esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper investigates the leader-follower formation tracking control of underactuated surface vessels (USVs) with input saturation. Each vessel is subject to the uncertainties induced by model uncertainties and environmental disturbances. First, an event-triggered extended-state observer (ETESO) is used to recover the velocity, yaw rate and uncertainties. Then, an estimator is used to estimate the velocity of the leader. An event-triggered controller (ETC) is constructed based on the estimator, the observer and extra variables. Specifically, extra variables are used to solve the problems of underactuation and input saturation. Stability analysis of the control system is conducted to prove that all signals are bounded. Simulations demonstrate that the ETESO can accurately estimate the uncertainties, velocity and yaw rate, and the ETC can largely reduce the action times of actuator.  
leader-follower; event-triggered controller; underactuated surface vessels; formation tracking +
Footnote †: journal: Applied sciences
## 3 System Model
### Near Space Segment  
The near space segment consists of a number of HAPs. In general, the HAPs are urgently launched across the disaster area. The number of launched HAPs usually depends on the communication requirements. Therefore, the main cost of the emergency communication system is the cost of the HAPs in our proposed system model. Due to its inexpensive reusable platform (_circa_ millions of dollars) compared to a costly satellite [31], a large coverage area and easily rapid deployment compared to the ground base station, the HAP is cost-effective. The payloads of the HAP contain two parts:  
* LTE/WiMAXcommunication payload for the user links [32];
* Communication payload supporting the Ka/Ku band or laser relay links to the satellite [8].  
The most promising energy subsystems for battery-powered HAPs are the regenerative fuel cell (RFC) and the electrical battery. In fact, most HAP programs worldwide are considering the use of the RFC onboard HAPs for unlimited operation endurance. Battery power is particularly suited to small-sized UAVs with timescales of days or weeks. Furthermore, as mentioned in Section 2, for HAPs with a solar panel, any surplus of energy captured by solar cells during the day can be stored in electrical batteries or in RFCs.
# Time of Flight Distance Sensor-Based Construction Equipment Activity Detection Method
## 3 Methodologies
### Detecting Equipment Activity
#### 3.3.3 DCNN-Based Activity Recognitions
Softmax layer: Subsequent to the fully connected layer, a Softmax layer is employed to convert the raw class scores emanating from the fully connected layer into probabilities. The Softmax function ensures that the output scores are normalized, resulting in a probability distribution over the class labels. This step is critical for multi-class classification tasks, as it provides a clear probabilistic interpretation of the model's predictions. Classification layer: The final component of the classification stage is the classification layer, which serves to quantify the discrepancy between the predicted class probabilities and the true class labels. This layer computes the loss during the training process, facilitating the optimization of the network's parameters. By minimizing this loss, the model learns to improve its predictions, enhancing its ability to accurately classify incoming samples. * Output section The output section serves as the definitive assessment of the network's classification capabilities. It not only provides a clear indication of the predicted class for each input but also offers a probabilistic insight into the model's confidence regarding its predictions. In this study, the network is tasked with the classification of six distinct activities: Waiting, Scraping, Excavating (under), Excavating (front), Dumping, and Repositioning. * Training options configuration The classification stage directly addresses the task of assigning class probabilities to the input samples. The model training was conducted in the MATLAB environment, using the Deep Learning Toolbox. For the training process, we selected the Adam optimization algorithm for its effectiveness with complex datasets. The parameters defined through the training options are shown in Table 3.
## 2 Literature Review
### Three-Level Proficiency Classification Framework  
The Three-Level Proficiency Classification Framework (Novice, Intermediate, and Expert) aligns with the established cognitive science literature, particularly models of skill acquisition, such as the Dreyfus Model and Cognitive Load Theory. This framework emphasizes the developmental stages of expertise as follows:  
* Novice: Individuals at this level demonstrate rule-based behavior that requires explicit instruction and guidance. They frequently experience substantial cognitive load during task execution. Research indicates that Cognitive Load Theory significantly influences learning outcomes, particularly in problem-solving contexts [40]. For example, studies suggest that appropriate cognitive load management can improve problem-solving skills by creating conducive learning environments for novices [40].
* Intermediate: At this proficiency level, individuals can perform tasks independently in familiar contexts, having developed procedural skills. However, they may still struggle with new or complex situations. Evidence suggests that intermediate learners exhibit varying levels of cognitive workload that affect their efficiency in managing routine tasks. Monitoring cognitive workload via physiological metrics, such as HRV has been examined in settings where cognitive task demands vary, indicating that these fluctuations can lead to performance inconsistencies in unfamiliar scenarios [21; 41].
