{"id":"https://openalex.org/W7163880921","doi":"https://doi.org/10.48550/arxiv.2606.06917","title":"Belief-Aware Scheduling for Predictive Wildfire Hazard Mapping under Sparse-Window Telemetry","display_name":"Belief-Aware Scheduling for Predictive Wildfire Hazard Mapping under Sparse-Window Telemetry","publication_year":2026,"publication_date":"2026-06-05","ids":{"openalex":"https://openalex.org/W7163880921","doi":"https://doi.org/10.48550/arxiv.2606.06917"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.06917","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.06917","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.06917","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138199815","display_name":"Xun Shao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shao, Xun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138116234","display_name":"Kohsuke Yamakawa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yamakawa, Kohsuke","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5061100382","display_name":"Cheah Wai Shiang","orcid":"https://orcid.org/0000-0002-1647-6703"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shiang, Cheah Wai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13553","display_name":"Age of Information Optimization","score":0.16439999639987946,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13553","display_name":"Age of Information Optimization","score":0.16439999639987946,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10080","display_name":"Energy Efficient Wireless Sensor Networks","score":0.14030000567436218,"subfield":{"id":"https://openalex.org/subfields/1705","display_name":"Computer Networks and Communications"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.10480000078678131,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.6693999767303467},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6211000084877014},{"id":"https://openalex.org/keywords/separable-space","display_name":"Separable space","score":0.45019999146461487},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.4239000082015991},{"id":"https://openalex.org/keywords/edge-device","display_name":"Edge device","score":0.392300009727478},{"id":"https://openalex.org/keywords/model-predictive-control","display_name":"Model predictive control","score":0.3646000027656555},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.35920000076293945},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.334199994802475}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.692799985408783},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.6693999767303467},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6211000084877014},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.5274999737739563},{"id":"https://openalex.org/C70710897","wikidata":"https://www.wikidata.org/wiki/Q680081","display_name":"Separable space","level":2,"score":0.45019999146461487},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.4239000082015991},{"id":"https://openalex.org/C138236772","wikidata":"https://www.wikidata.org/wiki/Q25098575","display_name":"Edge device","level":3,"score":0.392300009727478},{"id":"https://openalex.org/C172205157","wikidata":"https://www.wikidata.org/wiki/Q1782962","display_name":"Model predictive control","level":3,"score":0.3646000027656555},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.35920000076293945},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.334199994802475},{"id":"https://openalex.org/C28761237","wikidata":"https://www.wikidata.org/wiki/Q7805321","display_name":"Time horizon","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.32010000944137573},{"id":"https://openalex.org/C2776836416","wikidata":"https://www.wikidata.org/wiki/Q1364844","display_name":"False alarm","level":2,"score":0.3109999895095825},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.305400013923645},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3041999936103821},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.2770000100135803},{"id":"https://openalex.org/C2779119184","wikidata":"https://www.wikidata.org/wiki/Q294350","display_name":"ALARM","level":2,"score":0.2709999978542328},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2662999927997589},{"id":"https://openalex.org/C2778134712","wikidata":"https://www.wikidata.org/wiki/Q1047307","display_name":"Bundle","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.2563999891281128},{"id":"https://openalex.org/C49261128","wikidata":"https://www.wikidata.org/wiki/Q1132455","display_name":"Hazard","level":2,"score":0.25380000472068787},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.25360000133514404},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.2515000104904175}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.06917","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.06917","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.06917","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.06917","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"An":[0],"edge":[1],"node":[2],"monitoring":[3],"a":[4,9,46,59,70,131,144,170,176,191,241],"wildfire":[5],"observes":[6],"more":[7],"than":[8],"duty-limited":[10],"or":[11],"windowed":[12],"down-link":[13],"can":[14],"carry.":[15],"The":[16],"receiver":[17],"must":[18],"predict":[19],"the":[20,26,31,51,90,94,107,139,141,161,164,182,199,206,212,223,248,251],"H-step-ahead":[21],"hazard":[22],"map":[23],"from":[24,93],"whatever":[25],"link":[27],"delivers.":[28],"We":[29,66],"argue":[30],"operative":[32,166],"design":[33],"problem":[34,75,253],"is":[35,121,151,186],"not":[36,122,220],"which":[37,120],"neural":[38],"architecture":[39],"to":[40,44],"use":[41],"but":[42],"how":[43],"derive":[45,86],"structured":[47,91,162,177,213],"belief":[48,92,185,249],"sufficient":[49],"for":[50],"receiver's":[52],"prediction":[53],"task":[54,237],"and":[55,85,117,148,175,190,209,230,239,250],"maintain":[56],"it":[57],"through":[58],"scheduler":[60],"that":[61],"anticipates":[62],"future":[63],"transmission":[64],"opportunities.":[65],"formalize":[67],"this":[68,236],"as":[69],"partially":[71],"observed":[72],"sequential":[73],"allocation":[74],"with":[76],"three":[77],"coupled":[78],"per-region":[79],"action":[80],"axes":[81],"(sensing,":[82],"representation,":[83],"transmission),":[84],"each":[87],"component":[88,167],"of":[89],"H-step":[95],"forward":[96],"operator's":[97],"input":[98],"requirements.":[99],"Identifying":[100],"these":[101],"mechanisms":[102],"requires":[103],"independent":[104],"control":[105],"over":[106,222],"window":[108],"period":[109],"P,":[110],"per-window":[111],"capacity":[112],"C,":[113],"predictive":[114,228],"horizon":[115],"H,":[116],"fuel":[118],"composition,":[119],"separable":[123],"in":[124,153,188,226],"real-landscape":[125],"data;":[126],"we":[127],"therefore":[128],"evaluate":[129],"on":[130,205,211],"physics-calibrated":[132],"synthetic":[133],"environment.":[134],"Three":[135],"empirical":[136],"observations":[137],"support":[138],"principle:":[140],"gap":[142],"between":[143,169],"non-myopic":[145],"activity-paced":[146,201],"reference":[147,202],"uniform":[149],"pacing":[150],"unimodal":[152],"window-period":[154],"sparsity,":[155],"peaking":[156],"at":[157],"intermediate":[158],"spacing;":[159],"ablating":[160],"belief,":[163],"dominant":[165],"flips":[168],"default":[171,207],"landscape":[172,178,208],"(temporal":[173],"staleness)":[174],"(static-risk":[179],"prior),":[180],"while":[181],"per-cell":[183],"intensity":[184],"redundant":[187],"both;":[189],"40":[192],"k-parameter":[193],"lightweight":[194,224],"cross-region":[195],"attention":[196],"encoder":[197,218,225],"exceeds":[198],"FAIR":[200],"by":[203],"~28%":[204],"~11%":[210],"landscape.":[214],"A":[215],"deeper":[216],"Transformer":[217],"does":[219],"improve":[221],"mean":[227],"loss":[229],"exhibits":[231],"higher":[232],"training-seed":[233],"variance.":[234],"Within":[235],"class":[238],"regime,":[240],"modest":[242],"architectural":[243],"inductive":[244],"bias":[245],"suffices":[246],"when":[247],"scheduling":[252],"are":[254],"correctly":[255],"posed.":[256]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-09T00:00:00"}