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NASA NTRS2001

Aerosol Measurements by the Globally Distributed Micro Pulse Lidar Network

Full time measurements of the vertical distribution of aerosol are now being acquired at a number of globally distributed MP (micro pulse) lidar sites. The MP lidar systems provide full time profiling of all significant cloud and aerosol to the limit of signal attenuation from compact, eye safe instruments. There are currently eight sites in operation and over a dozen planned. At all sited there are also passive aerosol and radiation measurements supporting the lidar data. Four of the installations are at Atmospheric Radiation Measurement program sites. The network operation includes instrument operation and calibration and the processing of aerosol measurements with standard retrievals and data products from the network sites. Data products include optical thickness and extinction cross section profiles. Application of data is to supplement satellite aerosol measurements and to provide a climatology of the height distribution of aerosol. The height distribution of aerosol is important for aerosol transport and the direct scattering and absorption of shortwave radiation in the atmosphere. Current satellite and other data already provide a great amount of information on aerosol distribution, but no passive technique can adequately resolve the height profile of aerosol. The Geoscience Laser Altimeter System (GLAS) is an orbital lidar to be launched in early 2002. GLAS will provide global measurements of the height distribution of aerosol. The MP lidar network will provide ground truth and analysis support for GLAS and other NASA Earth Observing System data. The instruments, sites, calibration procedures and standard data product algorithms for the MPL network will be described.

Spinhirne, James↗

NASA NTRS1986

Queueing Network Models for Parallel Processing of Task Systems: an Operational Approach

Computer performance modeling of possibly complex computations running on highly concurrent systems is considered. Earlier works in this area either dealt with a very simple program structure or resulted in methods with exponential complexity. An efficient procedure is developed to compute the performance measures for series-parallel-reducible task systems using queueing network models. The procedure is based on the concept of hierarchical decomposition and a new operational approach. Numerical results for three test cases are presented and compared to those of simulations.

Mak, Victor W. K.↗

NASA NTRSDate not supplied

Implementing Delay/Disruption Tolerant Networking for NASA’s Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) Mission

NASA’s Plankton, Aerosol, Clouds, ocean Ecosystem (PACE) mission will be the ﬁrst NASA science mission to use Delay/Disruption Tolerant Networking (DTN) for routine operations. The DTN Bundle Protocol (BP) is being integrated into the core Flight Software (cFS) for the transfer of house-keeping ﬁles. DTN nodes will also be integrated into the Near Space Network (NSN) ground stations and the PACE Mission Operations Center. This paper will describe the DTN implementations, the PACE DTN operations concept and how this mission is a signiﬁcant step towards the Solar System Internet.

dtn↗

NASA NTRS2019

Overview of the New Version 3 NASA Micro-Pulse Lidar Network (MPLNET) Automatic Precipitation Detection Algorithm

Precipitation modifies atmospheric column thermodynamics through the process of evaporation and serves as a proxy for latent heat modulation. For this reason, a correct precipitation parameterization (especially for low-intensity precipitation) within global scale models is crucial. In addition to improving our modeling of the hydrological cycle, this will reduce the associated uncertainty of global climate models in correctly forecasting future scenarios, and will enable the application of mitigation strategies. In this manuscript we present a proof of concept algorithm to automatically detect precipitation from lidar measurements obtained from the National Aeronautics and Space Administration Micropulse lidar network (MPLNET). The algorithm, once tested and validated against other remote sensing instruments, will be operationally implemented into the network to deliver a near real time (latency <1.5 h) rain masking variable that will be publicly available on MPLNET website as part of the new Version 3 data products. The methodology, based on an image processing technique, detects only light precipitation events (defined by intensity and duration) such as light rain, drizzle, and virga. During heavy rain events, the lidar signal is completely extinguished after a few meters in the precipitation or it is unusable because of water accumulated on the receiver optics. Results from the algorithm, in addition to filling a gap in light rain, drizzle, and virga detection by radars, are of particular interest for the scientific community as they help to fully characterize the aerosol cycle, from emission to deposition, as precipitation is a crucial meteorological phenomenon accelerating atmospheric aerosol removal through the scavenging effect. Algorithm results will also help the understanding of long term aerosol–cloud interactions, exploiting the multi-year database from several MPLNET permanent observational sites across the globe. The algorithm is also applicable to other lidar and/or ceilometer network infrastructures in the framework of the Global Aerosol Watch (GAW) aerosol lidar observation network (GALION).

Simone Lolli↗

NASA NTRS1992

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations

As part of the Research Institute for Computing and Information Systems (RICIS) activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This interim report provides the status of the project and outlines the future plans.

