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Record W2903676724 · doi:10.5515/kjkiees.2018.29.9.722

Analysis of a Target’s Power-Spill Patterns Using Squint SAR Images

2018· article· en· W2903676724 on OpenAlexaff
Ji-Hwan Hwang, Duk‐jin Kim, Seung-Hoon Han, Jae‐Hyoung Cho, Hyoi Moon

Bibliographic record

VenueThe Journal of Korean Institute of Electromagnetic Engineering and Science · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced SAR Imaging Techniques
Canadian institutionsKootenay Association for Science & TechnologyUniversity of Manitoba
Fundersnot available
KeywordsAzimuthComputer scienceSIGNAL (programming language)Synthetic aperture radarRotation (mathematics)Remote sensingRange (aeronautics)Projection (relational algebra)Computer visionGeologyOpticsPhysicsAlgorithmEngineering

Abstract

fetched live from OpenAlex

This paper presents an analysis technique for estimating the properties of a target’s power-spill patterns observed in reconstructed SAR images, which in turn depend on the setup squint angle of the FMCW signal-based SAR system. The target responses observed in the reconstructed SAR images were affected by the range-direction and azimuth-direction of a wave projected on the ground, and the obtained results were analyzed by applying three-dimensional squinted SAR geometry. Furthermore, the rotation patterns were verified through simulations based on the FMCW signal model and back-projection algorithm. This paper summarizes the obtained evaluation results as a function of SAR geometry and squint angle.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.429

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
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 head0.007
GPT teacher head0.236
Teacher spread0.229 · 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 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

Citations0
Published2018
Admission routes1
Has abstractyes

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