esa-sceva/satcom-chunk-collection huggingface dataset:
# Relief Aircraft Dispatch Strategies Based on Different Levels of Information Sharing Systems
## 4 Dispatch Strategy Based on the Information Sharing System
### Level Three and Corresponding Transportation Method  
In Level three, not only can the shelters report the number of injuries to the airport, but pilots can also access real-time information about the rescue shelters, as shown in Figure 6. In this case, the aircraft can perform dynamic routing based on real-time demand information. This means that the aircraft could be assigned to another shelter if it still has sufficient capacity left after severing the first shelter.  
In the dynamic routing planning strategy, first, aircraft are assigned to a shelter based on requests, similar to the \"one-destination\" method. Then, after severing the planned shelter, the aircraft pilot determines whether there is any remaining capacity. If so, the pilot connects to the dispatch center and identifies another shelter. Considering the fuel and injured already on board, dynamic routing in this study is performed only once in one flight mission.  
From the description, we can consider that this method is more flexible than the \"one-destination\" and \"multiple-destination\" strategies. If there is remaining capacity after servicing one shelter, the pilot can report to the headquarters to take-off to another shelter, which is better than the \"one destination\" method. If the aircraft arrives at one shelter and finds that the injured are more than the reported request, it serves to full capacity at that location. However, in the same situation, the \"multiple-destination\" method would have to collect persons and travel to the next planned shelter. Thus, this is better than the \"multiple-destinations\" strategy. Although we did not conduct numerical experiments,  
Figure 6: Information flow in Level three.  
from the analysis above we can conclude that the \"dynamic routing\" is more flexible and effective in managing the uncertainty between the reported requests before takeoff and the actual mission at the shelter.  
In fact, we proposed transport methods based on the characteristic of corresponding level of information sharing. Some methods are being used in relief activities, such as \"reconnaissance and rescue\" and \"one flight one destination\". Others, such as \"multiple destinations\" and \"dynamic\", have been used in practice in ground transport or discussed in literature [36]. Further, the reason we choose these methods is that they represent the characteristics of different levels of sharing information. We want to compare different information systems by comparing different transport methods, which is the main purpose of this research.
## 3 Simulation Results and Analysis
### Simulation and Analysis of the Long-Term Collision Probability
#### 3.2.2 Results and Analysis
(2) During the 7-year evolution period of the Starlink constellation, the average collision probability after the deployment of the Starlink constellation does not increase compared with that for the nondepployed constellation, indicating that a lower constellation orbit height can improve the orbital safety and space environment stability of a low-orbit giant constellation. (3) The occurrence of a collision greatly increases the density of space debris, and the space debris generated by the collision is small and stays in orbit for a long time, resulting in a greater threat of secondary collisions to constellation satellites.
## 5 Constant Fuel Flow
### Constant Oxygen Stoichiometry Ratio  
We consider a stoichiometric ratio, \(S\), for oxygen:  
\[S=\frac{q_{O_{2}}^{in}}{q_{O_{2}}^{react}} \tag{41}\]  
where \(S\) is held constant (unlike Section 5.1 where \(r_{HO}\) is kept constant). The fuel flow rate is kept constant, as before.  
Using the stoichiometric ratio, we can express oxygen partial pressure as:  
\[p_{O_{2}} = \frac{1}{K_{O_{2}}}\left(q_{O_{2}}^{in}-q_{O_{2}}^{react}\right) \tag{42}\] \[= \frac{1}{K_{O_{2}}}(S-1)q_{O_{2}}^{react}\] (43) \[= \frac{1}{K_{O_{2}}}(S-1)\frac{I_{FC}}{4F} \tag{44}\]  
Two different oxygen partial pressures have the following ratio:  
\[\frac{p_{O_{2}}^{final}}{p_{O_{2}}^{initial}}=\frac{I_{FC}^{final}}{I_{FC}^{ initial}} \tag{45}\]  
Figure 10: Validation results for the constant fuel flow operation; mean absolute relative error = 3.96%.  
