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Record W2215387059 · doi:10.1002/mop.29596

Analysis of antenna gain enhancement using new frequency selective surface superstate

2015· article· en· W2215387059 on OpenAlex
Moufida Bouslama, Moubarek Traii, Ali Gharsallah, Tayeb A. Denidni

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicrowave and Optical Technology Letters · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsAntenna gainAntenna (radio)Radiation patternHigh-gain antennaTunable metamaterialsMicrowaveAntenna measurementMaterials scienceAntenna factorRadiationAntenna efficiencyOpticsAcousticsOptoelectronicsElectronic engineeringEngineeringElectrical engineeringPhysicsTelecommunicationsMetamaterial

Abstract

fetched live from OpenAlex

ABSTRACT In this article, a new high‐gain resonant cavity (RC) antenna with a frequency selective surface (FSS) superstrate is presented at 6 GHz operating frequency. The high‐gain FSS antenna is constructed by a new FSS superstrate layer, providing more freedom to improve the radiation pattern characteristics. A hybrid FSS element placed on top of the antenna is also investigated to estimate the required number of unit cells. The achieved simulation results for this antenna are thoroughly studied and the FSS superstrate enhancing the proposed antenna gain is discussed. Moreover, parametric studies for the patch antenna with and without FSS superstrate are also presented to demonstrate the effect of the new FSS superstrate on the radiation pattern characteristics. The measured results are achieved to support the simulation ones. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:448–453, 2016

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.

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 score0.163
Threshold uncertainty score0.784

Codex and Gemma teacher scores by category

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

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 head0.024
GPT teacher head0.247
Teacher spread0.223 · 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