esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Spatial Homogeneity of Biospeckle Activity  
Graphical outcomes of homogeneity in BA in analysed groups of samples are presented in Figure 5a-c, and maps of spatial variability of IM are presented in Figure 5d-f. Graphical representation of biospeckle activity is very useful, but from a practical point of view quantitative measures are more desirable for ease of interpretation. For this reason, we decided to carry out the homogeneity test using inertia moment as an activity indicator to localize regions homogeneous enough to establish ROI for further analysis. As expected, regions of highest homogeneity of BA, as well as the lowest values of IM, were observed in uninfested samples (Figure 5a,d). As for samples of the F24 (Figure 5b) and F72 (Figure 5c) series, homogeneity decreased with increasing area of the higher IM indicator. Due to the local nature of occurrence of potential biospeckle activity sources, we decided to localize ROIs around the global maxima contained in areas with the highest IM values, taking into account information on homogeneity to avoid the analysis of transition areas.  
Figure 5: Example maps of homogeneity percentage (**a–c**) and spatial variability of inertia moment (**d–f**) calculated for a frame of 5 \(\times\) 5 pixels. Maps from left to right present stages F0 (not infested sample), F24 (24 h after exposure), and F72 (72 h after exposure) of example fruit No. 03. Dark blue and red colours correspond respectively to the lowest and the highest values respectively.
# Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint
## 3 System Model
### Channel Model  
In the underwater environment, acoustic signal experiences both attenuation and ambient noise. We adopt a classic channel model first presented in [21]. This model has been widely used in most previous studies, such as [5; 22; 23; 24; 25; 26; 27; 28]. In order to ensure that the paper is self-contained, we present the channel model including the attenuation component in Appendix A and the ambient noise component in Appendix B.
## 2 Materials and Methods
### Verification of the VLES Model  
To verify the feasibility of the VLES method to address the oscillating cylinder, we simulated a canonical case of a cylinder oscillating in a fluid initially at rest. We choose the oscillation cylinder adopting the same parameters as reported by [PERSON] et al., such as \(Re=100\), \(KC=5\), \(U=0.01\) m/s, \(D=0.01\) m,\(f_{e}=0.2\) Hz, where \(KC\) is a Keulegan-Carpenter number [24]. The cylinder is given by a harmonic motion oscillating in the horizontal direction, and its translational displacement satisfies \(X(t)=-\mathrm{A_{e}}\mathrm{sin}(2\pi f_{e}t)\), where \(\mathrm{A_{e}}\) is the oscillating amplitude. The cylinder initially moves towards the negative x coordinate with a velocity \(U_{X}=-2\pi f_{e}\mathrm{A_{e}}=-U\), \(U_{Y}=0\). \(U_{X}\) and  
The instantaneous contours of the pressure and vorticity of the fluid around the oscillating cylinder at four different phase angles are shown in Figure 3. The four phase angles (\(\gamma=2\pi f_{e}t\)) adopted are \(\gamma=0^{\circ}\), \(\gamma=96^{\circ}\), \(\gamma=192^{\circ}\), and \(\gamma=288^{\circ}\), respectively. These vorticity contours show the generation and shedding of the wake vortices of the oscillating cylinder. It is evident that the computed results (two columns on the right) using the VLES method are very consistent with results reported by [PERSON] et al. (two columns on the left) in Figure 3. Combined with the comparisons in Figures 2 and 3, it can be seen that the VLES method can be used to effectively handle the dynamic boundary problems, such as an oscillating cylinder. To further compare the numerical and experimental results, Figure 4 provides local velocity information for three phase angles. The comparison shows a good agreement between them, and therefore the VLES method is applicable to calculate the flow past an oscillating cylinder.  
Figure 3: Contours of pressure (the third column) and vorticity (the fourth column) at four phase angles (\(\gamma=2\pi\ell t\)) as compared with the results (the first column and second column) of [PERSON] et al. (2014), respectively. (**a**) \(\gamma=0^{\circ}\); (**b**) \(\gamma=96^{\circ}\); (**c**) \(\gamma=192^{\circ}\); and (**d**) \(\gamma=288^{\circ}\).  
Figure 2: Time history of the hydrodynamic force (red line) calculated in this paper, including the resultant force (solid line), its pressure (dashed line), and viscous (dash dot line) components as compared with the simulation results of [PERSON] et al. (2014) (black circle).
