esa-sceva/satcom-chunk-collection huggingface dataset:
# A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2
## 3 The Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2
### Chinese Traffic Sign Dataset  
There is little related research on Chinese traffic signs due to the lack of Chinese traffic sign datasets previously available. The German Traffic Sign Detection Benchmark (GTSDB) is generally accepted by researchers, which divides traffic signs into three categories, containing mandatory signs with blue color and circular shape, danger signs with red color and triangular shape, and prohibitory signs with red color and circular shape. There are 900 images with the size of 1360 \(\times\) 800 in GTSDB (600 for training and 300 for testing). Similar to the GTSDB, the Chinese traffic sign dataset (CTSD) was recently published by the Chinese Academy of Sciences, and was collected from videos of city roads. The CTSD contains 1100 images with the size of 1024 \(\times\) 768 and 1280 \(\times\) 720 (700 for training and 400 for testing). It also divides Chinese traffic signs into three categories, including mandatory signs with blue color and circular or rectangular shape, danger signs with yellow color and triangular shape, and prohibitory signs with red color and circular shape.  
The network structure is deeper, the more features are learned, and the model performance is superior. With the deepened network structure, the demand for the amount of training data increases greatly. For deep convolutional networks, the number of training images of CTSD is not enough to train the model. So, we decided to acquire, process, and mark new images to expand the CTSD, called the CSUST Chinese traffic sign detection benchmark (CCTSDB). It is available at [[https://github.com/csustYZhangjm/CCTSDB](https://github.com/csustYZhangjm/CCTSDB)]([https://github.com/csustYZhangjm/CCTSDB](https://github.com/csustYZhangjm/CCTSDB)). In CCTSDB, we added about 5200 images collected from the highway with a size of 1000 \(\times\) 350. The road condition of highways differs in some attributes compared to city roads, as shown in Table 2. To increase the diversity of the training data, we processed the initial images for data augmentation. For example, we added Gaussian noise to the initial 700 training images. We also transformed the scale by the factor of 1.5 and 0.5 corresponding to weight and height. To learn more features from illumination changes, we darkened the initial 700 training images, while we lightened the data collected from highways due to the poorer light conditions.  
In addition, the test set of CTSD in Reference [6] contains 139 mandatory signs, 264 prohibitory signs, and 129 danger signs. Concerning the test set for CCTSDB--excepting tiny traffic signs that are not clear enough to divide into subclasses--we found that the test set contained a total of 209 mandatory signs, 376 prohibitory signs, and 151 danger signs. This observed difference is mainly because the CTSD only marks 10 subclasses of mandatory signs, 23 subclasses of prohibitory signs, and 15 subclasses of danger signs to detect.
## 3 Time-Efficient Method to Determine an Optimal Scaling Factor and the Encoder Resolution
### Automatic Scaling Factor  
The time-efficient method that is used to automatically adjust the scaling factor is based on the estimation of an auxiliary error vector \(\epsilon_{ijk}(n)\)[13]. For the FXECAP-L algorithm, the auxiliary vector is defined as \(\epsilon_{ijk}(n)=\mathbf{X}_{ijk}(n)\mathbf{C}[\mathbf{e}_{k}(n)]\). Thus, the algorithm for the coefficient update can be rewritten as  
\[\mathbf{w}_{ji}(n+1)=\mathbf{w}_{ji}(n)+\sum_{k=1}^{K}\mu_{ijk}\epsilon_{ijk} (n). \tag{11}\]  
In this way, the proposed automatic scaling factor can be expressed as  
\[sf_{ijk}=Res_{max}\frac{\|p_{ijk}(n)\|^{2}}{\|p_{ijk}(n)\|^{2}+R}, \tag{12}\]  
where \(Res_{max}\) is the initial value of the encoder resolution, \(p_{ijk}(n)\) is an estimation of \(\epsilon_{ijk}(n)\) that is obtained as \(p_{ijk}(n)=\alpha p_{ijk}(n-1)+(1-\alpha)\epsilon_{ijk}(n)\) with a smoothing factor (\(0<\alpha<1\)) and is a parameter related to the projection order and is approximated by \(L_{ij}/\text{SNR}\), where SNR is the signal-to-noise ratio. The proposed time-efficient method uses \(Res_{max}\) (see Equation (12)) to guarantee the stability of the filter while another method uses a maximum step-size parameter. Therefore, the proposed time-efficient method establishes that the variation of \(sf\) (see Equation (12)) does not depend on the value of \(sf_{max}\) in order to avoid the divergence of the algorithm. Hence, the convergence is achieved by regularizing the step size \(\mu_{ijk}\) by means of the resolution. The proposed time-efficient method to compute the step size \(\mu_{ijk}\) is as follows:  
