esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of CAI Using Ordinary Kriging Method  
In this study, the ordinary kriging method was used to determine the spatial distribution of the predicted CAI over South Korea. A variogram is expected to square the difference between two points separated by a lag. The variogram measures the similarity of data at a certain distance and decreases as the distance between two points decreases. As the distance increases, the variogram increases or the correlation is lost and, thus, has a meaningless value. Therefore, the isolation distance at which the correlation and tendency are lost in the variogram is defined as a range. The variogram value at that distance is defined as a sill. There are empirical variograms and theoretical variograms, and a theoretical variogram that minimizes prediction error was selected for considering spatial interrelationships. In the case of ordinary kriging, the sill converges to the variance of the entire dataset. The sill was set as the variance of all the data, and kriging was applied using an exponential variogram.  
The work configuration for using the Surfer software is as follows: The coordinate system was set as Korea 2000/Unified CS-EPSG:5179. The cell size was set to 500 m \(\times\) 500 m; the raster size was set to the \(X\)-axis (from 746,000 m to 1,394,500 m) and \(Y\)-axis (from 1,458,500 m to 2,068,500 m); the lag size was 15,500 m; and the number of lags was set to 16 (Figure 14). The Surfer software was used to calculate the range of the variogram model. The variogram is the expected value of the square of the difference in data values between two points separated by a separation distance (h). The variogram appears smaller as the distance between two points increases, and as the distance increases, the variogram increases or the correlation is lost and thus has a meaningless value. Therefore, the separation distance at which the correlation and tendency are lost in the variogram is called the range, and the variogram value at that distance is defined as the sill. In ordinary kriging, the threshold theoretically converges to the variance of the entire dataset. Based on the above theory, the sill was set as the variance of the entire dataset in Surfer, and kriging was applied to the CAI using an exponential variogram.  
If a prediction is made using data from a point station, there is a disadvantage in that only a value located at one point can be predicted. However, if ordinary kriging is used, the ground station data are spatially smoothed, and the local area can be predicted. Therefore, a comprehensive air-quality index was predicted using the six predicted pollution data points in this study. We propose a methodology that enables the spatial distribution of predicted data using ordinary kriging to identify predicted values in the spatial distribution of predicted CAI in South Korea (Figure 15).
# Human-Robot Cooperative Control Based on Virtual Fixture in Robot-Assisted Endoscopic Sinus Surgery
## 3 Human-Robot Cooperative Control Based on Virtual Fixture  
### Interactive Force Acquisition and Filtering  
Figure 7 shows the schematic diagram of human-robot cooperative control based on VFs. The surgeon adjusts the endoscope to the desired posture by dragging the operating handle at the end of the robot. During the dragging process, the robot is restrained and guided by the VFs to prevent the endoscope at the end of the robot from interfering with the nasal tissue. The human-robot interaction process of the control method is the same as that when no robot is involved in the surgical operation, but the advantage is that after the endoscope of the robot is dragged to the proper posture, the robot can be maintained in the current posture without operating buttons or pedals. With the control method, the surgeon can free his left hand and carry out complex surgical operations with both hands, effectively reducing the surgeon's operational burden.  
Firstly, the surgeon drags the handle of the robot to move, and the control system obtains the surgeon's drag force through the six-dimensional force sensor (ATI Industrial Automation Company, USA):  
\[F=[f_{x}\ f_{y}\ f_{z}\ m_{x}\ m_{y}\ m_{z}]^{T} \tag{17}\]  
Considering the manual operation is easy to shake, the obtained force signal is generally not smooth and needs to be filtered. Considering the computational efficiency, the force signal can be smoothed with the moving average filtering as follows:  
\[\overline{F}=f(F_{1},F_{2},\dots,F_{n}) \tag{18}\]  
where \(n\) is the length of the intercepted window; the larger the \(n\) is, the smoother the force signal obtained. However, excessive \(n\) is likely to cause a control response delay. Assuming that the system control period is \(T_{\text{control}}\), the force sensor sampling period is \(T_{\text{sample}}\), then \(n\) can be determined by \(n=T_{\text{control}}/T_{\text{sample}}\).
