esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Progress of Blockchain Consensus Algorithm:
## 3 Blockchain Consensus Algorithm
### Non-Byzantine Fault-Tolerant Consensus Algorithm
#### 3.2.1 VR Algorithm  
The Viewstamped Replication algorithm is called the VR algorithm, which is a non-Byzantine fault-tolerant algorithm dedicated to solving the consistency problem in 1988. The working principle of the VR algorithm is to perform log replication operations between each node and to ensure the consistency between nodes in the blockchain network by replicating the state. The VR algorithm can tolerate no more than \(f\) nodes failing at the same time in a network with a total of \(2\,f+1\) nodes. During the working process of the VR algorithm, one node will be selected as primary, and the other nodes will be used as backup. The most important thing in the work process is to let all nodes execute commands in the same order; the primary node determines the order of commands, and other backup nodes accept the order determined by the primary node. When the primary node fails, the VR algorithm will perform a process called view change, which selects a new primary node. The election of the new primary node requires voting. At least \(f+1\) nodes need to communicate and vote to determine the new primary node. The new primary node will perform the new consensus process and replace the old primary node. The VR algorithm stipulates that a node that is down or has an error in the network will be set to the recovering state after restarting. In this state, it will not participate in any voting. The node will generate a Recovery request with a unique number and send it to all working nodes in the network. All nodes can respond to the request. The primary node replies to the error node with the content of the replicated node. The requesting node needs to receive at least\(f+1\) responses, receive the information of the primary node, and then update its own log information to return to the normal state.  
The VR algorithm comes with a main election phase, and it requires that only one leader exists in each consensus process, and the leader is responsible for operations such as sequencing. This greatly simplifies the difficulty of algorithm design and implementation, and it is an early consensus algorithm with relatively simple implementation.
###### Abstract  
High-resolution remote-sensing images can be used in human activity analysis and criminal activity monitoring, especially in sparsely populated zones. In this paper, we explore the applicability of China's Gaofen satellite images in the land cover classification of Xinjiang, China. First of all, the features of spectral reflectance and a normalized radar cross section (NRCS) for different types of land covers were analyzed. Moreover, the seasonal variation of the NRCS in SAR (Synthetic Aperture Radar) images for the study area, Dunkuotam Village of Yuli County, China, was demonstrated by the GEE (Google Earth Engine) platform accordingly. Finally, the CART (classification and regression trees) algorithm of a DT (decision tree) was applied to investigate the classification of land cover in the western area of China when both optical and SAR images were employed. An overall classification accuracy of 83.15% with a kappa coefficient of 0.803 was observed by using GF-2/GF-3 images (2017-2021) in the study area. The DT-based classification procedure proposed in this investigation proved that Gaofen series remote-sensing images can be engaged to effectively promote the routine workflow of the administrative department.  
land cover; classification; Gaofen satellite; Google Earth Engine; CART; decision tree; Xinjiang 20231525315233  
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Citation [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Land Cover Classification by Gaofen Satellite Images Based on CART Algorithm in Yuli County, Xinjiang, China.  
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## 6 Experimental Environment Deployment
### ROS Connection to WEB  
Load the ROSlib library in the WEB. ROSlib is a JavaScript client library that enables communication with the ROS in the browser. By using ROSlib, a new 'ROS' object can be created, which will connect to the server running the ROS via the WebSocket protocol:  
var ros = new ROSLIB.Ros((url: 'ws://<your-ros-bridge-server><port>'));  
Enter the IP address of the ROS to connect to the robot already in communication with the ROS. Next, create a new 'Topic' object for the robot's status information. It subscribes to the corresponding topic in the ROS as a listener to receive real-time data from the robot, as shown in Figure 11.  
