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The theoretical concept, based on modern photogrammetric methods, underlying the design of the MISR science data processing system responsible for the autonomous and continuous georectification of multi-angle imagery is the subject of this paper.
A new paradigm for data science has emerged, with quantum data, quantum models, and quantum computational devices. This field, called quantum machine learning (QML), aims to achieve a speedup over traditional machine learning for data analysis. However, its success usually hinges on efficiently training the parameters in quantum neural networks, and the field of QML is still lacking theoretical scaling results for their trainability. Some trainability results have been proven for a closely related field called variational quantum algorithms (VQAs). While both fields involve training a parametrized quantum circuit, there are crucial differences that make the results for one setting not readily applicable to the other. In this work, we bridge the two frameworks and show that gradient scaling results for VQAs can also be applied to study the gradient scaling of QML models. Our results indicate that features deemed detrimental for VQA trainability can also lead to issues such as barren plateaus in QML. Consequently, our work has implications for several QML proposals in the literature. In addition, we provide theoretical and numerical evidence that QML models exhibit further trainability issues not present in VQAs, arising from the use of a training dataset. We refer to these as dataset-induced barren plateaus. These results are most relevant when dealing with classical data, as here the choice of embedding scheme (i.e., the map between classical data and quantum states) can greatly affect the gradient scaling.
A data reduction program was developed and debugged which presents the Aerospace particle data and the UCLA magnetometer data in a variety of formats suitable for analysis as well as further data processing. Fifty days of data were processed through this program and analysis of these data were begun. In addition, modifications were made to an old ATS-1 program in order to enable this program to process ATS-1 data obtained simultaneously with ATS-6 data acquisitions during 1974. Some analysis of ATS-1 data and comparison with ATS-6 data also went forward. Preparations were begun to transfer ATS-6 data to the National Space Science Data Center. These efforts are described in detail.
The primary data reduction effort during the reporting period was to process summary plots of the IMP 8 plasma wave data and to submit these data to the National Space Science Data Center. Features of the electrostatic noise are compared with simultaneous observations of the magnetic field, plasma and energetic electrons. Spectral characteristics of the noise and the results of this comparison both suggest that in its high frequency part at least the noise does not belong to normal modes of plasma waves but represents either quasi-thermal noise in the non-Maxwellian plasma or artificial noise generated by spacecraft interaction with the medium.
Earth Scientists lack adequate tools for quantifying complex relationships between existing data layers and studying and modeling the dynamic interactions of these data layers. There is a need for an earth systems tool to manipulate multi-layered, heterogeneous data sets that are spatially indexed, such as sensor imagery and maps, easily and intelligently in a single system. The system can access and manipulate data from multiple sensor sources, maps, and from a learned object hierarchy using an advanced knowledge-based geographical information system. A prototype Knowledge-Based Geographic Information System (KBGIS) was recently constructed. Many of the system internals are well developed, but the system lacks an adequate user interface. A methodology is described for developing an intelligent user interface and extending KBGIS to interconnect with existing NASA systems, such as imagery from the Land Analysis System (LAS), atmospheric data in Common Data Format (CDF), and visualization of complex data with the National Space Science Data Center Graphics System. This would allow NASA to quickly explore the utility of such a system, given the ability to transfer data in and out of KBGIS easily. The use and maintenance of the object hierarchies as polymorphic data types brings, to data management, a while new set of problems and issues, few of which have been explored above the prototype level.
To date four OFAD (Optical Field Angle Distortion) calibrations have been performed on FGS3 in M35 and analyzed by the Astrometry Science Team. Two have been performed since the last HST (Hubble Space Telescope) Calibration Workshop. The ongoing Long Term Stability Tests have also been analyzed and incorporated into the calibration. A lateral color calibration has been derived from calibration and science data. Descriptions of these tests and the results of our analysis of the resulting data are given. The astrometric science supported by these calibrations is briefly reviewed.
