esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Calibration Method for the Angular Positioning Deviation of the Rotary Table
### Optimizing Coordinates of Laser Tracer Stations  
In order to improve the accuracy of the coordinates of laser tracer stations obtained by the self-calibration algorithm, the Singular Value Decomposition (SVD) transformation [16; 17] of the covariance matrix is used for plane fitting. The coordinates of \(m\) laser tracer stations obtained by self-calibration algorithm are fitted into a plane. In the fitting plane, the distances from the coordinates of \(m\) laser tracer stations to the fitting plane should have the minimum residuals. The optimized coordinates of laser tracer stations can be obtained by projecting \(m\) coordinates of the laser tracer onto the fitting plane.  
The principle of SVD of covariance matrix is provided as  
\[A=U\Sigma V^{\mathrm{T}} \tag{18}\]  
where \(U\) is the left singular orthogonal vector matrix; \(\Sigma\) is the diagonal singular value matrix; \(V\) is the right singular orthogonal vector matrix.  
The fitting plane is set as  
\[aX+bY+cZ+e=0 \tag{19}\]  
The singular vector corresponding to the minimum singular value is the normal vector direction \(\overset{\rightarrow}{n}=(a,b,c)\) of the fitting plane equation.  
By solving SVD, the fitting plane equation coefficients \(a\), \(b\), \(c\), and \(e\) can be obtained, and then the fitting plane equation \(aX+bY+cZ+e=0\) is obtained.  
The optimized coordinates of laser tracer stations are set as \(P_{j}\big{(}X_{j}^{\prime},Y_{j}^{\prime},Z_{j}^{\prime}\big{)}\). According to the parallelism between straight line \(P_{j}P_{j}^{\prime}\) and normal vector \(\overset{\rightarrow}{n}=(a,b,c)\) of the plane \(aX+bY+cZ+e=0\), the parametric equation of straight line \(P_{j}P_{j}^{\prime}\) is  
\[\left\{\begin{array}{ll}X^{\prime}_{j}=X_{j}+a\lambda\\ Y^{\prime}_{j}=Y_{j}+b\lambda\\ Z^{\prime}_{j}=Z_{j}+c\lambda\end{array}\right. \tag{20}\]  
After substituting \(\big{(}X_{j}^{\prime},Y_{j}^{\prime},Z_{j}^{\prime}\big{)}\) into plane equation \(aX+bY+cZ+e=0\), we can get  
\[\lambda=-\frac{aX^{\prime}_{j}+bY^{\prime}_{j}+cZ^{\prime}_{j}+e}{a^{2}+b^{2} +c^{2}} \tag{21}\]  
After substituting \(\lambda\) into Equation (20), the optimized coordinates \(P_{j}\big{(}X_{j}^{\prime},Y_{j}^{\prime},Z_{j}^{\prime}\big{)}\) of laser tracer stations are obtained.
# A Novel Adaptive Image Data Hiding and Encryption Scheme Using Constructive Image Abstraction
## 3 Experimental Results and Analysis
### Analysis of Security
#### 3.2.3 Analysis of Correlation  
Adjacent pixels in plain images are typically highly correlated, making it feasible to predict a pixel using the nearby pixels. To prevent malicious attacks, a suitable encryption  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Image**} & \multirow{2}{*}{**Channel**} & \multicolumn{2}{c}{**VOH**} & \multicolumn{2}{c}{\(\chi^{2}\) **Value**} \\ \cline{3-6}  & & **Plain** & **Ciphered** & **Plain** & **Ciphered** \\ \hline \multirow{4}{*}{507} & R & 78,703,629.406 & 2810.023 & 6,757,715.622 & 224.635 \\  & G & 79,139,306.367 & 2668.469 & 6,795,124.075 & 289.866 \\ (HDR) & B & 80,909,521.609 & 3241.375 & 6,947,119.749 & 249.191 \\  & E & 281,099,137.109 & 3114.953 & 24,135,964.816 & 292.041 \\ \hline NCHU Main & R & 121,376,328.734 & 2373.852 & 12,252,500.062 & 239.632 \\ Gate & G & 124,007,220.664 & 2610.609 & 12,518,079.058 & 263.532 \\ (LDR) & B & 133,176,246.500 & 2740.508 & 13,443,658.953 & 276.644 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Variance of Histogram (VOH) and \(\chi^{2}\) values for the abstracted stego images.  
