esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Modeling
### Static Sensitivity Modeling
The capacitance per unit length of the sensor can be expressed as:  
\[C=\varepsilon\varepsilon_{0}\frac{K(k)}{K^{\prime}(k)} \tag{2}\]  
Figure 1: (**a**) Basic structure of the interdigital capacitive humidity sensor. (**b**) Sensitivity mechanism of the sensor. (**c**) Key structural parameters of the sensor. (**d**) Two-dimensional electric field model of the sensor interdigitated structure. where \(K^{\prime}(k)=K(k^{\prime})\), \(k^{\prime}=\sqrt{1-k^{2}}\). \(K(k)\) is a complete elliptic integral of the first kind, \(K(k)=\int_{0}^{\frac{\pi}{2}}\frac{d\theta}{\sqrt{1-l\sin^{2}\theta}}\). The cross-sectional view of the interdigital capacitive humidity sensor is shown in Figure 1b. The thicknesses \(h_{1}\), \(h_{2}\), and \(h_{3}\) of the humidity-sensitive layer, the electrode layer, and the substrate layer correspond to the dielectric constants \(\varepsilon_{1}\), \(\varepsilon_{2}\), and \(\varepsilon_{3}\), respectively. The number of fingers in the interdigital capacitive humidity sensor is \(N\) and the length is \(L\). The capacitance expression of the interdigital capacitance is:  
\[C=NL\Bigg{[}\varepsilon_{0}\frac{K(k_{0})}{K^{\prime}(k_{0})+\varepsilon_{0} (\varepsilon_{1}-1)\frac{K(k_{1 in})}{K^{\prime}(k_{1 in})}+\varepsilon_{0}( \varepsilon_{2}-1)\frac{K(k_{2 out})}{K^{\prime}(k_{2 out})}+\varepsilon_{0}( \varepsilon_{3}-\varepsilon_{2})\frac{K(k_{2 out})}{K^{\prime}(k_{3 out})}} \Bigg{]} \tag{3}\]  
where \(k_{in}\) and \(k_{out}\) are the coefficients inside and outside the electrode, respectively. \[k_{\text{in}}=2\Bigg{\{}\bigg{[}\Big{(}\frac{a}{2}\Big{)}^{2}+h_{i}^{2}\bigg{]} ^{\frac{\theta}{2\varphi}}\bigg{(}\frac{a}{2}+\frac{b}{2}\bigg{)}^{\frac{ \theta}{\varphi}}\Bigg{\}}^{-\frac{1}{2}} \tag{4}\]  
\[k_{\text{out}}=2\Bigg{\{}\bigg{[}\Big{(}\frac{a}{2}\Big{)}^{2}+h_{i}^{2}\bigg{]} ^{\frac{\theta}{2(2-\varphi)}}+\bigg{(}\frac{a}{2}+\frac{b}{2}\bigg{)}^{\frac {\theta}{(2-\varphi)}}\Bigg{\}}^{-\frac{1}{2}} \tag{5}\]  
In establishing theoretical models, sensitive materials also affect the system's performance to a certain extent. Considering the particularity of atmospheric environment detection, we need to choose sensitive materials with lower cost and more accessible access as much as possible. Polyimide has the advantages of high sensitivity, good linearity, hysteresis characteristics, low cost, etc., and the production process is mature and straightforward. In this model, we take polyimide material as an example to further improve the sensor electric field equation. The dielectric constant \(\varepsilon_{1}\) of this material can be written as a function of ambient humidity \(RH\) and ambient temperature \(T\) as \(\varepsilon_{1}(RH,T)\). The capacitance expression combined with the interdigital capacitive humidity sensor constructed by the conformal mapping method is:  
\[C=f_{C}(a,b,h_{1},h_{2},L,N,RH,T) \tag{6}\]  
By definition, the sensor sensitivity expression is:  
\[S=\frac{\partial C}{\partial RH}=f_{S}(a,b,h_{1},h_{2},L,N,RH,T) \tag{7}\]
# Assessment of Heavy Metal Distributions in Sand Beaches in the Maltese Islands
## 3 Results and Discussion
### Chemical Analysis
#### 3.1.2 Correlation Analysis  
Spearman-Rho correlation analysis performed on the data obtained, illustrated in Figure 1, showed that the highest correlation was between Fe and Mn concentrations, with r = 0.682. This shows a strong linear relationship between these heavy metals, possibly due to them having a similar source. This corresponds to the homogeneity results obtained for Mn proving its predominant lithogenic origin. Both Fe and Mn content was also observed to be higher along the eastern coast of the islands, most probably originating from the natural degradation of sedimentary rock. In fact, the second-most prevailing winds for Malta and Gozo are south-easterly winds; thus, more sea waves tend to strike the islands from that direction, increasing the wear rate in those coordinates by noticeable factors [39]. The Mn and Sr relationship is also strong but negative (r = \(-\)0.639). The correlation factor of Sr with Fe is moderately high with r = \(-\)0.4. This proves that Sr also hints towards a lithogenic origin, as outlined in the Kruskal-Wallis test results described. Such results match those recorded in other studies, namely by [PERSON] et al., where strong associations (r > 0.7) between Fe, Mn, and Sr were recorded [35], and by [PERSON] and [PERSON], where the correlation between Mn and Pb was high (r = 0.816) [36].  
