esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Argan Tree Deforestation Detection Using Sentinel-2 Time Series and Machine Learning
## 2 Materials and Methods
### Accuracy Assessment  
Evaluating the accuracy of results and comparing them provides insights into the model's and data's effectiveness [123]. Evaluation is done by comparing classification results with ground truth data [53]. Table 7 details the data split used for training and testing. All data originate from the study area but were collected from various locations within it, ensuring diversity and offering a more comprehensive representation of the region. In general, a larger portion of the data was used for training to provide a sufficiently large sample size for effectively training the model. The remaining data were used for testing to evaluate model performance. For fair comparisons between models, it was critical to train them on the same training samples and evaluate them using identical test samples. We used the most commonly employed metrics in similar studies (Table 8) [108]: Overall accuracy (OA), precision, and the kappa coefficient. These metrics were key to determining the best-performing model.  
The OA is a primary indicator of the likelihood of correct classification, calculated from the sum of the confusion matrix's diagonal elements (Figure 6). Precision assesses the accuracy of each class individually. The kappa coefficient, a more complex metric, assesses observed accuracy against random chance, considering not just the diagonal but also the off-diagonal elements of the confusion matrix. The \"sklearn\" package facilitated the evaluation by providing functions for each of the used metrics.  
During the final testing phase of organ tree detection, the fully labeled reference image was used to evaluate the performance of the most robust models. The best-performing model was determined based on its ability to correctly classify this reference image, achieving the highest number of accurately classified pixels compared to other classifiers.
# Determination of the Waterway Parameters as a Component of Safety Management System
## 4 Materials and Methods
### Research Results of Determining the Width of the Fairway by INSMS \"NEPTIN\" Application
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Fairway Section**} & \multirow{2}{*}{**Beginning of Section (km)**} & \multirow{2}{*}{**PNS**} & \multicolumn{2}{c}{**Restricted Visibility**} & \multicolumn{2}{c}{**Good Visibility**} & \multirow{2}{*}{**Fairway Widths**} \\  & & **Section (km)** & & **(Radar—** & **(Terrestrial—** & **(Terrestrial—** & **Widths** \\  & & & **Daytime)** & **Daytime)** & **(Night)** \\ \hline Swinoujscie Entrance & \(-0.5\) & 120.00 & 158.03 & 121.26 & 121.91 & 150 \\ Kosa Turn & 2.7 & 146.00 & 159.62 & 133.54 & 133.61 & 150 \\ Mileinski Canal & 5.4 & 120.00 & 145.08 & 120.14 & 120.19 & 150 \\ Milein Turn & 6.275 & 146.00 & 152.48 & 132.82 & 132.86 & 150 \\ Karsibórz Ferry & 7.55 & 120.00 & 154.94 & 120.89 & 121.35 & 150 \\ Paprotno Turn & 10.2 & 146.00 & 150.67 & 132.53 & 132.55 & 150 \\ Piastowski Canal & 11.1 & 120.00 & 172.20 & 123.60 & 125.28 & 110 \\ Zalew Szczecinski “A” part & 16.8 & 120.00 & 168.78 & 122.96 & 124.37 & 110 \\ Zalew Szczecinski “B” part & 21.9 & 120.00 & 165.66 & 122.41 & 123.59 & 