esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Optimization Algorithm Combing
## 4 Experiments
### Effects of the Colony Size on the Performance of GABCG  
In order to analyze the effect of colony size on convergence speed and accuracy, the algorithm programs were run with different colony sizes. At first, we studied the three algorithms with 80, 160, and 240 colony sizes. Both the numbers of employed bees and onlooker are half of the colony size, and the number of scout bees is one. In this test, the programs were executed on five benchmark functions. Schaffer function and Rosenbrock function were tested with 30 variable elements, and the other functions were tested with 60 variable elements. Each experiment was repeated 30 times by the ABC, GABC, and GABCG algorithms.  
Figure 3: Convergence curves of the ABC, GABC, and GABCG algorithms for benchmark functions.  
The mean and standard deviations of optimization results are presented in Table 4. It can be found that as the colony size increases, the accuracy of optimization results become better, and our GABCG algorithm always outperforms the ABC and GABC algorithms. To make the effects on GABCG clear, we present the convergence curves of the GABCG algorithm with different colony sizes in Figure 4. As can be seen, there are no significant improvement from 80 to 240. However, in these tests, the running time of program is almost proportional to the colony size. Therefore, in the practical application of the algorithms, an excessively large population will consume a large amount of computing resources without greatly improving the accuracy of results. This is also found in [PERSON]'s study [3] on the traditional ABC algorithm, which found that a colony size of 50-100 can provide an acceptable convergence speed for search. It gives us the confidence to use a relatively small colony size for efficiency.  
Figure 4: Convergence curves of the GABCG algorithm with different colony sizes.
## 4 Results and Discussion
### Influence of Turbulence Intensity on Aerodynamic Performance of Wind Turbines  
The shear wind considering turbulence intensity is in line with the real environment for normal wind turbine operation compared to the uniform wind in ideal conditions. The main consideration is the unstable inlet wind input. The variation of wind speed and wind direction has a great impact on the aerodynamic performance of the wind turbine. In order to simulate the actual operating conditions of the wind turbine more realistically, the aeroelastic response of the wind turbine blade under turbulent conditions is further calculated in this section. In this paper, the Turbsim [25] program is used to simulate turbulent winds, which is a set of professional pre-processing software developed by the Renewable Energy Laboratory in the United States to simulate turbulent winds. The parameters of the turbulent wind field were established in combination with the CFD flow field size used in the previous paper, as shown in Table 9:  
Figure 18: Equivalent stress distribution of blades. (**a**) Stress distribution on blade surface at 11.4 m/s. (**b**) Stress distribution on blade surface at 10.0 m/s. (**c**) Stress distribution of web at 11.4 m/s. (**d**) Stress distribution of web at 10.0 m/s.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Wind Speed/m\(\cdot\)s\({}^{-1}\)** & \
\begin{tabular}{c} **Maximum Stress on Blade** \\ **Surface/MPa** \\ \end{tabular} &
\begin{tabular}{c} **Maximum Stress of** \\ **Web/Mpa** \\ \end{tabular} \\ \hline
11.4 & 125.2 & 253.8 \\
10.0 & 103.2 & 213.0 \\
8.0 & 71.0 & 148.8 \\
7.0 & 62.2 & 120.7 \\
5.0 & 42.5 & 79.8 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Maximum equivalent stress of blade surface and web.  
According to the above table, turbulent wind fields were created using the IEC-Von Karman wind spectrum model with turbulent intensities of 5%, 10% and 15% in that order. The wind shear coefficients were considered in building the wind fields. The mean wind speed was used at the hub center and its height was used as the reference height to output a two-dimensional turbulent wind field. Figure 19 shows the wind speed changes of hub center height and two-dimensional section under different turbulence intensities. Then, the velocity inlet boundary conditions were input into STAR CCM+ for calculation through post-processing. Considering the computational efficiency of the fluid-solid coupling method, the simulation time of this paper is 40 s.
# Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery
## 2 Materials and Methods
### Accuracy Assessment  
We conducted a systematic accuracy assessment of our finalized mangrove map. We evaluated our map against reference data at a total of 2000 control points (1000 mangrove points and 1000 non-mangrove points). To create the mangrove control points, we randomly generated 1000 points within the areas classified as mangrove on our map. To create the non-mangrove control points, we randomly generated 1000 points within a 100 m buffer of the areas classified as mangrove on our map. We used high-resolution (1 m or finer) Google Earth imagery captured in 2020 as reference data [37].  
