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At least 1,675 records · Page 93
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Collaborative Platforms Aid Emergency Decision Making

Terra. Aqua. Cloudsat. Landsat. NASA runs and partners in many missions dedicated to monitoring the Earth, and the tools used in these missions continuously return data on everything from shifts in temperature to cloud formation to pollution levels over highways. The data are of great scientific value, but they also provide information that can play a critical role in decision making during times of crisis. Real-time developments in weather, wind, ocean currents, and numerous other conditions can have a significant impact on the way disasters, both natural and human-caused, unfold. "NASA has long recognized the need to make its data from real-time sources compatible and accessible for the purposes of decision making," says Michael Goodman, who was Disasters Program manager at NASA Headquarters from 2009-2012. "There are practical applications of NASA Earth science data, and we d like to accelerate the use of those applications." One of the main obstacles standing in the way of eminently practical data is the fact that the data from different missions are collected, formatted, and stored in different ways. Combining data sets in a way that makes them useful for decision makers has proven to be a difficult task. And while the need for a collaborative platform is widely recognized, very few have successfully made it work. Dave Jones, founder and CEO of StormCenter Communications Inc., which consults with decision makers to prepare for emergencies, says that "when I talk to public authorities, they say, If I had a nickel for every time someone told me they had a common operating platform, I d be rich. But one thing we ve seen over the years is that no one has been able to give end users the ability to ingest NASA data sets and merge them with their own."

Source record↗
NASA NTRS2012

Cost-Effective Telemetry and Command Ground Systems Automation Strategy for the Soil Moisture Active Passive (SMAP) Mission

Soil Moisture Active Passive (SMAP) is an Earth-orbiting, remote-sensing NASA mission slated for launch in 2014.[double dagger] The ground data system (GDS) being developed for SMAP is composed of many heterogeneous subsystems, ranging from those that support planning and sequencing to those used for real-time operations, and even further to those that enable science data exchange. A full end-to-end automation of the GDS may result in cost savings during mission operations, but it would require a significant upfront investment to develop such comprehensive automation. As demonstrated by the Jason-1 and Wide-field Infrared Survey Explorer (WISE) missions, a measure of "lights-out" automation for routine, orbital pass ground operations can still reduce mission cost through smaller staffing of operators and limited work hours. The challenge, then, for the SMAP GDS engineering team is to formulate an automated operations strategy--and corresponding system architecture--to minimize operator intervention during operations, while balancing the development cost associated with the scope and complexity of automation. This paper discusses the automated operations approach being developed for the SMAP GDS. The focus is on automating the activities involved in routine passes, which limits the scope to real-time operations. A key subsystem of the SMAP GDS--NASA's AMMOS Mission Data Processing and Control System (AMPCS)--provides a set of capabilities that enable such automation. Also discussed are the lights-out pass automations of the Jason-1 and WISE missions and how they informed the automation strategy for SMAP. The paper aims to provide insights into what is necessary in automating the GDS operations for Earth satellite missions.

remote-sensing↗
NASA NTRSDate not supplied

TPSAS-NF1676L-32124-DND

The NASA DEVELOP National Program seeks to simultaneously build capacity to use Earth observations in early career and transitioning professionals while building capacity with institutional partners to apply Earth observations in conducting operations, making decisions, or informing policy. This is done through 10-week feasibility projects, conducted by the DEVELOP teams in collaboration with decision makers. The program carries out 60-80 projects each year, engaging with over 120 partners from local, state, and federal governments to academic institutions and NGOs. To best understand project partner needs, projects begin with a thorough proposal development process in which partners share their current practices, needs, and capabilities. Throughout the project life cycle, the DEVELOP teams engage with the partners to ensure communication, feedback, and understanding. DEVELOP’s model of conducting rapid feasibility projects is an effective way to show decision maker show they can use NASA Earth science data in new ways to help them make informed decisions. Because the projects are conducted by a DEVELOP team in collaboration with end users, the partners are introduced to new data products and methodologies that they otherwise might not be able to explore within their own resource constraints. This presentation will discuss project examples, success stories, and best practices for engaging with decision makers on applied science projects.

