esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Filtering Power Divider with Simple Symmetric Structure Using Stubs
## 6 Conclusions  
In this paper, a new structure of WPD with a wide suppression band is designed and fabricated, which follows the modern communication system's specifications. The proposed device operates at 2 GHz, which shows 30 dB and 20 dB attenuation for second and third harmonics, respectively. Open-ended and short-ended stubs are used for the performance improvement of the presented divider. The open- and short-ended stubs were used between the divided output resistor in the proposed power divider for the first time, which resulted in achieving a controllable bandwidth, high levels of harmonic suppression, and low insertion loss. Moreover, two resistors were used between output ports to improve the isolation between output ports. The results showed that desirable parameters of the divider were obtained, in terms of input return loss, output return loss, and isolation.  
Conceptualization, S.R. ([PERSON]) and S.R. ([PERSON]); Formal analysis, Y.S.M. and S.R. ([PERSON]); Methodology, S.I.Y. and L.W.Y.L.; Resources, J.R.; Software, S.R. ([PERSON]) and J.R.; Writing--original draft, [PERSON] ([PERSON]) and S.R. ([PERSON]); Writing--review & editing, S.I.Y., Y.S.M. and L.W.Y.L. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
All the material conducted in the study is mentioned in the article.  
The authors declare no conflict of interest.
# Structural Augmentation in Seismic Data for Fault Prediction
## 3 Seismic Fault Detection Based on U-Net
### Loss Function  
The estimation of fault zones can be considered as an image segmentation problem, and the MSE is a common evaluation metric used to measure the difference between predictions and labels as follows:  
\[Loss=\frac{1}{N}\sum_{k=1}^{N}(y_{k}-p_{k})^{2}, \tag{15}\]  
where \(N\) represents the number of pixels in the input seismic data. The predicted fault probability \(p_{k}\) and theoretical fault label \(y_{k}\) at each pixel measure the computational error of the updated neural network.
## 5 Limitations  
The first limitation of this study is related to the research object, which is a 3D point cloud of the subway station model. The point cloud comes from a subway station 3D model, which is not easy to obtain because a subway station 3D model is often a confidential file. Therefore, it is difficult to determine how to obtain a large number of 3D models of subway stations. The 618 models used in this paper come from secondary modeling of metro station plans. Because these are not the source files for the 3D model of the project, the realism of the secondary modeling cannot be fully guaranteed, and in addition, a large number of metro stations need to be modeled twice, which will produce a huge workload. If the source file models of metro stations can be analyzed and studied, or if the BIM models of metro station design can be added to this process, then both the quantity and quality will be improved qualitatively. The second limitation relates to the division and labeling of the functional space in the metro station which, in this paper, only involve eight kinds of space including non-paying areas, paying areas, entrances and exits, platform levels, tracks, accessible elevators, and escalators and stairs, which cover the main functional partitions of the metro station. Some auxiliary rooms, toilets, etc., are not added; the reason for this is that they are not universal. There are also smaller partition modules such as ticket offices and gates that are not added. If they can be further refined in subsequent studies, the modeling process will surely be more effective. In addition, this paper only deals with the study of functional partitioning of building space in subway stations. The research object is the point cloud of such general data, and the same method can be expanded to plumbing, heating and electricity, and other related professions. If other parts can also be added to expand the dataset in future studies, then artificial intelligence can be achieved for the entire professional design assistance of the subway station.  
Regarding the overfitting problem mentioned in this paper, the main reasons could be the long training time and the result of high model complexity, insufficient data, and lack of regularization, which was discussed to some extent in this study.  
In this paper, MIOU and accuracy are used as indicators of success, but there is no discussion of the impact of data diversity or potential bias in the data on the results of the study. Good performance on these metrics does not necessarily guarantee good model generalization, and further work is needed to find other metrics that are more suitable for point cloud data evaluation.  
Finally, the visualization of the prediction-generated model involved in this paper mainly uses MeshLab for the transformation of point clouds to mesh. Relative to the architectural model, the details are insufficient, and if the point cloud can be transformed into a fine metro station model, then this deep learning framework will be more genuine and down-to-earth for architect assistance.
