esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Acquisition of Raw Data
### Data Processing
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Method Data Collection** & **Classification** & **k-NN (k = 3)** &
\begin{tabular}{c} **Random Forest** \\ **Classifier** \\ \end{tabular} & **SVM** \\ \hline
20 samples (1 s) with different & 1/4 & 92.5 & 93.2 & 92.3 \\ swallow positions & 2/4 & 93.7 & 93.1 & 92.0 \\
3/4 & 94.2 & 95.1 & 93.7 \\ \hline
30 samples (1.5 s) with different & 1/5 & 93.7 & 93.8 & 92.9 \\
30 samples (1.5 s) with different & 2/5 & 94.1 & 94.3 & 93.0 \\ swallow positions & 3/5 & 94.2 & 94.6 & 93.3 \\
4/5 & 94.4 & 95.4 & 93.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Classifier performance with 20- and 30-sample windows. Figure 6: Comparison of data processing techniques: (**a**) Proposed data processing technique; (**b**) Previous work data processing technique [4,7]. cycle before a new piece of food was ingested. In contrast, in the most similar work given by [10,11], swallowing was set in the middle of the window, so the window included silent phases along with related eating patterns; thus, the algorithm could not attain a high accuracy. Discriminating features are obtained from informative frames carrying information about the swallow event and the prior chewing sequence.
# DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
## 2 Related Works
### AI-Based Research
Figure 3, shows the outcome of our investigations and reviews in terms of mean Average Precisions (mAP), and the speed (Frame Per Second--FPS) on some of the most successful object detection models such as RCNN [46], fast RCNN [47], faster RCNN [48], SSD: Single Shot MultiBox Detector [49], YOLOv1-v4 [50; 51; 52; 53] tested on the Microsoft Common Objects in Context (MS COCO) [54] and PASCAL Visual Object Classes (VOC) [55] data sets under similar conditions. Otherwise, the performance of the systems may vary depending on various factors such as backbone architecture, input image size, resolution, model depth, software, and hardware platform. As can be seen from Figure 3, some of the models such as SSD and YOLOv2 perform in a contradictory manner in dealing with COCO and VOC12 datasets. They may seem good in one, and weak in another one. One of the possible reasons could be attributed to the different number of object categories in COCO and VOC12 (80 categories vs. 20). This makes the VOC12 dataset an easier goal to learn and less challenging. However, when it comes to the higher number of classes, the performance of the system may seem irregular, depending on the feature complexity of each object (good in some detections and weak in some other). Since the social distancing topic is very recent, there has not been much dedicated research regarding the accuracy of people detection and inter-people distance estimation in the crowd, no experiment on challenging datasets has been performed, no standard comparison has been conducted on common datasets, and no analytical studies or post-processing have been considered after the people detection-phase to analyse the risk of infection distribution. Considering the above-mentioned research gaps, we propose a new model which not only performs more accurate and faster than the state-of-the-art but also will be trained and tested using a large and comprehensive dataset, in challenging environments and lighting conditions. This will ensure the model is capable of performing in real-world scenarios, particularly in covered shopping  
Figure 3: Mean Average Precision (mAP) and Speed (FPS) overview of eight most popular object detection models on Microsoft Common Objects in Context (MS-COCO) and PASCAL Visual Object Classes (VOC) datasets. centres where the lighting conditions are not as ideal as the outdoor lighting. Furthermore, we offer post-detection and post-processing analytical solutions to mitigate the spread of the virus.
