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At least 487 records · Page 27

Bose-Einstein-condensed scalar field dark matter and the gravitational wave background from inflation: New cosmological constraints and its detectability by LIGO

Here, we consider an alternative to weakly interacting massive particle (WIMP) cold dark matter (CDM), ultralight bosonic dark matter ($m$ ≳ 10 -22 eV /$c$ 2 ) described by a complex scalar field (SFDM) with a global $U(1)$ symmetry, for which the comoving particle number density, or charge density, is conserved after particle production during standard reheating. We allow for a repulsive self-interaction. In a Λ SFDM universe, SFDM starts relativistic, evolving from stiff ($w$ = 1) to radiation-like ($w$ = 1/3), before becoming nonrelativistic at late times ($w$ = 0). Thus, before the familiar radiation-dominated era, there is an earlier era of stiff-SFDM-domination. During both the stiff-SFDM-dominated and radiation-dominated eras, the expansion rate is higher than in Λ CDM. SFDM particle mass $m$ and quartic self-interaction coupling strength λ, are therefore constrained by cosmological observables, particularly $N$ eff , the effective number of neutrino species during big bang nucleosynthesis, and $z$ eq , the redshift of matter-radiation equality. Furthermore, since the stochastic gravitational wave background (SGWB) from inflation is amplified during the stiff-SFDM-dominated era, it can contribute a radiationlike component large enough to affect these observables, by further boosting the expansion rate after the stiff era ends. Remarkably, this same amplification makes detection of the SGWB possible at high frequencies by current laser interferometer experiments, e.g., aLIGO/Virgo and LISA. For SFDM particle parameters that sat isfy these cosmological constraints, the amplified SGWB is detectable by LIGO for a broad range of reheat temperatures $T$ reheat , for values of the tensor-to-scalar ratio $r$ currently allowed by CMB polarization measurements. For a given $r$ and λ/(mc 2 ) 2 , the marginally allowed Λ SFDM model for each $T$ reheat has the smallest $m$ that satisfies the cosmological constraints and maximizes the present SGWB energy density for that $T$ reheat . This SGWB is then maximally detectable for values of $T$ reheat for which modes that reenter the horizon when reheating ended have frequencies today that lie within the LIGO sensitive band. For example, for the family of marginally allowed models with $r$ = 0.01 and λ/($mc$ 2 ) 2 = 10 -18 eV -1 cm 3 , the maximally detectable Λ SFDM model has $T$ reheat ≃ 2×10 4 GeV and $m$ ≃ 1.6×10 -19 eV/c 2 , for which we predict an aLIGO O1 run detection with signal-to-noise ratio of ~ 10. We show that the null detection of the SGWB recently reported by the aLIGO O1 run excludes the parameter range 8.75 × 10 3 ≲ $T$ reheat (GeV) ≲ 1.7 × 10 5 for this illustrative family at 95% confidence, thereby demonstrating that GW detection experiments can already place a new kind of cosmological constraint on SFDM. A wider range of SFDM parameters and reheat temperatures should be accessible to aLIGO/Virgo O5, with the potential to detect this unique signature of the ΛSFDM model. For this same illustrative family, for example, a 3σ detection is predicted for 600 ≲ $T$ reheat (GeV) ≲ 10 7 .

79 ASTRONOMY AND ASTROPHYSICS↗

Regression for Radioactive Source Detection

Detection of radioactive sources is an important capability that led to the deployment of networks for detecting and localizing low-level, hazardous radiation sources. It is generally expected that such networks outperform the individual detectors by intelligently fusing information from several dispersed sensors. In this paper, we develop a network detection method that uses the slope of a linear regression fit as a test statistic for detecting a point radioactive source within a field of detectors. In our regression model, we compute a least-squared linear fit between the average radiation counts at the detectors and the inverse-squared distances of known detector locations to an estimated source location. We show that the slope of this regression fit is an estimate of the source intensity and can be used as a threshold for source detection purposes. We compare performance of our proposed detection method with that of a fusion-based Sequential Probability Ratio Test (SPRT) method. For performance analyses, two datasets from the Domestic Nuclear Detection Ofces Intelligence Radiation Sensors Systems (IRSS) outdoor tests are used. Each of these tests consists of several runs of a single radioactive source moving in and out of a detector network. We present receiver operating characteristic (ROC) curves and optimal threshold values for the performance each detection method, and determine that our detection method using linear regression fit has slightly better overall performance than SPRT method.

