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Record W2206366520

Evaluation of RADARSAT-2 Yaw Steering for SMTI Applications

2010· article· en· W2206366520 on OpenAlexaff
Pierre D. Beaulne, Charles E. Livingstone

Bibliographic record

VenueSynthetic Aperture Radar (EUSAR), 2010 8th European Conference on · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsDefence Research and Development Canada
Fundersnot available
KeywordsGeodesyComputer scienceGlobal Positioning SystemState vectorSatelliteRemote sensingPosition (finance)Computer visionRadarOffset (computer science)Earth's rotationArtificial intelligenceGeographyEngineeringPhysicsAerospace engineering
DOInot available

Abstract

fetched live from OpenAlex

RADARSAT-2 is mechanically steered in yaw to provide a measure of earth rotation compensation and thus bring the data Doppler centroids (DC) near zero Hz. The satellite attitude control uses an algorithm whose inputs are GPS measured satellite position and the star-tracker measured attitude of the satellite. The GPS data are used to generate an orbit state vector and the star-tracker data are used to generate an attitude state vector both of which are reported with the radar signal data. Examination of DC distribution and stationary-world phase from along-track interferometry (ATI) from 53 RADARSAT-2 SMTI (Surface Moving Target Indication) scenes has been used to explore systematic and random steering angle discrepancies between radar observations and the reported attitude state vector data. Systematic pointing offset components derived from these data provide an improved earth-rotation compensation model for ocean target measurements.

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)0.002
metaresearch head score (Gemma)0.003
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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
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)0.0020.001

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 head0.057
GPT teacher head0.258
Teacher spread0.201 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

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Same venueSynthetic Aperture Radar (EUSAR), 2010 8th European Conference onSame topicGeophysics and Gravity MeasurementsFrench-language works237,207