Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “dissemination”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,405 records · Page 78

Identifying Emerging Safety Threats Through Topic Modeling in the Aviation Safety Reporting System: A COVID-19 Study

The NASA Aviation Safety Reporting System (ASRS) is a voluntary, confidential aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants, and others involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts, and a short descriptive synopsis is written to describe the safety issue. The de-identified reports are then disseminated to the aviation community in many ways, including via an online database, Safety Alert Bulletins, For Your Information Notices, and the CALLBACK newsletter. In this work, we consider whether we can improve the grouping, linking, and understanding of safety concerns through topic modeling. Specifically, we use topic modeling as a building block to identify emerging safety threats over time. This unsupervised approach, we argue, offers the flexibility to identify new emerging themes in this large dataset by constructing different timelines based on the content similarity of ASRS report narratives. This method's unsupervised nature improves upon related research, which is limited to pre-defined labels and therefore can not fully capture emerging safety threats. We apply our method to all ASRS reports in 2020 to assess if the generated timelines can highlight COVID-19 as it is emerging as a safety threat in incoming ASRS reports. We perform both a quantitative and qualitative evaluation of the automatically constructed timelines. The qualitative evaluation is performed by describing the evolution of top terms in the timelines, generated by our method, which we found explicitly convey the themes of COVID-19. Separately, we use a set of 1,213 COVID-19 reports from 2020 that were manually identified by ASRS analysts to quantitatively evaluate the COVID-19 reports distribution across the timelines. Our results have shown that COVID-19 emergence can be identified using the top terms that were generated by topic modeling. The top terms in topic modeling therefore can serve as a summary alternative to manually inspecting reports. Moreover, leveraging the manually identified COVID-19 reports, we found the manually identified timelines accounted for over 70% of the COVID-19 reports curated by the ASRS analysts, which demonstrates the potential of this approach for facilitating the understanding of safety concerns as they emerge and evolve. This method shows great potential to understand aerospace safety threats and other narrative- driven incident report databases.

ASRS↗

An Integrated Model of Models for Global Flood Alerting

A dramatic increase in frequency of minor to major flooding since 2000 has caused significant damage to infrastructure and economic losses across the world. To mitigate and recover from these losses, actions have been taken to build resilient communities and infrastructures. Situational awareness in near real-time is essential to ensure community resilience and enhance response and recovery efforts. Several hydrologic and hydraulic flood models are available at various spatial and temporal resolutions at regional to global scale. Given the global coverage of two operational flood models - GloFAS (Global Flood Awareness System) and GFMS (Global Flood Monitoring System), the first component of this project first focuses on integrating the outputs from these two models to classify flood severity and send alerts based on potential for impacts similar to the USGS PAGER (used for severity alerting and impact analysis for earthquakes). The second component of the project focuses on using flood outputs derived from earth observation data to validate, update and add additional exposure and impact products to flood alerts. The flood impacts and severity information will be disseminated as alerts and maps through the DisasterAWARE platform, operated by the Pacific Disaster Center (PDC), that provides global multi-hazard alerting and Situational Awareness information to the emergency management community and public. In this presentation, the following objectives will be covered: (i) the effective integration of the flood models, (ii) accuracy of the flood model outputs (flood extent and depth) for specific flood events both in the United States and globally in comparison with each other and earth observation data.

Glasscoe, Margaret↗

Learning from Past Missions for Today’s Case Studies

As interest in small spacecraft and its community continue to grow, the extensive selection of subsystem parts and service providers can present as many opportunities as unique challenges for mission design and implementation. These challenges often include inadequate trade studies, missing lessons learned, and unknown solutions to common pitfalls. Trade studies frequently fall short if products and services ultimately do not meet customer expectations, especially if the actual performance of a particular service or technology is not shared with others seeking similar products or services. It is difficult to quantify the viability and robustness of desired products and services, truly understand what solutions exist, and account for unique failures from previous SmallSat missions when experiences are not shared and captured. This noted, one reason for the lack of information as input for trade studies is that it is problematic to capture and disseminate the relevant lessons learned, experienced anomalies, and programmatic issues in both the laboratory and on-orbit setting, as these types of information can be extremely sensitive and are specific to each unique mission. Often, they are not made publicly available. The adoption and use of existing tools and databases, as well as contributions toward and sharing of information available in literature can help distribute useful information for future SmallSat missions to help avoid these common pitfalls. This paper will provide the required framework for current and future SmallSat mission implementation by providing best practices and identifying helpful resources to assist with subsystem parts and services as part of trade study selections.

