esa-sceva/satcom-chunk-collection huggingface dataset:
# Incorporating Grey Total Influence into Tolerance Rough Sets for Classification Problems
## 1 Introduction  
Rough set theory [1; 2] can effectively deal with the formulation of vague concepts [3; 4; 5; 6; 7; 8; 9; 10]. Traditional rough-set-based methods use discretization methods to deal with quantitative attributes. Given that no one method of discretization is optimal, the tolerance rough set (TRS) has proven effective in dealing with problems involving numerical attributes [11; 12; 13]. Several TRS-based classifiers (TRSCs) use the traditional TRS with a simple distance measure to estimate similarity between any pair of patterns, viewing each category in a classification problem as a concept [11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21]. To expand the applicability of TRSCs, instead of the simple distance measure, several measures such as flows [22] and relationships [19; 23] among patterns have been proposed to improve classification performance.  
Direct relationships have been effectively measured in grey TRSC [19] through grey relational analysis (GRA) from the viewpoint of relationships obtained between patterns. However, direct as well as indirect relationships can exist between patterns. The widely used Decision-Making Trial and Evaluation Laboratory (DEMATEL) can effectively verify interdependencies among patterns or variables [20; 21; 22]. Furthermore, the total influence matrix plays an important role in the DEMATEL, and can be used to indicate direct/indirect influences among patterns. This is why the DEMATEL has been widely applied to various decision problems [24; 25]. This motivated us to use the total influence matrix to realize direct/indirect relationships when constructing a TRS-based classifier. This article thus proposes a grey-total-influence-based TRS (GTI-TRS) for pattern classification. Furthermore,a genetic algorithm is used to determine the parameters required to construct the proposed classifier with high classification accuracy.  
The remainder of this paper is organized as follows. Section 2 introduces a traditional similarity measure for the TRS and its computation steps. In Section 3, we present the proposed grey total influence matrix and GTI-TRS for pattern classification. In Section 4, we provide a genetic algorithm to construct the proposed GTI-TRS-based classifier (GTI-TRSC). Some real-world datasets were used to determine the classification accuracy of the proposed method. The experiments and results described in Section 5 show that the proposed GTI-TRSC can perform well compared to rough-set-based methods considered. Section 6 contains a discussion of the results and the conclusions of this study.
# Cost-Effective Design Modification of a Sleeve Bearing with Large Bearing Clearance
## 3 Numerical Simulation
### Model Reduction  
Model reduction was employed to improve the computational efficiency of the simulation without significantly affecting the dynamics of the system, particularly the lower frequency modes. Thus, the number of nodes of the full shaft model was reduced from 54 to 7. The shaft nodes corresponding to the three bearings as well as the nodes at the generator and runner were retained, and the DOFs were reordered as master (denoted by an '\(m\)' subscript) and slave nodes (denoted by an '\(s\)' subscript) in Equation (3):  
\[\begin{bmatrix}\mathbf{M}_{mm}&\mathbf{M}_{ms}\\ \mathbf{M}_{sm}&\mathbf{M}_{ss}\end{bmatrix}\begin{bmatrix}\tilde{\mathbf{q}}_{ m}\\ \tilde{\mathbf{q}}_{s}\end{bmatrix}+\begin{bmatrix}\mathbf{K}_{mm}&\mathbf{K}_{ ms}\\ \mathbf{K}_{sm}&\mathbf{K}_{ss}\end{bmatrix}\begin{cases}\mathbf{q}_{m}\\ \mathbf{q}_{s}\end{cases}=\begin{cases}\mathbf{f}_{m}\\ \mathbf{0}\end{cases} \tag{3}\]  
The DOFs of the system were reduced and expressed by the master nodes (\(\mathbf{q}_{m}\)), as shown in Equation (4), by disregarding the inertial terms and considering the lower equation from the stiffness term:  
\[\begin{cases}\mathbf{q}_{m}\\ \mathbf{q}_{s}\end{cases}=\mathbf{T}_{s}\mathbf{q}_{m} \tag{4}\]  
where  
\[\mathbf{T}_{s}=\begin{bmatrix}\mathbf{I}\\ -\mathbf{K}_{ss}^{-1}\mathbf{K}_{sm}\end{bmatrix}\]  
