esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Plant Cultivation Phase  
The quadratic regressions used to summarize the experiment showed no statistically significant differences (\(p\geq 0.05\)) in the intercepts and curvatures of the growing curve for most of the growing variables (plant height and dry weight of above-below ground biomass) for both species (Figure S2). The only exception was the height of _V. tiinus_, which showed a simple linear relationship, although not significant, among treatments (see Table S2). A different behavior was observed for the below-ground dry biomass of _V. tiinus_ only (Table 4 and Figure 2). The GM1 substrate showed a significantly lower growth rate (\(-0.104\) g per day, \(p=0.0174\)), which yielded a final value of 37.184 g less than that of CGM (\(p=0.0112\)). In addition, GM5 showed a similar reduced final value (\(-28.578\) g), but with a slightly different significance (\(p=0.049\)) and a growth rate very close to statistical significance (\(-0.079\) g per day, \(p=0.0680\)).  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multicolumn{2}{c}{**Below-Ground**} & \multirow{2}{*}{**Std. Error**} & \multirow{2}{*}{**\(t\)-Value**} & \multirow{2}{*}{\(p\)**-Value**} \\  & **Dry Biomass (g)** & & & & \\ \hline (Intercept) & 143.034 & 10.3718 & 13.7907 & \(<\)1 \(\times\) 10\({}^{-3}\) \\ GM1 & \(-37.184\) & 14.2611 & \(-2.6074\) & 0.0112 \\ GM2 & \(-16.528\) & 14.2611 & \(-1.1589\) & 0.2505 \\ GM3 & \(-12.948\) & 14.2611 & \(-0.9079\) & 0.3671 \\ GM4 & \(-22.004\) & 14.2611 & \(-1.5429\) & 0.1274 \\ GM5 & \(-28.578\) & 14.2611 & \(-2.0039\) & 0.0490 \\ GM6 & 21.506 & 14.2611 & \(1.5080\) & 0.1361 \\ DAYS & 0.637 & 0.0523 & 12.1627 & \(<\)1 \(\times\) 10\({}^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The first line (intercept) is the mean of below-ground dry biomass achieved by _V. tiinus_ plant growth on CGM substrate, while lines 2–7 refer to the difference against CGM at the end of the experiment. Lines DAYS and DAYS\({}^{2}\) (\(p<1\times 10^{-3}\)) indicate the linear and quadratic terms of CGM, respectively. The subsequent lines are the differences between each substrate and CGM in the linear term of the regression. The graphic results are depicted in Figure 2.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Substrates**} & \multicolumn{4}{c}{**IT Legislation**} \\  & **CGM** & **GM1** & **GM2** & **GM3** & **GM4** & **GM5** & **GM6** & **Mixed Media** \\ \hline BD\({}^{*}\) & \(0.3\pm 0.09\) a & \(0.6\pm 0.02\) b & \(0.6\pm 0.05\) bc & \(0.3\pm 0.02\) a & \(0.7\pm 0.02\) c & \(0.8\pm 0.06\) cd & \(1.0\pm 0.08\) d & \(\leq\)0.95 \\ pH\({}^{*}\) & \(4.8\pm 0.1\) a & \(7.0\pm 0.1
# Crude Oil Price Forecasting Model Based on Neural Networks and Error Correction
## 4 Application Analysis  
### Real-Time Adaptability  
Deep learning and reinforcement learning are utilized in our model. Hence, the training process was time-consuming. To further evaluate the proposed model, this section contains analysis of its real-time performance and computational efficiency. All experiments were carried out on computers equipped with Core i7-9800X 3.8 GHz CPU, 16 G memory, and a single GPU 2080 Ti. Table 13 presents the computational efficiency of different models. Analyzing the data in the table led to the following conclusions.  
