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At least 577 records · Page 32

Comets, Carbonaceous Meteorites, and the Origin of the Biosphere

Evidence for indigenous microfossils in carbonaceous meteorites suggests that the paradigm of the endogenous origin of life on Earth should be reconsidered. It is now widely accepted that comets and carbonaceous meteorites played an important role in the delivery of water, organics and life critical biogenic elements to the early Earth and facilitated the origin and evolution of the Earth's Biosphere. However; the detection of embedded microfossils and mats in carbonaceous meteorites implies that comets and meteorites may have played a direct role in the delivery of intact microorganisms and that the Biosphere may extend far into the Cosmos. Recent space observations have found the nuclei of comets to have very low albedos (approx.0.03) and. these jet-black surfaces become very hot (T approx. 400 K) near perihelion. This paper reviews recent observational data-on comets and suggests that liquid water pools could exist in cavities and fissures between the internal ices and rocks and the exterior carbonaceous crust. The presence of light and liquid water near the surface of the nucleus enhances the possibility that comets could harbor prokaryotic extremophiles (e.g., cyanobacteria) capable of growth over a wide range of temperatures. The hypothesis that comets are the parent bodies of the CI1 and the CM2 carbonaceous meteorites is advanced. Electron microscopy images will be presented showing forms interpreted as indigenous-microfossils embedded' in freshly. fractured interior surfaces of the Orgueil (CI1) and Murchison (CM2) meteorites. These forms are consistent in size and morphologies with known morphotypes of all five orders of Cyanobacteriaceae: Energy Dispersive X-ray Spectroscopy (EDS) elemental data shows that the meteoritic forms have anomalous C/O; C/N; and C/S as compared with modern extremophiles and cyanobacteria. These images and spectral data indicate that the clearly biogenic and embedded remains cannot be interpreted as recent biological contaminants and'therefore are indigenous'microfossils in the meteorites.

Hoover, Richard B.↗

Network-Capable Application Process and Wireless Intelligent Sensors for ISHM

Intelligent sensor technology and systems are increasingly becoming attractive means to serve as frameworks for intelligent rocket test facilities with embedded intelligent sensor elements, distributed data acquisition elements, and onboard data acquisition elements. Networked intelligent processors enable users and systems integrators to automatically configure their measurement automation systems for analog sensors. NASA and leading sensor vendors are working together to apply the IEEE 1451 standard for adding plug-and-play capabilities for wireless analog transducers through the use of a Transducer Electronic Data Sheet (TEDS) in order to simplify sensor setup, use, and maintenance, to automatically obtain calibration data, and to eliminate manual data entry and error. A TEDS contains the critical information needed by an instrument or measurement system to identify, characterize, interface, and properly use the signal from an analog sensor. A TEDS is deployed for a sensor in one of two ways. First, the TEDS can reside in embedded, nonvolatile memory (typically flash memory) within the intelligent processor. Second, a virtual TEDS can exist as a separate file, downloadable from the Internet. This concept of virtual TEDS extends the benefits of the standardized TEDS to legacy sensors and applications where the embedded memory is not available. An HTML-based user interface provides a visual tool to interface with those distributed sensors that a TEDS is associated with, to automate the sensor management process. Implementing and deploying the IEEE 1451.1-based Network-Capable Application Process (NCAP) can achieve support for intelligent process in Integrated Systems Health Management (ISHM) for the purpose of monitoring, detection of anomalies, diagnosis of causes of anomalies, prediction of future anomalies, mitigation to maintain operability, and integrated awareness of system health by the operator. It can also support local data collection and storage. This invention enables wide-area sensing and employs numerous globally distributed sensing devices that observe the physical world through the existing sensor network. This innovation enables distributed storage, distributed processing, distributed intelligence, and the availability of DiaK (Data, Information, and Knowledge) to any element as needed. It also enables the simultaneous execution of multiple processes, and represents models that contribute to the determination of the condition and health of each element in the system. The NCAP (intelligent process) can configure data-collection and filtering processes in reaction to sensed data, allowing it to decide when and how to adapt collection and processing with regard to sophisticated analysis of data derived from multiple sensors. The user will be able to view the sensing device network as a single unit that supports a high-level query language. Each query would be able to operate over data collected from across the global sensor network just as a search query encompasses millions of Web pages. The sensor web can preserve ubiquitous information access between the querier and the queried data. Pervasive monitoring of the physical world raises significant data and privacy concerns. This innovation enables different authorities to control portions of the sensing infrastructure, and sensor service authors may wish to compose services across authority boundaries.

