esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Resonant Converter with Three Half-Bridge Legs for Wide Voltage Operation
###### Abstract  
This paper studied a hybrid resonant converter with three half bridge legs for wide input voltage operation. Compared to the conventional resonant converters with narrow voltage operation, the presented converter can achieve wider voltage operation. On the basis of the proper switching status of power switches, the developed converter can operate at half-bridge resonant circuit under high input voltage range and the other two full-bridge resonant circuits under medium and low input voltage ranges. Each resonant circuit has a 2:1 (\(V_{in,max}=2V_{in,min}\)) input voltage operation range. Therefore, the developed converter can achieve an 8:1 (\(V_{in,max}=8V_{in,min}\)) wide voltage operation. The main advantage of the studied converter is the single-stage direct current (DC)/DC power conversion instead of the two-stage power conversion to achieve wide voltage operation. Because the equivalent resonant tank of the adopted converter is controlled by frequency modulation, the soft switching operation on power switches or rectifier diodes can be realized to improve circuit efficiency. The performance of the proposed circuit was confirmed and verified by experiments with a laboratory circuit.  
DC/DC converters; zero voltage switching; frequency modulation; resonant converters Article
## 4 Conclusions  
Two different LOHC compounds were taken into consideration, namely, H18-DBT and H12-NEC, which have different characteristics and properties. A 0D model was used for calculating the dehydrogenation power \(P_{des}\) and the minimum exhaust gas temperatures \(T_{exh}\) for the dehydrogenation process to take place. We demonstrated that obtaining sufficient energy to dehydrogenate the LOHC compounds is possible by exploiting the exhaust gases at certain values of \(T_{exh}\) calculated and indicated beforehand. We also showed that the dehydrogenation power \(P_{des}\) increases with the rise in the rotational speed for both H18-DBT and H12-NEC. As the CH\({}_{4}\) volumetric fraction increased, \(T_{exh}\) started to decline. The required power for dehydrogenation was calculated at a higher value for H18-DBT at all rotational speeds than it was observed for H12-NEC.  
The simulations carried out within this work can be further refined by exploiting special engineering software for calculating the temperatures reached in the chamber during the combustion process for an engine cycle, as well as by carrying out suitable experimental tests in order to compare the results and possibly validate everything. Finally, it should be noted that the implemented model requires a tank containing the LOHC+ liquid and a catalyst that allows the dehydrogenation process to occur. Therefore, it is advisable to design and validate these components, as well, paying attention to their size and weight, as discussed for the automotive sector. For this reason, the study and use of geometries and materials that allow the process to take place should be optimized.  
Conceptualization, S.P., A.M. and M.Z.; methodology, S.P., A.M. and M.Z.; software, S.P., A.M., M.Z. and A.F.K.; validation, S.P., A.M., M.Z. and A.F.K.; writing--original draft preparation, A.F.K.; writing--review and editing, S.P., A.M. and A.F.K.; visualization, S.P., A.M. and A.F.K.; supervision, A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case Number** & **H18-DBT** & **H12-NEC** \\ \hline \(1\) (100\% H\({}_{2}\)) & \(T_{exh}=1150\) K & \(T_{exh}=990\) K \\ \(P_{des,max}=56.113\)kW & \(P_{des,max}=42.900\)kW \\ \hline \(2\) (75\% H\({}_{2}\) + 25\% CH\({}_{4}\)) & \(T_{exh}=875\) K & \(T_{exh}=770\) K \\ \(P_{des,max}=27.533\) kW & \(P_{des,max}=21.049\) kW \\ \hline \(3\) (50\% H\({}_{2}\) + 50\% CH\({}_{4}\)) & \(T_{exh}=710\) K & \(T_{exh}=640\) K \\ \(P_{des,max}=13.639\) kW & \(P_{des,max}=10.427\) kW \\ \hline \(4\) (25\% H\({}_{2}\) + 75\% CH\({}_{4}\)) & \(T_{exh}=630\) K & \(T_{exh}=575\) K \\ \(P_{des,max}=5.426\) kW & \(P_{des,max}=4.148\) kW \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results summary.
