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Record W4401633696 · doi:10.1117/12.3017646

Innovative octave band quad ridge feed horn and vacuum window for next-generation radio observatories

2024· article· en· W4401633696 on OpenAlexaff
Sara Salem Hesari, Nasrin Tasouji, Ivan Wevers, В. А. Решетов, Jens Børnemann, L. B. G. Knee, A. Densmore

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsUniversity of VictoriaHerzberg Institute of Astrophysics
Fundersnot available
KeywordsBandwidth (computing)Feed hornOctave (electronics)WidebandOpticsReturn lossRadio astronomyRadio telescopeSuiteWindow (computing)TrimmingComputer scienceEngineeringPhysicsElectrical engineeringTelecommunicationsRadiation patternAntenna (radio)Mechanical engineering

Abstract

fetched live from OpenAlex

In the effort to push the boundaries of radio astronomy observations, technological innovations are essential for future generations of observatories. This paper introduces an innovative design for future radio receivers in major observatories like ngVLA, ALMA, and SKA. The concept centers around an Octave Band Quad Ridge Feed Horn and Vacuum Window, designed to operate within the 25 to 50 GHz (2:1) frequency range. This cutting-edge feed model uses dielectric loading to enhance phase and polarization efficiency, thereby significantly increasing the overall aperture efficiency across the bandwidth. The dielectric rod, features a solid construction with comb-shaped ridges located in the throat of the feed horn. To complete the entire optic model an HDPE vacuum window with three custom designed antireflection layers to minimize the signal reflections, is designed and presented. To confirm the performance of the proposed model, we use two different 3D full-wave electromagnetic simulators, CST Studio Suite and HFSS. The results showcase a return loss exceeding 25dB with exceptional beam symmetry across the entire bandwidth.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.799
Threshold uncertainty score0.653

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 head0.045
GPT teacher head0.262
Teacher spread0.216 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations2
Published2024
Admission routes1
Has abstractyes

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