esa-sceva/satcom-chunk-collection huggingface dataset:
# Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information
## 4 Feature Layer Fusion of Multi-Source Heterogeneous Sensors Based on 1D-CNN
### Model Building and Training
#### 4.4.2 Model Training  
In this paper, the multi-source heterogeneous sensor information has been standardized and input into the established one-dimensional convolutional neural network model to train the model. The Relu function is used as the activation function to simplify the calculation process and avoid gradient disappearance. SGD (i.e., stochastic gradient descent method) is used to update the weights of each neural node in the neural network. The Softmax function is used as the classification function of the output layer. Moreover, the Dropout layer is introduced in the fully connected layer to avoid over-fitting [22]. At the same time, the Dropout layer can also be considered a product of noise and a fully  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Level Number**} & \multirow{2}{*}{**Layer Categories**} & \multicolumn{2}{c}{**Number of**} & \multirow{2}{*}{**Postscript**} \\  & & & **Cores/Size/Step Size** & \\ \hline
1 & Input layer & \(6\times 1000\times 1\) & Channel \(\times\) dimension \\
2 & Convolution layer & \(16/201\times 1/1\) & Relu \\
3 & Pooling layer & \(16/2\times 1/1\) & Average pooling \\
4 & Convolution layer & \(64/101\times 1/1\) & Relu \\
5 & Pooling layer & \(64/2\times 1/1\) & Average pooling \\
6 & Convolution layer & \(128/51\times 1/1\) & Relu \\
7 & Pooling layer & \(128/2\times 1/1\) & Average pooling \\
8 & Convolution layer & \(256/31\times 1/1\) & Relu \\
9 & Pooling layer & \(256/2\times 1/1\) & Average pooling \\
10 & Flatten layer & \(256\) & \\
11 & Fully connected layer & \(256\) & Relu \\
12 & Output layer & \(7\) & Softmax \\ \hline \hline \end{tabular}
\end{table}
Table 4: Specific structure parameters of the 1D-CNN model.  
Figure 7: One-dimensional convolutional neural network model for multi-sensor fusion.  
connected matrix, thus guaranteeing the high robustness of the algorithm even under high-noise conditions.  
In addition, the monitor system data set is divided into the training set and the test set according to the 4:1 scale. The learning rate was 0.1. The maximum number of iterations was 20. The batch size was 40, and every mini-batch training of the model would update the weights of each neural node.
# A Study on the Design and Control of the Overhead Hoist Railway-Based Transportation System
## 3 Design of the Whole System
### Mechanical System
#### 3.1.2 The Shuttle Design  
The principle diagram of the shuttle is introduced in Figure 4 below.  
Figure 3: The schematic diagram of the system.  
The drive part uses a timing belt which is affordable and easy to fabricate. Eight wheels are used to operate each shuttle, ensuring that the vehicle stays stable and does not tip over while moving on the rail. The hanger arm's ball-bearing joints ensure that the gravity vector of the goods is always in a stable state when operating. The shuttle-base design and fabrication are shown in Figure 5 below in accordance with the basic specifications introduced in Table 1.  
The specifications of the experimental shuttle are as follows: size, 220 \(\times\) 100 \(\times\) 80 mm; mass, 3.5 kg; payload, 10 kg; max speed, 0.3 m/s; actuator, DC Planet Gear motor.
