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We present a novel method for estimating the surface horizontal velocity on ice shelves using laser altimetrydata from the Ice Cloud and land Elevation Satellite (ICESat; 20032009). The method matches undulations measured at crossover points between successive campaigns.
Renewable energy systems continue to be one of the fastest growing segments of the energy industry. This paper focuses on the understanding of how photovoltaic (PV) technology behaves under dc arc conditions. Emphasis is placed on the electrical safety aspect of DC arc flash incident energy evaluation. Because of the fast proliferation of PV systems and the lack of formal equivalent calculation guidelines such as IEEE 1584 for AC systems, it has been necessary to rely on different equations and models presented by various researchers over the last few years. This paper discusses the behavior of PV systems under arc conditions and presents the results of available methods to estimate the dc arc flash incident energy. This paper provides a comparative analysis of a proposed arc-flash incident energy calculation method against different laboratory tests including those performed by NREL. Detailed explanations are provided regarding the effect of PV module I-V and P-V curves under arcing conditions. Examples of the application of the proposed calculation method to the test measurements are included.
We propose a method for estimating the composition, i.e. the relative amounts of leptons and protons, of extragalactic jets which exhibit Chandra - detected knots in their kpc scale jets. The method relies on measuring, or setting upper limits on, the component of the Cosmic Microwave Background (CMB) radiation that is bulk-Comptonized by the cold electrons in the relativistically flowing jet. These measurements, along with modeling of the broadband knot emission that constrain the bulk Lorentz factor GAMMA of the jets, can yield estimates of the jet power carried by protons and leptons. We provide an explicit calculation of the spectrum of the bulk-Comptonized (BC) CMB component and apply these results to PKS 0637 - 752 and 3C 273, two superluminal quasars with Chandra - detected large scale jets. What makes these sources particularly suited for such a procedure is the absence of significant non-thermal jet emission in the 'bridge', the region between the core and the first bright jet knot, which guarantees that most of the electrons are cold there, leaving the BC scattered CMB radiation as the only significant source of photons in this region. At lambda = 3.6 - 8.0 microns, the most likely band to observe the BC scattered CMB emission, the Spitzer angular resolution (approximately 1" - 3") is considerably smaller than the the 'bridges' of these jets (approximately 10"), making it possible to both measure and resolve this emission.
Despite the wide application of meta-analysis in ecology, some of the traditional methods used for meta-analysis may not perform well given the type of data characteristic of ecological meta-analyses. We reviewed published meta-analyses on the ecological impacts of global climate change, evaluating the number of replicates used in the primary studies (ni) and the number of studies or records (k) that were aggregated to calculate a mean effect size. We used the results of the review in a simulation experiment to assess the performance of conventional frequentist and Bayesian meta-analysis methods for estimating a mean effect size and its uncertainty interval. Our literature review showed that ni and k were highly variable, distributions were right-skewed, and were generally small (median ni =5, median k=44). Our simulations show that the choice of method for calculating uncertainty intervals was critical for obtaining appropriate coverage (close to the nominal value of 0.95). When k was low (<40), 95% coverage was achieved by a confidence interval based on the t-distribution that uses an adjusted standard error (the Hartung-Knapp-Sidik-Jonkman, HKSJ), or by a Bayesian credible interval, whereas bootstrap or z-distribution confidence intervals had lower coverage. Despite the importance of the method to calculate the uncertainty interval, 39% of the meta-analyses reviewed did not report the method used, and of the 61% that did, 94% used a potentially problematic method, which may be a consequence of software defaults. In general, for a simple random-effects meta-analysis, the performance of the best frequentist and Bayesian methods were similar for the same combinations of factors (k and mean replication), though the Bayesian approaches had higher than nominal (>95%) coverage for the mean effect when k was very low (k<15). Our literature review suggests that many meta-analyses that used z-distribution or bootstrapping confidence intervals may have over-estimated the statistical significance of their results when the number of studies was low; more appropriate methods need to be adopted in ecological meta-analyses.
The problems of Doppler parameter estimation in the processing of spaceborne synthetic aperture radar (SAR) data are examined. The phase history of a target can be approximated by a linear FM signal with two parameters: the Doppler centroid and the Doppler frequency rate. Two methods to estimate these parameters automatically from the radar echo are presented. The accuracies of these methods were evaluated using the SEASAT SAR data. It is shown that the method for Doppler centroid determination may be applicable in the measurement of ocean current velocities. The concept behind this technique is presented and the accuracies of the measurements required are examined. Some preliminary results of testing this technique with the SEASAT data are presented.