110 \\ Zalew Szczecinski “C” part & 26.45 & 120.00 & 159.16 & 121.42 & 122.13 & 110 \\ Zalew Szczczczinski “D” part & 29.85 & 120.00 & 171.91 & 123.55 & 125.21 & 110 \\ Zalew Szczecinski “E” part & 35.5 & 120.00 & 165.95 & 122.46 & 123.66 & 110 \\ Zalew Szczecinski “F” part & 40.1 & 120.00 & 147.20 & 120.24 & 120.35 & 110 \\ Zulawy Turn & 41.36 & 146.00 & 151.34 & 132.82 & 132.86 & 150 \\ Marków & 42.4 & 120.00 & 161.98 & 121.82 & 122.73 & 150 \\ Szeroki Nurt & 46.3 & 120.00 & 155.22 & 120.93 & 121.40 & 150 \\ Police Turn & 49 & 146.00 & 153.58 & 132.82 & 132.86 & 150 \\ Domiaza & 50.5 & 120.00 & 148.29 & 120.31 & 120.45 & 110 \\ Mnisi Turn & 51.956 & 146.00 & 152.33 & 132.82 & 132.86 & 150 \\ Skolwinski Ostrow & 53.2 & 120.00 & 147.98 & 120.29 & 120.42 & 110 \\ Wyspa Zurawia Turn & 54.6 & 146.00 & 153.09 & 132.82 & 132.86 & 150 \\ Stolczyn & 56 & 120.00 & 160.29 & 121.57 & 122.36 & 110 \\ \hline Radolin Turn & 59.6 & 146.00 & 169.05 & 134.24 & 134.34 & 150 \\ Przekop Mielenski & 64.1 & 120.00 & 155.22 & 120.93 & 121.40 & 110 \\ Parnica Turn & 66.8 & 146.00 & 157.08 & 133.54 & 133.61 & 150 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: List of the fairway widths in m of the safe manoeuvring areas for the “maximum vessel” at the seabed with a depth of 12.5 m for the current navigational markings, \((1-\alpha)=0.95\). Figure 8: Fairway widths of the safe manoeuvring areas for a bulk carrier going from Swinoujšcie to Szczecin with good visibility and two positioning systems (PNS, terrestrial). Figure 9: Fairway widths of the safe manoeuvring areas for a bulk carrier going from Swinoujšcie to Szczecin with good visibility at night with two positioning systems (PNS, terrestrial).
## 7 Optimization of the Four-Layer Three-Segment Tube
### Maximization of the Distance between Natural Frequencies and a Selected Frequency  
The last example discussed in detail is the maximization of the distance between natural frequencies and an arbitrarily selected excitation frequency (see Equation (8)). The considered external excitation frequencies are \(p=90\), \(p=100\), \(p=110\), \(p=120\), and \(p=130\) Hz. These values of \(p\) are assumed to be within the frequency variation limits, which range from \(f_{1}\) to \(f_{10}\). Figure 10 shows the results (as a box-and-whisker diagram) of three optimization loops in the application of either ANN\({}_{123}^{10}\) or ANN\({}_{1234}^{10}\) for an external excitation frequency of \(p=120\) Hz.  
The improvement is clearly visible, especially for the first network. The ANN\({}_{123}^{10}\) network, which was trained on a data set that was smaller than the one used to train ANN\({}_{1234}^{10}\), finally yields equally precise results after three CL loops.  
The results for the maximization of the distance between natural frequencies and the considered excitation frequencies are presented in Table 12. The minimum difference between any natural frequency and any of the arbitrarily selected excitation frequencies is significantly higher than the desired 10% of the excitation frequency (as in the case for the MODEL1 structure). In every case, the difference is almost 20% of the excitation frequency \(p\).  
Figure 9: The accuracy of (**a**) ANN\({}_{3}^{1}\) and (**b**) ANN\({}_{123}^{1}\) after subsequent curriculum learning (CL) loops.
