esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Feature Selection and Model Development
#### 3.3.3 The Development of a Simplified Assessment Protocol  
Based on our feature importance analysis, we developed a simplified assessment protocol that balanced the classification performance with clinical practicality. Through random forest feature importance ranking, we identified the five most influential features: air length during clock-drawing (importance score = 10.66), MMSE score (8.72), the air-to-paper-length ratio during the connecting line task (8.14), P-SDMT score (7.41), and years in education (6.70).  
This simplified feature set requires only two drawing tasks (clock-drawing and connecting lines), alongside basic demographic information and the standard cognitive screening, potentially reducing the assessment time by approximately 35-40% compared to that for the complete protocol. To validate this approach, we developed an optimized SVM-RBF model using only these five features and compared its performance to that of the complete model using all eight features.  
Table 5 presents the performance comparison between our complete model, the simplified model, and the baseline model, with 95% confidence intervals for key metrics. The simplified five-feature model achieved an AUC of 0.921 (95% CI: 0.853-0.989), which was comparable to--and slightly higher than--the complete eight-feature model's AUC of 0.910 (95% CI: 0.853-0.967). While the simplified model showed a marginally lower sensitivity (75.0% vs. 76.5%) and specificity (82.8% vs. 91.1%), the overlapping confidence intervals suggested these differences were not statistically significant.  
Figure 11 shows the confusion matrix for the simplified model, which correctly classified 24 of 32 cognitive impairment cases (75.0% sensitivity) and 48 of 58 cognitively normal cases (82.8% specificity). The F1-score decreased from 0.800 (95% CI: 0.679-0.895) in the complete model to 0.727 (95% CI: 0.590-0.836) in the simplified model, representing a non-significant 9.1% relative reduction.  
Figure 10: A model performance comparison across disease groups. The SVM-RBF model for MS patients and the XGBoost model for NMOSD patients both achieved high AUC values, with strong sensitivity and specificity.  
Notably, the simplified model's feature composition maintains diversity across assessment domains: traditional cognitive screening (MMSE score), cognitive processing speed (P-SDMT score), demographic information (years in education), and two handwriting dynamics measures from different drawing tasks. This balanced approach captures complementary aspects of cognitive-motor function while reducing the assessment burden.  
The comparable performance of our simplified protocol, despite a 37.5% reduction in the feature count, suggests that a focused assessment of key handwriting dynamics alongside the traditional measures could provide an efficient approach to cognitive screening in clinical settings. This simplified protocol offers potential advantages in its reduced administration time and complexity while maintaining robust classification capabilities.
## 2 Materials and Methods
### Proposed Model Architecture  
Article [13] proposed a high-level reference architecture of software-defined VANETs. The proposed architecture consists of three planes including the data plane, a control plane, and an application plane. The data plane is made up of switches, RSUs, cellular network nodes, and vehicular devices. The control plane includes different types of controllers: OpenFlow controllers and controllers tailored to enforce the policies required by the applications. The OpenFlow protocol is used by the data plane elements to communicate with the control plane. Furthermore, the control plane communicates with the application plane using the Northbound Interface (NBI) such as REST APIs.  
Our proposed architecture is depicted in Figure 2. The components of this architecture cooperate using 5G slicing technology. In addition, MEC technology is implemented by connecting the safety servers directly to the RSUs. Our proposed framework is made up of four main modules: IoV layer, edge computing device layer, data plane and control plane (SDN network core), and QoS application. The IoV layer consists of autonomous vehicles that generate three types of traffic: traffic belonging to the safety application, which in our research is remote monitoring and detection of a drowsy driver using AI models, implemented on the edge computing device, infotainment traffic and best-effort traffic. Once a drowsy driver is detected, a message is sent to the safety server (RCC) to turn the vehicle into teleoperation mode. We evaluate the implementation of safety servers using the MEC concept to support the ultra-low delay requirement of this type of traffic. The OpenFlow switches use the destination port number to classify the traffic flows. Each switch's meter table, which the RYU controller installed, is used to allocate bandwidth to various traffic classes. In the event that MEC technology is not active, the meter table settings prioritize safety traffic to ensure prompt transmission across the data plane to the RCC. The deployed QoS module is adaptive in that it gathers unused bandwidth from each class and redistributes it to the class that is most in need of it, giving precedence to safety traffic to ensure fewer packet loss and lower average RTT. The bandwidth needed to transfer the entire video of the monitored driver to the RCC and make the decision there is reduced when a sleepy driver is identified at the edge. Our proposed frameworkarchitecture components are explained in the following subsections and our design choices are justified.
