Marine Communications Channel Modeling Using the Finite-Difference Time Domain Method
Bibliographic record
Abstract
Broad area maritime surveillance (BAMS) is a current interest area for the application of unmanned aerial vehicles (UAVs). Robust communications is a primary concern that impedes the general acceptance of UAVs by the Federal Aviation Administration (FAA), as loss of communications link is generally perceived as a loss of vehicular control. Thus, to gain an increased understanding of the communications channel UAVs' experience during low-level maritime operations, a channel-modeling effort using the finite-difference time domain method (FDTD) is conducted. The focus of this effort has been to assess the effects of sea surface shadowing conditions on the marine communications channel. A 2-D electromagnetic (EM) simulator has been developed, utilizing modified Pierson–Moskowitz (PM) spectral models to generate a random sea surface in a deep-water location from which multipath scattering is produced. Data analysis conducted on the transient EM simulation results has produced generalized path loss exponent, standard deviation, mean excess delay, and root mean square delay models as a function of frequency and observable sea surface height for fixed transmitter and receiver locations.
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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".