esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Engineering Verification
### Stability Inspection  
Prior to the commencement of concrete pouring during the construction process, ultrasonic inspections were conducted to assess the trench wall's verticality. The results of the inspection for the I-1 trench segment are presented in Figure 18. The visual representation illustrates that the overall verticality of the ground connection wall is deemed satisfactory and meets the criterion of 1/650 for verticality. Additionally, no indications of instability were detected along the trench wall. These observations serve as evidence that the chosen slurry weight was effective in ensuring the stability of the trench wall.  
The location of the collapsed hole in the trench wall can also be judged with the help of the rate of liquid level rise during concrete pouring. The construction records are analyzed using section I-11 of this project as an example. The I-11 underground diaphragm wall is 98.2 m deep and the width type is double-angled, referring to Figure 15. The rate of concrete level rise during pouring is shown in Figure 19.  
Figure 19 compares the calculated rising height of concrete with the actual rising height of concrete, as well as identifying the presence of any collapsed holes in the underground continuous wall. The calculated rising height of concrete represents the theoretical height of the liquid surface that should rise after pouring concrete, whereas the actual rising height of concrete refers to the measured height of the liquid surface on-site. A slow-down in the slope of the actual ascending height line may suggest the presence of a collapsed hole. Our results demonstrate that the slope of the actual ascending height line tends to  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Overall Stability** & **Local Stability** \\ \hline
**Double-Angle Trench Wall** & **Rectangular Section** & \\ \hline
10.58 & 10.06 & 11.14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The critical gravity of slurry obtained by different calculation methods (unit: kN/m\({}^{3}\)).  
Figure 19: Real-time concrete level rise curve.  
slow down at the depth of 9-15 m and 77-82 m from the top of the wall, indicating the suspected location of the collapsed hole in the wall. This indicates that a heavier slurry is needed here for containment. This finding was found to be consistent with the results of ultrasonic trenching inspection records depicted in Figure 11, verifying the correctness of the 3D sliding force balance analysis proposed in Section 2.
## 4 Results and Discussion
### Evaluation Indicators  
To validate the effectiveness of our model, we employed various evaluation metrics, including Precision, Recall, Average Precision (AP), and mean Average Precision (mAP). Precision is a measure of the proportion of predicted true positive samples over the entire sample of positive cases with positive predictions. It is calculated as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{9}\]  
\[Recall=\frac{TP}{TP+FN} \tag{10}\]  
The precision rate is calculated using the number of true cases, false positive cases, and false negative cases. It reflects the proportion of true cases among the predicted positive samples. On the other hand, the recall rate reflects the proportion of positive samples correctly predicted by the model to the total positive samples. Recall is another evaluation metric that we use to assess the performance of our model. It measures the proportion of  
Figure 5: DOTA dataset description.  
predicted true positive samples over the entire sample of true cases with positive results. The P-R curve is a plot of the precision rate against the recall rate. For a particular category, the recall rate is set as the horizontal coordinate and the precision rate as the vertical coordinate. The area under this curve is the AP value of the category as follows:  
\[AP=\int_{0}^{1}P(R)dR \tag{11}\]  
The mean Average Precision (mAP) is a comprehensive evaluation metric for target detection algorithms on a particular dataset. It is calculated as the average of the AP values of all categories and is currently the most important metric for evaluating the performance of the model. The mAP is denoted as follows:  
\[mAP=\frac{1}{C}\sum_{i=0}^{C}AP_{i} \tag{12}\]
# Locating the Source of Diffusion in Complex Networks via Gaussian-Based Localization and Deduction
## 3 Experiments and Analysis  
To quantify the validity of the proposed algorithm, we present numerical results on the success rate of source localization on arbitrary trees and general graphs. In real implementations, the diffusion delay \(\theta_{uv}\) along each edge is sampled via an independent and identically truncated Gaussian distribution to ensure that \(\theta_{uv}>0\). Since no prior knowledge is available on the diffusion source \(s\) and observers \(O\), they are chosen randomly among the network. The results are obtained by averaging over 100 independent realizations.
## 3 Results
### Common Method Bias Test and Mediation Analysis  
#### 3.4.1 Common Method Bias Test  
This study further investigated the underlying mechanisms through which digital technology use affects teaching quality. To assess potential common method bias, [PERSON]'s single-factor test was applied to all 69 items from four separate questionnaires. The results showed that seven factors had eigenvalues greater than 1, and the variance explained by the first factor after rotation was 27.47%, which is below the critical threshold of 40%, suggesting that common method bias was not a significant issue in this study. The Pearson correlation analysis revealed significant positive correlations among digital technology use, perceived teacher support, academic self-efficacy, and teaching quality (all \(p<0.01\); see Table 2).  