Hybrid models require longer computation time than single models. This is primarily due to hybrid models' intricate structure, which prolongs computation time compared with individual models. Moreover, using RL for ensemble optimization is slower than employing heuristic algorithms. This occurs because RL requires extensive training iterations during ensemble optimization to explore optimal strategies. During each decision-making step, RL not only evaluates the action values of the current state but also predicts the state and action values of the next step, thereby updating the current strategy. These procedures increase computational complexity. In contrast, heuristic algorithms optimize directly using rules or prior knowledge, requiring fewer iterations and thus achieving higher computational efficiency. However, the maximum time for RL was 105.36 s, which was considerably shorter than the model's time interval (1 day). Therefore, despite the longer computation time for RL, such durations remain acceptable given the more excellent forecasting performance achieved.  
A highly adaptive real-time model can rapidly respond to market fluctuations and promptly update forecast outcomes, thus providing investors and decision-makers with timely and accurate references. As shown in the accompanying table, the model's computational time was in the range of [663.46 s, 1297.93 s] across three datasets. The longest recorded computation time was 300 s. Notably, the shortest dataset interval in this study was one day, indicating that the model's computation time is considerably shorter than the minimum data update interval. Consequently, the proposed model demonstrated robust real-time adaptability. Furthermore, the proposed model shows substantial potential for real-world applications, providing strong technical support for accurate crude oil price forecasting.
## 2 Background and Previous Work
An intelligent computer-aided diagnosis framework for the DR grading of retinal fundus images is implemented, and the framework does not need any segmentation process for the retinal fundus images. The proposed framework has two parts. The first part embeds the DR lesion structures in a pre-trained CNN model. The second part uses the extracted features of retinal fundus images in a fuzzy inference system (FIS), which significantly reduces the model complexity and data dependency and measures the severity percentage, so multiple uses of the system can provide the progression rate in this case. Table 1 summarizes the related works' techniques, findings, and limitations compared to the proposed framework. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **References** & **Data Used** & **Performance (Accuracy)** & **Limitation** \\ \hline \multirow{3}{*}{DenseNet} & \multirow{3}{*}{[33]} & \multicolumn{2}{c}{APTOS dataset} & \multirow{3}{*}{0.9580} & \multirow{3}{*}{Model implementation used small and imbalanced datasets.} \\  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \multirow{3}{*}{Inception-ResNet} & \multirow{3}{*}{[24]} & \multirow{3}{*}{Customized dataset.} & \multirow{3}{*}{91.61} & The non-proliferate symptoms are \\  & & & & not visible on retina images. \\ \cline{1-1}  & & & & Small dataset. \\ \hline \multirow{3}{*}{ResNet-50} & \multirow{3}{*}{[34]} & \multirow{3}{*}{
\begin{tabular}{c} Messider \\ EyePACS \\ \end{tabular} } & \multicolumn{2}{c}{The accuracy achieved ranges from 96\%, on a two-category Messider-2 dataset, to} \\  & & & 75.09\% on a five-category EyePACS & The model is highly demanding. \\ \cline{1-1}  & & & & deep learning. \\ \hline Xception & [35] & IDRiD, & 84\% on the binary classification of IDRiD. & Shortage in performance. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of studies conducted with diabetic retinopathy image datasets and the proposed study.
# Gaussian Processes for Signal Processing and Representation in Control Engineering
## 8 Various Optimization Problems
### Model Predictive Control  
Many control problems are based on designing a stabilized feedback with minimized performance criterion which satisfies constrains of the controls and states. The best solution would be to look for a closed solution for the feedback. Unfortunately, often closed solutions cannot be found analytically. A known approach to solving this problem is the repeated solution of the open-loop optimal control problem in a specific state. So the first part of this open-loop result is implemented and then the whole process is repeated. A control approach that utilized this strategy is called model predictive control (MPC) [45].  
The GP based model of predictive control is the main purpose of the [46] article. The authors propose that kind of solution, mostly because of the measure of confidence offered by GP. That feature is very helpful in non-linear MPC (NMPC) design. They also note that GP is a probabilistic non-parametric black-box model that provides information about prediction uncertainties, which are difficult to evaluate appropriately in non-linear parametric models. The whole GP was set based on complex and different approaches, which are feasible in order to create a model for NMPC. A detailed description can be found in the article [46].  
