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At least 361 records · Page 20

Java-based Graphical User Interface for MAVERIC-II

A computer program entitled "Marshall Aerospace Vehicle Representation in C II, (MAVERIC-II)" is a vehicle flight simulation program written primarily in the C programming language. It is written by James W. McCarter at NASA/Marshall Space Flight Center. The goal of the MAVERIC-II development effort is to provide a simulation tool that facilitates the rapid development of high-fidelity flight simulations for launch, orbital, and reentry vehicles of any user-defined configuration for all phases of flight. MAVERIC-II has been found invaluable in performing flight simulations for various Space Transportation Systems. The flexibility provided by MAVERIC-II has allowed several different launch vehicles, including the Saturn V, a Space Launch Initiative Two-Stage-to-Orbit concept and a Shuttle-derived launch vehicle, to be simulated during ascent and portions of on-orbit flight in an extremely efficient manner. It was found that MAVERIC-II provided the high fidelity vehicle and flight environment models as well as the program modularity to allow efficient integration, modification and testing of advanced guidance and control algorithms. In addition to serving as an analysis tool for techno logy development, many researchers have found MAVERIC-II to be an efficient, powerful analysis tool that evaluates guidance, navigation, and control designs, vehicle robustness, and requirements. MAVERIC-II is currently designed to execute in a UNIX environment. The input to the program is composed of three segments: 1) the vehicle models such as propulsion, aerodynamics, and guidance, navigation, and control 2) the environment models such as atmosphere and gravity, and 3) a simulation framework which is responsible for executing the vehicle and environment models and propagating the vehicle s states forward in time and handling user input/output. MAVERIC users prepare data files for the above models and run the simulation program. They can see the output on screen and/or store in files and examine the output data later. Users can also view the output stored in output files by calling a plotting program such as gnuplot. A typical scenario of the use of MAVERIC consists of three-steps; editing existing input data files, running MAVERIC, and plotting output results.

Seo, Suk Jai↗

Global Interconnectivity Between Mobile Satellite and Terrestrial Users: Call Signalling Issues and Challenges

This paper will discuss some of the challenges in connecting mobile satellite users and mobile terrestrial users in a cost efficient manner and with a grade of service comparable to that of satellite to fixed user calls. Issues arising from the translation between the mobility management protocols resident at the satellite Earth station and those resident at cellular switches - either GSM (Group Special Mobile) or IS-41 (used by U.S. digital cellular systems) type - will be discussed. The impact of GSM call routing procedures on the call setup of a satellite to roaming GSM user will be described. Challenges facing provision of seamless call handoff between satellite and cellular systems will be given. A summary of the issues explored in the paper are listed and future work outlined.

mobile satellite usersGroup Special MobileGSMdigit↗

Wake Vortex Inverse Model User's Guide

NorthWest Research Associates (NWRA) has developed an inverse model for inverting landing aircraft vortex data. The data used for the inversion are the time evolution of the lateral transport position and vertical position of both the port and starboard vortices. The inverse model performs iterative forward model runs using various estimates of vortex parameters, vertical crosswind profiles, and vortex circulation as a function of wake age. Forward model predictions of lateral transport and altitude are then compared with the observed data. Differences between the data and model predictions guide the choice of vortex parameter values, crosswind profile and circulation evolution in the next iteration. Iterations are performed until a user-defined criterion is satisfied. Currently, the inverse model is set to stop when the improvement in the rms deviation between the data and model predictions is less than 1 percent for two consecutive iterations. The forward model used in this inverse model is a modified version of the Shear-APA model. A detailed description of this forward model, the inverse model, and its validation are presented in a different report (Lai, Mellman, Robins, and Delisi, 2007). This document is a User's Guide for the Wake Vortex Inverse Model. Section 2 presents an overview of the inverse model program. Execution of the inverse model is described in Section 3. When executing the inverse model, a user is requested to provide the name of an input file which contains the inverse model parameters, the various datasets, and directories needed for the inversion. A detailed description of the list of parameters in the inversion input file is presented in Section 4. A user has an option to save the inversion results of each lidar track in a mat-file (a condensed data file in Matlab format). These saved mat-files can be used for post-inversion analysis. A description of the contents of the saved files is given in Section 5. An example of an inversion input file, with preferred parameters values, is given in Appendix A. An example of the plot generated at a normal completion of the inversion is shown in Appendix B.

