Auroral Cluster: A Space Physics Mission for Multiple, Electronically Tethered Small Satellites

Auroral Cluster is a space physics mission that has been identified by the NASA Space Physics Strategic Implementation Study as a candidate for flight in the next decade. Auroral Cluster will employ multiple spacecraft outfitted with similar complements of science instruments allowing simultaneous multipoint plasma measurements in the Earth\u27s auroral regions. Co-orbiting small satellites (mass \u3c 400 kg each) that are electronically tethered to share distributed spacecraft systems represent an efficient approach for achieving the science goals of the Auroral Cluster mission. Multisatellite missions represent a new trend in gathering space science data and pose many new and difficult challenges for the space systems engineer. The results of an Auroral Cluster feasibility study, which discusses a variety of mission trade-offs, are presented. A discussion of the science background and mission goals is used to identify the technical drivers for the design of the multiple spacecraft system. A mission plan and some considerations for a Auroral Cluster satellite design are presented. Special consideration is given to the spacecraft subsystems that will allow the system to be operated as a network of electronically tethered interdependent small satellites. These subsystems include attitude determination, spatial separation knowledge and control, data storage, and intersatellite communication

Modular Synthesis of Sketches Using Models

One problem with the constraint-based approaches to synthesis that have become popular over the last few years is that they only scale to relatively small routines, on the order of a few dozen lines of code. This paper presents a mechanism for modular reasoning that allows us to break larger synthesis problems into small manageable pieces. The approach builds on previous work in the verification community of using high-level specifications and partially interpreted functions (we call them models) in place of more complex pieces of code in order to make the analysis modular. The main contribution of this paper is to show how to combine these techniques with the counterexample guided synthesis approaches used to efficiently solve synthesis problems. Specifically, we show two new algorithms; one to efficiently synthesize functions that use models, and another one to synthesize functions while ensuring that the behavior of the resulting function will be in the set of behaviors allowed by the model. We have implemented our approach on top of the open-source Sketch synthesis system, and we demonstrate its effectiveness on several Sketch benchmark problems.National Science Foundation (U.S.) (Grant NSF-1116362)National Science Foundation (U.S.) (Grant NSF-1139056)United States. Dept. of Energy (Grant DE-SC0005372

Sigref – A Symbolic Bisimulation Tool Box

We present a uniform signature-based approach to compute the most popular bisimulations. Our approach is implemented symbolically using BDDs, which enables the handling of very large transition systems. Signatures for the bisimulations are built up from a few generic building blocks, which naturally correspond to efficient BDD operations. Thus, the definition of an appropriate signature is the key for a rapid development of algorithms for other types of bisimulation. We provide experimental evidence of the viability of this approach by presenting computational results for many bisimulations on real-world instances. The experiments show cases where our framework can handle state spaces efficiently that are far too large to handle for any tool that requires an explicit state space description. This work was partly supported by the German Research Council (DFG) as part of the Transregional Collaborative Research Center “Automatic Verification and Analysis of Complex Systems” (SFB/TR 14 AVACS). See www.avacs.org for more information

OBDD-Based Representation of Interval Graphs

A graph $G = (V,E)$ can be described by the characteristic function of the edge set $\chi_E$ which maps a pair of binary encoded nodes to 1 iff the nodes are adjacent. Using \emph{Ordered Binary Decision Diagrams} (OBDDs) to store $\chi_E$ can lead to a (hopefully) compact representation. Given the OBDD as an input, symbolic/implicit OBDD-based graph algorithms can solve optimization problems by mainly using functional operations, e.g. quantification or binary synthesis. While the OBDD representation size can not be small in general, it can be provable small for special graph classes and then also lead to fast algorithms. In this paper, we show that the OBDD size of unit interval graphs is $O(\ | V \ | /\log \ | V \ |)$ and the OBDD size of interval graphs is $O(\ | V \ | \log \ | V \ |)$which both improve a known result from Nunkesser and Woelfel (2009). Furthermore, we can show that using our variable order and node labeling for interval graphs the worst-case OBDD size is$\Omega(\ | V \ | \log \ | V \ |)$. We use the structure of the adjacency matrices to prove these bounds. This method may be of independent interest and can be applied to other graph classes. We also develop a maximum matching algorithm on unit interval graphs using$O(\log \ | V \ |)$operations and a coloring algorithm for unit and general intervals graphs using$O(\log^2 \ | V \ |)$ operations and evaluate the algorithms empirically.Comment: 29 pages, accepted for 39th International Workshop on Graph-Theoretic Concepts 201

Hierarchical Set Decision Diagrams and Regular Models

This paper presents algorithms and data structures that exploit a compositional and hierarchical specification to enable more efficient symbolic model-checking. We encode the state space and transition relation using hierarchical Set Decision Diagrams (SDD) [9]. In SDD, arcs of the structure are labeled with sets, themselves stored as SDD. To exploit the hierarchy of SDD, a structured model representation is needed. We thus introduce a formalism integrating a simple notion of type and instance. Complex composite behaviors are obtained using a synchronization mechanism borrowed from process calculi. Using this relatively general framework, we investigate how to capture similarities in regular and concurrent models. Experimental results are presented, showing that this approach can outperform in time and memory previous work in this area

Band structure model of magnetic coupling in semiconductors

We present a unified band structure model to explain magnetic ordering in Mn-doped semiconductors. This model is based on the $p$-$d$ and $d$-$d$ level repulsions between the Mn ions and host elements and can successfully explain magnetic ordering observed in all Mn doped II-VI and III-V semiconductors such as CdTe, GaAs, ZnO, and GaN. This model, therefore, provides a simple guideline for future band structure engineering of magnetic semiconductors.Comment: 4+ pages, 5 figure

A Temporal Logic Based Theory of Test Coverage and Generation

This paper presents a theory of test coverage and generation from specifications written in extended finite state machines (EFSMs). We investigate a family of coverage criteria based on the information of control flow and data flow in EFSMs and characterize them using the temporal logic CTL. We discuss the complexity of minimal cost test generation and describe a simple heuristic which uses the capability of model checkers to construct counterexamples. Our approach extends the range of applications of model checking from automatic verification of finite state systems to automatic test generation from finite state systems

Subsumer-First: Steering Symbolic Reachability Analysis

Abstract. Symbolic reachability analysis provides a basis for the veri-fication of software systems by offering algorithmic support for the ex-ploration of the program state space when searching for proofs or coun-terexamples. The choice of exploration strategy employed by the anal-ysis has direct impact on its success, whereas the ability to find short counterexamples quickly and—as a complementary task—to efficiently perform the exhaustive state space traversal are of utmost importance for the majority of verification efforts. Existing exploration strategies can optimize only one of these objectives which leads to a sub-optimal reach-ability analysis, e.g., breadth-first search may sacrifice the exploration ef-ficiency and chaotic iteration can miss minimal counterexamples. In this paper we present subsumer-first, a new approach for steering symbolic reachability analysis that targets both minimal counterexample discovery and efficiency of exhaustive exploration. Our approach leverages the re-sult of fixpoint checks performed during symbolic reachability analysis to bias the exploration strategy towards its objectives, and does not require any additional computation. We demonstrate how the subsumer-first ap-proach can be applied to improve efficiency of software verification tools based on predicate abstraction. Our experimental evaluation indicates the practical usefulness of the approach: we observe significant efficiency improvements (median value 40%) on difficult verification benchmarks from the transportation domain.