esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Response Analysis of a Curved Bridge under Near-Fault and Far-Field Ground Motions
## 1 Introduction
They found that structural displacement increases significantly in the excitation of near-fault ground motion, and vertical seismic action is obvious. [PERSON] et al. (2019) analyzed the seismic characteristics of a long-span steel box arch bridge for near-fault spatial seismic excitation by a shaking table test. The study showed that the structural response is significantly affected by the spatial change in near-fault ground motions and should be taken seriously. [PERSON] et al. (2019) analyzed the seismic response law of a tall pier bridge in the excitation of NP ground motions with different pulse periods and numbers through shaking table tests. They determined that the seismic fragility of the pier is correlated with pulse period and pier height, while the pulse number is not obvious. [PERSON] et al. (2019) established a soil-cable-stayed bridge model with a similarity ratio of 1:70, analyzed the structural response characteristics in the excitation of near-fault and FF ground motions, and compared the model with numerical methods. They found that NP ground motions can increase the structural displacement and the failure probability of the bridge tower. [PERSON] et al. (2019) established a 1/10-scale model of three-span steel-concrete composite rigid-frame bridge and carried out a series of shaking table experiments. They identified that NP ground motions can increase structural response, and numerical analysis proved that the steel-concrete composite rigid-frame bridge has enhanced seismic performance. [PERSON] et al. (2019) established a 1:25 curved bridge collision model, analyzed the collision characteristics of the curved bridge under NP, near-fault non-pulse (NN), and FF ground motions, and found that the collision response under NP ground motions was more significant and more unfavorable to the curved bridge. According to the above literature, the seismic response of bridges is greatly affected by near-fault and FF ground motions. At present, most studies on curved bridges with longitudinal slopes used numerical analyses. Thus, the authors conduct a shaking table test on a 5% longitudinal slope curved bridge with a ratio of 1:10. The main purpose of this paper is to study the seismic performance of continuous curved bridges in excitations of near-fault and FF ground motions by analyzing the dynamic characteristics, mid-span displacement, pier displacement, beam end displacement, and bearing deformation; the results can provide some guidance and data reference for the seismic analysis of curved bridges.
# Flexible Phased Antenna Arrays: A Review
## 3 Mostly Used Materials
The comparison of these materials, including their mechanical, electrical, and application-driven motivations, is provided in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Materials Used** & **Mechanical Properties** & **Electromagnetic Properties** & **Reason for Use** \\ \hline Chromium & Durable, Thin & Highly Conductive & Conductivity \\ \hline Rogers® RTS880 & Semi-flexible, Lightweight & Dielectric constant = 2.2, Loss & \\  & & tangent = 0.0009 & \\ \hline DuPont Pyralux® & Flexible, Lightweight & Dielectric constant = 3.4, Loss & \\  & & tangent = 0.003 & Flexible, thin, low profile \\ \hline Copper & Durable & Conductive, Low resistivity & \begin{tabular}{c} Conductivity, reliability, compatible \\ with PCB standard manufacturing \\ \end{tabular}  \\ \hline LCP & Foldable, Lightweight, Deployable & \ \begin{tabular}{c} Dielectric constant = 3.0–3.3, Loss \\ tangent = 0.0049 \\ \end{tabular}  & Low loss, flexible \\ \hline Rogers® 4003C & Rigid, Lightweight & Dielectric constant = 3.38–3.55, Loss & \\  & & tangent = 0.0027 & Low loss \\ \hline Rogers® 3003 & Semi-Flexible, Durable, Lightweight, & Dielectric Constant = 3.00, Loss & \\  & Thermally-conductive &
