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All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Space Operations Learning Center

The Space Operations Learning Center (SOLC) is a tool that provides an online learning environment where students can learn science, technology, engineering, and mathematics (STEM) through a series of training modules. SOLC is also an effective media for NASA to showcase its contributions to the general public. SOLC is a Web-based environment with a learning platform for students to understand STEM through interactive modules in various engineering topics. SOLC is unique in its approach to develop learning materials to teach schoolaged students the basic concepts of space operations. SOLC utilizes the latest Web and software technologies to present this educational content in a fun and engaging way for all grade levels. SOLC uses animations, streaming video, cartoon characters, audio narration, interactive games and more to deliver educational concepts. The Web portal organizes all of these training modules in an easily accessible way for visitors worldwide. SOLC provides multiple training modules on various topics. At the time of this reporting, seven modules have been developed: Space Communication, Flight Dynamics, Information Processing, Mission Operations, Kids Zone 1, Kids Zone 2, and Save The Forest. For the first four modules, each contains three components: Flight Training, Flight License, and Fly It! Kids Zone 1 and 2 include a number of educational videos and games designed specifically for grades K-6. Save The Forest is a space operations mission with four simulations and activities to complete, optimized for new touch screen technology. The Kids Zone 1 module has recently been ported to Facebook to attract wider audience.

Lui, Ben↗
NASA NTRS1995

Improved Adjoint-Operator Learning For A Neural Network

Improved method of adjoint-operator learning reduces amount of computation and associated computational memory needed to make electronic neural network learn temporally varying pattern (e.g., to recognize moving object in image) in real time. Method extension of method described in "Adjoint-Operator Learning for a Neural Network" (NPO-18352).

Toomarian, Nikzad↗
NASA NTRS2012

Space Operations Learning Center Facebook Application

The proposed Space Operations Learning Center (SOLC) Facebook module, initially code-named Spaceville, is intended to be an educational online game utilizing the latest social networking technology to reach a broad audience base and inspire young audiences to be interested in math, science, and engineering. Spaceville will be a Facebook application/ game with the goal of combining learning with a fun game and social environment. The mission of the game is to build a scientific outpost on the Moon or Mars and expand the colony. Game activities include collecting resources, trading resources, completing simple science experiments, and building architectures such as laboratories, habitats, greenhouses, machine shops, etc. The player is awarded with points and achievement levels. The player s ability increases as his/her points and levels increase. A player can interact with other players using multiplayer Facebook functionality. As a result, a player can discover unexpected treasures through scientific missions, engineering, and working with others. The player creates his/her own avatar with his/her selection of its unique appearance, and names the character. The player controls the avatar to perform activities such as collecting oxygen molecules or building a habitat. From observations of other successful social online games such as Farmville and Restaurant City, a common element of these games is having eye-catching and cartoonish characters, and interesting animations for all activities. This will create a fun, educational, and rewarding environment. The player needs to accumulate points in order to be awarded special items needed for advancing to higher levels. Trophies will be awarded to the player when certain goals are reached or tasks are completed. In order to acquire some special items needed for advancement in the game, the player will need to visit his/her neighboring towns to discover the items. This is the social aspect of the game that requires the player to go out of his/her own establishment to explore what is in the neighborhood. Spaceville will take advantage of Facebook s successful architecture to inspire a new audience of scientists and engineers for the future.

Lui, Ben↗
NASA NTRS1984

The Impact of Pictorial Display on Operator Learning and Performance

The effects of pictorially displayed information on human learning and performance of a simple control task were investigated. The controlled system was a harmonic oscillator and the system response was displayed to subjects as either an animated pendulum or a horizontally moving dot. Results indicated that the pendulum display did not effect performance scores but did significantly effect the learning processes of individual operators. The subjects with the pendulum display demonstrated more vertical internal models early in the experiment and the manner in which their internal models were tuned with practice showed increased variability between subjects.

Miller, R. A.↗
NASA NTRS1993

Adjoint-Operator Learning For A Neural Network

Electronic neural networks made to synthesize initially unknown mathematical models of time-dependent phenomena or to learn temporally evolving patterns by use of algorithms based on adjoint operators. Algorithms less complicated, involve less computation and solve learning equations forward in time possibly simultaneously with equations of evolution of neural network, thereby both increasing computational efficiency and making real-time applications possible.

