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This dataset contains the event log table with 10-minute wind statistics from the scanning lidar at AWAKEN's site A2. This is a good dataset to start from for people unfamiliar with the AWAKEN project.
This dataset contains the event log table that provides 10-minute wind statistics from the scanning lidars at AWAKEN's sites A1, A2, and H. This is a good dataset to start from for people unfamiliar with the AWAKEN project.
The automated software system for archiving, analyzing, and interrogating data from the CCD/Transit Instrument (CTI) is described. The CTI collects up to 450 Mbytes of image-data each clear night in the form of a narrow strip of sky observed in two colors. The large data-volumes and the scientific aims of the project make it imperative that the data are analyzed within the 24-hour period following the observations. To this end a fully automatic and self evaluating software system has been developed. The data are collected from the telescope in real-time and then transported to Tucson for analysis. Verification is performed by visual inspection of random subsets of the data and obvious cosmic rays are detected and removed before permanent archival is made to the optical disc. The analysis phase is performed by a pair of linked algorithms, one operating on the absolute pixel-values and the other on the spatial derivative of the data. In this way both isolated and merged images are reliably detected in a single pass. In order to isolate the latter algorithm from the effects of noise spikes a 3x3 Hanning filter is applied to the raw data before the analysis is run. The algorithms reduce the input pixel-data to a database of measured parameters for each image which has been found. A contrast filter is applied in order to assign a detection-probability to each image and then x-y calibration and intensity calibration are performed using known reference stars in the strip. These are added to as necessary by secondary standards boot-strapped from the CTI data itself. The final stages involve merging the new data into the CTI Master-list and History-list and the automatic comparison of each new detection with a set of pre-defined templates in parameter-space to find interesting objects such as supernovae, quasars and variable stars. Each stage of the processing from verification to interesting image selection is performed under a data-logging system which both controls the pipe-lining of data through the system and records key performance monitor parameters which are built into the software. Furthermore, the data from each stage are stored in databases to facilitate evaluation, and all stages offer the facility to enter keyword-indexed free-format text into the data-logging system. In this way a large measure of certification is built into the system to provide the necessary confidence in the end results.
Over, the years, NASA has played an important role in providing to and the development of automated nutrient delivery and monitoring, systems for growing crops hydroponically for long term space missions. One example are the systems used in the Biomass Production Chamber (BPC) at Kennedy Space Center (KSC). The current KSC monitoring system is based on an engineering workstation using standard analog/digital input/output hardware and custom written software. The monitoring system uses completely separate sensors to provide a check of control sensor accuracy and has the ability to graphically display and store data form past experiment so that they are available for data analysis [Fortson, 1992]. In many cases, growing systems have not been fitted with the kind of automated control systems as used at KSC. The Center for Food and Environmental Systems for Human Exploration of Space (CFESH) located on the campus of Tuskegee University, has effectively grown sweetpotatoes and peanuts hydroponically for the past five years. However they have adjusted the pH electrical conductivity and volume of the hydroponic nutrient solution only manually at times when the solution was to be replenished or changed out according to its protocol (e.g. one-week, two-week, or two-day cycle). But the pH of the nutrient solution flowing through the channel is neither known nor controlled between the update, change out, or replenishment period. Thus, the pH of the nutrient solution is not held at an optimum level over the span of the plant's growth cycle. To solve this dilemma, an automated system for the control and data logging of pH data relative to sweetpotato production using the nutrient film technique (NFT) has been developed, This paper discusses a microprocessor-based system, which was designed to monitor, control, and record the pH of a nutrient solution used for growing sweetpotatoes using NFT.
The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2021 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store). This submission includes the background data used to generate the 2021 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2021 model run is also included, which contains the statements at the DEBUG level and above.
The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2020 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilizes the appdirs Python dependency (https://pypi.org/project/appdirs/). An overview of the ElectricityLCI data stores may be found on the README (https://github.com/USEPA/ElectricityLCI/blob/v2.0/README.md#data-store). This submission includes the background data used to generate the 2020 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: python -c "import appdirs; print(appdirs.user_data_dir())"). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2020 model run is also included, which contains the statements at the DEBUG level and above.
The ElectricityLCI v2 Python package (https://github.com/USEPA/ElectricityLCI/tree/v2.0) was used to generate the 2022 electricity baseline: a regionalized life cycle inventory model of U.S. electricity generation, consumption, and distribution using standardized facility and generation data. ElectricityLCI implements a local data store for downloading and accessing public data on an individual's computer. The data store follows the folder definition provided by USEPA's esupy Python package (https://github.com/USEPA/esupy), which utilized the appdirs Python dependency (https://pypi.org/project/appdirs/). This submission includes the background data used to generate the 2022 electricity baseline inventory. Each zip archive stores the source files as found in their data stores. Sub-folders in each of the data stores are archived separately. For example, stewi.zip contains the JSON files, while stewi.facility.zip is the 'facility' sub-folder of stewi data store that stores the parquet files. To reproduce the data store, extract each zip file and drag-and-drop sub-folders in to their appropriate root folders to recreate the data stores, then copy the root folders to your data store folder (as returned by running the following on the command line: `python -c "import appdirs; print(appdirs.user_data_dir())"`). The main five data stores include: 'electricitylci', 'facilitymatcher', 'fedelemflowlist', 'stewi', and 'stewicombo'. The log file generated by the 2022 model run is also included, which contains the statements at the DEBUG level and above.
