esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Furthermore, electron backscatter diffraction (EBSD) enables precise mean grain size analysis in aluminum alloys, providing a notable advantage over optical microscopy, particularly with respect to the identification of fine grains [16; 17]. Other valuable techniques for fracture-analysis-related projects include the calculation of recrystallization amounts and mapping of grain boundary angle misorientation, as evidenced in the existing literature [18; 19; 20; 21; 22; 23; 24]. Extruded profiles are frequently characterized by a high degree of texturing, with the nature and intensity of this texturing varying depending on the specific alloy employed [25; 26; 27; 28; 29; 30; 31]. Notably, disparities can be observed between the surface texture and that of the material's mid-thickness. In order to better understand and evaluate these variations, the application of inverse pole figure (IPF) plots has proven to be an invaluable analytical tool. Through the utilization of such techniques, it is possible to discern the presence of both single and double fiber textures, which can arise as a direct result of the hot extrusion process. The surface texture is affected by a high amount of shear deformation and temperatures higher than 580 \({}^{\circ}\)C in the extrusion die. The recrystallization percentage varies among the alloys based upon the presence of grain inhibitors, and it can be also different in different areas of the same profile. With regard to automotive alloys, studies have demonstrated that deformation texture on the surface zones of extruded profiles is conducive to the formation of shear bands, whereas random texture is associated with delayed fracture initiation. Additionally, the size and distribution of constituent particles on the free surface of bent specimens have been shown to significantly influence bending performance, as reported in previous investigations [18; 19]. The study conducted by [PERSON] et al. [13] has highlighted the valuable contribution of finite element analysis (FEM) in elucidating the results of three point bend and tensile tests. Specifically, the FEM approach enables the visualization of the stress fields that emerge between intermetallic particles and the surrounding aluminum matrix, which in turn act as stress raisers depending on their morphology. According to the findings, globular particles can function as constant stress raiser points, irrespective of the loading direction, while non-equiaxed particles exhibit a higher sensitivity to the direction of the applied load. In the current research, the focus has been given to the comparison of the texture and the grain boundary misorientation between the surface zone and the mid-thickness areas (core) and to the correlation of precipitation potential with grain boundaries misorientation. In this aspect, the boundaries are divided to three different categories, i.e., high-angle grain boundaries (HAGBs, >15 degrees), low-angle grain boundaries (LAGBs, 5-15 degrees), and sub-grain boundaries (SGBs, 2-5 degrees). HAGBs have not been further categorized, but this will be performed in future work.
## 3 Materials and Methods
### YOLOv586 Yarn Hairiness Improvements
#### 3.4.1 CBG Module--Activation Function  
Here, the replacement of the SiLU activation function by GeLU is discussed [47]. The decision to switch to GeLU was based on better results obtained from a comprehensive mathematical analysis performed beforehand (Figure 5B).  
This CBG block is used in several other blocks (Figure 5C) and in two different bottleneck blocks (Figure 5D).  
The C3 module is a crucial part for feature extraction, consisting of three CBG modules and several stacked Bottleneck blocks. The C3_x notation indicates that there are x number of stacked Bottleneck blocks. In Figure 5E [45; 46; 47], the feature map is split into two branches after entering the C3 module. One branch passes through the CBG and Bottlenecks, while the other passes through the CBG only. The two branches are then concatenated and processed through another CBG module.  
Each Bottleneck block consists of two CBG modules: the first performs a 1 \(\times\) 1 convolution to halve the number of channels, and the second performs a 3 \(\times\) 3 convolution to double them. Reducing the dimensionality first allows the convolutional kernel to better understand the feature information, while increasing the dimensionality helps extract more detailed features. The residual structure adds input and output, preventing the vanishing gradient problem. The main purpose of the C3 function is to capture complex patterns and details as the network depth increases. Here, 3 \(\times\) 3 convolutions are widely used because they enable the neural network to learn richer, more abstract representations. After extensive testing, the GeLU activation function was selected for the optimized YOLOv5 algorithm for the following reasons:* **Improved Non-linearities:** GeLU captures complex patterns of loop and protruding fibers better than SiLU, enhancing the detection of various yarn hairiness types.
