esa-sceva/satcom-chunk-collection huggingface dataset:
# CDA-Net for Automatic Prostate Segmentation in MR Images
### Contributions  
Some of the above algorithms have achieved promising performance in prostate MR segmentation. Notwithstanding, in terms of prostate images with fuzzy or even missing edge information, as well as images at both ends of the sequence with small gland proportions and weak edge features, prostate segmentation is still a challenge. In addition, exploiting the limited dataset more effectively to improve the segmentation performance is also a problem worth exploring.  
To this end, this paper proposes a cascaded dual attention network (CDA-Net) for prostate MR segmentation. The algorithm adopts a cascade segmentation method, including two stages of ROI extraction and fine segmentation. Firstly, the improved Faster R-CNN with sequence correlation postprocessing (named RAS-FasterRCNN) is utilized to perform preliminary localization of the prostate. Secondly, an encoder-decoder architecture (called RAU-Net) that integrates residual learning and soft attention mechanism is used to achieve precise segmentation of the prostate. The algorithm was evaluated on two public databases, presenting excellent segmentation accuracy and generalization performance.  
The main contributions of this work can be summarized as follows:  
(1) The algorithm uses a cascaded dual attention mechanism to progressively advance the extraction of the precise contours of the prostate organs. Firstly, bounding box regression adjustment is applied to achieve ROI extraction. This hard attention mechanism excludes the influence of adjacent tissues around the prostate and other redundant background information well. Secondly, a trainable soft attention gate mechanism is introduced in the subsequent segmentation network. It dynamically learns the implicit attention distribution in the feature maps to enable the network to focus on the organ areas, thereby improving the accuracy of segmentation. Compared with the single-stage end-to-end segmentation method, this algorithm is more robust and flexible.  
(2) The algorithm combines the advantages of segmentation methods in 2D and 3D manners. The application of 2D segmentation allows the network to be fully trained under small-scaledatasets. Meanwhile, in view of the weak interslice correlation and consistency of organ positions, the algorithm adds the spatial curve fitting strategy in the sequence direction in the ROI extraction phase, which significantly improves the initial localization effect, establishing a good foundation for subsequent segmentation.  
The rest of this paper is organized as follows: in Section 2, the source of the datasets and data augmentation methods are described. Section 3 details the entire framework of the algorithm. Qualitative and quantitative experimental results obtained on two public datasets are shown in Section 4. Section 5 presents conclusions about the contributions and segmentation performance as well as directions for future research.
# Study on Reinforcement Learning-Based Missile Guidance Law
## 5 Simulation
### Passing Controller Model  
The simulation of this part is implemented via the acceleration command passing through the controller model. As it is mentioned in Section 2.3 the controller is simply modeled with 1 st order differential equation i.e., (9). For the 1 st order, controller model is formulated with condition of \(\tau\in\{0,\) 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.75\(\}\). We carried out a simulation with those predefined parameters and graphed its results including the trend lines. The miss cost is the average \(ZEM^{*}\) for the 100 initial conditions, and the energy cost is the average of integral values for the square of derived acceleration with each initial condition. The simulation results are as follows  
Note that the RL-based is trained without any controller model and has an equivalent energy cost of PNG that has navigation constant 3-4. In terms of the miss cost, RL-based seems less robust to the system. Yet, the time constant of 0.75 is quite a harsh condition. Nevertheless, they have a hit rate of 100% for all initial conditions. RL-based and PNG both far exceed the requirement even if they have been engaged with a system that we had intended to push them to the limit. Therefore, the robustness on ZEM* does not matter. On the other hand, the energy consumption of RL-based seems more robust to the system than the PNG is. As shown in Figures 13-15, the energy cost trend line of PNG with respect to time constants shows a larger gradient than the energy cost trend line of RL-based. We consider a robustness criterion as follows:  
\[\eta[\%]=\frac{\
abla f_{PNG}-\
abla f_{RL}}{\
abla f_{PNG}+\
abla f_{RL}} \times 100 \tag{16}\]  
where \(f\) is the linear trend line of each cost for each guidance law that their subscript denotes, and \(\
abla\) indicates the gradient of each trend line. With the criterion of the robustness, RL-based shows 64%, 12%, and 12% better robustness than PNG with \(N=2\), \(N=3\), and \(N=4\) respectively. We suspect that it is because the RL is trained with uncertainties and may bring policies that are robust to the system. Contrary to ZEM* robustness, energy robustness matters in the engagement scenario, because less energy consumption suggests farther radius of action of a missile.  
