Ammonia is an important compound with many uses, such as in the manufacture of fertilizers, explosives and pharmaceuticals. As an archetypal hydrogen-bonded system, the properties of ammonia under pressure are of fundamental interest, and compressed ammonia has a significant role in planetary physics. We predict new high-pressure crystalline phases of ammonia (NH(3)) through a computational search based on first-principles density-functional-theory calculations. Ammonia is known to form hydrogen-bonded solids, but we predict that at higher pressures it will form ammonium amide ionic solids consisting of alternate layers of NH(4)(+) and NH(2)(-) ions. These ionic phases are predicted to be stable over a wide range of pressures readily obtainable in laboratory experiments. The occurrence of ionic phases is rationalized in terms of the relative ease of forming ammonium and amide ions from ammonia molecules, and the volume reduction on doing so. We also predict that the ionic bonding cannot be sustained under extreme compression and that, at pressures beyond the reach of current static-loading experiments, ammonia will return to hydrogen-bonded structures consisting of neutral NH(3) molecules.
The development of equations-of-state and transport models in areas such as shock compression and fusion energy science is critical to DOE programs. Notable shortcomings in these activities are phase transitions in highly compressed metals. Fully characterizing high energy density phenomena using pulsed power facilities is possible only with complementary numerical modeling for design, diagnostics, and data interpretation.
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Compressed sensing (CS) is a recent mathematical technique that leverages the sparsity in certain sets of data to solve an underdetermined system and recover a full set of data from a sub-Nyquist set of measurements of the data. Given the size and sparsity of the data, radar has been a natural choice to apply compressed sensing to, typically in the fast-time and slow-time domains. Polarimetric synthetic aperture radar (PolSAR) generates a particularly large amount of data for a given scene; however, the data tends to be sparse. Recently a technique was developed to recover a dropped PolSAR channel by leveraging antenna crosstalk information and using compressed sensing. In this dissertation, we build upon the initial concept of the dropped-channel PolSAR CS in three ways. First, we determine a metric which relates the measurement matrix to the l2 recovery error. The new metric is necessary given the deterministic nature of the measurement matrix. We then determine a range of antenna crosstalk required to recover a dropped PolSAR channel. Second, we propose a new antenna design that incorporates the relatively high levels of crosstalk required by a dropped-channel PolSAR system. Finally, we integrate fast- and slow-time compression schemes into the dropped-channel model in order to leverage sparsity in additional PolSAR domains and overall increase the compression ratio. The completion of these research tasks has allowed a more accurate description of a PolSAR system that compresses in fast-time, slow-time, and polarization; termed herein as highly compressed PolSAR. The description of a highly compressed PolSAR system is a big step towards the development of prototype hardware in the future.
The evolution of the DOS of compressed As in the strongly stable bcc phase at 300, 400, 800, 1000, 1400, 1600, and 2000 GPa with the same MT sphere of 1.77 bohrs. Panels show the pressure dependence of the (a) total DOS, (b) s states, (c) p states, (d) d states, (e) eg states, and (f) t2g states.
The single-pixel imaging technique uses multiple patterns to modulate the entire scene and then reconstructs a two-dimensional (2-D) image from the single-pixel measurements. Inspired by the statistical redundancy of natural images that distinct regions of an image contain similar information, we report a highly compressed single-pixel imaging technique with a decreased sampling ratio. This technique superimposes an occluded mask onto modulation patterns, realizing that only the unmasked region of the scene is modulated and acquired. In this way, we can effectively decrease 75% modulation patterns experimentally. To reconstruct the entire image, we designed a highly sparse input and extrapolation network consisting of two modules: the first module reconstructs the unmasked region from one-dimensional (1-D) measurements, and the second module recovers the entire scene image by extrapolation from the neighboring unmasked region. Simulation and experimental results validate that sampling 25% of the region is enough to reconstruct the whole scene. Our technique exhibits significant improvements in peak signal-to-noise ratio (PSNR) of 1.5 dB and structural similarity index measure (SSIM) of 0.2 when compared with conventional methods at the same sampling ratios. The proposed technique can be widely applied in various resource-limited platforms and occluded scene imaging.
