esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on SWIM Services Dynamic Migration Method +
## 6 Conclusions  
Aiming at the complexity of network environment and the importance of SWIM service, this paper first analyzes the security threats faced by SWIM. From the perspective of survivability enhancement, this paper proposes a SWIM service dynamic migration method, which can conceal nodes and increase the difficulty of malicious attacks. In this mechanism, a trigger mechanism combining time interval and pseudo-random sequence is designed to ensure the continuity and controllability of service triggering. Then the destination node selection strategy is studied, and the SWIM core service is used to ensure the spatial randomness and security balance of the SWIM service. Compared with the SASS model and the TOKEN model, it is proved that the SWIM service dynamic migration method has strong invulnerability, and the mechanism can meet the performance requirements of SWIM. This study has certain significance for SWIM emergency response. In the face of large-scale network attacks, the SWIM service dynamic handover mechanism relies on the handover cluster behind it to share the attack pressure to ensure maximum network performance in the harsh network environment. This requires that each SWIM service provider itself be somewhat resistant to aggression. In view of the existing problems in this paper, in the future research, we can also analyze and study the security threat scenario of large scale damage of SWIM network, and design a SWIM service rapid recovery mechanism by using cloud services, cloud storage and other technologies to improve the emergency response capacity of the system. The future is an era of informatization and digitization. Big data, blockchain, artificial intelligence and other new generation information technologies are widely used in all walks of life, creating great economic and social value. In the future, the new generation of information technology can be used for in-depth analysis and processing of SWIM security threats to ensure safe and efficient operation.  
[PERSON], [PERSON], [PERSON] and [PERSON] conducted the research along with analysis and results.  
This research was funded by the National Natural Science Foundation, grant number U1533107, and the Major Program of Natural Science Foundation of Tianjin, grant number 17 JCZDJC30900.  
The authors declare no conflict of interest.
## 3 WSN Protocols
### TEEN (Threshold-Sensitive Energy Efficient Sensor Network)  
The Threshold-sensitive Energy Efficient sensor Network (TEEN) protocol employs a hierarchical clustering approach to optimise energy efficiency and data transmission in Wireless Sensor Networks [42]. This protocol utilises a data-centric methodology combined with a two-level hierarchical clustering scheme. At the first level, multiple sensor nodes are grouped into clusters, and one node from each cluster is selected as the CH. At the second level, the CHs from the first level form a higher-level cluster; one of these CHs is designated  
Figure 4: Results of implementing a standard SEP protocol using MATLAB: cluster heads are marked as bold plus signs, other nodes are marked as coloured rhombuses, and the base station is marked as a red circle in the centre.  
as the second-level CH. The second-level CH aggregates data from the first-level CHs and transmits them to the base station.  
Due to its hierarchical clustering mechanism, as illustrated in Figure 5, the TEEN protocol is classified as a reactive protocol [18; 43; 44]. In this protocol, the CHs within each cluster collect data from their respective cluster members. Once the CHs are selected, the nodes within each cluster begin sensing and transmitting data to their respective CHs, which then broadcast the aggregated data to other cluster areas or the base station [45].  
The TEEN protocol differs significantly from protocols such as LEACH and SEP in terms of functionality. Specifically, TEEN introduces two thresholds--Hard Threshold (HT) and Soft Threshold (ST)--to control data transmission and reduce energy consumption. The HT is defined as a user-specified value representing the range of interest for a sensed attribute. When the sensed attribute exceeds the HT, the sensor node transmits data to the CH during the current round. soft threshold is the minimum change of the sensed attribute value, which triggers the sensor's transmitter. The soft threshold reduces the data transmission frequency because it allows for little change in the sensed attribute. The value of ST in the network can be changed according to the user. If the ST is smaller, the network will be more accurate and have a balanced power consumption [46]. Two conditions allow nodes to transmit data: the value of the hard threshold should be less than the sensed attribute's current value, and the soft threshold should be equal to or less than the value of the sensed attribute [45]. Figure 5 shows the implementation of the standard TEEN protocol using MATLAB.
