esa-sceva/satcom-chunk-collection huggingface dataset:
# Finite Element Model of Soliosis Brace with Increased Utility Characteristics
## 3 Results  
### Computational Examinations of the Original Orthosis  
The numerical model was tested with loading conditions similar to those used in previous FEM studies by [PERSON] and [PERSON], specifically a 36 N load applied as \(F_{1}\), which is consistent with physiological loading conditions [3]. The numerical model of the original coset was verified experimentally on a test stand using ESPI [3]. In future studies, the modified corset will undergo additional verification tests. The generated results gave the von Mises stress distribution shown in Figure 2. Excluding the minimal surface areas where the load and supports were located, the majority of the orthosis exhibited stress levels below 6.5 MPa (Figure 2). This stress level is consistent with the linear segment of the stress-strain curve for polypropylene [13; 14], confirming previous assumptions about the linearity of the model.  
Figure 2 shows that simply identifying isolated regions of elevated von Mises stress does not sufficiently elucidate the functional mechanics of the orthotic shell. More informative is the use of the mapped distribution of principal stress vectors, particularly for constructing stress trajectories. Figure 3 illustrates these principal stress vectors, focusing on the maximum tensile stresses, denoted as \(\sigma_{1}\). This figure plots the paths of these \(\sigma_{1}\) stresses, with splines drawn to align with the directions of the \(\sigma_{1}\) vectors, starting and ending near the points where external forces were applied.  
Figure 2: von Mises stress distributions in the corset shell under corrective loading.  
Based on the shapes of these principal stress \(\sigma_{1}\) trajectories derived from numerical analyses, and the shell regions where these trajectories correspond to the highest stress values, it can be hypothesized that these specific regions primarily contribute to the brace's corrective function. Conversely, other shell regions are likely to play an auxiliary role, such as stabilizing the orthosis relative to the torso, and should not significantly affect the brace's corrective capabilities. These auxiliary regions could potentially be removed without significantly affecting the stiffness of the shell.  
To validate this hypothesis, one could remove those parts of the shell in which the principal stress trajectories correspond to relatively low \(\sigma_{1}\) stress values and then perform a numerical analysis similar to that performed for the original orthosis. When the mechanical properties, deformation behavior under corrective loading, and stiffness values of the modified brace are close to those of the original structure, the hypothesis would be considered confirmed. Minor adjustments, such as a slight change in the shell thickness in critical areas, may be necessary. These minor modifications would account for the small contributions of the removed areas to the shell's load capacity, and would require some form of compensating action after their removal.
# Real-Time Farm Surveillance Using IoT and YOLOv8 for Animal Intrusion Detection
## 2 Methodology and Implementation
### Intrusion Detection  
In agricultural contexts, the phrase \"intruders\" usually refers to animals and humans [24]. The possible consequences of these incursions in this environment are many. Animals, whether wild or domesticated, represent a severe hazard to crops by devouring or harming them. Human invaders, on the other hand, may have far from benevolent intentions, frequently motivated by the illegal act of crop stealing. Figure 3 depicts the architecture of the Smart Farm Surveillance System, showcasing its incorporation of IoT components, cloud computing, and real-time monitoring functionalities intended to protect agricultural fields from invasions, including by cattle. The device comprises two ESP32 microcontrollers, each fitted with cameras and audio, that incessantly surveil the area and photograph any intruders. Live data are transferred to the cloud, where machine learning algorithms, like YOLOv8, evaluate it in real time to detect possible threats. Upon the identification of an intruder, the cloud system interfaces with the ESP32 microcontrollers, initiating alarms and activating the speakers to repel the intruders. In addition, the farmers receive alerts via a  
Figure 2: Proposed methodology of the system.  
cloud-based messaging system. The arrows in the figure represent the flow of data between the sensors and the cloud, emphasizing the system's ability to react promptly and improve farm security.  
The primary goal of this system is to provide an efficient and resilient tool for detecting intrusions on agricultural property. When such intrusions are detected, the essential premise is to trigger a rapid and responsive warning and alarm system. This method is critical in guaranteeing the quick interception of illegal entities, whether animals or people, preventing possible harm and theft [25].  
