esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Proposed Mechanism Underlying Hempseed Protein Extraction  
A pair of cysteine residues forming an SS bond can be crucial for native protein folding and stability. A previous investigation by [PERSON] et al. [73] comparing SS bond formation in vivo and in vitro suggested that the oxidative thiol-to-disulphide mechanism can be different in in vitro conditions and result in more rapid formation. To gain further insights into the extraction mechanism occurring during hempseed protein extraction, an intracellular antioxidant sodium L-ascorbate was used to determine whether a redox reaction also occurred. As evidenced in Figure S2, no significant difference was observed between the control and the antioxidant-involved extraction. The absence of a significant difference in the rate of SS formation between the presence and absence of an antioxidant in this system suggests that a redox mechanism may not be the primary contributor to the observed phenomenon. Therefore, we consider the underlying mechanism of the thiol/disulphide exchange process in this study to involve an SN2 transition state, as illustrated in Figure 9 [74]. The free sulfhydryl groups were first deprotonated by hydroxide to thiolate anions (S\({}^{-}\)). This highly reactive form attacks an existing disulphide bond in the system and forms the SN2 transition form (Cys-S-S-R). Subsequently, a new disulphide bond is formed by displacing the other sulphur atom in the transition form. The native state of disulphide in plant protein is displayed as intra-molecular [75], which indicates the transition form can only displace the last sulphur atom in intra-molecular disulphide at the initial reaction. However, the proportion of intermolecular SS bonds increases as the extraction proceeds, and S\({}^{-}\) might attack the newly formed inter-SS instead of the intra-SS via disulphide shuffling procedure [76]. The protein aggregation caused by inter-SS formation via the disulphide shuffling process would cease upon introducing hydrogen ion (H\({}^{+}\)) into the system, as evidenced in Figure S1.  
Figure 9: Schematic representation of the proposed mechanism of protein alterations occurring during alkaline extraction of hempseed proteins.
## 4 Results
### LoS/NLoS Conditions  
The standard deviation is larger in NLoS conditions, suggesting greater variability in measurement distances. Under LoS conditions, the average measurement distances are closer to the reference values of 1 m and 2 m; the min and max values are also closer to the values expected in relation to NLoS (Figure 8).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Distance** & **Distance** & **Distance** & **Distance** & **Distance** \\  & **0.5 [m]** & **1 [m]** & **1.5 [m]** & **2 [m]** & **2.5 [m]** \\ \hline count & 937 & 602 & 1024 & 1039 & 993 \\ mean & 0.58 & 1.10 & 1.42 & 1.88 & 2.39 \\ std & 0.01 & 0.01 & 0.02 & 0.01 & 0.02 \\ min & 0.55 & 1.05 & 0.99 & 1.84 & 2.32 \\ max & 0.62 & 1.13 & 1.46 & 1.92 & 2.44 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distance measurements—multi-axis test position for 2 m.  
Figure 8: Distance distribution histogram for LoS and NLoS measurements.  
The spread of values is smaller in NLoS conditions, but the distribution of the histogram on the axis deviates from the actual distances. Measurement accuracy below 10 cm was achieved only for 1 m under LoS conditions. Moreover, the number of measurements received in the same time interval (300 s) is almost three times higher (887 for 1 m and 863 for 2 m for NLoS conditions) compared to the LoS conditions.
## 4 Methodology
### Complexity Analysis
#### 4.3.3 Total Complexity Analysis  
If the original graph is divided into \(M\) partitions, then for each partition, the complexity of simulated annealing is \(O\left(\mathcal{K}_{multi-SA}\left(\frac{N}{M}\right)^{3}\right)\). Without considering the optimization of parallel operations, the total time complexity is \(O\left(\mathcal{K}_{multi-SA}\frac{N^{3}}{M^{2}}\right)\). To sum up, without omission, the time complexity of GPSA is  
\[O\left(\mathcal{K}_{SA}\mathcal{N}^{3}+\mathcal{K}_{multi-SA}\frac{N^{3}}{M^{2 }}+\mathcal{K}_{GP}\mathcal{N}^{2}+\mathcal{N}^{4}\right) \tag{16}\]  
The total number of iterations \(\mathcal{K}_{SA}\) for the main simulated annealing algorithm is greater than the number of iterations \(\mathcal{K}_{multi-SA}\) for partial simulated annealing. The \(\mathcal{K}_{SA}\mathcal{N}^{3}\) term is also much larger than the \(\mathcal{K}_{GP}\mathcal{N}^{2}+\mathcal{N}^{4}\) term. Overall, GPSA, like regular simulated annealing, still has the complexity bottleneck of \(O\left(\mathcal{N}^{3}\right)\). In actual simulations, it also meets theoretical expectations, as shown in the next section.