We calculated the overall accuracy, Kappa coefficient, mangrove commission error, and mangrove omission error for our mangrove map [36]. Overall accuracy quantifies how accurately the classification process differentiated between mangrove and non-mangrove features. The Kappa coefficient quantifies the overall success of the classification relative to a random classification. Mangrove commission error quantifies the likelihood that a given mangrove pixel on the map is not mangrove, while mangrove omission error quantifies the likelihood that a given non-mangrove pixel on the map is mangrove.
# An Image-Encryption Algorithm Based on Stage-Merging Bit Scrambling
## 3 Security Analysis
### Encryption Time  
For image-encryption algorithms, on the basis of ensuring security, encryption time is also a significant indicator to measure the quality of the algorithm.  
In the simulation environment of this paper, the encryption time test is carried out on the same image with the size of 256 \(\times\) 256, and the average value of multiple test results is taken. Table 8 shows the results. Compared with single- and adjacent-pixel bit scrambling, stage-merging bit scrambling significantly shortens the scrambling time, and the scrambling speed is faster.
## 5 Applications of BC-based Photocatalysts
### TiO\({}_{2}\)-BCPs  
During recent decades, TiO\({}_{2}\) has been the most used photocatalyst in the field of water purification owing to the advantages of high photo-activity, price moderate, non-toxicity, low cost and good chemical stability [102]. However, pristine TiO\({}_{2}\) still suffers from the low response of visible light and high charge recombination. In recent years, biochar modified TiO\({}_{2}\) composites (BC-TiO\({}_{2}\)) have attracted great attention due to the physical adsorption, electrostatic interaction and suppression of electron/hole charges recombination granted by BC.  
[PERSON] et al. [30] synthesized need straw biochar-based TiO\({}_{2}\) by sol-gel method. The coupling of TiO\({}_{2}\) and BC suppressed the recombination of e'/h\({}^{+}\) pairs and further promoted photocatalytic degradation efficiency. The sulfamethoxazole removal rate was 91.27%, compared with that of pure TiO\({}_{2}\) (58.47%). Furthermore, the obtained BC-TiO\({}_{2}\) were successfully applied in real water with a removal rate of 65.70%. Very recently, [PERSON] et al. [39] fabricated a series of BC-TiO\({}_{2}\) photocatalysts by direct hydrolysis method and pyrolysis of waste walnut shells for the removal of methyl orange. It showed that catalyst with the weight ratio of 0.2/1 (BC/Ti) exhibited the best decolorization efficiency of 92.45% and mineralization efficiency of 76.56 % after recycling 5 times. [PERSON] et al. [41] prepared TiO\({}_{2}\)-based wood and straw-derived biochar photocatalysts via an ultrasound-assisted route. The prepared composite pyrolyzed at the temperature of 700 \({}^{\circ}\)C had plate-like particles structure, which was beneficial for light use and electron-hole separation. Through Kubelka-Munk function, the band gap energy (E\({}_{g}\)) of TiO\({}_{2}\)-BC varied from 2.12 to 2.50 eV depending on different biomass feedstocks and temperatures. At the same time, the red-shift in absorption edge of BCPs was observed (Figure 7). They suggested the improved photoabsorption performance and the extended absorption edge was attributed to the introduction of biochar as well as the intimate interfacial contact caused by ultrasound procedure.
## 3 Results  
### Characterization of MuSC-EVs  
To determine the size and number of extracellular vesicles (EVs) released from MuSCs, media was collected from cultured MuSCs 24 h after plating cells (1.0-2.0 \(\times\) 10\({}^{4}\) cells/cm\({}^{2}\)). EVs were isolated from the media and characterized via nanoparticle tracking analysis (NTA). Over this 24 h period each MuSC released \(2.35\times 10^{5}\pm 3.10\times 10^{4}\) EVs, calculated using data derived from NTA and seeding density of the MuSCs. Since numerous EV subpopulations exist, we examined the size distribution of the MuSC-EVs. Our data show MuSC-EVs have a mean size of \(125.7\pm 1.7\) nm and modal size of \(99.30\pm 2.6\) nm, indicating MuSCs primarily release EVs in the size range of small and medium-sized EVs (Figure 1A,B). Further, to visualize the morphology of the EVs, TEM was performed on a subset of MuSC-EVs (Figure 1C,D).
