esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Histological and Histomorphometric Evaluation
Observations were very similar for osteopororotic and healthy animals. The data of the histomorphometric BA% and BIC% analysis are given in Table 2, Figures 6 and 7. Statistical testing revealed that BA% in OVX (9.2% \(\pm\) 2.4%) and SHAM (11.1% \(\pm\) 3.5%) rats for the saline controls did not differ significantly (\(p\) > 0.05) between rats (Table 2 and Figure 6). On the other hand, as presented in Table 2 and Figure 7A, three weeks of daily subcutaneous injection of alendronate, simvastatin, or combination led to a significant increase (\(p\) < 0.05) in bone area (BA%) around titanium implants compared to the saline controls in healthy as well as in osteopororotic animals. In addition, in osteopororotic conditions, alendronate administration resulted in an enhanced BA% around implants compared to simvastatin and the combination of alendronate and simvastatin treatment. In SHAM rats, no significant differences in BA% existed between the various treatment groups. Statistical analysis of the BIC% data demonstrated that systemic administration of alendronate and simvastatin, alone or in combination, did not alter bone-implant contact (BIC%) compared to saline controls in both osteopororotic and healthy conditions (Table 2 and Figure 7B. Finally, using a non-parametric Mann-Whitney rank sum test for the pooled data of the various drug treatment groups, a significant difference (\(p\) < 0.01) in BA% between OVX and SHAM rats (16.6% \(\pm\) 6.6% vs 20.1% \(\pm\) 5.1%) was observed, while similar BIC% between OVX and SHAM rats (50.1% \(\pm\) 27.2% vs. 49.3% \(\pm\) 25.8%) were observed (Figure 8). Figure 5: Representative histological images stained with methylene blue and basic fuchsin, showing non-decalcified (pMMA embedded) cross-sections displaying the varying amount of tight bone contact to implant in different treatment groups. (**A**) osteopororotic condition, alendronate group. (**B**) healthy condition, combination group. Figure 6: Box-and-whisker plot shows the histomorphometric bone area (BA%) in saline groups in both osteopororotic and healthy conditions.
## 3 Experiments
### Baseline Models  
To assess the efficacy of the EG-DGATN, several baselines were set up in this study, including traditional statistical models and state-of-the-art deep learning models:  
* ARIMA: A statistical-based time series forecasting method.
* STGCN [24]: Spatiotemporal graph convolutional network, which utilizes convolutional structures to extract spatiotemporal correlations from time series.
* ASTGCN [25]: An attention-based spatiotemporal graph convolutional network with attention mechanisms for traffic flow prediction, which is used to analyze the spatiotemporal features of the time series.
* DCRNN [26]: Diffusion convolutional recurrent neural network employs diffusion convolution to capture spatial correlations and combines Seq2 Seq architecture to capture temporal correlations.
* GNNLSTM [27]: Combines a graph neural network with the LSTM model to learn latent time series patterns on spatiotemporal graphs and address the dependency issues of time series.
* GraphWave [28]: Learns an adaptive adjacency matrix from data through end-to-end supervised training, retaining hidden spatial correlations through this adaptive adjacency matrix.
* R-DGATN: Replaces the Granger causality test graph-building process with a randomly generated adjacency matrix.
* S-DGATN: Replaces the Granger causality test graph-building process using the adjacency matrix of spatial adjacencies.
# Video Super-Resolution Network with Gated High-Low Resolution Frames
## 4 Experimental Results  
### Implementation Details  
The training network in this paper uses the open-source dataset Vimeo-90K, containing 64,612 video sequences (each sequence contains 7 consecutive frames of size 448 \(\times\) 256). To obtain low-resolution video frames of size 64 \(\times\) 64 LR, the high-resolution sequence maps were uniformly cropped to 256 \(\times\) 256 and then blurred with a Gaussian kernel of 1.6 and further down-sampled using a 4-fold scale.  
