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At least 1,081 records · Page 60

Analysis of terrestrial conditions and dynamics

Land spectral reflectance properties for selected locations, including the Goddard Space Flight Center, the Wallops Flight Facility, a MLA test site in Cambridge, Maryland, and an acid test site in Burlington, Vermont, were measured. Methods to simulate the bidirectional reflectance properties of vegetated landscapes and a data base for spatial resolution were developed. North American vegetation patterns observed with the Advanced Very High Resolution Radiometer were assessed. Data and methods needed to model large-scale vegetation activity with remotely sensed observations and climate data were compiled.

Goward, S. N.↗

User friendly database for Neptune planetary radio astronomy observations

Planetary Radio Astronomy (PRA) data from the Voyager Neptune encounter were cleaned and reformatted in a variety of formats. Most of these formats are new and have been specifically designed to provide easy access and use of the data without the need to understand esoteric characteristics of the PRA instrument or the Voyager spacecraft. Several data sets were submitted to the Planetary Data System (PDS) and have either appeared already on peer reviewed CDROM's or are in the process of being reviewed for inclusion in forthcoming CD-ROM's. Many of the data sets are also available online electronically through computer networks; it is anticipated that as time permits, the PDS will make all the data sets that were a part of this contract available both online and on CD-ROM's.

Evans, David R.↗

Micromechanical Modeling of the Thermal Expansion of Graphite/copper Composites with Nonuniform Microstructure

Two micromechanical models were developed to investigate the thermal expansion of graphite/copper (Gr/Cu) composites. The models incorporate the effects of temperature-dependent material properties, matrix inelasticity, initial residual stresses due to processing history, and nonuniform fiber distribution. The first model is based on the multiple concentric cylinder geometry, with each cylinder treated as a two-phase composite with a characteristic fiber volume fractions. By altering the fiber volume fraction of the individual cylinders, unidirectional composites with radially nonuniform fiber distributions can be investigated using this model. The second model is based on the inelastic lamination theory. By varying the fiber content in the individual laminae, composites with nonuniform fiber distribution in the thickness direction can be investigated. In both models, the properties of the individual regions (cylinders or laminae) are calculated using the method of cells micromechanical model. Classical incremental plasticity theory is used to model the inelastic response of the copper matrix at the microlevel. The models were used to characterize the effects of nonuniform fiber distribution on the thermal expansion of Gr/Cu. These effects were compared to the effects of matrix plasticity, choice of stress-free temperature, and slight fiber misalignment. It was found that the radially nonuniform fiber distribution has little effect on the thermal expansion of Gr/Cu but could become significant for composites with large fiber-matrix transverse CTE and Young's modulus mismatch. The effect of nonuniform fiber distribution in the through-thickness direction of a laminate was more significant, but only approached that of the stress-free temperature for the most extreme cases that include large amounts of bending. Subsequent comparison with experimental thermal expansion data indicated the need for more accurate characterization of the graphite fiber thermomechanical properties. Correlation with cyclic data revealed the presence of a mechanism not considered in the developed models. The predicted response did, however, exhibit ratcheting behavior that has been observed experimentally in Gr/Cu. Finally, simulation of the actual fiber distribution of particular specimens had little effect on the predicted thermal expansion.

Bednarcyk, Brett A.↗

Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis

Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.

Image Processing; Pattern Recognition↗

Planetary Crater Detection and Registration Using Marked Point Processes, Multiple Birth and Death Algorithms, and Region-Based Analysis

Because of the large variety of sensors and spacecraft collecting data, planetary science needs to integrate various multi-sensor and multi-temporal images. These multiple data represent a precious asset, as they allow the study of targets spectral responses and of changes in the surface structure; because of their variety, they also require accurate and robust registration. A new crater detection algorithm, used to extract features that will be integrated in an image registration framework, is presented. A marked point process-based method has been developed to model the spatial distribution of elliptical objects (i.e. the craters) and a birth-death Markov chain Monte Carlo method, coupled with a region-based scheme aiming at computational efficiency, is used to find the optimal configuration fitting the image. The extracted features are exploited, together with a newly defined fitness function based on a modified Hausdorff distance, by an image registration algorithm whose architecture has been designed to minimize the computational time.

Image Processing:Pattern Recognition↗

Enabling Dynamic Data Governance in Science: Design, Implementation, and Future Directions of the Modern Data Governance Framework

As scientific data volumes exponentially grow, dynamic, flexible and open approaches to data governance are needed. In this paper, we describe our efforts to build an open, scientific Modern Data Governance Framework (mDGF) that streamlines and makes actionable data governance requirements for projects and data providers. We present the goals and design of the mDGF. We also share our envisioned usage for the mDGF and planned future work.