Figueroa, Fernando↗

Fabricating Composite-Material Structures Containing SMA Ribbons

An improved method of designing and fabricating laminated composite-material (matrix/fiber) structures containing embedded shape-memory-alloy (SMA) actuators has been devised. Structures made by this method have repeatable, predictable properties, and fabrication processes can readily be automated. Such structures, denoted as shape-memory-alloy hybrid composite (SMAHC) structures, have been investigated for their potential to satisfy requirements to control the shapes or thermoelastic responses of themselves or of other structures into which they might be incorporated, or to control noise and vibrations. Much of the prior work on SMAHC structures has involved the use SMA wires embedded within matrices or within sleeves through parent structures. The disadvantages of using SMA wires as the embedded actuators include (1) complexity of fabrication procedures because of the relatively large numbers of actuators usually needed; (2) sensitivity to actuator/ matrix interface flaws because voids can be of significant size, relative to wires; (3) relatively high rates of breakage of actuators during curing of matrix materials because of sensitivity to stress concentrations at mechanical restraints; and (4) difficulty of achieving desirable overall volume fractions of SMA wires when trying to optimize the integration of the wires by placing them in selected layers only.

Turner, Travis L.↗

Smart Sensors' Role in Integrated System Health Management

During the last decade, there has been a major effort in the aerospace industry to reduce the cost per pond of payload and become competitive in the international market. Competition from Europe, Japan, and China has reduced this cost to almost a third from 1990 to 2000. This cost has leveled in recent years to an average price of around $12,000/pound of payload. One of NASA's goals is to promote the development of technologies to reduce this cost by a factor of 10 or more Exploration of space, specially manned exploration missions, involves very complex launch and flight vehicles, associated ground support systems, and extensive human support during all phases of the mission. When considering the Space Shuttle Program, we can see that vehicle and ground support systems' processing, operation, and maintenance represent a large percentage of the program cost and time. Reducing operating, processing and maintenance costs will greatly reduce the cost of Exploration programs. The Integrated System Health Management (ISHM) concept is one of the technologies that will help reduce these operating, processing and maintenance costs. ISHM is an integrated health monitoring system applicable to both flight and ground systems. It automatically and autonomously acquires information from sensors and actuators and processes that information using the ISHM-embedded knowledge. As a result, it establishes the health of the system based on the acquired information and its prior knowledge. When this concept is fully implemented, ISHM systems shall be able to perform failure prediction and remediation before actual hard failures occurs, preventing its costly consequences. Data sources, sensors, and their associated data acquisition systems, constitute the foundation of the system. A smart sensing architecture is required to support the acquisition of reliable, high quality data, required by the ISHM. A thorough definition of the smart sensor architectures, their embedded diagnostic agents, and communication protocols need to be established and standardized to allow the embedding and exchange of health information among sensors and ISHM. This workshop is aimed to foster the exchange of ideas and lessons learned between government, industry and academia to aid in the establishment of ISHM (and smart sensors) standards and guidelines as well as to identify present technology gaps that will have to be overcome to successfully achieve this goal.

