esa-sceva/satcom-chunk-collection huggingface dataset:
## Appendix A Interaction between the Wind and the Transmission Line Towers  
Four types of interaction parameters are concerned, the combined wind factor (\(\mu_{z}\) and \(\mu_{z,w}\)), accounting for the effect of the altitude and terrain on the wind field, the gust response factor (\(\beta_{z}\) and \(\beta_{z,w}\)), accounting for the load effect due to the turbulence of the wind and the dynamic amplification of the tower and wires, the shape factor (\(\mu_{s}\) and \(\mu_{s,w}\)), accounting for the effect of structural members (e.g., the member size, shape and solidity), and the span factor (\(\alpha\)), accounting for the reduction effect due to the non-uniform distribution of the wind speed along the line. In this work, these interaction parameters are calculated according to the design codes/specifications (Table A1), referring to the IEC Standard 60826 [21], the ASCE-74 [38], the Chinese load code (GB 50009-2012) [39], and the Chinese code for design of 110 kV-750 kV overhead transmission line (GB 50545-2010) [40]. To determine the interaction parameters, four types of data are necessary: the wind intensity data (e.g., the 10-min averaged wind speed at 10 m over the ground); structural data, including the geometric and mechanical properties of the tower and wires; spatial data, including the direction and span of the line; and terrain data, which affects the altitude-dependent wind distribution and wind turbulence.
# Effect of Process Parameters on Distortions Based on the Quantitative Model in the SLM Process
## 4 Discussion and Conclusions  
In this study, a transient thermo-structural model is used to calculate the residual stress, plastic strain, and distortion in the SLM process. We propose a theoretical model considering displacements, plastic strains, and thermal strains to investigate the mechanism of distortion. Before removing the substrate, there is significant tensile stress in the component. The residual stress is released when the substrate is removed and the part shrinks. Meanwhile, the difference in axial residual stress (residual stress gradient) leads to distortion. The distortions of thin-walled and plate models, fabricated with different layers, scanning speeds, stiffness of support structures, and scanning strategies, are investigated. As the number of layers increases, the distortion significantly decreases. This is because as the height goes up, the increase rate of the moment of inertia is greater than the bending moment. The distortion degree increases slightly (from 9.13 to 11.3) with the scanning speed, reaches the maximum at 700 mm/s, and decreases (from 11.3 to 8.3). The scanning speed of 1000 mm/s results in the lowest distortion. As the stiffness of the support structures decreases, the distortion degree increases from 13.4 to 18.3. Therefore, increasing the stiffness of the support structures is beneficial to reducing the distortion. Since the scanning directions are consistent in Case (a), the residual stress distributions in the x-direction and the y-direction are nearly the same among layers. Therefore, Case (a) has the minimum distortion. Compared with Case (b), where scanning directions are changed among layers and the distortion is high, it can be indicated that changing the scanning direction among layers is useless to reduce the distortion. Case (c) deduces the maximum distortion where the maximum displacement in the vertical direction is 19.3 \(\upmu\)m. This is because the scanning direction is at a 45\({}^{\circ}\) angle to the boundary, which is equivalent to extending the scan length. It can be indicated that long scan lengths should be avoided to reduce distortion.  
Conceptualization, S.Z. and X.X.; methodology, S.Z. and L.P.; software, S.Z. and C.X.; validation, S.Z., L.P., C.X. and X.X.; formal analysis, S.Z. and X.X.; investigation, S.Z. and L.P.; resources, S.Z. and X.X.; data curation, S.Z. and C.X.; writing--original draft preparation, S.Z. and X.X.; writing--review and editing, S.Z., L.P., C.X. and X.X.; visualization, S.Z. and X.X.; supervision, S.Z., L.P., C.X. and X.X.; project administration, S.Z., L.P., C.X. and X.X.; funding acquisition, S.Z., L.P., C.X. and X.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The first author, [PERSON], gives thanks to the financial assistance from the Fundamental Research Funds for the Central Universities of China (2016-JL-012).  
The authors declare no conflict of interest.
## 5 Conclusions
### Characterization and Verification of Bonding Parameters  
3. A quadratic regression mathematical model was developed to investigate the relationship between the compressive strength of the bonded model and the bonding parameters. This was achieved by combining the Box-Behnken design (BBD) with the response surface methodology (RSM). Consequently, the optimal solution for the bonding parameters has been determined. The normal stiffness per unit area is 7.48 \(\times\) 10\({}^{11}\) N/m\({}^{3}\), the shear stiffness per unit area is 1.29 \(\times\) 10\({}^{10}\) N/m\({}^{3}\), the critical normal stress is 4.61 \(\times\) 10\({}^{9}\) Pa, and the critical shear stress is 6.93 \(\times\) 10\({}^{8}\) Pa. Furthermore, the validity of the bonding parameters was confirmed through simulation tests.  