Lai, David↗

Propulsion Diagnostic Method Evaluation Strategy (ProDiMES) User's Guide

This report is a User's Guide for the Propulsion Diagnostic Method Evaluation Strategy (ProDiMES). ProDiMES is a standard benchmarking problem and a set of evaluation metrics to enable the comparison of candidate aircraft engine gas path diagnostic methods. This Matlab (The Mathworks, Inc.) based software tool enables users to independently develop and evaluate diagnostic methods. Additionally, a set of blind test case data is also distributed as part of the software. This will enable the side-by-side comparison of diagnostic approaches developed by multiple users. The Users Guide describes the various components of ProDiMES, and provides instructions for the installation and operation of the tool.

Simon, Donald L.↗

Analysis of Factors for Incorporating User Preferences in Air Traffic Management: A system Perspective

This paper presents an analysis of factors that impact user flight schedules during air traffic congestion. In pre-departure flight planning, users file one route per flight, which often leads to increased delays, inefficient airspace utilization, and exclusion of user flight preferences. In this paper, first the idea of filing alternate routes and providing priorities on each of those routes is introduced. Then, the impact of varying planning interval and system imposed departure delay increment is discussed. The metrics of total delay and equity are used for analyzing the impact of these factors on increased traffic and on different users. The results are shown for four cases, with and without the optional routes and priority assignments. Results demonstrate that adding priorities to optional routes further improves system performance compared to filing one route per flight and using first-come first-served scheme. It was also observed that a two-hour planning interval with a five-minute system imposed departure delay increment results in highest delay reduction. The trend holds for a scenario with increased traffic.

Sheth, Kapil S.↗

A New User Interface for On-Demand Customizable Data Products for Sensors in a SensorWeb

A SensorWeb is a set of sensors, which can consist of ground, airborne and space-based sensors interoperating in an automated or autonomous collaborative manner. The NASA SensorWeb toolbox, developed at NASA/GSFC in collaboration with NASA/JPL, NASA/Ames and other partners, is a set of software and standards that (1) enables users to create virtual private networks of sensors over open networks; (2) provides the capability to orchestrate their actions; (3) provides the capability to customize the output data products and (4) enables automated delivery of the data products to the users desktop. A recent addition to the SensorWeb Toolbox is a new user interface, together with web services co-resident with the sensors, to enable rapid creation, loading and execution of new algorithms for processing sensor data. The web service along with the user interface follows the Open Geospatial Consortium (OGC) standard called Web Coverage Processing Service (WCPS). This presentation will detail the prototype that was built and how the WCPS was tested against a HyspIRI flight testbed and an elastic computation cloud on the ground with EO-1 data. HyspIRI is a future NASA decadal mission. The elastic computation cloud stores EO-1 data and runs software similar to Amazon online shopping.

Mandl, Daniel↗

Power User Interface

Power User Interface 5.0 (PUI) is a system of middleware, written for expert users in the Earth-science community, PUI enables expedited ordering of data granules on the basis of specific granule-identifying information that the users already know or can assemble. PUI also enables expert users to perform quick searches for orderablegranule information for use in preparing orders. PUI 5.0 is available in two versions (note: PUI 6.0 has command-line mode only): a Web-based application program and a UNIX command-line- mode client program. Both versions include modules that perform data-granule-ordering functions in conjunction with external systems. The Web-based version works with Earth Observing System Clearing House (ECHO) metadata catalog and order-entry services and with an open-source order-service broker server component, called the Mercury Shopping Cart, that is provided separately by Oak Ridge National Laboratory through the Department of Energy. The command-line version works with the ECHO metadata and order-entry process service. Both versions of PUI ultimately use ECHO to process an order to be sent to a data provider. Ordered data are provided through means outside the PUI software system.

