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A soft decision analyzer system is operable to interconnect soft decision communication equipment and analyze the operation thereof to detect symbol wise alignment between a test data stream and a reference data stream in a variety of operating conditions.
At the Goddard Distributed Active Archive Center, we have recently added a 35-Year record of output data from the North American Land Assimilation System (NLDAS) to the Giovanni web-based analysis and visualization tool. Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) offers a variety of data summarization and visualization to users that operate at the data center, obviating the need for users to download and read the data themselves for exploratory data analysis. However, the NLDAS data has proven surprisingly resistant to application of the summarization algorithms. Algorithms that were perfectly happy analyzing 15 years of daily satellite data encountered limitations both at the algorithm and system level for 35 years of hourly data. Failures arose, sometimes unexpectedly, from command line overflows, memory overflows, internal buffer overflows, and time-outs, among others. These serve as an early warning sign for the problems likely to be encountered by the general user community as they try to scale up to Big Data analytics. Indeed, it is likely that more users will seek to perform remote web-based analysis precisely to avoid the issues, or the need to reprogram around them. We will discuss approaches to mitigating the limitations and the implications for data systems serving the user communities that try to scale up their current techniques to analyze Big Data.
The first transatlantic radio transmission, demonstrated by Marconi in December of 1901, revealed the essential role of the ionosphere for radio communications. This ionized layer of the upper atmosphere controls the amount of radio power transmitted through, reflected off of, and absorbed by the atmospheric medium. Low-frequency radio signals can propagate long distances around the globe via repeated reflections off of the ionosphere and the Earth's surface. Higher frequency radio signals can punch through the ionosphere to be received at orbiting satellites. However, any turbulence in the ionosphere can distort these signals, compromising the performance or even availability of space-based communication and navigations systems. The physics associated with this distortion effect is analogous to the situation when underwater images are distorted by convecting air bubbles. In fact, these ionospheric features are often called 'plasma bubbles' since they exhibit some of the similar behavior as underwater air bubbles. These events, instigated by solar and geomagnetic storms, can cause communication and navigation outages that last for hours. To help understand and predict these outages, a world-wide community of space scientists and technologists are devoted to researching this topic. One aspect of this research is to develop instruments capable of measuring the ionospheric plasma bubbles. Figure 1 shows a photo of the Charge Analyzer Responsive to Local Oscillations (CARLO), a new instrument under development at NASA Marshall Space Flight Center (MSFC). It is a frequency-domain ion spectrum analyzer designed to measure the distributions of ionospheric turbulence from 1 Hz to 10 kHz (i.e., spatial scales from a few kilometers down to a few centimeters). This frequency range is important since it focuses on turbulence scales that affect VHF/UHF satellite communications, GPS systems, and over-the-horizon radar systems. CARLO is based on the flight-proven Plasma Local Anomalous Noise Environment (PLANE) instrument, previously flown on a U.S. Air Force low-Earth orbiting satellite, which successfully measured ion turbulence in five frequency decades from 0.1 Hz to 10 kHz (fig 2).
A soft decision analyzer system is operable to interconnect soft decision communication equipment and analyze the operation thereof to detect symbol wise alignment between a test data stream and a reference data stream in a variety of operating conditions.
The Major Constituent Analyzer (MCA) is a mass spectrometer based system that measures the major atmospheric constituents on the International Space Station. A number of limited-life components require periodic change-out, including the ORU 02 analyzer and the ORU 08 Verification Gas Assembly. The most recent ORU 02 and ORU 08 assemblies in the LAB MCA are operating nominally. For ORU 02, the ion source filaments and ion pump lifetime continue to be key determinants of MCA performance. Finally, the Node 3 MCA is being brought to an operational configuration.
The spectre of cyber attacks on aerospace systems can no longer be ignored given that many of the components and vulnerabilities that have been successfully exploited by the adversary on other infrastructures are the same as those deployed and used within the aerospace environment. An important consideration with respect to the mission/safety critical infrastructure supporting space operations is that an appropriate defensive response to an attack invariably involves the need for high precision and accuracy, because an incorrect response can trigger unacceptable losses involving lives and/or significant financial damage. A highly precise defensive response, considering the typical complexity of aerospace environments, requires a detailed and well-founded understanding of the underlying system where the goal of the defensive response is to preserve critical mission objectives in the presence of adversarial activity. In this paper, a structured approach for modeling aerospace systems is described. The approach includes physical elements, network topology, software applications, system functions, and usage scenarios. We leverage Model-Based Systems Engineering methodology by utilizing the Object Management Group's Systems Modeling Language to represent the system being analyzed and also utilize model transformations to change relevant aspects of the model into specialized analyses. A novel visualization approach is utilized to visualize the entire model as a three-dimensional graph, allowing easier interaction with subject matter experts. The model provides a unifying structure for analyzing the impact of a particular attack or a particular type of attack. Two different example analysis types are demonstrated in this paper: a graph-based propagation analysis based on edge labels, and a graph-based propagation analysis based on node labels.
