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NASA Near Earth Network (NEN) and Space Network (SN) Support of CubeSat Communications
There has been a historical trend to increase capability and drive down the Size, Weight and Power (SWAP) of satellites and that trend continues today. NASA scientists and engineers across many of NASAs Mission Directorates and Centers are developing exciting CubeSat concepts and welcome potential partnerships for CubeSat endeavors. From a Telemetry, Tracking and Command (TTC) Systems and Flight Operations for Small Satellites point of view, small satellites including CubeSats are a challenge to coordinate because of existing small spacecraft constraints, such as limited SWAP and attitude control, and the potential for high numbers of operational spacecraft. The NASA Space Communications and Navigation (SCaN) Programs Near Earth Network (NEN) and Space Network (SN) are customer driven organizations that provide comprehensive communications services for space assets including data transport between a missions orbiting satellite and its Mission Operations Center (MOC). This paper presents how well the SCaN networks, SN and NEN, are currently positioned to support the emerging small small satellite and CubeSat market as well as planned enhancements for future support.
Quantum Wrapper Networking for Entanglement Management and Control in Transparent Optical Quantum Networks
The overarching goal of this three-year project was to design and experimentally demonstrate a quantum wrapper networking (QWN) protocol and architecture for entanglement management and control in coexisted classical-quantum networks, facilitating a global quantum Internet. In QWN, the quantum payload (qubits) is wrapped with classical bits as a header. The classical bits facilitate end-to-end transport of the quantum payload without having to read or alter the quantum data payload at intermediate switches and routers.
Advancing the understanding of coastal disturbances with a network-of-networks approach
Coastal ecosystems are at the nexus of many high priority challenges in environmental sciences, including predicting the influences of compounding disturbances exacerbated by climate change on biogeochemical cycling. Extreme events such as hurricanes, flooding, landslides, and wildfires influence biogeochemical cycling in these systems. However, while research in coastal science is fundamentally transdisciplinary – as drivers of biogeochemical and ecological processes often span scientific and environmental domains – traditional place-based approaches are still often employed to understand coastal ecosystems. In this perspective, we argue that integration among distributed research sites from a macrosystem perspective is crucial to understand how compounding disturbances affect coastal ecosystems. We identify a roadmap for the implementation of an integrated network-of-networks framework that leverages existing research network sites in coastal ecosystems to advance continental-scale process understanding for studying extreme events and global change. We also identify specific ways that existing research efforts can maximize mutual benefit, and where additional infrastructure investments might increase return-on-investment along the coast, using the coastal continental US as a case study.
Competing endogenous RNA network associated with oxygen-induced retinopathy: Expression of the network and identification of the MALAT1/miR-124–3p/EGR1 regulatory axis
Retinopathy of prematurity (ROP) is a severe retinal dysfunction in prematurely born babies. The relationship between non-coding RNAs and retinopathy of prematurity (ROP) remain unclear. Microarray analysis of lncRNAs, miRNAs, and mRNAs was conducted in a mouse model of ROP. A competing endogenous RNA (ceRNA) network was constructed. The relationship among MALAT1, miR-124–3p, and Early growth response protein 1 (EGR1) was assessed in hypoxia-induced primary human umbilical vein endothelial cells (HUVECs) and ROP mouse model. In the study, we found 2252 lncRNAs, 1239 mRNAs, and 36 miRNAs were differentially regulated. ceRNA network consisting of 21 lncRNAs, 10 miRNAs, and 19 mRNAs was established. Of the most down-regulated miRNAs, miR-124–3p was selected for additional study. miR-124–3p ceased the migration and proliferation of primary HUVECs in hypoxic conditions, and directly suppressed EGR1. Additionally, MALAT1 directly sponged miR-124–3p. Knockdown of MALAT1 decreased EGR1 expression and inhibited the migration and proliferation of primary HUVECs in hypoxia. Furthermore, these changes were rescued by depletion of miR-124–3p. In vivo, intravitreal injection of miR-124–3p, shMALAT1 decreased EGR1 expression and markedly suppressed retinal neovascularization in OIR models. Intravitreal injection of shMALAT1 and miR-124–3p antagomir at the same time can promote retinal neovascularization, which reversed the suppression of retinal neovascularization functioned by shMALAT1. In conclusion, the expression profiles of lncRNAs and miRNAs and the ceRNA network in a mouse model of ROP may be indicative of the underlying mechanisms of retinal angiogenesis and neural activity. The MALAT1/miR-124–3p/EGR1 regulatory axis is partly responsible for retinal neovascularization, which may provide a novel theoretical basis for the pathogenesis of ROP.
Versatile Synthetic Platform for Polymer Membrane Libraries Using Functional Networks
Poly(ethylene glycol) (PEG) diacrylate copolymer networks containing pentafluorophenyl active esters can be quantitatively substituted with a wide variety of primary amines, enabling the development of a versatile synthetic platform for the preparation of polymer hydrogel and membrane libraries. Here, by tuning the starting network, a high degree of control over cross-linking density, water uptake, and functional group incorporation can be reproducibly achieved, which is vital for elucidating structure–property relationships in ion transporting membranes. From the same cross-linked scaffold, a diversity of basic, acidic, and solute-chelating moieties were obtained through functionalization, which allows for tailored uptake of basic and acidic organic dyes and metal chloride salts. Ion permeation and sorption measurements for a series of polymer networks with controlled cross-linking density and varied imidazole grafting densities illustrate the ability of this platform to isolate the effect of chemical functionality on ion transport from the effects of cross-linking density and water content.