Conceptualization, F.Y.; Methodology, X.W.; Formal analysis, B.C.; Investigation, W.Y.; Resources, W.Y.; Writing--original draft, J.S.; Writing--review & editing, J.S.; Project administration, R.G.; Funding acquisition, R.G.; All authors have read and agreed to the published version of the manuscript.  
Figure 7: The change of compressive force in the uniaxial compression process.  
**Funding:** This work was funded by the Frontier Exploration Project of Longmen Laboratory (Project number: LMQYTSKT036).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data generated or analyzed during this study are included in this published article.  
**Conflicts of Interest:** The authors declare no competing interests.
## 6 Evaluation Experiment  
### Setup  
For this study, we evaluated the proposed framework quantitatively and qualitatively using five experiments, including baseline evaluation. Table 2 presents a summary of the data source combinations for each experiment. First, semantic segmentation was conducted using only the source images. We considered this result as the baseline. Subsequently, Experiment A was conducted using images generated from CUT [71] as an unconditional GAN. Experiment B was conducted using images generated from pix2 pixHD [65] as a conditional GAN. Experiment C was conducted using images generated from both CUT and pix2 pixHD. Finally, Experiment D was conducted using all the images.  
Figure 14: I2L and L2I style transfer results for the respective GANs in Soybeans.  
Table 3 presents hyperparameters of each model for the experiments. The CUT and pix2 pixHD parameters were set through the literature [65; 71] and our preliminary experiments. The DeepLabV3+ parameters were set empirically through our earlier studies [114; 115] and several references [129; 130; 131]. These settings were not necessarily optimal values because we used no automated parameter setting tools [132; 133; 134].  
We used two platforms for implementing and executing our framework. First, we used Colaboratory (Colab) (Google LLC; Mountain View, CA, USA), which is a cloud platform for accelerating DL applications [135]. The major Colab specifications are the following: 12 GB Tesla K80 (NVIDIA Corp., Santa Clara, CA, USA), GPU; XEON 2.20 GHz (Intel Corp., Santa Clara, CA, USA), CPU; Ubuntu 18.04.5 LTS, OS; 12.69 GB, RAM; 107.72 GB, HDD for CPU and TPU; and 78.19 GB, HDD for GPU. The second platform was a Windows desktop computer. The major specifications of this platform are the following: 48 GB RTX A6000 (NVIDIA Corp., Santa Clara, CA, USA), GPU; Ryzen Threadripper 3970X 3.7 GHz (Advanced Micro Devices, Inc., Santa Clara, CA, USA), CPU; Ubuntu 18.04.5 LTS on Windows Subsystem for Linux 2 (Microsoft Corp., Redmond, WA, USA), OS; 32 GB, RAM; and 512 GB, HDD.  
Although numerous GAN and semantic segmentation models have been proposed, a tradeoff exists between the performance and computational cost. For example, StyleGAN2 [136] and StyleGAN3 [137] were calculated using eight GPUs (Tesla V100, NVIDIA Corp., Santa Clara, CA, USA). For our implementation and execution environment, we used the models and parameters presented in Table 3. These models can be changed arbitrarily according to the computational platform because the proposed method is the framework depicted in Figure 1.
