OEXP exploration studies technical report. Volume 3: Special reports, studies, and indepth systems assessments

The Office of Exploration (OEXP) at NASA has been tasked with defining and recommending alternatives for an early 1990's national decision on a focused program of manned exploration of the Solar System. The Mission analysis and System Engineering (MASE) group, which is managed by the Exploration Studies Office at the Johnson Space Center, is responsible for coordinating the technical studies necessary for accomplishing such a task. This technical report, produced by the MASE, describes the process used to conduct exploration studies and discusses the mission developed in a case study approach. The four case studies developed in FY88 include: (1) a manned expedition to PHOBOS; (2) a manned expedition to MARS; (3) a lunar surface observatory; and a lunar outpost to early Mars evolution. The final outcome of this effort is a set of programmatic and technical conclusions and recommendations for the following year's work

Foundations of Software Science and Computation Structures

This open access book constitutes the proceedings of the 24th International Conference on Foundations of Software Science and Computational Structures, FOSSACS 2021, which was held during March 27 until April 1, 2021, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2021. The conference was planned to take place in Luxembourg and changed to an online format due to the COVID-19 pandemic. The 28 regular papers presented in this volume were carefully reviewed and selected from 88 submissions. They deal with research on theories and methods to support the analysis, integration, synthesis, transformation, and verification of programs and software systems

On the discardability of data in Support Vector Classification problems

We analyze the problem of data sets reduction for support vector classification. The work is also motivated by distributed problems, where sensors collect binary measurements at different locations moving inside an environment that needs to be divided into a collection of regions labeled in two different ways. The scope is to let each agent retain and exchange only those measurements that are mostly informative for the collective reconstruction of the decision boundary. For the case of separable classes, we provide the exact conditions and an efficient algorithm to determine if an element in the training set can become a support vector when new data arrive. The analysis is then extended to the non-separable case deriving a sufficient discardability condition and a general data selection scheme for classification. Numerical experiments relative to the distributed problem show that the proposed procedure allows the agents to exchange a small amount of the collected data to obtain a highly predictive decision boundary