esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 FE Model of a Bridge
### Bridge Models  
The use of real structures and physical models is difficult to investigate structural behavior of considered partially earth-anchored cable-stayed bridges with a new key segment closing method. Instead of use of real structures, this study uses the finite element (FE) model, which can correctly and reliably reproduce the real behavior of the structure. The method using FE model is frequently applied for predicting the behavior of long-span cable bridges in various fields [10; 11; 12; 13].  
Analysis of cable-stayed bridges is much more complicated than that of conventional bridges because of the former's nonlinear structural behavior due to cable sag, large deflections, and compression effects in the pylons and girders [14; 15]. In the present study, RM Bridge, which is a well-known 3-D finite element program for cable-stayed bridges, is adopted to simulate the complicated  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Location**} & **Elastic Modulus** & **Poisson** & **Weight Density** \\  & & **(\(\mathbf{E_{CS}}\), GPa)** & **Ratio (\(\mathbf{v}\))** & **(\(\mathbf{\gamma_{s}}\), kN/m\({}^{3}\))** \\ \hline Concrete (50 MPa) & \multirow{2}{*}{Pylon} & 31 & 0.167 & 24.53 \\ Steel (SM520) & \multirow{2}{*}{Girder} & 210 & 0.300 & 77.00 \\ Cable (1770 MPa) & \multirow{2}{*}{Stay cable} & 200 & 0.300 & 77.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of a bridge.  
Figure 4: Outline of the sample bridge (units: m). (**a**) Side view; (**b**) Cross-section of the girder; (**c**) Pylon; (**d**) Cross-sections of the pylon; (**e**) Bearing conditions.  
behavior of cable-stayed bridges [16; 17]. This program, which is widely used in analyzing long-span cable-stayed, can simulate the cable nonlinear effect, P-delta effect, and the construction sequence of cable-stayed bridges easily by simple beam elements and cable elements. Girders and pylons are modeled as beam elements based on beam theory. Stay cables are modeled as cable elements, which consider only normal forces and shear forces or bending moments cannot occur in cable elements. In addition, spring elements are used to model bearings and supports. In order to consider the curved cable geometry (nonlinear deformation behavior of the cables), the stay cable nonlinear option of the program is used. The P-delta effect is also included in the analysis.  
Figure 5 illustrates the configuration of the FE model with a partially earth-anchored cable system, where the partially earth-anchored cable-stayed bridge model has six pairs of earth-anchored cables at the abutments (L-EA-6).
## 3 Results  
### Digitally Enhanced Phantom  
The original and post-processed CT and CBCT phantom images in three different transverse sections are shown in Figure 2. Table 1 reports the HU values of OARs in the original and processed CT and CBCT phantom images. Both post-processed phantom image sets have been made publicly available as additional downloadable material for this paper, as Supplementary Material.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**ORGAN** & \multicolumn{4}{c}{**Original Phantom CT Images (HU)**} & \multicolumn{4}{c}{**Processed Phantom CT Images (HU)**} \\ \hline  & Mean & Std & Min & max & mean & Std & min & Max \\ \hline Parotid Glands & 50.0 & 6.0 & 35.0 & 73.0 & 26.4 & 7.4 & \(-\)1.0 & 53.0 \\ Larynx & 42.2 & 5.8 & 25.0 & 57.0 & 72.5 & 28.5 & 21.0 & 108.0 \\ OralCavity & 48.1 & 4.2 & 37.0 & 60.0 & 40.0 & 5.3 & 25.0 & 53.0 \\ BrainStem & 48.4 & 7.7 & 32.0 & 74.0 & 38.6 & 13.5 & 10.0 & 66.0 \\ Eyes & 75.1 & 8.4 & 53.0 & 100.0 & 101.9 & 9.5 & 62.0 & 135.0 \\ \hline
**ORGAN** & \multicolumn{4}{c}{**Original Phantom CBCT Images (HU)**} & \multicolumn{4}{c}{**Processed Phantom CBCT Images (HU)**} \\ \hline  & Mean & Std & Min & max & mean & Std & min & Max \\ \hline Parotid Glands & 61.6 & 14.8 & 17.0 & 100.0 & 18.1 & 14.1 & \(-\)16.0 & 56.0 \\ Larynx & 112.9 & 14.8 & 58.0 & 154.0 & 185.9 & 66.6 & 85.0 & 278.0 \\ OralCavity & 88.3 & 19.1 & 25.0 & 147.0 & 96.7 & 18.8 & 39.0 & 150.0 \\ BrainStem & 91.7 & 27.7 & 23.0 & 182.0 & 44.8 & 65.2 & \(-\)85.0 & 182.0 \\ Eyes & 27.7 & 20.5 & \(-\)28.0 & 92.0 & 40.9 & 20.5 & \(-\)16.0 & 104.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Original and post-processed CT and CBCT transverse sections of the CIRS ATOM Max Dental and Diagnostic Head Phantom Model-711 used in the method presented for QA of DIR. The HU values of parotid glands, larynx, oral cavity, brainstem and eyes were digitally modified using MATLAB and CERR tools, as described in Section 2.1. HU values for original and post-processed CT and CBCT phantom images reported in the table referred to a ROI of 20 \(\times\) 20 pixels.  
