esa-sceva/satcom-chunk-collection huggingface dataset:
# Forecasting the Number of Road Accidents in Polish Provinces Using Trend Models
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data/Model** & **Annual Data** & **Monthly Data** \\ \hline Exponential & y = \(1384.8\)e\({}^{-0.04\text{ks}}\) & y = \(313.34\)e\({}^{22}\)\({}^{-05\text{s}}\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.9257\) & R\({}^{2}\) = \(0.0597\) \\ \hline Linear & y = \(-379.996\)x + \(1306.3\) & y = \(0.0201\)x \(-\)\(25.195\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.909\) & R\({}^{2}\) = \(0.0728\) \\ \hline Logarithmic & y = \(-270\)ln(x) + \(1471.8\) & y = \(826.22\)ln(x) \(-\)\(7974.5\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.797\) & R\({}^{2}\) = \(0.0703\) \\ \hline Polynomial of 2 nd degree & y = \(0.5472\)x\({}^{2}\) \(-\)\(50.034\)x + \(1352.4\) & y = \(1E\)\(\cdot\)\(05\)x\({}^{2}\) \(-\)\(1.1854\)x + \(25138\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.9145\) & R\({}^{2}\) = \(0.1476\) \\ \hline Polynomial of 3 rd degree & y = \(0.0029\)x\({}^{3}\) + \(0.451\)x\({}^{2}\) \(-\)\(49.168\)x + \(1350.7\) & y = \(-3E\)\(\cdot\)\(09\)x\({}^{3}\) + \(0.0004\)x\({}^{2}\) \(-\)\(15.737\)x + \(227570\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.9145\) & R\({}^{2}\) = \(0.153\) \\ \hline Polynomial of 4 th degree & y = \(-0.0389\)x\({}^{4}\) + \(1.7163\)x\({}^{3}\) \(-\)\(24.176\)x\({}^{2}\) + \(77.988\)x + \(1181.8\) & y = \(-8E\)\(-12\)x\({}^{4}\) + \(1E\)\(\cdot\)\(06\)x\({}^{3}\) \(-\)\(0.078\)x\({}^{2}\) + \(2184.9\)x \(-\)\(2E\)\(+\)\(07\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.9353\) & R\({}^{2}\) = \(0.2282\) \\ \hline Polynomial of 5 th degree & y = \(0.0044\)x\({}^{5}\) \(-\)\(0.281\)x\({}^{4}\) + \(6.5105\)x\({}^{3}\) \(-\)\(65.24\)x\({}^{2}\) + \(219.85\)x + \(\quad\) & y = \(4E\)\(\cdot\)\(15\)x\({}^{5}\) \(-\)\(8E\)\(\cdot\)\(10\)x\({}^{4}\) + \(7E\)\(\cdot\)\(05\)x\({}^{3}\) \(-\)\(2.8588\)x\({}^{2}\) + \(60215\)x \\ \cline{2-3}  & 1043.9 & \(-\)\(5E\)\(\cdot\)\(+\)\(08\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.9423\) & R\({}^{2}\) = \(0.2645\) \\ \hline Polynomial of 6 th degree & y = \(-0.0006\)x\({}^{6}\) + \(0.0425\)x\({}^{5}\) \(-\)\(1.2443\)x\({}^{4}\) + \(18.152\)x\({}^{3}\) \(-\) & y = \(4E\)\(\cdot\)\(18\)x\({}^{6}\) \(-\)\(1E\)\(\cdot\)\(12\)x\({}^{5}\) + \(1E\)\(\cdot\)\(07\)x\({}^{4}\) \(-\)\(0.0064\)x\({}^{3}\) + \(199.59\)x\({}^{2}\) \\ \cline{2-3}  & 133.54x\({}^{2}\) + \(393.19\)x + \(910.74\) & \(-\)\(3E\)\(\cdot\)\(06\)x + \(2E\)\(\cdot\)\(10\) \\ \hline \multirow{2}{*}{Potentiometric} & y = \(1636\)x\({}^{-0.3}\) & y = \(0.0415\)x\({}^{0.9283}\) \\ \cline{2-3}  & R\({}^{2}\) = \(0.7549\) & R\({}^{2}\) = \(0.0575\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Trend models for Opole province.
