# Using Artificial Intelligence for Space Challenges: A Survey
## 4 State of the Art
### Works on Mission Design and Planning
In [61], a support vector machine for regression is applied to data coming from the Egyptsat-1 satellite launched in April 2007 to analyze the satellite performance and build a fault diagnosis system to determine the origin of this satellite failure. The authors of [62] envision a new deep learning approach for telemetry analysis of space operations. The proposed architecture consists of three modules developed for automatic feature extraction, anomaly detection, and telemetry prediction. First, an autoencoder neural network is created for feature vector extraction and anomaly detection. Then, a multi-layer long short-term memory network is evaluated for telemetry forecasting and anomaly prediction. The study in [63] provides an analysis aimed at improving the autonomy level of space missions, and in particular of small satellites. The author presents several case studies such as event and failure detection tradespace exploration where neural networks, expert systems, and genetic algorithms are used. AI-Express is a space system providing services to demonstrate AI algorithms, qualify technologies, and validate mission concepts, in-orbit and as-a-service [64]. The system is built to consider the requirements of small satellite missions and explore innovative approaches in communications, planning, tasks, and data processing. The authors of [65] envision the need to integrate machine learning with medical care during a space mission. The current human missions and future deep space missions open several challenges so that the medical care of the crew needs to become more and more independent and autonomous. Aligned with this need, improving astronauts' work efficiency and mental health becomes crucial for space missions. Indeed, monitoring crew members' stress and reliability in isolation is an essential parameter for the success of a human mission. In [66], natural language processing is exploited to monitor the crew's psychological health in stressful conditions during long missions. In [67; 68; 69], in-cabin astronaut assistance and person-following nanosatellites are studied and developed for the China Space Station. The satellite follows assisted flying autonomously and offers immediate assistance. Furthermore, a deep convolution neural network (DCNN)-based detection module and a probabilistic-model-based tracking module are trained to accomplish the cited tasks. In conclusion, we observed that gathering and retrieving information for a space mission is widely achieved with the support of expert systems coupled with natural language processing techniques. These solutions aim to enable access to useful information in a reasonable time. However, natural language processing techniques and convolutional neural network approaches are exploited to support astronauts by offering immediate assistance in conditions of need. Concerning satellite health monitoring, several proposals show how dimensionality reduction, clustering, and deep learning can be applied for telemetry analysis of space operations to improve the usability and the exploitation of these data.