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Introduction

A description is given of the navigation of Mariner 9, which included not only precision flight path control but also pointing of the scientific instruments mounted on a two-degree of freedom scan platform. Flight path control involved the determination of the spacecraft trajectory and the design and execution of the propulsive maneuvers required to effect the necessary changes in the trajectory. Radiometric tracking data provided by the Deep Space Network (DSN) were the principal data type used in the orbit determination process. During the Mars approach phase, optical tracking data were also used, but only on an experimental basis.

Oneil, W. J.↗

Remote Maintenance Monitoring

Automated system gives new life to aging network of computers. Remote maintenance monitoring system developed to diagnose problems in large distributed computer network. Consists of data links, displays, controls, software, and more than 200 computers. Uses sensors to collect data on failures and expert system to examine data, diagnose causes of failures, and recommend cures. Designed to be retrofitted into launch processing system at Kennedy Space Center. Reduces downtime, lowers workload and expense of maintenance, and makes network less dependent on human expertise.

Owens, Richard C.↗

Stable Kalman filters for processing clock measurement data

Kalman filters have been used for some time to process clock measurement data. Due to instabilities in the standard Kalman filter algorithms, the results have been unreliable and difficult to obtain. During the past several years, stable forms of the Kalman filter have been developed, implemented, and used in many diverse applications. These algorithms, while algebraically equivalent to the standard Kalman filter, exhibit excellent numerical properties. Two of these stable algorithms, the Upper triangular-Diagonal (UD) filter and the Square Root Information Filter (SRIF), have been implemented to replace the standard Kalman filter used to process data from the Deep Space Network (DSN) hydrogen maser clocks. The data are time offsets between the clocks in the DSN, the timescale at the National Institute of Standards and Technology (NIST), and two geographically intermediate clocks. The measurements are made by using the GPS navigation satellites in mutual view between clocks. The filter programs allow the user to easily modify the clock models, the GPS satellite dependent biases, and the random noise levels in order to compare different modeling assumptions. The results of this study show the usefulness of such software for processing clock data. The UD filter is indeed a stable, efficient, and flexible method for obtaining optimal estimates of clock offsets, offset rates, and drift rates. A brief overview of the UD filter is also given.

Clements, P. A.↗

Comparison of column water vapor measurements using downward-looking near-infrared and infrared imaging systems and upward-looking microwave radiometers

Remote soundings of precipitable water vapor from three systems are compared with each other and with ground truth from radiosondes. Ancillary data from a mesoscale network of surface observing stations and from wind-profiling radars are also used in the analysis. The three remote-sounding techniques are: (a) a reflectance technique using spectral data collected by the Airborne Visible-Infrared Imaging Spectrometer; (b) an emission technique using Visible-Infrared Spin Scan Radiometer Atmospheric Sounder (VAS) data acquired from the NOAA's GOES; and (c) a microwave technique using data from a limited network of three ground-based dual-channel microwave radiometers. The data were taken over the Front Range of eastern Colorado on 22-23 March 1990. The generally small differences between the three types of remote-sounding measurements are consistent with the horizontal and temporal resolutions of the instruments. The microwave and optical reflectance measurements agreed to within 0.1 cm; comparisons of the microwave data with radiosondes were also either as good or explainable. The largest differences between the VAS and the microwave radiometer at Elbert were between 0.4 and 0.5 cm and appear to be due to variable terrain within the satellite footprint.

Gao, Bo-Cai↗

Space and Earth Sciences, Computer Systems, and Scientific Data Analysis Support, Volume 1

