esa-sceva/satcom-chunk-collection huggingface dataset:
The achievements to improve charging efficiency are attributed to novel topologies, optimal design and control strategies [1; 2; 3; 4]. Non-isolated battery charging topologies are able to achieve high performance like the Cockcraft-Walton multiplier and switched inductors-capacitors [5; 6; 7]. However, they are not recommended for high power application owing to high switch stress and lack of galvanic isolation. Among isolated battery charging topologies, many converters adopt the full bridge (FB) structure and its variants [8]. A hybrid FB converter integrated with a half-bridge (HB) structure or another FB structure on the primary side is presented in [9; 10] where the reduction of conduction loss is realized due to the reduction of circulating current. In order to overcome the narrow zero-voltage switching (ZVS) range of the conventional FB converter at light load, an additional output inductor or clamping capacitor is adopted in [11; 12]. However, these solutions increase the manufacturing cost and control complexity. Because of attractive features such as wide ZVS range of primary switches, zero-current switching (ZCS) of secondary diodes, and good voltage regulation capabilities, the FB resonant converters have attracted lots of attention [13; 14; 15]. The typical topology is the LLC-type (inductor-inductor-capacitor) FB resonant converter [16; 17], which is generally controlled by frequency modulation. However, due to the wide range variation of output voltage for the battery charger, the switching frequency may have to be away from the resonant frequency. This will cause a series of problems, such as high circulation current, increasing transformer loss and lower efficiency. Therefore, the LLC-type FB resonant converter with frequency modulation is not suitable for the battery charger with wide variation of voltage and current. In order to avoid the above problems, modified topologies and modulation strategies of FB resonant converters are presented in [18; 19; 20]. In [21; 22], a phase-shifted modulation method is used to increase the efficiency of FB resonant converter under a light load condition. However, these methods increase the control complexity. In [20; 23; 24], higher order resonant tanks are used in the FB resonant converters. Nevertheless, complex topologies create difficulties in terms of optimum design, which will increase the cost of the system as well. This paper focuses on the dual-bridge resonant converter (DBRC) introduced firstly in [25; 26]. A DBRC has two FB structures with eight active switches that are implemented on the two sides of a high-frequency (HF) isolated transformer, which provides flexible control options. Two different modulation methods are then proposed for the DBRC with full soft-switching capability and low resonant current, both of which can realize CV and CC charging. The design principles and procedures customized for the two modulation methods are also presented. Verifications of two modulation methods are exemplified through both simulation and actual experimental tests on a 600 W prototype converter.
## 3 Results
### Combined Analysis of HS-SPME\(\times\) GC-O-MS and HS-GC-IMS
#### 3.3.2 Determination of the Key Aroma Compounds in Yellow Horn Seed Oil  
Forty kinds of VOCs with VIP \(>\) 1 in GC-IMS and 23 kinds of main aroma compounds with combined OAVs \(\geq\) 1 and sniffing in GC-O-MS were selected as screening criteria. Six VOCs were screened, including hexanal, 2,5-dimethylpyazine, heptanal, 2-pentylfuran, 1-hexanol and 1-octen-3-ol. They were considered to be the key aroma compounds in yellow horn seed oil. Hexanal in flaxseed oil contributes fatty, oily, sweet, nutty, and green odors [61]. It was reported that 2,5-dimethylpyrazine was a key aroma compoundin oils such as canola oil [62], nut oil [59], and olive oil [13] and provided roasted and nutty aromas. In a roasted peanut oil study conducted by [PERSON] et al. [63], it has been confirmed that heptanol is the product of fatty-acid oxidation and one of the important aroma compounds, providing fatty and oily aromas for roasted peanut oil. 2-pentylfuran is formed via the cyclization of peroxidation free radicals in the oxidation process of linoleic acid [64], which was considered as an important index of lipid oxidation. 2-pentylfuran can impart fruity and green aromas to the yellow horn seed oil. Through thermal oxidation studies on different varieties of olive oil, [PERSON] et al. [65] reported that 2-pentylfuran was found in thyme-flavored olive oil during thermal oxidation. [PERSON] et al. [66] reported that 1-hexanol provided a typical fresh odor for raw flaxseed powders, giving green-like, fresh-like, mint-like aromas via the aroma description of a microwave treatment of flaxseed meal. It was reported that linoleic acid (C2:10)-derived 10-L(S)-hydroperoxy-cis-9 and trans-11-octadecadieuoic acid via enzymatic oxidation, which then converted to 1-octen-3-ol [32]. Meanwhile, 1-octen-3-ol mainly contributed to mushroom and sweet odors to food. As shown in Figure 5D, the abundances of hexanal and heptanal in the 150 \({}^{\circ}\)C and 160 \({}^{\circ}\)C samples were significantly different from the other groups (\(p<0.05\)). Moreover, 2-pentylfuran, 1-octen-3-ol, and 2,5-dimethylpyrazine in the 160 \({}^{\circ}\)C sample were significantly more abundant than in the other groups. To sum up, these six VOCs were important aroma substances that provided key aromas to yellow horn seed oil.