Bruce Yost↗

Advanced Air Mobility Vertiport Considerations: A List and Overview

Advanced air mobility (AAM) is an emerging field in aeronautics that involves utilizing small aircraft for everyday transportation and other services, and many AAM aircraft are envisioned to take off and land at new infrastructure termed vertiports. This paper describes a variety of considerations related to AAM vertiports that need to be considered in the planning for and deployment of vertiports in practice. The factors include siting, design, regulations, safety, environmental impact, social acceptance, equity, and operational integration factors. Over 450 considerations were compiled from Subject Matter Experts (SMEs) participating in NASA’s AAM Ecosystem Working Groups (AEWGs) in October 2021. This paper consolidates these considerations and broadly disseminates the valuable knowledge of these SMEs. These considerations can be used by researchers to conduct demand and network analysis, local transportation planners to develop AAM networks for their community, and the AAM ecosystem members to identify policy, standards, and research gaps.

vertiport considerations↗

Natural Language Understanding and Extraction of Flight Constraints Recorded in Letters of Agreement

This paper presents an automated information extraction and inference technique using natural language processing for extracting flight operational procedures and constraints embedded in heritage air traffic management documents. The extracted flight constraints can be digitized and fit into existing airspace information exchange models such as the Aeronautical Information Exchange Model (AIXM). This approach offers a digitized solution to disseminate airspace operating conditions to diverse air users and stakeholders in the National Airspace System (NAS). Furthermore, the digitized flight procedures can provide operational flexibility for emerging advanced air mobility providers and reduce traffic controller workload while maintaining current safety standards. To demonstrate this process, 1,972 Letters of Agreement (LOAs) have been selected for processing, named entity extraction, constraint identification and extraction. This dataset is derived from a subset of documents related to Air Route Traffic Control Centers (ARTCC) operations. We experimented with various traditional information extraction techniques, state-of-the-art machine learning and deep learning models to perform named entity recognition and pattern recognition on our dataset. We present the results from our experiments and demonstrate 99.0% F-1 score for named entity recognition, and a 96.6% accuracy for our entire workflow up to named entity recognition. We also discuss constraint definitions using generic patterned templates and extensions to this work in applying entity linking to digitally extracting relevant constraints.

Natural Language Processing↗

Natural Language Understanding and Extraction of Flight Constraints Recorded in Letters of Agreement

This paper presents an automated information extraction and inference technique using natural language processing for extracting flight operational procedures and constraints embedded in heritage air traffic management documents. The extracted flight constraints can be digitized and fit into existing airspace information exchange models such as the Aeronautical Information Exchange Model (AIXM). This approach offers a digitized solution to disseminate airspace operating conditions to diverse air users and stakeholders in the National Airspace System (NAS). Furthermore, the digitized flight procedures can provide operational flexibility for emerging advanced air mobility providers and reduce traffic controller workload while maintaining current safety standards. To demonstrate this process, 1,972 Letters of Agreement (LOAs) have been selected for processing, named entity extraction, constraint identification and extraction. This dataset is derived from a subset of documents related to Air Route Traffic Control Centers (ARTCC) operations. We experimented with various traditional information extraction techniques, state-of-the-art machine learning and deep learning models to perform named entity recognition and pattern recognition on our dataset. We present the results from our experiments and demonstrate 99.0% F-1 score for named entity recognition, and a 96.6% accuracy for our entire workflow up to named entity recognition. We also discuss constraint definitions using generic patterned templates and extensions to this work in applying entity linking to digitally extracting relevant constraints.