# Attention-Based Mechanism and Adversarial Autoencoder for Underwater Image Enhancement
## 3 Experimentation and Analysis  
### Experimental Environment and Parameter Design  
To validate the effectiveness of the algorithm, this experiment was implemented on the Pycharm simulation platform, and the network was trained using the PyTorch deep learning framework. The UW-AAE model was executed on a computer equipped with an Intel(R) Core(TM) i5-7300 HQ CPU @ 2.50 GHz and a GTX 1080 Ti GPU. The network training process took approximately 42 h, while the testing time was approximately 6 min. Random rotation and horizontal flipping were employed for data augmentation during UW-AAE training. Additionally, all input images in the training set were resized to 256 \(\times\) 256 pixels. The model parameter batch was set to 16, the learning rate to 0.0002, and the hidden vector dimension to 128. To determine an appropriate value for the number of epochs, Figure 8 illustrates the evolution of validation loss and training loss functions during the training process of the method proposed in this study. As the number of epochs increases, the loss function values exhibit a consistent decline. Around epoch 120, the loss value shows a tendency to stabilize, indicating the onset of convergence of the loss function. This observation suggests that our method demonstrates a favorable fitting performance. During adversarial training, the generator and discriminator are trained alternately. The discriminator is trained once, and the generator is updated twice in each iteration. Both the generator and discriminator use the Adam optimizer, with \(\beta\)1 set to 0.5 and \(\beta\)2 set to 0.999. The adversarial learning rate (\(lr\)) is set to 0.0002. To mitigate overfitting during the training process, a dropout technique is applied to the autoencoder (with a dropout ratio of 0.3).  
The training set used in this experiment consists of 5200 real underwater images and their corresponding clear images, which were obtained from Underwater-ImageNet [28]. Due to the varying attenuation of different wavelengths of light in seawater, real underwater images often exhibit blue-green color tones. Similarly, the dataset provided by Underwater-ImageNet also exhibits these blue-green attenuation characteristics. The test set is divided into two subsets: test set A, containing 60 real underwater images without reference, and test set B, containing 45 underwater images with reference images for evaluation.
## 2 Materials and Methods
### Relational Mapping of Convolutional Neural Networks to Change-Point Detectors  
The main types of neural networks are feedforward networks (FFN), recurrent neural networks (RNN), long short-term memory (LSTM), auto encoder (AE), generative adversarial network (GAN), and convolutional neural networks (CNN) [50]. By analyzing the structure of neural networks, we found that the unique architecture of CNN is relatively suitable to integrate with energy operator methods to construct change-point detectors.  
The architecture of the simplest typical CNN generally consists of an input layer, a convolutional layer (CONV), a ReLU layer, a pooling layer, a fully connected layer (FC), and an output layer [51; 52], and the CONV layer and ReLU layer are often collectively referred to as the convolutional layer. In more detail, CONV is a filter that extracts signal features and filters the signal. The filter is equivalent to a convolution kernel for data in 2-D matrix form, defining the \"vision\" of the detector and assigning weights to each data point. The pooling layer pools the signal features from the CONV output, extracts the main features, and implements feature compression. After CONV and pooling, the data are abstracted into features with higher information content, and FC is subsequently used to classify these features.  
The architecture of a typical change-point detector is almost identical to that of CNN. First, a filter (Butterworth, Baseline detrending, TKEO, etc.) is needed for the noise reduction or feature enhancement of the raw signal; then, the signal is framed by a sliding window to extract features such as the mean or variance of each frame. Finally, the features are classified by a classifier. Due to the one-to-one correspondence between the functional components of CNN and the traditional change-point detector, the mapping can be established intuitively, as shown in Figure 1.  
In the CNN algorithm, the parameters (such as weights and biases) of neurons in the CONV, pooling layer, and FC need to be updated through training with a large amount of labelled data. The training process generates most of the computation demand in CNN and is the main source of algorithmic complexity. Furthermore, although CNN is nonlinear, functional components such as the CONV, pooling layer, and FC are linear, and the nonlinear properties of neural network algorithms mainly come from activation functions.  
