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The logistic support of space facilities is described, with special attention given to the problem of sizing the inventory of ready spares kept at the space facility. Where possible, data from the Space Shuttle Orbiter is extrapolated to provide numerical estimates for space facilities. Attention is also given to repair effort estimation and long duration missions.
The strategic routing of aircraft through navigable and controlled airspace to minimize adverse noise impact over sensitive areas is critical in the proper management and planning of the U.S. based airport system. A major objective of this phase of research is to identify, inventory, characterize, and analyze the various environmental, land planning, and regulatory data bases, along with potential three dimensional software and hardware systems that can be potentially applied for an impact assessment of any existing or planned air route. There are eight data bases that have to be assembled and developed in order to develop three dimensional aircraft route impact methodology. These data bases which cover geographical information systems, sound metrics, land use, airspace operational control measures, federal regulations and advisories, census data, and environmental attributes have been examined and aggregated. A three dimensional format is necessary for planning, analyzing space and possible noise impact, and formulating potential resolutions. The need to develop this three dimensional approach is essential due to the finite capacity of airspace for managing and planning a route system, including airport facilities. It appears that these data bases can be integrated effectively into a strategic aircraft noise routing system which should be developed as soon as possible, as part of a proactive plan applied to our FAA controlled navigable airspace for the United States.
We set out to evolve ultra-low power air quality sensing technologies developed at JSC to add a highly accurate positioning sensor based on SBIR technology to give a self-surveying air quality monitoring platform with years-long lifetime on a small, disposable coin cell battery. Using Radio Frequency Identification (RFID) technology for data transport, the system can take advantage of RFID-based inventory management systems in place on lunar exploration assets to provide this capability with extremely small SWAP impacts. Years-long operational lifetimes enable flexible, autonomous environmental monitoring during lengthy intervals between and unprecedented situational awareness during crewed missions. Software integration of the localization system into the JSC RFID sensing platform was advanced, but the COVID-19 pandemic complicated and slowed maturation of the localization system SBIR product, and center closure indefinitely deferred a final hardware integration and system demonstration. In the meantime, progress was made to mature the air-quality sensing platform for flight, including hardware, software, antenna, and mechanical improvements. The underlying RFID sensing capability was also adapted to a drawer motion sensing system, which is currently (FY21) being taken toward an ISS flight demonstration as part of the RFID Enhanced Autonomous Logistics Management (REALM)-3 experiment.
Meteorites from the Moon, Mars, and many types of asteroid bodies have been identified among our global inventory of meteorites, however samples of Mercury and Venus have not been identified. The absence of mercurian and venusian meteorites could be attributed to an inability to recognize them in our collections due to a paucity of geochemical information for Venus and Mercury. In the case of mercurian meteorites, this possibility is further supported by dynamical calculations that suggest mercurian meteorites should be present on Earth at a factor of 2-3 less than meteorites from Mars [1]. In the present study, we focus on the putative mineralogy of mercurian meteorites using data obtained from the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft, which has provided us with our first quantitative constraints on the geochemistry of planet Mercury. We have used the MESSENGER data to compile a list of mineralogical and geochemical characteristics that a meteorite from Mercury is likely to exhibit.
Wood pallets play a critical role in the movement and storage of goods worldwide. They are an important component in the complex global supply chain and used by almost every industry. It is therefore important to assess the environmental implications of the wood pallet supply chain and identify optimization strategies that can be implemented. In this paper, primary 2018 annual production data collected from U.S. pallet manufacturers were used to develop the first industry-average life-cycle inventory (LCI). A new functional unit was proposed to perform a more refined and accurate environmental life-cycle assessment of the wood pallet supply chain. Using the LCI data developed, a cradle-to-grave industry-average life-cycle impact assessment was performed. This novel approach quantifies environmental impacts of a generic multi-use pallet, including repair and remanufacturing. The total global warming impact was 10.4 kg CO 2 e per 45.4 t of pallet loads of product delivered using wood pallets. The manufacturing stage contributed the most, about 35%, followed by the raw material supply stage. About 41% of total primary energy consumption was from renewable sources, with most sourced from biomass. Fossil fuels comprised about 52% of the total (225 MJ per functional unit) primary energy consumption. Total environmental impact was significantly affected by two main parameters: reference service life and load-bearing capacity. Pallet repair was also found to be an important component of the wood pallet supply chain, which has a low environmental footprint compared with the overall impact of a pallet and enables mitigation of overall impact by extending the reference service life. Finally, at end-of-life, common industry practices demonstrated substantial potential environmental benefits that can minimize overall environmental impact.
