esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Area Description  
The area of the former refrigerator manufacturer is situated in the western part of Slovakia, in the north of Zlate Moravce city (Figure 1). Refrigerators and related componentshave been produced since the 1950s; production ended in 2002 [70,71]. The study area included the former factory (source area, area A), agricultural land (area B) and residential zone (area C). The former factory is located in the north of the area, residential zone is in the southern part of the area and the central part is an agricultural land (Figure 1). Smaller engineering and repair companies are still in operation in the area of former refrigerator production.  
Figure 1: Location of the study area within Slovakia and investigated zones: area A = source area (the former refrigerator manufacturer, Calex Company, City of Zlate Moravce, Slovakia), area B = agricultural land and area C = residential zone, including also sampling sites for semi-quantitative measurements of chemical parameters in soil gas (orange circles) and existing and newly installed wells (green and light-blue circles, respectively) for groundwater sampling. Yellow square shows sampling sites of soil gas for GC-MS analyses.  
The aquifer is formed by Quaternary fluvial sediments, mainly clayey and silty gravels with inter-granular permeability. The aquifer has a thickness ranging from 2 m to 5 m within the entire area, and the mean depth of the water table is 4.0 m below ground surface (b.g.s). The direction of groundwater flow is from northeast to southwest. A layer of silts and sandy silts with a mean thickness of 2.4 m is above the aquifer, and the source area is covered by a 0.5 to 1.0 m-thick layer of anthropogenic sediments. Below the aquifer, at a depth of approximately 6.0 to 8.0 m b.g.s., Neogene sediments consisting of diverse clays of firm consistency are deposited, representing an impermeable layer, i.e., aquitard (Figure S1 in Supplementary Materials (SM)).  
Previous hydro-geochemical monitoring confirmed a serious contamination of groundwater with CLHCs. According to [70], groundwater in the source area (area A in Figure 1) contained 21.7-6482 ug/L PCE, 53-85,200 ug/L TCE, 32-34,190 ug/L _cis_-DCE and 11-5186 ug/L VC. However, VC was below the detection limit at most monitoring sites. Concentrations of CLHCs above the EU drinking water limit were also confirmed in the residential area C (15-186 ug/L PCE and up to 80 ug/L _cis_-DCE). Free-floating oil product (0.14 to 0.29 m thick) was found at the groundwater table in two wells located in source area A [30]. Soil analyses found high concentrations of total petroleum (TP; 1040-17,900 mg/kg) and benzene, toluene, ethylbenzene and xylenes (BTEX; 190 mg/kg); however, contaminated soils were excavated and removed [70]. Due to the persistent and extensive groundwater contamination in the area, remediation is necessary.
# Computation of Implicit Representation of Volumetric Shells with Predefined Thickness
## 5 Examples
### Circle  
A circle can be defined in a Cartesian coordinate system by the implicit function  
\[f(x,y)=x^{2}+y^{2}-R^{2}, \tag{13}\]  
with \(R\) being the radius. For the zero contour of \(f(x,y)\), it is straightforward to compute the two contours with offset \(h\) by replacing the radius with \((R-h)\) and \((R+h)\), respectively.  
In order to validate the proposed method, we compute the first- and second-order offset functions from Equation (13):  
\[\phi^{(1)}(x,y,h) = x^{2}+y^{2}-R^{2}-2h\sqrt{x^{2}+y^{2}} \tag{14}\] \[\phi^{(2)}(x,y,h) = x^{2}+y^{2}-R^{2}-2h\sqrt{x^{2}+y^{2}+\frac{h^{2}}{4}-h\sqrt{x^ {2}+y^{2}}}. \tag{15}\]  
Rewriting Equations (14) and (15) in terms of polar coordinates gives  
\[\phi^{(1)}(\rho,\theta,h) = \rho^{2}-R^{2}-2h\rho \tag{16}\] \[\phi^{(2)}(\rho,\theta,h) = \rho^{2}-R^{2}-2h\sqrt{\rho^{2}+\frac{h^{2}}{4}-h\rho}. \tag{17}\]  
At the specified location of the new surface, the offset functions are  
\[\phi^{(1)}(R+h,\theta,h) = -h^{2} \tag{18}\] \[\phi^{(2)}(R+h,\theta,h) = 0, \tag{19}\]  
which means that the second-order offset function provides the exact solution while the error of the first-order offset function is within the predicted range.
