esa-sceva/satcom-chunk-collection huggingface dataset:
### Related Works
#### 1.2.1 Eccentric Photorefraction
The best-performing model achieved an overall spherical refractive error prediction accuracy of 75%. Although the model's performance was not outstanding, their approach demonstrated good feasibility in estimating refractive errors using red-reflection images. The datasets used in these studies typically contained only 300 to 500 samples or involved repeated data collection from a small group of individuals. Consequently, the lack of a labeled real-human-eye refractive error dataset has become a major obstacle to the advancement of these methods. The distribution of refractive error images is uneven, with particularly scarce data for high-diopter hyperopia. Manual collection of eccentric photorefraction pupil images requires significant labor costs and raises personal biometric security concerns. Moreover, due to individual variations in pupil characteristics and unclear bright-region boundaries, even professional ophthalmologists find it complex and time-consuming to directly calculate refractive errors from eccentric photorefraction images. Additionally, no publicly available datasets of eccentric infrared pupil images exist for research. Therefore, we developed a method to simulate eccentric photorefraction pupil images for various refractive errors using model-eye images, as detailed in Section 2.3. Furthermore, due to the large intra-class variation and small inter-class differences in this type of data, the small bright crescent area in the images is difficult for deep neural networks to recognize. Theoretically, refractive error prediction relies on the ratio of global to local features, where even a few pixel changes can lead to significant prediction deviations. However, current studies have not specifically addressed distinguishing these fine-grained features. Therefore, research on AI algorithms for refractive error measurement remains insufficient. There is significant potential for exploring adaptive and accurate neural network architectures for refractive error measurement based on eccentric photorefraction images.
# Accumulative Roll Bonding--A Review
## 4 Properties
### Corrosion  
Materials with high microstructural defects, such as those UFG structures produced by ARB, usually have low corrosion resistance, however, it is still controversial to say that UFG materials have a poor corrosion resistance in comparison with their polycrystalline counterparts. The corrosion resistance of UFG materials depends on their alloy system and corrosive medium. [PERSON] et al. [129] investigated the corrosion behavior of UFG Al-Mn alloy produced by an ARB operation. The results showed that the size and the number of pitting corrosion sites observed on the material are less than those coarse grain (CG) counterparts. The corrosion potential of UFG alloy was more positive than that of CG samples and the current density was much lower by \(\sim\)33%. Immersed in 3.5% NaCl solution for 168 h, UFG Al alloy retained nearly unchanged tensile strength, while the degradation of total elongation was significant. Uniform pits formation was attributed to the energy balance between non-equilibrium grain boundaries and intragranular defects as well as reduction in particle size. The results showed that UFG Al-Mn alloy exhibit better resistance in than CG samples in 3.5% NaCl solution.  
[PERSON] et al. [130] studied the corrosion behavior of ARBed Al/SiC\({}_{\text{p}}\) nanocomposite and found that the corrosion resistance in both the surface and cross section of samples was enhanced for higher  
Figure 20: (**a**) Wear behavior of ARBed Al/SiC\({}_{\text{p}}\) nanocomposites processed by different rolling cycles, (**b**) wear behavior of monolithic material and seven-cycle ARBed Al/Al\({}_{2}\)O\({}_{3}\) composite versus sliding distance [127].  
number of ARB cycles. In addition, the corrosion resistance of surface samples was relatively better than those obtained from the cross samples for a specific ARB cycle. [PERSON] et al. (2019) evaluated the corrosion of ARBed Al/nano-SiO2p nanocomposite sheets where the corrosion resistance strongly weakened after the first cycle but gradually resumed when the number of ARB cycles increased. The grain boundaries were found not to be active sites rather than grains causing the formation of more uniform and denser passive oxide layers on the specimens. In other research conducted by [PERSON] et al. (2019), the corrosion behavior of finely dispersed and highly uniform ARBed Al/B4C-SiC hybrid composite in 3.5 wt.% NaCl solution was examined and it was shown that performing ARB leads to a lower corrosion current density of the fabricated hybrid composites as well as ARBed un-reinforced samples. Employing the ARB also led to a higher polarization resistance in all samples.