# Bounded Attitude Control with Active Disturbance Rejection Capabilities for Multirotor UAVs
## Appendix B Proof of Proposition 2
\] (A16)  
Choosing \(\kappa\) such that \(k_{1}>0\), which satisfies inequality (A13), and using the following Young's Inequalities with \(n_{1},m_{1}>0\)  
\[ \begin{split}\omega_{e_{1}}\tilde{q}_{1}&\leq n_{1 }\omega_{e_{1}}^{2}+\frac{\tilde{q}_{1}^{2}}{4n_{1}}\\ \omega_{e_{1}}\Delta_{1}&\leq m_{1}\omega_{e_{1}}^{ 2}+\frac{\Delta_{1}^{2}}{4m_{1}}\end{split} \] (A17)  
one obtains  
\[ \begin{split}\dot{V}_{1}&\leq-\tilde{\lambda}_{1} \omega_{e_{1}}^{2}-k_{1}\bigg{(}n_{1}\omega_{e_{1}}^{2}+\frac{\tilde{q}_{1}^{2 }}{4n_{1}}\bigg{)}+m_{1}\omega_{e_{1}}^{2}+\frac{\Delta_{1}^{2}}{4m_{1}}\\ &=-(\underbrace{\tilde{\lambda}_{1}+k_{1}n_{1}-m_{1}}_{K_{1}}) \omega_{e_{1}}^{2}-k_{1}\tilde{q}_{1}^{2}+\frac{\Delta_{1}^{2}}{4m_{1}}\end{split} \] (A18)  
The parameters \(\lambda_{1},m_{1},n_{1}\) can be chosen such that \(K_{1}>0\). The same arguments are applied to \(\dot{V}_{2}\) and \(\dot{V}_{3}\), and (A8) becomes  
\[ \begin{split}\dot{V}&=\dot{V}_{1}+\dot{V}_{2}+\dot{V }_{3}\\ &\leq-\omega_{e}^{T}K_{\omega}\omega_{e}-\tilde{q}_{v}^{T}K_{q} \tilde{q}_{v}+\frac{1}{4}\Delta^{T}M\Delta\end{split} \] (A19)  
with \(K_{\omega}=\text{diag}(K_{1},K_{2},K_{3})\), \(K_{q}=\text{diag}(k_{1},k_{2},k_{3})\), \(M=\text{diag}(\frac{1}{m_{1}},\frac{1}{m_{2}},\frac{1}{m_{3}})\) and \(\Delta=\xi-\hat{\xi}=(\Delta_{1}\,\Delta_{2}\,\Delta_{3})^{T}\). Defining the error state as \(z=(\omega_{e}^{T}\,\tilde{q}_{v}^{T})^{T}\) and \(K_{T}=\text{diag}(K_{\omega},K_{q})\), (A19) can be written as  
\[\dot{V}\leq-\underbrace{z^{T}K_{T}}_{W(z)}+\frac{1}{4}\Delta^{T}M\Delta\] (A20)  
Since \(W\) is proper and positive definite, there exists a class \(\mathcal{K}_{\infty}\) function \(\bar{\gamma}\) such that \(W(z,t)\geq\bar{\gamma}(\|z\|)\) and therefore  
\[\dot{V}\leq-\bar{\gamma}(\|z\|)+\frac{1}{4}\Delta^{T}M\Delta\] (A21)Then, if \(\|z\|\geq\tilde{\gamma}^{-1}\Big{(}\frac{1}{2}\Delta^{T}M\Delta\Big{)}\), it follows that  
\[\hat{V}\leq-\frac{1}{2}\tilde{\gamma}(\|z\|) \tag{10}\]  
Consequently, by Definition 1 and Theorem 1, the system (10) with control law (16) is ISS with respect to \(\Delta\), i.e., it is robustly stable due to the ability of the system to maintain its behavior despite the external disturbances.
# A Robust Cover Song Identification System with Two-Level Similarity Fusion and Post-Processing
###### Abstract  
Similarity measurement plays an important role in various information retrieval tasks. In this paper, a music information retrieval scheme based on two-level similarity fusion and post-processing is proposed. At the similarity fusion level, to take full advantage of the common and complementary properties among different descriptors and different similarity functions, first, the track-by-track similarity graphs generated from the same descriptor but different similarity functions are fused with the similarity network fusion (SNF) technique. Then, the obtained first-level fused similarities based on different descriptors are further fused with the mixture Markov model (MMM) technique. At the post-processing level, diffusion is first performed on the two-level fused similarity graph to utilize the underlying track manifold contained within it. Then, a mutual proximity (MP) algorithm is adopted to refine the diffused similarity scores, which helps to reduce the bad influence caused by the \"hubness\" phenomenon contained in the scores. The performance of the proposed scheme is tested in the cover song identification (CSI) task on three cover song datasets (Covers80, Covers40, and Second Hand Songs (SHS)). The experimental results demonstrate that the proposed scheme outperforms state-of-the-art CSI schemes based on single similarity or similarity fusion.  
Keywords:cover song identification; similarity network fusion; mixture Markov model; mutual proximity +
Footnote †: journal: Applied Sciences
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 5 Performance Evaluation
### Simulation Environment  
The simulation parameters are listed in Table 4. The number of UAV nodes varies from 20 to 100. The weighting coefficients of CH election strategy are \(a_{1}=0.45\), \(a_{2}=0.43\) and \(a_{3}=0.12\). The initial energy of a node is set as 10 J. When the remaining energy of the node is less than 2 J, it will be regarded as a dead node; when the number of dead nodes reaches half, the network will be considered invalid. The energy parameters of flying consumption are set as \(m_{\text{UAV}}=0.7\) kg, \(n_{w}=4\), and \(r_{w}=0.15\) m [21].
