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At least 1,531 records · Page 85

Evolution of a high-performance storage system based on magnetic tape instrumentation recorders

In order to provide transparent access to data in network computing environments, high performance storage systems are getting smarter as well as faster. Magnetic tape instrumentation recorders contain an increasing amount of intelligence in the form of software and firmware that manages the processes of capturing input signals and data, putting them on media and then reproducing or playing them back. Such intelligence makes them better recorders, ideally suited for applications requiring the high-speed capture and playback of large streams of signals or data. In order to make recorders better storage systems, intelligence is also being added to provide appropriate computer and network interfaces along with services that enable them to interoperate with host computers or network client and server entities. Thus, recorders are evolving into high-performance storage systems that become an integral part of a shared information system. Data tape has embarked on a program with the Caltech sponsored Concurrent Supercomputer Consortium to develop a smart mass storage system. Working within the framework of the emerging IEEE Mass Storage System Reference Model, a high-performance storage system that works with the STX File Server to provide storage services for the Intel Touchstone Delta Supercomputer is being built. Our objective is to provide the required high storage capacity and transfer rate to support grand challenge applications, such as global climate modeling.

Peters, Bruce↗

An integrated manifold learning approach for high-dimensional data feature extractions and its applications to online process monitoring of additive manufacturing

As an effective dimension reduction and feature extraction technique, manifold learning has been successfully applied to high-dimensional data analysis. With the rapid development of sensor technology, a large amount of high-dimensional data such as image streams can be easily available. Thus, a promising application of manifold learning is in the field of sensor signal analysis, particular for the applications of online process monitoring and control using high-dimensional data. The objective of this study is to develop a manifold learning-based feature extraction method for process monitoring of Additive Manufacturing (AM) using online sensor data. Due to the non-parametric nature of most existing manifold learning methods, their performance in terms of computational efficiency, as well as noise resistance has yet to be improved. To address this issue, this study proposes an integrated manifold learning approach termed multi-kernel metric learning embedded isometric feature mapping (MKML-ISOMAP) for dimension reduction and feature extraction of online high-dimensional sensor data such as images. Based on the extracted features with the utilization of supervised classification and regression methods, an online process monitoring methodology for AM is implemented to identify the actual process quality status. Finally, in the numerical simulation and real-world case studies, the proposed method demonstrates excellent performance in both prediction accuracy and computational efficiency.

36 MATERIALS SCIENCE↗

Automated Performance Characterization of DSN System Frequency Stability Using Spacecraft Tracking Data

This software provides an automated capability to measure and qualify the frequency stability performance of the Deep Space Network (DSN) ground system, using daily spacecraft tracking data. The results help to verify if the DSN performance is meeting its specification, therefore ensuring commitments to flight missions; in particular, the radio science investigations. The rich set of data also helps the DSN Operations and Maintenance team to identify the trends and patterns, allowing them to identify the antennas of lower performance and implement corrective action in a timely manner. Unlike the traditional approach where the performance can only be obtained from special calibration sessions that are both time-consuming and require manual setup, the new method taps into the daily spacecraft tracking data. This new approach significantly increases the amount of data available for analysis, roughly by two orders of magnitude, making it possible to conduct trend analysis with good confidence. The software is built with automation in mind for end-to-end processing. From the inputs gathering to computation analysis and later data visualization of the results, all steps are done automatically, making the data production at near zero cost. This allows the limited engineering resource to focus on high-level assessment and to follow up with the exceptions/deviations. To make it possible to process the continual stream of daily incoming data without much effort, and to understand the results quickly, the processing needs to be automated and the data summarized at a high level. Special attention needs to be given to data gathering, input validation, handling anomalous conditions, computation, and presenting the results in a visual form that makes it easy to spot items of exception/ deviation so that further analysis can be directed and corrective actions followed.