## 2 Materials and Methods
### Equipment  
The Raman spectra were obtained using a DXRTM Raman spectrometer equipped with a microscope from a Thermo Scientific (Waltham, MA, USA). In addition, a motorized microscope stage sample holder and an excitation laser source at 532 nm were used, while the detector consisted of a charge-coupled device. The Raman microscope was first automatically aligned and calibrated to ensure and maintain optimal results. Both neon and polystyrene standards were used in the calibration process with the tool provided with the system. The following conditions were used for SERS measurements: 10\(\times\) objective (0.4 NA), aperture was 25 \(\upmu\)m pinhole, the grating was 900 lines-mm\({}^{-1}\), and the spectral range was 3500-50 cm\({}^{-1}\). The optimal laser power was set to 5 mW, and 20 exposures of 30 s each were selected to acquire all spectra. The following options were applied: white light correction, fluorescence correction, cosmic ray rejection, and a smart background option. The camera temperature was \(-\)51 \({}^{\circ}\)C. Omnic v. 9.2 software from Thermo Fisher Scientific was used for data collection and analysis. Spectra were compared using the common scale mode.  
Scanning electron microscopy images of the silver substrate were obtained with a Carl Zeiss (Jena, Germany) MERLINTM field emission scanning electron microscope (FE-SEM) with an accelerating voltage of 20.0 kV.  
Absorbance of TBARS samples was measured using a Shimadzu UV1700 Pharmaspec spectrophotometer (Kyoto, Japan).
## 5 Discussion  
The experimental results demonstrate that our proposed SubChain Salp Swarm Algorithm (SSSA) has the advantages of minimizing truckload costs while retaining the same level of customer satisfaction. Although the ratio of both waiting time and delivery time satisfaction is slightly lower than those of the other selected optimization methods (about 5%), transportation costs are greatly reduced (over 2 k degrees lower).  
In contrast to traditional metaheuristic algorithms, our proposed method fully considers probable matching relationships between cargo and trucks, the sequences of receiving points, and the departure time of trucks. With the consideration of these basic conditions, our SSSA method is more robust when facing various unexpected conditions, and the matching strategy is more accurate and realistic. We have divided the operating costs into basic fees, fuel costs, handling costs, and overtime penalties, which constitute general costs in logistics applications.  
In developing our method, we designed the SubChain mechanism with a selection strategy and an updating strategy. The design of the selection strategy utilizes a stochastic element to select the appropriate candidate for local selection and uses an updating strategy to change the locations of the disconnected individuals. By employing these SubChain operations, the connection between local and global optima is strengthened, and the reliance on local optima is greatly reduced. In this case, our proposed method is more applicable to solving the LTL issue.  
Compared with other metaheuristic algorithms, our proposed method has advantages in dealing with timely issues to make better decisions regarding trucking loads, and utilizing our method in LTL problems can provide more accurate and robust suggestions for delivering cargo with limited trucks.  
While our method has demonstrated its effectiveness on the selected datasets, it needs to be validated in practical applications in real-world logistics scenarios, as real-life logistics problems typically include dynamic and unpredictable issues such as traffic conditions, shipment delays, and fluctuating market demands, which are not fully captured by static simulated environments. In future research, we will need to pay more attention to actual freight routines with more complex real-time data inputs and evaluate the method's performance in dynamic situations, which will also help improve the scalability and robustness of our algorithm.
# Worldwide Connectivity for the Internet of Things Through LoRaWAN
## 3 The Back-End of the LoRaWAN(tm) System  
The architecture shown in Figure 1 can scale without problems as the network size grows, including nationwide deployments. For example, a national utility provider may deploy GW over an entire country, connecting all of them to a unique NS that manages the entire LoRaWAN(tm) network. Of course, the actual implementation of the NS needs to take into account issues like, e.g., fault tolerance, scalability, capability of handling a huge number of messages, and these challenges are particularly important in a national network setup.  
However, as pointed out in the Introduction, a device may need to roam between different networks, and the architecture of Figure 1 cannot cope with this requirement. As a consequence, a complete architecture that is capable of supporting a global/worldwide roaming has been included in the latest specification of the LoRaWAN(tm) system. A graphical representation of such an architecture is provided in Figure 2.  
Figure 1: The architecture of a LoRaWAN(tm) network including the access part and back-end part. Notice that the “yellow” end device is “connected” to two distinct gateways (GW).  
Going deep into the details of the procedures involving the different entities in the architecture would be too heavy, and also quite boring. Thus, in the following of this paper we will list all the entities with a light description of each of them. In particular, in the remainder of this section we will go through the three kinds of NS. Moreover, let us remark that two further entities are introduced in Figure 2: the application server and the join server, whose functions will become clear in the description of how an end device joins a LoRaWAN\({}^{\text{TM}}\) network, which will be given in the next section.
