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On-demand Command and Control of ASTERIA with Cloud-based Ground Station Services

ASTERIA (Arcsecond Space Telescope Enabling Research in Astrophysics) was a 6-unit CubeSat technology demonstration mission that deployed from the International Space Station on November 20th, 2017. After successfully completing its 90-day primary mission that demonstrated arcsecond-level line-of-sight pointing and focal plane thermal stability for exoplanet detection, it entered an extended mission performing onboard software demonstrations to mature technology both in space and on the ground. One of the technologies was a completely cloud-based ground system leveraging Amazon Web Services (AWS) Ground Station service.Announced in December 2018 and launched in May 2019, AWS Ground Station is a fully managed ground station service that aims to reduce the overhead associated with developing and maintaining ground system infrastructure throughout the mission lifecycle. AWS Ground Station makes available the suite of features required for any ground system in support of low-Earth orbit (LEO) and medium-Earth Orbit (MEO) satellite operations on-demand and without setting up or maintaining long-term contracts. Charges are incurred on a per-minute basis for antenna usage during scheduled tracks. Support is available for S-band uplink and downlink, along with X-band narrowband and wideband downlink. Missions that use the service may reserve tracks with any licensed AWS Ground Station antennas located across each service region and have direct access to any AWS services in support of mission operations.The cloud-based architecture built around the AWS Ground Station service greatly enhanced ASTERIA mission operations by enabling end-to-end pass automation, on-demand contact scheduling and contingency planning, along with more efficient data downlink through station availability and station-to-station handovers. It incorporated open-source software, particularly NASA's AMMOS Instrument Toolkit (AIT) and Open Mission Control Technologies (OpenMCT), along with the AWS application programming interfaces (API) to the Ground Station, Elastic Compute Cloud (EC2) and Simple Storage Service (S3) services. After showcasing operability in August 2019, the team continued using and improving this novel ground system architecture until the end of mission in December 2019. This paper describes the cloud-based ground system, how it was designed, tested, and evaluated with an in-orbit spacecraft, the operational capabilities that it enabled, along with lessons learned and recommendations for future missions.

Fesq, Lorraine↗

Developing and Validating Measurement Scales During Pandemic Conditions: A Case Study with the Scale for Habitat Usability

At NASA, habitat evaluations often employ subjective measures. Some measures are frequently used, well established tools, whereas others are homegrown measures tailored to specific projects. The variety of measures used makes evaluation comparisons across projects difficult. Additionally, some of these measures are burdensome, may be too specialized, or may require an expert to use and interpret, limiting their utility. Taken together, these drawbacks suggest the need for a new measurement tool. To that purpose, a team at NASA worked on developing a new scale for measuring habitat usability, the Scale for Habitat Usability (SHU). The SHU is intended to be a quick, multi-faceted measure for evaluating habitat usability across the development lifecycle. However, like many research projects, the development of the SHU faced setbacks due to the COVID-19 pandemic. Pandemic prevention protocols precluded in-person data collection, forcing the team to take some non-traditional approaches to scale development. This paper reports the steps the team took to complete the project.

Ian Robertson↗

On-demand Command and Control of ASTERIA with Cloud-based Ground Station Services