* **Mitigation of Vanishing Gradients:** GeLU's smooth derivatives prevent vanishing gradients, ensuring the more stable training of deep neural networks.
* **Improved Training Stability:** by approximating the cumulative Gaussian distribution, GeLU enhances the training stability, leading to more precise weight adjustments and better detection accuracy.
* **Superior Performance:** GeLU outperformed other activation functions in metrics, as shown in Section 4 of this study.
## 6 Simulation Results
### Simulation Results of WFRT
#### 6.1.2 Permanent dc Line-Line Fault on Line L\({}_{41}\)  
During the second test, HVdc circuit breakers CB\({}_{12}\) and CB\({}_{41}\) operate on both sides of the line-line fault within WF ring on line L\({}_{41}\). This situation results in the disconnection of WF-VSC\({}_{1}\) and GS-VSC\({}_{1}\) from the MT-HVdc grid. Dashed lines in Figure 10 represent the power of the detached VSCs after isolation. The permissible range of MT-HVdc grid voltage is not violated as long as power transmission is balanced. However, WFRT may not fulfill the maximum power loss standard during permanent line-line fault within the WF ring as every transmission system operator (TSO) implies its own measures and constraints.  
Figure 9: Disconnection of GS-VSC\({}_{1}\) from WFRT.  
Considering the above-described control and operation during the tests, it is proved that the designed system architecture of WFRT is not applicable for the applications where sudden and permanent GS-VSC disconnection or a permanent dc-link fault within WF ring is expected. Therefore, exploring and developing new topology is the need of the time to meet system needs under steady-state and dynamic situations.
## 3 Results
### Extraction of Bioactive Phenolic Compounds from Different Ecotypes of Burdock Roots

Table 3: Comparison of extraction yield, total phenolic content (Folin–Cicol-etu assay), antioxidant activity (DPPH and ORAC assays), and anticholinergic capacity (AChE and BChE assays) of OLE, SPLE, and SPME of burdock root extracts obtained using SLE and PLE at 50 \({}^{\mathrm{c}}\)C.  
Figure 2: HPLC-DAD chromatograms at 280 nm of phenolic compounds recovered from OLE, SPLE, and SPME burdock roots by PLE at 50 \({}^{\mathrm{o}}\)C. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **Compound** & **Rt (min)** & **[M–H]\({}^{-}\)** & **MS2 Ions** & **OLE PLE 50 \({}^{\circ}\)C** & **SPLE PLE 50 \({}^{\circ}\)C** & **SPLE PLE 50 \({}^{\circ}\)C** \\  & & & & **(g/100 g of Dried & **(g/100 g of Dried & **(g/100 g of Dried \\  & & & & **Extract)** & **Extract)** & **Extract)** \\ \hline
1 & Chlorogenic acid & 7.9 & 374.9 & 3532, 190.7, 178.7 & 1.06 \(\pm\) 0.03 b  & 1.12 \(\pm\) 0.06 b  & 2.12 \(\pm\) 0.03 a  \\
2 & 4-O-Caffeoylquinic acid & 8.32 & 353.0 & 190.8, 178.8, 135.0 & 0.0095 \(\pm\) 0.0002 b  & 0.020 \(\pm\) 0.002 a  & 0.0081 \(\pm\) 0.0006 b  \\
3 & Cyanrine & 10.25 & 515.1 & 353.4, 190.8, 178.8, 135.0 & 0.099 \(\pm\) 0.05 b  & 0.17 \(\pm\) 0.09 b  & 2.05 \(\pm\) 0.03 a  \\
4 & Dicaffeoylmaloylquinic acid & 17.9 & 631 & 573.3, 514.9, 468.6, 447.7, 379. & 0.27\(\pm\)0.03 c  & 0.30 \(\pm\) 0.01 b  & 1.105\(\pm\)007 a  \\
5 & Dicaffeoylmaloylquinic acid & 19.3 & 652.6 & 631.514, 949.09, 469.1, 451.1, & 0.13 \(\pm\) 0.02 b  & 0.089 \(\pm\) 0.006 c  & 0.308 \(\pm\) 0.005 a  \\  & isomer 1 * & 19.3 & [M–H+Na]\({}^{-}\) & 353.4, 191.0, 173.1, 135 & 0.065 \(\pm\) 0.008 b  & 0.055 \(\pm\) 0.007 b  & 0.086 \(\pm\) 0.001 a  \\
