A technique for the determination of the dipole moment of a spacecraft from a limited amount of near field data
Field measurements by rotating spacecraft for dipole moment determination
Eichhorn, W. L.↗
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Identifying Improvements to Airborne and Field Data Stewardship from Data User and Producer Needs (Findings from the March 2022 Airborne and Field Data Workshop)
Workshop Purpose: to gather valuable feedback from data users and data producers in order to improve the acquisition and use of NASA’s airborne and field data. Workshop Goals: - Identify new ways to improve the discovery, access, and reuse of NASA’s airborne and field data - Intentionally include the airborne community in future planning to ensure the community needs are met - Understand how NASA can better help researchers realize the full value of airborne and field data - Enable Open Source Science and determine what this means for airborne and field data communities
Geoffrey Stano↗
Comparison of Noise Source Localization Data with Flow Field Data Obtained in Cold Supersonic Jets and Implications Regarding Broadband Shock Noise
Phased array noise source localization have been compared with 2 types of flow field data (BOS and PIV). The data show that: 1) the higher frequency noise in a BBSN hump is generated further downstream than the lower frequency noise. This is due to a) the shock spacing decreasing and b) the turbulent structure size increasing with distance downstream. 2) BBSN can be created by very weak shocks. 3) BBSN is not created by the strong shocks just downstream of the nozzle because the turbulent structures have not grown large enough to match the shock spacing. 4) The point in the flow where the shock spacing equals the average size of the turbulent structures is a hot spot for shock noise. 5) Some of the shocks responsible for producing the first hump also produce the second hump.
Podboy, Gary↗
The synchronous orbit magnetic field data set
The magnetic field at synchronous orbit is the result of superposition of fields from many sources such as the earth, the magnetopause, the geomagnetic tail, the ring current and field-aligned currents. In addition, seasonal changes in the orientation of the earth's dipole axis causes significant changes in each of the external sources. Main reasons for which the synchronous orbit magnetic field data set is a potentially valuable resource are outlined. The primary reason why synchronous magnetic field data have not been used more extensively in magnetic field modeling is the presence of absolute errors in the measured fields. Nevertheless, there exists a reasonably large collection of synchronous orbit magnetic field data. Some of these data can be useful in quantitative modeling of the earth's magnetic field. A brief description is given of the spacecraft, the magnetometers, the standard graphical data displays, and the digital data files.
Mcpherron, R. L.↗
Application of Acoustical Holography for Projecting Near-Field Data to the Far Field
In this report, the application of the acoustical holography (AH) technique for projecting near-field acoustic data to the far field is discussed. Test problems will be used to showcase the accuracy and utility of this technique and to demonstrate its superiority over the more common technique of spherical spreading projection. Three versions of the technique will be presented: planar, cylindrical, and spherical. Depending on the nature of the acoustic field, one or more of these versions may be useful and practical to use. Since the cylindrical and spherical versions depend on the accurate numerical computations of certain special functions, such as the Hankel functions and the associated Legendre polynomials, the implementation of the high-precision numerical computation of the required special functions on a computer are included in the appendixes at the end of the report. In some cases, the material presented in these appendixes represents a substantial extension of the results previously published in the open literature. As appropriate, special considerations associated with the use of the AH technique in practical situations are discussed.
Holography↗
Detection of reflector surface error from near-field data: Effect of edge diffracted field
The surface accuracy of large reflector antennas must be maintained within certain tolerances if high gain/low sidelobe performance is to be achieved. Thus the measurement of the surface profile is an important part of the quality control procedure when constructing antennas of this type. An efficient method for surface profile measurement has been proposed, i.e., the reflector surface is calculated from the measured near-field phase data using the theory of geometric optics. For a surface profile calculation of this kind, it is necessary to know the margin of error built into the method of calculation. This will enable a specification of the tolerance from which the surface profile can be determined. When calculating the surface profile from near-field phase data, there are two main sources of error. The first is the measurement error in near-field phase data. The second arises from the edge diffracted fields that are superimposed on the reflected fields in the measured near-field data. The error in the calculated surface profile produced by the edge diffracted fields is examined.
