esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Raw Material and Thermal Treatment  
The first step in the extraction of calcium phosphate from fish waste was to evaluate the yield values of wet and dry bones recovered from discards and fish waste. These values are summarized in Table 1.  
As expected, the main component of the proximal analysis was the ashes (mineral fraction); however, there was still a considerable percentage of organic matter in the samples. The colour and smell of the samples confirm that they kept a significant amount of organic matter, over 30%, as can be seen in Table 1. Therefore, a thermal treatment was applied to all species to eliminate these remaining organic parts from the samples.  
After the thermal process, a significant mass reduction was also observed in the samples of every selected fish species. The mass loss in the heat treatment process is listed in Table 2.  
This fact showed that most of the organic compounds that remained in the initial powder were removed from the samples. The change in colour after pyrolysis (see Figure 2) proves the importance of this step to the success of the process. Moreover, the mass loss  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Y\({}_{\textbf{w}}\)** & **Y\({}_{\textbf{d}}\)** & **Hu (\%)** & **Ash (\%)** & **OM (\%)** \\  & **(\%, _ww/w_)** & **(\%, _dw/w_)** & **Hu (\%)** & **Ash (\%)** & **OM (\%)** \\ \hline
**Scorp** & 11.2 \(\pm\) 0.4 & 4.7 \(\pm\) 0.1 & 1.70 \(\pm\) 0.32 & 67.02 \(\pm\) 0.46 & 31.29 \(\pm\) 0.78 \\ \hline
**Trach** & 8.5 \(\pm\) 0.7 & 3.7 \(\pm\) 0.1 & 4.03 \(\pm\) 0.89 & 62.33 \(\pm\) 0.92 & 33.64 \(\pm\) 0.03 \\ \hline
**Salmo** & 11.6 \(\pm\) 0.4 & 6.0 \(\pm\) 0.5 & 2.80 \(\pm\) 0.29 & 59.24 \(\pm\) 0.82 & 37.96 \(\pm\) 1.11 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Yields of bones recovered from the initial substrate.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fish Sample** & **Temperature of Thermal Treatment (\({}^{\circ}\)C)** & **Mass Loss (\%)** \\ \hline _Scorpaena scrofa_ & 750 & 35.7 \\ \hline _Trachurus trachurus_ & 750 & 39.9 \\ \hline _Salmo salar_ & 750 & 45.3 \\ \hline _Scorpaena scrofa_ & 950 & 35.9 \\ \hline _Trachurus trachurus_ & 950 & 40.4 \\ \hline _Salmo salar_ & 950 & 45.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mass loss of fish bones at 750 and 950 \({}^{\circ}\)C.  
percentage remains practically constant between both temperature levels. Hence, organic mass is interpreted to be fully eliminated at 750 \({}^{\circ}\)C.  
Scanning electron microscopy (SEM) images reveal porous-shaped structures without significant differences between the three fish species under study and the two temperatures of the thermal treatment. However, an additional porosimetry study is required to measure the pore size distribution, porosity, and the specific surface area, as well as determine the relationship between the calcination temperature and porosity. As shown in Figure 3, granules of irregular morphologies can be found in samples from all three studied species. Furthermore, some granules show a porous structure (Figure 3b-e).
