esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 6 Experimental Results
### Comparison with State-of-the-Art Methods  
#### 6.2.1 Camera-IMU Calibration Methods  
In this section, we first compare our method (under two parameter configurations) with those presented in [37; 50; 51] on BoardF. We calibrated each method on the portion data for calibration in hall, balcony and BoardF and then evaluated them on the corresponding evaluation data. Although we assert that the use of a gyroscope is unnecessary for the estimation task, we also evaluated a recent gyroscope-based camera-IMU calibration method, namely CRISP [44]. Because it requires video and gyroscope input, CRISP was calibrated on Motion.  
The calibration methods were evaluated by estimating the downtilt and azimuth angles of the checkerboards by applying the calibration results (i.e., the rotations between the camera and the IMU sensors) to an uncalibrated phone. Statistics are presented in Table 2 as the mean error and standard error with respect to the ground truth.  
As seen from Table 2, our method always shows the best or very close to the best performance in downtilt estimation. Meanwhile, for azimuth estimation, none of the three existing methods can reduce the error effectively, whereas our method always achieves a large improvement by considering the possible accelerometer-magnetometer misalignment.  
Moreover, we also report the results for the accuracy of our calibration method when it is calibrated on a proper dataset, BoardL, in which no restrictions are placed on the checkerboard orientations. We tested configurations of either keeping or removing the variables describing the relative rotation between the accelerometer and magnetometer in our calibration, as well as varying the weighting parameter \(w\) from 0.05 to larger values with a fixed step size of 0.01.  
In Figure 6, the two solid blue lines (calibration without accelerometer-magnetometer DoFs) for the downtilt and azimuth angles show opposite tendencies: when \(w\) is low, the downtilt estimation is improved relative to the baseline in green lines, whereas the azimuth error is very large, and when \(w\) is high, the situation is reversed. When the additional DoFs between the accelerometer and magnetometer are added, both types of error are effectively reduced simultaneously when \(w\) is below 0.3, as shown by the red lines. This indicates the existence of rotation between the accelerometer and magnetometer.  
Furthermore, the curves of the downtilt/azimuth error versus \(w\) represented by the red lines show broad plateaus, thereby demonstrating the robustness of the method with respect to \(w\).  
Therefore, we report our method to work best with the additional DoFs between accelerometer and magnetometer and with \(w\) set to a low value approximately in the range \([0.1,0.3]\). With these configurations, the downtilt and azimuth accuracies are around respectively 0.35 and 3.4 degrees on average with standard deviations of 0.25 and 3.4 degrees, which are generally superior to those reported by the state-of-the art in Table 2.
## 3 Numerical Method and Validation  
The numerical method used in this paper is validated with the results of field measurement [35]. A train with 8 carriages was used in the test, which had a total length of 201.4 m and a cross-section area of 11.2 m\({}^{2}\), passing through the tunnel at 300 km/h. The tunnel had the same cross-section as that mentioned in this paper, and a total length of 1005 m with ballast-less tracks laid inside. The atmospheric pressure of the test ground was 1.02 \(\times\) 10\({}^{5}\) Pa, and the ambient temperature was 8.9 \({}^{\circ}\)C. During the testing, measurement points were arranged at 140 m from the entrance with a height of 1.5 m to monitor the pressure variation.  
The convergence of the mesh is studied first. The grid sizes may affect calculation accuracy; therefore, four sets of mesh with different quantities (denoted as \(N\)) are modelled by changing the sizes of grids. _ANSYS Fluent_ is used to solve the fluid governing equation. The coupling between velocity and pressure is dealt with using the _Semi-Implicit Method for Pressure Linked Equations_, and the gradient term is discretized by the _Green-Gaussian Cell-Based_ method. The pressure term is discretized using the _standard_ format, and the _second-order upwind_ format is adopted in the discretization of the other spatial terms. In addition, the _second-order implicit_ format is used in the time discretization.  
Figure 4: Grid distribution of the model: (**a**) Mesh of tunnel and train; (**b**) Mesh of hood.  
Figure 3: Monitoring points in the simulation: (**a**) Hood at tunnel entrance; (**b**) hood at tunnel exit.  
