esa-sceva/satcom-chunk-collection huggingface dataset:
# CI-UNet: Application of Segmentation of Medical Images of the Human Torso
## 5 Conclusions  
This paper proposes a deep learning-based image segmentation network for empirically predicting stemmed medical segmentation images with minor improvements in segmentation edges and accuracy. We can summarize our main contributions as follows. First, we built a deep learning-based prediction model to predict abdominal organ segmentation images. We explored the enhanced dataset using pre-ablation and cross-validation experiments to obtain the optimal parameters. Secondly, the model can better reflect the edge segmentation of organs and slightly improve the accuracy. Thirdly, the application of CI-UNet to spine image segmentation with better results was approved by physicians. In addition, it is expected that these re- results will help to develop spine segmentation images and assist in calculating spine bending angles.  
There are some potential limitations of our study. In spine segmentation and angle calculation, the spine prediction results are misjudged when our predicted images have some stray or misjudged points; we will fix our calculation method and expand our spine dataset to find better training parameters for the network. Later, we will conduct more and better deep-learning research around orthopedic diseases in the local population of Guizhou province to better serve doctors' diagnoses.  
Conceptualization, Z.H., X.W. and J.Q.; methodology, X.W. and J.Q.; software, X.W., D.M. and J.Q.; validation, Q.W., Z.H. and J.Q.; formal analysis, J.Q.; investigation, Q.W., X.W. and J.Q.; data curation, D.M. and J.Q. writing--original draft preparation, D.M. and J.Q.; writing--review and editing, Y.T. and J.Q.; visualization, X.W., Y.T. and J.Q.; supervision, X.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported in part by the National Natural Science Foundation of China under grant 61861007 and 61640014; in part by the Guizhou Province Science Technology Support Plan under grant [2022]264; in part by the Guizhou Education Department Innovation Group Project under grant KY [2021]012; and in part by the Guizhou Province Science and Technology Planning Project ZK [2021]303.  
**Informed Consent Statement:** Informed consent was obtained from all subjects participating in the study, and written informed consent was also obtained from patients for the publication of this article.  
**Data Availability Statement:** The data are currently being withheld due to the privacy of the individuals for whom the dataset was designed.  
**Acknowledgments:** We would like to thank [PERSON] from the Guizhou Branch of Beijing Jishuitan Hospital for his medical help and guidance on this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 3 Data and Methods
### Method
#### 3.2.7. Vertical Integrated Water Vapor Flux (VIWVF) and Water Vapor Flux Divergence  
VIWVF refers to the mass of water vapor passing through a vertical air column of unit width (from the surface to the top of the atmosphere, such as 1000-300 hPa) per unit time, which reflects the direction and intensity of atmospheric moisture transport [58]. Its calculation formula is as follows:  
\[f_{u}=\frac{1}{g}\bigg{/}p_{p_{top}}^{p_{surf}}\ q\cdot udp,f_{v}=\frac{1}{g} \bigg{/}p_{p_{top}}^{p_{surf}}\ q\cdot udp \tag{20}\]  
where \(f_{u}\) and \(f_{v}\) are zonal and meridional VIWVF, respectively, \(g\) is gravitational acceleration, with a value of 9.81 m/s\({}^{2}\), \(q\) is specific humidity, and \(p_{surf}\) and \(p_{top}\) are the air  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **DAR** \\ \hline Territorial drought & DAR \(\geq\) 50\% \\ Regional drought & 33\% \(\leq\) DAR \(<\) 50\% \\ Partial regional drought & 25\% \(\leq\) DAR \(<\) 33\% \\ Localized drought & 10\% \(\leq\) DAR \(<\) 25\% \\ Near normal & DAR \(<\) 10\% \\ \hline \hline \end{tabular}
\end{table}
Table 3. Drought category according to DAR.  
pressure from the surface to the top of the atmosphere, with values of 1000 hPa and 100 hPa, respectively.  
