esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experiments  
### SFRC Material  
The concrete used in the study was provided by a ready-mixed concrete company, and its compressive strength was designed as 27 MPa. The water-to-cement ratio was 0.54, and the slump value was 150 mm. The mix proportion of the concrete is given in Table 1. All of the materials except the steel fibers were mixed in a ready-mixed concrete truck for around 30 min until reaching the site where specimens were manufactured. The steel fibers were then added into the truck at the site and mixed with the concrete mixture. The steel fibers used in the study had hooked ends that are commonly used in practice. The length and diameter of the fibers were 60 and 0.75 mm, respectively, and thus the aspect ratio was 80. The ultimate tensile strength of the fiber was 1100 MPa.  
The compressive strength of concrete was measured using cylinder specimens with a diameter of 10 mm and length of 20 mm. The average compressive strength of the non-reinforced plain concrete measured from four cylinder specimens was approximately 28 MPa. The average compressive strengths of the SFRC with fiber volume percentages of 0.33, 0.67, and 1.00%, measured from four cylinders for each fiber volume fraction, were in the range of 26 to 28 MPa. Thus, the measured compressive strength showed very good agreement with the design strength of 27 MPa. Little difference was found between the non-reinforced and SFRC, which means that there was little influence of the steel fibers on the compressive strength of the concrete.
# A Fuzzy Path Selection Strategy for Aircraft Landing on a Carrier
## 3 Landing Path Selection Method Based on FMAGDM  
To solve the landing path selection problem, mathematical descriptions of the essential elements are needed to establish the model. Since it is difficult to describe the real-time environmental information quantitatively, the judgment of a single pilot or a single LCO is often inaccurate. Therefore, it is necessary to develop a comprehensive decision-making method which integrates the judgments of both the pilot and the LCO to make the final decision more scientific and reasonable. In view of the abovementioned characteristics, the FMAGDM method can describe the information pertaining to the environment and the weights of decision-makers [22; 23; 24] and is suitable for solving the path selection problem. To be specific, the fuzzy TOPSIS approach [25; 26] is developed, and the triangular membership function is applied to denote the PRCF (performance ratings of contributing factors) and the current environmental vector. Then, the mathematical model is built to evaluate alternative landing paths. To combine the decisions of the pilot and the LCO, the group decision-making method based on the TOPSIS approach is developed to rank the alternative landing paths. Finally, the optimal landing path under the current environment is obtained.
# Even Dispersion Design for a Compact Linear Loudspeaker Array with Adaptive Genetic Algorithm
###### Abstract  
Even dispersion is important for live sound reinforcement systems; however, it needs to be carefully designed when using a linear loudspeaker array. This is because the audience area is often large, while the loudspeakers are placed centrally in this case for convenience, and thus both the level and the frequency balance may not remain reasonably constant for all audiences. To solve this problem, the adaptive genetic algorithm is firstly introduced in the parameters optimization. Secondly, taking the radiation characteristics at different frequencies into account, a linear-phase non-uniform filter bank is proposed to decompose the broad frequency band into several sub-bands. The audio is re-synthesized with the optimized parameters in each frequency band for a linear loudspeaker array. To show the validity of the proposed method, the simulations and the experiments are conducted to demonstrate that the sound pressure level in the audience area is distributed within \(\pm\)1.33 dB, ranging from 200 Hz to 4000 Hz.  
Keywords:linear loudspeaker array; constant sound pressure level; broadband +
Footnote †: journal: Applied Sciences
# SVDC: Synchronous Update with the Division and Combination of SRv6 Policy
## 3 Policy-Dividing and Combination Mechanism in SUDC
### SRv6 Policy Pushing
#### 3.2.1 Push Initiation  
Push initiation involves multicasting the policy push initiation signal to all target nodes involved in this policy distribution through BIERv6 technology. The BitString of the push initiation signal is encoded in such a way that all the bits corresponding to the nodes that need to perform the policy update are set to 1. The specific type of signal is not specified in this scheme, which can be a specific multicast Internet Protocol (IP) address or a specific User Datagram Protocol (UDP) port.
