esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Summarized Tables on AI Techniques and Performance  
In order to facilitate a clear comparison of the diverse AI-based methods employed for predictive maintenance in the mining sector, we have created a series of tables that consolidate the key algorithms, their reported performance metrics, computational costs, and most frequent mining applications. Table 5 provides an overview of selected studies spanning traditional machine learning (e.g., Random Forest, Support Vector Machine) and more advanced deep learning (e.g., CNNs, RNNs, Autoencoders) approaches, focusing on fault detection, anomaly identification, and real-time monitoring across critical mining assets such as conveyors, crushers, and ventilation systems [1; 3; 6; 84]. As shown in Table 5, classical methods like Random Forest (RF) and Support Vector Machines (SVM) remain popular due to their balance of accuracy and moderate computational demands [3; 6]. Meanwhile, deep learning techniques such as CNNs and RNNs often exhibit superior diagnostic accuracy, albeit at the cost of greater computational complexity and longer inference times [24; 25]. In addition, multiple studies propose hybrid methods that fuse domain-specific physics with ML algorithms to achieve robust fault detection in harsh mining environments [55]. These insights underscore the importance of selecting algorithms that match both the operational constraints and the specific fault detection objectives. Key Observations:
* ML techniques with ensemble strategies (e.g., XGBoost, RF) frequently exhibit strong performance across a variety of data modalities. * CNN and RNN/LSTM architectures excel when rich sensor inputs (vibration, acoustic, thermal) must be processed in real time [5; 8]. * Hybrid solutions incorporating physical models can mitigate data scarcity issues and enhance robustness under non-stationary conditions [55; 77]. Cited Literature on Techniques and Results  
[PERSON] et al. [25], [PERSON] et al. [24], [PERSON] et al. [67], etc., collectively demonstrate that ML algorithms, when supplemented with domain-driven feature extraction, can reach
## 4 Discussion
However, there was no considerable difference between experimental group 3, where only PRF was applied, and the unfilled control group in this study. Similar results were observed in another study, where platelet-rich fibrin did not increase bone regeneration in non-critical size defects in rabbit tibia. The authors suggested that this may be partially due to difficulty in retention of PRF at the defect site because of the anatomy of the tibia [40]. The differences in the biology and physiology can also affect the outcomes. Furthermore, no statistically significant difference was observed between groups 1 and 2. The experimental difference between the two groups was the application of PRF. There is a need to further investigate the effects of PRF when used in bone defects. To increase the release of growth factors, an appropriate scaffold is required. The tooth biomaterial led to the successful formation of new bone [10]. The powder form of a tooth biomaterial can be used for a narrow bony defect with or without PRF. Bioactive glass or HA biomaterials do not biodegrade fully and tooth biomaterials also require a comparatively longer duration for complete biodegradation [35]. In this study, a tooth biomaterial was successfully applied for restoration of peri-implant defects. We proved that peri-implant defects were successfully restored by using the tooth biomaterial enriched with PRF. Tooth biomaterial alone also exhibited satisfactory results as compared to PRF alone and empty defects. These results were supported by the implant stability test. More extensive clinical studies are needed to assess the application of tooth derived biomaterial as a bone graft material for bone tissue engineering. Further studies applying PRF in tissue engineering techniques should be carried out to verify this treatment method. Conceptualization, M.H.J. and E.S.L. ; methodology, E.S.L. ; validation, M.H.J., J.H.L., H.B.J., and E.S.L. ; formal analysis, J.H.L. and H.B.J. ; investigation, J.H.L. and P.W. ; resources, J.H.L. and P.W. ; data curation, M.H.J. ; writing--original draft preparation, P.W. ; writing--review and editing, P.W., H.S.J., and E.S.L. ; visualization, H.S.J. and E.S.L. ; supervision, H.B.J. ; project administration, E.S.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors would like to acknowledge Korea University for providing all the research facilities to carry out the research. The authors declare no conflict of interest.
## 4 Experimental Results
### Evaluation of the Model with XAI Methods  
As seen in Table 7, the best performance was obtained using the ANOVA FS, SMOTE sampling, and EXT classification algorithms. In this step, LIME and SHAP, both XAI methods, were employed to transform the successful model into an explainable and interpretable form. The flow of this proposed model is summarized in Figure 3.  
Figure 3: Proposed Model Flow.  
The best ML model was interpreted by performing XAI analysis using the LIME and SHAP methods.
