Results for “Complex Networks” — Page 25

NASA NTRS2001

Recent Troposheric Ozone Observations from Satellite and In-Situ Measurements: An Overview

The past 4-5 years have seen an unprecedented increase in tropospheric ozone (and related) observations thanks to focused campaigns, new satellite data, and to innovative approaches to climatology (commercial aircraft sampling, networks). The result has been new insights into the complex chemistry and dynamics affecting tropospheric ozone in the non-urban environment: global pollution, subtropical stratospheric folds, disturbances associated with the 1997 El-Nino. A synthesis based on selected examples of field and satellite data will be presented.

Thompson, A.↗

NASA NTRS2001

New Insights into Tropospheric Ozone from Satellites and Soundings

The past 4-5 years have seen an unprecedented increase in tropospheric ozone (and related) observations thanks to focused campaigns, new satellite data, and to innovative approaches to climatology (commercial aircraft sampling, networks). The result has been new insights into the complex chemistry and dynamics affecting tropospheric ozone in the non-urban environment: global pollution, subtropical stratospheric folds, disturbances associated with the 1997 El-Nino. A synthesis based on selected examples of field and satellite data, much of it from Goddard's tropospheric work, will be presented.

Thompson, A.↗

NASA NTRS2020

On Development of Three-Dimensional Visualization Capabilities in Glenn Research Center Communication Analysis Suite

With NASA’s upcoming mission to return to the Moon sustainably by 2024 and using that success as a means to step onto the barren world of Mars, it remains more important than ever to conduct research and planning as thoroughly and efficiently as possible. In a mission as complex as landing humans onto another celestial body, a network of orbiting satellites and ground stations must accurately and reliably communicate with each other, enabling crucial data communications throughout the mission. Visualizing this important data communication increases the understanding of the data and can accelerate analyses efforts. The purpose of this software development is to create an interactive visualization with data taken MATLAB® scripts in the GRC Communication Analysis Suite that is easy to understand, can show all necessary data, and display the data accurately. The main types of data to visualize are from the State Propagation, Line of Sight and Dynamic Link Margin scripts. These all show positions and orbits of satellites and ground stations, while the Line of Sight data shows when they have the ability to communicate with each other based on their respective antenna positions and fields of view. Additionally, the Dynamic Link Margin mode color-codes the communication link performance onto the Line of Sight access lines. Visualization requires a graphics language that is easily accessible, has the needed features, and able to easily read data produced by the GRC Communication Analysis Suite MATLAB® scripts. ThreeJS, a graphics library for Web Graphics Library, coded in JavaScript was selected for the visualization. The next part of the software development was to move the data from MATLAB® to the JavaScript. The best way to accomplish this was to implement a MATLAB® function converting the output data of the scripts to a JavaScript Object Notation file. A key part of the development was creating the visualization within JavaScript and ThreeJS to visualize any combination of planets, moons, orbits, satellites, ground stations, line of sight links, and handle future features without changing major parts of the code. The current visualization capability runs directly from MATLAB®, and can dynamically create any scene. This software development currently supports the lunar communications analysis underway by NASA, and can be easily expanded upon in the future to aid any analysis requirements to help plan current and future space missions.

Visualization↗

NASA NTRS2017

A Framework for Extending the Science Traceability Matrix: Application to the Planned Europa Mission

One of the most critical functions of the systems engineering requirements process for a large multi-instrument science-driven space mission is to successfully communicate customer expectations into a comprehensive and traceable science requirements flowdown. These requirements are essential to communicating the constraints on the scope of the science investigations and clarifying how multiple instruments contribute to a given science goal. They also provide insight into how the science goals of the whole mission are affected by design choices. There is little specific guidance available on best practices for developing this science-driven flowdown. A unified Science Traceability Matrix (USTM) contains a significant amount of information that can be leveraged for that purpose, but the USTM was not designed to directly produce a complete science requirements flowdown. Thus, starting with the principles codified in a USTM, the authors propose a framework that directly maps into the requirements flowdown and supports broader systems engineering processes while retaining its meaning to the science team. This Science Traceability and Alignment Framework, or STAF, defines a set of common definitions and valid relationships to structure communication across the project. In addition, STAF populates a network of information that can be useful to support complex mission analysis activities such as fault protection. This work discusses the highest-level implementation of the STAF, the project-domain or P-STAF, which describes an approach to decomposing customer requirements into science requirements. The planned Europa Mission is used as a case study for the implementation of this framework and its potential benefits to a project.

