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At least 1,027 records · Page 57

In-Situ Resource Utilization for Space Exploration: Resource Processing, Mission-Enabling Technologies, and Lessons for Sustainability on Earth and Beyond

As humanity begins to reach out into the solar system, it has become apparent that supporting a human or robotic presence in transit andor on station requires significant expendable resources including consumables (to support people), fuel, and convenient reliable power. Transporting all necessary expendables is inefficient, inconvenient, costly, and, in the final analysis, a complicating factor for mission planners and a significant source of potential failure modes. Over the past twenty-five years, beginning with the Space Exploration Initiative, researchers at the NASA Glenn Research Center (GRC), academic collaborators, and industrial partners have analyzed, researched, and developed successful solutions for the challenges posed by surviving and even thriving in the resource limited environment(s) presented by near-Earth space and non-terrestrial surface operations. In this retrospective paper, we highlight the efforts of the co-authors in resource simulation and utilization, materials processing and consumable(s) production, power systems and analysis, fuel storage and handling, propulsion systems, and mission operations. As we move forward in our quest to explore space using a resource-optimized approach, it is worthwhile to consider lessons learned relative to efficient utilization of the (comparatively) abundant natural resources and improving the sustainability (and environment) for life on Earth. We reconsider Lunar (and briefly Martian) resource utilization for potential colonization, and discuss next steps moving away from Earth.

Specific impulse↗

Incorporation of Human Risk Directed Acyclic Graphs (DAG) With Mishap Investigations to Un-Silo Knowledge

NASA’s Human System Risk Board (HSRB) has been a central driver in efforts to understand, mitigate, and communicate the 29 human systems risks monitored by the board. As a result of the collaboration between research, operations, and technical authorities, large bodies of knowledge have been collected and digested to represent the current understanding of the risks. As a part of these bodies of knowledge, directed acyclic graphs (DAGs) have been developed to communicate the current understanding of the causal relationship of the hazards, contributing factors, countermeasures, other risks, and outcomes that contribute to the overall risk. This risk knowledge is applied in a theoretical sense for potential incidents during exploration even while informed by surveillance data. However, there have been mishaps and close calls during past space exploration that intersect with one or more of the Human System Risks DAGs and knowledge bases. The purpose of this exercise was to un-silo this risk knowledge and connect it to the close call of EVA 23 through the development of a DAG representing the intersection of the HSRB Risks and the events of the close call. The development of the DAG occurred through an iterative process, with each iteration expanding and/or refining the nodes and connections described by the source materials. In addition to the risk documentation developed by the HSRB, lessons learned and other mishap investigation documents were utilized to understand the events that led to water entering the helmet of a crewmember on the EVA. New nodes specific to the events of EVA 23 were interconnected with existing HSRB DAG nodes and edges. Nodes within the DAG were defined within a “DAG-tionary” with any updates to a definition that may have previously existed from the HSRB DAGs, and edges were recorded in a matrix. Both the DAG-tionary and matrix describe where nodes and edges are present across the Risk and Mishap DAG. This DAG will then be reviewed by experts outside of HSRB and HRP to confirm that interpretations of the non-health related events (such as the engineering nodes) are represented accurately. DISCUSSION This process highlighted a method by which the knowledge generated among the contributing members of the HSRB Risks can be effectively adapted and utilized through the tools employed by the Risk Custodian teams. By leveraging these tools, new context and insights to the information at hand can be brought forward to address current spaceflight challenges. Moreover, un-siloing this knowledge through future DAGs and other efforts can drive interprofessional collaboration and foster communication. This will enable teams to work together more effectively, leveraging their diverse expertise to tackle the complex challenges of space exploration and human research. Ultimately, this collaboration will bring NASA closer to achieve agency goals and contribute to the overall shared mission and vision.

Samuel Jacobs↗

CAMAS 2025: Continuing to Advance Arctic Marine Science

The Consortium for the Advancement of Marine Arctic Science (CAMAS) held its second annual Workshop and Early-Career School in Seattle, WA, on April 15-18, 2025. The workshop attracted 74 participants, including a dozen scientists from Europe (8) and Asia (4). The goal of CAMAS is to facilitate and enhance international collaboration on marine Arctic science, in order to advance the understanding and model representation of key marine Arctic processes that contribute to the rapid changes in the Arctic Earth system. These rapid changes have profound impacts on operations in the Arctic, including those associated with the national and energy security of the United States. The Early-Career School started the event on Tuesday April 15. Thirty-three early-career scientists (postdocs and students) gathered for lectures and discussions on topics like high-resolution Arctic Ocean and sea ice modeling; biogeochemistry of the Arctic; Machine Learning for Arctic Earth system modeling; and an Arctic perspective on geo-engineering.

