esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper first analyzes the selection criteria for an attacker's position from a theoretical perspective, providing a foundation for rational attackers when choosing between attacking the superior UC or the TSM. This lays the theoretical groundwork for developing defense strategies. From a data perspective, it proposes an attack detection algorithm based on a GAN model, establishing a Bayesian game model with the attacker, UC, and sTSM as participants. The paper examines the conditions for Nash Equilibrium under various strategic combinations. Traffic flow sample data and attack sample data were collected, and a joint simulation of information attacks on road network data was conducted using MATLAB-VISSIM to simulate abnormal situations, which validated the effectiveness of the algorithm. Additionally, a comparison was made between the SVM model and the proposed traffic GAN model. It was found that SVM did not misclassify any abnormal samples as normal, achieving a higher precision than the method proposed in this paper. However, SVM misclassified more normal samples as abnormal, while the recall rate of the proposed algorithm was higher. Ultimately, the F1 score of the proposed method exceeded that of SVM, indicating that the detection performance of the proposed method is superior to that of the SVM algorithm. In the future, we will further explore the practical problems of urban road traffic control and continue to revise the model and database to prepare for application in practical projects.  
Conceptualization, Data curation, Methodology, Writing--review & editing, K.L.; Conceptualization, Investigation, Writing--review & editing, K.P.; Formal analysis, Project administration, Software, W.X.; Investigation, Resources, Visualization, M.L.; Formal analysis, Software, Writing--review & editing, Z.H.; Conceptualization, Methodology, Writing--original draft, L.W. All authors have read and agreed to the published version of the manuscript.  
This work is supported by National Key R&D Program Project, grant number 2022 YFB2601804 and Yuxiu Innovation Project of NCUT (No. 2024 NCUTYXCX218). This research was also supported by North China University of Technology.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Norm** & **GAN/\%** \\ \hline determine Accuracy Rate & 97.6 \\ RMSE & 13.9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation Indicators.
## 3 Results and Discussion
### Surface Roughness
#### 3.1.1 Top Surface  
Looking at Figure 8, laser power had the greatest effect on the top surface roughness with increasing laser power also increasing the roughness due to the increased LED, as explained above. This result agrees with the results of the analysis of variance (ANOVA), which indicate the interaction of laser power and scan speed as the most significant (\(p\)-value: 0.037), followed by laser power (\(p\)-value: 0.044). Generally, it can be said that all three parameters increased surface roughness proportionally.  
In Figure 9, the interactions of the three parameters are displayed. Scan speed and laser power did seem to have a strong interaction effect on surface roughness, since varying them caused large variations in roughness, while hatch spacing did not seem to have a significant effect.
## 4 Future Prospects for China's Weather Routing Services
### Enhancing Weather Routing Support for Intelligent Ships  
The rapid advancement of shipbuilding technologies, coupled with breakthroughs in information systems and AI, has positioned intelligent and autonomous ships as a pivotal direction for the future of maritime transportation. It is important to distinguish between these concepts: \"Maritime Autonomous Surface Ships\" (MASS) are defined by the IMO with different levels of autonomy, while \"intelligent ships\" is a broader term for vessels equipped with advanced data processing, connectivity, and automation systems, which may not be fully autonomous. As a critical enabler of vessel operational performance, weather routing technology requires deep integration with autonomous navigation systems onboard intelligent ships. By interfacing hydrometeorological data with intelligent ships' autonomous control systems, onboard operating platforms can dynamically adjust routes, speeds, and navigation modes in response to real-time environmental conditions, optimizing trajectories to avoid severe weather or marine hazards. In complex meteorological scenarios, weather routing services can further collaborate with AI-driven collision avoidance systems to execute autonomous risk mitigation decisions, significantly enhancing navigational safety and operational efficiency.  