## 3 EM Analytical Model
### EM Models for Non-Segmented PMS Machines
#### 3.1.3 Electromotive Forces and Electromagnetic Torque  
A classical method to determine electromotive forces (EMF) \(E_{i}\) (4) is based on the estimation of the no-load flux \(\phi_{i}\) (5) of each phase [7].  
\[E_{i}(\theta_{r})=\frac{d\phi_{i}(\theta_{r})}{d\theta_{r}} \tag{4}\]  
\[\phi_{i}(\theta_{r})=R_{s}\times L_{z}\int_{\theta=0}^{2\pi}W_{i}(\theta) \times B_{/R}(\theta,\theta_{r})d\theta \tag{5}\]  
In (4) and (5) \(\theta_{r}=\Omega t\), \(\Omega\), and \(W_{i}(\theta)\) are the rotor position, rotor speed and winding function of the phase \(i\), respectively.  
The knowing of the EMF allows the determination of the electromagnetic torque \(T_{em}\) by (6).  
\[T_{em}(\theta_{r})=\sum_{i=1}^{m}\frac{E_{i}(\theta_{r})\times I_{i}(\theta_{ r})}{\Omega} \tag{6}\]  
\(E_{i}(\theta_{r})\) and \(I_{i}(\theta_{r})\) are, respectively the EM forces and the currents associated with the phase \(i\), and \(m\) is the number of phase.
###### Abstract  
Cold atmospheric plasma (CAP) has shown good potentiality for the decontamination and stabilization of fresh fruit and vegetable products; however, information about its effect on nutritional quality is still scarce. The aim of this research was to evaluate the impact of a form of indirect treatment known as Dielectric Barrier Discharge (DBD) on apple slices--more specifically, the polyphenolic profile and antioxidant activity of fresh-cut Pink Lady apples. Atmospheric plasma was generated using air as feed gas, and directed to apple slices for up to 30 min. The effect of plasma treatment on physico-chemical parameters was mainly observed as a slight acidification of the tissue and reduction of browning after an extended period of exposure. The samples' phenolic profile was significantly affected after 10 min of treatment, both in quantitative (an approximately 20% increase) and qualitative terms, while with increasing exposure time a progressive decrease of all polyphenol classes was observed. The antioxidant activity, evaluated by different in-vitro methods, followed a similar trend, increasing after 10 min of processing and then decreasing. Results highlighted how plasma exposure promotes a metabolic response of the fresh tissue, and the importance of carefully controlling the exposure time in order to minimize the loss of nutritional properties.  
Keywords:fresh-cut apples; cold plasma; polyphenols content; antioxidant activity +
Footnote †: journal: Applied Physics Letters
## 3 Discussion  
As shown in Figure 23, compared with the traditional product debugging mode, the virtual simulation debugging of the automobile connecting rod production line based on the digital twin proposed in this article can effectively save equipment debugging time in the process of product development, reduce the cost of production materials and manpower in the process of product debugging, and make the manufacturing products seize a larger market share with the advantage of a shorter design and manufacturing cycle.  
In Figure 23, it can be seen that the advantage of virtual simulation-based debugging of the automobile connecting rod production line based on the digital twin is that virtual debugging can be carried out when the production line is assembled in the workshop. Virtual debugging will find many problems that will be encountered in the actual production debugging of the production line after the assembly is completed, such as the unreasonable planning of the crane running time mentioned in the case of Section 2.5.4 and the accumulation of the workpiece to be processed in the production line mentioned in the case of Section 2.6. The advantage of virtual debugging is that it can find problems in advance and solve them, which will greatly reduce the actual debugging time of the workshop production line. Among them, t1 is the time required for traditional product debugging, and t2 is the time required for virtual simulation debugging of the automobile connecting rod production line based on the digital twin. The value of saving time is the difference between t1 and t2, and the time saved is represented by the transparent rectangular box of the dotted line in the graph. Through comparison, it can be concluded that the use of virtual simulation debugging of the automobile connecting rod production line based on the digital twin will greatly reduce the actual debugging time of the production line, thus speeding up the research and development progress and improving industrial competitiveness.
