esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
According to the previous economic analysis of PV or PV/BESS works of literature, the traditional methods directly apply the load demand or electricity price evaluations to set PV capacity, but the constraints of the excess electricity fraction are less considered to the correspondence between renewable fraction (RF) and excess power. Usually, the operation of excess power uses power curtailment by the solar inverter and reveals a waste of excess PV power. Therefore, the excess electricity fraction is limited to 3% to a novel precise analysis and effective use of energy for the economic benefit evaluation of PV/BESS. The energy arbitrage revenue can be obtained by calculating the optimal economical capacity allocation of a PV system and BESS in the conditions of meet load demand and PV's RF constraints at the same time. The HOMER software is a simulation analysis tool. The analysis method requires users to plan according to their needs and it can carry out relevant research and analysis according to different research needs. This study uses HOMER simulation to propose the economic benefit analysis method of PV/BESS applied to BTM, which is to consider the effective use of renewable energy. Especially an analysis method is considered by the restriction of excess electricity, and to filter out the solutions that meet the restriction of excess electricity from the simulation results (excess electricity is set to 3% in this study) to find out the capacity of PV/BESS solutions under the conditions of different proportions of renewable energy. The analysis method to most project benefits is the part that has not been considered in the economic benefit analysis literature in the past, and this method is proposed for the engineer application of renewable energy to the BTM economic benefit analysis. The design plan is under comprehensive considerations for the electricity users to evaluate and choose the system plan.
###### Abstract  
Marine transportation of liquefied carbon dioxide (LCO\({}_{2}\)) is crucial for Carbon Capture, Transportation, Utilization, and Storage (CCTUS) technology, aiding in CO\({}_{2}\) emission reduction and greenhouse effect control. This study investigates the thermodynamic and fluid dynamic characteristics of LCO\({}_{2}\) in Type C storage tanks using numerical simulations, focusing on heat transfer, flow phenomena, and boil-off gas (BOG) generation under varying storage pressures. Results show that heated liquid rises along the tank wall, forming vortices, while gas-phase vortices are driven by central upward airflow. Over time, liquid velocity near the wall increases, enhancing flow field mixing. Gas-phase temperatures rise significantly, while liquid-phase temperature gradients remain minimal. Higher storage pressures reduce fluid velocity, vortex range, and thermal response speed. BOG generation is higher at low pressures and decreases as pressure rises, slowing beyond 1.5 MPa. Under sloshing conditions, interfacial fluctuations enhance heat and mass transfer, reducing thermal stratification. Resonance periods amplify interfacial disturbances, improving thermal mixing and minimizing temperature gradients (\(\Delta\)T \(\approx\) 0.1 K). Higher filling rates suppress surface rupture, while lower rates exhibit gas-dominated instabilities and larger thermal gradients (\(\Delta\)T \(\approx\) 0.3 K).  
carbon dioxide cargo; BOG; fluid dynamics; numerical simulation +
Footnote †: journal: Applied Sciences
# Substitute Seed Nodes Mining Algorithms for Influence Maximization in Multi-Social Networks
## 5 Simulation
### Analysis of Multi-Social Networks Influence Maximization  
In the simulation, CELF++ is used to solve the problem of influence maximization and the propagation model we selected is the LT model. For the edge (\(u\), \(v\)) existing in the graph \(G\), the probability \(p_{uv}\) the node \(u\) to activate its neighbor node \(v\) is calculated by the reciprocal of the number of node \(v\)'s in-degrees, that is \(p_{uv}=1/\left|in(v)\right|\), \(v\in out(u)\). Each time the influence of the seed set was calculated, the propagation process was simulated by 10,000 Monte Carlo methods.  
To compare with the information spread in multi-social networks, we first show the spread of influence in a single social network, as shown in Figure 4. From the experimental results we can see that, as the number of seed nodes increases, the influence increases.  
Next, the experiments verify the information spread in multiple social networks. However, we cannot get the social network data sets with the annotated _Bridge Users_. In the experiments, we choose the node with the same ID from the three social networks and assume these nodes (with the same ID) belong to one user, this user is regarded as a _Bridge User_. In the following simulation, the BU is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **NetHEPT** & **Epinions** & **Slashdot** \\ \hline Number of nodes & 15,233 & 75,879 & 77,360 \\ Number of edges & 62,796 & 508,837 & 905,468 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Three social network datasets used in the simulation.  