Figure 6: Distribution of predicted teaching quality scores across usage frequencies of four digital tools. (**A**) Management software; (**B**) Websites; (**C**) Multimedia materials; (**D**) Multimedia courseware. Boxplots show the predicted scores of teaching quality across different usage intervals, while bar charts indicate the number of corresponding samples in each interval.
## 2 Point-to-Point Joint Motion via Multiple Points with Constrained Velocities
### Point-to-Point Joint Motion via Multiple Points with Constrained Velocities  
When there are multiple intermediate target points between the start and end points [18], and the end-effector needs to pass through these intermediate points at specified speeds, we briefly call it PTP motion with constrained speed. When the end-effector moves in speed-constrained PTP mode, joints correspondingly perform velocity-constrained PTP rotation, and both angular displacements and angular velocities at intermediate target points of joints must be specified, so as to meet position, orientation and speed requirements of the end-effector at the target points.  
As shown in Figure 3, the end-effector moves from start point \(P_{a}\) to end point \(P_{d}\). It needs to pass through two intermediate target points \(P_{b}\) and \(P_{c}\) at specified speeds, and joints should rotate accordingly. Taking joint 1 as an example, joint 1 correspondingly rotates from the start point \(A\) to end point \(D\), passing through the intermediate target points \(B\) and \(C\) at constrained velocities. Therefore, joint 1 carries out PTP motion via multiple points at constrained velocities. By solving the inverse kinematics function, all joints' angular displacements and angular velocities at each target point can be obtained from the positions, orientations and speeds of the end-effector, so that the rotation trajectory of joint 1 can be piecewise-planned, from point \(A\) to point \(B\), from \(B\) to \(C\), and from \(C\) to \(D\). Other joints can carry out similar rotation plannings. Finally, the movement of the end-effector is synthesized by all joints' rotations together.  
Figure 2: Relationship between PTP end-effector motion and PTP motion plannings of joints.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 7 Land-Based Systems Implementation-Evolution: Early Rail Defect Detection Capability
### Adaptive SAFE Model for Rail Parameter Estimation
#### 7.5.1 Problem Statement  
Since the performance of any monitoring system that makes use of ultrasonic guided waves is influenced by some characteristics of the propagating environment, it would be useful to estimate these characteristics from the signals acquired by the system itself. In fact, the dispersion curves of a rail depend on the properties of its material, as well as on its geometry, neither of which are known with a sufficient level of accuracy. Moreover, the rail geometry changes over time because of wear and regular maintenance operations (including rail grinding of the crown) [52].
## 3 Results of Simulation Experiments and Discussion
### Performance Analysis with Small-Scale Problem
#### 3.2.2 Analysis of Average Time Consumption  
Figure 7 shows the average time consumption (\(t_{avg}\)) for the whole transportation and manufacturing process in 20 sets of experiments using our proposed methods and  
Figure 6: The relationship between success rate and the number of tasks (\(n\)) for our methods and the NBS-ISPS method with different numbers of robots (\(r\)) and time limits: (**a**) time limit = 100 s; (**b**) time limit = 200 s.  
the NBS-ISPS method with different numbers of tasks and robots and varying weights. Baseline means a task will be assigned to a machining center at the time point right after the last task assigned to this machining center is finished.  
Figure 7 indicates that the average time consumption of the proposed LSS method is close to that of the NBS-ISPS method. The RSS method performs better than the NBS-ISPS method, and the Baseline method performs the worst. This is potentially because the RSS method tries to reduce the total time consumption by reorganizing the processing sequences of machining centers, while the NBS-ISPS method does not more effectively account for the time consumed by machining centers. Usually, the time consumed in smart factories concentrates on the processing of workpieces and parts, while the time spent transporting materials accounts for a relatively smaller proportion of the total time consumption. Therefore, the NBS-ISPS method may not be applicable when the time consumed by machining centers is a main concern.
# Hierarchical Federated Learning for Edge-Aided Unmanned Aerial Vehicle Networks
## 1 Introduction  
### Motivations  
Owing to the recent advances in the Internet of Things (IoT), there has been a bursting increase in heterogeneous and private data generated from various sensors, mobile phones, smart home appliances and unmanned aerial vehicles (UAVs) [1]. Particularly, UAVs are proving to be one of the key enablers in gathering data from applications, such as road extraction [2], traffic management [3] and remote sensing, due to their mobility and cost effectiveness [4]. Naturally, therefore, there is a huge demand to construct machine learning models for such data while keeping the privacy.  
