esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Antenna Design  
It is critical for vehicular antennas for WAVE communication systems to exhibit a high gain over the field of view (FOV) of the beam pattern, with a low return loss. To satisfy these two main objectives, we designed an array antenna structure, as shown in Figure 1. This structure consists of four small rectangular segments that are used to generate the required resonant frequency.  
To achieve high efficiency and to ensure the radiation of an elliptical beam pattern, an array structure with four elements, with an area of 15.5 \(\times\) 8.5 mm\({}^{2}\) per rectangular patch, is used in the ground plane and the feed plane, as shown in Figure 2.  
Table 1 lists the characteristics of the WAVE antenna structure that are determined experimentally to obtain an optimal beam pattern while maintaining the resonant frequency.
###### Abstract  
This paper proposes and demonstrates the use of filtered microwave band-pass windows situated at 2.1 GHz and 4.2 GHz as electrical carriers to transmit digital signals. The use of an appropriate microwave photonic filter (MPF) allows for the generation of the microwave band-pass windows. The key parameters of the filtering effect are the intermodal separation of a multimode laser diode (MLD), the chromatic dispersion parameter of the optical link, and its own length. Experimentally, it is demonstrated that the filtered band-pass windows can be used as electrical carriers to transmit digital signals at frequencies of 50 MHz, 100 MHz, and 150 MHz over 25.31 km of single-mode-standard-fiber (SM-SF). The quality Q-factor, jitter, and bit-error-rate are the parameters that allow for the evaluation of the quality of the digital signal transmission. The obtained results allow for the proposition of this photonic architecture in a passive optical network (PON) to distribute services like Internet Protocol (IP) telephony, internet, streaming video, and high definition television.  
microwave photonic filter; digital signal transmission; optical communication systems; passive optical network +
Footnote †: journal: Accepted: 2 April 2020; Published: 18 May 2020
# A Heterogeneous Multi-Satellite Dynamic Mission Planning Method Based on Metaheuristic Algorithms
## 4 Simulation Analysis  
### User Generation  
To simulate a user base with varying demands in the real world, 1000 users were randomly generated at ten different time intervals. At each time interval, some users disconnect, and new users are generated, as shown in Figure 4. To closely mimic real-world conditions, 90% of the users are distributed between 60 degrees north and 60 degrees south latitude. This distribution pattern reflects actual conditions, as mid- and low-latitude regions are typically densely populated and economically active, leading to higher communication demands. In contrast, high-latitude regions have fewer inhabitants and natural constraints, resulting in a relatively lower demand for satellite communication resources.  
The user base comprises 50% fixed users and 50% mobile users. Fixed users remain stationary, while mobile users have randomly generated directions and speeds, with their positions updated at each time step based on these parameters. In the figure, blue dots represent fixed users, and red arrows represent mobile users. The length and direction of the arrows indicate the speed and movement direction of the users, respectively. Both the priorities and the preferences of users are randomly generated within specified ranges, reflecting diverse demands. Specifically, the initial demand for each user is adjusted according to the input target's total demand. At each time step, existing users disconnect based on a certain probability, and new users are generated based on another probability. The number of new users and their demand are dynamically adjusted according to the current demand gap.
## 3 Multi-Sensor Fusion
### Fusion of Horizontal Lidar and Vertical Lidar
#### 3.3.2 The Adaptive Time Synchronization Algorithm  
In this paper, the lidar time synchronization is achieved through hardware time synchronization and GNSS is used as the master clock. However, there is a time offset between the two lidars. As the time offset is constant, an adaptive timestamp synchronization algorithm is used to solve it. The adaptive time synchronization algorithm is used to match its timestamp and realize the simultaneous localization and mapping of the lidars. The timestamps of the dual lidar is shown in Figure 4, where lidar_201/scan represents the timestamp of the horizontal lidar and lidar_202/scan represents the timestamp of the vertical lidar.  
