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Software package consists of FORTRAN subroutines that perform universal noiseless coding and decoding of integer and binary data strings. Purpose of this type of coding to achieve data compression in sense that coded data represents original data perfectly (noiselessly) while taking fewer bits to do so. Routines universal because they apply to virtually any "real-world" data source.
Ultra-low frequency (ULF) wave observations across the heliosphere often rely on the sign of correlations between plasma (density/pressure) and magnetic field perturbations to distinguish between fast and slow magnetosonic modes. However, the assumptions behind this magnetohydrodynamic result are not always valid, particularly within the magnetosphere which is inhomogeneous and supports standing waves along the geomagnetic field. Through theory and a global simulation, we find both effects can result in anomalous plasma–magnetic field correlations. The interference pattern in standing waves can lead both body and surface magnetosonic waves to have different cross-phases than their constituent propagating waves. Furthermore, if the scale of gradients in the background are shorter than the wavelength or the waves are near-incompressible, then advection by the wave of inhomogeneities can overcome the wave's inherent sense of compression. These effects need to be allowed for and taken into account when applying the typical diagnostic to observations.
Distributed Acoustic Sensing (DAS) is an emerging sensing technology that records the strain-rate along fiber optic cables at high spatial and temporal resolution. This technique is becoming a popular tool in seismology, hydrology, and other subsurface monitoring applications. However, due to the large coverage (10’s of km) and high density of measurements (1m spacing at 100’s of Hz), a DAS installation could produce terabytes of data records per day. Because many DAS instruments are deployed in remote locations, this large data size poses significant challenges to its transfer and storage. In this paper, we explore lossless compression methods to reduce the storage requirement in both real-time and post-hoc scenarios. Here we propose a two-stage compression method to improve the compression ratio and compression speed. This two-stage compression method could reduce the storage requirement by 40%, which is 20% more than other lossless methods, such as ZSTD. We demonstrate that the compression method could complete its operation well before the DAS instrument needs to output the next file, making it suitable for real-time DAS acquisition. We also implement a parallel compression method for a post-hoc scenario and demonstrate that our method could effectively utilize a parallel computer. With 256 CPU cores, our parallel compression method achieves the speed of 26GB/second.
proposed system would perform multiple coordinated functions in regulating the pressure of the oxidant gas (usually, pure oxygen) flowing to a fuelcell stack and in removing excess product water that is generated in the normal fuel-cell operation. The system could function in the presence or absence of gravitation, and in any orientation in a gravitational field. Unlike some prior systems for removing product water, the proposed system would not depend on hydrophobicity or hydrophilicity of surfaces that are subject to fouling and, consequently, to gradual deterioration in performance. Also unlike some prior systems, the proposed system would not include actively controlled electric motors for pumping; instead, motive power for separation and pumping away of product water would be derived primarily from the oxidant flow and perhaps secondarily from the fuel flow. The net effect of these and other features would be to make the proposed system more reliable and safer, relative to the prior systems. The proposed system (see figure) would include a pressure regulator and sensor in the oxidant supply just upstream from an ejector reactant pump. The pressure of the oxidant supply would depend on the consumption flow. In one of two control subsystems, the pressure of oxidant flowing from the supply to the ejector would be sensed and used to control the speed of a set of a reciprocating constant-displacement pump so that the volumetric flow of nominally incompressible water away from the system would slightly exceed the rate at which water was produced by the fuel cell(s). The two-phase (gas/liquid water) outlet stream from the fuel cell(s) would enter the water separator, a turbinelike centrifugal separator machine driven primarily by the oxidant gas stream. A second control subsystem would utilize feedback derived from the compressibility of the outlet stream: As the separator was emptied of liquid water, the compressibility of the pumped stream would increase. The compressibility would be sensed, and an increase in compressibility beyond a preset point (signifying a decrease in water content below an optimum low level) would cause the outflow from the reciprocating pump to be diverted back to the separator to recycle some water.
Fast detection and characterization of seismic sources is crucial for decision-making and warning systems that monitor natural and induced seismicity. However, besides the laying out of ever denser monitoring networks of seismic instruments, the incorporation of new sensor technologies such as Distributed Acoustic Sensing (DAS) further challenges our processing capabilities to deliver short turnaround answers from seismic monitoring. In response, this work describes a methodology for the learning of the seismological parameters: location and moment tensor from compressed seismic records. In this method, data dimensionality is reduced by applying a general encoding protocol derived from the principles of compressive sensing. The data in compressed form is then fed directly to a convolutional neural network that outputs fast predictions of the seismic source parameters. Thus, the proposed methodology can not only expedite data transmission from the field to the processing center, but also remove the decompression overhead that would be required for the application of traditional processing methods. An autoencoder is also explored as an equivalent alternative to perform the same job. We observe that the CS-based compression requires only a fraction of the computing power, time, data and expertise required to design and train an autoencoder to perform the same task. Implementation of the CS-method with a continuous flow of data together with generalization of the principles to other applications such as classification are also discussed.
