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Purpose of the Space Network Ground Segment Sustainment (SGSS) is to implement a new modern ground segment that will enable the NASA Space Network (SN) to deliver high quality services to the SN community for the future The key SGSS Goals: (1) Re-engineer the SN ground segment (2) Enable cost efficiencies in the operability and maintainability of the broader SN.
Lightweight and high resolution mirrors are needed for future space-based X-ray telescopes to achieve advances in high-energy astrophysics. The slumped glass mirror technology in development at NASA GSFC aims to build X-ray mirror modules with an area to mass ratio of approx.17 sq cm/kg at 1 keV and a resolution of 10 arc-sec Half Power Diameter (HPD) or better at an affordable cost. As the technology nears the performance requirements, additional engineering effort is needed to ensure the modules are compatible with space-flight. This paper describes Flight Mirror Assembly (FMA) designs for several X-ray astrophysics missions studied by NASA and defines generic driving requirements and subsequent verification tests necessary to advance technology readiness for mission implementation. The requirement to perform X-ray testing in a horizontal beam, based on the orientation of existing facilities, is particularly burdensome on the mirror technology, necessitating mechanical over-constraint of the mirror segments and stiffening of the modules in order to prevent self-weight deformation errors from dominating the measured performance. This requirement, in turn, drives the mass and complexity of the system while limiting the testable angular resolution. Design options for a vertical X-ray test facility alleviating these issues are explored. An alternate mirror and module design using kinematic constraint of the mirror segments, enabled by a vertical test facility, is proposed. The kinematic mounting concept has significant advantages including potential for higher angular resolution, simplified mirror integration, and relaxed thermal requirements. However, it presents new challenges including low vibration modes and imperfections in kinematic constraint. Implementation concepts overcoming these challenges are described along with preliminary test and analysis results demonstrating the feasibility of kinematically mounting slumped glass mirror segments.
Design and analysis results are reported for a panel that is a 16th arc-segment of a full 33-ft diameter cylindrical barrel section of a payload fairing structure. Six such panels could be used to construct the fairing barrel, and, as such, compression buckling testing of a 16th arc-segment panel would serve as a validation test of the buckling analyses used to design the fairing panels. In this report, linear and nonlinear buckling analyses have been performed using finite element software for 16th arc-segment panels composed of aluminum honeycomb core with graphiteepoxy composite facesheets and an alternative fiber reinforced foam (FRF) composite sandwich design. The cross sections of both concepts were sized to represent realistic Space Launch Systems (SLS) Payload Fairing panels. Based on shell-based linear buckling analyses, smaller, more manageable buckling test panel dimensions were determined such that the panel would still be expected to buckle with a circumferential (as opposed to column-like) mode with significant separation between the first and second buckling modes. More detailed nonlinear buckling analyses were then conducted for honeycomb panels of various sizes using both Abaqus and ANSYS finite element codes, and for the smaller size panel, a solid-based finite element analysis was conducted. Finally, for the smaller size FRF panel, nonlinear buckling analysis was performed wherein geometric imperfections measured from an actual manufactured FRF were included. It was found that the measured imperfection did not significantly affect the panel's predicted buckling response
Key challenges of a future large aperture, segmented Ultraviolet Optical Infrared (UVOIR) Telescope capable of performing a spectroscopic survey of hundreds of Exoplanets will be sufficient stability to achieve 10^-10 contrast measurements and sufficient throughput and sensitivity for high yield Exo-Earth spectroscopic detection. Our team has collectively assessed an optimized end to end architecture including a high throughput coronagraph capable of working with a segmented telescope, a cost-effective and heritage based stable segmented telescope, a control architecture that minimizes the amount of new technologies, and an Exo-Earth yield assessment to evaluate potential performance. These efforts are combined through integrated modeling, coronagraph evaluations, and Exo-Earth yield calculations to assess the potential performance of the selected architecture. In addition, we discusses the scalability of this architecture to larger apertures and the technological tall poles to enabling it.
