esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Experimental Results
There are five targets moving on road-1 with ground radial velocities \(v_{r}=2,4,-4,-8,-12\) m/s, according to the road marking in Figure 6b; six targets moving on road-2 with \(v_{r}=6,9,12-1,-5,-10\) m/s; two targets moving on road-4 with \(v_{r}=1.5,8\) m/s; one target moving on road-5 with \(v_{r}=4.5\) m/s; and four targets moving on road-6 with \(v_{r}=1,3,5,13.5\) m/s. Figure 7b shows the traditional AMF technique detection results use Equations (20) and (21) with \(P_{fa}=10^{-5}\). In Figure 7b, the \"red dots\" represent the accurate detection results and the \"green dots\" represent the false alarms. In Figure 7b, it can be observed that some \"green dots\" for the residual clutter regions and the eighteen real moving targets have been detected completely. Figure 7c shows the detection results of the proposed method with \(P_{fa}=10^{-5}\), in which the \"red dots\" represent the accurate detection results and the \"green dots\" represent the false alarms. It can be observed that the eighteen moving targets have been detected completely with some false alarms. Figure 7d shows the relocation results of the proposed method, whose detection results are shown in Figure 7c. In Figure 7d, the \"red dots\" represent the accurate detection and \"green dots\" represent the false alarms. The \"blue dots\" represent the accurate detection and false relocation results. A bit of accurately detected results and false alarms, whose relocated positions are the same, have been marked by \"red dots\". It can be observed that sixteen moving targets have been correctly relocated and two targets have been relocated in the wrong roads because the probability of correct classification is low for low input SCNR cases. Based on the proposed technique, the false alarms are also relocated to the corresponding roads. Some false alarms, whose relocated positions are very close to each other or close to the false relocation dots, have been overlapped by each other or overlapped by the \"blue dots\". Figure 6: Master satellite SAR image, road map, interferometric phase map, and azimuth displacement map derived from road database. (**a**) Master satellite SAR image; (**b**) road map; (**c**) interferometric phase map; (**d**) azimuth displacement map derived from road database.
## 3 Mathematical Modeling of ProposedDC Architecture  
The proposed DC microgrid consists of four different sources (PV, wind, fuel cell, and battery). The PV power mainly depends upon solar irradiance and the ambient temperature. Wind power depends on wind velocity. PMSG is used to generate electrical power from the turbine. Wind power is interconnected to the DC microgrid by the AC/DC converter. Fuel cell power depends upon parameters such as distribution, flow rate, partial pressure of fuel cell gases, and the required number of stack cells. Mathematical modeling of each source is briefly discussed in this section.
###### Abstract
Motivated by this need, this paper proposes a methodology that can be used to evaluate the condition of welding electrode tips during the spot welding process. First, the waveform is extracted from the dynamic resistance during the spot welding process. To maximize the representation capability of waveforms, one-dimensional (1D) waveforms are converted into two-dimensional (2D) images. The overall procedure consists of (1) waveform imaging; (2) unsupervised feature extraction; and (3) welding electrode condition assessment. The rest of this paper is organized as follows. Section 2 describes the proposed methodology. Section 3 examines the proposed method using a case study of a real spot welding system. Section 4 concludes this paper and discusses future work. The contributions of this paper can be summarized into two primary areas. First, a new recurrence plot (RP) that converts 1D waveforms into 2D images is proposed to increase the sensitivity of weak signals, while suppressing the effect of external noise. Second, a new methodology is proposed to assess the health condition of welding electrode tips in an unsupervised manner. Waveform signals  
Figure 1: Waveform types [17]. collected from healthy welding electrode tips in a spot welding system are the only requirement of the proposed method.
