esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Digital Traffic Environment
### Coordinate Systems  
In this work, we adopt the coordinate system depicted in Figure 1. The geodetic fixed coordinate system XOY is established with \((\lambda_{0},\varphi_{0})\) as the origin coordinates. The positive directions of the X and Y axes point to the true east and true north, respectively. The coordinate system XOY fixed to the own ship (OS) is established. The center of gravity of the OS is set as the origin, and the positive directions of the x and y axes point to the starboard abeam and bow of the OS, respectively. True course (\(TC\)) of OS is the angle between the Y and y axes, true bearing (\(TB\)) of target ship (TS) is the angle from Y-axis to the bearing line, and relative bearing (\(Q\)) is the angle from OS heading line to the bearing line. The relationship between \(TB\), \(Q\), and \(TC\) is shown in Equation (1).  
\[(TB=Q+TC) \tag{1}\]  
The transformation equation of the coordinate system from x-o-y to XOY is shown in Equation (2).  
\[(X,Y)=(x,y)\times B+(\lambda_{\varphi},\varphi_{0}) \tag{2}\]  
Figure 1: Coordinate systems transformation (Coordinate system XOY and xoy).  
where B is the transformation matrix, as shown in Equation (3).  
\[B=\begin{bmatrix}cos\ (TC)&-sin\ (TC)\\ sin\ (TC)&cos\ (TC)\end{bmatrix} \tag{3}\]  
The coordinates of the ship in the XOY coordinate system can be obtained by transforming its longitude and latitude (\(\lambda,\varphi\)) through Equation (4).  
\[\left\{\begin{aligned} X=R*arcos\Big{[}cos^{2}\varphi_{0}* cos\ (\lambda-\lambda_{0})+sin^{2}\varphi_{0}\Big{]}\\ Y=R*arcos[cos\ \varphi*cos\ \varphi_{0}+sin\ \varphi_{0}]\end{aligned}\right. \tag{4}\]  
where \(R\) is the radius of the Earth.
## 1 Introduction  
Access to safe and clean water is increasingly threatened by diverse environmental pressures and intense human activity. Industrial discharges, agricultural runoff, and continuously increased amounts of municipal wastewater introduce a broad spectrum of chemical pollutants into natural waters, many of which are resistant to degradation and not effectively removed by conventional treatment systems [1, 2]. Among these, emerging contaminants (ECs) represent a growing category of concern due to their persistence, bioactivity, and potential to cause long-term ecological and human health effects [3, 4].  
Detected at concentrations ranging from nanograms to micrograms per liter, these substances often escape regulatory monitoring frameworks and are continuously introduced into aquatic environments. Their resistance to conventional biological and physicochemical treatments has been widely documented, along with the risk of generating transformation byproducts that may be equally or more toxic than the parent compounds [5; 6]. Studies have confirmed their presence in surface waters, groundwater and even drinking water supplies, raising concerns over chronic exposure, endocrine disruption and the propagation of antimicrobial resistance [7; 8].  
These risks are further amplified by the limited efficiency of standard treatment plants, which were not designed to remove complex organic micropollutants. Even where partial degradation occurs, byproducts may retain or exceed the toxicity of the parent compound, contributing to bioaccumulation and ecosystem stress [9; 10]. Additionally, the detection of pharmaceuticals, artificial sweeteners, and industrial additives in effluents and downstream drinking water underscores the inadequacy of current systems to prevent environmental dissemination [10; 11].  
Recent global assessments estimate that more than 2 billion people rely on drinking water sources contaminated by fecal or chemical pollutants, while nearly 80% of wastewater is discharged untreated into the environment [11]. Monitoring campaigns have identified pharmaceuticals, polyfluoroalkyl substances (PFAS), and personal care products in rivers and lakes across multiple continents [12]. For instance, a European survey reported frequent detection of antibiotics and anti-inflammatory drugs at concentrations exceeding ecotoxicological safety thresholds [13].  
In response to these challenges, attention has increasingly turned toward advanced treatment processes capable of degrading complex contaminant mixtures at trace levels. Heterogeneous photocatalysis has emerged as a promising approach due to its capacity for complete mineralization of organic pollutants, operation under mild conditions, and compatibility with solar energy. It offers significant advantages in environmental compatibility, treatment efficiency, and adaptability to diverse water matrices, making it a compelling candidate for sustainable water purification.