## 3 High-Power and Narrow Divergent Beam Performance Using a Long Cavity Structure
The I-L plots obtained from both types of device having 5.5 \(\upmu\)m apertures during CW operation at 20 \({}^{\circ}\)C are presented in Figure 9a. Figure 8: Temperature dependence of the threshold current under CW operation. These data demonstrate that the 5A-cavity VCSEL exhibited a maximum power, threshold current density and threshold current of 6.6 mW, 9.7 kA/cm\({}^{2}\) and 2.3 mA, respectively. The smaller aperture size gives a lower threshold current and maximum power due to the volume effect, compared to the 8-\(\upmu\)m aperture VCSEL in Figure 6a. This tendency is the same for the 10-\(\uplambda\) cavity VCSEL. From Figure 9a, the threshold current, threshold current density and maximum power of the 10-\(\uplambda\) cavity VCSEL were 2.8 mA, 11.7 kA/cm\({}^{2}\) and 12.5 mW, respectively. These data also indicate that the high power performance of the 10-\(\uplambda\) cavity VCSEL is superior to that of the 5\(\uplambda\)-cavity VCSEL with a small aperture due to the improvement in the thermal resistance. Figure 9b shows the FWHM divergence angle of the 5\(\uplambda\)- and 10\(\uplambda\)-cavity VCSELs with 5.5-\(\upmu\)m aperture as a function of their LOP. The insets show the FFP images at around 1 mW and 5 mW. An extremely small divergence angle of 4.2\({}^{\circ}\) was observed for the 10\(\uplambda\)-cavity VCSEL with around 1 mW. Furthermore, a narrow beam of 5.1\({}^{\circ}\) in FFP angle was observed even at a relatively high LOP of 5 mW, as shown in Figure 9b. The FFP image of the 5\(\uplambda\)-cavity VCSEL at 5 mW, on the other hand, was clearly different and shows a doublet multi-mode shape, although a small divergence angle of 4\({}^{\circ}\) was observed at around 1 mW. In this case, the divergence angle gradually spread out with an increase in LOP, indicating that the component of the higher-order-mode became dominant due to spatial refractive index changes, such as spatial hole burning and thermal lens effects. The improvement of thermal resistance and the large asymmetry of the mirror design possibly suppressed the spatial refractive index change in the 10\(\uplambda\)-cavity VCSEL. It is very interesting that such high power technologies established by edge emitting lasers are also effective in GaN-based VCSELs.
## 1 Introduction
The DT represents the interconnection and convergence between a physical entity and its digital representation, and it can exchange information in real-time in both directions; in this way, the digital entity can control the physical entity and vice versa [10]. There are also hybrid DTs that use a combination of physics-based models, artificial intelligence, and data to create a more accurate tool, which could even be simpler to create if the dynamics are unknown [11]. One of the main reasons for using a DT is that manufacturers can observe in real time the manufacturing/logistics environment, allowing for optimization and cost reduction. They have the potential to provide real-time status updates on machine and production line performance; plus, they are useful tools for monitoring and control because they provide a framework to support the high demands currently presented during the industrial revolution. By using a DT, mechanical damage during implementation or late detection of the problem in the early stages of the project can be avoided [12]. The contribution of the present work is the implementation of an architecture for the monitoring and control of industrial processes based on an immersive MES and DT system. For the design of the architecture, CPS, MES, robotics, IoT, AR, VR, and industrial communication protocols in OPC UA technologies will be used. To integrate these technologies and enhance the functionalities of the DT, providing greater benefits for users and customers, the proposal will be subsequently validated through experimental tests in the Industry 4.0 laboratory to verify the correct operation and show the advantages and challenges presented by the immersive DT in a continuous production line.
# A Novel MPPT Based Reptile Search Algorithm for Photovoltaic System under Various Conditions
## 2 PV System Configuration  
The PV system topology in this study is depicted in Figure 1. It is a large-scale, three-phase, grid-connected system composed of a PV array, a DC-DC boost converter with an MPPT controller, and a three-phase VSC inverter. The PV array consists of 60 parallel strings, each composed of 5 modules connected in series, and it is rearranged into three PV arrays connected in series. The system is designed to generate a maximum output of 100.37 KW under Standard Test Conditions (STC) (irradiance 1000 KW/m\({}^{2}\) and temperature 25 \({}^{\circ}\)C).  
Impact of Irradiance and Shading on the PV SystemFigure 2a illustrates the effect of uniform solar radiation on the P-V curves of the PV array. As the irradiance increases, the generated power also increases and vice versa [28]. There exists a single point, referred to as the MPP, where the power generated is at its maximum, and to operate the PV array at this point, the impedance of the PV and the load must be matched with the use of an MPPT tracker [29].  
