esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup
### Experimental Models  
The dimensions of the CAARC high-rise building standard model are 101.6 mm \(\times\) 152.4 mm \(\times\) 609.6 mm (width \(\times\) length \(\times\) height). The arrangement of measurement points typically requires a denser distribution in areas with high local wind suction, such as corners, while other areas are evenly distributed. Therefore, the measurement point layout for each layer in this study is based on existing research [11]. The arrangement of the pressure measurement points is shown in Figure 2. For the measurement points on the model, a total of nine levels of measurement points were arranged along the height of the model. These levels are positioned at H/60, H/9, 2H/9, H/3, H/2, 2H/3, 7H/9, 8H/9, and 59H/60. Each level consists of 20 pressure taps, resulting in a total of 180 pressure taps, and the length of the tubing was below 0.2 m, avoiding the resonance and damping effects [27]. The experiments are conducted in the wind tunnel laboratories of Chongqing University with a blockage ratio of 2.15% and Harbin Institute of Technology with a blockage ratio of 0.77%.  
The instantaneous pressures were measured utilizing a PSI synchronous multi-pressure sensing system with a sampling frequency of 330 Hz at CQU, whereas a Scanivalue system was employed with a sampling frequency of 550 Hz at HIT. The sampling time is consistently set to 120 s. All equipment used in both wind tunnel laboratories was calibrated and zeroed efficiently, ensuring the reliability of the instruments. The wind speed scale ratio in this study was chosen to be 1:5, with a time scale ratio of 1:60. The sampling interval for the full-scale structure was typically 1 s, so the actual sampling frequency should be no less than 60 Hz. Additionally, according to the Nyquist sampling theorem, the sampling frequency should be greater than twice the signal frequency, which means it should be at least 120 Hz. Due to differences in the equipment of the wind tunnel  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Laboratory** & **Scale Ratio** & **Turbulence Integral Scale (m) L\({}_{u}^{\mathrm{x}}\)** & **D (m)** & **L\({}_{u}^{\mathrm{x}}\)/D** & **\(\beta\) = 0\({}^{\circ}\)** & **\(\beta\) = 90\({}^{\circ}\)** \\ \hline HIT & 1/300 & 0.400 & 0.1524 & 0.1016 & 2.625 & 3.937 \\ \hline CQU & 1/300 & 0.340 & 0.1524 & 0.1016 & 2.231 & 3.346 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of the longitudinal turbulent flow fields at (2/3) H.  
Figure 2: Dimensions of the CAARC model and tap distributions for the CAARC model.  
laboratories, the sampling frequencies could not be kept consistent, but they all meet the minimum requirement. Therefore, the results can be considered valid.
## 2 Methodology
### Prediction System Architecture  
The feature data extracted from the ECG and TMT-ECG signals were used to develop the prediction system for the CAD, using convolution neural network (CNN) modeling. This prediction system was developed on two considerations. The first one was based on single-lead signals and the other on 12-lead signals (I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, V6). In the design of the CNN model, the data obtained through the data extraction procedure array of the time series voltage datasets, including the ECG feature data, were used as input. The amplitude values of the signal, QRS complex, ST segment, P, Q, R, S, and T values, were included.  
Initially, in the single-lead CNN model, a total of 815 time-series data extracted from individual ECG graphs were normalized and fed into the input layer to obtain an output during the training cycles. Iterations were repeated for the designated number of training data in a similar way. For the 12-lead ECG, a similar approach was adopted but, in this case, the number of time series data extracted from a single multi-lead (12-lead) ECG graph was 9792. These data were obtained as a one-dimensional dataset and were used in the development of the CNN model. Considering various CNN architectures, each with different layer numbers and combinations of activation functions, an optimal CNN network was arrived at. Three subcategories were made based on the number of layers used for the optimization procedure, i.e., (1) a one-layer CNN architecture, (2) two-layer CNN architecture, and (3) three-layer CNN architecture [40].
## 2 Materials and Methods
### Global Datasets, Their Inputs, and Underlying Approach  
Simulated ET from our models was compared with three established global ET datasets: MOD16A2-ET, GLEAM, and TerraClimate. Table 7 summarizes their resolution, temporal frequency, input data, and primary algorithms used for ET estimation. MOD16A2-ET employs the Penman-Monteith approach to derive canopy conductance and ET. It utilizes MODIS land and vegetation products alongside gridded weather data from The Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) [43].  
