esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Study Analysis and Results
### Spearman Correlation Coefficient
The Empirical Bayes method exhibits overall stronger positive correlations, varying in a broad range from 0.406 to 0.840, while the PSI method consistently demonstrates the highest correlations, ranging from 0.738 to 0.893. In the total sample, the proposed method shows a relatively strong positive correlation (0.815), whereas the Empirical Bayes method (0.928) and PSI method (0.942) display even stronger positive correlations with crash history. This indicates a higher association between all three methods and the crash history \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Group/Method**} & \multicolumn{3}{c}{**Spearman’s Rank Correlation Coefficient**} \\ \cline{2-4}  & **Proposed Method** & **EB Method** & **PSI Method** \\ \multicolumn{4}{c}{**Crash Density-Based Analysis**} \\ \hline Upper Tail (20) & 0.744 & 0.815 & 0.958 \\ Upper Tail (40) & 0.587 & 0.786 & 0.954 \\ \hline Upper Tail (60) & 0.638 & 0.813 & 0.959 \\ Upper Tail (80) & 0.655 & 0.828 & 0.959 \\ Upper Tail (100) & 0.605 & 0.825 & 0.958 \\ Total Sample (377) & 0.683 & 0.816 & 0.879 \\ \multicolumn{4}{c}{**Crash Frequency-Based Analysis**} \\ \hline Upper Tail (20) & 0.502 & 0.406 & 0.738 \\ Upper Tail (40) & 0.562 & 0.768 & 0.878 \\ \hline Upper Tail (60) & 0.65 & 0.84 & 0.893 \\ Upper Tail (80) & 0.703 & 0.823 & 0.87 \\ Upper Tail (100) & 0.702 & 0.829 & 0.867 \\ Total Sample (377) & 0.815 & 0.928 & 0.942 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative analysis of rank correlation for proposed method versus the EB method versus the PSI method. when the total sample is analyzed. The difference in performance between the proposed method and the other existing methods can be explained by properly understanding the formulation of the EB and the PSI methods. Specifically, observed crash history is a major contributor to the EB and the PSI methods, which explains the higher correlation between crash history and these methods. On the other hand, observed crash history is not an input to the proposed method.
# Lower Limb Muscle Activation in Young Adults Walking in Water and on Land
## 3 Results
### Main Effects  
#### 3.3.1 Environment  
Significant main effects of environment were observed on several dependent measures, including stance time (\(F=15.68\), \(p<0.001\)), swing time (\(F=811.53\), \(p<0.001\)), stride rate (\(F=733.18\), \(p<0.001\)), stride length (\(F=446.97\), \(p<0.001\)), stance phase GM RMS (\(F=59.11\), \(p<0.001\)), stance phase Co-A (\(F=41.26\), \(p<0.001\)), swing phase TA RMS (\(F=49.91\), \(p<0.001\)), and swing phase Co-A (\(F=13.42\), \(p<0.001\); Table 5).  
Post hoc comparisons revealed that stance time (\(p<0.001\)), swing time (\(p<0.001\)), stride length (\(p<0.001\)), stance phase Co-A (\(p<0.001\)), and swing phase TA RMS (\(p<0.001\)) were significantly greater in water compared to land, while stride rate (\(p<0.001\)), stance phase GM RMS (\(p<0.001\)), and swing phase Co-A (\(p<0.001\)) were significantly greater on land compared to in water. There were no main effects of the environment on stance phase TA RMS (\(F=0.03\), \(p=0.866\)) or swing phase GM RMS (\(F=3.39\), \(p=0.074\); Table 5).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measure** & **Land** & **Water** & **2.5 mph** & **3.5 mph** \\ \hline Stance time (s) & 0.70 (0.07) & 0.72 (0.10) a  & 0.78 (0.06) & 0.63 (0.04) b  \\ Swing time (s) & 0.37 (0.03) & 0.59 (0.07) a  & 0.50 (0.14) & 0.46 (0.11) b  \\ Stride length (m) & 1.41 (0.16) & 1.73 (0.19) a  & 1.44 (0.20) & 1.71 (0.19) b  \\ Stride rate (strides\(\times\)s\({}^{-1}\)) & 0.95 (0.08) & 0.77 (0.09) a  & 0.79 (0.10) & 0.93 (0.10) b  \\ TA RMS Stance (uV) & 797 (0.39) & 80.3 (8.0) & 61.0 (22.21) & 99.0 (34.3) b  \\ GM RMS Stance (uV) & 130 (95.62) & 83.4 (34.43) a  & 86.6 (45.5) & 127.7 (56.8) b  \\ Co-A Stance (uV) & 69.4 (34.5) & 122.9 (67.2) a  & 97.5 (67.8) & 94.8 (50.5) \\ TA RMS Swing (uV) & 116.1 (54.4) & 164.8 (82.2) a  & 104.8 (35.5) & 176.1 (84.1) b  \\ GM RMS Swing (uV) & 18.2 (15.8) & 13.4 (8.9) a  & 13.6 (14.4) & 18.0 (11.2) b  \\ Co-A Swing (%) & 17.0 (13.5) & 8.9 (7.2) a  & 14.1 (14.0) & 11.8 (8.3) b  \\ \hline \hline \end{tabular}
\end{table}
Table 5: Central tendency and dispersion results collapsed across speed and environment.
