With the continuous advancements in commercial off-the-shelf small satellite technology, there has been a significant increase in proposed missions and an ongoing trend towards rapid development and launch. Traditionally, mission operations of larger spacecraft utilize established products, adapting the operational interfaces and software to the needs of the specific mission. This paper reports the creation of the mission operations interface for a CubeSat mission from scratch to a fully operational system in about seven months under the voluntary commitment of about six students. We will first give a short introduction into the traditional software development used extensively in the space sector before we briefly describe CubeSats and our own CubeSat project, called MOVE-II. The used software development process will be described using the agile methodology Scrum as a baseline. It will be explained how this process was implemented utilizing tools like Trello, GitLab and Slack. Afterward the software design, as well as used frameworks and software, are briefly described to demonstrate how these go hand in hand with our development process. Subsequently, our operations software, as well as satellite operations with our software, are described with a focus on advantages and disadvantages of our software development approach and general lessons learned that we gathered during our project.

Although we can measure muscle activity and analyze their activation patterns, we understand little about how individual muscles affect the joint torque generated. It is known that they are controlled by circuits in the spinal cord, a system much less well-understood than the cortex. Knowing the contribution of the muscles toward a joint torque would improve our understanding of human limb control. We present a novel framework to examine the control of biomechanics using physics simulations informed by electromyography (EMG) data. These signals drive a virtual musculoskeletal model in the Neurorobotics Platform (NRP), which we then use to evaluate resulting joint torques. We use our framework to analyze raw EMG data collected during an isometric knee extension study to identify synergies that drive a musculoskeletal lower limb model. The resulting knee torques are used as a reference for genetic algorithms (GA) to generate new simulated activation patterns. On the platform the GA finds solutions that generate torques matching those observed. Possible solutions include synergies that are similar to those extracted from the human study. In addition, the GA finds activation patterns that are different from the biological ones while still producing the same knee torque. The NRP forms a highly modular integrated simulation platform allowing these in silico experiments. We argue that our framework allows for research of the neurobiomechanical control of muscles during tasks, which would otherwise not be possible.