esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Fuzzy-Based Fault-Tolerant Control of a Continuum Robotic System for Maximly Sinus Surgery
## 1 Introduction
: _To reduce the effect of a fault in the robot surgery, which techniques are employed in this study?_  
**Solution 3**. : _In this study, the main idea of the fault-tolerant controller is feedback linearization control. The proposed observation technique is applied to this controller to improve the power of fault reduction. To improve the robustness in uncertain and noisy condition, a fuzzy adaptive technique is applied to the robust observation-based feedback linearization controller (Section 3.3)._  
The rest of this research paper is organized as follows. In Section 2, the surgical robot for maxillary sinus surgery is modeled based on the fuzzy ARX-Laguerre procedure. The proposed adaptive fuzzy ARX-Laguerre T-S fuzzy robust feedback linearization observation for surgical robot fault detection, estimation, identification, and tolerant control are presented in Section 3. Section 3 includes three main steps. In the first step, the fuzzy ARX-Laguerre T-S fuzzy robust feedback linearization observer based on a variable structure algorithm is utilized. In the second step, fault detection, estimation, and identification are proposed. For the fault-tolerant algorithm, an adaptive fuzzy advanced observation-based feedback linearization algorithm is recommended in the third step. In Section 4, fault detection, estimation, identification, and tolerant control results for the surgical robot for maxillary sinus surgery are analyzed. Finally, the conclusions are provided in the last section.
## 4 Evaluation of Seismic Performance
### Energy Dissipation  
During an earthquake, when a structure comes into the elastic-plastic phase, the seismic energy is mainly dissipated by the hysteretic behavior of the components (beams, columns, and walls) of  
Figure 13: Evolution of normalized stiffness degradation.  
the structure. Therefore, the energy dissipation through hysteretic damping, \(E_{d}\), is commonly used to evaluate the seismic performance of the shear wall. In this paper, the energy dissipation of the specimens is calculated by the area of the hysteresis loop (the horizontally hatched area) of each first loading cycle in the test, which is shown in Figure 14.  
The energy dissipation of the test wall in one loading cycle is shown in Figure 15a where the hysteretic energy in the last cycles of the test was omitted because the hysteresis loops were either unstable or incomplete due to serious damage of the specimens. The figure shows that the energy dissipation of both traditional and precast RMSWs improved with the increasing loading displacement. At low displacement levels, precast shear walls exhibited nearly the same low energy dissipation as traditional shear walls. As the loading continued, the dissipated energy of both traditional and precast shear walls increased significantly due to yielding of the vertical rebar at the sides and the inelastic deformation of the shear wall. The figure shows that precast RMSWs have an energy dissipation capacity similar to that of traditional RMSWs.  
Figure 14: Energy dissipation of the specimen.  
Figure 15: _Cont._  
Normalized energy dissipation of each specimen, which is defined as the ratio between the hysteresis energy of the test wall at a certain displacement and the hysteresis energy at the yield displacement, is plotted versus the ratio of the post-yield displacement \(D\) to the yield displacement \(D_{y}\) in Figure 15b. The figure shows that, although the construction method and axial compressive stress were different for the specimens, a nearly linear increase of the normalized energy dissipation was observed with the increase of \(D\)/\(D_{y}\) until the displacement of the specimens was more than six times the yield displacement. A similar finding in traditional RMSWs has also been reported by [PERSON] and [PERSON] [28; 29]. The hysteretic energy at the displacement of 3 \(D_{y}\), was on average about five times the energy dissipation at yield of the test walls.
# Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging
## 3 Experimental Results and Discussions
### Overfitting
#### 3.3.1 Model Evaluation and Overfitting Detection
Logistic Regression and SVM, with closely aligned training and test accuracies (Logistic: 85.25% training, 84.36% test; SVM: 86.38% training, 86.03% test), exhibited minimal overfitting. CatBoost also showed a relatively small gap between training (92.56%) and test accuracy (87.15%), although there was still a slight tendency to overfit. LightGBM demonstrated a larger gap (97.05% training, 86.03% test), but it remained within a reasonable range. These results underscore the tradeoff between model complexity and generalization. Models like Random Forest and XGBoost, which are more complex, tend to overfit unless properly regularized, while simpler models like Logistic Regression and SVM generalize better. This emphasizes the importance of using validation techniques such as cross-validation, ensembling, or data augmentation to improve model robustness and mitigate overfitting in real-world applications where the model may fail when exposed to new data. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline Logistic & 0.84 & 0.84 & 0.77 & 0.80 \\ Regression & 0.86 & 0.89 & 0.76 & 0.82 \\ \hline KNN & 0.83 & 0.83 & 0.73 & 0.78 \\ \hline Random Forest & 0.84 & 0.84 & 0.77 & 0.80 \\ XGBoost & 0.83 & 0.81 & 0.77 & 0.79 \\ LightGBM & 0.86 & 0.85 & 0.81 & 0.83 \\ CatBoost & 0.87 & 0.88 & 0.80 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model Performance Metrics. Figure 4: Overfitting check by all models: (**a**) ROC curve, (**b**) P-R curve.
## 3 Results
### Analysis of Vertical Distribution of Air Quality
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Altitude [m]**} & \multicolumn{5}{c}{**Location**} \\ \cline{2-7}  & **Point 1** & **Point 2** & **Point 3** & **Point 4** & **Point 5** & **Point 6** & **Point 7** \\ \hline \multirow{2}{*}{0–5} & 32.72 \(\pm\) 1.23 & 34.55 \(\pm\) 2.1 & 46.37 \(\pm\) 0.32 & 47.91 \(\pm\) 0.52 & 42.45 \(\pm\) 0.48 & 28.46 \(\pm\) 0.88 & 36.81 \(\pm\) 1.64 \\  & bcF & abcE & abB & abcA & aC & dG & dD \\ \multirow{2}{*}{5–10} & 33.92 \(\pm\) 1.07 & 33.85 \(\pm\) 1.08 & 46.12 \(\pm\) 0.31 & 47.48 \(\pm\) 0.3 & 41.75 \(\pm\) 0 & 28.23 \(\pm\) 0.27 & 37.18 \(\pm\) 1.09 \\  & aE & bcE & abcB & bcA & cC & dF & cD \\ \multirow{2}{*}{10–15} & 33.35 \(\pm\) 0.87 & 33.15 \(\pm\) 2.04 & 45.84 \(\pm\) 0.64 & 47.31 \(\pm\) 0 & 42 \(\pm\) 0.29 & 28.56 \(\pm\) 0 & 36.76 \(\pm\) 1.19 \\  & abE & bcE & abcdB & cA & bcC & cdF & dD \\ \multirow{2}{*}{15–20} & 32.95 \(\pm\) 0.93 & 32.85 \(\pm\) 3.13 & 44.93 \(\pm\) 1.12 & 47.31 \(\pm\) 0.47 & 42.25 \(\pm\) 0.33 & 28.89 \(\pm\) 0.27 & 37.51 \(\pm\) 0.82 \\  & bcE & bcE & deB & cA & abC & cF & bcdD \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Mean and standard deviation of PM\({}_{2.5}\) concentrations across different geographic locations and altitudes, along with [PERSON]’s post hoc test results for significant differences. For each column, different lowercase letters indicate significantly different at \(p\leq 0.01\). For each row, different capital letters indicate significant differences at \(p\leq 0.01\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multicolumn{2}{c}{**Type III Sum**} & \multirow{2}{*}{**df**} & \multicolumn{2}{c}{**Mean**} & \multirow{2}{*}{**F**} & \multirow{2}{*}{**Sig**} \\  & **of Squares** & & & **Square** & & \\ \hline Altitude & 37.416 & 10 & 3.742 & 2.593 & \textless{}0.01 \\ Location & 31,870.211 & 6 & 5311.702 & 3681.345 & \textless{}0.01 \\ Altitude \(\times\) Location & 346.246 & 60 & 5.771 & 4 & \textless{}0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Two-way ANOVA location and altitude and their interaction on PM\({}_{2.5}\) concentrations.
