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Scattering from Uniaxial 3D Objects with a Smooth Boundary Using an Equivalent Source Method

2020· article· en· W3130510036 on OpenAlexaff
Kai Wang, Jean‐Jacques Laurin, Ke Wu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsRadar cross-sectionScatteringBoundary (topology)RadarComputer scienceScheme (mathematics)Boundary value problemBistatic radarSoftwareAcousticsComputational scienceOpticsMathematical analysisPhysicsMathematicsRadar imagingTelecommunications

Abstract

fetched live from OpenAlex

The formulations for three-dimensional (3D) scattering from uniaxial objects with a smooth boundary using an equivalent source method (ESM) are introduced. The proposed technique uses two sets of sources distributed inside and outside of a scatterer to simulate outside and inside scattered fields, respectively. The uniaxial dyadic Green's functions are deployed in the ESM in order to obtain the scattered fields. A stable solution is achieved by using the proposed multiple-layered distribution scheme of sources. Two numerical examples are present to study bistatic radar cross section (RCS) responses under different scenarios. Excellent agreements are achieved by comparing numerical results with those obtained from commercial software packages, while the CPU time and the required memory are drastically reduced by using the ESM.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.270
Teacher spread0.246 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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