Figure 12: (a) Simulation trajectories; (b) Trajectories near target.  
Every single simulation easily satisfies the hit condition. Additionally, understandably, they show poorer energy performance than intact guidance laws do. Meanwhile, even with this scenario, the RL-based algorithm is more robust to the controller model. In the same manner as the criterion of robustness described in Equation (16), LOS-rate-only RL-based shows 44% and 10%, respectively, better than LOS-rate-only PNG with \(\widetilde{N}=1200\) and \(\widetilde{N}=1600\). The results of the simulation suggest that the RL-based missile guidance law works better than the PNG when the actuation delay goes past a certain threshold point.
## 3 Results and Discussions
### Thermogravimetric Analysis  
Figure 4 shows the thermal stability of activated carbon at all ranges of temeperature. At a temperature range of 110-250 \({}^{\circ}\)C, the presence of free water molecules in the interlayer region was evaporated, resulting in the removal of physically adsorbed water from the produced samples. TGA analysis of activated carbon produced initially showed that phosphoric acid had been transformed into an anhydrous material. At 213 \({}^{\circ}\)C, the two-step process begins, phosphoric acid self-polymerizing into pyrophosphate. The dehydration process was completed at 200-300 \({}^{\circ}\)C, and the degradation and pyrolysis process as an oxidant with hemicellulose, cellulose, and lignin structure to produce the pore structure in the activated carbon was completed at 350-400 \({}^{\circ}\)C [24]. Finally, full thermal deterioration  
Figure 3: XRD analysis of activated carbon olive trees and _Lantana camara_.  
begins due to the coagulation process. In case of olive tree thermal degradation, more than 50% weight is lost at approximately 450 \({}^{\circ}\)C and almost fully degrades at 575 \({}^{\circ}\)C. In the case of _Lantana camera_ activated carbon, thermal degradation starts at 270 \({}^{\circ}\)C and full degradation occurs at above 450 \({}^{\circ}\)C.
## 3 Results
The relationship between thickness (width and volume) expansion and charge capacity at this stage was expressed in the following formulae through a linear fitting analysis of Figure 4b-d:  
\[T=0.1159\mathrm{x}+46.926, \tag{1}\]  
\[W=0.0675\mathrm{x}+71.702, \tag{2}\]  
\[V=0.34\mathrm{x}, \tag{3}\]  
where x represents the charge capacity, and T, W, and V represent the thickness, width, and volume expansion, respectively. Comparing Figure 4b with Figure 4c, we found that the variation of Si/C anodes in the direction of thickness was still linear in the subsequent lithiation process, while there was no expansion in the width direction due to the space limitations. This assumes that there is enough space to release the expansion of Si, and the above linear relationships are always followed in the later stage of lithiation. The equations will be used to optimize the design of 3D-line-structured anodes. According to the theoretical capacities of silicon (3579 mAh/g) and graphite (370 mAh/g), the theoretical capacity of Si/C composite (20 wt.% silicon nanoparticles, 60 wt.% graphite, 10 wt.% conductive agent, and 10 wt.% binder) prepared in the present study was 1172 (=0.25 \(\times\) 3579 + 0.75 \(\times\) 370) mAh/g. As shown in Figure 5a, when the thickness is 45 \(\upmu\)m, the width of each line of composite material is 75 \(\upmu\)m, and the width of the respective channel is 25 \(\upmu\)m. However, the expansion is blocked when the lithium intercalation capacity is 260 mAh/g. A higher capacity would lead to a larger expansion of electrodes. Assuming the theoretical capacity could be achieved (meaning the electrode materials could be fully utilized as shown in Figure 5b) without structural damage, the thickness and width expansion could be evaluated using Equations (1) and (2). \[\mathrm{T_{optimized}}=0.1159\times 1172+46.926=183\upmu\mathrm{m}, \tag{4}\]  
\[\mathrm{W_{optimized}}=0.0675\times 1172+71.702=150\upmu\mathrm{m}. \tag{5}\]  
As shown in Figure 5b, if the thickness of the electrode material and the width of each line structure remains unchanged, an optimized channel width might be 75 (=150-75) \(\upmu\)m. Here, we need to point out that this evaluation was very coarse because of the penetration depth of lithium, and whether stress corresponding to a higher capacity would produce defects even without space limitations was unclear. Therefore, we define the above optimization strategy as a \"suggested route to optimization\". In ongoing research, further optimization will be performed.
