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NASA NTRSDate not supplied

Integrated Design Results for the MSR SRC Mars Ascent Vehicle

The primary mission of the NASA Mars Sample Return (MSR) Campaign is to return samples of the Martian surface to Earth for scientific study. As part of this campaign, NASA is developing a Mars Ascent Vehicle (MAV). This vehicle must survive an approximate two year journey to the Martian surface as a payload aboard a separate lander spacecraft. After residing on the surface for another year, the MAV will carry a payload of samples into orbit. From there, following ejection from the MAV, the samples will rendezvous with an Earth return spacecraft for capture, and ultimately, return to Earth.The design of the MAV represents a number of unique challenges, as no launch vehicle has ever left the surface of a planet other than Earth. Although conceptual designs for a MAV have been in various levels of development since the 1970s, none have achieved the level of fidelity and support that exists in the current MSR-MAV design. Early MSR-MAV concept studies examined multiple methods of propulsion, ultimately deciding that a Two Stage to Orbit (TSTO) solid propulsion vehicle would provide the most capable performance in a Martian environment. Following this key architecture decision, the vehicle design was further matured to a Solid-Solid Guided-Guided (SSGG) architecture for NASA Key Decision Point A (KDP-A). Although the SSGG design was able to meet all mission constraints, concerns were raised regarding limited mass margin on other elements of the MSR campaign at such an early phase. A design challenge was issued to reduce MAV total mass by as much as possible. It was ultimately determined that by moving a number of components of the vehicle second stage to the first stage, the overall vehicle mass could be reduced significantly. The new design featured a much smaller and completely unguided second stage. This paper describes the resultant Solid-Solid Guided-Unguided (SSGU) MAV design concept developed as part of the Systems Requirement Cycle (SRC). This design was developed primarily by NASA Marshall Space Flight Center (MSFC), in association with NASA Jet Propulsion Laboratory (JPL) and NASA Langley Research Center (LaRC). The TSTO vehicle includes one solid rocket motor per stage. As the vehicle second stage is unguided, it features spin-stabilization to maintain vehicle stability during flight. An electromechanically actuated Thrust Vector Control (TVC) and a monopropellant Reaction Control System (RCS) is employed for active guidance on the first stage. The vehicle is designed to deliver up to 0.47kg of Martian samples to a Mars circular orbit of 380km at 27° inclination. Due to the extremely unique design constraints of this mission, and a recent transition to a Risk Class A posture, the MAV team was compelled to devise unconventional solutions to the vehicle design. The detailed design and analysis of these subsystems and the vehicle as a whole are discussed in this paper relative to all of the engineering disciplines involved.

MSR↗

NASA NTRSDate not supplied

Integrated Design Results for the MSR SRC Mars Ascent Vehicle

The primary mission of the NASA Mars Sample Return (MSR) Campaign is to return samples of the Martian surface to Earth for scientific study. As part of this campaign, NASA is developing a Mars Ascent Vehicle (MAV). This vehicle must survive an approximate two-year journey to the Martian surface as a payload aboard a separate lander spacecraft. After residing on the surface for another year, the MAV will carry a payload of samples into orbit. From there, following ejection from the MAV, the samples will rendezvous with an Earth return spacecraft for capture, and ultimately, return to Earth. The design of the MAV represents a number of unique challenges, as no launch vehicle has ever left the surface of a planet other than Earth. Although conceptual designs for a MAV have been in various levels of development since the 1970s, none have achieved the level of fidelity and support that exists in the current MSR-MAV design. Early MSR-MAV concept studies examined multiple methods of propulsion, ultimately deciding that a Two-Stage-to-Orbit (TSTO) solid propulsion vehicle would provide the most capable performance in a Martian environment. Following this key architecture decision, the vehicle design was further matured to a Solid-Solid Guided-Guided (SSGG) architecture for NASA Key Decision Point A (KDP-A). Although the SSGG design was able to meet all mission constraints, concerns were raised regarding limited mass margin on other elements of the MSR campaign at such an early phase. A design challenge was issued to reduce MAV total mass by as much as possible. It was ultimately determined that by moving a number of components of the vehicle second stage to the first stage, the overall vehicle mass could be reduced significantly. The new design features a much smaller and completely unguided second stage. This paper describes the resultant Solid-Solid Guided-Unguided (SSGU) MAV design concept developed as part of the Systems Requirement Cycle (SRC). This design was developed primarily by NASA Marshall Space Flight Center (MSFC), in association with NASA Jet Propulsion Laboratory (JPL) and NASA Langley Research Center (LaRC). The TSTO vehicle includes one solid rocket motor per stage. As the vehicle second stage is unguided, it features spin-stabilization to maintain vehicle stability during flight. An electromechanically actuated Thrust Vector Control (TVC) system and a monopropellant Reaction Control System (RCS) are employed for active guidance on the first stage. The vehicle is designed to deliver up to 0.47kg of Martian samples to a Mars circular orbit of 380km at 27° inclination. Due to the extremely unique design constraints of this mission, and a recent transition to a Risk Class A posture, the MAV team was compelled to devise unconventional solutions to the vehicle design. The detailed design and analysis of these subsystems and the vehicle as a whole are discussed in this paper relative to all of the engineering disciplines involved.