Natural Language Processing↗

NASA Satellites and Data Fusion: A Case Study with Coral Reefs

As the world is experiencing a significant rise in both AI, Climate, and Space start-up companies, we are in a new wave of limitless innovation. NASA's statutory responsibility is to "provide for the widest practicable and appropriate dissemination of information concerning its activities and the results thereof." (51 U.S.C. § 20112) In particular, through machine learning, the public data drawn from NASA’s space assets can provide insights for addressing climate-related problems here on Earth. And many climate start-up companies can benefit from leveraging this data, either for proof of concepts or their own missions. In the Fall of 2021, several scientists from NASA and Coral Vita led a Practicum with the Georgia Institute of Technology Masters in Data Analytics program. The Practicum saw two teams of students develop and implement machine learning models to infer, from CALIPSO satellite imagery, vitality properties upon satellite pass. This kind of capability can lead to real-time mapping of coral health around the world, giving organization an understanding of where to prioritize reef reconstitution.. In this presentation, we will highlight this use case and discuss other concrete applications in which space data is used to solve problems here on Earth.

Earth Sciences↗

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 aviation safety reporting system. The ASRS receives reports from pilots, air traffic controllers, flight attendants and other involved in aviation operations. The reports are de-identified and coded by ASRS expert safety analysts. The de-identified reports are then disseminated to the aviation community in a number of ways including entry into an online database. Augmenting the discovery of topics of user interest in this online database would therefore be beneficial to the community it serves. Aim: We propose and execute an experiment to assess the use of seed term topic modeling using the database narratives to identify UAS reports. The use of seed term topic modeling would enable users to identify groups of related narratives associated to a topic of their interest. Method: We use a newly curated field in ASRS reports which identify UAS from non-UAS reports in combination of different set of UAS related terms to assess if seed topic modeling can be used in ASRS.

LDA↗

Learning from Past Missions for Today’s Case Studies

As the small spacecraft industry continues to grow, the extensive selection of subsystem parts and service providers can present unique challenges in mission implementation. These challenges include inadequate trade studies, missing lessons learned, and unknown solutions to common pitfalls. Trade studies frequently fall short because products and services ultimately do not meet customer expectations; the actual outcomes of a particular service or technology are not always shared with others seeking similar practices. It is difficult to: quantify the viability and robustness of the desired products and services, truly understand what solutions exist, and account for unique failures from previous smallsat missions. One reason for the lack of information as input for trade studies is that it is problematic to capture and disseminate the relevant lessons learned, experienced anomalies, and programmatic issues in both the laboratory and on-orbit setting as these types of information can be extremely sensitive and are specific to each unique mission. Therefore, they are not always made publicly available. The utilization and sharing of existing tools, databases, and literature can help distribute useful information for future smallsat missions to hopefully avoid these common pitfalls. This paper will provide the required framework for current and future smallsat mission implementation by providing best practices and identifying helpful resources to assist with subsystem parts and services trade study selection.

Small Spacecraft↗

Interoperable Services to Mitigate Lunar Position, Navigation, and Timing Challenges

Across the Earth, both civilian and government endeavors enjoy a built-in reliance on a position, navigation, and timing (PNT) infrastructure to which they are largely blind. Whether walking, driving, flying, or orbiting, Earth-centric PNT systems that have evolved over decades provide a core functionality to which we have grown accustomed for these pursuits. As NASA joins with other government space agencies and commercial partners to return humans to the Moon in a sustained manner within the current decade, expectations for PNT knowledge and timeliness at the Moon rival those on Earth. The need exists to develop a viable lunar-centric PNT infrastructure to support the planned human and robotic exploits. Navigating within the influence of the Moon presents its own set of challenges. Identification, understanding, and use of a unified reference frame and time system on which navigation is based becomes a fundamental need at the Moon. In addition to the unified foundational elements, measurement liability, dynamic conditions that require Earth-independent autonomous operations, and standards for PNT signals and message-based data exchange each represent distinct challenges to navigation in a burgeoning operational lunar environment. The PNT services planned as part of the lunar communications and navigation relay architecture known as LunaNet aid in surmounting the challenges. LunaNet interoperability specifications stipulate standards for signal parameters, messages, and lunar reference systems for PNT services. Within LunaNet’s defined interoperability resides the concept of a Reference Signal to provide communication signals specifically structured to enable measurement of pseudorange, Doppler, and time transfer by the recipient. One such signal type, the Augmented Forward Signal (AFS), functions as the mainstay for LunaNet PNT, while also serving the data needs for ubiquitous broadcast of network access and rapid unscheduled dissemination of alerts and messages. The presence of a geometrically distributed network of orbiting nodes transmitting the AFS forms the basis for the Lunar Augmented Navigation System (LANS), that delivers both radio navigation and data to multiple users in the lunar environment simultaneously. After reviewing the challenges associated with lunar navigation, this paper will describe the concepts and rationale behind the LunaNet PNT services. By borrowing techniques from Earth-centric Global Navigation Satellite Services (GNSS), the Tracking and Data Relay Satellite System, and the Consultative Committee for Space Data Standards, PNT from LunaNet aids to overcome challenges faced for accurate lunar navigation.

