esa-sceva/satcom-chunk-collection huggingface dataset:
# A Blockchain Solution for the Internet of Vehicles with Better Filtering and Adaptive Capabilities
## 1 Introduction
Issues such as low transaction throughput and high latency, stemming from traditional consensus mechanisms, exacerbate performance constraints in resource-constrained vehicular environments. Additionally, the continuous growth of blockchain ledgers, combined with the limited bandwidth of in-vehicle communication networks, can result in network congestion, reduced throughput, and increased storage costs. Furthermore, most blockchain systems are designed for relatively static networks and struggle to adapt to the rapidly changing topologies of vehicular networks. These challenges collectively hinder the performance, scalability, and adaptability of blockchain technology in such dynamic environments. Several existing studies have proposed various blockchain-based security mechanisms for IoV, introducing lightweight and reputation-based schemes, as well as lightweight consensus protocols built on distributed blockchains. These approaches utilize reputation-proof mechanisms to enhance trust in data sharing among vehicles and partially mitigate these limitations. In practical applications, it is feasible to implement a collaborative and consistent database within RSUs. This setup not only ensures effective trust value management within IoV but also minimizes the risks posed by external attacks on the overall system. The AE-PBFT algorithm proposed in this paper is specifically designed for the IoV environment and effectively manages trust-related tasks. It enables vehicles to quickly and in real-time update their trust values, thereby enhancing the role differentiation among nodes and improving both consensus efficiency and overall system performance. This paper makes the following contributions:  
* We propose AE-PBFT, an algorithm suitable for IoV, which includes a new credit model and a dynamic consensus group mechanism. The algorithm outperforms previous algorithms in node screening efficiency and fault tolerance;
* We propose a model of IoV system based on AE-PBFT, which has higher reliability and security than the previous model;
* Through the analysis of the results obtained from simulation experiments, it can be seen that the proposed algorithm outperforms previous consensus algorithms in terms of consensus delay, throughput, communication overhead, and fault tolerance, and is more suitable for the operating environment of the IoV system. The rest of this paper is organized as follows: Section 2 introduces work related to IoV authentication technology, trust management models, and blockchain consensus algorithms. Section 3 introduces the Practical Byzantine Fault Tolerance consensus algorithm(PBFT). Section 4 introduces the improved AE-PBFT algorithm suitable for IoV. Section 5 introduces the IoV model based on the AE-PBFT algorithm. Section 6 relates our theoretical analysis. Section 7 analyzes our experimental results. Section 8 explores the potential applications of the proposed algorithm in various other fields. Section 9 summarizes this paper.
## 4 Classification of Auto-Configuration Protocols
### Stateless Protocols
#### 4.2.3 Arod  
In _Address auto-configuration with address Reservation and Optimistic duplicated address Detection_ (AROD) [24], the address reservation is based on the existence of nodes that have an IP address reserved to deliver it to the new nodes that enter. Two types of nodes will exist:  
* Agents type 1 with a reserved IP address, apart from the IP address that has its network interfaces. When a node joins the network, this reserved IP will be assigned to it immediately.
* Agents type 2, which do not have reserved IP addresses. If a node that joins newly asks one of these for an IP address, this node borrows the reserved address of one of its neighbours who is of type 1, and it is assigned to the new one immediately.  
It establishes a mechanism so that the network does not remain without nodes of type 1, whenever an IP address is assigned to a new node, the node that has delivered the address generates two random IP addresses (one for itself and another one for the new node that has joined) and does a DAD process to detect if these addresses are unique. Once the process has been carried out the following possibilities can arise:  
* The IP addresses are unique; therefore, the two nodes turn into node type 1.
* If only one is unique, the one that gave the IP address will be turned into node type 1.
* If none is unique, the two nodes will remain as type 2.  
This protocol considers its process of duplicated address detection as an optimistic DAD process, because it is only carried out once when a node joins and a reserved IP address is assigned to it. It contemplates the possibility of changing the reserved IP address number to the node type 1. If this number increases, the latency of IP address assignment is minor, but on the contrary the overhead increases due to having had to undergo more DAD processes, and _vice versa_. This possibility allows swapping latency _versus_ overhead.