# A Facility Layout Algorithm for Logistics Scenarios Driven by Transport Lines
## 3 Force-Directed Logistics Layout Algorithm
### Decoding and Filtering  
The decoding process utilized by the algorithm determines the layout solutions for all working facilities within the scene, and Equation (15) uses matrix encoding for the horizontal coordinates, the vertical coordinates, and the orientation of the \(n\) working facilities. In the equation, the current encoding of the algorithm at temperature \(t\) is represented by \(E_{t}\), while the horizontal coordinates of working facility \(i\) in the workshop are denoted by \(x_{i}\), the vertical coordinates of working facility \(i\) in the workshop are denoted by \(y_{i}\), and the orientation information of working facility \(i\) in the workshop is denoted by \(o_{i}\).  
\[E_{t}=[\{x_{1},\ldots,x_{i},\ldots,x_{n}\},\{y_{1},\ldots,y_{i},\ldots,y_{n}\},\{o_{1},\ldots,o_{i},\ldots,o_{n}\}] \tag{15}\]  
The MOSA-FD algorithm uses different ways of expanding the neighborhood solutions depending on the current temperature. At high temperatures conditions, the MOSA-FD algorithm initially explores coordinates within the workshop area randomly. However, as the temperature decreases, the exploration is limited to the neighborhoods surrounding each facility represented by the current solution. In the early stages of the algorithm, a code-swapping operation is employed to exchange the coding information between any two working facilities in the workshop, thereby facilitating the search for a better solution.  
Figure 4: Algorithmic framework for logistics facility layout.  
In the mid to later stages of the algorithm, a neighborhood search operation is employed to maintain the current solution representation and search the neighborhood by fine-tuning the coordinates of a working facility within a certain range.  
The solution filtering is based on the multi-objective optimization strategy of Pareto dominance relation. The calculation of the acceptance probability involves comparing the new solution with the dominance value obtained from both the archive solution set and the current solution and combining this with the current temperature. In the initial period of high temperature, the new solution is readily accepted based on the dominance relation. However, as the temperature decreases, the acceptance probability gradually diminishes as well. To enhance the variety of feasible solutions in the algorithm, a non-dominated solution is chosen from the archive as the current solution after a designated number of iterations.
## 4 Experimental Results
### Filter Results  
To verify the performance of our adaptive filter, we used a trained mask to filter the noise of the two captured signal components separately, as shown in Figure 7.  
The angles of the two sources are \(-89^{\circ}\) and \(89^{\circ}\), respectively. The SNR is set to \(-10\) dB and the number of snapshots is 200. Due to the influence of colored noise, which exhibits a non-uniform power distribution across the frequency spectrum, the original time series lose the characteristics of monoharmonic signals, leading to a remarkable similarity between them. This similarity can weaken the differentiation ability of neural networks, thereby potentially compromising their performance in signal processing tasks. However, more discriminative features are generated by SSAM, making the network easier to train. Therefore, SSAM can indeed filter unimportant frequency components, and improve the ability of neural networks to distinguish between two sources.
# Double Trapezoidal Wave Transmitting System with Controllable Turn-Off Edge
## 3 Double Trapezoidal Wave Transmitting System  
### Overall Structure of the Transmitting System  
We need the emission current with different turn-off times to induce the induced polarization field, and want the emission current to have high linearity and good quality. The overall structure of the double trapezoidal wave transmitting system with a controllable turn-off edge is shown in Figure 3.  
