esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Wear Resistance of Ti-6 Al-4V Alloy Composite Coating Prepared by Laser Alloying
## 4 Conclusions  
In this paper, the wear properties of Ti-6 Al-4V alloy and composite coating composed of Ni60A, TiN, Al, and Si, were investigated by studying the friction coefficient, abrasion loss, wear morphology, wear volume, and specific wear rate. Conclusions are as follows:  
1. At room temperature (25 \({}^{\circ}\)C), with the increase of load (3 N, 6 N, 9 N), the wear width and depth of Ti-6 Al-4V alloy increase, and the friction coefficient decreases from 0.47-0.53 to 0.39-0.44, The results show that the wear resistance of the alloyed coating is better than that of Ti-6 Al-4V Alloy at room temperature.
2. With the increase of temperature (25 \({}^{\circ}\)C, 350 \({}^{\circ}\)C, 700 \({}^{\circ}\)C), the wear width and depth of Ti-6 Al-4V alloy and alloying coating decrease. The friction coefficient of Ti-6 Al-4V alloy decreases from 0.41-0.46 to 0.36-0.40. The friction coefficient of alloying coating becomes unstable with the increase of temperature. The wear rate of alloying coating is lower than that of Ti-6 Al-4V alloy at 25 \({}^{\circ}\)C. At 350 \({}^{\circ}\)C, the wear rate of the alloy coating is similar to that of Ti-6 Al-4V alloy, but at 700 \({}^{\circ}\)C, the wear rate is higher than that of Ti-6 Al-4V alloy. The reason is that with the increase of temperature, the surface alloy material softens, the hardness decreases, the surface oxidation occurs seriously, and the oxide film breaks away from the metal surface under the action of normal stress and shear stress, resulting in the insufficient wear resistance of the alloying coating.
3. Under the high temperature condition, the wear extent, wear volume, and specific wear rate of the alloying coating are lower than the room temperature, but the wear resistance is not satisfactory compared with the Ti-6 Al-4V alloy under high temperature condition, indicating that the alloying coating, with good oxidation resistance under high temperature conditions, failed to get better wear resistance.  
Conceptualization, A.H.; methodology, A.H.; validation, Q.C.; formal analysis, Q.C. and J.Z.; investigation Q.C. and P.W.; writing--original draft preparation, Q.C. and J.Z.; writing-review and editing, Q.C. All authors have read and agreed to the published version of the manuscript.  
This research was financially supported by the Key R&D Projects (National Key projects) (No. 2017 YFE0100100).  
Ethical review and approval were waived for this study, due to \"Not applicable\" for studies not involving humans or animals.  
Not applicable.  
Did not report any data.  
The authors gratefully acknowledge the State Key Laboratory of Material Processing and Die & Mould Technology for their technical and equipment support.  
The authors declare no conflict of interest.
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
## 2 Related Work
### Node Scheduling  
In WSNs, scheduling can be used in the multiple access control layer and the network layer [16]. In this paper, we discuss the use of sensor node scheduling in the network layer. Scheduling sensor activities is an effective way to extend the life of wireless sensor networks. Only activating the necessary sensor nodes at a certain moment can save much energy. Various efforts have been made in order to optimize wake-up scheduling in sensor networks. These methods are generally divided into two categories. The first category is to find the largest number of disjoint coverage sets to extend the life of the network. The second type maximizes the total working time of disjoint cover sets to extend the life of the network [17]. The authors of [11] used GA to divide sensor nodes into some disjoint sets, each of them contains the minimum number of sensor nodes to cover all MTPs. Only one set keeps active at one time, and the remaining sets will enter sleep mode. Therefore, the lifetime of WSNs can be extended. However, the author assumed that the entire set will be discarded when there exist a sensor node in current active set runs out of energy. A new set will be activated in order to continue the sensing work. This will lead to the energy waste of sensor nodes, since other sensor nodes in the abandoned set may still have much residual energy. In [10], a sensor node scheduling method that is based on improved GA is proposed to achieve network coverage and connectivity. Sensor nodes are densely deployed in the network area, activating a group of sensor nodes with more residual energy for coverage. By introducing a new mutation operation, redundant sensor nodes can be dormant. Based on the ant colony algorithm, the authors of [12] proposed an algorithm to deal with the SCP. The energy of the network can be saved through activating sensor nodes in the minimum coverage set that satisfies the coverage requirement by turns. Then a local wake-up scheduling method is also proposed. Once an active sensor node runs out of energy, its neighbor nodes in the disjoint coverage set will be activated. The network performance can be indeed improved; however, the author did not consider network connection constraints and routing strategies.
