esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Design  
Generally, a web-based geovisual analytical system includes front-end clients, application services and back-end data repositories [17]. To implement each component, the first step is to analyze the functional requirements of geovisual analytical for climate data. The typical information that statistical analysis should detect from climate data are mean values, correlation between variables, stationarity over time series, quality of forecasting, and spatiotemporal patterns [29]. Most analyses only use model simulation data, but the validation of simulation also requires observation data in addition to model simulation data. Except mean calculation, all the other analyses are performed based on the mean value over a certain time period. Therefore, certain frequently used mean values should be pre-calculated and stored as initial statistics. Data analysis can be divided into simple and high-level types. Simple analyses like intuitively observing spatiotemporal patterns or difference between simulation and observation can be implemented directly through data comparisons. High-level analyses such as detecting correlation and anomalies need to be sent to the server side for complex processing. Graphic representation of results is required for final results presentation.  
Therefore, our system is designed to include three functional components (Figure 2): (1) the data repository to store data or metadata from simulation, observation and initial statistics; (2) the application server to provide data processing and high-level analytical functions; and (3) the web-based client to perform simple analysis and display visualization results with interactive tools.  
Figure 2: System architecture.
# Optimizing Electric Vehicle Routing Efficiency Using K-Means Clustering and Genetic Algorithms
## 3 Methodology and Problem Definition  
### Route Representation  
The route is represented by a sequence of points defined by their \((x_{i},y_{i})\) coordinates. The route connects a starting point \((x_{i=0},y_{i=0})\), several checkpoints \((x_{i},y_{i},i=1\ldots n)\), and an ending point \((x_{i=G},y_{i=G})\), allowing it to mimic a realistic path. Importantly, these points not only define the route but also act as exit points to the charging stations. These exit points are essentially junctions where vehicles can leave the main route to access nearby charging stations and then return to the route after charging. An example of the route is illustrated in Figure 1. Additionally, each route point is assigned to the cluster whose centroid is closest to it. Thus, when a vehicle reaches a specific route point, it is within the cluster to which the route point is assigned.  
The first step in managing charging stations along the route is to generate random points representing the locations of the charging stations. To simulate varying queueing times, each station is assigned a random queueing time value ranging from 0 to 20 min. Queueing times remain fixed throughout a single GA execution and are not randomized, ensuring stability in the optimization process and preventing disruptions to convergence. Instead, they are dynamically recalculated only when the EV's position changes during route updates.  
The Euclidean distance between each charging station and every point on the route is then calculated to determine the closest \((RP_{i})\). This closest \((RP_{i})\) on the route acts as the exit point \((EP_{i})\) for the charging station, facilitating access and integration into the route optimization process. This mapping ensures that each charging station is effectively associated with a specific point on the route \(RP_{i}\). There are two key implications of this route definition for the optimization process. First, if the algorithm selects a charging station for a stop, the station can only be accessed via its corresponding \((RP_{i})\), which serves as the designated \(EP_{i}\) to that station. This ensures a clear and structured connection between the route and charging infrastructure. Second, each \(RP_{i}\) may provide access to a varying number of charging stations. All stations associated with the same \(RP_{i}\) are sorted based on their proximity, maintaining a consistent sequence. This proximity-based sorting is essential to the subsequent GA process, as it enables efficient evaluation and selection of charging stations during the optimization phase.
## 4 Performance Analysis
### Secrecy Outage Probability (SOP)
#### 4.2.1 Exact Analysis
The SPSC performance of the system under the different shadowing fading effect is illustrated in Figure 9 under the heterodyne detection at HAP. It can be observed that the system becomes degraded as the shadowing fading effect get severer. The results also demonstrate that the analytical results perfectly agree with the simulation results, which justify the accuracy of the derived expressions.