Figure 7: All 10 abstracted-encrypted stego images pass the \(\chi^{2}\) test in all channels.  
algorithm should eliminate as many as possible those correlations. We can measure the correlation among pixels using the correlation coefficient (\(r_{x,y}\)) specified in Equation (10):  
\[r_{x,y}=\frac{E(x-E(x))E(y-E(y))}{\sqrt{D(x)}\sqrt{D(y)}} \tag{10}\]  
In this equation, \(x\) and \(y\) represent the adjacent pixel pair, while \(E(x)\) and \(D(y)\) correspond to the expectation and variance of \(x\) and \(y\) over these samples, respectively. The value of \(r_{x,y}\) ranges from \(-1\) to \(+1\), with values closer to zero indicating lesser correlation between paired pixels. Moreover, when \(r_{x,y}>0\), it indicates a direct correlation between the pixels, whereas \(r_{x,y}<0\), the pixels are inversely related.  
We calculate the \(r_{x,y}\) for R, G, and B channels horizontal (H), vertical (V), and diagonal (D) directions, using 5000 pairs of adjacent pixels. For an RGBE image, we compute the \(r_{x,y}\), which are all floating-point values in the R, G, and B channels. The results for the plain and ciphered images are presented in Table 5. The statistics indicate that the \(r_{x,y}\) have been substantially reduced in the encrypted image and its values are approaching zero. This outcome demonstrates the success of our encryption algorithm in disrupting the pre-existing correlation in the plain image.  
Figure 8 displays the correlation coefficients of all 10 encrypted abstracted stego test images. The results are found to be very close to zero, indicating that the proposed encryption algorithm has significantly reduced the correlation in all three directions, thereby improving the security of the encrypted images.
# Residual Stress Relaxation of DRWDs in OSDs under Constant/Variable Amplitude Cyclic Loading
## 3 Analysis Model of the Residual Stress Relaxation Effects
### A Coupled Stress Analysis Model That Considers the Welding Residual Stress and Mechanical Stress  
In this paper, a coupled stress analysis model that considers the welding residual stress and mechanical stress of OSD is established; the specific method (Figure 10) is as follows:  
1. The Shell63 elastic shell element is used to establish the global model (GM) of steel box girders, including the top decks, ribs, bottom decks, and transverse diaphragm plates of the steel box girder system. The element size is 150 mm and the total number is 91,090. The length of the global model along the bridge is equal to five times the spacing of diaphragm plates and the width is 32.5 m; the length of the global model is 18.75 m. The thickness of the diaphragm plate is 14 mm.  
2. The local fine model (LFM) of DRWDs is established with Jiangyin Yangtze River Highway Bridge as the engineering background. The eight-node solid element provided by ANSYS is used to perform a FEM simulation on the top decks, ribs, and weld structure in 1.5 U ribs. During the modeling process, the Solid70 elements and Solid185 elements are adopted for heat transfer analysis and mechanical analysis, respectively. The welding seam details are shown in Figure 1. The length of local fine model is 900 mm and the width is 300 mm.
3. The multi-scale model of entire OSDs is modeled by embedding the LFM into the GM as shown in Figure 10a. In this model, the shell elements and solid elements are rigidly connected and the multipoint constraint algorithm is used in contact calculation. In contact connection area, the target element (Targe170) and contact element (Conta175) are defined as solid element (Solid185) surface and shell element (Shell63) nodes, respectively. All translational degrees of freedom (DOFs) are constrained at both ends of the multi-scale model of entire OSDs.
4. The thermal structure coupling method is adopted to simulate the temperature field and stress field of welding process in the multi-scale model of entire OSDs. This process is the same as that in Section 2.2, so will not be repeated here. Then, the external fatigue loading is applied to the deck surface of the multi-scale model to obtain the true coupled stress time history of DRWDs. The blue distributed loads in Figure 10b are the external cyclic loads, which are applied on top of the welded joint in a 50 mm \(\times\) 300 mm area, to simulate the effect of cyclic loading on the residual stress relaxation.  
Figure 10: A multi-scale coupled stress analysis model: (**a**) build model; (**b**) load model (unit: mm).
# A New Algorithm for Computing the Distance and the Diameter in Circulant Graphs
## 5 Upper Bound for the Diameter of Circulant Graphs  
In 1990, [PERSON] et al. [10] gave the following upper bound on the diameter of \(C_{n}(1,s)\):  
\[diam(C_{n}(1,s))\leq\max\{\lfloor\frac{n}{s}\rfloor+1,n-\lfloor\frac{n}{s} \rfloor s-2,(\lfloor\frac{n}{s}\rfloor+1)s-n-1\}. \tag{1}\]  
Another upper bound was given by [PERSON] and [PERSON] [11]:  
\[diam(C_{n}(1,s))\leq diam(C_{n}(1,2))=\lfloor\frac{n+2}{4}\rfloor. \tag{2}\]  
Next, we present our upper bound on the diameter of \(C_{n}(1,s)\) for \(n\) and \(s\).  
