esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations  
The mechanical testing of the samples was carried out ex vivo outside of wet chambers and at room conditions. This setup comes with a number of limitations, shortening the life span of the tissues, as the mechanical properties differ from those of living tissue. As a consequence, the missing tissue surrounding the tendons in the center may have resulted in diminished hydration effects. Despite moistening of the samples prior to the testing and maintaining a moist environment, drying effects may occur. The water content of the tissue may vary throughout the preparation and testing process. To keep the natural fiber orientation intact without creating a likely failure site, the tendon specimens were left intact. This as a result led to unintended failure sites but was necessary to allow for fatigue testing without premature failure. The main failure site observed was in the clamping area, potentially due to shearing or squeezing of the sample considering the sample to be uniformly thick. This needs to be considered in future studies. The natural tapering of the tendons was randomly assigned to the rigid and moving clamping side, which may have influenced the results. The cross-sectional area was calculated by assuming a rectangular shape and averaging three areas of the tendons, which moreover influenced the calculation of the peak stresses. In future studies, the smallest cross-sectional areas could, for example, be detected and measured by optical methods. During the testing, 38% of the 3D-printed clamps failed, mainly on the edge areas. As this can potentially influence the results by acting as a stress concentrator and lead to lower tolerable maximum forces of such tissues, further analyses to quantify these effects, e.g., using image correlation techniques, is recommended for future studies. While failure on the edges of the clamps was assessed as not having a major effect on the results, it does preclude them from being reused for further testing. Optimizing the design and/or using other materials than PLA for the manufacturing of the clamps could enable multiple use as well as reduced damage of the clamps and therefore reduced material costs.
# Influence of Earthquakes on Landslide Susceptibility in a Seismic Prone Catchment in Central Asia
## 3 Results
### Relationship between Landslide Metrics and Environmental Variables  
The relative importance of each environmental variable for the explanation of landslide metrics was performed using the machine-learning-based random forest analysis. The results indicated that the explanatory powers of LSI (ranged between 28.47% and 87.85%) were significantly higher than that of VOL (ranged between 11.07% and 19.22%) (Figure 6). The explanatory power reduced with the increase in the earthquake level, except in the extreme scenario of VOL (Figure 6). Overall, the most important environmental variable was distance to faults (ranged between 30.28% and 46.05%) for the explanation  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable/Class**} & \multicolumn{6}{c}{**Landslide Susceptibility Index**} & \multicolumn{6}{c}{**Volume of Landslide (m3)**} \\ \cline{2-11}  & **Moderate** & **Strong** & **Severe** & **Violent** & **Extreme** & **Moderate** & **Strong** & **Severe** & **Violent** & **Extreme** \\ \hline
**Elevation (m)** & & & & & & & & & & & \\
576–1000 & 2.03 & 1.45 & 0.96 & 0.56 & 0.32 & \(2.0\times 10^{7}\) & \(2.3\times 10^{7}\) & \(3.1\times  
of LSI, followed by slope, annual precipitation, and elevation (Figure 6a), whereas annual precipitation (ranged between 10.91% and 37.14%) was identified as the most important environmental variable for the explanation of VOL, followed by elevation, slope, and distance to faults (Figure 6b). The explanatory powers of all the other five environmental variables were relatively low, and the variable with the lowest explanatory power was aspect (LSI: 5.59\(-\)16.16%; VOL: 1.04\(-\)4.86%; Figure 6).
## 3 Materials and Methods
### EIM Data Processing and Classification
#### 3.3.3 Feature Extraction and Classification  
Each of the variation maps generated using the previous integration and fusion methods offers a representation of the changes captured in the breast tissues, merging all the information contained in the EIM layers. The proposed EIM image analysis method focuses on distinguishing between cases with malignant lesions and those without (healthy). It is expected that a simplified representation of the EIM layers will enable an integrated view that helps to improve the performance in automatic breast cancer classification based on a complementary non-invasive sieving technique. In addition, a variability map simplifies the application of machine learning techniques, particularly automatic classification. Each image was resized to 224 \(\times\) 224 and normalized to standardize the input for the classifiers.  