## 2 Case Study
### In Situ and Laboratory Investigation  
Several in situ and laboratory investigations were conducted in order to identify the soil profile and evaluate the soil's physical and engineering properties. In order to achieve a detailed design, 37 boreholes were drilled at the proposed location using rotary-drilling techniques. In situ tests such as standard penetration tests (SPT) and shear-vane tests were carried out in 24 and 21 locations, respectively, to provide data on geotechnical properties. Through the utilisation of SPT, thick-walled sample tubes were driven into the ground to depths ranging between 150 mm and 450 mm in order to obtain undisturbed samples. During the geotechnical investigation, boreholes were prepared, and laboratory tests were carefully performed for factors such as water content, density, specific gravity, Afterberg limits, geodometer and triaxial tests.
# Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map
## 2 Related Works
### Retinex Theory  
Retinex theory [19] divides an image into a reflection component and an illumination component:  
\[S^{c}=R^{c}\times L, \tag{1}\]  
where \(S\), \(R\) and \(L\) are original image and reflection image and illumination image, respectively; and \(c\) denotes three color channels of RGB (red, green, blue). The algorithm based on retinex theory aims to enhance the illuminance component to improve the quality of low-light images [27].
# Polarization Transmission of Visible Light in Inhomogeneous Sea Fog Particle Environment
## 5 Conclusions  
In conclusion, we show the polarization transmission characteristics' variation law under the environment of inhomogeneous sea fog particles using theory and experimental methods. We first calculated the equivalent refractive index of the inhomogeneous sea fog particles through the equivalent analysis method and the relative humidity formula. Then we improved the Monte Carlo method to construct an inhomogeneous particle scattering model. After that, we set indoor simulation experiments, and we recorded the DOP of polarized light under different wavelengths and relative humidity conditions. Finally, we calculate the conformity of the simulation and experiment to verify the feasibility.  
Both the simulated and experimental results show that in the visible band, the DOP decreases with the increase of the wavelength. DOP\({}_{450}\) is approximately 3-10% higher  
Figure 13: Comparison of polarization transmission characteristics between homogeneous sea fog particles and inhomogeneous sea fog particles: (**a**) same wavelength; (**b**) same relative humidity.  
than DOP\({}_{532}\), and DOP\({}_{532}\) is approximately 5% higher than DOP\({}_{671}\). The relative humidity increases from 45% to 85%, and the DOP increases by 10-15%. Therefore, the wavelength is inversely proportional to DOP; relative humidity is proportional to DOP. This will provide theoretical support for polarization imaging and polarization detection.  
In the follow-up research, external field experiments must be conducted to verify further the inhomogeneous particle scattering model. We will replenish the influence of the actual outdoor environment on polarization characteristics.  
Methodology, S.Z.; Formal analysis, W.Z.; Writing--original draft, S.B.; Writing--review & editing, J.Z.; Supervision, Y.L., Q.F. and J.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (No. 61890963); National Natural Science Foundation of China (No. 62127813); Key Projects of Jilin Provincial Department of Education (No. JJKH20220738 KJ, JJKH20220737 KJ); Project of Science and Technology Department of Jilin Province (No. 20210201093 CX, 20200201261 JC).  
Not applicable.  
The authors declare no conflict of interest.