Another issue that affects the efficiency of the PV system, besides variations in weather conditions, is partial shading. This phenomenon has a great and serious influence on the characteristics of the PV module; it occurs when some of the PV modules connected in a string receive a different level of insolation [30; 31]. To prevent hotspot problems and current mismatches caused by partial shading, bypass diodes are placed in parallel with each PV module. Additionally, a blocking diode is connected to prevent current flow from the unshaded modules to the shaded modules [32].  
While the bypass diode allows the PV system to operate efficiently under partial shading conditions, it also makes the curves of the PV array complex, resulting in multiple peaks with only one global peak known as the GMPP (Figure 2b), making it challenging to track the maximum power. After examining the nonlinear characteristics of the PV array and the effects of various factors on its efficiency, an MPPT tracker with appropriate optimization techniques is deemed critical to enhancing the performance of the system. It extracts the available maximum power by operating the PV array at the GMPP.
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 3 Methodology
### Foundation-Damage Classification Approach  
#### 3.4.1 Deep Learning of Noise-Contaminated Databank  
Several factors can affect the vibration test, such as noise (including environmental noise and electrical noise from the monitoring devices) and inconsistencies in the excitation source. The data are enriched by obtaining a large number of ensembles to capture as much variation in the excitation source as possible. Despite efforts to control the noise and the magnitude of the vibration source, variations in vibration data are inevitable. Conducting  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & **Type** & **Depth** & **Filter** & **Stride** & **No.** & **Type** & **Depth** & **Filter** & **Stride** \\ \hline
1 & Conv1 & 6 & 1 \(\times\) 4 & 1 & 8 & ReLU & - & - & - \\
2 & ReLU & - & - & - & 9 & Maxpool3 & - & \(1\times 2\) & 2 \\
3 & Maxpool1 & - & 1 \(\times\) 2 & 2 & 10 & Flatten & & & \\
4 & Conv2 & 4 & 1 \(\times\) 4 & 1 & 11 & Fc1 & 48 & - & - \\
5 & ReLU & - & - & - & 12 & Fc2 & 16 & - & - \\
6 & Maxpool2 & - & 1 \(\times\) 2 & 2 & 13 & Fc3 & 4 & - & - \\
7 & Conv3 & 5 & 1 \(\times\) 8 & 1 & 14 & Classification & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of 1-D CNN layers.  
Figure 4: Schematic of a 1-D CNN classification model: (**a**) 1-D CNN architecture; (**b**) data preparation and training procedures.  
comprehensive experiments that account for all of these variables is challenging and costly. Data augmentation by injecting Gaussian noise into the measured signals is a practical solution for simulating realistic measurement conditions. Introducing noise into the data is also an effective method for enriching the dataset. Several noise levels injected ensure that the CNN model is exposed to a wider range of data during training. It leads to a more robust and general model that can perform on unseen and real-world vibration data.  
Figure 5 illustrates the process for configuring the noise-contaminated databank. The vibration data were obtained in 15 ensembles for each foundation damage level. Eighty percent (80%) of the total vibration data was used for training and validation datasets. Data generation used Gaussian noise with standard deviations of 0%, 1%, 2%, 3%, 4%, and 5% of the signal amplitude. The remaining 20% of the total vibration data had noise levels injected ranging from 1% to 16% (in 1% increments) to form the evaluation dataset. This evaluation dataset was used to examine the effect of noise on the accuracy of the 1-D CNN model.
## 3 Results and Discussion
### AC Susceptibility as a Function of DC Field: \(\chi_{1}^{\prime\prime}(H_{DC})\)  
An interesting approach that can be used to detect the granularity in a sample is to plot the imaginary part of the fundamental harmonic \(\chi_{1}^{\prime\prime}\) as a function of the DC magnetic field. In the main panel of Figure 7, \(\chi_{1}^{\prime\prime}\) vs. H\({}_{\rm DC}\) curves at different AC amplitudes, at a fixed temperature equal to 77 K and fixed frequency equal to 1567.9 Hz, have been reported. The presence of an anomalous trend of the curves is evident because usually these curves are characterized by the presence of a standard dome-shaped curve [33]. In our case, after the anomalous initial decrease in the \(\chi_{1}^{\prime\prime}\) signal [33] due to the inter-grain contribution, a peak at high DC fields is present for all the reported curves and can be ascribable to the intra-grain contribution. The crossover between the two contributions is identifiable by considering the minimum of the curves as indicated by the colored arrows in the inset of Figure 7.  