Figure 4: The spread of influence in a single social network. (**a**) The spread of influence in NetHEPT; (**b**) The spread of influence in Epinions; (**c**) The spread of influence in Slashdot.  
obtained by this method. Then the above three social networks \(G_{1}(V_{1},E_{1}),\;G_{2}(V_{2},E_{2}),\;G_{3}(V_{3},E_{3})\) can be aggregated into one network \(G(V,E)\) by algorithm 1. When aggregation, the number of selected _Bridge User_ is set as 0, 1000, 2000, 3000, 4000, and 5000 respectively. The information spread with a different number of _Bridge Users_ in multi-social networks is shown in Figure 5.  
As the result shown in Figure 5, we can find that, as the number of seed nodes increases, the spread of influence becomes larger. As discussed before, we have analyzed that _Bridge Users_ can spread information from social network \(G_{1}\) to \(G_{2}\). When comparing the influence spreading of a different number of _Bridge Users_, we can see that the more _Bridge Users_, the wider influence spreading, which indicates the self-spread of _Bridge User_ expands the scope of information spreading. These results confirm that the problem of influence maximization is different from previous, multi-social networks can be connected by _Bridge Users_.
## 3 Results and Discussion
### Position and Temperature Dependence  
The Electrode response as a function of its position with respect to the beam entrance and the water temperature was evaluated.  
In order to study the position dependence, the detector was irradiated in four different positions, as shown in Figure 6 where a sketch of the water tank (view from the top) and the Electrode positions are presented. In each position, ten consecutive irradiations, corresponding to a released dose of 4 Gy at the entrance position of the water tank, were performed. A maximum percentage difference of 5.3% was found for the detector response in the four positions.  
The detector dependence with respect to the temperature was investigated in the temperature range of 20-40 \({}^{\circ}\)C. Differences lower than 3% between the lowest and highest values were observed.
# Antimicrobial and Odour Qualities of Alkylpyrazines Occurring in Chocolate and Cocoa Products
## 2 Pyrazines in Chocolate and Cocoa  
### The Way of Formation of Pyrazine in Cocoa and Cocoa Products  
The odour of a wide variety of goods, including cocoa, coffee, cooked meat, mushrooms, bread, sesame oil and seeds, roasted nuts (macadamia, peanuts, hazelnut), almonds, popcorn, tobacco, potato chips, rum, whiskey, soy sauce and vegetables (beans, spinach, sweet corn, green bell peppers, red peppers, cucumbers, green peas, lettuce, tomatoes, cabbage, carrots and pumpkin) is affected majorly by pyrazines. Scientists began investigating the widespread occurrence of alkylpyrazines in natural goods in the middle of 1960, and as a result, knowledge of pyrazines and their olfactory properties began to expand. During that time, the process of creating alkylpyrazines during the production of cocoa was identified [12]. The genetic background of the seed, harvesting, fermenting, drying and roasting all play an essential role in creating the aroma of cocoa. Raw cocoa beans taste and smell like vinegar, and different flaws such as the processing of unripe or overripe fruit, inadequate aeration, the absence of mixing of the fruit, infection with microbial pathogens and/or smoke damage as a consequence of inappropriate drying may impair the distinctive bitter and astringent taste and the residual sweetness of fermented beans [3,13]. Following the application of a crude fermentation process to the fresh seeds, accompanied by drying and roasting, the characteristic aroma of cocoa is created [7].
# Predicting Employee Absence from Historical Absence Profiles with Machine Learning
## 5 Results
### Sick Leave Prediction  
#### 5.2.1. Short-Term Predictive Performance (Single-Target Prediction)  
To assess the predictive accuracy of sick leave one week in advance based on our data representation, we compared the performance of the baseline model with regression trees and various ensemble methods using four evaluation metrics. The results, presented in Figure 9, show a clear distinction in performance between models (detailed results are available in Table S6 of the Supplementary Materials).  
The baseline model, as expected, produced the poorest results across all metrics, emphasizing the need for more advanced predictive approaches. The regression tree method shows a significant improvement over the baseline.  
