esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Magnet Stress Simulation and Analysis
### Optimization of Preload Parameters Based on Three-Factor Orthogonal Test  
As mentioned in the previous subsection, manual tuning of the preload parameters in such gradient magnets poses challenges due to the response surface properties of the tuning parameters. To optimize these parameters for each coil, an experimental design based on three-factor orthogonal testing was implemented using the orthogonal parametric sweep function in COMSOL. The experimental design consisted of setting the highest stress value for coils 1-3 as the dependent variable, upper, and lower bounds on the design parameters and the number of levels, as detailed in Table 7.  
We established a parameter scan in COMSOL, supplemented the manual parameterization, and conducted a parametric analysis of the preload forces P1, P2, and P3 of coils 1-3. Taking into account the actual working conditions of aluminum alloy strip winding (in large-size coil manufacturing, the preload force provided by the aluminum alloy strip is difficult to exceed 12 MPa), especially for the preload layer of coil 3, the upper limit of the preload force is set to 10 MPa.  
The purpose of establishing a scan in this study is to quickly find the target interval value. Therefore, a between-parameters sensitivity analysis is not necessary. Since, in  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Level** & **Minimum** & **Maximum** \\ \hline P1 & 21 & 40 [MPa] & 50 [MPa] \\ P2 & 21 & 55 [MPa] & 65 [MPa] \\ P3 & 11 & 5 [MPa] & 10 [MPa] \\ \hline \hline \end{tabular}
\end{table}
Table 7: Upper- and lower-parameter limits and levels of factors.  
Figure 7: (**a**) Radial stress distribution in magnet coil; (**b**) equivalent stresses in magnet mandrel.  
COMSOL, a single simulation time is very short, the weights of the preload forces P1, P2, and P3 can be set the same; they are quickly calculated by encrypting their respective intervals to simplify the process of parametric analysis. Using this method, 4851 sets of orthogonal parameter tests were generated, and the processing time was about 5 min.  
After solving, 4851 sets of maximum stress values of coils 1-3 can be obtained. After processing the data, the P1P2, P2P3, and P1P3 factor response surfaces are obtained, as shown in Figure 8. It can be seen from the figure that the response surface plot is tile-shaped. In the optimal solution area, 30 groups of solutions with negative stress peaks can be obtained, all of which are valid solutions. The contour line in the figure is the sum of the maximum radial stresses on the three coils. It can be seen that the effective solutions are distributed in the dark-blue area, and the darkest area is the theoretical optimal solution.  
For all valid solutions, the maximum radial stress of the three coils can be close to 0. In order to reduce the difficulty of winding and the burden on the skeleton, this paper selects the parameters with the smallest total preload force in the solution area as the final design parameters, that is, P1 = 44.5 MPa, P2 = 57.5 MPa, and P3 = 9.5 MPa.
# Hybrid Precoder Using Stiefel Manifold Optimization for Mm-Wave Massive MIMO System
## 3 Problem Formulation
### Proposed Methodology  
Let us consider that\(f\) is a real-valued function on set \(\mathcal{M}\) of various points from \(n\times p\) matrices in real time without any particular structure. Here, \(p\) represents the orthonormal vectors in space \(\mathbb{R}^{n}\). So, an optimization problem can be formed for \(\mathbf{X}\) of the \(n\times p\) matrix that satisfies the condition of \(\mathbf{X}^{T}\mathbf{X}=I\).  
\[\begin{array}{c}\min_{\mathbf{X}\in\mathbb{R}^{n\times p}}f(X)\\ \text{s.t}\ \mathbf{X}^{T}\mathbf{X}=I\end{array} \tag{6}\]The set of p orthonormal vectors in space \(\mathbb{R}^{n}\) is known as the Stiefel manifold, represented as \(St(p,\,n)\).  
\[St(p,\,n)=\big{\{}\textbf{X}\in\mathbb{R}^{n\times p}:\,\,\textbf{X}^{T}\textbf{X}=I \big{\}} \tag{7}\]  
This manifold becomes a unit sphere, \(S^{n-1}\), i.e., a simple nonlinear manifold, when \(p\) becomes 1.  