MAV↗

NASA NTRSDate not supplied

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↗

NASA NTRSDate not supplied

A Model-Based Systems Engineering Journey to Developing a Concept of Operations

Starting in 2017, NASA’s Human Research Program (HRP) Exploration Medical Capability (ExMC) element began a systems engineering transition from traditional, document-centric development to model-centric development when defining its foundation medical systems. These foundation medical systems define a Concept of Operations (ConOps) and identify the generic requirements for a medical system based on assumptions about a generic crew and mission environments and guidance from NASA standards (e.g., Medical “Levels of Care”). By making the transition, ExMC intends to improve communication among stakeholders about foundation medical system requirements and content. In addition, this transition will enable ExMC to lower both development and crew treatment risks for future, mission-specific medical systems. ExMC followed a Model Based Systems Engineering (MBSE) paradigm when developing the foundation medical systems. A model-based approach provides several advantages over a traditional, document-centric approach. First, when Systems Engineers (SE) develop diagrams in a model using a standard modeling language, they produce information dense pictures that facilitate understanding much more efficiently with less room for misinterpretation than text. Second, due to the evolving nature of projects, documentation becomes out of date the minute it is published. This can result in people making decisions based on information that is no longer current, especially if they are referencing a locally-stored copy of a document. A model, on the other hand, is always up to date with the latest approved changes and information. It serves as a single point of truth. Third, a model-centric approach centralizes all important information in one place. Rather than having to flip through separate ConOps documents, design specifications, requirements specifications, and the like to coordinate information, a model captures the content in one, integrated spot. This integration makes tracing information from end-to-end easier with greater reliability. The ExMC Systems Engineering Lifecycle follows a well-defined process. ExMC Systems Engineers perform all major steps of the process, regardless of the development methodology. One of the first steps in the process is developing the ConOps that describes the operation of the system from the point of view of the users. It includes a list of the users and their needs, the goals of the medical system, key assumptions about the system, and definitions of the medical system’s operational environments. For this development effort, ExMC chose to replace the traditional text-based ConOps document with a model. While the decision to change the development workflow was not difficult, implementing the structural and organizational workflows were. It required showing ExMC’s users, most of whom are not Systems Engineers, how the information they require would be presented in the model and to gain their acceptance of this approach. This paper documents key lessons learned during the ConOps transformation by focusing on how the model represents information, the agile workflow used by SEs when developing the model and how it integrates into a project plan, how leadership influenced key users to accept the transformation, and how the users interact with the model information.

Jeffrey Robert Cohen↗

NASA NTRS2015

Resource Prospector (RP) - Early Prototyping and Development

The Resource Prospector (RP) is an In-Situ Resource Utilization (ISRU) technology demonstration mission under study by the NASA Human Exploration and Operations Mission Directorate's (HEOMD) Advanced Exploration Systems (AES) Division. The mission, currently planned to launch in 2020, will demonstrate extraction of oxygen from lunar regolith to validate ISRU capability. The mission will address key Strategic Knowledge Gaps (SKGs) for robotic and human exploration to the Moon, Near Earth Asteroids (NEAs), and ultimately Mars, as well as meet the strategic goals of the Global Exploration Roadmap (GER), offered by the International Space Exploration Coordination Group (ISECG). In this roadmap, the use of local resources is specifically addressed relating to human exploration. RP will provide knowledge to inform the selection of future mission destinations, support the development of exploration systems, and reduce the risk associated with human exploration. Expanding human presence beyond low-Earth orbit to asteroids and Mars will require the maximum possible use of local materials, so-called in-situ resources. The moon presents a unique destination to conduct robotic investigations that advance ISRU capabilities, as well as providing significant exploration and science value. Lunar regolith contains useful resources such as oxygen, water, silicon, and light metals, like aluminum and titanium. Oxygen can be separated from the regolith for life support (breathable air), or used to create rocket propellant (oxidizer). Regolith can be used to protect against radiation exposure, be processed into solar cells, or used to manufacture construction materials such as bricks and glass. RP will characterize the constituents and distribution of water and other volatiles at the poles of the Moon, enabling innovative uses of local resources, in addition to validating ISRU capabilities. This capability, as well as a deeper understanding of regolith, will be valuable in the exploration of near-Earth asteroids (NEAs) and Mars. In order to reduce risk and explore system designs, the RP project is attempting two-fold approaches to development as it looks towards flight. We continue to explore flight planning, requirements, and interfaces definition by using Engineering Test Units (ETUs), looking towards lunar deployment, while also using fiscal year 2015 to develop, build and test an earth-terrestrial prototype rover and payload system. This terrestrial prototype, called "RP15", is built to both inform the system design, and to be a partnership advocacy tool for this unique mission. RP15 must be affordable within the resource and time constraints of fiscal year 2015, while working to the following Needs, Goals, and Objectives provided by HEOMD/AES: 1. Demonstrate rover mobility in a 1g environment 2. The Surface Segment (prototype rover + payload system) shall represent the flight system concept with as much fidelity as affordable (limited by cost and schedule) - Surface Segment shall be the approximate size/dimension/footprint -Surface Segment shall package all the expected devices (instruments, systems, etc.), even if some facets are mocked-up due to time/cost constraints -Overall Surface Segment fidelity negotiable to make achievable 3. Priority should be given to illustrating mission functionality over support functionality, which exists solely to support mission functionality This paper will provide an overview of RP project developments, including the design and build, capturing the development and initial integrated testing of RP15 in relevant environments.

