On subsonic compressible flows by a method of correspondence I - methods for obtaining subsonic circulatory compressible flows about two-dimensional bodies
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Gelbart, Abe↗
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Studies of von Karman's similarity theory and its extension to compressible flows : a similarity theory for turbulent boundary layer over a flat plate in compressible flow
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Lin, C C↗
Studies of von Karman's similarity theory and its extension to compressible flow : investigation of turbulent boundary layer over a flat plate in compressible flow by the similarity theory
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Shen, S F↗
: the Linear Perturbation Theory of Axially Symmetric Compressible Flow with Application to the Effect of Compressibility on the Pressure Coefficient at the Surface of a Body of Revolution
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Herriot, John G↗
An experimental investigation of two internal-compression air-inlet designs which use fluid boundaries as a means of supersonic compression
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Howell, Robert R↗
What to Support when You're Compressing: The State of Practice, Gaps, and Opportunities for Scientific Data Compression
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Computer science↗
Lossless Astronomical Image Compression and the Effects of Random Noise
In this paper we compare a variety of modern image compression methods on a large sample of astronomical images. We begin by demonstrating from first principles how the amount of noise in the image pixel values sets a theoretical upper limit on the lossless compression ratio of the image. We derive simple procedures for measuring the amount of noise in an image and for quantitatively predicting how much compression will be possible. We then compare the traditional technique of using the GZIP utility to externally compress the image, with a newer technique of dividing the image into tiles, and then compressing and storing each tile in a FITS binary table structure. This tiled-image compression technique offers a choice of other compression algorithms besides GZIP, some of which are much better suited to compressing astronomical images. Our tests on a large sample of images show that the Rice algorithm provides the best combination of speed and compression efficiency. In particular, Rice typically produces 1.5 times greater compression and provides much faster compression speed than GZIP. Floating point images generally contain too much noise to be effectively compressed with any lossless algorithm. We have developed a compression technique which discards some of the useless noise bits by quantizing the pixel values as scaled integers. The integer images can then be compressed by a factor of 4 or more. Our image compression and uncompression utilities (called fpack and funpack) that were used in this study are publicly available from the HEASARC web site.Users may run these stand-alone programs to compress and uncompress their own images.
Pence, William↗
Low-Complexity Lossless and Near-Lossless Data Compression Technique for Multispectral Imagery
This work extends the lossless data compression technique described in Fast Lossless Compression of Multispectral- Image Data, (NPO-42517) NASA Tech Briefs, Vol. 30, No. 8 (August 2006), page 26. The original technique was extended to include a near-lossless compression option, allowing substantially smaller compressed file sizes when a small amount of distortion can be tolerated. Near-lossless compression is obtained by including a quantization step prior to encoding of prediction residuals. The original technique uses lossless predictive compression and is designed for use on multispectral imagery. A lossless predictive data compression algorithm compresses a digitized signal one sample at a time as follows: First, a sample value is predicted from previously encoded samples. The difference between the actual sample value and the prediction is called the prediction residual. The prediction residual is encoded into the compressed file. The decompressor can form the same predicted sample and can decode the prediction residual from the compressed file, and so can reconstruct the original sample. A lossless predictive compression algorithm can generally be converted to a near-lossless compression algorithm by quantizing the prediction residuals prior to encoding them. In this case, since the reconstructed sample values will not be identical to the original sample values, the encoder must determine the values that will be reconstructed and use these values for predicting later sample values. The technique described here uses this method, starting with the original technique, to allow near-lossless compression. The extension to allow near-lossless compression adds the ability to achieve much more compression when small amounts of distortion are tolerable, while retaining the low complexity and good overall compression effectiveness of the original algorithm.