To perform feature extraction, a VGG19 architecture pre-trained on ImageNet [39] was used as a fixed feature extractor (include_top=False). This configuration excluded the fully connected layers and retained the convolutional base, which consisted of 16 convolutional layers organized into five blocks. Each block was followed by a max-pooling layer, progressively reducing the spatial dimensions while increasing the abstraction. The resized EIM images were passed through this convolutional base, and the resulting feature maps output by the final block were flattened into one-dimensional vectors, preserving the hierarchical and spatial structure learned by the network. This approach followed the standard design and usage patterns of CNNs, as described in [40]. In the context of medical image analysis, using deep networks as fixed extractors has been shown to enhance the classification robustness, particularly when working with uncertainty-aware or noise-sensitive data [41]. Even in related domains such as ECG signal classification, the extraction of deep, abstract features using pre-trained architectures has proven helpful in improving the prediction quality [42]. After processing an EIM image, the output of the last convolutional block in VGG19 provided a vector with a length of \(7\times 7\times 512=25\),088 features, which served as the input for the classifiers. In this way, the deep visual descriptors encapsulated the most relevant spatial and semantic information in the image, allowing traditional classifiers to operate effectively in this transformed feature space. For experimental purposes, the performance of several classifiers, including Logistic Regression, Random Forest, bagging, and XGBoost [43; 44; 45; 46], were tested with the features described in the variability maps.
# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 3 Methodology
### Global Attention Model  
The attention mechanism is a process that simulates how humans allocate attention resources. Its core idea is to focus on key areas while diminishing attention to irrelevant information, thereby enhancing the overall performance of the model. By filtering and amplifying key features, it effectively filters out noise or unimportant information, significantly improving the model's ability to recognize important information and its prediction accuracy. Unlike traditional methods that assign random weights, the attention mechanism dynamically assigns weights to each input element using a probability distribution, ensuring that the model focuses on the information that is most relevant to the task. This mechanism not only addresses the potential information loss issue in LSTM when processing very long sequences but also better preserves the global and local dependencies in temporal features, thus optimizing the model's expressive power and predictive performance. Additionally, the attention mechanism is highly flexible and can be combined with various neural network architectures, further enhancing the model's adaptability and generalization ability. Figure 4 visually illustrates the structure of this mechanism.  
As shown in Figure 4, \(x_{t}\) represents the input from the LSTM model, while \(h_{t}\) represents the hidden layer output obtained from the LSTM. The attention probability distribution applied to the LSTM hidden layer output is denoted as \(\alpha_{t}\), and the output of the LSTM  
Figure 3: The structure diagram of multi-head self-attention.  
optimized by the attention mechanism is denoted as \(y\). The weight calculation process of the attention mechanism is as follows:  
\[w_{t}=\lambda tanh(\eta h_{t}+\sigma), \tag{11}\]  
\[\alpha_{t}=\frac{exp(w_{t})}{\sum\limits_{j=1}^{t}w_{j}}, \tag{12}\]  
\[\varphi_{t}=\sum\limits_{t=1}^{i}\alpha_{t}h_{t}, \tag{13}\]  
where the attention scoring function \(w_{t}\) is used to calculate the correlation between the grid load and the output vector of the LSTM network at time step \(t\). The function \(tanh\) represents the hyperbolic tangent activation function. The weight coefficients are denoted as \(\lambda\) and \(\eta\), while the bias coefficient is denoted as \(\sigma\). \(exp\) represents the exponential function, which is used to compute the attention weight score. The output of the attention layer at time step \(t\) is denoted as \(\varphi_{t}\).