Lunar↗

NASA NTRS2015

2015 Space Radiation Standing Review Panel

The 2015 Space Radiation Standing Review Panel (from here on referred to as the SRP) met for a site visit in Houston, TX on December 8 - 9, 2015. The SRP met with representatives from the Space Radiation Element and members of the Human Research Program (HRP) to review the updated research plan for the Risk of Radiation Carcinogenesis Cancer Risk. The SRP also reviewed the newly revised Evidence Reports for the Risk of Acute Radiation Syndromes Due to Solar Particle Events (SPEs) (Acute Risk), the Risk of Acute (In-flight) and Late Central Nervous System Effects from Radiation Exposure (CNS Risk), and the Risk of Cardiovascular Disease and Other Degenerative Tissue Effects from Radiation (Degen Risk), as well as a status update on these Risks. The SRP would like to commend Dr. Simonsen, Dr. Huff, Dr. Nelson, and Dr. Patel for their detailed presentations. The Space Radiation Element did a great job presenting a very large volume of material. The SRP considers it to be a strong program that is well-organized, well-coordinated and generates valuable data. The SRP commended the tissue sharing protocols, working groups, systems biology analysis, and standardization of models. In several of the discussed areas the SRP suggested improvements of the research plans in the future. These include the following: It is important that the team has expanded efforts examining immunology and inflammation as important components of the space radiation biological response. This is an overarching and important focus that is likely to apply to all aspects of the program including acute, CVD, CNS, cancer and others. Given that the area of immunology/inflammation is highly complex (and especially so as it relates to radiation), it warrants the expansion of investigators expertise in immunology and inflammation to work with the individual research projects and also the NASA Specialized Center of Research (NSCORs). Historical data on radiation injury to be entered into the Watson “big data” study must be used with caution. The general scientific issues of reproducibility, details of experimental methods and data analysis from preclinical and basic research laboratories have been raised broadly over the last few years (not specific to this work) and indicate that caution must be applied in the ways these data are used. This pertains to preclinical data and also to phase 3 clinical trials in radiation oncology and medical oncology. Of course, appropriate use and analysis of these “big-data” sets also offer the potential of pinpointing limitations and extracting remaining useful information. Emphasis should be placed on the latter possibility. A key target is risk reduction from radiation exposure. Progress of the entire space program, now moving towards the Mars mission, requires timely answers to key components of human risk, which are known to be complex. Periodic review of progress should be conducted with additional resources directed into achieving critical milestones. Turning the long red bars to yellow and green (or for some risks such as CNS possibly to grey) must be high priority. That such progress will require new science and not engineering means that it should be viewed in a knowledge-based light. The technology-based aspects of engineering issues are certainly as important, however, science and knowledge-based problems are solved in a different way than engineering. Timelines for engineering are more predictable, while for science, progress can be methodical with occasional major incremental findings that can rapidly change the rate of progress. As opportunities for rapid incremental changes arise, periodic enhancement of investment is strongly recommended to enable such new knowledge to be quickly and efficiently exploited. Collaborations and linkages with National Institute of Allergy and Infectious Diseases (NIAID), the Biomedical Advanced Research and Development Authority (BARDA) and the Department of Defense (DoD) are in place and more are encouraged, where possible, with the radiation injury and medical countermeasure studies. This could include utilizing some of their animal model testing contracts to facilitate obtaining results using common platforms. Such approach will facilitate the comparison of results among laboratories, and will facilitate and accelerate the development of medical countermeasures. It is particularly noteworthy that the NASA Space Radiation Element is reaching out to the Multidisciplinary European Low Dose Initiative (MELODI) platform coordinating low dose radiation risk research, and to other international agencies that are studying low dose radiation effects in an effort to fill the void generated by the cancelation of the Department of Energy (DOE) low dose radiation program. While NASA is working actively with NIAID and BARDA to integrate their relevant findings of radiation mitigator investigations to NASA programs, the committee notes its disappointment that the United States currently lacks a dedicated low dose radiation program with clear mechanistic orientation and aimed at the quantification and mitigation of human radiation risk on Earth. This void gives to the NASA Space Radiation Program Element special societal value, but also makes its overall design more challenging.

Steinberg, Susan↗

NASA NTRSDate not supplied

Independent Technical Assessment of NASA and External Quantum Sensing Capability

The recent FY 2020 federal Research and Development Budget Priorities memo addresses the leadership need in Quantum Information Science (QIS) directing agencies to “prioritize QIS research and development (R&D), which will build the technical and scientific base necessary to explore the next generation of QIS theory, devices, and applications.” Quantum Sensing (QS) is an integral part of QIS. NASA is a key part of the directive to forward American space exploration and commercialization by providing “capabilities that have broad potential applications in space and on Earth.” QS provides an arena for NASA to demonstrate leadership in both areas of the administrative directive. QS uses quantum properties to achieve unprecedented measurement sensitivity and performance, including quantum-enhanced methodologies that outperform their classical counterparts. Typical quantum sensors exploit techniques such as atomic systems, matter waves, quantum entanglement, quantum superposition of states, quantum illumination methods, and manipulation of photons and atoms, in general. Guided by advancements in our ability to generate, manipulate, and control quantum systems, the emerging quantum sensing technologies promise unrivalled sensitivity, resolution, and precision, potentially leading to game-changing applications. Significant gains include technologies important for a range of NASA missions such as remote sensing, in situ measurements, metrology, interferometry, quantum communication, ranging, imaging, radar and lidar receivers, and gravity measurements. NASA Engineering and Safety Center has convened an independent external panel, comprising of Quantum Sensing Experts from Government, DoD, academia, and Federally funded Research and Development Center to conduct an independent technical assessment of the agency's capabilities in Quantum Sensing to understand NASA's internal needs and competencies related to Quantum Sensing and compare agency capabilities with those available externally including industry, academia, and other government agencies. The outcomes of the assessment will help the agency in establishing appropriate strategies and investments to develop and maintain the state-of-the-art sensing competence and capabilities required to meet the agency’s future needs. The External Experts Panel (EEP) is collaborating with HQ, various NASA Center and the representatives in the NASA Quantum Sensing Community of Practice (QS CoP), a part of NASA Sensors and Instrumentation Technical Fellow Technical Discipline Team, in obtaining common, current understanding of agency mission needs where QS can be an important enabler for future needs, and any programs, projects, assets, and technologists working in QS. The EEP organized a Quantum Sensing Workshop of practitioners from industry, academia, other government agencies, external experts, and interested NASA personnel to gather the assessment information. EEP also conducted information gathering on the industry at large, educational institutions, and other government agency research efforts for capture in the assessment database. At the conclusion of this assessment, EEP team will develop findings and conclusions describing NASA’s capabilities for the mission needs, NASA's relative position on new, enabling technologies, and an analysis of the gaps that may present any risks to near-term or far-term mission needs. This presentation will give details of the NASA and External Quantum Sensing Assessment outcomes, findings, observations, and its recommendation to NASA as how it can advance Quantum Sensing technologies and techniques for its science and explorations related missions.