LunaNet↗

Interoperable Services to Mitigate Lunar Position, Navigation, and Timing Challenges

Across the Earth, both civilian and government endeavors enjoy a built-in reliance on a position, navigation, and timing (PNT) infrastructure to which they are largely blind. Whether walking, driving, flying, or orbiting, Earth-centric PNT systems that have evolved over decades provide a core functionality to which we have grown accustomed for these pursuits. As NASA joins with other government space agencies and commercial partners to return humans to the Moon in a sustained manner within the current decade, expectations for PNT knowledge and timeliness at the Moon rival those on Earth. The need exists to develop a viable lunar-centric PNT infrastructure to support the planned human and robotic exploits. Navigating within the influence of the Moon presents its own set of challenges. Identification, understanding, and use of a unified reference frame and time system on which navigation is based becomes a fundamental need at the Moon. In addition to the unified foundational elements, measurement liability, dynamic conditions that require Earth-independent autonomous operations, and standards for PNT signals and message-based data exchange each represent distinct challenges to navigation in a burgeoning operational lunar environment. The PNT services planned as part of the lunar communications and navigation relay architecture known as LunaNet aid in surmounting the challenges. LunaNet interoperability specifications stipulate standards for signal parameters, messages, and lunar reference systems for PNT services. Within LunaNet’s defined interoperability resides the concept of a Reference Signal to provide communication signals specifically structured to enable measurement of pseudorange, Doppler, and time transfer by the recipient. One such signal type, the Augmented Forward Signal (AFS), functions as the mainstay for LunaNet PNT, while also serving the data needs for ubiquitous broadcast of network access and rapid unscheduled dissemination of alerts and messages. The presence of a geometrically distributed network of orbiting nodes transmitting the AFS forms the basis for the Lunar Augmented Navigation System (LANS), that delivers both radio navigation and data to multiple users in the lunar environment simultaneously. After reviewing the challenges associated with lunar navigation, this paper will describe the concepts and rationale behind the LunaNet PNT services. By borrowing techniques from Earth-centric Global Navigation Satellite Services (GNSS), the Tracking and Data Relay Satellite System, and the Consultative Committee for Space Data Standards, PNT from LunaNet aids to overcome challenges faced for accurate lunar navigation.

Navigation↗

A Strategy for Advancing Earth Independent Medical Operations

Exploration medical operations to the Moon and Mars present unprecedented challenges for providing in-mission medical care. The greater distance from Earth is the primary hazard that drives the need for a medical operations paradigm shift from low earth orbit mission. Increasingly complex long duration and long distance missions will have resource constraints (ex: mass, power, volume, data), a paucity of resupply or evacuation opportunities, and disruptions in real-time communications. In order to advance a more autonomous medical approach, a multi-faceted strategy will need to optimize all aspects of human health and performance in space. This strategy will include: increasing onboard medical autonomy through the development of novel crew health and performance systems; decision support capabilities to augment astronauts’ abilities in preventing, diagnosing, and treating medical conditions; creating new procedures and training tools for skill maintenance and just-in-time training; and enabling rapid crew access to data from all on-board systems, leading to better-informed, real-time, autonomous decisions. Collectively, this approach can be referred to as “Earth Independent Medical Operations” (EIMO). The Exploration Medical Capability Element of NASA’s Human Research Program has undertaken a longitudinal planning process to consensus around EIMO. Technical interchange meetings will occur with key constituents in late 2022 and again in early 2023 to define EIMO in practical terms and identify key elements of an EIMO system, with greater conceptual articulation and dissemination planned for the second half of 2023. We will share insights into the strategy for internal and external consensus building around EIMO, with a particular focus on the approach to partner stakeholders within NASA as well as commercial agencies.

