esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Temperature Evolution at Different Time Periods (T1-T6)
#### 3.3.2 Evolution of Temperature in the Coronary Band Area (Bx3-Bx4)  
The temperatures in the coronary bands reached values close to those observed after training (Bx3 = 28.55 \(\pm\) 0.21 \({}^{\circ}\)C; Bx4 = 28.7 \(\pm\) 0.26 \({}^{\circ}\)C), even though they were not similar beforehand (Figure 10). At T3, the temperature in the Bx3 area of the study limb decreased by 3.98 \(\pm\) 0.25 \({}^{\circ}\)C (Z = \(-\)3.336; \(p\) = 0.001) after the ice boot pack treatment compared to T2. A slow increase in temperature was observed at T4, followed by a progressive decrease at T5 and T6. In the control limb (Bx4 area), the temperature continued to increase after treatment, with a small difference in temperature at T3 compared to T2 (+1.16 \(\pm\) 0.37 \({}^{\circ}\)C; Z = +2.421; \(p\) = 0.015), and then progressively decreased at T4, T5, and T6. By T6, the temperatures for both areas (Bx3 and Bx4) were close to each other and comparable to the initial temperatures recorded at T1 (Bx3 = 21.48 \(\pm\) 0.24 \({}^{\circ}\)C; Bx4 = 20.80 \(\pm\) 0.16 \({}^{\circ}\)C; Figure 11). However, although small, the differences (0.76 \(\pm\) 0.38 \({}^{\circ}\)C for Bx3 and 0.51 \(\pm\) 0.22 \({}^{\circ}\)C for Bx4) remained near the significance threshold, according to the non-parametric Mann-Whitney U-test (Z = \(-\)1.950 and \(p\) = 0.051 for Bx3; Z = \(-\)2.055 and \(p\) = 0.040 for Bx4).  
Figure 11: Value and variability of temperatures in the metacarpal (**a,b**) and coronary band (**c,d**) areas: boxplot distributions for the (**a**) Bx1 area; (**b**) Bx2 area; (**c**) Bx3 area; (**d**) Bx4 area.  
Figure 10: Evolution of the local average temperature (\(y\) axis) in the coronary band area over time (\(x\) axis). Bx1, right (study) limb; Bx2, left (control) limb; T1, before training; T2, immediately after training; T3, after applying the cooling method; T4, 10 min after training; T5, 20 min after training; T6, 30 min after training.  
The variability in the temperature values in the metacarpal area is shown in Figure 11a,b. Similar results were observed using the Wilcoxon score for the mean, showing comparisons between time points (Figures S1 and S2).  
The variability in the temperature values in the coronary band areas are shown in Figure 11c,d. In addition, box plot diagrams showed the comparative distributions of mean temperature values between the coronary band areas at different time points (Figure 11c,d). Similar results were observed using the Wilcoxon score for the mean, showing comparisons between time points (Figures S3 and S4).
## 3 Results and Discussion
### Degradation Kinetics of PFOS  
The regression of the PFOS concentration was generated under the assumption that PFOS's degradation by CP followed the kinetics of a first-order exponential decay reaction. Subsequent regression analysis revealed that the degradation kinetic constant of PFOS was 3.09 h\({}^{-1}\) (kPFOS). These results are reflected in Table 4, which compares the first-order degradation rate constants of AOPs when treating non-biodegradable substances and PFOS, as determined in prior research.  
A literature review reveals that antibiotics degrade faster than PFOS. This is attributed to the greater oxidation power required to break the strong C-F chain of PFOS compared to antibiotics. Although the rate of PFOS degradation has been consistent across prior studies, it was lower than the rate observed in this study, highlighting CP's superior oxidizing abilities. However, our regression analysis shows that the CP application achieved a 62.5% removal efficiency within 1 h, with subsequent results indicating only incremental PFOS removal. This was hypothesized to be the result of the by-products produced by PFOS acting as radical scavengers. To verify this theory, an acute toxicity test was performed to assess the effect of these by-products according to the PFOS degradation time.  