Pham, Timothy T.↗

Automated Performance Characterization of DSN System Frequency Stability Using Spacecraft Tracking Data

This software provides an automated capability to measure and qualify the frequency stability performance of the Deep Space Network (DSN) ground system, using daily spacecraft tracking data. The results help to verify if the DSN performance is meeting its specification, therefore ensuring commitments to flight missions; in particular, the radio science investigations. The rich set of data also helps the DSN Operations and Maintenance team to identify the trends and patterns, allowing them to identify the antennas of lower performance and implement corrective action in a timely manner. Unlike the traditional approach where the performance can only be obtained from special calibration sessions that are both time-consuming and require manual setup, the new method taps into the daily spacecraft tracking data. This new approach significantly increases the amount of data available for analysis, roughly by two orders of magnitude, making it possible to conduct trend analysis with good confidence. The software is built with automation in mind for end-to-end processing. From the inputs gathering to computation analysis and later data visualization of the results, all steps are done automatically, making the data production at near zero cost. This allows the limited engineering resource to focus on high-level assessment and to follow up with the exceptions/deviations. To make it possible to process the continual stream of daily incoming data without much effort, and to understand the results quickly, the processing needs to be automated and the data summarized at a high level. Special attention needs to be given to data gathering, input validation, handling anomalous conditions, computation, and presenting the results in a visual form that makes it easy to spot items of exception/deviation so that further analysis can be directed and corrective actions followed.

Pham, Timothy T.↗

High-Speed Data Recorder for Space, Geodesy, and Other High-Speed Recording Applications

A high-speed data recorder and replay equipment has been developed for reliable high-data-rate recording to disk media. It solves problems with slow or faulty disks, multiple disk insertions, high-altitude operation, reliable performance using COTS hardware, and long-term maintenance and upgrade path challenges. The current generation data recor - ders used within the VLBI community are aging, special-purpose machines that are both slow (do not meet today's requirements) and are very expensive to maintain and operate. Furthermore, they are not easily upgraded to take advantage of commercial technology development, and are not scalable to multiple 10s of Gbit/s data rates required by new applications. The innovation provides a softwaredefined, high-speed data recorder that is scalable with technology advances in the commercial space. It maximally utilizes current technologies without being locked to a particular hardware platform. The innovation also provides a cost-effective way of streaming large amounts of data from sensors to disk, enabling many applications to store raw sensor data and perform post and signal processing offline. This recording system will be applicable to many applications needing realworld, high-speed data collection, including electronic warfare, softwaredefined radar, signal history storage of multispectral sensors, development of autonomous vehicles, and more.

Taveniku, Mikael↗

20 Year Daily Average Modeled Velocity and Discharge for Openwater Season at Five Communities on Kuskokwim River, Alaska

This dataset includes modeled velocity and discharge at five communities in the middle Kuskokwim River region: Aniak, Chuathbaluk, Crooked Creek, Red Devil and Stony River. Modeled velocities and discharge represent daily averages calculated for the openwater season (OWS) from June 1 - October 18 over the 20 year period 2000-2019 using the raw data described below and included in this archive; full details of methodology are described in (Brown et al. submitted to Renewable Energy). Raw data inputs to inform the modeling process include in-situ measurements of 1) discharge with an acoustic Doppler current meter (ADCP, 600kHz Workhorse Rio Grande by Teledyne RD Instruments) and a global positioning receiver (GPS, Trimble 5700, 5800 and R8) utilizing Real Time Kinematic (RTK) GPS mode over 1-2 days at each site in 2009 or 2010 (Ravens 2014), and 2) river stage with a water level logger (HOBO U20-001-01 by Onset) over 2-9 weeks at each site (Ravens 2014), 3) in addition to a 20 year long-term discharge record collected at the USGS stream gage site in Crooked Creek (USGS 2016). Raw data (discharge and stage) are included in this archive for two additional communities: Lower Kalskag and Sleetmute, where modeled velocities were not calculated due to equipment failure or loss. The USGS stream gage data at Crooked Creek (USGS 2016) and stream gage methodology (Turnipseed and Sauer 2010) are publicly available online, so the data are not duplicated here.

16 TIDAL AND WAVE POWER↗

Streaming Analytics for Anomaly Detection in Large-Scale Data

Anomalous behavior poses serious risks to assured performance and reliability of complex, high-consequence systems. For spaceborne assets and their state-of-health (SOH) telemetry, the challenges of high-dimensional data of varying data types are compounded by computational limitations from size, weight, and power (SWaP) constraints as well as data availability. Automated anomaly detection methods tend to perform poorly under these constraints, while current operational approaches can introduce delays in response time due to the manual, retrospective processes for understanding system failures. As a result, presently deployed space systems, and those deployed in the near future, face situations where mission operations might be delayed or only be able to operate under degraded capabilities. Here, we examine a near-term lightweight solution that provides real-time detection capabilities for rare events and assess state-of-the-art anomaly detection techniques against real SOH telemetry from space platforms. This report describes our methodology and research, which could support more automated capabilities for comprehensive space operations as well as for other resource-constrained edge applications.

