esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing the Use of RTKLIB for Smartphone-Based GNSS Measurements
## 3 Results
Figure 2: Illustration of the different scenarios: Highway (**left**), Street (**middle**), and Urban Canyon (**right**). Each scenario is representative of various GNSS environments: open-sky, mixed, and multipath. In order to see how the RTKLIB solution compares to the other participants, the test set results were uploaded to the official Kaggle website. Although the competition is over, the Kaggle site can still be used to assess the test data. For the test data, based on the private leaderboard score, Google's baseline solution scored 5.42 for Kaggle, which is in 692 th place out of 810 participants, while the proposed RTKLIB PPK solution scored 2.15 m, which resulted in 5 th place, as shown in Figure 4. The private leaderboard score is provided instead of the public leaderboard counterpart, because different subsets of data are used for each scoring scheme, and the private score is used to determine the winners of the contest. Figure 4: Screenshot of the official Kaggle private leaderboard results for the proposed RTKLIB PPK solution. Figure 3: CDF plots of the performance of baseline solutions, unmerged RTKLIB solutions, and final merged solutions for each scenario and the entire training dataset: (**1**) Highway, (**2**) Street, (**3**) Urban Canyon, (**4**) Full Data. The unmerged RTKLIB solution was better than the baseline positions in all scenarios, and the merge further improved the accuracy of the RTKLIB solution. The main differences between the baseline solutions that Google provided as an example and the proposed solutions are how the GNSS measurements are computed and how the computed position solutions are processed [10]. For the navigation engine, the Google baseline solution used all satellite constellations, including GPS, GLONASS, BeiDou, Galileo, and QZSS, while the RTKLIB used GPS, GLONASS, and Galileo. For the raw measurements, the baseline solution used only pseudorange, but the RTKLIB also used carrier phase measurements for greater measurement precision. For the measurement processing, the baseline solution used the Klobuchar ionosphere model, EGNOS troposphere model, and ephemeris satellite clock model for error mitigation in single point positioning (SPP), while RTKLIB used double-differencing to remove atmospheric, clock, and orbital errors in its PPK solution. Moreover, while the baseline solution used weighted least squares for data processing, a Kalman filter was used for the RTKLIB in order to alleviate the impacts of measurement noise on the position solution. For the position solution processing, instead of providing the position solution from each device directly, the solutions were merged between devices based on the expected accuracies of the navigation solutions. This is done to mitigate the sections of data where carrier phase measurements from the phones are unreliable or unavailable. This process is difficult for the baseline solution, as no metrics are provided for the expected accuracy of the computed GNSS positions, but in the case of RTKLIB, estimates for the accuracy are provided by the navigation engine.
## 4 Conclusions  
This paper has primarily reviewed the research status of metal-film reflection gratings and multilayer-dielectric-film transmission and the reflection gratings that have been designed and developed. Diffraction grating structural design parameters, fabrication technologies, diffraction efficiency, and anti-laser-damage thresholds have been introduced and analyzed. The analysis shows that metal-film gratings are not able to achieve high diffraction efficiency and anti-laser-damage threshold values due to the inherent absorption characteristics of materials. However, continuous improvements in the theoretical basis and fabrication technologies of diffraction gratings have led to certain breakthroughs. The diffraction efficiencies and anti-laser-damage thresholds of multilayer-dielectric-film gratings have steadily improved, resulting in practical applications. The preparation of diffraction gratings with high damage resistance by optimizing the design of the film structure of the multilayer dielectric film, the groove structure of the diffraction grating, and the near-field distribution of the electromagnetic field is the main focus of future research.  
Writing--original draft preparation, Y.W.; visualization and supervision, X.F., L.Q., Y.N., Y.C. and L.W. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Ministry of Science and Technology Key R&D Program (2020 YFB2205902); the National Natural Science Foundation of China (NSFC) (62090051, 62090052, 62090054, 11874353, 61935009, 61934003, 61904179, 62004194); the Science and Technology Development Project of Jilin Province (20200401069 GX, 20200401062 GX, 20200501006 GX, 20200501007 GX, 20200501008 GX); the Key R&D Program of Changchun (21 ZGG13, 21 ZGN23); the Innovation and entrepreneurship Talent Project of Jilin Province (2021Y008); the Key R&D Program of Changchun (21 ZGG13, 21 ZGN23); the Special Scientific Research Project of Acade-mication Innovation Platform in Hainan Province (YSPTZX202034); and the \"Lingyan\" Research Program of Zhejiang Province (2022C01108).  