Cordone, G.↗

Towards Malware Detection via CPU Power Consumption: Data Collection Design and Analytics

This paper presents an experimental design and data analytics approach aimed at power-based malware detection on general-purpose computers. Leveraging the fact that malware executions must consume power, we explore the postulate that malware can be accurately detected via power data analytics. Our experimental design and implementation allow for programmatic collection of CPU power profiles for fixed tasks during uninfected and infected states using five different rootkits. To characterize the power consumption profiles, we use both simple statistical and novel, sophisticated features. We test a one-class anomaly detection ensemble (that baselines non-infected power profiles) and several kernel-based SVM classifiers (that train on both uninfected and infected profiles) in detecting previously unseen malware and clean profiles. The anomaly detection system exhibits perfect detection when using all features and tasks, with smaller false detection rate than the supervised classifiers. The primary contribution is the proof of concept that baselining power of fixed tasks can provide accurate detection of rootkits. Moreover, our treatment presents engineering hurdles needed for experimentation and allows analysis of each statistical feature individually. This work appears to be the first step towards a viable power-based detection capability for general-purpose computers, and presents next steps toward this goal.

Bridges, Robert↗

Feasibility of vibro-acoustography with a quasi-2D ultrasound array transducer for detection and localizing of permanent prostate brachytherapy seeds: A pilot ex vivo study

Purpose: Effective permanent prostate brachytherapy (PPB) requires precise placement of radioactive seeds in and around the prostate. The impetus for this research is to examine a new ultrasound-based imaging modality, vibro-acoustography (VA), which may serve to provide a high rate of PPB seed detection while also effecting enhanced prostate imaging. The authors investigate the ability of VA, implemented on a clinical ultrasound (US) scanner and equipped with a quasi-2D (Q2D) array US transducer, to detect and localize PPB seeds in excised prostate specimens. Methods: Nonradioactive brachytherapy seeds were implanted into four excised cadaver prostates. A clinical US scanner equipped with a Q2D array US transducer was customized to acquire both US and C-scan VA images at various depths. The VA images were then used to detect and localize the implanted seeds in prostate tissue. To validate the VA results, computed tomography (CT) images of the same tissue samples were obtained to serve as the reference by which to evaluate the performance of VA in PPB seed detection. Results: The results indicate that VA is capable of accurately identifying the presence and distribution of PPB seeds with a high imaging contrast. Moreover, a large ratio of the PPB seeds implanted into prostate tissue samples could be detected through acquired VA images. Using CT-based seed identification as the standard, VA was capable of detecting 74%–92% of the implanted seeds. Additionally, the angular independency of VA in detecting PPB seeds was demonstrated through a well-controlled phantom experiment. Conclusions: Q2DVA detected a substantial portion of the seeds by using a 2D array US transducer in excised prostate tissue specimens. While VA has inherent advantages associated with conventional US imaging, it has the additional advantage of permitting detection of PPB seeds independent of their orientation. These results suggest the potential of VA as a method for PPB imaging that ultimately may allow US-based real-time intraoperative dosimetry.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Automated contouring error detection based on supervised geometric attribute distribution models for radiation therapy: A general strategy

