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NASA NTRSDate not supplied

An Integrated Design Tool for Tow-Steered Laminates of Composites in Abaqus and MSC.Patran/Nastran

Tow-steered composites can be tailored for optimal mechanical performance of lightweight structures. However, there are no commercial-grade design tools for tow-steered composite structures, which hinders the design innovation of tow-steered composites in realistic structures. The novelty of this paper is to develop an integrated design framework along with the development of graphical user interface (GUI) plug-ins in commercial finite element (FE) software Abaqus and MSC.Patran/Nastran. The GUI plug-ins take all the design setups and communicate with external codes for the material modeling and optimization, and hence provide a unified design environment within the FE codes. The mechanics of structure genome (MSG) plate model computes shell element properties based on user-defined fiber paths and layup, which are defined via the GUI plug-ins. The optimization is performed by an open-source code, Dakota, from Sandia National Laboratories (Sandia), which also coordinates the structural analysis, material modeling, and optimization in design iterations. Two examples are presented to demonstrate the user-friendliness and versatility of the developed GUI plug-ins. The developed tools will ease the design process and facilitate the application of tow-steered composites in realistic aerospace structures.

Xin Liu↗

NASA NTRS2021

A Practical Guide to Writing a Radiative Transfer Code

Using our decades-long experience in radiative transfer (RT) code development for Earth science, we endeavor to reduce the knowledge gap of bringing RT from theory to code quickly. Despite numerous classic and recent literature, it is still hard to develop anRT code from scratch within a few weeks. It is equally hard to understand, not to mention modify, an existing “monster” RT code, for which the developer is either located remotely or has retired. Following the format of “Numerical Recipes” by Press et al., we collocate in this paper small pieces of necessary theory with corresponding small pieces of RT code. These are arranged in an order that is natural for code development, which is often opposite of the natural order for laying out the theoretical basis. We focus on the transfer of unpolarized monochromatic solar radiation in a plane-parallel atmosphere over a reflecting surface. Both the surface and the atmosphere are homogeneous (uniform) at all directions. The multiple scattering is numerically solved using the deterministic method of Gauss-Seidel iterations. Except for the presented Python-Numba open-source RT code gsit, the paper does not report any new scientific results, but rather serves as an academic demonstration. If development time is an issue or the reader is familiar with basic concepts of RT theory, we recommend proceeding directly to Sec.3 “RT code development.

multiple light scattering↗

NASA NTRS2022

An Advanced Open-Source Platform for Air Quality Analysis, Visualization, and Prediction

Ambient air pollution is the largest environmental health risk factor, leading to several million premature deaths globally per year. The challenge of combating poor air quality is exacerbated by growing urban populations, changing emissions, and a warming climate. While there have been many advances monitoring and modeling of atmospheric composition, reflected in the dramatic increase in archived Earth Observations, there is no single measurement or method that alone can provide an accurate depiction of the entire atmosphere. The rapidly growing collections of observational and modeling data require us to be smarter about what data to include, and how such data is used. In recent years, NASA has invested significantly in advancing the concepts for Analytics Collaborative Framework (ACF) [5] and New Observing Strategies (NOS) [4] to tackle our software infrastructure need for harmonized data management and dynamic acquisition of diverse measurements for on-demand, interactive, multivariate analysis, and access [3]. It is not enough to have a big data, standalone analytics solution; it is critical that we start integrating data from remote sensing, modeling, and in-situ networks in a harmonized manner that enables timely and data-driven decision-making for air quality management. This work presents the design and development of an Air Quality Analytics Collaborative Framework (AQ ACF), as part of NASA’s Advanced Information Systems Technology (AIST) effort, to establish a data, machine-learning, and numerically driven platform for air quality analysis, visualization, and prediction.