# Intelligent Arabic Handwriting Recognition Using Different Standalone and Hybrid CNN Architectures
## 5 Discussion and Comparisons
### Arabic MNIST Dataset  
For the standalone models trained using the Arabic MNIST dataset, most of the models performed excellently, with accuracies above 95%. The best performances were observed for XGBoost (with values for precision, recall, F1-score, and accuracy of 1), Logistic Regression (with precision, recall, and F1-score values of 1.0 and an accuracy of 0.99957), the SVMs (with precision, recall, and F1-score values of 1 and an accuracy of 0.99628), followed by the transfer-learning model (with an accuracy of 0.99743) and the CNN (with an accuracy of 0.99063, and precision, recall, and F1-score values of 1). AdaBoost, on the other hand, obtained the lowest performance, with an accuracy of 0.696.  
Considering the hybrid models, see Figure 9, the best classification performance was observed for the combination of CNN and SVM (with an accuracy of 0.9710, and precision, recall, and F1-score values of 0.97), followed by the CNN and Random Forest (with an accuracy of 0.9390), CNN and Logistic Regression (accuracy of 0.9388), and CNN and XGBoost (accuracy of 0.9320). The CNN and AdaBoost hybrid model obtained the weakest performance, with an accuracy of 0.5470, precision of 0.57, recall of 0.55, and an F1-score of 0.54.
# Reconstruction and Measurement of Irregular Karst Caves Using BLST along the Shield Metro Line
###### Abstract  
This study investigated the application of the borehole laser scanning technology (BLST) method in the detection of both dry and water-filled karst caves. In order to solve the problem of excessive laser attenuation during the detection, we designed a test for the characteristics of multiwavelength laser attenuation in water-filled karst caves and studied the influence exerted by various factors, including different wavelengths, different laser power levels, different suspended media, and effect of turbidity on the attenuation coefficient. During the test, we discovered the existence of a \"blue-green window\" with low turbidity and a \"near infrared window\" with high turbidity in karst cave water environments. Based on the general survey results of drilling and comprehensive geophysical prospecting, a quantitative method using targeted drilling was proposed to detect the spatial morphology of karst caves in complex environments. We also investigated the effects of complex environmental factors such as suspended media and high turbidity on the laser detection distance and accuracy in karst caves, and established a quantitative matching model of laser wavelengths, laser power, and complex environmental parameters. Based on this, we obtained the best acquisition mode for detecting lasers in different karst development environments. A high-precision, three-dimensional visualized model of a real karst cave was established to quantitatively obtain the characteristic parameters, such as accurate position, three-dimensional shape, space volume, and cave filling type, which was applied to the detection of karst caves along the Jinan subway line.  
irregular karst cave; measurement; 3D parameters; BLST method; shield metro
# Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods
## 5 Experimental Results
### SOC Prediction Using FMI-Based EV Simulation
\tag{34}\]  
\[I_{B}(t)=\frac{E_{B0}}{2\times R_{Bi}}-\sqrt{\left(\frac{E_{B0}}{2\times R_{ Bi}}\right)^{2}-\frac{P_{bc}}{R_{Bi}}} \tag{35}\]  
\[Q(t)=\int_{0}^{t}I_{B}dt \tag{36}\]  
\[C=C_{0}*(1-K_{Ct}*t_{bu})*\left(1+alphaC*\left(T_{curr}-T_{ref}\right)\right) \tag{37}\]\[\text{SOC}(t)=\frac{\text{C}-Q(t)}{\text{C}} \tag{38}\]  
To verify the effectiveness of our speed prediction model for the final SOC prediction, we compared the proposed model results with the speed and SOC prediction obtained with the Seasonal Autoregressive Integrated Moving Average (SARIMA) model. The final SOC prediction results are shown in Table 7. As can be seen from the table, the RMSE of TA-LSTM drops 56.69%, 84.97%, 82.34%, and 91.62% compared to SARIMA in the AtoC, CtoD, DtoE, and EtoF sections, respectively. The predicted SOC results made by TA-LSTM and SARIMA for the driving cycle of each road section are shown in Table 8 and Figures 12-15. From Table 8, we can see that TA-LSTM performed better than SARIMA in every driving cycle. In addition, the RMSE results of TA-LSTM for all driving cycles in the CtoD and DtoE sections are less than 0.0004. Although the RMSE for the AtoC and EtoF section is slightly worse than the first three sections, the RMSE results are less than 0.0006. The comparison between the real SOC and the predicted values shows that the predicted results of our model are very close to the real values. This indicates that our proposed method is able to perform SOC prediction properly. In addition, in order to simulate the battery model by reflecting more realistic situations, we checked the change in SOC by varying the ambient temperature and battery usage time. The real driving cycle, the parameters of the battery model, and the ambient temperature and battery operating time values for the simulation are shown in Table 9.  