Figure 2: Original and post-processed CT and CBCT transverse sections of the CIRS ATOM Max Dental and Diagnostic Head Phantom Model-711 used in the method presented for DIR accuracy evaluation. Sections were chosen to visualize organs of interest. The HU values of parotid glands, larynx, oral cavity, brainstem and eyes were digitally modified using MATLAB and CERR tools, as described in Section 2.1.
## 3 Results and Discussion
Unfortunately, the lower measuring frequency of the color pyrometer does not allow for detailed changes in the temperature course to be recorded and, furthermore, leads to a temporal uncertainty as intensity is collected over a larger time interval. This temporal uncertainty of the measurement is conterminous with a spatial uncertainty of the temperature measurement in the welding direction. The small scales of the melt pool and keyhole geometries, especially in the frontal part, demand a high spatial resolution of the pyrometer in this direction, which the color pyrometer cannot provide. Taking into consideration the fact that the small-band pyrometry is prone to positioning errors and measures a spatially averaged temperature perpendicular to the welding direction, including colder regions, the results from the two different measuring methods do not necessarily have to be perceived as contradictory. Rather, the measurement performed with the small-band pyrometer gives more qualitative information about the course of the temperature curve, whereas the color pyrometer measures a reliable maximum temperature while lacking sufficient temporal resolution.
# Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet
###### Abstract  
Palm vein recognition has received some considerable attention regarding its use in biometric identification. Palm vein characteristics offer a superior level of security and reliability in personal identification compared to extrinsic methods such as fingerprint, face, and palm print recognition, as vein patterns are difficult to duplicate and do not change throughout one's lifetime. This study proposes both segmentation and recognition methods to enhance the authentication performance and achieve correct identification using palm vein features. First, we propose a segmentation model based on the U-Net model, enhanced with an attention gate, to effectively segment palm vein patterns. The incorporation of both the attention gate and residual block allows the segmentation model for the learning of the essential features required for specific segmentation tasks. The Hessian-based Jerman filtering method is used for ground-truth labeling. The segmentation model extracts the palm vein patterns and filters out the irrelevant and noisy pixels for the purpose of recognition. The efficient channel attention residual network is trained to learn discriminative features for personal identification using combined margin-based loss functions for palm vein recognition. The channel attention module enhances the useful information and suppresses irrelevant features in the feature maps, which overcomes the problem of rotation, position translation, and scale transformation, as well as improves the recognition rate. The combined loss function used in this study increases the similarity between the intra-class samples and the diversity between inter-class samples. The proposed recognition model achieved 100% accuracy for palm vein recognition and an equal error rate of 0.018 for palm vein verification.  
