esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analytical Modeling of the 2-DOF CPM
### Motion Stroke  
The motion strokes along the \(x\)- and \(y\)-axes of the 2-DOF CPM are the same. Considering the stiffness of the actuator, the input stiffness of the CPM in the \(x\)-axis is given in Figure 11.  
The relationship between the actual and nominal displacement output of the actuator can be obtained according to Figure 11 as  
\[\delta_{a1}=\frac{k_{n1}}{k_{n1}+K_{x-Fx}^{In}}\cdot\delta_{n1}=\frac{1}{1+K_{ x-Fx}^{In}/k_{n}}\cdot\delta_{n1} \tag{23}\]  
where \(k_{n1}\) is the axial stiffness of the actuator, and \(K_{x-Fx}^{In}\) is the input axial stiffness of the CPM along the \(x\)-axis.  
The efficiency of the motion transferred from the actuation point to the terminal platform should also be calculated to obtain the motion stroke of the CPM. According to Figure 10, utilizing a force equilibrium condition of point \(O\) can obtain the relationship between the displacement output of the terminal platform and the deformation at the actuation point \(A_{1}\) as  
\[{}^{O}\!\mathbf{K}_{T1}\delta_{o}+{}^{O}\!\mathbf{K}_{1-2}(\delta_{o}-\delta_{a1})=0 \quad\Rightarrow\quad\delta_{o}=\left[\left({}^{O}\!\mathbf{K}_{1-2}+{}^{O}\!\mathbf{ K}_{T1}\right)^{-1}\!\mathbf{K}_{1-2}\right]\delta_{a1} \tag{24}\]  
The motion stroke of the CPM along the \(x\)-axis can be calculated according to Equations (22) and (23) as  
\[\delta_{ox}=\frac{\left[\left({}^{O}\!K_{1-2}+{}^{O}\!\mathbf{K}_{T1}\right)^{-1} {}^{O}\!\mathbf{K}_{1-2}\mathbf{e}_{1}\right]^{\mathrm{T}}\!\mathbf{e}_{1}}{1+K_{x-Fx}^{In }/k_{n}}\cdot\delta_{n} \tag{25}\]  
where \(\mathbf{e}_{1}=[1,0,0]^{\mathrm{T}}\) is a unit vector.  
Additionally, the maximum stress generated inside the flexure hinges should be kept within the allowable value when the CPM moves to the maximum displacement. The maximum stress of Parts I and II can be calculated as [22]  
\[\sigma_{1}^{\mathrm{max}}=\alpha\frac{12\delta_{a}^{\mathrm{max}}Et_{1}}{L^{2} }\leq\frac{\sigma_{y}}{n}\,,\ \sigma_{2}^{\mathrm{max}}=\alpha\frac{12\delta_{o}^{\mathrm{max}}Et_{2}}{L^{2} }\leq\frac{\sigma_{y}}{n} \tag{26}\]  
where \(\alpha\) is the stress concentration factor chosen as 2, \(\delta_{a}^{\mathrm{max}}\) and \(\delta_{o}^{\mathrm{max}}\) are the maximum displacements of the actuation platform and terminal platform, \(\sigma_{y}\) is the yield stress of the material, and \(n\) is the safety factor chosen as 1.5.  