Figure 10: Overall performance and motion diagram of industrial robot.  
var listener = new ROSLIB.Topic({  
ros: ros,  
name: '/robot_joint_states',  
messageType:'sensor_msgs/JointState'  
});  
listener.subscribe(function(message) {  
});  
The ROS uploads the robot's joint positions, speeds, torques, temperatures, status data, target positions, path planning, motion commands, and timestamps for synchronizing robot status and motion trajectory data. This information allows the model in the WEB to simulate the industrial robot's status in real time.  
The collaboration of the entire system relies on these seamless information flows from the robot to the ROS, and then to the WEB application, and the information is finally displayed in 3D form on the user interface through Three.js. This lays the foundation for information flow synchronization for the future industrial metaverse. One of the obstacles encountered during this part of the research is the need to first examine the packets sent by the physical robot to determine which packet contains the arm position parameters and which one contains the movement trajectory of the arm. If the manufacturer's development documentation does not provide this information, it will be necessary to identify it through manual comparison.
## 3 Framework
### Overall Model Architecture  
As illustrated in Figure 1, CLEAR integrates four synergistic components in a cascaded architecture:  
1. **Relation-aware input constructor**: Processes document paths through dual-stage filtering that dynamically weights sentences based on entity co-occurrence patterns and semantic relevance to potential relations. This generates compressed, relation-focused sequences (Section 3.3).
2. **Contextual encoder**: Transforms filtered inputs into deep semantic representations using pre-trained language models (RoBERTa). This component extracts target and bridge entity embeddings critical for cross-document reasoning (Section 3.4).
3. **Cross-path integration core**: The framework's nexus features our novel Path Distance-Weighted Attention (PDWA) mechanism. This computes pairwise semantic distances between document paths and dynamically adjusts attention weights to prioritize information from semantically aligned paths while suppressing noisy connections (Section 3.5).
4. **Relation prediction module**: Aggregates refined path representations, applies multi-instance pooling, and generates final predictions through adaptive threshold classification (Section 3.6).  
Figure 1: Our CLEAR model structure diagram.  
The architecture follows a unidirectional information flow: (1) Filtered inputs \(\rightarrow\) (2) contextual encoding \(\rightarrow\) (3) cross-path integration \(\rightarrow\) (4) relation prediction. Crucially, the PDWA mechanism (component 3) receives contextualized representations from all paths simultaneously, enabling global reasoning about cross-document relationships. This integrated design addresses both evidence preservation (via relation-aware filtering) and cross-path reasoning (via distance-weighted attention) in a unified framework.
# Extensive Experiments on Opportunistic Routing in Wireless Sensor Networks
## 3 Proposal: ODYSSE
### Distance Computation  
The RSSI is a relevant basis for computing link metric. In fact, it provides a good compromise between path lengths and links quality. It is also an indicator of packet loss [44].  
Consequently, each link is characterized by a metric denoted \"link_metric\" which is defined by Algorithm 1 as follows:  
```
1:if (RSSI < RSSI_THRESHOLD) then
2: link_metric = \(1+\gamma\)
3:else
4: link_metric = 1
5:endif
```  
**Algorithm 1** link_metric computation  
The parameters RSSI_THRESHOLD and \(\gamma\) are tuned experimentally. From this algorithm, we can see that the link_metric is equal to 1 if the link RSSI is higher than a given threshold. Otherwise this link is penalized and its distance is equal to \(1+\gamma\). The objective is to avoid \"long links\", that is links which are of a bad quality (physical distance which is approximated by the RSSI is relatively high).  
From this metric characterizing a given link, a distance vector protocol computes distances from the gateway as follows: The gateway initiates the process to allow each node to estimate its distance denoted (\"gateway_distance\") towards this gateway. This process is based on the broadcast of a message called \"Level\", sent periodically by the gateway. The latter initialises its distance to zero and broadcasts the Level message. Receiving this message from any node \(v\), each node \(u\) performs the following steps:1. The gateway_distance is potentially updated as illustrated in Algorithm 2:
2. The first step is iterated for a predefined period called LEVEL_PERIOD to potentially receive Level messages from neighbors. After this period, in case the node has updated its distance, it generates and broadcasts a new Level message including its distance. Otherwise, the Level message received is not repeated.  
