esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Magnesium and Its Alloys: A Review
## 2 Production Techniques
### Carbothemic Reduction as a New Production Technique  
However, the current state-of-the-art Pidgeon process is known for its complex management and high operating cost [17]. In addition, it is notorious for consuming a massive amount of energy per production batch [18] to the extent that [PERSON] and [PERSON] estimated the global warming impact of producing magnesium metal in China to be \(\sim\)60% higher than producing aluminum [19]. This led to a search for more efficient and sustainable production technologies. An identified alternative production route is a carbothermic reduction, where magnesia reacts with carbon to produce magnesium and carbon monoxide vapor.  
Although this production method has had large-scale industrial trials in the mid-20 th century, it has only garnered more attention in the recent decades as (a) carbon is a cheaper reductant than ferrosilicon, (b) carbon is readily available, and (c) if successful, the overall production cost is estimated to be significantly cheaper than the Pidgeon method [20]. Therefore, the condensation of magnesium vapors and its separation from carbon monoxide still presents a significant challenge.  
Additionally, there is also a growing research interest in solar carbothermic reduction. Recent preliminary studies have concluded the process to consume less energy and emit less carbon dioxide at 32.3 mega-joules (MJ) of primary energy and 5.31 kg (kg) of CO\({}_{2}\) per kg of magnesium as compared to 181.4 MJ and 15.9 kg CO\({}_{2}\) in the Pidgeon process [21].
# An Optimal Resonant Frequency Band Feature Extraction Method Based on Empirical Wavelet Transform
## 2 Theoretical Background
### Scale Space Histogram  
In summary, the scale space approach introduces a parameter in the data analysis and defines the size of the parameter. Then, the data information corresponding to different parameters is obtained through constantly changing scale parameters, and the intrinsic characteristics of the data information are excavated. [PERSON] [30] and [PERSON] [31] noted that the discrete Fourier transform \(X(f)\) of the finite-length discrete-time vibration signal \(x(t)\) can be filtered by a kernel function, and some parameters of the kernel function are used as a scale, whose corresponding discrete scale-space representation \(L(x,t)\) is defined as the following equation:  
\[L(x,t)=\sum_{n=-\infty}^{+\infty}g(n;t)\otimes X(f) \tag{7}\]where \(\otimes\) denotes the convolution product; \(g(n\!t)\) denotes the Gauss kernel function, given by Equation (8). and the parameter \(t\) in the function is the scale, which means that we can obtain \(L(x,\!t)\) on different scales by an iterative convolution:  
\[g(n;t)=\frac{1}{\sqrt{2\pi t}}e^{-n^{2}/(2t)} \tag{8}\]  
In practice, in order to have a finite impulse response, the range of the filtering is reduced from \(-\infty-+\infty\) to \(-M\!-\!+\!M\). When \(M\) is sufficiently large, the approximate error of the Gauss is negligible. A common choice is to set \(M\) to \(3<\) C \(<6\) (which means that the size of the filter increases relative to \(t\)). This paper determines C = 6, to ensure an approximation error of less than \(10^{-9}\). The sampling of \(t\) is conducted as \(\sqrt{t}=s\sqrt{t_{0}}\), where \(s=1,\ldots,s_{\max}\), and \(\sqrt{t_{0}}=0.5\) represents half of the distance between the two samples. Because we use a finite length signal, there is no interest in studying more than \(\sqrt{t_{\max}}=x_{\max}\), which means \(s_{\max}=2x_{\max}\).  
The scale-space representation of the histogram gives a key property, where the number of minimum values of \(L(x,\!t)\) relative to \(x\) is a decreasing function of \(t\) and \(s\). Therefore, each local minimum produces a scale-space curve of different lengths. Since the scale-space feature between two local minimum values in the histogram has a sufficient length, the distinguishing meaningful empirical modes are quantified to find the scale-space feature whose length is longer than the threshold \(T\)[16]. The remaining problem is to develop an adaptive method to determine the appropriate value of \(T\) for different signals. This can be defined as a two-coalescing class problem on a collection. In this paper, \(k\)-means clustering is selected to determine the appropriate value of \(T\).
