Payload design requirements analysis (study 2.2). Volume 1: Executive summary

Research was conducted to provide data on ways to effectively realize the projected cost reductions for payloads to be developed and operated in the shuttle era. Prior studies have indicated that the Shuttle concept of satellite operations will lead to a large reduction in overall payload cost. This study provides the data and insight into the methods of accomplishing these economic benefits. The study examined only payloads that will be launched on the shuttle/tug/sortie lab combinations. These payloads are of four types: (1) expendable; (2) ground refurbishable; (3) on-orbit maintainable; and (4) sortie. The expendable payloads are intended specifically for the shuttle/tug and not for expendable launch vehicles. Economic comparisons were made only between these four types of shuttle payloads and not between these payloads and current expendable launch vehicle payloads

Towards an ontology-based platform-independent framework for developing KBE systems in the aerospace industry

Aerospace engineering is considered to be one of the most complex and advanced branches of engineering. The use of knowledge based engineering (KBE) technologies has played a major role in automating routine design activities in view of supporting the cost-effective and timely development of a product. However, technologies employed within KBE systems are usually platform-specific. The nature of these platform-specific models has significantly limited knowledge abstraction and reusability in KBE systems. This research paper presents a novel approach that illustrates the use of platform-independent knowledge models for the development of KBE systems in the aerospace industry. The use of semantic technologies through the definition of generic-purposed ontologies has been employed to support the notion of independent knowledge models that strengthens knowledge reusability in KBE systems. This approach has been validated qualitatively through experts’ opinion and its benefit realised in the abstraction, reusability and maintainability of KBE systems

On the Modularization of ExplorViz towards Collaborative Open Source Development

Software systems evolve over their lifetime. Changing conditions such as requirements or customer requests make it inevitable for developers to perform adjustments to the underlying code base. Especially in the context of open source software where everybody can contribute, demands can change over time and new user groups may be addressed. In particular, research software is often not structured with a maintainable and extensible architecture. In combination with obsolescent technologies, this is a challenging task for developers, especially, when students are involved. In this paper, we report on the modularization process and architecture of our open source research project ExplorViz towards a microservice architecture, which facilitates a collaborative development process for both researchers and students. We describe the modularization measures and present how we solved occurring issues and enhanced our development process. Afterwards, we illustrate our modularization approach with our modernized, extensible software system architecture and highlight the improved collaborative development process. Finally, we present a proof-of-concept implementation featuring several developed extensions in terms of architecture and extensibility

Ensuring Supply Chain Sustainability from the Perspective of Environmental and Social Performance

Indonesia has as of late received best maintainable business performs to profit organizations, atmosphere and partners. Consequently, this investigation shows ways to supportable SC triple main concern progress got from linkages of official burdens and authoritative personality (OI) to atmosphere and social supportable practices. It demonstrates that the superordinate objective of SSCM is met disregarding unwavering outer weights and alluring OI driving the explicit ecological and dependable social practices of firms. A review approach was received to assemble the information need for this examination. A sum of 203 finished polls were gotten from respondents, who were directors and senior officials in SCM in Malaysia. The information was investigated utilizing basic condition demonstrating with PLS-SEM rendition 3.0. Our discoveries demonstrated that all speculated connections are noteworthy aside from both process-and market-based social practices couldn't straightforwardly affect economic performance. This is in sharp complexity to ecological practices. Be that as it may, the critical connection between social practices with performance is a pathway for the previous to economic performance. Subsequently, the economic benefits got from social practices are rescued through its performance results. The epic and helpful effect of institutional weights on practices of SCM are likewise uncovered

SANTO: Social Aerial NavigaTion in Outdoors

In recent years, the advances in remote connectivity, miniaturization of electronic components and computing power has led to the integration of these technologies in daily devices like cars or aerial vehicles. From these, a consumer-grade option that has gained popularity are the drones or unmanned aerial vehicles, namely quadrotors. Although until recently they have not been used for commercial applications, their inherent potential for a number of tasks where small and intelligent devices are needed is huge. However, although the integrated hardware has advanced exponentially, the refinement of software used for these applications has not beet yet exploited enough. Recently, this shift is visible in the improvement of common tasks in the field of robotics, such as object tracking or autonomous navigation. Moreover, these challenges can become bigger when taking into account the dynamic nature of the real world, where the insight about the current environment is constantly changing. These settings are considered in the improvement of robot-human interaction, where the potential use of these devices is clear, and algorithms are being developed to improve this situation. By the use of the latest advances in artificial intelligence, the human brain behavior is simulated by the so-called neural networks, in such a way that computing system performs as similar as possible as the human behavior. To this end, the system does learn by error which, in an akin way to the human learning, requires a set of previous experiences quite considerable, in order for the algorithm to retain the manners. Applying these technologies to robot-human interaction do narrow the gap. Even so, from a bird's eye, a noticeable time slot used for the application of these technologies is required for the curation of a high-quality dataset, in order to ensure that the learning process is optimal and no wrong actions are retained. Therefore, it is essential to have a development platform in place to ensure these principles are enforced throughout the whole process of creation and optimization of the algorithm. In this work, multiple already-existing handicaps found in pipelines of this computational gauge are exposed, approaching each of them in a independent and simple manner, in such a way that the solutions proposed can be leveraged by the maximum number of workflows. On one side, this project concentrates on reducing the number of bugs introduced by flawed data, as to help the researchers to focus on developing more sophisticated models. On the other side, the shortage of integrated development systems for this kind of pipelines is envisaged, and with special care those using simulated or controlled environments, with the goal of easing the continuous iteration of these pipelines.Thanks to the increasing popularity of drones, the research and development of autonomous capibilities has become easier. However, due to the challenge of integrating multiple technologies, the available software stack to engage this task is restricted. In this thesis, we accent the divergencies among unmanned-aerial-vehicle simulators and propose a platform to allow faster and in-depth prototyping of machine learning algorithms for this drones