# Fault Diagnosis of HV Cable Metal Sheath Grounding System Based on LSTM
## 3 Analytical Calculations of Sheath Currents under Fault Conditions
### Sheath Loops Open Circuit Fault  
An open circuit fault occurs in the sheath loops, and the metal sheath is not effectively grounded and cannot form a closed loop. The causes of sheath loop open circuit faults include loose cable joints, improper installation, or third-party Sabotage [15]. Take the example of an open circuit fault at the first end of the sheath loop L1, as shown in Figure 5 (the leakage currents of the sheath loops L2 and L3 are not marked in the figure).  
The value of \(I_{\mathrm{i1}}\) is zero when an open circuit fault occurs in the sheath loop L1, but there is also a leakage current component in the sheath loop, and the sheath current of the grounding box is shown in Equation (14).  
\[\left\{\begin{array}{l}I_{\mathrm{sa1}}=0\\ I_{\mathrm{sa2}}=-I_{\mathrm{2}}-(I_{\mathrm{CRB1}}+I_{\mathrm{CRC2}}+I_{ \mathrm{CRA3}})+(I_{\mathrm{CLB1}}+I_{\mathrm{CLC2}}+I_{\mathrm{CLA3}})\\ I_{\mathrm{sa3}}=-I_{\mathrm{3}}-(I_{\mathrm{CRC1}}+I_{\mathrm{CRA2}}+I_{ \mathrm{CRB3}})+(I_{\mathrm{CLC1}}+I_{\mathrm{CLA2}}+I_{\mathrm{CLB3}})\\ I_{\mathrm{sa4}}=I_{\mathrm{i2}}+(I_{\mathrm{CRB1}}+I_{\mathrm{CRC2}}+I_{ \mathrm{CRA3}})-(I_{\mathrm{CLB1}}+I_{\mathrm{CLC2}}+I_{\mathrm{CLA3}})\\ I_{\mathrm{sa5}}=I_{\mathrm{i3}}+(I_{\mathrm{CRC1}}+I_{\mathrm{CRA2}}+I_{ \mathrm{CRB3}})-(I_{\mathrm{CLC1}}+I_{\mathrm{CLA2}}+I_{\mathrm{CLB3}})\\ I_{\mathrm{sa6}}=I_{\mathrm{CA1}}+I_{\mathrm{CB2}}+I_{\mathrm{CC3}}\end{array}\right. \tag{14}\]
## 3 Analysis and Results
### Qualitative Analysis
#### 3.2.2 Categories by Location Information  
During the measurement phase of the pilot project, researchers investigated the location information and work proportions of the issues handled by BIM (Figure 3). Apart from uncategorized information, the issues in Project 1 were, in order, tunnel (21.90%), construction road (5.01%), and existing structure (2.64%). In Project 2, the issues were, in order, tunnel (58.05%), underground passage (6.83%), and existing structure (6.10%). Therefore, it can be observed that in the construction phase of railway projects applying BIM, the most commonly addressed issues are related to tunnels.
## 2 Materials and Methods
### Frequency Analysis  
The common vibrational exposure of some off-road agricultural vehicles with or without implement is summarized in Table 5. Sometimes, unconsidered factors also affect vibration exposure, which might not be associated harmonically. This type of phenomenon cannot be anticipated and accurately predicted in advance. Therefore, some intensive analysis, i.e., power spectral density (PSD) is required to understand the reason for the repetition of the random vibration, and its effect on the targeted operation. In this regard, data transformation (time domain to frequency domain) is essential [51; 52]. In this study, Fast Fourier Transform (FFT) was used for this purpose. FFT is a computational method which converts a signal from its original domain (time or space) to a frequency domain and vice versa [53]. Sometimes, FFT cannot describe the non-stationery signals. A power spectral density (PSD) is widely used in research in those situations. PSD helps understanding of the distribution pattern or frequency variations of the time signal energy with differing spectral resolutions [54]. In this study, PSD was obtained through [PERSON]'s overlapped segments averaging estimator [55], using windows of 1000 data with an overlap of 600 data on a segment of 1000 discrete Fourier transform (DFT) points extracted from the middle of each record. Then, each PSD was normalized by its median to reduce the effect of different offsets in the recordings. Equal numbers of data were considered before and after the central data point of the recorded acceleration. The used formula of DFT and PSD are mentioned in Equations (13) and (14), respectively. Notations, definitions, and relevant units of all variables used in this manuscript to evaluate the vibration exposure were listed in the nomenclature section.  
