esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Applications and Discussions
### Identification of Electrical Water Heater
#### 5.2.2 Simulation and Discussion  
In this example, 700 data samples from \(t=101\) to \(t=800\) are used to test the method. The results of the identification are shown in Figure 3. It shows that the agents can run effectively in a fixed working condition. The main initial values and indexes of the identification systems under different conditions are list in Table 5. The 700 data samples of Figure 3c are consisted with two different conditions, The first 350 data samples come from condition 1, the second 350 data samples from condition 2. The RMSE of the ABFT-SMM in this time is 0.0107. It is very close to the RMSE value of condition 1 or condition 2, which means the ABFT-SMM method runs efficiently in the condition-changed situation, while more complex systems need to be used to test the approach.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Conditions** & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{b_{1}}\) & \(\mathbf{b_{2}}\) & \(\mathbf{\
u_{0}}\) \\ \hline
1 & \(-1.8\) & 0.8112 & 0.0093 & 0.0019 & 20.83 \\
2 & \(-1.817\) & 0.8302 & 0.0032 & 0.0086 & 21.35 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of water heater example under different working conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Conditions** & \(\mathbf{r_{a}}\) & \(\mathbf{s_{i}}\) & Centers & RMSE \\ \hline
1 & 0.18 & 1 & 21 & 0.01 \\
2 & 0.2 & 1 & 17 & 0.0096 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Initial values and indexes of the identification systems.  
Figure 2: Schematic diagram of water heater system.
# Dual-Band Single-Layered Frequency Selective Surface Filter for LTE Band with Angular Stability
## 2 Proposed Design of FSS Configuration
### Design Methodology with Its Evolution Stages
This deliberate deviation from symmetry is a tactical design decision to enhance functionality, enabling the structure to resonate at two separate frequencies, as necessitated for dual-band applications. Consequently, asymmetry augments the design's versatility and efficacy, which would not have been attainable with a symmetric configuration. The increase in the discrete elements with each development step results in a shifting of resonant frequency toward the lower side, as tabulated in Table 2.
## 4 Understanding Centralised Identity Systems
### Security Considerations  
The identity includes sensitive details such as the user's name, age, bank details, and other personal information, which, if accessed by unauthorised individuals, could be misused. Therefore, ensuring its security is of paramount concern. Several mechanisms are employed to keep identity data and the transactions involving these data secure:  
* **Encryption**: Identity data such as credentials and session tokens are encrypted both in transit and at rest to prevent unauthorised access [12; 15].
* **Access Control**: Strict policies are enforced centrally, allowing access only to resources based on the roles and permissions of the user. Role-based access control (RBAC) ensures that the user is granted permissions as per their assigned roles. These are managed by the identity provider (IdP) and enforced across all service providers (SPs) [12].
* **Session Management**: Tokens issued after authentication are time-limited and need to be refreshed after a specified time. This reduces the risk of session hijacking [12; 13].
* **Logging and Monitoring**: Centralised servers allow for easy collation and storage of audit trails, including transactions, authentication attempts, and access requests. These logs help monitor suspicious activity and aid in debugging issues in centralised identity management systems (CIMSs) [12].
# Stochastic Modelling of Selfish Mining in Proof-of-Work Protocols +
## 3 Agent-Based Model and Simulation
### ABM of Selfish Mining  
#### 3.1.1 Peer-to-Peer Network  
The foundation of the agent-based model (ABM) is a set \(\mathcal{N}\) of \(N\) nodes interconnected through a static graph \(G(\mathcal{N},\mathcal{E})\) representing the system's peer-to-peer network, which we will henceforth refer to as Bitcoin. The set \(\mathcal{E}\) includes all connections among nodes, so that they can communicate with one another. The Bitcoin protocol specifies a default connection size of eight peers for a new node joining the network. But the average number of connections is effectively higher than the default value, because a node may be open to new incoming connections, meaning that over time the number of peers of a node tends to increase [16]. For the simulations, we assume an average degree of 10. To account for heterogeneity in the degree distribution, we consider different network topologies, such as uniform random, Erdos-Renyi, Barabasi-Albert [17, 18].  