By introducing the functional components of CNN, it can be observed that, if the weights and biases of CNN are pre-set in advance, and the activation function part is retained, not only can the complexity of the algorithm be reduced, but also the non-linear property of the detector can be preserved.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 4 An INS Cyber-Physical Range
### Simulation, Emulation, and Analysis Tools  
The Bridge Command, which is an open-source ship simulator software for Windows, Linux, and macOS, operating systems is used in the eMIR [8; 131]. Compatible strings with NMEA 0183 are broadcasted by the Bridge Command. Moreover, detailed guidelines are available to extend the functions of the Bridge Command [132; 121; 133]. The Orocos Toolkit can be used to manage data received from sensors [134].  
The MATLAB Simulink allows simulation, continuous test, automatic code generation, and verification of embedded systems [135]. The developed model can run on the MATLAB Simulink [32]. The Simulink Real-Time is used to create real-time applications from the Simulink model [136].  
Various virtual bridge elements that control and monitor the bridge console, such as bow thruster, main propulsion, coming, heading indicator GPS, VHF, light controls, machine telegraph, steering lever, rudder angle indicator can be developed [8].  
The Cognitive Architecture for Safety Critical Task Simulation (CASCaS) in the eMIR platform is a Windows-based software to simulate human behavior such as a navigator on a ship bridge [137]. A virtual user interface as a user interface representation in the CASCaS may be developed [33; 138].  
A simulation tool for marine traffic may be developed to implement, execute, and observe the motion of multiple vessels and the effects of environmental conditions (e.g., waves, currents, and winds) in a realistic marine environment [31; 37].  
Development of a tool like the Distributed Controlling Toolkit (DistriCT) could be pretty useful for three tasks, namely program control, simulator control, and simulation assessment. Program control is to set simulation components. Simulation control is to manage simulation behavior. Simulation assessment is to access and analyze the simulation states [37].  
The behavior of rigid objects (e.g., cargo) in the simulated environment can be simulated by an N-Body simulator [31]. Software for socio-technical analysis including bridge, crew, and organizational aspects can be developed [33].  
By using an eclipse-based editor, an initial static scene consisting of essential components, actors, and environmental factors in accordance with the pre-defined scenario can be developed. For instance, a 3D vessel can be inserted into the scene [23; 37].  
Unreal Engine is a comprehensive solution for real-time 3D creation and is used in many industries, such as games, architecture, automotive, film, training, simulation [139]. Due to its performance and graphics quality, Unreal Engine can be used to illustrate real-time ship motions in the simulation environment [140].
# Automatic Fact Checking Using an Interpretable Bert-Based Architecture on COVID-19 Claims
## 2 Related Work  
Fact verification is a common strategy used to debunk false information in digital media. Usually, it is performed manually due to the difficulties in finding reliable and available evidence supporting or refuting a particular statement and the not trivial decision needed to define a claim as false. Nevertheless, this task is resource intensive and time consuming [5]. For this reason, it is necessary to support this labor through technological tools that promote acceleration in this process. Information Retrieval (IR) systems are already an adequate help, but are not always sufficient for this complex task. Many automatic fact verification approaches follow a three-stage scheme [6]; Figure 1 describes these stages. The first step aims to retrieve relevant documents from a knowledge base or a document collection. These databases usually include tens of thousands of documents, so the retrieval process needs to be efficient. In [7], the authors apply Anserini [[https://github.com/castorini/anserini](https://github.com/castorini/anserini)]([https://github.com/castorini/anserini](https://github.com/castorini/anserini)) (accessed on 17 October 2022) to retrieve documents. Anserini is an IR API based on Apache Lucene [[https://lucene.apache.org/](https://lucene.apache.org/)]([https://lucene.apache.org/](https://lucene.apache.org/)) (accessed on 17 October 2022). Some approaches rely on weights such as TF-IDF [8]. Others use an external search API [9].  