This Final Progress Report covers the specific technical activities of Hughes STX Corporation for the last contract triannual period of 1 June through 30 Sep. 1993, in support of assigned task activities at Goddard Space Flight Center (GSFC). It also provides a brief summary of work throughout the contract period of performance on each active task. Technical activity is presented in Volume 1, while financial and level-of-effort data is presented in Volume 2. Technical support was provided to all Division and Laboratories of Goddard's Space Sciences and Earth Sciences Directorates. Types of support include: scientific programming, systems programming, computer management, mission planning, scientific investigation, data analysis, data processing, data base creation and maintenance, instrumentation development, and management services. Mission and instruments supported include: ROSAT, Astro-D, BBXRT, XTE, AXAF, GRO, COBE, WIND, UIT, SMM, STIS, HEIDI, DE, URAP, CRRES, Voyagers, ISEE, San Marco, LAGEOS, TOPEX/Poseidon, Pioneer-Venus, Galileo, Cassini, Nimbus-7/TOMS, Meteor-3/TOMS, FIFE, BOREAS, TRMM, AVHRR, and Landsat. Accomplishments include: development of computing programs for mission science and data analysis, supercomputer applications support, computer network support, computational upgrades for data archival and analysis centers, end-to-end management for mission data flow, scientific modeling and results in the fields of space and Earth physics, planning and design of GSFC VO DAAC and VO IMS, fabrication, assembly, and testing of mission instrumentation, and design of mission operations center.

Estes, Ronald H.↗

ACTS Ka-Band Earth Stations: Technology, Performance, and Lessons Learned

The Advanced Communications Technology Satellite (ACTS) Project invested heavily in prototype Ka-band satellite ground terminals to conduct an experiments program with the ACTS satellite. The ACTS experiment's program proposed to validate Ka-band satellite and ground station technology. demonstrate future telecommunication services. demonstrate commercial viability and market acceptability of these new services, evaluate system networking and processing technology, and characterize Ka-band propagation effects, including development of techniques to mitigate signal fading. This paper will present a summary of the fixed ground terminals developed by the NASA Glenn Research Center and its industry partners, emphasizing the technology and performance of the terminals (Part 1) and the lessons learned throughout their six year operation including the inclined orbit phase of operations (Full Report). An overview of the Ka-band technology and components developed for the ACTS ground stations is presented. Next. the performance of the ground station technology and its evolution during the ACTS campaign are discussed to illustrate the technical tradeoffs made during the program and highlight technical advances by industry to support the ACTS experiments program and terminal operations. Finally. lessons learned during development and operation of the user terminals are discussed for consideration of commercial adoption into future Ka-band systems. The fixed ground stations used for experiments by government, academic, and commercial entities used reflector based offset-fed antenna systems ranging in size from 0.35m to 3.4m antenna diameter. Gateway earth stations included two systems, referred to as the NASA Ground Station (NGS) and the Link Evaluation Terminal (LET). The NGS provides tracking, telemetry, and control (TT&C) and Time Division Multiple Access (TDMA) network control functions. The LET supports technology verification and high data rate experiments. The ground stations successfully demonstrated many services and applications at Ka-band in three different modes of operation: circuit switched TDMA using the satellite on-board processor, satellite switched SS-TDMA applications using the on-board Microwave Switch Matrix (MSM), and conventional transponder (bent-pipe) operation. Data rates ranged from 4.8 kbps up to 622 Mbps. Experiments included: 1) low rate (4.8- 1 00's kbps) remote data acquisition and control using small earth stations, 2) moderate rate (1-45 Mbps) experiments included full duplex voice and video conferencing and both full duplex and asymmetric data rate protocol and network evaluation using mid-size ground stations, and 3) link characterization experiments and high data rate (155-622 Mbps) terrestrial and satellite interoperability application experiments conducted by a consortium of experimenters using the large transportable ground stations.

Reinhart, Richard C.↗

Aerosol and Cloud Detection Using Machine Learning Algorithms and Space-Based Lidar Data

Clouds and aerosols play a significant role in determining the overall atmospheric radiation budget, yet remain a key uncertainty in understanding and predicting the future climate system. In addition to their impact on the Earth’s climate system, aerosols from volcanic eruptions, wildfires, man-made pollution events and dust storms are hazardous to aviation safety and human health. Space-based lidar systems provide critical information about the vertical distributions of clouds and aerosols that greatly improve our understanding of the climate system. However, daytime data from backscatter lidars, such as the Cloud-Aerosol Transport System (CATS) on the International Space Station (ISS), must be averaged during science processing at the expense of spatial resolution to obtain sufficient signal-to-noise ratio (SNR) for accurately detecting atmospheric features. For example, 50% of all atmospheric features reported in daytime operational CATS data products require averaging to 60 km for detection. Furthermore, the single-wavelength nature of the CATS primary operation mode makes accurately typing these features challenging in complex scenes. This paper presents machine learning (ML) techniques that, when applied to CATS data, (1) increased the 1064 nm SNR by 75%, (2) increased the number of layers detected (any resolution) by 30%, and (3) enabled detection of 40% more atmospheric features during daytime operations at a horizontal resolution of 5 km compared to the 60 km horizontal resolution often required for daytime CATS operational data products. A Convolutional Neural Network (CNN) trained using CATS standard data products also demonstrated the potential for improved cloud-aerosol discrimination compared to the operational CATS algorithms for cloud edges and complex near-surface scenes during daytime.