From the results in Figure 16, it can be confirmed that the battery SOC decreased faster because the battery capacity decreases as the temperature decreases. From the results of Figure 17, it can be confirmed that the battery SOC decreased more quickly because the battery capacity decreases as the battery usage time increases. \begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Description** & **Values** \\ \hline Driving cycle & Real driving cycle & Cycle 8 \\ \(K_{ct}\) & Linear \(t\) dependency of the capacity \(C\) & 6.084436 \(\times\) 10\({}^{-10}\) \\ \(t_{bu}\) & battery usage time (s) & 0, 157,698,305 s (5 years), \\ \(T_{ref}\) & Reference temperature (\({}^{\circ}\)C) & 20 \({}^{\circ}\)C \\ \(T_{curr}\) & Ambient temperature (\({}^{\circ}\)C) & 0, 20, 30 \({}^{\circ}\)C \\ \(alphaC\) & Linear temperature coefficient of capacity & 0.03 (0) \\ \hline \end{tabular}
\end{table} 
Table 9: Parameters for the battery model simulation. Figure 14: SOC prediction of route D to E: (a) cycle 8; (b) cycle 9; (c) cycle 10; (d) cycle 11. Figure 15: SOC prediction of route E to F: (a) cycle 8; (b) cycle 9; (c) cycle 10; (d) cycle 11.
# A One-Phase Tree-Structure Method to Mine High Temporal Fuzzy Utility Itemsets
## 1 Introduction
In the first phase, based on their designed upper-bound model, candidate itemsets are found in level-wise processing. In the second phase, the method scans the database to ensure whether the candidates are truly high. In order to solve the TFUIM (temporal fuzzy utility itemset mining) problem, a two-phase algorithm based on a tree structure was previously proposed to improve the performance efficiency in mining [18]. Its main goal is to reduce the number of candidates compared with that in [16]. The first phase is to hold candidate itemsets with the upper bounds satisfying the threshold in the tree. With traversing the tree, candidate itemsets can be found. They then need to be decided for being high itemsets in the second phase. However, generating the possible candidates in the first phase in [18] is also time-consuming. This study thus proposes a one-phase algorithm based on a tree structure with the array list to overcome the execution shortcoming without scanning the database in the second phase. The proposed tree structure holds all the required information. It needs additional memory to store mining information but can run faster than the previous one. The proposed mining approach based on the designed tree structure can find desired high itemsets without scanning the database again. The rest of the paper is organized as follows. Section 2 presents relevant works related to the proposed approach; Section 3 explains the TFUIM problem and some terms; Section 4 describes the proposed approach and uses an example to illustrate the approach; Section 5 shows some experimental comparison on three real-life datasets; Lastly, a conclusion is provided in Section 6.
# Data-Aware Path Planning for Autonomous Vehicles Using Reinforcement Learning +
## 1 Introduction
Through rigorous testing across various parameters, we demonstrate that our reinforcement learning approach can learn efficient and effective solutions. These solutions are shown to be superior to baseline models that do not account for the heterogeneity of traffic and bandwidth. Our findings indicate not only the viability of using reinforcement learning for this complex optimization problem but also its potential to significantly outperform traditional path planning methods. The key contributions of this paper are as follows:  
* A novel reinforcement learning framework that jointly optimizes for travel time and data transfer efficiency in heterogeneous urban environments. * Implementation of a realistic simulation environment using GraphML [7], incorporating real map data and vehicle mobility patterns for robust evaluations of path planning strategies. * Experimental validation demonstrating the superiority of our approach over traditional baselines in heterogeneous traffic and bandwidth scenarios. The remainder of this paper is organized as follows: Section 2 presents a review of related work in the field. Section 3 details the core of our research, providing a comprehensive overview of the problem formulation. This section elaborates on the principles of reinforcement learning, details the construction of our reinforcement learning environment,and explains the experimental setup. In Section 4, we present and analyze the simulation results. Finally, Section 5 summarizes our findings and discusses their implications for the future of autonomous vehicle path planning.
