RADIOSCIENCE LAB. THE CALCULATION OF ELECTRON DENSITY PROFILES FROM TOPSIDE SOUNDER RECORDS
Calculation of electron density profiles - data conversion of topside radio wave refraction records from ground-based ionosondes
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Calculation of electron density profiles - data conversion of topside radio wave refraction records from ground-based ionosondes
The new experimental measurements of beam spin asymmetry were performed for the deeply virtual exclusive $π$ 0 production in a wide kinematic region with the photon virtualities $Q$ 2 up to 8 GeV 2 and the Bjorken scaling variable $x$ $B$ in the valence regime. The data were collected by the CE BAF Large Acceptance Spectrometer (CLAS12) at Jefferson Lab with longitudinally polarized 10.6 GeV electrons scattered on an unpolarized liquid-hydrogen target. Sizable asymmetry values indicate a substantial contribution from transverse virtual photon amplitudes to the polarized structure functions. The interpretation of these measurements in terms of the Generalized Parton Distributions (GPDs) demonstrates their sensitivity to the chiral-odd GPD $\overline{E}$ $T$ , which contains information on quark transverse spin densities in unpolarized and polarized nucleons and provides access to the proton’s transverse anomalous magnetic moment. Additionally, the data were compared to a theoretical model based on a Regge formalism that was extended to the high photon virtualities.
The Astromaterials Research and Exploration Science Directorate at the NASA Johnson Space Center (JSC) has laboratory instrumentation that mimic the capabilities of corresponding flight instruments to enable interpretation of datasets returned from Mars robotic missions. The lab instruments have been and continue to be applied to datasets for the Moessbauer Spectrometer (MB) on the Mars Exploration Rovers (MER), the Thermal & Evolved Gas Analyzer (TEGA) on the Mars Phoenix Scout, the CRISM instrument on the Mars Reconnaissance Orbiter Missions and will be applied to datasets for the Sample Analysis at Mars (SAM), Chemistry and Mineralogy (CheMin) and Chemistry & Camera (ChemCam) instruments onboard the Mars Science Laboratory (MSL). The laboratory instruments can analyze analog samples at costs that are substantially lower than engineering models of flight instruments, but their success to enable interpretation of flight data depends on how closely their capabilities mimic those of the flight instrument. The JSC lab MB instruments are equivalent to the MER instruments except without flight qualified components and no reference channel Co-57 source. Data from analog samples were critical for identification of Mg-Fe carbonate at Gusev crater. Fiber-optic VNIR spectrometers are used to obtain CRISM-like spectral data over the range 350-2500 nm, and data for Fephyllosilicates show irreversible behavior in the electronic transition region upon dessication. The MB and VNIR instruments can be operated within chambers where, for example, the absolute H2O concentration can be measured and controlled. Phoenix's TEGA consisted of a calorimeter coupled to a mass spectrometer (MS). The JSC laboratory testbed instrument consisted of a differential scanning calorimeter (DSC) coupled to a MS configured to operate under total pressure (12 mbar), heating rate (20 C/min), and purge gas composition (N2) analogous to the flight TEGA. TEGA detected CO2 release at both low (400-680 C) and high (725-820 C) temperature and an endothermic reaction in concert with the high temperature release. The high-temperature thermal decomposition is consistent with calcite, dolomite, or ankerite, (3-6 wt.%) or any combination of these phase based upon laboratory testbed experiments. Recent laboratory experiments suggest that the low temperature CO2 release was caused by a reaction between calcium carbonate and hydrated magnesium perchlorate; although, CO2 release by the oxidation of organic materials and Fe-/Mg-rich carbonates cannot be ruled out. MSL landed in Gale crater on August 5, 2012. Although numerous analog samples have been analyzed on the JSC laboratory testbeds, no SAM, CheMin, or ChemCam analyses have been acquired by MSL to date. The JSC SAM laboratory testbed consists of a thermal analyzer coupled with a MS configured to operate under total pressure (30 mbar), heating rate (35 C/min), and purge gas composition (He) analogous to the flight SAM. The CheMin and ChemCam laboratory testbeds were developed and built by inXitu, Inc. and Los Alamos National Laboratory, respectively, to acquire datasets relevant to the MSL CheMin and ChemCam flight instruments.