Polyvinyl chloride (PVC) gels have recently been found to exhibit mechanoelectrical transduction or sensing capabilities under compressive loading applications. This phenomenon is not wholly understood but has been characterized as an adsorption-like phenomena under varying amounts and types of plasticizers. A different polymer lattice structure has also been tested, thermoplastic polyurethane, which showed similar sensing characteristics. This study examines mechanical and electrical properties of these gel sensors and proposes a mathematical framework of the underlying mechanisms of mechanoelectrical transduction. COMSOL Multiphysics is used to show solid mechanics characteristics, electrostatic properties, and transport of interstitial plasticizer under compressive loading applications. The solid mechanics takes a continuum mechanics approach and includes a highly compressive Storakers material model for compressive loading applications. The electrostatics and transport properties include charge conservation and a Langmuir adsorption migration model with variable diffusion properties based on plasticizer properties. Results show both plasticizer concentration gradient as well as expected voltage response under varying amounts and types of plasticizers. Experimental work is also completed to show agreeance with the modeling results.
We describe a model initially presented by Sittler et al. [2006] which attempts to explain the global response of Saturn's magnetosphere and its corresponding auroral behavior to variations in the solar wind. The model was derived from published simultaneous Hubble Space Telescope (HST) auroral images and Cassini upstream measurements taken during the month of January 2004. These observations show a direct correlation between solar wind dynamic pressure and (1) auroral brightening toward dawn local time, (2) an increase of rotational movement of auroral features to as much as 75% of the corotation speed, (3) the movement of the auroral oval to higher latitudes and (4) an increase in the intensity of Saturn Kilometric Radiation (SKR). This model is an alternative to the reconnection model of Cowley et al. [2004a,b; 2005] which is more Earth-like while ours stresses rotation. If angular momentum is conserved in a global sense, then when compressed the magnetosphere will tend to spin up and when it expands will tend to spin down. With the plasma sheet outer boundary at L approximates 15 we argue this region to be the dominant source region for the precipitating particles. If radial transport is dominated by centrifugal driven flux tube interchange motions, then when the magnetosphere spins up, outward transport will increase, the precipitating particles will move radially outward and cause the auroral oval to move to higher latitudes as observed. The Kelvin-Helmholtz instability may contribute to the enhanced emission along the dawn meridian as observed by HST. We present this model in the context of presently published observations by Cassini.
A device combines video feeds from multiple cameras to provide wide-field-of-view, high-resolution, stereoscopic video to the user. The prototype under development consists of two camera assemblies, one for each eye. One of these assemblies incorporates a mounting structure with multiple cameras attached at offset angles. The video signals from the cameras are fed to a central processing platform where each frame is color processed and mapped into a single contiguous wide-field-of-view image. Because the resolution of most display devices is typically smaller than the processed map, a cropped portion of the video feed is output to the display device. The positioning of the cropped window will likely be controlled through the use of a head tracking device, allowing the user to turn his or her head side-to-side or up and down to view different portions of the captured image. There are multiple options for the display of the stereoscopic image. The use of head mounted displays is one likely implementation. However, the use of 3D projection technologies is another potential technology under consideration, The technology can be adapted in a multitude of ways. The computing platform is scalable, such that the number, resolution, and sensitivity of the cameras can be leveraged to improve image resolution and field of view. Miniaturization efforts can be pursued to shrink the package down for better mobility. Power savings studies can be performed to enable unattended, remote sensing packages. Image compression and transmission technologies can be incorporated to enable an improved telepresence experience.
Future space-based, remote sensing systems will have data transmission requirements that exceed available downlinks necessitating the use of lossy compression techniques for multispectral data. In this paper, we describe several algorithms for lossy compression of multispectral data which combine spectral decorrelation techniques with an adaptive, wavelet-based, image compression algorithm to exploit both spectral and spatial correlation. We compare the performance of several different spectral decorrelation techniques including wavelet transformation in the spectral dimension. The performance of each technique is evaluated at compression ratios ranging from 4:1 to 16:1. Performance measures used are visual examination, conventional distortion measures, and multispectral classification results. We also introduce a family of distortion metrics that are designed to quantify and predict the effect of compression artifacts on multi spectral classification of the reconstructed data.
Previous work has shown that a bidirectional solar wind electron heat flux is one of the more prominent signatures of a coronal mass ejection event in the solar wind at 1 AU. Using ISEE 3 solar wind electron measurements obtained during 1978 and 1979, this signature was used to identify the fast coronal mass ejecta driving 19 interplanetary shocks. In 17 of the 19 shock events an eastward deflection of the ejection plasma (apparent arrival from west of the sun) was observed. The average eastward deflection for all of the events was about 3 deg, corresponding to a typical transverse velocity of 25 km/s. Usually an oppositely directed (i.e., westward) flow deflection of comparable magnitude was observed within the compressed ambient plasma ahead of the ejecta. The sense of these deflections - first westward within the compressed ambient plasma and then eastward within the ejecta - is the same as is observed near the leading edges of quasi-stationary, corotating high speed streams.