The output of the adaptive time synchronization algorithm only depends on the timestamp, not on the arrival time of dual lidar point cloud messages. It means that the adaptive time synchronization algorithm can be safely used on dual lidar point cloud messages that have suffered arbitrary processing delays. As shown in Figure 5, time goes from left to right, with the first row representing the horizontal lidar timestamp and the second row representing the vertical lidar timestamp. Each dot represents the lidar point cloud with the timestamp. The blue dot represents the pivot of the lidar point clouds, with the broken line linking the dual lidar point clouds in a set. Suppose the horizontal lidar point clouds' queue is \(\mathbf{PL_{P}}\) and the vertical lidar point clouds' queue is \(\mathbf{PV_{P}}\), with lidar point clouds inserted in a topic-specific queue (\(\mathbf{PL_{P}}\) or \(\mathbf{PV_{P}}\)) as they arrive. Once each topic-specific queue contains at least one message, the latest message is found at the head of the queues, known as the pivot. Assume that the queue with pivots is \(\mathbf{PL_{P}}\), which matches \(\mathbf{PV_{P}}\), being the smallest difference between the timestamps, \(\mathbf{T}\). The two queues are combined into a set; finally, this set is synchronous output.  
Figure 4: Timestamps of dual lidar.  
Figure 3: Dual lidar point clouds’ information after external parameter calibration.
## Preliminaries
### Onion Routing and Garlic Routing  
Onion routing is a technique for anonymous communication. In an onion network, messages are encapsulated in multiple layers of encryption like an onion. The encrypted message will pass through a series of onion routers, each of which decrypts one single layer, uncovering the next node [52]. Only the original sender knows where the destination is, and only the original sender and the destination node know the plaintext message. All intermediate nodes know only the immediately preceding and following nodes, hence achieving the anonymity of the original sender and the message.  
Garlic routing is a variant of onion routing. Different to onion routing, it groups messages and disperses them through multiple tunnels, which further increases the difficulty of attack.
# UAV-BS Site Planning Based on Circular Coverage Strategy
## 2 Background and Related Work
### UAV Aerial Base Station
#### 2.3.2 Research on UAV-BS Autonomous Control  
[PERSON] et al., aiming at the scenario in which UAVs act as aerial base stations to provide wireless communication services, studied a scheme for the joint optimization of user scheduling, multi-resource coordination, and UAV travel path, so as to meet the requirements of user experience quality while improving energy efficiency as much as possible [18]. On this basis, an iterative optimization algorithm based on the Dinkelbach algorithm and block coordinate descent was proposed, and the problem was divided into four sub-problems for gradual optimization. [PERSON] et al. proposed a UAV-enabled mobile edge computing system that ensures interaction between UAVs, Internet devices, and edge clouds. At the same time, they use successive convex approximations to jointly optimize UAV position, trajectory, and computational resources to provide the best service [19]. [PERSON] et al. proposed a technique based on deep reinforcement learning to find the best trajectory and throughput in a specific coverage area. After training, the UAV is able to autonomously collect all data from the user node at a significant summation rate while minimizing the associated resources used [20]. [PERSON] et al. proposed a new general UAV trajectory and communication co-design framework, which has a flexible number of node optimization variables to reduce the computational complexity of path planning for long period/distance UAV flight [21].
## 3 Results
### Study on Seismic Failure Modes of Cable-Stayed Pipe Bridge Oil and Gas Pipeline Structure  
In general, the seismic damage of large-scale, complex oil and gas pipeline engineering structures often neglects the interaction between the oil and gas pipelines and cable-stayed pipe bridge structures. The oil and gas pipeline serves as a critical structural component within the cable-stayed pipe bridge system. To ensure its safety, this study analyzed the failure mechanisms of oil and gas pipelines under strong earthquakes in such systems, revealing their vulnerable points. However, compared to highway or railway bridges, cable-stayed pipe bridges with integrated oil and gas pipelines exhibit lower lateral stiffness and more intricate vibration characteristics. This complex structural system comprises various components, including bridge towers, cables, truss beams, supports, and pipelines, that interact during earthquake events, resulting in coupled, random, and multifaceted forms of seismic damage. Failure in one component may trigger failures in others due to interdependencies among them, leading to diverse failure modes within the pipe bridge system when subjected to strong earthquakes.
