esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability Analysis of the Huasushu Slope Under the Coupling of Reservoir Level Decline and Rainfall
## 8 Conclusions  
The following conclusions were drawn from our analysis of three-dimensional cloud maps of stress, displacement, pore pressure, and equivalent plastic strain for Huasushu slope and two-dimensional slope stability calculations for Cases 1 to 5:  
(1) The three-dimensional deformation and failure analysis showed that the combined effect of a drop in the reservoir water level and heavy rainfall can significantly change the seepage field within this slope, leading to displacement in the slope mass. The deformations in the Huasushu slope and the increased equivalent plastic shear strains reached peak values when the reservoir water level gradually dropped from 175 m to 145 m and the 50-year rainfall occurred concurrently. Therefore, Case 3 was the most adverse for the Huasushu slope's stability. In this case, slope displacement was mainly concentrated in the middle.  
(2) In Case 3, the slope experienced local tensile-shear failure in the middle sections and the anterior border, but not an overall failure. The sliding belt in the middle and rear sections was steeper than in the anterior border, and the scope of the damage was more extensive. The horizontal displacement at surface monitoring point GPS2-1 was 5 mm with a vertical displacement of 30 mm.  
(3) Our analyses demonstrated that an increase in the rate of decline in the reservoir water level will reduce the slope stability factor, which will decrease further with an increase in the intensity and duration of rainfall.  
(4) According to the two-dimensional stability analysis, the most dangerous condition for the occurrence of a landslide at Huasushu is Case 3 (a slight decrease in the reservoir water level from 175 m to 145 m accompanied by 50-year rainfall). Under these conditions, the minimum value of the slope stability factor is 1.022, indicating that the landslide is basically in a stable state. The two-dimensional calculation results of the landslide further verify the correctness of the calculation results for the three-dimensional model.  
(5) Comprehensive two- and three-dimensional simulations and analyses of their results showed that changes in the reservoir water level had little effect on the overall stability of the Huasushu slope, but may lead to local damage in the wading area. Meanwhile, heavy rainfall has a more significant impact on overall slope stability. Therefore, in subsequent monitoring, increased attention should be paid to heavy rainstorms.  
Methodology, Y.L.; Investigation, J.W.; Data curation, H.Y.; Writing--review & editing, H.Y.; Funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript. This research received support from the National Natural Science Foundation of China (Grants No. 42071264 and 41641027), for which the authors are grateful. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.
# A History of Channel Coding in Aeronautical Mobile Telemetry and Deep-Space Telemetry
## 3 Deep-Space Telemetry
### Cassini-Huygens
The _Huygens_ probe was the first spacecraft to land on a body in the outer solar system. Prior to the landing, scientists were not certain that beneath the Titan's opaque organic haze, there was a solid surface to land on [73]. Another interesting fact about the probe component of the mission was a problem called the \"radio relay anomaly\" [73; 74]. Transmitter or receiver motion creates time compression (when moving toward each other) or time dilation (when moving away from each other). In most cases, the impact of the time compression/dilation on the underlying pulse train is negligible and is safely ignored. The resulting time-varying phase causes a frequency shift in the RF carrier known as a Doppler shift. What was overlooked during the design stage was the fact that the relative velocity of the orbiter as it flew past Titan while the probe descended was so large that the time compression/dilation could not be ignored. The compression/dilation was enough to cause cycle slips in the timing PLLs. The cycle slips would have caused large quantities of data to be lost. The obvious compensation, increasing the bandwidth of the timing PLL in the PSK demodulators in the orbiter's PSE, was not possible because the timing PLL loop bandwidth was fixed in hardware. An innovative solution was developed by the _Huygens_ Recovery Task Force: change the profile of the _Cassini_ fly-by to reduce the relative velocity between the orbiter and the probe [73; 74]. An interesting story on the ESA engineer who identified this problem, [PERSON], was published in [75]. The radio relay anomaly illustrates an important characteristic of channel coding that can be easily overlooked: many non-coding things have to work well before performance gains of coding can be realized.