Figure 1: Scatterplot matrix on multivariate correlations.  
Correlations between most of the other heavy metals assessed were moderate and in the range 0.2 < r < 0.5. Results from studies by [PERSON] et al. [35] showed higher correlations than those obtained in this study.  
Relationships between Sr and other elements assessed were negative, except for the link with Pb which was weak but positive with r = 0.118; this proved that while Sr concentration decreased, Pb concentration decreased with a marginally similar pattern. On the other hand, other elemental concentrations tend to increase while Sr concentration decreases, proving an inverse relationship. This outcome differs from that obtained by [PERSON] et al. in their coastal sediment analysis performed in Egypt [35], but partly matches previously documented local work undertaken on agricultural soil in Malta [10].
# Weigh-in-Motion Method Based on Modular Sensor System and Axle Recognition with Neural Networks
## 3 Results and Discussion in Axle Recognition
### Training Process  
#### 3.1.1 Vehicle Data Acquisition and Selection  
To facilitate field testing and verification, a modular WIM system was installed at the entrance lane of the Epang Palace highway toll station in Xi'an, Shaanxi, China. Approximately 1800 vehicles traveled through the lane every day, and we collected data from both rainy and sunny days. The weighing signals of 10,000 vehicles were randomly selected as the training and verification sets of the axle recognition model, and the weighing signals of 1853 vehicles were used as the test set. The dataset included multiple axle configurations, such as two-axle passenger cars, two-axle to six-axle trucks, and a small number of ultra-long trucks with more than six axles with extreme weight, achieving comprehensive coverage of vehicles at highway toll stations.  
As shown in the sensor module structure diagram in Figure 2, WIM sensors were installed at four corners of each weighing platform. The sum of the four sensors was referred to as the weight in one platform, the sum of multi-platforms occupied by vehicles was referred to as the combined weighing signal, the sum of the two sensors in front was referred to as the entering weighing signal, and the sum of the two sensors in the rear was called the exiting weighing signal. Weight fluctuations were obvious in the entering and exiting weighing signals. Therefore, the entering and exiting weighing signals were selected for axle recognition. Figure 4a shows the entering and exiting weighing signals and the recognition results.
## 3 Finite Element Analysis
### Loads and Boundary Conditions  
Four contact pairs are established for the finite element analysis: the O-ring and the gasket, the O-ring and the steel shell, the O-ring and the piston, and the gasket and the piston. In the contact analysis setting, the contact type is chosen as Frictional or Bonded, with Asymmetric selected for contact behavior. The augmented Lagrange method is employed for advanced rubber material detection at Gauss points. In terms of the contact surfaces, the hard material surface is designated as the target surface, while the soft one (rubber) is treated as the contact surface. The Coulomb friction model is adopted, with specific friction coefficients set at f = 0.1 between the O-ring and the steel parts, f = 0.04 between the gasket and the steel parts, and f = 0.02 between the O-ring and the gasket [14]. It is assumed that the friction coefficient of 316 stainless steel and polyethylene does not change with temperature at the limit temperature.  
