esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Dataset and Metric  
**Dataset**. We first evaluated the performance of CMD-Net on the GPDNet dataset [7]. The test set of this dataset was composed of 10 categories selected from ShapeNet [45] repository: _airplane_, _bench_, _car_, _chair_, _lamp_, _pillow_, _rife_, _sofa_, _speaker_, and _table_. Each category of the test set involved 10 instances, each of which was subjected to three levels of Gaussian noise: low-level (\(\sigma=0.01\)), medium-level (\(\sigma=0.015\)), and high-level (\(\sigma=0.02\)). Each instance consisted of 30,720 points. Note that, every instance in this dataset was pre-aligned to the canonical space, and there was no rotated instance. Since our method CMD-Net is capable of canonicalizing arbitrarily rotated clouds, we were unable to measure the performance of our method on the GPDNet dataset regarding canonicalization. To this end, we created a new dataset committed to the problem of _category-level 3D shape denoising through canonicalization_: We first took 10 instances of the _airplane_ category from the GPDNet dataset. Then, we rotated the low-level, mid-level, and high-level noise-corrupted samples (30 in total) of each instance around the \(x\) axis (\(\theta_{x}\)), the \(y\) axis (\(\theta_{y}\)), and the \(z\) axis (\(\theta_{z}\)), respectively. We applied the set \(\Theta\) that involved five set of rotations \(\Theta=\{(\theta_{x_{i}},\theta_{y_{i}},\theta_{z_{i}})\}_{i=1}^{5}\) where all numbers were in degree:  
\[\Theta=\{(10,10,10),\,(15,-20,-30),(30,20,40),(50,45,-20),(-20,-20,-20)\} \tag{11}\]  
After performing the same procedure for 10 categories of the GPDNet dataset, we finally obtained a test set that involved 1500 samples of 100 instances of 10 categories. We call this dataset the \"CMD-Net dataset\", and will make it publicly available. Sample instances are shown in Figure 4.  
Figure 4: Samples from CMD-Net Dataset: (**left-most column**) noise-free 3D point clouds in the canonical space; rotated instances (**2 nd column**) with \(\sigma=0.01\); (**3 rd column**) with \(\sigma=0.015\); (**right-most column**) with \(\sigma=0.02\).  
**Metric.** We evaluated the denoising performance of the proposed method using the Chamfer Measure (cloud-to-cloud distance) [7] that produces the score C2C for the average distance of the denoised points \(\mathbf{H}_{u}^{can}\) from the ground truth cloud of the underlying surface \(\mathbf{C}^{can}\):  
\[C2C=\frac{1}{2M}\left[\sum_{i=1}^{M}\min_{j}\left|\left|\mathbf{u}_{i}-\mathbf{ c}_{j}\right|\right|_{2}^{2}+\sum_{j=1}^{M}\min_{i}\left|\left|\mathbf{u}_{i}- \mathbf{c}_{j}\right|\right|_{2}^{2}\right]\,,\,\mathbf{u}_{i}\in\mathbf{H}_{u} ^{can}\,,\,\mathbf{c}_{j}\in\mathbf{C}^{can}. \tag{12}\]  
The lower the score of C2C, the better the denoising performance of the method is.
## 2 Related Work
### Routing in LEO Satellite Networks  
A load-balancing routing method based on the extended link states (LRES) algorithm, which uses a congestion avoidance mechanism, has been proposed to select a path from multiple paths between a source and a destination node in satellite constellations [29]. A cooperative data downloading (CDD) routing algorithm for LEO networks that use the inter-satellite laser link between the visible satellites to schedule feeder links' bandwidth resources has been proposed to optimize remote sensing data downloading [30]. An autonomous on-board routing algorithm that makes routing decisions on each hop using the link state information of the ISLs between neighboring satellites was proposed for LEO satellite networks [31]. The location-assisted on-demand routing protocol (LAOR) computes the shortest path for each communication request to estimate a better network state and avoid congestion in the LEO satellite network [18]. An adaptive routing for the non-geostationary satellites network was proposed for dynamic network topology changes and traffic load fluctuations on the ISLs using a link cost metric comprising propagation delay and traffic load [17]. A clustering routing scheme based on the Nash bargaining solution that contains intra-cluster and inter-cluster routing phases was proposed using an agent-based clustering framework [32]. An extreme-learning-machine-based distributed routing (ELMDR) strategy was proposed to make routing decisions on LEO satellites based on the traffic forecast at the satellite nodes using the ground traffic load [33].
