esa-sceva/satcom-chunk-collection huggingface dataset:
# VDCrackGAN: A Generative Adversarial Network with Transformer for Pavement Crack Data Augmentation
## 3 Methods
### Experiment Results  
#### 4.3.1 Model Comparison Experiment  
To verify the effectiveness of the method proposed in this paper, model comparison experiments were carried out, and the experimental results are shown in Table 5. The experimental data show that the VAE-DCGAN model, which combines VAE with DCGAN, improves the IS score and FID score by 8% and 11.2% over the original DCGAN model, respectively. The introduction of spectral normalization is effective. The IS score and FID score of the VAE-DCGAN+SN model are improved by 1.3% and 1.8%, respectively, compared with the VAE-DCGAN model, and by 9.7% and 12.8%, respectively, compared with DCGAN. Finally, the introduction of the Swin Transformer is also effective, again improving the IS score and FID score by 2.8% and 15.6% on top of VAE-DCGAN+SN, and the optimization improvement effect of this paper's method on IS score and FID score reaches 13.6% and 26.4%, respectively, when compared to the original DCGAN. In addition, the IS and FID indicators of VDCrackGAN outperform those of SAGAN and WGAN in the crack generation task, as shown in Table 5. Moreover, the IS scores of the generated samples of VDCrackGAN are very close to the real crack samples, which also indicates the high quality of the generated crack images by VDCrackGAN.  
\begin{table}
\begin{tabular}{c c} \hline
**Type** & **Configuration** \\ \hline CPU & Intel i9-10900X 3.7 GHz \\ GPU & NVIDIA GeForce RTX 3090 24 GB \\ RAM & DDR4 3000 MHz 64 GB \\ Soft & Python 3.7.6, Pytorch 1.9.0, Cuda 11.3 \\ \hline \end{tabular}
\end{table}
Table 4: Experiment environment.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Models** & \multicolumn{2}{c}{**IS**} & \multicolumn{2}{c}{**FID**} \\ \cline{2-5}  & **Score** & **std** & **Score** & **std** \\ \hline real crack dataset & 3.259 & 0.216 & — & — \\ SAGAN & 3.212 & 0.036 & 27.139 & 0.075 \\ WGAN & 3.176 & 0.048 & 38.762 & 0.092 \\ DCGAN & 2.923 & 0.037 & 36.414 & 0.085 \\ VAE-DCGAN & 3.106 (\(\uparrow\)8.0 *) & 0.046 & 32.331 (\(\uparrow\)11.2) & 0.081 \\ VAE-DCGAN +SN & 3.146 (\(\uparrow\)9.7) & 0.039 & 31.751 (\(\uparrow\)12.8) & 0.081 \\ VAE-DCGAN +SN + Swin Transformer (our VDCrackGAN) & 3.235 (\(\uparrow\)13.6) & 0.040 & 26.812 (\(\uparrow\)26.4) & 0.072 \\ \hline \end{tabular}  
* The arrow \(\uparrow\) represents the improvement in performance index, and the number after \(\uparrow\) represents the percentage improvement in performance index relative to DCGAN.  
\end{table}
Table 5: Comparison of different generative models.
## 7 Method Application 2: Iron Losses Computation
### Measurements Comparison
After the correction of the high frequency losses, the comparison with no-load measurements for the several motors is shown in Figure 18.  
Figure 17: First comparison between measurements and simulation in the no-load test of the 11 kW machine. The measurements have been done according to the standard, increasing the stator voltage from 30 % to 115 % of the rated one. During the on-load operation, the stator iron losses increase due to the rotor slot harmonics, produced by the rotor magnetic load. Furthermore, in the cage, the induced current due to the stator harmonic fields produces additional Joule losses. The on-load additional iron losses are computed as:  
\[P_{\text{fe,OL}}^{\text{add}}=P_{\text{fe,OL}}-P_{\text{fe,NL}} \tag{30}\]  
where \(P_{\text{fe,OL}}\) are the on-load iron losses and \(P_{\text{fe,NL}}\) are the no-load iron losses. The additional cage losses can be estimated analytically, using the harmonic equivalent circuits. From measurements, the additional losses are computed according to the Standard. Comparison with the simulations results is reported in Figure 19.  
Finally, the skewing influence on iron losses can be neglected, as suggested in [20].
