esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Indoor Localization Based on Extracting Trusted Fingerprint
## 5 Experiment Analysis
### Effects of Different Impaired Levels  
In the experiment, we placed an impaired AP for which the antenna of the AP was broken and an AP with poor power connection, respectively. First of all, we used the traditional fingerprint localization algorithm for localization and then used the safe fingerprint localization algorithm for localization. The localization results are shown in Figure 9.  
Figure 8: Voting results.  
Figure 9 is mainly used to observe the influence of different impaired levels on the location results. In order to analyze the impact of impaired AP on the localization results, we first conducted a single localization and analyzed the impact of impaired AP on the localization algorithm according to localization error, as shown in Figure 9a. Then, we selected multiple positions in the localization area for localization, where the localization error for each location is the average of multiple localization errors. Figure 9b shows the overall localization accuracy of the algorithm.  
Figure 9a shows that 10 localization experiments were conducted at the same location. The black line indicates the localization result of the traditional fingerprint localization algorithm when the antenna of the AP is broken. The green line indicates the localization result of the traditional fingerprint localization algorithm for the AP with poor power connection. Since the RSSI of the impaired AP is unstable, the localization result in the same location is not stable, and the localization error largely changes. The red line and the blue line indicate the localization error of the algorithm proposed in this paper in the presence of different impaired levels in the environment. It can be clearly seen from the Figure 9a that the algorithm in this paper can guarantee better localization results when there are different impaired levels in the environment.  
Figure 9b shows the average localization error for multiple runs at 10 different locations. It is mainly used to reflect the stability of the localization algorithm. In Figure 9b, the abscissa represents the different location, and the ordinate represents the average localization error of each location. For the AP with poor power connection, the black line represents the localization result of the traditional fingerprint localization algorithm, and the red line represents the localization result of the algorithm proposed in this paper. When the antenna of the AP is broken, the green and blue lines indicate the localization results of the traditional fingerprint localization algorithm and the proposed algorithm, respectively. As can be seen from Figure 9b, the localization algorithm proposed in this paper can effectively filter impaired APs and reduce the impact of impaired APs on the location result.
# The Development of Key Technologies in Applications of Vessels Connected to the Internet
## 2 Internet of Vessels Architecture
### The Technical Architecture of the Networks Layer
#### 2.3.5 Underwater Acoustic Communication Networks  
As well as the land communication, the underwater communication can be divided into wire communication and wireless communication. Laying electric and optical fiber submarine cables realizes the underwater wire communication, while the underwater wireless communication networks can be regarded as delay tolerant networks (DTNs) due to their sparse deployment, node mobility and some severely impaired bandwidth-limited underwater acoustic communication channels [36]. As a reference implementation of the DTNs protocol, DTN2 was designed and implemented [31]. Based on the DTN2 research platform, an underwater convergence layer is developed to interconnect acoustic networks [37]. The most advanced underwater acoustic communication equipment is now applied in military, special commercial and scientific research [38]. The distance of signal transmission can be several thousand meters.
## 3 Hardware Implementation
### ECG R-Peak Detection Engine
#### 3.2.2 Process Element  
Computation is performed by the process element (PE). As shown in Figure 9, the PE comprises a multiplier, an accumulator, a subtracter, a saturation and truncation unit, and an input/output multiplexer. The operation of PE follows the INT16 format. To address the issue of overflow, we employ saturation and truncation.  
Figure 8: (**a**) State diagram of the ECG R-peak detection engine (**b**) State diagram of the filter operation.  
Figure 9: Hardware architecture of the PE.
## 4 Results and Analysis
### Robustness Results on Other Models  
We show the corruption robustness of five other vehicle detection models on test data in Tables 8-10, in which they represent the evaluation results under light, moderate, and severe conditions, respectively.  
