esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 LECP-CP Analysis
Take the condition where the heterogeneous nodes are randomly deployed in the network into consideration: the expected number of CHs in the practical network is taken as \(M_{\rm exp}=\left\lceil\frac{4A}{3\sqrt{3}R_{a}^{2}}\right\rceil\). **Theorem 3:** If \(N\) nodes are randomly deployed over a square field, the area of which is \(A\), then when the cluster radius is set as the optimal value \(R_{opt}=\sqrt[4]{\frac{4\sqrt{3}A(6E_{elce}+E_{sen}+E_{com})}{9N_{fs}}}\), the energy consumption of the entire network can be minimized. **Proof:** In Section 5, we have obtained that in a round of the CH rotation process, the total energy consumption in a cluster is:  
\[E_{all-total} = \{(6n+6)\,E_{elec}+(n+1)\,(E_{sen}+E_{com}) \tag{17}\] \[+(21+n)\varepsilon_{fs}R_{a}^{2}\}l.\]  
Obviously, if the energy consumption in each cluster of one round is the minimum, then the total energy consumption in the network is the minimum. According to Lemma 2, the expected number of CHs in the practical network is taken as \(M_{\rm exp}=\left\lceil\frac{4A}{3\sqrt{3}R_{a}^{2}}\right\rceil\); thus, the energy consumption of all clusters in one round is:  
Figure 4: The maximum cluster area and minimum cluster area. Figure 3: A case of inter-cluster and intra-cluster transmission range. \[E_{total} = M_{\exp}E_{all-total} \tag{18}\] \[= M_{\exp}[(6n+6)\,E_{elec}+(n+1)\,(E_{sen}+E_{com})\] \[+(21+n)\varepsilon_{fs}R_{a}^{2}]l\] \[= M_{\exp}[(\frac{N}{M_{\exp}}+1)(6E_{elec}+E_{sen}+E_{com})\] \[+(21+\frac{N}{M_{\exp}})\varepsilon_{fs}R_{a}^{2}]l\] \[E_{all-total} = \{(6n+6)\,E_{elec}+(n+1)\,(E_{sen}+E_{com}) \tag{17}\]0 \[E_{all-total} = \{(6n+6)\,E_{elec}+(n+1)\,(E_{sen}+E_{com}) \tag{17}\]1  
Taking the derivative of Formula (18) over \(R_{a}\) and making the derivation result be zero, then we can obtain the optimal cluster radius \(R_{opt}\):  
\[E_{all-total} = \{(6n+6)\,E_{elec}+(n+1)\,(E_{sen}+E_{com}) \tag{17}\]2  
**Theorem 4:** The overhead complexity of control messages in the network is \(O(N)\). **Proof:** At the beginning of each round, each node broadcasts a \(Node\_Msg\). Thus, there are \(N\)\(Node\_Msgs\) in the whole network. In each round, each cluster member broadcasts a \(Join\_Msg\), while each CH broadcasts a \(Head\_Msg\), a \(Schedule\_Msg\) and a \(Rout\_Msg\). Suppose the number of generated CHs is \(k\); then, the total number of \(Join\_Msgs\) is \(N-k\), and the numbers of \(Head\_Msgs\), \(Schedule\_Msgs\) and \(Rout\_Msgs\) messages are all \(k\). Thus, the total number of control messages in the entire network is \(N+(N-k)+k+k+k=2N+2k\). Therefore, the overhead complexity of control messages in the network is \(O(N)\). **Theorem 5:** The time complexity of LECP-CP is \(O(1)\). **Proof:** LECP-CP adopts a distributed clustering strategy. Thus, the time complexity of the entire network is equal to that of a single node \(O(1)\). In other words, the time complexity is a constant and has nothing to do with the network size.
# On the Vaneless Space Vortex Structures in a Kaplan Turbine Model Operating at Speed No Load
## 2 Experimental Setup  
### Test Stand Description and Instrumentation  
A 1:3.875 reduced-scale replica of the Porjus Kaplan U9 prototype was selected for this study. Figure 1 shows the shaft line below the generator, a hydraulic bearing supporting the bottom of the shaft, the penstock, the spiral casing and the entire draft tube. The runner has 6 blades and a reference diameter, \(D\), of 400 mm. The distributor is composed of 18 unequally distributed stay vanes and 20 equally distributed guide vanes. The vaneless space presents a height, \(B_{0}\), of 165.6 mm and a width, \(V_{0}\), of 480 mm as shown in the bottom of Figure 1.  
