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Record W2980247784 · doi:10.1029/2019rs006868

Investigation of a 256‐Monopole Transmit Antenna Array for Over‐the‐Horizon Radar in Canada

2019· article· en· W2980247784 on OpenAlexaffabout
Simon Henault, R. J. Riddolls

Bibliographic record

VenueRadio Science · 2019
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsClutterRadarOver-the-horizon radarAzimuthComputer scienceCalibrationRemote sensingDoppler effectAntenna arrayAntenna (radio)AcousticsGeologyPhysicsTelecommunicationsOptics

Abstract

fetched live from OpenAlex

Abstract A 256‐monopole planar array is investigated as a potential transmit antenna covering the entire 3‐ to 18‐MHz band in an over‐the‐horizon radar (OTHR) operating at high latitudes in Canada. This array is relatively inexpensive, simple to build, and offers the azimuth and elevation resolutions necessary to defeat the auroral clutter which has traditionally limited the operation of OTHR at high latitudes. It is demonstrated that excellent clutter rejection ratios are possible with this antenna array and accurate calibration, but significant performance degradation occurs with calibration errors in particular in the lower portion of the operating frequency band. Various calibration approaches and error mechanisms are studied. A new approach combining multiple‐input‐multiple‐output (MIMO) radar techniques at lower frequencies and conventional transmit null steering at higher frequencies is proposed and evaluated. This approach enhances the robustness of the array to calibration errors while preserving the radar unambiguous range‐Doppler space across the operating frequency band.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.917
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.186
Teacher spread0.178 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations8
Published2019
Admission routes2
Has abstractyes

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