The peak-peak pressure differential of the measurement point inside the tunnel and the amplitude of the MPW 20 m outside the tunnel exit with different meshes are shown in Table 1. The results show that when \(N>7.990\times 10^{6}\), the variation in \(\Delta p\) and \((\Delta p_{\text{m}})_{\text{max}}\) can be ignored. Therefore, the grid sizes used in _Mesh 3_ are taken in the subsequent calculation. The minimum size of the grids on the train surface is 60 mm, which is set to 100 mm to the surfaces of the tunnel and hood. Boundary layers with a total number of 5 and a height of 1 mm for the first layer are arranged near the surfaces of train, tunnel, and hood.  
Using the mentioned numerical model, the calculated pressure of the monitoring points 140 m and 400 m away from the tunnel entrance are compared with the filed measurement results, as shown in Figure 5. The pressure variation obtained by numerical simulation is basically consistent with that from field measurements. The relative errors of the peak-peak pressure differential compared to the experimental data are 0.74% and 2.86%, respectively. The numerical calculation results are in good agreement with the filed measurement data, indicating that the numerical method used in this paper can accurately simulate the aerodynamic effects caused by a train passing through a tunnel.
# The Thermo-Pressure Concept: A New Model in Diabetic Foot Risk Stratification
###### Abstract  
Elevated foot pressure and high temperatures have been proposed as predictive factors for diabetic foot ulceration; however, on their own, their predictive value is still questionable. This pilot study hypothesized that combining these two measurements in a thermo-pressure model could result in a more reliable risk assessment tool for providing risk categorization aimed to determine whether there is a correlation between foot plantar pressure and temperature in patients living with diabetes mellitus (DM) when compared with healthy individuals following a 15 min walk. Foot pressure and thermographic images were taken from 48 participants who were categorised into four groups following clinical evaluation into healthy individuals (Group A), living with DM with no complications (Group B), DM with peripheral arterial disease (Group C) and DM patients with neuropathy (Group D). This study demonstrated a positive correlation between pressure and temperature; as pressure increases, so does temperature. This was more pronounced in the groups with DM when compared to the healthy group. More research is warranted to further develop this innovative thermo-pressure model, which could hopefully be more sensitive in categorizing and identifying patients living with DM who are at risk of developing foot ulceration to initiate prompt care.  
foot pressure; thermography; diabetic foot; diabetes; ulceration Article
## 4 Model Comparison and Validation
### Comparison Results of the Proposed Models
The average of the correlation coefficients (\(R^{2}\)) of Lognormal, Hyperbolic, and Gumbel-constitutive models were less than 0.8, and the average correlation coefficient of the other five models was more than 0.8. It indicates that most of the above distributions were suitable for describing the damage evolution of type A curves. The average correlation coefficient (\(R^{2}\)) of the Gamma-constitutive model was 0.8609, indicating that Gamma distribution was the optimum for describing the damage evolution of weak softening type curve; The following optimal functions are Weibull-constitutive model (\(R^{2}\) = 0.8408) and Exponential-constitutive model (\(R^{2}\) = 0.8382). The type B curves are the strong softening curves with a large softening degree. The simulated curves of the Lognormal, Hyperbolic, and Weibull-constitutive models show that the peak displacement deviates to the left (Figure 10), which indicates that the damage degree of these distributions reached the peak damage degree faster than the actual state with the displacement. It means that these models are not suitable for strong softening curves. The fitting results of other constitutive models show that the peak displacement of these models deviates slightly to the right under low normal stress (200 kPa and 300 kPa). While the fitting results under high normal stress are better, the average correlation coefficients of these models were greater than 0.8 (Figure 11b), indicating that these distributions conformed to the damage evolution law of type B curves. The average correlation coefficient of Gamma-constitutive model was 0.9744, which was the optimal constitutive model. The following are Exponential-constitutive model and Logistic-constitutive model, and the average correlation coefficient was 0.9303 and 0.9253, respectively. In sum, the Gamma-constitutive model was the optimal shear constitutive model in both the strong softening curve and the weak softening curve, indicating that Gamma distribution was the most suitable for describing the dynamic damage process of slip zone soil. In addition, Exponential distribution was also one of the best models, which is a special case of Gamma distribution. Therefore, the superiority of Gamma distribution over other models can also be proved from this point. However, the commonly used Weibull-constitutive model was not suitable for the strong softening curve because of the large peak offset; it is only suitable for weak softening curves but is not the optimal model. The peak displacement of the strong softening curve fitting in most models deviates to the left or right. The results show that the damage evolution process of the strong softening shear curve was relatively unique, and there were few applicable damage distribution models. On the contrary, the weak softening curve was applicable to many models, and the deviation of peak displacement was small.
