EssentialSAT: Lessons Learned from a Space Systems Engineering Learning Laboratory

Simultaneous and collaborative development of satellites and Information Systems to support their lifecycle processes

This provides an overview of space science and space systems engineering education at Penn State University. Students at the graduate and undergraduate levels participate in an educational program consisting of three interdependent components: space systems-related courses, space systems project work, and research. Courses range from Introduction to Space Physics to Spacecraft Design. Student project involvement is realized through a number of student-driven space systems projects completed throughout the past two decades. Students are provided opportunities to do independent study projects, honors theses, M.S. theses, and Ph.D. theses on a number of space science and space systems-related topics. Our educational goal is to prepare students at the undergraduate and graduate levels for productive careers in technical and nontechnical fields relating to space systems. Due to student interest, we are developing a certificate in Space Systems Engineering for undergraduates in the College of Engineering. Strengthening Penn State's position in space engineering are its Center for Space Research Programs, membership in the USAF's Space Education Consortium, and its role in serving as the lead institution within the Commonwealth for the Pennsylvania Space Grant Consortium

The engineering of space systems is a collaborative, iterative process that integrates various domain-specific viewpoints to represent the final system. To ensure consistency across these viewpoints, the European Space Agency (ESA) employs Model-Based System Engineering (MBSE) and Semantic-Based System Engineering (SBSE) methodologies together to improve digital continuity and interoperability across collaborative space system developments. One significant application of semantic engineering in SE is the ESA MBSE Methodology. The ESA MBSE Methodology provides a standardized approach aligned with the European Cooperation for Space Standardization (ECSS), promotes interoperability across MBSE methodologies and tools, and overcomes integration challenges. ESA MBSE Methodology is the input for the Overall Semantic Modeling for Space System Engineering (OSMoSE) which leverages interoperability in the space community. Case studies, such as the EagleEye Earth Observation mission, demonstrate practical applications, highlighting how semantic models enhance efficiency in complex space systems. This paper discusses the importance of semantics and data management in SE and presents a practical solution derived from the ESA MBSE Methodology.

For many engineering systems, flexibility is an important attribute that allows them to adapt to emerging changes. The existence of flexibility can help space systems adapt themselves to internal/external changes, or even take advantage of new possibilities while in space. Given the potential upfront cost of designing an engineering system to be more flexible, there is a need for a comprehensive framework that allows decision‐makers to measure the value of flexible systems design in its different dimensions. Based on insights from the flexibility literature, this paper proposes a unified and comprehensive framework for measuring the value of flexibility in space systems based on six fundamental elements through which flexibility in engineering systems can be mapped. While the illustrative case study presented in this paper focuses on the value of flexibility in a space system, DARPA's Orbital Express program, this framework could generally be applicable for many other engineering systems. © 2006 Wiley Periodicals, Inc. Syst Eng 10: 26–44, 2007

Parks College of Engineering and Aviation of Saint Louis University [1i] has a tradition of offering an outstanding aerospace engineering education to prepare students at the undergraduate and graduate level for careers in commercial aviation, defense systems, and space systems fields. Courses are offered across the engineering spectrum (aerospace, electrical and computer engineering, and physics departments) ranging from an introduction to aerospace engineering to spacecraft design, spacecraft communications, and space physics. Students participate in courses that include orbital mechanics, space dynamics, spacecraft engineering, and space systems. Senior capstone project work is also included. A separate Astronautics Engineering track as well as a Minor in Space Systems Engineering for non-aerospace engineering students is currently being developed. A, number of student-driven space systems projects are in process that involve design, development, and test of small satellites similar to those recently highlighted in the March 2009 Systems article entitled The First one Hundred University-Class Spacecraft 1981 - 2008. Reference [4] identifies student spacecraft launched over the past 27 years.

Missile and Space Systems Engineering

Missile and Space Systems Engineering

Conceptual design of a compact multipurpose space simulation system

Three-spacecraft autonomous orbit determination and observability analysis with inertial angles-only measurements

In the mid‐1990s, the Federal Government pursued “Acquisition Reform,” which resulted in significantly reduced government technical oversight of contractors. This caused less technical personnel to be hired in the government program offices for the last ten years. Recent investigations of space problems have recognized the need to revitalize the systems engineering workforce within the government program offices. Two years ago, the National Reconnaissance Office (NRO) embarked on the development of a professional development and certification program for space systems engineering. The NRO workforce is heterogeneous; it is comprised of military and civilian members of all DoD services as well as several intelligence community agencies. Our objective was to develop a program that maximized the synergy with parent‐agency programs and avoided any redundant training requirements. A three‐level certification program was established that required technical education, systems engineering experience, and systems engineering training. The training selected is a combination of existing NRO courses, off‐the‐shelf academic courses, commercial training classes, newly developed classes and INCOSE CSEP certification. After the first year, over 375 employees have attended at least one training class and we are certifying systems engineers at the rate of 10–12 per month. The success of this program has led to potential expansion into other areas of the government.

