esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The SeNPs Prevent Apoptosis in Renal Parenchyma Induced by cadmium  
The _Bcl-2_ family proteins play a crucial role for the mitochondria dependent apoptotic pathway [57]. _Bcl-2_ is known as an anti-apoptotic protein which protects the cells from apoptosis, whereas the pro-apoptotic proteins such as _Bax_ promote the programmed cell death.  
Exposure to Cd causes the activation of apoptosis-related mitochondrial signaling and DNA damage response [58], through unbalancing _Bcl-2_/_Bax_ ratio which determines cell death [59], and considering a valuable tool to assess renal protective effects of SeNPs and LSeNPs against the pro-apoptotic activity of Cd.  
As shown in Figure 5, Cd exposure increased immunopositivity of pro-apoptotic _Bax_ protein, especially at tubular level and decreased expression of anti-apoptotic _Bcl-2_ protein, however, co-treatment with SeNPs and LSeNPs induced a marked reduction in positivity of _Bax_ and increased _Bcl-2_ protein positivity in a dose-dependent manner.  
Mitochondria is the major intracellular target for Cd toxicity, inhibiting directly Na/K pump [60]; accumulation of Cd2+ is followed by inhibition of the respiratory chain by acting at the level of complex III [61] and resulting in the generation of reactive oxygen species (ROS) [62], and mitochondrial damage [63] with release of cytochrome c [64], leading to caspase activation, causing cell death by apoptosis and necrosis [65; 66; 67]. Moreover, previous studies showed that exposure to Cd down-regulated anti-apoptotic _Bcl-2_ protein and upregulated pro-apoptotic _Bax_ protein, suggesting that Cd-induced apoptosis via regulations of _Bcl-2_ and _Bax_, while selenium has an ability to inhibit mitochondrial apoptotic pathway in oxidative stress mediated kidney dysfunction caused by cadmium and rebalanced the _Bcl-2_/ _Bax_ ratio [68], as we showed in our experiment.
# Bioengineered Skin Substitutes: Advances and Future Trends
## 3 Cell-Based Skin Substitutes
### Cell-Laden Commercial Skin Templates
#### 3.1.2 Cultured Epithelial Sheets  
Recent advances in the tissue engineering field enabled the production of cultured epithelial autografts (CEA) used for wound coverage and healing [34; 35]. CEA is composed of the patient's own keratinocytes (autologous) or donor cells (allografts) (sheets prepared from the skin of an unrelated donor). Thus, they represent an important life-saving treatment option for large burn injuries as well as for chronic ulcers [34; 35]. In the case of burn wounds greater than 50% of the total body surface area, donor skin is limited [36]. Thus, cultured epithelial autografts offer potential coverage to support wound closure [35].  
A first step toward the application of CEA was made by [PERSON] and [PERSON], who have isolated and cultured human keratinocytes under in vitro culture conditions [37; 38]. They also introduced irradiated 3T3 ( murine fibroblasts) into the serial culture of human primary epidermal cells [37]. Finally, the achievements by [PERSON] and [PERSON] paved the way for the development and clinical application of CEA in the 1970s/1980s [36; 38; 39; 40; 41; 42]. Since then, CEA was widely used for burn and reconstructive surgery worldwide [40; 41; 42; 43].  
However, the application potential of CEA is limited due to their inconsistent graft take rates, infection risk, and often unsatisfactory functional and aesthetic results [40; 44]. The main reason for these problems is the lack of a functionally competent dermal component [44].
## 3 Results and Discussion
### Photocatalytic \(H_{2}\) Evolution Performance  
The photocatalytic performance of GaN:ZnO solid solution was evaluated by water splitting for H\({}_{2}\) production under visible light. Figure 4a shows the production of H\({}_{2}\) of the water splitting reaction on Rh\({}_{2}\),Cr\({}_{y}\)O\({}_{3}\)-loaded GaN:ZnO solid solution under xenon lamp irradiation (\(\lambda>400\) nm). The H\({}_{2}\) evolution performance rate in 6 h is presented in Figure 4b, and the average evolution rates of H\({}_{2}\) for S\({}_{750}\), S\({}_{850}\), and S\({}_{950}\) were about 47.44, 53.44, and 42.15 umOL-g\({}^{-1}\).h\({}^{-1}\), respectively. The decreased activity for S\({}_{950}\) may be attributed to its nanocrystalline having a larger size, lower BET surface areas, and higher degree of crystallization. As for S\({}_{750}\), the lower photocatalytic efficiency can be attributed to the incomplete transformation to GaN:ZnO solid solution, associated with the results of XRD and SEM. In addition, the photocatalytic stability of S\({}_{850}\) was evaluated by the recycling test, which is important for the long-term application of photocatalytic materials. As shown in Figure 4c, over five consecutive cycles, the catalyst exhibited negligible loss of hydrogen evolution activity.  
