esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Heart Rate Variability (HRV) Study
### Method  
The study was performed in a swimming pool with a water depth of \(<\)1 m and stairs. The average water temperature was 30.2 \({}^{\circ}\)C with a chlorine content of 0.05 mg/L. The measurements outside the swimming pool were also performed in the same room with an average air temperature of 26.3 \({}^{\circ}\)C. All measurements were performed between 1 pm and 4 pm in the afternoon to reduce influences of the circadian rhythm [24] and provide standardized conditions across all subjects.  
We recorded the ECG of all subjects using our described current ECG system and repeated the same procedure for each of them. This is described in the following and also shown in the flowchart (Figure 6) and pictures (Figure 7). After preparation of the skin, the subjects sat in a resting position for 5 min outside the water to slow down the heart rate and ensure equal conditions for all subjects. Then, there was a 10-min measurement period in a sitting position outside the water, then 10-min measurement in supine position outside the water. Then, the subjects were immersed in water. We measured the ECG for 10 min in a sitting position inside the water, then 10 min in a supine position. According to [3], a five minute evaluation period is required for HRV measurements. Therefore, we recorded for 10 min and used a centered window of 5 min within this measurement, where the heart rate was most stable, for each evaluation, as was done in previous work [4]. We also recorded an additional 2-min backstroke exercise for five subjects. This was done to verify that measurements could also be taken during a dynamic task performed in water.  
In order to allow a fair comparison to methods using potential-based measurements, e.g., [4], we placed the electrodes in an Einthoven lead II configuration, although, based on the current-circuit's principle, it would be more favorable for the current ECG to have the measurement electrode placed directly above the heart on the left side of the lower sternum.  
After the measurement, which was stored on the internal SD-card of the device, the signals were extracted, all R-peaks determined by an automated algorithm [25, 26] and manually checked for outliers. All found outliers were corrected manually. The resulting tachogram was equidistantly resampled using a cubic spline interpolation method at a frequency of 6.8 Hz and detrended using a polynomially fitted trend-curve, in order to calculate the spectral power density (PSD) using Fast Fourier Transform with a Hanning window. From the resulting PSD, the frequency parameters were derived.
## 4 Results and Discussion
### Results Discussion  
The present study introduces association rule technique into complex network and develops a directed weighted network of seafarers' unsafe acts and their influencing factors with 53 nodes and 260 edges, which has a high clustering coefficient (0.6203) and a short average path length (1.8048). The results show that the interaction network developed belongs to a typical small-world network, indicating that the network has great connectivity, and the connection between nodes has randomness and uncertainty, which leads to the fast and wide risk propagation of seafarers' unsafe acts involved in maritime accidents. Meanwhile, certain seafarers' unsafe acts and their influencing factors play an important role in the development of maritime accidents, and should be given focused control. The small-world nature of the network increases the difficulty of maritime safety risk management, and it is necessary to identify the nodes in the network that have high risk propagation ability and clarify the risk propagation path of typical seafarers' unsafe acts.  
Then, the weighted PageRank algorithm is used to clarify the criticality of seafarers' unsafe acts and influencing factors in the network, and the results show that failure to make proper sound and light signals (U16) achieves the highest PageRank value, followed by failure to take effective collision avoidance action early (U25), and improper emergency response measures (U20). In addition, the top 10 nodes all belong to seafarers' unsafe acts, indicating that effective control of unsafe acts with higher criticality would interrupt the propagation of risks and thereby reduce the occurrence of maritime accidents. Finally, based on the community detection method of random walk, 5 communities in the interaction network are identified and 13 typical risk propagation paths of seafarers' unsafe acts are clarified. The results facilitate maritime stakeholders to understand the evolution mechanism of seafarers' unsafe acts from a \"complex system\" perspective, and to develop safety management strategies for high-risk nodes, so as to improve maritime transportation safety.  
