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The NASA Lewis Research Center's Remote Access Control Room (RACR) uses off-the-shelf video conferencing software integrated with existing facility data systems to provide access to the test data by networking from virtually anywhere in the country. The system allows research engineers in remote locations to participate in tests and monitor data in real time just as if they were present in the control room.
Multi-mission Encrypted Communication System (MECS) is a computer program that enables authorized, geographically dispersed users to gain secure access to a common set of data files via the Internet. MECS is compatible with legacy application programs and a variety of operating systems. The MECS architecture is centered around maintaining consistent replicas of data files cached on remote computers. MECS monitors these files and, whenever one is changed, the changed file is committed to a master database as soon as network connectivity makes it possible to do so. MECS provides subscriptions for remote users to automatically receive new data as they are generated. Remote users can be producers as well as consumers of data. Whereas a prior program that provides some of the same services treats disconnection of a user from the network of users as an error from which recovery must be effected, MECS treats disconnection as a nominal state of the network: This leads to a different design that is more efficient for serving many users, each of whom typically connects and disconnects frequently and wants only a small fraction of the data at any given time.
The Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) instrument is an airborne hyperspectral spectrometer measuring backscattered solar radiation in the ultraviolet (290–400 nm) and visible (415–695 nm) wavelength regions. This paper presents high-resolution sulfur dioxide (SO2) maps over the Korean Peninsula, produced by SO2 retrievals from GeoTASO measurements during the Korea–United States Air Quality Field Study (KORUS-AQ) from May to June 2016. The highly sensitive GeoTASO instrument with a spatial resolution of ~250 m × 250 m can detect point emission sources of SO2 within its fields of view, even without merging multiple overlapping observations. To retrieve SO2 vertical columns from the GeoTASO measurements, we apply an algorithm based on principal component analysis (PCA), which is effective in suppressing noise and biases in SO2 retrievals. The retrievals successfully capture SO2 plumes and various point sources such as power plants, a petrochemical complex, and a steel mill, located in South Chungcheong Province, some of which are not detected by a ground-based in situ measurement network. Spatial distributions of SO2 from GeoTASO observations in source areas are consistent with those from the Stack Tele-Monitoring System reports and airborne in situ SO2 measurements. Comparisons of SO2 retrievals from GeoTASO and existing satellite sensors demonstrate the significance of high-resolution SO2 observations, by indicating that GeoTASO detects small SO2 emission sources that are not precisely resolved by single overpasses of satellites. To assess future geostationary SO2 observations having a higher spatial resolution, we upscale the GeoTASO SO2 retrievals to a spatial resolution of the Geostationary Environment Monitoring Spectrometer (GEMS). Since the upscaled GeoTASO retrievals also detect SO2 plumes clearly, we expect from GEMS to identify even small SO2 emission sources over Asia.
The NASA Orbital Debris Program Office (ODPO) relies primarily on ground-based radar measurements to characterize the distribution of small debris, down to approximately 3 mm depending upon altitude and the sensor used, in low Earth orbit (LEO). Since the early 1990’s, the Massachusetts Institute of Technology (MIT) Lincoln Laboratory (LL) has been collecting radar measurements for the NASA ODPO under agreements with the U.S. Department of Defense. The Haystack Ultrawideband Satellite Imaging Radar (HUSIR) is the primary ground-based radar sensor used by the ODPO and provides data on orbital debris down to an approximate size of 5.5 mm below 1000km altitude using the NASA size estimation model (SEM). Since orbital debris of this size are a significant risk to both human and robotic missions in LEO, the sensitivity of this radar makes it a high-value sensor. The NASA ODPO radar measurements are conducted on a continual basis for monitoring and enabling modeling of the orbital debris environment over time. HUSIR observations from 2019 are the most recent snapshot of the environment that has been measured and analyzed to date. In recent years, HUSIR measurements indicated relatively stable populations for the orbital debris objects that it is able to detect. In 2019, several interesting events happened on-orbit, including the start of large constellation deployments into LEO, as well as the Indian anti-satellite test with Microsat-R (International Designator 2019-006A, U.S. Strategic Command Space Surveillance Network catalog number 43947). Due to these events, coupled with a general increase in the number of missions and participants launching missions in recent years, continual monitoring is necessary to determine the effects of this increased activity on the orbital debris environment. This paper will explore the results of the 2019 HUSIR radar measurements, including above-average flux measurements at lower LEO altitudes and the evolution of the flux during the time of observations.
