esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Impedance Estimation: Challenges and Considerations
## 3 Harmonic Synchrophasor Estimation
### Illustrative Results  
Figure 7 shows the performance of the FFT with a 1 cycle window and sampling frequency of 15,360 kHz for HSpE, considering a signal contaminated by SNR of 40 dB and harmonics, from the 3 rd to the 49 th components with an amplitude of 5% related to the fundamental component and variations at the system frequency of 57 to 63 Hz. The TVE graph is shown for each harmonic for oversampling values L = 1 (no-oversampling), L = 4, L = 16, and L = 64. Each of the circles shows different off-nominal frequencies. To estimate the harmonics, a 3 rd order Lagrange interpolator [37] was used, followed by the FFT. Note that the TVE error is around 6% for L = 1, reducing to 1.5% when L = 64. It shows that noise reduction is an important pre-processing in the harmonic phasor estimation process.  
Figure 8 presents the comparison among three time-domain resampling techniques: MSVA, Lagrange (\"Lagran\" in the plot), and B-Spline (\"Bspline\" in the plot). The SNR was considered 60 dB, and 6000 cases were generated for each off-nominal frequency varying from 55 to 66 Hz (1 Hz of step-changing). Additionally, the phase was sorted from 0 to 2\(\pi\), using a uniform distribution, and the magnitude of all harmonic, up to the 50 th harmonic, was kept constant and equal to 5% off the fundamental component. Then the average and the maximum TVE were obtained.  
Figure 6: Oversampling method.  
Figure 7: TVE for the harmonic synchrophasor for a signal with noise and harmonics.  
As shown in Figure 8, the B-spline interpolation algorithm presented the best results, followed by the MSVA. The maximum TVE was lower than 2% for the B-Spline algorithm and reached more than 3% for the two other algorithms. Only the B-Spline algorithm complied with the PMU standard for the fundamental frequency. Although there is no standard for the maximum TVE error for the harmonic phasor estimation, the results show a challenge to move the error to below than 1% for all harmonic components.
## 2 Materials and Methods  
### Rock Sample Preparation  
The rock material was obtained from a roadway of a lead-zinc polymetallic mine in Baoshan city, Yunnan Province, China. The lead-zinc polymetallic deposit, located in the northern part of Baoshan in the Sanjiang region, is a skarn-type polymetallic deposit hosted within Early Paleozoic carbonate rocks. The geographical coordinates are approximately 99\({}^{\circ}\)10'12''-99\({}^{\circ}\)11'31'' E and 25\({}^{\circ}\)33'46''-25\({}^{\circ}\)24'51'' N, with the mining area spanning 3.005 km2, situated directly north of the Baoshan urban area at a straight-line distance of 40 km. The Baoshan block is delineated to the west by the Lushi suture zone, to the east by the Changing-Menglian suture zone, to the north by the intersection of the Nujiang-Ruili fault and the Lancangjiang fault, and to the south by the Dian-Burma-Thai-Malay block. Overall, it exhibits a wedge-shaped configuration that is narrower in the north and broader in the south, interposed between the Tengchong-Ruili block and the Lanping-Simao basin [21, 22]. The block has undergone a geological history characterized by the formation, evolution, and closure of the Tethys Ocean, followed by the continental-continental collision orogen between the Eurasian and Indian plates. This unique and complex tectonic environment, coupled with favorable metallogenic geological conditions, has created an advantageous setting for the concentration and mineralization of various useful metallic elements [23].  
