# Using Artificial Intelligence for Space Challenges: A Survey
## 4 State of the Art
### Works on Mission Design and Planning  
In the early stages of a space mission, the development of AI presents several advantages. To enhance information retrieval and knowledge reuse in the field of space missions, the study presented in [48] proposes a semi-supervised Latent Dirichlet Allocation (LDA) model for space mission design. First, the general model is trained and then used to generate specific LDA models focused on different spacecraft subsystems or topics. The authors of [49] analyze how knowledge management and human-machine interaction, machine learning, and natural language processing methods can improve the responsible use of information during a concurrent engineering study and design an AI assistant, called the design engineering assistant, to support knowledge management strategies. A natural language processing system, called AstroNLP, is presented in [50]. The solution can automatically extract and visualize useful information from the mission in a direct graph to optimize the documentation's inspection of a mission. The study in [51] stresses the importance of developing an expert system used as a design engineering assistant in the field of space mission design. The detailed software architecture of both front and back ends and the tool requirements are presented. Given the diversification and number of available data (e.g., implicit and explicit knowledge), the highlighted problem consists in querying and finding the needed information in a reasonable time. Considering the perspective of the ground segment, the increase in volume and type of data, processes, and procedures requires innovative solutions and methodologies. Therefore, in [52], an expert system is proposed to tackle the challenges of systems engineering, optimization of development, management, and implementation of the ground system. In [53], an AI-driven design tool for the transfer of orbital payload via a space tether is proposed. The authors investigate the recent proposals on space tethers, utilized in applications such as the orbiting or deorbiting of space vehicles, payload transfer, inter-planetary propulsion, plasma physics, and electrical science in the Earth's upper layer atmosphere. In addition, AI tools support recent space elevator projects. Indeed, the proposed solution relies on a computational-intelligence-assisted design framework that exploits the Artificial Wolf Pack algorithm with logsig randomness. The optimization of resource-constrained Internet-of-Space-Things [54] during next-generation space missions requires a flexible and autonomous framework that overcomes the limitations of existing space architecture. In this regard, the authors of [55] propose an AI-based cognitive cross-layer decision engine to bolster next-generation space missions. The system exploits deep reinforcement learning and cross-layer optimization. The papers [56; 57] review existing satellite health monitoring solutions and present an analysis of remote data mining and AI techniques used to address the needs in this field. Usually, satellite telemetry data and integrated information are used to produce an advanced health monitoring system and prevent unexpected failures. The authors of [58] present a guideline for intelligent health monitoring solutions exploiting data mining techniques. Furthermore, the study contains operations and applications of such systems related to the ground control station. Multimodality and high dimensionality are two critical characteristics of satellite data. In [56; 59], the reduction and clustering of probabilistic dimensionality are applied to telemetry data of the Japan Aerospace Exploration Agency (JAXA)'s Small Demonstration Satellite-4. These methods analyze anomalies and understand the health status of the system. The study [60] investigates the importance of integrating a fault-diagnosis system into a space vehicle to isolate, detect, and classify faults in the system.