## 4 Discussion
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Name of Model** & **2D/3D** & **Coarse/Fine** & **Mean DSC** & **Data Splitting** \\ \hline Holistically Nested 2D FCN Stage-1 [11] & 2D & coarse & 0.768 \(\pm\) 0.111 & 4-fold CV \\ \hline
2D FCN [13] & 2D & coarse & 0.803 \(\pm\) 0.09 & 4-fold CV \\ \hline Coarse-scaled Model 2D FCN [14] & 2D & coarse & 0.757 \(\pm\) 0.105 & 4-fold CV \\ \hline Single Model 3D U-net [12] (trained from scratch) & 3D & coarse & 0.815 \(\pm\) 0.057 &
\begin{tabular}{c} 61 training and 21 test sets \\ randomly selected \\ \end{tabular} \\ \hline Single Model 3D Attention U-net [12] (trained from scratch) & 3D & coarse & 0.821 \(\pm\) 0.068 & 61 training and 21 test sets \\ \hline Coarse-scaled Model 3D U-net [15] & 3D & coarse & 0.819 \(\pm\) 0.068 & 4-fold CV \\ \hline Proposed model & 2D & coarse & 0.789 \(\pm\) 0.083 & 4-fold CV \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of coarse-scaled models using the Pancreas-CT dataset. as a data augmentation method needs some consideration. In this regard, computational cost of mixup and RICAP is relatively low, and mixup and RICAP are easy to implement. Accuracy of pancreas segmentation was visually evaluated by the two radiologists in the current study. To our knowledge, there was no study of deep learning to evaluate the segmentation accuracy of pancreas structure visually. The results of visual scores mean that automatic segmentation model of the current study was good. It is expected that the proposed model may be useful for clinical cases if the clinical CT images have similar condition and quality to those of the Pancreas-CT dataset. In the current study, we evaluated automatic pancreas segmentation using the public dataset called Pancreas-CT. Although this dataset was used in several studies as shown in Table 3, the manually annotated labels of four or eight cases were scored as not perfect based on the visual assessment of the current study. In most of the cases, the labels for the pancreas head were assessed as low-quality. It is presumed that the low-quality labeling is caused by the fact that annotators did not fully understand the boundary between the pancreas and other organs (e.g., duodenum). To evaluate the segmentation accuracy, reliable labeling is mandatory. For this purpose, a new database for pancreas segmentation is desirable. There were several limitations to the present study. First, we investigated the usefulness of data augmentation only in segmentation models. The usefulness of data augmentation should be evaluated for other models such as classification, detection, and image generation. Second, the 3D fine-tuned model of pancreas segmentation was not evaluated. Because U-net, mixup, and RICAP were originally suggested for 2D models, we constructed and evaluated the 2D model of pancreas segmentation. We will apply the proposed methods to the 3D fine-tuned model in future research.
## 3 Implementation and Design
### Kinematic Design  
This section explains the kinematic design of the proposed uterine manipulation robot. Figure 9a shows the spherical coordinates with the origin (**O**) or pole fixed at the cervix. According to **Roll** and **Pitch** motions on the selected uterine manipulator, the movement would occur in spherical coordinates (\(\mathbf{r}\), \(\boldsymbol{\theta}\), \(\boldsymbol{\varphi}\)), where the radial distance (\(\mathbf{r}\)) is the tip length, the azimuthal angle (\(\boldsymbol{\theta}\)) made by the **Roll** motion and polar angle (\(\boldsymbol{\varphi}\)) made by the **Pitch** motion of a point (\(\mathbf{P}\)) with respect to a unit sphere with a fixed point (\(\mathbf{O}\)), as shown in Figure 9b. The movements at the handle that cause **Roll** and **Pitch** motions have been illustrated in blue and green, respectively.  
As can be seen in the Spherical coordinates, with known \(\mathbf{r}\) for radius, \(\boldsymbol{\theta}\) for **Roll** motion and \(\boldsymbol{\varphi}\) for **Pitch** motion, it is sufficient for a coordinate to be located anywhere on the spherical object area, hence there is no need for a direct Yaw motion transition. However, with regard to the control interfacing, the Cartesian coordinate system, in which the coordinates are perpendicular to one another with the same unit length on all **XYZ**-axes, is essential. Therefore, conversion between Spherical coordinates and Cartesian coordinates is needed. Equations (2)-(4) are the calculation of (\(\mathbf{r}\), \(\boldsymbol{\theta}\), \(\boldsymbol{\varphi}\)) coordinates from the Cartesian coordinates while Equations (5)-(7) are the calculation of (x, y, z) coordinates from the Spherical coordinates. Next, the simulation results from this kinematic design have been evaluated by a mathematical model and 3D models.  
\[\mathbf{r}^{2}=\mathbf{x}^{2}+\mathbf{y}^{2}+\mathbf{z}^{2} \tag{2}\]  
Figure 8: Coverage of uterine manipulator movement; (**a**) is the front view showing the \(\pm 150^{\circ}\) in the **Roll** panel with the **Pitch** angle fixed to \(90^{\circ}\), (**b**) is the side view showing the (0\(\sim\)90\({}^{\circ}\)) in the **Pitch** panel, (**c**) is the front view showing how the distal **Yaw** motion could be achieved and (**d**) is the side view showing all \(\pm 90^{\circ}\) in the **Pitch** panel.  
