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Record W2589103532 · doi:10.1109/antem.2000.7851705

Dual-polarized lens antenna for LMCS applications

2000· article· en· W2589103532 on OpenAlexaff
A. Petosa, J. Shaker, S. Thirakoune, S. Jetté, John Bradley, M. Cuhaci

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsCommunications Research Centre Canada
Fundersnot available
KeywordsMicrostrip antennaDual-polarization interferometryOpticsPatch antennaAntenna feedPolarization (electrochemistry)MicrostripDirectional antennaAntenna (radio)Reconfigurable antennaCoaxial antennaAcousticsComputer scienceElectronic engineeringPhysicsEngineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper presents a lens antenna configuration designed at 30 GHz. Separate microstrip-fed cavity elements are used for the transmit and receive signals. The elements are separated in space and orthogonal linear polarization is used to improve the isolation between the transmit and receive channels. Further isolation is achieved by placing polarization filters in front of each of the feed antennas, to help suppress the cross-polarized field. A polarization-sensitive surface placed between the lens and the feeds is used to direct the transmit and receive signals to the appropriate feed antenna. Active devices are also to be integrated with the feed antennas to reduce losses due to transmission line lengths, and improve the noise figure of the antenna.

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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.003

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 head0.011
GPT teacher head0.209
Teacher spread0.198 · 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

Citations3
Published2000
Admission routes1
Has abstractyes

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