# Array Design and Transmitter Coils Selection for Underwater Wireless Power Transfer System
###### Abstract  
This paper proposes a method for array design and optimal transmitting coil selection of underwater wireless power transfer systems. This method is divided into three steps. Firstly, by analyzing the influence of different ratio side lengths of the transmitting coil and receiving coil on mutual inductance, the optimal ratio side length coil is selected. Secondly, by analyzing the relative size of the reflection impedance of the power supply coil and its surrounding coils, the optimal coil activation criterion is derived. Finally, by estimating the position of the receiving coil without communication, the switching of the power supply coil is realized. According to the proposed method, it was verified on the experimental platform. Under a rated power of 300 W with a load resistance of 20 \(\Omega\), the system maintains efficiency \(\geq\) 80% even under horizontal offsets up to 150 mm (75% of the transmitting coil side length) and two-dimensional offsets up to 200 mm (100% of the transmitting coil side length).  
underwater wireless power transfer system; array coupling mechanism; receiving coil position estimation; array switching control 2025156368  
1
# Efficient Parallel Algorithms for 3D Laplacian Smoothing on the GPU
## 1 Introduction
Two special compute unified device architecture (CUDA) kernels were designed to solve the race condition in neighbor search and data dependence in smooth iteration. Experimental results have indicated that Form A, which needs to swap intermediate nodal coordinates is always slower than the form that does not swap data. [PERSON] et al. [22] further optimized the smart Laplacian smoothing algorithm with the help of CUDA dynamic parallelism. This nested CUDA code can greatly simplify the comparison of mesh quality so that the iteration process can be optimized. However, because a parent core needs to call two subcores, the performance of parallel computing is not fully utilized, which results in this version being slightly faster than the original GPU version. It is remarkable that most of the above studies focused on two dimensional cases. However, it is obvious that mesh composed of tetrahedrons or hexahedrons is more suitable for various kinds of numerical simulations. Therefore, a parallel solution for Laplacian smoothing in three dimensional applications is necessary. This paper presents an efficient three dimensional Laplacian smoothing based on GPU acceleration. With the help of parallel sort and unique, we search and store the first-order neighbors in the subarea to avoid conflicts. The effects of different data structures are also considered, including the array of non-aligned structures, array of aligned structures, and structure of arrays. In addition, new iteration kernels have been designed, which make full use of the power of the GPU and thus significantly reduce the number of iterations. The rest of paper is organized as follows. Some basic concepts and principles of Laplacian smoothing and data layout is given in Section 2. The next Section 3 presents some key issue and implementation details of 3D parallel Laplacian Smoothing. Sections 4 and 5 mainly elaborate on the results of several groups of experimental tests and the discussion of GPU acceleration performance of each part. Finally, in Section 6 we present our conclusions.
# Characterizing the Impact of Data-Damaged Models on Generalization Strength in Intrusion Detection
## 2 Materials and Methods
### Training and Evaluation Procedure
#### 2.2.1. Included Algorithms  
Pretrained models for a total of twelve supervised classifiers were included. The algorithms are separable into three families. All tree-based methods used gini-impurity to make splitting decisions. The abbreviations next to the methods are used throughout the rest of the text and in the figures. The sequence of decision tree-based classifiers includes important innovations made to them over time. The methods from other families were added for comparative purposes. Detailed information on the inner workings of every algorithm can be read in these references [31, 32, 33].  
1. Tree-based methods: * Decision tree (dtree); * Decision trees with bagging (bag); * Adaboost (ada); * Gradient-boosted trees (gradboost); * Regularized gradient boosting (xgboost); * Random forest (rforest); * Randomized decision trees (extratree).
