esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
We proposed an analytical method that calculates the TE of a spur gear pair by considering various materials and statistically analyzed the range of the TE caused by various uncertainties. In contrast to the conventional approach, which considers only the mechanical uncertainty, we additionally considered the thermal deformation for tooth contact analysis. This approach is appropriate in that thermal deformation can generate a large geometric error according to the material. To demonstrate the importance of the geometric error caused by thermal deformation, the magnitudes of geometric errors caused by mechanical uncertainty and thermal deformation were compared in the case of a plastic gear. Through various numerical examples, we investigated the tendency and influence of each uncertainty on the TE. We believe that considering the uncertainties can lead to more precise gear design in terms of predicting the performance range and managing the gear quality. Although steel gears are not susceptible to thermal deformation, we demonstrated that the TE of a spur gear pair with a comprehensive error is greater than that of a spur gear pair with mechanical uncertainties due to thermal deformation. In this study, we only investigated a numerical method that calculates the TE of a spur gear pair with uncertainties. However, this approach should be validated experimentally, and the influence of thermal deformation on the TE should be examined using the proposedmethod. Furthermore, the proposed method can be extended to other gear systems, such as helical, worm, and harmonic drive systems, since the approach is based on the kinematic relationship between the rack and the gear. Studying spur gear pairs that are susceptible to uncertainties in the tooth geometry is an important direction for future research.  
Conceptualization and numerical analysis, J.-H.L., H.-S.C., and J.-G.K., writing-original draft preparation, J.-H.L., H.-S.C., writing--review and editing, J.-G.K., J.-H.S., G.-H.L., D.-I.P.; funding acquisition, D.-I.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Technology Innovation Program NO.1415168894 supported by the Ministry of Trade, Industry and Energy of the Korean Government.  
Not applicable.  
Data Availability StatementData available on request due to privacy/ethical restrictions. The data presented in this study are available on request from the corresponding authors. The data are not publicly available due to their containing information that could compromise the privacy of research participants.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 Test Equipment and Analysis Method
### Measurement and Analysis of Pressure Fluctuation  
As shown in Figure 9, the pressure pulsation characteristics of a straight outlet conduit without a division pier were tested; four CY302 high-precision digital pressure sensors were set on the wall of a straight outlet conduit, which were distributed in the vertical and horizontal directions of the straight outlet conduit. The angle of the two adjacent pressure sensors was 90\({}^{\circ}\), and the pressure pulsation test was conducted at the impeller speed of 2200 r/min.  
Generally, a Fourier transform is used to analyze pressure fluctuation signals, which has the disadvantages of being non-stationary, of short duration and with time domain and frequency domain localization. The wavelet packet method is a more detailed decomposition and reconstruction method of signals based on wavelet analysis; the high and low-frequency parts are decomposed by high and low-pass filters, respectively, and wavelet packet decomposition can adaptively select the corresponding frequency band according to the signal characteristics and analysis requirements and match with the signal spectrum. The sampling frequency of the pressure fluctuation test is \(f_{\rm s}=1000\) Hz; according to the Nyquist sampling theorem, the highest analysis frequency is \(f=f_{\rm s}/2=500\) Hz, which is divided into 16 sub bands. The frequency bandwidth of each wavelet packet is \(d=f/24=31.25\) Hz, and the wavelet packet energy spectrum  
Figure 9: Installation positions of pressure sensor.  
wavelet to decompose the signal to a specified number of layers and then solve the energy of each node decomposition coefficient. The solution steps and corresponding calculation formula are described in [30; 31; 32], and the four-level decomposition tree of the pulsating wavelet packet of each measuring point in a straight outlet conduit is shown in Figure 10.
## 2 Materials and Methods
### Technical Indicators
#### 2.2.2. Ensemble Models  
* **Random Forest (RF)**: RF [27] fits several decision trees on random samples of the data and averages them to obtain a final result. Individual trees are fit by recursively splitting the data in such a way that maximises the information gain. The hyperparameters used by RF are the number of fitted estimators, the maximum number of features, and the minimum sample leaf size.
* **AdaBoost (AB)**: AB [28] is an adaptive boosting algorithm used to sequentially train an ensemble of weak learners. The algorithm begins by fitting a weak learner, and continues by training copiesof this learner, placing a greater instance weight on incorrectly predicted values. The algorithm proceeds until the final model, a weighted sum of all trained weak learners, becomes a strong learner. We use the algorithm to train an ensemble of decision trees.
