esa-sceva/satcom-chunk-collection huggingface dataset:
# XGBoost-Enhanced Graph Neural Networks: A New Architecture for Heterogeneous Tabular Data
## 2 Related Work  
### Tree-Based Table Model  
Tree-based tabular integration models, including GBDT, XGBoost, CatBoost, and LightGBM, are one of the more popular methods for processing tabular data nowadays. [PERSON] et al. (2016) combine multiple weak learners, usually decision trees, to create strong learners. The main principle of GBDT is to construct a series of weak learners step by step, and each of them adjusts its prediction based on the residuals (prediction errors) of the previous round to adjust the predictions, thus gradually improving the model's performance. In a variety of machine learning and data science tasks, [PERSON] et al. (2016) proposed the machine learning algorithm XGBoost, a decision tree ensemble algorithm based on the gradient boosting principle. [PERSON] et al. (2017) made an improvement algorithm for classification and regression trees that uses the gradient boosting principle. This resolved the issue of prediction bias resulting from special target leakage. [PERSON] et al. [8] proposed LightGBM, including two new techniques: gradient-based unilateral sampling (GOSS) and exclusive feature bundling (EFB). They say that it speeds up traditional GBDT training by more than 20 times while maintaining almost the same accuracy. However, why do tree-based models still outperform deep learning on tabular data? When dealing with non-smooth functions or decision boundaries, tree-based tabular models can identify sharp points in the search space as well as some random functions [9]. Additionally, they are capable of discerning the usefulness of features and avoiding those that are unnecessary in order to select the optimal path. When dealing with rotated data sets, tree models do not have local invariance, which makes them better able to handle tabular data without properties.
# Statistical Validation Framework for Automotive Vehicle Simulations Using Uncertainty Learning
## 6 Application Domain
### Application Decision-Making  
The application decision-making model \(G_{dec}\) in Equation (31) decides if the individual result of the simulation is precise enough for the application or not.  
\[G^{\text{a}}_{\text{dec}}:\big{(}\hat{y}^{\text{a}},t^{\text{a}}_{\text{y}} \big{)}\mapsto\hat{u}^{\text{a}}=\begin{cases}1&\text{if }\max\big{[}t^{95}_{\overline{\hat{u}}}(\hat{y}^{\text{a}})\big{]}-\min \big{[}t^{95}_{\overline{\hat{u}}}(\hat{y}^{\text{a}})\big{]}\leq t^{\text{a}} _{\text{y}}\\ 0&\text{else}\end{cases} \tag{31}\]  
The example assumes that the threshold for the width of the 95% confidence interval is \(t^{\text{a}}_{\text{y}}=\mu_{g^{\text{a}}_{\text{m}}}\cdot\) 30%, while \(\mu_{g^{\text{a}}_{\text{m}}}\) is the mean of the p-box. A conservative 95% confidence interval can be calculated based on the p-box \(B_{\hat{y}^{\text{a}}_{\text{m}}}(y^{\text{a}}_{\text{s}})\) shown in Figure 9 that represents the total prediction uncertainty. The lower and upper limits of the interval are estimated by the 2.5 percentile of the p-box's left CDF and the 97.5 percentile of the p-box's right CDF.  
Table 3 summarises all results and shows that \(t^{95}_{\overline{\hat{u}}_{\text{m}}}(y^{\text{a}}_{\text{s}})\leq t^{\text{ a}}_{\text{y}}\). Under this condition, the result is valid and precise enough for the current individual application. Other tolerance values are possible. Another criterion for a different use-case where the consumption should be lower than a specific value with a confidence of 95% is also possible.  
Figure 9: Total prediction uncertainty \(\hat{y}^{\text{a}}\) of the consumption.  
