esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Controller Deployment in SDN-Enabled 6G Space-Air-Ground Integrated Network
## 1 Introduction
### In Terms of Multi-Controller Deployment Strategies  
Researchers focused on optimizing network and controller loads in [27; 28; 29]. [PERSON], [PERSON], et al. in [30] abstract the controller deployment problem as an NP-hard problem, and propose a learning automata-based heuristic with the goal of minimizing the propagation delay between switches and controllers in the network. This is a static deployment, with no consideration for topo dynamics. [PERSON] in [31] and [PERSON] et al. in [32] aimed to minimize the propagation delay of switches and controllers. The former propose an SDN-enabled SAGIN architecture, and optimize the K-means algorithm to solve the controller deployment problem of the dynamic topology network composed of UAV in the air-based domain, hereinafter referred to as n-k-means. The latter proposed a clustering algorithm based on improved density peaks to solve the multi-controller deployment problem. The algorithm has achieved significant results in optimizing the delay, but the load of a single controller cannot be controlled, which is a disaster for the scarcity of satellite resources. [PERSON], [PERSON], et al. in [26] proposed a strategy to migrate switch nodes under the administration of an idle controller and high-load controller to an adjacent low-load controller (hereinafter referred to as CCLDS) to reduce network delay and improve network throughput, considering the large load difference between SDN-based satellite network controllers. The load balancing effect of the controller is significant, but the disconnection and connection process between the switch node and the controller is time consuming, resulting in a delay that is not well controlled. [PERSON], [PERSON], et al. in [33] considered the routing times of a switch controller to the switch of the routing number and the number of routes between controllers, based on the load balancing factor of the controller, by minimizing the cost factors and traffic request of the linear function, which determines the location of the controller and the change in the load factor. However, the disadvantage of this is, in the case of given a network topology, he could not determine the number of controllers. To sum up the previous research on multi-controller deployment strategies based on SDN, there are mainly several problems: whether to consider the dynamics of network topology, how to consider both network delay and controller load, and whether to have a low-time-complexity algorithm. In response to these issues, specific solutions are given in the following chapters of this paper.
# Trace Elements and Mineralogy of Upper Permian (Zechstein)
## 1 Introduction  
New technologies intensively use several trace elements, e.g., lithium (Li), strontium (Sr), rubidium (Rb), boron (B), caesium (Cs), or gallium (Ga), which are considered in the world as strategic and critical ore materials [1; 2; 3; 4; 5; 6]. These elements are incorporated within many minerals, some of which occur in evaporitic deposits, such as rock and potash salts and sulphates. These sediments, and the recent and past environments of their accumulation, are in the interest of many modern countries e.g., China, the USA, and the European Union (e.g., [7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18]).  
Nowadays, a large lithium volume is obtained due to the processing of brines and salts from intracontinental saline lakes in two giant salars of Bolivia [19; 20; 21]: Salar de Uyuni and Salar de Coipasa, with lithium reserves estimated at 21 mln tonnes (23.6% of the world reserves in 2021 [22]). Brines of the Salar de Atacama in Chile, which is two times smaller, contain 1000-7000 mg/L [23] or 1000-5000 mg/L of lithium (average--1400 mg/should be \"L\" - the symbol of \"litre\"), and in 2017 they represent 27% of the world reserves [6; 24; 25]. Brines of salars in Altiplano have from 57 to 570 mg/L up to 8.9 g/L of lithium and 10.8 g/L of boron [26; 27; 28; 29]. Brines of Quarham saline lake (Quaidam Basin, western China) contain 51.6-138.4 mg/L of boron [30].  
Poland has large documented and predicted salt reserves, mainly of the Upper Permian (Zechstein) age [31]; therefore, it is a real necessity to estimate their efficacy as an economic source of the required trace elements. So far, geochemical studies of salt deposits are limited to determining the content of the main elements and components (e.g., [32; 33; 34; 35; 36; 37; 38]) e.g., sodium (Na), calcium (Ca), magnesium (Mg), potassium (K), chlorine (Cl), bromine (Br), and iodine (I), which are significant in rock classification for human consumption, roadsalt, or fertiliser. First analyses of selected trace elements (Br, B, I, Li, Cs, Co, Cr, Fe, Mn, Ni, Rb, Sr, V, and Cu) content in various evaporitic rocks of the Permian and Neogene age from Poland were done from the late 1980s [39; 40; 41; 42; 43; 44; 45; 46] to the early 2000's [47; 48]. Over the last twenty years, such geochemical studies were conducted on some Upper Permian salts from salt domes (e.g., [49; 50; 51; 52; 53]).  