Water vapor flux divergence refers to the rate of water vapor convergence (negative divergence) or divergence (positive divergence) within an air column, which directly determines the potential for PRE [59]. The expression is as follows:  
\[div_{WVF}=\frac{\partial f_{u}}{\partial x}+\frac{\partial f_{v}}{\partial y} \tag{21}\]  
where \(div_{WVF}\) is the water vapor flux divergence and \(x\) and \(y\) correspond to the longitude and latitude directions, respectively.
## 3 The Proposed Local-Moment-Driven Robust Reversible Data-Hiding Scheme
### Embedding Method
#### 3.1.3 Auxiliary Information for Blind Decoding  
To recover the original image and extract the secret information in a lossless manner, some auxiliary information needs to be embedded in the image along with the secret data. This information consists of the following components:  
* Details of location map: Though the proposed LM-RRDH scheme embeds the secret data in the MSB plane of the image, the problem of overflow (pixel value \(>\sum\limits_{k=n}^{7}2^{k}\) where \(n=0,\ 1,\ 2,\ 3,\dots,\ 7\) or \(MAX\)) and underflow (pixel value \(<0\)) can still arise due to the pixel modification for embedding the secret information. Therefore, a location map \((LM)\) is constructed to avoid such problems, in which all the pixels of the original image that is either equal to '0' or to \(MAX\) are marked by '1', otherwise by '0', as given in Equation (21): \[LM(l)=\left\{\begin{array}{c}1\ if\ P_{l}=0,\\ 1\ if\ P_{l}=MAX,\\ 0\ else.\end{array}\right.\] (21) where \(l\) is the number of the pixel scanned in the raster-scan order of the image, \(I^{MSB}\). Further, the values of '0' and \(MAX\)-valued pixels are increased and decreased by '1', respectively, using Equation (22), so that these pixels can also be used for embedding the secret information while avoiding the problem of underflow/overflow. \[P_{l}=\left\{\begin{array}{c}P_{l}+1\ if\ P_{l}=0,\\ P_{l}-1\ if\ P_{l}=MAX.\end{array}\right.\] (22) Next, the \(LM\) is compressed using JBEG/arithmetic encoding to obtain a compressed location map \((CLM)\). The length of the \(CLM\) is denoted by \(LCLM\).
* _Maximum pixel value of \(I^{LSB}\):_ As discussed above, the proposed LM-RRDH scheme first decomposes the cover image \((I)\) into two images, \(I^{MSB}\) and \(I^{LSB}\) cover images \((I)\), by segregating a certain number of LSBs from the MSBs of each pixels of the image and then embeds the secret data in \(I^{MSB}\). Therefore, the number \((n)\) of LSBs to generate the \(I^{LSB}\) must be shared with the receiver so that they can decompose the image and start the extraction process. The value of \(n\) can be encoded, at a maximum, by 3 bits.
* _LSBs of the border pixels:_ The LM-RRDH scheme embeds some of the information, such as number \((n)\) of LSBs, to generate the \(I^{LSB}\) in the LSBs of border pixels of the cover image \((I)\), as the border pixels are not used for embedding the secret information. Therefore, the LSBs of the border pixels need to be embedded in the \(I^{MSB}\) along with the secret data as auxiliary information, so that extraction and recovery can be performed blindly.
###### Abstract  
In today's digital age, it is crucial to secure the flow of information to protect data and information from being hacked during transmission or storage. To address this need, we present a new image encryption technique that combines the Kronecker xor product, Hill cipher, and sigmoid logistic Map. Our proposed algorithm begins by shifting the values in each row of the state matrix to the left by a predetermined number of positions, then encrypting the resulting image using the Hill Cipher. The top value of each odd or even column is used to perform an xor operation with all values in the corresponding even or odd column, excluding the top value. The resulting image is then diffused using a sigmoid logistic map and subjected to the Kronecker xor product operation among the pixels to create a secure image. The image is then diffused again with other keys from the sigmoid logistic map for the final product. We compared our proposed method to recent work and found it to be safe and efficient in terms of performance after conducting statistical analysis, differential attack analysis, brute force attack analysis, and information entropy analysis. The results demonstrate that our proposed method is robust, lightweight, and fast in performance, meets the requirements for encryption and decryption, and is resistant to various attacks.  
