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DOE OSTI2026

Cation Data for the East River Watershed, Colorado (2014-2025)

This data package contains mean values for cation concentration for water samples taken from the East River Watershed in Colorado. Inductively coupled plasma mass spectrometry (ICP-MS) has been used to measure the concentrations of elements of interest simultaneously for the East River Watershed, Colorado groundwater and surface water samples to inform insights on the biogeochemistry processes within the watershed. The East River is part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United States. For samples collected prior to 06-16-2021, the instrumentation, Elan DRC II, PerkinElmer SCIEX, automatically switches among the three models necessary to analyze all 37 elements. These 37 elements include: (1) Lithium (Li), Beryllium (Be), Boron (B), Sodium (Na), Magnesium (Mg), Aluminium (Al), Silicon (Si), Phosphorus (P), Titanium (Ti), Cobalt (Co), Nickel (Ni), Copper (Cu), Zinc (Zn), Germanium (Ge), Arsenic (As), Rubidium (Rb), Strontium (Sr), Zirconium (Zr), Molybdenum (Mo), Silver (Ag), Cadmium (Cd), Tin (Sn), Antimony (Sb), Caesium (Cs), Barium (Ba), Europium (Eu), Lead (Pb), Thorium (Th), Uranium (U) using standard model, argon Ar as reaction gas, (2) Potassium (K), Calcium (Ca), Vanadium (V), Chromium (Cr), Manganese (Mn), Iron (Fe) using dynamic reaction cell (DRC) model, ammonia NH3 as reaction gas, and (3) Phosphorus (P) and Selenium (Se) using DRC model, oxygen O2 as reaction gas. Note for the samples with higher concentrations of chloride (Cl-), asenic (As) concentrations were analysed with DRC model (oxygen O2 as reaction gas) to avoid the interference of chloride. For samples collected on and after 06-16-2021, an advanced Agilent 8900 triple quadrupole inductively coupled plasma mass spectrometry system (Agilent 8900 QQQ ICP-MS, Agilent Technologies) has been used to measure the concentrations of interested 36 elements simultaneously for environmental samples, including (1) Lithium (Li), Beryllium (Be) and Boron (B) using standard no gas mode, (2) Sodium (Na), Magnesium (Mg), Aluminium (Al) Phosphorus (P), Potassium (K), Chromium (Cr), Manganese (Mn), Iron (Fe), Cobalt (Co), Nickel (Ni), Copper (Cu), Zinc (Zn), Germanium (Ge), Arsenic (As), Rubidium (Rb), Strontium (Sr), Zirconium (Zr), Molybdenum (Mo), Silver (Ag), Cadmium (Cd), Tin (Sn), Antimony (Sb), Cesium (Cs), Barium (Ba), Europium (Eu), Lead (Pb), Thorium (Th) and Uranium (U) using standard helium (He) collision mode, (3) Titanium (Ti) and Vanadium (V) using high Energy (HEHe) helium (He) collision mode, and (4) Silicon (Si), Calcium (Ca) and Selenium (Se) using standard H2 reaction mode. All samples were prepared/diluted with 2% (v/v) ultrapure nitric acid in Milli-Q water (18.2 mega ohm-cm), and analyzed under a rigorous quality assurance and quality control (QA/QC) process. This data package contains (1) a zip file (cation_data_2014_2025.zip) containing a total of 5,849 files: 5.848 data files of cation data from across the Lawrence Berkeley National Laboratory (LBNL) Watershed Function Scientific Focus Area (SFA) which is reported in .csv files per location and a locations.csv (1 file) with latitude and longitude for each location; (2) a file-level metadata (v6_20260901_flmd.csv) file that lists each file contained in the dataset with associated metadata; (3) a data dictionary (v6_20260901_dd.csv) file that contains terms/column_headers used throughout the files along with a definition, units, and data type; (4) PDF and docx files for the detemination of Method Detection Limits (MDLs) for ICP-MS PerkinElmer DRC II instrumentation (Detemination_of_Method_Detection_Limits__MDLs__for_ICP_MS__PerkinElmer_Elan_DRC_II__LBL_Bldg74_Lab214D) for samples before November 2021; (5) PDF and docx files for the determination of MDLs for ICP-MS Agilent 8900 QQQ instrumentation (ICP_MS_Analysis_detection_limits_and_QA_QC_WenmingDong_updated_2026-08-06) for samples November 2021 and onward. Missing values within the anion data files are noted as either "-9999" or "0.0" for not detectable (N.D.) data. There are a total of 113 locations containing cation data. Update on 2021-04-11: Added Detemination of Method Detection Limits (MDLs) for ICP-MS document, which can be accessed as a PDF or with Microsoft Word. Update on 2022-06-10: versioned updates to this dataset was made along with these changes: (1) updated cation data for all locations up to 2021-12-31, (2) removal of units from column headers in datafiles, (3) added row underneath headers to contain units of variables, (4) removed suffix and prefix on two variables (“aqberylliumion_asberyllium” and “aqlithiumion_aslithium”), (5) added -9999 for empty numerical cells, and (6) the addition of the file-level metadata (flmd.csv) and data dictionary (dd.csv) were added to comply with the File-Level Metadata Reporting Format. Update on 2022-09-09: Updates were made to reporting format specific files (file-level metadata and data dictionary) to correct swapped file names, add additional details on metadata descriptions on both files, add a header_row column to enable parsing, and add version number and date to file names (v2_20220909_flmd.csv and v2_20220909_dd.csv). Update on 2023-08-08: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-01-05. The file level metadata and data dictionary files were updated to reflect the additional data added. Update on 2024-03-11: Updates were made to both the data files and reporting format specific files. New available anion data was added, up until 2023-10-16. Further, revisions to the data files were made to remove incorrect data points (from 1970 and 2001). The reporting format specific files were updated to reflect the additional data added. Updated versions of the PDF and docx files for determination of MDLs for ICP-MS data were added to this dataset for samples starting in November 2021. Update on 2025-05-15: Updates were made to both the data files and reporting format specific files. New available cation data was added, up until the end of WY2024 (September 30, 2024). International Generic Sample Numbers (IGSNs), when registered, were added to the data files. The reporting format specific files were updated to reflect the additional data added. Update on 2026-09-01: Updates were made to both the data files and reporting format specific files. New available cation data was added, up until the end of WY2025 (September 30, 2025). Updated versions, as of 2026-08-06, of the PDF and docx files for determination of MDLs for ICP-MS data were added to this dataset for samples starting in November 2021.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1990