The whole idea was implemented on the gas-liquid separation plant example. The results of this approach were successful. The authors note that thanks to the confidence interval, the GP model can highlight input spaces, where prediction quality is poor, for instance due to data complexity or lack of it. This is a very attractive feature in control design. The only drawback of this solution is a computational complexity for industrial purposes. For the presented case (model contained about 1000 points to identify), the calculation of covariance matrix inversion was time consuming. The model barely managed to keep time constraints, but as the authors say, it should not detract from the overall value of GP usage [46].
# Solar Photovoltaic Integration in Monopolar DC Networks via the GNDO Algorithm
## 1 Introduction
### Motivation  
The efficient integration of renewable energy resources in electrical networks effectively reduces greenhouse gas emissions into the atmosphere caused by fossil sources [6]. In this research, the optimal integration of PV generation units is explored as an opportunity to provide sustainable alternatives in order to ensure global development for future generations [7; 8]. PV sources are a reliable and mature electricity generation technology with long useful life periods (higher than 20 years) and low maintenance in non-seasonal countries. Hence, this makes them an attractive technology for home applications (self-consumption) and microgrids and general distribution grids from the point of view of utility companies, given the tax reductions provided by the regulations of the electrical sector in countries where the presence of renewable generation is insignificant in comparison with conventional generation sources [9].  
The integration of PV sources in electrical distribution networks, even if operated with DC monopolar technology, is not an easy task since its mathematical modeling belongs to the family of mixed-integer nonlinear programming (MINLP) models. The nature of these models makes it necessary to develop efficient solution methodologies that address complexities with reduced computational effort. Therefore, the MINLP model that represents the studied problem is solved in this research by applying a master-slave optimization methodology. The main advantage of using metaheuristic optimizers is the possibility of guiding the exploration and exploitation of the solution space by using a fitness function that allows exploring infeasible solution regions in the search for local and global optimal solutions.
## 5 Summary and Conclusions  
This study simulated the horizontal freezing and strengthening of the tunnel between the Yixianqiao and Daxinggong sections of Nanjing Metro Line 2 to create a thermo-mechanical coupling calculation model for the changes in the soil temperature and displacement fields during freezing construction. To forecast how much soil will undergo frost heave and thaw settlement, the authors employed the DEM simulation approach. The findings of the study demonstrate the following:  
(1) The DEM numerical model developed can accurately simulate the freezing-method tunnel construction process and achieve an effective simulation of the ground surface deformation caused by the freezing-method construction, which offers fresh perspectives and direction for the forecasting of ground surface displacement in future projects.  
(2) The frozen soil wall is initially formed with the freezing tube as the center of the circle at the beginning of the freezing cycle and thereafter expands with the increase in freezing time. After 40 days of freezing, a cup-shaped frozen soil wall was formed, meeting the design requirements for freezing reinforcement.  
(3) As the freezing (thawing) days rises, the soil experiences larger frost heave (thaw settlement) displacement. Typically, the frost heave displacement is smaller than the  
Figure 23: Thawing settlement displacement curve with tunnel buried depth: (**a**) vertical displacement; (**b**) horizontal displacement.  
Figure 24: Maximum thaw settlement displacement about the tunnel buried depth: (**a**) vertical displacement; (**b**) horizontal displacement.  
thaw settlement displacement. The vertical displacement caused by frost heave and thaw settlement grows almost symmetrically around the tunnel's center, gradually decreasing towards the ends until it reaches zero. The maximum surface frost heave displacement of 8 mm and the maximum thaw settlement displacement of 18 mm occur directly above the center of the tunnel for the case modeled. People can set up heating limit pipes and thawing grouting pipes right above the center of the tunnel to effectively reduce the displacement.  
(4) The depth of tunnel embedment has a major influence on the frost heave and thaw settlement of the soil caused by the freezing construction. The greater the burial depth of the tunnel, the smaller the surface heave and settlement movement. If there is a need to reduce the amount of heave and settlement movement deformation in subsequent freezing work, the depth of tunnel embedment should be increased appropriately.  
