esa-sceva/satcom-chunk-collection huggingface dataset:
## Methods
### Data Processing
#### 3.1.2 Standardization of Input Variables  
When input variables are linearly related to each other and the output variable, it is unnecessary to normalize or standardize them for ML model training. Otherwise, when the input variables show a nonlinear relationship with each other and the output variable, the adjusted weights and biases of the model are dramatically affected by the variables at a large magnitude in model training, which degrades the training rate and causes local optimization [29]. Furthermore, utilizing extremely small weights could induce the uncertainties of calculating the floating point with a computer [30]. For resolving these limitations, standardization or normalization is generally used, and there are no fixed methods for standardization and normalization. Using standardized or normalized input variables improves the training rate and reduces the possibility of local optimization. Therefore, we applied standardization to input variables in this study as follows:  
\[V^{\prime}=\frac{(V-V_{mean})}{V_{std}}, \tag{9}\]where \(V\) denotes the unstandardized input variable; \(V^{\prime}\) is defined as the standardized input variable; \(V_{mean}\) represents the mean value of the input variable; and \(V_{std}\) refers to the standard deviation of the input variable. When applying the standardization, input variables showed similar ranges and magnitudes.  
Because the objective of this study was to build an ML model for the retrieval of SSI in real time, the ML model was trained for the ability to calculate accurate SSI for a longer period based only on the data for previous training periods. Thus, when standardizing the input variables, their mean and standard deviation were calculated based on the training data from 25 July 2019 to 31 July 2020.
# Real-Time Maintenance Optimization with Industrial Internet of Things
## 3 Models of Conventional and Real-Time Optimization
### Real-Time Maintenance Strategy Optimization in IIoT-Supported Production Systems
The described problems can be solved using a real-time optimizer, Section 4.2 shows the numerical results supported by an OpenSolver-supported simulation. Various solvers are available for optimization tasks, ranging from commercial software to open-source solutions. OpenSolver, a free add-in for Microsoft Excel, offers a robust platform for solving linear, integer, and nonlinear optimization problems. Other widely used solvers, such as Gurobi 12.0.2 and CPLEX 22.1.2, are renowned for their high-performance capabilities but often require licensing fees. OpenSolver offers several key advantages, making it an attractive choice for optimization tasks. First and foremost, it is a cost-effective solution, as it is completely free to use, eliminating the need for expensive commercial solvers. Its seamless integration with Microsoft Excel is another significant benefit, as it allows users to directly solve optimization problems within their existing spreadsheet models without needing to switch between different platforms or tools. We have included a summary table listing the key hyperparameters used in the Solver configuration (see Table 2). These parameters influence optimization behavior, convergence, and the risk of local vs. global optima. The default values were used unless stated otherwise. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyper Parameter** & **Description** & **Value** \\ \hline \multirow{2}{*}{Convergence} & Threshold for stopping optimization when changes & \multirow{2}{*}{0.0001} \\  & in the objective are minimal & \\ \multirow{2}{*}{Derivatives calculation method} & How derivatives are approximated & Forward \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameters and settings used in the GRG nonlinear Solver.
## 5 Open Research Issues and Challenges
### Implementing Cognitive Radio Attributes to Sensors  
Radio-equipped sensors with cognitive capabilities may incur higher energy consumption, while performing cognitive actions, such as learning, sensing and adapting. Secondary users have to switch their respective channel frequencies and transmission powers according to the type of operating network (e.g., IEEE 802.11 [27] or IEEE 802.16 [28]). For a cognitive environment, all network devices related to sensors and other controllers may efficiently perform cognitive operations, but increase the design complexity of network devices. A number of CRBAN architectures have been proposed, as detailed in Section 3, where cognitive attributes have been applied to the network controller rather than the sensors. CRBANs will become fully cognitive by introducing cognitive attributes to all sensors, which will help sensors make independent decisions in order to reduce interference and improve QoS [29].
## 5 Performance Evaluation
### Convergence Behavior  
We analyze the convergence of computation resource allocation from the perspective of the performance gap of the last two iterations. Algorithms requiring the assistance of ES include BR-CMCRA, LESE, and LSVLESE. As shown in Table 3, the gap of our proposed BR-CMCRA algorithm fluctuates around 0.01, which means the performance cannot improve a lot to some degree. In addition, the maximum gap of the LESE algorithm is 0.0312, which is acceptable. The LSVLESE algorithm's gap is significant because its iteration times are five times the amount of TaVs, whereas the other algorithms are set to ten times. The iterations are fewer in the LSVLESE algorithm because when a task is chosen to be executed in Loc + Edge mode, the allocated computation resources must exceed a certain amount, or the TaV may prefer to choose Loc + SeV mode.
