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This viewgraph information provides information on establishing Quality of Service (QoS) for satellite communication networks.
As small spacecraft venture out of Earth orbit, they will encounter challenges not experienced or addressed by the numerous low Earth orbit (LEO) CubeSat and Smallsat missions staged to date. The LEO CubeSats typically use low-cost, proven CubeSat radios, antennas, and university ground stations with small apertures. As more ambitious yet cost-constrained space mission concepts to the Moon and beyond are being developed, CubeSats and smallsats have the potential to provide a more affordable platform for exploring deep space and performing the associated science. Some of the challenges that have, so far, slowed the proliferation of small interplanetary spacecraft are those of communications and navigation.
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As future manned and robotic space explorations missions involve more complex systems, it is essential to verify, validate, and optimize such systems through simulation and emulation in a low cost testbed environment. The goal of such a testbed is to perform detailed testing of advanced space and ground communications networks, technologies, and client applications that are essential for future space exploration missions. We describe the development of new technologies enhancing our Multi-mission Advanced Communications Hybrid Environment for Test and Evaluation (MACHETE) that enable its integration in a distributed space communications testbed. MACHETE combines orbital modeling, link analysis, and protocol and service modeling to quantify system performance based on comprehensive considerations of different aspects of space missions. It can simulate entire networks and can interface with external (testbed) systems. The key technology developments enabling the integration of MACHETE into a distributed testbed are the Monitor and Control module and the QualNet IP Network Emulator module. Specifically, the Monitor and Control module establishes a standard interface mechanism to centralize the management of each testbed component. The QualNet IP Network Emulator module allows externally generated network traffic to be passed through MACHETE to experience simulated network behaviors such as propagation delay, data loss, orbital effects and other communications characteristics, including entire network behaviors. We report a successful integration of MACHETE with a space communication testbed modeling a lunar exploration scenario. This document is the viewgraph slides of the presentation.
System Visualization Tool (SVT) computer program developed to provide systems engineers with means of graphically representing networks. Generates diagrams illustrating structures and states of networks defined by users. Provides systems engineers powerful tool simplifing analysis of requirements and testing and maintenance of complex software-controlled systems. Employs visual models supporting analysis of chronological sequences of requirements, simulation data, and related software functions. Applied to pneumatic, hydraulic, and propellant-distribution networks. Used to define and view arbitrary configurations of such major hardware components of system as propellant tanks, valves, propellant lines, and engines. Also graphically displays status of each component. Advantage of SVT: utilizes visual cues to represent configuration of each component within network. Written in Turbo Pascal(R), version 5.0.
Computer-assisted reconstructions of small parts of the macular neural network show how the nerve terminals and receptive fields are organized in 3-dimensional space. This biological neural network is anatomically organized for parallel distributed processing of information. Processing appears to be more complex than in computer-based neural network, because spatiotemporal factors figure into synaptic weighting. Serial reconstruction data show anatomical arrangements which suggest that (1) assemblies of cells analyze and distribute information with inbuilt redundancy, to improve reliability; (2) feedforward/feedback loops provide the capacity for presynaptic modulation of output during processing; (3) constrained randomness in connectivities contributes to adaptability; and (4) local variations in network complexity permit differing analyses of incoming signals to take place simultaneously. The last inference suggests that there may be segregation of information flow to central stations subserving particular functions.
The relaxation of two-dimensional quasicrystalline elastic networks when their constituent bonds are perturbed homogeneously is studied. Whereas ideal, quasi-periodic networks are stable against such perturbations, significant accumulations of strain in a class of disordered networks generated by a growth process are found. The grown networks are characterized by root mean square phason fluctuations which grow linearly with system size. The strain accumulation observed in these networks also grows linearly with system size. Finally, dependence of strain accumulation on cooling rate is found.
Linear network analysis by topological formulas for derivation of k-tree terms without generation of k-trees of graph
The dislocation networks generated during high-temperature creep of several single-crystal nickel-based superalloys are analyzed. The networks continually evolve during creep at relatively low temperatures or eventually reach a more stable configuration at high temperatures. Specifically, the role of these networks in directional coarsening processes are studied, along with their formation kinetics, characteristics, and stability during creep. The results of this study combined with previous findings suggest that the directional coarsening process is strongly influenced by elastic strain energy. The dislocation networks formed during primary creep are found to be stable during all subsequent creep stages. Aspects of these dislocation networks are determined to be a product of both the applied creep stress and coherency strains.
We propose a modified diffusion-limited aggregation (DLA) model for the evolution of fluvial drainage networks. Random walkers are introduced randomly on a grid, and each two-dimensional random walk proceeds until the walker finds a drainage network on which to accrete. This model for headward growth of drainage networks generates drainage patterns remarkably similar to actual drainages. The model also predicts statistical features which agree with actual networks, including the frequency-order (bifurcation) ratio (R(sub b) = 3.98) and the stream length-order (R(sub r) = 2.09). Using the definition of network fractal dimension D = log R(sub b)/log R(sub r), we find that our DLA model gives D = 1.87, near the observed range of D approximately equal to 1.80 - 1.85.
NASA's Space Geodesy Project (SGP) is developing a prototype core site for a next generation Space Geodetic Network (SGN). Each of the sites in this planned network co-locate current state-of-the-art stations from all four space geodetic observing systems, GNSS, SLR, VLBI, and DORIS, with the goal of achieving modern requirements for the International Terrestrial Reference Frame (ITRF). In particular, the driving ITRF requirements for this network are 1.0 mm in accuracy and 0.1 mm/yr in stability, a factor of 10-20 beyond current capabilities. Development of the prototype core site, located at NASA's Geophysical and Astronomical Observatory at the Goddard Space Flight Center, started in 2011 and will be completed by the end of 2013. In January 2012, two operational GNSS stations, GODS and GOON, were established at the prototype site within 100 m of each other. Both stations are being proposed for inclusion into the IGS network. In addition, work is underway for the inclusion of next generation SLR and VLBI stations along with a modern DORIS station. An automated survey system is being developed to measure inter-technique vectorties, and network design studies are being performed to define the appropriate number and distribution of these next generation space geodetic core sites that are required to achieve the driving ITRF requirements. We present the status of this prototype next generation space geodetic core site, results from the analysis of data from the established geodetic stations, and results from the ongoing network design studies.
In this paper, Multilayer Feedforward Networks are applied to the robot inverse kinematic problem. The networks are trained with endeffector position and joint angles. After training, performance is measured by having the network generate joint angles for arbitrary endeffector trajectories. A 3-degree-of-freedom (DOF) spatial manipulator is used for the study. It is found that neural networks provide a simple and effective way to both model the manipulator inverse kinematics and circumvent the problems associated with algorithmic solution methods.