The Mars Organic Molecule Analyzer (MOMA), a dual-source, ion trap-based instrument capable of both pyrolysis-gas chromatography mass spectrometry (pyr/GC-MS) and laser desorption/ionization mass spectrometry (LDI-MS), is the core astrobiology investigation on the ExoMars rover. The MOMA instrument will be the first spaceflight mass analyzer to exploit the LDI technique to detect refractory organic compounds and characterize host mineralogy; this mode of analysis will be conducted at Mars ambient conditions. In order to achieve high performance in the Martian environment while keeping the instrument compact and low power, a number of innovative designs and components have been implemented for MOMA. These include a miniaturized linear ion trap (LIT), a fast actuating aperture valve with ion inlet tube, and a Microelectromechanical System (MEMS) Pirani sensor. Advanced analytical capabilities like Stored Waveform Inverse Fourier Transform (SWIFT) for selected ion ejection and tandem mass spectrometry (MS/MS) are realized in LDI-MS mode, and enable the isolation and enhancement of specific mass ranges and structural analysis, respectively. We report here the technical details of these instrument components as well as system-level analytical capabilities, and we review the applications of this technology to Mars and other high-priority targets of planetary exploration.
Greenhouse gas (GHG) concentrations have increased over the past decades and are linked to global temperature increases and climate change. These changes in climate have been suggested to have varying effects, and uncertain consequences, on agriculture, water supply, weather, sea-level rise, the economy, and energy. To counteract the trend of increasing atmospheric concentrations of GHGs, the state of California has passed the California Global Warming Act of 2006 (AB-32). This requires that by the year 2020, GHG (e.g., carbon dioxide (CO2) and methane (CH4)) emissions will be reduced to 1990 levels. To quantify GHG fluxes, emission inventories are routinely compiled for the State of California (e.g., CH4 emissions from the California Greenhouse Gas Emissions Measurement (CALGEM) Project). The major sources of CO2 and CH4 in the state of California are: transportation, electricity production, oil and gas extraction, cement plants, agriculture, landfills/waste, livestock, and wetlands. However, uncertainties remain in these emission inventories because many factors contributing to these processes are poorly quantified. To alleviate these uncertainties, a synergistic approach of applying air-borne measurements and chemical transport modeling (CTM) efforts to provide a method of quantifying local and regional GHG emissions will be performed during this study. Additionally, in order to further understand the temporal and spatial distributions of GHG fluxes in California and the impact these species have on regional climate, CTM simulations of daily variations and seasonality of total column CO2 and CH4 will be analyzed. To assess the magnitude and spatial variation of GHG emissions and to identify local “hot spots”, airborne measurements of CH4 and CO2 were made by the Alpha Jet Atmospheric eXperiment (AJAX) over the San Francisco Bay Area (SFBA) and San Joaquin Valley (SJV) in January and February 2013 during the Discover-AQ-CA study. High mixing ratios of GHGs were observed in-flight with a high degree of spatial variability. To provide an additional method to quantify GHG emissions, and analyze AJAX measurement data, the GEOS-Chem CTM is used to simulate SFBA/SJV GHG measurements. A nested-grid version of GEOS-Chem will be applied and utilizes varying emission inventories and model parameterizations to simulate GHG fluxes/emissions. The model considers CO2 fluxes from fossil fuel use, biomass/biofuel burning, terrestrial and oceanic biosphere exchanges, shipping and aviation, and production from the oxidation of carbon monoxide, CH4, and non-methane volatile organic carbons. The major sources of CH4 simulated in GEOS-Chem are domesticated animals, rice fields, natural gas leakage, natural gas venting/flaring (oil production), coal mining, wetlands, and biomass burning. Preliminary results from the comparison between available observations (e.g., AJAX and CALGEM CH4 emission maps) and GEOS-Chem results will be presented, along with a discussion of CO2 and CH4 source apportionment and the use of the GEOS-Chem-adjoint to perform inverse GHG modeling.
