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NASA NTRSDate not supplied

Establishing Trust in NASA’s Artemis Campaign Computer-Human Interface (CHI) Implementation

The NASA Artemis program will return humans to the Moon. This time, with the help of commercial and international partners, the program's objective is a permanent moon base. The moon base infrastructure, including an orbiting moon station and moon surface assets, will be developed for astronauts to stay for the long haul to learn to live and work on another planet in preparation for an eventual Humans-to-Mars mission. As the roundtrip communication delays increase in deep space exploration, the crew will need more onboard systems autonomy and functionality to maintain and control the vehicle or habitat. These mission constraints will change the current Earth-based spacecraft to ground control support approach that will demand more safe, efficient, and effective Computer-Human Interface (CHI) control. For Artemis, CHI is defined as the elements that the crew interfaces with: audio, video, lighting, and crew controls subsystems. Understanding how CHI will need to evolve to support deep space missions will be critical for the Artemis program--especially crew controls, which is the focus of this paper. How does NASA ensure crew controls are reliable enough to control complex systems and prevent a catastrophic event due to human error--especially when the astronauts could be physiologically and/or psychologically impaired? NASA's approach to mitigating catastrophic hazards in human spaceflight system development such as crew controls, is through a holistic system engineering and Human System Integration methodology that focuses on incorporating NASA's Human-Rating Requirements-that ensures human performance characteristics to control/safely recover the crew from hazardous situations within the human interface design are considered. This paper discusses, at a high level, CHI for the Artemis program. Next, a discussion of what it means to human-rate a space system crew controls and how trust in the human-computer interface begins with the NASA human rating requirements. Finally, a discussion on how systems engineering and the human system integration process ensures that crew control implementation incorporates the NASA human-rating requirements.

artemis↗
NASA NTRS2000

Animated software training via the internet: lessons learned

The Mission Execution and Automation Section, Information Technologies and Software Systems Division at the Jet Propulsion Laboratory, recently delivered an animated software training module for the TMOD UPLINK Consolidation Task for operator training at the Deep Space Network.

simulattions training animations flash↗
NASA NTRSDate not supplied

Medical Lessons Learned from the Exploration Atmospheres Study

Background: The National Aeronautics and Space Administration’s (NASA) Exploration Atmospheres study (EA) was done to evaluate alternative cabin atmospheres for future spacecraft designs and planetary surface exploration of the Moon, Mars and beyond. Deep space exploration involves creating habitats and environments safe for human occupancy and means to explore the outside environment (extravehicular activities, EVA). In order to validate alternative atmospheres and pre-spacewalk procedures, the EA study was conducted to evaluate factors such as hypoxia risks, denitrogenation protocols, food limitations, medications, and the impact of other factors on human performance. Overview: Space travel is constrained by mass, volume, power and the cost of vehicle development, which creates tradeoffs in various capabilities, including breathing gasses. Higher atmospheric pressure in a vehicle means more gas, and a stronger containment vessel/habitat; while lower pressure requires higher oxygen partial pressure, which may increase fire risk. NASA’s EA study evaluated a proposed alternative cabin environment (8.2 psia, 34% Oxygen), for future spacecraft habitat, and planetary EVAs. EA included both a 3-day and a 11-day trial. These trials included a depressurization and saturation to 8.2psi at 34% O2 with additional depresses to 4.3 psia at 85% O2 for simulated EVAs, (1 EVA during the 3-day and 5 during the 11-day trials). Discussion: Planning for and executing the medical monitoring and response plan for a trial of this scope was a huge undertaking with no prior practice to fall back on. Food obstacles, sleeping issues, medications, joint injury, equipment limitations, medical privacy, multiple cases of decompression sickness, and even a COVID outbreak among the support team proved challenging. Conclusion: Testing of this nature is an essential part NASA’s preparation for the upcoming Lunar Artemis missions. As spaceflight transitions beyond low earth orbit, to planetary, even more trials of this nature will be required to learn what are the optimal atmospheric and associated operational constraints to maintain the optimal health of the crew and achieve mission objectives.