Cherrette, Alan R.↗
Distribution of ULF energy (f is less than 80 mHz) in the inner magnetosphere - A statistical analysis of AMPTE CCE magnetic field data
Magnetic field measurements made with the AMPTE CCE spacecraft are used to investigate the distribution of ULF energy in the inner magnetosphere. The data base is employed to examine the spatial distribution of ULF energy. The spatial distribution of wave power and spectral structures are used to identify several pulsation types, including multiharmonic toroidal oscillations; equatorial compressional Pc 3 oscillations; second harmonic poloidal oscillations; and nightside compressional oscillations. The frequencies of the toroidal oscillations are applied to determine the statistical radial profile of the plasma mass density and Alfven velocity. A clear signature of the plasma pause in the profiles of these average parameters is found.
Takahashi, Kazue↗
Catalog of particles and fields data 19581965
Available particles and fields data, covering the period 1966 to 1973 inclusive, are announced. Most data result from individual experiments carried on board individual spacecraft. A variety of user-oriented data are included. A newly created composite interplanetary magnetic field data set is discussed and other data products, that may interest the particles/fields community are mentioned, including geomagnetism, magnetopause and bow shock positions, and magnetospherically trapped particles.
King, M. L.↗
Field Data Collection: an Essential Element in Remote Sensing Applications
Field data collected in support of remote sensing projects are generally used for the following purposes: (1) calibration of remote sensing systems, (2) evaluation of experimental applications of remote sensing imagery on small test sites, and (3) designing and evaluating operational regional resource studies and inventories which are conducted using the remote sensing imagery obtained. Field data may be used to help develop a technique for a particular application, or to aid in the application of that technique to a resource evaluation or inventory problem for a large area. Scientists at the Forestry Remote Sensing Laboratory have utilized field data for both purposes. How meaningful field data has been collected in each case is discussed.
Pettinger, L. R.↗
Comparing field data using Alpert multi-wavelets
In this paper we introduce a method to compare sets of full-field data using Alpert tree-wavelet transforms. The Alpert tree-wavelet methods transform the data into a spectral space allowing the comparison of all points in the fields by comparing spectral amplitudes. The methods are insensitive to translation, scale and discretization and can be applied to arbitrary geometries. This makes them especially well suited for comparison of field data sets coming from two different sources such as when comparing simulation field data to experimental field data. We have developed both global and local error metrics to quantify the error between two fields. Additionally, we verify the methods on two-dimensional and three-dimensional discretizations of analytical functions. Furthermore, we then deploy the methods to compare full-field strain data from a simulation of elastomeric syntactic foam.
42 ENGINEERING↗
Analysis of DE-1 PWI electric field data
The measurement of low frequency electric field oscillations may be accomplished with the Plasma Wave Instrument (PWI) on DE 1. Oscillations at a frequency around 1 Hz are below the range of the conventional plasma wave receivers, but they can be detected by using a special processing of the quasi-static electric field data. With this processing it is also possible to determine if the electric field oscillations are predominately parallel or perpendicular to the ambient magnetic field. The quasi-static electric field in the DE 1 spin/orbit plane is measured with a long-wire 'double probe'. This antenna is perpendicular to the satellite spin axis, which in turn is approximately perpendicular to the geomagnetic field in the polar magnetosphere. The electric field data are digitally sampled at a frequency of 16 Hz. The measured electric field signal, which has had phase reversals introduced by the rotating antenna, is multiplied by the sine of the rotation angle between the antenna and the magnetic field. This is called the 'perpendicular' signal. The measured time series is also multiplied with the cosine of the angle to produce a separate 'parallel' signal. These two separate time series are then processed to determine the frequency power spectrum.
