esa-sceva/satcom-chunk-collection huggingface dataset:
# Tensor Rank Regularization with Bias Compensation for Millimeter Wave Channel Estimation
## 7 Conclusions  
In this paper, we presented a novel CP decomposition-based method to jointly estimate both tensor rank and component matrices of the received signal for the mmWave hybrid MIMO-OFDM systems. The proposed method is able to estimate channel parameters accurately without the prior knowledge of number of multipaths. Our approach differs from most existing works that assume the number of channel paths is known, instead, we aimed to determine the tensor rank or the number of multipath in the mmWave channel. We proposed a novel rank regularization method with a weighting parameter that can control the impact of the estimates from the previous iteration and compensate the tensor estimation bias. Compared to the baseline \(I1\) regularization LS method, our proposed method shows that we can significantly increase the rank estimation success by reducing rank estimation bias. Compared to the RALS, our proposed method is capable of promoting sparsity for low rank CP tensor with selected values of the weighting parameter. Numerical experiments show that the proposed method outperforms the conventional \(I1\) strategy in terms of accuracy and robustness in high SNR situation with a marginal cost to computation.  
Conceptualization, F.H., A.H. and L.Y.Y.; formal analysis, F.H., methodology, F.H., A.H. and L.Y.Y.; writing--original draft preparation, F.H.; writing--review and editing, F.H., A.H. and L.Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
# Research on Optimal Voltage Control of Distribution Network with the Participation of EVs and PVs
## 2 Materials and Methods
### Energy Management Schemes of EV Charging  
#### 2.3.1 Analysis of EV Charging Demands  
To use EVs for grid voltage support, it is essential to investigate the spatiotemporal uncertainties of EV charging demands which are determined by the type of EVs, the initial EV battery, and the EV arrival time at and departure time from the charging station. Depending on vehicle application, EVs can be divided into four types: private cars, buses, taxis, and exclusively-used cars, resulting in diversified traveling patterns and hence the charging demands. According to the national household travel survey (NHTC) conducted by the US Department of Transportation, the EV charging start time is close to a normal distribution as in (8) [37; 38]. Here, except for buses, all the other three EV types are assumed to have their arrival time at the charging station following a normal distribution. EVs of bus type have their traveling patterns following a uniform distribution. According to a survey of EV charging behaviors conducted by the China Electric Power Research Institute, the characterization of EV traveling patterns is tabulated in Table 3, providing preliminary data for estimating the EV charging demands.  
Figure 6: Illustration of the voltage droop control applied to smart PV inverters.  
\[f(t_{ini})=\left\{\begin{array}{l}\frac{1}{\sigma_{t}\sqrt{2\pi}}\text{exp}\left[- \frac{(t_{ini}-u_{t})}{2\sigma_{t}^{2}}\right],(u_{t}-12)<t_{ini}\leq 24\\ \frac{1}{\sigma_{t}\sqrt{2\pi}}\text{exp}\left[-\frac{(t_{ini}+24-u_{t})}{2 \sigma_{t}^{2}}\right],0<t_{ini}\leq(u_{t}-12)\right\}\end{array}\right\} \tag{8}\]  
where \(t_{ini}\) is the EV arrival time at the charging station while \(u_{t}\) is its expected value, \(u_{t}\) is the standard deviation.  
Based on the characterization of EV traveling patterns in Table 1, the daily charging demands for the four types of EVs can be estimated via the Monte Carlo approach. For each type of EV, the random sampling of the initial battery SoC and the EV arrival time to the charging station is first applied by using the probability distribution parameters tabulated in Table 1. By applying the Monte Carlo approach, 1000 diversified charging profiles are generated for each type of EV, with their aggregate charging profiles forming the daily charging demands, as illustrated in Figure 7. It is observed that different types of EVs have their unique patterns of charging demands, indicating that they may present a difference of potential in the application of grid voltage support.