This paper presents an archive and published data on the content of selected trace elements in the Upper Permian (Zechstein) potash-bearing deposits in Poland, as well as reports new data. The geochemical characteristics of the analysed evaporites were supplemented with the description of the main minerals detected, some of them being responsible for a higher content of several elements in the rocks.
# Real-Time Motion Blur Using Multi-Layer Motion Vectors
## 2 Motion Blur with Multilayer Images
### Motion Blur Generation Using Multi-Layer Image
#### 2.2.1 Calculating Search Area  
To calculate the motion-blurred color of a pixel we should find pixels that pass through the current pixel between \(T-1\) and \(T\). Rather than checking all pixels, we calculate the search area based on the average and standard deviation of motion vectors. If a mipmap level n is read from a pixel, it is an average of \(2^{n}\times 2^{n}\) pixels centered on that pixel [16; 17]. The standard deviation is then calculated as the square root of the difference between the average of the squared motion vector and the square of the average of the motion vector. In Figure 4, we illustrate the search area(blue box) generated according to the motion vector map.
## 3 Results and Analyses  
### Topographic and Vegetation Heterogeneities at Two Scales  
Figure 3 shows the spatial distributions of topographic factors and the LAI on Julian day 193 at 500 m and 1 km resolutions. Although the values of these variables at two resolutions presented similarity on the whole distributions, the spatial variations at a 500 m resolution are more pronounced than those at a 1 km resolution.  
The statistics of the topographic factors and the LAI on Julian day 193 at 500 m and 1 km are listed in Table 1. The altitude at a 500 m resolution varied between a minimum of 2606 m and a maximum of 4744 m; at a 1 km resolution, the altitude varied between a minimum of 2647 m and a maximum of 4671 m. The mean altitudes at the two resolutions are similar, while the range and deviation in altitude at 500 m is larger than that at a 1 km resolution. The slope had a maximum of 38\({}^{\circ}\) at a 500 m resolution, which varied from the 28\({}^{\circ}\) maximum at a 1 km resolution. The mean slope value at 500 m (18\({}^{\circ}\)) was greater than that at a 1 km resolution (12\({}^{\circ}\)), showing that there was a considerable loss of topographic information at a coarse resolution. The statistical results also show that the LAI on Julian day 193 at 1 km had a smaller deviation than that at 500 m, and the mean value of the LAI at 500 m (7.0 m\({}^{2}\) m\({}^{-2}\)) was greater than that at a 1 km resolution (5.7 m\({}^{2}\) m\({}^{-2}\)). In addition, the aspect at 500 m had a similar range, mean value and deviation to those at 1 km.
This method has proven effective in mitigating temporal feature instability, thereby enhancing the overall feature information available for EMG signal decoding [20; 21]. EMG signals' capability to classify human actions hinges on information gathered from various anatomical locations and the intrinsic features embedded within the signals. However, when information acquisition is restricted to a single channel, as noted in [PERSON]'s seminal work on motor control in 2004 [23], limitations arise. Each sEMG channel corresponds to the collective activity of numerous muscles, making it imperative to augment the number of channels. This augmentation allows for a finer dissection of the information contained within an individual channel, resulting in a more abundant resource of spatial and temporal information for subsequent feature extraction. This, in turn, contributes to the effectiveness and precision of EMG signal decoding. While current methods have yielded promising results, they often overlook the spatial characteristics inherent in EMG channels. In this paper, we introduce a novel Space-to-space (SPS) framework for sEMG feature extraction. By adopting a spatial perspective, we integrate inter-channel correlations and employ a multi-view feature fusion approach to decode electromyographic signals for pattern recognition. Our model exhibits a significant performance improvement when compared to conventional methods. Our contribution to this work can be summarized as follows:  
* This paper designs a new method for processing sEMG signals, which is based on expanding the original EMG signal segments in the space domain, thus releasing more feature information within a single channel to improve the accuracy of gesture action classification. * A novel framework for sEMG-based gesture classification is established using inter-channel correlation (ICC) matrix representation extracted by the spatial sEMG information. * Multi-information fusion involving time-domain and frequency-domain feature information with ICC matrix is implemented to build a pattern-recognition framework composed of multiple information. * The effectiveness of the proposed approach is verified on hand gestures of six public datasets with various sEMG sensors and data formats, and a comprehensive analysis of the performance of multiple feature extraction combinations is completed.