The experimental setup comprised a large number of sensors installed on the rotor (on-board) and on fixed parts of the test stand (off-board) to study the dynamic response of the turbine in off-design operating conditions as presented in detail by [PERSON] et al. [35]. Among them, the following sensors shown in Figure 2 were selected for the present study:  
* 1 accelerometer on the draft tube cone wall in radial direction (A0y).
* 1 pressure transducer on the vaneless space in axial direction (PVSA).
* 1 torque sensor on a guide vane stem (TGV).
* 1 strain gauge on the pressure side of a runner blade (SBR).
* 1 strain gauge on the shaft measuring the torque strain (SST).
# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
## 3 Signal Acquisition and Feature Extraction
### Feature Extraction
#### 3.2.3 Time-Frequency Domain Feature Parameters  
The Empirical Mode Decomposition (EMD) improved by mirror extension is used to process the vibration signal [46]. The advantage of this method is the suppression of endpoint effects. The energy entropy, which is suitable for non-stationary and non-linear complex signals [47], is extracted from the processed vibration signal.  
Assume [48]:  
\[E_{i}=\int\lvert{c_{i}(t)}\rvert^{2}dt=\sum\limits_{k=1}^{n}{\lvert{x_{k}^{2} \rvert}}, \tag{25}\]  
\[E=\sum\limits_{i=1}^{n}{E_{i}}, \tag{26}\]  
\[p_{i}=\frac{E_{i}}{E}, \tag{27}\]The equation of energy entropy \(P_{i}\) is as follows:  
\[P_{i}=-\sum_{i=1}^{n}p_{i}\log_{10}p_{i} \tag{28}\]  
where, \(i=1,2,3\ldots n\); \(x_{ik}\) is the amplitude of each discrete point; \(E_{i}\) is the energy of each Intrinsic Mode Function (IMF) component, and \(E\) is the total energy of the signal. After vibration signals at different times being decomposed by IEMD, the number of IMF may be different. Therefore, the correlation coefficient method is used to determine the effective number of IMF components. Assume that the non-stationary signal \(x(t)\) is decomposed into a finite number of uncorrelated components \(x_{i}(t)\) by IEMD. The correlation coefficient between each IMF and the original signal can be calculated as:  
\[\rho_{x_{i}x}=\frac{\sum\limits_{t=0}^{\infty}x_{i}(t)x(t)}{\left[\sum\limits _{t=0}^{\infty}x_{i}^{2}(t)\sum\limits_{t=0}^{\infty}x^{2}(t)\right]^{2}} \tag{29}\]  
where, \(i=1,2,\ldots,n\) and \(0\leq\left|\rho_{x_{i}x}\right|\leq 1\). The larger the correlation coefficient, the greater the degree of correlation and vice versa. When \(\left|\rho_{x_{i}x}\right|>0.1\), it usually indicates that the IMF has a good correlation with the original signal [49].  
The stator current signal obtained from the simulation model is decomposed into 4 layers by the Dual-tree Complex Wavelet Transform (DTCWT) [50]. The obtained 5 sub-band signals are reconstructed and the sample entropy is calculated. Sample entropy can obtain better and more stable results with less data and has stronger robustness [51]. For finite sequences \(N_{t}\), the equation for the sample entropy \(Se_{i}\) is as follows:  
\[Se(m,r,N_{t})=-\ln\frac{\overline{B}^{m+1}(r)}{\overline{B}^{m}(r)} \tag{30}\]  
where, \(m\) is the number of dimensions of the constructed vector, \(r\) is a given threshold, and \(\overline{B}^{m}(r)\) is the average of the maximum distance between two \(m\)-dimensional vectors.
## 1 Introduction
### Photogrammetry  
Data processing in photogrammetry implies the 3D reconstruction geometry of an object from a set of images using some software. Nowadays, improvements in highly automated reconstruction methods have made it possible to achieve 3D model representations from a set of photographs and in a shorter time compared to the traditional CAD modeling techniques. This has contributed to the widespread usage of popular photogrammetry software packages which are user friendly and can be used by non-specialists. However, the results of such approaches are quite unreliable in terms of metrics and accuracy.  
