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The first-order bistatic high frequency radar scattering cross section of the ocean surface for the case of a floating platform

2015· article· en· W1545982354 on OpenAlexaff
Yue Ma, Eric W. Gill, Weimin Huang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Waves and Remote Sensing
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBistatic radarRadar cross-sectionCross section (physics)PhysicsTransmitterRadarAcousticsRadar imagingOpticsScatteringComputer scienceTelecommunicationsChannel (broadcasting)

Abstract

fetched live from OpenAlex

The first-order bistatic high frequency radar cross section of ocean surface is derived for the case of a fixed receiver and a floating, but tethered, transmitter. A general expression for the bistatically received first-order electric field is obtained from earlier work based on fixed antennas. A small displacement caused by the platform motion is included in the source term to modify the fixed-antenna model. Based on the assumption that the ocean surface can be described as a Fourier series with coefficients being random variables, the first-order bistatic radar cross section is derived. The cross section model is found to contain a sum of Bessel functions, varying in order from zero to infinity. Simulation results depict the effect of platform motion in the Doppler spectrum. It is shown that the location of the motion-induced peaks are symmetrically distributed in the spectrum and the magnitude decreases with increasing order of the Bessel functions. These peaks caused by the swaying motion of the transmitter platform are seen to have less energy in the bistatic cross section than those in the monostatic 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.988

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 head0.023
GPT teacher head0.236
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2015
Admission routes1
Has abstractyes

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