Theorem 8: For all \(n\) and \(s\),  
\[diam(C_{n}(1,s))\leq\lfloor\frac{\lfloor\frac{n}{s}\rfloor}{s}\rfloor+\lceil \frac{s}{2}\rceil. \tag{3}\]Proof.: If \(i\leq\lfloor\frac{n}{2}\rfloor\), then \(\ell^{1}(i)\leq r+\lfloor\frac{\lfloor\frac{n}{2}\rfloor}{s}\rfloor\) and \(\ell^{2}(i)\leq 1+s-r+\lfloor\frac{\lfloor\frac{n}{2}\rfloor}{s}\rfloor\). Since, by Lemma 4, \(d(i)\leq\min(\ell^{1}(i),\ell^{2}(i))\), then \(d(i)\leq\lfloor\frac{\lfloor\frac{n}{2}\rfloor}{s}\rfloor+\lceil\frac{n}{2}\rceil\). In fact, \(\min(\ell^{1}(i),\ell^{2}(i))=\ell^{1}(i)\) if \(r\leq\lceil\frac{s}{2}\rceil\), and \(\min(\ell^{1}(i),\ell^{2}(i))=\ell^{1}(i)\leq\lceil\frac{n}{2}\rceil+\lfloor \frac{\lfloor\frac{n}{2}\rfloor}{s}\rfloor\). If \(r>\lceil\frac{s}{2}\rceil\), then \(\min(\ell^{1}(i),\ell^{2}(i))=\ell^{2}(i)<1+s-\lceil\frac{s}{2}\rceil+\lfloor \frac{\lfloor\frac{n}{2}\rfloor}{s}\rfloor\leq\lceil\frac{s}{2}\rceil+\lfloor \frac{\lfloor\frac{n}{2}\rfloor}{s}\rfloor\). Thus, for \(i\leq\lfloor\frac{n}{2}\rfloor\), \(d(i)\leq\lfloor\frac{\lfloor\frac{n}{2}\rfloor}{s}\rfloor+\lceil\frac{s}{2}\rceil\). Consequently, we conclude that \(diam(C_{n}(1,s))\leq\lfloor\frac{\lfloor\frac{n}{2}\rfloor}{s}\rfloor+\lceil \frac{s}{2}\rceil\).  
By combining (1), (2), and (3), we obtain the following result.  
Theorem 5.1: _For all \(n\) and \(s\),_  
\[diam(C_{n}(1,s))\leq\min(\max(\lfloor\frac{n}{s}\rfloor+1,n-\lfloor\frac{n}{s} \rfloor s-2,(\lfloor\frac{n}{s}\rfloor+1)s-n-1),\]  
\[\lfloor\frac{n+2}{4}\rfloor,\lfloor\frac{\lfloor\frac{n}{2}\rfloor}{s}\rfloor+ \lceil\frac{s}{2}\rceil).\]
# Prediction of Glioma Grades Using Deep Learning with Wavelet Radionic Features
###### Abstract  
Gliomas are the most common primary brain tumors. They are classified into 4 grades (Grade I-II-III-IV) according to the guidelines of the World Health Organization (WHO). The accurate grading of gliomas has clinical significance for planning prognostic treatments, pre-diagnosis, monitoring and administration of chemotherapy. The purpose of this study is to develop a deep learning-based classification method using radiomic features of brain tumor glioma grades with deep neural network (DNN). The classifier was combined with the discrete wavelet transform (DWT) the powerful feature extraction tool. This study primarily focuses on the four main aspects of the radiomic workflow, namely tumor segmentation, feature extraction, analysis, and classification. We evaluated data from 121 patients with brain tumors (Grade II, \(n=77\); Grade III, \(n=44\)) from The Cancer Imaging Archive, and 744 radiomic features were obtained by applying low sub-band and high sub-band 3D wavelet transform filters to the 3D tumor images. Quantitative values were statistically analyzed with Mann-Whitney U tests and 126 radiomic features with significant statistical properties were selected in eight different wavelet filters. Classification performances of 3D wavelet transform filter groups were measured using accuracy, sensitivity, F1 score, and specificity values using the deep learning classifier model. The proposed model was highly effective in grading gliomas with 96.15% accuracy, 94.12% precision, 100% recall, 96.97% F1 score, and 98.75% Area under the ROC curve. As a result, deep learning and feature selection techniques with wavelet transform filters can be accurately applied using the proposed method in glioma grade classification.  
