esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Verification and Discussion
### Experimental Verification  
In the proposed method, the precondition of the maximum efficiency tracking control is that the mutual inductance can be accurately estimated. Therefore, the mutual inductance estimation method is experimentally verified first. Figure 9 shows the comparison between the estimated and measured mutual inductance values under the conditions  
Figure 10: Experimental waveforms of the inverter and system output voltages and currents under the conditions of different load resistances. (**a**) \(M=45.30\)\(\mu\)H, \(R_{\rm L}=15\)\(\Omega\). (**b**) \(M=45.30\)\(\mu\)H, \(R_{\rm L}=24\)\(\Omega\). (**c**) \(M=45.30\)\(\mu\)H, \(R_{\rm L}=32\)\(\Omega\).  
In addition, the inverter phase shift angle also changes with the load resistance or the mutual inductance. These values of the inverter phase shift angle are calculated based on (11) to maintain system transmission efficiency at the maximum points.  
Figure 11: Experimental waveforms of the inverter and system output voltages and currents under the conditions of different mutual inductances. (**a**) \(R_{\text{L}}=40\)\(\Omega\), \(M=45.30\)\(\mu\)H. (**b**) \(R_{\text{L}}=40\)\(\Omega\), \(M=37.75\)\(\mu\)H. (**c**) \(R_{\text{L}}=40\)\(\Omega\), \(M=28.27\)\(\mu\)H.  
Finally, comparisons of experimental system transmission efficiencies with and without the maximum efficiency tracking control are shown in Figure 12 under the conditions of load resistance and mutual inductance variations.  
As shown in Figure 12a, without the maximum efficiency tracking control method, the system transmission efficiency generally decreases with the load resistance, until reaching 85.52% at 96 \(\Omega\). When the maximum efficiency tracking control method is adopted, system transmission efficiency remains relatively stable despite changes in load resistance. When the load resistance is 96 \(\Omega\), system transmission efficiency is 88.63%, representing a 3.11% improvement, compared to the conditions that the method is not adopted. Further calculations suggest that the average system transmission efficiency is increased by 1.80%, and the efficiency fluctuation is decreased by 2.67%, in the load resistance range of 12 \(\Omega\) to 96 \(\Omega\).  
As shown in Figure 12b, the system transmission efficiencies have been improved with the maximum efficiency tracking control method. This improvement is evident under the conditions of different mutual inductances, and the maximum efficiency increase is 3.50%. Further calculations suggest that the average system transmission efficiency is increased by 1.80%, and efficiency fluctuation is decreased by 3.14%, over the range of the mutual inductance variation from 28.27 \(\mu\)H to 45.30 \(\mu\)H.  
Therefore, the experimental results show that the proposed coordinated control method can achieve constant voltage outputs and improve system transmission efficiencies, under the conditions of different load resistances and mutual inductances.
## 4 Application of Drive-by Methodology to the Railway Vehicle Subsystem
### Wheelset Damage Identification
Figure 15: Machine learning problem for wheel out-of-roundness (OOR) defect detection: (**a**) implementation framework and (**b**) architecture of OORNet model [85]. Besides the WDD, another issue regarding the wear of train wheels is the change of the equivalent conicity profile necessary to keep the vehicle aligned to the track. Aiming to monitor this parameter based on onboard instrumentation, [PERSON] et al. [87] proposed a model-based approach to identify the equivalent conicity of the wheels from the lateral acceleration of the car body and lateral acceleration and yaw rate of the bogies. An analytical formulation of the vehicles' lateral response, which among other parameters depends on the conicity, was derived and used a nonlinear state estimator, the constrained unscented Kalman filter (CUKF), to infer the conicity value from measured data. The proposed method was validated using simulated data from the commercial software SIMPACK(r) 18.4 and could successfully capture variations at the conicity value considering different track adhesion levels.
# Flexible Phased Antenna Arrays: A Review
## 4 Major Challenges
### Fabrication and Manufacturing Challenges  
Fabricating FPAs involves significant challenges related to substrate handling, multilayer integration, and precision assembly, all of which are critical for achieving flexibility. In particular, multilayer PCB integration introduces issues such as alignment accuracy and interlayer crosstalk. For example, a deployable phased-array transmitter using a four-layer LCP substrate integrated RF, ground plane, Serial Peripheral Interface (SPI) control, and patch antennas to achieve compactness. However, this configuration revealed the inherent difficulties in maintaining uniformity during the multilayer lamination process [65]. These critical challenges include layer-to-layer alignment sensitivity due to LCP dimensional instability under thermal cycling. This can be mitigated by utilizing low-temperature lamination techniques to reduce the expansion/contraction of LCP or by using pre-conditioned LCP sheets to stabilize thermal expansion prior to assembly.  