## 3 Datasets and Methodology
### Methodology
#### 3.2.2 Data Processing Workflow  
The overall procedure for obtaining ground deformation information primarily comprises two stages: data preprocessing and SBAS-InSAR processing. In this study, Sentinel-1 SAR data were processed using the SARscape module in the ENVI 5.3 platform [3]. SARscape, developed by the Swiss company samrap (Caslano, Switzerland), is a professional radar image processing software that has been widely used for various satellite-borne radar datasets, including ERS-1/2, RADARSAT-1/2, ENVISAT ASAR, ALOS PALSAR, and Sentinel-1 [24]. The selection of this software is based on its efficient radar data processing capabilities and its extensive application experience, particularly in handling large-scale SAR data and monitoring land subsidence. SARscape is capable of accurately performing interferometric measurements, time series analysis, and ground deformation extraction, making it the tool of choice in this study to ensure the accuracy and reliability of data processing.  
The Sentinel-1 data preprocessing steps were as follows [25]: (1) importing the SAR scenes into the standard SARscape format; (2) mosaicking SAR images acquired on the same date; and (3) cropping the data to the boundary of the study area. The subsequent SBAS-InSAR processing included the following steps: (1) pairing the input scenes using an optimal combination strategy; (2) performing interferometric processing on the selected image pairs; (3) re-flattening all interferograms using control points; (4) removing atmospheric phase artifacts and estimating deformation velocity; and (5) geocoding, which involves projecting the deformation results into a geographic coordinate system (Figure 2).
# A Tile-Based Multi-Core Hardware Architecture for Lossless Image Compression and Decompression
## 2 Compression and Decompression Algorithms Based on Hybrid Strategies
### Compression Workflow  
The compression workflow is illustrated in Figure 1. During the compression phase, the image is processed sequentially from left to right and top to bottom. Each tile is decomposed into its constituent pixel components: Alpha (transparency), red, green, and blue (ARGB), which correspond to the respective color channels. These components are read and compressed according to a predefined pattern, indicated by the directional arrows in Figure 1. The compression mechanism involves analyzing the current pixel component \"x\" in relation to its neighboring components: \"A\" (immediately preceding), \"B\" (directly above), \"C\" (upper left), and \"D\" (upper right). Each compression strategy is associated with a unique binary flag: '00B' is assigned to run-length coding, '01B' signifies non-coding, and '1B' is reserved for predictive coding. To determine the appropriate compression strategy, the algorithm uses computational formulas, denoted as Equations (1) and (2), to calculate the parameters \(P\) and \(mErrval\). These parameters play a critical role in deciding whether predictive coding or non-coding should be applied. Once the compression mode is selected, the encoded pixel component \"x\" is output along with its corresponding flag bit, which indicates the encoding strategy, followed by the actual encoded data.
## 3 Results and Discussion
### Effects of Resin Content  
#### 3.4.1 Flexural (Static Bending)  
Figure 13 presents the MOR and MOE as a function of the resin content. The bending properties of the boards were strongly dependent on the resin content. The results prove that a decreased resin content led to improving the bending properties. The lower resin content of the particleboards led to a higher number of contact points (inter-bonding between fibers), so that the bending strength was improved. For particleboard produced with a higher resin content (14%), the MOR value reached 1.3 MPa, and the MOE value was 213.6 MPa. Lower resin content (10%) in the produced particleboard led to improve bending properties, causing MOR to increase to 2.1 MPa and the MOE value to increase to around 344.97 MPa. The bending properties decreased with an increasing resin content; this was attributed to increase solidification of particleboard.