The convolutional layers of this network without special specification are trained end-to-end using a 3 \(\times\) 3 convolution accompanied by the activation function Relu, using the L1 loss function. In the testing phase, the benchmark dataset Vid4 [28], SPMCS [8], which is commonly used and publicly available in video superscore, was used for evaluation.  
The deep learning framework used in this experiment was PyTorch; the hardware environment was two NVIDIA DELL V100 GPUs. The batch size of the training model  
Figure 7: Schematic diagram of the hierarchical hybrid multi-attention blocks (HHMAB). Conv3 denotes a convolutional layer consisting of 3 \(\times\) 3 convolutional kernels and the Relu activation function, Conv1 denotes the convolution of 1 \(\times\) 1 convolutional kernels. CA stands for channel attention module and SA stands for spatial attention module. \(\oplus\) denotes the summation of elemental correspondences.  
was 4, and the Adam optimizer was used, with a setting of \(\beta 1=0.9\) and \(\beta 2=0.999\). The learning rate was initially set to \(1\times 10^{-4}\), and after 50 epochs to 70 epochs, it was divided by 10.  
The training loss curve and the test performance evaluation curve are shown in Figure 8. As can be seen from the graph, when the number of training iterations is low, the loss is lower, and the test performance evaluation metric is higher; when the number of iterations exceeds 20, the loss value fluctuates, and the test performance evaluation also starts to show a fluctuating increase.
# Improved PR Control Without Load Current Sensors and Phase-Locked Loops for APFs
## 4 Simulation Verification
### Simulation Analysis of Proposed PR Control Method  
In the simulation, the proposed PR controller adopts a single-frequency resonant structure with the resonant frequency set to 50 Hz. The proportional gain \(K_{\text{p}}\) is set to 20, the resonant gain \(K_{\text{r}}\) to 150, the quality factor \(Q\) to 10, and the sampling period \(T_{s}\) to 50 \(\upmu\)s. These parameter settings are based on the root locus analysis presented in Section 3 and are intended to ensure system stability while achieving satisfactory reference current tracking and harmonic compensation performance.  
Figure 8 shows the simulation waveform of the proposed PR control strategy under steady-state conditions. After the APF was put into operation at t = 0.1 s, the THD of the grid current dropped rapidly from the initial 28% to 2.3%, further improving the harmonic suppression effect compared with the traditional PR control method (4.5%). Figure 8e further provides a spectrum analysis of the grid current, showing that the main low-order harmonics have been effectively suppressed  
To evaluate the performance of the proposed control strategy under dynamic conditions, Figure 9 shows the load being suddenly reduced from 120 \(\Omega\) to 60 \(\Omega\) at t = 0.2 s to simulate a load mutation. As can be seen from Figure 9, the grid current waveform remains stable at the moment of load mutation, without significant overshoot, and the system quickly converges to a new steady state within about 15 ms, reflecting that the proposed control strategy has excellent dynamic response performance. The THD of the grid current before and after the mutation is 2.3% and 1.8%, respectively. The corresponding spectrum analysis is shown in Figure 9e, which further verifies the effectiveness of the control strategy under dynamic conditions.  
The proposed PR control strategy simplifies the control structure by eliminating multiple harmonic-specific PR controllers, the PLL module, and load-side current sensors. This reduces system complexity and computational burden, enhances dynamic response and robustness, and allows accurate control using only grid-side measurements--ultimately lowering hardware cost and easing system maintenance.  
Figure 8: \(Cont.\)  
Figure 8: Steady-state situation of proposed PR control method: (**a**) grid voltage; (**b**) grid current; (**c**) DC voltage; (**d**) grid connection point voltage; (**e**) FFT of the proposed PR control grid current; before mutation.  
Figure 9: Load mutation of proposed PR control method: (**a**) grid voltage; (**b**) grid current; (**c**) DC voltage; (**d**) grid connection point voltage; (**e**) FFT of grid current after the mutation.