Purpose: One of the most critical steps in radiation therapy treatment is accurate tumor and critical organ-at-risk (OAR) contouring. Both manual and automated contouring processes are prone to errors and to a large degree of inter- and intraobserver variability. These are often due to the limitations of imaging techniques in visualizing human anatomy as well as to inherent anatomical variability among individuals. Physicians/physicists have to reverify all the radiation therapy contours of every patient before using them for treatment planning, which is tedious, laborious, and still not an error-free process. In this study, the authors developed a general strategy based on novel geometric attribute distribution (GAD) models to automatically detect radiation therapy OAR contouring errors and facilitate the current clinical workflow. Methods: Considering the radiation therapy structures’ geometric attributes (centroid, volume, and shape), the spatial relationship of neighboring structures, as well as anatomical similarity of individual contours among patients, the authors established GAD models to characterize the interstructural centroid and volume variations, and the intrastructural shape variations of each individual structure. The GAD models are scalable and deformable, and constrained by their respective principal attribute variations calculated from training sets with verified OAR contours. A new iterative weighted GAD model-fitting algorithm was developed for contouring error detection. Receiver operating characteristic (ROC) analysis was employed in a unique way to optimize the model parameters to satisfy clinical requirements. A total of forty-four head-and-neck patient cases, each of which includes nine critical OAR contours, were utilized to demonstrate the proposed strategy. Twenty-nine out of these forty-four patient cases were utilized to train the inter- and intrastructural GAD models. These training data and the remaining fifteen testing data sets were separately employed to test the effectiveness of the proposed contouring error detection strategy. Results: An evaluation tool was implemented to illustrate how the proposed strategy automatically detects the radiation therapy contouring errors for a given patient and provides 3D graphical visualization of error detection results as well. The contouring error detection results were achieved with an average sensitivity of 0.954/0.906 and an average specificity of 0.901/0.909 on the centroid/volume related contouring errors of all the tested samples. As for the detection results on structural shape related contouring errors, an average sensitivity of 0.816 and an average specificity of 0.94 on all the tested samples were obtained. The promising results indicated the feasibility of the proposed strategy for the detection of contouring errors with low false detection rate. Conclusions: The proposed strategy can reliably identify contouring errors based upon inter- and intrastructural constraints derived from clinically approved contours. It holds great potential for improving the radiation therapy workflow. ROC and box plot analyses allow for analytically tuning of the system parameters to satisfy clinical requirements. Future work will focus on the improvement of strategy reliability by utilizing more training sets and additional geometric attribute constraints.

60 APPLIED LIFE SCIENCES↗

Mosquito-Borne Viruses and Insect-Specific Viruses Revealed in Field-Collected Mosquitoes by a Monitoring Tool Adapted from a Microbial Detection Array

Several mosquito-borne diseases affecting humans are emerging or reemerging in the United States. The early detection of pathogens in mosquito populations is essential to prevent and control the spread of these diseases. Here, we tested the potential applicability of the Lawrence Livermore Microbial Detection Array (LLMDA) to enhance biosurveillance by detecting microbes present in Aedes aegypti, Aedes albopictus, and Culex mosquitoes, which are major vector species globally, including in Texas. The sensitivity and reproducibility of the LLMDA were tested in mosquito samples spiked with different concentrations of dengue virus (DENV), revealing a detection limit of >100 but <1,000 PFU/ml. Additionally, field-collected mosquitoes from Chicago, IL, and College Station, TX, of known infection status (West Nile virus [WNV] and Culex flavivirus [CxFLAV] positive) were tested on the LLMDA to confirm its efficiency. Mosquito field samples of unknown infection status, collected in San Antonio, TX, and the Lower Rio Grande Valley (LRGV), TX, were run on the LLMDA and further confirmed by PCR or quantitative PCR (qPCR). The analysis of the field samples with the LLMDA revealed the presence of cell-fusing agent virus (CFAV) in A. aegypti populations. Wolbachia was also detected in several of the field samples (A. albopictus and Culex spp.) by the LLMDA. Our findings demonstrated that the LLMDA can be used to detect multiple arboviruses of public health importance, including viruses that belong to the Flavivirus, Alphavirus, and Orthobunyavirus genera. Additionally, insect-specific viruses and bacteria were also detected in field-collected mosquitoes. Another strength of this array is its ability to detect multiple viruses in the same mosquito pool, allowing for the detection of cocirculating pathogens in an area and the identification of potential ecological associations between different viruses. Finally, this array can aid in the biosurveillance of mosquito-borne viruses circulating in specific geographical areas.