After these steps, we find that nodes are forming a logical tree (i.e., DAG) defining their distances towards the gateway taking into account the RSSI (Received Signal Strength Indicator) values.
## 2 Materials and Methods
### Cloud Server Architecture  
The cloud server features a modular design that integrates four core functions:  
1. Web Server: Responsible for handling HTTP requests from multiple user devices and providing a unified user interface. The system allows users to log in through various platform browsers to view real-time monitoring data, watch underwater video streams, and send remote control commands. Figure 4 shows the system control interface that integrates these comprehensive monitoring and control capabilities.
2. MQTT Service v2.0: This service utilizes the lightweight MQTT communication protocol, which is responsible for bidirectional data transmission. It manages the transmission of motor status control commands for the underwater ROV while also receiving and distributing water quality sensor data from the ROV system. The MQTT protocol is particularly well-suited for mobile communication environments as it can maintain reliable connections even under limited bandwidth and unstable network conditions. This ensures the accurate delivery of control commands and timely updates of sensing data. By leveraging MQTT's publish/subscribe mechanism, the system achieves precise control of the underwater ROV and the synchronized monitoring of environmental parameters.  
Figure 3: Central control room setup at National Taiwan Ocean University. (**a**) Operator monitoring ROV data via multi-screen station. (**b**) Researchers analyzing real-time data collaboratively.  
RISP Service: This service is responsible for real-time underwater video streaming, ensuring the timeliness and stability of image data. The system integrates FFmpeg technology v4.4 for video conversion and compression, optimizing transmission efficiency. It is capable of maintaining image transmission in 4G mobile communication network environments.
* MySQL Database: Provides long-term storage and management functions for sensor data and the operational status of underwater ROV systems.
## 3 Results of SHPB Tests
### Dynamic Elastic Modulus  
The variation in the dynamic modulus of elasticity with strain rate for the water-saturated fissured sandstone and natural fissured sandstone specimens is shown in Figure 9.  
Figure 8: Variation in dynamic strain with strain rate in sandstone specimens.  
As shown in Figure 9, the dynamic modulus of elasticity of the water-saturated fissured sandstone and natural fissured sandstone specimen increases slowly with the increase in strain rate. The dynamic modulus of elasticity of water-saturated fissured sandstone specimens is generally greater than that of natural sandstone specimens, and the difference between the two ranges from 1.1 GPa to 2.9 GPa, with a relatively small change. The dynamic modulus of elasticity is related to the type of material, whereas sandstone is relatively dense, thus the strain rate effect of the modulus of elasticity is not significant at different strain rates. When the dynamic elastic moduli are fitted, they are found to increase linearly with the increase in strain rate. The fitting relationship is expressed in Equation (4), which indicates a reasonable fitting relationship.  
\[\begin{cases}E_{d1}=0.3628\dot{e}+9.0354(R^{2}=0.9934)\\ E_{d2}=0.3051\dot{e}+11.360(R^{2}=0.9843)\end{cases} \tag{4}\]  
where \(E_{d1}\) is the dynamic modulus of elasticity of water-saturated fissured sandstone, GPa; \(E_{d2}\) is the dynamic modulus of elasticity of natural fissured sandstone, GPa; and \(\dot{e}\) is the strain rate, s\({}^{-1}\).