## 2 Data and Methods
### Physical Mono-Window Model
RTTOV simulations for model atmospheres with 21 pressure levels (1000-1 hPa) were performed using the ERA-Interim profiles closest in time and space to each satellite observation.
## 4 Results and Analysis of Spot Area Detection  
### UV Detection Image Processing  
The experimental results presented earlier indicate that the stronger the discharge intensity, the higher the photon count. The discharge point around the conductor appears as a spot in the ultraviolet image. Therefore, the area of the ultraviolet spot can be used to reflect the discharge intensity of the discharge location. As shown in Figure 8a, the ultraviolet detection images captured by the ultraviolet detector are RGB images. Their characteristic is that each pixel can be represented by three equally sized 2D arrays: R (red), G (green), and B (blue). To simplify the subsequent processing, the RGB image was converted into a grayscale image, as it contains more image information. Additionally, the k-means clustering algorithm was employed to segment the ultraviolet detection image. Unlike traditional image segmentation methods, the primary idea behind the k-means algorithm is to iteratively classify the data set into different clusters to achieve optimal  
Figure 7: The variation in the photon count in relation to the gain under different applied voltages: (a) 52.5 kV; (b) 53.5 kV; (c) 54.5 kV.  
clustering capability [26]. Therefore, it is more suitable for segmenting ultraviolet detection images that are complex in shape and lack regularity.  
In practical situations, when performing ultraviolet detection on power transmission lines, there are various challenges due to the complex field conditions and the presence of high-voltage electrical equipment. The ultraviolet images obtained from the ultraviolet imaging device are susceptible to interference, resulting in the presence of many noise points in the images. This interference can make it difficult for detection personnel to accurately determine the discharge locations on the power transmission lines. Therefore, this study employed mathematical morphological filtering algorithms to filter the ultraviolet detection images. The algorithm utilizes the opening operation to dilate the segmented ultraviolet image, eliminating discrete discharge points smaller than the structural element around the discharge area. It then applies the closing operation to fill in the cracks and concave regions outside of the discharge area, smoothing the discharge region [27]. The process of handling the ultraviolet detection image is illustrated in Figure 8.
# Intelligent Reflecting Surface Aided Wireless Systems with Imperfect Hardware
## 1 Introduction
### Related Studies
Furthermore, to improve the performance of wireless systems equipped with multiple antennas, several works have noted that RIS can improve spectrum efficiency [10; 43; 44] of such networks. For example, the authors in [43] studied RIS-aided single-cell systems equipped with multiple antennas. As the main result, one can reduce active RF chains. Regarding the data rate, it can be achieved with similar performance compared with the massive MIMO without RIS. The authors in [10] confirmed the improvement of cell-edge users, and inter-cell interference can be mitigated in the context of a RIS-aided multi-cell MIMO system. In [44], when the RIS is activated for both functions of wireless information and power transfer, the system can improve its performance in terms of energy harvesting. Furthermore, in the context of millimeter-wave (mmWave) communications relying on RIS, the accurate acquisition of the CSI is important to achieve high RIS beamforming gain. Ref. [45] considered channel estimation in RIS-aided mmWave communications assuming small-scale fading. The authors characterized statistically fluctuating two-ray (FTR) distributions. Ref. [46] addressed channel estimation in high-speed railway networks relying on RIS-aided mmWave communication. The authors proposed a reinforcement-based approach combining long short-term memory (LSTM) and deep deterministic policy gradient (DDPG) to design the base station transmit beamforming and RIS phase shifts. Ref. [47] proposed an alternating direction method of multipliers (ADMM)-based channel estimation method for RIS-enabled millimeter-wave communication. Simulation results demonstrated that the proposed ADMM approach is beneficial for channel estimation and also reduces training overhead. For other emerging RIS-aided multi-user communication systems such as RIS-UAV [48] and RIS-CR [49], novel channel estimation techniques have been proposed. In [48], the authors studied the secure communication of RIS-aided UAV communication in the presence of eavesdroppers. In addition, the authors assumed that the CSI of the eavesdropper is imperfect. The authors maximized the average worst-case secrecy rate by jointly designing the trajectory of the UAV, the RIS phase shifts, and the legitimate users' transmit power. The successive convex approximation (SCA), \(\mathcal{S}\)-Procedure, and semidefinite relaxation (SDR) methods are proposed to solve the formulated non-convex problem. In [49] the authors considered maximizing the secondary user (SU) achievable rate by jointly designing SU beamforming and RIS reflecting coefficients subject to SU transmit power constraint and PU interference temperature constraint in a multiple RIS-aided MISO CR network. The network consists of a single SU coexisting with a primary network containing multiple PUs.