# Reliable Positioning and mmWave Communication via Multi-Point Connectivity +
## 3 PEB Computation and Channel Estimate
### FIM: Channel Parameters
Using Equation (19), the noiseless received signal \(r_{n}\) at \(n\)th carrier can be written as  
\[r_{n} = \sqrt{\frac{P_{k}}{N}}\sum_{n=1}^{N}\mathbf{w}_{b}^{\mathrm{H}} \mathbf{h}_{b,k}e^{-j2\pi n\Delta_{f}\tau_{b,k}} \tag{21}\] \[= \sqrt{\frac{P_{k}}{N}}\sum_{n=1}^{N}g_{b,k}\mathbf{w}_{b}^{ \mathrm{H}}\mathbf{a}_{R}(\phi_{b,k})e^{-j2\pi n\Delta_{f}\tau_{b,k}}\] \[= \sqrt{\frac{P_{k}}{N}}\sum_{n=1}^{N}g_{b,k}q_{b,k}e^{-j2\pi n \Delta_{f}\tau_{b,k}}\.\]  
As given in Equation (18), to derive the expression for the first entry \(J_{\tau_{b,k}\tau_{b,k}}\) of the FIM in Equation (17), first we need to compute \(\frac{\partial}{\partial\tau_{b,k}}r_{n}\), given as  
\[\frac{\partial}{\partial\tau_{b,k}}r_{n}=\sqrt{\frac{P_{k}}{N}}\Big{(}-j2\pi n \Delta_{f}g_{b,k}q_{b,k}\,e^{-j2\pi n\Delta_{f}\tau_{b,k}}\Big{)}. \tag{22}\]  
Using Equation (22) in (18), \(J_{\tau_{b,k}\tau_{b,k}}\) is computed as  
\[J_{\tau_{b,k}\tau_{b,k}}=\frac{P_{k}^{c}}{\sigma^{2}}|g_{b,k}|^{2}(2\pi\Delta_ {f})^{2}n^{2}|q_{b,k}|^{2},\] (23a) following the same steps, all other entries in Equation ( 17 ) are computed as \[J_{\phi_{b,k}\phi_{b,k}} = \frac{P_{k}^{c}}{\sigma^{2}}|g_{b,k}|^{2}|q_{b,k}|^{2}\, \tag{23b}\] \[J_{\phi_{b,k}\tau_{b,k}} = \frac{P_{k}^{c}}{\sigma^{2}}\Re\{g_{b,k}^{*}q_{b,k}q_{b,k}^{*}\}\,\] (23c) \[\boldsymbol{\eta}_{b,k}=  
where \(u(t)\) is the noiseless part of the received signal during the training phase, given by  
\[u_{b,k}(t)=\mathbf{w}_{b}^{\mathrm{H}}(t)\mathbf{h}_{b,k}x_{k}^{p}(t-\tau_{b,k}) \,\ \forall k,\ \forall b. \tag{19}\]0 (23d) \[\boldsymbol{\eta}_{b,k}=  
where \(u(t)\) is the noiseless part of the received signal during the training phase, given by  
\[u_{b,k}(t)=\mathbf{w}_{b}^{\mathrm{H}}(t)\mathbf{h}_{b,k}x_{k}^{p}(t-\tau_{b,k}) \,\ \forall k,\ \forall b. \tag{19}\]1 (23e) \[\boldsymbol{\eta}_{b,k}=  
where \(u(t)\) is the noiseless part of the received signal during the training phase, given by  
\[u_{b,k}(t)=\mathbf{w}_{b}^{\mathrm{H}}(t)\mathbf{h}_{b,k}x_{k}^{p}(t-\tau_{b,k}) \,\ \forall k,\ \forall b. \tag{19}\]2  
where \(q_{b,k}=\mathbf{w}_{b}^{\mathrm{H}}\mathbf{a}_{R}(\phi_{b,k})\) and \(\dot{q}_{b,k}=\mathbf{a}_{R}^{\mathrm{H}}(\phi_{b,k})\mathbf{D}_{b,k}^{\mathrm{ H}}\mathbf{w}_{b}\), in which \(\mathbf{D}\ \in\ \mathbb{C}^{N_{R}\times N_{R}}\) is a diagonal matrix with the \(l\)th entry given by \(D(l)\ =\ j\pi(l-1)cos(\phi_{b,k})\). \(P_{k}^{c}=P_{k}/N\) is the power per sub-carrier from user \(k\), \(\Delta_{f}\) is inter-carrier spacing and \(n\) denotes the sub-carrier index in the OFDM frame. All the remaining entries in Equation (17) are zero.