\[\mu_{ijk}=\frac{\parallel\mathbf{X}_{ijk}(n)\mathbf{e}_{k}(n)\parallel^{2}}{ \parallel\mathbf{X}_{ijk}^{t}(n)\mathbf{X}_{ijk}(n)\mathbf{e}_{k}(n)\parallel ^{2}}Res_{ij}\cdot sf_{ijk}. \tag{13}\]  
Using the previous time-efficient method, the proposed algorithm requires \((3N+1)IJK\) multiplications more than the existing FXECAP-L algorithm with an evolving order at each iteration. Therefore, it is clear that this strategy contributes little to the overall computational complexity. Here, the computational complexity is defined in terms in the number of additions and multiplications per iteration. As can be observed in Table 1, the proposed method requires 1204 and 3218 more multiplications than the conventional FXECAP-L algorithm and the filtered-x affine projection-like-I (FXAPI-I) algorithm, respectively, by considering a typical case (\(M=128\), \(N=100\), \(L=5\), \(I=1\) and \(J=K=2\)). On the other hand, the conventional FXECAP-L and FXAPL-I algorithms demand 800 and 2400 fewer additions, respectively, compared with the proposed method, as shown in Table 2. However, the computational cost of the proposed method decreases when the steady-state is reached, while the computational cost of the FXAPL-I algorithm is always constant.
# Efficient Wireless Charger Deployment for Wireless Reshapeable Sensor Networks
## 7 Conclusions  
In this paper, we study the WCDO problem concerning with how to deploy as few as possible chargers to make a WRSN sustainable. We have proposed two heuristic algorithms, namely the GCC algorithm and the ACC algorithm, to provide approximate solutions to the problem. We consider two forms of energy requirements: coverage requirement and power requirement. For the coverage requirement scenarios, GCC outperforms ACC in terms of the number of chargers deployed. On the contrary, for the power requirement scenarios, ACC outperforms GCC in terms of the number of chargers deployed. However, for both forms of requirements, ACC has shorter execution time than GCC, as ACC has \(O(m^{2}n^{3})\) time complexity and GCC has \(O(m^{2}n^{5})\) time complexity, where \(n\) is the number of sensor nodes and \(m\) is the number of grid points.  
In the proposed GCC and ACC algorithms, the chargers are restricted to be deployed on grid points of a grid, which may limit the application of the proposed algorithms. Moreover, the charger's charging space is modeled as a cone with a specific generatrix length and a specific opening angle, which may not be accurate enough for calculating the energy provision offered by the charger. We are now designing more efficient and flexible algorithms, such as meta-optimization algorithms, to solve the problem without the charger deployment restriction or the cone modeling of the charging space.  
Figure 23: The comparisons of GCC and ACC in terms of the execution time for: (**a**) low; (**b**) medium; and (**c**) high power requirement.  
Such enhanced algorithms are suitable to WSNs in a closed space or room like space capsule in which sensor nodes have known and fixed positions and energy requirements. Redundant sensor nodes may be applied to such WSNs to prolong the network life time; however, the weight of those redundant nodes may be a burden on the space capsule. The WRSNs that rely on WPT to power sensor nodes are thus suitable for the environment, to which the enhancements of the GCC and ACC algorithms can be applied.  
We have noticed a related solution [29] using Voronoi diagrams to help solve the directional coverage problem in WSNs whose sensor nodes are associated with rotatable directional sector-shaped sensing areas. However, the solution is for 2D environments and cannot be applied directly for solving the WCDO problem which considers 3D environments. We thus plan to study how to apply 3D Voronoi diagram algorithms to solve the WCDO problem in the future.  
This work was supported in part by the Ministry of Science and Technology (MOST), Taiwan, under Grant Nos. 104-2221-E-008-017-, 105-2221-E-008-078- and 105-2218-E-008-008-.  
[PERSON] conceived, designed and performed the experiments; [PERSON] and [PERSON] designed the algorithms, analyzed the data, and wrote the paper.  
The authors declare no conflict of interest.
