Compression Scenario Research Articles

Oriented Acicular Rutile Inclusions in Eclogites: Exsolutions From Majoritic Garnet or Shock Needles?

AbstractOriented rutile needles (ORNs) forming a triangular network on the cross sections of garnet crystals have been widely used together with omphacite inclusions as evidence for exsolution from a majoritic garnet and exhumation of the host rocks from great depths (>200 km) in the Earth. A coronitic eclogite at Yangkou in the Chinese Su‐Lu high‐pressure metamorphic belt contains ORNs that are only found in the reddish cores of garnet porphyroblasts. The texture formed by the ORNs is not restricted to garnet but extends into the coexisting other minerals, which together form pseudomorphs after augite. Therefore, the ORNs are not specifically related to the host garnet and cannot be exsolutions therefrom. The outer zones of the garnet porphyroblasts in contact with plagioclase pseudomorphs are pale and rutile‐free but contain minute inclusions of omphacite, quartz, kyanite, phengite, and K‐feldspar, typical of coronitic garnet between augite and plagioclase. Electron backscatter diffraction reveals no optimum matching of the low index crystallographic directions of rutile and garnet as required by an exsolution mechanism. On the other hand, the ORNs resemble the amorphous lamellae in quartz and zircon in meteorite and seismic shocked rocks, and are inferred to have crystallized earlier in seismic shocked augite and were then overgrown by the host minerals. By contrast, the rutile particles in garnet cataclasites in a nearby eclogite breccia display deformed and explosive patterns and random crystallographic orientations. All these observations are best explained by the seismic shock compression and rarefaction scenario proposed earlier.

Plasticity analysis and a homogenized constitutive model of compressible multi-layer structure of battery

A homogenized constitutive model for the compressible multi-layer structure of battery (CMLSB) under external loading is essential for optimizing the structural design of electric assemblies. Currently, there is no specific constitutive model that is both mechanically explanatory and operationally applicable to CMLSB under varied loading conditions. In this study, due to limited understanding of the in-plane behavior of CMLSB, an analytical model was developed to investigate plasticity in this specific direction using the strain probing method. The observed plastic characteristics inspired the formulation of a novel two-dimensional constitutive framework for CMLSB in the in-plane direction.By integrating this new constitutive framework with one-dimensional plastic descriptions, a hybrid constitutive model was introduced and implemented in finite element software. Calibration and validation of the model were performed using a commercial pouch cell battery and its segments under various loading conditions. Finite element simulations with the hybrid model demonstrated remarkable accuracy in predicting the mechanical behavior of the cell under various in-plane and out-of-plane compression scenarios. Additionally, simulations were carried out to analyze the impact of cell packaging and air pressure. The new hybrid battery model is considered a user-friendly, physically interpretable, and high-fidelity tool, poised to significantly facilitate the comprehensive design of electric devices.

Investigation on the bending performance of sandwich beams with re-entrant diamond auxetic core

ABSTRACT Auxetic metamaterials, characterised by a negative Poisson’s ratio (NPR), exhibit unique mechanical properties where they expand under tension and contract under compression. These structures hold significant potential with applications spanning various industries such as biomedical, construction, sports, aerospace and defence. The re-entrant diamond auxetic metamaterial represents a modified unit cell structure designed to mitigate the bending-dominated failure observed in conventional re-entrant structures. Notably, this modification demonstrates advantages, particularly in uni-axial compression scenarios. However, its performance concerning energy absorption under bending loads has not been explored until now. This study examines the bending behaviour of a re-entrant diamond auxetic metamaterial, both as a core and in sandwich configurations. The samples are fabricated using additive manufacturing (3D printing) and compression moulded glass-epoxy composite skins are added to these cores. Experimental assessments are conducted to evaluate bending performance, facilitated by advanced digital image correlation (DIC) techniques for real-time displacement measurement on the sample surface. The results reveal remarkable 14, 11 and 4-times improvement in energy absorption, specific energy absorption and failure displacement in the sandwich construction compared to the core configuration. Additionally, the study investigates the impact of eccentric loading on the flexural performance of the re-entrant diamond auxetic metamaterial.

