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Record W2016231366 · doi:10.1109/oceans.2014.7003151

The first-order FMCW HF radar cross section model for ionosphere-ocean propagation

2014· article· en· W2016231366 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRadar Systems and Signal Processing
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsRadarIonosphereClutterRemote sensingGeologySkywaveContinuous-wave radarRadar horizonRadar cross-sectionRadar imagingGeophysicsTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

High frequency surface wave radar (HFSWR) is becoming accepted as an important remote sensing device for sea state monitoring, and the frequency-modulated continuous wave (FMCW) has been widely used as the radar transmitted waveform. However, the performance of HFSWR may be significantly impacted by unwanted echoes, of which ionospheric clutter is one of the main sources. During transmission, a portion of the radar radiation may travel upwards to the ionosphere from the transmitting antenna. This may be partially reflected back to the receiving antennas directly or via the ocean surface, the latter being refered to as ionosphere-ocean propagation. The purpose of this paper is to investigate the physical mechanism of how the ionosphere clutter might be analytically characterized within the radar backscatter spectrum. The first-order HF radar clutter power and its radar cross section of ionosphere-ocean propagation for an FMCW source will be derived theoretically, and then simulated and compared for a variety of ionosphere velocities and wavelengths.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score0.308

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 studies0.0000.000
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 head0.217
Teacher spread0.207 · 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

Quick stats

Citations7
Published2014
Admission routes1
Has abstractyes

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