esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### UMAP Transformation and Feature Distribution  
UMAP inherently clusters similar data points, ideally reflecting shared attribute values in the low-dimensional space. However, its dependence on initialization might not correctly cluster the samples. Hence, since the methodology is based on the UMAP transformation for the validation, a preliminary visualization of the data after UMAP transformation is crucial to confirm a correct initialization. This starting check will play a vital role in the synthetic data validation phase, as generated data points are validated based on their alignment with neighboring points in the UMAP-reduced space.  
This methodology uses UMAP as a dimensionality reduction tool to extend the capabilities of a previously proposed partially synthetic data generation method [24], which is already based on UMAP. Alternative dimensionality reduction techniques, such as t-distributed stochastic neighbor embedding (t-SNE) and TriMap, could also be considered for adaptation in future studies. In the original methodology, these alternative techniques were tested on prostate and breast cancer datasets, as detailed in [24]. The exploration of other dimensionality reduction tools could open interesting research directions, especially for applications in different data domains or structures.  
For now, the focus remains on adapting the UMAP-based partially synthetic methodology to fully synthetic data generation. However, for the currently used datasets, UMAP  
Figure 2: Algorithm workflow.  
is still the best choice. To illustrate this, Figure 3 compares the transformations achieved by UMAP, t-SNE, and TriMap for cardiovascular disease data. Of the three, UMAP provides the clearest distinction between clusters, further supporting its suitability for the proposed method.
## Appendix A Implementation Details
### Advantages of Utilizing a \(224\times 224\) RGB Image as Input  
The reasons for choosing an RGB image with a resolution of \(224\times 224\) as the network input are as follows:* Dataset compatibility: The majority of images in existing publicly available 3D reconstruction datasets are based on a resolution of \(224\times 224\). Therefore, selecting RGB images with a resolution of \(224\times 224\) as input ensures better alignment with the dataset, leading to improved training efficacy of the network.
* Resource constraints: Higher resolution images as input increase computational and memory requirements, resulting in longer training times and higher hardware demands. By opting for RGB images with a resolution of \(224\times 224\) as input, computational resource consumption is reduced while maintaining higher performance levels.
* Information preservation: 3D reconstruction involves processing and analyzing input images to extract relevant features. By choosing RGB images with a resolution of \(224\times 224\) as input, more detailed information can be preserved, resulting in enhanced 3D reconstruction performance.
# Fragility Assessment of Traffic Embarkments Exposed to Earthquake-Induced Liquefaction
## 2 Methodology for the Derivation of Fragility Curves
### Estimation of the Fragility Function Parameters  
An example of the results of a seismic response assessment using the employed approach is presented in Figure 1a. As can be seen from the figure, the results can be used to estimate the conditional probability of exceeding a selected damage state at a given IM level, which is computed based on the ratio between the number of cases exceeding a selected damage state and the total number of GMs (Figure 1a). As suggested by [PERSON] [19], the obtained results can be used to estimate the fragility parameters using the maximum likelihood method. Assuming the independence of the observations obtained for different GMs and a lognormal distribution of the fragility function (Equation (1)), the likelihood function is defined as the product of binomial probabilities at multiple IM levels, as follows:  
\[\textit{Likelihood}=\prod_{j=1}^{m}\left(\begin{array}{c}n_{j}\\ z_{j}\end{array}\right)\Phi\left(\frac{ln\left(x_{j}/\theta\right)}{\beta} \right)^{2j}\left(1-\[PERSON]\left(\frac{ln\left(x_{j}/\theta\right)}{\beta} \right)\right)^{n_{j}-z_{j}} \tag{2}\]  
where \(m\) denotes the number of IM levels, \(z_{j}\) is the number of cases exceeding a certain damage state at the \(m\)th intensity level, \(n_{j}\) is the number of used GMs, and \(\prod\) is the product of all individual likelihoods [19]. Estimates of the fragility function parameters are obtained by maximizing the likelihood function from Equation (2) or, equivalently, by maximizing the logarithm of the likelihood function as follows:  
\[\left\{\hat{\theta},\hat{\beta}\right\}=\begin{array}{c}\underset{\theta,\beta} {\operatorname{argmax}}\\ \theta,\beta\end{array}\sum_{j=1}^{m}\left\{ln\left(\begin{array}{c}n_{j}\\ z_{j}\end{array}\right)+z/\ln\Phi\left(\frac{ln\left(x_{j}/\theta\right)}{ \beta}\right)+\left(n_{j}-z_{j}\right)\ln\left(1-\Phi\left(\frac{ln\left(x_{j}/ \theta\right)}{\beta}\right)\right)\right\}\end{array} \tag{3}\]  
An example of the fragility curve obtained using the above methodology is presented in Figure 1b.  