## 5 Conclusions  
In this work, a series of numerical studies have been conducted to investigate the wave propagation behaviour in the wood poles. Two interfacial methods, namely, friction contact and bonded contact, were proposed to simulate the interfacial behaviour between a wood pole and its surrounding soil for guided wave propagation. Experimental testing was also conducted to verify the numerical findings. The results showed that the bonded contact method can produce better overall wave propagation patterns in simulating the guided wave behaviour in the wood pole, even though the frictional contact method is able to accurately generate first two peaks of the wave pattern, which is crucial in determination of the underground embedment length. Then, parametric sensitivity is conducted to investigate the relationships between wave propagation behaviour and material properties as well as the excitation location. The results show that higher elastic modulus in the longitudinal direction always generates higher guided wave velocity regardless of the material characteristics while elastic modulus change in the radial or tangential direction has little effect on the wave propagation. Unlike the elastic modulus, the density has reverse phenomenon that lower density will yield higher wave velocity despite the material characteristics. Besides, the change of Poisson's ratio has much more influence on wave velocity in the orthotropic material model than the isotropic material model. For the excitation location, the wave propagation in the isotropic material model is clear and can be easily analysed despite the change of excitation location due to the simplicity of the material characteristics. For the orthotropic material model, complicated wave patterns occur because of the generation of different types of waves and different modes of waves, which makes the signal analysis very complex. Furthermore, hammer impact excitation generates broad-band frequency signals, and accordingly, the generated waves in a wood pole possess a wide frequency range. To solve this issue, narrow-band signals should be considered in the following study. For the purpose of damage detection in wood utility poles, wave behaviour in damaged wood poles and advanced damage identification technique should also be investigated in the next stage. Besides, the damping of the soil and the moisture factor, that can directly influence the mechanical behaviour of  
Figure 22: Wave trace in 12 m wood pole and soil.  
the wood, will be also considered in the FE modelling, and more experimental tests will be conducted to further verify the performances of the proposed FE models.  
This research is supported by University of Technology Sydney via Faculty of Engineering & Information Technology (FEIT) Blue Sky project. The authors would like to thank the financial support from the funding body.  
[PERSON] and [PERSON] designed the finite element models and conducted experimental testing; [PERSON] analysed the model parameter sensitivities; [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### The Effect of Oil Flow Rate  
As shown in Figure 11, the oil-gas two-phase distribution inside the bearing at different oil flow rates is presented. The bearing rotating speed is 11,000 rpm, the oil viscosity is 0.0046 Pa-s, and the oil density is 938.6 kg/m\({}^{3}\). With the increase in oil flow rate, the oil phase inside the bearing gradually increases, and it mainly concentrates near the outer ring. Figure 12 shows the volume fraction of the oil phase inside the bearing along the circumference at different oil flow rates. Under the action of the cage and balls, the oil gathered at the supply holes increases with the increase of the oil flow rate. The fluctuation of the oil volume fraction along the circumference increases, and the uniformity decreases. The radial distribution of oil inside the bearing remains unchanged, mainly concentrated near the outer ring (as shown in Figures 13 and 14). The distribution gap between the outer and inner rings of oil increases with the increase in flow rate, as shown in Figure 15. The decrease in the lubricating oil flow rate will lead to insufficient lubrication of bearings, and  
Figure 10: The ratio of oil volume fraction between the zone of outer and inner rings at different bearing rotating speeds.  
Figure 9: Oil volume fraction near the zone of the inner ring and outer ring at different bearing rotating speeds.  
it is difficult to take away the heat generated; the increase in oil flow rate will lead to an increase in fluid viscous friction. Therefore, for the oil flow rate, it is necessary to ensure lubrication while taking away the heat generated by the bearing.  
Figure 11: Oil and gas distribution at different oil volume flow rates.  
Figure 12: Circumferential oil volume fraction at different oil volume flow rates.  
Figure 13: Radial oil volume fraction at different oil volume flow rates.
