esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Collaborative Edge Computing for Vehicular Network Using Clustering Service
## 4 The CCVEC Framework  
This section illustrates the design of the framework by presenting the general steps and components involved in this research to achieve the desired objectives. The main aim of the Cluster Collaboration Vehicular Edge Computing (CCVEC) framework is to provide efficient vehicle mobility that moves quickly from one region to another under a cluster of edge computing servers. Edge servers cooperate to smoothen a vehicle's mobility by initially sharing/exchanging its information. The CCVEC framework implements cluster management to ease the sharing of information among edge computing servers [20, 21], which in turn ensures high connectivity and low latency for vehicle device data streaming, especially with high-speed mobility and intersections [26]. The framework steps are explained in detail in the following:  
1. Initially, all edge servers (cluster nodes) present themselves with the cloud point (cluster head). The cluster head populates the geolocation topology information of each edge node. Also, the cluster head updates the clusters' information when any server announces that they are joining/leaving the range;
2. Once the vehicle is connected by the edge server, this server (current) shares the vehicle's information with the next potential server. At the same time, the previous server keeps the same information active to keep track of vehicle paths;
3. Next, the edge server notifies the cloud of the new vehicle's position for the calculation of the next position;
4. When the vehicle moves to the next server, repeat Step 2.  
From the steps above, the framework ensures that the vehicles' information will be pre-populated and pre-processed in the next station edge server prior to its arrival. This avoids extra connection setup and at the same time reduces unnecessary data connections with the cloud. Figure 3 illustrates the major components involved in this framework.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Year** & **IoT Type** & **Algorithm** \\ \hline Ref. [26] & 2022 & Vehicle-dynamic traffic map & Dynamic map system \\ Ref. [20] & 2018 & Vehicular services & CVEC \\ Ref. [27] & 2021 & Healthcare & edge-fog-cloud \\ Ref. [28] & 2020 & Healthcare & MobMBAR \\ Ref. [13] & 2024 & IoV and IoFV & Review \\ Ref. [14] & 2022 & 5G IoV & Review \\ \hline \hline \end{tabular}
\end{table}
Table 2: Related works summary.  
Figure 3: CCVEC framework components.
## 3 Results and Discussion
### Deployment and Optimization of Advanced Energy Storage Systems
#### 3.3.2 Optimal Sizing and Placement of Energy Storage  
Optimizing the sizing and placement of ESSs is essential to maximize their economic and technical benefits. Various studies propose advanced optimization algorithms, including genetic algorithms and mixed-integer linear programming, to determine the ideal capacity and location of storage units within microgrids [22; 47; 86]. Properly sized storage systems enhance grid stability, reduce operational costs, and minimize energy curtailment [23; 27]. Additionally, optimal placement supports efficient energy distribution, reduces transmission losses, and facilitates local voltage and frequency regulation [48; 61].  
Multi-objective optimization approaches have gained prominence, balancing technical objectives such as reliability and efficiency with economic considerations, including investment and operational costs [34; 42]. However, the real-world application of these models faces challenges related to computational complexity and the availability of accurate demand and generation data [43; 70].
## 2 Materials and Methods
### Oxidative Stability Study of Packaged Fried Almonds
#### 2.6.2 Hexanal Content  
Hexanal content was determined by headspace-solid-phase microextraction (HS-SPME) followed by GC analysis. An Agilent 7820A GC System (Palo Alto, CA, USA) equipped with a FID detector was used. Fried almonds were ground using a domestic electric grinder (Moulinex, Barcelona, Spain). Ground samples (1.00 \(\pm\) 0.01 g) were placed in a 20 mL vial with a micro-stirring bar and they were mixed with 2 mL of NaCl 2M and 40 mL of the internal standard (4-methyl-2-pentanone, 8 mg kg\({}^{-1}\)). The vial was sealed with an aluminium crimp cap provided with a polytetrafluoroethylene/silicone septum [3]. A DVB/CAR/PDMS SPME fibre was used (50/30 \(\upmu\)m, StableFlex, 1 cm long) mounted to an SPME manual holder assembly (Supelco, Bellefonte, PA). The sample vial was placed in a water bath at 50 \({}^{\circ}\)C and 500 rpm. After 10 min of sample equilibration, the SPME fibre was inserted and exposed to the vial headspace. After 30 min of extraction time, the fibre was immediately desorbed into the GC-FID injection port at 250 \({}^{\circ}\)C for 12 min (splitless mode). A SPB-5 column (30 m \(\times\) 0.32 mm \(\times\) 0.25 \(\upmu\)m; Supelco, Bellefonte, PA) was used which was programmed from 50 \({}^{\circ}\)C to 70 \({}^{\circ}\)C (hold 1 min) at 5 \({}^{\circ}\)C min\({}^{-1}\) to 200 \({}^{\circ}\)C (hold 10 min) at 35 \({}^{\circ}\)C min\({}^{-1}\). Helium was used as the carrier gas (1 mL min\({}^{-1}\)) and the FID temperature was 300 \({}^{\circ}\)C. Hexanal was quantified by using a standard calibration curve as described elsewhere [44]. Hexanal stock (30 mg kg\({}^{-1}\)) and working solutions were prepared using distilled water as solvent.