# Gyrotrons as High-Frequency Drivers for Undulators and High-Gradient Accelerators
###### Abstract  
Gyrotrons are used as high-power sources of coherent radiation operating in pulsed and CW regimes in many scientific and technological fields. In this paper, we discuss two of their numerous applications. The first one is in gyrotron-powered electromagnetic wigglers and undulators. The second one is for driving high-gradient accelerating structures in compact particle accelerators. The comparison, between the requirements imposed by these two concepts on the radiation sources on one hand and the output parameters of the currently available high-performance gyrotrons on the other hand, show that they match each other to a high degree. We consider this as a manifestation of the feasibility and potential of these concepts. It is believed that after the first successful proof-of-principle experiments they will find more wide usage in the advanced FEL and particle accelerators.  
gyrotron; gyro-devices; gyro-klystron; microwave wigglers; microwave undulators; high-gradient accelerating structures +
Footnote †: journal: _sciences_  
present the potential of gyrotron-based electromagnetic wigglers/undulators and compact high-gradient particle accelerators. Both concepts have been proposed a long time ago but nowadays we are witnessing a renewed interest in their realization, which is facilitated by the progress in the development of high-performance advanced gyrotrons as well as by their increased availability in comparison to the situation several decades ago. In this review paper, we present both the pioneering works and the recent progress on these topics.  
The rest of the paper is organized as follows. In Section 2 we present the concept of microwave wigglers and undulators. The gyrotron-based wigglers and undulators are considered in Section 3. Then, in Section 4 compact high-gradient particle accelerators powered by gyrotrons are discussed. Finally, in Section 5 we present an outlook and conclusion.
# Underwater Sensor Network Redeployment Algorithm Based on Wolf Search
## 4 RAWS Algorithm
### RAWS Basic Principle  
The objective of this study is to deploy sensor nodes in a certain 3D underwater space. When the UWSN node was deployed randomly, the redeployment scheme was designed according to the WSA algorithm, as shown in Figure 2.  
Active coverage: When node \(x(j)\) detects the presence of coverage target \(x(z)\) in the sensing range, the node moves to the target with a certain step. In the absence of a common target, any two nodes will not move in the same direction.  
Passive coverage: It uses distributed coverage when it doesn't have a target in the sensing range. If node \(x(l)\) does not have a neighbor node with a hop, then node \(x(l)\) can move in any direction with a certain step. If node \(x(i)\) has a one hop neighbor node and the neighbor node \(x(j)\) has more targets of monitoring points than node \(x(i)\), then node \(x(i)\) moves along the direction of the best neighbor node \(x(j)\) in a certain step.  
Escape mechanism: If node \(x(k)\) is aware of obstacles of \(x(b)\), then it moves in a random direction with a large sensing radius of the step size to avoid obstacles. This mechanism can prevent the algorithm from falling into a local optimum of the considered objective function solution.
# Robust Analysis and Laser Stripe Center Extraction for Rail Images
## 3 Experiments
### Efficiency  
A short execution time is essential in industrial environments. In the experiments conducted in the present study, the program was run on an Intel(R) Core (TM) i3-4150U CPU at 3.5 GHz with 4 GB of RAM. The compared methods were coded in MATLAB(r)[26] r2014a compiled for 32 bits on Windows 7. The compared methods were run 20 times, and the average times of different center extraction methods were recorded. The UM method, the Steger method, and the proposed method took 1.09 s, 0.792 s, and 0.20 s to process the image in Figure 2 with a resolution of 330 \(\times\) 570 pixels, respectively. For the iterative endpoint method in UM, quadratic function was used for fitting, with a threshold of \(\epsilon=2\). The main reason why our method is faster than the UM method is that we make use of the shape characteristics of the rail stripe in the process of optimizing the centerline of the light stripe. The Steger method needs to convolve the whole image with Gaussian kernels five times, so it takes more time than our method. Furthermore, the proposed method  
Figure 10: (**a–c**) Experimental results of center extraction for different rail images.  
Figure 9: Comparison of experimental results achieved by (**a**) the proposed method, (**b**) the UM method, and (**c**) the Steger method.  
was implemented sequentially, and the run time would be significantly reduced if they are implemented in a parallel manner on multiple processors. Specifically, it was implemented by multi-thread flow acceleration and the serialized output constraints program in [17].