The second step focuses on extracting relevant text spans from retrieved documents. Some approaches measure the similarity between the claim and the statement to evaluate the span relevance [7]. Enhanced Sequential Inference Model (ESIM) [10]-based approaches use recurrent neural networks to classify the relevance [9]. Additionally, methods that fine-tune pretrained language models are available [11], and nowadays, these models have achieved the most competitive results. In [PERSON] et al. [11], a model that uses _BERT_ to extract evidence is presented, and it is the state of the art in relevance using neural networks. The difference between our architecture and the architecture by [PERSON] et al. is the interpretation capability that we included by computing attention among the tokens of the claims and the evidence.  
The third and last step aims to evaluate the claim against retrieved evidence and classify it as supported, refuted, or without information. Approaches aimed at evaluating relevance can also be used to verify the claim. The only difference is that the former uses two classes, while the latter uses three.
## 5 Validation of the Proposed Method
### Performance Metrics  
Some metrics were chosen to evaluate the performance of the proposed method during the experiments in this section, including precision \(p\), recall \(r\), F1 score, and the processing speed \(v\).  
\[p=\frac{TP}{TP+FP} \tag{8}\]  
\[r=\frac{TP}{TP+FN} \tag{9}\]  
\[f=\frac{2\times p\times r}{p+r} \tag{10}\]  
\[v=\frac{N_{d}}{t_{d}} \tag{11}\]  
where \(TP\) (True Positive) indicates the number of correctly detected anomalies, \(FP\) (False Positive) is the number of incorrectly detected anomalies, \(FN\) (False Negative) denotes the number of missed detected anomalies, \(N_{d}\) is the total number of samples, and \(t_{d}\) is the time used to process the samples.
## 3 Results and Analysis
### Simulation Experiment and Result Analysis
#### 3.3.3 Stability Analysis of Algorithms  
In order to observe and compare the stability of each algorithm at higher dimensions more intuitively, functions \(f_{1}\sim f_{11}\) are run 50 times in order to develop line graphs (D = 200), as shown in Figure 5a-k. The more stable the curve, the better the stability of the algorithm. The smaller the value of the curve, the better the optimization ability of the algorithm.  
For unimodal functions \(f_{1}\sim f_{4}\), the fluctuation of the MDBAS algorithm (blue) and the AFS algorithm (orange) is large, and the curve is far from the optimal value. Combined with Table 5, it can be seen that the stability and optimization accuracy of the AFS-MDBAS (green) and AFS-MMSBAS algorithm (red) are better than those of the individual algorithms. This shows that the hybrid algorithm composed of the AFS and BAS algorithms achieves superior performance for unimodal functions. For the multimodal functions \(f_{5}\sim f_{10}\), the advantages of the AFS-MDBAS algorithm in terms of stability and optimization accuracy are not obvious compared with the AFS and MDBAS algorithms, but the advantages of the AFS-MMSBAS algorithm are quite obvious. The curve is the most stable and is below all the other curves, which further demonstrates that the AFS-MMSBAS algorithm has the best stability and optimization ability.  
Figure 5: D = 200; comparison of stability of functions \(f_{1}\sim f_{11}\). (**a**) Comparison of stability of four algorithms for function \(f_{1}\); (**b**) Comparison of stability of four algorithms for function \(f_{2}\); (**c**) Comparison of stability of four algorithms for function \(f_{3}\); (**d**) Comparison of stability of four algorithms for function \(f_{4}\); (**e**) Comparison of stability of four algorithms for function \(f_{5}\); (**f**) Comparison of stability of four algorithms for function \(f_{6}\); (**g**) Comparison of stability of four algorithms for function \(f_{7}\); (**h**) Comparison of stability of four algorithms for function \(f_{8}\); (**i**) Comparison of stability of four algorithms for function \(f_{9}\); (**j**) Comparison of stability of four algorithms for function \(f_{10}\); (**k**) Comparison of stability of four algorithms for function \(f_{11}\).