From Equations (25), (33) and (45), we obtain the following expression for \(\Delta V\) for the constant-\(S\) mode:  
\[\Delta V=\frac{RT}{2F}\ln\left(\frac{q_{H_{2}}^{in}-\frac{I_{FC}^{final}}{2F}}{q_ {H_{2}}^{in}-\frac{I_{FC}^{initial}}{2F}}\sqrt{\frac{I_{FC}^{initial}}{I_{FC}^{ final}}}\right) \tag{46}\]  
The above equation shows that for \(I_{FC}^{final}>I_{FC}^{initial}\), the change in voltage is negative, implying a loss. As before, expressing currents in Amperes and setting \(I_{FC}^{initial}=1\) A and \(I_{FC}^{final}=I_{FC}\), we have for \(I_{FC}>1\) A the following expression for the output voltage:  
\[V\approx E_{Nernst}+\frac{RT}{2F}\ln\left(\frac{q_{H_{2}}^{in}-\frac{I_{FC}}{2F }}{q_{H_{2}}^{in}-\frac{1}{2F}}\sqrt{\frac{1}{I_{FC}}}\right)-rI_{FC} \tag{47}\]  
where \(E_{Nernst}\) is given by Equation (6).  
The polarization plot for the constant oxygen stoichiometry (and constant fuel flow) condition is shown in Figure 11 where the Nernstian loss is plotted separately (the Nernstian loss is plotted as a negative value). The polarization curve shows characteristics similar to those in the literature: after the initial drop, the voltage shows an approximately linear decay for much of the operating range before dropping heavily at high currents. In Figure 11, \(q_{H_{2}}^{in}=0.0009\) mol/s. Figure 12 shows the effect of the (constant) fuel flow rate on the polarization performance. Power-versus-current behavior for the constant-fuel-flow-constant-oxygen-stoichiometry condition is presented in Figure 13. Figures 11-13 use the relevant data from Table 1 and \(E_{Nernst}=1\) V. Figures 12 and 13 use the following \(q_{H_{2}}^{in}\) values: 0.004 mol/s, 0.002 mol/s, and 0.0009 mol/s.  
Figure 11: Polarization characteristics (constant hydrogen flow and constant oxygen stoichiometry); the Nernst loss is plotted separately.  
Figure 12: Effect of the hydrogen flow rate on polarization (constant hydrogen flow and constant oxygen stochiometry).  
Figure 13: Effect of the fuel flow rate on the power-versus-current behavior (constant hydrogen flow and constant oxygen stochiometry).
## 2 Results and Discussion
### Backpressure and Permeability Studies for PS-co-DVB polyHIPEs  
Backpressure profiles were plotted for solvents commonly encountered in reversed phase HPLC (acetonitrile (ACN), MeOH and H\({}_{2}\)O) at flow rates of 0 to 1.2 mL/min\({}^{-1}\) in 0.2 mL/min\({}^{-1}\) increments. Predictably, there was a linear dependence of column pressure on flow rate, demonstrating a good mechanical stability of the monoliths, with the slopes of the backpressure plots being lowest for ACN and highest for H\({}_{2}\)O due to differences in solvent viscosity. However, the decrease in backpressure due to solvent viscosity differences was not as large as expected, being only 34% from H\({}_{2}\)O to ACN for Column #1 despite a 62% difference in H\({}_{2}\)O and ACN viscosities at 25 \({}^{\circ}\)C. This is proposed to be due to different levels of swelling of the polymer monolith in acetonitrile. Interestingly, the backpressure for Column #1 was higher than that of Column #2 for all solvents as illustrated in Figure 3. Where permeability decreases, it is most likely due to shrinkage of the pores resulting from swelling due to the solvent used and so permeability studies using Darcy's Law (Equation 1) were also conducted:  
\[k=\frac{QnL}{\Delta PA} \tag{1}\]  
Here, \(k\) is the permeability coefficient (m\({}^{2}\)), \(Q\) is the flow rate (m\({}^{3}\)/s), \(\eta\) is the viscosity of a given solvent (Pa\(\cdot\)s), \(\Delta P\) is the change in pressure (Pa), \(L\) is the column length (m) and \(A\) is the area of the column (m\({}^{2}\)). Typical organic polymer monoliths usually have permeabilities within the range of \(10^{-14}\) to \(10^{-15}\)[51; 52; 53].  
Figure 3: Backpressure profiles of PS-co-DVB polyHIPE monoliths in silcosteel tubing for water (**a**); methanol (**b**); and acetonitrile (**c**). Blue plots represent Column #1 and green plots represent Column #2. Backpressure measurements were made in triplicate at each flow rate.  
