esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of Explosion Mitigation by Deployed Metal Combined with Water Curtain
## 4 Discussion
Moreover, the study [4] on blast mitigation using a water wall, in which walls made of plastic bags, filled with water and having a thickness of 5 to 8 cm, it was shown that the mitigation was obtained by obstruction, reflection and diffraction of the blast wave. The mitigation mechanism was comparable to that of a rigid wall, thus the mitigating effect of energy exchange with water was not primarily responsible for the effect. The results obtained here could also be compared to those obtained by [PERSON] et al. [6] and [PERSON] et al. [5]. In these two studies, blast mitigation observed when using a single grid was very low and this could be explained by the high porosity of the grids used. Indeed, the first study [6] used a stainless steel ring mesh with a porosity of 63% and, in the second, a woven wire mesh having a relative opening fraction of 60.2%. In the setup using ring mesh, adding a water curtain also enhanced the attenuation of both peak overpressure and positive impulse. The authors also claimed that when a blast wave hits ring mesh covered by water, the water layer forms a closed surface that reflects the blast wave to a greater extent. In the case of a higher-charge explosive, the phenomenon of shock wave transmission/reflection would likely be the same and equivalent mitigation performance would be achieved. However, if the grids are not sufficiently resistant to a high loading, the grids could deform and tear. Some debris could impact the structure behind such grids, and consequently the use of gridded plates could have prejudicial effects. The grids and their attachment system must be sized to guarantee their resistance and their structural integrity with respect to anticipated blast size.
## 5 Discussion  
Hyperparameter tuning and cross-validation play a key role in implementing BESS modeling and achieving accurate estimations of the target variable, all because of the different KPIs and level of mutual correlation. Furthermore, Feature Engineering is a helpful process that can provide promising insights not only to analyze the dataset, but also to obtain a new subset of features in the charge and discharge of a BESS.  
Regarding the binary classifiers, the approach that consists of taking the voltage and current as input features showed an elevated level of performance metrics, which is supported by the fact that constant values of current during the charge and discharge represent a potential trend to predict the binary output. The second approach, which considers the KPIs generated in the Feature Engineering process, showed slightly lower but optimistic performance compared to the first approach, and it is concluded that although input features are randomized to avoid easily processing patterns for unbalanced datasets, multiple BESS indicators must be provided to achieve performance metrics at the same numerical level for sensitivity, specificity, and accuracy. Logistic Regression and Decision Tree achieved a prominent level of performance metrics that is not dependent on the quantity of selected features, all because of the hyperparameter tuning, however, Naive Bayes provided a lower specificity by considering the KPIs generated in the Feature Engineering process, being the assumption of independence features a cause.  
Finally, regression algorithms provided a similar performance in the cross-validation and testing steps, demonstrating the promising results of the BESS modeling based on the RUL. Compared to the binary classifiers implementation, hyperparameter tuning and cross-validation do not manifest computational expensive outputs, which is explained by a lower quantity of predictors. In this approach, the quantity of cycles, discharging capacity, and the calculated SOH are the indicators of a mathematical relationship that fits a linear model, which can also be implemented in more advanced research approaches that consider the external degradation mechanisms during the operation of a BESS.
# Experimental Tests and Aeroacoustic Simulations of the Control of Cavity Tone by Plasma Actuators
## 2 Methodologies
### Numerical Simulations
#### 2.4.5 Prediction of the Far Acoustic Field  
The porous Ffowcs, Williams, and Hawkings (FW-H) method [23; 24; 25] was used to predict the acoustic pressure at the measurement point (\(x/L=6.75\) and \(y/L=21.5\)). The pressure \(p\) was sampled at a semi-cylindrical surface of radius \(r=2.5L\), near the center of the cavity (\(x/L=0.5\) and \(y=0\)).  
A spanwise computational domain of width \(W_{\mathrm{c}}=3L\), which was chosen to be smaller than that of the experiment, \(W=7.5L\), was utilized in order to reduce computational resources. To take the effects of this difference into account, the sound-pressure levels, \(SPL\) (\(f\)), were corrected by using the equivalent coherence length, \(L_{\mathrm{c}}\) (\(f\)), which was computed based on the coherence of the velocity along the center of the cavity (\(x/L=0.5\), \(y=0\)) in the spanwise direction. The detailed method is described in [5].
