esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Concept of Detection DDoS Attacks in SDN  
Distributed denial-of-service (DDoS) attacks are generally carried out by several machines. These attacks follow a similar pattern to a basic denial-of-service (DoS) attack. However, the use of multiple machines simultaneously as separate origins of attack amplifies the attack's impact, while making it challenging to locate the attackers. The attacker forms a network of machines, consisting of a master (Master) and many remote hosts (Slaves). During the course of the attack, the attacker connects to the master, which sends an order to all remote hosts. Then, these hosts attack the target using a technique chosen by the attacker [21].  
Defense mechanisms against DDoS attacks have become one of the most significant challenges in network security. Consequently, a large number of defense classifications and taxonomies have emerged. One important way to categorize defense options is through the main characteristics of their defense. This results in three main categories and policies: (1) Stopping attacks before they reach the target [22] with firewalls as an example of this prevention mechanism; (2) attacking detection through the identification of anomalies in the traffic entering the network; (3) identifying the attack's ultimate origin. This last technique is complicated by two aspects of the IP protocol [23]. First, it can be easy for an attacker to spoof source IP addresses. Second, one cannot know the full end-to-end path of a packet. SDN presents a solution to these shortcomings, thanks to its holistic view of the entire network. Additionally, SDN makes it possible to organize a set of OpenFlow switches through a single controller, allowing the centralization of the network control plane. This centralization makes it more viable to trace the end-to-end path of a packet, as the controller has a global vision of the network [24].  
Deep-learning (DL) algorithms are used for threat detection, bandwidth optimization, power efficiency, and network traffic management. In machine learning, data are of the utmost importance for decision-making, as opposed to specific conditions presented by the algorithm [25]. DL algorithms are classified into three types: Supervised, unsupervised, and reinforcement learning. In supervised learning, labeled data are utilized for classification and regression. Unsupervised learning focuses on the classification of unlabeled data into distinct classes [26]. This work is focused on investigating the use of two common deep-learning techniques, LSTM and CNN, and their integration with an autoencoder.
# Long-Distance Shield Tunnelling Performance Prediction Based on Informer
## 2 Methodology
### LSPP Model Design
#### 2.2.2 Data Preparation  
1. Selection of construction parameters  
There are many factors affecting the penetration in shield construction, including geological parameters, mechanical conditions, and tunnelling parameters [30]. Table 1 summarizes the parameters used in some literature on shield tunnelling performance prediction from 2021 to 2024. As seen from Table 1, many features affect shield tunnelling performance, more than thirty of them, and they are relatively scattered. To elucidate the salient influencing factors while circumventing the intricacies of the model, the present study methodically screens these factors from the perspectives of both theoretical underpinnings and engineering feasibility. _CS_, _CT_, _EP_, _SS_, and _CC_ are routine monitoring data during shield construction; _CS_ and _CT_ directly affect the cutting ability and rock breaking efficiency of the shield machine; and _EP_ directly reflects the force of the stratum on the shield machine, which affects the stability of digging and cutting efficiency, especially under complex geological conditions, and plays a significant role. _SS_ affects the efficiency of soil discharge, which is closely related to the stability and efficiency of the shield machine. _CC_ is an approximate characterization  
Figure 2: LSPP model framework. of \(CP\), which reflects the blade loading and cutting difficulty, and is an important index in evaluating the energy consumption and performance of shield tunnelling. In addition to these direct construction parameters, a number of calculation-based metrics better reflect the performance of shield tunnelling. \(PR\) and \(SE\) can reflect the comprehensive performance of the shield, which can be obtained through the calculation of conventional monitoring data. \(PR\) is calculated as shown in Equation (1), which reflects the penetration ability of the cutter plate of the shield machine to the stratum in the process of digging. \(SE\) synthesizes the thrust, torque, and radius of the cutter plate and quantifies the energy consumption required to excavate a unit volume of soil in units of \"MJ/m\({}^{3}\)\". Since it is not convenient to count the actual energy consumed and the volume removed by the shield in actual construction, this paper adopts an approximation of the calculation as shown in Equation (2), where \(R\) is the radius of the cutter head (in metres); \(TF\) is the total