Perotti, Jose M.↗

Characterization of Aerodynamic Performance of Boundary-Layer-Ingesting Inlet Under Crosswind

NASA has been studying future transport concepts, envisioned to be technically realizable in the timeframe of 2020-2030, to meet environmental and performance goals. One concept receiving considerable interest involves a propulsion system embedded into a hybrid wing-body aircraft. While offering significant advantages in fuel savings and noise reduction by this concept, there are several technical challenges that are not encountered in the current fleet and must be overcome so as to deliver target performance and operability. One of these challenges is associated with an inlet system that ingests a significantly thick boundary layer, developing along the wing-body surface, into a serpentine diffuser before the flow meeting fan blades. The flow is subject to considerable total pressure loss and distorted at the fan face, much more significantly than in the inlet system of conventional aircraft. In our previous studies [1, 2], we have shown that through innovative design changes on the airframe surface, it is possible to simultaneously increase total pressure recovery and decrease distortion in the flow, without resorting to conventional penalty-ridden flow control concepts, such as vortex generator or boundary layer bleeding/suction. In the current study, we are interested in understanding the following issues: how the embedded propulsion system performs under a crosswind condition by studying in detail the flow characteristics of two inlets, the baseline and another optimized previously under the cruise condition. With the insight, it is hoped that it can help in the follow-on study by devising effective strategies to minimize flow distortion arising from the integration of an embedded-engine system into an airframe to the level acceptable to the operation and fuel consumption before 2030. To achieve these demanding goals, non-conventional concepts are called for; but technology gap is too big that it requires evolutionary approach by focusing various concepts and technologies needed in the next three generations of aircraft, respectively named as N+1, N+2, and N+3. Noticeably, considerable reduction in each category of 1 is required in N+2 (relative to Boeing 777-200 and GE90 engines) and N+3 (relative to Boeing 737-800 and CFM56-7B engines). In this study, concepts for N+2 is our interest. A concept that has potential to achieve these metrics and has been under intensive study is the hybrid wing body (HWB) airframe with a tightly integrated propulsion system, see Fig. 1. The inlet is non-circular at the entrance and the entering flow, no longer uniform or free of disturbances, and is now carrying with it a boundary layer developing along the fuselage; the inlet is thus known as boundary-layer-ingesting (BLI) inlet.

Liou, Meng-Sing↗

Improved Displacement Transfer Functions for Structure Deformed Shape Predictions Using Discretely Distributed Surface Strains

In the formulations of earlier Displacement Transfer Functions for structure shape predictions, the surface strain distributions, along a strain-sensing line, were represented with piecewise linear functions. To improve the shape-prediction accuracies, Improved Displacement Transfer Functions were formulated using piecewise nonlinear strain representations. Through discretization of an embedded beam (depth-wise cross section of a structure along a strain-sensing line) into multiple small domains, piecewise nonlinear functions were used to describe the surface strain distributions along the discretized embedded beam. Such piecewise approach enabled the piecewise integrations of the embedded beam curvature equations to yield slope and deflection equations in recursive forms. The resulting Improved Displacement Transfer Functions, written in summation forms, were expressed in terms of beam geometrical parameters and surface strains along the strain-sensing line. By feeding the surface strains into the Improved Displacement Transfer Functions, structural deflections could be calculated at multiple points for mapping out the overall structural deformed shapes for visual display. The shape-prediction accuracies of the Improved Displacement Transfer Functions were then examined in view of finite-element-calculated deflections using different tapered cantilever tubular beams. It was found that by using the piecewise nonlinear strain representations, the shape-prediction accuracies could be greatly improved, especially for highly-tapered cantilever tubular beams.