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 3 Feature-Based Modulation Recognition Methods
### Chaotic Mapping and Autocorrelation Estimation
\(\sigma_{p}\) and \(\sigma_{q}\) are the standard deviations of the pixel values in images \(p\) and \(q\), measuring the degree of dispersion of the pixel values. Experiments were conducted to evaluate the classification performance of this method across a SNR range of 5-30 dB, encompassing both low and high SNR scenarios. The results demonstrate that this method achieves an area under the receiver operating characteristic curve (AROC) of 1 at an SNR of 5 dB. This indicates that the model can perfectly distinguish between positive and negative cases; specifically, for any positive case, its score is higher than that of any negative case. The equal error rate (EER) is below 0.01, and the method exhibits robustness against phase noise. By enhancing inter-class differences through nonlinear projection, this scheme provides a practical low-power modulation recognition solution for OWC systems with lower complexity than traditional deep learning models. However, the randomness of chaotic mapping may lead to fluctuations in results. In dynamic time-varying channels, distortions such as varying degrees of rotation and phase shifts in the original constellation diagram can also be amplified by chaotic mapping, resulting in misclassification. Additionally, when handling new modulation formats, this method may require the retraining or adjustment of reference templates, potentially increasing maintenance costs.
## 3 Status and Application
### Single-Pass Interference  
The single-pass mode enables the simultaneous acquisition of dual-channel SAR data in a single flight by installing two antennas on the same platform. It has the advantages of being unaffected by time decoherence, having less atmospheric interference, and having a stable baseline. The single-pass mode can be further classified into Cross-Track Interferometry (XTI) and Along-Track Interferometry (ATI) based on the different baseline structures.  
Figure 8: SBAS-InSAR results. (**a**) Mean deformation velocity map. (**b**) Zoom-in of Sierra Negra caldera. The white marks indicate the locations of the stations in the continuous GPS network that has been deployed since 2002. (**c**) Comparison between the SBAS-InSAR time-series (black triangles) and the LOS projected GPS measurements (red line) in correspondence with the GPS station labeled as GV01 [its location is shown in (**b**)] [53].  
XTI refers to the working mode where the baseline is perpendicular to the heading direction, as shown in Figure 9a, and in this mode, one antenna transmits electromagnetic waves while the other two antennas simultaneously receive the reflected information from the ground. The interference phase is caused by the path difference between the two antennas and the ground target, and this path difference has a geometric relationship with the terrain. Therefore, this mode is used to obtain elevation information of the ground surface. ATI mode refers to the working mode where the baseline is parallel to the heading direction, as shown in Figure 9b, and the interference phase is mainly caused by the displacement changes of the ground target within two observations. Thus, this mode is often used for detecting ground target movement, mapping sea surface currents, etc. It is difficult to build two SAR systems with a certain baseline distance on the spacecraft platform, so this mode is mainly used on airborne platforms.
# Point Cloud Repair Method via Convex Set Theory
## 2 Methods
### Weakening of Convex Set  
Because the position of the 3D point is uncertain, a looser uncertainty analytical method is needed to deal with this ill-posed problem. Nonprobabilistic set theory can effectively deal with various uncertainty problems. If a set is convex, it is called a convex set. In the 3D point cloud, to reasonably distribute new filling points, we weakened the general definition of the convex set, so that it could be used in point-cloud repair and avoid the confusion of point distribution due to the uncertainty of the 3D point.  
**Definition 1**.: _In the repair of a 3D point cloud, let \(S\) be a 3D vector space containing \(x_{i}\) and \(x_{j}\). For the segment formed by the connection of \(x_{i}\) and \(x_{j}\) in subset \(s\) of \(S\), when all new filling points contained in \(s\) are in the direction domain of the line segment, \(s\) is a convex set. As shown in Figure 4,  
Figure 3: Example of point-cloud holes.  
