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819 records · Page 46

A Modeling Pattern for Layered System Interfaces

Communications between systems is often initially represented at a single, high level of abstraction, a link between components. During design evolution it is usually necessary to elaborate the interface model, defining it from several different, related viewpoints and levels of abstraction. This paper presents a pattern to model such multi-layered interface architectures simply and efficiently, in a way that supports expression of technical complexity, interfaces and behavior, and analysis of complexity. Each viewpoint and layer of abstraction has its own properties and behaviors. System elements are logically connected both horizontally along the communication path, and vertically across the different layers of protocols. The performance of upper layers depends on the performance of lower layers, yet the implementation of lower layers is intentionally opaque to upper layers. Upper layers are hidden from lower layers except as sources and sinks of data. The system elements may not be linked directly at each horizontal layer but only via a communication path, and end-to-end communications may depend on intermediate components that are hidden from them, but may need to be shown in certain views and analyzed for certain purposes. This architectural model pattern uses methods described in ISO 42010, Recommended Practice for Architectural Description of Software-intensive Systems and CCSDS 311.0-M-1, Reference Architecture for Space Data Systems (RASDS). A set of useful viewpoints and views are presented, along with the associated modeling representations, stakeholders and concerns. These viewpoints, views, and concerns then inform the modeling pattern. This pattern permits viewing the system from several different perspectives and at different layers of abstraction. An external viewpoint treats the systems of interest as black boxes and focuses on the applications view, another view exposes the details of the connections and other components between the black boxes. An internal view focuses on the implementation within the systems of interest, either showing external interface bindings and specific standards that define the communication stack profile or at the level of internal behavior. Orthogonally, a horizontal view isolates a single layer and a vertical viewpoint shows all layers at a single interface point between the systems of interest. Each of these views can in turn be described from both behavioral and structural viewpoints.

Shames, Peter M.↗

NASA’s Space Launch System: Building A Capability for Science and Exploration

NASA’s Space Launch System, designed for human exploration of deep space and offering enabling benefits for a variety of science missions, is entering the final stages of preparation for its first launch, while simultaneously making progress toward future missions. SLS offers robust payload mass, volume, and characteristic energy that can be used not only for human exploration but for a variety of science missions, including probes to the outer solar system and beyond. In addition, while the vehicle is optimized to be a super-heavy lifter for lunar orbit as a staging area for the lunar surface or Mars, the addition of commercially available propulsion systems as third and/or fourth stages allows SLS to deliver unmatched performance for ultra-high C3 missions. Studies show that while the baseline SLS Block 2 vehicle can deliver about 8 t directly to the Jovian system at a C3 of 83, the addition of a Centaur upper stage would raise that mass to more than 15 t. The New Horizons spacecraft, with mass less than 0.5 t, launched toward Pluto with a record C3 of 158 km2/sec2. By comparison, an SLS Block 2 with an Orion 30B and Star 48BV payload stages could launch equivalent mass to a C3 more than double that of the New Horizons launch. (While the study has been conducted based on contemporary cryo stages and solid stages, this analysis provides real-world data for the range of performance this capability enables even as the specific stages available evolve.) Studies have shown it may be possible to reach 80-90 AU within a decade. The initial Block 1 vehicle for the first launch, Artemis I, is completely manufactured and all the elements are at NASA’s Kennedy Space Center (KSC) with the exception of the core stage, and manufacturing is in progress for the next several flights. Following the completion of the Green Run test series at NASA’s Stennis Space Center, the Artemis I core stage will be refurbished and delivered to KSC for stacking for launch in the second half of 2021. With the Artemis I vehicle fully manufactured and assembly of solid rocket boosters beginning, NASA and its industry partners have made significant progress manufacturing successive vehicles. Hardware for the next two launches is currently in production. NASA is committed to SLS as a key component of its launch architecture. Agency planning manifests outline the launch vehicle’s role in human lunar exploration over the next decade as it moves from its Block1 configuration to its intermediate Block 1B configuration and its ultimate Block 2 configuration.In addition, NASA has issued contracts with prime contractors for SLS hardware for delivery well into the 2030s.