According to Equation (23), the maximum displacement of the terminal platform is always smaller than that of the actuation platform. Therefore, the motion stroke of the CPM should satisfy  
\[\delta_{o}^{\mathrm{max}}\leq\min\left\{\frac{\sigma_{y}L^{2}}{12n\alpha Et_{ 1}}\,,\ \frac{\sigma_{y}L^{2}}{12n\alpha Et_{2}}\right\} \tag{27}\]
## 1 Introduction
### Motivation  
The primary strength of EC in IoT network security lies in its decentralized architecture. EC reduces the need for data to travel, thus reducing the potential attack surface. Hence, due to its precise control over data processing locations and methods, the EC can uphold data privacy regulations like the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA). Therefore, EC enables the implementation of security protocols and analytics directly at the device level, facilitating real-time threat detection and response. EC is expected to possess flexible, lightweight, secure, and self-adaptive data analytics mechanisms based on user data sensitivity. However, the decentralized architecture of EC poses significant trust management challenges due to the issues related to collecting and managing evidence information from edge devices [49]. ML-enabled EC can make data-driven inferences, predictions, and decisions based on acquired knowledge from past data. Also, ML is a preferred choice for IoT services' privacy and data security due to its analytical capability. However, ML-based security schemes possess serious limitations due to the need for huge training datasets and privacy issues. So, there is an urgent need to devise an ML-based security scheme with low computation and communication costs [50]. ML-based modeling through anomaly detection techniques offers more generic and robust security solutions against unknown attacks. However, ML-based security solutions may be vulnerable to ever-evolving attacks like adversarial ML [51]. With a large amount of data generated by edge devices, there is a need for ML models that can run on resource-constrained edge devices. Also, techniques should exist for compressing the ML models that can make these models lighter and faster while making them suitable for edge deployment. Traditional security solutions rely on cryptographic methods where a secret key is stored within the device. However, the entire security system can be breached if this key is compromised. In contrast, a Physical Unclonable Function (PUF) uses the hardware itself as the medium to generate a unique secret key. The core principle of PUFs is based on the unique, device-level variations introduced during the manufacturing process. PUFs can be applied in various security protocols, including unique identifiers, secret keys, device authentication, intellectual property protection, and pseudo-random bit generators (PRNGs) [52].
# Optimized Downlink Scheduling over LTE Network Based on Artificial Neural Network
## 3 Downlink Scheduling in LTE
### Maximum Largest Weighted Delay First (MLWDF) Metric  
This channel-aware technique serves several data access streams with different QoS needs. MLWDF considers fair distribution, delay reduction, and ensuring system throughput. This algorithm handles real-time and non-real-time data flow, wherein the PF is used for the non-real-time flow, and the following weighting expression is used for the real-time data flow by applying the following weighting metrics, as in Equation (2).  
\[M_{i,k}^{M-LDWF}= \alpha_{i}\ D_{HOL,i}*M_{i,k}^{PF} \tag{2}\] \[\alpha_{i}= -\frac{log\delta_{i}}{\tau_{i}}\]  
wherein, \(D_{HOL,i}\) denotes the head-of-line delay of packet faced at time t, for the \(i\) user, \(\tau_{i}\) denotes the delay threshold for a packet for each user \(i\) that was considered for real-time data, and \(\delta_{i}\) denotes the maximal possibility of an HOL packet delay that may cause the user \(i\)th to exceed their threshold delay [23].
# Numerical Study on Redbed Slope Stability Under Multi-Factor Conditions
## 2 Numerical Modeling and Setup
### Numerical Simulation Parameters  
In this study, mechanical parameters of highly weathered argillaceous siltstone, moderately weathered argillaceous siltstone, and moderately weathered conglomerate rock in their natural state were derived from engineering investigation reports, which include both field and laboratory test data (such as standard penetration tests and triaxial shear tests). The elastic modulus and Poisson's ratio were determined through laboratory tests. The Mohr-Coulomb model relies on the shear modulus and bulk modulus as the primary physical deformation parameters for geotechnical materials, which are crucial for characterizing their behavior under loading conditions. These parameters are calculated from the elastic modulus and Poisson's ratio. The physical and mechanical parameters for each geotechnical layer, as well as the rock mass modulus, are provided in Tables 1-3.  
