esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Decision Forest Models for Customer Churn Prediction
###### Abstract  
Customer churn is a critical issue impacting enterprises and organizations, particularly in the emerging and highly competitive telecommunications industry. It is important to researchers and industry analysts interested in projecting customer behavior to separate churn from non-churn consumers. The fundamental incentive is a firm's intent desire to keep current consumers, along with the exorbitant expense of gaining new ones. Many solutions have been developed to address customer churn prediction (CCP), such as rule-based and machine learning (ML) solutions. However, the issue of scalability and robustness of rule-based customer churn solutions is a critical drawback, while the imbalanced nature of churn datasets has a detrimental impact on the prediction efficacy of conventional ML techniques in CCP. As a result, in this study, we developed intelligent decision forest (DF) models for CCP in telecommunication. Specifically, we investigated the prediction performances of the logistic model tree (LMT), random forest (RF), and Functional Trees (FT) as DF models and enhanced DF (LMT, RF, and FT) models based on weighted soft voting and weighted stacking methods. Extensive experimentation was performed to ascertain the efficacy of the suggested DF models utilizing publicly accessible benchmark telecom CCP datasets. The suggested DF models efficiently distinguish churn from non-churn consumers in the presence of the class imbalance problem. In addition, when compared to baseline and existing ML-based CCP methods, comparative findings showed that the proposed DF models provided superior prediction performances and optimal solutions for CCP in the telecom industry. Hence, the development and deployment of DF-based models for CCP and applicable ML tasks are recommended.  
telecommunication; customer churn; decision forest; machine learning; ensemble
## 8 Conclusions  
This work addresses a fundamental challenge in stereo-based image-based visual servoing (IBVS): the presence of local minima arising from the overdetermined nature of aligning the 6-DOF camera poses with a higher number of image feature constraints. Through a formal reformulation of the stereo camera pose estimation problem as a Perspective-3-Point (P3P) case, we clarify the minimum information needed to uniquely resolve the camera pose and highlight the risk of instability when controlling more image features than available degrees of freedom. To overcome this, a cascaded control framework was introduced, combining a feedforward joint-space controller with an adaptive Youla-parameterized feedback loop. The feedforward loop accelerates convergence by directly commanding joint configurations associated with the desired pose, while the feedback loop ensures robustness and fine-tuned tracking under disturbances or modeling uncertainties. When compared to existing solutions, the proposed method offers a number of compelling advantages. Unlike hybrid switched controllers, which require explicit mode-switching logic and may suffer from discontinuities, our approach provides a unified framework that avoids local minima without controller mode changes. In contrast to 2% D visual servoing, which blends pose and image features into a hybrid Jacobian, our design maintains the pure IBVS structure while preserving real-time feasibility. Moreover, compared to model predictive control (MPC) approaches that require high-frequency optimization, our strategy achieves comparable performance using linear control theory and avoids excessive computational overhead. The Table 2 below summarizes the key differences between the four approaches:  
Despite these advantages, several practical considerations must be acknowledged. The open-loop nature of the feedforward controller can introduce overshoot, particularly in systems with low damping or compliance in joints. While this is mitigated by tuning time constants and damping ratios, it underscores the importance of dynamic modeling accuracy. Additionally, the feedback controller's reliance on linearized approximations restricts global validity, though the proposed adaptive update strategy helps maintain performance over a broad state space. Implementation in real hardware would also require precise stereo calibration and real-time computation of joint-space inverse kinematics, both of which may present engineering challenges. Overall, this work demonstrates that combining classical control design with geometric insights and modern parameterization techniques can yield a robust, efficient, and scalable solution to a longstanding problem in visual servoing. Future research may explore multi-rate updates, gain-scheduled switching between linearized controllers, or simultaneous \(H_{\infty}\) feedforward-feedback design [41] to further enhance robustness and applicability in unstructured environments.