6 & Dicaffeoylmaloylquinic acid * & 20.2 & [M–H+Na]\({}^{-}\) & 515.1, 375.354, 17.1, 137.1, 135.4 & 0.065 \(\pm\) 0.008 b  & 0.055 \(\pm\) 0.007 b  & 0.086 \(\pm\) 0.001 a  \\
7 & 3.5-Dicaffeoylquinic acid & 21.1 & 515 & 353.19, 190.9, 178.9 & 0.145 \(\pm\) 0.007 b  & 0.09 \(\pm\) 0.01 c  \\
8 & 1.5-Dicaffeoylquinic acid & 21.7 & 515 & 353.1, 190.9, 178.9 & 2.1 \(\pm\) 0.1 b  & 2.0 \(\pm\) 0.1 c  \\  & Dicaffeoylmaloylquinic acid & 515, 490.9, 469.4, 449. 353, & 0.13 \(\pm\) 0.01 b  & 0.119 \(\pm\) 0.006 b  & 0.488 \(\pm\) 0.006 a  \\  & isomer 2 * & 22.6 & 631 & 191.7, 173.5 & 0.13 \(\pm\) 0.01 b  & 0.148 \(\pm\) 0.006 a  \\
10 & Dicaffeoylmaloylquinic acid & 24.7 & 747 & 631.5, 469.4, 451.3, 353, & 0.35, & \\  & acid * & 335, 191 & 0.30 \(\pm\) 0.02 b  & 0.25 \(\pm\) 0.01 c  & 0.473 \(\pm\) 0.004 a  \\ \hline \hline  & & & & & & & \\ \end{tabular}
\end{table} 
Table 4: Mass data spectra and quantification (g/100 g of dried extract) of phenolic compounds in the three different burdock ecotypes (OLE, SPLE, and SPME) obtained by PLE at 50 \({}^{\circ}\)C using HPLC-DAD-IT-MS.
# A Novel Method of Adaptive Kalman Filter for Heading Estimation Based on an Autoregressive Model
## 3 The Calculation of Measurement Vector for Heading Estimation
### Fused Heading Estimation  
The heading estimated by single sensor is noisy due to sensor performance and external disturbance, and a reliable heading can be determined by fusing headings calculated by different sensors [22]. A heading estimation by fusing these headings that are calculated by a magnetometer, obtained by gyroscope and stored by previous estimate can be expressed as follows.  
\[\psi_{k}=\left\{\begin{array}{l}\beta^{msp}\left\{\beta^{msp}\psi_{k}^{mag} +\beta^{gyro}\psi_{k}^{gyro}+\beta^{prev}\psi_{k-1}^{prev},\psi_{\Delta}^{cor} \leq\psi_{\delta}^{cor},\psi_{\Delta}^{mag}\leq\psi_{\delta}^{mag}\\ \beta^{msp}\left\{\beta^{mag}\psi_{k}^{mag}+\beta^{gyro}\psi_{k}^{gyro},\psi_{ \Delta}^{cor}\leq\psi_{\delta}^{cor},\psi_{\Delta}^{mag}>\psi_{\delta}^{mag}\\ \psi_{k-1}^{prev},\psi_{\Delta}^{cor}>\psi_{\delta}^{cor},\psi_{\Delta}^{mag} \leq\psi_{\delta}^{mag}\\ \beta^{gp}\left\{\beta^{gyro}\psi_{k}^{gyro}+\beta^{prev}\psi_{k-1}^{prev}, \psi_{\Delta}^{cor}>\psi_{\delta}^{cor},\psi_{\Delta}^{mag}>\psi_{\delta}^{mag} \end{array}\right.\right.\end{array}\right. \tag{32}\]  
where  
\[\beta^{msp}=(\beta^{mag}+\beta^{gyro}+\beta^{prev})^{-1} \tag{33}\]  
\[\beta^{mg}=(\beta^{mag}+\beta^{gyro})^{-1} \tag{34}\]  
\[\beta^{gp}=(\beta^{gyro}+\beta^{prev})^{-1} \tag{35}\]  
\[\varphi_{\Delta}^{cor}=\left|\psi_{k}^{mag}-\psi_{k}^{gyro}\right| \tag{36}\]  
\[\varphi_{\Delta}^{mag}=\left|\psi_{k}^{mag}-\psi_{k-1}^{mag}\right| \tag{37}\]  
where \(\beta^{prev}\), \(\beta^{mag}\), and \(\beta^{gyro}\) are the respective weights of the heading stored by the previous estimate, the heading calculated by the outputs of the current magnetometer and the heading obtainedby the gyroscope--\(\psi_{k}^{mag}\) is the current heading calculated by Equation (26), \(\psi_{k}^{gyro}\) is the current heading calculated by Equation (31), \(\psi_{k-1}^{prev}\) is the heading of previous time updated by KF, \(\psi_{\delta}^{cor}\) and \(\psi_{\delta}^{mag}\)--are thresholds.
