Bibliographic record
Abstract
The nuclear inventory of Russia and the USA currently comprises 12,685 warheads in a large network of vehicles; and the interconnected network is managed by a command and control communication system. This command and control communication system (C3) must also relay information from numerous airborne, space-born, and ground sensors throughout the network in potentially degraded environments and are nonetheless meant to securely hold transmissions that must be held to the highest standards of encryption. C3 systems are also arguably one of the most challenging systems to develop, since they require far more security, reliability, and hardening compared to typical communication systems, because they typically must (absolutely) work while other systems fail. Systems used for C3 are not always cutting-edge technology, but they must be upgraded at crucial junctures to keep them at peak performance. This manuscript outlines a blueprint of a way to embed current and future systems with revolutionary encryption technology. This will transform the security of the information we pass to our C3 assets adding redundancy, flexibility, and enhanced speed and insure vehicles and personnel in the system receive network message traffic. Quantum key distribution (QKD) has the potential to provide nearly impregnable secure transmissions, increased bandwidth, and additional redundancy for command and control communication (C3). While QKD is still in its adolescence, how QKD should be used or C3 must be charted out before it can be engineered, tested, and implemented for operations. Following a description QKD functionality, its pros and cons, we theorize the best implementation of a QKD system for C3.
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 | 0.003 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".