esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Offshore Wind Resources, Based on Improved Particle Swarm Optimization
## 1 Introduction
[15] applied numerical simulations to estimate the wind resources near Jeju Island in China, but the estimated results were significantly affected by the choice of the data set. This is because the study only focused on determining the most appropriate wind field for hindcast simulations. [PERSON] et al. [16] put forward a comprehensive prediction method, based on the improved wavelet transform. [PERSON] et al. [17] proposed a hybrid model to estimate the wind speed at different heights, based on the measurements at lower heights using the particle swarm optimization (PSO) and long short-term memory (LSTM). [PERSON] et al. [18] analyzed the predictability of wind speed with heights and employed the recurrent neural network (RNN) model for predicting the wind speed 12 h ahead of time, using 48 previous hourly values. [PERSON] et al. [19] put forward a short-term, multi-dimensional prediction of the wind speed, based on LSTM. However, the accuracy of LSTM may be compromised with the inclusion of exogenous features in the training sets and the duration of the prediction ahead. Back propagation (BP) [20; 21; 22; 23] and improved particle swarm optimization (IPSO) [24; 25; 26; 27] have been widely used in the wind energy resource evaluation, but the BP neural network (NN) has the shortcomings of a local minimization and a slow convergence speed. Combining the BPNN model with the PSO algorithm can effectively avoid the local optimal problem [28; 29]. The adaptive PSO can combine the advantages of different training algorithms, to evaluate the wind resources, has stronger learning capabilities, global search capabilities, and can avoid the shortcomings of the BPNN from falling into the local optimum [30]. The wind resource assessment method, based on the hybrid NN not only comprehensively considers the geographical location information around the wind farm, but also improves the accuracy of wind resource assessment [31]. Through using the PSO algorithm to optimize the BPNN, this can accurately and effectively identify the type of wind turbine gearbox fault [32]. In summary, the combination of the PSO algorithm and the BPNN is a theoretically feasible modelling method. However, the hybrid model, based on the above two algorithms,is less used in the wind resource assessment. Therefore, an IPSO-BP hybrid intelligent algorithm prediction model is established, based on the IPSO and BPNN in this study. The number of nodes in the input and output layers of the BP module is determined by the wind measurement data and the prediction accuracy. In addition, the global optimal solution is obtained by the IPSO, so as to evaluate the wind resources in the Luchao Port area off the coast of China. Finally, a more practical wind speed variation law is obtained. This provides significant guidelines to help engineers make decisions in designing and manufacturing wind farms, from the assessment of offshore wind resources.
# A Review of Traffic Flow Prediction Methods in Intelligent Transportation System Construction
## 3 Traditional Machine Learning Models  
### Introduction to Machine Learning Methods  
In recent years, machine learning methods have enabled significant advances in various fields, such as industrial control, finance, and healthcare. This tool has also proven highly effective in the transportation domain, particularly in the development and application of intelligent transportation systems (ITSs). In the context of traffic flow prediction, machine learning methods, compared to traditional statistical techniques, are better able to adapt to the evolving nature of traffic networks. Through the use of historical data, pattern recognition, and the establishment of dynamic nonlinear models, machine learning methods play a vital role in enabling precise traffic flow predictions.