\[X(f)=\frac{1}{N}\sum_{n=0}^{N-1}x[N]e^{-2\pi\frac{f}{N}},\;f=0,\;1,\;2,\;\ldots, \;N-1 \tag{13}\]  
\[PSD=\frac{a_{RMS}{}^{2}}{\Delta f} \tag{14}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Common Vibration Levels \({}^{\text{a}}\)**} & \multicolumn{2}{c}{**Safe Exposure Range \({}^{\text{b}}\)**} \\ \hline
**Vehicle/Implement** & **WBV (ms\({}^{-2}\))** & **A(8) (ms\({}^{-2}\))** & **R\({}^{\text{A}}\) (ms\({}^{-2}\))** \\ \hline Self-propelled sprayer & 0.5–0.69 & \\ Tractor-mounted sprayer & 0.5–0.74 & \\ Tractor-mounted plough & 0.73–0.89 & \\ Tractor–trailer transport & 1.05–1.32 & Exposure Action Value: 0.50 & Low: \(<\) 0.8 \\ Tractor-mounted cultivator & 1.2–1.49 & Exposure Limit Value: 1.15 & Medium: 0.8 \(<\) RA \(<\) 1.2 \\ All-terrain vehicles & 0.85–1.39 & & High: \(>\) 1.2 \\ Tow tractor & 0.2–1.50 & & \\ Backhoe loader & 0.2–1.65 & & \\ \hline \hline \end{tabular} \({}^{\text{a}}\)[56; 57; 58], \({}^{\text{b}}\)[44].  
\end{table}
Table 5: Common vibrational exposure of agricultural vehicle during on-farm operation and a safe exposure zone.
# Online Phase Measurement Profilometry for a Fast-Moving Object
## Principle
### Super-Resolution Image Reconstruction Algorithm Based on MAP
#### 2.2.3 Iterative Solution  
Since it is hard to directly solve \(\hat{I}\) when \(\Theta(\hat{I})\) is minimum, we can choose to estimate \(\hat{I}\) iteratively by using the gradient descent [23], and projecting the negative gradient of the objective function into the constraint space at each iteration, thus \(\hat{I}\) converges to the local minimum and to the global minimum as far as possible.  
The whole iterative process can be described as:  
1. firstly, the low-resolution image is interpolated by bicubic interpolation, and the initial estimation \(\hat{I}^{0}\) is obtained. When \(m=0\), where \(m\) is the number of iterations, the mean square error (\(MSE\)) of the initial negative gradient \(MSE_{0}\) is calculated.
2. calculate the gradient of the objective function \(g_{m}=\
abla\Theta(\hat{I}_{m})\).
3. calculate the projection map to the constraint space \(p_{m}\), \(p_{m}=-Pg_{m}\), where \(P\) is the projection operator, and \(P=E-H_{k}^{T}\big{(}H_{k}H_{k}^{T}\big{)}^{-1}H_{k}\), where \(E\) is the identity matrix.
4. using the gradient of the objective function to constrain \(\hat{I}_{m}\), and the expression is \[\hat{I}^{m+1}=\hat{I}^{m}+(-\beta p_{m})\] (13) where \(\beta\) is the learning rate, which is the coefficient of the negative gradient of the objective function.
5. calculate the MSE of the negative gradient of this iteration \(MSE_{m}\). If \(\frac{MSE_{m}}{MSE_{0}}\leq\varepsilon\), \(\hat{I}^{m}\) is the best estimation, where \(\varepsilon\) is the iteration termination flag. Otherwise, return to step \(2^{\circ}\). \(MSE\) is defined as: \[MSE=\frac{1}{L_{1}L_{2}}\sum_{x,y}[\hat{I}(x,y)-{I^{\prime}}_{r}(x,y)]^{2}\] (14)where \(L_{1}\), \(L_{2}\) are the width and height of the high-resolution deformed pattern. \(I^{\prime}_{r}\) is the reference of high-resolution deformed pattern, which is obtained by interpolating the first frame of low-resolution images. The whole process of MAP is shown in Figure 1.
## 3 The Proposed Imputation Method
### Preprocessing
#### 3.1.2 Data Filtering (**Df**)  
The data filtering step focuses on removing instances (rows) containing a significant proportion of missing feature values, especially when the percentage of missing features exceeds \(\alpha_{X}\), which we set to 50% in all our experiments. Such cases are likely to differ significantly from their original observations, which may reduce the quality of the imputation process. Let \(M_{\mathbf{x}_{i}}\) be the number of missing values in the instance \(\mathbf{x}_{i}\), where \(0\leq M\mathbf{x}_{i}<\hat{m}\). The missing percentage is then calculated as \(MP_{\mathbf{x}_{i}}=M\mathbf{x}_{i}/\hat{m}\), where \(\hat{m}\) is the number of features after the feature filtering step. The resulting dataset, denoted as \(D_{p2}\), is obtained by applying the data filtering process (**DF**) to the intermediate data \(D_{p1}\). This can be expressed as the set of instances such that each instance belongs to \(D_{p1}\) and has a missing percentage \(MP_{\mathbf{x}_{i}}\) that does not exceed the threshold \(\alpha_{X}\).