## 3 Results  
### Microstructure in As-Cast and Homogenized States  
Figure 2a demonstrates the BSE images of the studied alloy in the as-cast state. Several phases distinguished by contrast were revealed in the images. The aluminum solid solution (Al) was the predominant microstructural feature. The phases with dark and bright contrast were found on the periphery of dendrite cells. The bright areas predominantly corresponded to the Cu-rich phases (CuAl\({}_{2}\) and Q(Al\({}_{5}\)Cu\({}_{2}\)Mg\({}_{8}\)Si\({}_{6}\))) while the dark areas correspond to the Mg\({}_{2}\)Si phase. The volume fraction of Cu-rich phases and Mg\({}_{2}\)Si phase were 1.8 \(\pm\) 0.2% and 2.1 \(\pm\) 0.1%, respectively. The two-step homogenization annealing provided for near complete dissolution of the Cu-rich phases (Figure 2b). The Mg\({}_{2}\)Si particles were partially fragmented and spherodzied. After homogenization, the volume fraction of the Mg\({}_{2}\)Si phase was 2.0 \(\pm\) 0.1% that was similar to as-cast state. The small fraction of particles enriched with Sc were also defined after annealing (Figure 2b). The size of particles was varied from 0.2 to 2 \(\upmu\)m.  
Figure 2: (**a,b,e**) SEM-BSE and (**c,d**) TEM micrographs of the (**a**) as-cast and (**b–e**) homogenized alloy; (**c**) bright field image; (**d**) dark filed image for (**c**) obtained in the reflection marked-up with a green circle in the SAED (insert in (**d**)); (**e**) SEM-EDS elemental distribution maps for the alloying elements.  
The TEM studies of the as-homogenized alloy revealed nanoscale dispersions (Figure 2a,b). Typical for the L12-Al3(Sc,Zr) phase superlattice reflections were observed in the selected area electron diffraction (SAED) (Figure 2d insert). The ordered position of the superlattice reflections against the reflections of Al confirm at least partial coherency of the dispersoids and the matrix. The mean dispersoid size was 10 \(\pm\) 1 nm.
# A Computer-Aided Detection System for the Detection of Lung Nodules Based on 3D-ResNet
## 3 Method
### False-Positive Reduction: 3D Depth Residual Network  
In existing CAD systems [12], the 2D convolutional neural network is mostly adopted in the false positive reduction stage. However, 2D convolutional neural networks cannot make full use of 3D image information. Studies have shown that the convolutional neural network depth is an extremely important factor in determining its performance [23]. The performance of a convolutional neural network will increase as its depth increases. At the same time, deeper networks become more difficult to train and degenerate. In order to solve these problems, a deep residual network [24] was proposed by [PERSON] et al. Based on the above discussion, we designed a 3D deep residual network in order to improve the performance of reducing false positives.  
The structure of the 3D deep residuals network is shown in Figure 1b. The main network structure refers to the 34-layer residual network in [24]. The original residual network was a 2D residual network and the CT involved in this paper provides 3D image information. The original 2D network cannot make good use of spatial information, so we improved the original 2D residual network and propose a 3D residual network. At the same time, the number of original network convolution kernels is large. We properly reduced the number of convolution kernels under the existing data set to prevent over-fitting and to prevent difficulty in training. The improved 3D residual network was composed of 33 convolution layers and a fully-connected layer. The convolution layer used \(3\times 3\times 3\) convolution kernels and the initial convolution kernel was 16. For the same size output, the number of convolution kernels does not change. We doubled the number of convolution kernels when the size of feature map was reduced by half. We achieved down-sampling by setting the step size to 2. We implemented residual operations by inserting shortcut connections between convolutional layers. While the input and output of the residual block were the same, we added the input characteristics and output characteristics of the residual block directly before passing through the activation function. When the output size of the residual block increased, we let the input feature map pass through a \(1\times 1\times 1\) convolutional layer of step size 2 to uniform size and then added it to the output. At the end of the network was a global average pooling and a fully-connected layer using the sigmoid's activation function.  
In the training process, we used an efficient first-order stochastic gradient optimization algorithm (Adam [25]) to optimize the model. The cross-entropy error was used as a measure of the loss. The number of samples per batch (mini-batches) was 40, and batch normalization [26] was used to accelerate training before each convolutional layer's activation function. The weight initialization strategy is the standard initialization proposed by [PERSON] et al. [27]. Training was stopped when the accuracy in the crossing validation dataset did not improve after three epochs.