## 3 Proposed Protocol
### Data Forwarding Algorithm
#### Determining the Base Path
```
0: source, d: destination, \(\epsilon\): the stretch factor OutputP: base path candidate set \(P\); shortest_len\(\leftarrow\) Shortest_path (s, d) ; ifd_core == null!! d_hole!= nullthen \(\Gamma_{1}=\frac{(n-2)\pi}{2n}(1+\epsilon)\) ; else \(p_{\mathcal{C}(t)}\leftarrow\) the perimeter of d_core; \(e_{\mathcal{C}(t)}\leftarrow\) the length of the longest edge of d_core; \(d_{\mathcal{C}(t)}(s)\leftarrow\) the shortest distance from \(s\) to an edge of d_core; \(\Gamma_{1}=Max\left\{1,\frac{\sin\frac{(n-2)\pi}{2n}\left((1+\epsilon)d_{ \mathcal{C}(t)}(s)-\frac{P_{\mathcal{C}(t)}}{2}\right)}{d_{\mathcal{C}(t)}(s)+ \frac{C_{\mathcal{C}(t)}}{2}}\right\}\)  end for for\(p\in P\)do if\(|p|>\Gamma_{1}\times\) shortest_len \(P\).remove(\(p\)) ;  end for convex_hull \(\leftarrow\) the convex hull of all_core_polygon_list; \(V_{\epsilon}\leftarrow\) vertices of convex_hull; \(n\leftarrow\) the number of the vertices of \(V_{\epsilon}\) ; for\(p\in P\)do \(n_{1}\leftarrow\) the number of \(p\)'s vertices belonging to \(V_{\epsilon}\); \(p.priority\leftarrow\frac{n_{1}}{n}\)  end for returnP
```  
**Algorithm 1**Base path candidate determination algorithm.
## 3 Results
### Impact of Focal Position when Cutting the Samples in Air and While Covered with a Water Layer
The asymmetry when translating the focal position in both directions relative to the surface of the sample cannot be explained in the conventional case. It is a result of the non-linear propagation of the beam within the water layer and its transformation from a Gaussian distribution, as described previously. When the beam is focused precisely at the surface of the sample, the beam undergoes the transformation process from Gaussian to semi flat-top, however this happens at the cost of energy losses at the central part of the beam, as described previously. When modelling this scenario, it was noticed that in this case the beam exhibits the largest losses in the central part of the beam as compared with the other cases, and although this does produce ablation, a more efficient setting can be found. When the focal position is set above the water, the energy losses are not as great as compared with the previous case, however the beam still expands because of the initial curvature of the wavefront present as a result of focusing with the lens. Therefore, the beam arrives at the surface having a larger radius, and the ablation process is slower as compared with the third case of focusing due to the lower intensity. When the geometrical focal position is set below the surface of the sample, due to the larger radius of the beam travelling through the water layer, energy losses are not as great as compared with the first case, however the radius of the beam decreases because of the curved wavefront due to the lens focusing. In this case, the energy losses are lower as compared with the first case of focusing, whereas, the diameter of the beam at the surface is lower than the second case of focusing. In addition, the minimal radius is achieved at a certain depth of the sample. This balance between the differences of the radius and non-linear losses produces the flat plateau seen in Figure 8 with no clear maximum. It is worth mentioning that, even though the best results (in terms of micromachining throughput) are obtained when the focal position is set 0.75 mm below the surface of the sample, since the beam is slightly defocused at the surface a larger crater (\(\sim\)80 \(\upmu\)m), by approximately 30%, is produced as compared with a setting at position 0. The formation of the craters (as described in Figure 6) is not impacted by the defocusing and the same V-shape craters are created when focusing in air, whereas, the craters with a blunt apex are created when focusing in water.
# Measurement and Modeling of 3D Solar Irradiance for Vehicle-Integrated Photovoltaic
## 4 Discussion
### Partial Shading Issue  
Since calculated power and related loss are affected by probability variables, the calculated result is not a single number but shows a distribution. Figure 20 shows an example of the distribution of the normalized efficiency of several array designs of the car roof PV, affected by the mismatching loss result from partial shading. The ratio of the power output of the partially shaded module is less than the ratio of the area in the sun. The reduction ratio corresponds to the mismatching loss. The loss factor is a function of the number of strings, curvature of the car roof, and latitude.
## 3 Experiment Description
### Measurement Campaign  
We used GPS positioning to determine the field positions of working base points S1 and S2 in the two monitoring directions on the basis of field investigation, the intervisibility condition between radar and the monitored object, and the main monitoring building of the WGC within the main lobe of radar scanning. Working base points S1 and S2 were located in the north and west directions of the building, respectively, as shown in Figure 3.  
We installed the IBIS-S-1 system on working base point S1, and the distance between the radar installation location and the WGC was 246 m. S1 was located at the roof of a two-story building of a farmer, as shown in Figure 4. The IBIS-S-2 system was rigidly fixed on working base point S2, which was located in the Wuchang River Beach Park with a distance of 241 m from the monitored building, as shown in Figure 5.  