# A Study on the Roof-Cutting and Pressure Releasing Technology of Roof Blasting
## 3 Simulation of Hard Roof Blasting
### Analysis of Simulation Results
#### 3.3.2 Failure Zone Analysis of Rock Elements  
In this discussion, a model with a charge diameter of 60 mm is used as an example to analyze the variations of the equivalent stress of the rock after the main charge detonation. Six rock units were selected along the center of the borehole with an interval of 0.5 m (from monitoring point to borehole distance), as shown in Figure 2. The monitoring data are shown in Table 4. The shock wave arrived at element A, the equivalent stress reached 11.65 GPa, and the element was completely destroyed. Then, the shock wave continued to propagate; the equivalent stress first decreased, then rose and finally decreased, and the amplitude of the last stage decreased with the increase of distance, which can reflect the gradual decrease of the damage degree of the rock mass.  
Figure 4 and Table 4 indicate that the radius of the crushing zone increases with the enlargement of the diameter of the charge. The radius of the crushing zone is five times the diameter of the charge borehole, equivalent to the cylindrical charge's theoretical calculation value. Furthermore, the rock can be broken under the combined action of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Unit Number** & **Distance (m)** & **Peak Stress (MPa)** &
\begin{tabular}{c} **Equilibrium Equivalent** \\ **Stress (MPa)** \\ \end{tabular} \\ \hline A & 0.5 & 11.65 & 9.69 \\ B & 1.0 & 13.40 & 9.65 \\ C & 1.5 & 14.86 & 9.63 \\ D & 2.0 & 16.74 & 9.62 \\ E & 2.5 & 10.39 & 9.58 \\ F & 3.0 & 10.18 & 9.56 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Equivalent stress and distance relationship of rock unit.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Density** & \(C_{0}^{\prime}\) & \(C_{1}^{\prime}\) & \(C_{2}^{\prime}\) & \(C_{3}^{\prime}\) & \(C_{4}^{\prime}\) & \(C_{5}^{\prime}\) & \(C_{6}^{\prime}\) & \(E_{\xi}\) & \(V_{0}\) \\ (g/cm\({}^{3}\)) & (Mpa) & (Mpa) & (Mpa) & (Mpa) & (Mpa) & (Mpa) & (Pa) & (Pa) & \(V_{0}\) \\ \hline
1.29 & 0 & 0 & 0 & 0 & 0.4 & 0.4 & 0 & 2.5 \(\times\) 10\({}^{5}\) & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Air material parameters.  
impact pressure and detonation gas, as long as the impact pressure exceeds the dynamic compressive strength. Under the premise of the same non-coupling index, the scope of the crushing zone increases with the increase of the diameter of the charge.  
The radius range of the fracture zone and crushing area of each group of models were extracted and are shown in Figure 3. The relationship curve of the charge diameter is shown in Figure 4.  
Figure 4: The variation law of the rock damage zone with the diameter of the charge.  
Figure 3: Stress nephograms of different diameters of charge at \(t\) = 1.0 ms.
# Modeling Network-Controlled Device-to-Device Communications in SimuLTE
###### Abstract  
In Long Term Evolution-Advanced (LTE-A), network-controlled device-to-device (D2D) communications allow User Equipments (UEs) to communicate directly, without involving the Evolved Node-B in data relaying, while the latter still retains control of resource allocation. The above paradigm allows reduced latencies for the UEs and increased resource efficiency for the network operator, and is therefore foreseen to support several services, from Machine-to-machine to vehicular communications. D2D communications introduce research challenges that might affect the performance of applications and upper-layer protocols, hence simulations represent a valuable tool for evaluating these aspects. However, simulating D2D features might pose additional computational burden to the simulation environment. To this aim, a careful modeling is required to reduce computational overhead. In this paper, we describe our modeling of network-controlled D2D communications in SimuLTE, a system-level LTE-A simulation library based on OMNeT++. We describe the core modeling choices of SimuLTE, and show how these allow an easy extension to D2D communications. Moreover, we describe in detail the modeling of specific problems arising with D2D communications, such as scheduling with frequency reuse, connection mode switching and broadcast transmission. We document the computational efficiency of our modeling choices, showing that simulation of D2D communications is not more complex than simulation of classical cellular communications of comparable scale. Results show that the heaviest computational burden of D2D communication lies in estimating the Sidelink channel quality. We show that SimuLTE allows one to evaluate the interplay between D2D communication and end-to-end performance of UDP- and TCP-based services. Moreover, we assess the accuracy of using a binary interference model for frequency reuse, and we evaluate the trade-off between speed of execution and accuracy in modeling the reception probability.  