59 BASIC BIOLOGICAL SCIENCES↗

Quantum-enhanced detection of viral cDNA via luminescence resonance energy transfer using upconversion and gold nanoparticles

Abstract The COVID-19 pandemic has profoundly impacted global economies and healthcare systems, revealing critical vulnerabilities in both. In response, our study introduces a sensitive and highly specific detection method for cDNA, leveraging Luminescence Resonance Energy Transfer (LRET) between upconversion nanoparticles (UCNPs) and gold nanoparticles (AuNPs), and achieves a detection limit of 242 fM for SARS-CoV-2 cDNA. This innovative sensing platform utilizes UCNPs conjugated with one primer and AuNPs with another, targeting the 5′ and 3′ ends of the SARS-CoV-2 cDNA, respectively, enabling precise differentiation of mismatched cDNA sequences and significantly improving detection specificity. Through rigorous experimental analysis, we established a quenching efficiency range from 10.4 % to 73.6 %, with an optimal midpoint of 42 %, thereby demonstrating the superior sensitivity of our method. Our work uses SARS-CoV-2 cDNA as a model system to demonstrate the potential of our LRET-based detection method. This proof-of-concept study highlights the adaptability of our platform for future diagnostic applications. Instrumental validation confirms the synthesis and formation of AuNPs, addressing the need for experimental verification of the preparation of nanomaterial. Our comparative analysis with existing SARS-CoV-2 detection methods revealed that our approach provides a low detection limit and high specificity for target cDNA sequences, underscoring its potential for targeted COVID-19 diagnostics. This study demonstrates the superior sensitivity and adaptability of using UCNPs and AuNPs for cDNA detection, offering significant advances in rapid, accessible diagnostic technologies. Our method, characterized by its low detection limit and high precision, represents a critical step forward in developing next-generation biosensors for managing current and future viral outbreaks. By adjusting primer sequences, this platform can be tailored to detect other pathogens, contributing to the enhancement of global healthcare responsiveness and infectious disease control.

Esmaeili, Shahriar [Institute for Quantum Science ↗

Exploring the feasibility of crack detection in plate weldments through an interposed layer of viscoelastic material.

Development of new approaches in the nondestructive evaluation of welded steel plates for large naval structures continues to be an area of interest for the DoD and the military complex. In this report we will evaluate an elastic-microwave based detection and imaging method using both numerical finite element (3D FEM) and experimental methods. The evaluation was performed on two test articles that were designed to represent critical structural components with flaws of interest. Details of the materials and the mechanical drawings were provided with the test articles. Flaws, cracks and weld defects have been included in the test articles to determine detection sensitivity and accuracy of the proposed method. Our approach uses a microwave interferometer (MI) to record the scattered response of flaws in the steel plated as it is driven by an incident elastic field. The MI can “see” (penetrate) through the viscoelastic coating to the upper surface of the plate. Out-of-plane displacement amplitudes of 10nm in the frequency range (1kHz to 25kHz) are readily observable, with the key feature being, that the surface bond between the coating and the steel plate are undisturbed. The non-contact aspect of the interferometer allows for large surface regions to be accurately and efficiently scanned in space and time. Spatiotemporal data sets of this kind and specialized wavefield processing provide powerful detection and imaging modalities. From these wavefield data sets, a plate thickness mapping capability has been demonstrated that can detect thickness changes on the order of 0.79 mm (1/32”) with spatial resolution on the order of the spatial sampling rate, 15mm (~5/8”). We have also shown that a topological energy analysis of the wavefield data can detect and locate small flaws, on the order of 5-10 mm (0.025-0.40”) in the welded joint of a 1.5” thick T-plate. As before, these results are obtained through a 2” thick viscoelastic coating without altering the integrity of coating or the coating bond. These results show that we are detecting and locating damage (flaws) in the plate that are much smaller than the wavelength of the propagating guided modes. Classical scattering theory places a (λ/2) resolution limit on the detectability of flaws in terms of the incident field [22], however, since the spatial resolution of the scanned region is much smaller, (Δx=Δy=15mm) and the inherent natural focusing of the time reversal operation, we are able to detect smaller flaws on the order of (λ/10). It is important to realize that we are not imaging the flaw but detecting and localizing a difference between a reference (pristine) sample and the measured (damaged) sample, relative to a spatial grid on the surface. The current scan resolution is 15mm x 15mm. At present we cannot expect to resolve individual flaws within a grid space only their cumulative effect. Even with the current limitations, this imaging approach appears to be a promising alternative to current methods where the coating layer is removed.

