esa-sceva/satcom-chunk-collection huggingface dataset:
# Ionization of Xenon Clusters by a Hard X-ray Laser Pulse
###### Abstract  
Ultrashort pulse X-ray free electron lasers (XFFLs) provided us with an unprecedented regime of X-ray intensities, revolutionizing ultrafast structure determination and paving the way to the novel field of non-linear X-ray optics. While pioneering studies revealed the formation of a nanoplasma following the interaction of an XFEL pulse with nanometer-scale matter, nanoplasma formation and disintegration processes are not completely understood, and the behavior of trapped electrons in the electrostatic potential of highly charged species is yet to be decrypted. Here we report the behavior of the nanoplasma created by a hard X-ray pulse interacting with xenon clusters by using electron and ion spectroscopy. To obtain a deep insight into the formation and disintegration of XFEL-ignited nanoplasma, we studied the XFEL-intensity and cluster-size dependencies of the ionization dynamics. We also present the time-resolved data obtained by a near-infrared (NIR) probe pulse in order to experimentally track the time evolution of plasma electrons distributed in the XFEL-ignited nanoplasma. We observed an unexpected time delay dependence of the ion yield enhancement due to the NIR pulse heating, which demonstrates that the plasma electrons within the XFEL-ignited nanoplasma are inhomogeneously distributed in space.
# A Self-Organizing Area Coverage Method for Swarm Robots Based on Gradient and Grouping
## 6 Experiment and Analysis
### Qualitative Analysis of Different Coverage Algorithms  
In this paper, the GGC algorithm has great advantages in coverage-rate and convergence speed and is also superior to other coverage algorithms in environmental scale,  
Figure 23: Coverage time curve of different number of robots in failure.  
Figure 24: Comparison of regional coverage rates.  
complexity, communication cost, and stability. As shown in Table 3, we can see that other coverage algorithms that do not participate in the quantitative analysis are not suitable for large-scale robot environments and cannot cover complex areas. For CVT, LFS, DD, CADT and other algorithms, there is a large amount of communication between each other and communication delay, and these algorithms require much computation to cover the region. By contrast, GGC does not just adapt to the coverage model of large-scale swarm robots, but also in the same complex area environment, and swarm robots can carry out coverage work with small communication costs and minimal computation. Moreover, because GGC has good robustness, its stability is better than other algorithms.
# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 5 Literature Review
### Results and Insights
#### 5.4.4 Spatial Scale  
Resilience frameworks need to combine qualitative and quantitative analyses at various temporal and spatial scales [156]. The temporal aspect is treated widely through monitoring of performance evolution with time, however, more effort should be put into considering time horizons of different events which directly relate to the system resilience and the efficiency of quantification methods [136]. For a small service area, the same failure probability of each component is considered when the distribution system suffers from natural disasters [131]. In the case of larger areas, it becomes very important to consider the spatial distribution of an event, in order to better estimate the hazard impact and recovery duration [105]. This can be achieved by defining multiple impact zones and use of failure probability or N-k contingency constraint [123; 132]. Other methods use a model for event path [128], the spatial distribution of the number of outages [129], and spatio-temporal random processes [143]. Since post-disruption electric grid performance is highly sensitive to event spatial characteristics [105], the spatial dimension should be explicitly incorporated into performance function, unlike most related works.