Investigating the damage characteristics of kiwifruits through the integration of compressive load and finite element method

Fruits are susceptible to various forms of mechanical damage after harvesting. Compression-induced bruising is the most common damage incurred, and it crucial to mitigate this for reducing economic losses associated with fruit. This study investigates the impact of compression on kiwifruit. Experiments combined with finite element method simulations were conducted to analyze the relationship between compression distance and bruise volume of kiwifruits under different storage times. This study's results found that compression distance and storage time are linearly positively correlated with the bruise volume of kiwifruit. The difference in bruising volume between simulation results and actual results is less than 14 %. The aforementioned results indicate that our employed compression model is capable of simulating real compression scenarios. This study provides significant theoretical insights into predicting and evaluating potential damages that may occur during post-harvest processing of kiwifruits under compression conditions.

EFFICIENT VIDEO COMPRESSION USING HEVC AND NON-LINEAR CONVOLUTIONAL MOBILEVNET BASED RATE-DISTORTION OPTIMIZATION

With the surge in demand for high-quality video content over various platforms, efficient video compression techniques have become indispensable. High-Efficiency Video Coding (HEVC) has been a cornerstone, yet further enhancements are essential for optimal compression. Despite HEVC’s advancements, achieving optimal compression while maintaining video quality remains challenging. Additionally, existing methods often overlook the computational complexity, hindering real-time applications. We propose a novel approach integrating HEVC with Non-Linear Convolutional MobileNet (NLCM) for enhanced compression efficiency. Our method employs a rate-distortion optimization framework, leveraging the capabilities of both HEVC and NLCM to achieve superior compression performance. NLCM provides adaptive filtering, enhancing spatial and temporal correlations, while HEVC ensures high compression efficiency. Through experimentation on standard video datasets, our method demonstrates significant improvements over existing techniques. Compared to HEVC alone, our approach achieves up to 30% reduction in bitrate at equivalent perceptual quality levels. Moreover, computational complexity is reduced by 15%, enabling real- time applications without compromising performance. The proposed method exhibits competitive results across various resolutions and frame rates, making it versatile for diverse video compression scenarios.

Dark Matter searches with photons at the LHC

We unveil blind spot regions in dark matter (DM) direct detection (DMDD), for weakly interacting massive particles with a mass around a few hundred GeV that may reveal interesting photon signals at the LHC. We explore a scenario where the DM primarily originates from the singlet sector within the Z3-symmetric Next-to-Minimal Supersymmetric Standard Model (NMSSM). A novel DMDD spin-independent blind spot condition is revealed for singlino-dominated DM, in cases where the mass parameters of the higgsino and the singlino-dominated lightest supersymmetric particle (LSP) exhibit opposite relative signs (i.e., κ < 0), emphasizing the role of nearby bino and higgsino-like states in tempering the singlino-dominated LSP. Additionally, proximate bino and/or higgsino states can act as co-annihilation partner(s) for singlino-dominated DM, ensuring agreement with the observed relic abundance of DM. Remarkably, in scenarios involving singlino-higgsino co-annihilation, higgsino-like neutralinos can distinctly favor radiative decay modes into the singlino-dominated LSP and a photon, as opposed to decays into leptons/hadrons. In exploring this region of parameter space within the singlino-higgsino compressed scenario, we study the signal associated with at least one relatively soft photon alongside a lepton, accompanied by substantial missing transverse energy (ɆT) and a hard initial state radiation jet at the LHC. In the context of singlino-bino co-annihilation, the bino state, as the next-to-LSP, exhibits significant radiative decay into a soft photon and the LSP, enabling the possible exploration at the LHC through the triggering of this soft photon alongside large ɆT and relatively hard leptons/jets resulting from the decay of heavier higgsino-like states.

A model for the temperature-dependent creep/recovery behavior of dry fiber fabric considering in-plane tension

In the manufacturing process of fiber-reinforced composite materials, the preforming of dry fiber fabrics is typically carried out under specific out-of-plane pressure, in-plane tension, and at designated temperatures. The fabric exhibits significant time and temperature-dependent permanent deformation as well as creep/recovery behavior. Gaining a better understanding of these phenomena and harnessing predictive capabilities will be instrumental in achieving more precise control over fiber volume fraction and material composition. In this paper, creep/recovery experiments and cyclic loading-unloading tests were conducted on CF3031 dry fiber preforms under various in-plane tensions and temperatures, measuring in-plane strains using Fiber Bragg Grating (FBG) sensors. Based on this data, a modified Burgers viscoelastic-plastic model has been proposed to describe the time-dependent creep/recovery behavior of dry fiber fabric preforming under the coupled effects of in-plane tension, out-of-plane pressure, and temperature variations. This model employs a single set of equations and parameters to represent the complete creep/recovery process of the material. The experimental results align well with the predicted outcomes across different compression scenarios.