# Combined MvdXML and Semantic Technologies for Green Construction Code Checking
## 2 Research Background
### Code-Checking Approaches in Construction Industry
#### 2.3.2 Code Checking Based on Semantic Web
Therefore, considering the particularity of green construction standards and the limitation in current code-checking approaches, in this paper, BIM, logical rule, ontology, and semantic web query language were integrated to conduct green construction code checking. The conceptual model ontology, work item ontology, and construction condition ontology for green construction were built, while the missing information for green construction code checking was added using IfcDoc, and various kinds of green construction rules were checked using an approach combining mvdXML and semantic technologies. Users can modify the parameters of each rule to match their local regulations using the approach proposed in this paper. Once the rule structure is defined, it is available for multiple code-checking projects.
## 3 Results and Discussion
### Characteristics of Filter Residues
#### 3.6.6 Proposed Interaction Mechanism Between PVA and Iron Oxides  
The interaction between PVA and iron oxides can be described by a multistage coordination-templating mechanism, as shown in Figure 10, that explains both the observed FTIR spectral changes and the XRD crystallinity patterns. In the initial coordination stage, PVA hydroxyl groups (-OH) act as electron donors that coordinate with Fe\({}^{2+}\) and Fe\({}^{3+}\) ions through their oxygen atoms [40; 41; 42]. This coordination is evidenced by the observed changes in the O-H stretching vibration peak (2750-3000 cm\({}^{-1}\)) in our FTIR results, which became more pronounced at lower PVA concentrations (3-4 g/L). At lower PVA concentrations (3-5 g/L), a nucleation control mechanism dominates, where polymer chains adsorb onto the surface of forming iron oxide nuclei. The hydroxyl groups of PVA form hydrogen bonds with the surface hydroxyl groups of iron oxide nuclei, creating a steric  
Figure 9: TEM images of sample B1: (**a,b**) after drying at 100 \({}^{\circ}\)C, showing dispersed nanoparticles and fibrous networks; (**c,d**) after calcination at 650 \({}^{\circ}\)C, revealing rod-like structures and particle aggregation. The morphological evolution correlates with the enhanced magnetic properties and crystallinity observed through other characterization methods.  
barrier that inhibits crystal growth and leads to the amorphous characteristics observed in XRD patterns. This explains the broadened diffraction peak around 35\({}^{\circ}\) seen in our samples. Conversely, at higher PVA concentrations (6-7 g/L), a template-directed growth process occurs. PVA molecules form ordered structures due to intermolecular hydrogen bonding, creating templates that guide the growth of specific crystal planes. This explains the emergence of preferential growth along certain crystallographic directions, as evidenced by the sharp diffraction peak at 31.72\({}^{\circ}\) (corresponding to the (220) crystal plane of Fe\({}_{3}\)O\({}_{4}\)) in our XRD analysis. The thermal transformation pathway further supports this mechanism. Upon heating, the PVA template undergoes thermal decomposition, which explains the morphological evolution observed in TEM from dispersed nanoparticles within fibrous networks at 100 \({}^{\circ}\)C to rod-like structures and aggregated particles at 650 \({}^{\circ}\)C. This transformation follows the sequence: PVA-Fe complex \(\rightarrow\) PVA-stabilized Fe\({}_{3}\)O\({}_{4}\) nanoparticles \(\rightarrow\) thermally reorganized Fe\({}_{3}\)O\({}_{4}\)/\(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) structures. This proposed mechanism aligns with our experimental observations while providing a theoretical framework that connects the molecular interactions to the observed macroscopic properties of the magnetic precipitates.
