esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Underwater Routing Protocols
### Localization-Based Routing Protocols  
These protocols require that the two- or three-dimensional coordinates information of the sensor nodes be known for determining the routes from the bottom to the surface of the water. Without the coordinates information, the distances among nodes and the routing trajectories cannot be determined in these protocols. These protocols are further classified into the following types based on the problems they address, resolve or the parameters they utilize to make the routing decisions. Table 2 shows a summary of these protocols. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protocol** & **Routing Strategy** & **Problem** & **Metrics** & **Demerits** & **Year** \\ \hline \multirow{4}{*}{VBF} & Selects forwarder nodes & \multirow{4}{*}{Nodes’ mobility} & \multirow{4}{*}{Energy efficiency, scalability} & Nodes in the pipe die quickly due to high data load, which creates energy holes (dead nodes) & \multirow{4}{*}{2006} \\  & within a pipe from the source to the destination & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \multirow{4}{*}{SBR-DLP} & Combines the forwarding probability of a packet with packet holding time in a routing zone formed by the angle among the source, forward and destination & Nodes’ movement & Energy efficiency & \multirow{4}{*}{2009} \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \multirow{4}{*}{TC-VBF} & Modifies the VBF protocol to select forwarders in response to nodes’ density & Nodes’ mobility & Energy efficiency & \multirow{4}{*}{2017} \\ \cline{1-1}  & The network is divided into layers, and nodes forward data packets from the bottom to top within a routing pipe in these layers & Nodes’ mobility for localization of forwarder nodes in a routing pipe in the pipe & \multirow{4}{*}{High throughput} \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \multirow{4}{*}{HH-VBF} & Rather than defining a single routing pipe as in node defined for forward node & Reduction of data load on the nodes in the forward node & Minimization of energy hole formation in VBF by controlling data load on the nodes & High computational delay due to defining the routing pipe for every node, compromised \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \multirow{4}{*}{REBAR} & Creates an adaptive cylindrical path from the source to the destination with a varied radius to choose forwarders & Energy balancing and early death of nodes close to the water & Energy efficiency and void hole control & Error in position calculation of nodes when they move & \multirow{4}{*}{2007} \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Localization-based routing protocols. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
# Optimization of Design Parameters in LSTM Model for Predictive Maintenance
## 2 Literature Survey
### Various Approaches for Predictive Maintenance
#### 2.1.2 Statistical Model-Based Approaches  
This category predicts the state of machinery with a random coefficient model or stochastic process model, which is free from physical properties or principles. [PERSON] et al. [8] used a regression model which indicates the present and past degradation state of bearing components as a linear relationship and error term. A proportional hazard (PH) model which calculates the survival probability of machinery by the product of a baseline hazard function and covariate function is a representative model belonging to this category [9]. Designing a nonlinear model with a random coefficient and enhancing the coefficient with various estimation methods is another common approach. [PERSON] and [PERSON] [10] considered a Bayesian approach for modeling the deterioration process of wind turbine blades. Methods including maximum likelihood estimation [11] and Monte Carlo simulation [12] were also considered for the same purpose. In addition, a certain stochastic process, as well as a random coefficient model, has been used to predict the state of machinery. [PERSON] and [PERSON] [13] developed a model for predictive maintenance based on the Wiener process and validated it by using lithium-ion battery degradation data. [PERSON] et al. [14] defined the fatigue state of a milling process with a hidden Markov model (HMM) and adopted MLE to estimate the parameters of the HMM. These methods do not require any domain knowledge and can reflect uncertainty inherent in degradation procedures of machinery. Nevertheless, basic assumptions of a statistical model should be satisfied, which imposes a limitation on application. In addition, a lack of measures for theoretical validation, such as the principle of physics, is an obstacle for parameter estimation. [PERSON] et al. (2015) intended to resolve this problem by considering both physics and statistical models.