* **Gradient Boosting (GB)**: Another boosting algorithm, GB [29] focuses on sequentially improving model predictions by fitting copies of learners to residuals. Residual predictions are repeatedly added to model predictions until the sum of residuals stops decreasing. Similarly to AB, we choose to apply the GB algorithm to train an ensemble of decision trees.
## 1 Introduction
Furthermore, unlike the conventional communication network, the SCNs of SGs must satisfy the minimum delay requirement of the power grid due to the time-critical nature of synchrophasor applications [13]. These dynamic challenges may lead to failure in the communication links of SCNs, which disrupt the operation of SGs. However, the extent of such failure should be minimized as much as possible. To achieve the objective of mitigating the impact of link failure or to evade any dire consequences of it, SCNs are designed to be resilient. Further, a highly resilient synchrophasor communication network can respond to such failures. The paper's remainder is organized as follows: The motivation and related work within the paradigm of the present work are discussed in Section 2. The system modelling of the SCN and parameterization for estimating the resiliency are discussed in Section 3. Section 4 proposes a graph-theoretic approach for the resiliency analysis of SCNs. The simulation results and discussion are included in Section 5. Finally, the conclusion of the work with future direction is presented in Section 6.
# Simulation of Wave Propagation Using Finite Differences in Oil Exploration
In this sense, splitting the set of sources and using a cluster of 16 computers to obtain the resulting seismogram along the profile is proposed. [PERSON] [8] developed a numerical simulation of elastic wave propagation in azimuthally anisotropic media using a multi-level finite difference method. [PERSON] [9] employed a GPU-based cluster environment for the development of scalable solvers for a 3D wave-propagation problem with finite difference methods. [PERSON] [10] presented an algorithm for the numerical solution of an initial-boundary value problem for a symmetric t-hyperbolic system of partial differential equations, which describes the propagation of seismic waves in a porcelastic medium saturated with a fluid. [PERSON] [11] developed a fourth-order finite difference scheme for the numerical modeling of elastic waves in the frequency domain, which can provide a stable numerical solution with fewer grid points per wavelength. These papers demonstrate the potential of finite difference methods for simulating wave propagation in oil exploration. In addition, other authors contribute significantly to oil exploitation studies, using simulation methods that facilitate the understanding of the associated variables and their behavior in situ. [PERSON] [12] discusses using the finite difference method in the numerical simulation of stress wave propagation in the drill string and how factors, such as temperature and material properties, affect stress wave propagation characteristics. [PERSON] [13] utilizes a higher-order, step-grid finite difference method to simulate seismic wave propagation and thereby analyzes the correct identification of low-level faults to detect the distribution of remnant oil. [PERSON] [14] proposes the use of the finite difference method to investigate stress wave interactions in fractured rocks for petroleum extraction goals. Finally, [PERSON] [15] uses the finite difference method to simulate the propagation of seismic waves and establishes a nonlinear model using a neural network to predict geological information. Altogether, these results indicate that the finite difference method is a valuable tool for simulating wave propagation in oil exploration. Moreover, it can be effectively employed to examine the influence of different factors on wave propagation characteristics.
# A GNSS/5G Integrated Three-Dimensional Positioning Scheme Based on D2D Communication
## 2 Methods
### GNSS/5G Integrated Three-Dimensional Positioning Scheme  
In this subsection, we propose a GNSS/5G integrated three-dimensional positioning scheme. As Figure 2b indicates, an area bounded by three intersecting circles is represented by a shade of gray in the two-dimensional positioning, when the positions of the reference nodes are inaccurate. Similarly, a stereo region will be formed in the three-dimensional positioning. As earlier mentioned, the positions of the cooperative nodes are not accurate enough to act as the reference nodes, and then, the positioning results of the requesting nodes will be affected. If we choose four reference nodes to position a requesting node each time, there will be a lot of position results with some errors. DBSCAN can keep the high-density data points and remove the outliers in the sample data set. Then, the outlying results with large errors can be identified when a large number of calculated positions of a node are available. Thus, according to the GNSS and TOA/RSS measurements, we firstly exploit DBSCAN to obtain relatively accurate positions of the requesting nodes. Meanwhile, we can also reduce the position deviation of the cooperative nodes. Then, the results from the DBSCAN algorithm are input as the initial state of the MUs in PF, which further integrate with the TOA/RSS measurements.
