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At least 289 records · Page 16

Dissemination and Use of NPOESS Data in AWIPS II

Real-time satellite information provides one of many data sources used by NWS forecast offices to diagnose current weather conditions and to assist in short-term forecast preparation. While GOES satellite data provides relatively coarse spatial resolution coverage of the continental U.S. on a 10-15 minute repeat cycle, polar orbiting data has the potential to provide snapshots of weather conditions at high-resolution in many spectral channels. The multispectral polar orbiting satellite capabilities allow for the derivation of image and sounding products not available from geostationary orbit. The utility of these polar orbiting measurements to forecasters has been demonstrated with NASA EOS observations as part of the Short-term Prediction and Research Transition (SPORT) program at Marshall Space Flight Center. SPORT scientists have been providing real-time MODIS data to NWS forecasters on an experimental basis to address a variety of short-term weather forecasting problems since 2003. The launch of the NPOESS Preparatory Project (NPP) satellite in 2009 will extend the continuity of high-resolution data provided by the NASA EOS satellites into future operational weather systems. The NPP data will be available in a timeframe consistent with the early installation of the next generation Advanced Weather Information Processing System (AWIPS) under development by Raytheon for the NWS. The AWIPS II system will be a JAVA-based decision support system which preserves the functionality of the existing systems and offers unique development opportunities for new data sources and applications in the Service Orientated Architecture (SOA) environment. The poster will highlight some of the advanced observing and display- capabilities of these new systems such as plug-ins for NASA and NPP datasets, and the development of local applications which are not well handled in the current AWIPS (e.g., 3D displays of LMA data, generation and display of 3-channel color composites, etc.).

Jedlovec, Gary↗

Some practical universal noiseless coding techniques

Some practical adaptive techniques for the efficient noiseless coding of a broad class of such data sources are developed and analyzed. Algorithms are designed for coding discrete memoryless sources which have a known symbol probability ordering but unknown probability values. A general applicability of these algorithms to solving practical problems is obtained because most real data sources can be simply transformed into this form by appropriate preprocessing. These algorithms have exhibited performance only slightly above all entropy values when applied to real data with stationary characteristics over the measurement span. Performance considerably under a measured average data entropy may be observed when data characteristics are changing over the measurement span.

Rice, R. F.↗

Chimera D-Series Gravitational Wave Emission Sourced from Matter

Gravitational wave data sourced from the the time-dependent fluid quadrupole motion, in the Chimera D-Series three-dimensional core collapse supernova simulations. Data from three models initiated from three different progenitors are presented: D9.6-3D, D15-3D, and D25-3D. Please see the README for more information about the data structure and progenitors.

79 ASTRONOMY AND ASTROPHYSICS↗

Alternate symbol inversion for improved symbol synchronization in convolutionally coded systems

Inverting alternate symbols of the encoder output of a convolutionally coded system provides sufficient density of symbol transitions to guarantee adequate symbol synchronizer performance, a guarantee otherwise lacking. Although alternate symbol inversion may increase or decrease the average transition density, depending on the data source model, it produces a maximum number of contiguous symbols without transition for a particular class of convolutional codes, independent of the data source model. Further, this maximum is sufficiently small to guarantee acceptable symbol synchronizer performance for typical applications. Subsequent inversion of alternate detected symbols permits proper decoding.

Simon, M. K.↗

What Happened, and Why: Toward an Understanding of Human Error Based on Automated Analyses of Incident Reports

The objective of the Aviation System Monitoring and Modeling (ASMM) project of NASA s Aviation Safety and Security Program was to develop technologies that will enable proactive management of safety risk, which entails identifying the precursor events and conditions that foreshadow most accidents. This presents a particular challenge in the aviation system where people are key components and human error is frequently cited as a major contributing factor or cause of incidents and accidents. In the aviation "world", information about what happened can be extracted from quantitative data sources, but the experiential account of the incident reporter is the best available source of information about why an incident happened. This report describes a conceptual model and an approach to automated analyses of textual data sources for the subjective perspective of the reporter of the incident to aid in understanding why an incident occurred. It explores a first-generation process for routinely searching large databases of textual reports of aviation incident or accidents, and reliably analyzing them for causal factors of human behavior (the why of an incident). We have defined a generic structure of information that is postulated to be a sound basis for defining similarities between aviation incidents. Based on this structure, we have introduced the simplifying structure, which we call the Scenario as a pragmatic guide for identifying similarities of what happened based on the objective parameters that define the Context and the Outcome of a Scenario. We believe that it will be possible to design an automated analysis process guided by the structure of the Scenario that will aid aviation-safety experts to understand the systemic issues that are conducive to human error.