Similarly, the full mass and stiffness matrices were reduced by applying, \(\mathbf{M}_{\mathbf{R}}=\mathbf{T}_{s}^{\mathrm{T}}\mathbf{M}\mathbf{T}_{s}\) and \(\mathbf{K}_{\mathbf{R}}=\mathbf{T}_{s}^{\mathrm{T}}\mathbf{K}\mathbf{T}_{s}\) multiplications. The static reduction is adequate at zero excitation frequency. However, for large frequencies, the inertial term becomes more significant, and an approximation cannot be valid. In the improved reduction system (IRS) method [20], the static reduction method has been improved by taking the inertia term as pseudo-static forces, seen in Equation (5):  
\[\mathbf{T}_{\mathbf{IRS}}=\mathbf{T}_{s}+\mathbf{S}\mathbf{M}\mathbf{T}_{s} \mathbf{M}_{\mathbf{R}}^{-1}\mathbf{K}_{\mathbf{R}} \tag{5}\]  
where  
\[\mathbf{S}=\begin{bmatrix}\mathbf{0}&\mathbf{0}\\ \mathbf{0}&\mathbf{K}_{ss}^{-1}\end{bmatrix}\]  
Like in the static reduction method, the mass, gyroscopic, damping, and stiffness matrices are reduced using \(\mathbf{M}_{\mathrm{IRS}}=\mathbf{T}_{\mathrm{IRS}}^{\mathrm{T}}\mathbf{M} \mathbf{T}_{\mathrm{IRS}}\), \(\mathbf{G}_{\mathrm{IRS}}=\mathbf{T}_{\mathrm{IRS}}^{\mathrm{T}}\mathbf{G} \mathbf{T}_{\mathrm{IRS}}\), \(\mathbf{C}_{\mathrm{IRS}}=\mathbf{T}_{\mathrm{IRS}}^{\mathrm{T}}\mathbf{C}_{ \mathrm{IRS}}\), and \(\mathbf{K}_{\mathrm{IRS}}=\mathbf{T}_{\mathrm{IRS}}^{\mathrm{T}}\mathbf{K} \mathbf{T}_{\mathrm{IRS}}\), respectively. To further improve the accuracy, an iteration scheme is commonly employed.
# Blockchain Implementation Method for Interoperability between CBDCs
## 2 Related Work  
Cross-border payments are highly inefficient, and blockchain technology can play a role in improving them. One approach may be through the interoperability of CBDCs. To date, only one central bank has issued CBDCs, so interoperability between multiple CBDCs is conceptual envisaging.  
The Bank of the Bahamas has launched Sand Dollar, the world's first CBDCThe Bahamas have more mobile phones than bank accounts. Sand Dollar is designed for financial integration. Customers can pay with Sand Dollar to any vendor with a digital wallet certified by the Central Bank of the Bahamas [14]. The National Bank of Cambodia started the Bakong system. The Bakong system will be issued in digital currency by Cambodian partner organizations, not by the central bank. Anyone with a phone number under their name in Cambodia can use the Bakong system and pay Cambodian riel or dollars [15].  
Bank for International Settings (BIS) Innovation Hub works on The Multiple CBDC (mCBDC) Bridge Project [16]. mCBDC Bridge is a multi-currency cross-border project that explores the capabilities of distributed ledger technology (DLT) and studies the application of CBDC to strengthen financial infrastructure. The participating authorities of the project are the Hong Kong Monetary Authority, the Bank of Thailand, the Digital Currency Institute of the People's Bank of China, and the Central Bank of the United Arab Emirates. This project explores how CBDC can be used to address the challenges of cross-country funding delivery, including inefficiency, high cost, low transparency, and compliance.  
The BIS conceptually proposes three models of compatible CBDC systems (model 1), interlinked CBDC systems (model 2), and a single system for mCBDC (model 3) [17]. Compatible standards allow payment systems to reduce friction and barriers to cross-border monetary services. Model 1 is probably very similar to a traditional payment system. CBDC, like payment methods, can reduce the operational responsibility of participating in multiple systems through common technical standards such as message format, encryption technology, data requirements, and user interfaces. Linking payment systems are complex tasks and often require compatibility measures. Model 2 would simply not work together to bring the system together. Two forms can be taken for implementation of the model: (i) a shared technology interface (ii) a common deletion mechanism (a distributed shared account or a more centralized universal payment agent or system). Finally, a multinational payment system with CBDCs is possible. Model 3 potentially allows for more operational capabilities and efficiency, but increases governance and control hurdles. Through the shared network, participants can make payments across borders through deposit receipts linked to CBDCs held in the domestic system. However, a single mCBDC system creates policy problems for the central bank. For example, the scope of CBDC issource for monetary policy and financial stability and payment policy.
# Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models
## 4 Development of Data-Driven Models
### ML Model Calculations
Thus, the following error metrics are defined, which are suitable for regression analysis ([17]):  
* Coefficient of determination, \(R^{2}\) \[R^{2}(y,\hat{y})=1-\sum_{i=1}^{n}{(y_{i}-\hat{y_{i}})^{2}}/\sum_{i=1}^{n}{(y_ {i}-\overline{y})^{2}}\] (15) where \(\overline{y}=\frac{1}{n}\sum_{i=1}^{n}{y_{i}}\) and \(\sum_{i=1}^{n}{(y_{i}-\hat{y_{i}})^{2}}=\sum_{i=1}^{n}{\epsilon_{i}}^{2}\)
* Mean absolute error, \(MAE\) \[MAE(y,\hat{y})=\frac{1}{n}\sum_{i=1}^{n}{|y_{i}-\hat{y_{i}}|}=\frac{1}{n}\sum_{ i=1}^{n}{|\epsilon_{i}|}\] (16)
* Root mean squared error, RMSE: \[RMSE(y,\hat{y})=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}{(y_{i}-\hat{y_{i}})}^{2}}}= \sqrt{\sum_{i=1}^{n}{\epsilon_{i}}^{2}}\] (17)
* Mean absolute percentage error, \(MAPE\): \[MAPE(y,\hat{y})=\frac{1}{n}{\sum_{i=1}^{n}{|y_{i}-\hat{y_{i}}|}}/{max(\epsilon,{|y_{i}|})\cdot 100\%}\] (18) where \(\epsilon\) is an arbitrarily small positive number to avoid zeroing the denominator. Moreover, in Figure 12, the SHP values predicted by the model in the test set are plotted against the actual SHP values, and the points are clustered around the 45-degree line. Similarly, in Figure 13, the residual SHP values (actual minus predicted) in the test set are plotted against the predicted SHP values, and the points are clustered around the horizontal line through zero. A comparison of the results of Model-1 and Model-2 in both the test set and cross-validation revealed slightly better performance of the model with the Days feature, which was due to the additional information provided by the temporal feature. \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Model-1** & **Model-2** \\ \hline R\({}^{2}\) & 0.979 & 0.981 \\ \hline MAE [kW] & 56.6 & 54.1 \\ \hline MAPE & 1.12\% & 1.11\% \\ \hline RMSE [kW] & 115.1 & 109.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance evaluation of the extra trees ensemble method for Model-1 and Model-2 on the test set, using a variety of error metrics. Figure 12: Plot of the predicted SHP values against the actual SHP values of the test set: (**a**) for Model-1, and (**b**) for Model-2. Figure 13: Plot of residual SHP values against the predicted SHP values on the test set: (**a**) for Model-1, and (**b**) for Model-2.
## 3 What Is SDN?
### Working Principle  
Communication between the SDN controller and the data planes is facilitated by the OpenFlow protocol. This protocol is a standard method for the SDN controller to interact with networking devices, such as routers and switches, whether they are OpenFlow-based or use P4 Runtime. Through the OpenFlow or P4 Runtime protocols, the SDN controller instructs these network devices on how to handle incoming data packets, make forwarding decisions, and modify packet headers if necessary. OpenFlow, a crucial component of SDN, provides centralized control, enabling SDN controllers to manage network devices and enforce policies efficiently. Its programmability allows administrators to dynamically define forwarding rules, ensuring that the network can be customized and adapted to meet evolving requirements. By decoupling the control and data planes, OpenFlow enhances the network's flexibility and scalability. Operating on a flow-based forwarding model, OpenFlow classifies traffic into flows and directs devices on how to process packets. This contributes to the agility, efficiency, and adaptability of SDN networks [63; 64].
