On Crossover Distance for Optical Wireless Satellite Networks and Optical Fiber Terrestrial Networks
Bibliographic record
Abstract
Optical wireless satellite networks (OWSNs) can provide lower latency data communications compared to optical fiber terrestrial networks (OFTNs). The crossover function enables to calculate the crossover distance for an OWSN and an OFTN. If the distance between two points on Earth is greater than the crossover distance, then switching or crossing over from the OFTN to the OWSN results in lower latency for data communications between these points. In this work, we extend the previously proposed crossover function for a scenario such that intermediate satellites (or hops) are incorporated between ingress and egress satellites in the OWSN for a more realistic calculation of the crossover distance in this scenario. We consider different OWSNs with different satellite altitudes and different OFTNs with different optical fiber refractive indexes, and we study the effect of the number of hops on the crossover distance and length of a laser inter-satellite link (LISL). It is observed from the numerical results that the crossover distance increases with an increase in the number of hops, and this increase is higher at higher satellite altitudes in OWSNs and lower refractive indexes in OFTNs. Furthermore, an inverse relationship between the crossover distance and length of a LISL is observed. With an increase in the number of hops, the length of a LISL decreases as opposed to the crossover distance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".