## 3 Methodology
### Data Analysis  
First of all, this study conducted a pretest and instruments reliability test, as presented earlier. The data collected through the main survey, following the preliminary survey, were analyzed using the following procedure. To confirm respondents' demographic characteristics, frequency analysis was conducted via SPSS 22.0 software. The current study conducted a two-step statistical procedure using AMOS 22.0 software to analyze the data. Before the hypothesis tests, confirmatory factor analysis (CFA) was conducted to verify the unidimensionality of the constructs. Then, the hypotheses associated with causal links were tested by conducting structural equation modeling (SEM), and the hypotheses associated with moderating roles were tested by conducting multiple-group analysis.
## 4 Results and Discussion
### Compressive Strength  
Figure 3 depicts the results of replacing a portion of 0, 15, 25, 35, and 50% cement with lime. In the first part, represented by mixtures (1, 2, 3, 4, and 5), it was demonstrated that an increase in the percentage of lime replacement for cement results in a significant decrease in compressive strength at ages 7, 28, and 90 days. This is due to the fact that lime has a lower compressive strength than cement. As the percentage of lime in the mixture increased, the overall strength of the mortar decreased [32]. Such behavior is consistent with many studies ([33; 34; 35]). In the second part, the use of 25% lime as a partial replacement for cement, along with 10%, 20%, and 30% granite powder as a partial replacement for fine aggregate in mixes 6, 7, and 8, resulted in a significant improvement in compressive strength. This enhancement results from the synergistic actions of lime, which facilitates the pozzolanic reaction and improves binding characteristics, and granite powder, which serves as a filler, refining the concrete's microstructure [36].  
Mix no. 8, containing 25% lime as a partial replacement for cement and 30% granite powder as a replacement for fine aggregate, gave better results than the rest of the mixes.  
Figure 3: Compressive strength for various mixes at ages 7, 28, and 90 days.  
Figure 2: Flow of various mixes.  
Mix no. 8 was characterized by an intense increase in compressive strength and a combination of viscous consistency as well as an increase in the density of the microstructure due to the increase in hydration products. Increasing the proportion of fine aggregate replaced with granite powder to more than 30% has an important adverse effect on the loss in compressive strength.
# Cable Temperature Prediction Based on RF-GPR for Digital Twin Applications
## 3 RF-GPR Prediction Model of Cable Temperature
### Model Evaluation Index  
The data set calculated by the finite element method for the cable temperature is preprocessed and randomly divided into a training set and a test set in proportion, of which  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter Name** & **Numerical Value** \\ \hline Max\_features & 10 \\ Max\_depth & 10 \\ Min\_samples\_split & 2 \\ Min\_samples\_leaf & 1 \\ Min\_weight\_fraction\_leaf & 0 \\ Max\_leaf\_nodes & 5 \\ \hline \end{tabular}
\end{table}
Table 2: Parameter settings of random forest model.  
\begin{table}
\begin{tabular}{c c} \hline
**Variable Numbers** & **Input Characteristic Variables** & **Importance (\%)** \\ \hline
1 & X coordinate of the point & 14.68 \\
2 & Y coordinate of the point & 37.10 \\
3 & \(x\)-axis coordinates of the cable core center & 0.85 \\
4 & \(y\)-axis coordinates of the cable core center & 1.23 \\
5 & Excitation current & 28.52 \\
6 & Cable core conductivity & 5.10 \\
7 & Relative permeability & 0.65 \\
8 & Thermal conductivity of insulation & 5.95 \\
9 & Convective heat transfer coefficient & 4.45 \\
10 & Ambient temperature & 1.48 \\ \hline \end{tabular}
\end{table}
Table 3: The importance of each feature variable in model training.  
the training set accounts for 80%, and the test set accounts for 20%. The training set is used to train the prediction model, and the test set is used to validate the model and evaluate the performance. The criterion to judge the accuracy of the model is expressed by accuracy and heel mean square deviation [25; 26; 27].  
The calculation formula of accuracy \(r^{2}\) is as follows:  
\[r^{2}=1-u/v \tag{11}\]  
where \(u\) is the sum of squares of the difference between the true value and the predicted value, \(u=\sum\Bigl{(}T_{true}-T_{predict}\Bigr{)}^{2}\); \(T_{true}\) is the real value; \(T_{predict}\) is the predicted value; and \(v\) is the sum of squares of the difference between the true value and the true average.  
