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Stainless Steel Laser-Sintered 3D Printed Corrugated Horn Antennas at Ka Band

2023· article· en· W4388038904 on OpenAlexafffund
Ian Goode, Aditya Singh, Carlos E. Saavedra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSuperconducting and THz Device Technology
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFrench hornHorn antennaKa bandConical surfaceMaterials scienceFeed hornAntenna (radio)OpticsOptoelectronicsDirectional antennaAcousticsElectrical engineeringSlot antennaEngineeringPhysicsComposite materialMicrostrip antennaPeriscope antenna

Abstract

fetched live from OpenAlex

The viability of stainless steel as the constituent material for 3D printed corrugated conical horn antennas at Ka Band is explored in this work. Three identical corrugated antennas are fabricated to assess the impact of different levels of post processing on the metal surface. A fourth conical horn antenna without corrugations was fabricated using the same metal 3D printing process. This ‘basic’ horn antenna is used as a baseline performance comparator for the corrugated horns. Measurements show that the 3D printed corrugated horn can deliver a peak realized gain of 19.3 dBi at 38.5 GHz and a cross-polar discrimination (XPD) in the $\vec{E}$ plane below 22.8 dB at 28 GHz and below 27.5 dB at 40 GHz. The XPD performance in the $\vec{H}$ is below 23 dB at 28 GHz and below 32.8 dB at 40 GHz.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.695
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.023
GPT teacher head0.260
Teacher spread0.237 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2023
Admission routes2
Has abstractyes

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