image encryption; Hill cipher; chaotic map; logistic map; Kronecker product Article
## 3 Results and Discussion
### Status of Waste-to-Energy Energy and Biomass Energy in Southeast Asian Countries
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Description**} & \multicolumn{1}{c}{**Brunen**} & \multicolumn{1}{c}{**Cambodia**} & \multicolumn{1}{c}{**Indonesia**} & \multicolumn{1}{c}{**Laos**} & \multicolumn{1}{c}{**Malaysia**} & \multicolumn{1}{c}{**Myanmar**} & \multicolumn{1}{c}{**Philippines**} & \multicolumn{1}{c}{**Singapore**} & \multicolumn{1}{c}{**Thailand**} & \multicolumn{1}{c}{**Vietnam**} \\ \hline
**Population (2015)** & 423,188 & 15,577,899 & 255,993,674 & 6,802,023 & 30,331,007 & 53,897,154 & 100,998,376 & 5,540,000 & 67,959,259 & 91,700,000 \\ \hline
**Urban Population** & 77\% & 21\% & 54\% & 39\% & 75\% & 34\% & 44\% & 100\% & 50\% & 34\% \\ \hline
**Per Capita GDP (USD)** & 31,164.6 & 1162.9 & 3331.7 & 2134.7 & 9955.2 & 1287.4 & 3001.0 & 55,646.6 & 5840.0 & 2085.1 \\ \hline
**Waste Generation (ions/year)** & 210,000 & 1,089,000 & 22,500,060 & 77,000 & 10,680,000 & 1,130,040 & 14,400,000 & 7,670,000 & 26,850,000 & 12,800,000 \\ (2015) & 210,000 & (2014) & (2012) & & & & & & & \\ \hline
**Per Capita Waste Generation** & \multirow{2}{*}{0.87} & \multirow{2}{*}{0.6} & \multirow{2}{*}{0.52} & \multirow{2}{*}{0.7} & \multirow{2}{*}{1.52} & \multirow{2}{*}{0.44} & \multirow{2}{*}{0.5} & \multirow{2}{*}{1.49} & \multirow{2}{*}{1.76} & \multirow{2}{*}{1.46} \\ (kg/capita/day) & & & & & & & & & & \\ \hline
**Source Segregation** & \(<\)50\% & \(<\)50\% & \(<\)50\% & \(<\)50\% & \(<\)50\% & 50\% & 50-70\% & \(<\)50\% & \(<\)50\% \\ \hline
**Collection Rate** & 90\% & 80\% & 56–75\% & 40–70\% & \(>\)70\% & \(<\)50\% & 40–90\% & \(>\)90\% & \(>\)80\% & 80–82\% \\ \hline
**Reused and UUilized** & na & na & 7\% & na & na & na & na & na & 17.80\% & na \\ \hline
**Recycling** & \% & na & 20\% & 7\% & 9\% & 5\% & 28\% & 47\% & 14\% & 8.20\% \\ \hline
**Compost** & \% & 2\% & na & - & 15\% & 1\% & na & na & 0\% & 10\% & na \\ \hline
**Incineration** & \% & na & na & 2\% & na & 1\% & na & 39\% & 5\% & 5.40\% \\ \cline{2-11}  & **No. of plants** & na & na & na & 4 & 1 & na & 4 & 3 & na \\ \hline
**Sanitary Landfill** & \% & na & na & na & na & na & na & 15\% & na & na \\ \hline
**No. of plants** & na & na & 10 & na & 8 & na & na & 1 & 91 & 17 \\ \hline
**Controlled Landfill** & \% & na & na & na & na & na & na & - & na & na \\ \hline
**Solid Waste** & \multirow{2}{*}{**\%**} & \multirow{2}{*}{70\%} & \multirow{2}{*}{20\%} & \multirow{2}{*}{84\%} & \multirow{2}{*}{61\%} & \multirow{2}{*}{93\%} & \multirow{2}{*}{90\%} & \multirow{2}{*}{65\%} & \multirow{2}{*}{0\%} & \multirow{2}{*}{70\%} & \multirow{2}{*}{na} \\ **Disposal** & & & & & & & & & & \\ \hline
**Others** & \% & 28\% & 60\% & 9\% & 13\% & 6\% & 4\% & 5\% & 8\% & 1\% & na \\ \hline \hline \end{tabular} Note: Recycling data for Cambodia are based on Phnom Penh only; for Laos PDR, Vietnamese; and for Myanmar, Yangon. The disposal method of Vietnam is based on Hanoi. na: not accessible.  