During the simulation of the combined sealing structure's operating state, the piston remains fixed and the steel shell moves relative to the piston. Load steps are set in finite element analysis:  
* The piston is fully constrained in displacement throughout all stages;
* Installation stage of 15 \({}^{\circ}\)C: The steel shell is displaced in the negative \(Y\)-axis direction to reach the initial installation position, completing the installation pre-compression of the O-ring;
* Inflation stage of 15 \({}^{\circ}\)C: The steel shell is displaced in the negative \(Y\)-axis direction to reach the lower position, then 70 MPa gas pressure is applied to the upper half of the exposed O-ring;
* Inflation stage of 130 \({}^{\circ}\)C: The steel shell is displaced in the negative \(Y\)-axis direction to reach the lower position, then 87.5 MPa gas pressure is applied to the upper half of the exposed O-ring;  
Figure 6: Finite element model of the combined seal.  
* Deflation stage of 15 \({}^{\circ}\)C: The steel shell is displaced in the positive \(Y\)-axis direction to reach the upper position, then 70 MPa gas pressure is applied to the lower half of the exposed O-ring;
* Deflation stage of \(-60\)\({}^{\circ}\)C: The steel shell is displaced in the positive \(Y\)-axis direction to reach the upper position, then 56 MPa gas pressure is applied to the lower half of the exposed O-ring.
# Saturation-Based Airlight Color Restoration of Hazy Images
## 3 Proposed Dehazing Method
### Extraction of Color Components in Airlight
#### 3.1.3 Airlight Extraction Based on Area Scores  
As shown in Figure 4, the clustering area sorted in Section 3.1.2 selects the optimal sky area by calculating the area score of these clusters. The sky area shows bright pixels in the image [1; 2; 3; 4; 5; 6] with very low scattering [30]. Further, it has a relatively low saturation value compared with neighboring areas due to floating particles [5; 31]. In this paper, the sky area consists of a set of bright, low variance, and low saturation pixels.  
When the mean and variance of the L channel of the clustered group and the mean of the a and b channels are given as \(\mu(L_{i})\), \(\sigma(L_{i})\), and \(\mu(\mu(a_{i}),\mu(b_{i}))\), they represent the brightness, variance, and saturation of the clusters, respectively. First, \(\mu(L_{i})\) represents the mean of the L channel, so the larger the \(\mu(L_{i})\), the larger the brightness value of the sample pixels in the cluster. Second, \(\sigma(L_{i})\) represents the distribution of brightness values, signifying the distribution of pixels in the cluster. Finally, the \(\mu(\mu(a_{i}),\mu(b_{i}))\) represents the mean of the saturation channels, and the higher the value, the more saturated the cluster. Therefore, \(score_{i}\), the area score for airglow estimation, is defined by Equation (12).  
\[\begin{array}{c}score_{i}=\mu(L_{i})-\sigma(L_{i})-\mu(\mu(a_{i}),\mu(b_{i}) )\\ i=1,\ldots,k\end{array} \tag{12}\]  
Figure 4: Clustering results of the color-cast image: (**a**) original hazy image, (**b**) clustering using only a and b channels, and (**c**) clustering using the LAB color image.  
Here, \(\mu(\cdot)\) and \(\sigma(\cdot)\) represent the average and scattering values of each cluster. Based on the characteristics of the sky area, the cluster with the highest value of \(score_{i}\) is selected as the airlight area. The selected airlight area is shown in Figure 3c. As shown in Figure 3c, the selected airlight area is dominant in the image; thus, the top 1% of the pixels in this cluster are selected as the final airlight \(A\), and their L, a, and b channel averages of the final airlight are entered into the 1 \(\times\) 3 airlight matrix.
## 3 Result and Discussion  
### Multispectral Data Processing  
Vegetation indices from multispectral images before and after the fire were derived via the indices. However, four months after the fire, we can see that vegetation regenerates in some areas, such as the white part in (b). It can be better understood by viewing the differences in (c). The locations marked in green in Figure 3c are areas where the difference in pixel values is negative, indicating that vegetation regenerated for four months after the fire.