Pfister, Robin↗

A Survey of Health Management User Objectives Related to Diagnostic and Prognostic Metrics

One of the most prominent technical challenges to effective deployment of health management systems is the vast difference in user objectives with respect to engineering development. In this paper, a detailed survey on the objectives of different users of health management systems is presented. These user objectives are then mapped to the metrics typically encountered in the development and testing of two main systems health management functions: diagnosis and prognosis. Using this mapping, the gaps between user goals and the metrics associated with diagnostics and prognostics are identified and presented with a collection of lessons learned from previous studies that include both industrial and military aerospace applications.

Wheeler, Kevin R.↗

ISS Remote User Payload Operations Training and Support

For more than ten years hundreds of payloads have been, and are currently being, successfully operated onboard the ISS. These payloads are operated by a diverse set of users all over the world. Due to the current international economic environment payload operations are being streamlined, in more and more cases, by using the payload investigators and scientists to also fill the role of operators. Taking this into consideration, increasingly, we have payload operators that are new to space operations and practices, therefore ground systems training and support have become a more critical aspect in ensuring a successful payload mission. The ISS ground systems payload interface is the Payload Operations and Integration Center (POIC), located at Marshall Space Flight Center. ISS ground systems training for all remote ISS payload operators, as well as the ISS POIC CADRE, are centralized at this facility. The POIC is the starting point for a remote payload operator to learn how to integrate, and operate their payload, successfully onboard the ISS. Additionally, the CADRE that supports the payload user community are trained and operate from this facility. This paper will give an overview of the ISS ground systems at the POIC, as it relates to the payload user/operator and CADRE community. The entire training process from initial contact with the POIC to in-flight operations will be reviewed and improvements to this process will be presented. More importantly we will present current training methods and proposed methodology whereby the user community will be trained more efficiently and thoroughly. Also, we will discuss how we can more effectively support users in their operations concept to programmatically conduct certain aspects of payload operations to reduce costs.

Roth, Karl↗

Multi-User Space Link Extension (SLE) System

The Multi-User Space (MUS) Link Extension system, a software and data system, provides Space Link Extension (SLE) users with three space data transfer services in timely, complete, and offline modes as applicable according to standards defined by the Consultative Committee for Space Data Systems (CCSDS). MUS radically reduces the schedule, cost, and risk of implementing a new SLE user system, minimizes operating costs with a lights-out approach to SLE, and is designed to require no sustaining engineering expense during its lifetime unless changes in the CCSDS SLE standards, combined with new provider implementations, force changes. No software modification to MUS needs to be made to support a new mission. Any systems engineer with Linux experience can begin testing SLE user service instances with MUS starting from a personal computer (PC) within five days. For flight operators, MUS provides a familiar-looking Web page for entering SLE configuration data received from SLE. Operators can also use the Web page to back up a space mission's entire set of up to approximately 500 SLE service instances in less than five seconds, or to restore or transfer from another system the same amount of data from a MUS backup file in about the same amount of time. Missions operate each MUS SLE service instance independently by sending it MUS directives, which are legible, plain ASCII strings. MUS directives are usually (but not necessarily) sent through a TCP-IP (Transmission Control Protocol Internet Protocol) socket from a MOC (Mission Operations Center) or POCC (Payload Operations Control Center) system, under scripted control, during "lights-out" spacecraft operation. MUS permits the flight operations team to configure independently each of its data interfaces; not only commands and telemetry, but also MUS status messages to the MOC. Interfaces can use single- or multiple-client TCP/IP server sockets, TCP/IP client sockets, temporary disk files, the system log, or standard in, standard out, or standard error as applicable. By defining MUS templates in ASCII, the flight operations team can include any MUS system variable in telemetry or command headers or footers, and/or in status messages. Data fields can be arranged within messages in different sequences, according to the mission s needs. The only constraints imposed are on the format of MUS directive strings, and some bare minimum logical requirements that must be met in order for MUS to read the mission control center's spacecraft command inputs. The MUS system imposes no limits or constraints on the numbers and combinations of missions and SLE service instances that it will support simultaneously. At any time, flight operators may add, change, delete, bind, connect, or disconnect.

