esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Drift Support Analysis
### Estimation of Support Pressure  
Due to the overlying strata and the superposition of the structure in the strata, it is necessary to estimate the supporting pressure when supporting the drift. The calculation of support pressure plays an important role in analyzing the stability of drift. The support pressure can easily be obtained through the rock mass classification system  
[PERSON] et al. [44] proposed Equation (5) for estimating the short-term support pressure for arched underground openings in both squeezing and non-squeezing ground conditions in tunneling by conventional blasting methods using steel rib supports.  
\[\mathrm{p_{v}}=\frac{7.5\mathrm{B^{0.1}}\cdot\mathrm{H^{0.5}}-\mathrm{RMR}}{20 \mathrm{RMR}} \tag{5}\]  
where \(\mathrm{p_{v}}\) is short-term roof support pressure, MPa; B is span of opening, m; H is overburden or tunnel depth, m; and RMR is actual (disturbed) post-excavation rock mass rating just before supporting.  
[PERSON] et al. [23] plotted support capacities of 200 underground openings against the rock mass quality (Q) and derived the following empirical correlation for ultimate support pressure:  
\[\mathrm{P_{v}}=(0.2/\mathrm{J_{r}})\mathrm{Q^{-1/3}} \tag{6}\]  
[PERSON] and [PERSON] [45] suggested the following correlation for predicting support pressure in tunnels through poor rock masses (such as Q < 4):  
\[\mathrm{p_{v}}=\frac{40\mathrm{B}}{\mathrm{J_{r}}}\cdot\mathrm{Q^{-1/3}} \tag{7}\]  
where B is the diameter or span of the tunnel in m. Equation (7) shows that the support pressure increases with tunnel size in poor rock masses. [PERSON] et al. [46] proposed the following correlation for ultimate tunnel support pressure:  
\[\mathrm{p_{v}}=\frac{0.2}{\mathrm{J_{r}}}\cdot\mathrm{Q^{-\frac{1}{3}}}\cdot \mathrm{f}\cdot\mathrm{f}^{\prime} \tag{8}\]  
\[\mathrm{f}=1+(\mathrm{H}-320)/800\geq 1\]  
where \(\mathrm{p_{v}}\) is ultimate tunnel support pressure, MPa; f is the correction factor for overburden; f\({}^{\prime}\) is correction factor for tunnel closure; and H is overburden above crown or tunnel depth below ground level, m. Different equations are provided to calculate the support pressure; the results are shown in Table 6. Through field investigations, it was found that Equation (8) is more similar to the rock mass characteristic value under field conditions.
# Dual-Dimensional Gaussian Splatting Integrating 2D and 3D Gaussians for Surface Reconstruction
## 3 Methods  
### Background  
3D Gaussian Splatting (3 DGS) [9] generates 3D Gaussians from a sparse point cloud to represent a scene. Each Gaussian is defined by its mean \(\mathbf{\mu}\), representing its position, and its covariance matrix \(\Sigma\), which encodes its shape and orientation in 3D space:  
\[G(\mathbf{x})=\exp(-\frac{1}{2}(\mathbf{x}-\mathbf{\mu})^{T}\Sigma^{-1}(\mathbf{x} -\mathbf{\mu})). \tag{1}\]  
The covariance matrix  
To address this issue, 2D Gaussian Splitting (2 DGS) [10] incorporates a method based on homogeneous coordinate transformation. The \(4\times 4\) homogeneous transformation matrix, encoding the scale and rotation information of the 2D Gaussians, is mapped to screen space by multiplying it by a \(4\times 4\) world-to-screen transformation matrix. This precise 2D-to-2D mapping ensures the accurate projection of the Gaussians. However, representing curved surfaces with flat 2D Gaussians is challenging and often results in over-smoothed reconstructions.
