esa-sceva/satcom-chunk-collection huggingface dataset:
# Reinforcement Learning for Semi-Active Vertical Dynamics Control with Real-World Tests
## 5 Control System Verification
### Design and Optimization of the Benchmark Controller  
In order to assess the trained controller on the real vehicle, a benchmark controller was necessary (see [20]). In this work, we used a slightly modified version of the SH/GH controller originally presented in [2]. The original version of the SH/GH controller postulated a virtual damper between the sky and the chassis as well as a virtual damper between the road and the wheel mass. The virtual forces, which would result from the two virtual dampers, were then applied by the semi-active damper, if the damper characteristics allowed it. Otherwise, the forces were clipped to the maximum applicable forces. The parameters of the controller were the damping coefficients of both the virtual skyhook damper and the virtual groundhook damper.  
Even though this concept is appealing, the application was highly dependent on the measurement quality of the damper velocity. The SH/GH implementation described above output a desired damper force, but the interface for controlling the damper was a desired damper current. To obtain the desired damper current for a given force, the damper characteristics depicted in Figure 3 had to be inverted. The calculation was then highly dependent on the damper velocity. We found that this dependency affected the applicability of the SH/GH controller implementation, in which the controller output a desired damper force.  
We used a modified version of the SH/GH controller with a current interface to minimize the dependency on the damper velocity measurement. In this controller variant, the controller directly output a desired damper current, instead of a damper force. The structure of the benchmark controller is depicted in Figure 9, where the SH current demand \(i_{\text{SH}}\) is calculated as  
\[i_{\text{SH}}=\begin{cases}k_{\text{SH}}\,v_{\text{c}}&\text{if }v_{\text{c}} \cdot v_{\text{d}}\geq 0\\ 0&\text{else}\end{cases} \tag{17}\]  
and the GH current setpoint \(i_{\text{GH}}\) is calculated as  
\[i_{\text{GH}}=\begin{cases}k_{\text{GH}}\,v_{\text{w}}&\text{if }v_{\text{w}} \cdot v_{\text{d}}<0\\ 0&\text{else}\end{cases}. \tag{18}\]  
In this equation, \(v_{\text{w}}\) denotes the vertical wheel velocity and \(v_{\text{d}}\) denotes the damper velocity. In our convention, \(v_{\text{d}}<0\) represents a compression of the damper.  
To ensure a fair comparison, we optimized the controller parameters \(k_{\text{SH}}\) and \(k_{\text{GH}}\) in Dymola using the Optimization Library [26]. The optimization was performed on the QVM described in Section 3 and on different artificial road excitations. We optimized a separate parameter set for each vehicle corner using integral variants of the objectives \(J_{\text{c}}\) and \(J_{\text{rh}}\) from Equations (14) and (15).
## 5 R-DPAP: Resource-Constrained Directed Path Allocation Problems
### Framework Definition
The nodes in the graph correspond to the time windows associated with each request and each satellite. For instance, node \(\mathtt{a}_{1}\) in graph \(g_{\mathtt{a}}\) corresponds to time window \(\mathtt{a}_{1}=[1,4]\). Formally, \(d_{\mathtt{g}_{\mathtt{a}}}(\mathtt{a}_{1})=2\) (because an observation duration equal to 2 is required), \(\mathit{ws}_{g_{\mathtt{a}}}(\mathtt{a}_{1})=1\), \(\mathit{w}_{g_{\mathtt{a}}}(\mathtt{a}_{1})=4\) (corresponding to time window \([1,4]\)) and \(c_{\mathtt{g}_{\mathtt{a}}}(\mathtt{a}_{1})=\mathit{sat}_{1}\)._  