# Green Approach in Water-Based Drilling Mud Design to Increase Wellbore Stability
## 2 Materials and Methods
### Laboratory Test Equipment and Test Procedures  
The used equipment and testing procedures are shown in Figure 5.  
The prepared drilling mud (Figure 5a) was poured into the cell of an API filter press (Figure 5b), and pressure of 0.6895 MPa (100 psi) was applied for a period of 30 min. The filtrate volume extracted from the drilling mud through a filter paper (Whatman No. 50, filter area 45.8 cm\({}^{2}\)) was collected in a laboratory beaker, while a mud cake was formed on the paper (Figure 5c). The collected filtrate volume is expressed as API filtration.  
The capability of drilling mud to plug pores in a ceramic disc was determined using a permeability plugging tester (PPT) (Figure 5d) and ceramic discs with permeabilities of 0.4 \(\upmu\)m\({}^{2}\) (400 mD) and 0.75 \(\upmu\)m\({}^{2}\) (750 mD). The tests were performed at a differential pressure of 3.447 MPa and a temperature of 88 \({}^{\circ}\)C. During the test, the filtrate was collected in a measuring cylinder after 7.5 min and after 30 min. According to the manufacturer's instructions, to calculate PPT filtration, the collected volume after 30 min must be multiplied by 2 (Equation (1)), while the volume of collected fluid before the formation of a drilling mud cake (spurt loss) is calculated by using Equation (2) [45]:  
Figure 5: Used equipment and test procedures.  
\[\text{PPT filtrate volume = 2\cdot V_{30}} \tag{1}\] \[\text{Sput loss = 4\cdot V_{7.5}-2\cdot V_{30}} \tag{2}\]  
where PPT filtrate volume, mL; V\({}_{7.5}\), fluid volume collected after 7.5 min, mL; V\({}_{30}\), fluid volume collected after 30 min, mL; spurt loss, fluid volume collected before forming a drilling mud cake, mL.  
To determine their swelling, the bentonite-quartz pellets were removed from the compactor cell after the compression time of 30 min had elapsed, and placed in the dynamic linear swell meter cell (Figure 5e). The swelling test for each pellet in the selected drilling mud was designed and run for 24 h in the linear swell meter (Figure 5f). In the first case, the swelling of the pellets was determined at room temperature. To determine the effect of temperature on pellet swelling, in the second case, pellet swelling at 90 \({}^{\circ}\)C was determined. After 24 h, the swollen pellet was removed from the cell of the dynamic linear swell meter (Figure 5g).
