esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Employment of Multi-Directional Image Data  
Using multi-directional images can enhance prostate segmentation by addressing visibility variations. For example, the apex and base of the prostate often have ambiguous boundaries on transverse US images but appear clearer in the sagittal and coronal directions. [PERSON] et al. (2022) [22] proposed the Multi-xCpert Fusion (MXF) framework, which uses input images from all directions to improve segmentation results. The authors trained three 2D U-Nets on axial, sagittal, and coronal TRUS images, creating view-specific segmentation volumes of the prostate. These volumes served as input for a subsequent fusion network, which generated view-specific confidence maps to create the final segmentation. Similarly, [PERSON] et al. (2019) [29] trained three view-specific V-Nets and introduced a Multi-Directional Contour Refinement (MDCR) method to post-process the view-specific volumes and estimate the PV. The core concept of MDCR is to define the final boundary contour by averaging boundary intersection coordinates from axial, sagittal, and coronal directions. This approach increased the DSC score from 0.912 to 0.919 (\(p<0.001\)).  
[PERSON] et al. (2020) [32] proposed a novel 3D segmentation method by utilizing a modified U-Net to segment twelve 2D radial slices, which were subsequently used for 3D volume reconstruction. The choice to obtain radial prostate segmentations, as opposed to  
Figure 3: Example TRUS images in (**a**) with shadow artifacts in yellow arrows: adapted from [PERSON] et al. (2018) [20], with permission from Elsevier, and in (**b**,c**) with missing/blurry boundaries in purple arrows: adapted from [PERSON] et al. (2022) [40], with permission from Elsevier.  
transverse segmentations, was motivated by the observation that the prostate presents more clearly when the center of the gland is in-plane. Figure 4 illustrates the difference between radial, sagittal, and axial image slices. A modified U-Net was trained on 6773 2D images obtained randomly from axial, coronal, sagittal, and radial planes. The resulting 12 segmentations were placed in the initial 3D space, and the 3D prostate boundary was reconstructed. The resulting 3D segmentation was compared to a traditional V-Net, and the modified U-Net showed improved segmentation performance (DSC = 0.94 vs. 0.91). Although the V-Net was not rigorously optimized, this comparison suggests that segmentation based on 3D information can yield better results than less spatially dense image information.  
In clinical practice, adjacent slices in a 2D acquisition are often considered during manual segmentation to distinguish the prostate boundary from surrounding tissue. [PERSON] et al. (2018) explored the integration of neighboring slices as input to a traditional U-Net to improve 2D TRUS segmentation. Their evaluation showed that the DSC increased significantly by 1% when two adjacent slices on each side of the input image were added. However, integrating three adjacent slices did not show significant improvement, indicating that adding two adjacent slices is sufficient to enhance segmentation performance.
# A landslide Warning Method Based on K-Means-ResNet Fast Classification Model
###### Abstract  
Landslide early warning is a key technology for effective landslide prevention and control. However, the traditional landslide early warning mainly makes decisions through thresholds, and if the thresholds are not selected properly, it will lead to missing alarms and false alarms frequently. To resolve this problem, this study proposes a landslide early warning algorithm based on a K-means-ResNet model. This method uses the K-means method to cluster the landslide deformation state, and then uses ResNet to classify the landslide rainfall and deformation data, so as to realize the threshold-free judgment and early warning of landslide deformation state. The model was applied to the Zhongma landslide, Guangxi Province, China, and the Shangmao landslide, Hunan Province, China, for validation and evaluation. The results showed that the accuracy, precision and recall of the proposed model can reach 0.975, 0.938, 0.863 and 0.993, 0.993, 0.925, respectively, for classifying the deformation states of the two landslides, and the classification results are better than those of the baseline model. Compared with the threshold-based early warning method, the proposed early warning method does not require artificial determination of threshold parameters and can effectively identify landslide deformation states, which can not only reduce false alarms and missing alarms but also improve the reliability of early warning.  
