esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Sampling Methods to Deal With the Big Data Multi-Class Imbalance Problem
## 3 Sampling Class Imbalance Approaches
### Smote+Enn\({}^{*}\)  
Artificial Neural Networks (ANN) are computational models that have become a popular tool used in classification and regression tasks [57]. Nevertheless, it is well know that their maximum performances in supervised classification depend on the quality of the training dataset [68]. Typical methods to improve the training dataset quality are based in the nearest neighbor rule (for  
Figure 1: Flowchart of hybrid sampling methods.  
example: ENN, CNN, TL or OSS), which work in the feature space of the dataset. In other words, the decision region is defined in the feature space [69]. ANN sets the decision boundary in the hidden space or transformed data (see Equations (1)-(3) [55], even the deep learning ANN [70]. Thus, in this research, we consider the most appropriate scheme seeking the noise or overlap samples in the ANN hidden space (transformed data) or in the ANN output; for simplicity, we chose the last one to propose the SMOTE+ENN* approach.  
SMOTE+ENN* is presented in Algorithm 1, which works as follows: first, the dataset is processed with SMOTE (Step 2); then, it is used to train the neural network (Step 5); after that, the neural network output is processed with ENN method (Step 7); i.e., the samples (of the training dataset) related to neural network output (which are considered noise) are deleted (Step 8). Finally, the neural network is trained again with the resultant dataset (Step 9). Source code of the proposed strategy is available in website ([[https://github.com/ccastore/SMOTE-ENN_out.git](https://github.com/ccastore/SMOTE-ENN_out.git)]([https://github.com/ccastore/SMOTE-ENN_out.git](https://github.com/ccastore/SMOTE-ENN_out.git))).  
```
Input: Training dataset X; Test dataset TS; // X \(\cap\) TS \(==\os\), i.e., they are a disjunts sets. Output:\(g\)-mean values;
1: Read MLP, SMOTE and ENN configuration files;  INIT():
2:X\({}_{smo}=\) SMOTE(X);
3:index\(=\{0,1,2,3,...,\|\textbf{X}_{smo}\|\}\);
4:X\({}_{idx}=(\textbf{X}_{smo}\mid\textbf{index}^{\textbf{T}})\); // To add index to X\({}_{smo}\)
5:OUT = MLP (X\({}_{idx}\), \(I_{init}\)); // \(I_{init}\) is the number the initial epochs
6:OUT\({}_{idx}=(\textbf{OUT}\mid\textbf{index}^{\textbf{T}})\);
7:OUT\({}_{emn}=\) ENN(OUT\({}_{idx}\));
8:X\(=\) REMOVE(X\({}_{idx}\), OUT\({}_{emn}\)) ; //Remove samples (from X\({}_{idx}\)) related to neural network output that are considered noise
9:\(g\)-mean = MLP (X, TS, \(I_{end}\)); // To train the MLP with \(I_{end}\) epochs and to test it with TSEND()  REMOVE(\({}_{idx}\), OUT\({}_{emn}\)):
10:/* Q and \(N\) are the number of samples and features in X\({}_{idx}\), respectively.*/
11:for\(q=0\) to \(q<Q\)do
12:if\(X_{idx}[q][N]==OUT_{em}[q][N]\)then
13:for\(n=0\) to \(n<(N-1)\)do
14:// (N-1) because, the add index is deleted (Step 4).
15:\(X_{tmp}[q][n]=X_{idx}[q][n]\);
16:endfor
17:endif
18:endfor
19:return X\({}_{tmp}\);
```  
**Algorithm 1**SMOTE+ENN*
# Hyperspectral Imaging for Minced Meat Classification Using Nonlinear Deep Features
## 2 Material and Methods
### Hyperspectral Imaging System (HSI)
#### 2.2.3 Spectral Pre-Processing  
The formulated reflectance cube contains random noises due to the scattering of light falling on the minced meat. These effects of light can cause undesired spectral variations in the spectrum, which may not be equivalent to the studies and cause unreliable effects in the classification process. Therefore, to normalize the irregularities in the spectra Savitzky Golay Filtering method is applied by using two-degree polynomial and eleven point filters respectively [32]. Thus, a smooth spectrum is formulated that contains the maximum information regarding the chemical composition of each minced meat type.