# A Validated Thermal Computational Fluid Dynamics Model of Wine Warming in a Glass
###### Abstract
The study focuses on one configuration with one specific glass and filling quantity to closely investigate the physical process and the applied computational methods. Experiments are presented providing reference data to evaluate and validate the simulation approach. The importance of modeling the different heating mechanisms is examined in detail. The temperature increase of wine is determined for various combinations of the initial wine temperature after pouring and ambient temperature. The results can inform sommeliers, restaurateurs, and wine enthusiasts about how wines should be tempered to reach the optimal drinking temperature under different room and storage conditions. For example, one could determine how long a red wine has to remain in a glass to reach a certain temperature when the bottle was stored cool at a known temperature. Red wine could also be stored at a specific low temperature to let it breathe in a glass for a given period while reaching the ideal drinking temperature. To optimize the drinking experience, especially on hot summer days, bars or restaurants could adjust the storage temperature of white wines based on ambient temperature and the time it takes to serve them. Besides practical advice, this study also gives insights into the physical effects of wine warming as the local and temporal influence of different heating mechanisms are investigated. The presented simulation approach could be utilized to develop new wine glasses with regard to their thermal behavior. Thus, producers could optimize the glass shape to potentially reduce wine warming. The investigated configuration of wine warming in a glass is specified in Section 2. Experiments were conducted for the same configuration. The experimental setup, procedure, and results are presented in Section 2.1 and the following sections. Section 3 outlines the conjugate heat transfer simulation approach, including the governing equations, the material modeling and properties and the simulation setup. The simulation approach is validated with the experimental data in Section 4. Furthermore, the influence of different heating mechanisms and fluid properties is investigated. Section 5 presents the main resultsand findings. The warming process is analyzed, and a parameter study for different initial and surrounding temperatures is performed. Section 6 summarizes the results and gives an outlook on potential further research questions.
## 4 Case Study
### Operations  
The proposed DSP-based approach first learns medium access control (MAC) addresses, then builds a link table for each QuadBox pair, and finally finds and establishes dual paths between QuadBox pairs by applying the DSP algorithm to the link table.
## 4 Performance Evaluation of Improved SOSK-Means  
Forty-two datasets were considered for validating the proposed ISOSK-means algorithm for automatic clustering, of which 24 were real-life datasets and 18 were artificial datasets. All the algorithms were programmed using MATLAB R2018b, running on Windows 10 operating system, installed on a 3.60 GHz Intel(r) Core i7-7700 processor computer system with 16 GB memory size. For the study, the _eco_size_ was set at \(40+2g\). The algorithms were executed 40 times with 200 iterations in each run. The performance results are presented using the minimum, maximum, average, and standard deviation of the fitness values, as well as the average computational time. The improved SOSK-means algorithm's effectiveness was measured and compared using the following criteria:  
* The average best fitness value measured the algorithm's quality of the clustering solutions.
* The performance speed used the algorithm's computational cost and convergence curve.
* The statistical significance difference was over 40 replications.  
The results were compared with the standard K-means, the standard SOS algorithm, the existing SOSK-means and other state-of-the-art hybrid and non-hybrid metaheuristic algorithms. The best results among the compared algorithms are presented in bold format. The values of the common control parameters were the same for the K-means, standard SOS algorithm and the SOSK-means algorithms. The control parameters for the competing metaheuristic algorithms obtained from literature in [61] are shown in Table 1.
[27] investigated a method for determining the dynamic strength safety margin and fatigue durability of pipeline structures under combined loading from pre-stressing and random excitation. [PERSON] et al. [28] proposed that under high-pressure fluid pulsation and strong random vibration excitation, high-vibration stress can cause cumulative damage to pipeline structures. [PERSON] et al. [29] provided the excitation parameters and vibration response under multi-source excitation. In summary, the prediction of fatigue life in pipelines by simulating maximum stress under a single excitation is predominantly favored by most contemporary scholars. This preference stems from the inherent complexity associated with concurrently conducting frequency-domain random vibration analysis and time-domain fluid pulsation vibration analysis. Currently, there is a scarcity of effective evaluation methods to assess the fatigue strength of pipelines subjected to composite excitation. To address these issues, this article proposes the utilization of the optimal transport meshless algorithm (OTM) within a robust fluid-structure coupling framework. The incorporation of composite excitation into this framework serves to validate the structural integrity and fatigue durability of pipelines, while also enabling accurate estimation of their service life post-design. Resonance excitation and pulsation excitation are identified as the most critical loads in pipelines. Therefore, within the optimal transport meshless method for strong fluid-structure coupling calculations, this study evaluates stress under composite excitations such as pipeline resonance and pulsation. Subsequently, lifespan estimation is performed using the hazard point method (as depicted in Figure 1).
