esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Impact of the Number of Users and the Pilot Reuse Factor on Area Throughput
Figure 16 shows that the effect of the pilot reuse factor on the area throughput varied across the number of antennae per AP at different numbers of UE when the pilot reuse factor was raised to two. The proposed estimator achieved area throughput = 1476 Mbit/km\({}^{2}\) at N = 4 and M = 80, whereas for the existing estimator, the area throughput = 1399 Mbit/km\({}^{2}\) at N = 4 and M = 80. The proposed estimator improves the performance by 5.50% at this point. At N = 3 and M = 40, the area throughput achieved by the existing and proposed estimators became 1100 Mbit/km\({}^{2}\) and 1142 Mbit/km\({}^{2}\), respectively. At N = 2 and M = 20, the area throughput achieved by the existing and proposed estimators became 837.3 Mbit/km\({}^{2}\); i.e., at this point the performance of the two estimators is the same. The area throughput values attained by the proposed and existing estimators for N < 2 are very close. As the pilot reuse factor increased from w = 1 to w = 2, the area throughput decreased. When the number of APs increased with the increased pilot reuse factor, the area throughput increased up to some point and diminished after a certain point. The pilot length became 20 since K = 10, N = 4, and w = 2, which means that the pilot length decreased as the pilot reuse factor increased. Figure 14: Area throughput versus the number of access points for fully centralized processing with the MMSE combiner at K = 15, L = 4, \(\tau_{c}\) = 200, and w = 1. Figure 15: Area throughput versus the number of antennas per UE for fully centralized processing with the MMSE combiner at K = 10, L = 4, \(\tau_{c}\) = 200, and w = 2. The use of multi-antenna access points (APs) and user equipment (UE) significantly increases hardware costs, particularly in centralized processing architectures. The multi-antenna configuration at the UE side also adds computational burden, as it requires more complex signal processing. Moreover, establishing efficient connectivity between the APs and the central processing unit (CPU) poses a major challenge. Since the number of APs is typically much larger than the number of UE, this requires high-capacity, low-latency connections such as fiber-optic links with substantial bandwidth. However, deploying and maintaining such a dense and high-speed backhaul network is both technically complex and costly.
# A Complete Pipeline for Heart Rate Extraction from Infant ECGs
## 2 Materials and Results
### The ECG Pipeline  
The main focus of the ECG pipelines analysed here was to accurately extract a set of R-peak locations from an ECG signal, identifying the peak within each QRS complex (known as peak detection). Some pipelines can also search for other ECG complexes, which are not analysed here. A pipeline typically starts with preprocessing, a mathematical manipulation of the ECG signal to allow the QRS complex to be more easily identifiable.  
Once the signal has undergone preprocessing, the resulting preprocessed ECG contains R-peak locations that differ slightly from the raw ECG, due to the mathematical operations that occur during preprocessing. In order to readjust the peaks back to the original location on the raw ECG (i.e., the R-peak location on the raw ECG, rather than the R-peak location on the preprocessed ECG), we introduce a \"local peak-correction\" operation. Additionally, we introduce a square wave specific filter for those devices where it is appropriate.
# A Gas-Kinetic BGK Scheme for Natural Convection in a Rotating Annulus
## 3 Numerical Results and Discussions
### Effect of Step Size \(\Delta t\)  
Different from the used N-S solver where the (physical) time step size is not a sensitive parameter to affect the simulation results, in the BGK scheme \(\Delta t\) needs to be carefully determined. A direct reason is that \(\Delta t\) and \(\tau=\mu/p\) are decoupled in the BGK scheme, but \(\Delta t\) only and the ratio between them are involved in the evaluations of time-dependent distribute function \(f\) and fluxes \(\mathbf{F}\) as shown in Equations (24) and (25). Physically, it is required that \(\Delta t/\tau\) should be within a reasonable range in order to correctly reflect the physical reality. For the low-speed viscous flow, sufficient particle collisions \(\Delta t\) occur at the cell interface and thus \(\Delta t\) should be greatly larger than \(\tau\). However, a too large \(\Delta t\) could lead to violation of the stability condition. On the other hand, a small \(\Delta t\) may decrease the computational efficiency and even implies rarefied flow, where the present Maxwellian-based BGK scheme no longer works. In [PERSON]'s work for Rayleigh-Benard simulation [16], they kept the collision time \(\tau\) to be around \(10^{-1}\Delta t\). [PERSON] et al. [11] suggested that \(\Delta t/\tau\) is about 10\(\sim\)100 for any practical simulations.  