## 2 Materials and Methods
### Elimination of a Mixture of Contaminants of Emerging Concern  
Solutions containing 5 mg/L of acetylsalicylic acid (ASA), ibuprofen (IBU), and sodium naproxen (NAP) were each treated in the presence of 50 mM Na\({}_{2}\)SO\({}_{4}\), used as electrolyte. The reported concentration represents the average of three measurements, with the error expressed as the standard error of the mean (SEM). To assess the CECs' adsorption capacity of the surface of the material, the catalyst was dispersed in the solution before starting the treatment and stirred for 30 min. The optimization of the process was assessed by studying different currents (10, 20, and 50 mA) and different catalyst loads (0, 10, 20, and 50 mg L\({}^{-1}\)). The concentration of each contaminant was followed by Ultra Performance Liquid Chromatography (UPLC), using a Perkin Elmer equipment, model Altus-30 (Waltham, MA, UA), coupled to a photodiode array (PDA) detector and controlled by an Empower software (v3). Each compound was studied at \(\lambda\) = 225 nm, and their retention times (\(t_{\text{R}}\)) were 2.6, 5.6, and 8.6 min for ASA, NAP, and IBU, respectively. A Phenomenex Kinetex(C18, 100 \(\times\) 2.1 mm, 1.7 \(\upmu\)m, 100 A) LC column (Torrence, CA, USA) was used at 30 \({}^{\circ}\)C. A flow of 0.3 mL min\({}^{-1}\) and a gradient elution program of (A) Formic Acid (0.1% in water) and (B) Acetonitrile was used and is described as follows: \(t_{\text{min}}\)/A%; \(t_{0.0}\)/80; \(t_{0.5}\)/80; \(t_{1.0}\)/60; \(t_{5.5}\)/60; \(t_{8.0}\)/25; \(t_{9.0}\)/80; \(t_{12.0}\)/80.  
The adsorption capacity of the material was studied following Equation (11) [46]:  
\[q=\frac{(C_{0}-C_{t})\cdot V}{W} \tag{11}\]where \(q\) is the amount of each catalyst uptake (in mg g\({}^{-1}\)); \(C_{0}\) is the initial concentration of each compound, and \(C_{t}\) is the concentration at time \(t\) (both in mg L\({}^{-1}\)); \(V\) is the solution volume (in L), and \(W\) is the mass of the catalyst (in g).  
The concentration of each contaminant in the function of time during the EF process was followed by Equation (12):  
\[Normalized\ concentration=\frac{C_{t}}{C_{0}} \tag{12}\]  
where \(C_{0}\) and \(C_{t}\) are the initial concentration of each compound, and \(C_{t}\) is the concentration at time \(t\) (both in mg L\({}^{-1}\)), respectively.  
When organic compounds are attacked by \({}^{\bullet}\)OH radicals (Equation (3)), it could be described by the Langmuir-Hinshelwood model (Equation (13)) [47; 48], which on integration can be written as either Equations (14) or (15).  
\[-\frac{dC}{dt}=\ \mathrm{k}\ [\mathrm{OH}^{\bullet}]\ [\mathrm{CECs}]\ =\ \mathrm{k}_{\mathrm{app}}\ [ \mathrm{CECs}] \tag{13}\]  
\[\ln\ \frac{C_{0}}{C_{t}}=\ \mathrm{k}_{\mathrm{app}}\cdot\mathrm{t} \tag{14}\]  
\[\ln\ \frac{C_{t}}{C_{0}}=\ -\mathrm{k}_{\mathrm{app}}\cdot\mathrm{t} \tag{15}\]  
Furthermore, the kinetic degradation of the mixture of CECs was determined by the pseudo-first order rate constants, also known as apparent rate constant, \(\mathrm{k}_{\mathrm{app}}\).
# Inference Acceleration with Adaptive Distributed DNN Partition over Dynamic Video Stream
## 4 The Proposed ADDP Framework
### Device Intracluster Feature Map Division
#### 4.3.3 Data Segmentation  
In order to improve the parallelism of all devices within a device cluster, it was necessary to divide the computational tasks per unit of data according to the actual computational capacity of the devices. Different devices within the same cluster capture different quantities of data, which can be seen as all devices jointly processing the captured video frames by a model vertical partitioning technique based on feature map partitioning. For example, in a device cluster with \(n\) devices, where device \(D_{i}\) acquires \(P_{i}\) frames per unit time, the device cluster can be regarded as acquiring \(\sum_{i}P_{i}\) frames of data per unit time. We only needed to divide each data frame assigned to these n devices according to the actual computational speed of \(D_{i}\). Based on the regression model built in Section 3, we could estimate the inference time of devices at each input data size to search for the partitioning configuration that minimized the variance of inference time for all \(D_{i}\). However, this search approach had a significant time overhead, prolonging the inference time delay. DeepSlicing [26] demonstrated that the input data size on the slice dimension was roughly proportional to the inference latency of multiple consecutive layers in a DNN. Therefore, we directly sliced the input data on the W dimension based on the ratio of device arithmetic power, achieving near-optimal device parallelism.
