Edge Part Research Articles

Antarctic ice-shelf meltwater outflows in satellite radar imagery: ground-truthing and basal channel observations

Abstract Ice shelves regulate the flow of the Antarctic ice sheet toward the ocean and its contribution to sea-level rise. Accurately monitoring the basal and surface melting of ice shelves is therefore essential for predicting the ice sheet's response to climatic warming. In this study, we utilize Sentinel-1A synthetic aperture radar satellite imagery combined with shipboard measurements of water temperature and salinity to investigate the presence of surficial meltwater plumes along the Antarctic coastline. Our approach reveals a strong correlation between areas of pronounced low radar backscatter extending from ice shelves and significant decreases in water temperature and salinity, suggesting meltwater-enriched ocean waters. We propose that the low radar backscatter signature of meltwater outflows is caused by stable stratification of the upper water column, driven by density contrasts from buoyant, low-salinity meltwater and surface current shear that reduce Bragg scattering waves. The resulting smooth water surfaces were observed adjacent to the surface expression of deep basal channels, documented in a helicopter survey along part of the Bellingshausen Sea ice edge. We present high-temporal resolution satellite radar as a tool for identifying meltwater release from beneath ice shelves, capable of all-weather, day-and-night imaging.

Cost-effective task offloading and trajectory optimization in UAV assisted edge networks with DDPG

PurposeUnmanned aerial vehicles (UAVs), known for their exceptional flexibility and maneuverability, have become an integral part of mobile edge computing systems in edge networks. This paper aims to minimize system costs within a communication cycle. To this end, this paper has developed a model for task offloading in UAV-assisted edge networks under dynamic channel conditions. This study seeks to efficiently execute task offloading while satisfying UAV energy constraints, and validates the effectiveness of the proposed method through performance comparisons with other similar algorithms.Design/methodology/approachTo address this issue, this paper proposes a task offloading and trajectory optimization algorithm using deep deterministic policy gradient, which jointly optimizes Internet of Things (IoT) device scheduling, power distribution, task offloading and UAV flight trajectory to minimize system costs.FindingsThe analysis of simulation results indicates that this algorithm achieves lower redundancy compared to others, along with reductions in task size by 22.8%, flight time by 34.5%, number of IoT devices by 11.8%, UAV computing power by 25.35% and the required cycle for per-bit tasks by 33.6%.Originality/valueA multi-objective optimization problem is established under dynamic channel conditions, and the effectiveness of this approach is validated.

Border-Enhanced Triple Attention Mechanism for High-Resolution Remote Sensing Images and Application to Land Cover Classification

With the continuous development and popularization of remote sensing technology, remote sensing images have been widely used in the field of land cover classification. Since remote sensing images have complex spatial structure and texture features, it is becoming a challenging problem to accurately categorize them. Land cover classification has practical application value in various fields, such as environmental monitoring and protection, urban and rural planning and management, and climate change research. In recent years, remote sensing image classification methods based on deep learning have been rapidly developed, in which semantic segmentation technology has become one of the mainstream methods for land cover classification using remote sensing image. Traditional semantic segmentation algorithms tend to ignore the edge information, resulting in poor classification of the edge part in land cover classification, and there are numerous attention mechanisms to make improvements for these problems. In this paper, a triple attention mechanism, BETAM (Border-Enhanced Triple Attention Mechanism), for edge feature enhancement of high-resolution remote sensing images is proposed. Furthermore, a new model on the basis of the semantic segmentation network model DeeplabV3+ is also introduced, which is called DeepBETAM. The triple attention mechanism BETAM is able to capture feature dependencies in three dimensions: position, space, and channel, respectively. Through feature importance weighting, modeling of spatial relationships, and adaptive learning capabilities, the model BETAM pays more attention to edge features, thus improving the accuracy of edge detection. A remote sensing image dataset SMCD (Subject Meticulous Categorization Dataset) is constructed to verify the robustness of the attention mechanism BETAM and the model DeepBETAM. Extensive experiments were conducted on the two self-built datasets FRSID and SMCD. Experimental results showed that the mean Intersection over Union (mIoU), mean Pixel Accuracy (mPA), and mean Recall (mRecall) of DeepBETAM are 63.64%, 71.27%, and 71.31%, respectively. These metrics are superior to DeeplabV3+, DeeplabV3+(SENet), DeeplabV3+(CBAM), DeeplabV3+(SAM), DeeplabV3+(ECANet), and DeeplabV3+(CAM), which are network models that incorporate different attention mechanisms. The reason is that BETAM has better edge segmentation results and segmentation accuracy. Meanwhile, on the basis of the self-built dataset, the four main classifications of buildings, cultivated land, water bodies and vegetation were subdivided and detected, and good experimental results were obtained, which verified the robustness of the attention mechanism BETAM and the model DeepBETAM. The method has broad application prospects and can provide favorable support for research and application in the field of surface classification.

