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TEMPO Data Distribution by ASDC
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TEMPO Data Distribution at ASDC
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Applying Machine Learning and Bayesian Inference to Identify and Locate Moving Anthropogenic Sources Using Distributed Acoustic Sensing Data
Distributed acoustic sensing (DAS) systems, which use existing telecommunication fibers, offer high‐resolution capabilities ideal for recording anthropogenic sources. However, the complexity of urban environments and the large amount of data recorded by DAS require automated methods to efficiently detect and categorize anthropogenic sources. Here, we evaluate how well three machine learning models (k‐nearest neighbor [k‐NN], convolutional neural networks, and recurrent‐convolutional neural networks) can identify various anthropogenic sources recorded by DAS. Our findings reveal that both k‐NN and neural network methods perform well in high signal‐to‐noise ratio (SNR) settings. However, their accuracy decreases at SNRs <4. We also use Kalman filtering, a form of Bayesian inference, on backprojected locations of these sources to recover locations that generally fall within standard smartphone Global Positioning System errors. By combining machine learning and Kalman filter results, we calculate a multidimensional model of moving anthropogenic sources. These results demonstrate the potential of DAS data in urban seismology for accurately identifying and locating such sources. Depending on the research objectives, these sources can be further studied or filtered out to improve the quality of seismic data for earthquake studies. Such methods provide a valuable tool for urban seismology and seismic hazard analysis.
Towards the Development of a Unified Distributed Date System for L1 Spacecraft
The purpose of this grant, 'Towards the Development of a Unified Distributed Data System for L1 Spacecraft', is to take the initial steps towards the development of a data distribution mechanism for making in-situ measurements more easily accessible to the scientific community. Our obligations as subcontractors to this grant are to add our Faraday Cup plasma data to this initial study and to contribute to the design of a general data distribution system. The year 1 objectives of the overall project as stated in the GSFC proposal are: 1) Both the rsync and Perl based data exchange tools will be fully developed and tested in our mixed, Unix, VMS, Windows and Mac OS X data service environment. Based on the performance comparisons, one will be selected and fully deployed. Continuous data exchange between all L1 solar wind monitors initiated. 2) Data version metadata will be agreed upon, fully documented, and deployed on our data sites. 3) The first version of the data description rules, encoded in a XML Schema, will be finalized. 4) Preliminary set of library routines will be collected, documentation standards and formats agreed on, and desirable routines that have not been implemented identified and assigned. 5) ViSBARD test site implemented to independently validate data mirroring procedures. The specific MIT tasks over the duration of this project are the following: a) implement mirroring service for WIND plasma data b) participate in XML Schema development c) contribute toward routine library.
Packaging and distributing ecological data from multisite studies
Studies of global change and other regional issues depend on ecological data collected at multiple study areas or sites. An information system model is proposed for compiling diverse data from dispersed sources so that the data are consistent, complete, and readily available. The model includes investigators who collect and analyze field measurements, science teams that synthesize data, a project information system that collates data, a data archive center that distributes data to secondary users, and a master data directory that provides broader searching opportunities. Special attention to format consistency is required, such as units of measure, spatial coordinates, dates, and notation for missing values. Often data may need to be enhanced by estimating missing values, aggregating to common temporal units, or adding other related data such as climatic and soils data. Full documentation, an efficient data distribution mechanism, and an equitable way to acknowledge the original source of data are also required.