## 4 Analysis of the Non-Ideal Gain Effect and Sensitivity Performance  
Considering only the influence of the non-ideal gains (\(\alpha=\beta\
eq 1\)), the characteristics \(\omega_{o}\) and \(Q\) of the proposed filter will be modified as:  
\[\omega_{o}\ =\ \sqrt{\frac{\alpha_{1}\alpha_{2}\beta_{1}g_{m1}S_{m2}}{C_{1}C_{ 2}}}\quad, \tag{21}\]  
and  
\[Q\ =\ R_{2}\sqrt{\frac{\alpha_{1}\alpha_{2}\beta_{1}g_{m1}S_{m2}C_{2}}{C_{1}}}, \tag{22}\]  
where \(\alpha_{i}\) and \(\beta_{i}\) (\(i=1\), \(2\)) are the parameters \(\alpha\) and \(\beta\) of the \(i\)-th VDBA, respectively.  
The sensitivity analysis of \(\omega_{o}\) and \(Q\) with respect to active and passive components is also carried out, and the calculation results are obtained as:  
\[S_{a_{1}}^{\omega_{0}}\ =\ S_{a_{2}}^{\omega_{0}}\ =\ S_{\beta_{1}}^{\omega_{0}}\ =\ S_{\beta_{m1}}^{\omega_{0}}\ =\ S_{\beta_{m2}}^{\omega_{0}}\ =\ \frac{1}{2}, \tag{23}\]  
\[S_{C_{1}}^{\omega_{0}}\ =\ S_{C_{2}}^{\omega_{0}}\ =\ -\frac{1}{2}, \tag{24}\]  
\[S_{a_{1}}^{Q}\ =\ S_{a_{2}}^{Q}\ =\ S_{\beta_{1}}^{Q}\ =\ S_{\beta_{m1}}^{Q}\ =\ S_{\beta_{m2}}^{Q}\ =\ \frac{1}{2}, \tag{25}\]  
and  
\[S_{R_{2}}^{Q}=1,\quad S_{C_{1}}^{Q}\ =\ -S_{C_{2}}^{Q}\ =\ -\frac{1}{2}. \tag{26}\]  
It can be easily deduced that all the sensitivity coefficients of \(\omega_{o}\) and \(Q\) are not greater than one in all four modes of operation.
# Automatic Needle Route Proposal in Preoperative Neck CT for Injection Laryngoplasty
## 2 Methodology
### Optimal Needle Route Search  
From this refined set of routes, we seek the route that faces minimal obstacles or rigidness so that the needle can be pushed through easily. Therefore, we define a cost function to represent this rigidness and find the lines from \(\Omega_{r}\) and \(\Omega_{l}\) that minimize the cost. It is important to note that we conduct separate searches for the right and left vocal folds using their respective search spaces. As a result, we obtain two distinct optimal needle routes, each used to reach a specific vocal fold.  
In CT, intensity is directly proportional to the rigidness of a material. Therefore, a simple definition of the cost can be the sum of intensities over the route, which is as follows:  
\[\begin{split}\Gamma_{r}&=\arg\min_{\Gamma\in \Omega_{r}}\sum_{\mathbf{p}\in\Gamma}I(\mathbf{p})\\ \Gamma_{l}&=\arg\min_{\Gamma\in\Omega_{l}}\sum_\mathbf{{p}\in\Gamma}I(\mathbf{p})\end{split} \tag{6}\]  
where \(\Gamma_{r}\) and \(\Gamma_{l}\) indicate the optimal needle routes for right and left folds.  
However, summation may normalize thin rigidness (e.g., tiny calcifications) when the intensity sum is minimal despite containing a few high-intensity voxels. Hence, we use the maximum intensity over a route as the cost. Thus, the proposed optimal needle routes can be defined as follows:  
\[\begin{split}\Gamma_{r}^{*}&=\arg\min_{\Gamma\in \Omega_{r}}\max_{\mathbf{p}\in\Gamma}I(\mathbf{p})\\ \Gamma_{l}^{*}&=\arg\min_{\Gamma\in\Omega_{l}}\max _{\mathbf{p}\in\Gamma}I(\mathbf{p})\end{split} \tag{7}\]  
This helps avoid calcifications even if they are small. In addition, the needle insertion point is also ensured to be in the cricthyroid membrane region. Any route that instead passes through the surrounding thyroid and cricoid cartilages would give a larger cost due to the higher intensity of the cartilages. If the intensity sum is used, this high intensity is not reflected because cartilage are usually thin.