Key challenges of a future large aperture, segmented Ultraviolet Optical Infrared (UVOIR) Telescope capable of performing a spectroscopic survey of hundreds of Exoplanets will be sufficient stability to achieve 10-10 contrast measurements and sufficient throughput and sensitivity for high yield Exo-Earth spectroscopic detection. Our team has collectively assessed an optimized end to end architecture including a high throughput coronagraph capable of working with a segmented telescope, a cost-effective and heritage based stable segmented telescope, a control architecture that minimizes the amount of new technologies, and an Exo-Earth yield assessment to evaluate potential performance.
The National Aeronautics and Space Administration (NASA) Near Earth Network (NEN) Project is implementing a new launch communications ground segment to provide services for the next generation of human and robotic space exploration systems. It will deliver unique and advanced capabilities to accelerate the transformation of Kennedy Space Center into a multi-user spaceport in cooperation with the United States Air Force (USAF). The project has leveraged commercial technologies and remote operations concepts matured in NASAs orbiting satellite ground systems to achieve dramatic lifecycle cost efficiencies as compared to the space shuttle-era ground segment. The purpose of this paper is to discuss the development history, capabilities and anticipated use cases of the NEN Launch Communications Segment (NEN LCS).The NASA Kennedy Space Center is co-located with the USAF Eastern Launch Range at Cape Canaveral, Florida. The USAF operates two launch communications ground stations, but they are not designed to transmit voice, commands or other data to the launch vehicle or astronauts. The bi-directional uplink-downlink communications responsibility for human missions has historically resided with the Goddard Space Flight Center in Greenbelt, Maryland. Several market analyses and feasibility studies investigating concepts to provide NASAs next generation launch communications services were performed during the Constellation Program prior to its cancellation in 2009, and as part of the Kennedy Space Centers follow-on efforts to transform itself into a 21st century multi-user spaceport. In 2012, the Kennedy Space Center and the USAF 45th Space Wing jointly led a study to analyze the market needs of current and future launch systems and assess the operational deficiencies of the Eastern Range infrastructure. The study team issued several recommendations, two of which ultimately became driving operational capability requirements for the NEN LCS: increased telemetry data rates of at least 20 Mbps, and S-band uplink capability. Additional capabilities identified in the requirements development process include spread spectrum modulation support, LDPC 12 and 78 error correction codes, support for IRIG-106 and CCSDS data formats, automated best source selection, and Space Link Extension (SLE) services for data distribution. The NEN LCS is comprised of two permanent ground stations, the new Kennedy Uplink Station (KUS) and refurbished Ponce de Leon (PDL) station. Both stations are remotely operated from the Global Monitor and Control Center at Wallops Flight Facility. This core architecture is extensible through host-tenant arrangements with the U.S. Air Force and deployable assets, enabling agile, tailored and robust solutions to meet the needs of civil, commercial or military customers. The NEN LCS has three use cases:1.To provide agile, tailored and robust launch communications solutions to Florida spaceport customers2.To provide orbital communications services to near-earth customers 3.To provide an experimental proving ground for Space Mobile Network concepts and technologies The NEN LCS driving mission is to support the bi-directional link with the Orion crew capsule and two 20 Mbps telemetry links from the Space Launch System core stage on Exploration Mission-1, the first integrated flight of NASAs flagship human exploration systems.
"This work presents updates to the coronagraph and telescope components of the Segmented Aperture Interfer-ometric Nulling Testbed (SAINT). The project pairs an actively-controlled macro-scale segmented mirror withthe Visible Nulling Coronagraph (VNC) towards demonstrating capabilities for the future space observatoriesneeded to directly detect and characterize a significant sample of Earth-sized worlds around nearby stars inthe quest for identifying those which may be habitable and possibly harbor life. Efforts to improve the VNCwavefront control optics and mechanisms towards repeating narrowband results are described. A narrative isprovided for the design of new optical components aimed at enabling broadband performance. Initial work withthe hardware and software interface for controlling the segmented telescope mirror is also presented."