R Sanders↗
NASA NTRSDate not supplied

Modeling Ring-Opening Polymerization with Machine-Learned Committors

Many important industrial and biological processes depend on ring-opening polymerization (ROP), including large-scale production polymers, such as nylon 6, and a variety of biodegradable polymers and high-performance resins. Large-scale simulations (>200K atoms) of ROP were performed using a deep neural network-based method that captured the effect of the local chemical environment of the reactive sites using training data from quantum chemical methods. The utility of this method was demonstrated by modeling the cationic ROP of bis-benzoxazine, a promising resin for use in advanced high-temperature application composites (>200°C). Density functional theory calculations were used to map out the various ring opening and polymerization reaction paths that occur during the thermal processing of bis-benzoxazine. The probabilities of these reactions, as a function of local atomic configurations, were used to construct committor functions using a neural network approach. The learned committor functions were then used to determine when a particular reaction occurred within the REACTER framework for reactive molecular dynamics simulations. The experimentally observed exotherm that occurs during bis-benzoxazine ROP was captured in an effective manner in the simulations by imposing a local heating of the system that corresponds to the enthalpy of reaction for each reaction. The dependence of the final polymer morphology on the ROP reaction temperature was analyzed at the molecular scale. The results presented in this work indicate that machine-learned committor functions are a promising approach for incorporating high-fidelity reactivity criteria that capture the effect of local chemical environments into large-scale polymerization simulations.

machine learning, polymer simulations, molecular d↗
NASA NTRS2021

Pointing Error Budget Development and Methodology on the Psyche Project

The Psyche mission was selected by NASA as the 14th mission in the Discovery Program in 2017. The Psyche spacecraft utilizes solar electric propulsion, and will journey to the asteroid (16) Psyche during a 3.5 year trajectory after its planned 2022 launch. The spacecraft instrument suite includes a magnetometer, a multispectral imager, a gamma ray neutron spectrometer, and an X-band radio telecommunications system. It also includes the Deep Space Optical Communication technical demonstration. These instruments along with other spacecraft components require pointing accuracy to meet their scientific and engineering performance requirements. Early on in the project development, the team established a methodology by which pointing accuracy (knowledge and control) is analyzed against the system requirements by means of pointing error budgets and requirement allocations. A margin policy was implemented to ensure the instrument and engineering component pointing accuracy requirements will be met during verification and in flight. Psyche’s pointing management framework defines detailed rationales for the system and subsystem error allocations of the top level pointing accuracy requirements, with sufficient project level pointing margin, and supports end-to-end pointing requirement verification. This paper will present an overview of the Psyche project’s pointing error budget development process, and discuss the rationale behind the methodology. Psyche’s pointing budget methodology integrates best practices and lessons learned from heritage missions, while focusing on the specific needs of the Psyche spacecraft and its science instruments. Key challenges in the pointing error budget development will be reviewed, and a deep dive into two key Psyche pointing budgets are presented. The systems engineering of Psyche’s pointing budget methodology outlined in this paper will serve as a resource for future deep space missions.

Lai, Peter↗
NASA NTRS2019

Biological CubeSats: What Have We Learned so Far and What Is Next?

Since Apollo 17 in 1972, NASA has sent no humans or other biological organisms outside of Earth's protective magnetosphere. Recently, NASA has set its sights on human exploration in deep space, with an ambitous plan to put astronauts back on the Moon by 2024 and to eventually land human missions on Mars. Such missions will require significant countermeasures, likely both technological and biomedical, to protect biology from chronic radiation exposure. CubeSats can inform these countermeasures by querying relevant space environments with model organisms.NASA has launched five biological CubeSat missions into low-Earth orbit (LEO). GeneSat-1 was launched in 2006 to study gene expression and increase our knowledge of how spaceflight affects microbes. Similar life-support technologies were then used in PharmaSat and O/OREOS, which launched in 2009 and 2010, respectively. PharmaSat contained optical systems to examine how yeast cells responded to an antifungal treatment. One of O/OREOS payloads, SESLO (Space Environment Survivability of Living Organisms), housed dormant microorganisms, which were rehydrated on orbit to track alterations to growth and metabolism induced by microgravity and radiation. In 2014, NASA launched SporeSat to study the mechanisms of plant cell gravity sensing using lab-on-a-chip devices. Most recently, in 2017, NASA launched EcAMSat (E. coli AntiMicrobial Satellite), which investigated the effects of microgravity on antibiotic resistance of a pathogenic bacterium. Each one of these missions increased our understanding of the biological effects of spaceflight in LEO, while refining technologies and imparting valuable lessons to the next generation of CubeSats.CubeSats housing translational biological models are therefore ideal for defining the hazards of deep space travel, as they can provide critical data over relevant durations. BioSentinel, a next-generation deep-space CubeSat, is planned to launch as a secondary payload on Artemis 1 in 2020. BioSentinel will study the DNA damage response to deep space radiation in yeast.