It should be noted that the approach used to estimate fragility is somewhat approximate due to the use of the same set of GMs at different IM levels. However, the change in surface ground motion characteristics with increasing seismic intensity is partly taken into account by applying GMs at the base of the model (bedrock) and using non-linear material models for soil layers.
# Estimation of Intelligent Commercial Vehicle Sideslip Angle Based on Steering Torque
## 1 Introduction
In addition, this novel algorithm can be extended to leverage information from other sensors, such as cameras and lidar, to improve the reliability and accuracy of vehicle condition observations [19]. Most of the vehicle-model-based methods mentioned above are designed based on the vehicle model of steering wheel angle or front wheel angle. Different from the steering system of passenger vehicles, which usually uses an electric power steering (EPS) system, commercial vehicles usually use an electric-hydraulic power steering (EHPS) system because of their large loads. However, for intelligent commercial vehicles equipped with EHPS, the relationship between the steering wheel angle and front wheel angle is nonlinear due to the hydraulic system [20; 21]. The method of estimating sideslip angle based on torque signal proposed in this paper takes this nonlinearity into account, so it has great potential for the wide application of electric-hydraulic power steering (EHPS) systems of intelligent commercial vehicles. In order to solve the problem that autonomous vehicles cannot use GNSS and multi-sensor fusion methods to accurately and timely obtain the vehicle sideslip angle in special environments, this paper proposes a novel method for estimating the sideslip angle of intelligent commercial vehicles based on a dynamic model. The main contributions of this paper are as follows. (1) Different from the existing methods, which consider the vehicle steering system as a whole, the dynamic modeling of the electric-hydraulic power steering system in the steering system is carried out in this paper, and the transfer function from the steering wheel to the front wheel angle is obtained. (2) Considering the nonlinear problem of the vehicle system, the EKF based on the steering torque estimation is designed to estimate the sideslip angle of the intelligent commercial vehicle. The rest of this paper is organized as follows. In Section 2, the dynamics of the EHPS system and 2-DOF linear vehicle model are established, and the frequency of sideslip angle-steering angle and sideslip angle-steering torque transfer functions are discussed. In Section 3, a nonlinear observer of EKF is designed based on the magic tires formula. Section 4 details the simulations and experiments conducted on a test bench and real vehicle, and the results of two estimation methods are compared. Finally, Section 5 concludes the work.
## 4 Results and Discussion
### Comparative Analysis of Composite Sleeper and Type-III Sleeper-Ballasted Track
At many center frequencies, the power flow of the composite sleeper at the rail-seat section is the largest, while the power flow to the center and the end of the sleeper gradually decreases. This is due to the energy being transferred from the rail to the sleeper through the fastener system; however, the power flow of the concrete sleeper distribution is not obvious, and the power flow is more evenly distributed along the longitudinal direction. This is due to the fact that the concrete sleeper is closer to rigid than the composite sleeper and closer to the overall vertical vibration. It can be observed from Figure 10 that the vibration power flow of the composite sleeper is quite different. The power flow of the composite sleeper at the rail-seat section is large, which is likely to cause fatigue damage under long-term vehicle loading. This issue requires attention. The ballast bed power flow of the composite sleeper and concrete sleeper-ballasted track is shown in Figure 11. The ballast bed power flow values of the composite sleeper-ballasted track in the ranges of 80-125 Hz and 250-400 Hz are smaller than those of the concrete sleeper-ballasted track; however, in the ranges of 40-63 Hz and 800-1000 Hz, the former's values are larger than the latter's, and the ballast bed power flow values of the two tracks are closer at the other center frequencies. From the perspective of power flow, the use of composite sleepers consumes a great deal of the vibration from the sleeper itself, which reduces the vibration energy in the ballast bed, especially in the middle frequency bands. This is beneficial to slowing down the pulverization of the ballast bed. Figure 9: Sleeper power flow: (**a**) sleeper input and output power flow; (**b**) sleeper consumption power flow. Figure 11: Ballast bed power flow. Figure 10: Sleeper longitudinal power flow distribution: (**a**) composite sleeper; (**b**) type-III prestressed concrete sleeper.
