Plasma-based thrusters: Electrostatic and electromagnetic coupling
Bibliographic record
Abstract
Summary form only given. Electric propulsion for spacecraft offers many advantages compared to other traditional counterparts such as chemical propulsion. Plasma-based propulsion devices can be useful for satellites for the purpose of station-keeping, attitude control, formation-flying and possibly end-of-life de-orbiting for space debris mitigation. There is a growing interest to have a propulsion system for small satellites (microsatellites and nanosatellites) as their mission capabilities can drastically be increased. Satellites with masses of a few kilograms approximately require only tens of micronewtons of thrust for station-keeping and attitude control, implying that electric propulsion could be an excellent candidate. There are three main types of electric propulsion: electrostatic, electromagnetic and electrothermal, each with its own advantages and characteristics [1,2] depending on the application and space mission. In the present work, the different types of thrusters will be presented with an aim to design a hybrid thruster coupling the advantages of the three modes of electric thrusters. The simulations will detail the plasma evolution within the thrusters and help optimize the governing parameters such as electric and magnetic field profiles.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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".