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 4 Results and Discussion
### Comparative Evaluation of Mamdani, Sugeno, and ANFIS Controllers for Solar-Powered Service Systems  
A comparison between different controllers or Solar-Powered Service Systems is presented in Table 6.  
Figure 9: Defuzzification result: solar power performance evaluation.  
Observation:  
* Mamdani FIS is best when you want interpretable, rule-based control (e.g., deciding when to initiate vacation based on solar input and queue length).
* Sugeno FIS is better when you need fast, optimized responses (e.g., inverter control or linear output estimation)
* ANFIS is ideal for systems with large training data, where real-time prediction, learning, and system adaptation (e.g., MPPT or dynamic load switching) are essential.
## 3 Model Structure
### Data Preprocessing  
To enable prediction of the target's positional region, it was necessary to extract the target areas from the dataset. We applied the SLIC superpixel segmentation method to perform superpixel processing on the original image data [25]. In this method, K-means clustering was used for center sampling and the image was segmented into different pixel blocks with the cluster center as the seed point. The superpixel replaced the original pixel level data, which significantly reduced the computational amount of the subsequent processing [26]. Through the superpixel segmentation method, the region of interest (ROI) of the tracked target was marked. Meanwhile, for subsequent visualization analysis, we retained the feature matrix of each pixel block and the positional information of each superpixel block. The superpixel segmentation result \(S\) could be expressed as follows:  
\[S=\{s_{n}\}_{n=1,\ldots,N} \tag{1}\]  
Figure 3: I-GCN Overall framework of model.  
\[s_{k}=\phi_{n}(U) \tag{2}\]  
where \(n\) was the number of superpixel blocks after image segmentation, \(\phi_{n}\)(0) was the superpixel segmentation function, \(U\) was the original image data, \(s_{n}\) was the superpixel graph of a single image, and \(N\) was the \(n\)th superpixel obtained by applying function \(\phi_{n}\)(0) to the segmentation of \(U\). To ensure that the output size of the model matched the original size, it was necessary to store the relevant information in \(s_{n}\). Therefore, \(s_{n}\) could be expressed as follows:  
\[s_{n}=\{n,L_{n},P_{n}\} \tag{3}\]  
where \(n\) was the index of the superpixel block, \(L_{n}\) was the average brightness value of the pixels in the nth superpixel block, and \(P_{n}\) was the set of positions of all the pixels that made up the nth superpixel block. The SLIC method used in this paper performed superpixel segmentation of the satellite target's continuous tracking image data at multiple scales. To better validate the model's performance, five scales were used for superpixel segmentation of the target image, namely, \(N\in\{100,196,256,324,1024\}\). Some of the results of the superpixel segmentation are shown in Figure 4.
## 5 Classification Performed by Machine Learning Algorithms  
### Selection of the Algorithms  
At the outset of this study, it was planned to compare the performance of human listeners against that of CNN only, assuming that this deep learning algorithm would substantially outperform the traditional classification methods. The outcomes of the pilot tests, however, not only undermined this assumption but also showed that for some conditions, the CNN and traditional algorithms could complement each other (for some groups of recordings CNN may show strong performance whereas traditional techniques may exhibit weaknesses and vice versa). Therefore, it was decided to include in this study both the deep learning technique based on CNN as well as the three traditional machine learning algorithms, namely SVM, XGBoost, and Logit. SVM and XGBoost algorithms represent the state-of-the-art traditional classification methods, whereas Logit was included here for its simplicity and a possibility of using L1 regularization [42], which is advantageous from the viewpoint of feature selection.