# Uncertainties and Perspectives on Forest Height Estimates by Sentinel-1 Interferometry
## 2 Materials and Methods
### Sentinel 1 Data  
Satellite missions, such as the European Union (EU) Copernicus Sentinels, generate a significant volume of data that may be proficiently used to aid the support of environmental studies and land management. Nevertheless, technological transfer, presently, appears to be limited, even if web-based services based on these data are becoming operational [21; 22; 23]. In the general context of satellite open data, SAR imagery shows a further weakness in entering operational services mainly due to the complexity of data processing [24; 25] and data availability.  
S1 is one of the current largest space-borne missions providing free and open accessible SAR data. Furthermore, the European Space Agency (ESA) has made the free software SNAP (Sentinel Application Platform) available for users, enabling an easier and focused exploitation of products from the Copernicus Programme [17; 26]. The S1 mission is a two-satellite constellation (Sentinel-1A and Sentinel-1B) acquiring a microwave C-band (5.6 cm wavelength). Its main acquisition mode over land is the interferometric wide swath (IW) recording backscattered signal in dual pole mode (VV and VH). Data are natively recorded as complex values (I/Q components) and in-SAR geometry (range and azimuth). In particular, its medium-high spatial resolution and high revisit time (6 days) make S1 mission useful for a wide range of applications, including forest mapping.  
In the literature, many methods based on SAR interferometry have been proposed to estimate forest height and can be summarized into three main categories: (a) the Pol-InSAR methodology [27; 28; 29]; (b) coherence-based methodology [30; 31; 32]; and (c) interferometric phase-based methodology [19; 20]. Unfortunately, S1 data are not suitable for (a) due to the lack of quad-pol channels that would be required [21; 33]. Approach (b) is mainly applied using tandem acquisition mode that guarantees a higher coherence over vegetation. SinceS1 does not support tandem acquisition, coherence values over forests are generally low, limiting this approach. Consequently, approach (c) appears to be the most promising, because it is somehow consistent with the technical features of S1 data. S1 main technical features [34; 35; 36; 37] are reported in Table 1, and used for the following simulations.
## 2 Materials and Methods
### Models Equations and Conditions
The acoustic properties of the subdomains of this model are shown in Table 1. The time dependent ultrasound propagation was determined with FEM by solving the acoustic wave Equation (2) given by  
\[\
abla^{2}p-\frac{1}{c^{2}}\frac{\partial^{2}p}{\partial t^{2}}=0, \tag{2}\]  
where \(p\) is the acoustic pressure (Pa), and \(c\) is the speed of sound in the media (m s\({}^{-1}\)). The acoustic field was also modeled under harmonic conditions to determine the effect of standing waves for different setup inclinations. Although the ultrasound transducer was driven in pulsed regime, we can assume acoustic harmonic conditions based on the large number of sine-cycles into each pulse, e.g., 10,000 sine-cycles in 1-MHz tone-burst with 10% DF at 10 Hz PRF. The amplitude of the radiated pressure of boundary 1 was \(P_{0}=155\) kPa, which is equivalent to 1.6 W/cm\({}^{2}\) at 50% DF for this transducer [22]. The acoustic field was determined based on the harmonic acoustic wave equation for attenuating media [21]. The heat produced in the culture dish was studied for the largest intensity used in the experiments (1.6 W/cm\({}^{2}\) at 50% DF). From Figure 1b, \(TD=1.0\) cm and \(CM=2.0\) mm. Materials' properties for acoustic and thermal models are shown in Table 1. The temperature in \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Acoustic Attenuation** & **Speed of Sound** & **Heat Capacity** & **Thermal Conductivity** & **Density** \\  & **[Np m\({}^{-1}\)]** & **[m s\({}^{-1}\)]** & **[J