# An Application of an Unequal-Area Facilities Layout Problem with Fixed-Shape Facilities
## 4 Computational Results
### Test Problem P50 Results and Comparisons  
The 50 facility test problem, P50, available in the literature from the commercial software VIP-PLANOPT, was solved once (one run) using the TS/BSH presented in [PERSON] and Hakobyan [18], and five times using the proposed GA. Recall, the floor space is unconstrained, and the Euclidean distance measure is used to determine the distances between pairs of facilities. See Table 6 for a summary of the results. The proposed GA obtained the minimum TWD of 71,031.24 and improved the best-found TWD in the literature (71,291.4), which is bolded in Table 6, by 0.36% in less CP time. Although each of the five runs were 60 min for the proposed GA, only one run did not produce a better result than TS/BSH.
# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
## 2 Proposed Approach  
This section describes sensing hardware development and software and a cloud-based data analytic method for long-term bridge bearing data acquisition. The proposed system integrates CV technology, a trigger board equipped with a schedule-based \"event-driven system\" (EDS) for power management, and cloud computing for data management and processing. The overall architecture of the system is shown in Figure 1.  
Figure 1: Overview of the proposed system.
# A Gene Expression Programming Model for Predicting Tunnel Convergence
## 1 Introduction
In recent years, artificial intelligence (AI) methods have been extensively used in many fields of engineering and science [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43] on account of the ability of these methods to generate nonlinear relationships between input and output data. Research has also been conducted aiming to predict tunnel convergence by means of AI approaches. By developing an adoptive neuro fuzzy inference system (ANFIS) model, [PERSON] and [PERSON] [44] proposed an approach to predict tunnel convergences and convergence velocity of NATM tunnels, by using collected datasets from two different high-speed railway tunnels in China. The results of this research demonstrated the ability of the proposed ANFIS model to predict tunnel convergence and convergence velocity. [PERSON] and [PERSON] [45] proposed a back propagation artificial neural network (ANN) model to predict the convergences of TBM tunnels, using several datasets from convergence monitoring measurements in different sections of the Ghomroud water conveyance tunnel. The collected datasets were utilized to estimate the unknown non-linear relationships between the rock properties and the tunnel convergence. The results demonstrated that the developed model is fairly able to predict tunnel convergence. Further research has also been conducted to forecast tunnel convergence by means of AI approaches [46; 47; 48]. In this paper, a gene expression programming (GEP) model is developed to predict tunnel convergence in NATM tunnels. The advantages of the proposed model, in relation to the aforementioned researches, is that a mathematical equation can be derived from the proposed model to predict tunnel convergence in NATM tunnels. In typical AI approaches, such as ANNs, a model can be proposed, in contrast to an equation, which, thus, may not be useful for researchers and engineers in practice. In the following sections, after some explanations regarding the case study under examination, the principles of GEP will be described. Then, the used database, with detailed input and output parameters, together with GEP model development, will be discussed. Finally, an evaluation of the developed GEP equation will be given in detail.
# Real-Time Monitoring Method for Traffic Surveillance Scenarios Based on Enhanced YOLOv7
## 4 Experimental Analysis  
### Experimental Platform and Dataset  
All of our detection experiments were conducted on the Windows platform using an i7-12/700F CPU, 32 GB of memory, and an NVIDIA GeForce RTX 3060 GPU processor with 12 GB of memory. The experimental framework was built using the Python programming language. The deep learning development environment includes Python 3.9, PyCharm 2023, Pytorch-GPU 2.0.0, CUDA 11.8, and cuDNN 8.0.  
We validated the vehicle detection algorithm using the UA-DETRAC [45] dataset. This dataset belongs to large-scale traffic monitoring system datasets, where all the images of 100 challenging video clips are captured by low-position ordinary surveillance cameras in real scenes. The dataset includes cars, buses, trucks, and vehicles of four other categories, with a total of 8250 vehicles. The scenes cover daytime, nighttime, sunny, cloudy, and rainy weather conditions, with a total of 1.21 million annotated bounding boxes and 138,252 images. The dataset is randomly divided into training, validation, and test sets in a 6:2:2 ratio, with 82,950 images in the training set and 27,651 images each in the validation and test sets. The distribution of weather conditions in each group is shown in Figure 4(a), and the distribution of vehicle targets is shown in Figure 4(b). The proportions of different weather conditions and vehicle targets in the training, validation, and test sets are consistent, which improves the model's generalization capability and ensures algorithm robustness.
