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NASA NTRS2016

Using Natural Language to Enhance Mission Effectiveness

The availability of highly capable, yet relatively cheap, unmanned aerial vehicles (UAVs) is opening up new areas of use for hobbyists and for professional-related activities. The driving function of this research is allowing a non-UAV pilot, an operator, to define and manage a mission. This paper describes the preliminary usability measures of an interface that allows an operator to define the mission using speech to make inputs. An experiment was conducted to begin to enumerate the efficacy and user acceptance of using voice commands to define a multi-UAV mission and to provide high-level vehicle control commands such as "takeoff." The primary independent variable was input type - voice or mouse. The primary dependent variables consisted of the correctness of the mission parameter inputs and the time needed to make all inputs. Other dependent variables included NASA-TLX workload ratings and subjective ratings on a final questionnaire. The experiment required each subject to fill in an online form that contained comparable required information that would be needed for a package dispatcher to deliver packages. For each run, subjects typed in a simple numeric code for the package code. They then defined the initial starting position, the delivery location, and the return location using either pull-down menus or voice input. Voice input was accomplished using CMU Sphinx4-5prealpha for speech recognition. They then inputted the length of the package. These were the option fields. The subject had the system "Calculate Trajectory" and then "Takeoff" once the trajectory was calculated. Later, the subject used "Land" to finish the run. After the voice and mouse input blocked runs, subjects completed a NASA-TLX. At the conclusion of all runs, subjects completed a questionnaire asking them about their experience in inputting the mission parameters, and starting and stopping the mission using mouse and voice input. In general, the usability of voice commands is acceptable. With a relatively well-defined and simple vocabulary, the operator can input the vast majority of the mission parameters using simple, intuitive voice commands. However, voice input may be more applicable to initial mission specification rather than for critical commands such as the need to land immediately due to time and feedback constraints. It would also be convenient to retrieve relevant mission information using voice input. Therefore, further on-going research is looking at using intent from operator utterances to provide the relevant mission information to the operator. The information displayed will be inferred from the operator's utterances just before key phrases are spoken. Linguistic analysis of the context of verbal communication provides insight into the intended meaning of commonly heard phrases such as "What's it doing now?" Analyzing the semantic sphere surrounding these common phrases enables us to predict the operator's intent and supply the operator's desired information to the interface. This paper also describes preliminary investigations into the generation of the semantic space of UAV operation and the success at providing information to the interface based on the operator's utterances.

Trujillo, Anna C.↗

NASA NTRS2016

Using Natural Language to Enable Mission Managers to Control Multiple Heterogeneous UAVs

The availability of highly capable, yet relatively cheap, unmanned aerial vehicles (UAVs) is opening up new areas of use for hobbyists and for commercial activities. This research is developing methods beyond classical control-stick pilot inputs, to allow operators to manage complex missions without in-depth vehicle expertise. These missions may entail several heterogeneous UAVs flying coordinated patterns or flying multiple trajectories deconflicted in time or space to predefined locations. This paper describes the functionality and preliminary usability measures of an interface that allows an operator to define a mission using speech inputs. With a defined and simple vocabulary, operators can input the vast majority of mission parameters using simple, intuitive voice commands. Although the operator interface is simple, it is based upon autonomous algorithms that allow the mission to proceed with minimal input from the operator. This paper also describes these underlying algorithms that allow an operator to manage several UAVs.

Trujillo, Anna C.↗

NASA NTRS2018

ESIP Documentation Cluster Session: GCMD Keyword Update

The Global Change Master Directory (GCMD) Keywords are a hierarchical set of controlled Earth Science vocabularies that help ensure Earth science data and services are described in a consistent and comprehensive manner and allow for the precise searching of collection-level metadata and subsequent retrieval of data and services. Initiated over twenty years ago, the GCMD Keywords are periodically analyzed for relevancy and will continue to be refined and expanded in response to user needs. This talk explores the current status of the GCMD keywords, the value and usage that the keywords bring to different tools/agencies as it relates to data discovery, and how the keywords relate to SWEET (Semantic Web for Earth and Environmental Terminology) Ontologies.

data discover↗

NASA NTRS2018

Considerations for Implementing Voice-Controlled Spacecraft Systems Through a Human-Centered Design Approach

