esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of On-Site Monitoring Results of Shield Tunneling under High-Speed Railway Bridges
### Analysis of Monitoring Results  
#### 4.2.1 Analysis of Surface Subsidence Monitoring Results  
Set up surface settlement monitoring points along the elevated bridge for shield tunneling and monitor the surface settlement of the left and right tunnel lines when the shield tunneling passes through the left and right pipes. The monitoring results are as follows:  
The surface settlement along the process of the left and right shield tunneling passing through the elevated bridge is shown in Figures 13 and 14. During the process of shield tunneling crossing the left or right line, the settlement of Piers 104 and 105 always fluctuates within the range of (1, 1) mm. The maximum settlement did not exceed \(\pm\) 1 mm, which meets the safety control standards. In the final settlement curve of the measuring points along the left line, the overall trend shows a slight uplift, followed by accelerated settlement, and finally a slow and stable decline. The upwelling part in the early stage mainly fluctuates within a range of 1 mm, and as the shield tunneling passes through, the surface settlement rapidly decreases to \(-\)4 mm. Subsequently, during the rapid descent phase, the disturbance of the shield tunnel to the soil gradually decreases. In the later monitoring period, the fluctuation gradually decreased from \(-\)4 mm to \(-\)6 mm and finally maintained a steady-state fluctuation. The overall change in surface subsidence was within the range of (1, \(-\)7) mm, which has not yet reached the yellow warning stage and meets safety control standards. The overall settlement process of the right line is similar to that  
Figure 11: Schematic diagram of stress and soil pressure gauge measurement point layout.  
Figure 12: Automated real-time monitoring and early warning system.  
of the left line. The difference is that the downward trend of settlement on the right track is relatively slow, and the overall variation is more uniform due to the influence of shield tunneling. The final settlement value is also lower than the left line, only reaching about \(-5\) mm. The overall variation of surface settlement is within the range of (0, \(-7\)) mm. It has not yet reached the yellow warning stage and meets safety control standards.
## Materials and Methods
### Fracture Tests
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Specimen** & **Material** & \
\begin{tabular}{c} **2\(\alpha\)** \\ **(deg)** \\ \end{tabular} &
\begin{tabular}{c} **Specimen** \\ **Radius, R** \\ \end{tabular} & \(\mathbf{P_{f1}}\) & \(\mathbf{P_{f2}}\) & \(\mathbf{P_{f3}}\) & \(\mathbf{P_{f,average}}\) \\ \hline \multirow{9}{*}{PMMA} & \multirow{4}{*}{30} & 5 & 593.4 & 564.3 & 587.6 & 581.8 \\  & & 10 & 979.7 & 1083.7 & 1062.8 & 1042.0 \\  & & 20 & 1981.6 & 2001.0 & 1845.6 & 1942.7 \\  & & 35 & 3341.5 & 3129.6 & 3308.9 & 3260.0 \\ \cline{2-7}  & & 5 & 458.0 & 435.5 & 453.5 & 449.0 \\  & & 10 & 795.9 & 880.6 & 863.6 & 846.7 \\  & & 20 & 1559.0 & 1574.4 & 1452.0 & 1528.5 \\  & & 35 & 2701.4 & 2530.0 & 2675.0 & 2635.5 \\ \cline{2-7}  & & 5 & 301.3 & 283.3 & 303.4 & 296.0 \\  & & 10 & 517.8 & 564.8 & 575.3 & 552.6 \\  & & 20 & 1045.4 & 1057.8 & 1001.9 & 1035.0 \\  & & 35 & 1816.9 & 1685.2 & 1836.4 & 1779.5 \\ \cline{2-7}   RVNBD & & 5 & 365.0 & 343.0 & 367.5 & 358.5 \\  & & 10 & 623.6 & 680.0 & 692.8 & 665.5 \\  & & 20 & 1270.0 & 1285.2 & 1217.3 & 1257.5 \\  & & 35 & 2130.3 & 1975.9 & 2153.3 & 2086.3 \\ \cline{2-7}  & & 5 & 298.0 & 280.0 & 300.0 & 292.7 \\  & & 10 & 514.0 & 560.8 & 571.2 & 548.7 \\  & & 20 & 988.8 & 1000.5 & 947.7 & 979.0 \\  & & 35 & 1741.5 & 1615.3 & 1760.3 & 1705.7 \\ \cline{2-7}  & & 5 & 192.9 & 181.4 & 194.2 & 189.5 \\  & & 10 & 345.0 & 376.4 & 383.4 & 368.3 \\  & & 20 & 709.5 & 718.0 & 680.0 & 702.5 \\  & & 35 & 1164.7 & 1080.2 & 1177.2 & 1140.7 \\ \hline \multirow{9}{*}{RPMA} & \multirow{4}{*}{45} & 5 & 208.0 & 195.5 & 209.4 & 204.3 \\  & & 10 & 346.4 & 377.8 & 384.9 & 369.7 \\  & & 20 & 681.0 & 689.2 & 652.8 & 674.3 \\  & & 35 & 1110.6 & 1030.0 & 1122.6 & 1087.7 \\ \cline{2-7}  & & 5 & 219.4 & 198.8 & 212.9 & 210.4 \\  & & 10 & 358.4 & 394.8 & 383.4 & 378.9 \\  & & 20 & 651.0 & 688.8 & 695.6 & 678.5 \\  & & 35 & 1116.3 & 1072.0 & 1127.3 & 1105.2 \\ \cline{2-7}  & & 5 & 120.9 & 114.9 & 119.7 & 118.5 \\  & & 45 & 10 & 223.2 & 246.9 & 242.0 & 237.4 \\  & & 20 & 437.0 & 441.4 & 407.1 & 428.5 \\  & & 35 & 701.0 & 656.6 & 694.3 & 684.0 \\ \cline{2-7}  & & 5 & 128.8 & 119.7 & 129.5 & 126.0 \\  & & 10 & 235.0 & 257.9 & 256.2 & 249.7 \\  & & 20 & 437.8 & 440.4 & 419.6 & 432.6 \\  & & 35 & 702.0 & 662.7 & 708.3 & 691.