esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Intermetallics Formation and Joint Performance of Laser Welded Ni to Al
## 3 Results  
### Evaluation of Weld Quality  
Figure 3a-d show cross-sections of welds prepared by using the CW and PW laser sources, respectively. Comparing Figure 3a,b, it can be seen that the Ni sheet was completely melted and a deep weld was formed due to the high laser power, whether using a continuous laser or a pulsed laser. The severely dynamic melting pool during welding was unable to fill the weld crater during solidification. Therefore, high porosity and voids were formed in these welds. By comparing Figure 3a,c, it is very obvious that by controlling the power of the PW laser, the porosity in the weld can be significantly reduced, but the pores generated by the PW laser during the welding process could not be completely eliminated. In comparison of Figure 3b,d, it can be observed that when the CW laser was properly defocused, a Ni-Al weld-braze joint without any defect could be obtained. These results suggested that both laser power and focus position have significant effect on the occurrence of weld defects, as well as controlling the penetration depth and weld width when welding thin Al-Ni sheets. Using the CW laser and defocused beam, the penetration depth is easier to control to avoid the boiling of the melted Al and mixing with Ni, so that a clear weld interface without obvious defects can be achieved, as shown in Figure 3d. The widths of the weld joint shown in Figure 3c,d are 600 \(\upmu\)m and 1700 \(\upmu\)m, respectively. The penetration depth into the Al side shown in Figure 3c is 140 \(\upmu\)m, whereas a much shallower penetration depth into the Al can be seen in Figure 3d. It is difficult to identify the weld joint interface in Figure 3a,b, due to the large pores formed. Thus, the dimensions of these two joints were not analyzed.  
In addition, it can be seen that almost all obvious voids concentrate near the fusion boundaries at the Al side. This is because the aluminum alloy very easily generates plasma during the high-energy laser welding process. On the one hand, these plasmas reduced the  
Figure 2: Weld strength testing set-ups: (**a**) shear test; (**b**) 90-degree peel test.  
penetration depth of the weld, and on the other hand, the generated plasma was concentrated on the aluminum alloy side of the fusion line boundary, resulting in the formation of voids.
## 3 Results and Discussion
### Thermal Properties of GO/AMT-CE Composites  
The thermal stability of the GO/AMT-CE composites was estimated through the TGA measurement. Typical traces with different contents of GO/AMT-CE were performed under nitrogen, as shown in Figure 5, which exhibited a major mass loss from 400 to 550 \({}^{\circ}\)C. Td\({}_{5\%}\) represented the corresponding temperature when the weight loss was 5%, while Td\({}_{10\%}\) referred to 10%. With the increase of GO/AMT in CE resin systems, the temperature of Td\({}_{5\%}\) increased from 403 to 431 \({}^{\circ}\)C, and the temperature of Td\({}_{10\%}\) increased from 428 to 442 \({}^{\circ}\)C. The char yield at 800 \({}^{\circ}\)C of pure CE was 34%, while that of GO/AMT-CE composites was 38-42%. The enhancement of the thermal stability could be mainly ascribed to the excellent thermal stability of GO/AMT and the increasing cross-linking degree of the GO/AMT-CE system under the catalytic effect of GO/AMT in CE resin. Therefore, the mobility of polymer segments at the interface between the GO/AMT and CE matrices was inhibited by strong interactions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Samples** & **T\({}_{\text{i}}\)(\({}^{\circ}\)C)** & **T\({}_{\text{p}}\)(\({}^{\circ}\)C)** & **T\({}_{\text{f}}\)(\({}^{\circ}\)C)** & **\(\Delta\)H(J/g)** \\ \hline Pure CE & 267.34 & 310.09 & 332.35 & 828.5 \\
0.3\% GO/AMT & 233.31 & 281.49 & 321.88 & 794.9 \\
0.5\% GO/AMT & 207.95 & 250.46 & 272.37 & 669.1 \\
0.7\% GO/AMT & 176.91 & 229.96 & 264.45 & 600.1 \\
1.0\% GO/AMT & 163.67 & 189.48 & 258.85 & 671.7 \\ \hline \hline \end{tabular}  
* Ti-The initial exothermic peak temperature; Tp-The maximum exothermic peak temperature; Tf-The final exothermic peak temperature.  
\end{table}
Table 1: Sample formulation and Differential scanning calorimetry (DSC) date of various graphene oxide/adamantane-cyanate ester (GO/AMT-CE) composites at the heating rate of 10 \({}^{\circ}\)C/min.  
