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The Mars Network provides proximity based communications and navigation services to support Mars exploration. The network will be comprised of science orbiters with a MN relay transceiver, and potentially, dedicated telecommunication orbiters. The common MN transceiver, called Electra, is currently in deployment, and is being designed for both communications and radiometric tracking.
Networks are vital tools for understanding and modeling interactions in complex systems in science and engineering, and direct and indirect interactions are pervasive in all types of networks. However, quantitatively disentangling direct and indirect relationships in networks remains a formidable task. Here, we present a framework, called iDIRECT (Inference of Direct and Indirect Relationships with Effective Copula-based Transitivity), for quantitatively inferring direct dependencies in association networks. Using copula-based transitivity, iDIRECT eliminates/ameliorates several challenging mathematical problems, including ill-conditioning, self-looping, and interaction strength overflow. With simulation data as benchmark examples, iDIRECT showed high prediction accuracies. Application of iDIRECT to reconstruct gene regulatory networks in Escherichia coli also revealed considerably higher prediction power than the best-performing approaches in the DREAM5 (Dialogue on Reverse Engineering Assessment and Methods project, #5) Network Inference Challenge. In addition, applying iDIRECT to highly diverse grassland soil microbial communities in response to climate warming showed that the iDIRECT-processed networks were significantly different from the original networks, with considerably fewer nodes, links, and connectivity, but higher relative modularity. Further analysis revealed that the iDIRECT-processed network was more complex under warming than the control and more robust to both random and target species removal ( P < 0.001). As a general approach, iDIRECT has great advantages for network inference, and it should be widely applicable to infer direct relationships in association networks across diverse disciplines in science and engineering.
The universe is growing at an accelerated rate, yet the several experiments and science cases used to measure this property yield conflicting results for the rate. Known colloquially as the Hubble tension problem, this discrepancy calls for a novel measurement of the Hubble Constant, H0, in order to be resolved. The Dark Energy Survey Gravitational Wave group (DESGW) aims to relieve this tension by performing a standard sirens measurement of H0 independent of traditional methods. To do so in the current era of data abundance and limited telescope time demands a robust, optimized, and complete science software pipeline to carefully balance the transition from gravitational wave data to state-of-the-art images, images to photometric data, and photometric data to identification and analysis of electromagnetic counterparts to gravitational wave signals. This thesis details the contributions of the author to the DESGW Search & Discovery Pipeline for the fourth observing run of the LIGO/Virgo/KAGRA gravitational wave detector network, as well as the science applications that have been pursued.
The data storage and retrieval demands of space and Earth sciences researchers have made the NASA Center for Computational Sciences (NCCS) Mass Data Storage and Delivery System (MDSDS) one of the world's most active Convex UniTree systems. Science researchers formed the NCCS's Computer Environments and Research Requirements Committee (CERRC) to relate their projected supercomputing and mass storage requirements through the year 2000. Using the CERRC guidelines and observations of current usage, some detailed projections of requirements for MDSDS network bandwidth and mass storage capacity and performance are presented.
Projected for launch in the latter part of 1998, the Advanced X-ray Astrophysics Facility (AXAF), the third satellite in the Great Observatory series, promises to dramatically open the x-ray sky as the Hubble and Compton observatories have done in their respective realms. Unlike its companions, however, AXAF will be placed in a high altitude, highly elliptical orbit (10,000 x 100,000 km), and will therefore be subject to its own unique environment, spacecraft and science instrument constraints and communication network interactions. In support of this mission, ground operations personnel have embarked on the development of the AXAF Offline System (OFLS), a body of software divided into four basic functional elements: (1) Mission Planning and Scheduling, (2) Command Management, (3) Altitude Determination and Sensor Calibration and (4) Spacecraft Support and Engineering Analysis. This paper presents an overview concept for one of these major elements, the Mission Planning and Scheduling subsystem (MPS). The derivation of this concept is described in terms of requirements driven by spacecraft and science instrument characteristics, orbital environment and ground system capabilities. The flowdown of these requirements through the systems analysis process and the definition of MPS interfaces has resulted in the modular grouping of functional subelements depicted in the design implementation approach. The rationale for this design solution is explained and capabilities for the initial prototype system are proposed from the user perspective.
Here, we extend the canonical problems of simulation and optimization of steady-state gas flows in pipeline networks with compressors to the transport of mixtures of highly heterogeneous gases injected throughout a network. Our study is motivated by proposed projects to blend hydrogen generated using clean energy into existing natural gas pipeline systems as part of efforts to reduce the reliance of energy systems on fossil fuels. Flow in a pipe is related to endpoint pressures by a basic Weymouth equation model, with an ideal gas equation of state, where the wave speed depends on the hydrogen concentration. At vertices, in addition to mass balance, we also consider mixing of incoming flows of varying hydrogen concentrations. The problems of interest are the heterogeneous gas flow simulation (HGFS), which determines system pressures and flows given fixed boundary conditions and compressor settings, as well as the heterogeneous gas flow optimization (HGFO), which extremizes an objective by determining optimal boundary conditions and compressor settings. We examine conditions for uniqueness of solutions to the HGFS, as well as compare and contrast mixed-integer and continuous nonlinear programming formulations for the HGFO. We develop computational methods to solve both problems, and examine their performance using four test networks of increasing complexity.
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In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.