Amanda Clayton↗
DOE OSTI2023

Neural network-based control of an ultrafast laser

With the recent advances in machine learning (ML) and data science (DS), the control, modeling, and analysis of these complex systems continues to improve. In this work, we report on the optimization of the intensity of a femtosecond laser using feedforward neural networks (FFNN) that model the input–output relationships of the data. The input parameters of the system were optimized to achieve the required performance of the femtosecond laser. We propose a neural network-based control system to model the relationship between the spectral amplitude and phase of the input laser pulse at the amplifier input and the shape of the output pulse. Low-jitter laser parameter inputs and the resulting laser pulse duration were modeled, and the resulting correlation between the input and output data was used to optimize the laser pulse. Here, we demonstrate improved processing and laser control performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

ESS-DIVE Reporting Format for Dataset Package Metadata

ESS-DIVE’s (Environmental Systems Science Data Infrastructure for a Virtual Ecosystem) dataset metadata reporting format is intended to compile information about a dataset (e.g., title, description, funding sources) that can enable reuse of data submitted to the ESS-DIVE data repository. The files contained in this dataset include instructions (dataset_metadata_guide.md and README.md) that can be used to understand the types of metadata ESS-DIVE collects. The data dictionary (dd.csv) follows ESS-DIVE’s file-level metadata reporting format and includes brief descriptions about each element of the dataset metadata reporting format. This dataset also includes a terminology crosswalk (dataset_metadata_crosswalk.csv) that shows how ESS-DIVE’s metadata reporting format maps onto other existing metadata standards and reporting formats.Data contributors to ESS-DIVE can provide this metadata by manual entry using a web form or programmatically via ESS-DIVE’s API (Application Programming Interface). A metadata template (dataset_metadata_template.docx or dataset_metadata_template.pdf) can be used to collaboratively compile metadata before providing it to ESS-DIVE.Since being incorporated into ESS-DIVE’s data submission user interface, ESS-DIVE’s dataset metadata reporting format, has enabled features like automated metadata quality checks, and dissemination of ESS-DIVE datasets onto other data platforms including Google Dataset Search and DataCite.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2014

Research Data Alliance: Understanding Big Data Analytics Applications in Earth Science

The Research Data Alliance (RDA) enables data to be shared across barriers through focused working groups and interest groups, formed of experts from around the world - from academia, industry and government. Its Big Data Analytics (BDA) interest groups seeks to develop community based recommendations on feasible data analytics approaches to address scientific community needs of utilizing large quantities of data. BDA seeks to analyze different scientific domain applications (e.g. earth science use cases) and their potential use of various big data analytics techniques. These techniques reach from hardware deployment models up to various different algorithms (e.g. machine learning algorithms such as support vector machines for classification). A systematic classification of feasible combinations of analysis algorithms, analytical tools, data and resource characteristics and scientific queries will be covered in these recommendations. This contribution will outline initial parts of such a classification and recommendations in the specific context of the field of Earth Sciences. Given lessons learned and experiences are based on a survey of use cases and also providing insights in a few use cases in detail.

Riedel, Morris↗
NASA NTRS2022

Geodetic Investigations of the Mission Concept MAGIC to Reveal Callisto’s Internal Structure

Geodetic and geophysical investigations of the Galilean moon Callisto address fundamental questions regarding the formation and evolution of the Jovian system. Callisto's evolution and internal structure appear to significantly differ from the other Jovian satellites. Similarly-sized Ganymede is a highly evolved ice-rock moon with a differentiated interior, intrinsic magnetic field, and abundant surface evidence of internal activity. In contrast, Callisto's surface is ancient, and Galileo spacecraft data suggest its interior is only incompletely differentiated, despite the presumed presence of a sub-surface ocean. These properties make Callisto uniquely able to constrain the timing and nature of the Jovian system formation. The Magnetics, Altimetry, Gravity, and Imaging of Callisto (MAGIC) mission concept is conceived to fully characterize the properties of this enigmatic moon from its deep interior to the icy shell. Three main instruments are included as a scientific payload. Highly accurate measurements of Callisto's topography, magnetic field, and morphology are obtained by the onboard laser altimeter, magnetometer, and camera, respectively. The telecommunication system supports an additional gravity and radio science investigation. Long- and short-wavelength gravity anomalies afford powerful constraints on internal differentiation and the properties of the hydrosphere (water and ice). Comprehensive numerical simulations and covariance analyses of MAGIC mission scenarios presented in this paper show that the gravitational degree-2 normalized coefficients and the pole obliquity enable the determination of the moment of inertia with an accuracy better than 0.015%. The combination of gravity and altimetry measurements acquired by MAGIC are essential to the characterization of Callisto's interior if – as is likely – the degree-2 gravity includes non-hydrostatic terms. MAGIC's radio science data yield the estimation of Callisto's gravity field with spatial resolutions of <100 km. The combination of gravitational and deformation tides that are retrieved by the radio science and altimetry investigations, respectively, leads to the recovery of the rigid ice shell thickness to within ∼3 km. Together these datasets would resolve ambiguities inherent in Galileo flyby data, revealing Callisto's interior structure as well as the existence and properties of its postulated internal ocean.

