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This document is a short summary of the steps required for MicroCT evaluation of a specimen. This includes data acquisition through image analysis, for the EXD HME program. Expected outputs for each stage are provided. Data shall be shipped to LLNL as described herein.
PLANT (Polarimetric-interferometric Lab and Analysis Tools) is a new collection of software tools developed at the Jet Propulsion Laboratory to support processing and analysis of Synthetic Aperture Radar (SAR) data for ecosystem and land-cover/land-use change science and applications. PLANT inherits code components from the Interferometric Scientific Computing Environment (ISCE) to generate highresolution, coregistered polarimetric-interferometric SLC stacks from Level-0/1 data for a variety of airborne and spaceborne sensors. The goal is to provide the ecosystem and land-cover/land-use change communities with rigorous and efficient tools to perform multi-temporal, polarimetric and tomographic analyses in order to generate calibrated, geocoded and mosaicked Level-2 and Level-3 products (e.g.,maps of above-ground biomass and forest disturbance). In this paper we introduce the capabilities of PLANT and report first results obtained with the tools developed up to date.
In situ visualization, i.e., visualizing simulation data as it is generated, is an emerging processing paradigm in response to trends in the area of high-performance computing. This paradigm holds great promise in its ability to access increased spatio-temporal resolution and leverage extensive computational power. However, the paradigm is also widely viewed as limiting when it comes to exploration-oriented use cases and further will require visualization systems to become more and more complicated and constrained. Additionally, there are many open research topics within situ visualization. The Dagstuhl seminar 18271 "In Situ Visualization for Computational Science" brought together researchers and practitioners from three communities (computational science, high-performance computing, and scientific visualization) to share interesting findings, to identify lines of open research, and to determine a medium-term research agenda that addresses the most pressing problems. This report summarizes the outcomes and findings of the seminar.
In situ visualization, i.e., visualizing simulation data as it is generated, is an emerging processing paradigm in response to trends in the area of high-performance computing. This paradigm holds great promise in its ability to access increased spatio-temporal resolution and leverage extensive computational power. However, the paradigm is also widely viewed as limiting when it comes to exploration-oriented use cases and further will require visualization systems to become more and more complicated and constrained. Additionally, there are many open research topics with in situ visualization. The Dagstuhl seminar 18271 "In Situ Visualization for Computational Science" brought together researchers and practitioners from three communities (computational science, high-performance computing, and scientific visualization) to share interesting findings, to identify lines of open research, and to determine a medium-term research agenda that addresses the most pressing problems. This report summarizes the outcomes and findings of the seminar.
Abstract AmeriFlux is a network of research sites that measure carbon, water, and energy fluxes between ecosystems and the atmosphere using the eddy covariance technique to study a variety of Earth science questions. AmeriFlux’s diversity of ecosystems, instruments, and data-processing routines create challenges for data standardization, quality assurance, and sharing across the network. To address these challenges, the AmeriFlux Management Project (AMP) designed and implemented the BASE data-processing pipeline. The pipeline begins with data uploaded by the site teams, followed by the AMP team’s quality assurance and quality control (QA/QC), ingestion of site metadata, and publication of the BASE data product. The semi-automated pipeline enables us to keep pace with the rapid growth of the network. As of 2022, the AmeriFlux BASE data product contains 3,130 site years of data from 444 sites, with standardized units and variable names of more than 60 common variables, representing the largest long-term data repository for flux-met data in the world. The standardized, quality-ensured data product facilitates multisite comparisons, model evaluations, and data syntheses.
Neutron imagers are important diagnostics for the inertial confinement fusion implosions at the National Ignition Facility. They provide two- and three-dimensional reconstructions of the neutron source shape that are key indicators of the overall performance. To interpret the shape results properly, it is critical to estimate the uncertainty in those reconstructions. There are two main sources of uncertainties: limited neutron statistics, leading to random errors in the reconstructed images, and incomplete knowledge of the instrument response function (the pinhole-dependent point spread function). While the statistical errors dominate the uncertainty for lower yield deuterium-tritium (DT) shots, errors due to the instrument response function dominate the uncertainty for DT yields on the order of 10 16 neutrons or higher. In this work, a bootstrapping method estimates the uncertainty in a reconstructed image due to the incomplete knowledge of the instrument response function. The main reconstruction is created from the fixed collection of pinhole images that are best aligned with the neutron source. Additional reconstructions are then built using subsets of that collection of images. Variations in the shapes of these additional reconstructions originate solely from uncertainties in the instrument response function, allowing us to use them to provide an additional systematic uncertainty estimate.