# Opportunities and Limitations in Recycling Fossil Polymers from Textiles
## Recycling Fossil Polymers
### Enzymatic Recycling: To Monomers or New Products
The past few years have witnessed the discovery of several enzymes able to catalyse the hydrolysis of amorphous PET, including several hydrolases, lipases, and esterases. After the work of [PERSON] et al[PERSON] [160] and the enzyme stabilization in compost investigated by [PERSON] et al. [161], [PERSON] et al. [162] studied the enzymatic degradation of PET and obtained a very good result by using leaf-branch compost cutinase (LCC), yet used to hydrolyse amorphous PET. The enzymatic step was preceded by the melting and cooling in liquid nitrogen of the sample, so that PET was in its amorphous state. When physical-chemical treatments are combined with enzymatic ones, the full process can be defined as chemonezymatic [163]. This methodology could be applied more extensively to textiles in the future. With a similar approach based on cutinase, [PERSON] et al. [164] produced terephthalatic acid from PET/cotton fabrics. Cotton was thus separated from PET. Interestingly, a comparison of terephthalatic acid production from petrol to that from enzymatic PET depolymerization revealed a 69% lower energy requirement and 17% lower greenhouse gas emissions for the latter, suggesting that the development of biocatalytic recycling processes be continued [165]. On the contrary, [PERSON] et al. [166] used the selective digestion of wool fibres from wool/polyester mixed fabrics. Complete degradation of wool fibres was achieved by using a keratinase in a two-step process with the addition of a reducing agent and undigested polyester fibres. The obtained nutrient-rich keratin hydrolysate could be used in microbial growth media or incorporated into bio-fertilisers or animal feed, contributing to the development of the circular economy. [PERSON] considered a mixed fabric containing wool, cotton and PET [167] and suggested a first hydrolysis step of wool using proteases followed by a second enzymatic hydrolysis of cellulose using cellulases, and finally by poly(ethylene terephthalate) (PET) hydrolysis using cutinases. [PERSON] et al. [168], after the enzymatic degradation of cotton in PET/cotton fabric, produced a recycled polyester that was fed back into the textile processing chain, and finally towels were obtained. Polyamides can be also degraded by enzymatic processes [169]. Carbios company [170] and Novozyme [171] are currently working in the field of enzymatic degradation of textiles.
# Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data
## 2 Literature Review
### Traffic Information Forecasting Methods  
For traffic information forecasting methods, several studies used data mining techniques (e.g., linear regression [26; 27], logistic regression (LR) [28], Bayesian classifier [29; 30], \(k\)-nearest neighbors (kNN) [31; 32; 33], artificial neural network (ANN) [34; 35; 36; 37], _etc._) to analyze the historical traffic information and obtain the forecasted traffic information.  
For traffic information forecasting methods based on regression approaches, some studies used and applied linear regression and LR approaches to predict the future vehicle speed, and the experimental results in these studies showed that the average accuracy of vehicle speed forecasting was higher than 90% for highways [26; 28]. However, these regression approaches are unsuitable to analyze the non-linear distribution of data, so the accuracy of the vehicle speed prediction-based regression approach was only 73.3% for an urban road network in [27]. Furthermore, the regression approaches are the special cases of ANN without hidden layers, and the accuracy of the traffic information forecasting method based on ANN may be higher than regression approaches [34; 35; 36; 37].  
For traffic information forecasting methods based on probability approaches, Bayesian classifier approaches were proposed and used to analyze traffic information and conditions [29], and these approaches could be combined with regression approaches to improve the accuracy of traffic information prediction [30]. However, the assumption and limitation of Bayesian classifier approaches were the independence between input characteristics. Therefore, Bayesian classifier approaches may not be applied to a variety of practical cases.  
For traffic information forecasting methods based on the distances between data attributes, a kNN approach was proposed to analyze the previous arrival time, dwell time and delay time for bus arrival time prediction [31; 32]. Moreover, some studies used a kNN approach to analyze the relationship of traffic information and weather conditions for the improvement of vehicle speed prediction [33]. Although the results showed that the mean relative errors of using the kNN method were lower, the power consumption and time complexity of the kNN method were higher with big data at runtime.  
For traffic information forecasting methods based on ANN, some studies proposed and applied ANN to forecast short-term traffic information, and the experimental results showed that the accuracy of using ANN was better than using other approaches [35; 36; 37]. Although a previous study classified road segments into several road types for reducing ANN models [35], the diverse traffic characteristics of road segments may exist in the same type of road. Moreover, using ANN to analyze CFVD for short-term traffic information prediction has not been investigated. Therefore, this study proposed a traffic information forecasting method based on ANN to analyze and predict the traffic information of each road segment from CFVD.