Jay Lemery↗

Assessment of Geo-Kompsat-2A Atmospheric Motion Vector Data and Its Assimilation Impact in the GEOS Atmospheric Data Assimilation System

Korea’s second geostationary meteorological satellite, Geo-Kompsat-2A (Geostationary-Korean Multi-Purpose Satellite-2A, GK2A), was successfully launched on 4 December 2018. GK2Agenerates Atmospheric Motion Vectors (AMVs) every 10 min in the full disk area. This data hasbeen disseminated via Global Telecommunication System (GTS) since 25 October 2019. This articleevaluates the quality of GK2A AMVs in the Goddard Earth Observing System (GEOS) atmosphericdata assimilation system (ADAS). The data show slow wind speed biases at 200–300 hPa and 600–800hPa in the northern and southern hemispheres. These biases are caused by observation heightassignment errors near jet streams. The Equivalent Blackbody Temperature (EBBT) method of GK2Atends to assign clouds at higher altitude, which mainly causes slow wind speed biases, especially inthe lower atmosphere. The IR/WV intercept method of GK2A assigns clouds slightly lower in theatmospheric layers below the altitude of 400 hPa, which causes positive biases. Quality control (QC)criteria to select the most suitable GK2A AMV data for assimilation are presented based on thesequality assessments. A new QC criterion utilizing height errors within the GEOS ADAS is introducedto exclude data with slow wind speed biases and large errors. GEOS forecast accuracy is slightlyimproved after assimilating GK2A AMVs along with other conventional, radiance, and satellitewinds which include AMVs made by the Himawari-8 satellite in nearly the same observational areaof GK2A. Additionally, the present work shows that GEOS forecasts can be significantly improved,especially in the tropics and southern hemisphere after assimilating GK2A data in the absence ofHimawari-8 AMVs. This study demonstrates that GK2A AMV data is a valuable data source toenhance the robustness of GEOS ADAS.

Atmospheric Motion Vectors↗

Having a Come-Apart: Lessons Learned from Additively Manufactured Hardware Failures

NASA has been engaged with additively manufactured (AM) process and component development since the 2000’s. AM offers various technical advantages, such as enhanced hardware design complexity, part consolidation, and processing of novel alloys in addition to programmatic advantages for reduction in processing time and cost. The focus of much of the AM development at NASA has been to mature the various processes, characterize material properties, develop standards, produce demonstrator parts, and integrate AM hardware in liquid rocket engines. These aspects have been demonstrated through process and design iterations using a methodical characterization, test-fail-fix cycles, as well as application and dissemination of lessons learned. In addition to these fundamental demonstrations of the AM process and hardware development, alloys that provide performance advantages in the high temperature and high-pressure environments have been matured for use in rocket engines. These environments are challenging for any alloy and any design, and the AM process is required to fully meet the intended design requirements. The importance of proper AM process was made evident in the failure of a Laser Powder Bed Fusion (L-PBF) copper-alloy combustion chamber during a hot-fire test due to a degraded material quality resulted from an AM process issue. The hot-fire test aimed to demonstrate high duty cycle under a risk-tolerant development project, where consequences of component failure would be minimal. However, the unintentional component failure emphasized the necessity of robust material characterization and rigorous process control procedures for the safe use of AM components in critical applications. In part, such concerns motivate the AM certification approach that NASA has recently adopted in NASA-STD-6030 “Additive Manufacturing Requirements for Spaceflight Systems”. This presentation provides an overview of the previously mentioned failure, a discussion on the evaluation of the failed chamber and supplemental chambers produced at the same time, a representative material samples that included intentional build witness lines, and a summary of the key results and recommendations from the evaluations. NASA continues to approach AM processes and designs with a level of risk and acceptance of failures that is appropriate for the project objectives, with the overall goal of safe implementation of AM technology and transferring AM technology into commercial space applications. The objective of this presentation is to provide awareness to the community working critical and non-critical AM components and the lessons learned on proper implementation of AM.

