esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Figure 1e shows a picture of the arc generated by the generator, and Table 2 summarizes the resistances, capacitances, and inductances used in the decoupling network, as well as module and line impedances. The single-phase and three-phase inverters used in the experiments are composed of an insulated-gate bipolar transistor (IGBT) module (SKM50 GB123D, SEMIKRON, Nuremberg, Germany). A resistance and an inductance of 10 \(\Omega\) and 10 mH are used as the loads for three-phase and single-phase inverter; Table 3 shows the arc generation test conditions for each arc current. To use the concept of switching frequency for the arc current with spread frequency fluctuations, the average switching frequency was calculated by measuring the number of times that the switch was operated during a certain period. In Table 3, the current is represented by the arc current magnitude before arc generation. For reference, the arc current before arc is the inverter input current, and after arc generation, the inverter input current becomes the arc current. Figure 2 shows the flowchart for the data accumulation and frequency analysis in this work. To collect arc current data, first, a DC voltage is applied to drive an inverter load, as in Figure 1a. Then, the arc rods are separated by the step motor connected to the rods. Then, the arc current data are stored at 250 kHz using the oscilloscope (Tektronix MSO3054, OR, USA) so that the data before and after arcing are included. The current probe used for the current measurement is Tektronix TCP312 (Tektronix, OR, USA) that can measure frequency components from 0 to 100 MHz. Using the saved data, frequency analysis was performed in MATLAB, and the fast Fourier transform (FFT) was used for the analysis. \begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline \(R_{1}\), \(R_{2}\), \(R_{3}\), \(R_{4}\) & 0.5 \(\Omega\) \\ \(L_{1}\) & 12 mH \\ \(L_{2}\), \(L_{3}\) & 60 uH \\ C\({}_{1}\) & 20 uF \\ C\({}_{2}\), C\({}_{3}\) & 22 nF \\ \(L_{4}\), \(L_{5}\) & 50 uH \\ C\({}_{4}\) & 10 nF \\ C\({}_{5}\), C\({}_{6}\) & 1 nF \\ \hline \end{tabular}
\end{table} 
Table 2: Parameters used in the decoupling network as well as module and line impedances. Figure 2: Flowchart of data accumulation and frequency analysis in this study.
## 3 Materials and Methods
### Assessment of Flood Hazard Mapping and Resilience  
To assess the effects of climate and hydrological factors on flood hazards in Ayutthaya, the following six thematic-layer factors were deployed to generate a flood risk map using ArcGIS 9.3: maximum daily rainfall (mm/d), runoff (m\({}^{3}\)/s), slope of the area (%), past flood events (yr), land use, and road density (km/km\({}^{2}\)). The important weights of the following factors were determined by expert pairwise comparison. By applying the AHP technique, all experts (i.e., representatives from the Office of the Ayutthaya Municipality, Ayutthaya Provincial Office for Natural Resources and Environment, Disaster Prevention and Mitigation Office in Ayutthaya, Royal Irrigation Department, and Ayutthaya Provincial Public health office, Thailand; \(n=5\)) were asked to complete a pairwise comparison of each factor using a nine-point scale ranging from 1 to 9 (i.e., less important variables were valued from 1 to 9) (Equation (1)). Both the consistency index (_CI_) and consistency ratio (_CR_) were computed to avoid any incidental judgment in the pairwise comparison (Equations (2) and (3)) [40]. The comparison is acceptable with an estimated CR value of less than 0.1:  
\[A=[aij]=\left\{\begin{array}{ccc}1&aij\ldots&a1n\\ 1/aij&1&a2n\\ 1/a1n&1/a2n&1\end{array}\right\} \tag{1}\]  
where \(A\) is a representation of the pairwise comparison matrix of experts' opinions on each factor associated with flood hazards over an alternative \(a_{\bar{i}}\) and all comparisons \(ij=1,2,\ldots,n\):  
\[CI=\frac{\lambda\max-n}{n-1} \tag{2}\]  
where \(\lambda\max\) is the largest eigenvalue of the pairwise comparison matrix and \(n\) is the number of factors associated with flood hazards:  
\[CR=\frac{CI}{RI} \tag{3}\]  
where \(CI\) is the consistency index and \(RI\) is the random consistency index (e.g., the \(RI\) for 6 factors is 1.24).  
