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Analysis of data systems and computer programs for nuclear rocket shielding methods, modification, updating, and data input preparation - Vol. 1
Intensity mapping is a promising technique for surveying the large-scale structure of our universe from z = 0 to z ∼ 150, using the brightness temperature field of spectral lines to directly observe previously unexplored portions of our cosmic timeline. Examples of targeted lines include the 21 cm hyperfine transition of neutral hydrogen, rotational lines of carbon monoxide, and fine-structure lines of singly ionized carbon. Recent efforts have focused on detections of the power spectrum of spatial fluctuations, but have been hindered by systematics such as foreground contamination. This has motivated the decomposition of data into Fourier modes perpendicular and parallel to the line of sight, which has been shown to be a particularly powerful way to diagnose systematics. However, such a method is well-defined only in the limit of a narrow-field, flat-sky approximation. This limits the sensitivity of intensity mapping experiments, as it means that wide surveys must be separately analyzed as a patchwork of smaller fields. In this paper, we develop a framework for analyzing intensity mapping data in a spherical Fourier–Bessel basis, which incorporates curved sky effects without difficulty. We use our framework to generalize a number of techniques in intensity mapping data analysis from the flat sky to the curved sky. These include visibility-based estimators for the power spectrum, treatments of interloper lines, and the “foreground wedge” signature of spectrally smooth foregrounds.
Several methods of estimating heat fluxes from nonadiabatic sources distributed over a subjacent surface were discussed. Data calculated by synoptic analysis for the entire Northern Hemisphere from aerological soundings at the AT500 and AT400 level, along with the temperature of the subjacent surfaces, were used. A polytropic model of the atmosphere is used in order to avoid the problems posed by the lack of any complete theory of heat transfer in the lower atmosphere.
Seismic data quality assessment (QA) is the first and one of the most important steps before conducting any further data analysis. Traditional methods involve checking various metrics, such as spike detection and power spectral density, by setting strict thresholds or comparing data against synthetic benchmarks. However, these approaches often rely on pre-existing knowledge and assumptions about data anomalies, leading to potential misclassification of unusual cases. Here, in this study, we propose a deep autoencoder model, an unsupervised learning approach that evaluates data quality without making assumptions about normal and anomalous data, which can be used to identify deviations in recorded data that may indicate nascent instrument failure. We test the model with the U.S. International Monitoring System (IMS) seismic stations and demonstrate the capability of detecting anomalies on a monthly scale. This could prompt station operators to examine potential problems early, allowing sufficient time for instrument maintenance to prevent data outages. In addition, we use a new manually selected testing dataset to compare our model performance against two supervised machine learning (ML) approaches and a standard QA package, as baseline models. When applied to the dataset containing known data anomalies, performance of the supervised and unsupervised ML approaches is similar, with an accuracy of 88.1% for our model compared to ∼90% for the supervised ML approach and 78.2% for the standard QA package. Our model outperforms the baseline models when applied to new stations, where new types of data anomalies can be station-specific and not included in the training dataset. Finally, we show model transferability by training the model with data from the Global Seismograph Network only and applying it to the IMS network data. The results suggest that our model is generalizable and can be applied to new stations with good accuracy.
Many spacecraft are designed to spin about an axis along the flight path as a means of stabilizing the attitude of the spacecraft via gyroscopic stiffness. Because of the assembly requirements of the spacecraft and the launch vehicle, these spacecraft often spin about an axis corresponding to a minor moment of inertia. In such a case, any perturbation of the spin axis will cause sloshing motions in the liquid propellant tanks that will eventually dissipate enough kinetic energy to cause the spin axis nutation (wobble) to grow further. This spinning slosh and resultant nutation growth is a primary design problem of spinning spacecraft and one that is not easily solved by analysis or simulation only. Testing remains the surest way to address spacecraft nutation growth. This paper describes a test method and data analysis technique that reveal the resonant frequency and damping behavior of liquid motions in a spinning tank. Slosh resonant frequency and damping characteristics are necessary inputs to any accurate numerical dynamic simulation of the spacecraft.