## 5 Conclusions  
In this study, using small-scale flume tests, we evaluated the effect of the configuration of cylindrical baffles behind a rigid barrier on run-up height and the impact force. For this, a series of small-scale flume tests were performed according to different baffle heights and numbers of rows. The findings from this study are as follows:  
1. The experimental results showed that the use of baffle arrays reduced the run-up height and impact force on the terminal rigid barrier because of the suppression of boulders and flow;
2. Increasing the number of baffle rows created a greater run-up and impact force reduction because it impeded the mobility of boulders and flow. In comparison with the existing analytical approaches, the test with no baffles agreed well with the trend  
Figure 9: Comparison of the residual static impact force.  
line by the energy principle model, whereas the results with baffles approached the trend line by the momentum-based approach model, which was dominated by the frictional force;
3. Increasing the number of baffle rows provided more energy dissipation and impact force reduction. Furthermore, the use of baffles promoted a reduction in the run-up height and overflow; however, it did not produce a higher flow deposition on the back of the rigid barrier;
4. Cylindrical baffles behind the rigid barrier can be utilized as efficient countermeasures to reduce the high impact forces by boulders entrained by debris flows. For the field application of baffles, however, the transverse blockage ratio of the baffles needs to be varied with the size of boulders. Thus, an additional study on impact force characteristics considering the interaction between baffles and flows including various size of boulders will be needed.  
Data curation, B.-J.K. and D.K.; Conceptualization, B.-J.K.; Methodology, B.-J.K. and C.-Y.Y.; Visualization, B.-J.K. and D.K.; Formal analysis, B.-J.K.; Writing-review & editing, B.-J.K., D.K. and C.-Y.Y.; Funding acquisition, C.-Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1A6A1A03044326 and 2021R1A6A3A01086753).  
Not applicable.  
The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.
## 3 Results and Discussion
### Optimization of the W1 WQMP - Results and Discussion
The WQMP revealed that it is possible to obtain a consistent spatio-temporal portrait of the cyanobacteria community with the proposed sampling strategy and sampling frequency and recurrence. In addition, sufficient data were collected to attribute the species to the trophic state of the lake. It is also possible to eliminate one station where generally two samples were taken (C01), for a total of 20 samples per year. In addition, it was shown that the sampling could be reduced to a four-week interval. However, the recommendations are to:  
* Ensure that changes in the water quality of the tributaries (in particular road salts) and the ensuing impact on Lac Saint-Charles are detected in order to reinstate the present cyanobacteria program and to observe changes which they may announce. * Verify whether other objectives followed within this WQMP are not compromised by these decisions.
## 1 Introduction  
In UAV [1] agricultural plant protection spraying operations, the droplet deposition characteristics [2] determine the effectiveness and efficiency of plant protection spraying, and are important indicators for evaluating and optimising spraying operations. Due to the high droplet density during spraying and the influence of natural conditions, the phenomenon of sticky droplets often occurs on the crop surface [3]. The study of accurate segmentation of overlapping droplets on crop leaves was of great significance in guiding the in-depth understanding of droplet deposition distribution characteristics [4]. At present, the field detection of overlapping-droplet-deposition distribution characteristics is mainly divided into two methods: direct detection and indirect detection [5]. Although the direct detection method can directly obtain the amount of droplet deposition on the leaf, there is uncertainty in its elution effect. In contrast, indirect detection methods are favoured for their intuitive nature and wide applicability. Among them, water-sensitive test paper has become the most commonly used overlapping-droplet collector due to its advantages such as obvious colour development, easy image processing, and storage [6; 7; 8]. Segmentation of overlapping droplets on water-sensitive test papers is a relatively complex process, and many strategies and methods have been proposed to improve and optimize the analysis process to obtain more accurate information [9]. Currently, several methods have been developed for overlapping-object-image-segmentation techniques, mainly including the concave-point method, watershed algorithm, and morphological algorithm [10]. Table 1 provides a systematic comparison and analysis of various methods. Morphological segmentation methods achieve overlapping-object differentiation through morphological operators. [PERSON] et al. [11] proposed an improved image-segmentation algorithm based on morphological reconstruction and the fuzzy C-mean algorithm, which effectively improves the segmentation time and enhances the segmentation efficiency, but shows large differences when dealing with overlapping objects with different structures. [PERSON] et al. [12] developed a morphology-based cell-image-segmentation algorithm, which is simple and fast and which has high segmentation accuracy for mild to moderate overlapping cells, but is less effective in dealing with complex overlapping situations. The watershed algorithm is suitable for grey-scale image segmentation with the structure of mountain peaks and valleys, but gradient transformation is required for images that do not conform to this structure. The algorithm responds well to weak edges, but it is easily affected by image noise, local irregularities, and subtle grey-scale changes on the object surface, resulting in under-segmentation and over-segmentation phenomena. [PERSON] et al. [13] proposed a distance-transform-based marker-controlled watershed algorithm, which achieves the simultaneous segmentation of red blood cells and white
# Study on the Deployment of Ultra-Wideband Positioning Base Stations in Pig Farms
## 2 UWB Positioning Principles  
The UWB positioning system used in this study utilizes the Time of Arrival (TOA) technique, which estimates the time delay from the node to be located to the reference base stations, thereby determining the distance between them. The main advantage of the TOA positioning method is its ability to exploit the high time-division rate inherent in ultra-wideband signals to accurately detect signal time delays at the receiver end, thereby allowing the distances between nodes and reference base stations to be estimated. Once these distances are estimated, the coordinates of the target node can be calculated usinga basic UWB positioning algorithm. In three-dimensional space, the TOA positioning model follows a spherical representation that requires a minimum of four base stations for accurate localization. In addition, precise clock synchronization between base stations has a significant impact on positioning accuracy.  
