esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Validation
### Analysis
Figure 14: _Cont._  
Related to the average density, a higher density should be obtained in the initial stages when the beam has experienced only minor damages. However, whenever damage progresses, the data points should spread out, decreasing the density. More dispersion might be the symptom of an anomalous performance of the beam, possibly due to damage. From the blue histograms in Figure 14, it is evident that, except for some cases (sensors 3 and 4), only significant variations in this parameter were obtained for the last loading stage, which is a symptom of the sudden debonding of the FRP plate. For sensor 3, from load step 7, an important deterioration occurred which was maintained until the last stage. For sensor 4, a sudden and very significant decrease in volume broke out in the ninth step. Debonding originated close to this sensor; therefore, this could have been a warning before the final failure. Although it holds less interest than clusterability itself, in the first column of Figure 14, the average density was also plotted for a 95% confidence interval. The volume of the convex hull and the density of the points in the latent space provide complementary information about the state of the beam. Although both indicators might be inversely related in some cases, volume brings an additional dimension to the analysis by considering the total spatial extent and shape of the data, which might be crucial for more complex diagnoses. This parameter showed higher fluctuations than density throughout the loading steps, which might mean that it is very sensitive to the complex loading-unloading dynamic and that its efficiency is not as high as that of density. Finally, ellipse overlap histograms (third column of Figure 14 in green) show how the similarity in data dispersion varied between consecutive loading steps. In general, a high degree of overlap between consecutive steps indicates that the beam structure has not changed significantly, while a low degree of overlap suggests notable variations, possibly due to damage. By analyzing these histograms, specific patterns of how the beam responds to different load levels can be identified. For the sensors located on the left side of the FRP strip, the overlap level between the last two loading stages was zero or near-zero, which is a symptom of the  
Figure 14: Damage indicators vs. load steps, considering all the points (original and synthetic): (**a**) sensor 1; (**b**) sensor 2; (**c**) sensor 3; (**d**) sensor 4; (**e**) sensor 6; (**f**) sensor 7; (**g**) sensor 8; (**h**) sensor 9; (**i**) sensor 10.  
subsequent sudden debonding of the strip. It is remarkable that there was zero overlap among the last four loading stages for sensor 4, the origin of the subsequent debonding. From this study, it can be concluded that a decrease in density, an increase in the hull volume, and a reduction in ellipse overlaps are complementary indicators suitable to determine the health of a structure, which agrees with what can be intuited with the visualization of the latent space.
# Fabrication of Eco-Friendly Graphene Nanoplatelet Electrode for Electropolishing and Its Properties
## Materials and Methods
### Characterization  
#### 2.3.1 M-GNP Film Characterization  
The chemical composition of the M-GNP was analyzed by X-ray Photoelectron Spectroscopy (XPS) (K-alpha plus, Thermo Fisher Scientific, Waltham, MA, USA) and Confocal Micro-Raman (FEX, NOST, Gyeonggi, Korea) Spectroscopy. The microstructure M-GNP and pristine GNP were analyzed by Scanning Electron Microscopy (SEM) (APREO, FEI, Hillsboro, OR, USA). The mechanical strength of the GNP film was measured using a micro-force testing machine (Micro Tester 8848; Instron, Seoul, Korea). The conductivity of the M-GNP film was measured using a 4-point probe (CMT-SR1000N, Advanced Instrument Technology, Cumming, GA, USA).