simulation; LTE; direct communication; D2D; SimuLTE 2018 18 3551 doi:10.3390/s18103551
# Determining Optimal Power Flow Solutions Using New Adaptive Gaussian TLBO Method
## 1 Introduction  
Performing power networks efficiently is one of the essential goals in the electricity sector. One of the most crucial tools for accomplishing this task is to use the optimal power flow (OPF) method to optimize the control variables of the power systems. Under various operating conditions in the power systems, it is crucial to find the optimal values for these variables to maintain secure and cost-effective operations. Meanwhile, OPF problems are complex, nonlinear, and nonconvex, with uneven and non-derivative components. As a result, traditional approaches cannot find suitable solutions. This, therefore, has always required a robust optimization approach, which has been realized and investigated [1]. The complexity of optimization problems across multiple disciplines cannot be solved using a universally applicable solution method in today's engineering environment. In recent years, hundreds of novel approaches have been proposed, and many have proven successful in resolving certain optimization challenges [2; 3; 4]. Over the past half-century, electrical engineering experts have focused their attention on the classic OPF problem. Its goals have evolved significantly over the years. By analyzing this problem and considering nonlinear constraints, the power of generation units and settings of controllable devicescan be controlled. Traditionally, linear, and nonlinear programming have been used to solve this problem. The problem's nonlinear, convex, and non-derivative nature has recently resulted in a trend towards new optimization methods. This study improves teaching-learning-based optimization (TLBO) to be more effective for a wide range of different OPF problems. Numerous constrained optimization issues have recently been addressed using population-based metaheuristic algorithms such as engineering design and operation. In power system operation, a significant concern is the optimal flow of power, which is one of the main issues addressed by many implementations of these algorithms.
## 8 Smart Grid Security  
Security of smart grids is very important since smart grid exchange power and information online using various technologies explained in earlier sections. A smart grid's vulnerabilities are more popular in smart meters, smart electronic systems, and IP-based components that are more likely to attract the fact that many stakeholders are involved [55]. Various organizations such as NIST, IEEE Power and Energy Society, IEC Smart Grid Standardization, and National Standard of the People's Republic of China [56] created several safety standards for smart grids. The authors of [57] conducted a study on cybersecurity challenges for IoT-based smart grid networks and the financial and economic losses from cyber-attacks. The multiple types of security threats to smart grids, such as physical attacks and cyber-attacks leading to a violation of consumer privacy, energy theft, power blackouts, infrastructure failure, and operating staff protection, are discussed in [58].  
The smart grid protection system literature is published in [30; 31; 59; 60; 61; 62; 63; 64]. Work in the literature includes cyber-security assessment criteria for smart grids, security risk assessment for smart-grid-enabled software-defined networking (SDN), location-based protection for smart grid applications, ortho-code privacy mechanisms in smart grids using ring communication architecture, integrated smart grid systems security threat models, cyber-security of a power grid, analysis of smart grid communication infrastructure and smart grid cyber-security, and smart grid information security for the protection of smart grid knowledge.
## 5 Simulation Results  
### Simulation Settings  
The simulations are conducted in a scenario where a UAV located in a hot cell captures images and performs distributed inference with a swarm of surrounding UAVs. The UAVs are assumed to perform the mission while maintaining a fixed height \(z=40\) m. The UAV swarm operate in a 100 m \(\times\) 100 m area, divided into 25 cells, with one of the 25 cells set as a hot cell. Three CNN models were employed in the simulation: AlexNet [33], VGG16 [34], and YOLO [35]. These models were selected because they are widely used in the object classification or object detection. The AlexNet and VGG16 were trained using the ILSVRC 2014 dataset [36], and YOLO was trained using the PASCAL VOC 2007 dataset [37]. The parameters and depths of these architectures are listed in Table 1. Here, M refers to one million. It is assumed that each UAV has two memory usage levels (low: 1 GB; high: 2 GB) and one computational capacity level (11.2 GFLOPS). The inoperable state parameter is set to be two, and the model is trained in an environment where zero to two UAV becomes inoperable during each episode. Parameters for training the proposed DRL-RLD model are listed in Table 2. The values of the training parameters were determined empirically through extensive trials. The bandwidth and transmission power were configured to assume communication channel within a low-power, resource-constrained environment, as typically encountered in disaster response and surveillance applications [26]. The noise power spectral density was selected based on the standard thermal noise level. The end-to-end latency is employed to evaluate the performance of the proposed DRL-RLD system. This evaluation metric measures the total time required for an image acquired in a hot cell to undergo distributed inference across the layers assigned to the UAV swarm and for the final output to reach the ground device.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Parameters** & **Depth** \\ \hline AlexNet [33] & 74.7 M & 8 \\ VGG16 [34] & 169.8 M & 16 \\ YOLO [35] & 295.5 M & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of CNN models.