Compressive sensing chaotic encryption algorithms for OFDM-PON data transmission
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Bibliographic record
Abstract
In this paper, we propose chaotic compressive sensing (CS) encryption algorithms for orthogonal frequency division multiplexing passive optical network (OFDM-PON), aiming at compressing the transmitted data and enhancing the security of data transmission. Bitstream transmission using CS directly is restricted due to its inability to satisfy the sparsity in neither time nor frequency domain. While the sparsity of the transmitted data can be constructed when transmitting the multimedia. A sensor can be then used to identify whether the data is multimedia. If it is, the CS technique is used, and the sensor's result is set as side information inserted into the pilot and transmitted to the terminal simultaneously. For encryption processing, a 2-dimensional logistic-sine-coupling map (2D-LSCM) is used to generate pseudo-random numbers to construct the first row of a measurement matrix to encrypt the system. Four transform formats are then applied to generate the sparsity of the transmitted data. Due to the restriction of data transmission in the physical layer, the discrete cosine transform (DCT) is chosen to conduct the CS technique. Four approximation algorithms are also proposed to optimize the performance of compressing the length of bits. We find that 'Round + Set negative to 0' shows the best performance. The combination of this chaotic CS encryption technique with the OFDM-PON systems saves the bandwidth and improves the security.
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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 it