Ko, William L.↗

Method for making a microporous membrane

A method for making a microporous membrane comprises the steps of: providing a plurality of carbon nanotubes having a hollow interior diameter of 20 Angstroms or less; sonicating the plurality of carbon nanotubes utilizing a solution comprising deionized, distilled water and a surfactant that coats at least one of the plurality of carbon nanotubes; collecting the coated carbon nanotubes; forming a matrix that supports the plurality of carbon nanotubes; embedding the coated carbon nanotubes into the matrix; rinsing the coated nanotubes to remove at least a portion of the surfactant; curing the nanotube-matrix assembly; and cutting the nanotube-matrix assembly to a particular thickness so as to open the ends of the embedded nanotubes. The hollow interiors of the plurality of embedded carbon nanotubes comprise the pores of the microporous membrane.

Gavalas, Lillian Susan↗

Delay Tolerant, Radio Frequency Identification (RFID )-enabled Sensing

Radio Frequency Identification (RFID) technology offers a completely passive method to transmit fixed data sequences from an RFID tag, which typically doesn’t have its own power supply, to an interrogator. Radio frequency (RF) energy harvested from the interrogator is rectified by the tag and used to charge an integrated circuit (IC). The IC then modulates the received signal with the data stored on the tag and reflects the energy back to the interrogator. RFID has seen great proliferation in terrestrial inventory management applications, and it has recently made the jump to spaceflight applications onboard the International Space Station, augmenting an existing optical bar‐code infrastructure for tracking supplies. A number of advanced automated logistics management (ALM) concepts employing RFID are currently being developed and evaluated, including so‐called “smart” shelves, cubbies, and trash receptacles using low‐power, embeddable RFID interrogators. An infrastructure where both crew members and robotic assistants, such as autonomous free flyers, are similarly equipped with small RFID interrogators seems likely. It therefore behooves us to consider extending this infrastructure beyond ALM to applications such as low power, embedded sensing. Typically, data on an RFID tag can only be written by an RFID interrogator, using interrogator energy. In recent years, however, a few efforts have focused on using that energy to drive data acquisition from the tag IC, allowing the tag to modify its stored data sequence with sensor data before replying to an interrogator. In this way, the tag can act as a completely passive sensing device. One problem exists with this approach, however: the tag cannot gather data when an interrogator is not present. Thus, strictly passive RFID sensing tags cannot gather data at regular intervals, in the manner of a typical wireless sensor network, without careful, and impractical, planning of mobile interrogator movements. To address this shortcoming, we look to a recent advance in RFID technology which allows an external microprocessor to power the tag IC and write directly into its RFID memory using a wired serial interface. In this paradigm, data gathering is driven by a small, on‐board power supply (using batteries or harvested energy), and data transfer is provided passively through the RFID interrogation service. Since communication typically consumes the lion’s share of power in WSNs, such a technique has the potential to enable extremely long‐lived, embedded wireless sensing when used with extremely low‐current microcontrollers. Since the communication channel is only open when an interrogator is present and actively interrogating the RFID sensing tag, transport of periodically‐sampled sensor data presents itself as a delay/disruption‐tolerant networking (DTN) problem. In this paper, we present the design of a DTN‐like overlay on the common EPC Global, Class 1, Generation 2 RFID standard. This overlay allows seamless, guaranteed data transfer using the EPC Global protocol, supporting extremely low‐power, embedded sensing using an infrastructure likely to be already in place for ALM applications. We evaluate a prototype implementation of a complete end‐to‐end system using a robotic RFID interrogation agent, and we present future directions for the development of this sensing technique.

Raymond S. Wagner↗

Elastomeric Sensing of Pressure with Liquid Metal and Wireless Inductive Coupling