Jani, Yashvant↗

NASA NTRS1992

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Translational controller results

The reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we also use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use two terms interchangeable to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS). This report is the deliverable D3 in our project activity and provides the test results of the fuzzy learning translational controller. This report is organized in six sections. Based on our experience and analysis with the attitude controller, we have modified the basic configuration of the reinforcement learning algorithm in ARIC as described in section 2. The shuttle translational controller and its implementation in fuzzy learning architecture is described in section 3. Two test cases that we have performed are described in section 4. Our results and conclusions are discussed in section 5, and section 6 provides future plans and summary for the project.

Jani, Yashvant↗

NASA NTRS1993

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Special approach/docking testcase results

As part of the RICIS project, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Maximum Mission (SMM) satellite simulation. In utilizing these fuzzy learning techniques, we use the Approximate Reasoning based Intelligent Control (ARIC) architecture, and so we use these two terms interchangeably to imply the same. This activity is carried out in the Software Technology Laboratory utilizing the Orbital Operations Simulator (OOS) and programming/testing support from other contractor personnel. This report is the final deliverable D4 in our milestones and project activity. It provides the test results for the special testcase of approach/docking scenario for the shuttle and SMM satellite. Based on our experience and analysis with the attitude and translational controllers, we have modified the basic configuration of the reinforcement learning algorithm in ARIC. The shuttle translational controller and its implementation in ARIC is described in our deliverable D3. In order to simulate the final approach and docking operations, we have set-up this special testcase as described in section 2. The ARIC performance results for these operations are discussed in section 3 and conclusions are provided in section 4 along with the summary for the project.

Jani, Yashvant↗

NASA NTRS1992

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Attitude control results

As part of the RICIS activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This report is deliverable D2 Altitude Control Results and provides the status of the project after four months of activities and outlines the future plans. In section 2 we describe the Fuzzy-Learner system for the attitude control functions. In section 3, we provide the description of test cases and results in a chronological order. In section 4, we have summarized our results and conclusions. Our future plans and recommendations are provided in section 5.

Jani, Yashvant↗

NASA NTRS1965

Boron trifluoride nuclear detector preamplifier uses single-cable connection

Preamplifier for a nuclear particle detector operates with a single interconnecting cable. Isolating and bypass networks permit this single cable operation.

Heckelman, J. D.↗

NASA NTRSDate not supplied

Completion of NASA’s Two-Year Laser Communications Relay Demonstration (LCRD) Experiment Program

The National Aeronautics and Space Administration’s (NASA) Laser Communications Relay Demonstration (LCRD) completed its two-year experiment program in June 2024. The LCRD geosynchronous payload includes two laser communications terminals interconnected via an onboard electronic switch. The payload relays data between two optical ground stations located in California and Hawaii and can also exchange data with an RF ground station in New Mexico. The addition of the Integrated LCRD Low-Earth Orbit (LEO) User Modem and Amplifier Terminal (ILLUMA-T) on the International Space Station in 2023 enabled data relay demonstrations between a laser terminal in low Earth orbit and either one of the optical ground stations or the RF ground station. The experiment program has included a variety of experiments originating within the LCRD project, NASA, and industry. The experiments have included measurements of the effects of the atmospheric optical channel (turbulence, weather) on the performance and availability of laser communications, adaptive optics characterization, demonstration of optimetrics techniques, and demonstration of Delay/Disruption Tolerant Networking (DTN). Furthermore, various future operational scenarios including robotic and exploration missions and various network service configurations have been emulated. This conference paper provides an overview and highlights of the LCRD experiment program with an emphasis on activities and results since the start of the ILLUMA-T testing in December 2023. A discussion of the expectations for the next phase of the LCRD mission, as well as how LCRD results may be applied to future applications is also included. LCRD is a joint project involving NASA Goddard Space Flight Center (GSFC), the California Institute of Technology Jet Propulsion Laboratory (JPL), and Massachusetts Institute of Technology Lincoln Laboratory (MIT LL).

