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Record W4312936528 · doi:10.1109/access.2022.3229050

Physical Design and Experimental Verification of a Huygens’ Metasurface Two-Lens System for Phased-Array Scan-Angle Enhancement

2022· article· en· W4312936528 on OpenAlexafffund
Jaemin Kim, Gleb A. Egorov, George V. Eleftheriades

Bibliographic record

VenueIEEE Access · 2022
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoCanada Foundation for InnovationGovernment of Ontario
KeywordsPhased arrayOpticsRadomeLens (geology)DirectivityMetamaterialPhased-array opticsAntenna (radio)PhysicsBeam steeringPlanarPlanar arrayBeam (structure)Computer scienceTelecommunications

Abstract

fetched live from OpenAlex

Over the past decades, many radome designs to extend the angular scan range of phased-array antennas have been devised by utilizing dielectric materials and metamaterials. More recently, metasurface technology such as planar lenses and beam deflectors have been applied to phased arrays, enabling scan-angle enhancers to have a low profile. In this work, a physical Huygens’ metasurface (HMS) two-lens system for scan-angle doubling of a phased array is presented. For the HMS unit cells, the wire-loop topology is deployed to achieve high transmission for the required phase-angle shift. The proposed two-lens system is analyzed by full-wave simulations and experiments. The simulation results demonstrate that the scan angle doubles when the incident angle is below 15° in accordance to the design specification. Furthermore, the directivity degradation of the refracted beams by the two-HMS lenses is in good agreement with theory. Finally, a fabricated two-lens system with two 15λ long by 15λ wide metasurface lenses and a 16 × 16-element patch antenna array as a source is experimentally verified at 10 GHz. The experimental results are in good agreement with the simulated results by showing angle-doubling performance with ±2° scan errors.

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)0.000
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: none
Teacher disagreement score0.574
Threshold uncertainty score0.465

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.042
GPT teacher head0.300
Teacher spread0.258 · 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

Citations15
Published2022
Admission routes2
Has abstractyes

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