# Posterior Probability-Based Symbol Detection Algorithm for CPM in Underwater Acoustic Channels
## 2 System Model  
Figure 1 depicts the architecture of the CPM UWA communication system. Preceding CPM, the transmitted data must be added with a tail symbol and CP, containing a unique word. Following CPM signal that passes through the UWA channel, the signal is captured by the receiver, wherein competent time-frequency synchronization and Doppler compensation are assumed to be executed. Subsequently, the received data are extracted through matched filtering, channel equalization, and symbol detection.
## 3 Finite Element Method (FEM)
### Influencing Factors
#### 3.2.4 Pile Spacing  
The pile spacing was 4\(D\) of the fixed value in the SP\(-\)PSC pile group tests. In this section, the influence of pile spacing on the soil compaction coefficient among the pile group is systematically studied using the finite element method. In the numerical analyses, the pile spacing gradually increased from 1\(D\) to 8\(D\), and the pile diameter, pile length, and soil moisture content were fixed at 0.56 m, 7 m, and 18%, respectively. As two special points in the pile group, the soil compaction coefficients at the centroid and the edge midpoint are given in the following.  
Figure 8: The relationship between the compaction coefficient and \(\omega\).  
Figure 7: The relationship between the compaction coefficient and \(H\).  
Figures 9 and 10 illustrate the variation in the soil compaction coefficients at the centroid and the edge midpoint, and the pile spacing, in which the focused points were taken at the place 3 m from the ground, where the abscissa is the multiple of the pile diameter. It can be found from Figures 9 and 10 that the soil compaction coefficients gradually decreased with increases in the pile spacing, but the rate of decrease gradually decreased. When the pile spacing is greater than 3, the distance between the centroid (or edge midpoint) of the SP\(-\)PSC pile group and the compaction piles is relatively large. Therefore, based on the Saint-Venant principle, the impact of the pile spacing on the compaction coefficient is insignificant when the pile spacing exceeds 3. Comparing the soil compaction coefficients at the centroid and the edge midpoint, it is obvious that the compaction coefficient at the centroid is smaller than that at the edge midpoint, which is because the centroid is the farthest from each pile core.
## 6 Experimental Evaluation
### Results
[3] on the same test images ( Results are taken from Table 2 of [3] with respect to rectangular regions) using U-Net-based semantic segmentation, trained on synthetic data and fine-tuned on human-annotated pages, followed by a watershed algorithm to separate the individual characters. Two of the U-Net backbones are outperformed (VGG-16 and ResNet-50), while the Inception-ResNet-V2 backbone achieves higher results than our method. Table 5 shows the detailed results. Overall, we observe that comparable results are achieved with the proposed alignment method, despite the fact that 10 pages of the test set were used to fine-tune the system in [3], while no fine-tuning to the test manuscripts has been performed for our system. This is a promising outcome for the proposed approach since no human annotations were required to achieve a good generalization to unseen data. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Acc** & **P** & **R** & **F1** & **IoU0** & **IoU** \\ \hline YOLOv5s & 89.31 & 94.45 & 92.20 & 93.12 & 73.76 & 79.63 \\ YOLOv5m & 89.16 & 93.92 & 93.16 & 93.38 & 73.07 & 79.32 \\ YOLOv51 & 86.77 & 92.67 & 91.22 & 91.72 & 71.15 & 78.05 \\ YOLOv5x & 87.74 & 93.81 & 90.58 & 91.96 & 72.19 & 79.32 \\ YOLOv5s-scr & 90.73 & 94.99 & 93.61 & 94.17 & 69.16 & 77.61 \\ YOLOv5m-scr & 90.96 & 95.51 & 93.50 & 94.40 & 69.59 & 77.96 \\ YOLOv5l-scr & 87.25 & 95.41 & 88.24 & 91.45 & 68.39 & 76.57 \\ YOLOv5x-scr & 86.18 & 94.32 & 87.57 & 90.54 & 68.32 & 75.