Among the ensemble methods, Random Forests achieved the best results, showing the lowest RMSE and highest correlation coefficient. Bagging also performed well, with slightly lower RMSE. BagSubspaces and Random Subspaces demonstrated moderate improvements, performing better than the baseline but not outperforming the other ensemble methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**One-Week Ahead Prediction** & **Two-Week Ahead Prediction** & **One-Month Ahead Prediction** \\ \hline Previous12 MonthSickLeaveDay5 & Previous1 MonthVacationLeaveDay6 & Week\_SpringSeason\_K+2 \\ \hline Previous12 MonthVacationLeaveDay5 & Week\_WinterSeason\_K+1 & Week\_WinterHoliday\_K+3 \\ \hline Previous12 MonthVacationLeaveDay4 & Previous2 WeekSickLeaveDay6 & Week\_WinterSeason\_K+4 \\ \hline Previous12 MonthSickLeaveDay4 & Previous12 MonthVacationLeaveDay4 & Week\_WinterSeason\_K+3 \\ \hline Previous12 MonthVacationLeaveDay3 & Previous12 MonthSickLeaveDay2 & Week\_SpringSeason\_K+3 \\ \hline Previous6 MonthVacationLeaveDay4 & Week\_WinterSeason\_K+2 & Week\_SpringSeason\_K+1 \\ \hline Previous12 MonthVacationLeaveDay1 & Previous12 MonthSickLeaveDay5 & Week\_SpringSeason\_K+4 \\ \hline Week\_WinterSeason\_K+1 & Previous3 MonthSickLeaveDay5 & Week\_WinterHoliday\_K+2 \\ \hline Previous3 MonthVacationLeaveDay5 & Previous6 MonthVacationLeaveDay5 & Week\_WinterSeason\_K+2 \\ \hline Previous12 MonthSickLeaveDay1 & Week\_AutumnSeason\_K+2 & Week\_SpringHoliday\_K+4 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Attribute importance using Random Forests for vacation leave prediction with forecasting horizons of one week, two weeks, and one month. Attributes are presented in descending order of importance, as determined by the impurity function. Only the top ten attributes are shown.
## 3 Results  
### Finite Element Analysis (FEA)  
FEA was used to validate the analytical model and help provide a thorough understanding of the dynamic behaviour of slitted beams fitted with PZT. The FEA simulations were conducted using ANSYS software version 14.5, concentrating on beams with dimensions and material properties to match those used for the analytical model.  
The model was constructed using the following dimensions: overall beam length of 142.0 mm, width 28.0 mm, with slit width 2.0 mm, and the slit commencing at a distance 50.0 mm from the fixed end (slit length 92.0 mm from the free end). The beams were composed of a 0.70 mm thick substructure topped with a 0.66 mm thick PZT-5A layer. To ensure that the stress and strain distributions, especially at the slitted regions and the free end of the beam, were accurately represented, the model used a fine mesh.  
The fixed end of the beam was fully constrained, while the free end was able to vibrate freely. An ACEL command was used to apply base vibrations to the beam from the beam base, with an acceleration magnitude of \(-1\) m/s\({}^{2}\) in the y-direction to simulate real-world vibrations.  
With 500 substeps, a harmonic analysis covering a frequency range of 0 to 50 Hz was carried out to guarantee a thorough capture of the slitted beams' resonance and anti-resonance behaviour. The electrical circuit's load resistance was adjusted to 10 k\(\Omega\).  
Figure 2 shows FEA results for slitted beams under various loading conditions and vibrational modes, generated using ANSYS software. The figure illustrates different deformation patterns and mode shapes, with higher deformations observed at the free end of the beam, both slitted and without a slit. These results confirm the analytical model and provide insights into the structural dynamics and optimization of slitted beams.  
The frequency response functions of slitted beams with one tip mass of 5 g and conventional beams with PZT layer and a tip mass of 5 g, as determined using ANSYS simulation, are shown in Figure 3. The regular beam shows a robust vibrational mode (resonance peak) close to 30 Hz, while the slitted beam shows resonance peaks at roughly 27 Hz and 43 Hz, and a sharp dip or anti-resonance between these peaks at about 31 Hz. The presence of the slit dramatically changes the dynamic behaviour of the beam, as demonstrated by this complex response in the slitted beam. The slit's impact on energy dissipation at particular frequencies is demonstrated by the presence of the strong anti-resonance peak. Our findings confirm how significantly the vibrational properties and energy harvesting capability of beams with PZT can be affected by the inclusion of a slit and tip mass.