biometrics authentication; palm vein recognition; attention gate; efficient channel attention
# Accurate Tracking Algorithm for Cluster Targets in Multispectral Infrared Images
## 5 Conclusions  
This paper proposes an accurate tracking algorithm for small targets in infrared image clusters based on optical flow estimation that solves the technical problems of a large number of targets, a low target recognition rate, and poor tracking accuracy in the tracking process. Infrared weak small target tracking is susceptible to the environment, background, and target complexity. In the target tracking process, the target shape, size, texture, and other items of feature information are difficult to obtain due to the distance between the target and the detector, and the target in the image has a point source feature and is easily drowned by noise. The algorithm proposed in this paper, through the infrared image subspace model and SIR particle filtering target observation model, uses the PigNet network built and trained in the PigNet model to achieve fast segmentation and tagging of the target and then uses the improved optical flow estimation algorithm to determine the movement of the target pixel position by the time-domain variation and correlation of the pixel intensity data in the image sequence to achieve high-accuracy cluster weak target tracking. Through a large amount of experimental data analysis, the comparative analysis of the proposed algorithm and the currently popular algorithms detailed in this paper shows that MOTA accuracy reached 79.7%--a higher value than SiamFC's 2.7%. The FPS index reached 42.3, which was higher than SiamFC's 3.8. The IDF1 reached 77.5 and ranked first; the ID value was 9.9% lower than the best MOT algorithm; and the ML index ranked second, which proves that the proposed algorithm has the desired effect. Through the optimization of the network structure and parameter adjustment, the tracking accuracy of the proposed algorithm was further improved. At the same time, the operating space of the algorithm was reduced to realize the application of embedded systems, which can expand the application field of the algorithm not only in medical, industrial, security, and safety fields, but also in military and aerospace fields.  
Not applicable.  
The authors declare no conflict of interest.
# Joint Location-Allocation Model for Multi-Level Maintenance Service Network in Agriculture
## 5 Case Study
### Optimal Location-Allocation Plan  
In this section, the Benders decomposition solution approach is developed to solve the proposed MLFLAPs, and all computing programs are coded on a personal computer with an AMD 4.00 GHz processor.  
First, the commercial solver GUROBI is introduced to solve the proposed MLFLAPs directly, but the optimal results can only be obtained after about 20 h (71,933 s) due to the massive decision variables and constraints, which may not be applicable in reality. The developed Benders decomposition approach is then applied to this problem. The optimal solutions are obtained with three iterations in 8727 s (2.4 h), and the minimal service costs in this case is 5.836 \(\times\) CNY 10\({}^{5}\). The location-allocation results of service stations and spare part centres are shown in Table 1 and Figure 5.  
Figure 4: Maintenance record in 251 service units.  
Figure 3: Three provinces with 251 service units.  
As shown in Figure 5, 25 service stations and two spare part centres are built in this service network. First, service stations are usually selected in these service units with a higher number of service demands, such as service units 1, 12, 79, 85, 106, 138, 153, 202, and 238. Service units served by one service station are distinguished by different colours, and all 251 of these service units are always allocated to the nearest service stations. For example, the service station in 73 provides maintenance service for three service units, e.g., 73, 76, and 78. Otherwise, the service station in 220 provides maintenance service for seventeen service units, e.g., 3, 178, 206, 215, 216, 218, 220, 221, 222, 223, 239, 240, 241, 242, 243, 244, and 246.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**NO.**} & **Service** & **Number of** & **Service Units** & **Spare Part Centres** \\  & **Stations** & **Vehicles** & **Service Units** & **Spare Part Centres** \\ \hline
1 & 1 & 2 & 1, 2, 57, 217, 224, 225 & 207 \\ \hline
2 & 4 & 2 & 4, 5, 6, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 61 & 207 \\ \hline  
In addition, there are 60 service vehicles are allocated to these selected service stations based on the service demands in their service area. For example, four service vehicles are allocated to the service station in 12 with a total maintenance demand of 1168, while only one service vehicle is allocated to the service station in 94 with a total maintenance demand of 246.  
As for spare part centres, two spare part centres are set in service units 118 and 207, and the service area of these two spare part centres is shown in Figure 5 with the black dotted lines. They are responsible for supplying spare parts for service stations in the top and bottom areas in Figure 5, respectively. In this case, service stations in 8, 10, 12, 43, and 138 can be served by these two spare part centres.
# RRT*-Fuzzy Dynamic Window Approach (RRT*-FDWA) for Collision-Free Path Planning
## 5 Experimental Results
### FDWA Lobal Path Planning  
In order to verify its effectiveness, the FDWA algorithm was simulated. First, the initial values of the weight coefficients in the evaluation function were set at \(\alpha\) = 0.2, \(\beta\) = 0.4, and \(\gamma\) = 0.4; the maximum linear and angular velocities were also set to \(v\) = 0.5 m/s and \(\omega\) = 0.3 rad/s. \(a\) and \(b\) represent the reward and punishment function \(K\): \(a\) = 0.5 and \(b\) = 1.5. The time for forward simulation was 3 s and dt = 0.1.  
