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Record W2891024552 · doi:10.1109/lawp.2018.2869580

Investigation of the 3D Printing Roughness Effect on the Performance of a Dielectric Rod Antenna

2018· article· en· W2891024552 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 Antennas and Wireless Propagation Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMaterials scienceDielectricFabricationSurface roughnessAntenna (radio)OpticsOptoelectronicsComposite materialElectrical engineeringEngineering

Abstract

fetched live from OpenAlex

In this letter, the roughness effect of a three-dimensional (3-D) printed dielectric rod antenna is investigated. One of the issues of 3-D printing in microwave components and antennas is the limited printing resolution, which creates a surface roughness on printed devices and deteriorates the performance. The effect of surface roughness on the radiation characteristics and impedance matching of a dielectric rod antenna is studied. The roughness as a perturbation brings the antenna out of its optimum design by changing the E-field intensity alongside the rod. The whole antenna consisted of a coaxial to waveguide adapter and the dielectric rod is 3-D printed and the adaptor is dipped into a low viscosity solution of silver epoxy and isopropanol to coat a conductor layer on the inner surfaces of the waveguides. The fabrication method is cost effective and much easier than conventional methods.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.092
Threshold uncertainty score0.322

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 head0.011
GPT teacher head0.201
Teacher spread0.190 · 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