esa-sceva/satcom-chunk-collection huggingface dataset:
# GPSR: Gradient-Prior-Based Network for Image Super-Resolution
## 3 Method
### Detail-Preserving Gradient-Guided Strategy (GGS)
#### 3.2.2 Gradient-Guided (GG) Loss  
To avoid blindly restoring high-frequency details, we propose a compact GG loss that drives the network to generate appropriate gradient information at the suitable position. The proposed GG loss includes two items that affect the gradient branch and the SR branch, respectively.  
On the one hand, inspired by ProSR [37] and LapSRN [38], we impose the deep multi-scale supervision strategy on the proposed gradient branch to provide accurate spatial structure information for the SR process. Specifically, the spatial structure feature map \(F_{grad}^{s}\) generated by the \(s\)-th gradient module passes through a Conv, yielding the corresponding gradient map \(G_{s}\):  
\[G_{s}=f_{conv}\left(F_{grad}^{s}\right), \tag{12}\]  
where \(s=1,2,\cdots,N\). Each \(G_{s}\) has its own loss and corresponding label \(G_{s}^{\prime}\), which is obtained via downsampling and gradient feature extraction operations on HR images \(I_{HR}\):  
\[G_{s}^{\prime}=g(f_{down}(I_{HR})), \tag{13}\]  
where \(f_{down}(\cdot)\) represents the downsampling function with bicubic kernel. Since each \(G_{s}\) and corresponding \(G_{s}^{\prime}\) have the same shape, the gradient branch can be optimized by \(L_{SR}^{pix}\):  
\[L_{grad}^{pix}=\sum_{s=1}^{N}\mathbb{E}\big{\|}G_{s}-G_{s}^{\prime}\big{\|}_{ 1}, \tag{14}\]  
where \(\left\|\cdot\right\|_{1}\) denotes the \(\ell_{1}\) norm.  
On the other hand, we impose a gradient restriction on the reconstructed images, which enables the network to intensively learn the image gradient space. Such a scheme cannot only enhance the structure preservation capacity, but also alleviate over-smoothing or over-sharpening restoration. Specifically, we restrain the reconstructed image \(I_{SR}\) by minimizing the distance of the gradient map between the HR image \(I_{HR}\) and the reconstructed image \(I_{SR}\),  
\[L_{SG}^{pix}=\mathbb{E}\|g(I_{SR})-g(I_{HR})\|_{1}. \tag{15}\]  
Then, a compact GG loss \(L_{gg}\) is formed by combining \(L_{grad}^{pix}\) and \(L_{SG}^{pix}\):  
\[L_{gg}=\alpha L_{grad}^{pix}+\beta L_{SG}^{pix}, \tag{16}\]  
where \(\alpha\) and \(\beta\) are the parameters to balance the \(L_{grad}^{pix}\) item and the \(L_{SG}^{pix}\) item, respectively. Empirically, \(\alpha\) and \(\beta\) were set to 0.1 and 0.001, respectively. Consequently, an overall loss \(L_{GP}\) is proposed by combining pixel-wise and gradient-wise errors:  
\[L_{GP}=L_{SR}^{pix}+L_{gg}, \tag{17}\]  
where \(L_{SR}^{pix}\) is the \(L_{1}\) loss that minimizes the pixel-wise distance between the HR image \(I_{HR}\) and its reconstructed image \(I_{SR}\), and can be formulated as:  
\[L_{SR}^{pix}=\mathbb{E}\|I_{SR}-I_{HR}\|_{1}. \tag{18}\]  
Ultimately, the proposed GPSR is able to effectively recover gradient information and produce the reconstructed images with sharp edges and textures by minimizing \(L_{GP}\).
## 4 Experimental Results and Discussion
### Statistical Analysis of Data
#### 4.1.1 Model 1: Estimation of the Ex Variable Using the Two-State Logit Model  
The estimation results of the two-state logit model for predicting the probability of patients dying (variable \"Ex\") are presented below. The mathematical model incorporates the independent variables that affect the variable in question  
\[Ex=-0.469+(0.2721*cavaucakvkn)-(0.4111*hilar)-(0.8010*biopsy3)-\] \[(0.4795*biopsy4)+(0.7721*diabetes1)+(0.8469*diabetes2)\]  
The interpretation of the model will be based on the odds ratio (OR) values. In this context, when the OR \(<1\), it indicates a decreasing effect compared to the reference group, while an OR \(>1\) suggests an increasing effect compared to the reference group. In the current study, a statistically significant relationship was observed between cavaucakvkn1 and the dependent variable \"Ex\". The odds ratio analysis revealed that patients with cavaucakvkn1 end-to-end anastomosis had a higher likelihood of mortality compared to patients without this type of anastomosis (OR = 1.313; \(p<0.05\)). Moreover, patients without hilar invasion but with alveolar invasion were less likely to experience mortality compared to patients with hilar invasion (OR = 0.663; \(p<0.01\)).  
