esa-sceva/satcom-chunk-collection huggingface dataset:
# The Bivariate Empirical Mode Decomposition and Its Contribution to Grinding Chatter Detection
## 3 Application of a Feature Vector Extraction Method for Simulating Chatter Signals
### Selection of the Ideal Number of Projection Directions  
According to the simulated signal, variation in the correlation coefficient, frequency and calculation time, along with the change in direction number, can be calculated as shown in Figure 7. The trends of correlation coefficient and frequency variation tend to be stable when the maximum projection number is set to 32. Higher numbers are not discussed in this paper.  
The degree of stability of the correlation coefficient and frequency for the true IMFs is shown in Figure 8.  
Figure 8: (**a**) The stability degree of correlation coefficient and frequency from IMF1; (**b**) the stability degree of correlation coefficient and frequency from IMF2. Blue solid lines represent the correlation coefficient, red dashed lines represent the frequency components of the real part of IMF1, yellow dotted lines represent the frequency components of the imaginary part of IMF2.  
Figure 7: (**a**) The correlation coefficient of each true IMF; (**b**) the frequency of each true IMF; (**c**) calculation time for the BEMD procedure.  
In light of the indicators from the above figures, it is clearly seen that the correlation coefficient and frequency components of the signals are almost stable when the direction number is greater than 16. On the other hand, the computational efficiency of the BEMD algorithm gradually reduces with increasing projection number. In general, the ideal number of projection directions is 16, which provides a basis for subsequent BEMD operations.
# A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence
## 1 Literature Review
### Deep Neural Network
#### 1.1.3 Long Short-Term Memory (LSTM)  
An LSTM is an architecture for deep neural networks that uses forget gates to solve the problems of vanishing gradients or bursting. Ref. [39] states that it is essential to note that, in contrast to standard recurrent neural network topologies, LSTM facilitates loss backpropagation over a restricted amount of time steps in numbers. A standard LSTM unit comprises three gates: an entry gate, an exit gate, and a gate that can be forgotten. The cell itself is also included in this unit. Through monitoring the gates' operation, the cell can determine which data should be stored and when information should be granted to other units. The LSTM transition is built on the foundation established by the subsequent equations [38]:  
\[i_{t}=\sigma(W^{i}X_{t}+U^{i}h_{t-1}+b^{i}) \tag{2}\]  
\[f_{t}=\sigma(W^{i}X_{t}+U^{i}h_{t-1}+b^{i}) \tag{3}\]  
\[o_{t}=\sigma(W^{o}X_{t}+U^{o}h_{t-1}+b^{o}) \tag{4}\]  
\[u_{t}=tanh(W^{m}X_{t}+U^{m}h_{t-1}+b^{m}) \tag{5}\]  
\[c_{t}=i^{o}t_{t}+f^{o}i_{t-1} \tag{6}\]  
\[h_{t}=o^{o}ttanh(c_{t-1}) \tag{7}\]  
The input vector to the LSTM unit is denoted as \(x_{t}\), the activation vector for the forget gate is denoted as \(f_{t}\), the activation vector for the input gate is denoted as \(i_{t}\), and the activation vector for the output gate is denoted as \(o_{t}\).  
The cell state vector is denoted as \(c_{t}\), whereas the hidden state vector is represented as \(h_{t}\). In this model, the letter \(W\) represents the weight matrices, whereas the letter \(b\) represents the parameters of the bias vector [40]. Bi-LSTM employs bidirectional hidden layers to encompass both historical and future information. As a result, there is an enhanced acquisition of knowledge inside the network and a more robust and reliable exchange of information in both directions over time.
## 4 Mobility Model of the MS  
The path passed by the MS in the wireless sensor network is an essential and influencing factor in the data collection process. Hence, we need to determine the mobility model to implement it in the MS. To do so, we base on the mobility model used in a previous publication [15], in which the MS passes by all sensor nodes. Thus, we adopt this model mobility to cluster-based wireless sensor network in order to make MS passes only by CHs in the network field. Figure 2 shows an example of how the MS moves through the network.  