Susca, Sara↗

NASA NTRSDate not supplied

DeepONet-Assisted Optimization of Surface Topography for Transition Delay in a Mach 4.5 Boundary Layer

We use deep learning, an ensemble variational technique (EnVar), and direct numerical simulations(DNS) to design an optimal topography for a two-dimensional roughness element that delays the on-set of laminar-turbulent transition in a Mach 4.5 flat-plate boundary layer. Deep operator networks (DeepONets), which have the known ability to learn complex nonlinear operators within dynamical systems, are used for machine learning. For the baseline configuration of a smooth flat plate, the second-mode waves at the DNS inflow cause a quick nonlinear breakdown of the high-speed boundary layer within the computational domain. Results reported in the present study validate the ability of DeepONets to model the transition delay via a given topography of the roughness element. The computing cost to optimize the rough-ness element for minimal skin-friction drag is substantially lowered by the DeepONets-based reduced-order model. In comparison to the baseline method of EnVar optimization based on DNS alone, the DeepONets-based EnVar optimizer is able to delay transition past the outflow boundary of the computational domain while utilizing almost 5–6 times fewer DNS.

Machine Learning↗

NASA NTRS2008

Distributing Data to Hand-Held Devices in a Wireless Network

ADROIT is a developmental computer program for real-time distribution of complex data streams for display on Web-enabled, portable terminals held by members of an operational team of a spacecraft-command-and-control center who may be located away from the center. Examples of such terminals include personal data assistants, laptop computers, and cellular telephones. ADROIT would make it unnecessary to equip each terminal with platform- specific software for access to the data streams or with software that implements the information-sharing protocol used to deliver telemetry data to clients in the center. ADROIT is a combination of middleware plus software specific to the center. (Middleware enables one application program to communicate with another by performing such functions as conversion, translation, consolidation, and/or integration.) ADROIT translates a data stream (voice, video, or alphanumerical data) from the center into Extensible Markup Language, effectuates a subscription process to determine who gets what data when, and presents the data to each user in real time. Thus, ADROIT is expected to enable distribution of operations and to reduce the cost of operations by reducing the number of persons required to be in the center.

Hodges, Mark↗

NASA NTRS1999

BIO-Plex Information System Concept

This paper describes a suggested design for an integrated information system for the proposed BIO-Plex (Bioregenerative Planetary Life Support Systems Test Complex) at Johnson Space Center (JSC), including distributed control systems, central control, networks, database servers, personal computers and workstations, applications software, and external communications. The system will have an open commercial computing and networking, architecture. The network will provide automatic real-time transfer of information to database server computers which perform data collection and validation. This information system will support integrated, data sharing applications for everything, from system alarms to management summaries. Most existing complex process control systems have information gaps between the different real time subsystems, between these subsystems and central controller, between the central controller and system level planning and analysis application software, and between the system level applications and management overview reporting. An integrated information system is vitally necessary as the basis for the integration of planning, scheduling, modeling, monitoring, and control, which will allow improved monitoring and control based on timely, accurate and complete data. Data describing the system configuration and the real time processes can be collected, checked and reconciled, analyzed and stored in database servers that can be accessed by all applications. The required technology is available. The only opportunity to design a distributed, nonredundant, integrated system is before it is built. Retrofit is extremely difficult and costly.

Jones, Harry↗

NASA NTRS2022

Predicting Unreinforced Fabric Mechanical Behavior with Recurrent Neural Networks

Unreinforced woven fabrics are widely employed in various high-performance applications, including parachute deployment systems, airbags, and ballistic armor. The analysis of such materials is inherently complex due to the multiscale structure of these materials, and the dependence of macroscale behavior on changes that occur at lower scales. Previously, NASA’s Multiscale Analysis Tool (NASMAT) showed its capability in predicting unreinforced fabric behavior at the macroscale by capturing finite rotations that occur at the mesoscale. Though effective, the tool can face high computational cost for large, complex problems, motivating the need for the development of a surrogate model that can capture the same behavior. A recurrent neural network (RNN) was developed and trained on virtual NASMAT data to mimic the physics-based solutions while improving the computational runtime. The architecture of the RNN to best simulate the fabric behavior was carefully crafted based on heuristic knowledge of predicting physics-based temporal data, manual hyperparameter case studies, and Hyperband optimization.. The resultant model was able to predict a variety of stress-strain curves for fabrics with different mesoscale geometries, and was further validated by comparing to experimental data for the K706 style Kevlar plain-weave fabric, demonstrating the ability of the model to effectively capture the geometric changes in the fabric without explicitly calculating them, as is done in NASMAT. Furthermore, the tool showed its ability to improve on the runtime by a factor of 10 for fabric solutions compared to the multiscale tool, which would further enable the simulation of complex loading scenarios on unreinforced fabrics.