Abstract. Initial steps in statistical downscaling involve being able to compare observed data from regional climate models (RCMs). This prediction requires (1) regridding RCM outputs from their native grids and at differing spatial resolutions to a common grid in order to be comparable to observed data and (2) bias correcting RCM data, for example via quantile mapping, for future modeling and analysis. The uncertainty associated with (1) is not always considered for downstream operations in (2). This work examines this uncertainty, which is not often made available to the user of a regridded data product. This analysis is applied to RCM solar radiation data from the NA-CORDEX (North American Coordinated Regional Climate Downscaling Experiment) data archive and observed data from the National Solar Radiation Database housed at the National Renewable Energy Lab. A case study of the mentioned methods over California is presented.
The NOVA/IOPS (Input-Output Processor Simulator) is used to control all interface testing of the MDM (Multiplexer Demultiplexer) Breadboard S10 10M (serial input-output-input module) and the Matra Space Lab Inerface Unit (SL/BIU). The software handles bookkeeping such as word error rates, types of errors, display of error buffers, data display and test identification.
The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. In this presentation, we explore the output characteristics of the dataset and various validation analyses. We also present and discuss plans for the integration of this data into power system planning models using a decision-making under deep uncertainty (DMDU) methodology.
In the 1990's space plasma physics studies will increasingly involve correlative analysis of observations from multiple instruments and multiple spacecraft. The solar terrestrial physics missions in the 1990's will be designed around simultaneous observations from spacecraft monitoring the solar wind, the polar magnetosphere and the near and distant magnetotail. Within these regions, clusters of spacecraft flying in formation will increasingly involve comparative magnetospheric studies. No single lab will have the expertise to process and analyze all of the different types of data so the data repositories will be distributed. Catalog and browse systems will be required to help select events for study. Data compression techniques may be useful in designing the data bases used for selecting events for study. Data compression onboard the spacecraft will be necessary, since instrument data rates will be much larger than available telemetry rates. However, considerable care will be necessary to avoid losing valuable data when applying data compression algorithms.
The Wide-Field Imaging Interferometry Testbed (WIIT) at NASA's Goddard Space Flight Center was designed to demonstrate the practicality and application of techniques for wide-field spatial-spectral ("double Fourier") interferometry. WIIT is an automated system, and it is now producing substantial amounts of high-quality data from its state-of-the-art operating environment, Goddard's Advanced Interferometry and Metrology Lab. In this paper, we discuss the characterization and operation of the testbed and present the most recent results. We also outline future research directions. A companion paper within this conference discusses the development of new wide-field double Fourier data analysis algorithms.
NASA Marshall Space Flight Center's experience with different GPS simulators and receivers over the last 10 years has shown a need for testing the receivers in more than just a nominal mission. The Spaceliner 100 program is researching blended INS/GPS data tuned specifically for launch vehicles and orbital deployments. The paper will discuss layout of the testing lab, the test equipment, test scenarios that all receivers will be evaluated under, and a discussion of receiver types planned to test. It will conclude with a discussion of some of the current tests and goals of future testing.