To produce precise, detailed and photo-realistic 3D digital reconstructions useful for visualization, documentation, and analysis, certain criteria have to be met [4; 13; 29]. SfM is a method for photogrammetric surveying in which the determination of camera positions and orientation parameters are done automatically. Nowadays, there is a variety of image-based modeling software based on the SfM, such as the open-source ones: VisualSFM, AliceVision, OpenMVS, COLMAP, and Regard3D; and the popular commercial solutions: Agisoft Metashape (new version of Agisoft PhotoScan), Reality Capture, Autodesk Recap Photo, and Context Capture [12; 30; 31; 32].  
This method can be used for creating highly detailed and precise models. Since the precision of SfM models depends on the quality and properties of the available photographs, to achieve reliable and accurate 3D point measurements, the photographs have to be properly acquired. Thus, particular attention has to be given to the design of the surveying plan [4; 13]. When the photographs are created, the photogrammetric reconstruction is done in several main steps, which imply building dense point cloud, generating a mesh, and finally projecting texture from the images. The final result of the photogrammetric modeling is a highly detailed 3D reconstruction of an object geometry, with realistic texture representation.
# An Effective Wide-Bandwidth Channel Access in Next-Generation WLAN-Based V2X Communications
## 3 Proposed Scheme and Its Variants
### Alternative Channel Access Schemes for 20-MHz Transmission
#### 3.2.1 Modified Conventional Wideband Channel Access Scheme with AIFS Period (Start AIFS + End AIFS)  
Since the conventional AIFS-based wideband channel access scheme does not perform back-off operations in secondary channels, in order to consider fairness carefully in secondary channels under an OCB environment, the conventional AIFS-based wideband channel access scheme must be modified. The modification of the conventional AIFS method includes the sensing of both 10-MHz channels before performing the back-off procedure, as illustrated in Figure 6.  
As in the conventional AIFS method, channel sensing in the primary channel can be performed with PD and ED sensing. In the proposed modification, since the modified conventional wideband channel access scheme only requires one transceiver, ED-based channel sensing can only be used in the secondary channel because PD sensing in the secondary channel is not general. When a busy channel is detected with ED sensing in the secondary channel, the busy-channel duration cannot be known. Therefore, in order to minimize the impact of 20-MHz frame trans  
Figure 5: Proposed channel access scheme for 20-MHz transmission.  
Figure 6: Access variant scheme for 20-MHz channel with modification of conventional wideband channel access scheme.  
channel and protect the ongoing frame transaction (i.e., data frame + ACK) of the secondary channel, a relatively long waiting period of the EIFS duration must be applied. The rest of the channel access procedure after the back-off operation is conducted as in the conventional AIFS-based wideband channel access method.
# A Fast Computer-Generated Holographic Method for VR and AR Near-Eye 3D Display
## 4 Discussion  
The VR results of our method are faster (38 times faster) than the traditional point cloud method. The VR results also show that the proposed method is able to achieve focus and defocus of the 3D images. Similarly, AR results also show that the proposed method is able to achieve a good image quality with 3D display capability as well. More importantly, use of LED as illumination source results in speckle noise-free VR/AR 3D images so that the image quality is better than the use of coherent source as illumination for holographic display. Another advantage of this method is that the depth of the 3D image can be easily controlled through the SLM.  
It is worthwhile to point out that the calculation time is determined by the number of views of the projected image and the distance from the projection plane to the holographic plane. The computation time is a trade-off between the number of the projected images and the distance between the projection plane and the holographic plane. Further increase of computational speed is still possible through the use of GPU acceleration for the convolution.  
It is also worth noting that the difference between the display modes of AR and VR can be easily changed with different eyepieces, and all the underlying computation should be identical. This offers convenience for easily switching between VR and AR devices for practical applications. The 3D object can be set between the holographic plane and projected plane or the projected plane can be set between the 3D object and holographic plane. In the experiment we used the former method. For an extreme situation, the holographic plane is the projection plane. Under this condition, the convolution in spatial domain is not possible because the PSF cannot be calculated. However, the calculation of the hologram can be performed in the frequency domain [4].  