Figure 9: Coordinate system design; (**a**) spherical coordinates and (**b**) mapping on the uterine manipulator.  
\[\mathbf{\varphi} =\cos^{-1}(\frac{\mathbf{x}}{\mathbf{r}}) \tag{3}\] \[\mathbf{\theta} =\tan^{-1}(\frac{\mathbf{y}}{\mathbf{z}})\] (4) \[\mathbf{x} =\mathbf{r}\times\cos(\mathbf{\theta})\] (5) \[\mathbf{y} =\mathbf{r}\times\sin(\mathbf{\varphi})\sin(\mathbf{\theta})\] (6) \[\mathbf{z} =\mathbf{r}\times\sin(\mathbf{\varphi})\cos(\mathbf{\theta}) \tag{7}\]
## 4 Trusted Network Secure Routing Algorithm
### Communication Delay  
The routing protocol used in a wireless sensor network has a close relationship with the communication delay: the longer the delay is, the greater the impact on the network's normal communication will be. In this paper, the distance between nodes is considered to represent the communication delay \(T_{ij}\). One might simply use the physical distance between two nodes to represent their communication delay; however, a situation can arise in which the physical distance between the two nodes is very short but the node voltage is close to the minimum voltage value of the sensor, causing the communication ability of the nodes to be very poor. Thus, in this case, the effective distance between the two nodes is very far. Therefore, in this paper, we propose the following concept of the effective distance to provide a more reasonable representation of the communication delay:\[T_{ij}=\left[\frac{1}{V_{0}-V_{\min}}\times\left(d_{ij}\right)^{2}\right]^{-1} \tag{16}\]  
where \(V_{0}\) is the current operating voltage of the sensor, which is initialized at 3 V and, when the system is running, is equal to the voltage value of the node in the wireless sensor network; \(V_{\min}\) is a constant that specifies a critical voltage and is generally set to 2.7 V; and \(d_{ij}\) represents the Manhattan metric distance between node \(i\) and node \(j\), which is defined in Equation (17). The above formula considers not only the relationship between the energy and the distance between the nodes but also the change in voltage. This formula causes the effective distance between the nodes to change very quickly when the voltage is near the minimum value, which makes the formula very useful.  
Manhattan metric: Suppose that \(i=\left(x_{i1},x_{i2},\ldots,x_{ip}\right)\) and \(j=\left(x_{j1},x_{j2},\ldots,x_{jp}\right)\) are two objects described by \(P\) numerical attributes. The definition of the Manhattan metric between objects \(i\) and \(j\) is shown below:  
\[d(i,j)=\left|x_{i1}-x_{j1}\right|+\cdots+\left|x_{ip}-x_{jp}\right| \tag{17}\]
## 2 Materials and Methods
### Centrifugal Contactor Setup  
The demonstration of the process was carried out using 1 cm annular miniature centrifugal contacts produced by the Institute of Nuclear Energy Technology, Tsinghua University, Beijing, China, with the rotors made of titanium and the stator housings made of stainless-steel [33; 34]. The process was run in countercurrent mode with a rotator speed of 4500 rpm, and the speed was checked with a stroboscope tachometer regularly during the experiment. The contactor battery setup consists of four batteries with four stages each, resulting in a total available number of 16 stages. As the calculated flowsheet comprised 32 stages, the test was split into two parts as described below. Calibrated syringe pumps (Kent Scientific Corp., Torrington, CT, USA) were used to deliver the organic and aqueous flows.  
