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Record W2298573220 · doi:10.1364/josaa.32.001700

Electrically thin flat lenses and reflectors

2015· article· en· W2298573220 on OpenAlexafffund
Miguel Ruphuy, Omar Siddiqui, Omar M. Ramahi

Bibliographic record

VenueJournal of the Optical Society of America A · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversidad de Costa Rica
KeywordsOpticsLens (geology)DielectricReflector (photography)Materials scienceFocal pointTransformation opticsFocus (optics)Interference (communication)PermittivityParabolic reflectorCompensation (psychology)Cardinal pointElectrical impedanceMetamaterialOptoelectronicsPhysicsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

We introduce electrically thin dielectric lenses and reflectors that focus a plane wave based on the principles of phase compensation and constructive wave interference. Phase compensation is achieved by arranging thin rectangular slabs having different dielectric permittivity according to a permittivity profile obtained through analytic design equations. All incident rays parallel to the optical axis converge to a focal point with equalized optical paths resulting in constructive interference. Plane wave simulations indicate strong focusing, even in the presence of impedance mismatch between free space and the dielectric layers composing the lens. We demonstrate focusing at 9.45 GHz using a lens fabricated with commercially available dielectric materials. In addition to focusing, the flat lens proposed here demonstrates relatively high power gain at the focal point. We also present a flat reflector based on the same concept. We believe that the proposed dielectric lens and reflector are strong candidates to replace heavy metallic dishes and reflectors used in a variety of applications, especially satellites.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.194

Codex and Gemma teacher scores by category

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

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 head0.032
GPT teacher head0.283
Teacher spread0.251 · 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 teacher head, 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

Citations19
Published2015
Admission routes2
Has abstractyes

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