Further, we make the simplifying assumption of a static topology, meaning that miners do not enter or exit the network. The assumption also implies that miners do not drop or form any new connections with their peers in a certain time period. For selfish mining, the defining time scale is that of emerging consensus (block creation and propagation), which should be considerably lower than that of nodes altering their connections, or entering and exiting the network altogether. We, therefore, argue this to be a reasonable assumption.  
In this peer-to-peer network, we call all nodes _miners_. A miner is either an honest or selfish type. The former will follow the standard Bitcoin protocol, the latter will perform the selfish mining strategy. For the sake of simplicity, we assume a single selfish miner in the simulations, which implies that \((N-1)\) miners are honest.
# Robotics in Gastrointestinal Endoscopy
## 2 Review of Robot Assisted Endoscopic Devices (Robot Assisted ESD System)
### Robotic Endoscopic Multitasking Platform
#### 2.1.4 K-FLEX (EasyEndo Surgical, Daejeon, Korea)  
The K-FLEX (EasyEndo Surgical, Daejeon, Korea) is a robotic endoscopic platform for advanced endoluminal surgery. The platform is composed of a bendable overtube, two exchangeable surgical instrument modules, a driving robot arm, and a master console (Figure 4) [20]. The motion of each robot arm was controlled to bend up and down, left, and right with an intuitive thumb stick. The robot arm allows two independent bends at an angle of more than 100\({}^{\circ}\) in each direction, allowing multidirectional traction. The robot arm has a 2.8 mm working channel and a joint configuration that allows precise movement. The robot's left arm was equipped with a grasping end effector and the right arm with a monopolar cautery. The master console monitor has a graphic simulator showing approximate location of the robot arm in the GI tract. Ex vivo porcine stomach ESD experiments were successfully conducted [21]. CE or FDA approval was not obtained.  
Figure 4: K-FLEX (EasyEndo Surgical, Daejeon, Korea). The K-FLEX system is composed of two surgical instruments, overtube, driving robot arm, and a master console [20].  
Figure 3: Flex Robotic System (Medrobotics, Raynham, MA, USA) [18]. (**a**) The robot’s flexible distal end including two working channels with a needle-knife and grasper. (**b**) Performing ESD with the robotic system using three-dimensional high-definition visualization. A—Consele with the physician controller. B—3-Dimensional high-definition monitor. C—Base and drive of the robot. D—Instrument support assembly.
# Energy Storage in Carbon Fiber-Based Batteries: Trends and Future Perspectives
## 4 Conclusions  
Carbon fiber-based batteries are poised to play a transformative role in the pursuit of more sustainable energy solutions. Their unique ability to merge structural functionality with energy storage not only offers significant weight reduction and efficiency gains but also opens up new avenues for innovation across various industries. By integrating carbon fibers into battery systems, particularly as anodes, many of the critical challenges in battery performance can be addressed more effectively.  
Recent research has primarily focused on three main areas within the development of carbon fiber-based batteries. The most investigated area is the development of anodes for lithium-ion batteries, driven by the high global demand and the significant challenges these anodes present, such as dendrite formation during charging, significant volume changes leading to crack formation, and side reactions with the electrolyte that can degrade battery performance.  
Analysis of the research contributions reveals that China, the United States, Sweden, South Korea, and Australia are leading countries in this field, with China significantly ahead in both publication volume and intra-country collaborations. Despite this fact, international collaboration remains limited, underscoring the need for increased global cooperation. Notably, countries such as Israel, Australia, and Denmark exhibit higher citation impacts, reflecting the high quality and influence of their research.  
Carbon fiber-based batteries are still in a developmental stage, with substantial research needed to realize their full potential. Future research directions should prioritize optimizing fiber-matrix interfaces to enhance electron and ion transport, scaling manufacturing processes for broader application, and developing standardized protocols to ensure consistency and reliability. Increased international collaboration will be vital in accelerating technological progress and addressing existing challenges. As the field matures, carbon fiber-based batteries hold significant promise for advancing sustainable energy systems and contributing to a decarbonized future.  
Conceptualization, A.d.B. and D.V.; methodology, G.L.; formal analysis, A.d.B., D.V., G.L. and P.F.-A.; data curation, G.L.; writing--original draft preparation, A.d.B., G.L. and P.F.-A.; writing--review and editing, A.d.B., D.V., G.L. and P.F.-A.; supervision, D.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 15: Key trends and future perspectives in energy storage technologies based on carbon fi-ber-based batteries.  
Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors. The authors declare no conflicts of interest.
# Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost
## 3 Dataset Creation
### Finite Element Modeling  
When analyzing the mechanical properties of the steel deck pavement system using the finite element method, a local model is typically used as the computational model. Therefore, this study uses a local model of the steel deck pavement system for calculation and analysis. The bridge under consideration is Highway Mainline Bridge No. 1, which is 2.8 m high and 16.31 m wide. The standard cross-section of a steel box girder is shown in Figure 2. The standard thickness of the steel box girder top plate is 16 mm, while that of the bottom plate is 14 mm. The top plate stiffening ribs are U-shaped stiffening ribs. The whole bridge model is simplified to obtain the local finite element model, which has a size of 4200 mm \(\times\) 9000 mm and includes seven U-shaped stiffening ribs and four cross-sectional plates. The steel structure, such as the steel bridge deck, U-shaped stiffening ribs, and cross-partitions, are in shell units, while the pavement layer is in solid units. Table 1 shows the parameters of the local finite element model of the steel deck pavement system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Parameters** & **Values** & **Model Parameters** & **Values** \\ \hline Thickness of upper layer of pavement & 30 mm & U-shape stiffening rib & 600 mm \\  & & spacing & \\ Thickness of lower layer of pavement & 30 mm & Height of U-shaped & 280 mm \\  & & stiffening ribs & \\ Modulus of elasticity of upper & & Width of upper opening of & 300 mm \\  & & U-shaped stiffening rib & 170 mm \\  & & Thickness of U-shaped & 8 mm \\  & & stiffening ribs & \\ Poisson’s ratio of lower & & Spacing of horizontal spacer & 3000 mm \\  & & Thickness of steel bridge panel & 16 mm \\ Modulus of elasticity of steel bridge panel & 206,000 MPa & Height of horizontal spacer & 12 mm \\  & & Height of horizontal spacer & 1000 mm \\ Poisson’s ratio of steel bridge panel & 0.3 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Finite element model parameters.  
Figure 2: The standard cross-section of a steel box girder.  
According to the Specification for the Design of Highway Steel Bridges (JTGD64-2015), the load type for a single-axle two-wheel set is selected as 140 KN, and the two-wheel tire load weighs 70 KN [28]. Assuming that the vehicle load is uniformly distributed on the contact surface between the tires and the pavement, the contact pressure is related to the contact area. The contact surface is simplified to a rectangle, and the load equivalent action form is shown in Figure 3. The boundary conditions of the finite element model are as follows: the horizontal displacement of the steel bridge panel and pavement is restricted, while the vertical displacement is not restricted. The bottom of the cross-partition is solidified. The number of elements is 20,624. The number of elements nodes is 30,142. The finite element model is shown in Figure 4.
# Mixer U-Net: An Improved Automatic Road Extraction from UAV Imagery
## 2 Preliminaries
### Building Blocks
#### 2.1.4 ConvMixer Inception Block  
The Inception module [11] is one of the ground-breaking architectures in the deep learning realm. Prior studies built deeper networks by stacking convolutional layers on top of each other to achieve superior performance. However, the larger and deeper the model becomes, the more prone it is to overfitting and vanishing gradients [39]. On top of that, the increase in model size results in an increase in computational cost as well. The inception network was designed to overcome these obstacles. Another important aspect of this architecture is that it allows the model to learn the input image at different scales simultaneously. Figure 3a illustrates the naive inception module. The input (base) proceeds through \(1\times 1\), \(3\times 3\), \(5\times 5\) convolutions, and \(3\times 3\) max-pooling independently, and the outputs are concatenated to be used as an input to the next layer.  