Figure 6: First harmonic Cole–Cole plots at a fixed AC frequency \(\upgamma=508.94\) Hz and AC amplitude h\({}_{\rm AC}=10\) Oe. The two dome-shaped curves evidence the inter-grain (closed symbols) and the intra-grain contributions (open symbols).  
Figure 7: \(\chi_{1}^{\prime\prime}\) as a function of the DC field at different AC amplitudes, a fixed frequency \(\upgamma=1597.9\) Hz, and T = 77 K plotted on a semi-log field scale. Inset: the arrows indicate the local minimum of the curves corresponding to the crossover between the inter-grain and intra-grain contributions.  
The detected points, named \(H_{DC}^{\beta}\), are reported as a function of the AC amplitude in the contour plot of the \(\chi_{1}^{{}^{\prime\prime}}\) (see Figure 8). The obtained curve can be fitted with the power law \(H_{DC}^{\beta}(h_{AC})=H_{DC}^{\beta}(0)+bh_{AC}^{c}\) where \(H_{DC}^{\beta}(0)\) is the contribution separation when h\({}_{\rm AC}=0\) Oe, \(b\) is a coefficient, and \(c\) is the exponent. Particularly, \(H_{DC}^{\beta}(0)=10.4\) T, \(b=-6.24\) T/Oe, and c = 0.16. In Figure 8, the black solid line separates the intergranular and the intragranular regions in the case of having T = 77 K, which can be interesting for different superconducting applications. It is important to note that at 77 K, the intragranular response is dominant, especially at high AC amplitudes, which makes this material interesting for several types of applications, including, among others, energy transmission [55], signal filtering [56], and magnetic confinement fusion [57].
# Blasting Vibration Control and Signal Analysis of Adjacent Existing Deterioration Tunnels
## 1 Introduction
Wavelet packet analysis can show the frequency and energy distribution characteristics of blasting vibration signals in a more comprehensive and detailed way. Previous studies focused on reducing blasting vibration mainly around blasting charge, electronic detonator time-sharing blasting, and bench blasting. In this paper, a new blasting method is proposed for the existing deterioration tunnel: mechanical cutting combined with ordinary detonator blasting instead of full-section detonator blasting. With the help of numerical simulation and field experiments, the blasting effect of the two blasting schemes was simulated and analyzed. The vibration reduction effect of the mechanical cutting method was judged by the velocity response of the existing tunnel. Wavelet packet is used to analyze the relationship between frequency, velocity, and energy distribution of vibration signals in deterioration tunnels. Determining the frequency interval of peak vibration velocity and peak energy is helpful in guiding the design of the blasting scheme to ensure the safety of the existing deterioration tunnel.
## 3 Methods
### Dynamic Adaptive Focal Loss
When the proportion of positive samples is high, the value of \(\alpha_{t}^{\prime}\) increases, approaching \(\alpha_{initial}\), giving lower weight to the positive samples and reducing the penalty on easy samples. When the proportion of positive samples is low, \(\alpha_{t}^{\prime}\) decreases, approaching \(1-\alpha_{initial}\), assigning higher weight to the positive samples and increasing the focus on difficult samples. This dynamic adjustment effectively balances the weights between positive and negative samples, allowing the model to learn effectively even in the presence of class imbalance. The formula for dynamically adjusting the parameter \(\gamma\) is  
\[\gamma_{t}^{\prime}=\gamma_{initial}\times(1-\overline{y}_{smooth}) \tag{22}\]  
This equation adjusts \(\gamma_{t}^{\prime}\) based on the change in the proportion of positive samples \(\overline{y}_{smooth}\) When the proportion of positive samples is low, indicating that there are more difficult-to-classify samples in the current batch, the value of \(\gamma_{t}^{\prime}\) increases, approaching \(\gamma_{initial}\), which gives higher weight to these difficult samples. When the proportion of positive samples is high, \(\gamma_{t}^{\prime}\) decreases, approaching zero, thus reducing the penalty on easy-to-classify samples. Through this dynamic adjustment, the modified Focal Loss can adaptively increase the loss weight for difficult samples in class-imbalanced situations, significantly improving the model's classification performance. The dynamic Focal Loss calculation is designed as follows:  
\[DAFL(p_{t})=-\alpha_{t}^{\prime}\times(1-p_{t})^{\gamma_{t}^{\prime}}\times \log(p_{t}) \tag{23}\]  