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Synthetic character detection results on the test set. The performance is measured in terms of character detection accuracy (Acc), precision (P), recall (R), F1 score (F1), and mean intersection over union before (IoU0) and after (IoU) shrinking the detected bounding boxes. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Acc** & **P** & **R** & **F1** & **IoU0** \\ \hline YOLOv5s & 94.73 & 98.07 & 96.47 & 96.95 & 78.19 & 83.18 \\ YOLOv5m & 96.43 & 98.64 & 97.11 & 97.85 & 78.83 & 85.13 \\ YOLOv5l & 93.51 & 98.09 & 93.91 & 95.85 & 75.61 & 83.93 \\ YOLOv5x & 93.01 & 97.85 & 93.47 & 95.42 & 75.67 & 83.77 \\ YOLOv5s-scr & 96.53 & 97.89 & 97.98 & 97.93 & 73.73 & 77.56 \\ YOLOv5m-scr & 94.34 & 98.09 & 95.37 & 96.67 & 75.77 & 79.08 \\ YOLOv5l-scr & 93.81 & 98.43 & 95.59 & 96.39 & 75.25 & 77.68 \\ YOLOv5x-scr & 88.82 & 94.69 & 95.30 & 93.84 & 71.07 & 74.68 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Adapted character detection results on the test set. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Acc** & **P** & **R** & **F1** & **IoU0** \\ \hline Printed (1 class) & 96.43 & 98.64 & 97.11 & 97.85 & 85.13 \\ Printed (4855 classes) & 95.72 & 98.31 & 96.70 & 97.47 & 85.26 \\ Handwritten (CASIA) & 95.68 & 98.26 & 96.49 & 97.35 & 85.54 \\ Handwritten (Nom) & 94.30 & 97.94 & 94.96 & 96.38 & 85.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Adapted character detection results with YOLOv5m on the test set. Comparison of printed and handwritten page synthetization (see Figure 2(b)).
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 4 Results and Discussion
### POI Recognition  
The results for each point of interest, as detailed in Appendix A, are summarized in the scores presented in Table 1. The recall rate shows that 80% of all broadcasted POIs are correctly detected, while the precision of about 79% indicates that few misdetections are made. To elaborate on this number, all broadcasted POI detections were correct, but a few false positives were generated at times where the helicopter view was not being broadcasted. On average, each POI is recognized 15 s before the director switches to the helicopter view, demonstrating an effective and timely recognition process. When using the MedianFlow tracker, POIs are followed for approximately 6 s after the director switches away from the helicopter view. This results in a completeness of 95%, ensuring that POIs are monitored nearly throughout the entire shot. The SAM 2 tracker improves this with a  
Figure 10: Graphical overview of the calculated metrics per POI.  
mean end offset of almost 6 s longer, showing the effectiveness of this tracker. The mean completeness stays the same since the missing 5% is due to a late detection of a POI.  
Examining the failure cases more closely, we observe that the _Sint-Sebastiaankerk_ in _Michelbeke_ was not recognized because the saliency model did not allocate sufficient attention to it, thereby impeding the subsequent stages of the recognition pipeline. In another instance, the first appearance of the chapel on top of the _Muur van Geraardsbergen_ fails at an even earlier stage of the pipeline. This failure occurred because the POI was filmed from a considerable distance, well beyond the 500 m range. As discussed in Section 3.2.1, increasing the range would resolve this issue, but it would also result in a less resource-efficient system.
## 2 Refrigeration Cycles, Models and Assumptions  
The cascade refrigeration cycle and the mechanical subcooling (MS) cycle can be represented by the refrigeration scheme detailed in Figure 1. Essentially, both systems include these main components with the following operating characteristics:  
* A main cycle, working with CO\({}_{2}\) as refrigerant, which absorbs energy from the cold source.
* A CO\({}_{2}\) compressor, subcritical-rated for the cascade configuration and transcritical-rated for the MS configuration.
* A CO\({}_{2}\) gas-cooler, which performs heat rejection to the hot sink.