## 3 Results and Discussion
### \(O_{2}\) Production System Design Process Results
#### 3.2.3 MIEC Membrane  
Once the final O\({}_{2}\) production system is established and evaluated for all engine operating points, the membrane area sweep is performed to select the most suitable one for the final design. Figure 16 shows the ratio of fuel used in the burners to engine fuel consumption for each area evaluated. Modifying the membrane surface area leads to a better permeability since it favors the adsorption and dissociation of oxygen molecules by reducing the resistance of their transport through the membrane; for this reason, larger membranes require less pressure and temperature to operate [25]. The tendency of curves is asymptotic, which means that from a specific membrane area onward, its increase does not bring about significant improvements in fuel consumption; this phenomenon occurs as there are certain minimum air requirements (temperature, pressure) for the membrane to operate.  
Regarding the differences in fuel consumption between the engine operating points, the points at lower engine speed present a higher relative fuel consumption. This is mainly due to the fact that the flow lines of the O\({}_{2}\) production system coming from the engine transfer much less heat to the incoming air than at higher engine speeds. Based on this analysis, the membrane area selected considering all engine operating points is 14,000 m\({}^{2}\).
# Discovering Trends in the Digitalization of Shipping:
## 4 Analysis
### Exploration of the Data
#### 4.1.2 Word Network  
Network analysis allows the detection of influential keywords, identification of main topics, and insights into the structure of the text. The top 15 feature words and their relationships can be seen in the graph in Figure 2. The most important features were system, security, risk, service, management, company, solution, operation, cyber, information, port, ping, control, business, and industry. A bit further down the list is also mentioned cost.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Year** & **Presentations** \\ \hline
2020 & 10 \\
2021 & 12 \\
2022 & 51 \\
2023 & 45 \\ Total & 593 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Number of presentations by year (The higher the frequency, the darker the color).  
Figure 2: Word network.  
In Figure 2, the word network, together with the centrality outlined in Table 2, reveals a complex tapestry of interconnected themes that include systems, security, technology, networks, information, business, and risk. This intricate network underscores a strong emphasis on cybersecurity, highlighted by the frequent co-occurrence of terms such as \"system\", \"security\", \"cyber\", \"risk\", and \"technology\". This emphasis points towards a primary concern with safeguarding against threats. Furthermore, the inclusion of terms like \"company\", \"business\", \"control\", \"industry\", \"solution\", and \"operation\" embeds this focus on cybersecurity within a broader business context, indicating that these protective measures are not just technical concerns but are integral to the operational and strategic frameworks of businesses.  
The analysis also points to a global perspective on these issues, with words like \"global\", \"satellite\", and \"network\" hinting at the need for measures that span across internationally interconnected systems, including the potential use of satellite-based technologies. Additionally, the mention of \"management\", \"service\", \"port\", and \"ping\" within the network indicates a focus on the technical management of networks, service delivery, and the diagnostics critical for maintaining system integrity.  
Lastly, the presence of \"cost\" within the network highlights an awareness of the financial implications, suggesting that economic considerations are crucial in the planning and implementation of security measures. This multifaceted analysis reveals a comprehensive view of cybersecurity that encompasses technical, business, and global considerations, alongside the practical aspects of management and cost-efficiency.