# Data-Driven Personalized Learning Path Planning Based on Cognitive Diagnostic Assessments in MOOCs
## 5 Evaluation
### Learning Effect  
Finally, the total online learning time and average exercise test score of the general student group and training path group were calculated, and the statistics are demonstrated as follows.  
Table 3 shows that the average exercise test score of the training path group was significantly higher than that of the general student group. This indicates that the proposed learning path dynamic planning algorithm may improve the students' online learning efficiency (i.e., improve the exercise score and reduce the learning time). Meanwhile, by comparing the average total learning time of students in the same score band, it is found that the average learning time of the training path group is shorter than that of the general student group. The experiments proved that the proposed learning path dynamic planning algorithm may effectively improve students' online learning efficiency. This answers the first research question, showing that personalized learning paths help to improve learners' learning effectiveness.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Statistics** & **Illustration** & **General Student Group** & **Training Path Group** \\ \hline \(scof_{final}\) & Average score of final & 54.5 & **75.2** \\  & test. & & \\ \hline \(num_{final}^{sco\geq 60}\) & Average online learning time of students with a score \(\geq\) 60. & 11.3 h & **9.1 h** \\ \end{tabular}
\end{table}
Table 3: Statistics of the average online learning time and the final test average scores of students.
## 3 Numerical Simulation
### Computation Domain and Boundaries
#### 3.2.2 Meshes and Boundaries  
Meshes was dealt by the Trimmed Mesher embedded in the software. Taking account of both calculation accuracy and computational resources cost, local meshes at some special regions, like the tunnel hood, train nose, and train tail, are refined (as shown in Figure 5). Grid size is defined with four levels, i.e., 0.5 m, 1.0 m, 2.0 m, and 4.0 m, giving to the total number of cells being 4,072,472 and 5,541,780 for the scenarios that passage of a single train and intersection of two trains, respectively. To improve the solution of flow field at boundary layer and give the consideration to \(k-\omega\) model which belongs to the low-Reynold model requiring \(y^{+}\leq 1\), eight hexagonal prism layer meshes with growing ratio being 1.2 are applied to the train body where the first layer height at train body is 1.5 \(\times\) 10\({}^{-6}\) m. Similarly, six layer meshes with growing ratio being 1.5 are utilized at tunnel surface where the first layer height is 2.0 \(\times\) 10\({}^{-5}\) m. The above settings result in the exact value of \(y^{+}\) near the wall and train bodies respectively being 0.44 and 0.35.  
Surfaces of tunnel linings, floor, and train body are defined as non-slip wall boundaries. Pressure outlets are applied to tunnel portals, surfaces of external flow domain (except the floor). Nearby the wall boundaries, solution of turbulence is very sensitive to the settings. To simulate the realistic situation, roughness of tunnel sidewalls and floor are respectively 2.0 mm and 5.0 mm. Besides, roughness is set as 1.0 mm for all the train surfaces so that friction at train body can be deemed to be evenly distributed. For the simulated scenario of single train's passage, HST is initially placed 200 m away from the tunnel entrance portal to ensure the external flow field being not disturbed as much as possible. By changing the initial location of Train B, the crossing-point of train's intersection is predefined to be tunnel center only.
## 5 Conclusions and Future Research Directions  
### Conclusions  
An extensive review of different FD-physical layered systems and MAC layered protocols is carried out. Physical layered FD systems are mainly OFDM-based, digital modulation-based and spread spectrum-based. The FD MAC layered protocols are categorized as contention-free and contention-based. Contention-free protocols include CDMA, FDMA, and hybrid CDMA, while contention-based protocols are divided into two types: HS and RA. Physical and MAC FD-UWAC systems and protocols are affected by various  
Figure 7: Various FD-PHY and MAC-based UWASNs in a cross-layered perspective.  
challenges, according to the review. Different FD physical and MAC FD-UWAC systems and protocols are compared based on various metrics, as well as their implementation, benefits, and drawbacks in various environments.  
In the following section, we discuss various open research problems in these FD-physical layered and MAC layered protocols, as well as potential research directions. We pointed out that this field of research is still in its early stages and needs to be further developed.