deep learning; radiomics; wavelet; grading Article
## 3 Results  
### \(F^{-}\) Level Measurement Using EPMA Analysis  
The F\({}^{-}\) content of each sample was quantified using EPMA analysis to confirm the effect of NCP on APF application, as shown in Figure 4a. It confirmed that the F\({}^{-}\) value of the APF-treated group after only one application of NCP for 3 min was significantly higher by approximately two times than that of the APF-only group (\(p\) < 0.001). Moreover, in the group treated with APF after NCP pre-treatment, the F\({}^{-}\) content of the enamel specimen increased significantly with the increase in the number of treatments (approximately 0.18% for one time, 0.36% for two times, 0.53% for three times, and 0.56% for four times). In contrast, in the case of enamel specimens treated with APF alone, F\({}^{-}\) content was maintained at approximately 0.9 to 0.1% for one to three applications, but a slight increase in the F\({}^{-}\) content to approximately 0.2% was observed at four applications.
# PCCAU-Net: A Novel Road Extraction Method Based on Coord Convolution and a DCA Module
## 3 Experiments and Results
### Comparative Experiment Results and Analysis  
This chapter is devoted to an exhaustive inquiry into the practical utility of the PCCAU-Net model in the realm of road detection, achieved through systematic evaluation and analysis. To rigorously and comprehensively assess the performance of our model in the task of road extraction, this study engages a suite of benchmark models commonly employed in image segmentation, namely U-Net [16], Residual U-Net [18] (ResU-Net), DeepLab V3+ [22], and SDUNet [44]. These models are widely influential and recognized within this field as comparative benchmarks. U-Net, notable for its simplicity and efficiency, excels in small-scale datasets, but falls short in tackling complex, multi-scale challenges. In contrast, ResU-Net enhances convergence speed and accuracy by incorporating residual connections, although it risks overfitting in smaller datasets. DeepLab V3+ employs a sophisticated multi-module architecture, excelling in capturing multi-scale contextual information, albeit at the cost of elevated computational and storage overheads. SDUNet amplifies its capability to capture both local and global information through spatial attention mechanisms and dense connections; however, its efficacy may wane when dealing with highly imbalanced or heterogeneous data. These comparative models furnish a broad performance baseline and facilitate a nuanced understanding of the strengths and limitations of our proposed approach from multiple perspectives. These models are subjected to empirical validation using two datasets that present distinct characteristics and challenges: the Massachusetts Roads Dataset and the DeepGlobe Road Dataset. We aim to illuminate the exceptional efficacy and unique advantages of PCCAU-Net in the context of high-resolution remote sensing imagery for road detection.
## 2 Results and Discussion
### Method Validation  
Components in the matrix can influence the ionization when ESI is used, and the stable isotope dilution assay is the best method available to compensate for the matrix effect [37]. However, most of the isotope internal standards are usually not available and expensive. In this study, the standard curves were established by plotting five concentration levels in the range of 0.01 to 0.5 \(\mathrm{\SIUnitSymbolMicro m}\)\(\mathrm{L}^{-1}\) against peak areas in pure acetonitrile and in a blank jujube sample. According to [PERSON] [36], if the slope ratio of the matrix/solvent for each analyte was in the range of 0.8-1.2, it indicated that there is no matrix effect, otherwise a strong matrix effect exists. The slope ratios shown in Table 2 indicated a strong matrix effect for each _Alternaria_ toxin. Thus, matrix-matched calibration standard curves were adopted to quantify _Alternaria_ toxins in jujube samples. Calibration coefficients (R\({}^{2}\)) ranged from 0.9866 to 0.9997. Limit of Detection (LOD) and Limit of Quantitation (LOQ) varied in the range of 0.14-0.26 \(\mathrm{\SIUnitSymbolMicro kg}^{-1}\) and 0.47-0.87 \(\mathrm{\SIUnitSymbolMicro kg}^{-1}\), respectively, which were comparable with those obtained by  
Figure 3: Effect of (**a**) the amount of MgSO\({}_{4}\) and (**b**) NaCl on the extraction recoveries (ERs%) of four _Alternaria_ toxins: TeA, AOH, AME and TEN.  
liquid liquid extraction-liquid chromatography (LLE-LC)-MS/MS [5; 10], solid phase extraction-liquid chromatography (SPE-LC)-MS/MS [2; 8] and QuEChERS-LC-MS/MS [25; 27]. Recovery experiments were conducted via spiking three different concentrations (40, 80, and 160 \(\upmu\)g kg\({}^{-1}\)) of _Alternaria_ toxins for a jujube sample (S-7), which had the lowest _Alternaria_ toxins content. Satisfactory recoveries of all the toxins were obtained in the range of 83.5-109.6% (Relative standard deviation, RSD \(\leq\) 6.5%). These results described above demonstrated that the developed method was reliable for the analysis of _Alternaria_ toxins in jujube. Figure S3 shows the LC-MS/MS chromatograms of the blank and spiked (80 \(\upmu\)g kg\({}^{-1}\)) jujube sample (S-7).