Scalability was identified as another challenge, and fabrication methods such as IP, while cost-effective, are limited in scalability for large arrays. This can be dealt with by the division of multilayer architecture into separate modules connected through flexible interposers to reduce complexity. Another way is to utilize the modular rigid-flex architectures to isolate sensitive components from deformation zones.  
Furthermore, the design of wideband stacked patch antenna array using IP emphasized the challenges in achieving uniformity and consistency in multilayer designs [41]. The fabrication process involved IP on PET. It posed the challenge of Silver conductivity limitation, photonic curing, stack alignment (the ground and radiating layers were printed on separate PET films, necessitating accurate alignment during assembly). IP is an efficient fabrication technique, but it also introduces alignment and attachment challenges [63]. A possible solution for future works can be the utilization of conformal pick-and-place systems with 3D vision feedback to map surface topology and guide placement.  
In addition to aligning and attaching ICs on 3D-printed surfaces, addressing the surface roughness of 3D-printed materials to improve the metallization quality posed another challenge [42]. The surface roughness of printed materials (that depends on the printing technology as well as on the working frequency of the final device) has a significant impact on metallization quality. It can cause non-uniform copper deposition, leading to higher surface resistance and degradation of RF performance. Sanding and surface finishing techniques are utilized to counter this to smooth the substrate before electroplating.
# A Multi Brain Tumor Region Segmentation Model Based on 3D U-Net
## 2 Related Work
In this paper, an enhanced segmentation model of different brain tumor regions based on a 3D U-Net was proposed to address the above problems and to achieve better segmentation performance of different brain tumor regions. The main contributions of this paper are as follows:  
1. An enhanced encoding module and decoding module were proposed to increase the extraction and utilization of image features. 2. The hybrid loss function combining the binary cross-entropy loss function and dice similarity coefficient was adopted to speed up the model's convergence and to achieve accurate and fast automatic segmentation. 3. The model's performance in the segmentation of brain tumor's WT region, TC region, and ET region from MR images was studied. The results showed that the proposed 3D U-Net model can achieve better segmentation, especially for the TC and ET tumor regions. In order to present the algorithm in this article more clearly, the advantages and disadvantages of the above algorithms and the algorithm in this article are summarized in Table 1. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Advantage** & **Disadvantage** \\ \hline Multi U-Net Integrated & Three U-Nets work together with strong feature learning ability & Cross-validation resulted in overfitting of the network and incorrect prediction of the tumor core area \\ \hline \multirow{4}{*}{2D-3D [14]} & Combining short distance 3D context and long distance 2D context, overcoming the limitations of specific selection of neural network architecture & The network is relatively complex and does not fully utilize tumor features \\ \cline{1-1}  & non-enhanced tumors & \\ \hline DR-U-Net [15] & Replacing convolutional blocks with stacked residual blocks to enhance feature transfer & Class imbalance leads to a significant average DSC standard deviation between the validation and testing sets \\ \hline \multirow{3}{*}{Improving U-Net [18]} & Injecting gradient signals deep into the network through deep supervision, extensively expanding data to prevent overfitting & Long network training time limits the number of architectural variants and data \\ \cline{1-1}  & Adding VAE branches to provide additional guidance and standardization for the encoder output section & Cropping the image smaller in order to increase batch processing results in poor network feature extraction performance \\ \hline \multirow{3}{*}{Ours} & Using enhanced encoding and decoding modules to improve the extraction and utilization of image features. The mixed loss function is used to accelerate the rate of convergence of the model & Blocking processing loses some information between blocks, resulting in poor segmentation performance for the whole tumor area \\ \cline{1-1}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of advantages and disadvantages of brain tumor segmentation methods.
# Recent Progress in Fast Distributed Brillouin Optical Fiber Sensing
## 4 Developments of the Fast BOTDA Schemes
### Slope-Assisted Fast BOTDA
#### 4.3.2 Enlarging the Monotonous Slope of the Sensing Curve
It can be clearly seen that the time traces are in good agreement with the reference time trace in Figure 13a-c while several distortion points (the blue ellipse in Figure 13d) and severe waveform distortion (zoom-in view in Figure 13e) are generated for the frequency interval of 180 MHz. Therefore, the maximum frequency interval for this scheme is 160 MHz, which is twice of that of the BGS-based multi-slope-assisted BOTDA scheme [42]. This results in a doubled sampling rate. The dynamic strain with a maximum variation of about 5372.9 \(\upmu\)\(\varepsilon\) (254.3 MHz) and a 5.58 Hz main vibration frequency is obtained at the sampling rate of 1 kHz, as illustrated in Figure 13f. Figure 13: The experimental results of the dynamic strain measurement using the multi-slope-assisted BOTDA based on the KS and the OFA [11]. The time traces for frequency intervals of (**a**) 80 MHz, (**b**) 120 MHz, (**c**) 160 MHz, and (**d**) 180 MHz. The reference time traces (the black and blue dots) are calculated by curve-fitting the BGS and KS. (**e**) A zoom-in view of the time traces within the red blocks. (**f**) The power spectra at the frequency interval of 160 MHz.