Beetle assemblage distribution along edge–forest gradient in a managed oak forest

Abstract Forest management practices creating small‐scale forest edge habitats in managed forests have substantial impacts on the formation of beetle assemblages. In 2021, we conducted research in a managed oak woodland in Central Slovakia using three flight interception traps (FITs) to study beetle assemblages. Two FITs were installed at the forest edge, facing both, the clearing and the closed‐stand forest, while the third FIT was positioned 100 m within the closed‐canopy forest. We employed a novel methodological approach, which involved separately collected subsamples within the ecotone using FITs differentiated as ‘ecotone inwards’ and ‘ecotone outwards’ to capture distinct directional movements at the edge. We recorded 5.997 beetle specimens, encompassing 417 species across 284 genera and 58 families. We found that the species diversity and abundance of beetles were not significantly different between the FITs. However, the species composition was significantly different. The partial detrended canonical correspondence analysis suggests that these compositional differences might be closely associated with the distribution of trophic guilds, indicating varied responses to habitat modifications induced by forest edge creation. The results of our study showed that phloephagous, saproxylophagous, xylomycetophagous, xylophagous and zoophagous beetles exhibited a positive association with the forest edge, while mycophagous, phytophagous and saprophagous groups were distinctly associated with the closed‐canopy forest. Our analyses indicated that the ‘ecotone outward’ part of forest edges of managed forest may attract higher number of trophic groups, while the ‘ecotone inward’ part of the forest edge hosted high abundances of phloephagous and xylomycetophagous species.

A Machine-Learning-Assisted Classification Algorithm for the Detection of Archaeological Proxies (Cropmarks) Based on Reflectance Signatures

The detection of subsurface archaeological remains using a range of remote sensing methods poses several challenges. Recent studies regarding the detection of archaeological proxies like those of cropmarks highlight the complexity of the phenomenon. In this work, we present three different methods, and associated indices, for identifying stressed reflectance signatures indicating buried archaeological remains, based on a dataset of measured ground spectroradiometric reflectance. Several spectral profiles between the visible and near-infrared parts of the spectrum were taken in a controlled environment in Cyprus during 2011–2012 and are re-used in this study. The first two (spectral) methods are based on a suitable analysis of the spectral signatures in (1) the visible part of the spectrum, in particular in the neighborhood of 570 nm, and (2) the red edge part of the spectrum, in the neighborhood of 730 nm. Machine learning (decision trees) allows for the deduction of suitable wavelengths to focus on in order to formulate the proposed indices and the associated classification criteria (decision boundaries) that can enhance the detection probability of stressed vegetation. Noise in the signal is taken into account by simulating reflectance signatures perturbed by white noise. Applying decision tree classification on the ensemble of simulations and basic statistical analysis, we refine the formulation of the indices and criteria for the noisy signatures. The success rate of the proposed methods is over 90%. The third method rests on the estimation of vegetation/canopy reflectance parameters through inversion of the physical-based PROSAIL reflectance model and the associated classification through machine learning methods. The obtained results provide further insights into the formation of stress vegetation that occurred due to the presence of shallow buried archaeological remains, which are well aligned with physical-based models and existing empirical knowledge. To the best of the authors’ knowledge, this is the first study demonstrating the usefulness of radiative transfer models such as PROSAIL for understanding the formation of cropmarks. Similar studies can support future research directions towards the development of regional remote sensing methods and algorithms if systematic observations are adequately dispersed in space and time.