Co-design across the Exascale Computing Project (ECP) has been critical for both enabling science applications and bringing disparate communities together. Developing and porting applications to the various high-performance computing (HPC) architectures on pre-exascale and exascale computers has been quite challenging due to the diversity of hardware features and software stacks. The Co-design Center for Particle Applications (CoPA) has developed and enhanced the Cabana and PROGRESS/BML libraries to facilitate the creation of new particle applications, make existing particle applications exascale capable, and allow teams to explore new capabilities. Particle methods from atomistic, mesoscale, continuum, through cosmological scales have been built with Cabana, along with new possibilities for application coupling. Similarly, the PROGRESS/BML library has enabled quantum particle applications with linear algebra solvers to use advanced hardware. Across these CoPA-developed libraries, the co-design abstraction layer combines performance portability with math library support to facilitate separation of concerns and directly support science runs.
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The Large Early Galaxy Census (LEGA-C) is a Public Spectroscopic Survey of ∼3200 K-band selected galaxies at redshifts z = 0.6 − 1.0 with stellar masses M{sub ∗}>10{sup 10} M{sub ⊙}, conducted with VIMOS on ESO’s Very Large Telescope. The survey is embedded in the COSMOS field (R.A. = 10h00; decl. = +2 deg). The 20 hr long integrations produce high-signal-to-noise ratio continuum spectra that reveal ages, metallicities and velocity dispersions of the stellar populations. LEGA-C’s unique combination of sample size and depth will enable us for the first time to map the stellar content at large lookback time, across galaxies of different types and star formation activity. Observations started in 2014 December and are planned to be completed by mid 2018, with early data releases of the spectra and value-added products. In this paper we present the science case, the observing strategy, an overview of the data reduction process and data products, and a first look at the relationship between galaxy structure and spectral properties, as it existed 7 Gyr ago.
Instrumentation of science subsystem of Mariner- Mars 1964 mission - data collection instruments and data processing
The objectives, functions, and organization of the Deep Space Network are summarized. The deep space instrumentation facility, the ground communications facility, and the network control system are described. Other areas reported include: Helios Mission support, DSN support of the Mariner Mars 1971 extended mission, Mariner Venus/Mercury 1973 mission support, Viking mission support, radio science, tracking and ground-based navigation, network control and data processing, and deep space stations.
Experiments proposed for the Apollo-Soyuz space mission are discussed. Data focus of space processing and manufacturing, earth surveys, and life sciences. Special efforts were made to test the compatibility of the rendezvous and docking systems for manned spacecraft. Mission planning programs, personnel training, and spacecraft modifications for both spacecraft are included.
A description of the structure and uses of the Long Duration Exposure Facility (LDEF) is given. The LDEF was designed to provide a large number of economical opportunities for science and technology experiments that require modest electrical power and data processing while in space and which benefit from postflight laboratory investigations with the retrieved experiment hardware. Like the Shuttle, the LDEF is reusable, and repeat missions are planned, each with a new complement of experiments. The LDEF is essentially a free-flying cylindrical structure. The experiments on LDEF are totally self-contained in trays mounted on the exterior of the structure. LDEF can accommodate 86 experiment trays, 72 around the circumference and 14 on the two ends. The LDEF is delivered to Earth orbit by the Shuttle. In orbit, the Shuttle remote manipulator system removes the LDEF from the Shuttle payload bay and places it in a gravity-gradient-stabilized attitude. After an extended period in orbit, which is set by experiment requirements, the LDEF is retrieved on a subsequent Shuttle flight.
The High Data Rate Instrument Study was a joint effort between the Jet Propulsion Laboratory (JPL) and the Goddard Space Flight Center (GSFC). The objectives were to assess the characteristics of future high data rate Earth observing science instruments and then to assess the feasibility of developing data processing systems and communications systems required to meet those data rates. Instruments and technology were assessed for technology readiness dates of 2000, 2003, and 2006. The highest data rate instruments are hyperspectral and synthetic aperture radar instruments which are capable of generating 3.2 Gigabits per second (Gbps) and 1.3 Gbps, respectively, with a technology readiness date of 2003. These instruments would require storage of 16.2 Terebits (Tb) of information (RF communications case of two orbits of data) or 40.5 Tb of information (optical communications case of five orbits of data) with a technology readiness date of 2003. Onboard storage capability in 2003 is estimated at 4 Tb; therefore, all the data created cannot be stored without processing or compression. Of the 4 Tb of stored data, RF communications can only send about one third of the data to the ground, while optical communications is estimated at 6.4 Tb across all three technology readiness dates of 2000, 2003, and 2006 which were used in the study. The study includes analysis of the onboard processing and communications technologies at these three dates and potential systems to meet the high data rate requirements. In the 2003 case, 7.8% of the data can be stored and downlinked by RF communications while 10% of the data can be stored and downlinked with optical communications. The study conclusion is that only 1 to 10% of the data generated by high data rate instruments will be sent to the ground from now through 2006 unless revolutionary changes in spacecraft design and operations such as intelligent data extraction are developed.
