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Record W2113309249 · doi:10.1109/tap.2011.2109694

Lenses for Circular Polarization Using Planar Arrays of Rotated Passive Elements

2011· article· en· W2113309249 on OpenAlexafffund
Rudi H. Phillion, M. Okoniewski

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Calgary
FundersCanada Research Chairs
KeywordsOpticsLens (geology)PhysicsCircular polarizationPolarization (electrochemistry)PlanarAperture (computer memory)Planar arrayCylindrical lensMicrostripFocal lengthAcousticsComputer science

Abstract

fetched live from OpenAlex

A planar array of passive lens elements can be phased to approximate the effect of a curved dielectric lens. The rotational orientation of each element can provide the required phase shift for circular polarization. The array elements must be designed so that the hand of circular polarization changes as the electromagnetic wave passes through the lens. An element is presented that is based on an aperture-coupled microstrip patch antenna, and two lenses are designed. Each lens has a diameter of 254 mm and contains 349 elements. The elements have identical dimensions but the rotational orientation of each element is selected to provide a specific lens function. The first lens is designed to collimate radiation from a feed horn into a beam pointing 20°from broadside. At 12.9 GHz the aperture efficiency is 48%. The second lens acts as a Wollaston-type prism. It splits an incident wave according to its circular polarization components.

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)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.040
GPT teacher head0.239
Teacher spread0.200 · 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

Citations153
Published2011
Admission routes2
Has abstractyes

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