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Coupled Resonator Configuration for Enhanced Reflectarray Magnitude and Phase Control

2023· article· en· W4378842766 on OpenAlexaff
Mohamed K. Emara, Debidas Kundu, Leandro Rufail, Shulabh Gupta

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMetamaterials and Metasurfaces Applications
Canadian institutionsCarleton University
Fundersnot available
KeywordsResonatorCapacitive sensingResistive touchscreenOpticsResonance (particle physics)Materials sciencePhase (matter)DipoleSplit-ring resonatorBeam steeringOptoelectronicsPhysicsReflection (computer programming)Beam (structure)Electrical engineeringEngineeringComputer scienceAtomic physics

Abstract

fetched live from OpenAlex

A reflectarray metasurface unit cell consisting of two coupled resonators and three variable controls is proposed. The configuration consists of an outer split-ring resonator (SRR) responsible for resonance at the desired frequency and includes two variable elements; one capacitive and one resistive. A dipole-ring resonator (DRR) is inserted inside the SRR with a capacitive element, creating a resonance at a higher frequency than the SRR. Variation of the DRR resonance affects both the magnitude and the phase of the lower resonance of the SRR. For any given constraint on the available capacitive or resistive values, the coupled resonator configuration provides a significantly enhanced reflection magnitude and phase variations compared to a single isolated resonator. Control over magnitude and phase is used to demonstrate four cases of beamforming in the X-band at 9 GHz: side-lobe level (SLL) reduction, beam-steering, beam-steering with SLL reduction, and dual-beam generation.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.033
GPT teacher head0.338
Teacher spread0.305 · 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 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

Citations4
Published2023
Admission routes1
Has abstractyes

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