Additive Manufacturing↗

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↗

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

LDA↗

State-of-the-Art: Small Spacecraft Technology

When the first edition of NASA’s Small Spacecraft Technology State-of-the-art report was published in 2013, 247 CubeSats and 105 other non-CubeSat small spacecraft under 50 kilograms (kg) had been launched worldwide, representing less than 2% of launched mass into orbit over multiple years. In 2013 alone, around 60% of the total spacecraft launched had a mass under 600 kg, and of those under 600 kg, 83% were under 200 kg and 37% were nanosatellites (1). Of the total 1,849 spacecraft launched in 2021, 94% were small spacecraft with an overall mass under 600 kg, and of those under 600 kg, 40% were under 200 kg, and 11% were nanosatellites (1). Since 2013, the fight heritage for small spacecraft has increased by over 30% and has become the primary source to space access for commercial, government, private, and academic institutions. The total number of spacecraft launched in the past 10 years is 5,681 and 45% of those had a mass. As with all previous editions of this report, the 2022 edition captures and distills a wealth of new information available on small spacecraft systems from NASA and other publicly available sources. This report is limited to publicly available information and cannot reflect major advances in development that are not publicly disclosed. We encourage any opportunity to publish mission outcomes and technology development milestones (e.g., via conference papers, press releases, company website) so they can be reflected in this report. Overall, this report is a survey of small spacecraft technologies sourced from open literature; it does not endeavor to be an original source, and only considers literature in the public domain to identify and classify devices. Commonly used sources for data include manufacturer datasheets, press releases, conference papers, journal papers, public filings with government agencies, news articles, presentations, the compendium of databases accessed via NASA’s Small Spacecraft Systems Virtual Institute (S3VI) Information Search, and engagement with companies. Data not appropriate for public dissemination, such as proprietary, export controlled, or otherwise restricted data, are not considered. As a result, this report includes many dedicated hours of desk research performed by subject matter experts reviewing resources noted above. Content in this 2022 edition is based on data available by October 2022. This report should not be considered as a comprehensive overview of all the technologies but a great reference for the current state-of-the-art SmallSat technologies. The organizational approach for each chapter is relatively consistent with previous editions and includes an introduction of the technology, current development status of the technology’s procurable systems, and summary tables of technologies surveyed. The content in each chapter is uniquely organized to present a mini-stand-alone report on spacecraft subsystems. As in previous years, chapters include information from previous editions but are updated with new and maturating technologies and reference missions. Tables in each section provide a convenient summary of the technologies discussed, with explanations and references in the body text. The authors have attempted to isolate trends in the small spacecraft industry to point out which technologies have been adopted after successful demonstration missions. Lastly, the authors tried to use the terms “SmallSat,” “microsatellite,” “nanosatellite,” and “CubeSat” in a consistent manner, even as these terms are often used interchangeably in the space industry. Every subsystem chapter contains updated information to reflect the growth in the small spacecraft market. Significant changes are included in several chapters. The “Complete Spacecraft Platforms” chapter now includes information on the two main market options, hosted payload services and dedicated buses. The “Power” chapter provides information on the development of solid-state batteries with significantly higher energy than the current state-of-theart lithium-ion batteries. A large effort was made to update the “Communications” chapter to appropriately capture the recent technology maturation of optical communications for SmallSats. The “Ground Data Systems and Mission Operations” chapter was updated to reflect the recent establishment of the Near Space Network and influx of SmallSat Optical Ground Stations. The “Guidance, Navigation and Control” chapter was updated to include Lidar sensor technology. The “Deorbit Systems” chapter includes a discussion of recently proposed changes by the Federal Communications Commission (FCC) to limit a spacecraft’s lifetime to no longer than 5 years after end-of-mission. The “Identification and Tracking” Chapter includes updated information on the progress of SmallSat tracking. Finally, this report now encompasses technology funded by NASA’s Small Spacecraft Technology (SST) program’s SmallSat Technology Partnerships (STP) initiative which is described further in this Introduction. The reader can find the included SST technology in the “On the Horizon” section of the “Thermal Systems”, “Communications”, and “Guidance, Navigation, and Control” chapters. A central element of this report is to list state-of-the-art technologies by NASA standard Technology Readiness Level (TRL) as defined by the 2020 NASA Engineering Handbook, found in NASA NPR 7123.1C NASA Systems Engineering Processes and Requirements. The authors have endeavored to independently verify the TRL value of each technology by reviewing and citing published test results or publicly available data to the best of their ability. Where test results and data disagree with vendors’ own advertised TRL, the authors have attempted to engage the vendors to discuss the discrepancy. Readers are strongly encouraged to follow the references cited in the literature describing the full performance range and capabilities of each technology. Readers of this report should reach out to individual companies to further clarify information. It is important to note that this report takes a broad system-level view. To attain a high TRL, the subsystem must be in a flight-ready configuration with all supporting infrastructure—such as mounting points, power conversion, and control algorithms—in an integrated unit. An accurate TRL assessment requires a high degree of technical knowledge on a subject device, and an in-depth understanding of the mission (including interfaces and environment) on which the device was flown. There is variability in TRL values depending on design factors for a specific technology. For example, differences in TRL assessment based on the operating environment may result from the thermal environment, mechanical loads, mission duration, or radiation exposure. If a technology has flown on a mission without success, or without providing valid confirmation to the operator, such claimed “flight heritage” was discounted. The authors believe TRLs are most accurately determined when assessed within the context of a program’s unique requirements. While the overall capability of small spacecraft has matured since the 2021 edition of this report, technologies are still being developed to make deep space SmallSat missions more routine and more cost effective. Future editions of this report may include content dedicated to the rapidly growing fields of assembly, integration, and testing services, and mission modeling and simulation–all of which are now extensively represented at small spacecraft conferences. Many of these subsystems and services are still in their infancy, but as they evolve and reliable conventions and standards emerge, the next iteration of this report may also evolve to include additional chapters.