Xie, Hua↗
Pilot Field Test: Use of a Compression Garment During a Stand Test After Long-Duration Space Flight
Orthostatic intolerance (OI) is a concern for astronauts returning from long-duration space flight. One countermeasure that has been used to protect against OI after short-duration bed rest and space flight is the use of lower body and abdominal compression garments. However, since the end of the Space Shuttle era we have not been able to test crewmembers during the first 24 hours after landing on Earth. NASA's Pilot Field Test provided us the opportunity to test cardiovascular responses of crewmembers wearing the Russian Kentavr compression garment during a stand test at multiple time points throughout the first 24 hours after landing. HYPOTHESIS We hypothesized that the Kentavr compression garment would prevent an increase in heart rate (HR) >15 bpm during a 3.5-min stand test. METHODS: The Pilot Field Test was conducted up to 3 times during the first 24 hours after crewmembers returned to Earth: (1) either in a tent adjacent to the Soyuz landing site in Kazakhstan (approx.1 hr) or after transportation to the Karaganda airport (approx. 4 hr); (2) during a refueling stop in Scotland (approx.12 hr); and (3) upon return to NASA Johnson Space Center (JSC) (approx.24 hr). We measured HR and arterial pressure (finger photoplethysmography) for 2 min while the crewmember was prone and throughout 3.5 min of quiet standing. Eleven crewmembers consented to participate; however, 2 felt too ill to start the test and 1 stopped 30 sec into the stand portion of the test. Of the remaining 8 crewmembers, 2 did not wear the Russian Kentavr compression garment. Because of inclement weather at the landing site, 5 crewmembers were flown by helicopter to the Karaganda airport before initial testing and received intravenous saline before completing the stand test. One of these crewmembers wore only the portion of the Russian Kentavr compression garment that covered the lower leg and thus lacked thigh and abdominal compression. All crewmembers continued wearing the Russian Kentavr compression garment during the second testing session in Scotland, but none wore it during testing at JSC. RESULTS: The mean Delta HR from the supine to standing position in the 8 crewmembers measured pre-flight or 60 days after return from long-duration space flight was 9.8 bpm. During the first few hours after landing from long-duration space flight, the mean Delta HR of the 6 crewmembers who wore the Russian Kentavr compression garment in Kazakhstan or Karaganda was +14 bpm and the change in mean arterial pressure (Delta MAP) was +0.8 mmHg, while the 2 crewmembers who did not wear the Russian Kentavr compression garment had a Delta HR of +38 bpm and a Delta MAP of +1.1 mmHg. In Scotland, 4 crewmembers wore the Russian Kentavr compression garment and had a Delta HR of +7.4 bpm while the 3 crewmembers who did not wear it had a Delta HR of +25.0 bpm. Seven crewmembers were tested upon return to JSC approx. 24 hr after landing, but none wore the Russian Kentavr compression garment and their Delta HR was 16.0 bpm. CONCLUSIONS: These are the first stand-test data to be collected from long-duration crewmembers during the first 24 hr of re-adaptation to gravity on Earth. The Delta HR measured in crewmembers who completed the stand-test while wearing Kentavr within the first approx.4 hours after returning to Earth was only slightly elevated from pre-flight Delta HR, while the few subjects who did not wear the Russian Kentavr compression garment had a much larger increase in HR in order to maintain arterial pressure throughout 3.5-min of standing. These data demonstrate the effectiveness of a compression garment in preventing large increases in HR during a 3.5 min stand test after long-duration space flight. However, the fact that three crewmembers were too ill to complete the test or was not able to complete 3.5 min of standing despite wearing the Russian Kentavr compression garment indicates that wearing a compression garment does not resolve all problems crewmembers face during the period of re-adaptation immediately after return to Earth's gravity.