Additionally, the estimation results of the two-state logit model showed a statistically significant relationship between biopsy and the dependent variable \"Ex\". Patients with hepatitis had a lower likelihood of mortality compared to patients with cirrhosis (OR = 0.449; \(p<0.05\)). Similarly, patients with tumors had a lower likelihood of mortality compared to patients with cirrhosis (OR = 0.619; \(p<0.05\)). Furthermore, a statistically significant relationship was identified between diabetes and the dependent variable \"Ex\" based on the estimation results of the two-state logit model. It was determined that patients using diabetes_insulin had a higher likelihood of mortality compared to patients using insulin and OAD (OR = 2.164; \(p<0.05\)). Similarly, patients using diabetes_OAD had a higher likelihood of mortality compared to patients using insulin and OAD (OR = 2.332; \(p<0.05\)).
# Temperature Dependence of Density and Viscosity of Biobutanol-Gasoline Blends
## 3 Results and Discussion
### Mathematical Models
#### 3.4.2 Mathematical Models for Dynamic Viscosity  
The temperature dependence of the dynamic viscosity of the biobutanol-gasoline blends were modelled using four mathematical models, see Section 2.4 for more details. The coefficients of these models are shown in Tables 4-7. Thus, it was shown that with an increasing temperature, the dynamic viscosity of the biobutanol-gasoline blends decreases [32]. The difference between the Arrhenius model and Newtonian model is only in the use of the Universal gas constant \(R\), so it holds that the coefficient \(b\) (Newton model) multiplied by the Universal gas constant \(R\) equals the Arrhenius activation energy \(E_{a}\). However, knowledge of the Arrhenius activation energy \(E_{a}\) of the biobutanol-gasoline  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Exponential** & \(\rho_{0}\) **(kg\(\cdot\)m\({}^{-3}\))** & \(b\) **(K\({}^{-1}\))** & \(R^{2}\) & **SSE** \\ \hline B0-95 & 842.7 & \(-\)0.0003653 & 0.9675 & 7.094 \\ B5-95 & 918.5 & \(-\)0.0006602 & 0.9108 & 67.90 \\ B10-95 & 939.7 & \(-\)0.0007296 & 0.9800 & 17.37 \\ B85-95 & 1054 & \(-\)0.0009530 & 0.9793 & 33.96 \\ B0-98 & 950.2 & \(-\)0.0007528 & 0.9705 & 27.75 \\ B5-98 & 989.9 & \(-\)0.0008632 & 0.9720 & 35.31 \\ B10-98 & 973.6 & \(-\)0.0007975 & 0.9710 & 31.33 \\ B85-98 & 1057 & \(-\)0.0009501 & 0.9824 & 28.75 \\ B100 & 1074 & \(-\)0.0009863 & 0.9745 & 45.80 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Coefficients of exponential volumetric mass density model and statistical indicators (\(R^{2}\)—coefficient of determination, SSE—sum of squared estimate of errors).  
Figure 1: Exponential model for volumetric mass density of biobutanol-gasoline (RON 95) blends.  
Figure 2: Exponential model for volumetric mass density of biobutanol-gasoline (RON 98) blends.  
blends is important and can be used for further calculations and applications [27; 28; 29]. In Table 7 (the coefficients of the Vogel model), several statistically insignificant coefficients can be seen, despite the Vogel model being the most accurate according to the highest average value of the coefficient of determination \(R^{2}\) (0.9784) and average value of sum of squared estimate of errors SSE (0.0067). The Newton and Arrhenius models are in the next rows, which have an identical average value of \(R^{2}\) (0.9234) and SSE (0.0514) and the exponential model has the lowest accuracy (but still very high), according to the results of the average value \(R^{2}\) (0.8991) and SSE (0.0168).
