esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
However, thicker absorbers also increase the overall weight and dimensions of the structures. Today, most noise absorbers are porous membranes, cavities, perforated panels, and composite absorbers in the form of open- and closed-cell foams, fiberglass, cloth, mineral wool, acoustic ceiling tiles, and wood fibers. Also, metamaterials have been recently proposed for aircraft lining panels that seems to be promising in reducing the cabin noise [19]. An insight into the composite materials for passive sound absorption can be found in [20]. Within the field of advanced technologies for PNC, metamaterials [21; 22], 3D printed components [23], and electrospun blankets [24; 25] have recently attracted great attention for sound absorption. In fact, electrospinning is the only technique that nowadays can give micrometric or sub-micrometric fibers, that are, therefore, at least 10 times thinner than the traditional soundproofing materials. Blankets are typically produced as woven non-woven mats for which the thin fiber diameter gives a high surface-to-volume ratio, which is an ideal property for lightweight and porous materials. When a layer of microfibers is exposed to incident sound waves, the friction dissipation inside the micro-pores and the scattering of waves interacting with fibers dissipate the sound energy. Furthermore, the electrospun layers act as an acoustic resonance membrane: the membrane resonates at given frequencies, thus in turn deeply increasing the rate of sound energy conversion into thermal energy. The development of electrospun sound absorbers is very promising and very much needed, as there are increasingly higher demands on noise reduction in new products, especially for the aircraft and automotive industries. Research is focusing on tests to assess the sound absorption properties of electrospun blankets [26; 27]. Recently, silica particles were added to dimensionally stabilize the PVP mats during the cross-linking thermal treatment necessary to obtain water resistance and fire self-extinguishing behavior [28]. In fact, PVP blankets are obtained through a green electrospinning process that exploits solubility of the PVP polymer in ethanol [26; 27]. The solubility in polar solvent, and therefore in water, is obviously a severe drawback in the blanket applications. This problem was overcome through a proper cross-linking heat treatment of the silica/PVP blankets [28]. The addition of silica also allows to obtain self-extinguishing materials that satisfy the severe Federal Acquisition Regulation. The cabin noise reduction can be achieved by using high performance materials in combination with advanced computational approaches, such as BEM, to evaluate upfront the performances of several PNC implementations.
# Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators
## 3 Materials and Methods
### Performance Metrics  
The performance metrics used in this research work, namely, accuracy, mean absolute error (MAE), sensitivity (recall), fallout, precision, F-measure, specificity, and ROC area, are discussed here. The confusion matrix shown in Table 6 depicts various performance metrics for evaluating a classifier. True positives are the responses equal to the positive class that are correctly predicted as positive. True negatives are the responses equal to the negative class that are correctly predicted as negative. False positives are the responses equal to the negative class but are predicted as positive. False negatives are the responses equal to the positive class but are predicted as negative.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{TN}+ \text{FN}}\times 100\% \tag{2}\]  
\[\text{MAE}=\frac{\sum|\text{Predicted value}-\text{Actual value}|}{\text{ Number of predictions}} \tag{3}\]  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}}\times 100\% \tag{4}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}}\times 100\% \tag{5}\]  
\[\text{Fallout}=\frac{\text{FP}}{\text{TN}+\text{FP}}\times 100\% \tag{6}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\times 100\% \tag{7}\]  
\[\text{F}-\text{Measure}=\frac{2\times\text{Precision}\times\text{Recall}}{ \text{Precision}+\text{Recall}} \tag{8}\]  
ROC Area: The area under the ROC curve measures the quality of a model's predictions regardless of what classification threshold is chosen. The ROC curve represents the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \begin{tabular}{c} **Predicted Class** \\ **High Risk (1)** \\ \end{tabular} & \multicolumn{1}{c}{**Low Risk (0)**} \\ \hline Actual class & \begin{tabular}{c} High risk (1) \\ Low risk (0) \\ \end{tabular} & \
\begin{tabular}{c} True Positive (TP) \\ False Positive (FP) \\ \end{tabular} &
\begin{tabular}{c} False Negative (FN) \\ True Negative (TN) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 6: Confusion matrix.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Attribute No.** & **Attribute Name** & **Rank** \\ \hline
12 & ca & 0.18812 \\
3 & cp & 0.17789 \\
13 & thal & 0.11452 \\
2 & sex & 0.09307 \\
11 & slope & 0.06898 \\
9 & exang & 0.06667 \\
7 & restecg & 0.05842 \\
10 & oldpeak & 0.02350 \\
8 & thalach & 0.02118 \\
1 & age & 0.01786 \\
4 & trestbps & 0.01577 \\
6 & fbs & 0.01386 \\
5 & chol & 0.00181 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Attribute sets that were obtained from the Relief attribute evaluation.  
