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Dual-polarized fixed-beam high-gain array antenna for microwave and mm-wave applications

2017· article· en· W2766612594 on OpenAlexaff
Abhijit Bhattacharya, Rodney G. Vaughan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsArray gainBroadsideOpticsAntenna gainBandwidth (computing)MicrowaveSlot antennaRadiationPhased arrayAntenna apertureReflective array antennaAperture (computer memory)Antenna arrayPolarization (electrochemistry)Dual-polarization interferometryExtremely high frequencyMaterials scienceRadiation patternPhysicsAntenna (radio)Computer scienceAcousticsTelecommunications

Abstract

fetched live from OpenAlex

There is a long-standing need for low-profile antennas with fixed-direction high gain in both polarizations. The design challenge for such an an array is that the elements and feed should be low loss and low cost. A new design using crossed slot elements for such a broadside fixed array is presented. The structure comprises an array of sub-arrays of slot elements with waveguide feeding. The design has excellent radiation properties (gain, pattern shape, polarization purity, bandwidth) and is suitable for microwave and millimeter-wave frequencies. An example design is presented that has a gain of 12 dBi, aperture efficiency of 54%, and a polarization isolation of 40 dB, all over a bandwidth close to 10%. An advantage of this structure is its scalability to increase its size and provide higher gain without major modifications and without increasing the loss significantly - a major advantage of waveguide-fed slot arrays. For example, the radiation efficiency of a 4×4 array using aluminum is ~99% from simulation.

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: 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
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.003

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 head0.014
GPT teacher head0.221
Teacher spread0.207 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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