## 4 Network Model
### Mathematical Programming Formulation  
To find the optimal routing and scheduling policies for the network flow problem described in the previous section, we formulated the minimum cost integer program described by Equations (7-11) below.  
\[(\mathrm{P1})\,\mathrm{minimize} \sum_{i\mapsto j\in L}c_{ij}x_{ij} \tag{7}\] \[\mathrm{subject\ to} \sum_{j:u\mapsto j\in L_{1}}x_{ij}\leq 1\;\;\forall\,i,\;i\in V_{p}\] (8) \[\sum_{k:k\mapsto i\in L_{ki}}x_{ki}\leq 1\;\;\forall\,i,\;i\in V _{p}\] (9) \[\sum_{j:u\mapsto j\in L}x_{ij}-\sum_{k:k\mapsto i\in L}x_{ki}=b(i )\;\;\forall\,i,\;i\in V\] (10) \[0\leq x_{ij}\leq u_{ij},\;\;x_{ij}\in\mathbf{Z} \tag{11}\]  
For problem (P1), the optimal forwarding paths are computed for the competitive parameters of total communication energy consumption and end-to-end message delivery delay. Constraints (8) and (9) are the out-degree and in-degree restrictions for all mobile nodes. Equation (10) is the flow conservation constraint and (11) defines the flow variable. It can be shown that the constraint matrix formed by Equations (8-11) is totally unimodular and thus the linear programming relaxations of this problem is guaranteed to give integer solutions. In effect (P1) can be solved efficiently in time.
size to object distance [20]. The phase delay from the measurement point can be retrieved in TDS from the Fourier spectrum [21]. A THz-TDS image thus contains both the amplitude and phase of the signal, so that a holographic reconstruction [5] to the object plane can be performed.  
In this paper, we present THz-TDS imaging combining a robotic scanning method with continuous signal acquisition and holographic reconstruction of the object to improve the resolution. We apply the method to a metallic Siemens star in order to quantify resolution and to wood samples with birefringent optical properties [22] to demonstrate the technique on a structured non-metallic object.
## 5 Experimental Results
### Impact of Gaussian Blur on Fall-Detection Performance  
In our fall-detection system, Gaussian blur is employed to ensure privacy protection and test the OpenPose module's resilience in key point detection under varying levels of image obfuscation. We conducted detailed experiments with different Gaussian blur kernel sizes to find the optimal balance between these requirements--privacy and detection accuracy.  
Figure 9: This figure displays the results from the GradCAM module, highlighting random regions during not fall sequences and continuous regions of key points during fall sequences. The \(X\)-axis denotes the frame number from 1 to 50 in the sequence, and the \(Y\)-axis represents the temporal variance.  
The following table demonstrates how the performance of the fall-detection system varies with the degree of Gaussian blur applied:  
As the data in Table 4 illustrates, smaller kernel sizes like 5 \(\times\) 5 offer the highest performance in terms of precision, recall, and overall accuracy. With an increase in kernel size, there is a notable trend towards reduced precision and accuracy, though recall remains relatively high. This trend is attributed to the blurring effect obscuring key points, making it challenging for the OpenPose module to detect and classify key points accurately. Particularly with a kernel size of 31 \(\times\) 31, there is a noticeable drop in performance metrics, indicating significant difficulty in key point detection due to excessive blurring.  
The kernel size of 21 \(\times\) 21 represents a compromise, providing adequate privacy while maintaining reasonable accuracy in fall detection. However, as the kernel size increases to 31 \(\times\) 31, the decline in performance underscores the critical trade-off faced. While stronger blurring enhances privacy, it significantly impairs the system's ability to effectively detect and analyze fall incidents.  