This project describes resistance-based soft sensors filled with liquid metal, which permit measurements of large strains (0 percent to 110 percent), associated with small forces of less than 30 Newtons. This work also demonstrates a methodology for wireless transfer of these strain measurements without connected electrodes. These sensors allow intermittent detection of pressure on soft membranes with low force. Adapting these sensors for passive wireless pressure sensing will eliminate the need for embedded batteries, and will allow the sensors to transmit pressure data through non-conductive materials including glass and acrylic. The absence of batteries allows us to embed these sensors into materials for long-term use because the sensors only use passive analog circuit elements. We found the oxidation of the liquid metal (eutectic gallium indium) plays a role in the repeatability of the soft sensors. We investigated how the oxidation layer affected the behavior of the sensor by encapsulating materials (silicone, fluorosilicone, and PVC) with varied permeabilities to oxygen. We measured the effects of mechanical loading on the oxidation layer and the effects of wireless inductive coupling on the oxidation layer. We concluded our research by investigating the effects of embedding self-resonant circuits into polydimethylsiloxane (PDMS). Efforts to design engineered systems with soft materials are a growing field with progress in soft robotics, epidermal electronics, and wearable electronics. In the field of soft robotics, PDMS-based grippers are capable of picking up delicate objects because their form-fitting properties allow them to conform to the shape of objects more easily than conventional robotic grippers. Epidermal devices also use PDMS as a substrate to hold electronic components such as radios, sensors, and power supply circuits. Additionally, PDMS-based soft sensors can monitor human motion with liquid metal embedded within micro-channels. Passive wireless sensors have applications in structural health monitoring and medical health monitoring. Doctors can take wireless blood pressure measurements inside arteries to monitor the progression of heart disease. Glaucoma patients can use this technology to monitor the pressure in their eyes to track the progression of the disease.

Dick, Jacob↗

Copilot 3

Ultra-critical systems require high-level assurance, which cannot always be guaranteed in compile time. The use of runtime verification (RV) enables monitoring these systems in runtime, to detect property violations early and limit their potential consequences. The introduction of monitors in ultra-critical systems poses a challenge, as failures and delays in the RV subsystem could affect other subsystems and threaten the mission as a whole. This paper presents Copilot 3, a runtime verification framework for real-time embedded systems. Copilot monitors are written in a compositional, stream-based language with support for a variety of Temporal Logics (TL), which results in robust, high-level specifications that are easier to understand than their traditional counterparts. The framework translates monitor specifications into C code with static memory requirements, which can be compiled to run on embedded hardware. This paper presents version 3 of the Copilot language, demonstrates its suitability with a number of examples, and discusses its use in larger applications. Additionally, it describes the framework?s architecture, its implementation as a Domain Specific Language (DSL) embedded in Haskell, and the progress of the project over the years.

Ivan Perez↗

Evaluations of Damaged High-Pressure Compressor Blades in Two Turbine Engines of NASA DC–8

A series of flights were performed by the NASA DC-8 for the FIREX-AQ missions to observe the effects of wildfires and agricultural fires on air quality and climate in selected locations of the United States. Borescope evaluations after these flights indicated accelerated damage had occurred over this flight series to the blades in the high pressure compressor section of all four CFM56-2C1 turbine engines. This erosion and impact damage appeared to vary in severity, and appeared most severe for engines 3 and 4 located on the right wing of the DC-8.Engine 3 had the most flight hours and cycles since last overhaul of 7,152 h and 2,502 cycles. Engine 4 had the lowest flight hours and cycles since last overhaul of 1,354 h and 369 cycles. For these reasons, three noticeably damaged blades were selected from the high pressure compressor for each of these two engines, and then evaluated at NASA GRC using optical and scanning electron microscopy. Engine 3 compressor blades had both erosion and impact damage that were observed and characterized. The erosion damage was associated with embedded particles 13 μm to 42 μm in sectioned width, composed of varied compositions ranging from SiO2 to SiO2 also containing Ca, Mg, and Al (CMAS). The impact damage was associated with the impacts of larger objects at least 650 μm to 850 μm in width, but only small fragments 5 μm to 15 μm in sectioned width were embedded in the impact surface, also composed of SiO2 to SiO2 containing Ca, Mg, and Al (CMAS). On the other hand, engine 4 blades had only impact damage that was observed and characterized. This impact damage was associated with impacts of objects at least 1,480 μm to 2,080μm in width. Small fragments 5 μm to 15 μm in sectioned width were embedded in the impact surface. For one impact, the fragments were again composed of SiO2 to SiO2 containing Ca, Mg, and Al (CMAS). For two other impacts, the fragments were often composed of Fe, and less frequently Cu.