## 4 Discussion
### The Model's Disadvantages  
Regarding the model's weaknesses, its stability could be the focus of improvement. As indicated by the new fused-data learning-curve graph in Figure 11, the cross-validation scores approximate the training set scores, in a fluctuating course with the first training samples reaching the model's stability. This could indicate that the model needs more training data samples for improved learning. Further, a **t-stat score of -1.81** indicates a significant difference in recall between the cross-validation and test set scores. As mentioned above, the model shows longer inference times compared to those of the initial model because of its complexity in having to process both visual and audio features for making predictions, thus increasing computational time. Further, no mitigation strategies were used to handle the class imbalance, providing a fair comparison with the initial model. This signifies a minor bias toward the positive class. Moreover, in Figures 14-16, the statistical analysis indicates that the combined late fusion model shows slight declines in some metrics, main taining a balanced improvement in performance. This does not interfere with the model's functionality and is something that could be improved with a larger and more balanced training sample. Moreover, the introduction of larger real clinical data containing audio and visual information from patients with dementia could resolve these issues [60; 61].
# Research on the Jiamusi Area's Shallow Groundwater Recharge Using Remote Sensing and the SWAT Model
## 3 Materials and Methods
### Research Methods
#### 3.2.3 Shallow Aquifer Reservoir Variable Calculation Method  
Based on the calculation principle of groundwater balance, the calculation formula for shallow aquifer reservoir variables is as follows:  
\[\Delta S_{gw}=PERC-GWQ-REVAP-DARC \tag{3}\]  
where \(\Delta S_{gw}\) is the shallow aquifer reservoir variable (mm); \(PERC\) is the leakage water of the vadose zone (mm); \(REVAP\) is the water quantity of aquifer regression vadose zone (mm); \(DARC\) is the seepage volume of a deep aquifer (mm); \(GWQ\) is the contribution of underground runoff to the main river course (mm).  
The groundwater runoff modulus method was used to calculate the natural supply of the basin, and the specific formula is as follows:  
\[Q=M\cdot F\cdot t\cdot 10^{-7} \tag{4}\]  
where \(Q\) is the natural supply amount (10,000 m\({}^{3}\)); \(M\) is the groundwater runoff modulus (L/(s\(\cdot\)km\({}^{2}\))); \(F\) is the catchment area (km\({}^{2}\)); \(t\) is time (s). The groundwater runoff modulus was calculated as follows:  
\[M_{year}=w_{year}/F\times t\times 1000 \tag{5}\]  
where \(W_{year}\) is the average under-land runoff in time step (m\({}^{3}\)); \(F\) is the catchment area (km\({}^{2}\)); \(t\) for time (s)The data and research techniques used in this work are listed above. Two methods were used in this paper. First, groundwater recharge estimation and recoverable amount calculation were carried out using the remote sensing water balance method. The precipitation data are TRMM data after comparison with the rain measuring station. The evapotranspiration data were synthesized by mod16, and the net flow depth data were simulated and synthesized by GLADS-2.1 software and finally brought into the formula to obtain the results. Using the SWAT model for the laboratory, first, the HWSD soil database, CMADS meteorological database, and land use data were imported into the SWAT model for database construction. SPAW software (6.02.75 version) was used to calculate soil level in the construction of the soil type database. After running the model, parameter calibration was carried out, and the range of selected parameters was continuously reduced to adjust parameters, and finally, the optimal parameters were obtained. Then, the simulated results were substituted into the groundwater water balance and the groundwater runoff modulus calculation equations, and the groundwater runoff modulus and groundwater recharge were obtained, respectively. Then, the obtained results were compared with those obtained by the remote sensing water balance method, and the groundwater resources in the study area were comprehensively evaluated. The details are shown in Figure 4 below:  
Figure 4: Technology roadmap. (I indicates the optimal parameter range for the first run of the model lock, II indicates that the parameter range is shortened for rerun).