Fabric↗

NASA NTRS2004

Examples of Current and Future Uses of Neural-Net Image Processing for Aerospace Applications

Feed forward artificial neural networks are very convenient for performing correlated interpolation of pairs of complex noisy data sets as well as detecting small changes in image data. Image-to-image, image-to-variable and image-to-index applications have been tested at Glenn. Early demonstration applications are summarized including image-directed alignment of optics, tomography, flow-visualization control of wind-tunnel operations and structural-model-trained neural networks. A practical application is reviewed that employs neural-net detection of structural damage from interference fringe patterns. Both sensor-based and optics-only calibration procedures are available for this technique. These accomplishments have generated the knowledge necessary to suggest some other applications for NASA and Government programs. A tomography application is discussed to support Glenn's Icing Research tomography effort. The self-regularizing capability of a neural net is shown to predict the expected performance of the tomography geometry and to augment fast data processing. Other potential applications involve the quantum technologies. It may be possible to use a neural net as an image-to-image controller of an optical tweezers being used for diagnostics of isolated nano structures. The image-to-image transformation properties also offer the potential for simulating quantum computing. Computer resources are detailed for implementing the black box calibration features of the neural nets.

Decker, Arthur J.↗

NASA NTRS1991

From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

Ross, Muriel D.↗

NASA NTRSDate not supplied

NASA Delay Tolerant Networks: Operational, Evolving, an Ready for Expansion

The future of humanity’s presence beyond Earth depends on the successful commercialization of space. For commercialization to succeed, companies need cost-efficient architectures to support their business models and minimize risks for human capital, design, development, and operations. An ongoing challenge to any space enterprise is the reality that terrestrial network technologies are insufficient to provide reliable communications between assets in space. Whether you need to ensure your valuable data is safely transmitted to the ground or reliably delivered between platforms in orbit, ensuring data integrity over intermittent communication links is a necessity. Current solutions to space communications rely heavily on manual recording, storing, and retrieval of data from spacecraft. The current standard in space communication protocols, Consultative Committee for Space Data Systems (CCSDS) Space Packet standard, is reliant on inflexible network architectures based around mission-critical infrastructure to ensure data delivery. However, by automating the recording, storing, retrieval, and verification of data with Delay Tolerant Networks (DTN), the operator is freed from the dependence on manual data management and expensive mission critical infrastructure. NASA has been developing delay tolerant systems since the late 1990’s. Multiple DTN implementations have been established during that time, each suited to different use cases. Most notably, the DTN deployment for the International Space Station (ISS) includes demonstration of two DTN technologies: Interplanetary Overlay Network (ION) and Delay Tolerant Network Marshall Enterprise (DTNME). Beyond ISS, there are even more NASA DTN deployments being considered. Now that DTN implementations are maturing, it is appropriate to reflect upon these decades of work, review the integration and performance of the existing ISS deployment, and explore the future possibilities for DTN deployment industry-wide. The ISS DTN deployment is a complex architecture consisting of different DTN implementations for the onboard and ground network environments. The ION DTN implementation is being used in the on-board network. The Huntsville Operations Support Center (HOSC) DTN implementation, DTNME, is used by the ground network supporting ISS and will soon be a second onboard gateway too. The two implementations work cooperatively to provide high fidelity data services to flight operations users and payload developers across the globe. Though the two implementations yield a quality service, limitations are evident. Data rate, data storage, and device management are constrained by the services themselves and the complex nature of the deployment. Evolution of operations concepts will improve system capabilities and stability, but significant improvement will require additional development to the implementations themselves and to the overall deployment architecture. Taking advantage of the ongoing development and operation of the ISS DTN service will be central to the success of the future evolutions of NASA DTN deployments while demonstrating the benefits of DTN’s low-cost reliable data communication protocols for the growing commercial space industry. A broad effort on DTN integration and support is necessary to promote expansion beyond existing applications. NASA is developing several useful DTN implementations across a number of different systems: ION, DTNME, High-Rate DTN (HDTN), Bundle Protocol Library (BPLib), and others. To prevent fragmentation, DTN implementation teams need to communicate, collaborate, and integrate with one another to build a solid operational foundation for new DTN deployments. The establishment of a group that can assist new DTN users with understanding the purpose of each DTN implementation, provide best practices, and serve as a general knowledge base is paramount. Potential use of DTN on Gateway and other future NASA missions further drives the need for streamlined communication between DTN implementation teams. A well-integrated and highly engaged NASA DTN working group should help provide system architects the best DTN solutions for future commercial space efforts. This paper will first review the history of DTN implementations, explore the shortcoming of current space networking solutions given available limits in technology, and therefore establish the need for Delay Tolerant Networking in space communications. Secondly, the authors will explore NASA’s array of DTN implementations and highlight their usefulness to space applications. Thirdly, this paper will establish general DTN implementation distinguishing factors. Fourthly, the authors will discuss attempts to create a generic DTN comparison matrix, and the authors will review potential future topics in DTN innovation and collaboration, highlighting several key future efforts. Finally, this paper will describe how the institution of a NASA DTN Working Group will benefit DTN adoption across the governmental and commercial space sector. The goal of this paper is to encourage enthusiasm for DTN, share strategies for improving DTN on both current and future applications, promote the collaboration of DTN implementation groups within the international space operations community, and open the conversations about DTN, priorities, complexities, and innovation to the wider spaceflight industry.