## 1 Introduction
This paper presents a system that automatically recovers applications in a cluster. The system detects when a cluster loses functionality because of a disaster and automatically restores the services that were operating in the affected cluster to another cluster. Accordingly, even if a disaster occurs and the cluster loses functionality, automatic recovery is immediately initiated without the need for user intervention. Conventional disaster recovery involves delays due to human intervention [12], which can be minimized in an automated recovery, thus providing the advantage of faster recovery. Compared to manual recovery, an automated system responds immediately and quickly restores services to normal. In addition, it prevents mistakes that can occur during manual operations and ensures consistent recovery. Our research has two technical contributions. First, it automates the entire recovery process, i.e., from event detection, including disasters and attacks, to querying backup files, selecting clusters for restoration, and executing restoration tasks. To automate all these functions, the system integrates Kubernetes management platforms with Kubernetes backup and restoration tools. Thus, user-intervention time is eliminated. This automation prevents mistakes that can occur during manual operations because of the complexity of the recovery process and ensures consistent recovery. In addition, automated recovery uses pre-allocated resources to perform tasks most optimally, thereby improving the system's overall performance and stability. Second, when selecting a cluster for the restoration task, machine learning is used to predict the CPU usage required for selecting a cluster. While clusters can be selected using algorithms or rules, the time taken for restoration tasks must also be considered. Therefore, machine learning is used to predict the CPU usage of clusters to select the cluster for successful restoration. This minimizes service disruption with quick recovery times, positively affecting RTOs. Finally, the automated-recovery solution reduces management complexity and enhances the overall system stability by providing consistent recovery procedures across various systems or applications. This approach prevents mistakes that can occur during manual operations and ensures consistent recovery. The remainder of this paper is structured as follows. Section 2 reviews the current related research, highlighting the importance of recovery automation in cloud environments and presenting the unique aspects of our research. Section 3 describes the architecture of the proposed system, which integrates Kubernetes management platforms with backup and restoration tools. Section 4 introduces the data used in the experiments and explains the pre-processing methods used. Section 5 discusses the Long Short-Term Memory (LSTM) and how to train time-series data on the LSTM. Section 6 details the experimental environment and methods used. In Section 7, we present and analyze the experimental results. Finally, conclusions are provided in Section 8.
## 4 Discussion and Conclusions
Fine-tuning the proposed modified VGG19 improved the overall classification accuracy compared to that obtained from training the original VGG19 from random initialization. The overall accuracy increased from 83.76% using original VGG19 to 88.46% using transfer learning on the modified VGG19 although it was applied from non-medical data. This study is one of the few works dealing with CNV screening on OCTA data using only images at the choriocapillaris slab. Obtained results revealed a promising application of CAD systems to diagnose CNV on OCTA choriocapillaris images in clinical routine using DL-based methods. In order to produce more reliable results to clinicians and to help them quantify the CNN uncertainty, we aim to measure the CNN prediction uncertainty in further works to identify how much a CNN could be trusted in diagnosis [34, 35] and to avoid using images not suitable for diagnosis when high uncertainty is detected [35]. Further studies on this topic will focus on data augmentation, as well as including more CNV images to work on larger datasets and different imaging modalities to improve classification performance for ambiguous cases. Conceptualization, K.T., A.M., E.S., E.P. and Y.C. ; methodology, K.T. ; validation, E.S., E.P. and Y.C. ; investigation, K.T., A.M. and A.S.; resources, A.M., A.S. and E.S. ; writing--original draft preparation, K.T. ; writing--review and editing, K.T., A.M., E.S., E.P. and Y.C. ; supervision, E.S., E.P. and Y.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted according to the guidelines of the Declaration of Helsinki and current French legislation and with approval of our local ethics committee. The authors declare no conflict of interest. The authors declare no conflict of interest.