It can be observed that different corruption factors exhibit similar effects on various vehicle detection models, indicating that these corruptions have a universal impact across all models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & Type (mAP50) (\%) & SSD & CenterNet & Faster R-CNN & Mask R-CNN & RetinaNet \\ \hline \multirow{6}{*}{**Fron**} & Clean & 72.0 & 67.1 & 79.7 & 80.6 & 78.6 \\ \cline{2-7}  & Rain & 67.2 & 63.2 & 75.8 & 75.9 & 74.4 \\  & Fog & 66.1 & 63.0 & 74.7 & 74.4 & 76.1 \\  & Frost & 66.5 & 64.2 & 76.0 & 76.6 & 74.2 \\  & Snow & 65.8 & 64.3 & 75.5 & 77.3 & 75.4 \\ \hline \multirow{6}{*}{**Fron**} & Brightness & 70.3 & 66.7 & 79.0 & 78.4 & 75.1 \\  & Dark & 68.4 & 62.5 & 74.6 & 73.4 & 73.7 \\  & Saturate & 69.0 & 64.9 & 76.8 & 76.6 & 75.7 \\ \hline \multirow{6}{*}{**Fron**} & GaussianNoise & 68.3 & 65.2 & 76.4 & 76.3 & 75.6 \\  & SpeckleNoise & 67.9 & 65.1 & 75.7 & 76.0 & 74.6 \\  & ZoomBlur & 56.0 & 53.3 & 67.1 & 68.2 & 65.1 \\  & MotionBlur & 65.4 & 62.3 & 73.2 & 73.7 & 72.9 \\ \hline \multirow{6}{*}{**Fron**} & ElasticTransform & 68.3 & 63.2 & 75.7 & 75.9 & 74.2 \\  & JpegCompression & 67.9 & 64.0 & 76.4 & 76.8 & 75.9 \\ \cline{1-1}  & Occlusion & 21.3 & 20.4 & 33.4 & 35.7 & 30.6 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The benchmark results of remaining models. We show robustness metric (mAP50) under each corruption based on the car, truck, and bus classes at light level.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & Type (mAP50) (\%) & SSD & CenterNet & Faster R-CNN & Mask R-CNN & RetinaNet \\ \hline \multirow{6}{*}{**Fron**} & Clean & 72.0 & 67.1 & 79.7 & 80.6 & 78.6 \\ \cline{2-7}  & Rain & 62.4 & 58.6 & 71.4 & 72.6 & 69.0 \\  & Fog & 61.2 & 59.1 & 70.7 & 70.9 & 68.9 \\  & Frost & 62.0 & 55.5 & 70.7 & 71.8 & 67.4 \\  & Snow & 60.6 & 57.0 & 69.2 & 70.4 & 67.8 \\ \hline \multirow{6}{*}{**Fron**} & Brightness & 62.7 & 58.3 & 70.4 & 72.0 & 70.1 \\  & Dark & 59.3 & 55.1 & 69.4 & 71.5 & 68.7 \\  & Saturate & 63.0 & 58.6 & 70.6 & 74.4 & 71.4 \\ \hline \multirow{6}{*}{**Fron**} & GaussianNoise & 61.6 & 57.0 & 69.4 & 71.4 & 70.0 \\  & SpeckleNoise & 61.0 & 58.4 & 67.1 & 70.6 & 69.8 \\ \cline{1-1}  & ZoomBlur & 33.7 & 28.9 & 35.7 & 38.8 & 36.4 \\ \cline{1-1}  & MotionBlur & 59.9 & 56.4 & 67.5 & 69.5 & 68.4 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The benchmark results of remaining models. We show robustness metric (mAP50) under each corruption based on the car, truck, and bus classes at middle level.
## 4 Experiments and Analysis
### Results Analysis and Discussion  
This section provides baseline comparisons to evaluate the model accuracy, loss value, and effectiveness in minimizing overfitting. Moreover, we applied five different variation models using TF-IDF-glove word representation and various deep learning algorithms with a batch size of 256, epochs remaining 50, and the learning rates to 0.001 for all the datasets for satisfactory accuracy.
## 6 Performance Comparison
### Comparison of Temperature Distribution  
A suitable heat dissipation system must be used to ensure rotor safety and reliability. In this paper, the temperature distribution of the high-speed IPM is simulated using the thermal network method in motor-CAD. As shown in Figure 20, a water cooling system with a spiral waterway outside the stator is adopted; the water temperature and flow rate are set to 25 \({}^{\circ}\)C and 1.2 m\({}^{3}\)/h, respectively. The direction of the arrows in the figure represents the direction of flow of the cooling liquid.  
