  The use of Deep Learning (DL) algorithms has improved the performance of
vision-based space applications in recent years. However, generating large
amounts of annotated data for training these DL algorithms has proven
challenging. While synthetically generated images can be used, the DL models
trained on synthetic data are often susceptible to performance degradation,
when tested in real-world environments. In this context, the Interdisciplinary
Center of Security, Reliability and Trust (SnT) at the University of Luxembourg
has developed the 'SnT Zero-G Lab', for training and validating vision-based
space algorithms in conditions emulating real-world space environments. An
important aspect of the SnT Zero-G Lab development was the equipment selection.
From the lessons learned during the lab development, this article presents a
systematic approach combining market survey and experimental analyses for
equipment selection. In particular, the article focus on the image acquisition
equipment in a space lab: background materials, cameras and illumination lamps.
The results from the experiment analyses show that the market survey
complimented by experimental analyses is required for effective equipment
selection in a space lab development project.