The identification of intrusions into agricultural systems is critical. It is now feasible to reduce the danger of crop loss due to animal depredation and human malice by implementing sophisticated intrusion detection systems. The capacity to identify incursions in real time allows the agricultural community to preserve important crops and, as a result, the livelihoods of individuals who rely on these lands for food and economic well-being. Furthermore, intrusion detection has ecological importance because it reduces the harmful effect of unregulated animal infiltration on local ecosystems. As a result, the significance of such systems extends well beyond the agricultural field, making them a critical component of sustainable and secure farming operations.
## 3 Equilibrium and Thermodynamic Aspects of Cr(VI) Sorption
### Maximum Sorption Capacity  
A most important parameter for evaluating sorbent efficiency is the maximum (saturation) sorption capacity, \(q_{m}\), as determined from the plateau level of the experimental isotherm. A list of values of \(q_{m}\) for different sorbents is given in Table 1. It is important to note that the \(q_{m}\) values reported here may not be strictly comparable because, as discussed above, the Cr(VI) sorption capacity may vary with operative conditions such as pH and temperature. The highest maximum sorption capacity for Cr(VI), 12.0 mmol g\({}^{-1}\), was reported by [PERSON] et al. [62] for a polypyrrole graphene oxide nanocomposite. High values of \(q_{m}\) were also found for nanosilica immobilized-fungi (\(q_{m}=10.1\) mmol g\({}^{-1}\)) [36], grafted aerobic granular sludge (\(q_{m}=7.7\) mmol g\({}^{-1}\)) [60], and chitosan/poly(vinyl amine) cryogel (\(q_{m}=6.1\) mmol g\({}^{-1}\)) [63].
## 5 Obstacles and Challenges in Evolving V2X Communication  
### RAN Obstacles and Challenges  
#### 5.1.1 The Used Frequencies  
Due to the cooperative property of the V2X network, it would be essential to use the same access layer technology in all vehicles. The C-V2X and the ITS-G5 technologies target the same 5.9 GHz frequency range, which is divided into channels. This frequency range is dedicated to ITS communication. The lack of backward compatibility of Release 16 can also be a problem since the parallel usage of Releases 14/15 and Release 16 would require even more bandwidth. There are concerns about the parallel use of these technologies on subsequent channels as well [133].
# Reliable Internet of Things: Challenges and Future Trends
## 3 Major Use Cases of IoT
### Internet of Smart Grids  
Internet of Smart Grids (IoSG) improves electricity usage and distribution by connecting the home devices with the grid. Smart meters can also be placed at homes that share electricity usage information with the grid. IoSG can reduce electricity usage, thus facilitating the customers as well as improving the carbon footprints and conserving electricity resources [77; 78; 79; 80; 81; 82].  
The primary data sensed in IoSG is the amount of electricity consumed in Kilowatt-hour (KWH). These data can be sensed at a home level or individual devices within a home level. For distribution, the electric loss is also an important parameter [83; 84; 85; 86; 87].  
Cellular communications are mostly used to share electricity data from a smart meter to the electric grid. The data rate and latency requirements for this type of data may not be high. In this regard, 5G technology is the most suited to share electricity data periodically.  
IoSG can analyze the electricity data and provide useful insights such as scheduling different loads at different times of the day. Moreover, it can predict the amount of bill based on current usage and allow users to take actions if needed [88; 89; 90].
## 3 Results and Discussion
### Effect of Irradiation
#### 3.7.2 Irradiation Stability of SC4A/CHCl\({}_{3}\)  
Irradiation stability of SC4A/CHCl\({}_{3}\) was investigated in the present study. First, SC4A/CHCl\({}_{3}\) was irradiated with varying doses (200 Gy\(\cdot\cdot\cdot\cdot\)min\({}^{-1}\)), and then 1.0 \(\times\) 10\({}^{-3}\) mol\(\cdot\)L\({}^{-1}\) irradiated SC4A/CH\({}_{3}\)Cl was employed to extract Cs(I) or Sr(II) from an aqueous phase of equal volume. The \(D_{\mathrm{Cs}}\) and \(D_{\mathrm{Sr}}\) values under different irradiation doses are listed in Table 6. The values of \(D_{\mathrm{Cs}}\) and \(D_{\mathrm{Sr}}\) are almost constant until the irradiation dose reached 5.0 \(\times\) 10\({}^{5}\) Gy. The results indicate that SC4A/CH\({}_{3}\)Cl was hardly impaired by irradiation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Irradiation dose (Gy)** & **0** & **1.0 \(\times\) 10\({}^{4}\)** & **1.0 \(\times\) 10\({}^{5}\)** & **5.0 \(\times\) 10\({}^{5}\)** & **1.1 \(\times\) 10\({}^{6}\)** \\ \hline D\({}_{\mathrm{Cs}}\) & 27.59 & 26.92 & 27.55 & 20.67 & 16.27 \\ D\({}_{\mathrm{Sr}}\) & 20.86 & 21.13 & 20.62 & 15.36 & 11.51 \\ \hline \hline \end{tabular} Conditions: \(T\) = 298 K, [SC4A]\({}_{\mathrm{(o)}}\) = 1 \(\times\) 10\({}^{-3}\) M, [Metal]\({}_{\mathrm{(a)}}\) = 1 \(\times\) 10\({}^{-4}\) M, pH = 6, contact time = 60 min, shaking speed: 150 rpm.  