The significance of biomass burning as a source of carbon monoxide and ozone in the Southern Hemisphere tropics - A satellite analysis

Carbon monoxide mixing ratios obtained by the October 1984 Measurement of Air Pollution from Satellites (MAPS) experiment are compared with the distribution of October 1984 Total Ozone Mapping Spectrometer (TOMS) ozone concentrations. The TOMS and MAPS data show coincident high values of ozone and carbon monoxide over central South America and southeastern Africa. The 1984 MAPS data are also compared with tropospheric ozone concentrations derived from 6 years of TOMS and Stratospheric Aerosol and Gas Experiment (SAGE) I and II measurements. Examination of the October climatological distribution of tropospheric ozone also reveals high concentrations over central South America and southeastern Africa. These coincident high values of CO and ozone in the rural southern tropics are most likely due to biomass burning and the subsequent transport of CO and ozone by large-scale weather patterns. It appears that both CO and ozone are being transported thousands of kilometers from their source regions by the prevailing winds.

Watson, Catherine E.↗
DOE OSTI2020

Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements

Physics-informed neural networks (PINNs) were recently proposed in [18] as an alternative way to solve partial differential equations (PDEs). A neural network (NN) represents the solution, while a PDE-induced NN is coupled to the solution NN, and all differential operators are treated using automatic differentiation. Here, we first employ the standard PINN and a stochastic version, sPINN, to solve forward and inverse problems governed by a non-linear advection–diffusion–reaction (ADR) equation, assuming we have some sparse measurements of the concentration field at random or pre-selected locations. Subsequently, we attempt to optimise the hyper-parameters of sPINN by using the Bayesian optimisation method (meta-learning) and compare the results with the empirically selected hyper-parameters of sPINN. In particular, for the first part in solving the inverse deterministic ADR, we assume that we only have a few high-fidelity measurements, whereas the rest of the data is of lower fidelity. Hence, the PINN is trained using a composite multi-fidelity network, first introduced in [12], that learns the correlations between the multi-fidelity data and predicts the unknown values of diffusivity, transport velocity and two reaction constants as well as the concentration field. For the stochastic ADR, we employ a Karhunen–Loève (KL) expansion to represent the stochastic diffusivity, and arbitrary polynomial chaos (aPC) to represent the stochastic solution. Correspondingly, we design multiple NNs to represent the mean of the solution and learn each aPC mode separately, whereas we employ a separate NN to represent the mean of diffusivity and another NN to learn all modes of the KL expansion. For the inverse problem, in addition to stochastic diffusivity and concentration fields, we also aim to obtain the (unknown) deterministic values of transport velocity and reaction constants. The available data correspond to 7spatial points for the diffusivity and 20 space–time points for the solution, both sampled 2000 times. We obtain good accuracy for the deterministic parameters of the order of 1–2% and excellent accuracy for the mean and variance of the stochastic fields, better than three digits of accuracy. In the second part, we consider the previous stochastic inverse problem, and we use Bayesian optimisation to find five hyper-parameters of sPINN, namely the width, depth and learning rate of two NNs for learning the modes. Here, we obtain much deeper and wider optimal NNs compared to the manual tuning, leading to even better accuracy, i.e., errors less than 1% for the deterministic values, and about an order of magnitude less for the stochastic fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Fractional delay filter for a digital signal processing system