## 3 Results and Discussion
### Effect of Electrode Design on Longitudinal Tool Wear (TLW)  
Length of each electrode was measured before and after machining. The difference in lengths is named as longitudinal tool wear. It was noticed that the maximum too wear was found in the conventional tool design (\(\mathrm{D_{C}}\)) as can be seen in Figure 10. On the other end the tool length reduction was found to be less in the case of all the modified tool designs compared with the conventional tool design. The main reason behind this reduction in tool wear was the machining time. Since the machining time for modified tool designs was smaller as compared to the conventional tool. Low machining time indicates that the time for the tool to be under machining conditions is small and hence the length of tool reduction becomes smaller. Within the category of relief angled tool designs (\(\mathrm{D_{R}}\)) as the relief angle increased the tool wear increased, except for 45 degree relief. A similar pattern was observed in the case of the tool design named as the relief angle with land (\(\mathrm{D_{RL}}\)). The reason behind this increasing trend again could be the change in discharge behavior of each electrode design. There is a possibility that by introducing the relief angle the discharge behavior changed from regular (as in case of conventional tool design) to irregular. Since, the shank of the tool was uniform throughout the length whereas the footing end of the modified tool was tapered. The change in the cross sectional area was involved. Due to this change in the cross section area the resistance to the discharge current varied. As a result, there was a possibility that the discharge characteristics were changed due to which the machining time as well as the tool wear was directly affected. A change in cross sectional area is different for different relief angles, thus the effect of relief angle over the current and discharge characteristics needs to be explored further. However, from the results it could be inferred that the minimum tool wear in longitudinal direction was found to be in the case of \(\mathrm{D_{R10}}\) amounting to 790 \(\mathrm{\SIUnitSymbolMicro m}\).  
The effect of tool length reduction was directly over the radial undercut at the exit point of the machined hole. As the electrode length reduced due to the wear at the footing end, the diameter of the remaining tool reduced because of the presence of relief angle. The concept is further illustrated in Figure 11 wherein the length of tool before and after machining is presented schematically. Moreover, the actual tool before and after machining is shown as a sample. When the length reduced, the remaining diameter became smaller and as a result the diameter of the machined hole at the exit point was smaller than the diameter at the entrance. In this way, a radial undercut was observed. A change in tool length and diameter was greater with the increase in the relief angle, which is why the radial undercut also seemed to be larger in the case of large relief angles.
## 3 Model Construction and Solving Algorithm
### Solving Algorithm  
#### 3.2.1 NSGA-II-TOPSIS-GCA  
In this paper, the NSGA-II-TOPSIS-GCA algorithm was constructed to solve the MOODPR problem. The core of NSGA-II is to coordinate the relationship between objective functions, and the NSGA-II introduces the concepts of individual non-dominated sorting, individual crowding, and the parent elite retention strategy, which can adapt to multi-objective problem solving and improve search efficiency. The principle of TOPSIS is to compare the distance closeness degree of the scheme in the non-inferior solution set (alternative scheme set) so as to sort the schemes. However, TOPSIS ignores the correlation among attribute indexes. For this problem, a new hybrid closeness degree is constructed by combining the GCA with TOPSIS, which not only considers the distance closeness degree but also takes into account the correlation among attribute indexes and the implicit relationship among the data, which makes the decision schemes more reasonable and feasible.  
The overall idea of the new algorithm is as follows: Firstly, we input the MOODPR problem, basic data, and parameters. Secondly, the MOOM is constructed, and the non-inferior solution set can be obtained by NSGA-II. Thirdly, we establish the decision matrix according to the appropriate attribute indexes, and the matrix is weighted and normalized. Then, the TOPSIS-GCA is used to sort all the schemes according to the hybrid closeness degree. Finally, the scheme that has the largest closeness degree is taken as the final decision scheme. Figure 5 shows the main flow diagram of the NSGA-II-TOPSIS-GCA algorithm.  
