A conceptual study of the SSV/GN&C system data bus
Development of time shared data bus concept for use with space shuttle guidance, navigation, and control system
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Development of time shared data bus concept for use with space shuttle guidance, navigation, and control system
This paper explores the potential of distributed ledger technology (DLT) to improve fault-tolerant grid operations by leveraging its core features as an immutable, decentralized ledger, a distributed, consensus-based agreement process, and a distributed state-replication engine. Distribution power systems deliver electricity to millions of customers; however, they are susceptible to various threats that can result in customer interruptions. These include faults caused by adverse weather conditions, natural disasters, vegetation growth, equipment failure, and malicious attacks. To minimize the effects of these faults, fault-handling approaches rely on network knowledge to isolate affected areas and reconnect unaffected areas, reducing the number of affected customers while maintaining safety. Here, we present a trusted data-sharing architecture that enables independent, distributed actors to reconstruct the pre-fault system state by enabling distributed resources to make appropriate decisions with limited network/system information. Although the process requires some data sharing between switch-delimited areas, the approach limits the amount of private information shared, preserving customers' privacy and business-sensitive information. We include three use cases that form a foundation for third parties to develop functional solutions that can eventually be deployed in the field. The gross error detection method used within switch-delimited areas can identify sensor errors and accurately detect circuit breaker states. The evaluation of possible reconnection while preserving data ownership resulted in a voltage magnitude difference smaller than 0.001% from the OpenDSS power flow solution that has full system knowledge, which is below the expected power flow tolerance. The approach offers a promising opportunity for improving fault-tolerant distribution grid operations.
Purpose: Visualization and processing of medical images and radiation treatment plan evaluation have traditionally been constrained to local workstations with limited computation power and ability of data sharing and software update. We present a web-based image processing and planning evaluation platform (WIPPEP) for radiotherapy applications with high efficiency, ubiquitous web access, and real-time data sharing. Methods: This software platform consists of three parts: web server, image server and computation server. Each independent server communicates with each other through HTTP requests. The web server is the key component that provides visualizations and user interface through front-end web browsers and relay information to the backend to process user requests. The image server serves as a PACS system. The computation server performs the actual image processing and dose calculation. The web server backend is developed using Java Servlets and the frontend is developed using HTML5, Javascript, and jQuery. The image server is based on open source DCME4CHEE PACS system. The computation server can be written in any programming language as long as it can send/receive HTTP requests. Our computation server was implemented in Delphi, Python and PHP, which can process data directly or via a C++ program DLL. Results: This software platform is running on a 32-core CPU server virtually hosting the web server, image server, and computation servers separately. Users can visit our internal website with Chrome browser, select a specific patient, visualize image and RT structures belonging to this patient and perform image segmentation running Delphi computation server and Monte Carlo dose calculation on Python or PHP computation server. Conclusion: We have developed a webbased image processing and plan evaluation platform prototype for radiotherapy. This system has clearly demonstrated the feasibility of performing image processing and plan evaluation platform through a web browser and exhibited potential for future cloud based radiotherapy.
The GLobal Integrated Design Environment (GLIDE) is a client-server software application purpose-built to mitigate issues associated with real time data sharing in concurrent engineering environments and to facilitate discipline-to-discipline interaction between multiple engineers and researchers. GLIDE is implemented in multiple programming languages utilizing standardized web protocols to enable secure parameter data sharing between engineers and researchers across the Internet in closed and/or widely distributed working environments. A well defined, HyperText Transfer Protocol (HTTP) based Application Programming Interface (API) to the GLIDE client/server environment enables users to interact with GLIDE, and each other, within common and familiar tools. One such common tool, Microsoft Excel (Microsoft Corporation), paired with its add-in API for GLIDE, is discussed in this paper. The top-level examples given demonstrate how this interface improves the efficiency of the design process of a concurrent engineering study while reducing potential errors associated with manually sharing information between study participants.