# Augmented EMTCNN: A Fast and Accurate Facial Landmark Detection Network +
## 3 Materials and Methods
### Network Architecture
#### 3.1.3 CoordConv Layer  
In general, the CNN consists of several convolution layers and a pooling layer used to find significant features in the input images. So far, it has exhibited unprecedented performance in object  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Input Size** & **Number of Parameters** \\ \hline VGG-19 [47] & \(224\times 224\) & 143,667,240 \\ \hline Hyper Face [40] & \(227\times 227\) & 29,677,932 \\ \hline ResNet-18 [48] & \(224\times 224\) & 11,689,512 \\ \hline MTCNN [12] & - & 481,336 \\ \hline EMTCNN [2] & - & 6,029,938 \\ \hline Augmented EMTCNN & - & 6,083,186 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the number of parameters.  
Figure 2: Dilated convolution.  
detection and image classification. Nevertheless, this structure has some limitations. For instance, convolution filters can detect the noticeable features in an image, but they do not consider positional relationships among the feature maps. Such relationships can be utilized if they are common to all face images. For instance, the position of the human eye is above the mouth. Even though positional information is significant, the EMTCNN model does not consider it. Such spatial coordinate information can be incorporated into the model by augmenting the CoordConv layer. In the CoordConv layer, two additional channels are added to each feature map as shown in Figure 2(a). Figure 2(b) shows sample Coord channels with height and width equal to 8. As shown in the figure, each channel represents a nominalized coordinate value between -1 and 1 and has the same size as the feature map. One channel represents coordinate information on the horizontal axis, and the other channel represents coordinate information on the vertical axis.
# Shadow Fleets: A Growing Challenge in Global
## 5 Discussion
According to the Centre for Research on Energy and Clean Air, shadow tankers have been involved in 50 incidents from the Danish Straits to Malaysia since the start of Russia's full-scale invasion of Ukraine, with these vessels frequently operating with inadequate or no protection and indemnity insurance while transporting vast volumes of crude oil [64]. Recent incidents include the December 2024 Black Sea oil spill involving aing Russian tankers Volgoneft-212 and Volgoneft-239, which resulted in approximately 2400-5000 tons of oil spillage and highlighted the risks posed by aging vessels in severe weather conditions [65]. The International Tanker Owners Pollution Federation reports that total global tanker spills reached approximately 10,000 tons in 2024 [66]. The proliferation of aging, poorly maintained vessels operating outside traditional insurance and oversight frameworks increases risks to marine ecosystems and coastal communities. The regulatory challenges posed by dark and gray fleets are substantial. These fleets exploit flags of convenience, leveraging lax regulatory practices in countries like Panama, Liberia, the Marshall Islands, Malta, and Cameroon. This exploitation creates difficulties in enforcing sanctions and safety standards [67]. There is a pressing need for advanced technological solutions for vessel tracking and identification, as well as for addressing the challenges in analyzing complex ownership structures and vessel histories. International collaboration is crucial, necessitating coordinated efforts to close regulatory loopholes and develop unified guidelines for transparency, accountability and safety. Addressing shadow fleet challenges requires specific, actionable measures. We recommend implementing blockchain technology for mandatory ownership transparency, requiring real-time, immutable ownership documentation for all vessels engaging in international trade. Enhanced satellite surveillance should establish mandatory correlation between AIS data and satellite imagery through artificial intelligence systems capable of detecting AIS manipulation and unauthorized ship-to-ship transfers. The international community must develop a binding International Shadow Fleet Protocol under IMO auspices, establishing criminal liability for shadow fleet operations, mandatory information sharing between flag states, and standardized enforcement mechanisms. Flag state accountability mechanisms should implement graduated sanctions against persistently non-compliant flag states. Economic measures should include comprehensive asset forfeiture programs enabling the seizure of vessels and cargo engaged in shadow fleet operations.
## 4 Results  
### Results of the Imagery Classification  
By appliyng the supervised classification described in Section 3.1, the land use/land cover for 1989 (Figure 7a) and 2019 (Figure 7b) was derived.  
