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Substrate Integrated Waveguide Power handling Capability at Millimeter Wave with Surface Roughness Consideration

2021· article· en· W4212791779 on OpenAlexaff
Ahmed Moulay, Abdelkader Zerfaine, Tarek Djerafi

Bibliographic record

Venue2021 IEEE MTT-S International Microwave and RF Conference (IMARC) · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsExtremely high frequencyConductorMaterials scienceMicrostripWaveguideMillimeterElectric power transmissionOhmic contactSurface roughnessPower (physics)DielectricSubstrate (aquarium)AcousticsElectrical conductorElectronic engineeringOptoelectronicsOpticsElectrical engineeringEngineeringLayer (electronics)PhysicsComposite material

Abstract

fetched live from OpenAlex

Abstract-This study investigates the power handling capability of the substrate integrated waveguide (SIW) transmission lines in millimeter-wave frequencies. The average power handling capability (APHC) which refers to self-heating is considered. The ohmic and dielectric losses expressions are used to predict the APHC as a function of the geometrical parameters and used material. In fact, small dimensions and the increased losses at millimeter-waves dramatically reduce the power handling capability. Moreover, the additional effect of the surface roughness doubles the conductor loss at millimeter-wave frequencies. The proper material and dimensions selection for SIW, which could increase its APHC are discussed. In addition, guidelines for investigating the oversized version of SIW are also presented. To investigating the oversized version of SIW are also presented. To validate the simulations, a set of transmission lines (microstrip, The good agreement of measured losses with the calculated validates this study.

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 score0.002

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)0.0010.000

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 head0.018
GPT teacher head0.216
Teacher spread0.198 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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