The GLEAM product is derived from microwave satellite observations. Vegetation optical depth (VOD) and soil moisture data are sourced from passive and active C- and L-band microwave sensors. GLEAM first calculates cover-dependent potential evaporation rates (Ep) using the Priestley-Taylor (1972) equation based on air temperature and net radiation. This captures sub-grid land cover heterogeneity. Subsequently, Ep is transformed to actual transpiration or bare soil evaporation at each grid cell using a cover-dependent  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Surface and Aerodynamic Parameters**} \\ \hline aPT & Priestley–Taylor coefficient & 1.26 \\ fg & Green vegetation fraction & NDVI based \\ fc & Fractional cover & LC based \\ wc & Canopy width to height ratio & 1 \\ XLAD & [PERSON] 1990 LIDF chi & 1 \\ hc & Canopy height (m) & LC based \\ z0 soil & Bare soil aerodynamic roughness length (m) & 0.01 \\ hv & Average/effective leaf width (m) & LC based \\ b & Soil surface resistance (Rs) constant & 0.012 \\ c & Soil surface resistance (Rs) constant & 0.0025 \\ C\({}^{\prime}\) & Constant in total boundary resistance (Rx) & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameterization used for TSEB application in Mediterranean region.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**PROSPECT Model**} \\ \hline Cab & leaf chlorophyll content & 40 \\ Cea & leaf carotenoid content & 10 \\ Cdm & leaf mass per area (dry matter) & 0.012 \\ Cw & equivalent leaf water thickness (leaf water content) & 0.009 \\ Cs & senescent material (brown pigments) & 0 \\ Cant & leaf anthocyanin content & 1 \\ Cp & leaf protein content & 0 \\ Cbc & leaf brown pigment content & 0 \\ N & mesophyll structure parameter & 1.5 \\ rho\_thermal & leaf thermal reflectance & 0.01 \\ tau\_thermal & leaf thermal transmittance & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._TerraClimate is built upon the Thornthwaite water balance model. It integrates soil and land properties with daily climatological data from the GRIDMET-A Dataset of Daily High-Spatial Resolution Surface Meteorological data to provide water budget and recharge estimates for each grid within the surface water flow direction. ET is then derived by solving the water balance equation for each grid, with available soil water capacity acting as a major constraint [45].
## 3 Results
### Seasonal Dynamics of SIF and GPP at the Canopy Scale  
Figure 5 shows the processed data for two consecutive years at the DM observation site including the seasonal variations in \(\text{SIF}_{760}\), GPP, PAR, CI, AT, and VPD for the maize canopy. The seasonal pattern of \(\text{SIF}_{760}\) was consistent with that of the GPP over the whole growing season in 2017 and 2018 (Figure 5a). Both \(\text{SIF}_{760}\) and GPP exhibited gradual increases as the canopy developed and showed a significant decline from leaf senescence to harvest. In addition, \(\text{SIF}_{760}\) and GPP generally remained relatively stable with the exception of some cloudy sky days (Figure 5b). This can be seen from the decrease in GPP around DOY 206 to DOY 207 in 2017, and DOY 198 to DOY 199 in 2018 (marked with arrows in Figure 5a), for example. Therefore, we concluded that the seasonal fluctuations in \(\text{SIF}_{760}\) and GPP were also primary driven by the variations in PAR; this explained most of the changes in \(\text{SIF}_{760}\) and GPP. Similar seasonal trajectories were observed for both the clearness index (CI) and PAR over the whole growing season (Figure 5b). Therefore, the value of CI can be used as a proxy for the light conditions. In addition, AT and VPD presented similar seasonal patterns and their relationships with the ratio of GPP to \(\text{SIF}_{760}\) were relatively weak (Table 1).  
Figure 4: Relationships between PAR and canopy GPP and \(\text{SIF}_{760}\) based on individual day data acquired over the whole growing season at the DM maize field in 2017. The GPP–PAR relationships can be well fitted using a hyperbolic function, while the \(\text{SIF}_{760}\)–PAR relationships can be well fitted using the linear regression method. The DOY indicates the day of the year.  