###### Abstract  
This study investigates the potential benefits of polar-transformed ECG spectrograms in comparison with conventional ECG spectrograms in terms of the visualization of R-R intervals and deep learning predictions of cardiac arrhythmia. The results suggest that polar-transformed ECG spectrograms could serve as an alternative approach for on-device AI applications in heart rhythm monitoring.
## 5 Connectivity Manager
### Integrated Satellite-Terrestrial Connectivity  
The connectivity system of an autonomous vessel is a hybrid architecture, comprising satellite and terrestrial components. The connectivity manager decides which data are sent over which route depending on the QoS requirements of the data and availability of the links. Connection to the outside world is required to be very robust to ensure safety at sea and efficient transport of goods anytime, anywhere in the world.  
Satellite and high-frequency (HF) networks are able to provide connectivity in the deep-sea conditions. Further, future digital VHF networks called VHF data exchange systems (VDESs) have both satellite and terrestrial components and will be useful in those environments. The first practical tests have been conducted recently [45]. In addition, digital ultra-high-frequency (UHF) connectivity may provide redundant private network critical connections. Mobile operator networks such as LTE and 5G are mostly used close to the shoreline where they can provide high-capacity links for the vessels. There are on-going activities to develop maritime-specific mobile operator networks (see, e.g., [37]). Reference [40] aiming to up 100 km coverage. It can be calculated that the maximum line-of-sight (LoS) distance \(d\) in kilometers is:  
\[d=3.57(\sqrt{Kh_{1}}+\sqrt{Kh_{2}}) \tag{1}\]  
Figure 3: Connectivity manager for integrated satellite–terrestrial system in a future maritime scenario.  
where \(K=4/3\) is an adjustment factor to account for the refraction, and \(h_{1}\) and \(h_{2}\) are antenna heights (in meters) from the ground (or sea level). A typical maritime communication channel consists of a 2-ray channel where, in addition to an LoS link, another signal is reflected from the water surface [46].  
The maximum LoS distance for ship-to-ship communications when both ships are equipped with identical antenna heights is shown in Figure 4.  
The maximum LoS range would be 36.8 km (approximately 20 nautical miles) if it is assumed that vessels have high masts that are 20 m in length. Very high antennas are required if link distances above 80 km are targeted. Thus, there is a clear dependence on the antenna heights in order to achieve long ranges. One may need, e.g., a drone or a balloon to raise the antenna from the ship to achieve a long-range connection without a satellite. In practical tests, a coverage up to 70 km from the shore has been achieved using a high mast that is 350 m in length on the shore and a high antenna that is 10 m in length on the ship.  
One of the most demanding places for connectivity is the port area due to extensive traffic. High-data-rate networks using private 5G and beyond cellular networks and WiFi are expected in the future. The 5G rollouts for local port networks are already on going. The private network can provide special services in the area, supporting harbor operations with surveillance, logistics, and other services. Thus, the autonomous vessel would be connected to the port operator network when approaching the land. Finally, radio-equipped buoys can also act as relays for connectivity.