## 2 TH-2 Spaceborne GNSS Data Assessment
### Tracking Ability of GNSS Satellites  
The GPS and BDS2 observations from the TH-2A and TH-2B during day of year (DOY) 305-322 in 2019, with a sampling interval of 1s, are used. Since the receivers on the two satellites are of the same type, the observation tracking performance of the TH-2A is similar to that of the TH-2B. Figure 2 shows the daily number of GPS and BDS2 observations from the TH-2A. For the GPS, owing to the semi-codeless tracking method of the receiver for the L2 frequency [22; 23], the number of GPS L2 observations is about 9.7% less than that of the L1. For the BDS2, there is no obvious discrepancy in the tracking performance between the BDS2 B1 and B3 frequencies. The stable tracking abilities provide good support for the orbit determination.  
In November 2019, GPS consisted of 32 medium Earth orbit satellites (MEOs), while BDS2 included five geostationary Earth orbit satellites (GEOs), seven inclined geostationary orbit satellites (IGSOs), and three MEOs. Their differences in observation geometry can be reflected by the position dilution of precision (PDOP). When the cutoff elevation angle is set to \(5^{\circ}\), the percentage of the epochs with the PDOP of GPS below six is about 99% along the trajectories of the TH-2A and TH-2B, whereas the corresponding percentage for BDS2 is about 60%, which means a weaker observation geometry compared with that of GPS. In addition, as GPS is a global constellation, there is no significant difference in the number of observed satellites in different areas, which is about 7-10, as shown in Figure 3. On the contrary, since the distribution of BDS2 is unbalanced, and most satellites provide  
Figure 2: Daily numbers of GPS and BDS2 observations for TH-2A.  
service in the Asia-Pacific region from 55\({}^{\circ}\)S to 55\({}^{\circ}\)N, and 55\({}^{\circ}\)E to 180\({}^{\circ}\)E [24], the number of BDS2 satellites varies from region to region. The number of BDS2 satellites observed in the Asia-Pacific region is about 8.8, while the number in the Western Hemisphere is only 0-2.  
Since the ionospheric-free combination of the pseudorange and carrier phase is chosen as the basic measurement in the POD, we define an available satellite with observations from two frequencies that can be tracked simultaneously. As shown in Figure 4, there are about 99.98% of epochs with at least four available GPS satellites. The average number of available satellites per epoch is approximately 8.2. For the BDS2, it can be found that there is a 3-6% probability with no observation in an epoch, with about 50% of epochs with at least four available satellites. The average number of available BDS2 satellites is significantly smaller than that of GPS, which is about 3.5 per epoch.
## 4 Results and Discussion
### Influence Analysis of Pipeline Parameters
#### 4.3.2 Pipeline Wall Thickness  
Figure 13a-c show the vertical deformation, horizontal deformation, stress and axial force of the pipeline when the pipeline wall thickness was set to n = 50 mm, n = 75 mm and n = 100 mm. In the three working conditions, the maximum vertical deformation of the pipeline was 17.3, 17.7, and 20.0 mm, the maximum horizontal deformation of the pipeline was 4.14, 4.25, and 4.76 mm, the maximum stress of the pipeline was 1.11, 0.94, and 0.86 MPa, and the maximum axial force of the pipeline was 54.8, 69.6, and 85.0 kN. The vertical deformation, horizontal deformation and maximum axial force of the pipeline increased with the increase of pipeline wall thickness. With the increase of pipeline wall thickness, the maximum stress of pipeline caused by tunnel excavation decreased gradually. The distribution of pipeline deformation, stress and axial force were basically unchanged.  
Figure 11: Vertical deformation, horizontal deformation, stress and axial force of pipelines with different diameters: (**a**) d = 400 mm; (**b**) d = 600 mm; (**c**) d = 1000 mm.  
Figure 12: Vertical deformation of pipelines with different diameters.  
Figure 13: Cont.  
The settlement of the top of pipeline 1 above the central axis of the tunnel is shown in Figure 14. Under the same conditions of other factors, the greater the pipeline wall thickness, the greater the settlement of the pipeline caused by tunnel excavation. The vertical deformation of the pipeline with a wall thickness of 100 mm increased by 3.07 mm at most compared with that of the pipeline with a wall thickness of 50 mm. With the increase of pipeline wall thickness, the self-weight of the pipeline increases. After the excavation of double-arch tunnel, there was an unloading surface under the pipeline. Due to the self-weight, the pipeline deformed along with the excavation, and the settlement of thick pipeline with large self-weight was greater than that of thin pipeline with small self-weight. Therefore, more attention should be paid to the deformation of pipelines with larger wall thickness.  