Furthermore, in their updated _inception-v2_ and _inception-v3_ versions [40], the authors incorporated several upgrades to the inception module, which resulted in a boost in performance and a remarkable reduction in model size. Specifically, one of the important changes was to factorize \(5\times 5\) convolution operation into two \(3\times 3\) convolutions since two stacked \(3\times 3\) convolutions produce the same effect of a single \(5\times 5\) convolution for a reduced cost of computation. We leveraged this idea in designing our architectures. However, instead of using standard convolutions, we adopt ConvMixer layers (see Figure 3b) with \(3\times 3\) filter size to learn image features. In addition, we replaced the \(3\times 3\) max-pooling operation with \(7\times 7\) standard convolution. Following [40], \(5\times 5\) and \(7\times 7\) convolutions are represented by a series of two and three ConvMixer layers accordingly. In the original implementation, the ratio of \(3\times 3\), \(5\times 5\) convolutions, and \(3\times 3\) max-pooling layer within a module increases as it becomes deeper. However, for the sake of simplicity, we keep it equal for all filter sizes across the entire model. The method for building the network architectures of the proposed models is explained in detail in the next section.  
Figure 2: The architecture of the ConvMixer layer (CML) that substitutes standard \(3\times 3\) convolutional layers in the proposed ConvMixer inception block. Here, \(\bigoplus\) stands for the element-wise addition of the input and the output of the Depthwise convolution followed by the ReLU activation function and Batch normalization.
## 8 Implementation and Discussion
### Conceptual Efficiency of the BFS System  
The BFS framework aims to improve efficiency in financial reporting and liquidity management through its architecture. However, it is important to note that as a conceptual framework, specific efficiency metrics such as runtime, memory consumption, or energy usage cannot be quantified without full implementation and testing. Instead, the efficiency of the BFS system can be discussed in terms of process streamlining and data accessibility. For instance, the dual-blockchain approach potentially improves efficiency in data processing by separating public and private information flows, reducing the computational overhead typically associated with processing all data on a single blockchain. The real-time validation capability of the BFS system could enhance transactional efficiency by reducing the time and resources needed for reconciliation and auditing processes. Additionally, the automated smart contract functionality may increase operational efficiency by reducing manual interventions and the associated risk of errors. However, it is fundamental to emphasise that these efficiency gains are theoretical at this stage and would require empirical validation in future research. The true efficiency of the BFS system across various metrics can only be determined through rigorous testing and comparison with existing systems once a full implementation has been developed.
## 5 Conclusions  
In this investigation, three RF models optimized by ALO, WVO, and GOA were developed to predict _GS_. To avoid data redundancy and improve the scientific rigor of the data, the Pearson correlation analysis was used to exclude feature parameters with strong correlation prior to database construction. The database contains seven input parameters, namely \(H\), \(AR\), \(EP\), \(Sm\), \(MC\), \(E\), and \(GP\).  
After completing model construction, the ALO-RF, WVO-RF, and GOA-RF models were evaluated using the evaluation indices MAE, MAPE, R\({}^{2}\), and RMSE. The GOA-RF (Pop = 10) model exhibited the best performance in the overall _GS_ prediction, with MAE (Train set: 2.8224; Test set: 2.3507), RMSE (Train set: 4.93; Test set: 3.1576), R\({}^{2}\) (Train set: 0.9487; Test set: 0.9282), and MAPE (Train set: 40.5629; Test set: 38.5637). Finally, the importance of each input parameter is calculated using the best prediction model (GOA-RF), and the results indicated that \(MC\), \(AR\), \(Sm\), and \(GP\) play a crucial role in the _GS_ prediction.  
In future research, more geological, design, and operational parameters should be taken into account to enrich the database and make the prediction of ground settlement more accurate and efficient, therefore providing a more solid scientific foundation for the prevention and control of disasters related to ground settlement. In addition, the proposed GOA-RF model can inspire the development of prediction models in other disciplines.  
Figure 10: Importance distribution of each parameter of the optimal model.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **R\({}^{2}\)** \\ \hline Training set & \\ \hline GOA-RF(Pop = 10) & 0.9487 \\ RF & 0.7912 \\ MARS Methodology [61] & 0.906 \\ \hline Test set & \\ \hline GOA-RF(Pop = 10) & 0.9282 \\ RF & 0.7195 \\ MARS Methodology [61] & 0.721 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the prediction effect of GOA-RF (Pop = 10) and previous studies.  