## 3 Results and Discussion
### Compressive Strength
The values observed in the graph that plots compressive strength for most of the specimens were higher than the minimum required by NBR 12646 [52], which determines the compressive strength for walls of foamed concrete. However, it is important to emphasize that there is no Brazilian standard to evaluate this property in blocks of foamed concrete. The lowest value for compressive strength was found for the test specimens developed with a foam content of 9.5% of the total mass and 28 days of curing time, the average value being compressive strength of 1.85 MPa. Figure 3c shows that, for a 28-day curing time, the superplasticizer specimens had a mean compressive strength of 3.48 MPa; and that the air incorporator and the plasticizer presented lower compressive strength values than the superplasticizer, namely, 3.1 MPa and 3.4 MPa, respectively. The specimens made without additives had the lowest compressive strength, this being 2.9 MPa. There was little variation in the compressive strength due to the presence of additives, except for the specimens that used superplasticizer additive in their composition, which were evaluated after 365 days. This is because the presence of superplasticizer in the concrete mix leads to a higher strength due to the reduced void size and pore connectivity [53]. The results also show that using CCW to substitute natural sand may have influenced the compressive strength, since CCW absorbs more water than natural sand, which causes foam bubbles to coalesce and, consequently, increase in size, thereby reducing the homogenous distribution of CCW in the mixture [22; 54]. Another observation is that the compressive strength results are smaller than the results obtained by [PERSON] et al. [55], [PERSON] et al. [56], and [PERSON] et al. [57], but the materials these authors, used as aggregates have different characteristics to the CCW used in the present work.
# Velocity-Aware Handover Self-Optimization Management for Next Generation Networks
## 3 Velocity-Based Optimization Algorithm
### HO Problem
```
1:ifthen\(RSRP_{TBS}>RSRP_{SBS}+\Delta\mathcal{HOM}_{t}\)
2:ifthenTrigger\(timer\geq TTT\)
3:\(HO\)Decision\(\longleftarrow\)True
4: Send HO request
5:elseif
6:then\(HO\)Decision\(\longleftarrow\)false
7: Run Trigger Timer
8:endif
9:endif
```  
**Algorithm 2** HO Trigger and Decision  
Finally, the main HO performance metrics are calculated and evaluated. The averages of HOP, HOPP, and HOF in each simulation for all UEs are calculated as:  
\[\overline{HOP}=\frac{\sum_{j=1}^{N_{u}}HOP}{N_{u}}\quad\forall\;\;j^{th}UE, \tag{5}\]  
where \(N_{u}\) denotes number of UEs overall simulation time. The average of the HOPP per UE are expressed as follows:  
\[\overline{HOPP}=\frac{N_{HOPP}}{N_{C}+N_{F}}, \tag{6}\]  
where \(N_{HOPP}\) represents the number of HOPPs over the entire simulation. \(N_{F}\) and \(N_{C}\) represent the number of failed and successful HO, respectively. RLF is considered when the UEs lose connectivity with the BS during the HO process. The main source of RLF is the failure during HO initialization, which disrupts or causes the radio link to fail. The average probability of the RLF from all UEs is expressed as:  
\[\overline{P(RLF)}=\frac{\sum_{j=1}^{N_{u}}P(RLF)}{N_{u}}\quad\forall\;\;j^{th}UE \tag{7}\] \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Conditions** & **Speeds** & \(\Delta\mathcal{HOM}\) & \(\Delta\mathcal{TTT}\) \\ \hline \multirow{3}{*}{Condition 1} & \(V_{u,t}>V_{r}\) & \(-\)Step & \(-\)Step \\  & \(V_{u,t}=V_{r}\) & \(+\)Step & \(+\)Step \\  & \(V_{u,t}<V_{r}\) & \(+\)Step & \(+\)Step \\ \hline \multirow{3}{*}{Condition 2} & \(V_{u,t}>V_{r}\) & \(-\)Step & \(-\)Step \\  & \(V_{u,t}>V_{r}\) & \(None\) & \(None\) \\  & \(V_{u,t}<V_{r}\) & \(+\)Step & \(+\)Step \\ \hline \multirow{3}{*}{Condition 3} & \(V_{u,t}>V_{r}\) & \(-\)Step & \(-\)Step \\  & \(V_{u,t}=V_{r}\) & \(-\)Step & \(-\)Step \\ \cline{1-1}  & \(V_{u,t}<V_{r}\) & \(-\)Step & \(-\)Step \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Adjusting the table of HCPs.
# Development of a Prediction Model for Tractor Axle Torque during Tillage Operation
## 3 Results
### Model Verification  
The eight prediction models, developed earlier, were verified using the measured validation set. Figure 8 shows the relationship between the actual torque and the prediction torque for each prediction model. The results were compared based on a 1:1 line. The prediction Model A shows that the distribution of the actual torque and the predicted torque deviates from a 1:1 line, and there is some vertical distance (i.e., error) from a 1:1 line. The other prediction models show a 1:1 line with a close distribution. Thus, it was found that the prediction model makes it possible to predict the actual axle torque.  