# TSViT: A Time Series Vision Transformer for Fault Diagnosis of Rotating Machinery
## 4 Experimental Results and Evaluation
### Hyperparameters  
The structure of the transformer-based model is complex, and the hyperparameters are dependent on each other, making the parameter adjustment process challenging. Therefore, it is necessary to analyze the impact of the hyperparameters in the TSViT. We adjusted the hyperparameters using the PBR, CWRU, and XJTU datasets. Table 6 presents the fault diagnosis results using the TSViT model with various hyperparameter values under the same training condition. Figure 15 illustrates the results. There are two crucial hyperparameters in the patch embedding phase: the size of a patch \(L_{p}\) and the dimensions of the time series patch embedding \(m\). The number of patches \(n\) is inversely proportional to \(L_{p}\), and this relationship influences the subsequent self-attention calculations. We assume that the length of signal samples \(L\) must be evenly divisible by \(L_{p}\) to ensure that a signal sample can be divided into integer patches. \(L_{p}\) is varied between 16, 32, 64, and 128 to identify the optimal value for diagnostic testing. It can be seen from Table 6 that \(L_{p}\) should not be excessively large. When \(L_{p}\) is too large, the number of patches decreases, reducing the computational load of self-attention. However, this is not conducive to extracting features, and it also results in a decrease in the model's accuracy. It should not be excessively small either. If \(L_{p}\) is too small, the overall model's computational load will increase exponentially, which is not favorable for model training and may lead to overfitting. Consequently, the accuracy of the model will also decrease. Figure 15a illustrates the results. Table 6 presents that \(m\) is related to the parameter quantity of the entire model and should not be excessively large. When \(m\) is too large, there will be too many parameters, leading to overfitting. The average accuracy on the datasets is the highest when \(m\) is set to 192 and \(L_{p}\) is set to 32. Figure 15b illustrates the results. The model's performance is slightly affected by the position embedding. The experimental results indicate a 0.02% decrease in average accuracy on the three datasets. Additionally, the dropout following the position embedding also plays a role in influencing performance. Without dropout, the model tends to overfit, resulting in a decrease in average accuracy on the three datasets.
## 1 Introduction
's [9] experimental results from laser weeding indicate that the optimal time for weed removal is when the weed stem diameter is less than 2 mm during the 3-4 leaf stage, necessitating precise targeting of the weed stem and leaves with lasers for effective removal. This introduces new requirements for control processing. Laser weeding equipment requires an understanding of the complete plant shape to accurately locate the boundaries of weed stems and leaves, addressing the issue of bounding box localization in target detection algorithms, which fails to control the laser for precise cutting. Thus, this paper first applies image segmentation technology to the field of laser weeding and proposes an improved YOLOv8 instance segmentation algorithm based on bidirectional feature fusion and deformable convolution (BFFDC-YOLOv8-seg). This improved instance segmentation algorithm supports precise localization of weeds during the laser weeding process, effectively reducing crop damage rates. In order to conduct this study, the research consists of the following tasks:  
1. The dataset images are processed by adding Gaussian noise and adjusting the color space to enhance the model's generalization and robustness towards weed edge features in agricultural environments. 2. By obtaining model scales compatible with laser weeding equipment and developing pretrained weights more suited for agricultural settings, the accuracy and speed of model training are enhanced. 3. It introduces the Bidirectional Feature Pyramid Network (BiFPN) [24], an efficient weighted bidirectional framework for cross-scale connections and fast normalized feature fusion method. This approach significantly enhances the network's ability to focus on small targets, effectively addressing the challenge of detecting inconspicuous features in complex backgrounds. * DSConv is integrated to enhance the network's capability to segment irregular edges of plant stems and leaves, enabling accurate weed segmentation.
## 3 Experimental Results and Discussion
### Multiwavelength Visible Lasers Generation
Compared to some of the commercial wavelength tunable laser products which are based on SHG and OPO techniques, such as PHOCUS (OPOTEK, 690-950 nm), FPPO (MICROTECH, 740-1000 nm), and PhotoSonus X (EKSPLA, 665-1064 nm), this discretely tunable multiwavelength visible laser system adopts a new technique and can generate shorter tunable wavelengths varying from 579.5 to 658.4 nm with the same 1064 nm pumping laser and SHG part. Furthermore, the conversion efficiency of SRS and SHG/SFG could be improved by optimizing of time-domain waveform, beam quality, and oscillator structure in the future work. Additionally, we will strive to increase the output energy of visible-wavelength lasers for wide applications, such as spectroscopy, diagnostics, photoacoustics, multiphoton imaging, and medical research. \begin{table}
\begin{tabular}{c c c c c c} \hline \(\mathbf{A_{1}}\) (nm) & \(\mathbf{A_{2}}\) (nm) & **Nonlinear Process** & \(\mathbf{\lambda_{\text{cal}}}\) (nm) & \(\mathbf{\lambda_{\text{exp}}}\) (nm) & \(\mathbf{\theta}\) (\({}^{\circ}\)) \\ \hline  
G & 747 & Not Detected & 50.3 \\ \hline \end{tabular}
\end{table} 
Table 1: Calculated and measured output wavelengths with different phase-matching angles.