Photoluminescence spectroscopy was employed for the investigation of the charge separation efficiency of the samples in the process of the photocatalytic reaction. Figure 4d shows that the emission intensity follows the order of S\({}_{850}<\)S\({}_{750}<\)S\({}_{950}\). The sequence is the opposite of the photocatalytic activity order. Owing to this, the fluorescence was produced by recombination of the holes in the valence band (VB) and the electrons in the conduction band (CB), and the most diminished PL intensity of S\({}_{850}\) indicates that the recombination of photogenerated charge carriers was maximally retarded [41]. Moreover, the photoelectrochemical measurement further supports the transfer and separation behavior of the photo-induced charge carriers of the samples. As shown in the photocurrent response curves and EIS Nyquist plots in Figure 4e,f, S\({}_{850}\) displayed the strongest photocurrent density and the smallest semicircle in comparison with S\({}_{750}\) and S\({}_{950}\), suggesting the lowest  
Figure 3: (a,b) TEM and (c,d) HRTEM images of S\({}_{850}\).  
interfacial electron-transfer resistance and the highest photo-induced charge transfer and separation efficiency, which leads to the highest photocatalytic performance.
## 2 Predictions of the Stability of Headings in the LGCB Mines
Two variants of loads acting on the group of headings were assumed for the numerical calculations. The flat, rectangular plate with openings shaped to correspond to the shapes of the analysed excavations located inside was assumed to be loaded on its edges:  
* Load variant 1 (heading depth H = 1000 m b.g.l. ):
* side edges: \(p_{x}\) = 22.11 MPa;
* upper edge and bottom edge: \(p_{z}\) = 22.11 MPa;
* direction perpendicular to plate surface: \(p_{y}\) = 22.11 MPa. * Load variant 2 (heading depth H = 1300 m b.g.l. ):
* side edges: \(p_{x}\) = 30.12 MPa;
* upper edge and bottom edge: \(p_{z}\) = 30.12 MPa;
* direction perpendicular to plate surface: \(p_{y}\) = 30.12 MPa. The edges of the analysed plate were provided with supports which do not slide either in the vertical or in the horizontal direction. The numerical modelling employed finite elements having three nodes and a triangular shape. The plate edges were assumed to be at a 100 m distance from the extreme points on each side of the analysed excavations (the roof, the floor and the side walls). In all numerical models, the plate was 288.0 m \(\times\) 203.5 m. Owing to the assumed dimensions of the plate, its edges (pillars) were not located excessively close to the excavations and thus had no influence on the results of calculations of the area of the excavations. In order to increase the accuracy of numerical calculations, smaller-size finite elements were used in the middle of the plate, in the area where the headings were driven (region of dense finite elements) (Figure 2). In each numerical model, the calculations were performed in two steps:  
* Step 1: original state of the rock mass (no mining excavations present in the analysed plate);
* Step 2: state with the excavations present (four mining excavations present in the rock mass). In the numerical simulations, it was assumed that the optimal measure of heading stability is the range of the yielded rock mass zone in the roof of the heading. \begin{table}
\begin{tabular}{c c c c} \hline
**Depth** & **Vertical Stresses** & **Horizontal Stresses** & **Horizontal Stresses** \\
**H [m]** & \(\sigma_{z}\) [MPa] & \(\sigma_{x}\) [MPa] & \(\sigma_{y}\) [MPa] \\ \hline
1000 & 22.11 & 22.11 & 22.11 \\
1300 & 30.12 & 30.12 & 30.12 \\ \hline \end{tabular}
\end{table} 
Table 7: Primary stresses for two depths in the LGCB mines.
# Concept Drift Detection Based on Deep Neural Networks and Autoencoders
## 3 Methodology
### Pretrain  
This section focuses on the overall process of the pretraining stage, as shown in Figure 3 and Algorithm 2. First, a DNN model, referred to as Model1, is initialized (refer to statement 2 of Algorithm 2), and the model is trained using static data (non-drift data) \(D\) (refer to statements 3 through 8 of Algorithm 2) to obtain and save the outputs \(a^{(L)}\) from the last hidden layer. Simultaneously, the connection weights \(W,b\) between all neurons in the hidden layers are collected and stored (indicated by the purple box in the figure). The final dimension of the obtained hidden layer outputs is a matrix formed by multiplying the number of neurons in the last hidden layer by the batch size, represented by the matrix composed of red neurons in Figure 3. We select the last 5000 sets of outputs from the saved last hidden layer (as shown in the yellow box in Figure 3) as training data for drift detection. Due to the time-intensive nature of the training process, we selected only a portion of the collected weights. Additionally, in the later stages of neural network training, the model is able to fit the data to a certain extent, with the hidden layer output partially representing the distribution of data under the old concept. Unlike streaming data, this output is static, generated from previous batch training using non-drift data.  