## Materials and Methods
### Design and Analysis of AFPMSM
Axial component of magnetic flux density (\(B_{\rm z}\)) is determined by Equation (8) [9]:  
\[B_{\rm z}=\frac{B_{\rm r}}{1+\mu_{\rm rec}\big{(}\frac{d_{\rm ag}+0.5d_{\rm s} }{h_{\rm PM}}\big{)}k_{\rm sat}} \tag{8}\]  
where \(B_{\rm r}\) is the remanent magnetic flux density, \(d_{\rm ag}\) the air gap thickness (between rotor and stator), \(d_{\rm s}\) the stator thickness, \(\mu_{\rm rec}\) the recoil permeability, \(h_{\rm PM}\) the PM thickness and \(k_{\rm sat}\) the saturation factor. Ratio between PM pitch and pole pitch (\(\alpha_{\rm i}\)) is independent of the radius and can be written as Equation (9) [9]:  
\[\alpha_{\rm i}=\frac{\tau_{\rm PM}}{\tau_{\rm p}} \tag{9}\]  
where \(\tau_{\rm p}\) is the pole pitch and \(\tau_{\rm PM}\) the PM pitch. Magnetic flux (\(\Phi\)), induced voltage and electromagnetic torque can be determined using Equations (10)-(12), respectively [9]:  
\[\Phi=\alpha_{\rm i}B_{\rm z}\frac{\pi}{8p}\big{(}D_{o}^{2}-D_{\rm i}^{2}\big{)} \tag{10}\]  
\[E=\pi\sqrt{2}fN_{1}k_{\rm w}\Phi \tag{11}\]  
\[T_{\rm em}=\frac{1}{4}\alpha_{\rm i}ml_{\rm a}N_{1}k_{\rm w}B_{\rm z}\big{(}D_ {o}^{2}-D_{\rm i}^{2}\big{)} \tag{12}\]  
where \(B_{\rm z}\) is the axial component of magnetic flux density, \(p\) the number of poles, \(D_{\rm o}\) and \(D_{\rm i}\) the outer and inner PM diameter, \(E\) the induced voltage, \(f\) the network frequency, \(N_{1}\) the number of turns per coil,\(k_{\rm w}\) the winding factor, \(\varPhi\) the magnetic flux, \(T_{\rm em}\) the electromagnetic torque, \(\alpha_{\rm i}\) the PM pitch-to-pole pitch ratio, \(m\) the number of phases, and \(I_{\rm a}\) the effective value of stator current.
# Recent Advances in Hydrothermal Oxidation Technology for Sludge Treatment
## 2 Results and Discussion
### Research on Influencing Factors of Sludge Treatment by HTO
Moreover, pH can affect sludge dewatering performance, ammonia nitrogen conversion, and subsequent resource recovery. Therefore, treating sludge under optimal pH conditions can improve both dewatering performance and resource recovery efficiency.
###### Abstract
Under physiological conditions, osteoblast-produced LPA is present in bone tissue, and under some pathophysiological conditions, such as fracture healing, bone cells are exposed to high levels of platelet-derived LPA [22]. In different bone cells, LPA induces various cellular effects, including proliferation, differentiation, survival, and migration [23]. Furthermore, LPA contributes to angiogenesis which is also a key step during the bone regeneration process [24]. Due to these effects, LPA is a promising candidate for applications in bone regeneration [25]. In recent years, some biomaterial studies have supported the potential of LPA for use in bone tissue engineering applications. [PERSON] et al. found that albumin-bound LPA and calcitriol co-operate to promote osteoblast maturation on titanium and hydroxyapatite-coated surfaces [26] and improve human osteoblasts [16]. A study reported by [PERSON] et al. indicates that LPA and 1\(\alpha\),25-dihydroxy vitamin D3-enriched injectable scaffold can fasten bone fragments, leading to their apposition and new bone formation [27]. Furthermore, [PERSON] et al. showed that physically entrapped LPA containing alginate hydrogel enhanced human bone marrow mesenchymal stem cell survival in vivo [28]. The very first criterion of any scaffold for tissue engineering is that it must be biocompatible [29]. Therefore, the present study aimed to investigate the biocompatibility of LPA and to determine the effect of the most abundant, albumin-bound 16:0, 18:1, and 18:2 LPA species on the proliferation and migration of human bone-marrow-derived mesenchymal stem cells.