## 3 Results and Discussion
### Engine Performance
#### 3.1.2 Combustion Phasing
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Engine** & \multirow{2}{*}{**Test**} & **Pilot** & \multicolumn{2}{c}{**Mass Fraction Burned (ATDC)**} & \multicolumn{2}{c}{**Flame-**} & \multicolumn{2}{c}{**Rapid-**} \\
**Load** & **Fuel** & \multirow{2}{*}{**Timing**} & **CA10** & **CA50** & **CA90** & **Development** & **Burning** \\ \cline{3-10}
**(Nm)** & & & (\({}^{\circ}\)**CA**) & (\({}^{\circ}\)**CA**) & (\({}^{\circ}\)**CA**) & (\({}^{\circ}\)**CA**) & (\({}^{\circ}\)**CA**) & (\({}^{\circ}\)**CA**) & **(ms \({}^{1}\))** & (\({}^{\circ}\)**CA**) & **(ms \({}^{1}\))** \\ \hline
0 & PD0 & \(-\)20 & \(-\)9.2 & 5.1 & 12.2 & 10.8 & 2.40 & 21.4 & 4.76 \\  & PD10 & \(-\)20 & \(-\)8.9 & 5.2 & 12.5 & 11.1 & 2.47 & 21.4 & 4.76 \\  & PD30 & \(-\)20 & \(-\)8.8 & 5.4 & 12.8 & 11.2 & 2.49 & 21.6 & 4.80 \\  & PD100 & \(-\)20 & \(-\)9.1 & 5.1 & 12.2 & 11.0 & 2.44 & 21.2 & 4.72 \\ \hline
40 & PD0 & \(-\)20 & \(-\)7.2 & 7.4 & 14.5 & 12.8 & 2.84 & 21.7 & 4.82 \\  & PD10 & \(-\)20 & \(-\)7.1 & 7.3 & 14.5 & 12.9 & 2.87 & 21.6 & 4.80 \\  & PD30 & \(-\)20 & \(-\)7.0 & 7.5 & 14.6 & 13.0 & 2.89 & 21.6 & 4.80 \\  & PD100 & \(-\)20 & \(-\)5.0 & 8.3 & 15.4 & 15.0 & 3.33 & 20.4 & 4.53 \\ \hline \hline \end{tabular}  
* The flame-development and rapid-burning duration can be converted to the time by Time (ms) = \({}^{\circ}\)CA/(0.006 \({}^{\circ}\) N). Here N is the engine speed (rpm).  
\end{table} 
Table 6: Combustion phases under test conditions. Figure 5: Mass fraction burned at engine load of **(a)** 0 Nm, **(b)** 40 Nm.
## 4 Discussion
### Relationship Between Lower Limb Joint Mobility, Explosiveness, and Advanced Layup Success Rates  
During the process of enhancing lower limb explosiveness, the flexion angles of the hip, knee, and ankle joints played a critical role. The range of motion and synergistic effects of these joints directly determined the efficiency of force transmission and the performance of explosiveness [52]. Especially in explosive movements, the flexion angle of the hip joint had a significant impact on the load distribution of the lower limb muscle groups and the generation of explosiveness. It was also closely related to the synergistic operation of theknee and ankle joints [53]. The study by [PERSON] et al. indicated that an appropriate hip flexion angle promoted the stretch response of muscles, significantly enhancing lower limb explosiveness and further optimizing athletic performance [54]. Additionally, the research conducted by [PERSON] et al. supported this perspective, indicating that optimal joint flexion angles contributed to athletes' ability to achieve maximum explosiveness [55], which aligned closely with the findings of this study. This study significantly improved the lower limb joint mobility and explosiveness of participants through PT. Specifically, after training, the flexion angles of the hip, knee, and ankle joints in the participants were significantly optimized. This finding aligned with the results of [PERSON] et al., indicating that PT effectively improved the functionality of the lower limb kinetic chain, thereby increasing layup height and hang time [56].  
Further analysis revealed a strong correlation between the success rate of advanced layups and lower limb joint mobility as well as explosiveness. Using a multiple linear regression model analysis, the coefficient of determination (\(R^{2}\)) ranged from 0.50 to 0.72. This indicated that the mobility and explosiveness of the hip, knee, and ankle joints explained 50% to 72% of the variance in advanced layup success rates. This result was consistent with the study by [PERSON], further confirming the effectiveness of PT in improving explosiveness and technical performance [2]. Its coordination and stability directly influenced the efficiency of force transmission and athletic performance [52]. PT improved lower limb joint mobility and explosiveness, significantly enhancing the overall functionality of the kinetic chain, thereby increasing the success rate of advanced layups. This training method not only strengthened muscle power and elasticity but also optimized neuromuscular control, enabling athletes to transmit force more efficiently and reduce energy loss during high-intensity movements. These comprehensive improvements provided basketball players with significant advantages in competition while also offering a scientific basis for future training design.