Santa Maria, Sergio R.↗
NASA NTRS2002

Observations and Parameterizations of Particle Size Distributions in Deep Tropical Cirrus and Stratiform Precipitation Clouds: Results from In-Situ Observations in TRMM Field Campaigns

In this study, we report on the evolution of particle size distributions (PSDs) and habits as measured during slow, Lagrangian-type spiral descents through deep subtropical and tropical cloud layers in Florida, Brazil, and Kwajalein, Marshall Islands, most of which were precipitating. The objective of the flight patterns was to learn more about how the PSDs evolved in the vertical and to obtain information of the vertical structure of microphysical properties. New instrumentation yielding better information on the concentrations of particles in the size (D) range between 0.2 and 2 cm, as well as improved particle imagery, produced more comprehensive observations for tropical stratiform precipitation regions and anvils than have been available previously. Collocated radar observations provided additional information on the vertical structure of the cloud layers sampled. Most of the spirals began at cloud top, with temperatures (T) as low as -50 C, and ended at cloud base or below the melting layer (ML). The PSDs broadened from cloud top towards cloud base, with the largest particles increasing in size from several millimeters at cloud top to one centimeter or larger towards cloud base. Some continued growth was noted in the upper part of the ML. Concentrations of particles less than 1 mm in size decreased with decreasing height. The result was a consistent change in the PSDs in the vertical. Similarly, systematic changes in the size dependence of the particle cross-sectional area was noted with decreasing height. Aggregation-as ascertained from both the changes in the PSDs and evolution of particle habits as observed in high detail with the cloud particle imager (CPI) probe-was responsible for these trends. The PSDs were generally well-represented by gamma distributions of the form N = N0 gamma D microns e- lambda gamma D that were fitted to the PSDs over 1-km horizontal intervals throughout the spirals. The intercept (N0 gamma), slope (lambda gamma), and dispersion (microns) values were derived for each PSD. Exponential curves (N = N0e- lambdaD; micron = 0) were also fitted to the distributions. The lambda gamma values for given spirals varied systematically with temperature as did the values of lambda (exponential), and the data generally conformed to values found in previous studies involving exponential fits to size distributions in mid-latitude frontal and cirrus layers. Considerable variability often noted in the PSD properties during the loops of individual spirals was manifested primarily in large changes in N0 gamma and N0, but micron, lambda gamma and lambda remained fairly stable. Temperature is not found to be the sole factor controlling lambda gamma or lambda but is a primary one. Direct relationships were found between lambda gamma and N0 gamma or lambda gamma and micron for the gamma distributions and lambda and N0 for the exponential. The latter relationship was not found as distinctly in earlier studies; observed PSDs in this study had better fidelity with less scatter. The micron values changed monotonically with T over the range of temperatures and were directly related to N0 gamma or lambda gamma, thereby reducing the number of variables in the PSD functional equation to two. In the upper part of the ML, N0 and lambda continued to decrease, and in the lower part these values began to increase as the largest particles melted. We developed general expressions relating various bulk microphysical, radar, and radiative transfer-related variables to N0 gamma and lambda gamma, useful for both tropical and mid-latitude clouds. These relationships facilitate the specification of a number of bulk properties in cloud and climate models. The results presented in this paper apply best to temperatures between 0 and -40 C, for which the measured radar reflectivities fall in the range of 0 to 25 dBZe.

Heymsfield, Andrew J.↗
NASA NTRS2015

Implementing NASA's Capability-Driven Approach: Insight into NASA's Processes for Maturing Exploration Systems