External Quantum Sensing Capability↗

NASA NTRS2023

Adaptive Independent Verification and Validation (IV&V) Reduces Risk of Software Impacting Safety in Artemis Missions

The National Aeronautics and Space Administration (NASA) is asking more of its human spaceflight programs than ever before through the collective Artemis Missions. The NASA Independent Verification and Validation (IV&V) Program contributes to NASA’s human spaceflight goals by providing IV&V services for NASA’s critical spacecraft and ground software. The IV&V Program is tasked with providing assurance from both individual and integrated mission software perspectives. The Artemis IV&V organization is actively supporting six distinct development efforts: Orion, the Space Launch System (SLS), Exploration Ground Systems (EGS), Mission Control Center (MCC), the Lunar Gateway, and the Human Landing System (HLS), representing a wide diversity of developer organizations, management structures, and development approaches. With much of this extremely complex flight and ground software being essential to human safety both on the ground and in space, Artemis IV&V is likewise challenged to provide more value-added assurance to future Artemis missions within a constrained budget. To meet this challenge, Artemis IV&V employs a variety of novel and evolving “Adaptive IV&V” approaches for planning and executing IV&V analysis to increase both the efficiency and effectiveness of the IV&V Program’s assurance activities, and to address the difficulties imposed by assuring software for a large, highly integrated, multi-mission enterprise managed and executed by physically and organizationally distinct programs. Instilling agile principles like iterative planning cycles, self-organizing teams, and regular retrospectives, into IV&V planning and execution has led to a more rapid turnaround of a minimum viable assurance product and allowed for increased alignment of assurance activities with development progress. Adopting an assurance case methodology has led to greater consistency and clearer communication of assurance design and provided a foundation for long-term maintenance of assurance plans, products, and results across missions. The IV&V-developed Assurance / Safety Case Analytical Network (A-SCAN) framework and tool has enabled the quantification and tracking of system/software risk and confidence. These confidence measures provide a means to repeatedly express the impact of planned and completed assurance work and the remaining residual risk. Applied as part of a “Follow-the-Risk” organizational ethos, this allows consistent rightsizing of analysis rigor and intensity commensurate with the perceived risk of defects, as well as appropriate targeting of the highest risk areas of the software to find safety issues before they can manifest. Finally, the development of the IV&V Advanced Risk Reduction Integrated Software Test and Operations Tri-program Lightweight Environment (ARRISTOTLE), an integrated software-only simulation of Orion, SLS, and EGS systems, has made it possible to independently test integrated pad and flight scenarios and inject faults to observe how the Artemis multi-program, mission software behaves in degraded modes and in response to hazards. These adaptive IV&V investments have enabled Artemis IV&V to become more efficient and effective in IV&V planning and execution and respond more readily to changes in the risk landscape, increasing the breadth and depth of risk reduction possible within the available resources. Residual risk tracking allows IV&V to communicate more effectively with stakeholders, both internal and external at all levels, and inform key decision-making personnel. This evolving assurance design approach provides IV&V surety that work is performed in the highest risk, most value-added areas of the software, to keep our astronauts and ground crews safe and ensure mission success.

Gerek A Whitman↗

NASA NTRS2023

Adaptive Independent Verification and Validation (IV&V) Reduces Risk of Software Impacting Safety in Artemis Missions

Gerek Whitman↗

NASA NTRS1996

Evaluation of an Anthropometric Human Body Model for Simulated EVA Task Assessment