The temperature distributions of the initial design and optimized scheme are shown in Figure 21. For the high-speed IPM motors, the maximum temperature of the motor is located at the rotor core. The maximum motor temperature of the optimized scheme is 129.6 \({}^{\circ}\)C, which is 37.1 \({}^{\circ}\)C lower than the original design. The temperature of the PM decreased from 158.7 \({}^{\circ}\)C to 125.3 \({}^{\circ}\)C, a decrease of 33.4 \({}^{\circ}\)C.  
The comparisons of the detailed performance between the initial and optimized schemes are listed in Table 7. Compared to the initial design,  
Figure 19: Comparison of the rotor stress before and after optimization. (**a**) Initial; (**b**) optimal.  
Figure 20: Motor cooling system design.  
mance is significantly improved in all aspects. Among them, motor efficiency increases from 95.96% to 96.34%, with an increase of 0.38%. The optimized power factor rises to 0.972. The rotor safety factor increases from 1.03 to 1.29. In addition, the temperature of the winding decreases from 105.1 \({}^{\circ}\)C to 96.7 \({}^{\circ}\)C.
# Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting
## 2 Materials and Methods
### Datasets
#### 2.1.1 Electric Demand in Spain  
The first dataset used in the experimental study covers the national electrical energy demand in Spain ranging from 2014-01-02T00:00 to 2019-11-01T23:50. During this period, the consumption was measured every ten minutes which makes a time series with a total length of 306,721 measurements. The data was provided by Red Electrica de Espana (the Spanish public grid) and is publicly available at [28]. The dataset was divided into two sets: the training set contains 245,376 samples corresponding to the period from 2014-01-02T00:00 to 2018-09-02T00:50; and the test set contains 61,343 samples comprising the period from 2018-09-02T01:00 to 2019-11-01T23:40. As it has been done in previous studies using this data [29], we defined the forecasting horizon to be 4 hours, which involves a prediction of 24 time-steps.  
Figure 1 plots the electric demand data at different scales. As can be seen, the time series presents both weekly and daily seasonality. In general, the demand is higher at weekdays than at weekends and suffers a severe drop during night-time every day.
# Conservation Integrals in Nonhomogeneous Materials with Flexoelectricity
## 2 Basic Equations in Nonhomogeneous Flexoelectric Materials  
The field equations for flexoelectric solids are, with \(f_{i}\) being the body force and \(\rho_{e}\) being the free electric charge [45]  
\[\sigma_{ij,j}-\tau_{ijm,mj}+f_{i}=0 \tag{1}\]  
\[D_{i,i}-Q_{ij,ji}-\rho_{e}=0 \tag{2}\]  
where \(\sigma_{ij}\), \(\tau_{ijm}\), \(D_{i}\), and \(Q_{ij}\) are the stress, the high-order stress, the electric displacement, and the high-order electric quadrupole, respectively. In this paper, the subscript comma indicates differentiation with respect to the spatial variables, all the derivations are discussed in rectangular Cartesian coordinates and the Latin indices run from 1 to 3.  
