esa-sceva/satcom-chunk-collection huggingface dataset:
# Earthquake Prediction for the Duzce Province in the Marmara Region Using Artificial Intelligence
## 1 Introduction  
Natural phenomena are in constant evolution. The earthquake monitoring agencies monitor these changes and the data obtained are recorded. One of the natural events is an earthquake [1]. Over the centuries, many large and small earthquakes have occurred in every corner of the world. These data are recorded in earthquake monitoring centers investigating seismic station's signals [2]. The recorded data are analyzed and scaled by seismologists [3]. However, when an earthquake occurs, the magnitude can be calculated precisely. Aftershocks can affect the calculation of magnitude, as they could cover part of the signal that maybe used to calculate the magnitude [4].  
There is still the need for a straightforward method for earthquake prediction and pre-scaling. This is because it is tough to predict an earthquake due to the uncertainty of the event and its sudden occurrence [5]. Many methods, probability calculations, algorithms, and analysis techniques have been used to predict earthquakes. Artificial intelligence methods and machine learning are most commonly used in earthquake prediction [6]. However, in engineering, as in many other fields, artificial intelligence is used effectively on big data [7]. The examination of research in recent years shows that artificial intelligence is used to predict human or natural events and solve problems in different fields [8]. The distribution of the data to be used during the estimation according to time and classification are essential. Time distribution is a technique primarily used in data classification. We can call it a \"time series\", a data collection organized in a chronological order [9]. It provides an opportunity to evaluate and analyze the data generated using a series [10].  
In this study, seismic data from the Duzce Province in Turkey, covering the years 1990-2022, were utilized. Duzce Province was selected due to it being located on the NorthAnatolian Fault Zone, which is a primary seismic region, and because of the limited number of studies conducted in this area. The accuracy rates were improved by calculating the b and d coefficients of the seismic data. The data were subjected to prediction and testing processes using the RNN method. The results were compared with the actual data.
## 2 System Description
### Pseudo-Open-Loop Reconstruction  
The closed-loop state represents the standard operational state of the GLAO system. Detection of atmospheric turbulence in this state necessitates reconstructing pseudo-open-loop data from closed-loop data. Figure 5 illustrates the flowchart for transforming closed-loop data into pseudo-open-loop data.  
Figure 4: Flow chart for data acquisition and preprocessing in the measurement system.  
We define open-loop data as closed-loop data with a DM voltage of zero. As a result, the measurement system no longer distinguishes between open-loop data and closed-loop data, and calculations can be made with the closed-loop data. Using the region method, we measured the slope response of the MD-WFS on the DM for each actuator unit voltage.  
\[S_{i}=\left[x_{1}^{1},y_{1}^{1},x_{1}^{2},\ldots,y_{1}^{N},x_{2}^{1},\ldots,y_ {M}^{N}\right]^{\mathrm{T}}, \tag{1}\]  
In the formula, \(S_{i}\) represents the slope response of the \(i\)-th actuator, \(x\) represents the slope in the horizontal direction, and \(y\) represents the slope in the vertical direction. For a set of slope data, there are a total of \(N\) sub-apertures, each containing \(M\) guide regions. For the GLAO system, the slope data include 48 \(\times\) 9 \(\times\) 2 floating-point numbers. For the 127 units on the DM, their response slopes can form a response matrix.  
\[Z=[S_{1},S_{2},\ldots,S_{i},\ldots,S_{127}], \tag{2}\]  
Through the response matrix, a relationship can be established between the control voltage of the DM and the slope of the MD-WFS. The slope information corresponding to the control voltage can be calculated from this. The final pseudo-open-loop data comprise the correction slope information calculated by the control voltage \(V\) and the residual slope information \(S_{close}\).  