The permeabilities of the PS-co-DVB polyHIPEs lay within a range of \(10^{-16}\) to \(10^{-17}\) and were therefore lower than literature values for more traditional polymer monoliths [52; 53]; however, they were still considered acceptable, as the deviation most likely resulted from using a column of larger diameter than previously reported for capillary columns [51]. In the case of both Column #1 and Column #2, permeability was an order of magnitude lower for acetonitrile relative to water (Column #1: \(1.7\times 10^{-16}\) for water and \(8.3\times 10^{-17}\) for ACN; Column #2: \(2.0\times 10^{-16}\) for water and \(9.0\times 10^{-17}\) for ACN). It is proposed that the polyHIPE exhibited some pore shrinkage in ACN due to the wettability of the polymer (solvated polymer chains), which also accounts for the smaller than anticipated drop-off in backpressure during backpressure studies. Despite the effect of pore shrinkage observed when permeability studies were carried out, the general linear trend of the backpressure plots (Figure 3) would suggest that pore shrinkage was transient and did not permanently alter the polyHIPE morphology.
# Seismic Risk in Alborz: Insights from Geological Moment Rate Estimation and Fault Activity Analysis
## 2 Tectonic Setting and Seismicity
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Fault** & **T** & **L** & **R** & **Fault** & **L** & **R** \\
**Name** & **T** & **(Km)** & **(m)** & **Name** & **T** & **(Km)** & **(m)** \\ \hline Sangavar & Earthquake Fault & 81 & 360 & F2 = Hokm Abad-Baba & Quaternary Fault & 152 & 366 \\ Masuleh & Active, & 105 & 253 & Jajarm & Active, & 78 & 435 \\ F33 = Rudbar fault branch & Earthquake Fault & 10 & 252 & F36 = Siah Kuh & Quaternary Fault & 100 & 392 \\ Talesh & Active and Relative & 250 & 250 & Rabat Garbil & Quaternary Fault & 119 & 382 \\ F33 = Rudbar fault branch & Earthquake, & 30 & 219 & Sfarayen & Active Fault & 40 & 512 \\ Thrust and Reverse & & & & & Active, & 7 & 498 \\ F32 = Kabateth fault branch & Active Fault & 42 & 190 & Doruneh1 & Active Fault & 31 & 481 \\ Lahijan & Active Fault & 42 & 217 & Doruneh2 & Strike-Slip & 35 & 477 \\ F31 = North Alborz fault & Active Fault & 45 & 196 & Torud & Active Fault & 80 & 241 \\ North Rudbar & Quaternary Fault & 100 & 162 & F8 = Qods & Active Fault & 23 & 280 \\ Bonan & Active Fault & 76 & 112 & F9 = Aliakhan & Active Fault & 64 & 277 \\ Alamutrud & Active, & 133 & 78 & F10 = Mehrkardu & Active, & 33 & 348 \\ F21 = Daryasar-Dikin & Active Fault & 19 & 118 & F11 = Mozaffar Abad & Thrust and Reverse & 69 & 336 \\ F22 = Veneda-Veres & Active Fault & 37 & 85 & Ghachab & Thrust and Reverse & 40 & 158 \\ F23 = North of Ghazvin & Active Fault & 10 & 105 & Pishva & Quaternary Fault & 35 & 50 \\ F24 = Gharib Mazzeah-Veres & Active Fault & 21 & 111 & Garmsar & Active, & 70 & 65 \\ North Ghazvin & Active, & 64 & 129 & Eyyanaki & Thrust and Reverse & 94 & 9 \\ Zanjan & Quaternary Fault & 104 & 196 & Kahrizak & Active, & 4 & 21 \\ F35 = Gmorkan & Quaternary Fault & 15 & 193 & South Tehran & Thrust and Reverse & 21 & 14 \\ F34-F33 = Baghestan- & Quaternary Fault & 25 & 170 & Siah Kuh & Quaternary, & & \\ Oeshab-Aldaibu-Shinin & Quaternary, & 116 & 216 & F27 = Abdareh-Khar Rud & Quaternary Fault & 41 & 147 \\ Soltanieh & Thrust and Reverse & Active, & 6 & 111 & Parandak & Quaternary Fault & 42 & 111 \\ F25 = Hasan Abad & Thrust and Reverse & & 80 & 51 & E8 = Avai-Ahmad & Quaternary Fault & 47 & 190 \\ Kandovan & Thrust and Reverse & & 75 & 74 & F29 = Indes Ardmin & Quaternary Fault & 7 & 185 \\ F14 = Ghasre Firozeh & Quaternary Fault & 12 & 7 & Kushk+e Nosrat & Quaternary Fault & 116 & 105 \\ F15 = Sorkhe Hesar & Quaternary Fault & 12 & 12 & F29 = Indes-Aremin & Quaternary Fault & 31 & 151 \\ \hline \end{tabular}
\end{table} 
Table 1: Active faults [13,14] of the study area with the active fault type (T), fault length (L), and nearest fault distance to Tehran (R).
