esa-sceva/satcom-chunk-collection huggingface dataset:
# Unbiased 3D Semantic Scene Graph Prediction in Point Cloud Using Deep Learning
## 3 Methods
### Network Design
#### 3.2.2 ENA-GNN: Node and Edge Cross-Attention
The initial message is passed through the following feature message encoding [11; 14]:  
\[\left(X_{V_{i}}^{l},X_{V_{j}}^{l}\right)=\left(X_{V}^{l}\odot A_{E}\right), \tag{6}\]  
\[m_{V_{j}}^{l}(v)=LN\left(\varphi_{p}\left(X_{E_{(i,j)}}^{l}\right)+\varphi_{s }\left(X_{V_{j}}^{l}\right)\right), \tag{7}\]  
For the \(E(i,\ j)\), the message-passing process is calculated as follows:  
\[m_{E_{(i,j)}}^{l}(u)=LN\left(\varphi_{s}\left(X_{V_{j}}^{l}\right)+\varphi_{o }\left(X_{V_{j}}^{l}\right)\right), \tag{8}\]  
where \(\varphi_{s}\), \(\varphi_{p}\) and \(\varphi_{o}\) are the three nonlinear transformation MLPs with different dimensions as the subject, predicate and object, respectively. Feature layer normalization (LN) [39] is used to smooth the computational process of message transfer after feature fusion, and \(m^{l}\) is the preliminary encoded message. Figure 5: Edge cross-attention, incorporating edge features. After that, the message of node \(V\) is characterized by \(m_{n}\), which is the node used as the subject and object, respectively:  
\[m_{n}^{l}(v)=\frac{1}{\left|R_{v}^{i}\right|+\left|R_{v}^{j}\right|}\left(\sum_{u \in R_{v}^{i}}\left(m_{V_{i}}^{l}(v)\right)+\sum_{u\in R_{v}^{j}}\left(m_{V_{i} }^{l}(v)\right)\right), \tag{9}\]  
where \(\mid\cdot\mid\) denotes the base (calculated quantity), and the \(R_{v}^{i}\) and \(R_{v}^{j}\) are the sets of connections of node v as the subject and object, respectively. Eventually, a gated recurrent unit (GRU) [37] is used to update the hidden state to retain the initial information. In order to calculate all nodes \(v\in V\) in each layer and all directed edges \(u\in\ E(i,\ j)\) of the potential representations \(X_{V}^{l}\) and \(X_{E_{(i,j)}}^{l}\) of the message-passing process, the message propagation of the nodes and edges is as follows:  
\[X_{V}^{l+1}=GRU_{n}^{l}\ \left(X_{V}^{l}\odot A_{E},\ m_{n}^{l}(v)\right),\]  
\[X_{E_{(i,\ j)}}^{l+1}=GRU_{E}^{l}\left(X_{E_{(i,\ j)}}^{l},m_{E_{(i,\ j)}}^{l} (u)\odot A_{V}\right), \tag{10}\]  
where \(\odot\) denotes the Hadamard product for interacting with the cross-attention of nodes and edges with the obtained information of this layer. \(GRU(\cdot,\cdot)\) denotes a layer of gated recurrent network units for feature message delivery and updates.
This study quantitatively evaluates the effects of different factors--including the type of fusion layer, the number of layers, and the window shift size--on the performance of the PRI-SWT model using multiple classification metrics. Notably, the real and imaginary components typically represent different types of modal information; thus, effectively fusing both modalities enables the model to more accurately distinguish CFRP defect features, resulting in a substantial improvement in detection performance. The results reveal that a single-layer linear fusion provides optimal performance, with all evaluation metrics achieving their highest values. The structure of the paper is as follows: Section 2 outlines the concept and classifications of multi-modal fusion. Section 3 introduces the Support Vector Machine (SVM) model, the Vision Transformer (ViT) model, and the Parallel Real-Imaginary/Swin Transformer (PRI-SWT) model for classifying CFRP defect detection images. Section 4 evaluates the performance of these models in terms of classifying CFRP defects and analyzes the impact of various factors on the classification performance of the PRI-SWT model.