The James Webb Space Telescope Primary Mirror Segment Assemblies (PMSAs) and Secondary Mirror Assembly (SMA) were cleaned at the Johnson Space Center (JSC) in January 2018. In order to quantify the effectiveness of the cleaning, the same cleaning process was performed on the PMSA and SMA traveling witness wafers. These wafers have accompanied their respective mirror segments from their arrival at the Goddard Space Flight Center, through transport to JSC, and ultimately their exposure in Chamber A for cryogenic testing. The traveling wafers were analyzed using an Image Analysis automated microscope both prior to and after the cleaning. The resulting data showed that the PMSA wafers' Percent Area Coverage (PAC) reduced by 83.5% on average, from 0.1524 PAC to 0.0251 PAC. The SMA wafer's PAC decreased by 97.2%, from 0.1194 PAC to 0.0034 PAC. Further analysis of the particle size bins was completed in order to calculate their particle distribution slopes. The slope of the PMSA wafers increased by 0.025 on average, and the SMA wafer slope increased by 0.066. This indicates that the ratio of large to small particles slightly increased after the cleaning across all mirror segments. Visual inspections of the wafers and the flight PMSAs and SMA showed considerable and comparable particulate coverage improvements, thus leading to the conclusion that the average PAC on the PMSAs and SMA improved by the same factor as their respective wafers.
The goal of directly imaging Earth-like planets in the habitable zone of other stars has motivated the design of coronagraphs for use with large segmented aperture space telescopes. In order to achieve an optimal trade-o between planet light throughput and di racted starlight suppression, we consider coronagraphs comprised of a stage of phase control implemented with deformable mirrors (or other optical elements), pupil plane apodization masks (gray scale or complex valued), and focal plane masks (either amplitude only or complex-valued, including phase only such as the vector vortex coronagraph). The optimization of these optical elements, with the goal of achieving 10 or more orders of magnitude in the suppression of on-axis (starlight) di racted light, represents a challenging non-convex optimization problem with a nonlinear dependence on control degrees of freedom. We develop a new algorithmic approach to the design optimization problem, which we call the "Auxiliary Field Optimization" (AFO) algorithm. The central idea of the algorithm is to embed the original optimization problem, for either phase or amplitude (apodization) in various planes of the coronagraph, into a problem containing additional degrees of freedom, speci cally ctitious "auxiliary" electric elds which serve as targets to inform the variation of our phase or amplitude parameters leading to good feasible designs. We present the algorithm, discuss details of its numerical implementation, and prove convergence to local minima of the objective function (here taken to be the intensity of the on-axis source in a "dark hole" region in the science focal plane). Finally, we present results showing application of the algorithm to both unobscured o -axis and obscured on-axis segmented telescope aperture designs. The application of the AFO algorithm to the coronagraph design problem has produced solutions which are capable of directly imaging planets in the habitable zone, provided end-to-end telescope system stability requirements can be met. Ongoing work includes advances of the AFO algorithm reported here to design in additional robustness to a resolved star, and other phase or amplitude aberrations to be encountered in a real segmented aperture space telescope.
Globally, forest fires remain a significant threat to human and environmental wellbeing. Towards mitigating the impacts of forest fires, it is critical that accurate and updated information regarding not only the fire line, but also nearby human settlements, vegetation, and water sources is reported quickly to emergency services. However, while existing UAS-based fire detection methods are effective, they largely do not report the contextual environmental information necessary to best serve nearby communities in disaster response. Additionally, modern advancements in deep learning offer new approaches for image segmentation which may improve classification accuracy beyond current pixel-wise indices. In this work, we benchmark the performance of these modern segmentation techniques in locating both fire lines and environmental features in historical Autonomous Modular Sensor imagery. Furthermore, we augment these outputs with satellite imagery segmentation towards developing a robust contextual mapping tool for rapid emergency fire response and decision making.
