esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Path Analysis
Figure 5: The **(a)** path diagram and **(b)** SEM of all the observed variables (ET, R\({}_{n}\), T\({}_{a}\), VPD, WS, and P) and latent constructs (dryness and wetness) within the path analysis with positive (black) and negative (red) path coefficients for hourly timescale across all seasons. The dashed line indicates direct contribution. The * sign indicates significance level and n.s. indicates non-significance. Similar to Figure 6, the path analysis results were compared for all the variables with significant path coefficients across seasons. The distributions of path coefficients for each variable during the spring, summer, autumn, and winter seasons are shown in Figure 7. R\({}_{n}\) had the highest regression coefficient for the autumn season, followed by summer and spring. In winter, the independent contribution of R\({}_{n}\) was the lowest in determining the variability in ET. However, VPD showed the least contribution to ET variability in the range of \(\sim\)1000-10000 years. Figure 6: Distributions of regression coefficients from all significant results of path analysis SEM at hourly, daily, and weekly timescales with sample size (n = 12) for each variable. Black solid lines represent median values and boxes represent interquartile range. the autumn season, which became maximum in the summer season. This revealed the VPD-dependent driving force in summer seasons when there is an abundant amount of water vapor in the atmosphere. The effect of temperature was observed to be greater during the spring and winter seasons compared to the summer and autumn seasons. Windspeed and rain had the highest impact during the summer season. The lesser effect of rain on ET variability during spring and autumn suggested there was sufficient water available for ET.
# Dual-Branch Multimodal Fusion Network for Driver Facial Emotion Recognition
## 4 Experiment
### Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Multiscale**} & \multicolumn{4}{c}{**Scale = 1**} & \multicolumn{4}{c}{**Scale = 2**} & \multicolumn{4}{c}{**Scale = 3**} & \multicolumn{4}{c}{**Scale = 4**} \\ \hline
**Encoder** & **Acc** & **Pr** & **Re** & **F1** & **Acc** & **Pr** & **Re** & **F1** & **Acc** & **Pr** & **Re** & **F1** \\ \hline Resnet 18 & 0.5742 & 0.5667 & 0.5866 & 0.5765 & 0.6735 & 0.6721 & 0.6374 & 0.6543 & 0.5354 & 0.5298 & 0.5158 & 0.5227 & 0.6978 & 0.6874 & 0.6773 & 0.6823 \\ \hline Resnet 34 & 0.6372 & 0.6301 & 0.5936 & 0.6113 & 0.6323 & 0.6333 & 0.572 & 0.6011 & 0.6102 & 0.6072 & 0.5875 & 0.5972 & 0.6073 & 0.5991 & 0.5804 & 0.5896 \\ \hline Resnet 50 & 0.6311 & 0.6298 & 0.5753 & 0.6013 & 0.6102 & 0.6117 & 0.652 & 0.6312 & 0.6428 & 0.6329 & 0.6389 & 0.6359 & 0.6416 & 0.6412 & 0.6229 & 0.6319 \\ \hline Resnet 101 & 0.6519 & 0.6501 & 0.681 & 0.6652 & 0.6801 & 0.6797 & 0.6652 & 0.671 & 0.7059 & 0.6989 & 0.6935 & 0.6962 & 0.6398 & 0.6385 & 0.6171 & 0.6276 \\ \hline \hline \end{tabular} Note: Acc represents accuracy, Pr represents precision, Re represents recall, and F1 represents F1-score.  
\end{table} 
Table 8: DMFNet model evaluation parameters. summary, the results of the driver emotion recognition experiments and the ablation analysis show that the proposed DMFNet model achieves significant improvements compared to the baseline network.
# Pursuit-Evasion Problem of Unmanned Surface Vehicles in a Complex Marine Environment
## 6 Conclusions  
Combining the guidance algorithm and the underactuated characteristics of a USV, this paper proposes an interception algorithm for escaping targets. For the escaping target with a flexible escape route, the algorithm can still achieve rapid interception. According to  
Figure 11: Experimental results of the pursuit trajectory. (**a**) The pursuer sensed the evader and began to intercept it. (**b**) The pursuer gradually approached the evader. (**c**) The pursuer successfully intercepted the evader.  
the Lyapunov stability theory, it is validated that the designed controller can ensure the convergence of the motion error of USV in a complex marine environment. Finally, the simulation and experimental results validate the effectiveness of the interception algorithm. In this paper, the uncertainty of the environment and the flexibility of the escaping target are comprehensively considered. In addition, the missile guidance method is applied to the interception field, and the trajectory of the escaping target is further predicted by combining the Kalman algorithm.  