Processing parameters and machine log data for the MCPC LDRD Agile investment is collected material samples processed. This dataset captures the selected processing parameters, machine logs captured during material processing, and descriptions of how characterization samples were extracted from processed plates of material. The collect characterization data is captured in other datasets.
Two autopilots providing autonomous flight capabilities are presented herein. The first is the Pico-Pilot, demonstrated for the 12-inch size class of micro air vehicles. The second is the MicroPilot MP2028(sup g), where its integration into a 36-inch Zagi airframe (tailless, elevons only configuration) is investigated and is the main focus of the report. Analytical methods, which include the use of the Advanced Aircraft Analysis software from DARCorp, were used to determine the stability and control derivatives, which were then validated through wind tunnel experiments. From the aerodynamic data, the linear, perturbed equations of motion from steady-state flight conditions may be cast in terms of these derivatives. Using these linear equations, transfer functions for the control and navigation systems were developed and feedback control laws based on Proportional, Integral, and Derivative (PID) control design were developed to control the aircraft. The PID gains may then be programmed into the autopilot software and uploaded to the microprocessor of the autopilot. The Pico-Pilot system was flight tested and shown to be successful in navigating a 12-inch MAV through a course defined by a number of waypoints with a high degree of accuracy, and in 20 mph winds. The system, though, showed problems with control authority in the roll and pitch motion of the aircraft: causing oscillations in these directions, but the aircraft maintained its heading while following the prescribed course. Flight tests were performed in remote control mode to evaluate handling, adjust trim, and test data logging for the Zagi with integrated MP2028(sup g). Ground testing was performed to test GPS acquisition, data logging, and control response in autonomous mode. Technical difficulties and integration limitations with the autopilot prevented fully autonomous flight from taking place, but the integration methodologies developed for this autopilot are, in general, applicable for unmanned air vehicles within the 36-inch size class or larger that use a PID control based autopilot.
The project Characterization of Most Promising Sequestration Formations in the Rocky Mountain Region is one of 9 site characterization projects that were implemented as part of ARRA (American Recovery and Reinvestment Act). Data from this project was used to improve resolution of data in NATCARB in the area of study. Data related to this study has already been incorporated in NATCARB Atlas. The Rocky Mountain Carbon Capture and Storage (RMCCS) project investigated multiple geologic formations and characterized a local site on the Colorado Plateau for future CCS opportunities. The RMCCS project focused on the Cretaceous Dakota, Jurassic Entrada, and Pennsylvanian Weber Sandstones, the three largest regional formations. All formations in this project are potential CO2 storage resources for future power plants, natural gas processing plants, cement plants, and oil shale development projects. The area adjacent to Craig, Colorado, (Sand Wash Basin) was the area selected for detailed geologic characterization on the RMCCS project. The basin was selected in part because the geology can be extrapolated to other sites on the Colorado Plateau. Field mapping and seismic surveys were conducted to identify and evaluate the basin's structural configuration. A 9,745-foot deep characterization well was drilled to collect 131 feet of core and a suite of geophysical well log data. Petrophysical tests on samples of core were used to calibrate geophysical log data, which can be used to obtain storage resource estimates and evaluate associated uncertainty as well as simulate the hydrologic behavior of injected CO2. A detailed analysis of the primary formations (Dakota, Entrada and Weber sandstones) yielded a more accurate CO2 storage resource assessment for these formations within the Colorado Plateau; RMCCS estimates indicate a total CO2 storage resource of more than 38,000 million metric tons. The characterization of the Sand Wash Basin (2-D seismic surveys, multiple well logs and lithological, petrophysical and geochemical analyses) allowed for a detailed 3-D model to be constructed. The model served as the framework for analyses ranging from CO2 storage resource, injectivity, and subsurface flow to uncertainty estimates to evaluation of risk.