lidar↗

The Xpress Transfer Protocol (XTP): A tutorial (short version)

The Xpress Transfer Protocol (XTP) is a reliable, light weight transfer layer protocol. Current transport layer protocols such as DoD's Transmission Control Protocol (TCP) and ISO's Transport Protocol (TP) were not designed for the next generation of high speed, interconnected reliable networks such as fiber distributed data interface (FDDI) and the gigabit/second wide area networks. Unlike all previous transport layer protocols, XTP is being designed to be implemented in hardware as a VLSI chip set. By streamlining the protocol, combining the transport and network layers, and utilizing the increased speed and parallelization possible with a VLSI implementation, XTP will be able to provide the end-to-end data transmission rates demanded in the high speed networks without compromising reliability and functionality. This tutorial briefly describes the operation of the XTP protocol and in particular, its error, flow and rate control; inter-networking addressing mechanisms; and multicast support features, as defined in the XTP Protocol Definition Revision 3.4.

Sanders, Robert M.↗

Description and Status of the North Alabama Lightning Mapping Array

The North Alabama Lightning Mapping Array (LMA) is a network LMA detectors that detects and maps lightning using VHF radiation (TV Channel 5) in a region centered about Huntsville, Alabama that includes North Alabama, Central Tennessee and parts of Georgia and Mississippi. The North Alabama LMA has been in operation since late 2001, and has been providing real time data to regional National Weather Service (NSF) Weather Forecast Offices (WFOs) since mid 2003 through the NASA Short-term Prediction Research and Transition (SPoRT) center. Data from this network (as well as other from other LMA systems) are now being used to create proxy Geostationary Lightning Mapper (GLM) data sets for GOES-R risk reduction and algorithm development activities. In addition, since spring 2009 data are provided to the Storm Prediction Center in support of Hazardous Weather Testbed and GOES-R Proving Ground activities during the Spring Program. Description, status and plans will be discussed.

Blakeslee, Richard J.↗

The role of data management in discipline-independent data visualization

The common data format (CDF) is described in terms of its support applications for the database management of visualization systems. The CDF is a self-describing data abstraction technique for the storage and manipulation of multidimensional data that are based on block structures. The discipline-independent approach is designed to manage, manipulate, archive, display, and analyze data, and can be applied to heterogeneous equipment communicating different data structures over networks. An improved CDF version incorporates a hyperplane access allowing random aggregate access to subdimensional blocks within a multidimensional variable. The visualization pipeline is also discussed, which controls the flow of data and permits the visualization of different classes of data representation techniques. The system is found to accommodate a large variety of scientific data structures and large disk-based data sets.

Treinish, Lloyd A.↗

Wavelet and Multiresolution Analysis for Finite Element Networking Paradigms

This paper presents a final report on Wavelet and Multiresolution Analysis for Finite Element Networking Paradigms. The focus of this research is to derive and implement: 1) Wavelet based methodologies for the compression, transmission, decoding, and visualization of three dimensional finite element geometry and simulation data in a network environment; 2) methodologies for interactive algorithm monitoring and tracking in computational mechanics; and 3) Methodologies for interactive algorithm steering for the acceleration of large scale finite element simulations. Also included in this report are appendices describing the derivation of wavelet based Particle Image Velocity algorithms and reduced order input-output models for nonlinear systems by utilizing wavelet approximations.