An analysis is presented that illustrates how the JWST fine-phasing process can be carried out using NIRSpec data collected at the science focal plane. The analysis is based on a multi-plane diffraction model which properly accounts for the microshutter diffractive element placed at the first relay position of the spectrograph. Wavefront sensing results are presented based on both simulated as well as real data collected from the NASA Goddard Microshutter Testbed.
The National Virtual Observatory (NVO) Extensible Secure Scalable Service Infrastructure (NESSSI) is a Web service architecture and software framework that enables Web-based astronomical data publishing and processing on grid computers such as the National Science Foundation's TeraGrid. Characteristics of this architecture include the following: (1) Services are created, managed, and upgraded by their developers, who are trusted users of computing platforms on which the services are deployed. (2) Service jobs can be initiated by means of Java or Python client programs run on a command line or with Web portals. (3) Access is granted within a graduated security scheme in which the size of a job that can be initiated depends on the level of authentication of the user.
It has been almost 50 years since humans last set foot on the Moon. With NASA’s Artemis program, the United States and its international and commercial partners are embarking on a new endeavor to explore the lunar surface. Before we return, we will have simulated every aspect of these future missions. Many of these models and simulations (M&S) will rely on well-known and commonly-used technologies, some of which trace their origins back to the Apollo program. However, M&S has advanced significantly, as have the underlying computational capabilities. As a result, we are able to model many more aspects of the Artemis vehicles and support systems with significantly improved detail and confidence. Accurately and efficiently modeling the lunar environment will be critical to simulating the Artemis elements and mission activities. This includes characterizing and modeling lunar topography, smaller craters, exposed surface rocks, lunar regolith, surface lighting, and ambient thermal environment. These are all necessary for understanding fundamental behaviors and performance of vehicles and support systems in the lunar environment and are often determining factors in the selection of exploration sites and defining mission profiles. The Astromaterials Research and Exploration Sciences (ARES) team in the Exploration Integration and Science Directorate at NASA’s Johnson Space Center (JSC) and the NASA Exploration Systems Simulations (NExSyS) team in the Simulation and Graphics Branch at JSC are developing and maintaining the Digital Lunar Exploration Sites (DLES) data, documentation, and software packages. This data is being fed directly into a diverse collection of graphics and simulation environments, where it is used to construct the closest known truth for numerous potential Lunar South Pole landing sites. DLES is available to projects across NASA and particularly the Artemis program to support coordinated digital representations of the lunar environment. This paper describes the fundamental need for DLES, the science data sets that are going into DLES, some of the processes used to integrate this data into DLES products, the basic products that constitute DLES, and some examples of DLES in use.
After an almost 50-year absence, NASA along with a group of international and commercial partners will return humans to the surface of the Moon as part of the Artemis program. As with the preceding Apollo program, modeling and simulation (M&S) will be an enabling technology for achieving the Artemis mission objectives. Fortunately, M&S has advanced considerably in the past half century, permitting much more detailed and encompassing integrated representations of the Artemis systems. One modeling area of critical importance to simulating the Artemis elements and mission activities is the accurate and efficient modeling of the operational lunar environment. This is particularly challenging since the Artemis program is considering exploration sites in the area of the Lunar South Pole (LSP), far away from any previous surface exploration sites. Fortunately, we now have considerably more and better data from recent lunar sensing missions. A planetary science team and a human exploration simulation team at NASA’s Johnson Space Center are developing a suite of products called the Digital Lunar Exploration Sites (DLES). DLES is intended to provide some of the necessary lunar environmental data products. This paper describes the fundamental need for DLES, the science data sets that are going into DLES, some of the processes used to integrate this data into DLES products, the basic products that constitute DLES, and some examples of DLES in use.