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NASA NTRS2019

Generating Excitement and Increasing Awaressness of NASA Planetary Science and Astromaterials Assets

Students, educators, the public, and the scientific community are so often inspired by NASA science and exploration. Millions have joined NASA during live mission event broadcasts and also follow NASA on social media. Exploration of worlds in our solar system enable the scientific community to obtain and analyze data that provide clues to better understand the history and evolution of our solar system. Missions that collect and return samples to Earth from a target solar system body provide scientists with samples they can research and analyze in their laboratories. For those who are not planetary scientists, they may not understand the significance of these samples and/or the importance of sample return missions. The Astromaterials Research and Exploration Science (ARES) Science Engagement team, through work supported by NASA’s Science Mission Directorate (SMD) Science Education Cooperative Agreement Notice NNH15ZDA004C, provides access to samples from NASA’s Astromaterials Collections through NASA sponsored exhibits at educator and scientific conferences, NASA relevant public outreach events, and collaborations with other Science Activation Teams supported by the SMD Cooperative Agreement Notice. The goal of this work is to generate excitement while enhancing knowledge and awareness of NASA’s unique assets, thus highlighting NASA planetary science and exploration.

Graff, P. V.↗
NASA NTRS2018

Aurorasaurus Database of Real-Time, Crowd-Sourced Aurora Data for Space Weather Research

This technical report documents the details of Aurorasaurus citizen science data for the period spanning 2015 and 2016 as well as its routine data filtering protocols. Aurorasaurus citizen science data is a collection of auroral sightings submitted to the project via its website or apps and mined from social media. It is a robust data set and particularly abundant during strong geomagnetic storms when auroral precipitation models have the highest uncertainty. These data are offered to the scientific community for use through an openaccess database in its raw and scientific formats, each of which is described in detail in this technical report. Furthermore, by demonstrating its scientific utility, we aim to encourage its integration into auroral research.

Citizen science↗
NASA NTRS2019

Building Foundations for International Collaboration Through Educational Outreach Initiatives

As human space exploration pursues journeying to the Moon and Mars, international and intercultural collaboration is key for the success of such ambitious goals. Since 2014, NASA's Johnson Space Center (JSC) Hispanic Employee Resource Group (HERG) has established relationships and participated in outreach events with emerging space organizations in Latin America with educational and public outreach as a common goal. Such efforts align with strategic goals to empower the next generation of innovators and explorers and to champion the development of space exploration capabilities. This paper discusses the partnerships developed to introduce Spanish speaking populations to science, space technology, aeronautics, research and development through television, webcasts and social media. The authors share accomplishments, challenges, and lessons learned to build foundations for international collaboration to ignite research and innovation and propel human space exploration. The methodology makes use of Space Act Agreements to leverage existing outreach materials and programs and translate the content into Spanish for the benefit of students, educational organizations and the public in general. The plan is to apply the same model in other regions and countries to create a Latin American network of language translation and diffusion of science and space technology content targeting emerging space sectors. This model could be used to reach out to other international communities to provide cultural and educational outreach for space exploration and research.

Fontanot, Carlos↗
NASA NTRS2019

Citizen Science Twitter Data Management for Earth Science Applications

Social media data can provide useful real-time and historical information relating to the natural world, but managing this data poses challenges. Scientists at GES DISC are exploring the potential of Twitter data to augment precipitation data from the Global Precipitation Measurement (GPM) mission. However, the format of Twitter data is unconventional in the context of NASA data centers, resulting in frustration for scientists who need to work with the data. This study investigated procedures and standards needed to properly manage Twitter data to make them compatible with these data centers. After comparing databases, the study found that the MongoDB database was best suited for the storage of raw Twitter data due to its flexibility, ability to be accessed by multiple users, and querying functionality. The study used the Python package Zarr to transform processed Twitter data into a gridded format similar to that of satellite data. Each Tweet was mapped onto a time-space grid; each grid location contained information about Tweet attributes and precipitation. The study developed a pipeline for downloading, storing, and gridding Twitter data and transformed Twitter data into an understandable format for users of NASA satellite data.