## 4 Discussion
### Significance of Understanding Spatial Heterogeneity and Footprint Modeling
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**FIVT of the**} & \multicolumn{2}{c}{**Mean FIVT within**} \\  & **Source Area** & **the Pixel** & **Bias** \\ \hline
2010.10.3 & 0.9125 & 0.5075 & 0.4050 \\
2010.10.8 & 0.9594 & 0.5036 & 0.4558 \\
2010.10.15 & 0.8982 & 0.5793 & 0.3189 \\
2010.10.17 & 0.9414 & 0.5742 & 0.3672 \\
2010.10.20 & 0.8248 & 0.5616 & 0.2632 \\
2010.10.27 & 0.8744 & 0.5724 & 0.3020 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of FTVT within the flux source area and the pixels of GOME-2 SIF data. Figure 9: The location of the study site (red point) and the rectangular area is the solar-induced chlorophyll fluorescence (SIF) pixel (40 km \(\times\) 80 km) of the Global Ozone Monitoring Experiment-2 (GOME-2) in the same period, and the background is an EVI image derived from Landsat TM, 4 th October 2010. It can be seen that the SIF pixel position shift of GOME-2 is large, and the value of EVI within each pixel is relatively different.
# Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot
###### Abstract  
Recurrence-plot (RP) analysis is a graphical tool to visualize and analyze the recurrence of nonlinear dynamic systems. By combining the advantages of the RP and a convolutional neural network (CNN), a fault-classification scheme for planetary gear sets is proposed in this paper. In the proposed approach, a vibration is first picked up from the planetary-gear test rig and converted into an angular-domain quasistationary signal through computed order tracking to eliminate the frequency blur caused by speed fluctuations. Then, the signal in the angular domain is divided into several segments, and each segment is processed by the RP to constitute the training sample. Moreover, a two-dimensional CNN model was developed to adaptively extract faulty features. Experiments on a planetary-gear test rig with four conditions under three operating speeds were carried out. The results of measured vibration demonstrated the validity of CNN and recurrence plot analysis for the fault classification of planetary-gear sets.  
convolutional neural network; fault classification; order-tracking technique; planetary-gear set; recurrence-plot analysis +
Footnote †: journal: Applied Sciences
# A Review of Deep Learning Applications for Railway Safety
## 8 Discussion and Conclusions
### Performance Optimization
#### 8.1.2 Processing Time  
Beyond model accuracy, processing time can be one of the essential requirements in artificial intelligence for railway safety. In particular, real-time processing can be required for high-speed train applications. For example, [PERSON] et al. [81] suggested further studies to develop a real-time system that recognizes moving obstacles by combining railway area recognition and obstacle detection steps. In addition, [PERSON] et al. [88] expected that their system could be utilized for real-time train control to reduce the risk of train derailment.
# Numerical Analysis of Fatigue Life of Wind Turbine Blades Reinforced with Graphene Platelets
## 2 Finite Element Model of a 5 MW Wind Turbine Blade
### Validation  
In this study, midas-NFX 2024R1, a commercial finite element analysis software, was used to conduct fatigue load spectrum analysis [29]. A four-node quad was used for the elements of the analysis model, and the element size was set at 0.08 m \(\times\) 0.08 m, as in the report by [PERSON] [26]. To validate the created finite element model, the mass, natural frequency, and bending and torsion deformations of the analysis model were compared with the results from the existing literature. As listed in Tables 4 and 5, the present analysis models and the previous studies have very similar masses and natural frequencies.  
Figure 3 shows the results of comparing flapwise deflection and torsional deformation between the present analysis models and the reference solutions. The aerodynamic loads applied to the wind turbine blade model were calculated based on the rated wind speed (11.4 m/s). In this process, the BEMT to be introduced in Section 3 was utilized. The analysis model of this study showed high similarity to those of previous studies in terms of mechanical behavior, such as deflection and torsion, as well as the mass distribution and natural frequency of the blade, confirming that its results are sufficiently reliable.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{8}{c}{**Mass (kg)**} \\ \cline{2-7}  & **Gelcoat** & **E-LT-5500** & **Saertex DB** & **SNL Triax** & **Foam** & **UD Carbon** & **Total** \\ \hline Present & 29 & 338 & 921 & 8726 & 4160 & 2655 & 16,829 \\ [PERSON] et al. [30] & 29 & 376 & 916 & 8784 & 3953 & 2638 & 16,696 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mass distributions in the SNL 61.5 m wind turbine blade.  