Wildfire occurrences have been increasing for the past decade, leaving devastating traces across the world. In the recent efforts, remote sensing and airborne missions have been utilized to better understand and manage wildfires. This has resulted in an exponential increase in volume of remote sensing data, which has pushed the need for intelligent automation of data extraction for wildfire studies. Machine learning offers accurate automation in detecting such natural anomalies and enable decision-makers to take actions in a timely manner. Recent advances in machine learning algorithms, namely probabilistic generative methods, allow researchers and decisionmakers to step beyond detection and study “what-if” scenarios for wildfire occurrences. Additionally, they offer better imitations to the stochastic behavior of nature, and wildfire events. However, optimizing the performance of these probabilistic generative models is a computationally expensive process, specially using digital computers. On the other hand, quantum computers have recently shown a promise to reduce computationally costly training of such models and provide performance improvements. There is a body of research investigating the potential for improved machine learning methods in which key operations are performed on a quantum computer. In this study, we propose a probabilistic image-toimage segmentation approach combining a very well-known segmentation method, U-NET, with a Conditional Variational Auto-Encoder (CVAE) to not only detect wildfires but also describe the stochasticity of the phenomenon and be capable of running “what-if” scenarios. Our proposed model is compatible with training on quantum computers, which results in a quantum-assisted image-to-image segmentation approach and can be used to benchmark the potential benefit of quantum computing over the classical one.
This study examined the improvement of microscopy segmentation intersection over union accuracy by transfer learning from a large dataset of microscopy images called MicroNet. Many neural network encoder architectures were trained on over 100,000 labeled microscopy images from 54 material classes. These pre-trained encoders were then embedded into multiple segmentation architectures including UNet and DeepLabV3+ to evaluate segmentation performance on created benchmark microscopy datasets. Compared to ImageNet pre-training, models pre-trained on MicroNet generalized better to out-of-distribution micrographs taken under different imaging and sample conditions and were more accurate with less training data. When training with only a single Ni-superalloy image, pre-training on MicroNet produced a 72.2% reduction in relative intersection over union error. These results suggest that transfer learning from large in-domain datasets generate models with learned feature representations that are more useful for downstream tasks and will likely improve any microscopy image analysis technique that can leverage pre-trained encoders.
A method for digital image segmentation is provided. The method comprises training a neural network for image segmentation with a labeled training dataset from a first domain, wherein a subset of nodes in the neural net are dropped out during training. The neural network receives image data from a second, different domain. A vector of N values that sum to 1 is calculated for each image element, wherein each value represents an image segmentation class. A label is assigned to each image element according to the class with the highest value in the vector. Multiple inferences are performed with active dropout layers for each image element, and an uncertainty value is generated for each image element. The label of any image element with an uncertainty value above a predefined threshold is replaced with a new label corresponding to the class with the next highest value.
Taiwan Research Reactor will be dismantled in the period of 2022 to 2027 according to the approved dismantling plan. Based on the sampling and analyzing results, some internals of Taiwan Research Reactor must be segmented under the water because of their higher radiation, and the calandria is one of them. To dismantle this calandria, Institute of Nuclear Energy Research develops an underwater disc saw that is driven by a hydraulic pump and will be used to segment the shell of the calandria. In this article, Taiwan Research Reactor and the structure of the calandria are briefly depicted, and the segmentation processes of this calandria and the design of this disc saw are also included. Besides, the trial of cutting the shell of the simplified full size calandria mockup is briefly demonstrated in this article. Further tests will be performed in the near future to figure out which parts or functions of this disc saw need to be modified to get better cutting performance. (authors)
About 50% of buildings in the US were constructed before energy codes were introduced. Modular overclad panel retrofits, in which a new envelope is constructed over the existing building, are a promising solution given that it minimizes occupant disruption and shortens construction time at the jobsite. Current state-of-the-art retrofit panel layout and dimensioning consists of three steps: 1) 3D point cloud data generation of the building envelope using commonly available surveying equipment, 2) manual segmentation of 3D point cloud data by a trained professional to identify and dimension window openings, door openings, and other architectural features, and 3) modular panel layout optimization and dimensioning by an architect or engineer. Among these steps, the second one remains the most difficult and costly because it is very labor-intensive. We propose a methodology to automatically label 3D point cloud data to reduce the time and expense spent in manual segmentation. Machine learning methods were employed to classify the point cloud data into distinct groups, each of which corresponds to different features of the building envelope. After classification, a segmentation algorithm was developed to perform boundary detection and separate the components of the façade. Finally, the algorithm returns the relative positions and dimensions of the features in the building envelope. The measurements obtained with the proposed automated method were compared against the actual dimensions to determine the overall algorithm accuracy. The proposed algorithm can then be used to reduce manual efforts for 3D point cloud labeling before modular panel layout optimization is performed.