The coordinate of the \(j\)th base station is \(\left(x_{j},y_{j},z_{j}\right)\). Solving for label coordinates \(\left(x,y,z\right)\). Assuming the synchronization condition, i.e., no clock difference, when there are \(n\) measured values, \(R_{j}\) represent the distance between the tag and \(j\)th base station  
\[R_{j  
The covariance of d\(x\) is:  
\[\begin{array}{ll}cov(dx)&=E\big{(}dxdx^{T}\big{)}\\ &=E\Big{[}\big{(}H^{T}H\big{)}^{-1}H^{T}drdr^{T}H\big{(}H^{T}H\big{)}^{-1}\Big{]} \\ &=\big{(}H^{T}H\big{)}^{-1}H^{T}cov(dr)H\big{(}H^{T}H\big{)}^{-1}\\ &=\big{(}H^{T}H\big{)}^{-1}\sigma_{r}^{2}\\ \\ &\qquad cov(dr)=1\sigma_{r}^{2}\end{array} \tag{10}\]  
In the measurements, the errors are random, independent in zero mean, and the RMS in common.  
\(\sigma_{r}\) is the root-mean-square (RMS), the error covariance matrix is:  
\[Q=\sigma_{r}^{2}I \tag{12}\]  
\[H=\begin{bmatrix}a_{x1}&a_{y1}&a_{z1}\\ a_{x2}&a_{y2}&a_{z2}\\ &\vdots&\vdots&\vdots\\ a_{xn}&a_{yn}&a_{2n}\end{bmatrix} \tag{13}\]  
The matrix of weight coefficients \(I\) is:  
\[I=\big{(}H^{T}H\big{)}^{-1}=\begin{bmatrix}i_{11}&i_{12}&i_{13}\\ i_{21}&i_{22}&i_{23}\\ i_{31}&i_{32}&i_{33}\end{bmatrix} \tag{14}\]  
\[Q=\sigma_{r}^{2}I=\sigma_{r}^{2}\begin{bmatrix}i_{11}&i_{12}&i_{13}\\ i_{21}&i_{22}&i_{23}\\ i_{31}&i_{32}&i_{33}\end{bmatrix}=\begin{bmatrix}q_{11}&q_{12}&q_{13}\\ q_{21}&q_{22}&q_{23}\\ q_{31}&q_{31}&q_{33}\end{bmatrix} \tag{15}\]
## 3 Effect of the Particle Size Ratio on the Macroscopic Mechanical Behavior
### Effect of the Particle Size Ratio on the Elastic Modulus  
The elastic modulus of models with different particle size ratios are extracted, and the average and standard deviation of the elastic modulus of four specimens with same particle size ratio and different particle arrangements are calculated, as shown in Figures 20 and 21.  
The average elastic modulus decreases with an increase in the particle size ratio, from 2.07 GPa to 1.27 GPa, with a decline amplitude of 38.89%. This is because when the particle size ratio is 1.0, the particles are aligned and pressed against each other. The force conduction between particles is clear and the particles' displacement is small; so, the model's deformation is small and the elastic modulus is larger. When the particle size ratio is less than 1.5, the elastic modulus decreases at a fast rate, and the elastic modulus difference of specimens with different IDs is relatively large. When the particle size ratio  
Figure 21: Variation in the standard deviation of the elastic modulus with the particle size ratio.  