To clearly show the effect of \(\Delta t\) on the simulation result, we study the steady-state case with \(Ra=10^{4}\) by using four different step sizes corresponding to the ratio \(\Delta t/\tau\) of 10, 50, 100 and 200. As shown in Figure 7, for \(\Delta t/\tau=10\) and 50, a stable \(Nu\) is obtained equaling to 2.5019 and 2.5131, respectively. The difference between them is about 0.2%. But, for \(\Delta t/\tau=100\), a periodic oscillation appears and it seems not to decay with time. Although its time-averaged value still agrees well with the previous stable values, this oscillation is physically not true. When \(\Delta t/\tau=200\) is used, a more intensive oscillation is found with the larger amplitude and higher frequency, which provides further evidence that the \"fake\" oscillation is entirely due to the numerics. Therefore it is concluded that when using the present BGK scheme for such low-speed thermal flows, \(\Delta t\) cannot be chosen as large as for subsonic/transonic flows. The current used step size \(\Delta t=1\times 10^{-7}\) leads to the value of \(\Delta t/\tau\approx 50\), which is a suitable choice.  
Figure 7: The effect of step size \(\Delta t\) on variation of \(Nu\) with time \(t\) for \(Ra\)\(=10^{4}\).
# Remediation of Heavy Metal Contaminated Farmland Soil by Biodegradable Chelating Agent GLDA
## 1 Introduction
However, up until now, studies have mainly focused on EDTA, and there are few studies on the biodegradable chelator, GLDA. There are few studies on the pH regulation of GLDA by citric acid, the influence of different leaching times of GLDA, and the amount of GLDA on the leaching effect in the relevant literature. During the study, various parameters, such as the contact time, pH value, chelator concentration, stability constant, and metal species distribution, affected the metal performance expression of chelators. In the experiment, we used the complexation reaction of heavy metals with biodegradable chelating agents to remove heavy metals from the soil and tested the physical and chemical properties of the soil before and after removal. In order to further improve the leaching removal efficiency of Pb from the soil by GLDA, and to explore the influence of different pH levels on the leaching effect of heavy metals in the soil by GLDA solution, citric acid and a GLDA solution were mixed, and four soil samples from different places were selected for soil leaching. The leaching effect of the combination of GLDA and citric acid was explored to determine whether it was better than that of the separate leaching effects. Further, on this basis, the influence of dosage and oscillation time on the leaching effect was explored, as well as the effect of the combination of GLDA and different salts, so as to determine the leaching condition with the best effect and the lowest cost. In this study, the effect of GLDA on the removal of heavy metal Pb from the soil by soil leaching under different application conditions was discussed for the first time. This method can be applied not only to metal Pb but also to other metals, such as Cd.
# Remote Crop Mapping at Scale: Using Satellite Imagery and UAV-Acquired Data as Ground Truth
## 3 Results
### Intercropped Maize (iMaize)  
A similar analysis was performed to compare spectral signatures of intercropped maize (denoted as iMaize) with the other agricultural categories. Maize in Rwanda is most often intercropped with beans and cassava, although intercropping patterns can vary widely between fields both in terms of the crops being intercropped with maize and the ratio of maize to other crops (Figure 4). Given this variability and the relatively low density of maize in these examples, it is not surprising that such intercropped maize has a spectral signature that better matches the signature of the crop that it is intercropped with than the signature of pure maize (Figure 5). As a result, intercropped maize was likely classified as another crop type (i.e., beans or cassava). This is consistent with the labeling procedure used in this study in which we assigned the label according to the dominant crop type.  
Figure 3: Boxplots of S2-MSI and S1-SAR features for all labeled training samples (**a**–**c**) and 2D S2-MSI feature space for the representative training samples of each labeled class (**d**).
# Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models
## 3 Results and Discussion
### Correlation Matrix of Input Features and Methanol Injection Rate  
To explore the relationships between the five input features (flow rate, temperature, pressure, gas specific gravity, and water content) and the minimum methanol injection rate required, a correlation matrix analysis was conducted. Visualized in Figure 12, this analysis  
Figure 11: Feature importance scores: (**a**) RF; (**b**) XGBoost; (**c**) DT.  
used Pearson correlation coefficients (r) from \(-1\) to \(1\) to quantify linear dependencies, shedding light on the feature influences on methanol dosing for hydrate inhibition. These findings complemented the machine learning models' predictions and feature importance analysis, offering a linear perspective to guide operational decisions.  
The matrix revealed clear patterns in methanol correlations. Flow rate showed the strongest positive correlation (r = 0.33), indicating that higher flows increase methanol needs due to elevated hydrate risks and distribution demands [4]. Temperature exhibited a moderate negative correlation (r = \(-0.52\)), suggesting that lower temperatures raise methanol requirements by stabilizing hydrates [3]. Pressure (r = 0.26), gas specific gravity (r = 0.20), and water content (r = 0.28) showed weaker positive correlations, reflecting milder effects. Water content's modest r-value, despite its role in hydrate formation [26], likely reflected its narrow dataset range, limiting the linear impact.  
Inter-feature correlations were near zero (e.g., flow rate vs. temperature; r = \(-0.01\)), confirming their independence and suitability as distinct predictors in ML models and minimizing multicollinearity [17]. These results aligned with Section 3.2, where flow rate and temperature dominated across the XGBoost, RF, and DT models, although water content's weaker linear role highlighted ML's nonlinear strength.  
Practically, this can inform methanol dosing. Flow rate adjustments significantly affect inhibitor needs, while temperature management reduces use in colder sections. Pressure, specific gravity, and water content play secondary roles. Integrating these insights with ML predictions optimizes hydrate prevention, balancing costs and environmental efficiency in gas pipelines.
## 3 Control Strategy Description
### Basic Theory of Sliding Mode Controller  
In this paper, different sliding mode controllers are designed to realize the speed tracking control of PMSM. In the design process of the controller, the most traditional sliding mode surface is generally designed as a linear sliding mode surface, and its expression is as follows:  
\[s=c_{1}e+x_{2} \tag{7}\]  
where \(s\) represents the sliding mode surface function and \(c_{1}\) is the parameter of the sliding mode surface, and its value cannot be zero. In order to achieve the strong robustness of the PMSM servo system, and to ensure the study of this paper does not lose generality, this paper selects the constant approximation law, and obtains the state equation of sliding surface as shown in Equation (8):  
\[\dot{s}=c_{1}\dot{e}+\dot{x}_{2} \tag{8}\]  
The control output signal of the design backstepping sliding mode controller is as follows:  
\[u=-g^{-1}[(ac_{1}+c)e+(bc_{1}+d)x_{2}+(ac_{1}+c)x_{1d}+K\text{sgn}(s)] \tag{9}\]  
where \(K\) is the constant velocity approach rate switching gain parameter.  
Combining Equations (6)-(9), the sliding mode surface function is derived as follows:  
\[\dot{s}=-K\text{sgn}(s)+c_{1}d(t) \tag{10}\]  
Equation (10) can be analyzed, if the value of the switching gain parameter of the constant velocity approach rate is greater than \(\mid\mid c_{1}d(t)\mid\mid\), the q-axis sliding mode surface equation of the PMSM can converge to zero in a finite time. However, the prerequisite for this inference is to ensure that the disturbance of the system mismatched channel is bounded. Although this condition makes the sliding mode control algorithm have some limitations, compared with the rest of the nonlinear algorithm, the condition of the bounded disturbance is more in line with the actual operating conditions of the PMSM constraints, which is one of the reasons the sliding mode control is widely used.  
The steady-state equation of the speed tracking control signal can be obtained by introducing \(s=0\) into Equation (6) as follows:  
\[\dot{e}=(a-bc_{1})e+ax_{ld}+d(t) \tag{11}\]  
The analysis of Equation (11) shows that the designed backstepping sliding mode controller can make the speed reach the sliding surface in a limited time, but the equilibrium point calculated by the equation is not the origin, which indicates that the system cannot reach the desired sliding surface. The system will only vibrate near the sliding surface. This inference also proves that the sliding mode control algorithm has a good suppression effect on the disturbance of the matched channel, but is more sensitive to the influence of the disturbance of the unmatched channel.