Maille, Nicolas P.↗

Development of the Centralized Storm Information System (CSIS) for use in severe weather prediction

The centralized storm information system is now capable of ingesting and remapping radar scope presentations on a satellite projection. This can be color enhanced and superposed on other data types. Presentations from more than one radar can be composited on a single image. As with most other data sources, a simple macro establishes the loops and scheduling of the radar ingestions as well as the autodialing. There are approximately 60 NWS network 10 cm radars that can be interrogated. NSSFC forecasters have found this data source to be extremely helpful in severe weather situations. The capability to access lightning frequency data stored in a National Weather Service computer was added. Plans call for an interface with the National Meteorological Center to receive and display prognostic fields from operational computer forecast models. Programs are to be developed to plot and display locations of reported severe local storm events.

Mosher, F. R.↗

Weather Research and Forecasting Model Sensitivity Comparisons for Warm Season Convective Initiation

Mesoscale weather conditions can significantly affect the space launch and landing operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). During the summer months, land-sea interactions that occur across KSC and CCAFS lead to the formation of a sea breeze, which can then spawn deep convection. These convective processes often last 60 minutes or less and pose a significant challenge to the forecasters at the National Weather Service (NWS) Spaceflight Meteorology Group (SMG). The main challenge is that a "GO" forecast for thunderstorms and precipitation at the Shuttle Landing Facility is required at the 90 minute deorbit decision for End Of Mission (EOM) and at the 30 minute Return To Launch Site (RTLS) decision. Convective initiation, timing, and mode also present a forecast challenge for the NWS in Melbourne, FL (MLB). The NWS MLB issues such tactical forecast information as Terminal Aerodrome Forecasts (TAF5), Spot Forecasts for fire weather and hazardous materials incident support, and severe/hazardous weather Watches, Warnings, and Advisories. Lastly, these forecasting challenges can also affect the 45th Weather Squadron (45 WS), which provides comprehensive weather forecasts for shuttle launch, as well as ground operations, at KSC and CCAFS. The need for accurate mesoscale model forecasts to aid in their decision making is crucial. This study specifically addresses the skill of different model configurations in forecasting warm season convective initiation. Numerous factors influence the development of convection over the Florida peninsula. These factors include sea breezes, river and lake breezes, the prevailing low-level flow, and convergent flow due to convex coastlines that enhance the sea breeze. The interaction of these processes produces the warm season convective patterns seen over the Florida peninsula. However, warm season convection remains one of the most poorly forecast meteorological parameters. To determine which configuration options are best to address this specific forecast concern, the Weather Research and Forecasting (WRF) model, which has two dynamical cores - the Advanced Research WRF (ARW) and the Non-hydrostatic Mesoscale Model (NMM) was employed. In addition to the two dynamical cores, there are also two options for a "hot-start" initialization of the WRF model - the Local Analysis and Prediction System (LAPS; McGinley 1995) and the Advanced Regional Prediction System (ARPS) Data Analysis System (ADAS; Brewster 1996). Both LAPS and ADAS are 3- dimensional weather analysis systems that integrate multiple meteorological data sources into one consistent analysis over the user's domain of interest. This allows mesoscale models to benefit from the addition of highresolution data sources. Having a series of initialization options and WRF cores, as well as many options within each core, provides SMG and MLB with considerable flexibility as well as challenges. It is the goal of this study to assess the different configurations available and to determine which configuration will best predict warm season convective initiation.