# Mapping Aquaculture Ponds for the Coastal Zone of Asia with Sentinel-1 and Sentinel-2 Time Series
## 3 Data and Methods
### Satellite Data Processing
#### 3.3.3 Segmentation  
An automatic segmentation of the median image was applied using a histogram-based thresholding algorithm by OTSU [54] that is suited for the separation of bimodal distributions [32]. OTSU's method [54] is non-parametric, unsupervised, and used for automatic threshold selection grey-level histograms. It calculates the optimal threshold separating two classes by minimizing the weighted sum of within-class variances for an assumed bimodal histogram (see Figure 7). In a following step, the binary output image of the histogram-based thresholding of the median \(\sigma\)0 values at VH polarization (segments based on pixel data) was converted from raster to vector features. The resulting objects were then used to extract the following features for each pond object: temporal metric features of the satellite data (SAR features and optical features of Sentinel-1 and Sentinel-2) and topographic features (mean of the elevation and slope derived from the DEM data), which we stacked into one combined feature data cube. The object-based pond dataset was processed for each coastal parcel and downloaded in GeojSON format automatically in a script written in the GEE JavaScript API. The final results of the 261 parcels were then merged to five macro-regions (see Section 2) instead of one single file in order to limit memory limitations on local machines and enable further processing.  
On the basis of DEM-derived terrain information and coastline data, potential pond aquaculture areas were identified and extracted in order to exclude other water bodies that are most likely to be confused with aquaculture ponds, such as small mountain lakes or reservoirs located in higher latitudes. The topographical features of elevation and slope derived from the high-resolution SRTM dataset were used to generate a static mask to extract flat and low-lying coastal areas where pond aquaculture could potentially occur (see Figure 6). For this purpose, the mean elevation and mean slope values were determined from digitized pond aquaculture samples along the coastal zone of Asia to derive representative threshold values for the static mask. We defined a height of less than 50 m above sea level and slope of less than 10 percent as threshold values for generating the topographic masks (altitude mask and slope mask).
## 3 Results
### Seasonal and Inter-Annual Variations over Local Sites
Satellite observations were extracted within a 3 \(\times\) 3 window centred at each flux tower site (SIF: 1.5\({}^{\circ}\)\(\times\) 1.5\({}^{\circ}\); EVI and EVI \(\times\) PAR: 0.15\({}^{\circ}\)\(\times\) 0.15\({}^{\circ}\)). \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Size** & **Data** & **2014-2015** & **2015-2016** & **2016-2017** & **2017-2018** & **2016-2019** & **2018-2020** & **MML\({}^{1}\)** & **2014-2013** & **2015-2016** & **2016-2017** & **2017-2018** & **2018-2019** & **MML\({}^{1}\)** \\ \hline AU-flow & SF-GP & 6 & 24 & \(-\)4 & \(-\)21 & 0 & 11 & \(-\)80 & 20 & 7 & 44 & \(-\)42 & 39 \\  & SF-GP & -17 & \(-\)12 & 10 & 3 & \(-\)6 & 10 & 9 & 74 & 83 & 24 & 15 & 41 \\ \hline AU-Dry & SF-GP & -86 & 3 & \(-\)35 & \(-\)38 & \(-\)10 & 32 & \(-\)43 & \(-\)71 & \(-\)42 & \(-\)47 & \(-\)45 & 40 \\  & SF-GP & -6 & 5 & \(-\)27 & \(-\)16 & \(-\)8 & 12 & \(-\)39 & \(-\)55 & \(-\)5 & \(-\)23 & \(-\)78 & 40 \\ \hline AU-Sdp & SF-GP & 2 & \(-\)21 & \(-\)33 & \(-\)21 & 19 & \(-\)5 & \(-\)14 & \(-\)14 & 6 & 8 \\  & SF-GP & -3 & 0 & -6 & 17 & 7 & 3 & 12 & 22 & 10 & 12 \\ \hline AU-ASM & SF-GP & 22 & 108 & \(-\)25 & & 52 & \(-\)11 & 9 & \(-\)31 & & 17 \\  & SF-GP & 37 & 88 & \(-\)10 & & 44 & 14 & 84 & 34 & & 44 \\ \hline AU-ITE & SF-GP & 15 & \(-\)134 & \(-\)13 & & 54 & \(-\)2 & 9 & \(-\)27 & 13 \\  & SF-GP & 67 & \(-\)25 & 1 & 31 & 20 & 84 & 42 & & **0** \\ \hline \hline \end{tabular}  
* MAE: mean absolute error.  
\end{table} 
Table 1: Summary of differences (days) in SOS and EOS derived from SIE, EVI compared with that derived from lower-based GPP. Negative values represent an earlier green-up (SOS) or senescence (ECS) of SIE, EVI relative to that of CPP.