The Phoenix Scout Lander mission investigated the north polar region of Mars in 2008 with the goal to study the history of water, assess the past/present Martian climate, search for organics, and evaluate the potential for past/present microbial habit-ability on Mars. To accomplish this goal, the Phoenix Lander’s Thermal and Evolved-Gas Analyzer (TEGA) instrument assessed the gas composition of the Martian atmosphere and evaluated the mineralogy of the Martian regolith. The TEGA instrument consisted of eight small ovens connected to a 4 channel magnetic sector mass spectrometer. The ovens heated soil samples from ambient to 1000°C where the gases (e.g., H2O, CO2, etc.) evolved from thermal decomposition of mineral phases were analyzed by the mass spectrometer. Minerals thermally decomposed at characteristic temperatures and the evolving gases indicated the presence of perchlorate, carbonate, and hydrated phases in the Phoenix landing site soils.
The ability to generate chlorophyll a (Chl a) assessments from ocean color orbital sensors, such as VIIRS and MODIS, that satisfy the requirements to be climate-quality data record (CDR) quality is contingent in part on the quality of the in situ ground or sea truth observations that serve as datasets for vicarious calibration and algorithm validation activities. NASA has a mandate to collect, analyze, and distribute in situ data of the highest possible quality with documented uncertainties and in keeping with established performance metrics. Using a dataset of over 18,000 HPLC phytoplankton pigment samples representing water collected in all major ocean basins analyzed a central laboratory (Field Support Group (FSG) of the Ocean Ecology Laboratory (OEL) at NASA Goddard Space Flight Center (GSFC)), we performed an assessment of the global precision among sample replicates of Chl a as well as major accessory pigments. We investigated the impacts of filtration volume, water basin, collection technique, pigment concentration, and different filtration volumes for replicate filters on replicate filter precision, as well as investigating any pigment-specific differences. Our results quantify sample variability with the goal of understanding any systemic biases or biogeographic influences.
With a revived focus to create a permeant presence on the moon and in preparation for future Mars exploration, it is imperative that all re-sources are utilized to their fullest potential. In-Situ Resource Utilization (ISRU) will be critical for future mission success as it would enable independent operation of missions while minimally relying on the complex supply chain created between the Earth, moon and Mars. One of the most critical resources that have been identified for ISRU is the creation of liquid oxygen (O2) for not only breathable air, but also for rocket fuel. On the moon and potentially Mars, one of the most O2 rich resources is in the presence of regolith. Although there are numerous minerals within regolith and various processes to extract O2, this is not the scope of this paper. Since the O2 extraction method can vary, a critical standardized analytical method is needed to verify O2 (or oxygenated precursor compounds) for extraction efficiency and purity. Our group utilizes a modified consumer off-the-shelf (COTS), Residual Gas Analyzer (RGA) Single Quadrupole Mass Spectrometer (QMS). Due to the current field mission requirements, the gas analysis module will need to rely only on mass-to-charge ratios (m/z) and peak intensities to differentiate and quantitate targeted gas-phase reactants or products, i.e. carbon monoxide (CO), carbon dioxide (CO2), methane (CH4) and hydrogen (H2). However, due to solar wind implanted volatiles and various other compounds present in lunar regolith, such as nitrogen (N2), helium (He), and deuterium (D2), deconvolution and quantification of isobaric compounds becomes quite difficult. Specifically, the presence of N2 makes quantification of CO, an oxygenated precursor, difficult due to both compounds having a peak at m/z 28. Approaches to differentiate isobaric components: Initially, the solution to overcome isobaric overlap was to use isotopic ratios, particularly be-tween carbon-13 and nitrogen-15. Although limited literature discuss isotopic composition and attempt to quantify the ratios, variation is particularly high and limited to a few lunar regolith samples. The following were identified as potential approaches to quantify isobaric species, N2 and CO, using our RGA. Threshold Ionization. CO and N2 both have peaks at m/z 28, but only N2 is capable of producing a peak at m/z 7. While a N2 peak at m/z 7 is generally rare, instrument parameters can be optimized in order to maximize the peak intensity. Typically a standard ionization energy of 70 eV is used to monitor a batch process and would produce reliable results for a majority of the reactants or products that we are interested in quantifying. However, to analyze the rare molecular fragment of N2 (N2+ at m/z 7), a different ionization energy may be beneficial. Threshold Ionization Mass Spectrometry (TIMS) will be explored to optimize the N2+ peak at m/z 7 by varying parameters such as ionization energy and pressure. Therefore, a quantitative value at m/z 7 will be used to determine the product gas concentration of N2, which will be subtracted from the total concentration determined at m/z 28 enabling us to approximate the amount of CO. Secondary Detector (infrared gas detector, IR). Another approach to quantitate and differentiate be-tween isobaric components is to leverage alternative modes of detection. Unlike mass spectrometry, which generally uses a separation step prior to analysis (i.e. gas chromatography), IR produces energy that is specifically absorbed depending on the bond and configuration of the molecule. This technique could be leveraged since it is generally only active on non-symmetric molecules, i.e. CO. Utilizing concentrations of CO determined from another detector could help correct concentrations of CO obtained from the QMS. However, the inclusion of another detector adds additional mission requirements, such as power, software changes and avionics updates that add additional cost and schedule requirements.