_applied sciences_  
Proof of Concept of Reconfigurable Solvent Vapor Sensor Tag with Wireless Power Transfer for IoT Applications  
**[PERSON] 1, [PERSON] 2, [PERSON] 2, [PERSON] 3 and [PERSON] 1**  
1 Department of Physics, Microwave Electronics Research Laboratory, Faculty of Sciences of Tunis, University of Tunis El Manar, Tunis 2092, Tunisia 2 Department of Electromagnetic Field, Faculty of Electrical Engineering, Czech Technical University in Prague, Technicka 2, 16627 Prague, Czech Republic 3 Military Research Center, Taieb Mihri City, Tunis 2045, Tunisia 4 Correspondence: [PERSON]SS]  
**Abstract:** In this paper, a concept of a reconfigurable chipless radio frequency identification (RFID) sensor tag for detecting solvent vapors/gas in IoT applications was presented. The concept was based on the authors' previously published rectangular loop structure equipped with a U-folded dipole loaded with a glide-symmetrical interdigital capacitor coated with a thin layer of tetrasulfonated copper phthalocyanine deposited as a sensing layer to improve the sensing capability in the presence of acetone vapor. In order to further maximize the sensitivity of the designed structure to the desired solvent, a circuit for a central frequency adjustment using a radio frequency varactor diode biased with a wireless power transfer (WPT) was designed. By varying the DC bias of the diode, a continuous tunable range of approximately 200 MHz was achieved. The proposed reconfigurable wireless sensor tag was manufactured and the frequency shift was verified by measurement. The proposed external frequency control can be applied to a wide class of electrical resonators.  
**Keywords:** chipless RFID; gas sensor; glide symmetry; Internet of Things (IoT); reconfigurability; U-folded dipole; varactor diode; wireless power transfer (WPT)
# Study on the Influence of Dust Removal Fan Structure Parameters on Dust Removal Performance
## 3 Numerical Simulation Scheme of Dust Removal Fan
### Comparison of Simulated and Experimental Values
These four groups of numerical simulation results are compared with those obtained by the test system, as shown in Table 4.  
Using the total pressure and static pressure of the dust removal fan as evaluation indicators, we draw a line chart to compare the numerical simulation results and the test results of the total pressure and static pressure of the dust removal fan, as depicted in Figure 8.  
The comparison between the numerical simulation results and the experimental results indicates that the results obtained by the numerical simulation are in good agreement with the experimental results. The change trend of the two is basically the same, and the relative error of the numerical simulation results is less than 5%. Therefore, the numerical simulation method adopted in this paper is accurate and reliable. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{\begin{tabular}{c} **Volume Flow** \\ **Rate/(m\({}^{3}\)/min)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Total** \\ **Pressure/Pa** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Static** \\ **Pressure/Pa** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Driving Impeller** \\ **Shaft Power/kW** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Total Pressure** \\ **Efficiency/\%** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Static Pressure** \\ **Efficiency/\%** \\ \end{tabular} } \\ \hline \multirow{2}{*}{1} & Simulation & \multirow{2}{*}{373.469} & \multirow{2}{*}{1634.58} & \multirow{2}{*}{1350.22} & \multirow{2}{*}{20.356} & \multirow{2}{*}{49.98} & \multirow{2}{*}{41.28} \\  & result & & & & & \\ \cline{1-1}  & Test result & & & & & & \\ \hline \multirow{2}{*}{2} & Simulation & \multirow{2}{*}{395.853} & \multirow{2}{*}{1534.19} & \multirow{2}{*}{1214.31} & \multirow{2}{*}{20.463} & \multirow{2}{*}{49.46} & \multirow{2}{*}{39.15} \\  & Test result & & & & & & \\ \cline{1-1}  & Test result & & & & & & \\ \hline \multirow{2}{*}{3} & Simulation & \multirow{2}{*}{403.690} & \multirow{2}{*}{1499.87} & \multirow{2}{*}{1167.34} & \multirow{2}{*}{20.510} & \multirow{2}{*}{49.20} & \multirow{2}{*}{38.29} \\  & result & & & & & & \\ \cline{1-1}  & Test result & & & & & & \\ \hline \multirow{2}{*}{4} & Simulation & \multirow{2}{*}{410.939} & \multirow{2}{*}{1456.81} & \multirow{2}{*}{1112.48} & \multirow{2}{*}{20.546} & \multirow{2}{*}{48.56} & \multirow{2}{*}{37.08} \\  & result & & & & & & \\ \cline{1-1}  & Test result & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between the simulation value and the test value of the dust removal fan under four working conditions. Figure 8: Comparison of simulated values and test values.