It should be pointed out that use of LED as the illumination source is also associated with potential drawbacks. For example, due to the large light-emitting area of the LED and poor spatial coherence, it is more difficult to produce a large depth of field image. Another disadvantage is that when the increase of the distance between the holographic plane and the 3D object is over a certain threshold (e.g., 20 mm), the blur of the VR/AR image becomes more evident. While this may not be significant for near-eye VR/AR 3D display, a careful control of coherence of the light source as well as more controllable propagation of the light can be potentially useful to solve these problems.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Proof That Our Method Uses the Minimum Number of Drones
#### 3.6.1 Deployment of the Initial Drone  
A straightforward idea in placing the first (or last) drone is that its coverage boundary should pass through the origin. In order to attain minimum wastage, we use the idea of dividing wastage equally along both axes. Consider a circle with a center \(C\) and radius \(r\), as in Figure 14:  
\[\angle ACB =\angle BCD=90^{\circ}\] \[Area(SegmentAB) =r^{2}(\frac{\theta\tau}{360}-\frac{sin\theta}{2})\] \[=Area(SegmentBD)\]  
hence, the wastage is equally distributed along both axes.  
Figure 14: Placing the first drone at \(C\).
# Research on Multiple-Axis Contour Error Suppression Method Based on Composite Layered Control
## 2 Design of a Single-Axis Controller Based on Sliding-Mode Disturbance Observer
### Design and Analysis of Sliding-Mode Approach Law Based on Improved S-Type Function (ISSMC)  
#### 2.2.1 The Proposal of ISSMC  
To better adapt the sliding-mode surface and system state variations, the proposed reaching law in this paper combines the advantages of the robustness of the power-reaching law and the rapid convergence of the exponential-reaching law, building upon the foundation of the traditional exponential-reaching law. Moreover, it is specifically designed based on the characteristics of the system used in this paper, as depicted below:  
\[\left\{\begin{aligned} \dot{S}&=-e|x|^{4}f(s)-k|s|^{ bf(s)}s\\ f(s)&=\frac{1-e^{-a(s-1)}}{1+e^{-a(s-1)}}\\ \lim_{t\to\infty}&|x|=0,\varepsilon>0,k>0,0<a<1,0< b<1\end{aligned}\right. \tag{9}\]  
where \(s\) is a sliding surface function, \(\varepsilon\), \(k\), \(a\), and \(b\) are the parameters to be designed, and \(x\) is a system state variable.  
As is well known, the sign function is a discontinuous switching function that undergoes instantaneous switching near the zero point, resulting in high-frequency oscillations of the system state as it approaches the stable point along the sliding-mode surface. To suppress the controller's flutter, replacing the sign function with the \(f(s)\) function is pro posed. The value of \(a\) in the \(f(s)\) function determines the change speed of the value of the smooth function near the origin, as shown in Figure 2. When \(a\) tends to infinity, the smooth function approximates the sign-switching function. When the value of \(a\) is very small, the thickness of the boundary layer is too large, resulting in a slow convergence rate. Therefore, \(a=2\) is chosen on the balance of these two aspects. The introduction of this function effectively reduces the flutter of the system.  
**Analysis:** When the system state is far away from the sliding-mode surface, that is, when \(s>1\), \(f(s)\approx 1\), the system state reaches the sliding-mode surface under the action of the variable speed term \(\varepsilon|x|^{a}f(s)\) and the variable index term \(k|s|^{bf(s)}s\). As \(|s|\) decreases and \(|s|^{b}s\) reaches \(1\), \(k|s|^{b}s\) converges to \(k\) and \(\varepsilon|x|^{a}\) converges to \(\varepsilon\). This means that when the state outside the stable point is close to the sliding-mode surface, the coefficient of the whole reaching law gradually decreases, thus reducing the flutter. When \(s<1\), \(f(s)\approx-1\), the speed of the variable exponential term in the reaching law is \(k|s|^{-b}s\); compared with \(k|s|^{b}s\) in reference [20], it is obvious that \(k|s|^{-b}s>k|s|^{b}s\) can be obtained, and the convergence law proposed in this paper is faster. In summary, from the initial state of the system state to reaching the sliding-mode surface and finally reaching the stable point, the approach law proposed in this paper consistently achieves a faster speed than the traditional approach law and suppresses the generation of high-frequency flutter.
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
## 4 Results and Discussion
### Summary of Findings  
A comparative summary of key performance metrics is presented in Table 2.  