## 3 Proposed Method
### Formula  
GCN can be expressed with the following formula:  
\[H^{(l+1)}=f\Big{(}H^{(l)},A\Big{)} \tag{4}\]  
where \(H^{(0)}=X\). For instance, taking a one-layer GCN with activation function ReLU as an example, \(W\) is a weight as in a normal neural network. \(A\), \(X\) are the adjacency matrix and feature matrix (input), respectively.  
\[f\Big{(}H^{(0)},A\Big{)}=f(X,A)=ReLU(AX\cdot W) \tag{5}\]  
In other words, \(AX\) can be seen as the adjacency matrix by feature matrix that is the sum of the feature values of the nodes connected to a certain node. With \(AX\), the feature value considering the surrounding nodes can be obtained. Then, it simply multiplies \(AX\) by the weight \(W\) and applies the nonlinear activation function ReLU like a normal neural network.  
GCN can be regarded as a special case of the Weisfeiler-Lehman algorithm with (1) parameters and (2) differentiability.
## 4 Performance Validation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Labels** & \(Acc_{uy}\) & \(Sens_{y}\) & \(Spec_{y}\) & \(F_{score}\) & **MCC** \\ \hline \multicolumn{6}{c}{**Training Phase (80\%)**} \\ \hline C-0 & 98.43 & 85.71 & 99.17 & 85.71 & 84.88 \\ C-1 & 96.46 & 79.49 & 99.53 & 87.32 & 85.85 \\ C-2 & 98.03 & 90.62 & 99.10 & 92.06 & 90.96 \\ C-3 & 97.24 & 96.30 & 97.50 & 93.69 & 91.99 \\ C-4 & 96.85 & 93.02 & 97.63 & 90.91 & 89.04 \\ C-5 & 96.46 & 91.30 & 97.60 & 90.32 & 88.16 \\ C-6 & 97.64 & 92.31 & 98.25 & 88.89 & 87.64 \\ \hline
**Average** & **97.30** & **89.82** & **98.40** & **89.85** & **88.36** \\ \hline \multicolumn{6}{c}{**Testing Phase (20\%)**} \\ \hline C-0 & 98.44 & 85.71 & 100.00 & 92.31 & 91.78 \\ C-1 & 98.44 & 100.00 & 98.25 & 93.33 & 92.72 \\ C-2 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ C-3 & 98.44 & 100.00 & 98.00 & 96.55 & 95.64 \\ C-4 & 96.88 & 100.00 & 96.43 & 88.89 & 87.83 \\ C-5 & 96.88 & 100.00 & 96.43 & 88.89 & 87.83 \\ C-6 & 92.19 & 54.55 & 100.00 & 70.59 & 70.60 \\ \hline
**Average** & **97.32** & **91.47** & **98.44** & **90.08** & **89.49** \\ \hline \multicolumn{6}{c}{**Training Phase (70\%)**} \\ \hline C-0 & 99.10 & 88.24 & 100.00 & 93.75 & 93.48 \\ C-1 & 98.20 & 96.67 & 98.44 & 93.55 & 92.57 \\ C-2 & 98.65 & 90.62 & 100.00 & 95.08 & 94.45 \\ C-3 & 94.59 & 87.76 & 96.53 & 87.76 & 84.29 \\ C-4 & 98.65 & 96.55 & 98.96 & 94.92 & 94.15 \\ C-5 & 96.40 & 92.31 & 97.27 & 90.00 & 87.84 \\ C-6 & 98.20 & 92.31 & 98.98 & 92.31 & 91.29 \\ \hline
**Average** & **97.68** & **92.06** & **98.60** & **92.48** & **91.15** \\ \hline \multicolumn{6}{c}{**Testing Phase (30\%)**} \\ \hline C-0 & 98.96 & 75.00 & 100.00 & 85.71 & 86.14 \\ C-1 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ C-2 & 98.96 & 100.00 & 98.85 & 94.74 & 94.32 \\ C-3 & 94.79 & 89.47 & 96.10 & 87.18 & 83.96 \\ C-4 & 98.96 & 95.45 & 100.00 & 97.67 & 97.05 \\ C-5 & 100.00 & 100.00 & 100.00 & 100.00 & 100.00 \\ C-6 & 97.92 & 90.91 & 98.82 & 90.91 & 89.73 \\ \hline Average & 98.51 & 92.98 & 99.11 & 93.74 & 93.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classifier outcome of BSHS-EODL approach with different metrics. The extensive comparative study of the BSHS-EODL technique takes place in Table 6[17; 30]. The results highlighted that the VGG-19 model shows least performance, whereas the DBN and YOLO-GC techniques reported moderately similar outcomes. In comparison, the ResNet and CDNN models attain a reasonable performance. Figure 5: TACY and VACY analysis of BSHS-EODL method. Figure 6: TLOS and VLOS analysis of BSHS-EODL approach. However, the BSHS-EODL technique gains maximum classification performance with \(accu_{y}\) of 98.51%, \(sens_{y}\) of 92.98%, and \(specy\) of 99.11%. These outcomes ensured the betterment of the BSHS-EODL method over other existing techniques in the IoMT environment.
