esa-sceva/satcom-chunk-collection huggingface dataset:
# Finite Element Analysis of Proposed Self-Locking Joint for Modular Steel Structures
## 3 Finite Element (FE) Model  
The accuracy of finite element modeling is important for simulation-based studies. Therefore, before the finite element analysis of the proposed connection is carried out, the validation process of the modeling technique is conducted. For this purpose, the experimental modular connection specimen and result from [18] as shown in Figure 3a is used.  
A three-dimensional finite element model of connection is developed to represent the actual test configuration. Models consist of square hollow sections (SHS) and columns (200 \(\times\) 200 \(\times\) 10 mm) connected with a floor beam (200 \(\times\) 180 \(\times\) 8 mm) and a ceiling beam (200 \(\times\) 180 \(\times\) 6 mm). Joint boxes were connected with both upper and lower columns. The nonlinear analysis was conducted to capture analogous simulation results. Another 3D finite element model of the proposed connection was developed using similar numerical modeling techniques and material properties discussed in subsequent sections. The model  
Figure 3: (**a**) Details of experimental specimen [18] and FE idealized model used in validation process, (**b**) specimen orientation of proposed connection and FE model idealization, and (**c**) details of the proposed connection.  
consists of columns, floor beams, ceiling beams, and connections with all its components (adapter, plug plate, bolt, and spring) as illustrated in Figure 3b,c. Dimensions of all sections adopted in the model are provided in Table 1.
## 3 Results  
### Monthly Temporal Correlations  
The mean remote sensing-based drought index performance statistics for all 344 climate divisions are summarized in Tables 3-5. In this study, we compared the correlations between _in situ_ drought indices (PDSI, Z-Index, 1-, 2-, 3-, 6-, 9-, 12-, and 24-month SPI) and seven remotely sensed drought indices (VIUPD-derived VCI, NDVI-derived VCI, TRMM-derived PCI, MODIS-derived TCI, VUA-derived SMCI and NSIDC-derived SMCI). Among the seven remotely sensed drought indices compared, VIUPD-derived VCI ranked first in most seven goodness-of-fit measures. As shown in Table 3, the VIUPD-derived VCI correlates best with PDSI, 3-, 6-, 9-, 12-, and 24-month SPI (\(r\) = 0.622, 0.564, 0.582, 0.584, 0.548, and 0.390 respectively), and Tables 4 and 5 indicated that it has less error with PDSI, 3-, 6-, 9-, 12-, and 24-month SPI (RMSE = 2.658, 1.075, 1.082, 1.058, 1.068, and 1.074, respectively, and MAE = 1.592, 0.892, 0.897, 0.873, 0.885, and 0.887, respectively) than those of NDVI-derived VCI, PCI, TCI and SMCI. However, in terms of correlations with the short-term drought indices (such as the Z-index and SPI-1), the performance of the VIUPD-derived VCI was worse than that of the PCI. The r-value for the VIUPD-derived VCI and SPI-1 was lower than that of the VUA-based SMCI (Table 3). This is mainly because the drought indices of PCI and VUA-based SMCI are meteorological indices. PCI is directly based on the precipitation data of TRMM and VUA-based SMCI is derived from soil moisture data from ARMS-E, while VIUPD is a vegetation index which measures the health condition of the vegetation. Previous studies revealed that there is a lag response of vegetation indices to moisture conditions and the lagged response of vegetation indices likely occurs because vegetation growth is controlled by soil moisture and therefore changes in vegetation growth are buffered by soil moisture [2;3]. 1- and 2-month SPI are more suitable for evaluating meteorological remote sensing-based drought indices such as PCI and VUA-based SMCI; PDSI, 3-, 6-, 9-, 12-, and 24-month SPI are more suitable for evaluating agricultural remote sensing-based drought indices [29;35].
## 4 Numerical Simulation
### Selection of the Main Virtual Distortions  
In the process of damage identification, the main virtual distortion of each substructure is analyzed and selected at first [39]. Using the FE model of the undamaged structure, the stiffness matrix of each substructure is assembled, and its eigenvalue decomposition is carried out. Then the positive eigenvalues, i.e., the number of virtual distortions are shown in Table 2, which is 261 virtual distortions in total. If all virtual distortions are used, a 261 \(\times\) 261 linear system of equations should be calculated to determine one virtual distortion when solving Equation (7). In order to reduce the computational cost, the correlation between the virtual distortions of each substructure and the distortion of the undamaged structure is analyzed. Finally, the correlations are sorted from large to small and cumulatively summed, and those virtual distortions are retained and further considered in computations, for which the accumulated correlation is below 99%. The main virtual distortions for the first six natural frequencies identified are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Order** & **Identified** & **Damaged Model** & **Undamaged Model** \\ \hline
1 & 1.972 & 1.961 & 2.133 \\
2 & 6.141 & 6.142 & 6.568 \\
3 & 11.254 & 11.257 & 11.472 \\
4 & 16.091 & 16.095 & 16.963 \\
5 & 22.007 & 22.006 & 23.052 \\
6 & 27.330 & 27.339 & 29.484 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The first six frequencies of structure in different cases (Hz).  