Based on these results, the 650 nm red laser diode exhibited the most favorable overall performance across all evaluated criteria. It combined high energy efficiency, a broad modulation bandwidth, and excellent compatibility with silicon photodetectors, making it the most practical candidate for short-range FSO systems. In contrast, the 405 nm violet laser offered adequate modulation capabilities but was hindered by lower energy efficiency and increased thermal sensitivity. The 532 nm green laser underperformed across all categories, exhibiting higher threshold currents, lower output power, and poor modulation stability.
# On the Rate-Distortion Theory for Task-Specific Semantic Communication
## 5 Experimental Results
### Results for Generation Tasks
#### 5.2.4 Perceptual Quality Under Different Divergences
Furthermore, the relative performance ranking among models, with KL and WD consistently outperforming the VAE baseline, aligns precisely with the visual comparisons shown in Figure 6. This consistency between quantitative metrics and human perceptual evaluation strengthens the validity of our experimental findings and provides comprehensive evidence for the superiority of our proposed approaches.
## 7 Proposed Extensions to Bio-2 FA-IoD
### Enhanced Session Key Agreement with Diffie-Hellman for Perfect Forward Secrecy
#### 7.1.3 Performance Considerations  
Integrating classical DH operations (exponentiation modulo a large prime) would significantly increase the computational cost compared to the current hash- and symmetric-key-only operations. A single modular exponentiation is considerably more expensive than a hash or AES operation. This would impact the \"Computational Cost\" figures presented in Section 5.1. Similarly, including DH public keys would slightly increase the \"Communication Cost\", although this is often a small overhead compared to the security benefits. The overall lightweight nature of the protocol would be somewhat affected, but the enhanced PFS is often a trade-off worth considering for critical IoD applications.
## 3 Discussion
### SVM-ML Configuration  
#### 3.1.1 RBF Kernel Determination  
The grid search results of the hyperparameter pair (\(\gamma\), C) for the RBF kernel (Figures 3 and 4) showed that the highest classification accuracies were obtained in the predefined range of the hyperparameter pair. Similar boundaries of the hyperparameter pair (\(\gamma\), C) were also applied in other SVM-based studies, e.g., [PERSON] et al. used the boundaries of (\(\mathrm{e^{-8}\leq\gamma\leq e^{8}}\), \(\mathrm{e^{-8}\leq C\leq e^{8}}\)) [66], and [PERSON] et al. selected the boundaries of (\(2^{-15}\leq\gamma\leq 2^{3}\), \(2^{-5}\leq\mathrm{C\leq 2^{15}}\)) [42]. This showed that the selected optimal hyperparameter pair (\(\gamma\), C) was in the traditional search space, and the SVM-ML model with the general search space was feasible for classifying the RSP features extracted from EEG during sensory assessments. The kernel scaling parameter \(\gamma\) determines the complexity of the classification decision function of the model [67]. For smaller values of \(\gamma\), the decision function is nearly linear, and for larger values of \(\gamma\), the function becomes more curved [67]. The optimal value of \(\gamma\) (\(2^{1}\)) chosen by the model was close to the upper boundary of the preset \(\gamma\) range, which  
Figure 6: Comparisons of the classification accuracies of the SVM-ML model with respect to fabric stimulations when considering arm differences, i.e., (**a**) SA, (**b**) SU, and (**c**) UD. The significant differences are indicated by \({}^{\star\star}\) for \(p<0.05\) and \({}^{\star\star\star}\) for \(p<0.001\) (Kruskal–Wallis with Bonferroni post-hoc test).  
suggested that there was a relatively strong nonlinearity among the EEGs of multiple upper-limb groups in the original feature space, and the model obtained the \"curved\" decision function by mapping the raw EEG to a higher dimensional space. The regularization parameter C defines the penalty degree of the model for the percentage of deviation from the misclassified trained data [67]. As the value of C increases, its penalty degree for the model becomes larger, and the percentage of deviation of the misclassified data is smaller during the training phase. The optimal value of C (\(2^{3}\)) selected by the model was relatively lower compared to the predefined range of C. This suggested that the model tolerated a greater percentage of misclassified training data when searching for the optimal decision function, indicating that there was an overlap among the different groups of EEG data points near the decision function. Meanwhile, the model with the optimal hyperparameter pair (\(\gamma\), C) achieved an accuracy of 76.8%, which was comparable to those reported in studies on multiclass classification of EEG using SVM-ML models with accuracies exceeding 71.0% [68, 69].