# Real-Time Object Detection Based on UAV Remote Sensing: A Systematic Literature Review
###### Abstract  
Real-time object detection based on UAV remote sensing is widely required in different scenarios. In the past 20 years, with the development of unmanned aerial vehicles (UAV), remote sensing technology, deep learning technology, and edge computing technology, research on UAV real-time object detection in different fields has become increasingly important. However, since real-time UAV object detection is a comprehensive task involving hardware, algorithms, and other components, the complete implementation of real-time object detection is often overlooked. Although there is a large amount of literature on real-time object detection based on UAV remote sensing, little attention has been given to its workflow. This paper aims to systematically review previous studies about UAV real-time object detection from application scenarios, hardware selection, real-time detection paradigms, detection algorithms and their optimization technologies, and evaluation metrics. Through visual and narrative analyses, the conclusions cover all proposed research questions. Real-time object detection is more in demand in scenarios such as emergency rescue and precision agriculture. Multi-rotor UAVs and RGB images are of more interest in applications, and real-time detection mainly uses edge computing with documented processing strategies. GPU-based edge computing platforms are widely used, and deep learning algorithms is preferred for real-time detection. Meanwhile, optimization algorithms need to be focused on resource-limited computing platform deployment, such as lightweight convolutional layers, etc. In addition to accuracy, speed, latency, and energy are equally important evaluation metrics. Finally, this paper thoroughly discusses the challenges of sensor-, edge computing-, and algorithm-related lightweight technologies in real-time object detection. It also discusses the prospective impact of future developments in autonomous UAVs and communications on UAV real-time target detection.  
U  
2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Intelligent Optimization of Process Parameters in Limit Gauge Hot Strip Rolling
## 5 Optimization of Process Parameters and Result Analysis
### Result Analysis
#### 5.2.2 Thickness of Intermediate Billet  
According to Figure 7a, in the rolling history samples of MRTRG00201_1276 strip, the thickness of the intermediate billet is mainly distributed around 43.7 mm, with the minimum of 43.70 mm and the maximum of 43.72 mm. The thickness of intermediate billet recommended by the model is 43.71 mm, which is at the position with the largest number of rolling samples, and conforms to the distribution law of rolling samples.  
Figure 6: Rolling rhythm. (**a**) Sample distribution; (**b**) Stability score.  
According to Figure 7b, the rolling stability score when the thickness of intermediate billet is 43.71 mm is significantly higher than that when other values are taken, while the optimal parameter obtained through model optimization is 43.71 mm, which can obtain the highest rolling stability.
# Introducing Cement-Enhanced Clay-Sand Columns under Footings Placed on Expansive Soils
## 4 Results and Discussion
### The Swelling Potential  
The swelling potential (SP), shown as a percentage, is the relationship between the sample's initial height (\(h\)) and its increased height (\(\Delta h\)) under the specified vertical stress (7 kPa, in this case) when the sample is entirely submerged. The following equation, which expresses the swelling potential, was calculated using [PERSON] 1963 formulation. It is expressed as  
\[SP(\%)=\frac{\Delta h}{h}*100 \tag{1}\]  
The swelling potential is found to decrease with the increase in cement content for all curing periods considered in this study.  
The swelling potential is reduced significantly when 4% and 8% cement additives were considered. In cases where the swelling percent needs to be reduced to a very low level, a 4% cement additive can be considered. One day of curing is not sufficient for the sand-clay mixture with cement to establish a better strength. The curing time is investigated here to tell when the construction of the foundation can proceed after applying the cement-stabilized soil columns. It can be recommended to wait for more time if the cement content is less than 4% (i.e., 28 days), while mixtures with rich cement contents can achieve good strength in 7 days or less.  