## 3 Results and Discussion
### Comparison with Existing Methods  
To evaluate the effectiveness of our technique, we compared IL10-Stack with the non-stacking method, IL10-Fuse, and the existing methods, IL-10 Pred and ILeukin10 Pred (Table 1). As shown in Table 1, the IL10-Stack model based on stacking achieved the best performance in independent testing, with ACC = 0.910 and MCC = 0.820, outperforming the IL10-Fuse model (ACC = 0.896 and MCC = 0.792).  
As shown in Figure 4, we employed multiple metrics to examine model performance (Figure 4A-F)). Relative to the existing method, IL-10 Pred, the ACC of our model was improved by 12.1% (Figure 4A), MCC improved by 39% (Figure 4B), AUC improved by 4.5% (Figure 4C), P improved by 33.7% (Figure 4D), Sp improved by 8.1% (Figure 4E), and Sn improved by 17.1% (Figure 4F). Moreover, relative to the existing method, ILeukin10 Pred, our model showed improvements of 2.4% in ACC (Figure 4A), 4.9% in MCC (Figure 4B), and 11.6% in Sn (Figure 4F). Our results demonstrate that IL10-Stack is one of the cutting-edge IL-10-induced peptide prediction techniques based on unified deep representation learning. Compared with existing non-stacking methods, IL10-Stack provides a more reliable and stable prediction of IL-10-induced peptides.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classifier** & **ACC** & **MCC** & **AUC** & **P** & **Sp** & **Sn** \\ \hline IL-10 Pred & 0.812 & 0.590 & 0.880 & 0.674 & 0.819 & 0.797 \\ ILeukin10 Pred & 0.875 & 0.755 & 0.931 & **0.927 \({}^{\text{a}}\)** & **0.947** & 0.804 \\ IL10-Fuse & 0.896 & 0.792 & **0.948** & 0.905 & 0.895 & 0.897 \\ IL10-Stack & 0.910 & 0.820 & 0.920 & 0.901 & 0.885 & **0.933** \\ \hline \hline \end{tabular} \({}^{\text{a}}\) The best performance values are indicated in bold and underlined.  
\end{table}
Table 1: Comparison of independent testing results between IL10-Stack and other existing methods.  
Figure 4: Comparison of independent testing performance between the IL-10 Pred, ILeukin10 Pred, IL10-Fuse, and IL10-Stack methods. (**A**) ACC, accuracy; (**B**) MCC, Matthews correlation coefficient; (**C**) AUC, the area under the receiver operating characteristic curve; (**D**) P, precision; (**E**) Sp, specificity; (**F**) Sn, recall.
# Digital Twins in Solar Farms: An Approach through Time Series and Deep Learning
## 4 Methodology
### Algorithms for Anomaly Detection  
In this paper, we propose two methodologies based on the detection of anomalies using the \(RE\). The first method (or local detection) captures the residual deviation of the model obtained with respect to the real system, establishing a threshold for labeling the anomaly between mild or serious. The second method (fine detection) checks the behavior in a period in order to establish if the supposed anomaly could be just an error in the measurement or if we are in the presence of an anomalous behavior (persistence of the anomaly in a period of time). They are briefly detailed below:  
* First method: A set of thresholds are established using as a criterion the degree of deviation \(\delta\) up to a 15% variation with respect to the \(RE\):
* If \(\delta>RE\ \rightarrow\ \text{slight anomaly}\).
