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At least 1,513 records · Page 84

Compositional Realizability Checking within FRET

A set of requirements for a reactive system is realizable if, for any sequence of inputs that satisfy the assumptions on the environment, the guarantees always hold. Realizability checking is essential to ensure that an implementation can be constructed that satisfies the requirements. We propose a framework that supports users in the non-trivial task of developing realizable requirements. Our framework uses architectural information to automatically de-compose a set of requirements into subsets that can be analyzed separately, and therefore more efficiently. It then integrates existing algorithms in order to detect unrealizability, identify minimal sets of conflicting requirements, and compute counterexamples. The capability to focus on minimal conflict sets is key for localizing and correcting the sources of unrealizability. Our approach supports this process by enabling users to interactively visualize and explore the produced conflict sets and counterexamples. We have implemented our framework in the open-source Formal Requirements Elicitation Tool (FRET), and have used it on a variety of industrial-level case studies, showcasing the strengths of our approach in terms of raw performance, as well as diagnostic potential.

FRET↗

Aero Fair Presentation

Diversity and Inclusion Outreach event to promote STEM, NASA, and Aeronautics to underrepresented middle school students in the Bay Area. Connect with the community. Engage with students to showcase NASA Ames Aeronautics and spark interest in a STEM education.

Aero Fair↗

ANALYSIS OF THE MSL/MEDLI ENTRY DATA WITH COUPLED CFD AND MATERIAL RESPONSE.

The Mars Science Laboratory (MSL) was protected during its atmospheric entry by an instrumented heat-shield using NASA's Phenolic Impregnated Carbon Ablator (PICA) material. PICA is a lightweight carbon fiber/polymeric resin material that offers out-standing performances for protecting probes during planetary entry. The Mars Entry Descent and Landing Instrument (MEDLI) suite on MSL offers unique in-flight validation data for models of material response and atmospheric entry. MEDLI recorded, among other things, time-resolved in-depth temperature data of PICA using thermocouple sensors assembled in the MEDLI Integrated Sensor Plugs (MISP). The objective of this work is to showcase and analyze the coupling between the material response and the aerothermal environment. As shown in Figure 1, the workflow is divided into the following steps. First, the aerothermal properties are computed in the Data Parallel Line Relaxation (DPLR) code [3] and used with the Nonequilibrium air radiation (NEQAIR) program [8] to compute radiative heating. Second, the thermal response inside the material is computed in the Porous material Analysis Toolbox based on Open-FOAM (PATO) using a fixed blowing correction parameter. Third, the pyrolysis gases computed in PATO are used as inputs to a blowing boundary condition within DPLR. Fourth, the new environment properties from DPLR are used in NEQAIR to provide an updated solution, then both the updated aerothermal environment and radiative heating are used in PATO without blowing correction. The third and fourth steps are then repeated until convergence in surface temperature is obtained. Convergence in the radiative heating is generally achieved before surface temperature, at which point the radiative heating is no longer updated. Char mass loss rates are forced to zero to produce a non-receding surface condition. For early time points in the trajectory, where flow around the MSL aeroshell is rarefied, the Direct Simulation Monte Carlo (DSMC) code, SPARTA, is used to compute the aerothermal environment. Iteration between PATO and SPARTA is not performed due to the computational cost of DSMC simulations. Preliminary results of the coupling between PATO and DPLR for the MSL heatshield atmospheric entry model are presented in Figures 2-4 at 65 seconds after entry interface. Figure 2 shows the surface temperature results from an uncoupled simulation in PATO with the blowing correction parameter applied (left) along with the coupled surface temperature after iteration (right). Figure 3 shows the surface temperature along the centerline from windward to leeward for easier comparison. Figure 4 shows the coupled and uncoupled pyrolysis gas blowing rate. Mars 2020 used a similar heatshield consisting of PICA for thermal protection during entry, descent, and landing. In preparation for Mars 2020 post-flight analysis, the predictive material response capability is benchmarked against flight data from MEDLI. This work represents an important milestone toward the development of validated predictive capabilities for designing thermal protection systems for planetary probes.

