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Record W2418446765 · doi:10.1109/eucap.2016.7481661

Low-profile antennas with 100% aperture efficiency based on cavity-excited omega-type biansiotropic metasurfaces

2016· article· en· W2418446765 on OpenAlexaff
A. J. Epstein, Joseph P. S. Wong, George V. Eleftheriades

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAperture (computer memory)OpticsPhysicsAntenna (radio)Reflection (computer programming)Reflection coefficientExcited stateAntenna apertureLossy compressionAcousticsComputer scienceDipole antennaTelecommunications

Abstract

fetched live from OpenAlex

We propose a novel concept for highly-directive low-profile antennas, based on a single localized source embedded in a cavity, covered by an omega-type bianistoropic metasurface (BMS). We show that such metasurfaces, which include subwavelength particles with electric and magnetic polarizabilities, and magnetoelectric coupling, allow control of both the aperture field phase and the BMS reflection coefficient, without requiring active or lossy components. Subsequently, we use this degree of freedom to exclusively excite the highest-order fast lateral mode, guaranteeing optimal aperture illumination efficiency for arbitrarily-large apertures, without incurring edge-taper losses. We verify our semianalytical calculations with full-wave simulations, showing that the proposed antenna can outperform our previously-introduced cavity-excited Huygens' metasurface antenna, offering a simple and efficient design for compact high-gain antennas.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.023
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.003

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 head0.245
Teacher spread0.227 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2016
Admission routes1
Has abstractyes

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