The calculation formula of the root-mean-square error (\(rmse\)) is as follows:  
\[rmse=\sqrt{\frac{1}{n}\sum_{i=1}^{n}{(T_{true}^{(i)}-T_{predict}^{(i)})^{2}}} \tag{12}\]  
where \(n\) is the number of samples, \(T_{true}\) is the true value, and \(T_{predict}\) is the predicted value.  
In order to eliminate the difference between the magnitude and dimension of the feature quantities and improve the accuracy of the training results, the training data are normalized [28]. The normalized formula is as follows:  
\[X_{norm}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{13}\]  
where \(X\) is the data set; \(X_{max}\) and \(X_{min}\) are the maximum and minimum values in the data, respectively; and \(X_{norm}\) is the normalized data value.
## 1 Introduction
In this paper, we extend the work and aim to quantify the energy consumption, network coverage, and throughput of multi-hop LoRaWAN. Considering the LoRaWAN specification, we define an analytical model. The evaluation of the multi-hop LoRaWAN performance is carried out by comparing the multi-hop scenario with the single-hop scenario. It is worth noting that the length of the area of both scenarios is set to 10 km since it is limited by how far the coverage of the single-hop gateway can reach. To fine-tune the hop range of the multi-hop LoRaWAN, three multi-hop scenarios are defined. However, since the coverage constraint does not apply here, we can extend the length of the study area to 88.6 km. The contributions of this work are as follows:  
* Estimating the energy consumption of the proposed multi-hop LoRaWAN solution based on equations that include LoRa parameters that affect the metrics of network performance. Other research considers similar models [10] but does not focus on optimizing energy consumption. * Estimating the maximum capacity of the proposed solution and computing the upper limits that can be realistically reached. This research highlights two possible limiting factors. One limiting factor is related to the bit rate, and it has been discussed in previous research. The other limiting factor is related to the duty cycle that has been described in [11]. Assessing the performance of the multi-hop LoRaWAN network by using the analytical model. This assessment is demonstrated by comparing single-hop and multi-hop LoRaWAN scenarios. * Studying the trade-offs involved in deploying a linear multi-hop LoRaWAN, pointing out some issues that need to be considered when deploying such networks. The study follows a scientific method, where three multi-hop LoRaWAN network scenarios are defined and utilized for the study. The rest of the paper is organized as follows. Section 2 presents some background about the LPWAN technologies and a brief description of LoRa technology. Work related to multi-hop LoRa used in monitoring networks is described in Section 3. Section 4 describes the case study, network architecture, and LoRaWAN configuration. Section 5 presents the proposed LoRaWAN multi-hop analytical model for linear sensor networks. Section 6 presents the model results and relevant discussion. Section 7 concludes the paper.
# Removal of Color-Document Image Show-Through Based on Self-Supervised Learning
## 4 Experimental Results and Analysis
### Quantitative and Qualitative Analysis on the Bleed-through Database  
This section utilizes the BTD dataset with ground truth for both quantitative and qualitative analysis of our proposed method and existing methods, including Hua [35], Mog [10], and Ro2 [36]. Figure 16 displays the restoration results for the dataset named NLILMSG311.265/6. It can be observed that, compared to other methods, our proposed approach effectively removes most of the show-through while preserving the foreground. Hua performs well in handling dark, translucent areas in isolation but often deletes foreground text in overlapping image regions, thereby reducing readability. Mog retains foreground information but struggles with dark, translucent regions, leaving visible show-through. Ro2 generally preserves foreground information well but still results in some residual show-through in most cases. Table 2 presents the quantitative analysis. Our method performs the best across all three metrics, indicating superior performance in show-through removal. Due to the Hua method removing a significant amount of foreground text while eliminating show-through, it has the highest average FgError. The results of the Mog and Ro2 methods exhibit some residual show-through.  
Figure 16: Qualitative analysis of NLILMSG311.265/6 from the BTD dataset.  
Figure 15: Qualitative analysis of MS dataset.