landside warning: ResNet; K-means; classification; cluster +
Footnote †: journal: Applied Sciences
## 2 Related Studies
[22] built a forecasting model using a Convolutional Neural Network (CNN) that utilized SoHO images to predict the Dst index with a True Skill Statistic (TSS) of 0.62 and a Matthews Correlation Coefficient (MCC) of 0.37. Table 1 presents a summary of the related studies. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Research Objective** & **Methodology Used** & **Result** & **Resource** \\ \hline Established a method to forecast the Dst index one hour in advance Proposed a method based on an Artificial Neural Network (ANN) combined with an analytical model of solar wind–magnetosphere interactions Combined the Support Vector Machine (SVM) with Distance Correlation (DC) coefficients to build a model Used Multivariate Relevance Vector Machines (MVRVM) to predict various indices Proposed an ANN technique to forecast the Dst index using 24 past hourly solar wind parameter values Used the Bagging integrated learning algorithms, ANN, SVM, and LSTM in advance Combined an empirical model and an ANN model to prediction model Built a forecasting model using a Convolutional Neural Network (CNN) that utilized SoHO images to predict the Dst index & Predicting Dst parameters feasible in the short term, but some bias remains & **[PERSON] et al. (2011)** [15] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the Dst-related studies that used deep learning. Although these neural network-based models demonstrated a good Dst forecasting ability, neural networks such as ANN, CNN, etc., mainly deal with independent and identically distributed data. At the same time, the Dst index belongs to time-series data, which exist as a specific periodical change in time. It thus does not satisfy the independent and identically distributed condition, which will finally lead to long-term change errors when using these neural networks to train the model. In order to solve these problems and consider the advantages of the LSTM method, this study uses the LSTM model and the EMD-LSTM model (the LSTM model with the addition of the EMD algorithm), which is trained to simulate the characteristics of the Dst index itself, to obtain better forecasting of the Dst index. The EMD-LSTM method has been applied to the prediction of some other space weather indices. For example, [PERSON] et al. [23] conducted a prediction of high-energy electron fluxes using this method, and it was found that the most minor prediction error can be obtained using the EMD-LSTM method compare with other models.
# Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening
###### Abstract  
AutoDock and Vina are two of the most widely used protein-ligand docking programs. The fact that these programs are free and available under an open source license, also makes them a very popular first choice for many users and a common starting point for many virtual screening campaigns, particularly in academia. Here, we evaluated the performance of AutoDock and Vina against an unbiased dataset containing 102 protein targets, 22,432 active compounds and 1,380,513 decoy molecules. In general, the results showed that the overall performance of Vina and AutoDock was comparable in discriminating between actives and decoys. However, the results varied significantly with the type of target. AutoDock was better in discriminating ligands and decoys in more hydrophobic, poorly polar and poorly charged pockets, while Vina tended to give better results for polar and charged binding pockets. For the type of ligand, the tendency was the same for both Vina and AutoDock. Bigger and more flexible ligands still presented a bigger challenge for these docking programs. A set of guidelines was formulated, based on the strengths and weaknesses of both docking program and their limits of validation.  
AutoDock 4; AutoDock Vina; molecular docking; CADD; virtual screening; computational chemistry
# Analytical Model to Calculate Radial Forces in Permanent-Magnet Synchronous Machines
## 4 Validation
### Analytical Results vs. FEM Results
#### 4.1.2 Magnetic Pressure  
In this section, magnetic pressures calculated in both the stator fixed frame and rotor moving frame are reported. In Figure 12, temporal and spatial waveforms of the magnetic pressure on the rotor are shown for the Qs48p8 machine. In Figure 13, the same results are plotted for the Qs36p15 machine. In the case of the Qs48p8 machine, the results obtained by the model were very similar to the ones obtained by FEM. In the case of the Qs36p15 machine, there were a few differences in the magnitudes, but the results can be considered very precise.  
Figure 11: Spectrum of magnetic flux density computed in the rotor moving frame: (**a**) Qs48p8, (**b**) Qs36p15.  
Figure 12: Magnetic pressure distribution created by stator coils on the rotor surface of the Qs48p8 machine: (**a**) temporal, (**b**) spatial.  
In Figure 14, the spectra of magnetic pressures on the rotor are plotted. As was expected after the analysis performed in Section 2, fractional harmonics appeared in the case of the fractional machine, whereas for the integer machine, all harmonics of the pressure were integers.  
Looking at the spectra of the magnetic pressures on the stator plotted in Figure 15, it can be concluded that all harmonics in both machines were integers and even. As was previously pointed out, the spectrum of the pressures on the rotor is modified depending on the winding configuration, whereas the spectrum of pressures on the stator does not depend on the type of winding. Hence, frequencies of harmonics are always the same and they do not depend on whether the machine is fractional or not.  
