esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Method
#### 2.3.3 Random Forest Regression  
Random forest regression was used to determine the 'Weight' of each independent variable in the dataset in relation to the dependent variable, also known as the 'class weights'. For each input sample, the predictions of all decision trees (tree = 1000) were weighted based on their 'experience' (i.e., the number and diversity of the samples on which they were based) and then summed up to obtain the result. The regression analysis used for our dataset was performed using 'RandomForestRegressor' from the scikit-learn library. We examined a total of 122 sites, with 38 datasets per site. The HDMs, YGP, SCB, and GXB contained 24, 48, 23, and 15 sites, corresponding to sample sizes of 912, 1824, 874, and 570, respectively. These data were divided into a training set and a test set, with each containing 50% of the total data. The optimal parameter combinations for the random forest regression model were determined using the training set. Subsequently, a random forest regression prediction model was built and tested using the test set. The 'friedman_mse' method was used. Only the absolute values of the class weights could be obtained. Therefore, it was necessary to combine these with a correlation analysis to investigate the positive and negative relationships between the dependent variables and the independent ones. The degree of linear fit between the training set and the test machine was determined by calculating the coefficient of determination (R\({}^{2}\)), in which an R\({}^{2}\) value closer to 1 indicates a superior fit of the model. The mathematical formulation of the model and R\({}^{2}\) are provided below for reference:  
\[\mathrm{F(x)}=\sum f_{i}(x), \tag{5}\]  
\[\mathrm{R}^{2}=1-\frac{\mathrm{\sum}_{i=1}^{N}\left(y_{i}-\hat{y}\right)^{2}}{ \mathrm{\sum}_{i=1}^{N}\left(y_{i}-\overline{y}\right)^{2}}, \tag{6}\]  
where \(\mathrm{F(x)}\) is the prediction result of the random forest, specifically the weight. \(\mathrm{f_{i}(x)}\) is the prediction result of the i-th decision tree within the ensemble; N is the number of samples; \(y_{i}\) is the actual value of the sample point; \(\hat{y}\) is the predicted value; \(\overline{y}\) is the average value.
# Line Structure Extraction from LiDAR Point Cloud Based on the Persistence of Tensor Feature
## 4 Experiments and Discussions
### The LCS Construction and Resampling  
It can be obviously observed that the point with high line saliency in Figure 5a is considered to show a line structure, while the point with low surface saliency in Figure 5a also turns out to show a line structure. Besides, the point with high point saliency in Figure 5c seems to be the key point of show line structure, although some noisy points also have high point saliencies. Hence, the \(LCS\) can be constructed based on the threshold values \(\left\{\sigma_{surface},\sigma_{line},\sigma_{point}\right\}\). According to the saliency of the computation result, \(\sigma_{surface}\) is set to be 3.27, and the point with saliency lower than the threshold is selected. However, \(\sigma_{line}\) is set as 3.56 and \(\sigma_{point}\) is set as 6.33, and the point with saliency higher than the threshold is selected. With the threshold of different geometric features, the \(LCS\) in different dimension is computed, and the statistic are depicted in Table 1.  
As seen from Table 1, the number of the \(LCS\) point computed using \(s_{surface}\), \(s_{line}\), and \(s_{point}\) is 89, 3326, and 982, respectively, and the number of ground truth \(LCS\) computed using the spatial relation (within the distance of 0.05 m) between the ground truth line structure and the point cloud, is 4006. Only using single geometric feature, the \(LCS\) cannot cover the entire line structure. In addition, as depicted in the fourth row of Table 1, the number of the LCS without duplication is 4155, which is a little higher than that of the ground truth \(LCS\). Hence, although there might be redundancy, it is reasonable to select the \(LCS\) based on Equation (10). Finally, the \(LCS\) is constructed, as shown in Figure 6.  