## 2 Materials and Methods
### OFDM System Working Principle
The FFT operation can improve the SNR of the photocurrent signal at this intensity point. The system SNR of the OFDM system is analyzed after FFT demodulation as follows. Define an output signal \(y(t)\) containing cosine signals of different frequencies with Gaussian white noise as:  
\[y(t)=A_{1}\cos(2\pi f_{1}t+\theta_{1})+A_{2}\cos(2\pi f_{2}t+\theta_{2})+\ldots+A _{M}\cos(2\pi f_{M}t+\theta_{2})+n(t) \tag{8}\]  
where \(A_{1},A_{2},\ldots,A_{M}\) represent the signal amplitude calculated by \(A=ca^{2}/2\), and \(\sigma_{n}^{2}\) is the variance of Gaussian white noise. Take \(T_{s}\) as the sampling period and discretize \(y(t)\) at point \(N\). \(y(k)\) is obtained, where \(k=0,1,2,\ldots,N-1\). \[y(k)=A_{1}\cos(2\pi f_{1}kT_{s}+\theta_{1})+A_{2}\cos(2\pi f_{2}kT_{s}+\theta _{2})+\ldots+A_{M}\cos(2\pi f_{M}kT_{s}+\theta_{2})+n(k) \tag{9}\]  
Then, the SNR of signal \(y(k)\) can be expressed as:  
\[SNR=\frac{P_{s}}{P_{n}}=\frac{A_{1}^{2}+A_{2}^{2}+\ldots+A_{M}^{2}}{2\cdot \sigma_{n}^{2}} \tag{10}\]  
After performing \(N\)-point FFT operation on signal \(y(k)\), the signal power \(P_{ss}\) is  
\[P_{ss}=\sum_{j=1}^{M}\left(\frac{N}{2}A_{j}\right)^{2} \tag{11}\]  
If the Gaussian white noise with standard deviation \(\sigma_{n}\) has a time domain length of \(N\), then after the \(N\)-point FFT operation, the standard deviation becomes \(\sqrt{N}\sigma_{n}\), so the noise power can be written as:  
\[P_{mn}=\left(\sqrt{N}\sigma_{n}\right)^{2}=N\sigma_{n}^{2} \tag{12}\]  
The \(\langle SNR\rangle\) is  
\[\langle SNR\rangle=\frac{P_{ss}}{P_{nn}}=\frac{N}{2}SNR \tag{13}\]
# Full-Field Mode Shape Identification Based on Subpixel Edge Detection and Tracking
## 2 Theory and Methodology
### Relocation of the Edge in Subpixel-Level  
The pixel-level edge-detection algorithms in the above step can only provide the location of the edge point at the pixel level, i.e., the coordinates of the edge points are integer pixels, which is not precise enough for the case with subtle vibrations. Thus, subpixel edge-detection techniques have been developed for precise edge localization, including four main categories, i.e., interpolation-based [36; 37], fitting-based [38; 39], moment-based [30; 31; 40; 41; 42; 43; 44], and the partial area effect (PAE) method [45]. Among them, the moment-based method is not sensitive to noise and is widely used. Hence, the subpixel edge-detection method based on the improved ZOM is adopted herein for relocating more precise edges, as elaborated below.
## 1 Introduction
### Broad & Narrow Tunnel  
**Broad Tunnel**. [PERSON] [7] proposed a general tunnel as a classic prisoner communication model in 1983 and described a network tunnel in detail. As shown in Figure 2, the OSI computer network model is based on a classic prisoner communication model. In this model, [PERSON] and [PERSON] communicate through two networked machines. Despite the fact that their communication data appears to be sent through an open communication channel, they can create a private tunnel that is only visible to them. This generalized tunnel is formally defined as \(\|A\circ B\|\). For any kind of tunnel \(t_{i}\), \(\forall t_{i}\in\|A\circ B\|,i\in R\), there is \(t_{i}=\left\{\begin{matrix}\|A\circ B\|_{\mathbb{I}}\\ \|A\circ B\|_{\mathbb{I}}\end{matrix}\right.\).  
