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Record W2901820321 · doi:10.1049/cp.2018.0734

A Systematic Circuit-based Approach to Efficiently Realize Singleand Dual-band Circular Polarizers

2018· article· en· W2901820321 on OpenAlexaff
Mehdi Hosseini, Sean V. Hum

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolarizerDual (grammatical number)Multi-band deviceComputer scienceTopology (electrical circuits)Electronic engineeringElectrical engineeringTelecommunicationsEngineeringOpticsPhysics

Abstract

fetched live from OpenAlex

This paper describes a systematic circuit-based approach which streamlines the realization of linear-to-circular polarizers. Two demonstrative designs are presented and the related challenges and opportunities are discussed. The first design is a low-loss polarizer, composed of two identical frequency selective surfaces (FSSs). The reduced number of layers yields a total profile of one-tenth of a wavelength, while providing high polarization purity and high transparency. This design features axial ratio of less than 3 dB (1 dB) over 10% (4%) fractional bandwidth, with low insertion loss of 0.5 dB (0.36 dB). The second design is a dual-band polarizer comprised of five FSS layers, which demonstrates low insertion loss of 0.6 dB and 4 / 2.7% bandwidth at 20 / 30 GHz. The unit cell used to realize the equivalent circuit elements of the polarizers is based on a subwavelength FSS grid, the so-called modified Jerusalem cross, recently introduced in the literature. The low profile and compactness of this cell (0.17λ 0 ×0.17λ 0 ×0.11λ 0 ) enhance the accuracy of the circuit-driven approach, relax the computational analysis, and could potentially reduce the sensitivity to the angle of incidence. In particular, the low insertion loss of such designs makes them appealing for a variety of terrestrial and satellite applications.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.881
Threshold uncertainty score0.798

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 head0.018
GPT teacher head0.219
Teacher spread0.200 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations16
Published2018
Admission routes1
Has abstractyes

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