Laurie, S. S.↗
Performance of a Discrete Wavelet Transform for Compressing Plasma Count Data and its Application to the Fast Plasma Investigation on NASA's Magnetospheric Multiscale Mission
Plasma measurements in space are becoming increasingly faster, higher resolution, and distributed over multiple instruments. As raw data generation rates can exceed available data transfer bandwidth, data compression is becoming a critical design component. Data compression has been a staple of imaging instruments for years, but only recently have plasma measurement designers become interested in high performance data compression. Missions will often use a simple lossless compression technique yielding compression ratios of approximately 2:1, however future missions may require compression ratios upwards of 10:1. This study aims to explore how a Discrete Wavelet Transform combined with a Bit Plane Encoder (DWT/BPE), implemented via a CCSDS standard, can be used effectively to compress count information common to plasma measurements to high compression ratios while maintaining little or no compression error. The compression ASIC used for the Fast Plasma Investigation (FPI) on board the Magnetospheric Multiscale mission (MMS) is used for this study. Plasma count data from multiple sources is examined: resampled data from previous missions, randomly generated data from distribution functions, and simulations of expected regimes. These are run through the compression routines with various parameters to yield the greatest possible compression ratio while maintaining little or no error, the latter indicates that fully lossless compression is obtained. Finally, recommendations are made for future missions as to what can be achieved when compressing plasma count data and how best to do so.
Particles↗
Hardware Implementation of Lossless Adaptive Compression of Data From a Hyperspectral Imager
Efficient onboard data compression can reduce the data volume from hyperspectral imagers on NASA and DoD spacecraft in order to return as much imagery as possible through constrained downlink channels. Lossless compression is important for signature extraction, object recognition, and feature classification capabilities. To provide onboard data compression, a hardware implementation of a lossless hyperspectral compression algorithm was developed using a field programmable gate array (FPGA). The underlying algorithm is the Fast Lossless (FL) compression algorithm reported in Fast Lossless Compression of Multispectral- Image Data (NPO-42517), NASA Tech Briefs, Vol. 30, No. 8 (August 2006), p. 26 with the modification reported in Lossless, Multi-Spectral Data Comressor for Improved Compression for Pushbroom-Type Instruments (NPO-45473), NASA Tech Briefs, Vol. 32, No. 7 (July 2008) p. 63, which provides improved compression performance for data from pushbroom-type imagers. An FPGA implementation of the unmodified FL algorithm was previously developed and reported in Fast and Adaptive Lossless Onboard Hyperspectral Data Compression System (NPO-46867), NASA Tech Briefs, Vol. 36, No. 5 (May 2012) p. 42. The essence of the FL algorithm is adaptive linear predictive compression using the sign algorithm for filter adaption. The FL compressor achieves a combination of low complexity and compression effectiveness that exceeds that of stateof- the-art techniques currently in use. The modification changes the predictor structure to tolerate differences in sensitivity of different detector elements, as occurs in pushbroom-type imagers, which are suitable for spacecraft use. The FPGA implementation offers a low-cost, flexible solution compared to traditional ASIC (application specific integrated circuit) and can be integrated as an intellectual property (IP) for part of, e.g., a design that manages the instrument interface. The FPGA implementation was benchmarked on the Xilinx Virtex IV LX25 device, and ported to a Xilinx prototype board. The current implementation has a critical path of 29.5 ns, which dictated a clock speed of 33 MHz. The critical path delay is end-to-end measurement between the uncompressed input data and the output compression data stream. The implementation compresses one sample every clock cycle, which results in a speed of 33 Msample/s. The implementation has a rather low device use of the Xilinx Virtex IV LX25, making the total power consumption of the implementation about 1.27 W.
Keymeulen, Didlier↗
Streaming Compression of Scientific Data via Weak-SINDy
Here, in this paper, a streaming weak-SINDy algorithm is developed specifically for compressing streaming scientific data. The production of scientific data, either via simulation or experiments, is undergoing a stage of exponential growth, which makes data compression important and often necessary for storing and utilizing large scientific data sets. As opposed to classical “offline” compression algorithms that perform compression on a readily available data set, streaming compression algorithms compress data “online” while the data generated from simulation or experiments is still flowing through the system. This feature makes streaming compression algorithms well suited for scientific data compression, where storing the full data set offline is often infeasible. This work proposes a new streaming compression algorithm, streaming weak-SINDy, which takes advantage of the underlying data characteristics during compression. The streaming weak-SINDy algorithm constructs feature matrices and target vectors in the online stage via a streaming integration method in a memory efficient manner. The feature matrices and target vectors are then used in the offline stage to build a model through a regression process that aims to recover equations that govern the evolution of the data. For compressing high-dimensional streaming data, we adopt a streaming proper orthogonal decomposition (POD) process to reduce the data dimension and then use the streaming weak-SINDy algorithm to compress the temporal data of the POD expansion. We propose modifications to the streaming weak-SINDy algorithm to accommodate the dynamically updated POD basis. By combining the built model from the streaming weak-SINDy algorithm and a small amount of data samples, the full data flow could be reconstructed accurately at a low memory cost, as shown in the numerical tests.