Face Forgery Detection with Long-Range Noise Features and Multilevel Frequency-Aware Clues

The widespread dissemination of high-fidelity fake faces created by face forgery techniques has caused serious trust concerns and ethical issues in modern society. Consequently, face forgery detection has emerged as a prominent topic of research to prevent technology abuse. Although, most existing face forgery detectors demonstrate success when evaluating high-quality faces under intra-dataset scenarios, they often overfit manipulation-specific artifacts and lack robustness to postprocessing operations. In this work, we design an innovative dual-branch collaboration framework that leverages the strengths of the transformer and CNN to thoroughly dig into the multimodal forgery artifacts from both a global and local perspective. Specifically, a novel adaptive noise trace enhancement module (ANTEM) is proposed to remove high-level face content while amplifying more generalized forgery artifacts in the noise domain. Then, the transformer-based branch can track long-range noise features. Meanwhile, considering that subtle forgery artifacts could be described in the frequency domain even in a compression scenario, a multilevel frequency-aware module (MFAM) is developed and further applied to the CNN-based branch to extract complementary frequency-aware clues. Besides, we incorporate a collaboration strategy involving cross-entropy loss and single center loss to enhance the learning of more generalized representations by optimizing the fusion features of the dual branch. Extensive experiments on various benchmark datasets substantiate the superior generalization and robustness of our framework when compared to the competing approaches.

GSC: efficient lossless compression of VCF files with fast query.

With the rise of large-scale genome sequencing projects, genotyping of thousands of samples has produced immense variant call format (VCF) files. It is becoming increasingly challenging to store, transfer, and analyze these voluminous files. Compression methods have been used to tackle these issues, aiming for both high compression ratio and fast random access. However, existing methods have not yet achieved a satisfactory compromise between these 2 objectives. To address the aforementioned issue, we introduce GSC (Genotype Sparse Compression), a specialized and refined lossless compression tool for VCF files. In benchmark tests conducted across various open-source datasets, GSC showcased exceptional performance in genotype data compression. Compared with the industry's most advanced tools (namely, GBC and GTC), GSC achieved compression ratios that were higher by 26.9% to 82.4% over GBC and GTC on the datasets, respectively. In lossless compression scenarios, GSC also demonstrated robust performance, with compression ratios 1.5× to 6.5× greater than general-purpose tools like gzip, zstd, and BCFtools-a mode not supported by either GBC or GTC. Achieving such high compression ratios did require some reasonable trade-offs, including longer decompression times, with GSC being 1.2× to 2× slower than GBC, yet 1.1× to 1.4× faster than GTC. Moreover, GSC maintained decompression query speeds that were equivalent to its competitors. In terms of RAM usage, GSC outperformed both counterparts. Overall, GSC's comprehensive performance surpasses that of the most advanced technologies. GSC balances high compression ratios with rapid data access, enhancing genomic data management. It supports seamless PLINK binary format conversion, simplifying downstream analysis.

BASICS: Broad Quality Assessment of Static Point Clouds in a Compression Scenario

BASICS: Broad Quality Assessment of Static Point Clouds in a Compression Scenario

Optimal Information Hiding: Advanced Bitstream‐Based Watermarking for Secure and Efficient Integration of Image Data in Digital Videos

