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Record W2508077009 · doi:10.1155/2016/9674742

High Gain Slot Array with Fabry-Perot Cavity Feeding Circuit

2016· article· en· W2508077009 on OpenAlexaff
Halim Boutayeb, Mourad Nedil

Bibliographic record

VenueInternational Journal of Antennas and Propagation · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversité du Québec en Abitibi-TémiscaminguePolytechnique Montréal
Fundersnot available
KeywordsFabry–Pérot interferometerResonatorAntenna (radio)ConductorSlot antennaMaterials scienceElectrical impedanceOptoelectronicsWavelengthDielectricImpedance matchingHigh-gain antennaSubstrate (aquarium)OpticsElectronic engineeringEngineeringElectrical engineeringPhysicsAntenna measurement

Abstract

fetched live from OpenAlex

A new approach for designing slot arrays using a Fabry-Perot cavity for the feeding circuit is presented. The proposed array has simpler and smaller feeding circuit compared to conventional feeding networks that have multiple dividers or combiners. The dividers and combiners are usually sources of losses. In addition, the profile of the proposed array is not limited by the half-wavelength resonance condition that exists for Fabry-Perot resonator antennas based on partially reflecting surfaces. The operating frequency is not sensitive to the profile of the antenna. A small profile can be achieved without the utilization of an artificial magnetic conductor or a substrate with high dielectric constant. To validate the proposed approach, full-wave numerical results are presented at 5.8 GHz showing good impedance matching, a high gain of about 22 dB, and an efficiency of 76%.

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 score0.493
Threshold uncertainty score0.252

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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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