Figure 7: Scatter plot of the standardized predicted values and standardized residuals for each regression Model (**A–H**): Model A = axle torque as a linear function of engine torque and engine speed; Model B = axle torque as a linear function of tillage depth; Model C = axle torque as a linear function of slip ratio and travel speed; Model D = axle torque as a linear function of engine torque, engine speed and tillage depth; Model E = axle torque as a linear function of engine torque, engine speed and travel speed; Model F = axle torque as a linear function of tillage depth and travel speed; Model G = axle torque as a linear function of engine speed, tillage depth and travel speed; and Model H = axle torque as a linear function of engine torque, tillage depth and travel speed.  
Table 10 shows the prediction performance evaluation results of the actual axle torque for each prediction regression model. It was found that the R\({}^{2}\) had a range of 0.265 to 0.832, and the MAPE was less than 2.8% for all the regression models. In addition, the RMSE and RD had ranges of 16.50 to 36.65 Nm and 1.53 to 3.39%, respectively. Model A showed the lowest accuracy with a MAPE of 2.73%. Model E showed the best performance, with an R\({}^{2}\) of 0.832, MAPE of 1.19%, RMSE of 16.50 Nm, and RD of 1.53%. Thus, it was found that axle torque could best be explained using three explanatory variables (engine torque, engine speed, and travel speed).
## 3 Method  
### Data Preprocessing  
The dataset used in this paper is the diabetes-related literature provided by Ruijin Hospital, which includes textbooks, research papers, and clinical guidelines related to diabetes. This dataset is a chapter-level text, in which there are 15 medical entities, including examination methods, etiology, clinical manifestations, medication methods, and sites.  
In NLP tasks, the sentences input to the model need to be of appropriate length. Too short sentence length will lose the feature information of the text, while too large sentence length is not conducive to the model training process. The diabetes-related medical literature used in this paper is chapter-level Chinese text, and an article with thousands or even tens of thousands of words cannot be directly input to the model for training, so it is necessary to divide the long diabetes text and transform it into data suitable for model input.  
Because of formatting conversion of the original text, there will be line breaks or periods between entities in the article, which leads to a lack of good sentence boundaries. Thus, directly using punctuation to divide the sentences may have the situation of dividing one entity in two sentences, resulting in incomplete entities. To address the problem, in this paper, we propose to use a sliding window size equal to the step size to slice the sentence, and extend the window left and right by a certain length of characters. Thus, the length of the sentence can be controlled and all have certain contextual information, avoiding the case of entity segmentation. The specific division method is shown in Figure 1, in which the size of the sliding window (\(window\_size\)) is 5, the number of characters extended to the left and right of the sentence (\(pad\_size\)) is 3, and the final sentence length obtained is \(window\_size+2*pad\_size=11\).  
The sentences obtained from the above division are labeled in BIO mode, and the characters in the sequence are labeled with \"B-X\", \"I-X\", or \"O\". The label \"B-X\" means the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Model** & **Task** \\ \hline Huang et al. [18] & BiLSTM + CRF & NER \\ Zhang et al. [19] & Lattice-LSTM+CRF & Chinese NER \\ Qiang et al. [20] & 6-Tag-Boundary-Pos & Chinese NER \\ Chiu et al. [13] & BiLSTM+CNN & Chinese NER \\ Ding et al. [22] & graph neural networks & Chinese NER \\ Strubell et al. [23] & IDCNNs & Sequence labeling\&NER \\ Zhang et al. [24] & BERT+BiLSTM-CRF & Medical NER for breast cancer \\ Li et al. [25] & BiLSTM+Attention & Entity extraction of Chinese EMR \\ Ji et al. [26] & Attention-BiLSTM-CRF & Chinese EMR NER \\ Li et al. [27] & BERT+BiLSTM+CRF & Clinical NER \\ Li et al. [28] & CRF & Chinese EMR NER \\ Zhou et al. [29] & BioBERT-CRF & Biomedical NER \\ \hline \hline \end{tabular}
\end{table}
Table 1: A summary of NER models.  
Figure 1: Schematic diagram of a sliding window.  
corresponding character is at the beginning of the entity, \"I-X\" means the corresponding character is in the middle of the entity, and \"O\" means the character does not belong to any entity.