* A second CO\({}_{2}\) heat exchanger acting as CO\({}_{2}\) condenser for the cascade system and as CO\({}_{2}\) subcooler for the MS configuration.
* An expansion system: composed of the'vessel + expansion valve' for the cascade configuration and of a 'back-pressure + vessel + expansion valve' for the MS cycle.
* An auxiliary single-stage refrigeration cycle: working with another refrigerant (HCs, HFOs, NH\({}_{3}\), HFCs) as high temperature cycle in the cascade configuration and as dedicated mechanical subcooling cycle for the MS configuration. The auxiliary system, whose refrigerant is not distributed to the cooling appliances, absorbs heat from the intermediate temperature level and performs heat rejection to the same hot sink as the main cycle. In the cascade configuration, the auxiliary cycle performs CO\({}_{2}\) condensation and in the MS it only subcools the CO\({}_{2}\) at the exit of the gas-cooler.  
The operation of the cycle of Figure 1 as cascade or as MS cycle will depend on the hot sink temperature (T\({}_{\rm H}\)) and on the high-pressure fixed by the back-pressure valve (P\({}_{\rm high}\)), as detailed in the following subsections.
## 2 Preliminaries  
For convenience, let \(\#h\) be the number of elements in \(h\). [PERSON] and [PERSON] [21] also used [PERSON]'s scale 1-9 to express the membership degree in HFS, and provided the definition of a hesitant multiplicative set (HMS).  
Definition 1: [21] Consider that a fixed set \(X\), a hesitant multiplicative set on \(X\), is defined by a function that gives a subset of \([1/9,9]\) when applied to \(X\).  
For easy understanding, [PERSON] and [PERSON] [21] represented the HMS as  
\[M=\{<x,b_{M}(x)>|x\in X\}\]  
where \(b_{M}(x)\) is the set of values from \([1/9,9]\) according to [PERSON]'s scale [19], indicating possible multiplicative membership degrees of \(x\in X\) of the set \(M\). They also called \(b=b_{M}(x)\) a hesitant multiplicative element (HME) and \(M\) the set of hesitant fuzzy elements. For further work, let \(\#b\) denote the number of elements in the HME \(b\).  
Example 1: Consider \(X=\{x_{1},\,x_{2},\,x_{3}\}\), \(E=\{<x_{1},\,\{0.2,\,0.4,\,0.9\}>,\,<x_{2},\,\{0.5\}>,\,<x_{3},\,\{0.1,0.6\}>\}\), \(M=\{<x_{1},\,\{1/6,\,2\}>,\,<x_{2},\,\{1,\,4,\,7\}>,\,<x_{3},\,\{1/3\}>\}\), \(h=\{0.2,\,0.4,\,0.9\}\), and \(b=\{1,\,4,\,7\}\). Then, \(E\) is HFS, \(M\) is HMS, \(h\) is HFE, and \(b\) is HME.  
The common problem in working in a hesitant fuzzy environment is that there is a different number of elements in different HFEs or HMEs. Take a fixed set \(X=\{x_{1},\,x_{2},\,x_{3},\,...,\,x_{n}\}\) of alternatives. The DMs can give their preferences as either HFE or HME. [PERSON] and [PERSON] [21] introduced the hesitant fuzzy preference relation (HFPR) and the hesitant multiplicative preference relation (HMPR):  
Definition 2: [21] For a fixed set \(X=\{x_{1},x_{2},...,x_{n}\}\), an HMPR is expressed by a matrix \(B=(b_{ij})_{n\times n}\subseteq X\times X\), where \(b_{ij}=\{b_{ij}^{s}\mid s=1,\,2,\,3,\,...,\,\#b_{ij}\}\) is an HFE giving all the possible preference degrees of the alternative \(x_{i}\) over \(x_{j}\) in [PERSON]'s scale. Additionally, \(b_{ij}\) satisfies the following conditions for all \(i,j\in N\):  
\[b_{ij}^{\sigma(s)}\times b_{ji}^{(\#b_{ij}-\sigma(s)+1)}=1,\,b_{ii}=\{1\},\,\#b _{ij}=\#b_{ji}\]  
where \(b_{ij}^{\sigma(s)}\) is the sth smallest value in \(b_{ij}\), and the elements of \(b_{ij}\) are arranged in increasing order.  
