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Record W2049461767 · doi:10.1109/antem.2012.6262351

Numerical rough surface scattering simulations using the FVTD method

2012· article· en· W2049461767 on OpenAlexaff
Dustin Isleifson, L. Shafai, David G. Barber

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsScatteringMonte Carlo methodPlanarRadar cross-sectionGaussianBoundary value problemTransformation (genetics)Field (mathematics)Computer scienceComputational physicsOpticsPhysicsMathematicsMathematical analysis

Abstract

fetched live from OpenAlex

A method for numerically calculating the scattering from randomly rough surfaces has been developed and implemented in a finite-volume time-domain (FVTD) computational engine. A formulation that computes the total-field for planar multi-layered media was implemented and used as the source in a scattered-field implementation of the FVTD method. Computational geometries with rough surfaces exhibiting Gaussian statistics were created. Monte Carlo simulations for the scattering from the rough surfaces were performed using a scattered-field formulation of the FVTD method, which represents the rough surface as contrast sources. These contrast sources generate the scattered-fields and are more easily absorbed by the absorbing boundary conditions (ABCs), improving efficiency and accuracy. A far-field transformation is made and the normalized radar cross-section is computed. The validity of the technique is demonstrated by showing favorable comparisons with the small perturbation model (SPM). We demonstrate the applicability with a practical example for sea ice remote sensing.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.543
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
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)0.0010.000

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 head0.026
GPT teacher head0.325
Teacher spread0.299 · 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.

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
Published2012
Admission routes1
Has abstractyes

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