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Record W4206447811 · doi:10.1109/tap.2021.3137483

Ka-Band Integrated Multilayer Pyramidal Horn Antenna Excited by Substrate-Integrated Gap Waveguide

2021· article· en· W4206447811 on OpenAlexaff
Syed M. Sifat, Shoukry I. Shams, Ahmed A. Kishk

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaterials scienceLeakage (economics)Horn antennaOpticsOptoelectronicsFrench hornPerfect conductorConductorDielectricRadiation patternAntenna (radio)Electrical engineeringAcousticsSlot antennaComposite materialPhysicsEngineering

Abstract

fetched live from OpenAlex

This work proposed a solution of structures built from stacking several dielectric substrates with the conducting cladding, aiming for electrical contact between them [perfect electric conductor (PEC)–PEC]. At mm-wave frequencies, surface roughness or imperfect flat surfaces present possible gaps that cause severe unexpected leakage. Many engineers overlooked this problem. To prevent this leakage, we propose transforming one of the PEC–PEC surfaces into artificial magnetic conductors (AMCs), creating a PEC–AMC layer that suppresses any leakage even with no contact between the surfaces where the gap is less than a quarter wavelength. A wideband multilayer pyramidal horn antenna using substrate-integrated gap waveguide (SIGW) technology is proposed as an example that highlights the proposed solution. In each layer of the horn, the opening is surrounded by periodic cells to suppress leakage and surface waves. In addition, the upper surface surrounding the horn’s opening is surrounded by EBG mushroom cells to act as a soft surface that suppresses the surface waves and reduces the edge diffraction, and, in turn, improves the radiation characteristics of the horn. The horn achieved a gain of 11.5 dBi and 20.5% bandwidth (28.5–35 GHz). The simulated and measured results show excellent agreement with each other.

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 categoriesMeta-epidemiology (narrow)
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 score0.744
Threshold uncertainty score1.000

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 head0.014
GPT teacher head0.214
Teacher spread0.200 · 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.

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

Citations29
Published2021
Admission routes1
Has abstractyes

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