Liu, Qian↗

NASA NTRS2019

Software Tool for Tracking & Mapping the NASA Orion AA-2 Test Flight Ejectable Data Recorders in Real Time

On 2 July 2019, the NASA Ascent Abort 2 flight took place off the Florida coast to test the emergency systems to separate the Orion Crew Module (CM) from the future Space Launch System rocket in the event of a malfunction. During this high-altitude test, instrumentation data was recorded on twelve customized buoyant Ejectable Data Recorders (EDRs) and subsequently jettisoned from the CM in mid-air. Upon release, the EDRs activated their GPS-Iridium beacon systems and began transmitting Short Burst Data (SBD) messages via the Iridium satellite network to relay their individual location and system health information. To locate, track and retrieve each EDR from the ocean surface in real-time, multiple open-source programming tools (Python and Linux shells) were developed for parsing the incoming Iridium binary SBD messages. For this, a Linux laptop was used to receive the Iridium-generated emails containing the SBD messages and autonomously execute the parsing tools. The received SBD data contained location, timestamp and health status information that was translated, saved, and subsequently used for simultaneously generating a continuously updated color-coded tabular display summary and unique KML files used with Google Earth to track their locations. Once their locations were known, dedicated recovery vessels retrieved all EDRs from the ocean. An additional tool was also developed in order to generate 5- and 10-minute geolocation predictions for each EDR by deriving the displacement distance, elapsed time, displacement heading and velocity based on the latest known information available. The recovery vessels were also tracked with the use of a separate commercial GPS beacon system. After jettison, 67% of the EDRs transmitted valid data by the time they were retrieved from the ocean. However, the real-time information presented by the plotting tool allowed for the ready depiction of EDR dispersal patterns and reference drift trajectories, which contributed to the recovery of all twelve EDRs and the AA-2 flight data. Lastly, the available data showed that the distance between the software’s reported drift/predicted locations and the recovery locations did not exceed 38 meters, therefore demonstrating the advantages of this software tool for supporting real-time tracking and recovery efforts of beacon devices.

Moxey, Lucas↗

NASA NTRSDate not supplied

A New Architecture for Parallelization of Complex Spacecraft Trajectory Optimization Scans

This paper describes CopScanner, a new component of the Copernicus ecosystem for spacecraft trajectory design and optimization. CopScanner is a Python library being developed at the NASA JSC which enables easy parallelization of Copernicus scans. CopScanner is currently being developed and implemented for production of Copernicus trajectory scans for upcoming Artemis Missions (Artemis II and beyond). On the backend, CopScanner utilizes Dask, an open-source Python library for parallel computing which enables parallelization over both multi-core local machines and large-scale distributed computing clusters. CopScanner abstracts the trajectory scanning process into a DAG which is constructed using a chain of individual subscans. Each node in the DAG executes a python module, called the callable, for which there are built-in defaults, or users may specify their own. Support for custom callables makes CopScanner a versatile trajectory optimization software. All output files and associated metadata from a CopScanner scan are compressed and stored in a two-file output, collectively called the FileStore, consisting of a SQLite database and a compressed JSON MessagePack file, for which CopScanner provides a Python class for interaction.