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The fracture loads of polymeric (PMMA and GPPS) RVNBD and RVCSB samples (dimensions in mm, load values in N).
# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
###### Abstract
* Unlike most methods, our method can obtain intrinsic parameters of the camera and extrinsic parameters of the system together. It only takes one capture of the camera and LiDAR to get the intrinsic parameters and extrinsic parameters of the system. Moreover, we employ an optimization algorithm to reduce errors by minimizing the distance between the 3D points and target plane. * More frames of the camera and LiDAR can improve accuracy by aligning the triangular pyramid planes of all frames. We also use simulation and incremental experiment to verify the precision and stability. To explain and explore the proposed calibration method, we organize the remainder of this paper as follows. In Section 2, we investigate previous work with regard to calibration between the camera and LiDAR briefly. In Section 3, the calibration target and the proposed method are described in detail. The experiments and results analysis on both simulation and real test are exhibited in Section 4. Finally, conclusions and future work are provided in Section 5.
## 5 Experimental Results and Analysis
### Benchmark Functions  
In order to validate the performance of the HE-DEPSO algorithm in solving different optimization problems, the set of single-objective test functions with boundary constraints CEC 2017 [58] was used. This set consists of 29 test functions whose global optimum is known. We have two unimodal functions (\(F_{1}\) and \(F_{3}\)), seven simple multimodal functions (\(F_{4}-F_{10}\)), ten hybrid functions (\(F_{11}-F_{20}\)), and ten composite functions (\(F_{21}-F_{30}\)). In all these optimization problems, we seek to find the global minimum (\(F_{min}\)) within the search space bounded in each dimension (\(D\)) by the interval \([-100,100]\). Information about this set of test functions is briefly presented in Table 2.
# Driver Behavior Classification System Analysis Using Machine Learning Methods
## 2 Driver Behavior Overview
### Driving Behavior Sensing Methods  
Several sensing modalities have been used in the literature to identify driving behavior. They have been classified into three types of data: vehicle control data, visual data and physiological data [10]. In the vehicle control data type, the identification is based on vehicle dynamics data such as the supervision of the steering wheel, the pedal position or the throttle hold rate [11; 12]. Several researchers have proposed steering wheel patterns to detect driver behavior. They stated that distracted drivers tend to increase the distance with the leading vehicle or tend to drive faster than normal [13]. Others proposed steering wheel metrics to detect abnormal behaviors [14], such as the standard deviation of the steering wheel angle, the steering wheel reversal rate [15], the high frequency of steering [16] and the steering entropy [17; 18]. In the visual data type, the driver behavior is deduced from images and videos of the driver's facial expressions [19; 20], the movements of the eye ball [21; 22; 23; 24; 25; 26], head movements [27; 28; 29; 30] and body movements [31]. In the physiological data type, a driver's abnormal behavior is detected by analyzing brain activity signals and their heart rate. These signals are used to detect the major distraction factor, which is driver fatigue [32; 33]. Physiological data can now be acquired using nano sensors embedded in human bodies [34; 35].