This analysis clearly demonstrates the need to carefully select the degree of Gaussian blur. It ensures that while the privacy of the individuals in the video footage is protected, the fall-detection capabilities of the system are not unduly compromised. The findings highlight the delicate balance between obscuring sensitive information and retaining sufficient image clarity for accurate fall detection, which is crucial for practical applications in environments where privacy and security are paramount.
# Green Production of Glycerol Ketals with a Clay-Based Heterogeneous Acid Catalyst
## 3 Results and Discussion
### Glycerol Ketalization Kinetic Studies
#### 3.3.1 System 1: Ketalization with Acetone. Preliminary Analysis
From a kinetics perspective, the initial reaction rate increases with temperature, following an exponential trend, not too steep, so the activation energy of the direct reaction (from glycerol and acetone to the ketal 1) should be moderate. Effect of Initial Molar Ratio of Reactants  
One of the classical strategies to shift the equilibrium position towards the products is to increase the molar excess of one reagent [17]. In this case, due to the interest in the full conversion of glycerol and the easy recovery of acetone, the acetone excess was studied. Therefore, three runs (in duplicate, as it has been the usual way in this work) were performed, selecting the acetone/glycerol molar ratio values of 3, 6 and 9. As shown in Figure 11, the yield to solketal increased from ~65 up to ~75% when the excess of acetone increased from 3 to 9; in fact, fitting yields to a rectangular hyperbola permits us to conclude that increasing acetone excess up to infinitum will not totally shift the equilibrium position towards the products (the equilibrium position at infinite acetone seems to be 92%, approximately). This fact could be explained if deactivation of the catalyst AC is present, as has been shown in the catalyst selection (Section 3.1). Figure 11: Influence of the molar excess of acetone on the yield of solketal 1 at 24 h and the initial reaction rate (Reaction conditions: acetone/glycerol molar ratio, 6:1; temperature, 25 \({}^{\circ}\)C). Figure 10: Influence of the reaction temperature on the yield of solketal 1 at 24 h and the initial reaction rate (Reaction conditions: acetone/glycerol molar ratio, 6:1; temperature, 25 \({}^{\circ}\)C). If we considered the product of the concentrations of the reagents, the initial reaction rate does not seem to be proportional to it, as r\({}_{0}\) should be approximately similar for the 3 and 6 acetone excess and about 15% lower for an excess of acetone of 9. Therefore, at least at the beginning of the reaction process, the reaction is not an elemental one, as it is well-known, as a hemiketal-like intermediate is firstly formed and, subsequently, it cycles to form the ketal 1.
## 2 Methodology
### Relationship-Based Weight Analysis
It enables more precise pouring monitoring and regulatory compliance review by introducing an approach that analyzes relationships between objects. It should be noted that the final integration of the three relationship factors (distance, attribute, and spatial relationships) is performed through mathematical formulation rather than trainable neural networks. While deep learning models are utilized in the preprocessing stages--specifically YOLOv11 for object detection and pose estimation algorithms for human skeleton extraction--the relationship-based weight calculation follows a deterministic approach using the weighted combination defined in Equations (8) and (9). This design choice ensures interpretability and computational efficiency in the final decision-making process while maintaining the benefits of deep learning-based feature extraction.
## 2 Testing Apparatus and Methods  
### Testing Apparatus  
The seepage testing apparatus mainly consists of a sample chamber, a triaxial loading system, and a water-soil collection system, as shown in Figures 2 and 3. The brief description of each component is presented in the following texts.  
* Sample chamber: The soil sample chamber, where the soil sample is loaded, is 150 mm in diameter, and 300 mm in height. The rubber membrane attaches the inner wall of sample chamber, which creates an enclosed space between the rubber membrane and soil chamber.
* Triaxial loading system: The triaxial load system independently provides axial pressure, confining pressure, and hydraulic pressure to simulate the triaxial stress state. The triaxial loads, up to 4.0 MPa, with the precision of 10 KPa, are individually supplied by three pneumatic pumps with pressure-servo control function. The confining pressure is applied by the pressureed water in the annulus space between the rubber membrane and sample chamber.
