Automatic scheduling techniques are becoming a crucial tool for the efficient planning of large astronomical surveys. A specific scheduling method is being designed and developed for the Atmospheric Remote-sensing Infrared Exoplanet Large-survey (Ariel) mission planning based on a hybrid meta-heuristic algorithm with global optimization capability to ensure obtaining satisfying results fulfilling all mission constraints. We used this method to simulate the Ariel mission plan, to assess the feasibility of its scientific goals, and to study the outcome of different science scenarios. We conclude that Ariel will be able to fulfill the scientific objectives, i.e. characterizing $$\sim$$
∼
1000 exoplanet atmospheres, with a total exposure time representing about 75–80% of the mission lifetime. We demonstrate that it is possible to include phase curve observations for a sample of targets or to increase the number of studied exoplanets within the mission lifetime. Finally, around 12–15% of the time can still be used for non-time constrained observations.
Amazonia-1 is a Brazilian remote sensing satellite providing mainly images, in order to observe and monitor deforestation, especially in the Amazon region. This paper describes the thermal control design, which uses passive and active concepts. The active thermal control is based on heaters regulated by software via thermistors. The passive thermal control consists of multi-layer insulation blankets and radiators, paints, surface finishes to maintain temperature level of the overall carrier components within an acceptable value. The thermal control design is supported by thermal analysis using thermal mathematical model. The temperatures and heater power are predicted for critical cases.
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