## 7 Experimental Verification
### Bearing Reliability Prediction
To validate the predictive accuracy of the AdaBoost-MKRVM model, this study employed Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Gated Recurrent Unit (GRU), Least Squares Support Vector Machine (LSSVM), and MKRVM models to predict the reliability of bearings 3-1, 3-2, 3-4, and 5. The results were compared with those of the AdaBoost-MKRVM model. In the LSTM neural network, the number of neural network layers is set to 3; the neuron excitation function is set to Sigmoid function; and the number of input neurons of each layer, the hidden layer, and the output layer are set to 10 layers, 14 layers, and 1 layer, respectively. The GRU settings are the same as LSTM. In LSSVM,the Gaussian kernel function is used, and the penalty factor and kernel function parameters are set to 9, 0.002. In RVM, the Gaussian kernel function is used, and the penalty factor and kernel parameter are set to 9, 0.002. In MKRVM, the Gaussian kernel function and linear kernel function are used. The penalty factor and kernel  
Figure 15: Four bearing reliability prediction results. (**a**) Bearing 3-1; (**b**) bearing 3-2; (**c**) bearing 3-4; (**d**) bearing 5. This study addresses the issue of reliability prediction for rolling bearings, starting with vibration signals. An improved CYCBD algorithm based on the Ivy optimization algorithm was proposed to efficiently remove signal noise. A reliability modeling method based on logistic regression was developed, and an ensemble learning model combining AdaBoost and MKRVM was further applied for precise reliability prediction of the bearings, demonstrating the effectiveness and accuracy of the proposed approach.
# Enhanced Trajectory Tracking via Disturbance-Observer-Based Modified Sliding Mode Control
## Appendix A ESO Error Convergence  
In state space, Equations (3)-(5) are presented as:  
\[\dot{x}=Ax+Bu, \tag{1}\]  
where  
\[A=\begin{bmatrix}0&1&0\\ 0&0&1\\ 0&0&0\end{bmatrix},\ B=\begin{bmatrix}0\\ 0\\ 1\end{bmatrix},\]  
the observer in the above system (Equation (1)) can be designed as:  
\[\dot{\hat{x}}=A\hat{x}+Bu+L\text{-}C\text{-}\text{sat}\left(\overset{\sim}{x }_{1}\right), \tag{2}\]  
where \(C=[1\quad 0\quad 0]\), and \(L=[l_{1}\quad l_{2}\quad l_{3}]\), is the observer-gain matrix. Assuming that the estimated error is within the saturation limit, \(\text{sat}\left(\overset{\sim}{x}_{1}\right)=\frac{\overset{\sim}{x}_{1}}{ \varepsilon_{o}}\). Equation (2) can be written as:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Characteristics** & **ESO-Based SMC** & **ESO-Based PIDSMC** \\ \hline Small tracking error & No & Yes \\ Effective against perturbation & No & Yes \\ Model-free control & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 6: Concluding summary.  
\[\dot{\hat{x}}=A\hat{x}+Bu+L\cdot\mathcal{C}\left(\frac{\overset{\sim}{x}_{1}}{e_{o}} \right), \tag{11}\]  
The error derivative is defined as:  
\[\dot{e}=\overset{\sim}{x_{1}}=\dot{x}-\hat{x}, \tag{12}\]  
by using Equations (10) and (11) in (12):  
\[\dot{e}=Ax+Bu-A\hat{x}-Bu-L\cdot\mathcal{C}\left(\frac{e}{e_{o}}\right), \tag{13}\]  
after solving further:  
\[\dot{e}=A(x-\hat{x})-L\cdot\mathcal{C}\left(\frac{e}{e_{o}}\right), \tag{14}\]  
\[\dot{e}=Ae-L\cdot\mathcal{C}\left(\frac{e}{e_{o}}\right), \tag{15}\]  
\[\dot{e}=e(A-L\cdot\mathcal{C}/e_{o}), \tag{16}\]  
The Laplace transformation of Equation (16) is as follows:  
\[pe_{(p)}-e_{(0)}=\bigg{(}A-\frac{LC}{e_{o}}\bigg{)}e_{(p)}, \tag{17}\]  
\[pe_{(p)}-\bigg{(}A-\frac{LC}{e_{o}}\bigg{)}e_{(p)}=e_{(0)}, \tag{18}\]  
\[e_{(p)}=\frac{e_{(0)}}{p-\bigg{(}A-\frac{LC}{e_{o}}\bigg{)}}, \tag{19}\]  
The Laplace inverse of Equation (19) is as follows:  
\[e_{(t)}=e_{(0)}ex\bigg{(}A-\frac{LC}{e_{o}}\bigg{)}t. \tag{20}\]
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 3 Non-Cooperative Relative Positioning  
This section focuses on egocentric or non-cooperative approaches, while the following section analyzes cooperative approaches. Egocentric techniques are passive or non-cooperative in the way that a target vehicle is not actively contributing to the estimation of its relative position.
# Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm
## 2 Basic Knowledge and Literature Review
### Parallel Propulsion Mode  
The parallel propulsion strategies of multiple nodes are coupled and affect each other. Common parallel propulsion modes include synchronous sequential propulsion, synchronous parallel propulsion and asynchronous parallel propulsion, as shown in Figure 9.  
In synchronous sequential propulsion, as shown in Figure 9a, each task node of the system is executed in accordance with the specified order. During the whole progress, only one task node can be executed, represented as the colored progress bar shown in Figure 9a, while other task nodes are in the state of waiting, represented as the grey progress bar shown in Figure 9a. The logic of this propulsion mode is simple without complex synchronization mechanisms. Since the tasks are executed one by one and the result of the previous task can be sent to the next task as a parameter, the entire progress has low traffic. However, this mode is suitable for tasks nodes that are sequentially dependent. Otherwise, it will cause unnecessary time consumption and worse operation efficiency because every task node has large amounts of idle waiting time.  
Figure 6: Communication between nodes in the classical island model.  
Figure 7: Communication between nodes in the Pool model.  
In synchronous parallel propulsion, as shown in Figure 9b, all task nodes are executed at the same time. Due to the different computing capacity of each task node, the propulsion speeds are different. The task nodes with faster propulsion speeds need to spend certain amount of time waiting to maintain synchronization with other task nodes. In this mode, all tasks are in the state of synchronous parallel operation, so the solving efficiency is greatly improved compared with synchronous sequential propulsion. Even if a task node is blocked during a stage, other task nodes can still be executed. It is obvious that there is a need to wait for synchronization.  
Figure 8: Communication between nodes in improved Pool model.  
Figure 9: Parallel propulsion modes: (**a**) synchronous sequential propulsion; (**b**) synchronous parallel propulsion; (**c**) asynchronous parallel propulsion.  
needs to be a synchronization mechanism to keep the system robust. This mode is suitable for task nodes that need to communicate with each other regularly.  
In Figure 9c, all task nodes are executed in parallel at their own propulsion speeds without any waiting time. This mode is more suitable for task nodes with high independence. Otherwise, communication data between nodes will be chaotic.
# A Delay-Aware and Reliable Data Aggregation for Cyber-Physical Sensing
###### Abstract  
Physical information sensed by various sensors in a cyber-physical system should be collected for further operation. In many applications, data aggregation should take reliability and delay into consideration. To address these problems, a novel Tiered Structure Routing-based Delay-Aware and Reliable Data Aggregation scheme named TSR-DARDA for spherical physical objects is proposed. By dividing the spherical network constructed by dispersed sensor nodes into circular tiers with specifically designed widths and cells, TSTR-DARDA tries to enable as many nodes as possible to transmit data simultaneously. In order to ensure transmission reliability, lost packets are retransmitted. Moreover, to minimize the latency while maintaining reliability for data collection, in-network aggregation and broadcast techniques are adopted to deal with the transmission between data collecting nodes in the outer layer and their parent data collecting nodes in the inner layer. Thus, the optimization problem is transformed to minimize the delay under reliability constraints by controlling the system parameters. To demonstrate the effectiveness of the proposed scheme, we have conducted extensive theoretical analysis and comparisons to evaluate the performance of TSR-DARDA. The analysis and simulations show that TSR-DARDA leads to lower delay with reliability satisfaction.  
physical sensing; reliability; delay; tiered structure routing +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Characterization of the Parylene C Coating on Cylindrical Zn Stents  
#### 3.2.1 Composition of the Deposited Parylene C Coating  
The chemical identity of the Parylene C after depositing a coating on cylindrical, rhombus design Zn stents was characterized by Fourier Transform Infrared Spectroscopy (FTIR). FTIR was conducted within the wavelengths ranging from 4000 to 400 cm\({}^{-1}\). FTIR analysis illuminates the bonding energy of the Parylene C coating. Figure 9 compares the spectra obtained by FTIR from a) a Parylene C-coated Zn stent, b) a pure Parylene C sample, and c) an uncoated Zn stent. Peaks at 1500-1600 cm\({}^{-1}\) represent energy bands of the C=C aromatic stretching. Other peaks at 1050 cm\({}^{-1}\) and 650 cm\({}^{-1}\) can be assigned as the bonding energy peaks of chlorobenzene. Peaks at 1450 cm\({}^{-1}\) are representative of the bonding energies of C=C and CH\({}_{2}\). Finally, peaks at 2860 cm\({}^{-1}\) and 2923 cm\({}^{-1}\) indicate bonding energy of the C-H bond in the methyl group and 3000 cm\({}^{-1}\) designates C-H stretching [28; 33; 44; 45; 46]. From the FTIR study, it was obvious that there was successful formation of a Parylene C polymeric film on the Zn stent.  