## 4 IoT-Cloud-Based EWS
Field testing can provide valuable insights into the system's performance under actual operating conditions, which may differ from those in a simulated environment. Field testing can also help to identify potential issues with the system, such as sensor malfunction or communication failures [206]. This technique can be time-consuming and resource-intensive, but it provides valuable data on the system's performance and reliability in real-world scenarios [207]. * Data-driven analysis involves analyzing large datasets generated by an EEWS system to identify patterns and trends, which can provide insights into its performance and reliability [208]. Data-driven analysis can be used to identify correlations between sensor data and earthquake characteristics, such as magnitude, duration, and intensity [209]. This technique can also be used to identify anomalies in sensor data, which may indicate issues with the system's performance or reliability [210]. Data-driven analysis can provide valuable insights into the performance and reliability of an EEWS system over long periods of time [211]. By using a combination of these techniques, researchers can gain a more comprehensive understanding of the performance and reliability of IoT-enabled EEWS systems. This can help to identify areas for improvement and ultimately improve the effectiveness of these systems in mitigating the impact of earthquakes. Integrating advanced technologies such as ML algorithms, distributed computing, and edge computing into EEWS systems can improve their accuracy and effectiveness. However, there are several challenges and considerations associated with these technologies. For example, ML algorithms require large amounts of data and computational resources to train and optimize, which may be difficult to obtain in the context of EEWS systems [212; 213]. Distributed computing can improve the scalability and fault tolerance of EEWS systems, but it also introduces additional complexity and overhead in terms of communication and coordination [214; 215; 216; 217]. Edge computing can improve the responsiveness and efficiency of EEWS systems by processing data closer to the source. Still, it also requires careful management of resources and trade-offs between processing power and energy consumption [218; 219]. In addition, the implementation of these advanced technologies can be complex and may require significant expertise and resources. Furthermore, there may be limitations associated with the hardware and software infrastructure of EEWS systems, such as sensor networks and communication protocols, which may need to be upgraded or modified to support these technologies. Therefore, while integrating advanced technologies into EEWS systems has the potential to improve their accuracy and effectiveness, careful consideration of the trade-offs and implementation complexities is necessary to ensure their successful deployment and operation.
## 3 The Proposed Method
### Cable Forces Predictor
#### 4.6.2 Training  
The data (i.e., 972 samples including their optimized cable force and counterweight results) were imported into MATLAB and trained with the Statistics and Machine Learning Toolbox. The RMSE and training time of variable \(d_{1}\) are shown in Table 5. Results show that Gaussian Process Regression should be the most suitable training model for our problem, because the RMSE is the least among the four concerned models. Though the training time of GPR is the longest, it was still acceptable in terms of absolute timespan. Polynomial Regression was inadequate in terms of accuracy to deal with optimum design problems, which further highlights the motivation of this work. Regression Tree was both relatively accurate in prediction results and efficient in training time. The fast training advantage of RT is rooted in its efficient binary tree data hierarchy, but it did not show up much because of the modest number of training data. Support Vector Regression was also inaccurate in predicting the results, demonstrated by its relatively large RMSE.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Boundary** & **Discretization** & **Detail** \\ \hline \(X_{1}\) & 0.20–0.26 & 4 & 0.20, 0.22, 0.24, 0.26 \\ \(X_{2}\) & 12 m–18 m & 3 & 12 m, 15 m, 18 m \\ \(X_{3}\) & 13 m–14 m & 3 & 13 m, 13.5 m, 14 m \\ \(X_{4}\) & 0.65–0.79 & 3 & 0.65, 0.72, 0.79 \\ \(X_{5}\) & 0.15–0.29 & 3 & 0.15, 0.22, 0.29 \\ \(X_{6}\) & 0.5–0.9 & 3 & 0.5, 0.7, 0.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: A total of 972 samples generated from design variables.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Type** & **Load Cases** & **Boundary** \\ \hline Concrete Design Strength of the towers & Strength & LCB1–LCB5 & \(-\)22.4 MPa–1.83 MPa \\ Steel Design Strength of the main girder & Strength & LCB1–LCB5 & \(-\)270 MPa–270 MPa \\ Safety Stress Factor of the cables & Strength & LCB1–LCB5 & \(\geq\)2.5 \\ Vertical Deflection of the main girder & Stiffness & VL1–VL4 & \(\leq\)2.045 m \\ Elastic Buckling Stability Factor & Stability & LCB6–LCB11 & \(\geq\)4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Model** & **RMSE** & **Training Time (s)** \\ \hline PR & 1964.4 & 5.8 \\ RT & 253.6 & **2.9** \\ SVR & 761.7 & 3.4 \\ GPR & **168.7** & 11.6 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Training model comparison.  
The results recorded the Gbest (refer to Appendix A) during the 500 iteration rounds. The values of variables were stable after around 320 iterations. Meanwhile, there were a total of 20 price decreases, as shown in Figure 9. After seven times of decrease, the theoretical material cost nearly stopped decreasing. The results indicate the fast convergence speed of PSO algorithm in optimum design problems.