true positive rate (sensitivity or recall) vs. the false positive rate (fallout) at every \(0\to 1\) threshold.
## 4 Experimental Results
In Table 8, findings for all bus routes serving the Asian side of Istanbul showed that incorporating one-hour lagged temperature and wind speed variables into the LSTM model yielded the lowest RMSE values, similar to the results observed on the Kadkoy-Pendik \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Previous Hour Input Variables**} & \multicolumn{6}{c}{**LSTM**} \\ \hline
**Model** & **Temporal** & **xTemp** & **xHumidity** & **xWind** & **MAPE** & **RMSE** & **TIC** & **STD\_AE** & **RAE** & **STD\_APE** \\ \hline
1 & ✓ & X & X & X & 0.0508 & 562 & 0.0249 & 378 & 0.107 & 6.504 \\
2 & ✓ & ✓ & X & X & 0.0504 & 555 & 0.0247 & 371 & 0.107 & 5.938 \\
3 & ✓ & ✓ & X & ✓ & **0.0474** & **542** & **0.0239** & **364** & **0.103** & **5.517** \\
4 & ✓ & X & X & ✓ & 0.0506 & 559 & 0.0248 & 376 & 0.105 & 5.868 \\
5 & ✓ & ✓ & ✓ & X & 0.0499 & 585 & 0.0261 & 401 & 0.109 & 6.190 \\
6 & ✓ & X & ✓ & ✓ & 0.0508 & 565 & 0.0251 & 380 & 0.109 & 6.234 \\
7 & ✓ & X & ✓ & X & 0.0492 & 575 & 0.0255 & 392 & 0.108 & 6.014 \\
8 & ✓ & ✓ & ✓ & ✓ & 0.0524 & 613 & 0.0273 & 416 & 0.11 & 5.765 \\ \hline  & & & & & & & & & & & \\ \end{tabular}
\end{table} 
Table 6: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Input Variables**} & \multicolumn{6}{c}{**LSTM**} \\ \hline
**Model** & **Temporal** & **xTemp** & **xHumidity** & **xWind** & **MAPE** & **RMSE** & **TIC** & **STD\_AE** & **RAE** & **STD\_APE** \\ \hline
1 & ✓ & X & X & X & 0.05384 & 2165 & 0.0292 & 1598 & 0.112 & 7.448 \\
2 & ✓ & ✓ & X & X & 0.0584 & 2271 & 0.0309 & 1603 & 0.1234 & 6.687 \\
3 & ✓ & ✓ & X & ✓ & **0.051** & **2112** & **0.0279** & **1508** & **0.1085** & **6.762** \\
4 & ✓ & X & X & ✓ & 0.0599 & 2260 & 0.0306 & 1575 & 0.1243 & 8.940 \\
5 & ✓ & ✓ & ✓ & X & 0.0608 & 2286 & 0.031 & 1618 & 0.124 & 8.159 \\
6 & ✓ & X & ✓ & ✓ & 0.06346 & 2392 & 0.0325 & 1683 & 0.1305 & 8.836 \\
7 & ✓ & X & ✓ & X & 0.06883 & 2546 & 0.0347 & 1768 & 0.1406 & 9.955 \\
8 & ✓ & ✓ & ✓ & ✓ & 0.0562 & 2299 & 0.0312 & 1694 & 0.119 & 9.791 \\ \hline  & & & & & & & & & & & \\ \end{tabular}
\end{table} 
Table 7: Public rail transportation on the Kadköy–Pendik route. route. Nevertheless, the percentage improvement in RMSE was more limited compared to the Kadikov-Pendik corridor. As shown in Table 9, for the three rail lines on Istanbul's Asian side (M4 and M5 metro lines, and the B1 Gebze-Uskidar commuter line), various combinations of weather parameters were evaluated in the LSTM model. Although a decrease in RMSE was observed when using one-hour prior temperature and wind speed, the percentage improvement remained below 1%. This suggests that the contribution of these parameters to model performance is relatively marginal in this context. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{8}{c}{Input Variables} & \multicolumn{8}{c}{LSTM} \\ \hline
**Model** & **Temporal** & **xTemp** & **xHumidity** & **xWind** & **MAPE** & **RMSE** & **TIC** & **STD\_AE** & **RAE** & **STD\_APE** \\ \hline  
& ✓ & ✓ & 0.0415 & 3800 & 0.0233 & 2764 & 0.092 &