###### Abstract  
Landslide susceptibility mapping is considered to be a prerequisite for landslide prevention and mitigation. However, delineating the spatial occurrence pattern of the landslide remains a challenge. This study investigates the potential application of the stacking ensemble learning technique for landslide susceptibility assessment. In particular, support vector machine (SVM), artificial neural network (ANN), logical regression (LR), and naive Bayes (NB) were selected as base learners for the stacking ensemble method. The resampling scheme and Pearson's correlation analysis were jointly used to evaluate the importance level of these base learners. A total of 388 landslides and 12 conditioning factors in the Lushui area (Southwest China) were used as the dataset to develop landslide modeling. The landslides were randomly separated into two parts, with 70% used for model training and 30% used for model validation. The models' performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and statistical measures. The results showed that the stacking-based ensemble model achieved an improved predictive accuracy as compared to the single algorithms, while the SVM-ANN-NB-LR (SANL) model, the SVM-ANN-NB (SAN) model, and the ANN-NB-LR (ANL) models performed equally well, with AUC values of 0.931, 0.940, and 0.932, respectively, for validation stage. The correlation coefficient between the LR and SVM was the highest for all resampling rounds, with a value of 0.72 on average. This connotes that LR and SVM played an almost equal role when the ensemble of SANL was applied for landslide susceptibility analysis. Therefore, it is feasible to use the SAN model or the ANL model for the study area. The finding from this study suggests that the stacking ensemble machine learning method is promising for landslide susceptibility mapping in the Lushui area and is capable of targeting areas prone to landslides.  
landslide susceptibility; stacking ensemble learning; Lushui 1 May 2020; 8 June 2020; Published: 10 June 2020
# Anomaly Detection Method in Railway Using Signal Processing and Deep Learning
## 3 Signal Processing to Improve Deep Learning Results
### A Flat Signal Overlapped with a Noise Signal  
Since it is difficult to obtain data from the driving rail vehicles with wheel flats, the noise signals of normal wheels obtained in past research [17] were overlapped with the simulation flat signals. The noise signals of normal wheels were obtained on the axle box using the Seoul Metro Line 7 vehicle. Figure 20 shows the location of the measuring equipment mounted on the axle box.  
Among the overlapped data section, the data of the most severe noise section was applied, and the overlapped result is shown in Figure 21. If the wheel flat size is small, the flat signal (Time-domain) is fully covered by the noise signal. It was found that it is difficult to determine the presence or absence of any small flat signal.  
Figures 22 and 23 show the FFT and STFT of the signal overlapped on the noise signal. As a result of the analysis, it was found that the frequency band by the flat signal was relatively low compared to the frequency band by the noise signal, and thus, it was confirmed that the frequency bands are separate from each other.  
Figure 21: Overlap result (Time domain).  
Figure 20: Location of the measuring equipment mounted on the axle box.  
Therefore, as a result of minimizing the influence of the noise signal by cropping only the frequency band indicated by the flats, it was confirmed that only a slight difference appears in the red square shown in Figure 24.  
Figure 23: STFT of the signal overlapped on the noise signal [(**a**): with noise, (**b**): without noise].  
Figure 22: FFT of the signal overlapped on the noise signal.
## 2 Materials
### Seahawk System  
Seahawk was developed by Geostory Inc. since 2014 to monitor wetlands and coasts in Korea. The system can measure co-registered green and NIR laser beams using a holographic optical element [32]. It was designed to measure more than one point on a \(2\times 2\) m\({}^{2}\) area at up to 35 m depth in clear water at a flight altitude of 400 m. The ABL point cloud is generated from raw observation data using its own software, namely the Lidar BAthymetry SyStem--Data processing (LBASSD); the data processing algorithms are not disclosed. Since its first successful flight on July 1, 2018, Seahawk has been used for bathymetric measurements of coastal zones, focusing on coastal erosion monitoring in South Korea. Details of the Seahawk system are listed in Table 1.  