## 4 Results
### Traffic Management  
The differences in traffic management provided by analyzed VTS system are also observed. Vessels' traffic lanes managed by the VTS Zatoka Gdanska and VTS Lawica Slupska systems are presented in Figure 4. In turn, the location of ports (brown circles)  
\begin{table}
\begin{tabular}{c c c c} \hline
**Technical Infrastructure** & **VTS Zatoka Gdanska** & **VTS Lawica Slupska** & **VTS \  
and selected fragments the of Swinoujscie-Szczecin fairway (green circles) are shown in Figure 5.  
A comparative analysis of the VTS systems taking into account traffic management is presented in Table 5. The features possessed and lacking by the analyzed systems were considered.  
Figure 4: Configuration of vessels’ traffic lanes. (**a**) VTS Zatoka Gdáńska. (**b**) VTS Lawica Shupska [11].  
Figure 5: Schematic location of ports along the Swinoujscie–Szczecin fairway. (**a**) General overview of the fairway, (**b**,**c**) enlarged characteristics of the two fairway sections (marked with green circles on (**a**)) (our own elaboration based on [11]).  
To sum up, it should be mentioned that the VTS Zatoka Gdanska system primarily focuses on managing sea routes and traffic monitoring, while more detailed port operations are handled by the Harbor Master's Office. In turn, VTS Lawica Slupska operates more as a monitoring system, providing the infrastructure necessary for safe vessels navigation without actively managing their movement. The complexity of traffic management within the VTS Swinoujsce-Szczecin system, where operators play an essential role in planning and coordinating activities at every stage of the vessel's traffic in ports, should be highlighted.
# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 4 Proposed Approach
### POI Extraction  
Unlike existing approaches, which heavily rely on clustering and are therefore dependent on their parameters, our method adopts a different strategy. Our method employs a Bayesian network to estimate the most probable POI based on a user's location history. The Bayesian network is capable of modeling and calculating the joint probability that a specific location is a POI, taking into account various significant factors including the day of the week, time of day, category of the place, frequency of visits, and duration of each stay. The Bayesian network, with its ability to integrate a priori knowledge and manage uncertainty, is particularly well-suited for this complex task. It considers how these various factors interact with each other and their combined influence on the probability of a location being a POI. For instance, it can recognize that certain categories of places are more likely to be POIs at specific times of the day or on certain days of the week, as well as that more frequent visits or longer stays may indicate a POI with a higher probability. The approach starts by constructing a Bayesian model structured around the causal and conditional relationships between the variables in question. Then, the network is fed historical location data to learn the conditional probabilities that underpin the model. Once the Bayesiannetwork is trained, it is capable of making real-time inferences to predict new POIs based on incoming observations. Using our method, one is able not only to identify the most visited locations but also to understand the context of these visits, which provides a richer comprehension of user's habits and preferences. This approach is particularly powerful due to its probabilistic nature, which effectively manages uncertainties and variations in user behavior, thereby providing robust and reliable predictions for POIs. In what follows, we recall the basic concepts of a Bayesian network before detailing how we build it.
###### Abstract  
We propose an intelligently structured receiver to achieve high-speed transmissions over optical code-division multiple access (OCDMA) networks. Employing spectral-amplitude coding (SAC) reduces multiuser interference (MUI) in OCDMA, but the network bit-rate is limited by phase-induced intensity noise (PIIN) coming from the incoherency of light sources. To mitigate PIIN, the receiver performs interference estimations and regenerations through consecutive stages. The MUI is removed by subtracting the estimated interference term from the received multiplexed signals. For PIIN variance, it is both dependent on and positively related to user number and bit-rate. Reducing the number of interference users allows the network to support transmissions with a higher speed under a given noise level. The proposed scheme has the advantages of all-optical signal processing and a compact structure. Additionally, the function of noise suppression is rarely studied in the existing MUI elimination schemes, such as serial interference cancellation (SIC) and parallel interference cancellation (PIC). The simulation results show the proposed receiver achieves significant increment in bit-rate than the conventional balanced detector in SAC-OCDMA networks.  
Keywords:optical code-division multiple access (OCDMA); spectral-amplitude coding (SAC); interference cancellation; noise suppression; bit-rate +
Footnote †: journal: Applied Sciences
# Mail Man Ferry: A Novel Routing Protocol in Intermittently Connected Networks
## 3 Related Works
### Routing Protocols  
Epidemic [9] is an opportunistic-based routing method. Messages are flooded in Epidemic until it reaches the message's destination. Message flooding is defined as sending a copy of a message to all nodes encountered. Another suggested routing system that uses an opportunistic strategy is spray and wait [10]. The spray and wait protocol limits the number of given copies of a message to L copies. The choice of L can be determined based on several factors, including the environment size and number of nodes. The main issue with opportunistic forwarding is the resource consumption in multiple copies of each message. So, a prediction-based routing protocol called PRoPHET (Probabilistic Routing Protocol) has been suggested [13]. In PRoPHET, nodes only receive a copy of a message if their chance of sending it to the destination is higher than the current message carrier's chance. The contact history with the destination is used to determine how likely each node will deliver the message. The more frequently a node meets a destination node, the more likely it is to meet the destination node again, making it more likely that the package will be delivered. Increasing the node delivery probability in PRoPHET makes a node a candidate to receive a copy of a message.  