Li, Rachel↗
NASA NTRS2019

Developing a Machine-Learning-Based Processing Framework for Twitter and Other Crowdsourced Data

Crowdsourced data streams such as Twitter and other social media are important sources of real-time and historical global information for Earth science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we have been exploring the Twitter data stream for its potential in augmenting the validation program of NASA's Global Precipitation Measurement (GPM) mission. To realize this potential, we need to increase the information density and enhance the quality of filtered precipitation tweets. We have implemented various components of a machine learning (ML)-based processing infrastructure for crowdsourced data that outputs, in this instance, useful and usable information derived from precipitation tweets. We have test enriched the Twitter stream with higher quality active tweets from those knowingly contributing to our effort and from existing crowdsourced programs (e.g., mPING, CoCoRaHS). We have experimented with various algorithms for processing tweets, including Naà ve Bayes, Convolutional Neural Network (CNN), Hierarchical Attention Network (HAN), and semi-supervised learning (with tri-training). Our current work focuses on (1) automated review of Earth science-related publications to determine relationships between discipline research needs and ML algorithms; (2) investigating Sequential Generative Adversarial Network (SeqGAN) for processing precipitation tweets for anomaly detection; and (3) managing crowdsourced data in a way that is compatible with existing NASA satellite data archives and using the data for ML applications. Key results include (1) network visualization of NLP-processed publications in various Earth science disciplines; (2) difference between GPM-linked, generated tweets and collected actual tweets that is small for GPM-determined light to moderate rain cases and high for GPM-determined heavy rain cases; and (3) identification of MongoDB for storing raw tweets and Zarr format for gridded tweets (compatible with GPM data). Our results have taken us a step closer to an operational ML-based tweet processing infrastructure and have already demonstrated that tweet-derived precipitation information is potentially useful for validation of Earth science satellite data.

Teng, William↗
NASA NTRSDate not supplied

Artificial Neural Networks and AI in high Assurance Applications: Gaps and Techniques

In recent years, capabilties of Deep Neural Networks (DNN) and Artificial Intelligence (AI) systems have grown tremendously. They are now applied in many areas ranging from game playing, social media, science, to robotics, automotive, and aerospace applications.Based upon requirements for safety of DNN and AI in high assurance automotive and aerospace applications, I will discuss the necessity to ensure that AI technqiues for the analysis of Earth observation data and reasoning are working correctly and reliably.In this talk I will present modern techniques for the verification and validation (V&V) of DNN and other AI components as well as approaches for interpretable AI. I will discuss how these techniques can help to ensure quality of the AI results, improve confidence in their application, and facilitate human-AI interaction and collaboration.

Johann Schumann↗
NASA NTRSDate not supplied

A Worldwide Celebration: International Observe the Moon Night

International Observe the Moon Night is a worldwide public engagement program that has been held annually since 2010. Every autumn, we ask people to observe the Moon in whatever way makes sense to them (via binoculars, telescopes, unaided eye, images, artwork, songs, stories, etc.). The event occurs when the Moon is in or near a first-quarter phase, which provides excellent viewing opportunities along the terminator (the line between night and day), as long shadows place lunar features into great relief. Hundreds of thousands of individuals all around the globe participate in the event as a collective whole, learning about lunar science and exploration, taking part in celestial observations, and honoring cultural and personal connections to the Moon. People participate in a variety of ways, including hosting or attending virtual or in-person events and observing the Moon from home. Participants also have the opportunity to connect with other lunar observers around the world through our Facebook page (facebook.com/observethemoon/), our Flickr group for images (flickr.com/groups/observethemoon2021/), and through the hashtag #ObserveTheMoon across social media platforms.

S L Tiedeken↗
NASA NTRSDate not supplied

NASA DEVELOP’s Communication Strategy for Sharing Applied Science Outcomes

NASA’s DEVELOP Program conducts rapid feasibility projects that enable the future workforce and current decision makers to collaborate and build capacity to use Earth science data to enhance environmental management. The program communicates its results and applications to a broad audience through multiple methods: social media, website blog posts, print materials, short project videos, an “Ambassador Corps,” scientific and policy conference presentations, community engagement activities, end-of-project presentations, project “hand-offs,” traditional publications (both gray literature and peer-reviewed), and a project archive gallery. This presentation will describe the various methods employed by DEVELOP to communicate the program’s scientific outputs, target audiences, general statistics, community response, and lessons learned.