## 5 Case Study
### Comparison of Three Semi-Empirical Modeling Methods
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scenario** & \multicolumn{2}{c}{**Unknown ASOC (\%)**} & \multicolumn{2}{c}{**Known \(\Delta SOC\) (\%)**} \\ \hline Scenario 1 & [20, 100] & [10, 90] & [0, 100] & [80, 100] & [40, 60] & [0, 20] \\ Scenario 2 & [80, 100] & [40, 60] & [0, 20] & [20, 100] & [10, 90] & [0, 100] \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Scenario combination for a comprehensive comparison of three methods. improved semi-empirical model has high experimental data requirements and requires the calculation of the aging factor based on measured data with the same DOD but different average SOCs. At the same time, the more comprehensive the experimental data, the higher the fitting accuracy and applicability of this method. To sum up, the three methods have their respective suitable application scenarios: When the \(\Delta SOC\) is known to be very limited, it is more suitable to use the interval number similarity-based semi-empirical modeling method to approximate the capacity degradation  
Figure 11: Comparison of three methods under six cases. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Unknown \(\Delta SOC\)**} & \multicolumn{2}{c}{**Parameter Interval Number**} & \multicolumn{2}{c}{**Model Interval Number**} & \multicolumn{2}{c}{**Improved Semi-Empirical**} \\  & & **Similarity Method** & \multicolumn{2}{c}{**Similarity Method**} & \multicolumn{2}{c}{**Modeling Method**} \\ \cline{3-6}  & \(\mathbf{R^{2}}\) & \(\mathbf{RMSE}\) & \(\mathbf{R^{2}}\) & \(\mathbf{RMSE}\) & \(\mathbf{R^{2}}\) & \(\mathbf{RMSE}\) \\ \hline \multirow{3}{*}{Scenario 1} & \([20,100]\) & 0 & 0.0121 & 0.7472 & 0.0045 & 0 & 0.0185 \\  & \([10,90]\) & 0.8989 & 0.0012 & 0 & 0.0142 & 0 & 0.0079 \\  & \([0,100]\) & 0.8974 & 0.0019 & 0 & 0.0137 & 0 & 0.0135 \\ \hline \multirow{3}{*}{Scenario 2} & \([80,100]\) & 0 & 0.0213 & 0.8322 & 0.0075 & 0 & 0.0249 \\  & \([40,60]\) & 0 & 0.0275 & 0.9137 & 0.0042 & 0 & 0.0197 \\  & \([0,20]\) & 0.8469 & 0.0113 & 0 & 0.0329 & 0 & 0.0504 \\ \hline \hline \end{tabular}  
* Notes: \(R^{2}=0\) means that the fitting effect of the function is worse than the average value.  
\end{table} 
Table 9: The goodness of fit of three methods in two scenarios with very limited data. of retired batteries. When the known \(\Delta SOC\) can cover the unknown \(\Delta SOC\) more fully, we can choose the model interval number similarity method, and when the known \(\Delta SOC\) cannot adequately cover the unknown \(\Delta SOC\), we can choose the parameter interval number similarity method. When the \(\Delta SOC\) of the measured data is more comprehensive, that is, two types of measured data with the same average SOC but different DODs and the same DOD but different average SOCs are known, a more accurate retired battery capacity degradation model can be obtained by means of the improved semi-empirical model.
## 5 Implementation of the \"Muting\" Safety Function in a Dual Architecture Based on sPLC
### Reviving and Testing Dual Architecture  
Since the functions of the comparator (Figure 5) and the safety function \"Muting\" (Figure 6) were thoroughly tested at the model level in the Matlab Simulink software tool, the generated software was revived on the dual architecture according to Figure 7 without any difficulties and interventions into the generated software (such additional interventions would conflict with the safety requirements). Subsequently, test scenarios were created to verify the correct operation of the safety function and also to verify the reaction to simulated faults. The verification of the behavior of the \"Muting\" function during simulated faults is particularly important because the safe reaction to the fault is the main goal of the proposed and implemented dual architecture.  
All tests were recorded using tables, where each row of the table represents one test case and also evaluates this test case (compliant/non-compliant). In the case of the tested safety function, all were evaluated as compliant. The tables with the individual test cases are not part of this article because they are too extensive.