Since the total prediction uncertainty in the form of the p-box includes a lot of information for current and future use-cases, it should always be kept together with the validation result. This information is essential to improve the M+S process. The shape of the p-box enables a systematic reduction of the total prediction uncertainty of the SRQ. The authors recommend four strategies to efficiently improve automotive M+S processes:  
1. Measure the epistemic parameters more precisely to reduce the blue area.
2. Control the test setup to reduce the natural variation of the aleatory parameters, resulting in a smaller width of the s-curve.
3. Use a more detailed model to reduce the inherent model error and validate it in more validation parameter configurations to reduce the prediction uncertainty. This results in a smaller green area.
4. Use finer steps to reduce numerical uncertainties.  
With these suggestions and the information in the p-box, the user can choose the most efficient way to reduce uncertainties and improve the simulation model and reliability assessment.
# A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles
## 5 Roll Stability Control in Off-Road Vehicles
### Roll State Evaluation and Rollover Warning  
Active safety technologies play a crucial role in reducing the rollover risk of agricultural machinery. Figure 6 illustrates the layout of a typical tractor active roll stability control system, which includes sensor observation, state estimation, rollover risk monitoring, and stability control [17]. To achieve effective rollover warning and active rollover control, the system must first acquire key vehicle state information, such as the roll angle, center of gravity height, and LTR. The acquisition of this information relies on sensor measurements and parameter estimation, which provide the foundational data for subsequent control decisions.  
Current research on tractor rollover prevention focuses on real-time monitoring and early warning systems, leveraging inertial sensors like accelerometers and gyroscopes to track the roll angle, yaw rate, and other key states during operation on slopes or uneven terrain [39]. [PERSON] et al. [120] developed a static and dynamic stability analysis algorithm based on mechanical modeling and 3D geometry, which calculates the critical rollover angle under varying speeds and turning radii. While effective for stability evaluation, traditional methods are limited by their reliance on current state variables, hindering their predictive capabilities. To address this, [PERSON] et al. [51] introduced the Rollover Critical Position (RCP) index and applied exponential terminal sliding mode control (SMC) to enhance response speed. Building on this, [PERSON] et al. [121] proposed a rollover stability indicator derived from a time-varying posture model, enabling early risk detection by monitoring the roll angle relative to its critical threshold and activating active steering when necessary. Further advancing system responsiveness, [PERSON] et al. [122] developed a control system that integrates active steering with a 3-DOF rollover dynamic model to adjust the front-wheel angle in real time based on a calculated stability index, effectively reducing  
Figure 6: Representative configuration of active roll stability control systems in tractors [17; 51].  
the rollover risk. In terms of practical deployment, [PERSON] et al. [123] created an iOS-based app, SafeDriving, capable of real-time rollover monitoring and emergency alerting via onboard or external sensors, showing strong performance across varied tractor types and terrains. [PERSON] et al. [40] proposed a low-cost adaptive observer method using hydraulic pressure data to estimate the lateral load transfer rate; rollover risk is flagged when \(\mid\) LLT\(\mid\)\(\geq\) 0.8. This approach adapts to changes in load, terrain, and center-of-gravity height, offering a robust and affordable solution for rollover risk detection in adjustable agricultural vehicles like harvesters and tractors. Overall, these advances significantly enhance the foresight, adaptability, and practicality of tractor rollover warning systems.
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 2 Related Knowledge
### Rhombus Pattern Based PEE  
The rhombus prediction error extension [25] is to divide the image pixels into overlapping pixel groups. Pixels of a group are further divided into \"\(\times\)\" and \"\(\bullet\)\" sets. Pixels of \"\(\times\)\" set are used for data embedding and pixels of \"\(\bullet\)\" set are used for prediction. Figure 2 illustrates that a pixel \(p_{i,j}\) in the \"\(\times\)\" set is predicted by its four neighboring pixels \((p_{i,j-1},p_{i,j+1},p_{i-1,j},p_{i+1,j})\) in the \"\(\bullet\)\" set. The embedding, extraction and recovery procedures are given as follows.  
Figure 2: Pixel prediction procedure for a group.
# Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks
## 3 Adaptive Sub-Network Organization
### Adaptation to the Changes of the Sub-Network  
The aggressiveness of the adaptation process for the transmission of the beacons is controlled by setting the following protocol parameters:  
1. \(I_{min}\)--the minimum beaconing interval,
2. \(I_{max}\)--the maximum beaconing interval,
3. \(k\)--minimum number of consistent messages that a device needs to receive within a given period to become consistent.  
The parameters \(I_{min}\) and \(I_{max}\) control the beaconing rate. Recall that devices that are characterized as inconsistent use the minimum interval \(I_{min}\). If the higher-layer application requires that the IoT network adapts quickly to topological changes, the network administrator is advised to set the value of \(I_{min}\) very low value. However, it should be noted that a very low value of \(I_{min}\) will cause congestion and degradation of the communication channels. On the other hand, the parameter \(I_{max}\) affects those devices that are characterized as consistent devices and controls the reductions in energy consumption due to the beaconing mechanism in networks where no topological changes occur. While setting the \(I_{max}\) parameter, it is important to keep in mind that very long beaconing periods lead to very slow responses to possible changes.  
The parameter \(k\) controls the threshold that evaluates the consistency of a device. An internal counter \(c\) is used to count the number of consistent messages received. Given a sufficient number of consistent messages received (\(c>=k\)), there is no need to send a beacon as _the network is characterized stable._ However, if the threshold value \(k\) is not reached (\(c<k\)), the network is said to be unstable due to outdated or incomplete information on the local topology. The network immediately transmits a new beacon. At the beginning of each current beaconing interval, the counter \(c\) is reset.  
When these parameters are set, it is important to have an understanding of the expected density of the IoT deployment. In dense deployments, \(k\) should be set to a high value to keep track of the state of each sub-network. For sparse deployments, however, a very high value for \(k\) may lead to cases where very few sub-networks become stable.
## 4 Electrochemical Analysis  
### Cyclic Voltammetry  
To understand the interaction process of Li\({}^{+}\) with the developed ternary composite electrode, cyclic voltammograms (CV) were evaluated for the first four cycles. In this electrochemical test, the oxidation and reduction reactions occurring within the coin cell can be detected and analyzed. Figure 9 shows the CV results for the Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/C composite fibers. For the initial cathodic scan (corresponding to Li\({}^{+}\) insertion), a peak at a potential of 1.8 V is present (R1). This peak can be attributed to the initial insertion of lithium into the \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\) and TiO\({}_{2}\) components of the electrode, as shown in Equations (1) and (2) [53]:  
\[Fe_{2}O_{3}+xLi^{+}+xe^{-}\to Li_{x}Fe_{2}O_{3} \tag{1}\]  
\[TiO_{2}+xLi^{+}+xe^{-}\leftrightarrow Li_{x}TiO_{2} \tag{2}\]  
Figure 8: (**A**) Thermogravimetric analysis (TGA) and (**B**) X-ray diffraction patterns of \(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/carbon composite fibers demonstrating the material crystal structure.  
Figure 9: Cycle voltammetry curves of Fe\({}_{2}\)O\({}_{3}\)/TiO\({}_{2}\)/C composite fibers between 0.01 and 3.0 V at a scan rate of 0.1 mVs\({}^{-1}\).  