* \(0.1\delta>RE\ \rightarrow\ \text{- If }0.15\delta>RE\ \rightarrow\ \text{ serious anomaly}\).
* Second method: The \(RE\) is defined as threshold, and the following criteria are established for labeling the anomaly:
* If at least 80% of the signals at the same instant of time \(t\) show \(\delta>RE\).
* If the deviation \(\delta\) hasbeen repeated in at least \(\pm 5t\rightarrow\ \text{ serious anomaly}\). The steps implemented for the anomaly detection system are described below: 1. Obtain the reconstruction set, \(\hat{X}_{t}=\{\hat{x}_{1t},\ \ldots,\ \hat{x}_{kt}\}\) with the prediction of our model using the training \(X_{t}=\{x_{1t},\ \ldots,\ x_{kt}\}\). 2. With this reconstruction set, the \(ER\) reconstruction error on the training set is obtained. 3. Obtain the reconstruction set, \(\hat{X}_{t}=\{\hat{x}_{1t},\ \ldots,\ \hat{x}_{kt}\}\) with the prediction of our model using the test set \(X_{t}=\{x_{1t},\ \ldots,\ x_{kt}\}\). 4. On the set \(\hat{X}_{t}\), apply the first method. 5. On the set \(\hat{X}_{t}\), apply the second method. Subsequently, an evaluation of the effectiveness in detecting anomalies of the models obtained through the different processing techniques was carried out. For this purpose, a set of manually labeled samples was used according to the criteria of experts, and using metrics such as precision, recall or AUC, the proposed methods were evaluated.
## 3 Results
### Accuracy Values of the IOS  
The results of the accuracy evaluation and the distribution of those are shown in Table 4 and Figure 8. Trueness values ranged from 22.5 \(\upmu\)m (\(\pm\)4.8 \(\upmu\)m) OC non-p to 12.8 \(\upmu\)m (\(\pm\)0.7 \(\upmu\)m) Tr3 non-p, whereby the results for precision varied between 19.5 (\(\pm\)4.5) \(\upmu\)m OC non-, Tr non-p and 10.7 \(\upmu\)m (\(\pm\)5.2 \(\upmu\)m) OC p.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **IOS** & \
\begin{tabular}{c} **Trueness [n = 6]** \\ **Mean (\(\pm\)SD) [\(\upmu\)m]** \\ \end{tabular} &
\begin{tabular}{c} **Precision [n = 15]** \\ **Mean (\(\pm\)SD) [\(\upmu\)m]** \\ \end{tabular} \\ \hline \multirow{4}{*}{t0} & OC non-p & 22.5 (\(\pm\)4.8) & 19.5 (\(\pm\)4.5) \\  & OC p & 14.6 (\(\pm\)0.9) & 13.6 (\(\pm\)7.6) \\  & Tr3 non-p & 14.9 (\(\pm\)1.8) & 19.5 (\(\pm\)7.1) \\  & Tr3 p & 15.7 (\(\pm\)1.2) & 15.1 (\(\pm\)3.7) \\  & TD & 17.1 (\(\pm\)1.0) & 11.9 (\(\pm\)2.8) \\ \hline \multirow{4}{*}{t1} & OC non-p & 22.5 (\(\pm\)1.4) & 12.1 (\(\pm\)3.2) \\  & OC p & 14.3 (\(\pm\)0.4) & 15.3 (\(\pm\)7.5) \\ \cline{1-1}  & Tr3 non-p & 12.8 (\(\pm\)0.7) & 18.0 (\(\pm\)5.3) \\ \cline{1-1}  & Tr3 p & 15.7 (\(\pm\)0.6) & 17.2 (\(\pm\)7.0) \\ \cline{1-1}  & TD & 20.6 (\(\pm\)1.4) & 17.5 (\(\pm\)4.2) \\ \hline \multirow{4}{*}{t2} & OC non-p & 21.8 (\(\pm\)1.7) & 14.7 (\(\pm\)3.8) \\  & OC p & 17.6 (\(\pm\)0.8) & 11.2 (\(\pm\)3.5) \\ \cline{1-1}  & Tr3 non-p & 14.5 (\(\pm\)1.2) & 14.6 (\(\pm\)4.5) \\ \cline{1-1}  & Tr3 p & 14.2 (\(\pm\)1.3) & 13.9 (\(\pm\)5.4) \\ \cline{1-1}  & TD & 19.9 (\(\pm\)1.4) & 15.3 (\(\pm\)5.0) \\ \hline \multirow{4}{*}{t3} & OC non-p & 19.2 (\(\pm\)1.4) & 16.7 (\(\pm\)4.2) \\ \cline{1-1}  & OC p & 21.8 (\(\pm\)1.3) & 10.7 (\(\pm\)5.2) \\ \cline{1-1}  & Tr3 non-p & 13.6 (\(\pm\)1.3) & 14.5 (\(\pm\)4.3) \\ \cline{1-1}  & Tr3 p & 18.3 (\(\pm\)2.2) & 12.2 (\(\pm\)5.2) \\ \cline{1-1}  & TD & 21.3 (\(\pm\)1.2) & 11.6 (\(\pm\)3.3) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy (trueness and precision) values for the applied IOS.