An Offload NIC for NASA, NLR, and Grid Computing
This work addresses distributed data management and access dynamically configurable high-speed access to data distributed and shared over wide-area high-speed network environments. An offload engine NIC (network interface card) is proposed that scales at nX10-Gbps increments through 100-Gbps full duplex. The Globus de facto standard was used in projects requiring secure, robust, high-speed bulk data transport. Novel extension mechanisms were derived that will combine these technologies for use by GridFTP, bandwidth management resources, and host CPU (central processing unit) acceleration. The result will be wire-rate encrypted Globus grid data transactions through offload for splintering, encryption, and compression. As the need for greater network bandwidth increases, there is an inherent need for faster CPUs. The best way to accelerate CPUs is through a network acceleration engine. Grid computing data transfers for the Globus tool set did not have wire-rate encryption or compression. Existing technology cannot keep pace with the greater bandwidths of backplane and network connections. Present offload engines with ports to Ethernet are 32 to 40 Gbps f-d at best. The best of ultra-high-speed offload engines use expensive ASICs (application specific integrated circuits) or NPUs (network processing units). The present state of the art also includes bonding and the use of multiple NICs that are also in the planning stages for future portability to ASICs and software to accommodate data rates at 100 Gbps. The remaining industry solutions are for carrier-grade equipment manufacturers, with costly line cards having multiples of 10-Gbps ports, or 100-Gbps ports such as CFP modules that interface to costly ASICs and related circuitry. All of the existing solutions vary in configuration based on requirements of the host, motherboard, or carriergrade equipment. The purpose of the innovation is to eliminate data bottlenecks within cluster, grid, and cloud computing systems, and to add several more capabilities while reducing space consumption and cost. Provisions were designed for interoperability with systems used in the NASA HEC (High-End Computing) program. The new acceleration engine consists of state-ofthe- art FPGA (field-programmable gate array) core IP, C, and Verilog code; novel communication protocol; and extensions to the Globus structure. The engine provides the functions of network acceleration, encryption, compression, packet-ordering, and security added to Globus grid or for cloud data transfer. This system is scalable in nX10-Gbps increments through 100-Gbps f-d. It can be interfaced to industry-standard system-side or network-side devices or core IP in increments of 10 GigE, scaling to provide IEEE 40/100 GigE compliance.
Ground System for Solar Dynamics Observatory (SDO) Mission
NASA s Goddard Space Flight Center (GSFC) has recently completed its Critical Design Review (CDR) of a new dual Ka and S-band ground system for the Solar Dynamics Observatory (SDO) Mission. SDO, the flagship mission under the new Living with a Star Program Office, is one of GSFC s most recent large-scale in-house missions. The observatory is scheduled for launch in August 2008 from the Kennedy Space Center aboard an Atlas-5 expendable launch vehicle. Unique to this mission is an extremely challenging science data capture requirement. The mission is required to capture 99.99% of available science over 95% of all observation opportunities. Due to the continuous, high volume (150 Mbps) science data rate, no on-board storage of science data will be implemented on this mission. With the observatory placed in a geo-synchronous orbit at 36,000 kilometers within view of dedicated ground stations, the ground system will in effect implement a "real-time" science data pipeline with appropriate data accounting, data storage, data distribution, data recovery, and automated system failure detection and correction to keep the science data flowing continuously to three separate Science Operations Centers (SOCs). Data storage rates of approx. 45 Tera-bytes per month are expected. The Mission Operations Center (MOC) will be based at GSFC and is designed to be highly automated. Three SOCs will share in the observatory operations, each operating their own instrument. Remote operations of a multi-antenna ground station in White Sands, New Mexico from the MOC is part of the design baseline.
Coarrars for Parallel Processing
The design of the Coarray feature of Fortran 2008 was guided by answering the question "What is the smallest change required to convert Fortran to a robust and efficient parallel language." Two fundamental issues that any parallel programming model must address are work distribution and data distribution. In order to coordinate work distribution and data distribution, methods for communication and synchronization must be provided. Although originally designed for Fortran, the Coarray paradigm has stimulated development in other languages. X10, Chapel, UPC, Titanium, and class libraries being developed for C++ have the same conceptual framework.