\end{table} 
Table 1: Demographic context, waste generation, and waste management in Southeast Asian countries [1,12,42,46,51].
# Data Protection and Privacy of the Internet of Healthcare Things (IoHTs)
## 4 Healthcare Data Protection Legislation and Framework
### Major Healthcare Initiative
#### 4.1.5 General Data Protection Regulation (GDPR)  
In May 2018, a new General Data Protection Regulation (GDPR) replaced directive 95/46 [73], consolidating and innovating data protection rules. The introduced GDPR is considerably more comprehensive and establishes requirements for internal compliance mechanisms that did not exist in the legislation. It applies to all sectors of the economy, all broadly defined personal data, and every sector that controls or processes data. Moreover, it applies protective standards throughout the lifespan of the data. GDPR is designed to enable people to better control their data. Although the EU already established its data protection directive in 1995, it was not completely reasonable for all the member states of the EU. To remove all the reservations, the GDPR has been established and is applicable throughout the EU.  
The liability of the GDPR's impact is likely to be for health organizations, hospitals, and other healthcare organizations that process healthcare data. GDPR requires accountability for how healthcare data is processed. Data controllers are not only responsible for the compliance, but processors are also accountable for any data breach of their work and direction. Furthermore, health organizations will need to be clearer and attentive in profiling data processing activities and will require their staff to ensure a secure flow of data [74].
# OQPSK Synchronization Parameter Estimation Based on Burst Signal Detection
## 3 Performance Evaluation
### Performance Simulation of Synchronization Parameter Estimation  
#### 3.2.1 Performance Simulation of Frequency Offset Estimation  
Figure 2 shows the Mean Squared Error (MSE) comparison of different frequency offset estimation algorithms. Simulation conditions: the added normalized frequency offset is 0.02; \(N=108\); and the segment length \(L\) of the algorithm in this article is 18, 12, 9, and 6, respectively. The figure implies that the L&R and Fitz algorithms have the highest estimation accuracy, and their performance is very close to the MCRB, while the L&W algorithm has the worst performance. The Kay algorithm only gradually gets better in the case of a high SNR. The performance of the algorithm in this article is proportional to the length of \(L\). We can see that when \(L=18\), the accuracy of the algorithm in this article is very close to that of the L&R and Fitz algorithms and is far better than the L&W algorithm. In addition, when the SNR is lower than 12 dB, its performance is also better than the KAY algorithm.  
Figure 3 shows the comparison of the estimation range of the algorithm in this article and several frequency offset algorithms with high accuracy. Simulation conditions: the added normalized frequency offset is \([-0.4\):\(0.4]\), and its step size is 0.001; \(N=108\); and the segment length \(L\) of the algorithm in this article is 18, 12, 9, and 6, respectively. It can be seen from the figure that under the current simulation conditions, the estimation ranges of Fitz and L&R are \((-0.035\):\(0.035)\) and \((-0.07\):\(0.07)\), respectively. In practical applications, the range of the estimated frequency offset should be at least \((-0.1\):\(0.1)\); thus, the L&R and Fitz algorithms become invalid in the presence of large frequency offset. While the estimation range of the algorithm in this article can still reach \((-0.11\),\(0.11)\) even in the case of the highest accuracy (\(L=18\)). In the following simulations, we select \(L=12\), since the \(L=12\) case is more robust to frequency offset \((-0.16\):\(0.16)\), and there is not much difference in the MSE performance between \(L=12\) and \(L=18\).  
In short, as long as the appropriate \(L\) is selected, the frequency offset estimation algorithm in this paper not only has an accuracy close to the L&R and Fitz algorithms, but it also has a much larger estimation range. In addition, the frequency offset estimation algorithm is calculated based on the correlation results generated during the burst signal detection process, so its complexity drops significantly.  