# Electronically Controllable Phase Shifter with Progressive Impedance Transformation at K Band
## 2 Materials and Methods: Phase Shifter Working Principle and Design
### Reflective LC Circuit with Progressive Impedance Transformation  
The key element of the phase shifter that provides its reconfigurability is a reflective circuit. It is composed of a LC circuit with a short-circuit to the ground plane at the end. The capacitive effect is provided by a varactor while the inductive effect is provided by narrowing a section in the microstrip line (high impedance section). The varactor used for this design is the MA46585 varactor, with a capacity range of 0.13 pF to 2.2 pF. The connection to the ground plane is made with a metallic via. Due to the low resistive value of the varactors (\(\sim\)9 \(\Omega\)), it is necessary to add a progressive impedance transformation to the reflective circuit, in order to improve the performance of the reflective element. For the 9-to-50 \(\Omega\) transformation an impedance conversion based on two cascaded quarter-wavelength transformers is designed. The sequential transformation of the impedance in two steps impedance (i.e., transformation of 50 \(\Omega\) to 25 \(\Omega\) and 25 \(\Omega\) to 9 \(\Omega\)) requires higher microstrip widths. These widths are impracticable in a size-reduced device. The alternative is a non-sequential transformation in two steps: to avoid low line impedances, a new relation between impedances can be set up, according to Equation (1). This principle of impedance transformation is illustrated in Figure 3, where \(\text{Z}_{\text{x}}\) are the characteristic impedances of the microstrip sections.  
\[\text{Z}_{\text{in}}=\frac{\text{Z}_{0}{}^{2}}{\text{Z}_{\text{x}}}=\frac{ \text{Z}_{0}{}^{2}}{\text{Z}_{1}{}^{2}/\text{Z}_{0}}=\frac{\text{Z}_{0}{}^{3} }{\text{Z}_{1}{}^{2}} \tag{1}\]  
The non-sequential transformation requires a quarter-wavelength section of impedance \(\text{Z}_{1}\) of 118 \(\Omega\), which means a thinner microstrip section. Therefore, two cascaded quarter-wave impedance transformers with characteristic impedance of 118 \(\Omega\) and 50 \(\Omega\), respectively, are used to transform the circuit impedance from 50 \(\Omega\) to 9 \(\Omega\) along a width bandwidth. Figure 4 provides the circuit model of the non sequential transformer, and its design outcomes.  
Figure 3: Non-sequential cascaded \(\lambda\)/4 impedance transformers according to Equation (1).  
Figure 2: Three decibel hybrid coupler: (**a**) microstrip design; (**b**) design performance (\(\text{S}_{\text{XX}}\)\(\mid\) values).  
The total phase variation is of around 290\({}^{\circ}\) for the complete capacity range [0.13-2.2 pF], but with a non-linear behavior. However, this non-linearity is compensated by the DC-voltage/capacity relation. Compared to the case without progressive impedance transformation, the latter provides a phase shift of around 220\({}^{\circ}\). This means an improvement of at least 70\({}^{\circ}\) due to the progressive impedance transformation, for the same capacity range.
# Topology Comparison Study of Five-Phase Wound-Field Doubly Salient Fault Tolerant Generators
## 3 Comparative Study and Simulation of Five-Phase WFDSG  
In order to present the optimized core parameters of the five-phase WFDSG, three parts are described in this section. Section 3.1. shows the slot fill factor, split ratio, and magnetic density characteristic constraints. Section 3.2. is the comparative study of two WFDSGs with 30/24-pole and 20/16-pole. And the rotor pole is optimized considering the fringing effect with equivalent width analysis and FEA method in Section 3.3.