One of the more mission-critical tasks performed in space is extravehicular activity (EVA) which requires the astronaut to be external to the station or spacecraft, and subsequently at risk from the many threats posed by space. These threats include, but are not limited to: no significant atmosphere, harmful electromagnetic radiation, micrometeoroids, and space debris. To protect the astronaut from this environment, a special EVA suit is worn which is designed to maintain a sustainable atmosphere (at 1/3 atmosphere) and provide protection against the hazards of space. While the EVA suit serves these functions well, it does impose limitations on the astronaut as a consequence of the safety it provides. Since the astronaut is in a virtual vacuum, any atmospheric pressure inside the suit serves to pressurize the suit and restricts mobility of flexible joints (such as fabric). Although some of the EVA suit joints are fixed, rotary-style joints, most of the mobility is achieved by the simple flexibility of the fabric. There are multiple layers of fabric, each of which serves a special purpose in the safety of the astronaut. These multiple layers add to the restriction of motion the astronaut experiences in the space environment. Ground-based testing is implemented to evaluate the capability of EVA-suited astronauts to perform the various tasks in space. In addition to the restriction of motion imposed by the EVA suit, most EVA activity is performed in a micro-gravity (weight less) environment. To simulate weightlessness EVA-suited testing is performed in a neutral buoyancy simulator (NBS). The NBS is composed of a large container of water (pool) in which a weightless environment can be simulated. A subject is normally buoyant in the pressurized suit; however he/she can be made neutrally buoyant with the addition of weights. In addition, most objects the astronaut must interface with in the NBS sink in water and flotation must be added to render them "weightless". The implementation of NBS testing has proven to invaluable in the assessment of EVA activities performed with the Orbiter and is considered to be a key step in the construction of the International Space Station (ISS). While the NBS testing is extremely valuable, it does require considerable overhead to maintain and operate. It has been estimated that the cost of utilizing the facility is approximately $10,000 per day. Therefore it is important to maximize the utility of NBS testing for optimal results. One important aspect to consider in any human/worksite interface is the considerable wealth of anthropometric and ergonomic data available. A subset of this information specific to EVA activity is available in NASA standard 3000. The difficulty in implementing this data is that most of the anthropometric information is represented in a two-dimensional format. This poses some limitations in complete evaluation of the astronaut's capabilities in a three-dimensional environment. Advances in computer hardware and software have provided for three-dimensional design and implementation of hardware with the advance of computer aided design (CAD) software. There are a number of CAD products available and most companies and agencies have adopted CAD as a fundamental aspect of the design process. Another factor which supports the use of CAD is the implementation of computer aided manufacturing (CAM) software and hardware which provides for rapid prototyping and decreases the time to product in the design process. It is probable that most hardware to be accessed by astronauts in EVA or IVA (intravehicular activity) has been designed by a CAD system, and is therefore represented in three-dimensional space for evaluation. Because of the implementation of CAD systems and the movement towards early prototyping, a need has arisen in industry and government for tools which facilitate the evaluation of ergonomic consideration in a three-dimensional environment where the hardware has been designed by the CAD tools. One such product is Jack which was developed by the University of Pennsylvania with funding from several government agencies, including NASA. While the primary purpose of Jack is to model human figures in a ground-based (gravity) environment, it can be utilized to evaluate EVA-suited activities as well. The effects of simulated gravity must be turned off by turning off "behaviors". Although Jack provides human figures for manipulation, the primary instrument to be evaluated for EVA mobility is the work envelope provided by the EVA suit. An EVA Jack suit model has been developed by NASA-JSC and was utilized in this study. This suit model provided a more restrictive motion environment as expected for an EVA suited subject. As part of this study, the anthropometric dimensions for a 50th percentile male were compared with basic anthropometric data and were found to be representative for the population group expected in the NASA flight program. The joints for the suit were created in a manner which provided consistent performance with EVA reach envelopes published in NASA standard #3000.

Etter, Brad↗

NASA NTRS1999

Sustained Partial Sleep Deprivation: Effects on Immune Modulation and Growth Factors

The vulnerability to medical emergencies is greatest in space where there are real limits to the availability or effectiveness of ground based assistance. Moreover, astronaut safety and health maintenance will be of increasing importance as we venture out into space for extended periods of time. It is therefore critical to understand the mechanisms of the regulatory physiology of homeostatic systems (sleep, circadian, neuroendocrine, fluid and nutritional balance) and the key roles played in adaptation. This synergy project has combined aims of the "Human Performance Factors, Sleep and Chronobiology Team"; the "Immunology, Infection and Hematology Team"; and the "Muscle Alterations and Atrophy Team", to broadly address the effects of long term sleep reduction, as is frequently encountered in space exploration, on neuroendocrine, neuroimmune and circulating growth factors. Astronaut sleep is frequently curtailed to averages of between 4- 6.5 hours per night. There is evidence that this amount of sleep is inadequate for maintaining optimal daytime functioning. However, there is a lack of information concerning the effects of chronic sleep restriction, or reduction, on regulatory physiology in general, and there have been no controlled studies of the cumulative effects of chronic sleep reduction on neuroendocrine and neuroimmune parameters. This synergy project represents a pilot study designed to characterize the effects of chronic partial sleep deprivation (PSD) on neuroendocrine, neuroimmune and growth factors. This project draws its subjects from two (of 18) conditions of the larger NSBRI project, "Countermeasures to Neurobehavioral Deficits from Cumulative Partial Sleep Deprivation During Space Flight", one of the projects on the "Human Performance Factors, Sleep and Chronobiology Team ". For the purposes of this study, to investigate the effects of chronic sleep loss on neuroendocrine and neuroimmune function, we have focused on the two extreme sleep conditions from this larger study: a 4.2 hour per night condition, and a 8.2 hour per night condition. During space flight, muscle mass and bone density are reduced, apparently due to loss of GH and IGF-I, associated with microgravity. Since >70% of growth hormone (GH) is secreted at night in normal adults, we hypothesized that the chronic sleep restriction to 4 hours per night would reduce GH levels as measured in the periphery. In this synergy project, in collaboration with the "Muscle Alterations and Atrophy Team ", we are measuring insulin-like growth factor-I (IGF-I) in peripheral circulation to test the prediction that it will be reduced by chronic sleep restriction. In addition to stress modulation of immune function, recent research suggests that sleep is also involved. While we all have the common experience of being sleepy when suffering from infection, and being susceptible to infection when not getting enough sleep, the mechanisms involved in this process are not understood and until recently have gone largely overlooked. We believe that the immune function changes seen in spaceflight may also be related to the cumulative effects of sleep loss. Moreover, in space flight, the possibility of compromised immune function or of the reactivation of latent viruses are serious potential hazards for the success of long term missions. Confined living conditions, reduced sleep, altered diet and stress are all factors that may compromise immune function, thereby increasing the risks of developing and transmitting disease. Medical complications, which would not pose serious problems on earth, may be disastrous if they emerged in space.