Robert W. Stough↗

Embossed Teflon AF Laminate Membrane Microfluidic Diaphragm Valves

A microfluidic system has been designed to survive spaceflight and to function autonomously on the Martian surface. It manipulates microscopic quantities of liquid water and performs chemical analyses on these samples to assay for the presence of molecules associated with past or present living processes. This technology lies at the core of the Urey Instrument, which is scheduled for inclusion on the Pasteur Payload of the ESA ExoMars rover mission in 2013. Fabrication processes have been developed to make the microfabricated Teflon-AF microfluidic diaphragm pumps capable of surviving extreme temperature excursions before and after exposure to liquid water. Two glass wafers are etched with features and a continuous Teflon membrane is sandwiched between them (see figure). Single valves are constructed using this geometry. The microfabricated devices are then post processed by heating the assembled device while applying pneumatic pressure to force the Teflon diaphragm against the valve seat while it is softened. After cooling the device, the embossed membrane retains this new shape. This solves previous problems with bubble introduction into the fluid flow where deformations of the membrane at the valve seat occurred during device bonding at elevated temperatures (100-150 C). The use of laminated membranes containing commercial Teflon AF 2400 sheet sandwiched between spun Teflon AF 1600 layers performed best, and were less gas permeable than Teflon AF 1600 membranes on their own. Spinning Teflon AF 1600 solution (6 percent in FLOURINERT(Registered TradeMark) FC40 solvent, 3M Company) at 500 rpm for 1.5 seconds, followed by 1,000 rpm for 3 seconds onto Borofloat glass wafers, results in a 10-micron-thick film of extremely smooth Teflon AF. This spinning process is repeated several times on flat, blank, glass wafers in order to gradually build a thick, smooth membrane. After running this process at least five times, the wafer and Teflon coating are heated under vacuum at 220 C for one hour in order to drive off any residual solvent present in the composite film. After this, a second blank, glass wafer is brought down from above and the stack is held under vacuum at 3 atm mechanical pressure for ten 10 hours.

Willis, Peter↗

Preliminary Application of Formal Verification to An Autonomy Architecture for Unmanned Aircraft

There is a desire to design autonomous systems in such a way that capabilities can be easily added or re-combined to produce new behaviors while preserving their safety properties. ICAROUS, a prototype software architecture for building safety-centric autonomous unmanned aircraft applications, is designed to support this type of extensibility and re-configurability. In ICAROUS, core capabilities are implemented as individual soft- ware services, so that enabling access to new capabilities simply requires adding new services. To make use of these capabilities, ICAROUS includes a specialized service that provides a general framework for config- uring the relative priorities, conditions, and rules that govern how different modules should be engaged and disengaged during flight. The inherent complexity of coordinating multiple modules under changing conditions makes it difficult to determine whether a particular configuration could have erroneous behaviors in certain circumstances. A robust set of integration tests can help discover errors, but testing can only realistically cover a relatively small proportion of total system behaviors. Developing good tests and interpreting the results to pinpoint the cause of errors when they arise can also be very time-consuming. To supplement testing, formal methods can be used to model and analyze complex systems, achieving better coverage and simplifying the process of finding, understanding, and fixing errors. To demonstrate these benefits, this paper explores the ap- plication of formal methods to ICAROUS. In particular, the Spin model checker is used to specify requirements for and model portions of the system, then verify whether the model satisfies the requirements and find and fix errors when it does not.

Formal Methods↗

Preliminary Application of Formal Verification to An Autonomy Architecture for Unmanned Aircraft

There is a desire to design autonomous systems in such a way that capabilities can be easily added or re- combined to produce new behaviors while preserving their safety properties. ICAROUS, a prototype software architecture for building safety-centric autonomous unmanned aircraft applications, is designed to support this type of extensibility and re-configurability. In ICAROUS, core capabilities are implemented as individual soft- ware services, so that enabling access to new capabilities simply requires adding new services. To make use of these capabilities, ICAROUS includes a specialized service that provides a general framework for config- uring the relative priorities, conditions, and rules that govern how different modules should be engaged and disengaged during flight. The inherent complexity of coordinating multiple modules under changing conditions makes it difficult to determine whether a particular configuration could have erroneous behaviors in certain circumstances. A robust set of integration tests can help discover errors, but testing can only realistically cover a relatively small proportion of total system behaviors. Developing good tests and interpreting the results to pinpoint the cause of errors when they arise can also be very time-consuming. To supplement testing, formal methods can be used to model and analyze complex systems, achieving better coverage and simplifying the process of finding, understanding, and fixing errors. To demonstrate these benefits, this paper explores the ap- plication of formal methods to ICAROUS. In particular, the Spin model checker is used to specify requirements for and model portions of the system, then verify whether the model satisfies the requirements and find and fix errors when it does not.