However, this study still has several shortcomings. First, although the controller considering uncertain external disturbance is designed in the algorithm, due to the lack of equipment, this function is not realized in the experiment. Second, the route of the escaping target should be adjusted in real time according to the state of the pursuer. In this paper, the escaping target moves according to the predefined route in advance. In addition, obstacles are an important factor affecting interception. In future research, we plan to consider the above shortcomings. Future work will further consider the game strategy between the evader and the pursuer, and the obstacle avoidance algorithm will be applied to the pursuit algorithm in more complex environments.  
Conceptualization, Z.S. and H.S.; Formal analysis, Z.S. and X.L.; Funding acquisition, H.S.; Methodology, Z.S. and H.S.; Software, L.D. and P.L.; Writing--original draft, Z.S.; Writing--review and editing, X.L. and P.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 52071100), by the National Natural Science Foundation of China (Grant No. 51909103), and by The Fundamental Research Funds for the central Universities (No. 307202 QBZ0102).  
Not applicable.  
The authors declare no conflict of interest.
# Cleaning Phenolic Compounds Present in Water Using Salting-Out Effect with DCA-Based Ionic Liquids
## 4 Conclusions  
In this work, the extraction capability of two hydrophilic ILs, [EMim] [DCA] and [BMim] [DCA], was evaluated for the removal of three phenolic compounds, phenol, o-cresol, and chlorophenol, and a mixture of these compounds (PCM) from water using three different concentrations of salt, (20, 30 and 40% of K\({}_{3}\)PO\({}_{4}\)), and ATPS.  
For all the studied systems, the extraction efficiencies were generally high, with extraction yields greater than 90% for all the initial studied concentrations of phenolic compounds at all concentrations of salt.  
The legal limit established for these compounds in water is 1 mg\(\cdot\)L\({}^{-1}\), and it was reached in only one extraction cycle for 2-chlorophenol and PCM for initial concentrations lower than 1000 mg\(\cdot\)L\({}^{-1}\) and for 500 mg\(\cdot\)L\({}^{-1}\) of phenol and o-cresol. For the other systems with two extraction cycles, it was possible to obtain dischargeable water.  
It was observed that an increase in the salt concentration increased the extraction efficiencies, and an increase in the initial concentration of the phenolic compound increased the efficiencies.  
Regarding the alkyl chain length of the cation of the IL, there were slight differences between the extraction results--both are good extraction agents for phenolic compounds in  
Figure 6: Final concentration (\(C_{j}\)) versus initial concentration (\(C_{i}\)) for the extraction of PCM using (a) [EMim] [DCA] and (b) [BMim] [DCA] with 20% ( ), 30% ( ) and 40% ( ) of K\({}_{3}\)PO\({}_{4}\).  
water--nevertheless, in this work, better results were observed when [EMim][DCA] was used as the extraction solvent.  
Both ILs presented high extraction efficiencies when the mixture of phenols (PCM) was extracted from the water, which means that they could be applied in real industrial wastewater where these compounds are normally present as a mixture.  
The best result was obtained for the extraction of 2-chrolophenol with 40% salt using [EMim] [DCA] as an extraction agent.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13032009/s1](https://www.mdpi.com/article/10.3390/app13032009/s1)]([https://www.mdpi.com/article/10.3390/app13032009/s1](https://www.mdpi.com/article/10.3390/app13032009/s1)).  
Investigation, O.G.S.; Methodology, B.G. and A.D.; Supervision, B.G. and A.D.; Writing--original draft, O.G.S.; Writing--review and editing, B.G. and A.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Comision Interministerial de Ciencia y Tecnologia (Spain), grant number CTM2013-46093-P.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Chromatographic Results and UV-Vis Spectra  
Vitamins D\({}_{2}\), D\({}_{3}\), E and K\({}_{1}\) were well separated using the optimized chromatographic system and produced distinct \(\text{R}_{\text{F}}\) values: \(\text{R}_{\text{F}}\) 0.20 for vitamins D\({}_{2}\) and D\({}_{3}\), 0.46 for vitamin E, and 0.68 for vitamin K\({}_{1}\). To determine their absorbance maxima (\(\lambda_{\text{max}}\)) for optimal detection, the UV-Vis spectrum of each fat-soluble vitamin was recorded. The \(\lambda_{\text{max}}\) values for vitamin  
Figure 2: HPTLC image visualized at 254 nm: track 1: Vit D\({}_{2}\); track 2: Vit D\({}_{3}\); track 3: Vit E; track 4: Vit K\({}_{1}\); track 5: Over spotting of vitamins D\({}_{2}\), D\({}_{3}\), E, and K\({}_{1}\).  
