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Record W4400877660 · doi:10.1109/tap.2024.3428729

Reconfigurable Metasurface Reflectors Using Split-Ring Resonators With Co-Designed Biasing for Magnitude/Phase Control

2024· article· en· W4400877660 on OpenAlexafffund
Mohamed K. Emara, Debidas Kundu, Keigan MacDonell, Leandro Rufail, Shulabh Gupta

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2024
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsCarleton University
FundersMinistère de la Défense Nationale
KeywordsBiasingResonatorPhase (matter)OpticsOptoelectronicsReconfigurable antennaOptical ring resonatorsMaterials scienceRing (chemistry)Phase controlAntenna (radio)PhysicsComputer scienceVoltageDipole antennaTelecommunications

Abstract

fetched live from OpenAlex

A novel reconfigurable metasurface reflector with a simple architecture is proposed based on either p-i-n or varactor diodes for magnitude and phase control, respectively. Each metasurface reflector is based on a single-dielectric two-metal-layer printed-circuit board (PCB), with a tunable resonator on the top metal layer and biasing on the bottom metal layer. The metasurface reflectors are designed for reconfigurability on a column-by-column basis (i.e., single-plane beamforming). Biasing is integrated in the design of the unit cell and RF-dc isolation is implemented at the ends of each reflector column. The bias lines are designed to have minimal effect on the resonator characteristics, while appropriate architectures are devised for each configuration involving p-i-n or varactor diodes. To demonstrate various functionalities of the metasurfaces, the p-i-n-based metasurface reflector is used to demonstrate magnitude control, sidelobe level control, and binary amplitude grating to generate three symmetrical beams. The varactor-based metasurface reflector is used to demonstrate single beam-steering and binary phase grating to generate two symmetrical beams.

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 score0.460
Threshold uncertainty score0.714

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 studies0.0010.000
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 head0.050
GPT teacher head0.326
Teacher spread0.276 · 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

Citations14
Published2024
Admission routes2
Has abstractyes

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