## 3 Sound Quality Analysis of Pattern Noise
### Sound Quality Analysis Based on Predicted Pattern Noises  
The sound metrics (loudness and sharpness in this study) were compared with the simple scan method [15] to analyze the effect of the proposed method. The coefficient of determination \(r^{2}\) of the simple scan method and adaptive filter method are compared, as shown in Figures 9 and 10. In the figures, the y-axis represents estimated data, the x-axis is the measured data, and the yellow dots represent the value of each pattern. Figure 7 shows data from the simple data scan method, and Figure 8 shows data from the adaptive filter method proposed in this study. In the plot, the correlation value of the adaptive filter is significantly higher than that of the simple scan method. This means that the adaptive filter method is more effective for estimating tire pattern noise.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Non-Standardization Coefficient**} & \multicolumn{2}{c}{**Standardization Coefficient**} & \multirow{2}{*}{\(\mathbf{t}\)} & \multirow{2}{*}{\(\mathbf{p}\)} \\ \cline{2-2} \cline{5-5}  & \(\mathbf{B}\) & **Standard Error** & & & \\ \hline (constant) & 13.743 & 1.151 & & 11.943 & 0.000 \\ \hline Loudness & \(-\)0.088 & 0.026 & \(-\)1.027 & \(-\)3.328 & 0.013 \\ \hline Sharpness & \(-\)0.104 & 0.358 & 0.090 & 0.292 & 0.0779 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of multiple linear regression analysis.  
Figure 8: Correlation plot of the regression analysis and sound metrics; (**a**) loudness vs. subjective evaluation; (**b**) sharpness vs. subjective evaluation.  
In Figure 9, the loudness and sharpness values of the simple scan data method and their correlation values are presented. The estimating abilities of the simple scan method for loudness and sharpness are 0.18 and 0.26, respectively. In Figure 10, the loudness and sharpness values of the adaptive filter method and their correlation values are presented. The estimating abilities of the adaptive filter method for loudness and sharpness are 0.96 and 0.74, respectively. From these results, loudness and sharpness are highly accurately estimated. There are other data for sound metrics, but the most influential factor, loudness, has the highest correlation coefficient. As a result, loudness and sharpness are applicable factors for the SQI of tire pattern noise.
## 3 Results and Discussion
### Scenario 2  
The range of the road profile was extended as shown in Figure 8, and online parameter estimation was included. The new road profile is shown in Figure 8a. \(b_{s}\) and \(b_{us}\) were assumed to be changing frequently at a rate of 0.5 Hz. \(b_{s}\) varied between 4 and 9 s/m, and \(b_{us}\) varied between 3 and 7 s/m. Noise was added to the parameters to simulate noisy measurements. Figure 9a,b shows that the system successfully estimated the parameters in less than 100 ms. Despite the changing of parameters and the bumpy road profile, the actor network was able to maintain excellent performance and provided 6.14% lower overall acceleration compared to the optimum PID. The average acceleration values obtained were 0.6861 m/s\({}^{2}\) for the actor network and 0.7310 m/s\({}^{2}\) for the optimum PID.  
Figure 7: Scenario 1 results.
## 4 Experimental Results and Discussion of IHSSAO Performance Verification  
In this section, the performance of the IHSSAO in solving numerical optimization problems is examined by selecting 23 classical benchmark functions and 17 CEC2017 benchmark functions for comparison experiments with other meta-heuristic algorithms. All simulation experiments are conducted in MATLAB R2017a in Microsoft Windows 10 with a computer hardware platform configuration of Intel(r) CoreTM i7-9750H @ 2.60 GH and RAM 8 G.