Quentin Moore↗

NASA NTRSDate not supplied

Simulated Multipath Using Software Generated GPS Signals

Depending on the environment, multipath can be one of the largest error sources contributing to degradation in Global Navigation Satellite System (GNSS) (e.g., GPS) performance. Multipath is a phenomenon that occurs as radio signals reflect off of surfaces, such as buildings, producing multiple copies of the original signal. When this occurs with GPS signals, it results in one or more delayed signals arriving at the receiver with or without the on-time/direct GPS signal. The receiver measures the composite of these signals which, depending on the severity of the multipath, can substantially degrade the accuracy of the receiver's calculated position. Multipath is commonly experienced in cities due to tall buildings and its mitigation is an ongoing area of study. This research demonstrates a novel approach for simulating GPS multipath through the modification of an open-source tool, GPS-SDR-SIM. The resulting additional testing capability could allow for improved development of multipath mitigating technologies. Currently, open-source tools for simulating GPS signals are available and can be used in the testing and evaluation of GPS receiver equipment. These tools can generate GPS signals that, when used by a GPS receiver, result in computation of a position solution that was pre-determined at the time of signal generation. That is, the signals produced are properly formed for the pre-determined location and result in the receiver reporting that position. This allows for a GPS receiver under test to be exposed to various simulated locations and conditions without having to be physically subjected to them. Additionally, while these signals are generated by a software simulation, they can be processed by real or software defined GPS receivers. This work utilizes the GPS-SDR-SIM software tool for GPS signal generation and while this tool does implement some sources of error that are inherent to GPS, it cannot inject multipath. GPS-SDR-SIM was modified in this effort to produce additional copies of signals with pre-determined delays. These additional delayed signals mimic multipath and represent what happens to GPS signals in the real world as they reflect off of surfaces and arrive at a receiver in place of or alongside the direct GPS signal. A successful proof of concept was prototyped and demonstrated using this modified version of GPS-SDR-SIM to produce simulated GPS signals as well as additional simulated multipath signals. The generated data was processed using a software defined GPS receiver and it was found that the introduction of simulated multipath signals successfully produced the expected characteristics of a composite multipath signal. Further maturation of this work could allow for the development of a GPS receiver testing and evaluation framework and aid in the development of multipath mitigating technologies.

GPS↗

NASA NTRS2016

Advancing the Power and Utility of Server-Side Aggregation

During the upcoming Summer 2016 meeting of the ESIP Federation (July 19-22), OpenDAP will hold a Developers and Users Workshop. While a broad set of topics will be covered, a key focus is capitalizing on recent EOSDIS-sponsored advances in Hyrax, OPeNDAPs own software for server-side realization of the DAP2 and DAP4 protocols. These Hyrax advances are as important to data users as to data providers, and the workshop will include hands-on experiences of value to both. Specifically, a balanced set of presentations and hands-on tutorials will address advances in1.server installation,2.server configuration,3.Hyrax aggregation capabilities,4.support for data-access from clients that are HTTP-based, JSON-based or OGC-compliant (especially WCS and WMS),5.support for DAP4,6.use and extension of server-side computational capabilities, and7.several performance-affecting matters.Topics 2 through 7 will be relevant to data consumers, data providers andnotably, due to the open-source nature of all OPeNDAP softwareto developers wishing to extend Hyrax, to build compatible clients and servers, andor to employ Hyrax as middleware that enables interoperability across a variety of end-user and source-data contexts. A session for contributed talks will elaborate the topics listed above and embrace additional ones.

aggregation↗

NASA NTRSDate not supplied

Preservation of Provenance and Context to Ensure Future Understandability of Airborne Earth Observations and Derived Data Products

Open-source science goes beyond making data from scientific projects (e.g., on-orbit/satellite missions, airborne and field investigations, and other data producing activities) openly available after they are generated, but involves and open sharing of information throughout the project lifecycle. Preservation of the data and associated information required for understanding and reusing the data well after the scientific projects is a contributor to open-source science as well. Considering the high investment in the on-orbit/satellite missions, we had developed a document titled “NASA Earth Science Data Preservation Content Specification (PCS)” in 2011. This document has been used as a requirement for recent on-orbit/satellite missions by NASA. Recently it became clear that the specifications should be applied to other scientific projects as well. Therefore, the document was revised to cover other types of projects, and a Preservation Content Implementation Guidance (PCIG) document was also developed. The revised PCS, and the PCIG, were published in 2022. The purpose of this presentation is to highlight the contents of these documents as they apply to suborbital/airborne investigations. The PCS calls for content preservation in eight general categories - Measuring Instrument/Platform Description, Instrument and Science Data Products and Metadata, Science Raw Data, Product and Algorithm Documentation, Instrument Calibration, Science Algorithm Software, Science Data Product Algorithm Inputs, Science Data Product Validation, and Science Data Access and Analysis Tools. While all these categories apply to various types of projects, a few clarifying sentences have been added to the descriptions of contents in each of the categories to show which categories are especially important to airborne and field investigations and where some contents are not applicable (or difficult to obtain). The PCIG document provides some general guidance applicable to all types of projects and specific guidance in a separate section for airborne and field investigations. This section calls out typical artifacts produced during such investigations that can meet the spirit of the various PCS categories.