Kaylin Bugbee↗

Accelerator-Based Studies of Heavy Ion Interactions Relevant to Space Biomedicine

Evaluation of the effects of space radiation on the crews of long duration space missions must take into account the interactions of high energy atomic nuclei in spacecraft and planetary habitat shielding and in the bodies of the astronauts. These heavy ions (i.e. heavier than hydrogen), while relatively small in number compared to the total galactic cosmic ray (GCR) charged particle flux, can produce disproportionately large effects by virtue of their high local energy deposition: a single traversal by a heavy charged particle can kill or, what may be worse, severely damage a cell. Research into the pertinent physics and biology of heavy ion interactions has consequently been assigned a high priority in a recent report by a task group of the National Research Council. Fragmentation of the incident heavy ions in shielding or in the human body will modify an initially well known radiation field and thereby complicate both spacecraft shielding design and the evaluation of potential radiation hazards. Since it is impractical to empirically test the radiation transport properties of each possible shielding material and configuration, a great deal of effort is going into the development of models of charged particle fragmentation and transport. Accurate nuclear fragmentation cross sections (probabilities), either in the form of measurements with thin targets or theoretical calculations, are needed for input to the transport models, and fluence measurements (numbers of fragments produced by interactions in thick targets) are needed both to validate the models and to test specific shielding materials and designs. Fluence data are also needed to characterize the incident radiation field in accelerator radiobiology experiments. For a number of years, nuclear fragmentation measurements at GCR-like energies have been carried out at heavy ion accelerators including the LBL Bevalac, Saturne (France), the Synchrophasotron and Nuklotron (Dubna, Russia), SIS-18 (GSI, Germany), the Alternating Gradient Synchrotron at Brookhaven National Laboratory (BNL AGS) and the Heavy Ion Medical Accelerator (HIMAC) in Chiba, Japan. Until fairly recently most of these experiments were done to investigate fundamental problems in nuclear physics, but with the increasing interest in heavy charged particles on the part of the space flight, radiobiology and radiotherapy communities, an increasing number of experiments are being directed at these areas. Some of these measurements are discussed in references therein. Over the past several years, our group has taken cross section and fluence data at the AGS and HIMAC for several incident beams with nuclear charge, Z, between 6 and 26 at energies between 290 and 1050 MeV/nucleon. Iron (Z = 26) has been studied most extensively, since it is the heaviest ion present in significant numbers in the GCR. Targets have included tissue-equivalent and proposed shielding materials, as well as a variety of elemental targets for cross section measurements. Most of the data were taken along the beam axis, but measurements have been made off-axis, as well. Here we present selected data and briefly discuss some implications for spacecraft and planetary habitat design.

Miller, J.↗

High dimensional feature reduction via projection pursuit

The recent development of more sophisticated remote sensing systems enables the measurement of radiation in many more spectral intervals than previously possible. An example of that technology is the AVIRIS system, which collects image data in 220 bands. As a result of this, new algorithms must be developed in order to analyze the more complex data effectively. Data in a high dimensional space presents a substantial challenge, since intuitive concepts valid in a 2-3 dimensional space to not necessarily apply in higher dimensional spaces. For example, high dimensional space is mostly empty. This results from the concentration of data in the corners of hypercubes. Other examples may be cited. Such observations suggest the need to project data to a subspace of a much lower dimension on a problem specific basis in such a manner that information is not lost. Projection Pursuit is a technique that will accomplish such a goal. Since it processes data in lower dimensions, it should avoid many of the difficulties of high dimensional spaces. In this paper, we begin the investigation of some of the properties of Projection Pursuit for this purpose.

Jimenez, Luis↗

The Virtual Climate Data Server (vCDS): An iRODS-Based Data Management Software Appliance Supporting Climate Data Services and Virtualization-as-a-Service in the NASA Center for Climate Simulation

Scientific data services are becoming an important part of the NASA Center for Climate Simulation's mission. Our technological response to this expanding role is built around the concept of a Virtual Climate Data Server (vCDS), repetitive provisioning, image-based deployment and distribution, and virtualization-as-a-service. The vCDS is an iRODS-based data server specialized to the needs of a particular data-centric application. We use RPM scripts to build vCDS images in our local computing environment, our local Virtual Machine Environment, NASA s Nebula Cloud Services, and Amazon's Elastic Compute Cloud. Once provisioned into one or more of these virtualized resource classes, vCDSs can use iRODS s federation capabilities to create an integrated ecosystem of managed collections that is scalable and adaptable to changing resource requirements. This approach enables platform- or software-asa- service deployment of vCDS and allows the NCCS to offer virtualization-as-a-service: a capacity to respond in an agile way to new customer requests for data services.

