{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003327441,0.0003593668,0.0002732229,0.0003033468,0.0001487213,0.0003359525,0.0004925911,0.0003334234,0.0009932787],"category_scores_gemma":[0.0005573794,0.0001120494,0.0002259898,0.0003742224,0.0002162956,0.0002995115,0.0002598674,0.0003158257,0.0002049846],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004048939,"about_ca_system_score_gemma":0.0004364617,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005751274,"about_ca_topic_score_gemma":0.00492764,"domain_scores_codex":[0.9998559,0.00003328026,0.000008031718,0.00001907425,0.00006882244,0.00001502805],"domain_scores_gemma":[0.9996336,0.0001253803,0.0000431128,0.00005956834,0.0001206149,0.0000177255],"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":[0.0002112091,0.0001509569,0.009543354,0.0001856431,0.00004485299,0.0002208801,0.0002151696,0.9391443,0.02639619,0.00200389,0.00111747,0.02076617],"study_design_scores_gemma":[0.00001893089,0.0001748578,0.002243181,0.000008744998,0.00001661221,0.00002459306,0.00004972352,0.9833802,0.01285468,0.0001584392,0.001059998,0.00001005067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8900726,0.0002020082,0.09851258,0.0001373249,0.00005293579,0.0001237373,0.0007831872,0.001067363,0.00904818],"genre_scores_gemma":[0.9860011,0.000120823,0.01200846,0.00001510742,0.000002173991,0.00006620176,0.0004602835,0.00002139787,0.001304621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005751274,"threshold_uncertainty_score":0.01143563,"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."}}