## 3 Construction of the Basic Network
### Treatment Approaches for Malfunctioning Nodes and Edges  
As outlined in Section 2.4, following the failure of the road network, it is essential to assess the overall vulnerability of the system. This section elucidates two scenarios of unit failure within the road network and the corresponding mechanisms for addressing these failures.  
1. Node Failure: When a network node is designated as failed, all edges associated with the failed node are concurrently classified as failed. In the topological structure of the network, the edges connected to a node may not be distinct. Consequently, node failure exerts a considerable influence on the vulnerability of the road network.
2. Edge Failure: When a network edge is designated as failed, it becomes impassable, rendering the two nodes it connects unaffected by this edge. The implications of edge failure on the network cannot be uniformly assessed; rather, they necessitate a thorough examination of the remaining nodes and edges within the network. Consequently, it is essential to propose a robust calculation method to evaluate the impact of edge failure on the overall vulnerability of the network.  
In summary, the procedure of the failure unit processing algorithm can be articulated as Algorithm 1:  
```
Input: Road network \(\text{G}=(\text{V},\text{E})\), F as set of failed units Output: Road network \(\text{G}^{\prime}\) following a failure 1. Initialize \(\text{G}^{\prime}=\text{G}\) 2. For each failed unit \(\text{f}\in\text{F}\): 2.1 If f is a node:  Mark f as a failed node in \(\text{G}^{\prime}\), mark the edges connected to f as failed edges, and set the weight of the failed edges to infinity Inf 2.2 If f is an edge:  Mark f as a failed edge in \(\text{G}^{\prime}\), and set the weight of the failed edge to infinity Inf 3. Return \(\text{G}^{\prime}\)
```  
**Algorithm 1** Algorithm for Processing Failures in Road Network Units
## 5 Case Study
### Simulation
#### 5.3.3 Prediction and Integration Processes  
The SVR model optimized by using the IJaya algorithm was employed as an independent prediction tool to capture the nonlinear patterns of each subseries after the optimal input variables were identified by using the PACF. The predicted value of grouting power was then obtained by summing the predicted results of the subseries. The search ranges of the SVR algorithm were set to \(C\) = (1,1000), \(g\) = (0,10), and \(\varepsilon\) = (0,0.2). The statistical population size, \(N_{pop}\), was set to 30 and the maximum number of iterations, \(N_{max}\), was set to 100. The final prediction results are presented in Figure 7.  
Based on accurate, real-time prediction of grouting power during the grouting process, construction workers can regulate the grouting pressure and injection rate reasonably to maintain the grouting power within a certain range, thereby avoiding the occurrence of defective conditions. From the curves of predicted data and actual data in Figure 7, it can be seen that the prediction error of grouting power based on the EPIJaya-SVR model is small (the average absolute error being 0.2165 MPa-L/min). The predicted and actual data have good consistency, which can meet actual engineering needs, and thus the model proposed here can provide practical assistance for guiding grouting construction.  