In Figures 16 and 17, the magnetic pressures on the stator are shown for the Qs48p8 and Qs36p15 machines, respectively. Notice that pressures were analyzed at different points of the tooth base, in the edge and in the middle of the slot.  
Figure 14: Spectra of magnetic pressures computed on the rotor surface: (a) Qs48p8, (b) Qs36p15.  
Figure 13: Magnetic pressure distribution created by stator coils on the rotor surface of the Qs36p15 machine: (a) temporal, (b) spatial.  
Figure 15: Spectra of magnetic pressures computed in the middle of a tooth base: (a) Qs48p8, (b) Qs36p15.  
According to the reported results, it is concluded that the model is able to compute magnetic pressures with rather high accuracy, especially taking into account that saturation effects were not considered.
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding
### Damson  
The aforementioned ionospheric adverse effects take place anywhere in the world; however, multipath dispersion and Doppler spread is more severe at high latitudes. In 1993, there were very little bibliography about the severity and the frequency of occurrence of these effects at high latitudes. Therefore, the DAMSON (Doppler And Multipath SOunding Network) experiment was deployed to make measurements over a number of mid and high latitude paths [34]. The DAMSON project was a collaboration of the UK's Defence Evaluation Research Agency (DERA), Norway's Forsvarets Forskningsinstitutt (FFI), Sweden's Forsvarets Forskningsanstalt (FOA) and Canada's Communications Research Centre (CRC).
# Porous Doped Carbons from Anthracite for High-Performance Supercapacitors
## 3 Results and Discussion
### Effect of Nitrogen Content on Supercapacitor Performance
The relatively lower D/G intensity ratio for KAC-800-900 can also give shape to a reduced nitrogen content and simultaneously a higher carbon graphitization degree. The consistent conclusion can further be espoused by thermo-gravimetric experiment. From the DTA (Figure 8b) and DTG curves (Figure S5), the KAC-800-900 shows the higher temperature of exothermic peak under the air flow, pinpointing its higher crystallization extent [36]. Supercapacitive properties for all porous carbon electrodes were further probed by electrochemical impedance spectroscopies (EIS) characterization and the corresponding Nyquist plots are supplied in Figure 9. In the low-frequency region, the Nyquist plots of KAC-800 and KAC-800-900 describe the typical features of EDLCs with the nearly vertical lines, denoting an ideal capacitive behavior and low electrolyte diffusion resistance in the electrode material [37]. KAC-800 owns a more vertical straight line along the imaginary axis, articulating its lower electrolyte diffusion resistance than the KAC-800-900 electrode [38]. The inset in Figure 9 sketches a magnified high-frequency region for KAC-800 and KAC-800-900. The equivalent series resistance (\(R_{ESR}\)) is calculated from \(x\)-intercepts with the real axis of the Nyquist plots in the high-frequency region. It can be ascertained that the KAC-800 electrode harbored a lower \(x\)-intercept than KAC-800-900, deciphering its higher electrical conductivity and more excellent pore accessibility for electrolyte ions [39]. This could be attributed to the presence of nitrogen-containing functional groups in the KAC-800 that can encourage the wettability and electrical  
Figure 8: (**a**) Raman spectra and (**b**) DTA curves of KAC-800 and KAC-800-900. conductivity and therefore furnish the minimized diffusive resistance to electrolyte ion conveyance [40]. In short, combined with the preceding explorations, our vivid comparison study between KAC-800 and KAC-800-900 does spell out the vital implications that the nitrogen dopant has upon expediting the ion kinetics and ion access to the electrode surfaces during the electrochemical events.