Watson, Leela R.↗

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

60 APPLIED LIFE SCIENCES↗

HarDWR - Harmonized Water Rights Records

A dataset within the Harmonized Database of Western U.S. Water Rights (HarDWR). For a detailed description of the database, please see the meta-record v2.0. Changelog v2.0 - Recalculated based on data sourced from WestDAAT - Changed using a Site ID column to identify unique records to using aa combination of Site ID and Allocation ID - Removed the Water Management Area (WMA) column from the harmonized records. The replacement is a separate file which stores the relationship between allocations and WMAs. This allows for allocations to contribute to water right amounts to multiple WMAs during the subsequent cumulative process. - Added a column describing a water rights legal status - Added "Unspecified" was a water source category - Added an acre-foot (AF) column - Added a column for the classification of the right's owner v1.02 - Added a .RData file to the dataset as a convenience for anyone exploring our code. This is an internal file, and the one referenced in analysis scripts as the data objects are already in R data objects. v1.01 - Updated the names of each file with an ID number less than 3 digits to include leading 0s v1.0 - Initial public release Description Here we present an updated database of Western U.S. water right records. This database provides consistent unique identifiers for each water right record, and a consistent categorization scheme that puts each water right record into one of seven broad use categories. These data were instrumental in conducting a study of the multi-sector dynamics of inter-sectoral water allocation changes though water markets (Grogan et al., *in review*). Specifically, the data were formatted for use as input to a process-based hydrologic model, Water Balance Model (WBM), with a water rights module (Grogan et al., *in review*). While this specific study motivated the development of the database presented here, water management in the U.S. West is a rich area of study (e.g., Anderson and Woosly, 2005; Tidwell, 2014; Null and Prudencio, 2016; Carney et al., 2021) so releasing this database publicly with documentation and usage notes will enable other researchers to do further work on water management in the U.S. West. We produced the water rights database presented here in four main steps: (1) data collection, (2) data quality control, (3) data harmonization, and (4) generation of cumulative water rights curves. Each of steps (1)-(3) had to be completed in order to produce (4), the final product that was used in the modeling exercise in Grogan et al. (*in review*). All data in each step is associated with a spatial unit called a Water Management Area (WMA), which is the unit of water right administration utilized by the state in which the right came from. Steps (2) and (3) required use to make assumptions and interpretation, and to remove records from the raw data collection. We describe each of these assumptions and interpretations below so that other researchers can choose to implement alternative assumptions an interpretation as fits their research aims. Motivation for Changing Data Sources The most significant change has been a switch from collecting the raw water rights directly from each state to using the water rights records presented in WestDAAT, a product of the Water Data Exchange (WaDE) Program under the Western States Water Council (WSWC). One of the main reasons for this is that each state of interest is a member of the WSWC, meaning that WaDE is partially funded by these states, as well as many universities. As WestDAAT is also a database with consistent categorization, it has allowed us to spend less time on data collection and quality control and more time on answering research questions. This has included records from water right sources we had previously not known about when creating v1.0 of this database. The only major downside to utilizing the WestDAAT records as our raw data is that further updates are tied to when WestDAAT is updated, as some states update their public water right records daily. However, as our focus is on cumulative water amounts at the regional scale, it is unlikely most records updates would have a significant effect on our results. The structure of WestDAAT led to several important changes to how HarWR is formatted. The most significant change is that WaDE has calculated a field known as `SiteUUID`, which is a unique identifier for the Point of Diversion (POD), or where the water is drawn from. This separate from `AllocationNativeID`, which is the identifier for the allocation of water, or the amount of water associated with the water right. It should be noted that it is possible for a single site to have multiple allocations associated with it and for an allocation to be able to be extracted from multiple sites. The site-allocation structure has allowed us to adapt a more consistent, and hopefully more realistic, approach in organizing the water right records than we had with HarDWR v1.0. This was incredibly helpful as the raw data from many states had multiple water uses within a single field within a single row of their raw data, and it was not always clear if the first water use was the most important, or simply first alphabetically. WestDAAT has already addressed this data quality issue. Furthermore, with v1.0, when there were multiple records with the same water right ID, we selected the largest volume or flow amount and disregarded the rest. As WestDAAT was already a common structure for disparate data formats, we were better able to identify sites with multiple allocations and, perhaps more importantly, allocations with multiple sites. This is particularly helpful when an allocation has sites which cross WMA boundaries, instead of just assigning the full water amount to a single WMA we are now able to divide the amount of water between the number of relevant WMAs. As it is now possible to identify