A second reaction can be observed at a potential of 0.8 V (R2), which is attributed to the complete reduction of the Fe\({}_{2}\)O\({}_{3}\) to elemental Fe and Li\({}_{2}\)O (Equation (3)). The decrease in the peak sharpness confirms the large specific capacity loss during the initial discharge cycle due to the conversion reaction (Equation (3)) [23]:  
\[Fe_{2}O_{3}+6 Li^{+}+6e^{-}\to 2\ Fe+3 Li_{2}O. \tag{3}\]  
The final irreversible peak observed at the first cycle at a potential of 0.4 V (R3) is attributed to the formation of the solid electrolyte interface (SEI) layer at the electrode/electrolyte interface. In the later cycles, the peaks from the irreversible reactions R1 and R3 are no longer present, where only the reduction and oxidation peaks at 0.8 and 2.1 V are observed (R4, O1). These peaks are associated with the alloying/de-alloying between Li and elemental Fe, as well as the intercalation between lithium ions and TiO\({}_{2}\) and carbon (Equations (2) and (4)):  
\[6C+xLi^{+}+xe^{-}\leftrightarrow Li_{x}C_{6} \tag{4}\]
# Indoor Wavelet OFDM VLC-MIMO System: Performance Evaluation
## 2 System Model
### The Receiver
#### 2.3.1 Received Wavelet-OFDM Data  
DWT receiver is the reverse operation of IDWT. It performs a DWT according to the MATLAB toolbox. The transmitted signal \(\cup\) [k] is the front-end receiver successive digitally modulated symbols to be transformed, the data is decomposed across filters, LPF and HPF related to CA in addition to the CD. CA is the output signal of the approximation coefficients or LPF and CD are the output data of the detailed coefficients or HPF to execute that operation. The data is transposed before converting from S/P and the wavelet family used is Demy wavelet.  
\(\cup\)[k] contains some zeros elements which are decomposed from CD information produced in the transmitter comparing to CA data. So, the CD data will be considered as imaginary data and the CA will be considered as real data to avoid any loss of data. That is all go via the DWT-OFDM receiver which can be seen in Figure 5.  
Figure 4: A Wavelet-OFDM indoor imaging VLC-MIMO receiver by utilizing the hybrid Pre-SDE system.  
The forward DWT is used to reconstruct the data symbol. The DWT-OFDM now could be obtained by a weighted sum of wavelet similar to scale carriers, which can be expressed as:  
\[\mathrm{W(t)}=\sum_{\mathrm{j}\leq\mathrm{j}}\sum_{\mathrm{k}}\mathrm{w}_{\mathrm{ j}\mathrm{k}}(\mathrm{t}).\Psi_{\mathrm{j}\mathrm{k}}(\mathrm{t})+\sum_{\mathrm{k}} \mathrm{a}_{\mathrm{j}\mathrm{k}}\Phi_{\mathrm{j}\mathrm{k}}(\mathrm{t}), \tag{12}\]  
where \(\mathrm{w}_{\mathrm{j}\mathrm{k}}(\mathrm{t})\) is the wavelet coefficients existing at the \(\mathrm{k}^{\mathrm{th}}\) position from the scale \(\mathrm{j}\), \(\Psi_{\mathrm{j}\mathrm{k}}(\mathrm{t})\) is the IDWT modulator as a sequence of wavelet, \(\Phi_{\mathrm{j}\mathrm{k}}(\mathrm{t})\) is the scaling function, and \(\mathrm{a}_{\mathrm{j}\mathrm{k}}\) is the approximation coefficient. The output of DWT consists of two vectors: the approximation coefficients (AC) and the detail coefficients (DC).  
The wavelet modulation (IDWT) in addition to the wavelet demodulation is obtained according to the Mallet algorithm [14]. The IDWT depends on up-sampling and DWT depends on down-sampling by a factor of two to each other, and filtering the approximated coefficients as shown in Equation (10), relative to LPF/HPF, respectively.
## 3 LIG-Based Wearable Systems for Multifunctional and Intelligent HCI
### Intelligent Artificial Throat  
Most traditional sound sources and acoustic detectors are separated. However, to enhance integration and achieve a multifunctional acoustic HCI system, it is beneficial to incorporate both sound collection and generation functions into a single device.  
Figure 12: The intelligent speech recognition and motion control system. Reproduced with permission from [18]. Copyright 2022 American Chemical Society.  