Perkins, Toby↗

Engineering of Data Acquiring Mobile Software and Sustainable End-User Applications

The criteria for which data acquiring software and its supporting infrastructure should be designed should take the following two points into account: the reusability and organization of stored online and remote data and content, and an assessment on whether abandoning a platform optimized design in favor for a multi-platform solution significantly reduces the performance of an end-user application. Furthermore, in-house applications that control or process instrument acquired data for end-users should be designed with a communication and control interface such that the application's modules can be reused as plug-in modular components in greater software systems. The application of the above mentioned is applied using two loosely related projects: a mobile application, and a website containing live and simulated data. For the intelligent devices mobile application AIDM, the end-user interface have a platform and data type optimized design, while the database and back-end applications store this information in an organized manner and manage access to that data to only to authorized user end application(s). Finally, the content for the website was derived from a database such that the content can be included and uniform to all applications accessing the content. With these projects being ongoing, I have concluded from my research that the applicable methods presented are feasible for both projects, and that a multi-platform design for the mobile application only marginally drop the performance of the mobile application.

Smith, Benton T.↗

GPS Space Service Volume: Ensuring Consistent Utility Across GPS Design Builds for Space Users

GPS availability and signal strength originally specified for users on or near surface of Earth with transmitted power levels specified at edge-of-Earth, 14.3 degrees. Prior to the SSV specification, on-orbit performance of GPS varied from block build to block build (IIA, IIRM, IIF) due to antenna gain and beam width variances. Unstable on-orbit performance results in significant risk to space users. Side-lobe signals, although not specified, were expected to significantly boost GPS signal availability for users above the constellation. During GPS III Phase A, NASA noted significant discrepancies in power levels specified in GPS III specification documents, and measured on-orbit performance. To stabilize the signal for high altitude space users, NASA DoD team in 2003-2005 led the creation of new Space Service Volume (SSV) definition and specifications.

Volume↗

Center Planning and Development: Multi-User Spaceport Initiatives

The Vehicle Assembly building at NASAs Kennedy Space Center has been used since 1966 to vertically assemble every launch vehicle, since the Apollo Program, launched from Launch Complex 39 (LC-39). After the cancellation of the Constellation Program in 2010 and the retirement of the Space Shuttle Program in 2011, the VAB faced an uncertain future. As the Space Launch System (SLS) gained a foothold as the future of American spaceflight to deep space, NASA was only using a portion of the VABs initial potential. With three high bays connected to the Crawler Way transportation system, the potential exists for up to three rockets to be simultaneously processed for launch. The Kennedy Space Center (KSC) Master plan, supported by the Center Planning and Development (CPD) Directorate, is guiding Kennedy toward a 21st century multi-user spaceport. This concept will maintain Kennedy as the United States premier gateway to space and provide multi-user operations through partnerships with the commercial aerospace industry. Commercial aerospace companies, now tasked with transporting cargo and, in the future, astronauts to the International Space Station (ISS) via the Commercial Resupply Service (CRS) and Commercial Crew Program (CCP), are a rapidly growing industry with increasing capabilities to make launch operations more economical for both private companies and the government. Commercial operations to Low Earth Orbit allow the government to focus on travel to farther destinations through the SLS Program. With LC-39B designated as a multi-use launch pad, companies seeking to use it will require an integration facility to assemble, integrate, and test their launch vehicle. An Announcement for Proposals (AFP) was released in June, beginning the process of finding a non-NASA user for High Bay 2 (HB2) and the Mobile Launcher Platforms (MLPs). An Industry Day, a business meeting and tour for interested companies and organizations, was also arranged to identify and answer any additional questions posed by potential proposers. After amending the AFP and posting additional material for potential users to consider, proposals are being accepted until July 31, at which point they will be evaluated to determine the proposer which best meets the objectives of the government. By identifying VAB HB2 as available and seeking proposals from the commercial sector for VAB HB2 and MLP use, Center Planning and Development is ensuring Kennedy Space Centers relevance in the evolving launch industry of the 21st century.

