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A modal current correlation approach for designing dual-frequency slotted cavity antennas

2017· article· en· W2765887449 on OpenAlexaff
Abhijit Bhattacharya, Rodney G. Vaughan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsBandwidth (computing)ModalWaveguideAcousticsFrequency bandMicrowaveOpticsSlotted waveguideModal analysisMulti-band deviceRadarSlot antennaDirectional antennaElectronic engineeringComputer sciencePhysicsMaterials scienceEngineeringVibrationTelecommunicationsAntenna (radio)

Abstract

fetched live from OpenAlex

Slotted metallic waveguide and cavity antennas are widely used for microwave and higher frequencies, mainly due to their simple construction and low loss nature. These antennas provide good radiation characteristics for communications and radar applications, but their single, narrow bandwidth is a restriction. A few dual-band antennas utilizing metal waveguide structures can be found in the literature, but they sacrifice the structural simplicity of the single mode configuration. We present a new design method to arrive at a dual-frequency design which can preserve the simple construction of single band cavities. The method is based on the correlation between the modal surface currents in the metallic cavity. Numerical results show promising outcomes for an example of a rectangular cavity structure. This method could be applied to other common waveguide structures for circular or cylindrical antennas.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.029
GPT teacher head0.249
Teacher spread0.220 · 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
Published2017
Admission routes1
Has abstractyes

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