36 MATERIALS SCIENCE↗

Screening IMS Seismic Detections using Dynamic Correlation Processor: Progress report for 2020

LLNL completed the FY20 workplan entailing application of the Dynamic Correlation Processor (DCP) to International Monitoring System (IMS) primary seismic data in 2019 and 2020. The effective analysis period was curtailed to January 1 through November 12, 2019 due to a data compression change that rendered later data unusable. Fixing the data compression error is a priority for our data source, and we plan to extend the period of data analysis when the fix is implemented. We found that the percentage overlap between detections generated by the International Data Centre (IDC) DFX software and detections generated by DCP varied widely by station. Overlap is 30-40% at some stations, suggesting that a large percentage of DFX detections could be screened (removed) from the detection-event association algorithm. However, detection overlap was much lower, a few percent, at many stations and the percentage overlap was low at stations known to have many mines and other local sources of seismicity. Unexpectedly low detection overlap at many stations prompted us to examine results for the ARCES station in detail. There is considerable mine activity near ARCES, but our initial FY20 analysis found only ~2% DCP/DFX detection overlap. Differences in the pre-processing (e.g. beam recipes) used by DCP and DFX appear to be the cause of low detection overlap at ARCES. DCP uses wideband filters to improve detector robustness and the IDC uses narrow-band pre-filtering to improve sensitivity. After reprocessing ARCES using the IDC beam recipe, DCP/DFX overlap increased to ~74%. This result shows that we must reprocess the IMS network using the IDC station-specific beam recipes if we are to effectively screen DFX detections and improve automatic event building performance at the IDC.

58 GEOSCIENCES↗

Vegetation Detection Using Deep Learning and Conventional Methods

Land cover classification with the focus on chlorophyll-rich vegetation detection plays an important role in urban growth monitoring and planning, autonomous navigation, drone mapping, biodiversity conservation, etc. Conventional approaches usually apply the normalized difference vegetation index (NDVI) for vegetation detection. In this paper, we investigate the performance of deep learning and conventional methods for vegetation detection. Two deep learning methods, DeepLabV3+ and our customized convolutional neural network (CNN) were evaluated with respect to their detection performance when training and testing datasets originated from different geographical sites with different image resolutions. A novel object-based vegetation detection approach, which utilizes NDVI, computer vision, and machine learning (ML) techniques, is also proposed. The vegetation detection methods were applied to high-resolution airborne color images which consist of RGB and near-infrared (NIR) bands. RGB color images alone were also used with the two deep learning methods to examine their detection performances without the NIR band. The detection performances of the deep learning methods with respect to the object-based detection approach are discussed and sample images from the datasets are used for demonstrations.

58 GEOSCIENCES↗

Single-Atomic Site Catalyst Enhanced Lateral Flow Immunoassay for Point-of-Care Detection of Herbicide

Point-of-care (POC) detection of herbicides is of great importance due to their impact on the environment and potential risks to human health. Here, we design a single-atomic site catalyst (SASC) with excellent peroxidase-like (POD-like) catalytic activity, which enhances the detection performance of corresponding lateral flow immunoassay (LFIA). The iron single-atomic site catalyst (Fe-SASC) is synthesized from hemin-doped ZIF-8, creating active sites that mimic the Fe active center coordination environment of natural enzyme and their functions. Due to its atomically dispersed iron active sites that result in maximum utilization of active metal atoms, the Fe-SASC exhibits superior POD-like activity, which has great potential to replace its natural counterparts. Also, the catalytic mechanism of Fe-SASC is systematically investigated. Utilizing its outstanding catalytic activity, the Fe-SASC is used as label to construct LFIA (Fe-SASC-LFIA) for herbicide detection. The 2,4-dichlorophenoxyacetic acid (2,4-D) is selected as a target here, since it is a commonly used herbicide as well as a biomarker for herbicide exposure evaluation. A linear detection range of 1-250 ng/mL with a low limit of detection (LOD) of 0.82 ng/mL has been achieved. Meanwhile, excellent specificity and selectivity towards 2,4-D have been obtained. The outstanding detection performance of the Fe-SASC-LFIA has also been demonstrated in the detection of human urine samples, indicating the practicability of this POC detection platform for analyzing the 2,4-D exposure level of a person. We believe this proposed Fe-SASC-LFIA has potential as a portable, rapid, and high-sensitive POC detection strategy for pesticide exposure evaluation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Comparative Evaluation of Four Presumptive Tests for Blood to Detect Epithelial Injury on Fish