# Use of Covariance Analysis in Electroencephalogram Reveals Abnormalities in Parkinson's Disease
## 5 Discussion
### Differential Anteroposterior Power Gradient between CL and PD Patient Groups  
In CL groups, the dominance of the \(\alpha\) brainwave due to the normal cortical activity is characteristic of the eyes-closed resting state in frontal channels, together with the low cortical activity of the posterior channels, caused by the absence of visual stimuli [16; 45]. We showed for the first time with our power anomalies analyses a decreasing anteroposterior power gradient characteristic of CL groups, from the high powers of the Fp, F and T channels to the low powers of the P and O channels.  
The decrease in the slope of the anteroposterior power gradient in CL >60 was mainly due to the decreased dispersion in power of the \(\delta\) brainwave. This is consistent with the decrease in \(\delta\) brainwave activity found as a function of age [52] and physiologically associated with a loss of connectivity between the frontal subcortical and cortical areas caused by changes in grey matter (neuronal death) and white matter (leukoaraiosis) [53], to frontal vascular alterations [54] and a loss of dopamine neurotransmission characteristic of normal aging [55].  
In both PD patient groups, the anteroposterior power gradient was inverted. Despite PD patients <60 presenting similar features to CL <60 in terms on the global wavelet curves, main frequencies and brainwave power profiles, the power anomalies analysis shows an inverted anteroposterior power gradient due to reduced dispersion of the \(\delta\) brainwave frequency and power in the Fp and F channels and increased power in the in P and O channels. The inversion could be attributed to the instability of the frontal \(\delta\) brainwave caused by the malfunction of the inhibitory thalamic-cortical pathways identified in PD [56]. In the case of PD patients >60, the inverted gradient was related to a synchronized \(\delta\) brainwave in all channels. We suggest this due to an attempt to re-establish the thalamic-cortical inhibitory circuits by means of the use of anti-parkinsonian drugs regularly used in advance PD patients, which have extensive effects throughout the brain. This is in addition to the notable dispersion in power and frequency of the \(\alpha\) brainwave, indicating a significant loss of normal cortical neuronal synchronization [46].  
In the global wavelet curves profiles of PD patients, an increase in the \(\beta\) brainwave was also observed. It was not found in the CL groups. This has been previously observed and associated with an abnormal functioning of the basal ganglia [15] and with the use of Levodopa [47]. The notable increase in the \(0\) brainwave observed in the PD patient >60 might also be related to alterations in the limbic circuit [57].
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 4 Details of the Implementation
### Passage Entering Algorithm  
This algorithm constructs a path that passes through the passage between two obstacles when one penetrates the convex hull of the other one, as shown in Figure 15(a). The contour of the convex hull of the obstacle \(O_{2}\) is shown as a thick black line and its interior is colored in pale gray. We note that there are somewhat more complex situations in which both obstacles have parts of them penetrating into each other's convex hull. These cases can be dealt with in a similar manner as the present case. Figure 15(b) shows the four bitangents that connect these two obstacles.  
The construction of a passage path, from left to right, is relatively straightforward when its starting point lies in one of the two regions illustrated in Figure 16(a),b. Thus, when it starts at any point in the region shown in pale gray, in Figure 16(a), the path can reach, tangentially, the boundary of O\({}_{2}\) in the clockwise direction. It can then follow a tangent convex arc on \(O_{2}\) to reach the bitangent \(T_{3}\) and then exit to the right between \(O_{1}\) and \(O_{2}\). Similarly, upon starting from any point in the region shown in pale gray, in Figure 16(b),  
Figure 16: Obstacle \(O_{1}\) is imbedded in \(O_{2}\).  
Figure 15: Definition of the tangent convex arc.  
the path can reach, tangentially, the boundary of \(O_{1}\) in the counter-clockwise direction, from which point it can follow the tangent convex arc on \(O_{1}\) until it can leave the passage between \(O_{1}\) and \(O_{2}\).  