allocations with water used in multiple WMAs, it is no longer practical to store this information within a single column. Instead the stAllocationToWMATab.csv file was created, which is an allocation by WMA matrix containing the percent Place of Use area overlap with each WMA. We then use this percentage to divide the allocation's flow amount between the given WMAs during the cumulation process to hopefully provide more realistic totals of water use in each area. However, not every state provides areas of water use, so like HarDWR v1.0, a hierarchical decision tree was used to assign each allocation to a WMA. First, if a WMA could be identified based on the allocation ID, then that WMA was used; typically, when available, this applied to the entire state and no further steps were needed. Second was the spatial analysis of Place of Use to WMAs. Third was a spatial analysis of the POD locations to WMAs, with the assumption that allocation's POD is within the WMA it should belong to; if an allocation still had multiple WMAs based on its POD locations, then the allocation's flow amount would be divided equally between all WMAs. The fourth, and final, process was to include water allocations which spatially fell outside of the state WMA boundaries. This could be due to several reasons, such as coordinate errors / imprecision in the POD location, imprecision in the WMA boundaries, or rights attached with features, such as a reservoir, which crosses state boundaries. To include these records, we decided for any POD which was within one kilometer of the state's edge would be assigned to the nearest WMA. Other Changes WestDAAT has Allowed In addition to a more nuanced and consistent method of assigning water right's data to WMAs, there are other benefits gained from using the WestDAAT dataset. Among those is a consistent categorization of a water right's legal status. In HarDWR v1.0, legal status was effectively ignored, which led to many valid concerns about the quality of the database related to the amounts of water the rights allowed to be claimed. The main issue was that rights with legal status' such as "application withdrawn", "non-active", or "cancelled" were included within HarDWR v1.0. These, and other water rights status' which were deemed to not be in use have been removed from this version of the database. Another major change has been the addition of the "unspecified water source category. This is water that can come from either surface water or groundwater, or the source of which is unknown. The addition of this source category brings the total number of categories to three. Due to reviewer feedback, we decided to add the acre-foot (AF) column so that the data may be more applicable to a wider audience. We added the ownerClassification column so that the data may be more applicable to a wider audience. File Descriptions The dataset is a series of various files organized by state sub-directories. In addition, each file begins with the state's name, in case the file is separate from its sub-directory for some reason. After the state name is the text which describes the contents of the file. Here is each file described in detail. Note that st is a placeholder for the state's name. stFullRecords_HarmonizedRights.csv: A file of the complete water records for each state. The column headers for each of this type of file are: state - The name of the state to which the allocations belong to. FIPS - The two digit numeric state ID code. siteID - The site location ID for POD locations. A site may have multiple allocations, which are the actual amount of water which can be drawn. In a simplified hypothetical, a farm stead may have an allocation for "irrigation" and an allocation for "domestic" water use, but the water is drawn from the same pumping equipment. It should be noted that many of the site ID appear to have been added by WaDE, and therefore may not be recognized by a given state's water rights database. allocationID - The allocation ID for the water right. For most states this is the water right ID, and what is recommended to use should a right be looked up on a given state's water rights database. The water amounts associated with these IDs tend to be finer scaled than those associated with siteID. It should be noted that some allocations may be extracted from multiple sites, particularly for larger Places of Use. ownerClassification - A classification of the types of owners for water rights. The most common is `Private` which incorporates a wide range of entities. Several classifications would be grouped into a government category, most of which are for the U.S. Federal Government. These allocations could be listed as "Federal", "United States of America", or as the names of any number of federal agencies. The last major grouping of entities is for "Native American"s. priorityDate - The date we use as the water right priority date for our modeling analysis. This is the legal priority date when it is available. However, for some rights, specifically from California and New Mexico, we used a pseudo priority date (e.g. well completion date or start of well drilling date) when a legal priority date was not available. The most questionable dates come from New Mexico, where the only date associated with certain water right records was the date the allocation was recorded in the database. As the allocation record creation tended to be within a few months of the filing of the application of the water right, from manually double checking the water rights, and our analysis focuses on aggregating water rights on the timescale of years, we determined it was acceptable to use such dates to include as many records as possible. primaryBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories WestDAAT. This column is the original WaDE category for the primary water use at the PoD site. allocationBeneficialUse - From the numerous state water use categories, WaDE categorized them into 21 categories for WestDAAT. This column is the original WaDE category

