esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Mitochondrial Dysfunction in AAI-Induced Kidney Diseases
### Investigation Methods of Mitochondrial Function
#### 3.2.5 Measurement of Calcium  
The cytotoxic effects of aristolochic acid I (AAI) are potentiated by increased concentrations of Ca2+ [2]. Exposure of kidney homogenates to AAI (50 \(\upmu\)M) along with Ca2+ for 24 h resulted in mitochondrial swelling, Ca2+ efflux, membrane depolarization, cytochrome C release, decreased ATP production, and high caspase 3 activity [5; 6]. In another study, a higher concentration of AAI (100 \(\upmu\)M) was used on a canine kidney cell line (MDCK), which induced cellular stress, increased intracellular Ca2+ concentration, and altered mitochondrial function [23]. Ca2+ efflux was measured after isolated mitochondria from rat kidneys were energized with succinate in the presence of AAI, which induced significant swelling that was prevented by the addition of cyclospor A (CsA) and ADP. The release of Ca2+ in response to AAI occurred in a dose-dependent manner, indicating the induction of mitochondrial permeability transition (MPT). AAI caused damage to HK-2 cells through a mitochondria-dependent mechanism, evidenced by mitochondrial swelling, release of Ca2+ from kidney mitochondria, and membrane depolarization in both mitochondria and HK-2 cells. A low concentration of Ca2+ and AAI alone cannot induce MPT in isolated mitochondria. However, Ca2+ levels are enhanced in tubular cells treated with AAI, suggesting tissue-specific toxicity of AAI that may account for its MPT-induced effects on renal tubular cells [20].  
In a study performed on MH1C1-A23187 treated cells, a delay in the increase in cytosolic Ca2+ concentration ([Ca2+]\({}_{c}\)) was observed, and this delay was prevented by the addition of AAI [32]. AAI increased intracellular Ca2+ concentration ([Ca2+]\({}_{l}\)) in MDCK cells in the presence of extracellular Ca2+; even in the absence of extracellular Ca2+, AAI could still induce an increase in [Ca2+]\({}_{l}\). The AAI-induced increase in [Ca2+]\({}_{l}\) led to the release of endoplasmic reticulum (ER) stores and the influx of extracellular Ca2+ [23].
## 2 Methodology
### Color Space Selection  
The displacement calculation is based on the two registration point clouds to find monitoring points with the same characteristics on the slope, monitoring points recognition through the color value, the initial data are expressed in RGB color space by numerical, but some disadvantages was found in the actual application, under the color space, monitoring points with the same characteristics will be under different light conditions present a  
Figure 13: The system functional framework.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & \multicolumn{1}{c}{**Tool Classification**} & \multicolumn{1}{c}{**Tool Name**} \\ \hline \multirow{4}{*}{Hardware device} & UAV & DJI Matrice 600 Pro \\  & TLS & Trimble TX8 \\  & Digital Camera & Canon 5D MarkIII \\  & Workstation & Dell PowerEdge R930 \\ \hline \multirow{4}{*}{Software} & Flight control software & DJI Go 4 \\  & TLS data processing software & Trimble Realworks 10.0.1 \\  & UAV point cloud generation software & Pix4D 4.4.12 \\ \hline \multirow{4}{*}{Development environment} & System development platform & Visual Studio 2015 \\  & Programming language & Visual C++ \\ \cline{1-1}  & Front-end interface framework & Qt 5.7.0 \\ \cline{1-1}  & Image processing algorithm library & OpenCV, PCL \\ \cline{1-1}  & Graphics processing and rendering & Visualization Toolkit \\ \hline \hline \end{tabular}
\end{table}
Table 3: The development requirements of the system.  
different color value. For monitoring points in different time periods, RGB colors are different, and different RGB colors extracted inevitably complicates the identification process of monitoring points and affects the accuracy of identification.  