In practical engineering, the contact surfaces between rock strata are highly complex, and their parameters are influenced by various factors, including lithology, tectonic stress, and the lubricating effect of water flow along the contact layers. Determining accurate contact surface parameters is challenging. Therefore, in this study, the contact surface parameters are referenced from engineering experience and the recommendations of previous researchers [29, 30, 31, 32]. Specifically, the normal stiffness and shear stiffness of the contact surfaces are set as 10 times the equivalent stiffness of the \"hardest\" adjacent region, while the cohesion and internal friction angle are taken as 0.8 times the minimum values of the surrounding soil layers. Additionally, in fluid-solid coupling calculations, the contact surface parameters are appropriately reduced using a \"weakening method\".
# MPVF: Multi-Modal 3D Object Detection Algorithm with Pointwise and Voxelwise Fusion
## 4 Experiments and Results
### Evaluation Metrics  
We adopt the official KITTI benchmark evaluation metrics [16], including Average Precision (AP) and mean Average Precision (mAP), strictly following the KITTI evaluation standards to ensure the effectiveness of our method. To ensure consistency in detection accuracy, different IoU thresholds were established for various object categories: 0.7 for cars and 0.5 for both pedestrians and cyclists. Due to their larger size and fixed shape, vehicles allow for stricter localization requirements. In contrast, pedestrians and cyclists, with their smaller size and varying postures, pose greater challenges for accurate detection. Adopting a uniform IoU threshold of 0.5 may reduce the accuracy of vehicle detection. To ensure the reproducibility and fairness of the experiments, this study provides a detailed IoU-based evaluation method. The precision is determined by comparing the IoU of predicted boxes with ground truth boxes against the set thresholds; if the IoU exceeds the threshold, it is classified as a true positive (_TP_), otherwise it is classified as a false positive (_FP_). Consequently, the recall and precision can be expressed as follows:  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{3}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{4}\]  
Here, _FN_ denotes false negatives. We adopt the latest AP40 [41] standard from KITTI and compute AP using an interpolation method to enhance the reliability of the evaluation, as shown in Equations (5) and (6). For multi-class detection, the mAP serves as a comprehensive evaluation metric, obtained by calculating the average AP across all categories, as indicated in Equation (7).  
\[P_{interp}(r)=\underset{rr\geq r}{\text{max}}P(r\prime) \tag{5}\]  
\[\text{AP}_{i}=\frac{1}{|R_{40}|}\underset{r\in R_{40}}{\sum}P_{interp}(r) \tag{6}\]  
\[\text{mAP}=\frac{1}{k}\underset{i=1}{\overset{k}{\underset{i=1}{\sum}}}AP_{i} \tag{7}\]  
Here, _P\({}_{interp}(r)\)_ represents the interpolation function, \(i\) denotes the object category index, \(k\) is the total number of categories, \(r\) indicates the sampled recall values, and _R\({}_{40}\)_[41] represents the set of 40 recall values distributed evenly within the range [0, 1].
## 2 Results and Discussions
### Comparative Analysis Between Different Techniques
SPC evaluates changes in the \"a\" coordinate within the CIELAB color space, providing a practical and straightforward means to monitor the visual degradation of reddish dyes. DIP builds on this by offering a pixel-by-pixel analysis of color changes across the entire surface, delivering enhanced sensitivity and precision. On the other hand, UV-Vis measures a reduction in absorbance at a specific wavelength, offering a precise and quantitative assessment of chemical degradation, which aligns closely with the ISO methodology. However, the challenges presented by cementitious materials underscore the need for methodological adjustments. Cemertitious surfaces, unlike ceramics, exhibit high porosity and low dye adsorption, which can obscure the actual photocatalytic efficiency. Techniques such as UV-Vis may underestimate efficiency on such substrates due to limitations in pollutant adsorption. This limitation highlights the need for pre-treatment steps or calibration to ensure accurate results. Lighting conditions are another topic of divergence. ISO 10678 specifies strict standards for light intensity and spectral distribution, ensuring consistent and reproducible results. The techniques in the study employ controlled UV-Vis irradiation but do not always adhere to the same stringent standards, which may introduce variability and necessitate standardization for broader applicability. Ultimately, the comparison reveals that, while ISO 10678 provides a strong foundation for dye degradation studies, its focus on smooth, non-porous materials limits its applicability to substrates like cementitious surfaces. The integration of complementary techniques, such as those explored in this study, can address these limitations, enabling a more comprehensive evaluation of photocatalytic efficiency across diverse materials. This approach is essential for advancing the development and adoption of self-cleaning technologies in real-world applications. Another critical factor is the potential photocatalytic activity of the substrate itself, combined with photolysis effects, which can independently contribute to pollutant degradation. This interaction is particularly relevant for cementitious materials, where inherent chemical properties, such as alkalinity and porosity, may influence dye degradation under UV exposure. The ISO 10678 standard addresses this indirectly by requiring untreated control samples to isolate the specific contribution of the photocatalytic coating or treatment. However, it does not explicitly elaborate on the interactions between substrate properties and photolysis. For accurate evaluation, experiments must include control samples and adapt the methodology to account for these effects, especially when studying complex substrates like cementitious materials.