The broad objective of the research was to study individual characteristics of human adaptation to long duration spaceflight and possibilities of their correction using autonomic conditioning. The changes in autonomic state during adaptation to microgravity can have profound effects on the operational efficiency of crewmembers and may result in debilitating biomedical symptoms. Ground-based and inflight experiment results showed that certain responses of autonomic nervous system were correlated with, or consistently preceded, reports of performance decrements or the symptoms. Autogenic-Feedback-Training Exercise (AFTE) is a physiological conditioning method that has been used to train people to voluntary control several of their own physiological responses. The specific objectives were: 1) To study human autonomic nervous system (ANS) responses to sustained exposure to microgravity; 2) To study human behavior/performance changes related to physiology; 3) To evaluate the effectiveness of preflight autonomic conditioning (AFTE) for facilitating adaptation to space and readaptation to Earth; and 4) To archive these data for the NASA Life Sciences Data Archive and thereby make this information available to the international scientific community.
The Moderate Resolution Imaging Spectroradiometer (MODIS) has 36 spectral bands with a total of 490 detectors, covering spectral regions in the visible (VIS), near-infrared (NIR), short-wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR). MODIS is a cross-track scanning radiometer which collects data using a rotating scan mirror (both sides) over a wide range of scan angles. The VIS, NIR, and SWIR bands (bands 1-19 and 26) make measurements of daytime surface reflected radiances, thus are referred to as the reflective solar bands (RSB). MODIS was built with a complete set of on-board calibrators, capable of providing radiometric, spatial, and spectral calibration and characterization during its entire mission. The RSB on-orbit calibration is primarily provided using a solar diffuser (SD) and a solar diffuser stability monitor (SDSM). The SD and SDSM calibration system is operated on a regular (weekly to bi-weekly) basis. The spectro-radiometric calibration assembly (SRCA) is another on-hoard calibrator that also provides RSB radiometric calibration support. For this purpose, the SRCA is operated in a radiometric mode on a monthly basis. A complete SRCA radiometric calibration is performed using different lamp configurations, or different radiance levels, to cover the range of RSB gain. Two additional SRCA modes with slightly different configurations are designed and operated for sensor on-orbit spectral and spatial characterization. In addition to its on-hoard calibrators, each MODIS makes monthly lunar observations to monitor RSB radiometric calibration stability. The MODIS lunar observations are made through its space view (SV) port at nearly the same lunar phase angles via spacecraft roll maneuvers. The SD, SRCA, and lunar measurements are made at different scan angles and data samples are collected for all spectral bands and detectors using both sides of the scan minor. Since launch, Terra and Aqua MODIS have operated successfully for more than 8 years and 6 years, respectively. Many SD/SDSM, SRCA, and lunar observations have been made by each instrument and used to derive RSB on-orbit calibration parameters, enabling corrections for sensor response changes and changes of the response versus scan angle (RVS). In general, the RSB calibration parameters are updated regularly into the MODIS Level 1B (LIB) code in support of continuous data processing for all MODIS science data products. This paper provides a brief description of MODIS RSB calibration methodologies and approaches, and summarizes on-orbit changes of their responses (gains), particularly for the VIS spectral bands.
Since launch, the Terra and Aqua moderate resolution imaging spectroradiometer (MODIS) instruments have successfully operated on-orbit for more than 9 and 6.5 years, respectively. Iv1ODIS, a key instrument for the NASA's Earth Observing System (EOS) missions, was designed to make continuous observations for studies of Earth's land, ocean, and atmospheric properties and to extend existing data records from heritage earth-observing sensors. In addition to frequent global coverage, MODIS observations are made in 36 spectral bands, covering both solar reflective and thermal emissive spectral regions. Nearly 40 data products are routinely generated from MODIS' observations and publicly distributed for a broad range of applications. Both instruments have produced an unprecedented amount of data in support of the science community. As a general reference for understanding sensor operation and calibration, and thus science data quality, we ;provide an overview of the MODIS instruments and their pre-launch calibration and characterization, and describe their on-orbit calibration algorithms and performance. On-orbit results from both Terra and Aqua MODIS radiometric, spectral, and "spatial calibration are discussed. Currently, both instruments, including their on-board calibration devices, are healthy and are expected to continue operation for several }rears to come.
The space radiation environment poses a certain risk to all electronic components on Earth-orbiting and planetary mission spacecraft. In recent years, there has been increased interest in the use of high-density, commercial, nonvolatile flash memories in space because of ever-increasing data volumes and strict power requirements. They are used in a wide variety of spacecraft subsystems. At one end of the spectrum, flash memories are used to store small amounts of mission-critical data such as boot code or configuration files and, at the other end, they are used to construct multi-gigabyte data recorders that record mission science data. This report examines single-event effect (SEE) and total ionizing dose (TID) response in single-level cell (SLC) 32-Gb, multi-level cell (MLC) 64-Gb, and Triple-level (TLC) 64-Gb NAND flash memories manufactured by Micron Technology with feature size of 25 nm.
