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Record W3150008303 · doi:10.2478/jee-2021-0008

A single-layer metallo-dielectric superstructure for enhancing the performances of EBG cavity antenna

2021· article· en· W3150008303 on OpenAlexaff
Abdelhalim Chaabane, Hussein Attia, F. Djahli, Tayeb A. Denidni

Bibliographic record

VenueJournal of Electrical Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsInstitut National de la Recherche Scientifique
FundersDirection Générale de la Recherche Scientifique et du Développement Technologique
KeywordsMaterials scienceSuperstructureRadiation patternAntenna (radio)OptoelectronicsOpticsDielectricAntenna efficiencyMicrostrip antennaCenter frequencyElectrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract A novel single-layer metallo-dielectric superstructure is proposed in this paper. It is constructed by two asymmetric unit- cells optimally arranged on the same layer to construct a partially reflective surface to be placed over a multilayer microstrip slot antenna named feed antenna for enhancing its performances. The radiation is expected to be maximum at the center of the formed layer. Thus, to maintain a high-gain performance, the unit-cells placed at the center are designed to provide a quasi-optimal reflection phase with high reflectivity at the frequency band of interest. A prototype of the proposed antenna operating at 10 GHz with overall size of 2.133λ 0 ×2.133λ 0 ×0.56λ 0 is successfully designed and fabricated. The calculated and measured antenna gain results indicate that the proposed antenna exhibits a wider radiation bandwidth performance of about 41.15% and 36.15%, respectively.

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 score0.000
Threshold uncertainty score0.001

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)0.0000.000

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 head0.013
GPT teacher head0.217
Teacher spread0.203 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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