54 ENVIRONMENTAL SCIENCES↗

Artificial Intelligence for Data Center Operations (AIOps): Cooperative Research and Development (Final Report)

High performance computing data centers will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence and machine learning approaches provide the means to improve HPC data center operational efficiency, by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. NREL has developed methods of real-time collection, aggregation and streaming of these data in the ESIF HPC Data Center and has collected a significant dataset of relevant metrics across computer systems, racks, environmental, building and utility sources for research into various predictive analytics problems. HPE's Advanced Technology Group (ATG) is doing comprehensive research into exascale monitoring and management for High Performance Computing (HPC) systems (hereinafter HPE's Data Monitoring/ Management Technology). NREL and HPE will collaborate to add Artificial Intelligence (AI) to NREL's real-time data collection/ aggregation/ streaming system and HPE's Data Monitoring/ Management System, with the goal of improving the operational efficiency of NREL's Energy Systems Integration Facility (ESIF) HPC Data Center through data analytics on both historical and real-time data from IT systems and facilities operations. This collaboration will consist of efforts in Data Management, Data Analytics, and AI/ML Optimization for both manual and autonomous intervention in data center operations. This will be a multi-year, multi-staged effort with a goal towards building capabilities for an Advanced Smart Facility, and demonstration of these techniques in the NREL ESIF HPC Data Center.

97 MATHEMATICS AND COMPUTING↗

Constraints on the Physical Properties of GW190814 through Simulations Based on DECam Follow-up Observations by the Dark Energy Survey

On 2019 August 14, the LIGO and Virgo Collaborations detected gravitational waves from a black hole and a 2.6 solar mass compact object, possibly the first neutron star–black hole merger. In search of an optical counterpart, the Dark Energy Survey (DES) obtained deep imaging of the entire 90% confidence level localization area with Blanco/DECam 0, 1, 2, 3, 6, and 16 nights after the merger. Objects with varying brightness were detected by the DES Pipeline, and we systematically reduced the candidate counterparts through catalog matching, light-curve properties, host-galaxy photometric redshifts, Southern Astrophysical Research spectroscopic follow-up observations, and machine-learning-based photometric classification. All candidates were rejected as counterparts to the merger. To quantify the sensitivity of our search, we applied our selection criteria to full light-curve simulations of supernovae and kilonovae as they would appear in the DECam observations. Because the source class of the merger was uncertain, we utilized an agnostic, three-component kilonova model based on tidally disrupted neutron star (NS) ejecta properties to quantify our detection efficiency of a counterpart if the merger included an NS. We find that, if a kilonova occurred during this merger, configurations where the ejected matter is greater than 0.07 solar masses, has lanthanide abundance less than 10 -8.56 , and has a velocity between 0.18c and 0.21c are disfavored at the 2σ level. Furthermore, we estimate that our background reduction methods are capable of associating gravitational wave signals with a detected electromagnetic counterpart at the 4σ level in 95% of future follow-up observations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Expedition Memory: Towards Agent-based Web Services for Creating and Using Mars Exploration Data.

Explorers ranging over kilometers of rugged, sometimes "feature-less" terrain for over a year could be overwhelmed by tracking and sharing what they have done and learned. An automated system based on the existing Mobile Agents design [ I ] and Mars Exploration Rover experience [2], could serve as an "expedition memory" that would be indexed by voice as wel1 as a web interface, linking people, places, activities, records (voice notes, photographs, samples). and a descriptive scientific ontology. This database would be accessible during EVAs by astronauts, annotated by the remote science team, linked to EVA plans, and allow cross indexing between sites and expeditions. We consider the basic problem, our philosophical approach, technical methods, and uses of the expedition memory for facilitating long-term collaboration between Mars crews and Earth support teams. We emphasize that a "memory" does not mean a database per se, but an interactive service that combines different resources, and ultimately could be like a helpful librarian.