# A Novel Deep Learning Approach for Machinery Prognostics Based on Time Windows
## 3 Experiment and Analysis  
### Experimental System  
NASA C-MAPSS dataset generated by the aircraft engine simulation models is often used for comparison of different approaches [29]. The C-MAPSS dataset contains four sub-datasets that correspond to different simulation conditions and engine failure modes. Each sub-dataset has one training set and one testing set. The training set contains temporal data of 21 outputs of the aircraft engine simulation model from the unknown state of the model to the stage of failure. These 21 outputs include diverse types of data such as temperature data, speed data and pressure data at different positions of the engines, etc. The information of the C-MAPSS dataset is given in Table 2. The failure modes of FD001 and FD002 are only high-pressure compressor (HPC) degradation, while the failure modes of FD003 and FD004 include high-pressure compressor degradation and fan degradation.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Layers**} & \multirow{2}{*}{**Input**} & \multirow{2}{*}{**Output**} & \multicolumn{6}{c}{**The Number of the Different Kernels**} \\ \cline{4-7}  & & & **-** & **\(\mathbf{1\times 1}\)** & **\(\mathbf{1\times 1}\)** & **\(\mathbf{1\times 1}\)** & **\(\mathbf{1\times 1}\)** & \\  & & & \(\mathbf{1\times 1}\)** & **\(\mathbf{3\times 1}\)** & **\(\mathbf{5\times 1}\)** & **\(\mathbf{7\times 1}\)** & **\(\mathbf{9\times 1}\)** & **Sum** \\ \hline First convolution–pooling layer & \(30\times\mathrm{S}\times 8\) & \(15\times\mathrm{S}\times 80\) & - & 4 & 4 & 4 & 4 & \\  & & 16 & 16 & 16 & 16 & 16 & 80 \\ \cline{3-7} Second convolution–pooling layer & \(15\times\mathrm{S}\times 80\) & \(7\times\mathrm{S}\times 160\) & - & 16 & 16 & 16 & 16 & \\  & & 32 & 32 & 32 & 32 & 32 & 160 \\ Third convolution–pooling layer & \(7\times\mathrm{S}\times 160\) & \(3\times\mathrm{S}\times 256\) & - & 32 & 32 & 32 & - & \\  & & 64 & 64 & 64 & 64 & - & 256 \\ Fourth convolution layer & \(3\times\mathrm{S}\times 256\) & \(3\times\mathrm{S}\times 256\) & - & 64 & - & - & - & \\  & 128 & 128 & - & - & - & - & 256 \\ \cline{3-7} Subsequent layers & \(3\times\mathrm{S}\times 256\) & \(3\times\mathrm{S}\times 256\) & - & 64 & - & - & - & \\  & & 128 & 128 & - & - & - & 256 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of different kernels in each layer (time window 30).  
As shown in Table 3, only 14 sets among the 21 outputs were selected for the study, the others were constant during the life of the engines from health to failure and did not provide any valuable information for RUL prediction [23]. Therefore, as shown in Table 3, 14 sets of time-varying variables with indices 2, 3, 4, 7, 8, 9, 11, 12, 13, 14, 15, 17, 20 and 21 were selected in the study. The state of the aircraft engine was healthy during the early operation period. In the initial stage, it was assumed that the RUL of the aircraft engine was a constant value. Based on the degradation mechanism of aircraft engines in this dataset [23], the constant value of RUL in the early stage was set to 125 in this paper.
# Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management
## 2 Review of the State-of-the-Art Technology
### Detection by Acoustic Signals
In the array, it is important to know the number of microphones, which usually ranges from 4 to 16 microphones (in steps of two), and the distance between sensors, which usually ranges from 0.3 to 0.6 meters in increments of 0.05 meters. A commercial C-UAS solution from Dedrone enterprise is Dedrone DroneTracker [36], which is a multiple-sensor unit that may integrate an ultrasonic audio detector. Its specifications are shown in Table 8.