# Optical Fiber Current Sensors Based on FBG and Magnetotstrictive Composite Materials
###### Abstract  
Optical fiber current sensors are widely used in the online monitoring of a new generation power system because of their high electrical insulation, wide dynamic range, and strong anti-electromagnetic interference ability. Current sensors, based on fiber Bragg grating (FBG) and giant magnetostrictive material, have the advantages of high reliability of FBG and high magnetostrictive coefficient of giant magnetostrictive material, which can meet the monitoring requirements of digital power systems. However, giant magnetostrictive materials are expensive, fragile, and difficult to mold, so giant magnetostrictive composite materials have replaced giant magnetostrictive materials as the sensitive elements of sensors. High sensitivity, high precision, wide working range, low response time, and low-cost optical fiber current sensors based on magnetostrictive composites have become a research hotspot. In this paper, the working principle of the sensor, the structure of the sensor, and the improvement of magnetostrictive composite materials are mainly discussed. At the same time, this paper points out improvements for the sensor.  
optical fiber current sensor; magnetostrictive composite material; fiber bragg gratings; sensor structure +
Footnote †: journal: Applied Physics Letters  
**Keywords:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
# A Method for Degenerate Primer Design Based on Artificial Bee Colony Algorithm
## 3 The Proposed Algorithm
### Representation of the Search Space  
For the degenerate primer design problem, each candidate solution is a primer pair, including a forward candidate primer and a reverse candidate primer, and the lengths of the forward primer and the reverse primer are between 18 and 26. Therefore, the search space for degenerate primer design can be expressed as a fully connected graph structure with 55 layers, as shown in Figure 3.  
Figure 3: Representation of the search space.  
The diagram contains four types of node: start node, intermediate node, sentinel node, and end node. The middle nodes of each layer are successively set as an effective combination of four base pairs, such as nucleotide \"A\", \"T\", \"C\", \"G\", \"AT\", \"AC\", \"AG\", \"TC\", \"TG\", \"CG\", \"ATC\", \"ATG\", \"TCG\", \"ACG\", \"ATCG\", etc. Sentinel nodes are mainly used to separate the forward primer and reverse primer, representing the end of the forward primer and the beginning of the reverse primer.  
Setting up multiple sentinel nodes and end nodes is mainly performed to deal with the problem caused by variable primer length and to facilitate the completion of forward and reverse primer design. In each layer, appropriate base combination branches are selected according to the rules of base pairing and the constraints of coverage and degeneracy, and the cycle is iterated until appropriate primer pairs have been searched for and output results have been obtained or the maximum number of iterations has been reached. Then, the output results are filtered on the basis of primer design constraints, and the primer pairs and parameters of the forward and reverse primers that meet the requirements of the constraints are finally output.  
In the process of constructing the search space, each bee constructs a candidate food source from the starting node in Figure 3, and calculates the selection probability of the next optional node in accordance with the probability state transition formula adopted by the next optional node according to its category (i.e., employed bee, onlooker bee, or scout bee). The next node to visit is then selected by means of a roulette game. For each bee, including the intermediate nodes in the next layer, several bases or base combinations that meet the requirements are screened on the basis of the input template DNA sequence and the base matching principle, and then the selection probability of the corresponding nodes is calculated. In this way, nodes are selected layer by layer until an end node is encountered. Therefore, in addition to the above 15 base combinations, an end node is also set at nodes 46 to 54, so that the construction of the candidate food sources can be completed at any time. In this way, each bee completes the construction process of a candidate food that completes the design of a candidate primer pair.
# Application of a Hybrid Model in Landslide Susceptibility Evaluation of the Western Tibet Plateau
## 2 Materials and Methods
### Conditioning Factors
#### 2.3.1 DEM and Derivatives  
Many common factors can be found using DEM, such as slope, slope aspect, and elevation [51]. In this area, the slope generally ranged from 0\({}^{\circ}\) to 60\({}^{\circ}\), and was grouped into 7 classes: 0-10\({}^{\circ}\), 10-20\({}^{\circ}\), 20-30\({}^{\circ}\), 30-40\({}^{\circ}\), 40-50\({}^{\circ}\), 50-60\({}^{\circ}\), >60\({}^{\circ}\) (Figure 3a). The slope aspect affecting sunlight and wind was classified into nine categories: flat, north, northeast, east, southeast, south, southwest, west, northwest (Figure 3b). The elevation was between 3869 and 6357 m, which was divided into eight classes with intervals of 300 m (Figure 3c).
## 2 Materials and Methods
### Rotating Polarization-MPSK (RP-MPSK) Modulation  
BER performance of RPW remains unaffected if RP-QPSK is used instead of RP-BPSK, but more transmitted power is required because two quadrature carriers are used [24]. To further improve the data rate and the energy efficiency, RP-MPSK modulation is proposed. The data rate increases at the cost of BER performance. The problem can be mitigated if RPW receiver samples the received signal at higher sampling rates to combat BER degradation.  
Let the order of modulation be \(M=2^{k}\) when there are \(k=\log_{2}M\) bits per symbol \(s_{m}\in\{s_{0},s_{1},s_{2},\ldots,s_{m-1}\}\). The step-by-step process from modulation to detection is given below.