esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Accurate predictions of radio frequency electromagnetic field (RF-EMF) levels can help implement measures to reduce exposure and check regulatory compliance. Therefore, this study aims to predict the RF-EMF levels in the medium using an artificial neural network (ANN). The work was conducted at Ondokuz Mayis University, Kurupelit Campus, where the measurement location has line-of-sight to the base stations. Band selective measurements were also performed to assess the contribution of 2G/3G/4G services to the total RF-EMF level, which was found to be the highest among all services within the total band. Long-term RF-EMF measurements were carried out for 35 days within the frequencies of 100 kHz to 3 GHz. Then, an ANN model with Levenberg-Marquardt (LM) and Bayesian Regulation (BR) algorithms was proposed, which utilized inputs from real-time RF-EMF measurements. The performance of the models was assessed in terms of mean squared error (MSE) and regression performance. The average MSE and regression performances of the models were similar, with the lowest testing MSEs of 2.78 \(\times\) 10\({}^{-3}\) and 3.76 \(\times\) 10\({}^{-3}\) for LM and BR methods, respectively. The analysis of the models showed that the proposed models help to predict the RF-EMF level in the medium with up to 99% accuracy.  
RF-EMF; RF-EMF measurement; base station; ANN; LM; BR Article
## 2 Material and Methods
### Proposed Deep Learning Model  
In this section, a deep learning model is proposed and explained. Before inputting the variables into the proposed deep learning model, a normalization step is required to ease the learning stage of the model weights. The variables are all normalized using the min-max scaling method before being input into the model. The five variables \(d_{V2X}\), \(d_{I2X}\), \(P_{V}\), \(SINR_{th}\), and \(U\) are input to the model and the output parameter \(P_{I}\) is calculated from the final dense layer. The model consists of three types of phases: 1D-CNN, flattening, and dense layers, as shown in Figure 3. The normalized input parameters are passed through two 1D-CNN layers, first with 32 filters and a kernel of size 5 and the other with 16 filters and a kernel of size 1. Each 1D-CNN layer produces padded results to maintain the width of the output matrix. The output from the second 1D-CNN is then inputted to a flattening layer, which reforms the dimension, thus preparing it for input to the dense layers. Three dense layers follow the flattening layer, to output the regression result. The choice of the number of filters in the 1D-CNN and the number of nodes in the dense layers was based on a grid search to test for multiple combinations.  
Considering the choice of activation functions, the grid search also involved testing the different methods that could follow the hidden layers in the proposed model. The best results were achieved when the output of each hidden layer was inputted to a parametric rectified linear unit (PReLU) activation function. For the final layer, the linear activation function was the suitable choice to adapt to the range of the \(P_{I}\) previously shown in Table 4, since it ranges between \(-16.1\) and \(+46.84\) dBm.  
Figure 2: Pearson correlation coefficients of each input parameter (\(d_{V2X}\), \(d_{I2X}\), \(P_{V}\), \(SINR_{th}\), and \(U\)) and the output \(P_{I}\).  
The optimization used in the proposed model is the adaptive moment (Adam) as it can adaptively learn the required parameters according to the learning process [40]. Since the problem defined is a regression problem, the loss function used was to minimize the mean absolute error as the model learns. The experiments applied to train, test, and evaluate the proposed model will be explained in the following section.
## 3 Results and Discussions
### Chemical Analysis  
In Figure 5, FT-IR spectra of unmodified asphalt binder and after rejuvenating with fresh and waste engine oil has been presented. Noticeable differences can be clearly seen in intensities of functional groups before and after waste oil addition.  