As computational power and speech recognition algorithms improve, the consumer market will see better-performing speech recognition applications. The cell phone and Internet-related service industry have further enhanced speech recognition applications using artificial intelligence and statistical data-mining techniques. These improvements to speech recognition technology (SRT) may one day help astronauts on future deep space human missions that require control of complex spacecraft systems or spacesuit applications by voice. Though SRT and more advanced speech recognition techniques show promise, use of this technology for a space application such as vehicle/habitat/spacesuit requires careful considerations. There are still recognition challenges to overcome such as background noise, human speech variability, and task loading. However, implemented correctly, a voice-controlled spacecraft system (VCSS) can provide a useful, natural and efficient form of human-machine communications during complex tasks such as when the hands and eyes are busy or as an aid for vehicle situational awareness inquiries or collaborative human-robot tasks. This document provides considerations and guidance for the use of SRT in VCSS applications for space missions, specifically in command-and-control (C2) applications where the commanding is user-initiated. First, current SRT limitations as known at the time of this report are given. Then, highlights of SRT used in the space program provide the reader with a history of some of the human spaceflight applications and research. Next, an overview of the speech production process and the intrinsic variations of speech are provided. Finally, general guidance and considerations are given for development of a VCSS using a human-centered design approach for space applications that includes vocabulary selection and performance testing, as well as VCSS considerations for C2 dialogue management design, feedback, error handling, and evaluation/usability testing. Available from

Salazar, George A.↗

NASA NTRS2019

Development Considerations for Implementing a Voice-Controlled Spacecraft System

As computational power and speech recognition algorithms improve, the consumer market will see better-performing speech recognition applications. The cell phone and Internet-related service industry have further enhanced speech recognition applications using artificial intelligence and statistical data-mining techniques. These improvements to speech recognition technology (SRT) may one day help astronauts on future deep space human missions that require control of complex spacecraft systems or spacesuit applications by voice. Though SRT and more advanced speech recognition techniques show promise, use of this technology for a space application such as vehicle/habitat/spacesuit requires careful considerations. This paper provides considerations and guidance for the use of SRT in voice-controlled spacecraft systems (VCSS) applications for space missions, specifically in command-and-control (C2) applications where the commanding is user-initiated. First, current SRT limitations as known at the time of this report are given. Then, highlights of SRT used in the space program provide the reader with a history of some of the human spaceflight applications and research. Next, an overview of the speech production process and the intrinsic variations of speech are provided. Finally, general guidance and considerations are given for the development of a VCSS using a human-centered design approach for space applications that includes vocabulary selection and performance testing, as well as VCSS considerations for C2 dialogue management design, feedback, error handling, and evaluation/usability testing.

Salazar, George↗

NASA NTRSDate not supplied

Enabling Exchange and Adequate Use of Data for Observation Based Atmospheric Research

Systematic long-term field observations have played a vital role in advancing atmospheric research over the past several decades. The use of these observations has expanded from primarily characterizing atmospheric processes and trends to evaluating satellite measurements, assessing models, and improving air quality forecasts. Consequently, the demand for atmospheric chemistry observational data have dramatically increased in terms of scope and coverage of measurements (i.e., parameters/species, spatiotemporal extent). In addition to high quality measurements, certain data reporting standards need to be agreed to ensure the data can be readily exchanged and are sufficiently documented to enable adequate use in different research activities. To this end, WMO has developed and implemented measurement guidelines and community practices for meteorology, climatology, atmospheric and hydrological sciences. In addition, the WMO Expert Team on Metadata Standards manages and evolves the existing metadata standards for the WMO Information System WIS and WMO Integrated Global Observing System WIGOS to support consistent and interoperable data descriptions, ensure relevance to research, and to apply data science principles. This team draws on a wide range of expertise from the research community, including atmospheric measurements, modeling, data management, and data science. The current activities include development of key performance indicators, vocabularies for metadata and the evolution of metadata standards to lower the barrier of application to weather/climate/water/environment data for all communities and the weather enterprise. This presentation intends to promote awareness of ongoing progress and actively solicit community feedback.

Field Observations↗

NASA NTRSDate not supplied

Semantic Search with Sentence-BERT for Design Information Retrieval

Managing and referencing design knowledge is a critical activity in the design process. However, reliably retrieving useful knowledge can be a frustrating experience for users of knowledge management systems due to inherent limitations of standard keyword-based searches. In this research, we consider the task of retrieving relevant lessons learned from the NASA Lessons Learned Information System (LLIS). To this end, we apply a state-of-the-art natural language processing (NLP) technique for information retrieval (IR): semantic search with sentence-BERT, which is a modification of a Bidirectional Encoder Representations from Transformers (BERT) model that uses siamese and triplet network architectures to obtain semantically meaningful sentence embeddings. While the pre-trained sBERT model performs well out-of-the-box, we further fine-tune the model on data from the LLIS so that it learns on design engineering-relevant vocabulary. We quantify the improvement in query results using both standard sBERT and fine-tuned sBERT over a keyword search. Our use case throughout the paper is to use queries related to specific requirements from a NASA project. Fine tuning the sBERT model on LLIS data yields a mean average precision (MAP) of 0.807 on queries based on information needs from a real NASA project. Results indicate that applying state-of-the-art natural language processing techniques, especially when finetuned using engineering data, to design information retrieval tasks shows significant promise in modernizing design knowledge management systems.

