esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Results of Digital Twin Model Construction
#### 3.1.2 Construction Results of the Application Layer  
The application layer for the digital twin model dedicated to solid-wood-panel processing encapsulates two key functionalities: the simulation platform and real-time mapping.  
The simulation platform, developed in the Unity3D environment specifically for solid-wood-panel processing (as detailed in Figure 18), adopts a dynamic production Gantt chart. This chart adjusts and reshapes according to the manufacturing process's variations, ensuring accurate reflection of the physical operations and demonstrating simulation capabilities. Here, users can alter parameters such as AGV speed within defined fields and observe the effects on the production simulation, enabling interactive exploration of the production possibilities.  
Shifting to the real-time mapping aspect, this functionality mirrors the physical production line in real time (as illustrated in Figure 19), tracing the operational status of various equipment, monitoring the progress status of tasks, and providing immediate alarm information. This meticulous tracking functionality plays a dual role: maintaining an accurate visualization of the physical layer at the user interface and demonstrating a potent ability for real-time problem detection.  
Figure 17: Real-time mapping of the physical layer to the virtual layer.  
Figure 18: Interactive simulation interface of the application layer for solid-wood-panel processing.  
The solid-wood-panel processing production has a real-time status monitoring page for the entire production workshop. Based on the solid-wood-panel processing production status monitoring data sensing system, this interface can reflect the manufacturing state. The mapping effects of the individual processing units are depicted in Figure 20. The virtual-actual mapping of the inspection unit is demonstrated in Figure 20a, while the outbound unit's virtual-actual mapping is shown in Figure 20b. The mapping for the processing unit is presented in Figure 20c, and the packaging unit's mapping is displayed in Figure 20d.  
By integrating simulation and real-time mapping within a single application layer, the digital twin model offers a comprehensive tool for solid-wood-panel processing that delivers insights and capabilities for real-time monitoring and simulation.
# Color Structured Light Stripe Edge Detection Method Based on Generative Adversarial Networks
###### Abstract  
The one-shot structured light method using a color stripe pattern can provide a dense point cloud in a short time. However, the influence of noise and the complex characteristics of scenes still make the task of detecting the color stripe edges in deformed pattern images difficult. To overcome these challenges, a color structured light stripe edge detection method based on generative adversarial networks, which is named horizontal elastomeric attention residual Unet-based GAN (HEAR-GAN), is proposed in this paper. Additionally, a De Bruijn sequence-based color stripe pattern and a multi-slit binary pattern are designed. In our dataset, selecting the multi-slit pattern images as ground-truth images not only reduces the labor of manual annotation but also enhances the quality of the training set. With the proposed network, our method converts the task of detecting edges in color stripe pattern images into detecting centerlines in curved line images. The experimental results show that the proposed method can overcome the above challenges, and thus, most of the edges in the color stripe pattern images are detected. In addition, the comparison results demonstrate that our method can achieve a higher performance of color stripe segmentation with higher pixel location accuracy than other edge detection methods.  
GAN-based method; edge detection; structured light; pixel location accuracy Article
## 5 Implementation Results  
### Estimation BER for Optical-OFDM Technologies  
The symmetric clipping procedure in optical-orthogonal frequency-division multiplexing (O-OFDM) should be conducted in DCO-OFDM [19]. ACO-OFDM systems would also be used to reduce error in the bias cutting at the top of the OFDM signal.  
We specified \(K\) as the clipping process attenuation factor and \(\beta_{bottom}\), \(\beta_{top}\) as the top-cutting and bottom-cutting levels. We take the equation in [20,21]:  
\[K=\frac{Cov(s,\psi(s))}{\sigma^{2}}=Q(\beta_{bottom})-Q(\beta_{top}) \tag{8}\]  
where \(Cov[.]\) denotes the covariance operator, \(\sigma^{2}\) denotes the signal's variance, and \(\psi(.)\) presents the normalized nonlinear transfer function.  