## 4 Antenna Array
### Normal Array
#### Uniform Amplitude and Phase Distribution  
The geometrical arrangement of the linear array with the 180\({}^{\circ}\) rotation of the antenna elements 2 and 4 is depicted in Figure 11. The amplitude and phase values of each antenna feeding port in order to achieve a broadside radiation are listed in Table 4. It can be noticed that the ports of the rotated elements \(H_{2}\), \(V_{2}\), \(H_{4}\), \(V_{4}\) are fed out-of-phase. In order to provide the inversion feeding, these elements are fed by means of cables with a length of half the guided wavelength regarding the cable lengths that feed the other antenna ports.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Port H** & **Amplitude** & **Phase** & **Port V** & **Amplitude** & **Phase** \\ \hline \(H_{1}\) & 0 dB & 0\({}^{\circ}\) & \(V_{1}\) & 0 dB & 0\({}^{\circ}\) \\ \(H_{2}\) & 0 dB & 180\({}^{\circ}\) & \(V_{2}\) & 0 dB & 180\({}^{\circ}  
A comparison of this configuration with the normal array in terms of co-polar and cross-polar patterns is shown in Figure 12. The measured co-polar patterns of the array with the 180\({}^{\circ}\) rotation are really similar to the measured patterns of the normal array. The measured cross-polarization, especially for the horizontal polarization, is higher than the simulated value. The overall cross-polarization performance of this configuration is comparable to the normal array, but it presents a higher sidelobe level. The cross-polarization suppression for both configurations are alike.
###### Abstract  
A low-profile dual-polarized shared-aperture phased array antenna is proposed for Ku-band satellite communications in this paper. The stacked octagonal patches loaded with Via-rings are proposed as dual-polarized shared-aperture radiation elements, with the characteristics of wide impedance bandwidth, high gain, and weak coupling. Furthermore, innovative minimized three-port ring couplers are utilized for the differential-fed antenna array, further suppressing the cross-polarization component. Substrate integrated coaxial line (SICL) and microstrip line (MS) feed networks are employed for the excitation of transmitting band (Tx) horizontal polarization and receiving band (Rx) vertical polarization, respectively. The non-uniform subarray architecture is optimized to minimize the sidelobe levels with the reduced number of transmitter and receiver (T/R) radio frequency phase-shifting modules. As proof-of-concept examples, 16 \(\times\) 24 and 32 \(\times\) 24 array antennas are demonstrated and fabricated. The measured impedance bandwidths of the proposed phased array antennas are around 21.1%, while the in-band isolations are above 36.7 dB. Gains up to 29 dBi and 32.4 dBi are performed by two prototypes separately. In addition, the T/R phase-shifting modules are utilized to validate the beam-scanning characteristic, which is of value for dynamic satellite communications.  
dual-polarized; phased array; Ku-Band satellite communications; shared-aperture; nonuniform subarray; differential-fed; substrate integrated coaxial line +
Footnote †: journal: Sensors  
0000-0002-0002-3238-660X][PERSON]  
25253986  
Footnote †: journal: Sensors
## 5 Numerical Results  
Next, we report some dimensioning results to verify the effectiveness of the proposed solution in reducing the number of wavelengths used. We also verify the impact that the offered traffic amount and type (business and residential) and the transport network topology (i.e., number of optical rings) have on the wavelength dimensioning. We consider an area of 1 km\({}^{2}\) handled by a central office that contains a DU pool and Ethernet switches. We assume that the area is divided into \(T=200\) squared sub-areas and each sub-area is covered with a given number of RRUs and RBSs that generate CPRI and GEthernet flows, respectively.
