MétaCan
Menu
Back to cohort
Record W2474917794 · doi:10.1080/07038992.2016.1177449

The Second-Order Bistatic High-Frequency Radar Cross Section of Ocean Surface for an Antenna on a Floating Platform

2016· article· en· W2474917794 on OpenAlexaffvenue
Yue Ma, Weimin Huang, Eric W. Gill

Bibliographic record

VenueCanadian Journal of Remote Sensing · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBistatic radarAntenna (radio)Radar cross-sectionPhysicsTransmitterRadarCross section (physics)AcousticsRadar imagingRemote sensingGeologyComputer scienceTelecommunicationsChannel (broadcasting)

Abstract

fetched live from OpenAlex

. The 2nd-order bistatic high-frequency radar cross section of ocean surface is derived for the case of a fixed receiver and a floating transmitter. The 2nd-order radar cross section contains both the hydrodynamic contribution and the electromagnetic contribution. The 2nd-order hydrodynamic contribution is obtained based on the 1st-order bistatic model with antenna motion. The derivation of the 2nd-order electromagnetic contribution begins with a general expression for the bistatically received 2nd-order electric field for the case of a floating antenna. A new bistatic electromagnetic coupling coefficient, which, unlike some earlier versions, produces no nonphysical singularities in the radar cross section, is derived. The new bistatic radar cross section model is verified by comparing it with that of a monostatic swaying antenna case and that of the bistatic case without antenna motion. The effect of platform motion on simulated Doppler spectra is considered for a variety of sea states and operating frequencies. The motion-induced 2nd-order peaks appearing in the spectrum are seen to have significantly less energy than those in the 1st-order case.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.984
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.018
GPT teacher head0.227
Teacher spread0.209 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations12
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Remote SensingSame topicOcean Waves and Remote SensingFrench-language works237,207