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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 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 categoriesMeta-epidemiology (narrow)
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 score0.506
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 head0.003
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 teacher head, not a consensus.

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
Published2021
Admission routes1
Has abstractyes

Explore more

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