# A Global Continuous Multi-Scale Terrain Contour Simplification Method Based on the Level Set
## 3 Experiments and Results
### Results and Analysis
#### 3.2.4 Comparison of Position Change  
Generally, simplifying contour lines can trigger changes in shape, and a higher degree of simplification corresponds to a higher amount of distortion. One way of measuring the error induced by simplification is by calculating the location difference between the original and simplified lines. The position change of a curve mainly represents the geometric accuracy of the linear features. The simplified contours generated by the three simplification methods were compared based on the mean, standard, and max differences of position. The change in the position of the simplified contours was calculated by using the computational positional error method proposed by [PERSON] [45]. For each original point of the initial contour, the positional error was calculated as the perpendicular difference between that point and the corresponding line segment of the simplified contour. The distance \(d\) from the point \(P_{0}(x_{0},y_{0})\) to the corresponding line segment \(l:A_{x}+B_{y}+C=0\) was defined as Equation (13). Applying these simplification algorithms consistently produced low positional errors.  
\[d=\frac{\left|A_{x_{0}}+B_{y_{0}}+C\right|}{\sqrt{A^{2}+B^{2}}} \tag{13}\]  
For the validation, we used 20 randomly generated contour lines (described in Section 3.2.3) as experimental data. We compared three position differences, namely, the standard, average, and max position differences, among the simplified results generated by the three simplification methods and the initial contours at the same scale. As shown in Table 3, CCSM obtained the smallest standard positional error (208.199 m) and maximum positional error (778.478 m) among the three simplification methods, thereby suggesting that CCSM can effectively maintain the global shape characteristics of the contours better. Given that the real value of the mean positional error is susceptible to the local extremum, the mean positional error values may not stably reflect the overall displacement [44].  
Figure 11: Average linear densities of the 20 selected contours generated by the three methods.
## 2 Problem Description and Methods  
### Ship Trajectory Pattern Definition  
The ship trajectory prediction problem can be abstracted as a ship time series prediction problem. After cleaning and resampling a large amount of AIS data, the ship trajectory data consist of a series of discrete data points in a given time range, as shown in Figure 1.  
In our setup, \(T\) is a time-ordered observation sequence, the discrete point \(p_{\rm i}\) is a four-dimensional data point, which includes SOG (Speed Over Ground), COG (Course Over Ground), LAT (latitude), and LON (longitude), and \(t_{\rm i}\) is the time scale corresponding to the four-dimensional discrete data points, as shown in Equation (1).  
\[\left\{\begin{array}{c}T:\left((p_{1},t_{1}),(p_{2},t_{2}),\cdots,(p_{n},t_{ n})\right),\\ p_{i}:\left(lon_{i},lat_{i},SOG_{i},COG_{i}\right)\end{array}\right., \tag{1}\]  
where \(i\) is the index of the trajectory sequence, and \(n\) is the total number of trajectory sequences. Thus, a dataset containing \(N\) trajectories can be expressed as \(T_{N}=\{(p_{i},t_{i})\}_{i=1}^{N}\), where \(p_{\rm i}\) is the four-dimensional feature vector defined in Equation (1) and \(t_{\rm i}\) is the observation timestamp of the corresponding AIS data.  
Figure 1: Schematic diagram of ship trajectory pattern.  
The ship trajectory prediction problem can be described as inputting the actual observed \(s\) trajectories into the model, with the aim of letting the model learn the variation function of the ship trajectory and obtaining the \(k\) predicted trajectories output by the model, as shown in Equation (2).  
\[\left\{\begin{aligned} Input:T_{s}&=\{(p_{i},t_{i}) \}_{i=1}^{s},\\ Output:T_{k}&=f(T_{s})\end{aligned}\right. \tag{2}\]  
The mapping relationship can be expressed in a mathematical formula such as Equation (3).  
\[y_{i,k}=f_{s,k}(x_{i,s}). \tag{3}\]  
where \(x_{i,s}\) indicates that at the \(i\)-th trajectory point, \(s\) observation sequences are input to the model for prediction, \(f_{s,k}\) indicates that the model predicts \(k\) trajectories from the input \(s\) trajectories, and \(y_{i,k}\) indicates that the model predicts \(k\) trajectories at the \(i\)-th trajectory point, and the data are input to the model for training through the sliding time window, as shown in Figure 2.
## 5 Conclusions and Discussion  
In summary, we first recovered the lost information of the existing decoy-state method when detecting the weaker version of a PNS attack in the decoy-state MDI-QKD and extracted a normal distribution statistic via statistical hypothesis testing. Based on this information, we proposed a new method to detect the weaker version of a PNS attack. Most importantly, the error probability of detection was precisely calculated by our method, and we also gave the calculation. Finally, according to the judgment result, the corresponding secure key rate was provided. In particular, compared with the existing decoy-state MDI-QKD protocols, the secure key rate with our method has been highly improved if the judgment result is no weak PNS attack. Meanwhile, the associated experimental results also verified the correctness of our method.  