## 4 Time Differenced Filter Algorithm
### State Equation
Then the state equation in the discrete form is given by  
\[\mathbf{X}_{t_{i}}=\mathbf{\Phi}_{t_{i-1},t_{i}}\cdot\mathbf{X}_{t_{i-1}}+ \mathbf{\Gamma}_{t_{i-1}}\Delta\mathbf{X}_{t_{i-1},t_{i}} \tag{3}\]  
In the equation, \(\mathbf{\Phi}_{t_{i-1},t_{i}}\) is the transition matrix from \(t_{i-1}\) to \(t_{i}\) and is a unit vector; \(\mathbf{\Gamma}_{t_{i-1}}\) is the input matrix given below. If multi-GNSS observations are used, such as GPS/Galileo/QZSS/BeiDou, vector \(\mathbf{X}_{t_{i}}\) is extended to \(\mathbf{X}_{t_{i}}=\left(x_{t_{i}}\,y_{t_{i}}\,z_{t_{i}}\,dt_{r,t_{i}}^{GPS}\, dt_{r,t_{i}}^{Callio}\,dt_{r,t_{i}}^{Bellou}\right)^{T}\), where \(dt_{r}^{GPS}\) is the receiver clock offset for GPS and QZSS, as the inter system bias between GPS and QZSS is nearly zero, and can be safely neglected [34], \(dt_{r}^{Callio}\) and \(dt_{r}^{Bellou}\) are the receiver clock offsets for Galileo and BeiDou, respectively. When there are not enough usable TDCP measurements to calculate the \(\Delta\textbf{X}_{t_{i-1},t_{i}}\), it is obtained by multiplying the Doppler-derived speed with \(\Delta t\). The input matrix is  
\[\boldsymbol{\Gamma}_{t_{i-1}}=\left[ \begin{array}{cccc}1&0&0&0\\ 0&1&0&0\\ 0&0&1&0\\ 0&0&0&1\\ 0&0&0&1\\ 0&0&0&1\end{array} \right] \tag{4}\]  
Clearly, if there are carrier phase observations at two successive epochs, the IEVP can be calculated. The algorithm makes full use of the continuous phase observations, and thus is more suitable for the urban environment, in which the satellite signals are vulnerable to dense trees, high buildings, and other shelters.
## 3 Applying the Methodology to the Engineering Case
### FE Model for the Engineering Case  
To minimize the effects of boundary conditions, the distance between the lateral boundaries was set to be 3 to 5 times the final excavation depth, in accordance with the recommendations of [PERSON] et al. [26]. As a result, the model domain was defined as 280 \(\times\) 240 \(\times\) 60 m\({}^{3}\), as shown in Figure 6. Consequently, the 3D numerical model consists of 232,895 elements. Meanwhile, the lateral boundaries were constrained in the horizontal direction, and the bottom boundary was fixed in both the vertical and horizontal directions.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Layer**} & \(\gamma\) & \(c\) & \(\varphi\) & \(E_{\text{so}}\)\({}^{\prime\prime\prime}\) & \(E_{\text{o}}\)\({}^{\prime\prime\prime}\) & \(E_{\text{ur}}\)\({}^{\prime\prime\prime}\) & \(G_{\text{o}}\)\({}^{\prime\prime\prime}\) & \(\gamma_{0.7}\) \\  & **(kN/m\({}^{3}\))** & **(kPa)** & **(\({}^{\circ}\))** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(MPa)** & **(\(10^{-3}\))** \\ \hline
3a Silty sand & 21.5 & 6 & 30 & 8.58 & 8.58 & 34.32 & 68.64 & 0.1 \\
3b Silty clay & 21.5 & 5 & 29 & 7.75 & 7.75 & 54.20 & 139.5 & 0.1 \\
4a Silty clay & 20.5 & 6 & 27 & 4.48 & 4.48 & 31.36 & 80.64 & 0.1 \\
5a Silty clay & 21.3 & 6 & 28 & 6.12 & 6.12 & 42.84 & 110.16 & 0.1 \\
6a Silty clay & 20.5 & 6 & 27 & 5.50 & 5.50 & 38.50 & 99.00 & 0.1 \\
6b Clay & 20.7 & 5 & 26 & 4.41 & 4.41 & 30.87 & 79.38 & 0.1 \\
7b Sand & 21.8 & 7 & 28 & 7.52 & 7.52 & 52.64 & 96.96 & 0.1 \\
8a Sand & 22.6 & 1 & 33 & 12.12 & 12.12 & 48.48 & 82.48 & 0.1 \\