The results of this study are consistent with previous studies. For example, all of the top 10 critical events in grounding accidents belong to contributing events, i.e., events that directly lead to the occurrence of ship grounding [30]. Similarly, the top 10 critical nodes in this study all belong to seafarers' unsafe acts. However, [PERSON] et al. [30] stated that \"Deviated from the ships' route\" was the riskiest hazardous event. The reason for this difference may be the different types of maritime accident addressed. Additionally, [PERSON] et al. [55] employed Bayesian network to model the interaction among maritime risk factors, and most probable explanation (MPE) was used to explore the most probable risky scenario. However, it is difficult to specify the risk propagation path, because its results may be changed by the introduction of irrelevant variables.
## 4 Experiments
### Experimental Results
Laplacian Filter: Figure 5 shows clearer clustering tendencies among different age groups after incorporating the Laplacian filter in the encoder. Encoder with Joint Loss Optimization: Figure 5 further displays the embedding effect after the joint optimization of graph reconstruction loss and clustering loss, where the clustering of each age group is more pronounced, indicating the effectiveness of the joint optimization strategy in enhancing clustering outcomes. Semi-supervised Learning Approach: Figure 5 reveals the evolution of embeddings with semi-supervised learning, where boundaries between clusters are clearer and nearly non-overlapping, showcasing the significant impact of semi-supervised learning methods on improving embedding quality and clustering performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model Variants**} & \multicolumn{4}{c}{**CHBMP**} \\  & **ACC** & **NMI** & **ARI** & **F-Score** \\ \hline GAAE and spectral clustering & 0.654 & 0.424 & 0.332 & 0.503 \\ +Laplacian filtering & 0.660 & 0.430 & 0.336 & 0.621 \\ +Joint optimization & 0.692 & 0.467 & 0.402 & 0.653 \\ +Semi-supervised learning & 0.754 & 0.479 & 0.423 & 0.663 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation experiment. Figure 4: (a) Number of hidden neurons vs. accuracy; (b) Number of attention heads vs. accuracy. Figure 5: Evolution of the model’s clustering effects. (a) Original signal distribution; (b) +Laplacian filtering; (c) +Joint optimization mechanism; (d) +Semi-supervised learning.
# A Multi-Input Fusion Model for Privacy and Semantic Preservation in Facial Image Datasets
## 5 Experimentation and Results
### Evaluation of the De-Identified Facial Image Dataset for Machine Learning  
In the problem of the utility of generated processed dataset, our method aims to retain information related to quality, maintain the similarity between the original image and the processed image as much as possible, and erase only privacy-related information, which can ultimately benefit the effectiveness of the image.  
As an illustration of the effectiveness of our method, we used facial expression recognition as a service and employed three different de-identification methods: direct output images without post-processing (DOP), images with post-processing (GFP) using the GFP-GAN technique [6] for facial enhancement, and images with relabels (RL) to validate the usability of the proposed model. To evaluate the performance of our method, we utilized three state-of-the-art facial expression algorithms, RTCNN [9], DACL [10], and DAN [11], and selected the best quality-preserving model that was trained for 50 rounds with \(\alpha=0.4\) for analysis.  
For the dataset processed using DOP and GFP, we employed the aforementioned facial expression recognition algorithms to detect and compare the results with the original dataset labels. If they matched, the semantics of the face were considered preserved. The number of images with matching labels was compared to the total number of images to obtain a value for utility preservation. For RL, we first performed facial expression recognition on the processed dataset to obtain new labels. We then used the new labels and processed images as inputs to retrain the facial expression recognition algorithm, followed by testing the testing set for facial expression recognition. The number of images with matching labels of the testing set was then calculated and compared to obtain the value. Let \(N_{matched}\) be the number of generated images with matching labels and \(N_{total}\) represent the total number of images in the dataset. The preserved utility \(PU\) is calculated according to Equation (22). The results are shown in Table 4.  
\[PU=\frac{N_{matched}}{N_{total}} \tag{22}\]  
Figure 9: The error bar diagrams of output images processed by different methods.  
The data presented in Table 4 reveal that the accuracy rates for most of the above methods utilizing the generated dataset are similar to the accuracy rate achieved on the original dataset. This indicates that our method is successful in protecting the utility of images with a concealed identity.  