## 5 Quantitative Seismic Characterization Based on the RPM-FCNN Framework
A bigger value  
Figure 15: Quantitative seismic characterization results using the RPM-FCNN framework: (**a**) Organic matter mixture aspect ratio \(\alpha_{k}\); (**b**) Organic matter porosity \(\Phi_{k^{\prime}}\) (**c**) Seismic anisotropy parameters \(\varepsilon\); (**d**) Seismic anisotropy parameters \(\gamma\); (**e**) Total porosity \(\Phi\); (**f**) Clay content _Vclay;_ (**g**) Kerogen content _Vkerogen_. indicates a more elongated shape, which represents greater connectivity and facilitating shale gas flow. Figure 15b presents the predicted spatial distribution of organic matter porosity \(\Phi_{k}\). Organic matter porosity is related to kerogen maturity, with a higher porosity indicating greater maturity and reflecting a better gas content of shale. Figure 15c,d illustrate the seismic anisotropy parameters \(\varepsilon\) and \(\gamma\), respectively, which reflect the intrinsic anisotropy associated with the structural organization of the shale gas reservoir. The seismic anisotropy parameters also provide knowledge of mechanical properties in shale. Figure 15e-g represent the spatial distribution of total porosity \(\Phi\), clay content _Vclay_, and kerogen content _Vkerogen_ in the study area. The total porosity is associated with the potential of the shale gas reservoir. The content of clay affects the bedding structure in shale. The content of kerogen reflects the existence of organic matter, which is connected with the production of shale gas. These parameters are essential for the quantitative evaluation of the sweet spots in shale gas reservoirs.
# Smart Patch for Structural Health Monitoring of Composite Repair
## 3 Damage Detection and Localization
### Delamination  
Figures 5 and 6 compare the results of damage detections and localizations for two artificial delamination positions at 250 kHz by using three and four PZT transducers, respectively. The results show that the DI results using both three and four PZT transducers can detect the damage. The DAS results show that the artificial delamination can be located accurately using three PZT transducers but cannot when using four PZT transducers. Two factors affected the locating results during the measurements. First, the repair patch cannot be placed in the same position as the baseline status after inserting the KAPTON(r) films into bonding positions. In addition, the thickness of the contact gel used to bond the repair patch and the host structure cannot be kept at the same value since the contact gel will come back again every time after hardly pushing the repair patch. Both factors would affect the measured signals. Therefore, using four or more PZT transducers will cause the inaccuracy of damage localization due to the inconsistency caused by the contact gel.  
However, the smart repair patch will be bonded to the host structure permanently by resin film for the actual situation. First, the repaired structure bonded by the resin film will make the patch maintain its original position when delamination happened and retain the same thickness of the bonding area. Furthermore, the selection of the contact glue to bond the patch and host structure is a compromising way under laboratory conditions and no alternative material can be found to bond the repaired structure together and peel them after measuring. Hence, the above two factors would not affect the measuring results when using four PZT transducers, and it can be confirmed that the DAS results would be accurate when using four PZT transducers for the actual repair.  
Figure 4: Schematics of (**a**) artificial delamination and (**b**) bonding with blue contact gel for the bonded repair structures.  
Figure 5: Delamination detection using (**a**) three PZT transducers and (**b**) four PZT transducers, and localization using (**c**) three PZT transducers and (**d**) four PZT transducers at 250 kHz for the position \(1\) (where the “\(\bigcirc\)” is the position for real damage and the “\(\times\)” is the position for predicted damage).  
Figure 6: _Cont._
## 5 Results and Discussion
### Subjective Human Evaluation  
In addition to objective metrics, we conducted a structured user study involving 15 participants (aged 21-45, with normal or corrected vision), selected from a university population. Each participant reviewed 10 randomly selected low-light scenes, enhanced by five different models (SID, Retinex-Net, Zero-DCE, LLFormer, and Ours). Images were randomized and rated using a 5-point Likert scale across three dimensions: brightness, naturalness, and sharpness. All evaluations were conducted under consistent ambient lighting on calibrated displays.  
Participants rated each image on a scale from 1 to 5 for the following aspects:  
* Brightness: Is the image sufficiently illuminated?
* Naturalness: Does the image look visually plausible?
* Sharpness: Are textures and edges clearly preserved?  
Mean scores across all scenes are reported in Table 5. Our model received the highest scores in all categories, indicating a favorable perception among human viewers.  