8b Silty clay & 22.0 & 1 & 33 & 10.31 & 10.31 & 41.23 & 199.98 & 0.1 \\ \hline \hline \end{tabular} Note: \(\gamma\) is the unit weight, \(c\) is the cohesion force, \(\varphi\) is the internal friction force, \(E_{\text{so}}\)\({}^{\prime\prime\prime}\) is the secant stiffness in standard drained triaxial test, \(E_{\text{sd}}\)\({}^{\prime\prime\prime}\) is the tangent stiffness for primary oedometer loading, \(E_{\text{ur}}\)\({}^{\prime\prime\prime}\) is the unloading/reloading stiffness, \(G_{\text{o}}\)\({}^{\prime\prime\prime}\) is the reference shear modulus at extremely small strains, and \(\gamma_{0.72}\) is the threshold shear strain at which \(G_{\text{s}}\) = 0.722\(G_{\text{o}}\).  
\end{table}
Table 2: _Cont._  
Figure 6: Three-dimensional finite element model: (**a**) model, (**b**) structure, and (**c**) mesh map.
## 6 Experiment and Result Analysis
### Limitations and Future Research Work
#### 6.10.2 Limitation 2: Computational Constraints for High-Resolution Content  
Processing 4K-resolution video streams incurs significant computational overhead, particularly in the Gram matrix calculations of the texture analysis branch. The quadratic complexity of high-dimensional feature processing creates a performance bottleneck, hindering real-time deployment in resource-constrained scenarios. This reflects the broader challenge of balancing model complexity with high-fidelity media operation efficiency.
# Creep Model of Weakly Cemented Soft Rock Considering Damage and Secondary Development in FLAC3D
## 4 Secondary Development of Creep Damage Constitutive Model
### Verification of Axial Stress-Strain
#### 4.2.1 Numerical Modeling Setup  
The simulated tests used international standard specimens of 50 \(\times\) 100 mm, with the bottom end fixed, no circumferential constraints, and loading applied at the top. The numerical model was constructed using a mesh comprising 3200 elements and 3421 nodes to ensure sufficient accuracy while maintaining computational efficiency. Boundary conditions were applied by fixing the bottom of the model in the Z-direction to prevent vertical displacement, while the top surface was subjected to axial loading. The lateral boundaries were treated according to the loading conditions: unconstrained in the uniaxial case and constrained in the triaxial case. For the uniaxial simulations, the axial stress was increased stepwise from 5.5 MPa to 9.5 MPa, whereas in the triaxial condition, the loading ranged from 11.5 MPa to 20 MPa. Each loading step was maintained for a duration of 6 h to replicate the conditions of the laboratory creep tests. Tables 5 and 6 provide parameters used for numerical analysis.  
FLAC3D was used in this study to obtain numerical results. FLAC3D was chosen due to its robust built-in support for time-dependent constitutive models and flexibility in secondary development via user-defined FISH functions. Unlike ABAQUS or ANSYS, which are more suited for solid mechanics problems with complex geometry but limited time-dependent rock creep features, FLAC3D is tailored for geotechnical problems involving large deformations, making it more practical for simulating underground excavation scenarios. UDEC, while useful for jointed rock, lacks the 3D continuum capabilities required for this study.