**Example 5**. : _Figure 5(a) illustrates an allocation for the problem presented in Example 4. A valid allocation could be \(\tau_{\mathtt{ex}}=\{g_{\mathtt{a}}\mapsto\{\mathtt{s}_{\mathtt{a}},\mathtt{a }_{1},\mathtt{a}_{2},\mathtt{f}_{\mathtt{a}}\},\mathtt{gb}\mapsto\{\mathtt{s}_{ \mathtt{b}},\mathtt{b}_{1},\mathtt{b}_{4},\mathtt{f}_{\mathtt{b}}\}\}\). In fact, we can consider two functions \(\tau_{\tau_{\mathtt{ex}},\delta_{\mathtt{a}}}\) and \(\tau_{\tau_{\mathtt{ex}},\delta_{\mathtt{b}}}\) that assign start dates to nodes in \(\tau_{\mathtt{ex}}\) without any conflict in resources. As illustrated in Figure 5(b), it is possible to have \(\tau_{\tau_{\mathtt{ex}},\delta_{\mathtt{a}}}(\mathtt{a}_{1})=1\) (i.e., a slot starting at time 1 and ending at time 3 is booked within time window \(\mathtt{a}_{1}\)), \(\tau_{\tau_{\mathtt{ex}},\delta_{\mathtt{a}}}(\mathtt{a}_{2})=7\), \(\tau_{\tau_{\mathtt{ex}},\delta_{\mathtt{b}}}(\mathtt{b}_{1})=3\), and \(\tau_{\mathtt{ex}},\tau_{\mathtt{ex},\delta_{\mathtt{b}}}(\mathtt{b}_{4})=9\), which results in a non-conflicting access to the resources \(\mathit{sat}_{1}\) and \(\mathit{sat}_{2}\)._
# Deep Morphological Anomaly Detection Based on Angular Margin Loss
## 6 Conclusions  
We proposed a novel DAD model to extract the dominant morphological features of \"normal\" images. The proposed algorithm is based on a combination of morphological transformations and a self-supervised learning algorithm. In addition, to improve the discriminative power of the proposed DAD model, we adopted an angular margin on the classification loss function. The experiments confirmed that the proposed algorithm achieves higher performance in the industrial anomaly detection dataset than the previous algorithms. In addition, the proposed algorithm yields slightly better performance than the previous reconstruction-based DAD models. The results validate the satisfactory performance of the proposed algorithm in real-world industrial anomaly inspection applications. In the future, we plan to combine the proposed algorithm with the semantic feature-based algorithm.  
Conceptualization, T.K.; methodology, T.K.; software, T.K.; validation, T.K.; and Y.C.; formal analysis, T.K.; investigation, T.K. and E.H.; resources, T.K.; data curation, T.K.; writing--original draft preparation, T.K. and E.H.; writing--review and editing, T.K.; visualization, T.K.; supervision, Y.C.; project administration, Y.C.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This work was supported by the Technology development Program (S2798925) funded by the Ministry of SMEs and Startups (MSS, Korea).  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **Compared Algorithm** & \(n\) & \(W_{\text{critical}}\) & \(W_{-}\) & \(W_{+}\) & \(W\) & \(H_{1}\) \\ \hline \multirow{4}{*}{MVTec} & [PERSON] et al. [7] & 15 & 30 & 119 & 0 & 0 & [PERSON] \\  & [PERSON] et al. [9] & 15 & 30 & 119 & 0 & 0 & Reject \\  & [PERSON] et al. [32] & 15 & 30 & 106.5 & 13.5 & 13.5 & Reject \\  & [PERSON] et al. [33] & 15 & 30 & 119 & 0 & 0 & Reject \\ \hline \multirow{4}{*}{MNIST} & [PERSON] et al. [11] & 10 & 11 & 54 & 1 & 1 & Reject \\  & [PERSON] et al. [35] & 10 & 11 & 51 & 4 & 4 & [PERSON] \\  & [PERSON] et al. [13] & 10 & 11 & 40.5 & 14.5 & 14.5 & - \\  & [PERSON] et al. [33] & 8 & 6 & 26 & 10 & 10 & - \\ \hline \multirow{4}{*}{Fashion-MNIST} & [PERSON] et al. [11] & 10 & 11 & 55 & 0 & 0 & [PERSON] \\  & [PERSON] et al. [35] & 10 & 11 & 46 & 9 & 9 & [PERSON] \\  & [PERSON] et al. [13] & 10 & 11 & 45 & 10 & 10 & [PERSON] \\  & [PERSON] et al. [33] & 10 & 11 & 37 & 18 & 18 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the Wilcoxon signed-rank test on the proposed algorithm and related algorithms on MVTec, MNIST, and Fashion-MNIST datasets. \(W_{\text{critical}}\), \(W_{-}\), \(W_{+}\), \(W\), and \(H_{1}\) are critical value, the sum of the ranks of the negative differences, the sum of the ranks of positive differences, the test statistic for the Wilcoxon signed test, and the null hypothesis. The level of significance \(\alpha\) is 0.05.