DTN↗

NASA NTRSDate not supplied

NASA Delay Tolerant Networks: Operational, Evolving, an Ready for Expansion

The future of humanity’s presence beyond Earth depends on the successful commercialization of space. For commercialization to succeed, companies need cost-efficient architectures to support their business models and minimize risks for human capital, design, development, and operations. An ongoing challenge to any space enterprise is the reality that terrestrial network technologies are insufficient to provide reliable communications between assets in space. Whether you need to ensure your valuable data is safely transmitted to the ground or reliably delivered between platforms in orbit, ensuring data integrity over intermittent communication links is a necessity. Current solutions to space communications rely heavily on manual recording, storing, and retrieval of data from spacecraft. The current standard in space communication protocols, Consultative Committee for Space Data Systems (CCSDS) Space Packet standard, is reliant on inflexible network architectures based around mission-critical infrastructure to ensure data delivery. However, by automating the recording, storing, retrieval, and verification of data with Delay Tolerant Networks (DTN), the operator is freed from the dependence on manual data management and expensive mission critical infrastructure. NASA has been developing delay tolerant systems since the late 1990’s. Multiple DTN implementations have been established during that time, each suited to different use cases. Most notably, the DTN deployment for the International Space Station (ISS) includes demonstration of two DTN technologies: Interplanetary Overlay Network (ION) and Delay Tolerant Network Marshall Enterprise (DTNME). Beyond ISS, there are even more NASA DTN deployments being considered. Now that DTN implementations are maturing, it is appropriate to reflect upon these decades of work, review the integration and performance of the existing ISS deployment, and explore the future possibilities for DTN deployment industry-wide. The ISS DTN deployment is a complex architecture consisting of different DTN implementations for the onboard and ground network environments. The ION DTN implementation is being used in the on-board network. The Huntsville Operations Support Center (HOSC) DTN implementation, DTNME, is used by the ground network supporting ISS and will soon be a second onboard gateway too. The two implementations work cooperatively to provide high fidelity data services to flight operations users and payload developers across the globe. Though the two implementations yield a quality service, limitations are evident. Data rate, data storage, and device management are constrained by the services themselves and the complex nature of the deployment. Evolution of operations concepts will improve system capabilities and stability, but significant improvement will require additional development to the implementations themselves and to the overall deployment architecture. Taking advantage of the ongoing development and operation of the ISS DTN service will be central to the success of the future evolutions of NASA DTN deployments while demonstrating the benefits of DTN’s low-cost reliable data communication protocols for the growing commercial space industry. A broad effort on DTN integration and support is necessary to promote expansion beyond existing applications. NASA is developing several useful DTN implementations across a number of different systems: ION, DTNME, High-Rate DTN (HDTN), Bundle Protocol Library (BPLib), and others. To prevent fragmentation, DTN implementation teams need to communicate, collaborate, and integrate with one another to build a solid operational foundation for new DTN deployments. The establishment of a group that can assist new DTN users with understanding the purpose of each DTN implementation, provide best practices, and serve as a general knowledge base is paramount. Potential use of DTN on Gateway and other future NASA missions further drives the need for streamlined communication between DTN implementation teams. A well-integrated and highly engaged NASA DTN working group should help provide system architects the best DTN solutions for future commercial space efforts. This paper will first review the history of DTN implementations, explore the shortcoming of current space networking solutions given available limits in technology, and therefore establish the need for Delay Tolerant Networking in space communications. Secondly, the authors will explore NASA’s array of DTN implementations and highlight their usefulness to space applications. Thirdly, this paper will establish general DTN implementation distinguishing factors. Fourthly, the authors will discuss attempts to create a generic DTN comparison matrix, and the authors will review potential future topics in DTN innovation and collaboration, highlighting several key future efforts. Finally, this paper will describe how the institution of a NASA DTN Working Group will benefit DTN adoption across the governmental and commercial space sector. The goal of this paper is to encourage enthusiasm for DTN, share strategies for improving DTN on both current and future applications, promote the collaboration of DTN implementation groups within the international space operations community, and open the conversations about DTN, priorities, complexities, and innovation to the wider spaceflight industry.