## 3 Nonlinear Polarization Rotation Principle  
The structure of nonlinear polarization rotation principle (NPR) mode clamping is shown in Figure 6, including two polarization controllers (PC) and a polarization-dependent isolator (PDI). PC1 is equivalent to a 1/2 wave plate, PC2 is equivalent to a 1/4 wave plate, and the PDI has three functions, namely as an isolator, polarizer, and polarization detector. Its working principle is as follows: light after PDI is converted into linear polarized light and after PC2, into elliptically polarized light. Elliptical polarized light can be regarded as the superposition of two orthogonal linear polarized lights; the light intensity of these two decomposed lights is different. Due to the influence of nonlinear effects, different intensities of light produce different nonlinear phase shifts in the transmission of optical fibers. When the light pulse returns to PC1, the polarization direction of the optical pulse is changed by adjusting PC1, so that the part with higher light intensity near the center of the light pulse passes through with low loss, and the part with weak light intensity near the edge of the light pulse is filtered; thus, the light pulse is continuously narrowed in multiple cycles. Finally, a stable mode-locked pulse laser output is obtained.
# Next-Generation Dual Transceiver FSO Communication System for High-Speed Trains in Neon Smart City
## 3 System Description  
The FSO communication system comprises three key stages: an optical signal transmitter, a free-space transmission channel, and a receiver. To facilitate full-duplex communication, two transceivers are equipped on both the BS and the train. A visual representation of the line-of-sight considerations for a dual transceiver optical wireless G2T communication link operating on a single straight-line track is shown in Figure 1. On top of the train, there are two strategically placed optical transceivers, one at the front and one at the back. These transceivers are separated by a distance \(\mathrm{L_{T}}\), which represents the length of the train. Similarly, two transceivers are installed at each BS to ensure coverage in both right and left directions. Between the two coverage areas of the BS, there is a coverage gap area \(\mathrm{L_{2}}\). In order to maintain an unobstructed LoS connection between the BS and the train, the BS transceivers are tilted with angle \(\gamma\), which represents the tilt angle between the optical beam axis and the parallel horizontal track axis [24].  
The BSs are placed at distance \(\mathrm{L_{v}}\) away from the track. The BSs are linked to the fiber cable of the network backbone, enabling smooth distribution of data to the Centralized Traffic Controller (CTC). The CTC plays a crucial role in managing the system to enhance network performance and ensure efficient operations [31]. The BSs operate in an on-off mode, activating only when the train is within their transmission range in order to conserve energy. The laser beams emitted with divergence angle \(\mathrm{\theta_{T}}\) from the BSs are precisely focused on the roof of the train, propagating over a transmission distance \(\mathrm{z}\) defined as [19]:  
\[\mathrm{z}=\mathrm{L_{c}}\mathrm{cos}(\gamma)+\sqrt{\mathrm{L_{v}^{2}+L_{H}^{2 }}}\mathrm{cos}(\mathrm{\theta_{T}}/2) \tag{1}\]  
where \(\mathrm{L_{c}}\) represents the track's effective coverage distance for single transceiver and \(\mathrm{L_{H}}\) represents the horizontal distance between the BS and the minimum coverage point of the laser source.