## 2 Theory and Methodology
### Univariate Interpolation Schemes
#### 2.2.2 Hierarchical Univariate Interpolation  
We further demand that the interpolation nodes form sequences of nested grids for increasing interpolation levels, such that \(Z_{i-1}\subset Z_{i}\), \(\forall i>0\). In that case, we may define a hierarchical interpolation scheme as follows. We first define the operator \(\Delta_{i}\) as the difference between two consecutive univariate interpolants, i.e.,  
\[\Delta_{i}=\mathcal{I}_{i}-\mathcal{I}_{i-1}, \tag{4}\]where \(\mathcal{I}_{-1}\left[g\right]\left(y\right)=0\). The interpolation in Equation (3) can now be given as a sum of interpolant differences, such that:  
\[\mathcal{I}_{i}\left[g\right]\left(y\right)=\sum_{k=0}^{i}\Delta_{k}\left[g \right]\left(y\right). \tag{5}\]  
The hierarchy of formula in Equation (5) is obvious if we consider two consecutive interpolation levels, \(i-1\) and \(i\), in which case the level-\(i\) interpolation reads:  
\[\mathcal{I}_{i}\left[g\right]\left(y\right) =\mathcal{I}_{i-1}\left[g\right]\left(y\right)+\sum_{j:y_{ij} \in Z_{i}\setminus Z_{i-1}}\left(g\left(y_{i,j}\right)-\mathcal{I}_{i-1} \left[g\right]\left(y_{i,j}\right)\right)l_{i,j}\left(y\right) \tag{6a}\] \[=\mathcal{I}_{i-1}\left[g\right]\left(y\right)+\sum_{j:y_{ij}\in Z _{i}\setminus Z_{i-1}}s_{i,j}l_{i,j}\left(y\right), \tag{6b}\]  
where the interpolation coefficients \(s_{i,j}\), given by:  
\[s_{i,j}=g\left(y_{i,j}\right)-\mathcal{I}_{i-1}\left[g\right]\left(y_{i,j} \right),\;\;y_{i,j}\in Z_{i}\setminus Z_{i-1}, \tag{7}\]  
are called hierarchical surpluses. In the particular case of \(m(i)=i+1\), which can in fact be accomplished using the Leja sequences discussed in Section 2.3, a single grid point is added for each new interpolation level. Then, the index \(j\) can be dropped and Equation (6) is simplified to \(\mathcal{I}_{i}\left[g\right]\left(y\right)=\mathcal{I}_{i-1}\left[g\right] \left(y\right)+s_{i}l_{i}\left(y\right)\).  
The obvious advantage of using nested grids and the hierarchical Equations (5) or (6), is that at each new level \(i\) the QoI must be evaluated only at the new nodes \(y_{i,j}\in Z_{i}\setminus Z_{i-1}\). Moreover, univariate hierarchical interpolation rules constitute the backbone of the sparse adaptive multivariate interpolation algorithms discussed in Section 2.4.
## 2 Literature Review
\begin{table}
\begin{tabular}{c l l l} \hline
**Space** & **Objective** & **Solution Approach** & **Reference** \\ \hline \multirow{8}{*}{2D} & Max. Coverage & Particle swarm optimization (PSO) & [7] \\ \cline{2-4}  & Max. Coverage, Min. Cost & Greedy algorithm and Dual sampling algorithm & [2] \\ \cline{2-4}  & \multirow{4}{*}{Max. Weighted Coverage} & Binary integer programming (BIP) & [8] \\ \cline{2-4}  & & Genetic algorithm, Self-orientation algorithm, & [9] \\ \cline{2-4}  & Multi-Objective (Min. Cost, Min. Motion, and Min. Maximum Brightness) & \multirow{2}{*}{Covariance matrix adaptation-evolution strategy (CMA-ES)} & [10] \\ \cline{2-4}  & Multi-Objective (Max. Effective Coverage, Min. Cost, and Max. Total Visibility) & & Non-dominated sorting Genetic algorithm (NSGA-II) & [11] \\ \hline \multirow{8}{*}{3D} & Max. Coverage & Greedy algorithm based on an Image-based model & [3] \\ \cline{2-4}  & \multirow{4}{*}{Max. Weighted Coverage} & Simulated annealing (SA)-based area coverage & [12] \\ \cline{2-4}  & & enhancing algorithm & \\ \cline{2-4}  & \multirow{4}{*}{Max. Weighted Coverage} & Binary integer programming (BIP) & [13] \\ \cline{2-4}  & & Genetic algorithm, Octree-based search, and Random search & [14] \\ \cline{2-4}  & & Collaboration-based local search algorithm (COLSA) & [15] \\ \cline{2-4}  & Max. Weighted Coverage, Min. Number of Camera & Binary quadratic programming (BQP) & [16] \\ \cline{2-4}  & Min. Cost & Particle swarm optimization (PSO) & [17] \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of previous literature about the camera placement problem. to maximize the coverage of user-defined priority areas with the optimum values of parameters that included pan, tilt, zoom, and camera location. [PERSON] et al. [20] addressed two issues common in surveillance applications: occlusions and the reconfiguration of PTZ cameras. They also considered new relevance maps, which are computed by identifying the occluded areas for each camera. Although numerous camera placement problems have been studied, only a few studies have investigated the problem in the context of large-scale surveillance in the 3D space and using real-world data. Furthermore, studies addressing the camera placement problem with different specifications of PTZ cameras and occlusions are rare in the literature. Therefore, the goal of our study is to bridge the gap between theoretical studies and real-world problems that have more realistic requirements for surveillance activities. In this manuscript, Section 3 describes the constraints, including PTZ cameras, occlusions, and the characteristics of the surveillance targets. Section 4 presents efficient algorithms for solving the problem within a reasonable amount of time in a scenario.