ASTERIA (Arcsecond Space Telescope Enabling Research in Astrophysics) was a 6-unit CubeSat technology demonstration mission that deployed from the International Space Station on November 20th, 2017. After successfully completing its 90-day primary mission that demonstrated arcsecond-level line-of-sight pointing and focal plane thermal stability for exoplanet detection, it entered an extended mission performing onboard software demonstrations to mature technology both in space and on the ground. One of the technologies was a completely cloud-based ground system leveraging Amazon Web Services (AWS) Ground Station service. Announced in December 2018 and launched in May 2019, AWS Ground Station is a fully managed ground station service that aims to reduce the overhead associated with developing and maintaining ground system infrastructure throughout the mission lifecycle. AWS Ground Station makes available the suite of features required for any ground system in support of low-Earth orbit (LEO) and medium-Earth Orbit (MEO) satellite operations on-demand and without setting up or maintaining long-term contracts. Charges are incurred on a per-minute basis for antenna usage during scheduled tracks. Support is available for S-band uplink and downlink, along with X-band narrowband and wideband downlink. Missions that use the service may reserve tracks with any licensed AWS Ground Station antennas located across each service region and have direct access to any AWS services in support of mission operations. The cloud-based architecture built around the AWS Ground Station service greatly enhanced ASTERIA mission operations by enabling end-to-end pass automation, on-demand contact scheduling and contingency planning, along with more efficient data downlink through station availability and station-tostation handovers. It incorporated open-source software, particularly NASA's AMMOS Instrument Toolkit (AIT) and Open Mission Control Technologies (OpenMCT), along with the AWS application programming interfaces (API) to the Ground Station, Elastic Compute Cloud (EC2) and Simple Storage Service (S3) services. After showcasing operability in August 2019, the team continued using and improving this novel ground system architecture until the end of mission in December 2019. This paper describes the cloud-based ground system, how it was designed, tested, and evaluated with an inorbit spacecraft, the operational capabilities that it enabled, along with lessons learned and recommendations for future missions.