# Hailstorm Detection by Satellite Microwave Radiometers
## 3 The Hail Detection Model
### The Hail Detection Model
The distribution of probability starts with a carrying capacity about 0.40, which roughly corresponds to TB150 \(>\) 270 K and is quite far from hail signatures as found in Table 6. Thus, for finding a numerical value that fits the hail signal, Equation (4) is adjusted on the basis of TB150 values displayed in Table 6 used as input to Figure 8b. The hail detector starts with probability values \(>\) 0. 36 (TB150 = 181.30 K) generally associated with graupels or small hail aggregates to large hail (2 \(<\) d \(<\) 10 cm) then reaches values around 0.53 when TB150 = 152.51 K, which is associated to the transition between large hail (d \(<\) 10 cm) to super hail (d \(>\) 10 cm) and finally the model stops (saturation condition) when TB150 approaches the critical value 103.70 K, i.e., the absolute minimum found in the training dataset. It is then clear the advantage of using a compact formula (Equation. (4)) in approximating such a complex interaction mechanism between hail and radiation field which typically is no-linear. As a final comment, ought to be said that since the model has been developed and tested during the \"warm\" months when hail typically forms (Mar-Sep) such possible spurious effects can be arisen over from frozen soils. Experimental applications demonstrate very few false alarms when the hail detector is integrated into the MWCC computational scheme which is able to screen out the surface effects. Figure 8: Distribution of hail probabilities calculated from eq. 4 when applied to the whole dataset (**a**) and the hail model as a function of TB150 (**b**). Figure 7: Distribution of probability model (Equation (2)) when applied to data in Table 6. The model realistically approximates the probability distribution (**a**) and the carrying capacity (**b**) when the model runs with the whole dataset.
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
## 2 Communication Architectures  
Communication architecture plays an important role in the intelligent control and autonomous collaboration of UAV swarms. In the primary stage of the development of a UAV swarm, a central station is available and powerful enough to communicate with the whole UAV swarm. Therefore, the centralized approach was extended from the traditional single-UAV system to UAV swarm communication architecture. As the swarm grows in size, the decentralized approach is proposed. The decentralized approach has a more complex structure and organization, but it does reduce the swarm's dependence on central stations [36]. Many experts and scholars have devoted themselves to the research of UAV swarm communication architectures. In this section, we review these researches and systematically introduce common communication architectures available for UAV swarm systems. Additionally, we also analyze the pros and cons of these architectures.
## 5 Challenges and Limitations in Carotenoids Analysis
### Challenges in Sample Preparation  
Sample preparation is critical for ensuring efficient carotenoid recovery while minimizing degradation, environmental impact, and overall analytical performance. However, several limitations exist:  
* Exhaustive extraction requirements: Carotenoids are strongly bounded to plant cell matrices and lipophilic compounds, requiring high solvent volumes and prolonged processing times in techniques such as maceration, Soxhlet extraction, and solvent-assisted extraction. This increases the risk of degradation and variability in extraction efficiency [53, 54, 55, 56, 57, 58, 115]. To improve extraction selectivity and reduce solvent usage, techniques such as SWE and SFE have gained attention, as they offer cleaner, eco-friendly alternatives with reduced processing times [77, 78, 79, 80, 85]. * Extensive use of organic solvents and chemicals: Traditional extraction relies on solvents like hexane and acetone, posing environmental and safety concerns [48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58]. While DES and ionic liquids (ILs) provide promising green alternatives, their validation for food applications remains incomplete. Further research is needed to standardize low-toxicity, biodegradable solvents for regulatory approval [42, 59]. * Stability and degradation issues: Carotenoids are susceptible isomerization, and thermal degradation, affecting quantification accuracy [116]. Enzymatic-assisted extraction (EAE) and pulsed electric field (PEF) technologies have demonstrated the potential to reduce degradation by operating under mild conditions [81, 110]. Additionally, nitrogen flushing and low-temperature extractions help preserve carotenoid integrity during sample preparation [117, 118]. * Lack of universally applicable extraction methods: Due to matrix-dependent variations in carotenoid interactions with proteins, fibers, and lipids, extraction conditions must be tailored. [119]. A single standardized method applicable to all matrices is currently lacking, making interlaboratory comparisons and method validation even more complex. A promising solution lies in emerging hybrid approaches, such as combining UAE with enzymatic treatments, which have improved recovery in complex food matrices [75, 86]. Further optimization of multi-step protocols is necessary for interlaboratory standardization. Table 5 summarizes the key advantages and limitations of commonly used extraction techniques and the carotenoids most frequently extracted using each method.