\(\|A\circ B\|_{\mathbb{I}}\) represents the hidden tunnel of network storage. As the most commonly used tunnel, this tunnel is also the tunnel most discussed in our paper because it changes the header fields and payloads of various network protocols to hide information. Both communication parties will only deal with keywords and optional parameters with this tunnel, specified ahead of time, and any keywords or optional parameters not specified in the protocol will be automatically discarded. It is also beneficial for covert communication to hide more information in the message payload.  
\(\|A\circ B\|_{\mathbb{I}}\) is a network time covert tunnel which encodes covert information based on a control time interval. For example, data packets' delivery times are directly changed, but data packet timestamps are changed indirectly. In this tunnel, data packet arrival time  
Figure 1: Our review of tunnel detection process.  
depends on network stability. As time jitter, packet disorder, and loss often lead to decoding failure, we are not focusing on these tunnels.  
**Narrow Tunnel**. In a narrow tunnel, the passenger protocol is encapsulated in a payload and transmitted to the channel. This is purely responsible for the protocol encapsulation and decapsulation of data packets like the edge of the network topology. Our paper formally defines this narrow tunnel as \(|A\circ B|\) in Figure 3. Moreover, the encryption of \(|A\circ B|\) is more prevalent.
# A Review on SIW and Its Applications to Microwave Components
## 2 Modes in SIW  
Substrate-integrated waveguides and the traditional air-filled waveguide have comparable characteristics, as both technologies provide for \(TE_{i,0}\) modes including \(TE_{1,0}\), the dominant mode. Nevertheless, as opposed to the traditional air-filled waveguide which can maintain \(TM\) and \(TE_{i,j}\) (\(j\
eq 0\)) modes, the substrate-integrated waveguide is not able to maintain these modes due to the discontinuity in the side walls. Consequently, only the \(TE_{i,0}\) modes can be supported in the SIW structure, as explained in [15]. The external current circulation of a conventional waveguide, with metallic posts on the thin walls, can be used to describe the fact of the presence of \(TE_{1,0}\) mode in the substrate-integrated waveguide, as shown in Figure 2. This is due to surface currents being formed in guided wave constructions because of mode formation. According to [15], significant energy radiation should happen when the holes cut all the length of the current's transverse path. Alternatively, only an extremely small amount of radiation should happen when the holes cut through the path of current flow. It can be observed from Figure 2 that the holes do not cut the surface current on the side metallic wall. Hence, the \(TE_{1,0}\) mode is maintained in the structure, which clarifies why the \(TE_{i,0}\) modes can happen in the substrate-integrated waveguide structure.
# Lithological Discrimination of Khyber Range Using Remote Sensing and Machine Learning Algorithms
## 4 Results
### Band Ratios  
In order to improve the spectral emissivity pattern of quartz, we used the quartz index (QI), a ratio proposed in [89] which is based on bands 10-13 of ASTER, and the result is shown in Figure 10. Quartz has a more pronounced spectral emissivity pattern with emissivities in the range 0.30-1.00 (Figure 1). Quartz has a lower spectral emissivity in bands 10 and 12, and the spectral emissivity patterns of quartz-bearing rocks also exhibit the Reststrahlen property.  
Figure 8: (**a**) ASTER color composite (RGB:632). (**b**) ASTER color composite (RGB:468). The boundaries of geological formations (Figure 2) are marked in white color in these figures to show PCA classification in relation to the geological map.  
Figure 7: (**a**) Landsat OLI RGB of PC1, PC3, PC4. (**b**) Landsat OLI RGB of PC2, PC4, PC7. The boundaries of geological formations (Figure 2) are marked in white color in these figures to show PCA classification in relation to the geological map.  
