Microgravity eases several constraints limiting experiments with ultracold and condensed atoms on ground. It enables extended times of flight without suspension and eliminates the gravitational sag for trapped atoms. These advantages motivated numerous initiatives to adapt and operate experimental setups on microgravity platforms. We describe the design of the payload, motivations for design choices, and capabilities of the Bose-Einstein Condensate and Cold Atom Laboratory (BECCAL), a NASA-DLR collaboration. BECCAL builds on the heritage of previous devices operated in microgravity, features rubidium and potassium, multiple options for magnetic and optical trapping, different methods for coherent manipulation, and will offer new perspectives for experiments on quantum optics, atom optics, and atom interferometry in the unique microgravity environment on board the International Space Station.

This paper presents the software responsible for the design and execution of the experiments in the Bose-Einstein Condensate and Cold Atom Laboratory (BECCAL) mission, an experiment with ultra-cold and condensed atoms on the International Space Station. The software consists of two parts: the experiment control software and the experiment design tools. The first corresponds to the software running on the payload and is in charge of controlling and executing the experiments, while the latter are the tools used by the scientists to create the experiment definition that will be later uploaded to the instrument to be executed. To overcome the challenge of developing software with such complexity, it was decided to follow a modeldriven development approach. Several domain-specific languages (DSLs) have been created to allow scientists to describe their experiments in a domain-specific way. These descriptions are then uploaded and executed by different interpreters onboard. The paper details the architecture of the experiment control software and the different modules that compose it, as well as the developed languages and tools used to describe new experiments. The paper also discusses and evaluates some important aspects of the software, such as how resilient it is to failures, as well as the advantages and disadvantages of the selected approach compared to other approaches used in similar missions. The developed software will also be used for the MAIUS-2/3 missions.

Space systems are complex and consist of multiple subsystems. Research and development teams of such complex systems are usually distributed among various institutions and space agencies. This affects the quality of the On-board Software (OBSW) since testing it without having all required subsystems at the software development site can be troublesome. In this paper, we present a data-driven method which can be used to synthesize parts of a system or even an entire system as a black-box model. We exploit the data collected from the real hardware to derive a model using a Machine Learning (ML) algorithm. The proposed model can easily be distributed among development teams and is dedicated to emulate the system for testing the OBSW.