Figure 4: Differential scanning calorimetry (DSC) curves of GO/AMT-CE composites with different GO-AMT contents.
## 2 Materials and Methods
### Overlap Similarity Algorithm  
In this study, the NDVI shown mangrove patterns are treated as training samples and the status of mangrove populations, provided by the Management Board of Can Gio Mangrove Biosphere Reserve, is used as a testing sample. For the determination of the signals of the NDVI that show the mangrove population, the overlap similarity algorithm was applied. Overlap similarity is considered as a correlation coefficient in conducting a spatial correlation between two variables [48] and it stretches a decision case [49]. It is defined as the size of the intersection between two sets, divided by the size of the smaller of the two sets [50]. The formula of overlap similarity is calculated using Equation (2):  
\[O(A,B)=\frac{|A\cap B|}{\min(|A|,|B|)} \tag{2}\]  
where \(A\) and \(B\) are the NDVI of mangrove and referenced mangrove, respectively.  
First, a novel sampling method named controlled random sampling is adopted to generate the training sets. The controlled random sampling can reduce the overlap between the training and testing samples. Then, the super-pixel segmentation method is adopted to divide the Sentinel-2 imagery into many smoothed small classifications [51]. The training samples with the same label will be used to form a set. The overlap index assigns a real number between 0 and 1 to the similar dataset. A zero value means that the documents are dissimilar completely, whereas one indicates that the documents are practically identical [50]. Therefore, if the NDVI is a subset of mangrove references, which are supposed to be the status of mangrove species by management, or vice-versa, it shows the overlap coefficient value as 1.
## Appendix A
### a.2  
The \(F\) statistic allows to obtain these scores, which we can see in Equation (A2).  
\[F=\frac{\frac{SSBG}{d\hat{f}_{1}}}{\frac{SSWG}{d\hat{f}_{2}}}.\] (A2)  
\(SSBG\) = Sum of squares between groups:  
\[SSBG=\sum_{j=1}^{m}\sum_{i=1}^{n_{j}}(y_{ij}-\hat{y}_{j})^{2}.\] (A3)\(SSWG=\) Sum of squares within groups:  
\[SSWG=\sum_{j=1}^{m}n_{j}(\hat{y}_{j}-\hat{y})^{2}. \tag{10}\]  
In addition, \(df_{1}=m-1\), \(df_{2}=n-m\), \(m\) is the number of levels of our target variable and \(n\) is the number of observations, \(\hat{y}_{j}\) is the mean in group \(j\), \(\hat{y}\) is the total mean [66].  
*Balanced accuracy** For binary classification this value is equal to the arithmetic mean of the sensitivity and specificity.  
\[balanced\_accuracy=\frac{1}{2}\bigg{(}\frac{TP}{TP+FN}+\frac{TN}{TN+FP}\bigg{)}. \tag{11}\]  
\(TP\) are the true-positives. \(TN\) are the true negatives. \(FN\) are the false-negatives. \(FP\) are the false-positives.  
**Sensitivity** Represents the proportion of true-positives that the model predicted correctly (Positive Class 1: Dissatisfied).  
\[Sensitivity=\frac{TP}{(TP+FN)}. \tag{12}\]  
**Specificity** Represents the proportion of true negatives that the model predicted correctly (Negative Class 0: Satisfactory).  
\[Specificity=\frac{TN}{(TN+FP)}. \tag{13}\]  
**Positive predictive value (PPV)** Also called precision, represents the probability of an instance being of the positive class having been predicted as positive by the model.  
\[PPV=Precision=\frac{TP}{(TP+FP)}. \tag{14}\]  
**Negative predictive value (NPV)** Represents the probability of an instance being of the negative class having been predicted as negative by the model.  
\[NPV=\frac{TN}{(TN+FN)}. \tag{15}\]  
**Cohen's kappa coefficient** Measures the agreement between classifiers.  
\[kappa=\frac{(Pr(a)-Pr(e))}{(1-Pr(e))}. \tag{16}\]  
where, \(Pr(a)\): the percentage of agreement observed.  
\(Pr(e)\): is the probability that the inter-rater agreement is due to chance.  
**Area under the ROC curve (AUC).** Indicates how good the model is at discriminating instances of the positive class and the negative class. This value fluctuates between 0 and 1. According to the Ref. [67], the closer this value is to 1, the greater the discriminating capacity of the model.