# A Novel Method for Estimating the Dosage of Cold Atmospheric Plasmas in Plasma Medical Applications
## 3 Experimental Results
### Cell Viability in Response to the CAP Treatment  
Since the discharge power and the initial number of cells in each well were both fixed, the different values of \((W/N)\) in Figure 5, i.e., 19.44, 29.16, 34.02, 38.88, 43.74, 48.60, 68.04, and 97.20 mJ/cell, actually corresponded to different plasma treatment times of 2, 3, 3.5, 4, 4.5, 5, 7, and 10 min. The experiments were repeated three times on different days in this study. It could be seen from Figure 5 that: (i) with increasing in the treatment time, the cell viability decreased obviously; (ii) the value of \((W/N)\) corresponding to LD\({}_{50}\) should lie between 30 and 45 mJ/cell.
# Experimental Investigation of Anisotropic Diffusion Applied in Ghost Imaging Reconstruction
## 1 Introduction
CGI greatly simplifies the imaging system of ghost imaging and it is of great significance both for academic research and for applications, such as three-dimensional imaging, single pixel imaging and lidar and so forth because of its depth resolution. As far as all the methods above simplifying the system and optimizing the image speed, background noise remains the major factor disturbing the imaging quality, so further denoising techniques to improve the quality of the reconstructed image remains the focus of research. The IPGI algorithm takes the reconstruction result of PGI as the initial image to construct the interference noise term. Theoretically, image quality will keep getting better with successive iterations, so continuous iteration can effectively suppress the noise interference [8; 9; 10; 11]. After several iterations, the noise will be greatly suppressed. However, one of the problems with the IPGI algorithm is that the image used to construct the noise interference term should be as clear as possible, otherwise the iterative operation may generate more noise. Anisotropic diffusion, described by the Perona-Malik equation [29], has a good effect on reducing noise and preserving edge information. Therefore, in order to make the constructed noise interference terms close to the real noise, we introduce anisotropic diffusion to smooth the reconstructed images before each iterative operation, thus further suppressing the noise. Compared with IPGI, this proposed method can not only make the objects more visible, but also well protects the reconstructed image edge details.
# Experimental and Theoretical Study of One-Way Cyclic Lateral Responses of Piles in Sloping Ground
## 3 Results and Discussion
### Cyclic Lateral Loading Tests
In conclusion, both the increase of load amplitude and slope angle can increase the maximums of \(M_{\alpha}\) and \(p_{\alpha}\), with greater pile deflections; however, only the increase in slope angle w leads to the increased depth of these maximums. Figure 13: Dimensionless bending moment and subgrade reaction profiles under different cycle numbers. (**a**) bending moment profiles; (**b**) subgrade reaction profiles. Similarly, the FBEM is also employed to predict the pile responses in cyclic lateral loading tests including the dimensionless pile deflection profiles. For simplicity, only the predicted dimensionless pile deflection and bending moment profiles under different cycle numbers in test C2 are demonstrated in Figure 15. The pile deflections evidently demonstrate a characteristic of flexible piles; in addition, they gradually increase with the increasing cycle number when the number of cycles \(n\) is greater than 100 but are insensitive to the cycle number when it is greater than 100 and especially greater than 600. Excellent agreement was observed between the FBEM predictions and measured results, proving that the FBEM can well predict the one-way cyclic lateral responses of piles in sloping ground (Figure 16). Explanations for two points are necessary. The first point is the discrepancy in terms of the locations of the maximum bending moment of piles; the measured maximum bending moment seems to occur at about \(z_{\alpha}=1.75\) in Figure 13a, whereas the locations of the predicted ones are varied in Figure 15b (at \(z_{\alpha}=1.25\) when \(n=1\) and at \(z_{\alpha}=2\) when \(n>100\)). This is mainly because the curves directly connecting the scattered points of measurement are inherently discontinuous and thus probably fail to capture the real extreme points of interest, although they can also provide a possible pattern reflecting the development of the bending moment to some extent. To make up for shortcomings of the scattered measurement and obtain a full picture of relatively real bending moment profiles, FBEM can work as a supplementary analysis means, as previously testified. The second is the unfavorable prerequisite for the use of FBEM; although FBEM is effective in solving the aforementioned problem, it requires the measurement of the bending moment at the ground surface for each case when the subgrade reaction modulus is uncertain, thus preventing a generalized application. To summarize, FBEM in this paper is only used as a supplementary analysis method for the experimental method. In a follow-up study, a more systematic theoretical method including the assessment method of subgrade reaction modulus under cyclic load will be proposed by comparative analysis of more in-depth tests and numerical studies. Figure 14: Dimensionless bending moment and subgrade reaction profiles under different slope angles and load amplitudes. (**a**) bending moment profiles; (**b**) subgrade reaction profiles.