Figure 2: The overview of our method.  
the new fill (blue) points were located in a direction of the line segment connected by \(x_{i}\) and \(x_{j}\). In the repair of 3D point clouds, the properties of convex sets are as follows: (1) The intersection of any convex set is also a convex set._  
_(2) If \(s\) is a convex set, any new filling point \(x_{new}\) in \(s\) is located on the side of the line segment formed by \(x_{i}\) and \(x_{j}\)._  
Considering the gradualness of the spatial point-cloud data change, it was assumed that the point-cloud data of the area to be filled and the hole edge point cloud had similar distribution rules, and the distribution of the points in the area to be filled could be inferred from the geometric information of the edge point set. When the set consisting of new filling points and edge points in a specific area meet the appeal definition, the set is convex.
# Delamination Effect on the Buckling Behaviour of Carbon-Epoxy Composite Typical Aeronautical Panels
## 4 Results and Discussion
### CD1-CD4 Configurations  
In Table 3, the buckling loads and displacement obtained from the linear eigenvalue buckling analyses are reported for each configuration.  
The stiffness of the panels remains almost the same. As expected, the configurations with delamination under two layers (CD1 and CD3) experience the local buckling of delamination at loads, that are 3.7 kN and 6.5 kN, respectively, much lower than the configurations with delamination under four layers (CD2 and CD4), where the loads are 21.4 and 38.2 kN. The skin buckling and the global buckling (which includes stringers) are very similar in terms of loads and displacements. Figure 5 shows the deformed shape in terms of out-of-plane displacements for the CD1-CD4 configurations.  
The initiation of propagation occurs earlier in the CD1 and CD3 configurations, but in all of the configurations, the delamination starts to propagate at loads (108 and 111.6 kN) considerably higher than the skin buckling (83.3 and 86.5 kN) and very close to the global buckling (123.3 and 124.6 kN), as can be noticed form the deformed shape in Figure 5. Indeed, propagation initiates but does not evolve due to the global buckling of the panel, as shown in Figure 6. All of the non-linear static analyses performed with the SMXB procedure stopped due to non-convergence after the global buckling of the panel.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Configuration** & \multirow{2}{*}{**Stiffness**} & \multicolumn{2}{c}{**Buckling Circular**} & \multirow{2}{*}{**Buckling Skin**} & \multirow{2}{*}{**Buckling Stringer**} & \multirow{2}{*}{**Delamination Initiation**} \\
**ID** & & & & & \\ \hline CD1 & 281,094 & 3.7 kN & 83.3 kN & 123.3 kN & 108 kN \\  & N/mm & 0.013 mm & 0.296 mm & 0.439 mm & 0.4 mm \\ CD2 & 281,060 & 21.4 kN & 76.9 kN & 122  
Figure 5: Out-of-plane displacement contour plot: (1) buckling of circular delamination, (2) buckling of the skin, (3) global buckling including stringers and (4) delamination initiation, with (**a**) CD1, (**b**) CD2, (**c**) CD3 and (**d**) CD4.  
Figure 6: _Cont._
## 2 Materials and Methods
### Workflow Description
#### 2.3.1 Data Integration  
The LULC map of the study area was obtained from the Azuay province at a scale of 1:5000 [53], which was constructed from the aerial orthophotography of the SIGTIERRAS project of Ecuador [52]. The _Polylepis_ occurrence data for the study area were sourced from georeferenced records available in online repositories, including the Global Biodiversity Information Facility (GBIF) and Herbario Azuay, collected in 2016. These initial records were validated through field verification conducted between July 2016 and October 2017. The final dataset comprises 244 presence records of the _Polylepis_ genus distributed as follows: 55 records for _P. incana_, 86 for _P. languisonsa_, 50 for _P. reticulata_, and 53 for _P. weberbueri_. Of the total dataset, 73% represents field-verified observations, while the remaining 27% corresponds to botanical collection data or herbarium records.  
Based on this LULC map, we generated random points in two targets, multiclass and binary, for the classification processes (Table 1). In the binary target, the No _Polylepis_ class groups classes such as native forests, pine, shrub vegetation, and non-forest classes. Finally, seventy percent of the points were allocated for training, while the remaining thirty percent were reserved for validation.
# Effect of Environmental Temperature on the Insulating Performance of Epoxy/MgO Nanocomposites
## 3 Results and Discussion
### Dielectric Performance
With the increase in environmental temperature, the conductive loss is inversely proportional to the frequency at lower frequencies, and the dielectric loss of the composites decreased rapidly. As the frequency increased, the conductive loss decreased, and the relaxation polarization loss has a smaller effect at high temperatures, resulting in a decrease in the dielectric loss [20].
