esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
[34] used the genetic price algorithm (GPA) to monitor the movement of contaminants in the subsurface. Additionally, [35] introduced a hybrid approach by using the results of the local optimization method as an input to the genetic algorithm for the modeling of the SR data. This novel algorithm by [35] overcame the problem of being stuck in the local minima. It optimized the computational cost of the genetic algorithm using the multicore parallel computing method. Similarly, local and global (hybrid) optimization has been applied to complement each other to subdue their shortcomings [35; 36; 37]. Most previous work has considered an objective function from a different perspective to perform joint inversion using a global optimization method, such as when using a local optimization algorithm. For instance, [PERSON] et al. [38] and [PERSON] et al. [39] considered the objective function for the inversion of electromagnetic data in two terms. First, they tried to minimize the data misfit and the roughness of the model at the same time. [PERSON] et al. [40] applied a non-dominated sorting genetic algorithm in a joint 1D parametric inversion involving magnetic resonance and vertical electrical sounding. In previous studies, joint inversion/interpretation was applied by using either local or global optimization methods. Moreover, local and global (hybrid) optimization has been applied to complement each other to subdue their shortcomings. However, based on our knowledge, the joint modeling of different geophysical data by using the results from the inversion (local optimization) to constrain the search space of the global optimization approach has never been reported in the literature. Thus, a procedure that constrains the global part of the combined optimization algorithm by using the local optimization to define a close search space to the real model parameters was proposed and designed. In this way, the search space of the model parameters has been limited to a more reliable range, drastically reducing the computation time. This study presents the combined local and global optimization approach to jointly model SR and DCR data. With this proposed approach, the multi-objective (i.e., integration of the DCR and SR misfit functions) global optimization algorithm is used for the first time for the two-dimensional joint inversion of two different geophysical datasets, i.e., SR and DCR data. Specifically, in this algorithm, the global part of the combined optimization algorithm is constrained by using the output of the local optimization to define a search space, significantly improving its run time and mitigating model instability. In addition to the improved computational cost that resulted from applying the combined local and global optimization methods, we made the multi-objective global optimization algorithm run on parallel computing. This process further optimized the computational cost and devised the optimum technique using the combined optimization algorithm. We tested and discussed the efficacy of our proposed algorithm using synthetic SR and DCR data.
# Flow-Induced Vibration Analysis by Simulating a High-Speed Train Pantograph
## 3 Pantograph Structural Modal Simulation Analysis
### Pantograph Structural Modal Simulation Model  
The high-speed pantograph, serving as the contact interface providing electrical power to high-speed trains, possesses a relatively complex structure. The physical model of the pantograph includes numerous small components such as bolts, washers, and fillets, which have minimal impact on the modal analysis of the structure. Therefore, in modeling, the pantograph can be simplified. The simplified pantograph, as shown in Figure 12, mainly comprises components like the bow head, carbon strip, bow head support, upper arm, balancing rod, lower arm, pull rod, base frame, and transmission mechanism.  
Figure 11: The resistance, lateral force, and uplift force (average values) of the pantograph under the knuckle-upstream operating condition at different operating speeds.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{4}{c}{**Uplift Force [N]**} \\ \cline{2-5}  & **400 km/h** & **450 km/h** & **500 km/h** & **600 km/h** \\ \hline Pantograph head & 156.89 & 200.56 & 248.05 & 345.22 \\ Pantograph head bracket & 58.11 & 75.12 & 91.63 & 133.9 \\ Upper arm & \(-\)43.16 & \(-\)55.2 & \(-\)69.34 & \(-\)103.92 \\ Link arm & \(-\)39.73 & \(-\)50.82 & \(-\)63.34 & \(-\)91.1 \\ Lower arm & 89.45 & 113.89 & 140.53 & 196.54 \\ Stabilizer & 40.39 & 51.24 & 63.26 & 88.4 \\ Whole & 264.73 & 337.29 & 413.62 & 554.15 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The average uplift force of pantograph under the knuckle-upstream operating condition at different running speeds.
