Low-cost square cross section helical antennas
Bibliographic record
Abstract
Square and other cross section helical antennas have several advantages over the conventional circular cross section helical antennas used in satellite communication and other applications. The linear segments which make up a. square cross section helical antenna can be easily supported on a structure which can be cheaply designed and manufactured even for variable cross section or pitch helical antennas. This structure has the additional advantage of having minimal conductor-dielectric proximity, thus virtually eliminating unwanted frequency shift and loss due to dielectric loading. The linear elements are also well suited to simulation using the method of moments, in which cylindrical conductors are nearly always standard elements. Other polygonal cross sections allow some improvement in the axial ratio performance of the helical antennas. The theory and history of helical antennas is discussed, and axial-mode helical antenna models designed to illustrate the relative performance of circular and square helical antennas are presented. Simulations of these models and others with other cross sections were performed using WIPL, a method-of-moments simulation software package, and these simulation results are discussed. Circular and square helical antennas were constructed and measured, and the effects of dielectric loading immediately within the helical winding measured. These simulation and measurement results illustrate the performance advantages of square and other non-circular cross sections for helical antennas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".