Flood disaster resilience was also assessed based mainly on AHP and experts' opinions (\(n=3\)), including representatives from the Office of the Ayuthaya Municipality, Ayuthaya Provincial Office for Natural Resources and Environment, and Disaster Prevention and Mitigation Office in Ayuthaya. The 10 essentials for making cities resilient proposed by UNDRR (Figure S1) were modified into 15 factors for the AHP-pairwise comparison. Applying Equation (1), each individual factor in a paired comparison was considered an expression of the expert's preference for one alternative. For instance, when an expert decided that alternative \(i\) (i.e., factor \(i\) of modified UNDRR flood resilience) was equally important as alternative \(j\) (i.e., factor \(j\) of modified UNDRR flood resilience), a comparison  
Figure 1: Research case study: Ayuthaya Province, Thailand.  
represented by \(a_{\bar{y}}=a_{\bar{y}}=1\) or factor \(i=\) factor \(j\) was observed [41]. Through the AHP method, the highest-ranked factor was considered crucial in determining community flood resilience. In contrast, the lowest-ranked factor was considered the least important by the panel of experts.
## 3 Results
### Method Validation
#### 3.2.3 Precision and Accuracy  
Precision and accuracy were evaluated in the term of relative standard deviation (RSD %) for precision and the term of recovery percentage of nominated concentrations for accuracy. Both precision and accuracy were evaluated by analysis of three concentrations of QC samples (LLQC, MQC, HQC) and LLOQ, six times in one day (intra-day) and for three successive days (inter-days). The results of accuracy and precision for ERDA are presented in Table 2. The low values of % RSD indicated that the precision of the developed method was within the acceptable range required by the concerning guidelines for the determination of EDRA in human plasma.
## 2 Materials and Methods
### Gas Chromatography-Olfactometry (GC-O)  
GC-O analysis was performed using an Agilent 7890B GC (Agilent Technologies Inc., Santa Clara, CA, USA) equipped with a flame ionization detector (FID), a sniffing port (ODP3; Gerstel, Mulheim an der Ruhr, Germany), a cool on-column injector and a split/splitless (S/SL) injector. Separations were performed on a fused silica capillary column (either Rtx(r)-Wax, 30 m length \(\times\) 0.32 mm i.d. \(\times\) 0.25 \(\upmu\)m film, Restek; or HP-5, 30 m length \(\times\) 0.32 mm i.d. \(\times\) 0.25 \(\upmu\)m film; Agilent Technologies Inc., USA). The sample (1.0 \(\upmu\)L) extracted by DSE-SAFE was injected cold on-column at 35 \({}^{\circ}\)C in track oven mode, and that extracted by HS-SPME was injected S/SL injector at 250 \({}^{\circ}\)C in splitless mode. Nitrogen was used as carrier gas at a fixed flow rate of 1.53 mL/min. The oven temperature was programmed from 35 \({}^{\circ}\)C, increased by 2 \({}^{\circ}\)C/min to 130 \({}^{\circ}\)C, then increased to 250 \({}^{\circ}\)at 8 \({}^{\circ}\)C/min and held for 10 min. The flow of the carrier gas was split in a 1:1 ratio at the end of the capillary column. One part was directed to FID (250 \({}^{\circ}\)C) and the other to the sniffing port (200 \({}^{\circ}\)C). A series of n-alkanes C7-C30 for the DB-wax and the HP-5 was used to determine linear retention indices (RI).