## 2 Materials and Methods  
This paper investigated how and if AM technologies can be a sufficient method in energy industry production lines, spare parts management, and prototyping. An initial investigation indicated that most components in GIS products are made of aluminum alloys. Therefore, this study focused on aluminum AM and 3D printing methods and their potential. As the first step, a careful evaluation of the AM market and available technologies for aluminum alloys was performed. Table 1 shows the results of the market evaluation and the pros and cons of each technology. However, the results are based on the claims of the companies, without printing samples for all techniques. Evaluation outcomes show the maturity of PBF methods and the significant challenges of other AM technologies for aluminum alloys. In addition, Directed Energy Deposition (DED) and hybrid printing methods that combine near-net shape printing and machining to achieve the final design and tolerances can be an optimum solution in some cases. The selection of the proper method needs some consideration including the complexity of design, post-process requirements, and required surface roughness. As the second step, a decision chart was proposed to consider the printability of each aluminum part without deep consideration of cost aspects (Table 2). As a case study, one product, a gas-insulated high-voltage switchgear (ELK-04/145 kV), was considered to verify the accuracy of the proposed charts. GIS-ELK-04/145 kV is the smallest GIS product in Hitachi Energy that is more AM-friendly in comparison with other models. However, the results of using the chart/flowchart on the candidate case study can be generalized to the other GIS products because of their similar designs, functionality, and technical requirements. The key parameters to determine printability include the level of complexity, size, type of aluminum alloy, quantity of the order, standard requirements, re-design possibilities, lead time limitations, required average surface roughness, additional requirements for machining or post-processing, and electrical/mechanical property requirements. The complexity classification is based on unique figures that AM enabled us to design. For example, TPMS/gyroid structures, internal surfaces, and any complexity that cannot be covered by CM are categorized as high complexity. Simple complexity includes any pipes, tubes, simple cylinders, plates, boxes, etc. Any geometry that can be manufactured by CM methods and needs some manufacturing steps is categorized in medium- complexity level. According to the market evaluation and decision chart, a decision flowchart was proposed to be implemented in the case study (Figure 1). In this step, the presented flowchart was used for all aluminum parts of GIS-ELK-04/145 kV to obtain the fraction of parts that can be printed. Next, printable aluminum parts were investigated in a cost analysis considering routine production, spare parts production, and prototyping for R&D, and technology development projects.
# An Efficient Trajectory Planning Method for High-Speed Interception of Invasive Drones
## 3 Air Resistance Approximation and Motion Primitive Generation
### Motion Primitive Generation
#### 3.2.3 Single Axis Trajectory Generation
Then, by rearranging Equation (31), the unknown constant \(C\) is separated:  
\[\big{(}k_{rp}-1\big{)}^{2}\mathbf{\phi} \begin{bmatrix}C_{1}\\ C_{2}\\ C_{3}\end{bmatrix} = \begin{bmatrix}\Delta p\\ \Delta v\\ \Delta a\end{bmatrix}  \tag{33}\]where  
\[\boldsymbol{\phi}=\left[ \begin{array}{cc}-\frac{e^{-k_{rb}t}+k_{rb}t^{-}\frac{k_{rb }t^{2}}{k_{rb}}^{2}-1}{k_{rb}}&-\frac{\sinh(k_{rb}t)-k_{rb}t}{k_{rb}}&\frac{t^{3 }}{6k_{rb}}^{2}\\ \frac{e^{-k_{rb}t}+k_{rb}t-1}{k_{rb}}&-\frac{2\sinh\left(\frac{k_{rb}t}{t^{2}} \right)^{2}}{k_{rb}}&\frac{t^{2}}{2k_{rb}}\\ -\frac{t}{k_{rb}\left(k_{rp}-1\right)}&\frac{k_{rb}t^{3}}{k_{rb}}^{3}\left(k_{ rp}-1\right)&-\frac{t\left(k_{rb}t+2\right)}{2k_{rb}}^{2}(k_{rp}-1)\end{array} \right] \tag{33}\]  
\[ \begin{array}{c}\left[\begin{array}{c}\Delta p\\ \Delta v\\ \Delta a\end{array}\right]=\left[\begin{array}{c}p_{f}\\ v_{f}\\ a_{f}\end{array}\right]-\left[\begin{array}{c}\frac{p\left(k_{rb}t^{5}+p_{b}k _{rb}^{4}}{k_{rb}}^{4}-\frac{\sigma_{1}}{k_{rb}}-\frac{a_{0}\left(k_{rip}t-1 \right)\left(k_{rp}-1\right)}{k_{rb}}\\ \frac{\sigma_{1}}{k_{rb}}-\frac{a_{0}\left(k_{rp}-1\right)}{k_{rb}}\\ a_{0}\end{array}\right]\end{array} \]  
Solving for unknown coefficient \(C\)  
\[\left[ \begin{array}{c}C_{1}\\ C_{2}\\ C_{3}\end{array} \right]=\boldsymbol{\phi}^{-1}\left(k_{rp}-1\right)^{-2}\left[ \begin{array}{c}\Delta p\\ \Delta v\\ \Delta a\end{array} \right] \tag{34}\]  
At this point, generating motion primitives only requires multiplying the above matrices for each axis, and then finding the optimal state trajectory corresponding to the primitive by solving (31). For partially defined terminal translation states, it is possible to make the co-states of undefined states equal to zero at the end time when solving for the optimal input trajectory [31], thereby allowing the corresponding \(\vartheta\) to be free. These additional situations are usually not of practical significance in the research scenario of this article, and therefore will not be further discussed here. The single axis cost of formula (25) can be explicitly calculated as follows. The lowest cost sports primitive can be considered the most reasonable in the sense of formula (23). \[ \begin{array}{c}J=\frac{\left(k_{rp}-1\right)^{2}}{6}\times\left(\frac{3C_ {2}^{2}e^{2k_{rb}t}}{k_{rb}{}^{5}t}-\frac{6\left(2C_{2}e^{k_{rb}t^{2}}\left(C _{3}t+C_{1}\right)-C_{3}{}^{2}t\right)}{k_{rb}{}^{4}t}\right.\\ \left.\left[1 ex\right]+\frac{6C_{3}\left(C_{3}t+2C_{1}\right)}{k_{rb}{}^{3}}+ \frac{2\left(\left(C_{3}t+C_{1}\right)^{2}+C_{1}\left(C_{3}t+2C_{1}\right) \right)}{k_{rb}{}^{2}}\right)\end{array}  \tag{35}\]  
This cost applies to all combinations of fully or partially defined terminal translation variables.