Figures 7-10 indicate the effect of the curing time on the swelling potential. It worth mentioning here that there is an optimum clay content beyond which the swelling potential will increase again. A 30% clay content is an ideal maximum to be considered if the cement additive is 2% or less.  
Figure 7: The swelling potential versus the clay content at 1 day of curing.  
Figure 8: The swelling potential versus the clay content at 7 days of curing.  
Figure 10: The swelling potential versus the clay content at 90 days of curing.  
Figure 9: The swelling potential versus the clay content at 28 days of curing.  
Figure 11a-d indicate the curing time for variable amounts of cement additives. The graphs also reflect the clay content influence. It can be observed that at a high cement content (8%) the swelling potential is not changing beyond 10 days of curing time. For 30% clay and beyond, the swelling potential can increase with the increasing curing time.  
The short-term strength increase is mostly due to the hydration reaction, which lowers the mixture's moisture content and forms the main bonds. On the other hand, the pozzolanic processes start when the pore fluid reaches a specific alkalinity level and an appropriate concentration of hydroxide ions is generated [37; 38].
# Channel Characterization and SC-FDM Modulation for PLC in High-Voltage Power Lines
###### Abstract  
Digital communication over power lines is an active field of research and most studies in this field focus on low-voltage (LV) and medium-voltage (MV) power systems. Nevertheless, as power companies are starting to provide communication services and as smart-grid technologies are being incorporated into power networks, high-voltage (HV) power-line communication has become attractive. The main constraint of conventional HV power-line carrier (PLC) systems is their unfeasibility for being migrated to wideband channels, even with a high signal-to-noise ratio (SNR). In this scenario, none of the current linear/non-linear equalizers used in single carrier schemes achieve the complete compensation of the highly dispersive conditions, which limits their operation to 4 kHz channels. In this paper, a new PLC-channel model is introduced for transmission lines incorporating the effects of the coupling equipment. In addition, the use of the single-carrier frequency-division modulation (SC-FDM) is proposed as a solution to operate PLC systems in a wide bandwidth, achieving transmission speeds above those of the conventional PLC system. The results presented in this paper demonstrate the superior performance of the SC-FDM-PLC over conventional PLC systems, obtaining a higher transmission capacity in 10 to 30 times.  
corona noise; high-voltage power lines; modal analysis; multiconductor lines; OFDM; power line communications; PLC system; PLC channel; SC-FDM +
Footnote †: journal:  
_Future Internet_  
Footnote †: journal:
# A Review of Lithium-Sulfur Batteries Based on Metal-Organic Frameworks: Progress and Prospects
### Overview of Lithium-Sulphur Batteries
#### 1.1.2 Development Potential of Lithium-Sulphur Batteries  
The development potential of LSBs as a new high energy density battery technology is receiving more and more attention. Since the theoretical energy density of LSBs is much higher than that of conventional lithium-ion batteries, this gives them significant potential for use in electric vehicles (EVs) and portable electronic devices [30; 31]. Companies such as Oxis Energy and Sion Power, for example, are developing LSBs for the automotive and aerospace industries, aiming to provide longer-lasting, lighter power supplies. Lyten, Inc. of the United States (San Jose, CA, USA) has developed the LytCell lithium-sulfur battery platform, which utilizes 3D graphene materials to significantly increase the energy density of the battery to three times that of conventional lithium-ion batteries. In addition, Lyten has addressed the cycle life of lithium-sulfur batteries through innovative material design. Sulfur is an abundant and low-cost resource, and its cost advantage over the cobalt and nickel used in lithium-ion batteries may lead to more economical energy storage solutions, especially in large-scale applications such as grid storage. The global LSBs market size is estimated at USD 33.8 million in 2023 and is expected to grow at a CAGR of 25.5% from 2024 to 2030. This indicates that the LSBs market is expanding rapidly and is expected to grow significantly in coming years.  