The core of the control circuit is a STM32 chip based on the ARM core. Keyboard and OLED are responsible for setting parameters and display system status. All the control of the system is based on the PWM technology of the control circuit. The PWM wave is converted to 15 V by the optocoupler, which not only plays the role of level conversion but also electromagnetic isolation. The transmitting bridge is powered by a 12 V battery. Four MOSFETs that constitute the transmitting bridge are controlled by optocoupler to generate positive and negative trapezoidal waves on the transmitting coil. When the emission current is turned off, the control circuit generates signals to control the MOSFET, which constitutes the absorption circuit and the turn-off edge and can suppress the current oscillation and obtain the emission current with good quality at different turn-off times on the transmitting coil.
## 5 Experiments and Analysis
### Algorithm Comparison
Its ability to address Q-value overestimation and optimize resource allocation in a multi-agent environment highlights its potential as a robust and efficient solution for task offloading and resource allocation in multi-UAV MEC systems. These findings validate the superiority of MATD3-TORA and its applicability to real-world scenarios requiring dynamic and adaptive resource management.
# UAV Communication Recovery under Meteorological Conditions
## 3 Methods  
In this section, we evaluate the impact of meteorological conditions on UAV-ground communications in four selected regions from the coverage radii of UAVs. The evaluation is based on the derived implicit function of the coverage radius of the UAV with respect to the elevation angle. Firstly, we analyze the relationship between the coverage radius of the UAV and the elevation angle under the influence of the atmosphere only, based on excessive path losses with different numbers of reflections. We then examine the effect of specific attenuation due to rain, fog, and dry snow and analyze the relationship between the UAV coverage radius and the elevation angle. At the same time, we give the numerical results of the coverage radius of the UAV under different weather conditions in the four selected regions, and the results are quantitatively analyzed and discussed. Moreover, we give the numerical results of the UAV coverage area, optimal height, and compensated path loss under different rainfall rates, liquid water densities, and snowfall rates, respectively. Finally, we quantitatively analyze and discuss these results.  
From the geometric relationship between the UAV and a ground user in one of the four selected regions, as shown in Figure 4, the distance between the UAV and ground user can be calculated as \(d=C_{r}\cdot\sec\theta\). Since we need to explore the relationship between the coverage radius (\(C_{r}\), in m) and elevation angle (\(\theta\), in degree) of the UAV under the influence of different weather in the four regions, based on Equation (5), we give the implicit function of the coverage radius of the UAV with respect to the elevation angle, as shown in Equation (10).  
\[\begin{split} PL_{\mathrm{max}}&=\left[i\cdot\sin( j\theta+k)+l\cdot\sin(m\theta+n)\right]\times\left(\eta_{LoS}-\eta_{NLoS}\right)+20\log(C_{r} \cdot\sec\theta)\\ &+20\log\!\left(\frac{4\pi}{\lambda}\right)+\eta_{NLoS}+\frac{( \beta+\gamma)(C_{r}\cdot\sec\theta)}{1000},(0\leq\theta\leq 70^{\circ}), \end{split} \tag{10}\]  
where \(PL_{\mathrm{max}}\) represents the maximum allowable path loss, and its value is set to 114 dB [17], and \(\eta_{NLoS}=\eta_{NLoS1},\eta_{NLoS2},\eta_{NLoS3}\), \(\gamma=\gamma_{\mathrm{rain}},\gamma_{\mathrm{fog}},\gamma_{\mathrm{ snow}}\).  
Figure 4: The geometric relationship between the UAV and a ground user in Region 2.
## 4 Conclusions  
This study analyzed the corrosion resistance of the oil impregnated AAO surface of aluminum 7075 to suggest a way to improve the corrosion protection for subsea application. The cylindrical shape was investigated as well as the plate shape. The contact angle was measured, and the interfacial tension between the water and the surfaces were calculated using the proposed equations. The salt spray test was conducted in the case of the plate shape, whereas the pressure test was carried out in the case of the cylindrical shape. Although salt spray and pressure tests were conducted, the oil in the pores of the AAO surface remained. The contact angle of the oil impregnated AAO surface was increased after the salt spray and pressure tests. That is, the nanoporous structure and the remaining oil in the pores help to maintain a high contact angle. The calculated interfacial tension coincided with the measurement of the contact angle. Corrosion was not indicated in the oil impregnated AAO surface for the 720 h salt spray test. The impregnated oil in the pores can endure a high-pressure condition (600 bar). Enhanced aluminum 7075 by AAO and oil impregnation can be used for subsea equipment.  
Conceptualization, J.-Y.J.; Methodology, J.C.; Validation, S.L.; Formal Analysis, Y.S.; Writing--Original Draft Preparation, Y.S.; Writing--Review & Editing, J.-Y.  