# Modeling and Analysis of Contactless Solar Evaporation for Scalable Application
## 2 Methodology
### Modelling Framework
The heat transfer and mass transfer were coupled through Eqs. 8 and 9, the flow and heat transfer are coupled through Equations (10) and (11). \[q_{\mathrm{evap}}=-L_{v}g_{\mathrm{evap}} \tag{8}\]  
\[Q_{p}=\alpha_{p}T(\frac{\partial p}{\partial t}+\mathbf{u}\cdot\
abla p) \tag{9}\]  
\[\alpha_{p}=-\frac{1}{\rho}(\frac{\partial\rho}{\partial T})_{p} \tag{10}\]  
Figure 2:0  
where \(q_{\mathrm{evap}}\), \(L_{v}\) and \(g_{\mathrm{evap}}\) are the phase change heat flux, latent heat of evaporation and evaporative vapor flux, respectively. \(\alpha_{p}\) and \(\tau\) are the coefficient of thermal expansion and the viscous stress tensor. To solve the multiple-physical fields coupling problem, the laboratory SCE model applied the following boundary conditions: a constant heat flux (\(Q_{\mathrm{in}}\) one-sun solar radiation) boundary on the solar absorber surface. The radiative heat transfer process of infrared emitter and water surface is represented by setting boundary heat sources based on Stefan-Boltzmann law on the infrared emitter and water surface, respectively. The expressions of boundary heat sources are shown in Equations (12) and (13). Convective heat flux boundary was applied on the sidewall of laboratory SCE, considering a constant ambient temperature of 25 \({}^{\circ}\)C. The open boundaries were applied on the outer boundaries of the air domain. Figure 2:1  
Figure 2:2  
where \(F\), \(\varepsilon\) and \(\sigma\) represent the view factor, emittance and Stefan-Boltzmann constant, respectively. \(T_{\mathrm{e}}\) and \(T_{\mathrm{wat}}\) are the temperature of emitter and water surface. \(\varphi\) is the hole area ratio. As shown in Equation (14), the convective heat flux boundary in laboratory SCE was then replaced by periodic boundary conditions on the lateral surfaces in the scalable SCE,to simulate the large-scale application, whereas the rest settings were consistent in both SCE models. Figure 2:3  
The stable evaporation rate is the primary metric used to assess the performance of SCE system. Hence, the above modelling framework were solved in COMSOL Multiphysics with a stationary solver. The relative tolerance is 1e\({}^{-3}\) considering both computational cost and accuracy. The material properties including thermal conductivity were also called from the COMSOL material library. The main input parameters for the two models are shown in Figure 2:4  
Evaporation rates under different mesh configurations were studied to ensure mesh-independent results, and the results for scalable SCE model is shown in Figure 2:5 After increasing the element number from M\({}_{5}\) (455724) to M\({}_{6}\) (1778605), the difference in evaporation rate is less than 1%, indicating a mesh-independent result. The same method was also applied to the laboratory SCE model. With these analyses, the element numbers of 455724 and 2185976 were confirmed for the scalable and laboratory SCE models, respectively.