## 4 Simulation Verifications
### Validations of ADRC and Improved MPC  
To verify the presented ADRC + improved MPC control strategy, the ADRC double closed-loop control strategy, and the PI double closed-loop control strategy are taken as representatives to achieve comparisons. The PI control parameters use the optimal parameters selected in Figure 6. The waveforms of DC bus voltage, powers of the battery and capacitor when ADRC + improved MPC control strategy, ADRC double closed-loop control strategy, and the PI double closed-loop control strategy are utilized, respectively, are shown in Figures 17-19. At 1 s, the power of PV generation decreases from 2400 W to 4100 W, the DC bus voltage overshot of ADRC + MPC is smaller than ADRC and PI, and the recovery time of ADRC + MPC is shorter than the others. Simultaneously, the power fluctuations of the battery and the supercapacitor are also smaller when ADRC + MPC is adopted. At 2 s, the load power increases from 3100 W to 5000 W, and at 3 s, the load power decreases to 4100 W. During these dynamic procedures, the characteristics of the DC bus voltage, the battery power, and the supercapacitor power using ADRC + MPC are superior to these using DRC or PI.  
Based on Figures 17-19, the proposed ADRC combined with improved MPC for HESS significantly reduces voltage overshoots during disturbances and, consequently, increases the disturbance immunity. The MPC-based inner current control loop accelerates response speed and simultaneously eliminates unnecessary switching losses. The presented DC bus voltage compensator effectively increases the voltage drops and maintains the DC bus voltage constant at the rated value.  
The simulation results indicate the excellent dynamic characteristics of ADRC combined with improved MPC.  
Figure 16: Waveforms before and after the proposed DC bus voltage compensator are added.  
Figure 17: The waveforms of DC bus voltage when ADRC combined with improved MPC control strategy, ADRC double closed-loop control strategy and the PI double closed-loop control strategy are utilized, respectively.
## 2 A2G Channel Measurement Campaign  
### Measurement Equipment  
In this section, we present the A2G channel sounding measurements for the fixed-wing vertical takeoff and landing (VTOL) UAV conducted in rural area of Zigong, China. The transmitter system consists of a high performance Universal Software Radio Peripheral (USRP) Ettus X310, an on-board computer controlling the USRP, a rubidium clock, an Inertial Measurement Unit (IMU) navigation system recording the coordinates and UAV attitude, a power amplifier (PA) accompanied with a bandpass filter, and an omni-directional antenna. The transmitter mounted on the UAV emits a 36 dBm sounding signal with rectangular shaped spectrum periodically with bandwidth of \(B=25\,\mathrm{MHz}\) at 2.7 GHz. An IMU navigation system with centimeter-level positioning accuracy was used to record the position and attitude of UAV. The omni-directional transmitter antenna was fixed below the airframe to minimize interference of the fuselage on the signal.  
The ground receiver system consists of the NI vector signal transceiver PXIe-5841 controlled by PXIe-8861 via LabVIEW, a low-noise amplifier (LNA), a rubidium clock, a high accuracy global navigation satellite system (GNSS) receiver, and an omni-directional antenna. A GNSS receiver with sub-meter accuracy was used to record the position of the receive antenna, which was approximately 1.5 m above the ground as visualized in Figure 1.  
The same type of rubidium clocks providing high precision 10 MHz frequency standards to all relevant radio devices for synchronization. Before the channel measurement, both clocks were pre-adjusted in order to synchronize frequency offset between two clocks. Further, a calibration of all used front-end including radio devices and cables was performed by directly connecting transmitter and receiver in order to mitigate the front-end effect. The channel measurement parameters are listed in Table 1.  
The signals acquired during the measurements were processed using MATLAB software 2023(b) to extract the frequency domain channel transfer function. channel impulse response (CIR) was obtained by inverse Fourier transform of the frequency response by using the window function to reduce impact of the sidelobe of the effect. Through the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Carrier frequency \(f_{c}\) & 2.7 GHz \\ Bandwidth & 25 MHz \\ Number of sub-carriers \(N\) & 512 \\ Transmit power & \(\sim\) 36 dBm \\ OFDM signal period & 20.48 μs \\ Measurement time grid & 1.024 ms \\ Transmitter antenna & Omni-directional, 6 dBi (V-pol.) \\ Receiver antenna & Omni-directional, 3 dBi (V-pol.) \\ UAV speeds & 27 μs/s \\ UAV maximum altitude & 700 m \\ UAV maximum distance & 3 km \\ \hline \hline \end{tabular}
\end{table}
Table 1: Channel measurement parameter.  
channel measurement campaign, a total number of 2,800,000 CIR snapshots were collected, covering different UAV flight phases.