The \(BER\) simulation of M-QAM O-OFDM is represented as:  
\[BER=\frac{4(\sqrt{M}-1)}{\sqrt{M}\log_{2}(M)}Q(\sqrt{\frac{3\log_{2}(M)}{M-1} \Gamma_{i(dec)}})+\frac{4(\sqrt{M}-2)}{\sqrt{M}.\log_{2}(M)}Q(3\sqrt{\frac{3 \log_{2}(M)}{M-1}\Gamma_{i(dec)}}) \tag{9}\]  
with \(M\) is the number constellation of symbol QAM. In M-QAM DCO-OFDM, \(\Gamma_{b(dec)}\) stands for receiver electrical SNR per bit on enabled subcarriers and is distinguished as follows:  
\[\Gamma_{b(dec)}=\frac{K^{2}P_{b(dec)}/G_{B}}{\sigma_{clip}^{2}+\frac{G_{B} \sigma_{AMGN}^{2}}{g_{(opt)}^{2}G_{DC}}}=\frac{K^{2}}{\frac{G_{B}\sigma_{clip }^{2}}{P_{b(dec)}}+\frac{G_{B}\gamma_{b(dec)}^{-1}}{g_{h(opt)}^{2}G_{DC}}} \tag{10}\]  
where \(\gamma_{b(dec)}=E_{b}/N_{0}\) is the electrical SNR in each bit; \(\sigma_{clip}^{2}\) is the cutting noise variance; \(G_{DC}\) is the attenuation of the electrical signal power; \(g_{h(opt)}\) is the optical path gain coefficient; \(G_{B}\) is the ratio of used for DCO-OFDM, \(G=\frac{N-2}{N}\); and \(N\) is the length of OFDM symbol. In addition to that, with ACO-OFDM, \(G_{B}=0.5\).  
Figures 6 and 7 depict the estimated BER curves of ACO-OFDM and DCO-OFDM versus pixel \(E_{b}/N_{0}\) with bottom- and top-cutting levels appropriate for the proposed scheme's deployed parameters. The figure shows that to attain a BER of O-OFDM schemes, the pixel \(E_{b}/N_{0}\) must be at least 23 dB with DCO-OFDM and 18 dB with ACO-OFDM. The relationship between \(E_{b}/N_{0}\) and \(SNR\) are as follows:  
\[SNR=\frac{E_{b}}{N_{0}}\frac{R}{B} \tag{11}\]  
where \(R\) is the data rate of the OCC system (including error correction information and the other protocol overhead) and \(B\) is the bandwidth of the OCC system. As previously stated, the pixel \(SNR\) values are be raised by selecting a longer exposure time, but this would restrict the communication bandwidth; therefore, communication bandwidth control must be carefully examined [22].
## Results
### Outcomes  
Figure 3 shows the means and standard deviations of the study outcomes for both the experimental and control groups at baseline and post-intervention. The experimental group showed statistically significant improvements in the overall cognitive level (MMSE), working memory (DBS), and depressive symptoms (GDS) after adhering to the _Neuro-World_ training for 12 weeks, while the control group did not. No adverse effects were observed during the study period. More specifically, a two-way mixed model ANOVA showed that there was a significant interaction between the effects of intervention (experimental vs. control) and time (baseline vs. post-intervention) in the total MMSE scores (\(p<0.01\)). [PERSON]'s post-hoc tests further revealed that there was a statistically significant increase in the mean MMSE scores between baseline (BASE) and post-intervention (POST) in the experimental group (BASE: \(21.64\pm 1.08\) vs. POST: \(27.57\pm 1.45\), \(t.ratio(27)=6.48\), \(p<0.01\)), while there was a statistically significant decrease in the control group (BASE: \(20.87\pm 1.30\) vs. POST: \(16.40\pm 4.22\), \(t.ratio(27)=-5.06\), \(p<0.01\)). We performed a comparative analysis between the observed improvements in the total MMSE score vs. clinical information, including the brain lesions, the types of stroke (ischemic vs. hemorrhagic), and chronicity, but did not find any significant correlations. Furthermore, there was a statistically significant difference in the mean MMSE between the experimental and the control groups at post-intervention (EXP: \(27.57\pm 1.45\) vs. CON: \(16.40\pm 4.22\), \(t.ratio(27)=-12.43\), \(p<0.1\)) but with no differences at baseline (EXP: \(21.64\pm 1.08\) vs. CON: \(20.87\pm 1.30\), \(t.ratio(27)=-0.86\), \(p=0.82\)).
## 4 Outage Probability Analysis
### Data Rate and Capacity Analysis  
In general, the end-to-end capacity of the multi-hop system depends on the weakest link; however, in this case, the relays are operating in virtual duplex mode, so the capacity will be the  
Figure 11: State transition matrix for K = 2 and L = 2.  
average capacities of the two links. The instantaneous capacity \(C\) of a link from the source-to-relay or from the relay-to-destination is given by Shannon's capacity relation as:  
\[C_{(i,j)}=log_{2}(1+\gamma_{ij}), \tag{39}\]  
where \(i\) and \(j\) represent the source-to-relay and relay-to-destination links, respectively. In one time slot, the end-to-end capacity is \(C=min(c_{i},c_{j})\), but on average, the capacity will vary and will be the average of the two links, that is:  
\[c_{steady\ state}=avg(c_{i},c_{j}). \tag{40}\]
## 4 Performance Analysis of Islanding Detection Methods
### Suitability with Miscellaneous IDMs  
The integration of multiple DGs in the power system is becoming more common which also creates new opportunities to use different IDMs that are linked together on the same feeders or next-door feeders. Therefore, adding new DGs on the same feeder requires considering the integration among the different types of islanding detection methods that are used. Passive methods do not affect other methods but can easily be affected by other methods, such as active methods.
