Modeling Multiple HF Antennas on the C-130/Hercules Aircraft. II
Bibliographic record
Abstract
Long-range transport aircraft such as the C-130/Hercules require reliable HF communication systems for their global missions. In some cases dual HF systems are installed using two separate antennas, in a SIMOP mode: one antenna for transmitting and the other for receiving, at arbitrary frequencies over the 2-30 MHz HF frequency range. Candidate antennas for such HF systems are probes on the vertical stabilizer or the wingtips and dorsal or wing-root notch antennas. Previous work has modeled the HF performance of these antennas by executing spectral sweeps of models at 0.1 MHz increments using the NEC4 and MBC computer codes. These results were also correlated with scale model radiation pattern and impedance measurements. In this paper the results of explicit modeling of the SIMOP mode are presented for the candidate antenna pairs. The receive antenna terminals are terminated in 50 ohms and transmit and receive currents are extracted from the solution file and each pair is plotted vs. frequency. It is seen that peaks in the antenna coupling correspond to the resonant peaks in plots of impedance reported earlier. Insights provided by current distribution plots on the wire-grid model for each antenna allow the degree of antenna coupling to be well understood. This exceptional use of the "CEM virtual antenna range" can lead to very cost-effective HF SIMOP installation designs.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".