To deal with unstable quality point cloud and unevenly distributed point density, the \(LCS\) is further resampled as the regular grid point of the space \(S\). The more complex the scene, the higher the grid size \(\kappa\) to preserve the detailed line structure, and vice versa. However, it is not a good idea to set \(\kappa\) with a very high value since it may cause broken line segments in the result. For example, if grid size \(\kappa\) is set to be too high such that gird interval is smaller than the minimum distance between two neighboring points, there will be vacant grids and the consecutive line segment will become broken. Hence, the suggested ranging domain is [10; 100]. With grid size \(\kappa=45\), the point in \(LCS\) is resampled and becomes a regularly distributed \(LCS^{\prime}\) in the space \(S\). Besides, the computation complexity for the line structure extraction is also simplified and refined.
# Soft Periodic Convolutional Recurrent Network for Spatiotemporal Climate Forecast
## 4 Experiment Settings
### Comparison Methods  
**Convolutional neural networks (CNN)**[50]: A CNN is a standard deep-learning method for next video frame prediction. It is also widely used and applied as a model for climate prediction. In this experiment, we used five convolutional layers, the last being the output layer with an output channel of one.  
Figure 4: A sample of the monthly spatial plot of January 2018. (**a**) The Northern Hemisphere of ZH300 data, where deep blue represents the lower value of ZH300. (**b**) The entire globe in the spatial grid of SST data, where deep red represents high temperature, and the terrain area (white) is ignored. Note that these projections are just for visualization.  
**Convolution gated recurrent unit (ConvGRU)**[51]: We applied a ConvGRU as a baseline method by replacing the first two layers with ConvGRU from the CNN baseline model; that is, two layers of ConvGRU and three layers of CNN. Then we applied consecutive a prior spatial input to the model.  
**ConvLSTM**: To determine the benefit of using an LSTM structure that captures change over time, we adopted a ConvLSTM as a baseline method. The first two layers of the CNN were replaced by ConvLSTM layers, the same way as we applied the ConvGRU.  
**SP-CRN (GRU) and SP-CRN (LSTM)**: As described in Section 3, we applied the ConvGRU and the ConvLSTM as the CRN component for the SP-CRN model. The SP-CRN (LSTM) will undergo further experimentation for our periodicity proposals.
# Prediction of the Yield Strength of RC Columns Using a PSO-LSSVM Model
## 4 Model Development and Verification
### Evaluation of the PSO-LSSVM Prediction Performance
#### 4.4.2 Evaluation of the PSO-LSSVM Prediction Results  
Before evaluating the proposed model, the dataset composed of 382 test results is randomly divided into two sets: the training set (305 data samples) and the testing set (77 data samples). With the optimal parameters in Table 4 adopted, the training performance and testing results of the PSO-LSSVM are displayed in Figures 5 and 6, respectively. The scatter-plots in the two figures show that the proposed model has yielded prediction results that present quite excellent agreement with the experimental test results in both training and testing processes. Moreover, it also can be seen that the PSO-LSSVM earned relatively high values of \(R^{2}\) for testing, the corresponding values of the coefficient of determination (\(R^{2}\)) are 0.99 and 0.96 for the training and testing cases of yield strength, respectively (Figures 5a and  
In addition, the mean absolute error (MAE) and root mean square error (RMSE) for evaluating the predictive accuracy of the PSO-LSSVM model, are also presented in Figures 5 and 6. The PSO-LSSVM model attains good perdition performances in both yield strength and yield displacement reflected in the low values of MAE and RMSE. Nevertheless, the values of MAE and RMSE in the testing process of the PSO-LSSVM are low enough already, which demonstrates that the new model has a good capability of predicting yield strength and displacement accurately. To sum up, the results indicate that the estimated yield strength and displacement are trustworthy and reliable due to the high \(R^{2}\) and low RMSE and MAE. Thus, the proposed PSO-LSSVM model is deemed suited for predicting yield strength and displacement of RC columns as a new and effective method. Meanwhile, the results shown in Figures 5 and 6 also indicate that the PSO algorithm adopted for searching optimal solutions to the LSSVM model is quite effective and meets the calculation accuracy requirement in both training and testing results.