## 4 Case Validation and Results
Last, this study obtained 63 initial concept sketches, from which the expert group selected and removed those that clearly did not conform to the PI and those that had similar designs, resulting in 48 concept sketch alternatives, as shown in Figure 11.  
**The second phase: Extraction of E-moped Kansei CRs**  
_Step 3: Constructing the Kansei dictionary._ The Chinese online shopping platform JD.com and the American platform Amazon.com are both large-scale platforms with a vast amount of reviews [5]. To ensure the diversity of data collection, the Octopus tool was used to crawl data from the two platforms, selecting review data from mainstream brands such as Yade, Aima, Niu, and Luyuan under the female E-moped category [47], and a total of 6181 reviews were collected. After filtering out duplicates, meaningless content, and expressions that only conveyed good or bad sentiments, a Kansei dictionary consisting of 72 words with a frequency higher than 50 was constructed, as shown in Table 2.  
Figure 11: The side view of screened sketch alternatives. Figure 7.0 The interface of Midjourney for sketch generation. _Step 4: Clustering Kansei factors_. Cluster the Kansei words using FA to identify the user's concerns. A focus group of 15 E-moped and product design experts with over 5 years of experience screened female E-moped search results on JD and Amazon, and then applied the KJ method to refine the Kansei dictionary to 10 Kansei words, as shown in Figure 7.1  
Since this study explores the impact of emotional needs on form design, Photoshop was used to desaturate product images and eliminate the interference of background and perspective differences in E-mopeds. Three selected products with significant differences are shown in Figure 7.2  
The obtained 10 Kansei words and 3 typical samples were used to create a conformity questionnaire, which was then distributed to female consumers for scoring. The questionnaires were designed on a 5-point Likert scale with a score of 1 for not conforming, 2 for less conforming, 3 for average, 4 for more conforming, and 5 for conforming. The questionnaire collected a total of 60 subjects, of which 30 were female students majoring in product design, and the other 30 were middle-aged and young women from society. Data analysis was conducted using the SPSSAU software, and the [PERSON]'s \(\alpha\) coefficient was 0.934, indicating the high reliability of the questionnaire data. The Kaisere-Meyere-Olkin (KMO) measure and [PERSON]'s test of sphericity were conducted, resulting in a KMO value of 0.754, indicating that FA can be used to process the data. The [PERSON]'s test
# Visual MAV Tracker with Adaptive Search Region
## 4 Results  
The tracking performance was evaluated in terms of accuracy, robustness, and the expected average overlap curve (EAOC) [33]. To calculate these performance indices, the starting points are selected every 50 frames. The algorithm then starts to track a target from each starting point to the final frame or initial frame, as shown in Figure 4. For example, S1 and S2 are closer to the initial frame than to the final frame. For these starting points, tracking is conducted in the forward direction. S3 and S4 are closer to the final frame; thus, backward tracking is performed. The group of frames from the starting point to the final (or initial) frame is called a sub-sequence.  
The tracking accuracy for the \(k\)th starting point can be calculated as follows:  
\[A_{k}=\frac{1}{N_{k}^{F}}\sum_{i=1}^{N_{k}^{F}}\text{IOU}(i), \tag{4}\]  
where \(N_{k}^{F}\) is the number of frames before tracking failure occurs and IOU is the ratio of the intersected area to the union area of the estimated bounding box and the ground truth bounding box. The robustness of the \(k\)th starting point can be calculated as  
\[R_{k}=\frac{N_{k}^{F}}{N_{k}}, \tag{5}\]  
where \(N_{k}\) is the number of frames in a subsequence. Then, the total accuracy \(A_{\text{tot}}\) and robustness \(R_{\text{tot}}\) can be calculated by averaging \(A_{k}\) and \(R_{k}\) over the starting points and sequences.  
The EAO curve represents both accuracy and robustness with extended sub-sequences. If tracking fails in the middle of a sub-sequence, the corresponding extended sub-sequence is constructed with the original images of the sub-sequence and dummy frames. Dummy frames are needed to calculate the EAOC, and tracking cannot be performed in dummy frames; that is, the IOU is zero in dummy frames. The number of dummy frames is determined such that the length of the extended sub-sequence is the same as that of the original sequence. If tracking is completed in the final frame without failure in another sub-sequence, the corresponding extended sub-sequence does not contain any dummy frames. The EAOC value can then be calculated as:  
\[\text{EAOC}(i)=\frac{1}{n_{e}(i)}\sum_{k=1}^{n_{e}(i)}\text{IOU}(k,i), \tag{6}\]  
where \(\text{IOU}(k,i)\) represents the IOU value for the \(i\)th frame of the \(k\)th extended sub-sequence and \(n_{e}(i)\) is the number of extended sub-sequences that contain the \(i\)th frame. The \(i\)th frame can be either the original image or a dummy frame.  