The International Hydrographic Organization (IHO) has developed the S-100 Universal Hydrographic Data Model, which standardizes data formats and exchange protocols for marine information. These standards provide a unified framework for integrating weather routing products into intelligent ship systems, ensuring seamless interoperability and data sharing. To leverage this framework, efforts should prioritize the development of S-100 compliant weather routing systems that offer flexibility and scalability to accommodate diverse vessel types and operational environments [58]. Such systems will deliver precise meteorological insights to support intelligent ships in autonomous navigation, obstacle avoidance, and energy efficiency management, thereby advancing the industry's transition toward fully autonomous and sustainable maritime operations. Key barriers include achieving full interoperability between diverse systems, ensuring robust cybersecurity for interconnected platforms, developing clear regulations for autonomous operations, and guaranteeing the reliability of complex decision-making systems in all conditions.
## 5 Conclusions  
In this paper, two-stage scaling law theory was adopted to carry out dynamic centrifuge tests on two calcareous sand models with different relative densities (M1 and M2) under three different earthquakes by means of stepwise loading. Moreover, dynamic FE analyses were conducted on a medium dense site subjected to strong earthquakes. The dynamic responses of the horizontal saturated free field during earthquakes were analyzed with a focus on the characteristics of pore pressure development. The liquefaction potential of the engineering site is comprehensively evaluated, and three major conclusions are drawn as follows:  
Figure 10: Time histories of EXPPR at different observation points: (**a**) depth = 5 m; (**b**) depth = 10 m; (**c**) depth = 15 m.  
1. The maximum value of EXPPR for the saturated calcareous sand ground will decrease with the increase of soil's depth, while it will increase with the increase of the input ground motion's peak acceleration and duration. The improvement of the relative density of can effectively strengthen the liquefaction resistance of the ground, reduce the peak pore pressure, and accelerate the dissipation process.
2. When subjected to earthquakes with 0.3 g peak acceleration, the saturated calcareous sand ground model witnessed the peak values of EXPPR reaching up to around 1.0 at the depth of 5-10 m, indicating a high potential of liquefaction under the same circumstances.
3. Based on [PERSON]'s dynamic consolidation theory, the dynamic FE analysis using the PDMY constitutive model can well simulate the seismic response of the EXPP at the initial stage of pore pressure accumulation. However, generally, the pore pressure could build up and dissipate relatively faster in the numerical results.  
Conceptualization, W.L. and Y.Y.; methodology, P.C. and Y.Y.; software, X.L. and J.D.; validation, J.D. and Y.Y.; writing--original draft preparation, J.D.; writing--review and editing, Y.Y. and C.L.; visualization, J.D.; supervision, Y.Y. and C.L.; project administration, P.C. and X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (52061135112 and 51908425) and the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (311020009).  
Not applicable.  
The authors acknowledge the support from the Key Laboratory of Environment and Safety Technology of Transportation Infrastructure Engineering, CCCC.  
The authors declare no conflict of interest.
## Appendix A RL Agents  
### DQN  
The DQN [24] extends classical Q-learning by employing deep neural networks to approximate the Q-value function. The DQN estimates the action-value function \(Q(s,a)\), which represents the expected cumulative future reward for taking action \(a\) in state \(s\), and follows the [PERSON] [48] equation to update Q-values iteratively. A key feature of the DQN is its use of experience replay and target networks to stabilize training by breaking the correlation between samples and reducing Q-value variance.  
The Q-learning update in the DQN is defined by  
\[y_{t}^{DQN}=R_{t+1}+\gamma\max_{a}Q(S_{t+1},a;\theta^{-}) \tag{1}\]  
where \(\theta^{-}\) represents the parameters of the target network, which are periodically updated from the main Q-network.  
While the DQN can achieve human-level performance in many environments, especially in discrete action spaces, it suffers from overestimation bias due to the maximization step over noisy Q-value estimates. This overestimation can lead to suboptimal policies, especially in environments with high variance or noise in the reward signals.  