## 2 Materials and Methods
### Learning and Prediction  
The LSTM-RNN model was trained and implemented for KAM prediction under OS Ubuntu 20.04.5 LTS, CPU Intel Xeon W-2245, GPU NVIDIA Quadro RTX 5000, RAM 128 GB, Python 3.8.8, and PyTorch 2.0.1 [22]. Although Recurrent Neural Networks are used to identify patterns or meanings in sequential data, we chose LSTM layers to overcome the limitations of RNNs in effectively learning long-term dependencies in long sequence data, which can improve the prediction accuracy for temporally continuous data, such as KAM values that change with the gait cycle.  
As LSTM-RNNs operate on sequential data, a sliding window function is used to convert consecutive data points into overlapping sequences by shifting the window over the data one step at a time. This method allows the model to capture continuous patterns effectively and improve generalization. The transformed data are then converted to PyTorch tensors and utilized for model training via PyTorch's DataLoader, which handles batching and shuffling of the data to optimize the training process [22]. To facilitate training, the data are scaled using StandardScaler from scikit-learn [23], which normalizes the features based on the training data, adjusting all features to have a mean of 0 and a standard deviation of 1.  
To compare the predictive accuracy of the models, we evaluated two systems: a multi-model system that includes both a classification model and a regression model, and a single-model system that includes only a regression model. The multi-model system first  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Motion Lab Data** & **IMU Sensor Data** \\ \hline Motion sensor data & Accelerometer data \\ GRF data & Gyro sensor data \\ Baseline KAM data & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure of the RAW dataset.  
Figure 4: Collecting datasets with IMU and GRF sensors.  
classifies the input IMU sensor data into one of five gait pattern types: normal gait, toe-in gait, toe-out gait, trunk lean, and knee thrust. Once the pattern is classified into one of these five types, the IMU sensor data are then passed as the input to a KAM regression model that is specifically trained for that gait pattern to make the final prediction. A detailed description of each gait type, along with the encoding methods used for the gait types, can be found in Table 3.
## 4 Experiments
### Experiments about Influencing Factors
#### 4.2.2 Results regarding SNR and Discussion  
Noise disturbance is a problem often encountered in data analysis and processing. Therefore, this subsection aims to analyze the impact of noise (Gaussian noise) on the performance of the classifiers, and to verify the robustness of the proposed classifier at different SNR levels. We train different RNNs-based classifiers with the same noise level (20 dB), and test them with different noise levels (0 dB to 30 dB, stepped by 2 dB).  
The classification results on the dataset \"OSU Leaf\" are shown in Figure 9. As shown in Figure 9, the classification accuracies of all methods are improved significantly with the increase in SNR, indicating that SNR is a key factor for TSC tasks. Additionally, BICORN-RNN maintains an obvious advantage when the SNR is greater than 5 dB, and it obtains more than 90% classification accuracy when the SNR is greater than 20 dB. As for 0 dB, the accuracies of all methods are around 20%. It is more inclined to classify data into 6 classes randomly because the signal is almost drowned out by noise.  
Relatively, convolutional operation has a slight superiority over random mapping and bidirectional transmission, assuming a low SNR; this evidence shows that convolution is an effective  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Experiments**} & **SNR** **(dB)** & **Length** & **Missing Ratio** & **Overlapping Ratio** \\  & **(dB)** & **(Time-Steps)** & **(\%)** & **(\%)** \\ \hline \multirow{2}{*}{4.2.2} & Range: 0–30 & \multirow{2}{*}{20} & \multirow{2}{*}{0} & \multirow{2}{*}{0} \\  & Step: 2 & & & \\ \hline \multirow{2}{*}{4.2.3} & \multirow{2}{*}{20} & Range: 5–100 & \multirow{2}{*}{0} & \multirow{2}{*}{0} \\  & & Step: 5 & & \\ \hline \multirow{2}{*}{4.2.4} & \multirow{2}{*}{20} & \multirow{2}{*}{20} & Range: 0–50 & \multirow{2}{*}{0} \\  & & Step: 2 & & \\ \hline \multirow{2}{*}{4.2.5} & \multirow{2}{*}{20} & \multirow{2}{*}{20} & \multirow{2}{*}{0} & Range: 0–100 \\  & & & & Step: 5 \\ \hline \hline \end{tabular} SNR: signal-to-noise ratio, is defined as the ratio of signal power to the noise power.  
\end{table}
Table 3: A summary of the experimental basic setup.  
Figure 9: Comparison of several improved classifiers, including the full structure BICORN-RNN, to standard RNN with different SNR.  
method for filtering noise. Whereas random mapping might bring out more instability, i.e., it may exacerbate or counterbalance noise disturbances.