## 5 Discussion
### Vulnerabilities Mitigation
#### 5.2.3 Insd  
An insider threat is anyone with authorized access to or knowledge of an organization's resources. The company trusts this person, who knows the company's fundamentals and has access to its assets. Because of that, a malicious insider can potentially cause great damage to the company imperceptibly. The average time taken for a company to detect an insider's malicious actions is 85 days [75].  
An insider may be classified as unintentional or intentional. As unintentional insider threats are more suitable, within the scope of this work, to DISC, PHYS, PORT, and START, in this section, we discuss mainly intentional malicious insiders.  
The main motivations for conducting an insider attack are financial benefits or espionage [76]. Insider-incident action is privilege abuse, while the actions are undertaken mainly via privilege abuse. Because of that, the least-privilege policy and Privileged Access Management (PAM) technology are helpful tools for preventing insider leakage.  
The leading adopted technologies used for mitigating insider threats are Data Loss Prevention (DLP), Privileged Access Management, User and Entity Behavior Analytics(UEBA), Security Information and Event Management (SIEM), Endpoint Detection and Response (EDR), and Insider Threat Management (ITM) [75].  
Administrative security controls may also be implemented. A background check and an employee screening upon hiring may reveal a mischievous past history for the candidate, enabling the company to cancel the employment process. If a person passes this investigation, enforcing a Non-Disclosure Agreement (NDA) signing is an additional countermeasure, as it will legally constitute their liability.  
After an employee is hired, other security measures should still be adopted. One is assessing the need to know for each employee, enforced through an access control mechanism. Granting more access to knowledge and data than the employee needs to perform their usual tasks exposes the information unnecessarily. A similar control is based on the principle of the least privilege, which grants a worker the minimum necessary privileges.  
Separation of duties is another form of mitigating inside intentional threats, which divides critical tasks among several employees, as per their department in the organization, for example. A job rotation policy, although sometimes infeasible, may also help manifest fraud, sabotage, or espionage. Terminating the contract with the employer is another critical step in preventing data leakage, and the company must ensure that the user's accounts are disabled, preferably during the exit interview, in which any equipment belonging to the organization should be returned, and after which the ex-employee should be escorted out of the facility.
## 3 Results
### Sinusoidal Fitting for Comparison between Heading and Pointing Tasks  
#### 3.5.1 Head in Space  
Sinusoidal fitting for the variation of head Euler angles as a function of target direction was significant in both movement tasks (r2 = 0.86 to 0.98 for heading; r2 = 0.73 to 0.99 for pointing). Figure 4 provides a summary of these variables (for numerical values, see Table S3, supplementary material). The amplitude of sinusoidal waves describing the orientation of the head in space varied significantly according to the task and was larger for heading than pointing (\(t\)-test, \(p<0.001\)). There was a significant negative offset which was smaller for heading (\(<\)\(-1^{\circ}\)) than for pointing (\(-2.06\) to \(-2.98^{\circ}\), Student \(t\) test; \(p<0.001\) for AP and LA, \(p<0.01\) for RO). This suggests that the decrease of head rotations for pointing relative to heading is due both to a decrease of the periodic variations with target directions and to a constant negative offset (i.e., a bias in anterior tilt, right tilt and right axial rotation). The mean fitted period was close to \(180^{\circ}\). Phase shift was approximately \(0^{\circ}\) (\(2.5\pm 4.1^{\circ}\)) for anteroposterior tilt (i.e., maximum for North), \(-90^{\circ}\) (\(-92\pm 20^{\circ}\)) for lateral tilt (maximum for East) and \(90^{\circ}\) (\(91\pm 7.9^{\circ}\)) for RO (maximum for West).  
Figure 4: Variation of the parameters of the sinusoidal fitting. The graphs show the amplitude and offset of the sinusoidal fitting of the Euler angles as a function of target direction. Each histogram represents the mean \(\pm\) SEM of 10 participants. Asterisks indicate statistical significance calculated by the fitting procedure (Origin Software, Levenberg Marquardt algorithm). Black asterisks vertically oriented compare tasks (pointing vs heading) and asterisks horizontally oriented compare reference frames (head orientation by reference to trunk vs space) (***: \(p<0.001\), ***: \(p<0.01\), *: \(p<0.05\)).