97 MATHEMATICS AND COMPUTING↗

Chemical nonequilibrium effects on the inviscid flow in the windward plane of symmetry of two simplified shuttle configurations

A theoretical study was made to delineate the effects of thermochemical nonequilibrium in the inviscid flow field of a representative space shuttle orbiter configuration. The study was based on experimental pressure data which was used as an input to a stream tube computer program. The pressure data from two configurations are tabulated. Calculations were restricted to the windward plane of symmetry and the calculations covered an altitude range from 200,000 to 250,000 feet at velocities of 16,000 to 24,000 feet per second respectively. Angles of attack of 20 and 40 degrees were included. The calculations show that the nonequilibrium effects are confined largely to the entropy layer expect at the highest altitude, where significant nonequilibrium effects are observed in the entire inviscid flow field.

Lordi, J. A.↗

Crew Field Notes: A New Tool for Planetary Surface Exploration

The Desert Research and Technology Studies (DRATS) field tests of 2010 focused on the simultaneous operation of two rovers, a historical first. The complexity and data volume of two rovers operating simultaneously presented significant operational challenges for the on-site Mission Control Center, including the real time science support function. The latter was split into two "tactical" back rooms, one for each rover, that supported the real time traverse activities; in addition, a "strategic" science team convened overnight to synthesize the day's findings, and to conduct the strategic forward planning of the next day or days as detailed in [1, 2]. Current DRATS simulations and operations differ dramatically from those of Apollo, including the most evolved Apollo 15-17 missions, due to the advent of digital technologies. Modern digital still and video cameras, combined with the capability for real time transmission of large volumes of data, including multiple video streams, offer the prospect for the ground based science support room(s) in Mission Control to witness all crew activities in unprecedented detail and in real time. It was not uncommon during DRATS 2010 that each tactical science back room simultaneously received some 4-6 video streams from cameras mounted on the rover or the crews' backpacks. Some of the rover cameras are controllable PZT (pan, zoom, tilt) devices that can be operated by the crews (during extensive drives) or remotely by the back room (during EVAs). Typically, a dedicated "expert" and professional geologist in the tactical back room(s) controls, monitors and analyses a single video stream and provides the findings to the team, commonly supported by screen-saved images. It seems obvious, that the real time comprehension and synthesis of the verbal descriptions, extensive imagery, and other information (e.g. navigation data; time lines etc) flowing into the science support room(s) constitute a fundamental challenge to future mission operations: how can one analyze, comprehend and synthesize -in real time- the enormous data volume coming to the ground? Real time understanding of all data is needed for constructive interaction with the surface crews, and it becomes critical for the strategic forward planning process.

Horz, Friedrich↗

DeepCare: Improving Patient Care using Deep Learning on Electronic Health Records

Coordinating patient care using electronic health records (EHR) data presents an exciting but formidable opportunity in data extraction, analysis and modeling. Traditional methods use a manual feature driven approach to model patients with age, family history and symptoms to predict disease outcomes. We propose a novel approach to model patients based on their streaming electronic health records data combined with information from medical knowledge bases, which has been gained over years of medical research. Using a combination of representation learning and long short term memory (LSTM) networks we plan to model patient evolution over time, leading to more accurate and individualized predictive models for patient’s diseases. Our approach will be transformative in providing critical decision support for patient care, enabling accurate understanding and evolution of diseases in patients.

60 APPLIED LIFE SCIENCES↗

Ice Flow in the Northeast Greenland Ice Stream

As the use of satellite data for icesheet research has accelerated, new discoveries have changed the way that we think about ice discharge, and new tools have changed the way that we measure it. We have begun to apply a combination of remote sensing and ice sheet modeling techniques to study ice flow in the recently identified northeast Greenland ice stream. This ice stream shows evidence of organized flow far into the interior and has low-slope areas of rapid flow and regions of enhanced shear that resemble the ice streams of West Antarctica. The onset of rapid flow close to the ice divide and the evolution of its flow pattern, however, make this ice stream unique. Our investigation is aimed at increasing our understanding of this ice stream and its origins and role in the discharge of ice from the ice sheet and the effects that discharge may have on the history and evolution of the ice in northern Greenland. The ice flow data comes from satellite radar interferometry tied to reference points. We have applied this technique to measure the velocity over the entire ice stream, which is more than 700 km in length. The velocity contours reveal distinct, well-developed shear margins, which are coincident with linear features in the Synthetic Aperture Radar (SAR) amplitude imagery. It is surprising to see such defined, enhanced flow so far inland and so close to the ice divide. The velocity data and topography data are being used to feed a modeling effort that allows us to study the dynamics and character rapid flow. Additional information is contained in the original.