Abundant evidence now shows that the buried Chicxulub structure in northern Yucatan, Mexico, is indeed the intensely sought-after source of the ejecta found world-wide at the Cretaceous-Tertiary (K/T) boundary. In addition to large-scale concentric patterns in gravity and magnetic data over the structure, recent analyses of drill-core samples reveal a lithological assemblage similar to that observed at other terrestrial craters. This assemblage comprises suevite breccias, ejecta deposit breccias (Bunte Breccia equivalents), fine-grained impact melt rocks, and melt-matrix breccias. All these impact-produced lithologies contain diagnostic evidence of shock metamorphism, including planar deformation features in quartz, feldspar, and zircons; diaplectic glasses of quartz and feldspar; and fused mineral melts and whole-rock melts. In addition, elevated concentrations of Ir, Re, and Os, in meteoritic relative proportions, have been detected in some melt-rock samples from the center of the structure. Isotopic analyses, magnetization of melt-rock samples, and local stratigraphic constraints identify this crater as the source of K/T boundary deposits.
This paper describes a series of small unmanned aerial system (sUAS) flights performed at NASA Langley Research Center in April and May of 2019 to test a newly added Steer-to-Clear feature for the Safe2Ditch (S2D) prototype system. S2D is an autonomous crash management system for sUAS. Its function is to detect the onset of an emergency for an autonomous vehicle, and to enable that vehicle in distress to execute safe landings to avoid injuring people on the ground or damaging property. Flight tests were conducted at the City Environment Range for Testing Autonomous Integrated Navigation (CERTAIN) range at NASA Langley. Prior testing of S2D focused on rerouting to an alternate ditch site when an occupant was detected in the primary ditch site. For Steer-to-Clear testing, S2D was limited to a single ditch site option to force engagement of the Steer-to-Clear mode. The implementation of Steer-to-Clear for the flight prototype used a simple method to divide the target ditch site into four quadrants. An RC car was driven in circles in one quadrant to simulate an occupant in that ditch site. A simple implementation of Steer-to- Clear was programmed to land in the opposite quadrant to maximize distance to the occupant’s quadrant. A successful mission was tallied when this occurred. Out of nineteen flights, thirteen resulted in successful missions. Data logs from the flight vehicle and the RC car indicated that unsuccessful missions were due to geolocation error between the actual location of the RC car and the derived location of it by the Vision Assisted Landing component of S2D on the flight vehicle. Video data indicated that while the Vision Assisted Landing component reliably identified the location of the ditch site occupant in the image frame, the conversion of the occupant’s location to earth coordinates was sometimes adversely impacted by errors in sensor data needed to perform the transformation. Logged sensor data was analyzed to attempt to identify the primary error sources and their impact on the geolocation accuracy. Three trends were observed in the data evaluation phase. In one trend, errors in geolocation were relatively large at the flight vehicle’s cruise altitude, but reduced as the vehicle descended. This was the expected behavior and was attributed to sensor errors of the inertial measurement unit (IMU). The second trend showed distinct sinusoidal error for the entire descent that did not always reduce with altitude. The third trend showed high scatter in the data, which did not correlate well with altitude. Possible sources of observed error and compensation techniques are discussed.
A recent well, GDC-36, was drilled in The Geysers Geothermal Field served in a Department of Energy-industry to demonstrate improved drilling performance with polycrystalline diamond compact (PDC) bits. Both PDC and roller cone drill bits were used to drill this well. Key challenges encountered during drilling included lost circulation in the mud-drilled section, and bit damage interfacial severity in the deeper, air-drilled section. The objective of this study is to evaluate the drilling performance in relation to the local geological characteristics using machine learning methods. By applying K-clustering to the sonic log data, we were able to identify areas correlated with measured lost circulation. Also, the boundaries defined by clustering of the mineralogical and lithological data from the mud logs correlate well with interfacial severity during drilling. A random forest model was employed to build correlation between drilling data and rock strength. The confined compressive strength (CCS) of the rock in the training of the machine learning model was inferred from the dipole sonic log. The R-squared of the testing data is 0.78, and the RMSE (Root Mean Squared Error) is 0.06. The trained model was used to forecast rock strength for the section where sonic log data are not available. CCS could also be inferred from mud logs provided the relationship between mineralogy and rock strength is established through core testing data.
The International Space Station (ISS) is a unique workplace environment for U.S. astronauts and Russian cosmonauts to conduct research and live for a period of six months or more. Noise has been an enduring environmental physical hazard that has been a challenge for the U.S. space program since before the Apollo era. Noise exposure in ISS poses significant risks to the crewmembers, such as; hearing loss (temporary or permanent), possible disruptions of crew sleep, interference with speech intelligibility and communication, possible interference with crew task performance, and possible reduction in alarm audibility. Acoustic measurements are made aboard ISS and compared to requirements in order to assess the acoustic environment to which the crewmembers are exposed. The purpose of this paper is to describe in detail the noise exposure monitoring program as well as an assessment of the acoustic dosimeter data collected to date. The hardware currently being used for monitoring the noise exposure onboard ISS will be discussed. Acoustic data onboard ISS has been collected since the beginning of ISS (Increment 1, November 2000). Noise exposure data analysis will include acoustic dosimetry logged data from crew-worn during work and sleep periods and also fixed-location measurements from Increment 1 to present day. Noise exposure levels (8-, 16- and 24-hr), LEQ, will also be provided and discussed in this paper. Discussions related to hearing protection will also be included. Future directions and recommendations for the noise exposure monitoring program will be highlighted. This acoustic data is used to ensure a safe and healthy working and living environment for the crewmembers aboard the ISS.