# Experimental Study on the Condensation Heat Transfer on a Wettability-Interval Grooved Surface
## 2 Description of Experiment
### Test Section  
The humid air condensation experimental platform was built to visually investigate the condensate dynamic behaviors and obtain the condensation heat flux on different functional surfaces. The experimental platform mainly consisted of a condensation chamber, a humidifier, a heat exchange plate, a water bath, and a high-speed CCD camera, as shown in Figure 3. In the condensation chamber, the surface to be tested was fixed to  
Figure 2: The wettability of samples. The // symbol in the figure indicates the apparent contact angle of this droplet stretching along the direction of the flat groove, whose viewing angle is shown to be in the vertical groove direction, while \(\bot\) is the opposite of it.  
the heat exchanger plate by screws in the vertical direction, on the top of which, four boreholes with the diameter of 0.5 mm and depth of 10 mm were drilled on the back of the sample to arrange thermocouples to measure the wall temperature of the surface. The distance between the condensation surface and the thermocouple was less than 1 mm. The water bath was connected to the inlet and outlet of the heat exchanger plate, and two thermocouples were arranged at the inlet and outlet of the heat exchanger plate separately to measure the temperature of the inlet and outlet water. The cooling water flow rate was measured by a turbine flow meter, and the wall temperature of the surface was controlled by adjusting the flow rate and temperature of the cooling water. The humidifier was used to increase the humidity in the chamber, and an exhaust fan was installed at the top of the chamber to enhance the air mixing. A temperature and humidity meter was arranged at 5 mm in front of the condensation surface to measure the humidity and temperature of the humid air. The heat exchange plate and pipes were wrapped with thermal insulation materials to reduce heat loss. The camera was used to record the condensation phenomenon on the surface.  
At standard atmospheric pressure, in a constant temperature room at 20 K, when the humidifier works stably, the relative humidity in front of the condensation surface can be maintained at 80 \(\pm\) 3%. Before the start of the experiment, the flow rate and temperature of cooling water were adjusted to the set value. When the temperature of the wall, the inlet, and the outlet water of the heat exchange plate became stable, the humidifier was turned on, the temperature data were recorded, and the surface condensation phenomenon was recorded by a camera.
# Daydirex: Translating Nighttime Scenes towards Daytime Driving Experience at Night
## 4 Proposed Solution
### Matching Perspective of Images  
Obtaining a part of street view image that matches the current location is not sufficient for providing appropriate hint information. It is because a street view image has different perspective from that taken in a normal car. Such perspective difference mainly results from the fact that street view images are captured using a specialized equipment and vehicle as shown in Figure 8a. The camera that takes street view images is installed at a high position as shown in the figure, which causes a big difference in the vertical position where a camera can be mounted in a normal car that we consider for our target scenario.  
To make the perspective of street view image match that of nighttime image input, it is necessary to perform perspective transformation. For the purpose, we need to use a trapezium-shaped window to crop the part to be used as hint, not a rectangle-shaped window shown in Figure 7. Figure 8b shows an example of trapezium-shaped window overlaid on a street view image. We empirically determine the size and initial position of the window. We apply perspective transformation to the cropped image using the window. We use OpenCV library to perform perspective transformation.  