Figure 5: **(a) Seasonal dynamics of canopy gross primary production (_GPP_, black) and solar induced fluorescence in the far-red band (\(\text{SIF}_{760}\), dark red) in the DM maize field in 2017 and 2018. Shaded regions indicate values within \(\pm\)_SE_ of the mean; **(b)** photosynthetically active radiation (PAR, purple) and clear sky index (CI, green); **(c)** air temperature (AT, orange) and vapor pressure deficit (VPD, blue). The arrows indicate the decrease in GPP on individual days. All maize canopy measurements represent the daily mean values during each daily sampling period.  
Next, we analyzed the relationships between \(\text{SIF}_{760}\) and GPP based on the half-hourly and daily mean data for the whole growing season. At the seasonal timescale, the relationships between maize canopy GPP and \(\text{SIF}_{760}\) exhibited strong hyperbolic correlations at both half-hourly (R\({}^{2}\) = 0.66, Figure 6a; R\({}^{2}\) = 0.66, Figure 6c) and daily (R\({}^{2}\) = 0.82, Figure 6b; R\({}^{2}\) = 0.76, Figure 6d) temporal resolutions. Additionally, we found that the relationships between \(\text{SIF}_{760}\) and GPP showed an improvement when the half-hourly mean data were aggregated to daily mean data (R\({}^{2}\) increased from 0.66 to 0.82, Figure 6a,b; R\({}^{2}\) increased from 0.66 to 0.76, Figure 6c,d) due to the reduction in noise.
## 3 IEEE 802.15.4
### IEEE 802.15.4 Based Solutions  
Several higher layer protocols have been defined directly on top of the IEEE 802.15.4 MAC sublayer. For completeness, a very brief overview of the most commonly used non-IETF solutions that have been built on top of IEEE 802.15.4 is given below.  
* ZigBee [10] builds upon the physical layer and medium access control defined in IEEE standard 802.15.4 for low-rate WPAN with additional network, security and application software layers. Predefined application services specify which actions a device can take, the main example being \"turn the lights on\" and \"turn the lights off\".
* Wireless HART [11] focuses on automation and industrial applications that require real time guarantees. To realize these goals, a time synchronized, self-organizing, and self-healing mesh architecture is used. The standard was initiated in early 2004 and developed by 37 _HART Communications Foundation_ (HCF) companies. In April 2010, WirelessHart was approved by the _International Electrotechnical Commission_ (IEC) unanimously, making it a wireless international standard as IEC 62591.
* The MiWi protocol stacks [12] are small foot-print alternatives to ZigBee (40K-100K), which makes them useful for cost-sensitive applications with limited memory. Although the MiWi software is free, there exists a unique restriction and obligation to use it only with Microchip microcontrollers.
* ISA100 [13] addresses wireless manufacturing and control systems (developed by the _Systems and Automation Society_ (ISA)). They defined ISA100.11a, a wireless networking standard that builds upon IEEE 802.15.4.  
Contrary to most of the above solutions, the IETF standards are fully open (no contributor fees are required). To ensure that each device is IP-addressable, an IPv6 layer (6 LoWPAN layer) is defined above the IEEE 802.15.4 MAC sublayer. This adaptation layer is discussed in the next section.