\begin{tabular}{c} •
# Phase Imbalance Analysis of GF-3 Along-Track InSAR Data for Ocean Current Measurement
## 2 Ocean Current Measurement Method Based on GF-3 ATI Data
### Model of GF-3 ATI Ocean Current Measurement
The large-scale wave orbital velocity changes periodically, and the average value is zero, which can be removed by the neighborhood average. The contribution of sea surface wind can be calculated by using the CDOPmodel, which is an empirical geophysical model function for predicting the Doppler frequency shift of sea surface wind proposed by [PERSON] et al. [20; 21]. \[f_{w}=\alpha_{pp}F(X(\theta,\phi,u_{10},pp))+\beta_{pp}, \tag{7}\]  
\[v_{w}=-\frac{\lambda}{2\sin\theta}f_{w}, \tag{8}\]  
where \(\theta\) is the incidence angle in degrees, \(\phi\) is the wind direction with respect to the antenna look angle in degrees, \(u_{10}\) is the wind speed, and \(pp\) represents the polarization. For the details of the formula, please refer to [21]. The phase velocities of Bragg-resonant waves can be removed by empirical models, given by [3; 22]:  
\[v_{p}=\sqrt{\frac{g}{|k|}+\frac{\tau|k|}{\rho}}, \tag{9}\]  
\[B=x_{2}-x_{1}=d/2, \tag{2}\]0  
where \(g\) is the gravitational acceleration constant, \(k\) is the Bragg wave number, \(\tau\) is the sea surface tension, and \(\rho\) is the seawater density. The speed measured by the radar is determined by the ratio of the spectral density of the forward waves and the backward waves in the resolution unit. \[B=x_{2}-x_{1}=d/2, \tag{2}\]1  
Among them, \(\alpha\) and \(1-\alpha\) represent the contribution ratio of forward and backward Bragg spectral density to radar echo, respectively. Generally speaking, the following model and wind information can be used to estimate the contribution of Bragg wave phase velocity to radial velocity. \[B=x_{2}-x_{1}=d/2, \tag{2}\]2  
\[B=x_{2}-x_{1}=d/2, \tag{2}\]3  
where \(n\) is usually 2\(\sim\)5; for C-band SAR, \(n=2.5\).
## 4 Discussion
### Effect of Dam Bodies on the Experimental Parameters  
Sediment transport in rivers is highly non-linear in time and space [52,53]. Under different experimental conditions, there will be different hydraulic parameters that vary along the cascade permeable dam bodies. For the small particle size, the water level in front of 1\(\mu\) dam is higher, which is about 56-89% of 1\(\mu\) dam height at the end of the experiment. The water level difference rises by 0.8-3.7 cm, and the permeability coefficient decreases by about 40-56%. The \(\eta\) of the dam reaches a maximum of 190 \(\mu\)g/(g/L) under Q = 300 L/H and C = 2.0 g/L. Under the action of the sediment-laden flow, the pore volume of 1\(\mu\) dam gradually decreases, reducing the sediment supply to the subsequent dams and causing the water level in front of 2-4\(\mu\) dam to rise by 38-74%, 26-63%, and 13-48% of the dam height. The rise in the water level difference of 2\(\#\) and 3\(\#\) dam is 0.3-2.4 cm and 0.1-2.5 cm, respectively. The decrease in coefficient of 2\(\#\) and 3\(\#\) dam is 23-41% and 10-32%, respectively. The turbidity removal rate of 1# dam ranged from 2.7% to 21.8%, and decreased instead of increasing due to the resuspension of sediment in the Deposition area. The removal rate of turbidity of 2-4# dam decreases step by step. At the beginning of the experiments, the sediment interception efficiency was good. As the experiment progressed, the pores of the dams were gradually blocked, and the overall \(\eta\) of the dam was \(\eta_{1}>\eta_{2}>\eta_{3}>\eta_{4}\). In summary, the more permeable dam bodies, the greater the total mass of intercepted sediment by the dams. But too many dams will reduce water fluidity, causing secondary damage to the lake water environment, as well as the unnecessary waste of construction and material costs.  
Figure 10: Sediment interception rate along the flow direction: (**a**) Q = 300 L/H; (**b**) Q = 500 L/H; (**c**) Q = 700 L/H.  
