created_at":"2026-07-05T07:57:33.097493+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.10436","citing_title":"Analytical performance evaluation of quantum radar architectures: From single-photon to entangled-noise radars","ref_index":64,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J5UEMBVL52WZDAHSSTCTJIPD3U","json":"https://pith.science/pith/J5UEMBVL52WZDAHSSTCTJIPD3U.json","graph_json":"https://pith.science/api/pith-number/J5UEMBVL52WZDAHSSTCTJIPD3U/graph.json","events_json":"https://pith.science/api/pith-number/J5UEMBVL52WZDAHSSTCTJIPD3U/events.json","paper":"https://pith.science/paper/J5UEMBVL"},"agent_actions":{"view_html":"https://pith.science/pith/J5UEMBVL52WZDAHSSTCTJIPD3U","download_json":"https://pith.science/pith/J5UEMBVL52WZDAHSSTCTJIPD3U.json","view_paper":"https://pith.science/paper/J5UEMBVL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.11875&json=true","fetch_graph":"https://pith.science/api/pith-number/J5UEMBVL52WZDAHSSTCTJIPD3U/graph.json","fetch_events":"https://pith.science/api/pith-number/J5UEMBVL52WZDAHSSTCTJIPD3U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J5UEMBVL52WZDAHSSTCTJIPD3U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J5UEMBVL52WZDAHSSTCTJIPD3U/action/storage_attestation","attest_author":"https://pith.science/pith/J5UEMBVL52WZDAHSSTCTJIPD3U/action/author_attestation","sign_citation":"https://pith.science/pith/J5UEMBVL52WZDAHSSTCTJIPD3U/action/citation_signature","submit_replication":"https://pith.science/pith/J5UEMBVL52WZDAHSSTCTJIPD3U/action/replication_record"}},"created_at":"2026-07-05T07:57:33.097493+00:00","updated_at":"2026-07-05T07:57:33.097493+00:00"}