# Time Coding OTDM MIMO System Based on Singular Value Decomposition for 5G Applications
###### Abstract  
For 5G and beyond cellular communication systems, new coding and modulation techniques are suggested to reach the requirements of high data rate and quality of service. In this paper, a new space-time coded orthogonal transform division multiplexing (STC OTDM) technique is proposed for 5G applications. The proposed system is used to enhance the data rate and performance of the orthogonal transform division multiplexing (OTDM) technique. The proposed system is based on using space-time coding (STC) with OTDM to increase the system diversity and consequently the system performance. The OTDM technique is based on transmitting data on orthogonal basis functions obtained from the Singular Value Decomposition (SVD) of the channel impulse response of the desired user. Various modulation techniques like QPSK, 64-QAM, and 256-QAM are investigated using different subcarriers and channel models. The simulation results show that the proposed system achieved a better performance when compared to classical and recent multicarrier techniques. The proposed technique increases the diversity gain resulting in a decrease in the fading effect of the multipath channel and an enhancement in the bit error rate (BER) performance. The proposed technique also provides a secure data transmission to the desired user as his data is sent on the basis functions extracted from his own channel impulse response that cannot be decoded by other users.  
bit error rate (BER); space-time coding (STC); Singular Value Decomposition (SVD); massive MIMO; orthogonal transform division multiplexing (OTDM); Toeplitz matrix +
Footnote †: journal: Applied Sciences
## 6 Case Study
### Discussion  
To demonstrate the feasibility and advantages of the proposed method, the proposed index _HCI_ is compared with the other individual index, and the results of the \(i\)-AHP method are compared with those of the traditional AHP method. Besides, \(i\)-GAM is developed to analyze the heterogeneity of _HCI_. The discussion can be summarized as follows:  
1. The index _HCI_ proposed in this paper is in accordance with the other individual physical and mechanical index (\(K,\rho_{d},E_{bd},E_{0}\) and \(K_{30}\)), and the results of the selected two blocks are comparable. Similar trends are found in the mean value and geostatistical data of each index, which indicates that there is close correlation between these indices. Moreover, results from the \(i\)-AHP method are generally the same as those from the traditional AHP method. In fact, the difference between the two methods is less than 0.3%. The above results show high consistency between the proposed method and the traditional method.
2. By taking both the physical and mechanical indices into account, the real-time hybrid index _HCI_ can reveal the differences between the physical and mechanical indices, and a low individual index does not result in a low hybrid index. Here, we compared the indices in Zone X, which indicate the relationship between the physical and mechanical indices is not deterministic, and it is crucial to comprehensively consider each index. By taking the subjectivity and uncertainty into account, the \(i\)-AHP method can provide more detailed interval information than the traditional AHP method. For the instances of Point A and Point B, decision makers can participate in the process, which will avoid the shortcomings of the traditional AHP method that has an extreme rating based on a precise number.
3. For the compaction quality heterogeneity analysis, semivariogram model parameters \(C\) and \(a\) can reflect the compaction continuity, and smaller \(C\) and larger \(a\) can represent better compaction continuity and less variability. With the improvement of fitting accuracy, results show that the \(C\) and \(a\) of _HCI_ have similar trends with the other individual index.
## 4 Experimental Results
### Limitations of DRST-Net  
Although DRST-Net demonstrates promising results in weld seam recognition, it does encounter some limitations that need to be addressed in future work. One of the main drawbacks of DRST-Net is its high computational cost. Due to the dual-branch architecture, the model requires significant computational resources, especially during training and inference. The combined use of ResNet50 for local feature extraction and Swin Transformer for global semantic modeling leads to increased memory usage and processing time. This may limit the model's practicality for real-time applications in industrial environments where fast processing is essential.  
DRST-Net's performance heavily relies on the availability of high-quality annotated data for training. While the model is capable of learning complex features, the accuracy and robustness of its predictions depend on the quality and diversity of the training data. In real-world industrial settings, obtaining such high-quality annotated data can be challenging and time-consuming, especially for large-scale datasets.  
