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Record W2972833010

A Two-Port Microstrip Sensor Antenna for Permittivity and Loss Tangent Measurements

2019· article· en· W2972833010 on OpenAlexaff
Mohammad Mahdi Honari, Rashid Mirzavand, Hossein Saghlatoon, Pedram Mousavi

Bibliographic record

VenueEuropean Conference on Antennas and Propagation · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrowave and Dielectric Measurement Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDissipation factorMicrostrip antennaPatch antennaAntenna (radio)AcousticsCoaxial antennaAntenna measurementPermittivityAntenna factorMaterials scienceRadiation patternAntenna efficiencyElectronic engineeringOpticsComputer sciencePhysicsTelecommunicationsEngineeringOptoelectronicsDielectric
DOInot available

Abstract

fetched live from OpenAlex

A two-port microstrip sensor antenna system is presented for characterizing different materials. The proposed sensor antenna can estimate both permittivity and loss tangent of a sample under test (SUT). Compared to the single port sensor antenna, the proposed sensor antenna system is more practical since the working frequency of the system can be adjusted easily. The permittivity of material has a huge impact on the antenna resonance, while its loss tangent affects power transmitted to the second port. Therefore, by finding out the antenna resonance and the level of transmission response, one can characterize the materials. The results show that the antenna radiation pattern is not deteriorated for samples with different permittivities and loss tangents.

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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.050
GPT teacher head0.249
Teacher spread0.199 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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