Modeling and Simulation of a Spacecraft Payload Hardware Using Machine Learning Techniques

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

A pooled analysis of sequential therapies with sorafenib and sunitinib in metastatic renal cell carcinoma

OBJECTIVE: To evaluate the optimal sequence for the receptor tyrosine kinase inhibitors (rTKIs) sorafenib and sunitinib in metastatic renal cell cancer. METHODS: We performed a retrospective analysis of patients who had received sequential therapy with both rTKIs and integrated these results into a pooled analysis of available data from other publications. Differences in median progression-free survival (PFS) for first- (PFS1) and second-line treatment (PFS2), and for the combined PFS (PFS1 plus PFS2) were examined using weighted linear regression. RESULTS: In the pooled analysis encompassing 853 patients, the median combined PFS for first-line sunitinib and 2nd-line sorafenib (SuSo) was 12.1 months compared with 15.4 months for the reverse sequence (SoSu; 95% CI for difference 1.45-5.12, p = 0.0013). Regarding first-line treatment, no significant difference in PFS1 was noted regardless of which drug was initially used (0.62 months average increase on sorafenib, 95% CI for difference -1.01 to 2.26, p = 0.43). In second-line treatment, sunitinib showed a significantly longer PFS2 than sorafenib (average increase 2.66 months, 95% CI 1.02-4.3, p = 0.003). CONCLUSION: The SoSu sequence translates into a longer combined PFS compared to the SuSo sequence. Predominantly the superiority of sunitinib regarding PFS2 contributed to the longer combined PFS in sequential use