remote sensing↗

NASA NTRS2022

The Earth Model Column Collaboratory (EMC2) v1.1: An Open-Source Ground-Based Lidar and Radar Instrument Simulator and Subcolumn Generator for Large-Scale Models

Climate models are essential for our comprehensive understanding of Earth's atmosphere and can provide critical insights on future changes decades ahead. Because of these critical roles, today's climate models are continuously being developed and evaluated using constraining observations and measurements obtained by satellites, airborne, and ground-based instruments. Instrument simulators can provide a bridge between the measured or retrieved quantities and their sampling in models and field observations while considering instrument sensitivity limitations. Here we present the Earth Model Column Collaboratory (EMC2), an open-source ground-based lidar and radar instrument simulator and subcolumn generator, specifically designed for large-scale models, in particular climate models, but also applicable to high-resolution model output. EMC2 provides a flexible framework enabling direct comparison of model output with ground-based observations, including generation of subcolumns that may statistically represent finer model spatial resolutions. In addition, EMC2 emulates ground-based (and air- or space-borne) measurements while remaining faithful to large-scale models' physical assumptions implemented in their cloud or radiation schemes. The simulator uses either single particle or bulk particle size distribution lookup tables, depending on the selected scheme approach, to perform the forward calculations. To facilitate model evaluation, EMC2 also includes three hydrometeor classification methods, namely, radar- and sounding-based cloud and precipitation detection and classification, lidar-based phase classification, and a Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP) lidar simulator emulator. The software is written in Python, is easy to use, and can be straightforwardly customized for different models, radars, and lidars. Following the description of the logic, functionality, features, and software structure of EMC2, we present a case study of highly supercooled mixed-phase cloud based on measurements from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE). We compare observations with the application of EMC2 to outputs from four configurations of the NASA Goddard Institute for Space Studies (GISS) climate model (ModelE3) in single-column model (SCM) mode and from a large-eddy simulation (LES) model. We show that two of the four ModelE3 configurations can form and maintain highly supercooled precipitating cloud for several hours, consistent with observations and LES. While our focus is on one of these ModelE3 configurations, which performed slightly better in this case study, both of these configurations and the LES results post-processed with EMC2 generally provide reasonable agreement with observed lidar and radar variables. As briefly demonstrated here, EMC2 can provide a lightweight and flexible framework for comparing the results of both large-scale and high-resolution models directly with observations, with relatively little overhead and multiple options for achieving consistency with model microphysical or radiation scheme physics.