The complication is that there is, in general, a particular region, shown in white and labeled as \(R\) in Figure 16(c), from which the interior of the passage cannot be reached with glancing tangents to \(O_{1}\) or \(O_{2}\). The following procedure deals with this situation.  
1. Draw the glancing tangent from point \(A\) to obstacle \(-O_{2}\), which arrives at point \(B\), and the glancing tangent from \(A\) to \(+O_{1}\), which arrives at point \(C\). These two lines define the region \(o_{2}\) in \(O_{2}\), as shown darker in Figure 16(c).
2. Starting at \(A\), construct the glancing tangent to \(-O_{2}\), which arrives at point \(D\) on \(O_{2}\).
3. From this point, the path can follow the tangent convex arc in the clockwise direction on \(o_{2}\) to reach the bitangent \(T_{3}\) to get to point \(C\), and proceed to exit through the passage. We then call the tangent from \(A\) to \(O_{2}\) a _passage entering tangent_.
## 2 Materials and Methods
### Model Evaluation  
In the sensitivity experiments, we used three quantitative statistics to evaluate model performance, including the percent bias (_pbias_), root mean square error (_RMSE_), and Nash-Sutcliffe efficiency (_NSE_). The equations for these metrics are as follows:  
\[pbias=\frac{\sum_{i=1}^{n}\left(x_{i}-y_{i}\right)}{\sum_{i=1}^{n}y_{i}}\times 1 00\% \tag{18}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(x_{i}-y_{i}\right)^{2}} \tag{19}\]  
\[NSE=1\frac{\sum_{i=1}^{n}\left(x_{i}-y_{i}\right)^{2}}{\sum_{i=1}^{n}\left(y_{ i}-\overline{y}\right)^{2}} \tag{20}\]  
where x and y are the simulated and observed values at time step \(i\), respectively; \(\overline{y}\) is the mean of the observed values at time step \(i\), respectively; and n is the total number of time steps. In this study, the model simulation could be judged as satisfactory if _pbias_\(\leq\pm 25\%\) and _NSE_\(\geq 0.50\)[68].
## 5 Evaluation Experiment
### Experiment C  
Figure 15 shows the experimental setup used for Experiment C. Position estimation was conducted in a rectangular area of 30 m in width and 20 m in length. The two microphone arrays, M1 and M2, were installed, respectively, at coordinates (0, 20) and (30, 0). The microphone frontal orientation of M1 and M2 was set to the equivalent of their diagonals to use the small error range around 0\({}^{\circ}\) effectively. The position detection interval was 5 m horizontally and vertically. For Experiment C, the position that could not be calculated on the diagonal was merely (15, 10). This field consists of a rectangular  
Figure 14: Scatter plots showing the GT coordinates and estimated coordinates for Experiment B.  
Figure 13: Positional distributions for the GT positions and estimated positions for Experiment A.  
shape, unlike the square field for Experiment A. The estimation target for Experiment C had 32 positions: P1-P32.  
Figure 16 depicts the angle estimation results obtained for each group with similar vertical positions to those shown in Figure 15. The respective results demonstrated that distinct peaks shown as filled purple circles from the output waves were obtained at the source direction angles.  
Figure 16: _Cont._  
Figure 15: Setup of sound source positions and microphone arrays for Experiment C.  
Figure 17 portrays the distributions for the GT positions and estimated positions. This tendency indicated that the error values were significant at positions near the diagonal between M1 and M2.  
Figure 18 portrays the scatter plots between the GT coordinates and estimated coordinates for the respective axes. The distribution variation shown for the results of Experiment C was higher than that of the scatter plots for Experiment B. By contrast, the distribution variation was low in the lower left and upper right regions, which were more distant from both microphone arrays.