This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications.
The space-time integral of the thermodynamic pressure plays the role of the thermodynamic potential for compressible, adiabatic flow in the sense that the pressure integral for stable flow is less than for all slightly different flows. This stability criterion can be converted into a variational minimum principle by requiring the molar free-enthalpy and the temperature, which are the arguments of the pressure function, to be generalized velocities, that is, the proper-time derivatives of scalar spare-time functions which are generalized coordinates in the canonical formalism. In a fluid context, proper-time differentiation must be expressed in terms of three independent quantities that specify the fluid velocity. This can be done in several ways, all of which lead to different variants (canonical transformations) of the same constraint-free action integral whose Euler-Lagrange equations are just the well-known equations of motion for adiabatic compressible flow.
The following technologies are considered for automatic processing of earth resources data: (1) registration of multispectral and multitemporal images, (2) digital image display systems, (3) data system parameter effects on satellite remote sensing systems, and (4) data compression techniques based on spectral redundancy. The importance of proper spectral band and compression algorithm selections is pointed out.
Same equation with three adjustable parameters applies to different materials. Improved equation of state describes pressure on solid as function of relative volume at constant temperature. Even though types of interatomic interactions differ from one substance to another, form of equation determined primarily by overlap of electron wave functions during compression. Consequently, equation universal in sense it applies to variety of substances, including ionic, metallic, covalent, and rare-gas solids. Only three parameters needed to describe equation for given material.
Compression of Advanced Very high Resolution Radiometers (AVHRR) imagery operating in a lossless or nearly-lossless mode is evaluated. Several practical issues are analyzed including: variability of compression over time and among channels, rate-smoothing buffer size, multi-spectral preprocessing of data, day/night handling, and impact on key operational data applications. This analysis is based on a DPCM algorithm employing the Universal Noiseless Coder, which is a candidate for inclusion in many future remote sensing systems. It is shown that compression rates of about 2:1 (daytime) can be achieved with modest buffer sizes (less than or equal to 2.5 Mbytes) and a relatively simple multi-spectral preprocessing step.
The amount of data collected from spaceborne remote sensing has substantially increased in the last years. During same time period, the ability to store or transmit data has not increased as quickly. At this time, there is a growing interest in developing compression schemes that could provide both higher compression ratios and lower encoding/decoding errors. In the case of the spaceborne Synthetic Aperture Radar (SAR) earth observation system developed by the French Space Agency (CNES), the volume of data to be processed will exceed both the on-board storage capacities and the telecommunication link. The objective of this paper is twofold: to present various compression schemes adapted to SAR data; and to define a set of evaluation criteria and compare the algorithms on SAR data. In this paper, we review two classical methods of SAR data compression and propose novel approaches based on Fourier Transforms and spectrum coding.
Moiré materials open an entirely new platform for exploring the interplay between band structure, interactions, symmetry and topology. Strong effects of interaction can result from antiferromagnetic correlations as is commonly found in high temperature superconductors. Alternatively, the effects of interactions can be strengthened by reducing the role of kinetic energy as is commonly done in quantum Hall systems. Interestingly, moiré materials are a new class of materials where both types of effects can be present simultaneously, even at zero magnetic field, leading to a plethora of new correlated topological phases. Here we plan to harness the expertise of our groups in synthesis, fabrication, and novel measurement techniques to unravel and elucidate some of the mysteries of moiré materials. Our goal in this proposal has been to deepen our understanding of correlated phases in moiré materials using a variety of experimental tool developed in the PI’s labs. We used thermodynamic probes based on local and global electrostatic sensing to provide direct information on compressibility, entropy, magnetization and topology. Revealing the fundamental principles of correlated topological matter may pave the way towards a new class of materials with superior electronic characteristics with possible application in quantum science, engineering and energy harvesting platforms.
Continuing advances in space and Earth science requires increasing amounts of data to be gathered from spaceborne sensors. NASA expects to launch sensors during the next two decades which will be capable of producing an aggregate of 1500 Megabits per second if operated simultaneously. Such high data rates cause stresses in all aspects of end-to-end data systems. Technologies and techniques are needed to relieve such stresses. Potential solutions to the massive data rate problems are: data editing, greater transmission bandwidths, higher density and faster media, and data compression. Through four subpanels on Science Payload Operations, Multispectral Imaging, Microwave Remote Sensing and Science Data Management, recommendations were made for research in data compression and scientific data applications to space platforms.