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Record W4309603261 · doi:10.3390/cryst12111646

C-Band Linear Polarization Metasurface Converter with Arbitrary Polarization Rotation Angle Based on Notched Circular Patches

2022· article· en· W4309603261 on OpenAlexaff
Tao Zhang, Haoran Wang, Chongmei Peng, Zhaohui Chen, Xiaoyi Wang

Bibliographic record

VenueCrystals · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsPolytechnique Montréal
FundersNational Key Research and Development Program of China
KeywordsPolarization (electrochemistry)OpticsLinear polarizationCircular polarizationOblique casePolarization rotatorPhysicsMaterials scienceMicrostripChemistry

Abstract

fetched live from OpenAlex

This paper presents a linear polarization metasurface converter with arbitrary polarization rotating angle and stable oblique incident response, which is based on the concept of linear-to-circular polarization decomposition and recombination. A metasurface unit cell is proposed using a notched circular patch with two metallized vias connected to the bottom-layer coplanar waveguide (CPW) transmission lines. By changing the length of the two transmission lines, different polarization rotation angle may be realized. The proposed metasurface polarization converter is theoretically analyzed and full-wave simulated. A 45∘ metasurface polarization converter with 8×8 unit cells is designed and experimentally demonstrated. The experimental results agree well with the simulation, showing that the proposed metasurface polarization converter can achieve a high polarization conversion ratio (PCR) larger than 85% under up to 50∘ oblique incident wave.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.018
GPT teacher head0.233
Teacher spread0.214 · 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 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

Citations20
Published2022
Admission routes1
Has abstractyes

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