# VNF Placement Optimization at the Edge and Cloud +
## 6 Results and Discussions
### A Hybrid Simulation Approach
By sending the traffic and configuring the routing path, we can extract real processing and queuing delays, while the propagation delays will be simulated and added to the overall delay communication by simulating the distances between the communicating servers. In the same manner, the transport network will be simulated as a link connecting the two data centers, adding the corresponding propagation delay. Finally, for the chaining of the VNFs, two different approaches will be used according to the placement decision. In the first case, assuming that two consecutive VNFs are allocated on the same server, a Virtual Ethernet Bridging (VEB) technique will be used by creating a bridge at the hypervisor level that will facilitate the interconnection of the VNFs. In the second case, the traffic forwarding between two VNFs residing on different servers will be performed through the switch, using a Virtual Ethernet Port Aggregator (VPA) technique. The CVI-SIM will monitor and gather real-time metrics (e.g., delay, throughput) and will extract the necessary results after the completion of the experiment. Nonetheless, the major challenge in this hybrid system is the response time to interconnect and synchronize the different modules. We need to make sure that the communication of the involved modules will be performed seamlessly without adding any significant delays, especially since we are measuring the response-time of mission critical IoT applications.
## 2 Materials and Methods
### Random Forests  
The RF model is based on an ensemble of single decision trees first developed by [PERSON] [22]. A decision tree is a type of model used for classification and regression. It operates by using the values of the input features to recursively partition the data into subsets. For every division, select the feature that divides the data most effectively. The tree continues to grow until all the data in a subset belong to the same class, or until a stopping criterion has been met. The final tree can be used to classify new data by following the splits from the root to a leaf node. The class label is assigned to the leaf node. The subdivisions in a decision tree are chosen to maximize the reduction in impurity. Either Gini impurity or entropy is commonly used to measure this reduction in impurity based on the training data. The impurity gives insight into the separability of the data. For more information about the Gini impurity and entropy in RFs, we recommend the PhD thesis from [PERSON] [23]. If only one decision tree is used, the process is prone to overfitting, as it will be specifically fit to the dataset. To tackle this issue, an ensemble of decision trees called random forest is used.  
For the implementation of the model, we used the scikitlearn-library [24].  
For a given dataset, the hyperparameters of RFs were optimized through grid search. The inputs to the RF classifier wee:  
1. n_estimators: number of trees in the forest varied from 15 to 350.
2. max_features: number of features looked at for the best split (we tested \"sqrt\" and \"log2\").
3. max_depth: maximum depth of a single tree, varied from 15 to 150.
4. bootstrap: the whole dataset used for a single tree, either \"True\" or \"False\".
## 2 Mathematical Modeling
### Dilution of Precision
\[\frac{\partial\alpha_{\text{Az,k}}}{\partial x_{\text{E}}}=\frac{\partial \left(\arctan\left(\frac{y_{\text{E}}-y_{\text{S}_{\text{k}}}}{x_{\text{E}} -x_{\text{S}_{\text{k}}}}\right)\right)}{\partial x_{\text{E}}}=\frac{-\left(y _{\text{E}}-y_{\text{S}_{\text{k}}}\right)}{\left(x_{\text{E}}-x_{\text{S}_{ \text{k}}}\right)^{2}+\left(y_{\text{E}}-y_{\text{S}_{\text{k}}}\right)^{2}} \tag{16}\]  