As part of the NASA Aviation Safety Program (AvSP), a novel pitot-static calibration method was developed to allow rapid in-flight calibration for subscale aircraft while flying within confined test areas. This approach uses Global Positioning System (GPS) technology coupled with modern system identification methods that rapidly computes optimal pressure error models over a range of airspeed with defined confidence bounds. This method has been demonstrated in subscale flight tests and has shown small 2- error bounds with significant reduction in test time compared to other methods. The current research was motivated by the desire to further evaluate and develop this method for full-scale aircraft. A goal of this research was to develop an accurate calibration method that enables reductions in test equipment and flight time, thus reducing costs. The approach involved analysis of data acquisition requirements, development of efficient flight patterns, and analysis of pressure error models based on system identification methods. Flight tests were conducted at The University of Tennessee Space Institute (UTSI) utilizing an instrumented Piper Navajo research aircraft. In addition, the UTSI engineering flight simulator was used to investigate test maneuver requirements and handling qualities issues associated with this technique. This paper provides a summary of piloted simulation and flight test results that illustrates the performance and capabilities of the NASA calibration method. Discussion of maneuver requirements and data analysis methods is included as well as recommendations for piloting technique.
Analyzing mass spectrometry imaging data can be laborious and time consuming, and as the size and complexity of datasets grow, so does the need for robust automated processing methods. We here present a method for comprehensive, semi-targeted discovery of molecular distributions of interest from mass spectrometry imaging data, using widely available image similarity scoring algorithms to rank images by spatial correlation. A fast and powerful batch search method using a MATLAB implementation of structural similarity (SSIM) index scoring with a pre-selected reference distribution is demonstrated for two sample imaging datasets, a plant metabolite study using Artemisia annua leaf, and a drug distribution study using maraviroc-dosed macaque tissue. .
We report the results of a VAMAS (Versailles Project on Advanced Materials and Standards) inter-laboratory study on the measurement of the shell thickness and chemistry of nanoparticle coatings. Peptide-coated gold particles were supplied to laboratories in two forms: a colloidal suspension in pure water and; particles dried onto a silicon wafer. Participants prepared and analyzed these samples using either X-ray photoelectron spectroscopy (XPS) or low energy ion scattering (LEIS). Careful data analysis revealed some significant sources of discrepancy, particularly for XPS. Degradation during transportation, storage or sample preparation resulted in a variability in thickness of 53%. The calculation method chosen by XPS participants contributed a variability of 67%. However, variability of 12% was achieved for the samples deposited using a single method and by choosing photoelectron peaks that were not adversely affected by instrumental transmission effects. The study identified a need for more consistency in instrumental transmission functions and relative sensitivity factors, since this contributed a variability of 33%. The results from the LEIS participants were more consistent, with variability of less than 10 % in thickness and this is mostly due to a common method of data analysis. The calculation was performed using a model developed for uniform, flat films and some participants employed a correction factor to account for the sample geometry, which appears warranted based upon a simulation of LEIS data from one of the participants and comparison to the XPS results.
We present the Dark Energy Survey (DES) Science Portal, an integratedweb-based data interface designed to facilitate scientific analysis. Wedemonstrate how the Portal can provide a reliable environment to accesscomplete data sets, provide validation algorithms and metrics in the case ofmultiple methods and training configurations, and maintain the provenancebetween the different steps of a complex calculation, while ensuringreproducibility of the results. We use the estimation of DES photometricredshifts (photo-$z$s) as an example. A significant challenge facingphotometric surveys for cosmological purposes, such as DES, is the need toproduce reliable redshift estimates. The choice between competing algorithmsand configurations and the maintenance of an up-to-date spectroscopic databaseto build training sets, for example, are complex tasks when dealing with largeamounts of data that are regularly updated and constantly growing. We show howthe DES Science Portal can be used to train and validate several photo-$z$algorithms using the DES first year (Y1A1) data. The photo-$z$s estimated inthe Portal are used to feed the creation of catalogs for scientific workflows.While the DES collaboration is still developing techniques to obtain precisephoto-$z$s, having a structured framework like the one presented here iscritical for the systematic vetting of DES algorithmic improvements and theconsistent production of photo-$z$s in future DES releases.