ERTS-1 satellite imagery has proved a valuable data source for land use as well as natural and cultural resource studies on a regional basis. ERTS-1 data also provide an excellent base for mapping resource related features and phenomena. These investigations are focused on a number of potential applications which are already showing promise of having operational utility.
The author has identified the following significant results. LANDSAT was used as a basis for inventorying land use within each of the Ohio-Kentucky-Indiana regional commissions, 225 drainage areas, and nine counties. Computer tabulations were produced to obtain the area covered by each of 16 land use categories within 225 drainage areas. The 16 categories were merged into ten categories and mapped at a scale of 1 inch = 5,000 ft, with detail to 0.44 hectares for the 2,700 sq mi region. These products were produced in less than 90 days, at a cost of $20,000.
The author has identified the following significant results. LANDSAT and aircraft data were compared for differences in absolute numbers of water bodies in the two data sets. This comparison was done using a linear regression analysis. The coefficients of determination were 0.65 and 0.74 for data of May and July, respectively. Standard statistical procedures were used to adjust the LANDSAT pond counts to actual pond numbers based on regression relationships.
A variety of conventional and remotely sensed data for the 25 million acre California Desert Conservation Area (CDCA) have been integrated and analyzed to estimate range carrying capacity. Multispectral classification was performed on a digital mosaic of ten Landsat frames. Multispectral classes were correlated with low level aerial photography, quantified and aggregated by grazing allotment, land ownership, and slope.
Mangroves are recognized for their valued ecosystem services to coastal areas, and the functional linkages between those services and ecosystem carbon stocks have been established. However, spatially explicit inventories are necessary to facilitate management and protection of mangroves, as well as providing a foundation for payment for ecosystem service programs such as REDD+. We conducted an inventory of carbon stocks in mangroves within Pongara National Park (PNP), Gabon using a stratified random sampling design based on forest canopy height derived from TanDEM-X remote sensing data. Ecosystem carbon pools, including aboveground and belowground biomass and necromass, and soil carbon to a depth of 2 m were assessed using measurements and samples from plots distributed among three canopy height classes within the park. There were two mangrove species within the inventory area in PNP, Rhizophora racemosa and R. harrisonii. R. harrisonii was predominant in the sparse, low-stature stands that dominated the west side of the park. In the east side of the park, both species occurred in tall-stature stands, with tree height often exceeding 30 m. Canopy height was an effective means to stratify the inventory area, as biomass was significantly different among the height classes. Despite those differences in aboveground biomass, the soil carbon density was not significantly different among height classes. Soils were the main component of the ecosystem carbon stock, accounting for over 84% of the total. The ecosystem carbon density ranged from 644 to 943 Mg C ha−1 among the three height classes. The ecosystem carbon stock within PNP is estimated to be 40,588 Gg C. The combination of pre-inventory information about stand conditions and their spatial distribution within the assessment area obtained from remote sensing data and a spatial decision support system were fundamental to implementing this relatively large-scale field inventory. This work exemplifies how mangrove carbon stocks can be quantified to augment national C reporting statistics, provide a baseline for projects involving monitoring, reporting and verification (i.e., MRV), and provide data on the forest composition and structure for sustainable management and conservation practices.
The aim of nuclear safeguards is to ensure that special nuclear material is used for peaceful purposes. Historically, nuclear material accounting (NMA) has provided the quantitative basis for monitoring for nuclear material loss or diversion, and process monitoring (PM) data is collected by the operator to monitor the process. PM data typically support NMA in various ways, often by providing a basis to estimate some of the in-process nuclear material inventory. We develop options for combining PM residuals and NMA residuals (residual = measurement - prediction), using a hybrid of period-driven and data-driven hypothesis testing. The modified statistical tests can be used on time series of NMA residuals (the NMA residual is the familiar material balance), or on a combination of PM and NMA residuals. The PM residuals can be generated on a fixed time schedule or as events occur.
A new emissions inventory of particulate matter (PM) is being derived mainly from remote sensing data using fire radiative power (FRP) and aerosol optical depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument, as well as wind data from the Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis dataset, which spans the satellite era. This product is generated using a coefficient of emission, C(sub e), that has been produced on a 1x1 degree global grid such that, when it is multiplied with satellite measurements of FRP or its time-integrated equivalent fire radiative energy (FRE) retrieved over a given area and time period, the corresponding PM emissions are estimated. This methodology of using C(sub e) to derive PM emissions is relatively new and advantageous for near real-time air quality applications compared to current methods based on post-fire burned area that may not provide emissions in a timely manner. Furthermore, by using FRP to characterize a fire s output, it will represent better accuracy than the use of raw fire pixel counts, since fires in individual pixels can differ in size and strength by orders of magnitude, resulting in similar differences in emission rates. Here we will show examples of this effect and how this new emission inventory can properly account for the differing emission rates from fires of varying strengths. We also describe the characteristics of the new emissions inventory, and propose the process chain of incorporating it into models for air quality applications.
