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Record W2891713728 · doi:10.1109/access.2018.2869718

Modeling and Design Empirical Formulas of Microstrip Ridge Gap Waveguide

2018· article· en· W2891713728 on OpenAlexaff
Abdelmoniem T. Hassan, Mohamed A. Moharram Hassan, Ahmed A. Kishk

Bibliographic record

VenueIEEE Access · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
Fundersnot available
KeywordsMicrostripDielectricGround planeConductorSurface waveRidgePropagation constantElectrical conductorElectrical impedanceDispersion (optics)Materials scienceCharacteristic impedanceSurface (topology)OpticsExtremely high frequencyComputer scienceGeometryOptoelectronicsMathematicsPhysicsTelecommunicationsElectrical engineeringEngineeringAntenna (radio)Composite materialGeology

Abstract

fetched live from OpenAlex

Recently, interest in the microstrip ridge gap waveguide (MRGW) has increased due to the need for a self-packaged and low-loss structures for millimeter-wave applications. The MRGW consists of a grounded textured surface, which is artificially representing a perfect magnetic conductor surface loaded with a thin low dielectric constant substrate with a printed strip. This is topped with another dielectric substrate covered with a conducting plate at the top as a ground plane. Currently, the full-wave and optimization tools are used to design the MRGW structure. Consequently, an efficient modeling and design tool for the MRGW is proposed in this paper via curve fitting. Closed form empirical expressions for the effective dielectric constant, characteristic impedance, and the dispersion effect are provided. The developed expressions are generalized for arbitrarily chosen MRGW parameters. The expressions are verified with the full-wave solution. The results show the potential of the proposed approach in modeling the MRGW structure.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.450
Threshold uncertainty score0.657

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 head0.078
GPT teacher head0.311
Teacher spread0.233 · 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 designSimulation or modeling
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

Citations19
Published2018
Admission routes1
Has abstractyes

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