{"id":"https://openalex.org/W4416749039","doi":"https://doi.org/10.1109/taes.2025.3637788","title":"Switching Spatio-Temporal Deep Variational Model for Radar Target Detection in Complex Clutter","display_name":"Switching Spatio-Temporal Deep Variational Model for Radar Target Detection in Complex Clutter","publication_year":2025,"publication_date":"2025-11-27","ids":{"openalex":"https://openalex.org/W4416749039","doi":"https://doi.org/10.1109/taes.2025.3637788"},"language":null,"primary_location":{"id":"doi:10.1109/taes.2025.3637788","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taes.2025.3637788","pdf_url":null,"source":{"id":"https://openalex.org/S193624734","display_name":"IEEE Transactions on Aerospace and Electronic Systems","issn_l":"0018-9251","issn":["0018-9251","1557-9603","2371-9877"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Aerospace and Electronic Systems","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039852750","display_name":"Xueling Liang","orcid":"https://orcid.org/0000-0003-2494-5896"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueling Liang","raw_affiliation_strings":["National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-2494-5896","affiliations":[{"raw_affiliation_string":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Lixing Shi","orcid":"https://orcid.org/0009-0009-3095-9718"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lixing Shi","raw_affiliation_strings":["National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0009-0009-3095-9718","affiliations":[{"raw_affiliation_string":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Wenchao Chen","orcid":"https://orcid.org/0000-0002-8875-1161"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenchao Chen","raw_affiliation_strings":["National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0002-8875-1161","affiliations":[{"raw_affiliation_string":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101514500","display_name":"Kun Qin","orcid":"https://orcid.org/0009-0006-3901-7207"},"institutions":[{"id":"https://openalex.org/I4210105312","display_name":"Shanghai Power Equipment Research Institute","ror":"https://ror.org/01pw44479","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210105312"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kun Qin","raw_affiliation_strings":["Shanghai Aerospace Electronic Communication Equipment Institute, Shanghai, China","Shanghai Aerospace Electronic Communication Equipment Institute, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Aerospace Electronic Communication Equipment Institute, Shanghai, China","institution_ids":["https://openalex.org/I4210105312"]},{"raw_affiliation_string":"Shanghai Aerospace Electronic Communication Equipment Institute, China","institution_ids":["https://openalex.org/I4210105312"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101558735","display_name":"Bo Feng","orcid":"https://orcid.org/0000-0003-0935-6604"},"institutions":[{"id":"https://openalex.org/I4387153688","display_name":"Beijing Institute of Radio Metrology and Measurement","ror":"https://ror.org/02c8ctc21","country_code":null,"type":"company","lineage":["https://openalex.org/I15089104","https://openalex.org/I4387153688"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Feng","raw_affiliation_strings":["Beijing Institute of Radio Measurement, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Institute of Radio Measurement, Beijing, China","institution_ids":["https://openalex.org/I4387153688"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Bo Chen","orcid":"https://orcid.org/0000-0001-5151-9388"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Chen","raw_affiliation_strings":["National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0001-5151-9388","affiliations":[{"raw_affiliation_string":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100411962","display_name":"Hongwei Liu","orcid":"https://orcid.org/0000-0003-4046-163X"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongwei Liu","raw_affiliation_strings":["National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China"],"raw_orcid":"https://orcid.org/0000-0003-4046-163X","affiliations":[{"raw_affiliation_string":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]},{"raw_affiliation_string":"National Laboratory of Radar Signal Processing, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.41105952,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"62","issue":null,"first_page":"2381","last_page":"2402"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10891","display_name":"Radar Systems and Signal Processing","score":0.41940000653266907,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10891","display_name":"Radar Systems and Signal Processing","score":0.41940000653266907,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.392300009727478,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12131","display_name":"Wireless Signal Modulation Classification","score":0.14219999313354492,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/clutter","display_name":"Clutter","score":0.9034000039100647},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.6001999974250793},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.544700026512146},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5314000248908997},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.4408000111579895},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.438400000333786},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.4318000078201294},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4318000078201294},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42179998755455017}],"concepts":[{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.9034000039100647},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7106999754905701},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7077999711036682},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.6001999974250793},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.544700026512146},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5314000248908997},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.4408000111579895},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.438400000333786},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4318000078201294},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4318000078201294},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42179998755455017},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.4171999990940094},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.40380001068115234},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.39809998869895935},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.39340001344680786},{"id":"https://openalex.org/C162416716","wikidata":"https://www.wikidata.org/wiki/Q446180","display_name":"Moving target