Mullington, Janet M.↗

NASA NTRS2015

An Integrated 0-1 Hour First-Flash Lightning Nowcasting, Lightning Amount and Lightning Jump Warning Capability

Lightning one of the most dangerous weather-related phenomena, especially as many jobs and activities occur outdoors, presenting risk from a lightning strike. Cloud-to-ground (CG) lightning represents a considerable safety threat to people at airfields, marinas, and outdoor facilities-from airfield personnel, to people attending outdoor stadium events, on beaches and golf courses, to mariners, as well as emergency personnel. Holle et al. (2005) show that 90% of lightning deaths occurred outdoors, while 10% occurred indoors despite the perception of safety when inside buildings. Curran et al. (2000) found that nearly half of fatalities due to weather were related to convective weather in the 1992-1994 timeframe, with lightning causing a large component of the fatalities, in addition to tornadoes and flash flooding. Related to the aviation industry, CG lightning represents a considerable hazard to baggage-handlers, aircraft refuelers, food caterers, and emergency personnel, who all become exposed to the risk of being struck within short time periods while convective storm clouds develop. Airport safety protocols require that ramp operations be modified or discontinued when lightning is in the vicinity (typically 16 km), which becomes very costly and disruptive to flight operations. Therefore, much focus has been paid to nowcasting the first-time initiation and extent of lightning, both of CG and of any lightning (e.g, in-cloud, cloud-to-cloud). For this project three lightning nowcasting methodologies will be combined: (1) a GOESbased 0-1 hour lightning initiation (LI) product (Harris et al. 2010; Iskenderian et al. 2012), (2) a High Resolution Rapid Refresh (HRRR) lightning probability and forecasted lightning flash density product, such that a quantitative amount of lightning (QL) can be assigned to a location of expected LI, and (3) an algorithm that relates Pseudo-GLM data (Stano et al. 2012, 2014) to the so-called "lightning jump" (LJ) methodology (Shultz et al. 2011) to monitor lightning trends and to anticipate/forecast severe weather (hail > or =2.5 cm, winds > or =25 m/s, tornadoes). The result will be a time-continuous algorithm that uses GOES satellite, radar fields, and HRRR model fields to nowcast first-flash LI and QL, and subsequently monitors lightning trends on a perstorm basis within the LJ algorithm for possible severe weather occurrence out to > or =3 hours. The LI-QL-LJ product will also help prepare the operational forecast community for Geostationary Lightning Mapper (GLM) data expected in late 2015, as these data are monitored for ongoing convective storms. The LI-QL-LJ product will first predict where new lightning is highly probable using GOES imagery of developing cumulus clouds, followed by n analysis of NWS (dual-polarization) radar indicators (reflectivity at the -10 C altitude) of lightning occurrence, to increase confidence that LI is immanent. Once lightning is observed, time-continuous lightning mapping array and Pseudo-GLM observations will be analyzed to assess trends and the severe weather threat as identified by trends in lightning (i.e. LJs). Additionally, 5- and 15-min GOES imagery will then be evaluated on a per-storm basis for overshooting and other cloud-top features known to be associated with severe storms. For the processing framework, the GOES-R 0-1 hour convective initiation algorithm's output will be developed within the Warning Decision Support System - Integrated Information (WDSS-II) tracking tool, and merged with radar and lightning (LMA/Psuedo-GLM) datasets for active storms. The initial focus of system development will be over North Alabama for select lightning-active days in summer 2014, yet will be formed in an expandable manner. The lightning alert tool will also be developed in concert with National Weather Service (NWS) forecasters to meet their needs for real-time, accurate first-flash LI and timing, as well as anticipated lightning trends, amounts, continuation and cessation, so to provide key situational awareness and decision support information. The NASA Short-term Prediction Research and Transition (SPoRT) Center will provide important logistical and collaborative support and training, involving interactions with the NWS and broader user community.