Earth Model Column Collaboratory↗

NASA NTRS2011

InSAR Scientific Computing Environment

This computing environment is the next generation of geodetic image processing technology for repeat-pass Interferometric Synthetic Aperture (InSAR) sensors, identified by the community as a needed capability to provide flexibility and extensibility in reducing measurements from radar satellites and aircraft to new geophysical products. This software allows users of interferometric radar data the flexibility to process from Level 0 to Level 4 products using a variety of algorithms and for a range of available sensors. There are many radar satellites in orbit today delivering to the science community data of unprecedented quantity and quality, making possible large-scale studies in climate research, natural hazards, and the Earth's ecosystem. The proposed DESDynI mission, now under consideration by NASA for launch later in this decade, would provide time series and multiimage measurements that permit 4D models of Earth surface processes so that, for example, climate-induced changes over time would become apparent and quantifiable. This advanced data processing technology, applied to a global data set such as from the proposed DESDynI mission, enables a new class of analyses at time and spatial scales unavailable using current approaches. This software implements an accurate, extensible, and modular processing system designed to realize the full potential of InSAR data from future missions such as the proposed DESDynI, existing radar satellite data, as well as data from the NASA UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar), and other airborne platforms. The processing approach has been re-thought in order to enable multi-scene analysis by adding new algorithms and data interfaces, to permit user-reconfigurable operation and extensibility, and to capitalize on codes already developed by NASA and the science community. The framework incorporates modern programming methods based on recent research, including object-oriented scripts controlling legacy and new codes, abstraction and generalization of the data model for efficient manipulation of objects among modules, and well-designed module interfaces suitable for command- line execution or GUI-programming. The framework is designed to allow users contributions to promote maximum utility and sophistication of the code, creating an open-source community that could extend the framework into the indefinite future.

Rosen, Paul A.↗

NASA NTRS2023

Space Networking Implementation for Lunar Operations

The High-Rate Delay Tolerant Networking (HDTN) project at NASA has developed a performance optimized and open-source Delay Tolerant Networking (DTN) implementation. The primary goal is to create a scalable networking solution to increase the scientific data return rate of space missions. To reach this goal, HDTN must span multiple edge cases in space networking by including tools and configurations to accommodate a wide range of space systems. Typically, HDTN evaluations are conducted on a laboratory emulation test bed, made up of hardware accelerated x86 based systems capable of data rates over 10 Gbps. HDTN must have an effective implementation process on a wide range of systems to increase the sustainability of the design. One important implementation option is with low-level embedded systems which could be used on small robotic missions. This paper details the implementation process, benchmark testing, and performance results of HDTN in multiple configurations on Raspberry Pi 4 devices. By implementing HDTN on a Raspberry Pi 4, a process for building HDTN onto ARM processors was developed and utilized to conduct benchmark tests in multiple network configurations, achieving a data rate performance exceeding 600 Mbps. Based on these results, HDTN proved to run on small ARM based systems with slight modifications to the build procedure. These results were then extended to evaluating an implementation of the HDTN software parsed across several Raspberry Pi 4 nodes. To test this capability, HDTN was configured in a simplified cut-through setup and distributed among multiple Raspberry Pi 4 processors. This distributed architecture was benchmark tested in a similar fashion to the testing of a singular HDTN implementation. The results from the benchmark testing are used to examine how these implementation options and capabilities can expand the use cases for DTN, and particularly with small robotic missions.

Space Networking↗

NASA NTRS2014

Preparing GMAT for Operational Maneuver Planning of the Advanced Composition Explorer (ACE)

The General Mission Analysis Tool (GMAT) is an open-source space mission design, analysis and trajectory optimization tool. GMAT is developed by a team of NASA, private industry, public and private contributors. GMAT is designed to model, optimize and estimate spacecraft trajectories in flight regimes ranging from low Earth orbit to lunar applications, interplanetary trajectories and other deep space missions. GMAT has also been flight qualified to support operational maneuver planning for the Advanced Composition Explorer (ACE) mission. ACE was launched in August, 1997 and is orbiting the Sun-Earth L1 libration point. The primary science objective of ACE is to study the composition of both the solar wind and the galactic cosmic rays. Operational orbit determination, maneuver operations and product generation for ACE are conducted by NASA Goddard Space Flight Center (GSFC) Flight Dynamics Facility (FDF). This paper discusses the entire engineering lifecycle and major operational certification milestones that GMAT successfully completed to obtain operational certification for the ACE mission. Operational certification milestones such as gathering of the requirements for ACE operational maneuver planning, gap analysis, test plans and procedures development, system design, pre-shadow operations, training to FDF ACE maneuver planners, shadow operations, Test Readiness Review (TRR) and finally Operational Readiness Review (ORR) are discussed. These efforts have demonstrated that GMAT is flight quality software ready to support ACE mission operations in the FDF.