# U-SSD: Improved SSD Based on U-Net Architecture for End-to-End Table Detection in Document Images
## 4 Experiment
### Evaluation
#### 4.3.1 Evaluation on Our Dataset  
The dataset provided by a Taiwanese Law Firm was evaluated, and the results of the model under IoU = 0.5 are shown in Table 2. Regarding precision, while the one-stage object detection model was much higher than the two-stage model, the difference was not significant. On the contrary, while the recall of the two-stage model was relatively high, the values were not far apart. In terms of the comprehensive performance of F1-score and AP, when IoU is 0.5, all the above six models can perform well, among which U-SSD received good scores. The Precision-Recall Curve is shown in Figure 6.  
Due to the large difference between the Table and the natural object, the higher the overlap rate between the predicted position and the real position, the better. Therefore, the results of the model under IoU = 0.8 are quite important, as shown in Table 3. The one-stage model performed better than the two-stage model in precision, recall, F1-score, and AP. The U-SSD model, as proposed in this paper, performed better than the other models in comprehensive comparison. The Precision-Recall Curve is shown in Figure 7.  
Generally, as the IoU threshold increases, the performance of detection decreases; however, the smaller the position offset of the predicted table, the higher the accuracy, which is more important for Table detection, as shown in Table 4. The average precision of  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Specification** \\ \hline Operating System & Linux Ubuntu 18.04.3 \\ CPU & Intel® CoreTM i7-9700 CPU @ 3.00 GHz \(\times\) 8 \\ GPU & GeForce RTX 2080 Ti/PCle/SSE2 \\ Programming Language & Python \\ Deep learning framework & PyTorch \\ \hline \hline \end{tabular}
\end{table}
Table 1: The system information of experiment environment.  
U-SSD can achieve over 90% performance at IoU = 0.5 to 0.9. Figure 8 shows that when the IoU threshold was set below 0.7, the performance of all models was relatively average. As the threshold was increased, with the exception of U-SSD, the other models obviously decreased, while U-SSD remained relatively stable and accurate, which shows that its performance is more robust compared with other models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**IoU = 0.5**} \\ \cline{2-5}  & **Precision** & **Recall** & **F1-Score** & **AP** \\ \hline Faster R-CNN + VGG & 62.50\% & 98.48\% & 0.76 & 96.80\% \\ \hline Faster R-CNN + ResNet50 & 71.13\% & 98.48\% & 0.83 & 97.40\% \\ \hline YOLOv3 & 96.85\% & 93.18\% & 0.95 & 96.20\% \\ \hline YOLOv3 + U-Net & 96.18\% & 95.45\% & 0.96 & 96.93\% \\ \hline SSD & 98.37\% & 91.67\% & 0.95 & 95.61\% \\ \hline U-SSD & 98.39\% & 92.42\% & 0.95 & 96.85\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The comparison at IoU = 0.5 on our dataset.  
Figure 6: P-R curve at IoU = 0.5 on our dataset.  
Figure 8: The average precision from IoU = 0.5 to IoU = 0.9 on our dataset.  
Figure 7: P-R curve at IoU = 0.8 on our dataset.
## 5 Simulation Results and Analysis
### Simulation Setting  
We evaluate and compare the performance of DOIDS. The simulation parameters are listed in Table 2, and opportunistic routing in [26] is adopted. We randomly deployed 100 nodes in a [5500 \(\times\) 5500 \(\times\) 5500] m\({}^{3}\) 3-D network. As shown in Figure 7, the sink node is deployed at the purple pentacle, and the points in different colors represent different hops to the sink node. Each node (the node directly connected to the sink node can be ignored) is embedded with DOIDS to monitor the energy consumption, forwarding, and link information of each node in its candidate node set. The parameters of the hardware setting listed in Table 2 for underwater communication systems are obtained from [27].  
The employed performance evaluation metrics are:  
* The true positive rate (TPR, %): Ratio of the number of malicious nodes detected to the total number of malicious nodes in the candidate node set of all nodes. The larger the TPR, the higher the detection rate of malicious nodes.
* The false positive rate (FPR, %): Ratio of the number of normal nodes that have been misjudged to the total number of normal nodes in the candidate node set of all nodes. The smaller the FPR, the lower the false detection rate of malicious normal nodes.