## 1 Introduction
The research shows that the surface landslide displacement process of the slope with a jointed leveling layer is mainly controlled by the slope angle. As the joint inclination angle increases, it shows more pronounced permanent displacement, which results in reduced stability. Most models that research the characterization method of the slope joint network model assume that the rock mass structural plane on the joint section is flat and linear. The current research on the stability of complex jointed slopes is mostly based on this assumption due to the limitations of related methods, which neglects the rough characteristics of the structural plane of the slope rock mass. [PERSON] et al. [15] and [PERSON] et al. [16] demonstrate that the joint roughness of rock mass is genuine and significantly affects the rock mass's mechanical properties. In order to investigate the impact of rock joint roughness on the stability of open-pit slopes, [PERSON] et al. [17] established a relationship formula between rock shear strength and joint roughness based on indoor experiments. They further validated the correlation between joint roughness and slope shear performance through simulation studies. To further characterize the fracture network of a rock mass, [PERSON] et al. [18] proposed a rough discrete fracture network (RDFN) model that accounts for the roughness of the joint surfaces. Fracture network models have been successively constructed based on different geometric types of RDFN models and modeling methods; these include sine curves, broken lines, and fractals. Moreover, [PERSON] conducted the laboratory tests and numerical simulations to model the complex structural plane network of the rock mass. The results indicate significant differences in mechanical properties and failure modes between the RDFN model with different joint shapes and the straight DFN model. Rock masses with distinct joint shapes exhibit varying mechanical strength. The RDFN model and associated methodology provide a new method to model rock slopes with intricate joint network distribution while taking joint roughness into account. This study proposes a characterization method for a rough structural plane model to investigate the stability of a bedded slope, utilizing the complex fractured rock mass of the Shilu Iron Mine. The method is based on the principle of the Fourier transform, referring to the RDFN model and modeling method. A rough discrete fracture network model is established based on in situ measurement and characterization of structural planes, reflecting the distribution characteristics of the planes. The model is optimized using the PFC code and gravity field increase method. The study exposes the failure mode characteristics for different joint fracture geometries and spatial distribution conditions. Additionally, it analyzes the influence of the joint inclination angle on the stability of the layered slope. These research results have a guiding value for engineering modeling and stability analysis of similarly complex jointed slopes.
## 3 Results and Discussion
### Polypropylene  
Figure 7 shows the interval plots for the thickness and strength of polypropylene welds when the control factors were maintained at different levels. Considering the amplitude coefficient (Figure 7a), the histogram shows amplitude levels varying from 24 \(\upmu\)m to 40 \(\upmu\)m (60% to 100%), while the other factors were fixed (Table 1). The thickness of the weld did not change much (0.02 mm to 0.03 mm), while the strength of the weld tended to decrease at 70% (40 Mpa) and 100% (45 Mpa) and peaked at 65% (70 Mpa) and 90% (90 Mpa). Similar to the polyamide material, grade 7 with 90% margin provided the best results for the material. In terms of the weld pressure factor (Figure 7b), the welding pressure levels varied from 2.0 kg/cm\({}^{2}\) to 6.0 kg/cm\({}^{2}\). When the welding pressure increased, the strength of the weld tended to decrease, while the thickness fluctuated in the range of 0.02 mm to 0.03 mm. For this factor, when weld strength was still a priority, level 2 (2.5 kg/cm\({}^{2}\)) provided the most optimal value. In the five samples tested at level 5, one result for the weld strength (about 80 MPa) was higher than the rest (about 25 to 30 MPa). This may be due to the fact that the mechanical properties of the plastic fibers chosen for the test were not uniform with the rest of the test pieces. This noise signal, although yielding positive results, unintentionally causes the confidence interval of the interval plot to be negative. This problem does not affect the aim of the experiment much. In order for the experimental results to be objective, this noise signal is still kept. In terms of the holding time factor (Figure 7c), the histogram shows varying hold time levels from 0.1 s to 0.5 s, while the other factors remained fixed. The welding process remained relatively satisfactory with changing the time; the strength of the weld was the best at level 4 (0.25 s), while the thickness of the welds did not differ significantly. Considering the speed factor (Figure 7d), the graph shows that the levels of the speed factor varied from 3.0 mm/s to 7.0 mm/s. Similar to the polyamide material, when the speed increased, the thickness and strength of the weld tended to increase markedly. However, if the thickness of the weld was larger, the strength of the weld decreased (grades 8 and 9). Therefore, the best result was at level 7 (6 mm/s) with an average weld strength of 100 Mpa and a thickness of 0.06 mm. In general, the strength of polypropylene welds was better than that of polyamide, but the thickness of the weld is reduced, which is due to the filament (PA) and multifilament (PP) structure. The best three levels of control factors will be selected and are shown in Table 2.