Clancey, William J.↗

The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update

Galaxy (https://galaxyproject.org) is deployed globally, predominantly through free-to-use services, supporting user-driven research that broadens in scope each year. Users are attracted to public Galaxy services by platform stability, tool and reference dataset diversity, training, support and integration, which enables complex, reproducible, shareable data analysis. Applying the principles of user experience design (UXD), has driven improvements in accessibility, tool discoverability through Galaxy Labs/subdomains, and a redesigned Galaxy ToolShed. Galaxy tool capabilities are progressing in two strategic directions: integrating general purpose graphical processing units (GPGPU) access for cutting-edge methods, and licensed tool support. Engagement with global research consortia is being increased by developing more workflows in Galaxy and by resourcing the public Galaxy services to run them. The Galaxy Training Network (GTN) portfolio has grown in both size, and accessibility, through learning paths and direct integration with Galaxy tools that feature in training courses. Code development continues in line with the Galaxy Project roadmap, with improvements to job scheduling and the user interface. Environmental impact assessment is also helping engage users and developers, reminding them of their role in sustainability, by displaying estimated CO 2 emissions generated by each Galaxy job.

97 MATHEMATICS AND COMPUTING↗

Gen IV Education and Training Working Group Webinars’ Initiative

Collaboration and support among national laboratories, industry, universities, and research and development organizations are vital to not only maintain a skilled and competent nuclear workforce but also to avert the risk of human resource shortages. As stated in a recent IAEA paper [1], the world nuclear electrical generating capacity is projected to increase to 554 GW(e) by 2030 and up to 874 GW(e) by 2050 (Fig. 1). This represents a 42% increase over current levels by 2030 and a doubling of the current capacity by 2050. To promote Education and Training (E&T) on Gen IV reactor systems and other nuclear related topics of interest, the Gen IV Education and Training Working Group (ETWG) identifies and advertises training courses; engages collaboration with other international education and training organizations; delivers webinars dedicated to Gen IV systems; and maintains a modern social medium platform to exchange information and ideas on Gen IV R&D topics, as well as on related GIF education and training activities. At the end of June 2020, the GIF ETWG has produced, podcasted and posted forty-one webinars covering the six Gen IV systems and various subjects addressing e.g. the economics of the nuclear fuel cycle, sustainability aspects of Gen IV systems, nuclear fuels and materials challenges, the thorium fuel cycle, energy conversion systems, and lessons learned for knowledge management and preservation. This paper describes the development of these webinars from the initial concept to its full realization, with future webinars planned in 2021, addressing topics such as materials and fuels performance for advanced reactors, small modular reactor.

Paviet, Patricia D.↗

Advanced Analytics and Big Earth Data

NASA's Earth Science Data Systems process, archive and distribute petabytes of Earth Observation data to a variety of end users. These end users will face dramatically increased data size in the near future, bringing about new challenges and opportunities in analyzing those data. One area of particular ferment currently is Machine Learning. Many Machine Learning methods are black boxes, limiting direct insight into the data's properties. However, they can be used for a variety of data enhancement purposes, such as parameter retrieval, data fusion and image classification and segmentation. The Earth Observing System Data and Information System is also evolving to host large data volumes in the cloud, enabling data proximal analysis. As part of this effort, an Analytics framework is being developed to support and enhance user analysis of the data. By using standards based services in the framework, diverse user communities can be served, while also allowing inter-system collaboration in the analysis process.

Cloud Computing↗

BETO 2021 Peer Review - Inverse Bioproduct Design Through Machine Learning and Molecular Simulation

This work aims to identify performance advantaged bioproducts (PABPs) through property prediction, which will guide experimental synthesis. The impact of this work will be faster market adoption of bioproducts with greater performance relative to incumbent products. We have identified >106 bioproduct candidates, but only some will have superior performance to create a market pull. High-throughput property prediction, enabled by machine learning, and elucidation of structure-function relationships, enabled by molecular simulation, provide a hypothesis driven approach for down selection of candidate biomolecules to pursue experimentally. To enable machine learning and molecular simulation for bioproduct discovery, automated structure generation and embedding must capture relevant features for prediction, databases must cover domains applicable to biobased products, and best practices for simulation of polymer systems must be developed. To address these challenges, we have established bioproduct relevant datasets, developed high-throughput polymer structure generation, and built end-to-end neural networks that have predicted 8 properties for >1.4 x 106 biopolymers. A molecular simulation pipeline for building, running, and analyzing polymers and polymer additives is being used to predict performance and develop design principles of biobased products. In collaboration with the PABP synthesis project, these computational tools are guiding synthesis and informing design of PABPs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