Mecikalski, John↗

NASA NTRSDate not supplied

Advancement of Entry System Modeling to Support Exploration of Giant Planets

This paper describes NASA’s efforts to advance entry system modeling and simulation capabilities to support future exploration of Giant planets. The Giant planets are key destinations of interest to the planetary science community for their potential to provide insight into the formation and evolution of our Solar System, as well as extrasolar planetary systems. To date, the Galileo atmospheric probe is the only purpose-built entry probe to a Giant planet. Post-flight analysis of Galileo’s performance showed that there was significant recession of the thermal protection system (TPS), well beyond what was anticipated on the flank, and this was due in part to insufficiently accurate capability for estimating the flight environment and TPS response. While Galileo ultimately survived its flight, the example serves to highlight the great challenge of designing successful missions for environments that are poorly understood or where models have not yet been validated. An important means to reduce mission risks is the incorporation of physics-based modeling with well-quantified uncertainties. The emphasis on physics-based modeling – in contrast to empirically-driven models – is motivated by the fact that it is impossible to completely replicate entry environments through ground tests and, therefore, extrapolation to the flight environment is required. Basing analysis in fundamental physics removes the bias of ground test limitations, though one must then be careful to properly characterize model inputs, simplifying assumptions, and the limits wherein the model is valid. NASA’s Entry Systems Modeling (ESM) Project is tasked with investigating such considerations for planetary science missions across the Solar System, and in recent years has begun to do so for Giant planets. The most distinctive features of the Giant planets, from an entry system perspective, are the atmospheres composed primarily of hydrogen and helium. The entry velocities of proposed missions are generally very large and can therefore be expected to result in significant convective and radiative heating generated by the vehicle’s shock layer. Yet thermochemical behavior of the hydrogen-helium system is not well understood under such conditions. The ESM project is leading efforts to develop accurate thermochemical databases based on state-of-the-art measurements in the Electric Arc Shock Tube and detailed computational chemistry. The large heat fluxes anticipated by missions has driven interest in new TPS materials, in particular woven materials, which may be enabling but have never been flown before. Consequently, multiscale models are in development to describe properties and performance of the materials from micro- to system-scale. The goal is to not only provide accurate thermal response but also to inform thermostructural reliability predictions for extreme entries. Additionally, new computational models have been developed to evaluate performance of non-destructive evaluation techniques which are vital to establishing acceptance of systems to be free of manufacturing faults like material cracking, voids, and debonding. Finally, in the area of guidance and control, aerocapture has been shown conceptually to provide a number of mission benefits, including reducing transit time and increasing payload fraction. The ESM project is building a launch-to-landing trajectory simulation capability to enable detailed studies of aerocapture maneuvers in the context of Giant planets missions. The final presentation and paper will describe each of these topics in detail, including discussion of specific gaps and the technical approach to solving them. In addition, the final paper will briefly discuss ongoing coordination between ESM project work and an ESA-funded technology development activity comprised of validation testing in the Oxford T6, IRS PWK and IST ESTHER tunnels, as well as state-to-state modeling of the shock layer to better represent non-Boltzmann energy distributions leading to non-equilibrium radiation.