kg\({}^{-1}\) K\({}^{-1}\)]** & **[W m\({}^{-1}\) K\({}^{-1}\)]** & **[kg m\({}^{-3}\)]** \\ \hline Water & 0.23 & 1523 & 4183 & 0.610 & 993 \\ DMEM & 0.23 & 1543 & 4183 & 0.610 & 998 \\ Polystyrene & 4.03 & 2350 & 1195 & 0.115 & 1053 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Acoustic and thermal properties of materials used in simulations [12; 23; 24; 25; 26]. the system was modeled using the parameters and conditions described in [22]. Boundaries 1 to 3 of Figure 1b were set with a constant temperature of 37 \({}^{\circ}\)C; boundaries 4, 5, 10, 11 and 12 were set with zero heat transfer assuming null heat losses by both radiation and thermal conduction to the air. Interior boundaries were set with a continuity condition. Natural convective heat transfer was assumed small with a velocity component of 0.1 mm/s in the \(z\)-direction [27]. Mesh was kept the same as in the acoustic models. The time dependent heating simulation was computed from 0 to 300 s (5 min) at steps of 1 s. The acoustic attenuation was assumed produced by only absorption (zero scattering).
# Fragile Watermarking of 3D Models in a Transformed Domain
## 6 Experimental Results
#### Comparison with Related Works  
Among the related works, we selected for comparison the ones that reported numerical (objective) measures of distortion introduced by their algorithms, as there were no visible modifications to the model after watermark embedding. It should be pointed out that an exact comparison was not really possible as every paper used different sets of models with respect to one another, so we only compared with the best results they reported. Moreover, we emphasize that there were not many works on fragile watermarking reporting parameters to compare to in a fair way.  
All in all, we can say that our proposed method was superior to all the compared methods in terms of quality (in many cases, more than two orders of magnitude of the considered measure) of the resulting models and the fragility of the watermark.  
In [14], the authors reported the best PSNR value of 84.47 dB, which was far less than the smallest PSNR (\(>\)299 dB) our algorithm produced.  
In [12], the authors reported distortion (the same as our MAE values) for various settings of their method's parameters; again, an exact comparison was difficult for two main reasons. First, the set of models they used was different from our set, and the model precision was \(10^{-6}\), whereas the precision of our models was \(10^{-4}\). Anyway, their best result was \(1.3\times 10^{-5}\), which was worse than our worst result (horse, \(2.30\times 10^{-6}\)) by one order of magnitude. In [18], the same authors reported a distortion of 0.01, four orders of magnitude worse than our distortion results.  
In [10], the authors reported a mean distance error (averaged over five models) of \(2.33\times 10^{-4}\), which was two orders of magnitude greater than the mean distance error (averaged over 10 models) computed by our models (\(1.92\times 10^{-6}\)).  
In [15], the authors reported an average RMS error (\(3.74\times 10^{-4}\)) that was two orders of magnitude greater than our reported RMSE (\(3.19\times 10^{-6}\)), while our _RMSRatio_ (\(1.07\times 10^{-7}\)) was comparable with theirs. It should be pointed out, anyway, that our payload (five bits per vertex) was five times that of [15], but the distortion induced was the same. Moreover, the work in [15] had the disadvantage of requiring the centroid of the original model to perform the integrity verification, thus requiring out-of-band information to be kept per-model.  
In [20], the authors reported an average (over five models) SNR of 124.24 dB that was far smaller than our average (over 10 models) SNR of 231.14 dB.  
Finally, we performed several other experiments on the same models with different precisions, and in general, we could say that the proposed method resulted in a distortion that was in the order of \(1/100^{\text{th}}\) of the precision of the original model.