# High Precision Indoor Positioning System for Mobile Asset Management and Safety +
## 2 Methodology
### Comparison between WiFi Device and DecaWave  
A BLE device, a Wi-Fi device, and a DecaWave module simultaneously measure the distance of an object from the fixed point. The graphs show the differences in the accuracy  
Figure 3: Tags tracking.  
of these techniques. The most accurate readings belong to the DecaWave module. The most accurate of the three techniques is the DecaWave, and the worse technique is the BLE.
## 3 Results and Discussion
### [PERSON]'s Rule of Five Evaluation  
[PERSON]'s rule of five was applied to evaluate the similarity of test compounds to orally active drugs in humans. This rule serves as a predictive guideline for assessing drug permeability. According to [PERSON]'s criteria, an oral drug candidate should meet the following conditions: molecular weight \(\leq\) 500 g/mol, lipophilicity (MLogP) \(\leq\) 4.15, H-bond donors (NH or OH) \(\leq\) 5, and H-bond acceptors (N or O) \(\leq\) 10 [32]. A molecular weight above 500 g/mol can reduce compound concentration at the intestinal epithelium surface, limiting absorption in the gastrointestinal tract and the central nervous system (CNS) [57]. Similarly, a partition coefficient (MLogP) value of more than 4.15 indicates excessive lipophilicity, which can hinder recognition by target enzymes and increase toxicity. However, a very low partition coefficient value can also be problematic, as it may reduce membrane permeability [58; 59]. Further, hydrogen-bond donors (\(\leq\)5) and acceptors (\(\leq\)10) are crucial for passive diffusion. An excessive number of hydrogen bonds can hinder the transition of molecules from the water-soluble phase into the lipid bilayer, reducing membrane permeability [60; 61]. Drug-likeness results can be determined based on how many of [PERSON]'s parameters are violated. A compound that violates one criterion may still be considered drug-like, but violations of two or more indicate it is unlikely to be an orally active drug candidate [62; 63; 64].  
The prediction results (Table 2) show that all PBD compounds met the criteria of [PERSON]'s rule of five, with the exception of PBD-16 to PBD-21, which exhibit molecular weights (MW) exceeding 500 g/mol. This increase in molecular weight is attributed to the addition of carbon atoms in PBD-16, PBD-17, and PBD-18 and nitrogen atoms in PBD-19, PBD-20, and PBD-21. Although a molecular weight below 500 g/mol is traditionally preferred, this threshold is not an absolute requirement in modern drug development [65]. Given that this is the only violation observed, all PBD compound are still considered to meet the drug-likeness criteria.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Compound** & **MW** & **MlogP** & **HBD** & **HRA** & **Violation** & **Compound** & **MW** & **MlogP** & **HBD** & **HBA** & **Violation** \\ \hline
4-OHT & 387.51 & 4.49 & 1 & 3 & 1 & PBD-22 & 486.59 & 2.58 & 1 & 5 & 0 \\ \hline Ralcovifene & 473.58 & 3.21 & 2 & 5 & 0 & PBD-23 &
## 5 Results and Discussions
### Anomaly Pattern Detection
The interquartile range (9.00 km to 18.10 km) further supports the prevalence of shallow to moderate-depth seismicity. With a mean magnitude of 1.88 and a standard deviation of 1.03, the data show that most recorded events are small. The interquartile range (1.10 to 2.50) reinforces this observation. However, the maximum magnitude of 7.80 indicates that the region can produce significant earthquakes, particularly in the Gulf of Aqaba area. The wide range of magnitudes (0.01 to 7.80) suggests a complex seismotectonic environment, possibly reflecting various tectonic processes including rifting and fault movements. The newly developed seismicity catalog of the Red Sea clearly depicts the region's active seismicity, characterized predominantly by frequent minor events but with the potential for occasional large earthquakes. The shallow depth profile aligns with the area's geological setting as a divergent plate boundary. The spatial distribution likely correlates with the rift axis and associated fault systems. The comparative analysis of seismicity parameters derived from the refined catalog, following machine-learning-based outlier removal, versus the original dataset from the three seismographic networks inclusive of anomalous entries, reveals both notable distinctions and intriguing similarities across key seismicity attributes. In terms of magnitude distribution, the refined catalog exhibits a more constrained range and a lower mean magnitude. This suggests that the machine-learning outlier removal process effectively eliminated spurious seismic events, likely artifacts of instrumentation errors or misidentification. Consequently, this refinement provides a more representative portrayal of the region's typical seismic energy release patterns. Depth profiles between the two catalogs show marked differences, with the cleaned dataset presenting a shallower mean focal depth and reduced standard deviation. This adjustment aligns more closely with the expected crustal structure of the Red Sea rift zone, offering a more accurate representation of the depth distribution of seismogenic sources in the region. Geographical distribution parameters demonstrate subtle yet significant shifts post-outlier removal. The refined catalog exhibits a more concentrated spatial clustering, evidenced by reduced standard deviations in both latitudinal and longitudinal dimensions. This spatial consolidation likely reflects a more accurate delineation of the active tectonic boundaries and associated fault systems within the Red Sea region. Despite these differences, certain similarities persist between the two catalogs, particularly in the overall temporal distribution of events and the general trends in magnitude-frequency relationships. These enduring similarities lend credence to the fundamental patterns of seismic activity in the region. However, the observed differences underscore the critical importance of rigorous data cleaning and outlier removal in seismological studies.