Barhen, Jacob↗
NASA NTRS2016

Human Flight to Lunar and Beyond - Re-Learning Operations Paradigms

For the first time since the Apollo era, NASA is planning on sending astronauts on flights beyond LEO. The Human Space Flight (HSF) program started with a successful initial flight in Earth orbit, in December 2014. The program will continue with two Exploration Missions (EM): EM-1 will be unmanned and EM-2, carrying astronauts, will follow. NASA established a multi-center team to address the communications, and related tacking/navigation needs. This paper will focus on the lessons learned by the team designing the architecture and operations for the missions. Many of these Beyond Earth Orbit lessons had to be re-learned, as the HSF program has operated for many years in Earth orbit. Unlike the Apollo missions that were largely tracked by a dedicated ground network, the HSF planned missions will be tracked (at distances beyond GEO) by the DSN, a network that mostly serves robotic missions. There have been surprising challenges to the DSN as unique modern human spaceflight needs stretch the experience base beyond that of tracking robotic missions in deep space. Close interaction between the DSN and the HSF community to understand the unique needs (e.g. 2-way voice) resulted in a Concept of Operations (ConOps) that leverages both the deep space robotic and the Human LEO experiences. Several examples will be used to highlight the unique challenges the team faced in establishing the communications and tracking capabilities for HSF missions beyond Earth Orbit, including: Navigation. At LEO, HSF missions can rely on GPS devices for orbit determination. For Lunar-and-beyond HSF missions, techniques such as precision 2-way and 3-way Doppler and ranging, Delta-Difference-of-range, and eventually possibly on-board navigation will be used. At the same time, HSF presents a challenge to navigators, beyond those presented by robotic missions - navigating a dynamic/"noisy" spacecraft. Impact of latency - the delay associated with Round-Trip-Light-Time (RTLT). Imagine trying to have a 2-way discussion (audio or video) with an astronaut, with a 2-3 sec or more delay inserted (for lunar distances) or 20 minutes delay (for Mars distances). Balanced communications link. For robotic missions, there has been a heavy emphasis on higher downlink data rates, e.g. bringing back science data. Higher uplink data rates were of secondary importance, as uplink was used only to send commands (and occasionally small files) to the spacecraft. The ratio of downlink-to-uplink data rates was often 10:1 or more. For HSF, a continuous forward link is established and rates for uplink and downlink are more similar.

Kenny, Edward (Ted)↗
NASA NTRS2016

Human Flight to Lunar and Beyond - Re-Learning Operations Paradigms

For the first time since the Apollo era, NASA is planning on sending astronauts on flights beyond Low-Earth Orbit (LEO). The Human Space Flight (HSF) program started with a successful initial flight in Earth orbit, in December 2014. The program will continue with two Exploration Missions (EM) to Lunar orbit: EM-1 will be unmanned and EM-2, carrying astronauts, will follow. NASA established a multi-center team to address the communications, and related navigation, needs. This paper will focus on the lessons learned in the team, planning for the missions' parts that are beyond Earth orbit. Many of these lessons had to be re-learned, as the HSF program after operated for many years in Earth orbit. Fortunately, the experience base from tracking robotic missions in deep space by the Deep Space Network (DSN) and close interaction with the HSF community to understand the unique needs (e.g. 2-way voice) resulted in a ConOps that leverages of both the deep space robotic and the Human LEO experiences. Several examples will be used to highlight the unique operational needs for HSF missions beyond Earth Orbit, including: - Navigation. At LEO, HSF missions can rely on Global Positioning System (GPS) devices for orbit determination. For Lunar-and-beyond HSF missions, techniques such as precision 2-way and 3-way Doppler and ranging, Delta-Difference-of-range, and eventually on-board navigation will be used. - Impact of latency - the delay associated with Round-Trip-Light-Time (RTLT). Imagine trying to have a 2-way discussion (audio or video) with an astronaut, with a 2-3 sec delay inserted (for Lunar distances) or 20 minutes delay (for Mars distances). - Balanced communications link. For robotic missions, there has been a heavy emphasis on the downlink data rates, bringing back science data from the instruments on-board the spacecraft. Uplink data rates were of secondary importance, used to send commands to the spacecraft. The ratio of downlink-to-uplink data rates was often 10:1 or more. For HSF, rates for uplink and downlink, at least for high-quality video, need to be similar.