Figure 9: Distribution of the intercepted sediment mass along the flow direction: (**a**) 1# Dam; (**b**) 2# Dam; (**c**) 3# Dam; (**d**) 4# Dam.
# Real-Time Monitoring Method for Traffic Surveillance Scenarios Based on Enhanced YOLOv7
## 3 ETS-YOLOv7 Algorithm
### Algorithm Overview  
The framework of the proposed ETS-YOLOv7 algorithm is shown in Figure 1, where YOLOv7 [29] is chosen as the backbone network. To reduce the influence of complex backgrounds and improve the model's ability to extract vehicle features, channel attention and spatial attention mechanisms are applied sequentially to enhance the focus on different dimensional channel information and vehicle scale features, forming the CBAM attention mechanism [38]. This improved feature is then fed into the neck network. In addition, in large-scale traffic monitoring systems, the aspect ratio of the same vehicle in surveillance videos may vary significantly due to its driving state and monitoring angle. To avoid the larger aspect ratio penalty inherent in the standard CIoU [39] loss function, we adopt the WIoUv3 [40] loss function, which features a dynamic non-monotonic focusing mechanism(FM) and uses outlier degree as a criterion. This loss function also mitigates the impact of high and low quality anchor boxes and increases the network's attention to medium quality anchor boxes. Finally, we replace the original ELAN module [41] with the CLAN module to adapt to edge devices, reduce redundant computation, and improve network robustness and computation efficiency. The CBAM, the WIoUv3 loss function, and the CLAN module are discussed in detail in the following sections.
## 4 Experiment  
### Experiment Setup  
Open-source decoder-based models are adopted and the model size (i.e., the number of trainable parameters) ranges from 7B to 13B. The models are Llama3.1-8B, Llama2-7B, Llama2-13B, Gemma2-9B, Falcon3-7B, and Falcon3-10B. We employed the AdamW optimizer [40], the batch size is 2, and the number of epochs for tuning is 3. Given the limited training data and computational resources, we adopted LoRA (Low-Rank Adaptation) [41] for efficient fine-tuning. The detailed hyperparameter settings used for training are provided in Table 5. We used the PyTorch library (version 2.2.2+cu121) and Hugging Face open-source models for implementation and training [42; 43]. We accessed GPT-3.5-Turbo and GPT-4o via the OpenAI API to generate neutral utterances and to perform automatic evaluation, respectively. Our machine is equipped with RTX 3090 Ti (4 EA) and 256 GB RAM. We arbitrarily set the number of center characters \(k\) (i.e., cluster size) to be 50, and the representative characters of the two clusters are [PERSON] and [PERSON]. For inference, we set the temperatures 0.7 and 1.1 for [PERSON] and [PERSON], respectively. The dataset statistics used for instruction-tuning and model evaluation are detailed in Table 6. The training dataset consists of 144 utterances, and the test dataset used for evaluation contains 50 utterances.  
Figure 5: The distribution of words per sentence, where the x-axis represents the word count per sentence and the y-axis indicates the number of sentences corresponding to each word count. Specifically: word count distribution of prompt sentences \(P_{t}\)**(top)** word count distribution of response sentences \(R_{s}\)**(bottom)**.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hyperparameter** & **Value** \\ \hline Learning rate & \(5\times 10^{-5}\) \\ Batch size (per device) & 2 \\ Epochs & 3 \\ Optimizer & AdamW \\ LR scheduler & linear \\ LoRA target modules & q\_proj, v\_proj \\ LoRA rank & 4 \\ LoRA alpha & 16 \\ LoRA dropout & 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyperparameter settings used for instruction tuning with LoRA.