Antonio Genova↗
DOE OSTI2023

The Quick Rise and Fall of LK-99 as a Room Temperature Superconductor [Slides]

A swift determination of superconductivity vs non-superconductivity of LK-99 demonstrates the importance of reproducibility in science. This has benefited from 4 decades study of high-temperature cuprate mechanism. Holy grail of room-temperature ambient-pressure superconductor remains to be pursued. Caution is needed about DFT calculations. Crucial old experimental data should be referenced. With the existing amount of experimentally discovered superconductors, could data science make a stride?

36 MATERIALS SCIENCE↗
NASA NTRS1987

AVIRIS onboard data handling and control

The timing and flow of detector and ancillary data for the Airborne Visible/Infrared imaging spectrometer (AVIRIS) are controlled within the instrument by its digital electronics assembly. In addition to providing detector and signal chain timing, the digital electronics receives, formats, and rate-buffers digitized science data; collects and formats ancillary (calibration and engineering) data; and merges both into a single tape record. Overall AVIRIS data handling is effected by a combination of dedicated digital electronics to control instrument timing, image data flow, and data rate buffering and a microcomputer programmed to handle real-time control of instrument mechanisms and the coordinated preparation of ancillary data.

Steinkraus, Ronald E.↗
NASA NTRS1988

AVIRIS onboard data handling and control

The timing and flow of detector and ancillary data for the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) are controlled within the instrument by its digital electronics assembly. In addition to providing detector and signal chain timing, the digital electronics receives, formats, and rate-buffers digitized science data; collects and formats ancillary (calibration and engineering) data; and merges both into a single tape record. Overall AVIRIS data handling is effected by a combination of dedicated digital electronics to control instrument timing, image data flow, and data rate buffering and a microcomputer programmed to handle real-time control of instrument mechanisms and the coordinated preparation of ancillary data.

Steinkraus, Ronald E.↗
NASA NTRS2015

Ultra-Compact Ka-Band Parabolic Deployable Antenna for RADAR and Interplanetary CubeSats

Over the past several years, technology and launch opportunities for CubeSats have exploded, enabling a wide variety of missions. However, as instruments become more complex and CubeSats travel deeper into space, data communication rates become an issue. To solve this challenge, JPL has initiated a research and technology development effort to design a 0.5 meter Ka-band parabolic deployable antenna (KaPDA) which would stow in 1.5U (10 x 10 x 15 cu cm) and provide 42dB of gain (50% efficiency). A folding rib architecture and dual reflector Cassegrainian design was selected as it best balances RF gain and stowed size. The design implements an innovative telescoping waveguide and gas powered deployment. RF simulations show that after losses, the antenna would have over 42 dB gain, supported by preliminary test results. KaPDA would create opportunities for a host of new CubeSat missions by allowing high data rate communication which would enable using high fidelity instruments or venturing further into deep space, including potential interplanetary missions. Additionally KaPDA would provide a solution for other small antenna needs and the opportunity to obtain Earth science data. This paper discusses the design challenges encountered, the architecture of the solution, and the antennas expected performance capabilities.

Sauder, Jonathan↗
NASA NTRSDate not supplied

Anomaly Detection in Flight Operational Data Using Deep Learning

In this session, we demonstrate two recently developed deep learning models for anomaly detection in flight operational data by the Data Sciences Group at NASA Ames Research Center. The first model is Convolutional Variational Auto-Encoder (CVAE) [1], which is an unsupervised deep encoder-decoder model, designed specifically for finding anomalies in heterogeneous multivariate time series data. We will demonstrate its application to finding anomalies in streaming data from NASA’s Digital Information Platform’s Fuser source. CVAE identifies data instances that are not representative of expected nominal behavior as anomalous. Since it is an unsupervised approach, the flagged anomalies will need to be reviewed by the subject matter experts (SMEs) for validation and labeling and is designed to assist with vulnerability discovery within Safety Monitoring System programs. The second model is Robust and Explainable Semi-supervised Anomaly Detection (RESAD) model [2], which builds on CVAE to allow learning from both minimally labeled data (previously reviewed by the SMEs) as well as majority unlabeled data. RESAD takes advantage of graph theoretic techniques to propagate the labels from the labeled data to the unlabeled data based on a pre-defined similarity metric and structures the learned feature space from flight time-series so that data of the same class would cluster tightly together. This model characteristic is enabled by training with an augmented loss function and allows learning of a more informative feature space for down-stream tasks such as search and active learning. We demonstrate RESAD using data from the NASA DASHlink project [3].

anomaly detection↗
DOE OSTI2023

Science &Technology Review (October/November 2022)

Livermore researchers seek to understand the disease mechanism of amyotrophic lateral sclerosis (ALS) and, with that knowledge, identify therapies to cure this always-fatal disease. As the article beginning on p. 4 describes, the ALS research effort combines Laboratory core competencies in bioscience, engineering, computational modeling, and data science.

