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Record W2805298745 · doi:10.1155/2018/3469730

Line Defect-Layered EBG Waveguides in Dielectric Substrates

2018· article· en· W2805298745 on OpenAlexafffund
Hassna Ouassal, J. Shaker, Langis Roy, Khelifa Hettak, Reza Chaharmir

Bibliographic record

VenueInternational Journal of Antennas and Propagation · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsOntario Tech UniversityCommunications Research Centre CanadaCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDielectricWaveguideMaterials scienceSlabPhotonic crystalMicrowaveOptoelectronicsMetamaterialOpticsEngineeringPhysicsTelecommunicationsStructural engineering

Abstract

fetched live from OpenAlex

A dielectric-based multilayer structure composed of U-shaped rings (ML-UR) is used to develop a class of novel electromagnetic band gap (EBG) slab waveguide. The structure has two band gaps that narrow down as dielectric constant is increased. The EBG slab waveguide is created by embedding a single-layer line defect inside the 3D crystal of the EBG slab guide. Unlike our previously published foam-based EBG structure, the use of dielectric spacer in the EBG waveguides offers significant advantages in terms of overall size, structure reliability, and design flexibility. The waveguide structures reported in this paper are designed to operate at X-band (8–12 GHz) while being fed by coplanar-slotline transitions. Prototypes were fabricated and characterized experimentally. The insertion loss decreases by decreasing the number of full lattices of ML-UR surrounding the channels. The proposed waveguide has potential in microwave components such as directional couplers, phase shifters, and antenna array feeding networks.

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: Empirical
Teacher disagreement score0.248
Threshold uncertainty score0.224

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 head0.012
GPT teacher head0.281
Teacher spread0.269 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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