During the field investigation, as shown in Figure 1a, multiple intermittent joints were observed on the rock mass surface. Due to blast vibrations and excavation disturbance, it was found that the discontinuous joints were connected, increasing the risk of geo-disaster from spalling and collapse occurring at the roadway. The connection of the intermittent joints poses a huge threat to the stability of the roadway, and could affect normal mine production. As a result, the influence of intermittent joints on rock fracture exposed to dynamic loading needs to be thoroughly investigated. At the laboratory scale, marble samples were prepared with double-stepped fissures using a water jet (Figure 1b) to mimic the rock bridge structure. The water jet had a weak disturbance effect on the intact marble samples, ensuring the preparation quality of the tested marble samples. The combination of high-pressure water and garnet abrasive, emitted from a nozzle with a diameter of 0.75 mm, can create a defect with an aperture measuring 1 mm. The inclination of the fissures was determined from the field measurement of rock joints, and the fissure angle (\(\alpha\)) was set to 30\({}^{\circ}\). The rock bridge approach angle (\(\beta\)) was set to 75\({}^{\circ}\). Four kinds of marble samples with different rock bridge lengths were prepared, i.e., 10 mm, 20 mm, 30 mm, and 40 mm. Figure 1c shows typical marble samples containing double-stepped fissures with different rock bridge lengths of 10, 20, 30, and 40 mm.
## 1 Introduction
For example, devices classified as moving along relatively regular or polynomial trajectories employ regression to estimate the mobility pattern. On the other hand, if the device exhibits highly complex or non-linear movements that make accurate prediction extremely difficult, the existing resource allocation scheme is employed [15]. By combining decision tree-based classification with pattern-specific regression, the proposed scheme could enable more accurate mobility pattern predictions and reduce packet loss caused by topology changes. To exploit these predictions effectively, we perform graph construction and resource allocation for communication with mobile devices on all devices based on [15]. Through the pre-resource allocation scheme, when a mobile device transmits data to the network manager via a nearby device, the network manager could collect the location information of the mobile devices and the exact transmission time. Based on the information, the network manager constructs a routing graph and allocates resources based on the time-specificnetwork topology predicted by the decision tree-based classification and the regression. This scheme ensures that the network can proactively adapt to network changes, thus providing higher reliability and managing resource efficiently in industrial wireless sensor network environments. The key contributions of this paper are as follows:  
* Our scheme enhances the accuracy of mobility pattern classification to overcome the limitations of the traditional scheme, which relies on a single regression/prediction model to cover all mobility patterns. * The proposed scheme improves prediction accuracy and computational efficiency by employing customized predictive models tailored to each classified type. * Based on these advantages, the proposed scheme enables an improved transmission success ratio and more efficient resource allocation. The remainder of this paper is organized as follows. In Section 2, we explain the operating process of industrial wireless sensor networks represented by WirelessHART and the problems caused by the movement of devices in the existing network. In Section 3, we explain a mobility support scheme based on a decision tree-based pattern classification and regression to solve the problems, and performance evaluation is described in Section 4. Finally, we summarize this paper and outline future works in Section 5.
## 2 Literature Review
### Integration of MCDM Methods with Other Techniques  
Researchers have integrated MCDM methods with various techniques to enhance their applicability and address complex decision-making problems. Fuzzy set theory has been incorporated into MCDM methods to deal with the vagueness and imprecision of human judgments [10; 11]. Grey theory has also been combined with MCDM methods to handle incomplete and uncertain information [5]. These integrations have expanded the capabilities of MCDM methods to tackle real-world decision problems more effectively.
# Time Series Classification with Shapelet and Canonical Features
## 2 Related Work
### Classification Method Based on Shapelet  
The classification method of the time series classification algorithm based on Shapelet distinguishes between different categories based on whether the discriminating subsequence (i.e., Shapelet [8]) appears in the time series, regardless of where it appears. The original method of [PERSON] et al. [8] used information gain as an evaluation criterion to search for optimal Shapelet by enumerating all subsequences. Since the high computational cost of the method severely hampers Shapelet's practicality, much of the research on Shapelet has focused on how to accelerate the discovery of Shapelet. For example, [PERSON] et al. [13] employed an intelligent caching technique that traded space for time, reducing the time complexity of an exact search for Shapelet by an order of magnitude. The fast Shapeletmethod [9] uses SAX technique [20] to discretize subsequences and search for \"approximate\" optimal Shapelet in low-dimensional spaces using random projection techniques, reducing the temporal complexity of searching for Shapelet to \(O(n\cdot l^{2})\). When [PERSON] et al. [10] constructed each node of a decision tree, only randomly selected k subsequences (k are much less than the number of all subsequences). In [11], Shapelet pre-screening was performed based on variance located at key points in the time series at each time point. Ref. [12] applied local Fisher discriminant analysis (LFDA) to find key dimensions in time series to reduce the Shapelet search space. Another method of improving efficiency is called LearningShapelet [1], which learns Shapelet by optimizing strategies rather than directly using subsequences in the dataset as candidates.  