## 2 Compact Dual-Polarized Pillbox Feeding Structure with High Isolation
Figure 6: Detailed configuration and field block effects of blocking vias: (**a**) specification of blocking vias and (**b**) field distribution inside the blocking vias. Moreover, Figure 7 exhibits the field distributions on the middle layer (Sub 2) with and without blocking vias. As shown in Figure 7a, the y-polarized fields reflected from the parabolic reflecting surface combine with the \(x\)-polarized fields, degrading the isolation and causing phase distortion. Conversely, Figure 7b demonstrates that fields separated by blocking vias cannot be coupled with orthogonally polarized fields. As a result, the feed horn antenna can produce a low-distortion cylindrical wave. In addition, the proposed dual-polarized HA can achieve high isolation because of the low coupling between each port. Figure 8 shows a comparison of the simulated results with and without blocking vias in the middle layer and it shows that the isolation between the feed ports (S\({}_{21}\)) is approximately 15 dB larger than that without the blocking vias. However, previous field distribution results represent guided waves inside the waveguide. Therefore, the feeding structure should use a metasurface of the unmodulated impedance surface to convert it to a surface wave simulation and verify the phase progression. Figure 9 presents the field distribution results on the top layer using a metasurface. Figure 9a illustrates the field distribution resulting from the propagation of a surface wave in a metasurface with an unmodulated surface impedance. The cylindrical wave from the SIW horn antenna is reflected by the reflector on the under layers (Sub 1 or Sub 2) and produces a plane wave at the top layer (Sub 3). Furthermore, once the coupled field from the lower layers is integrated into the upper layer, it is subsequently converted by the metasurface into a TM\({}_{0}\) mode surface wave that acts as a reference wave, as shown in Figure 9a. In addition, Figure 9b,c shows the phase distribu  
Figure 8: Comparison of transmission coefficients between each polarization feeding port with and without blocking vias. Figure 7: Field distribution of pillbox feeding structures (**a**) without and (**b**) with blocking vias. corresponding to each polarization mode. In particular, the phase distribution in each mode acts as a plane wave perpendicular to the direction of wave propagation. As a result, plane waves with an accurate phase can be realized, suppressing unwanted radiation and enabling continuous scanning in various directions, including the broadside, with a high gain.
## 2 Materials and Methods
### Modeling  
#### 2.2.1 The Kinetic Model  
The kinetic model describes the attachment of SS to, and its detachment from, a filter medium in terms of the specific SS deposit per unit volume of the medium (\(\sigma\), kg/m\({}^{3}\)) [37], as given by Equation (1):  
\[\frac{\partial\sigma}{\partial t}\ =\ k_{a}uC_{0}(\sigma_{\rm m}-\sigma)-k_{d}\sigma \tag{1}\]  
where \(\sigma\) is the specific SS deposit (kg/m\({}^{3}\)) at time \(t\) (h), \(\sigma_{m}\) is the maximum specific SS deposit (kg/m\({}^{3}\)), \(C_{0}\) is the SS concentration of the influent (kg/m\({}^{3}\)), \(u\) is the filtration velocity (m/h), \(k_{a}\) is the attachment constant (h\({}^{-1}\)), and \(k_{d}\) is the detachment constant (h\({}^{-1}\)). It is assumed that the local fluid velocity and shear stress increase due to the decrease in pore space when \(\sigma\) reaches a critical level. The elevated fluid velocity and shear stress induce the breakage of deposited SS aggregates and, hence, the discharge of the solids [38]. Consequently, the amount of SS in the effluent is increased, and the overall SS deposit, i.e., \(\sigma\), is decreased.
## 3 Methods
### Loss Function and Optimizer  
We combine the cross-entropy loss function [31] and Lovasz-Softmax [32] as the loss function and use stochastic gradient descent (SGD) as the optimizer of the network. The semantic segmentation problem is actually classifying each point in the point cloud, and the cross-entropy loss function can be applied to the multi-classification problem. Lovasz loss is an effective additional loss term that can be used for different machine-learning tasks such as object detection and semantic segmentation. Therefore, we combine the Lovasz loss with the cross-entropy loss function to achieve better model training results. The final loss function can be expressed by Equation (6):  
\[L_{total}=-\sum_{i}1/\sqrt{v_{i}}P(y_{i})\text{log}\,P(\hat{y}_{i})+1/|C|\sum_{ c\in C}J(e(c)) \tag{6}\]  
where \(v_{i}\) represents the frequency of each class, \(P(\hat{y}_{i})\) and \(P(y_{i})\) represent the corresponding predicted and true value probabilities, and \(J\) represents the extension function of the Lovasz function for the semantic segmentation metric IOU (intersection over union). \(|C|\) represents the total number of classes, and \(e(c)\) represents the error vector of class \(c\), which can be expressed as:  
\[\text{e}_{i}(c)=\left\{\begin{aligned} & 1-x_{i}(c)&& \text{if }c=y_{i}(c)\\ & x_{i}(c)&&\text{otherwise}\end{aligned}\right. \tag{7}\]  
where \(x_{i}(c)\in[0,\,1]\) denotes the probability that point \(i\) is the prediction of class \(c\), and \(y_{i}(c)\in\{-1,1\}\) denotes the corresponding ground-truth label.  