Double Q-learning [38], originally proposed to address the overestimation bias in classical Q-learning, was later adapted to the DQN. In the Double DQN, the key idea is to decouple the action selection from the action evaluation to reduce the overestimation bias in the Q-values. Instead of using the same Q-values for both selecting and evaluating actions, the Double DQN selects actions using the main Q-network and evaluates them using the target Q-network. This modification reduces overestimation and improves stability. The update rule in the Double DQN is modified as follows:  
\[y_{t}^{DoubleDQN}=R_{t+1}+\gamma Q(S_{t+1},\arg\max_{a}Q(S_{t+1},a;\theta), \theta^{-}) \tag{2}\]  
Here, the action is selected based on the online network parameters \(\theta\), while the evaluation is performed using the target network \(\theta^{-}\).  
In comparison to the vanilla DQN, the Double DQN reduces overestimation substantially, improving policy evaluation and performance, especially in noisy environments.  
The Dueling DQN [49] architecture further improves upon the standard DQN by separating the estimation of the state-value function \(V(s)\) and the advantage function \(A(s,a)\), which measures the relative benefit of an action in a given state compared to the average action. The key advantage of this separation is that the value function can be learned more effectively, even in states where the choice of action has little impact on the reward.  
The Q-value function is decomposed as  
\[Q(s,a;\theta,\alpha,\beta)=V(s;\theta,\beta)+\left(A(s,a;\theta,\alpha)-\frac {1}{|\mathcal{A}|}\sum_{a^{\prime}}A(s,a^{\prime};\theta,\alpha)\right) \tag{3}\]  
This decomposition allows the agent to learn state values without needing to evaluate the impact of every possible action. This is particularly useful in environments where many actions lead to similar outcomes. The Dueling DQN often outperforms the standard DQN and Double DQN in terms of both learning speed and final performance.
# Phase Balancing and Reactive Power Support Services for Microgrids
## 1 Introduction
The inverters should be able to intentionally deliver reactive power and asymmetrical currents while injecting the active power of the PV or ESS into the grid. The existing solutions in the literature prioritize reactive power support and in case of available capacity by the inverter then it is utilized for phase balancing [24]. Therefore, the compensation of asymmetric currents is limited according to the provision of reactive power support. In this paper a novel sharing algorithm is developed at the local controller for sharing the Q support and PB according to the loading conditions at the MG's PCC. Therefore, the available capacity of each inverter is distributed for each ancillary service according to the loading conditions. The proposed MGCC requires ICT technology to be able to receive measurements from a smart meter installed at the MG's PCC and send the estimated set-points for compensating MG's asymmetries and reactive power to the PV/ESS inverters. Finally, the proposed MGCC and the advanced inverter sharing strategy can bring significant benefits for the distribution system operator regarding its power quality, efficiency and maximization of its capacity through fair provision of Q-PB compensation. The rest of the paper is organized as follows. The MG architecture is demonstrated in Section 2. Section 3 presents the proposed sharing algorithm and experimental results in a laboratory scale inverter. Section 4 demonstrates the centralized controller and the effectiveness of the solution through a real-time hardware in the loop investigation. The paper concludes in Section 5.