The results from a concentrated computational effort are presented with the primary objective being directed at evaluating the vortical-flow-prediction capability of an unstructured-grid Euler solver. Both viscous and inviscid solutions, obtained from an established structured-grid method, along with experimental wind-tunnel data are used as benchmark measures to assess the validity of the unstructured-grid Euler results. Viscous effects on vortical flows are first identified by comparing the viscous and inviscid solutions obtained from the structured-grid method. Computational data analysis is then presented which reveals excellent correlations between the inviscid structured and unstructured-grid results in terms of off-surface flow structures, surface pressure distribution, and the predicted longitudinal aerodynamic characteristics. The sensitivity of the unstructured-grid inviscid solutions to grid refinement is also discussed along with an analysis of the convergence and performance characteristics for each method.
The results from a concentrated computational effort are presented with the primary objective being directed at evaluating the vortical-flow-prediction capability of an unstructured-grid Euler solver. Both viscous and inviscid solutions, obtained from an established structured-grid method, along with an experimental wind-tunnel data are used as bench-mark measures to assess the validity of the unstructured-grid Euler results. Viscous effects on vortical flows are first identified by comparing the viscous and inviscid solutions obtained form the structured-grid method. Computational data analysis are then presented which reveal excellent correlations between the inviscid structured and unstructured-grid results in terms of off-surface flow structures, surface pressure distribution and the predicted longitudinal aerodynamic characteristics. The sensitivity of the unstructured-grid inviscid solutions to grid refinement is also discussed along with an analysis of the convergence and performance characteristics for each method.
Confocal Raman microscopy (CRM) is applied ex vivo for imaging of the spatial distribution of different skin components in skin sections containing hair follicles. For multivariate data analysis, different methods are used in order to spectrally decompose the reference spectra of the skin components (dermis, viable epidermis, stratum corneum and hair). Classical least squares regression (CLS) and multivariate curve resolution–alternating least squares (MCR-ALS) are chosen as suitable methods. In comparison to other optical methods, the advantage of CRM is molecular specificity and dispensability of labelling dyes, which is e.g. necessary in fluorescence microscopy. Therefore, a useful future application of CRM in combination with multivariate data analysis lies in the analysis of penetration routes of topically applied substances, such as cosmetic formulations or drugs into the skin, which is particularly interesting in and around hair follicles. (paper)
The parallax of all stars of visual magnitude greater than about 6.5 has already been measured. If Nemesis is a main-sequence star 1 parsec away, this requires Nemesis's mass to be less than about 0.4 solar masses. If it were less than about 0.05 solar masses its gravity would be too weak to trigger a comet storm. If Nemesis is on the main sequence, this mass range requires it to be a red dwarf. A red dwarf companion would probably have been missed by standard astronomical surveys. Nearby stars are usually found because they are bright or have high proper motion. However, Nemesis's proper motion would now be 0.01 arcsec/yr, and if it is a red dwarf its magnitude is about 10 - too dim to attract attention. Unfortunately, standard four-color photometry does not distinguish between red dwarfs and giants. So although surveys such as the Dearborn Red Star Catalog list stars by magnitude and spectral type, they do not identify the dwarfs. Every star of the correct spectral type and magnitude must be scrutinized. Our candidate list is a hybrid; candidate red stars are identified in the astrometrically poor Dearborn Red Star Catalog and their positions are corrected using the Hubble Guide Star Catalog. When errors in the Dearborn catalog make it impossible to identify the corresponding Hubble star, the fields are split so that we have one centering on each possible candidate. We are currently scrutinizing 3098 fields, which we believe contain all possible red dwarf candidates in the northern hemisphere. Since our last report the analysis and database software has been completely rebuilt to take advantage of updated hardware, to make the data more accessible, and to implement improved methods of data analysis. The software is now completed and we are eliminating stars every clear night.