## 3 Results and Discussion
### Conductivity Anisotropy
Some decrease of the value after vacuum annealing is caused primarily by the increase of the transversal component: high temperature leads to the dissipation of interlayer inclusions, the concentration of interstitial donors increases, and their ionization increases \(\sigma_{\parallel C}\). On the contrary, for the intercalated crystals we find a significant increase in \(\Delta E_{b}\) (Table 2). However, for all the samples, the values of the pre-exponential factor (\(A\approx 20\div 100\)) obviously do not correspond to the effective mass ratio, as it is not below 1 (\(m^{*}_{\parallel C}/m^{*}_{\perp C}=0.615\)). According to [25; 27; 42], high conductivity anisotropy of indium monoselenide is not related to a highly anisotropic scattering mechanism, but is caused by the presence of a significant concentration of 2D electrons, which do not participate in charge transfer across the layers, in contrast to 3D carriers. Within this model, expression (8) takes the form  
\[\sigma_{\perp C}=e(n_{2}\mu_{2\perp C}+n_{3}\mu_{3\perp C}), \tag{11}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \(\sigma_{\parallel C}\) (**Ohm\({}^{-1}\).cm\({}^{-1}\))** & \(\sigma_{\perp C}\)/\(\sigma_{\parallel C}\) & \(\Delta E_{b}\) & \multirow{2}{*}{**A**} \\  & **80 K** & **300 K** & **80 K** & **300 K** & **(meV)** & \\ \hline
1 & 0.09375 & 0.01826 & 65.1 & 62.1 & 7.6 & 21.7 \\
2 & \(1.183\times 10^{-4}\) & \(9.88\times 10^{-4}\) & 708 & 154 & 15.3 & 77.5 \\
3 & \(1.218\times 10^{-5}\) & \(1.964\times 10^{-4}\) & 2575 & 248 & 23.1 & 97.0 \\
4 & 0.1182 & 0.0387 & 50.0 & 34.3 & 4.6 & 25.9 \\
5 & 0.1367 & 0.0549 & 38.8 & 27.9 & 3.5 & 234 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Conductivity across the layers and conductivity anisotropy for different samples of \(n\)-InSe. and the anisotropy depends on the relative value of the concentrations of 2D and 3D electrons and corresponding mobilities:  
\[\frac{\sigma_{\perp C}}{\sigma_{\parallel C}}=\frac{n_{2}\mu_{2\perp C}+n_{3} \mu_{3\perp C}}{n_{3}\mu_{3\parallel C}}exp(\Delta E_{b}/kT). \tag{12}\]  
Because of the limitations of the available experimental data, it is impossible to determine the separate mobility components contained in Equation (12). However, taking into account the high values of the preexponential factor, we can assume that 2D carriers are dominant.
## 2 System Description
### Farm  
The array could be directly connected to the grid as it outputs standard AC power. However, since the power utilization is only about 50%, it will be beneficial to further improve the power quality by creating a WEC farm that consists of several arrays. The arrays must be positioned in the farm according to the same principles of the WEC placement in the array. Hence, no array must shield another array from the predominant wave direction. Also, for effective power smoothing, the incoming wave along the predominant wave direction should only interfere with one WEC at a time. A proposed farm configuration consisting of six arrays that complies with this is shown in Figure 9(a), and implemented in the following simulation work. Some interference is expected between the absorbers, which in general  
Figure 8: Artistic impression of array based on Lifesaver.  
reduces the power output from the rear WECs. The wedge-shaped layout could compensate some of this by amplifying the incoming wave, but this has not yet been verified by hydrodynamical analysis, and must be treated with care.  
While the smoothing effect of the array mainly comes from evening out the power fluctuations within a single wave, the smoothing effect for the farm comes from balancing out the differences between waves in an incoming wave train. As demonstrated by [PERSON] ([PERSON], 1988) [4], smoothing of about 100-200 seconds is required to effectively smooth out wave train fluctuations. This corresponds to a fetch of 1-2 km and is far greater than the farm configuration suggested here. Some cyclical power fluctuations must therefore be expected also on the farm output, and a peak-to-average ratio of approximately 1.25 is expected. The proposed electrical configuration for the farm is shown in Figure 9(b) and shows how the medium voltage power from each array is collected for export. A second step-up transformer may be included to further increase the output voltage. The main benefit of the improved power quality from the farm is better utilization of the shore cable connecting to the grid.