This research was supported by a grant from the Endowment Project of \"Development of the Surface Modification Technologies based on Metal-Oxide for Offshore Equipment\" (PES9320) and \"Development of Technology to Support the Rapid Search and Rescue of Marine Accidents\" (PES3130) funded by Korea Research Institute of Ships and Ocean Engineering.  
The authors declare no conflict of interest.  
Figure 9: Contact angles of AAO (anodic aluminum oxide), AAO-O (oil impregnation), and AAO-O-PT (pressure test) surfaces for cylindrical shape of aluminum 7075.
## 3 Results  
### Dose Linearity and Sensitivity to MV X-rays  
Irradiations revealed a linear response within a dose range of 25 to 500 cGy for the central pixel of the edgeless diode array. This linearity was confirmed with a coefficient of determination R\({}^{2}\) =1. Figure 4 shows the linearity measurement at SCCC. The result obtained at SGHCC is shown in Figure S2 in the Supporting Information.  
The sensitivity measurements for diode D13 at the SCCC LINAC yielded a value of 7.0 \(\times\) 10\({}^{-3}\)\(\pm\) 1.7 \(\times\) 10\({}^{-6}\) Gy/nC, while the sensitivity at SGHCC was found to be 6.728 \(\times\) 10\({}^{-3}\) Gy/nC. On average, across all diodes, the sensitivity for the SCCC LINAC was 6.95 \(\times\) 10\({}^{-3}\)\(\pm\) 2.3 \(\times\) 10\({}^{-5}\) Gy/nC, whereas for SGHCC, it was 6.726 \(\times\) 10\({}^{-3}\)\(\pm\) 1.4 \(\times\) 10\({}^{-5}\) Gy/nC.  
Despite the measurements being taken in two different radiation facilities, from two different LINACs, the calibration factors registered by the sensor were found to be in close agreement, differing by only 3.6%.  
Figure 3: The experimental setup for the angular dependence study. The edgeless array is enclosed within a PMMA adaptor facing the beam direction (X-axis) at 0\({}^{\circ}\) (gantry angle is 90\({}^{\circ}\)). The incident beam is striking the array’s edge at 90\({}^{\circ}\) (Z-axis) when the beam is directed towards the floor.
## 4 Results and Discussion
### Effectiveness of Data Augmentation Based on Mix-up  
We first confirmed the effectiveness of the data augmentation based on mix-up. The validation was performed on ResNet50 with the conventional loss function (\(\lambda\) = 0.00). We performed three experiments: without data augmentation, with data augmentation based on conventional translation and inversion, and with data augmentation based on mix-up. The comparison results are shown in Table 3. The effectiveness of the data augmentation was statistically tested in a \(t\)-test. Both methods with data augmentation outperformed the method without data augmentation (translation and inversion, \(p\) = 0.003; mix-up, \(p\) = 0.0001), and the results were statistically significant (\(p\) < 0.05). Moreover, the data augmentation based on mix-up outperformed the conventional augmentation based on translation and inversion (\(p\) = 0.0052 < 0.05). To our knowledge, we present the first evidence that mix-up data augmentation improves medical image classification. Mix-up-based data augmentation was therefore applied in our further experiments.
# Optimal Solution of Environmental Economic Dispatch Problems Using QPGPSO-\(\omega\)
## 4 Results and Discussion
### Case 1 of Only RESs without Constraints  
In case 1, QPGPSO-\(\omega\) was employed to solve the EDP for the power system comprising only RESs without considering any constraints.  