# CA-CGNet: Component-Aware Capsule Graph Neural Network for Non-Rigid Shape Correspondence
## 4 Experiments and Evaluation
### Correspondence Results
In order to clearly demonstrate the corresponding effect, we visualize the correspondence results of different methods on these three datasets, as shown in Figure 12. Our method shows more accurate matching results, which indicates that our method has better generalization. Figure 10: Quantitative correspondence performance of different methods on the original FAUST dataset and the remeshed FAUST dataset. (**a,b**) are the evaluation on the original FAUST dataset, (**c,d**) are the evaluation on the remeshed FAUST dateset. Considering the large gap between the animal model and the human model, the proposed method is tested on the animal model in the TOSCA dataset to verify the adaptability of the method. Figure 13 shows the corresponding results of reference shapes and deformable shapes on the non-human models. By contrast with FMNet, SURFMNet, and MGCN, our method is closer to ground truth in corresponding details. Figure 11: Quantitative correspondence performance of proposed methods on three remeshed datasets. (**a**) is the evaluation on the remeshed SCAPE dataset, (**b**) is the evaluation on the remeshed TOSCA dataset, (**c**) is the evaluation on the remeshed KIDS dataset. Figure 12: Visualization of the corresponding results of our method and other algorithms on the human model in remeshed SCAPE, TOSCA, and KIDS datasets. (**a**) SCAPE. (**b**) and (**c**) TOSCA. (**d**,**e**) KIDS. Corresponding points show the same color. In Table 4, we show the average geodesic error of several methods on four resampled datasets. The bold represents the minimum error in each column. In the remeshed FAUST dataset, the error of our method is 78.72% less than FMNet, 56.60% less than SURFMNet, and 47.43% less than MGCN. In remeshed SCAPE, it is 48.88% less than FMNet, 45.42% less than SURFMNet, and 27.71% less than MGCN. In the remeshed KIDS dataset, it is 62.32% less than FMNet, 65.66% less than SURFMNet, and 57.74% less than MGCN. In remeshed TOSCA, it is 74.65% less than FMNet, 76.16% less than SURFMNet, and 71.87% less than MGCN. It can be found that our model achieves better results than those of the compared methods, which demonstrates that our model is more robust on non-rigid 3D models with lower resolution.
###### Abstract  
This paper describes a practical approach to the transformation of Base Transceiver Stations (BTSs) into scalable and controllable DC Microgrids in which an energy management system (EMS) is developed to maximize the economic benefit. The EMS strategy focuses on efficiently managing a Battery Energy Storage System (BESS) along with photovoltaic (PV) energy generation, and non-critical load-shedding. The EMS collects data such as real-time energy consumption and generation, and environmental parameters such as temperature, wind speed and irradiance, using a smart sensing strategy whereby measurements can be recorded and computing can be performed both locally and in the cloud. Within the Spanish electricity market and applying a two-tariff pricing, annual savings per installed battery power of 16.8 euros/kW are achieved. The system has the advantage that it can be applied to both new and existing installations, providing a two-way connection to the electricity grid, PV generation, smart measurement systems and the necessary management software. All these functions are integrated in a flexible and low cost HW/SW architecture. Finally, the whole system is validated through real tests carried out on a pilot plant and under different weather conditions.  
base transceiver stations (BTS); microgrid; green communications; energy management systems (EMS); IEC61850 standard; embedded systems for Internet of Things (IoT); monitoring and control systems; photovoltaic distributed generation
## 2 Basic Theory
### Common Approach of TDG Methods
\begin{array}{c}\int_{x^{(k-1)}}^{x^{(k)}}\boldsymbol{\Phi}\boldsymbol{U}^ {n+1}\text{d}x=\int_{x^{(k-1)}}^{x^{(k)}}\boldsymbol{\Phi}\boldsymbol{U}^{n} \text{d}x-\Delta t\,\boldsymbol{\Phi}\boldsymbol{f}^{\mu}|_{x^{(k-1)}}^{x^{(k) }}+\Delta t\int_{x^{(k)}}^{x^{(k)}}\frac{\partial\boldsymbol{\Phi}}{\partial x }\overline{f}^{\mu}\text{d}x\\ +\frac{\Delta t^{2}}{2}\int_{x^{(k-1)}}^{x^{(k)}}\boldsymbol{\Phi}\overline{u }^{n}_{tt}\text{d}x+\frac{\Delta t^{2}}{6}\int_{x^{(k-1)}}^{x^{(k)}}\boldsymbol {\Phi}\overline{u}^{n}_{tttt}\text{d}x.\end{array}  \tag{6}\]  
To handle the high-order temporal derivatives, \(\partial^{2}\boldsymbol{u}/\partial t^{2}\) and \(\partial^{3}\boldsymbol{u}/\partial t^{3}\), appearing in Equation (5), the flux Jacobian matrix \(\boldsymbol{A}(\boldsymbol{u})\) is defined as:  
\[\frac{\partial\overline{f}}{\partial\boldsymbol{u}}=\boldsymbol{A}(\boldsymbol {u}), \tag{7}\]  
We can use:  
\[\frac{\partial\overline{f}}{\partial t}=\frac{\partial\overline{f}}{\partial \boldsymbol{u}}\frac{\partial\boldsymbol{u}}{\partial t}=\boldsymbol{A}\frac{ \partial\overline{u}}{\partial t}=-\boldsymbol{A}\frac{\partial\overline{f}}{ \partial x}. \tag{8}\]  
From Equation (8), the time-derivative terms \(\overline{u}^{n}_{tt}\) and \(\overline{u}^{n}_{ttt}\) can be transformed into spatial derivatives by \(\boldsymbol{A}(\boldsymbol{u})\) and its associated operators. The benefit of this approach is that all the temporal derivative terms in Equation (6) are converted into spatial derivatives,making it possible to obtain a higher-order estimate of the solution by one-step spatial integration. However, it can also be observed that increasing the order of the time Taylor expansion (using the Lax-Wendroff-type formulation) makes the operators related to \(\mathbf{A}(\mathbf{u})\) more complex and consequently increases the number of numerical flux computations. To address this issue, the following sections will demonstrate a detailed explanation of how the optimized TDG method differs from the conventional TDG method, along with an outline of its computational procedure.