## 2 Research Methodology  
### Gradient Boosting Regression Tree  
Gradient Boosting Regression Tree represents an integrated algorithm with strong learning strategies. Despite an original aim to solve classification problems, it was successfully applied in the field of regression [21].  
\[F_{m}(x)=F_{m-1}(x)+h_{m}(x) \tag{1}\]  
where \(h_{m}(x)\) represents the basis function of the weak learner. In GBRT, the basis function \(h_{m}\) is a small regression tree of fixed size, for which the GBRT model \(F_{m}(x)\) can be considered as the sum of \(m\) small regression trees. As illustrated in Figure 1, for each iteration \(m\), a new tree generated. A simple tree is determined by the deviation between the experimental measurement and all the previous models (i.e., gradient) prediction. Then, the regression tree is incorporated into the GBRT model.  
The algorithm is purposed to estimate the response \(Y_{i,t+k}\) corresponding to the training set, which means that the better \(h_{m}\) needs to satisfy the formula:  
\[F_{m}(x_{i,t})=F_{m-1}(x_{i,t})+h_{m}(x_{i,t})=Y_{i,t+k}. \tag{2}\]  
Converted to  
\[h_{m}(x_{i,t})=Y_{i,t+k}-F_{m-1}(x_{i,t}) \tag{3}\]  
\(h_{m}\) in Equation (1) is a model for fitting the residual \(r_{m,i,t}=Y_{i,t+k}-F_{m-1}(x_{i,t})\) at m iterations. The current residual can be expressed as a negative gradient of the squared error loss function:  
\[-\frac{\partial\frac{1}{2}\Big{(}Y_{i,t+k}-F_{m-1}(x_{i,t})\Big{)}^{2}}{ \partial F_{m-1}(x_{i,t})}=Y_{i,t+k}-F_{m-1}(x_{i,t}). \tag{4}\]  
Figure 1: Schematic of tree-based gradient boosting.  
It can be concluded from Equation (4) that \(h_{m}\) is converted to a negative gradient of the squared loss function. In addition, Equation (4) proves that the gradient lifting algorithm of the least square error loss function is a gradient descent algorithm. By substituting the squared error with different loss functions and their gradients, they are generalized to other loss functions. The gradient lifting and gradient boosting regression trees are described in detail in the literature [21; 34]. For each lift and iteration, a regression tree is fitted to the current residual. The addition of enough regression trees into the final model can give rise to arbitrarily small training errors. To avoid overfitting, a simple regularization strategy is adopted to scale the contribution of each regression tree by a factor of m.  
\[F_{m}(x)=F_{m-1}(x)+Vh_{m}(x)\quad V\in[0;1] \tag{5}\]  
The parameter \(V\) is known as the learning rate, as \(V\) reduces the step size during the gradient descent. The interaction taking place between \(V\) and the number of iterations \(M\) is significant. Smaller \(V\) requires more iterations, as a result of which, more basis functions are required to converge the training error. Empirical evidence suggests that a small \(V\) value has the capability to achieve better prediction accuracy. [PERSON] et al. [21] recommended setting the learning rate to a small value and choosing to stop \(M\) early. The interaction between \(V\) and \(M\) can be found in the literature [34].