The simulation was conducted using specific power plants located in different regions of Pakistan. Four biofuel power plants located in Rahim Yar Khan (RYK), Punjab; five PV plants commissioned in Bahawalnagar and Saihiwal, Punjab; and six wind-based frameworks deployed in Jhimpir, Sindh, were selected for the study. The climatic conditions and applied requisites of the considered resources were taken from [20,64,65] and presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Parameter** & **Range** & **Value** \\ \hline
1 & C\({}_{1}\), C\({}_{2}\) & [1.5–3] & 2.5 \\ \hline
2 &
\begin{tabular}{c} Uniform pdf (probability \\ density function), r \\ \end{tabular} & [0–1] & 0.5 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters and their corresponding values for QPGPSO-\(\omega\).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Regional** & **Project** & \
\begin{tabular}{c} **Capacity** \\ **(MW)** \\ \end{tabular} & **Area** &
\begin{tabular}{c} **Irradiance/Rated** \\ **Velocity** \\ \end{tabular} \\ \hline \multirow{4}{*}{Region 1} & M/S R.Y.K Mills Limited & 30 & Rahim Yar Khan & \\ \cline{2-5}  & M/S Hamza-Sugar Mills Limited & 15 & Rahim Yar Khan & \\ \cline{2-5}  & M/S J.D.W Sugar-Mills (Unit II) & 26.4 & Rahim Yar Khan & \\ \cline{2-5}  & M/S Cheniot-Power Limited & 62.4 & Rahim Yar Khan & \\ \hline \multirow{4}{*}{Region 2} & Harrapa Solar (private) Limited & 18 & Saihiwal & Rad min = 150 \\ \cline{2-5}  & Adam-jee Power-Generation (private) & & Rad max = 1000 & Rad max = 1000 \\ \cline{2-5}  & Limited. & 10 & Noorsar, Bahawalnagr & P.V Mean = 5.2 \\ \cline{2-5}  & Asia-Petroleum Limited. & 30 & Noorsar, Bahawalnagr & P.V Std. Dev. = 0.6 \\ \cline{2-5}  & M/S Safe Solar Power (private) Limited & 10.3 & Bahawalnagr & P.V Over est. = 1.5 \\ \cline{2-5}  & Asia-Petroleum Limited. & 30 & Bahawalnagr & P.V direct cost = 1.6 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters for the RESs utilized for test case 1.  
The QPGPSO-\(\omega\) algorithm was simulated for 30 sequential trials, and the average results obtained from these trials are presented in Figure 2. The figure illustrates the convergence behavior of the algorithm, indicating the progressive improvement in cost values for the same power demand (PD) as the iteration's progress. The 17 th iteration is identified as the point of convergence where the algorithm achieves optimal cost values. Table 3 provides a comprehensive power and cost comparison between the QPGPSO-\(\omega\) algorithm and other prominent optimization techniques implemented for the case of RES-only systems [66].
## 2 Theoretical Background
### Customer Participation
#### 2.1.2 Customer Participation in Service Recovery  
[PERSON], [PERSON], and [PERSON] (2016) first introduced customer participation in service recovery and defined it as \"the degree to which the customer is involved in taking actions to respond to a service failure\". Scholars have verified that customer participation can lead to positive post-recovery behavior, such as perceived service quality ([PERSON] et al., 2016), satisfaction and repurchase intention ([PERSON] et al., 2016; [PERSON] et al., 2016), positive word of mouth ([PERSON] et al., 2016; [PERSON] et al., 2016), experience with service recovery ([PERSON] et al., 2016), and intention for future co-creation ([PERSON] et al., 2016; [PERSON] et al., 2016).  
Because customer participation can bring the above advantages, scholars have found that customer participation can enhance the effect on customers' cognition, including perceived justice ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016), perceived match ([PERSON] et al., 2016), perceived value ([PERSON] et al., 2016), perceived control ([PERSON] et al., 2016), and perceived co-creation ([PERSON] et al., 2016). Furthermore, positive and negative emotions ([PERSON] et al., 2016) and affect ([PERSON] et al., 2016) have been used to explain the role of customer participation in customers' emotions. Additionally, the roles of recovery effort ([PERSON] et al., 2016) and customer participation readiness ([PERSON] et al., 2016) have been discussed to better understand the impact of individual factors on the effect of customer participation.  
The moderator variables that can influence the positive relationship between customer participation and post-recovery evaluation have also been studied, including the customer's attitude about co-recovery ([PERSON] et al., 2016), the initiator of co-recovery ([PERSON] et al., 2016), recovery urgency ([PERSON] et al., 2016), brand equity ([PERSON] et al., 2016), culture ([PERSON] et al., 2016), and recovery process communication ([PERSON] et al., 2016). However, [PERSON] and [PERSON] ([PERSON] and [PERSON], 2016) have noted that existing studies on customer participation pay less attention to the moderators, especially employees' personal resources, such as their communication style and emotional intelligence ([PERSON] et al., 2016). Limited studies have explored the moderating role of employees' personal resources in customer participation and the service outcome ([PERSON] et al., 2016). Thus, in this study, we use speech act theory to analyze the moderating role of employees' different expressive modes of apology in customer participation and perceived justice in online recovery.