## 5 Performance Prediction for E-CSS of Horizontal Well  
In view of the successful application of high-power electric heating technology under foreign heavy oil wells in the oil sands of the Congo, the United States and Canada, they all have an increase in the production of heavy oil wells. Taking the actual reservoir model of A, a typical horizontal well of CSS [23; 24; 25; 26] A in the FC well area as an example, the prediction and comparison of conventional CSS and E-CSS are carried out. The effective thickness of the oil layer corresponding to the well is 10.9 m, porosity is 32.6%, permeability is 573 mD, the viscosity of crude oil degassed at 50 \({}^{\circ}\)C is 20,203 mPa\(\cdot\)s and the length of the horizontal segment is 255 m. The peak power of the electric heater is 2000 W/m, and the maximum surface heating temperature is 300 \({}^{\circ}\)C.  
Figure 10 shows the comparison of the oil rate and oil recovery factor for CSS and E-CSS. From Figure 10, this comparison indicates that during the E-CSS process, the oil layer temperature in the near-well area continues to heat up, and the flow resistance of the horizontal section into the well is significantly reduced; the oil increase in the CSS cycle becomes more and more obvious. By the 13 th round, the peak of oil increase is reached,  
Figure 9: Wellbore porous flow resistance reduction ratio under different electrically enhanced temperatures.  
and then gradually decreased. After the 20 th round, electric heating assistance has no obvious effect. The reason is that the formation thermal field near the wellbore area in the late stage of multi-cycle CSS has been established, and there is no obvious effect in continuing electric heating to heat up the near-well area. According to the statistics, the recovery of the 20 cycles of E-CSS is 9.4%, which is higher than that of conventional CSS, and the cumulative oil-steam ratio has increased from 0.14 of conventional CSS to 0.23, indicating that electric heating can greatly increase the CSS; the oil-steam ratio and the recovery factor have important potential for improving the CSS of horizontal wells.
# Secure and Lightweight Cloud-Assisted Video Reporting Protocol over 5G-Enabled Vehicular Networks
## 7 Conclusions  
In this paper, we noted that although there exist a few research works for secure video reporting service in 5G-enabled vehicular networks, their usage is limited because of public key certificates and expensive pairing operations are required. To overcome the limitation, we proposed a new secure and lightweight protocol for cloud-assisted video reporting service in 5G-enabled vehicular networks. Based on a fined-grained access control, the proposed protocol allowed the designated vehicles to recover the recorded video files without any prior communication. By providing security and privacy for the participating entities, the proposed protocol prevents malicious users from tracking, revealing or impersonating the system entities. Also, by using a new certifiateless signature scheme, the proposed protocol assured the authentication of legitimate vehicles. With anonymous credentials instead of public key certificates, the proposed protocol guaranteed the authorization of participating entities. From the security and performance analysis results, we showed that the proposed protocol took a lightweight overhead compared to the state-of-the-art works. From these analysis results, we believe that the proposed protocol will help to realize timely and secure cloud-assisted video reporting service over 5G-enabled vehicular networks.  
This work was supported by BK21 PLUS, Creative Human Resource Development Program for IT Convergence, by basic science research program through national research foundation korea (NRF) funded by the ministry of science, ICT and future planning (NRF-2015R1D1A1A01057888), and by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2012-0-00265, Development of high performance IoT device and Open Platform with Intelligent Software).  
[PERSON] identified the drawbacks in [PERSON] et al protocol. Then he proposed how the protocol would be improved to overcome the outpointed weaknesses. [PERSON] contributed by proposing a new orientation on how the protocol would be enhanced in terms of security primitives. [PERSON] contributed for cryptographic analysis and organizing the sections and subsections of the paper. In this article, all authors have read the final version of the manuscript for approval purpose.  
All the authors confirm that there is no conflict of interest.