Materials data science using CRADLE: A distributed, data-centric approach
Abstract There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE’s capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed. Graphical abstract
A Web Service and Android Application for the Distribution of Rainfall Estimates and Earth Observation Data
The full potential of Satellite Rainfall Estimates (SRE) can only be realized if timely access to the datasets is possible. Existing data distribution web portals are often focused on global products and offer limited customization options, especially for the purpose of routine regional monitoring. Furthermore, most online systems are designed to meet the needs of desktop users, limiting the compatibility with mobile devices. In response to the growing demand for SRE and to address the current limitations of available web portals a project was devised to create a set of freely available applications and services, available at a common portal that can: (1) simplify cross-platform access to Tropical Rainfall Measuring Mission Online Visualization and Analysis System (TOVAS) data (including from Android mobile devices), (2) provide customized and continuous monitoring of SRE in response to user demands and (3) combine data from different online data distribution services, including rainfall estimates, river gauge measurements or imagery from Earth Observation missions at a single portal, known as the Tropical Rainfall Measuring Mission (TRMM) Explorer. The TRMM Explorer project suite includes a Python-based web service and Android applications capable of providing SRE and ancillary data in different intuitive formats with the focus on regional and continuous analysis. The outputs include dynamic plots, tables and data files that can also be used to feed downstream applications and services. A case study in Southern Angola is used to describe the potential of the TRMM Explorer for SRE distribution and analysis in the context of ungauged watersheds. The development of a collection of data distribution instances helped to validate the concept and identify the limitations of the program, in a real context and based on user feedback. The TRMM Explorer can successfully supplement existing web portals distributing SRE and provide a cost-efficient resource to small and medium-sized organizations with specific SRE monitoring needs, namely in developing and transition countries.
A relational data-knowledge base system and its potential in developing a distributed data-knowledge system
A new approach used in constructing a rational data knowledge base system is described. The relational database is well suited for distribution due to its property of allowing data fragmentation and fragmentation transparency. An example is formulated of a simple relational data knowledge base which may be generalized for use in developing a relational distributed data knowledge base system. The efficiency and ease of application of such a data knowledge base management system is briefly discussed. Also discussed are the potentials of the developed model for sharing the data knowledge base as well as the possible areas of difficulty in implementing the relational data knowledge base management system.
A general purpose subroutine for fast fourier transform on a distributed memory parallel machine
One issue which is central in developing a general purpose Fast Fourier Transform (FFT) subroutine on a distributed memory parallel machine is the data distribution. It is possible that different users would like to use the FFT routine with different data distributions. Thus, there is a need to design FFT schemes on distributed memory parallel machines which can support a variety of data distributions. An FFT implementation on a distributed memory parallel machine which works for a number of data distributions commonly encountered in scientific applications is presented. The problem of rearranging the data after computing the FFT is also addressed. The performance of the implementation on a distributed memory parallel machine Intel iPSC/860 is evaluated.
High-speed data duplication/data distribution: An adjunct to the mass storage equation
The term 'mass storage' invokes the image of large on-site disk and tape farms which contain huge quantities of low- to medium-access data. Although the cost of such bulk storage is recognized, the cost of the bulk distribution of this data rarely is given much attention. Mass data distribution becomes an even more acute problem if the bulk data is part of a national or international system. If the bulk data distribution is to travel from one large data center to another large data center then fiber-optic cables or the use of satellite channels is feasible. However, if the distribution must be disseminated from a central site to a number of much smaller, and, perhaps varying sites, then cost prohibits the use of fiber-optic cable or satellite communication. Given these cost constraints much of the bulk distribution of data will continue to be disseminated via inexpensive magnetic tape using the various next day postal service options. For non-transmitted bulk data, our working hypotheses are that the desired duplication efficiency of the total bulk data should be established before selecting any particular data duplication system; and, that the data duplication algorithm should be determined before any bulk data duplication method is selected.
OhioView: Distribution of Remote Sensing Data Across Geographically Distributed Environments
Various issues associated with the distribution of remote sensing data across geographically distributed environments are presented in viewgraph form. Specific topics include: 1) NASA education program background; 2) High level architectures, technologies and applications; 3) LeRC internal architecture and role; 4) Potential GIBN interconnect; 5) Potential areas of network investigation and research; 6) Draft of OhioView data model; and 7) the LeRC strategy and roadmap.