esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Real-Time Monitoring and Fault Diagnosis Technology
### Real-Time Monitoring  
Condition-based maintenance (CBM), which can grasp the working conditions of equipment in real time, is welcomed by more and more manufacturers [103]. The Optimine system in the Sandvik company collects the real-time operating information of the scraper and integrates all the data into one platform to make them visualized and under control. The intelligent monitoring system for an underground LHD with bucket volume of 8 m\({}^{3}\), which was developed by the Jinchuan Group in China, could achieve the operation status monitoring in real time and record, save and transmit the running data as well. Moreover, it was also reported to have the functions of fault alarming and maintenance prompting [104]. Academically, Loughborough University's [PERSON] et al. [105] monitored the dynamic data in  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Advantage** & **Disadvantage** \\ \hline A* algorithm & It responds quickly to the environment & It has large amount of computation, poor real-time performance and long operation time \\ Fast Random Search Tree Algorithm & The search is highly efficient and is adaptable to different scenes & It is nonautonomous and time consuming for the path planning \\ Genetic Algorithm & Easy to plan for the global optimal path & The calculating speed is slow with low search efficiency \\ Neural Network Algorithm & high fault tolerated and generalization ability & Huge training data is required and there may be some unexpected data which is difficult to be handled \\ Ant Colony Algorithm & The optimal path can be searched at multiple points in the global area at the same time & Easy to fall into local optimum and slow convergence \\ Particle Swarm Algorithm & Fast search speed and good environment & Easy to result in local optimum and low convergence accuracy \\ Artificial Potential Field & Simple structure, convenient for bottom real-time control & Easy to simply obtain a local optimal solution and “chattering” phenomenon \\ Fuzzy Logic Algorithm & The uncertainty and ambiguity for data processing can be overcame, exhibiting good real-time performance & It is expert in experience and requires large amount of calculation for complicated situations \\ \hline \hline \end{tabular}
\end{table}
Table 3: Characteristics of path planning methods.  
the hydraulic system for mobile machinery through embedded particle pollution sensors academically and determined the wear and tear degree of the machine and corresponding locations according to the size and shape of metal particles in the hydraulic oil. [PERSON] et al. [106] proposed an algorithm for detecting the technical state change in the scraper based on temperature data. It was proved by tests that the algorithm can describe the condition of the scraper's cooling system well and is suitable for various types of LHD.
## 3 Results and Discussion
### Modelling of the Sauter Mean Diameter  
Table 2 lists the coefficients and their corresponding confidence intervals of Equations (8) and (9), which were determined in this work. Table 3 summarizes the quality of the re-correlated models. The coefficient \(C_{\text{S}}\) of the viscosity in Equation (9) was taken from the data of [PERSON] et al. [51], because the influence of the viscosity is not studied in this work.  
Both models show a good agreement with the measured values (see Figure 8), which is confirmed by high coefficients of determination, \(R^{2}>0.95\). The model of [PERSON] et al. [51] represents the experimental data slightly better (higher value of \(R^{2}\)). The deviation from the model to the experimental data is less than 12% absolute and 4.0% as mean deviation for [PERSON] et al. [51] and less than 22% absolute and 5.7% as mean deviation for [PERSON] [50]. The errors are randomly distributed with the approach of [PERSON] et al. [51], over all data. The standard approach by [PERSON] [50] has the highest errors for \(\varphi=5\) vol.-%, which is a result of the linear dependence of the phase fraction in the model. The value of the Weber exponent is for both models around \(C_{9}\approx-0.65\), which is close to the theoretical one of \(-0.6\) being obtained if coalescence is neglected [47]. The results with a Weber exponent \(C_{9}=-0.6\) are shown in the Supplementary Material in Tables S1 and S2 and in Figure S1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Calculated Errors**} \\ \hline \multicolumn{1}{c}{\begin{tabular}{c} **Maximum** \\ **Relative Error** \\ \end{tabular} } & \multicolumn{1}{c}{\begin{tabular}{c} **Mean** \\ **Relative Error** \\ \end{tabular} } & \multicolumn{1}{c}{\
\begin{tabular}{c} **Maximum** \\ **Absolute Error** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **Mean** \\ **Absolute Error** \\ \end{tabular} } \\ \hline \(\Delta d_{32}^{max,\%}\) in \% & \(\begin{array}{c}-mean,\%\\ \Delta d_{32}\end{array}\) in \% & \(\begin{array}{c}\Delta d_{32}^{max,\mu\mu}\text{ in }\mu\text{m}\\ \Delta d_{32}\end{array}\) in \(\begin{array}{c}\mu\text{m}\end{array}\) & \(\begin{array}{c}-mean,\mu\text{m}\\ \Delta d_{32}\end{array}\) in \(\begin{array}{c}\mu\text{m}\end{array}\) \\ \hline Values Equation (8) & \(21.9\) & \(5.7\) & \(63\) & \(24\) \\ Values Equation (9) & \(11.4\) & \(4.0\) & \(50\) & \(18\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Absolute and relative deviations of the models.  