```
0: Non-drift data \(D=\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots,(x_{m},y_{m})\}\), the number of layers \(L\), the number of neurons per layer \(M_{F},M_{S},M_{L}\), the learning rate \(\eta_{1}\), the loss function \(\mathcal{L}_{\mathcal{P}}\), the optimizer \(optim_{1}\), the delay rate \(\gamma_{1}\), the learning rate update step size \(s_{1}\), the iterations \(pretrain\_iters\).
0: Last hidden outputs matrix \(a^{(L)}=[a^{(L)}_{1},a^{(L)}_{2},\ldots,a^{(L)}_{m}]\), weights and bias associated with all hidden layers \(W=[W^{(F)},W^{(S)},\ldots,W^{(L)}]\), \(b=[b^{(F)},b^{(S)},\ldots,b^{(L)}]\), F, S, L represent the number of layer, \(W^{(I)}=[w^{(I)}_{1},w^{(I)}_{2},\ldots,w^{(I)}_{m}]\), \(b^{(I)}=[b^{(I)}_{1},b^{(I)}_{2},\ldots,b^{(I)}_{m}]\).
1: Processing data \(D\) with StandardScaler.
2: Initialize weight matrix \(W\) with random values.
3:for\(epoch=1\)to\(pretrain\_iters\)do
4:for\((x_{i},y_{i})\)in\(D\)do
5: Obtain the prediction \(\hat{y}_{i}\).
6: Calculate the loss \(\mathcal{L}_{\mathcal{P}}(y_{i},\hat{y}_{i})\) between prediction \(\hat{y}_{i}\) and true label \(y_{i}\).
7: Perform loss backward and update all the hidden layers' parameters \(W\), \(b\).
8:endfor
9:endfor
```  
**Algorithm 2** Pretrain process
## 3 Results
### Calibration of the RS-CFLUX Model at the Two Stations  
The prior value and range of the parameters were obtained from the literature or from conventional knowledge. The posterior value and range of the parameters in the RS-CFLUX model were estimated using the MCMC method (see Table 2, Figures 4 and 5). From the posterior distribution of parameters, we found that \(\mathrm{LUE_{max}}\) was well constrained at both stations. The value of 2.70 g C/MJ of \(\mathrm{LUE_{max}}\) at the Daman superstation was much higher than the value of 2.15 g C/MJ at the Zhangye wetland station. \(\mathrm{LST_{max}}\), \(\mathrm{LST_{opt}}\) and \(\mathrm{LST_{min}}\) were not well constrained. \(\mathrm{LST_{max}}\) and \(\mathrm{LST_{min}}\) were very similar at the two stations, but \(\mathrm{LST_{opt}}\) was obviously higher for maize than for reed. The range from \(\mathrm{LST_{min}}\) to \(\mathrm{LST_{max}}\) for maize was smaller than that for reed because the growing season for maize is shorter than for reed. Respiration rate at reference temperature Rref was also well constrained at both stations, but empirical parameter of respiration function E0 and a were not well constrained at either site.  
Figure 3: Seasonal dynamics of the GPP and NEP at the Daman superstation and Zhangye wetland station.  
Figure 4: Histograms of the RS-CFLUX model parameters at the Daman superstation (maize).  