indication","level":5,"score":0.39320001006126404},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.38100001215934753},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3797000050544739},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.3612000048160553},{"id":"https://openalex.org/C51196962","wikidata":"https://www.wikidata.org/wiki/Q16966352","display_name":"Stationary target indication","level":5,"score":0.35830000042915344},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.35830000042915344},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.3499000072479248},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.328900009393692},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.3248000144958496},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.30239999294281006},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.30059999227523804},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.29019999504089355},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2720000147819519}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/taes.2025.3637788","is_oa":false,"landing_page_url":"https://doi.org/10.1109/taes.2025.3637788","pdf_url":null,"source":{"id":"https://openalex.org/S193624734","display_name":"IEEE Transactions on Aerospace and Electronic Systems","issn_l":"0018-9251","issn":["0018-9251","1557-9603","2371-9877"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Aerospace and Electronic Systems","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1425959807","display_name":null,"funder_award_id":"QTZX24003","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G3406886181","display_name":null,"funder_award_id":"(B18039)","funder_id":"https://openalex.org/F4320327912","funder_display_name":"Higher Education Discipline Innovation Project"},{"id":"https://openalex.org/G6191674428","display_name":null,"funder_award_id":"62576266","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6520012262","display_name":null,"funder_award_id":"QTZX23018","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G7382222742","display_name":null,"funder_award_id":"62571396","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7794727296","display_name":null,"funder_award_id":"6220010437","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7843506534","display_name":null,"funder_award_id":"U21B2006","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327912","display_name":"Higher Education Discipline Innovation Project","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W394601756","https://openalex.org/W639708223","https://openalex.org/W1522734439","https://openalex.org/W1531532259","https://openalex.org/W1570170791","https://openalex.org/W2044017192","https://openalex.org/W2045374177","https://openalex.org/W2052240371","https://openalex.org/W2066762715","https://openalex.org/W2097168866","https://openalex.org/W2101560942","https://openalex.org/W2112708316","https://openalex.org/W2114418710","https://openalex.org/W2148749846","https://openalex.org/W2153594606","https://openalex.org/W2166543177","https://openalex.org/W2169170471","https://openalex.org/W2171277075","https://openalex.org/W2194775991","https://openalex.org/W2336589871","https://openalex.org/W2394863419","https://openalex.org/W2398053359","https://openalex.org/W2586283610","https://openalex.org/W2743617586","https://openalex.org/W2785362611","https://openalex.org/W2898064738","https://openalex.org/W2963091558","https://openalex.org/W2969558369","https://openalex.org/W2975094552","https://openalex.org/W2981753322","https://openalex.org/W3012620966","https://openalex.org/W3015959599","https://openalex.org/W3046476394","https://openalex.org/W3162095947","https://openalex.org/W3208042922","https://openalex.org/W3212451694","https://openalex.org/W4226502288","https://openalex.org/W4283205333","https://openalex.org/W4296437559","https://openalex.org/W4312309864","https://openalex.org/W4317038594","https://openalex.org/W4324116547","https://openalex.org/W4385224036","https://openalex.org/W4386973902","https://openalex.org/W4387448347","https://openalex.org/W4399039570","https://openalex.org/W4404848750","https://openalex.org/W4406016617","https://openalex.org/W4409494512"],"related_works":[],"abstract_inverted_index":{"Radar":[0],"target":[1,83,209],"detection":[2,20,66,84,210],"in":[3,46,85],"real-world":[4],"environments":[5,49],"is":[6],"fundamentally":[7],"challenged":[8],"by":[9,218],"the":[10,58,162,177,188,194,207,213],"spatial":[11,145],"heterogeneity":[12],"and":[13,50,64,107,125,136,146,154,187,200,221],"temporal":[14,147],"nonstationarity":[15],"of":[16,180,191,212],"clutter.":[17],"Traditional":[18],"multi-frame":[19],"methods":[21,42],"often":[22],"suffer":[23],"from":[24,111,169],"performance":[25],"degradation":[26],"due":[27],"to":[28,44,129,150,166],"rigid":[29],"modeling":[30],"assumptions":[31],"that":[32,103,120],"break":[33],"down":[34],"under":[35],"parameter":[36],"mismatch.":[37],"Meanwhile,":[38],"supervised":[39],"deep":[40],"learning":[41],"struggle":[43],"generalize":[45],"complex":[47,86],"clutter":[48,87,132,170,198],"require":[51],"extensive":[52],"labeled":[53,160],"data.":[54],"These":[55],"limitations":[56],"highlight":[57],"need":[59],"for":[60,80],"a":[61,73,96,115,138],"flexible,":[62],"robust,":[63],"annotation-free":[65],"framework.":[67],"In":[68],"this":[69],"work,":[70],"we":[71],"propose":[72],"Switching":[74],"Spatio-Temporal":[75],"Deep":[76],"Variational":[77],"Model":[78],"(SSTDet)":[79],"unsupervised":[81],"radar":[82,112,181],"environments.":[88],"The":[89],"model":[90,119,131,195],"comprises":[91],"three":[92],"key":[93],"components:":[94],"(1)":[95],"Complex-Valued":[97],"Convolutional":[98],"Neural":[99],"Network":[100],"(CV-CNN)":[101],"backbone":[102],"preserves":[104],"amplitude-phase":[105],"structure":[106],"extracts":[108],"compact":[109],"features":[110],"echoes;":[113],"(2)":[114],"switching":[116],"spatio-temporal":[117,172,192],"generative":[118],"leverages":[121],"Gumbel-Softmax":[122],"latent":[123],"variables":[124],"Gaussian":[126],"mixture":[127],"priors":[128],"adaptively":[130],"dynamics":[133],"across":[134],"frames;":[135],"(3)":[137],"dualbranch":[139],"structured":[140],"inference":[141],"network":[142],"with":[143],"decoupled":[144],"non-local":[148],"attention":[149],"capture":[151],"long-range":[152],"dependencies":[153],"enhance":[155],"feature":[156,202],"separability.":[157,203],"Trained":[158],"without":[159],"data,":[161],"framework":[163],"effectively":[164],"learns":[165],"distinguish":[167],"targets":[168],"through":[171],"representation":[173],"modeling.":[174],"By":[175],"exploiting":[176],"relative":[178],"stability":[179],"echoes":[182],"over":[183],"short":[184],"time":[185],"intervals":[186],"complementary":[189],"nature":[190],"features,":[193],"significantly":[196],"improves":[197],"suppression":[199],"enhances":[201],"Simulation":[204],"results":[205],"confirm":[206],"superior":[208],"capability":[211],"proposed":[214],"approach,":[215],"as":[216],"evidenced":[217],"both":[219],"qualitative":[220],"quantitative":[222],"assessments.":[223]},"counts_by_year":[],"updated_date":"2026-08-01T09:00:35.917206","created_date":"2025-11-28T00:00:00"}