## 3 Modal Identification  
### Beam Theory  
The transverse displacement of a beam due to bending vibration, \(v(x,t)\), can be decomposed on a modal basis [24] as:  
\[v(x,t)=\sum_{i=1}^{\infty}b_{i}(t)\cdot\phi_{i}(x) \tag{2}\]  
Figure 5: Zoom on an image captured by the camera.  
Figure 6: Example of deflections obtained for the clamped–clamped beam at different times.  
where \(b_{i}(t)\) is the modal response of the \(i\)th mode and \(\phi_{i}(x)\) is the \(i\)th mode shape admitting for generic solution:  
\[\phi_{i}(x)=C_{1}\cos(\beta_{i}x)+C_{2}\sin(\beta_{i}x)+C_{3}\cosh(\beta_{i}x)+C _{4}\sinh(\beta_{i}x) \tag{3}\]  
The coefficients \(C_{i}\) depend on the boundary conditions. Applying the boundary conditions for a clamped-clamped beam leads to:  
\[\cos(\beta_{i}L)\times\cosh(\beta_{i}L)=1 \tag{4}\]  
and for a cantilever beam:  
\[\cos(\beta_{i}L)\times\cosh(\beta_{i}L)=-1 \tag{5}\]  
The values \(\beta_{i}L\) are the roots of these equations and give the different natural frequencies, with \(L\) the length of the beam. For these two boundary conditions, the \(i\)th mode shape can then be expressed by:  
\[\phi_{i}(x)=\cosh(\beta_{i}x)-\cos(\beta_{i}x)-\sigma_{i}\cdot(\sinh(\beta_{i} x)-\sin(\beta_{i}x)) \tag{6}\]  
with \(\sigma_{i}=\frac{\cosh(\beta_{i}L)-\cos(\beta_{i}L)}{\sinh(\beta_{i}L)-\sin( \beta_{i}L)}\) for a clamped-clamped beam and \(\sigma_{i}=\frac{\sinh(\beta_{i}L)-\sin(\beta_{i}L)}{\cosh(\beta_{i}L)+\cos( \beta_{i}L)}\) for a cantilever beam.  
The first six natural frequencies expected for the studied beam, in both configurations, are reported in Table 1, as well as the values of \(\beta_{i}L\) and \(\sigma_{i}\). Moreover, the mode shapes are shown on Figure 7 for both configurations.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Clamped–Clamped Beam**} & \multicolumn{3}{c}{**Cantilever Beam**} \\ \hline \(i\) & \(\beta_{i}L\) & \(\sigma_{i}\) & \(f_{i}\) [Hz] & \(\beta_{i}L\) & \(sigma_{i}\) & \(f_{i}\) [Hz] \\ \hline
1 & 4.730 & 0.9825 & 124.6 & 1.875 & 0.7341 & 15.37 \\
2 & 7.853 & 1.0008 & 343.4 & 4.694 & 1.0185 & 96.3 \\
3 & 10.996 & 1.0000 & 673.3 & 7.855 & 0.9992 & 269.7 \\
4 & 14.137 & 1.0000 & 1112.9 & 10.996 & 1.0000 & 528.6 \\
5 & 17.279 & 1.0000 & 1662.6 & 14.138 & 1.0000 & 873.7 \\
6 & 20.421 & 1.0000 & 2322.1 & 17.280 & 1.0000 & 1305.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Expected natural frequencies.  
Figure 7: Theoretical mode shapes for clamped–clamped and cantilever beams.
# Target-Oriented Multi-Agent Coordination with Hierarchical Reinforcement Learning
## 5 Experiments
### Ablation Studies  
To better understand the individual contributions of each component within the RHMC framework, we conducted detailed ablation studies. These studies further validate the importance of the regulation mechanism and the target-conditioned filter in enhancing the RHMC's performance. As illustrated in Figures 2 and 10, the full RHMC method, incorporating both the regulation mechanism and the target-conditioned filter, achieved a final reward of 70. In contrast, the variants without these components achieved rewards of 55 and 35, respectively. This indicates that the regulation mechanism contributes to a 50% performance improvement, while the target-conditioned filter contributes to a 21.4% improvement.  
Table 2 presents ablation study results showing final rewards and performance improvements across different methods.  