NASA is engaged in transforming human spaceflight. The Agency is shifting from an exploration-based program with human activities focused on low Earth orbit (LEO) and targeted robotic missions in deep space to a more sustainable and integrated pioneering approach. Through pioneering, NASA seeks to address national goals to develop the capacity for people to work, learn, operate, live, and thrive safely beyond the Earth for extended periods of time. However, pioneering space involves more than the daunting technical challenges of transportation, maintaining health, and enabling crew productivity for long durations in remote, hostile, and alien environments. This shift also requires a change in operating processes for NASA. The Agency can no longer afford to engineer systems for specific missions and destinations and instead must focus on common capabilities that enable a range of destinations and missions. NASA has codified a capability driven approach, which provides flexible guidance for the development and maturation of common capabilities necessary for human pioneers beyond LEO. This approach has been included in NASA policy and is captured in the Agency's strategic goals. It is currently being implemented across NASA's centers and programs. Throughout 2014, NASA engaged in an Agency-wide process to define and refine exploration-related capabilities and associated gaps, focusing only on those that are critical for human exploration beyond LEO. NASA identified 12 common capabilities ranging from Environmental Control and Life Support Systems to Robotics, and established Agency-wide teams or working groups comprised of subject matter experts that are responsible for the maturation of these exploration capabilities. These teams, called the System Maturation Teams (SMTs) help formulate, guide and resolve performance gaps associated with the identified exploration capabilities. The SMTs are defining performance parameters and goals for each of the 12 capabilities, developing maturation plans and roadmaps for the identified performance gaps, specifying the interfaces between the various capabilities, and ensuring that the capabilities mature and integrate to enable future pioneering missions. By managing system development through the SMTs instead of traditional NASA programs and projects, the Agency is shifting from mission-driven development to a more flexible, capability-driven development. The process NASA uses to establish, integrate, prioritize, and manage the SMTs and associated capabilities is iterative. NASA relies on the Human Exploration and Operation Mission Directorate's SMT Integration Team within Advanced Exploration Systems to coordinate and facilitate the SMT process. The SMT Integration team conducts regular reviews and coordination meetings among the SMTs and has developed a number of tools to help the Agency implement capability driven processes. The SMT Integration team is uniquely positioned to help the Agency coordinate the SMTs and other processes that are making the capability-driven approach a reality. This paper will introduce the SMTs and the 12 key capabilities they represent. The role of the SMTs will be discussed with respect to Agency-wide processes to shift from mission-focused exploration to a capability-driven pioneering approach. Specific examples will be given to highlight systems development and testing within the SMTs. These examples will also show how NASA is using current investments in the International Space Station and future investments to develop and demonstrate capabilities. The paper will conclude by describing next steps and a process for soliciting feedback from the space exploration community to refine NASA's process for developing common exploration capabilities.

Williams-Byrd, Julie↗
NASA NTRS2016

Decision Analysis Methods Used to Make Appropriate Investments in Human Exploration Capabilities and Technologies

NASA is transforming human spaceflight. The Agency is shifting from an exploration-based program with human activities in low Earth orbit (LEO) and targeted robotic missions in deep space to a more sustainable and integrated pioneering approach. Through pioneering, NASA seeks to address national goals to develop the capacity for people to work, learn, operate, live, and thrive safely beyond Earth for extended periods of time. However, pioneering space involves daunting technical challenges of transportation, maintaining health, and enabling crew productivity for long durations in remote, hostile, and alien environments. Prudent investments in capability and technology developments, based on mission need, are critical for enabling a campaign of human exploration missions. There are a wide variety of capabilities and technologies that could enable these missions, so it is a major challenge for NASA's Human Exploration and Operations Mission Directorate (HEOMD) to make knowledgeable portfolio decisions. It is critical for this pioneering initiative that these investment decisions are informed with a prioritization process that is robust and defensible. It is NASA's role to invest in targeted technologies and capabilities that would enable exploration missions even though specific requirements have not been identified. To inform these investments decisions, NASA's HEOMD has supported a variety of analysis activities that prioritize capabilities and technologies. These activities are often based on input from subject matter experts within the NASA community who understand the technical challenges of enabling human exploration missions. This paper will review a variety of processes and methods that NASA has used to prioritize and rank capabilities and technologies applicable to human space exploration. The paper will show the similarities in the various processes and showcase instances were customer specified priorities force modifications to the process. Specifically, this paper will describe the processes that the NASA Langley Research Center (LaRC) Technology Assessment and Integration Team (TAIT) has used for several years and how those processes have been customized to meet customer needs while staying robust and defensible. This paper will show how HEOMD uses these analyses results to assist with making informed portfolio investment decisions. The paper will also highlight which human exploration capabilities and technologies typically rank high regardless of the specific design reference mission. The paper will conclude by describing future capability and technology ranking activities that will continue o leverage subject matter experts (SME) input while also incorporating more model-based analysis.

