The GPM Validation Network and Evaluation of Satellite-Based Retrievals of the Rain Drop Size Distribution
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Patrick Noel Gatlin↗
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The GPM Validation Network and Evaluation of Satellite-Based Retrievals of the Rain Drop Size Distribution
Explore the source record for details and available documents.
Patrick N. Gatlin↗
The R-Shell approach - Using scheduling agents in complex distributed real-time systems
Large, complex real-time systems such as space and avionics systems are extremely demanding in their scheduling requirements. The current OS design approaches are quite limited in the capabilities they provide for task scheduling. Typically, they simply implement a particular uniprocessor scheduling strategy and do not provide any special support for network scheduling, overload handling, fault tolerance, distributed processing, etc. Our design of the R-Shell real-time environment fcilitates the implementation of a variety of sophisticated but efficient scheduling strategies, including incorporation of all these capabilities. This is accomplished by the use of scheduling agents which reside in the application run-time environment and are responsible for coordinating the scheduling of the application.
Natarajan, Swaminathan↗
Zapiary: Creating Visibility in IOT Networks
Zigbee and Z-Wave are the main networking protocols used by low-power Internet of Things (IOT) devices. These protocols use low frequencies. Mesh architecture, and unique address formats that make them not compatible with traditional network traffic tools like IX-Discovery Tools. Zapiary is a software that takes CSV files with Zigbee and Z-Wave traffic and generates Structured Threat Information eXpression (STIX) JSON bundles illustrating the communication within IOT networks. The bundles can then be viewed within Structured Threat Intelligence Graph (STIG) or used with AI/ML models to provide deeper visibility into nodes that make up the network and the ability to trend the mesh network over time.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Efficient File Sharing by Multicast - P2P Protocol Using Network Coding and Rank Based Peer Selection
In this work, we consider information dissemination and sharing in a distributed peer-to-peer (P2P highly dynamic communication network. In particular, we explore a network coding technique for transmission and a rank based peer selection method for network formation. The combined approach has been shown to improve information sharing and delivery to all users when considering the challenges imposed by the space network environments.
military↗
Nodes
Nodes is a technology demonstration mission that is scheduled for launch to the International SpaceStation no earlier than Nov.19, 2015. The two Nodes satellites will be deployed from the Station in early 2016 todemonstrate new network capabilities critical to the operation of swarms of spacecraft. They will demonstrate the ability ofmulti spacecraft swarms to receive and distribute ground commands, exchange information periodically, andautonomously configure the network by determining which spacecraft should communicate with the ground each day ofthe mission.
Nodes↗
Gammon - A load balancing strategy for local computer systems with multiaccess networks
Consideration is given to an efficient load-balancing strategy, Gammon (global allocation from maximum to minimum in constant time), for distributed computing systems connected by multiaccess local area networks. The broadcast capability of these networks is utilized to implement an identification procedure at the applications level for the maximally and the minimally loaded processors. The search technique has an average overhead which is independent of the number of participating stations. An implementation of Gammon on a network of Sun workstations is described. Its performance is found to be better than that of other known methods.
Baumgartner, Katherine M.↗
Achieving ultrahigh modulus of resilience and enhanced thermal stability in ZnO x /SU-8 interpenetrating network polymer nanocomposite nanopillars
The modulus of resilience, a mechanical property that quantifies the maximum strain energy density a material can store during elastic deformation, is a crucial parameter for materials used in flexible displays, micro/nano-electro-mechanical system (M/NEMS) actuators, and ultra-sensitive pressure sensors. In this study, ZnO x /SU-8 nanocomposite nanopillars with a diameter of 300 nm, fully infiltrated with a uniformly distributed, interpenetrating amorphous ZnO x filler network, were synthesized via vapor-phase infiltration (VPI). In-situ uniaxial nano-compression tests revealed that the modulus of resilience of ZnO x /SU-8 reaches ∼ 12 MJ/m 3 , which is an ultrahigh value among all engineering materials with comparable strength. In addition, the synthesis fidelity, inorganic infiltration depth, and mechanical performance were all significantly improved compared to VPI-synthesized AlO x nanocomposites. Thermal stability, another key requirement for M/NEMS device materials operating under extreme environments, was also notably enhanced. Furthermore, partial crystallization of the amorphous ZnO x fillers during annealing contributed to an additional increase in modulus of resilience, reaching up to ∼ 13.9 MJ/m 3 . This work presents an effective fabrication strategy for producing nanostructured organic–inorganic hybrid nanocomposites with ultrahigh modulus of resilience and superior thermal stability, paving the way for their integration into next-generation flexible displays and high-performance M/NEMS devices working under harsh environments.