The authors declare no conflict of interest.
# The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm
## 5 Empirical Research and Result Analysis
### Comparison and Analysis of Long-Term Forecast Results  
In this paper, we analyze the long-term forecasting performance of the three models for 18 stock indices using the data of the last 250 trading days within the selected full sample period as the forecast set for the long-term forecasting performance empirical evidence and the other data within the sample period as the training set. Tables 11-13 show the SVR parameters obtained from the long-term bat algorithm search, the evaluation metrics of the long-term forecasting performance of the three models, and the comparison of the mean values of the evaluation metrics, respectively. From Table 11, the optimal penalty factors for the 18 stock indices obtained by the bat algorithm and the SVR model mixed with the free parameter search optimization range from 41.5925 to 98.7044. The absolute values of the insensitive loss factors range from 0.0941 to 1.4540. The optimal range of the bandwidth parameter of the Gaussian radial basis kernel function is between 0.7957 and 2.6268. Bringing the free parameters obtained from the long-term bat algorithm search in Table 11 into the SVR model, we can obtain, in Table 12, the long-term forecasting results for the 18 stock indices for 250 trading days and the long-term forecasting effects of the two control models. \begin{table}
\begin{tabular}{c c c c} \hline
**Index** & \(\mathbf{C}\) & \(\mathbf{\epsilon}\) & \(\mathbf{\sigma}\) \\ \hline SSE & 46.7841 & 1.8062 & 1.2239 \\ \hline SSE A shares & 55.3020 & 0.0941 & 1.4064 \\ \hline SSE B shares & 98.7044 & 0.9020 & 2.1627 \\ \hline SSE 380 & 97.0419 & 1.1014 & 2.6268 \\ \hline SSE 180 & 55.3028 & 0.7625 & 1.3201 \\ \hline SSE 50 & 41.5926 & 0.8020 & 0.9744 \\ \hline CSI 300 & 95.5773 & 1.4353 & 1.5305 \\ \hline CSI 1000 & 97.7517 & 0.2103 & 2.7517 \\ \hline CSI 100 & 57.9451 & 1.2245 & 0.7957 \\ \hline CSI 500 & 47.6028 & 1.4540 & 2.4417 \\ \hline CSI 800 & 95.0546 & 0.8172 & 2.0737 \\ \hline SZCI & 70.1796 & 1.3687 & 1.4546 \\ \hline SZSE B shares & 92.4955 & 0.6667 & 1.5814 \\ \hline SZSE & 72.8900 & \(-\)0.4267 & 1.0070 \\ \hline SZSE A shares & 94.2792 & 0.2899 & 0.8999 \\ \hline SME & 95.4082 & \(-\)0.6240 & 1.9613 \\ \hline SME 300 & 87.2609 & 0.8486 & 1.7831 \\ \hline SME CI & 72.4881 & 0.8511 & 1.5453 \\ \hline \end{tabular}
\end{table} 
Table 11: SVR parameters obtained by long-term bat algorithm optimization.
# Advancements and Prospects of Electronic Nose in Various Applications: A Comprehensive Review
## 2 E-Nose System
### The Electronic Nose's Brain: Algorithms and Visualization
#### 2.4.4 Support Vector Machines  
SVM is a powerful supervised machine learning algorithm utilized primarily for classification tasks, though it also extends to regression. The core principle of an SVM involves mapping labeled data points onto an N-dimensional space with the aim of maximizing the separation between distinct categories. When new data points are introduced, they are projected into this space and assigned to a category based on their position within that space [88].  
Operating in a binary fashion, SVM handles two classes at a time, seeking to identify a hyperplane that can optimally segregate the classes. If a straightforward hyperplane does not suffice, SVM identifies the hyperplane that maximizes the distance between data points from the two considered classes, utilizing support vectors. If a clear hyperplane is not possible even in the expanded feature space, SVM employs nonlinear transformations through kernels to elevate dimensionality of the data, facilitating effective classification without excessive data manipulation.  
In regression scenarios, SVM applies comparable principles to those used in classification, though with some nuances. The central objective remains minimizing errors by defining a hyperplane (a subspace of N \(-\) 1 dimensions) that maximizes the margin while accounting for potential error tolerance [89; 90; 91; 92]. By harnessing SVM's capabilities both as a classifier and a regressor (with independent regressors for each class), exceptional outcomes are attainable for classification and quantification tasks alike.