Figure 5: Histograms of the RS-CFLUX model parameters at the Zhangye wetland station (reed).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Prior** & **Posterior (Daman)** & **Posterior (Wetland)** \\ \hline LST\({}_{\rm min}\) (\({}^{\circ}\)C) & \(-3(-10,5)\) & \(-2.75\) (\(-9.44,4.35\)) & \(-3.47\) (\(-9.54,4.02\)) \\ LST\({}_{\rm opt}\) (\({}^{\circ}\)C) & \(15(5,30)\) & \(19.27\) (\(7.16,29.07\)) & \(12.78\) (\(5.52,26.30\)) \\ LST\({}_{\rm max}\) (\({}^{\circ}\)C) & \(35(30,50)\) & \(40.44\) (\(30.93,49.26\)) & \(39.98\) (\(30.94,49.16\)) \\ LUE\({}_{\rm max}\) (gC/MJ) & \(2(0,3.5)\) [34,35] & \(2.70\) (\(1.55,3.44\)) & \(2.15\) (\(1.42,3.08\)) \\ Ref (gC/m\({}^{2}\)/day) & \(2(0,20)\) [11] & \(1.89\) (\(0.15,4.46\)) & \(0.98\) (\(0.17,1.97\)) \\ E0 (\({}^{\circ}\)C) & \(200(0,500)\) [11] & \(185.49\) (\(13.90,445.41\)) & \(263.70\) (\(50.34,471.57\)) \\ a (gC/m\({}^{2}\)/day) & \(0.8(-1,1)\) [11] & \(-0.04\) (\(-0.93,0.91\)) & \(-0.01\) (\(-0.92,0.92\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Prior distribution (initial value and range) and posterior distribution (mean value and 95% confidence interval) of the parameters of the RS-CFLUX model at the Daman superstation (Daman) and Zhangye wetland station (Wetland).
# A Comprehensive Survey of the Harmony Search Algorithm in Clustering Applications
## 4 Clustering Applications of the HSA
### Clustering Protocol for Wireless Sensor Network Applications  
WSNs comprise small battery-powered machines with insufficient power supplies. Once used, the small sensor connections are generally unavailable to the user; therefore, the power supplier cannot be replaced [103; 104; 105; 106]. Protocols for WSNs are being developed to reduce power damage [107]. The clustering method is one of the most used processes for increasing the lifetime of the network by data gathering and for trading off the energy expenditure between sensor connections of the network [108]. [PERSON] et al. [78] utilized the HSA for decreasing the intracluster variety and finding the optimal power expenditure of the network. HSA is a music-based optimization algorithm, which is similar to the track discovery approach, in which a participant continues to clean the pitches to achieve better consistency (harmony). Other similar cluster techniques were assessed for the clustering protocol in WSNs; the techniques were Low-Energy Adaptive Clustering Hierarchy (LEACH), PSO, GA, K-means, and FCM clustering algorithms. The results illustrated that the proposed approach using HSA decreased electricity expenditure and increased the network lifetime.  
[PERSON] et al. in [84] proposed a new framework to facilitate the continuous improvement of localized cluster protocols using the HSA for WSNs. In the proposed framework, the clustering protocol applied the HSA in real-time. The experiments were performed with the same cluster protocols for WSNs using LEACH-centralized (LEACH-C) and a clustering protocol for the FCM algorithm. The results of the proposed approach with the HSA illustrated the security and monitoring applications for constructing ecosystems. The obtained results showed that the WSNs' lifespan had been increased with the proposed approach, compared with other similar methods.  
[PERSON] et al. [89] introduced a new clustering method based on an improved HSA (IHS) to increase the lifespan of WSNs. Its performance was evaluated via simulations on a less powerful WSN. The WSNs were partitioned into clusters by using the proposed IHS algorithm; the network lifetime and amount of information sent to the Base Station (BS) were assumed. The obtained results revealed that the proposed method provided better results.  
[PERSON] et al. in [95] introduced a clustering method based on HSA. They proposed a fitness function with the power, range, and node space as the parameters. Next, a possible function for the distribution of non-CH (cluster head) nodes to CHs was determined. Then, the HSA was used for routing purposes by adjusting the parameters of the fitness function; this method was called CRHS. Three standard tests were performed to evaluate the performance, and the proposed algorithm was compared with other similar existing techniques. The obtained results showed that the performance of the proposed CRHS was superior compared with other techniques.
# Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
## 4 Performance Evaluation
### Robustness
#### 4.3.1 Network Size  
Experiments were carried out by varying the network field sizes such as 60 \(\times\) 60 m\({}^{2}\), 90 \(\times\) 90 m\({}^{2}\), 120 \(\times\) 120 m\({}^{2}\) and 150 \(\times\) 150 m\({}^{2}\) containing 80, 180, 320 and 500 nodes, respectively. The size of the region of interest was kept fixed, _i.e._, the value of \(\alpha\) was 9. The rate of the query was varied from 0.1 to 0.5 queries per sector per second. In Figure 14, the rate of the query is represented by \(\beta\). Figure 14a,b demonstrates that energy consumption and latency is exponentially proportional to the value of \(\beta\). However, when \(\beta\) is constant, they grow linearly with the size of the network. Figure 14c shows the percentage of accuracy as a function of network size. The accuracy drops slightly with the scale of the network.  