Finding the moving plan for the MS passing by all CHs in the network is similar to the well-known traveling salesman problem (TSP). TSP is considered an NP-hard problem. Thus, the problem of finding the shortest path for the MS is an NP-hard problem, where the MS acts as salesman and CHs are the cites to be visited by the MS [15]. We assume that the MS knows the topology of the network and it calculates the shortest path passing by all CHs in the cluster-based wireless sensor network. The authors in [15], used mobility models based on metaheuristic algorithms. Metaheuristic algorithms are optimization methods used for solving NP-hard class problems. These methods search solutions for problems in reasonable calculation time. These solutions may be optimum solutions or very close to the optimum solutions.  
We have chosen a mobility model from the previous publication [15] based on a tabu search algorithm, and the authors used a multi-objective function with different constraints to solve the  
Figure 2: Moving plan in Cluster-Based Wireless Sensor Network.  
problem of finding a suitable trajectory for MS. In this work, we are interested in finding the shortest path for the MS based on distance. Therefore, we only use the part of calculating the shortest trajectory of the multi-objective proposed in [15]. The objective function used in this work is presented in Equation (2) which minimizes the path of MS in terms of distance. The metaheuristic tabu search method used in this paper for calculating the trajectory is presented in Algorithms 1 and 2.  
Problem StatementThe metaheuristic algorithm used to calculate the optimal shortest path passing by all CHs in order to collect data by taking into consideration some related constraints.
## 1 Introduction
The results can contribute to our understanding of satellite precipitation retrieval in climate change and hydrological monitoring.
## 2 Materials and Methods
### HS-GC-IMS Analysis  
The VOCs and polymers of low molecular weight in the sample were detected using the FlavourSpec(r) (Melbourne, Australia) flavor analyzer, which employs GC-IMS technology. The FlavourSpec(r) flavor analyzer was equipped with a gas-phase ion mobility spectrometry unit and an automatic headspace sampling unit. The sample preparation procedure was as follows: 1 g of the sample was weighed and placed in a 20 mL headspace vial, followed by the addition of 20 \(\upmu\)L of the internal standard ethyl decanoate (100 ppm) and mixing. The sample was then incubated at 60 \({}^{\circ}\)C for 15 min before injecting 500 \(\upmu\)L of the headspace. The incubation speed was set to 500 rpm, and the headspace injection needle temperature was maintained at 85 \({}^{\circ}\)C. Each sample was analyzed in triplicate to ensure reproducibility.  
The gas chromatography conditions were as follows: an MXT-5 column (15 m \(\times\) 0.53 mm \(\times\) 1.0 \(\upmu\)m, Restek, Bellefonte, PA, USA) was used. The column temperature was maintained at 60 \({}^{\circ}\)C, with nitrogen (purity 99.999%) as the carrier gas. The total chromatographic run time was 20 min, with the carrier gas flow rate gradient set to an initial flow rate of 2 mL/min maintained for 2 min, and then linearly increased to 100 mL/min over the remaining 18 min. For ion mobility spectrometry, the drift tube temperature was set at 45 \({}^{\circ}\)C, and nitrogen (purity \(\geq\)99.999%) was used as the drift gas, with a flow rate of 150 mL/min.  
The qualitative analysis of volatile substances was conducted using the VOCal software integrated with the National Institute of Standards and Technology (NIST), Flavors and Fragrances of Natural and Synthetic Compounds (FFNSC) library, and the IMS database from G.A.S. (Dortmund, Germany). Retention index (RI) values of the target VOCs were calculated using n-alkanes (C4-C9) as external standards. The identification of VOCs was achieved by matching RI and drift time (Dt) against the NIST and IMS database. The internal standard method was used for quantitative analysis.