Figure 6: The PACFs of the original series and the subseries decomposed by EWT.  
Figure 7: The PACFs of the original series and the subseries decomposed by EWT.
## 4 Results and Discussion
### Liquid Velocity for Different Media with Varying \(B_{0}\)
#### 4.1.1 Medium 1  
At the initial stage (\(t=10\) s), the normalized velocity (\(w_{l}/W_{0}\)) in Medium 1 was independent of \(B_{0}\). At \(t=450\) s, \(w_{l}/W_{0}\) decreases \(\sim\)20% from the original value, not showing a great dependency of the magnetic flux density. Only for \(B_{0}=1\) T, the \(w_{l}/W_{0}\) value was slightly lower than the no-field condition (4.1% lower). For longer times (at \(t=900\) s, 1800 s, and 3600 s), the \(w_{l}/W_{0}\) ratio became more affected by \(B_{0}\). At the latest evaluated time (3600 s), the normalized velocity decreased 28.3% at 1 T with respect to the no-field condition.
## 2 Materials and Methods  
In order to assess the human exposure to the EMF emitted by wearable antennas, such an antenna for on-body communication has been simulated, tuned at f = 2.45 GHz. The first step of the study consisted in simulations with the antenna in free space, then the simulations have been performed by placing the antennas near a human model in different positions. The antenna has been simulated with both human models, male and female adult model of the Virtual Family.
# Challenges for the Implantation of Symbiotic Nanostructured Medical Devices
## 1 Introduction  
The foundation to harness nanotechnology for the engineering of biomedical systems was pioneered by [PERSON] in 1903, when he first used the term \"nanometer\" in the characterization of the gold nanoparticles he made and then measured with his ultramicroscope [1]. Subsequently, the introduction of the concept of \"biomimetics\" by [PERSON] in the 1960s has stimulated a great many possible applications for the engineering of biomedical devices [2]. Though nanotechnology was not explicitly mentioned in the text of the famous lecture by [PERSON], he described a future where small-scale machines might be constructed, controlled, and manipulated. He highlighted the medical relevance for these types of machines by quoting his friend, [PERSON], who suggested the concepts of a miniaturized surgical device and \"small machines that might be permanently incorporated in the body to assist some inadequately-functioning organ\" [3]. All of those previous concepts introduced by [PERSON], [PERSON], [PERSON], and [PERSON] have provided pillars upon which we can advance our ideas about nanostructured medical devices for implantation.  
In this short review, we introduce the concept of \"symbiotic nanostructured medical devices,\" since our starting point was whether the implanted device is intended to have any two-way communication of energy or materials with the body. If the intended application is only one-way communication from the medical device to the body, such as for the delivery of a drug, the challenge is to avoid the degradation or encapsulation of the medical device. However, if the implanted medical device is intended to properlyintegrate with the body, then it needs to mimic the two-way (duplex) communication that is required for transplanted living organs or cells [4]. Such an implanted medical device is one that we consider symbiotic. Examples of such implantable duplex communicating systems include biofuel cells or open-loop feedback devices, where a molecule from the body is utilized by the duplex communicating system to produce a different material (e.g., molecule or energy) for use by the body (Figure 1). There is clearly a major technical component in engineering such duplex communicating systems to provide the appropriate performance and lifetime when implanted inside the body. Nonetheless, those functions of such duplex systems depend on maintaining the unfettered two-way transport communication of materials when the system is implanted in the body or, indeed, if the device is externally positioned to maintain tight contact with the body. Maintaining such an unfettered two-way transport in order to design long-term functional performance requires overcoming the challenges of biocompatibility. We discuss the relative significance of those two challenges from the perspective of an implanted enzymatic biofuel cell. We have several years of expertise in enhancing its performance inside the body of mammals for this duplex symbiotic system.