Kurdila, Andrew J.↗

The GEOS-5 Neural Network Retrieval (NNR) for AOD

One of the difficulties in data assimilation is the need for multi-sensor data merging that can account for temporal and spatial biases between satellite sensors. In the Goddard Earth Observing System Model Version 5 (GEOS-5) aerosol data assimilation system, a neural network retrieval (NNR) is used as a mapping between satellite observed top of the atmosphere (TOA) reflectance and AOD, which is the target variable that is assimilated in the model. By training observations of TOA reflectance from multiple sensors to map to a common AOD dataset (in this case AOD observed by the ground based Aerosol Robotic Network, AERONET), we are able to create a global, homogenous, satellite data record of AOD from MODIS observations on board the Terra and Aqua satellites. In this talk, I will present the implementation of and recent updates to the GEOS-5 NNR for MODIS collection 6 data.

Castellanos, Patricia↗

Evaluating Diurnal Ozone Emissions: A Comparative Analysis of DSCOVR EPIC, PANDORA, and TOLNet Data for Space-Based Monitoring Assessment: A Case Study by ASDC

Stratospheric ozone occurs naturally in the upper atmosphere, forming a protective layer that shields us from the sun's harmful ultraviolet rays. Tropospheric ozone is not emitted directly into the air but is created by chemical reactions between nitrogen oxides (NOx) and volatile organic compounds (VOC). This reaction happens when pollutants emitted by cars, power plants, industrial boilers, refineries, chemical plants, and other sources chemically react in sunlight [1]. Therefore, increased levels of tropospheric ozone indicate the presence of pollutants in the air. While daily anthropogenic activity causes an increase of ground-level ozone, variation of stratospheric ozone changes happens much slower, so that variation of the total ozone column reflects the variation of the tropospheric column due to natural, e.g., wildfires and anthropogenic air pollution. Both total and tropospheric ozone column products are used in this study. The reflectance spectra measured by the Earth Polychromatic Imaging Camera (EPIC) instrument aboard the Deep Space Climate Observatory (DSCOVR) spacecraft are compared with a set of radiative transfer-derived lookup tables for the EPIC filter transmission functions and a wide range of ozone values to retrieve ozone with a maximum resolution of 18 km at the sub-satellite point [2]. EPIC provides total column ozone in level 2 and level 4 products and tropospheric column ozone in level 4 products. Both EPIC Ozone products [2] are available at the Atmospheric Science Data Center (ASDC) at NASA Langley Research Center [3, 4]. Pandora spectrometer instrument measures columnar amounts of trace gases in the atmosphere. These gases (O3, NO2, CH2O) absorb light from the sun at specific wavelengths in the ultraviolet-visible spectrum [5]. Using the theoretical solar spectrum as a reference, Pandora determines trace gas amounts using differential optical absorption spectroscopy (DOAS). Pandora ozone retrievals are available from the Pandonia Global Network [6]. Pandora data from North American major metropolitan areas, New York, NY, Washington DC, Los Angeles, CA, and Mexico City. Tropospheric Ozone Lidar Network (TOLNet) was established in 2012 to provide high spatiotemporal observations of tropospheric ozone to (1) better understand physical processes driving the ozone budget in various meteorological and environmental conditions and (2) validate the tropospheric ozone measurements of space-borne missions [7]. TOLNet data are available at ASDC [8]. While EPIC provides global coverage of ozone retrievals several times daily, temporal resolution may miss some features in daily ozone variations. Ground-based sensors such as Pandora spectrometers and TOLNet lidars provide better temporal resolution while missing continuous spatial coverage. The forthcoming ozone retrieval from the TEMPO mission [9] will provide better spatial and temporal coverage of air quality (including ozone) over North America. This study investigates whether EPIC ozone products can detect diurnal air quality variations and compare ozone temporal development with retrieval by ground-based instruments.

diurnal ozone emissions, DSCOVR EPIC, PANDORA, spa↗

CFDP for Interplanetary Overlay Network

The CCSDS (Consultative Committee for Space Data Systems) File Delivery Protocol for Interplanetary Overlay Network (CFDP-ION) is an implementation of CFDP that uses IO' s DTN (delay tolerant networking) implementation as its UT (unit-data transfer) layer. Because the DTN protocols effect automatic, reliable transmission via multiple relays, CFDP-ION need only satisfy the requirements for Class 1 ("unacknowledged") CFDP. This keeps the implementation small, but without loss of capability. This innovation minimizes processing resources by using zero-copy objects for file data transmission. It runs without modification in VxWorks, Linux, Solaris, and OS/X. As such, this innovation can be used without modification in both flight and ground systems. Integration with DTN enables the CFDP implementation itself to be very simple; therefore, very small. Use of ION infrastructure minimizes consumption of storage and processing resources while maximizing safety.