Changes in climate in the Himalayan region cause variability in temperature, precipitation, and phenology. It can also impact the health of coniferous forest ecosystems including increased damage due to aggressive forest pests. Forest disturbance from bark beetle is a major concern in Bhutan, sometimes causing extensive tree mortality to pine and spruce forests. Climatological trends and changes in vegetation phenology were analyzed and incorporated into a tool in Google Earth Engine that identified patches of forest disturbance in Bhutan. Preprocessed phenology and meteorological data from the Advanced Very High-Resolution Radiometer (AVHRR) and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS), along with Climate Hazards Center Infrared Precipitation with Station (CHIRPS), Famine Early Warning System Network Land Data Assimilation System (FLDAS), Sentinel-2 Multispectral Instrument (MSI), and Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM)+, and Landsat 8 Operational Land Imager (OLI) were used within the tool. Changes in temperature, precipitation, and phenology were analyzed throughout Bhutan, and forest disturbance caused by bark beetle was investigated in two districts of the country
The sub-surface structures of water-bearing icy worlds (e.g., Europa, Enceladus, and Titan) and the structures of icy regions on Mars contain critical information about their origin and evolution. These bodies have been identified as high-priority targets in the NRC Planetary Science Decadal Survey, which noted a lack of technology readiness as one impediment to related missions. An Elastic Wave Analyzer for Icy Sub-surfaces (EWAIS) instrument is being developed to acquire data that can provide answers to fundamental science questions related to these bodies. The EWAIS instrument generates and receives elastic waves using an array of piezoelectric transducers capable of operating at temperatures as low as 30K. A developed breadboard acquires reflected signals from discontinuities in the traveled materials’ impedance (the product of wave velocity and density) along the wave path. Analysis of the acquired data can be used to determine the elastic properties, presence of cracks, locations of cavities and other discontinuities, as well as the thicknesses of ice and liquid layers. The EWAIS instrument is being developed with a novel dual-frequency transmitter array that will enable reaching distances of kilometers through ice, as well as resolving smaller acoustic reflectors nearer the EWAIS instrument. The selected dual frequencies correspond to two depth ranges that can be analyzed, with resolutions of tens of centimeters in the region of tens to hundreds of meters and tens of meters on the kilometers scale, respectively. The EWAIS will be applicable for operation from any in-situ platform, including surface assets (e.g., lander and rover) and sub-surface assets (e.g., melting probes).
FRET is an open source tool, developed at NASA Ames, for writing, understanding, formalizing, and analyzing requirements. In practice, requirements are typically written in natural language, which is ambiguous and consequently not amenable to formal analysis. Since formal, mathematical notations are unintuitive, requirements in FRET are entered in a restricted, natural language, called FRETish with precise unambiguous meaning. FRET helps users write FRETish requirements both by providing grammar information and examples during editing, but also through English and diagrammatic explanations to clarify subtle semantic issues. For each requirement, FRET automatically produces formalizations and supports interactive simulation of produced formalizations to ensure that they capture user intentions. Through its analysis portal, FRET connects to analysis tools by exporting verification code. Currently FRET connects to (1) the CoCoSim automated analysis tool for the verification of Simulink and Stateflow models, and (2) the Copilot runtime monitoring tool for the analysis of C programs. FRET also supports the consistency/realizability analysis of requirements for identifying conflicting requirements. In this tutorial, we introduce FRET and learn to speak and analyze FRETish through several examples.