A processing element for implementation in a digital signal processing system is provided. The processing element is configured to receive a first data stream comprising a plurality of digital values where each value represents a sample of an analog signal. The processing element is further configured to receive a second data stream comprising a series of digital values where each value represents a sample of the analog signal. The processing element is configured to filter the first data stream via a first Farrow-structured fractional delay (FD) filter and output a filtered first data stream; filter the second data stream via a second Farrow-structured FD filter and output a filtered second data stream; and temporarily store values from the second data stream and output the stored values to the first Farrow-structured FD filter so that the stored values can be used to filter the first data stream.

Stanley, Dennis L.↗
DOE OSTI2022

Parametric dynamic mode decomposition for reduced order modeling

Dynamic Mode Decomposition (DMD) is a model-order reduction approach, whereby spatial modes of fixed temporal frequencies are extracted from numerical or experimental data sets. The DMD low-rank or reduced operator is typically obtained by singular value decomposition of the temporal data sets. For parameter-dependent models, as found in many multi-query applications such as uncertainty quantification or design optimization, the only parametric DMD technique developed was a stacked approach, with data sets at multiple parameter values were aggregated together, increasing the computational work needed to devise low-rank dynamical reduced-order models. Here in this paper, we present two novel approach to carry out parametric DMD: one based on the interpolation of the reduced-order DMD eigen-pair and the other based on the interpolation of the reduced DMD (Koopman) operator. Numerical results are presented for diffusion-dominated nonlinear dynamical problems, including a multiphysics radiative transfer example. All three parametric DMD approaches are compared.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

In Situ Soil Moisture and Thaw Depth Measurements Coincident with Airborne SAR Data Collections, Barrow and Seward Peninsulas, Alaska, 2017

The in-situ soil moisture and thaw depth measurements provided in this dataset were collected coincident with airborne overflights of L- and P-band SAR instruments at the NGEE Arctic study site near Barrow, on the North Slope, and at the three study sites on the Seward Peninsula, Alaska. Field measurements and flights were conducted during the summer of 2017 as a collaboration between the NASA ABoVE Project's Airborne SAR Campaign and the NGEE Arctic Project. ABoVE protocols for establishing field measurement plots were followed. NGEE Arctic plots for the ground-based measurements are located at existing study sites where SAR data would also add value to current monitoring and characterization efforts of the NGEE Team. The ground-based data will be used by ABoVE to analyze, calibrate and validate the remote sensing products. In November 2020, a Version 2 (V2) set of in situ soil moisture and thaw depth measurement data files were released. This dataset contains 1 pdf, 2 zip files, and 7 csv files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort 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).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

In Situ Soil Moisture and Thaw Depth Measurements Coincident with Airborne SAR Data Collections, Seward Peninsula, Alaska, 2019

The in-situ soil moisture and thaw depth measurements provided in this dataset were collected coincident with airborne overflights of L- and P-band SAR instruments at the Teller and Kougarok NGEE Arctic study sites on the Seward Peninsula, Alaska. Field measurements and flights were conducted in August 2019 as a collaboration between the NASA ABoVE Project's Airborne SAR Campaign and the NGEE Arctic Project. ABoVE protocols for establishing field measurement plots were followed. NGEE Arctic plots for the ground-based measurements are located at existing study sites where SAR data would also add value to current monitoring and characterization efforts of the NGEE Team. The ground-based data will be used by ABoVE to analyze, calibrate and validate the remote sensing products. This dataset follows the format and collection guidelines of the collaboration effort in 2017. Contained in this dataset are *.csv (including data dictionaries), .zip, .kml, .jpgs, .py, and .pdf files.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort (with some overlap with Covid-19 pandemic) 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).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1973