# Intrusion Detection Model Based on Improved Transformer
## 4 Results  
### Experimental Environment and Datasets  
The experimental hardware environment utilized in this paper was equipped with an Intel Core i5-10300H 64-bit processor, 16 GB of RAM, and a GTX1660 ti graphics card. The experimental platform employed TensorFlow 2.2.0 and Keras 2.3.1 frameworks, and Python 3.7 was utilized for coding implementation.  
The NSL-KDD dataset [31] is a commonly used dataset in intrusion detection research. It is an improved version of the KDD-CUP-99 dataset, with duplicate and redundant records removed. The dataset contains both normal and anomalous network traffic and is divided into training and testing subsets. The training set consists of 125,973 samples, while the test set consists of 22,543 samples.  
The distribution of normal and anomalous samples in the training set of the NSL-KDD dataset is highly imbalanced, with only a small proportion of samples being abnormal. The distribution is presented in Figure 6. To address this issue, the hybrid sampling strategy of KNN under-sampling and Borderline-SMOTE over-sampling is used to balance the dataset. After hybrid sampling, the ratio between normal and abnormal samples in the dataset was balanced at 1:1. The distribution of normal and abnormal samples after hybrid sampling is shown in Figure 7.
## 3 Results and Discussion  
### Core Content of Microcapsules  
Table 5 shows the core content of cerevisiae wax containing E-44 epoxy resin microcapsule (WM1) and nano-CaCO\({}_{3}\)/ceresine wax containing E-44 epoxy resin microcapsule (WM2). The core contents of WM1 and WM2 were 75.4% and 80.6%, respectively. This is mainly due to the different viscosities of the shell materials of these two microcapsules. As can be seen from Table 3, the viscosity of cerevisiae wax was relatively low (5.5 mPa\(\cdot\)s) at the preparation temperature (130 \({}^{\circ}\)C), and the material can be uniformly dispersed after melting by heat. After adding the coolant, the particle size of the formed microcapsules was slightly smaller, resulting in a small amount of healing agent (E-44 epoxy resin) remaining outside the shell, therefore the core content of the microcapsule was relatively low. The viscosity of the nano-CaCO\({}_{3}\)/ceresine wax mixture (130 \({}^{\circ}\)C, 35 mPa\(\cdot\)s) was higher than that of cerevisiae wax after the addition of nano-CaCO\({}_{3}\) to cceresine wax, which reduced the dispersibility of the material. The addition of nano-CaCO\({}_{3}\) also reduced the solidification time of the shell and improved the encapsulation ability, which resulted in a higher core content of the microcapsules.
# Comparison of Traffic Flow Models with Real Traffic Data Based on a Quantitative Assessment
## 4 Results
[PERSON] and [PERSON]'s model has one of the lowest estimation errors; however, it underestimates the values of \(q_{max}\) and \(v_{opt}\). The problem with [PERSON]'s model is that traffic volume increases significantly for densities of the order of 150 pc/km and that speed does not tend to zero; instead, it tends to a certain minimal value which in the case if the field data is 12 km/h (Figure 7). In turn, this goes against Polish studies that have shown that for densities of the order of 50 pc/km/lane, vehicle stream speed does not exceed 10 km/h, and for densities of the order of 100 pc/km/lane, the speed is close to zero. Figure 8: Fundamental diagram represented by two-regime models. \begin{table}