* Expert: Experts demonstrate highly flexible and effective performance, marked by a comprehensive understanding of their tasks and systems. They can foresee potential issues and make informed decisions, relying on a reduced perceived cognitive load for the routine components of tasks [42]. Research suggests that the assessment of cognitive workload is crucial for understanding expert performance, indicating that experts can maintain high performance despite dynamic cognitive demands [41; 42]. Moreover, effective feedback is critical during the skill acquisition process for experts, enhancing their learning outcomes [43].
# Dynamic Analysis of Tethered Satellites with a Payload Moving Along a Flexible Tether
## 3 Discretized Equations of Motion  
After discretizing the kinetic and potential energies using the ANCF, they were substituted into [PERSON]'s equation to derive the corresponding discretized equations of motion. First, we addressed the discretization of the kinetic energy using the ANCF. In the discretized model, the position vectors of satellites A and B correspond to \(\mathbf{r}_{1}\) and \(\mathbf{r}_{N+1}\), respectively. For a model discretized into \(N\) elements, as shown in Figure 2, the kinetic energy, originally expressed by Equation (4), can be represented by the following equation:  
\[K=\frac{1}{2}\sum_{e=1}^{N}\int_{s_{e}}^{s_{e+1}}\rho A\dot{\mathbf{r}}_{e} \cdot\dot{\mathbf{r}}_{e}\mathrm{d}s+\frac{1}{2}m_{A}\dot{\mathbf{r}}_{1} \cdot\dot{\mathbf{r}}_{1}+\frac{1}{2}m_{B}\dot{\mathbf{r}}_{N+1}\cdot\dot{ \mathbf{r}}_{N+1}+\frac{1}{2}m_{v}\dot{\mathbf{r}}_{v}\cdot\dot{\mathbf{r}}_{ v}. \tag{15}\]  
By substituting Equations (10) and (14) into Equation (15), the kinetic energy of the tethered satellite system can be expressed as  
\[K=\frac{1}{4}\sum_{e=1}^{N}\rho A\dot{\mathbf{d}}_{e}^{\mathrm{T}}\left(\int_ {-1}^{1}\mathbf{N}^{\mathrm{T}}\mathbf{N}\mathrm{d}_{e}^{x}\right)\dot{ \mathbf{d}}_{e}+\frac{1}{2}m_{A}\dot{\mathbf{d}}_{1}^{\mathrm{T}}\left( \mathbf{N}^{\mathrm{T}}\mathbf{N}\right)_{\xi=-1}\dot{\mathbf{d}}_{1}\]  
\[+\frac{1}{2}m_{B}\dot{\mathbf{d}}_{N}^{\mathrm{T}}\left(\mathbf{N}^{\mathrm{T} }\mathbf{N}\right)_{\xi=1}\dot{\mathbf{d}}_{N}+\frac{1}{2}m_{v}\dot{\mathbf{d} }_{i}^{\mathrm{T}}\left(\mathbf{N}^{\mathrm{T}}\mathbf{N}\right)_{\xi=\xi(t)} \dot{\mathbf{d}}_{i}\]  
\[+(2V/h)m_{v}\;\mathbf{d}_{i}^{\mathrm{T}}\left(\mathbf{N}^{\prime\prime} \mathbf{N}\right)_{\xi=\xi(t)}\dot{\mathbf{d}}_{i}+\frac{1}{2}(2V/h)^{2}m_{v} \mathbf{d}_{i}^{\mathrm{T}}\left(\mathbf{N}^{\prime\mathrm{T}}\mathbf{N}^{ \prime}\right)_{\xi=\xi(t)}\mathbf{d}_{i}. \tag{16}\]  
Similarly, the potential energy of a satellite system can be described using a discretized model.
## 5 Conclusions  
This study used numerical analysis to investigate the effects of changing water depth on a full-formed low-speed ship with a bottom groove at various speeds. The resulting insightful conclusions are as follows:  
1. The use of a bottom groove in combination with jetting can significantly reduce drag under various water depth circumstances. Both the relative and absolute drag reduction rates exceeded 30% and 20%, even in extremely shallow water conditions, demonstrating the tremendous drag reduction potential of jetting in both shallow and extremely shallow waters.