Figure 5: Flow diagram of NSGA-II-TOPSIS-GCA algorithm.
# A Review of the Water Desalination Technologies
## 2 The State of the Art
### Capacitive Deionization (CDI)  
Like electrodialysis reversal, in Capacitive Deionization (CDI) an electric field is produced between two carbon electrodes supplied with direct current voltage. As a consequence of the electric field, the dissolved ions are absorbed into the carbon micropores of the electrodes [54]. To regenerate them, a reverse voltage is applied, releasing ions from the electrodes to the saltwater. The co-ion adsorption phenomenon limits the efficiency of this technology, i.e., the adsorption of ions by electrodes having the same surface charge. To improve the energy efficiency, an anion exchange membrane and a cation exchange membrane could be installed on the electrodes, as shown in Figure 25 [54]. This solution is called Membrane Capacitive Deionization (MCDI) [55].  
According to the literature, this technology requires a lower energy consumption for the desalination of brackish water and lower maintenance operations than the electrodialysis reversal units [56]. Despite these advantages, CDI is a process under investigation, as it is a recent introduction in the context of saltwater desalination [54; 57].
# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 4 Experimental Setup and Results
### Cross-Corpora Evaluation  
We conducted a cross-corpora experiment to check the generalizability aptitude of the proposed SEEN-B4 model for multi-class wound classification. It has been noted that the existing works on wound classification have not investigated the aspect of the cross-corpora evaluation of the model. So, we performed a cross-corpora evaluation for multi-class wound classification. For this experiment, we considered the three classes of wounds that are common in the AZH and Medetec datasets, e.g., Diabetic, Pressure, and Venous wounds. This experiment is conducted on AZH, Medetec, Extended AZH, and Extended Medetec datasets.  
We achieved this experiment in four phases, where at each phase, we trained the model on a training set of one dataset and tested it on a testing set of another dataset. For instance, in the first phase, we trained the model on the AZH dataset and then tested it on the Medetec dataset. Table 6 states the results of the cross-corpus experiment that demonstrate the generalization aptitude of our proposed SEEN-B4 model. The highest accuracy of 77% is achieved for this experiment when the model is trained on the Extended AZH dataset tested for the Extended Medetec dataset. It is worth noting that in the case of cross-corpora evaluation, the accuracies of the extended datasets are also higher compared to the original imbalance AZH and Medetec datasets. This indicates that the balanced dataset and more wound samples positively impact the cross-dataset evaluation. From Table 6, it is noticeable that the performance of our model drops for the cross-corpus experiment, despite having satisfactory performance on the individual datasets. The AZH and Medetec datasets are collected under different environments and conditions. For  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Accuracy (\%)** & **Balanced Accuracy (\%)** \\ \hline Swish-SeLU & 80.28 & 77.64 \\ Swish-ReLU & 81.69 & 78.58 \\ Swish-Swish & 85.91 & 84.39 \\ Swish-ELU (Our Method) & 87.32 & 86.05 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation Study.  
example, the Medetec dataset includes stock images, while the AZH dataset images are gathered from the wound care center.  
Additionally, the images are captured from distinct devices and have different lighting conditions, color modes, background settings, varying angles, and distances from the capturing devices. All these variations among the images of the other datasets have made it challenging to achieve as good results, as has been attained for the individual datasets. Considering the cross-corpus scenario and the diversity among datasets, the results around 59-77% are encouraging, and supports the robustness of our method.
## 3 Results and Discussion
### Simulation Evaluation
As shown in Figure 8, we can see that the proposed algorithm is also the best for packet drop ratio under the assumed simulation scenario, whose packet drop ratio is almost 0% until the number of 50 nodes. In the efficient AODV without SDN, a querying node has to select the next node for every hop until it reaches the destination. By doing so, it may come across several fragile links due to the random and fast movement of vehicles. However, in other SDN-based schemes, the SDN controllers  
Figure 6: Routing overhead comparison. keep the global view of the VANET, which means the SDN controllers manage all the information about idle channels and relay nodes for each segment of the road. Therefore, SDN controllers can provide more informed routing decisions to each querying node. Furthermore, our proposed protocol increases network stability by providing a more stable route solution, for this reason, the least number of packets dropped compared to the other three protocols. Note that, the result above is obtained in the simulation environment we set, not in all simulation scenarios, and we did not take into account other factors not mentioned in this paper [27]. Figure 9 shows the impact of the proposed scheme on average throughput. It can be clearly observed that as number of vehicles increases, the throughput of our proposed algorithm is relatively stable and higher than efficient AODV, SVAO, and SDGR. Specifically, the throughput of our algorithm is in the range of 25,000-30,000 bit/s for the considered range of vehicle nodes (50-above 70). This is because the proposed algorithm is aware of stable links and the future position of nodes, which result in lower packet loss. Furthermore, it does not consume more bandwidth and gives opportunity to other packets to transmit in the inter-vehicle network. Obviously, our algorithm can receive a greater number of data packets than the other three protocols. Figure 8: Packet drop ratio. Figure 7: Average end-to-end delay.