## 4 Results and Discussions
### Effect of Falling Angle under Different Submergence Conditions
#### 4.2.2 Cases of Partial Subaerial Landslides  
The falling angles (\(\theta\)) refer to the angle between the sliding direction of the slider along the slope and the positive x direction, and are set as \(20^{\circ},30^{\circ},40^{\circ},50^{\circ},60^{\circ}\) and \(70^{\circ}\), respectively, and the submergence conditions of landslides are partial subaerial landslides (the falling height is \(-0.2\) m). When the falling angles are \(30^{\circ},50^{\circ}\) and \(70^{\circ}\), the temporal evolution of water elevation in the G\({}_{1}\) and G\({}_{2}\) point are shown in Figure 12. Similar to the case of subaerial landslide, the oscillation amplitude of partial subaerial landslide-induced wave behind the wave train at G\({}_{1}\) increases with the increase in falling angle, and the significant attenuation of the fluctuation also existed at G\({}_{2}\). Different from subaerial landslides, the wave train amplitude of partial subaerial landslide-induced wave at G\({}_{1}\) and G\({}_{2}\) are generally smaller than that of subaerial landslide-induced waves, which, of course, is also related to the smaller initial falling height of partial subaerial landslides. The other particular thing is that the maximum amplitude of G\({}_{1}\) at \(30^{\circ},50^{\circ}\) and \(70^{\circ}\) is not significantly different (only slightly reduced at \(70^{\circ}\) compared with \(30^{\circ}\) and \(50^{\circ}\)), which is completely different from subaerial landslides. Compared to the maximum amplitude at G\({}_{1}\), there is a slightly noticeable attenuation of that at G\({}_{2}\) with the increase in the falling angle. This pattern of maximum amplitude variation is quite different from that of subaerial landslides and will be discussed later in conjunction with the change of maximum falling angles with falling height.
## 2 Materials and Methods
### Direct Laser Writing 3D Lithography  
Direct laser writing (DLW) 3D lithography experiments were conducted employing a Pharos laser (515 nm, 300 fs, 200 kHz, Light Conversion Ltd, Vilnius, Lithuania), 20 \(\times\) NA = 0.8 objective and the combined movement of the linear stages and Galvano-scanners. A detailed description of the experimental setup can be found in a previous publication [45]. The goal was to figure out if AESO and VDM monomer based resins can be suitable for ultra-fast laser pulses initiated by 3D polymerization in a confined space. An investigation test to assess the optimal fabrication parameters was performed. A 3D model of so-called resolution bridges (RB) was programmed accordingly. It consisted of five rectangle-shaped columns which were 15 \(\upmu\)m wide, 60 \(\upmu\)m long and 15 \(\upmu\)m high. Gaps between the columns varied from 5 \(\upmu\)m to 20 \(\upmu\)m every 5 \(\upmu\)m. The five straight lines perpendicular to the columnar long edges formed in the gaps. Such a structure corresponds to the RB suspended between the columns. Each line was polymerized from a single laser beam scan. RB were obtained with different longitudinal and lateral sizes by varying the laser power (\(P\)), which corresponded to the light intensity (\(I\)) at thesample and the scanning velocity (\(v\)). It enabled the evaluation of the smallest features and their dependence on the applied \(I\) which could be formed in AESO and VDM based resins. The capability to form 3D microporous woodpile structures was demonstrated. They consisted out of two layers separated via vertical 20 \(\upmu\)m height columns. The layers were made of a 15 \(\upmu\)m wide and 75 \(\upmu\)m long log pattern with a 15 \(\upmu\)m gap between the logs, resulting in a 30 \(\upmu\)m period. Each log was made from multiple scans whose number depended from the distance \(d_{xy}\) between scans. \(d_{xy}\) was 0.25-2 \(\upmu\)m every 0.25 \(\upmu\)m. During the fabrication, the resin was placed between two glass slides, as shown in Figure 1. After the exposure the samples were developed in 4-methyl-2-pentanone for 15 min, removing the uncured resin and leaving only the formed structures on the substrate. The fabricated structures were characterized using a scanning electron microscope (SEM, Hitachi TM-1000, Tokyo, Japan).
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 2 Image Encryption Algorithms
### Chaotic Systems
#### 2.2.6 Hyperchaotic Systems  
Hyperchaotic maps are high-dimensional nonlinear dynamical systems, defined as any chaotic system with at least two positive Lyapunov exponents [25]. The Lyapunov exponent of a dynamical system is a parameter that measures the rate of separation of two infinitesimally close trajectories [26]. Hyperchaotic maps are considered to be better than simple chaotic maps due to their higher degree of non-periodicity and unpredictability [27].  