# Estimating Eddy Dissipation Rate with QAR Flight Big Data
## 2 Data and Methodology
### EDR Estimation
#### 2.3.3 Estimating EDR with QAR Data
(3) Estimate the Acceleration Response Energy  
First of all, perform an inverse Fourier transform on the band pass filter function \(h_{bp}(t)\), as shown in Equation (18):  
\[h_{bp}(t)=\frac{1}{2\pi}\int_{-\infty}^{\infty}H_{bp}(\omega_{1},\omega_{2}, \omega)e^{i\omega t}d\omega. \tag{18}\]  
Then, according to the Parseval theorem (assuming local stationarity), an approximation of the acceleration response energy \(\hat{\sigma}_{\tilde{z}}\) can be obtained by convolution, as shown in Equation (19):  
\[\hat{\sigma}_{\tilde{z}}{}^{2}(t)\approx\frac{1}{2T}\int_{t-T}^{t+T}\left[\int _{0}^{\tau_{1}}h_{bp}(\tau_{2})VRTG(\tau_{1}-\tau_{2})d\tau_{2}\right]^{2}d\tau _{1} \tag{19}\]  
where \(T\) takes 10 s, and \(VRTG(t)\) is a curve equation obtained by fitting the VRTG value of the recorded points in \(2T\). (4) Estimate EDR  
Finally, EDR \(\varepsilon^{\frac{1}{3}}\) can be estimated via the Equation (20) [32]:  
\[\varepsilon^{\frac{1}{3}}=\sqrt{\frac{\hat{\sigma}_{\tilde{z}}{}^{2}}{0.7 TAS^{ 2/3}I(\omega)}}. \tag{20}\]  
In this way, EDR could be estimated with the QAR data, where all the parameters are calculated accordingly. In the next section, this procedure will be exemplified with QAR data collected from 1 January 2016 to 31 December 2018.
## 3 Proposed Architecture: Multi-Model IDS Based on Traffic Load
### Performance Evaluation
#### 3.8.3 Hyperparameters in the Model  
The risk of overfitting was reduced by tuning hyperparameters and using k-fold cross-validation. ML models perform better when hyperparameters are properly selected, since they influence an algorithm's ability to fit the model to the data. In order to optimize the performance of the model, it is necessary to select appropriate hyperparameters. The hyperparameters of a model must be tuned to maximize its performance [70]. ML models are built by setting hyperparameters, which are settings that are not learned from data and are determined before training begins [71]. The following are critical components of hyperparameters [71]:  
1. The following types of hyperparameters can be identified: * Characteristics specific to a particular model may include the depth of decision trees (DTs) or the number of hidden layers within a neural network, for example. * Training-specific factors include the learning rate or batch size in gradient descent.
2. A hyperparameter tuning approach involves finding the best parameters to optimize a model's performance on a given task. It can be carried out through grid search, Bayesian optimization, or random search.
3. In terms of importance, the following apply: * Optimizing hyperparameters can significantly improve a model's performance. * Controlling overfitting and underfitting can be achieved by altering hyperparameters, such as the regularization parameter.
4. The challenge of tuning hyperparameters can be computationally expensive and time-consuming, especially for models with a large number of parameters and complex interactions among them.  
With GridSearchCV, tuning hyperparameters in ML efficiently and effectively is possible. A systematic evaluation of hyperparameters creates a grid of possible combinations. Combinations that perform best are evaluated by using cross-validation and selected according to performance. In this way, hyperparameter tuning can be automated, improving model performance and reducing the need for manual trial and error [70].  
A description of each hyperparameter and its values is provided in Table 8. In addition, k-fold cross-validation can enhance the model's robustness and generalizability when used during the evaluation process. To train the model, k = 5 subsets of the datasets were divided into one set, and the model was trained on the sets of data. By repeating this procedure five times, the cross-validation performance metrics for each fold were obtained. In addition, the results of the cross-validation process are not discussed in detail in this study.
## 4 Results and Discussion
### Model Parameter Initialization and Validation of Fitness Function Values  
After the data correlation analysis is performed to determine the characteristic data, the initialization parameter values of the DE-GWO-SVR model need to be determined. Five initialization hyperparameters were set in the experiments of this paper, in which the initialization population size (nPop) was 30, the maximum number of iterations (MaxIt) was 500, the independent variable dimension (nVar) was 2, the crossover probability (pCR) was 0.2, and the scaling factor (Beta) was between 0.2 and 0.8. This setting not only satisfies  
Figure 4: Univariate linear fit between temperature, conductivity, salinity, total dissolved solids, and dissolved oxygen. (**a**) DO and temperature, (**b**) DO and conductivity, (**c**) DO and salinity, (**d**) DO and total dissolved solids.  
