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This report summarizes the data and analysis of well development and testing (WDT) activities for the well in the context of ongoing hydrogeological studies and long-term groundwater monitoring of the Pahute Mesa area within, and in the vicinity of, the Nevada National Security Site (NNSS) in Nye County, Nevada.
Purpose: To develop and investigate whether the logarithmic barrier (LB) method can result in high-quality reconstructed CT images using sparsely-sampled noisy projection data Methods: The objective function is typically formulated as the sum of the total variation (TV) and a data fidelity (DF) term with a parameter λ that governs the relative weight between them. Finding the optimized value of λ is a critical step for this approach to give satisfactory results. The proposed LB method avoid using λ by constructing the objective function as the sum of the TV and a log function whose augment is the DF term. Newton's method was used to solve the optimization problem. The algorithm was coded in MatLab2013b. Both Shepp-Logan phantom and a patient lung CT image were used for demonstration of the algorithm. Measured data were simulated by calculating the projection data using radon transform. A Poisson noise model was used to account for the simulated detector noise. The iteration stopped when the difference of the current TV and the previous one was less than 1%. Results: Shepp-Logan phantom reconstruction study shows that filtered back-projection (FBP) gives high streak artifacts for 30 and 40 projections. Although visually the streak artifacts are less pronounced for 64 and 90 projections in FBP, the 1D pixel profiles indicate that FBP gives noisier reconstructed pixel values than LB does. A lung image reconstruction is presented. It shows that use of 64 projections gives satisfactory reconstructed image quality with regard to noise suppression and sharp edge preservation. Conclusion: This study demonstrates that the logarithmic barrier method can be used to reconstruct CT images from sparsely-amped data. The number of projections around 64 gives a balance between the over-smoothing of the sharp demarcation and noise suppression. Future study may extend to CBCT reconstruction and improvement on computation speed.
Disclosed herein are systems, methods, and non-transitory computer-readable storage media for simulating propagation of an electromagnetic field, performing phase retrieval, or sampling a band-limited function. A system practicing the method generates transformed data using a discrete Fourier transform which samples a band-limited function f(x) without interpolating or modifying received data associated with the function f(x), wherein an interval between repeated copies in a periodic extension of the function f(x) obtained from the discrete Fourier transform is associated with a sampling ratio Q, defined as a ratio of a sampling frequency to a band-limited frequency, and wherein Q is assigned a value between 1 and 2 such that substantially no aliasing occurs in the transformed data, and retrieves a phase in the received data based on the transformed data, wherein the phase is used as feedback to an optical system.
Six Apollo missions landed on the Moon from 1969-72, returning to Earth 382 kg of lunar rock, soil, and core samples. These samples are among the best documented and preserved samples on Earth that have supported a robust research program for 45 years. From mission planning through sample collection, preliminary examination, and subsequent research, strict protocols and procedures are followed for handling and allocating Apollo subsamples, resulting in the production of vast amounts of documentation. Even today, hundreds of samples are allocated for research each year, building on the science foundation laid down by the early Apollo sample studies and combining new data from today's instrumentation, lunar remote sensing missions and lunar meteorites. Much sample information is available to researchers at curator.jsc.nasa.gov. Decades of analyses on lunar samples are published in LPSC proceedings volumes and other peer-reviewed journals, and tabulated in lunar sample compendia entries. However, for much of the 1969-1995 period, the processing documentation, individual and consortia analyses, and unpublished results exist only in analog forms or primitive digital formats that are either inaccessible or at risk of being lost forever because critical data from early investigators remain unpublished.
The Astromaterials Acquisition & Curation Office at NASA's Johnson Space Center (JSC) is the designated facility for curating all of NASA's extraterrestrial samples. Today, the suite of collections includes the lunar samples from the Apollo missions, cosmic dust particles falling into the Earth's atmosphere, meteorites collected in Antarctica, comet and interstellar dust particles from the Stardust mission, asteroid particles from Japan's Hayabusa mission, solar wind atoms collected during the Genesis mission, and space‐exposed hardware from several missions. To support planetary science research on these samples, JSC's Astromaterials Curation Office hosts NASA's Astromaterials Curation digital repository and data access portal [http://curator.jsc.nasa.gov/], providing descriptions of the missions and collections, and critical information about each individual sample. Our office is designing and implementing several informatics initiatives to better serve the planetary research community. First, we are re‐hosting the basic database framework by consolidating legacy databases for individual collections and providing a uniform access point for information (descriptions, imagery, classification) on all of our samples. Second, we continue to upgrade and host digital compendia that summarize and highlight published findings on the samples (e.g., lunar samples, meteorites from Mars). We host high resolution imagery of samples as it becomes available, including newly scanned images of historical prints from the Apollo missions. Finally we are creating plans to collect and provide new data, including 3D imagery, point cloud data, micro CT data, and external links to other data sets on selected samples. Together, these individual efforts will provide unprecedented digital access to NASA's Astromaterials, enabling preservation of the samples through more specific and targeted requests, and supporting new planetary science research and collaborations on the samples.
