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Record W4285237182 · doi:10.1109/ojap.2022.3173161

3D Printed Linearly Polarized X-Band Conical Horn Antenna and Lens

2022· article· en· W4285237182 on OpenAlexafffund
Ian Goode, Carlos E. Saavedra

Bibliographic record

VenueIEEE Open Journal of Antennas and Propagation · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceHorn antennaOpticsFrench hornConical surfaceLens (geology)Ku bandFeed hornBandwidth (computing)MetamaterialOptoelectronicsRadiation patternAntenna (radio)Slot antennaElectrical engineeringEngineeringAcousticsTelecommunicationsPhysicsComposite materialPeriscope antenna

Abstract

fetched live from OpenAlex

A low-cost method to 3D print and metalize circular waveguide components is demonstrated using polylactic acid (PLA) and aluminum adhesive backed tape. A gradient index (GRIN) lens, an externally metalized thin-walled conical horn, and a WR90 rectangular to linearly polarized circular waveguide transition are simulated, fabricated, and measured. The horn and lens were both monolithic prints that were externally metallized to simplify the metallization process. Both the horn and lens have a measured operational bandwidth of 8.2 GHz to 12.4 GHz with an input reflection less than -15 dB and peak gain of 18.7 dBi at mid-band. The walls of the thin-wall horn are printed at a thickness such that the dielectric layer does not impact the performance of the horn while being robust enough to support external metallization. The lensed horn functioned as the support for the aluminum foil while also improving the radiation pattern by improving the E SLL by up to 15 dB compared to the thin-walled horn 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.

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 score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
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 head0.018
GPT teacher head0.233
Teacher spread0.215 · 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

Citations29
Published2022
Admission routes2
Has abstractyes

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Same venueIEEE Open Journal of Antennas and PropagationSame topicMicrowave Engineering and WaveguidesFrench-language works237,207