# Dynamic Alternations in Mother-Infant Dyad's Gut Microbiota over the Period of Six Months
## 1 Introduction  
The human gut harbours trillions of symbiotic bacteria [1]. Everyone has hundreds of different gut microbial species. To date, about 1500 different microbial species have been identified from the human gut. Since birth, the human microbiome plays a crucial role in the maturing immune system and regulates health. Various maternal factors, vertical microbial transmission from the mother, horizontal environmental transmission, and internal factors associated with the infant play an indispensable role in regulating the gut microbiome [2].  
Gut microbiome ecology and function are dynamic and highly unstable during infancy, especially during the first year of life. The gut microbiota changes dramatically through interactions with the developing immune system in the gut [3]. The healthy microbiome maintains the health of the host. The interaction between the microbiome and the host is substantially critical in early life because the abundance and composition of the microbiome undergo major changes in early life, and these changes become stable and remain constant throughout life, thereby determining the healthy adult life [4]. In early life, the gut microbiome is relatively active, and these initial inhabitants have a key impact on the health of the host throughout the life cycle [5]. Understanding the composition and structure of the microbiome in the intestine and the factors that influence them in early life could provide a platform for developing strategies to maintain the host's health by restoring the gut microbiota.  
High-throughput sequencing is a powerful method that unfolds both the cultivable and uncultivable microbial population in samples. Metagenomic analysis has been used to describe the taxonomic composition and functional potential of microbial communities and to assemble the entire genome sequence of a specific strain [6]. Metagenomic data analysis has also enabled us to gain a comprehensive understanding of microbial diversity, composition, community function, and their relationship with the colonic environment. Cost-effective and high-throughput metagenomic techniques have been widely used, and deep phylogenetic characteristics of intestinal microbiome have been successfully analysed. It could be used as a nifty approach to analyse the structure and function of intestinal microflora in infants.  
In this study, microbial metagenomic DNA was extracted from one mother-infant dyad's faecal samples using a DNA kit, and a metagenomic library containing genomic DNA was constructed and sequenced by Illumina sequencer. The dynamic changes in the intestinal microbiota of the mother before and after delivery were analysed, along with the infant's microbiota over 6 months. Furthermore, the relationship between the structure and function of the intestinal microflora of mother and infant was analysed to explore the influencing factors of intestinal microflora composition and establishment in early life.
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 8 Conclusions  
In this paper, we have proposed a novel design for personal content retrieval systems to jointly optimize content recommendation and content delivery. Optimal algorithms with exponential complexities have been introduced to solve the joint optimization problem. A linearization technique has been proposed to reduce the computational complexity of the optimal algorithms. Moreover, several heuristic algorithms have been presented to tackle the joint optimization problem with polynomial complexity. The fundamental performance of the proposed system has been characterized by theoretical bounds and evaluated via simulations. Theoretical and simulation results have shown that the proposed system has the potential to achieve both high user interest and low transmission outage probability. Moreover, it has been demonstrated that the best performing heuristic algorithm can well approximate the optimal performance of the system. We conclude that the proposed system can effectively balance the conflicting goals of maximizing user interest and minimizing transmission outage; hence, it is a promising design paradigm for personalized content retrieval systems.  
**Acknowledgments:** The authors acknowledge the support from the Natural Science Foundation of China (Grant Nos. 61571378 and 61601388).  
**Author Contributions:** [PERSON], [PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. All authors have read and approved the final manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 9: Performance comparison with different distributions of individual user’s interest in a piece of content across multiple users (file size distribution is subject to \(Lognorm(9.357,1.318)\), \(P_{T}=1\) W).