## 4 Experimental Validation
### Test Specimens  
As shown in Figure 7, three different sandwich beam specimens are prepared to measure the dynamic mechanical properties of one self-adhesive viscoelastic damping material (VDM). The asymmetry types include the asymmetry of base beams' thickness and the asymmetry of materials. The short asymmetrical specimen is 180-mm-long and consists of a 0.90-mm-thick steel base beam and a 1.02-mm-thick steel base beam; the long asymmetrical specimen is 240 mm long and contain a 0.97-mm-thick steel substrate beam and a 0.90-mm-thick aluminium substrate beam; the long symmetrical specimen is 240 mm long and is composed of two 0.97-mm-thick steel base beams. All the test specimens are 10 mm wide and has identical self-adhesive VDM core with the thickness of 0.5 mm. The densities of the steel, aluminium and VDM are measured as 7830 kg/m\({}^{3}\), 2710 kg/m\({}^{3}\), 1600 kg/m\({}^{3}\), respectively.  
To simulate the cantilever boundary condition, thick root sections of 25 mm length, 16 mm thickness are designed. Aluminium sheets with the same thickness of the sandwich cores are adhered to the root spacers between the adjacent root sections, aiming at giving firm and ideal cantilever support.  
Figure 6: Experimental setup.  
Figure 7: Sandwich beam specimens: (**a**) short asymmetrical specimen H\({}_{1}\)\(\
eq\) H\({}_{3}\), (**b**) long asymmetrical specimen H\({}_{1}\)\(\
eq\) H\({}_{3}\) and (**c**) long symmetrical specimen H\({}_{1}\)\(=\) H\({}_{3}\).
# Evaluation of Multi-Source High-Resolution Remote Sensing Image Fusion in Aquaculture Areas
## 2 Materials and Methods
### Raff Culture Zone Fusion Evaluation
#### 3.2.2 GF-2 Fusion Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**GF-1**} & \multirow{2}{*}{**Band**} & **Gray Mean** & **Standard** & **Information** & **Average** & **Correlation** & **Spectral** & **Bias Index** \\  & & **Value** & **Deviation** & **Entropy** & **Gradient** & **Coefficient** & **Distortion** & **Bias Index** \\ \hline \multirow{4}{*}{MS} & Band1 & 256.57 & 4.53 & 4.20 & 0.78 & 0 & 0 & 0 \\  & Band2 & 246.70 & 6.61 & 4.76 & 1.21 & 0 & 0 & 0 \\  & Band3 & 143.39 & 6.55 & 4.69 & 0.88 & 0 & 0 & 0 \\  & Band4 & 67.99 & 4.87 & 4.29 & 0.97 & 0 & 0 & 0 \\ \hline \multirow{4}{*}{GS} & Band1 & 195.62 & 4.82 & 4.27 & 1.43 & 0.17 & 50.95 & 0.21 \\  & Band2 & 195.48 & 6.08 & 4.61 & 1.49 & 0.28 & 51.22 & 0.21 \\  & Band3 & 102.91 & 5.06 & 4.35 & 1.13 & 0.29 & 40.47 & 0.28 \\  & Band4 & 26.52 & 4.53 & 4.16 & 1.26 & 0.12 & 41.47 & 0.61 \\ \hline \multirow{4}{*}{NNNDiffuse} & Band1 & **245.11** & **6.81** & **4.73** & **2.43** & 0.14 & 6.74 & 0.03 \\  & Band2 & **245.42** & **7.88** & **4.96** & **2.42** & 0.23 & 9.06 & 0.04 \\ \cline{1-1}  & Band3 & **141.54** & **5.56** & **4.49** & **1.43** & 0.28 & 7.63 & 0.05 \\ \cline{1-1}  & Band4 & **67.82** & **3.81** & **3.95** & **0.80** & 0.20 & 5.42 & 0.80 \\ \hline \multirow{4}{*}{PCA} & Band1 & 190.01 & 4.81 & 4.28 & 1.42 & 0.18 & 56.58 & 0.23 \\ \cline{1-1}  & Band2 & 189.88 & 6.02 & 4.59 & 1.46 & 0.28 & 56.82 & 0.23 \\ \cline{1-1}  & Band3 & 98.57 & 5.05 & 4.35 & 1.14 & 0.28 & 44.82 & 0.31 \\ \cline{1-1}  & Band4 & 22.51 & 4.62 & 4.20 & 1.24 & 0.12 & 45.48 & 0.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quantitative evaluation results for GF-1 data from a floating raft culture area.
