Secure Optical Communication Using a New 5D Chaotic Stream Segmentation
Bibliographic record
Abstract
According to its complex properties like ergodicity, unpredictability, and sensitivity to its initial states, chaotic systems are attracting more and more attention and are widely used for security purposes. Moreover, the chaotic signals are considered suitable for spread spectrum modulation due to their wideband properties. It is able to reduce the peak to average power ratio (PAPA). This paper presents a new Dynamic Diffeo-Difference Multi-Dimensional (DDD-MD) system. It is used as a key for a new cryptosystem designed based on the chaotic stream segmentation (CSS) method. The proposed system provides the best trade-off between efficiency, robustness, and high data rate transmission. The behavior of the proposed chaotic system is evaluated numerically by analyzing the Lyapunov exponent spectrum, complexity, and attractor phase diagram. Besides, it is practically assessed based on the principle of system circuit design; the circuit diagram of the system is prepared and simulated by Multisim, which shows high consistency with the numerical simulation. These evaluations show that the proposed system has a rich dynamics behavior to be realized in an encryption system and provides a foundation for many engineering and physical applications.
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) | 0.001 | 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".