# An Improved Rectifier Circuit for Piezoelectric Energy Harvesting from Human Motion
## 4 Results and Discussion
### Cycling Test  
#### 4.2.1 Effect of Varying Cycling Speed  
In the cycling test, the performance of H-Bridge and FBR circuits is studied when the source of excitation is a person performing cycling motion (Figure 11) at a different speed (RPM). The OC voltage or input voltage (i.e., voltage produced by PD) to both circuits at different cycling speed is shown in Figure 20. It is observed that the voltage produced by PD increases with cycling speed, but due to the complicated nature of human motion, the rate of increase is not very consistent across the different speed.  
In order to further understand the performance of both circuits, a closer look is taken at the rectified voltage and the output power produced at different cycling speed, as shown in Figure 21. It is evident that regardless of the type of rectifier circuit, both the rectified voltage and power increases with cycling speed. This is due to the fact that, at different cycling speed, the energy input is different and the PD is expected to produce a higher voltage at higher cycling speed (i.e., higher input energy) [44; 45].  
Similar to the shaker test, both output parameters produced by the H-Bridge circuit is significantly higher than the FBR circuit for all cycling speed. The advantage of the H-Bridge circuit is particularly pronounced at higher cycling speed. At a higher speed, say 70 and 80 RPM, it is noted that the input energy increases disproportionately (Figure 20). However, the rectified voltage and power through the FBR circuit is still limited. The H-Bridge circuit, on the other hand, is able to rectify the voltage and power more efficiently.  
Figure 20: Generated input voltage at different speed.
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 4 Test Results and Analysis
### Image Acquisition Module Calibration Experiment  
The position information obtained by the image acquisition module is the plane two-dimensional data in the image coordinate system in pixels. Thus, the image coordinate system needs to be calibrated to restore the true deformation value of the measured point [36].
## 5 The Improved BPNN
### The Improved Momentum Method  
The standard BPNN algorithm has the problem of slow convergence and is easy to run into a local minimum as a result of the adoption of the gradient descent method. This paper brings in the momentum method to adjust the weights and the biases as shown below:  
\[\Delta w(t+1)=-\eta(1-\gamma)\frac{\partial e_{err}}{\partial w(t)}+\gamma \Delta w(t) \tag{22}\]  
\[\Delta b(t+1)=-\eta(1-\gamma)\frac{\partial e_{err}}{\partial b(t)}+\gamma \Delta b(t) \tag{23}\]  
\[\Delta w(t+1)=w(t+1)-w(t) \tag{24}\]  
\[\Delta b(t+1)=b(t+1)-b(t) \tag{25}\]  
where \(\Delta w(t+1)\) and \(\Delta b(t+1)\) are the increments of the weights and the bias, respectively. \(\gamma\) ranging from 0 to 1 denotes the momentum factor. As shown in (22), the added momentum \(\gamma\Delta w(t)\) can reduce the oscillation of the training process and thus, the convergence can  
Figure 6: The structure of BPNN.  
be improved. To further enhance the performance of the method, we propose an improved momentum method as follows:  
\[\Delta w(t+1)=-\eta(1-\gamma)\frac{\partial e_{err}}{\partial w(t)}+\gamma\Delta w (t)+\sigma\Delta w(t-1) \tag{26}\]  
\[\Delta b(t+1)=-\eta(1-\gamma)\frac{\partial e_{err}}{\partial b(t)}+\gamma \Delta b(t)+\sigma\Delta b(t-1) \tag{27}\]  
where \(\sigma\) is a constant that should be smaller than \(\gamma\). However, the learning rate \(\eta\) is a fixed value in the method, which can be improved because during the training process, the learning rate should be higher when the learning process needs to be accelerated and it should be lower when the algorithm stability is the priority. Therefore, this paper employs an adaptive adjustment method for the learning rate, which is presented by:  
\[\eta_{w}(t+1)=\eta_{w}(t)\times 2^{f_{sig1}} \tag{28}\]  
\[\eta_{b}(t+1)=\eta_{b}(t)\times 2^{f_{sig2}} \tag{29}\]  
\[f_{sig1}=sign[(-\frac{\partial e_{err}}{\partial w(t)})\times(-\frac{ \partial e_{err}}{\partial w(t-1)})] \tag{30}\]  
\[f_{sig2}=sign[(-\frac{\partial e_{err}}{\partial b(t)})\times(-\frac{ \partial e_{err}}{\partial b(t-1)})] \tag{31}\]  
\[sign(x)=\left\{\begin{array}{c}1,x>0\\ 0,x=0\\ -1,x<0\end{array}\right. \tag{32}\]  
where \(\eta_{w}(t+1)\) and \(\eta_{b}(t+1)\) denote the adaptive learning rate for the weights and the bias, respectively. The adaptive adjustment method can coordinate the training speed and the algorithm stability, thereby improving the convergence performance and finding the optimal solution.
