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Record W4322576852 · doi:10.1109/tcsii.2023.3249561

Substrate Integrated Waveguide-Based Dual-Polarized Self-Diplexing Antenna Array

2023· article· en· W4322576852 on OpenAlexaff
Hassan Naseri, Peyman PourMohammadi, Noureddine Melouki, Fahad Ahmed, Amjad Iqbal, Tayeb A. Denidni

Bibliographic record

VenueIEEE Transactions on Circuits & Systems II Express Briefs · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsAntenna (radio)OpticsWaveguideMaterials scienceCapacitive sensingSubstrate (aquarium)OptoelectronicsAntenna arrayRadiationPhysicsElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

Substrate Integrated Waveguide (SIW) cavity backed self-diplexing slot antenna array is proposed with dual-polarized capability. The antenna array is composed of four linearly placed full-duplex elements operating at 3.5 and 4 GHz independently. The antennas are designed inside a Half Mode SIW (HMSIW) cavity, and two capacitive slots adjust the resonant frequencies. Furthermore, two 1 by 4 power dividers all including 50-ohm transmission lines are designed to feed the radiators, thus resulting in a single layer antenna array integrated with feeding networks. Upon evaluating the simulated and measured results, the minimum reflection coefficients of −25 dB were observed at both operating frequencies. In addition, the data showed that the suggested structure brings about a high isolation level of more than 23 dB between two bands. It is also worth expressing that maximum gains of 10 and 11.5 dBi are obtained at 3.5 and 4 GHz, respectively. And, the radiation efficiencies are 0.78 and 0.9 for the low and high frequency bands.

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 score0.001
Threshold uncertainty score0.003

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.215
Teacher spread0.197 · 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

Citations16
Published2023
Admission routes1
Has abstractyes

Explore more

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