Finally, the overall loss function for the AASNet network is divided into three parts and can be expressed as  
\[L_{total}=\lambda_{cls}*L_{cls}+\lambda_{box}*L_{box}+\lambda_{mask}*L_{mask} \tag{24}\]  
where \(\lambda_{cls}\), \(\lambda_{box}\), and \(\lambda_{mask}\) are the weighting factors for the classification, bounding box regression, and segmentation losses, respectively. In our experiments, we assign a weight of 0.5 to the \(\lambda_{cls}\), 7.5 to the \(\lambda_{box}\), and 0.2 to the \(\lambda_{mask}\). To address the extreme imbalance in underwater instance segmentation datasets, the classification loss \(L_{cls}\) uses our proposed Dynamic Adaptive Focal Loss (DAFL); the Dynamic Adaptive Focal Loss schematic illustration diagram is shown in Figure 4. For the bounding box regression loss \(L_{box}\), we adopt the CIoU loss [33] and Distribution Focal Loss [34] to enhance the prediction accuracy and alignment between the predicted and ground truth boxes. The segmentation loss \(L_{mask}\) employs binary cross-entropy loss to measure the difference between the predicted and ground truth masks. These two loss functions are designed to be consistent with the original YOLOv9 approach.
# A Variable Step Size Normalized Least-Mean-Square Algorithm Based on Data Reuse
## 3 Variable Step Size NLMS Algorithm
Furthermore, for more challenging signals such as speech (i.e., highly correlated and nonstationary), the value of \(l\) could be even higher, e.g., \(0.5\leq l\leq 1\). The computational complexity of the proposed VSS-NLMS algorithm is similar to the complexity of the conventional NLMS benchmark, since the values of the step size parameters of the VSS-NLMS algorithm are a priori computed and scheduled based on (10) and (12); thus, these values can be used directly in Steps 1 and 2 of the algorithm, as describe before. The only extra computational amount is required in Step 3 of the VSS-NLMS algorithm, which is mainly related to the power estimates from (14) and (15). Nevertheless, this step requires only 5 multiplications, 2 additions, and 1 comparison, which represents a negligible computational effort as compared to the overall complexity of the conventional NLMS algorithm that is proportional to \(\mathcal{O}(L)\), especially for very large values of \(L\) as in the context of AEC. In addition, the proposed VSS-NLMS does not require additional parameters from the environment, so it can be considered a nonparametric algorithm [14]. Of course, it would be very useful to find more practical ways to set (or adjust) the parameter \(l\) within the algorithm, based on the convergence status of the adaptive filter, but this will be the subject for future works.
## 5 Analysis of Displacement Effect
### Analysis of the Influence Factors  
Under two theoretical models, the influence laws of the grouting pressure, groundwater pressure, grout water-cement ratio and penetration coefficient of porous media on the diffusion radius are shown in Figures 5-7, respectively.  
Figure 5: Relationship between diffusion distance and time under different pressures. Two groups of data exist in the figure: the data considering the displacement effect and the data without considering the displacement effect. The two groups of data in Figures 5–7 have the same meaning as Figure 5.  
Figure 6: Schematic diagram of the diffusion radius with different groundwater pressures.  
It can be deduced from Figures 4-8 that:  
1. With the increase in grouting time, the diffusion radius obtained by the two theoretical models increases nonlinearly with time. The increment speed of diffusion radius gradually decreases with time. The increment of diffusion radius in the early period is much larger than that in the late period.
2. The grouting pressure, groundwater pressure, water-cement ratio of Bingham cement grout and penetration coefficient of porous media significantly affect the diffusion radius under the two theoretical models. The relationship between groundwater pressure and diffusion radius is reverse. The greater the groundwater pressure, the smaller the diffusion radius. However, the grouting pressure, penetration coefficient of porous media and water-cement ratio are positively associated with the diffusion radius of Bingham cement grout. The greater the grouting pressure, porous media penetration coefficient and water-cement ratio of Bingham cement grout, the greater the diffusion radius.