## 4 Discussion
The attenuation is about 3 to 6 dB higher than those of moulded and folded masks for the octave-wide bands with centre frequencies 4 and 8 kHz, which together carry 43% of the articulation index. The calculation result confirms intuition: the tighter-fitted mask stronger suppresses waves of higher frequency. Figure 7 demonstrates the remarkable difference between the attenuation of M1204 V and the other masks in the 1/3 octave-wide bands with centre frequencies of 2 kHz and 5 kHz. M1204 V attenuates the higher-frequency band stronger than the lower-frequency one. Such a difference is much smaller for the folded vertically M1200 VB, while the opposite relation occurs for the remaining five FFPs. Figures 8 and 9 illustrate how the studied FFPs disrupt the normalized speech spectra reported in ANSI 3.5-1997 standard [11]. Doubtless, speaking persons should raise their voice by at least one \"loudness level\" to compensate for the attenuation by an FFP. That would cause changes in the voice timbre, which may result in a worsened understanding by the listener despite sufficient loudness. The listener's experience could be compared to that of a person with hearing loss. The latter concerns mainly frequencies above 1 kHz [19]. Figure 7: Attenuation of sound by the seven studied FFPs in 1/3 octave-wide bands with centre frequencies of 2 kHz (diagonal hatching) and 5 kHz (cross-hatching). Figure 8: Disruptions on the ANSI speech spectra caused by the moulded FFPs. Empty symbols—ANSI spectra, filled symbols—spectra with the FFPs corrections: \(L_{f}\) (ANSI) – \(\Delta L_{f}\). Lines are guides for the eye only. Speech levels: Nor.—normal, Ra.—raised, Lo.—loud, Sh.—shouted.
# Semi-Supervised Learning for Intrusion Detection in Large Computer Networks
## 3 Methodology
### Proposed Semi-Supervised Learning Models
#### 3.3.10. Combining These Concepts  
In semi-supervised learning with co-training and a random forest base estimator, the workflow typically includes the following:  
1. **Initial Training**: Train two random forest classifiers on different views of a small labeled dataset.
2. **Prediction**: Use each random forest classifier to predict labels for the unlabeled instances.
3. **Confidence Selection**: Select the instances with the highest confidence from each classifier's predictions.
4. **Teaching**: Add the confidently labeled instances from one classifier to the training set of the other classifier.
5. **Model Update**: Retrain each random forest classifier with its expanded training set.
6. **Iteration**: Repeat the process of prediction, selection, teaching, and retraining until a stopping criterion is met (e.g., a certain number of iterations, achieving a performance threshold, or exhausting the labeling budget).  
The workflow is summarized in the following Algorithm 3.  
```
1:Labeled dataset \(\mathcal{L}\), Unlabeled dataset \(\mathcal{U}\), Feature views \(V_{1}\) and \(V_{2}\);
2:Trained Random Forest models RF1 and RF2;
3:Initialize: Split features into two disjoint views \(V_{1}\) and \(V_{2}\);
4:Train initial Random Forest models RF1 on view \(V_{1}\) and RF2 on view \(V_{2}\) using labeled set \(\mathcal{L}\);
5:while stopping criterion not met do
6:for each instance \(x\in\mathcal{U}\)do
7: RF1 predicts the label for \(x\) using view \(V_{1}\);
8: RF2 predicts the label for \(x\) using view \(V_{2}\);
9:endfor
10: Select instances from \(\mathcal{U}\) with the highest confidence from RF1 and RF2;
11: Add the confidently labeled instances from RF1 to the training set of RF2, and vice versa;
12: Update the labeled datasets for RF1 and RF2 with newly labeled instances;
13: Retrain RF1 using the updated labeled set from view \(V_{1}\), and RF2 using the updated labeled set from view \(V_{2}\);
14:endwhile
15:Note:\(\in\) is the mathematical symbol for \"element of\".