To discriminate carbonate minerals in the study area, we used ASTER L2 TIR ratio band 13/band 14 (Figure 11), as employed in [89] to successfully delineate carbonate minerals in Nevada. The emissivity feature of carbonate minerals is a result of CO\({}_{3}\) vibrations in the TIR region at ASTER band 14. Because of the influence of temperature information,  
Figure 10: Quartz index = (band 11/(band 10 + band 12))*(band 13/band 12). The bright pixels represent quartz dominance.  
Figure 9: (**a**) OLI color composite (RGB-754). (**b**) OLI color composite (RGB-147). The boundaries of geological formations (Figure 2) are marked in white color in these figures to show PCA classification in relation to the geological map.  
carbonate minerals' spectral emissivity is weaker than quartz's and may be difficult to identify in the ASTER L1B daytime TIR FCC image.
## 8 Performance Analysis  
The mobility framework is used to test SF (Section 3.1), SF MM (Section 3.2), SF MM PR (Section 6.1) and SF MM PR 3N (Section 6.2). The analysis focuses on how using different network naming schemes affects the performance metrics described below:  
* **Mean delay experienced by the consumer**: A low overall delay would ensure a pleasant viewing experience for streamed content.
* **Mean goodput experienced by the consumer**: For the consumer to have a good viewing experience, the goodput performance should be maintained close to the actual data streaming rate of the content. We chose to compare goodput percentage to the desired ideal because it summarizes the application-level throughput. Goodput is calculated by using the formula shown in Equation (1): \[\text{Goodput}=\frac{\text{\# of PDUs received by the Rx}}{\text{in sequence}\times\text{PDU payload size}}.\] (1)
* **Network timed out Interests to satisfied Consumer Interests ratio**: Interest timeout in ICN means that the network can track a consumer's request by using the PIT to remove Interest requests that cannot be satisfied after a specified timeout period. If a forwarding strategy has low network Interest time out to satisfy consumer Interests ratio, it means that a generated Interest is generally locating the requested Data. This also implies that the consumer does not put a considerable burden on the whole network by forwarding the Interest via unnecessary interfaces.
# Strained Silicon Single Nanowire Gate-All-Around TFETs with Optimized Tunneling Junctions
## 2 Device Fabrication  
Single nanowire GAA TFETs were fabricated on strained silicon-on-insulator (sSOI) wafers with 15 nm biaxially strained silicon (\(\epsilon_{\mathrm{biax,tensile}}=0.8\%\)) and 145 nm buried oxide (BOX). Strained silicon, due to its lower effective mass \(\mathrm{m^{*}}\) and lower bandgap \(\mathrm{E_{g}}\), can achieve better tunneling current [10]. Figure 1 shows the process steps as well as schematics of the final device structure. The single NW was fabricated by a top-down process including e-beam lithography, subsequent reactive ion etching (RIE), and HF dip to suspend the nanowire. The final NW cross-section dimensions are 35 nm \(\times\) 10 nm. After cleaning the substrate using RCA procedure, 3 nm HfO\({}_{2}\) by atomic layer deposition (ALD) and 60 nm TiN using atomic vapor deposition (AVD) were deposited as the high-k/metal gate stack. Then it was patterned using e-beam lithography and etched back by dry and wet etching. A very thin layer of 1.7 nm Ni was deposited by physical vapor deposition (PVD) to form a high quality NiSi\({}_{2}\) layer at the source and drain by annealing the substrate at 550 \({}^{\circ}\)C in forming gas for 30 s. The unreacted Ni residuals were selectively removed in a mixture of H\({}_{2}\)O:H\({}_{2}\)SO\({}_{4}\). Ni silicide formed by this method yields good gate alignment without encroachment of silicide in the channel region [11]. Implantation was carried out into the silicide using boron and phosphorous ions with various energies and tilt angles. For each step of the ion implantation, the other side of the device was protected by polymethyl methacrylate (PMMA) photoresist in order to form p\({}^{+}\) and n\({}^{+}\) junctions. After implantations, the dopants were activated at various temperatures in N\({}_{2}\) environment, as will be discussed in the next section. Finally, the substrate was passivated by SiO\({}_{2}\) and final Al contacts were formed. Figure 2 shows a side view scanning electron microscope (SEM) image of the finished structure, where the gate completely wraps around the nanowire, leading to improved electrostatic control.  