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
## 4 High-Order QAM and APSK Modulation and Demodulation
### QAM Soft Demodulation
\tag{27}\]Therefore, for the real part bit sequence of the 16 QAM constellation mapping symbols in Figure 4, the symbol positions corresponding to the real part bit \(b_{1}=0\) are \(1\) and \(3\), and the symbol positions corresponding to the real part bit \(b_{1}=\)\(1\) are \(-1\) and \(-3\); the symbol positions corresponding to the real part bit \(b_{2}=\)\(0\) are \(-1\) and \(1\), and the symbol positions corresponding to the real part bit \(b_{2}=\)\(1\) are \(-3\) and \(3\). According to (25), the LLR of the first bit \(b_{1}\) in the received symbol \(y\) is:  
\[LLR(b_{1})=\log\left(\frac{\exp\left[-\frac{{{h^{2}}(y-1)}^{2}}{2{\sigma^{2}}} \right]+\exp\left[-\frac{{{h^{2}}(y-3)}^{2}}{2{\sigma^{2}}}\right]}{\exp\left[- \frac{{{h^{2}}(y+3)}^{2}}{2{\sigma^{2}}}\right]+\exp\left[-\frac{{{h^{2}}(y+1) }^{2}}{2{\sigma^{2}}}\right]}\right) \tag{28}\]  
Similarly, the LLR of the second bit \(b_{2}\) in the received symbol \(y\) is:  
\[LLR(b_{2})=\log\left(\frac{\exp\left[-\frac{{{h^{2}}(y-1)}^{2}}{2{\sigma^{2}} }\right]+\exp\left[-\frac{{{h^{2}}(y+1)}^{2}}{2{\sigma^{2}}}\right]}{\exp \left[-\frac{{{h^{2}}(y-3)}^{2}}{2{\sigma^{2}}}\right]+\exp\left[-\frac{{{h^{2 }}(y+3)}^{2}}{2{\sigma^{2}}}\right]}\right) \tag{29}\]  
Since the mapping numbers for the real and imaginary bits are the same, the LLRs of the imaginary bits can be obtained directly from the results of the real bits. That is, the LLRs of the third bit \(b_{3}\) and the fourth bit \(b_{4}\) in the received symbol \(y\) are:  
\[ \begin{cases}LLR(b_{3})=LLR(b_{1})\\ LLR(b_{4})=LLR(b_{2})\end{cases}  \tag{30}\]  
Then, according to the decision criterion in (27), the \(LLR(b_{1})\), \(LLR(b_{2})\), \(LLR(b_{3})\), \(LLR(b_{4})\) can be used to make decisions and demodulate the 4-bit values in the 16 QAM modulation symbol.
# Improving Geotechnical Properties of Expansive Subgrade Using Sugar Cane Molasses and Cement
## 4 Results and Discussion  
### Effect of Stabilizers on pH Values  
Several specimens were produced in the manner described in Table 4, and the soil-stabilizer slurries' pH values were measured in line with ASTM D-6276 [20]. The table presents the pH values of soil samples following a one-hour mix with varying percentages of molasses and cement as stabilizers. The native soil, with a baseline pH of 8.20, serves as a reference point. The data demonstrate a clear trend in which the addition of stabilizers influences the soil's pH, with specific combinations of molasses and cement yielding distinct results. For instance, the S\({}_{4,4}\) combinations, consisting of 4% molasses and 4% cement, reduce the pH slightly to 7.84, making the soil less alkaline than the native sample. As the cement percentage increases, the pH also rises, as seen in the S\({}_{4,8}\) (8.07) and S\({}_{4,12}\) (9.11) combinations. This trend continues with higher percentages of molasses, where the S\({}_{8,4}\) (13.19) and S\({}_{8,12}\) (13.74) combinations exhibit significant increases in pH, indicating a strong alkalizing effect. Notably, the highest pH values occur when both stabilizers are used in substantial quantities, such as in the S\({}_{12,8}\) (13.18) and S\({}_{12,12}\) (13.48) samples, reflecting a synergistic interaction between molasses and cement that amplifies the alkaline reaction in the soil. These findings highlight the impact of varying stabilizer concentrations on soil pH values, providing critical insights for soil stabilization practices in civil engineering.
## 2 System Model and Problem Formulation
### System Model
#### 2.1.3 Delay Cost Model  
For MUs downloading the target content workload, we mainly consider the transmission delay as the delay cost due to the limitations of the backhaul capacity of the BS. It costs less in terms of transmission delay for a MU downloading the target content from the MECS to a MU (denoted as \(T_{MEC}\)),as compared to that from the CN to a MU (denoted as \(T_{Back}\)). In order to quantify the delay performance of services without restricting our model to any particular metric, we utilize an average response time to represent the delay cost:  
\[T_{Aver}=\eta\Lambda\beta^{T} \tag{6}\]  
\[\eta=[T_{MEC},T_{Back}] \tag{7}\]  
where \(T_{MEC}=\frac{L}{C_{MEC}}\), \(T_{Back}=\frac{L}{C_{B}}\) represents the duration of one MU finishing a download on interested content from the MECS and finishing a download on interested content from the CN, respectively.  