\[S_{pseudo}=ZV+S_{close}, \tag{3}\]
# High-Temperature Mechanical Properties of 4.5%Al \(\delta\)-TRIP Steel
## 3 Results and Discussion  
### Hot Ductility  
Figure 3 shows the measured tensile strength relative to the temperature. \(\sigma_{\mathrm{max}}\) was decreased with the increasing temperature. Between 700 and 950 \({}^{\circ}\)C, \(\sigma_{\mathrm{max}}\) decreased with temperature, while the decreasing speed of the \(\sigma_{\mathrm{max}}\) at the temperatures above 950 \({}^{\circ}\)C was significantly slower. The \(\sigma_{\mathrm{max}}\) value was the smallest at 1350 \({}^{\circ}\)C, and its value was 1.54 MPa. The measured tensile strength curve was extended by the slope of the final two data points. The ZST of the sample was around 1405 \({}^{\circ}\)C.  
The trend of the RA of the specimen as a function of temperature is shown in Figure 4. In order to better determine the RA of the third brittle zone, the test temperature interval was changed from 50 to 25 \({}^{\circ}\)C, and several specimens tested at small temperature intervals were added in the first brittle zone. At a temperature range between 700 and 1300 \({}^{\circ}\)C, the RA was basically higher than 40%. In this  
Figure 3: Maximum tensile strength as a function of temperature.  
Figure 2: Thermal schedule for the hot tensile tests.  
temperature range, the minimum value of RA reached approximately 44% at 925 \({}^{\circ}\)C. The RA reached its maximum value of 78% at 1250 \({}^{\circ}\)C. The RA dropped rapidly between 1250 and 1350 \({}^{\circ}\)C. The RA was the smallest at 1350 \({}^{\circ}\)C, and its value was 2%. It was indicated that the sample had good resistance to the high-temperature cracking. The RA-temperature curve can be extrapolated. The ZDT of the sample was around 1355 \({}^{\circ}\)C.  
\(\Delta\)T is the difference between the ZST and the ZDT, which is the temperature range of the crack-sensitive zone. [PERSON] et al. [26] thought the embrittlement in zone I of a titanium alloy is related to the solidification cracking and the sensitivity of the solidification cracking may be negligibly small if \(\Delta\)T is less than 50 \({}^{\circ}\)C. [PERSON] et al. [27] found the \(\Delta\)T of TWIP steel with 0.05% C, 25% Mn, 3% Al, and 3% Si is about 50 \({}^{\circ}\)C, which is wider compared with those of other steels. The result also indicates that this TWIP steel has stronger high-temperature crack sensitivity. In this research, the \(\Delta\)T of the sample was 50 \({}^{\circ}\)C. The high-temperature crack sensitivity of the 4.5% Al-containing \(\delta\)-TRIP steel is excellent.  
The research of [PERSON] et al. [28] shows the steel is not easy to crack for RA > 60% and the sensitivity of the crack increases for RA < 60%. Based on the RA of 60% as a critical value for dividing ductility, we found that the sample had three temperature zones: the first brittle zone (1300-1350 \({}^{\circ}\)C), the second ductility zone (1000-1250 \({}^{\circ}\)C), and the third brittle zone (800-975 \({}^{\circ}\)C).
# Improvement of Power Converters Performance by an Efficient Use of Dead Time Compensation Technique
## 5 Conclusions  
The research endeavor successfully models and implements a novel dead time technique rooted in dead time compensation for enhancing the power quality parameters. The overall efficiency improvement of power converters has been achieved from cumulative improvements in several power quality parameters such as sinusoidal load current, phase angle, fundamental voltage magnitude, harmonic distortion. Further, the mitigation in a motor operation like torque pulsation smoothening, current waveform restoration, and speed enhancement are also enhanced. The parametric equations for all the parameters are derived for normal scenarios and dead time compensation scenarios. The model is simulated in MATLAB with two different cases and the results, with and without the proposed model application, are drawn. Finally, two separate case studies of the performance relative to the pre-model and major changes in all dimensions of the power quality parameters and the output of the power converters deficiency are found by the implementation of the dead time compensation technique. The proposed DTC results in significant improvement in the following parameters as exhibited in Figures 9-15:  
1. [label=()]
2. Sinusoidal load current waveform has been achieved by applying dead time compensation, leading to the removal of distortions accruing from harmonics
3. Fundamental voltage magnitude has been significantly restored through the proposed DTC implementation.
4. The phase angle has been improved through the proposed DTC strategy. Additionally, the third harmonic distortion has been significantly alleviated 3.77%, through the proposed DTC strategy in Case 1 and 3.04% in Case 2.