Based on literature review, it is found that each color in HSL color space is represented by a unique set of indicators, namely, hue \(H\), saturation \(S\) and brightness \(L\). To verify the above view, the RGB color and HSL color of five monitoring points in two time periods are recorded through experiments (Table 4). It can be seen from the Table 4 that light has a great influence on the color value of RGB space, light has a great influence on the \(L\) value of HSL color space, and little influence on \(H\) and \(S\). Therefore, in this study, \(H\) and \(S\) were extracted to eliminate the interference of light on the extraction of monitoring points. The conversion relations are shown in Equations (19)-(21) [36]. The formula for converting RGB color space into HSL color space is as follows:  
\[H=\left\{\begin{array}{cc}\theta&G\geq B\\ 2\pi-\theta&G<B\end{array}\right.\text{ where }\theta=\cos^{-1}\left(\frac{2R-G-B}{2 \sqrt{\left(R-G\right)^{2}+\left(R-B\right)\left(G-B\right)}}\right) \tag{19}\]  
\[S=1-\frac{3\min\left(R,G,B\right)}{R+G+B} \tag{20}\]  
\[L=\frac{R+G+B}{3\times 255} \tag{21}\]
## 1 Introduction
[20] investigated the correlation between landslides in the Fiumarella di Corleto watershed in the southern Apennine Mountains, Italy, and several predisposing factors including lithology, slope gradient, aspect, and utilization of land. The findings of this study demonstrate that the spatial distribution and types of landslides can be more effectively characterized through a combination of geomorphological analysis and GIS data processing. The susceptibility analysis of landslides in the region of Yiyang, which is highly prone to landslides, is usually based on expert knowledge assessment, evaluation of cataloged data, and physical evaluation of landslide processes [21]. However, there is a lack of specific and authentic susceptibility maps based on comprehensive study. And there exist great challenges in the applicability of the existing landslide susceptibility analysis method based on this specific area due to great differences from other areas in topography, geology, environment, hydrology, and meteorological conditions. With regard to that, landslide susceptibility in Yiyang was analyzed by using machine learning techniques in combination with a geographical information system in this study. The primary contributions of this study are as follows: (1) A comprehensive dataset of landslides in Yiyang was constructed. (2) A landslide susceptibility model is proposed based on weighted information entropy by employing XGBoost to calculate the information value of predisposing factors and weighing them, thereby obtaining the landslide risk of the slope unit. (3) The landslide susceptibility maps of Anhua County, Heshan District, and Taojiang County in Yiyang City generated in this study may be used for natural disaster management and land-use planning to prevent or mitigate the losses caused by landslides and help future development. In brief, the main innovation of this study is that we obtained landslide density and frequency ratios of different landslide susceptibility areas. We also found that area percentages of very low susceptibility areas, low susceptibility areas, moderate susceptibility areas, high susceptibility areas, and very high susceptibility areas in the study region were 13.32%, 23.76%, 23.22%, 21.73%, and 17.97%, respectively. It seems to be the first time that landslide susceptibility maps were generated for Heshan District and Taojiang County. This study fills the gap in this aspect.
## 3 Data Preparation and Description  
### Production Segmentation  
Due to the limited samples provided (only 33 perforated sections) in the study areas, it was difficult to find the relationship between logging data and production and obtain a reliable production prediction model. Therefore, the production segmentation was adopted to enlarge the quantity of samples. The production was divided based on the formation coefficient (the product of permeability, gas saturation, and porosity). The model assumes that the effect of perforation friction at different depths is not considered. The production division is based on the following criteria:  
\[Q_{j}=Q.\frac{K_{j}S_{j}\varnothing_{j}}{\sum_{i=1}^{n}K_{i}S_{i}\varnothing_{i}} \tag{14}\]  
where \(Q_{j}\) represents the gas production of a sampling point after partitioning (m\({}^{3}\)/d), \(Q\) represents the total gas production of the perforation segment (m\({}^{3}\)/d) \(K\) is the permeability (md), \(S\) is the oil saturation (%), and \(\varnothing\) represents the porosity (%).  
The well-logging data and productivity data that this study relies on were derived from a designated gas well in the Lixing Gas Field located in Luliang City, Shanxi Province, China. The gas field has a significant geographical location, located on the eastern edge of the Ordos Basin. Its geological structure and resource reserves provide rich and representative data resources for this study. As shown in Figure 4, the total production of the perforation section from 1485.40 to 1491.40 m is 2800 m\({}^{3}\)/d. After subdivision, we determined the production of each sampling point from 1485.40 to 1491.40 m, with an interval of 0.1 m.  
After dividing the production according to the above formula, a total of 2663 samples were obtained from 33 perforation sections, which were used to train the production prediction model.