**Author Contributions:** Conceptualization, J.Z.; Investigation, P.Y., C.L. and K.P.; Methodology, P.Y. and J.Z.; Project administration, W.W. and J.Z.; Software, P.Y. and C.L.; Supervision, K.P. and J.Z.; Visualization, P.Y., W.Y., W.W. and Y.Q.; Writing--original draft, P.Y.; Writing--review & editing, J.Z., W.W., W.Y., C.L. and Y.Q. All authors have read and agreed to the published version of the manuscript.  
This research is partially supported by the National Natural Science Foundation Project of China (42177164), the Distinguished Youth Science Foundation of Hunan Province of China (2022)J10073).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The authors do not have permission to share data.  
**Acknowledgments:** This research was funded by the National Science Foundation of China (42177164), and the Distinguished Youth Science Foundation of Hunan Province of China (2022)J10073).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Adaptive Pitch Control of Variable-Pitch PMSG Based Wind Turbine
## 3 Perturbation Observer-Based Nonlinear Adaptive Controller Design
### NAC Design of WT
#### 3.1.2 Definition of Perturbation and State  
For this subsystem, a perturbation term including all subsystem uncertainties, nonlinearities and interactions among subsystems is defined.  
Define perturbation term \(\Psi_{1}(x)\) as:  
\[S_{1}:\ \ \ \left\{\begin{array}{rcl}\Psi_{1}(x)&=&F_{1}(x)+(B1(x)-B1(0))u_{1} \\ B1(0)&=&\dfrac{AE\beta}{\tau_{\beta}}[-0.088e^{-12.5\tau}-\dfrac{0.08V^{2}}{F }+\dfrac{0.105\beta^{2}}{(1+\beta^{3})^{2}}]\end{array}\right. \tag{17}\]  
where \(B1(0)\) is nominal value of \(B1(x)\).  
Defining the state vectors as \(z_{11}=y_{1},z_{12}=y_{1}^{(1)},z_{13}=\Psi_{1}\), and control variable as \(u_{1}=\beta_{\mathrm{r}}\). The dynamic equation of the subsystem \(S_{1}\) becomes as  
\[S_{1}:\ \ \left\{\begin{array}{rcl}z_{11}&=&y_{1}\\ \dot{z}_{11}&=&z_{12}\\ \dot{z}_{12}&=&\Psi_{1}(x)+B1(0)u_{1}\\ \dot{z}_{13}&=&\Psi_{1}(x)\end{array}\right. \tag{18}\]  
For subsystem \(S_{1}\), several types of perturbation observers, e.g., linear Luenberger observer, sliding mode observer and high-gain observer, have been proposed [19; 29; 36]. High-gain observers proposed in [29] are used to estimate states and perturbations in this paper.
## 6 Results and Discussions
### Case 2: Non-Cascaded Non-Pumped STHTS Considering Transmission Losses [25]

Table 6: Comparison of some performance parameters between APSO and the improved APSO on non-cascaded non-pumped STHTS problem. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Interval** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline Demand MW & 1200 & 1500 & 1100 & 1800 & 950 & 1300 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Load demand for each scheduling interval (each interval is 12 hours, making a total scheduling period of 3 days). slightly higher average time to converge to the solution as compared to the simple APSO algorithm. However, the difference in computation time was not very significant. Therefore, if some more tuning parameters were properly adjusted, both algorithms would have the capability to find robust approximations to global optimum solutions with low standard deviations, for the non-cascaded non-pumped STHTS problem while considering transmission line losses, improved APSO having more ability to find a better approximation to global minima. Table 8 gives the best cost result of improved APSO's performance. The convergence characteristics of the simple APSO and improved APSO are given in Figures 6 and 7, respectively, on the non-cascaded non-pumped STHTS problem considering transmission losses. These figures are taken for the successful trials for both the algorithms. Figure 8 presents the convergence characteristics of the improved APSO algorithm on the non-cascaded non-pumped STHTS problem for five different instances. The algorithm has shown good performance in reaching good approximates of the global optimum solution for four instances, whereas the algorithm could not reach a good solution for one instance. The number of particles was taken to be equal to 200 for both implementations. The convergence characteristics of both the algorithms were quite similar and showed the general nature of PSO variants. The algorithms, as their names suggest, are accelerated in nature, i.e., they are fast at finding good approximates of global optima.