Satellite remote sensing technology has contributed to the transformation of multiple earth science domains, putting space observations at the forefront of innovation in earth science. With new satellite missions being launched every year, new types of earth science data are being incorporated into science models and decision-making systems in a broad array of organizations. Policy guidance can influence the degree to which user needs influence mission design and when, and ensure that satellite missions serve both the scientific and user communities without becoming unfocused and overly expensive. By considering the needs of the user community early on in the mission-design process, agencies can ensure that satellites meet the needs of multiple constituencies. This paper describes the mission development process in NASA and ESA and compares and contrasts the successes and challenges faced by these agencies as they try to balance science and applications within their missions.
This presentation describes GIS relevant data at NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), GES DISC Services and Support for GIS Users, and use cases of GES DISC data in ArcGIS.
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.
The Kepler and K2 missions collected Fine Guidance Sensor (FGS) data in addition to the science data, as discussed in the Kepler Instrument Handbook (KIH, Van Cleve and Caldwell 2016). The FGS CCDs are frame transfer devices (KIH Table 7) located in the corners of the Kepler focal plane (KIH Figure 24), which are read out 10 times every second. The FGS data are being made available to the user community for scientific analysis as flux and centroid time series, along with a limited number of FGS full frame images which may be useful for constructing a World Coordinate System (WCS) or otherwise putting the time series data in context. This document will describe the data content and file format, and give example MATLAB scripts to read the time series. There are three file types delivered as the FGS data.1. Flux and Centroid (FLC) data: time series of star signal and centroid data. 2. Ancillary FGS Reference (AFR) data: catalog of information about the observed stars in the FLC data. 3. FGS Full-Frame Image (FGI) data: full-frame image snapshots of the FGS CCDs.
The effective applications of land surface models (LSMs) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a preprocessor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land-surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand-alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions and meteorological boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational datasets and data analytics algorithms to aid land surface modeling and data assimilation.
The Kepler and K2 missions collected Fine Guidance Sensor (FGS) data in addition to the science data, as discussed in the Kepler Instrument Handbook (KIH, Van Cleve and Caldwell 2016). The FGS CCDs areframe transfer devices located in the corners of the Kepler focal plane, which are read out 10 times every second. The FGS data are being made available to the user community for scientific analysis as flux and centroid time series, along with a limited number of FGS full frame images which may be useful for constructing a World Coordinate System (WCS) or otherwise putting the time series data in context. This document will describe the data content and file format, and give example MATLAB scripts to read the time series.
The Stratospheric Aerosol and Gas Experiment III (SAGE III) instrument, installed on the International Space Station (ISS), has completed over a year of data collection and production of science data products. The SAGE III/ISS is a solar and lunar occultation instrument, scanning the light from the sun and moon, through the limb of the Earth’s atmosphere. It was launched in February 2017 and provides data from June 2017 to the present. It continues SAGE's legacy of ozone, aerosol and water vapor profile measurements and extends the lengthy records for monitoring constituents important for understanding stratospheric ozone trends. This presentation shows the preliminary validation results of comparing SAGE III/ISS ozone and water vapor vertical profiles with those of NOAA ESRL/GMD and NIWA mission-funded ozonesondes and frost point hygrometers, and comparisons with other correlative data.
The Stratospheric Aerosol and Gas Experiment III (SAGE III) instrument, installed on the International Space Station (ISS), has completed over a year of data collection and production of science data products, including the newly released version 5.1 data product. The SAGE III/ISS is a solar and lunar occultation instrument, scanning the light from the sun and moon, through the limb (edge) of the Earth's atmosphere. It was launched in February 2017 and provides data from June 2017 to the present. It continues SAGE's legacy of ozone, aerosol and water vapor profile measurements and extends the lengthy records for monitoring constituents important for understanding stratospheric ozone trends. This presentation shows the preliminary validation results of comparing SAGE III/ISS ozone vertical profiles with those of mission funded ozone sondes, associated lidar, and comparisons with other correlative data.