# Sparse Multiuser Receiver Design in Large Scale Array System
## 1 Introduction
A multiplicative update (MU) algorithm based on proportional fair proxy is proposed in [10], which is essentially the geometric smoothing of the minimum required SNR. Experimental results in [10] show that the minimum attainable SNR of MU-SLA is equivalent to that of SLA, but higher than those of all other algorithms. The average calculation time of MU-SLA is lower than that of SLA, but longer than those of all other gradient-based methods. Based on the proposed analog matrix, we further discuss the simultaneous demodulation of simple modulated signals transmitted between the BS and users with fewer RF chains. This is an optimization problem of underdetermined observations with modulation constraints, and we propose a GAMP method, which is a demodulation method realized by iterative message-passing algorithms [11; 12].
## 5 Analysis and Discussion of Numerical Simulation Results
### Study on Mechanical Behavior Characteristics of the Surrounding Rock at Stratigraphic Boundary
#### 5.1.1 Study on the Distribution Characteristics and Evolution Law of the Stress Field
Meanwhile, to study the stress characteristics of surrounding rock at different distances from the excavation face, the maximum principal stresses at different distances from the monitoring line (A-A', B-B', C-C', D-D', E-E', F-F', G-G') were recorded to calculate the corresponding SCD, and the results are shown in Figure 13 (according to previous studies, the region 0-1.5 m from the monitoring line is considered a key stress zone [34]). On the roadway profile face, the surrounding rock of different strata is in a stress-released state, with greater stress release around the surrounding rock of the C1d formation, with the greatest stress release at the arch bottom (point E), which is close to zero. At 0.5 m from the roadway, the stress state of the surrounding rock vault A in the D\({}_{3}\)zg formation changes and is in the state of stress concentration, while all other points are still in the state of stress release. At 1.0 m from the roadway, at vault point A in the D\({}_{3}\)zg formation reaches a peak  
Figure 12: Distribution curve of the secondary stress field in the vault at different monitoring sections. SCD value of 1.3. The vault region of the D\({}_{3}\)zg formation exhibits a significantly higher degree of stress concentration compared to other positions along the roadway and is the main location of the abnormal stress zone.. In contrast, the surrounding rock of the C\({}_{1}\)d formation is mainly in the state of stress release near the free face, and the range of the stress release zone is larger than that in the D\({}_{3}\)zg formation, as also observed in Figure 11.  
It can be concluded that differences exist in the stress field distribution characteristics of the surrounding rock when the roadway crosses different formations. When the roadway does not cross the stratigraphic boundary and remains within the C\({}_{1}\)d formation, the stress release zone in the surrounding rock is wider, and the stress concentration zone is located farther away from the free face of the excavation, in contrast, with relatively low stress accumulation. Conversely, when the roadway crosses into the D\({}_{3}\)zg formation, the stress release zone is significantly smaller, and the stress concentration zone is located closer to the free face of the excavation, with a relatively high degree of stress accumulation. In addition, when the roadway crosses the stratigraphic boundary, an abnormal stress zone (associated with a local region of relatively hard rock) is formed in the lower plate of the stratigraphic boundary. This zone is primarily concentrated near the vault, making the rock mass in this area more susceptible to cumulative damage. Meanwhile, this abnormal stress zone creates a favorable external environment and mechanical conditions for the accumulation of high energy, thus increasing the risk of rockburst disasters.
# Backlight Unit Excessive Dimming Based on Perceptual Image Analysis
## 3 Methods
### Backlight Unit Simulator and Image Quality Measure
#### 3.2.2 Image Quality Measure  
Using the backlight simulator, image quality degradation caused by excessive dimming and compensation was evaluated by using measures such as PSNR, distance measure, and SSIM, which are widely used in backlight dimming [7,8]. PSNR can measure a change in the brightness and the color of the pixels compared to the original image due to the excessive dimming. The distortion measure focuses on brightness to measure the degree of distortion of the image by comparing the standard deviation or the mean of brightness. Because human eyes are sensitive to changes in brightness, it is important to check for changes in picture quality through distortion measure. SSIM is a measure of image quality similar to that of humans' eye, which separates the object through changes in color or  
Figure 5: Simulator structure.  
brightness and compares the shape of the object to measure the similarity between the two images.  