# Balanced Parallel Exploration of Orthogonal Regions
## 5 Analysis
### Perimeter Scan
#### 5.1.4 Conversion to Tree
Since \(R_{t}\) is the parent of \(R_{p}\) we have that \(R_{t}\) is the first room that contains \(R_{p}\), we have \(q_{i_{0}r}<q_{i_{0}p}\) and \(q_{i_{0}p+j_{0}p}<q_{i_{0}r+j_{0}r}\). Also, from connected we know that either \(q_{i_{0}p}=q_{i_{0}r+j_{0}r}\) or \(q_{i_{0}r}=q_{i_{0}p+j_{0}r}\). Thus, \(q_{i_{0}r}<q_{i_{0}r+j_{0}r}\) or \(q_{i_{0}r}<q_{i_{0}r+j_{0}r}\). Therefore, from our projections \(R_{t}\) and \(R_{u}\) are not disjoined or connected, namely one contains the other. Suppose that \(t<u\). Therefore, \(R_{u}\) contains \(R_{t}\). Since \(R_{t}\) contains \(R_{p}\), we have that \(R_{u}\) contains \(R_{p}\), which is a contradiction, since \(R_{u}\) and \(R_{p}\) are connected. Therefore, \(t>u\). Consequently, \(R_{t}\) must also contain \(R_{u}\). Suppose that there is another room \(R_{\gamma}\) that is the parent of \(R_{u}\). Therefore \(q_{i_{0}r}<q_{i_{0}r}\) as rooms \(R_{u}\) and \(R_{p}\) are connected then \(R_{\gamma}\) contains \(R_{p}\), \(q_{i_{0}r}<q_{i_{0}p}\) As \(R_{t}\) is the parent of \(R_{p}\), \(R_{t}\) must be contained in \(R_{\gamma}\). This results in a contradiction because \(R_{t}\) contains \(R_{u}\) and is a descendant of \(R_{\gamma}\). From this, \(R_{t}\) is the parent of \(R_{p}\) and \(R_{u}\). If the room discovered in \(Q_{r-1}\) was not the room the agents began the exploration in, then there are two connected rooms that are the roots of two trees. Lemma 2: _For disjoint rooms \(R_{p}\) and \(R_{t}\) where \(p<t\), the subsequence that generates \(R_{p}\) must appear earlier than \(R_{t}\) in \(Q_{0}\)._  
Proof: Let's assume that it is possible for \(R_{t}\) subsequence, \(q_{i_{0}r},\ldots,q_{i_{0}r+j_{0}r}\), to appear earlier than \(R_{p}\) subsequence, \(q_{i_{0}p},\ldots,q_{i_{0}p+j_{0}r}\), in \(Q_{0}\); \(q_{0},\ldots,q_{i_{0}r},\ldots,q_{i_{0}r},\ldots,q_{i_{0}r},\ldots,q_{i_{0}r +j_{0}r},\ldots,q_{l}\). As traversal is from left to right, at some \(Q_{c}\) such that \(c<p\) the subsequence, \(q_{i_{0}r},\ldots,q_{i_{0}r+j_{0}r}\) would be traversed and then at \(Q_{p}\) the subsequence \(q_{i_{0}p},\ldots,q_{i_{0}p+j_{0}r}\) would be traversed. Thus, it is not possible for an earlier subsequence that makes up a disjoined room to be explored after a later disjoined room. All points will be covered by agents after the entire perimeter is traversed. Given Lemmas 1 and Lemma 2 the whole area is divided into room which are explored in a post-order traversal of \(T\). As we see from Figure 6a, from the agent's start position and traversal direction, the agents discover room \(0,1,2\), and \(3\) which after generating the tree structure, Figure 6b, traverses the left subtree, then the right subtree, and finally the root, a post-order traversal. Corollary 1: _The area is divided into rooms through the traversal of the perimeter. Following in reverse order create a tree \(T\) of rooms which are explored following post-order traversal of the tree._
# Optimizing Finite-Difference Operator in Seismic Wave Numerical Modeling
## Appendix A FD Operator
### The FD Operator through Taylor's Expansion  
According to [PERSON] and [PERSON] [24], the finite-difference formulas for the derivatives can be obtained from [PERSON]'s expansion.  
\[\begin{split}& f(x)=f(x_{0})+f^{\prime}(x_{0})(x-x_{0})+\frac{f^{ \prime\prime}(x_{0})}{2}(x-x_{0})^{2}+O((x-x_{0})^{3}),\\ & f(x_{0}+n\Delta x)=f(x_{0})+f^{\prime}(x_{0})n\Delta x+\frac{f^ {\prime\prime}(x_{0})}{2}(n\Delta x)^{2}+O((n\Delta x)^{3}),\end{split} \tag{10}\]  
\(x_{0}\) is a differentiable point on \(f\). \(\Delta x\) is the sampling interval, \(O((n\Delta x)^{n})\) is the approximate error. Next, we calculate the normal derivative formula.  