36 MATERIALS SCIENCE↗
Distributed earth model/orbiter simulation
Distributed Earth Model/Orbiter Simulation (DEMOS) is a network based application developed for the UNIX environment that visually monitors or simulates the Earth and any number of orbiting vehicles. Its purpose is to provide Mission Control Center (MCC) flight controllers with a visually accurate three dimensional (3D) model of the Earth, Sun, Moon and orbiters, driven by real time or simulated data. The project incorporates a graphical user interface, 3D modelling employing state-of-the art hardware, and simulation of orbital mechanics in a networked/distributed environment. The user interface is based on the X Window System and the X Ray toolbox. The 3D modelling utilizes the Programmer's Hierarchical Interactive Graphics System (PHIGS) standard and Raster Technologies hardware for rendering/display performance. The simulation of orbiting vehicles uses two methods of vector propagation implemented with standard UNIX/C for portability. Each part is a distinct process that can run on separate nodes of a network, exploiting each node's unique hardware capabilities. The client/server communication architecture of the application can be reused for a variety of distributed applications.
Geisler, Erik↗
Neural Network Burst Pressure Prediction in Graphite/Epoxy Pressure Vessels from Acoustic Emission Amplitude Data
Acoustic emission (AE) data were taken during hydroproof for three sets of ASTM standard 5.75 inch diameter filament wound graphite/epoxy bottles. All three sets of bottles had the same design and were wound from the same graphite fiber; the only difference was in the epoxies used. Two of the epoxies had similar mechanical properties, and because the acoustic properties of materials are a function of their stiffnesses, it was thought that the AE data from the two sets might also be similar; however, this was not the case. Therefore, the three resin types were categorized using dummy variables, which allowed the prediction of burst pressures all three sets of bottles using a single neural network. Three bottles from each set were used to train the network. The resin category, the AE amplitude distribution data taken up to 25 % of the expected burst pressure, and the actual burst pressures were used as inputs. Architecturally, the network consisted of a forty-three neuron input layer (a single categorical variable defining the resin type plus forty-two continuous variables for the AE amplitude frequencies), a fifteen neuron hidden layer for mapping, and a single output neuron for burst pressure prediction. The network trained on all three bottle sets was able to predict burst pressures in the remaining bottles with a worst case error of + 6.59%, slightly greater than the desired goal of + 5%. This larger than desired error was due to poor resolution in the amplitude data for the third bottle set. When the third set of bottles was eliminated from consideration, only four hidden layer neurons were necessary to generate a worst case prediction error of - 3.43%, well within the desired goal.
Hill, Eric v. K.↗
Multimessengers from the Radioactive Decay of r -process Nuclei
The radioactive β-decay of nuclei synthesized in the rapid neutron capture process (r-process) releases a variety of particles, including electrons, γ-rays, neutrinos, and neutrons. These particles provide a rich set of multimessenger signals that carry information about the astrophysical environments where neutron-rich nucleosynthesis occurs. In this work, we calculate from first principles the emission spectra resulting from the β-decay of r-process nuclei. Our approach incorporates detailed nuclear structure and decay data to model the energy distributions of each particle species. We couple the spectra with a nuclear reaction network simulation to obtain the temporal evolution of these distributions. We find that the emission distributions vary significantly in time and are nonthermal, with substantial average energies. We investigate these nuclear signals as a direct probe of heavy element formation and show that they are complementary observables to kilonova.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Management of the Space Station Freedom onboard local area network
An operational approach is proposed to managing the Data Management System Local Area Network (LAN) on Space Station Freedom. An overview of the onboard LAN elements is presented first, followed by a proposal of the operational guidelines by which management of the onboard network may be effected. To implement the guidelines, a recommendation is then presented on a set of network management parameters which should be made available in the onboard Network Operating System Computer Software Configuration Item and Fiber Distributed Data Interface firmware. Finally, some implications for the implementation of the various network management elements are discussed.
Miller, Frank W.↗
A Framework for Dimensioning VDL-2 Air-Ground Networks
This paper describes a framework developed at MITRE for dimensioning a Very High Frequency (VHF) Digital Link Mode 2 (VDL-2) Air-to-Ground network. This framework was developed to support the FAA's Data Communications (Data Comm) program by providing estimates of expected capacity required for the air-ground network services that will support Controller-Pilot-Data-Link Communications (CPDLC), as well as the spectrum needed to operate the system at required levels of performance. The Data Comm program is part of the FAA's NextGen initiative to implement advanced communication capabilities in the National Airspace System (NAS). The first component of the framework is the radio-frequency (RF) coverage design for the network ground stations. Then we proceed to describe the approach used to assess the aircraft geographical distribution and the data traffic demand expected in the network. The next step is the resource allocation utilizing optimization algorithms developed in MITRE's Spectrum ProspectorTM tool to propose frequency assignment solutions, and a NASA-developed VDL-2 tool to perform simulations and determine whether a proposed plan meets the desired performance requirements. The framework presented is capable of providing quantitative estimates of multiple variables related to the air-ground network, in order to satisfy established coverage, capacity and latency performance requirements. Outputs include: coverage provided at different altitudes; data capacity required in the network, aggregated or on a per ground station basis; spectrum (pool of frequencies) needed for the system to meet a target performance; optimized frequency plan for a given scenario; expected performance given spectrum available; and, estimates of throughput distributions for a given scenario. We conclude with a discussion aimed at providing insight into the tradeoffs and challenges identified with respect to radio resource management for VDL-2 air-ground networks.