## 3 Methodology
### Detection of a Single Satellite
#### 3.1.1 Determination of the \"Falling Moments\" and \"Rising Moments\"  
We define the moment at which \(C/N_{0}\) is higher than that at the next moment as the falling moment. This is denoted as \(a_{i}(1\leq i\leq N)\), where \(N\) is the number of falling moments. The set of falling moments, which is denoted as \(A\), can be expressed as \(A=\{a_{i}|1\leq i\leq N\}\). Likewise, the moment at which \(C/N_{0}\) is higher than that at the previous moment is defined as the rising moment and denoted \(b_{j}(1\leq j\leq M)\). For this expression, \(M\) is the number of rising moments, and the set of rising moments, which is denoted as \(B\), can be expressed as \(B\ =\ \{b_{j}|1\leq j\leq M\}\). To illustrate the model with an example, the \(C/N_{0}\) values of G13 at the GPS L2 frequency observed at station CEDU during the SRB event on 13 December 2006 are shown in Figure 4. The green points are the falling moments, while the purple points represent the rising moments.
## 4 Experiments and Result Analysis
### Model Performance Experiments
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Supervised Classification Learning (SCL) & 95.81 \(\pm\) 0.15 & 95.72 \(\pm\) 0.12 & 95.63 \(\pm\) 0.23 & 95.47 \(\pm\) 0.19 \\ Self-Supervised Classification Learning (SSCL) & 95.76 \(\pm\) 0.11 & 95.37 \(\pm\) 0.16 & 95.56 \(\pm\) 0.17 & 95.46 \(\pm\) 0.19 \\ DBS-NET (Proposed) & 97.54 \(\pm\) 0.12 & 97.47 \(\pm\) 0.16 & 97.23 \(\pm\) 0.20 & 97.22 \(\pm\) 0.13 \\ \hline \hline \end{tabular} Note: All the evaluation results use ResNet18 as encoder.  
\end{table} 
Table 4: The performance of birdsong classification with different methods. Figure 7: Comparative visualization of T-SNE visual effects using three different models, Each number in a cluster represents the corresponding category.
## 3 Discussion
### GC \(\times\) GC/HR-TOF/MS Analysis
In accordance with the results, sulfur-containing volatiles in black truffle (0.040 \(\upmu\)g\(\cdot\)g\({}^{-1}\) in total) included dimethyl disulfide and 3-(methylthio) propanoic acid, while compounds in white truffle (2.894 \(\upmu\)g\(\cdot\)g\({}^{-1}\) in total) included methional, (methylthio)-cyclohexane, 3-methylthio-1-propanol, 3-(methylthio) propanoic acid and benzothiazole. The diversity of sulfur-containing volatiles will surely play an important role in the formation of aromatic profiles due to their low olfactory threshold, as well as to distinguish thesamples [24]. Dimethyl disulfide has been identified in many truffles to date [7; 33]. Methional and benzothiazole were found in some kinds of _T. magnatum_ species, but varied according to geographical areas [34]. (Methylthio)-cyclohexane has been reported in anchovy sauce [35]. 3-Methylthio-1-propanol was detected in _T. borchii_ and _T. melanosporum_, as reported via SBSE [6], which has a powerful, sweet, soup or meat-like odor and flavor in high dilution. 3-(Methylthio)propanoic acid was once identified in fresh pulp of pineapple [36], but there was no relevant literature about it in truffle by far. Therefore, further determination still needs to be made to confirm whether it actually came from the fresh truffle or was generated during transportation or storage. In general, the various concentration of volatile compounds led to the formation of different aroma profiles of the two samples.