This Vision 20/20 paper considers what computational advances are likely to be implemented in clinical radiation oncology in the coming years and how the adoption of these changes might alter the practice of radiotherapy. Four main areas of likely advancement are explored: cloud computing, aggregate data analyses, parallel computation, and automation. As these developments promise both new opportunities and new risks to clinicians and patients alike, the potential benefits are weighed against the hazards associated with each advance, with special considerations regarding patient safety under new computational platforms and methodologies. While the concerns of patient safety are legitimate, the authors contend that progress toward next-generation clinical informatics systems will bring about extremely valuable developments in quality improvement initiatives, clinical efficiency, outcomes analyses, data sharing, and adaptive radiotherapy.
The Bioenergy Knowledge Discovery Framework (BioenergyKDF) is a scalable, web-based collaborative environment for scientists working on bioenergy related research in which the connections between data, literature, and models can be explored and more clearly understood. The fully-operational and deployed system, built on multiple open source libraries and architectures, stores contributions from the community of practice and makes them easy to find, but that is just its base functionality. The BioenergyKDF provides a national spatiotemporal decision support capability that enables data sharing, analysis, modeling, and visualization as well as fosters the development and management of the U.S. bioenergy infrastructure, which is an essential component of the national energy infrastructure. The BioenergyKDF is built on a flexible, customizable platform that can be extended to support the requirements of any user community especially those that work with spatiotemporal data. While there are several community data-sharing software platforms available, some developed and distributed by national governments, none of them have the full suite of capabilities available in BioenergyKDF. For example, this component-based platform and database independent architecture allows it to be quickly deployed to existing infrastructure and to connect to existing data repositories (spatial or otherwise). As new data, analysis, and features are added; the BioenergyKDF will help lead research and support decisions concerning bioenergy into the future, but will also enable the development and growth of additional communities of practice both inside and outside of the Department of Energy. These communities will be able to leverage the substantial investment the agency has made in the KDF platform to quickly stand up systems that are customized to their data and research needs.
Award DE-FE-0027702 from the Department of Energy to the Ground Water Protection Council (GWPC) focused on state and federal priorities in the areas of state Risk Based Data Management System (RBDMS) development, connectivity between state systems and FracFocus.org, and data sharing initiatives across agencies. The primary objective was to enhance the RBDMS by adding new components relevant to current environmental topics such as hydraulic fracturing, increasing field inspection capabilities, creating linkages between FracFocus and state programs, upgrading eForm capabilities, and analyzing potential for data sharing. The recipient worked with state agencies developing RBDMS module(s) that meet these needs.
The Stability Graph is a widely used tool for the design of open stopes in underground mining. Many users of the Stability Graph still apply this design method manually. Although the manual approach has benefits, using multiple graphs and stability number computation charts for each stope surface is time-consuming, even for the experienced mining engineer. Current practice in the use of the method also limits data sharing. This paper presents a StopeSoft web-based tool for open stope stability prediction that is developed on the basis of the Stability Graph method and is available at openstope.com. StopeSoft incorporates flexibility in terms of Stability Graph options and incorporates additional critical factors often overlooked. As a web-based tool, StopeSoft encourages and makes data sharing possible globally, focused on expanding the database and improving the current limitations of the Stability Graph to provide practical, reliable solutions for mining engineers, consultants, and academics. The StopeSoft automated process facilitates the process of open stope stability prediction, saving time and minimizing potential human errors. Statistical treatment of the data accounts for the variability of input parameters to emphasize the probabilistic nature of the Stability Graph method. The probabilistic interpretation of the stability states of stope surfaces eliminates the false feeling of absolute stope performance based on its location on the Stability Graph , as implied by the deterministic approach.