Figure 8: Mechanical behavior of Zn stent with rhombus, U and Omega design under compression after balloon expanding.  
The presence of Parylene C coating on the stent was also evaluated by determining the nature of the carbon bonding in the polymer groups of the Parylene C coating using Raman spectroscopy, analyzing the peaks in the range from 3200 to 160 cm\({}^{-1}\). As illustrated in Figure 10a, characteristic peaks at 3100 cm\({}^{-1}\) represent the aromatic C-H stretching, and the band between 1265-1365 cm\({}^{-1}\) can be assigned to a CH in-plane deformation [47].  
Figure 10: Raman spectroscopy of: (**a**) Parylene_C Dimer as a control; (**b**) Uncoated Zn metal; (**c**) Parylene C-coated stent on random spot 1; (**d**) Parylene C coated stent on random spot 2.  
Figure 9: FTIR spectra of: (**a**) Parylene C-coated Zn stent on random spot 1; (**b**) Parylene C-coated Zn stent on random spot 2; (**c**) Pure Parylene C sample; (**d**) Uncoated Zn stent.  
Figure 10b illustrates the spectrum of the uncoated Zn stent without any prominent peaks. Figure 10c-d displays the spectra of Parylene C coating on Zn stents obtained by probing random spots on the device surface. The peak at 1600 cm\({}^{-1}\) represents the substitution (para) of chlorine on the benzene rings, while the peaks around 1350-1500 cm\({}^{-1}\) can be assigned to CH\({}_{2}\) twisting or C-C skeletal in-plane vibrations of the aromatic ring. The peaks within the range of 750-1000 cm\({}^{-1}\) illustrate the out-of-plane deformation of CH [48]. This Raman study further proved that a Parylene C polymeric film was formed on the Zn stent.
## 4 Results
### Results of GR Calculations and Measurements
In the one-rod GS designs with an LRM (1 LRMR), the GR reduction ranged from the original value to 5.34 \(\Omega\) (43%) in low-resistivity soils such as S1, with a resistivity of 24 \(\Omega\)-m, to 129.64 \(\Omega\) (81%) in high-resistivity soils such as S8, with a resistivity of 302 \(\Omega\)-m.  
For a more complex design than the previous case, as in the triangular grid configuration with an LRM (3 LRMRT), the GR reduction ranged from its original value of 1.00 \(\Omega\) (21%) in low-resistivity soils such as S1, with a resistivity of 24 \(\Omega\)-m, to 30.62 \(\Omega\) (72%) in high-resistivity soils such as S8, with a resistivity of 302 \(\Omega\)-m.  
In each design, the GR of the LRM (\(\Delta\)GR) decreased as the soil resistivity increased, as shown in the columns of Table 11. In this study, the vertical rods were covered with premixed (P) and dry (D) LRMs. As shown above,  
Figure 16: GR vs. GS design in S4. Figure 14: GR vs. GS design in S2. Figure 15: GR vs. GS design in S3. These results show that, for example, in the one-rod design with an LRM (1 LRMR) in a low-resistivity soil such as S1, with a resistivity of 24 \(\Omega\cdot\)m, the GR decreased from its original value to 5.34 \(\Omega\) (43%). In contrast, in the triangular grid design with an LRM (3 LRMRT) in the same soil, the GR decreased to 1 \(\Omega\) (21%). In a high-resistivity soil such as S8, with a resistivity of 302 \(\Omega\cdot\)m, the GR decreased from its original value in the one-rod design with an LRM (1 LRMR) to 129.64 \(\Omega\) (81%), whereas for the 3 LRMRT design in the same soil, the GR decreased to 30.62 \(\Omega\) (72%). Thus, as the complexity of the GS design increased, the reduction in the GR of the LRM decreased. In addition, the LRM effect was assessed on two rectangular grids of six-rod GSs. One grid was placed in soil 5 (S5), with a resistivity of 175 \(\Omega\cdot\)m, with the premixed LRM covering each of the vertical rods, and the GR decreased from its original value of 5.22 \(\Omega\). The other grid was placed in soil 6 (S6), with a resistivity of 190 \(\Omega\cdot\)m, with the dry LRM covering each of the vertical rods, and the GR decreased to 0.60 \(\Omega\). A considerable GR reduction was not obtained despite the high resistivity of the soil because the GS design was even more complex. Figure 17: GR vs. GS design in S7. Figure 18: GR vs. GS design in S8.
