Propagation models for mobile terrestrial VHF communications
Bibliographic record
Abstract
A family of propagation models for the tactical VHF radio channel is presented based on propagation measurements conducted under the conditions expected for mobile land-tactical communications. The appropriate configuration for tactical communications is transmit and receive antennas on vehicular rooftops, or on backpacks, with mobile platforms. The phenomena modelled are multipath group and delay structure, and probability distribution of the multipath group attenuations. Models are given for different terrain environments: rural, suburban, littoral, and urban, and the results are specifically optimized to the operating frequencies within this band. The models are presented in a manner making them easy to implement in software or hardware for simulation or emulation. Because the wavelength varies by several factors over this band, the propagation is observed to be highly dependent on the frequency of operation. For example, at 30 MHz the wavelength of 10 metres is large as compared with many structures facing the propagating wave, while at 100 MHz, the wavelength of 3 metres is more comparable the size of ground-based structures in many environments, and subject to reflection and diffraction. The terrestrial propagation environment is therefore nonhomogeneous over this band, and models presented in this paper are frequency-dependent, thereby taking this into account.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".