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Record W2965188159 · doi:10.1109/access.2019.2933154

Design of Substrate Integrated Gap Waveguide and Their Transitions to Microstrip Line, for Millimeter-Wave Applications

2019· article· en· W2965188159 on OpenAlexafffund
Jing Zhang, Xiupu Zhang, Ahmed A. Kishk

Bibliographic record

VenueIEEE Access · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsMicrostripTransmission lineExtremely high frequencyAttenuationMicrowaveConductorWaveguideDielectricComputer scienceMaterials scienceSubstrate (aquarium)Electrical conductorAntenna (radio)Electric power transmissionElectronic engineeringOptoelectronicsAcousticsOpticsElectrical engineeringTelecommunicationsPhysicsEngineering

Abstract

fetched live from OpenAlex

This paper offers an approximate, but very convenient and accurate, manner to find the desired strip width for substrate integrated gap waveguide (SIGW) with a given characteristic impedance and the conductor and dielectric attenuation constants, without any complicated manual calculations or time-consuming full-wave simulation and optimization iterations. Moreover, the investigation of the transition between SIGW and microstrip lines will prove that an additional transition structure, such as a conventional microstrip taper, is not required any more at millimeter-wave frequencies for the desired transmission performance. This is a useful feature in circuit design and compactness. Both of the above works will be of great help to realize future feeding networks for SIGW antenna arrays or other types of cost-effective SIGW passive components at high frequencies. Two SIGW prototypes, working at Ka and V bands, are fabricated and offer experimental verifications, which present good agreement with the simulation results.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.767
Threshold uncertainty score0.693

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 head0.042
GPT teacher head0.256
Teacher spread0.214 · 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 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

Citations10
Published2019
Admission routes2
Has abstractyes

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