Using the assumption of linear theory for the flexoelectric solids, the bulk internal energy density function \(u\) can be written as [3,4]  
\[\begin{array}{l}u=\frac{1}{2}C_{ijkl}\varepsilon_{ij}\varepsilon_{kl}+r_{ijklm }\varepsilon_{ij}w_{klm}+d_{ijk}\varepsilon_{ij}D_{k}+e_{ijkl}\varepsilon_{ij} Q_{kl}\ +\ \frac{1}{2}g_{ijklm}w_{ijk}w_{lmn}\\ \  
Thus, the constitutive equations for the bulk can be expressed in terms of the electric Gibbs free energy as  
\[\begin{array}{l}\sigma_{ij}=\frac{\partial g^{E}}{\partial\epsilon_{ij}}=C_{ijkl} \epsilon_{kl}+r_{ijklm}w_{klm}-d_{ijk}E_{k}-e_{ijkl}V_{kl}\\ \tau_{ijm}=\frac{\partial g^{E}}{\partial w_{lm}}=r_{klijm}\epsilon_{kl}+g_{ ijlmkl}w_{kml}-f_{kjim}E_{k}-\eta_{klijm}V_{kl}\\ D_{i}=-\frac{\partial g^{E}}{\partial E_{i}}=d_{jkl}\epsilon_{jk}+f_{ijkl}u_{ jkl}+a_{ij}E_{j}+h_{ijk}V_{jk}\\ Q_{ij}=-\frac{\partial g^{E}}{\partial V_{ij}}=e_{klij}\epsilon_{kl}+\eta_{ ijkm}w_{kmn}+h_{kij}E_{k}+b_{ijkl}V_{kl}\end{array} \tag{9}\]  
Using constitutive equations, the bulk electric Gibbs free energy density function \(g^{E}\) can be rewritten as  
\[g^{E}=\frac{1}{2}\sigma:\epsilon+\frac{1}{2}\dot{\mathbf{\tau}}\dot{\mathbf{w}}-\frac {1}{2}\mathbf{D}\cdot\mathbf{E}-\frac{1}{2}\mathbf{Q}:\mathbf{V} \tag{10}\]  
Internal energy density and electric Gibbs free energy density can be used to obtain the conservation laws or dual conservation laws [46], which will be obtained in the following sections.
## 3 Results and Discussion
### Establishment of Collision Impact Force Model and Regression Analysis  
The drop impact test of the seedlings was carried out with different test factors (contact material, drop height and plug specification). As shown in Table 3, orthogonal tests were designed by [PERSON]. Multiple regression fitting analysis was performed on the test results. A quadratic multinomial regression model of the plug specification (\(X_{1}\)), contact material (\(X_{2}\)), and drop height (\(X_{3}\)) was established, as shown in Equation (10).  
\[\left\{\begin{matrix}F=6.20\ -\ 1.21\ \times\ X_{1}\ -\ 0.23\ \times\ X_{2}+0.83\ \times\ X_{3}+0.038\ \times\ X_{1}X_{2}+0.22\ \times\ X_{1}X_{3}\\ -\ 0.12\ \times\ X_{2}X_{3}+0.59\ \times\ X_{1}{}^{2}\ -\ 0.81\ \times\ X_{2}{}^{2}\ -\ 0.074\ \times\ X_{3}{}^{2}\end{matrix}\right. \tag{10}\]  
where, \(F\) is the collision impact force, N, and \(X_{1}\), \(X_{2}\), and \(X_{3}\) are the plug specification, contact material and drop height (mm), respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{3}{c}{**Test Factor Level Value**} & \multirow{2}{*}{**Collision Impact Force/N**} \\ \cline{2-2} \cline{4-4}  & \(\mathbf{X_{1}}\) & \(\mathbf{X_{2}}\) & \(\mathbf{X_{3}}\) & \\ \hline
1 & 0 & 0 & 0 & 6.08 \\
2 & \(-1\)  
The analysis of variance (ANOVA) and significance were carried out on the quadratic regression model of the collision impact force, and the results are shown in Table 4. The regression model of collision impact force was \(p<0.0001\), indicating that the model was extremely significant. The regression equation determination coefficient \(R^{2}=0.9877\) and the adjusted determination coefficient adjusted \(R^{2}=0.9718\) both were close to 1. Therefore, the regression equation had high reliability. The lack of fit \(p=0.6484>0.05\) indicated that the regression equation fit well and could better reflect the relationship between the collision impact force \(F\) and \(X_{1}\), \(X_{2}\) and \(X_{3}\). In this model, \(X_{1}\), \(X_{3}\), \(X_{1}\)\({}^{2}\), and \(X_{2}\)\({}^{2}\) had very significant impacts on the collision impact force, and \(X^{2}\) had a significant effect on the collision impact force. The influence on the collision impact force was on the order of plug specification > drop height > contact material.  