## Materials and Methods
### MDMIP Synthesis  
Fe\({}_{3}\)O\({}_{4}\) nanoparticles were prepared using a modified version of a previously reported hydrothermal method [43]. FeCl\({}_{3}\cdot\)6H\({}_{2}\)O (0.665 g), anhydrous sodium acetate (1.80 g), PEG 1000 (0.5 g), and ethylene glycol (20 mL) were placed in a Teflon-lined stainless-steel autoclave. The autoclave was heated at 180 \({}^{\circ}\)C for 8 h. The Fe\({}_{3}\)O\({}_{4}\) nanoparticles were separated magnetically and washed several times with ethanol and water. The precipitate was dried at 60 \({}^{\circ}\)C in a vacuum oven.  
The Fe\({}_{3}\)O\({}_{4}\) particles were modified with SiO\({}_{2}\) using the method reported by [PERSON] et al. [44]. Fe\({}_{3}\)O\({}_{4}\) (200 mg) was dispersed in 0.1 M hydrochloric acid solution (50 mL) by sonication for 10 min. The Fe\({}_{3}\)O\({}_{4}\) was separated using an external magnet and mixed with ethanol (160 mL) and ultrapure water (40 mL) by sonication for 15 min. Ammonium hydroxide (5.0 mL, 25 wt %) and TEOS (0.7 mL) were then added sequentially. The mixture was stirred at 40 \({}^{\circ}\)C for 12 h. The Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) nanoparticles were collected magnetically, thoroughly washed with ethanol and water, and dried in a vacuum for 24 h.  
The Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) nanoparticles were modified with vinyl groups. The nanoparticles (200 mg) were dissolved in methanol (50 mL) by sonication for 15 min, followed by addition of MPS (3 mL). The mixture was incubated at room temperature for 24 h. The Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@MPS nanoparticles were separated using an external magnet, washed with water, and dried in a vacuum for 24 h.  
The MDMIPs were prepared using the surface molecular imprinting method [45]. Typically, 0.25 mm 2,4-diamino-6-methyl-1,3,5-triazine and 1 mm MAA were dispersed in chloroform (30 mL) containing dimethyl sulfoxide (1 mL) and ethylene glycol (2 mL) by sonication for 15 min, and the mixture was cultivated at 4 \({}^{\circ}\)C for 3 h. Acetonitrile (10 mL) containing Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)@MPS nanoparticles (80 mg) was mixed and then ultrasonicated for 30 min.  
EGDMA (5 mm) and AIBN (25 mg) were then added sequentially to the mixture. The solution was deoxygenated with N\({}_{2}\) for 10 min and the reaction was performed at 60 \({}^{\circ}\)C for 24 h, with mechanical stirring. The MDMIPs were separated magnetically and washed with methanol/acetic acid (9:1, \(v/v\)) until no template was found by HPLC. Magnetic dummy molecularly non-imprinted polymers (MDNIPs) were prepared using the same procedures but without the dummy template. The procedure for MDMIP preparation is shown in Figure 1A.
# Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer
###### Abstract  
The lidar ceilometer estimates cloud height by analyzing backscatter data. This study examines weather detectability using a lidar ceilometer by making an unprecedented attempt at detecting weather phenomena through the application of machine learning techniques to the backscatter data obtained from a lidar ceilometer. This study investigates the weather phenomena of precipitation and fog, which are expected to greatly affect backscatter data. In this experiment, the backscatter data obtained from the lidar ceilometer, CL51, installed in Boseong, South Korea, were used. For validation, the data from the automatic weather station for precipitation and visibility sensor PWD20 for fog, installed at the same location, were used. The experimental results showed potential for precipitation detection, which yielded an F1 score of 0.34. However, fog detection was found to be very difficult and yielded an F1 score of 0.10.  
backscatter data; lidar ceilometer; weather detection; machine learning Article Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer [PERSON] et al. (2020), 10, 6452; doi:10.3390/app10186452
## 3 Methodology  
This study proposes an all-weather Land Surface Temperature (LST) generation method based on the Random Forest and Ridge Regression (RFRR) ensemble algorithm, aimed at leveraging the nonlinear fitting capability of machine learning to enhance the practical application value of FY-3C LST.  