2. Neighbor methods: * K-nearest-neighbors (knn); * Nearest-centroid (ncentroid).
3. Other methods: * Linear kernel SVM (linsvc); * RBF-kernel SVM (rbfsvc); * Logistic regression (binlr).
## 2 Related Work
Aiming to the performance anomaly problem, there have been some significant efforts to improve the system throughput and channel fairness. In [21], the authors verify that modifying the CWmin of station is inversely proportional to the transmission rate that can approximately provide the effectiveness for airtime fairness. Therefore, setting the CWmin of nodes based on their data-rate can achieve the airtime fairness. In [22], the authors propose a distributed average-CW algorithm, which implements time fairness by adjusting the CW of each node in real-time. In [23], the authors present an Idle-Sense algorithm to achieve an approximate time fairness, which dynamically adjusts CW according to the number of estimated idle slot and nodes' rates. In [3], the authors propose an objective function that maximizes system throughput under time fairness condition and, thus, obtains an optimal CW value for each node, which results in high computational cost when some nodes frequently join or leave the network. In [24], the authors propose an algorithm that runs multiple virtual DCF instances and its number is proportional to the node's data-rate. Therefore, the high-rate nodes can obtain more channel access opportunities. In [25] and [26], some approaches by adjusting the size of the packet are developed for achieving time fairness. Fundamentally, they let the size of packet be proportional to the node's data rate. However, the throughput of low-rate nodes can be severely restrained due to its size of packet always being suppressed, even though the highest rate node always transmits the packet with the Maximum Transmission Unit (MTU). Recently, a hybrid slotted-CSMA/CA time-division multiple access (TDMA) has been proposed in [27], which focuses on solving the massive registration of IoT devices at a single centralized AP. However, none of the above works propose a comprehensive solution to simultaneously solve the channel collision and performance anomaly problem, because the optimal CW is sometimes hard to satisfy these two purposes and the nodes often need a complex distributed cooperation. In our initial work [13], we propose a decoupling channel access algorithm to overcome this limitation. Similar to the CSMA/CQ proposed in [20], we partition the channel utilization into contention and transmission process. However, unlike CSMA/CQ, our algorithm only operates in one channel, thus it is more flexible to be deployed in many single channel scenarios. When compared with our previous work in [13], in this paper, we also address the channel airtime fairness in dense multi-rate scenarios and present the numerical analytical results to theoretically verify its effectiveness of this decoupling channel access mechanism.
## 3 Experimental Section
### Comprehensive Performance Analysis of UOCC System  
After grayscale conversation, de-bubbling and selection of column matrix, gamma correction, linear interpolation, thresholding, sampling adjustment, and recovering the data logic, the BER performances of RSE-based UOCC under different underwater environments (according to Table 2) could be evaluated. In this study, the number of pixel rows occupied by a bit was 15 after interpolation, so we corrected the fringes based on the multiple of 15. Payload bit lengths of 60 bits, 80 bits, 100 bits, 120 bits, and 140 bits were compared. The transmitted symbol rate was 10.8 kBaud (60 (data bits/frame) \(\times\) 60 (frames/second) \(\times\) 3 (repeat 3 times) = 10.8 kBaud), 14.4 kBaud, 18 kBaud, 21.6 kBaud, and 25.2 kBaud. The net data rates after the header and redundant packets removal can be calculated by the bit length in the payload per image frame times the frame rate. Hence, the corresponding transmitted data rate was 3.6 Kbit/s, 4.8 Kbit/s, 6 Kbit/s, 7.2 Kbit/s, and 8.4 Kbit/s.
