BID Preprocessor
Computer program for processing BID data for use by other programs in Automated Sneak Program System
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Computer program for processing BID data for use by other programs in Automated Sneak Program System
The development of rapid and accurate wind tunnel techniques to measure convective heating distributions in complex reentry configurations is discussed. Major emphasis was put on the infrared camera technique. Its essence is the measurement of infrared emission from the surface of a wind tunnel model as a function of time. Prior calibration of the infrared camera relates the emission to the surface temperature of the model. The time history of the surface temperature can then be related to the heating rate by standard techniques. The output of the camera is an electrical signal that is tape-recorded in analog form, then digitized and processed by computer, so that automated and relatively rapid data reduction can be accomplished. In addition, the camera produces real-time visual displays of the infrared emission as pictures on an oscilloscope screen. These pictures give immediate indications of hot and cool spots on the model.
A short-term technical study was performed by the MSC Earth Observations Division to determine the feasibility of the proposed Agricultural Stabilization and Conservation Service Automatic Remote Sensing/Compliance Determination System. For the study, the term automatic was interpreted as applying to an automated remote-sensing system that includes data acquisition, processing, and management.
Range scheduling for satellite control presents a classical problem: supervisory control of a large-scale dynamic system, with unwieldy amounts of interrelated data used as inputs to the decision process. Increased automation of the task, with the appropriate human-computer interface, is highly desirable. The development and user evaluation of a semi-automated network range scheduling system is described. The system incorporates a synergistic human-computer interface consisting of a large screen color display, voice input/output, a 'sonic pen' pointing device, a touchscreen color CRT, and a standard keyboard. From a human factors standpoint, this development represents the first major improvement in almost 30 years to the satellite control network scheduling task.
The purpose was to examine, jointly, cockpit automation and social processes. Automation was varied by the choice of two radically different versions of the DC-9 series aircraft, the traditional DC-9-30, and the glass cockpit derivative, the MD-88. Airline pilot volunteers flew a mission in the simulator for these aircraft. Results show that the performance differences between the crews of the two aircraft were generally small, but where there were differences, they favored the DC-9. There were no criteria on which the MD-88 crews performed better than the DC-9 crews. Furthermore, DC-9 crews rated their own workload as lower than did the MD-88 pilots. There were no significant differences between the two aircraft types with respect to the severity of errors committed during the Line-Oriented Flight Training (LOFT) flight. The attitude questionnaires provided some interesting insights, but failed to distinguish between DC-9 and MD-88 crews.
Integral Systems, Inc.'s EPOCH 2000 forms the core of NASA's Near Earth Asteroid Rendezvous (NEAR) mission's command and control center. EPOCH 2000, which allows ground operators to monitor and control satellites over a wide area network, owes part of its heritage from work completed to support Goddard Space Flight Center. The software automates telemetry processing, commanding, anomaly detection, and archiving collected data. The NEAR spacecraft, launched in February 1996, will rendezvous in early 1999 and orbit the Asteroid Eros for a year. Integral Systems also provided Low Earth Orbit Autonomous Ground Terminals (LEO-Ts) to NASA. The LEO-T is designed to make it easier and less expensive for principal investigators to obtain telemetry, tracking and control services for their science missions. The company products have supported well over 70 satellite missions aimed at scientific research, meteorology, or communications applications.
The accuracy of single-frequency ocean altimeters benefits from calibration of the total electron content (TEC) of the ionosphere below the satellite.
