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CubeSats, nanosatellites, including microsatellites with a moisture content of up to 60 kg have all contributed to the fast expansion of the Earth Observation sector. This development has also been aided by the reduction in cost associated with reaching space. Image data that has been acquired serves as a vital source of information in a variety of fields. As more remote sensing data is collected, the available bandwidth capabilities for the data transfer, known as the downlink, will eventually be used up. Under this article, we explain six different methodologies, including Pruning, Quantization, Information Distillation, Present Sample, Tensor Decomposing, and Sub-quadratic Converter based approaches, for compaction of such modeling techniques to enable their implementations in real industry NLP projects. These methods include information extraction, present sample, tensor decomposition, and parametric sharing. We believe that this survey organises the vast amount of work that has been done in the field of "deep learning for natural language processing" over the past couple of years and introduces it as a coherent story. This is especially important in light of the important need to build implementations with effectual and small designs, as well as the huge portion of newly published work in this area. Examples are shown using three-channel remote sensing and pictures obtained using RS that are included in multispectral data. It has been proved that the quality of pictures compressed using Discrete Atomic Transform may be adjusted and controlled by adjusting the greatest absolute deviation. This parameter also has a direct and tight relationship with more conventional metrics such as root mean square error (RMSE) and peak transmission ratio (PSNR), all of which are within the control of the user. Nevertheless, the majority of attention is being paid to several antenna applications, including millimetre wave, body-centric, radiofrequency, satellite, unmanned aircraft systems, gps devices, and textiles. The objective of this study is to investigate the recent trends in research within this sphere. We look at a variety of optimization strategies that are presently used to cram resource-constrained embedded and mobile systems with computation- and memory-intensive algorithms and examine how these strategies may be improved. [ABSTRACT FROM AUTHOR]

The encryption strength and compression efficiency are sometimes two opposing concepts in simultaneous picture compression and encryption methods. In this research, we suggest a novel lossy JPEG-based combined picture compression and encryption strategy that strives to increase encryption capability while preserving JPEG's compression efficiency. A covert orthogonal transform encrypts an original picture as a collection of coefficients. Since the image has a sparse representation in the conventional transform domain, it can be reconstructed from a small number of measurements, and thus allows the encrypted picture data to be compressed into a collection of measurement data. A secondary encryption approach based on the Fourier transform is used to a series of photographs to further strengthen their security. In addition, a fractional-order chaotic system with more complex chaotic behavior is constructed for application in picture compression and encryption. From what we can tell from experiments, the algorithm is robust and secure. First, we employ content-adaptive interpolation to acquire missing bitplanes and rejected pixels in order to rebuild the original picture. The first three MSBs are recovered losslessly by a combination of LDPC decoding, decryption, and Markov random field (MRF) exploitation. [ABSTRACT FROM AUTHOR]