Williams-Byrd, Julie↗
NASA NTRS2017

Space Launch System Update

NASA has a phased approach to ensure our nation's leadership in space exploration, beginning in Earth orbit, developing our skills in lunar space, and extending those skills and technologies to a human mission to Mars. We're currently in Phase 0, using the ISS to better understand living and working in space. You may have heard about our "twin study" with astronauts Scott and Mike Kelly that's giving us valuable information on the effects of microgravity environments on the human body during long stays in LEO. During Phase 1 in the 2020s, SLS will be used to lift the pieces of a "deep space gateway" outpost to lunar orbit. Developing and operating the gateway will get us to Mars in a step-by-step fashion, with lessons learned in each phase of the process informing the next steps. First step of moving humans farther into the solar system is completing and flying SLS and Orion.

Cobb, Sharon↗
NASA NTRS2020

Using Reinforcement Learning to Design Missed Thrust Resilient Trajectories

From ion thrusters to solar sails, spacecraft continue to adopt new and more efficient forms of propulsion. As these low-thrust propulsion meth- ods have become more prevalent, new challenges have arisen. Depending on the mission, low-thrust propulsion elements may need to thrust con- tinuously for days/months. During these thrusting periods, external fac- tors, such as a micro-meteoroid impact or a software glitch, may cause the spacecraft to prematurely cease its thrust stage. Half of all deep space missions enter a safe mode where they cannot thrust every four months. These missed thrust events can result in the complete loss of a space- craft for time-dependent trajectories like planetary rendezvous. This paper demonstrates how neural networks, trained using reinforcement learning, can autonomously correct for missed thrust events during an interplanetary trajectory.

Laipert, Frank E.↗
NASA NTRSDate not supplied

NASA SmallSat LEARN Forum Briefing: BioSentinel

We will focus on the technologies developed for BioSentinel, primarily the science payloads. One of them, the BioSensor, is a microfluidics-based instrument that houses a series of fluidic cards and manifolds. The fluidic cards can be loaded with microbial organisms to study the effect of the space environment on biology. For BioSentinel, we used the budding yeast as our model organism to study the response to the radiation environment in both low Earth orbit (ISS) and in deep space. The second instrument is a radiation spectrometer. This sensor allows the quantification of ionizing radiation as well as the characterization of the different particles based on the linear energy transfer or LET. Given that BioSentinel launched this past November (Artemis I), part of the presentation will include lessons learned and how we will apply them to future missions, including LEIA. LEIA was recently selected for a mission to the lunar surface on a CLPS lander vehicle, and is primarily based at NASA Ames.

BioSentinel↗
NASA NTRS2007

Cloud Vertical and Horizontal Structure from ICESat/GLAS and MODIS

To accurately model radiative fluxes at the surface and within the atmosphere, we need to know both vertical and horizontal structures of cloudiness. While MODIS provides accurate information on cloud horizontal structure, it has limited ability to estimate cloud vertical structure. ICESat/GLAS on the other hand, provides the vertical distribution and internal structure of clouds as deep as the laser beam can penetrate and return a signal. Having different orbits, MODIS and GLAS provide few collocated measurements; hence a statistical approach is needed to learn about 3D cloud structures from the two instruments. In the presentation, we show the results of the statistical analysis of vertical and horizontal structure of cloudiness using GLAS and MODIS cloud top(s) data acquired in October-November 2003. We revisit the (H1, C1) plot, previously used for analyzing cloud liquid water data, and illustrate cloud structure for single and multiple-layer clouds.

Marshak, Alexander↗
NASA NTRS2021

Deep Domain Adaptation based Cloud Type Detection using Active and Passive Satellite Data

Domain adaptation techniques have been developed to handle data from multiple sources or domains. Most existing domain adaptation models assume that source and target domains are homogeneous, i.e., they have the same feature space. Nevertheless, many real world applications often deal with data from heterogeneous domains that come from completely different feature spaces. In our remote sensing application, data in source domain (from an active spaceborne Lidar sensor CALIOP onboard CALIPSO satellite) contain 25 attributes, while data in target domain (from a passive spectroradiometer sensor VIIRS onboard Suomi-NPP satellite) contain 20 different attributes. CALIOP has better representation capability and sensitivity to aerosol types and cloud phase, while VIIRS has wide swaths and better spatial coverage but has inherent weakness in differentiating atmospheric objects on different vertical levels. To address this mismatch of features across the domains/sensors, we propose a novel end-to-end deep domain adaptation with domain mapping and correlation alignment (DAMA) to align the heterogeneous source and target domains in active and passive satellite remote sensing data. It can learn domain invariant representation from source and target domains by transferring knowledge across these domains, and achieve additional performance improvement by incorporating weak label information into the model (DAMA-WL). Our experiments on a collocated CALIOP and VIIRS dataset show that DAMA and DAMA-WL can achieve higher classification accuracy in predicting cloud types.