## 4 Case Study of Punjab OSM Data
### Data Preparation and Tools  
During this study, two types of datasets were used: (1) history dump; (2) routing network data. The repositories were explored by downloading the required data, i.e., history planet dump ([[http://planet.openstreetmap.org/](http://planet.openstreetmap.org/)]([http://planet.openstreetmap.org/](http://planet.openstreetmap.org/))) and regular region-wise extracts ([[http://download.geofabrik.de/](http://download.geofabrik.de/)]([http://download.geofabrik.de/](http://download.geofabrik.de/))). Further, in this study, OSM data of the Punjab (India) region were used for analysis. The following sections elaborate the methods and tools for extracting the test data:
# Assessment of Cardiovascular Disease in Autosomal Dominant Polycystic Kidney Disease
## 9 Cardiopulmonary Exercise Testing (CPET)  
Cardiopulmonary exercise testing (CPET), also referred to as a VO\({}_{2}\) (oxygen consumption) test, is a specialized type of stress test or exercise test that measures exercise ability. This test provides information about the heart and lung function to understand if patients' response to exercise is normal. CPET defines the maximum exercise capacity through the measurement of peak oxygen uptake (VO\({}_{2}\)). Oxygen uptake (V'O\({}_{2}\)), carbon dioxide production (V'CO\({}_{2}\)), and minute ventilation (V'E) are determined. CPET consists of a steady-state resting period, then one minute of warm-up without a load, followed by a stepwise protocol in which the work rate is increased in 1 min intervals by increments of 10 Watt. The exercise test is considered maximal for a value of respiratory exchange ratio (RER) > 1.05. The Lactic Threshold (LT) is detected individually using the V-slope method [58]. Workload (W), LT, maximal oxygen consumption (V'O\({}_{2}\)max), HR peak values, and BP are evaluated and compared with those obtained in a group of healthy subjects matched for age, height, weight, and gender. Oxygen uptake and ventilatory patterns obtained during the submaximal portion of CPET also give valuable information because of the possibility to evaluate the ability to perform activities of daily living during low-level exercise. CPET has become an important clinical tool to evaluate exercise capacity and to predict outcome in patients with heart failure and other cardiac conditions. It provides the assessment of the integrative exercise responses involving pulmonary, cardiovascular, and skeletal muscle systems, which are not adequately reflected through the measurement of individual organ system function. CPET is being used increasingly in a wide spectrum of clinical applications for the evaluation of undiagnosed exercise intolerance and for the objective determination of functional capacity and impairment [58].  
Few studies assessed the role of CPET in ADPKD, showing reduced tolerance to stress and decreased anaerobic thresholds in patients with preserved kidney function [59; 60]. VO\({}_{2}\) peak and anaerobic threshold are the major predictors of all-cause and cardiovascular disease, and in ADPKD patients, they are often out of the normal range. It is important to confirm that exercise capacity is impaired in ADPKD with preserved kidney and cardiac function.  
Therefore, early and non-invasive markers of cardiovascular risk and CPET should be performed in ADPKD patients, in the early stages of disease, despite the cost implication. Note that [PERSON] et al. [61] reported a case of subarachnoid hemorrhage from a ruptured berry aneurysm after stress testing in a patient with ADPKD.
# Numerical Study on Cooling Performance of a Steam-Cooled Blade Based on Response Surface Method
## 4 Analysis of the Results of the Response Surface Model Fitting
### Significance Analysis of Working Parameters  
The central composite design (CCD)-response surface method (RSM) was used to analyze the significance of the design parameters (\(T_{\rm g},p_{\rm gov},p_{\rm r},T_{\rm r}\) and \(M_{\rm r}\)) when the responses were the average cooling efficiency \(\varepsilon_{\rm ave}\), temperature non-uniformity \(\zeta\), and average dimensionless temperature \(\eta_{\rm ave}\) of the steam-cooled blade, respectively. The factor effect diagrams for the average dimensionless temperature \(\varepsilon_{\rm ave}\), temperature non-uniformity \(\zeta\), and average dimensionless temperature \(\eta_{\rm ave}\) of the steam-cooled blade are given in Figures 19-21, where the Pareto effect and normal effect diagrams are shown in Figure 19a,b, Figure 20a,b and Figure 21a,b, respectively. In the Pareto effect diagram, the positive effects are distributed to the right of the red line and the negative effects are distributed to the left of the red line. In the normal effects diagram, the effects of each factor are arranged in a sequence from small to large, showing the significance of each work parameter. As can be seen from Figure 19, the sensitivity levels of the effective terms for the average cooling efficiency  
followed by \(M_{\rm r}\), \(p_{\rm r}\) and \(p_{\rm g0}\). \(M_{\rm r}\) is the most significant parameter affecting the average dimensionless temperature of the blade, followed by \(T_{\rm r}\), \(p_{\rm r}\) and \(p_{\rm g0}\). In addition, \(T_{\rm g}\) has almost no effect on the performance of the steam-cooled blade.