By 1997, the Thomas Jefferson National Accelerator Facility will collect over one Terabyte of raw information per day of Accelerator operation from three concurrently operating Experimental Halls. When post-processing is included, roughly 250 TB of raw and formatted experimental data will be generated each year. By the year 2000, a total of one Petabyte will be stored on-line. Critical to the experimental program at Jefferson Lab (JLab) is the networking and computational capability to collect, store, retrieve, and reconstruct data on this scale. The design criteria include support of a raw data stream of 10-12 MB/second from Experimental Hall B, which will operate the CEBAF (Continuous Electron Beam Accelerator Facility) Large Acceptance Spectrometer (CLAS). Keeping up with this data stream implies design strategies that provide storage guarantees during accelerator operation, minimize the number of times data is buffered allow seamless access to specific data sets for the researcher, synchronize data retrievals with the scheduling of postprocessing calculations on the data reconstruction CPU farms, as well as support the site capability to perform data reconstruction and reduction at the same overall rate at which new data is being collected. The current implementation employs state-of-the-art StorageTek Redwood tape drives and robotics library integrated with the Open Storage Manager (OSM) Hierarchical Storage Management software (Computer Associates, International), the use of Fibre Channel RAID disks dual-ported between Sun Microsystems SMP servers, and a network-based interface to a 10,000 SPECint92 data processing CPU farm. Issues of efficiency, scalability, and manageability will become critical to meet the year 2000 requirements for a Petabyte of near-line storage interfaced to over 30,000 SPECint92 of data processing power.
A summary of technical accomplishments from the Livewire Data Platform since the 2024 Annual Merit Review.
This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”.The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models.This package includes:(1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models.(2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts.(3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’.(4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at(4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and(4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab).- See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions.- All ipython scripts and python files are written in and require PYTHON language software.
In the past several years, new tropical tropospheric ozone data products have been developed from TOMS and other satellites. Global chemical-transport models have been developed for interpretation of satellite data and to predict future ozone levels in the troposphere and stratosphere. However, the lack of ozone profile measurements for validation and evaluation of these data sets and models is critical in regions like the tropics. In 1998 NASA/Goddard Space Flight Center, in partnership with NOAA/CMDL (Climate Monitoring and Diagnostics Lab) and other nations, began a 2-year project to collect weekly ozonesonde measurements at southern hemisphere tropical sites and make the data available to the scientific community at a single electronic location: http://code9lQ.gsfc.nasa.gov/Data services/Shadoz/shadoz hmpq2.htmi A summary of data from the SHADOZ sites will be presented: Ascension Island, Fiji, Tahiti, Galapagos, American Samoa, Natal (Brazil), Reunion Island, Watukosek (Java), Nairobi and Irene, South Africa. SHADOZ is designed to meet other needs: (1) Provide the first climatology of tropical ozone along the equatorial zone for the wave-one pattern in total ozone; (2) Supplement field project observations. (3) Guide algorithm development for future satellite instruments; (4) Train scientists and educators in southern hemisphere tropical locations. From time to time, intensive tropical campaigns are making data available to SHADOZ. Data from the first half of 1999 will include INDOEX (Indian Ocean Experiment), SOWER (Stratospheric Ozone and Water in the Equatorial Region) at Christmas Island (2N, 157W), and a cruise from Norfolk, Virginia to Cape Town and Mauritius on NOAA's RN 'Ronald H Brown.'
Our research generates a vast amount of data that scientists must gather, store, analyze and interpret. These activities require expertise and the right tools. Fermilab is home to one of the largest tape robotic systems available today, providing more than 800 petabytes of storage capability. The lab is growing the tape facilities to meet expected demands of over 1,000 petabytes of additional data per year by the end of the decade. Fermilab also has the technology and computing power to efficiently pr ocess these data to facilitate scientific discoveries. Our computation al experts work closely with experimenters and the scientific commu - nity at large to provide and facilitate the use of the cutting-edge computing tools necessary for these discoveries.
NASA's Dryden Flight Research Facility (Ames-Dryden), operates a diverse fleet of research aircraft which are heavily instrumented to provide both real time data for in-flight monitoring and recorded data for postflight analysis. Ames-Dryden's existing automated calibration (AUTOCAL) laboratory is a computerized facility which tests aircraft sensors to certify accuracy for anticipated harsh flight environments. Recently, a major AUTOCAL lab upgrade was initiated; the goal of this modernization is to enhance productivity and improve configuration management for both software and test data. The new system will have multiple testing stations employing distributed processing linked by a local area network to a centralized database. The baseline requirements for the new AUTOCAL lab and the design approach being taken for its mechanization are described.