Economics↗

Methods for multisource data analysis in remote sensing

Methods for classifying remotely sensed data from multiple data sources are considered. Special interest is in general methods for multisource classification and three such approaches are considered: Dempster-Shafer theory; fuzzy set theory; and statistical multisource analysis. To apply statistical multisource analysis successfully it is necessary to characterize the reliability of each data source. Separability measures and classification accuracy are used to measure the reliability. These reliability measures are then associated with reliability factors included in the statistical multisource analysis to multispectral scanner data where different segments of the electromagnetic spectrum are treated as different sources. A discussion is included concerning future directions for investigating reliability measures.

Benediktsson, Jon Atli↗

Information Power Grid: Distributed High-Performance Computing and Large-Scale Data Management for Science and Engineering

We use the term "Grid" to refer to distributed, high performance computing and data handling infrastructure that incorporates geographically and organizationally dispersed, heterogeneous resources that are persistent and supported. This infrastructure includes: (1) Tools for constructing collaborative, application oriented Problem Solving Environments / Frameworks (the primary user interfaces for Grids); (2) Programming environments, tools, and services providing various approaches for building applications that use aggregated computing and storage resources, and federated data sources; (3) Comprehensive and consistent set of location independent tools and services for accessing and managing dynamic collections of widely distributed resources: heterogeneous computing systems, storage systems, real-time data sources and instruments, human collaborators, and communications systems; (4) Operational infrastructure including management tools for distributed systems and distributed resources, user services, accounting and auditing, strong and location independent user authentication and authorization, and overall system security services The vision for NASA's Information Power Grid - a computing and data Grid - is that it will provide significant new capabilities to scientists and engineers by facilitating routine construction of information based problem solving environments / frameworks. Such Grids will knit together widely distributed computing, data, instrument, and human resources into just-in-time systems that can address complex and large-scale computing and data analysis problems. Examples of these problems include: (1) Coupled, multidisciplinary simulations too large for single systems (e.g., multi-component NPSS turbomachine simulation); (2) Use of widely distributed, federated data archives (e.g., simultaneous access to metrological, topological, aircraft performance, and flight path scheduling databases supporting a National Air Space Simulation systems}; (3) Coupling large-scale computing and data systems to scientific and engineering instruments (e.g., realtime interaction with experiments through real-time data analysis and interpretation presented to the experimentalist in ways that allow direct interaction with the experiment (instead of just with instrument control); (5) Highly interactive, augmented reality and virtual reality remote collaborations (e.g., Ames / Boeing Remote Help Desk providing field maintenance use of coupled video and NDI to a remote, on-line airframe structures expert who uses this data to index into detailed design databases, and returns 3D internal aircraft geometry to the field); (5) Single computational problems too large for any single system (e.g. the rotocraft reference calculation). Grids also have the potential to provide pools of resources that could be called on in extraordinary / rapid response situations (such as disaster response) because they can provide common interfaces and access mechanisms, standardized management, and uniform user authentication and authorization, for large collections of distributed resources (whether or not they normally function in concert). IPG development and deployment is addressing requirements obtained by analyzing a number of different application areas, in particular from the NASA Aero-Space Technology Enterprise. This analysis has focussed primarily on two types of users: the scientist / design engineer whose primary interest is problem solving (e.g. determining wing aerodynamic characteristics in many different operating environments), and whose primary interface to IPG will be through various sorts of problem solving frameworks. The second type of user is the tool designer: the computational scientists who convert physics and mathematics into code that can simulate the physical world. These are the two primary users of IPG, and they have rather different requirements. The results of the analysis of the needs of these two types of users provides a broad set of requirements that gives rise to a general set of required capabilities. The IPG project is intended to address all of these requirements. In some cases the required computing technology exists, and in some cases it must be researched and developed. The project is using available technology to provide a prototype set of capabilities in a persistent distributed computing testbed. Beyond this, there are required capabilities that are not immediately available, and whose development spans the range from near-term engineering development (one to two years) to much longer term R&D (three to six years). Additional information is contained in the original.

