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Readout technique increases density of stored information for projection onto facsimile reproduction. Data stored on line structures is scanned at 90 deg. angle over area larger than recorded format to ensure complete recovery of information.
There has been an increasing demand by the health community for improved data on many different environmental factors relevant to the links between the environment and disease occurrence and transmission. These data are important for GIS-based monitoring, risk mapping, and surveillance of epidemiological parameters on a large number of different spatial, temporal, and spectral resolutions. Accordingly, NASA is developing new approaches to data collection and distribution in order to improve access to multiple sources of data streams to increase spatial and temporal coverage. Methods are being developed to incorporate different, scalable capabilities to handle multiple data sources by adding, deleting and replacing components as required as well as associated tools for their management. An approach has been to search for innovative solutions focused on the creation, use and manipulation of data stored in many different archives. These include data transformation and combination as well as data and information tools that can assist the public health and science community to use existing and anticipated products in new and flexible ways. This presentation will provide an inventory of geophysical parameters derived from satellite remote sensing sensors that are useful for GIS-based public health studies. The presentation will also discuss the physical and scientific limitations of access to and use of these data for health applications such as resolution and format differences, lack of software interoperability, data access problems. Finally, there will be a summary of the recent steps the NASA program has taken to bring NASA-generated satellite products to a wider range of users in the GIS community.
A system and method for cognitive processing of sensor data. A processor array receiving analog sensor data and having programmable interconnects, multiplication weights, and filters provides for adaptive learning in real-time. A static random access memory contains the programmable data for the processor array and the stored data is modified to provide for adaptive learning.
Flight and ground system operability has been a focus area on the Europa Clipper Project since early in its formulation phase. This has given the operations team the opportunity to influence the design, with a goal of increasing overall system operability. This paper presents example operability challenges, opportunities, and solutions arising from the pre-Critical Design Review (CDR) system design. The integrated wing assembly design directly couples a scientific instrument (the REASON sounding radar) to the spacecraft’s power source (solar array wing panels). Impacts to mission operations of this design include: increased slew durations; solar array pointing constraints during inner cruise, Europa flybys, and orbit trim maneuvers; and stray light intrusions into the stellar reference units’ keep out zones. The use of CCSDS File Delivery Protocol (CFDP) Class-2 for reliable downlink of the large volume of Europa Clipper science data is described, along with nominal and off-nominal use cases. The effort to improve post-launch spacecraft visibility by adding a third low-gain antenna to the spacecraft is detailed. The design of the bulk data store has necessitated the implementation of accountable data products (ADPs), accountability identifiers (AIDs), and metadata packets to provide end-to-end science data accountability. To streamline and automate the flight rules generation and checking process, a first order and temporal logic-based solution of expressing flight rules without ambiguity, and whose programmatic implementation can be automated, is proposed. The focus on operability has had a positive influence on Europa Clipper design decisions, although cost, schedule, budget, heritage, and other technical concerns have many times outweighed operability concerns. However, experience to date demonstrates that this approach to operability results in more thorough, balanced consideration of the effect of early design trades and decisions on the operations phase of a mission than seen in many previous missions, and provides operations development insight into prioritizing work to go.
Flight and ground system operability has been a focus area on the Europa Clipper Project since early in its formulation phase. This has given the operations team the opportunity to influence the design, with a goal of increasing overall system operability. This paper presents example operability challenges, opportunities, and solutions arising from the Critical Design Review (CDR) system design. The integrated wing assembly design directly couples a scientific instrument (the REASON sounding radar) to the spacecraft’s power source (solar array wing panels). Impacts to mission operations of this design include: increased slew durations; solar array pointing constraints during inner cruise, Europa flybys, and orbit trim maneuvers; and stray light intrusions into the stellar reference units’ keep out zones. The use of CCSDS File Delivery Protocol (CFDP) Class-2 for reliable downlink of the large volume of Europa Clipper science data is described, along with nominal and off-nominal use cases. The effort to improve post-launch spacecraft visibility by adding a third low-gain antenna to the spacecraft is detailed. The design of the bulk data store has necessitated the implementation of accountable data products (ADPs), accountability identifiers (AIDs), and metadata packets to provide end-to-end science data accountability. To streamline and automate the flight rules generation and checking process, a first order and temporal logic-based solution of expressing flight rules without ambiguity, and whose programmatic implementation can be automated, is proposed. The focus on operability has had a positive influence on Europa Clipper design decisions, although cost, schedule, budget, heritage, and other technical concerns have many times outweighed operability concerns. However, experience to date demonstrates that this approach to operability results in more thorough, balanced consideration of the effect of early design trades and decisions on the operations phase of a mission than seen in many previous missions, and provides operations development insight into prioritizing work to go.