Background and motivation: The high-throughput genomics communities have been successfully using standardized spreadsheet-based formats to capture and share data within labs and among public repositories. The nanomedicine community has yet to adopt similar standards to share the diverse and multi-dimensional types of data (including metadata) pertaining to the description and characterization of nanomaterials. Owing to the lack of standardization in representing and sharing nanomaterial data, most of the data currently shared via publications and data resources are incomplete, poorly-integrated, and not suitable for meaningful interpretation and re-use of the data. Specifically, in its current state, data cannot be effectively utilized for the development of predictive models that will inform the rational design of nanomaterials. Results: We have developed a specification called ISA-TAB-Nano, which comprises four spreadsheet-based file formats for representing and integrating various types of nanomaterial data. Three file formats (Investigation, Study, and Assay files) have been adapted from the established ISA-TAB specification; while the Material file format was developed de novo to more readily describe the complexity of nanomaterials and associated small molecules. In this paper, we have discussed the main features of each file format and how to use them for sharing nanomaterial descriptions and assay metadata. Conclusion: The ISA-TAB-Nano file formats provide a general and flexible framework to record and integrate nanomaterial descriptions, assay data (metadata and endpoint measurements) and protocol information. Like ISA-TAB, ISA-TAB-Nano supports the use of ontology terms to promote standardized descriptions and to facilitate search and integration of the data. The ISA-TAB-Nano specification has been submitted as an ASTM work item to obtain community feedback and to provide a nanotechnology data-sharing standard for public development and adoption.
A set of programs was written to test the functionality and performance of the Alsys Ada implementation of the Catalogue of Interface Features and Options (CIFO), a set of optional Ada packages for real-time applications. No problems were found with the task id, preemption control, or shared-data packages. Minor problems were found with the dispatching control, dynamic priority, events, non-waiting entry call, semaphore, and scheduling packages. The Alsys implementation is derived mostly from Release 2 of the CIFO standard, but includes some of the features of Release 3 and some modifications unique to Alsys. Performance measurements show that the semaphore and shared-data features are an order-of-magnitude faster than the same mechanisms using an Ada rendezvous. The non-waiting entry call is slightly faster than a standard rendezvous. The existence of errors in the implementation, the incompleteness of the documentation from the published standard impair the usefulness of this implementation. Despite those short-comings, the Alsys CIFO implementation might be of value in the development of real-time applications.
The visual impairment and intracranial pressure syndrome (VIIP) is a newly described space flight-associated medical condition made up of a constellation of symptoms affecting at least 34% of American astronauts who have flown International Space Station (ISS) missions. VIIP is defined primarily by visual acuity deficits and anatomical changes to eye structures, and is thought to be related to elevated intracranial pressure secondary to space flightinduced cephalad fluid shifts. Loss of visual acuity could be a significant threat to crew health and performance and may be suggestive of other adaptations with implications for years post-flight. Our primary objective is to determine whether vascular compliance is altered by space flight and whether such adaptations are related to the incidence of VIIP. In particular, we will measure ocular parameters and vascular compliance in vessels of the head and neck in astronauts who have no space flight experience, in astronauts before, during, and after space flight, and in bed rest subjects with conditions similar to space flight. Additionally, we will analyze astronaut data from the Lifetime Surveillance of Astronaut Health (LSAH) archive to determine which factors might be predictive of the development of VIIP. The project will be conducted in four separate but related parts. To understand the baseline condition of astronauts without any prior space flight experience, we will study 10 astronauts who have never flown in space by performing a comprehensive evaluation of the vasculature of the head, neck and eyes. Hemodynamic data (stroke volume and blood pressure), ocular (tonometry and ocular ultrasound), venous and arterial parameters will be acquired across a range of tilt angles (20, 10, 0, -10, -20 degrees). Vessels to be studied include the temporal, jugular, and vertebral veins and the cerebral, carotid and vertebral arteries. Ophthalmic data from the annual physical will be obtained through data sharing. To examine the relation between vascular compliance in the head and neck and the development of VIIP after a long duration space flight, we will study 10 astronauts before, during, and after long-duration ISS missions. Pre- and post-flight testing will be identical to that described above. During flight, images of the same vessels of interest will be obtained for later analysis. Ophthalmic data including VIIP scores will be obtained through data sharing from medically-required tests. To investigate the effects of age and elevated sodium intake, two potential contributors to VIIP, we will study 24 men (in two age groups: 25-35 and 45-55) during a 14 day 6deg head-down bed rest, a well-accepted analog of space flight. Standard NASA bed rest conditions will be maintained except for dietary sodium. Sodium intake will be similar to that of ISS astronauts, which is higher than consumed in previous bed rest studies. Pre- and post-bed rest testing procedures will be identical to the testing protocol described above for astronauts. Ophthalmic testing (optical coherence tomography, fundoscopy, and tonometry) will be conducted on the same day that vascular compliance measures are obtained. To identify parameters that may relate to an increase in an astronaut's susceptibility to developing VIIP, we will use data mining techniques to evaluate astronaut data obtained from the LSAH. Medical history, family history, space flight history and its related exposures, and history of high performance jet aircraft exposure will be examined for their potential relationship to ocular data. We hypothesize that the cephalad fluid shift induced by space flight will result in structural and functional adaptations in head and neck vessels leading to decreased vascular compliance and related to the development of VIIP symptoms. Further, although VIIP has not been observed in previous bed rest studies, we hypothesize that an elevated sodium intake will increase the incidence of VIIP symptoms in this space flight analog. Finally, we hypothesize that data mining analyses will reveal relationships between health history, previous exposures (including space flight and high performance aircraft), and the development of VIIP in the astronaut population.