Energy densities of 500-600 Wh kg\({}^{-1}\) have been achieved in the lab, and commercial products have energy densities around 350-400 Wh kg\({}^{-1}\), such as batteries from OXIS Energy and Sion Power. Companies such as OXIS Energy and Sion Power have launched commercial lithium-sulfur batteries, which are mainly used in drones, aviation, and specialized vehicles, but commercialization still faces challenges such as polysulfide shuttling and a short cycle life. Commercialization still faces challenges such as polysulfide shuttle, lithium dendrite growth, and a short cycle life. In the future, through the optimization of cathode materials, negative electrode protection technology, electrolyte innovation, and battery design improvement, LSBs are expected to achieve large-scale applications in electric vehicles, aviation, and energy storage, and become an important choice for next-generation battery technology.
###### Abstract  
This study develops an applied optimization method to address practical challenges in Laser Radar station planning for automotive Body-In-White (BIW) manufacturing inspection. Focusing on the spatially constrained industrial environments and complex measurement specifications, the work reformulates Laser Radar inspection planning as a multi-constrained optimization problem challenge. Firstly, a parametric geometric modeling approach is developed to define measurement spaces for individual features, accompanied by an innovative maximal complete subgraph mining algorithm to intelligently identify shared feasible measurement regions among multiple features. Secondly, kinematic equations are formulated using Denavit-Hartenberg (D-H) parameters, while a hierarchical bounding volume collision detection mechanism is integrated to establish a comprehensive constraint. Therefore, unified optimization method synergizing measurement coverage, robotic manipulator reachability, and operational safety requirements are proposed. Through experimental validations utilizing BIW (BIW) component inspection, the research has demonstrated its industrial applicability and has achieved a 92% measurement coverage with robot trajectories free of collisions. Compared with traditional manual planning methods, the proposed approach reduces the number of required inspection stations by 35% and improves the computational efficiency to meet industrial real-time deployment requirements. Experimental validation demonstrates the method's effectiveness in measurement accuracy, operational safety, and equipment utilization for advanced manufacturing quality control systems.  
laser radar; station planning; feasible region; BIW Article  
1
## 3 Theoretical Modeling
### Electromagnetic Coupling Coefficient and Magnetic Force
 \tag{7}\]  
It is assumed that the centers of the two magnets are always vertically aligned during the vibration of the HEH. The magnetic force between the magnetic proof mass and magnetic oscillator can be calculated based on the dipole-dipole model [35], which is given as  
\[F_{\mathrm{m}}=\frac{3\mu_{0}m_{1}m_{2}}{2\pi\left[u_{1}-u_{2}+d_{0}\right]^{ 4}} \tag{8}\]  
where \(m_{1}\) is the magnitude of the magnetic dipole moment of magnetic proof mass. \(u_{1}\) and \(u_{2}\) are the displacements of the magnetic proof mass and magnetic oscillator relative to the base, respectively. \(d_{0}\) is the initial separation distance between the magnetic dipoles. The direction of the magnetic force depends on the magnetization directions of the magnets. Under the small-amplitude excitation, the difference of the displacements (\(u_{1}\)-\(u_{2}\)) is small. The Taylor's expansion of magnetic force \(F_{\mathrm{m}}\) around (\(u_{1}\)-\(u_{2}\)) can be expressed as  
\[F_{\mathrm{m}}=\frac{3\mu_{0}m_{1}m_{2}}{2\pi}\left[d_{0}^{-4}+(-4)d_{0}^{-5}( u_{1}-u_{2})+10d_{0}^{-6}(u_{1}-u_{2})^{2}+\cdots+\frac{F_{\mathrm{m}}^{(n)}(0)}{ n!}\left(u_{1}-u_{2}\right)^{n}\right] \tag{9}\]If the high-order terms (\(n\geqslant 2\)) are neglected, the magnetic force will only induce the linear equivalent stiffness to the harvester, just as a linear spring.