Furthermore, the classification accuracy was assessd through the confusion matrix created with the help of data from the testing sample. Thus, based on the values from the confusion matrix for 1989 and 2019 (Table 1), the overall classification accuracy and kappa index were calculated. For 1989, the overall classification accuracy was 93.7%, while the Kappa index was 0.92. Meanwhile, for 2019, the overall classification accuracy was 95.23%, while the Kappa index was 0.939. It should also be mentioned that for both analyzed years, the user accuracy and producer accuracy values were determined. Thus, for 1989, the user accuracy ranged between 87.7% (water bodies) and 96.9% (built-up areas), while the producer accuracy ranged between 84.4% (water bodies) and 97.1% (pastures). If we bring into discussion the year 2019, we observe that the user accuracy ranged between 87.4% (pastures)  
Figure 7: Land use/land cover: (**a**) 1989 and (**b**) 2019.  
and 98.9% (water bodies), while the producer accuracy was situated between 87.5% (fruit trees) and 98.6% (forests).
# Single Cell Chemotactic Responses of
## 3 Results and Discussion
### Impact of Urea on the Type and Frequency of H. pylori Movements
Figure 4 gives an example how the cells moved towards the side of the microfluidic device where the concentration of urea was greatest. A video was provided in the supplementary to give an example that cells were attracted by urea at a concentration of 100 mM. The chemotactic activities exhibited by _H. pylori_ presented in response to urea  
Figure 3: Images of _H. pylori_ in chemotaxis buffer within a time frame of 2 s, which shows that the cells exhibit complex moving behavior, including twirling and tumbling. The motility was documented using a phase contrast video microscope with a 60\(\times\) lens at room temperature. Arrows indicate the direction of the movement of the cell. Frame 2 and 3 show a tumbling in \(z\) direction. Frames 6–9 show a tumbling in _x-y_ direction. suggest that the proton motive force generated in the process is adequate for swimming in chemotaxis buffer. As discussed, adaptive mechanisms are responsible for the chemotactic movement toward attractants [30; 33]. In _H. pylori_, chemoattractant sensing is mediated by methyl-accepting chemotaxis proteins (MCP), which contain a large cytoplasmic signaling and adaptation domain. When an attractant binds to an MCP, the activity of CheA is inhibited, resulting in increased methylation of the Tlp receptor by CheR. Consequently, adaptation of the receptor returns signaling to a state similar to that prior to stimulus challenge, which generates periodical \"running\" induced by dephosphorylated CheA and CheY [30]. Attractant-bound MCP may suppress CheA activity, reducing the pools of CheY-P, which may explain why the _H. pylori_ exhibited less frequent tumbling when exposed higher concentrations of urea. Ultimately, this may help to explain how _H. pylori_ senses concentration gradients of urea to move towards higher concentrations. In combination with the restricted motility discussed earlier, this helps to explain the lower directness (reflecting less random movement).
# Design and Implementation of a Digital Twin System for Log Rotary Cutting Optimization
## 2 Materials and Methods
### Five-Dimensional Model
#### 2.1.4 Digital Twin Data  
As a bridge for information transmission, twin data establishes the connection between the physical entity of the log rotary cutting system, the virtual model, and the service system [17]. The fused data, formed by integrating physical data and virtual data, can provide support for cutting optimization, cutting simulation, remote operation and maintenance, and other services. The digital twin data comprises virtual entity data, physical entity data, and service-generated data during system operation, continuously undergoing real-time optimization and updates with changes in the digital twin system's operational state. It  
Figure 4: Virtual entity of the log rotary cutting system.  
consists of static and dynamic data. Static data includes the device dimensions, material information, and assembly relationships of the physical and virtual entities, representing the physical attributes and original design data. Dynamic data consists of data from the physical entity, including real-time motion data monitored by sensors, as well as simulation data from the virtual entity, such as simulated loads and rule constraints. Additionally, it includes operational data derived from the models of both the physical and virtual entities, such as the analysis data of log characteristics and processing parameters. The data composition is illustrated in Figure 5.
