TEFS: A flash file system for use on memory constrained devices
Bibliographic record
Abstract
A file system is used to manage data on storage media. The FAT (File Allocation Table) file system was originally designed for floppy drives that were less than 500KB in size, and these drives were not capable of fast random reads and writes. FAT has been adapted to work on other types of storage devices since, and it is still widely used today. It is the standard file system used by microprocessors and embedded devices with constrained resources. Micro-controllers, like the Arduino, only officially support the FAT file system when interacting with a SD card. FAT performs well when data is read or written sequentially, but when data is read or written randomly, there is an impact on performance for large files on page based flash devices that cannot utilize caching strategies. Applications that perform random reading and writing are impacted by this architectural issue. For example, flash data structures, like a B-tree, will have poor performance since random reading is utilized to look up values. TEFS (Tiny Embedded File System) uses a simplified tree indexing structure to take advantage of the fast random reads and writes of flash storage and guarantees that the number of page reads and writes will stay constant as the file size increases when randomly reading or writing. Experimental results show that TEFS has significantly better performance than FAT on the Arduino for random I/Os, and the more efficient TEFS page interface is even slightly faster than FAT for sequential reading and writing.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.016 |
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".