Figure 20: Variation in the elastic modulus with the particle size ratio.  
is greater than 1.5, the rate of the elastic modulus reduction slows down, and the elastic modulus difference of specimens with different IDs is small. For a fixed particle size ratio that is bigger than 1.5, the elastic moduli of specimens with different IDs are closely concentrated near the average value. It can be found from Figure 21 that the standard deviation of the elastic modulus for specimens with other particle size ratios remains at a low level, except for the particle size ratio equal to 1.0. The standard deviation of the elastic modulus is 0.055 for specimens with a particle size ratio of 1.0, and it rapidly decreases to around 0.02 when the particle size ratio equals 1.5, with a decrease of roughly 63.64%. This indicates that the larger the particle size ratio is, the smaller the elastic modulus is. When the particle size ratio is not less than 1.5, the elastic modulus can show lower dispersion. The influence of the particles' spatial arrangement is rapidly weakened, and the elastic modulus of specimens with different IDs can show stronger consistency, which improves the accuracy and reliability of the numerical simulation results.  
In summary, the larger the particle size ratio, the smaller the uniaxial compressive strength and elastic modulus. When the particle size ratio is larger than 1.5, the dispersion of the numerical simulation results can be greatly reduced. The selection of the particle size ratio in a particular numerical simulation study usually needs to consider material grading characteristics. However, in combination with the above analysis, to obtain a more comprehensive, accurate, and reliable numerical simulation of uniaxial compression, the particle size ratio should not be less than 1.5.
# 3D Approaches and Challenges in Facial Expression Recognition Algorithms--A Literature Review
## 3 Conventional and Deep Learning-Based Approaches
### Deep Learning Applied to FER
#### 3.3.1 Convolutional Neural Networks (CNN)  
Most of the deep learning methods use neural network architectures, and one of the well-known and used to recognize objects or faces is the Convolutional Neural Network (CNN). The three main factors that have contributed to the use of CNNs for deep learning are the elimination of manual extraction of features, the cutting-edge recognition results and the possibility of retraining existing networks for other recognition activities [1].  
A CNN is composed of an input layer, an output layer and up to 150 hidden layers in the middle (for this reason, it is called \"deep\") [94], while traditional neural networks have only 2-3. These layers are intended to find and learn features, repeating convolution, activation or Rectified Linear Unit (ReLU), and pooling operations; each hidden layer increases the complexity of the features of the image [95]. Below is a summary of the three main steps:  
* Convolution is a mathematical operation that recalls the functioning of a scanner and consists of applying a smaller matrix called kernel to the input matrices, the images. Each of the convolutional filters is translated with a specific translation stride and activates certain features of the pictures.
* After a convolutional layer, in most ConvNets, there is an activation function. The main purpose of this layer is introducing a non-linearity in the system, using non-linear functions, such as tanh, sigmoid, and ReLU. The Rectified Linear Units returns 0 if it receives any negative input, but for any positive value x it returns that value back, so it can be written as f(x) = max (0, x): only activated features are transferred to the next layer.
* The pooling performs a non-linear subsampling, reducing the size of the output matrices and the number of parameters that must be learned by the network. The most common operations are MaxPooling and AveragePooling.  
After the learning phase of the features, the classification phase begins. To provide the classification output, a fully connected layer, and a classification layer, such as SoftMax, are used. The penultimate layer generates a vector of dimensions equal to the number of classes, containing the probability for each one and computing the class score on the entire original image. The last layer of the CNN architecture assigns the decimal probabilities to each class, in a multi-class problem.
# Robust Federated Learning for Mitigating Advanced Persistent Threats in Cyber-Physical Systems
## 4 Experimental Results
### Results
By integrating the selected classifiers into the designed FL structure, different nodes of the network can collaboratively train a global detection model to detect APT samples. Here, the FL network used ten nodes, where each node trained a local model on only one of the datasets listed in Table 3. Moreover, all of these nodes used a common network structure. Figure 4 depicts the cross-entropy loss of the global model through the iterations of the collaborative training. As nodes shared their parameters and updated their local weights in each round, the global loss decreased on the training data. This ensured that the global model was a perfect fit that successfully captured the APT distributions of each node. Furthermore, the global model converged rapidly and reached the optimal loss in fewer than 50 rounds of training. This implies the practicality of using FL in the collaborative training of a global APT detection model in CPSs. This application is vital to more data-sensitive CPSs, such as power systems and industrial control systems, where sensitive operational parameters and communication protocols can be targeted by APTs. The obtained results shown in Figure 5 indicate a significant improvement in the detection performance of both the APT classes and malware. Regardless of the choice of model architecture at the edge of the network, the proposed FL framework outperformed the stand-alone models. While all three models exhibited outstanding results in detecting the APT classes, the 1D CNN outperformed the DAE and MLP combinations with the designed framework, as depicted in Figure 5. The only class whose detection accuracy fell below 90 percent was Sandwork. Nevertheless, this pattern can also be seen for both the MLP and DAE in Figure 5,c. The observed improvements in the previous analysis indicate the significance of FL application across different CPSs. To begin with, accurate APT detection can prevent significant operational disruptions in industrial control systems. Another notable application of FL is in smart grids, which rely on distributed communication and control. These systems are often vulnerable to malicious activities and can benefit from the timely and accurate identification of adversaries. Figure 4: Cross-entropy loss of training a global model using FL with adaptive clipping. Ten nodes participate in the FL training process.