Figure 10 displays a demonstration of our model on street pictures captured by unmanned aerial vehicles that include lots of faces of pedestrians. This showcases the complete process of maintaining utility while prioritizing privacy protection, as implemented in our model, providing a clear representation of our model's capability of utility maintenance and privacy protection.
# Three-Dimensional Multifaceted Attention Encoder-Decoder Networks for Pulmonary Nodule Detection
## 3 Methods
### Three-Dimensional Region Generation Network  
We use a 3D region generation network (RPN) to achieve the final discrimination of pulmonary nodule classes (positive and negative nodules), predict each nodule bounding box, and merge overlapping candidate boxes using non-maximal suppression. Specifically, two sets of vectors are generated for each pixel point using two convolutional layers (with a convolutional kernel of \(1\times 1\times 1\)) predictions \(y_{1},y_{2}\), where \(y_{1}\) is a one-dimensional vector indicating the probability that the pixel point is a positive nodule, and \(y_{2}\) is a four-dimensional vector predicting the offset of the anchor point of the spatial location and radius prediction. Because negative nodules will be much more significant than positive ones, we use a focal loss function to match positive and negative samples. The focal loss function is defined as follows:  
\[L_{cls}=-\delta(1-p_{t})^{\tau}log(p_{t}) \tag{16}\]  
In this study, when the label contains an accurate sample, \(p_{t}=y_{1}\); otherwise, \(p_{t}=1-y_{1}\). \(\delta\) is the balance factor and \(\tau\) is the focusing parameter (\(\delta=0.5,\tau=2\)).  
The regression loss function on the nodal bounding box is  
\[L_{reg}=\sum_{k}S(G_{k},P_{k}) \tag{17}\]  
Figure 4: Main structure of the MSF module.  
\[G_{k}=(\frac{x_{g}-x_{a}}{r_{a}},\frac{y_{g}-y_{a}}{r_{a}},\frac{z_{g}-z_{a}}{r_{a} },log(\frac{r_{g}}{r_{a}})) \tag{18}\]  
\[P_{k}=(\frac{x-x_{a}}{r_{a}},\frac{y-y_{a}}{r_{a}},\frac{z-z_{a}}{r_{a}},log( \frac{r}{r_{a}})) \tag{19}\]  
where \(x,y,z,r\) denote the predicted offsets and \(x_{a},y_{a},z_{a},r_{a}\) denotes the defined anchor point, and \(x_{g},y_{g},z_{g},r_{g}\) denote the true label. \(s\) denotes the smoothed L1 regularization function.
# ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles
## 3 Scenario Description and Problem Statement
However, in the specific scenarios, the effect of obstacles on the location of the optimal position needs to be analyzed differently. As shown in Figure 1, the road section \(\widehat{\text{BF}}\) is out of the sight of Point A due to the blocking by Obstacle O\({}_{1}\), so the sender at Point A can only use corner Point B as the optimal position to realize the relay-node selection in this curved road scenario. However, the road section \(\widehat{\text{EG}}\) has a good line-of-sight condition because of no blocks, so the sender at Point E can directly select the farthest Point G in its coverage area as the optimal position. Therefore, by considering the specific distribution of obstacles within the communication range, we can select the proper optimal position to achieve the maximum coverage of one-hop and reduce the delay of the relay-node selection. Thus, the second problem to be resolved is described as follows. Problem 2: how to determine the optimal position? As shown in Figure 1, there are two road sections that are not covered by the broadcast: one is road section 1 indicated by the blue solid line, which is within the communication range of Node S\({}_{4}\), but not covered by the signal of Node S\({}_{4}\) because of the obstruction of Obstacle O\({}_{1}\); another is road section 2 indicated by the black solid line, which is outside the communication range of Node S\({}_{3}\) and S\({}_{4}\). As we aim to achieve full coverage of RoI, the location of the optimal position ensures that the broadcasting message can cover these road sections, i.e., road section 1 and 2. It should be noted that we only consider relay-node selection in vehicle to vehicle (V2V), and nodes can obtain not only their own position by using GPS, but also the local information about roads and obstacles by using GIS. To solve the problems of relay-node selection in the scenarios described above, in the next section, we propose an adaptive relay-node selection method that adaptively selects a relay-node selection method suitable for the current scenario according to the road structures and obstacles within the communication range of the sender.