2. Due to the influence of shallow water effects, the velocity and pressure fields at the bottom of the ship have undergone significant changes; therefore, the efficiency of drag reduction and the form of the air layer on the ship's bottom are significantly impacted by variations in water depth in restricted waters. Lower water depths alter the air layer fluctuation and overflow patterns, which has an unfavorable impact on the stern's ordered gas overflow. These modifications do not, however, affect how stable the air layer is inside the groove.
3. As the water depth becomes shallower, the air layer's ability to reduce drag exhibits an increasing-then-decreasing trend, demonstrating that there is an ideal drag reduction depth. For various speeds, there are several ideal depths for reducing drag.  
Figure 12: Comparing the pressure contour maps on the ship’s surface before and after jetting (\(F_{r}\) = 0.091).  
* Reduced frictional resistance no longer predominates the overall resistance reduction in shallow water circumstances. Opening a groove with jetting causes noticeable changes in the pressure field surrounding the hull as the water depth becomes shallower. In the overall resistance reduction, the proportion of the decrease in viscous pressure resistance rises and can reach up to 4.8 times the decrease in frictional resistance.  
Conceptualization, Q.Y. and Y.O.; methodology, Q.Y. and G.X.; validation, G.X., J.C. and Y.O.; writing--original draft preparation, Q.Y.; writing--review and editing, Y.O. and G.X.; supervision, Y.O. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
# Individualized Interaural Feature Learning and Personalized Binaural Localization Model +
## 6 Experiments With Simulated Data
### 3-D Space Localization with Probabilistic Model
#### 6.2.4 Localization Performance with Reverberations  
To localize the sound source in a reverberant environment, the proposed method introduces the Direct Path Relative Transfer Function (DP-RTF) estimation method as a pre-processing front-end in the system. In the DP-RTF extraction, because the reverberation condition is assumed unknown, the CTF length \(Q_{k}\) is settled as 0.25 s and remains unchanged in all simulations. Figure 9 shows the compared localization performances with multiple reverberation configurations. Although all methods are degraded in more severe environments, the proposed methods have more accurate localization results for both feature vector types, and the best performance is achieved by using full length vectors. It can be concluded that the high-IPD features still contain source direction information and, with an appropriate application, can contribute to the source position estimation in a reverberant environment.  
Similar to the previous subsection, the azimuth and elevation estimations are compared separately in Table 3. In the azimuth comparison, the state-of-the-art method has better performance when using the ILPD feature vector, which indicates that the high-IPD features are crucial to the proposed method to tackle the interference caused by reverberation. As for the elevation localization, a considerably better elevation performance can be obtained by using the proposed methods with both vector types, which proves that mapping of elevation exploited by the RF algorithm can work well against the reverberate interference.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{6}{c}{**(a) Azimuth Accuracy Comparison**} \\ \hline \(T_{60}\) & \multicolumn{2}{c}{**200 ms**} & \multicolumn{2}{c}{**300 ms**} & \multicolumn{2}{c}{**400 ms**} & \multicolumn{2}{c}{**500 ms**} \\ \hline
**Tolerance** & \(\leq\)**2.5\({}^{\circ}\)** & \(\leq\)**5\({}^{\circ}\)** & \(\leq\)**2.5\({}^{\circ}\)** & \(\leq\)**5\({}^{\circ}\)** & \(\leq\)**2.5\({}^{\circ}\)** & \(\leq\)**5\({}^{\circ}\)** & \(\leq\)**2.5\({}^{\circ}\)** & \(\leq\)**5\({}^{\circ}\)** \\ \hline Proposed - FULL & 94.32\% & 97.92\% & 91.44\% & 96.72\% & 89.44\% & 95.76\% & 78.88\% & 89.12\% \\ PPAM - FULL & 81.04\% & 90.64\% & 79.84\% & 90.32\% & 77.84\% & 88.88\% & 66.35\% & 85.76\% \\ Proposed - ILPD & 74.24\% & 84.96\% & 72.08\% & 83.28\% & 66.32\% & 80.4\% & 53.04\% & 74.88\% \\ PPAM - ILPD & 86.72\% & 96.88\% & 96.40\% & 77.20\% & 77.2\% & 94.96\% & 58.00\% & 83.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Azimuth and elevation estimation accuracy comparison in reverberate environment.  
Figure 9: Comparing the localization accuracy between proposed method and PPAM with different \(T_{60}\). The angular error tolerance is 2.5\({}^{\circ}\).