For the optimizer, the SGD is chosen in this paper, the initial learning rate is set to 0.24, and the decay is \(10^{4}\) per round of training. To speed up the learning process, momentum is introduced and set to 0.9.
## 2 Health Feature Parameters
### Health Features Extraction
The probability density (PD) analysis method correlates voltage distribution with capacity decline by calculating probability density for each voltage value. Figure 2: Discharge voltage curve under actual vehicle operation. Figure 3c shows the correlation between peak PD curve and capacity decline. By observing Figure 3a-c, it is evident that the voltage data needed for feature extraction in Cell1 battery mainly ranges from 3.78 V to 3.80 V. This indicates that the prediction process does not necessitate complete charge voltage data and only a smaller amount of data needs to be collected, resulting in reduced data computation and improved processing efficiency. To show the above conclusions more clearly, trends in IC peaks over the entire cycle process of B0005 and B0006 batteries are summarized. Changes and curves over the cycles are plotted as shown in Figure 3d. The max IC trend gradually decreases with increasing cycles, while showing some volatility, manifested as a global decline and a local increase. The trend closely resembles the downward trend in the actual maximum battery capacity, which verifies the validity of the selected health feature parameters in this paper. Figure 3: The process of extracting health features: (**a**) The charging curves of battery Cell1; (**b**) The capacity increment curves of battery Cell1; (**c**) The voltage probability density curves of battery Cell1; (**d**) The Max IC curves of B0005 and B0006 batteries.
## 4 Simulation and Analysis
### Computational Efficiency Comparison  
To compare the efficiency of the full-state and the error-state EKFs in the error overboundings calculation, the filtering time at each epoch is recorded in Figure 8. The filtering time of the error-state EKF was lower and more stable than that of the full-state EKF. The average filtering time for the error-state EKF was approximately \(7.573\times 10^{-4}\) s, \(16.163\%\) less than that for the full-state error, approximately \(9.033\times 10^{-3}\) s. A detailed comparison can be seen in Table 2.  
Figure 6: Simulation of the obtained error overboundings for the east position error.  
Figure 7: Simulation of the obtained error overboundings for the north position error.  
Actually, the filtering time required for sparse matrix operations is usually proportional to the number of arithmetic operations in the number of nonzero elements. In the system state propagation period, the state transition matrix \(\mathcal{O}_{k}\) of the full-state EKF has more nonzero elements than that of the error-state EKF. As shown in Figure 8, taking, for example, the simulation times at 312.5 s and 357.5 s, there are distinct peaks for the full-state EKF because of the sparse matrices in the system propagation equation. Moreover, the convergence process of the EKF was evaluated by 3.66 times variance. As shown in Figure 9, the state error of the error-state EKF converged faster than the full-state EKF, the oscillation before convergence was smaller, and the convergence process was smoother.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Maximum** & **Minimum** & **Mean** & **Variance** \\ \hline Error-state & \(1.868\times 10^{-3}\) s & \(8.03\times 10^{-5}\) s & \(7.573\times 10^{-4}\) s & \(4.8364\times 10^{-8}\) \\ Full-state & \(3.954\times 10^{-3}\) s & \(9.21\times 10^{-5}\) s & \(9.033\times 10^{-4}\) s & \(1.4365\times 10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Filtering time comparison.  
Figure 8: The filtering time of the error-state and full-state EKFs.  
Figure 9: The state error of the error-state and full-state EKFs.  
To sum up, error-state EKF-based method and full-state EKF-based method are consistent in final equation expression and overbounding results of the error overboundings. However, the qualitative analysis result shows that error-state EKF-based method has a higher calculation efficiency and faster convergence speed, and the quantitative comparison result in Table 2 shows error-state EKF-based method has shorter filtering time.