# Geographical Distribution of Global Radiation and Sunshine Duration over the Island of Cyprus
## 3 Results and Discussion
### Daily Values
#### 3.1.3 Daily Clearness Index and Relative Sunshine Duration
\begin{table}
\begin{tabular}{l l c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & \\ \hline
**Station/Month** & **Class** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **Year** \\ \hline
**Coastal** & **Cloudy** & 4.6 & 5.1 & 7.2 & 10.5 & 9.9 & 12.1 & * & 10.6 & 9.4 & 5.9 & 5.1 & 4.1 & 6.5 \\
**\&** & **Pc\_D** & 8.3 & 10.5 & 14.0 & 16.1 & 18.1 & 20.1 & 20.6 & 19.2 & 15.6 & 11.3 & 8.9 & 7.9 & 13.3 \\
**Plain** & **Pc\_B** & 11.1 & 14.0 & 18.1 & 21.6 & 24.3 & 25.8 & 25.1 & 23.4 & 19.7 & 14.8 & 11.8 & 10.2 & 18.3 \\  & **Clear** & 13.2 & 16.4 & 21.2 & 25.8 & 28.4 & 29.3 & 28.4 & 25.7 & 21.9 & 17.9 & 13.5 & 11.5 & 21.1 \\ \hline
**Semi-Montainous** & **Cloudy** & 4.3 & 5.3 & 6.5 & 9.8 & 10.9 & 12.6 & 13.7 & 13.2 & 10.3 & 6.2 & 5.1 & 4.0 & 7.3 \\
**\&** & **Pc\_D** & 8.3 & 10.6 & 13.6 & 16.5 & 18.5 & 19.9 & 19.5 & 18.3 & 14.7 & 11.5 & 8.8 & 7.6 & 14.0 \\
**Montainous** & **Pc\_B** & 11.0 & 13.9 & 18.0 & 21.7 & 24.3 & 25.1 & 24.8 & 22.7 & 19.2 & 15.1 & 11.7 & 10.2 & 18.1 \\  & **Clear** & 13.2 & 16.5 & 21.2 & 26.1 & 28.4 & 29.3 & 28.6 & 26.4 & 22.4 & 18.2 & 14.0 & 11.7 & 21.3 \\ \hline \hline \end{tabular}
\begin{tabular}{l l c c c c c c c c c c} \hline \hline
**Station/Month** & **Class** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **Year** \\ \hline
**Coastal** & **Cloudy** & 5 & 2 & 2 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 2 & 6 & 21 \\
**\&** & **Pc\_D** & 10 & 5 & 6 & 4 & 1 & 1 & 0 & 1 & 1 & 3 & 8 & 8 & 49 \\
**Plain** & **Pc\_B** & 7 & 5 & 7 & 5 & 4 & 2 & 3 & 6 & 8 & 9 & 9 & 7 & 72 \\  & **Clear** & 6 & 12 & 14 & 19 & 25 & 26 & 27 & 23 & 22 & 18 & 9 & 7 & 208 \\ \hline
**Semi-Montainous** & **Cloudy** & 9 & 6 & 4 & 4 & 1 & 1 & 0 & 0 & 0 & 2 & 4 & 8 & 39 \\
**\&** & **Pc\_D** & 9 & 8 & 9 & 7 & 4 & 5 & 3 & 4 & 5 & 7 & 9 & 11 & 80 \\
**Montainous** & **Pc\_B** & 5 & 4 & 6 & 6 & 6 & 6 & 5 & 5 & 6 & 7 & 7 & 5 & 69 \\
**Clear** & 4 & 9 & 11 & 13 & 19 & 18 & 23 & 21 & 19 & 15 & 9 & 6 & 166 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: (a) Monthly daily average of global irradiation (MJ/m\({}^{2}\)) according to the classification of the daily daily clearness index (\(K_{T}\)). (b) Monthly number of days according to the classification of the daily clearness index (\(K_{T}\)). The asterisk denotes a missing value.
# TEDL: A Text Encryption Method Based on Deep Learning
###### Abstract  
Recent years have seen an increasing emphasis on information security, and various encryption methods have been proposed. However, for symmetric encryption methods, the well-known encryption techniques still rely on the key space to guarantee security and suffer from frequent key updating. Aiming to solve those problems, this paper proposes a novel symmetry-key method for text encryption based on deep learning called TEDL, where the secret key includes hyperparameters in the deep learning model and the core step of encryption is transforming input data into weights trained under hyperparameters. Firstly, both communication parties establish a word vector table by training a deep learning model according to specified hyperparameters. Then, a self-update codebook is constructed on the word vector table with the SHA-256 function and other tricks. When communication starts, encryption and decryption are equivalent to indexing and inverted indexing on the codebook, respectively, thus achieving the transformation between plaintext and ciphertext. Results of experiments and relevant analyses show that TEDL performs well for security, efficiency, generality, and has a lower demand for the frequency of key redistribution. Especially, as a supplement to current encryption methods, the time-consuming process of constructing a codebook increases the difficulty of brute-force attacks, meanwhile, it does not degrade the efficiency of communications.  