Formal Methods↗

Global 3D Data Visualization and Analysis Platform with Advanced Machine Learning Capabilities in Support of Lunar Exploration

Introduction: The science goals for NASA’s Artemis program include: a) Understanding the character and origin of lunar polar volatiles, b) Conducting experimental science in the lunar environment and c) Investigating and mitigating exploration risks. The permanently shadowed regions (PSRs) on the Lunar south pole are expected to host large quantities of water-ice and volatiles that are important for sustainable Lunar exploration. There are several missions such as onboard Korea Pathfinder Lunar Orbiter (KPLO: Korean name Danuri) with onboard ShadowCam camera, Astrobotic Peregrine Mission One [4], and other efforts underway to obtain high resolution topographic, minerals, volatiles and other information on the moon. We envision a need in immediate future for platforms to integrate these data sets, provide rendering and visualization capabilities in the context of a 3D Lunar globe for easier information access and analysis. NASA's Celestial Mapping System (CMS) is developed to address the need for 3D tools for planetary science investigations, mission planning, in-situ operations, in a 3D-first design constructed around a unified view of a planetary globe. At present CMS provides many critical functionalities that include: 1) equipment planning and optimized placement on Lunar surface 2) line-of-sight (LOS) analysis 3) powerful measurement tools based on 3D terrain with realistic 3D models to represent rovers, astronauts and equipment 4) visualization of derived mapping products (e.g. resource maps), and 5) a data engine for hosting new observations that are not available in other contemporary lunar data tools. Planetary Data Ingestion: CMS can consume and analyze data from locally hosted and external third party sources. It is compatible with Open Geospatial Consortium (OGC) data and file standards and currently integrates datasets from the Astrogeology Science Center of USGS. This includes global and local data acquired from NASA (LRO, Clementine, Lunar Orbiter) and JAXA (SELENE/Kaguya), with capability of integrating more datasets. In addition, users can specify other WMS-hosted data endpoints, which CMS can then query and stream data from automatically. To set-up an automated process for ingestion and accurate rendering, visualization and analysis of external 3rd party planetary datasets within CMS, we initiated the process of ingesting unique dataset of super-enhanced images of the permanently shadowed regions (PSRs) at the lunar poles which were produced by the Hyper-effective nOise Removal U-net Software (HORUS) tool. This tool was developed to enhance the extremely low-light images of the interior of PSRs and provide the ability to see within these regions at and discern surface features (i.e. boulders and craters) down to 3 meters in size. We focused on the Nobile region on the Lunar south pole, selected site for VIPER mission and stitched several images to create a high-resolution map within one of the PSR of Nobile crater. Figure 1 shows the dark PSR zone form the original NAC layer of LRO as the base layer (left image) and the illuminated areas within that crater (center) which was created by ingesting and merging several of HORUS generated images. At present we employ a semi-automated process to ensure spatial accuracy and merger of several overlapping zones. However, we are in the process of completely automating this process by employing AI based techniques that would rank, sort, and stack the images based on their information density. The georectification of the images would employ selected features. Analysis on Ingested Planetary Datasets: Once an external planetary data-set is successfully ingested, georectified and merged seamlessly as a data-layer; CMS’ numerous analysis tools can be used on this data. A Line of Sight (LOS) tool has been developed for CMS which analyzes terrain profiles and obstructions to determine visibility for remote observers. Figure 1 (right image) shows the viewshed analysis on the same PSR in the Nobile region. The yellow pin shows the observer location outside the PSR. The yellow area shows the visible part of PSR. The obstructed area with no visibility for the observer is shown in red. The Measurements tool allows the user to take area and distance measurements of features on the terrain using various shapes. Measurement type can be specified in a number of ways: Line, Path, Polygon, Circle, Ellipse, Square, Rectangle or Freehand. Once the shape is specified, elevation information can then be extracted along each of these shapes. Figure 2 (left) shows the measurements performed on a crater n illuminated PSR in Nobile region. The equipment placement tool allows the user to place a 3D equipment model at a desired location and analyze its coverage area. The equipment placement tool is coupled with LOS to determine the coverage. Figure 2 (right) shows an equipment placed on the Lunar terrain and it’s coverage area. The red rays are blocked sight lines and the green rays are non-obstructed sight lines with the cyan lines showing the point of intersection with the terrain. More details are provided in the video demonstrations in Reference 5. Overcoming Polar Distortions: 3D geospatial applications exhibit significant distortions in polar imagery due to several reasons: 1) distortions in the source imagery, 2) incompatible tessellation algorithms at the poles, and 3) map projections. We are leveraging new tessellation algorithms and reprojecting data using projections that are better suited for Lunar poles. The goal is to seamlessly switch to polar projections while maintaining 3D view and navigation.