# An Inter-Comparison Study of Multi- and DBS Lidar Measurements in Complex Terrain
###### Abstract  
Wind measurements using classical profiling lidars suffer from systematic measurement errors in complex terrain. Moreover, their ability to measure turbulence quantities is unsatisfactory for wind-energy applications. This paper presents results from a measurement campaign during which multiple WindScanners were focused on one point next to a reference mast in complex terrain. This multi-lidar (ML) technique is also compared to a profiling lidar using the Doppler beam swinging (DBS) method. First- and second-order statistics of the radial wind velocities from the individual instruments and the horizontal wind components of several ML combinations are analysed in comparison to sonic anemometry and DBS measurements. The results for the wind speed show significantly reduced scatter and directional error for the ML method in comparison to the DBS lidar. The analysis of the second-order statistics also reveals a significantly better correlation for the ML technique than for the DBS lidar, when compared to the sonic. However, the probe volume averaging of the lidars leads to an attenuation of the turbulence at high wave numbers. Also the configuration (i.e., angles) of the WindScanners in the ML method seems to be more important for turbulence measurements. In summary, the results clearly show the advantages of the ML technique in complex terrain and indicate that it has the potential to achieve significantly higher accuracy in measuring turbulence quantities for wind-energy applications than classical profiling lidars.  
multi-lidar; WindScanner; complex terrain; turbulence; wind energy +
Footnote †: journal: _Remote Sensing_, **2016**, \(8\), 782; doi:10.3390/rs08090782
# Sunspot Detection Using YOLOv5 in Spectroheliograph H-Alpha Images
## 2 Convolution Neural Networks and YOLO Architecture  
Convolution neural networks are computational models that became popular in digital image analysis (even though they can be adapted and used for different types of inputs besides images). They gained popularity due to their versatility, speed of inference and low error rate comparing to other neural networks and image analysis methods. The input image is subjected to a number of convolutions, which act as filters to select the relevant features from the background. Using the features extracted, the objects are then detected and classified in fully connected layers, which generate as output the result of the detection and classification.  
YOLO is an acronym for \"You Only Look Once\", and it is a CNN model created by [PERSON] et al., proposed for the first time in 2016 [23]. The reason for the name is the fact that the input image is processed only once through the CNN. That is different from other region proposal CNNs, which perform detection on several region proposals, resulting in many predictions for different areas in an image. The performance of YOLO is also very good for different types of problems, which makes YOLO a popular choice, especially for real-time operation.  
Different versions of YOLO have been proposed, with small improvements compared to previous implementations. YOLOv5, implemented over the PyTorch framework, was chosen for the present work. The version used is available at its GitHub repository ([[https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)]([https://github.com/ultralytics/yolov5](https://github.com/ultralytics/yolov5)) (accessed on 26 April 2023)). It comes in different variations, whose names correspond to the network size, from n (nano) to x (extra-large). A smaller architecture corresponds to a faster train and inference, while more complex models offer better performance for difficult tasks, at the cost of additional processing time.
## 6 Conclusions  
In this paper, we studied a control method using SOGI-FLL for a 3-phase AC/DC PWM converter that is used in a DC distribution system. The proposed control method comprises a SOGI-FLL, which performs positive sequence voltage extraction, phase angle extraction, and harmonic filtering without an additional filter.  