For the single centrifugal contactor tests, a single stage of the 1 cm annular miniature centrifugal contacts was used. The single centrifugal contactor tests were based on the reference flowsheet [27]. In all experiments, the contactor was first filled with inactive aqueous and organic phases of the same compositions as required, and then, either the aqueous and organic phase was replaced with the radioactive phase for the actual test. Table 2 shows the flow rates used in the different single centrifugal contactor tests. The single centrifugal contactor was operated until the steady state was reached. If needed, the contactor operation was continued to collect sufficient organic and/or aqueous phase for the upcoming experiment. After that, the contactor was stopped and the equilibrium distribution ratios were determined.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Section** & **Ag. Flow Rate** & **Org. Flow Rate** & **Contactor Filled with *** \\  & **[mL h\({}^{-1}\)]** & **[mL h\({}^{-1}\)]** & \\ \hline Extraction & 72 & 24 & 3 mol L\({}^{-1}\) HNO\({}_{3}\) \\ Mo scrubbing (Scrub 2) & 30 & 24 & Fresh Scrub 2 solution \\ MA stripping & 18 & 36 & Fresh Strip 1 solution \\ Ln re-extraction & 18 & 18 & Fresh Strip 1 solution \\ Ln stripping & 54 & 36 & Fresh 0.5 mol L\({}^{-1}\) TEDGA in 0.5 mol L\({}^{-1}\) HNO\({}_{3}\) \\ \hline \hline \end{tabular}  
* organic phase always fresh ALSEP solvent.  
\end{table}
Table 2: Flow rates used in the different single centrifugal contactor tests and composition of the aqueous phases used to start operation.
## 6 Experimental Setup
Table 6 enlists the parameters of the NN that results in the optimized performance of NN. The parameters discussed previously in Section 3.1 are utilized to define the structure of the proposed Optimized Neural Network and their values are mentioned in the Table 6. The last section of the table evaluates the complexity aspect of the NN by evaluating its expressions in terms of its coefficients. For a comparative analysis, the parameters of GMP and DVR are also mentioned below in Table 7. The proposed MSA-DPD technique and CPWL without modification is used with \(M=3\) and \(K=L=4\). Similarly, for comparison, we have used GMP method previously used in [34; 65] with the parameters \(K=Q=R=3\). For NN, \(N=10\), \(K=30\) is utilized. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline \multirow{2}{*}{5G NR Waveforms} & \(f_{c}\) = 3 and 20 GHz \\  & Flexible G/F-OFDM \\  & Constellation type = 256 QAM \\ \hline \multirow{2}{*}{Laser} & Wavelength = 1310 nm \\  & Transmitter Type = Mach Zehnder Modulator \\ \hline \multirow{3}{*}{Optical Fiber} & Type = SSMF \\  & Fiber Dispersion = 16 \(\frac{\text{ps}}{\text{mm}}\) \\  & Fiber Distance = 22 km \\  & Attenuation = 0.42 \(\frac{\text{dB}}{\text{km}}\) \\ \hline \multirow{2}{*}{Photodiode} & Responsivity = 0.71 A/W \\  & Bandwidth = 40 GHz \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Optical Link Parameters.
## 5 Results and Discussion
### Models for PM\({}_{2.5}\) Estimation
However, the model's underestimation in representing PM\({}_{2.5}\) peaks is not systematic, and in many cases, models reproduce satisfactorily the high PM\({}_{2.5}\) concentrations, even overestimating them (Figure 6). The residual analysis for the model validation datasets revealed that the frequency of residuals approached the normal distribution peaking around zero for all the models (Supplementary Materials Figure S1). However, the frequency distributions were slightly shifted towards negative values, as the highest model underestimations may reach to \(-60\%\), but for very rare cases. In the vast majority of the cases, the predictions were quite accurate, indicating that the used ML models are satisfactory for PM\({}_{2.5}\) estimations in Malaysia. Atmosphere is a complex system and composed by various substances like air molecules and solid and liquid particles of various sizes, shapes and chemical composition [141,142]. Therefore, combining many auxiliary data such as meteorological factors, aerosols, gases and land use allows for a better estimation of PM\({}_{2.5}\). To date, most PM prediction studies found that inclusion of meteorological factors has improved the PM estimations, because each meteorology parameter may modulate the PM concentrations in a different way [43,45,71]. Nowadays, ML techniques and RF models are frequently used in estimating PM concentrations at many regions around the world [39,116,119]. The statistical indicators from the models' calibration and validation in this study (Supplementary Materials Table S2; Figures 4 and 5) are mostly comparable to those presented in other studies using various methods and ML approaches for estimates of PM concentrations around the world (Table 2). It should be noted that this study obtained reasonable results at national scale without including land use information compared to previous works [143,144,145,128]. Besides that, the validation techniques may be also different, as for instance [143] used sample and site based 10 CV in order to assess the spatial performance, whilst our study only used sample based 10 CV since it can be used to reflect the overall predictive ability [130]. Figure 6: Time series (hourly) of measured (black) and predicted (red) PM\({}_{2.5}\) concentrations from the SVR model across Malaysia for urban/industrial (**a**), suburban/rural (**b**), dry season (**c**), wet season (**d**), April–May (**e**), October (**f**) and overall (**g**) models. The available data set for each group is mentioned in the panels. The X axis shows number of datasets.