This dataset includes associated information about individual pit and core samples (e.g., location, contact, date, data collected) sampled between 2016 and 2019 from NGEE Arctic sites at Teller (MM 27 and MM47), Kougarok (MM47 and MM56, Hillslope, and vegetation plot areas), and Council (MM71). The inventory is qualitative and excludes those datasets on soil pit cores that included time-series information. The package includes one each *.csv data file, *.kml and *.pdf. All quantitative data is available in the related datasets https://doi.org/10.5440/1417652, https://doi.org/10.5440/1423892, https://doi.org/10.5440/1342956, https://doi.org/10.5440/1854940, https://doi.org/10.5440/1544760, https://doi.org/10.5440/1346200, and https://doi.org/10.5440/1856042. The goal of this dataset is to help modelers understand when, where, and what data was collected with respect to physical soil properties on the Seward Peninsula, AK, USA over the course of NGEE Arctic’s campaigns in the region from 2016-2019. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
There are a growing number of land cover data available for the conterminous United States, supporting various applications ranging from biofuel regulatory decisions to habitat conservation assessments. These datasets vary in their source information, frequency of data collection and reporting, land class definitions, categorical detail, and spatial scale and time intervals of representation. These differences limit direct comparison, contribute to disagreements among studies, confuse stakeholders, and hamper our ability to confidently report key land cover trends in the U.S. Here we assess changes in cropland derived from the Land Change Monitoring, Assessment, and Projection (LCMAP) dataset from the U.S. Geological Survey and compare them with analyses of three established land cover datasets across the coterminous U.S. from 2008-2017: (1) the National Resources Inventory (NRI), (2) a dataset Lark et al. 2020 derived from the Cropland Data Layer (CDL), and (3) a dataset from Potapov et al. 2022. LCMAP reports more stable cropland and less stable noncropland in all comparisons, likely due to its more expansive definition of cropland which includes managed grasslands (pasture and hay). Despite these differences, net cropland expansion from all four datasets was comparable (5.18-6.33 million acres), although the geographic extent and type of conversion differed. LCMAP projected the largest cropland expansion in the southern Great Plains, whereas other datasets projected the largest expansion in the northwestern and central Midwest. Most of the pixel-level disagreements (86%) between LCMAP and Lark et al. 2020 were due to definitional differences among datasets, whereas the remainder (14%) were from a variety of causes. Cropland expansion in the LCMAP likely reflects conversions of more natural areas, whereas cropland expansion in other data sources also captures conversion of managed pasture to cropland. The particular research question considered (e.g., habitat versus soil carbon) should influence which data source is more appropriate.
Often, when classifying multispectral data, only one class or crop is of interest, such as wheat in the Large Area Crop Inventory Experiment (LACIE). Usual procedures for designing a Bayes classifier require that labeled training samples and therefore ground truth be available for the 'class of interest' plus all confusion classes defined by the multispectral data. This paper will consider the problem of designing a two-class Bayes classifier which will classify data into the 'class of interest' or the 'other' classes but will require only labeled training samples from the 'class of interest' to design the classifier. Thus, this classifier minimizes the need for ground truth. For these reasons, the classifier is referred to as a single-class classifier. A procedure for evaluating the overall performance of the single-class classifier in terms of the probability of error will be discussed.