# A Maximum Power Point Tracking Control Method Based on Rotor Speed PDF Shape for Wind Turbines
## 3 MPPT Control Based on PDF Shape  
At present, in terms of controller design, research on maximum wind energy tracking focuses on advanced control strategies, mainly including neural network control algorithm [22], adaptive control algorithm [23], predictive control algorithm [24], sliding mode control algorithm [25; 26], fuzzy logic control algorithm [27], and robust control [28]. However, due to the randomness of wind speed and the multi-variable, large inertia and nonlinear of wind turbines, the system has high requirements for control effect. Therefore, this output PDF shape control algorithm is very necessary to achieve high precision control and effectively solve the non-Gaussian problem caused by dynamic random variables. FPK equation is suitable for strong and weak non-linear systems, which is the most advantageous method to study the PDF shape control of nonlinear stochastic systems. In this PDF control algorithm based on the FPK equation, the PDF of rotor speed is selected as the output distribution. By controlling the input of the system, the output PDF shape can track the desired PDF shape, thus effectively solving the MPPT control problem caused by stochastic wind speed [29; 30].  
The design of MPPT control based on the PDF shape of rotor speed is shown in Figure 4. This novel MPPT control method of combining OT control and PDF shape control is marked as \"PDF-MPPT control\" in subsequent parts.  
Figure 4: The design block diagram of PDF-MPPT control.  
Figure 3: Block diagram of OT control principle. The “Optimal Torque Curve” is fitted by the experimental data of wind turbine manufacturers, and it will change with the environmental factors. The PI controller used in the “Controller” module adjusts the rotor speed of the wind turbine according to the error between \(T_{g}\) and \(T_{g}\)’. “Wind Energy System” includes wind turbine model and electrical system [21].
# Design of SWB Antenna with Triple Band Notch Characteristics for Multipurpose Wireless Applications
## 3 Parametric Analysis  
A parametric study was conducted for optimizing numerous design parameters. A comprehensive behavioral analysis of the designed antenna was accomplished with a view to evaluating the efficacy of the antenna and to identify the influencing parameters of the respective notch band. The impact of the notching factors on the respective notches was investigated thoroughly, whereas the rest of the factors remained unaltered.
## 3 Results and Discussion
For each CCD pixel, a model [9] can be constructed as follows:  
\[\begin{array}{ll}\text{BRDF}(\text{row},\text{col})&=\text{px}\text{0y}\text{0 }(\text{row},\text{col})+\text{px}\text{1y}\text{0}(\text{row},\text{col}).* \beta+\text{px}\text{0y}\text{1}(\text{row},\text{col  
Figure 10: _Cont._  
Figure 10: **(a–c)** Six fitting coefficients of diffuser 1 in the UV1, UV2, and VIS channels. **(a)** The fitting coefficients of diffuser 1 in the UV1 channel. **(b)** The fitting coefficients of diffuser 1 in the UV2 channel. **(c)** The fitting coefficients of diffuser 1 in the VIS channel.  
Figure 11: _Cont._  
Figure 11: Cont.  
Given that the same light source is considered in the Earth and solar mode calibrations at equal distances, the irradiance of the light source can be neglected according to the forward Formula (5). Various sources contribute to the uncertainty in the BRDF calibration process for the satellite-mounted diffuse reflector panels, including source non-uniformity, standard diffuse plate uncertainty, UVS non-stationarity, source non-stationarity, and solar simulator distance.  
Figure 11: (**a–c**) Six fitting coefficients of diffuser 2 in the UV1, UV2, and VIS channels. (**a**) The fit coefficients of diffuser 2 in the UV1 channel. (**b**) The fitting coefficients of diffuser 2 in the UV2 channel. (**c**) The fitting coefficients of diffuser 2 in the VIS channel.  
The absolute accuracy of the UVS encompasses factors such as the standard diffuser plate uncertainty at the National Institute of Metrology (NIM), UVS non-stationarity, source non-uniformity (including source angle non-uniformity and source plane non-uniformity), source non-stationarity, and solar simulator distance error. The error can be calculated using Formula (7). Table 2 summarizes these results.  
\[\delta=\sqrt{{x_{1}}^{2}+{x_{2}}^{2}+{x_{3}}^{2}+{x_{4}}^{2}\cdots+{x_{n}}^{2}} \times 100\% \tag{7}\]  
where \(\delta\) denotes the total uncertainty, and \(x_{n}\) denotes the uncertainty of each sub-component. The total uncertainty in the calibration is summarized in Table 2.  
The final accuracy for the UVS absolute BRDF calibration parameters, as determined on the ground using the methods described above, is less than 3%. This uncertainty arises from variations between measurements obtained with different external light sources.