Figure 5: The first six mode shapes of the theoretical undamaged model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number** & **All** & \(\mathbf{\omega_{1}}\) & \(\mathbf{\omega_{2}}\) & \(\mathbf{\omega_{3}}\) & \(\mathbf{\omega_{4}}\) & \(\mathbf{\omega_{5}}\) & \(\mathbf{\omega_{6}}\) \\ \hline
1 & 18 & 2 & 1 & 0 & 2 & 3 & 3 \\
2 & 35 & 8 & 8 & 7 & 3 & 6 & 7 \\
3 & 34 & 7 & 10 & 5 & 8 & 7 & 3 \\
4 & 35 & 6 & 7 & 7 & 7 & 6 & 7 \\
5 & 34 & 6 & 7 & 8 & 6 & 8 & 7 \\
6 & 35 & 5 & 6 & 5 & 7 & 7 & 3 \\
7 & 34 & 8 & 9 & 4 & 9 & 10 & 17 \\
8 & 36 & 6 & 5 & 6 & 7 & 7 & 7 \\ \hline Total & 261 & 48 & 53 & 42 & 49 & 54 & 54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The number of main virtual distortions corresponding to the first six frequencies of each substructure.  
The number of selected virtual distortions is about 20% of the total number under each frequency, which effectively reduces the computational complexity of solving the linear Equation (7). It has an even more significant advantage in the application to large complex structures.
# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 5 Discussion
### Analysis of Error Change Information in Experimental Results  
During verification, it was identified that the incorrectly extracted patches primarily fell into three categories: bare soil, water accumulation, and high-reflectance areas caused by crop growth. The backscattering characteristics of ground objects are mainly influenced by surface roughness and the complex dielectric constant [48]. Generally, rougher sur  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Year**} & **Number of Preliminary** & **Number of Correct** & \multirow{2}{*}{**Accuracy Ratio (\%)**} \\  & **Change Patches** & **Change Patches** & \\ \hline
2019–2020 & 133 & 70 & 52.63 \\
2020–2021 & 163 & 68 & 41.71 \\
2021–2022 & 105 & 66 & 62.85 \\
2022–2023 & 116 & 50 & 43.10 \\
2023–2024 & 156 & 85 & 54.48 \\ Total/mean & 1036 & 771 & 50.95 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results of optical image.  
Figure 12: Comparison of only optical and SAR collaborative optical experimental results.  
faces are more likely to exhibit volume scattering, resulting in stronger backscattering. Conversely, smooth surfaces tend to produce surface scattering. In addition, the greater the water content of the soil, the higher the complex dielectric constant and the stronger the backscattering. When the surface is dry and smooth, such as compacted soil, surface scattering dominates, leading to a reduction in backscattering; such characteristics in SAR imagery can resemble those of pushed-fill soil, resulting in false detections (Figure 13). On the other hand, when soil moisture reaches saturation and water accumulation occurs, surface scattering becomes the primary mechanism, reducing backscattering. This creates a signature similar to that of a pond, leading to false detections (Figure 14).  
At the same time, the growth of crops may also cause pseudo-changes, crops will show large fluctuations in their backscattering coefficients during the growth cycle, and the backscattering coefficients in the same polarization (VV) will be higher than those in the cross-polarization VH, which may be related to the crop's physical properties and growth status [49], and the crops producing high backscattering in the VV polarization may cause pseudo-changes. As shown in the figure, the area of vegetation growth shows green highlights in the pseudo-color SAR image, but by looking at its individual polarization information, although it is not clearly distinguishable from the surrounding features in the  
Figure 14: Legend for pseudo-changes due to water accumulation: (**a**) optical images at TI; (**b**) optical images at T2; (**c**) SAR images at T1; (**d**) SAR images at T2.  
Figure 13: Legend for pseudo-changes due to bare soil: (**a**) optical images at TI; (**b**) optical images at T2; (**c**) SAR images at TI; (**d**) SAR images at T2.  
VH polarization, it has high backscatter in the VV polarization. Therefore, the growth of vegetation may also cause pseudo-changes (Figure 15).