Systematic two-dimensional cascade tests. Volume 4: Cascade test data

The data pertaining to the performance characteristics of hydrofoils are presented. The hydrofoils are: (1) double circular arc, (2) multiple circular arc, and (3) slotted doubled circular arc. The experimental data are organized into a systematic presentation format and provided with scale factors and reference values for interpretation. The data include local pressure distributions upstream and downstream of the cascade test section, continuous records from flow angle and total pressure surveys, hydrofoil local pressure distributions, and cavitation data.

Murrin, T. A.↗
NASA NTRS1977

Preliminary geologic evaluation of L-band radar imagery: Arkansas test site

The relatively small angles of incidence (steep depression angles) of the L-band system provide minimal shadowing on terrain back-slopes and considerable foreshortening on terrain fore-slopes which sacrifice much of the topographic enhancement afforded by a more oblique angle of illumination. In addition, the dynamic range of the return from vegetated surfaces is substantially less for the L-band system, and many surface features defined primarily by subtle changes in vegetation are lost. In areas having terrain conditions similar to those of northern Arkansas, and where LANDSAT and shorter wavelength aircraft radar data are available, the value of the JPL L-band imagery as either a complimentary or supplementary geologic data source is not obvious.

Macdonald, H.↗
NASA NTRS1978

Performance characteristics of the electrochemical concentration cell ozonesonde

A large number of electrochemical concentration cell (ECC) ozonesondes were calibrated in relation to a UV photometric absorption ozone instrument prior to using the ozonesondes in atmospheric soundings. The two methods of measuring ozone were in reasonable agreement on the average, but there was considerable variation from one ozonesonde to another. Averaging the individual linear regressions gives an ECC ozone concentration equal to 0 + or - 8 nbar plus (0.96 + or - 0.10) times the concentration determined with the UV instrument, where uncertainties represent 90% confidence limits. Applying individual calibration corrections to atmospheric sounding data reduced the mean difference between Dobson spectrophotometric measurements of total ozone overburdens and corresponding values obtained from ozonesonde data from -8.5 + or - 8.2 to -1.4 + or - 7.3% for a series of measurements carried out in 1977. Corresponding uncorrected and corrected differences averaged 3.2 + or - 9.7 and 1.9 + or - 10.6%, respectively, for soundings carried out in 1976.

Torres, A. L.↗
NASA NTRS1987

Evidence for methane and ammonia in the coma of comet P/Halley

Methane and ammonia abundances in the coma of Halley are derived from Giotto ion mass spectrometer data using an Eulerian model of chemical and physical processes inside the contact surface to simulate Giotto high-intensity spectrometer ion mass spectral data for mass-to-charge ratios (m/q) from 15 to 19. The ratio m/q = 19/18 as a function of distance from the nucleus is not reproduced by a model for a pure water coma. It is necessary to include the presence of NH3, and uniquely NH3, in coma gases in order to explain the data. A ratio of production rates Q(NH3)/Q(H2O) = 0.01 = 0.02 results in model values approximating the Giotto data. Methane is identified as the most probable source of the distinct peak at m/q = 15. The observations are fit best with Q(CH4)/Q(Q2O) = 0.02. The chemical composition of the comet nucleus implied by these production rate ratios is unlike that of the outer planets. On the other hand, there are also significant differences from observations of gas phase interstellar material.

Allen, M.↗
NASA NTRS2016

Addressing the Big-Earth-Data Variety Challenge with the Hierarchical Triangular Mesh

We have implemented an updated Hierarchical Triangular Mesh (HTM) as the basis for a unified data model and an indexing scheme for geoscience data to address the variety challenge of Big Earth Data. We observe that, in the absence of variety, the volume challenge of Big Data is relatively easily addressable with parallel processing. The more important challenge in achieving optimal value with a Big Data solution for Earth Science (ES) data analysis, however, is being able to achieve good scalability with variety. With HTM unifying at least the three popular data models, i.e. Grid, Swath, and Point, used by current ES data products, data preparation time for integrative analysis of diverse datasets can be drastically reduced and better variety scaling can be achieved. In addition, since HTM is also an indexing scheme, when it is used to index all ES datasets, data placement alignment (or co-location) on the shared nothing architecture, which most Big Data systems are based on, is guaranteed and better performance is ensured. Moreover, our updated HTM encoding turns most geospatial set operations into integer interval operations, gaining further performance advantages.