# A Hybrid Model for Freight Train Air Brake Condition Monitoring
## 2 Materials and Methods
### Main Components Implemented in the Model
#### 2.3.2 Distributor
P\({}_{\rm mbp}\) is the pressure at the main brake pipe, p\({}_{\rm ar}\) is the pressure at the auxiliary reservoir, p\({}_{\rm d}\) is the pressure at the distributor outlet pipe, k\({}_{\rm s}\) is the equivalent spring term derived by the equivalent connection in a series of piston and valve springs, A\({}_{1}\) is the piston surface on which the control pressure and the main brake pipe pressure act, A\({}_{3}\) is the valve surface area on which the auxiliary reservoir pressure acts on, A\({}_{2}\) is the piston surface on which the distributor pressure acts, \(\Delta\)x is the spring elongation represented by the piston-valve assembly stroke, F\({}_{\rm pl}\) is the spring pre-load and F\({}_{\rm f}\) is the friction force coming from the mechanical system interconnection. Starting from Equation (1), it is possible to derive a simplified model of the distributor based on the following considerations. Given the piston masses in the order of 10\({}^{1}\) kg, to have the piston inertial contribution comparable with the pressure forces (in the order of 10\({}^{3}\) N), the acceleration of the piston-valve assembly during braking should be proportional to 10\({}^{2}\) m/s\({}^{2}\). This is not the case for air brake systems for freight train applications, where inertial terms are usually an order of magnitude lower than pressure forces terms. The inertial contribution can, therefore, be neglected. The spring force coming from the piston-valve assembly movement can be considered a constant force, like the spring pre-loading force, due to the limited stroke variation. In this way, since these two terms do not introduce a differential term during the pressure build-up, they can only affect the distributor activation and saturation pressure. Considering the distributor as an ideal mechanical component (i.e., sliding surface well lubricated), the friction forces can be set to zero. Moreover, since the auxiliary reservoir pressure acts on a surface much smaller than the others, its force contribution can be neglected. With these assumptions, it is possible to derive a proportionality law between the distributor pressure and the main brake pipe pressure, as reported in Equation (2). \[\rm P_{d}=\left(P_{\rm cr}-P_{mbp}\right)\frac{A_{1}}{A_{2}} \tag{2}\]  
Considering a 1:1 ratio between the distributor pressure area and the main brake pipe pressure area, it is possible to model the distributor pressure as the depression of the main brake pipe pressure from the control reservoir pressure. This assumption is based on the reasonable consideration that the two areas A\({}_{1}\) and A\({}_{2}\) are comparable. In Figure 8, a comparison between the main brake pipe pressure coming from the experimental monitoring system and the modelled distributor pressure is shown. Figure 8: Comparison between the main brake pipe pressure and the distributor pressure.
## 4 Experiments
### Dynamic Obstacle Avoidance  
This experiment was conducted to corroborate the effectiveness of our novel active collision avoidance method in bypassing dynamic obstacles to reach the target position. During the experiment, we positioned the obstacle to move in the path of the robotic arm, while the arm was required to traverse from the starting position to the same final goal position as the previous experiment, avoiding any collision on its path. The initial positions of the two obstacles are in the upper left side of the beam, moving towards the lower right side at different speeds, and the obstacle information is shown in Table 4. As demonstrated in Figure 12, the robotic arm initially attempts to pass underneath the obstacles. When the obstacles move down and block the path, it changes its direction and moves upwards, passing through the gap between the two obstacles. In the end, the robotic arm successfully navigates around the dynamic obstacles and reaches its target position smoothly.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Spherical Obstacle** & **Information** \\ \hline Coordinates of obstacle 1 & [6002 mm, 3106 mm, 1089 mm] \\ Coordinates of obstacle 2 & [6292 mm, 2556 mm, 2389 mm] \\ Velocity of obstacles 1 & [0, \(-\)10, \(-\)30] (mm/iteration) \\ Velocity of obstacles 2 & [0, \(-\)40, \(-\)10] (mm/iteration) \\ Obstacle radius & 300 mm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation information for dynamic obstacles.  
Figure 11: The closest distance between the robot and the obstacles. The red dotted line is the distance limit.  
Figure 12: Robotic arm motion process using our guidance APF method. The green arrow represents the direction of the temporary target pose given by the action model. From 1 to 6 represents the process of the robotic arm from the beginning to the end of the simulation.  
is uncertainty in the results of the movements generated by the neural network, which can lead to less smooth movements of the robotic arm. However, this does not detract from the possible application of the above methods to unmanned intelligent robotic arms. Applications under other robotic arms and avoidance of shaped obstacles have not been considered yet, and this will be our next research direction.  