Figure 2: Overview of PFOS degradation and its associated dynamics in the aeration-assisted CP system. (**a**) PFOS concentration dynamics and regressed degradation line over time. (**b**) Variation in the TOC concentration in CP over time. (**c**) Detection of fluoride and sulfate produced during PFOS degradation in the system. (**d**) Mass balance of fluoride and sulfate, comparing experimental data with theoretical calculations, during PFOS degradation.
# An Overview of Non-Isolated Hybrid Switched-Capacitor Step-Up DC-DC Converters
## 5 Hybrid SC Converters
### Demonstrative Experimental Results
\begin{table}
\begin{tabular}{c c} \hline _Capacitance of Capacitors_\(C_{1}\) = \(C_{2}\) = \(C_{3}\) & **10**\(\mu\)F \\ \hline _Inductance_\(L\) & 1 mH \\ \hline _MOSFET_ & _IRFP4127 PbF_ \\ \hline _Diodes (all 3)_ & _APT60520 BG_ \\ \hline _Switching frequency_ & 50 kHz \\ \hline _Output load resistor_\(R\) & 100 \(\Omega\) \\ \hline \end{tabular}
\end{table} 
Table 4: Parameters of simulation in Figure 24a. Figure 35 corresponds to the operating point in which the input voltage \(v_{g}=50\) V, the duty cycle was \(D=0.7\), the output voltage was measured as 321 V, the output power was measured as 371 W, and the efficiency as 94%.
## 2 Methods
### Multiscale Modeling Approach
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\mathbf{R_{p}}\) **(dyne-s/cm\({}^{5}\))** & **C (cm\({}^{5}\)/dyne)** & \(\mathbf{R_{d}}\) **(dyne-s/cm\({}^{5}\))** \\ \hline Left Middle Cerebral Artery (LMCA) & 4168.21 & 6.50368 \(\times\) 10\({}^{-6}\) & 41,682.1 \\ Right Middle Cerebral Artery (RMCA) & 3989.86 & 6.7944 \(\times\) 10\({}^{-6}\) & 39,898.6 \\ Left Anterior Cerebral Artery (LACA) & 6304.05 & 4.30021 \(\times\) 10\({}^{-6}\) & 63,040.5 \\ Right Anterior Cerebral Artery (RACA) & 5891.02 & 4.6017 \(\times\) 10\({}^{-6}\) & 58,910.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The values of optimal lumped parameters for the 0D model. All the models were discretized into tetrahedral elements. To capture the near-wall flow features, three layers of boundary prism boundary layers were generated on the arterial wall. To avoid the errors caused by discretization, mesh independent analyses were conducted. In the mesh independency study, the temporal-averaged volumetric flow rates of each efferent artery in the non-stenotic case were obtained under element numbers of 320,000, 400,000, 510,000, 690,000 and 900,000, respectively. Differences of flow rates at each outlet within 1% from the finest mesh was set as the convergence criterion. When the element number was higher than 690,000, the flow rates at all outlets became stable (Figure 3). Finally, the model was discretized into approximately 690,000 elements (Figure 4). The governing equations of the 0D/3D couple models were solved in an open-source solver (SimVascular, ). Figure 4: The final result of discretization. Figure 3: Mesh independency study results. ACAs: anterior cerebral arteries; MCAs: middle cerebral arteries. Figure 2: Schematic of boundary conditions.
## 2 SRPC Compensation Principle
### Reactive Power Compensation  
It is important to add a certain reactive current to phase \(x\), and phase \(y\), in order to make the three-phase currents balanced without NSC of currents. In this context, section \(x\) converter compensates a capacitive reactive power and section \(y\) converter compensates an inductive reactive power when V/V connection is implemented [6]. As presented in Equation (7), the reactive current components have the same RMS value.  