# All-in-Focused Image Combination in the Frequency Domain Using Light Field Images
## 3 Experimental Results
### All-in-Focused Image Combination
#### 3.2.6 On the Expanded Images of the 'Cup' Dataset  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Criteria**} \\ \cline{2-3}  & **FMI** & **QAB/F** \\ \hline Light Field Software & 0.8279 & 0.5945 \\ SML & 0.9255 & 0.8706 \\ DCT-STD & 0.9237 & 0.8655 \\ DCT-VAR-CV & 0.9268 & 0.8684 \\ SML-WHV & 0.9223 & 0.8577 \\ Agarwala’s Method & 0.9273 & 0.8756 \\ DCT-Sharp-CV & 0.9424 & 0.9097 \\ DCT-CORR-CV & 0.9427 & 0.9096 \\ DCT-SVD-CV & 0.9429 & 0.9098 \\ The Proposed Method & 0.9518 & 0.9143 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Objective evaluation of the image results (non-reference fusion metrics) for ’flower’ image.  
Figure 16: Comparison of ‘flower’ image results: (**a**) Light Field Software, (**b**) SML, (**c**) DCT-STD, (**d**) DCT-VAR-CV, (**e**) SML-WHV, (**f**) Agarwala’s method, (**g**) DCT-Sharp-CV, (**h**) DCT-CORR-CV, (**i**) DCT-SVD-CV, (**j**) The Proposed Method.  
Figure 17: _Cont._The performance summary of the different methods on the five image datasets using Q\({}^{\text{AB/F}}\) and FMI are listed in Tables 6 and 7, in which the top values are shown in bold. According to fusion metrics, Q\({}^{\text{AB/F}}\) and FMI, the performance of the proposed method was better than the other nine compared methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multicolumn{10}{c}{**Methods**} \\ \cline{2-11}  & [6] & [9] & [5] & [4] & [16] & [17] & [18] & [19] & [20] & **Proposed** \\ \hline Bag & 0.7965 & 0.8015 & 0.8171 & 0.8498 & 0.7994 & 0.8542 & 0.8626 & 0.8637 & 0.8635 & **0.8729** \\ Cup & 0.7413 & 0.8088 & 0.8252 & 0.8535 & 0.8176 & 0.8570 & 0.8706 & 0.8709 & 0.8708 & **0.8788** \\ Bike & 0.7394 & 0.7799 & 0.7897 & 0.8230 & 0.7734 & 0.8373 & 0.8443 & 0.8512 & 0.8505 & **0.8574** \\ Mouse & 0.6917 & 0.7451 & 0.7626 & 0.7819 & 0.7548 & 0.7863 & 0.7917 & 0.7964 & 0.7971 & **0.8077** \\ Flower & 0.5945 & 0.8706 & 0.8655 & 0.8684 & 0.8577 & 0.8756 & 0.9097 & 0.9096 & 0.9098 & **0.9143** \\ \hline Average & 0.7127 & 0.8012 & 0.8120 & 0.8353 & 0.8006 & 0.8421 & 0.8558 & 0.8584 & 0.8583 & **0.8662** \\ \hline \hline \end{tabular}
\end{table}
Table 6: The performance summary of different methods on the five image datasets, using Q\({}^{\text{AB/F}}\).  
Figure 17: The expanded image ‘Cup’ image results: (**a**) Light Field Software, (**b**) SML, (**c**) DCT-STD, (**d**) DCT-VAR-CV, (**e**) SML-WHV, (**f**) [PERSON]’s method, (**g**) DCT-Sharp-CV, (**h**) DCT-CORR-CV, (i**) DCT-SVD-CV, (**j**) The Proposed Method.