###### Abstract  
The gradual reduction of water reserves for irrigation has become a worldwide concern. To improve the irrigation of processing tomato, we conducted a study to evaluate a system that monitors both plant water status and soil moisture levels (PlaSoMan) compared to an evapotranspiration method (IrriMan) and an empirical farmer's management (FarMan) in a two-year field trial. The results showed that PlaSoMan saved around 30% and 7.5% of water, with a yield loss of only 10% and 2.5% compared to FarMan and IrriMan, respectively. Thus, PlaSoMan showed satisfactory irrigation water use efficiency (IWUE) over the two years. Moreover, IrriMan and PlaSoMan had lower blue water requirement (BWR) values than FarMan. Finally, PlaSoMan reached the highest value of the yield quality indicator, which combined total yield and brix degree. Thus, the new system, which assesses both plant water status and soil moisture levels, appears to be associated with high-quality and water-friendly tomato production representing an efficient solution for areas with limited water resources.  
water saving; irrigation scheduling; water use efficiency; blue water requirement; wireless sensors; yield quality; plant water status; soil moisture level; smart irrigation Article
## 3 Application of the Proposed Methodology
### Modal Analysis  
This is the next step of the proposed methodology (see H on Figure 2). Modal analysis was performed to determine the behaviour of the structure in terms of dynamic loading and to optimise it. It was based on the theory of free vibration, where the general mathematical model of an oscillating system with one degree of freedom with viscous damping for free vibration is described by Equation (4):  
\[\ddot{q}(t)+c\dot{q}(t)+kq(t)=0 \tag{4}\]  
where \(m\) is the mass matrix, \(c\) is the damping matrix, \(k\) is the stiffness matrix (these matrices are constant), and \(q\) is the generalised coordinate (deformation) [53]. Advanced simulation programs use FEM to determine the natural frequencies in most cases. The modal characteristics based on the shape and material of the element are determined following Equation (4). The harmonic oscillation is generally substituted into Equation (5), where \(Q^{-}\) is the column matrix of harmonic oscillation amplitudes and \(\Omega\) is the angular frequency of the oscillation.  
\[Q=Q^{-}\cdot e^{i\Omega t} \tag{5}\]  
After substitution and correction, Equation (6) is obtained:  
\[\left(\mathbf{K}-\Omega^{2}\mathbf{M}\right)\cdot\overline{\mathbf{Q}}=0 \tag{6}\]  
Eigen frequencies result from the condition of the zero determinant, which are described by Equation (7):  
\[det\left|\mathbf{K}-\Omega^{2}\mathbf{M}\right|=0 \tag{7}\]  
The natural frequency according to Equation (8) [54] is obtained as output in most FEM software:  
\[f=\Omega/2\pi \tag{8}\]The input to the modal analysis was a model identical to that used for the stress and stiffness analyses. The same boundary conditions were also applied to the model. The aim of the analysis was to determine the behaviour of the machine at excitation frequencies up to 50 Hz, corresponding to a maximum machine speed of 3000 rpm. The results are presented in Section 4. The orientation of the ribs was again modified based on the results in order to increase the natural frequencies of the machine.
## 2 Materials and Methods
### Data Examination and Preprocessing
#### 2.2.2 Examining Similarity  
The second step is to examine the similarity distribution between all the images within the dataset to learn about data correlations. After investigating the dataset, we found that several pictures had similar latent space representations. This similarity factor is measured by taking the distance between the latent space representations of the images after normalizing or standardizing the images; in this case, images were divided by the maximum possible value of the pixels (255) as the standard image normalization method.  
Comparison of the latent space similarities was carried out in feature space of the VGG19 model. A different pre-trained network could have been used, but since we are comparing the most similar images based on the extracted features of the model, then no notable gain would have been obtained from picking a different pre-trained model. We used the Euclidean distance between the representations of the images and concluded the following:  
* By setting a small distance threshold of 0.3 and less: 10 similar images were detected, meaning that these images have at least another significantly related sample in the dataset. For instance, one of the examples is shifted a few pixels from the other one. It is indistinguishable to the human eye, especially since the image had a large resolution, making these tiny details and differences hard to pick up by the experts.
