On the Transmission and Propagation of Low Attenuation Rate Electromagnetic Pulses in Debye Media
Bibliographic record
Abstract
In a dispersive medium, the appearance of the steady-state part of the signal is preceded by oscillations known as precursors. These early oscillations are the product of the interrelated effects of phase dispersion and frequency dependent attenuation. Inside water, the attenuation rate of the Brillouin precursor is sub-exponential, following the inverse square-root of the distance traveled. Based on that, a near-optimal pulse that could achieve this attenuation rate, and, hence, would lend itself to underwater detection and communication applications, was recently proposed. The ldquooptimalityrdquo of this pulse is shown to be related to the temporal support of the pulse and its spectral characteristics, rather than its shape. A family of alternative pulses is found to have the low attenuation feature of the ldquooptimalrdquo pulse, as they eventually evolve into the Brillouin precursor itself shortly after they enter water. In addition, this work considers the practical case when such a pulse would be generated in air, would impinge onto an air-water interface and then propagate inside water. It is shown how the presence of the interface affects the attenuation rate of the pulse inside water and a simple way to recover its low attenuation rate is suggested. The finite-difference time-domain technique is employed in all the simulations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".