MERRA/Max provides a feature selection approach to dimensionality reduction that enables direct use of global climate model outputs in ecological niche modeling. The system accomplishes this reduction through a Monte Carlo optimization in which many independent MaxEnt runs, operating on a species occurrence file and a small set of randomly selected variables in a large collection of variables, converge on an estimate of the top contributing predictors in the larger collection. These top predictors can be viewed as potential candidates in the variable selection step of the ecological niche modeling process. MERRA/Max’s Monte Carlo algorithm operates on files stored in the underlying filesystem, making it scalable to large data sets. Its software components can run as parallel processes in a high-performance cloud computing environment to yield near real-time performance. In tests using Cassin’s Sparrow (Peucaea cassinii) as the target species, MERRA/Max selected a set of predictors from Worldclim’s Bioclim collection of 19 environmental variables that have been shown to be important determinants of the species’ bioclimatic niche. It also selected biologically and ecologically plausible predictors from a more diverse set of 86 environmental variables derived from NASA’s Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) reanalysis, an output product of the Goddard Earth Observing System Version 5 (GEOS-5) modeling system. We believe these results point to a technological approach that could expand the use global climate model outputs in ecological niche modeling, foster exploratory experimentation with otherwise difficult-to-use climate data sets, streamline the modeling process, and, eventually, enable automated bioclimatic modeling as a practical, readily accessible, low-cost, commercial cloud service.
The four Magnetospheric Multiscale (MMS) spacecraft will collect a combined volume of approximately 100 gigabits per day of particle and field data. On average, only 4 gigabits of that volume can be transmitted to the ground. To maximize the scientific value of each transmitted data segment, MMS has developed the Science Operations Center (SOC) to manage science operations, instrument operations, and selection, downlink, distribution, and archiving of MMS science data sets. The SOC is managed by the Laboratory for Atmospheric and Space Physics (LASP) in Boulder, Colorado and serves as the primary point of contact for community participation in the mission. MMS instrument teams conduct their operations through the SOC, and utilize the SOC's Science Data Center (SOC) for data management and distribution. The SOC provides a single mission data archive for the housekeeping and science data, calibration data, ephemerides, attitude and other ancillary data needed to support the scientific use and interpretation. All levels of data products will reside at and be publicly disseminated from the SDC. Documentation and metadata describing data products, algorithms, instrument calibrations, validation, and data quality will be provided. Arguably, the most important innovation developed by the SOC is the MMS burst data management and selection system. With nested automation and 'Scientist-in-the-Loop' (SITL) processes, these systems are designed to maximize the value of the burst data by prioritizing the data segments selected for transmission to the ground. This paper describes the MMS science operations approach, processes and data systems, including the burst system and the SITL concept.
One of the critical phases in the development of a spacecraft attitude control system (ACS) is the testing of its flight software. The testing (and test verification) of ACS flight software requires a mix of skills involving software, knowledge of attitude control, and attitude control hardware, data manipulation, and analysis. The process of analyzing and verifying flight software test results often creates a bottleneck which dictates the speed at which flight software verification can be conducted. In the development of the Microwave Anisotropy Probe (MAP) spacecraft ACS subsystem, an integrated design environment was used that included a MAP high fidelity (HiFi) simulation, a central database of spacecraft parameters, a script language for numeric and string processing, and plotting capability. In this integrated environment, it was possible to automate many of the steps involved in flight software testing, making the entire process more efficient and thorough than on previous missions. In this paper, we will compare the testing process used on MAP to that used on other missions. The software tools that were developed to automate testing and test verification will be discussed, including the ability to import and process test data, synchronize test data and automatically generate HiFi script files used for test verification, and an automated capability for generating comparison plots. A summary of the benefits of applying these test methods on MAP will be given. Finally, the paper will conclude with a discussion of re-use of the tools and techniques presented, and the ongoing effort to apply them to flight software testing of the Triana spacecraft ACS subsystem.
The capability of a computer program which can be part of a larger computer program for a fully automated multiaxial testing facility is described. This computer program is designed to process test data from tubular or flat specimens made from isotropic or anisotropic materials including high modulus fiber composites. The program can receive data from a large number of strain gages and combinations of applied loads. Options are provided for single element, 90-degree, rectangular or Delta rosettes, or any combinations of these types of strain gages. Options are provided for strain gage transverse sensitivities. The program outputs include: structural axes strains and stresses, initial and strain dependent elastic constants, shift of principal strain direction with load, and local curvatures from back-to-back strain gages, and either calcomp or microfilm plots. The computer program is described with respect to its flow chart, input/output, embedment or linking with other programs and its possible utility in a fully automated testing system.
