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Record W3202889672 · doi:10.1109/aces53325.2021.00066

Scattering of EM Waves From Random Surfaces With Different Contrast and Surface Roughness

2021· article· en· W3202889672 on OpenAlexaff
Mohsen Eslami Nazari, Weimin Huang

Bibliographic record

Venue2021 International Applied Computational Electromagnetics Society Symposium (ACES) · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsScatteringSurface finishMathematical analysisSurface roughnessMathematicsGaussianRadar cross-sectionMethod of moments (probability theory)GeometryOpticsPhysicsComputational physicsMaterials scienceStatisticsQuantum mechanics

Abstract

fetched live from OpenAlex

In this paper, a solution for electromagnetic (EM) scattering over a two-dimensional random rough surface with large roughness height and different contrast based on the generalized functions approach is proposed. By assuming pulsed dipole antenna and a two-dimensional Gaussian surface distribution with different root mean square heights and correlation lengths, the scattered E-field and the radar cross-section are calculated for the Neumann boundary conditions. A numerical evaluation of the solution using the method of moments (MoM) indicates that the proposed solution is better than the small perturbation method (SPM) and Kirchhoff approximation (KA) for different roughness heights and contrast media.

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)0.001
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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.003
GPT teacher head0.198
Teacher spread0.194 · 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
Published2021
Admission routes1
Has abstractyes

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