There are a number of legacy pond and silo storage facilities on Sellafield Site that contain a broad range of ILW arising from the early Windscale Pile and Magnox Fuel Reprocessing Programmes. The waste comprises of solid Miscellaneous Beta Gamma Waste (MBGW) from operations (including filters, containerised waste, thermocouple wire, support struts, pumps, soft wastes) and associated sludges that have arisen from the corrosion of the fuel, Magnox Swarf cladding and the MBGW. It is intended to retrieve and package this waste to meet the requirements of interim storage and final disposal in the Box Encapsulation Plant (BEP) at Sellafield. As part of this process each package must be assigned a realistic and justifiable radionuclide and material inventory. These inventories can either be assigned by radiometric measurements including the associated errors or developed by fundamental data underpinning these assessments and a 'chain of custody' approach of knowing where the waste has been and how it has evolved. The subject of this paper is concerned with the development of the waste fingerprints for these legacy waste streams. As the wastes have arisen over a ∼70 year period there have been an assortment of different working practices and developments in equipment design that have led to significant variations in the waste condition and its composition. During storage the waste items have become mixed, corroded and sludge deposits have arisen. Because of this, and that early consignment records are either incomplete or missing, the task of assigning material and radionuclide fingerprints has been very challenging. In addition, to comply with regulatory requirements the inventory assignment has to be sufficiently robust to meet the processing envelope of the encapsulation process i.e. to allow controls to be made on the amounts of reactive materials (e.g. aluminium, Magnox and uranium) that can be processed within each product and allow identification of excluded items from being consigned from the donor plants. Also, as the waste comprises actinides, fission and activation products it was noted that whilst the UK standard inventory code FISPIN10 predicts the actinides and fission products well, there was a significant discrepancy in the prediction of activation products when compared with those predicted by the specialised fusion code FISPACT. It should be noted that the use of FISPACT to predict actinides and fission products is not recommended. To address these issues, - position statements were produced for each of the waste streams that would be consigned to BEP; - gaps within the data were assessed and where possible inactive trial work was identified to address it; - material and radionuclide fingerprint justification documents were developed from the position statements based upon available data, the experimental work and justified assumptions; - where FISPIN runs were required, these were run with a modified activation data library based on the European Activation File 2010 (EAF2010) to model the activation products. This paper summarises the BEP process, discusses examples of how the material and radionuclide waste stream fingerprints have been justified by revisiting the sample data, original consignment records, engineering drawings and recent remotely operated vehicle (ROV) pond surveys and making reasoned assumptions based on operational practices and non-active experimental work. Incorporation of the EAF2010 library within the FISPIN code has offered significant benefits to BEP and indeed other plants processing mixed fission and activated wastes as it allows comparable predictions of activation products compared with that made by FISPACT and for the plant to maintain one data processing stream rather than two if it had to utilise both modelling codes. This work will underpin the inventory submission in support of the Interim Letter of Compliance (ILoC) to Radioactive Waste Management Limited (RWM). The process of examining the original records, plant operation schedules and engineering drawings has enabled removal of some of the pessimisms that have been made in previous assessments. (authors)
In the face of increasing frequency of extreme events, enhancing resilience and reliability of energy infrastructure demands innovative solutions. Hydropower, with its inherent generation flexibility and grid-forming capability, offers significant potential for enhancing power grid resilience through the establishment of microgrids. To enable informed decision-making and strategic planning, we present the Hydropower Resilience Database (HRD) that integrates data from various sources such as Oakridge National Laboratory (ORNL) HydroSource, National Inventory of Dams, and US Western Grid database. By integrating relevant information on hydropower plant characteristics, including dams, reservoirs, and connected electrical grid, this resource enables hydropower plant owners, utilities, and community stakeholders to identify and evaluate the feasibility of using hydropower resources in microgrids to support nearby communities and critical infrastructure in various challenging scenarios. Using HRD, a set of metrics are evaluated to quantify the capability of hydropower plants to support essential microgrid functions. An interactive tool is developed using ArcGIS, enabling the visualization and analysis using HRD. Our research contributes to strategic planning efforts for fortifying energy infrastructure, ensuring reliable power supply during disruptions, and advancing the development of robust and resilient energy systems in regions susceptible to grid vulnerabilities.
In the face of increasing frequency of extreme events, enhancing resilience and reliability of energy infrastructure demands innovative solutions. Hydropower, with its inherent generation flexibility and grid-forming capability, offers significant potential for enhancing power grid resilience through the establishment of microgrids. To enable informed decision-making and strategic planning, we present the Hydropower Resilience Database (HRD) that integrates data from various sources such as Oakridge National Laboratory (ORNL) HydroSource, National Inventory of Dams, and US Western Grid database. By integrating relevant information on hydropower plant characteristics, including dams, reservoirs, and connected electrical grid, this resource enables hydropower plant owners, utilities, and community stakeholders to identify and evaluate the feasibility of using hydropower resources in microgrids to support nearby communities and critical infrastructure in various challenging scenarios. Using HRD, a set of metrics are evaluated to quantify the capability of hydropower plants to support essential microgrid functions. An interactive tool is developed using ArcGIS, enabling the visualization and analysis using HRD. Our research contributes to strategic planning efforts for fortifying energy infrastructure, ensuring reliable power supply during disruptions, and advancing the development of robust and resilient energy systems in regions susceptible to grid vulnerabilities.