# Communication Technologies for Smart Grid: A Comprehensive Survey
## 2 Overview of Smart Grid
### Smart Grid Applications  
SG applications for monitoring and grid management include Advanced Metering Infrastructure (AMI), Distributed Automation (DA), Distributed Generation (DG), Distributed Storage, Home Energy Management Systems (HEMS), Demand Response (DR),  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Application Layer** & \multicolumn{2}{c}{**Power Transmission and**} & \multicolumn{1}{c}{**Customer Applications**} \\ \hline  & & Neighborhood Area & Premise Area Network \\ Communication Layer & Wide Area Network & Network/Field Area & (Home Area Network, \\  & & Network & Building Area Network, \\ \hline Power Control Layer & \multicolumn{2}{c}{Power monitoring, control, and management systems} \\ \hline Power System Layer & \multicolumn{2}{c}{Power Generation} & \multicolumn{1}{c}{Power Distribution} & Customer \\  & and Transmission & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overview of SG communication layers [20].  
and Supervisory Control And Data Acquisition (SCADA). All depend on reliable wired and wireless communication interfaces to operate in the SG infrastructure.
# Distribution Grid Stability--Influence of Inertia Moment of Synchronous Machines
## 2 Description of a Modeled System  
The electromechanical swinging system consists of the rigidity of a synchronous machine magnetic field, the damping effects, and the moment of inertia, which are connected with a shaft as presented in Figure 2. The system in Figure 2 is described by the differential equation  
\[\ddot{I\beta}+B\dot{\beta}+k\beta=\frac{I}{P_{d}}\dot{\omega}, \tag{1}\]  
where \(\beta\) is the load angle of a synchronous machine, \(\omega\) is the grid frequency (s\({}^{-1}\)), \(I\) is the overall moment of inertia connected to a shaft (kg\(\cdot\)m\({}^{2}\)), \(B\) is the torsion damping constant (N\(\cdot\)m\(\cdot\)s), \(K\) is the torsion rigidity of a synchronous machine (N\(\cdot\)m), and \(P_{d}\) is the number of pole pairs in a synchronous machine.  
Figure 1: Examples of power spectral density \(S\omega(\Omega)\) at frequency fluctuation \(\Omega\).  