Hannah S. Walsh↗

NASA NTRSDate not supplied

Semantic Search with Sentence-BERT for Design Information Retrieval

Managing and referencing design knowledge is a critical activity in the design process. However, reliably retrieving useful knowledge can be a frustrating experience for users of knowledge management systems due to inherent limitations of standard keyword-based searches. In this research, we consider the task of retrieving relevant lessons learned from the NASA Lessons Learned Information System (LLIS). To this end, we apply a state-of-the-art natural language processing (NLP) technique for information retrieval: semantic search with sentence-BERT, which is a modification of a Bidirectional Encoder Representations from Transformers (BERT) model that uses siamese and triplet network architectures to obtain semantically meaningful sentence embeddings. While the pretrained sBERT model shows excellent out-of-the-box performance, we further fine-tune the model on data from the LLIS so that it learns on design engineering-relevant vocabulary. We quantify the improvement in query results using both standard sBERT and fine-tuned sBERT over the LLIS’s built-in keyword search. Additionally, we demonstrate a use case for the query system by searching for lessons learned relevant to specific requirements from a NASA project as part of a broader knowledge management and retrieval system. Results indicate that applying state-of-the-art natural language processing techniques, especially when fine-tuned using engineering data, to design information retrieval tasks shows significant promise in modernizing design knowledge management systems.

Hannah S. Walsh↗

NASA NTRSDate not supplied

Assessing the Use of UAS-Related Terms in ASRS Using Seed Topic Modeling

Context: The NASA Aviation Safety Reporting System (ASRS) is a voluntary confidential system that disseminates reports received from personnel involved in aviation operations after de-identifying them. These reports are used by the community to improve overall aviation system safety. Aim: We propose and execute an experiment to assess the use of seed term topic modeling over the database narratives to identify Unmanned Aircraft System (UAS) reports. The use of seed term topic modeling enables users to identify groups of conceptually similar narratives associated to a topic of their interest. Method: We use a collection of narratives, expert-selected words, and report metadata that separates UAS from non-UAS reports to assess if seed topic modeling can be used to improve ASRS searches. Results: For simpler queries, seed topic search observes a higher recall and lower precision than the existing DBOL (DataBase OnLine) search in operation. However, the best results are obtained when seed topic search is used as a search suggestion system to be executed on the DBOL. Conclusion: Utilizing a combination of both the existing method and the proposed method, users can expand their search vocabulary about subjects of interest while improving the quality of results.

Text Mining↗

NASA NTRSDate not supplied

Assessing the Use of UAS-Related Terms in ASRS using Seeds for Topic Modeling

LDA↗

NASA NTRSDate not supplied

ICARTT File Format Enhancements: Supporting FAIRness of Airborne and Field Campaign Data

The ICARTT (International Consortium for Atmospheric Research on Transport and Transformation) standards were developed to fulfill data management needs for the ICARTT campaign in 2004. The ICARTT file format is text-based and composed of a header with important data description information and the data section. The ICARTT format, built on the NASA Ames and GTE data formats, was created to facilitate data exchange and promote collaborations among the science teams for achieving the ICARTT campaign goals. Due to the success of the ICARTT campaign, the ICARTT file format was exposed to a broad range of airborne researchers and was adopted for use in many other field campaigns sponsored by NASA and other partner agencies. The ICARTT format standards became a NASA standard in 2010 and was amended in January 2017 providing many enhancements, including the requirement for variable standard names. Primarily designed for airborne field studies, ICARTT has been further utilized for ground-based studies. The ICARTT format can host metadata that is critical for proper use of the data, especially for in-situ measurements. However, the information that needs to be included is often in free text, meaning the information are human readable, but not machine interpretable. Furthermore, the amount and type of information provided can vary substantially between principal investigators and campaigns. To support interoperability and FAIR principles, further enhancements to the ICARTT standards are recommended. Possible recommendations include standardizing timestamps for easier data comparisons and analysis; potential use of controlled and consistent vocabulary for variable short name and certain common metadata elements; and providing guidance on variable measurement units and how they are reported.