## 4 Discussion
### Significance and Superiority of Our Work
#### 4.2.4 Superior Accuracy and Performance  
Studies relying on outdated datasets like KDD, NSL-KDD, or UNSW-NB15 typically report lower accuracy and lack relevance to IoT-specific challenges. Even works using the BoT-IoT dataset often fail to reach high accuracy due to insufficient preprocessing, inappropriate feature selection, and a lower volume of training datasets used. Achieving 99.2% accuracy with Random Forest surpasses most existing studies, even those using the BoT-IoT dataset. By addressing preprocessing and feature selection thoroughly, our model outperforms studies that rely on deep-learning models requiring greater computational resources and data volume.
# Kernel Learning by Spectral Representation and Gaussian Mixtures
## 2 The Concept of Kernels
### Stationary Kernels  
Stationary kernels [20] are defined as \(\kappa(\mathbf{x},\mathbf{x}^{\prime})=\kappa_{s}(\mathbf{x}-\mathbf{x}^{\prime})\). An important factor in such definition is its dependency on the lag vector which can be interpreted as generalizations of the Gaussian probability distribution functions which are used to represent distributions [15]. Additionally, [PERSON] [21] proved that a symmetric function \(\kappa_{s}\) is a positive definite in \(\mathbb{R}^{D}\), if and only if, it has the form:  
\[\kappa_{s}(\mathbf{x}-\mathbf{x}^{\prime})=\int_{\mathbb{R}^{D}}2^{2\pi i\mathbf{\omega}^ {T}(\mathbf{x}-\mathbf{x}^{\prime})}d\mu(\mathbf{\omega}), \tag{1}\]  
where \(\mu\) is a positive finite measure. Equation (1) is called the _spectral representation of \(\kappa_{s}\)_. Now, suppose \(\mu\) has a density \(F(\omega)\) and \(\mathbf{\tau}=\mathbf{x}-\mathbf{x}^{\prime}\). Thus, it is possible to obtain:  
\[\kappa(\mathbf{\tau}) =\int F(\mathbf{\omega})e^{2\pi i\mathbf{\omega}^{T}\mathbf{\tau}}d\mathbf{ \omega},\] \[F(\mathbf{\omega}) =\int\kappa(\mathbf{\tau})e^{-2\pi i\mathbf{\omega}^{T}\mathbf{\tau}}d(\mathbf{ \tau}).\]  
In other words, the kernel function \(\kappa_{s}\) and its spectral density \(F\) are Fourier dual of each other. Furthermore, if \(\kappa(\mathbf{0})=\int F(\mathbf{\omega})d\mathbf{\omega}\) and \(F\) is a probability measure, the unique condition to define a **valid** Gaussian process is \(\kappa(\textbf{0})=1\). In other words, we need this condition to ensure that the kernel \(\kappa\) and the function \(f\) are correctly correlated.
## 2 Methodology and Computational Model
### Computational Setup  
In the present study, the ALM was implemented in the ANSYS Fluent platform together with user-defined functions. A computational domain of 60 \(D\ \times\ 60\ D\ \times\ 60\ H\) was built up to eliminate the blockage effect, and the wind turbine was located at the center (Figure 3a). The boundary conditions were set as follows: a velocity inlet of 7 m/s with a turbulence intensity of 5%, a pressure outlet of zero gauge pressure and symmetry on the other four boundaries. A hexagonal mesh with 1,166,480 elements in total was generated in ANSYS ICEM CFD, which was composed of three zones (Figure 3b). An inner zone of 2 \(D\ \times\ 2\ D\ \times\ 2\ H\) contained the turbine, with an element size of 0.5 \(c\ \times\ 0.5\ c\ \times\ 0.5\ c\). A transition zone of 3 \(D\ \times\ 3\ D\ \times\ 3\ H\) was adjacent to the former, with a double element size  
Figure 1: Different coordinate system arrangements: (**a**) inertial coordinate system (ICS) and unfolded blade coordinate system (BCS); (**b**) top view of the ICS and BCS; and (**c**) BCS and inclined pitch axis coordinate system (PCS) [4].  