AFP↗

Unified User Interface to Support Effective and Intuitive Data Discovery, Dissemination, and Analysis at NASA GES DISC

Goddard Earth Sciences Data and Information Services Center (GES DISC) has been providing access to scientific data sets since 1990s. Beginning as one of the first Earth Observing System Data and Information System (EOSDIS) archive centers, GES DISC has evolved to offer a wide range of science-enabling services. With a growing understanding of needs and goals of its science users, GES DISC continues to improve and expand on its broad set of data discovery and access tools, sub-setting services, and visualization tools. Nonetheless, the multitude of the available tools, a partial overlap of functionality, and independent and uncoupled interfaces employed by these tools often leave the end users confused as of what tools or services are the most appropriate for a task at hand. As a result, some the services remain underutilized or largely unknown to the users, significantly reducing the availability of the data and leading to a great loss of scientific productivity. In order to improve the accessibility of GES DISC tools and services, we have designed and implemented UUI, the Unified User Interface. UUI seeks to provide a simple, unified, and intuitive one-stop shop experience for the key services available at GES DISC, including sub-setting (Simple Subset Wizard), granule file search (Mirador), plotting (Giovanni), and other services. In this poster, we will discuss the main lessons, obstacles, and insights encountered while designing the UUI experience. We will also present the architecture and technology behind UUI, including NodeJS, Angular, and Mongo DB, as well as speculate on the future of the tool at GES DISC as well as in a broader context of the Space Science Informatics.

web portal↗

Incorporating Speech Recognition into a Natural User Interface

The Augmented/ Virtual Reality (AVR) Lab has been working to study the applicability of recent virtual and augmented reality hardware and software to KSC operations. This includes the Oculus Rift, HTC Vive, Microsoft HoloLens, and Unity game engine. My project in this lab is to integrate voice recognition and voice commands into an easy to modify system that can be added to an existing portion of a Natural User Interface (NUI). A NUI is an intuitive and simple to use interface incorporating visual, touch, and speech recognition. The inclusion of speech recognition capability will allow users to perform actions or make inquiries using only their voice. The simplicity of needing only to speak to control an on-screen object or enact some digital action means that any user can quickly become accustomed to using this system. Multiple programs were tested for use in a speech command and recognition system. Sphinx4 translates speech to text using a Hidden Markov Model (HMM) based Language Model, an Acoustic Model, and a word Dictionary running on Java. PocketSphinx had similar functionality to Sphinx4 but instead ran on C. However, neither of these programs were ideal as building a Java or C wrapper slowed performance. The most ideal speech recognition system tested was the Unity Engine Grammar Recognizer. A Context Free Grammar (CFG) structure is written in an XML file to specify the structure of phrases and words that will be recognized by Unity Grammar Recognizer. Using Speech Recognition Grammar Specification (SRGS) 1.0 makes modifying the recognized combinations of words and phrases very simple and quick to do. With SRGS 1.0, semantic information can also be added to the XML file, which allows for even more control over how spoken words and phrases are interpreted by Unity. Additionally, using a CFG with SRGS 1.0 produces a Finite State Machine (FSM) functionality limiting the potential for incorrectly heard words or phrases. The purpose of my project was to investigate options for a Speech Recognition System. To that end I attempted to integrate Sphinx4 into a user interface. Sphinx4 had great accuracy and is the only free program able to perform offline speech dictation. However it had a limited dictionary of words that could be recognized, single syllable words were almost impossible for it to hear, and since it ran on Java it could not be integrated into the Unity based NUI. PocketSphinx ran much faster than Sphinx4 which would've made it ideal as a plugin to the Unity NUI, unfortunately creating a C# wrapper for the C code made the program unusable with Unity due to the wrapper slowing code execution and class files becoming unreachable. Unity Grammar Recognizer is the ideal speech recognition interface, it is flexible in recognizing multiple variations of the same command. It is also the most accurate program in recognizing speech due to using an XML grammar to specify speech structure instead of relying solely on a Dictionary and Language model. The Unity Grammar Recognizer will be used with the NUI for these reasons as well as being written in C# which further simplifies the incorporation.