## 6 Experimental Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Benchmark** & **Auto** & **One-Hot** & **JEDI** & _PTY_ & **Our Approach** & **Complexity** \\ \hline bbara & 8.811 & 8.811 & 8.352 & 9.394 & 9.601 & C1 \\ bbsse & 10.096 & 9.642 & 9.213 & 9.763 & 9.924 & C1 \\ bbtas & 8.497 & 8.497 & 8.451 & 9.497 & 9.497 & C0 \\ beecount & 9.605 & 9.605 & 8.941 & 9.568 & 9.740 & C1 \\ cse & 10.558 & 9.840 & 9.343 & 9.570 & 9.764 & C1 \\ dk14 & 8.821 & 9.395 & 8.762 & 9.964 & 9.070 & C1 \\ dk15 & 8.797 & 8.998 & 8.735 & 9.890 & 9.009 & C1 \\ dk16 & 9.491 & 9.320 & 8.672 & 9.327 & 9.539 & C1 \\ dk17 & 8.617 & 9.587 & 8.617 & 9.617 & 9.617 & C0 \\ dk27 & 8.325 & 8.424 & 8.369 & 9.325 & 9.325 & C0 \\ dk512 & 8.566 & 8.566 & 8.477 & 9.566 & 9.566 & C0 \\ donfile & 9.033 & 9.034 & 8.509 & 7.916 & 7.628 & C1 \\ ex1 & 10.425 & 10.955 & 9.454 & 8.496 & 8.496 & C2 \\ ex2 & 8.635 & 8.635 & 8.596 & 9.566 & 9.738 & C1 \\ ex3 & 8.731 & 8.731 & 8.707 & 9.731 & 9.731 & C0 \\ ex4 & 9.214 & 9.315 & 8.874 & 9.745 & 9.902 & C1 \\ ex5 & 9.147 & 9.147 & 9.119 & 10.147 & 10.147 & C0 \\ ex6 & 9.564 & 9.772 & 9.330 & 9.701 & 9.863 & C1 \\ ex7 & 8.598 & 8.578 & 8.584 & 9.582 & 9.751 & C1 \\ keyb & 10.121 & 10.699 & 9.666 & 10.063 & 10.174 & C1 \\ kirkman & 10.971 & 10.392 & 10.280 & 10.621 & 10.300 & C2 \\ lion & 8.539 & 8.501 & 8.541 & 9.595 & 9.595 & C0 \\ lion9 & 8.470 & 8.998 & 8.444 & 9.427 & 9.427 & C0 \\ mark1 & 9.825 & 9.825 & 9.343 & 9.942 & 10.063 & C1 \\ mc & 8.688 & 8.719 & 8.682 & 9.688 & 9.688 & C0 \\ modulo12 & 8.302 & 8.302 & 8.299 & 9.302 & 9.302 & C0 \\ opus & 9.684 & 9.684 & 9.275 & 10.290 & 10.353 & C1 \\ planet & 11.264 & 11.264 & 9.073 & 9.897 & 9.791 & C2 \\ pma & 10.634 & 10.634 & 9.681 & 10.015 & 9.963 & C2 \\ s1 & 10.623 & 11.154 & 10.156 & 10.669 & 10.308 & C2 \\ s1488 & 11.013 & 11.372 & 10.155 & 10.314 & 10.299 & C2 \\ s1494 & 10.487 & 10.654 & 9.878 & 10.630 & 10.163 & C2 \\ s1a & 10.313 & 9.462 & 9.704 & 10.385 & 10.185 & C2 \\ s208 & 9.503 & 9.434 & 9.361 & 9.859 & 9.684 & C2 \\ s27 & 8.672 & 8.862 & 8.662 & 9.671 & 9.832 & C1 \\ s386 & 9.676 & 9.494 & 9.311 & 9.905 & 10.198 & C1 \\ s420 & 9.864 & 9.780 & 9.755 & 9.719 & 9.632 & C4 \\ s510 & 9.742 & 9.742 & 9.155 & 9.689 & 9.115 & C4 \\ s820 & 10.691 & 10.641 & 9.775 & 10.317 & 10.416 & C1 \\ s832 & 10.975 & 10.638 & 9.866 & 9.697 & 9.233 & C4 \\ s840 & 9.195 & 9.228 & 9.158 & 9.108 & 9.032 & C4 \\ sand & 12.390 & 12.390 & 11.652 & 10.995 & 10.895 & C3 \\ shiftreg & 8.302 & 7.265 & 7.091 & 8.802 & 8.802 & C0 \\ sse & 10.096 & 9.642 & 9.455 & 10.165 & 10.260 & C1 \\ styr & 11.067 & 11.497 & 10.666 & 11.540 & 11.646 & C2 \\ tma & 9.831 & 10.495 & 9.821 & 10.247 & 10.197 & C2 \\ \hline
**Sum** & 453.73 & 454.88 & 431.08 & 460.81 & 458.25 & \\ \hline
**Percentage, \%** & 99.01 & 99.27 & 94.07 & 100.56 & 100.00 \\ \hline \end{tabular}
\end{table} 
Table 8: Experimental results (cycle time in nanoseconds).