## 2 Experimental Section
### Synthesis of **6b**  
Using a similar procedure as described for the synthesis of compound **5a**, reaction of **4** (1.77 g, 5.0 mmol) with 2\(H\),2\(H\),3\(H\),3\(H\)-perfluoroheptanoyl chloride (1.55 g, 5.0 mmol) provided **6b** as a white solid (1.92 g, 61%): mp. 163.5-165.5 \({}^{\circ}\)C; \({}^{1}\)H NMR (DMF-d7): \(\delta\) 8.80 (bs, 1H), 8.0 (bs, 1H), 7.96 (bs, 1H), 4.91 (q, \(J\) = 6.0 Hz, 1H), 3.32 (m, 4H), 2.87-2.70 (m, 4H), 1.63 (m, 4H), 1.45 (bs, 20 H), 0.75 (t, distorted, 6H); \({}^{19}\)F NMR (DMF-d7): \(\delta\) -81.8 (m, 3F), \(-\)114.9 (m, 2F), \(-\)125.9 (m, 2F), \(-\)126.6 (m, 2F).
## 3 Results and Discussion  
### Samples Characterization  
According to XRD data presented in Figure 2, both the bulk and film samples of SZY possess an orthorhombic perovskite-type structure. For SZY film, except for the peaks of the orthorhombic SrZrO\({}_{3}\) phase, small reflexes of NiO and YSZ phases caused by the substrate response were recorded.  
SEM images of the pellet and film surfaces are depicted in Figure 3. The ceramic sample sintered at 1650 \({}^{\circ}\)C demonstrates dense grained microstructure with grains up to 4-5 \(\upmu\)m (Figure 3a). The film sintered at a lower temperature (1000 \({}^{\circ}\)C) has small-grained morphology with grains of 100-200 nm (Figure 3b). As can be seen in Figure 3a,b, there are no cracks or fractures on the sample's surfaces, however, a few pinholes can be observed on the film surface. That is why the deposition of several layers is required for achieving sufficient gas-tightness of the film. The film thickness evaluated from the cross-section image of the NiO-YSZ supported film was 2.5 \(\pm\) 0.1 \(\upmu\)m (Figure 3c).  
The chemical composition of NiO-YSZ supported SZY film was analyzed using SEM/EDX. The atomic ratio of Sr, Zr and Y elements was 0.95:0.98:0.02. So far as the effective probe depth of EDX is about 1 \(\upmu\)m, the obtained results characterize the outer 1 \(\upmu\)m layer of the 2.5 \(\upmu\)m -thick film. The EDX data indicate that Ni concentration in the outer film layer is 1.5-2 at.%, which can be caused by nickel diffusion from the substrate during the film synthesis. These results correlate with the study of  
Figure 2: XRD patterns for the bulk Sr\({}_{0.98}\)Zr\({}_{0.95}\)Y\({}_{0.05}\)O\({}_{3-\delta}\) (SZY) sample and SZY film on NiO-yttria-stabilized zirconia (YSZ) substrate.  
Ni diffusion in 1 \(\upmu\)m thick CeO\({}_{2}\) film by secondary ion mass spectroscopy technique which has also shown the presence of Ni throughout the entire film after the heat treatment at 800 \({}^{\circ}\)C [16].  
The film gas-tightness was measured after the reducing firing of SZY-film/NiO-YSZ pellets. The gas leakage coefficient was in the range of (2-5) \(\cdot\) 10\({}^{-17}\) m\({}^{2}\) that is comparable with the values reported for YSZ films deposited by the low pressure and atmospheric plasma spraying and supposed to be sufficient for the SOFC membranes [33].
## 3 Context-Aware Positioning Model
### Context-Aware Positioning
#### 3.3.2 Request Collaborative Positioning  
If the recommendation is to perform collaborative positioning, the model will use the contextual information of the summary to select viable neighboring devices with known positions as reference points. Then, it proceeds to estimate the device's position using either tri-lateration, or minimum bounding box approximation (MBB).  
Lateration is a type of triangulation in which the position of a resource is estimated by measuring its distance to several reference points with known positions. Using the direction or length of the vector drawn between the location to be estimated and the reference points, it is possible to calculate the absolute position of the desired resource [29]. Figure 4b shows an example of tri-lateration under the model. The green nodes know their positions with fitness scores over the threshold of 60; red nodes are those that do not know their positions, or that have a low fitness score and therefore cannot collaborate with the target. The solid lines link the target to the nodes chosen as reference points, while the dotted lines link nodes that have been acknowledged but are not part of the collaboration process.  