Analysis of Collision Contact Stress Distribution between Oil Sunflower Plug Seedings and Contact Materials
## 3 Results  
### Fabrication and Characterization of the GC Hybrid Implant  
To confirm the effects of GC coating on the titanium, we used pure titanium discs as a model implant. The GC solution was applied onto the surface of titanium discs through a spin-coating technique to improve the functionality of pure titanium as a dental implant (e.g., improved cell attachment and anti-biofilm formation). The hybrid implants were fabricated with three different concentrations of GC solution (1%, 3%, and 5%) and one chitosan solution-treated surface was also prepared as a control. To confirm the presence of GO on the surfaces of the hybrid implants, Raman spectroscopy analysis was conducted. Figure 1a shows the Raman spectra of the 1%, 3%, and 5% GC hybrid implants, demonstrating representative peaks of GO at the G and D peaks (near 1350 and 1590 cm\({}^{-1}\)). The G peak indicates an in-plane vibration from the sp\({}^{2}\) hybridization of well-ordered materials, and the D peak represents the out-of-plane vibration from the sp\({}^{3}\) bonding and structural defects [26]. This result demonstrated that the surfaces of the hybrid implant were successfully integrated with GO. Next, we investigated the water contact angle and the roughness. The surface properties, including suitable wettability and roughness, are essential to improve cell attachment and to suppress bacteria attachment [27; 28; 29]. As shown in Figure 1b,c, the contact angle of the chitosan solution-coated titanium was 61.5\({}^{\circ}\), and the contact angles of the GC hybrid implants were 56.2\({}^{\circ}\) (1% GC), 54.6\({}^{\circ}\) (3% GC), and 54.5\({}^{\circ}\) (5% GC). The average roughness was 114.6 \(\pm\) 7.9 nm for the chitosan-coated surface, 124.6 \(\pm\) 4.7 nm (1% GC), 140.7 \(\pm\) 8.8 nm (3% GC), and 149.0 \(\pm\) 7.2 nm (5% GC). Interestingly, the wettability and roughness were directly changed by the concentration of graphene, with a gradual increase as the concentration of graphene increased. Therefore, by combining the graphene and chitosan solutions, the surface properties, including wettability and roughness, were successfully controlled.
# Structural Pattern Analysis in _Patella vulgata_ Shells Using Raman Imaging
## 3 Results and Discussion
### Raman Band Assignment in P. vulgaris Shell  
Figure 6 shows the averaged Raman spectrum from a cross-section map of _P. vulgaris_ shell.  
According to [PERSON] et al., the strongest peaks that clearly differentiate areaonite from calcite are at 208 cm\({}^{-1}\), exclusive to aragonite, and 283 cm\({}^{-1}\), characteristic of calcite, with only a minor contribution from aragonite [13]. The region around 700 cm\({}^{-1}\) can also be used for differentiation, as aragonite exhibits a doublet at 700-704 cm\({}^{-1}\), while calcite presents a single peak at 711-712 cm\({}^{-1}\). However, distinguishing these peaks is often challenging due to resolution limitations. Furthermore, in the Raman spectra of CaCO\({}_{3}\), the most intense and narrow band corresponds to the symmetric stretching vibration (\(\
u_{1}\)) of the carbonate group, occurring at 1085 cm\({}^{-1}\) for aragonite and 1086 cm\({}^{-1}\) for calcite [40].  
Besides carbonate-related peaks, several bands originate from carotenoids. These pigments exhibit the following four major vibrational modes: \(\
u_{1}\) (1450-1680 cm\({}^{-1}\)), \(\
u_{2}\) (1070-1210 cm\({}^{-1}\)), \(\
u_{3}\) (1285-1315 cm\({}^{-1}\)), and \(\
u_{4}\) (1000-1015 cm\({}^{-1}\)) [34]. In particular, strong carotenoid peaks appear at 1130 cm\({}^{-1}\) and 1521 cm\({}^{-1}\), corresponding to the vibrational modes of conjugated C-C single bonds and C=C double bonds, respectively [15; 36]. The intensities of these bands are enhanced due to resonant coupling with the laser source, allowing their detection even at low concentrations [54].  