## 5 High Voltage Direct Current
### HVDC Configurations  
As shown in Figure 3, multiple arrangements can be achieved for HVDC links according to the number of conductors and their polarities. Some of which are monopole, bipole, and homopole configurations [45].  
While the monopole configuration has poor availability due to relatively higher outages and homopole configurations are not frequently used because of the main disadvantage of presenting huge ground return currents, bipole configuration is used when power transfer capacity exceeds the monopole configuration or higher reliability is needed during the operation [46].  
On the other hand, several configurations can be achieved with the arrangement of monopole or bipole configuration depending on its length, the number of terminals, and its purpose. These configurations are briefly described in Table 1, where, for long-distance land power transmission, bipole is a good choice as it can transmit 50% of its rated power during a fault on one of its poles. Bipole ground return topology is cheaper and more reliable than bipole with metallic return. Earth electrodes can also be used in bipolar HVDC  
Figure 2: Generation capability curve. Graphic depictions of this kind ease the comparison of the effectiveness of different restoration schemes.  
systems, and they have very low current in nearly balanced pole voltages [46]. As having zero or almost zero ground current is an important advantage for a HVDC link, bipolar links are the most common arrangement [45].
## 1 Introduction
* Based on the two real-world datasets from the Beijing subway, the comparative experiments demonstrate that the proposed PIANet outperforms the state-of-the-art deep learning methods for short-term OD prediction in URT, and the ablation studies show that the proposed OAMs and feature interaction play an important role in improving the short-term OD prediction performance. The rest of this paper is organized as follows. Section 2 surveys deep learning methods for short-term OD prediction in URT and the attention mechanism. Section 3 describes the proposed PIANet in detail, including problem formulation, the proposed OAM, the dense compression attention block (termed as DCAB), the overall architecture of the proposed PIANet, and the loss function. In Section 4, the basic settings including datasets, implementation details, evaluation metrics, and comparison methods are introduced. Then, the comparative experimental results among the proposed PIANet and the comparison methods are shown and analyzed. After that, the ablation studies are performed to verify the effectiveness of the proposed OAMs and feature interaction. In Section 5, this paper is summarized.
# Multivariable Adaptive Super-Twisting Guidance Law Based on Barrier Function
## 4 Simulation
### Simulation Set-Up  
Considering the limited capacity of the real actuator, the lateral acceleration of the missile can be formulated as:  
\[\mathbf{a}_{M}=\left\{\begin{array}{ll}\mathbf{a}_{M,\text{max}}sign(\mathbf{a}_{M}),& |\mathbf{a}_{M}|\geq\mathbf{a}_{M,\text{max}}\\ \mathbf{a}_{M},&|\mathbf{a}_{M}|<\mathbf{a}_{M,\text{max}}\end{array}\right. \tag{44}\]  
where \(\mathbf{a}_{M,\text{max}}\) is the ultimate maximum lateral acceleration, and we denote that \(\mathbf{a}_{M,\text{max}}=300\text{ m/s}^{2}\). In order to avoid the discontinuity problem of the sign function \(sign(x)\), a sigmoid function is utilized instead in this paper, which is given by:  
\[sigmoid(x)=\frac{2}{1+e^{-x}}-1 \tag{45}\]  
In addition, the simulation examples in this paper are set up as follows: (1) the initial missile-target engagement condition: \(r_{0}=8000\text{ m}\), \(\dot{r}_{0}=-800\text{ m/s}\), \(\theta_{0}=30^{\circ}\), \(\phi_{0}=10^{\circ}\), \(\dot{\theta}_{0}=\dot{\phi}_{0}=0^{\circ}\)/s, \(\theta_{d}=45^{\circ}\) and \(\phi_{d}=25^{\circ}\); (2) the target maneuvering condition: \(\mathbf{a}_{T\theta}=\mathbf{a}_{T\phi}=-30\text{ m/s}^{2}\), and \(\mathbf{a}_{T\tau}=0\text{ m/s}^{2}\); (3) the parameters of sliding mode manifold and barrier function-based super-twisting controller are given in Table 1; (4) the fixed step size of the fourth-order Runge-Kutta solver is 0.001 s.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(k_{1}\) & 0.5 & \(l_{10},\ l_{20}\) & 0.1 \\ \(k_{2}\) & 0.1 & \(l_{11},\ l_{21}\) & 1 \\ \(k_{3}\) & 2.5 & \(\varepsilon_{1}\) & 0.1 \\ \(\xi\) & 0.2 & \(\varepsilon_{2}\) & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of sliding mode manifold and barrier function-based super-twisting controller.