When I create PNG files with very small disk size, I tend to wonder if the file size becomes less important than the time viewers would need to decompress the image. Technically that would be trivial too, but I've wondered about it for a long time. We all know that more-compressed PNG images take longer to compress, but do they take longer to decompress?
I then wrote a script to convert the image from png to tif (on the assumption that TIF is a relatively uncompressed file format so quite fast) 200 times and timed the output.In each case I ran the script quickly and aborted it after a few seconds so any system caching could come into effect before running the full test, thus reducing the impact of disk io (and my computer happens to use SSD which also minimizes that impact. The results were as follows:
But, this does not take into account the time taken to download the file. This will, of-course, depend on the speed of your connection, the distance to the server and the size of the file. If it takes more then about 0.5 seconds more to transmit the large file then the small file, then (on my system - which is an older ultrabook, so quite slow thus giving a conservative scenatio), it is better to send the more highly compressed file. In this case - this means sending 5.8 megabytes a second, which equates to - very roughly, 60 megabits per second - excluding latency issues.
Conclusion for large files - if you are on a lightly used LAN it is probably quicker to use the less compressed image, but once you hit the wider Internet using the more highly compressed file is better.
This columnar compression engine is based on hypertables, which automatically partition your PostgreSQL tables by time. At the user level, you would simply indicate which partitions (chunks in Timescale terminology) are ready to be compressed by defining a compression policy.
In TimescaleDB 2.3, we started to improve the flexibility of this high-performing columnar compression engine by allowing INSERTS directly into compressed data. The way we did this at first was by doing the following:
With this approach, when new rows were inserted into a previously compressed chunk, they were immediately compressed row-by-row and stored in the internal chunk. The new data compressed as individual rows was periodically merged with existing compressed data and recompressed. This batched, asynchronous recompression was handled automatically within TimescaleDB's job scheduling framework, ensuring that the compression policy continued to run efficiently.
The newly introduced ability to make changes to data that is compressed breaks the traditional trade-off of having to plan your compression strategy around your data lifecycle. You can now change already-compressed data without largely impacting data ingestion, database designers no longer need to consider updates and deletes when creating a data model, and the data is now directly accessible to application developers without post-processing.
However, with the advanced capabilities of TimescaleDB 2.11, backfilling becomes a straightforward process. The company can simulate or estimate the data for the new parameters for the preceding months and seamlessly insert this data into the already compressed historical dataset.
New Zealand sphagnum moss is the highest quality moss in the world and the best choice for orchid growing. These highly compressed blocks are great for general repotting needs. To use, rehydrate the entire block until the moss is moist and easy to work with. You can store it still moist in a closed container, and it will not go bad. Alternatively, you can let the unused portion dry out before storing it. (I always store mine moist and ready to use.) I suggest hydrating the whole cake, not cutting off a bit to use because that shortens the fibers unnecessarily.
High quality New Zealand sphagnum moss is the medium of choice for the vast majority of Neo growers, including those in Japan. Sphagnum offers high acidity, high humidity, and good air circulation around the roots. Sphagnum is an excellent growing medium for many orchids making it a useful medium for your whole collection. These highly compressed blocks consist of primarily short strands and are an excellent choice for general potting needs. For moss with a much higher proportion of long strands, choose the supreme blocks.
Alibaba Cloud ApsaraDB for RDS for MySQL supports the TokuDB engine to store data that is compressed to 5 to 10 times smaller than its original size. It also supports highly concurrent writes through the caching of intermediate nodes.
TokuDB is an optional storage engine for RDS for MySQL. With a disk-optimized index structure Fractal Tree, TokuDB's intermediate nodes can cache data processing requests (insert/update/delete/on-line add index/on-line add column), improving the performance of high-concurrency writes by three to nine times. The node size is 4 MB (configurable), and data can be compressed by 5 to 10 times through a variety of compression algorithms such as zlib/quicklz/lzma/zstd/snappy. TokuDB also supports multiple versions of MVCC and the four isolation levels UR, RC, RR, and Serializable. 0852c4b9a8
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