# A VPN-Encrypted Traffic Identification Method Based on Ensemble Learning
## 1 Introduction
### Related Works
The use of ensemble learning for the research of encrypted traffic identification, compared with the traditional machine learning supervised identification method, adds a lot of nonlinear transformations and does not need to do complex feature engineering and feature transformation, which can greatly improve the VPN-encrypted traffic identification results. In the supervised learning methods, the ensemble learning algorithms have more linear transformations. It combines multiple weak classifiers to build a strong classifierfor ensemble voting and can obtain a higher identification rate. The ensemble learning method is to first generate multiple learners through certain rules, then use a certain integration strategy to combine, and finally comprehensively judge and output the final result. Literature [31; 32; 33] all use ensemble learning methods. The Xgboost used in this article is an ensemble learning framework. The method is an excellent implementation of boosting tree, using the CART regression tree as the basic learner. The tree model complexity is added as a regular term to the optimization objective, and a good balance is found between the loss function and the regular term. Xgboost is a type of serially generated ensemble learning, its next learner is related to the previous learner. Finally, after obtaining many learners, the sum is obtained to obtain the final learning result. Xgboost framework sorts the data in advance and stores it in block form. It is easy to parallel computing and optimize the architecture, thus it is suitable for the identification of encrypted traffic. However, the traditional Xgboost framework still has some problems in identifying encrypted traffic. There is a problem with the redundancy of encrypted traffic data features due to the high correlation between the original data features. The data classes of encrypted traffic are unbalanced, and the accuracy of the Xgboost model for VPN-encrypted traffic identification needs further improvement. Therefore, this paper proposes to use a VPN-encrypted traffic identification method with an improved Xgboost model.
## 4 Discussion
In order to obtain a classifier with better classification ability, we improved the convolutional layer of the traditional convolutional neural network to a parallel convolution layer [42]. The parallel convolution layer adds multiple convolution kernels of different sizes to filter out the P300 EEG signal features. The number of convolution kernels determines the output of the convolution layer, so the convolution layer needs to appropriately increase the number of convolution kernels in order to more fully extract the features of signals [43]. In the previous works on the convolution neural network, such as CNN-1 [24] and BN3 [28], a single convolution kernel layer was used in the convolution part. However, when the number of signals is too large, the effect of a single convolution kernel layer in filtering features will become worse, and it is easy to ignore some features. The parallel convolution layer could increase the data capacity of the network, and may overcome the lack of features caused by improper selection of the convolution kernel size [44]. Based on the traditional convolution neural network, this paper constructed the algorithm PCA-CNN, a new algorithm for the P300 EEG signals classification. Compared with some traditional convolutional neural algorithms, the PCA-CNN algorithm has a higher classification accuracy rate for the P300 EEG signals' recognition. As shown in Tables 3 and 4, and Figure 3, the results show that the PCA-CNN algorithm has good classification performance in datasets I and II. As shown in Figures 4 and 5, the ITR of PCA-CNN is higher than the traditional SVM classification algorithm, which proves the stability of the algorithm performance. The PCA-CNN algorithm can obtain the classification accuracy rate higher than 90% on both datasets I and II; when the classification accuracy rate of P300 EEG signals is higher than 80%, the classification algorithm is effective [45]. The proposed PCA-CNN algorithm can be employed in a brain-computer interface system, and be applied for people with disability in daily lives. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Subjects** & **Accuracy Rate** \\ \hline \multirow{2}{*}{**PCA**} & A & **98** \\  & B & **96** \\ \hline \multirow{2}{*}{**NO-PCA**} & A & 96 \\  & B & 93 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison of PCA and NO-PCA character recognition accuracy rate in dataset I.  
Figure 6: The comparison of PCA and NO-PCA characters recognition accuracy rate in dataset II.
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 8 Challenges
### Challenge 5  
Some smart healthcare applications have strict real-time requirements, such as in the case of remote monitoring and telemedicine, which can make QoS optimization challenging. Real-time constraints in smart healthcare systems can be met by applying QoS optimization techniques. These techniques include priority-based scheduling, efficient bandwidth and resource allocation, and the application of network protocols that facilitate real-time communication [80]. The system can also be made more responsive and able to meet real-time demands by using edge computing and a distributed design. It is also vital to assess and monitor the system's performance to make sure that the goal QoS is being met and to adjust as needed. It is important for healthcare organizations to carefully consider these trade-offs and limitations when selecting and implementing QoS optimization methods to ensure that they meet the needs of the organization and its users [81].  
In addition, authors in [82; 83; 84; 85; 86; 87; 88] presented several interesting and emerging research challenges related to the QoS, medical QoS, and QoE and then proposed various frameworks, methods, and algorithms. The effective and adaptive QoS optimization methods are proposed in [89; 90; 91; 92; 93]. The AI- and ML-driven classification and regression methods are the key role players in medical QoS and end-user perception analysis [94; 95; 96; 97; 98].
# Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens
## 2 Materials and Methods
### Machine Learning Methods
#### 2.4.1 \(k\)-Nearest Neighbor (\(k\)NN)  
The \(k\)-nearest neighbors (\(k\)NN) algorithm operates on the principle that akin data points tend to possess analogous labels or values [31]. In the training phase, the algorithm assimilates the complete training dataset as a reference. Subsequently, when generating predictions, it computes the distance between the input data point and all instances within the training dataset, employing a selected distance metric, such as the Euclidean distance.