## 2 System Model for Pairwise Clock Synchronization
### Two-Way Message Exchange Mechanism  
The two-way message exchange is a classic mechanism for communicating the timing messages between two adjacent nodes and has been widely used in the literature [12; 13; 14; 15]. For pairwise synchronization, one of the sensor nodes is selected as the reference node. For example, a two-way message exchange model between Node A and Node B is shown in Figure 3 in the situation when Node A is serving as the reference node. During message exchange round \(i\), the synchronization begins at Node A, and a synchronization message containing the sending time \(T_{i}^{a}\) is sent to Node B. Next, Node B records the reception of the message at its clock time \(T_{i}^{b}\) and sends an acknowledgment message to Node A at \(T_{i}^{c}\). The acknowledgment message contains the timestamps \(T_{i}^{b}\) and \(T_{i}^{c}\). At last, Node A receives the acknowledgment message at \(T_{i}^{d}\), and the message exchange round \(i\) ends. This process is repeated \(N\) times, where \(N\) stands for the required number of observations.  
Based on the procedure depicted above, the clock synchronization problem can be mathematically modeled as  
\[\begin{split} T_{i}^{b}=& f(T_{i}^{a}+d+X_{i})+\theta\\ T_{i}^{c}=& f(T_{i}^{d}-d-Y_{i})+\theta\end{split} \tag{2}\]  
where \(\theta\) and \(f\) represent the clock offset and clock skew, respectively. Moreover, \(X_{i}\) and \(Y_{i}\) denote the random delays from Node A to Node B and from Node B to Node A, respectively, and \(d\) stands for the fixed portion of delays. In addition, \(T_{i}^{a},T_{i}^{d}\) and \(T_{i}^{b},T_{i}^{c}\) are time stamps recorded at Nodes A and B, respectively.  
Figure 2: Clocks of sensor nodes.  
If we consider only the clock (phase) offset between two adjacent nodes, _i.e._, \(f=1\), the system model in Equation (2) can be expressed as:  
\[\begin{split} T_{i}^{b}=& T_{i}^{a}+d+X_{i}+\theta\\ T_{i}^{c}=& T_{i}^{d}-d-Y_{i}+\theta\end{split} \tag{3}\]  
The equations in Equation (3) can be further simplified as:  
\[\begin{split} U_{i}=& d+\theta+X_{i}\\ V_{i}=& d-\theta+Y_{i}\end{split} \tag{4}\]  
where \(U_{i}\) and \(V_{i}\) contain the clock information and are defined as \(U_{i}=T_{i}^{b}-T_{i}^{a}\) and \(V_{i}=T_{i}^{d}-T_{i}^{c}\), respectively.
## 3 Results
### Person Correlation Results  
The next step was determining the Pearson correlation level with the coefficient \(\rho\) supported by the statistic test \(p_{value}\) using the equations in Section 2.4. The evidence for Pearson correlation with intensity, R, G, B, W, and frequency was sufficient as \(p_{value}\leq 0.05\), but the duty correlation was unreliable because \(p_{value}>0.05\). Thus, duty cycle was the first variable eliminated (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Intensity (A)** & \multicolumn{3}{c}{**Light Color Percentage (\%)**} & **Frequency** & **Duty Cycle** & **Energy** \\ \cline{2-6}
**(\(\mathbf{\mathrm{\mu mol}^{-2}\ \mathrm{s}^{-1}}\))** & **R** & **G** & **B** & **W** & **(Hz)** & **(\%)** & **Consumption (Wh)** \\ \hline
50 & 45 & 0 & 5 & 0 & 0 & 0 & 23.5 \\
50 & 41.5 & 0 & 8.5 & 0 & 0 & 0 & 23.4 \\
50 & 0 & 0  
Figure 4 shows a correlation heat map of the input variables and the energy consumption output to identify the strongest correlations graphically. There is a lower correlation where the graph color is darker.