## 3 Results and Discussion
The peaks in the gamma spectrum were assigned to the characteristic gamma peaks at  
Figure 4: TEM and EDS data of the nanostructured Sm\({}_{2}\)O\({}_{3}\) before and after neutron irradiation. Figure 5: XRD data of the nanostructured Sm\({}_{2}\)O\({}_{3}\) without calcinations. 103 keV and 69.7 keV of Sm-153 (Figure 6). These results showed that the radioisotope Sm\({}_{2}\)O\({}_{3}\) NPs were successfully synthesized by the neutron irradiation of the as-prepared Sm\({}_{2}\)O\({}_{3}\) NPs that were synthesized by the microemulsion process and the calcination process. The successful synthesis of the Dy\({}_{2}\)O\({}_{3}\) powder with nanorod structures obtained by the redox process and the calcination process is shown by the results of the TEM image and EDS data (Figure 7). The morphology of Dy\({}_{2}\)O\({}_{3}\) powder exhibited nanorod-like structures with dimensions of 3 \(\times\) 15 nm in the TEM image. As EDS data shows, the percentage (%) of the Dy element and O element was 28.9% and 71.1%, respectively. After neutron irradiation, the morphology was not changed. From this result, the prepared Dy\({}_{2}\)O\({}_{3}\) was shown to have high stability under neutron irradiation. Figure 6: Gamma spectrum of the radioisotope Sm\({}_{2}\)O\({}_{3}\) NPs prepared by neutron irradiation. Figure 7: TEM and EDS data of the nanostructured Dy\({}_{2}\)O\({}_{3}\) before and after neutron irradiation. XRD data of the Dy\({}_{2}\)O\({}_{3}\) powder after the calcination process (Figure 8) showed that the diffraction peaks of the Dy\({}_{2}\)O\({}_{3}\) powder were very weak, indicating an amorphous structure after the calcination process. All the peaks assigned by the diffraction of crystalline Dy\({}_{2}\)O\({}_{3}\) at 2\(\Theta\) = 20.4\({}^{\circ}\), 29.0\({}^{\circ}\), 31.4\({}^{\circ}\), 33.6\({}^{\circ}\), 43.2\({}^{\circ}\), 48.2\({}^{\circ}\), and 57.3\({}^{\circ}\) correspond to the (211), (222), (321), (400), (431), (440), and (622) planes (JCPDS card 22-0612). Evident broad peaks were a mixture of the amorphous and nanocrystalline nature of the samples. For gamma spectroscopy analysis, a 1.74-mg sample of the as-prepared Dy\({}_{2}\)O\({}_{3}\) powder was activated for 10 s by neutron irradiation, and the gamma spectrum was then measured after cooling for 10 min. The peaks in the gamma spectrum were assigned to the characteristic gamma peaks at 94.7 keV, 361.7 keV, 633.4 keV, and 715.3 keV of Dy-165 (Figure 9). These results showed that the radioisotope Dy\({}_{2}\)O\({}_{3}\) nanorods were successfully synthesized by the neutron irradiation of the as-prepared Dy\({}_{2}\)O\({}_{3}\) nanorods that were synthesized by the redox process and the calcination process. We expect that the radioisotope Dy\({}_{2}\)O\({}_{3}\) nanorods can be used as radiotracers in scientific, environmental, engineering, and industrial fields. Figure 8: XRD data of the nanostructured Dy\({}_{2}\)O\({}_{3}\) without calcinations. Figure 9: Gamma spectrum of the radioisotope Dy\({}_{2}\)O\({}_{3}\) NPs prepared by neutron irradiation.