* The Area Under Curve (AUC): It is a generic classification metric that is also used to evaluate node detection capability within IDS models. The closer the AUC is to one, the better the IDS is at distinguishing between normal and malicious nodes. The specifics of the metric are described in [28].  
Figure 6: (a,b) Change curve of decision value with Tag value.
###### Abstract  
Satellite remote sensing images contain adequate ground object information, making them distinguishable from natural images. Due to the constraint hardware capability of the satellite remote sensing imaging system, coupled with the surrounding complex electromagnetic noise, harsh natural environment, and other factors, the quality of the acquired image may not be ideal for follow-up research to make suitable judgment. In order to obtain clearer images, we propose a dual-path adversarial generation network model algorithm that particularly improves the accuracy of the satellite remote sensing image super-resolution. This network involves a dual-path convolution operation in a generator structure, a feature mapping attention mechanism that first extracts important feature information from a low-resolution image, and an enhanced deep convolutional network to extract the deep feature information of the image. The deep feature information and the important feature information are then fused in the reconstruction layer. Furthermore, we also improve the algorithm structure of the loss function and discriminator to achieve a relatively optimal balance between the output image and the discriminator, so as to restore the super-resolution image closer to human perception. Our algorithm was validated on the public UCAS-AOD datasets, and the obtained results showed significantly improved performance compared to other methods, thus exhibiting a real advantage in supporting various image-related field applications such as navigation monitoring.  
remote sensing image; super-resolution; attention mechanism; navigation monitoring
## 5 Conclusions  
In this study, a new method based on the triangulation method combined with bump geometry parameter analysis was proposed for measuring bump height, in which an incoherent light is used to reduce the influence of the speckle effect, and the refined designs and assembly of an optical system of illumination and imaging models ensured the acquisition of the high-quality real-time images. A line-scanning model was also adopted in order to accomplish measurement of the whole wafer by scanning along the optimized scanning path driven by the high-precision motion platform. The experimental results showed that the measurement accuracy was within 1.5 \(\upmu\)m, and the repeatability measurement accuracy could reach 0.82 \(\upmu\)m, which can meet the needs of industrial bump height measurement. The method proposed in the paper can not only be used in measuring the height of bumps, but may also be used in measuring the height difference between planes and spheres or arcs, as well as the height displacement between different surfaces of different shapes.  
Conceptualization, F.M. and Z.Z.; Methodology, Z.Z., Y.K. and C.C.; Software, F.M. and Y.K.; Validation, C.C. and D.W.; Investigation, F.M.; Writing--Original Draft Preparation, D.W. and X.Z.; Supervision, W.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by National Key Research and Development Project (2019 YFB2006700).  
**Data Availability Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & \begin{tabular}{c} **Height Measured by** \\ **SENSOFAR, A** \\ **(\(\upmu\)m)** \\ \end{tabular} & \
\begin{tabular}{c} **Height Measured by** \\ **Proposed Method, B** \\ **(\(\upmu\)m)** \\ \end{tabular} &
\begin{tabular}{c} **Biases between A** \\ **and B** \\ **(\(\upmu\)m)** \\ \end{tabular} \\ \hline
1 & 49.37 & 50.74 & 1.37 \\
2 & 49.44 & 49.73 & 0.29 \\
3 & 49.84 & 49.07 & 0.77 \\
4 & 49.88 & 48.43 & 1.45 \\
5 & 49.95 & 49.25 & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of measurement results of proposed method with SENSOFAR instrument.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No. Times of** & **Height Measured** & **Average Value (\(\upmu\)m)** & **Standard Deviation** \\
**Require** & **(\(\upmu\)m)** & **Average Value (\(\upmu\)m)** & **(\(\upmu\)m)** \\ \hline
1 & 49.78 & & \\
2 & 51.39 & & \\
3 & 50.06 & & \\
4 & 50.45 & & \\
5 & 50.00 & & \\
6 & 48.91 & & \\
7 & 48.72 & & \\
8 & 49.12 & & \\
9 & 49.16 & & \\
10 & 49.25 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Repeatability of measurements taken 10 times for each micro bump.