Fesq, Lorraine↗

Uncrewed Lunar Surface Operations and Support Activities

Sustained human presence on the surface of the Moon and future missions to Mars require increased independence from surface crews and Earth-based mission control to operate efficiently, safely, and reliably. The time for surface crews to perform tasks will be limited. Extravehicular activities by surface personnel are burdensome and time-consuming, even when a continuous human presence on the surface occurs. Identifying and balancing human/automation roles and tasks and infusing automation and autonomy practices early in a system’s lifecycle will be essential to achieve mission objectives. Among these objectives are attaining a sustained human presence, improving performance and mission effectiveness, reducing operations and maintenance (O&M) costs, and ensuring operations that are robust to communication delays. To achieve these objectives, an operational shift toward increased automation and autonomy with less reliance on humans is needed. Uncrewed lunar surface operations and support activities occur when surface crews are not present or are independent of surface crew timeline activities requiring no surface crew oversight or intervention. These uncrewed surface opportunities can also be planned to minimize crew workload that avoids routine maintenance and support tasks, thus maximizing crew exploration time. Uncrewed preparations such as staging and prepositioning equipment and materials before the crew arrives could improve crew task efficiency. Additional opportunities exist to conduct uncrewed science, exploration, and utilization. Uncrewed surface architecture functions can include science and exploration; habitation; launch and landing support; surface communication and navigation; surface power generation and distribution; human surface mobility; lifting, handling, manipulating; excavation, construction, and site preparation; logistics management; maintenance and repair; surface resource utilization; integrated site operations and shared support services (e.g., site scheduling/prioritization, dust mitigation/contamination control, and surface safety). Early robotic lunar surface campaigns will provide information on the availability of resources, such as oxygen and water, and demonstrate surface-based technologies. After the Artemis III human lunar return mission, a series of landers will deliver surface systems, cargo, supplies, science packages, spare parts, and commodities. A balance of crewed and uncrewed surface operations will enable a sustained lunar surface presence at the South Pole of the Moon at a site that will be known as the Artemis Base Camp (ABC). It is envisioned that base camp operations on and around the Moon will then help prepare for the mission durations and activities needed to support the first human mission to Mars. Before long-duration crew missions to the base camp can occur, the necessary surface infrastructure will be pre-deployed and verified operational. Surface assets will be teleoperated and remotely managed from Earth. Additionally, robotic and short-duration crewed missions to the ABC will ensure the site’s merit to achieve long-term science objectives, availability of usable resources, and that terrain, seasonal variations, and illumination conditions are acceptable. ABC will consist of different areas where specific functions and services are rendered, including: • Launch and Landing Area • Habitation Area • Power Production Area • Resource Areas Launch and Landing Area—The launch and landing area will support associated functions for the arrival and departure of vehicles, such as crewed landing and ascent and uncrewed cargo deliveries and offloading. It will evolve from an unimproved site at the beginning of the exploration campaign to a more sustainable landing and launch area that can support repeated arrivals and departures. Initial uncrewed Lunar Terrain Vehicle (LTV) surface operations may include emplacement of navigation beacons and communication equipment, real-time video and photography of landing/liftoff events, and element repositioning, such as portable utility power (PUP) (applicable for other landed assets at other areas). Site preparations, such as surface leveling, soil compaction, and berm/path construction, may be needed for a more sustainable launch and landing area capable of accommodating vehicles that are increasingly more reusable and reduce the effects of plume surface interactions and ejecta impacts on nearby surface assets. During the ABC missions, cargo and logistics will be delivered to the lunar surface via robotic cargo landers before the crew arrives. These shipments, which can arrive in pressurized logistics carriers, will deliver the logistics necessary to support a crewed mission and include items such as food, water, equipment spares, etc. Providing the capability to retrieve, offload, and transport the logistics closer to the ABC site before the arrival of the crew will increase the overall efficiency of crew operations once they arrive. In the sustained phase of exploration, other supporting services may be needed, such as lander propellant servicing, surface power services, commodity refreshes, and additional inspection, maintenance, and repair capabilities, to sustain a cadence of extended personnel stays and cargo arrivals and departures. Habitation Area—Uncrewed support to surface habitation could involve supporting activation and pre-entry operations of the habitat while the crew is in orbit at the Gateway outpost preparing for a surface landing. Surface Habitat (SH) uncrewed operations may include bringing the cabin environment to a habitable temperature and air mix and activating other critical crew support systems. Potential crop production uncrewed tasks in the SH could also include autonomous watering and tending. Additionally, when the crew departs, the SH enters dormancy for the long period of uncrewed operation. A logistical staging area could also be collocated near the SH. If so, staging operations for crew supplies, waste re-location, and recycling operations may be opportunities for uncrewed operations. Power Production Area—The Fission Surface Power (FSP) element and its supporting distribution equipment provide power to surface elements as needed across the ABC to supplement day-to-day operations and survive lunar nights. Uncrewed support of this power system includes any initial LTV-assisted deployments of cables and other distributed equipment, associated electrical connections, and system testing and activation operations. Robotically performing some inspections, maintenance, or repair tasks on the power distribution equipment could reduce the surface crew workload. Resource Area— Uncrewed resource prospecting, mapping, and characterizing possible resource sites is likely to be time-consuming and represents an opportunity for uncrewed operations between crewed missions. Uncrewed mobile equipment operations will be needed in the extreme environments of permanently shadowed locations where resource extractions occur. As In-Situ Resource Utilization (ISRU) pilot plant operations begin, uncrewed surface support activities with available mobile and portable assets (LTV, PUP, etc.) will better support these operations. Any produced commodities can be stored at a centralized storage location for future use. Also associated with these operations is the use of mobile robotic excavators for resource acquisition and robotic/autonomous regolith processing. The waste tailings generated during excavation and regolith processing would also need to be transported and deposited at a dedicated location. Surface assets will continue operating between crew visits to maintain surface capabilities, conduct lunar surface science, technology demonstrations, and public outreach opportunities. Additionally, certain sustaining tasks that would consume valuable crew time could be performed before crew arrival, or after their departure. This capability may offer more affordable options to construct, activate, test, and maintain a broad set of surface assets. Telerobotically operated human surface mobility systems, such as the LTV and Pressurized Rover (PR), can be utilized for various tasks. Surface environmental conditions pose a distinct challenge for all these activities. Surface illumination and localized shadows are one such factor. Night-survival operations could consist of thermal management, battery pre-charging, and load shedding. Some surface systems may hibernate through the night and then awake and continue nominal operations. Uncrewed mobile assets may use a more adaptive approach to optimize their power and operations; one method is to follow the sunlight. Night-survival operations may be initiated remotely by teleoperation, automated, or accomplished by supervised autonomous operation. The ability to pre-deploy and control remote assets in orbit or on Mars before the arrival of the mission crew is a key capability that can be simulated on the moon. The base camp provides a venue where these advanced operational concepts, technologies, and autonomous methods and techniques, including the incorporation of time delays to simulate Earth-Mars latency can be replicated to help buy down future Mars mission risks. This paper will examine the evolution of uncrewed lunar surface operations and support activities. It will also discuss the lunar surface environmental conditions (thermal, lighting, terrain, topography, communications) along with the challenges they pose on uncrewed surface operations, and the performance of these activities with limited to minimal human interaction and/or teleoperation. Since lunar missions include Mars mission analogs, such investigation provides the framework for future uncrewed Mars mission support.