## 4 Simulation Results
### Introducing Studied System  
In this paper, solar generators, wind turbines, and fuel cells are considered units. Investment costs and other issue data are presented in Table 1. The cost of purchasing energy from the national grid is USD 0.2 per kilowatt on a one-year planning horizon. The fixed cost of installing a reactive power supply \(fc_{i}\) is estimated at USD 1000, and the variable cost of a reactive power supply \(c_{i}\) is estimated at USD 30 per kilowatt.  
Figure 7: Flowchart of the proposed method in problem-solving.  
Other assumptions of the problem are:  
* For the microgrid to succeed, at least 60% of the load must be supplied by the non-probabilistic output, which is included as a constraint.
* Renewable units are operated with a unit power factor [37].
* There is no energy storage option, so if necessary, non-renewable sources are adjusted according to the load requirement, i.e., no excess is allowed. This strategy only applies during the operation of the island. However, when connected to the network, the franchisee uses the program to inject the generated power into the system.
* In the case of an upstream error, the load-cutting strategy is only performed in the case of islands with priority loads.
* All network loads are considered with a constant power factor.
* In the planning stage, according to the costs of purchasing energy and the cost of installing distributed products, the amount of energy production within the microgrid is determined. In the case of connected to the network, their utilization is determined according to the cost of production of distributed products and the energy purchased.
* In this work, the improvement of potential microgrid reliability due to line capacity liberalization is considered.  
In the standard 34-bus distribution system, demand of 6.456 MW was adopted to evaluate the proposed method. Voltage and apparent power are the basis of choice for 12.66 kV and 100 MV analysis.
# A Method for Detecting the Yarm Roll's Margin Based on VGG-UNet
## 4 Experiments
### Comparative Experiment  
In this study, the encoder part of the U-Net model can be enhanced by utilizing different architectures to improve its feature extraction capabilities. Two models, VGG-UNet and Res-UNet [30], were constructed by replacing the encoder with efficient feature extraction networks VGG16 and ResNet [31], respectively. To investigate the specific impact of pre-trained weights on model performance, an experimental variable was introduced to assess the use of pre-trained weights. Under the same training conditions and parameter settings, these enhanced models were compared to the original UNet model through comparative experiments. Among them, ResNetUNet-pre and VGGUNet-pre represent the ResNet-UNet and VGG-UNet models loaded with pre-trained weights, respectively. The experimental results are shown in Figure 8. The results indicate that models using pre-trained weights exhibit significantly faster convergence during training and smoother loss value reduction, further confirming the effectiveness of pre-trained weights.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **True** & **False** \\ \hline True & True Positive (TP) & False Negative (FN) \\ False & False Positive (FP) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusing matrix of detection.  
To validate the effectiveness of the model in yarn segmentation tasks, we evaluated its performance on the test dataset. The specific results are depicted in Figure 9. Without the application of transfer learning, the Res-UNet model demonstrated the best performance, achieving an average Intersection over Union (mIoU) of 90.26%. In contrast, the original VGG-UNet model exhibited inferior performance, achieving only 79.73% mIoU, indicating room for improvement in its segmentation effectiveness. However, upon introducing pre-trained weights, the VGG-UNet model's performance significantly improved in all aspects, surpassing the Res-UNet model in metrics such as mPrecision, mRecall, mPA, and mIoU, surpassing the Res-UNet model in these metrics. Particularly, compared to the VGG-UNet model without pre-trained weights, the model fine-tuned with pre-trained weights showed an increase of 9.52% in average precision, 26.23% in average recall, and 18.3% in mIoU for yarn region segmentation tasks. This outcome clearly demonstrates that under conditions of limited sample size and few epochs, the strategy of fine-tuning the model with pre-trained weights is significantly superior to training from scratch.  
Figure 8: Comparison of training loss curves for various models.  
Figure 9: Comparison of evaluation metrics among various models.  
From Table 3, it can be observed that the errors in determining the yarn roll's margin for different types of yarn bobbin using the algorithm proposed in this study are all less than 3 mm. Additionally, regardless of whether the margin increases or decreases, the relative error in margin remains within 4%, meeting the actual production requirements of the factory.
