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Record W1984183809 · doi:10.1109/tap.2013.2292503

A Practical Approach to Locate Offset Reflector Focal Point and Antenna Misalignment Using Vectorial Representation of Far-Field Radiation Patterns

2014· article· en· W1984183809 on OpenAlexaff
Z. Allahgholi Pour, L. Shafai, B. Tabachnick

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2014
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCassegrain antennaOffset (computer science)Offset dish antennaOpticsPeriscope antennaReflector (photography)Focal pointParabolic antennaRadiation patternFeed hornParabolic reflectorNear and far fieldAmplitudePhase centerPhysicsComputer scienceCardinal pointAntenna (radio)Telecommunications

Abstract

fetched live from OpenAlex

A direct and unambiguous approach to determine the focal point of a single offset reflector is presented. The proposed approach makes use of the vectorial form of far-field radiation patterns, both amplitude and phase. The method is based on iteratively defocusing the primary feed laterally and axially. First, mathematical expressions of the offset reflector with a small lateral and axial defocused feed are reviewed. Then, the numerical results are presented. It is shown how the corresponding far-field phase pattern information can be utilized to optimally locate the focal point of an offset parabolic reflector. The method can also be used to place the phase center of an unknown feed on the reflector focal point, and align the reflector on a test tower.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.035
GPT teacher head0.273
Teacher spread0.238 · 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

Citations9
Published2014
Admission routes1
Has abstractyes

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