Bibliographic record
Abstract
In free-space optical satellite networks (FSOSNs), free-space optical links are employed between satellites and between satellites and ground stations.In this thesis, we analyze several system parameters and performances for FSOSNs, including phasing parameter, optical satellite link budget, tradeoff between network latency and satellite transmission power, and network latency minimization based on satellite transmission power constraints.We investigate the phasing parameter for Starlink Phase 1 Version 3 and Kuiper Shell 2 constellations.We look for the best value of the phasing parameter in these constellations that provides the maximum value of the minimum distance to ensure intra-constellation avoidance of collisions between satellites.We investigate the effect of link distance and link margin on laser inter-satellite link (LISL) transmission power, and the effect of slant distance, elevation angle, and link margin on laser uplink/downlink transmission power.We model these optical links and compute the results for various parameters.We examine the tradeoff between satellite transmission power and network latency in FSOSNs drawing on the Starlink Phase 1 Version 3 and Kuiper Shell 2 constellations for different LISL ranges and different inter-continental connections.We use appropriate system models for calculating the average satellite transmission power and network latency.We investigate the minimization of total network latency in the FSOSN resulting from the Starlink Phase 1 Version 3 constellation.We develop mathematical formulations for different LISL ranges and different satellite transmission power constraints for multiple simultaneous inter-continental connections.We use appropriate system models for calculating network latency and satellite optical link transmission power, and we formulate the problem as a binary integer linear program.First, I would like to express my great gratefulness to my supervisor, Professor Halim Yanikomeroglu.I joined Professor Yanikomeroglu's research group in April 2021.He leads me to this optical satellite network field and helps me to learn knowledge in this research area.Professor Yanikomeroglu is such a kind and gentle leader who is always nice and considerate to students and research group members.He always tries his best to help students including me in both study and life.I am very impressed with Professor Yanikomeroglu's knowledge and outsight in academia.I may not be a researcher if I have not met and supervised by him.I could not be wished for a better supervisor.I also want to give my great appreciations to Dr. Aizaz U. Chaudhry.Without him, I cannot get all the achievements in my research work.He offers me such a great help in my study, research, papers, thesis, and my life in many details.He helps me on finding papers, learning simulation tools, coding algorithms, writing and revising paper, presenting publishing, daily life advice, basically everything in my research.He spends a lot of time and effort to help me on my research work.He guides me on how to be a good researcher and publish paper works.It is really my good luck to being guided by Dr. Chaudhry.I also want to express
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".