SciDB↗
DOE OSTI2017

The Value of Transparency in Distributed Solar PV Markets

Market transparency refers to the degree of customer awareness of product options and fair market prices for a given good. In The Value of Transparency in Distributed Solar PV Markets, we use residential solar photovoltaic (PV) quote data to study the value of transparency in distributed solar PV markets. We find that improved market transparency results in lower installation offer prices. Further, the results of this study suggest that PV customers benefit from gaining access to more PV quotes.

14 SOLAR ENERGY↗
NASA NTRS2013

Detection of Extremes with AIRS and CrIS

Climate change is expected to be detected first as changes in extreme values rather than in mean values. The availability of data of from two instruments in the same orbit, AIRS data for the past eleven years and AIRS and CrIS data from the past year, provides an opportunity to evaluate this using examples of climate relevance: Desertification, seen as changes in hot extremes, severe storm, seen as a change in extremely cold clouds and the warming of the polar zone. We use AIRS to establish trends for the 1%tile, the mean and 99%tile brightness temperatures measured with the 900 cm(exp -1) channel from AIRS for the past 11 years. This channel is in the clearest part of the 11 micron atmospheric window. Substantial trends are seen for land and ocean, which in the case of the 1%tile (cold) extremes are related to the current shift of deep convection from ocean to land. Changes are also seen in the 99%tile for day tropical land, but their interpretation is at present unclear. We also see dramatic changes for the mean and 99%tile of the North Polar area. The trends are an order of magnitude larger than the instrument trend of about 3 mK/year. We use the statistical distribution from the past year derived from AIRS to evaluate the accuracy of continuing the trends established with AIRS with CrIS data. We minimize the concern about differences in the spectral response functions by limiting the analysis to the channel at 900 cm(exp -1).While the two instruments agree within 100 mK for the global day/night land/ocean mean values, there are significant differences when evaluating the1% and 99%tiles. We see a consistent warm bias in the CrIS data relative to AIRS for the 1%tile (extremely cold, cloudy) data in the tropical zone, particularly for tropical land, but the bias is not day/night land/ocean consistent. At this point the difference appears to be due to differences in the radiometric response of AIRS and CrIS to differences in the day/night land/ocean cloud types. Unless the effect can be mitigated by a future reprocessing the CrIS data, it will significantly complicate the concatenation of the AIRS and CrIS data records for the continuation of trends in extreme values.

sampling bias↗
DOE OSTI2013

A Systematic Evaluation of Traditional and Non-Traditional Data Streams for Integrated Global Biosurveillance (Final Report)

Living in a closely connected and highly mobile world presents many new mechanisms for rapid disease spread and in recent years, global disease surveillance has become a high priority. In addition, much like the contribution of non-traditional medicine to curing diseases, non-traditional data streams are being considered of value in disease surveillance. Los Alamos National Laboratory (LANL) was tasked by the Defense Threat Reduction Agency (DTRA)-Joint Science and Technology Office (JSTO) to determine the relevance of data streams for an integrated global biosurveillance system through the use of defined metrics and methodologies. The long-term, broad objective of this project was to provide information and analysis that can be leveraged from existing and developing national and international disease surveillance systems and methodologies, to create a global disease monitoring system, ultimately providing decision makers with timely information to prepare for and mitigate the spread of disease. Specifically, this project evaluated data streams that are currently being used in surveillance systems and data streams that had the potential for being used in surveillance to enable early disease detection. LANL’s focus was on infectious disease surveillance and the term “biosurveillance” will be used to refer to this scope hereafter. Data stream evaluation was conducted using two different methods: Multi criteria decision analysis (MCDA) using a tool called Logical Decisions ® that assigns utility scores to data streams based on weighted metrics and assigned values specific to data stream categories; and a Surveillance Window concept developed at LANL that assigns a window or windows of time specific to a disease within which information coming from various data streams can be determined to have utility. A cross method analysis was performed between the surveillance window based evaluation and the MCDA-based evaluation to identify data stream categories that showed high utility for both methods. In addition, algorithms that can integrate useful data streams to facilitate early disease detection were also examined. This project provided an understanding of data streams relevant to early warning, early detection and monitoring of disease outbreaks. The report describes the results of LANL’s evaluation of 16 data stream categories using the two methods, and recommendations for next steps. The project evolved significantly over its course, and significant improvements in our approaches were made compared to the initial proposed processes. A key outcome of this effort was that LANL was able to bring together several diverse entities involved in disease surveillance and lay the foundation for new collaborations that straddled military and civilian health surveillance. The robust evaluation framework developed by LANL has generated significant interest and there is interest in adopting this framework for various applications both within disease surveillance as well as other public health initiatives. Finally, LANL developed the Biosurveillance Resource Directory (BRD), a relational database that underwent pilot testing by members of the human, plant and animal disease surveillance community. The BRD is intended to be a global resource to facilitate rapid information access.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1996