Megan Buzanowicz↗

NASA NTRSDate not supplied

ICARTT File Format Enhancements: Supporting FAIRness and Data Discovery of Suborbital Campaign Data

Suborbital campaigns aim to accomplish a wide variety of goals and can include a variety of platforms, instruments, and parameters measured. In 2004, the ICARTT (International Consortium for Atmospheric Research on Transport and Transformation) standards were developed to fulfill data management needs for the ICARTT campaign. The ICARTT file format is text-based and composed of a header with important data description information and the data section. Built on the NASA Ames and GTE data formats, the ICARTT format was created to facilitate data exchange and promote collaborations among the science teams for achieving the ICARTT campaign goals. Due to its success and adaptation for use in many other field campaigns, the ICARTT file format became a NASA standard in 2010 and was amended in January 2017. These changes provided many enhancements, including the requirement for variable standard names. Primarily designed for airborne field studies, ICARTT has been further utilized for ground-based studies. NASA has made a commitment to build an inclusive open science community over the next decade. Open-source science strives to make publicly funded scientific research transparent, inclusive, accessible, and reproducible. The ICARTT format can host metadata that is critical for proper use of the data, particularly for in-situ measurements, and can enhance data discovery and accessibility. However, the required fields are often free text, meaning that the information is human readable, but not machine interpretable. Furthermore, the amount and type of information provided can vary significantly between principal investigators and campaigns. To support FAIR principles and interoperability, enhancements to the ICARTT standards are recommended. Possible recommendations include potential use of controlled and consistent vocabulary for variable standard name and certain common metadata elements; standardizing timestamps for easier data comparisons and analysis; and providing guidance on variable measurement units and how they are reported. Enhancing ICARTT metadata can further streamline the process to make suborbital data more readily available to the data user and improve variable-level metadata. Providing more variable-level metadata can enhance data searching and discovery, supporting NASA’s Open-Source Science Initiative (OSSI).

Megan Buzanowicz↗

NASA NTRSDate not supplied

WIS and WIGOS Metadata as the Foundation for a Sustainable Framework for Global Greenhouse Gas Watch Data Exchange

Metadata (data about data) is a critical component of data discovery, description, evaluation, documentation, and preservation. Developing and propagating metadata standards has been a longstanding area of activity in WMO and beyond. The WIS2 and WIGOS metadata models are being actively developed and maintained by dedicated task teams, established under the WMO Expert Team on Metadata. The metadata representations and vocabularies are governed by well-established processes within WMO. These standards are being used in a number of metadata/data exchange activities (e.g., WMO Information System 2.0 (WIS2), WIGOS (WMDR), Climate Data Management Systems (CMDS), etc.). It should also be noted that the application of the WIS2 and WIGOS standards fully support the WMO Unified Data Policy and open data policy as well as greatly enhance the value of observations by fostering data F.A.I.R.ness. Furthermore, the WMO metadata standards can serve as the foundation for a framework that will facilitate metadata mapping between the existing schemas used in well-established data centres, e.g., WMO WDCGG (World Data Centre for Greenhouse Gases) and NOAA ObsPack (Observation Package Data Products) and to automate metadata exchange between data centres as well as with WMO. These activities will play a central role in integrating measurements sponsored by various member countries and organizations to provide a more comprehensive characterization of the temporal and spatial distribution of the greenhouse gases. At the same time, this metadata exchange can lead to member countries and partner organizations improving their current metadata collection process for data discoverability, interoperability, and (re)usability. This presentation will describe metadata activities in the context of WIS2 and WIGOS and how they apply to GGGW data integration via metadata mapping and exchange.

Gao Chen↗

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

Aiden Szeto↗

NASA NTRSDate not supplied

NASA’s Top Human System Research and Technology Needs for Mars

NASA is working with industry and international partners to return humans to the moon and eventually enable a human Mars mission. Within NASA, several organizations work together to identify, prioritize, fund, execute, and operationalize the crew health and performance (CHP) research and technology development (R&D) necessary to enable these future missions. These organizations include flight programs as well as other organizations such as the Health and Medical Technical Authority (HMTA), Human Research Program, Space Technology Mission Directorate, and System Capability Leadership Teams, each of which existed for several years prior to the creation of the Moon-to-Mars (M2M) Program Office in 2023. A variety of constructs, vocabularies, and processes exist for the management of risks and strategic planning across these organizations. For example, M2M objectives, program risks, human system risks, human research gaps, capability gaps, and envisioned futures are all constructs currently used within NASA for identifying and prioritizing R&D needs. Alignment and evolution of these strategic planning constructs is ongoing and will allow for alignment and traceability between M2M objectives and R&D investments at a detailed level. While this reorganization is ongoing, and even after it is established, there is a recognized need among stakeholder organizations to identify and communicate the highest CHP R&D priorities in a unified and digestible way that synthesizes the considerations and perspectives of the CHP community across NASA. To achieve this, the HMTA facilitated a series of group discussions with representatives of NASA’s CHP community, which culminated in a product identifying the eight highest priority mission-enabling CHP capabilities for human missions to Mars, referred to as the “Top Human System Capability Needs for Mars”. The list includes Earth-Independent Human Operations; Mars Duration Food System; Mars Duration Effects on Human Physiology; Risks Mitigations for Vehicle Atmospheres; Computational Injury & Anthropometric Models; Exploration Exercise Countermeasures; Individual Variability in Responses to Spaceflight; and Sensorimotor Countermeasures. Existing strategic planning and risk management tools and processes were considered, as well as practicalities of technical, cost, and schedule feasibility associated with potential R&D risk mitigations and technology solutions. The product is not owned by any one organization and does not replace existing strategic or program planning processes; rather it aims to complement and inform them with a unified set of community generated priorities. It is anticipated that this list will be re-evaluated periodically based on R&D progress and the evolving M2M architecture.

