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Record W2411575532 · doi:10.1038/srep19268

A True Metasurface Antenna

2016· article· en· W2411575532 on OpenAlexafffund
Mohamed El Badawe, Thamer S. Almoneef, Omar M. Ramahi

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsResonatorAntenna (radio)Impedance matchingCoupling (piping)OpticsPhysicsAcousticsElectrical impedanceReconfigurable antennaDirectional antennaWavelengthDipole antennaComputer scienceMaterials scienceCoaxial antennaTelecommunications

Abstract

fetched live from OpenAlex

We present a true metasurface antenna based on electrically-small resonators. The resonators are placed on a flat surface and connected to one feed point using corporate feed. Unlike conventional array antennas where the distance between adjacent antennas is half wavelength to reduce mutual coupling between adjacent antennas, here the distance between the radiating elements is electrically very small to affect good impedance matching of each resonator to its feed. A metasurface antenna measuring 1.2λ × 1.2λ and designed to operate at 3 GHz achieved a gain of 12 dBi. A prototype was fabricated and tested showing good agreement between numerical simulations and experimental results. Through numerical simulation, we show that the metasurface antenna has the ability to provide beam steering by phasing all the resonators appropriately.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.207
Teacher spread0.194 · 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

Citations105
Published2016
Admission routes2
Has abstractyes

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