Johnston, William E.↗

The development of the July 1989 1 deg x 1 deg and 30' x 30' terrestrial mean free-air anomaly data bases

In June 1986 a 1 x 1 deg/mean free-air anomaly data file containing 48955 anomalies was completed. In August 1986 a 30 x 30 min mean free-air anomaly file was defined containing 31787 values. For the past three years data has been collected to upgrade these mean anomaly files. The primary emphasis was the collection of data to be used for the estimation of 30 min means anomalies in land areas. The emphasis on land areas was due to the anticipated use of 30 min anomalies derived from satellite altimeter data in the ocean areas. There were 10 data sources in the August 1986 file. Twenty-eight sources were added based on the collection of both point and mean anomalies from a number of individuals and organizations. A preliminary 30 min file was constructed from the 38 data sources. This file was used to calculate 1 x 1 deg mean anomalies. This 1 x 1 deg file was merged with a 1 x 1 deg file which was a merger of the June 1986 file plus a 1 x 1 deg file made available by DMA Aerospace Center. Certain bad 30 min anomalies were identified and deleted from the preliminary 30 min file leading to the final 30 min file (the July 1989 30 min file) with 66990 anomalies and their accuracy. These anomalies were used to again compute 1 x 1 deg anomalies which were merged with the previous June 86 DMAAC data file. The final 1 x 1 deg mean anomaly file (the July 89 1 x 1 deg data base) contained 50793 anomalies and their accuracy. The anomaly data files were significantly improved over the prior data sets in the following geographic regions: Africa, Scandinavia, Canada, United States, Mexico, Central and South America. Substantial land areas remain where there is little or no available data.

Kim, Jeong-Hee↗

SimScape Terrain Modeling Toolkit

This paper describes the SIMSCAPE middleware toolkit that has been developed recently to provide a common infrastructure for importing terrain model data from multiple data sources and making them available to simulation applications. The SIMSCAPE infrastructure simplifies the overall simulation design by eliminating the traditional need for custom terrain model interfaces to terrain data sources and simulation users. SIMSCAPE provides a collection of libraries and tools to use and manage terrain environment models within a wide range of simulation applications.

terrain↗

Correlation of Canopy Distortion with Asymmetric Loading in Large Diameter Ringsail Parachutes

The second generation of the main parachutes for the Orion Capsule Parachute Assembly System (CPAS) were subjected to multiple flight tests. Three of the tests included were instrumented with the Tension Measuring System (TMS) to record main dispersion bridle loads. While these units were not able to resolve the individual suspension line loads, they were able to record a course representation of the asymmetric loading of the main parachutes during inflation. These tests were also equipped with upward looking cameras mounted on the test vehicles to collect detailed imagery of main deployment and inflation. When analyzed independently, both data sources exhibit similar features. These similarities provide evidence that the suspension line loads and the distortions in the canopy geometry are not independent. This paper will examine both data sources simultaneously to establish a correlation between asymmetric loading at the canopy skirt and the deformations of the canopy. This correlation will be used to build a model which can estimate individual suspension line loads from a measured riser load and detailed imagery.