Multiple Use One-Sided Hypotheses Testing in Univariate Linear Calibration

Consider a normally distributed response variable, related to an explanatory variable through the simple linear regression model. Data obtained on the response variable, corresponding to known values of the explanatory variable (i.e., calibration data), are to be used for testing hypotheses concerning unknown values of the explanatory variable. We consider the problem of testing an unlimited sequence of one sided hypotheses concerning the explanatory variable, using the corresponding sequence of values of the response variable and the same set of calibration data. This is the situation of multiple use of the calibration data. The tests derived in this context are characterized by two types of uncertainties: one uncertainty associated with the sequence of values of the response variable, and a second uncertainty associated with the calibration data. We derive tests based on a condition that incorporates both of these uncertainties. The solution has practical applications in the decision limit problem. We illustrate our results using an example dealing with the estimation of blood alcohol concentration based on breath estimates of the alcohol concentration. In the example, the problem is to test if the unknown blood alcohol concentration of an individual exceeds a threshold that is safe for driving.

Krishnamoorthy, K.↗
DOE OSTI2023

Control of matrix converters using machine learning

A method of controlling a matrix converter system is provided. The method includes receiving an operating condition and consulting a trained Q-data structure for reward values associated with respective switching states of the switching matrix for an operating state that corresponds to the operating condition. The Q-data structure is trained using Q-learning to map a reward value predicted for respective switching states to respective discrete operating states. The method further includes sorting the reward values predicted for the respective switching states mapped to the operating state that corresponds to the operating condition, selecting a subset of the set of the mappings as a function of a result of sorting the reward values associated with the switching states of the operating state, evaluating each switching state included in the subset, and selecting an optimal switching state for the operating condition based on a result of evaluating the switching states of the subset.

Chamie, Mahmoud El↗
DOE OSTI2011

KEPLER AND GROUND-BASED TRANSITS OF THE EXO-NEPTUNE HAT-P-11b

We analyze 26 archival Kepler transits of the exo-Neptune HAT-P-11b, supplemented by ground-based transits observed in the blue (B band) and near-IR (J band). Both the planet and host star are smaller than previously believed; our analysis yields R{sub p} = 4.31 R{sub +} {+-} 0.06 R{sub +} and R{sub s} = 0.683 R{sub sun} {+-} 0.009 R{sub sun}, both about 3{sigma} smaller than the discovery values. Our ground-based transit data at wavelengths bracketing the Kepler bandpass serve to check the wavelength dependence of stellar limb darkening, and the J-band transit provides a precise and independent constraint on the transit duration. Both the limb darkening and transit duration from our ground-based data are consistent with the new Kepler values for the system parameters. Our smaller radius for the planet implies that its gaseous envelope can be less extensive than previously believed, being very similar to the H-He envelope of GJ 436b and Kepler-4b. HAT-P-11 is an active star, and signatures of star spot crossings are ubiquitous in the Kepler transit data. We develop and apply a methodology to correct the planetary radius for the presence of both crossed and uncrossed star spots. Star spot crossings are concentrated at phases -0.002 and +0.006. This is consistent with inferences from Rossiter-McLaughlin measurements that the planet transits nearly perpendicular to the stellar equator. We identify the dominant phases of star spot crossings with active latitudes on the star, and infer that the stellar rotational pole is inclined at about 12{sup 0} {+-} 5{sup 0} to the plane of the sky. We point out that precise transit measurements over long durations could in principle allow us to construct a stellar Butterfly diagram to probe the cyclic evolution of magnetic activity on this active K-dwarf star.

79 ASTRONOMY AND ASTROPHYSICS↗