# Short-Term and Long-Term Travel Time Prediction Using Transformer-Based Techniques
## 5 Results and Discussions
### Performance Comparison on Road Segments of Varying Complexities
Notably, the proposed SLIT exhibits the most remarkable improvements in all metrics for road segments of varying complexities, particularly in complex road scenarios. These areas present more significant forecasting challenges due to greater variability and unpredictability in traffic patterns. Moreover, the proposed SLIT demonstrates the most notable improvements in all metrics across road segments of varying complexities, with the most notable advancements observed in complex road scenarios. These environments cause greater forecasting challenges due to their increased variability and unpredictability in traffic conditions. Table 7 presents the average overall performance for long-term predictions from 1 to 7 days across road segments of varying complexities. The proposed SLIT consistently achieves the best results for both MAE and SMAPE metrics. Although SLIT often matches or exceeds the performance of PASS2S in long-term predictions, it is noteworthy that, in high variability road segments, SLIT ranks second to PASS2S regarding RMSE. This difference can possibly be explained by the design orientations of the two systems: while SLIT is designed to adapt across various prediction horizons, PASS2S focuses on long-term prediction with an attention mechanism that handles each segment independently, perhaps making it particularly effective in highly variable environments. SLIT's broader attention mechanism, focusing on flexibility across different horizons, may not specifically target the  
Figure 4: Travel time variation by road segment complexity organized in a descending step line chart. The chart displays the standard deviation (std) of travel time as a blue line and the coefficient of variation (cv) as a red dashed line, plotted against the left and right y-axes, respectively. Segments are annotated on the chart with thresholds of 100 and 50 to indicate their variability categories—high, moderate, or low. variabilities in complex traffic scenarios as effectively as PASS2S, where RMSE becomes a critical metric. This observation suggests that although SLIT provides robust long-term prediction capabilities, there may be opportunities to refine its attention mechanism to better manage scenarios with high variability. Exploring these refinements could be a valuable direction for future work, potentially leading to enhanced RMSE management in complex traffic conditions for SLIT. As we conclude our primary analysis of SLIT's performance across various traffic scenarios, we have also included detailed experimental results for peak and off-peak hours in Appendix C. These results are aligned with the PASS2S [5] settings for consistency. These additional results provide a deeper dive into the model's adaptability to different traffic densities and times, offering insights that complement the main findings discussed above.
## 2 Materials and Methods
### Classification Methods  
#### 2.3.1 Random Forest  
The Random Forest (RF) algorithm uses a multiplicity of binary decision trees [16]. Each tree is built using a set of training data, and for each node, a random set of variables considered for the best split is chosen. With reference to Figure 1, a dataset is randomly split and used to build a certain number of decision trees. The final result of the classification is represented by a specific voting step among all the trees that have been built.  
Random Forest builds a large collection of decorrelated trees and then averages them. Given B as the number of trees to generate, for b = 1, 2,..., B, the RF algorithms can be briefly described as follows.  
* A bootstrap sample Z* of size n is drawn from the training set
* A random-forest tree Tb is grown from the bootstrapped data, by recursively repeating the following steps for each terminal node of the tree, until the minimum node size, nmin, is reached:
* Selection of q variables at random forest from the d variables.
* Choice of the best variable/split-point among q (internal feature selection).
* Splitting of the node into two daughter nodes.
* Output of the ensemble of trees [Tb]B  
Given a new point x, let Cb(x) be the class prediction of the bth random forest tree, the prediction of RF on this new sample is given by y = majority vote Cb(x)B.  
Examples of application of Random Forest are given in [17; 18].
## 3 Results and Discussion
### Closed Industrial Waste Landfill with No Liner Installed  
The landfill is located in the central part of Poland, within the Warsaw plateau mesoregion, consisting of tills, glacial sands, silts, and clay deposits. In the close vicinity of the landfill, there is a number of environmentally valuable protected areas, and this is why there are plans to restore it. The entire site is going to be adapted for recreational and commercial purposes, after the reclamation works are accomplished.  
