Implementation of Shared Laser–LED Sources in a Free Space Optics (FSO) Network under Environmental Impact
Bibliographic record
Abstract
This paper is devoted to evaluating the combined coherent and incoherent sources (CCIS) technique for different applications in the optical domain and future optical code division multiple access (OCDMA) networks. Spectral amplitude coding (SAC) has gained significant attention in optical processing systems due to its increased capabilities in dealing with multiple-access interference (MAI) efficiently. Fixed right shift (FRS) is adopted as a signature code in this study. Furthermore, performance analysis is studied in terms of bit error rate (BER) for the system using CCIS in both the free space optics (FSO) and sky mesh network using an aerial altitude platform system (AAPS). Simulation results confirmed that a CCIS design significantly improves system performance with moderate cost. An acceptable BER value of 10−9 at 1.25 Gbps data rate and 60 km, 38 km, and 6 km distances for the laser, CCIS, and LED sources, respectively, can be supported. In particular, at Q-factor ~4.5, the FSO ranges under low haze, moderate haze, and heavy haze are, respectively, 3.7 km, 2.5 km, and 1.5 km. The reason is that a CCIS design causes an increase in the effective transmitted power. It can be summarized that a CCIS design can provide reliable solutions and an affordable cost for future optical fiber and wireless network applications.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".