Figure 10: The learning curve of our method with its ablations in multi-sensor multi-target coverage Task.  
Figure 9: The result of our method with baselines in different number of sensors with five targets.  
The regulation mechanism plays a critical role in stabilizing the high-level policy learning process and mitigating the non-stationarity introduced by dynamic changes in low-level policies. Meanwhile, the target-conditioned filter reduces the noise from irrelevant information, accelerating the learning process and improving overall efficiency. Without the target-conditioned filter, the system is more susceptible to noise from extraneous data, leading to lower final rewards, slower learning rates, and diminished efficiency. Notably, after 1 million training steps, the RHMC method with both components demonstrates superior policy learning and gradual convergence, whereas the variant without the target-conditioned filter requires 3 million steps to achieve convergence.  
These results underscore the significant roles these components play in enhancing the performance of the RHMC method. By stabilizing the training process and reducing the interference from irrelevant information, the regulation mechanism and target-conditioned filter collectively improve the overall efficiency and robustness of the RHMC framework, allowing for better coordination and performance in multi-agent environments.
###### Abstract
Moreover, studies have been conducted to select a model for predicting the ground subsidence risk level in urban areas in Korea through machine learning, after selecting influencing factors such as the number of years used and pipeline diameter among attribute values of underground utilities, and then to suggest a risk map [1]. Researchers have used various ways to predict risk levels in order to prevent accidents related to ground subsidence. However, they have had difficulty deriving highly accurate and reliable results, as ground subsidence occurs in complex ways and is caused by various factors in a wide range of areas. Thus, this study aims to propose a machine learning-based ground subsidence risk prediction model by selecting the following as influence factors among the attribute information of underground utility lines in representative urban areas in Korea: the number of years used, pipeline diameter and length, and the density of pipelines, which are likely to have a close correlation with ground subsidence. We compared the results of machine learning models by applying datasets with a range of conditions and selected the model that exhibited optimal performance. Furthermore, we aimed to present the importance of each influencing factor, which was used when classifying the ground subsidence risk levels by the machine learning model through the selected model.
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 3 Problem Statement
### Single Path or Multi-Path Routing
#### 3.1.2 First-Hop Multi-Path (FMP)  
FMP routing can mitigate these problems by selecting a connection link to the first hop. However, if network congestion or a link failure occurs in another hop, it cannot deal with the problem because the SD-WAN does not have the authority to control the path in the other hops. For example, SD-WAN is an overlay with the ability to use different underlays, that is, the Internet, MPLS, VPLS, and LTE. Because SD-WAN cannot control all that is happening between the selected internet service providers (ISPs), customers may even lose connections to sites for minutes or even hours.
## 2 Materials and Methods
### Methods
#### 2.3.2 Future Change Trend Analysis Based on Pixels  
The Hurst exponent [53], based on the rescaled range (\(R/S\)) analysis method, is an important indicator for quantitatively describing the long-term dependence of time series data, and its basic principle [54; 55; 56] is that, with a time series \(\xi(t)\) (\(t=1,2\)... ), for any positive integer \(\tau\geq 1\), there is a mean series:  
\[<\xi>_{t}=\frac{1}{\tau}\sum_{t=1}^{\tau}\xi(t)\ (\tau=1,2,\cdots) \tag{5}\]  
The cumulative deviation \(X\) (\(t\)) is thus obtained as:  
\[X(t,\tau)=\sum_{t=1}^{t}[\xi(t)-<\xi>_{t}]\ (1\leq t\leq\tau) \tag{6}\]  
The polar difference \(R\) is defined as:  
\[R(\tau)=\max_{1\leq t\leq\tau}X(t,\tau)-\min_{1\leq t\leq\tau}X(t,\tau)\ (\tau=1,2,\cdots) \tag{7}\]  
The standard deviation \(S\) is defined as:  
\[S(\tau)=\left[\frac{1}{\tau}\sum_{t=1}^{\tau}(\xi(t)-<\xi>_{\tau})^{2}\right] ^{\frac{1}{2}}\ (\tau=1,2,\cdots) \tag{8}\]\(R\), \(S\), and \(\tau\) satisfy the general relationship:  
\[R(\tau)/S(\tau)=c\cdot\tau^{H} \tag{9}\]  
Least squares fitting:  
\[\log(R/S)_{\tau}=\log c+H\cdot\log\tau \tag{10}\]  
where: \(c\) is a constant; \(R(\tau)/S(\tau)\) is the rescaled range; \(H\) is the Hurst exponent. The Hurst exponent indicates the range of the cumulative deviation from the mean of the time series over time. The Hurst exponent of the time series is between 0 and 1. There are three main scenarios: (1) 0 \(<H<\) 0.5, indicating that the future trend of the vegetation NPP is anti-persistence, i.e., opposite of the past trend; (2) 0.5 \(<H<\) 1, indicating that the future trend of the vegetation NPP is persistence, i.e., consistent with the past trend; (3) \(H\) = 0.5, and the time sequence is random.