## 3 Monitoring Results and Analysis
### Axial Force in the Anchor Bolts  
Figure 21 has shown the evolution of the axial forces at the end of the anchor bolts. In the same manner as the anchor cables, the anchor bolts also experienced a rapid drop in the axial forces in the initial installation period caused by the compaction of the rock, with the values accounting for 3.6-14.8% of the initial tensile forces. This process lasted for about 15 days, about three times longer than that of the anchor cables. This can be attributed to the relatively smaller prestress of the anchor bolts and the relatively larger excavation disturbance of the rock surrounding the anchor bolts extending the compaction time of the rock. Apart from the anchor bolts at the Monitoring Points A and B, the axial forces of the other anchor bolts were obviously affected by the subsequent excavation, and the monitoring values had obvious fluctuations. After the completion of the excavation, the axial forces in anchor bolts were stable.  
Figure 22 has shown the distribution of the axial forces at the end of the anchor bolts. The axial forces of the anchor bolts reached 95% of the design value. The maximum axial force in the anchor bolt was 120.4 kN at the left haunch, and the minimum value was 95.3 kN at the left spandrel. The yield load of the anchor bolts was 159 kN; therefore it could be seen that the axial forces in the anchor bolts reached 60.0-75.7% of the yield load, indicating that they had a higher utilization rate than the anchor cables.  
Figure 21: Evolution of the axial forces at the end of the anchor bolts.  
Figure 20: Final distribution of the axial forces in the cables.
## 1 Introduction
However, instead of using two completely different classification schemes depending on how much detail one needs, it may be more practical to use one scheme, which allows for more or less detail. The Magono-Lee classification is such a scheme since it has different levels, and the main level with its eight categories could constitute one such coarse classification with less detail. Using Magono-Lee enables comparison with many previous studies that have used this classification. Additionally, using a less detailed classification defined by a few shape groups, which are based on the detailed Magono-Lee scheme, results in a classification method that is both flexible and compatible with previous classifications. On the one hand, updating the highly detailed underlying classification of Magono-Lee by only adding new shapes would guarantee compatibility with previous studies. On the other hand, grouping the shapes into shape groups would make it possible to accommodate the needs of studies that do not require or desire such a high level of detail. Regrouping for specific studies is very easy since the underlying shapes use the same classification, and the eight main-level categories of Magono-Lee would only define one of many possible ways of grouping. Therefore, this study aims to first update the previous snow shape classification scheme of Magono-Lee by adding newly found shapes, then to sort particles into different shape groups, and finally to study the average microphysical properties of these shape groups. Hence, the study effectively aims to analyze the shape dependence of the snow particles' microphysical properties. For this, we present a dataset of falling snow particles that have been collected in Kiruna in northern Sweden with the new ground-based instrument Dual Ice Crystal Imager (D-ICI) [22] in Section 2. Section 3 describes how to classify these particles into shapes by updating the Magono-Lee classification scheme with shapes found by [PERSON] et al. [21] and [PERSON] [23] as well as shapes found in Kiruna as part of this study. The shape groups are defined in Section 4. Section 5 investigates the shape dependence of microphysical properties by using these shape groups. Finally, this study is summarized and concluded in Section 6.
# GPS Time Series Analysis from Aboa the Finnish Antarctic Research Station
## 2 Materials and Methods
### Observation Data  
Daily GPS observation files have been logged continuously at Aboa since 1 February 2003. The files have been retrieved on a yearly basis following the annual Antarctic expedition. The last observation day in the current archive is 8 December 2017. As a result, the covered period accounts for almost 15 years. The on-site observations were logged into the receiver manufacturer format and translated to RINEX (Receiver INdependent EXchange) format, version 2.11 using teqc utility [44] upon retrieval. The files include GPS-only observations at the epoch rate of 30 s. Epoch wise, the number of tracked satellites varied between 4 and 15, with a median number of 10 satellites per each epoch of observation.  