The NASA Orbital Debris Program Office (ODPO) relies primarily on ground-based radar measurements to characterize the distribution of small debris, down to approximately 3 mm depending upon altitude and the sensor used, in low Earth orbit (LEO). Since the early 1990’s, the Massachusetts Institute of Technology (MIT) Lincoln Laboratory (LL) has been collecting radar measurements for the NASA ODPO under agreements with the U.S. Department of Defense. The Haystack Ultrawideband Satellite Imaging Radar (HUSIR) is the primary ground-based radar sensor used by the ODPO and provides data on orbital debris down to an approximate size of 5.5 mm below 1000km altitude using the NASA size estimation model (SEM). Since orbital debris of this size are a significant risk to both human and robotic missions in LEO, the sensitivity of this radar makes it a high-value sensor. The NASA ODPO radar measurements are conducted on a continual basis for monitoring and enabling modeling of the orbital debris environment over time. HUSIR observations from 2019 are the most recent snapshot of the environment that has been measured and analyzed to date. In recent years, HUSIR measurements indicated relatively stable populations for the orbital debris objects that it is able to detect. In 2019, several interesting events happened on-orbit, including the start of large constellation deployments into LEO, as well as the Indian anti-satellite test with Microsat-R (International Designator 2019-006A, U.S. Strategic Command Space Surveillance Network catalog number 43947). Due to these events, coupled with a general increase in the number of missions and participants launching missions in recent years, continual monitoring is necessary to determine the effects of this increased activity on the orbital debris environment. This paper will explore the results of the 2019 HUSIR radar measurements, including above-average flux measurements at lower LEO altitudes and the evolution of the flux during the time of observations.
Machine learning is a powerful tool for data exploration and model building with large data sets. This project aimed to use machine learning techniques to explore the inherent structure of data from rotorcraft gear tests, relationships between features and damage states, and to build a system for predicting gear health for future rotorcraft transmission applications. Classical machine learning techniques are difficult, if not irresponsible to apply to time series data because many make the assumption of independence between samples. To overcome this, Hidden Markov Models were used to create a binary classifier for identifying scuffing transitions and Recurrent Neural Networks were used to leverage long distance relationships in predicting discrete damage states. When combined in a workflow, where the binary classifier acted as a filter for the fatigue monitor, the system was able to demonstrate accuracy in damage state prediction and scuffing identification. The time dependent nature of the data restricted data exploration to collecting and analyzing data from the model selection process. The limited amount of available data was unable to give useful information, and the division of training and testing sets tended to heavily influence the scores of the models across combinations of features and hyper-parameters. This work built a framework for tracking scuffing and fatigue on streaming data and demonstrates that machine learning has much to offer rotorcraft health monitoring by using Bayesian learning and deep learning methods to capture the time dependent nature of the data. Suggested future work is to implement the framework developed in this project using a larger variety of data sets to test the generalization capabilities of the models and allow for data exploration.
The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Prelimary results show significant LW improvement.
The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Preliminary results show significant LW improvement.
This volume (2 of 4) contains the specification, structured flow charts, and code listing for the protocol. The purpose of an autonomous power system on a spacecraft is to relieve humans from having to continuously monitor and control the generation, storage, and distribution of power in the craft. This implies that algorithms will have been developed to monitor and control the power system. The power system will contain computers on which the algorithms run. There should be one control computer system that makes the high level decisions and sends commands to and receive data from the other distributed computers. This will require a communications network and an efficient protocol by which the computers will communicate. One of the major requirements on the protocol is that it be real time because of the need to control the power elements.