Schnase, John L.↗

Quantum-Compatible Variational Segmentation for Image-to-Image Wildfire Detection Using Satellite Data

Wildfire occurrences have been increasing for the past decade, leaving devastating traces across the world. In the recent efforts, remote sensing and airborne missions have been utilized to better understand and manage wildfires. This has resulted in an exponential increase in volume of remote sensing data, which has pushed the need for intelligent automation of data extraction for wildfire studies. Machine learning offers accurate automation in detecting such natural anomalies and enable decision-makers to take actions in a timely manner. Recent advances in machine learning algorithms, namely probabilistic generative methods, allow researchers and decisionmakers to step beyond detection and study “what-if” scenarios for wildfire occurrences. Additionally, they offer better imitations to the stochastic behavior of nature, and wildfire events. However, optimizing the performance of these probabilistic generative models is a computationally expensive process, specially using digital computers. On the other hand, quantum computers have recently shown a promise to reduce computationally costly training of such models and provide performance improvements. There is a body of research investigating the potential for improved machine learning methods in which key operations are performed on a quantum computer. In this study, we propose a probabilistic image-toimage segmentation approach combining a very well-known segmentation method, U-NET, with a Conditional Variational Auto-Encoder (CVAE) to not only detect wildfires but also describe the stochasticity of the phenomenon and be capable of running “what-if” scenarios. Our proposed model is compatible with training on quantum computers, which results in a quantum-assisted image-to-image segmentation approach and can be used to benchmark the potential benefit of quantum computing over the classical one.

quantum↗

Image-to-Image Wildfire Detection via Quantum-Compatible Variational Segmentation from Remotely-sensed Data

Over the last decade, the incidence of wildfires has surged, causing widespread destruction globally. To better comprehend and manage these incidents, remote sensing and aerial missions have been implemented in recent efforts. However, this has resulted in an exponential rise in the amount of remote sensing data utilization, leading to a need for intelligent automation of data extraction in wildfire studies. Machine learning provides an accurate automated approach for detecting these natural anomalies and facilitates decision-makers to take prompt actions. To make insightful decisions in wildfire management, it is imperative to move beyond simple detection and explore the potential of probabilistic generative machine learning for creating "what-if" scenarios for various wildfire conditions. Such models offer improved representation of the stochastic nature of wildfire events. However, the optimization of these models can be computationally expensive, especially when using classical computers. Quantum computers have recently emerged as a promising solution to reduce the computational cost of training such models and improve their performance. In this study, we aim to utilize quantum-compatible machine learning techniques to implement our probabilistic generative approach. To that end, we propose a supervised probabilistic variational model consisting of a U-NET-based image-to-image component along with encoder and decoder networks which work as a variational autoencoder (VAE) component. Additionally, we explore the type of latent distribution type in the VAE component and implement different means for modeling the prior distribution. We further investigate the quantum-compatible versions of the model compared to the classical counterpart and benchmark potential benefits of quantum compatibility over the classical model.

quantum machine learning↗

Role of CEOS Working Group on Calibration and Validation in Analysis Ready Data Products

The Committee on Earth Observation Satellites (CEOS) is leading the CEOS Analysis Ready Data for Land (CARD4L) initiative. A goal of analysis ready data products is to limit the effort needed by users to pre-process the data allowing them to concentrate on the end products. CARD4L provides a set of specifications that data providers need to meet to be considered to satisfy CARD4L. One of the working groups within CEOS, the Working Group on Calibration and Validation (WGCV) is providing a peer review process to evaluate the documentation of the data providers validation and data product accuracy assessment. The approach makes use of the expertise within WGCV to collaborate with both the CEOS Land Surface Imaging Virtual Constellation and the data providers to work towards acceptance of the...

Thome, K.↗

Analyzing a 35-Year Hourly Data Record: Why So Difficult?