5. Significant improvements have been achieved in the induction motor parameters post the DTC strategy application. Parameters like torque pulsation, speed, and THD which cause hindrance in smooth motor operation have been demonstrably improved through the novel technique.  
Conceptualization, S.I.; Data curation, M.A., M.A., and S.A.A.R.; Formal analysis, M.U.J., M.A.A., M.A., and N.A.S.; Funding acquisition, A.X.; Investigation, H.U.R., S.S., S.A.A.R., and N.A.S.; Methodology, S.I. and A.X.; Project administration, A.X.; Resources, A.X.; Software, S.I.; Supervision, A.X.; Validation, M.U.J., and M.A.A.; Visualization, H.U.R., and S.S.; Writing--original draft, S.I.; Writing--review & editing, A.X., M.U.J., and M.A.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the 2016 national key R & D program of China to support low-carbon Winter Olympics of integrated smart grid demonstration project (2016 YFB0900500) and Beijing Natural Science Foundation (3182037). The Fundamental Research Funds for the Central Universities under Grant 2019 QN041.  
The authors declare no conflict of interest.
# From Brown-Field to Future Industrial Networks, a Case Study
## 2 Network Analysis and Modeling
### Characterization and Modeling
#### 2.2.2 Modeling with Volume and Dynamics Mixture  
Another approach that reduces the complexity of the flow-level characterization and modeling aims for a more abstract view of the network and targets different types of traffic, and their grouped flow characteristics. This type of approach profiles traffic either based on the type of services and traffic class or based on network virtualization traffic. The focus here changes from individual communication flow to general communication patterns from sub-systems in the network. The network traffic is usually classified into three categories: cyclic or time-triggered, stochastic and burst. The profiling of traffic classes considers the characteristics of each class, such as packet size and time intervals between transmissions. The cyclic traffics are small size packets with fixed generation time intervals, where stochastic traffics are event-triggered with small packet size and short duration and unknown transmission interval. The burst traffic flows are defined with large data transmissions for short intervals and self-similar transmission patterns in longer time intervals.
# The Use of Machine Learning Methods in Road Safety Research in Poland
###### Abstract  
Every year, thousands of accidents occur in Poland, often resulting in severe injuries or even death. The implementation of solutions supporting road safety analysis and management processes is necessary to reduce the risk of accidents and minimize their consequences. One of the rapidly developing tools that can play a key role in this area is machine learning. The aim of this study was to develop mathematical models based on ML algorithms describing road safety in Poland. First, variables with the strongest impact on safety were extracted. Then, mathematical modeling was performed using the k-Nearest Neighbors, Random Forest, and RPart algorithms. The best choice for imbalanced data, especially when the goal is to identify rare classes, is the RF model. The KNN model provides a compromise in situations where the highest overall accuracy is desired. On the other hand, the RPart model can be used as a fast, basic model, but it requires improvements to handle rare classes. The results not only identified factors that significantly affect the severity of injuries or the number of fatalities in accidents but, above all, also demonstrated the ability of ML-based models to predict threats and their consequences.  
road safety; transportation; Poland; machine learning; Random Forest; k-Nearest Neighbors; RPart algorithm Article
## Materials and Methods
### Remote Terminal Unit
#### 2.4.2 Real-Time Clock  
An RTC is an essential component of the RTU because the solar position algorithm receives the time and date as input data. Therefore, any error in the RTC time impacts on the solar vector calculation. The RTU uses an RTC DS1307. It is an RTC with I2C (Inter-Integrated Circuit) serial interface, low-power consumption, leap year correction, and a Battery-Backed supply with automatic power-fail detection.