## 1 Introduction
### Contribution to the Body of Knowledge  
Based on the results of the proposed model, warehouse managers can plan the allocation of products in the warehouse. This allocation will be optimal for such factors as actual demand, the allocation of other products, as well as client orders and products parameters. Managers will reduce the time of planning the product allocation and reduce the distances and order-picking time for products. The usefulness of the proposed model will allow the reducing of the total working time of order pickers, which means that the efficiency of the order-picking process will get higher. Following the simulation results of the real case study, it can be observed that the order-pickingtime is reduced from 10 to 16 per cent. The real cost-savings of the order-picking time depends on the company.  
The proposed model can be implemented as a module in a WMS system, or can work separately. The warehouse managers can use this model to optimize the allocation of a product in the warehouse and reduce order-picking distances, and consequently order-picking times. The proposed version of the model is limited to the homogeneous type of racks or for storing the homogeneous type of pallets on the ground. In future work, it is planned to improve the analytical description of the warehouse structure, with the objective that the model could be used for any type of warehousing. The proposed model uses just one type of transport means, with one velocity profile and with the normal distribution. The newest research papers pay attention to the reorganization of the warehouse with regard to the Internet of Things (IoT) ideology. The researchers [16] analyze the processes in traditional warehouses and modern ones. They propose a business process model of a smart restocking process. The authors pay attention to the optimization of the intralogistics.  
The model proposed in this paper uses the clients' orders, the products' demand and the products' properties to optimize the allocation of the product. This model can be implemented as part of the warehouse management system (WMS), or can be used as a separate software. This research is consistent with the ideology of Industry 4.0.
# Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning
## 3 Prediction Model of TBM Penetration Rate Based on Machine Learning
### Comparative Analysis of Prediction Performance of Penetration Rate Prediction Models  
In order to compare and analyze the prediction performance of the TBM penetration rate prediction models based on multiple regression, BPNN and SVR, the comparison curves between the predicted value of the TBM penetration rate and the field measured value of the three prediction models are drawn as shown in Figure 14, the absolute error change curve is shown in Figure 15 and the relative error change curve is shown in Figure 16.  
It can be seen from Figure 14 that both the BPNN model and SVR model can reflect the changing trend of the actual value of the TBM penetration rate, but the deviation between the predicted value and the actual value of the multiple regression prediction model is large, and the prediction ability to reflect the change trend of the actual value is poor.  
Figure 14: Scatterplot of the predicted and actual values of the three prediction models (the horizontal axis represents the number of thirty groups of samples in the test set).  
Figure 15: Comparison curve of absolute error between predicted value and actual value of three prediction models. (The horizontal axis represents the number of thirty groups of samples in the test set).  
the BPNN prediction model has better prediction performance and generalization ability, especially in higher prediction accuracy and stability.
## 3 Interference Scenarios and Parameters of Simulated Systems
The conducted power per element assumes 16 \(\times\) 8 \(\times\) 2 elements (power per H/V polarized element). To calculate the total transmitted power emitted towards the direction of the victim receiver's satellite, equivalent isotropic radiated power should be calculated using the following expression:  
\[\text{EIRP}_{\text{BS}}=\text{P}_{\text{tx}}+\text{G}_{\text{tx}}\]  
where P\({}_{\text{tx}}\) is a conducted power of a 5G base station (dBm), G\({}_{\text{tx}}\) gain towards the victim receiver (dBi), as expressed in Table 2 P\({}_{\text{tx}}\) equals 46 dBm, whereas the gain towards the victim receiver depends on the antenna pattern presented in Figures 4 and 5. Using that data, EIRP to each direction in azimuth and elevation plane can be considered. Figure 6 shows EIRP values in different directions when the antenna pattern is pointed toward the horizon. While scanning in the coverage area, the EIRP levels from each BS will vary in time and thus the interference levels will fluctuate depending on the beam direction of each BS. It should be noted that modern 5G BS uses digital beamforming. The major difference between analog and digital beamforming is that in digital beamforming multiple  
Figure 4: Antenna pattern of urban 5G NR BS (**a**) Horizontal plane; (**b**) Vertical plane. Figure 5: Antenna pattern of suburban 5G NR BS (**a**) Horizontal plane; (**b**) Vertical plane. independent beams steered in all directions can be formed in the digital beamforming processor [11; 12; 13; 14]. It is not expected for the 6425-7125 MHz range to be used in rural scenarios for providing contiguous coverage, however, some exceptions can be done and, and in such cases, base stations deployed in the rural area most likely will be isolated installations at some specific locations. When the rural deployment scenario is simulated in a sharing study, it should assume the BS density (per sector) of 0.001-0.006 BS per km\({}^{2}\). Other parameters for the rural deployment should be the same as the suburban.