Figure 4: Starting points and tracking direction for evaluation.  
In the following section, the performances of the four trackers are compared. The first model is the CSR tracker; hereinafter, this model is referred to as CSR. The second model is constructed by applying the SC to the CSR model and is referred to as CSR + SC. The third model is created by adding the SO to the second model and is referred to as CSR + SP + SC. The last model includes PP in addition to the third model and is referred to as CSR + SP + SC + PP.
# Robust Evolutionary-Game-Based Routing for Wireless Multimedia Sensor Networks
## 6 Conclusions  
In WMSNs, energy efficiency is an important design objective because the energy should support delivering large amounts of data while meeting a certain end-to-end delay requirement. This type of problem makes designing a routing protocol for WMSNs more challenging. In this study, we have proposed a new routing protocol for WMSNs, which is based on clustering technique using EG. The proposed EGR overcomes this challenge by introducing a data redundancy avoidance algorithm and intelligent CH election using EG in WMSNs with multiple sinks. Our extensive performance study shows that the EGR protocol achieves lower energy consumption, longer network lifetime, shorter end-to-end delay, and higher packet delivery ratio as compared to those in GEEC, EEREG, QMOR, AB routing, and LEACH-C. In summary, it can be concluded that EGR is more suitable for WMSNs that facilitate surveillance systems subjected to stringent QoS requirements. Furthermore, EGR can be deployed for environmental and battlefield monitoring, wherein faster data delivery is required.  
The individual contributions of authors are as follows. [PERSON] developed and simulated the EGR protocol for WMSNs with multiple sinks. [PERSON] directed the research and contributed to the refinement of the protocol and the interpretation of the simulation results. The paper was drafted by [PERSON] and subsequently revised and approved by [PERSON].  
This research was supported in part by research fund from Chosun University, 2019.  
The authors wish to thank the editor and anonymous referees for their helpful comments in improving the quality of this paper.  
The authors declare no conflict of interest.
# Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
## 5 Analysis
### RQ3: What Methods and Techniques Are Utilized to Detect IoT Botnets?  
As shown in Figure 10, the revised selection of studies from 2016 to 2020 that used various methods to detect IoT botnets numbered, on average, 7.5 each year. In the following section, we discuss the methods and techniques that have been used, and we summarize the relevant studies for each method according to categories of methods.  
In the overview, we concentrate on the concept behind each approach; the dataset used, if any; the outcome; the deficiencies, if found; and the standing in comparison to other studies. For the analysis, we classified the selected studies into a few main categories according to the methods proposed by the studies, as shown in Table 11. It explains these categories and the number of selected studies per category.  
Figure 9: The mechanism of DDoS.  
It is worth noting that most of the studies relied on artificial intelligence to suggest solutions for the problem of detecting botnets in the Internet of Things--78% of the studies. Figure 11 shows the percentage of selected studies per category.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Type of Used Method** & **Number of Studies** & **References** \\ \hline
1 & AI Supervised Learning & 15 & [37; 45; 48; 51; 52; 54; 55; 56; 61; 62; 64; 67; 69; 70] \\ \hline
2 & AI Unsupervised Learning & 3 & [60; 73; 74] \\ \hline
3 & AI Deep Learning & 11 & [46; 47; 49; 50; 53; 58; 59; 63; 65; 66; 68] \\ \hline
4 & Blockchain based & 3 & [71; 72; 78] \\ \hline
5 & SDN based & 1 & [75] \\ \hline
6 & Specification based & 3 & [76; 77; 80] \\ \hline
7 & Signature based & 1 & [79] \\ \hline  & Sum & 37 & - \\ \hline \hline \end{tabular}
\end{table}
Table 11: Number of studies selected per detection method.  
Figure 11: Number of selected studies per detection method.  
Figure 10: Number of publications per year.  
In the following sections, we describe these methods and describe the relevant selected studies. Figure 12 explains these methods' categories.