## 3 Multicarrier CDMA Based Modulation Schemes
### McC-DS-CDMA  
Multicarrier direct sequence CDMA (MC-DS-CDMA) transmitter spreads the serial to parallel converted data streams in time domain using a spreading code [46], as shown in Figure 7. Thus, MC-DS-CDMA exploits time diversity in wireless channel to improve the BER performance of the system. [PERSON] [55] proposed an interference avoidance code assignment strategy for the hierarchical 2-D spread MC-DS-CDMA based cognitive femtocell system. It was shown that, the proposed interference management scheme associated with the hierarchical system architecture can be a potential prototype of the CR-based femtocell system. [PERSON] _et al_. [56] proposed an interweave cognitive radio (CR) system based on a hierarchical wide-band two-dimensional spread MC-DS-CDMA scheme. The authors proposed a DSA algorithm to utilize the white and gray spreading codes to improve the spectrum efficiency without harming the primary user transmission. Multi-user detection (MUD) techniques are necessary at the receiver side to combat the channel effects which lead to loss of orthogonality among different spreading codes and interference among different users' signals. In [57], MUD schemes based on the zero forcing (ZF), minimum mean square error (MMSE) and interference cancellation (IC) techniques were proposed for MC-DS-CDMA based CR systems. The advantages of these schemes include low-complexity, high-efficiency, high-flexibility, and robustness to imperfect channel knowledge. Diversity techniques are generally used to improve the BER performance of the MC-DS-CDMA system. Figure 8 shows the BER performance of the MC-DS-CDMA system over Rayleigh fading channel with transmit antenna diversity (\(T\)) and frequency diversity (\(S\)). We observe that the BER performance is significantly improved by increasing the number of transmit antennas \(T\) from 1 to 2. To improve the BER performance of the system especially for high order modulations, the frequency diversity order is increased by increasing \(S\). However, when \(T=2\) and \(S\) becomes large, the improvement rate starts dropping, which is because the system approaches to its maximum capacity. Thus, considering trade-off with the complexity of the system due to the increased \(S\), Figure 8 suggests that \(S\) need not be larger than 4. Table 1 shows the comparison of various M  
Figure 7: Reconfigurable MC-DS-CDMA transmitter.
## 3 Methodology
### Performance Evaluation  
The performance of the classification algorithms was determined in the study; performance was based on the four standard evaluation metrics for accuracy, sensitivity, specificity and f-measure. A 10-fold cross-validation for comparison with the baseline method was used, splitting the data into 10 folds and using nine folds for training and one fold for testing. Confusion metrics were used for the analysis of supervised learning, where each column of the matrix represents the instances in a predicted class, while each row represents the instances in an actual class to avoid mislabeling as shown in Table 2.  
The entries in the confusion matrix have the following meaning in the context of a data mining problem: a is the correct negative prediction, also called true negative (TN), classified as failed by the model; b is the incorrect positive prediction, also called false positive (FP), classified as passed by the model; c is the incorrect negative prediction, also called false negative (FN), classified as failed by the model; and d is the correct positive prediction, also called true positive (TP), classified as passed by the model.  
The performance metrics according to this confusion matrix are calculated as follows.
## 3 Results and Discussion
### Multi-Layer Perceptron vs. Spray Drying
The results allowed for demonstrating that color characteristics have an impact on the effective differentiation of the research material consisting of the spray-dried powder from rhubarb juice with different powder content: 30, 40 and 50% and high (\"H\") and low (\"L\") level of saccharification of the selected carrier (maltodextrin). In 2021, research was conducted to test the effectiveness of fruit recognition at low inlet temperatures in spray drying. Based on the experience of previous work and using scanning microscopy techniques, image analysis, artificial neural networks, as well as modeling changes in water activity, [PERSON] et al. [23; 88; 89] carried out additional experiments on raspberry fruits. In one of the research, an endeavor was made to evaluate the impact of the type of carrier (maltodextrin, arabic gum and inulin) with dry mass content at the level of 50, 60 and 60% on the quality of raspberry powders [88]. Different types of powders were compared, taking into consideration the structure of micro-particles, water activity and moisture. It should be added that modern methods were applied in this research, such as low-temperature spray-drying and artificial intelligence with visual technique supported by an electron microscope. The aim of the study was to monitor the process of spray-drying in order to obtain raspberry powders characterized by high-quality properties. As a result of the research, it was possible to create the MLPN, for which the output layer of the network defined a nine-state variable, i.e., raspberry powder samples with different proportions and types of carriers. The MLP 8-44-9 network (Table 2) was determined from a set containing 8 neurons in the input layer (circularity, solidity, round, feret, W1, W2, water activity, moisture content) and 44 neurons in the hidden layer (Table 2). In summary, the qualitative assessment of the obtained powder, in terms of customer requirements, requires a wide range of physicochemical determinations. Producers of fruit and vegetable powders struggle with the problems of repeatability of nutritional properties of powders both between batches and within one production batch. Appropriate selection of input parameters in the MLPN resulted in high efficiency of quality assessment of fruit and vegetable powders, considering the amount and type of carriers. In order to obtain homogeneous powders, it is necessary to monitor the quality and drying parameters of food powders (including the selection of the amount of carrier) online using artificial intelligence.