Figure 7: Procedure of obtaining an image part to be used to provide hint information from a street view image.  
Figure 9 shows an example result of perspective transformation. An input nighttime image is shown in Figure 9 and the corresponding street view image is shown in Figure 9. To compare the difference between the perspective-transformed image and the one without transformation, we use two different cropping windows, the red trapezium-shaped window and the yellow rectangular window as shown in Figure 9. Two hint images from the windows are also shown, one is cropped using the yellow window without perspective transformation (Figure 9) and the other is cropped using the red window and perspective-transformed (Figure 9). We can check the difference between the two hint images based on the area (blue rectangle) that is bright and visible in the input nighttime image. In Figure 9, the streetlight that exists in the right side of the input image is not shown and the position of the tree in the center of the input image is slightly different. In contrast, Figure 9 shows the streetlight and the tree that are positioned similarly to the input image, which means that perspective transformation results in more appropriate hint images.
# A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy
## 4 Experimental Results and Settings
### Structure Similarity Index Measure (SSIM)  
SSIM is an index employed to measure the structure similarity between an original and an enhanced image [9]. A greater value of an SSIM indicates the best identical images with a high contrast rate. Equation (10) shows the calculation of the SSIM index:  
\[SSIM\ (X,Y)=[l(X,Y)]^{\alpha}\cdot[c(X,Y)]^{\beta}\cdot[s(X,Y)]^{\gamma}. \tag{10}\]  
In the above Equation (10), \(X,Y\) denote two windows with the same dimensions as the original and reconstructed image; \(\alpha>0\), \(\beta>0\), \(\gamma>0\) denote constant exponents used here as weight parameters; and \(l\), \(s\), \(c\) represent luminance, structure, and contrast components, computed as follows:  
\[l(X,\ Y)\ =\frac{2\mu_{x}\ \mu_{y+a_{1}}}{\mu_{x}^{2}+\mu_{y}^{2}+a_{1}}, \tag{11}\]  
\[s(X,\ Y)\ =\frac{\sigma_{xy+a_{3}}}{\sigma_{x}\sigma_{y+a_{3}}}, \tag{12}\]  
\[c(X,\ Y)\ =\ \frac{2\sigma_{x}\ \sigma_{y}\ +a_{2}}{\sigma_{x}^{2}+\sigma_{y}^{ 2}+a_{2}}. \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Statistical Parameter** & **Method** & **Image01** & **Image02** & **Image03** & **Image04** \\ \hline \multirow{6}{*}{**PSNR**} & Original & 6.55 & 7.97 & 9.77 & 7.98 \\  & HE & 7.04 & 9.89 & 10.78 & 9.89 \\  & AHE & 12.90 & 10.11 & 13.90 & 13.89 \\  & CLAHE & 15.80 & 13.89 & 13.92 & 13.90 \\  & ESHE & 15.90 & 17.90 & 17.90 & 19.90 \\  & **PROPOSED** & **20.10** & **21.23** & **22.67** & **23.78** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Peak signal-to-noise ratio (PSNR) comparison of proposed art with literature methods. A large value indicates high image quality.  
The terms \(\mu\) and \(\sigma\) indicate the average and variance of \(X\), \(Y\), respectively, in the aforementioned Equations (11)-(13), while \(a_{1},\ a_{2},\ \text{and}\ a_{3}\) are used as constant parameters to avoid instability between the average and variance of \(X\), \(Y\), and were specifically chosen as \(a=(KL)^{2}\). Here, \(L\) contains a dynamic range of pixel values, i.e., 255, and \(K<1\) is a small constant. In order to simplify the expression, the values of \(\alpha=\beta=\gamma=1\) and \(a_{3}=\frac{a_{2}}{2}\). An SSIM value of 1 means the best possible structure similarity between an original and enhanced image.  
As can be seen in Table 4, the proposed art achieved the highest SSIM values (near to 1) on tested images and outperformed the other methods. The performance of HE, AHE, and CLAHE in the SSIM domain were observed to be less significant in terms of having an identical structure to the original tested images. However, ESIHE nearly performed with the proposed art in enhancing contrast with acceptable structures of tested images. Finally, the obtained SSIM by the proposed art demonstrated the efficacy of the proposed art in enhancing image contrast without distortion of the image structures.