Mars Science Laboratory↗

IROSA 2B/4B EVA Robotics

The MAGIK Robotic analysis team provides kinematic feasibility assessments and robotic visualizations for the ISS. This animation was created with configurations provided by the CX Team and showcases the robotics for the IROSA install 2B and 4B EVAs. The ExtraVehicular Activities (EVAs) shown include the extraction of the two ISS Roll-Out Solar Arrays (IROSAs) from the ISS Power Augmentation (IPA) carrier by an astronaut on the end of the Space Station Remote Manipulator System (SSRMS) and subsequent handoff to a second astronaut on the ISS port side truss. Extravehicular activities not shown will complete the install of the IROSAs.

Alexander Radford Fryer↗

From Partial to Global Assume-Guarantee Contracts: Compositional Realizability Analysis in FRET

Realizability checking refers to the formal procedure that aims to determine whether an implementation exists, always complying to a set of requirements, regardless of the stimuli provided by the system’s environment. Such a check is essential to ensure that the specification does not allow behavior that can force the system to violate safety constraints. In this paper, we present an approach that decomposes realizability checking into smaller, more tractable problems. More specifically, our approach automatically partitions specifications into sets of non-interfering requirements. We prove that checking whether a specification is realizable reduces to checking that each partition is realizable.We have integrated realizability checking and implemented our decomposition approach within the open-source Formal Requirements ElicitationTool (FRET). A FRET user may check the realizability of a specification monolithically or compositionally. We evaluate our approach by comparing monolithic and compositional checking and showcase the strengths of our decomposition approach on a variety of industrial-level case studies

Anastasia Mavridou↗

The NASA AfriSAR Campaign: Airborne SAR and Lidar Measurements of Tropical Forest Structure and Biomass in Support of Current and Future Space Missions

In 2015 and 2016, the AfriSAR campaign was carried out as a collaborative effort among international space and National Park agencies (ESA, NASA, ONERA, DLR, ANPN and AGEOS) in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The NASA contribution to the campaign was conducted in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). A central motivation for the AfriSAR deployment was the common AGBD estimation requirement for the three future spaceborne missions, the lack of sufficient airborne and ground calibration data covering the full range of ABGD in tropical forest systems, and the intercomparison and fusion of the technologies. During the campaign, over 7000 km2 of waveform Lidar data from LVIS and 30000 km2 of UAVSAR data were collected over 10 key sites and transects. In addition, field measurements of forest structure and biomass were collected in sixteen 1 hectare sized plots. The campaign produced gridded Lidar canopy structure products, gridded aboveground biomass and associated uncertainties, Lidar based vegetation canopy cover profile products, Polarimetric Interferometric SAR and Tomographic SAR products and field measurements. Our results showcase the types of data products and scientific results expected from the spaceborne Lidar and SAR missions; we also expect that the AfriSAR campaign data will facilitate further analysis and use of waveform Lidar and multiple baseline polarimetric SAR datasets for carbon cycle, biodiversity, water resources and more applications by the greater scientific community.

AfriSAR↗

Polarization Difference - The Scientific Story Behind One Variable and Its Practical Applications

Cloud ice and snow microphysical characteristics play an important role in determining cloud radiative effect. They are also closely connected to the details of surface precipitation characteristics. Ice orientation is one microphysical property that is traditionally believed to have trivial impact on the weather systems/climate, and it is hard to measure from space. In this presentation, I will show how many scientific stories can be told from one variable – the brightness temperature difference between vertically-polarized and horizontally-polarized 166 GHz measurements from the Global Precipitation Measurement Microwave Imager (GPM-GMI). Through scrutinizing collocated CloudSat, GMI and GPM radar observations together with the aid of radiative transfer model simulations, we can not only tell apart the particle size, shape and orientation, but also understand the precipitation regime as well as the life stage of a a precipitation system. In the second half of my talk, I will showcase how this variable can be used for predicting precipitation flags and separating mixed-phase cloud from liquid and ice with the powerful machine learning/artificial intelligence (ML/AI) technique.