Video watermarking is a technique used to embed a visible or invisible mark onto a video file in order to claim ownership, protect intellectual property rights, and detect unauthorized users. The issue of video piracy arises due to the advancement of Internet services and the utilization of various storage technologies. This problem becomes particularly significant with the widespread propagation of media through online channels. The constant progress in sharing of data, videos, images, and applications necessitates the implementation of measures focused on security and the protection of intellectual ownership rights. To address this issue, we proposed a novel Bitstream video watermarking technique that integrates cutting‐edge methodologies to seamlessly embed image data within digital videos. This technique stands apart from less sophisticated watermarking methods by capitalizing on spatial and temporal domain concepts in video processing. An experiment is conducted to analyze the time required for the insertion and extraction processes to gain insights into the performance of bitstream watermarking in the video. While enhancing the existing approach, we systematically explored diverse strategies aimed at augmenting watermark capacity, bolstering robustness, and heightening visual imperceptibility. These strategies encompassed the intricate infusion of watermark fragments into the frame area through chaotic mechanisms, the integration of error‐correcting codes to ensure data fidelity, and the selective omission of dynamic video segments during information extraction processes. Rigorous experimentations are undertaken by the constraints of lossy compression scenarios, maintaining equivalent or reduced bitrate parameters. The findings substantiate the favorable outcomes stemming from the implemented enhancement, affirming the tangible suitability of the phase watermarking technique for the protection of video copyrights. In order to assess the quality of the bitstream video watermarking technique, the peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), and normalized correlation coefficient (NCC) are computed and examined by the testing data. The suggested model achieved a PSNR value up to 32.52 dB, SSIM value up to 0.97, and NCC value is 1. The dynamic range (DR) and spatial resolution (SR) are measured in the evaluation process. Security is enhanced significantly after implementing the multifaceted approach. The watermarking scheme is more robust and can effectively resist the potential attacks. In summary, this study advances the field of video watermarking by proposing a sophisticated bitstream technique and rigorously evaluating its performance. The outcomes underscore its practical viability for copyright protection, contributing to a more secure digital media landscape.

A viscoelastic-plastic model for the temperature-dependent creep and recovery behavior during dry fiber fabric compaction

The preforming process of dry fiber fabric usually involves compaction under constant pressure and specific temperature during fiber-reinforced composite manufacturing. The fabric exhibits time- and temperature-dependent considerable permanent deformation and creep/recovery behavior in compaction. A better understanding of these phenomena, combined with the ability to predict the behavior, will contribute to better control of fiber volume fraction and material composition. This study presented a universal viscoelastic-plastic model to represent the fabric’s visco-effect in compression and recovery stages. The proposed model consisted of a modified Burges element for time-dependent deformation and a plastic element for permanent deformation that occurs in all cases. It used only one set of equations and one set of parameters to characterize the cyclic loading/unloading and the compaction and relaxation phases. It could consider the material densification effect observed experimentally. The model was validated against experimental data for unidirectional (UD), plain woven, and twill woven fabrics. A reasonable match between experiments and prediction was achieved under different compression scenarios.

A fast and time-efficient machine learning approach to dark matter searches in compressed mass scenario

Various analyses for searching for the signature of SUSY or exotic particles have been carried out by the experiments at CERN. These analyses made use of traditional cut and count methods. While this method has yielded promising results, it has been challenging in the region where the mass difference between SUSY particles is small. Deep learning is currently widely employed in most data analysis tasks, including high energy physics, and has made significant advances in almost all fields for collecting and interpreting huge data samples. In this paper, a fast and time-efficient classification technique is proposed, utilizing machine learning algorithms to distinguish dark matter signal from SM background in compressed mass spectra scenarios at a center-of-mass energy of 14 TeV. A classification model was built in a short amount of time using 2D histograms produced with less amount of data, effectively reducing computational costs through the transfer learning of pre-trained deep models while maintaining a high level of classification accuracy.

Pressurized green hydrogen from water electrolysis: Compression before or after electrolysis? A comparison among different configurations

Storing the intermittent renewable energy in the form of green hydrogen is the key pathway toward achieving a complete shift to renewable energy dependence. This study compared and examined various settings to produce hydrogen from water electrolysis, considering pressure and temperature effects over a broad range that covers both liquid and steam phases. Pressurized and atmospheric electrolysis at low and high temperatures were investigated under both isothermal and multistage isentropic post-electrolysis gas compression. Modeling and analysis based on established correlations and equations of state were used to estimate the system’s overall power requirements for each scenario. Increasing the operating temperature significantly reduced the electrolysis work while the pressure had an opposite effect, especially at low pressures. The results conclude that compressing liquid water, heating, then electrolyzing requires less overall energy than atmospheric electrolysis followed by product gases compression. This was valid for isothermal or isentropic compression despite the fact that the isothermal required the least work. The difference between pre-and post-electrolysis compression scenarios becomes greater at high pressure and temperature values. For instance, to obtain hydrogen at 25 °C and 50 bar, the power saving from pressurized electrolysis was 2.5% compared with atmospheric electrolysis followed by gas compression, which increased up to 20% at 600 °C and 200 bar. This indicates that high pressure steam electrolysis integrated with renewable systems leads to producing green hydrogen ready to store, transport, or use at less cost and complexities, especially with a cheap heat source or waste heat utilization.