# Research on the Robustness of Active Headrest with Virtual Microphones to Human Head Rotation
## 2 Theory  
### Primary Sound Field  
The sound fields are assumed to be harmonic and the optimal control is derived through numerical calculations in frequency domains. Figure 1 demonstrates the spherical coordinates used in this paper where the head is modelled as a rigid sphere of radius \(a\) with the center of the sphere located at the origin. The primary sound field is assumed to be a diffracted diffuse field, and each of its samples is simulated by the superposition of plane waves with random phases and amplitudes from different directions. With the introduction of a rigid sphere, the sample of diffuse field pressure at \(\mathbf{r}=(r,\theta,\phi)\) can be expressed as [20]  
\[p_{p}(\mathbf{r})=\frac{1}{\sqrt{N_{p}}}\sum\limits_{i=1}^{N_{p}}P_{pi}( \mathbf{r}), \tag{1}\]  
\[\begin{array}{ll}P_{pi}(\mathbf{r})=&P_{Ai}e^{-ik\varphi_{0i}}\sum\limits_{l =0}^{\infty}{(-j)^{l}(2l+1)[j_{l}(kr)-\frac{j_{l}^{\prime}(ka)}{h_{l}^{\prime}( ka)}h_{l}(kr)]}\\ &\times\left\{P_{l}(\cos\theta_{i})P_{l}(\cos\theta)+\sum\limits_{m=1}^{l}{2( \frac{l-m}{l+m)!}P_{l}^{m}(\cos\theta_{i})P_{l}^{m}(\cos\theta)\cos[m(\phi- \phi_{i})]}\right\},\end{array} \tag{2}\]  
where \(k\) is the wave number, \(N_{p}\) is the number of plane waves, \(P_{Ai}\) and \(\varphi_{0i}\) are the amplitude and phase of the \(i\)th plane wave which are taken from uniform distributions of [0, 1] and \([-\pi,\pi]\), respectively. The incidence direction of the \(i\)th plane wave is expressed as \(\mathbf{n}_{i}=(1,\theta_{i},\phi_{i})\), where the elevation angle \(\theta_{i}\) is uniformly distributed in [0, \(\pi\)] for a total of \(N_{s}\), and, on the circumference of the circle where the elevation angle \(\theta_{i}\) is located, the azimuth angle takes uniform values in [\(-\pi\), \(\pi\)], for a total of \(2N_{s}\)sin\(\theta_{i}\). The factor sin\(\theta_{i}\) ensures that, on average, the incident waves are uniform at all angles. \(j_{l}(x)\) is the spherical Bessel function of order \(l\), \(h_{l}(x)\) is the spherical Hankel function of order \(l\), and \(P_{l}^{\prime m}(x)\) denotes the \(m\)th associated Legendre function of order \(l\), which becomes the Legendre polynomial for the case \(m=0\). When \(N_{p}\) is large enough, Equation (1) is an appropriate estimation of a sample of the primary diffuse field. In this paper, \(N_{s}\) is set to 30 when \(N_{p}=1116\), and the average of mean square sound pressure from 30 primary sound field samples is used for simulations.  
Figure 1: Coordinate of the rigid sphere.
## 3 Methodology
### Performance Metrics  
Four performance predictors, Precision, Recall, Accuracy, and F1 scores, were evaluated to demonstrate the performance of the trained model using Equations (1)-(4). Precision is the ratio of true positive (TP) outcomes to the sum of true and false positive (FP) outcomes. Recall is the ratio of true positive (TP) outcomes to the sum of true positive (TP) and false negative (FN) outcomes. The harmonic meaning of the precision and recall is known as the F1 score of the model. Accuracy shows the final count of the accurate predictions, representing the model's performance, and is used to calculate the loss of the model simultaneously. The positive outcome predicted as positive is known as a true positive (TP). An unfavorable outcome predicted as a positive output is known as a false positive (FP), and vice versa. The true negative result, which is negative, is called true negative (TN).  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{1}\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}) \tag{2}\]  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{FP}+\text{FN}+\text{ TN}) \tag{3}\]  
\[\text{F1}=2\times(\text{Precision}+\text{Recall})/(\text{Precision}\times \text{Recall}) \tag{4}\]