Figure 2: Schematic of ground-based real aperture radar (GB-RAR) working base points.  
Continuous dynamic monitoring of the WGC was conducted from 12:21 on 5 July 2017 to 12:14 on 7 July 2017, with a total duration of approximately 48 h. The times of starting and ending of the monitoring of the two IBIS-S systems are the same. Continuous and uninterrupted measurement was adopted for both IBIS-S systems. The two IBIS-S systems were configured to monitor the building up to a distance of 800 m with a sampling frequency of 20 Hz. The two systems used the same resolution (0.75 m) in range during the monitoring period. The configuration parameters of the two IBIS-S systems are the same, and the main configuration parameters of the measurement campaign are listed in Table 1. A weather station was fixed on the roof of the building (as shown in Figure 4b) to monitor the dynamic changes of meteorological parameters (such as temperature, atmospheric humidity, etc.) on the roof during the dynamic monitoring. The data collection time of the weather station ranged from 12:40 on 5 July 2017 to 09:40 on 7 July 2017, with a data sampling interval of 10 min.  
Figure 4: Location of the IBIS-S-1 system in the north–south direction. (**a**) View of the IBIS-S-1 interferometric radar on S1 (located in a farmhouse roof). (**b**) Weather station installed on the roof of the monitored building. (**c**) Ground view of the WGC from the location of the IBIS-S-1 interferometric radar.  
Figure 3: Distribution map of the two IBIS-S system installation locations (S1 and S2). The red triangle denotes the location of the IBIS-S system, and the monitored object (i.e., the WGC) is outlined by a red rectangle.
# A Design Method of Code Correlation Reference Waveform in GNSS Based on Least-Squares Fitting
## 1 Introduction
Interference is injected manually at the position of one second, and the tracking error is biased by 0.5 chips accordingly. Thus, the delay lock loop (DLL) of the BOC(1,1) signal would lock into the false steady states and cause biased tracking for the BOC(1,1) signal, but it returned to the correct positions for the BPSK(1) signal. This is due to the false lock point at the position of 0.5 chips of the W2 non-coherent discriminator for the BOC(1,1) signal in Figure 3.  
When comparing different multipath mitigation techniques, the common thread can be concluded that they are using locally-generated code replicas in the correlations to shape the code discriminator function (i.e., S-curve). In [12], the optimized S-curve for multipath mitigation is discussed. As a conclusion, the S-curve should return to the zero value at the left side of linear region to restrict the area of the multipath error envelope (MEE). Figure 1: Diagram of the BOC signal and the BPSK signal time series. In this sense, a least-squares approximation was performed by a linear combination of shifted code replicas to approach the ideal S-curve in [12]. Searching the suitable code correlation reference waveform was also performed, like the CCRW technique. However, the standard method of least-squares approximation used in [12] is not capable of handling the ill-conditioned problem brought by the band-limited effect on the coefficient matrix, which is composed of cross-correlation results. The ill-conditioned coefficient matrix could lead to an extremely large condition number in the least-squares approximation, which makes the solution computed with machine precision unreliable. Moreover, the length of the code tracking reference function is not restricted in the simulations, and only the simulations of the BPSK and BOC(n,n) signals coherent discriminator with infinite bandwidth are performed. The work in [13; 14] provides an unambiguous non-coherent discriminator for BOC signals using the S-curve shaping method with local linearly-combined codes, based on the least-squares solutions. However, the method has poor tracking precision and requires mass data storage digits. Figure 3: S-curves of non-coherent W2 discriminators (having 0.25 chips of gate width). Figure 2: Simulation results of the false lock problem when the CCRW W2 discriminator is applied in a noisy environment (input \(C/N_{0}=35\) dBHz, injecting a short period of strong interference at a time interval of 1 s, having 0.25 chips of gate width). This paper proposes a least-squares procedure for the code tracking reference function of CCRW, based on the truncated singular value decomposition (SVD) method. By introducing a measurement uncertainty in the low-rank approximation of the coefficient matrix, the condition number could be restrained to less than a preset value. The optimization results of CCRWs are presented and discussed over BPSK, BOC(1,1), BOC(2,1), BOC(6,1) and BOC(7,1) signals in narrow-band situations.