# Location Detection and Numerical Simulation of Guided Wave Defects in Steel Pipes
## 4 Pipeline Crack Location Algorithm and Quantitative Analysis
### Quantitative Analysis of Welds  
#### 4.2.1 Circumferential Defects  
By analyzing the reflection coefficient of UGWs in Section 2.2, the size of defects can be estimated. The radial depth and axial width of the pipeline defect are set to 1 mm and 5 mm, respectively. The circumferential length is changed; 30\({}^{\circ}\) is taken as the time step; the defects at 30\({}^{\circ}\), 60\({}^{\circ}\), 90\({}^{\circ}\), 120\({}^{\circ}\), 150\({}^{\circ}\), 180\({}^{\circ}\), 210\({}^{\circ}\), 240\({}^{\circ}\), 270\({}^{\circ}\), and 300\({}^{\circ}\) are set; and a simulation is performed in Ansys. The maximum displacement of the initial excitation wave and defect echo is shown in Table 2. The reflection coefficient of defects with different circumferential lengths is calculated, the correlation between the reflection coefficient and the circumferential length of pipeline defects is observed, and defects with different circumferential lengths are quantitatively analyzed to obtain partial data on the pipeline reception signals with different circumferential lengths, as shown in Figure 12a-c.  
With the increase in the circumferential length of pipeline defects, the echo displacement of cracks also shows an increasing trend. According to the relationship between defects with different circumferential sizes and the reflection coefficient of pipeline defects, the relationship  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Circumferential** & **Maximum Displacement of** & **Maximum Echo** & **Reflectance** \\
**Length** & **Initial Wave (mm)** & **Displacement (mm)** & **Reflectance** \\ \hline
30\({}^{\circ}\) & 0.3998 & 0.0225 & 0.05629 \\
60\({}^{\circ}\) & 0.3999 & 0.0423 & 0.10578 \\
90\({}^{\circ}\) & 0.3989 & 0.0607 & 0.15214 \\
120\({}^{\circ}\) & 0.3999 & 0.0807 & 0.20179 \\
150\({}^{\circ}\) & 0.3988 & 0.1022 & 0.25631 \\
180\({}^{\circ}\) & 0.3989 & 0.1205 & 0.30211 \\
210\({}^{\circ}\) & 0.3995 & 0.1435 & 0.35931 \\
240\({}^{\circ}\) & 0.3999 & 0.1635 & 0.40881 \\
270\({}^{\circ}\) & 0.3979 & 0.1802 & 0.45278 \\
300\({}^{\circ}\) & 0.3975 & 0.1999 & 0.50301 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relationship table of reflection coefficient between defects with different circumferential dimensions and pipeline defects.  
Figure 12: Quantitative analysis of circumferential defects in pipes with different lengths: (**a**) 30\({}^{\circ}\); (**b**) 60\({}^{\circ}\); (**c**) 180\({}^{\circ}\); (**d**) reflection coefficient relation–circumferential dimension curve.  
curve between the circumferential defect size and the reflection coefficient of defects is drawn as shown in Figure 12d. When the circumferential length of the defects increases, the reflection coefficient increases linearly. The reflection coefficient of defects presents a linear change trend with the circumferential length of the pipeline circumferential weld defects.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 5 Analysis
### Efficiency Comparative  
The parameters of the mission in the study are organized in Table 1. There is a significant difference in the number of captured images between the grid-type and contour-type flight plans. The contour-type captures 2-5 times more images than the grid-type, resulting in increased computational resources cost and data acquisition time. Specifically, in Case Study 1, the improvement in shooting distance and image overlap rate is not  
Figure 12: Results of contour route planning overlap rate. (**a**) Grid-type flying mission, (**b**) contour-type flying mission.  
Figure 13: Elevation difference statistics. (**a**) Grid-type, (**b**) contour-type.  
evident. However, in Case Study 2 and the field test, the effectiveness of improving average shooting distance (14 m, 27 m) and image overlap (17%, 15%) becomes more apparent due to the presence of complex and steep terrain.  
It is noteworthy that the contour-type has a higher percentage of images with elevation differences within the threshold value within the same image compared to the grid-type. In both Case Study 1 and Case Study 2, there was a 44% and 40% increase, respectively, in the percentage of images with elevation differences less than 30 m within the same image. In the field test, 100% of the images in the contour-type were less than the threshold of 150 m of elevation difference in the same image, while only 65% of the images in the grid-type did. The contour-type increased the percentage of images by 35% over the grid-type. Although the contour-type has two times more images, the contour-type flight route planning helps to improve the elevation difference in the acquired images so that the resolution of the images can be maintained at the same quality and the accuracy of the results can be improved.
