{"id":"W4214850970","doi":"10.3390/electronics11050781","title":"Quasi-Real RFI Source Generation Using Orolia Skydel LEO Satellite Simulator for Accurate Geolocation and Tracking: Modeling and Experimental Analysis","year":2022,"lang":"en","type":"article","venue":"Electronics","topic":"Inertial Sensor and Navigation","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Consortium de Recherche et d’innovation en Aérospatiale au Québec; École de technologie supérieure","keywords":"Geolocation; Computer science; GNSS applications; Mean squared error; Cramér–Rao bound; Satellite; Satellite system; Dilution of precision; Real-time computing; Simulation; Tracking (education); Global Positioning System; Monte Carlo method; Remote sensing; Algorithm; Engineering; Telecommunications; Estimation theory; Geography; Aerospace engineering; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001467801,0.0001166242,0.0001377327,0.0001002265,0.0003468405,0.00006986772,0.00003572834,0.00004562185,0.000005435657],"category_scores_gemma":[0.000005374705,0.0001383465,0.00004272291,0.0002251256,0.00000963497,0.0001534973,0.00001589006,0.0001109432,1.761113e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002120697,"about_ca_system_score_gemma":0.00002102719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005160498,"about_ca_topic_score_gemma":0.0000565613,"domain_scores_codex":[0.999265,0.00002831023,0.000190144,0.0001820806,0.0001107824,0.0002237075],"domain_scores_gemma":[0.9997928,0.00001845719,0.00003644175,0.00008122536,0.00003452741,0.00003658728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002244918,0.00001764837,0.0001170439,0.000008066834,0.00006179724,1.509395e-7,0.0007011115,0.8588805,0.1373533,0.0001452674,0.000001206771,0.00269142],"study_design_scores_gemma":[0.0003231951,0.00009211765,0.00003123836,9.980257e-7,0.0001411841,0.000003155181,0.0001383144,0.9649,0.03384624,0.00003890903,0.0003354545,0.0001491986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8422877,0.004876951,0.152536,0.00001157044,0.00004836494,0.0001629692,0.000005190662,0.00006509612,0.000006237767],"genre_scores_gemma":[0.9989766,0.0001979302,0.0004839293,0.00001879988,0.000109988,0.00002284232,0.0001490032,0.00002750403,0.00001337306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.156689,"threshold_uncertainty_score":0.5641605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02535077535496299,"score_gpt":0.2724571982568201,"score_spread":0.2471064229018571,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}