Energy-efficient Mott activation neuron for full-hardware implementation of neural networks
To circumvent the von Neumann bottleneck, substantial progress has been made towards in-memory computing with synaptic devices. However, compact nanodevices implementing non-linear activation functions are required for efficient full-hardware implementation of deep neural networks. Here, in this work, we present an energy-efficient and compact Mott activation neuron based on vanadium dioxide and its successful integration with a conductive bridge random access memory (CBRAM) crossbar array in hardware. The Mott activation neuron implements the rectified linear unit function in the analogue domain. The neuron devices consume substantially less energy and occupy two orders of magnitude smaller area than those of analogue complementary metal–oxide semiconductor implementations. The LeNet-5 network with Mott activation neurons achieves 98.38% accuracy on the MNIST dataset, close to the ideal software accuracy. We perform large-scale image edge detection using the Mott activation neurons integrated with a CBRAM crossbar array. Our findings provide a solution towards large-scale, highly parallel and energy-efficient in-memory computing systems for neural networks.
Citation network datasets for benchmarking spiking graph neural networks on experimental neuromorphic hardware
Spiking neural networks (SNNs) running on neuromorphic computers offer an energy-efficient alternative for AI tasks. Recently, spiking graph neural networks (S-GNNs) have been shown to produce encouraging results on benchmark citation network datasets such as Cora, CiteSeer, and PubMed for node classification tasks. These S-GNNs were run on SNN simulators only because they contain up to tens of thousands of neurons and up to millions of synapses, translating poorly to neuromorphic hardware. Therefore, in this paper, we create a suite of benchmark datasets from the CiteSeer dataset that can be accommodated on current neuromorphic hardware platforms. Our contribution consists of a collection of three datasets. First, we have an induced subgraph of CiteSeer, which we call MiniSeer, containing 2110 papers, 3604 binary features, and 6 topics. Second, MicroSeer is a very small dataset consisting of 84 papers, 1227 features, and 6 topics. Lastly, BiteSeer is a collection of 15 binary classification datasets. We present creation of these datasets along with accuracies, running times, and spike counts when simulated. We believe that our results in this paper will be used by the neuromorphic community to benchmark, test, and develop neuromorphic hardware and simulators.
DeepGraphONet: A Deep Graph Operator Network to Learn and Zero-Shot Transfer the Dynamic Response of Networked Systems
This article develops a deep graph operator network (DeepGraphONet) framework that learns to approximate the dynamics of a complex system (e.g., the power grid or traffic) with an underlying subgraph structure. Here, we build our DeepGraphONet by fusing the ability of graph neural networks to exploit spatially correlated graph information and deep operator networks to approximate the solution operator of dynamical systems. The resulting DeepGraphONet can then predict the dynamics within a given short/medium-term time horizon by observing a finite history of the graph state information. Furthermore, we design our DeepGraphONet to be resolution independent. That is, we do not require the finite history to be collected at the exact/same resolution. In addition, to disseminate the results from a trained DeepGraphONet, we design a zero-shot learning strategy that enables using it on a different subgraph. Finally, empirical results on the transient stability prediction problem of power grids and traffic flow forecasting problem of a vehicular system illustrate the effectiveness of the proposed DeepGraphONet.
A Fast and Scalable Genetic Algorithm-Based Approach for Planning of Microgrids in Distribution Networks
As a result of climate change, extreme weather events are occurring more frequently and with increasing impact. This trend poses a significant challenge for distribution utilities and system operators to ensure that there is uninterrupted power supply to critical loads in their networks; thus, the level of proactive preparation of the distribution system to be able to handle severe impacts of extreme weather events represents the system's resilience. One method that distribution system planners can use to prepare for future extreme events is to plan multiple microgrids which can use local generation as much as possible to supply critical loads. But partitioning an existing distribution system such that multiple feasible islands are planned and which are capable of supporting critical loads is still challenging for distribution systems - first, because of the size of the network graph partitioning problem and, second, because of the difficulty in properly formulating the desired attributes of such islands or microgrids. Therefore, this paper presents a genetic algorithm based approach that facilitates incorporating multiple objectives for grid partitioning by formulating two types of problems - node allocation and edge elimination - and it considers multiple topological and resilience-enhancing objectives. The performance of the proposed genetic algorithm-based approach is numerically evaluated on multiple test systems as well as on a real distribution feeder in Colorado, United States.