Beyond the ordinary geological survey, the site investigation also included 32 geoelectrical tests. The purpose of the geophysical investigation was to determine the thickness of the industrial waste (part of the landfill is located on former sand pits), investigate the potential contamination of the transport paths, and to determine a depth of impermeable layers (Pliocene clays) within the subsoil. The geophysical test allowed an investigation down to 25 m below the surface level. Based on a quantity interpretation of the tests, the resistivity of each soil layer was evaluated. The results are presented in Table 5.  
The measured resistivity distribution indicated a long-term emission of contaminants from the landfill, within the first aquifer layer (the groundwater table at 6 m b.s.l). The results of the geoelectrical tests are presented in Figures 9 and 10, as subsoil cross-sections.  
The migration in the upper part of the zone is vertically directed; however, within the saturation zone, the contaminants flow in the direction of the groundwater stream.  
The zones of the cross-sections where the resistivity is low are hatched. The contaminated zones are mainly located in former sand pits where the cohesive top soil was removed, and which initially created weak liners of the landfill bottom. From Figure 10, it can be seen that the pattern of the contamination zone overlies the direction of groundwater flow and moves towards the top of the lower permeability soil layer.  
The geophysical investigation also allowed a detailed determination of the landfill base's precise location and verification of the cubic capacity of the waste disposed on the landfill (255,000 m\({}^{3}\)), permitting an evaluation of the amount of contaminated soil to be removed (110,000 m\({}^{3}\)) during reclamation works.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material Type** & **Resistivity (\(\Omega\)m)** \\ \hline Anthropogenic material (slag, slag mixed with waste) & 3–342 \\ Well graded dry sands & 176–1126 \\ Clean clays and sandy clays & 15–55 \\ Contaminated clays and sandy clays & 4–14 \\ Sands (fine, clayey, silty) & 135–135 \\ Contaminated sands (fine, clayey, silty) & 7–14 \\ Sand/clay deposits & 24–100 \\ Silty clays, impermeable layers & 4–45 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Resistivity characteristics of the soil layers.
## 3 Model Development
### Steady-State Model Development
#### 3.2.1 Instantaneous and Finite Rate Reactions  
The following set of equilibrium reactions describe the chemistry of CO2 absorption into the MEA solvent:  
\[\text{MEAH}^{+}+\text{H}_{2}\text{O}\ \leftrightarrow\text{MEA}+\text{H}_{3} \text{O}^{+} \tag{2}\]  
\[\text{MEACOO}^{-}+\text{H}_{2}\text{O}\leftrightarrow\text{MEA}+\text{HCO}_{3}^ {-} \tag{3}\]  
\[2\text{H}_{2}\text{O}\leftrightarrow\text{H}_{3}\text{O}^{+}+\text{OH}^{-} \tag{4}\]  
\[\text{CO}_{2}+2\text{H}_{2}\text{O}\leftrightarrow\text{HCO}_{3}^{-}+\text{H}_ {3}\text{O}^{+} \tag{5}\]  
\[\text{HCO}_{3}^{-}+\text{H}_{2}\text{O}\leftrightarrow\text{CO}_{3}^{-}+\text{H}_ {3}\text{O}^{+} \tag{6}\]Carbamate and bicarbonate reactions are kinetically limited; the following finite rate reactions are described as follows:  
\[\text{CO}_{2}+\text{OH}^{-}\rightarrow\text{HCO}_{3}^{-} \tag{7}\]  
\[\text{HCO}_{3}^{-}\rightarrow\text{CO}_{2}+\text{OH}^{-} \tag{8}\]  
\[\text{MEA}+\text{CO}_{2}+\text{H}_{2}\text{O}\rightarrow\text{MEA}\text{ACOO}^ {-}+\text{H}_{3}\text{O}^{+} \tag{9}\]  
\[\text{MEA}\text{ACOO}^{-}+\text{H}_{3}\text{O}^{+}\rightarrow\text{MEA}+ \text{CO}_{2}+\text{H}_{2}\text{O} \tag{10}\]  
According to the power-law expression shown in Equation (11), the reaction rate is governed by the pre-exponential factor (kmol/m\({}^{3}\)s), activation energy (kJ/mol), gas constant, system temperature, species activity, and reaction order. The kinetic expression values used in the current study were obtained from the work of [PERSON] et al. [19], available in Appendix A Equations (A5)-(A9). Aspen built-in models were used to calculate the transport properties such as viscosity, density, surface tension, binary diffusivity, and thermal conductivity. The properties were used in the correlations for the heat and mass transfer, hold up, pressure drop, and interfacial area, etc. [19].  