\[\begin{array}{l}I_{rxr}=\ I_{x1}\ \tan\frac{\pi}{6}=\frac{1}{2}\ (I_{Lx}+I_{Ly})\ \tan\frac{\pi}{6}\\ I_{ryr}=\ I_{y1}\ \tan\frac{\pi}{6}=\frac{1}{2}\ (I_{Lx}+I_{Ly})\ \tan\frac{\pi}{6} \end{array} \tag{7}\]  
The three-phase power grid currents after active and reactive power compensation have an equal magnitude as presented in Figure 3a. Accordingly, the currents at the secondary side of the V/V power transformer also have equal magnitudes as presented in Equation (8) and Figure 3b. After compensation, the instantaneous waveforms of the phase \(x\) and phase \(y\) currents are as in Equation (9).  
\[\begin{array}{l}\dot{I}_{x2}=\dot{I}_{x1}\ +\dot{I}_{rxr}\ =\ \frac{1}{\sqrt{3}}\ ( \dot{I}_{Lx}+\dot{I}_{Ly})\ e^{\beta 0^{\circ}}\\ \dot{I}_{y2}=\dot{I}_{y1}\ +\dot{I}_{ryr}\ =\ \frac{1}{\sqrt{3}}\ (\dot{I}_{Lx}+\dot{I}_{Ly})\ e^{- \beta 120^{\circ}}\\ \dot{I}_{z2}=\dot{I}_{x2}\ +\dot{I}_{y2}\ =\ \frac{1}{\sqrt{3}}\ (\dot{I}_{Lx}+\dot{I}_{Ly})\ e^{ \beta 120^{\circ}}\end{array} \tag{8}\]  
\[\begin{array}{l}i_{x2}=\sqrt{\frac{2}{3}}\ (I_{Lx}+I_{Ly})\ \sin(\omega t\ +\ 0)\\ i_{y2}=\sqrt{\frac{2}{3}}\ (I_{Lx}+I_{Ly})\ \sin(\omega t-\frac{2\pi}{3} )\end{array} \tag{9}\]  
Figure 3: Diagrams of the three-phase power grid currents: (**a**) When both catenary sections are unequally loaded and without compensation; (**b**) When the active power is balanced between sections.  
The total compensation currents injected by the SRPC can be obtained from Figure 3b and Equation (10). The positive direction of the compensation current is when it flows into the SRPC as clarified in Figure 1.  
\[\begin{array}{c}{i_{r\!x}}^{*}=\ i_{x2}-i_{Lx}\\ {i_{r\!y}}^{*}=\ i_{y2}-i_{Ly}\end{array} \tag{10}\]
# Design of an Integrated System for Spaceborne SAR Imaging and Data Transmission
### Design Concept
The echo signal, post matched filtering, can be expressed as:  
\[y_{\text{r},i}(t)=\beta_{i}x_{\text{r}}(t-\tau)e^{-\text{j}2\pi(f_{\text{c}} \tau)}+\omega_{\text{r},i} \tag{3}\]  
where \(y_{\text{r},i}(t)\) represents the demodulated baseband signal of the \(i\)-th SAR echo; \(R\) denotes the distance from the SAR satellite platform to the center of the target; \(v\) is the velocity of the satellite relative to the ground; \(\tau\) signifies the time delay, with \(c\) being the speed of light and \(\tau=\frac{2R}{c}\); \(\beta_{i}\) represents the channel coefficient associated with the \(i\)-th subcarrier and the autocorrelation coefficient of the ground reflection coefficients; and \(\omega_{\text{r},i}\) is the Gaussian noise in the channel. The data transmission signal received by the ground mobile terminal through OQAM demodulation is expressed as:  
\[r_{\text{c},i}(t)=\alpha_{i}x_{\text{c}}(t-\frac{\tau}{2})e^{-\text{j}2\pi(f_ {\text{c}}\frac{\tau}{2})}+\omega_{\text{c},i} \tag{4}\]  
where \(r_{\text{c},i}(t)\) denotes the \(i\)-th demodulated baseband signal; \(\alpha_{i}\) represents the channel coefficient associated with the \(i\)-th subcarrier and the autocorrelation coefficient of the ground reflection coefficients; and \(\omega_{\text{c},i}\) represents the Gaussian noise in the channel.