The capability of a computer program which can be part of a larger computer program for a fully automated multiaxial testing facility is described. The program was designed to process test data from tubular or flat specimens made from isotropic or anistropic materials, including high modulus fiber composites. Data from a large number of strain gages and combinations of applied loads can be used. Options are provided for single element, 90-degree, rectangular or Delta rosettes, or any combinations of these types of strain gages. Options are provided for strain gage transverse sensitivities. The program outputs include: structural axes strains and stresses, initial and strain-dependent elastic constants, shift of principal strain direction with load, and local curvatures from back-to-back strain gages, and either Calcomp or microfilm plots. The computer program is described with respect to its flow chart, input/output, embedding or linking with other programs.
An overview is presented of the evolution of the computer configuration at JPL's Image Processing Laboratory (IPL). The development of techniques for the geometric transformation of digital imagery is discussed and consideration is given to automated and semiautomated image registration, and the registration of imaging and nonimaging data. The increasing complexity of image processing tasks at IPL is illustrated with examples of various applications from the planetary program and earth resources activities. It is noted that the registration of existing geocoded data bases with Landsat imagery will continue to be important if the Landsat data is to be of genuine use to the user community.
The Spaceport Command and Control System (SCCS) is the National Aeronautics and Space Administration's (NASA) launch control system for the Orion capsule and Space Launch System, the next generation manned rocket currently in development. This system requires high quality testing that will measure and test the capabilities of the system. For the past two years, the Exploration and Operations Division at Kennedy Space Center (KSC) has assigned a group including interns and full-time engineers to develop automated tests to save the project time and money. The team worked on automating the testing process for the SCCS GUI that would use streamed simulated data from the testing servers to produce data, plots, statuses, etc. to the GUI. The software used to develop automated tests included an automated testing framework and an automation library. The automated testing framework has a tabular-style syntax, which means the functionality of a line of code must have the appropriate number of tabs for the line to function as intended. The header section contains either paths to custom resources or the names of libraries being used. The automation library contains functionality to automate anything that appears on a desired screen with the use of image recognition software to detect and control GUI components. The data section contains any data values strictly created for the current testing file. The body section holds the tests that are being run. The function section can include any number of functions that may be used by the current testing file or any other file that resources it. The resources and body section are required for all test files; the data and function sections can be left empty if the data values and functions being used are from a resourced library or another file. To help equip the automation team with better tools, the Project Lead of the Automated Testing Team, Jason Kapusta, assigned the task to install and train an optical character recognition (OCR) tool to Brandon Echols, a fellow intern, and I. The purpose of the OCR tool is to analyze an image and find the coordinates of any group of text. Some issues that arose while installing the OCR tool included the absence of certain libraries needed to train the tool and an outdated software version. We eventually resolved the issues and successfully installed the OCR tool. Training the tool required many images and different fonts and sizes, but in the end the tool learned to accurately decipher the text in the images and their coordinates. The OCR tool produced a file that contained significant metadata for each section of text, but only the text and coordinates of the text was required for our purpose. The team made a script to parse the information we wanted from the OCR file to a different file that would be used by automation functions within the automated framework. Since a majority of development and testing for the automated test cases for the GUI in question has been done using live simulated data on the workstations at the Launch Control Center (LCC), a large amount of progress has been made. As of this writing, about 60% of all of automated testing has been implemented. Additionally, the OCR tool will help make our automated tests more robust due to the tool's text recognition being highly scalable to different text fonts and text sizes. Soon we will have the whole test system automated, allowing for more full-time engineers working on development projects.
This paper presents machine learning-based approaches to automate and optimize the detection of volume loss for the downlink process of telemetry data from the Mars Curiosity Rover. The Curiosity observes volume loss and data corruption, requiring re-transmits from the rover and Ground Data System Analysts (GDSA) to monitor the data flow. To resolve this issue, we created a data pipeline to accumulate data from various data sources in the downlink process and detect where the data is missed. In this paper, we benchmarked different methodologies based on the accuracy and excitability of them to identify whether a downlink data that is received to the ground system is complete or incomplete. Our results show that machine learning methods can improve the performance of the GDSA by 55% while the user can diagnose why data is missed and provide an explanation for the data accountability problem.
