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Record W3167643157 · doi:10.1109/lawp.2021.3095469

Arbitrary Wave Transformations With Huygens’ Metasurfaces Through Surface-Wave Optimization

2021· preprint· en· W3167643157 on OpenAlexafffund
Vasileios G. Ataloglou, George V. Eleftheriades

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2021
Typepreprint
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWavefrontPhysicsOpticsBandwidth (computing)Aperture (computer memory)Lossless compressionHuygens–Fresnel principleElectromagnetic radiationPlane wavePower (physics)Surface (topology)Plane (geometry)Surface waveComputer scienceAcousticsMathematicsGeometryTelecommunications

Abstract

fetched live from OpenAlex

Huygens' metasurfaces (HMSs) have demonstrated the ability to tailor electromagnetic wavefronts with passive low-profile structures. The fundamental constraint enabling passive and ideally lossless solutions is the conservation of the normal real power locally along the metasurface. In this letter, we examine the use of auxiliary surface waves to design HMSs for wave transformations with different incident and output power density profiles. The developed method relies on the optimization of a surface-wave distribution that is utilized to redistribute the power at the input side of the metasurface without incurring any reflections. A full design example is presented with a linear patch array along the H-plane illuminating a metasurface that produces uniform output fields along the E-plane. A high aperture illumination efficiency of 92% is obtained despite the small distance between the source and the metasurface. Moreover, the effects of the evanescent spectrum to the losses and the bandwidth of the structure are discussed.

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 categoriesMeta-epidemiology (narrow)
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 score0.114
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.035
GPT teacher head0.238
Teacher spread0.203 · 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.

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

Citations0
Published2021
Admission routes2
Has abstractyes

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