Life Support↗

NASA NTRSDate not supplied

Discovering Research Areas in Dataset Applications Through Knowledge Graphs and Large Language Models

Scientific datasets are increasingly cited in peer-reviewed journal publications, facilitating easy access to research utilizing those datasets. Datasets undergo a life cycle where older versions of datasets are replaced by newer versions often due to improvements in data resolution, algorithms, and other factors. Unlike peer reviewed documents registered with a single Digital Unique Identifier (DOI), datasets can be updated over time and the newer version of the datasets are registered with a new DOI which is not necessarily linked to the previous version of the dataset. It is challenging when publications citing a dataset need to be traced over the entire life cycle of that dataset. We provide an innovative approach to link the dataset versions and publications using a knowledge graph (KG). KG can help to trace the dataset cited in publications over the entire dataset life cycle and shed light into dataset usage in various applied research areas. We fine-tuned the pretrained NASA IMPACTINDUS Large Language Model (LLM) on a set of labeled publications abstracts. Our results showed that 87% of the publications were classified into one of twenty applied research areas, while the remaining 13% were classified into non-applied research areas. By linking datasets to applied research areas through the KG and employing Global Change Master Directory(GCMD), a well-established controlled vocabulary of scientific keywords describing Earth science datasets, we contribute to a transparent and advanced search and discovery mechanism for datasets across the Earth data ecosystem. The integrated KG and LLM approach is now incorporated and operational in dataset publication management at one of NASA’s Earth science data archival centers.

data provenance↗

NASA NTRS2024

NASA’s Top Human System Research and Technology Needs for Mars

NASA is working with industry and international partners to return humans to the Moon and to eventually enable humans to explore Mars. Within NASA, several organizations work together to identify, prioritize, fund, execute, and operationalize the research and technology development (R&TD) that will be necessary to enable crew health and performance (CHP) during these future missions. These organizations include flight programs, the Health and Medical Technical Authority (HMTA), the Human Research Program, the Space Technology Mission Directorate, System Capability Leadership Teams, and other organizations, many of which existed for several years prior to the creation of the Moon-to-Mars (M2M) Program Office in 2023. A variety of constructs, vocabularies, and processes exist for managing risks and supporting strategic planning across these organizations. For example, M2M objectives, program risks, human system risks, human research gaps, capability gaps, and envisioned futures are all constructs currently used within NASA to identify and prioritize R&TD needs. These strategic planning constructs are evolving to allow M2M objectives and R&TD investments to be aligned and traced at a detailed level. A recognized need exists among stakeholder organizations to identify and communicate the highest CHP R&TD priorities in a unified and digestible way that addresses the perspectives of NASA’s CHP community. To achieve this, the HMTA arranged a series of discussions with representatives of NASA’s CHP community, during which the 8 highest priority CHP capabilities that will enable human missions to Mars, referred to as the “top human system capability needs for Mars”, were identified. The list includes Earth-independent human operations; Mars-duration food system; Mars-duration effects on human physiology; risk mitigations for vehicle atmospheres; computational injury and anthropometric models; exploration exercise countermeasures; individual variability in responses to spaceflight; and sensorimotor countermeasures. Existing tools and processes for strategic planning and risk management were evaluated, as well as the technical practicalities, cost, and schedule feasibility associated with potential R&TD investments in different capability need areas. This capability needs report is not owned by any one NASA organization and does not replace existing strategic or program planning processes; rather it aims to complement and inform them with a unified set of community generated priorities. These top capability needs will be re-evaluated periodically based on R&TD progress and the evolving M2M architecture.

technology gaps↗