# Water Use Efficiency Spatiotemporal Change and Its Driving Analysis on the Mongolian Plateau
## 3 Results
### Temporal and Spatial Trend Analysis of NPP, ET, and WUE
#### 3.1.4 Detection of Change Points in Trends in Annual WUE  
Breakpoints were detected only in typical steppes and broadleaf forests (Figure 4a-h). Steppes showed a sharp increase in 1992 and a decrease in 1999, with no breakpoints in NPP Season and WUE Season, possibly due to human disturbances. Broadleaf forests showed growth from 1999 to 2017, with no significant decrease between 2017 and 2018. The first WUE Season surge occurred from 1997 to 1998, with a rate of 0.135 gC/m\({}^{2}\cdot\)mm\(\cdot\)year, primarily in Eastern Mongolia and Central Inner Mongolia. A second surge happened from 2007 to 2009, at a rate of 0.121 gC/m\({}^{2}\cdot\)mm\(\cdot\)year, observed mainly in Eastern and Southern Mongolia. From 2009 to 2018, WUE Season showed significant growth (0.009 gC/m\({}^{2}\cdot\)mm\(\cdot\)year). Both sharp increases in WUE Season coincided with increased NPP Season and decreased ET Season. No short-term WUE Season changes were observed in the annual trend analysis, suggesting that human factors may have caused these short-term mutations. Overall, the WUE Season on the Mongolian Plateau showed significant increases in 1997-1998, 2007-2009, and 2009-2018, particularly in Eastern Mongolia and Central Inner Mongolia, linked to higher NPP Season and lower ET.  
Figure 3: Spatial trend analysis of WUE on the Mongolian Plateau over the past 37 years. (**a**) shows the annual average values of WUE; (**b**) shows the spatial distribution of the coefficient of variation in WUE; (**c**) shows the average ET over the years; (**d**) shows ET trend; (**e**) shows the average WUE over the years; (**f**) shows WUE trend; (**g**) shows the coefficient of variation of WUE; (**h**) shows DSI trend Note: Sd indicates a significant decrease, Si indicates a significant increase, NSd indicates a nonsignificant decrease, and NSi indicates a nonsignificant increase.
## 4 Simulation and Experimental Results
### Simulation Results  
The numerical evaluation was performed in MATLAB/SIMULINK using the Runge-Kutta method and a fixed step time of 50 \(\mu\)s with \(K_{d}=150\) and \(K_{c}=150\). This section presents the tracking speed, position, and torque trajectories. The tracking of torque was performed with a load.
# Regional Adaptability Analysis of Solar Roof Utilization Technologies in China
## 2 Methodology
### Selection of Typical Cities  
The definitions of the solar radiation grades (from 1 to 5) are given in Table 1. Grade 1 (6680-8400 MJ/m\({}^{2}\)) has the richest radiation resource and Grade 5 (3344-4190 MJ/m\({}^{2}\)) has the poorest solar radiation resource.  
A map of China showing the levels of economic development is shown in Figure 2. As shown in Table 1, the annual GDP of the highest economic level (Grade 1) is higher than 50,000 RMB per capita, while the annual GDP of the lowest economic level is 10,000-20,000 RMB per capita. The economic development level influences the willingness of the building owners to invest solar utilization systems on building roofs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Annual Duration of Sunshine (h)** & **Annual Radiation Quantity per m\({}^{2}\) (MJ)** & **GDP per Capita** & **Climate Zoning Name** \\ \hline Grade 1 & 3200–3300 & 6680–8400 & \textgreater{}50,000 & Hot-summer-and-warm-winter zone \\ Grade 2 & 3000–3200 & 5852–6680 & 40,000–50,000 & Temperate zone \\ Grade 3 & 2200–3000 & 5016–5852 & 30,000–40,000 & Hot-summer-and-cold-winter zone \\ Grade 4 & 1400–2200 & 4190–5016 & 20,000–30,000 & Cold zone \\ Grade 5 & 1000–1400 & 3344–4190 & 10,000–20,000 & Frigid zone \\ \hline \hline \end{tabular}
\end{table}
Table 1: Grade of solar radiation, GDP, and climate zoning.  
Figure 1: Map of China showing five grades of solar radiation resource.  
According to the average temperatures of the hottest month and the coldest month, the division of thermal climate zones in China is presented in Figure 3. A definition of the five climate zones is given in Table 1. The climate zone of the Grade 1 is characterized with a hot summer and a warm winter, while the climate zone of Grade 5 is characterized with an extreme cold winter. The thermal climate zone has a significant influence on the thermal conditions of buildings: for example, insulation and the energy use of the buildings.  
Considering various influential factors, all provinces and cities in China are divided into 13 zones; see Figure 4.  
Figure 3: Map of China showing different climate zones for building thermal design.  
Figure 2: Map of China showing different economic development levels.  
A division of 13 zones gives a good foundation for the evaluation of regional adaptability of different solar utilization technologies. Then, a typical city is identified for each zone, as shown in Table 2.