36 MATERIALS SCIENCE↗
NASA NTRS2000

Earth Science Enterprise Scientific Data Purchase Project: Verification and Validation

This paper presents viewgraphs on the Earth Science Enterprise Scientific Data Purchase Project's verification,and validation process. The topics include: 1) What is Verification and Validation? 2) Why Verification and Validation? 3) Background; 4) ESE Data Purchas Validation Process; 5) Data Validation System and Ingest Queue; 6) Shipment Verification; 7) Tracking and Metrics; 8) Validation of Contract Specifications; 9) Earth Watch Data Validation; 10) Validation of Vertical Accuracy; and 11) Results of Vertical Accuracy Assessment.

Jenner, Jeff↗
DOE OSTI2023

Resource frugal optimizer for quantum machine learning

Quantum-enhanced data science, also known as quantum machine learning (QML), is of growing interest as an application of near-term quantum computers. Variational QML algorithms have the potential to solve practical problems on real hardware, particularly when involving quantum data. However, training these algorithms can be challenging and calls for tailored optimization procedures. Specifically, QML applications can require a large shot-count overhead due to the large datasets involved. In this work, we advocate for simultaneous random sampling over both the dataset as well as the measurement operators that define the loss function. We consider a highly general loss function that encompasses many QML applications, and we show how to construct an unbiased estimator of its gradient. This allows us to propose a shot-frugal gradient descent optimizer called Refoqus (REsource Frugal Optimizer for QUantum Stochastic gradient descent). Our numerics indicate that Refoqus can save several orders of magnitude in shot cost, even relative to optimizers that sample over measurement operators alone.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1983

The Galileo spacecraft system design

The Galileo Orbiter differs markedly from its Mariner, Viking and Voyager predecessors in many aspects of its design, as dictated by its unique mission characteristics. Novel design features include a dual spin configuration, long life bipropellant propulsion, radioisotope thermoelectric generators, and a star scanner. Other major changes are related to Space Shuttle Orbiter compatibility. The selection of a distributed data system employing a high speed data bus represents a major advance over Voyager systems. The command, control and data handling functions for the Orbiter are merged into a single software-controlled subsystem. The paramount importance of software development in the design of Galileo subsystems is noted, in view of the requirement for total engineering and science data acquisition process reprogrammability.

Jones, C. P.↗
NASA NTRS2016

From Toes to Top-of-the-Atmosphere: Fowler Sneaker Index

Fowler Sneaker Index (FSI), developed by a NASA summer intern, is a new Ocean Color application that facilitates continuous monitoring of environmental conditions in the Chesapeake Bay. It builds on three decades of citizen science data collected by former Maryland State Senator Bernie Fowler, during his yearly "Wade-ins in the Patuxent River". FSI demonstrates how NASA's Earth-observing tools, in combination with a concerned and engaged public, can take science from the tips of our toes-to-top-of the atmosphere and back.

Optics Express↗
NASA NTRS2017

Infrared Free Space Communication - The Autonomous Testing of Free Space Infrared Communication

Fiber optics has been a winning player in the game of high-speed communication and data transfer in cable connections. Yet, in free space RF has been the repeated choice of communication medium of the space industry. Investigating the benefits of free space optical communication over radio frequency is worthwhile. An increase in science data rate return capabilities could occur if optical communication is implemented. Optical communication systems also provide efficiencies in power, mass, and volume over RF systems1. Optical communication systems have been demonstrated from a satellite in orbit with the moon to earth, and resulted in the highest data rates ever seen through space (622Mbps)2. Because of these benefits, optical communication is far superior to RF. The HiDRA (High Data Rate Architecture) project is researching Passive Misalignment Mitigation of Dynamic Free Apace Optical Communication Links. The goal of this effort is to enable gigabit per second transmission of data in short range dynamic links (less than 100 meters). In practice this would enhance data rates between sites on the International Space Station with minimal size, weight, and power requirements. This paper will focus on an autonomous code and a hardware setup that will be used to fulfill the next step in the research being conducted. The free space optical communications pointing downfalls will be investigated. This was achieved by creating 5 python programs and a top-level code to automate this test.

Heldman, Christopher↗