Figure 14: Vertical deformation of pipelines with different wall thicknesses.  
Figure 13: Vertical deformation, horizontal deformation, stress and axial force of pipeline with different wall thickness: (**a**) n = 50 mm; (**b**) n = 75 mm; (**c**) n = 100 mm.
These techniques collectively enhance our understanding of Earth's composition, tectonic activity, and resources [3]. Remote sensing captures data on Earth's surface without direct contact, using satellite or airborne sensors to measure electromagnetic radiation. It supports diverse fields like geology, ecology, agriculture, and urban planning [8]. The data, processed into images or maps, provide insights into surface features and environmental changes [9]. Sensors operate across wavelengths, including visible, infrared, and radar, with advanced algorithms extracting actionable information [9; 10]. In geophysics, remote sensing complements subsurface methods by offering large-scale, high-resolution datasets on surface conditions, such as topography and vegetation [11; 12]. While remote sensing detects surface changes, geophysical techniques probe subsurface dynamics [13]. This synergy enables holistic analyses of geological structures and natural hazards, including earthquakes and landslides [14; 15]. In dynamic environments like the Dead Sea, satellite-based systems provide critical data for monitoring geological and environmental changes [16; 17]. Geophysics addresses challenges in natural hazard prediction, resource exploration, environmental monitoring, and land use [5]. It locates earthquakes, identifies resources, and monitors pollution [6; 7]. Technological advances in sensors, algorithms, and machine learning improve imaging and data interpretation, expanding interdisciplinary research opportunities [3]. Combining geophysics and remote sensing strengthens our ability to understand and manage Earth's dynamic systems [16; 17; 18; 19; 20].
## 6 Conclusions  
1. Development and comparison of three machine learning algorithms based on CatBoost gradient boosting, k-nearest neighbors (KNN) and support vector regression (SVR) were used to predict the compressive strength of self-compacting concrete by applying our accumulated empirical database and data.
2. It has been established that artificial intelligence methods can be applied to determine the compressive strength of self-compacting concrete. The developed models showed a mean absolute percentage error (MAPE) in the range 6.15-7.89%.
3. Of the three machine learning algorithms, the smallest errors and the largest coefficient of determination were observed in the KNN algorithm: MAE was 1.97; MSE, 6.85; RMSE, 2.62; MAPE, 6.15; and the coefficient of determination R\({}^{2}\), 0.99.
4. Models can be verified and accepted for use in determining the compressive strength of self-compacting concrete, taking into account all available data.
5. The developed methods can be successfully implemented in the process of production and quality control of building materials, since they do not require serious computing resources and, in the future, based on artificial intelligence, an expert system can be created to summarize all of the accumulated experimental data, which can be located in an electronic environment university and provide data to interested workers and researchers for the development of the industry.  
Figure 9: Metric values for the developed regression models: **(a)** MAE; **(b)** MSE; **(d)** RMSE; **(c)** MAPE.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app122110864/s1](https://www.mdpi.com/article/10.3390/app122110864/s1)]([https://www.mdpi.com/article/10.3390/app122110864/s1](https://www.mdpi.com/article/10.3390/app122110864/s1)), Table S1: Dataset.  
Conceptualization, S.A.S., E.M.S., A.N.B. and I.R.; methodology, S.A.S., E.M.S. and I.R.; software, I.R., A.C. and N.B.; validation, I.R., S.A.S., E.M.S. and A.N.B.; formal analysis, I.R., A.C. and N.B.; investigation, I.R.M., S.A.S., E.M.S., A.N.B. and I.R.; resources, B.M.; data curation, S.A.S., E.M.S. and I.R.; writing--original draft preparation, I.R., S.A.S., E.M.S. and A.N.B.; writing--review and editing, I.R., S.A.S., E.M.S. and A.N.B.; visualization, I.R., S.A.S., E.M.S., A.N.B. and N.B.; supervision, L.R.M. and B.M.; project administration, L.R.M. and B.M.; funding acquisition, A.N.B. and B.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The study did not report any data.  
The authors would like to acknowledge the administration of Don State Technical University for their resources and financial support.  
The authors declare no conflict of interest.