\[C_{B}=\left\{\begin{array}{ll}C_{Back}&,\ if\ K_{Back}\leq B(t)\\ \frac{C_{max}}{K_{Back}},\ if\ K_{Back}>B(t)\end{array}\right. \tag{8}\]  
where \(K_{Back}^{n}\) is defined as the number of MUs which finishing a download on interested content from the CN. The maximal number of MUs that can download interested content at the same time by the backhaul network is defined as \(B(t)\). If \(K_{Back}^{n}\leq B(t)\), then each user's transmission rate of backhaul is \(C_{Back}\), else if \(K_{Back}^{n}>B(t)\), each user's transmission rate of backhaul is \(C_{B}=\frac{C_{max}}{K_{Back}^{n}}\), \(B(t)=\frac{C_{max}}{C_{Back}}\).  
\[\Lambda=\left[\begin{array}{llll}K_{MEC}^{1}&K_{MEC}^{2}&\cdots&K_{MEC}^{U(t )}\\ K_{Back}^{1}&K_{Back}^{2}&\cdots&K_{Back}^{U(t)}\end{array}\right] \tag{9}\]  
where \(K_{MEC}^{n}\) is defined as the number of MUs which finishing a download of interested content from the MECS. The average number of MUs requesting interested contents from the BS is \(E[U(t)]=K_{MEC}^{n}+K_{Back}^{n}\). Since \(U(t)\) yields to the Poisson process, \(E[U(t)]=E\bigg{[}\frac{(\lambda t)^{k}}{k!}e^{-\lambda t}\bigg{]}=\lambda t\).  
\[\beta=[P(1),P(2),\ldots,P(N)] \tag{10}\]  
where \(P(n)=\left(\begin{array}{ll}U\\ K_{MEC}^{n}\end{array}\right)\)\(H^{K_{MEC}^{n}}(1-H)^{K_{Back}^{n}}\) represents the probability that \(K_{MEC}^{n}\) users hit the cache simultaneously.  
As such, the formulation of average response can be written as:  
\[T_{Aver}=\sum\limits_{n=0}^{U(t)}\left(\begin{array}{c}U(t)\\ n\end{array}\right)Q^{n}(1-Q)^{U(t)-n}T_{MEC}+\sum\limits_{n=0}^{U(t)}\left( \begin{array}{c}U(t)\\ n\end{array}\right)Q^{n}(1-Q)^{U(t)-n}T_{Back} \tag{11}\]  
The Equation (11) can be rewritten as:  
\[T_{Aver}=\sum\limits_{n=0}^{U(t)}\left(\begin{array}{c}U(t)\\ n\end{array}\right)Q^{n}(1-Q)^{U(t)-n}\left(\frac{nL}{C_{MEC}}+\frac{(U(t)-n)L} {\min\{C_{Back},C_{Max}\}}\right) \tag{12}\]  
Proof. The proof is presented in Appendix B.
# Automated Generation of Hybrid Metaheuristics Using Learning-to-Rank
## 2 Related Work
### Hyper-Heuristics and Automated Algorithm Generation Based on Machine Learning
Moreover, recent studies on metaheuristics highlight two major types of learning mechanisms: (1) strategy-level learning mechanisms and (2) parameter control. [PERSON] et al. [21, 22] proposed online strategy ranking methods for ridesharing problems, where search strategies are dynamically evaluated and reordered during evolution. In contrast, [PERSON] and [PERSON] [23] and [PERSON] et al. [24] employed learning techniques to adapt algorithmic parameters, such as inertia weights or crossover/mutation rates. These studies represent two distinct directions in learning-enhanced metaheuristics. Unlike the online learning mechanisms or parameter-tuning approaches, our method introduces an offline Learning-to-Rank framework for strategy selection. While online updates can be responsive, they often rely on short-term performance and may suffer from instability in complex or high-dimensional problems. Our offline LTR model is trained on broader search history data, enabling more stable, generalizable, and feature-oriented decision-making. As far as we know, this study is the first to employ LTR for the development of hybrid metaheuristic frameworks, offering a novel and theoretically grounded pathway in the landscape of automated algorithm design. While our work shares the core goal of algorithm automation with hyperheuristics, it differs in modeling granularity and learning objectives. Traditional hyperheuristics typically focus on selecting or generating operators based on heuristic performance, often relying on manually designed rules. In contrast, our approach does not target operator selection but instead learns the mapping between candidate solution features and algorithm behaviors. This enables strategy-level integration of multiple metaheuristics.