## 8 Query/Data Protection Mechanism
### Improving Base Station Scalability  
The above results may seem to suggest that a single base station can support only a very small number of sending motes. However, the number of motes reliably handled by a base station can be improved significantly simply by attaching more than one receiving mote (mote \(B\) in Figure 7) to the PC. Each receiving mote is set to a different wireless channel and handles incoming packets only from motes sending on that channel. To demonstrate the feasibility of this approach, we repeated the above stress test with two and three receiving motes. As shown in Figure 13, we found that the base station's ability to handle multiple sending motes scales very well with the number of receiving motes attached. The overall throughput increases linearly with additional receiving motes. With four senders per receiver, for instance, a single receiving mote allows a throughput of about 46.9 kbps; this increases to 93.0 kbps and 142.9 kbps for two and three receiving motes, respectively. We also observed that the packet loss remains low (on the order of 1%) with the additional receiving motes.  
Figure 12: Packet loss at receiving mote and PC as a function of the number of senders (maximum possible sending rate).  
Figure 13: Aggregate throughput at PC as a function of the number of senders and receivers (maximum possible sending rate).
# Geographic Routing Decision Method for Flying Ad Hoc Networks Based on Mobile Prediction
## 6 Conclusions  
In this article, we propose a geographic routing decision method based on extended Kalman filtering and Q-learning, aimed at addressing the instability of links caused by frequent changes in node positions and energy limitations in flying ad hoc networks. By accurately predicting the movement trajectory of nodes, we have designed a mechanism for dynamically adjusting the Hello packet transmission time slot, thereby optimizing the neighbor discovery process and effectively reducing communication energy consumption. At the same time, combined with the Q-learning algorithm, we propose an intelligent routing decision strategy that comprehensively considers link stability, communication distance, and energy consumption. The simulation results have verified that the MP-QGRD method exhibits significant advantages over existing routing schemes in terms of data transmission success rate, latency, and energy consumption control. With the expansion of network scale, the computational complexity and real-time response capability of algorithms remain controllable.  
Conceptualization, G.W. and M.F.; Methodology, G.W. and M.F.; Software, M.F., X.W. and M.Y.; Validation, M.F.; Formal analysis, G.W., S.J. and M.F.; Investigation, M.F.; Resources, M.F.; Data curation, M.F.; Writing--original draft preparation, M.F. and G.W.; Writing--review and editing, G.W.; Visualization, M.F.; Supervision, L.W. and G.W.; Project administration, L.W. and G.W.; Funding acquisition, L.W., G.W. and M.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grant No. 62176113, in part by the Open Research Projects of National Engineering Research Center of Advanced Network Technologies under Grant No. ANT2024001, in part by the Key Research Projects of Higher Education Institutions in Henan Province under Grant No. 25A520026, in part by the Longmen Laboratory Frontier Exploration Project of Henan Province under Grant No. LMQYTSKT035, in part by the Henan University Laboratory Research Project under Grant No. ULAHN202135, and in part by the China Postdoctoral Science Foundation under Grant No. 2023M730980.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# UAV Image Small Object Detection Based on RSAD Algorithm
###### Abstract  
There are many small objects in UAV images, and the object scale varies greatly. When the SSD algorithm detects them, the backbone network's feature extraction capabilities are poor; it does not fully utilize the semantic information in the deeper feature layer, and it does not give enough consideration to the little items in the loss function, which result in serious missing object detection and low object detection accuracy. To tackle these issues, a new algorithm called RSAD (Resnet Self-Attention Detector) that takes advantage of the self-attention mechanism has been proposed. The proposed RSAD algorithm utilises the residual structure of the ResNet-50 backbone network, which is more capable of feature extraction, in order to extract deeper features from UAV image information. It then utilises the SAFM (Self-Attention Fusion Module) to reshape and concatenate the shallow and deep features of the backbone network, selectively weighted by attention units, ensuring the efficient fusion of features to provide rich semantic features for small object detection. Lastly, it introduces the Focal Loss loss function, which adjusts the corresponding parameters to enhance the contribution of small objects to the detection model. The ablation experiments show that the mAP of RSAD is 10.6% higher than that of the SSD model, with SAFM providing the highest mAP enhancement of 7.4% and ResNet-50 and Focal Loss providing 1.3% and 1.9% enhancements, respectively. The detection speed is only reduced by 3 FPS, but it meets the real-time requirement. Comparison experiments show that in terms of mAP, it is far ahead of Faster R-CNN, Cascade R-CNN, RetinaNet, CenterNet, YOLOv5s, and YOLOv8n, which are the mainstream object detection models; In terms of FPS, it slightly inferior to YOLOv5s and YOLOv8n. Thus, RSAD has a good balance between detection speed and accuracy, and it can facilitate the advancement of the UAV to complete object detection tasks in different scenarios.  