Figure 7: (a) Experimental volume distributions at different phase fractions, \(N=400\) rpm. The lines are a guide to the eye; and (b) experimental Sauter mean diameters of both experiments at different rpm (model of [PERSON] et al. [51] with the coefficients from Table 2).
## 4 Proposed Approach
### Pseudocode: The Operation Process Flow  
The algorithm is represent for anomaly detection in a dataset, where the target attribute is analyzed for anomalies such as DoS, brute force, malformed, flood, and slowite. The algorithm takes the dataset as input and performs various operations on it to obtain the accuracy, F1 score, precision, recall, training and testing time. The algorithm is designed to work in multiple stages. In the first stage, the algorithm checks each value in the target attribute and executes code when the category matches one of the anomaly values. If the value is an anomaly, it is labeled with the corresponding category name and stored in the 'anomaly_category' column for that row. Then, the frequency of each anomaly is counted and stored in a dictionary called 'anomaly_freq'. Anomalies with the highest frequency, such as DoS, brute force, and malformed, are selected and stored in a list called 'high_freq_anomalies' by iterating through the sorted list of anomalies until the count of high_freq_anomalies is equal to 3, shown in Figure 5.  
Figure 4: The attacks are prioritized as per the frequencies of anomaly attacks in the dataset, the three attacks used in this research are DOS, Brute-Force, and Malformed these attacks are more frequent and critical.  
In the next stage, the correlation matrix is calculated for the dataset, and the attributes with weak or zero correlation are removed from the dataset. The resulting dataset is saved as the Feature Selection (FS) for the corresponding attack type. Then, the dataset is split into training and testing sets, and various machine learning techniques such as RF, NB, BT, LR, MLP, ANN, and DNN are applied on the training set using the best FS for each attack. Finally, the accuracy, F1 score, precision, recall, training and testing time are calculated for each model, and the best performing algorithm is identified based on the evaluation metrics. The algorithm is designed to automate the process of detecting anomalies in a dataset, and it uses various techniques to identify the best performing algorithm. This algorithm can be applied to various datasets to detect anomalies, and it can be modified to include additional techniques or metrics to improve its performance. The algorithm is useful in detecting anomalies in various domains, including smart homes, finance, healthcare, and cybersecurity.
## 4 Experiment Verification
### Control System
\[\hat{\eta}(t)\triangleq\hat{\omega}(t)u(t)+\hat{\theta}(t)x(t)+\hat{\sigma}(t)\]  
It can be derived straightforwardly that \(L=150\),000, while \(\parallel G(s)\parallel_{\mathcal{L}1}L\) can be obtained. Figure 5 shows \(\lambda=\parallel G(s)\parallel_{\mathcal{L}1}L\) with respect to the bandwidth of the low-pass filter \(\omega\). Let \(\omega=0.8\), obtain \(k=80\) that satisfies the \(\mathcal{L}_{1}\)-gain upper bound. Table 2 has the constant values used in the \(\mathcal{L}_{1}\) adaptive controller. In the design of the \(\mathcal{L}_{1}\) controller, we needed to estimate the boundary of parameters. Here, we mainly used the experimental method to estimate the boundary of \(\omega,\ \theta\) and \(\sigma\). Take the parameter boundary estimation of \(\sigma\) as an example. First, set a larger boundary, then make the platform control input \(r=[0.2 sin(\pi t),\ 0.3 sin(\pi t),\ 0.48+0.14 sin(\pi t),\ \frac{\pi}{2}sin(\pi t),\ \frac{\pi}{6}sin(\pi t),\ \frac{\pi}{2}sin(\pi t)]\) and impose external disturbance on the moving platform to obtain the accurate boundary of parameter \(\sigma\) under the complex state of the moving platform. The curve data of parameter \(\sigma\) is  
Figure 5: \(\lambda\) with respect to \(\omega\). shown in Figure 6, and the bounds of \(\sigma\) is \(|\sigma_{i}(t)|\leq 400\,,\;i=[1,\;\ldots,6]\). Further, we can obtain the following conservative bounds for the unknown time-varying signals for the implementation of the projection operator, \(\omega_{i}\in[0.8\,1.2],\;i=[1,\;\ldots,6]\); \(|\theta_{i}(t)|\leq 3800\,,\;i=[1,\;\ldots,6]\).