Facing this need, [PERSON] et al. invented a LIG-based intelligent artificial throat that could achieve functions of emitting and detecting sound by using a single LIG film, and its sound-detecting range and sound-emitting range could cover the range of human hearing [28]. Figure 13 shows the fabrication method, function, and application scenario of the LIG-based artificial throat. The artificial throat consisted of the LIG of the PI substrate made by the laser direct writing technique (450 nm laser). When the artificial throat was used as a sound source, it worked by using the thermoacoustic effects of the graphene. By applying periodic AC voltages to it, the LIG could produce a controllable sound in a wide frequency range (100 Hz-40 kHz), covering most of the frequency range of the human voice, and had good high-frequency characteristics. Additionally, research has revealed that the thinner the LIG, the greater the sound pressure level it can produce. By working as a strain sensor, the LIG device can be used as a sound receptor. It can distinguish vocalizations, hums, coughs, etc. by detecting the laryngeal vibration and laryngeal muscle movements with high sensitivity as well as identify different words and sentences spoken by different wearers.  
The LIG-based intelligent artificial throat possesses the simultaneous capability to both detect and produce sound. When worn on the throat, the artificial throat can accurately detect the muscle movement and vocal cord vibration signals of the wearer, classify the signals through the machine learning algorithm, confirm the information that the wearer wants to express, and convert it into understandable language using the sound emitting function of the artificial throat. This means that the LIG-based artificial throat holds tremendous potential for application. For example, it can assist people with voice loss to rebuild their ability to communicate by turning their inarticulate throat vibrations into coherent speech and help them reintegrate into society as typical individuals. Furthermore, it is expected to play an important role in the field of wearable HCI such as voice control based on its acoustic/mechanical detection ability, speech information conversion ability, and sound emitting ability. In the next section, a new generation of artificial throats is discussed, where a bioinspired dual-channel system will show how to further improve the ability of speech recognition and the intelligence of the artificial throat.
# Multi-Scale Twin Networks for Coastal Zone Change Detection in Remote Sensing Imagery
## 3 Principles and Methods
### Contextual Information Integration
#### 3.2.2 Spatio-Temporal Difference Attention Module
Finally, the two tensors are multiplied element-wise with the absolute difference feature map \(F_{D}\) to obtain the intermediate feature map \(F^{\prime\prime}\). The formula indicates:  
\[F_{h}=pool^{h}(F_{D}) \tag{10}\]  
\[F_{w}=pool^{w}(F_{D}) \tag{11}\]  
\[f=\delta(\odot_{1}([F_{h},F_{w}])) \tag{12}\]  
\[F^{\prime}_{h},F^{\prime}_{w}=\text{Split}(f) \tag{13}\]  
\[F^{\prime\prime}_{h}=\sigma\Big{(}\odot_{1}^{h}\big{(}F^{\prime}_{h}\big{)} \Big{)} \tag{14}\]  
\[F^{\prime\prime}=\text{CA}(F_{D}) \tag{7}\]0  
\[F^{\prime\prime}=\text{CA}(F_{D}) \tag{7}\]1  
where \(pool^{h}\) and \(pool^{w}\) denote the average pooling in vertical and horizontal coordinates, respectively, \(\delta\) denotes the ReLU function, \(\sigma\) denotes the Sigmoid function, and \(\times\) denotes matrix multiplication. The connected branch consists of a \(3\times 3\) convolutional block for learning local information in the input feature map and a second \(1\times 1\) convolutional block to reduce the number of channels in the feature map, ensuring that the output matches the output features of the subtraction branch. This design not only helps capture small-scale variations and subtle features but also simplifies feature fusion by ensuring consistency in the feature dimensions between the two branches. Each convolution block consists of