# Cross-Entropy Method in Application to the SIRC Model
## 3 Description of the Numerical Results
### CE Method Version 1  
In the first method, the algorithm is shown at the end of Section 2.3 has been applied twice. Once for the calculation of the optimal function \(u(t)\) with a fixed \(v(t)\), and then \(v(t)\) with a fixed \(u(t)\). In the next step, the results were combined and the result for the SIRC model was calculated using these two functions. This approach is possible due to the lack of a combined restriction on \(u(t)\) and \(v(t)\). Features are not dependent on each other with resources.  
The cost index was 0.003086 for the start of the epidemic and 0.006443 for a developed epidemic. The Figure 4 show the graphs of functions \(u(t)\) and \(v(t)\) along with a graph of solutions for both moments during the epidemic.  
For functions \(u(t)\) the results are similar, only the decrease here is more linear. However, the solution of the SIRC model gives the same results. Function \(v(t)\) is much more interesting. The CE method showed that it should have very low values, even close to 0 throughout the entire period in both cases. However, this does not affect the cost index (it is even smaller than in the previous case) and the SIRC model. More on this subject will be included in the summary.  
Figure 2: Solutions for SIRC models without optimal control: (**a**) at the beginning of the epidemic (**b**) when the epidemic is developed.  
Figure 4: Values of control functions and solutions obtained for them. Solved by the cross-entropy method—version 1.  
Figure 3: Values of control functions and solutions obtained for them. Solved by the sequential quadratic programming method.
# Control for Autonomous Intersection Management Based on Adaptive Control Barrier Function
## 1 Introduction
### Autonomous Vehicles (Decentralized Control)
#### 1.3.1 Environmental Perception  
In autonomous vehicles, accurate real-time detection and classification of surrounding objects are critical. In [18], researchers propose an object classification method that fuses vision and LiDAR data using a convolutional neural network to obtain informative feature representation for object recognition. In [20], a multi-modal deep learning architecture was suggested to handle RGB-D pictures for object recognition by exploiting the complementary information among different modalities. To enhance nighttime driving scene perception, ref. [21] proposes a method for detecting vehicles while driving at night. Additionally, ref. [22] suggests a perceived model of the environment that classifies four types of moving objects, pedestrians, cyclists, cars, and trucks, improving accuracy in detection and classification for autonomous vehicles.
## 3 Formula-Driven Supervised Learning
### FDSL Datasets
#### 3.4.1 Fractal DataBase  
The fractal dataBase (FractalDB) [77] was developed for FDSL. It contains pairs of fractal images \(I\) and their respective category labels \(c\)[98], which are generated using an iterated function system (IFS) [98]. The IFS is defined over a metric space \(\chi\) as:  
\[\text{IFS}=\{\chi;w_{1},w_{2},\cdots,w_{N};p_{1},p_{2},\cdots,p_{N}\}, \tag{6}\]  
where \(w_{i}:\chi\rightarrow\chi\) is the transformation function, \(p_{i}\) is probability with summation 1, and \(N\) is the aggregate of transformations.  
In IFS, each fractal \(S=\{x_{t}\}_{t=0}^{\infty}\in\chi\) is randomly constructed [98] using a two-step algorithm by repeating it for \(t=0,1,2,\cdots\) from a starting coordinate \(\mathbf{x}_{0}\). First, predefined probabilities \(p_{i}=p(w^{*}=w_{i})\) are used to select a transformation \(w^{*}\) from \(\{w_{1},\cdots,w_{N}\}\) for determining the \(i^{th}\) transformation. Next, a fresh point \(\mathbf{x}_{t+1}=w^{*}(\mathbf{x}_{t})\) is generated.  
2D fractals are constructed using a Euclidean space \(\chi=\mathbb{R}^{2}\) by an affine transformation [98]. The transformation has six parameters \(\theta_{i}=(a_{i},b_{i},c_{i},d_{i},e_{i},j_{i})\) relating to rotation or shift:  
\[w_{i}(\mathbf{x};\theta_{i})=\begin{bmatrix}a_{i}&b_{i}\\ c_{i}&d_{i}\end{bmatrix}\mathbf{x}+\begin{bmatrix}e_{i}\\ f_{i}\end{bmatrix} \tag{7}\]  
The fractal image is generated by drawing dots on a uniform background.  