Fast estimation of the single-particle density matrix is key to many applications in quantum chemistry and condensed matter physics. The best numerical methods leverage the fact that the density matrix elements f(H) ij decay rapidly with distance rij between orbitals. This decay is usually exponential. However, for the special case of metals at zero temperature, algebraic decay of the density matrix appears and poses a significant numerical challenge. Here, we introduce a gradient-based probing method to estimate all local density matrix elements at a computational cost that scales linearly with system size. For zero-temperature metals, the stochastic error scales like S -(d+2)/2d , where d is the dimension and S is a prefactor to the computational cost. The convergence becomes exponential if the system is at finite temperature or is insulating.
Physical and budget constraints often result in irregular sampling, which complicates accurate subsurface imaging. Preprocessing approaches, such as missing trace or shot interpolation, are typically employed to enhance seismic data in such cases. Recently, deep learning has been used to address the trace interpolation problem at the expense of large amounts of training data to adequately represent typical seismic events. Nonetheless, most research in this area has focused on trace reconstruction, with little attention having been devoted to shot interpolation. Furthermore, existing methods assume regularly spaced receivers/sources failing in approximating seismic data from real (irregular) surveys. This work presents a novel shot gather interpolation approach which uses a continuous coordinate-based representation of the acquired seismic wavefield parameterized by a neural network. The proposed unsupervised approach, which we call coordinate-based seismic interpolation (CoBSI), enables the prediction of specific seismic characteristics in irregular land surveys without using external data during neural network training. Importantly, experimental results on real and synthetic 3-D data validate the ability of the proposed method to estimate continuous smooth seismic events in the time-space and frequency-wavenumber domains, improving sparsity or low-rank-based interpolation methods.
International Atomic Energy Agency Safeguards Analytical Services (IAEA-SGAS) oversees coordinating the second Controlled Potential Coulometry Quality Control Interlaboratory Comparison (CPC QC ILC) on behalf of the Nuclear Material Laboratory (NML). The purpose of the exercise is to: (1) verify the ability of participating laboratories to measure the Pu concentration in the sample solutions, and (2) estimate the method performance (repeatability and intermediate reproducibility). Sample shipping coordination was performed by IAEA-SGAS. The test sample is a plutonium nitrate solution (estimated molarity of 3-5 M), delivered in two PTFE screw-capped quartz ampoules. Each participating laboratory is to receive two ampoules. Each ampoule holds approximately 8 mL of solution containing approximately 10 mg Pu/g. The gross weights of the ampoules were provided. The test sample is assumed to have an iron content below the level of significance for the CPC. The isotopic composition of the plutonium test sample was provided; however, no analysis date was provided. In addition, IAEA-SGAS provided analytical instructions to participants. On September 20, 2022, SRNL received the samples sent by IAEA-SGAS.
The extent and duration of snow cover is predicted to be altered as the climate changes. Developing high-resolution estimates of snow cover change is crucial for estimating changes in snow cover and the effects of these changes on watershed and ecosystems processes. Remote sensing tools have been a common method for rapidly mapping snow covered area (SCA) across a landscape. The most common remote sensing method for estimating SCA uses satellite-based calculations of the normalized difference snow index (NDSI), which relies on spectral measurements in the shortwave-infrared wavelengths (SWIR). NDSI is effective at catchment- to regional-scale estimates of SCA, but due to spatial resolution limitations of SWIR measurements, NDSI cannot be used to assess fine-scale SCA. In this work, we develop a new algorithm, called the Blue Snow Threshold (BST) algorithm, that maps high-resolution SCA by calculating a threshold on the blue wavelengths from high-resolution satellite imagery. This data package includes Orthorectified IKONOS-2 imagery (IkonosTestImage.tif) from August 14, 2004 at 1.00 meters Ground Sample Distance for Cook Inlet, Alaska (59.966414 , -152.982975). The Blue Snow Threshold algorithm (BST.py) was then used to produce a snow cover estimate (IkonosTestImage_BST.tif) for this study area. Additional imagery metadata is included in the ImageInfo.txt file. See Thaler et al., 2023 (https://doi.org/10.1016/j.rse.2022.113403) for more information about the BST algorithm.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
A method of estimating frequency drift rate and removing its effect from Allan variance plots is given. When tried on a test of hydrogen masers, the methods gives consistent results. An error in the previous Allan variance computation algorithm is corrected.