# A Novel Hybrid Path Planning Method Based on Q-Learning and Neural Network for Robot Arm
## 3 Simulation and Experiment Results
### Simulation Results
In order to better understanding the trend of the cumulative reward and showing concise reward value, the average rewards were added to the diagram. In Table 1, the best actions provided by the Q-learning algorithm are shown for the current workspace. By following the sequence of actions from the start cell in yellow, the robot can reach the target cell in green. Given Table 1, the end-effector starts from the start cell in (3,1) and moves down to reach cell (4,1). Then, it moves downward four times to reach cells (5,1), (6,1), (7,1), and (8,1). Finally, it moves rightward three times and passes through the cells (8,2) and (8,3) to reach the target cell in cell (8,4). In summary, the obtained actions and passing cells are written as a set of sequence of actions and cells as reported in the following:  
In order to use actions in the neural network, we rewrite their numerical forms as follows:  
Where Up is denoted by 1, Down is denoted by 2, Left is denoted by 3, Right is denoted by 4. With the help of cells' indexes and actions obtained in the previous step, in step 4, a neural network that was trained by the data set is used to determine the joints angles. As mentioned, the number of neural network inputs is three, including row index of the cell (i), column index of the cell (j), and action. Thus, according to the obtained data, the neural network inputs are as follows:  
Where are written in the form of [I-index, J-index, Numerical form of Action]. The outputs should be the required angles to move from the cell (3,1) to down, move from the cell (4,1) to down, move from the cell (5,1) to down, move from the cell (6,1) to down, move from the cell (7,1) to down, move from the cell (8,1) to the right, move from the cell (8,2) to the right, and move from the cell (8,3) to the right, respectively. The angles of the start cell are determined as \([18^{\circ},47^{\circ},49^{\circ},1^{\circ},-6^{\circ},198^{\circ}]\). The joints angles of found actions via the trained neural network are as follows: \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cells Indexes** & **1** & **2** & **3** & **4** \\ \hline
1 & Right & Right & Right & Down \\
2 & Right & Right & Right & Down \\
3 & Down & Down & Down & Down \\
4 & Down & Down & Down & Down \\
5 & Down & Down & Right & Down \\
6 & Down & Down & **Disturbic** & Down \\
7 & Down & Down & Down & Down \\
8 & Right & Right & Right & Target \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The best actions provided by the Q-learning algorithm for each cell. The simulation of our hybrid method helps us to evaluate its performance and safety before implementing it on a real-world experimental setup. Therefore, we can identify joints angles implemented in the RoboDK simulator step by step from the start cell to the target cell, respectively. Figure 6 shows this process.
## 5 Experiment Verification and Analysis
### Experiment on Datasets of Rolling Bearing in Case Western Reserve University  
#### 5.1.1 Experiment Description  
The experimental data are from the bearing database of CWRU [42]. The experimental bearing type is deep groove ball bearing SKF6205. In this experiment, the mixed data under four kinds of load of drive end bearing 0-3 hp are selected. The bearing experimental platform is shown in Figure 4. A 16-channel acquisition instrument is selected to collect vibration signals, and the sampling frequency is 12 kHz. Three kinds of defect positions of bearings are machined by electro-discharge machining (EDM), which are the ball fault (B), outer race fault (OR), and inner race fault (IR). Each type of damage has three sizes: 0.007 inches, 0.014 inches, and 0.021 inches. Bearings with different fault locations and different degrees of damage are regarded as a separate category, including a total of 10 bearing types in the normal state. Data enhancement is used to construct 1000 samples for each type of signal features, and one-hot coding [43] is used to label ten types of bearing states. The experimental dataset is divided into a training set, verification set, and test set according to 6:1:3, and the dataset and samples are divided as shown in Table 1.  
The original time-domain waveforms of vibration signals in 10 types of rolling bearings are shown in Figure 5. According to the figure, it is difficult to judge the fault types of rolling bearings by time-domain analysis, so this paper uses a sparse representation method to sparsely decompose the original signal in each state, extract effective features, and then reconstruct the signal. Finally, combined with 1 DCNN, 10 kinds of faults of rolling bearings are classified and diagnosed.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Fault Location** & **Normal** & \multicolumn{3}{c}{**Ball (B)**} & \multicolumn{3}{c}{**Inner Race (IR)**} & \multicolumn{3}{c}{**Outer Race (OR)**} \\ \hline Category labels & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\ Fault diameter & 0 & 0.007 & 0.014 & 0.021 & 0.007 & 0.014 & 0.021 & 0.007 & 0.014 & 0.021 \\ Sample length & 2048 & 2048 & 2048 & 2048 & 2048 & 2048 & 2048 & 2048 & 2048 & 2048 \\ Load (hp) & 0,1,2,3 & 0,1,2,3 & 0,1,2,3 & 0,1,2,3 & 0,1,2,3 & 0,1,2,3 & 0,1,2,3 & 0,1,2,3 & 0,1,2,3 & 0,1,2,3 \\ Training sample & 600 & 600 & 600 & 600 & 600 & 600 & 600 & 600 & 600 & 600 \\ Validation sample & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\ Testing sample & 300 & 300 & 300 & 300 & 300 & 300 & 300 & 300 & 300 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the rolling bearing dataset and sample partition.  
