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Record W2324274346 · doi:10.1109/tap.2014.2330578

Reflectarray Design With Similarity-Shaped Fragmented Sub-Wavelength Elements

2014· article· en· W2324274346 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2014
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsCommunications Research Centre CanadaUniversity of Ottawa
Fundersnot available
KeywordsSimilarity (geometry)Antenna (radio)WavelengthReflection (computer programming)Aperture (computer memory)OpticsComputer scienceElement (criminal law)Image (mathematics)Materials sciencePhysicsAcousticsTelecommunicationsArtificial intelligence

Abstract

fetched live from OpenAlex

A new technique for synthesizing reflectarray antennas is presented. It utilizes fragmented elements in a manner that allows the elements of the reflectarray to be shaped-optimized so that a high degree of geometrical similarity is maintained between adjacent elements. The implication is that in a reflectarray of such elements each element will see an electromagnetic environment that more closely emulates the infinite periodic one used to compute the element properties. We show experimentally that this indeed results in aperture efficiencies closely approaching the upper bounds achievable for some selected feed system, and offers a significant improvement over that obtained with conventional reflectarrays (that is, those not using similarity-synthesized fragmented elements). It is also shown that a reflectarray surface that uses fragmented elements can be simultaneously patterned with a visual image while still closely maintaining the desired reflection phase from its surface to yield a high-gain antenna.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.881
Threshold uncertainty score0.639

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 head0.019
GPT teacher head0.223
Teacher spread0.205 · 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