# A Cumulant-Based Method for Acquiring GNSS Signals
## 3 Cumulant-Based Acquisition Method
### FFT-Cumulant Method for GNSS Signal Acquisition
While these measurements provide a general indication of the relative efficiency of the two approaches, a more rigorous analysis in a real-time processing environment would be necessary to quantify the exact performance gains in practical applications.
# GNSS Spoofing Detection Based on Wavelets and Machine Learning
## 3 Methodology  
This section presents the proposed approach used for spoofing attack detection. The approach is based on the combination of discrete wavelet transform and machine learning classifiers. The key components of the methodology are outlined below.
## 3 Methodology
### Platoon Topology Control Based on Graph Theory and Safety Potential Field Theory
Thus, the longitudinal and the lateral distances between CAVs can be kept in the safe range, and the velocity of each CAV can track the desired velocity._  
To describe the scenario in Figure 1, a typical graph-based control protocol is shown as follows:  
\[\Delta f=\mathcal{L}\left[ \begin{array}{c}\dot{s}\\ \dot{l}\end{array} \right]=\mathcal{L}\left[ \begin{array}{c}f(d_{s}-s_{a})\\ f(d_{l}-l_{a})\end{array} \right], \tag{7}\]  
where \(d_{s}\) and \(d_{l}\) are the desired longitudinal distance and lateral distance between two vehicles which are connected by a mutual communication connection, respectively. Correspondingly, \(s_{a}\) and \(l_{a}\) are the actual longitudinal distance and lateral distance between two communication connection vehicles, respectively. \(f(d_{s}-s_{a})\) and \(f(d_{l}-l_{a})\) express the effect function of the deviation between the desired distance and the actual distance on the vehicle acceleration. For a more detailed explanation of this function, please refer to the studies of [32; 34]. The control method mentioned above is conducive to the rapid formation of the vehicular platoon but only considers two factors in terms of the space distance and vehicle communication method. In many studies [15; 32], similar ideas have been used to ensurethe efficiency of platoon formation. However, it is worth noting that the movement status (the value of the acceleration, the direction of the velocity, the value of the steering angle, etc.) of each vehicle in the platoon and the information of the road environment (signal period of the intersection, the speed limit of the road, etc.) will affect the driving of the platoon. Therefore, it is essential to summarize and analyze the impact of those various factors on the forming of the vehicle platoon. For this, we developed a platoon formation control model and a platoon optimization control model based on graph theory and safety potential field theory, and more details are discussed in the following sections.
## 3 Results and Discussion  
### Extraction Yield of GV Extracts  
As shown in Table 2, the extraction yield of the GV extracts ranged from 3 to 44%, with the highest yield observed in the GVW at 43.4 \(\pm\) 1.7%. Agar, a polysaccharide matrix located within the cell walls of red algae, is sourced from _Gracilaria_ species and extracted at high temperatures [39]. The yield and quality of agar are contingent on factors such as the species of red algae, their growth environment, and extraction conditions [40; 41; 42]. High temperatures facilitate the aqueous extraction of agar, and methods such as alkaline treatment with 2% NaOH and 90-100 \({}^{\circ}\)C can alter agar's structure, enhancing its yield [43]. [PERSON] et al. [44] observed the efficacy of high-temperature extraction of agar in _Gracilaria vermiculophylla_. Furthermore, another study showed that a 75% methanol extract of _Gracilaria_ sp. yielded the highest extraction rate, suggesting that a combination of water and an organic solvent is beneficial for extracting compounds that are soluble in both media [45].  
Therefore, the superior yield of GVW was likely attributable to the dissolution of soluble components at high temperatures during the extraction process. Additional studies are warranted to compare the extraction yields of GV using other solvents under consistent temperature conditions.
# Application of Reinforcement Learning in Decision Systems: Lift Control Case Study
## 4 The Proposed Solution
### Learning Process  
We utilized a dual-network Deep Q-Learning algorithm with the Experience Replay method and an \(\varepsilon\)-greedy policy for training. The training process spanned 1000 episodes, each consisting of 20,000 time-steps. Both the online and target neural networks featured three fully connected layers, with 20 neurons (matching the state vector length), 128 neurons, and 3 neurons (representing the number of actions), respectively.  