cloud ice and snow microphysical characteristics↗

Analysis of MSL/MEDLI Entry Data with Coupled CFD and Material Response

The Mars Science Laboratory (MSL) was protected during its atmospheric entry by an instrumented heatshield using NASA's Phenolic Impregnated Carbon Ablator (PICA) material [1]. PICA is a lightweight carbon fiber/polymeric resin material that offers outstanding performances for protecting probes during planetary entry. The Mars Entry Descent and Landing Instrument (MEDLI) suite on MSL offers unique in-flight validation data for models of material response and atmospheric entry. MEDLI recorded, among other things, time-resolved in-depth temperature data of PICA using thermocouple sensors assembled in the MEDLI Integrated Sensor Plugs (MISP) [2]. The objective of this work is to showcase and analyze the coupling between the material response and the aerothermal environment. As shown in Figure 1, the workflow is divided into the following steps. First, the aerothermal properties are computed in the Data Parallel Line Relaxation (DPLR) code [3] and used with the Nonequilibrium air radiation (NEQAIR) program [8] to compute radiative heating. Second, the thermal response inside the material is computed in the Porous material Analysis Toolbox based on OpenFOAM (PATO) [4,5,6] using a fixed blowing correction parameter. Third, the pyrolysis gases computed in PATO are used as inputs to a blowing boundary condition within DPLR. Fourth, the new environment properties from DPLR are used in NEQAIR to provide an updated solution, then both the updated aerothermal environment and radiative heating are used in PATO without blowing correction. The third and fourth steps are then repeated until convergence in surface temperature is obtained. Convergence in the radiative heating is generally achieved before surface temperature, at which point the radiative heating is no longer updated. Char mass loss rates are forced to zero to produce a non-receding surface condition. For early time points in the trajectory, where flow around the MSL aeroshell is rarefied, the Direct Simulation Monte Carlo (DSMC) code, SPARTA [7], is used to compute the aerothermal environment. Iteration between PATO and SPARTA is not performed due to the computational cost of DSMC simulations. Preliminary results of the coupling between PATO and DPLR for the MSL heatshield atmospheric entry model are presented in Figures 2-4 at 65 seconds after entry interface. Figure 2 shows the surface temperature results from an uncoupled simulation in PATO with the blowing correction parameter applied (left) along with the coupled surface temperature after iteration (right). Figure 3 shows the surface temperature along the centerline from windward to leeward for easier comparison. Figure 4 shows the coupled and uncoupled pyrolysis gas blowing rate. Mars 2020 used a similar heatshield consisting of PICA for thermal protection during entry, descent, and landing. In preparation for Mars 2020 post-flight analysis, the predictive material response capability is benchmarked against flight data from MEDLI. This work represents an important milestone toward the development of validated predictive capabilities for designing thermal protection systems for planetary probes.

Thermal Protection Systems↗

Numerical Fluxes and Shocks

Accurately describing shocks within numerical partial differential equation solvers has been a significant computational hurdle for the field of applied mathematics. In our work we present and discuss the background and history of how shocks have been dealt with. We have analyzed numerical schemes and fluxes for the purpose of education and improved computational speed and accuracy. We demonstrate the need for specific methods to deal with shocks by showcasing some methods in a simple one dimensional case for burger’s equation. We aim to extend our knowledge to the euler equations.

Partial Differential Equations↗

A Brief Introduction to AI/ML Applications of Air Traffic Management Data at NASA Ames

This presentation will give a brief overview of several AI/ML This presentation will give a brief overview of several AI/ML projects that NASA Ames interns are exploring in partnership with NASA Aeronautic Research Institute (NARI) and the FAA. NASA is interested in Natural Language Processing (NLP) of various legacy text and speech data within air traffic management e.g., Notices To Airmen (NOTAMs), Letters of Agreement (LoAs), Standard Operating Procedures (SOPs), and Air Traffic Control Center audio briefings. Since our focus is on applying state of the art AI/ML tools to legacy air traffic management data, we first showcase the different data sources of interest followed by a brief introduction to the techniques and language models used. We present some exciting preliminary results on each topic including both unsupervised learning techniques (e.g., clustering) and other modern language models (e.g., BERT) that help extract useful information from these data sources that are interpretable by both man and machine.