Kenny, Ted↗
NASA NTRS2013

Space Operations Learning Center (SOLC) iPhone/iPad Application

This iPhone application, Space Junk Sammy, is intended to be an educational application designed for Apple iPhones and iPads. This new concept educates kids in an innovative way about how orbital debris affects space missions. Orbital debris is becoming a very significant concern for NASA and all Earthorbiting space missions. Spacecraft in low-Earth orbit are in constant danger of being potentially damaged or destroyed by debris. High-profile spacecraft such as the International Space Station (ISS) and Hubble Space Telescope are dealing with orbital debris on a regular basis. Other basic educational concepts that are portrayed are low-Earth orbits, satellites, ISS, attitude control, and other facts that can be presented in betweenlevel popup screens. The Orbital Debris Cleanup game is relatively simple from the user s technical standpoint. It is a 2D game where the user s avatar is a satellite buddy, named Sammy, in orbit around Earth. Sammy is controlled by the user with the device s gyroscope as well as touchscreen controls. It has equipment used for taking care of the space debris objects on the screen. Sammy also has a claw, a laser deflector, and hydrazine rockets to grab or push the debris objects into a higher orbit or into a lower orbit to burn up in the Earth s atmosphere. The user interface shows Sammy and space debris objects constantly moving from left to right, where Sammy is trying to catch the debris objects before they move off the right side of the screen. Everything will be in constant motion to increase fun and add to the realism of orbiting the Earth. The satellite buddy is used to clean up the space debris and protect other satellites. Later levels will include a laser deflector and hydrazine rockets instead of a robotic claw to push the orbital debris into a higher orbit and out of the path of other satellites

Binebrink, Daniel↗
NASA NTRS2002

Managing Risk in Safety Critical Operations - Lessons Learned from Space Operations

The Mission Control Center (MCC) at Johnson Space Center (JSC) has a rich legacy of supporting Human Space Flight operations throughout the Apollo, Shuttle and International Space Station eras. Through the evolution of ground operations and the Mission Control Center facility, NASA has gained a wealth of experience of what it takes to manage the risk in Safety Critical Operations, especially when human life is at risk. The focus of the presentation will be on the processes (training, operational rigor, team dynamics) that enable the JSC/MCC team to be so successful. The presentation will also share the evolution of the Mission Control Center architecture and how the evolution was introduced while managing the risk to the programs supported by the team. The details of the MCC architecture (e.g., the specific software, hardware or tools used in the facility) will not be shared at the conference since it would not give any additional insight as to how risk is managed in Space Operations.

Gonzalez, Steven A.↗
NASA NTRSDate not supplied

Why Learning From All Operations Is Imperative

Learning from All Operations – Panel Discussion Introduction: Tzvetomir Blajev, Flight Safety Foundation Why Learning from All Operations is Imperative: Jon Holbrook, NASA Implemented Approaches to Learning from All Operations American Airlines Japan Airlines Southwest Airlines Delta Air Lines Why Learning from All Operations is Imperative (Summary): Immanuel Barshi, NASA

Jon Holbrook↗
NASA NTRSDate not supplied

Advancing NASA SatCORPS Global Data Products with Cloud Computing and Machine Learning

Operational satellite imager radiances are valuable for deriving many different physical parameters that can be used for a variety of weather, aviation, and energy applications. The NASA Satellite ClOud and Radiation Property retrieval System (SatCORPS) applies a suite of algorithms to meteorological satellite data to provide cloud properties, radiative fluxes and other parameters on a global scale. The use of cloud computing has enabled recent enhancements to process a constellation of geostationary satellites at higher spatiotemporal resolutions than previously possible that meet low latency and near real-time needs. Data taken from Meteosat-8 and -11, Himawari, GOES-16, and -17 are processed and combined with operational polar orbiting satellite data and composited on a 3-km grid to provide global coverage. To improve the utility of the data products, machine learning and other innovative methods are applied in various ways to help minimize data product uncertainties under the most challenging conditions and to improve their consistency at all times of day. An update on recent SatCORPS enhancements is presented, highlighting the community benefits achieved with the use of cloud computing and machine learning.