* For a threshold of 0.6 and less: 27 similar images were identified, 10 of them were the same as in the previous case, and the others had some minor differences. For instance, in some cases, one of the two images had measurements written on them.
* By increasing the threshold to 1.0, we found 86 images. Some of the cases were the same as the previous ones. However, new instances emerged with significant differences. For example, two of the pictures were of the exact same device, one of them was with bolts, and the other one was not having bolts.  
Based on these observations, the decision was to keep all the images as they will have no significant effect on the model performance or the measured accuracy. Additionally, given that this is how the original dataset is stored, we did not want to artificially influence or manipulate the data distribution. Nonetheless, such functionality might prove useful in other cases, this remains up to the industrial domain experts to decide when to use it.
# An Experimental Study on the Migration of Pb in the Groundwater Table Fluctuation Zone
## 3 Results and Discussion  
### The Curve of Adsorption and Desorption Kinetics  
Figure 3 shows the curves of adsorption and desorption kinetics of Pb. The adsorption and desorption capacities of Pb\({}^{2+}\) in the three sand media were significantly dissimilar. The adsorption and desorption quantity reached equilibrium in 1440 min. The maximum adsorption capacity of fine sand, medium sand, and coarse sand were 2366.6 mg\(\cdot\)kg\({}^{-1}\), 1847.6 mg\(\cdot\)kg\({}^{-1}\), and 1543.8 mg\(\cdot\)kg\({}^{-1}\), respectively. The maximum desorption capacity of coarse sand, medium sand, and fine sand were 81.6 mg\(\cdot\)kg\({}^{-1}\), 62.38 mg\(\cdot\)kg\({}^{-1}\), and 29.18 mg\(\cdot\)kg\({}^{-1}\), respectively. Compared to the hyperbolic diffusion model, pseudo-second-order model and Weber-Morris model, the data fit well to the Elovich model against various time ranges because the minimum \(R^{2}\) value was 0.90. The fitting parameters are shown in Table 2. Based on the adsorption kinetics experiment data, Pb\({}^{2+}\) in the contaminated solution was adsorbed rapidly onto the sampling sand within 240 min. The adsorption quantity gradually was stable from 240 to 1440 min in the experiment; this implies several points that adsorbed Pb\({}^{2+}\) quickly on the medium surface at the initial stage. The effective points that adsorb Pb\({}^{2+}\) gradually decreased with increasing reaction time, which gradually weakened the adsorption capacity until it reached equilibrium. The experiment's result aligned with the study of [PERSON] [33]. The same conditions occurred in the desorption kinetics experiment.  
Figure 4 illustrates the effects of pH on the adsorption and desorption of sand medium. The adsorption capacity of Pb\({}^{2+}\) in three sand media gradually increased at pH range of 4 to 6. The desorption capacity gradually decreased with increasing pH and the desorption quantity steadily approached equilibrium when the pH was between 8 and 9. Fine sand's adsorption and desorption capacities showed almost no change at different pH levels compared with medium and coarse sand. When the pH increased, the competitive adsorption sites of hydrogen ions in the medium decreased, and heavy metals mainly existed in the combined state of hydroxide or carbonic acid. This state is not conducive to their migration in the medium and increases adsorption capacity. These experimental conclusions are consistent with the literature [30; 34].  
Figure 2: Water table fluctuations process.
## 5 Experiment Results and Discussions
### Detecting Unknown Saturation Attacks Using Semi-Supervised Machine Learning
Specifically, the variational autoencoder is effective in the detection of unknown saturation attacks and obtains comparable results with respect to the supervised classifiers in detecting known saturation attacks. Nonetheless, we demonstrated that the variational autoencoder classifier can detect unknown saturation attacks with relatively high accuracy. This is due to different reasons. First, the traffic of flooding attacks has a high degree of similarity [2]. Meanwhile, the normal OpenFlow traffic has a low degree of self-similarity and has different statistical characteristics. Second, the selected features are sensitive to any abnormal behavior of the OpenFlow traffic since they represent the key messages of the OpenFlow protocol.