* Water-Soil Collection System: Meshed perforated plate I, with the hole diameter of 5 mm, is placed on the top of soil specimen to guarantee the water pressure is uniformly distributed. The water and eroded soil particles passing through Meshed perforated plate II are individually collected by the water and soil collection device. The mesh size of perforated plate II adopted in this test is determined by the design criteria of geotextile filter's opening size. The threshold of O\({}_{90}\)/d\({}_{90}\) (O\({}_{90}\) is the geotextile pore opening size corresponding to d\({}_{90}\)) generally varies between 1.0 and 4.0, [PERSON] and [PERSON] [19] proposed an empirical formula to determine geotextile's pore opening size: O\({}_{90}\)/d\({}_{90}\) = \(-0.028\)C\({}_{\text{u}}\) + 2.34. For the soil sample adopted in this test, C\({}_{\text{u}}\) = 6.02, d\({}_{90}\) = 2.21 mm, thus the minimum mesh size of perforated plate II is 4.80 mm to avoid soil particles' bridge forming. Considering the results of pre-tests, the mesh size of perforated plate II is finally set as 10.0 mm, ensuring at least 90% sand particles to pass through without clogging.
# Multi-Scale Safety Helmet Detection Based on SAS-YOLOv3-Tiny
## 4 Experiments and Results Analysis
### Experimental Progress and Result Analysis  
#### 4.2.1 Training Setting  
This paper's experimental platforms were Intel(R) Core (TM) I7-9700 CPU @3.00 GHz processor and NVIDIA GeForce RTX 2080 Ti GPU. The programming language used for the algorithm in this paper was Python 3.8. The deep learning framework Pytorch 1.6.0 was used. The operating system used was Ubuntu18.04, and other dependent libraries were configured. Generally speaking, there were two training methods to train the model. One method was that random initial weights were used to train the model. The other is that pre-training weights were used to train the model. This paper used the first method to train the model to compare the different modification methods. In the experiment, the SAS-YOLOv3-tiny network was trained from scratch by using a self-built dataset. To ensure the fairness of the test, we retrained the YOLOv3-tiny, YOLO v3 and v4 [29] in the same experimental environment to obtain the corresponding detection model for experimental comparison results of the improved algorithm model on the validation set and the test set. Some experimental parameters were set as follows. In the experiment, the batch size was set to 4; 140 epochs were trained; the cosine learning rate strategy was used, which changed the learning rate from 0.01 to 0.0005; momentum was set to 0.937; weight decay was set to 0.000484. In addition, the multi-scale training strategy was adopted to improve the detection effect of the network for images of different input resolutions, and the cut size was selected at [320, 352, 384, 416, 448, 480, 512, 544, 576, 608, 640] for training in each iteration.
## 5 Results and Discussion
### Retrieval of SM
In Figure 11, it can also be seen that the modeling results of the multisatellite combination were better, with \(r\) greater than 0.918, i.e., 40-44% better than that of a single satellite. RMSE and MAE were less than 0.039, and Max was less than 0.077. It is not difficult to see that the multisatellite robust regression model could effectively restrain the influence of outliers and improve the SM retrieval accuracy. Further comparing scheme 2 to scheme 5, it is surprising that with the gradual improvement of the set range of \(r_{n}\), the changes of \(r\), RMSE, MAE, and Max were still small, although the number of satellite combinations used for modeling decreased. This may be related to both the number and the quality of the selected satellites. When the observation quality of the satellites used for modeling is good, a small number of combined satellites can achieve a higher modeling effect. Therefore, it is worth noting that when the selected satellites had \(r_{n}\) greater than 0.700, the observation quality of these satellites was generally good, and this value could be directly chosen to build multisatellite robust regression models. In this case, only few satellites would be required to improve the results; so, the complexity of modeling is decreased. When each satellite had an \(r_{n}\) of less than 0.600, the observation quality of the satellite was poor; so, combining more satellites would also be a good option.