The Seahawk system digitizes the received analog signal at 1.6 Giga samples per second; thus, the time bin resolution of the waveform is 0.625 ns. The Seahawk individual waveform is recorded in 2400 bins at 16 bits (0-65,536 digital number (DN)) per sample. The system assumes a 35 m underwater arrival time of 160 ns when the laser transmission angle is 20\({}^{\circ}\). Therefore, considering the round-trip propagation time, 1 bin (0.625 ns) of the waveform corresponds to a depth difference of approximately 0.068 m.
# Free Space Optic Receiver with Strongly Overlapped Photodetectors' Field of View
## 4 FSO Receiver Measurements
This can be explained by the higher probability of both noise voltages meeting in phase to produce noise spikes much higher than standard background noise. These peaks prevail in amplitude and in time, leading to longer data training dropouts. We examined the results of the comparison of the \(BER\) parameters with three or six operating photodetectors. The difference was more than three orders higher, which is considerable during mainly data transfer. Many papers in initial studies mention an optical channel with the FOV of the photodetectors not overlapping or only slightly overlapping. According to the BP104F photodiode datasheet, its FOV is \(130^{\circ}\). With the FOV of photodiodes not overlapping, three BP104F photodiodes should be enough for receiving. Highly overlapped FOVs in six operating photodetectors BP104F would have no adverse impact, and instead provide a benefit. Strongly overlapped FOVs lead to parallel information processing and eliminate the uncorrelated noise of photodetectors and follow-up amplifiers (PDAs). The operation of all six photodetectors in a multipath light signal propagation environment provides a high communication radius at very low \(BER\). This is proved by the measured average \(BER\) value of \(2.72\times 10^{-8}\) at ray trajectories equal to or longer than 6 m with a data stream as high as 2 Mbit\(\cdot\)s\({}^{-1}\). When evaluating multiple photodetector receivers, no phenomenon concerning the addition of photodetectors noise can be ignored. The block diagram in Figure 8, showing a peak-to-peak voltage detector, voltage reference \(V_{\textit{nrf}}\), comparator, variable gain amplifier, and its control voltage \(V_{\textit{diff}}\), can be helpful in describing this. Adding noise increases its resultant power according to RMS. Peak-to-peak noise values are described by the noise power distribution function. The higher the noise RMS value, the higher the peak-to-peak values. However, we observed a difference in the photodetectors adding noise, which can be seen in Table 4 and from the graph in Figure 14.  
Figure 14 shows that higher voltages \(V_{\textit{diff}}\) reduce VGA gain and vice versa. This means that an empty signal channel makes the \(V_{\textit{diff}}\) lowest and the VGA gain highest; similarly, a full signal channel makes the \(V_{\textit{diff}}\) highest and VGA gain lowest. The channel may be filled with a useful signal or with noise. According to Table 4, increasing the number of operating photodetectors (without a useful signal) cleared the channel, which was an unexpected but positive result. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Operating Photodetector Count** & **1** & **2** & **3** & **6** \\ \hline \(V_{\textit{diff}}\) voltage controlling VGA [V] & 1.002 & 0.935 & 0.896 & 0.867 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: VGA control voltage \(V_{\textit{diff}}\) over operating photodetectors count.
# A Novel Monocular Vision Technique for the Detection of Electric Transmission Tower Tilting Trend
## 2 Contour Extraction of Transmission Tower
### The RGB Converted to HSV Color Space  
The HSV color space is similar to how humans perceive color, encapsulating color information. Intuitively, color (Hue) represents color; saturation (Saturation) represents the purity of color; the higher the saturation, the purer the color, but it becomes gradually  
Figure 1: Flow chart of analysis process of tower tilt.  
grayer; lightness (Value) indicates the brightness of color, and the three together constitute the HSV color space.  
Since the captured raw images are in RGB format, they need to be converted from the RGB color space to the HSV color space.  