Since this paper optimizes dimming amount based on HVS, it is necessary to consider perceived image quality in addition to existing mathematical quality measures. The change in the brightness of the pixel due to the excessive dimming results in a lower value of PSNR. The proposed dimming method, which allows a little saturation to occur by considering cases where there is a large difference in brightness between pixels, may produce generally poor PSNR values. In the case of distortion measure, contrary to PSNR, the value is not significantly lower even if the brightness decreases, but it may not be accurate because it may still have high scores even if the image quality is not good. To compensate for this, since the mean opinion score (MOS) evaluated by humans shows relatively high correlation with SSIM, SSIM is suitable for human perceived quality assessment in objective quality measures [23].
## 6 Verification with Real Vessel Data  
To further validate the effectiveness of the proposed method, offshore experiments were conducted near Zhifu Island, Yantai (with a water depth of approximately 17 m,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Vessel A** & **Vessel B** & **Vessel C** \\ \hline Scale = 42 & 0.7611 & 0.9914 & 0.9184 \\ Scale = 43 & 0.7542 & 0.9914 & 0.9344 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Entropy values at different time scales.  
Figure 14: Multiscale permutation entropy of electric field signals from different vessels: (**a**) MPE at time scale 42 and (**b**) MPE at time scale 43.  
Figure 13: Comparison between PE values of original signals and IMF values with maximum average energy: (**a**) PE values of original signals and (**b**) PE values of IMFs with maximum average energy.  
B is approximately 0.5, while the difference between Vessel A and Vessel C is around 0.3. This indicates that this paper's proposed method can effectively distinguish target signals.  
According to the data analysis in Table 4, the entropy variance of each vessel is very low across the three feature extraction methods of PE value, PE value of maximum mean energy IMFs, and MPE value. This suggests that the entropy value of different vessels is stable. Among them, the PE value feature parameter of the IMF with maximum average energy is in different fluctuation intervals. The range difference between vessels A and B is about 0.53, and the range difference between vessels A and C is about 0.22, indicating good differentiation. When only the PE value is used as a feature parameter, the maximum difference between vessel A and vessel C is only about 0.005, making it difficult to differentiate the signal characteristics of different vessels. However, when MPE is used as a feature parameter, the fluctuation ranges of vessel B and vessel C overlap partially when the time scale is 34, so they cannot be differentiated. In summary, the PE value of the IMF with maximum mean energy is the best feature extraction method as it can better distinguish the three signals than the other two methods.
## 4 Discussion  
In this study, we quantitatively evaluated motor control characteristics for circular tracking movements in 3D space. We analyzed the temporospatial relationship in 3D space between the circular tracking movements and target motion for various speeds and two different rotation axes. We measured three kinematic metrics (parameters) based on polar coordinates: differences in distance from the fixed pole, \(\Delta R\); position angle, \(\Delta\theta\); and angular velocity, \(\Delta\omega\).  
We found that when the target speed increased, \(\Delta\theta\) and \(\Delta\omega\) (indicating temporal motor control characteristics in polar coordinates) increased for both the frontal and sagittal planes. This suggests that irrespective of the target rotation axis in 3D space, increasing the target speed makes it more difficult to synchronize the angle and angular velocities of the target and tracer temporally (Figure 4B,C and Figure 5B,C). Further, \(\Delta R\), which indicates spatial motor control characteristics in polar coordinates, increased for both the frontal and sagittal planes when the target speed increased. This suggests that, irrespective of the target rotational axis in 3D space, increasing the target speed over a specific velocity makes it more difficult to track the target spatially (Figure 3B,C).  
To investigate the impact of the depth on motor control in 3D space, we compared the three parameters on both planes at each target speed. On the sagittal plane, \(\Delta\theta\) and \(\Delta\omega\) were significantly higher than on the frontal plane at all target speeds (Figures 4A and 5A). However, \(\Delta R\) was significantly higher than that on the frontal plane over target speeds of 0.25 Hz (Figure 3A). Further, the differences between the two planes strongly increased along with the target speed (Figures 3A, 4A and 5A) for all parameters.  
This result shows that the depth information is significant for target-tracking movements, particularly at high speeds. In the following, we discuss the impacts of the target speed and depth.