## 3 Data Collection and Processing
### Coupling Mechanism Analysis
#### 3.1.3 Analysis of the Coupling Mechanism between Blast Furnace Permeability and Gas Flow  
The formation mechanism of blast furnace permeability primarily involves the physical and chemical properties of the charge, as well as the distribution and disposition of the charge. The formation mechanism of the blast furnace gas flow involves all aspects of the gas flow in the blast furnace, including the tuyere state, hearth structure, blast furnace charge, furnace temperature, furnace pressure, and other factors. Consequently, there are several interconnected factors between blast furnace permeability and the formation mechanism of blast furnace gas flow [17; 18; 19; 20; 21; 22; 23]. First, the permeability of the blast furnace, whether good or poor, significantly impacts the gas flow within it, influencing the speed and direction of the gas movement. Optimal permeability facilitates smooth gas flow, enabling efficient heat and material transfer in the blast furnace and enhancing production  
Figure 4: Blast furnace gas flow influence factors.  
efficiency. Conversely, inadequate permeability restricts gas flow speed, impeding heat and material transfer and subsequently reducing the production efficiency of the blast furnace. Second, the direction and speed of blast furnace gas flow also contribute to the formation of blast furnace permeability. Faster gas flow rates facilitate the movement of charge particles, causing the gaps between them to expand and thereby improving blast furnace permeability. Additionally, the direction of gas flow affects permeability. In the upper section of the blast furnace, where gas temperature is higher and gas density is lower, an upward airflow is formed. This upward airflow increases the voids in the upper charge pile, contributing to improved permeability of the blast furnace. Consequently, the formation mechanisms of blast furnace permeability and blast furnace gas flow are inherently interconnected, with each factor influencing the other. Mastering and controlling these mechanisms can improve the production efficiency and product quality of the blast furnace.
# Torque Requirements and the Influence of Pilot Holes on Orthodontic Miniscrew Microdamage
## 3 Results
### Maximum Insertion Torque and Pilot Hole  
There was no evidence that maximum insertion torque differed according to pilot hole status. There was an estimated 1.71 Ncm increase in maximum insertion torque when a pilot hole was present (95% CI (0.43, 3.85) \(p\) = 0.1144).
# An Automatic 3D Point Cloud Registration Method Based on Biological Vision
## 4 Research Methodology
### Surface Deformation Measurement  
A camera-projector system based on the proposed method, shown in Figure 7a, was developed to measure the surface deformation dynamically during ablation tests in an arc-heated wind tunnel. The resolutions of the projector and camera images were 1280 \(\times\) 800 px and 2456 \(\times\) 2058 px, respectively. The sizes of CCD and CCD pixel were 2/3\" and 3.45 \(\times\) 3.45 \(\upmu\)m, respectively. The focal length was 75 mm. The data processing  
Figure 6: Point cloud registration. (**a**) Reference point cloud; (**b**) test point cloud.  
platform comprised a Dell laptop with an Intel(R) Core(TM) i7-6700 HQ CPU @ 2.6 GHz and 16 GB RAM. The aim was to evaluate the system's precision. Firstly, a model with the size of 40 \(\times\) 40 \(\times\) 5 mm was fixed to a special device which had six cones, as shown in Figure 7b. Secondly, the device was placed on a translation stage, as shown in Figure 7c. The translation stage's precision was 0.01 mm. Thirdly, surface ablation could be simulated by moving the model on the translation stage. The camera-projector system was used to reconstruct the model surface and the six cones at time 0 and time \(k\), denoting the reference point cloud and test point cloud, respectively. Fourthly, the reference and test point clouds were aligned using the proposed registration method, and then the model's surface deformation could be measured. Lastly, the measurement result was compared with the reading from the translation stage, and the measurement error of the camera-projector system was calculated. Root-mean-square (RMS) was introduced to evaluate the above measurement error.