Laser communications↗

NASA NTRSDate not supplied

Completion of NASA’s Two-Year Laser Communications Relay Demonstration (LCRD) Experiment Program

"The National Aeronautics and Space Administration’s (NASA) Laser Communications Relay Demonstration (LCRD) completed its two-year experiment program in June 2024. The LCRD geosynchronous payload includes two laser communications terminals interconnected via an onboard electronic switch. The payload relays data between two optical ground stations located in California and Hawaii and can also exchange data with an RF ground station in New Mexico. The addition of the Integrated LCRD Low-Earth Orbit (LEO) User Modem and Amplifier Terminal (ILLUMA-T) on the International Space Station in 2023 enabled data relay demonstrations between a laser terminal in low Earth orbit and either one of the optical ground stations or the RF ground station. The experiment program has included a variety of experiments originating within the LCRD project, NASA, and industry. The experiments have included measurements of the effects of the atmospheric optical channel (turbulence, weather) on the performance and availability of laser communications, adaptive optics characterization, demonstration of optimetrics techniques, and demonstration of Delay/Disruption Tolerant Networking (DTN). Furthermore, various future operational scenarios including robotic and exploration missions and various network service configurations have been emulated. This conference paper provides an overview and highlights of the LCRD experiment program with an emphasis on activities and results since the start of the ILLUMA-T testing in December 2023. A discussion of the expectations for the next phase of the LCRD mission, as well as how LCRD results may be applied to future applications is also included. LCRD is a joint project involving NASA Goddard Space Flight Center (GSFC), the California Institute of Technology Jet Propulsion Laboratory (JPL), and Massachusetts Institute of Technology Lincoln Laboratory (MIT LL). "

Lasercom↗

NASA NTRS1993

Electronic Performance Support for Operational Systems: A Case Study of the Link Monitor and Control Operator Assistant

For complex operational systems, help needs to come from the inside out. It is often not realistic to call a help desk for problems that need immediate attention, especially for tasks that put a heavy cognitive load on the system operator. This session addresses the issues associated with providing electronic performance support for operational systems, including situations where the system is already fielded and can only change through evolution rather than revolution. We present a case study based on our experiences in developing the Link Monitor and Control Operator Assistant for NASA's Deep Space Network (DSN). The goals of the Operator Assistant are to improve the operability of the system and increase the efficiency of mission operations.

Hill, Randall W., Jr.↗

NASA NTRS2022

The Mars 2020 Ground Data System Architecture

The Mars 2020 Mission’s primary objective is to collect 20 geographically unique samples during its prime mission of one and a quarter Martian years, or just over 2 Earth years. Mission planners determined the project needed to develop a system that would enable the operations team to analyze engineering and science data, make science decisions, select viable rover targets at a millimeter resolution and validate an uplink bundle for a car sized rover with more complex science instruments than any previous Mars surface mission. All this had to be done within a five hour time frame. Doing this with a small team would be a challenge, but this had to be accomplished by a large team of engineers and scientists located across North America and Europe. Achieving this level of operational efficiency was unheard of in the prime mission. In addition, the mission had another set of requirements that had nothing to do with surface operations; the Mars 2020 Ground Data System (GDS) was also expected to comply with a new set of security requirements to keep up with the ever changing cybersecurity landscape. The Mars 2020 Ground Data System (GDS) is a re-architected version of the Mars Science Laboratory GDS. The primary goal was to integrate the lessons learned from previous Mars surface missions, accommodate a set of new requirements and capabilities required to ensure mission success, and comply with a new set of cybersecurity controls. The new architecture includes several unique qualities including a data lake, language-agnostic system-wide event-based operations, containerization, automated deployment, network segmentation, infrastructure-as-code, API-driven interfaces, and the first Mars surface GDS to operate primarily in the cloud. The new architecture enabled greater access to the system’s data, tighter integration with the operations team, and a higher level of traceability. The availability of the data also enabled a new set of capabilities previously not possible on surface missions. These new capabilities include an autonomous data to information, pipeline for downlink analysis, horizontal scaling of science data processing capabilities, autonomous round trip data tracking of science and engineering data, integration of flight system state into the tactical planning cycle, high fidelity targeting utilizing kinematic data, and hierarchical image and 3d meshes data representations. This paper will introduce the requirements for the Mars 2020 Mission, the heritage architecture, and the rationale for the changes to achieve the new architecture. The paper will continue to describe the fundamental changes made to the GDS architecture, how these changes enabled a more tightly integrated GDS, and the new capabilities that were enabled by the new architecture. The paper will conclude with the lessons learned from the process of rearchitecting a heritage GDS system and from the first 200 days of operations supporting over 800 users from around the world.

Lopez-Roig, Reynaldo↗

NASA NTRS1982

Field operations with cesium clocks in HF navigation systems

Networks of HF phase comparison marine navigation stations employing cesium clocks are discussed. The largest permanent network is in the Gulf of Mexico where some fourteen base stations are continuously active and others are activated as needed. These HF phase comparison systems, which operate on a single transmission path, require a clock on the mobile unit as well. Inventory consists of upwards of 70 clocks from two different manufacturers. The maintenance of this network as an operating system requires a coordinated effort involving clock preparation, clock environment control, station performance monitoring and field service.