# A High Thermal Conductivity of MgO-H\({}_{2}\)O Nanofluid Prepared by Two-Step Technique
## 2 Experimental Details
### The Stability and Particle Size Analysis  
Zeta potential was employed as an index of the surface charge of the MgO nanoparticles in the base fluid. The surface charge was considered to be an indicator of the dispersion stability of the MgO nanosuspension. The suspension with high zeta potential was stabilized, while the sample with low value tended to accumulate and suffer from sedimentation, according to ASTM standard (D4187-82) of zeta potential ranges in Table 2[68,69]. MgO-DW nanofluid samples at different concentration ratios (0.05 vol.%, 0.1 vol.%, 0.15 vol.%, 0.2 vol.%, and 0.25 vol.%) were mixed with the KCl solution, then, mechanically stirred for 30 min with ultrasonic agitation for 90 min, and then tested by the device. The same procedure was followed in the case of 0.15 vol.% MgO-DW nanofluid samples at different ultrasonication times (45, 90, 135, and 180 min).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Volume Concentrations Ratio** & **Weights of MgO Nanopowder (g)** \\ \hline
0.05 vol.\% & 0.1433 \\
0.1 vol.\% & 0.2867 \\
0.15 vol.\% & 0.4326 \\
0.2 vol.\% & 0.5772 \\
0.25 vol.\% & 0.7178 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Weights of MgO nanoparticles corresponding to the volume concentrations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Zeta Potential Range (mV)** & **Stability Behavior** \\ \hline \(<\)\(\pm\)5 & Rapid coagulation or flocculation \\ \(\pm\)10 to \(\pm\)30 & Incipient instability \\ \(\pm\)30 to \(\pm\)40 & Moderate stability \\ \(\pm\)40 to \(\pm\)60 & Good stability \\ \(>\)\(\pm\)61 & Excellent stability \\ \hline \hline \end{tabular}
\end{table}
Table 2: The ASTM standard of zeta potential ranges.  
The measurements were carried out using a zeta potential analyzer (Zeta Plus (21521), power 100-240 V 50/60 Hz, USA). To ensure data reliability, each experiment was repeated three times before the mean results were estimated. After four days of preparation, the zeta potential measurements were obtained. A particle size analyzer (Zetasizer, Brookhaven Instruments, New York, NY, USA) can also detect particle size by using a dynamic light scattering (DLS) process. The container is illuminated by a laser, and the particles scatter the light which is measured using a detector. A fundamental property of Brownian motion is that small particles move much faster than large particles. There is an association between the size of a particle and its velocity due to Brownian motion. Based on this physical feature, a particle size analyzer measures the Brownian motion of the particles in a specimen and connects this to a size based on established theories [70]. Therefore, a particle size analyzer was used to determine the average particle size of the prepared MgO nanopowder at room temperature. The scattering angle and polydispersity index were 15\({}^{\circ}\) and 0.061, respectively.