Nevertheless, all judgment results in this paper were obtained under the condition that the null hypothesis was no weak version of a PNS attack. In other words, we assume that the gain of signal or decoy state will change significantly after the PNS attack. However, we can do nothing about the stronger PNS attack, which retains the gain of signal and decoy state, such as a partial PNS attack [15], because the premise of the derivation no longer holds, and the Type II error probability of our method in this case will be poor even close to unity. For this reason, compared with the existing decoy-state method [29; 30; 31] to directly estimate the secure key rate, our method is not ready for practical application now; however, we provide a new direction to improve the secure key rate and efficiency.  
Figure 1: The schematic diagram of statistical hypothesis testing. The value of the test statistic \(v\) is 0.236. Given the significance level of the test \(\alpha=0.05\), the critical values are \(v_{[1-\alpha/2]}=1.96\) and \(-v_{[1-\alpha/2]}=-1.96\).  
**Author Contributions:** Conceptualization, X.C.; Formal analysis, L.C.; Methodology, L.C.; Writing--original draft, L.C.; Writing--review & editing, Y.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Development of Height Indicators using Omnidirectional Images and Global Appearance Descriptors
## 3 Development of Height Indicators Using Global Appearance Descriptors
### Method 2: 2D-DFT Vertical Phase
\begin{array}{cccc}0&135,1&-92,6&104,3\\ -45,3&108,7&121,6&-8,4\\ 151,4&147,7&41,2&-135,3\\ -67,4&23,0&-117,6&-106,5\end{array} \right);\;\;\;\Theta_{R}^{rotated}= \left( \begin{array}{cccc}0&135,1&-92,6&104,3\\ -27,3&126,7&139,6&9.6\\ -172,6&-176.3&77,2&-99,3\\ -13,4&77,0&-63,6&-52,5\end{array} \right). \tag{10}\]  
On the one hand, Equation (9) shows that both magnitudes matrices are identical, as expected according to Equation (2). On the other hand, the relationship between both arguments matrices meets Equation (7), as detailed in the next equation:  
Figure 7: (**a**) Original panoramic image \(im_{R}\) and (**b**) resulting image \(int_{R}^{rotated}\) after considering a circular shift of rows equivalent to \(S=18\) deg. \[\Theta_{R}^{rotated}=\Theta_{R}+S\cdot VRM=\Theta_{R}+\left( \begin{array}{cccc}0&0& 0&0\\ 18&18&18&18\\ 36&36&36&36\\ 54&54&54&54\end{array} \right). \tag{11}\]  
Taking all these facts into consideration the next steps are followed in the height estimation application. First, from the reference image \(im_{R}\), a set of rotated versions is generated \(\Theta_{R}^{rotated}\) considering \(S=[-180+\Delta S,-180+2\Delta S,\dots,180]\,deg\). In the experiments, \(\Delta S\) is given a value equal to \(0.5\,deg\). Second, when a new test image \(im_{T}\) arrives, the arguments matrix of its 2D-DFT is obtained \(\Theta_{T}\in\mathbf{R}^{N_{F}\times N_{F}}\) and compared with the set of matrices \(\Theta_{R}^{rotated}\) generated from the reference image. The coefficient \(S\) that produces the best match (the minimum distance) is a topological measurement of the relative altitude between images.
# Nanotechnology, Nanomedicine, and the Kidney
## 5 Nanotechnology for Renal Therapy
### Drug Delivery
#### 5.1.5 Nanoparticles
They also tested different total amounts of MNP. They found that at 25 mg/kg, the renal specificity was maximized, while at a dose of 5 or 50 mg/kg, a significant accumulation of MNP in other organs could be found [79]. These MNP, therefore, show great potential for targeted delivery of drugs to the kidney. In an approach to treat systemic hypertension, which originates in the renal tubules and vasculature, and to treat atherosclerotic lesions, [PERSON] et al., generated a nanoparticle transfection method for the target delivery of siRNA [80]. They developed a galactose polyethylenimine glycoprotein urethane (GPE) and used it to bind anti-angiotensin (AGT) siRNA. They transfected GPE-AGT siRNA nanoparticles into rats with spontaneously hypertensive rats and found AGT protein expression in the liver was significantly reduced 72 h after treatment. Serum concentration of AGT and the cleavage-product angiotensin II was decreased. Blood pressure dropped significantly when compared to baseline. They also found that atherosclerotic lesions were attenuated and that renal function was stable. Eleven days after the transfection, blood pressure started to rise again. The study demonstrated that GPE nanoparticles can be used to modulate the renin-angiotensin system via specific siRNA.