## 3 Experimental Results
Another advantage of the proposed quench detection method is that the quench detection time can be reduced by changing the window size (N) corresponding to an integer multiple of the EMF frequency at which the FFT analysis is performed. That is, by increasing the number of FFT analyses that can be performed at the same time, it is possible to increase the probability of detecting a quench signal. Figure 11: Experimental results: (**a**) voltage signal measured at \(R_{ext}\), (**b**) FFT analysis in the superconducting state, and (**c**) FFT analysis in the normal state.
## 3 Proposed Calibration Process
### Step 2: Image Processing Using the Cyclorama Illumination Analyzer (CYIA)
The size, position of the centroids and average color of each area is statistically analyzed, calculating the following data:  
* The size of the biggest region in the photograph. * The size of the smallest region in the photograph. * The size of the regions in the ideal image. * The average size of the regions in the photograph. * The biggest size difference between a region in the photograph and the ideal one. * The smallest size difference between a region in the photograph and the ideal one. * The standard deviation in the size of the regions. * The maximum distance between a centroid in the photograph and its correspondent in the ideal image. * The minimum distance between a centroid in the photograph  
* The standard deviation of the distance between the centroids and the geometric centers in the regions. Moreover, it gives for every region its size, its energy, the position of the centroid, the size of the corresponding region in the ideal image and the size difference between the ideal and the actual region size. Four images are generated in order to show this information and ease the visual analysis. * A cyclorama photograph including the regions and centroids. * An ideal image including the regions and centroids. * A monochrome luminance photography. * An image with arrows joining the centroids of the ideal image and their correspondences in the real one. With all this information and their visual representation, a non-expert user is able to visually interpret the images and conclude if the illumination is well calibrated or if there are over lighted or shadowed areas in the cyclorama based on the proposed reinterpretation of the median cut sampling algorithm results.