Research on the Mechanism of Frost Heaves Caused by Void Water Accumulation behind the Lining of High-Speed Railway Tunnels in Cold Regions

(1) Background: Lining voids and macroscopic water freezing are both prominent issues that threaten driving safety in high-speed railway tunnels. With the continuous expansion of the scale of high-speed rail tunnels in extremely cold areas in China, the issues of lining voids, water accumulation, and frost heaving have triggered heated discussions. The need to reveal the frost-heave mechanism is urgent. (2) Methods: Firstly, a simulation experiment of the frost heaving of accumulated water was carried out based on the discharge conditions of accumulated water. Secondly, a numerical model was used to study the evolution process of frost heaves caused by accumulated water in voids. Finally, a drainage coefficient was introduced to propose a method for calculating the frost-heave force of accumulated water in voids. (3) Results: The blockage of the drainage channel leads to the generation of frost-heave force; the freezing/thawing process of the void water develops from the thinnest part to the thickest part of the void edge, and the freeze/thaw cycle of the water body causes the frost-heave force to become greater and greater in the evacuated cavity. The higher the height and the closer the drainage channel is to the hollow bottom, the greater the frost-heave force when the accumulated water freezes. (4) Conclusions: When the evacuated water freezes, it develops from the thinner edge to the thicker center. The magnitude of the frost-heave force is affected by the freeze/thaw cycle, the height of the evacuated cavity, and the position of the drainage channel.

Intent-driven orchestration of serverless applications in the computing continuum

Following the emergence of Internet of Things (IoT) and edge computing technologies and the development of distributed applications with strict Quality of Service (QoS) requirements, a transition is underway from centralized computing models to distributed computing continuum systems. The latter provide abstractions of the available resources, while they lead to the development of orchestration mechanisms with increased distributed intelligence and autonomy characteristics. These characteristics make the computing continuum suitable for managing serverless computing applications, considering their loose coupling with the resources and the need for reactiveness and efficiency to serve dynamic workloads in the edge and cloud part of the infrastructure. This paper presents an intent-based approach for the orchestration of a function chain over an edge and cloud infrastructure. We propose a Reinforcement Learning (RL) based autoscaling method that incorporates a reward function that aims to provide optimal utilization of resources and timely autoscaling of the functions, taking advantage of methods for workload prediction. Based on a high-level intent, the proposed optimization problem dictates the scheduling of the functions, minimizing the communication overhead and transformation cost while also considering the energy efficiency of the infrastructure. The proposed solution is evaluated with other state-of-the-art techniques for autoscaling and scheduling of serverless functions in a small edge infrastructure. Our solution provides 1.52% QoS violations with a slight increase in the deployed resources, while also significantly reducing the total cost and power consumption for the deployment of the function chain.

Implementing and evaluating a new Silent Rank Attack in RPL-Contiki based IoT networks

Abstract IoT networks are witnessing a rapid growth in various domains of our daily life, offering more attractive features in terms of measurement accuracy, easy implementation and affordable deployment costs. This outstanding boom is not undoubtedly far away from different challenging issues that impede the network efficiency and quality. The security concern remains one among the prominent issues that affect both the edge and the core IoT network where risks increase in conjunction with the network expansion. RPL is the well-known routing protocol for the edge part of the IoT network, intended to meet the requirements of the constrained IoT devices. Despite its various advantages, RPL remains suffering from various security attacks targeting the topology, the traffic, and the nodes resources. Our work presents a new silent decreased rank attack against RPL-Contiki, as well as a lightweight countermeasure. The obtained results on a random studied topology show that almost half the existing nodes in the topology were attracted by the planted malicious node, through its falsified low rank. Moreover, an increase of 12.5% in the control overhead and an increase of 15% in the total consumed energy are recorded compared to the attack-free topology. On the other hand, the attack did not heavily affect the PDR, but the latency showed an increase of 45% compared to the attack free case. This damaging effect makes this modified rank attack a serious threat to IoT RPL based networks.