Capacity Building↗
NASA NTRS2022

Bringing Planetary Science Mission Outreach to the Deaf and Blind Communities

Introduction: Technology for enhancing outreach, like 3D printing, and science communication products, such as videos and podcasts, can be utilized within the planetary science community, especially for the engagement and excitement of current or upcoming planetary exploration missions. However, these communication products can also be further enhanced for the benefit of the blind and deaf communities. While such products may already be readily available, such projects are not easily accessible to blind and/or deaf certified educators, which often rely on making their own resources or do not have the funds to provide such resources (e.g., cost of 3D printers or cost of braille books). The planetary science community can have better practices to reach these broader audiences. Best practices can include transcripts from podcasts, transcripts in videos, and large-font captions. Images on websites and social media accounts should also include alt-text descriptive captions. 3D printing can also enhance planetary science for the blind community, through tactile posters, maps, and pamphlets. Planetary data can also be augmented by providing different tactile geological maps (e.g., topography or various datasets), and audio-visual videos freely available for educators. Visual Engagement: Visual engagement consists of several avenues to consider, the three main themes includes: 1) swag; 2) videos; 3) interactive exploration. Swag can include the fun visual take-home materials, such as stickers, posters, bookmarks, etc. Videos can include educational-specific videos (available freely via YouTube or by other educational-specific streaming avenues, such as Nebula or Curiosity Stream), provided they have Closed Captioning (CC). The use of QR codes to such videos or websites can also benefit to being added on swag. Interactive exploration can also be sub-divided by different types of engagement. A popular and still fairly new technology for public engagement is the use of virtual reality (VR). While this has been mainly for martian and lunar surface exploration [1], the deaf communities can benefit from VR through a more extensive look at our solar system and beyond (for example, a VR experience of the flight path, or visual map of the heliosphere/dynamics of our Sun). Audio Engagement: Audio tools can also be a useful avenue of communication, especially for the blind communities. Audio archiving can certainly be transcripts from the video engagements, but also the use of podcasts can also be a benefit. Podcasting can take on two forms: 1) interview engagement; and 2) update engagement. For interviews, scientists can communicate with STEM-specific podcast platforms to make other listener-bases aware of what is going on with a specific mission. For update-type communication, missions may opt to have an archived podcast of news, updates, and the teams involved. The most important aspect of such podcasts would be for the need of complimentary transcripts (including descriptive transcripts if sounds are included), and the limited use of jargon. Research Engagement: There have been several examples of involving the blind and low-vision communities in citizen science, such as through the NASA Heliophysics division. Examples include the NASA PUNCH (Polarimeter to Unify the Corona and Heliosphere) mission led by the Southwest Research Institute [2], which include blind and visually-impaired citizens to assist in the Sun’s coronal rhythms.” Another example is the Eclipse Soundscapes: Citizen Science Project (ES:CSP), which documents observations of acoustical changes of nature and ecosystems during solar eclipse events [3]. Inclusivity: A major theme that is necessary for public engagement is inclusivity and the awareness of reaching broader audiences. Outreach to include hearing/seeing impaired communities are still lacking in the sciences. There are several opportunities that the planetary sciences could take. Other projects that have emerged from the space sciences include adding transcripts to visual engagement [4], and the use of 3D printing for the visually-impaired [5]. References: [1] Olgin, J. (2020) 51st LPSC, Abstract 2137. [2] https://scitechdaily.com/outreach-for-nasa-punch-mission-embraces-ancient-and-modern-sun-watching-theme/ [3] https://science.nasa.gov/science-activation-team/eclipse-soundscapes [4] NASA International Observe the Moon Night (Blind and Deaf Accessible), Youtube Video ( https://www.youtube.com/watch?v=neHCfg0S3-Q) [5] Richardson, J., et al. (2018) AGU Fall Meeting, Abstract ED23F-0963.

C J Ahrens↗
NASA NTRSDate not supplied

Plant Habitat 04 Citizen Outreach: The Space Chile Grow a Pepper Plant Challenge (2020-2021)