# MMDL-Net: Multi-Band Multi-Label Remote Sensing Image Classification Model
## 3 Results  
### Dataset Introduction and Experimental Configuration  
BigEarthNet is a substantial dataset comprised of Earth Observation images, collaboratively developed by the Technical University of Berlin and the German Aerospace Center (DLR). The dataset is designed to advance research in machine learning and deep learning within the remote sensing domain. BigEarthNet encompasses over 590,000 multispectral images captured by the multispectral instrument aboard the Sentinel-2 satellite between the years 2017 and 2018. The dataset's coverage includes land areas from 10 European countries spanning Western and Northern Europe. Each image in the dataset consists of 13 spectral bands ranging from the visible to the near-infrared spectrum. BigEarthNet is publicly accessible, allowing researchers to download the dataset from its official website ([[http://bigearth.net/](http://bigearth.net/)]([http://bigearth.net/](http://bigearth.net/)), accessed on 10 August 2023). Detailed information regarding the experimental setup is presented in Table 2.
## 2 Materials and Methods
### MHC-PSPNet Network
#### 2.2.3 H-Swish Activation Function  
In the PSPNet model, the ReLU function is used as the activation function during both the feature extraction stage and other stages of the network. While ReLU helps accelerate training, it has limitations in terms of learning capability. The Swish activation function effectively overcomes these limitations by preserving more comprehensive information from the image. However, Swish is more computationally complex than ReLU. To reduce the computational load, the improved network model adopts the H-Swish activation function, which retains the benefits of Swish while lowering the computational requirements to some extent. The sigmoid function and the h-sigmoid function are illustrated in Figure 6. The formulas for the Swish and H-Swish activation functions are provided in Equations (1) and (2).  
Figure 4: CBAM attention mechanism.  
Figure 5: Channel attention and spatial attention modules.  
\[Swish(x)=x\cdot\sigma(x) \tag{1}\]  
\[HardSwish(x) =x\cdot HardSigmoid(x)=x\cdot\frac{ReLu6(x+3)}{6}\] \[=x\cdot\left\{\begin{array}{l}1,x\geq 3\\ \frac{x}{6}+\frac{1}{2},-3\leq x\leq 3\\ 0,x\leq-3\end{array}\right. \tag{2}\]  
Figure 7 illustrates that the H-Swish function shares similar properties with the Swish function, including boundedness, smoothness, and monotonicity. Additionally, the H-Swish function maintains linear characteristics similar to those of the ReLU function. As a result, the improved model utilizes the H-Swish activation function to replace the original ReLU function, thereby enhancing feature extraction capabilities. This improvement is especially beneficial in scenarios with limited resources and complex backgrounds, such as those encountered in rescue environments.
## 3 Results and Discussion
### Thermogravimetric Analysis (TGA)  
The thermal degradation events of the sol-gel silica materials, realized via the sol-gel route in acidic medium, were observed by thermogravimetric analysis (TGA) under nitrogen atmosphere. The results are presented in Figure 2. Table 3 shows the weight loss (wt. loss %) and maximum decomposition temperature (T\({}_{\text{max}}\)) of the sol-gel silica materials.  
Figure 1: FTIR spectra of the sol–gel silica materials (TEOS/DMVES/OTES (**A**), TEOS/DMVES/HIDTMES (**B**)) (as powders).  
From Figure 2, it can be seen that all samples exhibited a weight loss at \(\sim\)200 \({}^{\circ}\)C, which may have been due to the loss of physisorbed water and residual organic solvent. The weight loss that occurred in the temperature range of 350-700 \({}^{\circ}\)C may have been due to the thermal decomposition of organic moieties functionalized on the silica skeleton. This result reveals that the alkyl chains of silane may have been decomposed at the temperature of 650 \({}^{\circ}\)C. Thus, the silanol groups could remain on the surface of silica networks [30].  
The obtained results show that the silane-coupling agents were covalently attached to the silica matrix.  
Analyzing Table 3, it can be seen that the weight loss (below 700 \({}^{\circ}\)C) was 22.18 wt.% for sample B (TEOS/DMVES/HDTMES) and 29.75 wt.% for sample A (TEOS/DMVES/OTES). These results demonstrate that a chemical reaction occurred between TEOS and silane precursors.