## 3 Results
### Objective Evaluation  
The results of objective evaluation listed in Tables 2-4 show the effect of different approaches in enhancing Mandarin EL speech. The U/V confusion matrixes in Table 2, the log F0 RMSE and F0 CC in Table 3 show the F0-based enhancement; the MCD and CodeAP RMSE in Table 4 show spectrum enhancement. In Table 2, \(U_{tar}\) represents the unvoiced part of target speech; \(U_{est}\) represents the unvoiced part of converted speech; \(V_{tar}\) represents the voiced part of target speech; and \(V_{est}\) represents the voiced part of converted speech. The results of U/V analysis were calculated according to the testing data (referring to the work [23]). Table 2 leads to three findings. Firstly, the difference between traditional features and Mel-Spectrogram parameters is very small. Secondly, different approaches are close in the accuracy of voiced segments. Thirdly, our approach has the highest accuracy of unvoiced segments and the accuracy of enhanced speech with variable tone is higher than that of enhanced speech with fixed tone. In Table 3, log F0 RMSE and F0 CC are obtained based on the comparison of the converted and target F0. If log F0 RMSE is low and F0 CC is high, the enhancement of F0 pattern would be effective. Our proposed approach performs better than the two baseline (CLDNN-based and VC3-based) approaches. In addition, the F0 pattern enhancement of speech with variable tone is more effective than that of speech with fixed tone. This is because the difference between EL-VT and normal speech is much less than the difference between EL-FT and normal speech. Table 4 shows the results of spectrum enhancement. For both traditional features and Mel-Spectrogram, our proposed approach performs better than baseline approaches. For CycleGAN-based approaches (VC3 and our approach), Mel-Spectrogram parameters lead to better performance than traditional features. This is because CycleGAN-based approaches can effectively transform one Mel-spectrogram into another as a whole, not frame-by-frame. In particular, our proposed approach can capture more information, such as semantic contextual information and frequency information. However, if the Mel-Spectrogram is \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Speech Types**} & \multicolumn{2}{c}{**Traditional Features**} & \multicolumn{2}{c}{**Mel-Spectrogram**} \\ \cline{2-5}  & **MCD (dB)** & **CodeAP RMSE (dB)** & **MCD (dB)** & **CodeAP RMSE (dB)** \\ \hline CLDNN-FT & 5.5294 & 4.3523 & 5.6625 & 4.8072 \\ VC3-FT & 4.9205 & 4.1055 & 4.7032 & 3.7805 \\
2C1T2C-FT & 4.8135 & 3.9522 & 4.6305 & 3.7625 \\ CLDNN-VT & 5.3215 & 4.5778 & 5.5441 & 4.7931 \\ VC3-VT & 4.7720 & 3.9815 & 4.5625 & 4.3505 \\
2C1T2C-VT & 4.7026 & 3.3765 & 4.5211 & 3.5922 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Objective evaluations of spectrum features.
## 3 Results and Discussion
### Pore Evolution
#### 3.4.1 Spectrum Curve Analysis  
The \(T_{2}\) spectral curves of granite after different heat treatments are shown in Figure 14, from which it can be seen that the peaks of the \(T_{2}\) spectral curves are mainly bimodal, and most of the curves between the two peaks are smoothly and continuously connected, indicating that the pore connectivity of different dimensions within the granite is relatively good. Overall, the peaks and areas of the spectral curves gradually increase with increasing heat treatment temperature, indicating that the high-temperature cold shock can affect the pore structure of the granite and promote the development of pores [45]. Comparing the \(T_{2}\) spectral curves under different cooling modes, it is found that the elevation and right shift of the second peak of coarse and fine granite under liquid nitrogen cold shock are more significant, indicating that the size and number of pores have been greatly increased, and liquid nitrogen cold shock has effectively changed the distribution of pores; furthermore, the peaks and valleys between the two peaks have been shifted upward more obviously with the increase in heat treatment temperature, which means that compared with water-cooled shock, liquid nitrogen cold shock can effectively improve the connectivity between the pores of granite. This indicates that compared with water-cooled shock, liquid nitrogen cold shock can effectively improve the connectivity between the pores of granite.