Mark E Lewis↗

Updraft Dynamics and Microphysics: On the Added Value of the Cumulus Thermal Reference Frame in Simulations of Aerosol–Deep Convection Interactions

One fundamental question about atmospheric moist convection processes that remains debated is whether or under what conditions a relevant variability in background aerosol concentrations may have a significant dynamical impact on convective clouds and their associated precipitation. Furthermore, current climate models must parameterize both the microphysical and the cumulus convection processes, but this is usually implemented separately, whereas in nature there is a strong coupling between them. As a first step to improve our understanding of these two problems, we investigate how aerosol concentrations modify key properties of updrafts in eight large-eddy permitting regional simulations of a case study of scattered convection over Houston, Texas, in which convection is explicitly simulated and microphysical processes are parameterized. Dynamical and liquid-phase microphysical responses are investigated using two different reference frames: static cloudy-updraft grid cells versus tracked cumulus thermals. In both frameworks we observe the expected microphysical responses to higher aerosol concentrations, such as higher cloud number concentrations and lower rain number concentrations. In terms of the dynamical responses, both frameworks indicate weak impacts of varying aerosol concentrations relative to the noise between simulations over the observationally derived range of aerosol variability for this case study. On the other hand, results suggest that thermals are more selective than cloudy-updraft grid cells in terms of sampling the most active convective air masses. For instance, vertical velocity from thermals is significantly higher at upper levels than when sampled from cloudy-updraft grid points, and several microphysical variables have higher average values in the cumulus thermal framework than in the cloudy-updraft framework. In addition, the thermal analysis is seen to add rich quantitative information about the rates and covariability of microphysical processes spatially and throughout tracked thermal lifecycles, which can serve as a stronger foundation for improving subgrid-scale parameterizations.

Daniel Hernandez-Deckers↗

Enabling Simulation Interoperability between International Standards in the Space Domain

Today, the design and development of space systems are conducted cooperatively by historical space agencies in this area such as NASA, ESA, Roscosmos, and JAXA together with their industrial partners. The space system lifecycle is characterized by high costs, uncertain conditions, and dangerous scenarios. To mitigate these issues, space agencies rely heavily on Modelling and Simulation as a key technology to support the analysis, design, and operation of space systems. To support large-scale distributed simulations, the scientific community has developed several standards to support the reuse and interoperability of simulation models such as IEEE 1516 High-Level Architecture (HLA), Real-time Platform Reference Federation Object Model (RPR FOM), Simulation Model Portability (SMP), and the novel Space Reference Federation Object Model (SpaceFOM). While the SpaceFOM standard has been specifically conceptualized for handling space systems, the other ones are more general-purpose and can be used to design and simulate generic complex systems. As a consequence, there is a lake of rules and guidelines to enable interoperability among these standards. The paper presents solutions and experiences for enabling interoperability and transferability of HLA, RPR FOM, and SMP simulation models with SpaceFOM.

Simulation↗

Research Report: Building a File Observatory for Secure Parser Development

"Parsing untrusted data is notoriously challenging.Failure to handle maliciously crafted data correctly can (anddoes) lead to a wide range of vulnerabilities. The Languagetheoretic security (LangSec) philosophy seeks to obviate the needfor developers to apply ad hoc solutions by, instead, offeringformally correct and verifiable input handling throughout thesoftware development lifecycle. One of the key components indeveloping secure parsers is a broad coverage corpus that enablesdevelopers to understand the problem space for a given formatand to use, potentially, as seeds for fuzzing and other automatedtesting. In this paper, we offer an update on the developmentof a file observatory to gather and enable analysis on a diversecollection of files at scale. Specifically, we report on the additionof a bug tracker corpus and new analytic methods on our existingcorpus."