## 3 Methods  
This section outlines the architecture of a proposed fog system designed to manage and predict parking space availability. The architecture consists of three layers, as shown in Figure 1. The first layer is the IoT layer, which includes the parking lots themselves that provide real-time updates on their space availability. Users in their vehicles or on their mobile devices can receive predictions of the number of available parking spaces that will be available at a specific time, helping them to plan their parking ahead of time. The second layer is the fog layer, which receives information requests from the users and the parking data. This layer incorporates a predictor of the number of parking spaces that will be available in the future, which utilizes a ML algorithm based on AdaBoost. This algorithm helps to accurately predict the number of available parking spaces, based on historical data and real-time updates. Finally, the cloud layer receives data from the parking lots, stores it, and generates statistics on their usage. Additionally, based on the performance of the predictor, this layer is responsible for training or retraining the algorithm to improve its accuracy over time. The main types of fog agents considered are fog gateways, fog node/server, and fog storage [20]. With respect to the supported communication protocols between the fog layer and the IoT layer, the main protocols are TCP/IP and 5G or 6G, as the primary interaction with users will be through mobile applications. However, for communication between the parking lot and the fog node, the protocols must be compatible with the sensor network in the parking lot. Sensor networks can consist of individual space sensors or cameras covering a large area. The communication protocols can be wireless, such as TCP/IP, Zigbee, or Bluetooth, or they can be wired, using protocols like I2C and MQTT, among others. Based on the investigation in ref. [21], which explores the relationship between the optimal number of fog nodes and the path loss coefficient to account for the impact of shadowing and fading, we can estimate the required number of fog nodes. One of its experiments suggests that, on average, 1.52 fog nodes are needed for 262 end devices, with certain conditions given in ref. [21]. Therefore, for example, for parking lots with 1000 spaces, we recommend using approximately six fog nodes to ensure adequate coverage and performance.  
Overall, this proposed fog system for parking space management and prediction has the potential to significantly improve parking space utilization, reduce traffic congestion, and save time for drivers. By utilizing advanced technologies, such as IoT, fog computing, and ML, this system can provide real-time updates and accurate predictions to users, helping to streamline the parking experience and enhance overall driving efficiency.
## 3 Numerical Validation  
Since LBFS has the capability of simulating inviscid and viscid flow on steady meshes with comparable performance to other numerical methods, it is interesting to combine LBFS with ALE for unsteady flow on unstructured dynamic meshes. First, the performance of ALE-LBFS on dynamic meshes is tested by fluid-solid coupling with forced motion. In the case of forced pitching of the NACA 0012 airfoil, ALE-LBFS performs well in the 2D problems. The cases of uCRM-13.5 in harmonic motion and forced pitching of the BSCW wing are responsible for validating the ability in 3D problems. Furthermore, flutter boundaries for both NACA 64A010 airfoil and AGARD 445.6 wing are predicted. Their motions of structure respond to the aerodynamic forces.
# Maintenance Management of a Transmission Substation with Optimization
## 5 Results of Optimal RCM Maintenance
### Postprocessing of the Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Maintenance Tasks**} & \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Without**} & \multicolumn{2}{c}{**With**} & \multirow{2}{*}{\begin{tabular}{c} **Saving** \\ **(EUR/Year)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Saving** \\ **(EUR/Year)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Saving** \\ **(EUR/Year)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **(\%)** \\ **(\%)** \\ \end{tabular} } \\ \hline \multirow{3}{*}{Revisions} & \(x\) & 159,574 & 7347 & 152,227 & 95.40 \\  & \(x+1\) & 159,574 & 28,520 & 131,054 & 82.13 \\  & \(x+2\) & 159,574 & 131,054 & 28,520 & 17.87 \\ \hline Inspections & & 405,935 & 405,935 & – & – \\ \hline Replacements & & 2914 & 2914 & – & – \\ \hline \multirow{3}{*}{Total} & \(x\) & **568,423** & **416,196** & **152,227** & **26.78** \\  & \(x+1\) & **568,423** & **437,369** & **131,054** & **23.06** \\  & \(x+2\) & **568,423** & **539,903** & **28,520** & **5.02** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Revision and total costs with and without optimization for changed objective function weights in the optimization process.