The United Nations University (UNU) estimates that floods presently impacts greater than 520 million people per year worldwide, resulting in up to 25,000 annual deaths, extensive homelessness, disaster-induced disease, crop and livestock damage, famine, and other serious harm. Meanwhile, aid agencies such as the International Federation of Red Cross and Red Crescent Societies (IFRC) are increasingly seeking better information concerning flood hazards in order to plan for and help mitigate the effects of damaging floods. There is fertile ground to continue development of better remote sensing and modeling techniques to help manage flood related disasters. Disaster management and humanitarian aid organizations need accurate and timely information for making decisions regarding deployment of relief teams and emergency supplies during major floods. Flood maps based on the use of satellite data have proven extremely valuable to such organizations for identifying the location, extent, and severity of these events. However, despite extraordinary efforts on the part of remote sensing data providers to rapidly deliver such maps, there is typically a delay of several days or even weeks from the on-set of flooding until such maps are available to the disaster management community. This paper summarizes efforts at NASA to address this problem through development of an integrated and automated process of a) flood forecasting b) flood detection, c) satellite data acquisition, d) rapid flood mapping and distribution, and e) validation of flood forecasting and detection products.
Lake Victoria, the second largest fresh water lake in the Eastern part of Africa is a vital natural resource for the economic well being and prosperity of over 30 million people located in riparian regions of Uganda, Kenya and Tanzania. It covers a large area of about 68,870 km2 and produces a GDP of about US $30 billion per year. The region is also very much prone to natural disasters such as severe floods during heavy precipitation periods in the Eastern part of Africa. In addition to floods, the precipitation also produces large infestations of mosquito larvae due to the standing water in many areas. This further causes multiple vector borne diseases such as Malaria, Rift Valley Fever and more. These problems are of serious concern and require active and aggressive surveillance and management to minimize the loss of human and animal lives and property damage. Satellite imagery and observations along with the in situ measurements provide a great tool to analyze and study this area and inform the policy makers to make calculated policy decisions which are more beneficial to the environment. Recently, NASA and USAID have joined forces with the Regional Center for Mapping of Resources for Development (RCMRD) located in Nairobi, Kenya to utilize multiple NASA sensors such as TRMM, SRTM and MODIS to develop flood potential maps for the Lake Victoria Basin. The idea is to generate a flood forecasts and "nowcasts" that can be sent to the disaster management organizations of Uganda, Kenya, and Tanzania. Post flood event satellite imagery is becoming a common tool to assess the areas inundated by flooding. However, this work is unique undertaking by utilizing land imaging and atmospheric satellites to build credible flood potential maps. At same time, we are also studying the potential occurrence and spread of Rift Valley Fever disease based on the short term climate records and precipitation data. These activities require multi-nation coordination and agreements and multiple operational agencies within each respective country. It also requires credible in situ data such as precipitation, river flow rates and lake levels to further validate the global and regional Hood models and algorithms. This also requires a considerable amount of training and capacity building for the RCMRD experts who will help us validate the model results and eventually transition it for operational use. In a final analysis, Disaster management and humanitarian aid organizations need accurate and timely information for making decisions regarding deployment of relief teams and emergency supplies during major floods. Flood maps based on the use of satellite data have proven extremely valuable to such organizations for identifying the location, extent, and severity of these events. However, despite extraordinary efforts on the part of remote sensing data providers to rapidly deliver such maps, there is typically a delay of several days or even weeks from the on-set of flooding until such maps are available to the disaster management community. This paper summarizes efforts at NASA to address this problem through development of an integrated and automated process of a) flood forecasting b) flood detection, c) satellite data acquisition, d) rapid Hood mapping and distribution, and e) validation of Hood forecasting and detection products.
Application and processing of remotely sensed data are discussed. Areas of application include: pollution monitoring, water quality, land use, marine resources, ocean surface properties, and agriculture. Image processing and scene analysis are described along with automated photointerpretation and classification techniques. Data from infrared and multispectral band scanners onboard LANDSAT satellites are emphasized.