Entry systems↗

NASA NTRSDate not supplied

A Machine Learning Approach to Improve Air Traffic Management Initiatives

Collaborating closely with commercial air carriers and related organizations, the Federal Aviation Administration(FAA) regulates air traffic and ensures the safety and efficiency of air operations. Air traffic controllers make strategic decisions, such as delaying, rerouting, or canceling flights, partly based on guidance provided by the FAA’s Air TrafficControl System Command Center (ATCSCC). The guidance includes, among other things, control measures known asTraffic Management Initiatives (TMIs) designed to enhance safety and improve operational efficiency. TMIs play a crucial role in managing the demand and capacity within the U.S. National Airspace System (NAS). Two major TMIs that are routinely used (primarily to mitigate the adverse effects of bad weather) are Ground Delay Programs (GDPs) andGround Stops (GSs). In a GDP, flights destined for airports facing thunderstorm activity experience delays at their origin airports. This proactive approach minimizes the risk of routing aircraft through hazardous weather conditions and also replaces (fuel burning) airborne delays with ground delays. In a GS, a temporary restriction is imposed on the departure or arrival of aircraft at a specific airport or within a designated airspace. Although other TMIs (e.g., miles-in-trail) are also implemented as part of (air) traffic flow management in the NAS, the focus of this work is on GDPs and GSs. Since TMIs, by design, lead to flight delays or cancellations, it is crucial to put in place the right set of parameters(e.g., scope and duration of the GDP). For example, when the end time of a GDP extends beyond what is necessary, it imposes unnecessary delays on departing flights. This situation could occur as a result of inaccurate prediction of the(required) duration of the GDP based on the weather forecast. On the other hand, if a GDP ends prematurely before the underlying capacity constraints are resolved at the destination airport, it may result in airborne holding. The delicate balance lies in matching the termination of the GDP precisely with the resolution of capacity constraints, avoiding both the imposition of unnecessary ground delays and the need for airborne holding due to premature program termination.Failing to specify the right parameters for TMIs also leads to flight delays, creating a significant obstacle in managing the increasing traffic volumes causing increased work load for the controllers. To address this issue, we propose the integration of Machine Learning (ML) models in the traffic flow management(TFM) pipeline. In current operations, decisions are made by human experts based on extensive training, historical patterns, available traffic and weather data. Since we have an abundance of data from past events that tell us the likely impact of various TMIs, by ingesting historical data, properly trained ML models can offer valuable insights and aid human decision-making. With the FAA increasingly exploring advanced analytics, ML emerges as a focal point for enhancing TFM within the National Airspace System (NAS). As a first step, this study aims to provide traffic controllers with decision-making support for the issuance and adjustment of TMIs. Data analytics and machine learning have been previously employed to address some of the challenges associated with TMIs. Numerous studies have concentrated on various facets of TMI issuance, exploring factors influencing TMI parameters, including arrival rate, airport capacity, and delay prediction. For example, using weather forecasts, several statistical methods were used to produce probabilistic capacity profiles which in conjunction with deterministic models provided insights into the GDP planning process [1–4]. The downside of using deterministic models is that they rely on fixed inputs and predetermined rules, which lack the ability to account for the inherent uncertainty and variability present in real-world scenarios. In a separate series of studies, researchers aimed to predict the occurrences of GDPs and GSs. The majority of these studies utilized various supervised learning methods, including Decision Trees, Naive Bayes, Support VectorMachines, and Random Forests to analyze the influence of weather conditions and arrival demand on TMI incidents[5–8]. However, these studies primarily focused on predicting the incidence of TMIs without explicitly addressing the scope of TMIs, including their duration and their geographical coverage. Furthermore, the emphasis of these studies was largely on GDPs, given their higher frequency and longer duration when compared to GSs. A limited number of studies focused on predicting the parameters of TMIs, specifically addressing their duration and extent. In one such study focusing on optimizing the TMI parameters at San Francisco International Airport (SFO),the authors utilized a probabilistic forecast of fog [9]. They simulated various capacity scenarios based on the (fog)burn-off forecasts, selecting GDP parameters that minimized airborne and overall ground delays. However, this approach exclusively emphasizes stratus (fog) burn-off as the primary determinant of GDP and GS, neglecting other influential factors like severe weather events, runway closures, lower capacity than traffic demand, and other important variables. Given the complexity of predicting the TMI and determining its scope, we seek a more holistic approach. We aim to consider all significant factors that could impact TMIs and their parameters. What sets this research apart is the fusion of all data sources relevant to the issuance and adjustment of TMIs and it represents the first comprehensive attempt to optimize TMIs in this manner. Since this comprehensive solution involves various aspects, we break down the problem into smaller components and input all parameters into a unified model called the “TMI Adjuster”. Figure 1 shows the overall framework and the list of datasets used in each model. The objective of the TMI Adjuster module is to deliver reliable, consistent and expedited recommendations for the progression, adjustment, and termination of TMIs. The ML solution entails developing a pipeline capable of predicting the necessity of a TMI (e.g., GS or GDP) along with its various parameters. For example, in the case of a GS, this includes the scope of the GS either in terms of distance from the destination airport or based on pre-defined airspace sectors. Here, scope refers to those regions and departing airports that are subject to the GS. In this paper, we concentrate on the issuance of GSs in the three major airports in the New York area — LaGuardia(LGA), John F. Kennedy International (JFK), and Newark Liberty International (EWR). We fuse traffic, weather and other relevant aviation data from years 2017 to 2019 to train and validate the ML models. In particular, we use the following datasets: •Terminal Aerodrome Forecast (TAF): meteorological forecasts specific to each airport, issued four times a day, covering predefined time periods. •TMI data: includes all GSs and GDPs along with their respective parameters. •Aviation System Performance Metrics (ASPM): includes traffic related data such as aircraft delays, arrival, and departure rates. •Notices to Airmen (NOTAMs): utilized to extract runway closure data and manage interdependencies between terminals in close proximity. •Flight cancellation data •Airspace Flow Programs (AFP): includes information on flight airborne holdings caused by TMIs. The data preprocessing entails transforming ASPM, TMI, AFP, NOTAMs, and weather data into an hourly format and consolidating all datasets by merging them based on date and time as the primary key. The TMI Adjuster framework comprises two parallel models: one dedicated to GS and a second model focused on GDP. As previously mentioned, our specific focus is on the GS model as a multi-classification problem. In this framework, each data point of the GS model input summarizes ten hours of data. Specifically, the data loader for the GS model generates the input and output of the model as follows: at a given time step, the input includes the actual traffic, weather, and TMI data from the two-hour window before the time step, alongside the weather forecast and scheduled traffic for the next 8 hours starting from the time step. Based on this information, the output of the GS model for each time interval consists of three dimensions. The first dimension represents a binary decision on whether there should be a GS in place for the next hour or not. The second dimension is related to the scope of the GS in the United States, and the third dimension is related to the scope of the GS in Canada (i.e., to determine if the GS impacts airports in Canada).One of the challenges with TMI modeling is the sparsity of TMI events, particularly regarding its scope. To address this challenge in the scope of the GS model output, we implement grouping. The GS scope for the US region is defined based on a list of centers that should be included when the GS is in place. With 20 centers in the US, we utilized historical data to group them into 4 categories. In particular, we summarized our historical data in a graph format where nodes represent centers, and link weights are defined based on the co-occurrence of centers in the scope parameter ofTMIs. By identified strongly connected components in this graph, we were able to partition the centers into four groups. We consider two model structures for the GS Model. Firstly, a hierarchical classification model [10], where the human decision-making for a GS is of hierarchical nature. The decision-maker first decides whether there is a need fora GS, and if the answer is yes, determines the scope. A hierarchical classification model organizes the problem into a class hierarchy, typically a tree or a Directed Acyclic Graph (DAG) structure, and considers the dependency of the decision in the previous step to the next component [10]. Here, we employ the local classifier per level approach, which involves training one multi-class classifier for each level of the class hierarchy. The second structure is the independent structure. In this setting, as the name suggests, we do not consider the dependency of the decisions in the different dimensions of the output of the model. Instead, for each dimension, we train a multi-class classifier independently. Table 1 summarizes GS model statistics for training, validation and testing. The table documents the effect of limiting data to the time steps when there was actually a TMI in place or when a TMI had just terminated. This resulted in a more balanced distribution of the GS class(GS positive class)versus “No GS”(GS negative class), which might help the training process. While JFK and LGA follow very similar distributions, with 40% and 42% GS positive class respectively, EWR has proportionally fewer GS incidents at 28%. Our subsequent phase involves evaluating the performance of both hierarchical structure and independent structure using different state-of-the-art multi-class classifier models such as Random Forest, Decision Trees, K-nearest Neighbors, and Logistic Regression and forecast the duration and scope of the GSs.

Farzan Masrour Shalmani↗