Joughin, Ian↗

The influence of burn severity on dissolved organic carbon concentrations across a stream network differs based on seasonal wetness conditions

Large, high-severity wildfires in many regions across the globe have increased concerns about their impacts on carbon cycling in watersheds. Altered sources of carbon and changes in catchment hydrology after wildfire can lead to shifts in dissolved organic carbon (DOC) concentrations in streams, which can have negative impacts on aquatic ecosystem health and downstream drinking-water treatment. Despite its importance, post-fire DOC responses remain relatively unconstrained in the literature, and we lack critical knowledge of how burn severity, landscape elements, and climate interact to affect DOC concentrations. To improve our understanding of the impact of burn severity on DOC concentrations, we measured DOC at 129 sites across a stream network extending upstream, within, and downstream of a large, high-severity wildfire in Oregon, USA. We collected samples across the study sub-basin during four distinct seasonal wetness conditions. We used our high-spatial-resolution data to develop spatial stream network (SSN) models to predict DOC across the stream network and to improve our understanding of the controls on DOC concentrations. Spatially, we found no obvious wildfire signal – instead, we observed a pattern of increasing DOC concentrations from the high-elevation headwaters to the sub-basin outlet, while the mainstem maintained consistently low DOC concentrations. This suggests that effects from large wildfires may be “averaged” out at higher stream orders and larger spatial scales. When we grouped DOC concentrations by burn severity group, we observed a significant decrease in the variability of DOC concentrations in the moderate and high burn severity sub-catchments. However, our SSN models were able to predict decreases in DOC concentrations with increases in burn severity across the stream network. Decreases in DOC concentrations were also highly variable across seasonal wetness conditions, with the greatest (–1.40 to –1.64 mg L –1 ) decrease occurring in the high-severity group during the wetting season. Additionally, our models indicated that in all seasons, baseflow index was more influential in predicting DOC concentrations than burn severity was, indicating that groundwater discharge can obscure the impacts of wildfire in a stream network. Overall, our results suggested that landscape characteristics can regulate the DOC response to wildfire. Moreover, our results also indicated that the seasonal timing of sampling can influence the observed response of DOC concentrations to wildfire.

54 ENVIRONMENTAL SCIENCES↗

Wave-current interaction near the Gulf Stream during the surface wave dynamics experiment

This paper presents a case study on the wave-current interaction near the local curvature of a Gulf Stream meander. The wave data were obtained from in situ measurements by a pitch-roll discus buoy during the Surface Wave Dynamics Experiment (SWADE) conducted off Wallops Island, Virginia, from October 1990 to March 1991. Owing to the advection of the Gulf Stream by the semidiurnal tide, the discus buoy was alternately located outside and inside the Gulf Stream. The directional wave measurements from the buoy show the changes in wave direction, wave energy, and directional spreading when waves encountered the current in the Gulf Stream meanders. A wave refraction model, using the ray-tracing method with an estimated Gulf Stream velocity field and meandering condition, was used to simulate wave refraction patterns and to estimate wave parameters at relative locations corresponding to buoy measurements. The numerical simulation shows that a focusing zone of wave rays was formed near the boundary and behind the crest of a simulated Gulf Stream meander. The focusing of wave rays causes changes in wave direction, increases in wave energy, and decreases in wave directional spreading, which are in good agreement with the results from the buoy measurements.

Wang, David W.↗

Analog Testing of Operations Concepts for Mitigation of Communication Latency During Human Space Exploration