Current methods of fish epithelial injury detection are limited to gross macroscopic examination that has a subjective bias as well as an inability to reliably quantify the degree of injury. Fluorescein, a presumptive test for blood, has been shown to have the capability to detect and quantify fish epithelial injury. However, there are several other presumptive tests for blood (Bluestar*, phenolphthalein, and HemastixH) that may have benefits over the use of fluorescein, particularly for field research on wild fish. This study investigated the capabilities of these four tests to detect and quantify a variety of injuries commonly encountered by fish (abrasion, cuts, fin frays, and punctures) using the freshwater bluegill Lepomis macrochirus as a model. Fluorescein was consistently found to be the most reliable (i.e., detected the highest proportion of true positive results and rarely detected false positive reactions) of the four presumptive tests for blood compared. Further testing was conducted to examine the reliability of fluorescein. By 24 h after an injury was inflicted, the injury was no longer detectable by fluorescein, and when fluorescein was applied to an injured fish, the fluorescein was no longer detectable 3 h after application. In a comparison of two common anaesthetics used in fisheries research, there was no significant difference in the proportion of injury detected when 3- aminobenzoic acid ethyl ester methanesulfate (tricaine) was used compared with a clove oil and ethanol (1:9) solution. In summary, fluorescein was the most reliable presumptive test for blood examined in this study for the detection and quantification of recent (hours) fish epithelial injury.

Fluorescein, fish, epithelial damage↗

Robust global detection of forced changes in mean and extreme precipitation despite observational disagreement on the magnitude of change

Detection and attribution (D&A) of forced precipitation change are challenging due to internal variability, limited spatial, and temporal coverage of observational records and model uncertainty. These factors result in a low signal-to-noise ratio of potential regional and even global trends. Here, we use a statistical method – ridge regression – to create physically interpretable fingerprints for the detection of forced changes in mean and extreme precipitation with a high signal-to-noise ratio. The fingerprints are constructed using Coupled Model Intercomparison Project phase 6 (CMIP6) multi-model output masked to match coverage of three gridded precipitation observational datasets – GHCNDEX, HadEX3, and GPCC – and are then applied to these observational datasets to assess the degree of forced change detectable in the real-world climate in the period 1951–2020. We show that the signature of forced change is detected in all three observational datasets for global metrics of mean and extreme precipitation. Forced changes are still detectable from changes in the spatial patterns of precipitation even if the global mean trend is removed from the data. This shows the detection of forced change in mean and extreme precipitation beyond a global mean trend is robust and increases confidence in the detection method's power as well as in climate models' ability to capture the relevant processes that contribute to large-scale patterns of change. We also find, however, that detectability depends on the observational dataset used. Not only coverage differences but also observational uncertainty contribute to dataset disagreement, exemplified by the times of emergence of forced change from internal variability ranging from 1998 to 2004 among datasets. Furthermore, different choices for the period over which the forced trend is computed result in different levels of agreement between observations and model projections. These sensitivities may explain apparent contradictions in recent studies on whether models under- or overestimate the observed forced increase in mean and extreme precipitation. Lastly, the detection fingerprints are found to rely primarily on the signal in the extratropical Northern Hemisphere, which is at least partly due to observational coverage but potentially also due to the presence of a more robust signal in the Northern Hemisphere in general.

54 ENVIRONMENTAL SCIENCES↗

Nondestructive Modular Leak Detection in 3D Printed 316L Stainless Steel Pipes via Laser Powder Bed Fusion

This research investigates the leak detection features of 316L Stainless Steel pipe structures manufactured via Laser Powder Bed Fusion (LPBF). This work involves the design of a modular sensor system integrating nondestructive evaluation (NDE) methods, including thermal imaging and ultrasonic frequency detection to detect and characterize leaks in components. This aims to improve leak detection sensitivity within medium-pressure gas systems, during continuous operation without halting flow or introducing safety risks. The system could be adaptable for use on unmanned aerial vehicles (UAVs), enabling remote leak detection in active environments. A custom pneumatic system incorporating temperature and pressure sensors was assembled to detect leaks in LPBF-printed 316L SS tee pipes. Experimental results and simulations confirm the system’s effectiveness in leak detection and material evaluation. This research program also integrated a Python-based image recognition platform based on a metallography and optical microscopy to assess the porosity and complement the leak detection data on the printed structures. This allows a detailed analysis of pore distribution and internal leak paths, which could compromise structural integrity, critical for quality control during manufacturing. Findings suggest that the investigated approach holds potential for enhancing leak detection technologies and adapt them for advanced manufactured parts.