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The Information Science Experiment System (ISES) represents a new approach in applying advanced systems technology and techniques to on-board information extraction in the space environment. Basically, what is proposed is a 'black box' attached to the spacecraft data bus or local area network. To the spacecraft the 'black box' appears to be just another payload requiring power, heat rejection, interfaces, adding weight, and requiring time on the data management and communication system. In reality, the 'black box' is a programmable computational resource which eavesdrops on the data network, taking and producing selectable, real-time science data back on the network. This paper will present a brief overview of the ISES Concept and will discuss issues related to applying the ISES to the polar platform and Space Station Freedom. Critical to the operation of ISES is the viability of a payload-like interface to the spacecraft data bus or local area network. Study results that address this question will be reviewed vis-a-vis the solar platform and the core space station. Also, initial results of processing science and other requirements for onboard, real-time information extraction will be presented with particular emphasis on the polar platform. Opportunities for a broader range of applications on the core space station will also be discussed.
Abstract Predicting forced, long‐term radiative feedbacks from internal climate variability has been a decades‐long quest in climate science. We train a convolutional neural network (CNN) to predict annual‐ and global‐mean top of the atmosphere radiation anomalies from time‐varying maps of near‐surface temperature in climate models. Trained on internal variability alone, the nonlinear CNN can predict radiation under strong climate change, outperforms a regularized linear regression approach, and works within and across different climate models. We show with explainable artificial intelligence methods that the CNN draws predictive skill from physically meaningful regions but at much smaller spatial scales than currently assumed.
Reliable near real-time precipitation estimates are essential for monitoring and managing of natural disasters such as floods. Quality of inputs and capability of the retrieval algorithm are two important aspects for developing satellite-based precipitation datasets. Most retrieval algorithms utilize infrared (IR) information as their input due to its fine spatiotemporal resolution and near-instantaneous availability. However, their sole reliance on IR information limits their capability to learn different mechanisms of precipitation during training, resulting in less accurate estimates. Moreover, recent advances in the field of machine learning offer attractive opportunities to improve the precipitation retrieval algorithms. This work investigates the effectiveness of adding geographical information (i.e. latitude and longitude) to IR information and the application of a U-Net-based convolutional neural network for improving the accuracy of retrieval algorithms. This research suggests that applying an appropriate CNN architecture on geographical and IR information provides an opportunity to improve the satellite-based precipitation products.
Dr. Ved Chirayath's seminar will highlight new sensing technologies and airborne platforms he is developing for Earth & Space Science as director of the NASA Silicon Valley Laboratory for Advanced Sensing. His talk will feature recent work on Fluid Lensing, the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions, MiDAR, a next-generation active hyperspectral remote sensing and optical communications instrument, airborne gas sensing of multipollutant combustion sources, and a plasma actuated unmanned aerial vehicle (UAV) that utilized high-voltage dielectric discharge devices to achieve the first plasma controlled flight in history.Fluid Lensing and the NASA FluidCam instrument have been used extensively to provide distortion-free 3D multispectral imagery from UAVs of shallow marine systems around the world. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) as a new method to remotely sense living and nonliving structures in extreme environments as an analog for future Ocean Worlds robotic exploration missions. Finally, Chirayath will present preliminary results from NeMO-Net, a supercomputer-based neural network that uses a citizen science video game for global multimodal coral reef benthic habitat mapping, fusing remote sensing data from Fluid Lensing, MiDAR, NASA's Earth Observing System, and commercial satellites, to better understand the present and past dynamics of shallow marine systems. Together, these maturing technologies present promising new ways in which to explore terrestrial, marine, and aerial systems on Earth, and, ultimately, aid in the search for extraterrestrial life within our solar system and beyond.
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NASA's Museum Alliance is a nationwide network of informal educators at museums, science centers, and planetariums that present NASA information to their local audiences. Begun in 2002 as the Mars Museum Visualization Alliance with advisors from a dozen museums, the network has grown to over 300 people from 200 organizations, including a dozen or so international partners. The network has become a community of practice among these informal educators who work with students, educators, and the general public on a daily basis, presenting information and fielding questions about space exploration. Communications are primarily through an active listserve, regular telecons, and a pass word protected website. Professional development is delivered via telecons and downloadable presentations. Current content offerings include Mars exploration, Cassini, Stardust, Genesis, Deep Impact, Earth observations, STEREO, and missions to explore beyond our solar system.
The 2009 Mars Science Laboratory (MSL) will attempt the first precision landing on Mars using a modified version of the Apollo Earth entry guidance program. The guidance routine, Entry Terminal Point Controller (ETPC), commands the deployment of a supersonic parachute after converging the range to the landing target. For very dispersed cases, ETPC may not converge the range to the target and safely command parachute deployment within Mach number and dynamic pressure constraints. A full-lift up abort can save 85% of these failed trajectories while abandoning the precision landing objective. Though current MSL requirements do not call for an abort capability, an autonomous abort capability may be desired, for this mission or future Mars precision landers, to make the vehicle more robust. The application of artificial neural networks (NNs) as an abort determination technique was evaluated by personnel at the National Aeronautics and Space Administration (NASA) Johnson Space Center (JSC). In order to implement an abort, a failed trajectory needs to be recognized in real time. Abort determination is dependent upon several trajectory parameters whose relationships to vehicle survival are not well understood, and yet the lander must be trained to recognize unsafe situations. Artificial neural networks (NNs) provide a way to model these parameters and can provide MSL with the artificial intelligence necessary to independently declare an abort. Using the 2009 Mars Science Laboratory (MSL) mission as a case study, a non-adaptive NN was designed, trained and tested using Monte Carlo simulations of MSL descent and incorporated into ETPC. Neural network theory, the development history of the MSL NN, and initial testing with severe dust storm entry trajectory cases are discussed in Reference 1 and will not be repeated here. That analysis demonstrated that NNs are capable of recognizing failed descent trajectories and can significantly increase the survivability of MSL for very dispersed cases. NN testing was then broadened to evaluate fully dispersed entry trajectories. The NN correctly classified 99.7% of descent trajectories as abort or nonabort and reduced the probability of an unsafe parachute deployment by 83%. This second, broader testing phase is discussed in this paper.