\[r_{j}=k_{j}^{0}exp\left(-\frac{\epsilon_{j}}{R}\left[\frac{1}{T}-\frac{1}{298. 15}\right]\right)\prod_{i=1}^{N}a_{i}^{a_{i}} \tag{11}\]  
where \(r_{j}\) is the rate of reaction \(j\), \(k_{j}^{0}\) is the pre-exponential factor, \(\epsilon_{j}\) is the activation energy, \(R\) is the gas constant, \(T\) is the temperature measured in Kelvin, \(a_{i}\) is the activity of species \(i\), and \(a_{ij}\) is the reaction order of species \(i\) in reaction \(j\).
# Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images
## Image Rectification Using a Perspective Transformation Model
### The Oblique Cameras Structure and the Rotation Matrix Acquisition  
The oblique photogrammetric platform selected in this paper contains five cameras. As shown in Figure 2, \(C_{0}\) is a down-looking camera, whereas \(C_{1}\), \(C_{2}\), \(C_{3}\) and \(C_{4}\) represent four side-looking cameras whose intersection angles with respect to \(C_{0}\) is \(45^{\circ}\).  
Figure 1: Under ideal circumstances, the imaging situations achieved by stereo cameras of different viewing-angles on a rectangular building structure are as follows: (**a**) the imaging performance of two down-looking cameras; (**b**) the imaging performance of a down-looking camera and a side-looking camera; (**c**) the imaging performance of two side-looking cameras. The red line represents the edge of the building and the line segment to be matched.  
Using the \(Y\)-\(X\)-\(Z\) rotation system, through a series of conversions with POS data, the three angle elements of the camera, represented by omega(\(\omega\)), phi(\(\varphi\)), kappa(\(\kappa\)), and three line elements, represented by \(X_{S}\), \(Y_{S}\), \(Z_{S}\), respectively, can be obtained. The camera's rotation matrix can be expressed as follows:  
\[\mathbf{R}=\left[\begin{array}{ccc}r_{11}&r_{12}&r_{13}\\ r_{21}&r_{22}&r_{23}\\ r_{31}&r_{32}&r_{33}\end{array}\right] \tag{1}\]  
where  
\[\left\{\begin{array}{l}r_{11}=\cos\varphi\cos\kappa-\sin\varphi\sin\omega \sin\kappa\\ r_{12}=-\cos\varphi\sin\kappa-\sin\varphi\sin\omega\cos\kappa\\ r_{13}=-\sin\varphi\cos\omega\\ r_{21}=\cos\omega\sin\kappa\\ r_{22}=\cos\omega\cos\kappa\\ r_{23}=-\sin\omega\\ r_{31}=\sin\varphi\cos\kappa+\cos\varphi\sin\omega\sin\kappa\\ r_{32}=-\sin\varphi\sin\kappa+\cos\varphi\sin\omega\cos\kappa\\ r_{33}=\cos\varphi\cos\omega\end{array}\right. \tag{2}\]  
Under normal circumstances, on a stable aerial platform, the down-looking camera is approximately vertically projected. Similar to a down-looking camera in conventional aerial photogrammetry, the deformation of acquired down-looking image is very small, but the side-looking images show trapezoidal deformation.