Optimization of Multi-Layer Data Recording and Reading in an Optical Disc
Bibliographic record
Abstract
Storage capacity of a conventional optical disc can be enhanced significantly by recording data within the three-dimensional volume. However, spherical aberrations and light-scattering limit the number of layers that can be efficiently recorded and retrieved. In this paper, we show that by optimizing the multi-layer data recording and reading parameters, the number of fabricated layers could be tripled to 60 in commercial grade poly-methyl-methacrylate polycarbonate and reduce the read laser power by a factor of 7 by the 20th layer. Influence of spherical aberration on write laser spot size was studied by using vector diffraction theory and compared with experimental values obtained by monitoring the threshold energy of the write laser with depth. Scattering losses of the read laser were studied by simulating the propagation of light through multi-layered micro-structured material. The main advantage of our technique is its practical implementation, as it refrains from the use of index-matching liquids and specialized optics during the read–write process.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".