# Fuzzy Model Predictive Control for Unmanned Helicopter
## 3 Control Methodology
### Takagi-Sugeno Inference Algorithm
\[\sum_{i=1}^{N_{\mu}}\mu_{i}(V_{x})=1 \tag{23}\]  
Assumed membership functions \(\mu_{i}(V_{x})\) are presented in Figure 3. The centers of the membership functions are, respectively, 0, 10, and 20 \(\frac{\text{m}}{\text{s}}\). The standard deviation of each membership function is 10 \(\frac{\text{m}}{\text{s}}\). In real flight conditions, each local MPC regulator--derived from a local linear model--computes its control action based on the current state of the rotorcraft. Each local control output is then weighted by its corresponding membership function, and the global control signal is obtained as a weighted sum of all local controller outputs, ensuring smooth blending between operating regions. To guarantee the stability of the fuzzy control system, a necessary condition is that each local control loop is stable on its own. Moreover, a sufficient condition for the asymptotic stability of a Takagi-Sugeno fuzzy system was established by [PERSON] and [PERSON] [35]. It stats that in order to guarantee stability of a fuzzy system, there should be found a positive definite matrix \(\mathbf{P}\) that satisfies the inequality presented below in (24):  
\[\mathbf{D}_{i}^{T}\mathbf{P}\mathbf{D}_{i}-\mathbf{P}<\mathbf{0} \tag{24}\]where \(\mathbf{D}_{i}\) is a state matrix for a closed-loop control system under the \(i\)-th rule. For static state-feedback regulators, defined by Equation (25), the corresponding \(\mathbf{D}_{i}\) closed-loop matrix can expressed by Equation (26). \[\mathbf{u}=\mathbf{K}\mathbf{x} \tag{25}\]  
\[\mathbf{D}_{i}=\mathbf{A}_{i}+\mathbf{B}_{i}\mathbf{K}_{i} \tag{26}\]  
where  
* \(\mathbf{A}_{i}\in\mathbb{R}^{m\times m}\) is the state matrix of the \(i\)-th local model;
* \(\mathbf{B}_{i}\in\mathbb{R}^{m\times n}\) is the control matrix of the \(i\)-rule model;
* \(\mathbf{K}_{i}\in\mathbb{R}^{n\times m}\) is the corresponding state-feedback gain. However, when using MPC, the control law becomes time-varying, so the classical criterion proposed by [PERSON] and [PERSON] [35] no longer directly applies. For our MPC-based fuzzy control, stability must instead be established using more general Lyapunov-based methods that support time-varying controllers. This remains a notable limitation of traditional LMI-based stability criteria, and a promising direction for future research.
###### Abstract  
The main limitation of wireless sensor networks (WSNs) lies in their reliance on battery power. Therefore, the primary focus of the current research is to determine how to transmit data in a rational and efficient way while simultaneously extending the network's lifespan. In this paper, a hybrid of a fuzzy logic system and a quantum annealing algorithm-based clustering and routing protocol (FQA) is proposed to improve the stability of the network and minimize energy consumption. The protocol uses a fuzzy inference system (FIS) to select appropriate cluster heads (CHs). In the routing phase, we used the quantum annealing algorithm to select the optimal route from the CHs and the base station (BS). Furthermore, we defined an energy threshold to filter candidate CHs in order to save computation time. Unlike with periodic clustering, we adopted an on-demand re-clustering mechanism to perform global maintenance of the network, thereby effectively reducing the computation and overhead. The FQA was compared with FRNSEER, BOA-ACO, OAFS-IMFO, and FC-RBAT in different scenarios from the perspective of energy consumption, alive nodes, network lifetime, and throughput. According to the simulation results, the FQA outperformed all the other methods in all scenarios.  
wireless sensor networks (WSNs); clustering; fuzzy logic; routing; quantum annealing algorithm 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vol., pp. 204-205, 2008. External Links: Document Cited by: SS1.