## 3 Presentation and Discussion of the Results
### Maturity Test  
Figure 21 shows the graph with the correlation between the maturity days (M\({}_{\mathrm{20}}\)) and the average SCC compressive strength (f\({}_{\mathrm{cm}}\)) as well as the logarithmic curve (continuous curve) used to fit the results with a correlation coefficient of about 0.98. Based on the values presented in columns 2 and 3 of Table 7, a small lag can be observed between the maturity days M\({}_{\mathrm{20}}\) and the effective curing days after 7 days. This can be explained because the temperature of the curing chamber was, on average, slightly below 20 \({}^{\circ}\)C. Anyway, Figure 21 shows that the maturity test is effective to estimate the SCC compressive strength. Table 7 shows that S\({}_{\mathrm{d}}\) of five individual readings of M\({}_{\mathrm{20}}\) varied from 0.09 to 0.70 days with the average being 0.26 days, while the C\({}_{\mathrm{v}}\) varied from 3.7 to 7.1% with the average being 5.1%. From Table 7 it can be observed that, except for the first reading (one day), the C\({}_{\mathrm{v}}\) remains almost constant as SCC compressive strength increases, while the S\({}_{\mathrm{d}}\) tends to increase. These results can lead to the conclusion that C\({}_{\mathrm{v}}\) will better represents the repeatability of the maturity meter test.  
Figure 21: Maturity days versus average concrete compressive strength.
# Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform
## 4 Error Synthesis and Distribution  
According to Equations (4)-(12), the covariance matrix of the target coordinate position in the spatial Cartesian system after transformation is represented as follows:  
\[P_{e}=J_{n}^{e}\begin{bmatrix}J_{n}^{n}\begin{bmatrix}J_{c}^{n}P_{\Omega}(J_ {c}^{h})^{T}&O\\ O&P_{\Psi}\\ O&P_{gfc}\end{bmatrix}(I_{n}^{e})^{T} \tag{13}\]  
Therefore, the standard deviation of the target localization error in the space rectangular coordinate system is  
\[\sigma_{e}=\sqrt{trace(P_{e})} \tag{14}\]  
where \(trace(P_{e})\) represents the sum of the diagonal elements of the matrix \(P_{e}\).  
The total error in the spatial Cartesian coordinate system is determined by the combined effect of individual errors, so error allocation is performed to differentiate them and then correct the results. The commonly used error allocation methods equally use the influence and possibility principles; this study proposes using the improved dung beetle optimization algorithm to allocate the errors.
# Research on Wear Detection of End Milling Cutter Edge Based on Image Stitching
## 5 Conclusions  
The wear of end mills is analyzed through industrial cameras and the establishment of a tool cylinder acquisition model. The edge images of the end mill were spliced according to the sequence. Based on the VGG16 network model, a fully convolutional neural network-based automatic segmentation model of tool wear area was proposed using the Tensorflow deep learning framework. The stitched panorama is used as a sample set to train and test the FCN network model. The experimental results show that the FCN model has better segmentation ability than traditional image segmentation algorithms, and the average intersection ratio after segmentation reaches 0.76, which has the advantages of higher segmentation accuracy and better robustness. Comparing the wear value obtained based on the FCN model with the wear value detected by the digital microscope, the error rate of this method is in the range of 1.34-8.93%, and the average error rate is 5.23%, which effectively verifies the reliability of this method. The FCN network model built in this paper has a short training time, recognition time, and fewer parameters. It can be applied to the application scenario of accurately and quickly segmenting and calculating the wear value of the side edge of the end mill and can better meet the needs of practical applications.  
In the future, more images of tool wear will be obtained through experiments, and the effect of sample size on training accuracy will be analyzed. At the same time, more kinds of semantic segmentation models are used to find more suitable algorithms for tool wear analysis and to explore the transfer learning technology of tool wear, which can be more conveniently applied to the wear analysis of different types of tools.  
Conceptualization, X.W.; methodology, X.Z. and X.W.; software, L.Q.; validation, L.Q. and X.Z.; investigation, L.Q.; X.Z. and X.W.; data curation, L.Q. and X.Z.; writing--original draft preparation, L.Q. and X.W.; writing--review and editing, L.Q. and X.W.; visualization, L.Q. and X.Z.; project administration, X.W.; funding acquisition, X.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by \"National Natural Science Foundation of China\", Grant Number 51975545, 51575144; and \"University Nursing Program for Young Scholars with Creative Talents in Hei-longjiang Province\", Grant Number LGYC2018 JC042.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflict of interest.