Figure 2: Aboa GPS antenna (**right**) next to the gravity laboratory (**left**) where the receiver is located (Photo Credit: [PERSON], 2012).  
Figure 3 gives the yearly distribution of the existing observation files. The overall daily availability is 91% (i.e., 4924 available days out of 5425 possible days). The largest gap of 381 days occurred after the 2006/2007 expedition. All files in 2006 and the first 17 days of 2007 were lost due to an unknown reason. Moreover, two other medium-length gaps were identified. One of the gaps occurred in 2008 and counted for 52 days between day-of-year 174 and 226. This was identified with the loss of power during the Antarctic winter. The other gap happened in 2010 and counted for 23 days between day-of-year 013 and 036. Furthermore, 13 days are missing in July 2004 as well as the last 8 days of December 2004. The other identified gaps count for less than 4 days. All together there are 23 gaps in the daily time series. Despite being an unattended GPS station, there is 100% daily availability for five years (2009, 2013, 2014, 2015 and 2016).
# A Study on Defect Detection in Organic Light-Emitting Diode Cells Using Optimal Deep Learning
###### Abstract  
In this study, we applied an optimal deep learning algorithm to detect defects in OLED cells. This study aims to enhance the yield of OLEDs by reducing the number of defective products through defect detection in OLED cells. Defects in OLED cells can arise owing to various factors, but dark spots are predominantly identified and studied. Therefore, actual dark spot images were required for this study. However, obtaining real data is challenging because of security concerns in the OLED industry. Therefore, a Solver program utilizing the finite element method (FEM) was employed to generate 2000 virtual dark spot images. The generated images were categorized into two groups: initial images of dark spots and images obtained after 10,000 h. The pixel values of these dark spot images were adjusted for efficient recognition and analysis. Furthermore, CNN, ResNet-50, and VGG-16 were implemented to apply the optimal deep learning algorithms. The results showed that the VGG-16 algorithm performed the best. A defect detection model was created based on the performance metrics of the deep learning algorithms. The model was trained using 1300 dark spot images and validated using 600 dark spot images. The validation results indicated an accuracy of 0.988 and a loss value of 0.026. A defect detection model that utilizes the VGG-16 algorithm was considered suitable for distinguishing dark spot images. To test the defect detection model, 100 images of dark spots were used. The experimental results indicated an accuracy of 89%. The images were classified as acceptable or defective based on the threshold values. By applying the VGG-16 deep learning algorithm to the defect detection model, we can enhance the yield of OLED products, reduce production costs, and contribute significantly to the advancement of OLED display manufacturing technology.  
AI; OLED; machine learning; dark spot Article
# Smart Grid Fault Mitigation and Cybersecurity with Wide-Area Measurement Systems: A Review
###### Abstract  
Smart grid reliability and efficiency are critical for uninterrupted service, especially amidst growing demand and network complexity. Wide-Area Measurement Systems (WAMS) are valuable tools for mitigating faults and reducing fault-clearing time while simultaneously prioritizing cybersecurity. This review looks at smart grid WAMS implementation and its potential for cyber-physical power system (CPPS) development and compares it to traditional Supervisory Control and Data Acquisition (SCADA) infrastructure. While traditionally used in smart grids, SCADA has become insufficient in handling modern grid dynamics. WAMS differ through utilizing phasor measurement units (PMUs) to provide real-time monitoring and enhance situational awareness. This review explores PMU deployment models and their integration into existing grid infrastructure for CPPS and smart grid development. The review discusses PMU configurations that enable precise measurements across the grid for quicker, more accurate decisions. This study highlights models of PMU and WAMS deployment for conventional grids to convert them into smart grids in terms of the Smart Grid Architecture Model (SGAM). Examples from developing nations illustrate cybersecurity benefits in cyber-physical frameworks and improvements in grid stability and efficiency. Further incorporating machine learning, multi-level optimization, and predictive analytics can enhance WAMS capabilities by enabling advanced fault prediction, automated response, and multilayer cybersecurity.  
