{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EFCE24HBL46WC224ALBSKJLA7A","short_pith_number":"pith:EFCE24HB","schema_version":"1.0","canonical_sha256":"21444d70e15f3d616b5c02c3252560f827203d5a8eb8af59df84900ce1a8c82a","source":{"kind":"arxiv","id":"2308.13333","version":1},"attestation_state":"computed","paper":{"title":"Small Celestial Body Exploration with CubeSat Swarms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.EP","astro-ph.IM","cs.NA","math.NA"],"primary_cat":"cs.RO","authors_text":"Dario Izzo, Emmanuel Blazquez, Francesco Biscani, Franco Perez-Lissi, Roger Walker","submitted_at":"2023-08-25T12:09:31Z","abstract_excerpt":"This work presents a large-scale simulation study investigating the deployment and operation of distributed swarms of CubeSats for interplanetary missions to small celestial bodies. Utilizing Taylor numerical integration and advanced collision detection techniques, we explore the potential of large CubeSat swarms in capturing gravity signals and reconstructing the internal mass distribution of a small celestial body while minimizing risks and Delta V budget. Our results offer insight into the applicability of this approach for future deep space exploration missions."},"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":"2308.13333","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2023-08-25T12:09:31Z","cross_cats_sorted":["astro-ph.EP","astro-ph.IM","cs.NA","math.NA"],"title_canon_sha256":"5c7140b1d8066e130792753e5f467bfaa66845d3d6e6e31bdf62272a19e6eebd","abstract_canon_sha256":"125681d7f45d4b2fa784144e4f412590afbb5fdd78877734b4c9c6072a3ac77a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:44:42.398829Z","signature_b64":"14Hu+jv67h3oga9G1nEL5SmcXv6Z1uiDJW/DB2zQNUTX2EHcsub+8hf+yxTOXVgjPWbRmiY9Ly7sIqeAAHsVAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21444d70e15f3d616b5c02c3252560f827203d5a8eb8af59df84900ce1a8c82a","last_reissued_at":"2026-07-05T06:44:42.398341Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:44:42.398341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Small Celestial Body Exploration with CubeSat Swarms","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.EP","astro-ph.IM","cs.NA","math.NA"],"primary_cat":"cs.RO","authors_text":"Dario Izzo, Emmanuel Blazquez, Francesco Biscani, Franco Perez-Lissi, Roger Walker","submitted_at":"2023-08-25T12:09:31Z","abstract_excerpt":"This work presents a large-scale simulation study investigating the deployment and operation of distributed swarms of CubeSats for interplanetary missions to small celestial bodies. Utilizing Taylor numerical integration and advanced collision detection techniques, we explore the potential of large CubeSat swarms in capturing gravity signals and reconstructing the internal mass distribution of a small celestial body while minimizing risks and Delta V budget. Our results offer insight into the applicability of this approach for future deep space exploration missions."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.13333","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/2308.13333/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":"2308.13333","created_at":"2026-07-05T06:44:42.398402+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.13333v1","created_at":"2026-07-05T06:44:42.398402+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.13333","created_at":"2026-07-05T06:44:42.398402+00:00"},{"alias_kind":"pith_short_12","alias_value":"EFCE24HBL46W","created_at":"2026-07-05T06:44:42.398402+00:00"},{"alias_kind":"pith_short_16","alias_value":"EFCE24HBL46WC224","created_at":"2026-07-05T06:44:42.398402+00:00"},{"alias_kind":"pith_short_8","alias_value":"EFCE24HB","created_at":"2026-07-05T06:44:42.398402+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.04506","citing_title":"Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENs","ref_index":40,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EFCE24HBL46WC224ALBSKJLA7A","json":"https://pith.science/pith/EFCE24HBL46WC224ALBSKJLA7A.json","graph_json":"https://pith.science/api/pith-number/EFCE24HBL46WC224ALBSKJLA7A/graph.json","events_json":"https://pith.science/api/pith-number/EFCE24HBL46WC224ALBSKJLA7A/events.json","paper":"https://pith.science/paper/EFCE24HB"},"agent_actions":{"view_html":"https://pith.science/pith/EFCE24HBL46WC224ALBSKJLA7A","download_json":"https://pith.science/pith/EFCE24HBL46WC224ALBSKJLA7A.json","view_paper":"https://pith.science/paper/EFCE24HB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.13333&json=true","fetch_graph":"https://pith.science/api/pith-number/EFCE24HBL46WC224ALBSKJLA7A/graph.json","fetch_events":"https://pith.science/api/pith-number/EFCE24HBL46WC224ALBSKJLA7A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EFCE24HBL46WC224ALBSKJLA7A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EFCE24HBL46WC224ALBSKJLA7A/action/storage_attestation","attest_author":"https://pith.science/pith/EFCE24HBL46WC224ALBSKJLA7A/action/author_attestation","sign_citation":"https://pith.science/pith/EFCE24HBL46WC224ALBSKJLA7A/action/citation_signature","submit_replication":"https://pith.science/pith/EFCE24HBL46WC224ALBSKJLA7A/action/replication_record"}},"created_at":"2026-07-05T06:44:42.398402+00:00","updated_at":"2026-07-05T06:44:42.398402+00:00"}