## 6 Conclusions  
This work addresses the core challenges of routing in UAV swarm networks, including link instability, routing voids, and lack of adaptability under dynamic and mission-driven conditions. To overcome these issues, we propose a trajectory-informed and reinforcement learning-based routing framework that integrates future mobility knowledge into a time-aware two-hop neighbor structure. Coupled with an adaptive Q-learning mechanism and delay-aware queue scheduling, the proposed approach enhances routing stability and decision intelligence. In future work, we aim to extend the framework to handle uncertain or asynchronous trajectory updates and improve learning convergence under constrained communication conditions.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Number of Nodes** & **30** & **40** & **50** & **60** & **70** & **80** & **90** & **100** \\ \hline Traj-Q-GPSR & 4.08 & 6.65 & 9.81 & 13.57 & 17.93 & 22.87 & 28.42 & 34.56 \\ GPSR & 1.58 & 2.13 & 2.67 & 3.22 & 3.76 & 4.31 & 4.85 & 5.40 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Storage overhead (average number of neighbor entries) based on number of nodes.  
Figure 8: Curve of routing overhead vs. number of nodes.  
**Author Contributions:** Conceptualization, M.W. and S.C.; Methodology, M.W. and F.X.; Software, M.W. and T.P.; Validation, B.J., S.C., H.X. and T.P.; Formal analysis, H.X. and T.P.; Investigation, S.C.; Data curation, M.G.; Writing--original draft, M.W.; Writing-- review & editing, B.J., S.C., H.X., M.G. and F.X.; Visualization, M.G.; Supervision, B.J. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Key Research and Development Program of China under Grant 2024 YFB4504500.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest. All co-authors have seen and agree with the contents of the manuscript and there is no financial interest to report. We certify that the submission is original work and is not under review at any other publication.
# A Survey of the Real-Time Metaverse: Challenges and Opportunities +
## 6 Metaverse Engine
### Immersive Technologies
#### 6.1.5. Advanced Graphical Rendering Technologies  
Advanced graphical rendering (GR) technologies are crucial for creating highly realistic and visually stunning virtual environments in the metaverse [25]. One of the key advancements in GR is real-time ray tracing, a technique that simulates the way light interacts with objects in a scene to produce highly accurate reflections, refractions, and shadows [105; 175]. This level of detail enhances the realism of virtual worlds, making them more immersive and visually engaging. Real-time ray tracing requires significant computational power, but recent advances in GPUs and optimization techniques have made it feasible for real-time applications, allowing users to experience lifelike visuals in interactive settings such as gaming, virtual tours, and social interactions within the metaverse.  
Another significant development in advanced graphical rendering is using artificial intelligence (AI) to enhance graphics quality and performance [24; 108]. AI-driven techniques, such as deep learning-based upscaling (NVIDIA's deep learning super sampling (DLSS)), can improve frame rates and image quality by predicting and generating high-resolution frames from lower-resolution inputs. High resolution not only ensures smoother performance but also allows for more detailed and complex scenes without compromising on speed. AI is also used in procedural content generation, enabling the creation of vast and diverse virtual landscapes with minimal manual effort [176]. By leveraging these advanced rendering technologies, the metaverse can offer visually rich, dynamic, and interactive environments that push the boundaries of what is possible in digital experiences, making them more compelling and lifelike for users.
## 2 Related Work
### Attention Mechanism  
Traditional deep learning models, such as Recurrent Neural Networks (RNNs) [36] and convolutional neural networks, often struggled with long-range dependencies and efficiently processing relevant features in the input data. This led to the development of attention mechanisms, which were initially introduced in the context of machine translation. Bahdanau et al.'s Additive Attention [37] addressed the limitations of fixed-length context vectors in sequence-to-sequence models by dynamically computing alignment scores between the encoder and decoder hidden states. This allowed the model to focus on relevant parts of the input sequence during translation, significantly improving translation quality.  