Status [19] is social networking-based routing. Nodes discovered by this protocol send messages based on two factors: They send the message to the found node if it has a status, and the status indicates the node is heading to the location \(\backslash\)point of interest (PoI). Like a shopping mall or park, a PoI has multiple nodes. Second, a nearby node receives a message, and the status simplifies PRoPHET's calculations. Epidemic utilizes less resources, and Epidemic is more prevalent when resources are unlimited.
## 5 Conclusions  
In this paper, we propose environmental perception attention and multi-scale fusion modules for the task of object detection in complex traffic scenarios, and demonstrate the effectiveness of our approach in the experimental part. Environmental perception attention first captures long-range contextual relationships from the input feature map, and then learns weights from channel dependencies to selectively emphasize informative features and suppress less useful features. In the three-scale fusion module, we introduce low-level feature maps to help the model obtain detailed information such as edges and textures, and fully integrate them with other scale features to enhance the detection ability of objects of different scales. Experimental results show that our method can effectively improve the performance of the model on the autonomous driving road traffic dataset. In addition, the ablation experiment results also show that our method has a certain synergistic effect. Although our proposed method has a significant effect in improving model detection accuracy, its computational complexity and resource requirements have also increased, which may pose certain challenges in resource-constrained embedded devices or real-time processing scenarios. Therefore, future research should focus on optimizing the model structure, further reducing computational overhead, or using model compression, pruning, quantization, and other technologies to ensure that the inference speed is maintained efficiently while improving performance. In general, this study provides methods and ideas for improving the object detection accuracy of autonomous driving systems in complex traffic scenarios, and promotes the further application of object detection algorithms in the field of autonomous driving.  
Conceptualization, C.Y. and J.L.; methodology, J.L.; software, J.L. and H.W.; validation, C.Y., J.L. and H.W.; formal analysis, C.Y.; investigation, C.Y. and H.W.; resources, Q.Y.; data curation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, C.Y.; visualization, H.W.; supervision, Q.Y.; project administration, Q.Y.; funding acquisition, Q.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Tianjin Enterprise Science and Technology Commissioner Project, Technology Innovation Guidance Special Fund (Fund) grant number (24 YDTPJC00410).  
Figure 6: Comparison of model detection results before and after using the proposed method.
###### Abstract  
Active noise control (ANC) is a technique applied to eliminate an unwanted sound by superposing a signal of equal amplitude and opposite phase, sometimes defined as an anti-noise signal, computed through an adaptive algorithm. The study described herein aims to evaluate and compare the performance of some of the most popular algorithms based on the least mean squares (LMS) approach applied to a multichannel active noise control system in a small, enclosed space. The comparison is conducted through an experimental evaluation of the ANC algorithms' performance, carried out on a tractor cabin in a hemi-anechoic chamber, generating the unwanted sound field using a dodecahedron sound source placed outside the enclosure, emitting narrowband and broadband signals. The experimental analysis and the comparison with the results obtained in a free field condition have made it possible to show certain practical limitations when implementing the algorithms. The results show that the feed-forward systems allow for greater stability, avoiding the acoustic feedback from the control loudspeakers to the reference microphone when this is outside the cabin, while the feedback system is the slowest configuration to converge, requiring an internal modeling of the reference signal. With random signals, the feed-forward systems concentrate their performance in the range above 500 Hz, while the feedback system becomes ineffective.  
active noise control; multichannel systems; LMS; enclosure
## 3 Examples and Discussion  
In Section 3, the band-gap properties in the infinite LR plate are acquired by the theory in reference [21], and the vibration and radiation responses in the finite LR plate are obtained by the theory in Sections 2.1 and 2.2. Examples of the LR plate with two resonators in each periodic element (TRIEPE LR plate) are focused on, for a low-frequency vibro-acoustic performance. In the following examples, the base plate is made of steel with a material density \(\rho_{p}\ =\ 7800\,\text{kg}/\text{m}^{3}\), [PERSON]'s modulus \(E\ =\ 2.1\times 10^{11}\,\text{Pa}\), [PERSON]'s ratio \(\
u\ =\ 0.3\), and damping loss factor \(\eta_{p}\ =\ 0.001\). The thickness of the plate is set as \(h_{p}\ =\ 0.003\text{m}\) and the lattice constant \(a_{L}\ =\ 0.05\,\text{m}\).