Maps↗

Global 3D Data Visualization and Analysis Platform With Advanced Machine Learning Capabilities in Support of Lunar Exploration

Introduction: The science goals for NASA’s Artemis program include: a) Understanding the character and origin of lunar polar volatiles, b) Conducting experimental science in the lunar environment and c) Investigating and mitigating exploration risks [1]. The permanently shadowed regions (PSRs) on the Lunar south pole are expected to host large quantities of water-ice and volatiles that are important for sustainable Lunar exploration [2]. There are several missions such as onboard Korea Pathfinder Lunar Orbiter (KPLO: Korean name Danuri) with onboard ShadowCam camera [3], Astrobotic Peregrine Mission One [4], and other efforts underway to obtain high resolution topographic, minerals, volatiles and other information on the moon. We envision a need in immediate future for platforms to integrate these data sets, provide rendering and visualization capabilities in the context of a 3D Lunar globe for easier information access and analysis. NASA's Celestial Mapping System (CMS) [5] is developed to address the need for 3D tools for planetary science investigations, mission planning, in-situ operations, in a 3D-first design constructed around a unified view of a planetary globe. At present CMS provides many critical functionalities that include: 1) equipment planning and optimized placement on Lunar surface 2) line-of-sight (LOS) analysis 3) powerful measurement tools based on 3D terrain with realistic 3D models to represent rovers, astronauts and equipment 4) visualization of derived mapping products (e.g. resource maps), and 5) a data engine for hosting new observations that are not available in other contemporary lunar data tools [5, 7]. Planetary Data Ingestion: CMS can consume and analyze data from locally hosted and external third party sources. It is compatible with Open Geospatial Consortium (OGC) data and file standards and currently integrates datasets from the Astrogeology Science Center of USGS. This includes global and local data acquired from NASA (LRO, Clementine, Lunar Orbiter) and JAXA (SELENE/Kaguya), with capability of integrating more datasets. In addition, users can specify other WMS-hosted data endpoints, which CMS can then query and stream data from automatically. To set-up an automated process for ingestion and accurate rendering, visualization and analysis of external 3rd party planetary datasets within CMS, we initiated the process of ingesting unique dataset of super-enhanced images of the permanently shadowed regions (PSRs) at the lunar poles which were produced by the Hyper-effective nOise Removal U-net Software (HORUS) tool [8]. This tool was developed to enhance the extremely low-light images of the interior of PSRs and provide the ability to see within these regions at and discern surface features (i.e. boulders and craters) down to 3 meters in size. We focused on the Nobile region on the Lunar south pole, selected site for VIPER mission and stitched several images to create a high-resolution map within one of the PSR of Nobile crater. Figure 1 shows the dark PSR zone form the original NAC layer of LRO as the base layer (left image) and the illuminated areas within that crater (center) which was created by ingesting and merging several of HORUS generated images. At present we employ a semi-automated process to ensure spatial accuracy and merger of several overlapping zones. However, we are in the process of completely automating this process by employing AI based techniques that would rank, sort, and stack the images based on their information density. The georectification of the images would employ selected features. Analysis on Ingested Planetary Datasets: Once an external planetary data-set is successfully ingested, georectified and merged seamlessly as a data-layer; CMS’ numerous analysis tools can be used on this data. A Line of Sight (LOS) tool has been developed for CMS which analyzes terrain profiles and obstructions to determine visibility for remote observers [5,6]. Figure 1 (right image) shows the viewshed analysis on the same PSR in the Nobile region. The yellow pin shows the observer location outside the PSR. The yellow area shows the visible part of PSR. The obstructed area with no visibility for the observer is shown in red. The Measurements tool allows the user to take area and distance measurements of features on the terrain using various shapes. Measurement type can be specified in a number of ways: Line, Path, Polygon, Circle, Ellipse, Square, Rectangle or Freehand. Once the shape is specified, elevation information can then be extracted along each of these shapes. Figure 2 (left) shows the measurements performed on a crater n illuminated PSR in Nobile region. The equipment placement tool allows the user to place a 3D equipment model at a desired location and analyze its coverage area. The equipment placement tool is coupled with LOS to determine the coverage. Figure 2 (right) shows an equipment placed on the Lunar terrain and it’s coverage area. The red rays are blocked sight lines and the green rays are non-obstructed sight lines with the cyan lines showing the point of intersection with the terrain. More details are provided in the video demonstrations in Reference 5. Overcoming Polar Distortions: 3D geospatial applications exhibit significant distortions in polar imagery due to several reasons: 1) distortions in the source imagery, 2) incompatible tessellation algorithms at the poles, and 3) map projections. We are leveraging new tessellation algorithms and reprojecting data using projections that are better suited for Lunar poles. The goal is to seamlessly switch to polar projections while maintaining 3D view and navigation.