## 4 Simulation Results
### Applications of KC200 GT PV Module
#### 4.2.3 Case 3: The Effect of Varying the Weather on SD and DD Models
The I-V and P-V data sheets from the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{(d) Irradiance of 800 W/m\({}^{2}\) at temperature of 25 \({}^{\circ}\)C} \\ \hline Applied technique & GO & FPA & EVO & HTS & MGA \\ \hline \(I_{ph}\) (A) & 8.441114 & 8.441114 & 8.320163 & 8.410788 & 6.570051 \\ \hline \(R_{sh}\) (\(\Omega\)) & 1.602797 & 1.602796 & 11.25177 & 2.046745 & 7.485995 \\ \hline \(I_{S1}\) (A) & 0.000001 & \(1.00\times 10^{-6}\) & 0.000001 & \(1.00\times 10^{-6}\) & \(4.24\times 10^{-8}\) \\ \hline \(R_{S}\) (\(\Omega\)) & 0.005628 & 0.005628 & 0.005796 & 0.005674 & 0.004646 \\ \hline \(\eta_{1}\) & 1.212172 & 1.212172 & 1.211071 & 1.211876 & 1.246328 \\ \hline RMSE & 0.039212 & 0.039212 & 0.058525 & 0.040739 & 0.005127 \\ \hline \hline \multicolumn{6}{c}{(e) Irradiance of 1000 W/m\({}^{2}\) at temperature of 50 \({}^{\circ}\)C} \\ \hline Applied technique & GO & FPA & EVO & HTS & MGA \\ \hline \(I_{ph}\) (A) & 8.301133 & 8.298574 & 8.266066 & 8.296297 & 8.301133 \\ \hline \(R_{sh}\) (\(\Omega\)) & 5.62421 & 6.134123 & 33.15247 & 6.661412 & 5.624207 \\ \hline \(I_{S1}\) (A) & 6.15 \(\times 10^{-7}\) & \(6.83\times 10^{-7}\) & \(8.91\times 10^{-7}\) & \(7.23\times 10^{-7}\) & \(6.15\times 10^{-7}\) \\ \hline \(R_{S}\) (\(\Omega\)) & 0.00484 & 0.004803 & 0.004752 & 0.00479 & 0.00484 \\ \hline \(\eta_{1}\) & 1.310146 & 1.318481 & 1.339864 & 1.323004 & 1.208789 \\ \hline RMSE & 0.001562 & 0.002195 & 0.011639 & 0.002984 & 0.001562 \\ \hline \multicolumn{6}{c}{(f) Irradiance of 1000 W/m\({}^{2}\) at temperature of 75 \({}^{\circ}\)C} \\ \hline Applied technique & GO & FPA & EVO & HTS & MGA \\ \hline \(I_{ph}\) (A) & 8.441114 & 8.441114 & 8.320163 & 8.410788 & 8.441114 \\ \hline \(R_{sh}\) (\(\Omega\)) & 1.602797 & 1.602796 & 11.25177 & 2.046745 & 1.602796 \\ \hline \(I_{S1}\) (A) & 0.000001 & \(1.00\times 10^{-6}\) & 0.000001 & \(1.00\times 10^{-6}\) & \(1\times 10^{-6}\) \\ \hline \(R_{S}\) (\(\Omega\)) & 0.005628 & 0.005628 & 0.005796 & 0.005674 & 0.005628 \\ \hline \(\eta_{1}\) & 1.212172 &manufacturer have been adapted to those simulated curves. When it comes to determining the PV parameters at different temperatures and irradiance, the recommended MGA technique performs better than commercial PV systems. This indicates the efficacy of the suggested methodology and its capacity to produce highly consistent fitted I-V data with manufacturers' data sheets. \begin{table}  
\end{table} 
Table 8: Acquired parameters for the KC200 GT PV module of the DD model under various weather scenarios using MGA compared to others. Figure 12: Acquired I-V characteristics for SD model of KC200 GT PV module under various weather scenarios using MGA.