# High-Precision Single-Photon Laser Time Transfer with Temperature Drift Post-Compensation
## 4 Results and Discussion
### Discussion  
From the TDEV analyzed above, one can conclude that the linear-fitting compensation method works better with the data in the temperature stable period. In the temperature rising period,  
Figure 8: (**a**) All the compensated data over time. The upper panel is delay vs. temperature, and the lower panel is delay vs. time. (**b**) TDEV before and after compensation in the whole period.  
the propagation delay is a little complex (see Figure 6). This demonstrates the complexity of thermal conduction in the test system and the instruments. Although the compensation method is not perfect, the 240 ps delay change was nearly perfectly compensated and the detection delay coefficient was reduced from the original value of 20 ps/\({}^{\circ}\)C to \(-\)0.05 ps/\({}^{\circ}\)C. The time stability expressed in a form of time deviation (TDEV) was also greatly improved. In the temperature stable period shown in Figure 5, the short-term time stability after the averaging time of 100 s flattens down and achieves 1.2 ps from the original value of 16 ps over 40,000 s. In the temperature rising period, due to the different heating and thermal transition conditions at different parts of the system, the temperature changing is not uniform, and the one-order linear compensation is not sufficient. Nevertheless, the long-term time stability still improved by half an order of magnitude, from the original value of 6.8 ps to 2.1 ps over 40,000 s. Although the TDEV of compensated data is not perfect, this post-compensation method is effective for the delay drift of the whole laser time transfer system.  
The experiment is now carried out in the atmosphere where the heat is mainly transmitted through the air. The instruments are separated and have low heat transport within each other. In the space application, the entire system will be very small and compact. This is why their temperatures will be much closer to each other. In addition, the entire instrument will be thermally insulated by thermal cladding, which will help to reduce temperature changes and hence the system delay drift. Considering these facts, the temperature drift post-compensation will be more efficient.
## 4 Experimental Setup
### Baseline Experiment Description
The results of the baseline experiments will be analyzed in the following aspects:Model Performance Comparison: Comparing the performance of different models and algorithms across various tasks, analyzing their strengths, weaknesses, and applicable scenarios. Dataset Quality Assessment: Evaluating the quality and suitability of the dataset based on baseline experiment results, identifying potential issues and areas for improvement. Result Visualization: Utilizing charts and visualization tools to present experimental results, facilitating intuitive analysis and understanding. Exploration of Application Potential: Exploring the practical applications of the dataset based on baseline experiment results, providing insights for future research.