Weimer, Daniel↗
MBARI-WEC September and October 2022 Field Data
This data is needed to simulate a model of the MBARI-WEC (Monterey Bay Aquarium Research Institute, Wave Energy Converter device) in a simulation environment (e.g. Gazebo) for 56 observation dates in the time between September and October 2022, and to compare the simulation outputs to the corresponding field data of the physical MBARI-WEC. There were 50 observations chosen in Sept and 6 observations in Oct. To help understand terms below, a summary of the system can be found at the github link in the downloads section below. The Gazebo MBARI-WEC model is also provided, should users wish to simulate using this platform. There are 4 *mat files included. ................................................................................................................................................................................................................................... Spectrum and Simulation Inputs: September2022_spectrum_siminputs.mat and October2022_spectrum_siminputs.mat has data needed for simulation inputs in table format. These include the ocean spectrum for an observation and operating parameters of the MBARI-WEC during that observation. They are organized as rows representing an observation and columns representing data. For example, for the September *mat there are 50 rows. The first 7 columns are Datetime, sig_waveheight, peak_period, mean_period, heaveconedoor_status, pistonpos_mean, and scale_factor: - Datetime is the date and time the observation occurred in PST - sig_waveheight is the significant wave height of the ocean spectrum during that observation in meters - peak_period is the peak period of the ocean spectrum during that observation in seconds - mean_period is the mean period of the ocean spectrum during that observation in seconds - heaveconedoor_status is the status of the heave cone doors where 0 represents the doors are open and 1 represents they are closed - pistonpos_mean is the mean position of the PTO ram (piston) in meters - scale_factor is an additional factor of 0.5 --1.4 applied to a default damping relationship The next columns are data needed to represent the ocean spectrum. First are the frequencies [Hz] labeled as "f0-f38", then the variance density [m2/Hz] labeled as "vardens0-vardens38". October2022_spectrum_siminputs.mat follows as a similar format as above, but includes a larger amount of ocean spectrum frequencies and variance density elements. ................................................................................................................................................................................................................................... Field data: The field data is found in MBARIWEC_septdata.mat and MBARIWEC_octdata.mat for the observations of September and October, respectively. These contain data in a struct format. The struct contains the following fields for each observation: PC_BattCurr, PC_LoadCurr, PC_RPM, PC_Voltage, SC_Range, SC_Velocity, DateTime, where: - PC_BattCurr is the current flowing to or from the onboard batteries in Amps - PC_LoadCurr is the current flowing to the load dump in Amps - PC_RPM is the electric/hydraulic motor shaft speed (directly coupled) in RPM - PC_Voltage is the bus voltage at the power converter in Volts - SC_Range is the PTO ram (piston) position in meters where 0 is fully retracted and 2.03 is fully extended - SC_Velocity is the PTO ram (piston) velocity in meters/sec - DateTime is the date and time of the sampled field data in each observation in PST - Electric Power is equal to: PC_Voltage*(PC_BattCurr + PC_LoadCurr) in Watts For example, upon loading MBARIWEC_octdata.mat, the aforementioned fields would be loaded, each with {6x1} cells for the 6 observations chosen in October. Within the first cell of e.g. SC_Range would be sampled data representing the field data of the MBARIWEC PTO piston position for say, one hour, of the first October observation. The corresponding field DateTime would...
16 TIDAL AND WAVE POWER↗
Multiscale Data-Driven Seismic Full-Waveform Inversion With Field Data Study
Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration geophysics. In recent years, the computational cost of FWI has grown exponentially due to the increasing size and resolution of seismic data. Moreover, it is a nonconvex problem and can encounter local minima due to the limited accuracy of the initial velocity models or the absence of low frequencies in the measurements. To overcome these computational issues, we develop a multiscale data-driven FWI method based on fully convolutional networks (FCNs). In preparing the training data, we first develop a real-time style transform method to create a large set of synthetic subsurface velocity models from natural images. We then develop two convolutional neural networks with encoder-decoder structures to reconstruct the low- and high-frequency components of the subsurface velocity models, separately. To validate the performance of our data-driven inversion method and the effectiveness of the synthesized training set, we compare it with conventional physics-based waveform inversion approaches using both synthetic and field data. Finally, these numerical results demonstrate that, once our model is fully trained, it can significantly reduce the computation time and yield more accurate subsurface velocity models in comparison with conventional FWI.