Non-Boussinesq effect on the internal solitary wave propagation under a Lagrangian-like description for various pycnocline thickness conditions

The Dubreil–Jacotin–Long (DJL) equation is an exact model for the steady propagation of internal solitary waves (ISWs) under Boussinesq approximation. The aim of this study is to discuss the non-Boussinesq effect on ISWs initialized by DJL equation for various pycnocline thickness conditions. The simulated results show that the non-Boussinesq effect can indeed lead to instability of DJL equation, and the pycnocline thickness will affect the amplitude of re-stable ISWs and the separation process of tail waves. Moreover, in order to extract features of the non-Boussinesq instability and the location of the tail wave separation, we propose an effective analytical approach for the quasi-traveling wave problem inspired by the definition of classical Lagrangian coherent structure (LCS). The improved finite-time Lyapunov exponent with a moving window (FTLEmw) is a Lagrangian-like description with considering the prior information of physical phenomenon. For a steady ISW propagation in the Boussinesq system, the repelling and attracting FTLEmv fields are symmetrical and the field ridge behaves as a continuous spiral topology, which suggests that particles on different streamlines in the flow structure have different rotation periods, while the streamlines are not actually completely closed due to the forward propagation of the wave. During the main unstable stage of non-Boussinesq effect, the topology of separated tail wave which is disconnected from the ISW main part can be clearly distinguished. The tail wave separation locations identified by the improved FTLEmv field and the classical FTLE field are not the same, which illustrates the difference between the two perspectives of flow structure change and fluid particle motion. In addition, the leading edge, trailing edge and main part of ISWs will gradually be divided into three-part FTLEmv topologies in the thicker pycnocline conditions.

Effect of laser irradiance on opto-electrical properties of PVA embedded graphene copper ferrite nanocomposite strips

The polyviny alcohol (PVA) nanocomposite strips embedded with graphene nanosheets and copper-ferrite nanoparticles were synthesized using solution casting technique. Each synthesized strip was exposed to Q-switched Nd-YAG laser irradiations using three different laser pulse energies of 19 mJ/cm2, 25.5 mJ/cm2 and 31.8 mJ/cm2 for 12 min. The synthesized irradiated and non-irrediated strips were characterized using X-ray diffraction, scanning electron microscopy, energy dispersive X-ray spectroscopy, Fourier transform infrared spectroscopy, UV–visble spectroscopy and frequency response analyzer to investigate the effects of nanofillers incorporation and laser pulse irradiations on structural, optical and electrical modifications. A small incorporation of each nanofiller (0.25 wt %) into the PVA matrix and subsequent laser irradiations have interestingly tuned the structural, optical, and electrical properties including direct bandgap, indirect bandgap, absorption edge, Urbach energy, extinction coefficient, refractive indices, optical conductivity, real and imaginary part of optical dielctric constant, dielectric loss and dielectric constant. The synthesized nanocomposite strips might find their potential in optoelectronic devices.