## 7 Conclusions  
This study conducted trishear kinematic modeling and limit analysis to accurately constrain the dip angle of peripheral faults at the tips of mafic saucer-shaped sills and better understand the origin of these faults. This case study was based on a saucer-shaped sill and its associated peripheral faults imaged in seismic reflection profiles. The main conclusions of the case study are the following:  
1. The trishear kinematic model successfully reproduces the geometry of peripheral faults at the sill tip and associated folds by performing a fault displacement of 58 m on a vertical fault plane with a P/S ratio of 2.5, where the fault displacement is slightly larger than the value derived from seismic interpretation;
2. The seismic interpretation shows that the dip angle of the fault in the case study is 88\({}^{\circ}\) inward dipping. However, both the trishear kinematic model and limit analysis suggest a value of 90\({}^{\circ}\). The latter is believed to better represent the actual value;
3. In contrast to the classical model where the host rock deformation above mafic sills is dominated by elastic bending, the results support that peripheral faulting and associated uplifting can occur at the tips of the mafic sill as a result of sill inflation;
4. Trishear kinematic modeling and limit analysis are effective methods for studying the geometry and mechanical properties of peripheral faults at the sill tips, respectively.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14198621/s1](https://www.mdpi.com/article/10.3390/app14198621/s1)]([https://www.mdpi.com/article/10.3390/app14198621/s1](https://www.mdpi.com/article/10.3390/app14198621/s1)), Figure S1: Cross-section maps of damage zone with varying values of C.  
This research was supported by the National Natural Science Foundation of China (Grant 41906053).  
Not applicable.  
The data that support the findings of this study are available on request from the corresponding author upon reasonable request.  
The author acknowledges Optum CE for their academic permission to use OptumG2 in this study. Data mapping is supported by the software Generic Mapping Tools (GMT) [41].  
The anonymous reviewers are also thanked for their valuable comments and suggestions, which greatly improved the quality of this manuscript.  
The author declares no conflicts of interest.
###### Abstract
Furthermore, advanced modulation scheme technology like the OFDM modulation technique is employed within the CR system. These techniques work efficiently in multipath fading channels and achieve a high data rate within the operating wireless systems [21; 22; 23; 24; 25]. The rest of the paper is arranged as follows: In Section 2, spectrum management and graph signal processing are described. Section 3 explains models 1 and 2 of the proposed DSCA-MC-CR system, including the system topology, system parameters, and the RF spectrum representation. The mathematical representation of the proposed DSCA-MC-CR is explained in Section 4. The SINR calculations, bit error rate (BER) estimations, data rate evaluations, and packet interarrival time analysis are presented in Section 5. The simulation results and the discussions are presented in Section 6. Finally, the paper's conclusions are given in Section 7.
# Prediction of Tunnel Blasting Vibration Velocity Considering the Influence of Free Surface
## 4 Blast Velocity Formula Considering the Effect of Free Surface  
### The Proposed Correction Formula for Blast Vibration Velocity  
According to the field test results, the free surface factor has a significant impact on the tunnel blasting vibration velocity propagation. The peak vibration velocity falls with the number of free surfaces while increasing with the distance between burdens. In general, the cutting blasthole only has a free surface; at this moment, the nearby rock is tightly clamped, intensifying the vibration effect. In contrast, there are multiple free surfaces in non-cutting blasting, where the peak vibration velocity is more closely related to the distance of the burden. Tunnel blasting, the number of free surfaces, and the burden distance in the cutting blasthole and non-cutting blasthole of the change in the schematic are shown in Figure 5.  