Now, let us consider \(\mathcal{M}\) as a unit sphere for retractions as a Riemannian sub-manifold of \(\mathbb{R}^{n}\). For the above problem (1), vec(\(F_{RF}\in\mathbb{R}^{N_{T}\times N_{RT}}\) forms a complex Stiefel manifold on sphere \(St(p,\,\mathbb{C})\), where \(St(p,\,\mathbb{C})\) = \(\{\textbf{X}=[\textbf{x}1,\,\dots\,\dots\,\dots\,\,\textbf{x}p]\in\mathbb{C}^{n \times p},\,\text{with}\,\,p=N_{T}\times N_{RT}.\)  
A retraction on the Stiefel manifold is given by:  
\[R_{X}(\xi)=q\,f(\textbf{X}+\xi)\]  
where \(q\,f(\textbf{X}+\xi)\) indicates the Q factor of the decomposition of \((\textbf{X}+\xi)\in R^{n\times p}\).  
This retraction on the Stiefel manifold is used in the proposed algorithm as discussed in Section 3.3. The proposed algorithm 1 uses the Riemannian Steepest Descent for its simplicity.
# Integrating Elliptic Curve Cryptography with the Modbus TCP SCADA Communication Protocol
## 2 Related Work
To our knowledge, only one approach in this context, i.e., Elliptic Curve Cryptographytography and Modbus, exists in [19] that only focuses on the man-in-the-middle attack in SCADA systems when they allow participants which do not authenticate themselves as well as the communication keys they use.
## 3 System Model
### Data Preprocessing
#### 3.3.2 Elevation Angle between UE and Cell Centric  
As a handover triggering method proposed for mobility enhancement in 3 GPP in Rel 16, a method of utilizing the elevation angle between the cell center and the UE is proposed. Assuming that the distance between two points is d and the altitudes of the two points are \(alt1\) and \(alt2\), the formula for calculating the altitude angle between the two points is as follows.  
\[Elevation\ angle=\arctan\bigg{(}\frac{alt_{1}-alt_{2}}{d}\bigg{)} \tag{8}\]
## 4 Results and Discussion
### Horizontal Proposals for Policy Measures and Interventions
As a result, the appropriate risk management tools under the new CAP, in order to protect producers' income from its sharp, intense and unpredictable fluctuations, are activated due to specific risks [57]. These risks may concern economic damages to the producers caused by adverse climatic events, animal or plant diseases, infestation by pests or environmental events. To this end, the proposal for this measure focuses on the following interventions:  
* Subsidizing the insurance premiums paid by producers to cover against production losses (based on the calculation of the percentage of production reduction compared to the average of the last three or five years) due to climate change, as an incentive to manage risk, the corresponding adjustment is required of the statutes of the Hellenic Agricultural Insurance Organization (ELGA), in order to expand its activity in voluntary insurance, defining the new categories and the corresponding risk premiums based on a relevant actuarial study, while the possibility of private insurance's contribution in the same direction should also be examined. * Establishment of mutual aid funds, which will be subsidized for their management costs, as an incentive for their formation and the encouragement of producers' participation in them. The aim of mutual aid funds is to share the risk among their members, which usually concern groups of producers in the same sector, so that those affected are compensated when necessary and for the risks foreseen, from the contributions of all the members to the fund. * Activation of an income stabilization tool, in the form of financial contributions from producers to mutual aid funds, which will provide them with compensation in cases of a serious reduction in their income due to crises (mainly environmental, climate, sanitary, phyto-sanitary). This specific tool differs from the rest, because it concerns the granting of compensation based on the calculation of the percentage of reduction in income and not production, in relation to a predetermined period of time in the past, while it also requires a clear identification of the reasons that justify the compensation in question.
# A CSI-Based Multi-Environment Human Activity Recognition Framework
## 3 Results
### Data Preparation
#### 3.1.2 Segmentation  
We applied the segmentation algorithm on the principal components generated from each of the three streams. This segmentation procedure identifies the starting and ending points for each of the activities performed by the subjects. Applying the segmentation algorithm can result in one of the following four cases:  
Figure 12: The percentage of activities that could not be segmented.  
Figure 13: Effect of applying the smoothing technique on a recorded signal.  