Conceptualization, H.X.; methodology, H.X.; software, P.C.; validation, J.H. and G.Z.; formal analysis, H.X.; investigation, H.X.; resources, H.X.; data curation, H.X.; writing--original draft preparation, H.X.; writing--review and editing, H.X.; visualization, Z.W.; supervision, D.Z.; project administration, J.H.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Study Design
### Data Preprocessing  
All functional mappings' preprocessing steps were performed using the DPABI toolkit ([[http://rfmri.org/dpabi](http://rfmri.org/dpabi)]([http://rfmri.org/dpabi](http://rfmri.org/dpabi)), accessed on 3 August 2022) and custom scripts in MATLAB. First, T1 structural images were segmented into WM, GM and cerebrospinal fluid (CSF) using DPABI segmentation option and then registered to MNI standard space. Then, the functional images were processed as following: (1) The first 5 volumes (10 s) of data were discarded for magnetization equilibrium. (2) Slice time correction and head motion correction were as follows: the head movement of all subjects was less than 1.5\({}^{\circ}\) or 1.5 mm. (3) The noise was removed, and the signals of no interest (including cerebrospinal fluid signals and 24 head-movement parameters) were regressed with a linear regression model [23]. (4) To remove linear drift, regression to the linear trend was performed to correct signal drift. (5) We also excluded participants who have large mean framewise displacement (FD) (e.g., FD > 0.2) as suggested by [PERSON] et al. [24]. The temporal scrubbing was performed by using a linear regression model. (6) Bandpass filtering with a frequency of 0.01-0.15 Hz to reduce the influence of non-neuronal signals on BOLD signals. (7) Spatial smoothness followed. To separate the WM signal from the GM signal, spatial smoothing was performed within the WM mask and the GM mask, respectively. Firstly, the individual space T1 segmentation image of each subject was registered to the functional image of the corresponding subject, which was used to identify the WM and GM mask of the functional image (threshold was 0.5). Functional images were smoothed (Gaussian smooth kernel, FWHM = 4 mm) within GM and WM masks, respectively. (8) Spatial standardization came next: the smoothed image is registered to the standard MNI space and resampled until the voxel size is 3 \(\times\) 3 \(\times\) 3 mm\({}^{3}\). Steps 1-7 are processed on the individual space of each subject to effectively distinguish GM and WM signals at the individual level.
## 2 Materials and Methods
### Protocol and Instruments
#### 2.2.2 Kinetic, Kinematic and Electromyography Measurements  
The marker trajectories of the reflective marker during CM movements were recorded with an eight-camera Vicon motion capture system (Vicon Metrics Ltd., Oxford, UK) at 1000 Hz sampling frequency. Ground reaction forces were measured with an embedded AMTI force platform (AMTI, Watertown, MA, USA) fixed in the middle of the path with a sampling frequency of 200 Hz. Following previous studies [40; 41], 38 optical reflective markers (12.5 mm diameter) were carefully attached to the anatomical locations of each participant. Marker placement was consistently performed by the same operator. The cameras and lab setting were calibrated before each experimental session with a stable marker trajectory and minimal noise.  
The duration of the CM trials was measured using single beam electronic timing gates (Brower Timing Systems, Draper, UT, USA) placed on a 1-m-high tripod at each marker location of the sprint test.  
EMG of the rectus femoris (RECFEM), biceps femoris long head (BFLH), tibialis anterior (TIBANT), and gastrocnemius medialis (GASMED) were recorded at 1000 Hz sampling frequency using a Delsys EMG system (Delsys, Boston, MA, USA). EMG data was used to validate the OpenSim model [42].
## 2 Materials and Methods
### Chemometric Methods  
#### 2.4.1 Partial Least Squares Discriminant Analysis (PLS-DA)  
Partial least squares discriminant analysis (PLS-DA) Duplex (1993); Duplex (1993); Duplex (1993); Duplex (1993) is a discriminant classifier, and is particularly appropriate for handling correlated features (e.g., spectroscopic variables). Briefly, PLS-DA is based on expressing the classification problem as a regression one, which can be solved by PLS Duplex (1993); Duplex (1993); Duplex (1993). More specifically, a predictor block is used to estimate (by PLS) a binary response called dummy \(\mathbf{Y}\) (a binary response matrix encoding the class-belonging).  
