MétaCan
Menu
Back to cohort
Record W1971199266 · doi:10.1109/tap.2013.2277511

Electromagnetic Scattering From 3-D Targets in a Random Medium Using Finite Difference Frequency Domain

2013· article· en· W1971199266 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsLakehead University
Fundersnot available
KeywordsComputer scienceSolverScatteringBiconjugate gradient stabilized methodComputational electromagneticsFrequency domainRadar cross-sectionAlgorithmComputational scienceElectromagnetic fieldMathematical optimizationIterative methodMathematicsPhysicsOptics

Abstract

fetched live from OpenAlex

In this paper a three-dimensional (3-D) numerical technique based on the finite difference frequency domain (FDFD) is implemented to calculate the scattering from arbitrary shaped objects embedded in a continuous random medium. The total field/scattered field (TF/SF) algorithm is integrated with the FDFD to minimize the memory consumption and speed up the calculations. For validation purposes, the radar cross-section of a 2-D conducting cylinder in random medium is calculated using the FDFD technique and compared to previously published data based on the current generator method. An upgrade to a 3-D solver was then inspired once the idea of using the multi-grid technique was introduced to accelerate the convergence rate of the BICGSTAB iterative solver. Thus, allowing the FDFD technique to become a robust method to solve the scattering problem from large targets embedded in random medium. Therefore, using the introduced simulating scheme, one can easily elucidate any scattering information out of real life targets surrounded by random environmental effects.

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.

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 categoriesnone
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 score0.645
Threshold uncertainty score0.706

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 head0.009
GPT teacher head0.214
Teacher spread0.206 · 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