## 5 Effects of Micronutrient Supplementation on Weight Management and Metabolic Health in Obesity
### Doses of Micronutrients That Were Found to Be Effective in Aiding Weight Loss and Metabolic Health
Despite the popularity of supplements containing trivalent Cr compounds and the large number of RCTs studying their effects in humans, the toxicity of these compounds is still a subject of research [133]. Also, due to insufficient data, the Food and Nutrition Board did not propose UL for vitamin B1 [134]; however, for vitamin C, it was established at 2000 mg/day, as above this intake, a risk of osmotic diarrhea exists [135]. Doses for all micronutrients suggested as effective in aiding weight loss and/or improving metabolic health were all greater than the mentioned AR or AI for healthy adults proposed by EFSA and the Food and Nutrition Board. However, most of these values, namely for vitamins E, B6, B9, Mg, and Zn, exceeded UL thresholds as well. The dose established as UL is the \"maximum level of total chronic intake of a nutrient from all sources judged to be unlikely to pose a risk of adverse health effects in humans\" [136]. Therefore, above UL, there is a risk of micronutrient overdose. To enable the application of vitamins and minerals in obesity at doses greater than UL, more data are needed. Even though the majority of the RCTs that implemented such doses did not report adverse effects, care has to be taken to interpret the results since studies were performed on a limited number of participants meeting all exclusion and inclusion criteria, creating quite homogenous groups. A good example of the risk that micronutrient overdosing may pose is presented in a study by [PERSON] et al. (2021) [137]. The authors reported two cases of hair loss among 71 T2D patients who received vitamins A and E at doses of 50,000 IU (corresponding to 15.015 \(\mu\)g, which is approximately 5-fold higher than EFSA's UL) and 100 mg, respectively, as well as 25 mg of Zn (only 35 out of 71 participants). Even though the authors reported the beneficial effects of the mentioned supplementation on glucose homeostasis and insulin secretion, they have admitted that the dose of vitamin A will need refinement in future studies. While establishing effective and safe doses of micronutrient supplementation for the treatment of obesity and associated metabolic disorders might be challenging, another option to consider is enhancing the bioavailability of the minerals and vitamins that are clearly reduced in obesity.
# Experiment Information System Based on an Online Virtual Laboratory
## 2 Design and Methods
### Development of the Digital Circuit Virtual Laboratory  
#### 2.2.1 System Architecture  
The implementation of the virtual laboratory software needs to ensure that the software is portable and adaptable so that different experimental projects can be applied. Browser/Server (B/S) architecture was adopted in the current implementation. The user can log in to the virtual laboratory system anytime and anywhere through the browser, and can select different experimental items to operate. Meanwhile, since the virtual experiment does not involve physical devices, users can test various innovative experiments according to their own ideas without worrying about damaging the devices. Thus, the main components of the virtual laboratory include the client side (such as the 3D (three-dimensional) student client and the web-based teacher interface) and the server side, as shown in Figure 2. Specifically, after logging in the student client, students would enter a 3D virtual reality laboratory and develop the virtual experiments. Comparatively, the teacher client is not a 3D virtual environment but a web-based interface, mainly handling data from students' virtual experiments. In addition, the teacher also has permission to log into the 3D virtual laboratory for experiment design, development, and test. Finally, the server side is responsible for the computations and the data transmissions between the 3D virtual laboratory and server side, and the teacher interface and server side.