The Evans Canyon Fire occurred in Washington in August 2020 and burned 76,000 acres of the semi-arid shrub-steppe landscape at low to moderate severities. This fire burned across the Wenas Creek watershed, allowing for a sampling design that included a portion of the stream within the burned area and a reference site upstream of the burn. Monthly data was collected at an unburned (W10) and burned (W20) site from November 2020 to August 2022 for in situ hydrological and water chemistry sensor data, handheld sensor water chemistry data, general environmental context photos, and field metadata. This dataset is comprised of one folder with field photos and one main data folder containing (1) sensor and sample field protocols; (2) file-level metadata (flmd); (3) data dictionary (dd); (4) international geo-sample number (IGSN) mapping file; (5) field metadata; (6) readme; (7) methods codes; (8) dissolved organic carbon (DOC; measured as non-purgeable organic carbon; NPOC); (9) total nitrogen (TN); (10) total suspended solids (TSS); (11) sensor data (specific conductance, pH, total dissolved solids, temperature, pressure, dissolved oxygen, and turbidity) averages; and (12) sensor installation methods. All files are .csv, .jpg, .jpeg, or .pdf.
Purpose: To determine the optimum sampling strategy for retrospective reconstruction of 4-dimensional (4D) MR data for nonrigid motion characterization of tumor and organs at risk for radiation therapy purposes. Methods and Materials: For optimization, we compared 2 surrogate signals (external respiratory bellows and internal MRI navigators) and 2 MR sampling strategies (Cartesian and radial) in terms of image quality and robustness. Using the optimized protocol, 6 pancreatic cancer patients were scanned to calculate the 4D motion. Region of interest analysis was performed to characterize the respiratory-induced motion of the tumor and organs at risk simultaneously. Results: The MRI navigator was found to be a more reliable surrogate for pancreatic motion than the respiratory bellows signal. Radial sampling is most benign for undersampling artifacts and intraview motion. Motion characterization revealed interorgan and interpatient variation, as well as heterogeneity within the tumor. Conclusions: A robust 4D-MRI method, based on clinically available protocols, is presented and successfully applied to characterize the abdominal motion in a small number of pancreatic cancer patients.
We will develop an enhanced statistical framework to evaluate environmental swipe samples collected at nuclear facilities with the goal of identifying anomalies associated with potential undeclared activities. Our approach will be based on Bayesian methodology and time series analysis. This overall framework should result in a more cost effective and operationally effective approach to environmental swipe sample analysis and interpretation.
A timber inventory of the Eldorado National Forest, located in east-central California, provides an example of the use of a Geographic Information System (GIS) to stratify large areas of land for sampling and the collection of statistical data. The raster-based GIS format of the VICAR/IBIS software system allows simple and rapid tabulation of areas, and facilitates the selection of random locations for ground sampling. Algorithms that simplify the complex spatial pattern of raster-based information, and convert raster format data to strings of coordinate vectors, provide a link to conventional vector-based geographic information systems.
An OSSE should for the sake of realism incorporate at least some of the high-frequency, small-scale phenomena that are suppressed by atmospheric models; these phenomena should be present in the realistic atmosphere sampled by all observing sensor systems whose data are being used. Errors are presently generated for an OSSE in a way that encompasses representational errors, sampling, geophysical local bias, random error, and sensor filtering.
The purpose is to solve the Linear Quadratic Regulator (LQR) problem with random time sampling. Such a sampling scheme may arise from imperfect instrumentation as in the case of sampling jitter. It can also model the stochastic information exchange among decentralized controllers to name just a few. A practical suboptimal controller is proposed with the nice property of mean square stability. The proposed controller is suboptimal in the sense that the control structure is limited to be linear. Because of i. i. d. assumption, this does not seem unreasonable. Once the control structure is fixed, the stochastic discrete optimal control problem is transformed into an equivalent deterministic optimal control problem with dynamics described by the matrix difference equation. The N-horizon control problem is solved using the Lagrange's multiplier method. The infinite horizon control problem is formulated as a classical minimization problem. Assuming existence of solution to the minimization problem, the total system is shown to be mean square stable under certain observability conditions. Computer simulations are performed to illustrate these conditions.