# Parametric Analysis of Control Techniques for 15 MW Semi-Submersible Floating Wind Turbine
## 2 Target Floating Wind Turbine
### Verification of OWT Numerical Modeling  
To verify the operational performance of the numerically modeled offshore wind turbine (OWT) in OpenFAST, steady-state simulation was conducted. Figure 2 presents the comparison of steady-state simulation results between the IEA 15 MW numerical model provided as open source by NREL and the numerical model implemented in this study [20]. The results showed that the two models exhibited identical operational performance in terms of the rotor speed, blade pitch, generator torque, and generator power across the range from cut-in wind speed to cut-out wind speed.
# Multi-Camera Person Re-Identification Based on Trajectory Data
## 2 Materials and Methods
### Trajectory Extraction
#### 2.2.1 Object Detector  
For the detection of customers within our video frames, we used the widely recognized object detector \"You Only Look Once\" (YOLO), first proposed in [7]. The architecture of this model consists of a single convolutional neural network (CNN), capable of predicting multiple bounding boxes as well as associated class probabilities for each. Its name describes its ability to require just a single analysis of an image to infer the objects within the image and their locations. The main advantages over other object detectors, such as Region-based CNN [8] (including Fast [9] and Faster [10]), are its remarkable speed, its ability to consider the context objects appear in in the image, and its versatile approach to learning object representations. Over time, different variations of the YOLO algorithm have been proposed, the latest being version 8. However, for our work, we decided to use version 5 (YOLOv5), because of its stability, good performance, compatibility with our available computational power, and its ready accessibility through the PyTorch Hub's repository of pre-trained models.
## 4 Experimental Results and Analysis
### Analysis of the Two-Way Partitioning Results
#### 4.1.2 Analysis of Runtime  
In the circuit-partitioning optimization problem, the running time of different algorithms is an important indicator. To evaluate the time performance of the proposed algorithm, we compared the execution times of METIS, KL algorithm, genetic algorithm, and the proposed algorithm on several circuit instances. Table 4 shows the running times on different circuits (us).  
Compared to METIS, the average execution time of the proposed algorithm is 0.97 to 1.78 times that of METIS. On average, the execution time of the proposed algorithm is 1.13 times that of METIS, which is slightly higher, but the difference is minimal. Compared to the KL algorithm, the proposed algorithm performs better, with its execution time being 0.33 to 0.64 times that of KL. On average, the execution time of the proposed algorithm is 0.47 times that of KL, with a significant improvement in execution time, especially for medium- and small-scale circuits. Compared to the genetic algorithm, the proposed algorithm consistently exhibits lower execution times, ranging from 0.03 to 0.99 times, indicating that the proposed algorithm significantly reduces computation time in most circuits. Overall, the proposed algorithm demonstrates stable performance, with significant optimizations in execution time, especially for medium- and small-scale circuits, and outperforms both the KL algorithm and the genetic algorithm.
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 4 Proposed CCB-Guided Adaptive Channel Denoising Mechanism  
To adapt to the complex channel conditions under satellite-ground conditions, improve transmission quality, and reconstruct the image fidelity, we propose a denoiser module and a model parameter selection mechanism. The denoiser module dynamically adjusts the parameters and configuration of the model by modeling and storing different channel environment parameters to best adapt to different channel qualities. The model parameter selection mechanism judges the current actual channel parameters through the pilot signal and dynamically selects the appropriate denoiser model parameters to achieve the best transmission performance under different channel and SNR conditions, thereby improving the robustness and reliability of the system.