Burleigh, Scott C.↗

Initial Characterization of Optical Communications with Disruption-Tolerant Network Protocols

Disruption-tolerant networks (DTNs) are groups of network assets connected with a suite of communication protocol technologies designed to mitigate the effects of link delay and disruption. Application of DTN protocols to diverse groups of network resources in multiple sub-networks results in an overlay network-of-networks with autonomous data routing capability. In space environments where delay or disruption is expected, performance of this type of architecture (such as an interplanetary internet) can increase with the inclusion of new communications mediums and techniques. Space-based optical communication links are therefore an excellent building block of space DTN architectures. When compared to traditional radio frequency (RF) communications, optical systems can provide extremely power-efficient and high bandwidth links bridging sub-networks. Because optical links are more susceptible to link disruption and experience the same light-speed delays as RF, optical-enabled DTN architectures can lessen potential drawbacks and maintain the benefits of autonomous optical communications over deep space distances. These environment-driven expectations - link delay and interruption, along with asymmetric data rates - are the purpose of the proof-of-concept experiment outlined herein. In recognizing the potential of these two technologies, we report an initial experiment and characterization of the performance of a DTN-enabled space optical link. The experiment design employs a point-to-point free-space optical link configured to have asymmetric bandwidth. This link connects two networked systems running a DTN protocol implementation designed and written at JPL for use on spacecraft, and further configured for higher bandwidth performance. Comparing baseline data transmission metrics with and without periodic optical link interruptions, the experiment confirmed the DTN protocols' ability to handle real-world unexpected link outages while maintaining capability of reliably delivering data at relatively high rates. Finally, performance characterizations from this data suggest performance optimizations to configuration and protocols for future optical-specific DTN space link scenarios.

Space networking↗

Experimental Testing of Data Fusion in A Distributed Ground-Based Sensing Network for Advanced Air Mobility

Advanced Air Mobility (AAM) is an active area of development which foresees the integration of autonomous uncrewed aircraft into the civil airspace for air transportation of people and cargo. Safe integration requires significant technological developments and extensive testing phases of sensing and surveillance strategies in dense airspace. Compared to well-assessed manned aviation systems scenarios, surveillance strategies in the AAM and small Uncrewed Aircraft Vehicles(UAVs) context need to detect smaller platforms flying at lower altitude against cluttered backgrounds in dense airspace. Fusion of data provided by a network of distributed sensing nodes is a powerful tool to enable detection and tracking in such complex conditions. This paper contributes to this research direction by proposing a surveillance strategy for the AAM environment based on sensor fusion of data acquired by distributed ground-based radars. Specifically, experimental data collected with two independent radars, observing the flight of two small UAVs, are used. Data fusion at tracking level is based on a leader-helper strategy where the leader radar uses the helper’s measurements to increase the lifespan of its generated tracks. This solution shows promising results with a 10%increase in track coverage with respect to the standalone leader radar tracking solution. The paper also proposes an interference removal processing method which is applied on the data collected by one of the two radars.

Federica Vitiello↗

A Neural Network Approach for Identifying Particle Pitch Angle Distributions in Van Allen Probes Data

Analysis of particle pitch angle distributions (PADs) has been used as a means to comprehend a multitude of different physical mechanisms that lead to flux variations in the Van Allen belts and also to particle precipitation into the upper atmosphere. In this work we developed a neural network-based data clustering methodology that automatically identifies distinct PAD types in an unsupervised way using particle flux data. One can promptly identify and locate three well-known PAD types in both time and radial distance, namely, 90deg peaked, butterfly, and flattop distributions. In order to illustrate the applicability of our methodology, we used relativistic electron flux data from the whole month of November 2014, acquired from the Relativistic Electron-Proton Telescope instrument on board the Van Allen Probes, but it is emphasized that our approach can also be used with multiplatform spacecraft data. Our PAD classification results are in reasonably good agreement with those obtained by standard statistical fitting algorithms. The proposed methodology has a potential use for Van Allen belt's monitoring.

Souza, V. M.↗