A processor utilizes instruction based sampling to generate sampling data sampled on a per instruction basis during execution of an instruction. The sampling data indicates what processor hardware was used due to the execution of the instruction. Software receives the sampling data and generates an estimate of energy used by the instruction based on the sampling data. The sampling data may include microarchitectural events and the energy estimate utilizes a base energy amount corresponding to the instruction executed along with energy amounts corresponding to the microarchitectural events in the sampling data. The sampling data may include switching events associated with hardware blocks that switched due to execution of the instruction and the energy estimate for the instruction is based on the switching events and capacitance estimates associated with the hardware blocks.
We search for the decay B + → ℓ + νℓγ with ℓ + = e + or μ + using the full Belle data set of 772 × 10 6 BB¯ pairs, collected at the Υ(4S) resonance with the Belle detector at the KEKB asymmetric-energy e + e – collider. We reconstruct one B meson in a hadronic decay mode and search for the B + → ℓ + νℓγ decay in the remainder of the event. Here, we observe no significant signal within the phase space of E$^{sig}_{γ}$ > 1 GeV and obtain upper limits of B(B + → e + ν e γ) < 6.1 × 10 –6 , B(B + → μ + ν μ γ) < 3.4 × 10 –6 , and B(B + → ℓ + ν ℓ γ) < 3.5 × 10 –6 at 90% credibility level.
We introduce a novel semi-supervised learning approach, named Teacher-Student Bayesian Optimization (TSBO ), integrating the teacher-student paradigm into BO to minimize expensive labeled data queries for the first time. TSBO incorporates a teacher model, an unlabeled data sampler, and a student model. The student is trained on unlabeled data locations generated by the sampler, with pseudo labels predicted by the teacher. The interplay between these three components implements a unique selective regularization to the teacher in the form of student feedback. This scheme enables the teacher to predict high-quality pseudo labels, enhancing the generalization of the GP surrogate model in the search space. To fully exploit TSBO , we propose two optimized unlabeled data samplers to construct effective student feedback that well aligns with the objective of Bayesian optimization. Furthermore, we quantify and leverage the uncertainty of the teacher-student model for the provision of reliable feedback to the teacher in the presence of risky pseudo-label predictions. TSBO demonstrates significantly improved sample-efficiency in several global optimization tasks under tight labeled data budgets. The implementation is available at https://github.com/reminiscenty/TSBO-Official.
The measurement of the cross section for the inclusive production of isolated prompt photons in proton-antiproton collisions at $\sqrt{s}$=1.96~TeV is presented. The data set corresponds to an integrated luminosity of 9.5~fb$^{-1}$, collected with the Collider Detector at Fermilab in Run~II. The measurement is performed as a function of the photon transverse energy ($E_T^{\gamma}$) covering the range of 30~GeV$< E_T^{\gamma} <$500~GeV in the pseudorapidity region $|\eta^{\gamma}|<$1.0. To reduce the background coming from the decays of $\pi^0$'s, $\eta$'s and other hadrons, photons are required to be isolated in the calorimeter. The output distributions of an Artificial Neural Network are exploited to estimate the remaining contamination from jets faking isolated photons. Results are compared to leading-order and next-to-leading-order perturbative QCD calculations.
This data was created with open-source tooling for the purpose of running a public competition. This dataset is a sample of what can be generated for the commissioned competition runner to be able to set up their testing infrastructure. It contains images of publicly available spacecraft models in a Blender scene composed of a light source, a background image, and the spacecraft model with bounding box labels.
The frequency response method of analyzing control system performance is discussed, and the difficulty of obtaining the sampled frequency response of the continuous system is considered. An upper bound magnitude error equation is obtained which yields reasonable estimates of the actual error. Finalization of the compensator improvement program is also reported, and the program was used to design compensators for Saturn 5/S1-C dry workshop and Saturn 5/S1-C Skylab.
System design is based on development of standardized, interchangeable modules that minimize power dissipation, interconnecting cables and size. Maximum flexibility is achieved by using building block approach where all data modules are identical and are connected at any remote location and addressed in random fashion from central unit.