For simplification, the following will be assumed:  
* An unloaded synchronous machine;
* Linearity of the magnetic circuit;
* The exclusion of the influence of external voltage, power, and frequency control circuits;
* The possession of torque characteristics for small \(|\beta|\) (\(<0.1\) rad) in linearized solutions. The precision of the system is described by Equation (1) in Laplace's transformation [7; 8]: \[G(p)=\frac{pI}{P_{d}\left(p^{2}I+pB+k\right)}.\] (2) The power spectral density of the load angle fluctuation is \[S_{\beta}(\Omega)=\lim_{n\rightarrow\infty}\left(G(p)\right)^{2}S_{\omega}( \Omega),\] (3) where \(S_{\beta}(\Omega)\) is the power spectral density of the load angle fluctuation and \(S_{\omega}(\Omega)\) is the power spectral density of fluctuation for angular frequency fluctuations (s\({}^{-2}\)). \[S_{\beta}(\Omega)=\frac{\Omega^{2}I^{2}S_{\omega}(\Omega)}{P_{d}\left[\left(k -\Omega^{2}I\right)^{2}+\Omega^{2}B^{2}\right]}.\] (4) The natural frequency of the system is \(\Omega_{0}\): \[\Omega_{0}=\sqrt{\frac{k}{I}}.\] (5) The coefficient of a relative system damping \[a=\frac{B}{2\sqrt{kI}}\] (6) is very low for most synchronous machines; it is usually \(<0.1\), in which case the resonance is very selective. The system transmits just the natural frequency. A system response has almost a sinusoidal course of load density swinging with an amplitude \(\beta_{A}\). From Equation (4), the results for \(\Omega=\Omega_{0}\) can be written as \[S_{\beta}(\Omega_{0})=\beta_{A}^{2}=\frac{I^{2}}{P_{d}^{2}\,B^{2}}=S_{\omega}( \Omega_{0}).\] (7)  
Figure 2: Electromechanical vibrating set of a synchronous machine.
## 6 Conclusions  
Based on the model results presented in this study, the following conclusions are drawn:  
1. Lateral deflection of a pile-wall is over-estimated by more than 400% using two-dimensional (plane strain) finite element analysis in comparison with three-dimensional finite element analysis due to a lack of consideration of pile spacing and small shape ratios of adjacent footings. This issue is addressed by considering the out-of-plane geometries of a pile-wall and adjacent footing using three-dimensional finite element analysis. However, both two- and three-dimensional finite element analyses show a close prediction for the lateral deflection of the pile-wall portion below 8.0 m depth due to the small influence of adjacent footing.  
Figure 11: Relationship between observed and predicted \(\mathrm{B_{p}}/\delta_{\mathrm{LT}}\).  
Figure 12: Relationship between observed and predicted \(\mathrm{B_{p}}/\delta_{\mathrm{LH}}\).  
* Lateral deflection of the pile head decreases with increasing soil initial modulus in compression, footing-pile distance, and pile width, as well as with decreasing footing shape ratio. However, based on the results of three-dimensional finite element analysis, footing-pile distance and pile width have a small influence on the lateral deflection at the pile toe, while soil type and footing shape ratio have a greater influence on the pile toe deflection.
* Due to the influence of the adjacent footing, the peak lateral pressure of the pile at the depth of approximate 2 m decreases with decreasing footing shape ratio and pile width and increasing footing-pile distance.
* Based on the observed profiles of lateral pressure and deflection of pile-walls, the factors influencing the performance of a pile-wall are ranked as follows: soil type, footing shape ratio, footing-pile distance, and pile width.
* Long piles tend to deflect and bend laterally rather than rotate. They also minimize the influence of footing-pile distance and pile width on the pile toe deflection. Depths of pile bending along long piles are related to the pile embedment lengths and not influenced by soil type, footing shape ratio, footing-pile distance, and pile width.
* Two empirical equations are developed to predict the lateral deflections at the pile toe and head for a pile-wall located within the critical distance from an adjacent footing. These two equations are useful for the structural design of a pile-wall by providing the net deflection along a pile-wall. Values predicted using the present developed equations are in agreement with those values generated using the present three-dimensional finite element analysis. It should be noted that the present empirical equations are applicable only within the range of conditions covered in this study.  
Writing--original draft preparation, G.A.S.; writing--review and editing, M.J. and G.A.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data are contained within the present article.  
The authors declare no conflicts of interest.