# Seismic Wave Amplification Characteristics in Slope Sections of Various Inclined Model Grounds
## 3 Analysis of 1 g Shaking Table Experiment Results
### Comparative Analysis of Acceleration Amplification
#### 3.1.3 PGA Contours of the Model Ground  
Figure 6 shows the PGA contours at the peak points of each seismic wave, illustrating the acceleration amplification trends in the ground models. The spacing between accelerometers was interpolated using the spline interpolation method. The overall trend of seismic wave propagation indicates that in all four ground models, high accelerations start from the lower part of the slope and increase towards the top. The PGA trend in Case 1 and Case 2 shows that the amplification is greater and more pronounced in Case 1, which is relatively less dense. For the multi-layered Cases 3 and 4, additional amplification was observed at the points where the layers change. By comparing the ground models, it was confirmed that the amplification of seismic waves in the ground is influenced by density and that the layered structure of the ground plays a significant role in seismic wave amplification. Additionally, as shown in Figure 6, the differences in acceleration at various points can significantly impact the design depending on the acceleration at the specific points used.  
Furthermore, the PGA contour plots helped identify the extent to which PGA influences the slope. The impact of PGA was analyzed for the c  
Figure 6: PGA contours for each seismic wave.  
and 1400 mm) of the slope, revealing that the influence extended to 61.17-76.67% for the Gyeongju earthquake wave, 47.17-66.33% for the Ofunato wave, 56-70% for the Pohang wave, and 56.33-85% for the Hachinohe wave. In addition, the average PGA influence across the slope models was 65.08% for Case 1, 55.08% for Case 2, and 67% for Case 4. It was confirmed that the influence range of PGA was the smallest in the densest model ground, Case 2, indicating that the stability of the slope is higher in this dense model ground compared to the other two model grounds. Overall, the average PGA influence was measured to be approximately 62%.  
Based on these observations, it can be inferred that the overall influence of PGA on the slope is likely around 60%. Therefore, when designing for seismic conditions, it may be prudent to consider that around 60% of the total PGA influence could be concentrated in the topmost portion of the slope, which should be factored into slope design for more reliable safety measures.
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
## 5 Experimental Results
### Feature Selection Using Wrapper Method
#### 5.3.2 Recursive Feature Elimination with Cross-Validation  
Similar to RFE, RFECV is a feature selection method supported by linear or tree-based models that assign weights to the features. Hence, DT and RF were used among the four classification algorithms applied in this study. By maintaining a stratified K-fold, as in the training stage, the feature subset with the best performance was extracted based on the cross-validation score. Table 7 shows the number of features of the feature subset derived through RFECV as well as the classification evaluation results using the subset. It shows the score for each algorithm using the base model described in Section 5.1. A total of 41 features were selected for the DT algorithm as the subset with the greatest influence on  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **No. of Features** & **Accuracy** & **Precision** & **F1-Score** & **Recall** & **AUC** \\ \hline \multirow{3}{*}{DT} & 5 & 82.775\% & 88.696\% & 88.271\% & 92.427\% & 94.586\% \\  & 10 & 81.308\% & 87.188\% & 87.336\% & 93.113\% & 94.884\% \\  & 55 (all) & 81.289\% & 87.272\% & 87.258\% & 93.081\% & 94.866\% \\ \hline \multirow{3}{*}{RF} & 5 & 83.313\% & 89.442\% & 88.998\% & 93.771\% & 95.334\% \\  & 10 & 85.262\% & 90.029\% & 89.967\% & 94.247\% & 95.701\% \\ \cline{1-1}  & 55 (all) & 86.537\% & 91.012\% & 90.709\% & 94.513\% & 95.911\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Multiclass classification performance using the subset in 5 or 10 features selected through RFE.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **No. of Features** & **Accuracy** & **Precision** & **F1-Score** & **Recall** & **AUC** \\ \hline \multirow{3}{*}{DT} & 5 & 80.037\% & 86.512\% & 85.654\% & 91.075\% & 93.733\% \\  & 10 & 83.611\% & 89.503\% & 89.048\% & 93.663\% & 95.294\% \\  & 55 (all) & 81.289\% & 87.272\% & 87.258\% & 93.081\% & 94.868\% \\ \hline \multirow{3}{*}{RF} & 5 & 86.255\% & 90.762\% & 90.572\% & 94.513\% & 95.897\% \\  & 10 & 91.069\% & 94.001\% & 93.605\% & 95.684\% & 96.784\% \\ \cline{1-1}  & 55 (all) & 86.537\% & 91.012\% & 90.709\% & 94.513\% & 95.911\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Multiclass classification performance using features selected through feature importance.  
classification, whereas 39 features were selected for the RF algorithm. When the 41 features selected by the RFECV method were used by the DT algorithm, all scores, including the accuracy and F1-score, were higher than those of the base model. When 39 features selected by the RFECV method were used, the RF algorithm slightly increased in accuracy, but decreased in the F1-score. That is, compared with the base model, the F1-score decreased by 0.09%, and the number of features decreased by 29.09%. This result confirms the usefulness of the features selected using the RFECV method.