###### Abstract
For the underactuated problem, the dynamic and kinematic models of the formation were transformed, redefined, and decomposed into the attitude subsystem and position subsystem, thereby achieving independent control of each degree of freedom of UUSVs. 2. For the complex differential explosion problem caused by the virtual control law in the calculation process, DSC technology was adopted in the controller design to avoid the repeated differentiation of the virtual control law and reduce the computational complexity. At the same time, combining RBF neural network approximation with MLP-based adaptive law enhances the model's anti-disturbance ability. The rest of this paper is organized as follows: Section 2 introduces the basic knowledge of graph theory and RBF neural networks. Section 3 introduces the design process of the cooperative formation controller. Section 4 conducts simulation. Section 5 summarizes this paper. Notations: \(\mathrm{R}^{N}\) represents the \(N\)-dimensional real number space, given the vector \(\mathbf{x}=\left[x_{1},\ldots,x_{N}\right]^{\mathrm{T}}\), \(\left\|x\right\|_{1}\) represents the 1-norm of \(\mathbf{x}\), \(\left\|x\right\|_{2}\) represents the 2-norm of \(\mathbf{x}\), and \(\left\|x\right\|_{\infty}\) represents the \(\infty\)-norm of \(\mathbf{x}\). Define \(\tanh(\mathbf{x})=\left[\tanh(x_{1}),\cdots,\tanh(x_{N})\right]^{\mathrm{T}}\). Define vector \(\mathbf{h}=\left[\mathbf{h}_{1}^{\mathrm{T}},\ldots,\mathbf{h}_{N}^{\mathrm{T}}\right]^{ \mathrm{T}}\), where \(\mathbf{h}_{i}\), \(i=\)\(1,\ldots,N\) is also a vector. Define \(\mathbf{h}\circ\mathbf{h}=\left[\mathbf{h}_{1}^{\mathrm{T}}\mathbf{h}_{1},\ldots,\mathbf{h}_{N}^{ \mathrm{T}}\mathbf{h}_{N}\right]^{\mathrm{T}}\). \(I\) is a column vector with all elements being 1. \(A^{\mathrm{T}}\) represents the transposition of matrix \(A\). \(\lambda(A)\) represents the eigenvalues of matrix \(A\), \(A=\text{diag}\{A_{11},\ldots,A_{NN}\}\) represents the extraction of diagonal elements in matrix \(A\). \(A\otimes B\) represents the Kronecker product of matrices \(A\) and \(B\).
## 4 Discussion  
### Uncertainty Validation  
The objective of this study is to investigate the interplay between MODIS GPP data and the ERA5-Land reanalysis dataset. Previous research has demonstrated the utility of MODIS GPP data for assessing regional-scale vegetation carbon sequestration capacity [26; 47; 48]. Similarly, other studies have evaluated the feasibility of utilizing ERA5-Land reanalysis datasets to capture soil hydrothermal conditions in the Qilian Mountains area of the Tibetan Plateau. For instance, various satellite and reanalysis estimates of surface soil moisture (SSM) and root-zone soil moisture (RZSM) in the Qinghai-Tibet Plateau region from summer 2016 to 2018 have been evaluated [49]. The results proved that the ERA5-Land product exhibited the best performance, with the lowest median unbiased root mean squared error (RMSE) value of 0.04 m\({}^{3}\)/m\({}^{3}\) when compared to other soil moisture products. Additionally, scholars combined reanalysis datasets with observed soil temperature data to analyze long-term changes in the thermal regime of the uppermost soil layer at six sites in the central Tibetan Plateau [50]. They concluded that MERRA2 and ERA5-Land demonstrated the highest quality in matching the observed data at each site, exhibiting the largest correlation coefficient, smallest standard deviation, and smallest RMSE. Therefore, the utilization of MODIS GPP data and ERA5-Land data in this study for analyzing the Qilian Mountains area is deemed reliable based on these previous findings.