Figure 7 shows the FDWA algorithm planning diagram and demonstrates the effectiveness of the algorithm in the presence of unknown obstacles. The solid purple line indicates the path simulated by the FDWA algorithm in the constant forward direction, and the dashed line indicates the final completed trajectory. The blue solid circles indicate  
Figure 6: RRT*-FDWA global path planning.  
stationary obstacles; the blue solid hollow circles indicate the current position of the moving obstacle; and the blue dashed circles indicate the position of the moving obstacle at the previous moment. When the mobile robot simulates the path forward, an optimal local path is found according to the evaluation function and the local minimum problem can be successfully avoided.  
To further verify the effectiveness of the FDWA, this paper is validated by a real robot. Figure 8a shows the experimental robot tianbot_mini, a two-wheel differential drive robot with a drive wheel at the rear and a gimbal at the front, and with classic PD control for the drive wheel. It has a ydlidar x2 LIDAR for obstacle detection. The linear velocity is constrained, \(v\in(0,\,0.5\) m/s), and the FDWA algorithm is encapsulated as a local path planner for the ROS navigation package. The experimental environment is the same as the simulation environment, and the white mobile robot in Figure 8b is regarded as a moving obstacle with a handle control and a speed of \(0.10\) m/s. Figure 8b-f shows the FDWA path planning process, and the experimental results better validate the effectiveness of the algorithm.  
Figure 7: FDWA path planning diagram. (**a**) Position planning diagram at the moment \(t=0.5\). (**b**) Position planning diagram at the moment \(t=1\). (**c**) Position planning diagram at the moment \(t=3\). (**d**) Position planning diagram at the moment \(t=5\).  
Figure 8: FDWA real environment path planning map. (**a**) the experimental robot (**b**) The robot position map at the moment \(t=0\). (**c**) The robot position map at the moment \(t=3\). (**d**) The robot position map at the moment \(t=5\). (**e**) The robot position map at the moment \(t=8\). (**f**) The robot position map at the moment \(t=10\).
## 2 Preliminaries and Problem Formulation
### Barrier Lyapunov Function  
**Definition 1** ([26]).: _BLF is a scalar function, defined with respect to a system \(\dot{x}=f(x)\) on \(D\), which is continuous, positive definite, and an open region containing the origin. BLF has continuous first-order partial derivatives at every point of \(D\) and the property \(V(x)\to\infty\) as \(x\) approaches the boundary of \(D\), and satisfies \(V(x(t))\leq b\), \(\forall t\geq 0\) along the solution of \(\dot{x}=f(x)\) for \(x(0)\in D\) and some positive constant \(b\)._To deal with the output constraint, a BLF is introduced as  
\[V=\frac{1}{2}\text{ln}\frac{k^{2}}{k^{2}-z^{2}}, \tag{14}\]  
where \(k>0\) is a constant and \(z\) is variable of error.  
**Lemma 1**.: _If \(|z|\leq k,k\in\mathbb{R}\) is any constant [26], then_  
\[\text{ln}\frac{k^{2}}{k^{2}-z^{2}}<\frac{z^{2}}{k^{2}-z^{2}}. \tag{15}\]
# Experimental and Modeling of Residual Deformation of Soil-Rock Mixture under Freeze-Thaw Cycles
###### Abstract  
Projects in seasonal frozen soil areas are often faced with frost heaving and thawing subsidence failure, and the foundation fill of most projects is a mixture of soil and rock. Therefore, taking soil-rock mixture with different rock contents as research objects, the residual deformation of soil-rock mixture under multiple freezing-thawing cycles is studied. In addition, the deep learning method based on the artificial neural network was pioneered combined with the freezing-thawing test of the soil-rock mixture, and the Long short-term memory (LSTM) model was established to predict the results of the freezing-thawing test. The LSTM model has been verified to be feasible in the exploration of the freeze-thaw cycle law of a soil-rock mixture, which can not only greatly reduce the period of the freeze-thaw test, but also maintain a high prediction accuracy to a certain extent. The study found that the soil-rock mixture will repeatedly produce frost heave andthaw subsidence under the action of freeze-thaw cycles, and the initial frost heave andthaw subsidence changes hugely. With the increase of the number of freeze-thaw cycles, the residual deformation decreases and then becomes steady. Under the condition that the content of block rock in the soil-rock mixture is not more than 80%, with the increase of block rock content, the residual deformation caused by the freeze-thaw cycle will gradually decrease due to the skeleton function of block rock, while the block rock content's further increase will increase the residual deformation. Furthermore, the LSTM model based on an artificial neural network can effectively predict the freezing and thawing changes of soil-rock mixture in the short term, which can greatly shorten the time required for the freezing and thawing test and improve the efficiency of the freezing and thawing test to a certain extent.  