text encryption; deep learning; hyperparameter +
Footnote †: journal: Applied Sciences  
**TEDL: A Text Encryption Method Based on Deep Learning**  
**[PERSON]**  
1 School of Computer Science and Engineering, Southeast University, Nanjing 211189, China; [EMAIL_ADDRESS]  
2
## 2 Coupling Model of the GRIN Fiber Probe and HC-PCF
When the coupling distance \(z\) between the GRIN fiber probe and HC-PCF is equal to \(z_{\omega}\), Equation (5) shows that the maximum coupling efficiency is only related to the waist radius \(\omega_{f}\). If \(\gamma\) is set as the ratio of the waist diameter of the GRIN fiber probe to the MFD of HC-PCF \(2\omega_{f}d\), the maximum efficiency can be expressed as  
\[\eta=1-\exp[-2(\frac{1}{\gamma})^{2}] \tag{8}\]  
The coupling efficiency \(\eta\) becomes a function of the ratio \(\gamma\), and it is necessary to analyze the relationship between the two. If the waist diameter is much smaller than MFD of HC-PCF, higher order modes with higher transmission losses are excited and the simplified model may not suitable. The minimum \(\gamma\) is set to 0.9 and changed from 0.9 to 2.5. The corresponding coupling efficiency is shown in \[z_{\omega}=\frac{S_{1}\cos(2 gL)+S_{2}\sin(2 gL)}{S_{0}-S_{3}\cos(2 gL)-S_{4}\sin (2 gL)} \tag{1}\]0  
It can be concluded from Figure 2 that the coupling efficiency is 27.4% when the ratio \(\gamma\) is 2.5, and the coupling efficiency will gradually increase as the ratio decreases. When the ratio is 1, the coupling efficiency reaches 86.5%, and the coupling efficiency reaches 91.5% when the ratio is 0.9. The results show that reducing the waist radius helps to improve the coupling efficiency.
# Experimental Investigation into the Thermal Performance of Personal Cooling Mechanisms
## 2 Materials and Methods  
### Methodology  
Thermal manikins assess heat loss, thermal insulation, and breathability by maintaining a consistent temperature setpoint and a specific sweat delivery rate. When combined with a thermophysiological predictive model, these manikins can estimate a user's thermal comfort and sensation, two subjective parameters that usually require human testing, as well as other physiological factors such as mean skin temperature and core temperature. This study employs a thermal manikin and two thermophysiological prediction models to evaluate the thermal performance of four cooling mechanisms. The thermal performance is assessed based on the mean skin temperature, hypothalamus temperature, thermal comfort index, and thermal sensation index of the manikin wearing the different cooling garments.
# Prediction of Grain Porosity Based on WOA-BPNN and Grain Compression Experiment
## 4 Modeling Prediction of Grain Porosity
### Model Evaluation Indicators  
After establishing the model, four evaluation indices were selected to test the accuracy and quality of the prediction model. These included the coefficient of determination (R\({}^{2}\)), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). These indices were used to evaluate the performance of the grain porosity prediction model in this paper. Among them, R\({}^{2}\) is usually used to evaluate the linear fit of the model. The closer the value of R\({}^{2}\) is to 1, the better the fit of the model. The lower the RMSE, MAE, and MAPE, the greater the prediction accuracy of the model. The corresponding formulas are shown below:  
\[\text{R}^{2}=1-\frac{\sum\limits_{i=1}^{m}\left(Y_{i}-\hat{Y}_{i}\right)^{2}}{ \sum\limits_{i=1}^{m}\left(Y_{i}-\hat{Y}_{i}\right)^{2}} \tag{17}\]  
\[\text{RMSE}=\sqrt{\frac{\sum\limits_{i=1}^{m}\left(Y_{i}-\hat{Y}_{i}\right)^{ 2}}{m}} \tag{18}\]  
\[\text{MAE}=\frac{\sum\limits_{i=1}^{m}\left|Y_{i}-\hat{Y}_{i}\right|}{m} \tag{19}\]  
\[\text{MAPE}=\frac{1}{m}\sum\limits_{i=1}^{m}\left|\frac{Y_{i}-\hat{Y}_{i}}{Y_ {i}}\right| \tag{20}\]where \(m\) is the total number of samples, \(i\) is the number of sample sequences, \(\overline{Y_{i}}\) is the average of the actual values of the samples, \(Y_{i}\) is the actual value of the samples, and \(\hat{Y_{i}}\) is the predicted value of the samples.