William L Smith↗
NASA NTRS2012

CloudSat Anomaly Recovery and Operational Lessons Learned

Nov 2011: NASA/JPL declared CloudSat fully operational in DO ]OP Mode . CloudSat collects science data during sunlit portion of orbit, stable spin hibernation in eclipse . New CONOPS requires constant monitoring of thermal and power profiles, while allowing collection of 54 mins of science data per sunlit orbit

spacecraft systems↗
NASA NTRS1994

Operations for Learning with Graphical Models

This paper is a multidisciplinary review of empirical, statistical learning from a graphical model perspective. Well-known examples of graphical models include Bayesian net- works, directed graphs representing a Markov chain, and undirected networks representing a Markov field. These graphical models are extended to model data analysis and empirical learning using the notation of plates. Graphical operations for simplifying and manipulating a problem are provided including decomposition, differentiation, and the manipulation of probability models from the exponential family. These operations adapt existing techniques from statistics and automatic differentiation to graphs. Two standard algorithm schemes for learning are reviewed in a graphical framework: Gibbs sampling and the expectation maximization algorithm. Some algorithms are developed in this graphical framework including a generalized version of linear regression, techniques for feed-forward networks, and learning Gaussian and discrete Bayesian networks from data. The paper concludes by sketching some implications for data analysis and summarizing some popular algorithms that fall within the framework presented. The main original contributions here are the decomposition techniques and the demonstration that graphical models provide a framework for understanding and developing complex learning algorithms.

Buntine, Wray L.↗
NASA NTRS2009

Turning Operational Lessons Learned into Design Reality

The capabilities and limitations of a particular system design are well known by the people who operate it. Operational workarounds, operational notes and lessons learned are traditional methods for dealing with and documenting design shortcomings. The beginning of each new program brings the hope that hard-learned lessons will be incorporated into the next new system. But often operations personnel find their well-intentioned efforts frustrated by an inability to have their inputs considered by design personnel who have strictly-scoped requirements that are coupled with ambitious cost and schedule targets. There is a way for operational inputs to make it into the design, but the solution involves a combination of organizational culture and technical data. Any organization that utilizes this approach can realize significant benefits over the life cycle of their project.

Brady, David A.↗
NASA NTRS2010

Operational Lessons Learned from the Ares I-X Flight Test

The Ares I-X flight test, launched in 2009, is the first test of the Ares I crew launch vehicle. This development flight test evaluated the flight dynamics, roll control, and separation events, but also provided early insights into logistical, stacking, launch, and recovery operations for Ares I. Operational lessons will be especially important for NASA as the agency makes the transition from the Space Shuttle to the Constellation Program, which is designed to be less labor-intensive. The mission team itself comprised only 700 individuals over the life of the project compared to the thousands involved in Shuttle and Apollo missions; while missions to and beyond low-Earth orbit obviously will require additional personnel, this lean approach will serve as a model for future Constellation missions. To prepare for Ares I-X, vehicle stacking and launch infrastructure had to be modified at Kennedy Space Center's Vehicle Assembly Building (VAB) as well as Launch Complex (LC) 39B. In the VAB, several platforms and other structures designed for the Shuttle s configuration had to be removed to accommodate the in-line, much taller Ares I-X. Vehicle preparation activities resulted in delays, but also in lessons learned for ground operations personnel, including hardware deliveries, cable routing, transferred work and custodial paperwork. Ares I-X also proved to be a resource challenge, as individuals and ground service equipment (GSE) supporting the mission also were required for Shuttle or Atlas V operations at LC 40/41 at Cape Canaveral Air Force Station. At LC 39B, several Shuttle-specific access arms were removed and others were added to accommodate the in-line Ares vehicle. Ground command, control, and communication (GC3) hardware was incorporated into the Mobile Launcher Platform (MLP). The lightning protection system at LC 39B was replaced by a trio of 600-foot-tall towers connected by a catenary wire to account for the much greater height of the vehicle. Like Shuttle, Ares I-X will be stacked on a MLP and rolled out to the pad on a Saturn-era crawler-transporter. While Ares I-X was only held in place by the four hold-down posts on its aft skirt during rollout, a new vehicle stabilization system (VSS) attached to the vertical service structure kept the vehicle from undue swaying prior to launch at the pad, LC 39B. Following the launch, the flight test vehicle first stage was recovered with the aid of new parachutes resized to accommodate the five-segment-long first stage, which had a much greater length and mass than the Shuttle s reusable solid rocket boosters. After splashdown, recovery divers exercised extra care when handling the first stage to ensure that the flight data recorders in the fifth segment simulator were not damaged by exposure to sea water. The data recovered from the Ares I-X flight test will be very valuable in verifying the predicted environments and models used to design the vehicle. Lessons learned from Ares I-X will be shared with the Ares Projects through written and verbal reports and through integration of mission team members into the Project workforce.