## 5 Performance Evaluation
### Simulation Setup
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline  & **Movement** & **10 min** & **20 min** & **30 min** & **40 min** & **50 min** & **60 min** \\ \hline M-UAV 1 & Scan & (125, 1075) & (625, 1075) & (625, 650) & (125, 650) & (125, 225) & (625, 225) \\ M-UAV 2 & Oval & (375, 1175) & (200, 1075) & (125, 900) & (125, 650) & (125, 400) & (200, 225) \\ M-UAV 3 & Stay-at & (275, 800) & (175, 625) & (275, 425) & (475, 425) & (575, 625) & (475, 800) \\ M-UAV 4 & Eight & (625, 475) & (575, 250) & (375, 150) & (175, 250) & (150, 475) & (375, 650) \\ M-UAV 5 & Waypoint & (100, 100) & (150, 200) & (200, 300) & (250, 400) & (300, 500) & (350, 600) \\ M-UAV 6 & Scan & (625, 225) & (125, 225) & (125, 650) & (625, 650) & (625, 1075) & (125, 1075) \\ M-UAV 7 & Oval & (550, 1075) & (625, 900) & (625, 650) & (625, 400) & (550, 225) & (375, 150) \\ M-UAV 8 & Stay-at & (475, 800) & (575, 625) & (475, 425) & (275, 425) & (175, 625) & (275, 800) \\ M-UAV 9 & Eight & (150, 825) & (175, 1025) & (375, 1100) & (575, 1025) & (625, 825) & (375, 650) \\ M-UAV 10 & Waypoint & (650, 1200) & (600, 1100) & (550, 1000) & (500, 900) & (450, 800) & (400, 700) \\ \hline  & **Movement** & **70 min** & **80 min** & **90 min** & **100 min** & **110 min** & **120 min** \\ \hline M-UAV 1 & Scan & (125, 225) & (125, 650) & (625, 650) & (625, 1075) & (125, 1075) & (625, 1075) \\ M-UAV 2 & Oval & (375, 150) & (550, 225) & (625, 400) & (625, 650) & (625, 900) & (550, 1075) \\ M-UAV 3 & Stay-at & (275, 800) & (175, 625) & (275, 425) & (475, 425) & (575, 625) & (475, 800) \\ M-UAV 4 & Eight & (625, 825) & (575, 1025) & (375, 1100) & (175, 1025) & (150, 825) & (625, 475) \\ M-UAV 5 & Waypoint & (400, 700) & (450, 800) & (500, 900) & (550, 1000) & (600, 1100) & (650, 1200) \\ M-UAV 6 & Scan & (625, 1075) & (625, 650) & (125, 650) & (125, 225) & (625, 225) & (125, 225) \\ M-UAV 7 & Oval & (200, 225) & (125, 400) & (125, 650) & (125, 900) & (200, 1075) & (375, 1175) \\ M-UAV 8 & Stay-at & (475, 800) & (575, 625) & (475, 425) & (275, 425) & (175, 625) & (275, 800) \\ M-UAV 9 & Eight & (150, 475) & (175, 250) & (375, 150) & (575, 250) & (625, 475) & (625, 475) \\ M-UAV 10 & Waypoint & (350, 600) & (300, 500) & (250, 400) & (200, 300) & (150, 200) & (100, 100) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Trajectory of moving unmanned aerial vehicles (M-UAVs) every 10 minutes. Figure 2: (a) \(3\times 3\) Manhattan grid map with four charging towers and one ground station, and 1479 reference points of 25 m intervals (**b**) Scenario 1 (sparse lattice grid): 28 fixed UAVs without overlapping areas (**c**) Scenario 2 (dense lattice grid): 45 fixed UAVs with overlapping areas. * First, all of the UAVs perform their own major tasks (i.e., monitoring at their locations to the extent of their batteries). The charging tower controller checks the status of each battery of the UAVs and suspends the surveillance functionality if the battery remains below 30%. * Next, the UAVs with battery status values of less than 30% select the nearest charging tower from their locations if the charging tower has available charging panels.