## 2 Materials and Methods
### Effect of the Generated (Reactive) CAP Species on the Biofilms
#### Membrane Integrity  
As membrane damage is one of the most important CAP inactivation mechanisms reported in literature (e.g., [8, 18, 28, 30, 31]), it was assessed if the examined plasma characteristics had an influence on the integrity of the membrane of the biofilm-associated _Listeria monocytogenes_ and _Salmonella_ Typhimuriumium cells. For this purpose, CAP treated biofilms were removed from the surface using the cell scraping method and 1.5 mL of the obtained cell suspension was centrifuged at 13,200\(\times\) g for 10 min. Finally, 200 \(\upmu\)L of the supernatant was transferred to a UV-transparent microtiter plate (screenstar, F-bottom, Greiner Bio-One, Vilvoorde, Belgium) and the absorbance was measured at 260 nm using the VersaMax plate reader. Three independent biological replicates (\(n=3\)) were performed for each combination of plasma characteristics. An increase in absorbance in comparison to the untreated model biofilms (= the positive controls) was deemed to be the result of a damaged membrane, originating from the interaction of the generated CAP species with this cellular target.
# Advances and Challenges of Corrosion and Topology Detection of Grounding Grid
## 2 Corrosion Detection Method of Substation Grounding Grid
### Electric Network Method
#### 2.1.2 Sensitivity Analysis Method  
[PERSON] [7; 8; 9] proposed a sensitivity analysis method to diagnose grounding grid corrosion. In this method, conductor resistance increment of the grounding grid is taken as the fault parameter, and the fault diagnosis equation is established by sensitivity analysis method. The principle of minimum energy and optimization technology are introduced into the solution of the fault diagnosis equation.  
For grounding grids with b branches and r accessible nodes, a fault diagnosis Equation (2) is established.  
\[\Delta\mathrm{U_{r}}=\mathrm{U_{rb}}\cdot\mathrm{X} \tag{2}\]  
where \(\mathrm{U_{rb}}\) is the sensitivity matrix and \(U_{ij}\) represents the effect of branch \(j\) conductor resistance on the potential of measuring node \(i\). \(\Delta\mathrm{U_{r}}\) is the variation of node voltage before and after corrosion, which can be obtained by measurement. \(\mathrm{X}\) is a b-dimensional column vector, \(x_{j}\) represents the multiplier of the conductor resistance increment in the \(j\)-th segment, which is to be determined. For the actual grounding grid, the fault diagnosis equation is undetermined because the number of accessible nodes m is always less than the number of branches b. The sensitivity matrix is related to branch resistance; that is, the sensitivity matrix is not constant, so Formula (2) is a non-linear equation and is established only when resistance slightly changes.  
\[\mathrm{Min}\;\mathrm{P}=\sum_{i=1}^{b}I_{j}^{2}R_{j}^{*} \tag{3}\]  
The application of optimization theory to the solution of the undetermined equation can solve the problem of calculating the actual resistance of conductors in each segment of the grounding grid.
## 4 Conclusions and Future Research  
### Conclusions  
The combined effect of P-wave velocity (\(\
u_{\rm p}\)), saturated density (\(\rho_{\rm sat}\)), and porosity (n) on UCS was studied for saturated cylindrical specimens of limestone. Under the testing conditions, it was demonstrated that \(\
u_{\rm p}\) and n have a synergetic effect on UCS, whereas \(\rho_{\rm sat}\) does not have a statistically significant influence on the dependent variable.  