## 3 The Proposed Multi-Scale Demodulation Method
### Time-Frequency Representation (TFR) Calculation Based on Continuous Wavelet Transformation (CWT)  
TFR analysis methods are applied to study a signal in both the time and frequency domains simultaneously. Among the commonly used TFR calculation methods, CWT has been proven to be powerful for the analysis of non-stationary signals; it offers good frequency resolution at low frequencies and good time resolution at high frequencies. The application of CWT for mechanical fault diagnosis has attracted a great deal of attention over the past decades [26; 27; 28]. The detailed procedure of CWT is summarized as follows.  
Suppose \(x(t)\) is an energy signal, namely \(x(t)\in L^{2}(n)\); then, the wavelet transformation of \(x(t)\) is defined as the integral of the kernel function with \(\psi_{a,b}(t)\).  
\[W_{x}(a,b;\psi)=\int_{-\infty}^{\infty}x(t)\psi_{a,b}(t)dt,\qquad a>0 \tag{5}\]  
where \(\psi_{a,b}(t)\) is obtained from the wavelet function \(\psi(t)\) by expansion and translation.  
\[\psi_{a,b}(t)=a^{-1/2}\psi(\frac{t-b}{a}) \tag{6}\]  
where \(a(a>0)\) is the scale parameter, \(b\) is the location parameter and \(a^{-1/2}\) is the normalized constant factor to ensure the conservation of the signal energy. When \(a\) decreases, the time width of the wavelet function decreases and the frequency bandwidth increases; on the contrary, the frequency bandwidth of the wavelet function decreases and the time width increases. Therefore, CWT can yield good frequency resolution at low frequencies and good time resolution at high frequencies.  
Wavelet transform can be seen as a re-expression of the signal at different scales of the wavelet basis. Thus, the selection of the wavelet basis function has a direct effect on the TFR quality. A good wavelet basis function can match the impulsive shocks caused by machinery fault well and suppress the background noise. A Morlet wavelet is a double-side wavelet, which has been proven to be useful in the fault diagnosis of gearboxes and bearings [29; 30; 31]. It is defined in the time domain as follows, and the corresponding waveform is as shown in Figure 6.  
\[\psi_{Morlet}(t)=\pi^{-1/4}e^{-iw_{0}t}e^{-t^{2}/2},\qquad i=\sqrt{-1} \tag{7}\]
## 3 Results and Discussion
### Relative Errors at an Early Production Time  
The relative error of the predictions and the true value of shale gas production on pre-100 days are shown in Figure 9. For the MLP networks, the relative errors of the three groups are 3.98%, 6.18%, and 6.46%, respectively. The average relative error is 5.54%. The maximum relative errors of the three groups are on the 1 st day, 3 rd day, and 3 rd day, respectively. After that, the relative errors are decreased with the production time. The relative errors of case 1 and case 3 are slightly increased between 90 days and 100 days. For the LSTM networks, the relative errors of the three groups are 0.16%, 0.08%, and 0.55%, respectively. The average relative error is 0.26%. The maximum relative errors of the three groups are on the 100 th day, 8 th day, and 9 th day, respectively.
# BL0K: A New Stage of Privacy-Preserving Scope for Location-Based Services
## 2 Related Work
### Privacy of Bloom Filter
Additionally, the finished creation consists of multi-party protocols that have the capability to preserve the privacy of data based on the locality, which are identified by the public encryption arrays. This procedure is often viewed as an efficient method of protecting the privacy of users when looking at their exact location. Parts \(\Delta_{1}\), \(\Delta_{2}\), and \(\Delta_{3}\) are used to build an SBF, as shown in Figure 1, where the hash functions 1, 2, and 3 are used to link all elements to the filter, except for the first 10 elements of the SBF [33]. Figure 1: Spatial bloom filter (SBF). (**a**) Two BF elements joined to \(\Delta_{1}\) are processed through the use of the hash functions, leading to six one-value elements that can then be written into the SBF. The same method can be used in (**b**) and (**c**). In [PERSON] et al. [34] the protocol named Private Set Intersection (PSI) was introduced based on the intersection of OT and BF. A PSI is linearly complex and, for the most part, relies on the efficient symmetric key operations. The authors stated that their method provides high scalability due to its basic protocol capability as well as an enhanced protocol, which was proven in the semi-honest and the malicious model, respectively. BF has been proposed as a probabilistic data structure used for hiding sensitive data. Unfortunately, each request leaks a maximum of one bit of information and the hash function requires careful design and security. BF analysis is focused on the orthogonality and independence of the structure. The results show that the proposed hash functions are less dependent and permeable than the comparison method, while significantly improving performance [35].