There are two main classification strategies for the above methods. The first way is to fuse the Shapelet search process with the building process of the decision tree [8; 9; 10; 13], and the second is to transform the dataset using the multiple Shapelets found (i.e., using the Shapelet transformation technique [16], see Section 3.2 for details) and then classifying them using traditional classifiers such as SVM [12]. Although a series of Shapelet-based classification methods have been proposed one after another, a recent assessment by [PERSON] et al. [18] suggests that in terms of accuracy, the STC classification algorithm [17] based on Shapelet precision search and Shapelet transformation techniques (the converted data are used to train the rotating forest classifier [18]) represents the most advanced level of this type of method. In the experimental analysis in Section 5, we will show that the accuracy and efficiency of the RSFCF algorithm proposed in this paper on the UCR time series dataset exceed the STC algorithm.
## 2 Results and Discussion
### LC/ESI/QTOF-MS Identification of Phenolic Compounds in Moringa Seedlings
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Tissue** & **Temperature** & \begin{tabular}{c} **IC\({}_{\text{50}}\) DPPH** \\ **(\(\upmu\)/mL)** \\ \end{tabular} & \
\begin{tabular}{c} **IC\({}_{\text{50}}\) ABTS** \\ **(\(\upmu\)/mL)** \\ \end{tabular} &
\begin{tabular}{c} **IC\({}_{\text{50}}\) FRAP** \\ **(\(\upmu\)/mL)** \\ \end{tabular} \\ \hline \multirow{4}{*}{Leaves} & 25 \({}^{\circ}\)C & 6.34 \(\pm\) 0.22 \({}^{\text{b}}\) & 14.24 \(\pm\) 0.22 \({}^{\text{b}}\) & 125.22 \(\pm\) 1.22 \({}^{\text{b}}\) \\ \cline{2-5}  & 30 \({}^{\circ}\)C & 2.39 \(\pm\) 0.11 \({}^{\text{a}}\) & 10.34 \(\pm\) 0.35 \({}^{\text{a}}\) & 117.4 \(\pm\) 1.02 \({}^{\text{a}}\) \\ \cline{2-5}  & 35 \({}^{\circ}\)C & 9.77 \(\pm\) 0.32 \({}^{\text{c}}\) & 19.23 \(\pm\) 0.11 \({}^{\text{c}}\) & 257.5 \(\pm\) 1.26 \({}^{\text{c}}\) \\ \hline \multirow{4}{*}{Whole seedling} & 25 \({}^{\circ}\)C & 7.26 \(\pm\) 0.8 \({}^{\text{b}}\) & 12.87 \(\pm\) 0.23 \({}^{\text{b}}\) & 211.5 \(\pm\) 1.23 \({}^{\text{b}}\) \\ \cline{2-5}  & 30 \({}^{\circ}\)C & 4.56 \(\pm\) 0.4 \({}^{\text{a}}\) & 12.24 \(\pm\) 0.29 \({}^{\text{a}}\) & 184.3 \(\pm\) 1.12 \({}^{\text{a}}\) \\ \cline{2-5}  & 35 \({}^{\circ}\)C & 12.36 \(\pm\) 0.6 \({}^{\text{c}}\) & 20.69 \(\pm\) 0.12 \({}^{\text{c}}\) & 235.4 \(\pm\) 1.36 \({}^{\text{c}}\) \\ \hline \multirow{4}{*}{Roots} & 25 \({}^{\circ}\)C & 11.26 \(\pm\) 0.4 \({}^{\text{b}}\) & 15.15 \(\pm\) 0.12 \({}^{\text{b}}\) & 194.2 \(\pm\) 1.16 \({}^{\text{b}}\) \\ \cline{2-5}  & 30 \({}^{\circ}\)C & 9.24 \(\pm\) 0.7 \({}^{\text{a}}\) & 13.25 \(\pm\) 0.13 \({}^{\text{a}}\) & 165.8 \(\pm\) 1.10 \({}^{\text{a}}\) \\ \cline{2-5}  & 35 \({}^{\circ}\)C & 15.24 \(\pm\) 0.1 \({}^{\text{c}}\) & 24.25 \(\pm\) 0.15 \({}^{\text{c}}\) & 212.5 \(\pm\) 1.21 \({}^{\text{c}}\) \\ \hline \multirow{4}{*}{Seeds} & 25 \({}^{\circ}\)C & 13.23 \(\pm\) 0.6 \({}^{\text{b}}\) & 18.12 \(\pm\) 0.22 \({}^{\text{b}}\) & 225.1 \(\pm\) 1.22 \({}^{\text{b}}\) \\ \cline{2-5}  & 30 \({}^{\circ}\)C & 10.18 \(\pm\) 0.1 \({}^{\text{a}}\) & 15.21 \(\pm\) 0.13 \({}^{\text{a}}\) & 174.2 \(\pm\) 1.16 \({}^{\text{a}}\) \\ \cline{1-1} \cline{2-5}  & 35 \({}^{\circ}\)C & 21.28 \(\pm\) 0.7 \({}^{\text{c}}\) & 29.54 \(\pm\) 0.14 \({}^{\text{c}}\) & 235.6 \(\pm\) 1.31 \({}^{\text{c}}\) \\ \hline \hline \end{tabular} Different superscript letters within a column indicate significant differences (two-way ANOVA, post hoc Tukey’s test, \(p\leq 0.05\)).  
\end{table} 