Air Traffic Management↗

Improving Data Analyses for a Biological Mission to Deep Interplanetary Space

Onboard the Artemis 1 rocket, NASA plans to launch the first deep space bioscience mission past low Earth orbit (LEO) since 1972, BioSentinel, a 6-Unit (6U) biological CubeSat. BioSentinel’s goals are to assess the effect of deep space ionizing radiation (IR) on DNA and cell damage response, using Saccharomyces cerevisiae, or budding yeast, as a model organism. BioSentinel accomplishes this by measuring Optical Density (OD) in addition to metabolic activity using the redox dye alamarBlue, both of which will be read through light emitting diode (LED) lights of differing wavelengths. With the largest and most sophisticated energy-providing solar panels utilized on a biological CubeSat to date, BioSentinel will be equipped with an IR dosimeter to identify what doses of radiation the yeast is exposed to at any point in time, in addition to a transponder to send such data back to NASA Ames’ Multi Mission Operations Center (MMOC). Biology computational programs and data processing scripts are necessary to analyze this complex data once it is received, including automated subroutines to analyze duplication rate, alamarBlue reduction, and time periods when paired against numerous other variables. Overall, we have implemented several VisualBasic biocomputational programs to analyze such data in an organized and efficient manner so that conclusions can be made rapidly. Furthermore, we showcase why efficient analysis of particular parameters is important to better understand the risks that deep space IR poses to astronauts, especially when considering the upcoming Artemis missions.

Bijan Harandi↗

Science Objectives of the ACCP/AOS Millimeter- and Submillimeter-Wave Radiometers

Passive microwave radiometers provide highly useful information regarding the Earth system by measuring thermal emission (from the surface and atmosphere) that is reflected, absorbed, and scattered by the surface and the atmosphere. As such, radiometers have been instrumental across Earth-observing concepts with heritage tracing back to the Nimbus era, and clouds and precipitation have been key geophysical phenomena these sensors have targeted. As millimeter-wave (particularly ≥85 GHz) and submillimeter-wave technologies have advanced, the applicability of microwave radiometers has expanded to encompass falling snow and ice clouds, respectively. Given the strong heritage of microwave radiometry and the emergence of submillimeter-wave sensors for sensing falling snow and ice clouds, the National Academies’ 2017 Decadal Survey for Earth Science and Applications from Space recommended passive sensors covering these wavelengths be included in observing systems that address the Aerosols, Clouds, Convection, and Precipitation (ACCP) combined designated observable. To showcase the capabilities for addressing the ACCP science objectives, we will provide an overview of the passive microwave capabilities envisioned for the Atmosphere Observing System (AOS, part of the recently unveiled NASA Earth System Observatory), including descriptions of the radiometers for the inclined and polar orbits and the primary geophysical variables of interest related to ice-phase clouds and precipitation. We will also discuss secondary science objectives, such as precipitation mapping, that can be achieved with the ACCP/AOS radiometers. Examples from the analyses performed for the ACCP/AOS architecture study and subsequent sensor definition exercises will be presented, outlining the basis for the radiometer configuration within the context of the overarching ACCP/AOS goals to elucidate the connections between atmospheric dynamics, weather (including extreme events), air quality, and climate.

Ian S Adams↗

GeoNEX-SUBSETS: Geostationary Satellite Observations Over International Observing Network Sites