# A Review of Wireless Positioning Techniques and Technologies: From Smart Sensors to 6G
## 2 Localization Techniques
### Tracking Techniques
#### 2.5.2 Filters  
Another tracking approach is the utilization of filters, such as the particle filter (PF) and Kalman filter (KF). A PF utilizes a set of particles (samples) for representing the posterior distribution of some stochastic or random process [65]. Each particle represents a discrete state hypothesis of the state variable, and the set of all particles determine the final state estimation. The Bayesian state estimate is an example of PFs and utilizes the discrete particles for approximating the posterior distribution of the estimated state. Another example are the Monte Carlo (MC) methods, which were initially used for the localization of robots. The robot's position and orientation are estimated as it moves and senses the  
Figure 11: Step and Heading System (SHS), reproduced with permission from [PERSON], _Smartphone Sensor Based Indoor Positioning: Current Status, Opportunities, and Future Challenges_, published by MDPI, 2020.  
environment and each particle represents a possible state, which is a possible position of the robot. In MC methods, the robot's initial state is unknown, and it begins with a uniform random distribution of particles; thus, it is equally likely to be at any point in the space. Based on the robot's movements, the particles are shifted, so the new state can be predicted and a correlation with the actual one is made.  
Alternatively, the KF produces estimates of hidden variables based on inaccurate and uncertain measurements and predicts the future system state, based on the previous estimations. For instance, the hidden variables of the GPS receiver include the location and velocity, and the measurements include the satellite signal's arrivals. Additionally, in [66], the fingerprinting, together with KF, was used for enhancing the RSS estimations, leading to the result that the nearest neighbour estimates were shifted closer to the actual path. Moreover, the KF is based on linear dynamical systems in the state space format; therefore, the process and measurement models are assumed to be linear. For non-linear dynamic models, the extended Kalman filter (EKF) and unscented Kalman filter (UKF) can be used instead. The EKF was used in [40] for fusing the nonlinear RSSI and CSI ranging models for estimating the distance between the receiver and transmitter. It was used to filter the obtained RSSI and CSI values at each reference point, which were then combined with a distance-related mathematical ranging model for reducing the impact of noise on the system and improving the ranging accuracy. A 1.5 m distance error for over 70% of the reference points was reported for the tested indoor environment, together with rich multipath effects suppression.
# Precise Mission Process Control Based on a Novel Dual-Code Group Network Plan Diagram
## 4 Mission Process Control System Modeling
### System Equations  
#### 4.3.1 State Equation  
We define the mapping \(f:\mathbf{x}(t),\mathbf{u}(t),\mathbf{\omega}(t)\rightarrow\mathbf{x}(t+\Delta t)\) from the system state \(\mathbf{x}(t)\), the control quantities \(\mathbf{u}(t)\), and the disturbance \(\mathbf{\omega}(t)\), to the new state \(\mathbf{x}(t+\Delta t)\) as the system state equation. Since \(\mathbf{x}(t)\) responds to the duration of each mission, and both \(\mathbf{u}(t)\) and \(\mathbf{\omega}(t)\) are changes in the duration of each mission, the state equation of the system is essentially the update equation of the mission duration, which is  
\[\mathbf{x}(t+\Delta t)=\mathbf{x}(t)+\mathbf{u}(t)+\mathbf{\omega}(t) \tag{30}\]According to (10)-(15), (21) and (22), the set of plan parameters \(\mathcal{P}(t)\) can be obtained based on the known state quantity \(\mathbf{x}(t)\). Thus, we can define the mapping from \(\mathbf{x}(t)\) to \(\mathcal{P}(t)\) as the time-parameter operator \(\rho(\cdot)\), i.e.,  
\[\rho(\cdot):\mathbf{x}(t)\rightarrow\mathcal{P}(t) \tag{31}\]