\[\frac{\partial\alpha_{\text{Az,k}}}{\partial y_{\text{E}}}=\frac{\partial \left(\arctan\left(\frac{y_{\text{E}}-y_{\text{S}_{\text{k}}}}{x_{\text{E}} -x_{\text{S}_{\text{k}}}}\right)\right)}{\partial y_{\text{E}}}=\frac{\left(x _{\text{E}}-x_{\text{S}_{\text{k}}}\right)}{\left(x_{\text{E}}-x_{\text{S}_{ \text{k}}}\right)^{2}+\left(y_{\text{E}}-y_{\text{S}_{\text{k}}}\right)^{2}} \tag{17}\]  
\[\frac{\partial\alpha_{\text{Az,k}}}{\partial z_{\text{E}}}=\frac{\partial \left(\arctan\left(\frac{y_{\text{E}}-y_{\text{S}_{\text{k}}}}{x_{\text{E}} -x_{\text{S}_{\text{k}}}}\right)\right)}{\partial z_{\text{E}}}=0 \tag{18}\]  
In (19)-(21) are reported the derivatives of the Elevation angle \(\alpha_{\text{El,k}}\) with respect to the variables \(x_{\text{E}}\), \(y_{\text{E}}\), and \(z_{\text{E}}\). \[\frac{\partial\alpha_{\text{El,k}}}{\partial x_{\text{E}}}=\frac{\partial \left(\arctan\left(\frac{\sqrt{\left(x_{\text{E}}-x_{\text{S}_{\text{k}}} \right)^{2}+\left(y_{\text{E}}-y_{\text{S}_{\text{k}}}\right)^{2}}}{z_{\text{E} }-z_{\text{S}_{\text{k}}}}\right)\right)}{\partial x_{\text{El,k}}}= \tag{19}\]  
\[\frac{\left(z_{\text{E}}-z_{\text{S}_{\text{k}}}\right)\left(\left(x_{\text{E}} -x_{\text{S}_{\text{k}}}\right)^{2}+\left(y_{\text{E}}-y_{\text{S}_{\text{k}}} \right)^{2}\right)^{\frac{-1}{2}}\left(x_{\text{E}}-x_{\text{S}_{\text{k}}} \right)}{\left(z_{\text{E}}-z_{\text{S}_{\text{k}}}\right)^{2}+\left(x_{\text{ E}}-x_{\text{S}_{\text{k}}}\right)^{2}+\left(y_{\text{E}}-y_{\text{S}_{\text{k}}}\right)^{2}}\]  
\[\frac{\partial\alpha_{\text{El,k}}}{\partial y_{\text{E}}}=\frac{\partial\left( \arctan\left(\frac{\sqrt{\left(x_{\text{E}}-x_{\text{S}_{\text{k}}}\right)^{2}+ \left(y_{\text{E}}-y_{\text{S}_{\text{k}}}\right)^{2}}}{z_{\text{E}}-z_{\text{S} _{\text{k}}}}\right)\right)}{\partial y_{\text{El,k}}}= \tag{20}\]\[\frac{\partial\kappa_{\rm EL,k}}{\partial z_{\rm E}}=\frac{\partial \left(\arctan\left(\frac{\sqrt{\left(x_{\rm E}-x_{\rm S_{k}}\right)^{2}+\left(y_{ \rm E}-y_{\rm S_{k}}\right)^{2}}}{z_{\rm E}-z_{\rm S_{k}}}\right)\right)}{ \partial z_{\rm EL,k}}= \tag{21}\] \[\frac{-\sqrt{\left(x_{\rm E}-x_{\rm S_{k}}\right)^{2}+\left(y_{ \rm E}-y_{\rm S_{k}}\right)^{2}}}{\left(z_{\rm E}-z_{\rm S_{k}}\right)^{2}+ \left(x_{\rm E}-x_{\rm S_{k}}\right)^{2}+\left(y_{\rm E}-y_{\rm S_{k}}\right) ^{2}}\]  
Introducing the auxiliary quantities in (22) and (23) to compact the notation, the Geometric Design Matrix \(\mathbf{H}\) is as in (24). \[s_{\rm k}=\sqrt{\left(x_{\rm E}-x_{\rm S,k}\right)^{2}+\left(y_{\rm E}-y_{\rm S,k}\right)^{2}} \tag{22}\]  
\[u_{\rm k}=\sqrt{\left(x_{\rm E}-x_{\rm S,k}\right)^{2}+\left(y_{\rm E}-y_{\rm S,k}\right)^{2}+\left(z_{\rm E}-z_{\rm S,k}\right)^{2}} \tag{23}\]  
\[\mathbf{H}=\left[
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 3 Reflector Antenna Configurations in Spaceborne SAR Systems  
The current spaceborne SAR systems mainly adopt parabolic reflector antennas, elliptical reflector antennas and dual-reflector antennas as their antenna forms. Table 1 lists the specific system examples. Below, we will conduct a detailed discussion based on their deployment mechanisms and feed configurations.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## The Proposed Scheme
### Replay Attack  
In our proposed scheme, if an attacker \(A\) intercepts the REQUEST message \(\{H_{i},R_{c}\}\) and replays it later, the server will detect it by checking the timestamp \(T_{1}\). On the other hand, if \(A\) replays the ACCEPT message \(\{L_{i}\}\) from the server, then the client can recognize it by checking \(T_{2}\). Therefore, our proposed scheme resists replay attacks.