Stonebraker, Ryan↗

Lean Model-Based Systems Engineering on the NASA High-Density Vertiplex Subproject

The High Density Vertiplex (HDV) subproject of NASA’s Advanced Air Mobility (AAM) project adopted Model-Based Systems Engineering (MBSE)in July of 2020, prior to subproject formulation. A small and lean team of HDV Systems Engineers(SE) are utilizing MagicDraw to execute NASA SE processes via MBSE. The SEs learned how to use MagicDraw from scratch and HDV is the first project for which the SEs have utilized MagicDraw. This paper will demonstrate project technical execution via MBSE, utilizing the digital elements built into the SysML (Systems Modeling Language). SysML provides a model-centric means of carrying out the NASA SE common technical processes by providing tools for complete system modeling, including requirements and interface management and design capture. The authors also leverage and extend SysML to perform other SE tasks, such as Verification and Validation (V&V)tracking. MBSE has two main purposes for HDV: 1) documenting the subproject’s logical architecture for distribution outside of the subproject, 2) capturing the subproject’s physical architecture in a single-source-of-truth for use by the subproject’s members. This paper details the challenges, lessons learned, and solutions that were encountered in implementing MBSE in the first iteration on a multi-iteration, full-lifecycle design, build, fly project.

Demetrios Katsaduros↗

An Analysis of the Impact of Extreme Attitude Operation on a Turbofan Engine in a Regional Jet Aircraft

The objective of this work is to characterize the variability in engine performance at extreme attitudes, resulting from the use of different standard control variables, evaluated at different points in the engine’s lifecycle. This paper describes the procedure used to create a dynamic model of an aft-mounted jet engine on a T-tail regional jet aircraft. This model enables simulation of engine operation at extreme attitudes. The model was subsequently evaluated at various altitudes, Mach numbers, and power settings over a range of angles of attack and sideslip, and deterioration levels. Each case was simulated using both fan speed and engine pressure ratio as the engine control variable. The results show that the wing has a very large impact on the engine operation, overwhelming other sources of variation.

loss of control - inflight↗

Implementation of Human Systems Integration Technical and Management Process for the Lunar Gateway Program

NASA recognizes Human Systems Integration (HSI) as part of the overall systems engineering and acquisition strategy for space systems. The Lunar Gateway Program is implementing HSI technical and management process across the lifecycle of the mission, as required by NPR 7123.1C NASA Systems Engineering Processes and Requirements, and led by the Gateway HSI team as required by NPR 8705.2C Human-Rating Requirements for Space Systems, now HEOMD-003 Crewed Deep Space Systems Human Rating Certification Requirements and Standards for NASA Missions. NASA has been using HSI principles for many years and has applied them to many of its previous human spaceflight Programs. As NASA returns to the Moon in a more sustainable manner, the Gateway Program is maturing the application of HSI by implementing it more visibly as part of Artemis, with guidance from the NASA/SP-20210010952 NASA HSI Handbook. This paper discusses how HSI is being implemented in the Gateway Program, challenges faced with its implementation during the development phase, and strategies/approaches used to overcome those. The paper also covers HSI implementation for flight systems, vehicle processing, and interfaces across the six identified NASA HSI Domains: human factors engineering, operations, safety, training, maintainability and supportability, habitability and environment. The goal is to provide an overview of the implementation process of HSI in the Gateway Program as an example for other Programs/Projects/Missionsthat are looking to implement HSI.