In addition to numeric scores, we collected optional free-text feedback from participants to better understand their qualitative impressions. Selected representative comments are summarized below to illustrate common perceptions associated with each method:  
* \"This one feels most like a real photo\" (Ours).  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **NIQE**\(\downarrow\) & **BRISQUE**\(\downarrow\) \\ \hline SID & 5.72 & 38.1 \\ Retinex-Net & 5.90 & 42.6 \\ Zero-DCE & 5.21 & 32.8 \\ EnlightenGAN & 5.34 & 35.0 \\ LLFormer & 4.98 & 30.7 \\ Ours & 4.87 & 29.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative results on cross-dataset images. No-reference image quality scores on the DICM dataset. Lower is better. Our model achieves the best balance of naturalness and detail across unseen scenes. The direction of the arrows indicates the direction of better performance.  
Figure 6: Visual comparison on an unseen low-light image from a DICM-style dataset. Our model (rightmost) achieves enhanced brightness, natural color balance, and better contrast relative to prior methods, confirming its generalization to real-world low-light conditions beyond the training distribution.  
* \"Too bright, slightly artificial\" (SID).
* \"Details are a bit fuzzy\" (Retinex-Net).
* \"Nice balance of contrast and color\" (LLFormer).
## 4 Modeling and Analysis of Pipe with Vibration Dampers
### Flow Field Analysis  
Figures 5-7 show the contours of cases 3, 6, and 7, including the velocity contours and the pressure contours, and we then analyzed the influence of the vibration dampers on the flow field around the pipe.  
According to the velocity contours, the obvious Karman vortex street phenomenon was observed. In the velocity contour of the three cases, a circular low-velocity area appeared on the windward side of the pipe. Compared with case 3, after the vibration dampers were installed the backflow phenomenon occurred behind the vibration dampers. The high-velocity areas on the upper and lower sides of the pipe were destroyed by the vibration dampers, and the backflow range in the wake area increased, causing the distribution of the high-velocity area to also shift backward, as shown in Figures 6a and 7a.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cases** & \(f_{g}\)**/Hz** & \(St\) & \(\overline{C_{D}}\) & \(C_{Lrms}\) \\ \hline
6 & 2.56 & 0.244 & 0.698 & 0.103 \\
8 & 3.20 & 0.244 & 0.683 & 0.096 \\
9 & 3.87 & 0.246 & 0.622 & 0.089 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of results after installing vibration dampers at different wind speeds.  
the pressure of the pipe without the vibration dampers. In addition, the farther from the circular pipe in the wake area, the closer the pressure was to the overall flow field, as shown in Figures 6b and 7b.  
Figures 8-10 show the three-dimensional vorticity of cases 3, 6, and 7, plotted using the Q criterion [21]. When the wind flowed cross the pipe, the wake region shed vortices in opposite directions and alternated periodically, as shown in Figure 8. In case 6, the vibration dampers disturbed the vortex in the wake area of the pipe, and the first pair of alternating up and down vortices in the wake area were severely damaged, resulting in many small vortices around the pipe. Compared with case 3, the vortex shedding moved backward as a whole, as shown in Figure 9, but owing to the smaller size of the vibration dampers compared to the pipe and the concentrated installation, there was no obvious effect on the vortex shedding behind. In case 7, the small vortices around the pipe were not as dense as those in case 6, but the vortices shedding altern