Table 2: Antioxidant activity of _M. oleifera_ extracts. studies suggested that moringa is a good source of bioactive compounds such as flavonoids, phenolics, and their derivatives [45;46;49].
# Minimization of the Decoupling Circuit (DC-Link) in Induction Motors
## 2 Energy Flow Analysis of the Study Case
### Case Study Selection  
For the selection of the case study, an evaluation of the behavior of the energy flow in the system under different operating profiles of the electric vehicle is performed. The purpose of this is to carry out a comparison between the points with the highest energy return, to subsequently select the operating profile that represents the critical point with the highest energy stored by the decoupling system, which will be used for the development of the mathematical equation proposed in this article. The methodology used for the selection of the case study is listed below:  
1. The analysis is delimited to the motor operation mode of the electric machine.
2. The operation profile to be analyzed (torque variation or speed variation at constant load) is selected.
3. The analysis of the power flow behavior in the selected profile is carried out, starting from the inverter current.
4. The point with the highest energy return in the working profile is identified.
5. The results of the previous point are obtained in both operation profiles.
6. A comparison is made of the points of highest energy return obtained in both work profiles (torque variation or speed variation at constant load).
7. The critical case with the highest level of energy return obtained from the previous step is selected.
8. Mathematical development is performed considering the selected critical case.  
The mathematical development of the capacitance required by the decoupling system, using the critical point resulting from the abovementioned comparison, guarantees the correct operation of the system in less demanding work scenarios since it ensures that the calculated capacitor can operate in the case of a higher energy return. For the analysis of the operation profiles of the electric vehicle, a three-phase induction motor is used as load, whose specifications are shown in Table 1, for accurate and consistent results with the application.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Power load & 3 HP \\ Motor speed & 1800 RPM \\ Nominal motor current & 8 A \\ Number of poles & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Motor specifications.
# Evaluating Prediction Models for Airport Passenger Throughput Using a Hybrid Method
## 2 The Proposed Hybrid Evaluating Method  
Various approaches have been proposed to forecast the APT of airports. The modelling methods can be divided into four categories including time series models, causal models, artificial intelligent models and hybrid models. In addition, market share methods and analogy-based methods are also widely used for forecasting the construction scale in the field of engineering. In this paper, time series models, causal models, artificial intelligent models, market share methods and analogy-based methods are mainly studied. Taking the five methods as a whole, a hybrid evaluating method is built to see if the airport passenger throughput can be predicted. When the hybrid evaluating method of the five common modelling models shows high prediction accuracy, the best prediction result can be obtained using one of the appropriate models as long as the parameter selection is reasonable and the method selection is appropriate. However, other unique methods should be considered when the hybrid evaluating method shows bad prediction accuracy.