1. In the first case, the segmentation algorithm works correctly, and it was able to identify the start and the end of the activity within each of the communication streams. Thus, no further action is needed, and the starting and ending points are used to extract the segment;
2. The second case happens when two streams are segmented successfully, while the third stream is not segmented. In this case, we take the average of the starting points from the two successful streams and the average of the ending points from the two successful streams and use them as the starting and ending points for the third unsuccessful stream;
3. The third case occurs when only one stream is segmented successfully, while no segments were detected for the other two streams. In such a case, we assume that the segments for the two streams are the same as the segment for the stream we successfully found the segment in it; and
4. The last case comes about when the segmentation algorithm does not successfully extract the activity segment in any of the streams. The index that represents the beginning of the segment can then be computed as follows: \[S=\frac{MIN(index(H),index(L))}{2},\] (6) where \(S\) is the index of the segment beginning, \(H\) is the point associated with the highest value of the signal, and \(L\) is the point associated with the lowest value of the signal. On the other hand, the calculations for the segment endpoint are expressed as follows: \[E=A-\frac{A-MAX(index(H),index(L))}{2},\] (7) where \(E\) is the index of the segment end and \(A\) represents the index of the end of the signal. In other words, the segment end index is the midpoint between the end of the signal and the furthest occurrence of either the highest-peak or the lowest-peak values in that signal.
# AODV-EOCW: An Energy-Optimized Combined Weighting AODV Protocol for Mobile Ad Hoc Networks
## 3 AODV-EOCW Protocol
### Route Measurement and Scoring
#### 3.4.3. Hop Count  
The reason why the number of hops was selected as a routing metric is that if the number of hops is too large, the overhead of the entire network will increase, as will the delay. After fine-tuning based on daily experiences and experiments, this study employed the scoring method, as shown in Table 2, for the hop count.  
The scores (RH) presented in Table 2 are in the range of 0 to 1. RH scores in this range are used to ensure that the hop count metric is in the same range as the congestion degree and residual energy to facilitate subsequent calculations. Clearly, the higher the routing hop score, the fewer routing hops required.
## 3 Results and Discussion
### Breast Profile Segmentation  
The breast was successfully segmented in all 150 images. Three images (all malignant) contained a small amount of residual noise adjacent to the breast (see Figure 3); this number increased to 14 when the significance threshold was lowered to 5%, illustrating the importance of this parameter in the quality of the final segmentation. Conversely, increasing the significance threshold to 25% eliminated the noise; however, small holes began appearing in the segmented borders, as isolated cells near the edges of the image began to fall below the threshold. Minor adjustments to the density threshold therefore represent a tradeoff between the false dismissal and false alarm rates. Unfortunately, ground truth breast margins were not provided with the dataset, therefore we were unable to conduct an ROC analysis against this parameter.
## 2 Materials and Methods
### Methodology
#### 2.3.1 Pre-Processing of UAV-LiDAR Point Cloud Data  
The pre-processing steps of UAV-LiDAR point clouds we applied were georeferencing, strip alignment, strip merging, de-noising, and the interpolation. The coordinates of the initial point cloud data were generated based on two systems: real-time-kinematic (RTK) antenna carried on the UAV and paired with the corresponding global positioning system (GPS) base station; and the inertial measurement unit (IMU) in the inertial navigation system (INS) system. All flight strips were merged based on the iterative closest point (ICP) registration algorithm, and the noise points were filtered using the maximum distance from the point to its neighbors. After ground points were classified using an improved progressive triangulated irregular network (TIN) densification filtering algorithm [53], they were interpolated to the digital elevation model (DEM) using the TIN interpolate algorithm. Normalized point cloud data were generated by subtracting the DEM from the de-noised point cloud, which represents the absolute heights of trees and other undergrowth above the elevation. To eliminate spatial error caused by different time phases, we performed a fine registration of the normalized point clouds for leaf-off and leaf-on seasons using the ICP registration algorithm. CHM images with a spatial resolution of 0.1 m were generated from the normalized point clouds based on the _grid_canopy_ function in the _lidR_ package in R software [54].  
Figure 3: Methodology developed in this study. CHM: canopy height model; DBSCAN: density-based spatial clustering of applications with noise; kNN classifier: k-Nearest Neighbor classifier. Stage I: ITD and seed points detection under leaf-off conditions; Stage II: ITS in leaf-on conditions; Stage III: Refining processing to final segmentation results.