To make hesitant fuzzy sets more compatible with reality, [PERSON] and [PERSON] [15] defined the hesitant probabilistic fuzzy element (HPFE) and the hesitant probabilistic fuzzy set (HPFS).  
**Definition 3**.: _[_15_]_ _Consider a fixed set \(R\). The HPFS on \(R\) is defined as a mathematical symbol:_  
\[H_{P}=\{h(\gamma_{i}|p_{i})|\gamma_{i},p_{i}\} \tag{1}\]  
_where \(h(\gamma_{i}|p_{i})\) is the HPFE consisting of the elements of the form \(\gamma_{i}|p_{i}\), expressing the hesitant fuzzy information with probabilities to the set \(H_{p},0\leq\gamma_{i}\leq 1,i=1,2,3,...,\#h\), where \(\#h\) is the number of elements in \(h(\gamma_{i}|p_{i})\), \(p_{i}\in[0,1]\) is the respective hesitant probability for \(\gamma_{i}\), and \(\sum\limits_{i=1}^{\#h}p_{i}=1\)._
# Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?
## 5 Methods for Congestion Learning
### Recurrent Neural Networks (RNN)  
Considering the growing relevance of neural-network algorithms, we have also evaluated the performance of this type of solutions. In particular, we have exploited a Recurrent Neural Network (RNN), with Long Short-Term Memory (LSTM) neurons. The model consists of two LSTM layers, of 100 neurons each, followed by a _fully connected_ layer embracing 50 neurons. Opposed to previous algorithms, this solution yields an actual congestion value, rather than a classification. The predicted continuous values are then grouped, so that the performance of the RNN can be compared with those obtained by the other solutions.
## 5 Results
### Modelling Gully Erosion Susceptibility  
This study employed the MaxEnt method for modelling the gully erosion susceptibility within the Mesima catchment (southern Italy). To implement the susceptibility model, 12 predisposing factors (lithology, soil texture, soil bulk density, land use, drainage density, slope gradient, slope aspect, plan curvature, LS, SPI, TPI, and TWI) and a gully's inventory were used as input variables. Additionally, a 10-fold cross-validation approach was applied to test the predictive capability and stability (robustness) of the MaxEnt algorithm. The dataset was split into ten folds, nine of which were used for the calibration and one for the validation.  
The findings of the MaxEnt algorithm performance for the calibration and validation phases are summarised in Table 2. The results presented in Table 2 allowed performance results obtained by each one of the 10-fold cross-validation to be compared. Concerning the calibration phase, the results of the performance metrics, i.e., accuracy, kappa coefficient, and AUC, always have excellent goodness fit of the prediction model, with a mean value of kappa coefficient and AUC of 0.877 and 0.964, respectively. As for the validation phase, the results showed a similar ranking pattern to the calibration phase (Table 2) and, consequently, the susceptibility model displayed excellent predictive skill (mean AUC = 0.952). In addition, the low values of standard deviation, obtained for all three performance parameters (Table 2), showed that the effects of different calibration and validation sets on the MaxEnt method accuracy are not significant, highlighting a strong stability (robustness) of the susceptibility model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Predisposing Factors**} & \multicolumn{2}{c}{**Multi-Collinearity**} \\ \cline{2-3}  & **VIF** & **TOL** \\ \hline Lithology & 1.66 & 0.60 \\ Soil texture & 1.56 & 0.64 \\ Soil bulk density & 2.07 & 0.48 \\ Land use & 1.78 & 0.57 \\ Drainage density & 2.10 & 0.48 \\ Slope gradient & 2.96 & 0.34 \\ Slope aspect & 1.34 & 0.74 \\ LS factor & 3.10 & 0.32 \\ Plan curvature & 1.52 & 0.66 \\ SPI factor & 1.96 & 0.51 \\ TPI factor & 2.18 & 0.46 \\ TWI factor & 1.58 & 0.63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Multi-collinearity results among the predisposing factors selected for modelling gully erosion susceptibility.