convolution operations, batch normalization, and ReLU activation, which improves feature processing efficiency and enhances the model's stability. In addition, feature extraction and channel tuning in the connected branch reduce the model's dependence on a specific change detection dataset. In change detection tasks, where datasets often differ and phase subtraction operations may introduce inconsistency or noise, the connected branch further optimizes features through an effective convolutional structure, thus improving the model's generalization ability across different datasets. This design enables the connected branch to provide richer, more stable feature representations when paired with the subtraction branch, which allows the model to detect changes more accurately while reducing overfitting to specific datasets. Figure 6 shows examples of the coastal zone change detection dataset and the corresponding intermediate feature maps obtained after SDAM processing. The intermediate feature map examples are presented in the form of heatmaps, highlighting the areas of the feature map that the model focuses on during the Contextual Information Integration stage. Compared to Figure 3, it is evident that the model pays more attention to the change areas with higher precision, and false positives and missed detections are significantly reduced.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 4 Methodology
### Proposed Stacked Convolutional Autoencoder for Data Reconstruction
#### 4.3.1 Step 1: Recursive Pretraining of the Weights  
(1) Training subnet AE 1. The loss function of the MSE is used and minimized by the subnet AE 1 network model to make the output data \(\hat{Y}\) approximate the ground truth \(Y\) as closely as possible, as follows:  
\[\mathcal{L}_{1}(W_{\text{conv}},W_{\text{deconv}})=\frac{1}{K_{1}}\sum_{i=1}^ {K_{1}}||\hat{Y}_{i}-Y_{i}||^{2},\]  
where \(K_{1}\) is the number of training data samples in each batch of subnet AE 1.  
Backpropagation is used to update the weights of the convolution layers using the gradient descent method, as follows:  
\[\begin{cases}W_{\text{conv}}&=W_{\text{conv}}-\eta\
abla_{W_{\text{conv}}} \mathcal{L}_{1}\\ W_{\text{deconv}}&=W_{\text{deconv}}-\eta\
abla_{W_{\text{conv}}}\mathcal{L} _{1}.\end{cases} \tag{26}\]  
(2) Training subnet AE 2. We set \(Q\) as the output data of the encoder kernel in subnet AE 1 after completing the training process in Step 1. Subnet AE 2 uses output data \(Q\) as its input. Moreover, the subnet AE 2 network model also minimizes the loss function of the MSE to approximate the output data \(\hat{Q}\) as closely as possible to the output of subnet AE 1's encoder kernel. The loss function \(\mathcal{L}_{2}\) of subnet AE 2 is expressed as  
\[\mathcal{L}_{2}(W_{12},W_{23},W_{34},W_{45})=\frac{1}{K_{2}}\sum_{i=1}^{K_{2} }||\hat{Q}_{i}-Q_{i}||^{2},\]  
where \(K_{2}\) denotes the number of training data samples in each batch.  
Figure 6: Architecture of the SCAE.  
Backpropagation is used to update the weights of the dense layers using the gradient descent method, as follows:  
\[\begin{cases}W_{12}&=W_{12}-\eta\
abla_{W_{12}}\mathcal{L}_{2}\\ W_{23}&=W_{23}-\eta\
abla_{W_{23}}\mathcal{L}_{2}\\ W_{34}&=W_{34}-\eta\
abla_{W_{34}}\mathcal{L}_{2}\\ W_{45}&=W_{45}-\eta\
abla_{W_{45}}\mathcal{L}_{2}\end{cases} \tag{27}\]
# Acquisition and Processing of UAV Fault Data Based on Time Line Modeling Method
## 5 Experimentation
### Comparison with Related Data Sets
#### 5.1.2 Performance Comparison  
As the UAV ATTACK data set does not contain clear positive and negative sample labels, this paper utilized the K-Means algorithm to cluster the GPS information of UAV ATTACK and generated appropriate labels. The labeled GPS information was then subjected to experimentation using the KNN algorithm to determine its accuracy rate. Notably, the accuracy of KNN on the ECU-IoT data set was obtained from [15]. The results presented in Table 10 demonstrate that KNN achieved the highest accuracy rate on the proposed data set in this paper.