domain adaptation↗
NASA NTRS2017

Plant Growth Optimization by Vegetable Production System in HI-SEAS Analog Habitat

The Vegetable Production System (Veggie) is a scientific payload designed to support plant growth for food production under microgravity conditions. The configuration of Veggie consists of an LED lighting system with modular rooting pillows designed to contain substrate media and time-release fertilizer. The pillows were designed to be watered passively using capillary principles but have typically been watered manually by the astronauts in low-Earth orbit (LEO). The design of Veggie allows cabin air to be drawn through the plant enclosure for thermal and humidity control and for supplying CO2 to the plants. Since its delivery to the International Space Station (ISS) in 2014, Veggie has undergone several experimental trials by various crews. Ground unit testing of Veggie was conducted during an 8-month Mars analog study in a semi-contained environment of a simulated habitat located at approximately 8,200 feet (2,500 m) elevation on the Mauna Loa volcano on the Island of Hawaii. The Hawaii Space Exploration Analog and Simulation (HI-SEAS) offered conditions (habitat, mission, communications, etc.) intended to simulate a planetary exploration mission. This paper provides data and analyses to show the prospect for optimized use of the current Veggie design for human habitats. Lessons learned during the study may provide opportunities for updating the system design and operational parameters for current Veggie experiments being conducted onboard the ISS and for payloads on future deep space missions.

Ehrlich, Joshua W.↗
NASA NTRS2016

Auto-Gopher-2 - Wireline Deep Sampler Driven by Percussive Piezoelectric Actuator and Rotary EM Motors

Two of the key purposes of future NASA’s solar system exploration of planetary bodies are the search for potentially preserved bio-signatures and for habitable regions. To address these objectives, a biologically inspired wireline deep rotary-percussive drill, called Auto-Gopher, has been developed. This drill employs a piezoelectric-actuated percussive mechanism for generating impulsive stresses and breaking formations, and an electric motor to rotate the bit to break material and remove the cuttings. Initially, the drill was designed as percussive mechanism for sampling ice and was demonstrated in 2005 at Lake Vida, Antarctica, reaching about 2 meters depth. The lessons learned suggested there is a need to augment the percussive action with bit rotation in order to maximize the penetration rate. The first generation implementation of the rotary augmentation was focused on the demonstration of this capability. In 2012, during the 3-day field test, the drill reached a 3-meter deep in gypsum. A separate mechanism was used to break and remove the cores. The average drilling power consumption was in the range of 100-150 Watts, while the rate of penetration was approximately 2.4 meters per hour. Currently under development is the second-generation drill, called Auto-Gopher-2. The drill will be fully autonomous. In this paper, the capabilities that are being integrated into the Auto-Gopher-2 are described and discussed.

Auto-Gopher Drill↗
NASA NTRS2003

Exploring Asteroid Interiors: The Deep Interior Mission Concept

Deep Interior is a mission to determine the geophysical properties of near-Earth objects, including the first volumetric image of the interior of an asteroid. Radio reflection tomography will image the 3D distribution of complex dielectric properties within the ~1 km rendezvous target and hence map structural, density or compositional variations. Laser altimetry and visible imaging will provide high-resolution surface topography. Smart surface pods culminating in blast experiments, imaged by the high frame rate camera and scanned by lidar, will characterize active mechanical behavior and structure of surface materials, expose unweathered surface for NIR analysis, and may enable some characterization of bulk seismic response. Multiple flybys en route to this target will characterize a diversity of asteroids, probing their interiors with non-tomographic radar reflectance experiments. Deep Interior is a natural follow-up to the NEARShoemaker mission and will provide essential guidance for future in situ asteroid and comet exploration. While our goal is to learn the interior geology of small bodies and how their surfaces behave, the resulting science will enable pragmatic technologies required of hazard mitigation and resource utilization.

Asphaug, E.↗