The International Space Station (ISS) is a unique workplace environment for U.S. astronauts and Russian cosmonauts to conduct research and live for a period of six months or more. Noise has been an enduring environmental physical hazard that has been a challenge for the U.S. space program since before the Apollo era. Noise exposure in ISS poses significant risks to the crewmembers, such as; hearing loss (temporary or permanent), possible disruptions of crew sleep, interference with speech intelligibility and communication, possible interference with crew task performance, and possible reduction in alarm audibility. Acoustic measurements were made onboard ISS and compared to requirements in order to assess the acoustic environment to which the crewmembers are exposed. The purpose of this paper is to describe in detail the noise exposure monitoring program as well as an assessment of the acoustic dosimeter data collected to date. The hardware currently being used for monitoring the noise exposure onboard ISS will be discussed. Acoustic data onboard ISS has been collected since the beginning of ISS (Increment 1, November 2001). Noise exposure data analysis will include acoustic dosimetry logged data from crew-worn dosimeters during work and sleep periods and also fixed-location measurements from Increment 1 to present day. Noise exposure levels (8-, 16- and 24-hr), LEQ, will also be provided and discussed in this paper. Future directions and recommendations for the noise exposure monitoring program will be highlighted. This acoustic data is used to ensure a safe and healthy working and living environment for the crewmembers onboard the ISS.
The project Characterization of Pliocene and Miocene Formations in the Wilmington Graben, Offshore Los Angeles, for Large-Scale Geologic Storage of CO2 is one of 9 site characterization projects that were implemented as part of ARRA (American Recovery and Reinvestment Act). Data from this project was used to improve resolution of data in NATCARB in the area of study. Data related to this study has already been incorporated in NATCARB Atlas. The Los Angeles Basin presents an opportunity for large-scale geologic CO2 storage. Due to its large population and historical and geologic setting as one of the most prolific oil and gas producing basins in the United States, the region is home to more than 12 major power plants and oil refineries that produce more than 5 million metric tons of fossil fuel-related CO2 emissions each year. GeoMechanics Technologies worked to characterize the Pliocene and Miocene sediments in the Wilmington Graben, offshore of Los Angeles, California, for high-volume CO2 storage. The Graben is located offshore of the Los Angeles and Long Beach Harbor area, making it accessible yet geologically isolated from the nearby Wilmington oilfield and onshore areas. These sediments span more than 5,000 feet of vertical interval with an estimated storage resource of more than 100 million metric tons of CO2. The project team analyzed and interpreted existing geologic data within the region, including detailed exploration well log data and 2-D and 3-D seismic data. New seismic lines were acquired to fill in current data gap areas and two new characterization wells were drilled and logged. This information was integrated with existing geologic interpretations for adjacent onshore areas to help characterize optimal areas for CO2 storage and seals to safely store CO2. Integrated 3-D geologic and geomechanical models for the Wilmington Graben were developed to simulate the fate and transport of injected CO2 in the subsurface and to assess risks. This project contributed to the understanding of injectivity, containment mechanisms, rate of dissolution and mineralization, and storage capacity of the Wilmington Graben and associated analogous basins. This effort also provided greater insight into the potential for offshore geologic formations to safely and permanently store CO2.
The contents of this CD-ROM include: 1) Aero Data; 2) Data Documents (Daily Acoustic Data Logs, Test Documentation, Test Photos); 3) EPNL Data (All Core Tones Removed, All Core Tones Removed and Various Bypass Tones, Core BPF Tone Removed, Core Tones Present); 4) Far-Field Acoustic Data, 5) High Speed Fan Reports; 6) Sound Power Levels (As-Measured PWL, Lossless PWL).
Aerial photography and information system technology were used to generate information required for the effective operation of three water-related programs in Michigan. Potential mosquito breeding sites were identified from specially acquired low altitude 70 mm color photography for the city of Lansing; the inventory identified 35% more surface water areas than indicated on existing field maps. A comprehensive inventory of surface water sources and potential access sites was prepared to assist fire departments in Antrim County with fire truck water-recharge operations. Remotely-sensed land cover/use data for Windsor Township, Eaton County, were integrated with other resource data into a computer-based information system for regional water quality studies. Eleven thematic maps focusing on landscape features affecting non-point water pollution and waste disposal were generated from analyses of a four-hectare grid-based data file containing land cover/use, soils, topographic and geologic (well-log) data.