Q.X.: conceptualization, writing--original draft, data curation, formal analysis, validation, visualization. T.Z.: writing--review and editing, conceptualization, data curation, formal analysis, validation. K.Z.: writing--review and editing, conceptualization, data curation, Y.L.: writing--review and editing. W.J.: writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Pneumonia Detection on Chest X-ray Images Using Ensemble of Deep Convolutional Neural Networks
## 4 Experimental Study
### Data Set Description  
Pneumonia diseases have been verified for our experimental assessment, including the diagnosis of pneumonia from chest X-ray images. Figure 2, for instance, displays chest X-ray images from the selected data set. This data set is split into two sets: normal and pneumonia. The data set was provided by [PERSON] and [PERSON] [43] and based on a chest X-ray scan images from pediatric patients from one to five years of age at the Guangzhou Women and Children's Medical Center. In the chest X-ray images (Pneumonia) data set, which is publicly available at [[https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia)]([https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia)) (accessed on 4 January 2022), there are in total 5856 normal and pneumonia chest X-ray images. To provide a fair comparison between our proposed method and the different methods, the training set, the validation set, and the test set were previously divided. The chest X-ray database was divided into two classes (normal and pneumonia). This data set contains two subsets for each class. The training subset consists of 1341 normal patients and 3875 pneumonia patients. Moreover, it contains 234 patients as normal and 390 pneumonia patients for the test subset. These data also consist of 16 validation data images, including eight pneumonia patients and eight normal patients. Samples of normal and pneumonia chest X-ray images can be seen in Figure 2.  
Figure 1: The structure of the proposed ensemble learning method.  
The performance of the proposed classification method was evaluated based on precision, recall, F1-score, and accuracy, as introduced in Equations (2)-(5), respectively. These metrics are the most popular in medical image classification [26, 49]. The precision is measured as the percentage of exact data that conform to specified characteristics. The recall is measured as the percentage of actual statistics to quantities that should have been explicitly anticipated. The F1-score is an indication of imbalanced data between Recall and Precision. The amount produced across all expected amounts is known as accuracy.
## 5 Field Test of Vegetable Greenhouse Monitoring System  
Since 2008, an experimental wireless-sensor-network-based monitoring system for vegetable greenhouses has been running in the Ansai Hou Trench Gate Village demostration area. Since 2009, the long-term field-test has been carried out in the Olympic Games Science and Technology Park of Chinese Academy of Sciences, and satisfactory experimental data have been observed.  
With regards to the aspect of energy consumption, two No. 5 Nanfu shaped ring cells can work for more than six months under a 10-minute sampling frequency condition. As for the transmission distance and the ability to cross barriers, even if the base station is placed on 10-story towers, the communication distance can reach 2,000 m under barrier-free circumstances, and if there is a three-reinforced-concrete-tower barrier, the longest stable transmission distance can approach 852 m.  
Regardless of all kinds of human factors, the system can work normally for six consecutive months without maintenance. The bit error rate is found at \(\pm\)2% upon the analysis of the returned historical data.  
In the installation and operation procedure the following features were observed:  
(1) The quality of the 2.4G network is significantly affected by environmental factors, which is attributed to the reflection and absorption of electromagnetic waves by the surrounding environment. The greenhouses in the Ansai Hou Trench Gate Village demonstration area are built of the local loess; if the node and the antenna are both placed inside the shed, the network cannot be connected. It is found during the experiment that the loess could effectively absorb the electromagnetic wave. However, the experiments in the Olympic Games Science and Technology Park of the Chinese Academy indicate that two nodes in the network can still be connected even at a distance of 1.2 km with four buildings between them.  
(2) The 2.4G wireless network and Wi-Fi network will interfere with each other; because the Zigbee-based 2.4G wireless network and Wi-Fi network both operate within the ISM frequency band, interference is likely to occur.  
(3) The height of the node's antenna that connects to the 2.4G transmitter module cannot be higher than that of the base station antenna which is connected to the 2.4G receiver module. The whole system adopts omni-directional antenna. In the test, if the receiver's (base station) antenna height is lower than that of the transmitter (node), the signal will be poor and the transmission distance will be constrained [10].