As with many deep learning models, DRST-Net may overfit when trained on relatively small or imbalanced datasets. Despite using techniques like weight decay and data augmentation, the model might not generalize well to unseen data in such scenarios. This  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **m1 ou (\%)** & **mPre (\%)** & **mRecall (\%)** & **F1\_Score (\%)** \\ \hline CNN branch & 82.64 & 88.90 & 88.44 & 88.67 \\ Transformer branch & 83.71 & 89.13 & 90.28 & 89.70 \\ Double-branch & 84.28 & 89.45 & 92.08 & 90.75 \\ Double-branch+CaFF & 86.17 & 92.32 & 91.04 & 91.68 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation experiments.  
highlights the importance of utilizing larger, more diverse datasets for training to ensure the model's ability to generalize across different environments.  
To address these limitations, several strategies can be explored in future work: First, optimizing the model for computational efficiency; this can include reducing the model's complexity by utilizing lighter architectures or pruning techniques, which would help reduce both the training and inference times. Second, exploring semi-supervised learning methods; by leveraging unlabeled data, semi-supervised learning could reduce the dependency on high-quality annotated data, making the model more adaptable to industrial environments where labeled data are scarce. Third, improving data augmentation; further augmenting the dataset with synthetic data, or improving the diversity of the existing dataset, could help alleviate the overfitting problem and improve the model's generalizability.
## 4 Results and Discussions
From Figure 9, it is observed that both controllers perform likewise against setpoints changes. However, when plant time-varying parameters take place (at time \(t=200\) s), the designed Expert MPC gives a better performance compared to the standard MPC. In order to compare the robustness of the designed controllers, two performance indexes were used: (a) the Integral Absolute Error (IAE), which is an integral index of the output time response, and (b) the control effort (TV), which provides a measure of the smoothness of the control signals [80; 81; 82]. Table 1 summarized the comparative results of the performance indexes of both controllers against the effect of plant time-varying parameters. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Control System** & \(t_{s}\) **(s)** & \(M_{p}\) **(\%)** & \(IAE\) & \(TV\) \\ \hline First test scenario & - & - & - & - \\ Expert MPC & - & - & - & - \\ Permeate flow rate & 125 & 0 & 876.4 & 30.1 \\ Permeate conductivity & 115 & 12.5 & 861.2 & 24.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance indexes of the designed controllers. Figure 8: The control signals of the designed Expert MPC in the first test scenario, where: (**a**) High feed pressure control signal; (**b**) Brine flowrate control signal. From Table 1, it is observed that the lowest IAE with the least control effort is obtained with the designed Expert MPC; therefore, this controller exhibits the best robustness. This means that the Expert MPC makes it more possible to get faster and accurate responses for the stabilization and control of the pilot-scale plant critical variables than the standard MPC. Consequently, the designed Expert MPC will guarantee a better operation of the actual industrial scale RO desalinization plants, as well as a higher conversion rate, with lower energy consumption. Nevertheless, in these plants, there are more disturbances compared with our pilot scale plant; therefore, the Rule Base of the Expert System will need to be extended. \begin{table}
\begin{tabular}{c c c c c} \hline
**Control System** & \(\mathbf{t_{s}}\) **(s)** & \(\mathbf{M_{p}}\) **(\%)** & **IAE** & **TV** \\ \hline Second test scenario & - & - & - & - \\ Expert MPC & - & - & - & - \\ Permeate flow rate & 135 & 0 & 896.1 & 32.1 \\ Permeate conductivity & 120 & 14.1 & 873.3 & 25.8 \\ MPC & - & - & - & - \\ Permeate flow rate & 135 & 0 & 1384.3 & 40.6 \\ Permeate conductivity & 120 & 14.1 & 1192.4 & 33.4 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 10: The control signals of the designed Expert MPC y MPC in the second test scenario, where: (**a**) High feed pressure control signals; (**b**) Brine flowrate control signals. Figure 9: Comparative evaluation of the real-time closed-loop responses of the pilot-scale plant control system with the designed EMPC and model predictive controller (MPC) to simultaneous steps changes at the setpoints of the critical variables, where: (**a**) Permeate flowrate responses; (**b**) Permeate conductivity responses.