Satellite remote sensing is crucial for monitoring the Earth’s surface and for simulating global carbon and water cycles. Subsets of satellite data from sensors such as MODIS over established observing networks have been valuable for researchers for comparing the ground observed phenomena with satellite observations. Here, we introduce new subsets of the GeoNEX geostationary satellite datasets over locations of several ground observation networks. The NASA Earth Exchange group has been generating geostationary satellite products with our international partners and universities to cover the entire globe. In comparison to polar-orbiting satellite sensors such as MODIS, the new generation geostationary satellites observe target areas at a higher frequency (5-10 minutes), which also significantly increased the data volume. To reduce the burden of downloading and extracting geostationary time series data and provide easy access to the community, we provide subsets of GeoNEX products through our data portal [www.data.nas.nasa.gov]. The ready-to-use subset follows the same format as the MODIS fixed sites subset tools for users who are familiar with MODIS subset data and software. The selected networks include Fluxnet, AERONET, and Phenocam over the conterminous US. We demonstrate the usage of the GeoNEX fixed-site subset data and showcase thir advantages with three example studies. The first example is simulating the diurnal cycle of plant ecophysiology at Fluxnet sites. The high frequency of the GeoNEX time series allows us to run ecological models at sub-hour time steps and directly compare the simulated carbon and water fluxes with half-hourly Fluxnet data. The second example uses the geostationary data to track phenological changes. It highlights the high-frequent observations of geostationary satellites in helping mask cloud covers and capture the quick responses of vegetation to environmental changes. As such, these examples demonstrate the value of ready-to-use GeoNEX subsets data in terrestrial ecophysiology research.

GeoNEX↗

Organic Matter in Itokawa Particles

The first Hayabusa mission returned samples from the near-Earth S-type asteroid 25143 Itokawa to Earth in 2010[1]. Although Itokawa has a lithology related to ordinary chondrites(OCs) that typically have low organic contents, several Itokawa particles were found to contain organic matter (OM)[2-5]. However, there was not an explicit conclusion to the origin of the observed OMin these early studies. We have extended our search for OM into other Itokawa grains. Here, we report extraterrestrial OM (macromolecular carbon and amino acids) observed in six Itokawa particles(including a category1 particle: RA-QD02-0162[#62; also nicknamed “Amazon”],and five category3 carbon-rich particles:RA-QD02-0012[#12], RA-QD02-0078[#78], RB-CV-0029[#29], RB-CV-0080 [#80] and RB-QD04-0052[#52]).All allocated Itokawa samples were initially analysed by spot and point-by-point mapping Raman spectroscopic analysis at the Open University, UK. Amazon was then transferred and mounted into indium on an aluminium stub, which was studied with a NanoSIMS 50L ion microprobe for its H,C,N isotopic compositions. The rest of the samples were mounted in sterile gold foils, and the amino acid contents of their acid hydrolysed hot water extracts were obtained with a liquid chromatography with tandem fluorescence and accurate mass detection at NASA Goddard Space Flight Center, USA. Based on the observation of the Raman parameters(e.g. the peak locations and widths of the defect (D) and graphite (G) bands)[6], a significant variety of carbonaceous materials has been observed in Amazon. The carbonaceous materials include primitive and unaltered OM that shares similarity with the IOM in primitive (CI,CM,CR) carbonaceous chondrites, as well as OM that has been heavily graphitised. The organic structure of the heated material is best represented by nanocrystalline graphite, comparable to that observed for metamorphosed meteorites (e.g., L3–6 Inman, Tieschitz and New Concord,CV3 Allende, and EH4 Indarch),suggesting peak metamorphic temperatures (PMT) of >~600°C. The thermal history recorded in the graphitic OM agrees with PMT estimates for returned Itokawa regolith grains (600–800°C)[7].We have obtained the H,C,N isotopic compositions for the primitive OM in Amazon, which exhibits unambiguously extraterrestrial isotopic signatures (δD = +4868±2288‰; δ13C = −24±5‰; δ15N = +344±20‰), contrasting to the typically negative isotopic values obtained for terrestrial organic matter[8]. The δD and δ13C values of the organic material in Amazon are comparable to OCs, however, the δ15N value is higher than that typically observed for OCs (δ15N = −47 to +36‰), and is similar to that of CRs (δ15N = +153 to +309‰)[9].Our data suggest a genetic link between the primitive organic material observed in Itokawa to CRs and IDPs for they share similar D, 13C and 15N enrichments[10].The high carbon contents of the five category 3 Itokawa particles suggest potentially higher OM abundances, hence we extracted and analysed amino acids in these samples. Although terrestrial contamination was observed primarily as L-protein amino acids, several terrestrially uncommon non-protein amino acids were also observed at low abundances, such as β-aminoisobutyric acid (β-AIB),β-amino-n-butyric acid (β-ABA), and β-alanine. Itokawa amino acid content observed here was dissimilar to thermally altered OCs, but preliminarily analogous to more aqueously altered CR2s.Continued evolution of Itokawa is evident by the in fall of primitive organic material derived from CRs/IDPs, accounting for a complex interplay between the remnant Itokawa silicates with exogenous organics. The results reported here are the first evidence of extraterrestrial OM in asteroid material from a sample-return mission, showcasing a working protocol for analysing samples returned by the Hayabusa2 and OSIRIS-REx missions