The strong peaks between \(\
u\approx 3000\) cm\({}^{-1}\) to 2800 cm\({}^{-1}\) represent aliphatic -CH\({}_{3}\), -CH\({}_{2}\) and CH stretching vibrations. Peaks around \(\
u\approx 2359\) cm\({}^{-1}\) are due to CO\({}_{2}\) and its intensity can be controlled by experimental accuracies. Peak in the finger print region from \(\
u\approx 1000\) cm\({}^{-1}\) to \(\
u\approx 700\) cm\({}^{-1}\) indicates benzene rings in asphalt. Signals observed at \(\
u\approx 1739\) cm\({}^{-1}\) and \(\
u\approx 1028\) cm\({}^{-1}\) are representative bands of carbonyl and sulfoxide groups. Intensity changes at these bands are a direct indication of aging binder. In the unaged condition, only FO and UFO exhibited signals at \(\
u\approx 1739\) cm\({}^{-1}\), proving high concentrations of carbonyl functional group C=O due to severe oxidation and heat exposure of waste engine oil during usage in vehicle, which is in line with the previous research [20]. The peak at \(\
u\approx 1456\) cm\({}^{-1}\) is due to CH\({}_{2}\) and CH\({}_{3}\) bending vibration while \(\
u\approx 1375\) cm\({}^{-1}\) depicts symmetric stretching. The band at \(\
u\approx 1216\) cm\({}^{-1}\) represents C-O bond due to ester molecules in the lubricant oils.  
Figure 6 illustrates the chemical profile of the RTFO aged condition for all binders. A significant difference in intensity of carbonyl functional group can be identified at \(\
u\approx 1737\) cm\({}^{-1}\). FR has undergone a severe aging due to the fact that it contains the higher amount of unsaturated hydrocarbons. FO and UFO, containing already aged engine oil, provide less opportunity to produce oxidized products and can be expected to resist aging compared to FR. An increase in peak of FR at \(\
u\approx 1216\) cm\({}^{-1}\) was also observed, which reveals the increased intensity of ester molecules in aged binder.  
Figure 5: FTIR spectra comparison of unaged asphalt binders.
## 3 Overview of High Slope Operation
### Slope Deformation Characteristics Analysis  
Several GNSS monitors are arranged on the inlet slope of the flood-discharge structure of Lianghekou Hydropower Project to monitor the displacement, which are placed in the elevation range of 2900-3175 m. The monitoring data record the deformation characteristics of the slope during water impounding.  
In this study, six GNSS field monitoring data, TPXJ-10, TPXJ-17, TPXJ-49, TPXJ-24, TPXJ-29, and TPXJ-35 were selected to describe the displacement of slope, which are placed at 3175 m, 3100 m, 3050 m, 3050 m, 2975 m, and 2900 m, respectively (as shown in Figure 1). These GNSS field monitoring sites all acquired data every two weeks.  
The monitoring data of GNSS in Figure 2 show that the overall apparent displacement of the slope shows fluctuations within a certain range. After water impounding, the slope deformation increases compared with that before impounding, and the maximum value of cumulative displacement in the monitoring data is 20.6 mm. After the first stage of water impounding, the water level in the reservoir area rises 81.0 m, the growth rate of the water level is 5.5 m/day, and the rapid growth of the water level does not bring an obvious increase to  
Figure 1: Typical profile of the inlet slope of the flood-discharge structure.  
the slope deformation. After the second stage of water impounding, the water level growth rate was 1.0 m/day. After the water level growth, the deformation of the slope showed a hysteresis response, the displacement began to increase significantly, and the average values of deformation rates of each measurement point were 0.042 mm/day, 0.058 mm/day, 0.056 mm/day, 0.032 mm/day, 0.072 mm/day, and 0.061 mm/day, respectively.  
Through the characteristics shown by the monitoring data, it is analyzed that the slope did not show an obvious response after the water impounding in Stage I. The lowest elevation of the slope is 2742 m, and the water impounding height did not reach the slope range, so the slope did not show an obvious response. After the second period of water impounding, the reservoir water level reached 2785 m, the slope lagged to produce a response, and all the monitoring points showed an obvious increase, indicating that water impounding has an obvious influence on the stability of the slope.