\(\rm D_{2}\), \(\rm D_{3}\), \(\rm E\) and \(\rm K_{1}\) were found to be 263 nm, 270 nm, 272 nm and 255 nm, respectively (Figure S2). Although their \(\rm R_{F}\) values were identical, the \(\rm\lambda_{max}\) values of vitamin \(\rm D_{2}\) and \(\rm D_{3}\) differed slightly with 263 nm for vitamin \(\rm D_{2}\) and 270 nm for vitamin \(\rm D_{3}\), however, this difference was not sufficient to quantify them accurately individually at their respective absorbance maxima.
# Analysis of Shield Tunnels Undercrossing an Existing Building and Tunnel Reinforcement Measures
## 6 Effect of Grouting Pressure on Mechanical Behaviors of Soil, Segment, and Building
### Effect of Grouting Pressure on Internal Forces of Segment
From Figure 28, it can be found that the changing pattern of building subsidence with the shield tunneling process was the same for different grouting pressures, and the subsidence of the building reduced gradually with the increase of grouting pressure. For the grouting pressure was 50 kPa, the subsidence of the building was 12.14 mm, while the grouting pressure was 550 kPa, the subsidence of the building was 1.36 mm, which was 88.80% less than that for the grouting pressure of 50 kPa. From Figure 29, it can be found that different grouting pressures resulted in comparable patterns of change in the lateral displacement of the pile during shield tunneling, and the lateral displacement of the pile gradually reduced as grouting pressure increased. When the grouting pressure was 50 kPa, the lateral displacement of the pile was 5.07 mm, while the grouting pressure was 550 kPa, the lateral displacement of the pile was 2.36 mm, which was 53.45% less than that for grouting pressure of 50 kPa. It can be seen from this that the stability of the building and piles can be effectively guaranteed by appropriately increasing the grouting pressure when excavating the double shield tunnels successively.
## 3 Results and Discussion
### Determination of the Specimen Size  
To explore the influence of specimen size on the compressive behavior of waste rock specimens, the strain of specimens in compressors with different inner diameters under 5 MP, 10 MPa, 15 MPa, and 20 MPa was subjected to non-linear curve fitting with the inner diameter of corresponding compressors, then iteration to convergence. The fitted curves and their equations are shown in Figure 13.  
Analysis of Figure 13 shows that the inner diameter \(\Phi\) of compressors and the strain \(\epsilon\) of waste rock specimens can be expressed by this function: \(\epsilon=y_{0}+A_{1}e^{-\Phi/t_{1}}\). The curves change greatly in the initial stage while the variation of strain tends to stabilize when an inner diameter is 0.15 m. This means that the inner diameter of compressors influences the compressive behavior slightly when using circular load-bearing compressors with the inner diameter of 0.15 m to conduct tests on specimens with particle sizes of 0-10 mm. Similarly,  
Figure 12: Changes in the void ratio of specimens in compressors with different inner diameters.  
Figure 13: Fitted curves and equations of the inner diameter of compressors versus strain.  
the ratio of the inner diameter of load-bearing compressors to the maximum particle size of waste rock should be 15:1 (or above) in compression tests on specimens with other particle sizes, so as to reduce the influence of the inner diameter of compressors on the compressive behavior of waste rock specimens.
