{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:OJA7GI3LBXZZDQGPQUGARBXVGQ","short_pith_number":"pith:OJA7GI3L","schema_version":"1.0","canonical_sha256":"7241f3236b0df391c0cf850c0886f5343dd68b425e023f53b3e5a80a1d1995a0","source":{"kind":"arxiv","id":"1910.12901","version":2},"attestation_state":"computed","paper":{"title":"Waypoint Optimization Using Bayesian Optimization: A Case Study in Airborne Wind Energy Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Ali Baheri, Chris Vermillion","submitted_at":"2019-10-28T18:33:14Z","abstract_excerpt":"We present a data-driven optimization framework that aims to address online adaptation of the flight path shape for an airborne wind energy system (AWE) that follows a repetitive path to generate power. Specifically, Bayesian optimization, which is a data-driven algorithm for finding the optimum of an unknown objective function, is utilized to solve the waypoint adaptation. To form a computationally efficient optimization framework, we describe each figure-$8$ flight via a compact set of parameters, termed as basis parameters. We model the underlying objective function by a Gaussian Process (G"},"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":"1910.12901","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.SY","submitted_at":"2019-10-28T18:33:14Z","cross_cats_sorted":["cs.SY"],"title_canon_sha256":"812b1fade9b78a5143b8b972e901b63404cac223677192526d16a66f9180ec58","abstract_canon_sha256":"094100316d25a49f351eeacd9a21c22231f4540598bb0eea9a3b6ef7fd3313be"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:51:59.479862Z","signature_b64":"/yyXNwGodWcjBtObOERjtT573z46VG2Vg3COOh4KWgYHax3bOHnbdr+pT8GQJBx5BDJ4I+JU625ORgnym61SCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7241f3236b0df391c0cf850c0886f5343dd68b425e023f53b3e5a80a1d1995a0","last_reissued_at":"2026-07-05T01:51:59.479434Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:51:59.479434Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Waypoint Optimization Using Bayesian Optimization: A Case Study in Airborne Wind Energy Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SY"],"primary_cat":"eess.SY","authors_text":"Ali Baheri, Chris Vermillion","submitted_at":"2019-10-28T18:33:14Z","abstract_excerpt":"We present a data-driven optimization framework that aims to address online adaptation of the flight path shape for an airborne wind energy system (AWE) that follows a repetitive path to generate power. Specifically, Bayesian optimization, which is a data-driven algorithm for finding the optimum of an unknown objective function, is utilized to solve the waypoint adaptation. To form a computationally efficient optimization framework, we describe each figure-$8$ flight via a compact set of parameters, termed as basis parameters. We model the underlying objective function by a Gaussian Process (G"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.12901","kind":"arxiv","version":2},"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/1910.12901/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":"1910.12901","created_at":"2026-07-05T01:51:59.479492+00:00"},{"alias_kind":"arxiv_version","alias_value":"1910.12901v2","created_at":"2026-07-05T01:51:59.479492+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.12901","created_at":"2026-07-05T01:51:59.479492+00:00"},{"alias_kind":"pith_short_12","alias_value":"OJA7GI3LBXZZ","created_at":"2026-07-05T01:51:59.479492+00:00"},{"alias_kind":"pith_short_16","alias_value":"OJA7GI3LBXZZDQGP","created_at":"2026-07-05T01:51:59.479492+00:00"},{"alias_kind":"pith_short_8","alias_value":"OJA7GI3L","created_at":"2026-07-05T01:51:59.479492+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/OJA7GI3LBXZZDQGPQUGARBXVGQ","json":"https://pith.science/pith/OJA7GI3LBXZZDQGPQUGARBXVGQ.json","graph_json":"https://pith.science/api/pith-number/OJA7GI3LBXZZDQGPQUGARBXVGQ/graph.json","events_json":"https://pith.science/api/pith-number/OJA7GI3LBXZZDQGPQUGARBXVGQ/events.json","paper":"https://pith.science/paper/OJA7GI3L"},"agent_actions":{"view_html":"https://pith.science/pith/OJA7GI3LBXZZDQGPQUGARBXVGQ","download_json":"https://pith.science/pith/OJA7GI3LBXZZDQGPQUGARBXVGQ.json","view_paper":"https://pith.science/paper/OJA7GI3L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1910.12901&json=true","fetch_graph":"https://pith.science/api/pith-number/OJA7GI3LBXZZDQGPQUGARBXVGQ/graph.json","fetch_events":"https://pith.science/api/pith-number/OJA7GI3LBXZZDQGPQUGARBXVGQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OJA7GI3LBXZZDQGPQUGARBXVGQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OJA7GI3LBXZZDQGPQUGARBXVGQ/action/storage_attestation","attest_author":"https://pith.science/pith/OJA7GI3LBXZZDQGPQUGARBXVGQ/action/author_attestation","sign_citation":"https://pith.science/pith/OJA7GI3LBXZZDQGPQUGARBXVGQ/action/citation_signature","submit_replication":"https://pith.science/pith/OJA7GI3LBXZZDQGPQUGARBXVGQ/action/replication_record"}},"created_at":"2026-07-05T01:51:59.479492+00:00","updated_at":"2026-07-05T01:51:59.479492+00:00"}