As the concept and technology of attention mechanisms continued to evolve, a variety of attention mechanisms emerged. Attention mechanisms have not only achieved significant progress in natural language processing but have also been successfully applied to computer vision tasks, enhancing models' ability to focus on spatial and channel features. For example, the Convolutional Block Attention Module (CBAM) [38] sequentially applies channel and spatial attention to input feature maps, enabling the model to focus on the most informative parts of the feature maps, which has shown significant performance improvements in image classification and object detection tasks. The innovation of CBAM lies in its dual attention mechanism: the channel attention module focuses on \"what\" is important, while the spatial attention module focuses on \"where\" it is important, ensuring the network can effectively emphasize critical features. Similarly, Squeeze-and-Excitation Networks (SENets) [39] introduced a channel attention mechanism that adaptively recalibrates channel-wise feature responses by modeling interdependencies between channels. This approach significantly improves the performance of various convolutional neural network architectures on image recognition tasks. SENet's innovation lies in its squeeze operation, which compresses the spatial dimensions of the input, and the excitation operation, which learns the importance of each channel through fully connected layers, significantly boosting performance.  
Summary: These advancements underscore the versatility and efficacy of attention mechanisms in deep learning, highlighting their critical role in the ongoing development of more sophisticated and capable models. By allowing models to dynamically focus on the most relevant parts of input data, attention mechanisms have become indispensable tools for achieving high performance in various applications.
## 2 Materials and Methods
### Sampling and Sample Preparation  
The soil samples were collected using reusable stainless-steel trowels and shovels, cleaned with distilled water, sun-dried and sterilized with alcohol, after each collection to avoid cross-contamination. The sampling followed a protocol described in a previous study [16]. Twenty-five samples were collected to ensure adequate coverage of each targeted area, allowing for a statistically robust and representative analysis of contamination levels based on proximity to rutile deposits. The sampling points were strategically distributed among the rutile discovery site, surrounding villages, and urban peripheries to provide a comprehensive overview of areas potentially affected by trace element contamination. Samples from inhabited areas were taken in close proximity to houses. This approach helps to better understand soil contamination in relation to the proximity of rutile deposits and the potential risks to residents. A distance of approximately 150-250 m was considered between the different sampled sites to ensure characteristic evaluation.  
Each sample, consisting of 1 kg of soil, was collected from a depth of 5 to 15 cm and then stored in hermetically sealed polymer bags. The samples were subsequently placed in biodegradable containers and transported to the Nuclear Science and Technology Research Center (CRSTN) of the Geological and Mining Research Institute (IRGM) in Yaounde for conditioning. During sample conditioning, foreign plant debris, stones, gravel, and roots were removed. Afterward, the samples were dried for 24 h at 105 \({}^{\circ}\)C in an oven and then ground and sieved with appropriate machinery. Finally, the samples were placed in marked polyethylene bags, carefully sealed to prevent contamination, and transported for analysis by flame atomic absorption spectrometry (FAAS) at the Research Center for Nuclear Safety, Metrology, and Technology, National Agency for Research and Innovation of Indonesia (BRIN).
# Utilizing Soft Computing Techniques to Estimate the Axial Permanent Deformation of Asphalt Concrete
## 3 Materials and Testing
### Uniaxial Repeated Loading Test  
A pneumatic repeated load system (PRLS) was used to determine the permanent deformation resulting from repeated uniaxial loading. During the test, specimens were subjected to repetitive compressive stress, and the resulting axial permanent deformation was measured over a range of loading cycles. The compressive load was applied as a rectangular wave at a consistent frequency of 60 cycles per minute, using two different loading patterns: one with a 0.1 s load duration followed by a 0.9 s rest period, and the other with a 0.4 s load duration followed by a 0.6 s rest period. Six levels of compressive stress (44.82 kPa (6.5 psi), 68.95 kPa (10 psi), 89.63 kPa (13 psi), 137.89 kPa (20 psi), 186.15 kPa (27 psi), and 206.84 kPa (30 psi)) were applied, and the experiments were conducted at seven  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Test Results** \\ \hline Passing sieve No.200 (0.075 mm) & 96 \\ Specific gravity & 2.787 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physical properties of the limestone filler.  
distinct temperatures (20, 25, 40, 55, 60, 65, and 70 \({}^{\circ}\)C). Then, the permanent strain (\(\epsilon\)p) was estimated by employing the following equation:  
\[\epsilon p=\frac{p_{d}\times 10^{6}}{h} \tag{1}\]  
where \(\epsilon\)p is the axial permanent microstrain, p\({}_{\text{d}}\) is the axial permanent deformation, and h is the specimen height.