The basic assumption underlying the proposed method is that the relationship between surface temperature and other environmental variables established under clear conditions remains applicable under cloudy conditions. As shown in Figure 2, the overall framework of this method consists of five main steps:  
1. The filling of Orbital Gaps in FY-3C MWRI and PMW data for 25 km surface temperature using Singular Spectrum Analysis.
2. Temporal filtering and the daily reconstruction of FY-3C VIRR, using NVI 1 km resolution data based on the Savitzky-Golay method.
3. The downscaling of FY-3C MWRI and PMW 25 km resolution surface temperature data using the machine learning RFRR algorithm.
4. Training and optimizing the RFRR model to merge FY-3C MWRI and VIRR LST data for different land cover types.
5. Generating seamless daily FY-3C 1 km resolution surface temperature data based on the land cover-specific fusion models.  
Figure 2: Flowchart of training the RFRR model and prediction after FY-3C data preprocessing. RFRR refers to the integrated model of Random Forest and Ridge Regression.
# A Design Method of Code Correlation Reference Waveform in GNSS Based on Least-Squares Fitting
## 6 Conclusions  
An LS technique is proposed for the design of CCRW for BPSK and BOC signals in GNSS, using the truncated SVD method. The purpose of this technique is to find an appropriate receiving discriminator for unambiguous and anti-multipath tracking in band-limited situations. The results show that this technique is capable of generating relatively simple local reference waveforms for coherent and non-coherent discriminators in different bandwidth cases, except that the BOC(6,1) and BOC(7,1) only have coherent results. The corresponding S-curves are capable of avoiding the false lock points. Their multipath error envelopes of the coherent discriminator are about 60% of the W2 CCRW for the BPSK signal, about twice for the BOC(1,1) signal and about three times for the BOC(2,1) signal. Their multipath error envelopes of the non-coherent discriminator are about the same as the W2 CCRW for the BPSK signal, except that it is half of W2 CCRW with infinite bandwidth, about 150% for the BOC(1,1) signal and about twice for the BOC(2,1) signal. The standard tracking errors of acquired S-curves are usually larger than W2 CCRW, except for the cases of the BOC(1,1) signal. Therefore, the LS method can acquire S-curves without false lock points at the cost of MEE area and tracking precision.  
In this study, [PERSON], [PERSON] and [PERSON] conceived of and designed the experiments. [PERSON] performed the experiments, compiled the program codes, analyzed the data and wrote the paper. [PERSON] contributed analysis tools and modified English errors.  
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.
## 3 Measurement and Result Analysis
### E, H and Zurface Current Analysis of the Proposed Structure  
Fundamentally, the unit cell's radiating patch includes two SRRs, and each of them corresponds to a frequency. From Figure 12, we can say that the radiating patch of the proposed unit cell resonates at two different frequencies: 9.84 GHz and 11.46 GHz, correspondingly. The E-field is stronger on the circular-typed SRR, which corresponds to the 9.84 GHz frequency, and similarly, a strong E-field is formed on the square-typed SRR, which corresponds to the 11.46 GHz frequency. Finally, we can conclude that both E and H-fields for two frequencies are excited in the opposite direction, which is the principle satisfying the Maxwell equations.
# Robust U-Nets for Fetal R-Peak Identification in Electrocardiography
## 5 Comparative Assessment of Existing R-Peak Detection Methods
### Performance Relative to Robustness
In low SNR scenarios (0 dB and 6 dB), U-Net shows a small IQR and a high median on both databases, indicating that it is more robust to both physiological noise (ADFECGDB) and synthetic noise (FECGSYN) than other existing methods. Additionally, Table 5 provides a rigorous comparative analysis with explicit low-SNR evaluation. While existing methods achieve competitive F1-scores (>93%) on high-quality data, their performance degrades substantially under low-SNR conditions (36.18-73.80%). In contrast, our U-Net maintains robust detection accuracy (87.38-88.44% F1-score) without preprocessing, demonstrating 23.5-51.2% absolute improvement in challenging environments. \begin{table}
\begin{tabular}{c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Preprocessing Requirement**} & \multirow{2}{*}{**Dataset**} & **F1 (\%)** &