In DC distribution system, the power delivered to the load will depend on the 3-phase AC/DC PWM converter that is connected to the grid. However, the conventional control method with PLL used in 3-phase AC/DC PWM converter can not flexibly cope with the various conditions occurred in the grid. Unlike the conventional method, the proposed control method using SOGI-FLL can allow for the stable power to supply to the load in DC distribution even when various conditions occurred in the grid because it have stable and fast transient characteristics.  
Experiment and simulation applied to 50 kW 3-phase 2-level AC/DC PWM converter was performed to verify the feasibility and effectiveness of the proposed control method. As a result, stable and fast transient control was verified while using the proposed method, even if the input voltage includes various harmonics, the frequency changes and a voltage drop occurs. Given those results, it is expected that the proposed method based on SOGI-FLL is one of good candidates for DC distribution system.  
[PERSON], and [PERSON] conceived and designed the experiment; [PERSON] and [PERSON] performed the experiment; [PERSON], [PERSON], [PERSON] and [PERSON] analyzed the theory. [PERSON] and [PERSON] wrote the manuscript. [PERSON] and [PERSON] participated in research plan development and revised the manuscript. All authors have contributed to the manuscript. This research received no external funding. This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea. (No. 20162010103830). This work was supported by \"Human Resources Program in Energy Technology\" of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea. (No. 20184030202190)  
**Conflicts of Interest**: The authors declare no conflict of interest.  
Figure 29: Waveform of detected positive/negative sequence \(\alpha\beta\) voltage and phase angle \(\theta\) (before and after \(b\) phase voltage drop).
## 4 Simulation and Experimental Results
### Simulation Results  
The simulation circuit is constructed based on the proposed circuit in Figure 8. The circuit parameters are calculated by the aforementioned design procedure. However, the value of \(C_{\mathrm{p}}\) obtained from Equation (9) is not accurate enough. The reasons are as follows: for the Class-E PA, achieving its optimal-tuned state is significantly dependent on the values of \(C_{\mathrm{p}}\). The mathematical calculation formula provides only the initial calculation outline. Typically, the value of \(C_{\mathrm{p}}\) needs to be adjusted based on its switching waveforms. Therefore, the value of \(C_{\mathrm{p}}\) was fine-tuned by a simulation to attain the optimally tuned state. As a result, the value of \(C_{\mathrm{p}}\) is determined to be 65 pF. Table 1 lists the circuit parameters.  
Figure 10: Experimental platform for the proposed circuit.  
Figure 11 demonstrates the ZVDS and ZVS features during different data conditions. The drain-source voltage (\(V_{\text{d}}\)) of \(S_{1}\), the gate-source voltage (\(V_{\text{g}}\)) of \(S_{1}\), the resonant current (\(I_{\text{t}}\)), and the drain-source current (\(I_{\text{d}}\)) of \(S_{1}\) are shown in Figure 11a,b. Figure 11a depicts the waveforms of \(V_{\text{d}}\), \(V_{\text{g}}\), \(I_{\text{t}}\), and \(I_{\text{d}}\) when the data signal was at a high level. It is apparent that the \(S_{1}\) satisfies the conditions of ZVDS and ZVS. Similarly, these waveforms are depicted in Figure 11b when the data were at a low level. Clearly, the \(S_{1}\) can achieve ZVS in this state. Additionally, the resonant current waveform exhibits high sinusoidal behavior due to the design of a high load quality factor \(Q_{\text{L}}\) for Class-E PA, reducing the reactive power loss.  
Figure 11c illustrates the \(V_{\text{out}}\) waveform of the receiver coil when the data rate is 3 Mbps (random bit stream). The voltage amplitudes for high and low data signals (\(V_{\text{out,H}}=11.9\) V and \(V_{\text{out,L}}=9.6\) V) closely match the calculated values (\(V_{\text{out,H}}=12.1\) V and \(V_{\text{out,L}}=9.8\) V) based on Equations (18) and (20). Similarly, the simulated MI approximates were 10%, which is consistent with the value from Equation (16). This close agreement between simulations and theoretical calculations validates the feasibility of the proposed circuit design methodology.