```  
**Algorithm 3** Co-Training Workflow Using Random Forests on Split Feature Views
# Marine Microalgae for Potential Lutein Production
## 6 Microalgae Cultivation for Commercial Lutein Production and Challenges
was cultivated in a 55 L tubular photobioreactor and the highest lutein productivity was 40 g m\({}^{-2}\) d\({}^{-1}\). Moreover, _Scenedesmus almeriensis_ was also cultivated in a 4000 L tubular photobioreactor for lutein production and 290 mg lutein m\({}^{-2}\) d\({}^{-1}\) was observed [57]. Besides lutein, the main commercial carotenoid productions are \(\beta\)-carotene from _Dunaliella_ sp. and astaxanthin from _Haematococcus_ sp. Commercially, \(\beta\)-carotene from _Dunaliella_ sp was extracted by Betatene (Australia) and Western Biotechnology (Australia) in 400 and 240 ha extensive unmixed ponds, where 13-14 and 4-5 tonnes of \(\beta\)-carotene per year have been extracted, respectively. For commercial astaxanthin extraction, Cyanotech (Hawaii) uses a closed tubular photobioreactor followed by open ponds, whereas Algatechnologies (Israel) applies a closed tubular photobioreactor for _Haematococcus_ sp. cultivation [57]. In conclusion, a tubular photobioreactor can be suggested for commercial lutein production as was experimentally tested for _Muriellopsis_ and _Scenedesmus_ species, and also due to the fact that tubular photobioreactors have been successfully used for other carotenoid-producing microalgae.
## 2 Materials and Methods
### Surrogate Modeling and Validation
#### 2.2.1 Universal Kriging  
The universal kriging, also known as Gaussian process modeling, represents a statistical interpolation method that splits the random function into a linear combination of deterministic functions, known at any point of the domain input, and a random component, the residual random function, described by a Gaussian noise depending on the input vector. The model function \(\hat{M}(\mathbf{x})\) is then described by the following equation (the apex \"K\" stays for \"kriging\"):  
\[\hat{Y}^{K}=\hat{M}^{K}(\mathbf{x})=\sum_{j=1}^{p}\beta_{j}f_{j}(\mathbf{x})+Z(\mathbf{x}), \tag{3}\]  
where \(\sum_{j=1}^{p}\beta_{j}f_{j}(\mathbf{x})\) is a linear combination of a given functional basis \(f\) with non- zero coefficients \(\beta_{j}\) and represents the mean value of \(\hat{Y}^{K}\), and \(Z(\mathbf{x})\) represents the stationary Gaussian process with zero mean and stationary autocovariance:  
\[\text{E}\big{[}Z(\mathbf{x}),Z\big{(}\mathbf{x}^{\prime}\big{)}\big{]}=\sigma^{2}R \big{(}\mathbf{x}-\mathbf{x}^{\prime},\ \mathbf{\theta}\big{)}, \tag{4}\]  
Characterized by the (constant) variance \(\sigma^{2}\) of the Gaussian process, and by \(R\) which is its stationary autocorrelation depending on the difference between two sample points (\(\mathbf{x}-\mathbf{x}^{\prime}\)) and its hyperparameters \(\ \mathbf{\theta}\). In the present work, the Matern correlation function \(R\) was used and its hyperparameters \(\sigma^{2}\) and \(\mathbf{\theta}\) were estimated by cross-validation estimation and covariance matrix adaptation-evolution strategy (CMA-ES). Further details of the autocorrelation functions properties and of the universal kriging method can be found in [31; 32].