Figure 4: Diagram of variation in vibration velocity peak distribution with the serial number of blastholes.  
Figure 5: Schematic diagram of blasting of different blasting holes in the tunnel: (**a**) Burden variation. (**b**) Free surface variation.  
Both the tunnel blasting the free surface and the minimum burden has a large influence on the blasting vibration (Figure 5), and the peak vibration velocity is negatively correlated with the number of free surfaces and positively correlated with the burden distance. Therefore, when predicting the vibration velocity formula, the Sadovsky-modified the formula considering the influence of the free surface, which is established as:  
\[v=k\left(\frac{\sqrt[3]{Q}}{R}\right)^{\alpha}\left(\frac{W_{\min}}{mR}\right)^ {\eta} \tag{2}\]  
where \(W_{\min}\) is the minimum burden (m), m is the number of free surfaces, and \(\eta\) is the attenuation coefficient associated with the free surface. Equations (1) and (2) were linearized for regression analysis, and logarithms were taken on both sides of the equation.  
Sadovsky formula:  
\[\ln v=\ln k+\alpha\ln\left(\frac{\sqrt[3]{Q}}{R}\right) \tag{3}\]  
Correction formula:  
\[\ln v=\ln k+\alpha\ln\left(\frac{\sqrt[3]{Q}}{R}\right)+\eta\ln\left(\frac{W_ {\min}}{mR}\right) \tag{4}\]
## 5 Experiments and Results
### Multi-Person Pose Estimation and Tracking
#### 5.1.3 Results on the PoseTrack Benchmark  
As outlined in Section 4.2, our approach involves training the model on the COCO training dataset and subsequently fine-tuning it on the PoseTrack 2018 training dataset. We first performed a thorough analysis of the COCO keypoint detection dataset for human pose estimation and made a fair comparison with the most recent and state-of-the-art methods. The COCO dataset is annotated from the original COCO dataset, while the mixed dataset is obtained by integrating multiple datasets including COCO, PoseTrack,etc. The above dataset settings are designed to be aligned with the baseline methods. The results summarized in Table 2 show that our method achieves the best performance currently. Specifically, on the COCO dataset, our average precision (AP) reaches 76.7%, and AP at 0.5 to 0.75 is 92.5%, 83.2%, and 79.9%, respectively. At the same time, we found that our method not only performs best in accuracy but also in recall rate (AR). On the mixed dataset, the same conclusion can be drawn: AP is 78.5%, and AP at 0.5 to 0.75 is 93.1%, 84.9%, and 80.2%, respectively. Considering that we use ResNet-50 as the backbone network, its final latency is 59.2 ms.  
We present our method's performance on the PoseTrack 2017 and PoseTrack 2018 benchmarks. Table 3 displays the results for the multi-person pose estimation task, while Table 4 showcases the results for the multi-person pose tracking task. Our larger ResNet-152 network achieves superior performance, with a 77 mAP score and a 65.8 MOTA score on the PoseTrack 2018 validation dataset. When compared to the state-of-the-art PoseTrack framework, specifically the FlowTrack in the Simple Baseline [26], our framework exhibits a slight improvement of 0.3% (73.9 \(\rightarrow\) 74.2) on the PoseTrack 2017 test dataset. Although the improvement in mAP is not substantial, it is noteworthy that our framework operates under significantly more challenging conditions than FlowTrack, yet achieves comparable results (MOTA: 57.3 vs. 57.6) in the pose tracking task. Unlike FlowTrack, our spatiotemporal sampling module does not rely on large amounts of flow data or complex propagation processes based on optical flow between multiple frames during the tracking stage. While being lightweight and straightforward to implement, our spatiotemporal sampling module can seamlessly integrate into various high-performing person detection architectures. The fact that our framework successfully learns temporal correspondences and outperforms strong single-frame baselines in both mAP and MOTA is impressive, as depicted in Table 5. To the best of our knowledge, our approach is the first to explore the temporal relationship between video frames at the feature level for video person detection in the modern PoseTrack framework.