# Environmental Impacts on Hardware-Based Link Quality Estimators in Wireless Sensor Networks
## 4 Typical Hardware-Based LQEs: A Survey
### SNR Based LQEs and Their Mapping Models  
According to Figure 3, the theoretical model and logistic regression model (for short, LR model) are usually used as mapping models between \(\mathit{SNR}\) and \(\mathit{PRR}\) for \(\mathit{SNR}\) based LQEs. In [21], the theoretical model was used as the mapping model between averaged \(\mathit{SNR}\) (defined as \(\mu_{\mathit{surr}}\)) and \(\mathit{PRR}\), as shown in Equation (8).  
\[\mathit{PRR}=\left[1-Q\left(\sqrt{2\times\frac{B_{N}}{R}\times 10^{\frac{ \mathit{surr}}{10}}}\right)\right]^{l} \tag{8}\]  
where \(Q(\cdot)\) represents the Q function, \(l\) is the number of bits in a packet, \(R\) is the data rate in kb/s, and \(B_{N}\) is the noise bandwidth of the transceiver in kHz. The values of \(R\) and \(B_{N}\) are also 250 kb/s and 384 kHz, respectively.  
In [26], the mapping model between \(\mu_{\mathit{surr}}\) and \(\mathit{PRR}\) is obtained based on logistic regression. Using the measured data in the runway, a mapping model was obtained, as shown in Equation (9).  
\[\mathit{PRR}=\frac{1}{1+e^{3.4435-1.1047\times\mu_{\mathit{surr}}}} \tag{9}\]
# Power Transformer Inrush Current Analysis: Simulation, Measurement and Effects
## 3 Numerical Modelling Results  
The simulation of the transient phenomenon that occurs when the transformer is connected to the mains was carried out using the EMTP software, which allows a detailed analysis of the transient regimes in transformers [27; 28; 29].  
The measurement of irnush current is not a common test (it involves high costs), it is generally carried out at the factory tests, which validate the operation of the transformer, a fact that can guarantee the functionality of the transformer in the network. Aiming to analyze the transient phenomenon that occurs when the transformer is connected to the network, in order to validate the results obtained after the simulation with experimental results, we opted for a 15 MVA, 10.5/6.3 kV TTUS OFAF transformer, existing in our possession. The study was carried out on a 15 MVA, based on its magnetization curve.  
The power transformer--EMTP wiring diagram is presented in the Figure 1, and the transformer data used in the simulation phase are the follows:  
- rated voltage U1 = 10.5 kV;
- number of winding turns w1 = 223;
- no-load losses P0 = 13 kW;
- rated power SN = 15 MVA;
- ratio of no-load current to rated current k[%] = 0;
- short-circuit voltage Uk (%) = 7.5%;
- rated frequency f = 50 Hz;
- short-circuit losses Pk = 73 kW;
- residual flux \(\phi_{1 rem}=0\);
- column cross-section Sc = 1252.67 cm\({}^{2}\);
- transformer connection phase \(\gamma=0\) rad;
- no-load current I0 A;
- magnetization curve of the magnetic core.  
The simulation parameters were calculated based on the mathematical model presented in Section 2 of the work.  
The modelling was done for the worst case, at zero crossing, where the value of the residual flux is considered to be zero.  
The maximum value of the connection current was recorded on phase U, 3.444 kA at zero crossing, while the maximum values for phases V and W are much lower. On phase V the maximum value is 1.775 kA and on phase W the maximum value is 1.669 kA (see Figure 2).  
From the analytical model (Section 2) and the simulation presented above, we can draw some conclusions:  
- when the transformer is connected to the mains, a transient current occurs whose amplitude can reach values up to (3-10) % of the nominal current, depending on the resistance of the primary winding and the variation of the flux with time (824.8 A);
- in the simulation, according to the conditions imposed, the value of the residual flux is zero and the connection angle of the supply voltage is zero (at zero crossing), this situation is the most unfavorable, as a result the most significant irnush current was recorded on the U phase (Figure 2);
the maximum value of the inrush current is influenced by the value of the initial phase angle.  
Experimental tests were carried out to validate the simulation results.