Figure 3: Comparison of elastic stiffness of the SNL 61.5\(-\)m wind turbine blade [34; 35; 36; 37]: **(a)** flapwise deflection, **(b)** torsional deformation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{8}{c}{**Natural Frequency (Hz)**} \\ \cline{2-7}  & **1 st** & **1 st** & **2 nd** & **2 nd** & **3 rd** & **1 st** \\  & **Flapwise** & **Edgewise** & **Flapwise** & **Edgewise** & **Flapwise** & **Torsion** \\ \hline Present & 0.8415 & 0.9930 & 2.7269 & 3.5918 & 5.7255 & 6.7280 \\ [PERSON] [26] & 0.87 & 1.06 & 2.68 & 3.91 & 5.57 & 6.45 \\ [PERSON] et al. [31] & 0.90 & - & 2.85 & - & 6.41 & 6.65 \\ [PERSON] and [PERSON] [32] & 0.9194 & 1.0552 & 2.8106 & 3.8870 & 5.6904 & 6.7152 \\ [PERSON] and [PERSON] [33] & 0.84 & 0.969 & 2.41 & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Natural frequency of the SNL 61.5 m wind turbine blade.
# A Comprehensive Review of Micro UAV Charging Techniques
## 3 Battery Charging Techniques for UAVs
### Fuel Cell (FC)  
To achieve long missions and enhance the battery endurance of UAVs, researchers have suggested the implementation of fuel cells (FC) technology. A typical UAV LiPo battery has 250 Wh/kg of energy, whereas compressed FC has 1000 Wh/kg of energy [54]. Furthermore, refueling in FC is faster and more efficient than battery charging and swapping technology [55]. Several technologies are being implemented in the FC industry. Most of these are based on temperature characterization, electrolytes, catalyst and chemical criteria. In [56], [PERSON] et al. evaluated the performance of frequently used FCs in UAVs, e.g., Solid Oxide Fuel Cell (SOFC), Methanol Fuel Cell (MFC) and Proton Exchange Membrane Fuel Cell (PEMFC). It can be concluded that the most frequently used FCs in UAVs are PEMFC. Several leading research institutes have dedicated their efforts to FC technology. Georgia Tech University experimentally demonstrated a 500 W FC-powered UAV system, while the Office of Naval Research (ONR) set a breakthrough record for an FC-powered UAV for the highest endurance of 48 h [57]. Furthermore, a Korean aerospace research institute demonstrated an FC-powered UAV with landing, vertical takeoff, monitoring and hovering capabilities [58]. Although FC technology has appealing advantages, such as reduced power losses and reduced carbon dioxide, FC technology is hampered by a sluggish dynamic response causing voltage fluctuations [59]. Moreover, absorption of excessive energy is difficult, which leads to a cold start. Similarly, storage of some FC, such as Hydrogen FC as shown in Figure 7, under low temperature and high pressure is another critical concern.  
In [61], [PERSON] et al. briefly discussed different types of batteries, such as Ni-Mh, Ni-Cd and Li-ion. Authors carried out a comparison of these batteries with fuel cells considering different parameters such as endurance, efficiency, temperature effect, discharging and power densities. Figure 8 presents a comparison of FCs, super capacitors and batteries considering power density vs. energy density aspects. It clearly indicates that FCs outperform other resources in terms of energy density. In [62], the authors investigate a drone for mobile crane inspection. As the flight time of drones is short for commercial applications,  
Figure 7: Hydrogen-powered UAV [60].  
the authors used lithium-ion batteries and proton exchange membrane cells to overcome this short flight time. The authors carried out analysis of both the environmental impact and the economic point of view. Lightweight fuel cells resulted in being more expensive than Li-ion batteries.
# A Single Image Enhancement Technique Using Dark Channel Prior
## 2 Dark Channel Prior Algorithm  
### Atmospheric Scattering Model  
McCartney [13] simplified the atmospheric scattering model based on the Mie scattering theory, and then obtained the physical model of foggy images. The model consists of two parts, where the first part is called the incident light attenuation model, and the second part is called the atmospheric light enhancement model. In the process of an object light reaching the observation device, the atmospheric light in other directions will enter the observation device, so that the atmospheric light obtained by the observation device is enhanced.  
In the incident light attenuation model, the intensity of light reaching the observation device decreases exponentially as the depth of the scene increases, and the attenuation term of the incident light can be formulated as:  
\[E_{D}(d,\lambda)=E_{0}(\lambda)e^{-\beta(\lambda)d} \tag{1}\]  
where \(E_{D}(d,\lambda)\) represents the light intensity of the object away from the target, \(E_{0}(\lambda)\) denotes the intensity of the object light reflected by the target, \(\beta(\lambda)\) represents the atmospheric scattering coefficient, and \(d\) denotes the scene depth.  
