Design and implementation of an ATM traffic generation and capture card
Bibliographic record
Abstract
As a result of recent increases in demand for network bandwidth, it is becoming more important that high-speed telecommunication networks be installed, managed, and used as efficiently as possible. In order for these objectives to be realized, it is essential that network operation be studied and accurate models of its behavior developed. Unfortunately, the data acquired from testing with network equipment is often less than ideal due to the cost and capabilit es of test equipment currently available commercially. Testing suffers as a result of the equipments high cost, inability to generate network traffic with certain types of properties, and the limited amount of traffic which can be recorded for analysis. A network interface card designed specifically to overcome the limitations of commercial test equipment is one solution for improving the results of current network testing. This card would not duplicate all of the capabilities of existing test equipment. Instead, it would only strive to improve the traffic generation capabilities to allow all possible types of network traffic sources to be simulated, extend network traffic capture periods to provide a sufficient amount of data for analysis, and be built at a low-enough cost to be a viable alternative to commercial equipment. This thesis will describe the development of a card designed for generating and capturing ATM network traffic at OC-3 rates.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".