Технология инъекционной изоляции пластовых выработок угольных шахт

The basic composition of technological suspension (TS) for injection grouting of the edge part of the coal seam has been developed. The investigated water-solid TS actively show structural characteristics at different velocities of flat flow in the fracture. An increase in the TS concentration and introduction of ash-slag into the composition provides nonlinear growth of dynamic (ultimate) shear stress τ0 and is accompanied by a jump of its value in the range of water-solid ratio from 0.35 to 0.4. The schemes of grouting operations for different categories of coal seams stability have been developed, which differ in the application of spray-insulating coating, collector connection scheme and the evacuation system for the injection boreholes. Monitoring of the operating parameters with current operational assessment of the massif fracturing degree allows to control the flow movement and the TS injection pressure, reducing the solution losses while improving the quality of injection isolation.

Transverse mechanical properties and microstructure of 1500 mm wide AZ31 magnesium alloy plate and its evolution during industrial rolling

The 1500 mm-wide AZ31 magnesium alloy plate is an important material for the molding of large shell components.In the industrial rolling process, the wider the plate, the more uneven the temperature and stress distribution are in the width direction during the industrial rolling process, which results in poor mechanical properties and an uneven microstructure. In this paper, the mechanical properties and microstructure evolution along the width direction during a single-pass rolling process (6–3 mm) of this plate were qualitatively and quantitatively investigated. Additionally, a cellular automaton (CA) model was used to reproduce the grain distribution along the width direction. The result shows that there is a significant difference in the elongation of different parts of the plates along the wide direction, and the elongation of the center part is significantly higher than the edge, which is related to the uneven microstructure distribution. The average grain size from the edge to the center shows a tendency of increase and then decrease, and the degree of grain refinement has increased. The number of twins increased in the edge part, showing a typical mixed grain state with poor unevenness. Further CA shows that the average grain size and percentage of dynamic recrystallization increase and then decrease from the center to the edge. The parts 0–250 mm and 625–700 mm from the edge are fine-grained (average grain size less than 1 μm) and account for about 46 % of the plate. These parts are preferred for the subsequent further processing of rolled plates.

SD-AETO: Service-Deployment-Enabled Adaptive Edge Task Offloading Scheme in MEC

In recent years, edge computing, as an important pillar for future networks, has been developing rapidly. Task offloading is a key part of edge computing that can provide computing resources for resource-constrained devices to run computing-intensive applications, accelerate computing speed and save energy. An efficient and feasible task offloading scheme can not only greatly improve the quality of experience (QoE) but also provide strong support and assistance for 5G/B5G networks, the industrial Internet of Things (IIoT), computing networks, etc. To achieve these goals, this paper proposes an adaptive edge task offloading scheme assisted by service deployment (SD-AETO) focusing on optimizing the energy utilization ratio (EUR) and the processing latency. In the pre-implementation stage of the SD-AETO scheme, a service deployment scheme is invoked to assist with task offloading considering each service’s popularity. The optimal service deployment scheme is obtained by using the approximate deployment graph (AD-graph). Furthermore, a task scheduling and queue offloading design procedure is proposed to complete the SD-AETO scheme based on task priority. The task priority is generated by corresponding service popularity and task offloading. Finally, we analyze our SD-AETO scheme and compare it with related approaches, and the results show that our scheme has a higher edge offloading rate and lower resource consumption for massive task scenarios in the edge network.

Improved SOI-MESFET structures for enhanced efficiency and optimized DC/RF characteristics

ABSTRACT In this paper, two new silicon on insulator metal-semiconductor field effect transistor (SOI-MESFET) structures are presented. Two parallel layers of oxide and aluminium are added at the gate edge of these structures. Also, in the buried oxide part of the Aluminium Edge and Silicon-Well MESFET (AESW-MESFET) structure, a silicon well and two aluminium layers are added. Moreover, and to improve the DC and RF characteristics, as compared to the Conventional MESFET (C-MESFET) structure, a silicon well and a silicon layer are added in the box oxide section in the Silicon Edge and Silicon-Well MESFET (SESW-MESFET) structure. By these changes, the value of the breakdown voltage in the normal structure has increased from 15.8 V to 33.1 V and 30.9 V in the proposed AESW-MESFET and SESW-MESFET structures, respectively. In addition, the maximum output power has been associated with a significant increase of 4.44 and 5.24 times, respectively. Compared to the C-MESFET, the proposed structures reduce gate-source and gate-drain capacitors and significantly increases conductivity. The cut-off frequency values are increased from 19.3 GHz (the normal structure) to 37.3 GHz and 35 GHz (the proposed structures), and the maximum oscillation frequencies are increased from 80 GHz to 154 GHz and 102.3 GHz. Therefore, the results show that the proposed structures have good performance and the ability to work at high power and high frequency.