### Appendix A.2  
\[\begin{split}& PCC_{u,v}=\frac{\text{cov}(u,v)}{\sigma_{u} \sigma_{v}}\\ &=\frac{Exp((u-\overline{u})(v-\overline{v}))}{\sigma_{u}\sigma_ {v}}\\ &=\frac{\sum\limits_{i=t-1}\left(\frac{Exp(u,i)-\overline{Exp(u)} }{\sigma_{u}}\right)\times\left(\frac{Exp(v,i)-\overline{Exp(v)}}{\sigma_{v}} \right)}{T-1}\end{split}, \tag{13}\]  
where \(\overline{Exp(u)}\) represents the average expression of gene \(u\) at all times \(T\), and \(\sigma_{u}\) and \(\sigma_{v}\) are the standard variance of expression for gene \(u\) at all times \(T\). If \( PCC_{u,v}\) has a positive value, then genes \(u\) and \(v\) are positively correlated; if the value of \( PCC_{u,v}\) is negative, then genes \(u\) and \(v\) are negatively correlated.
## 2 Nonlinear Model Predictive Control
### Mathematical Model of Nonlinear System
#### 2.2.3 Objective Function  
The goal of the MPC algorithm is to calculate a control sequence that can minimize the objective function. The objective function has a similar form to the following equation:  
\[J=\ \sum\
olimits_{k=1}^{N}L(X(t+k|t),\ Y(t+k|t),\ u(t+k|t)) \tag{4}\]  
where \(X(t+k|t)\) is a future system state, \(Y(t+k|t)\) is a future system output, and \(u(t+k|t)\) is a future system input.  
The objective function of the trajectory tracking controller should reflect the ability of the controller to smoothly track the desired trajectory. At the same time, the objective function is expected to require a low amount of calculation, to avoid affecting the real-time performance of the algorithm. The objective function is expressed as  
\[J(U)=k_{1}\sum\
olimits_{i=1}^{n}[y_{i}-T(x_{i})]^{2}+k_{2}\sum\
olimits_{i=1 }^{n}[\theta_{i}-arctanT^{\prime}(x_{i})]^{2}+\ k_{3}\sum\
olimits_{i=1}^{n}{ u_{i}}^{2} \tag{5}\]  
where \(T(x)\) is the selected planning target trajectory, \(n\) indicates prediction horizon, \(k_{1}\), \(k_{2}\), and \(k_{3}\) are the weight coefficients of the lateral deviations, yaw angle deviations, and control inputs, respectively. The first two items reflect the system's ability to track the desired trajectory, and the third item reflects the requirements for stably changing control inputs.
# Analysis of Digital Skills and Infrastructure in EU Countries Based on DESI 2024 Data
## 3 Research Tools for Investigation
#### DESI Indicators  
The tool provides access to detailed digital indicators, grouped into four key components:  
* Human Capital--digital skills and ICT specialists;
* Connectivity--broadband internet and 5G infrastructure;
* Integration of Digital Technology--digitalization of business and adoption of advanced technologies;
* Digital Public Services--availability and use of e-government services.  
Users can customize the data selection (Figure 1) using the following parameters:  
* DESI Period--year of observation;
* Breakdown--structural disaggregation of the index;
* Select the countries--country selection for comparative analysis;
* Indicator--specific digital indicator;
* Unit of measure--unit of measurement (%, index, scale, etc.).  
The last component, the unit of measurement, also serves to normalize the data.  
It is used for preliminary hypothesis testing in further empirical research and is particularly valuable for uncovering inter-component relationships in the process of digital transformation.  
All figures generated by the official DESI analytical tool have a standardized format. This format is informative and ensures comparability of all visualized indicators. In particular, the horizontal axis (\(X\)-axis) consistently represents the EU member states, while the vertical axis (\(Y\)-axis) displays the value of the selected indicator, usually expressed as a percentage (e.g., percent of enterprises or percent of population). This approach ensures comparability of all visualized indicators.  
The presented DESI analytical tool is provided by the European Commission's Digital Decade Platform and is a reliable and versatile tool for assessing the state and dynamics of digital transformation in the countries of the European Union.  
Its modular structure allows the integration of statistical data, visual analytics, and policy insights. DESI is widely used in empirical research, international comparisons, and evidence-based policymaking in the field of the digital economy.  
Figure 1: DESI visualization tool interface.
# Considerations of Curvature for a Near-Eye Holo-Video Display
## 5 Design Graph for a Curved Near-Eye Display  
In the circular case shown above (Figure 6), the ROC was chosen to be 1.5 cm. We chose this distance because it is roughly similar to most eyewear currently on the market. As we can see from Figure 8, choosing any radius other than one that falls on the eye gives a much smaller field of view; therefore, we opt to stick with the full curvature.  