Bruce Yost↗

Updates in Developing a Prototype Science Pipeline and Full-Volume, Global Hyperspectral Synthetic Data Sets for NASA’s Earth System Observatory’s Upcoming Surface, Biology and Geology Mission

The Surface Biology and Geology (SBG) mission recently passed mission confirmation review and has entered phase A – design and development. SBG will acquire high resolution solar-reflected spectroscopy and thermal infrared observations at a data rate of ~2.5 TB/day and generate products at ~40 TB/day. Given that the per-day volume is greater than NASA’s total extant airborne hyperspectral data collection, collecting, processing, disseminating, and exploiting the SBG data present new challenges. To meet these challenges, we have developed a prototype science pipeline and a full-volume global hyperspectral synthetic data set to help prepare for SBG’s flight (see poster GC42D-0730). Our science pipeline is based on the science processing technology developed for NASA’s Kepler and TESS planet-hunting missions. The pipeline infrastructure, Ziggy, provides a scalable architecture for robust, repeatable, and replicable science and application products that can be run on a range of systems from a laptop to the cloud or a supercomputer. Ziggy is compliant with NASA Procedural Requirement (NPR) 7150.2C, is at a technical readiness level (TRL) of 7 and has been released to github.com/nasa/ziggy. We integrated Ziggy with EO-1/Hyperion workflows to build a prototype pipeline and ingested the 17-year mission archive that provides globally sampled visible through shortwave infrared spectra that are representative of SBG data types and volumes. We fully implemented the first stage and processed the entire 55 TB Hyperion data set from the raw data (Level 0) to top-of-the-atmosphere radiance (Level 1R). We are currently evaluating the ISOFIT atmospheric correction module to convert the L1R data to surface reflectance (Level 2) before reprocessing the full data set to L2. Crosschecks are being performed with RadCalNet as well as with coincident observations by AVIRIS. We are also investigating modern methods for georectifying the Hyperion scenes. Finally, we describe an analysis of the cost to conduct forward processing and reprocessing campaigns for SBG on HECC with dedicated compute and storage resources using the resurrected Hyperion pipeline as a proxy for full-volume SBG data. The analysis demonstrates that SBG L0 data can be processed to L2 on HECC with full reprocessing campaigns every two years for ~$2.6M over a 7-year lifespan. Moreover, 69% of the system capacity would be available for other activities, possibly enabling future open-source science activities, including algorithm development, L3+ processing, .etc.

ESD↗