Figure 2: Sign convention of the variables.  
using hanging nodes on the interface between zones. Lastly, an outer zone corresponded to the whole domain, with a quadruple element size growing larger toward the boundaries. The numerical settings in Fluent were decided based on previous studies [34; 35]. The sheer stress transport (SST) k-\(\omega\) turbulence model and semi-implicit method for pressure-linked equations consistent (SIMPLEC) algorithm were utilized. Other settings can be found in the previous literature of the authors [33].
# Evaluation of _Jatropha curcas_ and Pistachio Shell Particles as Modifier for Asphalt Binder
\begin{tabular}{c c c c c} \hline \hline
**Waste** & **Function** & **Phase** & **Findings** & **Reference** \\ \hline Sugarcane bagsasse, & & & Improves the chemical properties and the & & \\ rice husk, and & Additive & Mixture & adhesive bond between the binder and & & [PERSON] et al. (2018) \\ corncobs & & & the aggregates & & \\ Rice husk ash & Additive & Mixture & & & \\  & & & Causes significant changes in morphology, and & & \\  & & & & found that the dissipation energy of the binder & & \\  & & & & is related to its adhesive properties & \\  & & & Improves thermal sensitivity and the adhesive & & \\ Rice husk and bio-oil & Additive & Mixture & & & \\  & & & & force between binder and the aggregates with & & \\ Rice husk ash & Substitute of & & & 4% by wt. & \\  & & & & & \\  & & & & & \\ Rice husk & Additive & Mixture & Good compatibility between asphalt and rice & & \\  & & & filler & & husk particles, improving the Marshall quotient & \\  & & & Improves the Marshall stability, stiffness & & \\  & & & & modulus, rutting resistance, and & & \\  & & & & fatigue behavior & \\ Rice husk and & Substitute & & & Rheological data indicate that the asphalt & \\ coal waste & of filler & & & deformation was reduced. & \\  & & & & Marshall stability improved with 5\% by wt. & \\  & & & & and increased of resilient modulus, stiffness, & \\  & & & & and tensile strength & \\  & & & & & \\ Coconut shell and & & & & Coconut shell is better additive than palm oil & \\  & & & & shell, showing an improving resilient modulus & \\  & & & & Improvement in penetration, softening point, & \\  & & & & viscosity, and rheology. & \\  & & & & Enhancement of the rheological behavior of & \\  & & & & asphalt mixtures in terms of phase angle (8) & \\  & & & & and complex modulus (G\({}^{*}\)) & \\  & & & & & \\  & & & & \\  & & & & & \\ Coconut shell & & & & \\ Coconut shell and & & & & & \\ coconut fibers & Aggregate & Mixture & & Improvement in resilient modulus with 10\% of & \\  & & & & fiber and 15\% shell & \\  & & & & & \\ Eggshell waste & Additive & Mixture & & With eggshell powder, improvement in & \\  & & & & rheological properties and its softening point, & \\  & & & & penetration, ductility, and viscosity & \\  & & & & Improvement in stability-flow performance, as & \\  & & & & well as in penetration, rotation viscosity, & \\  & & & & and ductility & \\  & & & & \\  & & & & There is not a change in temperature & \\  & & & & susceptibility of base binder, and that served as & \\  & & & & & \\  & & & & \\  & & & & & \\  & & & & \\  & & & & \\  & & & & \\  & & & & \\ \end{tabular}  \ \begin{tabular}{c}  \\ \end{tabular} 
\begin{tabular}{Based on previous information, this work reports the modification of hot mix asphalt, using as modifiers pistachio and _latropha cruces_ shells. The main physical properties, such as ring and ball, phase separation, dynamic viscosity, and rheology, are evaluated. Furthermore, the dispersion of modifier particles by means of fluorescent microscopy are reported.