# Numerical Simulation Study on the Performance of Buried Pipelines under the Action of Faults
## 4 Analysis of Parameters Affecting Pipeline Response under Fault Action  
### Influence of Fault Type  
In this part, the pipeline with \(D\) = 1219 mm, \(t\) = 22 mm, an internal pressure of 12 MPa, and a fault angle of 60 degrees is evaluated, so as to study the influence of different fault types on the performance of the pipe. Figure 8 shows the changes in the maximum axial strain and the maximum von Mises stress with fault displacement under different types of faults.  
As shown in Figure 8a, the maximum axial tensile strain rises with the increase in the fault displacement. The growth rate of the axial tensile strain under different types of faults varies, strike-slip fault > strike-slip reverse fault > strike-slip normal fault > reverse fault > normal fault, and the maximum axial tensile strain of the pipe does not reach the critical value of pipeline tensile damage when the displacement of the fault is 2 m.  
The situation for the maximum axial compressive strain is different. The growth rate of the axial compressive strain under different types of faults varies as follows, strike-slip reverse fault > reverse fault > strike-slip fault > strike-slip normal fault. The growth rates under a strike-slip reverse fault and a reverse fault are obviously larger than others, which indicates that the pipeline suffers from more intense extrusion when the action of a reverse fault exists. When the fault displacement is only 0.82 m, the pipe undergoes shell buckling failure first under the action of a strike-slip reverse fault, and the  
Figure 6: Variation in maximum axial tensile strain under different fault displacements.  
Figure 7: Location of the maximum axial strain when fault displacement is 1.76 m.  
positive fault with the slowest growth rate of \(\epsilon_{\rm cmax}\) also reaches the critical compressive strain before the fault displacement reaches 2 m.  
The variation in the maximum von Mises stress is similar to that of the maximum compressive stress. From Figure 8b, it can be seen that the influence of the fault types on the maximum von Mises stress of the pipe is as follows, strike-slip reverse fault > reverse fault > strike-slip fault > strike-slip normal fault > normal fault, and the stress growth rates under a strike-slip reverse fault, reverse fault, and strike-slip fault are significantly higher than those of a normal fault and strike-slip normal fault before reaching the yield strength.  
In summary, in the case of the same fault displacement, the pipeline is more likely to suffer damage when a reverse fault exists. Compared to a single fault type, the combination of strike-slip reverse faults poses greater harm to the buried pipeline. Therefore, in the following research, only strike-slip reverse fault will be addressed.
# A Proof-of-Multiple-State Consensus Mechanism for Mobile Nodes in Internet of Vehicles
## 4 Consensus Mechanism Based on Integrated State  
### Speed Prediction Models  
The vehicle speed prediction method used in this paper is based on the Long Short-Term Memory (LSTM) network model [23], which aims to predict short-term vehicle speed. The model's training data comes from the simulation environment, which includes the state data of several vehicle nodes at different points in time, such as vehicle speed, acceleration and road vehicle density information at any time. The model focuses on short-term speed predictions rather than long-term macro predictions because the simulation environment cannot fully reproduce the special factors in real-world conditions such as weather changes and holidays. This short-term prediction method can more accurately reflect the real situation in the simulation environment. Vehicle the driving speeds over a historical period are \(\left[\mathrm{v}_{1}^{1},\mathrm{v}_{1}^{2},\cdots,\mathrm{v}_{1}^{n}\right]\), and the prediction model obtains the speed in the future \(\Delta t\) speed after a certain period of time, which will be the basis for subsequent state value calculations \(\left[\mathrm{v}_{1}^{1+\Delta t},\mathrm{v}_{1}^{1+\Delta t},\cdots,\mathrm{ v}_{1}^{1+\Delta t}\right]\). The outputs of these models describe the possible driving state of a specific vehicle i in the future, rather than the average speed of all vehicles in the  
Figure 3: Consensus implementation.  
Figure 2: Schematic diagram of grouping.  
network. This is due to the relatively short monitoring time for changes in vehicle status. We are concerned about the movement mode of each vehicle node rather than the overall state changes of traffic flow. This will be an important parameter for subsequent state value calculations.