Figure 3: PPMCC\(-\)based water quality data analysis diagram.  
the number of wolves in each generation in the GWO algorithm, but also satisfies the variation operation of the population in the DE algorithm, as well as balancing the time for optimization of the algorithm. In performing the experimental simulation, according to Equations (14) and (15), 100 times MSE value with R\({}^{2}\) value as the objective function value was selected for optimization, which can make sure that the optimization avoids the disappearance of error due to the lack of precision. Figure 5 shows the fitness function graph of the algorithm during 500 iterations of the experimental simulation. As shown, with the continuous iteration of the optimization algorithm, the mean square error value of the algorithm is decreasing and the fitness of the algorithm is increasing. After the first 400 rounds of optimization, the curve fluctuation amplitude is significantly reduced and tends to be stable, which indicate that the fitness of the algorithm at this time has reached an optimal state, and the fitness function value no longer continues to decline with the iteration of the algorithm.
# A Cooperative Control Method for Excavation Support Robot with Desired Position/Posture
## 3 Cooperative Control Method
### Position/Posture Detailed Description for ESR  
Considering the space environment conditions in which the ESR is located and its working process constraints, the position/posture deviation of different states is caused. The position/posture morphological motion trend sets of ESR are represented in Figure 9.  
As shown in Figure 9, in the pre-set presentable state, the declination and scroll angles of the ESRs on both sides are both \(0^{\circ}\), and the distance from the edge sidewall is within the expected limit. Under the influence of the space environment and ESR motion, the ESRs on both sides of the edge are prone to different degrees of deviation such as declination and lateral incline.
# Evolution of Water-in-Oil Droplets in T-Junction Microchannel by Micro-PIV
## 3 Results and Discussion  
### Evolution of Water Droplets at the Intersection of the Offset T-Junction Microchannel  
In general, less viscous water flows faster than more viscous oil. However, when both solutions were experimented on within the microchannel, the hydrophobic coating on the walls of the PMMA channel [45] created a small air gap between the inside wall of the channel and the outer surface of the liquids [46]. The air gap around the more viscous liquid is larger, which allowed food-grade palm olein to move through the channel faster than the less viscous distilled water.  
Figure 3 shows the growth of water droplets at the junction in an offset T-microchannel at t\({}_{1}\) = 91 ms until t\({}_{4}\) = 636 ms. The droplet in the microchannel with a radius of 400 \(\upmu\)m evolved faster over time compared to the droplet in the channel with a radius of 750 \(\upmu\)m, and at t\({}_{4}\) = 636 ms, the front tip of droplet started to exceed the junction point. This growth pattern of the droplet was found to be similar to the ones in the channel with a radius of 500 \(\upmu\)m. However, for the channel with a radius of 750 \(\upmu\)m, the evolution of the water droplet was much slower as time passed, and the droplet was still within the junction area and can be seen at t\({}_{4}\) = 636 ms. In terms of droplet size, the droplet becomes larger as the size of the microchannel increases. The water droplet is rounder and more solid-shaped within the microchannel's radius of 750 \(\upmu\)m. Its size is less than or nearly equal to the microchannel's width. Both channels showed that when the droplet reached the junction, the oil pushed the water droplet upwards and caused an irregular shape at the bottom of the droplet, as can be seen at t\({}_{3}\) = 455 ms. At this point, the droplet was likely starting to enter the break-up process. The droplet expanded mainly in the radial direction and slightly in the axial direction [1]. Hence, the length increases gradually while the width increases moderately. This point is named the thread expansion stage, and the period is  
\begin{table}
\begin{tabular}{c c} \hline
**Composition/Material** & **Polystyrene** \\ \hline Diameter & 10 \(\upmu\)m \(\pm\) 0.08 \(\upmu\)m \\ \hline Concentration & 0.2\% solids \\ \hline Density & 1050 kg/m\({}^{3}\) \\ \hline Refractive index & 1.59 at 589 nm (25 \({}^{\circ}\)C) \\ \hline \end{tabular}
\end{table}
Table 2: Specification of seeding particle [38].  
called expansion time [1]. As it reached t = t\({}_{4}\), the cross-flowing liquids drove the thread in its axial direction and a visible neck formed. The droplet in the microchannel with a radius of 400 \(\upmu\)m is faster to split and to break up compared to the 750 \(\upmu\)m channel.