Trajectory analysis↗

NASA NTRS2019

Preparing General Mission Analysis Tool for Operational Maneuver Planning of the Advanced Composition Explorer Mission

mission design↗

NASA NTRS2022

Kamodo’s Model-Agnostic Satellite Flythrough: Lowering the Utilization Barrier for Heliophysics Model Outputs

Heliophysics model outputs are increasingly accessible, but typically are not usable by the majority of the community unless directly collaborating with the relevant model developers. Prohibitive factors include complex file output formats, cryptic metadata, unspecified and often customized coordinate systems, and non-linear coordinate grids. Some pockets of progress exist, giving interfaces to various simulation outputs, but only for a small set of outputs and typically not with open-source, freely available packages. Additionally, the increasing array of tools built upon these sporadic interfaces are typically model-specific. We present Kamodo’s model-agnostic satellite flythrough capabilities as the solution to the utilization barrier for heliophysics model outputs. Developed at the Community Coordinated Modeling Center, these flythrough capabilities are built in Python upon a network of model-agnostic interfaces developed in collaboration with model developers, providing interpolation results the community can trust. Kamodo’s flythrough capabilities present the user with a growing variety of flythrough tools based upon a rapidly expanding library of heliophysics model outputs in several domains, currently including a variety of Ionosphere-Thermosphere-Mesosphere and global magnetosphere model outputs. Each capability is designed to be easily accessible via simplistic model-agnostic syntax, with the entire package freely available in the cloud on Github. Here, we describe the tools developed, include several sample applications for common science questions, demonstrate interoperability with selected packages, and summarize ongoing developments.

Software↗

NASA NTRS2012

New Techniques for High-Contrast Imaging with ADI: The ACORNS-ADI SEEDS Data Reduction Pipeline

We describe Algorithms for Calibration, Optimized Registration, and Nulling the Star in Angular Differential Imaging (ACORNS-ADI), a new, parallelized software package to reduce high-contrast imaging data, and its application to data from the Strategic Exploration of Exoplanets and Disks (SEEDS) survey. We implement seyeral new algorithms, includbg a method to centroid saturated images, a trimmed mean for combining an image sequence that reduces noise by up to approx 20%, and a robust and computationally fast method to compute the sensitivitv of a high-contrast obsen-ation everywhere on the field-of-view without introducing artificial sources. We also include a description of image processing steps to remove electronic artifacts specific to Hawaii2-RG detectors like the one used for SEEDS, and a detailed analysis of the Locally Optimized Combination of Images (LOCI) algorithm commonly used to reduce high-contrast imaging data. ACORNS-ADI is efficient and open-source, and includes several optional features which may improve performance on data from other instruments. ACORNS-ADI is freely available for download at www.github.com/t-brandt/acorns_-adi under a BSD license