## 2 Friction between Main Cable and Saddle
### Determination of Friction  
The calculation model of the cable saddle is shown in Figure 5. The coordinate system \(XOY\) is established with the center \(O\) of the groove in the cable saddle as the origin. In practical engineering, the main cable of the cable saddle section is not allowed to be completely separated from the saddle. Generally, the main cable and the cable saddle are fixed near the center line of the cable saddle. This paper calls it the fixed point of the cable saddle, expressed in \(Q\), as the known point.  
For the convenience of calculation, the independent variable vectors as shown in Figure 5 are established, \(Z=[Z_{1},Z_{2},Z_{3},Z_{4}]\). \(Z_{1}\) is the main cable tension \(T_{1}\) of the left main cable at the tangent point (left tangent point \(B\)) on the cable saddle. \(Z_{2}\) is the angle of fixed-point \(Q\) turns around from the origin \(O\) to the left tangent \(B\) along the arc anticlockwise.  
Figure 4: Schematic diagram of contact section between main cable and cable saddle.  
\(Z_{3}\) is the angle of the fixed-point \(Q\) turns around from the origin \(O\) to the right tangent \(C\) along the arc. \(Z_{4}\) is the unstressed length of main cable between point \(I\) and \(B\). \(I\) and \(J\) are the nodes. The coordinates of the left tangent point \(B\) can be expressed by the fixed point \(Q\) of the cable saddle as  
\[x_{B} =-R\sin\left(Z_{2}-\theta_{Q}\right) \tag{6}\] \[y_{B} =-R\cos\left(Z_{2}-\theta_{Q}\right) \tag{7}\]  
Similarly, the coordinates of the right tangent point \(C\) are  
\[x_{C} =R\sin\left(Z_{3}+\theta_{Q}\right) \tag{8}\] \[y_{C} =R\cos\left(Z_{3}+\theta_{Q}\right) \tag{9}\]  
The length of the cable segment from the left and right tangent point to the fixed point of the saddle is  
\[s_{1} =R\left(Z_{2}-\theta_{Q}\right) \tag{10}\] \[s_{2} =R\left(Z_{3}+\theta_{Q}\right) \tag{11}\]  
According to [PERSON]'s law, the friction between the cable saddle and main cable is considered. From Equations (3)-(11), the friction f between main cable and saddle is calculated as  
\[f=\frac{Z_{1}}{\mu\left(Z_{2}+Z_{3}\right)}\left[1-e^{-\mu\left(Z_{2}+Z_{3} \right)}\right] \tag{12}\]  
In the formula, \(x_{B}\) and \(y_{B}\) are the horizontal and vertical coordinates of the left tangent point \(B\), respectively. \(x_{C}\) and \(y_{C}\) are the horizontal and vertical coordinates of the right tangent point \(C\), respectively. \(\theta_{Q}\) is the angle between the fixed point \(Q\) and the center line of the saddle. \(R\) is the arc radius of the cable saddle.
## 3 Results and Discussion  
The proposed method is first tested on the CEPRI36 benchmark system in Power System Analysis Synthesis Program (PSASP). The single line diagram of the CEPRI36 system is given in Figure 3. Unless otherwise specified, the unit of power in the numerical test is p.u. The reference value of system power is 100 MVA. The power flow and the time-domain simulation are computed in PSASP. Model construction and interpretation are implemented in Python. Multi-layer perceptron (MLP) is selected as the DL model for testing. Rotor angle-based transient stability assessment (TSA) is taken as an example of DSA. Note that other stability assessments and deep learning models in power systems are also suitable for our method. The numerical test is done on a computer with an Inter(R) Core(TM) i7-10700F CPU and 16.0 GB RAM.  
Figure 2: The flow chart of online security control based on SHAP.