## 3 Results from the Numerical Calculations
### Results from Calculation of Lateral Forces between Rails and Vertical Columns of the Wagon
The results from the calculation of these additional forces for different wagons of a specialized train composition are presented in the next subsection. \begin{table}
\begin{tabular}{c c c c c} \hline
**Point** & \begin{tabular}{c} **Coordinate \(x\)** \\ **(m)** \\ \end{tabular} & \
\begin{tabular}{c} **Coordinate \(y\) = \(u_{iy}\)** \\ **(m)** \\ \end{tabular} &
\begin{tabular}{c} **Reaction** \\ **Identifier (Unit)** \\ \end{tabular} & **Value** \\ \hline A\({}_{0}\) & 0 & 0 & M\({}_{\text{A}0}\) (kN\(\cdot\)m) & \(-20.9627\) \\ A\({}_{0}\) & 0 & 0 & F\({}_{\text{A}0}\) (kN) & \(-20.9627\) \\ A\({}_{1}\) & 3.000 & 0 & F\({}_{\text{A}1}\) (kN) & \(15.4067\) \\ A\({}_{2}\) & 8.190 & \(-0.168\) & F\({}_{\text{A}2}\) (kN) & \(28.7388\) \\ A\({}_{3}\) & 12.690 & \(-0.525\) & F\({}_{\text{A}3}\) (kN) & \(-45.0839\) \\ A\({}_{4}\) & 17.190 & \(-0.883\) & F\({}_{\text{A}4}\) (kN) & \(22.1165\) \\ A\({}_{5}\) & 22.380 & \(-1.540\) & F\({}_{\text{A}5}\) (kN) & \(21.0936\) \\ A\({}_{6}\) & 26.880 & \(-2.320\) & F\({}_{\text{A}6}\) (kN) & \(-42.6837\) \\ A\({}_{7}\) & 31.380 & \(-3.100\) & F\({}_{\text{A}7}\) (kN) & \(21.6934\) \\ A\({}_{8}\) & 36.570 & \(-4.241\) & F\({}_{\text{A}8}\) (kN) & \(20.7446\) \\ A\({}_{9}\) & 41.070 & \(-5.436\) & F\({}_{\text{A}9}\) (kN) & \(-43.0170\) \\ A\({}_{10}\) & 45.570 & \(-6.631\) & F\({}_{\text{A}10}\) (kN) & \(25.6134\) \\ A\({}_{11}\) & 50.760 & \(-8.245\) & F\({}_{\text{A}11}\) (kN) & \(4.9837\) \\ A\({}_{12}\) & 56.260 & \(-10.200\) & F\({}_{\text{A}12}\) (kN) & \(-8.6432\) \\ \hline \end{tabular}
\end{table} 
Table 1: Coordinates of points A\({}_{\text{i}}\) along the bending line and values of reaction forces (and moment \(M_{\text{A}0}\)) in points A\({}_{\text{i}}\) without horizontal clearance between the rails and the support stanchions (\(\Delta h=0\)).
# ChurnKB: A Generative AI-Enriched Knowledge Base for Customer Churn Feature Engineering
## 4 Evaluation
### Evaluating Churn Classifier Performance
#### 4.2.3 Results  
For experimentation and evaluation, the data were processed through a PyCaret ([[https://github.com/pycaret/pycaret](https://github.com/pycaret/pycaret)]([https://github.com/pycaret/pycaret](https://github.com/pycaret/pycaret)) (accessed on 13 April 2025)) pipeline to compare different machine learning models and assess the effect of ChurnKB-enabled features on their analytical performance. PyCaret does not provide built-in support for deep learning models such as TensorFlow or PyTorch-based neural networks in its standard modules. However, it includes various machine learning models via scikit-learn, XGBoost, LightGBM, CatBoost, logistic regression, and decision trees. To further validate the effectiveness of ChurnKB features, we also experimented separately with neural network-based models. For performance comparison, we report only the three top-performing models in each scenario due to space limitations, along with Multilayer Perceptron (MLP) and TabNet, which are designed for tabular data. These deep learning models are more suitable for tabular data than traditional recurrent architectures like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs), which are optimized for sequential data. In addition, we utilised 31 sub-instances from ChurnKB, which can be supplied with an appropriate lexical source.  
We compared three scenarios, i.e., (A) a dataset with only numerical data, (B) a dataset with only textual data, and (C) a dataset that includes both textual and numerical data.  
The evaluation results are reported in Table 2 (A), (B), and (C), respectively. Among various classification models, XGBoost appears consistently across all comparisons, with its performance improving when textual features are included. In the first scenario, XGBoost achieves an F1 score of 0.5752, while in the second scenario, its performance improves to 0.6698. The most notable performance boost occurs in the third scenario, where the inclusion of both numerical and textual features leads to an F1 score of 0.7891. These results highlight the positive impact of textual features on predictive performance. Given XGBoost's strong and consistent performance across all settings, we selected it as the target classifier for our research.  
All the comparison results indicate that applying ChurnKB-enabled feature engineering positively impacts the performance of different classifiers, specifically the XGBoost model. While MLP [77] and TabNet [78] demonstrate competitive performance, XGBoost remains the highest-performing model across all evaluation metrics, including F1 score. The superior performance of XGBoost can be attributed to its ability to efficiently capturing complex feature interactions. Additionally, XGBoost requires fewer computational resources and less training time compared to deep learning models, making it a more practical choice for real-world churn prediction applications.