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Design of Broadband H-plane Horn Antenna with High-Order Mode Based on Substrate Integrated Waveguide (SIW) Technology

2024· article· en· W4402978762 on OpenAlexaff
Libo Wang, Shu Lin, Tong Xu, Xingqi Zhang, Xinyue Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBroadbandHorn antennaFrench hornSubstrate (aquarium)Antenna (radio)WaveguideOptoelectronicsMode (computer interface)Materials scienceOpticsDirectional antennaComputer scienceTelecommunicationsSlot antennaPhysicsAcousticsGeology

Abstract

fetched live from OpenAlex

This paper presents the development of a high-order mode, low-profile, broadband H-plane horn antenna utilizing substrate integrated waveguide (SIW) technology to increase its bandwidth. The design strategically modifies the antenna's hO$\text{rn}$dimensions and incorporates high-order modes, thereby introducing resonant frequencies over a range of bands and significantly broadening its operational bandwidth. Furthermore, enhancements in the antenna's bandwidth and radiation properties are achieved through the extension of the waveguide aperture, modification of electromagnetic boundary conditions, and integration of impedance-matching structures. The antenna demonstrates a notable performance, with a reflection coefficient below -10 dB in the 30–52 GHz range, and a relative bandwidth of 53.6%. Additionally, it maintains a consistent gain of over 10 dBi across the operating frequency band with excellent directional radiation characteristics. The proposed SIW-based high-order mode H-plane horn antenna offers high bandwidth, low profile, and ease-of-manufacturing attributes, making it suitable for millimeter-wave applications in the Ka-band.

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: none
Teacher disagreement score0.000
Threshold uncertainty score0.002

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)0.0000.001

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 head0.008
GPT teacher head0.197
Teacher spread0.190 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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