smart grid; power system monitoring; wide-area measurement systems; area measurement; cybersecurity; power system security; wide-area monitoring systems; cyber-physical systems; wide-area management systems; phasor measurement units +
Footnote †: journal: _Energies_  
0000-0002-319-7886][PERSON]. [PERSON]  
1 College of Engineering, Carnegie Mellon University Africa, Kigali BP 6150, Rwanda; [EMAIL_ADDRESS]
## 4 Result
### A Parameter Size Comparison Between RNN and G-CTRNN  
The G-CTRNN model is a sequence of layers. Each layer is a set of cells, and each cell has different parameters that control the behavior of those cells. This section compares the different G-CTRNN and RNN models regarding parameter count and accuracy (Table 2). G-CTRNN reached an accuracy of about 82% with a total number of 572 parameters. The RNN reaches 73%, using almost the same number of parameters. To reach an accuracy close to the RNN accuracy, the G-CTRNN has only four neurons in the first layer. In this case, the G-CTRNN parameters are only 152, about 27% of the RNN parameters (less than one-third of the parameters!). The subsequent inquiry in the investigation pertains to determining the requisite number of parameters for the RNN to achieve an accuracy of 81.9%. RNN, with a parameter count of 1,548, achieved an accuracy of 81.4%, which falls short of the performance exhibited by the G-CTRNN with only 572 parameters. Therefore, the G-CTRNN, while having just 38% of the parameters, showed superior performance compared to the RNN.
## 3 Results and Discussion  
### Validation of Finite Element Model  
For the validation of the transmission tower finite element model, the displacement and rotation of the first order vibration mode are computed and presented in Figures 4 and 5. The X, Y and Z directions in the finite element model of the transmission tower are marked in Figure 1. In Figure 4, the displacement in the Z direction is more primary than that in the X and Y direction. However, the rotation in the X direction is more substantial and closer to the sum vector result in Figure 5. The results demonstrated in Figures 4 and 5 are quite true to the facts.  
In addition, the displacement and rotation results of the transmission tower with settlement are computed by the finite element method and revealed in Figures 6 and 7. Different from the transmission tower without settlement, the settlement makes the natural frequencies of the transmission tower obviously amplified. Furthermore, even though the displacement in the Z direction plays an essential role in the sum vector result, the changes of the displacement in the X direction are evident in Figure 6 when compared with that in Figure 4. Besides, the most dangerous locations in the displacement in the X direction move up in Figure 5(a) when the settlement happens. The modifications in the rotation of the transmission tower caused by settlement are apparent in the Y and Z directions, as shown in Figure 7. The rotation in the X direction is more dominant in the sum vector result, as shown in Figure 5. The experimental test and numerical simulation in the reported literature [5] also prove that the X direction is most sensitive in detecting the settlement of the transmission tower. The displacement of the transmission tower in the X direction is obviously fluctuated after the settlement and can be cited as the effective evidence for the identification and judgment of the foundation settlement. Thus, the finite element model of the transmission towers is verified to analyze the settlement by the natural frequencies and vibration modes.  
Figure 4: Displacement of the transmission tower in the first order vibration mode; (**a–c**) are the results in the X, Y and Z directions, respectively, and (**d**) is the sum vector result.  
Figure 5: Rotation of the transmission tower in the first order vibration mode; (**a**–**c**) are the results in the X, Y and Z directions, respectively, and (**d**) is the sum vector result.  
Figure 6: Displacement of the transmission tower in the first order vibration mode with settlement; (**a**–**c**) are the results in the X, Y and Z directions, respectively, and (**d**) is the sum vector result.