# An Improved Crack Identification Method for Asphalt Concrete Pavement
###### Abstract
proposed an unsupervised method for pavement crack detection. After collecting the pavement crack image, this method uses a K-means clustering algorithm and Otsu threshold method to segment and realize crack identification [7]. However, this method does not preprocess the pavement crack image, resulting in insufficient identification accuracy. [PERSON] et al. put forward attribute fusion technology based on principal component analysis in their crack identification method [8]. This method uses the principal component analysis method to analyze the principal components in a pavement crack image to achieve pavement crack identification. However, this method is affected by the selection of parameters of the principal component analysis algorithm, resulting in a poor application effect. [PERSON] et al. proposed a fractal feature identification method for multimode and multiscale images of fracture networks [9]. This method collects a group of multimode and multiscale images of fracture samples and analogues, and calculates the fractal dimension and length distribution of fracture networks for each image data set. The pavement crack identification results are obtained based on the scale, samples and order of magnitude. However, there is a deviation in the calculation of the fractal dimension of the crack grid by this method, which leads to its poor application effect. In many road management systems, road crack data sometimes comprise the sole criterion for determining road quality. For road management systems used by local governments and urban areas, it is difficult to measure road friction and roughness due to a lack of equipment, high cost or relatively low efficiency. This also leads to an urgent need for effective road crack identification and detection technology in the field of road management. Therefore, in response to the shortcomings of traditional methods, with the aim of effectively detecting longitudinal cracks, transverse cracks, oblique cracks and net-shaped cracks, this paper proposes the methods of image feature enhancement and crack identification of asphalt concrete pavement cracks, in order to improve the level of asphalt concrete pavement crack identification technology. The rest of this paper is arranged as follows. In Section 2, we give the specific methods of image feature enhancement and crack identification in this paper. Section 3 gives the experimental setup and some experimental results to verify the proposed methods. Finally, Section 4 concludes this paper.
# Analysis of the Landfill Leachate Treatment System Using Arima Models: A Case Study in a Megacity
## 1 Introduction
However, ARIMA models were used in various studies associated with water quality. For example, [PERSON] et al. [26] used these models to study the temporal behaviour of water quality parameters (TSS, BOD\({}_{5}\), and COD) in a WTP. These researchers reported that the ARIMA models were useful for improving efficiency in the WTP relative to BOD\({}_{5}\) (R\({}^{2}\) = 0.990). [PERSON] [27] also used ARIMA models to simulate water quality parameters of a basin in Turkey. This research properly simulated water quality parameters for 108 months. The correlation coefficients obtained between observed and simulated data for temperature, boron, and dissolved oxygen were 0.909, 0.902, and 0.893, respectively. [PERSON] and [PERSON] [28] obtained satisfactory results when analysing parameters such as pH, BOD\({}_{5}\), and COD in surface water bodies with ARIMA models. These researchers demonstrated the effectiveness of ARIMA models in analysing the temporal behaviour of these parameters and forecasting. [PERSON] et al. [29] used these models to forecast conductivity, chlorine, and BOD\({}_{5}\) of the Ganges River in India. Lastly, [PERSON] et al. [30] studied the modelling of water quality time series in a river with nine parameters. The results of this study showed the good performance of the proposed ARIMA models for the water quality estimation. This research was developed in a landfill of a megacity in a developing country (Bogota, Colombia). The metropolitan population for 2020 was 10.7 million, and an average of 6400 tons/day were discharged into the study landfill. This facility was located inside the megacity (southern zone), and its tropical mountain climate (2600 m.a.s.l.) was characterized by significant hourly variations in temperature (up to 12 \({}^{\circ}\)C). The leachatetreated by the system under study was discharged into one of the three most important surface water sources of the megacity (Tunjuelo River). Worldwide, there is little research on the analysis of the functioning of LTSs in developing countries with this level of population and climate characteristics. This lack of knowledge, in part, motivated the development of this study. The objective of this paper is to show an analysis of the operation of a landfill LTS in a Latin American megacity (Bogota, Colombia) by means of ARIMA models. The time series analyses correspond to chemical water quality parameters, measured at the inlet and exit of LTS. This study will be relevant to deepening knowledge in relation to the following aspects: (i) the temporal behaviour of landfill leachate in a megacity of a developing country, (ii) the influence of the climate conditions of the study site on the LTS, and (iii) the use of ARIMA models to detect potential problems and opportunities for improvement during the operation of landfill LTSs.