# Analyzing the Impact of Variability and Uncertainty on Power System Flexibility
## 3 Scenarios for Variability and Uncertainty  
### Variability and Uncertainty and Their Relevance  
Variability commonly refers to how dispersed or spread out the data values are. In statistics, there are metrics for describing variability in a dataset: range, interquartile range, variance and standard deviation. The range is a measure of spread: it represents the difference between the highest and lowest values in a dataset. The interquartile range is a measure of where the \"middle fifty\" is in a dataset, that is, a measure of where the majority of the values lie. The variance is the mean square of the differences from the mean, and the standard deviation is the square root of the variance. These indices enable comparison of data in terms of variability. In power systems, a typical variability is the load variability, which has been represented using the standard deviation.  
Uncertainty exists when the measured value differs from the forecast value; uncertainty has been widely represented by the error, which is defined as the measured value minus the forecast value. The net load forecast error and the unexpected failure of generating units are major uncertainties in power systems. Based on a probabilistic approach, these uncertainties have been considered as random variables. The net load forecast error has been modelled as a normal random variable, which follows a normal probability distribution. Without uncertainty, there is no risk related to flexibility; only variability exists. The variation in the forecast net load can be perfectly covered by the scheduled outputs of generating units, and the RC is thus not required. However, in reality, uncertainty always exists and causes risk situations in which RC is required.  
It should be noted that uncertainty and variability are linked through the RC. The variation of the forecast net load may alter the operating conditions of generating units; some units may not be involved in the operation. This means that the RC of the system changes with variability; this, of course, changes the system's ability to respond to risk.
# Ambient Environmental Parameter Estimation for Reliable Diffusive Molecular Communications
###### Abstract  
Molecular communication is a promising communication technology that uses biomolecules such as proteins and ions to establish a communication link between nanoscale devices. In diffusive molecular communication, which uses diffusion characteristics of transfer molecules, the diffusion mechanism is mathematically derived as a Channel Impulse Response (CIR) to design an optimal detector structure. However, an ideal environment is assumed for deriving a CIR. Hence there is a concern that developed systems based on the derived CIR may not operate well in a realistic environment. In this study, based on the finite element method (FEM), we constructed a model of the environment with heterogeneous temperature distribution and actual volume of transmitting molecules to not only demodulate the bit information via maximum likelihood sequence estimation (MLSE) but also to estimate the temperature and volume of the transmitting molecules. Furthermore, in this study, we evaluated the performance of the MLSE method and investigated the effects of ambient environmental temperature distribution and volume of the transmitted molecules on diffusive molecular communication. The evaluation results demonstrated that the proposed method can improve the communication performance by approximately 9 dB by estimating the temperature and transmit molecule volume.  
diffusive molecular communications; MLSE; ambient environmental parameter estimation Article
# Failure Assessment of Embankment Dam Elements: Case Study of the Pirot Reservoir System
## 3 About Zavoj Dam  
The Zavoj dam and reservoir on the Visocica river are located 13 km northeast of Pirot city in the Republic of Serbia. The construction of the Zavoj dam began in 1983 and was completed in 1989. The main purpose of the accumulation is to provide water for the production of electricity in HPP Pirot. In addition to this primary purpose, the Zavoj reservoir serves to accept the flood wave and reduce sediments in the valley of the Juzna and Velika Morava river, water supply, and more [18,19].  
The Zavoj dam is a rockfill dam with an upstream and downstream support body made of compacted stone embankment, an upstream sloping clay core, and corresponding filter layers between the clay core and the stone embankment (Figure 2).  
Basic characteristics of the dam:  
* Dam crest elevation: 617.5 m asl,
* Maximum water level: 615.9 m asl,
* Operating water level: 612.5 m asl,
* Minimum operating level: 568.0 m asl,  
Figure 2: Zavoj dam cross-section with quasi-homogeneous zones.  
* Elevation of the dam foundation: 531.5 m asl,
* Length of the dam in the crest: 250.0 m,
* Width of the dam in the crest: 10.0 m,
* Spillway gates 3 m \(\times\) 9.0 m.  
The dam body is created in accordance with Figure 2 and consists of seven different materials. The surrounding rock mass consists of alternating layers of bank sandstones and sandy clays. However, due to the lack of data on the distribution of quasi-homogeneous zones, here it was assumed that the surrounding rock mass is homogeneous. A photo of the Zavoj dam with its surroundings is shown in Figure 3.  
The overflow section of the dam is an open channel spillway and is located on the left side of the dam. The spillway crest is at 606.0 m above sea level. The spillway has three gates, 9 m wide and 10.2 m high. The width of the pillars between the overflow fields is 2.5 m. The bridge that connects the dam with the left bank crosses the spillway gate section. The spillway is located in a deep cut, with a slope of 3:1, has trapezoidal cross-section, and is 25 m wide and 300 m long. At the end of the spillway there is a sky-jump bucket. All these structure elements are included in the developed finite element model of the dam with the surrounding rock-mass.