###### Abstract  
The unmanned development of agricultural land and urban areas poses threats to water quality, which can lead to the death of the aquatic species in rivers. The present study developed a novel framework by combining a soil and water assessment tool (SWAT) and evolutionary algorithms to optimize the cultivation pattern at the catchment scale in the Tajan River basin, with the aim of mitigating the environmental impacts of surface runoff from farms. This river basin is located in northern Iran, where quick agricultural development is one of the environmental challenges. We utilized a SWAT to simulate the nitrate concentrations for different crops at the river basin scale by applying the Nash-Sutcliffe model efficiency coefficient (NSE) as a measurement index. Then, a novel model was developed to optimize the cultivation pattern by applying different metaheuristic algorithms. Fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) was applied as a decision-making system to select the best optimization algorithm. The results demonstrated that using a SWAT in the optimization model structure is a robust method for the design of an optimal cultivation pattern. The Nash-Sutcliffe model efficiency coefficient (NSE) was 0.74, demonstrating the robust predictive skills of the water quality model. The decision-making system indicated that particle swarm optimization and shuffled complex evolution were the best evolutionary algorithms to optimize the cultivation pattern using the proposed method. The proposed method opens a new window regarding the optimization of cultivation patterns in agriculture and provides an environmental-based optimization to design cultivation patterns on the catchment scale.  
optimization; cultivation pattern; nitrate; SWAT; metaheuristic optimization; TOPSIS +
Footnote †: journal: Applied  
[PERSON], [PERSON] Mitigating Nitrate Concentration Using an Optimal Cropping Pattern Developed by Linking a Soil and Water Assessment Tool with Evolutionary Optimization.
## 3 Results
### Initial Measurements
#### 3.1.3 Transducers  
The selection of transducer size is crucial in eddy current (EC) testing. Figure 19 and Tables 3 and 4 illustrate a comparison of four different transducers.  
Across the entire spectrum of tested concrete cover thicknesses (_h_), larger transducers demonstrate greater usability, as evidenced by a higher correlation coefficient. Furthermore,  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{_h_**= 10 mm**} & \multicolumn{3}{c}{_h_**= 15 mm**} & \multicolumn{3}{c}{_h_**= 20 mm**} & \multicolumn{3}{c}{_h_**= 25 mm**} \\ \cline{2-13}  & \(\overline{\pi}\) & \(\sigma\) & \(R\) & \(\overline{\pi}\) & \(\sigma\) & \(R\) & \(\overline{\pi}\) & \(\sigma\) & \(R\) & \(\overline{\pi}\) & \(\sigma\) & \(R\) \\ \hline
**P5** & 8.0 & 0.32 & 0.6 & 9.4 & 0.25 & 0.6 & 11.2 & 0.31 & 0.6 & 13.0 & **0.29** & 0.6 & 15.0 & 0 & 0 \\ \hline
**P13** & 11.0 & 0.32 & 0.6 & 12.5 & **0** & 0 & 14.0 & 0.32 & 0.6 & 16.2 & 0.92 & 1.8 & 16.5 & 0.85 & 2.1 \\ \hline
**P20** & 11.7 & **0.18** & 0.6 & 13.4 & **0** & 0 & 14.6 & **0** & 0 & 16.2 & 0.32 & 0.6 & 17.6 & 0.18 & 0.6 \\ \hline
**P25** & 13.4 & 0.33 & 0.6 & 15.3 & 0.33 & 0.6 & 16.7 & 0.42 & 1.2 & 18.1 & 0.42 & 1.2 & 19.1 & 0.23 & 0.6 \\ \hline  & \multicolumn{3}{c}{_h_**= 35 mm**} & \multicolumn{3}{c}{_h_**= 40 mm**} & \multicolumn{3}{c}{_h_**= 45 mm**} & \multicolumn{3}{c}{_h_**= 50 mm**} & \multicolumn{3}{c}{_h_**= 55 mm**} \\ \cline{2-13}  & \(\overline{\pi}\) & \(\sigma\) & \(R\) & \(\overline{\pi}\) & \(\sigma\) & \(R\) & \(\overline{\pi}\) & \(\sigma\) & \(R\) & \(\overline{\pi}\) & \(\sigma\) & \(R\) & \(\overline{\pi}\) & \(\sigma\) & \(R\) \\ \hline
**P5** & 16.3 & 0.55 & 1.2 & 18.4 & 0.92 & 3.1 & 20.4 &the transducer's quality (especially the coil's winding) significantly impacts non-destructive testing (NDT) results. It was noted that despite being larger, the T25 transducer exhibited a lower correlation coefficient than the T20. This fact is also confirmed by Table 4, which provides statistics, including the mean, standard deviation, and range for different values of \(h\) and various transducers.  
The statistics presented in Table 4 confirm that smaller transducers generally gave better results with thin concrete cover; larger transducers tend to perform better at higher \(h\) and across a broader range. However, Table 4 also highlights the impact of the transducer's quality on results. Despite its relatively large size, the T20 performed exceptionally well across nearly all concrete cover thicknesses.