## 6 Limitations and Error Cases  
Despite demonstrating effectiveness and exceptional performance in underwater image enhancement tasks through experiments on multiple datasets, LPS-Net is still constrained compared to other tasks due to the challenges in collecting underwater datasets, resulting in insufficient dataset sizes and suboptimal model optimization. Specifically, LPS-Net may require further model design and optimization to overcome the overfitting issue caused by small datasets and improve its performance in handling complex underwater image details. For instance, the utilization of unsupervised training methods could eliminate the need for collecting a large number of expensive paired data. Additionally, although the proposed model is expected to exhibit efficiency and flexibility in underwater  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Setting** & **Model** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **MSE\(\downarrow\)** \\ \hline (a) & S1 & 23.624 & 0.910 & 0.0732 \\ (b) & AFF & 23.891 & 0.914 & 0.0721 \\ (c) & GFU & 23.905 & 0.915 & 0.0717 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ablation study on the Gated Fusion Unit. \(S_{1}\) means simply adding the results of the two branches. \(\uparrow\) represents that higher is better, and \(\downarrow\) represents that lower is better. Underlines indicate the best results.  
Figure 11: Metric visualization of the ablation study of the MLB during training.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Setting\_kernel\_size**} & \multicolumn{4}{c}{**Metrics**} & \multirow{2}{*}{**\# of Params**} & \multirow{2}{*}{**GFLOPs**} \\ \cline{2-2} \cline{6-9}  & **1** & & & & & **11** & **PSNR\(\uparrow\)** & **SSIM\(\uparrow\)** & **MSE\(\downarrow\)** \\ \hline (a) & ✓ & ✓ & & & & 23.535 & 0.909 & 0.0742 & 78.46 k & 92.91 G \\ (b) & ✓ & ✓ & & & 23.622 & 0.910 & 0.0736 & 78.53 k & 93.18 G \\ (c) & & ✓ & ✓ & & & 23.771 & 0.913 & 0.0725 & 79.18 k & 95.57 G \\ (d) & & & & ✓ & ✓ & 23.423 & 0.910 & 0.0744 & 81.34 k & 103.53 G \\ \hline Ours & & & ✓ & ✓ & & 23.905 & 0.915 & 0.0717 & 80.12 k & 99.02 G \\ \hline \hline \end{tabular}
\end{table}
Table 5: The ablation study of the MLB components. \(\uparrow\) represents that higher is better, and \(\downarrow\) represents that lower is better. Underlines indicate the best results.  
robotics, further experiments are needed to validate its performance and reliability in practical applications.  
Besides, in some uncommon cases, where the degraded images appear to be pink or orange, the DAEB, which was designed to process individual color channels, cannot effectively handle these particular colors, leading to unsatisfactory enhancement results. In the future, we will consider incorporating more color-processing branches to address the challenges posed by complex and diverse color environments.
## 4 Component-Based Mechanical Models for TSACWs
### Ultimate Moment Capacity  
The ultimate moment capacity \(M_{n}\) is one of the well-known fundamental parameters that affects the overall response of bolted beam-to-column connections. Several literature studies on TSACW specimens suggest that a rotation of 0.03 rad is sufficient to experience the full plastic moment capacity \(M_{p}\) of the connected beam [38]. The American Institute of Steel Construction [39], in this regard, recommends considering a relatively low rotation  
Figure 4: Proposed extension of component-based model for TSACW.  
during the analysis and design process for this connection typology. Accordingly, the amplitude of 0.03 rad is defined as the maximum rotation capacity of TSACWs.  
The component-based method can be extended to estimate the ultimate moment capacity \(M_{n}\) of TSACWs, as far as the contribution of influencing components is properly considered. These all affect the overall TSACW flexural resistance and are represented by: the column web panel in shear, the column web in compression, the column web in tension, the column flange in bending, the top cleat in bending, the web cleat in bending, the bolts in tension or in shear, the beam web in tension, and the beam flange and web in compression. Past research studies proposed different mechanical models to calculate \(M_{n}\)[30; 40; 41]. In most of those proposals, the failure of the top, seat, and web cleat's leg under maximum moment was only recognized, while other failure mechanisms (such as the failure of one or more bolts) were fully disregarded. In 2005, the EC3 standard [13] defined the required ultimate moment capacity \(M_{n}\) of TSACs and TSACWs as:  
\[M_{n}=F_{Rd}Z \tag{7}\]  
where \(Z\) is the lever arm and \(F_{Rd}\) is the design resistance of weak joint components. The second term can be governed by one of the following mechanisms: top cleat in bending (\(F_{tc,Rd}\)), bolts in tension, beam flange in tension and compression, and beam web in tension.  