Christy, E. H.↗

NASA NTRS2017

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

Signal Recognition↗

NASA NTRS2017

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

Signal Classification↗

NASA NTRS2013

A Validated Set of MIDAS V5 Task Network Model Scenarios to Evaluate Nextgen Closely Spaced Parallel Operations Concepts

The Closely Spaced Parallel Operations (CSPO) scenario is a complex, human performance model scenario that tested alternate operator roles and responsibilities to a series of off-nominal operations on approach and landing (see Gore, Hooey, Mahlstedt, Foyle, 2013). The model links together the procedures, equipment, crewstation, and external environment to produce predictions of operator performance in response to Next Generation system designs, like those expected in the National Airspaces NextGen concepts. The task analysis that is contained in the present report comes from the task analysis window in the MIDAS software. These tasks link definitions and states for equipment components, environmental features as well as operational contexts. The current task analysis culminated in 3300 tasks that included over 1000 Subject Matter Expert (SME)-vetted, re-usable procedural sets for three critical phases of flight; the Descent, Approach, and Land procedural sets (see Gore et al., 2011 for a description of the development of the tasks included in the model; Gore, Hooey, Mahlstedt, Foyle, 2013 for a description of the model, and its results; Hooey, Gore, Mahlstedt, Foyle, 2013 for a description of the guidelines that were generated from the models results; Gore, Hooey, Foyle, 2012 for a description of the models implementation and its settings). The rollout, after landing checks, taxi to gate and arrive at gate illustrated in Figure 1 were not used in the approach and divert scenarios exercised. The other networks in Figure 1 set up appropriate context settings for the flight deck.The current report presents the models task decomposition from the tophighest level and decomposes it to finer-grained levels. The first task that is completed by the model is to set all of the initial settings for the scenario runs included in the model (network 75 in Figure 1). This initialization process also resets the CAD graphic files contained with MIDAS, as well as the embedded operator models that comprise MIDAS. Following the initial settings, the model progresses to begin the first tasks required of the two flight deck operators, the Captain (CA) and the First Officer (FO). The task sets will initialize operator specific settings prior to loading all of the alerts, probes, and other events that occur in the scenario. As a note, the CA and FO were terms used in developing this model but the CA can also be thought of as the Pilot Flying (PF), while the FO can be considered the Pilot-Not-Flying (PNF)or Pilot Monitoring (PM). As such, the document refers to the operators as PFCA and PNFFO respectively.

task network model↗

NASA NTRS1998

The CERES S'COOL Project: Development and Operational Phases

As part of NASA's Mission to Planet Earth, the first Clouds and the Earth's Radiant Energy System (CERES) instrument will be launched on the Tropical Rainfall Measuring Mission (TRMM) spacecraft from the Tanegashima launch site in Japan in November 1997. The instrument will measure the radiation budget incoming and outgoing radiant energy - of the Earth. The major feature of interest is clouds, which play a very strong role in regulating our climate. CERES will identify clear and cloudy regions and determine cloud physical and microphysical properties using imager data from a companion instrument. Validation efforts for the remote sensing algorithms will be intensive. As one component of the validation, the S'COOL (Students' Cloud Observations On-Line) project will involve school children around the globe in making ground truth measurements at the time of a CERES overpass. They will report cloud type, height, fraction, and opacity, as well as the local surface conditions. Their observations will be collected at the NASA Langley Distributed Active Archive Center (DAAC) and made available over the Internet for educational purposes as well as for use by the CERES Science Team in validation efforts. Pilot testing of the S'COOL project began in January 1997 with two local schools in Southeastern Virginia and one remote site in Montana. National testing in April 1997 involved 8 schools (grades 3 to high school) across the United States. Global testing will be carried out in October 1997. Details of the S'COOL project, which is mainly Internet-based, are being developed in each of these phases according to feedback received from participants. In 1998, when the CERES instrument is operational, a global observer network should be in place providing useful information to the scientists and learning opportunities to the students. Broad participation in the S'COOL project is planned, both to obtain data from a wide range of geographic areas, and to involve as many students as possible in learning about clouds and atmospheric science. This paper reports on the development phase of the S'COOL project, including the reaction of the teachers and students who have been involved. It describes the operational state of the S'COOL network, and identifies opportunities for additional participants.

Chambers, Lin H.↗