Q. H. S. Chan↗

Using Computational Reflection in PVS

Computational reflection is a well-known and powerful way to speed up the proof process in an interactive theorem prover. This presentation introduces the concept using PVS, and showcases 2 of the strategies using the method.

formal methods↗

Vibrational and Rovibrational Spectroscopy Applied to Astrochemistry

The detection of molecules in astrophysical environments almost always requires remote sensing. While radioastronomical observation and associated rotational spectroscopy are powerful astronomical tools, infrared spectral analysis provides a unique means of examining the observable universe, especially for molecules where permanent dipole moments are small or even non-existent. The molecular vibrations of small molecules are now able to be modeled via quantum chemistry and electronic structure theory conjoined to vibrational analysis to within spectroscopic accuracy in many cases. This chapter will showcase this success and build upon it to show how such advances are now being leveraged to describe molecular vibrations for molecules containing dozens of atoms, electronically excited states, "hot bands," exoplanetary atmospheric opacity data, and even emission of polycyclic aromatic hydrocarbons. All of these are required to prune the interstellar spectral garden of its "weeds" in search of "flowers" that will provide the necessary fingerprints for astronomers to be able to probe the heavens for its past, present, and future secrets.

Ryan C. Fortenberry↗

BERT-E: An Earth Science Specific Language Model for Domain-Specific Downstream Tasks

Language models are fast approaching human-like understanding of natural language. They have been shown to perform equally, if not better than humans in a myriad of language tasks such as next sentence prediction, question answering, entity extraction etc. Part of the success of the models are owed to the fact that they have been trained on varied natural language text over the internet. By virtue of this, the models do not contain the semantic information present in Earth science literature. Hence, there is a lot of room for improvement when using these models for earth science specific tasks. In this work, we showcase our approach on developing Earth science specific language models. Furthermore, we justify the need for such a model by using the embeddings generated by the model to perform a domain specific downstream task that performs better than a generic model.

Prasanna Koirala↗

pyQuARC: Open Source Library for Earth Observation Metadata Quality Assessment

Metadata quality is essential to effective data discovery and has become increasingly vital as more Earth Science data sets become available. The Common Metadata Repository (CMR) hosts metadata describing NASA’s Earth Observation data products, which are archived across 12 Distributed Active Archive Centers (DAACs). The Analysis and Review of CMR (ARC) Team, located at Marshall Space Flight Center, conducts metadata quality assessments to ensure that these data products are discoverable, accessible, and usable. To achieve these goals, the ARC team has developed a metadata quality assessment framework to evaluate metadata completeness, correctness, and consistency. ARC uses a combination of manual and automated methods to assess these three components and identify areas of improvement; the team then collaborates with the DAACs to resolve any findings. To streamline this process, ARC is currently developing a host of scripts, known as pyQuARC, to automate metadata quality assessments as much as possible. pyQuARC is an open source library for Earth Observation Metadata Quality Assessment, and the tool utilizes ARC’s metadata quality assessment framework to make basic validation checks, pinpoint inconsistencies between dataset-level (i.e. collection) and file-level (i.e. granule) metadata, and identify opportunities for more descriptive and robust information. Since pyQuARC is also customizable, other users can make modifications as needed, and future metadata standards can also be implemented. Once pyQuARC is fully developed, it will support multiple schema types to serve the broader EOSDIS metadata community. This presentation will provide an overview of pyQuARC and its process of development while showcasing the tool’s valuable features and uses.

Jenny Wood↗