# Spatial Signal Analysis Based on Wave-Spectral Fractal Scaling: A Case of Urban Street Networks
## 3 Empirical Analysis
### Cases of Power Spectral Scaling Analysis
The fractal dimension belong to phase space. The results suggest that two types of cities possess higher self-similar fractal dimension values. One is the large cities, such as Beijing and Tianjin, and the other is the cities of self-affine growth such as Zhangjiakou and Qinhuangdao. The Hurst exponent values show that all the growing processes of all these cities bear long memory, moreover, the growing processes of all these cities but Handan bear persistence. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**City**} & \multicolumn{4}{c}{**Spectral Analysis**} & \multicolumn{4}{c}{**R/S Analysis**} \\ \cline{2-9}  & \(P_{0}\) & \(\beta\) & \(R^{2}\) & \(D_{s}\) & \(D_{f}\) & **Coefficient** & \(H\) & \(R^{2}\) \\ \hline Baoding & 1532.2018 & 1.3649 & 0.5605 & 1.8176 & 1.6824 & 1.0111 & 0.5982 & 0.9373 \\ Beijing & 25950.7422 & 1.8009 & 0.8385 & 1.5995 & 1.9005 & 0.9317 & 0.6642 & 0.9542 \\ Cangzhou & 1545.2501 & 1.3164 & 0.5322 & 1.8418 & 1.6582 & 0.9809 & 0.6280 & 0.9696 \\ Chengde & 194.4615 & 1.4499 & 0.7281 & 1.7750 & 1.7250 & 0.9598 & 0.6366 & 0.9152 \\ Handan & 1680.5985 & 1.4165 & 0.4976 & 1.7917 & 1.7083 & 1.2221 & 0.4628 & 0.7739 \\ Hengshui & 295.9529 & 1.2741 & 0.6029 & 1.8629 & 1.6371 & 0.9621 & 0.6091 & 0.9344 \\ Langfang & 2493.1480 & 1.4288 & 0.6164 & 1.7856 & 1.7144 & 1.0578 & 0.5842 & 0.9222 \\ Qinhuangdao & 447.7705 & 1.6063 & 0.7843 & 1.6968 & 1.8032 & 1.1647 & 0.5625 & 0.9301 \\ Shijiazhuang & 1866.2198 & 1.5853 & 0.6874 & 1.7074 & 1.7926 & 0.9727 & 0.6074 & 0.9366 \\ Tangshan & 3534.1318 & 1.5370 & 0.7094 & 1.7315 & 1.7685 & 1.0489 & 0.5620 & 0.9332 \\ \hline \hline \end{tabular} Note: The original nighttime light dataset (1992–2013) come from American NOAA National Centers  
for Environmental Information (NCEI).  
\end{table} 
Table 3: The power spectrum exponents, fractal dimension, and Hurst exponent of Chinese cities in Beijing-Tianjin-Hebei region based on time series of night light data (1992–2013). Figure 6: The power-spectrum scaling relations between frequency and power spectral density (1956–2019). Note: The time series comes between 1949 and 2019. The number of data points is \(N=71\). According to the requirement of FFT, the data point number must be \(N=2^{j+1}\), where \(j=0\), \(1\), \(3\),..., \(\mathrm{int}(\mathrm{ln}N/\mathrm{ln}(2))\pm 1\). The best number is \(N=64\). So, the first 7 data points were removed to meet the needs of the algorithm. Figure 7: Urban growth in Beijing–Tianjin–Hebei region reflected by night light data (1992–2013). **(a)** 1992; **(b)** 1995; **(c)** 2000; **(d)** 2005; **(e)** 2010; **(f)** 2013. Note: The changing process of total quantity of nighttime lights (NTL) reflects urban growth. Using time series of night light data, we can make power spectral analysis and estimate the self-affine record dimension for temporal signals. A set of temporal signals reflect spatial signals from cross-sectional angle of view.