The model requires at least three reference points to perform tri-lateration, although more points could be used to improve the accuracy of the estimation, if available (multi-lateration). An advantage of this method is that it involves a small setup effort [30].  
The smallest or minimum bounding box is, for a point set (\(S\)) in \(N\) dimensions, the box with the smallest measure (area, volume, or hypervolume in higher dimensions) within which all specified points of \(S\) lay. The MBB of a point set is the same as the MBB of its convex hull, a fact that may be used  
Figure 4: **(a)** Dead reckoning; **(b)** trilateration; **(c)** minimum bounding rectangle (MBR).  
heuristically to speed up computation [31]. The term \"box\" comes from its usage in the Cartesian coordinate system, where it is indeed visualized as a rectangle in two dimensions (2D), a rectangular parallelepiped in three dimensions (3D), _etc._ Figure 4c illustrates an example of MBB applied to 2D positioning. Using as many nodes that know their positions as possible, a series of rectangles is formed, each containing at least three nodes. Then, line segments are traced from the centers of these rectangles to each other, forming an irregular polygon (in this case, a triangle), which represents the area where the node is likely located. In some cases, this polygon provides better accuracy than using tri-lateration. However, the cost of calculating this polygon increases in direct relation with the number of rectangles formed.
# Rock Crack Types Identification by Machine Learning on the Sound Signal+
###### Abstract  
Sound signals generated during rock failure contain useful information about crack development. A sound-signal-based identification method for crack types is proposed. In this method, the sound signals of tensile cracks, using the Brazilian splitting test, and those of shear cracks, using the direct shear test, are collected to establish the training samples. The spectrogram is used to characterize the sound signal and is taken as the input. To solve the small sample problem, since only a small amount of sound signal spectrogram can be obtained in our experimental test, pre-trained ResNet-18 is used as a feature extractor to acquire deep characteristics of sound signal spectrograms. Gaussian process classification (GPC) is employed to establish the recognizing model and to classify crack types using the extracted deep characteristics of spectrograms. To verify the proposed method, the tensile and shear crack development processes during the biaxial test are identified. The results show that the proposed method is feasible. Moreover, this method is used to investigate the tensile and shear crack development during the rockburst process. The obtained results are consistent with previous research results, further confirming the accuracy and rationality of this method.  
rock mechanics; spectrogram; crack type; Gaussian process; deep learning Article
# GAO-FCNN-Enabled Beamforming of the RIS-Assisted Intelligent Communication System
## 1 Introduction
However, its local optimal solution was inherently confined [27; 28], and it was difficult to obtain the global optimization in the scenario of high-performance demands. In this paper, a deep learning-enhanced genetic algorithm (GA) is proposed to develop a sophisticated RIS-assisted intelligent communication system model leveraging fully connected neural networks (FCNNs). Our approach is driven by the high computational complexity of communication systems, and various challenges existed in conventional optimization techniques, especially for the beamforming optimization of both transmitters and receivers. Various literatures have underscored the nonconvex challenges of optimization problems in this field. And now, suboptimal solutions are often obtained due to the local minima. Fortunately, this issue is tackled by overcoming the nonconvex obstacles by using our GA optimization (GAO). In addition, the cooperative optimization of the RIS reflection matrix and the beam assignment vector is also studied here. Furthermore, the robust modeling capabilities of FCNNs are explored to enhance the capacity of complex patterns identification in the RIS-assisted wireless communication system. Based on this, the enhancement of the system efficiency and dependability is improved. Numerical simulation results indicate that our proposed method markedly surpasses existing techniques from the perspective of bit error rate (BER) performance. And the exceptional efficiency and reliability of the deep learning-augmented GA/GAO in this paper can be affirmed. Consequently, complex communication optimization challenges in the RIS-assisted system are overcome. Moreover, contributions are made in this paper to the ongoing development of the intelligent communication system, and it is particularly important for the evolution of 6G mobile information networks. Notation: The bold lowercase and uppercase letters are used to represent the vectors and the matrices, respectively. \(\mathbf{x}^{\mathrm{H}}\) is the conjugate transpose of the vector \(\mathbf{x}\), and \(\mathbf{x}^{\mathrm{T}}\) is the corresponding transpose.