# Influence of Open Differential Design on the Mass Reduction Function
## 4 Numerical Analysis of Differential Transmission
### Analysis of Stress and Displacement of Drive Shaft  
In order to assess the possibility of mass reduction of the drive shaft an initial numerical analysis is performed. The drive shaft was meshed with second-order SOLID187 tetrahedral finite elements with 501,870 elements and 712,238 nodes. Mesh sizing is preformed to satisfy accurate stress convergence. Meshed model and boundary conditions are shown in Figure 11 and results in the form of equivalent von Mises stress and displacements are obtained and shown in Figure 12.  
From the results of the initial analysis, it appears that large parts of the drive shaft can be reduced in the middle part where the stress values are low. It is proposed to design the drive shaft as a hollow shaft. The inner radius of the hollow shaft is proposed as a structural parameter and in the further course it is investigated how this parameter, among other defined ones, influences the mass reduction of the whole assembly.
## 2 Related Work
### Optic Disc Localization  
In fundus images, intensity variations can help to localize the optic disc. To take advantage of these variations, many researchers have used metaheuristic algorithms for optic disc detection in retinal fundus images. [PERSON] et al. [2] proposed some metaheuristic algorithms, namely Ant Colony Optimization Algorithm, Bacterial Foraging Optimization Algorithm, Firefly Algorithm, Cuckoo Search Algorithm, and Krill Herd Algorithms. [PERSON] et al. [5] used the Jaya algorithm to solve the problem of localizing the optic disc in retinal images. They also proposed a preprocessing technique to improve the quality of retinal images. A method proposed by [PERSON] et al. [6] uses the Fish School Search Algorithm to detect the optic disc. [PERSON] et al. [7] presented a hybrid nature-inspired model called swarm intelligence with preprocessing that includes multiple stages as background subtraction, median, and mean filtering. [PERSON] and [PERSON]'s [8] firefly algorithm used the intensity of the optic disc detection. [PERSON] et al. [9] presented a bat optimization algorithm. In their work, they used grayscale images as a preliminary stage to reduce the computation time, followed by an ellipse fitting approach to further improve the accuracy of disc detection. [PERSON] et al. [10] observed the Harris Corner Detection method refers to changes in the grayscale within a sliding window. The algorithm identifies the corner area of the optic disc with the highest changes in the grey levels as the location of the optical disk. It measures each channel in the ROI to determine the most appropriate for OD detection. [PERSON] and [PERSON] [11] presented an automatic hybridization using a combination of adaptively regularized kernel-based fuzzy cluster mean and Intuitionistic Fuzzy C-Means (IFCM) and Intuitionistic Fuzzy C-Means (IFCM), followed by a level set approach. [PERSON] and [PERSON] [12] used a new technique for segmenting the optic disc using a matching histogram template and OD size algorithm. The center of the optic disc was found and then the cross-correlation between the original image and the template was determined. After finding the rectangle size, a binary mask was created for segmenting the optic disc using the ROI. [PERSON] et al. [13] proposed a method for optic disc localization and segmentation using an active contour model based on the FCM clustering algorithm. In the first step, the ROI was extracted and preprocessed, such as selecting the red channel from the RGB image, and the center of the optic disc was localized; then, the active contour was applied to detect the OD edges and perform segmentation.