# The Origin and Biomedical Relevance of Cannabigerol
## 3 Structure of Compounds from the Cannabigerol-Type Group  
Taking into account their widespread presence in hemp and the significant structural similarity of many phytocannabinoidss to cannabinoid (CBG), a separate class of cannabinoid-type compounds has been created (Figure 2). In addition to CBG itself, this group includes cannabinoidacid (CBGA), CBG, and CBGA methyl ethers, as well as cannabinoid (CBGV) and its acid derivative, cannabinoid acid (CBGVA). CBGV is an analogue of CBG containing a three-carbon side chain--in comparison, the side chain of a cannabinoid molecule contains five carbon atoms [16].  
Figure 1: Chemical composition of different parts of _Cannabis sativa_ L. [17,18].  
The structure of CBG consists of 21 carbon atoms (22 carbon atoms in the case of the acidic form) [25], with the total chemical formula of C\({}_{21}\)H\({}_{32}\)O\({}_{2}\) and a molar mass of 316.48 g/mol. According to the IUPAC nomenclature, the systematic name of cannabigerol is 2-[(2E)-3,7-dimethylocta-2,6-dienyl]-5-pentylbenzene-1,3-diol. The compound was first isolated in 1964 from a hexane extract of hashish. Its structure and stereochemistry were subsequently confirmed through chemical synthesis [26; 27].  
The melting point of cannabigerol and its thermal degradation were determined using differential scanning calorimetry and thermogravimetric analysis. It was noted that CBG melts at 52 \({}^{\circ}\)C (in comparison, the melting point of cannabidiol is 68 \({}^{\circ}\)C) [28]. It was also observed that thermal degradation of cannabigerol occurs at approximately 150 \({}^{\circ}\)C (CBD thermally degraded at 250 \({}^{\circ}\)C). For both CBG and CBD, these temperatures are close to their boiling points [28]. In contrast, other studies have shown that cannabigerol (like \(\Delta^{9}\)-THC) exhibits greater solubility in supercritical CO\({}_{2}\) as the temperature increases (at a constant pressure), while cannabidiol shows the opposite trend [29].
## 1 Introduction
After a total 4 m free-space transmission, the qualitative analysis of VLC links was carried out by frequency response and real-time BER measurements with a simple OOK-modulation scheme. After passing through the relay node, the NLOS problem could be solved and the communication distance was then extended; the consequent inevitable cost was a reduction in modulation bandwidth from 880 to 715 MHz. This is mainly due to the noise introduced by AF and the long-distance attenuation. On the whole, the communication system also remained high data rates and a slightly reduced modulation bandwidth, while the cost of the dual-hop VLC system was acceptable. In addition, we also studied the influence of distance on the dual-hop system. To the best of our knowledge, this is the first real-time dual-hop link based on micro-LEDs. The results show that our proposed dual-hop VLC system with relay infrastructure can support future high-speed optical wireless networks by extending the coverage area for indoor NLOS-transmission applications. The rest of this paper is organized as follows: Section 2 describes the concept and channel-model dual-hop VLC system based on micro-LEDs. Section 3 shows the experimental setup for the proposed dual-hop VLC system. System communication performance including the modulation bandwidth and the BER results with real-time is also given in this section. Finally, we conclude the investigation in Section 4.