# Cognitively Driven Arabic Text Readability Assessment Using Eye-Tracking
## 2 Background
### Machine-Based Readability Measures  
There are many limitations associated with the traditional readability formulas, despite their simple assumptions, such as the ignorance of text noise. Hence, they assume a text is composed of only well-formed sentences [2]. Additionally, they are non-effective with non-traditional documents, such as Web pages [2]. Furthermore, although readability research began a century ago [36], readability formulas are built on superficial text features. They do not consider in their calculation all components of texts and languages that are theoretically anticipated to contribute to the comprehension and difficulty of texts [18; 33]. Many readability-related features, such as discourse coherence and syntactic ambiguity, are ignored [1; 2; 37]. Consequently, readability formulas might be misused by writers who wish to achieve higher readability scores by shortening their sentences. Conversely, these  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Formula Name** & **Year** & **Formula Mathematical Calculation** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Dawood Readability \\ Score [31] \\ \end{tabular} } & \multirow{3}{*}{1977} & \multirow{3}{*}{\begin{tabular}{c} Dawood Readability Score = \(-0.0533\times W-0.2066\times S+5.5543\times p-1.0801\) \\ \end{tabular} } \\  & & & \(5.5543\times p-1.0801\) \\ \cline{1-1}  & & & where “W” is the average number of characters per word, “S” is the average number of words per sentence, and “\(p\)” is the average frequency. \\ \hline \multirow{3}{*}{\begin{tabular}{c} Al-Heeti [32] \\ Grade Level \\ \end{tabular} } & \multirow{3}{*}{1984} & \multirow{3}{*}{\
\begin{tabular}{c} Al-Heeti Grade Level = (AWL \(\times\) 4.414) \(-\) 13.468 \\ where “AWL” is the average number of characters per word. \\ \end{tabular} } \\  & & & \\ \hline A Corpus-Based & \multirow{3}{*}{2013} & \multirow{3}{*}{
\begin{tabular}{c} Readability \\ \end{tabular} } \\  & & & \\ \cline{1-1}  & & & \\ \cline{1-  
texts tend to have lower cohesion and coherence, as sentences become very short and difficult to comprehend [6] given the previous limitations.  
Several technological tools have been proposed for analyzing and unraveling the semantic characteristics of documents [6]. The early 2000s, with the increasing amount of textual data and the development of more complex prediction models, saw the rise of what was called \"the 'AI' (Artificial Intelligence) approach to readability\" [2]. NLP technologies and machine learning (ML) methods have been adopted in much text readability assessment research. Compared to traditional readability measures, this integration has shown significant advancement in the accuracy and reliability of this assessment [1; 2; 36; 37].
## 3 Results
of wines from Vranec and Cabernet Sauvignon grape varieties and fermented by autochthonous and commercial yeast strains. Mean \(\pm\) STDV were calculated from two replicates of each fermentation type (see Table 1) and four analytical measurements. Wine type abbreviations are defined in Table 1. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Wine Designation**} & **Total Phenolic Compounds (TPC)** & **Total Anthocyanins** & \multirow{2}{*}{**Color Intensity (a.u.)**} \\  & **(mg\({}_{\text{analyt}}\)/L\({}_{\text{wine}}\))** & **(mg\({}_{\text{analyt}}\)/L\({}_{\text{wine}}\))** & \\ \hline V1 & 1512.7 \(\pm\) 23.55 b  & 653.3 \(\pm\) 11.08 a  & 31.31 \(\pm\) 4.21 a  \\ V2 & 1528.9 \(\pm\) 39.01 b  & 583.7 \(\pm\) 22.65 b  & 21.02 \(\pm\) 5.28 b  \\ V3 & 1617.2 \(\pm\) 24.72 a  & 637.1 \(\pm\) 19.87 a  & 23.62 \(\pm\) 3.97 b  \\ CS1 & 1495.9 \(\pm\) 23.55 b, c  & 540.7 \(\pm\) 12.04 c  & 31.05 \(\pm\) 2.99 a  \\ CS2 & 1450.5 \(\pm\) 34.21 c  & 472.9 \(\pm\) 20.11 c  & 21.01 \(\pm\) 4.01 b  \\ CS3 & 1610.8 \(\pm\) 13.90 a  & 573.8 \(\pm\) 17.91 b  & 32.72 \(\pm\) 3.87 a  \\ \multicolumn{4}{c}{ANOVA factors and interactions} \\ \(p\)-values & & & \\ Yeast strains & **0.04** & **0.01** & **0.02** \\ Grape varieties & 0.03 & **0.02** & 0.14 \\ Yeast strains x Grape varieties & 0.15 & **0.04** & 0.21 \\ \hline \hline \end{tabular} Note: Mean \(\pm\) STDV were expressed in mg\({}_{\text{analyt}}\)/L\({}_{\text{vine}}\) of two replicates and four analytical measurements. The different superscript letters (a–d) mean significant differences (\(p<0.05\)) among results in the same column. Significant \(p\) values (\(p<0.05\)) are shown in bold.  
\end{table} 
Table 5: Mean values and standard deviations (mean \(\pm\) STDV) of total phenolic compounds, total anthocyanins, and color intensity (IC) of wines from the Vranec and Cabernet Sauvignon grape varieties and fermented by autochthonous and commercial yeast strains. Wine type abbreviations are defined in Table 1.  