# Aerodynamic Analyses of Airfoils Using Machine Learning as an Alternative to RANS Simulation
## 2 Background Theory
### Back-Propagation Neural Network  
A back-propagation neural network (BPNN) is a feed-forward type of artificial neural network that consists of three different types of layers. The three layers are known as the input layer, followed by the hidden layer(s) and, finally, the output layer. The number of hidden layers can be adjusted depending on the nature of the problem that is involved. The number of nodes, which are also known as neurons, in the input layer is selected in such a way that it matches the quantity of input features; similarly, the number of nodes or neurons in the output layer is equivalent to the number of outputs [29]. All of the processing elements, which are also known as nodes or neurons, in every layer are fully interlinked to the neurons that are in the succeeding layer through weighted connections. The inputs to the hidden layers are multiplied by the weights that are associated with the respective neurons and are then added together by the hidden layer to produce a summed output. This summed output is then passed through an activation function, such as sigmoid, tan-sigmoid or threshold functions [30]. Due to the simplicity, efficient performance and ease of implementation, it is one of the most extensively used neural networks. The general architecture of a BPNN with one output is shown in Figure 2.  
Our BPNN was built upon a simplified Widrow-Hoff learning rule, which is also known as the generalized delta rule, and used a supervised learning technique. [31]. For training, we used the gradient descent method [32], which was applied by adopting the following procedure:  
1. Initialize all weights \(w_{ij}^{[l]}\) and biases \(b_{j}^{[l]}\) by assigning random values (_l = layer number, i. = 1 to N and j = 1 to M);  
Figure 1: Airfoils with unstructured meshes.  
Figure 2: General architecture of the BPNN.  
2. Feed the training dataset and output dataset into the artificial neural network and then compute the output of each layer using Equation (1): \[y_{jp}^{[IC1]}=f\left(\sum_{i=1}^{N1}w_{ij}^{[l+1]}y_{ip}^{[l]}+b_{j}^{[l+1]}\right)\] (1)
3. Compute the error term at the output of each layer using Equation (2): \[err_{jp}^{[L]}=f^{\prime}\left(y_{jp}^{[L]}\right)\left(d_{p}-y_{jp}^{[L]}\right)\] (2) in the \(i\)th hidden layer (\(i\) = \(L\)-1, \(L\)-2,... 1) \[err_{jp}^{[l]}=f^{\prime}\left(y_{j}^{[l]}\right)\sum_{k=1}^{N_{i+1}}err_{kp} ^{l+1}w_{jk}^{l+1}\] (3)
4. Compute the changes in weights and biases between the input and output layers using Equations (4) and (5): \[b_{ij}^{[l]}(n+1)=b_{i}^{[l]}(n)+NG.err_{jp}^{[l]}\] (4) \[w_{ij}^{[l]}(n+1)=w_{ij}^{[l]}(n)+NG.err_{jp}^{[l]}y_{ip}^{[l-1]}\] (5) It can be seen that the error term was back-propagated into the neurons of the previous layer while calculating the changes in the weights and biases;
5. Repeat Steps 2 to 5 until the error term falls below the minimum specified error criteria;
6. The sigmoid activation function that was used in the present study was given by Equation (6): \[f(x)=\ 1/(1+e^{-x})\] (6)
## 4 Experiments and Result Analysis
### Experiment Method and Result Analysis
The optimized CGK-based fuzzy granular model of the aggregated structure showed the highest prediction performance with a validation PI of 0.6069 when contexts were generated flexibly, and six contexts and six clusters were used. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model Types** & **IG Type** & \begin{tabular}{c} **Num. of** \\ **Contexts** \\ \end{tabular} & \begin{tabular}{c} **Num. of** \\ **Clusters** \\ \end{tabular} & \
\begin{tabular}{c} **Training PI** \\ \end{tabular} &
\begin{tabular}{c} **Testing PI** \\ \end{tabular} \\ \hline CGK-GM & Uniform & 6 & 5 & 0.5476 & 0.5502 \\ \hline A-CGK-GM & Flexible & 6 & 3 & 0.5287 & 0.5870 \\ \hline Optimal & \multirow{2}{*}{Uniform} & \multirow{2}{*}{6} & \multirow{2}{*}{5} & \multirow{2}{*}{0.5698} & \multirow{2}{*}{0.5733} \\ CGK-GM & & & & & \\ \hline Optimal & \multirow{2}{*}{Flexible} & \multirow{2}{*}{6} & \multirow{2}{*}{0.6100} & \multirow{2}{*}{0.6069} \\ A-CGK-GM & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Result of Boston House Prices prediction experiment. Figure 16: Performance index of the optimized CGK-based fuzzy granular model of the aggregated structure. The results of experiments using two benchmarking databases for prediction confirmed that the optimized fuzzy granular models proposed showed higher prediction performance than the conventional CGK-based fuzzy granular model and the CGK-based fuzzy granular model of the aggregated structure. The prediction performance improved when the IGs were optimized through the GA. Furthermore, the performance of the context generation method changed according to the characteristics of the database.