97 MATHEMATICS AND COMPUTING↗
Optimising the processing and storage of visibilities using lossy compression
The next-generation radio astronomy instruments are providing a massive increase in sensitivity and coverage, largely through increasing the number of stations in the array and the frequency span sampled. The two primary problems encountered when processing the resultant avalanche of data are the need for abundant storage and the constraints imposed by I/O, as I/O bandwidths drop significantly on cold storage. An example of this is the data deluge expected from the SKA Telescopes of more than 60 PB per day, all to be stored on the buffer filesystem. While compressing the data is an obvious solution, the impacts on the final data products are hard to predict. In this paper, we chose an error-controlled compressor – MGARD – and applied it to simulated SKA-Mid and real pathfinder visibility data, in noise-free and noise-dominated regimes. As the data have an implicit error level in the system temperature, using an error bound in compression provides a natural metric for compression. MGARD ensures the compression incurred errors adhere to the user-prescribed tolerance. To measure the degradation of images reconstructed using the lossy compressed data, we proposed a list of diagnostic measures, exploring the trade-off between these error bounds and the corresponding compression ratios, as well as the impact on science quality derived from the lossy compressed data products through a series of experiments. We studied the global and local impacts on the output images for continuum and spectral line examples. We found relative error bounds of as much as 10%, which provide compression ratios of about 20, have a limited impact on the continuum imaging as the increased noise is less than the image RMS, whereas a 1% error bound (compression ratio of 8) introduces an increase in noise of about an order of magnitude less than the image RMS. For extremely sensitive observations and for very precious data, we would recommend a 0.1% error bound with compression ratios of about 4. These have noise impacts two orders of magnitude less than the image RMS levels. At these levels, the limits are due to instabilities in the deconvolution methods. We compared the results to the alternative compression tool DYSCO, in both the impacts on the images and in the relative flexibility. MGARD provides better compression for similar error bounds and has a host of potentially powerful additional features.
Techniques: interferometric↗
Fpack and Funpack Utilities for FITS Image Compression and Uncompression
Fpack is a utility program for optimally compressing images in the FITS (Flexible Image Transport System) data format (see http://fits.gsfc.nasa.gov). The associated funpack program restores the compressed image file back to its original state (as long as a lossless compression algorithm is used). These programs may be run from the host operating system command line and are analogous to the gzip and gunzip utility programs except that they are optimized for FITS format images and offer a wider choice of compression algorithms. Fpack stores the compressed image using the FITS tiled image compression convention (see http://fits.gsfc.nasa.gov/fits_registry.html). Under this convention, the image is first divided into a user-configurable grid of rectangular tiles, and then each tile is individually compressed and stored in a variable-length array column in a FITS binary table. By default, fpack usually adopts a row-by-row tiling pattern. The FITS image header keywords remain uncompressed for fast access by FITS reading and writing software. The tiled image compression convention can in principle support any number of different compression algorithms. The fpack and funpack utilities call on routines in the CFITSIO library (http://hesarc.gsfc.nasa.gov/fitsio) to perform the actual compression and uncompression of the FITS images, which currently supports the GZIP, Rice, H-compress, and PLIO IRAF pixel list compression algorithms.