In January 2020, staff members from the Engagement and Performance Operations Center (EPOC) and the Lonestar Education And Research Network (LEARN) met with researchers and staff at Baylor University for the purpose of a Campus-Wide Deep Dive into research drivers. The goal of this meeting was to help characterize the requirements for five campus research use cases and to enable cyberinfrastructure support staff to better understand the needs of the researchers they support. Profiled scientific use cases included: - Experimental High Energy Physics (HEP) - Proton Computed Tomography (pCT) - Nutrition and Relation to Digestive Microbiome - Baylor University Core Research Facilities - Molecular Quantum-dot Cellular Automata (QCA), and Material Science of Quantum Computing - Modeling and Simulation of Low-Dimensional and Nano-Structured Materials - Computational Fluid Dynamics Material for this event included the written documentation from each of the research areas at Baylor University, documentation about the current state of technology support, and a write-up of the discussion that took place in person. The Case Studies highlighted the ongoing challenges that Baylor University has in supporting a cross-section of established and emerging research use cases. Each Case Study mentioned unique challenges which were summarized into common needs. These included: - Tradeoffs for network/software security, and usability of the resulting infrastructure. Better communication to set expectations and understand realities is required. - Computation use on campus is widespread and healthy. While no major problems were uncovered, upgrades to maintain current usage patterns and encourage growth will be required. - Storage is a critical need for enterprise use cases and research. In particular, a campus wide ‘storage architecture’ to support research use cases (e.g. instruments, data sharing) is required in the 2-5 year time window. - Instrumentation on campus is healthy and expanding. Technology must scale with this in the form of computation and storage. - Working with LEARN to upgrade network capacity (in multiples of 10G, or upgrades to 100G) will be required in the 1-3 year time frame. - Network monitoring and visibility will help to establish external science use cases. - Data sharing via portal systems is not currently a critical need, but growing in scope. EPOC can assist Baylor with options.
Recently, it has become easier to access climate data from satellites, ground measurements, and models from various data centers, However, searching. accessing, and prc(essing heterogeneous data from different sources are very tim -consuming tasks. There is lack of a comprehensive visual platform to acquire distributed and heterogeneous scientific data and to render processed images from a single accessing point for climate studies. This paper. documents the design and implementation of a Web-based visual, interoperable, and scalable platform that is able to access climatological fields from models, satellites, and ground stations from a number of data sources using Google Earth (GE) as a common graphical interface. The development is based on the TCP/IP protocol and various data sharing open sources, such as OPeNDAP, GDS, Web Processing Service (WPS), and Web Mapping Service (WMS). The visualization capability of integrating various measurements into cE extends dramatically the awareness and visibility of scientific results. Using embedded geographic information in the GE, the designed system improves our understanding of the relationships of different elements in a four dimensional domain. The system enables easy and convenient synergistic research on a virtual platform for professionals and the general public, gr$tly advancing global data sharing and scientific research collaboration.