## 2 The ARDU Denoising Model
### Output Layer of the ARDU Model  
The output layer of the ARDU model consists of convolution with a \(1\times 1\) convolution kernel and the softmax function. The output layer uses convolution to adjust the number of signal feature maps input by the middle layer and outputs the time-frequency mask \(\widehat{M}(t,f)\) of the denoised signal with the softmax function.  
As a supervised learning algorithm, the ARDU model is inspired by the mask [28; 29; 30] in the supervised speech denoising algorithm. In this paper, the ideal amplitude mask (IAM) is used as the training target of the network model to obtain the time-frequency relationship between the seismic signal with noise and the clean seismic signal. The predicted mask can be used to estimate the frequency band distribution of the noise and seismic signals more accurately, and it can effectively reduce the waveform distortion of seismic signals and significantly suppress noise. The mask used in this paper is shown in Formula (4), where the mask \(M(t,f)\) represents the proportion of the clean seismic signal in the seismic signal with noise, ranging from 0 to 1.  
\[M(t,f)=\frac{|X(t,f)|}{|X(t,f)|+|N(t,f)|} \tag{4}\]  
The time-frequency mask \(\widehat{M}(t,f)\) predicted by the model is calculated and passed through the noise-containing seismic signal \(Y(t,f)\) to obtain a clean seismic signal, as shown in Formula (5).  
\[\widehat{X}(t,f)=\widehat{M}(t,f)Y(t,f) \tag{5}\]  
Finally, the time-domain seismic signal \(\widehat{X}(t)\) is obtained with inverse short-time Fourier transform (STFT-1), as shown in Formula (6):  
\[\widehat{X}(t)=STFT^{-1}\Big{\{}\widehat{X}(t,f)\Big{\}} \tag{6}\]
# Enhancing Freeway Traffic Capacity: The Impact of Autonomous Vehicle Platooning Intensity
## 3 Numerical Analysis  
### Literature Review of Reaction Time Selection  
Research has emphasized the faster reaction time and shorter time headway of CAVs as a significant factor in increasing highway capacity. In accordance with [18], it is suggested that with the advancement of CAV technologies, it becomes feasible to achieve a time gap of 0.5 s between vehicles. This reduced time gap has the potential to significantly reduce network delays, as demonstrated in simulation studies. However, there are opposing opinions, with some studies suggesting that CAVs should maintain a greater distance to ensure safety [7]. The latest experiment conducted at the California PATH showed that vehicles in platoons can maintain a time gap as small as 0.6 s, compared to 1.5 s for conventional non-automated vehicles [17]. In contrast, the 0.6 s time gap is generally associated with aggressive driving behavior, as opposed to the average 1.5 s time gap observed among human drivers. This implies that human drivers tend to maintain a greater time gap between vehicles for safety purposes, whereas the smaller 0.6 s time gap is more commonly seen during aggressive driving maneuvers. Aside from value selection, headway distribution is also widely studied [20; 35; 37]. For example, Ref. [20] reviewed the typical headway distributions resulting from different car-following typeswithin heterogeneous traffic. CAVs can effectively increase traffic capacity due to their shorter time headway. In summary, the method and related reaction time parameters cited in the previous studies are summarized in Table 1. It also demonstrates the most common modes of utilizing simulation methodologies when investigating mixed traffic scenarios.
# An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking
## 4 Detailed Description of the Proposed Algorithm
### Task Allocation  
To allocate tasks to sensors reasonably, we propose, with the use of target priority information, a Q-learning-based multi-target-tracking task allocation mechanism. In this subsection, we first map the sensor network to the Q-learning model. Then, we present the self-driven process of the mechanism and detail the setting of the reward function, which can evaluate the performance of the strategy at each step in Q-learning. Finally, we improve the exploration-exploitation policy according to the principle of balancing task allocation.
# Operation Margin of the ITER Central Solenoid During the Plasma Scenario
## 5 Result and Discussion
### Temperature Margin in the Conductor During the Plasma Scenario  
The temperature margin (\(T_{margin}\)) is computed as the difference between the temperature in the conductor (\(T(x,t)\)) and the current-sharing temperature (\(T_{cs}(x,t)\)) at the same location and time instant (Equation (6)). Consequently, it depends on the value of the current in the module and on the magnetic flux density distribution.  
\[T_{margin}(x,t)=T(x,t)-T_{cs}(x,t) \tag{6}\]  
The minimum of the temperature margin is observed at the end of the plasma pulse, i.e., \(t=1800\) s. In Figure 13a, the distribution of the temperature margin at the end of the pulse is shown. The inner turns, particularly turn #1, exhibit a lower margin. These turns are characterised by the highest magnetic flux density values, as shown in Figure 13b.  