[PERSON] proposed an image encryption algorithm based on a 2D hyperchaotic map in [17]. First, two hyperchaotic sequences, x and y, are generated by the 2D hyperchaotic map. Next, the plaintext image rows and columns are permuted using chaotic sequence x. Finally, the pixel values of the permuted image are obscured through forward and backward diffusion using chaotic sequence y to produce the cipher image.  
In [18], [PERSON] et al. proposed a hyperchaos-based image encryption algorithm using pixel-level permutation, bit-level permutation, and diffusion. First, two chaotic sequences are generated by a 5D multi-wing hyperchaotic system, where the control parameters are related to the original image. Then, pixel-level permutation and bit-level permutation are performed on the plaintext image using the first chaotic sequence. Finally, the permuted image is diffused using the second chaotic sequence to produce the cipher image.
## 4 Experiments
### Evaluation Metric  
After getting the tracking results of different methods, HOTA [49], CLEAR metrics [10], IDF1 were used to evaluate different aspects of the tracking performance.  
* **HOTA**: HOTA [49] is currently the most comprehensive evaluation index for tracking algorithms, which measures how well the trajectories of matching detections align, and averages this over all matching detections, while also penalising detections that don't match.
* **DetA**: DetA is one part of HOTA, the detection performance of trackers.
* **MOTA**: MOTA (Multi-Object Tracking Accuracy) [8] is another widely used metric which combines three error sources: false positives, missed targets, and identity switches.
* **IDF1**: ID F1 Score measures the ratio of correctly identified detections to the average number of ground-truth and computed detections.
* **IDSW**: Number of Identity Switches.
## 3 Study on Scattered Surface Wave from Surface Crack Based on Reciprocity Theorem  
### Form of Scattered Surface Wave  
The normal and shear stresses are loaded on the crack surface, and they jointly determine the amplitude of the scattered surface waves. Similar to the forms of the surface waves expressed in Section 2.1, the forward-scattered surface waves can be represented as:  
\[u_{x}^{n+} =iA_{sc_{n}}^{+}U^{R}(z)e^{ikx},u_{z}^{n+}=A_{sc_{n}}^{+}W^{R}(z)e ^{ikx} \tag{13}\] \[\tau_{xx}^{n+} =A_{sc_{n}}^{+}T_{xx}(z)e^{ikx},\tau_{xz}^{n+}=iA_{sc_{n}}^{+}T_{ xz}(z)e^{ikx}\] (14) \[u_{x}^{s+} =iA_{sc_{n}}^{+}U^{R}(z)e^{ikx},u_{z}^{s+}=A_{sc_{n}}^{+}W^{R}(z)e ^{ikx}\] (15) \[\tau_{xx}^{s+} =A_{sc_{n}}^{+}T_{xx}(z)e^{ikx},\tau_{xz}^{s+}=iA_{sc_{n}}^{+}T_{ xz}(z)e^{ikx} \tag{16}\]  
The back-scattered surface waves can be represented as:  
\[u_{x}^{n-} =-iA_{sc_{n}}^{-}U^{R}(z)e^{-ikx},u_{z}^{n-}=A_{sc_{n}}^{-}W^{R}(z )e^{-ikx} \tag{17}\] \[\tau_{xx}^{n-} =A_{sc_{n}}^{-}T_{xx}(z)e^{-ikx},\tau_{xz}^{n-}=-iA_{sc_{n}}^{-}T_ {xz}(z)e^{-ikx}\] (18) \[u_{x}^{s-} =iA_{sc_{n}}^{-}U^{R}(z)e^{-ikx},u_{z}^{s-}=-A_{sc_{n}}^{-}W^{R}(z )e^{-ikx}\] (19) \[\tau_{xx}^{s-} =-A_{sc_{n}}^{-}T_{xx}(z)e^{-ikx},\tau_{xz}^{s-}=iA_{sc_{n}}^{-}T_ {xz}(z)e^{-ikx} \tag{20}\]  
For the convenience of calculation, the time item \(e^{\pm i\omega t}\) is omitted. The superscript \(n\) and \(s\) in Equations (13)-(20) indicate normal stress and shear stress, respectively. The symbols \"+\" and \"-\" denote forward- and backscattered surface waves, respectively. In addition, \(A_{SC_{n}}^{\pm}\) and \(A_{SC_{s}}^{\pm}\) are the amplitude of the surface waves radiated by normal stress and shear stress, respectively. Since normal stresses are symmetric, it can be obtained that \(A_{SC_{n}}^{+}\) equals \(A_{SC_{n}}^{-}\). For the antisymmetric shear stresses, it can be verified that \(A_{SC_{n}}^{\pm}\) equals \(-A_{SC_{n}}^{-}\).  