Another option available is to increase the ROC such that the display is further from the eye. The ROC, when compared to either the closest drawable point (from the eye) or the aperture size (holding the other fixed in either case), can be seen in Figure 9.  
These graphs indicate that increasing the ROC is beneficial, but only to a point which we now consider. Depending on the parameters chosen, this point will fluctuate, but two main observations can be made. The first is that the closest drawable point from the eye will improve as the display becomes flatter, but, after a certain point, the effects of flattening the display converge to the flat-screen case, and the linear effects of placing the display further from the eye, to compensate for the increased ROC increase. We generally want to avoid this linear region, not only because the drawable points sharply decrease, but also because the display ought to be reasonably close to the user in order to be considered a near-eye display.  
The second item of note is that, as ROC increases, the allowable aperture will likewise increase, but this begins to slow as the curvature increases. Thus, for the example chosen in Figure 9, anywhere past approximately ROC = 0.5 m, the aperture size will see diminished returns for the tradeoff of placing the display further from the user. Again, we want to keep the display close to the user, but because the space in which we do want to place the display shows such a large variation, we will try to place the display as far from the user as possible.  
We have made the code used for this graph available for readers as Code 1 [25].
## 3 Parameter Optimization and Performance Test
### Net Dropping Seedling Duration Measurement Test
#### 3.1.2 Measurement Method  
The nursery board was opened, and the potted seedlings were held with both hands so that the lower end of the pot was flush with the initial drop height, and the potted seedlings were then released to let them fall freely. During this period, the potted seedlings were filmed using high-speed camera equipment at 240 frames/second. After the shooting was completed, the generated video was 30 frames/s, which was eight times slower. After exporting the video, video clip software was used to process the video. The video start point was moved to the frame where the pot seedlings started to fall (Figure 11a), and the video end was moved to the frame where the pot seedlings completely fell out of the seedling cylinder (Figure 11b), and the video duration \(U_{i}\) was obtained. Equation (30) was used to calculate the net dropping duration \(t_{j}\) of the pot seedlings at each position. The theoretical calculation accuracy of this method was 0.0047 s, which was the corresponding time length of one frame at the shooting rate of 240 frames/s, that is, 0.0047 s. In the actual calculation process, the counting accuracy was 0.01 s.  
\[t_{j}=\frac{U_{i}}{8} \tag{30}\]  
Ten seedling tests were carried out at each position, and a total of fifty tests were performed at five positions. After the trials were completed, the average value, standard deviation, and variation coefficient of the seedling dropping duration were calculated according to Equation (30).  
Figure 10: Prototype of automatic seedling feeding mechanism and pot seedling for testing: (1) pepper pot seedling, (2) interval seedling dropping mechanism, (3) pneumatic seedling pick-up mechanism, (4) plate sending device, (5) 24 V switching power supply, (6) photoelectric controller and pneumatic control element, (7) drive motor.
## 2 Methodology
### Machine Learning Models
#### 2.3.2 Random Forest (RF)  
Random forest is an ensemble learning method. It performs well when processing high-dimensional data. It constructs multiple decision trees during the training process and improves overall accuracy by combining their classification results while controlling model overfitting [49]. Random forest model employs random sampling with replacement from the training dataset to construct different training datasets. This technique of random sampling enhances the diversity and robustness of the model while reducing the influence of individual samples. Then, during the construction of each decision tree, the random forest model adopts a feature random selection strategy. When splitting each node of the decision tree, a subset of features is randomly selected from the total feature set as candidate split attributes. This feature random selection helps reduce the correlation between features and improves the model's generalization ability. Finally, the random forest model makes classification decisions through voting. For classification tasks, each decision tree provides a prediction, and the final classification result is determined by the majority vote. Assuming the set of classes is \(\{c_{1},c_{2},\ldots,c_{N}\}\) and \(h_{i}^{j}(x)\) represents the prediction result of the decision tree \(h_{i}\) for class \(c_{i}\), the voting result can be expressed by:  
\[H(x)=\left\{\begin{array}{ll}c_{j},\sum\limits_{i=1}^{T}h_{i}^{j}(x)>0.5\sum \limits_{k=1}^{N}\sum\limits_{i=1}^{T}h_{i}^{k}(x)\\ reject,&others\end{array}\right. \tag{9}\]