DTN↗

NASA NTRS1994

Solar fine scale structures in the corona, transition region, and lower atmosphere

The American Science and Engineering Soft X-ray Imaging Payload and the Naval Research Laboratory High Resolution Telescope and Spectrograph (HRTS) were launched from White Sands on 1987 December 11 in coordinated sounding rocket flights. The goal was to investigate the correspondence of fine-scale structures from different temperature regimes in the solar atmosphere, and particularly the relationship between X-ray bright points (XBPs) and transition region explosive events. We present results of the analysis of co-aligned X-ray images, maps of sites of transition region explosive events observed in C IV 10(exp 5), HRTS 1600 A spectroheliograms of the T(sub min) region, and ground-based magnetogram and He I 10830 A images. We examined the relationship of He I 10830 A dark features and evolving magnetic features which correspond to XBPs. We note a frequent double ribbon pattern of the He I dark feature counterparts to XBPs. We discuss an analysis of the relationship of XBPs to evolving magnetic features by Webb et al., which shows that converging magnetic features of opposite polarity are the most significant magnetic field counterparts to XBPs. The magnetic bipolar features associated with XBPs appear as prominent network elements in chromospheric and transition region images. The features in C IV observations corresponding to XBP sites are in general bright, larger scale (approximately 10 arcsec) regions of complex velocity fields of order 40 km/s, which is typical of brighter C IV network elements. These C IV features do not reach the approximately 100 km/s velocities seen in the C IV explosive events. Also, there are many similar C IV bright network features without a corresponding XBP in the X-ray image. The transition region explosive events do not correspond directly to XBPs. The explosive events appear to be concentrated in the quiet Sun at the edges of strong network, or within weaker field strength network regions. We find a greater number of C IV events than expected from the results of a previous Spacelab 2 HRTS disk survey. We attribute this at least partly to better spatial resolution with the newer HRTS data. The full-disk X-ray image shows a pattern of dark lanes in quiet Sun areas. The number density of C IV events is twice as large inside as outside a dark lane (4.6 x 10(exp -3) vs. 2.3 x 10(exp -3) explosive events per arcsec (exp 2)). The dark lane corresponds to an old decaying magnetic neutral line. We suggest that this provides an increased opportunity for small-scale convergence and reconnection of opposite polarity magnetic field features, in analogy with the results of Webb et al. for XBPs but at a reduced scale of reconnection.

Moses, Dan↗

NASA NTRS2005

Adaptive Filtering Using Recurrent Neural Networks

A method for adaptive (or, optionally, nonadaptive) filtering has been developed for estimating the states of complex process systems (e.g., chemical plants, factories, or manufacturing processes at some level of abstraction) from time series of measurements of system inputs and outputs. The method is based partly on the fundamental principles of the Kalman filter and partly on the use of recurrent neural networks. The standard Kalman filter involves an assumption of linearity of the mathematical model used to describe a process system. The extended Kalman filter accommodates a nonlinear process model but still requires linearization about the state estimate. Both the standard and extended Kalman filters involve the often unrealistic assumption that process and measurement noise are zero-mean, Gaussian, and white. In contrast, the present method does not involve any assumptions of linearity of process models or of the nature of process noise; on the contrary, few (if any) assumptions are made about process models, noise models, or the parameters of such models. In this regard, the method can be characterized as one of nonlinear, nonparametric filtering. The method exploits the unique ability of neural networks to approximate nonlinear functions. In a given case, the process model is limited mainly by limitations of the approximation ability of the neural networks chosen for that case. Moreover, despite the lack of assumptions regarding process noise, the method yields minimum- variance filters. In that they do not require statistical models of noise, the neural- network-based state filters of this method are comparable to conventional nonlinear least-squares estimators.