## 5 Results
### Real-Time Online Taxi-Hailing Demand  
While we are forecasting online taxi-hailing demand by different models in Table 6, we ignore that the future taxi demand is unavailable. To realize the real-time online taxi-hailing demand prediction, we should predict the taxi demand before forecasting the online taxi-hailing demand by model \"BPNN + T + E\" and \"XGB + T + E\". The results of taxi demand prediction are as in Figure 9 and Table 9.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Training Set**} & \multicolumn{3}{c}{**Testing Set**} \\ \cline{2-7}  & **MAPE** & **RMSE** & \(\mathbf{R}^{2}\) & **MAPE** & **RMSE** & \(\mathbf{R}^{2}\) \\ \hline BPNN + T & 0.213 & 26.972 & 0.842 & 0.224 & 28.576 & 0.819 \\ BPNN + T + E & 0.178 & 23.692 & 0.851 & 0.190 & 23.921 & 0.845 \\ BPNN + T + E + TX & 0.127 & 18.632 & 0.893 & 0.132 & 18.696 & 0.866 \\ XGB + T & 0.302 & 24.701 & 0.836 & 0.333 & 26.296 & 0.833 \\ XGB + T + E & 0.188 & 19.718 & 0.872 & 0.197 & 21.206 & 0.857 \\ XGB + T + E + TX & 0.129 & 17.797 & 0.887 & 0.139 & 19.193 & 0.865 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Prediction effects of BPNN and XGB.  
Figure 7: Average mean absolute percentage error (MAPE) of online taxi-hailing demand prediction based on BPNN and XGB in different hours. (**a**) MAPE of the prediction models based on BPNN; (**b**) MAPE of the prediction models based on XGB.  
Figure 8: Average RMSE of online taxi-hailing demand prediction based on BPNN and XGB in different hours. (**a**) RMSE of the prediction models based on BPNN; (**b**) RMSE of the prediction models based on XGB.  
Figure 9: The taxi demand prediction based on “BPNN + T + E” and “XGB + T + E”. (**a**) Result of taxi demand prediction based on “BPNN + T + E”; (**b**) Result of taxi demand prediction based on “XGB + T + E”.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Training Set**} & \multicolumn{3}{c}{**Testing Set**} \\ \cline{2-7}  & **MAPE** & **RMSE** & \(\mathbf{R}^{2}\) & **MAPE** & **RMSE** & \(\mathbf{R}^{2}\) \\ \hline BPNN + T + E & 0.177 & 31.812 & 0.874 & 0.185 & 32.860 & 0.851 \\ XGB + T + E & 0.204 & 37.625 & 0.747 & 0.213 & 37.754 & 0.713 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Prediction effects of taxi demand prediction based on BPNN and XGB.  
Table 9 shows that the model \"BPNN + T + E\" performs better than model \"XGB + T + E\" in forecasting taxi demand. Based on the information on taxi demand prediction (\"PTX\"), we forecast the online taxi-hailing demand by model \"BPNN + T + E + PTX\" as Figure 10. From Table 10, we find that including the information of \"PTX\" leads to an MAPE reduction from 0.190 to 0.183 and an RMSE reduction from 23.921 to 21.050, and it increases the R\({}^{2}\) from 0.845 to 0.853. However, because \"PTX\" is the projected taxi demand, the performance of the model \"BPNN + T + E + TX\" is better than the model \"BPNN + T + E + PTX\". Furthermore, Figure 11 indicates that the performance of the model \"BPNN + T + E + PTX\" is better than the model \"BPNN + T + E\" for most hours.
###### Abstract  
Well placement optimization is one of the most important means to control the decline of oilfields and improve the recovery rate in the development process of deep and heterogeneous reservoirs, such as deep buried carbonate oil reservoirs. However, the mapping relationship from deployed well positions to actual profits is non-linear and multi-modal. At the same time, the injection and production relationship of new wells also affects the contribution of well positions to final profits. Currently, common algorithms include gradient-based and heuristic non-gradient algorithms, which have advantages, but face problems of high computational complexity, slow optimization speed, and difficulty in convergence. We propose an evolutionary algorithm for well placement optimization in carbonate reservoirs. This algorithm improves well placement optimization and computational speed by constraining the sampling process to effective sampling spaces, integrating prior knowledge to enhance sampling efficiency, strengthening local optima exploration, and utilizing parallel computing. Additionally, it refines the optimized variable content based on actual control factors, enhancing the algorithm's robustness in practical applications. A case study from a carbonate reservoir in northwestern China demonstrated that this algorithm not only improved the performance by 50% compared to the classic DE algorithm but also achieved 15% higher optimization effectiveness than the current state-of-the-art algorithms.  
joint optimization; evolutionary-algorithm; in-situ experience; data parallel +
Footnote †: journal:  
0000-0001-999-4881-67