# Experimental Study of Scour Hole Depth around Bridge Pile Using Efficient Cross-Section
## 3 Methodology
### Sieve Analysis  
Sediment size is a key parameter that affects both the scour depth and the scour hole dimensions. Although the most commonly used sediment size affecting the characteristics of local scour is the d\({}_{50}\) median grain size diameter, the adopted similitude analysis could not scale down the sediment size to this diameter. Therefore, an effort was made to select smaller diameter sediment that had a negligible influence on the scouring. In this context, there have been few attempts to assess the interaction of sediment size and attributes of local scour. [PERSON] et al. [40] have reported that the fine uniform particle of non-cohesive sand has an insignificant influence on scour depth. Subsequently, [PERSON] and [PERSON] [41] have drawn the same conclusion during their experimental investigations concerning the influence of fine sediments on scouring action.  
Consequently, a sieving operation is performed herein for determining the sediment size so that the selected median diameter does not influence the local scour. In this respect, to begin, two samples of bed material were collected and dried in an oven for 24 h; following this, the gradation curves were drawn separately; and finally, 0.419 mm of the median diameter of non-cohesive sand was chosen to characterize the desired grading of the sediment for filling the test section of the flume. The results of the sieve analysis are shown in Table 1 and Figure 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sieve No**} & **Sieve Size** & **Material** & **Percent** & **Percent** & **Percent** \\  & **(mm)** & **Retained** & **Retained** & **Cumulative** & **Cumulative** \\  & & **(gm)** & **Retained** & **Retained** & **Passing** \\ \hline \hline \multicolumn{8}{c}{**Total Amount of Sample = 500 gm**} \\ \hline \#8 & 2.36 & 8.00 & 1.60 & 1.60 & 98.40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sieve analysis of fine aggregate.  
Figure 2: (**a**) Existing Pakhtoon Gari Bridge. (**b**) Model prepared for laboratory test. (**c**) Model fitted in flume test section. (**d**) Fixing of piles with pile cap. (**e**) Front view of the test section. (**f**) Square piles subjected to water current. (**g**) Electronic velocity meter.
## 2 Research Methodology
### Data Sources  
A data source is the initial location where data originate or where physical information is first digitized. However, even highly refined data can act as a source if accessed and utilized by another process. Data sources can include databases, flat files, live measurements from physical devices, scraped web data, or various static and streaming data services available online. There are two types of data sources: (a) primary data and (b) secondary data.  
Primary data were collected by selecting 12 water sampling stations in the three study areas. With the help of GPS, the location of each sampling station was determined and the coordinates of the latitudes and longitudes were recorded. These coordinates were then imported into a GIS platform for further analysis. Twelve water samples (A1 to A12) were collected from the Okhla Industrial Area, Nangloi, and the Karol Bagh District of New Delhi (Table 1). The twelve sample stations were identified and fixed in the study area for water sampling.  
The four sample stations were identified and established in each study area for water sample collection. In Karol Bagh, water samples were collected from a hand pump on Padam Singh Road, a bore well in Hotel Southern, a bore well on Street 49 in Reghar Pura, and a bore well on Street 58 in Reghar Pura (Figure 2--Site B). In the Okhla Industrial Area, water samples were obtained from a bore well in Tehkhand Village, Indira Kalyan Vihar, Okhla Community Development Project, and J J Cluster in Sanjay Colony (Figure 2--Site C). In the last area, Nangloi, two water samples were collected from canal water for treatment at the Nangloi Treatment Plant, from the treated water supply at the Nangloi Treatment Plant, and two water samples were obtained from a hand pump near the Nangloi Treatment Plant (Figure 2--Site A).  
A comprehensive field survey was conducted within the study area during the premosoon, monsoon, and post-monsoon periods to observe variations in water quality and differences in various physical properties of water. Interviews were conducted with the public at the stations to assess their knowledge of groundwater quality. Pictures, water samples, field observations, and public interview notes constituted the primary data.  
Secondary data included previous research papers, journals, and newspaper articles related to the study's topic.  
Figure 2: Site (**A**)—Location map showing water sample stations of the Nangloi District of Delhi; Site (**B**)—location map showing water sample stations of the Karol Bagh District of Delhi; Site (**C**)—location map showing water sample stations of Oklahoma of Delhi (author’s creation).