Comprehension of spacecraft telemetry using hierarchical specifications of behavior ⋆

Abstract. A key challenge in operating remote spacecraft is that ground operators must rely on the limited visibility available through spacecraft telemetry in order to assess spacecraft health and operational status. We describe a tool for processing spacecraft telemetry that allows ground operators to impose structure on received telemetry in order to achieve a better comprehension of system state. A key element of our approach is the design of a domain-specific language that allows operators to express models of expected system behavior using partial specifications. The language allows behavior specifications with data fields, similar to other recent runtime verification systems. What is notable about our approach is the ability to develop hierarchical specifications of behavior. The language is implemented as an internal DSL in the Scala programming language that synthesizes rules from patterns of specification behavior. The rules are automatically applied to received telemetry and the inferred behaviors are available to ground operators using a visualization interface that makes it easier to understand and track spacecraft state. We describe initial results from applying our tool to telemetry received from the Curiosity rover currently roving the surface of Mars, where the visualizations are being used to trend subsystem behaviors, in order to identify potential problems before they happen. However, the technology is completely general and can be applied to any system that generates telemetry such as event logs.

A clustering method for graphical handwriting components and statistical writership analysis

Handwritten documents can be characterized by their content or by the shape of the written characters. We focus on the problem of comparing a person\u27s handwriting to a document of unknown provenance using the shape of the writing, as is done in forensic applications. To do so, we first propose a method for processing scanned handwritten documents to decompose the writing into small graphical structures, often corresponding to letters. We then introduce a measure of distance between two such structures that is inspired by the graph edit distance, and a measure of center for a collection of the graphs. These measurements are the basis for an outlier tolerant K‐means algorithm to cluster the graphs based on structural attributes, thus creating a template for sorting new documents. Finally, we present a Bayesian hierarchical model to capture the propensity of a writer for producing graphs that are assigned to certain clusters. We illustrate the methods using documents from the Computer Vision Lab dataset. We show results of the identification task under the cluster assignments and compare to the same modeling, but with a less flexible grouping method that is not tolerant of incidental strokes or outliers

On deformations of 2d SCFTs

Motivated by the representation of the super Virasoro constraints as generalized Dirac-K{\"a}hler constraints $(d \pm d^\dagger)|\psi> = 0$ on loop space, examples of the most general continuous deformations $d \to e^{-W} d e^W$ are considered which preserve the superconformal algebra at the level of Poisson brackets. The deformations which induce the massless NS and NS-NS backgrounds are exhibited. Hints for a manifest realization of S-duality in terms of an algebra isomorphism are discussed. It is shown how the first order theory of 'canonical deformations' is reproduced and how the deformation operator $W$ encodes vertex operators and gauge transformations.Comment: 44 pages, no figures, discussion of relation to canonical deformations and vertex operators added, typos correcte

Patterns for the design of secure and dependable software defined networks

In an interconnected world, cyber and physical networks face a number of challenges that need to be resolved. These challenges are mainly due to the nature and complexity of interconnected systems and networks and their ability to support heterogeneous physical and cyber components simultaneously. The construction of complex networks preserving Security and Dependability (S&D) properties is necessary to avoid system vulnerabilities, which may occur in all the different layers of Software Defined Networking (SDN) architectures. In this paper, we present a model based approach to support the design of secure and dependable SDN. This approach is based on executable patterns for designing networks able to guarantee S&D properties and can be used in SDN networks. The design patterns express conditions that can guarantee specific S&D properties and can be used to design networks that have these properties and manage them during their deployment. To evaluate our pattern approach, we have implemented executable pattern instances, in a rule-based reasoning system, and used them to design and verify wireless SDN networks with respect to availability and confidentiality. To complete this work, we propose and evaluate an implementation framework in which S&D patterns can be applied for the design and verification of SDN networks

Prediction of Evapotranspiration in a Mediterranean Region Using Basic Meteorological Variables

A critical need for farmers, particularly those in arid and semiarid areas is to have a reliable, accurate and reasonably accessible means of estimating the evapotranspiration rates of their crops to optimize their irrigation requirements. Evapotranspiration is a crucial process because of its influence on the precipitation that is returned to the atmosphere. The calculation of this variable often starts from the estimation of reference evapotranspiration, for which a variety of methods have been developed. However, these methods are very complex either theoretically and/or because of the large amount of parameters on which they are based, which makes the development of a simple and reliable methodology for the prediction of this variable important. This research combined three concepts such as cluster analysis, multiple linear regression (MLR), and Voronoi diagrams to achieve that end. Cluster analysis divided the study area into groups based on its weather characteristics, whose locations were then delimited by drawing the Voronoi regions associated with them. Regression equations were built to predict daily reference evapotranspiration in each cluster using basic climate variables produced in forecasts made by meteorological agencies. Finally, the Voronoi diagrams were used again to regionalize the crop coefficients and calculate evapotranspiration from the values of reference evapotranspiration derived from the regression models. These operations were applied to the Valencian region (Spain), a Mediterranean area which is partly semiarid and for which evapotranspiration is a critical issue. The results demonstrated the usefulness and accuracy of the methodology to predict the water demands of crops and hence enable farmers to plan their irrigation needs.This paper was possible thanks to the research project RHIVU (Ref. BIA2012-32463), financed by the Spanish Ministry of Economy and Competitiveness with funds from the State General Budget (PGE) and the European Regional Development Fund (ERDF). The authors also wish to express their gratitude to the Spanish Ministry of Agriculture, Food and Environment (MAGRAMA) for providing the data necessary to develop this study

Accounting for Graded Performance within a Discrete Search Framework

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98271/1/s15516709cog2004_2.pd

Commonality Preserving Multiple Instance Clustering Based on Diverse Density

Abstract. Image-set clustering is a problem decomposing a given im-age set into disjoint subsets satisfying specied criteria. For single vector image representations, proximity or similarity criterion is widely applied, i.e., proximal or similar images form a cluster. Recent trend of the im-age description, however, is the local feature based, i.e., an image is described by multiple local features, e.g., SIFT, SURF, and so on. In this description, which criterion should be employed for the clustering? As an answer to this question, this paper presents an image-set clus-tering method based on commonality, that is, images preserving strong commonality (coherent local features) form a cluster. In this criterion, image variations that do not affect common features are harmless. In the case of face images, hair-style changes and partial occlusions by glasses may not affect the cluster formation. We dened four commonality mea-sures based on Diverse Density, that are used in agglomerative clustering. Through comparative experiments, we conrmed that two of our meth-ods perform better than other methods examined in the experiments.