Jackelynne Silva-Martinez↗

NASA System-Wide Safety Wildland Firefighting Operations Workshop Report

On March 9-11, 2022, NASA’s System-Wide Safety Wildland Firefighting Operations Workshop engaged the broader wildland firefighting management ecosystem in a safety-oriented discussion via a virtual platform. This enabled a better understanding of how NASA and community expertise can be leveraged in the safe development of current and future firefighting systems and operations. The goals of the workshop were to: (1) identify and prioritize the top safety-oriented risks, gaps in capabilities, and emerging technologies to enhance wildland firefighting for both near-term and far-term concepts, with a specific focus on aviation operations and (2) engage the stakeholder community in defining emergent safety-oriented scope, roles, responsibilities, and procedures for agents undergoing increasingly complex wildland firefighting operations in information-rich, but uncertain environments. Workshop participants were solicited from wildland firefighting stakeholders across government, industry, and academia. All levels of government were engaged, as NASA sought attendees from federal, state, local, and tribal government agencies. Industry participants from traditional wildland firefighting domains such as data visualization and equipment manufacturers were invited, and corporate attendees from novel application domains such as aerial robotics and autonomous systems were present as well. The top three findings were as follows: (1) Enhancing situation awareness is a safety priority, especially in the use of aerial assets; (2) Timely access to information along with data fusion and integrated displays will enhance safety-critical decision-making both inside and outside aviation contexts; and (3) Tailorable standards and common operating pictures in the field will enhance inter-agency cooperation in the wildland firefighting lifecycle and enable the optimal use of limited resources such as aerial assets. The workshop helped inform NASA of the relevant safety-related wildland firefighting concerns and aided the broader ecosystem in understanding the potential safety-oriented role NASA might play in this community. Increased engagement with crucial governmental stakeholders (e.g., U.S. Forest Service, CAL FIRE, etc.) along with industry partners in cutting- edge information -centric domains is a fundamental next step. Additionally, the workshop findings will help define the first of a series of operationally challenging demonstrations, held in concert with strategic ecosystem partners, known as the Safety Demonstrator Series for NASA’s System-Wide Safety project. The first demonstration is set in the wildland firefighting application domain and will: (1) examine high risk operational scenarios to reduce their overall risk via services, functions or capabilities that act as risk mitigators (or transfer that risk to automated systems better able to tolerate it) and (2) explore novel tools and technologies that will enhance safety margins by enabling non-traditional or neoteric operational paradigms.

wildland firefighting↗

The Interplay of Humor and Team Cohesion Over Time

Team bonding and interpersonal relationships are increasingly important as we continue understanding the effects of the pandemic on team dynamics. Specifically, team cohesion, defined as having a sense of belonging and commitment towards the group, has been found to be important for key team outcomes such as performance, relationships, and productivity (Beal, Cohen, Burke, & McLendon, 2003; Bell & Marentette, 2011). Research has stressed the benefits of positive humor on cohesion, with humor creating positive emotions and decreasing social distance (e.g., Robert & Wilbanks, 2012; Robinson & Smith-Lovin, 2001; Romero & Pescosolido, 2008). However, research around the impact of negative humor on cohesion is scarce. This study aims to examine the impact of both positive and negative humor on cohesion for teams over time. The longitudinal design allows us to examine the interplay of humor and cohesion throughout a team’s lifecycle to guide relationship development and management (Romero & Pescosolido, 2008). Humor is defined as remarks recognized as amusing or funny (Martin, 2007). Positive humor can be categorized as good-natured aimed to create mutual agreement, while negative humor is defined as aggressive, hostile, and unreciprocated (Coan & Gottman, 2007). Research has concluded the many benefits of positive humor, showing it can increase open communication, social support, and create a pleasant environment (Martineau, 1972; Robinson & Smith-Lovin, 2001; Romero & Pescosolido, 2008). The Humor Wheel model suggests that positive humor is contagious due to the positive affect produced and mimicked (Lehmann-Willenbrock & Allen 2014). Research has suggested positive humor would increase over time based on the norm of reciprocity; those treated positively are likely to respond similarly (Gouldner, 1960). Multiple parties’ involvement in humor exchanges highlights the interpersonal nature of humor. Thus, long-term consequences on relationships should be considered, such as cohesion (Robert & Wilbanks, 2012). It would be instrumental to understand how humor interactions could weaken or strengthen group cohesion through relational and emotional bonds over time (Martineau, 1972; Romero & Pescosolido, 2008). Positive humor should fundamentally increase team cohesion (Mesmer-Magnus, Glew, & Viswesvaran, 2012; Walter & Baruch, 2008) by promoting group harmony, team member attractiveness, and ingroup identity (Holmes, 2006). Thus, it is expected there is a positive relationship between positive humor and team cohesion over time (hypothesis 1). Humor is not always beneficial, especially when it elicits negative emotions (Dikkers, Doosje, & de Lange, 2011). Negative humor can be used to humiliate, tease, or insult (Coan & Gottman, 2007; Collinson, 1988; Romero & Cruthirds, 2006). It tends to cause distress and damage relationships, and in turn, decrease organizational performance and production (Avolio, Howell, & Sosik, 1999; Janes & Olson, 2000; Yerkes, 2001). Research concludes negative humor may create subgroups and decrease cooperation (Romero & Arendt, 2011). Research has also indicated positive implications for negative humor under specific circumstances. Negative humor could help to solidify ingroup identity and strengthen bonds when directed outside the team (Terrion & Ashforth, 2002; Thomae & Pina, 2015). For example, sport teams may use negative humor towards their opponent before a game, which emphasizes their differences and lowers the status and power of their opponent, ultimately promoting solidarity and cohesion inside the group. For this study, it is anticipated that negative humor toward other teammates will decrease team cohesion over time due to the damage to relationships and negative affect produced (Duncan, Smeltzer, & Leap, 1990; Maples, Dupey, Torres‐Rivera, Phan, Vereen, & Garrett, 2001). We also predicted that negative humor focused on something or someone outside the team will increase cohesion by enhancing ingroup ties and creating a common identity (Thomae & Pina, 2015). Thus, the relationship between negative humor and cohesion will be moderated by the target of the humor. The relationship will be negative when the target is within the team and positive when the target is outside (hypothesis 2).