For a TSAC with a single-web cleat, [PERSON] and [PERSON] [29], in 1990, elaborated a model proposal for the detection of collapse mechanisms. [PERSON] and [PERSON] [14] further extended the method from [29], and presented a formulation for TSACWs:  
\[M_{n}^{top-seat}=M_{os}+M_{p}+V_{pt}d_{2}+V_{pt}d_{4} \tag{8}\]  
In Equation (8), \(M_{os}\) is the plastic moment capacity of the seat angle; \(M_{p}\) is the plastic moment capacity of the top angle; \(V_{pt}\) is the ultimate shear force acting on the top angle; \(d_{2}\) is a parameter related to the depth of the beam and thickness of top and seat cleats; \(V_{pt}\) is a parameter that depends on the ultimate shear force at the upper and lower edges of the web cleat; and \(d_{4}\), finally, is the distance between plastic shear at the lower edge of the web cleat to the center of compression. These parameters are schematized in Figure 6.  
Figure 5: Geometrical parameters of the reference TSACW: (**a**) top angle, (**b**) seat angle, (**c**) web angle. Reproduced from [14] with permission from Elsevier, copyright agreement with license number 5011231057466, February 2021.
## 4 Results and Discussion
### Hydrodynamics
#### 4.3.2 Model Validation  
At low Reynolds numbers, specifically in the Darcy or creeping flow regimes, the pressure drop is offset solely by the shear stress exerted on both the cylindrical wall and the particle surfaces. Upon increasing the Reynolds number and entering the Forchheimer regime, flow inertia becomes a factor, resulting in a total pressure drop comprised of both viscous and inertial elements. It is worth noting that in some instances in the literature, the terminology used to refer to these two flow regimes as laminar, and turbulent flow may be misleading [22].  
To ensure accuracy and reliability in the modeling process, it is imperative to address the particle-particle and particle-pipe wall contact points within the packed bed structure prior to meshing the overall structure. An irrelevance test was also conducted for bed pressure drop under different grid sizes using the aforementioned treatment method (i.e., the shrinking ball model method shown in Figure 3) for the contact points. To validate the irregularity of the grid for the particle accumulation segment, we tested grid sizes of 1/20, 1/22, 1/24, and 1/26 \(d_{p}\). The results indicate that a grid size of 1/24 \(d_{p}\) yields a mesh-independent solution in which the pressure drop of the entire bed remains constant (Figure 6).  
Figure 6: Effect of grid size on particle bed pressure drop.
# Effect of Shot Peening on the Evolution of Scale on T91 Steel Exposed to Steam
## 3 Results  
### Effect of Shot Peening on Microstructure PRIOR to Steam Exposure  
The equiaxed grain and uniformly distributed M\({}_{23}\)C\({}_{6}\) precipitates were observed in the original T91 specimen, as shown in Figure 1. Shot peening causes plastic deformation near the surface, hence it changes the microstructure and surface properties. Microstructural investigation of untreated and SP-treated specimens was performed by SEM and EBSD. Figure 2 shows the effect of shot peening on the surface microstructure. The original equiaxed grains in the untreated specimens are -10 \(\upmu\)m, as shown Figure 2a. The presence of smaller grains near the shot peened surface and a gradual increase in the grain size towards the core of the materials validates the grain refinement effect of the shot peen  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline Shot Peening & \multicolumn{2}{c}{Ball Size} & Amplitude & Frequency & Duration \\ \cline{2-10} Treatment & 2.0 mm & 50 \(\upmu\)m & 20 kHz & 10 min \\ \hline \multicolumn{10}{c}{Test durations} \\ \cline{2-10} durations & 100 h & 200 h & 400 h & 600 h & 800 h & 1000 h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Shot peening and steam exposure conditions of T91 specimens.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline
**Elements** & **C** & **Si** & **Mn** & **P** & **S** & **Ni** & **Cr** & **Mo** & **N** & **Al** & **Nb** & **V** & **Fe** \\ \hline \({}^{*}\)T91 & 0.07–0.14 & 0.20–0.50 & 0.30–0.60 & \(\leq\)0.020 & \(\leq\)0.010 & \(\leq\)0.40 & 8.00–9.50 & 0.85–1.05 & 0.03–0.07 & \(\leq\)0.02 & 0.06–0.10 & 0.18–0.25 & Bal. \\ \({}^{*}\)OES & 0.09 & 0.33 & 0.37 & - & - & 0.14 & 8.66 & 0.89 & - & - & 0.07 & 0.22 & Bal. \\ measured & & & & & & & & & & & & & & \\ \hline \end{tabular} \({}^{*}\) T91 = ASTM-A213, \({}^{**}\)OES: optical emission spectrometry.  