Monitoring the Total Organic Carbon (TOC) in spacecraft potable water will be of major importance in long-duration human space exploration. In-flight analysis of potable water produced from a regenerative water processor provides immediate feedback on the quality of reclaimed water for crew health as well as water processing system health monitoring. This paper updates the progress in development of the next generation Total Organic Carbon Analyzer (TOCA) designed for the unique requirements of an exploration-class mission. The current objective is to design, build, certify, deliver, and operate a TOCA technology demonstration on the International Space Station (ISS). The next generation analyzer system technology was previously developed and selected among a feasibility study of other options. The new system provides primary advantages of reduced mass and volume through reduced system complexity and reduced need for consumables; therefore, the flight project is named MiniTOCA. The project has recently completed design of the tech demo instrument and assembled and tested a flight-like engineering development unit. The engineering unit has undergone performance testing and environmental testing which provides confidence for the project to move forward with flight unit production and certification activities. Test results are summarized in this paper. The flight unit is targeted for delivery to ISS in late 2025.
Monitoring the Total Organic Carbon (TOC) in spacecraft potable water will be of major importance in long-duration human space exploration. In-flight analysis of potable water produced from a regenerative water processor provides immediate feedback on the quality of reclaimed water for crew health as well as water processing system health monitoring. This paper updates the progress in development of the next generation Total Organic Carbon Analyzer (TOCA) designed for the unique requirements of an exploration-class mission. The current objective is to design, build, certify, deliver, and operate a TOCA technology demonstration on the International Space Station (ISS). The next generation analyzer system technology was previously developed and selected among a feasibility study of other options. The new system provides primary advantages of reduced mass and volume through reduced system complexity and reduced need for consumables; therefore, the flight project is named MiniTOCA. The project has recently completed design of the tech demo instrument and assembled and tested a flight-like engineering development unit. The engineering unit has undergone performance testing and environmental testing which provides confidence for the project to move forward with flight unit production and certification activities. Test results are summarized in this paper. The flight unit is targeted for delivery to ISS in late 2025.
Understanding the intricate relationship between climate variability and agricultural production is crucial for ensuring food security. This study investigates the impact of climate parameters, such as temperature, precipitation, and soil moisture, on major US crop yields. Adopting an open science approach, the study analyzes the impact of climate on agricultural production in the United States. The Galaxy workflow engine serves as the primary tool for integrating climate data from the Goddard Earth Sciences Data and Information Services Center (GES DISC), retrieved via the Giovanni system, with yield statistics from the United States Department of Agriculture’s National Agricultural Statistics Service (USDA NASS). Extensions for reading, preprocessing, and analyzing external data have been developed, enabling the creation of workflows within the Galaxy platform. The development of a reproducible workflow allows for the calculation of seasonal climate averages, which are then assessed for their correlation with crop yields. This methodology ensures the replicability of the research, promoting transparency and collaboration in the scientific community. Correlational and regression analyses have been applied to different sub-zones and crops. The findings from this research offer valuable insights into the relationship between climate parameters and crop yields. These insights contribute to a deeper understanding of climate-crop relationships, providing a solid foundation for informed decision-making in the agricultural sector. The high correlation values indicate a significant relationship between climate parameters and crop yields, underscoring the importance of considering climate factors in agricultural planning and policymaking. This research also exemplifies the power of open science in advancing our understanding of complex environmental and agricultural phenomena. By leveraging open data and services, it provides a robust and replicable framework for future studies in this critical field.
Over vast oceans and remote continents, observations are often scarce and discontinuous. Satellite and model data play a critical role in research and applications. However, finding and accessing satellite and model data can be a daunting task for many, especially those outside the community. The NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC), one of 12 NASA Science Mission Directorate Data Centers, provides Earth science data, information, and services to everyone such as researchers, application users, educators, and students. GES DISC archives and supports datasets applicable to several NASA Earth Science Focus Areas including Atmospheric Composition, Water & Energy Cycles, Carbon Cycle & Ecosystem, and Climate Variability. To facilitate data discovery, evaluation, and exploration, GES DISC has developed the Geospatial Interactive Online Visualization ANd aNalysis Infrastructure (Giovanni), an online tool to analyze and visualize NASA remote sensing and model data without downloading data and software. As of this writing, over 2000 Earth satellite and model variables are available in Giovanni, including several wellknown NASA satellite missions (e.g., TRMM, GPM) and projects (e.g., MERRA-2, GPCP). Giovanni provides twenty-two plots that can be used to analyze, compare, and explore Earth data across different disciplines. Results can be shared with colleagues and downloaded for further analysis. Over the years, Giovanni has helped publish over 3000 referral papers. In this presentation, we will showcase key variables and plot types in Giovanni with examples. In particular, we will present several popular precipitation products from GPM and CPCP for evaluation and comparison.