NREL Marine Energy Desalination Research and Development Portfolio: Preprint

The National Renewable Energy Laboratory (NREL), in collaboration with the U.S. Department of Energy's (DOE's) Water Power Technologies Office (WPTO), has developed a unique R&D approach to advance marine energy desalination. Desalination is a foundational investment within WPTO's Powering the Blue Economy portfolio, and was the first investment within this portfolio. NREL's marine energy desalination spans techno-economic feasibility studies, numerical modeling, and laboratory testing at the component and subsystem level, as well as development of the Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC). This multilayered approach enables an innovative feedback loop where the data and lessons learned from laboratory and field experiments are used to refine modeling tools and analysis techniques, prioritize out-year activities, and refine strategic directions within NREL and across the WPTO portfolio. The primary objective of the NREL-led research is to identify key barriers associated with the commercialization of wave-powered desalination and develop solutions that can be adopted by the marine energy industry. In parallel, these R&D activities can help inform technical assistance and support of industry and academic technologies. These two tracks help build a common solver community approach, while also identifying key stakeholders, government agencies, and other organizations outside of the marine sector that will be necessary for developing a robust industry. For the Spanish version of this report, see NREL/CP-5700-88483 (https://www.nrel.gov/docs/fy24osti/88483.pdf).

analysis↗

Developing Tomorrows Nuclear Professionals - Apprentice and Graduate Development at PFCS, Sellafield - 19013

The Pile Fuel Cladding Silo (PFCS) at Sellafield, originally built in 1949/50 to receive and store fuel cladding from reprocessing Windscale Pile fuel, has been used to store this material and other waste from the UK nuclear programme ever since. However, the Silo is unsuitable for indefinite storage of the waste; its age, condition and inventory, combined with significant uncertainty in all these factors, rank the facility among the most hazardous in Western Europe. In 2012, Sellafield Ltd began a highly collaborative relationship with its supply chain partners Bechtel Cavendish Nuclear Solutions (BCNS), to undertake a suite of projects to create a capability for retrieving waste. In doing so, the team has discovered a shared passion for graduate student and apprenticed trades development, which has led to the team goal of becoming the number one destination for training secondments within the associated businesses. This paper describes how the team has leveraged the unique challenge of the project, the extensive experience of the delivery team, and the capabilities of the contributing organisations to create a structure secondment experience aimed at maximizing learning and generating enthusiasm for nuclear clean-up. The scheme was developed and implemented, based on some very simple but consistent feedback from the trainee community on what differentiates a good and bad secondment. The paper includes three case studies from individuals who have participated in the programme and the analysis of overall feedback from all programme trainees. Universally, the scheme has been well received with a significant number of trainees coming back to the project to begin their professional careers. The simple differentiators between a good and bad secondment provide a good basis for those wishing to develop similar schemes. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Battery Storage Unlocked: Lessons Learned From Emerging Economies

The Clean Energy Ministerial (CEM) is a global forum that promotes policies and programs that advance clean energy technology. The CEM's mission is to bring together a community of global leaders to scale clean energy, amplifying the impact to all the sectors of the economy and applying a whole-of-society approach to meet collective climate and clean energy goals. At COP28 in Dubai, United Arab Emirates, the CEM announced the Supercharging Battery Storage Initiative as a vehicle to accelerate battery storage deployment around the world. The initiative supports countries around the world in co-creating strategies that enhance policy, regulation, supply chain, manufacturing, and financing solutions for battery energy storage deployment. Additionally, the initiative seeks to reduce the cost of the technology and promote diversified, sustainable, and secure supply chains (CEM n.d.). Through international collaboration, the initiative supports the integration of renewable energy globally, while securing the stability and reliability of the electricity grid.