At the Goddard Distributed Active Archive Center, we have recently added a 35-Year record of output data from the North American Land Assimilation System (NLDAS) to the Giovanni web-based analysis and visualization tool. Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) offers a variety of data summarization and visualization to users that operate at the data center, obviating the need for users to download and read the data themselves for exploratory data analysis. However, the NLDAS data has proven surprisingly resistant to application of the summarization algorithms. Algorithms that were perfectly happy analyzing 15 years of daily satellite data encountered limitations both at the algorithm and system level for 35 years of hourly data. Failures arose, sometimes unexpectedly, from command line overflows, memory overflows, internal buffer overflows, and time-outs, among others. These serve as an early warning sign for the problems likely to be encountered by the general user community as they try to scale up to Big Data analytics. Indeed, it is likely that more users will seek to perform remote web-based analysis precisely to avoid the issues, or the need to reprogram around them. We will discuss approaches to mitigating the limitations and the implications for data systems serving the user communities that try to scale up their current techniques to analyze Big Data.

computational performance↗

MODIS-N - Moderate Resolution Imaging Spectrometer-Nadir

The Moderate Resolution Imaging Spectrometer-Nadir (MODIS-N) for the Earth Observing System (EOS) is intended to provide daily global surveys for the atmosphere, the oceans, and the land. To achieve this capability, MODIS-N requires an at-least 2300-km swath width, and provides geometric-instantaneous-fields-of-view (GIFOVs) that are either 856 m, 428 m, or 214 m in size with reference to a 705 km satellite altitude. The 214 m GIFOV may or may not be used depending on total data rate impact assessments traded with science needs. To achieve the data for the multiplicity of science investigations MODIS-N provides nominally 36 spectral bands that are selected for specific locations and bandpasses in the spectral range from the visible to the long wave infrared. Another driver of this instrument combination is the need for long term spectral and radiometric calibration stability. Specific calibration capabilities are to be built into MODIS-N to achieve calibration knowledge over a 5 year operational life.

Weber, Richard R.↗

KDD Services at the Goddard Earth Sciences Distributed Active Archive Center

NASA's Goddard Earth Sciences Distributed Active Archive Center (GES DAAC) processes, stores and distributes earth science data from a variety of remote sensing satellites. End users of the data range from instrument scientists to global change and climate researchers to federal agencies and foreign governments. Many of these users apply data mining techniques to large volumes of data (up to 1 TB) received from the GES DAAC. However, rapid advances in processing power are enabling increases in data processing that are outpacing tape drive performance and network capacity. As a result, the proportion of data that can be distributed to users continues to decrease. As mitigation, we are migrating more data mining and mining preparation activities into the data center in order to reduce the data volume that needs to be distributed and to offer the users a more useful and manageable product. This migration of activities faces a number of technical and human-factor challenges. As data reduction and mining algorithms are normally quite specific to the user's research needs, the user's algorithm must be integrated virtually unchanged into the archive environment. Also, the archive itself is busy with everyday data archive and distribution activities and cannot be dedicated to, or even impacted by, the mining activities. Therefore, we schedule KDD 'campaigns' (similar to reprocessing campaigns), during which we schedule a wholesale retrieval of specific data products, offering users the opportunity to extract information from the data being retrieved during the campaign.

Lynnes, Christopher↗

Atmospheric Pressure Corrections in Geodesy and Oceanography: a Strategy for Handling Air Tides

Global pressure data are often needed for processing or interpreting modern geodetic and oceanographic measurements. The most common source of these data is the analysis or reanalysis products of various meteorological centers. Tidal signals in these products can be problematic for several reasons, including potentially aliased sampling of the semidiurnal solar tide as well as the presence of various modeling or timing errors. Building on the work of Van den Dool and colleagues, we lay out a strategy for handling atmospheric tides in (re)analysis data. The procedure also offers a method to account for ocean loading corrections in satellite altimeter data that are consistent with standard ocean-tide corrections. The proposed strategy has immediate application to the on-going Jason-1 and GRACE satellite missions.

Ponte, Rui M.↗

The warped disk of Centaurus A in the near-infrared

We present infrared images of Cen A (NGC 5128) in the J, H, and K bands. The infrared morphology is primarily determined by the presence of a thin absorptive warped disk. By integrating the light of the underlying prolate galaxy through such a disk, we construct models which we compare with infrared and X-ray data. The geometry of the warped disk needed to fit the IR data is consistent with a warped disk which has evolved as a result of differential precession in a prolate potential. The disk has an inclination, with respect to the principal axis of the underlying elliptical galaxy, that is higher at larger radii than in the inner region. A scenario is proposed where a small gas-rich galaxy infalling under the force of dynamical friction is tidally stripped. Stripping occurs at different times during its infall. The orientation of the resulting gas disk depends upon the angular momentum of the infalling galaxy. We find that the resulting precession angle of the disk is well described by the precession model, but that the inclination angle may vary as a function of radius. We propose an orbit for the infalling galaxy that is consistent with the geometry of the warped disk needed to fit our infrared data, and rotation observed in the outer part of the galaxy.

Quillen, A. C.↗