## 7 Results and Discussion
### Scalability  
In our current environment, the time taken to schedule five clusters is less than 1 s. Judging from these results, scheduling more clusters would not result in significant delays. Even with more target clusters, LSTM predictions can be parallelized easily. The more critical issue would be the training of multiple customized LSTM models for a large number of target clusters. Still, the issue will also be resolved with dedicated training servers conducting online learning through periodic new data collections from target clusters.  
Suppose we apply our proposed methodology to a multi-resource (e.g., CPU and GPU) cloud environment. In that case, we can collect more detailed information about services running on a failed cluster and train more customized LSTM prediction models. For example, a service requiring 0.1 GPU resources and 0.5 CPU resources, where the values are all normalized, can be relocated to a target cluster having enough GPU and GPU resources by inferring GPU and CPU resource consumption prediction simultaneously. Either two separate LSTM models for GPU and CPU, respectively, or a single LSTM model predicting two types of resources at the same time can be developed. Likewise, any different types of resources can be handled.
# A Novel Cell-Based Adaptive Cartesian Grid Approach for Complex Flow Simulations
## 3 Numerical Methods
### Hanging Grids Discretization
Such methods are simple in form, easy to implement, and readily integrated with flow solvers [35]. As for the elements used to implement the boundary conditions, they can be located inside the physical surface, known as the ghost cell method [36], or the grid cells near the surface can be modified using wall function theories [37; 38]. The implementation of IBC principally includes the following four key aspects: (1) classifying the grid types based on their relative positions to the physical surface; (2) selecting and locating reference points; (3) interpolating at these reference points; and (4) obtaining the physical quantities for ghost/wall-modeled cells based on reference points and boundary conditions. For inviscid flows and flows with low Reynolds numbers, this paper adopted the ghost cell method proposed by [PERSON] [36] to handle physical surface boundary conditions, as shown in Figure 8a. For high Reynolds number wall-bounded turbulent flows, the idea of tangential velocity reconstruction described in references [37; 38] was used to modify the grid cells near the wall, as depicted in Figure 8b.
## 3 Archaeological Feature Extraction from SAR: Methodological Approaches  
The extraction of archaeological features from SAR data is a tough challenge. One of the main issues is the low signal/noise ratio, especially considering the complexity of the target and the fact that modern landscapes may mask them. In this study we used X-band (3.1 cm wavelength) COSMO-SkyMed data. It is an Earth observation satellite constellation system run by the Italian Space Agency (ASI), and is intended for both military and civilian use. The constellation includes four identical satellites equipped with SAR sensors with global coverage. The first satellite was launched on 8 June 2007, and the last one on 5 November 2010. There are three basic types of imaging modes, including ScanSAR, Stripmap and Spotlight. In the ScanSAR mode, the sensor acquires large coverage (200 \(\times\) 200 km) images with a coarse spatial resolution (pixel at 100 m). In the Stripmap mode the acquisition provides medium coverage (40 \(\times\) 40 km) with fine resolution (pixel ranging from 3 to 5 m) and finally, Spotlight mode provides small coverage (10 \(\times\) 10 km) with very high resolution (1 m).  
Two data processing approaches were adopted (see Figures 1 and 2). The first was based on the multitemporal processing of Stripmap HIMAGE data applied to a case study in China (Luoyang). The spatial resolution has been considered enough for the detection of the features expected in Luoyang (see Section 4.1).  
Figure 1: Multi-temporal Synthetic Aperture Radar (SAR) data procedures for the enhancement of archaeological marks.  
The second approach was based on single date analysis of Enhanced Spotlight data applied to Sabratha in Libya and Metapontum in Italy, where smaller archaeological features compared to Luoyang are expected. In these cases, the multitemporal approach has not been adopted because of the unavailability of a time serie of Spotlight data.  
The two approaches we adopted aim to provide two different data processing strategies to cope with the limited availibility of archived data and the size of the expected features.