## 4 Optimisation of Reinforcement Approach of Prestressed Anchor Cables on the Slope
Additionally, by analysing Figure 12, it can be found that after removing cables from the upper part of the slope, the safety factor was reduced. After decreasing the cable prestress in the middle part, the safety factor also fell slightly. However, the safety factor in this condition was still larger than the safety index of slope stability stipulated in codes [33]. According to the result revealed in Figure 11, by using the optimisation method for reinforcing slopes involving prestressed anchor cables, it can be found that:1. To reduce the number of constructed cables in the slope, decrease the difficulty of construction of such cables and save money, it is feasible to remove cables from the upper-third of the slope and decrease the cable prestress in the middle-third. 2. To solve the problem of the safety factor of the slope decreasing due to removal of prestressed anchor cables from the upper part and reducing the cable prestress in the middle part, the safety factor of the slope can be increased by improving the cable prestress at the lower part. However, high-strength cables must then be used in the lower part of such slopes. 3. When designing the reinforcement scheme involving prestressed anchor cables on the slope, one should first calculate the safety factor of the slope when different positions of the whole slope are reinforced by using prestressed anchor cables. Afterwards, cables in the upper-third of the slope should be removed. In this case, if the safety factor of the slope is lower than that stipulated in codes, the cable prestress at the lower part should be improved until the safety factor reaches codified requirements. However, the cable prestress in the lower part cannot increase indefinitely, so if the safety factor of the slope still did not reach codified requirements after increasing the cable prestress in the lower part to a certain level, one should increase the cable prestress in the middle part of the slope. Figure 11: (**a**) The relationship between the displacement of the slope foot and the strength reduction factor under different schemes of prestressed anchor cables; (**b**) enlarged drawings of the enlarged sections in Figure 11a.
# Modal Strain Energy-Based Structural Damage Detection Using Convolutional Neural Networks
## Appendix A  
The convolution process (Figure 11) is to multiply each element in the convolution kernel (red matrix) with the corresponding element in a sub-region (yellow part) of the input data of the convolution layer and sum up the products to obtain an element in the feature matrix (green part). Each time, the sub-region moves either down or right at a step size, and the process is repeated until all elements of the input data are involved; in the end, the convolution operation form a new matrix (i.e., the feature matrix).  
The pooling layer is used to reduce the dimensions of the input matrix. There are usually two different pooling methods: max pooling (Figure 12a) and mean pooling (Figure 12b). Max pooling was used in this paper because it is better than mean pooling [42]. As shown in Figure 12a, max pooling picks up the maximum value of a sub-matrix (\(2\times 2\)) to form an element of the feature matrix.  
Activation function: A leaky rectified linear unit (ReLU) function performs a threshold operation where any input value less than zero is multiplied by a fixed scalar. This operation is equivalent to:  
\[f(x)=\left\{\begin{array}{c}x,x\geq 0\\ \textit{scale}*x,\;\;\;x<0\end{array}\right. \tag{1}\]  
As shown in Figure 11, the input of the hidden layer \(u_{i}\) was as follows:  
\[u_{i}=\sum_{i=1}^{36}w_{ij}x_{j}+\theta_{i} \tag{11}\]  
According to the transfer function of the hidden layer, the output of the \(i\)-th node of the hidden layer can be expressed as follows:  
\[y_{i}=\phi(u_{i})=\phi\biggl{(}\sum_{i=1}^{36}w_{ij}x_{j}+\theta_{i}\biggr{)} \tag{12}\]  
Therefore, the input of the output layer \(v_{i}\) was as follows:  
\[v_{i}=\sum_{i=1}^{n}w_{in}y_{i}+a_{i}=\sum_{i=1}^{n}w_{in}\phi\biggl{(}\sum_{i =1}^{36}w_{ij}x_{j}+\theta_{i}\biggr{)}+a_{i} \tag{13}\]  
According to Equations (11)-(13), the output of the output layer \(O\) was as follows:  
\[O=\varphi(v)=\varphi\biggl{(}\sum_{i=1}^{n}w_{in}y_{i}+a_{i}\biggr{)}=\varphi \biggl{(}\sum_{i=1}^{n}w_{in}\phi\biggl{(}\sum_{i=1}^{36}w_{ij}x_{j}+\theta_{i }\biggr{)}+a_{i}\biggr{)} \tag{14}\]  
Assuming that the expected output of the output layer was \(T\), the output error \(E\) was as follows:  
\[E=\sum_{i=1}^{N}(T-O)^{2} \tag{15}\]  
where \(N\) was the number of samples.  
The training process of neural networks was the weights, and thresholds were constantly adjusted. Training terminated when the error does not change or was less than a certain value.