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations
We developed a new class of physics-informed generative adversarial networks (PI-GANs) to solve forward, inverse, and mixed stochastic problems in a unified manner based on a limited number of scattered measurements. Unlike standard GANs relying solely on data for training, here we encoded into the architecture of GANs the governing physical laws in the form of stochastic differential equations (SDEs) using automatic differentiation. In particular, we applied Wasserstein GANs with gradient penalty (WGAN-GP) for its enhanced stability compared to vanilla GANs. We first tested WGAN-GP in approximating Gaussian processes of different correlation lengths based on data realizations collected from simultaneous reads at sparsely placed sensors. We obtained good approximation of the generated stochastic processes to the target ones even if there is a mismatch between the input noise dimensionality and the effective dimensionality of the target stochastic processes. We also studied the overfitting issue for both the discriminator and the generator, and we found that overfitting occurs also in the generator in addition to the discriminator as previously reported. Subsequently, we considered the solution of elliptic SDEs requiring approximations of three stochastic processes, namely the solution, the forcing, and the diffusion coefficient. Here again, we assumed data collected from simultaneous reads at a limited number of sensors for the multiple stochastic processes. Furthermore, three generators were used for the PI-GANs: two of them were feed forward deep neural networks (DNNs), while the other one was the neural network induced by the SDE. For the case where we have one group of data, we employed one feed forward DNN as the discriminator, while for the case of multiple groups of data we employed multiple discriminators in PI-GANs. We solved forward, inverse, and mixed problems without changing the framework of PI-GANs, obtaining both the means and the standard deviations of the stochastic solution and the diffusion coefficient in good agreement with benchmarks. In this work, we have demonstrated the effectiveness of PI-GANs in solving SDEs for about 120 dimensions. In principle, PI-GANs could tackle very high dimensional problems given more sensor data with low-polynomial growth in computational cost.
Neural network denoising of HED x-ray images, with an introduction to neural networks [Slides]
We created and trained a new neural network denoiser model using estimates of noise in the data. We showed that our denoiser significantly reduces the combined blur and noise on the training and testing set. We demonstrated the efficacy of neural network denoisers for reducing noise on HED x-ray images. Changes to network architecture and improvements to the noise model in training could improve the model.
Hardware-in-the-loop Testing of Network Protectors for Low-Voltage Networks with Distributed Energy Resources
In this report, we developed and validated a network protector relay digital twin model and interfaced a commonly used network protector relay hardware with our real-time simulation system. Hardware-in-the-loop protection studies are performed to assess the impact of distributed energy resources (DER) and benchmark a rate-of-change-based mitigation strategy. Simulation results suggest that the network protector reverse trip and auto-reclose functions are negatively impacted by the high distributed energy resource penetration. To accommodate DER backfeed while remaining secure and reliable for faults on primary feeders, we recommend options for a rate-of-change-based blocking scheme and a protection setting change. Finally, future mitigation ideas and standard revisions are discussed.
NASF transposition network: A computing network for unscrambling p-ordered vectors
The viewpoints of design, programming, and application of the transportation network (TN) is presented. The TN is a programmable combinational logic network that connects 521 memory modules to 512 processors. The unscrambling of p-ordered vectors to 1-ordered vectors in one cycle is described. The TN design is based upon the concept of cyclic groups from abstract algebra and primitive roots and indices from number theory. The programming of the TN is very simple, requiring only 20 bits: 10 bits for offset control and 10 bits for barrel switch shift control. This simple control is executed by the control unit (CU), not the processors. Any memory access by a processor must be coordinated with the CU and wait for all other processors to come to a synchronization point. These wait and synchronization events can be a degradation in performance to a computation. The TN application is for multidimensional data manipulation, matrix processing, and data sorting, and can also perform a perfect shuffle. Unlike other more complicated and powerful permutation networks, the TN cannot, if possible at all, unscramble non-p-ordered vectors in one cycle.
The Network Information Management System (NIMS) in the Deep Space Network
In an effort to better manage enormous amounts of administrative, engineering, and management data that is distributed worldwide, a study was conducted which identified the need for a network support system. The Network Information Management System (NIMS) will provide the Deep Space Network with the tools to provide an easily accessible source of valid information to support management activities and provide a more cost-effective method of acquiring, maintaining, and retrieval data.
Network management tools for a GPS datalink network
The availability of GPS (Global Position Satellite) information in real-time via a datalink system is shown to significantly increase the capacity of flight test and training ranges in terms of missions supported. This increase in mission activity. imposes demands on mission planning in the range-operations environment. In this context, network management tools which can improve the capability of range personnel to plan, monitor, and control network resources, are of significant interest. The application of both simulation and artificial intelligence techniques is described to develop such network managements tools.
Scalable Hierarchical Network Management System for Displaying Network Information in Three Dimensions
A network management system has SNMP agents distributed at one or more sites, an input output module at each site, and a server module located at a selected site for communicating with input output modules, each of which is configured for both SNMP and HNMP communications. The server module is configured exclusively for HNMP communications, and it communicates with each input output module according to the HNMP. Non-iconified, informationally complete views are provided of network elements to aid in network management.
Combining Flux Balance and Energy Balance Analysis for Large-Scale Metabolic Network: Biochemical Circuit Theory for Analysis of Large-Scale Metabolic Networks
Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.