## 2 System Architecture and Methods
### Implementation Details  
This section offers a concise overview of the core technologies and methodologies used in the implementation, tracing the sequence from voice recognition to robot actions. It highlights the key technologies driving each process and explains the rationale behind their selection while adhering to strict safety and privacy guidelines.  
Figure 1: Detailed view of the experimental setup, showcasing its various components.  
Figure 2: Flowchart detailing the child–robot interaction sequence, from voice command to the robot’s action execution. The interaction initiates with a child’s voice command, recorded by a microphone, and converted into text by a STT system. This text is analyzed by an LLM to determine the necessary action. Simultaneously, an object detection module gathers information on the environment’s elements and their positions, essential for executing the action. This information, combined with the LLM’s analysis, enables the robot action module to guide the robot’s movements accurately.
# Experimental and Parametric Study on the Pull-Out Resistance of a Notched Perfobond Shear Connector
###### Abstract  
To ease the installation of perforating rebars through multi-holes, an alternative notched perfobond shear connector was proposed by cutting out the hole edge. This paper presents the test results of six pull-out specimens with conventional and notched perfobond shear connectors. The objective was to compare the failure modes and pull-out behaviors of perfobond shear connectors using circular holes and notched holes. Furthermore, the explicit finite element method was introduced and validated to generate parametric results for pull-out tests of notched perfobond shear connectors. A total of 33 parametric simulations were performed to further study the influences of several variables, including the hole diameter, the cut width, the perfobond thickness, the concrete strength, the diameter and strength of the rebar, and the strength of the structural steel. The experimental and numerical results were used to evaluate the previous equations for perfobond shear connectors. Finally, an alternative equation was proposed to estimate the pull-out resistance of notched perfobond shear connectors.  
Keywords:composite structures; perfobond connector; pull-out test; finite element analysis; notched hole +
Footnote †: journal: Applied Sciences
## 4 Numerical Results
### Linear Antenna Array Design  
The next step is to evaluate the selected SI algorithms by utilizing the optimization problem of linear array design. Generally speaking, the synthesis of antenna arrays in wireless communication systems is of significant importance. In this context, the sidelobe level suppression is a typical optimization task in antenna arrays. Hence, the synthesis of antenna arrays is one of the most classic, yet demanding, optimization problems that have been thoroughly studied in the literature by utilizing various analytical or stochastic techniques [19, 84, 85, 86, 87, 88, 89]. The vast majority of the well-known array synthesis techniques, including the Dolph-Chebyshev and Taylor methods, work only for equally spaced array elements and produce real amplitude. However, these methods produce large amplitude ratios that cause spurious radiations originating from the feed, thus mitigating the desired radiation pattern. Another drawback when designing antenna arrays with a non-uniform amplitude is the total radiated power. If we consider a specific number of elements in the antenna array design and take into account the total radiated power, we can conclude that the amplitude taper in the elements cannot generate an optimum design due to the functionality of the total power of the array elements [26]. Instead, if we apply uniform amplitudes in all the elements of the antenna array and vary the phases of the generated signals, the total radiated power in the broadside direction can be maximized [26]. Therefore, the use of phase-only synthesis is a better alternative technique to a non-uniform amplitude array for various wireless applications. However, if we use phase-only synthesis with equal inter-element spacing, then a larger number of elements is required to achieve a similar sidelobe level as the corresponding non-uniform amplitude array. This drawback can be bypassed using an array with unequally spaced elements and unequal phases, thus optimizing both positions and phases of the elements in an array, as it is described in [26, 90]. Nevertheless, in this case, the nonlinear and non-convex dependence of the array factor based on the element positions and phases imposes a significant step of difficulty in the synthesis of unequally spaced arrays. This type of problem is challenging and the use of a global optimization method like the evolutionary or the SI algorithms is a promising solution to address it.