## 4 Experimental Results and Discussion
### Analysis of the Surface Roughness and Hardness
Based on the established response surface model, the response surfaces reflecting the relationship of the target value (i.e., the surface roughness and hardness) and the interaction of each two parameters are drawn, as shown in Figure 11. The interaction \(f\times P\) of both two processes have a significant impact on the surface roughness and surface hardness, as shown in Figure 11c,f,i,l. The interaction \(n\times f\) of both processes has a small influence, as shown in Figure 11b,e,h,k. The interaction \(n\times f\) has no significant effect on surface roughness and surface hardness, as depicted in Figure 11a,d,g,j. The relationships between process parameters are nonlinear. The results show that for the composite process, the spindle speed is negatively correlated with the surface roughness, and the feed and static extrusion amount are positively correlated with the surface roughness. By comparing the color changes, the interaction between the spindle speed and the feed has a greater impact on the surface roughness, as shown in Figure 11a,b. Feed, spindle speed and static extrusion amount are positively correlated with surface hardness. The interaction between spindle speed and static extrusion amount has a greater impact on surface roughness, as shown in Figure 11j,h,i. For the ultrasonic extrusion process, the feed is negatively related to the surface roughness, the spindle speed and the static extrusion amount are positively related to the surface roughness, and the interaction between the feed and the static extrusion amount has a greater impact on the surface roughness, as shown in Figure 11d,e,f. Spindle speed is negatively correlated with surface hardness, and feed and static extrusion amounts are positively correlated with surface hardness. The interaction between feed and static extrusion amount has a greater impact on surface hardness, as shown in Figure 11j,k,l. This is of guiding significance for the design of optimal parameter combination and the processing of workpieces in practice. Figure 11: _Cont._
## 3 Results and Discussion  
### Composition Characteristics of Pharmaceuticals in Snow  
PhACs were found in all snow samples examined in the present study. The concentration levels, LOQ values, and detection frequencies of the detected target compounds are summarized in Table S3. The total concentration of PhACs in the snow sampled from the sampling sites near JZB ranged from 52.80 to 1616.02 ng/L (Figure 2), with an average concentration of 215.59 ng/L, which is higher than the concentration of PhACs in the seawater in JZB (54.82 ng/ L) [23].  
Figure 2: Cumulative levels of pharmaceutically active compounds (PhACs) at the different sampling stations.  
A total of 38 target compounds were identified and quantified in the snow samples, with concentrations ranging from ND-1500.00 ng/L and LOQ values ranging from 0.1-2.3 ng/L. The highest concentrations of up to 1500.00 ng/L were observed for tetracycline. According to Figure 3a and Table S3, tetracycline (125.81 ng/L, 58.36%), desacetylcefotaxime (17.73 ng/L, 21.95%),ronidazolee (8.79 ng/L, 8.22%) and triamcinolone diacetate (2.84 ng/L, 4.08%) were the preponderant PhACs. There were 14 substances (flunixin, sulfamonomethoxine, sulfameter, sulfamethoxypyridazine, nitrazepam, nonidazole, 5-chloride-1-methyl-4-nitroimidazole, 5-nitrobenzimidazole, salbutamol, oxolinic acid, roxithromycin, desacetylcefotaxime, ceftotiour, and tetracycline) that showed frequencies of detection above 50%. Sulfamonomethoxine, sulfameter, sulfamethoxypyridazine, nitrazepam, nonidazole, 5-chloro-1-methyl-4-nitroimidazole, 5-nitrobenzimidazole, salbutamol and desacetylcefotaxime were the most abundant compounds with detection frequencies of 100%, indicating that they were widespread in the snow of JZB. These results indicate that atmospheric deposition made a significant contribution to the migration and transportation of PhACs.
# Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning
###### Abstract  
The majority of railway operators still implement conventional maintenance for railway point machines (RPMs), which is one of the most vital pieces of equipment for ensuring the safety of train operation. The conventional maintenance method lacks accuracy, is less efficient, and has high labor costs. This study developed a cost-effective and accurate fault diagnosis (FD) method based on current data to increase the overall efficiency of RPM maintenance. The FD method for RPM equipment discussed in this paper consists of three working conditions: normal, working, and failure. The method was proposed based on time-series current signals, which were gathered when the RPM was in operation. Time-series data were extracted and filtered using time-domain feature extraction based on scalable hypothesis testing. The selected features became the datasets for machine learning modeling. Six machine learning algorithms were compared in order to find the algorithm with the best FD accuracy. The results showed 100% accuracy for the Decision Tree and Random Forest algorithms in the FD method. The results of the FD method could be important for maintenance teams in determining suitable maintenance activities based on RPM working conditions.  
railway point machine; fault diagnosis; machine learning; predictive maintenance
## 4 Analysis of Classification Results
### Comparison of Recognition Results Using Different Methods  
To demonstrate the superiority of the proposed method in this chapter, WT-DBN was compared with WT-BP and WT-SVM, and the recognition accuracy of the three methods was obtained as shown in Figure 13 and Table 3.  
Figure 12: Boxplot of classification accuracy based on different amounts of training data.  
Figure 13: Comparison of recognition accuracy of different algorithms.  
From Figure 13 and Table 3, it can be observed that the recognition performance of WT-DBN is superior to WT-BP and WT-SVM, with a higher recognition accuracy for partial discharge and corona interference, reaching over 98%. The SVM transforms nonlinear problems into linear problems in high-dimensional space using kernel functions to process data classification. The SVM has certain non-linear modeling capabilities, but its training process is relatively simple and its classification accuracy is not as good as BP and 2 DCNN. The BP neural network is a fully connected feedforward neural network trained through a backpropagation algorithm. In this signal recognition process, the recognition accuracy also reached 93.75%. But, the WT-DBN proposed in this article has more advantages in processing classification data because DBN can automatically learn the features of input data, has good extraction ability for hierarchical features of data, and has faster training speed and higher accuracy in processing large-scale datasets.
# Accelerating Image Classification using Feature Map Similarity in Convolutional Neural Networks
## 4 Experimental Results and Analysis
### Feature Vector Similarity  
To verify that feature vectors of the same class have a similarity, we chose 10 classes from the Caltech256 [21] dataset and selected 100 images per class. The 100 images for each class were only images that were classified under the same class by AlexNet. The selected classes are [\"gorilla,\" \"backpack,\" \"binoculars,\" \"warplane,\" \"joystick,\" \"flower pot,\" \"mountain bike,\" \"toaster,\" \"photocopier,\" and \"daisy\"]. Figure 3 shows an example of images for the 10 selected classes. Some of these images include those that were classified as the same class, although they were not, such as in \"joystick\" and \"flower pot.\"We then extracted feature vectors at each convolution layer of AlexNet and then measured the distance of each feature vector. The distance measurement involves two distances: (1) Distance between images of the same class. (2) Distance between images of different classes.  
Distances between images of the same class were obtained by dividing the 100 images of each class into two groups of 50 each, and subsequently measuring the distance between the images of one group to the other. A total of \(50\times 50=2500\) distances were measured. Distances between images of different classes were determined by selecting 50 images for each class and measuring a total of \(50\times 50=2500\) distances. Figure 4 shows the results of this experiment. The horizontal axis of the graph represents the five convolution layers of AlexNet. The vertical axis represents a distance with a real value between 0 and 1. A red dashed-dotted line represents the distance between images of the same class.  
In most cases, the distance between the images of the same class is the smallest. This is because the images classified into the same class have similar features generated by the convolution layer, and the intensity of the features is similar. In addition, it shows that our feature vector extraction method provides a discrimination that can distinguish each class. We have confirmed that the images of the same class have the smallest distances to each other, and this point can be used in the cache.  
Figure 3: Examples of Caltech256 dataset images classified under the same class by AlexNet.  
Figure 4: Distance between feature vectors for each class. The red dashed-dotted line is the distance between images of same class. The distances between images of the same class are less than those between images of different classes.