Figure 4: CWRU bearing fault test rig [42].
## 4 Correlations between Spatial Indicators and Lighting Parameters
### Correlations in the Case of Scenario 1 (Present Status)  
In Figure 12, the correlation between the \(I\) index and \(L_{uv}\) is shown. In this case, the \(R^{2}\) coefficient is negligible and the data are very scattered with respect to the regression line. However, the regression line slope suggests that as roads centrality increases, luminance values slightly increase as if roads of the centre need more light.  
In Figure 13, the correlation between the \(I\) index and \(E_{h,uv}\) is shown (black circles) together with the correlation between the \(I\) index and \(E_{h,min}\) (white circles) for the sidewalks on the same side of luminaires. In both cases the \(R^{2}\) coefficients are low and they results: \(R^{2}=0.16\) (in the case of \(E_{h,uv}\)) and \(R^{2}=0.33\) (in the case of \(E_{h,min}\)). The regression lines have a positive slope showing an increase of the illuminance values as roads centrality increases. The positive slopes suggest that the town centre is more enlightened while its suburb appears less enlightened. This is a normal and expected course because generally to the most central roads correspond the greater traffic volumes.  
In Figure 14, the correlations between the \(I\) index is shown (black circles) together with the correlation between the \(I\) index and \(E_{h,min}\) (white circles) for the sidewalks on the opposite side of luminaires. In both cases the \(R^{2}\) coefficients are negligible. The regression line slopes are negative in both cases showing a trend that is the inverse of the illuminance values on sidewalks on the same side of luminaires. The negative slopes of the regression lines suggest that as roads centrality increases,  
Figure 12: Scenario 1 (present status): correlation between \(I\) and \(L_{uv}\).  
Figure 13: Scenario 1 (present status): correlations between \(I\) and \(E_{h,uv}\) (black circles and a continuous line for the regression) and between \(I\) and \(E_{h,min}\) (white circles and a dotted line for the regression) on the sidewalks on the same side of luminaires.  
illuminance levels on sidewalk on the opposite side of luminaires decrease as if roads of the centre are less enlightened. This is an unexpected result because minimum lighting requirements are the same for both the sidewalks and they increase as roads centrality increases.
# Handover Decision-Making Algorithm for 5G Heterogeneous Networks
## 2 Literature Review
[18] proposed a UMTS-WLAN handover algorithm based on dwell time prediction, RSS, signal-to-noise ratio (SNR), and monetary costs. The dwell time, \(t\), is predicted for WLAN using Equation (3), where \(l\) is the estimated travelling distance across WLAN coverage and \(v\) is the MT moving speed. The algorithm initiates dwelling time prediction technique when the MT detects the \(RSS_{boundary}\) of WLAN, as shown in Figure 2. \(R\) is the radius of the predefined boundary of WLAN coverage \(RSS_{boundary}\) and \(r\) represents the radius of the predefined RSS threshold \(RSS_{th}\). The \(R\) and \(r\) values are computed using the log-distance path loss model. Available WLANs are considered candidates for handoverwhen their corresponding \(t\) is higher than the time threshold (2 s). The unnecessary handover occurs if \(t\) is less than 2 s.  
\[t=\frac{l}{v}=\frac{R^{2}\ -r^{2}-d}{vd}\qquad\text{ where }l=\frac{R^{2}\ -r^{2}-d}{d} \tag{3}\]  
The quality of candidate, \(k\) (\(Q_{k}\)), is evaluated based on the measured \(RSS_{k}\), \(SNR_{k}\), and monetary cost, \(C_{k}\), values by using Equation (4), where the network with the highest \(Q\) value is selected as the best network. \[Q_{k}=\frac{RSS_{k}*\big{(}SNR_{k}-SNR_{REQ_{WLAN}}\big{)}}{C_{k}},\ \ k=\{1,\,2,\ldots,n\} \tag{4}\]  
The simulation results show that the proposed algorithm has better throughput compared with the conventional handover scheme. Furthermore, the algorithm minimizes the probability of unnecessary handover while optimizing the connection time to WLAN networks. However, the network quality evaluation is based on RSS and SNR only. More QoS criteria such as bandwidth, data rate, delay, and bit error rate (BER) should be considered for a full network quality assessment.