In a two-class problem, as in the present study, \(\mathbf{Y}\) is a dummy vector (\(\mathbf{y}\)) presenting either 1 or 0 in the rows corresponding to objects belonging to Class 1 (Authentic rice) and Class 2 (Adulterated rice), respectively. Mathematically, the regression relation between the data matrix \(\mathbf{X}\) and the dummy vector \(\mathbf{y}\) for a two-class case can be represented by the model in Equation (1)  
\[\mathbf{y}=\hat{\mathbf{y}}+\mathbf{e}=\mathbf{X}\mathbf{b}+\mathbf{e} \tag{1}\]  
where \(\hat{\mathbf{y}}\), \(\mathbf{b}\), and \(\mathbf{e}\) are the vectors of predicted responses, regression coefficients, and residuals, respectively.  
After solving the regression problem in Equation (1) by PLS, a further step is needed to achieve classification, since the predicted responses, \(\hat{\mathbf{y}}\), are not binary, but continuous values: accordingly, a suitable classification rule has to be built based on the values of \(\hat{\mathbf{y}}\). One possibility is to use the predicted responses, \(\hat{\mathbf{y}}\), (or the PLS scores) as input for classification methods such as linear discriminant analysis (LDA) or quadratic discriminant analysis (QDA) [52]. In the present study, the threshold was calculated based on the probabilistic approach proposed by [PERSON] and colleagues [53].  
When new samples (test set) need to be classified, their predicted responses, \(\hat{\mathbf{y}}_{new}\), are calculated based on the measurements, \(\mathbf{X}_{new}\), and the regression coefficients, \(\mathbf{b}\), estimated on the training set, and the classification rule described above is then applied to assign each individual to one of the categories under study.
## 5 Discussion
### Future Research  
Future research could explore extending this evaluation to other AI-enhanced tools for qualitative data analysis or incorporating more diverse user populations to assess the generalization of these results. Additionally, examining the role of training and prior tool familiarity in shaping user experiences could offer deeper insights. Replication studies across different datasets or task types would be valuable for confirming these findings. Finally, future studies could explore establishing benchmarks or thresholds for emotional and cognitive responses to further standardize evaluations of the QDA tools. Extending the scope of analysis to include additional data, such as task completion times and error rates, could complement self-reported measures and offer a fuller understanding of QDA tools' user experience. Lastly, longitudinal studies tracking the long-term change in perception, usability and adoption of these tools could yield insights into their sustained effectiveness and user satisfaction.
## 2 Materials and Methods
### Hyaluronan Expression  
To analyze the expression of hyaluron (HA) in HaCaT cells depending on the media used, \(1.25\times 10^{6}\) cells/mL were seeded into PAM30, PAM60, and control media in a cell culture \(\mu\)-Dish 35 mm (ibidi GmbH) and cultivated for 3 or 6 h at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). After cultivation for 3 h, cells from the supernatant were collected by centrifugation (1200 rpm, 5 min) and pooled from 5 dishes for each PAM sample and 10 dishes each for control experiments with DMEM, AAM, and HAdase. HA expression was quantified by flow cytometry. For this purpose, cells were fixed with 4% paraformaldehyde (Merck, Steinheim, Germany), stained with 2 mg biotinylated HA binding protein (Calbiochem, San Diego, CA, USA) for 120 min, followed by incubation with 0.5 mg streptavidin-FITC (Becton Dickinson, San Diego, CA, USA) for additional 30 min at room temperature in the dark. An extra DMEM sample was incubated with 0.5 mg streptavidin-FITC only and served as a blank control. Measurements of the fluorescence and light scatter characteristics of 10,000 cells were performed by flow cytometry (ext. 488, em. 519; FACSCalibur, BD Bioscience, Heidelberg, Germany). Data were analyzed by FlowJoTM 7.6.5 for Windows (FlowJo LLC, Tree Star Inc., Ashland, OR, USA) for three independent experiments.  
After 6 h of culture, cells were fixed and stained for HA, as described for flow cytometry. In addition, the nucleus of the cells was stained with Hoechst 33342 (10 mg/mL, 1:1000 in PBS, Invitrogen), and cell images were taken with a confocal laser scanning microscope (LSM 780, Carl Zeiss, Jena, Germany; objective 63 \(\times\) oil).