# Compact Broadband Antenna with Vicsek Fractal Slots for WLAN and WiMAX Applications
###### Abstract  
This paper aims to design a compact broadband antenna for wireless local area network (WLAN) and worldwide interoperability for microwave access (WiMAX) applications. The suggested antenna consists of an octagonal radiator with Vicsek fractal slots and a partial ground plane, it is printed on FR-4 dielectric substrate, and its global dimension is \(50\times 50\times 1.6\) mm\({}^{3}\). The antenna is designed and constructed using both CST MICROWAVE STUDIG(r) and CADFEKO electromagnetic solver, and in order to validate the acquired simulation results, the antenna is manufactured and tested using vector network analyzer E5071C. The measurement results show that the designed antenna attains a broadband bandwidth (S\({}_{11}<-10\) dB) from 2.48 to 6.7 GHz resonating at 3.6 and 5.3 GHz, respectively. The broadband bandwidth covers the two required bands: WiMAX at the frequencies 2.3/2.5/3.3/3.5/5/5.5 GHz and WLAN at the frequencies 3.6/2.4-2.5/4.9-5.9 GHz. In addition, the suggested antenna provides good gains of 2.78 dBi and 5.32 dBi, omnidirectional measured radiation patterns in the E-plane and the H-plane and high efficiencies of 88.5% and 84.6% at the resonant frequencies. A close agreement of about 90% between simulation and measurement results is noticed.  
broadband; octagonal microstrip patch antenna; Vicsek fractal; WLAN; WiMAX +
Footnote †: journal: _Sciences_
## 3 Methodology
### Preprocessing Computer Outputs  
Computer simulation technology (CST) software, which uses finite element (FE), is exploited in this work to model and evaluate prior information of EM responses (e.g., magnitudes of electric (E)-field) in the SBL for the proposed GP-based inversion methodology. CST is part of SIMULIA, a Dassault Systemes brand, sourced from Darmstadt, Germany. According to [35], CST Studio Suite(r) exclusively uses the FE method as the EM simulation solver for low-frequency application. Nine SBL models with the same input properties, except the hydrocarbon depth, were simulated using the CST software. Details of the SBL data acquisition are presented in Appendix A. For demonstration, one SBL model replicated by the CST is depicted in Figure 11. The SBL models, known as target models, were replicated with three background layers such as air, seawater, and sediment. The nine different \"tried\" depths were between 200 m and 1000 m from the seafloor with an increment of 100 m each. All the computer input properties and parameters used in the SBL modeling are indicated in Table 1.  
As discussed by researchers in [28], interpretation of the SBL data relies on an assumption that the normalized magnitude versus offset (MVO) between the target responses (i.e., with presence of hydrocarbon) and reference responses (i.e., without presence of hydrocarbon) can pre-identify the presence of high-resistive bodies. The idea is that the magnitudes of target response are divided by the magnitudes of reference response along the offsets (i.e., source-receiver separation distances). Ratio of the normalization should be greater than one, which is associated with the existence of the potential reservoirs. Thus, all the generated CST outputs were preprocessed before being used for the forward GP modeling. By considering the ratio measurements and the symmetrical property of the developed SBL models, we chose nine different datasets with 73 datapoints per set, which are from an approximate distance of 11,840.80 m to an approximate distance of 19,004.97 m, for the data processing presented in the next subsection.  
\begin{table}
\begin{tabular}{c c} \hline
**Input Property or Parameter** & **Numerical Values in Unit** \\ \hline Electrical conductivity (Air) & \(1.0\times 10^{-11}\) S/m \\ Electrical conductivity (Seawater) & \(1.63\) S/m \\ Electrical conductivity (Sediment) & \(1.00\) S/m \\ Electrical conductivity (Hydrocarbon) & \(2.0\times 10^{-3}\) S/m \\ Thickness of layer (Air) & \(300\) m \\ Thickness of layer (Searwater) & \(1000\) m \\ Total thickness of layer (Sediment) & \(3500\) m \\ Thickness of layer (Hydrocarbon) & \(200\) m \\ Transmission frequency & \(0.125\) Hz \\ Current strength & \(1250\) A \\ Transmitter length & \(270\) m \\ Depth of elevation (Transmitter) & \(30\) m \\ Length of SBL models & \(2000\) m \\ Width of SBL models & \(2000\) m \\ Height of SBL models & \(5000\) m \\ \hline \end{tabular}
\end{table}
Table 1: Input properties and parameters of the seabed logging (SBL) models.