SEIS: A decision support system for optimizing spacecraft operations strategies

Keeping¹²³ the spacecraft (S/C) healthy and productive is the responsibility and the main concern of the S/C flight control team (FCT). Space weather includes effects and conditions that favour the aging of a S/C and its instruments, e.g. degradation of sensors and solar arrays by charged particles and single event upsets (SEU). It is worth to stress out that it is hard to detect when the environmental conditions of a S/C are safe and when they are hazardous. So far, the most widely used approach of a FCT to counteract these effects has been to play safe. Playing safe, i.e. invoking counter measures early enough and keeping them for a long enough period, is a means to reduce the risk, but it is not the most efficient one. The dynamics of space weather could lead to situations where the instruments are shielded hours before the conditions become really hazardous. On the other hand it might happen that the FCT assumes safe conditions long before they actually are. Clearly the S/C productivity - i.e. the observation time - is being affected by this approach. A major concern is the availability and acquisition of data. A lot of space weather data is available from many sources in different formats and predictions are scarce and often not directly applicable to an individual S/C. It is therefore almost impossible for a FCT member to consider all these sources. Furthermore the available sources for space weather data provide their data offline or at best near real-time. Therefore the S/C may be already in a hazardous environment when the FCT receives this information. A decision support system is currently being implemented for ESA. Entitled "Space Environment Information System for Mission Control Purposes (SEIS)" the system integrates a huge variety of space weather data from different sources as well as S/C telemetry data from running missions. All this data is stored it in a data-warehouse. Having S/C and space weather data available from one single source, facilitates the analysis of this data therefore enhancing user awareness of space weather effects and possible cause-effect relationships between space weather events and S/C anomalous conditions. The system provides an interface to external applications to include data produced by physical models for the space environment and its effects. Furthermore, trained artificial neural networks or other plug-in tools can access all the data and produce forecasts for certain parameters. These forecasts can be fed back into the data-warehouse and be used for monitoring, analysis and operations planning. The reference mission for SEIS is INTEGRAL, ESA's gamma ray observatory. It also includes data from other ESA missions: ENVISAT and XMM. The main objective for INTEGRAL is to maximize the payload instruments' time while operating in safe condition, resulting in an increase of the S/C's productivity. The system is a prototype whose objective is the proof of concept for new emerging technologies applicable to the mission operations domain, increasing the quality and effectiveness of mission operations management. The paper presents the system, focusing on the application of the new technologies to support the FCT in their critical decision making process and its expected impact in the INTEGRAL'S operations strategy.