Bibliographic record
Abstract
The development of smart platforms in the Internet-of-Things (IoT) paradigm requires a number of technological advances to go hand in hand. IoT devices and entities have very diverse set of capabilities in terms of memory, power, and processing and are connected via a wide range of links with various qualities and capacities. This work focuses on the design of state-of-the-art data transmission methodology with arguably the highest level of flexibility and adaptability. We present a novel fountain-based encoding technique using overlapped generations of Luby-Transform (LT) codes. The proposed overlapped LT (OLT) codes achieve significant gains in BER and/or code rate. They are highly energy-efficient, scalable, and robust. Our analysis shows in particular that by using OLT codes, we can modify the fountain degree distribution such that it does not contain degree-one packets starting from the second generation. Thus, we introduce new degree distributions to improve the performance of OLT codes; the new scheme is referred to as smart OLT (SOLT). Simulation results are provided to show the improvements of OLT and SOLT codes in AWGN channel in terms of error rate. For example, SOLT codes can achieve the same error performance as LT codes, but they require smaller transmission overhead, for instance, at SNR of 9 dB and error rate of 10-5, SOLT codes require a code rate of 0.476 while LT codes require a code rate of 0.417.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".