\begin{tabular}{l l c c c c c c} \hline  & \multicolumn{3}{c}{Single-Regime Models–Group 1} & \multicolumn{3}{c}{Single-Regime Models–Group 2} \\ \cline{2-7} Assessment Criteria & Krysek & Underwood & Northwestern & [PERSON] \& Konhauser & [PERSON] & [PERSON] \& [PERSON] \\ \hline
1 & _Sutified boundary conditions_ & & & & & & \\  & BC1 & + & + & + & + & + & + \\  & BC2 & - & - & - & - & - & - \\  & BC3 & n.a. & n.a. & n.a. & n.a. & n.a. & n.a. \\ \hline
2 & _Simplicity_ & & & & & & \\  & No. of parameters & 2 & 2 & 2 & 2 & 4 & 4 \\  & No. of equations & 1 & 1 & 1 & 1 & 1 & 1 \\  & \(\mathrm{c}\mathrm{a}_{2}\) & 1.00 & 1.00 & 1.00 & 1.00 & 0.80 & 0.80 \\ \hline
3 & _Empirical accuracy_ & & & & & & \\  & RMSE (km/h) & 9.16 & 7.74 & 6.53 & 8.58 & 6.47 & 6.02 \\  & MAPE (\%) & 15.25 & 11.30 & 11.28 & 15.14 & 10.99 & 9.88 \\  & \(\mathrm{c}\mathrm{a}_{2}\) & 0.79 & 0.95 & 0.95 & 0.79 & 0.96 & 1.00 \\ \hline
4 & _Parameter value (expected value)_ & & & & & & \\  & \(\mathrm{v}_{\mathrm{var}}\) (km/h) & 127 & 141 & 111 & 107 & 110 & 109 \\  & \(\mathrm{v}_{\mathrm{var}}\) (km/h) & 52 & 52 & 67 & 75 & 78 & 69 \\  & \(\mathrm{k}_{\mathrm{var}}\) (pc/km) & - & - & - & - & - & - \\  & \(\mathrm{k}_{\mathrm{var}}\) (pc/km) & 78 & 79 & 61 & 59 & 50 & 49 \\  & \(\mathrm{q}_{\mathrm{max}}\) (pc/h) & 4060 & 4071 & 4095 & 4407 & 4116 & 4010 \\  & \(\mathrm{c}\mathrm{c}\mathrm{a}_{4}\) & 0.00 & 0.00 & 0.20 & 0.40 & 0.60 & 0.40 \\ \hline Final assessment & 0.60 & 0.65 & 0.72 & 0.73 & 0.79 & 0.73 \\ \hline \end{tabular}  
* The highlighted cells (criterion 4) mean that the estimated values of the parameters are consistent with the expected value ranges (Table 3). **–** Weights for the criteria assessment were adopted as follows: \(\mathrm{w}_{\mathrm{m}}=\mathrm{w}_{\mathrm{m}}\simeq 0.5\mathrm{w}_{\mathrm{m}}= \mathrm{w}_{\mathrm{m}}=\mathrm{w}_{\mathrm{m}}=0.33\).  
\end{table} 
Table 6: Assessment and comparison of traffic flow models (unsatisfied BC2).
# Domain Adaptation Network with Double Adversarial Mechanism for Intelligent Fault Diagnosis
## 3 Proposed Method
### Maximum Classifier Discrepancy  
For the sake of achieving more comprehensive domain adaptation, the task-specific decision boundary should be considered at the same time to achieve class-level alignment. In the DAN-DAM model, this goal is achieved by the adversarial relationship between the feature extractor and the two label classifiers. The main steps are as follows: The first is to ensure that the two label classifiers can correctly classify the source domain samples. The second is to find the fixed feature extractor and the target domain samples far away from the source domain by maximizing the discrepancy between the two label classifiers, where the difference between the two label classifiers is realized by using Wasserstein distance. Finally, the two label classifiers are fixed and the feature extractor is updated to minimize the discrepancy between the two classifiers, so that the sample distribution of the target domain is closer to the sample distribution of the source domain. This is repeated until the unlabeled target domain samples can be correctly classified by the label classifier just like the source domain samples with the labels. The optimization objective functions, in turn, are expressed as follows:  
\[\text{min}L_{C1}(x^{s})+L_{C2}(x^{s})-L_{W}(p_{1}(x^{t}),p_{2}(x^{t})) \tag{15}\]  
\[\text{min}L_{W}(p_{1}(x^{t}),p_{2}(x^{t})) \tag{16}\]  
where \(L_{W}\) represents the Wasserstein distance loss.