\[\begin{split}&\sum_{n=-N/2}^{N/2}a_{n}f(x+n\Delta x)\approx f(x )\sum_{n=-N/2}^{N/2}a_{n}+\Delta xf^{\prime}(x)\sum_{n=-N/2}^{N/2}na_{n}+\frac{ f^{\prime\prime}(x)}{2}(\Delta x)^{2}\sum_{n=-N/2}^{N/2}n^{2}a_{n}\\ &\qquad\approx Af(x)+B\Delta xf^{\prime}(x)+C(\Delta x)^{2}f^{ \prime\prime}(x)\\ & A=\sum_{n=-N/2}^{N/2}a_{n},\qquad B=\sum_{n=-N/2}^{N/2}na_{n}, \qquad C=\frac{1}{2}\sum_{n=-N/2}^{N/2}n^{2}a_{n},\end{split} \tag{11}\]  
where \(a_{n}\) are constant coefficients. For the calculation derivative, the constant \(A\) should be 0. For first derivative:  
\[\begin{split}&\frac{\partial f}{\partial x}\approx\frac{1}{\Delta x }\sum_{n=-N/2}^{N/2}\frac{a_{n}}{B}f(x+n\Delta x)\approx\frac{1}{\Delta x}\sum _{n=-N/2}^{N/2}b_{n}f(x+n\Delta x),\\ &\sum_{n=-N/2}^{N/2}b_{n}=0,\qquad\sum_{n=-N/2}^{N/2}n^{2}b_{n}= \sum_{n=1}^{N/2}n^{2}(b_{n}+b_{-n})=0,\end{split} \tag{12}\]  
where \(b_{n}\) are the finite-difference coefficients for the first derivative. Let \(b_{-n}=-b_{n}\); then, we can get \(b_{0}=0\).  
For the second derivative  
\[\begin{split}&\frac{\partial^{2}f}{\partial x^{2}}\approx\frac{1}{( \Delta x)^{2}}\sum_{n=-N/2}^{N/2}\frac{a_{n}}{C}f(x+n\Delta x)\approx\frac{1}{ (\Delta x)^{2}}\sum_{n=-N/2}^{N/2}c_{n}f(x+n\Delta x),\\ &\sum_{n=-N/2}^{N/2}c_{n}=0,\qquad\sum_{n=-N/2}^{N/2}nc_{n}=\sum_ {n=1}^{N/2}n(c_{n}-c_{-n})=0,\end{split} \tag{13}\]  
where \(c_{n}\) are the finite-difference coefficients for the second derivative. Let \(c_{-n}=c_{n}\); then, we can get \(c_{0}=-2\sum_{n=1}^{N/2}c_{n}\).  
Then, the normal derivative formula is summarized as  
\[\frac{\partial f}{\partial x}\approx\frac{1}{\Delta x}\sum_{n=1}^{N/2}b_{n}(f_ {n}-f_{-n}), \tag{14}\]  
\[\frac{\partial^{2}f}{\partial x^{2}}\approx\frac{1}{(\Delta x)^{2}}\Bigg{[}c_ {0}f_{0}+\sum_{n=1}^{N/2}c_{n}(f_{n}+f_{-n})\Bigg{]}, \tag{15}\]  
where \(f_{n}=f(x+n\Delta x)\), \(N\) is the order, \(c_{n}\) and \(b_{n}\) are the FD coefficients. The higher the \(N\), the more approximate the result.
## 6 Simulation and Analysis  
### Simulation Model  
To verify the effectiveness of the proposed protection scheme, an active distribution network simulation model was established using PSCAD/EMTDC, as shown in Figure 16. R, B, and L indicate relay, bus, and load. L\({}_{\gamma}\) is the unmeasured load branch. DG\({}_{1}\), DG\({}_{2}\), and DG\({}_{3}\) are all IBDGs based on the PQ-control strategy, which has a low voltage ride-through function and can suppress the output of negative sequence currents. The maximum output currents of IBDGs are 1.5 times the rated currents. DG\({}_{4}\) in the simulation model is the SBDG. The fault points \(f_{1}\)-\(f_{4}\) are located at feeders B\({}_{4}\)B\({}_{5}\), B\({}_{5}\)B\({}_{6}\), B\({}_{1}\)B\({}_{2}\), and B\({}_{7}\)B\({}_{8}\). The parameters of this system model and feeders are selected according to the actual distribution network in a certain area of China, as given in Tables 1 and 2.  