# An Overview of Non-Isolated Hybrid Switched-Capacitor Step-Up DC-DC Converters
## 4 The Charge Interchange between Capacitors  
As mentioned before, one of the motivations for studying SC converters is the recent development of topologies with a relatively high efficiency at a relatively large power. For example, [104] reported an efficiency of 99% with a 22 kW prototype. Table 2 shows some of the references in which switched converters are reported to have achieved a high efficiency. It can be observed that in those cases (mentioned in Table 2), the converters operate with a resonant charge interchange. This section discusses the different types of charge interchange among SC circuits and the analysis of stored energy (before and after the paralleling of capacitors).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Topology**} & **Number of** & **Maximum** & **Number of** & **Maximum** & **Output** \\  & **Switches** & **Voltage on a** & **Switch** & **Capacitors** & **Capacitor** & **Voltage** \\ \hline \(5\times\)_Series-Parallel_ & & & & & \\ (Figure 10a) & 13 & 4\(\times\)\(V_{g}\) & 5 & 5\(\times\)\(V_{g}\) & 5\(\times\)\(V_{g}\) \\ \hline \(8\times\)_Fibonacci_ & & & & & \\ (Figure 11a) & 13 & 5\(\times\)\(V_{g}\) & 5 & 8\(\times\)\(V_{g}\) & 8\(\times\)\(V_{g}\) \\ \hline \(4\times\)_Ladder_ & & & & & \\ (Figure 12a) & 8 & \(V_{g}\) & 6 & \(V_{g}\) & 4\(\times\)\(V_{g}\) \\ \hline \(6\times\)_Ladder_ (no draw) & 12 & \(V_{g}\) & 10 & \(V_{g}\) & 6\(\times\)\(V_{g}\) \\ \hline \(3\times\)_Flying Capacitor_ & & & & & \\ (Figure 13a) & 6 & \(V_{g}\) & 3 & 3\(\times\)\(V_{g}\) & 3\(\times\)\(V_{g}\) \\ \hline \(5\times\)_Flying Capacitor_ & & & & & \\ (no draw) & 10 & \(V_{g}\) & 5 & 5\(\times\)\(V_{g}\) & 5\(\times\)\(V_{g}\) \\ \hline \(5\times\)_MMCC_ & & & & & \\ (Figure 15) & 13 & \(V_{g}\) & 5 & 5\(\times\)\(V_{g}\) & 5\(\times\)\(V_{g}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between transistorized pure switched-capacitor converters.  
It has been observed that traditional SC circuits have _discharging losses_. This section explains this term. It has also been observed and demonstrated that SC circuits with a resonant charge interchange seem not to have _discharging losses_. One of the contributions of this article is to provide a new demonstration (other demonstrations have been published before) based on the amperes per second and charge conservation principle. These principles are widely used and known for power electronics engineering, for which we think the analysis will be better understood.
## 3 Data Analysis and Model Training
### Indicators for Model Evaluation  
In order to objectively evaluate the comprehensive performance of the model, it is necessary to consider the fact that this study is essentially a regression problem. In order to accomplish this, _MSE_ (mean square error), _MAE_ (mean absolute error), _MAPE_ (mean absolute percentage error), and _VAF_ (variance ratio) are used to evaluate the comprehensive performance of the model. The mean squared error (_MSE_) and mean absolute error (_MAE_) are employed to quantify the discrepancy between the actual and observed values. A smaller value indicates greater accuracy. _MAPE_ is a measure of the average size of the prediction error relative to the true value. The smaller the value, the more accurate the prediction. The variance ratio (_VAF_) is employed to assess the extent to which the variance  
Figure 5: Microseismic monitoring data: (**a**) the daily maximum energy value of the first 10 days at time t; (**b**) the daily total energy value of the first 10 days before the time of t; (**c**) the daily maximum frequency of the first 10 days at time t.  
in the model's observed data is interpreted. The value of _VAF_ ranges from 0% to 100%. When the _VAF_ is close to 100%, the model is able to explain the variance in the observed data to a high degree of accuracy. When the _VAF_ is close to 0%, the model's ability to fit the observed data is severely limited, and the variance in the observed data cannot be explained. The calculation formula is as follows:  
\[MAE=\frac{1}{n}{\sum\limits_{i=1}^{n}{\left|{y_{i}-y_{i}^{\prime}}\right|}} \tag{17}\]  
\[MAPE=\frac{1}{n}{\sum\limits_{i=1}^{n}{\left|{\frac{{y_{i}-y_{i}^{\prime}}}{{ y_{i}}}}\right|}} \tag{18}\]  
\[MSE=\frac{1}{n}{\sum\limits_{i=1}^{n}{\left({y_{i}-y_{i}^{\prime}}\right)^{2}}} \tag{19}\]  
In this formula, _yi_ represents the actual value, _yi_\({}^{\prime}\) represents the predicted value, and n represents the total number of samples.  