The computational methods used to predict and optimize the thermal structural behavior of aerospace vehicle structures are reviewed. In general, two classes of algorithms, implicit and explicit, are used in transient thermal analysis of structures. Each of these two methods has its own merits. Due to the different time scales of the mechanical and thermal responses, the selection of a time integration method can be a different yet critical factor in the efficient solution of such problems. Therefore mixed time integration methods for transient thermal analysis of structures are being developed. The computer implementation aspects and numerical evaluation of these mixed time implicit-explicit algorithms in thermal analysis of structures are presented. A computationally useful method of estimating the critical time step for linear quadrilateral element is also given. Numerical tests confirm the stability criterion and accuracy characteristics of the methods. The superiority of these mixed time methods to the fully implicit method or the fully explicit method is also demonstrated.
The computational methods used to predict and optimize the thermal-structural behavior of aerospace vehicle structures are reviewed. In general, two classes of algorithms, implicit and explicit, are used in transient thermal analysis of structures. Each of these two methods has its own merits. Due to the different time scales of the mechanical and thermal responses, the selection of a time integration method can be a difficult yet critical factor in the efficient solution of such problems. Therefore mixed time integration methods for transient thermal analysis of structures are being developed. The computer implementation aspects and numerical evaluation of these mixed time implicit-explicit algorithms in thermal analysis of structures are presented. A computationally-useful method of estimating the critical time step for linear quadrilateral element is also given. Numerical tests confirm the stability criterion and accuracy characteristics of the methods. The superiority of these mixed time methods to the fully implicit method or the fully explicit method is also demonstrated.
We investigated the use of ocean color remote sensing to measure transport of dissolved organic carbon (DOC) by the Mississippi River to the Gulf of Mexico. From 2000 to 2005 we recorded surface measurements of DOC, colored dissolved organic matter (CDOM), salinity, and water-leaving radiances during five cruises to the Mississippi River Plume. These measurements were used to develop empirical relationships to derive CDOM, DOC, and salinity from monthly composites of SeaWiFS imagery collected from 1998 through 2005. We used river flow data and a two-end-member mixing model to derive DOC concentrations in the river end-member, river flow, and DOC transport using remote sensing data. We compared our remote sensing estimates of river flow and DOC transport with data collected by the United States Geological Survey (USGS) from 1998 through 2005. Our remote sensing estimates of river flow and DOC transport correlated well (r2 ~ 0.70) with the USGS data. Our remote sensing estimates and USGS field data showed low variability in DOC concentrations in the river end-member (7-11%), and high seasonal variability in river flow (~50%). Therefore, changes in river flow control the variability in DOC transport, indicating that the remote sensing estimate of river flow is the most critical element of our DOC transport measurement. We concluded that it is possible to use this method to estimate DOC transport by other large rivers if there are data on the relationship between CDOM, DOC, and salinity in the river plume.
We present high-spectral-resolution M-band spectra from iSHELL on NASA’s Infrared Telescope Facility along the line of sight to the debris disk host star HD 32297. We also present a Gemini Planet Imager H-band polarimetric image of the HD 131488 debris disk. We search for fundamental CO absorption lines in the iSHELL spectra of HD 32297, but do not detect any. We place an upper limit on the CO column density of ∼6×1015cm−2. By combining the column density upper limit, the CO mass measured with the Atacama Large Millimeter/submillimeter Array (ALMA), and the geometrical properties of the disk, we estimate the scale height of the CO to be 2au across the radial extent of the disk (∼80–120au). We use the same method to estimate the CO scale height of three other edge-on, CO-rich debris disks that all have CO observed in absorption with the Hubble Space Telescope as well as in emission with ALMA: β Pictoris, HD 110058, and HD 131488. We compare our estimated CO scale heights of these four systems to the millimeter dust scale heights and find that, under the assumption of hydrostatic equilibrium, there is a potential correlation between the CO and millimeter dust scale heights. There are multiple factors that affect the gas vertical structure such as turbulence, photodissociation with weak vertical mixing, as well as where the gas originates. One possible explanation for the potential correlation could be that the gas and dust are of a similar secondary origin in these four systems.