Hydrodynamic and Electrochemical Analysis of Compression and Flow Field Designs in Vanadium Redox Flow Batteries

This numerical study investigates compression and flow field design effects on electrode behaviour in vanadium redox flow batteries (VRFBs). Through 3D simulations and analysis of various flow field designs, including conventional, serpentine, interdigitated, and parallel configurations, this study investigates three compression scenarios: uncompressed, non-homogeneously compressed, and homogeneously compressed electrodes. Hydrodynamic and electrochemical analyses reveal the impact on velocity, pressure, current density, overpotential, and charge–discharge performance. Interdigitated flow field is found to display the lowest charging potential and highest discharging potential among all flow fields under all three compression scenarios. Moreover, uncompressed electrode condition shows the conservative estimates of an average charging potential of 1.3647 V and average discharging potential of 1.3231 V in the case of interdigitated flow field, while compressed electrode condition and the non-homogeneously compressed electrode condition show an average charging potential of 1.3922 V and 1.3777 V, and an average discharging potential of 1.3019 V and 1.3224 V, respectively. Results highlight the significance of non-uniform compression while modelling and analysing the performance of VRFBs as it is a more realistic representation compared to the no-compression or homogeneous compression of the electrodes. The findings of this work provide insights for optimising VRFB performance by considering compression and flow field design.

Learned Distributed Image Compression with Multi-Scale Patch Matching in Feature Domain

Beyond achieving higher compression efficiency over classical image compression codecs, deep image compression is expected to be improved with additional side information, e.g., another image from a different perspective of the same scene. To better utilize the side information under the distributed compression scenario, the existing method only implements patch matching at the image domain to solve the parallax problem caused by the difference in viewing points. However, the patch matching at the image domain is not robust to the variance of scale, shape, and illumination caused by the different viewing angles, and can not make full use of the rich texture information of the side information image. To resolve this issue, we propose Multi-Scale Feature Domain Patch Matching (MSFDPM) to fully utilizes side information at the decoder of the distributed image compression model. Specifically, MSFDPM consists of a side information feature extractor, a multi-scale feature domain patch matching module, and a multi-scale feature fusion network. Furthermore, we reuse inter-patch correlation from the shallow layer to accelerate the patch matching of the deep layer. Finally, we find that our patch matching in a multi-scale feature domain further improves compression rate by about 20% compared with the patch matching method at image domain.

Continuum modeling of nonlinear buckling behavior of CNT using variational asymptotic method and nonlinear FEA

Carbon nanotubes (CNTs) exhibit a unique buckling behavior due to their slender tubular geometry and thin-walled circular cross section. This study aims to analyze effect of nonlinear cross-sectional deformation on buckling of CNTs. To accomplish this, CNTs are modeled as beam structures, and the analysis is conducted using the Variational Asymptotic Method (VAM) and a geometrically exact beam theory, as well as nonlinear finite element analysis (FEA). The study considers various loading cases, including pure axial compression and combined loading scenarios, such as bending-axial compression and torsion-axial compression. The results of the study indicate that inclusion of cross-sectional deformation-induced nonlinearity reduces the critical buckling load of CNTs. The reduction is 2–5% for pure axial compression and 10–40% for combined loading cases. The results are validated against existing literature and commercial finite element software, ABAQUS®. Additionally, parametric studies with different slenderness and radius-to-thickness ratios were carried out to further understand the impact of these parameters on buckling of CNTs. Finally, the study presents 3D deformed shapes of CNTs during buckling by combining the results of the 1D analysis and the 2D cross section analysis. The findings show that nonlinearity associated with radius-to-thickness ratio has a significant impact on the cross-sectional ovalisation of CNTs and is critical in evaluating their buckling behavior. This aspect of nonlinearity is often overlooked in continuum modeling methods of CNTs, making this study an important contribution to the field.

