FSO channel—atmospheric attenuation and refractive index (Cn 2) modeling as the function of local weather data
Bibliographic record
Abstract
Free space optical (FSO) communication is an upcoming attractive alternative technology for transporting high-bandwidth data when the existing RF/fiber-optic communication is neither realistic nor viable. However, the presence of FSO channel turbulences such as fog, smoke, rain, dust, snow, and/or sand can critically degrade the quality of the FSO communication system. There is a great technical development in today's optical components such as LED, laser, optical detector, detector's sensitivity at high bandwidth, modulation techniques, power requirements, total weight, and total size. In spite of all these technological developments, the major limitation of FSO communication quality is the atmosphere. Optical absorption and scattering due to the FSO channel's components in the atmosphere drastically reduce the transmitted optical power. Further, the arbitrary atmospheric formation due to random fluctuation of optical turbulence can alter the wavefront quality of the traveling optical signal, develop the intensity fading and thus result in random signal losses and inter symbol interference (ISI) at the receiver plane. Weather conditions thus ultimately determine the FSO communication system quality not only in ground-to-ground FSO applications but also for deep space laser satellite optical communications because a portion of the optical beam always in the atmospheric turbulence medium that causes time-varying scattering effects.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".