\end{table}
Table 6: The effect of irradiated extraction system on \(D_{\mathrm{Cs}}\) and \(D_{\mathrm{Sr}}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Irradiation Dose (Gy)** & **0** & **1.0 \(\times\) 10\({}^{4}\)** & **1.0 \(\times\) 10\({}^{5}\)** & **5.0 \(\times\) 10\({}^{5}\)** & **1.1 \(\times\) 10\({}^{6}\)** \\ \hline \(D_{\mathrm{Cs}}\) & 27.62 & 27.48 & 27.42 & 28.37 & 31.27 \\ \(D_{\mathrm{Sr}}\) & 20.74 & 20.18 & 21.14 & 21.54 & 20.94 \\ \hline \hline \end{tabular} Conditions: \(T\) = 298 K, [SC4A]\({}_{\mathrm{(o)}}\) = 1 \(\times\) 10\({}^{-3}\) M, [Metal]\({}_{\mathrm{(a)}}\) = 1 \(\times\) 10\({}^{-4}\) M, pH = 6, contact time = 60 min, shaking speed: 150 rpm.  
\end{table}
Table 5: The effect of irradiated extractants on \(D_{\mathrm{Cs}}\) and \(D_{\mathrm{Sr}}\).
# A Microscopic Traffic Flow Data Generation Method Based on an Improved DCGAN
## 1 Introduction
### Contribution  
Manually designed scenarios do not necessarily meet the characteristics of naturalistic driving scenarios, and because naturalistic driving datasets are limited, they cannot be adequately tested. In addition, there is rarely an automated method that can output microscopic traffic flow scenarios in bulk.  
To address the above issues, the main contributions of this study are: (i) A data generation method based on an improved DCGAN network for vehicle simulation of natural traffic flow is proposed. (ii) Experiments are designed to compare with the original DCGAN to verify that the proposed neural network can generate more realistic microscopic traffic flow data.
## 2 Analysis of the Pore-Structure Characteristics of the Filter Layer
### Ideal Ellipsoidal-Particle Filter Layer
#### 2.2.1 Theoretical Maximum Pore Radius  
Under ideal conditions, the particles in the filter layer that form the theoretical maximize the effective pore radius are arranged as depicted in Figure 3. The analysis was conducted using the upper-left portion of the axisymmetric diagram. The long-axis radius, \(OA\), is denoted as \(a\), and the short-axis radius, \(OB\), is denoted as \(b\). The contact point between the two ellipsoidal particles is denoted as \(C\), with \(OC=c\). The effective pore radius of the filter for this particle arrangement is \(BD=r_{max}\).  
By geometric derivation, \(r_{\max}\) can be determined using Equation (3), as follows:  
\[r_{\max}=\sqrt{a^{2}+b^{2}}-b \tag{3}\]  
The relationship between \(\tan\theta_{1}\) and the radii \(a\) and \(b\) of the long and short axes of the ellipsoid can be derived mathematically. The final solution is given by Equation (4), as follows:  
\[\tan\theta_{1}=\frac{a^{2}}{b^{2}} \tag{4}\]  
When \(a=b\), the ellipsoid degenerates into a sphere and \(\theta_{1}\) is \(45^{\circ}\), which is consistent with the previously obtained calculation results.
# Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection
## 3 Material & Methods
### Classification of Relevant Features Using XGBoost  
Large feature sets can be bad for deep learning training since they can cause overfitting owing to data noise, resulting in subpar performance on new, untried data. In addition, the \"curse of dimensionality,\" which refers to the existence of high-dimensional data that make the data sparse and make it challenging for the model to model the underlying patterns in the dataset accurately, is another factor that may harm model performance.  
The dataset, which comprises 128 features due to one-hot encoding of the categorical data, may lead to the issues above. Therefore, the data were subjected to a feature selection technique to solve this issue. \"Feature selection\" refers to picking a subset of pertinent features to be used in the model. The objective is to identify a subset of traits that improve model performance while reducing data dimensionality.  
After XGBoost had been tested on the complete dataset, the idea of gain in XGBoost was employed to determine each attribute's importance. XGBoost [54], which stands for eXtreme Gradient Boosting, is a popular machine learning algorithm known for its efficiency and effectiveness in handling large feature sets. It is commonly used for feature selection tasks because of its ability to assess the importance of features in a dataset.  
The dataset consisted of 128 features after one-hot encoding of categorical data. This large number of features can lead to challenges such as overfitting and the curse of dimensionality. To address these issues, feature selection techniques are applied to identify a subset of relevant features that can improve model performance while reducing dimensionality. XGBoost was used for feature selection in this case. The gain in XGBoost was employed to determine the importance of each attribute. The gain represented the improvement in the model's performance achieved by including a particular feature. XGBoost built a model based on the full dataset and evaluated the gain of each feature to determine their relevance. The gain values across all decision trees in the model were then aggregated to determine the overall significance of each feature.  
XGBoost determined the primary functions of each feature by developing a model on the full dataset and then evaluating the gain of each feature to determine the relevance of each feature. Then, the gain across every decision tree in the model was aggregated to determine the significance of each feature.  
As seen in Figure 8, where Src Bytes is the most significant feature, 38 features were retrieved from the original 138 features. Therefore, the other characteristics were eliminated from the dataset after these features had been utilized for training the models.  
Figure 7: 2D visualization of the intrusion attacks.
## 3 Materials and Method
### Soil and Pipe Parameters  
In the analyses, embankment fill and backfill around the pipe and local soil were modeled by considering the Hardening Soil (HS) Model, using the uncoupled flow rule and the elastoplastic Mohr-Coulomb failure criteria. The material parameters of the soils are presented in Table 2.  
The reference secant modulus \((E_{50}^{ref})\) of backfill (sand, Cr. S) and local soil was calculated from Equation (1) below by considering the secant modulus \((E_{50})\) obtained from triaxial compression tests [63]. The reference unloading-reloading module \((E_{ur}^{ref})\) is \(E_{ur}^{ref}=3\times E_{50}^{ref}\) and the reference pressure \(p^{ref}\) = 100 kPa was taken into account [64].  
\[E_{50}=E_{50}^{ref}\left(\frac{c\text{Cos}\phi-\sigma_{3}^{r}\text{Sin}\phi}{c \text{Cos}\phi+p^{ref}\text{Sin}\phi}\right)^{m} \tag{1}\]  
The thermoplastic pipe wall was elastically modeled, and the parameters of the pipes are presented in Table 3. A and B are PVC; the others are HDPE.
# Effect of Fiber Optic Plate on Centroid Locating Accuracy of Monocentric Imager
## 2 Monocentric Imager with FOPs as Relay Image Transmission Elements
### Periodic Translation of the Imaging Spot  
In the field of the space-based space target surveillance, the use of FOPRIT scheme can realize the detection of space targets in a large field of view. A distant space target is imaged as a diffuse spot by a monocentric imager. However, the diffuse spot is not a large target on the entire image, and usually only a few image sensor pixels can receive all the energy. We cannot determine the precise position of the space target, and then cannot identify and locate the space target, which affects the measurement accuracy of the monocentric imager. We note that the position of the space target imaged on the image sensor is random, and the position of the same space target imaged on the image sensor at different times is also different. As image sensor pixels are arranged in a regular square lattice, the centroid locating error of the imaging spot is same as the centroid locating error of the imaging spot after being translated by one image sensor pixel; that is, the translation period of the imaging spot is one image sensor pixel. We call this process the periodic translation of imaging spot (PTOIS) (see Figure 2). We note that the selection of the relative initial position between the imaging spot and the image sensor pixel has no effect on the PTOIS simulation. For the convenience of observation, we take the intersection of the imaging spot center and a certain image sensor pixel center as the relative initial position. It is meaningless to simulate and analyze the situation of the centroid location of the imaging spot on a certain instantaneous position. Only by analyzing the periodic translation of the imaging spot can we get the most referential and accurate conclusion.