In Memoriam: Dr. Clive L. Dym (1942–2016)

The International Council on Systems Engineering (INCOSE) Space System Working Group (SSWG) has created the CubeSat System Reference Model (CSRM), a representation of the logical architecture of a CubeSat system, intended to be used by system architects and engineers as a starting point as they develop the logical architecture of the Space and Ground components of the CubeSat mission of interest to them. The CSRM is based on Model-Based System Engineering (MBSE) principles, is System Modeling Language (SysML) compliant, is hosted in a graphical modeling tool, and is intended to introduce quality enhancements and economies associated with reusability. The CSRM has been vetted by System Engineering professionals and has been introduced to the CubeSat mission development team community with favorable results. It has been submitted to the Object Management Group (OMG) as a CubeSat specification, and is being evaluated for that role. The SSWG has created a notional outline describing how the CSRM can be applied to a specific mission development effort; and has also identified possible future efforts to expand the applicability, value, and use of the CSRM by the satellite development community.

Event Abstract Back to Event Stochastic Adaptive Sampling in Sensory Information Encoding Zhuoyi Song1, 2, Marten Postma3, Stephen Billings1, Daniel Coca1*, Roger Hardie4* and Mikko Juusola2, 5* 1 University of Sheffield, Department of Automatic Control and Systems Engineering, United Kingdom 2 University of Sheffield, Department of Biomedical Science, United Kingdom 3 University of Amsterdam, Swammerdam Institute for Life Sciences, Netherlands 4 University of Cambridge, Department of Physiology, Development and Neuroscience, United Kingdom 5 Beijing Normal University, State Key Laboratory of Cognitive Neuroscience and Learning, China How are vastly varying light changes in natural environment efficiently represented by the limited dynamic range of photoreceptors? How do photoreceptors effectively encode light intensity changes in millisecond scale? These important questions are at the core of the quest to understand rapid and reliable sensory information encoding mechanisms. We recently uncovered such mysteries by generating biophysically realistic computational models of fly photoreceptors. The models explain how the cell’s ultrastructure and transduction machinery shape response dynamics and coding properties over a broad range of light intenisties [1]. A Drosophila photoreceptor integrates light information by adaptive stochastic sampling rule1. Its photo-sensitive waveguide (rhabdomere) consists of ~30,000 microvilli, each of which is capable of generating single photon responses (quantum bumps). Because a microvillus can only generate a bump at a time and become briefly refractory thereafter [2], a fly photoreceptor is essentially a photon counter, and its information coding capability is ultimately set by the size and number of samples it generates in a time unit. Efficient encoding emerges from adapting the sample sizes (bump waveforms) and numbers to ambient light intensity [3]. The refractory period of each microvillus opposes saturation; together these dynamically and stochastically adjust availability of microvilli (bump production rate: sample rate), whilst intracellular calcium and voltage adapt their bump amplitudes and waveforms (sample size). These adapting sampling principles result in robust encoding of natural light changes, which both approximates contrast constancy and enhances novel/surprising light changes at different light levels. Importantly, rather than treating stochasticity in transduction machinery simply as noise, our modelling framework enabled us to analyse its merits and costs within the signalling processes. The results revealed benefits of stochastic reactions and their compartmentalisation for generating reliably responses. While adaptation adjusts a photoreceptor’s signaling performance to light conditions, stochastic sampling of microvilli contributes to the prevention of saturation and equalizes the usage of available encoding range. Adaptive stochastic sampling accurately predicts information processing across a range of fly species with different visual ecologies, supporting its general role in encoding sensory information. It will be interesting to examine whether this encoding principle is also applicable to other sensory neurons, in which reaction cascades often share similar molecular constituents with compartmentalised sub-structures. This principle could also apply to information sampling/encoding by population of synapses in neurons/networks.

A challenging problem for software and systems engineers is to provide assurance of operations for a system that is critical but must operate in situations that cannot be easily created in the testing lab. For example, a space system cannot be fully tested in all operational modes until it is launched and nuclear power plants cannot be tested under real critical temperature overload conditions. This situation is particularly challenging when seeking to provide assurance in critical AI systems (CAIS) where the underlying algorithms may be very difficult to verify under any conditions. In these cases using systems that have a similar underlying application, operational profiles, user characteristics, and underlying AI algorithms may be suitable as testing proxies. For example, a robot vacuum may have significant operational and implementation similarities to act as a testing proxy for some aspects of an autonomous vehicle.In this work we discuss the challenges in assured autonomy for CAIS and suggest a way forward using proxy systems. We describe a methodology for characterizing CAIS and matching them to their non-critical proxy equivalent. Examples are given along with a discussion of the history of other kinds of proxy verification and validation

The following topics are dealt with: engineering systems; hardware; software; environmental hazards; vehicle health; human factors; air traffic control; space systems.