OBJECTIVES: NASA Extreme Environment Mission Operations (NEEMO) is an underwater spaceflight analog that allows a true mission‐like operational environment and uses buoyancy effects and added weight to simulate different gravity levels. Three missions were undertaken from 2014‐2015, NEEMO's 18‐20. All missions were performed at the Aquarius undersea research habitat. During each mission, the effects of varying operations concepts and tasks type and complexity on representative communication latencies associated with Mars missions were studied. METHODS: 12 subjects (4 per mission) were weighed out to simulate near‐zero or partial gravity extravehicular activity (EVA) and evaluated different operations concepts for integration and management of a simulated Earth‐based science backroom team (SBT) to provide input and direction during exploration activities. Exploration traverses were planned in advance based on precursor data collected. Subjects completed science‐related tasks including presampling surveys, geologic‐based sampling, and marine‐based sampling as a portion of their tasks on saturation dives up to 4 hours in duration that were to simulate extravehicular activity (EVA) on Mars or the moons of Mars. One‐way communication latencies, 5 and 10 minutes between space and mission control, were simulated throughout the missions. Objective data included task completion times, total EVA times, crew idle time, translation time, SBT assimilation time (defined as time available for SBT to discuss data/imagery after it has been collected, in addition to the time taken to watch imagery streaming over latency). Subjective data included acceptability, simulation quality, capability assessment ratings, and comments. RESULTS: Precursor data can be used effectively to plan and execute exploration traverse EVAs (plans included detailed location of science sites, high‐fidelity imagery of the sites, and directions to landmarks of interest within a site). Operations concepts that allow for presampling surveys enable efficient traverse execution and meaningful Mission Control Center (MCC) interaction across long communication latencies and can be done with minimal crew idle time. Imagery and information from the EVA crew that is transmitted real‐time to the intravehicular (IV) crewmember(s) can be used to verify that exploration traverse plans are being executed correctly. That same data can be effectively used by MCC (across comm latency) to provide further instructions to the crew from a SBT on sampling priorities, additional tasks, and changes to the plan. Text / data capabilities are preferred over voice capabilities between MCC and IV when executing exploration traverse plans over communication latency. Autonomous crew planning tools can be effective at modifying existing plans if the objectives and constraints are clearly defined.

Chappell, Steven P.↗

Developing an Effective Model for Predicting Spatially and Temporally Continuous Stream Temperatures from Remotely Sensed Land Surface Temperatures

Although water temperature is important to stream biota, it is difficult to collect in a spatially and temporally continuous fashion. We used remotely-sensed Land Surface Temperature (LST) data to estimate mean daily stream temperature for every confluence-to-confluence reach in the John Day River, OR, USA for a ten year period. Models were built at three spatial scales: site-specific, subwatershed, and basin-wide. Model quality was assessed using jackknife and cross-validation. Model metrics for linear regressions of the predicted vs. observed data across all sites and years: site-specific r2 = 0.95, Root Mean Squared Error (RMSE) = 1.25 °C; subwatershed r2 = 0.88, RMSE = 2.02 °C; and basin-wide r2 = 0.87, RMSE = 2.12 °C. Similar analyses were conducted using 2012 eight-day composite LST and eight-day mean stream temperature in five watersheds in the interior Columbia River basin. Mean model metrics across all basins: r2 = 0.91, RMSE = 1.29 °C. Sensitivity analyses indicated accurate basin-wide models can be parameterized using data from as few as four temperature logger sites. This approach generates robust estimates of stream temperature through time for broad spatial regions for which there is only spatially and temporally patchy observational data, and may be useful for managers and researchers interested in stream biota.

McNyset, Kristina↗

Weakly supervised anomaly detection in the Milky Way

ABSTRACT Large-scale astrophysics data sets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searches. To this end, we demonstrate how Classification Without Labels (CWoLa), a weakly supervised anomaly detection method, can help identify cold stellar streams within the more than one billion Milky Way stars observed by the Gaia satellite. CWoLa operates without the use of labelled streams or knowledge of astrophysical principles. Instead, it uses a classifier to distinguish between mixed samples for which the proportions of signal and background samples are unknown. As a proof of concept, we demonstrate that this computationally lightweight strategy is able to detect both simulated streams and the known stream GD-1 in data. Originally designed for high-energy collider physics, this technique may have broad applicability within astrophysics as well as other domains interested in identifying localized anomalies.

79 ASTRONOMY AND ASTROPHYSICS↗

The Infrared Astronomical Satellite /IRAS/ Scientific Data Analysis System /SDAS/ sky flux subsystem

The sky flux subsystem of the Infrared Astronomical Satellite Scientific Data Analysis System is described. Its major output capabilities are (1) the all-sky lune maps (8-arcminute pixel size), (2) galactic plane maps (2-arcminute pixel size) and (3) regional maps of small areas such as extended sources greater than 1-degree in extent. The major processing functions are to (1) merge the CRDD and pointing data, (2) phase the detector streams, (3) compress the detector streams in the in-scan and cross-scan directions, and (4) extract data. Functional diagrams of the various capabilities of the subsystem are given. Although this device is inherently nonimaging, various calibrated and geometrically controlled imaging products are created, suitable for quantitative and qualitative scientific interpretation.

Stagner, J. R.↗