Parlos, Alexander G.↗

NASA NTRS2008

Performance Characterization of Space Communications and Navigation (SCaN) Network by Simulation

As future space exploration missions will involve larger number of spacecraft and more complex systems, theoretical analysis alone may have limitations on characterizing system performance and interactions among the systems. Simulation tools can be useful for system performance characterization through detailed modeling and simulation of the systems and its environment.

effectiveness↗

NASA NTRS2001

Space Shuttle RTOS Bayesian Network

With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, guidance and navigation, communication, and orbiter landing support. Secondary functions include the provision of operational services for non-avionics systems such as data handling for the payloads and caution and warning alerts to the crew. Recently, a process to selection the optimal commercial-off-the-shelf (COTS) real-time operating system (RTOS) for the CAU was conducted by United Space Alliance (USA) Corporation, which is a joint venture between Boeing and Lockheed Martin, the prime contractor for space shuttle operations. In order to independently assess the RTOS selection, NASA has used the Bayesian network-based scoring methodology described in this paper. Our two-stage methodology addresses the issue of RTOS acceptability by incorporating functional, performance and non-functional software measures related to reliability, interoperability, certifiability, efficiency, correctness, business, legal, product history, cost and life cycle. The first stage of the methodology involves obtaining scores for the various measures using a Bayesian network. The Bayesian network incorporates the causal relationships between the various and often competing measures of interest while also assisting the inherently complex decision analysis process with its ability to reason under uncertainty. The structure and selection of prior probabilities for the network is extracted from experts in the field of real-time operating systems. Scores for the various measures are computed using Bayesian probability. In the second stage, multi-criteria trade-off analyses are performed between the scores. Using a prioritization of measures from the decision-maker, trade-offs between the scores are used to rank order the available set of RTOS candidates.

Morris, A. Terry↗

NASA NTRS1991

Architecture, performance, and implementation of the tandem banyan fast packet switch

The authors propose a space-division fast packet switch architecture based on banyan interconnection networks, called the tandem banyan switching fabric. It consists of placing banyan networks in tandem, offering multiple paths from each input to each output, thus overcoming in a very simple way the effect of conflicts among packets (to which banyan networks are prone) and achieving output buffering. From a hardware implementation perspective, this architecture is simple in that it consists of several instances of only two VLSI chips, one implementing the banyan network and the other implementing the output buffer function. The basic structure and operation of the tandem banyan switching fabric are described, and its performance is discussed. The authors propose a modification to the basic structure which decreases the hardware complexity of the switch while maintaining its performance. An implementation of the banyan network using a high-performance BiCMOS sea-of-gates on 0.8-micron technology is reported.

Tobagi, Fouad A.↗

NASA NTRS2017

Juno Gravity Science: Preparing for Data Collection at Jupiter

One of the primary goals of the Juno mission is to investigate Jupiter’s interior by mapping its gravitational field with the gravity science instrument. The Juno spacecraft has two radio science components that comprise the gravity science instrument: the X-band telecommunications system for a X-up/X-down link and a Ka-band Translator for a Ka-up/Ka-down link. The Deep Space Network’s DSS-25 beam waveguide antenna at the Goldstone Deep Space Communications Complex in California provides the X- and Ka-band uplink alongside an Advanced Water Vapor Radiometer to calibrate tropospheric effects. X-band and Ka-band downlink data are collected with both open-loop and closed-loop receivers located at the complex. Utilization of Ka-band provides scientific benefit to the Doppler measurements, but also adds operational challenges. Pointing of the uplink and downlink Ka-band signals requires additional systems to be calibrated and operated by the Deep Space Network; and the higher frequency of Ka-band means the signal dynamics are increased by a factor of four over X-band signals. Due to the spacecraft’s elliptical orbit and 4000 kilometer perijove altitude, it accelerates at an extreme rate as it approaches Jupiter, inducing large dynamic ranges in Doppler range of approximately 6 MHz over 3 hours at Ka-band. After the installation of a new Ka-band transmitter at DSS-25 for Juno was completed in 2015, end-to-end testing was conducted to ensure readiness for operations at Jupiter and provide an initial assessment of the performance. Cruise testing was conducted in the same operational configuration that the system will be used in during perijove passes. Processed open-loop data yielded uncalibrated Doppler residuals of 1.9 mHz at X-band and 6.0 mHz at Ka-band with 5-second compression time. Conduction of these tests has prepared the instrument and the operations team for science data collection during the science phase of the mission.

Buccino, Dustin↗