The successful development of high-performance, durable platinum group metal-free (PGM-free) electrocatalysts and electrodes for polymer electrolyte membrane fuel cells (PEMFCs) will ultimately improve the cost-competiveness of fuel cells in a wide range of applications. This is considered to be a critical development especially for automotive fuel cell applications in order to bring the system cost of an automotive fuel cell system down to the $30/kW cost target set by the U.S. Department of Energy (DOE). The platinum group metal (PGM) electrocatalysts are a major contributor to the system cost. Addressing the technical challenges to PGM-free electrocatalyst and electrode development, therefore, represents one of DOE's most pressing research and development (R&D) priorities. ElectroCat was formed by the DOE as part of the Energy Materials Network (EMN) in early 2016, and shares with other EMN consortia the goal of decreasing the time to market for advanced materials related to clean energy technologies, in the context of increasing U.S. fuel cell electric vehicle (FCEV) manufacturing competitiveness. To accomplish this, the consortium performs core research and development and provides universities and companies streamlined access to the unique, world-class set of tools and expertise relevant to early-stage applied PGM-free catalyst R&D of the member national laboratories. Moreover, ElectroCat fosters a systematic methodology by which prospective catalysts and electrodes are prepared and analyzed rapidly and comprehensively using high-throughput, combinatorial methods. Catalyst discovery is augmented by theory as well as foundational electrocatalysis and materials knowledge at the participating national laboratories. Furthermore, ElectroCat has developed a data sharing framework, requisite of all EMN consortia, for disseminating its findings to the public via a searchable database, to further expedite incorporation of PGM-free electrocatalysts into next-generation fuel cells by advancing the general understanding of the PGM-free electrocatalyst field.
Data sharing benefits the researcher, the scientific community, and the public by allowing the impact of data to be generalized beyond one project and by making science more transparent. However, many scientific communities have not developed protocols or standards for publishing, citing, and versioning datasets. One community that lags in data management is that of low-temperature geochemistry (LTG). This paper resulted from an initiative from 2018 through 2020 to convene LTG and data scientists in the U.S. to strategize future management of LTG data. Through webinars, a workshop, a preprint, a townhall, and a community survey, the group of U.S. scientists discussed the landscape of data management for LTG – the data-scape. Currently this data-scape includes a “street bazaar” of data repositories. This was deemed appropriate in the same way that LTG scientists publish articles in many journals. The variety of data repositories and journals reflect that LTG scientists target many different scientific questions, produce data with extremely different structures and volumes, and utilize copious and complex metadata. Nonetheless, the group agreed that publication of LTG science must be accompanied by sharing of data in publicly accessible repositories, and, for sample-based data, registration of samples with globally unique persistent identifiers. LTG scientists should use certified data repositories that are either highly structured databases designed for specialized types of data, or unstructured generalized data systems. Recognizing the need for tools to enable search and cross-referencing across the proliferating data repositories, the group proposed that the overall data informatics paradigm in LTG should shift from “build data repository, data will come” to “publish data online, cybertools will find”. Funding agencies could also provide portals for LTG scientists to register funded projects and datasets, and forge approaches that cross national boundaries. Finally, the needed transformation of the LTG data culture requires emphasis in student education on science and management of data.
Industry 4.0 is revolutionizing manufacturing through the integration of automation and real-time data sharing in cyber-physical systems. At the forefront of this revolution is large-format additive manufacturing. In large-format printing, parts are often designed to be an even number of beads wide to produce a completely dense part. However, voids can still arise. This is often due to the part not being an even number of bead widths wide in some areas, or in geometry containing acute angles, as the process of generating closed contours cannot completely fill the space. Voids can be tolerated in smaller models, but in large-format additive manufacturing they may cause mechanical defects. To fill these voids, open loop paths called skeletons are often used, but they are typically limited by the physical constraints defined in the slicing software. To address this, researchers at Oak Ridge National Laboratory have extended skeleton toolpaths via an adaptive methodology. These adaptive skeletons were found to better fill void spaces through manipulation of physical parameters of the build process and were calculated as part of the slicing process.
Authorship Guidelines were developed to help facilitate trust among team members as we span multiple institutions, scientific disciplines, and career stages. NGEE Arctic was built on a foundation of open science, data sharing, and collaboration. In Phase 4 of the project, it was particularly important to keep this foundation in mind as we develop new collaborations across the Arctic. Included in this package is one *.pdf. The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research. Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.