# Research of a Multi-Frequency Waveform Control Method on Double-Wire MIG Arc Welding
###### Abstract  
To improve the energy efficiency and quality of the welding seam during the double-wire metal inert-gas (MIG) arc welding production, a new multi-frequency waveform control method was proposed in this paper. In this method, a multi-frequency modulation was added to the waveform in the peak stage of trailing wire current, under the situation that the two wires current waveforms had the same frequencies but inverse phases. This new action can benefit the stability of welding process and improve the quality of welding seam. Actual experiments were conducted to validate the proposed method. Orthogonal experiments and corresponding mathematical analyses were also employed to seek an optimal parameters combination. Finally, an optimal combination was confirmed and a welding seam with satisfactory quality was obtained, which showed that the proposed control method and corresponding analyses can well serve the double-wire MIG arc welding, and then benefit the actual production in the future.  
double-wire MIG arc welding; multi-frequency modulation; current waveform control; orthogonal experiment +
Footnote †: journal: Applied Sciences
## 2 Model
### Gru-Dnn
#### 2.3.1 GRU Model  
Long Short-Term Memory [68] (LSTM) is a special type of Recurrent Neural Network (RNN) that can prevent gradient vanishing and exploding in the course of long-sequence training. Even though LSTM is widely used, it nevertheless has many parameters, which makes training more difficult. Compared with traditional RNN, LSTM applies the gating mechanism to memorize past information and selectively forget some unimportant information. Three gating signals, the input gate, the forget gate, and the output gate, are constructed to realize the above-mentioned gating mechanism. Therefore, each input vector needs to be mapped into four signals before being input into the cell, corresponding to the input gate, forget gate, output gate, and input vector, respectively. Consequently, the LSTM model will expand by about four times the number of parameters. GRU optimizes the gating mechanism based on LSTM. It uses only two gates, reset gate and update gate, to achieve the same function. It is computationally cheaper and reduces the problem of gradient disappearance while maintaining the same performance. Therefore, GRU is selected as part of the real-time risk assessment model in this paper. Figure 4 depicts the primary structural differences between the GRU and LSTM.  
Figure 3: Autoencoder for multimodal feature extraction.  
The GRU obtains the two gated states from the transmitted state \(h^{t-1}\) and the input \(x^{t}\) of the current node. These two gated states are used to control both the reset gated state \(r\) and the update gated state \(z\), as shown in Figure 5, below.  
Firstly, the reset gate signal was obtained by the current memory value \(h^{t-1}\) and input vector \(x^{t}\), as shown in Equation (3). Among them, the \(\sigma(x)\) refers to the sigma non-linear function. After obtaining the gated signal, we reset the gated state \(r^{t}\) to obtain the memory value after updating \(h^{t^{\prime}}\). This first resets the existing memory \(h^{t-1}\) and then stitches it with the current input \(x^{t}\). The transformed value is then applied with an activation transform to scale the data to a range of \(-1{\sim}1\), as presented in Equation (4). As a result, \(h^{t^{\prime}}\) is obtained, as shown in Figure 4. In this way, \(h^{t^{\prime}}\) is targeted to add to the current hidden state, i.e., the state at the current moment is memorized.  
\[r^{t}=\sigma(W_{r}{\cdot}[h_{t-1},x_{t}]) \tag{3}\]  
\[{h^{t^{\prime}}}=\tan\h\big{(}W_{h}^{\prime}{\cdot}[r^{t}{\cdot}h_{t-1},x_{t} ]\big{)} \tag{4}\]  
In the updating phase, first the update gate signal is acquired by the same means as Equation (5). Then, GRU uses the same gated \(z\) to forget and remember at the same time, and the update expression is as Equation (6).  
\[z^{t}=\sigma(W_{z}{\cdot}[h_{t-1},x_{t}]) \tag{5}\]  
\[h^{t}=(1-z)\odot h^{t-1}+z\odot h^{t^{\prime}} \tag{6}\]  
The range of \(z\) is [0, 1]. The closer \(z\) is to 1, the greater the amount of data that is remembered; the closer to 0, the more that is forgotten.
## 3 Adaptive TrimTree Mechanism
### TrimTree Module
Therefore for cloud applications, \"passive network subset\" is computed by augmenting the \"active network subset\" and selecting aggregation layer switches and links in the same pod for backup because there is very little out-of-rack traffic. While for other applications, TrimTree computes a 'passive network subset' which comprises of two aggregate switches in a 4-pod network as shown in Figure 3c which means alternate aggregate switches in fat-tree DCN along with its uplinks and downlinks. The \"passive network subset\" is computed as follows:  
_Passive network subset = {passive links + passive switches} Passive links = {passive core-aggregation links + passive aggregation-edge links} Passive switches = {passive core switches + passive aggregation switches} Passive links computation: Passive core-aggregation links = {0 for cloud application/ neighboring links of active links} Passive aggregation-edge links = {alternate neighboring links of active links} Passive switches computation: Passive aggregation switches = {alternate neighbor switches of active aggregate switches} Passive core switches = {0 for cloud application/neighboring core switch of active core switch} This information is passed to the Route Update and the Power Management modules. The roles of these two modules are discussed below.