soil-rock mixture; freeze-thaw cycle; residual deformation; rock content; long short-term memory network +
Footnote †: journal: Applied Sciences
## 5 Long-Term Evolution  
While modeling the historical space debris distribution, existing measurement data is used to validate the scientifically generated population. As a result, important population reference points are created that ensure model reliability. For the future population, referring to the extrapolated population that might stabilize in the next decades, this is not possible. Hence, a meaningful estimate must be made about the expected evolution of the space debris environment. The scientific challenge here is the development of algorithms for the long-term propagation of orbits. Both analytic and numerical methods are available at the Institute. A very important field of research is the investigation of algorithms for calculating the probability of a catastrophic collision. A catastrophic collision is defined as an impact event in which the target, e.g., an upper stage or active payload, is hit so severely that it is completely fragmented. The released debris in this process can cause further catastrophic collisions in the long-term; the Institute has done much research and made many contributions in this field during the last several decades [27; 28]. A crucial step in modeling future population trends is the prediction of future traffic in space and assessment of post-mission disposal rates. To be able to carry on safely with space exploration, it is necessary to know the dynamic behavior of the space debris environment. Long-term simulations provide the opportunity to gain insight into a temporal evolution of the future space debris environment. Usually, long-term simulations start with a so-called business-as-usual (BAU) scenario that forecasts the development of parameters such as launch rates, post mission disposal (PMD) rates, number of explosions and solid rocket motor firings. The history of these parameters are analyzed and extrapolated to establish future population models that are based on the current and past space flight activities. In most cases, it is reasonable to extrapolate the parameters for different categories (such as countries and orbits) separately to be capable of considering effects  
Figure 7: Evaluation of spatial density in LEO for different size regimes. (**a**) \(d>1\) mm; (**b**) \(d>1\) cm; (**c**) \(d>10\) cm.  
that influence only single categories. A famous example of such an effect is the end of the Soviet Union, but also the increasing number of launched objects from private companies in the past few years can be included in this way.
###### Abstract  
The objective of this study was to enhance the mechanical properties of gravelly soil and to consider the binding and filling effects of xanthan gum and calcium lignosulfonate. To this end, gravelly soil samples were prepared with various dosages of xanthan gum and calcium lignocellulosic, and their curing effects were investigated. The mechanical properties and strength parameters of the cured gravelly soil were investigated using unconfined compressive strength (UCS) tests and conventional triaxial compression tests. Furthermore, scanning electron microscopy (SEM) was employed to examine the microstructure and curing mechanisms of the gravelly soil treated with these additives. The findings demonstrate that as the dosage increases, both xanthan gum and calcium lignocellulosic markedly enhance the compressive strength and shear strength of the gravelly soil. The curing effect of xanthan gum was found to be more pronounced with higher dosages, while the optimal curing effect for calcium lignocellulosic was achieved at a dosage of 4%. The gravelly soil treated with xanthan gum exhibited superior performance compared to that treated with calcium lignocellulosic when the same dosage was used. Moreover, at elevated confining pressures, the binding effect of xanthan gum and calcium lignocellulosic on the gravelly soil was less pronounced than the strength effect imparted by the confining pressure. This suggests that the impact of dosage on the shear strength of the gravelly soil is diminished at higher confining pressures. The stabilization of crushed stone soil by xanthan gum is a complex process that involves two main mechanisms: bonding and cementation, and filling and film-forming. The curing mechanism of calcium lignocellulosic-cured gravelly soil can be summarized as follows: ion exchange, adsorption and encapsulation, and pore filling and binding effects. The findings of this research offer significant insights that are pertinent to the construction of high earth-rock dams and related engineering applications.  
stabilized gravel soil; xanthan gum; calcium lignocellulosic; strength; curing mechanism +
Footnote †: journal: Applied sciences  
Article