## 3 Results  
### Thermodynamic Characterization of SOM  
Table 1 shows the elemental composition, C, H, N, of the soil samples collected for this study. LF represents samples from the soil surface, while M corresponds to the mineral soils collected at 5 cm depth from the soil surface. SOM is the percentage determined by TG. The C content of samples, as well as SOM, depletes from the soil surface to the mineral samples. Moreover, there are some differences among sampling sites, despite all of them representing mature oaks ecosystems at similar environmental conditions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Samples** & **C\({}_{\rm tot}\) (\%)** & **C\({}_{\rm org}\) (\%)** & **H (\%)** & **N (\%)** & **SOM (\%)** \\ \hline ROG LF & 39 & 34 & 5.0 & 1.6 & 73 \\ BW LF & 46 & 41 & 6.5 & 2.5 & 81 \\ NF LF & 44 & 42 & 6.5 & 2.0 & 80 \\ DC LF & 50 & 42 & 5.5 & 1.4 & 95 \\ G LF & 51 & 46 & 5.3 & 1.7 & 97 \\ K LF & 51 & 40 & 6.4 & 1.7 & 96 \\ ROG M & 5 & 5 & 0.3 & 0.3 & 10 \\ BWM & 11 & 9 & 1.0 & 0.6 & 23 \\ NFM & 12 & 8 & 1.4 & 0.5 & 16 \\ DCM & 6 & 4 & 0.7 & 0.3 & 7 \\ G M & 5 & 4 & 0.5 & 0.2 & 9 \\ KM & 10 & 10 & 1.4 & 0.5 & 21 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Elemental composition of soil samples selected for this review. SOM percentages are determined by thermogravimetry (TG). LF indicates samples from the soil surface representing organic matter at a low degree of decomposition. M represents mineral soil samples taken in the same places as LF but at 5 cm depth from surface, where SOM is more transformed than LF layers. Samples ROG, BW, and NF come from the Alice Holt Research Station in the southeast of the UK. Samples DC, G, and K are from the southwest of Ireland and were used in previous work [26]. All of them represent mature forest oak ecosystems. Correlations among these components can yield information about the SOM composition, indicating the percentages of N and H attached to C and SOM to weigh the contribution of inorganic material. In this particular case, the interest relies on the correlation between C and SOM to obtain the conversion factor from grams of SOM to carbon mole to be compared to the individual C/SOM relation for each sample. Correlation among C\({}_{\text{tot}}\), C\({}_{\text{org}}\), and SOM gives r values of 0.98 at \(p<0.001\) in all cases and equations C\({}_{\text{tot}}\) = 0.530 SOM + 0.646 and C\({}_{\text{org}}\) = 0.462 SOM + 0.172, respectively. It will be used the conversion factor of 0.462 g C g\({}^{-1}\) SOM. The soil thermal properties are shown in Table 2. The heat of combustion, Q\({}_{\text{SOM}}\), is obtained directly by DSC in kJg\({}^{-1}\) OM, and corrected to yield the enthalpy of combustion of SOM, \(\Delta_{\text{c}}\)H\({}_{\text{SOM}}\), in kJ mol\({}^{-1}\) C, as shown in Figure 1.
# Optimization and Hydration Mechanism of Ecological Ternary Cements Containing Phosphogypsum
## 3 Results and Discussion
### Experimental Results
#### 3.1.2 XRD Analysis  
In the microstructure characterization of the PBM, the minerals and hydration products are the two main phases that were intensively investigated. The X-ray diffractograms of the hydration products of the ECP-1, 2, 3 and 4 at the curing age of 3, 7 and 28 days are shown in Figure 5, which shows that the spectral lines of the hydration products mainly took the form of dispersed peaks, and the phases mainly included HT (Hydrotalcite), C\({}_{2}\)S, C\({}_{3}\)S, C\({}_{4}\)AF, C\({}_{3}\)A and unhydrated mineral admixture phases.  