Chapa, Nicholas↗

Earthdata User Interface (EUI) Library

As more remote sensing data moves to the cloud, the design tools we use to build user experiences and visualizations need to change and adapt to make the best use this new data reality. EUI 2.0, the next major iteration of the Earthdata User Interface Library, aims to make creating rich user experiences around NASA's Earth Observation System Data and Information System (EOSDIS) data easy and user-friendly. Building on a solid framework of design components, EUI 2.0 will be open source and have off-the-shelf integration with the Common Metadata Repository (CMR), basic mapping and visualization tools, a refreshed design toolkit for building apps and websites that fit the Earthdata design theme and guidelines. EUI 2.0 will allow for the rapid development of websites and applications based on EOSDIS tools and data holdings. It will also make use of cloud data availability to enable data visualization and analysis without the need to download and sync data.

Siarto, Jeff↗

NASA Orbital Debris Engineering Model ORDEM 3.1 - Software User Guide

This National Aeronautics and Space Administration (NASA) Orbital Debris Engineering Model (ORDEM) 3.1 Software User Guide accompanies delivery of the latest upgraded version of the model, ORDEM 3.1. The user guide also provides a top-level program description and a list of capabilities. It includes descriptions of runtime error and information codes, input/output file formats, runtimes for different orbit configurations, and how to use uncertainty files. ORDEM 3.1 supersedes the previous NASA Orbital Debris Program Office (ODPO) models – ORDEM 3.0 (Stansbery, et al. 2014) and ORDEM2000 (Liou, et al. 2002). The availability of new sensor and in situ data, re-analysis of older data, and development of new analytical techniques has enabled the construction of this more comprehensive and sophisticated model. An upgraded graphical user interface (GUI) is integrated with the software. This upgraded GUI uses project-oriented organization and provides the user with graphical representations of numerous output data products. For example, these range from the conventional flux vs. average debris size (or altitude bin) for chosen analysis orbits (or views) to the more complex color-contoured, two-dimensional (2-D) directional flux diagrams in local spacecraft elevation and azimuth. The current model, ORDEM 3.1, supports spacecraft as well as telescope/radar project assessments. ORDEM 3.1 contains updated debris populations covering low Earth orbit (LEO, up to 2000 km altitude) to geosynchronous orbit (GEO, up to 40,000 km altitude) and can assess debris calculations up to year 2050, extending coverage past the previous limit of 2035 in ORDEM 3.0. Although populations differ from its predecessor, ORDEM 3.1 is functionally the same as ORDEM 3.0 and can support ORDEM 3.0 projects through backward compatibility.

Vavrin, Andrew B.↗

NASA Orbital Debris Engineering Model ORDEM 3.2 – Software User Guide

This National Aeronautics and Space Administration (NASA) Orbital Debris Engineering Model (ORDEM) 3.2 Software User Guide accompanies delivery of the latest upgraded version of the model, ORDEM 3.2. The user guide also provides a top-level program description and a list of capabilities. It includes descriptions of runtime error and information codes, input/output file formats, runtimes for different orbit configurations, and how to use uncertainty files. ORDEM 3.2 supersedes the previous NASA Orbital Debris Program Office (ODPO) models – ORDEM 3.0 (Stansbery, et al. 2014) and ORDEM2000 (Liou, et al. 2002). The availability of new sensor and in situ data, re-analysis of older data, and development of new analytical techniques has enabled the construction of this more comprehensive and sophisticated model. An upgraded graphical user interface (GUI) is integrated with the software. This upgraded GUI uses project-oriented organization and provides the user with graphical representations of numerous output data products. For example, these range from the conventional flux vs. average debris size (or altitude bin) for chosen analysis orbits (or views) to the more complex color-contoured, two-dimensional (2-D) directional flux diagrams in local spacecraft elevation and azimuth. The current model, ORDEM 3.2, supports spacecraft as well as telescope/radar project assessments. ORDEM 3.2 contains updated debris populations covering low Earth orbit (LEO, up to 2000 km altitude) to geosynchronous orbit (GEO, up to 40,000 km altitude) and can assess debris calculations up to year 2050, extending coverage past the previous limit of 2035 in ORDEM 3.0. Although populations differ from its predecessor, ORDEM 3.2 is functionally the same as ORDEM 3.0 and can support ORDEM 3.0 projects through backward compatibility.

Andrew Vavrin↗