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Record W2548910270 · doi:10.1109/aps.2016.7696356

Low-profile single-feed highly-directive antennas based on cavity-excited metasurfaces

2016· article· en· W2548910270 on OpenAlexaff
A. J. Epstein, George V. Eleftheriades

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOpticsAntenna (radio)Shielded cableAperture (computer memory)Reflector (photography)Radiation patternRadomeAntenna aperturePhysicsOptoelectronicsAcousticsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

We propose a novel antenna design combining the simplicity of shielded Fabry-Perot leaky-wave antennas (FP-LWA) with the performance of antenna arrays. The device is based on a probe-fed shielded FP-LWA structure, with the standard partially-reflecting surface replaced by a metasurface. The cavity excitation is optimized to uniformly illuminate the aperture, forming radiating hot spots half-a-wavelength apart, while the metasurface imposes the linear phase required to promote directive radiation. This yields a probe-fed low-profile antenna with near-unity aperture illumination efficiency and no edge-taper losses. In previous work we have shown that a Huygens' metasurface can be used to that end; in this report we present an improved design featuring an omega-type bian-isotropic metasurface. The compact device structure (with respect to lens or reflector antennas) and the simple and efficient feeding scheme (with respect to antenna arrays) make these cavity-excited metasurface antennas very appealing for industrial applications, such as satellite communication or automotive radars.

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: Empirical
Teacher disagreement score0.225
Threshold uncertainty score0.863

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 head0.015
GPT teacher head0.214
Teacher spread0.199 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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