# Research on Application of Improved Quantum Optimization Algorithm in Path Planning
## 4 Analysis of Evacuation Path Planning Model Experiment
### Experimental Analysis of Single-Robot Path Planning
#### 4.3.1 Results Analysis  
The visualization of the spatially optimal route of the evacuation path three-dimensional raster model is given in Figure 11. The best path route based on the ACA search took 1.362073 s to run, the best path route based on the GA search took 1.304934 s to run, and the best path route based on the BQGA search took 1.256061 s.  
In Figure 11, the trends of the best individual fitnesses under different algorithms are represented. Figure 11 shows the trend of the best individual fitness for the BQGA, from the initial 128 to 118, with a difference of 10, the trend of the best individual fitness for the ACA, from the initial 126 to 115, with a difference of 11, and the trend of the best individual fitness for the GA, from the initial 128 to 110, with a difference of 18.  
For ACA, it is easy to fall into local optimum prematurely, and the computational search time is too long because of its complexity. It is easy to encounter a stagnation  
Figure 11: (**a**) Trends in the best individual fitness of the BQGA model; (**b**) trends in the best individual fitness of the ACA model; (**c**) trends in the best individual fitness of the GA model.  
Figure 9: Evacuation path planning route.  
phenomenon, that is, after the search is carried out to a certain degree, it cannot search the solution space further, which is not conducive to discovering better solutions. ACA's optimization efficiency is low.  
For GA, the encoding of ordinary algorithms does not fully represent the constraints of the optimization problem, and therefore it requires the consideration of thresholds for infeasible solutions, which in turn increases the workload and solution time, making the iteration time larger.  
BQGA avoids the random characteristics brought by generating binary codes with measuring quantum bits, and expands the planar unit circle description of quantum bits to Bloch spherical description, so that the quantum behavior can be fully reflected. Then, the frequent binary number decoding process can be avoided, and the time will be reduced. Thirdly, BQGA expands the number of global optimal solutions and improves the probability of obtaining global optimal solutions. Finally, the number of global optimal solutions can be expanded and the probability of obtaining global optimal solutions will be improved.
## 4 Case Study
### MSPCA Fault Data Recognition Results  
MSPCA combines the wavelet analysis method on the basis of PCA, and carries out unified threshold processing after wavelet decomposition, which has a certain anti-jamming ability to noise. The simulation results are shown in Figure 9. MSPCA can more effectively resist the interference of noise, the false positives of fault are also significantly reduced, but, due to the MSPCA in establishing the main space of atoms and the residual subspace being single, in dealing with traffic flow data, many false positives still appeared.
# Utilizing Trusted Lightweight Ciphers To Support Electronic-Commerce Transaction Cryptography
## 1 Introduction
In this study, we focus on the security of commercial transactions using asymmetric and symmetric encryption protocols and algorithms. The system will provide strong security using the public key encryption algorithm (ElGamal) and the symmetric encryption algorithm (AES) to make e-commerce transactions more secure. Our main contributions to this research are:  
* Designing a lightweight protocol for customer data authentication in e-commerce applications based on ElGamal for key generation and the AES algorithm for data encryption as well as relying on CRT to improve key generation performance. * Protecting all stored security parameters associated with business application entities in a systematic and robust manner. * Testing the e-commerce application protocol in the SYTHER tool for verification and analysis of security results, as well as performance testing of the proposed protocol and proving its superiority over existing protocols. Figure 1: Various attacks against e-commerce sector. A quick description of how the paper is organized with the key points: Section 1 is the introduction to e-commerce and security risks. In Section 2, we describe related works of e-commerce security. The background about the used techniques with the proposed protocol is discussed in Section 3. Section 4 demonstrates the methods of the proposed e-commerce protocol. The proposed protocol results and discussion are explained in Section 5. In Section 6, we briefly provide the conclusions of this research, and future directions are presented in Section 7.