UAV images; RSAD; small object; self-attention mechanism; feature fusion Article  
UAV Image Small Object Detection Based on RSAD
## 3 Topological Path Planning (TPP)
### Evaluation Metrics  
Evaluation metrics based on both paths and trajectories are important to compare the method performances quantitatively and qualitatively. The metrics and the performance criteria are presented in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Metric** & **Description** \\ \hline Number of Path Points & Number of points contained in the path \\ Computing Time [s] & Algorithm computing time in seconds \\ Distance [cm] & Path length in cm measured from start \(q_{initial}\) to goal \(q_{final}\) \\ \(\sigma(\bar{\theta})\) & Standard deviation of the joint acceleration \(\bar{\theta}\) (std acc); \\ \(\sigma(\bar{\theta})\) & estimates the intensity of the changes in velocity along the path \\ \(\overset{\text{\textcent{\textcent{\textcent{\textcent{\textcent{\textcent{\textcent {\textcentcent{\textcent{\textcentcent{\textcentcent{\textcentcentcent{\textcentcentcent {\textcentcentcentcent}}}}}}}}}}}}}}_{max}\) & Maximum acceleration rate along the path (max jerk) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance metrics.  
Figure 5: The conservative roadmap using TPP*. (**a**) The environment shows obstacles (1, 2, and 3), start (green), goal (red), and the optimum path. Blue marks represent critical positions. (**b**) The free space \(F\). (**c**) The roadmap \(R\) is achieved by skeletonization. (**d**) The shortest path \(P\) depicted in purple, highlighting undesired detours.
## 2 Proposed Coverage Enhancement Modeling  
Using the advantages of particle swarm optimization and virtual force, we proposed a three-dimensional iterative enhancement for coverage hole recovery in UWSNs, as shown in Figure 1. First, the initial deployment of underwater nodes adopts an improved optimization search algorithm, and then virtual force is introduced to make underwater sensors move towards positions of greatest coverage. To avoid oscillations in the late iteration, the global interference mechanism is used to change the direction of particle motion.
# A Comprehensive Review and Benchmarking of Fairness-Aware Variants of Machine Learning Models
## 10 Conclusions  
These findings highlight the lack of a single, universally effective fairness-aware algorithm. Instead of pursuing a one-size-fits-all approach, practitioners need to tailor their choices based on the fairness criteria most relevant to their specific use cases, the nature of their data, and the desired trade-off between fairness and performance. The results indicate that ensemble techniques, especially gradient boosting methods, hold promise in delivering fair outcomes without heavily compromising accuracy. However, this also emphasizes the importance of selecting appropriate fairness metrics and conducting thorough evaluations across diverse datasets.  
Like all studies, this one also has certain limitations. Specifically, some valid variations of classical models may have been inadvertently overlooked. Furthermore, the scope of this study is restricted to tabular datasets and is solely focused on classification tasks. This study does not include fairness-enhanced algorithms designed for online streaming data, such as Fair Adaptive Random Forests [41], Fairness-Aware Hoeffding Trees [42], Fairness-Enhancing and Concept-Adapting Trees (FEAT) [43], Online Fair Naive Bayes [44], and Fair-CMNB [45]. Similarly, fairness-aware variants of neural networks [46; 47; 48; 49] and graph neural networks [50; 51] are also excluded from this review.
###### Abstract
Since the first published TR-SFX experiment in 2012 at LCLS [23], many studies have been reported [24; 25; 26; 27; 28; 29; 30; 31], including those conducted at the European XFEL that made use of its ultra-fast repetition rate [18; 32; 33]. As time-resolved studies require much more data to be collected in comparison to regular SFX (\(N\) times more, where \(N\) is the number of desired time points to obtain reliable difference electron density maps), the megahertz repetition rate represents a significant advantage in data collection time [14]. For time-resolved experiments, a method to trigger the enzymatic reaction is needed and the two most common methods are laser triggering (pump-probe) and mixing (mix-and-inject) [19; 20; 32]. Furthermore, a very promising field under development is the use of caged compounds, allowing the very fast laser triggering of a reaction with a much wider spectrum of applications [34; 35; 36]. This perspective review aims to describe recent work performed in fields related to TR-SFX at high repetition rate XFELs, including sample delivery methods, mix-and-inject and pump-probe experiments, as well as the development of caged compounds. After discussing their advantages and limitations, we highlight possible further developments to make TR-SFX more broadly available to user groups and applicable to more scientific questions.