Pence, W.↗
ICER-3D Hyperspectral Image Compression Software
Software has been developed to implement the ICER-3D algorithm. ICER-3D effects progressive, three-dimensional (3D), wavelet-based compression of hyperspectral images. If a compressed data stream is truncated, the progressive nature of the algorithm enables reconstruction of hyperspectral data at fidelity commensurate with the given data volume. The ICER-3D software is capable of providing either lossless or lossy compression, and incorporates an error-containment scheme to limit the effects of data loss during transmission. The compression algorithm, which was derived from the ICER image compression algorithm, includes wavelet-transform, context-modeling, and entropy coding subalgorithms. The 3D wavelet decomposition structure used by ICER-3D exploits correlations in all three dimensions of sets of hyperspectral image data, while facilitating elimination of spectral ringing artifacts, using a technique summarized in "Improving 3D Wavelet-Based Compression of Spectral Images" (NPO-41381), NASA Tech Briefs, Vol. 33, No. 3 (March 2009), page 7a. Correlation is further exploited by a context-modeling subalgorithm, which exploits spectral dependencies in the wavelet-transformed hyperspectral data, using an algorithm that is summarized in "Context Modeler for Wavelet Compression of Hyperspectral Images" (NPO-43239), which follows this article. An important feature of ICER-3D is a scheme for limiting the adverse effects of loss of data during transmission. In this scheme, as in the similar scheme used by ICER, the spatial-frequency domain is partitioned into rectangular error-containment regions. In ICER-3D, the partitions extend through all the wavelength bands. The data in each partition are compressed independently of those in the other partitions, so that loss or corruption of data from any partition does not affect the other partitions. Furthermore, because compression is progressive within each partition, when data are lost, any data from that partition received prior to the loss can be used to reconstruct that partition at lower fidelity. By virtue of the compression improvement it achieves relative to previous means of onboard data compression, this software enables (1) increased return of hyperspectral scientific data in the presence of limits on the rates of transmission of data from spacecraft to Earth via radio communication links and/or (2) reduction in spacecraft radio-communication power and/or cost through reduction in the amounts of data required to be downlinked and stored onboard prior to downlink. The software is also suitable for compressing hyperspectral images for ground storage or archival purposes.
Xie, Hua↗
Enabling IP Header Compression in COTS Routers via Frame Relay on a Simplex Link
NASA is moving toward a networkcentric communications architecture and, in particular, is building toward use of Internet Protocol (IP) in space. The use of IP is motivated by its ubiquitous application in many communications networks and in available commercial off-the-shelf (COTS) technology. The Constellation Program intends to fit two or more voice (over IP) channels on both the forward link to, and the return link from, the Orion Crew Exploration Vehicle (CEV) during all mission phases. Efficient bandwidth utilization of the links is key for voice applications. In Voice over IP (VoIP), the IP packets are limited to small sizes to keep voice latency at a minimum. The common voice codec used in VoIP is G.729. This new algorithm produces voice audio at 8 kbps and in packets of 10-milliseconds duration. Constellation has designed the VoIP communications stack to use the combination of IP/UDP/RTP protocols where IP carries a 20-byte header, UDP (User Datagram Protocol) carries an 8-byte header, and RTP (Real Time Transport Protocol) carries a 12-byte header. The protocol headers total 40 bytes and are equal in length to a 40-byte G.729 payload, doubling the VoIP latency. Since much of the IP/UDP/RTP header information does not change from IP packet to IP packet, IP/UDP/RTP header compression can avoid transmission of much redundant data as well as reduce VoIP latency. The benefits of IP header compression are more pronounced at low data rate links such as the forward and return links during CEV launch. IP/UDP/RTP header compression codecs are well supported by many COTS routers. A common interface to the COTS routers is through frame relay. However, enabling IP header compression over frame relay, according to industry standard (Frame Relay IP Header Compression Agreement FRF.20), requires a duplex link and negotiations between the compressor router and the decompressor router. In Constellation, each forward to and return link from the CEV in space is treated independently as a simplex link. Without negotiation, the COTS routers are prevented from entering into the IP header compression mode, and no IP header compression would be performed. An algorithm is proposed to enable IP header compression in COTS routers on a simplex link with no negotiation or with a one-way messaging. In doing so, COTS routers can enter IP header compression mode without the need to handshake through a bidirectional link as required by FRF.20. This technique would spoof the routers locally and thereby allow the routers to enter into IP header compression mode without having the negotiations between routers actually occur. The spoofing function is conducted by a frame relay adapter (also COTS) with the capability to generate control messages according to the FRF.20 descriptions. Therefore, negotiation is actually performed between the FRF.20 adapter and the connecting COTS router locally and never occurs over the space link. Through understanding of the handshaking protocol described by FRF.20, the necessary FRF.20 negotiations messages can be generated to control the connecting router, not only to turn on IP header compression but also to adjust the compression parameters. The FRF.20 negotiation (or control) message is composed in the FRF.20 adapter by interpreting the incoming router request message. Many of the fields are simply transcribed from request to response while the control field indicating response and type are modified.