A data acquisition system for electric-arc heated, high-temperature wind tunnel facilities has been developed; the high voltage isolation of the system exceeds 50 kV. A closed loop, optically coupled data system acquires transducer signals from high-temperature, arc-generated plasmas used for reentry testing. The data acquisition system monitors on-line tests, stores data on magnetic tape, and prints out a paper tape. The system has 400 data channels: 200 low-voltage programmable channels and 200 high-voltage sequential channels.
The standard formulation of Kanerva's sparse distributed memory (SDM) involves the selection of a large number of data storage locations, followed by averaging the data contained in those locations to reconstruct the stored data. A variant of this model is discussed, in which the predominant pattern is the focus of reconstruction. First, one architecture is proposed which returns the predominant pattern rather than the average pattern. However, this model will require too much storage for most uses. Next, a hybrid model is proposed, called tagged SDM, which approximates the results of the predominant pattern machine, but is nearly as efficient as Kanerva's original formulation. Finally, some experimental results are shown which confirm that significant improvements in the recall capability of SDM can be achieved using the tagged architecture.
Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(R) (MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.
Data mining is widely used to mine business, engineering, and scientific data. Data mining uses pattern based queries, searches, or other analyses of one or more electronic databases/datasets in order to discover or locate a predictive pattern or anomaly indicative of system failure, criminal or terrorist activity, etc. There are various algorithms, techniques and methods used to mine data; including neural networks, genetic algorithms, decision trees, nearest neighbor method, rule induction association analysis, slice and dice, segmentation, and clustering. These algorithms, techniques and methods used to detect patterns in a dataset, have been used in the development of numerous open source and commercially available products and technology for data mining. Data mining is best realized when latent information in a large quantity of data stored is discovered. No one technique solves all data mining problems; challenges are to select algorithms or methods appropriate to strengthen data/text mining and trending within given datasets. In recent years, throughout industry, academia and government agencies, thousands of data systems have been designed and tailored to serve specific engineering and business needs. Many of these systems use databases with relational algebra and structured query language to categorize and retrieve data. In these systems, data analyses are limited and require prior explicit knowledge of metadata and database relations; lacking exploratory data mining and discoveries of latent information. This presentation introduces MatLab(TradeMark)(MATrix LABoratory), an engineering and scientific data analyses tool to perform data mining. MatLab was originally intended to perform purely numerical calculations (a glorified calculator). Now, in addition to having hundreds of mathematical functions, it is a programming language with hundreds built in standard functions and numerous available toolboxes. MatLab's ease of data processing, visualization and its enormous availability of built in functionalities and toolboxes make it suitable to perform numerical computations and simulations as well as a data mining tool. Engineers and scientists can take advantage of the readily available functions/toolboxes to gain wider insight in their perspective data mining experiments.
A cooperative flight test campaign between NASA and the U.S. Army was performed from September 2014 to February 2015. The purposes of the testing were to: investigate the effects of altitude variation on noise generation, investigate the effects of gross weight variation on noise generation, establish the statistical variability in acoustic flight testing of helicopters, and characterize the effects of transient maneuvers on radiated noise for a medium-lift utility helicopter. This test was performed at three test sites (0, 4000, and 7000 feet above mean sea level) with two aircraft (AS350 SD1 and EH-60L) tested at each site. This report provides an overview of the test, documents the data acquired and describes the formats of the stored data.
A cooperative flight test campaign between U.S. Navy, Army, Marine Corps, and NASA was performed at Marine Corps Air Station (MCAS) Yuma from 19 January to 15 February 2022. The primary purpose of testing military vehicles was to characterize the acoustic signature of the MV-22 rotorcraft and Stalker unmanned aerial system for typical mission gross weight, configuration, and for a range of typical mission operating conditions. Additionally, a civilian MD530 vehicle was tested to characterize the acoustic emissions for both legacy blades and S411 main rotor blades. This report provides an overview of the test, documents the data acquired, and describes the formats of the stored data.