Figure 13: (**a**) Energy consumption; (**b**) latency; and (**c**) accuracy.
## 2 Materials and Methods
### Fuzzy Inference System  
A Fuzzy Inference System (FIS) incorporates the knowledge of an expert, during design a model in between input and output parameters. In FIS, the input-output relations are defined by a set of fuzzy rules, e.g., IF-THEN rules [36]. Fuzzy logic-reasoning involves the assignment of membership function to the input and output parameters; and the rule base which processes the fuzzy values of the inputs to fuzzy values of the outputs. The accurate selection of these membership function and the rules is one of the most critical stages in the FIS which needs expert knowledge. FIS consists of three segments viz. fuzzification, inference engine and defuzzifier. Fuzzification converts the numeric value of the input to a linguistic variable with the help of the membership functions e.g., triangular, trapezoidal, Gaussian, etc. The inference engine evaluates the degree of the membership function of the input variables (premise) to the fuzzy consequent part using the fuzzy IF-THEN rules. The conditional statement contains a premise, the if-part, and a conclusion, the then-part [37]. The knowledge involved in a fuzzy inference system contains a group of several rules [38]. At last, the defuzzifier converts the fuzzy output into a crisp value. The fuzzy inference engine is the core of FIS which can represent the human decision-making process [36].  
The Takagi-Sugeno (T-S) FIS has fuzzy inputs and a crisp output which is a linear combination of the inputs or constant. This method is computationally efficient and suitable to work with optimization and adaptive techniques [39]. The T-S method involves a systematic approach to generating fuzzy rules from a given input-output data set (Figure 4). It uses a membership function of the input variables for producing the consequent (then part). It uses the fuzzy rule: _IF x is A AND y is B THEN z is f(x,y) where x, y, and z are linguistic variables, A and B are fuzzy sets and f(x,y) is a mathematical function [39]. T-S FIS uses a weighted average to generate the crisp output._  
In this present work, zero order T-S was adopted for grading of basmati rice. The membership functions were taken as 'gbellmf' [40] for all inputs viz. eccentricity, equivalent diameter, perimeter and the major axis length/minor axis length (\(a/b\)); the outputs were taken as constant (for whole grain, output = 1 and broken grain output = 0). An example of fuzzy membership function is shown in Figure 5. The rules of the T-S method were taken as follows:  
* **Rule 1:** If (eccentricity is high) and (equivalent diameter is high) and (perimeter is high) and (\(a/b\) is high) then (output is Whole grain).
* **Rule 2:** If (eccentricity is low) and (equivalent diameter is low) and (perimeter is low) and (\(a/b\) is low) then (output is broken grain).  
Figure 3: Rice grain properties.
## 6 Testing Results
### Testing of the Detection Techniques Using Experimental Datasets  
Other tests were conducted with experimental datasets using the test bench shown in Figure 8 in order to compare and prove the effectiveness of all the architectures previously mentioned. It has been possible to generate several experimental datasets of the interruption and of the sag disturbances. The experimental measurments are shown in Figures 14 and 15 along with the plots of the classification results below. Each classification point consist of 1000 samples of the measured signal. Once again, the CNN-LSTM with adjusted hyperparameters was the most consistent in classifying all the disturbances without misclassification. As mentioned before, the identification algorithms were tested with exerimental datasets containing interruption and sag disturbances. Concerning the sag disturbance, all of the four architectures performed correct identification. Some misclassifications occurred when testing the interruption disturbance with the CNN-LSTM and with the LSTM. Combining these results with the ones previously mentioned, the CNN-LSTM with adjusted hyperparameters is the is the one which performed best.  
The event signal, shown in Figures 9c, 10c, 11c and 12c, is a voltage signal with a harmonic distortion, sag and an interruption. Each architecture had good results when tested using this signal. However, the LSTM classified the harmonics as a no fault and misclassified a section as a notch. The LSTM-CNN also showed the same problem. On the other hand the CNN misclassified the harmonic disturbance as a transient disturbance. While all architectures successfully classified only the Interruption and the Sag in the testing, the LSTM-CNN with adjusted hyperparameters was the one that had better results because it classified the harmonics, sag, interruption correctly without misclassification.  
Figure 13: Bar chart of the comparison of the precision and recall of the LSTM (blue), CNN (red), LSTM-CNN(yellow) and LSTM-CNN with adjusted hyperparameters(purple). (**a**) Precision Training Data, (**b**) Recall Training Data, (**c**) Precision Testing Data, (**d**) Recall Testing Data.