The Space Chile Grow a Pepper Plant Challenge (SCGAPPC) is a citizen outreach activity performed in conjunction with NASA technical demonstration, Plant Habitat (PH)-04 which grew a New Mexico chile pepper as the first fruit in the Advanced Plant Habitat (APH). Growing a related pepper variety from Chimayo, New Mexico, the activity tasked citizen “Space Farmers” to grow a pepper plant indoors, at home, or in controlled environments to document the details of growth, and search for solutions to growing consistently productive and flavorful peppers under these conditions. Launched in February of 2020, a scientist on the PH04 team shared an introduction on social media to provide information about the activity and instructions on how to participate. Each space farmer received an envelopecontaining seeds and a mission sticker. They conducted their own pepper growth trials to record environment and horticultural data and submit one pepper to the University of New Mexico for capsaicin analysis. This activity was available online at the onset of the COVID-19 shutdown. During 2020-2021, over 1,800 envelopes were mailed across the globe, tens of thousands of seed pouches were shared, the SCGAPPC was featured during a virtual Growing Beyond Earth Educator Workshop with the challenge of becoming a critical part of that program when students were working remotely. Citizen space farmers around the world contributed to space biology and crop production research while learning about CE agriculture. Members of the space biology community volunteered participation, skills, and interaction. An online community page provided a format for scientists, and the general publicto interact. A portion of data was documented in an online database. The primary outcome served to inform the publicabout NASA space crop production, while creating a channel to share experiences and compare results from a singlecrop growing under varying controlled environments.

Jacob Torres↗
NASA NTRSDate not supplied

Documentarian’s Support Role: Recording Active Research for Outreach

The Space Crop Production Team at NASA’s Kennedy Space Center recently conducted a campaign of parabolic flights to study the best techniques for harvesting and collecting microgreens in microgravity. During this process, the research team invited a student with scientific training to the field sites, contributing as the documentarian and aiding in recording audio visual data. The documentarian was tasked with photographing and filming the preparation of experiments for parabolic flight, hardware installation in the parabolic flight plane, the crew performing their experiments, and the harvesting hardware and samples post-flight. Through having a documentarian on the team during experiments, the processes of problem-solving, teamwork, and data collection were able to be captured. This allows for these methods to be reflected upon post-experiment as a means of improving the procedures for the future. Additionally, the picture and video evidence became data that were analyzed following the flight test. The Space Crop Production team benefited by having a documentarian present, as they were able to develop a 45 second social media video with the intent of being published through official NASA outlets. Longer videos for classroom use are also in work. In addition to outreach usage, the collection of footage and visual data the documentarian provides has the potential to be scientifically published both in video-based scientific journals, and as supplemental data in online journal publications. Capturing research in this manner helps share an unseen side of the research practices with a larger audience and increases the potential for scientific engagement and activation.

Christopher Bermudez↗
NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.

Aiden C. Szeto↗
NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.

Aiden Szeto↗
DOE OSTI2020

Performance and usability enhancements for continuous subgraph matching queries on graph-structured data

A query graph, which includes vertices and edges, represents a query on graph-structured data. The query graph is decomposed into query subgraphs. A network analysis tool performs continuous subgraph matching queries to facilitate analysis of computer network traffic, social media events, or other streams of data represented as a dynamic data graph (graph-structured data). This can help identify emerging trends in the data. Some features of the network analysis tool enhance performance by effectively utilizing distributed computing resources (including processing cores and memory at different nodes of a cluster) to speed up the process of updating the dynamic data graph and detecting matches of query subgraphs. Features of a query graph building tool enhance usability by providing intuitive ways to specify query graphs and their subgraphs. Features of a results visualization tool enhance usability by providing an intuitive way to present the results of continuous subgraph matching queries.

Choudhury, Sutanay↗
DOE OSTI2021

Deepfaked online content is highly effective in manipulating people’s attitudes and intentions

In recent times, disinformation has spread rapidly through social media and news sites, biasing our (moral) judgements of other people and groups. “Deepfakes”, a new type of AI-generated media, represent a powerful new tool for spreading disinformation online. Although Deepfaked images, videos, and audio may appear genuine, they are actually hyper-realistic fabrications that enable one to digitally control what another person says or does. Given the recent emergence of this technology, we set out to examine the psychological impact of Deepfaked online content on viewers. Across seven preregistered studies (N = 2558) we exposed participants to either genuine or Deepfaked content, and then measured its impact on their explicit (self-reported) and implicit (unintentional) attitudes as well as behavioral intentions. Results indicated that Deepfaked videos and audio have a strong psychological impact on the viewer, and are just as effective in biasing their attitudes and intentions as genuine content. Many people are unaware that Deepfaking is possible; find it difficult to detect when they are being exposed to it; and most importantly, neither awareness nor detection serves to protect people from its influence. All preregistrations, data and code available at osf.io/f6ajb.

