Applying Methods of Soft Computing to Space Link Quality Prediction Bastian Preindl and Lars Mehnen and Frank Rattay and Jens Dalsgaard Nielsen Abstract The development of nano- and picosatellites for educational and scien- tific purposes becomes more and more popular. As these satellites are very small, high-integrated devices and are therefore not equipped with high-gain antennas, data transmission between ground and satellite is vulnerable to several ascendancies in both directions. Another handicap is the lower earth orbit wherein the satellites are usually located as it keeps the communication time frame very short. To counter these disadvantages, ground station networks have been established. One input size for optimal scheduling of timeframes for the communication between a ground sta- tion and a satellite is the predicted quality of the satellite links. This paper introduces a satellite link quality prediction approach based on machine learning. 1 Background Within the last decade the educational and academic approaches in space science made huge steps forward. Driven by the development of small satellites for taking scientific or educational payload of any kind into space, universities all over the Bastian Preindl Institute of Analysis and Scientific Computing, Vienna Technical University, Austria, e-mail: bastian@preindl.net Lars Mehnen Institute of Analysis and Scientific Computing, Vienna Technical University, Austria, e-mail: mehnen@technikum-wien.at Frank Rattay Institute of Analysis and Scientific Computing, Vienna Technical University, Austria, e-mail: frank.rattay@tuwien.ac.at Jens Dalsgaard Nielsen Department of Electronic Systems, Aalborg University, Denmark e-mail: jdn@es.aau.dk