## 4 Discussion
### Weighting  
In Section 2.3, we presented a weight setting method based on the area of a spherical quadrilateral enclosed by four terminal points of unit vectors. For an inhomogeneous phase pattern, the further apart two directions are, the more likely the corresponding PCCs are to be significantly different. Our method used triple-differenced PCC measurements, i.e., a combination of the relative PCCs at four different directions. Consequently, the more scattered the four directions are, the more likely the value of triple-differenced PCC are to be larger. Note that the triple-differenced PCC differs from the triple-differenced PCC measurement, where the former includes only the differences between PCC values, while the latter also includes the rest noise in the triple-differenced observations.  
Figure 14 depicts the relationship between the size of the triple-differenced PCCs and the corresponding weightings. From the slopes of the fitted lines, we can see that in most cases (13 out of 16), the sizes of the triple-differenced PCCs were directly proportional to the weights. It therefore makes sense to use the area of a spherical quadrilateral to weight the spherical harmonics fitting. It should also be noted that according to the values of the coefficients of determination (R\({}^{2}\)), there was a considerable amount of triple-differenced PCC data that did not correspond to the fitted linear function, especially for the GPS case. As the tests for GPS and BDS used the same set of phase patterns, the differences in R\({}^{2}\) between GPS and BDS should be due to the fact that the coverage of the collected GPS observations was sparser. The low values of R\({}^{2}\) are reasonable because the values of the PCC in two directions far apart may also be very similar, i.e., when the area of the spherical quadrilateral is large, the value of triple-differenced PCC can also be potentially small.  
However, the value of the PCC is a continuous variable in the antenna hemisphere, so when the four directions are quite close, the value of triple-differenced PCC is very small. In this case, the accuracy of the triple-differenced PCC measurements will be severely affected by the rest noise in the triple-differenced observations. During the phase pattern fitting process, setting the weights according to the area of the spherical quadrilateral can relieve the above problem, thus reducing the impact of the triple-differenced noise as much as possible. In addition, the proposed weight setting method also helps to better characterize the phase pattern at larger scales in the phase pattern fitting results.
## 3 Results
### BSN Induced DNA Damage in HCC Cells  
Augmented DNA fragmentation was noticed in BSN-treated (200 \(\upmu\)mol) HepG2 and C3A cells (Figure 2D,E). DNA fragmentation was significantly higher in BSN-treated cells.  
Figure 2: DNA fragmentation from BSN inhibited migration, invasion, and colony formation. **(A)** For 24 h, the indicated concentrations of BSN were applied to C3A and HepG2 cells. Migration assays were used to test the wound-healing ability of HepG2 and C3A cells (Original magnification, \(\times\)100). **(B)** The invasiveness in each sample was determined by counting three microscopic fields. Cell invasion inhibitory percentage was calculated and expressed as 100% for untreated cells (control). **(C)** BSN interfered with colony formation (HepG2 and C3A cells). In the presence of BSN (0-200 \(\upmu\)mol), the cells were incubated for five days to observe if they would proliferate or form colonies. Calculated by defining the number of colonies in absence of BSN as 100%. DNA fragmentation in hepatic cancer cells was caused by BSN (**D**,**E). DNA fragmentation was measured in HepG2 and C3A cells (1 \(\times\) 10\({}^{6}\)) after treatment with the indicated concentrations of BSN for 48 h. The columns are DNA fragmentation. **(F)**\(\gamma\)-H2A activation of control and experimental groups. [PERSON]’s post hoc test was used in this study to analyze the data. \(p<0.05\) was considered significant. Graph Pad prism was used. Experiments were conducted in triplicate. Data are presented as the mean \(\pm\) SD. Furthermore, *\(p<0.05\) vs. control.  
Based on these results, BSN caused DNA fragmentation in HCC cells. In addition, by Western blotting of HCC cells, we tested whether BSN caused DNA damage. DNA damage marker \(\gamma\)-H2A was significantly elevated in BSN-treated cells, suggesting that BSN might be able to prevent the damage caused by BSN (Figure 2F).