# On Optimal Cell Flashing for Reducing Delay and Saving Energy in Wireless Networks
## 4 Simulation Results  
In this section, we verify cell flashing by extensive simulations. To evaluate the performance of the proposed scheme, we consider two-tier multi-cell networks composed of 19 hexagonal cells. Each BS has an omnidirectional antenna. The simulation parameters are summarized in Table 3 where power related parameters are from [22]. Since we do not consider fast fading, the average interference power spectral density is computed by the path loss from interfering cells.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**BS Type** & \(P_{t}\) **(W)** & \(P_{o}\) **(W)** & \(P_{s}\) **(W)** \\ \hline \multirow{3}{*}{RRH} & 5.0 & 103.0 & 69.0 \\  & 1.0 & 96.2 & 62.0 \\  & 0.25 & 13.6 & 8.6 \\ \hline \multirow{3}{*}{Pico} & 5.0 & 103.0 & 69.0 \\  & 1.0 & 96.2 & 62.0 \\ \cline{1-1}  & 0.25 & 13.6 & 8.6 \\ \hline \hline Femto & 0.1 & 9.6 & 5.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Power model parameters for different omnidirectional BS types from [22].  
Figure 2: The derivative function of load in interference limited regime.  
We analyze the optimal \(\beta^{*}\) with numerical simulations and compare four different cases depending on the use of cell flashing and the cell size as follows:  
* Case 1: Cell flashing is not applied for small cells with radius 500 m.
* Case 2: Cell flashing is applied for small cells with radius 500 m.
* Case 3: Cell flashing is not applied for macro cells with radius 1500 m.
* Case 4: Cell flashing is applied for macro cells with radius 1500 m.
## 8 Current Challenges in the Development of 3D-Printed Prostheses
### Customization and Adjustment of Prostheses  
While 3D printing has made it easier to customize prosthetics, there are still barriers in terms of perfect fit and dynamic fit. Individual differences in the anatomical and physiological characteristics of each person make the design of personalized prostheses a complex process. In many cases, even if an adequate initial adjustment is achieved, the prosthesis may not fit well over time due to changes in the user's body, such as growth or weight changes [16].  
Research into adaptive prosthetics, which can be adjusted automatically or by manual calibration, is ongoing, but the available solutions are expensive and difficult to implement in 3D-printed models. In addition, the lack of standardization in the methods of measuring and manufacturing custom prostheses adds complexity to the process.
## 4 Numerical Model
### Influence of Grouting Reinforcement Layer Thickness
#### 4.4.1 Ground Settlement Analysis  
Figure 12 shows the settlement curves of various monitoring points on the ground when the grouting reinforcement layer thickness was h = 0 m, 0.5 m, 1.0 m, 1.5 m, 2.0 m, and 2.5 m are shown in Figure 13. The position corresponding to x = 0 was the central axis of  
Figure 12: Settlement of the left tunnel vault in the field.  
the left tunnel, the negative direction was away from the tunnel, and the positive direction was close to the right tunnel. The ground settlement curve was roughly parabolic. When h = 0 m, 0.5 m, and 1.0 m, the maximum ground settlement was located on the ground directly above the central axis of the left tunnel. When h = 1.5 m, 2.0 m, and 2.5 m, the maximum ground settlement was located on the right ground 4 m away from the central axis of the left tunnel. With the increase of the grouting reinforcement layer thickness, the location of the maximum ground settlement gradually shifted from the ground center point corresponding to the central axis of the left tunnel to the right tunnel. Comparing the ground settlement curve when the reinforcement layer thickness was h = 0.5 m and h = 1.0 m, the maximum settlement changed greatly, indicating that the reinforcement effect changed greatly there.  