# Compaction Characteristics and Mechanical Response of Gravel-Glass-Rubber Mixtures +
## 3 Experimental Procedure
### Shear Strength  
Shear strength is another key characteristic contributing to the performance of geotechnical structures. The shear strength is the result of friction and interlocking between particles and bonding at particle contacts. Due to interlocking, GGRM may contract or expand in volume as they are subjected to shear strains.  
Direct shear box tests were conducted (in accordance with the ISO Standard [45]) on dry specimens prepared by the tamping method at 90-95% degree of compaction. The size of the soil box was 100 mm (width) \(\times\) 100 mm (length) \(\times\) 53 mm (height). The specimens were tested under 30, 60, and 100 kPa effective normal stress (\(\sigma\)n\({}^{\prime}\))--the latter one corresponding to the maximum load capacity of the device. After the normal stress was applied to the mixtures, sufficient time was allowed for the material to fully compress. The 100 kPa tests required the most time to consolidate, approximately two hours. Once the vertical settlement increment became negligible, the specimens were sheared at a constant horizontal displacement rate of 1 mm/min. Tests were concluded when the horizontal displacement (\(\Delta\)H) achieved 15-20 mm.  
It is important to mention here that, while \(\sigma_{n}\)\({}^{\prime}\) is limited to 100 kPa, it represents typical field stress conditions where GGRM can be used primarily as lightweight backfill materials, shallow underground layers for mitigation of liquefaction phenomena, and geotechnical seismic isolation for low-to-medium-rise lightweight structures/infrastructure [46, 47, 48, 49, 50, 51, 52, 53]. Testing GGRM under higher confining pressures was out of the scope of this study; nevertheless, additional tests may be necessary in the future when dealing with specific applications where higher normal stresses are envisioned.
# Key Distribution Scheme for Optical Fiber Channel Based on SNR Feature Measurement
## 4 Analysis of Simulation Results  
### Random Analysis  
The National Institute of Standards and Technology (NIST) random test results of key sequences are illustrated in Figure 5a to evaluate the randomness of key bits. We randomly selected a set of keys from the key sequence as the key test sequence, and 10 NIST sub-tests [25]. The return threshold of all 10 of the NIST sub-tests is above 0.99, which indicates that the key sequence has true randomness. In addition, for some tests that return multiple thresholds, we only give the minimum value. Figure 5b describes the 0/1 ratio performance of [PERSON] and [PERSON]'s key bits as a function of the SNR sampling interval. [PERSON] quantifies the key to receive the 0, 1 key. The noise sources, such as laser, optical amplifier, and physical fiber parameters, determine the randomness of the fiber channel. Consequently, the probability of keys 0 and 1 fluctuates around 0.5, further verifying the key's randomness.
# Attacks and Preventive Measures on Video Surveillance Systems: A Review
## 2 Attacks on VSS
### Visual Layer Attacks  
VSSs are used by applications for the recognition of facial features, the automatic reading of license plates, scanning and reading QR codes and the compression of image data. VSS has an additional level of abstraction, i.e., the visual layer. This layer is prone to a few types of attacks as they involve imagery semantics and image recognition. The attacks are spread/injected in a multitude of ways, such as preinstalled malware in the system or through a firmware update or remote command insertion [26].  
Malicious images can be embedded into the images taken by video cameras/sensors [27]. One of the most common attacks on a live feed from VSS is frame duplication attack. An attacker, once gaining access to a VSS system, can insert previously recorded \"normal\" looking frames in place of the live stream, to avoid the detection of ongoing suspicious activity. To detect these frame duplication attacks, spatial and temporal domain similarities between frames are extracted and analyzed using various correlation techniques. To achieve this, a massive database is required for storing a huge amount of data and an enormous amount of computation time is required to detect and prevent such attacks in real time.