Melissa M. Vazquez↗

Making & Breaking the Rules for DED Design

Design for Additive Manufacturing (DfAM) is widely used to describe design activities and guidelines employed in the creation of Additively Manufactured (AM) parts. Basic design rules for widely adopted processes like Powder Bed Fusion (PBF) are relatively understood including material performance, part size constraints, self-supporting angles, overhangs, wall thicknesses, and feature resolution. Complexity is often inherent with PBF using organically inspired solutions through generative design and topology optimization software to create designs that often cannot be realized via traditional manufacturing processes. The design rules for Directed Energy Deposition (DED) are less well known, and, in some cases, the lack of understanding is likely inhibiting broader application of the various DED processes. This presentation covers key design rules for DED fusion processes and elucidates opportunities like multi-material deposition and build approaches ranging from basic features to complex large-scale parts. Complementary to the opportunistic characteristics of DfAM for DED, Manufacture for AM (MfAM) encompasses the restrictive aspects of the manufacturing lifecycle with limitations due to bead width and wall thickness, self-supporting angles, surface texture, microstructural evolution, and residual stresses. AM enabled part design must always consider the entire value chain starting with incoming feedstock (e.g. plate, wire, or powder) and progressing through the printing process, post-processing (e.g. machining, inspection), and final system assembly. Key considerations, like the tradeoffs between feature resolution and build rate, for process and part selection and economic assessments will be discussed as DED is often traded against other AM processes. After highlighting typical DED design rules, we will also show how some of those rules can be broken via use cases and part designs that span the full spectrum of DED processes with examples of various DED part designs.

Additive Manufacturing↗

Architecting Safer Autonomous Aviation Systems

The aviation literature gives relatively little guidance to practitioners about the specifics of architecting systems for safety, particularly the impact of architecture on allocating safety requirements, or the relative ease of system assurance resulting from system or subsystem level architectural choices. As an exemplar, this paper considers common architectural patterns used within traditional aviation systems and explores their safety and safety assurance implications when applied in the context of integrating artificial intelligence (AI) and machine learning (ML) based functionality. Considering safety as an architectural property, we discuss both the allocation of safety requirements and the architectural trade-offs involved early in the design lifecycle. This approach could be extended to other assured properties, similar to safety, such as security. We conclude with a discussion of the safety considerations that emerge in the context of candidate architectural patterns that have been proposed in the recent literature for enabling autonomy capabilities by integrating AI and ML. A recommendation is made for the generation of a property-driven architectural pattern catalogue.