# A Performance Test and Internal Flow Field Simulation of a Vortex Pump
## 6 Experimental Verification  
In this paper, five spherical probes were used to extend into the vortex chamber, and five points were measured. The location of the measuring points is shown in Figure 12. The measuring system and principle of the probe are shown in Figure 11. The flow field calculation, measurement data, and the curve contrast are shown in Table 3 and Figure 13. Curves in Figure 13 are based on the coordinate system in Figure 1.  
It can be seen from the comparison and analysis that the numerical calculation is consistent with the experimental results, which can reflect the internal flow pattern of the vortex pump. The numerical simulation results are reliable, and can be applied to the qualitative and quantitative analysis of the internal flow field of the vortex pump.  
Figure 12: Probe measuring system. (**a**) Structure diagram of the five-hole spherical probe; (**b**) Connection system diagram of five-hole spherical probe and differential pressure meter.  
Figure 13: Calculated and measured curves of flow field curves on the axial-cross-section \(Z=12.5\) mm.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Calculated and** & **Discharge** & **Rotational Speed** & **Value Type** & **Absolute** & **Circunferential** & **Radial Velocity** & **Axial Velocity** & **Static Pressure** \\
**Measured Position** & **q\({}_{s}\)/m\({}^{3}\)-b\({}^{-1}\)** & **air-min\({}^{-1}\)** & **Volacity** & **v\({}_{m}\)/m\({}^{-1}\)** & **Velocity** & **v\({}_{m}\)/m\({}^{-1}\)** & **v\({}_{m}\)/m\({}^{-1}\)** & **v\({}_{m}\)/m\({}^{-1}\)** & **v\({}_{m}\)/m\({}^{-1}\)** & **v\({}_{m}\)/m\({}^{-1}\)** \\ \hline \(x=-55\) nm Measuring point a & 9.31 & 2851 & Calculated value & 9.806 & 9.512 & 1.612 & 1.782 & 64,126 \\  & & Measured value & 9.312 & 9.002 & 1.237 & 0.687 & 59,051 \\ \hline \(x=-25\) nm Measuring point b & 9.30 & 2851 & Calculated value & 8.267 & 7.567 & \(-0.816\) & \(-0.509\) & \(-192.216\) \\  & & Measured value & 9.005 & 8.536 & \(-1.341\) & \(-1.321\) & \(-2.0564\) \\ \hline \(x=0\) Measuring point c & 9.32 & 2850 & Calculated value & 3.006 & 0.905 & \(-0.223\) & \(-2.2972\) & \(-2.2138\) \\  & & Measured value & 3.902 & 1.264 & \(-0.663\) & \(-3.242\) & \(-26.842\) \\ \hline \(x=25\) nm Measuring point d & 9.31 & 2849 & Calculated value & 8.306 & 8.026 & 1.572 & 1.216 & \(-18.162\) \\  & & Measured value & 8.817 & 8.308 & 2.003 & 1.362 & \(-2.447\) \\ \hline \(x=-55\) nm Measuring point e & 9.31 & 2850 & Calculated value & 10.332 & 9.248 & 1.865 & 2.016 & 63,636 \\  & & Measured value & 9.769 & 8.705 & 2.355 & 2.100 & 85,100 \\ \hline \end{tabular}
\end{table}
Table 3: Calculated and measured values of flow field curves on the axial cross-section \(Z=12.5\) mm.