# A Comprehensive Review of Developments in Electric Vehicles Fast Charging Technology
## 2 Fast Charging Techniques for Electric Vehicles  
Research and development efforts in fast charging techniques for electric vehicles are increasingly vital, aiming to reduce charging times and enhance the convenience of EV ownership [43]. According to the International Energy Agency's 2022 report, global electric car sales have surged, reaching 14% of all new car purchases, up from being 9% in 2021 and less than 5% in 2020. The first quarter of 2023 saw over 2.3 million electric cars sold, a 25% increase from the previous year. Projections suggest that 14 million electric vehicles will be sold by the end of 2023, with expectations that they may constitute 18% of total car sales for the year. Forecasts under the IEA Stated Policies Scenario indicate that electric car sales could reach 35% globally by 2030. This increasing popularity underscores the importance of efficient fast charging methods to support widespread adoption. Addressing the time needed to recharge EV batteries is a key challenge as EV adoption grows. A survey by McKinsey & Company found that 80% of EV owners consider fast charging availability to be crucial to their purchase decisions. Fast charging infrastructure is crucial for commercial sectors like taxi fleets and delivery services to minimize vehicle downtime and enhance operational efficiency. Significant advancements have been made in fast charging systems for electric vehicles (EVs) to meet the growing demand for high-power charging. These innovations enable shorter charging times compared to traditional methods. Key charging techniques include inductive charging, ultra-fast charging, DC fast charging, Tesla Superchargers, bidirectional charging V2G (vehicle-to-grid) integration, and battery swapping, as illustrated in Figure 1.
# Blur Kernel Estimation by Structure Sparse Prior
## 6 Conclusions  
In this paper, a novel blur kernel estimation method is proposed, which incorporates the sparsity prior and the locality constraint. To avoid the weakness of sparse representation, the proposed method utilizes the correlations between different image pixels. A structure sparse prior is proposed by introducing the locality constraint into sparse representation framework. By utilizing the locality constraint and the sparsity regularization, the proposed method can preserve the structure information of image. The structure sparse prior can preserve the inherent attribute of the sharp image. Extensive experiments showed that the proposed method outperforms other deblurring methods.  
Conceptualization, X.Y., J.Z. and X.L.; Methodology, X.Y., J.Z. and X.L.; Software, X.Y.; Validation; X.Y., J.Z. and X.L.; Formal analysis, X.Y., J.Z. and X.L.; Investigation, X.Y.; Resources, X.Y.; Data Curation, X.Y.; Writing-original draft preparation, X.Y.; Writing-Review& Editing, X.Y., J.Z. and X.L.; Visualization, X.Y., J.Z. and X.L.; Supervision, X.Y., J.Z. and X.L.; Project Administration, X.Y., J.Z. and X.L.; Funding Acquisition, X.Y., J.Z. and X.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflicts of interest.  
Figure 10: SSIM values of various methods with different kernel size: (**a**) k = 7; (**b**) k = 9; (**c**) k = 15; and (**d**) k = 19.
# Cyberattack on Flight Safety: Detection and Mitigation Using LoRa
## 3 Flight Safety Framework
### Sense and Avoid Protocol  
The sense and avoid protocol functioned similarly to the TCAS presented in the Introduction, but it was implemented using the nodes described earlier with the following modification. A node was connected to an external power source and operated all the time. We assumed the node was integrated into an aircraft or into a moving object that had a power supply. The node continuously checked if there was another node in the area by sending an appropriate message. If a node responded in the positive, both nodes began measuring the distance from each other. If the distance measurement (Figure 6) recorded a result lower than a constant, the node displayed the proper alert.  
Figure 5: The wireless sensor network protocol diagram. Nodes A and B exchange information directly. Nodes A and C exchange information through Node B. The dotted rings represent the node’s transmit and receive ranges.  
At the same time, when a node received a positive answer regarding the distance measurement and started the process of measuring the distance, it was blocked from other nodes' distance measurement requests. In this system, we focused solely on the ability to detect two nodes approaching each other at a distance that posed a danger to the aircraft. While modern TCASs provide additional information to the pilots such as flying the aircraft at different altitudes to avoid an impact [18], this proposal was an additional layer of protection while the TCAS was under a cyberattack.