Design of a Quantization-Based DNN Delta Compression Framework for Model Snapshots and Federated Learning

Deep neural networks (DNNs) have achieved remarkable success in many fields. However, large-scale DNNs also bring storage costs when storing snapshots for preventing clusters’ frequent failures or incur significant communication overheads when transmitting DNNs in the Federated Learning (FL). Recently, several approaches, such as Delta-DNN and LC-Checkpoint, aim to reduce the size of DNNs’ snapshot storage by compressing the difference between two neighboring versions of the DNNs (a.k.a., delta). However, we observe that existing approaches, applying traditional global lossy quantization techniques in DNN's delta compression, can not fully exploit the data similarity since the parameters’ value ranges vary among layers. To fully explore the similarity of the delta model and improve the compression ratio, we propose a quantization-based local-sensitive delta compression approach, named QD-Compressor, by developing a layer-based local-sensitive quantization scheme and error feedback mechanism. Specifically, the quantizers and number of quantization bits are adaptive among layers based on the value distribution and weighted entropy of the delta's parameters. To avoid quantization error degrading the performance of the restored model, an alternative error feedback mechanism is designed to dynamically correct the quantization error during the training process. Experiments on multiple popular DNNs and datasets show that QD-Compressor obtains a higher 7×-40× compression ratio in the model snapshot compression scenario than the state-of-the-art approaches. Additionally, QD-Compressor achieves an 11×-15× compression ratio to the residual model of the Federated Learning compression scenario.

Limits on compression of cosmic rays in supernova remnants

ABSTRACTThe spectral shape of the gamma-ray emission observed for dynamically old supernova remnants that interact with molecular clouds triggered an exciting scenario of adiabatic compression and farther re-acceleration of Galactic cosmic rays (GCRs) in radiative shells of the remnants, which was extensively discussed and applied to various sources over recent years. Indeed, the observed gamma-ray spectrum from a number of remnants strongly resembles the expected spectrum of the gamma-ray emission from the compressed population of GCRs. In the following we discuss the feasibility of this scenario and show that it is very unlikely that compressed GCRs could produce sufficient amount of gamma-rays and that the observed spectral shape is putting strong limits on the allowed compression factors. Further, absence of curvature in featureless power-law spectra of evolved supernova remnants at radio wavelengths is strongly disfavouring the compression scenario for electrons and hence for hadrons. Our calculations show that the contribution of compressed electrons to the observed radio flux could reach at most $\sim 10\,$ per cent.

Modeling of anomalous gas transport in heterogeneous unconventional reservoirs using a nonlinear generalized diffusivity equation

Exploitation of unconventional reservoirs is enabled by multi-fractured horizontal wells. Hydraulic fractures are created with the purpose of enhancing fluid conductivity in such low permeability formation. These hydraulic fractures, along with the intricate structure of the reservoir, such as the presence of natural fractures, result in a highly heterogeneous system and introduces significant challenges with respect to obtaining a proper description of fluid movement. This paper proposes the use of anomalous transport concept based on fractional-time derivative to model gas flow through highly heterogeneous unconventional reservoirs. A modified Darcy’s law that accounts for non-local temporal effects, i.e. history, is adopted to account for the complex flow mechanisms encountered in such disordered media with a wide range of heterogeneity scales. Studies previously developed on this topic solely focused on slightly-compressible fluid transport without further consideration of the nonlinear dependency of fluid properties to pressure. In this regard, the development of predictive models simultaneously considering reservoir heterogeneity and the nonlinear nature of gas transport is yet to be accomplished. In this work, transient pressure diffusion is modeled by the so-called nonlinear Generalized Diffusivity Equation (nGDE), and an integral solution is derived based on Green’s function method. It is observed that for the cases of higher heterogeneity level, higher pressure drawdown is required to maintain a given specified (constant) rate, significantly increasing the deviation of the system response from its linear (slightly-compressible) behavior. This finding corroborates the importance of properly accounting for the nonlinear behavior of gas properties and highlights that available analytical solutions suffer drawbacks when extrapolated to compressible flow scenarios.