Figure 1: Fabrication process steps as well as schematics of the final device structure of the single nanowire (NW) tunnel field effect transistors (TFETs).
## 4 Results and Discussion
### Evaluation Metrics
#### 4.4.4 Root Mean Square Error (RMSE)  
The RMSE is defined as the average deviation of the estimations from the observed values, and it is the square root of the mean square error.  
\[RMSE=\sqrt{\frac{1}{b}\sum_{a=1}^{b}\left[\tau_{a}-H_{a}\right]^{2}} \tag{46}\]
# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
## 4 Simulation Results and Analysis
### Simulation Results
#### 4.2.2 Classification Accuracy: Proposed Method vs. Comparative Methods  
The simulation results are shown in Figure 9 and Table 3, and it can be seen that the proposed method has the highest classification accuracy and F1-Score. Compared with SVM and lightweight ResNet, the proposed method can achieve at least 30% improvement; compared with the non-lightweight network CVResNet, it can achieve about 2% improvement.
# Tuning of Multivariable Model Predictive Control for Industrial Tasks
## 4 Simulation Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Parameters** & **SSE\({}_{1}\)** & **SSE\({}_{2}\)** & \(\sigma_{1}\) & \(\sigma_{2}\) & \(H_{1}\) & \(H_{2}\) \\ \hline \(\psi_{p,1}=\psi_{p,2}=0.1\) & \(3.91\times 10^{6}\) & \(2.69\times 10^{6}\) & \(1.36\) & \(1.0489\) & \(6.58\times 10^{1}\) & \(5.80\times 10^{1}\) \\ \(\psi_{p,1}=\psi_{p,2}=1\) & \(2.21\times 10^{6}\) & \(1.73\times 10^{6}\) & \(1.02\) & \(8.23\times 10^{-1}\) & \(4.94\times 10^{1}\) & \(4.63\times 10^{1}\) \\ \(\psi_{p,1}=\psi_{p,2}=10\) & \(1.52\times 10^{6}\) & \(1.39\times 10^{6}\) & \(8.48\times 10^{-1}\) & \(7.32\times 10^{-1}\) & \(4.08\times 10^{1}\) & \(4.14\times 10^{1}\) \\ \(\psi_{p,1}=\psi_{p,2}=100\) & \(1.31\times 10^{6}\) & \(1.27\times 10^{6}\) & \(7.87\times 10^{-1}\) & \(6.92\times 10^{-1}\) & \(3.85\times 10^{1}\) & \(3.95\times 10^{1}\) \\ \(\psi_{p,1}=\psi_{p,2}=1000\) & \(1.28\times 10^{6}\) & \(1.25\times 10^{6}\) & \(7.79\times 10^{-1}\) & \(6.85\times 10^{-1}\) & \(3.83\times 10^{1}\) & \(3.94\times 10^{1}\) \\ \(\psi_{p,1}=\psi_{p,2}=10,000\) & \(1.28\times 10^{6}\) & \(1.25\times 10^{6}\) & \(7.78\times 10^{-1}\) & \(6.85\times 10^{-1}\) & \(3.82\times 10^{1}\) & \(3.92\times 10^{1}\) \\ \hline \(\psi_{p,1},\psi_{p,2}\) tuned & \(9.62\times 10^{5}\) & \(1.23\times 10^{6}\) & \(6.74\times 10^{-1}\) & \(6.99\times 10^{-1}\) & \(3.32\times 10^{1}\) & \(3.91\times 10^{1}\) \\ \hline \end{tabular}
\end{table} 
Table 1: The values of the considered control quality indices for different constant parameters \(\psi_{p,1}\), \(\psi_{p,2}\), as well as for the tuned one (for all 600,000 samples). Figure 8: The third step of tuning the coefficients \(\psi_{p,2}\): the influence of the coefficient \(a_{2}\) on the performance criteria SSE\({}_{1}\), SSE\({}_{2}\), \(\sigma_{1}\), \(\sigma_{2}\), H\({}_{1}\) and H\({}_{2}\) (for all 600,000 samples). In the second part of the simulations, we use the obtained set of tuning coefficients, but all gains of all input-output process channels are increased by 20%. Such a case is particularly interesting since the model used in MPC is (practically) never perfect. Table 