Figure 2: Schematic diagram of scattering wave superposition principle of surface crack: (**a**) total field of incident wave and scattered wave; (**b**) incident wave field and its resultant tractions; (**c**) crack under tractions and corresponding scattered wave field.
## 4 Discussion  
### Adding PPFD and CI to NIRv to Remotely Assess GPP  
The addition of information on radiation to NIRv improved the GPP estimates for different vegetation cover types. Only in grasslands did radiation not improve the estimates, although this was expected given that canopy development in grasslands occurs in parallel to photosynthetic activity [37]. In addition, changes in GPP are often linked to water availability rather than to radiation, since grasslands are often water-constrained [38; 39].  
The improvement in GPP after adding radiation to NIRv was related to foliage organization. When PPFD was included in the model, the improvement increased with decreasing CI (higher leaf organization). This is due to the light distribution and its use inside the canopy. If leaves are more clumped on shoots, branches and crowns, more light can penetrate the canopy, making it less light-saturated and more sensitive to light changes. The incorporation of a variable of vegetation structure, such as CI, into the NIRv + PPFD algorithm improved the global GPP estimate in terms of uncertainty and bias. NIRv addresses the distribution of the photochemical capacity of the complete canopy since plants allocate their photosynthetic capacity to be able to fully exploit captured sunlight, and so the photochemical capacity of the complete canopy is directly related to the performance of the topmost leaves in the canopy [40]. However, the effects of the changes in PPFD differ depending on how the radiation is distributed throughout the canopy as a result of differences in structure and leaf distribution. Leaf structure and distribution also affect the reabsorption intensity and the scattering directions of PPFD within the canopy [41; 42] and, therefore, its reflectance and NIRv. Thus, the addition of CI information for each vegetation type corrected for the effects of the PPFD distribution in the canopy on GPP and improved the global GPP estimates. The incorporation of vegetation structure and foliage organization into GPP models should be advantageous given their key role in light use efficiency.
## 3 Estimation Methods and Techniques
### Machine Learning Approaches
This allows the model to be more general and stable [131], including in estimating rainfall. Gradient boosting is also effective in managing missing data [130], which may be caused by weather radar and rain gauges, and can correct model weaknesses by reducing the gradient of the loss function [132, 133]. In the boosting process, the model focuses on more difficult samples and then gradually improves its performance; the next model tries to correct the errors made by the previous model [134]. This makes gradient boosting suitable for use in cases of complex data estimation and can produce more accurate rainfall estimates than decision tree, random forest, and adaptive boosting algorithms [122, 123]. Ensemble learning techniques have achieved state-of-the-art performance in diverse machine learning applications by combining the predictions from two or more base models [135]. Ensemble learning methods were also applied to produce better rainfall estimates. Although accurate in rainfall estimation, the limitation of ensemble learning techniques is that they require tuning hyperparameters [136, 137]. This can affect the overall performance and behavior of the model [138]. Hyperparameter optimization studies for rainfall estimation in Indonesia have been discussed [139]. Tuning the hyperparameters carried out in the XGBoost algorithm can improve the accuracy of the rainfall estimation model.