Aviation↗
Discrete Graph Structure Learning for Forecasting Multiple Time Series
Time series forecasting is an extensively studied subject in statistics, economics, and computer science. Exploration of the correlation and causation among the variables in a multivariate time series shows promise in enhancing the performance of a time series model. When using deep neural networks as forecasting models, we hypothesize that exploiting the pairwise information among multiple (multivariate) time series also improves their forecast. If an explicit graph structure is known, graph neural networks (GNNs) have been demonstrated as powerful tools to exploit the structure. In this work, we propose learning the structure simultaneously with the GNN if the graph is unknown. We cast the problem as learning a probabilistic graph model through optimizing the mean performance over the graph distribution. The distribution is parameterized by a neural network so that discrete graphs can be sampled differentiably through reparameterization. Empirical evaluations show that our method is simpler, more efficient, and better performing than a recently proposed bilevel learning approach for graph structure learning, as well as a broad array of forecasting models, either deep or non-deep learning based, and graph or non-graph based.
Shang, Chao↗
Understanding Customer Dissatisfaction with Underutilized Distributed File Servers
An important trend in the design of storage subsystems is a move toward direct network attachment. Network-attached storage offers the opportunity to off-load distributed file system functionality from dedicated file server machines and execute many requests directly at the storage devices. For this strategy to lead to better performance, as perceived by users, the response time of distributed operations must improve. In this paper we analyze measurements of an Andrew file system (AFS) server that we recently upgraded in an effort to improve client performance in our laboratory. While the original server's overall utilization was only about 3%, we show how burst loads were sufficiently intense to lead to period of poor response time significant enough to trigger customer dissatisfaction. In particular, we show how, after adjusting for network load and traffic to non-project servers, 50% of the variation in client response time was explained by variation in server central processing unit (CPU) use. That is, clients saw long response times in large part because the server was often over-utilized when it was used at all. Using these measures, we see that off-loading file server work in a network-attached storage architecture has to potential to benefit user response time. Computational power in such a system scales directly with storage capacity, so the slowdown during burst period should be reduced.
Riedel, Erik↗
Local and utility-wide cost allocations for a more equitable wildfire-resilient distribution grid
Climate-induced extreme weather conditions make electricity infrastructure more vulnerable. They increase the risk of power-line-ignited wildfires which can, in turn, jeopardize electric power delivery. Here, leveraging machine learning, we show that lower-income communities in California not only have lower fractions of power distribution lines undergrounded, but overhead lines and poles in their neighbourhoods are also more vulnerable to wildfires. Should they bear the cost of undergrounding fire-prone lines themselves, they would have to pay a disproportionately higher cost per household. We propose a cost allocation scheme with an income threshold below which the cost is borne by utility-wide ratepayers and above which the cost is borne locally. This scheme can not only minimize the average of undergrounding costs per household as a share of income, but also homogenize such cost–income ratios across communities. Furthermore, our research demonstrates the opportunity to appropriately integrate existing policies to make electricity infrastructure affordable, equitable and reliable amidst climate change.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Computational microscopy for fast widefield deep-tissue fluorescence imaging using a commercial dual-cannula probe
A solid-glass cannula serves as a micro-endoscope that can deliver excitation light deep inside tissue while also collecting emitted fluorescence. Then, we utilize deep neural networks to reconstruct images from the collected intensity distributions. By using a commercially available dual-cannula probe, and training a separate deep neural network for each cannula, we effectively double the field of view compared to prior work. We demonstrated ex vivo imaging of fluorescent beads and brain slices and in vivo imaging from whole brains. We clearly resolved 4 µm beads, with FOV from each cannula of 0.2 mm (diameter), and produced images from a depth of ∼1.2 mm in the whole brain, currently limited primarily by the labeling. Since no scanning is required, fast widefield fluorescence imaging limited primarily by the brightness of the fluorophores, collection efficiency of our system, and the frame rate of the camera becomes possible.
42 ENGINEERING↗
Adversarial sampling of unknown and high-dimensional conditional distributions
Many engineering problems require the prediction of realization-to-realization variability or a refined description of modeled quantities. In that case, it is necessary to sample elements from unknown high-dimensional spaces with possibly millions of degrees of freedom. While there exist methods able to sample elements from probability density functions (PDF) with known shapes, several approximations need to be made when the distribution is unknown. In this paper the sampling method, as well as the inference of the underlying distribution, are both handled with a data-driven method known as generative adversarial networks (GAN), which trains two competing neural networks to produce a network that can effectively generate samples from the training set distribution. In practice, it is often necessary to draw samples from conditional distributions. When the conditional variables are continuous, only one (if any) data point corresponding to a particular value of a conditioning variable may be available, which is not sufficient to estimate the conditional distribution. This work handles this problem using an a priori estimation of the conditional moments of a PDF. Herein, two approaches, stochastic estimation, and an external neural network are compared for computing these moments; however, any preferred method can be used. The algorithm is demonstrated in the case of the deconvolution of a filtered turbulent flow field. It is shown that all the versions of the proposed algorithm effectively sample the target conditional distribution with minimal impact on the quality of the samples compared to state-of-the-art methods. Additionally, the procedure can be used as a metric for the diversity of samples generated by a conditional GAN (cGAN) conditioned with continuous variables.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