Davis, Stephan R.↗
NASA NTRS2012

CloudSat Anomaly Recovery and Operational Lessons Learned

In April 2011, NASA's pioneering cloud profiling radar satellite, CloudSat, experienced a battery anomaly that placed it into emergency mode and rendered it operations incapable. All initial attempts to recover the spacecraft failed as the resultant power limitations could not support even the lowest power mode. Originally part of a six-satellite constellation known as the "A-Train", CloudSat was unable to stay within its assigned control box, posing a threat to other A-Train satellites. CloudSat needed to exit the constellation, but with the tenuous power profile, conducting maneuvers was very risky. The team was able to execute a complex sequence of operations which recovered control, conducted an orbit lower maneuver, and returned the satellite to safe mode, within one 65 minute sunlit period. During the course of the anomaly recovery, the team developed several bold, innovative operational strategies. Details of the investigation into the root-cause and the multiple approaches to revive CloudSat are examined. Satellite communication and commanding during the anomaly are presented. A radical new system of "Daylight Only Operations" (DO-OP) was developed, which cycles the payload and subsystem components off in tune with earth eclipse entry and exit in order to maintain positive power and thermal profiles. The scientific methodology and operational results behind the graduated testing and ramp-up to DO-OP are analyzed. In November 2011, the CloudSat team successfully restored the vehicle to consistent operational collection of cloud radar data during sunlit portions of the orbit. Lessons learned throughout the six-month return-to-operations recovery effort are discussed and offered for application to other R&D satellites, in the context of on-orbit anomaly resolution efforts.

cloud profiling radar satellite↗
NASA NTRS2006

Leadership Challenges in ISS Operations: Lessons Learned from Junior and Senior Mission Control Personnel

The International Space Station (ISS) is operated by a multi-national, multi-organizational team that is dispersed across multiple locations, time zones, and work schedules. At NASA, both junior and senior mission control personnel have had to find ways to address the leadership challenges inherent in such work, but neither have had systematic training in how to do so. The goals of this study were to examine the major leadership challenges faced by ISS mission control personnel and to highlight the approaches that they have found most effective to surmount them. We pay particular attention to the approaches successfully employed by the senior personnel and to the training needs identified by the junior personnel. We also evaluate the extent to which responses are consistent across the junior and senior samples. Further, we compare the issues identified by our interview survey to those identified by a standardized questionnaire survey of mission control personnel and a contrasting group of space station crewmembers. We studied a sample of 14 senior ISS flight controllers and a contrasting sample of 12 more junior ISS controllers. Data were collected using a semi-structured qualitative interview and content analyzed using an iterative process with multiple coders and consensus meetings to resolve discrepancies. To further explore the meaning of the interview findings, we also conducted new analyses of data from a previous questionnaire study of 13 American astronauts, 17 Russian cosmonauts, and 150 U.S. and 36 Russian mission control personnel supporting the ISS or Mir space stations. The interview data showed that the survey respondents had substantial consensus on several leadership challenges and on key strategies for dealing with them, and they offered a wide range of specific tactics for implementing these strategies. Interview data from the junior respondents will be presented for the first time at the meeting. The questionnaire data showed that the US mission control sample reported a level of support from their management that compared favorably to national norms. American mission control personnel and Russian crewmembers reported higher supervisor support than American crewmembers and Russian mission control personnel. We will present the specific issues underlying these findings and compare and contrast the results from the two datasets. Although specific to space station personnel, our results are consistent with recent management, cultural, and aerospace research. We aim to use our results to improve training for current and future mission control personnel.

Clement, James L.↗