## 4 Discussion
### Evaluation of Our Proposed Chamber Method  
Strictly speaking, there are some possible causes which may have led to the reduction of _Ne_ within the chamber. Firstly, incoming sunlight was attenuated through the chamber wall even though it was made of transparent material, and this would reduce photosynthetic carbon uptake. We estimated a reduction of 1.7% in _Ne_. Secondly, the saplings were exposed under high temperature and humidity conditions when the chamber was closed at noon, and the increased _VPD_ average value would inhibit photosynthesis, resulting in an underestimation of 3.5% of the total crown _Ne_ (Figures 3b and 9e,f). Similarly, increased temperature in the chamber increased \(R\). We found that the mean Q\({}_{10}\) of crown respiration under different nitrogen treatment levels was 1.67 (the respiration rate increased 1.67 times with a temperature increase of 10 \({}^{\circ}\)C; not listed in this article). Therefore, the increased temperature average value (Figure 3a) resulted in an underestimation of 0.3% of the total crown respiration. Through the above analysis, the chamber method underestimated the _Ne_ by 5.5% (the inhibitory effect of light on dark respiration was not considered in this paper). Yet the closed-circuit chamber method outlined here nevertheless potentially provides a scientific and accurate method to observe the carbon-water exchange in the crown (or other components), and the acquisition of accurate and high-frequency data cannot be replaced by other methods so far. More importantly, the chamber approach provides useful information about long-term responses because of short-term differences in plant crown-level CO\({}_{2}\) exchange under different nitrogen addition levels can scale up to long-term changes in forest ecosystem carbon cycle and atmospheric nitrogen deposition.
## 3 Numerical Simulations and Experiments
### KDP Crystal Pre-Surface Testing Based on Off-Axis PMD  
Due to the parasitic reflection in the detection of transparent optical elements, the structured light coding pattern projected by the screen is reflected by both the front and rear surfaces of the transparent optical elements. The CCD camera captures the fringe pattern on the front and rear surfaces superimposed on each other, and the fringe pattern no longer satisfies the sinusoidal distribution, so the traditional phase shifting method will fail to solve for the correct screen pixel coordinates. In order to better separate the front and rear surface information of KDP crystals, this paper adopts a binary pattern method based on off-axis PMD [8]. After obtaining the coordinates of the screen reflected through the front surface using the binary pattern method, the slope can be calculated using Equation (1). The measurement size is a 72 mm \(\times\) 72 mm square containing 400 \(\times\) 400 pixels, which are sampled and transformed into a 100 \(\times\) 100 matrix.  
The measurement results are shown in Figure 13. Figure 13d shows the RMS value of 84.21 nm for the difference between the first four terms removed after reconstructing the front surface of the KDP crystal using Southwell and the first four terms removed by the interferometer, while Figure 3e shows only the RMS value of 47.76 nm for the difference between the first four terms removed after reconstructing the front surface of the KDP crystal using RBF and the first four terms removed by the interferometer.  
Figure 12: The RMS of repeatability experiments with two methods at different angles.  
Figure 13: The results of KDP crystal front surface reconstruction. **(a)** The result of interferometer without the Chebyshev’s first four terms; **(b)** reconstruct wavefront without the Chebyshev’s first four terms using Southwell; **(c)** reconstruct wavefront without the Chebyshev’s first four terms using RBF; **(d)** is the difference between **(a,b)**; **(e)** is the difference between **(a,c)**.