The moments of the cometary-ion distributions are determined through a three-dimensional analysis of the Giotto IMS high-intensity spectrometer (HIS) data. The spectrometer is described, with emphasis on its angle analyzer and mass analyzer. The method of data analysis is outlined, with ion-flow vectors and temperatures being addressed. The results of the water group ion-flow profile are presented, and it is noted that, after crossing the cometopause region, the ions become gradually colder. At cometocentric distances larger than 130,000 km, the cometary-ion temperature is found to be in the area of 100 eV or higher, and derivations of the flow parameters are uncertain. The ion temperature and the flow speed become lower by about 50 eV after crossing the magnetic pile-up boundary. It is concluded that the observed velocity and temperature profiles can be explained on the basis of charge exchange processes.
Increased penetration levels of distributed variable renewable generation can cause random voltage fluctuations and violations at multiple nodes. Traditional methods of voltage control typically involve reactionary responses of capacitor banks, tap changers, and recently even smart inverters. But because of the lack of foresight in voltage violations, these controls are ineffective to completely mitigate the issue. Therefore, new methods of predicting voltage violations subject to random power injection changes in the distribution network are needed, which can be used to guide optimal and dynamic methods of voltage control. This work lays the foundation for such preemptive voltage monitoring and control by proposing an analytical and sensor data-driven voltage sensitivity analysis method. Driven by stochastic data and forecasts, the method can be used to develop probabilistic voltage sensitivities and consequently to predict system nodes with high likelihood of voltage limit violations. The effectiveness of this method is tested on IEEE 69-node distribution system integrated with distributed solar. The results demonstrate the proposed method's ability to successfully predict nodes with high probability of voltage violations for a specific time-series simulation. The results also demonstrate the ability to guide timely power injection control actions to mitigate future voltage violations.
The analysis of reaction rate data has as its goal the determination of the order rate constant which characterize the data. Chemical reactions with one reactant and present simplified methods for accomplishing this goal are considered. The approaches presented involve the use of half lives or other fractional lives. These methods are particularly useful for the more elementary discussions of kinetics found in general and physical chemistry courses.
Increased penetration of distributed variable re-newable generation may cause random voltage fluctuations and violations at multiple nodes. Traditional methods of voltage control typically involve reactionary responses of capacitor banks, tap changers, and recently even smart inverters. But due to the lack of foresight in voltage violations, these controls are ineffective to completely mitigate the issue. Therefore, new methods of predicting voltage violations subject to random power injection changes in the distribution network is needed, which can be used to guide optimal and dynamic methods of voltage control. This work lays the foundation for such preemptive voltage monitoring and control by proposing an analytical and sensor data-driven voltage sensitivity analysis method. Driven by stochastic data and forecasts, the method can be used to develop probabilistic voltage sensitivities and consequently to predict system nodes with high likelihood of voltage limit violations. The effectiveness of this method is tested on IEEE 69 node distribution system integrated with distributed solar. The results demonstrate the proposed method's ability to successfully predict nodes with high probability of voltage violations for a specific time series simulation. The results also demonstrate the ability to guide timely power injection control actions to mitigate future voltage violations.
A method of determining thermal diffusivity in thin plates is presented. The method, using infrared images of evolving thermal patterns previously injected with a laser, is noncontacting, one-sided, and remote. It does not require independent estimates of either the emissivity of the sample or the sample thickness. With a line-segment pattern for thermal input, it yields the inplane components of the diffusivity tensor in anisotropic materials and also the rate of heat loss to the environment of the plate. Two methods of data analysis are presented, one corresponding to a heating line of general cross section and the other considering a Gaussian cross section, thereby saving considerable computer time. Both methods produce a statistical evaluation of measurement quality as well as estimates of diffusivity and loss rate. Results are shown for plates of metals and graphite-epoxy composite materials. Principal components and orientation for the diffusivity tensor are obtained in the anisotropic graphite-epoxy sample.