To further investigate the impact of Experience Replay techniques on learning dynamics, we conducted three experiments using all variants discussed in Section 2.3.2: basic Replay buffer, Prioritized Replay buffer, and Memory Palace. In the Memory Palace variant, the rooms corresponded to different floors where the lift cabin was currently located. This design aimed to distribute the sampled experience among specific contexts, preventing the domination of more frequent phase states and enhancing the overall learning process.  
Both the basic Replay Buffer and the Prioritized Replay Buffer had a capacity of \(10^{6}\), while the Memory Palace consisted of seven rooms, each being a Replay Buffer with a capacity of \(10^{5}\). During training, every four time-steps, a mini-batch of 32 transitions was sampled from the experience replay for weight updates. When using the Memory Palace, the mini-batch comprised thirty-five elements, with five from each room.  
To enhance learning stability, network weights were synchronized between the online and target networks every 40,000 time-steps. To enhance the extensive environment exploration, the exploration factor, \(\varepsilon\), started at 1 and gradually decreased by 0.02 after each episode until reaching the final value of 0.1. The discount factor, \(\gamma\), was set to 0.99 to significantly prioritize future rewards. In the learning process, we applied the Huber Loss function, a robust alternative to Mean Squared Error, particularly suitable when dealing with errors in reinforcement learning that might exhibit outlier values [19]. Additionally, we employed the Adam optimizer, a popular optimization algorithm known for its efficiency in adapting learning rates for individual parameters. All the hyperparameter's values were inspired by the work of [PERSON] et al. [14].
# Effect of Venturi Structures on the Cleaning Performance of a Pulse Jet Baghouse
## 2 Materials and Methods
### Experimental Apparatus
The dust feeder was able to control the dust concentration in the dust-laden airflow of the baghouse by adjusting the amount of dust generated. The air volume and the filtration velocity in the baghouse were regulated with a fan and a flow meter in the power system. The baghouse operating resistance was monitored with a pressure transducer, and the dust emission was measured in 6 s intervals using a dust spectrometer (Model 1.108; Grimm Aerosol Technik GmbH & Co., Airring, Germany). The filtering unit was composed of six polyester filter bags with a diameter of 130 mm and length of 1500 mm. The experimental conditions were as follows: the dust concentration was maintained at 100 g/m\({}^{3}\); the dust for the test was made of alumina particles with a weight-mean diameter of 4 \(\upmu\)m; the filtration velocity in the baghouse was set to 2 m/min, and the operating pressure drop of the dust cleaning to  
Figure 4: Semi-industrial baghouse experimental device. Figure 3: Experimental bench approach for determining the pulse jet cleaning performance. (**a**) Schematic diagram of the pulse jet cleaning experimental device and (**b**) the distribution of the measuring points on the filter bag. P0–P8 are the numbers of the measuring points. 1000 Pa; and particulate matter in the dust-laden airflow was captured by the filter bag and deposited on its surface. This increased the operating resistance in the baghouse. When the filtration resistance measured by the pressure transducer reached the preset value of 1000 Pa, the system automatically released the compressed pulsing air to clean the filter bag, which continued filtering the dust-laden airflow. Once the baghouse filtration resistance reached again 1000 Pa, the system started performing the next cleaning step. The time between any two cleaning intervals of the system was called a cleaning cycle. When testing the cleaning performance of the baghouse with the two types of venturi tubes, the experiments were continuously operated for 24 h under the same filtration-cleaning cycle conditions. After system stabilization, the number of cleaning cycles (\(N\)) and the dust emission concentrations \(C_{i}\) (\(i\) = 1,2,3... 