Figure 2: Accuracy of different frequency offset estimation algorithms. MCRB, Modified Cramer–Rao Bound.
## 4 Experimental Results  
To validate the proposed synthetization approach, a 75 \(\Omega\)-50 \(\Omega\) IT (case 11) was designed, fabricated, and measured, where \(\theta_{c}=45^{\circ}\), RL = 20 dB, and the center frequency \(f_{0}\) = 2 GHz. The characteristic impedances and the physical parameters of the fabricated IT are listed in Tables 6 and A1.  
The slot-coupled directional (SCD) structure was used in the circuit fabrication. The 3D view of the proposed IT is shown in Figure 11. The structure was constructed on the Roger's RT/duroid 5880 substrate with a dielectric constant (\(\varepsilon_{r}\)) of 2.2, a loss tangent (tan\(\delta\)) of 0.0009, and a thickness of 0.787 mm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristic Impedance (\(\Omega\))** & **Coupling Strength (dB)** & **Physical Parameters (mm)** \\ \hline \(Z_{ev1}=192.23\) & & \(W_{1}=4.0\) \\ \(Z_{od1}=34.77\) & 3.18 & \(L_{1}=27.5\) \\  & & \(S_{1}=10.8\) \\ \hline \(Z_{2}=69.06\) & & \(W_{2}=1.4\) \\  & & \(L_{2}=27.0\) \\ \hline \(Z_{ev3}=152.43\) & & \(W_{3}=4.9\) \\ \(Z_{od3}=30.00\) & 3.46 & \(L_{3}=26.7\) \\  & & \(S_{3}=8.4\) \\ \hline \(Z_{4}=56.35\) & & \(W_{4}=2.1\) \\  & & \(L_{4}=26.0\) \\ \hline \(Z_{ev5}=130.33\) & & \(W_{5}=7.6\) \\ \(Z_{od5}=20.10\) & 2.82 & \(L_{5}=26.4\) \\  & & \(S_{5}=10.3\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Parameters of the fabricated IT.  
Figure 10: Circuit simulations of the 7 selected examples in Table 5. (**a**) Circuit simulations of \(S_{11}\) and \(S_{21}\). (**b**) Enlarged circuit simulations of \(S_{21}\) and group delay.  
Figure 12a,b shows the front- and back-side photographs of the fabricated IT. Its circuit simulation, EM simulation, and measured results are shown in Figure 13. The \(S_{11}\) of EM simulation and measured results were lower than \(-17.1\) dB and \(-14.5\) dB in the passband, respectively. The errors between the EM simulation and measured results were mainly caused by fabrication error and parasitic effect around the terminal \(75\)\(\Omega\) resistor. Four reflection zeros within the passband can clearly be observed in the measurement results.
## 3 Results
### Simulation Verification with ADAMS and MATLAB/Simulink
#### 3.1.2 Slope Motion Simulation  
In order to quantitatively evaluate the motion control performance of the improved CPG algorithm in sloping terrain, a standardized slope model with a slope of 10\({}^{\circ}\) was constructed. This model was created using SOLIDWORKS 2024 software and importedinto the ADAMS simulation environment. The simulation of the robot's slope motion is illustrated in Figure 11a, and the friction coefficient of the slope was adopted as the rubber-cement ground friction coefficient. The center of mass motion trajectory in the case of slope is illustrated in Figure 11b, and the joint motion curve is demonstrated in Figure 11c. Among them, the blue dashed line indicates that the robot is going uphill, and the black dashed line indicates that the robot is going downhill.  
Figure 8: Virtual model of the wheel-legged robot.  
As illustrated in the center-of-mass displacement curve derived from Figure 11b, the slope prediction algorithm yields a slope of 0.979852, which corresponds to an approximate angle of 10.2\({}^{\circ}\). The initial flat-to-ramp error is substantial, with the Z-axis exhibiting fluctuations up to 0.1159 m. However, subsequent stabilization within \(\pm\)0.01181 m is observed. To ensure high precision inspection, it is imperative that vertical fluctuation is maintained within \(\pm\)0.035 m. As illustrated in Figure 11c, during uphill locomotion, the hip of the front leg is shifted backward, the hip of the back leg is shifted forward, and the knee joints undergo a counter adjustment. The downhill strategy employed in this study is diametrically opposed to the conventional approach.