## 4 Research Methods
### Convolutional Neural Network (CNN)
#### 4.4.2 Pooling Layer  
The function of the pooling layer is relatively simple as compared to the convolution layer. By compressing the image further, the dimension of each image could be reduced while maintaining important features. Thus, the overall system is more efficient and resistant to interference and can reduce over-fitting. After the CNN is provided with the pooling layer, the computation complexity of the whole network is reduced, and more attention is paid to the coincident features in the image. The general pooling methods are max pooling and average pooling. Like the convolution layer, the pooling layer uses the kernel to obtain the values of various image regions for operation, but it is free of the activation function. Figure 14 shows the max pooling operation process.  
Max pooling was used as the pooling method in this study. The feature map from the convolution layer was filtered by n x n in the pooling layer and scanned in the stride of n. The maximum value was selected in each filter to the next layer, defined as Equation (8). In the rectangular region \(R_{ij}\), \(y_{kij}\) is the max pooling output of the k-th feature map, and \(x_{kpq}\) is the element at (p,q) in \(R_{ij}\).  
\[y_{kij}=\max_{(p,q)\in R_{ij}}x_{kpq} \tag{8}\]
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 1 Introduction
### Tower Companies
#### 1.1.4 Sharing RAN Resources and Responsibilities  
The pooling of resources implies that multiple logical RANs share the same infrastructure. To meet the operator's requirements, the hardware and software resources controlled by TowerCos are pooled and allocated dynamically to the different logical RANs. It is similar to putting together building blocks to provide a specific capability, functionality, or feature. It is important for shared platform services such as AI, data access, etc., offered by TowerCos. In the RANaaS scenario, as seen in Figure 4, the TowerCo is responsible for the resource infrastructure and service infrastructure layers, the ClusterRAN controller, and cluster service orchestration along with the cluster function and infrastructure management. The operator is a tenant of the logical RAN and is responsible for managing the RAN cluster instance.  
In the RANaaS scenario, as seen in RAN A in Figure 5, the operator is responsible for the assurance of the end-user connectivity services provided by the RANaaS. On the other hand, the TowerCo manages the creation and the life-cycle management of various cluster instances. It is different if the operator had ordered a RAN Service (RAN B in Figure 5) where the TowerCo is responsible for the creation, assurances of the end-user connectivity, and life-cycle management of all cluster instances and the operator only uses the RAN.