58 GEOSCIENCES↗
An Overview of NASA’s Airborne and Field Data Resource Center
A key recommendation from NASA’s 2022 Airborne and Field Data Workshop called for the development of a virtual Resource Center for all stakeholders across the data lifecycle of airborne and field Earth observations. The agency’s Earth Science Data and Information System (ESDIS) Project and Airborne Data Management Group (ADMG) have worked in concert to establish the newly launched NASA Airborne and Field Data Resource Center (AFDRC) to provide a single entry point for a wide assortment of information on and the effective, responsible stewardship of non-satellite observational data. The AFDRC compiles access to many existing resources, but does so in a newly organized way that integrates availability to increase efficiency and holistic understanding while simplifying users’ experience. Initially launched in fall of 2023, the AFDRC is a NASA Earthdata domain website that clarifies several previously disparate resources and provides newly updated information, including: Learning Resources: Educational resources to broaden understanding of the role airborne and field observations play in advancing understanding of our planet and NASA’s role in collecting and archiving these data. Support for data users to Find and Access Data: Advanced contextual browse/search capabilities that efficiently link researchers to data products suitable for their science objectives - this includes linking to NASA’s Catalog of Archived Suborbital Earth Science Investigations (CASEI). Working with Data: Tools specific to individual types of suborbital Earth Science data and their (inter-)disciplinary communities to provide access as well as guidance for their application. Stewardship Responsibilities: Resources for data producers with to lessen requirement burdens at the time of data transfer, and information for data stewards with consistent, authoritative guidance on best practices and agency- and/or community- specific archival procedures. This presentation will give an overview of the motivation for NASA’s AFDRC, approach for the design and content, iterative community-driven improvements, promote the use of the AFDRC, and solicit additional feedback from airborne and field data user communities.
Sara Lubkin↗
Transfer-Learnt Energy Models for Predicting Electricity Consumption in Buildings with Limited and Sparse Field Data
Modeling energy consumption is critical for energy-efficient utilization of the electric appliances in a building, smart grid programs (like demand-response), and many other smart home applications. State-of-the-art energy modeling techniques either rely on theoretical models, or extensive instrumentation of the building envelope to gather ``big" data to train a deep neural network. While theoretical models are often limited by their estimation accuracy, it is not always feasible to gather a significant amount of field data. In this paper, we explore transfer learning-based strategies to train much more accurate model for energy estimation when using a sparse field data. We transferred knowledge, in the form of data and parameters, from the simulation framework to the field data. We evaluated the efficacy of our approach on field data collected from six commercial buildings and our results indicate that transfer learning-based models trained over one month data can perform comparative (and in some cases better) than the state-of-the-art machine learning and deep learning solutions.
Jain, Milan↗
Integrating Models with Real-time Field Data for Extreme Events: From Field Sensors to Models and Back with AI in the Loop
Focal Area(s): This whitepaper is responsive to focal area (1) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). We discuss Artificial Intelligence and Machine Learning (AI/ML) enabled integration of real-time data into the extreme event modeling workflow to improve the predictive capabilities of these models, and deliver real-time feedback to remote sensors, including software and data engineering challenges.
54 ENVIRONMENTAL SCIENCES↗
Exploration of Domain Aware Machine Learning for Grid Analytics: Transfer-Learnt Energy Models to Assist Buildings Control with Sparse Field Data
Buildings are a primary consumer of energy in the United States and are also increasingly being perceived as providers of grid services such as load shifting, shedding and modulation. High fidelity models of building energy consumption are needed to set appropriate baselines for measurement and verification (M&V) of controllers designed for energy efficient operation of buildings and to enable buildings to provide grid services via. participation in demand response programs. State-of-the-art building energy modeling techniques either rely on Physics based models, or extensive instrumentation of the building envelope to gather “big” data to train machine learning based models such as deep neural networks. While Physics based models are often limited by their accuracy, it is not always feasible to gather a significant amount of field data required to train machine learning based models with sufficient accuracy. In this paper, we explore the use of transfer learning-based strategies to address unsatisfactory accuracy of models for estimating building energy consumption when available field data for training is sparse or of unacceptable quality. In particular, we transfer knowledge in the form of data and parameters, from Physics based simulation frameworks to the field to improve the model accuracy, thus resulting in a Physics-informed Machine Learning framework. We evaluated the efficacy of our approach on field data collected from six commercial buildings and our results indicate that the proposed transfer learning based models provide comparative (and in some cases better) accuracy than state-of-the-art machine learning and deep learning solutions, with just one month of field data.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Meteorological data fields 'in perspective'
Perspective display techniques can be applied to meteorological data sets to aid in their interpretation. Examples of a perspective display procedure applied to satellite and aircraft visible and infrared image pairs and to stereo cloud-top height analyses are presented. The procedure uses a sophisticated shading algorithm that produces perspective images with greatly improved comprehensibility when compared with the wire-frame perspective displays that have been used in the past. By changing the 'eye-point' and 'view-point' inputs to the program in a systematic way, movie loops that give the impression of flying over or through the data field have been made. This paper gives examples that show how several kinds of meteorological data fields are more effectively illustrated using the perspective technique.
Hasler, A. F.↗