## 5 Energy Consumption and System Optimization in WBSNs
### Cost Function  
The cost function is composed of energy consumption and latency. The energy consumption of sensor \(i\) in a time interval is given in Equation (3), which can be simplified into [49]:  
\[bEcirc_{i}=E_{0}+bEcirc_{i}T_{i} \tag{4}\]  
where the first term \(E_{0}\) will remain constant at each time interval \(T_{i}\), it will only vary from node to node depending on the vital sign that is being measured. The second term based on the overhead protocol, is the variable component. The other cost function component is the latency, \(L\), which is the time it takes a sensor between collecting a sample and transmitting it to the base station and is given by:  
\[L=\sum_{i=1}^{N}\beta_{i}\frac{T_{i}}{2} \tag{5}\]  
The final cost function is given by:  
\[C_{i}=\alpha_{i}bEcirc_{i}+\ \beta_{i}\lambda L_{i} \tag{6}\]  
where \(\alpha_{i}\) corresponds to the relative weight assigned to the power consumption in sensor \(i\), \(\beta_{i}\) is the coefficient indicating higher priority in order to minimize latency \(L_{i}\) of sensor \(i\), and \(\lambda\) is to give importance to latency relative to energy consumption term. The final optimization problem based on the cost function and the constraints is expressed as follows:  
\[\sum_{i=1}^{N}(\alpha_{i}bEcirc_{i}+\ \beta_{i}\lambda L_{i}) \tag{7}\]  
Constrained to:  
\[0\leq T_{i}\leq \min\Big{(}T_{ir},\frac{X_{i}B_{i}}{SFiMi},\frac{DT\cdot DR- \partial v}{SFiMi}\Big{)},\] \[\sum_{i=1}^{N}\frac{1}{T_{i}}\leq\frac{1}{T}\]The optimization problem considering the research work done in [18,49], is the basis to develop an adaptive optimization system, capable to evaluate the circumstances of monitoring required due to network conditions, patient's condition and the application scenario.
# Research Progress of Enhanced Thermal Evacuation and Cooling Technology for High-Speed Motors
## 1 Introduction  
High-speed motors are extensively utilized as driving devices for rotating machinery because of their small size and high power density [1]. Currently, high-speed motors have been applied in the following areas: electric devices, high-speed machine tools, and turbo-machinery [2, 3]. In recent years, high-speed motors have developed towards ultra-high speeds, high power density, and miniaturization.  
In the operation of a motor, there will be heat generation from core and winding losses. In addition, due to the compact structure, heat traps inside the motor lead to higher spindle temperatures and thermal elongation. Spindle thermal deformation caused by the error is often small, but the impact on the machining accuracy is prominent, and in serious cases, even causes damage to the spindle. Therefore, it is especially critical to provide efficient heat evacuation and address the thermal contortion of the motor spindle caused by temperature increase [4, 5, 6, 7, 8].  
To study the heat evacuation of high-speed motors, this paper lists the different applications and cooling requirements for high-speed motors. Moreover, the main heat evacuation paths within the motor are evaluated, and the major thermal resistances are summarized. For the main thermal resistances, different optimization approaches for motor heat evacuation are discussed. Finally, the future research direction of motor heat evacuation is predicted.
# Toward More Sustainable River Transportation in Remote Regions of the Amazon, Brazil
## 3 Challenges in the Amazon Region: The Case of School Transport
### Possible Economic and Environmental Impacts  
#### 3.1.1 Economic Impact  
As shown in Section 2, improving the sustainability and efficiency of the school boats depends on several factors. While a fuel reduction analysis is beyond the scope of this work, reducing the hull resistance of ships, also applicable to riverboats, may minimize the effort required by the engine and thus reduce the amount of fuel needed [22]. Small changes to the hull shape can reduce hydrodynamic resistance. Based on Figure 2, after hull optimization, it can be assumed that in specific working conditions, the fuel consumption of a typical Amazon school boat is reduced by \(\sim\)5%. In the long-term, this could produce substantial economic benefits, as the boats travel long distances in areas where access to fuel may be limited. If a typical motorboat consumes an average of \(\sim\)95 L per hour at cruising speed [58], and the current price of fuel in Brazil is 0.76 $USD/liter (6-26-2020, [59]), with constant activity 8 h per day, the estimated 5% reduction in fuel consumption would provide savings of \(\sim\)38 L (\(\sim\)29 $USD) per day or \(\sim\)13870 L (\(\sim\)10,541 $USD) per year for one boat. In a fleet of 10 boats, several thousand liters could be saved per year. This direct economic benefit also has an effect on daily activities in the Amazon region that use similar small boats, such as school transport, food commerce, fishing, health campaigns, and scientific activities.