## 3 Results and Discussion
### Perming efficiency
Considering the protein's secondary structure, better perming results were obtained for proteins with a high \(\alpha\)-helix content, such the keratin and the BSA proteins. Keratin presents 38-45% of the protein structure in its \(\alpha\)-helical conformation [15], while BSA presents a total content of 67% [37]. On the other hand, the proteins with the highest \(\delta\)-sheet contents (KP-UM, KP-Cryst and silk fibroin) presented lower perming capacities when compared with the chemical method. Despite the apparent contribution of the proteins' structure to their capacity to change the shape of hair, molecular dynamics simulations (MD) with pulling/stretching assays help to understand the mechanisms behind the effect of a protein structure on its ability to bind and to change the shape of hair. These MD studies will be subject of a future publication. An effect of protein size could also be hypothesized to influence the proteins' potential to change hair shape. The best results were obtained for proteins with an intermediate molecular weight between 44 and 67 kDa, particularly keratin and BSA (Table 1). Regarding silk fibroin, this protein has the highest molecular weight (416 kDa) and the lowest perming efficiency. Most likely, due to its size, SF is located mainly at the surface of the hair fiber. Although the KP-UM and KP-Cryst proteins, with molecular sizes around 24-26 kDa, might penetrate deeper into the fiber, their smaller size could diminish the number of possible interactions with the proteins from the hair, affecting their ability to style hair [38]. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Perming Efficiency Ratio (Sample/Chemical)**} \\ \hline
**Sample** & **pH 5** & **pH 7** & **pH 9** \\ \hline Keratin & 0.947 & 0.723 & 0.875 \\ KP-UM & 0.815 & 0.675 & 0.872 \\ Silk Fibroin & 0.736 & 0.698 & 0.698 \\ BSA & 0.982 & 0.500 & 0.882 \\ KP-Cryst & 0.711 & 0.723 & 0.750 \\ EtOH Formulation & 0.686 & 0.435 & 0.664 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Perming efficiency ratios of the hair treated with keratin, KP-UM, silk fibroin, BSA and KP-Cryst proteins prepared in phosphate buffer at different pH values. The ratios were calculated between the perming efficiency results of the hair treated with the protein formulations and the hair chemically treated. Generally, it seems that the capacity of each protein to shape hair is related to the proteins' amino acidic sequence, its conformation and size, the charge at the proteins' surface and their capacity to interact with the proteins from hair. The results obtained for the proteins' perming efficiencies are corroborated by the visual inspection presented in Figure 2. All of the protein's formulations improved the curliness of hair relatively to the respective ethanolic formulation. Moreover, some of the proteins presented perming results similar to that obtained for the chemical method.
# Video Interpolation-Based Multi-Source Data Fusion Method for Laser Processing Melt Pool
## 4 Results and Discussion
### Video Frame Interpolation Module Evaluation
#### 4.1.3 Analysis of Training Strategies Based on Pre-Training  
Table 3 compares the results of the MTVFI model with pre-training on visible-light data and the MTVFI model without pre-training. The pre-trained MTVFI model performs best in terms of both PSNR and SSIM metrics, indicating that the interpolated frames it generates have a high degree of credibility. Figure 8 displays the interpolated frames generated from the comparative experiment based on the pre-training strategy: (a) is the previous frame of the infrared source video; (b) is the next frame; (c) is the interpolated frame generated by the pre-trained video frame interpolation module; (d) is the interpolated frame generated by the module without pre-training. Due to the limited amount of infrared data, the module without pre-training struggles to effectively learn the non-linear motion characteristics of the melt pool, leading to interpolated frames that only capture linear motion, resulting in blurred edges and structures more similar to the next frame. In contrast, the interpolated frames generated by the pre-training strategy can accurately learn the non-linear motion characteristics of the melt pool, producing frames with a higher degree of credibility. In Figure 8c, it can be observed that the uneven temperature at the top of the melt pool leads to abnormal motion of the melt pool, causing an increase in the temperature gradient at the center of the front end of the melt pool, while the temperature gradients on both sides decrease. The pre-trained MTVFI model can fully learn these non-linear motion characteristics of the melt pool, thereby generating interpolated frames with a higher degree of credibility.  
Figure 8: Qualitative comparison of training strategies based on pre-training: (**a**) the previous frame of the infrared source video; (**b**) the next frame of the infrared source video; (**c**) the interpolated frame generated by the pre-trained MTVFI model; (**d**) the interpolated frame generated by the MTVFI model without pre-training.  
Figure 7: Qualitative comparison of the MTVFI model with two other advanced video frame interpolation methods: (**a**) the previous frame of the infrared source video; (**b**) the next frame of the infrared source video; (**c**) the ground truth of the intermediate frame; (**d**) the intermediate frame generated by the SuperSloMo model; (**e**) the intermediate frame generated by the IFRNet model; (**f**) the intermediate frame generated by the pre-trained MTVFI model.