2 shows the values of all six performance criteria. Additionally, we consider six cases in which all coefficients are constant and have the values 0.1, 1, 10, 100, 1000 and 10,000, respectively. For almost all considered indices (the only exception is \(\sigma_{2}\), but the difference is not significant), the discussed method gives the best results. For constant weights \(\psi_{p,1}=\psi_{p,2}\) greater than 10,000, the results do not change. The histograms of the control errors are given in Figure 11, and Figure 12 depicts the first 1000 samples of the control errors. Similar to the perfect model case (Figures 9 and 10), our tuning method gives the best results.
# Nonlinear Stability Analysis of Shallow-Buried Bias Tunnel Based on Failure Mode Improvement
## 6 Example Analysis
### Influence of Slope Top Load  
When the shallow-tunnel burial depth of the slope section \(H=20\) m, rectangular tunnel height \(h=10\) m, span \(b=10\) m, bulk density of surrounding rock \(\gamma=20\) kN/m\({}^{3}\), uniaxial tensile strength \(\sigma_{\rm t}=30\) kPa, cohesion of surrounding rock \(\sigma_{0}=10\) kPa, angle of internal friction in the surrounding rock \(\varphi=18^{\circ}\), slope surface inclination \(\delta=18^{\circ}\), ratio of horizontal support force to vertical support force of surrounding rock \(K=1.0\), nonlinear coefficients \(m=1.0\), \(1.1\), \(1.2\), \(1.4\), so that the slope top load \(p=0\)-\(200\) kPa, the changes in surrounding rock pressure and failure mode of the shallow-buried tunnel are illustrated in Figures 4 and 5.  
From Figure 4, it can be seen that with the increase in the load on the top of the slope, the perimeter rock pressure increases significantly. \(p\) varies from 0 kPa to 200 kPa, and when \(m=1.0\), the increased value of the perimeter rock pressure reaches a minimum of 28 kPa; when \(m=1.4\), the increased value of the perimeter rock pressure reaches a maximum of 84 kPa, and \(m\) varies from 1.0 to 1.4; when \(p=0\) kPa, the increased value of the perimeter rock pressure reaches a minimum of 107 kPa; when \(p=200\) kPa, the increased value of the perimeter rock pressure reaches a maximum of 163 kPa, which indicates that the larger the load at the top of the slope is affected by the nonlinear coefficient more significantly. It can be inferred that, in actual construction, especially in less sedimentation areas, the unique geological conditions make the surrounding rock more susceptible to instability and deformation under the external loads from traffic, equipment, and structures on the tunnel roof. These factors adversely affect the stability of the surrounding rock. Therefore, in such regions, it is imperative to strictly control engineering activities within the tunnel roof area to prevent the occurrence of tunnel collapse and other disasters. As can be seen from Figure 5, when the load on the top of the slope increases, the tunnel damage is shifted towards the shallower side of the slope. Consequently, the engineering protection of the shallower side should be emphasized.  
Figure 4: Influence of slope top load on tunnel surrounding rock pressure.  
Figure 5: Influence of slope top load on failure mode of surrounding rock (\(m=1.2\)).