# UAV Attitude Angle Measurement Method Based on Magnetometer-Satellite Positioning System
## 6 Test and Analysis
### Magnetometer Calibration  
The magnetometer was strapped in the axial direction of the three-axis turntable and collected 4250 sets of data. The initial distribution of the data is shown in Figures 4-6.  
\begin{table}
\begin{tabular}{c c} \hline Latitude: & 31.8597691\({}^{\circ}\) \\ Longitude: & 117.2710962\({}^{\circ}\) \\ Height: & 273.00 m \\ Total field: & 42965 nT \\ Magnetic declination: & \(-\)4.932\({}^{\circ}\) \\ Magnetic inclination: & 48.453\({}^{\circ}\) \\ X component: & 28391 nT \\ Y component: & \(-\)2450 nT \\ Z component: & 32155 nT \\ Horizontal component: & 28496 nT \\ \hline \end{tabular}
\end{table}
Table 1: Local magnetic field information.  
Figure 3: BN-250 positioning + antenna integrated module.  
From the distribution of the initial measurement data, it can be observed that the three axes of the magnetometer deviate from the theoretical value, and the measurement sensitivity of the three axes is not consistent.  
Figure 4: Spatial distribution of initial measurement data.  
Figure 5: X\(-\)Y plane distribution of initial measurement data.  
Figure 6: Y\(-\)Z plane distribution of initial measurement data.  
Using the least-squares fitting algorithm, the results of the fitting and calibration of the magnetometer are as follows. For the ellipsoid equation represented by Equation (7), the fitting coefficients are:  
a1 = 1/2565.8112; a2=1.8376/2565.8112; a3 = 1.8774/2565.8112; a4 = a5 = a6 = 0; a7 = 101.1578/2565.8112; a8 = 47.3103/2565.8112; a9 = -93.6223/2565.8112  
The coordinates of the center of the ellipsoid are: \(\left[-50.5790\quad-12.8727\quad 24.9344\right]\)  
The \(x-\)axis radius is: 38.2640  
The \(y-\)axis radius is: 28.2269  
The \(z-\)axis radius is: 27.9265  
Using the aforementioned calibration method, the data distribution after the calibration of the magnetometer is shown in Figure 7.  
As shown in Figure 4, the spatial distribution of the magnetometer measurement data after calibration is spherical, which is consistent with the theoretical spatial distribution.
# Aerodynamic Study of the NASA's X-43A Hypersonic Aircraft
## 4 Results
### Mesh Independence Study  
A mesh independence study was done to ensure a proper precision in the final results, thus three different meshes were properly designed and evaluated. Mesh 1, the least refined mesh, was dimensioned in order to capture the log layer effects, while Mesh 2 and Mesh 3 (the finer one) were designed with the objective of visualising the buffer layer effects. Table 4 shows the number of elements, the minimum and average \(y^{+}\) values and the Mach numbers of stages 1, 2 and 3 characterising the three different meshes evaluated. The angle of attack was maintained at zero degrees, \(AoA=0^{\circ}\)  
As seen in the table, the minimum and average \(y^{+}\) decreases as the mesh is further refined. Mach numbers in all three stages were quite similar regardless of the mesh chosen; however, the results obtained from meshes 2 and 3 presented a higher similarity between them. In case of the Mach number of stage 1, a deviation of 1% and 0.1% was observed between Mesh 3 and Mesh 1 and Mesh 3 and Mesh 2, respectively. Furthermore, Mesh 3 differed in a 0.5% from Mesh 1 and a 0.1% from Mesh 2 when the Mach number of stage 2 was considered. For the Mach number of stage 3, Mesh 3 presented a deviation of 0.2% from Mesh 1 but a zero deviation from Mesh 2. Therefore, as meshes 2 and 3 had a very close Mach number values and both of them were able to capture the buffer layer effects, and considering that Mesh 2 converged about 16% faster than Mesh 3, Mesh 2 was finally selected to perform all the CFD simulations of the article.