The primary science objective for the CHUVA lightning mapping campaign is to combine measurements of total lightning activity, lightning channel mapping, and detailed information on the locations of cloud charge regions of thunderstorms with the planned observations of the CHUVA (Cloud processes of tHe main precipitation systems in Brazil: A contribUtion to cloud resolVing modeling and to the GPM (GlobAl Precipitation Measurement) field campaign. The lightning campaign takes place during the CHUVA intensive observation period October-December 2011 in the vicinity of S o Luiz do Paraitinga with Brazilian, US, and European government, university and industry participants. Total lightning measurements that can be provided by ground-based regional 2-D and 3-D total lightning mapping networks coincident with overpasses of the Tropical Rainfall Measuring Mission Lightning Imaging Sensor (LIS) and the SEVIRI (Spinning Enhanced Visible and Infrared Imager) on the Meteosat Second Generation satellite in geostationary earth orbit will be used to generate proxy data sets for the next generation US and European geostationary satellites. Proxy data, which play an important role in the pre-launch mission development and in user readiness preparation, are used to develop and validate algorithms so that they will be ready for operational use quickly following the planned launch of the GOES-R Geostationary Lightning Mapper (GLM) in 2015 and the Meteosat Third Generation Lightning Imager (LI) in 2017. To date there is no well-characterized total lightning data set coincident with the imagers. Therefore, to take the greatest advantage of this opportunity to collect detailed and comprehensive total lightning data sets, test and validate multi-sensor nowcasting applications for the monitoring, tracking, warning, and prediction of severe and high impact weather, and to advance our knowledge of thunderstorm physics, extensive measurements from lightning mapping networks will be collected in conjunction with electric field mills, field change sensors, high speed cameras and other lightning sensors, dual-polarimetric radars, and aircraft in-situ microphysics which will allow for excellent cross-network inter-comparisons, assessments, and physical understanding.
The design, development, installation, and operation of the test equipment (MRVS) is discussed. The main feature of the MRVS is continuous recording on an instrumentation recorder data of up to 1500 parameters with a total sample rate of up to 10,000 samples per second. In order to compress the evaluation and calibration time period, the following additional requirements were set for two test systems: (1) recording of selected parameters, time tagged on computer compatible tape (CCT); (2) recording of selected high bandwidth signals and ad hoc parameters on analog tape; (3) onboard presentation of calibrated parameter data, in engineering units, in numerical as well as graphical form for: system checkout during pre-, in- and post-flight, and quick look analysis during in-flight; (4) real time presentation on the ground by telemetry for: flight monitoring, takeoff and landing measurements, and noise measurements; and (5) data processing on the ground on the Fokker-NLR computer network.
The validation of some proposed measures of operator workload in a large interactive computer system is described. The tests were conducted on MARK III deep space network (DSN) equipment using experienced operators as subjects. The significant operator workload measure are identified and useful guidelines are obtained on operator task scheduling.
In order to accommodate the increasing number of computerized subsystems aboard today's more fuel efficient aircraft, the Boeing Co. has developed the DATAC (Digital Autonomous Terminal Access Control) bus to minimize the need for point-to-point wiring to interconnect these various systems, thereby reducing total aircraft weight and maintaining an economical flight configuration. The DATAC bus is essentially a local area network providing interconnections for any of the flight management and control systems aboard the aircraft. The task of developing a Bus Monitor Unit was broken down into four subtasks: (1) providing a hardware interface between the DATAC bus and the Z8000-based microcomputer system to be used as the bus monitor; (2) establishing a communication link between the Z8000 system and a CP/M-based computer system; (3) generation of data reduction and display software to output data to the console device; and (4) development of a DATAC Terminal Simulator to facilitate testing of the hardware and software which transfer data between the DATAC's bus and the operator's console in a near real time environment. These tasks are briefly discussed.
The aim is to develop a comprehensive observational and analytical program to study solar system physics and meterology by measuring molecular lines in the millimeter and submillimeter spectra of planets and comets. A primary objective is to conduct observations with new JPL and Caltech submillimeter receivers at the Caltech Submillimeter Observatory (CSO) on Mauna Kea, Hawaii. A secondary objective is to continue to monitor the time variable planetary phenomena (e.g., Jupiter and Uranus) at centimeter wavelength using the NASA antennas of the Deep Space Network (DSN).
The first measurements are reported for a major earthquake by a continuously operating GPS network, the permanent GPS Genetic ARRY (PGGA) in southern California. The Landers and Big Bear earthquakes of June 28, 1992 were monitored by daily observations. Ten weeks of measurements indicate significant coseismic motion at all PGGA sites, significant postseismic motion at one site for two weeks after the earthquakes, and no significant preseismic motion. These measurements demonstrate the potential of GPS monitoring for precise detection of precursory and aftershock seismic deformation in the near and far field.