# Low-Cost COTS GNSS Interference Monitoring, Detection, and Classification System +
## 5 Algorithmic Design
### Post-Processing
#### 5.4.1 Visualization  
Figure 14 visualizes a scenario with a camera, reference data, and real-time spectrograms (a video of this test setup is also provided in the Supplementary Video S1 for this article). The Qualysis motion tracking system [60] provides both reference positions of all participants (see also the top view in the lower left corner) and an actual perspective for intuitive monitoring and visualization (the upper left corner). On the right, the GUI shows the synchronized feature streams of all sensor nodes in real-time. A person makes a trajectory around the blocking, reflective, and absorptive walls between listeners (Sensors 0 to 4) and carries a PPD transmitting an interference signal. Comparing the feature data streams, it is clear that the blue and green listeners show significant interference (significant increase in signal power). In contrast, the red listener is less affected (the walls block the line of sight). The signal strength of the yellow listener decreases as it increasingly loses sight of the interference signal.  
Figure 14: Exemplary GUI of the visualization component. Each sensor node is identified with a color (green, blue, pink, and yellow) in the photo in the top left, mapped to the ground plot in the bottom left, and the applicable data highlighted in the graphs in the right.
# Experimental Analysis of Shale Cuttings Migration in Horizontal Wells
## 5 Conclusions  
1. Increasing the fluid flow rate and drill tool rotation speed helps reduce the height of the cuttings bed and improves cuttings migration efficiency. However, higher annular eccentricity leads to increased cuttings accumulation, reduced migration efficiency, and greater uncertainty in the process.
2. Non-spherical (flaky) particles are more difficult to transport compared to spherical particles, especially for larger particle sizes. Non-spherical particles are more prone to forming cuttings beds and require a higher critical start-up velocity.  
Figure 16: Confidence interval analysis, corresponding to Tables 2–7 from left to right.  
3. The theoretical model showed high accuracy in predicting the critical start-up velocity for non-spherical particles, with an error range between 8.63% and 9.61%. For spherical particles, the model's predictions were less accurate, with errors ranging from 15.50% to 17.56%.
4. Under non-rotating conditions, the experimental data aligned well with the theoretical model, with an average error of approximately 8.32%. However, as annular eccentricity and particle size increased, the discrepancy between the theoretical and experimental data also grew.
5. Under drill tool rotation, the theoretical model maintained reasonable accuracy, with an average error of about 11.94%. However, rotation introduced more variability, leading to increased uncertainty in the actual cuttings migration process.  
Conceptualization, Q.F. and M.M.; methodology, D.X.; validation, X.N. and M.W.; investigation, M.M.; writing--original draft preparation, M.M.; writing--review and editing, Q.F.; visualization, M.W.; supervision, Q.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Research on The Coupling Mechanism and Control Method of Reservoir Pressure in Fractured Formation Based on Mesoscale, grant number 35800000-22-ZC0607-0027.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
## 3 Methodology
### Hydrological Model  
As a rainfall-runoff simulation model, the BTOP model was selected as it is a semi-physically based distributed hydrological model with only five parameters to be calibrated. That is crucial for applying it to the majority of poorly measured basins in the world. It is applicable to small to large basins with slopes that can form streamlines. The BTOP model was first developed in 1999 at the University of Yamanashi [49; 50; 51; 52]. The idea was to develop a robust and straightforward hydrological model to simulate runoff based on TOPMODEL [53] by extending it to large basins. After years of continuous development, the BTOP model has been applied in nearly 2300 watersheds worldwide [54; 55]. It can be used for multi-purpose such as rainfall-runoff simulation, flood forecasting, and pollutant migration [56; 57].  
The BTOP model was applied to simulate discharge at Kitamatsuo, catchment area 3331 km\({}^{2}\), and 11.6 km from the Fuji River's estuary. Spatially distributed potential evapotranspiration was calculated by using the Shuttle Worth-Wallace (S-W) model [58]. We selected five cases to simulate flood discharge (i.e., July 2002, August 2003, October 2004, July 2007, and October 2009) as they received the most massive monthly cumulative rainfall of each year and there is almost no precipitation at the beginning of the month, and the discharge was at a low flow stage. October 2004 with gauge rainfall was used for calibration as it received the most extensive monthly rainfall from 2002 to 2011. Then we used five types of precipitation datasets (Gauge, GSMaP-NRT, IMERG-Early, and best bias-corrected SPPs for each) as forcing data for BTOP to simulate flood discharge in all cases.