batteries↗

NASA Software Engineering Benchmarking Study

To identify best practices for the improvement of software engineering on projects, NASA's Offices of Chief Engineer (OCE) and Safety and Mission Assurance (OSMA) formed a team led by Heather Rarick and Sally Godfrey to conduct this benchmarking study. The primary goals of the study are to identify best practices that: Improve the management and technical development of software intensive systems; Have a track record of successful deployment by aerospace industries, universities [including research and development (R&D) laboratories], and defense services, as well as NASA's own component Centers; and Identify candidate solutions for NASA's software issues. Beginning in the late fall of 2010, focus topics were chosen and interview questions were developed, based on the NASA top software challenges. Between February 2011 and November 2011, the Benchmark Team interviewed a total of 18 organizations, consisting of five NASA Centers, five industry organizations, four defense services organizations, and four university or university R and D laboratory organizations. A software assurance representative also participated in each of the interviews to focus on assurance and software safety best practices. Interviewees provided a wealth of information on each topic area that included: software policy, software acquisition, software assurance, testing, training, maintaining rigor in small projects, metrics, and use of the Capability Maturity Model Integration (CMMI) framework, as well as a number of special topics that came up in the discussions. NASA's software engineering practices compared favorably with the external organizations in most benchmark areas, but in every topic, there were ways in which NASA could improve its practices. Compared to defense services organizations and some of the industry organizations, one of NASA's notable weaknesses involved communication with contractors regarding its policies and requirements for acquired software. One of NASA's strengths was its software assurance practices, which seemed to rate well in comparison to the other organizational groups and also seemed to include a larger scope of activities. An unexpected benefit of the software benchmarking study was the identification of many opportunities for collaboration in areas including metrics, training, sharing of CMMI experiences and resources such as instructors and CMMI Lead Appraisers, and even sharing of assets such as documented processes. A further unexpected benefit of the study was the feedback on NASA practices that was received from some of the organizations interviewed. From that feedback, other potential areas where NASA could improve were highlighted, such as accuracy of software cost estimation and budgetary practices. The detailed report contains discussion of the practices noted in each of the topic areas, as well as a summary of observations and recommendations from each of the topic areas. The resulting 24 recommendations from the topic areas were then consolidated to eliminate duplication and culled into a set of 14 suggested actionable recommendations. This final set of actionable recommendations, listed below, are items that can be implemented to improve NASA's software engineering practices and to help address many of the items that were listed in the NASA top software engineering issues. 1. Develop and implement standard contract language for software procurements. 2. Advance accurate and trusted software cost estimates for both procured and in-house software and improve the capture of actual cost data to facilitate further improvements. 3. Establish a consistent set of objectives and expectations, specifically types of metrics at the Agency level, so key trends and models can be identified and used to continuously improve software processes and each software development effort. 4. Maintain the CMMI Maturity Level requirement for critical NASA projects and use CMMI to measure organizations developing software for NASA. 5.onsolidate, collect and, if needed, develop common processes principles and other assets across the Agency in order to provide more consistency in software development and acquisition practices and to reduce the overall cost of maintaining or increasing current NASA CMMI maturity levels. 6. Provide additional support for small projects that includes: (a) guidance for appropriate tailoring of requirements for small projects, (b) availability of suitable tools, including support tool set-up and training, and (c) training for small project personnel, assurance personnel and technical authorities on the acceptable options for tailoring requirements and performing assurance on small projects. 7. Develop software training classes for the more experienced software engineers using on-line training, videos, or small separate modules of training that can be accommodated as needed throughout a project. 8. Create guidelines to structure non-classroom training opportunities such as mentoring, peer reviews, lessons learned sessions, and on-the-job training. 9. Develop a set of predictive software defect data and a process for assessing software testing metric data against it. 10. Assess Agency-wide licenses for commonly used software tools. 11. Fill the knowledge gap in common software engineering practices for new hires and co-ops.12. Work through the Science, Technology, Engineering and Mathematics (STEM) program with universities in strengthening education in the use of common software engineering practices and standards. 13. Follow up this benchmark study with a deeper look into what both internal and external organizations perceive as the scope of software assurance, the value they expect to obtain from it, and the shortcomings they experience in the current practice. 14. Continue interactions with external software engineering environment through collaborations, knowledge sharing, and benchmarking.

Rarick, Heather L.↗

FY20 Proxy App Suite Release: Report for ECP Proxy App Project Milestone ADCD-504-10

Version 4.0 of the ECP Proxy App Suite is practically unchanged from the previous release. The current set of proxies has proven useful for many aspects of benchmarking and co-design and we see little reason to alter the suite. Although there have been few changes to the ECP suite, the team has been hard at work in other areas. In the area of Machine Learning (ML) we have now created a separate proxy suite dedicated to this scientific applications of ML. The suite includes: miniGAN (Generative Adversarial Networks), miniRL (Reinforcement Learning), CRADL (inline inference), Cosmoflow-Benchmark (Convolutional Neural Network), and MLPerf-DeepCam (Climate Segmentation Benchmark). Section 3 contains more information about these proxies as well as the principles that are guiding the development of the suite. We have surveyed available proxies for several application domains including Computational Fluid Dynamics, Quantum Chemistry, Quantum Computing Simulation, Molecular Dynamics, Monte Carlo Transport, and Density Functional Theory to identify gaps in proxy coverage. Several new proxy apps are either already available or will be released soon to fill these gaps. Section 4 provides full details. Finally, section 5 reports on our continued collaboration with the ECP Continuous Integration (CI) effort to use proxy apps to help identify problems and roadblocks to cross-lab CI. We have also assisted the El Capitan Center of Excellence (COE) to stand up a CI system that will be used to test releases of HPE and AMD software stacks. We hope that the COE effort can serve as a model for ECP by showing how the Proxy App Team can work with AD and ST teams identify critical features, kernels, patterns, etc. and incorporate them into a CI system that will help ensure that Frontier and Aurora will provide those needed capabilities.