# Monitoring the Dynamics of Ephemeral Rivers from Space: An Example of the Kuiseb River in Namibia
## 4 Results and Discussion
### Temporal Variations of NDVI, NDMI and \(\sigma_{0}\)
We therefore used the surface backscattering model developed by [33] to better understand \(\sigma_{0}\) variations and to check if the variations in \(\sigma_{\rm o}\) in Sentinel-1 data, from \(-22\) to \(-17\) dB in the dry period versus \(-17\) to \(-12\) dB in the wet period, are related to only moisture variations without changes in roughness. We attempted to reproduce the observed variations by setting the IEM model for a dry environment (\(\xi=3\)), then varied parameter \(\sigma\) (the surface height standard deviation) from 0 to 3 cm and L\({}_{\rm corr}\) (the surface correlation length) from 0 to 10 cm. In the first simulation, only the impact of moisture, without roughness changes, was tested, but no value of \(\sigma\) and L\({}_{\rm corr}\) could explain the 5-7 dB variations in \(\sigma_{0}\). Figure 6a shows an example with settings of \(\sigma=2\) cm and L\({}_{\rm corr}=0.25\) cm under dry (\(\xi=3\)) and wet (\(\xi=15\)) conditions--the difference between the two curves is less than 1 dB. Thus, moisture alone cannot explain the observed 5-7 dB variations in our \(\sigma_{0}\) time-series of Sentinel-1 data. In a second simulation, we varied the input values for \(\sigma\) and L\({}_{\rm corr}\) in addition to moisture. Figure 6b shows how changes in the roughness parameter L\({}_{\rm corr}\) resulted in a difference of 5-7 dB when switching from a dry to a wet environment. A change in roughness is also necessary together with the change in moisture for the expected result of 5-7 dB variation in \(\sigma_{0}\) values. The change in roughness is probably due to the appearance of vegetation. Figure 6c shows the possible values of \(\sigma\) and L\({}_{\rm corr}\), outlined with black boxes, which produced a variation of 5-7 dB, wherein \(\sigma\) must be less than 0.5 or greater than 2.3 cm and L\({}_{\rm corr}\) must be between 0.1 and 0.8 cm. Thus, the variations we observed in the \(\sigma_{0}\) time-series from Sentinel-1 data of the lower Kuiseb River are not only linked to variations in moisture or water availability, but also to changes in surface roughness, which were caused by vegetation growth.
## 2 Synchrophasor Technology and State of the Art Applications
### Application of Synchrophasors in Modern Control Centers
* An operator training, addressing the understanding and the interpretation of dynamic phenomena, needs to be established to raise the level of operator awareness and to establish a flexible response to events, instead of a mainly rule-based operation.
## 2 Comparison of Long-Term Features
### b-Values Time Variations  
We examined the b-value time variations of the seismicity produced by the simulator in a long-term time interval before and after pivot events. In this case, the b-value was computed as the average obtained by the stacking procedure on the earthquakes that occurred within each of the 200 bins by which the total time window is divided. The previously mentioned stacking technique was applied for the computation of b-values of sets of events falling in the time bins and magnitude exceeding the thresholds listed in Table 3. The results of this analysis are depicted in Figure 4.  
Figure 4: (**a**) Average b-values with their standard deviations of M 4.2+ earthquakes preceding and following an M 5.2+ earthquake, obtained from the 100,000 years simulation in a long-term scale for the CNAFS. The long-term plot shows an increase in b-values before and a decrease after the strong event. (**b**) As in the top, for the M 5.2+ earthquakes preceding and following an M 5.8+ earthquake in the NMTFS. The error bars represent the standard deviation of the b-values computed using the formula proposed by [PERSON] and [PERSON] (1982) [29].  
Our plot, concerning the b-value on specific areas of the CNAFS, exhibits a clear increase starting about 100 years before any M 5.2+ earthquake. Additionally, a decrease is notable in the last bin lasting 5 years before the pivot events. The decrease continues after the pivot events for about 150 years until the b-value preceding the precursory increase is restored. For the NMTFS, the same pattern is observable but for pivot events of M 5.8+ and in a time scale approximately 30 times shorter.