# Anomaly Detection for IOT Systems Using Active Learning
## 5 Results
### Novel Model Design  
This study employed a fusion of active learning and machine learning techniques to identify anomalies in IoT systems. Active learning lowers the amount of labeled data needed to train an accurate model. This method selects and queries the most informative labeling data points iteratively. The model's lowest certainty determines data points for labeling in this study's uncertainty sampling active learning technique. A machine learning model was created using IoT device network traffic data. The researchers detected network intrusions using the freely available UNSW-NB15 dataset. Our work used a random forest classifier, an ensemble learning method that builds numerous decision trees and aggregates their predictions to improve model accuracy and robustness.  
Initially, the dataset was partitioned into training and testing sets using an 80:20 ratio. The ActiveLearner object was initialized, encapsulating both the machine learning model and the active learning mechanism. To identify the most valuable data points for labeling, we implemented the uncertainty sampling methodology. A query limit of 50 was set, indicating that 50 data points from the test set would be selected for labeling. Throughout the active learning process, we utilized the ActiveLearner object to extract the most informative data points from the test set for labeling. The model was retrained after the incorporation of labeled data into the training set. Upon conducting calculations to determine the accuracy of the model after each query, it was observed that accuracy gradually improved as the active learning process advanced.  
Ultimately, our approach exhibited superior performance to traditional machine learning techniques that do not incorporate active learning, with a test set accuracy of 99.7%. The primary advantage of our methodology lies in its ability to attain a high level of accuracy while utilizing a small quantity of labeled data. This characteristic is particularly valuable in the context of anomaly detection within the Internet of Things (IoT) systems, where the  
Figure 13: AUC-ROC curve of testing data.  
acquisition of labeled data might present challenges in terms of both feasibility and cost. Our innovative approach of integrating active learning with machine learning algorithms has the potential to enhance anomaly detection in IoT systems.
# Modulated Noncollinear Optical Parametric Amplifier Output Induced by Stimulated Raman Scattering
## 4 Results and Discussion
### Stimulated Raman Processes in Optical Parametric Amplification  
Figure 2 shows a two-dimensional plot of the amplified signal spectra against pump-seed delay time measured with a multichannel lock-in amplifier (MCLA) for different conditions that depend on the configurations of the precompression optical system of the seed pulse and the stretching optics of the pump pulse. Due to the limited spectral range of the MCLA, we obtained delay-time-dependent amplified spectra in spectral ranges shorter and longer than 600 nm of the output amplified spectra to ensure that the resolution was sufficiently high; consequently, the spectra in Figure 2 have signal intensity jumps around 600 nm. A pump-seed delay time dependent spectrum of the amplified signal was also measured using a spectrometer (Ocean Optics, USB4000). An example is shown in Figure 3a. Three examples of amplified spectra obtained using different pump-seed delay times are shown in Figure 3b.  
Figure 2: Two-dimensional plots of the pump-seed delay time dependence of the amplified signal spectra measured using a multichannel lock-in amplifier. (**a–h**) show the results for different experimental configurations, namely number of bounces at the chirped mirror in the white-light seed-compression system and across the length of the glass in the pump-pulse stretcher. Using (N, L) to denote the bouncing number (N) and glass length (L) in centimeters, (**a–h**) correspond to (0,0), (1,0), (2,0), (3,0), (4,0), (4,3), (4,4), and (4,5), respectively.