600) of the baghouse were continuously recorded for 1 h, and the average filtration cycle time \(\overline{T}\) and average dust emission concentration \(\overline{C}\) of the baghouse were calculated according to the Equations (2) and (3), respectively:  
\[\overline{T}=\frac{3600}{N} \tag{2}\]  
\[\overline{C}=\frac{\sum_{i=1}^{600}C_{i}}{600} \tag{3}\]
## 4 Numerical Results
The material and geometrical properties of the beam are considered to be \(E_{M}=2.85\) GPa, \(\rho_{M}=1200\text{Kg}/\text{m}^{3}\), \(E_{\text{GPL}}=1.01\) TPa, \(\rho_{\text{GPL}}=1062.5\text{Kg}/\text{m}^{3}\), \(w_{\text{GPL}}=1.5\) um, \(l_{\text{GPL}}=2.5\) um, and \(h_{\text{GPL}}=1.5\) nm. These properties are kept constant for the following examples. The numerical results are obtained for a different power-law parameter, length-to-total thickness of the beam ratio, as well as for a different total weight fraction of the GPLs reinforced in the beam (\(\Gamma_{b}\)). As mentioned before, \(\Gamma_{b}\) is defined such that the total amount of GPLs remains constant with respect to any change in the power-law parameter or thickness of the face sheets. According to Tables 4 and 5, an increased total weight fraction of GPLs (\(\Gamma_{b}\)) yields a meaningful increase of the nonlinear frequency of the beam, while decreasing the nonlinear-to-linear frequency ratio. In addition, an increased length-to-thickness ratio provides a decreasing effect on the nonlinear frequency of the system and its associated nonlinear-to-linear ratio. On the other hand, an increased power-law parameter gets an increased nonlinear frequency and a decreased nonlinear-to-linear frequency ratio. A non-uniform behavior can be observed, sometimes, for an increasing power-law parameter and for a large amount of \(\Gamma_{b}\). This aspect is illustrated in detail as follows. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(\boldsymbol{L/h_{t}}\)} & \multirow{2}{*}{\(\boldsymbol{\Gamma_{b}}\)} & \multicolumn{4}{c}{\(\boldsymbol{\kappa}\)} \\ \cline{3-6}  & & **0.5** & **1** & **2** & **5** \\ \hline \multirow{3}{*}{10} & 0.5 & 1.52551 & 1.53220 & 1.54115 & 1.54952 \\ \cline{2-6}  & 1 & 1.96610 & 1.97452 & 1.98470 & 1.99110 \\ \cline{2-6}  & 2 & 2.62940 & 2.63683 & 2.64207 & 2.63204 \\ \hline \multirow{3}{*}{20} & 0.5 & 0.38485 & 0.38708 & 0.39006 & 0.39311 \\ \cline{2-6}  & 1 & 0.49821 & 0.50165 & 0.50612 & 0.51036 \\ \cline{1-1} \cline{2-6}  & 2 & 0.66931 & 0.67405 & 0.67978 & 0.68391 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: First-order nonlinear frequency of a simply supported GPL-reinforced sandwich beam (\(l_{tc}/h_{t}=0.6,N=10,a/h_{t}=1\)). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\(\boldsymbol{L/h_{t}}\)} & \multirow{2}{*}{\(\boldsymbol{\Gamma_{b}}\)} & \multicolumn{4}{c}{\(\boldsymbol{\kappa}\)} \\ \cline{3-6}  & & **0.5** & **1** & **2** & **5** \\ \hline \multirow{3}{*}{10} & 0.5 & 1.59007 & 1.56735 & 1.54135 & 1.51106 \\ \cline{2-6}  & 1 & 1.55652 & 1.53643 & 1.51484 & 1.49074 \\ \cline{2-6}  & 2 & 1.54324 & 1.52830 & 1.51607 & 1.50875 \\ \hline \multirow{3}{*}{20} & 0.5 & 1.55431 & 1.53007 & 1.50227 & 1.47047 \\ \cline{2-6}  & 1 & 1.51103 & 1.48708 & 1.46023 & 1.42993 \\ \cline{1-1} \cline{2-6}  & 2 & 1.48700 & 1.46431 & 1.43990 & 1.41399 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: First-order nonlinear-to-linear frequency ratio of a simply supported GPL-reinforced sandwich beam (\(l_{tc}/h_{t}=0.6,N=10,a/h_{t}=1\)).