In the atmospheric light enhancement model, the intensity of light reaching the observation device increases exponentially as the depth of the scene increases, and the atmospheric light enhancement term can be formulated as:  
\[E_{A}(d,\lambda)=E_{\infty}(\lambda)(1-e^{-\beta(\lambda)d}) \tag{2}\]  
where \(E_{A}(d,\lambda)\) represents the intensity of atmospheric light away from the target, \(E_{\infty}(\lambda)\) denotes the total intensity of unscattered atmospheric light, \(\beta(\lambda)\) represents the atmospheric scattering coefficient and \(d\) denotes the scene depth.  
From the above analyses, the physical model of foggy images can be formulated as:  
\[E(d,\lambda)=E_{D}(d,\lambda)+E_{A}(d,\lambda) \tag{3}\]  
Substituting Equations (1) and (2) into Equation (3), we can get:  
\[E(d,\lambda)=E_{0}(\lambda)e^{-\beta(\lambda)d}+E_{\infty}(\lambda)(1-e^{- \beta(\lambda)d}) \tag{4}\]  
If \(I(x)=E(d,\lambda)\), \(J(x)=E_{0}(\lambda)\), \(t(x)=e^{-\beta(\lambda)d}\), and \(A=E_{\infty}(\lambda)\), the model of foggy images can be simplified as:  
\[I(x)=J(x)t(x)+A(1-t(x)) \tag{5}\]  
In the model, \(x\) represents the two-dimensional coordinate value of the pixel, \(I(x)\) denotes the intensity of the degraded image. \(I(x)\) is a foggy image, and it also represents the input for image defogging algorithms. \(J(x)\) means the intensity of the image before degradation, and it also represents the output of image defogging algorithms. \(A\) represents the atmospheric light intensity, \(t(x)\) denotes the transmittance and is used to describe the proportion of reflected light transmitted to the image acquisition device without being scattered on the surface of objects. Therefore, in order to obtain the defogged image \(J(x)\), \(t(x)\) and \(A\) need to be estimated from \(I(x)\).
## 3 Materials and Methods
### Distributed Deep Learning
Amdahl's Law is particularly relevant in understanding the impact of parallelizing only a portion of a computation. Amdahl's Law is stated mathematically as follows:  
\[Speedup=\frac{1}{\left(F_{s}+\frac{F_{p}}{P}\right)} \tag{1}\]  
where  
\(Speedup\) is the overall speedup achieved by parallelizing the computation. \(F_{s}\) is the fraction of the computation that must be executed sequentially. \(F_{p}\) is the fraction of the computation that can be executed in parallel;  
\(P\) is the number of processors or processing units, and  
Since this study does not have the \(F_{s}\) and \(F_{p}\) values in addition to the number of processors or processing units \(P\), Equation (1) can be homological with the empirical speedup represented by the following equation:  
\[Speedup=\frac{T_{s}}{T_{p}} \tag{2}\]  
where  
\(T_{s}\) is the execution time without parallelism;  
\(T_{p}\) is the execution time with a distributed strategy. This empirical speedup reflects the actual acceleration effect of distributed parallel training [20]. The key implication of Amdahl's Law for DDL is that the speedup of a distributed computing application is limited by the sequential fraction of the computation [44]. In the context of DDL, a sequential fraction may include tasks or processes that cannot be easily parallelized or exhibit diminishing returns when parallelized.
###### Abstract  
In this paper, new single/double-layer N-way Wilkinson power dividers (WPDs) were designed by using slow-wave structures such as narrow-slit-loaded and meandered transmission lines. For size reduction, the slit-loaded and meandered lines were used instead of the quarter-wavelength transmission lines of a conventional WPD. Based on the proposed approaches, two-, four-, and eight-way power dividers were designed, simulated, and fabricated. The fabricated 2-, 4-, and 8-way circuits were measured at the center frequencies of 2.03, 1.77, and 1.73 GHz, which are in excellent agreement with the predicted ones. The meandered transmission lines were also used to design WPD types with novel input/output port arrangements. For this purpose, two three-way WPDs were located on both sides of the same board to have different power-splitting ratios at different inputs and outputs in order to provide alternative solutions for antenna arrays. Furthermore, a five-way dual-layer WPD was introduced by locating the meandered transmission lines into two layers. The most important advantage of the proposed 3- and 5-way WPDs is that they allowed the input power at the next output port to be halved, in the order of P/2, P/4, P/8, P/16, and P/16. All the designed power-halving WPDs were simulated, fabricated, and successfully tested.  
N-way; Wilkinson power dividers (WPDs); meandered transmission line; power halving