## 3 Results  
### Thermogravimetric Analysis (TGA)  
Figure 1 presents the TGA and derivative of thermogravimetry (DTG) curves of the raw materials used: LDPE and GTR (nonregenerated (NRR) and regenerated (RR)) in air. Figure 1a shows that the weight loss of neat LDPE begins around 300 \({}^{\circ}\)C, and complete degradation occurs around 500 \({}^{\circ}\)C. On the other hand, the weight loss of GTR (NRR and RR) begins around 200 \({}^{\circ}\)C, and degradation is completed around 570 \({}^{\circ}\)C. Figure 1b shows that for LDPE, a single peak at 450 \({}^{\circ}\)C is observed, which is related to the neat nature of the matrix, while the GTR presents several peaks between 250 and 530 \({}^{\circ}\)C related to GTR decomposition and the complex structure (a mixture of several components). As expected, the presence of oxygen in air decreases the thermal stability of all materials. The highest decomposition temperature can be associated to the oxidation of carbon residues generated at lower temperatures, leading to the formation of carbon dioxide [25]. [PERSON] et al. [26] studied the existence of inorganic compounds like silicon, calcium, and zinc in the form of SiO\({}_{2}\), ZnO, and CaCO\({}_{3}\) in GTR, individually. Therefore, a peak at around 510 \({}^{\circ}\)C can be related to the oxidation of inorganic components in the GTR. Furthermore, it can be observed that GTR regeneration cannot prevent the oxidation of carbon in the GTR structure since the main effect of this process is to partially break up the sulfur bonds.  
Figure 2 shows the TGA and DTG curves of the raw materials in nitrogen. The DTG curve in nitrogen shows that LDPE still has a sharp peak with a maximum at 470 \({}^{\circ}\)C. For GTR, two peaks between 350 \({}^{\circ}\)C and 430 \({}^{\circ}\)C can be seen. In Figure 2b, the small hump around 220 \({}^{\circ}\)C is related to GTR degradation. Around 8% weight loss can be reported at this point. Also, Figure 2a shows that the ultimate weight loss is less than 100%. This may be due to the presence of residues/inorganics in the GTR.  
Figure 1: Thermogravimetric analysis (TGA) (**a**) and derivative of the thermogravimetric (DTG) curve (**b**) curves of low-density polyethylene (LDPE) and different ground tire rubber (GTR) in air: RR: regenerated rubber and NRR nonregenerated rubber.
# A Survey of Techniques for Discovering, Using, and Paying for Third-Party IoT Sensors
## 4 Gap Analysis and Directions for Novel Research  
In the previous section, we reviewed the related work that can contribute to the development of IoT device sharing and enable its objectives, which are IoT device discovery, use, and pay. This review has helped us identify existing gaps in the related work IoT device sharing. In this section, we analyze the related work gaps that have hindered the development of global IoT device sharing. Also, we provide directions for novel research that help researchers advance the development of global IoT device sharing. From reviewing the related work in Section 4 and Tables 3-8, we can see that most of the existing techniques do not address the challenges of achieving global IoT device sharing. From the reviewed techniques that are based on using, or on part of blockchain technology such as [30; 37; 38; 39; 40; 55; 56; 57; 59], we can say that blockchain technology is a possible approach to establishing global and IoT-owned IoT device sharing. Blockchains are a decentralized ledger that can allow for IoT applications to query IoT devices and data descriptions along with other needed information (e.g., endpoint) stored in them. Also, it can run layers or modules on top of it in a decentralized structure to support IoT device discovery, using and paying functionalities for IoT applications. Blockchain is not owned or controlled by individuals; it runs on globally distributed computers (i.e., nodes) and is controlled by a consensus. It can thus support the IoT-owned sharing of IoT devices. However, in our survey, there is no blockchain-based technique for indexing IoT devices. Also, the analyzed blockchain techniques did not provide proof of scalability for sharing a large number of IoT devices. Based on that, in this section, we describe the identified gaps that hindered the development of the global sharing of IoT devices and our vision to address these gaps. Figure 3 shows our envisioned architecture for IoT device sharing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scalable** & **IoT-Owned** & **Interoperable** &
\begin{tabular}{c} **Fully Support Paying for** \\ **IoT Devices** \\ \end{tabular} \\ \hline \multirow{4}{*}{[57; 58]} & \multirow{4}{*}{None} & None of the related work fully \\  & & & supports paying for IoT \\ \cline{1-1}  & & devices nor supports \\ \cline{1-1}  & & & pay-as-you-go \\ \hline \hline \end{tabular}
\end{table}
Table 8: A summary of the review of related work for paying Internet of Things devices.