Nguyen, Sam P.↗
Some Results Relevant to Statistical Closures for Compressible Turbulence
For weakly compressible turbulent fluctuations there exists a small parameter, the square of the fluctuating Mach number, that allows an investigation using a perturbative treatment. The consequences of such a perturbative analysis in three different subject areas are described: 1) initial conditions in direct numerical simulations, 2) an explanation for the oscillations seen in the compressible pressure in the direct numerical simulations of homogeneous shear, and 3) for turbulence closures accounting for the compressibility of velocity fluctuations. Initial conditions consistent with small turbulent Mach number asymptotics are constructed. The importance of consistent initial conditions in the direct numerical simulation of compressible turbulence is dramatically illustrated: spurious oscillations associated with inconsistent initial conditions are avoided, and the fluctuating dilatational field is some two orders of magnitude smaller for a compressible isotropic turbulence. For the isotropic decay it is shown that the choice of initial conditions can change the scaling law for the compressible dissipation. A two-time expansion of the Navier-Stokes equations is used to distinguish compressible acoustic and compressible advective modes. A simple conceptual model for weakly compressible turbulence - a forced linear oscillator is described. It is shown that the evolution equations for the compressible portions of turbulence can be understood as a forced wave equation with refraction. Acoustic modes of the flow can be amplified by refraction and are able to manifest themselves in large fluctuations of the compressible pressure.
Ristorcelli, J. R.↗
Survey of Header Compression Techniques
This report provides a summary of several different header compression techniques. The different techniques included are: (1) Van Jacobson's header compression (RFC 1144); (2) SCPS (Space Communications Protocol Standards) header compression (SCPS-TP, SCPS-NP); (3) Robust header compression (ROHC); and (4) The header compression techniques in RFC2507 and RFC2508. The methodology for compression and error correction for these schemes are described in the remainder of this document. All of the header compression schemes support compression over simplex links, provided that the end receiver has some means of sending data back to the sender. However, if that return path does not exist, then neither Van Jacobson's nor SCPS can be used, since both rely on TCP (Transmission Control Protocol). In addition, under link conditions of low delay and low error, all of the schemes perform as expected. However, based on the methodology of the schemes, each scheme is likely to behave differently as conditions degrade. Van Jacobson's header compression relies heavily on the TCP retransmission timer and would suffer an increase in loss propagation should the link possess a high delay and/or bit error rate (BER). The SCPS header compression scheme protects against high delay environments by avoiding delta encoding between packets. Thus, loss propagation is avoided. However, SCPS is still affected by an increased BER (bit-error-rate) since the lack of delta encoding results in larger header sizes. Next, the schemes found in RFC2507 and RFC2508 perform well for non-TCP connections in poor conditions. RFC2507 performance with TCP connections is improved by various techniques over Van Jacobson's, but still suffers a performance hit with poor link properties. Also, RFC2507 offers the ability to send TCP data without delta encoding, similar to what SCPS offers. ROHC is similar to the previous two schemes, but adds additional CRCs (cyclic redundancy check) into headers and improves compression schemes which provide better tolerances in conditions with a high BER.
Ishac, Joseph↗