Bibliographic record
Abstract
This paper provides a detailed description of the Educational Space Science and ENgineering Experiment (ESSENCE) mission's system design of a 3U-Earth Observation CubeSat. This CubeSat will be designed, built, and operated by Canadian Students for scientific research related to attitude control laws, and monitoring permafrost in the Northern Canadian and Arctic regions. The ESSENCE mission would be launched from the International Space Station (ISS) via a US launch provider called NanoRacks. Moreover, the CubeSat would be operated by students located at Canadian ground stations. The scope of this paper is to discuss the key concepts regarding systems engineering which are required to design a successful CubeSat mission. Some of the concepts include project scope, mission analysis and development of key engineering budgets. Other concepts also include discussions regarding component selection via trade study analysis. These factors often drive the mission and aid other subsystem such as mechanical, electrical, RF communication etc. to make decisions for tasks exclusive to them. Furthermore, an implementation plan of an optical payload will be discussed to capture high resolution earth images of the desired location mentioned above. Lastly, a comparison of two key system design methods will be discussed including waterfall and agile methods.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".