NASA's Dryden Flight Research Facility (Ames-Dryden), operates a diverse fleet of research aircraft which are heavily instrumented to provide both real time data for in-flight monitoring and recorded data for postflight analysis. Ames-Dryden's existing automated calibration (AUTOCAL) laboratory is a computerized facility which tests aircraft sensors to certify accuracy for anticipated harsh flight environments. Recently, a major AUTOCAL lab upgrade was initiated; the goal of this modernization is to enhance productivity and improve configuration management for both software and test data. The new system will have multiple testing stations employing distributed processing linked by a local area network to a centralized database. The baseline requirements for the new AUTOCAL lab and the design approach being taken for its mechanization are described.
NASA’s Office of Technology, Policy, and Strategy (OTPS) performs data-driven assessments to inform the Agency’s development of policy and strategy. OTPS works across NASA, the US government, industry, and academia to incorporate relevant stakeholder input to data-driven assessments regarding the future of NASA. OTPS conducted a study with the goal of identifying potential models to ensure that NASA and its government-sponsored user communities can successfully complete activities in low-Earth orbit (LEO) in a post-International Space Station (ISS) (i.e. 2030s) timeframe. Given the expectation that one or more Commercial LEO Destinations will replace the ISS by 2030, NASA leadership is considering next steps for microgravity research and other activities in LEO. This task is particularly challenging due to the uncertain future of the LEO ecosystem. Future scenarios may range from a robust environment with multiple platform options and new activities to an austere environment with few platform options and deflated momentum among user communities. Both extreme futures, along with any number of scenarios in between, have serious implications for the future of LEO and NASA’s role in it. It is therefore imperative that NASA consider its prioritized activities for the 2030 timeframe and identify potential business and operating models that will provide continuity for government-sponsored activities. The results of this NASA OTPS study will be presented, along with a discussion of how specific engagement models could enable NASA and other entities to achieve their goals in the 2030 timeframe.
Understanding the complexity of the multiple processes of mass, momentum, charge, and heat transport, and how these affect reaction kinetics at the electrode/electrolyte interface is one of the major challenges in the field of energy and catalysis. The rapid and rational scale-up of electrocatalytic systems to industrial scales require a detailed understanding of nonlinear transport-reaction processes, accessible only through the building of multi-physics models that capture with high fidelity the complexity of real-world devices. The gastight rotating cylinder electrode (RCE) reactor is a promising lab-scale tool that can decouple transport from intrinsic kinetics to generate data for first-principle models useful in the design of industrial, electrochemical reactors. Computational Fluid Dynamics (CFD) studies have previously been used to investigate the bulk flow in RCE reactors for simple corrosion and electroplating processes. However, the quantification of changes in local concentration within the viscous layer where catalysis takes place requires capturing the correct flow conditions inside the hydrodynamic boundary layer near the surface of the electrode. Further, this requires simulations with spatial resolution in the nm and μm scale and temporal resolutions between ms and s scales that are similar to the timescales for reactions on the electrode surface. In this study, experimental electrocatalysis is combined with CFD modeling to elucidate and parameterize the hydrodynamics in a gastight RCE reactor. CFD simulations of the electrochemical ferricyanide reduction reaction under mass transport limited conditions are used to evaluate the validity of the CFD model parameters by comparing calculated dimensionless mass transport descriptors to dimensionless correlations obtained experimentally. Justifications for assumptions and details of the simulation methods used in this study are presented to provide a detailed understanding of the effect that each model parameter has on the ability to accurately simulate electrocatalysis in RCE systems. The simulation methodology reported here is a first step towards the development of multi-scale models for the study of transport dependent electrocatalytic processes, such as the electrochemical transformation of CO 2 to fuels and chemicals.