Federated learning (FL) is an efficient and promising solution to realize such a goal [5], and its applications in unmanned aerial vehicle (UAV) networks have attracted significant attention in both industrial and academic sectors [4]. With the aid of FL, a global machine learning model can be efficiently trained without the need for each UAV to directly send its private data to a cloud (or edge) server. This can be realized through the following four cyclic steps: (i) training a global model locally at each UAV (i.e., local model) with its own data, (ii) reporting the trained local models of the UAVs to a centralized cloud server, (iii) aggregating the local models at the cloud server to update the global model, and (iv) sending the updated global model to the UAVs for training it at the UAVs locally again. Most of the FL systems use the FedAvg algorithm, which averages the model updates weighted by the number of samples that they are trained on. Although the FL (without the need to send the real-time data feed to the cloud server) can reduce the latency andbandwidth, the ever-growing size of deep learning (DL) models causes the transmission of model updates to be a bottleneck of the system [6]. Additionally, in practice, a very critical issue for the FL in the edge-aided UAV network is how to handle the non-independent and identically distributed (non-i.i.d.) nature of heterogeneous data acquired by various types of UAVs while ensuring the convergence of training the global model [7].
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline  & \multicolumn{2}{c}{**Water Content (\%)**} & \multicolumn{2}{c}{**Water Activity**} \\ \cline{2-4}  & **Control** & **Treated** & **Control** & **Treated** \\ \hline Sesame seeds & 5.12 \(\pm\) 0.21 \({}^{\text{a}}\) & 5.11 \(\pm\) 0.10 \({}^{\text{a}}\) & 0.36 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.32 \(\pm\) 0.01 \({}^{\text{a}}\) \\ Ground black pepper & 11.23 \(\pm\) 0.19 \({}^{\text{a}}\) & 10.62 \(\pm\) 0.30 \({}^{\text{a}}\) & 0.35 \(\pm\) 0.05 \({}^{\text{a}}\) & 0.28 \(\pm\) 0.05 \({}^{\text{ab}}\) \\ Ground red pepper & 13.34 \(\pm\) 0.35 \({}^{\text{a}}\) & 12.82 \(\pm\) 0.15 \({}^{\text{ab}}\) & 0.35 \(\pm\) 0.00 \({}^{\text{a}}\) & 0.34 \(\pm\) 0.01 \({}^{\text{ab}}\) \\ Embryo buds of rice & 8.34 \(\pm\) 0.21 \({}^{\text{a}}\) & 8.03 \(\pm\) 0.15 \({}^{\text{a}}\) & 0.35 \(\pm\) 0.01 \({}^{\text{a}}\) & 0.34 \(\pm\) 0.00 \({}^{\text{a}}\) \\ \hline \end{tabular} a,b, within a column, different letters within indicate significantly different value (\(p\leq\) 0.05).  
\end{table} 
Table 2: Measured water content and water activity of samples after IPL treatment. Data are mean \(\pm\) SEM values.
# Enhancing Real-Time Video Streaming Quality via MPT-GRE Multipath Network
## 1 Introduction
Our previous studies have shown that the tunnel throughput is roughly equal to the combined throughput of the two paths. This significantly improves data transmission rates, especially for high-bandwidth applications like 4K video streaming. While MPT-GRE provides significant advantages for improving network throughput and minimizing quality degradation, its use in transmitting high-resolution video has some limitations. The overhead associated with GRE encapsulation and tunneling can lead to higher bandwidth consumption, potentially offsetting the benefits of multipath aggregation, especially in constrained network environments. Also, variations in path characteristics, such as high packet loss rates or high latency, can also adversely affect synchronization, resulting in fluctuations in video quality. The main contributions of this paper are as follows:  
* An evaluation of real-time 4K video streaming performance in MPT-GRE multipath networks by calculating and comparing widely used video quality metrics SSIM, MSE,and PSNR before and after transmission via the first path (single path) and MPT-GRE tunnels (multipath) to measure the solution's effectiveness. * A demonstration of the benefits of throughput aggregation in the MPT-GRE network for enhancing 4K video streaming quality. The paper evaluates the performance and efficiency of real-time 4K video streaming using the MPT-GRE multipath network by comparing video quality when streamed through a single path versus the MPT-GRE tunnel. Video quality metrics SSIM, MSE, and PSNR were chosen for their complementary strengths in measuring signal fidelity, perceptual quality, and pixel-level error, providing a comprehensive assessment of video quality under multipath network conditions. These metrics demonstrate how the tunnel's throughput aggregation can significantly enhance video streaming quality.
# Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
## 6 Bias Estimation with Opportunity Traffic
### Bias Observability Discussion  
Ideally the observability of the bias parameters in the scenario can be tested with the matrix \(\left[H^{\prime}R^{-1}H\right]\) where \(H\) and \(R\) is composed by all the \(H_{b,k}\) and \(R_{k}\) as in Equation (68). The bias parameters will be observable if this matrix is a positive definite matrix [15]. In the initialization phase this matrix is composed in order to calculate Equation (67). In the recursive bias estimation phase this matrix is never calculated but the observability can be tested composing this matrix with all the measurements. The dimensions of the resultant matrix becomes in an unaffordable computational load. The objective of this discussion is show the geometrical requirements (presented in the next paragraphs) that a radar scenario should have for the stability of the estimation algorithms. For a more rigorous mathematical analysis of the observability in radar registration the modified Fischer Information Matrix can be studied [29].  
In the best case the targets have diversity in range, azimuth and altitude and the biases are perfectly determined. In many real cases the opportunity traffic is not uniformly distributed in the airspace as most of the aircraft are in the upper flight levels. LSE method tries to minimize the global errors for all the tracks. If most of the tracks are concentrated in the same zone, the method will estimate the bias model parameters to get the better approximation of the corrections for these tracks, but the estimated parameters could lead to a bad bias correction for the aircraft in areas with lower density traffic.  
Another issue about the bias observability is the variability of the measurement values. If the sensors are widely separated in space, tracks with common coverage are concentrated in a small range of azimuth values. For example, in the azimuth bias model presented in Section 3, we may observe that the effect of the antenna squint and the axis squint are similar. The single difference between them is that axis squint depends on the azimuth of the target. If every target has similar azimuth, the effects of both bias terms are difficult to separate and their estimation is highly correlated. In this case, the position measures biases are well corrected for the tracks used in the estimation (inside common sensor coverage). But for other tracks with azimuth far from common coverage, there could be position errors because the azimuth bias dependence is not well estimated.
## 2 Theoretical Modeling
### Excitation Matrix Model  
Excitation matrix, as a necessary condition for solving the FSI model through the aforementioned method, with the basic idea of excitation matrix reviewed in [21], mainly includes fluid excitation (closed end, pressure boundary, velocity boundary and flow pulsation) and solid excitation (mechanical shock and simple harmonic vibration). In this subsection, a general excitation matrix model and the flow pulsation excitation model of axial piston pump are proposed, which are suitable for solving the FSI fourteen-equation model by the discrete time transfer matrix method (DTTMM).  
The excitation vectors at both ends of the pipe are as follows:  
\[\left\{\begin{array}{l}\left[\mathbf{Q}_{0}\right]_{7\times 1}=\left[\begin{array}{cc}V(0,t_{k})&f_{ex} (0,t_{k-1})&f_{ex}(0,t_{k-1})&M_{xy}(0,t_{k-1})&f_{xy}(0,t_{k-1})&M_{ex}(0,t_{k-1 })&M_{ex}(0,t_{k-1})\\ \left[\mathbf{Q}_{L}\right]_{7\times 1}=\left[\begin{array}{cc}V(L,t_{k})&f_{ex} (L,t_{k-1})&f_{ex}(L,t_{k-1})&M_{xy}(L,t_{k-1})&f_{ey}(L,t_{k-1})&M_{ex}(L,t_{k- 1})&M_{ex}(L,t_{k-1})\\ \end{array}\right]^{\mathrm{T}}\end{array}\right. \tag{19}\]  
As an important characteristic of axial piston pump and a key excitation form, the flow pulsation model has been investigated in previous works [9; 37; 38]. The theoretical formula of piston pump flow is shown as the following:  
\[q_{t}=2 AznR\tan\gamma=\frac{\pi d^{2}}{2}znR\tan\gamma \tag{20}\]  
where \(A\) is the cross-section of the piston, \(z\) is the number of pistons, \(n\) is the speed of cylinder block, \(\gamma\) is the tilt angle of swash plate, and \(R\) is the diameter of the piston.  