Brandt, Timothy D.↗

NASA NTRSDate not supplied

INCREASING THE TRANSPARENCY AND REPRODUCIBILITY OF SPACE RADIATION SCIENCE: THE RADIATION BIOLOGY ONTOLOGY

Among the primary objectives of the Open/Open-Source Science paradigm are making scientific investigation data transparent and results reproducible [1], objectives shared by the FAIR principles [2]. To accomplish this, the conceptual framework that includes all the investigation objects needs to be accurately captured and communicated to all data consumers. A large part of this requires using metadata standards to annotate data collected. These standards should be readily accessible, informed by scientific community consensus and sufficiently specific to encompass all of the important aspects of the investigation. Starting in 2020 we have been co-leading an open consortium to develop a new metadata standard, the Radiation Biology Ontology (RBO), through the Open Biological and Biomedical Ontologies (OBO) Foundry [3]. We began by transforming many of the terms from the National Council on Radiation Protection and Measurement into concepts that can be formally related to existing OBO Foundry classes or attributes. We then identified and imported into the RBO existing OBO Foundry classes that have obvious relevance for radiation biomedicine (for example, concepts from the Environment Ontology that describe radiative processes, and concepts from the Gene Ontology dealing with molecular and cellular responses to radiation). Finally, we scrutinized datasets from investigations of radiation effects held in NASA GeneLab and LSDA repositories and added additional classes, instances, and attributes into the RBO that should be used to annotate these data. We developed the RBO using the open-source tools of GitHub and publish the RBO periodically through the NIH/NCBI BioPortal website, so systems worldwide can leverage the knowledge it contains [4]. This initial phase of concept modeling has yielded an RBO that at present has more than 300 declared concepts, with more than 3500 additional concepts imported from other OBO Foundry ontologies. While this first phase has focused on concepts for annotating samples, environments, exposures, and measurements, the next phase will center on supporting annotation of results and findings, such as concept models of molecular, cellular and tissue effects. The value of the RBO will be determined in part by our ability to engage the community in its development, and we have established a Radiobiology Informatics Consortium with unrestricted membership as the owner of the RBO in order to encourage investigators, system owners and other to join in this effort. Anyone can report issues or request new concept modeling or other features directly on GitHub. By using the BioPortal application programming interface, systems can pose dynamic queries to the latest version of the RBO for information on individual classes or entire hierarchies; this design eliminates the need for systems to be updated in order to use newer versions of the RBO. We hope to contribute to the advancement of open radiobiological science through the continued, open development of the RBO, that will provide more precise, machine-interpretable descriptions of investigations, as well as support data meta-analysis through machine learning or other artificial intelligence methods. REFERENCES [1] Open science in space. Nature Medicine, 2021. 27(9): p. 1485-1485. [2] Wilkinson, M.D., et al., The FAIR Guiding Principles for scientific data management and stewardship. Sci Data, 2016. 3: p. 160018. [3] Smith, B., et al., The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration. Nat Biotechnol, 2007. 25(11): p. 1251-5. [4] Whetzel, P.L., et al., BioPortal: enhanced functionality via new Web services from the National Center for Biomedical Ontology to access and use ontologies in software applications. Nucleic Acids Res, 2011. 39(Web Server issue): p. W541-5.

informatics↗

NASA NTRSDate not supplied

DELTA: An Open-Source Framework to Simplify Machine Learning with Satellite Imagery

DELTA (Deep Earth Learning, Tools, and Analysis) is an open-source framework developed at NASA to simplify running and training machine learning (ML) models on satellite imagery. Users new to machine learning can run existing ML models on satellite imagery with minimal setup and configuration. For experienced ML users, DELTA helps simplify data engineering, preprocessing steps, and reduces the need for boilerplate code that needs written to make satellite imagery datasets palatable for machine learning. This lets data scientists focus on model development while DELTA handles the imagery manipulation. This presentation will demonstrate DELTA’s functionality and share some examples from an active project using it for flood mapping using imagery from multiple satellite sources.

deep learning↗

NASA NTRS2022

Building a Transdisciplinary, Exascale-Capable Workforce for Geospace Science

Key Points: ●First-principles, self-consistent geospace modeling will require at least exascale-level computing capabilities; however, the technical skills necessary to develop such simulation codes are not taught as part of Heliophysics training/PhD programs. ●Developing exascale Heliophysics codes will require transdisciplinary collaborations between physicists, computer scientists, software engineers, data scientists, and applied mathematics. Such teams must be persistent and formed around specific skills, not specific problems. ●We must have stable, positive long-term career outcomes for Heliophysics scientific developers in order to retain knowledge of and promote future innovation in scientific algorithm development and advanced computing techniques. One such way to do this is by funding long-term scientific programming groups, akin to Heliophysics DRIVE Centers. ●We must have a balance between promoting innovation via funding short-term closed-source development and allowing the open-source community to benefit from and build on the newest scientific modeling techniques.

C Bard↗