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Record W4365597323 · doi:10.36227/techrxiv.22635577.v1

Coupled Resonator-Based Metasurface Reflector with Enhanced Magnitude and Phase Coverage

2023· preprint· en· W4365597323 on OpenAlexafffund
Mohamed K. Emara, Debidas Kundu, Keigan MacDonell, Leandro M. Rufail, Shulabh Gupta

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsCarleton University
FundersMinistère de la Défense Nationale
KeywordsResonatorReflector (photography)OpticsBeamformingPhysicsCapacitorMain lobePhase (matter)ResistorAcousticsOptoelectronicsElectrical engineeringElectronic engineeringEngineeringAntenna (radio)Voltage

Abstract

fetched live from OpenAlex

A metasurface reflector unit cell is proposed for achieving near-complete-range of complex reflectance (i.e., magnitude and phase) for versatile beamforming capabilities. The unit cell is based on coplanar coupled resonators: a split ring resonator (SRR) with a lumped capacitor and a lumped resistor, and a dipole ring resonator (DRR) with another lumped capacitor. The DRR inserted inside the SRR creates a coupled resonance configuration which results in an enhanced complex reflectance range at the desired frequency. To provide a physical insight and explain the operation principle of the structure, the response of the unit cell is modeled as a coupled Lorentz oscillator via the effective surface susceptibilities, where a unique plasma, damping constant, and resonant frequency can be attributed to each resonator. The proposed unit cell is demonstrated in an array configuration for linear-polarized beamforming, where full-wave simulations are used to demonstrate beam-steering, gain control, side-lobe level control and dual- and triple-beam generation, as illustrative examples. Finally experimental demonstration is performed to validate the full-wave results and obtain in-depth electrical characterization of the reflectors.

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 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 score0.298
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.292
Teacher spread0.259 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

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