Parachutes↗

Multiple aspects maintenance ontology-based intelligent maintenance optimization framework for safety-critical systems

Abstract Maintenance optimization is a process for improving the efficiency of maintenance strategies and activities, considering various aspects of the target system and components, such as the probabilities of system failures and the cost of repair and replacement of a failed component. The improvement of maintenance optimization algorithms generally requires information from various data sources. For example, it may require the system risk information derived from risk analysis tools or the residual lifetime of a component from fault prognosis tools. The requirements of data acquisition (DAQ) and aggregation pose new challenges for maintenance management systems (MMSs) that implement and use these maintenance optimization algorithms. This paper proposes a multiple aspects maintenance ontology-based framework to facilitate DAQ from MMSs, online monitoring systems, fault detection and discrimination tools, risk assessment tools, decision-making tools, and component identification tools, and accelerate the implementation and verification of contemporary maintenance optimization models and algorithms. The proposed framework consists of a multi-aspect maintenance ontology with critical information for maintenance optimization and application interfaces for collecting information from various data sources, such as fault prognosis tools, online monitoring tools, risk assessment tools, and decision-making algorithms. In addition, this paper proposes a heuristic method for integrating concepts and properties from other existing ontologies into the proposed framework when the existing ontology is not fully compatible with the ontology under construction. Finally, the paper verifies the proposed ontology framework using a feedwater system designed for nuclear power plants with valves and filters as the components under maintenance.

Diao, Xiaoxu (ORCID:0000000346726352)↗

Fine-Root Ecology Database (FRED): A Global Collection of Root Trait Data with Coincident Site, Vegetation, Edaphic, and Climatic Data, Version 3.

To address the need for a centralized root trait database, we compiled the Fine-Root Ecology Database (FRED) from published and unpublished data sources. We have continued to add to the FRED database since the release of FRED 2.0 in 2018, and a new version of FRED is now available. FRED 3.0 has more than 150,000 observations of more than 330 root traits, with data collected from more than 1400 data sources. FRED 3.0 has 45% more root trait observations than FRED 2.0, particularly in the categories of root anatomy, morphology, and microbial associations; ancillary data on associated site, vegetation, edaphic, and climatic conditions from across the globe have also increased concurrently. FRED is focused on fine roots (traditionally defined as roots less than 2 mm in diameter), as coarse roots are studied using different methodology, often at very different scales, and have different traits and trait interpretations. However, FRED accepts data collected from roots of all sizes, and already contains several observations of coarse roots. Data collection will continue for the foreseeable future.

54 ENVIRONMENTAL SCIENCES↗

National Energy Water Treatment and Speciation (NEWTS) Database & Dashboard

The Department of Energy's Office of Fossil Energy & Carbon Management (DOE/FECM) through the National Energy Technology Laboratory (NETL) has launched a free online tool, the National Energy Water Treatment and Speciation (NEWTS) Database and Dashboard, which can be utilized by community leaders and water researchers to better understand the composition of energy-related wastewater streams. The NEWTS Database and Dashboard provide public access to difficult-to-access datasets, including the original data sources and the processed data forms for input into aqueous chemistry modeling software. The data provided by the tool will help mitigate environmental risks and identify possible sources of valuable critical minerals (CM). The goal of this ASME Power presentation is to highlight the data and capabilities of this free online-tool for obtaining high quality water datasets in formats that are easy for modeling the treatment and recovery of valuable resources from effluent waste stream associated with energy operations.

Siefert, Nicholas↗

Pseudo noise code and data transmission method and apparatus

Pseudo noise ranging codes, having a predetermined chipping rate, and a pair of binary data sources, each having a bit rate no greater than one tenth the chipping rate, quadriphase, digitally modulate a suppressed carrier wave having a first frequency are examined. Two additional binary data sources, each having a bit rate that is not restricted by the chipping rate of the first carrier, quadriphase, digitally modulate a suppressed carrier wave having a second frequency. The first and second frequencies are only slightly displaced so that there is overlap in the frequency bands which modulate the two carriers. The two suppressed carrier waves are linearly combined and transmitted from a first station to a second station so that the amplitude of the transmitted first wave is controlled so as not to degrade the detectability of the second wave at the second station.

Deerkoski, L. F.↗