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Record W2147978116 · doi:10.1109/eucap.2014.6902204

Coupling estimation in receiving antenna systems for array calibration

2014· article· en· W2147978116 on OpenAlexaff
Simon Henault, Symon K. Podilchak, Said Mikki, Yahia M. M. Antar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsRoyal Military College of CanadaQueen's UniversityRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsAntenna (radio)Coupling (piping)CalibrationElectronic engineeringGround planeCompensation (psychology)Computer scienceAntenna arrayAcousticsConductorPatch antennaMicrostrip antennaPhysicsEngineeringTelecommunicationsMathematicsGeometry

Abstract

fetched live from OpenAlex

A general approach is examined to predict the operation of receiving antennas and arrays where a matrix formulation is used to characterize the antenna system under study. Developed concepts are also extended to coupling ratio predictions and array calibrations. The proposed methodology is validated by comparing calculated predictions to measurements and simulations of a practical antenna system, defined by a single 2.5 GHz conductor backed patch antenna operating above a ground plane platform. Investigations are also extended to a two and four element patch array. The developed procedures are also applicable to other antenna system configurations, complex scattering environments, and practical compensation techniques for array processing.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.210
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 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

Citations1
Published2014
Admission routes1
Has abstractyes

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