Figure 12: Pressure profile in the spiral at the inlet, middle point and outlet of pancake #20, module CS3U.  
The minimum values of the temperature margin at the end of the pulse in each pancake of the module are shown in Figure 14. These values, slightly below 2.0 K, are located in the central pancakes of the modules.  
The minimum margins of 1.79 K for module CS1L and 1.82 K for module CS1U are found at \(x\) = 8.03 m, corresponding to 0.4 m from the outlet section of turn #1. The lowest value of 1.79 K fulfils the requirement of a temperature margin above 0.7 K.  
Figure 14: Minimum value of the temperature margin over the pancakes of the CS at the end of the pulse, \(t\) = 1800 s.  
Figure 13: (**a**) Temperature margin and (**b**) magnetic flux density in the CS at the end of the pulse, \(t\) = 1800 s.
# Photon-In/Photon-Out X-ray Free-Electron Laser Studies of Radiolysis
## 3 Discussion and Applications
Though the NaOH components will not be observed directly in this manner, the way in which they perturb the water network will be. Analogously, the oxidation state of the Na can be independently probed on the ultrafast timescale giving a direct view into non-local decay processes at the metal-ion site. The specific questions we seek to answer include the impact of incomplete solvent shells as the nearest neighbors for ICD and ETMD processes are not necessarily a water molecule, the impact of ion networks on these processes when comparing dilute and concentrated solutions of similar composition, and the overall timescale of the processes resulting from alteration of the solution structure. Interpretation of ultrafast X-ray spectroscopic measurements is invariably linked with theory, which, for understanding structural dynamics of core-excited matter, is an ongoing challenge as described in some recent reviews [73; 74]. There are already challenges at a very basic level, e.g., to predict the X-ray absorption spectrum of ground-state species with sub-eV accuracy [75] and to locate the positions of double-hole states [76]. Here, advances in equation-of-motion coupled-cluster (EOM-CC) approaches [77; 78] have allowed a systematic convergence after application of the core-valence separation scheme [79] within the EOM-CC framework [80]. This general approach has allowed EOM-CC-CVS to achieve improved accuracy for single photon transitions [40; 81] and expand to two-photon RIXS calculations [82]. Beyond high-level calculations of electronic structure, capturing core-excited state dynamics poses a further challenge that has been largely pursued with simpler electronic structure methods [72; 83; 84]. Describing non-local multiple-hole dynamics in complex systems is likely to remain a frontier and foster synergistic research between experiment and theory.
# Optimizing Agricultural Data Analysis Techniques through AI-Powered Decision-Making Processes
## 1 Introduction  
Smart farming is an emerging innovative technology to manage farm operations, mainly using the Internet of Things, cloud computing, and artificial intelligence to improve the quantity and quality of products by optimizing the use of resources and reducing environmental impact. Farming has always been a cornerstone of human societies, providing nutrition for a growing population and producing essential supplies like medicine, fiber, and fuel. Smart farming integrates digital technologies, IoT, cloud computing, robotics, sensors, GPS, and artificial intelligence into farm operations [1]. A smart farm's infrastructureincludes sensors for environmental data collection and surveillance cameras. These data are transmitted to cloud-based service platforms accessible to farmers through gateways. Like other agricultural technologies, smart farming aims to enhance food production through precise control and optimization [2]. Smart farming has expanded to encompass various domains over the past decade, including large-scale field crops, controlled-environment agriculture (greenhouses), and dairy and poultry farming. Compared to traditional farming methods, smart farming offers significant advantages due to its integration of advanced technologies, modern facilities, and data gathered throughout the farming process. Proper monitoring enables the enhancement of the quality of agricultural products. For instance, potential pest infestations or diseases can be detected early using collected data. Historical patterns can support farm workforce planning, resource optimization, and decrease excessive pesticide and fertilizer use [3].