36 MATERIALS SCIENCE↗

Detection of x ray sources in PROS

The problem of detecting discrete sources in x-ray images has much in common with the problem of automatic source detection at other wavelengths. In all cases, one searches for positive brightness enhancements exceeding a certain threshold, which appear consistent with what one expects for a point source, in the presence of a (possibly) spatially variable background. Multidimensional point spread functions (e.g., dependent on detector position and photon energy) are also common. At the same time, the problem in x-ray astronomy has some unique aspects. For example, for typical x-ray exposures in current or recent observatories, the number of available pixels far exceeds the number of actual x-ray events, so Poisson, rather than Gaussian statistics apply. Further, extended cosmic x-ray sources are common, and one often desires to detect point sources in the vicinity or even within bright, diffuse x-ray emission. Finally, support structures in x-ray detectors often cast sharp shadows in x-ray images making it necessary to detect sources in a region of rapidly varying exposure. We have developed a source detection package within the IRAF/PROS environment which attempts to deal with some of the problems of x-ray source detection. We have patterned our package after the successful Einstein Observatory x-ray source detection programs. However, we have attempted to improve the flexibility and accessibility of the functions and to provide a graphical front-end for the user. Our philosophy has been to use standard IRAF tasks whenever possible for image manipulation and to separate general functions from mission-specific ones. We will report on the current status of the package and discuss future developments, including simulation tasks, to allow the user to assess detection efficiency and source significance, tasks to determine source intensity, and alternative detection algorithms.

Deponte, J.↗

Orion MPCV Touchdown Detection Threshold Development and Testing

A robust method of detecting Orion Multi ]Purpose Crew Vehicle (MPCV) splashdown is necessary to ensure crew and hardware safety during descent and after touchdown. The proposed method uses a triple redundant system to inhibit Reaction Control System (RCS) thruster firings, detach parachute risers from the vehicle, and transition to the post ]landing segment of the Flight Software (FSW). The vehicle crew is the prime input for touchdown detection, followed by an autonomous FSW algorithm, and finally a strictly time based backup timer. RCS thrusters must be inhibited before submersion in water to protect against possible damage due to firing these jets under water. In addition, neglecting to declare touchdown will not allow the vehicle to transition to post ]landing activities such as activating the Crew Module Up ]righting System (CMUS), resulting in possible loss of communication and difficult recovery. A previous AIAA paper gAssessment of an Automated Touchdown Detection Algorithm for the Orion Crew Module h concluded that a strictly Inertial Measurement Unit (IMU) based detection method using an acceleration spike algorithm had the highest safety margins and shortest detection times of other methods considered. That study utilized finite element simulations of vehicle splashdown, generated by LS ]DYNA, which were expanded to a larger set of results using a Kriging surface fit. The study also used the Decelerator Systems Simulation (DSS) to generate flight dynamics during vehicle descent under parachutes. Proto ]type IMU and FSW MATLAB models provided the basis for initial algorithm development and testing. This paper documents an in ]depth trade study, using the same dynamics data and MATLAB simulations as the earlier work, to further develop the acceleration detection method. By studying the combined effects of data rate, filtering on the rotational acceleration correction, data persistence limits and values of acceleration thresholds, an optimal configuration was determined. The lever arm calculation, which removes the centripetal acceleration caused by vehicle rotation, requires that the vehicle angular acceleration be derived from vehicle body rates, necessitating the addition of a 2nd order filter to smooth the data. It was determined that using 200 Hz data directly from the vehicle IMU outperforms the 40 Hz FSW data rate. Data persistence counter values and acceleration thresholds were balanced in order to meet desired safety and performance. The algorithm proved to exhibit ample safety margin against early detection while under parachutes, and adequate performance upon vehicle splashdown. Fall times from algorithm initiation were also studied, and a backup timer length was chosen to provide a large safety margin, yet still trigger detection before CMUS inflation. This timer serves as a backup to the primary acceleration detection method. Additionally, these parameters were tested for safety on actual flight test data, demonstrating expected safety margins.