Domination and effect of multi-parameters in direct chill casting based on establishment of thermo model by numerical simulation and experiment

In this paper, a mathematical model validated by in-situ temperature measurement data of the direct chill (DC) casting process has been established in order to study the domination and effect of multi-parameters including secondary cooling water flow rate (Q), casting speed (V), condition of primary cooling, and spraying water temperature (Tw) on DC casting index of sump depth (dsump), maximum and minimum temperature gradient (Tg,max and Tg,min) at mushy zone and shape of solidus line. Further, the height of impingement zone (Hip) is systematic quantitative analysis by a novel designed test (Hip=1.08865Q`+0.06272V−4.75597). The calculated temperature using the numerical simulation exhibits a good match with the actual temperature measurement data. The results of multi-factors on index by the designed orthogonal test indicate that V dominates the dsump (dsump=0.033+27.74V) and Tg,min (Tg,min=2569.15−766342.20V) at mushy zone, Q dominates the Tg,max (Tg,max=9195.12+2344770Q−27.83Tw) by analysing the range (R), variance (S) and significance (F). Likewise, by discussing the combination of R, S, F and microstructure observation, the priority influencing the shape of solidus line at center part was V. The Q dominates the shape of solidus line at the position between the center and edge part.

Numerical studies on applications of cavitation models in water hammer-induced cavitating flows in pipelines

Hydraulic transients caused by a fast-closing needle valve and accompanying water hammer-induced cavitation-vortex flow are numerically investigated using the shear stress transport (SST) k–ω model with a pressure–density equation considering the weak compressibility of water and the new cavitation models based on the Rayleigh–Plesset equation that takes into account the surface tension and kinematic viscosity effect on the bubble radius change rate, respectively. The predicted pressure evolutions exhibit reasonable agreement with the experimental data, and the interfacial surface tension-based model is the most accurate in predicting transient pressure evolutions, followed by the kinematic viscosity-based model and the Singhal cavitation model, based on three proposed error indices with minimum of 0.0515, 0.1337, and 0.0035. The wave speed in the upper part of the cavitation trailing edge is higher than that in the lower part, resulting in the evolution of inclination angle of the trailing edge. According to the convective transport mechanism of vorticity, the change rate of vorticity in non-cavitation regions is mainly dominated by the stretching–twisting term and the turbulent fluctuation–diffusion term. In cavitation regions, the dilatation–shrink term makes significant contributions to the change rate of vorticity, and the baroclinic-torque term mainly acts near the edge of the cavitation region.

Preparation and characterization of large-sized CdZnTe epitaxial single crystal

CdZnTe (CZT) epitaxial single crystal with a diameter of 2 inches and a thickness exceeding 1 mm was prepared by the closed spaced sublimation. In order to test the uniformity of the large-sized CZT epitaxial single crystal, the composition, crystalline quality, X-ray response performance and carrier transport property of the central and edge parts of the CZT crystal were characterized. The morphology images show that different areas have flat surfaces with dislocation densities between 3.5 × 104 cm−2 and 4.5 × 104 cm−2, respectively. The crystal quality shows a trend of better in the central region than in the edge. The bandgap is between 1.56 eV and 1.57 eV, and the resistivity is between 5 × 109Ω cm and 6 × 109Ω cm. The sensitivities of the detectors prepared with the edge and central areas to X-ray is 276.22 μCGy−1 cm−2 and 283.43 μCGy−1 cm−2, respectively. The carrier lifetime products of the edge and central parts were tested to be 3.932 × 10−4 cm 2 V−1 and 4.511 × 10−4 cm2 V−1, by fitting the Hecht equation. The lower (μτ)e values of the edge part may be higher dislocation density, which has a great impact on the transport process of charge carriers, as demonstrated by the photoluminescence (PL) results. The fabricated detectors have a stable response to 241Am@59.5 KeV gamma-ray, which shows great potential for future applications in the field of photon counting imaging.