## 3 Error Introduced by LEO Satellite Assistance
### Clock Accuracy of Sattelite and Receiver  
In addition to the relative movement between the satellite and the receiver, the oscillator frequency bias of the satellite and receiver also affects the measurement of the received carrier Doppler. It can be obtained as follows:  
\[f_{CLK\_e}=\gamma\times f_{carr} \tag{6}\]  
where \(\gamma\) represents the frequency accuracy of the clock. \(f_{carr}\) indicates the carrier frequency of the GNSS signal. The accuracy of the clock frequency will affect the receiver's Doppler search. The deviation of the clock frequency includes two parts: the satellite clock and the receiver clock. The LEO satellite clock's accuracy is about \(1~{}\times~{}10^{-9}\), which could cause the Doppler error at about \(1.58\) Hz. When the receiver uses the LEO satellite for positioning, it absorbs the clock frequency deviation of the LEO satellite into the receiver's clock. The deviation of the receiver's clock frequency accuracy will also cause the Doppler error to deviate from the true value. The GPS receiver clock frequency accuracy is about \(5~{}\times~{}10^{-9}\). Then, the introduced Doppler frequency shift error can be calculated to be about \(7.88\) Hz. Therefore, the Doppler shift error caused by the clock accuracy of LEO satellite and the receiver is within \(10\) Hz.  
Figure 5: Doppler shift deviation caused by initial positioning error.
## 2 Materials and Methods
### Commercial Distillation Test  
The commercial test was carried out at the LUKOIL Neftohim Burgas (LNB) fluid catalytic cracking (FCC) complex unit. It consists of feed hydrotreating, FCC reactor-regenerator and fractionation sections. A process diagram of the LNB FCC complex is presented in Figure 2. More details about the LNB FCC complex unit is presented in our earlier research [33].  
The commercial test consisted in processing of a definite quantity of a blend from H-Oil heavy atmospheric gas oil, light, and heavy vacuum gas oils which were characterized by the ASTM D 1160 method, and the ASTM D 7169 method in the stabilization column of the FCC feed hydrotreating section (see Figure 2). The stabilization column of the FCC feed hydrotreating section has three beds filled with Sulzer structured packing and operates at an absolute pressure of 1.5 bars. It fractionates the reaction mixture leaving the FCCPT reactors into gas, naphtha, diesel, and stabilized hydrotreated vacuum gas oil (VGO). The diesel fraction is separated from the second packing bed and is stripped with steam in a stripping column as shown in Figure 2. Two cases were studied in this work. The first one was that of the typical FCCPT stabilization column operation. The second one was that of the typical FCCPT stabilization column operation plus processing of additional 24.4 wt.% of H-Oil vacuum gas oils (of fresh FCCPT feed).  
Figure 3 presents a flow chart describing the methodology and procedures to validate the diesel fraction content in H-Oil VGO measured by ASTM D 7169 and ASTM D 1160.  
Figure 2: A process diagram of Feed hydrotreating, FCC Reactor-Regenerator and Fractionation sections in the LUKOIL Neftohim Burgas FCC complex unit.  
Legend explanation:  
G = content of diesel fraction, measured by ASTM D 7169, wt.%;  
E = content of diesel fraction, measured by ASTM D 1160, vol.%  
X = FCCPT fractionation section feed rate, t/h;  
Y = FCCPT fractionation section diesel product rate, t/h;  
Y + \(\Delta\)Y = FCCPT fractionation section diesel product rate when H-Oil VGO is processed, t/h;  
Z = H-Oil VGO processed in FCCPT fractionation section, t/h;  
B = H-Oil diesel flow rate estimated as a multiplication of H-Oil VGO flow rate by the content of diesel fraction, measured by ASTM D 7169, t/h;  
C = H-Oil diesel flow rate estimated as a multiplication of H-Oil VGO flow rate by the content of diesel fraction, measured by ASTM D 1160, multiplied by the density of H-Oil diesel, t/h;  
Comparison of \(\Delta\)Y with B, and C  
If \(\Delta\)Y \(\approx\) B (C), then ASTM D 7169 (ASTM D 1160) correctly quantifies diesel content in H-Oil VGO;  
If \(\Delta\)Y \(>\) B (C), then ASTM D 7169 (ASTM D 1160) underestimates the diesel content in H-Oil VGO;  
If \(\Delta\)Y \(<\) B (C), then ASTM D 7169 (ASTM D 1160) overestimates the diesel content in H-Oil VGO.