A selection of X-rays patterns between 10\({}^{\circ}\) and 20\({}^{\circ}\) (2\(\theta\)) for hydrated pastes in the ECP system was plotted and the characteristic peak of calcium silicate hydrates (C-S-H) appeared between 12\({}^{\circ}\)-13\({}^{\circ}\). The C-S-H phases had a variable composition that depended on the prevailing Ca/Si ratio in the ECP system, which can change by pozzolanic reaction and leaching caused by the ingress of water and/or chemical attack, such as HF.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen** & **3 Days** & **7 Days** & **28 Days** & **90 Days** & **180 Days** \\ \hline PH & 39.4 & 50.0 & 67.1 & — & — \\ PD & 36.6 & 45.9 & 61.0 & — & — \\ PP & 18.1 & 19.9 & 32.3 & — & — \\ PFA & 37.8 & 39.7 & 51.1 & — & — \\ PFD & 33.2 & 39.5 & 45.6 & — & — \\ PFP & 25.1 & 28.9 & 38.8 & — & — \\ ECP-1 & 11.5 & 20.8 & 29.3 & 42.7 & 58.6 \\ ECP-2 & 28.3 & 34.1 & 46.3 & 58.4 & 76.9 \\ ECP-3 & 37.2 & 47.8 & 60.4 & 73.6 & 85.7 \\ ECP-4 & 51.6 & 64.8 & 72.4 & 87.1 & 92.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Strengths (UCS/MPa) of specimen up to 180 days at different curing condition.  
There was a dispersion peak in the range of 2\(\theta\) from 14\({}^{\circ}\) to 16\({}^{\circ}\), which was more obvious at 3 days and 7 days. This indicates that the intensity of the diffraction peaks of C\({}_{2}\)S, C\({}_{3}\)S and mineral admixture phase components decreased continuously with the prolongation of the reaction time, when the composite cementitious materials were continuously hydrated to form AFt crystals during the curing period. The literature shows that [20] when a large amount of minerals are present, a variety of ions are replaced to form complex compounds. Due to the continuous dissolution and hydration of phosphogypsum calcium ions, a small amount of the HT\({}^{-}\) and CH phases appeared, and the crystallinity of CH generated by hydration was relatively poor. As the reaction progresses, it was absorbed by the cement hydration products, making it difficult for CH to reach saturation, resulting in the diffraction peak of CH being relatively weak and inconspicuous at 28 days. According to investigations by [PERSON] et al. [21], the C-S-H phase may sorb sulfate, alumina and alkalis. The partial substitution of FA slowed down the formation of the C-A-S-H binding gel as a consequence of Al and Si release over time along with the formation of hybrid C-N-A-S-H gels.
# Numerical Study on the Rising Motion of Bubbles near the Wall
## 2 Numerical Simulation Method
### Geometric Model  
In the calculation of the two-dimensional plane domain, in order to ensure that the bubble has obvious deformation, and will not cause the bubble rising instability due to excessive deformation, the initial bubble with a diameter of \(d=4\) mm is selected and simulated. The entire domain is 80 mm long and 160 mm high. The height of the liquid level is 140 mm and the initial height of the bubble is 20 mm. The distance from the bubble to the bottom surface is 5 times its diameter, and the distance from the non-researched side wall is greater than 10 times the diameter, which can eliminate the influence of the non-researched side wall and the bottom surface of the bubble motion as much as possible. Taking a bubble with an initial diameter of 4 mm and a bubble wall distance of 4 mm as an example, the calculation domain is shown in Figure 1. Other bubbles with different diameters are the same as in this case.  
The mesh of the model is drawn by ICEM software. The two groups of models in this article have regular structures and simple shapes, and the mesh is divided into structured grids.  
Figure 2 is the grid division of the two-dimensional calculation model. The quadrilateral structure grid is used. The minimum size of the grid is 0.2 mm. Due to the need to study the motion of the wall bubbles, boundary refinement is used, and the overall grid number is 150,000. The gas used in the simulation is air, and the liquid is liquid water. Both physical properties and parameters under normal temperature and pressure are used. Except for the verification of the VOF method, the same gravity and surface tension are used. The surface tension of liquid is 0.0728 N/m.  
In the two-dimensional model, the top is set to PRESSURE-OUTLET, and the gauge pressure is 0 Pa; the rest of the walls are set to WALL, and the wall uses no-slip boundary conditions.