## 3 Development and Production of Vehicle
### Methodology of Environment and Surroundings Preparation  
The terms environment and surroundings are characterized by two different definitions. The environment is defined as the space of the whole room and the surrounding as the immediate proximity of the person being scanned. Within the methodology of preparation of the environment and the surroundings during the scanning process, the preparation and diagnostic room, regulation of the temperature and humidity of the environment, temperature in the room and its equipment are monitored. The room in which the person is scanned is to meet the basic requirement of ensuring a constant ambient temperature through air conditioning. The temperature range should be kept from 18 to 25 \({}^{\circ}\)C. At lower temperatures, the scanned person could quake and at higher temperatures could perspire, which would significantly affect scanning.  
\begin{table}
\begin{tabular}{c c} \hline Resolution: & 0.5 mm \\ \hline Accuracy: & 0.1 mm \\ \hline Distance accuracy: & 0.03\% for 1000 mm \\ \hline Texture resolution: & 1.3 Mpx \\ \hline Light source: & Flash-light \\ \hline Working distance: & 400–1000 mm \\ \hline Field of view (closer): & \(214\times 148\) mm \\ \hline Field of view (farther): & \(536\times 371\) mm \\ \hline Maximum frame rate: & Up to 16 fps \\ \hline Exposure time: & 0.0002 s \\ \hline Scan speed: & 2 mil. points/s \\ \hline Output formats: & AOP, ASC, CSV, OBJ, PLY, STL, WRL \\ \hline Dimensions: & \(262\times 158\times 63\) mm (h \(\times\) w \(\times\) t) \\ \hline Minimal requirements: & Intel® Core i5m, NVIDIA GeForce 400 or higher (with 1 GB or higher memory): \\  & 12 Gb RAM, 300 MB free space on disk \\ \hline OS support: & Windows 7, 8 or 10, 64 bit \\ \hline Weight: & 1.2 kg \\ \hline Price: & 13.700 \(\epsilon\) \\ \hline \end{tabular}
\end{table}
Table 1: Basic parameters of the Artec Eva scanner [9; 17].  
Figure 3: Use of the Artec Evascanner.  
Another requirement is to darken the room in order to avoid sunlight when scanning, which is undesirable in the process itself. Sufficient artificial lighting significantly affects the quality of the obtained 3D model. The recommended artificial lighting is in the range of 30 to 50 lux. The noise of the room may also affect the model itself. This value should be a maximum of 50 dB so that the subject can concentrate. To work with the Artec Eva scanner, it is necessary to ensure a minimum distance of approximately 1 m between the scanner and the scanned person. Based on this requirement, the proposed room should have dimensions of at least 2 \(\times\) 2 m or more.
# Parametric Analysis on Landing Gear Strut Friction of Light Aircraft for Touchdown Performance
## 1 Introduction  
The aircraft landing gear plays a crucial role in the takeoff, landing impact energy-absorbing, and ground operations [1]. In the shock absorber strut, the friction force is an elemental force of the total strut force and converts kinetic energy into internal energy in the landing process [2]. Therefore, friction can affect landing performance, especially in the light aircraft landing gear [3], which has a smaller structure stiffness and total strut force. To improve the landing performance by designing friction in the strut, a dynamic model of the landing gear needs to be established to obtain the effect of friction load on the soft landing buffering; it is also required for the designer to determine the influence of landing gear structure parameters on strut friction.  
The dynamic modeling and analysis of a landing gear with an oleo-pneumatic shock absorber has received relatively large attention in the literature [4; 5; 6]. The analysis model with sprung and unsprung mass vertical degrees of freedom is established in Milwitzky [7]. Various studies have focused on predicting landing gear dynamics behavior during landing [8; 9; 10] based on the numerical model. Elemental forces, including gas spring stiffness, oleo damping, and friction, are expressed in the literature. In particular, the friction force is calculated as the absorber strut is regarded as a rigid body [9; 11; 12] or flexible body [13].  