\[\textit{VAF}=\frac{{SS_{\textit{reg}}}}{{SS_{\textit{tot}}}}\times 100\% \tag{20}\]  
\[SS_{\textit{res}}={\sum\
olimits_{i=1}^{n}{\left({y_{i}-\hat{y_{i}}}\right)^{ 2}}} \tag{21}\]  
\[SS_{\textit{tot}}={\sum\
olimits_{i=1}^{n}{\left({y_{i}-\overline{y}}\right)^ {2}}} \tag{22}\]  
In Equation (20), \(SS_{\textit{res}}\) represents the sum of squares of residuals, which is the variance in the difference between the predicted value of the model and the actual observed value. \(SS_{\textit{tot}}\), on the other hand, is the total sum of squares, which is the variance in the difference between the actual observed value and its mean value.  
In Equations (21) and (22), \(y_{i}\) is the \(i\)th observed value, \(\hat{y_{i}}\) is the predicted value of the model for the \(i\)th observed value, and \(\overline{y}\) is the mean value of the observed value.
###### Abstract  
With the rapid development of virtual reality (VR) technology, digital materials have become more realistic and controllable, offering new opportunities for material perception research. However, material parameters for physically based rendering (PBR), which are widely used in industry, are often derived from design experience and lack interpretability. This study aims to investigate the visual perception difference of material glossiness between real and virtual environments. A compensation method is proposed to bridge the glossiness perception gap between the real-world and PBR. Two psychophysical experiments are conducted. Experiment 1 measures the psychological perception of materials with different glossiness in the real world and VR using a two-alternative forced-choice (2 AFC) design. Experiment 2 establishes a connection between multiple parameters in PBR and the psychologically perceived glossiness obtained in Experiment 1 through a material matching adjustment experiment. Finally, a material glossiness mapping method for VR real-time rendering, Gloss-Bridge, is introduced. This method maps the physical quantity (measured by gloss meter) to various PBR parameters. By using physical parameters instead of empirical parameters, it eliminates tedious hyper-parameter adjustments. This achievement provides a design basis for the production of VR materials based on subjective perception and physical parameters.  
glossiness perception; material virtualization; hyper-parameter tuning; multiple regression analysis; virtual reality +
Footnote †: journal: Applied Sciences
# Dynamic Bending Behaviour of Sandwich Structures for Marine Applications
## 4 Results and Discussion
### Analytical Assessment of Impact Bending Performance
#### 4.4.3 Inter-Laminar Shear Stress Analysis  
From a practical engineering perspective, ILSS is usually adopted to evaluate the interfacial properties of composite structures. Due to the heterogeneity and anisotropy of FRPSS, the interfaces are prone to moisture uptake and its consequential effects. Figure 13 illustrates the effects of seawater exposure on the ILSS of the specimen. The shear strength decreased with exposure to seawater for all samples. The ILSS for unexposed GSP, GSV, and GSH specimens were 16.24 MPa, 17.81 MPa, and 5 MPa, respectively. As expected, GSH samples with a larger thickness exhibited lower ILSS owing to more evenly distributed stresses across the interfaces, unlike the GSP and GSV specimens with higher localised stress concentrations in the through-thickness direction [33]. Interestingly, after seawater exposure, results indicated a reduction in ILSS by 18.6%, 17.4%, and 6.6% for GSP, GSV, and GSH samples, respectively. This clearly showed that while interface weakening was aggravated in the samples by moisture uptake, leading to mechanical property degradation, GSH was the least affected by this phenomenon. This could be due to the additional stiffness provided by the horizontal adhesive layer under impact bending and the thicker core size, thereby ensuring better distribution of the concentrated stresses in the structure after seawater exposure. It can be inferred, therefore, that the addition of adhesive layers in the core of FRPSS, leading to an increased thickness (GSH), would improve its damage-resistant properties under LVI and limit the effects of seawater exposure on its interface properties.  
Figure 12: Bending stiffness of FRPSS—multiple impacts: (**a**) GSP, (**b**) GSV, and (**c**) GSH.