Black hole horizon edge partition functions

We extend a formula for 1-loop black hole determinants by Denef, Hartnoll, and Sachdev (DHS) to spinning fields on any (d + 1)-dimensional static spherically symmetric black hole. By carefully analyzing the regularity condition imposed on the Euclidean eigenfunctions, we reveal an unambiguous bulk-edge split in the 1-loop Euclidean partition function for tensor fields of arbitrary integer spin: the bulk part captures the “renormalized” thermal canonical partition function recently discussed in [1]; the edge part is related to quasinormal modes (QNMs) that fail to analytically continue to a subset of Euclidean modes with enhanced fall-offs near the origin. Since the edge part takes the form of a path integral on Sd−1, this suggests that these are associated with degrees of freedom living on the bifurcation surface in the Lorentzian two-sided black hole geometry. For massive higher spin on static BTZ and massive vector on Nariai black holes, we find that the edge partition function is related to the QNMs with lowest overtone numbers.

The relationship between lowering the Earth's surface and bearing pressure above the advancing longwall face

This work aims to develop a method for determining the increase in stresses above an advancing longwall face of Western Donbas mines. The paper presents a solution to the problem. It is based on the analysis of geodetic instrumental observations of the earth's surface lowering and rock mass deformation above the advancing longwall face. Length and propagation in the roof and floor of the extracted seam are the main geometrical parameters of the zone of high rock pressure. Currently, the quantitative parameters of this zone are not considered. And its length under the conditions of Western Donbas is determined with an accuracy of 50%. Thus, research in this direction is relevant. The experimental basis for the research includes the results of observations performed at two vertical borehole extensometers and the results of data processing obtained at more than 30 observation stations on the Earth's surface. Thus, the research specified the geometrical parameters of the zone of high rock pressure and the nature of the vertical stress distribution within this zone. The paper introduces a method to determine a coefficient of stress increase above the advancing longwall face of Western Donbas mines. We also established the empirical coefficients of the vertical stress distribution function within the abutment pressure zone. There is a relationship between the lowering of the earth's surface and the values of the stress increase in the borehole edge part. The reliability of the obtained results is confirmed by geophysical studies in Western Donbas, as well as by the results of field observations.

Edge-Preserved Low-Rank Representation via Multi-Level Knowledge Incorporation for Remote Sensing Image Denoising

The low-rank models have gained remarkable performance in the field of remote sensing image denoising. Nonetheless, the existing low-rank-based methods view residues as noise and simply discard them. This causes denoised results to lose many important details, especially the edges. In this paper, we propose a new denoising method named EPLRR-RSID, which focuses on edge preservation to improve the image quality of the details. Specifically, we considered the low-rank residues as a combination of useful edges and noisy components. In order to better learn the edge information from the low-rank representation (LRR), we designed multi-level knowledge to further distinguish the edge part and the noise part from the residues. Furthermore, a manifold learning framework was introduced in our proposed model to better obtain the edge information, as it can find the structural similarity of the edge part while suppressing the influence of the non-structural noise part. In this way, not only the low-rank part is better learned, but also the edge part is precisely preserved. Extensive experiments on synthetic and several real remote sensing datasets showed that EPLRR-RSID has superior advantages over the compared state-of-the-art (SOTA) approaches, with the mean edge protect index (MEPI) values reaching at least 0.9 and the best values in the no-reference index BRISQUE, which represents that our method improved the image quality by edge preserving.