Daum, Jared↗

Damage Detection Response Characteristics of Open Circuit Resonant (SansEC) Sensors

The capability to assess the current or future state of the health of an aircraft to improve safety, availability, and reliability while reducing maintenance costs has been a continuous goal for decades. Many companies, commercial entities, and academic institutions have become interested in Integrated Vehicle Health Management (IVHM) and a growing effort of research into "smart" vehicle sensing systems has emerged. Methods to detect damage to aircraft materials and structures have historically relied on visual inspection during pre-flight or post-flight operations by flight and ground crews. More quantitative non-destructive investigations with various instruments and sensors have traditionally been performed when the aircraft is out of operational service during major scheduled maintenance. Through the use of reliable sensors coupled with data monitoring, data mining, and data analysis techniques, the health state of a vehicle can be detected in-situ. NASA Langley Research Center (LaRC) is developing a composite aircraft skin damage detection method and system based on open circuit SansEC (Sans Electric Connection) sensor technology. Composite materials are increasingly used in modern aircraft for reducing weight, improving fuel efficiency, and enhancing the overall design, performance, and manufacturability of airborne vehicles. Materials such as fiberglass reinforced composites (FRC) and carbon-fiber-reinforced polymers (CFRP) are being used to great advantage in airframes, wings, engine nacelles, turbine blades, fairings, fuselage structures, empennage structures, control surfaces and aircraft skins. SansEC sensor technology is a new technical framework for designing, powering, and interrogating sensors to detect various types of damage in composite materials. The source cause of the in-service damage (lightning strike, impact damage, material fatigue, etc.) to the aircraft composite is not relevant. The sensor will detect damage independent of the cause. Damage in composite material is generally associated with a localized change in material permittivity and/or conductivity. These changes are sensed using SansEC. The unique electrical signatures (amplitude, frequency, bandwidth, and phase) are used for damage detection and diagnosis. An operational system and method would incorporate a SansEC sensor array on select areas of the aircraft exterior surfaces to form a "Smart skin" sensing surface. In this paper a new method and system for aircraft in-situ damage detection and diagnosis is presented. Experimental test results on seeded fault damage coupons and computational modeling simulation results are presented. NASA LaRC has demonstrated with individual sensors that SansEC sensors can be effectively used for in-situ composite damage detection of delamination, voids, fractures, and rips. Keywords: Damage Detection, Composites, Integrated Vehicle Health Monitoring (IVHM), Aviation Safety, SansEC Sensors

Dudley, Kenneth L.↗

Detection Thresholds of Falling Snow From Satellite-Borne Active and Passive Sensors

There is an increased interest in detecting and estimating the amount of falling snow reaching the Earths surface in order to fully capture the global atmospheric water cycle. An initial step toward global spaceborne falling snow algorithms for current and future missions includes determining the thresholds of detection for various active and passive sensor channel configurations and falling snow events over land surfaces and lakes. In this paper, cloud resolving model simulations of lake effect and synoptic snow events were used to determine the minimum amount of snow (threshold) that could be detected by the following instruments: the W-band radar of CloudSat, Global Precipitation Measurement (GPM) Dual-Frequency Precipitation Radar (DPR)Ku- and Ka-bands, and the GPM Microwave Imager. Eleven different nonspherical snowflake shapes were used in the analysis. Notable results include the following: 1) The W-band radar has detection thresholds more than an order of magnitude lower than the future GPM radars; 2) the cloud structure macrophysics influences the thresholds of detection for passive channels (e.g., snow events with larger ice water paths and thicker clouds are easier to detect); 3) the snowflake microphysics (mainly shape and density)plays a large role in the detection threshold for active and passive instruments; 4) with reasonable assumptions, the passive 166-GHz channel has detection threshold values comparable to those of the GPM DPR Ku- and Ka-band radars with approximately 0.05 g *m(exp -3) detected at the surface, or an approximately 0.5-1.0-mm * h(exp -1) melted snow rate. This paper provides information on the light snowfall events missed by the sensors and not captured in global estimates.

remote sensing↗