# Advanced Parameter-Setting-Free Harmony Search Algorithm
## 4 Performance Comparison by PSF Type
### Test in the Continuous Test Function
#### 4.1.4 Three Types of PSF Scheme Comparison
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Function** & **PSF Type** & **4000 iter** & **8000 iter** & **12,000 iter** & **16,000 iter** & **20,000 iter** \\ \hline Griewank & Conventional & 23.2639 & 4.4741 & 2.9851 & 2.3429 & 1.9419 \\  & Iteration & **2.0668** & **1.4455** & **1.2903** & **1.2084** & **1.1645** \\  & Object & 13.0668 & 7.8658 & 5.1469 & 3.7086 & 2.8577 \\ Salomon & Conventional & 6.8991 & 2.8425 & 2.0464 & 1.7192 & 1.5463 \\  & Iteration & **2.2150** & **1.4152** & **1.1541** & **1.0129** & **0.9207** \\  & Object & 3.9688 & 3.3026 & 2.7275 & 2.2429 & 1.8403 \\ Periodic & Conventional & 1.3657 & 1.1097 & 1.0789 & 1.0626 & 1.0523 \\  & Iteration & 1.0296 & 1.0004 & 1.0002 & 1.0001 & 1.0001 \\  & Object & **1.0004** & **1.0001** & **1.0000** & **1.0000** & **1.0000** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average of 10,000 results for 32 variables in each function using PSF-HS and suitable pitch adjusting bandwidths. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Function** & **PSF Type** & **4000 iter** & **8000 iter** & **12,000 iter** & **16,000 iter** & **20,000 iter** \\ \hline Griewank & Conventional & 28.0128 & 7.3089 & 5.2085 & 4.3156 & 3.6455 \\  & Iteration & **9.7959** & **3.4818** & **1.5153** & **0.5534** & **0.0889** \\  & Object & 13.0668 & 7.8658 & 5.1469 & 3.7086 & 2.8577 \\ Salomon & Conventional & 6.8026 & 3.7045 & 3.2354 & 2.9802 & 2.8125 \\  & Iteration & **3.8990** & **3.1242** & **2.7194** & 2.4590 & 2.2816 \\  & Object & 3.9688 & 3.3026 & 2.7275 & **2.2429** & **1.8403** \\ Periodic & Conventional & 1.5800 & 1.1179 & 1.0898 & 1.0721 & 1.0620 \\  & Iteration & 1.5342 & 1.2988 & 1.2093 & 1.1578 & 1.1258 \\  & Object & **1.0004** & **1.0001** & **1.0000** & **1.0000** & **1.0000** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average of 10,000 results for 32 variables in each function using PSF-HS and unsuitable pitch adjusting bandwidths. As shown in Table 4, in most cases it is better to use the advanced than conventional PSF scheme. The iteration PSF method shows the best results in almost all cases. On the other hand, in the case of object PSF HS, the result varied greatly depending on the type of function. The use of advanced \(\mathbf{b}\) had a significant effect on certain functions. This shows that it is better to set the pitch adjusting bandwidth of the function to a different value than 0.1%.
## 5 Conclusions  
This study presents a robust and generalized deep learning-based intrusion detection approach for terrestrial and satellite network environments. The increase of network intrusion attacks has also increased the need for an intuitive cybersecurity system to cope with the attacks in the modern network environment. The proposed RFMLP model leverages the advantages of RF and MLP and augments the outputs using the soft voting criterion. The proposed RFMLP model is tested and verified on three datasets, namely KDD CUP 99, NSL-KDD, and STIN. The proposed model improved the classification results and proves its superiority when it is compared with RF, LR, SVM, and other state-of-the-art models from the literature. RFMLP surpassed other models in each category of attacks in terms of accuracy on all three datasets. Detection of R2L and U2R attack types remains a challenge for previous research but the proposed RFMLP shows robust results on these two types of attacks as well. Moreover, the simple architecture of MLP in the proposed ensemble also reduces the computational complexity and makes it suitable for both terrestrial and satellite networks.
## 1 Introduction  
High-quality natural aggregate for concrete widely used in the construction industry is in short supply, and the use of natural aggregate has been restricted to preserve the environment. Therefore, alternative sources of concrete aggregate that can be supplied sustainably are needed [1, 2]. Alternative aggregates include waste-concrete aggregate obtained by crushing waste concrete, and blast-furnace slag aggregate derived from blast-furnace slag generated in the steelmaking process [1, 2, 3, 4]. The latter forms as the high-temperature discharge cools, and it is considered environmentally harmless [3, 4, 5]. Notably, blast-furnace slags are valuable as alternative concrete aggregates because they have specific gravities that are similar to those of natural aggregates [1, 3]. The use of blast-furnace slag aggregates in concrete has been studied since the 1980s [6, 7, 8, 9, 10]. Recent studies have focused on using waste slags (granulated water-quenched blast-furnace slags) as admixtures for cement and concrete [11, 12, 13, 14]. Research on use of air-cooled slag aggregates in concrete is not extensive, and most commercial applications have used recycled aggregates [1, 15, 16, 17, 18, 19, 20]. These new high-value-added aggregates for concrete offer the potential of lower loadings, and supplement the commonly used low-value-added slag aggregates. [5, 6, 7]. Replacing natural aggregates with air-cooled slag aggregates would consume large quantities of industrial byproducts and help the environment [1, 15, 16, 17, 18, 19, 20].  