## 4 An Indoor Positioning Algorithm Combining IMU and LBP-FFT-CNNs
### Inertial-Based Positioning Algorithm Using IMU
#### 4.1.1 Sensor Data Acquisition  
The IMU provides raw acceleration \(\left(\overset{\rightarrow}{a}_{raw}\right)\) and angular velocity \(\left(\overset{\rightarrow}{\omega}_{raw}\right)\) data at regular intervals.
## 3 Results and Discussion
### Carbon and Nitrogen Removal Study
#### 3.1.1 Cod
The similar start-up periods indicate that there is an active microbial community present in the winery wastewater that is capable of colonizing the media, as corroborated by [PERSON] et al. (2003) [PERSON] et al. (2003). Inoculation with wastewater biomass only slightly increases the treatment efficiency during the start-up period. The durations for the start-up period that were observed in this study align with those previously reported in the literature. In winery wastewater treatment, a start-up period of ten days has been observed for aerated lagoons, sequencing batch reactors, and membrane bioreactors [27; 67; 68]. Without inoculation, the aerated lagoon exhibited removal within the first ten days of operation and reached >90% by the 21 st day of operation [27]. The first aggregates were observed in the sequencing batch reactor [68], and a removal efficiency of 97% was expressed for the membrane bioreactor [67] after inoculation and ten days of operation. Changing the frequency of loading from four times between 8 am and 5 pm to loadings every six hours did not significantly impact the performance of the treatment systems. Similarly, treatment during the reduced temperature phase was not significantly different, showing that the surface loading rate of 5.18 kg COD/m\({}^{2}\)/d (\(1.06\times 10^{-2}\) lb COD/ft\({}^{2}\)/d) was appropriate for treatment during reduced temperatures. These results are consistent with other studies that used vertical flow constructed wetlands with different waste streams, including domestic wastewater [49], milking facility wastewater [45], and winery wastewater [46].
# PDDQN-HHVBF Routing Protocol Based on Empirical Priority DDQN to Improve HHVBF
## 4 PDDQN-HHVBF Protocol
### Establishment of the Virtual Pipeline  
This part mainly introduces the way of establishing the virtual pipeline. Before the AUV and each node in M-UWSNs transmit data, a virtual pipeline is established with the Sink node. The virtual pipeline is established as shown in Figure 3. As Figure 3, the radius of the pipeline is \(R\), and \(d\) is the distance that the node deliveries data to the relay node. This paper assumes that the transmission range of AUV and all nodes is the same, and the virtual pipeline radius and transmission range are the same as \(R\). The nodes in the pipeline are the nodes that the sending node can transmit to, that is, the candidate relay nodes.  
Figure 3: The schematic diagram of PDDQN-HHVBF pipeline.  
When nodes perform data transmission, the nodes inside the virtual pipeline can be used as candidate relay nodes, while the nodes outside the virtual pipeline can no longer be used as candidate relay nodes. As shown in Figure 4, when the node holding the data packet successfully transmits the data to the relay node, the relay node will act as the new node holding the data packet, and at this time, a virtual pipeline between the node and the Sink node is established again.