## 3 Results
### Campi Flegrei  
Campi Flegrei is a broad area affected by volcanic activity in the southern coast of Italy, close the city of Naples. It is a potentially dangerous site because of the complexity of the caldera and the density of the population. Over the centuries, it was affected by the phenomenon known as bradyseism, i.e., successions of great uplift and subsidence phases. In 2011-2015, it was characterized by a significant uplift phase with registered vertical uplift values up to 20 cm [29]. In this study, we exploited the large SAR dataset acquired by the COSMO-SkyMed (CSK) constellation along the ascending and descending orbits in order to constrain the 2011-2015 Campi Flegrei uplift. The descending dataset consisted of 56 SAR single-look complex (SLC) images acquired between February 2011 and March 2015, whereas the ascending one was composed of 264 images spanning from January 2011 to  
Figure 4: Deformation trend and time series for the Fiumicino area. These outcomes were retrieved by a dataset of 38 C-band ENVISAT images.  
September 2015. The datasets were processed with the IPTA-MB approach. The topographic phase contribution was removed by the 90-m Digital Elevation Model provided by the SRTM. In total, 681 and 283 interferograms were retrieved for the ascending and the descending orbit, respectively. The results regarding the deformation rate and time series are shown in Figure 5. The ascending and descending data highlight a clear uplift signal surrounding the Solfatara area starting from 2011.  
As shown by the analysis of time series, the uplift accelerated in 2013; then, it slowed down in 2014 up to the beginning of 2015, when it seemed to be going toward a new and fast uplift phase (points P1 and P3). Concerning the negative signal on the ascending data corresponding to the area of Bagnoli-Fuorigrotta (point P2), it is consistent with a significant eastward displacement component going away from the satellite due to the volcano inflation.
# Remanufacturing Shoemaking Machine: Feasibility Study Using AHP and DEMATEL Approach
## 3 Methodology
### DEMATEL Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \(R\) & \(D\) & \(D+R\) & \(D-R\) \\ \hline
2.1 High corporate image & 1.795 & 0.699 & 2.494 & \(-\)1.097 \\
2.2 Good business model & 1.596 & 1.298 & 2.894 & \(-\)0.298 \\
2.3 Complete recycle system & 1.423 & 1.817 & 3.24 & 0.394 \\
2.4 Appropriate product design & 1.271 & 2.271 & 3.542 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The final output. Source: Researcher extract from DEMATEL software (made by Onlineoutput.com, Dubai, United Arab Eminrates). Figure 4: Cause–effect diagram. Source: Researcher extract from DEMATEL software (made by Onlineoutput.com, Dubai, United Arab Emirates). Step 6: Interpret the results  
According to the diagram and table above, each factor can be assessed based on the following aspects:  
* The horizontal vector \((D+R)\) reflects the overall influence each factor exerts within the system. In other words, \((D+R)\) indicates both factor i's impact on the whole system and other system factors' impact on the factor. In terms of degree of importance, \"appropriate product design\" is ranked in first place, followed by \"complete recycle system\", \"good business model\", and \"high corporate image\". * The vertical vector \((D-R)\) represents the degree of a factor's influence on the system. In general, the positive value of \((D-R)\) represents a causal variable, and the negative value of \(D-R\) represents an effect. In the study, \"complete recycle system\" and \"appropriate product design\" are causal variables, while \"high corporate image\" and \"good business model\" are regarded as effects. A high \(D+R\) value for \"appropriate product design\" in DEMATEL analysis indicates its overall influence within the system of shoe machinery remanufacturing. This value can be useful in decision-making related to remanufacturing in several ways:  
* Prioritizing Design for Remanufacturing (DfR): A high \(D+R\) suggests that designing machinery with remanufacturing in mind is a critical factor for success. This emphasizes the importance of investing in DfR principles, such as modularity, use of readily remanufactureable materials, and easy disassembly. By incorporating DfR principles early in the design phase, manufacturers can create machinery that is more cost-effective and environmentally friendly to remanufacture at its end of life. * Optimizing remanufacturing processes: Analyzing the \(D+R\) value alongside the \(D-R\) values of other factors can help identify bottlenecks or areas for improvement in the remanufacturing process itself. * Cost-Benefit Analysis: When making decisions about remanufacturing specific machinery models, the \(D+R\) value of \"appropriate product design\" can be factored into the cost-benefit analysis.