However, to use air-cooled slag aggregates in concrete, certain physical and environmental properties must be present to satisfy the quality and performance requirements of the concrete [1]. Environmental factors, such as soil and groundwater contamination, must also be considered because these industrial byproducts come into contact with groundwater through exposure to rain. Therefore, we report herein the quality and environmental properties of air-cooled slag aggregates in a rural-road paving application [1]. The physical and mechanical properties were analyzed with a view toward improving the manufacturing process, and by extension, the end-use performance.
## Materials and Methods
### Partial Least Squares Discriminant Analysis  
Partial least squares discriminant analysis (PLS-DA) is an improved discrimination algorithm based on PLSR that assigns a reference value (dummy variable) to each classification sample. The key to this method is the regression modeling of multiple dependent variables and multiple independent variables. Introducing a residual matrix \(\mathrm{E}\), the equation relating the response variable \(\mathrm{Y}\) and the spectral matrix \(\mathrm{X}\) is as follows [35]:  
\[\mathrm{Y}=\mathrm{XB}+\mathrm{E} \tag{1}\]  
The main step to acquire the least-squares solution \(\mathrm{B}\) is to decompose both matrix \(\mathrm{X}\) and matrix \(\mathrm{Y}\), and eventually, the predictive value of samples can be calculated.  
Because using redundant LVs in the PLS model can lead to model overfitting, the number of LVs is chosen to correspond to that at which the cumulative variance in \(\mathrm{Y}\) first reached 95%. A threshold is introduced to determine classification. The model performance is evaluated by the accuracy rate (%) as follows:  
\[\mathrm{Accuracy}=\frac{\mathrm{TP}+\mathrm{TN}}{\mathrm{TP}+\mathrm{FN}+ \mathrm{FP}+\mathrm{TN}}\times 100\% \tag{2}\]  
where \(\mathrm{TP}\) is the number of true positive samples, \(\mathrm{TN}\) is the number of true negative samples, \(\mathrm{FP}\) is the number of false-positive samples, and \(\mathrm{FN}\) is the number of false-negative samples [36]. The \(\mathrm{F}_{1}\)-Score is the harmonic mean of precision and recall, calculated as follows:  
\[F_{1}=\frac{2\times\mathrm{TP}}{2\times\mathrm{TP}+\mathrm{FP}+\mathrm{FN}} \times 100\% \tag{3}\]  
[PERSON]'s kappa is an alternative measure that can be used to determine the accuracy rate for classification problems. It can be calculated as follows [37]:  
\[\mathrm{Kappa}=\frac{\mathrm{N}\sum_{i=1}^{m}\mathrm{C}_{ii}-\sum_{i=1}^{m} \mathrm{C}_{i}\,\mathrm{C}_{i}}{\mathrm{N}^{2}-\sum_{i=1}^{m}\mathrm{C}_{i} \,\mathrm{C}_{i}} \tag{4}\]  
where \(\mathrm{N}\) is the number of test samples, \(\mathrm{C}_{ii}\) is the number of TPs for each class in the main diagonal, \(\mathrm{C}_{i}\) is the sum of counts in the ith column, and \(\mathrm{C}_{i}\) is the sum of counts in the ith row. The calculated value of [PERSON]'s kappa ranges from \(-1\) (total disagreement) through \(0\) (random classification) to \(1\) (complete agreement). The closer the kappa value is to \(1\), the better the performance of the classification algorithm.