The instantaneous flow of piston pump could be expressed as:  
\[q_{sh}=\sum_{i=1}^{x_{0}}Av_{i}=AR\omega\tan\gamma\sum_{i=1}^{z_{0}}\sin\! \varphi_{i} \tag{21}\]  
where \(z_{0}\) is the number of pistons in the oil drain chamber, \(v_{i}\) is the axial velocity of piston located in the oil drain chamber and \(\varphi_{i}\) is the relative rotation angle of piston located in the oil drain chamber.  
The flow pulsation excitation could be represented by flow velocity, and the detailed calculation process is: the first n-order spectrum and its function expressions can be obtained by FFT transformation of the time-domain flow pulsation, then, add the function expressions corresponding to the first n-order harmonics together to obtain \(V\).  
\[V_{e}=V_{0}+V_{A1}\sin\omega_{1}t+V_{A2}\sin\omega_{2}t+V_{A3}\sin\omega_{3}t+ V_{A4}\sin\omega_{4}t \tag{22}\]
## 5 Experimental Procedure
### Experimental Results  
After a training process consisting of 156 minibatch iterations, which represents the average of the LAMBADA, CBT, WikiText, PTB, enwiki8, text8, and 1 BW dataset sizes, we evaluated the performance of six natural language generation models on various datasets, including LAMBADA, CBT, WikiText, PTB, enwiki8, text8, and 1 BW. Table 7 shows that EvoText can steadily improve the performance of eight natural language generation models. Notably, the training process with just 156 steps can produce better results without altering the model architecture or the initial pretraining method. It is surprising to see that EvoText improves the performance of the GPT\({}_{small}\) model to surpass that of the OPT\({}_{125M}\) model. These results indicate that EvoText can significantly enhance the performance of the model without requiring extensive modifications. Our approach demonstrates favorable performance compared to the current state-of-the-art \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**LAMBADA**} & \multicolumn{2}{c}{**CBT**} & \multicolumn{2}{c}{**WikiText**} & \multicolumn{1}{c}{**PTB**} & \multicolumn{1}{c}{**enwiki8**} & \multicolumn{1}{c}{**text8**} & \multicolumn{1}{c}{**1 BW**} \\ \cline{2-11}  & **PPL \(\downarrow\)** & **ACC \(\uparrow\)** & **ACC \(\uparrow\)** & **ACC \(\uparrow\)** & **NET** & **PPL \(\downarrow\)** & **(WikiText103)** & **PPL \(\downarrow\)** & **BPC \(\downarrow\)** & **BPC \(\downarrow\)** & **PPL \(\downarrow\)** \\ \hline \multicolumn{11}{l}{**Baseline**} & & & & & & & & & & \\ GPT-2\({}_{medium}\) & 15.60 & 55.48 & 92.35 & 87.10 & 22.76 & 26.37 & 47.33 & 1.01 & 1.06 & 55.72 \\ \hline \multicolumn{11}{l}{**Ours**} & & & & & & & & & & \\ GPT-2\({}_{medium}\)(G)+ & & & & & & & & & & \\ RoBERTa\({}_{large}\)(D) & **14.21** & **57.20** & **92.85** & 87.00 & 21.94 & **25.12** & **45.93** & **1.00** & **1.06** & **55.01** \\ \hline \multicolumn{11}{l}{**RoBERTa\({}_{large}\)+**} & & & & & & & & & & \\ Word2 Vec & 15.19 & 57.13 & 92.85 & 87.03 & 23.01 & 27.72 & 46.91 & 1.03 & 1.06 & 55.61 \\ \hline \multicolumn{11}{l}{**RoBERTa\({}_{large}\)+**} & & & & & & & & & & \\ Glove & 15.29 & 57.03 & 92.79 & **87.13** & 23.11 & 27.22 & 46.93 & 1.04 & 1.06 & 55.10 \\ \hline \multicolumn{11}{l}{**RoBERTa\({}_{large}\)+**} & & & & & & & & & & \\ Scratch-trained & 15.01 & 56.13 & 92.79 & 87.03 & **21.81** & 26.42 & 46.01 & 1.04 & 1.06 & 55.10 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: After 156 minibatch-sized EvoText sessions, we evaluated the performance of various natural language understanding models (D) with or without pretrained word embeddings and the same language generation model (GPT\({}_{medium}\)) on 7 natural language processing tasks using ZERO-SHOT. Each model was tested five times on each task, and the results were averaged.