# Seismic Risk in Alborz: Insights from Geological Moment Rate Estimation and Fault Activity Analysis
## 2 Tectonic Setting and Seismicity
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**No.** & **Date** & **Lat.** & **Long.** & **M** & **q** & **i0** & **r** & **r\({}^{\prime}\)** & **Ref.** \\ \hline
1 & c.1.8 Ma & 36.11 & 50.78 & \(>\)7 & c & \(>\)IX & - & - & B \\
2 & c. 38.500–10,000 Bp & 35.82 & 52.11 & \(>\)6.5 & c & \(>\)VIII & - & - & B \\
3 & c. 4500 Bp & 36.7 & 49.98 & \(>\)6.5 & c & \(>\)VIII & - & - & B \\
4 & c. 4000–300 Bp & 35.6 & 49.9 & - & - & - & - & - & A \\
5 & c. 4000–3350 Bp & 34.45 & 47.92 & - & b & - & - & - & B \\
6 & c. 2400–2300 Bp & 35.5 & 51.8 & 7.6 & - & 1 & - & - & A \\
7 & c. 4 th cent. & 35.5 & 51.8 & 7.6 & - & 1 & - & - & A \\
8 & 743 & 35.3 & 52.2 & 7.2 & c & 2 & 30 & - & A \\
9 & 855 & 35.6 & 51.5 & 7.1 & b & 2 & - & 540 & A \\
10 & January 864 & 35.7 & 51 & 5.3 & c & 3 & - & - & A \\
12 & 23 February 958 & 36 & 51.1 & 7.7 & b & 1 & 50 & 700 & A \\
13 & 27 April 1008 & 34.6 & 47.4 & 7 & c & 1+ & - & - & A \\
14 & 1052 & 36.6 & 50.3 & 6.8 & b & \(>\)VII+ & - & 400 & B \\
15 & 10 December 1119 & 35.7 & 49.9 & 6.5 & c & 2 & - & - & A \\
16 & May 1177 & 35.92 & 50.83 & 7.2 & c & - & - & 650 & B \\
17 & 15 October 1485 & 36.7 & 50.5 & 7.2 & a & 2 & 28 & 650 & A \\
18 & 3 July 1486 & 36.43 & 50.45 & - & c & - & - & - & B \\
19 & 20 April 1608 & 36.4 & 50.5 & 7.6 & b & 1 & 26 & 600 & A \\
20 & 1665 & 35.75 & 52.08 & 6.5 & c & 2 & - & - & B \\
21 & 1687 & 36.3 & 52.6 & 6.5 & e & 2 & - & - & A \\
22 & July 1721 & 35.68 & 50.2 & - & d & - & - & - & B \\
23 & 1805 & 36.2 & 52.4 & - & c & 2 & - & 100 & A \\
24 & 26 June 1808 & 35.3 & 54.5 & 6.6 & b & 2 & - & 320 & A \\
25 & 9 October 1808 & 36.2 & 52.4 & - & d & 5 & - & - & A \\
26 & 16 December 1808 & 36.4 & 50.3 & 5.9 & b & - & - & 200 & A \\
27 & 1809 & 36.3 & 52.5 & 6.5 & b & 2 & 15 & 290 & A \\
28 & June 1815 & 35.9 & 52.2 & - & d & 4 & 30 & - & A \\
29 & 1825 & 36.1 & 52.6 & 6.7 & b & 2 & 24 & 360 & A \\
30 & 27 March 1830 & 35.7 & 52.5 & 7.1 & b & 2 & 37 & 570 & A \\
31 & 6 April 1830 & 35.73 & 52.6 & 5.3 & b & 3 & - & - & B \\
32 & 1 November 1854 & - & - & - & - & VI & - & - & B \\
33 & 18 April 1887 & - & - & - & - & VI & - & - & B \\
34 & 1868 & 34.9 & 52.5 & 6.4 & c & - & - & 300 & A \\
35 & 20 October 1876 & 35.8 & 49.8 & 5.7 & b & 2 & 19 & 150 & A \\ \hline \end{tabular}
\end{table} 
Table 2: Historical earthquakes in the study area. M = Magnitude, q = The quality of determining the location of the center of the earthquake, i0 = The intensity of the earthquake in the center, r = Radius of perceptibility, r\({}^{\prime}\) = Epicenter distance, Ref. = Reference, A = [PERSON] and Melville, 1982, B = Berberian and Yeats, 1999.
# A Thermal Discomfort Index for Demand Response Control in Residential Water Heaters
## 5 Experimental Validation
### Methodology
#### 5.1.2 Validation of the Proposed TDI  
After determining the forecasting accuracy of the proposed models, the second step of the experimental evaluation consists of validating the TDI calculation and utilization in realistic scenarios in order to properly evaluate the thermal discomfort of users. For this purpose, three experiments were designed based on scenarios that adequately represent the real operation of water heaters. A water heater dynamic simulator was developed and used to generate real scenarios. The description of the simulator and the experiments performed are presented in Section 6.