## 4 Construction Object Automatic Classification Dataset based on Point Cloud Data Construction
### Building an Object Classification Network Establishment  
For training, we based our network on 'FCAF3D' ([[https://github.com/SamsungLabs/fcaf3d](https://github.com/SamsungLabs/fcaf3d)]([https://github.com/SamsungLabs/fcaf3d](https://github.com/SamsungLabs/fcaf3d)), accessed on 7 July 2021) [23]. While there are other networks like PointNet and SSPC-Net for classifying 3D objects, the 'SubRGBD' dataset is widely used as an evaluation metric for the performance of 3D object classification networks in the fields of computer vision and networks. The term 'SubRGBD' is an abbreviation for \"Subtractive RGB-D\", signifying RGB-D data processing and analysis techniques. RGB-D data typically refers to data types that combine color (RGB) and depth (D) information. This type of data provides depth information in 3D space alongside 2D images [24]. It denotes the methods used to extract desired information or eliminate unnecessary information from a given RGB-D dataset.  
Figure 8: An Example of an aging building’s 3D Point cloud data.  
The FCAF3D network has been exhibiting the best performance on the 'SubRGBD' dataset, and it offers reasonable training and model speed. It operates using units referred to as 'voxels', which are concepts that correspond to pixels in 2D images [12]. Voxels are used as virtual minimum units in 3D datasets, and the training allows for adjusting voxel sizes. In this study, we used a base unit size of 1 cm. Figure 10 illustrates the definition of voxels in a visualized form.  
Furthermore, Figure 11 represents the fundamental network architecture of FCAF3D.  
Figure 10: Definition of Multi-Cell; (**a–c**) illustrates the definition of voxels in a visualized form.  
Figure 9: An Example of Labeling individual Point data by forming them into an array.  
As mentioned above, FCAF3D's model is not significantly different from the models for semantic segmentation of 2D images. It learns the data through 3D convolution and pooling processes. In this study, we stacked 4 blocks for training, but for more precise training, you can increase the number of blocks. Conversely, when precision is not required, you can reduce the number of blocks for training. Increasing the number of blocks may slow down the training process, but it offers the advantage of detecting smaller objects.  
The overall framework of the FCAF3D is outlined, featuring three-dimensional and sparse convolutions and transposed convolutions. This specific design facilitates the processing of the input point cloud in a single forward pass.
## 5 Results
### Accuracy Analysis  
This study aimed to compare the effectiveness of two different training methods for participants learning the functionalities of ADAS. Two functions, AP-On and LKA, were taught through video-based training, while two others, CA and ACC, were explained through user manuals.  
Steering wheel controls (AP-On and CA): The results indicate a significant difference in the accuracy levels between the two functions located on the steering wheel. The AP-On function, taught through video, achieved a perfect accuracy rate of 100%, while the CA function, taught through the user manual, demonstrated a lower accuracy of 79%, as demonstrated in Table 4. This variation in results may indicate that visual and interactive teaching methods such as videos may enhance comprehension and retention of the functionality, leading to higher accuracy in identifying and using the control.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group Division–Training Method** & \begin{tabular}{c} **No. of** \\ **Participants in** \\ **Each Group** \\ \end{tabular} & \begin{tabular}{c} **AP-On Response** \\ **Accuracy in \%** \\ \end{tabular} & \begin{tabular}{c} **LKA Response** \\ **Accuracy in \%** \\ \end{tabular} & \
\begin{tabular}{c} **CA Response** \\ **Accuracy in \%** \\ \end{tabular} &
\begin{tabular}{c} **ACC Response** \\ **Accuracy in \%** \\ \end{tabular} \\ \hline Video based & 48 & 100 & 77 & NA & NA \\ \hline User manual & 48 & NA & NA & 79 & 68 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Participants’ average accuracies after being trained using different methods.  
Shifter module controls (LKA and ACC): Similarly, the two functions located on the shifter module, LKA and ACC, showed accuracy levels of 77% and 68%, respectively. This pattern follows the trend observed with the steering wheel controls, where the function taught through video, LKA had a higher accuracy rate compared to the one taught through the user manual, ACC, as shown in Table 4.  
The consistent trend observed across both sets of controls suggests that the video-based training method may be more effective in instructing participants on the specific ADAS functions when compared to the user manual method. This suggests that the more interactive and visual nature of the video tutorials may foster a better understanding and may ease the learning curve.