The shift of key calcite Raman bands can be influenced by various chemical species incorporated into the crystal lattice, including substituted ions (Mn\({}^{2+}\), Fe\({}^{3+}\), Pb\({}^{2+}\), Ce\({}^{3+}\), etc.), complex ions, organic molecules, and free radicals [55]. Among these, partial magnesium substitution is particularly relevant, as it has been demonstrated to affect bands such as the symmetric stretching mode \(\
u_{1}\) (1085 cm\({}^{-1}\)), which can undergo slight shifts, depending on the nature of the substitution [13; 54]. Several studies have reported a linear correlation between these shifts and the amount of Mg incorporated into the calcite lattice [33; 56].  
In addition to the shifts observed in carbonate Raman bands, variations in the spectral position of carotenoid-related bands have also been reported [15; 20; 34; 35]. These shifts depend on the number of conjugated carbon atoms in the polyene chain. Longer chains reduce the energy gap, leading to a shift toward lower frequencies; whereas, shorter chains result in higher vibrational frequencies due to increased force constants [34].
## 3 Simulation
### Simulation  
#### 3.2.1 Non-Obstruction Scenarios  
The starting point and end point of the autonomous vehicle are randomly selected in an infinite map range. Since Algorithms 1-4 are all variants of DQN, and their performance differences after training to convergence are not significant, we focus on comparing the planning time and planning path length between Algorithms 4 and 5 in the obstacle-free scenario. To conduct this comparison, we utilize five sets of starting and ending points as test environments. For detailed results, please refer to Table 5.  
The test was conducted using five sets of test environments, and each environment was tested for 20 rounds. The results were averaged to obtain the performance metrics. Specifically, we compared the planning time and planning path length between Algorithms 4 and 5 in these five test environments. The detailed comparison results can be found in Tables 6 and 7.  
The walking paths of Algorithms 4 and 5 in the five rounds of random environment tests are shown in Figure 5.  
As can be seen from Tables 7 and 8, as well as Figure 5, in the five rounds of random environments, the APF-D3 QNPER algorithm (Algorithm 5) achieved an average driving  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Env 1 (s)** & **Env 2 (s)** & **Env 3 (s)** & **Env 4 (s)** & **Env 5 (s)** \\ \hline Algorithm 4 & 13.420 & 13.566 & 4.828 & 6.713 & 6.870 \\ Algorithm 5 & 7.743 & 5.527 & 3.876 & 5.668 & 3.897 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Planning time.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm Number** & **Algorithm Name** \\ \hline Algorithm 1 & Dueling DQN Algorithm \\ Algorithm 2 & DQN Algorithm \\ Algorithm 3 & Double DQN Algorithm \\ Algorithm 4 & D3 QNPER Algorithm \\ Algorithm 5 & APF-D3 QNPER Algorithm \\ Algorithm 6 & A*+DWA Algorithm \\ Algorithm 7 & A*+TEB Algorithm \\ \hline \hline \end{tabular}
\end{table}
Table 4: Algorithm number.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Environment** & **Starting Point** & **End Point** & **Initial Angle (\({}^{\circ}\))** \\
**Number** & **Coordinates (m)** & **Coordinates (m)** & **Initial Angle (\({}^{\circ}\))** \\ \hline
1 & (1.12, \(-8.63\)) & (\(-13.78\), \(-3.08\)) & \(3.11\) \\
2 & (1.44, \(9.52\)) & (\(-11.27\), \(-7.50\)) & \(-0.06\) \\
3 & (\(-8.23\), \(9.23\)) & (\(-4.69\), \(-1.47\)) & \(-2.50\) \\
4 & (\(-12.36\), \(1.34\)) & (\(12.70\), \(-1.06\)) & \(2.64\) \\
5 & (6.73, \(-1.30\)) & (\(-7.72\), \(8.49\)) & \(0.56\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Environmental points.  
time of 5.342 s and an average planning path length of 31.978 m. When compared with Algorithm 4, the APF-D3 QNPER algorithm demonstrated a 41.16% reduction in driving time and a 34.85% reduction in planning path length. In an obstacle-free environment, where the optimal solution is a straight-line distance from the starting point to the end point, the planning route generated by the APF-D3 QNPER algorithm exhibits closer alignment with a straight line, resulting in improved performance.