Hughes, Sean↗
DOE OSTI2022

Domestic Extremism: How to Counter Threats Posed to Critical Assets

Although facilities with sensitive information, hazardous materials, critical infrastructure, or other sensitive items have security measures in place to protect those critical assets, the threat of domestic extremism (DE) has challenged those security measures in new ways. The project described in this brief report used focus groups to identify best practices, tools, or techniques relevant to preventing or countering DE and sought to identify gaps in security measures. Five focus groups with twenty-two subject matter experts from government, healthcare, and other stakeholder groups were conducted to understand how organizations protect critical assets against the DE threat. The results of the focus groups underscore the importance of organizational culture, codes of conduct, and behavior observation programs in prevention of DE events. In addition, results emphasized the value of multidisciplinary teams, employee assistance programs, and regular threat assessment as resources when responding to DE incidents. However, monitoring of social media and lack of adequate resources remain challenges when attempting to prevent or counter the DE threat.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset

Face recognition technology has advanced significantly in recent years due largely to the availability of large and increasingly complex training datasets for use in deep learning models. These datasets, however, typically comprise images scraped from news sites or social media plat-forms and, therefore, have limited utility in more advanced security, forensics, and military applications. These applications require lower resolution, longer ranges, and ele-vated viewpoints. To meet these critical needs, we collected and curated the first and second subsets of a large multi-modal biometric dataset designed for use in the research and development (R&D) of biometric recognition technolo-gies under extremely challenging conditions. Thus far, the dataset includes more than 350,000 still images and over 1,300 hours of video footage of approximately 1,000 sub-jects. To collect this data, we used Nikon DSLR cameras, a variety of commercial surveillance cameras, specialized long-rage R&D cameras, and Group 1 and Group 2 UAV platforms. The goal is to support the development of algorithms capable of accurately recognizing people at ranges up to 1,000 m and from high angles of elevation. These ad-vances will include improvements to the state of the art in face recognition and will support new research in the area of whole-body recognition using methods based on gait and anthropometry. This paper describes methods used to col-lect and curate the dataset, and the dataset's characteristics at the current stage.

Brogan, Joel↗
DOE OSTI2020

Community Engagement - A Citizen-Centric Approach to Seeking a Social License - 20017

The long regulatory road has finally been completed-after years of effort a Record of Decision has been signed and a Permit to Construct has been granted or a Permit to Operate has been obtained. But what about the other road that should be taken in parallel-community engagement? Working with the public is not a sequential activity, rather it is a consequential one and needs to be integrated with overall project planning. Unless you have effectively, meaningfully, and patiently engaged with the project's communities of impact, you may find your project trying to obtain a social license or a social license to operate (SLO). The SLO has its origins in the mining industry and its roots in the business model of corporate social responsibility and sustainability. These latter practices are well known to the US DOE and its contractor community. Aspects of the SLO are emerging as individuals and communities are becoming more informed and have increased expectations for being able to influence and shape decisions. When local community issues are not sought out, listened to, or addressed early, and questions are left unanswered, they can become agenda items for larger unaffiliated groups, and project loss (through delays and/or cancellation) can occur. Issues can transition from resolvable to intractable, a type of SLO face-off. Social media campaigns, serial negative media coverage, and protest signs at project sites opposing regulatory decisions already made are no longer anomalies. These types of incidents demonstrate the increasingly delicate relationship between approved regulatory/technical decisions and public acceptance of those decisions. A SLO is not a requirement. However, the building-blocks of a SLO - working with members of affected communities to build understanding, potentially to obtain and maintain community acceptance or even gain approval or support - are evidence of leadership by project sponsors. Actions taken, or not taken, by project sponsors truly have the ability to influence an outcome. While projects are not assured of success or failure, engaging the community in an empowered process is an investment of time and resources toward success for the project and thus for the communities of impact. Authors Note: Extensive literature searches were performed in developing this paper. The majority of the published literature on SLO as a 'movement' was found from sources in Canada, the European Union (Germany and France in particular), South Asia, South Korea, and many Latin American countries. The literature addressed resource development (mining), infrastructure, and energy projects. Articles written by US sources often spoke to origins and structural theory, specific project issues such as Not in My Back Yard (NIMBY) and to protests/demonstrations related to those projects, rather than systemic opposition/conflict phenomena. For these reasons many international SLO experiences and sources are cited in the discussion. The authors recognize these non-US experiences with SLO as having a 'forecast' value to US projects, especially those related to nuclear projects of any type, waste treatment and disposal, energy development (especially fossil fuels as well as alternative sources), mining, and infrastructure. The body of future SLO experiences in the US will shape the future responses to it. (authors)

99 GENERAL AND MISCELLANEOUS↗