Experimental results were analyzed through simple regression analyses in order to develop the best-fit analytical model that can represent UCS depending on \(\
u_{\rm p}\) (as developed by most authors). Subsequently, multiple regression analyses were performed in order to obtain a better model to predict UCS from \(\
u_{\rm p}\), n, and \(\rho_{\rm sat}\).  
Simple regression analyses (using only \(\
u_{\rm p}\) as the independent variable) showed a best \(R^{2}\) value of 78.40%. On the other hand, a multiple regression analysis resulted in a better \(R^{2}\) (84.90%), thus indicating a better fit when more independent variables are considered.  
Multiple regression analyses also were able to identify \(\rho_{\rm sat}\) as a statistically nonsignificant independent variable for predicting UCS from ultrasonic pulse transmission tests. That is, there was insufficient evidence for \(\rho_{\rm sat}\) contributing to variations of UCS under the tested conditions.  
Finally, the exponential with multiple exponent model featured the best fit when predicting UCS, showing that UCS depends directly on \(\
u_{\rm p}\) and inversely on \(n\).
## 3 Results and Discussion
### Driving Stability Analysis Based on SRS  
As a factor associated with driving stability, smooth road shake (SRS) is generated by the unbalanced mass that occurs during the assembly and manufacturing of components such as tires and wheels. This is represented by the response of the acceleration transmitted to the foot floor panel during vehicle operation.  
In this study, we analyzed the characteristics of the SRS response by varying the side view swing arm (SVSA) angle according to its sensitivity. To achieve this, we applied an unbalanced mass to the left wheel center in a downward direction with a force of 0.8 N (Table 8), referring to the relevant literature [8]. The SVSA angles obtained through the roll analysis are presented in Table 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Stiffener Curvature** & **Y-axis Displacement** & **Angle** & **Torsional Rigidity** \\  & **(mm)** & **(mm)** & **(\({}^{\circ}\))** & **(N-m/deg)** \\ \hline Base & 0 & 0.133 & 0.082 & 1124.7 \\ Type 1 & +40 & 0.229 & 0.142 & 651.8 \\ Type 2 & +50 & 0.193 & 0.119 & 777.8 \\ Type 3 & +60 & 0.178 & 0.110 & 841.5 \\ Type 4 & \(-\)40 & 0.0568 & 0.057 & 1629.5 \\ Type 5 & \(-\)50 & 0.0996 & 0.062 & 1500.2 \\ Type 6 & \(-\)60 & 0.105 & 0.065 & 1435.0 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Torsional rigidity results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Direction** & **Value (N)** \\ \hline  & \(x\)-axis & 0 \\ Linear & \(y\)-axis & 0 \\  & \(z\)-axis & \(-\)0.8 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Unbalanced mass condition.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type** & **Max(\({}^{\circ}\))** & **Min(\({}^{\circ}\))** \\ \hline Base & 3.205 & \(-\)3.549 \\ Type 1 & 3.226 & \(-\)3.566 \\ Type 2 & 3.223 & \(-\)3.564 \\ Type 3 & 3.301 & \(-\)3.447 \\ Type 4 & 3.156 & \(-\)3.502 \\ Type 5 & 3.172 & \(-\)3.518 \\ Type 6 & 3.120 & \(-\)3.467 \\ \hline \hline \end{tabular}
\end{table}
Table 9: SVSA angle in roll analysis based on SRS.  
Each configuration exceeded the initially set SVSA angle range. Under flat road conditions, all the configurations exhibited the same SVSA angle with a maximum of 3.389 deg and a minimum of \(-\)3.470 deg. However, different SVSA angles were observed for each case when the vertical displacements of the left and right wheel centers of the vehicle differed (Figure 9).  
Notably, in the roll analysis, the configurations with the convex curvature of the reinforcement beam (Type 1 to Type 3) had smaller negative SVSA angles than the baseline configuration, whereas configurations with concave curvature (Type 4 to Type 6) had larger negative SVSA angles than the baseline configuration. Among the seven types, Type 1 had the smallest SVSA angle, indicating the best driving stability according to the SRS criterion.