Maps↗

NASA’s Space Launch System: New Launch Capability for Artemis Lunar and Deep Space Science Missions

With stacking and integration of the initial Block 1 Space Launch System (SLS) expected to begin in 2020, NASA’s powerful new launch vehicle is ready to take center stage in the agency’s Artemis program to return astronauts to the Moon. Combining the highest launch thrust and largest payload capacity ever developed, SLS also enables a new generation of high-C3 science missions to destinations such as the gas and ice giants, the Kuiper Belt, and even beyond the solar system. Block 1 is only the beginning, as the vehicle has a planned evolution path to progressively more powerful variants. In addition to these block upgrades providing increased lift capability, the vehicle can be configured to fly in crew configuration with the Orion spacecraft or in cargo configuration with payload fairings for launching science mission or large infrastructure, providing a flexible launch option. For Artemis I, the first SLS flight, the Block 1 vehicle in the crew configuration will send an uncrewed Orion spacecraft to lunar orbit for a thorough systems checkout before the crewed Artemis II flight. The Block 1 vehicle uses a proven propulsion system consisting of solid rocket boosters and RS-25 engines to lift more than 27 metric tons [t] to trans-lunar injection (TLI). In its cargo configuration, Block 1 can be fitted with a 5 m payload fairing. The second variant, Block 1B, uses a more powerful upper stage to increase payload mass to TLI to 38-42 t, depending on crew or cargo configuration. In the crew configuration, a co-manifested payload of up to 10 t can ride along in the Universal Stage Adapter (USA), which has as much volume for payloads as a 5 m-class payload fairing. The Block 2 evolved variant will lift 43-46 t to TLI, depending on crew or cargo configuration. The Block 1B and Block 2 vehicles can be outfitted with an 8.4 m-diameter payload fairing, available in 19.1 m and 27.4 m lengths, providing unprecedented volume for payloads. Larger-diameter 10 m fairings may also be an option in the future on the Block 2 vehicle. The unrivalled mass, volume and high-energy launches of SLS can provide significant mission flexibility for payloads and/or additional upper stages to open trade space for a new generation of exploration missions. SLS was designed to meet requirements for launching large-volume infrastructure as outlined in numerous studies of missions to cislunar space or Mars. Mission concept studies from the science community also point toward new possibilities enabled by SLS. Probes with more robust science packages can be sent to the gas giants. Dual spacecraft can be manifested for missions to Uranus and Neptune. Additional third or fourth payload stages can be encapsulated in the payload fairings to achieve missions to the Kuiper Belt or beyond. In addition, the large volume can be used to design and deploy wide-aperture mirrors on future space telescopes and to enable nuclear-thermal propulsion missions. At AIAA Ascend, the SLS Program will provide technical information on vehicle capabilities as well as descriptions of ongoing discussions with mission planners for utilizing the vehicle for an array of deep space missions.

Stephen Creech↗

Mapping Organic-Mineral Associations in Jezero crater

The search for potential signs of life on Mars, a primary aim of the Mars 2020 mission, is greatly informed by the detection of organic matter1. The presence of organic matter also provides key information about the habitability and biological potential of the planet throughout its history. The Perseverance rover was designed for in situ science with the ability to collect a suite of promising samples for eventual return to Earth. One of its instruments, Scanning Habitable Environments with Raman and Luminescence for Organics and Chemicals (SHERLOC), is a deep ultraviolet (DUV) Raman and fluorescence spectrometer designed to map the distribution of organic molecules and minerals on rock surfaces at a resolution of 100 μm2. With its unique spectral mapping capabilities, SHERLOC enables a novel understanding of organic-mineral relationships on Mars to better determine their formation, deposition, and preservation mechanisms. The rover’s landing site within Jezero crater combines a high potential for past habitability as the site of an ancient lake basin with a diverse set of minerals, including carbonates and clays, that may preserve organic materials and potential biosignatures. The Jezero crater floor includes three formations (fm); two of these, Máaz and Séítah, were explored as part of the mission’s first campaign. Here, we report the detection of multiple species of aromatic organic molecules using Raman and fluorescence spectroscopy across ten targets in the two formations. This is the first evidence of organic molecules in Martian materials obtained using Raman spectroscopy, and among the first using fluorescence spectroscopy, beyond Earth3. We report specific spatial patterns and classes of organic molecules in these compositionally distinct formations, potentially indicating different fates of carbon in these environments. Our findings indicate that there is a diversity of aromatic molecules prevalent on the Martian surface and these materials persist despite exposure to surface conditions. These organic molecules are largely found within minerals linked to aqueous processes, suggesting that these processes may have had a key role in organic synthesis, transport from their point of origin, or preservation.

Sunanda Sharma↗