In polymer composites, the fiber–matrix interface is primarily influenced by the surface treatment of the fibers and polymer morphology. Previous studies have investigated the effect of surface treatment of carbon fiber on the mechanical properties of the resulting composites. However, very few studies have explored the chemical structural modification of polymer effect on the fiber–matrix adhesion. In this work, the interfaces of soft thermoplastic polyurethane (S-TPU) and hard-segmented TPU (H-TPU) were investigated through surface, thermal, and mechanical characterization. A textile-grade carbon fiber (TCF) with 1% concentration of epoxy sizing (an emerging material for nonaerospace applications with 450 K filament tows) was used as a reinforcement to investigate the structure–property relationship at the interface. Atomic force microscopy results showed 39% higher surface roughness for S-TPU than for H-TPU. X-ray photoelectron spectroscopy results revealed a 550% increase in C=O content, which can provide multiple hydrogen bonding networks in H-TPU, and these C=O bonds can produce a strong chemical bond at the fiber–matrix interface. Additionally, dynamic mechanical analysis and differential scanning calorimetry results confirmed the presence of hydrogen bonding that enhances the cross-linked density by 232% in H-TPU compared to S-TPU. The improved mechanical properties of H-TPU composites, such as flexural, impact and tensile by 30, 50, and 130% compared to S-TPU composites, prove that the crystallinity and hydrogen bonding significantly increase the load bearing capacity due to the strong interface. The mechanical properties of TCF–TPU composites validate the formation of chemical bonds through a nucleophilic addition reaction at the interface and improve the bond strength of the composites. Thus, tailoring the polyurethane structure broadens the performance of segmented TPU in conjunction with TCF reinforcement, which has implications for cost-effective, high-strength composites.
Understanding pitting corrosion is critical, yet its kinetics and morphology remain challenging to study from X-ray computed tomography (XCT) due to manual segmentation barriers. To address this, an automated pipeline leveraging deep learning for efficient large-scale XCT analysis is developed, revealing new corrosion insights. The pipeline enables pit segmentation, 3D reconstruction, statistical characterization, and a topological transformation for visualization. Here, the pipeline is applied to 87 648 XCT images capturing commercial purity aluminum (1100 Al) wire exposed to sodium chloride (NaCl) salt particles over a period of 122 h. The pipeline achieves complete feature extraction and statistical quantification across the entire XCT dataset, leveraging distributed computing environment for high efficiency. Global growth kinetics such as high-level stepwise sigmoidal volume loss patterns and granular individual pit developments are both captured for 36 detected pits. By combining automation, computer vision, and extensive XCT datasets, this research accelerates precise corrosion assessment to enable materials science discoveries at scale.
Previous studies of the cantilevered pipeline conveying fluid system have included motion-limiting constraints in the form of trilinear springs. While this is desirable in experimental scenarios, it may not be representative of real-world applications. Therefore, here, this study focuses on multi-segmented motion-limiting constraints. As this type of motion-limiting constraint has not been investigated with a cantilevered pipeline system, a wide variety of outer and inner constraint stiffness and constraint gap sizes are investigated in this study to gain a comprehensive understanding of how the multi-segmented constraints affect the dynamics of the cantilevered pipeline. In this effort, bifurcation diagrams, phase portraits, Poincare maps, time histories, and power spectra are used to investigate the dynamics of the system, and the fluid flow speeds where dynamic characteristics are considered. In general, it is found that critical flow speeds like when the pipe sticks in the constraints are reduced as the constraint stiffnesses are increased. Additionally, the sticking flow speed occurred at lower flow speeds as the gap sizes of the inner and outer constraints decrease, and a larger constraint offset results in a smaller inner gap size leading to critical behaviors occurring at earlier flow speeds.