According to Figure 3, the results in this current research effort revealed a significant influence of type of yeast strains employed during the fermentation in the oenochemical parameters as well as in the content of polyphenic compounds, total anthocyanins, and color intensity of the produced wines. Besides, the influence of the type of yeast strain during winemaking depends significantly on the grape varieties. In other words, the influence of the same autochthonous and commercial yeast strains in the final wines (under the same processing conditions) depends significantly on the type of wine grape (_viz._ Vranec and Cabernet Sauvignon varieties).
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 4 Challenges of DL for 5G Operations at the Mobile Edge
### Slicing
The technique was improved by using social media and other data sources to analyze the effect of key events in a city, such as sporting games, and how this affects the network demand [27]. From here, DL can be used to classify traffic without privacy-invading techniques, such as packet inspection or strict classification based on ports or packet signatures [28]. Once the traffic type is understood, network operators can take advantage of network virtualization to create E2E slices per application and dynamically meet each SLA independently [54], while still achieving optimal resource usage. Complete network slicing requires allocating virtual resources to a subset of traffic and isolating these resources from the rest of the network. Predicted demand influences which and how many resources are allocated per slice [55] and determines whether new users are permitted to join the network at the given station. These decisions are based on forecasts for available resources within the slice [56] in a process built on \"admission control\", which aims to increase revenue through efficient resource usage. DL can give some slices priority over others [57] and adapt the slicing decisions in anticipation of dynamic service demands to maximize resource use [53]. This task is complicated as unknown devices continue to join the network, but with the aid of deep learning, even these can be automatically assigned to slices to balance the network load and improve slice efficiency [103]. Maintaining efficient resource usage requires ongoing resource allocation between and within slices as discussed in the next section.
# Simulation of the Marangoni Effect and Phase Change Using the Particle Finite Element Method
## 2 Governing Equations and Solution Methods
### Latent Heat Term  
The latent heat of fusion that is absorbed or released upon melting and freezing, respectively, is non-linear and spatially very localized, with a high magnitude. In the context of numerical simulation, this can cause oscillations and convergence issues. In our work, the latent heat is treated as a separate source term, although some authors incorporate it into the heat capacity term. The specific enthalpy \(h\) stored in the material at any point is  
\[h=\Delta h_{L}f_{p}(T)+\int_{0}^{T}\rho c_{p}(T) \tag{19}\]  
where \(\Delta h_{L}\) is the latent heat of fusion (a material property) and \(f_{p}\) is the phase fraction, defined in Equation (12). The first term is the latent heat and the second term is the sensible heat of the material for any given temperature \(T\). The rate at which the latent heat is stored or released is thus  
\[L=\frac{d}{dt}\big{(}\Delta h_{L}f_{p}\big{)}=\Delta h_{L}\frac{df_{p}}{dT} \frac{dT}{dt}. \tag{20}\]  
In the worst case, with isothermal phase change the phase fraction function \(f_{p}(T)\) becomes a step function at the melting point \(T_{m}\), and the derivative with respect to temperature hence becomes infinite. Even when \(f_{p}(T)\) is not a step but rather a steep ramp, numerical problems can arise and regularization is needed, as in Figure 1a.  
A regularization is a smoothing of the discontinuous function to restore at least \(C^{1}\) continuity by replacing the discontinuous function \(f_{p}\) by a function \(\hat{f_{p}}\) that closely approximates the original but is also sufficiently smooth. We used a cubic function that is tangential at both ends of the regularized interval. In the case of non-isothermal phase change with a simple ramp function for \(f_{p}\), one can choose to either replace the entire ramp by one cubic function or to replace each side the ramp by an individual cubic function (Figure 1b). The second option was selected here, both for its flexibility and to preserve as much of the original function as possible.  
It is worth noting that when \(\hat{f_{l}}\) is not a good approximation for \(f_{l}\), the non-linear algorithm may not be able to approximate the physically correct solution very well and may prevent the iterative solution from converging, depending on how badly chosen the regularization is. Assuming that a cubic function is used for \(\hat{f_{l}}\), there is one freely chosen parameter, and it is this that determines the width, \(\Delta T_{reg}\), of the interval. It has been found that with a good spatial resolution and the Newton-Raphson non-linear iterations for the heat equation, a very small \(\Delta T_{reg}\) works very well and no oscillations are encountered.