## 3 Materials and Methods  
### Sampling and Analytical Methods  
Field sampling was conducted on the migmatitiseignes within the J21 and J22 quadrangles on the 1:25,000-scale geological map (Figure 1). A total of 90 rock samples were collected from various locations within the Kalkan Formation. These samples underwent geochemical analysis to determine the concentrations of major oxides, including SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), K\({}_{2}\)O, CaO, MgO, FeO, Fe\({}_{2}\)O\({}_{3}\), TiO\({}_{2}\), P\({}_{2}\)O\({}_{5}\), and MnO. The following elements are present in the compound: Cr\({}_{2}\)O\({}_{3}\), BaO, SrO, and trace elements, including Rb, Ni, Nb, Y, Ta, V, W, U, Th, Zr, Pb, Cu, Zn, Mo, Co, Cd, Re, Au, Ag, Hg, Sn, As, Sb, Cl, Se, and S. Descriptive statistics of the measured geochemical variables are presented in Appendix A. Magnetic susceptibility measurements were also conducted on these rock samples at both low and high frequencies, resulting in mass-dependent and frequency-dependent magnetic susceptibility values.  
Seismic data for the study area between 1 January 1900 and 27 March 2025 were acquired from the Bogazici University Kandilli Observatory and Earthquake Research Institute (KOERI) Earthquake Query System ([[http://www.koeri.boun.edu.tr/sismo/zeqdb/](http://www.koeri.boun.edu.tr/sismo/zeqdb/)]([http://www.koeri.boun.edu.tr/sismo/zeqdb/](http://www.koeri.boun.edu.tr/sismo/zeqdb/)), accessed on 27 March 2025). The earthquake magnitude and frequency distribution is as follows: 0-1 (4306 events), 1-2 (2434 events), 2-3 (2909 events), 3-4 (599 events), 4-5 (104 events), and 5-6 (6 events). Notably, no seismic events exceeding magnitude 6 were recorded in the region during the specified period.  
Additionally, the designated study area includes 59 geothermal boreholes, as reported by the General Directorate of Mineral Research and Exploration. Of these, approximately 10 are classified as high-temperature geothermal wells, while the remaining wells fall into the medium- to low-temperature categories.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 3 Methodology
### Machine Learning Algorithms for Fault Detection
#### 3.2.6 K-Nearest Neighbors (KNN) Accuracy  
KNN has been applied to four models: Fine, Medium, Coarse, Cosine, Cubic, weighted, and Optimizable to compare and obtain high-quality results. The accuracy of each model is displayed in the graph below, and each is discussed in the section that follows.  
As can be seen from Table 2, every KNN model except Coarse KNN, which performed quite poorly at 41.2%, performed admirably. The remaining models all achieved excellent accuracy validation (78.8%). The quality of the results is demonstrated by the average accuracy of 90% achieved by the remaining four models.
## 4 Application of the Proposed Method
### Case Study 1  
In this case study, \(F_{1}=1.5\), \(F_{2}=1.2,F_{3}=2\) are given. The topology obtained from Stage 1 is presented in Figure 4a, following the minimization of the compliance sum (Equation (14)), when considering each of the loading conditions. Figure 5a shows the evolution of the compliances during Stage 1, where the TEWS method was used. It is observed that at this stage, all compliances are reduced.  
Figure 4: Topologies in Case Study 1: (**a**) after Stage 1 (traditional equally weighted sum method); (**b**) after Stage 2.  
Figure 3: A 2D beam with three loading conditions: (**a**) Load case 1; (**b**) Load case 2; (**c**) Load case 3.  
The topology obtained from Stage 2 is shown in Figure 4b, which was changed from Stage 1's topology in order to withstand the worst loading condition better. In this case study, the worst loading condition was LC3 most of the time. The performance of the structure was improved after Stage 2 in terms of reducing the most critical compliance, considering all the loading conditions individually, as shown in Figure 5b.  
Figure 6 illustrates how the maximum compliance decreased during Stage 2, where the number shown in the graph is the index of the loading condition. For example, when number 3 is shown, the compliance function under LC3 is assigned as the objective. It can be observed in Figures 5b and 6 that the role of compliance, being the most critical, changed occasionally. Compliance under LC3 was assigned to be the objective function most frequently, whereas compliance under LC2 was chosen on only a few occasions. The algorithm was terminated when the compliances under LC2 and LC3 appeared to have the same values, as listed in Table 1. The table demonstrates that the maximum compliance was reduced from 85.39076 (TEWS) to 77.27681 (BCM), implying the effectiveness of the proposed BCM method.  
Figure 5: Evolution of compliances in Case Study 1: (a) during Stage 1 (traditional equally weighted sum method); (b) during Stage 2.  
Figure 6: Reduction of the maximum compliance in Case Study 1 during Stage 2.