The Research Institute for Advanced Computer Science (RIACS) was established by the Universities Space Research Association (USRA) at the NASA Ames Research Center (ARC) on 6 June 1983. RIACS is privately operated by USRA, a consortium of universities with research programs in the aerospace sciences, under contract with NASA. The primary mission of RIACS is to provide research and expertise in computer science and scientific computing to support the scientific missions of NASA ARC. The research carried out at RIACS must change its emphasis from year to year in response to NASA ARC's changing needs and technological opportunities. A flexible scientific staff is provided through a university faculty visitor program, a post doctoral program, and a student visitor program. Not only does this provide appropriate expertise but it also introduces scientists outside of NASA to NASA problems. A small group of core RIACS staff provides continuity and interacts with an ARC technical monitor and scientific advisory group to determine the RIACS mission. RIACS activities are reviewed and monitored by a USRA advisory council and ARC technical monitor. Research at RIACS is currently being done in the following areas: Parallel Computing, Advanced Methods for Scientific Computing, High Performance Networks and Technology, and Learning Systems. Parallel compiler techniques, adaptive numerical methods for flows in complicated geometries, and optimization were identified as important problems to investigate for ARC's involvement in the Computational Grand Challenges of the next decade.
Mid-atlantic Technology Application Center (MTAC) pursued a number of initiatives designed to enhance the strategic position of the Langley Research Center (LaRC) and NASA in industry. Among these was a closer association with the ISA, International Society for Measurement and Control. During 1997, MTAC placed articles regarding NASA-developed technologies in each In Tech magazine. The monthly magazine is sent to 46,000 sensors and instrumentation professionals. In addition, MTAC coordinated NASXs participation in the ISA Tech 97 Conference, securing $112,000 of free exhibit space, 1500 NASA sensors posters at no cost to NASA, and thousands of dollars of free publicity. MTAC was awarded a contract by ISA to operate its Technical Resource Center (TRC). The goal of this project is to determine what user needs are in order to identify opportunities for collaboration between NASA centers and companies. In addition, the TRC work will lay the groundwork for the Technology Development Consortium (TDC) proposed by MTAC. The purpose of the TDC is to: match current industry needs with NASA technologies available now, and to identify future needs of NASA and industry which may lead to dual use projects. The goal of these activities is twofold: to infuse NASA technologies into the sensors and instrumentation industry and to secure industry funds to support NASA technology development projects. The instrumentation and sensors industry is valued at $30 billion worldwide, with $12 billion in sales in the United States. The growth rate averages 13.5%, so that by the year 2000, the industry will produce products worth $49 billion. More than 80% of instruments, sensors and control systems are currently manufactured in the United States. NASA and the industry do not have a history of collaborative projects; MTAC's initiatives in this area are designed to foster working relationships between the two parties that will help maintain U.S. leadership in this field. Mid-atlantic Technology Applications Center (MTAC) continued to work on LaRC-SI and Thin Layer Unimorph Driver and Sensor activities. MTAC helped develop the "master license" concept and identified additional applications and potential clients. The goal of these activities was to increase the financial return to Langley Research Center, to ensure the diffusion of the technologies throughout the economy, and to attract partners for future NASA technology development efforts. In an attempt to market LaRC-SI and the Thin Layer Unimorph Driver and Sensor, MTAC developed and pioneered the use of the USRTTC Technology Commercialization Team. As a result, MTAC's sister RTTCs identified both applications and potential users for the two technologies. MTAC also benefitted from its affiliate network to locate companies throughout the region that were interested in the two technologies. MTAC is working with Allegheny Ludlum to monitor the progress of the installation and testing of ultrasonic equipment purchased as a result of Langley Research Center's assistance.
An intelligent system for monitoring the microgravity environment quality on-board the International Space Station is presented. The monitoring system uses a new approach combining Kohonen's self-organizing feature map, learning vector quantization, and back propagation neural network to recognize and classify the known and unknown patterns. Finally, fuzzy logic is used to assess the level of confidence associated with each vibrating source activation detected by the system.