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Record W4312590658 · doi:10.1109/tap.2022.3215859

Support Vector Regression-Enabled Optimization Strategy of Dual Circularly-Polarized Shaped-Beam Reflectarray With Improved Cross- Polarization Performance

2022· article· en· W4312590658 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaMinisterio de Ciencia e InnovaciónMinisterio de Ciencia, Innovación y UniversidadesGobierno del Principado de Asturias
KeywordsPolarization (electrochemistry)Linear polarizationOpticsCircular polarizationPhysicsComputer scienceChemistryMicrostrip

Abstract

fetched live from OpenAlex

This work presents the optimization of a dual circular-polarized (CP) shaped-beam reflectarray with improved performance. To that end, the design methodology leverages surrogate models based on support vector regression (SVR) of the electromagnetic response of the constituent unit cell for a direct layout optimization of the antenna. The dual CP capability is achieved using a linear polarization (LP) Jerusalem cross integrated with an LP-to-CP polarization converter. A full description of the reflectarray analysis in CP is given. We also provide a missing demonstration in the literature of the fact that the direct coefficients in CP shape the copolar (CO) pattern of the corresponding polarization. This is applied to the optimization of a dual CP reflectarray with an isoflux pattern, achieving a reduction of more than 9 dB in the crosspolar (XP) pattern.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.618
Threshold uncertainty score0.735

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)0.0000.000

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 head0.012
GPT teacher head0.232
Teacher spread0.219 · 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