Response surface methodology (RSM) is an assemblage of statistical and mathematical techniques utilized to develop, improve, and optimize processes of industrial production [14; 15]. It is commonly used in the design process of some complex objects such asplanet lander [16; 17] and aircraft [18; 19]. RSM can reduce the computational expense for analysis by constructing a substitute model to approximately conform to the numerical model response based on the design of experiment results [20]. To acquire the relation between the design parameters of landing gear with the strut friction force and the preferable touchdown performance, parametric studies on the half-axle aircraft main landing gear with oleo-pneumatic shock absorber are carried out using RSM. The studies are organized into two parts. Section 2 presents the dynamics model of the main landing gear, including the dynamics equation and the elemental force expression. The method for analyzing normal forces in bearing contact area considering strut flexibility is expressed in Section 2.2. Moreover, a drop test is conducted to validate the numerical model in two landing conditions. Section 3 first illustrates the effect of strut friction on landing gear touchdown performance. Second, Sections 3.2 and 3.3 present the response surface functions for the landing responses and the sensitivity analysis of the parameters, respectively. Finally, Section 3.4 presents a multi-objective optimization design of the parameters of the landing gear to improve the landing performance based on response surface functions.
# Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks
## 6 Simulation Results
### Third Scenario  
In the third position of the jammer in the quadrant FG45, for the first time, the cluster most affected by the jamming effect chooses a new CH for each one. Blue and yellow clusters move their CH from G2 to F2 and G7 to F7, respectively. On the other hand, green and pink clusters preserve the previous CH.  
Additionally, isolated nodes in blue and yellow clusters increase to approximately 44%. Therefore, the cooperation and FCPA algorithms choose new routes and relays to achieve isolated nodes. Figure 11 shows the new relays and paths. For green and pink clusters, we observe that isolated nodes decrease to only one for each cluster. Consequently, the jammer significantly damages blue and yellow clusters.  
The algorithm found a transmit power of 18.50 dBm to surpass the jamming effect based on the most affected nodes corresponding to H4 and H5. Therefore, the FP and PA strategies transmit 602,422 kb, while the residual energy is 11.76 J and 470.13 J for FP and PA strategies, respectively.  
For the cooperation strategy, the nodes E3, F1, G2, G3, and H3 are defined as relays for isolated nodes. However, the relays E3 and G3 are the first nodes to exhaust their batteries completely. These nodes have a higher number of associations than the other relay nodes. Consequently, they spend more energy on each transmission and reception process. As a result, the residual energy is almost the same for CP and PAC strategies with 533.49 J and 536.93 J, respectively. For the information transmitted, we acquire 118,192 kb and 192,278 kb for CP and PAC, respectively. Then, the FCPA algorithm provides the best scenario again,  
Figure 11: Third scenario: where the network adjusts to the presence of the jammer in the new position by the variation of the CH in blue and yellow clusters, increasing the number of isolated nodes for these clusters.  
the amount of information transmitted reaches 914,288 kb. The residual energy is 415.50 J. Therefore, the FCPA algorithm provides the best tradeoff between these metrics.
## 5 Discussion
### Node Deployment  
The structure and configuration of network nodes are fundamental considerations when establishing an IoT network. Three primary deployment architectures are commonly utilised in IoT systems: cluster-based, peer-to-peer, and master-slave configurations [40]. Selecting the appropriate deployment structure is critical for enhancing QoS and optimising various performance metrics such as data rate, bandwidth, and energy consumption.  
An inefficient choice of node deployment can lead to unnecessary energy consumption and prolonged processing times. For instance, deploying an excessive number of nodes can strain network resources, while certain nodes may become overloaded while others remain idle. To optimise the performance of IoT communications, designers should carefully consider factors such as the type and quantity of tasks, the number of IoT devices, and the anticipated workloads when selecting deployment structures [41]. Proper planning in node deployment can lead to significant improvements in energy efficiency and overall network performance.