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **FDI 26 (t3)** & **FDI 27 (t3)** \\ \hline WLP (reference) & 417 & 344 \\ OC non-p & 415 (\(\pm\)6) + 0\% & 371 (\(\pm\)4) + 8\% \\ OC p & 428 (\(\pm\)10) + 3\% & 387 (\(\pm\)7) + 13\% \\ Tr3 non-p & 441 (\(\pm\)6) + 6\% & 361 (\(\pm\)9) + 5\% \\ Tr3 p & 434 (\(\pm\)10) + 4\% & 390 (\(\pm\)12) + 13\% \\ TD & 432 (\(\pm\)4) + 5\% & 328 (\(\pm\)9) + 11\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Wear values at the wear step t3.  
Tr3 non-p showed statistically significant lower trueness results than TD and OC non-p, as well as the powdered variation of Tr3 in comparison to OC non-p.  
The statistical results concerning the precision of the different IOS did not differ significantly.
## 3 Results and Discussion
### Limitations and Future Research  
This study tends to focus on the formation of a relatively stable seepage field in a dry-stack tailings pond under the action of long-term rainfall. The main purposes are as follows: (1) to propose the concept and research framework of a relatively stable seepage field; (2) remind designers that a relatively stable infiltration line will be formed in the dam body under the effect of cumulative rainfall over many years. Designers should consider the influence of this line in the calculation of dam slope stability and drainage design to avoid potential hazards.  
In this study, the influence of tailings reservoir bottom leakage is ignored. Strictly, the leakage at the bottom of the tailings pond is the main way of pollutant transport and ignoring it will have an important impact on the research results of seepage water pollution. However, when studying from the perspective of the dam slope safety analysis, it is safer without considering the leakage at the bottom of tailings pond. Therefore, it is feasible to apply the conclusion of this study.  
Indeed, the relevant theories and applications of the relatively stable seepage field proposed in this paper need to be improved. In addition, it is difficult to study all of the related aspects such as the assessment of pollution brought by the leakage of the dam bottom and the discharge of the seepage pipeline, the influence of different drainage arrangements, the effects of flood confluence frequency, flood infiltration range, infiltration time, and other factors on the relatively stable seepage field of the dry-stack tailings pond under the action of rainfall.  
In the future, based on the study in this manuscript, the authors will conduct in-depth research on the safety and pollution problems caused by the seepage field with the leakage of the dam bottom and the discharge of the seepage pipeline through a series of experimental, simulation, and numerical studies. Predictably, with considering more specific problems, including the possible dam seepage, complex drainage pipes, water infiltration in the catchment area and other factors, it will facilitate in gradually establishing a more perfect design theory and method for dry-stack tailings ponds.