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Record W2546775350 · doi:10.1109/lgrs.2016.2618855

First-Order Bistatic High-Frequency Radar Power for Mixed-Path Ionosphere-Ocean Propagation

2016· article· en· W2546775350 on OpenAlexafffund
Shuyan Chen, Weimin Huang, Eric W. Gill

Bibliographic record

VenueIEEE Geoscience and Remote Sensing Letters · 2016
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBistatic radarClutterRadarIonosphereRadar horizonRemote sensingRadar imagingGeologyComputer scienceGeophysicsTelecommunications

Abstract

fetched live from OpenAlex

A theoretical mixed-path ionospheric clutter model for bistatic high frequency radar is presented. Based on previous monostatic work, the first-order received electric field for bistatic radar is derived by considering the scattering processes on both the ionosphere and the ocean surface. Then, the first-order received power model is developed by incorporating a vertically polarized pulsed dipole antenna. In order to investigate the power spectrum of this ionospheric clutter model and its relative intensity to that of the ocean clutter, a normalized ionospheric clutter power is simulated. Numerical simulation results are compared with that of monostatic radar looking at the same ocean scattering patch. Subsequently, the simulations show how the bistatic angle and the ionospheric conditions affect the power spectrum for this bistatic mixed-path ionosphere clutter.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.006
GPT teacher head0.192
Teacher spread0.186 · 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

Citations15
Published2016
Admission routes2
Has abstractyes

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