97 MATHEMATICS AND COMPUTING↗

A Review of Basic Energy Reconstruction Techniques in Liquid Xenon and Argon Detectors for Dark Matter and Neutrino Physics Using NEST

Detectors based upon the noble elements, especially liquid xenon as well as liquid argon, as both single- and dual-phase types, require reconstruction of the energies of interacting particles, both in the field of direct detection of dark matter (weakly interacting massive particles WIMPs, axions, etc.) and in neutrino physics. Experimentalists, as well as theorists who reanalyze/reinterpret experimental data, have used a few different techniques over the past few decades. In this paper, we review techniques based on solely the primary scintillation channel, the ionization or secondary channel available at non-zero drift electric fields, and combined techniques that include a simple linear combination and weighted averages, with a brief discussion of the application of profile likelihood, maximum likelihood, and machine learning. Comparing results for electron recoils (beta and gamma interactions) and nuclear recoils (primarily from neutrons) from the Noble Element Simulation Technique (NEST) simulation to available data, we confirm that combining all available information generates higher-precision means, lower widths (energy resolution), and more symmetric shapes (approximately Gaussian) especially at keV-scale energies, with the symmetry even greater when thresholding is addressed. Near thresholds, bias from upward fluctuations matters. For MeV-GeV scales, if only one channel is utilized, an ionization-only-based energy scale outperforms scintillation; channel combination remains beneficial. We discuss here what major collaborations use.

argon↗

Validation of the Version 1 NOAA/NASA Pathfinder Sea Surface Temperature Data Set

A high-resolution, global satellite-derived sea surface temperature (SST) data set called Pathfinder, from the Advanced Very High Resolution Radiometer (AVHRR) aboard the NOAA Polar Orbiters, is available from the Jet Propulsion Laboratory Physical Oceanography Distributed Active Archive Center (JPL PO.DAAC). Suitable for research as well as education, the Pathfinder SST data set is a result of a collaboration between the National Oceanographic and Atmospheric Administration (NOAA), the National Aeronautics and Space Administration (NASA) and investigators at several universities. NOAA and NASA are the sponsors of the Pathfinder Program, which takes advantage of currently archived Earth science data from satellites. Where necessary, satellite sensors have been intercalibrated, algorithms improved and processing procedures revised, in order to produce long time-series, global measurements of ocean, land and atmospheric properties necessary for climate research. Many Pathfinder data sets are available to researchers now, nearly a decade before the first launch of NASA's Earth Observing System (EOS). The lessons learned from the Pathfinder programs will facilitate the processing and management of terabytes of data from EOS. The Oceans component of Pathfinder has undertaken to reprocess all Global Area Coverage (GAC) data acquired by the 5-channel AVHRRs since 1981. The resultant data products are consistent and stably calibrated [Rao, 1993a, Rao, 1993b, Brown et al., 1993], Earth-gridded SST fields at a variety of spatial and temporal resolutions.

Smith, Elizabeth A.↗

Feature Selection in High-Dimensional Space with Applications to Gene Expression Data

Recent years have seen rapid growth in high-dimensional datasets. Most existing machine learning (ML) algorithms fail in high-dimensional settings where many features could be redundant. A critical process of feature selection is thus applied in such a setting that helps in identifying the most relevant features while removing redundant ones. With the increase in high dimensionality, one is also faced with problems of efficiency and interpretation in performing such selection methods. Therefore, this paper proposes a “novel” feature selection framework that uses an ensemble of interpretable ML algorithms to perform feature selection and the ranking of final features. Finally, this framework is applied to a gene expression dataset obtained through collaboration with the National Aeronautics and Space Administration (NASA)’s Biological and Physical Sciences (BPS) team and helps identify important and relevant genes contributing to specific target attributes through classification tasks.

Nishan Pantha↗