## 5 AI on Smart City Technologies
### AI Algorithms in Smart Cities
#### 5.1.4 Computer Vision (CV)  
In AI, CV algorithms encompass a range of mathematical and computational techniques that empower machines to analyze and comprehend visual data obtained from their surroundings [127; 128; 129]. These algorithms can recognize patterns and features in images, videos, and other visual data and use that information to make decisions and predictions. CV is a crucial area of research in AI and is used in a wide range of applications, including facial recognition, object detection, image segmentation, and autonomous vehicles. Some of the most commonly used CV algorithms include convolutional neural networks (CNNs), widely used for image and video recognition, and object detection algorithms, which can detect and identify objects in images and videos. Other CV algorithms include feature detection algorithms, which can identify specific visual features in an image, and segmentation algorithms, which can separate an image into different regions or objects. CV algorithms have many smart city applications that can help improve traffic management by analyzing camera feeds and giving real-time updates to drivers, detecting public safety threats through analyzing surveillance camera feeds, optimizing waste collection routes and reducing resources spent, monitoring energy consumption in buildings and identifying areas to promote energy efficiency and understand development patterns and land use in urban planning initiatives through satellite imagery analysis. By automating visual data analysis from various sources, CV algorithms can make smart cities more efficient, safe, and sustainable [130; 131; 132].
# A Complex Network Important Node Identification Based on the KPDN Method
## 4 Datasets
### Networks with Scale \(10^{3}\)-\(10^{4}\)  
(1) email-univ. The email-univ dataset from SNAP is an email communication network containing 1133 nodes and 5451 edges. Nodes represent university employees, and edges indicate communication between employees. This dataset is often used for social network analysis, with the feature of a high average clustering coefficient.  
(2) US-airports. The US-airports dataset from SNAP represents the airline network between airports in the United States, containing 1574 airport nodes and 17,215 airline edges. This dataset can be used for transportation network analysis, with a relatively small average shortest path length.  
(3) power-eris1176. The power-eris1176 dataset from KONECT is a power grid network containing 1176 power station nodes and 9864 power supply edges. This dataset is used for power grid modeling and analysis, with a small network diameter.  
(4) bio-grid-worm. The bio-grid-worm dataset contains 3507 protein nodes and 6531 functional association edges from the protein-protein interaction network of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **N** & **M** & **\textless{}k\textgreater{}** & **Density** & **Clustering** \\ \hline email-univ & 1133 & 5451 & 4.811120918 & 0.43\% & 22.02\% \\ power-eris1176 & 1176 & 9864 & 8.387755102 & 0.71\% & 43.20\% \\ fb-pages-government & 7057 & 89,455 & 12.67606632 & 0.18\% & 41.09\% \\ US-airports & 1574 & 17,215 & 10.93710292 & 0.70\% & 50.42\% \\ bio-grid-worm & 3507 & 6531 & 1.8622754 & 0.05\% & 4.88\% \\ p2p-Gnutella08 & 6301 & 20,777 & 3.297413109 & 0.05\% & 1.09\% \\ \hline \end{tabular}
\end{table}
Table 1: Relevant parameters of real networks (\(10^{3}\)–\(10^{4}\) ).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dataset** & **N** & **M** & **\textless{}k\textgreater{}** & **Density** & **Clustering** \\ \hline dimacs10-astro-ph & 16,046 & 121,251 & 7.55646267 & 0.05\% & 66.51\% \\ ca-Astroph & 18,771 & 198,050 & 10.55084971 & 0.06\% & 63.06\% \\ gotton-reuters & 38,677 & 978,174 & 25.29084469 & 0.07\% & 4.73\% \\ prosper-loans & 89,269 & 3,330,022 & 37.30322956 & 0.04\% & 0.49\% \\ facebook-wosn-links & 63,731 & 817,035 & 12.82005617 & 0.02\% & 22.10\% \\ \hline \end{tabular}
\end{table}
Table 2: Relevant parameters of real networks (\(10^{4}\)–\(10^{5}\) ).  
nematode Caenorhabditis elegans in the BioGRID database. This dataset is applied in bioinformatics network analysis with high network density.  
(5) p2p-Gnutella08. The p2p-Gnutella08 dataset from SNAP is a P2P file-sharing network containing 6301 user nodes and 20,777 file transfer edges. This dataset is used for P2P network modeling, with a relatively high clustering coefficient.  
(6) fb-pages-government. The fb-pages-government dataset from SNAP is a Facebook page network containing 7057 government organization page nodes and 89,455 inter-page edges. This dataset is often used for social network analysis, with a small network diameter and large clustering coefficient.