Large-scale data analytics workflows ingest massive input data into various data structures, including graphs and key-value datastores. These data structures undergo multiple transformations and computations and are typically reused in incremental and iterative analytics workflows. Persisting in-memory views of these data structures enables reusing them beyond the scope of a single program run while avoiding repetitive raw data ingestion overheads. Memory-mapped I/O enables persisting in-memory data structures without data serialization and deserialization overheads. However, memory-mapped I/O lacks the key feature of persisting consistent snapshots of these data structures for incremental ingestion and processing. The obstacles to efficient virtual memory snapshots using memory-mapped I/O include background writebacks outside the application’s control, and the significantly high storage footprint of such snapshots. To address these limitations, we present Privateer, a memory and storage management tool that enables storage-efficient virtual memory snapshotting while also optimizing snapshot I/O performance. Here, we integrated Privateer into Metall, a state-of-the-art persistent memory allocator for C++, and the Lightning Memory-Mapped Database (LMDB), a widely-used key-value datastore in data analytics and machine learning. Privateer optimized application performance by 1.22× when storing data structure snapshots to node-local storage, and up to 16.7× when storing snapshots to a parallel file system. Privateer also optimizes storage efficiency of incremental data structure snapshots by up to 11× using data deduplication and compression.
Each day, the global air transportation industry generates a vast amount of heterogeneous data from air carriers, air traffic control providers, and secondary aviation entities handling baggage, ticketing, catering, fuel delivery, and other services. Generally, these data are stored in isolated data systems, separated from each other by significant political, regulatory, economic, and technological divides. These realities aside, integrating aviation data into a single, queryable, big data store could enable insights leading to major efficiency, safety, and cost advantages. In this paper, we describe an implemented system for combining heterogeneous air traffic management data using semantic integration techniques. The system transforms data from its original disparate source formats into a unified semantic representation within an ontology-based triple store. Our initial prototype stores only a small sliver of air traffic data covering one day of operations at a major airport. The paper also describes our analysis of difficulties ahead as we prepare to scale up data storage to accommodate successively larger quantities of data -- eventually covering all US commercial domestic flights over an extended multi-year timeframe. We review several approaches to mitigating scale-up related query performance concerns.
Each day, the global air transportation industry generates a vast amount of heterogeneous data from air carriers, air traffic control providers, and secondary aviation entities handling baggage, ticketing, catering, fuel delivery, and other services. Generally, these data are stored in isolated data systems, separated from each other by significant political, regulatory, economic, and technological divides. These realities aside, integrating aviation data into a single, queryable, big data store could enable insights leading to major efficiency, safety, and cost advantages. In this paper, we describe an implemented system for combining heterogeneous air traffic management data using semantic integration techniques. The system transforms data from its original disparate source formats into a unified semantic representation within an ontology-based triple store. Our initial prototype stores only a small sliver of air traffic data covering one day of operations at a major airport. The paper also describes our analysis of difficulties ahead as we prepare to scale up data storage to accommodate successively larger quantities of data -- eventually covering all US commercial domestic flights over an extended multi-year timeframe. We review several approaches to mitigating scale-up related query performance concerns.
The dCache project provides open-source software deployed internationally to satisfy ever more demanding storage requirements of various scientific communities. Its multifaceted approach provides an integrated way of supporting different use-cases with the same storage, from high throughput data ingest, through wide access and easy integration with existing systems, including event driven workflow management. With this presentation, we will show some of the recent developments that optimize data management and access to maximise the gain from stored data.
This paper documents the workflow and supporting technologies that a large system dynamics model, the biomass scenario model, employs to streamline the data preparation, simulation, quality control, and analysis process at the National Renewable Energy Laboratory. The workflow centers on automation of routine aspects of the flow of data between data stores, simulations, and visualizations. It enforces quality checks on data, reproducibility of computations, and traceability of results, while maintaining complete archives of modeling and analysis artifacts. The resulting frictionless simulation/analysis environment supports large-scale sensitivity analysis, interactive creation of ensembles of simulations, and rapid visualization-based exploration of simulation results.
A mechanism is provided for efficient coherence state modification of cached data stored in a range of addresses in a coherent data processing system in which data coherency is maintained across multiple caches. A tag search structure is maintained that identifies address tags and coherence states of cached data indexed by address tags. In response to a request from a device internal to or external from the coherence network, the tag search structure is searched to identify address tags of cached data for which the coherence state is to be modified and requests are issued in the data processing system to modify a coherence state of cached lines with the identified address tags. The request from the external device may specify a range of addresses for which a coherence state change is sought. The tag search structure may be implemented as search tree, for example.
Improved technique was developed for transferring and storing data at faster than real time speeds on hybrid computer. Predominant advantage is combined use of electronic relays, track and store units, and analog-to-digital and digital-to-analog conversion units of hybrid computer.