turbine↗

Graph Convolutional Network-Strengthened Topic Modeling for Scientific Papers

Machine learning has been woven into statistics to modernize topic modeling over textual documents written in natural language, and scientific paper search and recommendation can consequently offer higher accuracy instead of counting on traditional keyword-based search. However, topic distribution of a paper resulted from existing topic modeling techniques only relies on the statistics of words contained in the paper itself. We argue that community users’ views of a paper may also provide insights at the time of recommendation. For example, if a paper on fake image detection has been cited heavily by machine learning papers, such a feature should be absorbed in the embedding of this paper, so that it can be recommended for future query on machine learning. In this paper, we present a Graph Convolutional Network-strengthened Topic Modeling (GCN-TM) method, which employs GCN technique to refine topic modeling of scientific papers. A citation-oriented knowledge graph is constructed, and topic modeling is mapped to feature embedding of the comprising papers. On top of its own topics carried in its content, each paper learns topics from its neighbors and revise its embedding accordingly. Our empirical studies over real-life scientific literature has proved the necessity and effectiveness of our proposed approach.

Jia Zhang↗

Towards an Implementation of Differential Dynamic Logic in PVS

This paper describes an ongoing effort to embed and verify differential dynamic logic (dL) in the Prototype Verification System (PVS). dL is a logic for specifying and formally reasoning about hybrid systems, which employ both continuous and discrete dynamics. There are several benefits of this effort. First, the embedding of dL in PVS offers an independent formal verification of the semantics and rules of dL. Second, the embedding is fully operational within PVS, giving PVS practitioners the ability to use dL in the formal specification and verification process. Third, the rich specification language, type system, and powerful interactive prover of PVS can be used on dL objects. In addition to the embedding and verification of dL, a custom extension for Visual Studio Code has been developed, so that a stylized dL syntax can be used to specify hybrid programs and their properties.

Differential Dynamic Logic↗

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

Assessment of Bacterial Spores in Solid Materials: Curriculum Improvements Partnership Award for the Integration of Research (CIPAIR)

This summer, we quantified the release, by cryogenic grinding at liquid nitrogen temperatures, of microbes present in 4 different spacecraft solids: epoxy 9309, epoxy 9394, epoxy 9396, and a silicone coating. Three different samples of each material were prepared: aseptically prepared solid material, powdered material inoculated with a known spore count of Bacillus atrophaeus, and solid material artificially embedded with a known spore count of Bacillus atrophaeus. Samples were cryogenically ground as needed, and the powders were directly cultured to determine the number of microbial survivors per gram of material. Recovery rates were found to be highly material-dependent, varying from 0.2 to 50% for inoculated material surfaces and 0.002 to 0.5% for embedded spores. A study of the spore survival rate versus total grinding time was also performed, with results indicating that longer grinding time decreases recovery rates of viable spores.

embedded bioburden↗

Gage monitors quality of cross-wire resistance welds

Gage nondestructively monitors the quality of cross-wire resistance welds during the welding operation. The gage gives a dial indication of the relative embedment of the cross wires during the actual welding operation. A direct relationship exists between the depth of embedment and both weld strength and consistency.

Etzel, J.↗

Computer-aided linear-circuit design.

Usually computer-aided design (CAD) refers to programs that analyze circuits conceived by the circuit designer. Among the services such programs should perform are direct network synthesis, analysis, optimization of network parameters, formatting, storage of miscellaneous data, and related calculations. The program should be embedded in a general-purpose conversational language such as BASIC, JOSS, or APL. Such a program is MARTHA, a general-purpose linear-circuit analyzer embedded in APL.

Penfield, P.↗