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Record W4210897305 · doi:10.1038/s41598-022-06259-8

Microstrip sensor and methodology for the determination of complex anisotropic permittivity using perturbation techniques

2022· article· en· W4210897305 on OpenAlexaff
Hector-Noel Morales-Lovera, José‐Luis Olvera‐Cervantes, Aldo-Eleazar Perez-Ramos, Alonso Corona‐Chávez, Carlos E. Saavedra

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsPermittivityMicrostripAnisotropyResonatorMaterials scienceRelative permittivityPhysicsOpticsAcousticsOptoelectronicsComputer scienceDielectric

Abstract

fetched live from OpenAlex

In this work, a sensor in microstrip technology and a methodology for measuring the real part and the imaginary part of the complex uniaxial permittivity of solid anisotropic samples are presented. The sensor is based on a pair of parallel lines coupled resonators and a cleft arranged in the coupling region which allows to hold the samples under test (SUTs). The proposed methodology relates the change in the even/odd resonance frequency with the real part of the permittivity in the vertical/horizontal direction, and the change in the Q factor of the even/odd mode with the imaginary part of the permittivity in the vertical/horizontal direction. The methodology was successfully verified with the characterization, at 2.43 GHz of anisotropic samples of printed PLA, Diclad 880, and RO4350B using the knowns materials: RT5870, PTFE and RO4003.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.124
GPT teacher head0.313
Teacher spread0.189 · 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

Citations28
Published2022
Admission routes1
Has abstractyes

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