IFS has a set of parameters along with their probabilities, given by:  
\[\Theta=\{(\theta_{i},p_{i})\}_{i=1}^{N} \tag{8}\]  
The authors of FDSL assume that every category has a unique \(\Theta\). They generate \(1\)k and \(10\)k random categories for the FractalDB-\(1\)k and the FractalDB-\(10\)k datasets, respectively. In these datasets, \(N\) is chosen from the distribution \(\mathbb{N}=\{2,3,4,5,6,7,8\}\). \(\theta_{i}\) has bounds \([-1,1]\) for \(i=1,2,\cdots,N\). \(p_{i}\) is of the form:  
\[p_{i}=\frac{\det(A_{i})}{\sum_{i=1}^{N}\det(A_{i})} \tag{9}\]  
where \(A_{i}=(a_{i},b_{i},c_{i},d_{i})\) is the affine rotation. Finally, the new category \(\Theta=\{(\theta_{i},p_{i})\}_{i=1}^{N}\) is accepted after further inspections.  
Figure 5 shows fractal 2D images from the FractalDB dataset.
## 2 Materials and Methods
### Carriers Tested  
Laminates were strip-shaped sheets covered with calcite (Figure 3a). They consisted of a carrier material coated with an adhesive layer made of biodegradable glue, the main components of which were water and carbohydrates [41]. A calcite layer of about 2 mm thickness was attached to the glue. This was achieved by spreading finely grained calcite material over the laminate surface. The glue layer was capable of keeping the calcite particles fixed in the laminate structure, still enabling the contact between the active surface and surrounding water. However, compared to the freely added calcite material (as powder), the laminates fixed 9-33% less OP depending on the properties of the calcite used (different grain size and surface area) and experimental conditions [41]. This was probably because the calcite particles were partially covered with a glue layer, which limited the active mineral surface contributing to the OP sorption [39,41].  
Modules (Figure 3b) consisted of tray-shaped surfaces fixed on a vertically oriented frame. The trays were arranged one above the other and filled with the reactive material and covered with a polyamide mesh. This type of mesh had been previously proven to prevent the calcite grains from being flushed out from the trays while enabling water penetration into the interiors of the trays [39]. Technically, the modules were similar to the solution proposed by [PERSON] et al. [38] for an inter stream water treatment system consisting of cartridges filled with a reactive material placed in mesh bags.  
Figure 3: Laminates (**a**) and modules (**b**).  
Figure 2: Orthophosphate (OP) removal by the raw material (limestone) and ground limestone (grinding time 30 min, grinding speed of 350 rpm, ball to sample mass ratio 10:1; planetary ball mill) in a batch test. Initial orthophosphate concentration = 3 mg PO\({}_{4}\)\({}^{3-}\) L\({}^{-1}\) (-1.3 mg P-PO\({}_{4}\) L\({}^{-1}\)); the limestone dosage = 1 g L\({}^{-1}\). The results were means from duplicate experiments.
## 4 Discussion
### Review of Land-Use Simulation Results  
The change of impervious surface area is the most obvious under three different scenarios, which is the main feature of land use change in urban agglomeration in the future and shows the mode of expanding from the existing construction land to the periphery.  
\"The Master Plan of Beijing (2016-2035)\" requires the total scale of the city's construction land (impervious surface, including urban and rural construction land, special land, external transportation land and some water conservancy facilities) will be controlled about 3670 square kilometers by 2035. The area of impervious surface under cropland protection scenario (Figure 4d) meets the requirements, nevertheless, there is still a certain gap with the centralized construction area planned in the future, such as the northernmost areas around Beijing Daxing International Airport which should be developed and constructed. Comparing the three scenarios, the baseline scenario has the most severe urban expansion and has more negative impact on NPP; cropland protection scenario is the only scenario in which cropland is not transformed into impervious surface in a large area, but all kinds of land basically maintain the current situation, lacking the space for urbanization development; the ecological protection scenario has good protection for forest, wetland and water body, but there is a situation that a number of cropland is turned into impervious surface. Therefore, the land use planning of urban agglomeration should comprehensively consider the scenarios of ecological protection and cropland protection, which can not only ensure regional ecological security, but also improve the quality of cultivated land and protect food security. The change of land use in the process of urbanization in Beijing is representative. The land use transfer in Beijing is similar to that in Europe from 1950 to 1990: the proportion of forest and semi-natural areas increased slightly; artificial surfaces and agricultural areas changed greatly, in which the proportion of artificial surfaces increased and the proportion of agricultural land decreased [53]. The results suggested different processes are taking place in different kinds of land use: the mountains northwest of Beijing are dominated by forest management; intensification and urbanization are mainly encountered on the plain. However, the international community has increasingly recognized the importance of maintaining \"sustainable\" ecosystems to protect the environment on which we depend. The researchers explored the methods and tools of integrated landscape management for nature conservation, physical/territorial planning, watershed management, land arrangement projecting and forestry planning [54; 55].