# Open-Set Signal Recognition Based on Transformer and Wasserstein Distance
###### Abstract  
Open-set signal recognition provides a new approach for verifying the robustness of models by introducing novel unknown signal classes into the model testing and breaking the conventional closed-set assumption, which has become very popular in real-world scenarios. In the present work, we propose an efficient open-set signal recognition algorithm, which contains three key sub-modules: the signal representation sub-module based on a vision transformer (ViT) structure, a set distance metric sub-module based on Wasserstein distance, and a class space compression sub-module based on reciprocal point separation and central loss. In this algorithm, the representing features of signals are established based on transformer-based neural networks, i.e., ViT, in order to extract global information about time series-related data. The employed reciprocal point is used in modeling the potential unknown space without using the corresponding samples, while the distance metric between different class spaces is mathematically modeled in terms of the Wasserstein distance instead of the classical Euclidean distance. Numerical experiments on different open-set signal recognition tasks show that the proposed algorithm can significantly improve the recognition efficiency in both known and unknown categories.  
open-set classification; signal recognition; transformer; ViT; Wasserstein distance +
Footnote †: journal: Applied Sciences  
1
Footnote 1: email: {[[https://doi.org/10.3300/app1302151](https://doi.org/10.3300/app1302151)]([https://doi.org/10.3300/app1302151](https://doi.org/10.3300/app1302151))}
# A Heuristics-Guided Simplified Discrete Harmony Search Algorithm for Solving 0-1 Knapsack Problem
## 2 Preliminaries  
### Problem Formulation  
Given a knapsack with maximum capacity \(W\) and a set of \(n\) items where each item \(i\) has a profit \(p_{i}\) and a weight \(w_{i}\), the objective of the 0-1 KP is to pack the knapsack with items such that a maximum profit is obtained under the constraint that the total weight of the items in the knapsack does not exceed the capacity of the knapsack. Mathematically, the 0-1 KP can be modeled as the following 0-1 integer programming problem:  
\[\max f(\mathbf{x}) = \sum_{i=1}^{n}p_{i}x_{i} \tag{1}\] \[s.t. \sum_{i=1}^{n}w_{i}x_{i}\leq W\] (2) \[x_{i}\in\{0,1\} \tag{3}\]  
where \(x_{i}\) indicates whether item \(i\) is packed in the knapsack. If \(x_{i}\) is equal to 1, then item \(i\) is packed in the knapsack; otherwise, it is discarded. Constraint (2) is the capacity constraint which guarantees that the total weight of the items in the knapsack is not greater than the knapsack's capacity, and constraint (3) is the whole constraint which guarantees that no item is broken.
# Steel Surface Defect Detection Based on YOLOv8-TLC
## 3 YOLOv8-TLC Algorithm
### Building the TSP Module
#### 3.3.2 TSP Module  
In order to adapt to the scenario of steel surface defect detection, and to solve the problem of repeated feature extraction and the degradation of detection accuracy of traditional convolutional neural networks under the operations of cropping, flipping, and scaling that may be caused by the input of fixed-size images, this paper proposes a TSP module. Spatial Pyramid Pooling (SPP) [38], which was proposed by [PERSON] et al., provides the basis for this research. On this basis, in order to further reduce the computation of the model and speed it up, Spatial Pyramid Pooling-Fast (SPPF) is proposed, which replaces the parallel pooling operation with different window sizes as the serial pooling operation with the same window size and improves the activation function. Cross-Stage Partial Connection (CSPC) [12] is a technique designed to enhance feature transfer and network efficiency. Introducing partial connections across different stages in the network accelerates feature propagation and better utilizes the information between low-level and high-level features. Inspired by SPPCSPC [39], the Triple-Space Pyramid module is proposed by combining the ability of TA to enhance important features for steel surface defects and the advantages of SPPF and CSPC. This module effectively addresses issues related to image distortion caused by cropping and scaling operations, as well as the problem of redundant feature extraction. Its structural diagram is shown in Figure 4.