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Record W4286377447 · doi:10.1109/tgrs.2022.3193015

Frequency Management System (FMS) for Over-the-Horizon Radar (OTHR) Using a Near-Real-Time Ionospheric Model

2022· article· en· W4286377447 on OpenAlexafffundabout
T. Thayaparan, Hannah Villeneuve, David R. Themens, Benjamin Reid, E.M. Warrington, Taylor Cameron, Robyn Fiori

Bibliographic record

VenueIEEE Transactions on Geoscience and Remote Sensing · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsNatural Resources CanadaUniversity of New BrunswickDefence Research and Development Canada
FundersNatural Resources CanadaCanadian Space Agency
KeywordsOver-the-horizon radarIonosphereRadarSkywaveTransmitterRemote sensingMeteorologyHigh frequencyEnvironmental scienceRadio waveLatitudeComputer scienceGeologyTelecommunicationsGeodesyGeophysicsGeography

Abstract

fetched live from OpenAlex

Sky-wave Over-the-Horizon Radar (OTHR) propagates radio waves off the ionosphere to provide long-range surveillance around the Earth’s curvature. Frequency selection for high-latitude and polar OTHRs is challenging unless there is an environmental monitor that addresses the significant ionospheric variability in high-latitude regions, a spectrum monitor that finds unoccupied frequencies in the high frequency (HF) radio wave band, and a frequency management system (FMS) that selects an optimal frequency from the merged results of the environmental monitor and spectrum monitor. This paper describes the first FMS for high-latitude OTHR that merges results from the environmental monitor and spectrum monitor in real-time. The environmental monitor uses the Assimilative Canadian High Arctic Ionospheric Model (A-CHAIM), which assimilates near-real-time data and is, to date, the most advanced ionospheric model for high-latitude regions. Despite remaining limitations, demonstrations of this real-time FMS between a transmitter and four targets during the day and night and during fall and summer show that it could be one of the tools for selecting frequencies for operational scenarios.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.828
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.222
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2022
Admission routes3
Has abstractyes

Explore more

Same venueIEEE Transactions on Geoscience and Remote SensingSame topicIonosphere and magnetosphere dynamicsFrench-language works237,207