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Estimation of Monostatic Radar Cross Section of Chaff Particles Using the Sequential Loading Method

2021· article· en· W3194381952 on OpenAlexaff
Husam Osman, Joey R. Bray

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsChaffRadar cross-sectionMethod of moments (probability theory)RadarComputer scienceAcousticsParticle (ecology)Cross section (physics)Moment (physics)Iterative methodElectronic engineeringEngineeringAlgorithmPhysicsMathematicsGeologyTelecommunications

Abstract

fetched live from OpenAlex

This study presents an efficient and reliable approach to rapidly estimate the scattering properties of chaff particles. This approach adopts the sequential loading method (SLM) which is an iterative post-processing technique that is capable of predicting the new solution of different chaff particles without having to reuse any expensive computational electromagnetic (CEM) tools. The SLM operates solely on the initial solution of a single chaff particle for which the admittance matrix is previously calculated, only once, via the moment method. The calculation of the spatially averaged radar cross section (RCS) of the chaff particles contained in the RR-129 cartridge is successfully obtained by including both reradiation and conductive power losses. Also, a comparison between the estimated results and measured ones is attempted for a single chaff particle of length 5.08 cm.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.327
Teacher spread0.306 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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