Predictions of the total energy deposited into a brain tumor through X-ray irradiation are notoriously error-prone. We investigate how this predictive uncertainty is affected by uncertainty in both the location of the region occupied by a dose-enhancing iodinated contrast agent and the agent's concentration. This is done within the probabilistic framework in which these uncertain parameters are modeled as random variables. We employ the stochastic collocation (SC) method to estimate statistical moments of the deposited energy in terms of statistical moments of the random inputs, and the global sensitivity analysis (GSA) to quantify the relative importance of uncertainty in these parameters on the overall predictive uncertainty. A nonlinear radiation–diffusion equation dramatically magnifies the coefficient of variation of the uncertain parameters, yielding a large coefficient of variation for the predicted energy deposition. This demonstrates that accurate prediction of the energy deposition requires a proper treatment of even small parametric uncertainty. Our analysis also reveals that SC outperforms standard Monte Carlo, but its relative efficiency decreases as the number of uncertain parameters increases from one to three. A robust GSA ameliorates this problem by reducing this number.
The application of an alternative laser-induced breakdown spectroscopy (LIBS) method based on transversely excited atmospheric (TEA) CO2 laser was investigated for the first time for estimating the hardness of metallic materials. The human eye-safe (TEA) CO2 laser, operating at 10.6 µm, was used for plasma generation. The LIBS spectra were recorded by employing a cost-effective CCD camera for the time-integrated and spatial resolved measurements. The cast iron and aluminum alloys samples with different hardness have been tested. The ratio between the magnesium ionic and neutral lines in LIBS spectra was applied for estimating the material hardness. In addition, the hardness of all samples included in this study was determined using the conventional method for material hardness determination, i.e. Vickers hardness test. The linear dependence of magnesium lines intensity ratio on the sample’s hardness was obtained for both kinds of materials. Profilometric measurements were used to verify that the newly-introduced method is almost nondestructive for the investigated metals. The results obtained in the present work confirmed the potential of using this original LIBS system not only for the fast elemental analysis but also for the direct estimation of the hardness of metals and alloys. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Abstract Density estimation for unmarked animals is particularly challenging, yet density estimates are often necessary for effective wildlife management. Raccoons ( Procyon lotor ) are the primary terrestrial wildlife reservoir for Lyssavirus rabies within the United States. The raccoon rabies variant (RRVV) is actively managed at landscape scales using oral rabies vaccination (ORV) within the eastern United States. To effectively manage RRVV, it is important to know the density of raccoons to appropriately scale the density of ORV baits distributed on the landscape. We compared methods to estimate raccoon densities from camera‐trap data versus more intensive capture‐mark‐recapture (CMR) estimates across 2 land cover types (upland pine and bottomland hardwood) in the southeastern United States during 2019 and 2020. We evaluated the effect of alternative camera configurations and durations of camera trapping on density estimates and used an N‐mixture model to estimate raccoon densities, including covariates on abundance and detection. We further compared different methods of scaling camera‐based counts, with the maximum number of raccoons seen on any given image within a day best explaining density. Camera‐trap density estimates were moderately correlated with CMR estimates ( r = 0.56). However, densities from camera‐trap data were more reliable when classifying category of density as an index used to inform management (83% correct when compared to CMR estimates), although the densities in our study fell into the 2 lowest density classes only. Using more cameras reduced bias and uncertainty around density estimates; however, if ≤6 camera traps were used at a site, a line transect approach proved less biased than a grid design. Camera trapping should be conducted for at least 3 weeks for more accurate estimates of raccoon population density in our study area (<5% bias). We show that camera‐trap data can be used to assign raccoon densities to management‐relevant density index bins, but more studies are needed to ensure reliability across a greater range of environmental conditions and raccoon densities.
The most widely known modern method for estimating gravity field values from observed data is least-squares collocation. Its advantages are that it can make estimates at arbitrary locations based on irregularly spaced observations, and that it makes use of statistical information about errors in the input data while providing corresponding information about the quality of the output estimates. Disadvantages of collocation include the necessity of inverting square matrices of dimension equal to the number of data values and the need to assume covariance models for the gravity field and the data errors. Fourier methods are an important alternative to collocation; having the advantage of greater computational efficiency, but requiring data estimates to be on a regular grid and not using or providing statistical accuracy information. The GEOFAST algorithm is an implementation of collocation that achieves high computational efficiency by transforming the estimation equations into the frequency domain where an accurate approximation may be made to reduce the workload. The forward and inverse Fast Fourier Transforms (FFTs) are utilized. The accuracy and computational efficiency of the GEOFAST algorithm is demonstrated using two sets of synthetic gravity data: marine gravity for an ocean trench region including wavelengths longer than 200 km; and local land gravity containing wavelengths as short as 5 km. These results are discussed along with issues such as the advantages of first removing reference field models before carrying out the estimation algorithm.