# Effects of Atmospheric Turbulence on OAM-POL-FDM Hybrid Multiplexing Communication System
## 2 Methods
### Structure of 32-Channel-Hybrid-Multiplexing System  
In this paper, as shown in Figure 2, a 32-channel OAM hybrid multiplexing communication system composed of modules of OAM, polarization, and frequency multiplexing was constructed. The specific structure is that every four vortex beams are combined into one beam by OAM multiplexing [26]. Then, two beams are combined by polarization multiplexing and each beam carries four OAM modes, so these eight beams converge into one. This method is used to implement the same processing on the remaining 24 beams so that 32 beam converge into four composite OAM beams. After that, these four beams are processed with FDM. At the end, the hybrid multiplexing of a 32-channel OAM can be realized by four different frequencies, and then the laser is transmitted to the atmosphere through the telescopic system.  
At the receiver, optical signals of different frequencies are separated by frequency division de-multiplexing and four beams processed by polarization multiplexing and OAM multiplexing are obtained. Then, each of them is processed by polarization de-multiplexing and divided into two beams so that the four beams are divided into eight beams. After that, each of the eight beams experiences OAM de-multiplexing and is separated into four beams so the number of beams becomes 32 (\(4\times 2\times 4\)). Ultimately, the optical signal is converted into an electrical signal by a photodetector.  
Figure 1: Schematic diagram revealing the calculation method of the vortex beam’s topological charge.  
(**a**) Phase distribution under weak turbulence. (**b**) Phase distribution under strong turbulence.  
Figure 2: The working principle of the 32-channel orbital angular momentum (OAM) multiplexing communication system. The OAM beam carrying the signal is multiplexed, and every four beams are combined into one beam. After polarization multiplexing, one beam is combined from every eight (\(4\times 2\)) beams. Next, the laser is transmitted through the atmospheric turbulence channel. At the receiver, the frequency division de-multiplexing, the polarization de-multiplexing, and the OAM de-multiplexing are sequentially performed to separate the signals. Finally, detection and decoding are implemented. OAM: orbital angular momentum; FSO: free space optical.  
The 32-channel multiplexing communication system is established by the OAM-POL-FDM hybrid multiplexing method. In this system, a continuous optical signal is generated by the transmitter and the transmission power is set in the range of \(-5\) to 10 dB, according to our requirements. The center frequency of the optical signal is 193.4 THz. The modulator is a Mach-Zehnder modulator and the devices that produce the vortex beams are vortex lenses. Spatial PIN photodetectors with a responsivity of 1 A/W and dark current of 10 \(\upmu\)A were adopted in the receiver.
# A CNN-Based Method for Heavy-Metal Ion Detection
## 2 HMI Detection Method
### Sampling Module  
The sampling module consists of a commercially available screen-printed electrode sensor external to the three-electrode potentiostat, as shown in Figure 2. The three-electrode potentiostat measures the solution according to the mode and parameters (e.g., the scanning speed, scanning mode, number of cycles, etc.) from the control module. It can perform the most commonly used square-wave voltammetry [31]. The parameters of the SWV include Init, Final, Increase, Amplitude, and Frequency, where Init represents the initial potential, Final represents the endpoint potential, and Incr represents the potential increment.  
The three-electrode potentiostat has a form factor of 1.2 \(\times\) 0.8 inches. It uses an STM32L432-KBUx embedded microprocessor as the main control chip, an LDO3.3V regulator to provide the operating current, and an external 8 MHZ crystal oscillator. The two precision op-amps integrated by the AD8606 amplifier form the potentiostat circuit and the microcurrent detection circuit, respectively. The voltages of the WE and RE electrodes are controlled by a dual-channel 12-bit DAC in the main control. The three-electrode potentiostat is connected to the screen-printed electrodes via an electrochemical electrode interface. The data collected by the screen-printed electrodes are converted to analog-to-digital by the chip's built-in ADC. Data communication between the three-electrode potentiostat and the control module is via a serial port.  
In this study, the solution was sampled by square-wave voltammetry during the test. In order to run a potential scan, the scan was defined as a sequence of well-timed \(\triangle t\) steps, where the voltage was adjusted in increments of \(\triangle V\). The current \(I\) obtained from the  
Figure 1: Block diagram of the electrochemical hardware system.  
Figure 2: Three-electrode potentiostat and SPE sensor.  
response was measured and transmitted. Our potentiostats can achieve odd increments \(\bigtriangleup V_{odd}\) that are different from even increments \(\bigtriangleup V_{even}\).  
\[\Delta V_{odd}=-2A \tag{1}\]  
\[\Delta V_{even}=2A-Increase \tag{2}\]  
The system can be also used to sample solutions using cyclic voltammetry and chronoamperometry (CA), depending on the practical needs.