# Minimum Interference Channel Assignment Algorithm for Multicast in a Wireless Mesh Network
###### Abstract  
Wireless mesh networks (WMNs) have been considered as one of the key technologies for the configuration of wireless machines since they emerged. In a WMN, wireless routers provide multi-hop wireless connectivity between hosts in the network and also allow them to access the Internet via gateway devices. Wireless routers are typically equipped with multiple radios operating on different channels to increase network throughput. Multicast is a form of communication that delivers data from a source to a set of destinations simultaneously. It is used in a number of applications, such as distributed games, distance education, and video conferencing. In this study, we address a channel assignment problem for multicast in multi-radio multi-channel WMNs. In a multi-radio multi-channel WMN, two nearby nodes will interfere with each other and cause a throughput decrease when they transmit on the same channel. Thus, an important goal for multicast channel assignment is to reduce the interference among networked devices. We have developed a minimum interference channel assignment (MICA) algorithm for multicast that accurately models the interference relationship between pairs of multicast tree nodes using the concept of the interference factor and assigns channels to tree nodes to minimize interference within the multicast tree. Simulation results show that MICA achieves higher throughput and lower end-to-end packet delay compared with an existing channel assignment algorithm named multi-channel multicast (MCM). In addition, MICA achieves much lower throughput variation among the destination nodes than MCM.  
wireless mesh network; multicast communication; channel assignment; multi-radio multi-channel wireless mesh network
## 3 Methodologies
### Data Preparation
(4) Finally, all features are classified into two categories: 'accepted' means that the importance score of a feature is consistently higher than the maximum feature score;'rejected' means that the importance score of a feature is consistently lower \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Features** & **Sign** & **Unit** & **Min** & **Max** & **Mean** & **Median** & **St. D** \\ \hline Particle size & D & nm & 12.00 & 282.00 & 30.68 & 13.00 & 47.40 \\ Temperature & T & K & 283.15 & 411.10 & 322.57 & 308.21 & 33.84 \\ Volume fraction of EG–Al2O3 & VF & vol\% & 0.00 & 3.99 & 1.24 & 0.80 & 1.30 \\ Thermal conductivity & / & W/(m\(\cdot\)K) & 0.24 & 0.48 & 0.30 & 0.29 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Descriptive statics of input and output features. Figure 2: EG–Al2O3 nanofluid preparation process, thermal conductivity measurement, and application fields. than the maximum feature score. This iterative process continues until all features are decisively accepted or rejected, or until the maximum number of iterations is reached. Figure 3 illustrates the comparison in Z-scores between shadow features (represented by blue boxes) and original features (represented by green boxes). The Z-scores of each feature were calculated 30 times and are presented using quartile statistics: Q1 value (boxplot bottom), median value, Q3 value (boxplot top), and outliers. The analysis revealed that all features had higher Z-scores than the maximum Z-score of the shadow features, indicating that all features were accepted by the Boruta algorithm and could be used to train prediction models in this study.
## 2 Methods
### Multi-Scale Detail Enhancement  
MPSR uses the multi-scale method to improve image details without artifacts. Firstly, the image is convoluted into three Gaussian blur kernels \(H_{1},H_{2},H_{3}\) of different scales, and the blurred images \(B_{1},B_{2},B_{3}\) are obtained as follows:  
\[B_{1}=H_{1}*X,B_{2}=H_{2}*X,B_{3}=H_{3}*X \tag{1}\]  
Then, the convoluted images are subtracted from the original image to obtain different levels of detail information:  
\[D_{1}=X-B_{1},\;D_{2}=X-B_{2},\;D_{3}=X-B_{3} \tag{2}\]  
Finally, the overall detail image is generated by merging the three layers:  
\[D=(1-a_{1}\times\text{sgn}(D_{1}))\times D_{1}+a_{2}\times D_{2}+a_{3}\times D _{3} \tag{3}\]  
the fine detail \(D_{1}\) expands the gray level differences near the edge, but may saturate the gray levels duo to its excessive overshooting. To overcome this problem, the sgn function is introduced.  
The data of \(a_{1}a_{2}a_{3}\) and the PSNR of the reconstructed image are obtained by simulation experiment. Figure 3 is drawn from the experimental data. It can be seen that when \(a_{1}a_{2}a_{3}\) are set to 0.1, 0.2 and 0.6 respectively, the reconstruction effect is the best.  
Figure 3: Relationship between \(\mathbf{a}_{1}\), \(\mathbf{a}_{2}\), \(\mathbf{a}_{3}\) value and peak signal-to-noise ratio value.  
Figure 2: MPSR algorithm block diagram.