## 2 Operation of the BB84 Protocol  
The BB84 QKD protocol belongs to a class of prepare and measure (P&M) protocols [2]. This protocol leverages the no-cloning theorem, which makes duplicating quantum states impossible. Thus, this hinders the eavesdropper from wiretapping the quantum communication channel or copying the quantum states, creating a key, and transmitting the original states to the receiver. Moreover, its security is based on the quantum measurement principle, which states that a measurement of a quantum system causes it to collapse into an eigenstate of the operator corresponding to the measurement. Thus, an eavesdropper trying to learn (i.e., perform a measurement) the information being transmitted will result in her/his detection. The BB84 QKD protocol is implemented through the quantum and classical phases. [PERSON] and [PERSON] use quantum mechanical signals, measurements, and the quantum channel during the quantum phase. Moreover, this employs four quantum states, which constitute the two Pauli eigenbases, i.e., the canonical \(Z\) basis \(\{|0\rangle,|1\rangle\}\) and the mutually unbiased \(X\) basis \(\{|+\rangle=\frac{1}{\sqrt{2}}(|0\rangle+|1\rangle),|-\rangle=\frac{1}{\sqrt{2} }(|0\rangle-|1\rangle)\}\).
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 2 Likelihood-Based Modulation Recognition Methods
### Hybrid Likelihood Ratio Test (HLRT)  
HLRT combines the advantages of ALRT and GLRT to address the bias issue associated with GLRT for nested modulation types. HLRT treats some parameters as deterministic and processes them using maximum likelihood estimation (MLE), while statistically averaging the remaining parameters. For example, in the context of OFDM-IM modulation classification [17], the active subcarrier index \(I_{S}\) is estimated first, and then the symbols are averaged. The decision metric can be simplified as:  
\[\Gamma_{\text{HLRT}}=E_{S_{g}}\Big{[}\Gamma\Big{(}\mathbf{R}\Big{|}H_{i}, \hat{I_{g}},S_{g}\Big{)}\Big{]}, \tag{9}\]  
where \(\mathbf{R}\) represents the multi-dimensional matrix of received signals, and \(\Gamma\big{(}\mathbf{R}|H_{i},\hat{I_{g}},S_{g}\big{)}\) represents the joint likelihood function given the modulation hypothesis \(H_{i}\), the active subcarrier index \(\hat{I_{g}}\), and the symbols \(S_{g}\). This is merely a highly simplified expression for a brief introduction. The detailed reasoning formulas can be found in [17]. However, the complexity of the HLRT method remains high, especially when multiple parameters need to be addressed simultaneously. Additionally, the HLRT method has a high dependence on the initial parameter estimates; inaccuracies in these estimates can adversely affect the final recognition results. Likelihood-based methods achieve high-precision classification through rigorous statistical modeling. Although they are optimal in the Bayesian sense, they face challenges in computational complexity and parameter estimation, limiting their practical application.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 8 Future Research Directions
### Novel Antenna Designs Techniques  
Novel antenna designs for UAVs should be designed to ensure high data rate communication. It is suggested to utilize small, aerodynamic antennas for UAVs cruising at higher speeds. Similarly, directional antennas can be used in smalls sized UAVs to overcome space and energy limitations. Moreover, tilted-beam circularly polarized antennas are widespread due to advantage of saving space. By using such antennas, performance is envisaged to be enhanced in terms of radiation pattern, axial ratio and return losses. WPT techniques and backscatter antennas can be used to enhance network lifetime. The incorporation of multiple adaptive antennas can also support communication in highly-mobile UAVs [126].
## 4 Experiments and Results
### Simulation Setup  
Figure 8 shows a snapshot of the Contiki Cooja simulator and a suggested scheme architecture for the first scenario (33 nodes), where node 33 is the control. Nodes 28 through 30 are malicious nodes, nodes 31 and 32 are sink nodes, and the number of nodes is typical. Table 3 explains the simulation environment set for the implementation.  
The simulations are performed using the following resources. Hardware resource: Core i7 processor; RAM: 8 GB; software resource: VMware Workstation 16 player is used to run the Contiki OS. We have used the Cooja 3.0 simulator for the implementation and evaluation of our trust model.  
Figure 8: Simulation Environment.