First, the range of the RGB is normalized from 0-255 to 0-1 according to Formula (1):  
\[\mathrm{R^{\prime}}\ =\frac{\mathrm{R}}{255},\ \mathrm{G^{\prime}}\ =\frac{ \mathrm{G}}{255},\ \mathrm{B^{\prime}}\ =\frac{\mathrm{B}}{255}, \tag{1}\]  
\(\mathrm{R^{\prime}}\), \(\mathrm{G^{\prime}}\), and \(\mathrm{B^{\prime}}\) are the normalized RGB color intervals.  
To obtain the maximum \(\mathrm{C_{max}}\) and minimum \(\mathrm{C_{min}}\) from Formula (2) and Formula (3), respectively:  
\[\mathrm{C_{max}}=\mathrm{max}\big{\{}\ \mathrm{R^{\prime}}\,\ \mathrm{G^{\prime}}\,\ \mathrm{B^{\prime}}\ \big{\}}, \tag{2}\]  
\[\mathrm{C_{min}}=\mathrm{min}\big{\{}\ \mathrm{R^{\prime}}\,\ \mathrm{G^{\prime}}\,\ \mathrm{B^{\prime}}\ \big{\}}, \tag{3}\]  
Calculate the maximum difference between the three according to Formula (4):  
\[\Delta=\mathrm{C_{max}}-\mathrm{C_{min}}, \tag{4}\]  
Finally, the values of hue H, saturation S, and value V are calculated respectively according to Formulas (5)-(7):  
\[\mathrm{H}=\left\{\begin{array}{cc}0&\Delta=0\\ 30\times\left(\frac{\mathrm{G^{\prime}}-\ \mathrm{B^{\prime}}}{\Delta}\right)& \mathrm{C_{max}}=\ \mathrm{R^{\prime}}\\ 30\times\left(\frac{\mathrm{B^{\prime}}-\ \mathrm{R^{\prime}}}{\Delta}+2 \right)&\mathrm{C_{max}}=\ \mathrm{G^{\prime}}\\ 30\times\left(\frac{\mathrm{R^{\prime}}-\ \mathrm{G^{\prime}}}{\Delta}+4 \right)&\mathrm{C_{max}}=\ \mathrm{B^{\prime}}\\ \end{array}\right. \tag{5}\]  
\[\mathrm{S}=\left\{\begin{array}{cc}0&\mathrm{C_{max}}=0\\ \frac{\Delta}{\mathrm{C_{max}}}\times 255&\mathrm{C_{max}}\
eq 0\\ \end{array}\right. \tag{6}\]  
\[\mathrm{V}=\mathrm{C_{max}}\times 255 \tag{7}\]  
In this paper, HSV images were extracted from the towers, and the results of RGB images converted to HSV images are shown in Figure 2.  
Figure 2: RGB images converted to HSV images.  
In order to improve the effectiveness of extracting the pole and tower, the HSV image enhancement algorithm is used to enhance the converted image. The enhancement results are shown in Figure 3.
# SSTrack: An Object Tracking Algorithm Based on Spatial Scale Attention
## 4 The Experiments
### Ablation Study and Analysis  
To validate the effectiveness of the SSTrack, this study conducted ablation experiments on the GOT-10k test set. The experimental environment and the main hyperparameters of the model were consistent, and the results are presented in Table 3.  
Firstly, based on the SSTrack, a success rate of 71.5% was achieved on the GOT-10k dataset. Subsequently, replacing the Exemplar Transformer used in the feature fusion stage with a regular Transformer led to a decrease in success rate to 71.2%. Following that, removing the spatial scale feature enhancement module in the feature extraction stage resulted in a success rate drop to 70.4%. This suggests that the covariance pooling and dilated convolutions, which provide local detailed information, contribute to obtaining more accurate feature representations of the target.  
Finally, replacing the backbone network with ResNet-50 led to a success rate decrease to 67.6%, demonstrating that the global feature extraction capability of Swin Transformer effectively enhances tracking performance.