Figure 14 shows the relationship between the maximum ground settlement and the grouting reinforcement layer thickness. When the thickness of the grouting reinforcement layer was h = 0 m, 0.5 m, 1.0 m, 1.5 m, 2.0 m, and 2.5 m, the corresponding maximum ground settlement was 35.7 mm, 31.3 mm, 18.0 mm, 12.5 mm, 10.7 mm, and 10.3 mm respectively, and the variation range of the maximum settlement was 4.4 mm, 13.3 mm, 5.5 mm, 1.8 mm, and 0.4 mm respectively. The maximum variation range of settlement was the largest between h = 0.5 m and h = 1.0 m, indicating that the reinforcement effect was significant when the reinforcement layer thickness h changed from 0.5 m to 1.0 m. With the increase of grouting reinforcement layer thickness, the maximum variation range of settlement decreased rapidly. When the grouting reinforcement layer thickness changed from 2 m to 2.5 m, the maximum variation range of surface settlement was only 0.4 mm and the reinforcement effect obtained was not obvious. Grouting reinforcement has a great impact on the ground settlement. The greater the thickness of the reinforcement layer, the better the reinforcement effect. Considering the maximum ground settlement and its variation range, when the thickness of the grouting reinforcement layer was h = 1.0 m and h = 1.5 m, the maximum ground settlement caused by excavation was smaller and the reduction range was large, the reinforcement effect obtained was better and the benefit was higher.  
Figure 13: Ground settlement at monitoring points.
# Fractional-Order Sliding Mode with Active Disturbance Rejection Control for UAVs
## 1 Introduction
In order to solve this problem, we designed the fractional-order reaching law sliding mode with an active disturbance rejection controller (FOSM-ADRC) with active interference suppression. The main contributions of this paper are as follows:  
* We introduce a fractional-order calculus operator into the reaching law of the SMC and replace the sign function with a hyperbolic tangent function to obtain strong robustness and immunity to interference. * We use the output of the FOSMC as the input to the ADRC, and the problem of high-frequency oscillation at the output of the FOSMC is well solved after ADRC processing. * The FOSM-ADRC strategy is proposed; the controller is divided into two rings. The inner ring uses ADRC to obtain a strong anti-interference ability, and the outer ring uses the FOSMC to obtain a faster response speed and strong robustness. In order to verify the effectiveness of the proposed controller, the dynamics model of the UAV was constructed in a Matlab/Simulink (version:R2022b) environment, and detailed simulation experiments were carried out in this paper. The experimental results show that the proposed FOSM-ADRC can not only effectively suppress the jitter phenomenon but also maintain a better control performance under different disturbance conditions, which proves its potential application in the field of UAV control.
# FONDUE: A Framework for Node Disambiguation and Deduplication Using Network Embeddings +
### Contributions  
In this paper, we make a number of related contributions:  
* We propose FONDUE, a framework exploiting the empirical observation that naturally occurring networks can be embedded well using state-of-the-art NE methods, to tackle two distinct tasks: node deduplication (FONDUE-NDD) and node disambiguation (FONDUE-NDA). The former, by identifying nodes as more likely to be duplicated if contracting them enhances the quality of an optimal NE. The latter, by identifying nodes as more likely to be ambiguous if splitting them enhances the quality of an optimal NE;
* In addition this conceptual contribution, substantial challenges had to be overcome to implement this idea in a scalable manner. Specifically for the NDA problem, through a first-order analysis we derive a fast approximation of the expected NE quality improvement after splitting a node;
* We implemented this idea for CNE [6], a recent state-of-the-art NE method, although we demonstrate that the approach can be applied for a broad class of other NE methods as well;
* We tackle the NDA problem, with extensive experiments over a wide range of networks demonstrate the superiority of FONDUE over the state-of-the-art for the _identification_ of ambiguous nodes, and this at comparable computational cost;
* We also empirically observe that, somewhat surprisingly, despite the increase in accuracy for _identifying_ ambiguous nodes, no such improvement was observed for the ambiguous node _splitting_ accuracy. Thus, for NDA, we recommend using FONDUE for the _identification_ of ambiguous nodes, while using an existing state-of-the-art approach for optimally splitting them;
* Experiments on four datasets for NDD demonstrate the viability of FONDUE-NDD for the NDD problem based on only the topological features of a network.