Figure 16: Schematic diagram of the active distribution network.  
Figure 15: Fault record of a three-phase short-circuit fault with 2 \(\Omega\) fault resistance occurs at \(f\). (**a**) Currents at R\({}_{1}\); (**b**) currents at R\({}_{2}\); (**c**) the mutation of A-phase current at R\({}_{1}\) and a three-phase current at R\({}_{2}\).
## 3 Results and Discussion
### Elastic Modulus  
The relationship between the modulus of elasticity (\(E\)) and modulus of rupture (\(R_{f}\)) is shown in Figure 8. There was a good relationship with a high coefficient of correlation (\(R^{2}\) = 0.71). The elastic modulus in this study ranged from 4.789 GPa to 11.801 GPa and the modulus of rupture ranged from 8.53 MPa to 12.96 MPa. In other words, the increase in modulus of rupture can lead to an increase in the modulus of elasticity. In a previous study, the same result was observed, with a good relationship between the two parameters [20,21]. A great modulus of elasticity enhances deformation capacity and indicates that the material can deform more flexibly under stress without cracking and can absorb and redistribute loads more effectively. The results also indicated that the elastic modulus was related to the modulus of rupture. In other words, the elastic modulus value increased when the modulus of rupture value increased. It can be noted that the coconut fibers were not significantly affected to the relation between E and R\({}_{f}\), but the high modulus of rupture led to a higher modulus of elasticity. The relationship between the two parameters is expressed by Equation (3):  
\[E=1.22R_{f}-4.91,\,R^{2}=0.71 \tag{3}\]  
where \(E\) is the modulus of elasticity (GPa) and \(R_{f}\) the modulus of rupture (MPa).
# Modeling Undrained Shear Strength of Sensitive Alluvial Soft Clay Using Machine Learning Approach
## 1 Introduction
Generally, machine learning has been recently used by geotechnical engineers in the prediction of skin friction of driven piles [14], the bearing capacity of shallow foundations [15], or the shear strength of soil [16; 17; 18]. ML models are rarely used to estimate undrained shear strength of soft soil based on different geotechnical properties [19; 20; 12]. In 2022, [PERSON] et al. [6] only utilized ML models in the prediction of the undrained shear strength for marine-sensitive soft soil, with a coefficient of correlation of 0.715. Predictions of undrained shear strength of the alluvial sensitive soft clay using ML advanced modeling have not yet been thoroughly studied by geotechnical researchers. Therefore, the main objective of the current research was to introduce an accurate ML model to predict the undrained shear strength of sensitive alluvial soft clay using many important soft clay soil properties. The ML models have been formulated using a realistic dataset. The advantage of the ML model is to make an accurate prediction for the undrained shear strength of alluvial sensitive soft clay, in order to avoid disturbance difficulty during its extracting and sampling. Moreover, handling and performing shear strength tests on very soft and soft soil, either in the field or in the laboratory, typically comes with high costs, is time-consuming, and requires hard work and effort from geotechnical staff. ML prediction of the undrained shear strength of sensitive clay is expected to introduce significant savings in the above-mentioned factors. The ML models predict undrained shear strength based on soft clay essential properties, which can be easily determined in the laboratory. Soft soil properties, acting as predicting features, include moisture content, specific gravity,void ratio, dry unit weight, liquid limit, plasticity and consistency indexes, and pocket penetration shear. In this context, the ML model has been formulated using precise realistic datasets (features) for the sensitive soft clay samples collected from the delta region of Egypt, located in northern areas of the Nile River. Generally, alluvial soft clay regions are very crowded all over the world, with various structures like roads, embankments, bridges, railways, tunnels, airports, buildings, etc. The accuracy of the ML models is evaluated by the coefficient of correlation method. An accurate ML prediction of the undrained shear strength will achieve sustainably for the structures on non-treated or treated sensitive soft clay by gathering between its long-term high performance and economic side. The used undrained shear strength values of the sensitive clay were determined from the results of precise field vane shear tests. Collecting realistic and feature-enriched datasets, along with the selection of the appropriate algorithm, led to a substantial improvement in the prediction accuracy of the model.