# Secure Audio-Visual Data Exchange for Android In-Vehicle Ecosystems
## 3 Protocol and Results
### Test Scenarios and Results
#### 3.3.1 In-Door Experiments with Smartphones
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**20 cm**} & \multicolumn{3}{c}{**50 cm**} & \multicolumn{3}{c}{**100 cm**} \\ \hline \hline
**Snd.**Rcv.** & **E6** & **S7** & **A3** & **Nexus** & **E6** & **S7** & **A3** & **Nexus** & **E6** & **S7** & **A3** & **Nexus** \\ \hline
**E6** & X & 0\% & 2.59\% & 0\% & X & 3.33\% & 8.14\% & 1.11\% & X & 4.85\% & 9.36\% & 1.22\% \\ \hline
**S7** & 5.92\% & X & 4.81\% & 0\% & 5.57\% & X & NW & 1.11\% & 9.36\% & X & NW & 1.85\% \\ \hline
**A3** & 0.37\% & 0\% & X & 9.36\% & 3.38\% & NW & X & 9.66\% & 6.29\% & NW & X & 9.36\% \\ \hline
**Nexus** & 5.16\% & 0\% & 0.37\% & X & 0\% & 0.37\% & 0\% & X & 3.71\% & 7.43\% & 0\% & X \\ \hline \end{tabular}
\end{table} 
Table 5: BER for indoors experiments (16.7–20 KHz range). Figure 12: The BER (**up**) and the percentage of correct keys (**down**) for each smartphone pair.
## 5 Experimental Verification
### Identical Resonant Frequency Receivers  
To analyze the effects of identical frequency the Rx1, Rx2 and Rx3, Rx4 are designed with the resonant frequency of 1 MHz and 2 MHz respectively. Initially, all the distance between the Tx and Rx are maintained at 5 c.m, and the operating frequency of the Tx is selected at 1.0 MHz. The load-1 and load-2 voltages are maximum at 1 MHz operating condition and load-3, load-4 voltages are less than 0.5 V as shown in Figure 17a,b. Next, the operating frequency is changed to 2 MHz the load-3 and load-4 voltages are at 4.6 V, 4.4 V respectively and the load-1, load-2 voltages are less than 0.4 V as shown in Figure 17c,d respectively. It is observed that the effect of cross coupling on each Rx is reduced and both the coil voltages are reached more than 4.4 V under the identical resonant frequency condition. The uniform power distribution to the selected Rx also obtained as the non-resonant coil voltages are less than 0.4 V.
# A Generative Adversarial Network-Based Fault Detection Approach for Photovoltaic Panel
## 4 Experiment
### Model Construction  
The construction of the generator and the discriminator in the generative confrontation network is a key task. The generator includes an encoder and a decoder, and the discriminator is an encoder-like structure. The details of the encoder parameters and decoder parameters are shown in Tables 1 and 2, respectively. The input image size of the encoder is 32 \(\times\) 32 \(\times\) 3, and a 4 \(\times\) 4 convolution kernel is used. The first three layers of convolution use edge padding. The core step size is 2, and the batch normalization layer [33], the LeakyReLU activation function layer [34] and CBAM layer [32] are added after the convolution layer. The last layer of convolution directly outputs a hidden space vector with a size of 1 \(\times\) 1 \(\times\) 100. The decoder is symmetrical to the encoder structure, the first layer of transposed convolution has no padding and step size, and the last three layers of transposed convolution padding is 1, and the step size is 2. After the first three transposed convolutional layers, the batch normalization layer and the ReLU activation function layer are added, and the output size is 32 \(\times\) 32 \(\times\) 3 of the reconstruction image.  
In this paper, three experiments are performed on where the CBAM layer is embedded. Firstly, the CBAM layer is embedded after the first convolution block of the encoder network \(G_{E}\). Secondly, the CBAM layer is embedded after both the first and second convolution blocks of \(G_{E}\). Thirdly, the CBAM layer is embedded after the first, second and third convolution blocks, as shown in Figure 3. The final results of the three different CBAM embedding methods are shown in Figure 10. It can be seen that the third embedding effect is the best, so the proposed ppFDetector model chooses the third CBAM embedding method, and the AUC reaches the maximum value.  
Figure 9: PV Segmentation images: (**a**) Positive sample; (**b**) Negative sample.