## 3 Results
### Model Comparison and Validation
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Accuracy** & **Recall** & **F1 Score** & **Log Loss** & **Optimized Hyperparameters** \\ \hline RF & 89.02\% & 89.66\% & 0.8909 & 0.2844 & n\_estimators = 500; max\_depth = 20; min\_samples\_split = 2; min\_samples\_leaf = 1; \\  & & & & max\_features ='sqrt' \\ \hline
**XGB** & 89.02\% & 89.82\% & 0.8910 & 0.2417 & n\_estimators = 200; max\_depth = 3; learning\_rate = 0.2; subsample = 0.9; \\  & & & & colsample\_bytes = 0.8 \\ \hline
**MLP** & 81.60\% & 83.20\% & 0.8188 & 0.3836 &
\begin{tabular}{l} hidden\_layer\_sizes = (64, 32); activation ='relu’; solver = ‘adam’; alpha = 0.01; \\ learning\_rate\_init = 0.001; max\_iter = 1000; batch\_size = 'auto’; early\_stopping = True \\ \hline
**KNN** & 84.26\% & 83.04\% & 0.8406 & n\_neighbors = 10; weights = ‘distance’; metric = ‘manhattan’ \\ \hline
**GBM** & 89.35\% & 90.15\% & 0.8942 & 0.2548 & n\_estimators = 500; max\_depth = 3; learning\_rate = 0.1; min\_samples\_split = 2; \\  & & & & subsample = 0.9 \\ \hline
**SVM** & 86.28\% & 86.75\% & 0.8633 & 0.3130 & kernel = ‘rbf’; C = 10; gamma = ‘scale’ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison and hyperparameter settings of machine learning models. The models generally perform well in terms of accuracy and ROC. Among them, the decision tree models XGB, GBM, and RF outperformed the others, all with accuracy, recall, and F1 scores exceeding 89% and relatively low log loss scores. The SVM model also performed reasonably well, with accuracy, recall, and F1 scores above 86% and a relatively low log loss. However, the KNN and MLP models performed poorly. Although their accuracy is acceptable, their high log loss indicates that their predictions deviate significantly from the actual distribution. Figure 7: (**a**) Factor contribution comparison. (**b**) XGB SHAP value visualization. (**c**) GBM SHAP value visualization.
# Network Attack Path Selection and Evaluation Based on Q-Learning
## 3 Information-Physical Cross-Layer Risk Spread Model  
In order to evaluate the impact of the best attack path found on the power CPS, the following information-physical cross-layer risk propagation model is established. Power CPS is a multi-dimensional heterogeneous system which fully integrates the physical network and information network of the power system [20]. Through the coordination of computing equipment, sensor equipment, and communication equipment. The overall operating performance of the power system is optimized by physical equipment, etc. The CPS structure under the power Internet of Things is shown in Figure 1 which can be divided into three levels from bottom to top: user load, terminal sensing, and control and decision. The physical system and information system realize the interaction between information flow and energy flow through intelligent terminals. On the one hand, the terminal equipment collects the electricity consumption data and equipment status data of different power users, which are used by the control center to analyze the operation status of the power system and formulate appropriate control plans [21]. On the other hand, they receive commands from the control center to regulate electrical primary equipment, such as increasing or decreasing generator output, adjusting transformer taps, etc. [22].  
Different from traditional physical grid cascading failures, information-physical dual-network cascading failures caused by cyber-attacks will cause more serious consequences [23,24]. In terms of security risks in the information space, smart terminal equipment is the only way to spread to the power space. Therefore, attackers usually choose widely distributed smart terminals as access points to attack at present. After obtaining permission, scanning software is used to obtain the security vulnerabilities that can be used by each host of the system. Eventually attackers initiate a multi-level invasion, modify the configuration file or business data in the server after entering the control center.  
Figure 1: Cyber-physical system (CPS) structure under the power Internet of Things.  
It will cause the control center to incorrectly perceive the current state of the physical power grid [25], and affect the operation of the physical power grid by issuing wrong control commands. This paper mainly researches the attacks of tampering with system data in this situation.