Architecture patterns↗

Machine Learning for NASA Advanced Information Systems

NASA's Advanced Information Systems Technology (AIST) Program is one of several Technology programs managed by the Earth Science Technology Office (ESTO) in the Earth Science Division (ESD). The AIST Program focuses on advanced information systems and novel computer science technologies that will be needed by NASA Earth Science in the next 5 to 10 years. The three main thrusts of the AIST Program deal with Novel Observing Strategies (NOS), Analytic Collaborative Frameworks (ACF) and Earth System Digital Twins (ESDT). For all these thrusts, Machine Learning (ML) is increasingly being used in multiple aspects of Earth science systems, e.g., for onboard autonomy and decision making, for the analysis of massive and diverse datasets as well as more recently for developing surrogate models that will represent one of the main components of future Digital Twins of the Earth. Particularly, ESDT technologies developed by the AIST Program will allow to develop integrated Earth Science frameworks that will mirror the Earth with state-of-the-art models (Earth system models and others), timely and relevant observations, and analytic tools. These information systems will be used for supporting near- and long-term science and policy decisions. ESDT frameworks will build on previously developed AIST capabilities and technologies to integrate interconnected models with continuous streams of observations, data analytics, data assimilation, simulations, advanced visualizations and the ability to conduct "what-if" scenarios. This talk will describe the three thrusts of the AIST Program with a special focus on Machine Learning and how it is being used at all steps of the Earth Science data lifecycle.

Mathematical and Computer Sciences (General)↗

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

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

remote sensing↗

Continued Exploration of the Electrified Aircraft Propulsion Design Space

A previous electrified aircraft propulsion (EAP) design space exploration study was continued to investigate the impact of mission constraints, further explore the design space of different sized aircraft, and improve the modeling process. Three aircraft were investigated, 18- and 48-passenger turboprops, and a 78-passenger turbofan. Baseline aircraft models were inherited from the earlier study and were hybridized by applying EAP technologies. A parametric study varying motor power and battery specific energy was conducted. For the 78-passenger turbofan, design range was also introduced as a parametric parameter. Key metrics for the study included equivalent lifecycle CO2 emissions and direct operating cost plus interest. Both turboprop aircraft demonstrated significant potential savings in block fuel consumption and CO2 emissions, but at increased operating costs compared to their baseline models. As the 78-passenger turbofan had shown no improvements in any metrics under reasonable technology assumptions, design range was shortened in an attempt to obtain performance benefits. When designed for a shorter range, the 78-passenger EAP vehicle showed potential improvements in block fuel when compared to a baseline with a similarly shortened design range, but not in any other metrics. Operating cost reductions were not achieved for any aircraft, but several designs came close to breaking even with their baselines. Smaller, shorter range aircraft and turboprops demonstrated larger performance benefits and smaller operating cost penalties, but a simple trade of electric energy storage in batteries for fuel is generally insufficient to produce a viable aircraft concept.

Electrified Aircraft Propulsion↗

Multihierarchy Gaussian Process Models for Probabilistic Aerodynamic Databases using Uncertain Nominal and Off-Nominal Configuration Data

Probabilistic aerodynamic databases are a crucial component of the development lifecycle for aerospace vehicles. A key challenge when building aerodynamic databases is that most data used to construct them represent various simplifications of the real flight vehicle. For example, wind tunnel models often simplify the vehicle geometry and surface roughness characteristics, while CFD computations often make simplifications to the physics being modeled, such as fully laminar or turbulent calculations. Multifidelity data fusion models rely on a user being able to define a hierarchy of fidelity levels anchored to some "truth" data. This approach is unsatisfactory when no data can be considered to accurately reflect real flight conditions. In this work, we provide an alternative approach by presenting a consistent mathematical framework for building probabilistic aerodynamic databases in the form of a conditional probability distribution described by an ensemble of multifidelity Gaussian Processes. Instead of relying on a single hierarchy of data fidelity levels, the presented framework identifies a "nominal" configuration and potential corrections to the nominal which represent specific physical phenomena not represented in the nominal data. The nominal and correction functions themselves are constructed as multifidelity Gaussian Processes and linearly combined to form an ensemble model which fuses the uncertainties associated nominal and correction models. Results obtained using the proposed framework on a simplified Orion Crew Module wind tunnel dataset demonstrate the predictive capability of the multihierarchy framework. We further demonstrate the benefits of such a probabilistic aerodynamic database approach through function sampling and computing the conditional distributions of derived quantities, such as the trim angle of attack and aerodynamic coefficients at trim.

Gaussian Processes↗