\end{table}
Table 2: Nominal and measured chemical composition (wt. %) of the T91.  
ing, which is shown in Figure 2c, where the grain size near the shot peened surface is \(\sim\)3 \(\upmu\)m. The refinement of the grains is likely to improve the hardness according to the Hall-Petch relationship. To characterize the surface hardening effect of the shot peening, the Vickers hardness was measured. The Vickers hardness of the topmost surface was \(\sim\)250 HV, decreasing to the original value of \(\sim\)216 HV with increasing depth as shown in Figure 3. During plastic deformation in shot peening, precipitates might also be refined, resulting in an increase in number density as reported for M\({}_{23}\)C\({}_{6}\)[8].  
Figure 1: Microstructure of the T91 specimens: (**a**) SEM and (**b**) TEM with corresponding SADP of S1, and (**c**) EDS results of S1 before shot peening.  
Figure 3: Microhardness of T91 untreated (black squares) and shot peened (red triangles) specimens.  
Figure 2: Surface microstructure of the specimens before steam oxidation: (**a,b**) SEM and EBSD IPF with orientation map of the untreated specimen, (**c,d**) SEM and EBSD IPF with orientation map of the shot peened specimen.
# Self-Recognition Grasping Operation with a Vision-Based Redundant Manipulator System
## 2 Self-Recognized Grasping Operation
### Steps of Pose Transformation  
With instance segmentation and pose estimation, the position in pixel coordinates and the orientation angle are obtained to locate the target object, which can be expressed as \((u,v,q)\). For  
Figure 2: Framework of instance segmentation and pose estimation.  
manipulator control, this should be transformed by two steps. Firstly, the pose expressed in pixel coordinates is transferred into the camera coordinate, as shown in Equation (1):  
\[\left\{\begin{array}{l}z_{c}\cdot\left[\begin{array}{c}u\\ v\\ 1\end{array}\right]=\textbf{K}\left[\begin{array}{c}x_{c}\\ y_{c}\\ z_{c}\end{array}\right]\\ \textbf{R}_{c}=\textbf{R}(z,q)\cdot\textbf{R}(y,0)\cdot\textbf{R}(x,0)\end{array}\right. \tag{1}\]  
where \(\textbf{K}\) expresses the internal parameters matrix of camera, \(z_{c}\) represents the depth information of image, \(\textbf{R}(\cdot)\) represents rotation matrix operator, and \(\textbf{R}_{c}\) represents the attitude matrix.  
The second step is as follows, where the pose expressed in camera coordinates is transferred into a joint angle sequence of the manipulator, as shown in Equation (2):  
\[\left\{\begin{array}{l}\left[\begin{array}{cc}\textbf{R}_{c}&\textbf{p}_{ c}\\ \textbf{0}&1\end{array}\right]={}_{b}^{c}\textbf{T}\left[\begin{array}{cc} \textbf{R}_{b}&\textbf{p}_{b}\\ \textbf{0}&1\end{array}\right]\\ \textbf{\theta}=f_{inv}\left(\left[\begin{array}{cc}\textbf{R}_{b}&\textbf{ p}_{b}\\ \textbf{0}&1\end{array}\right]\right)\end{array}\right. \tag{2}\]  
where \({}_{b}^{c}\textbf{T}\) represents the transformation matrix between the camera coordinates and the base coordinates of the manipulator. The position of the target object is expressed as \(\left(u,v\right)^{\text{T}}\) in pixel coordinates, \(\textbf{p}_{c}=\left(x_{c},y_{c},z_{c}\right)^{\text{T}}\) in camera coordinates, and \(\textbf{p}_{b}=\left(x_{b},y_{b},z_{b}\right)^{\text{T}}\) in the base coordinates of the manipulator. \(\theta=\left\{\theta_{i}\right\}\), \(i=1,\ldots,7\) represents the joint angle sequence, and \(f_{inv}\) represents the inverse kinematics function of the manipulator.