## 1 Introduction
### Failure State Observability  
When considering the measurement noise, the relationship between the measurement variable and the state variable in the distribution network is:  
\[z=H(x)+v \tag{6}\]  
where \(z\), \(v\), \(x\), and \(H\) are the measurement vector, measurement noise vector, state vector, and measurement function vector, respectively.  
From a mathematical point of view, in order to ensure the observability of the system and the accurate determination of the subsequent fault section location, the configuration of the intelligent terminal should make the \(H\) matrix full rank, that is, the number of measurement linear correlations is the smallest. At the same time, the measurement vector dimension must be larger than the state vector dimension:  
\[\begin{array}{l}\mathrm{R}(M^{T}M)=\mathrm{dim}(x)\\ \mathrm{dim}(z)>\mathrm{dim}(x)\end{array} \tag{7}\]  
where R(\(\cdot\)) represents the order of the square matrix, and \(\mathrm{dim}(\cdot)\) represents the dimension of the vector.  
It should be noted that a ground fault in the vicinity of a distribution transformer may lead to the transformer neutral point discharge, which would further cause a series of complex faults such as transformer gap breakdown, transformer tripping, and gap current transformer damage. The power supply of the corresponding power supply area can be directly determined by monitoring the transformer's operating state. As a result, in this paper, intelligent terminals are installed on the low-voltage side of the transformer. The distribution network is primarily radial at the same time. Considering that the configuration nodes of intelligent terminals, especially \(\upmu\)PMU measurement equipment and its adjacent nodes, are all observable nodes [27; 28], the nodes with more outgoing lines should be selected as configuration nodes as much as possible.
# On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
## 2 Proposed Method
### Practical Implementation  
In this part, we only consider the structured spike-and-slab model that is defined in (8) for \(\lambda_{j,q,b}=\gamma_{q}v_{b}\). The derivation of the Gibbs sampler for the other models is straightforward.  
Let \(\mathbf{\Theta}=\underset{0\leqslant q\leqslant Q}{\cup}\{\gamma_{q},\eta_{q},\phi_{q}, \xi_{q}\}\underset{1\leqslant p\leqslant B}{\cup}\{v_{b}\}\cup\{\mathbf{z},\mathbf{g}\}\) be the set of unknown parameters and hyperparameters of the proposed models. Figure 2 shows the generative graph of the proposed hierarchical model. The related joint prior distribution is given by  
\[\mathrm{p}(\mathbf{x},\mathbf{\Theta})=\mathrm{p}(\mathbf{x}|\mathbf{\Theta})\mathrm{p}(\mathbf{ \Theta}) \tag{9}\]  
where  
\[\mathrm{p}(\mathbf{\Theta})=\prod_{q=0}^{Q}\mathrm{p}(\gamma_{q}|\eta_{q})\mathrm{ p}(\eta_{q}|\phi_{q})\mathrm{p}(\phi_{q}|\xi_{q})\mathrm{p}(\xi_{q})\prod_{b=1}^{B }\mathrm{p}(v_{b})\mathrm{p}(\mathbf{z}|\mathbf{g})\mathrm{p}(\mathbf{g}) \tag{10}\]  
The resulting joint density reads  
\[\mathrm{p}(\mathbf{x},\mathbf{\Theta},\tau,\mathbf{y})=\mathrm{p}(\mathbf{x},\mathbf{\Theta}) \mathrm{p}(\mathbf{x}|\tau)\mathrm{p}(\tau) \tag{11}\]  
We propose designing a Gibbs sampler that is able to efficiently sample from the conditional distribution of the signal \(\mathbf{x}\), \(\tau\) and the set of hyperparameters \(\mathbf{\Theta}\). The main challenge with industrial signals is their large size due to the high sample rate of the target signal. To give the reader an idea, a vibrating signal from a mechanical system that resonates at 5 kHz needs at least 10,000 samples per second. We propose adopting the data augmentation strategy in [58] in order to tackle the tractability of sampling in such a high dimensional problem. Appendix B provides the derivation of conditional distributions and details about sampling steps.