esa-sceva/satcom-chunk-collection huggingface dataset:
# The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges
## 4 WSN Systems in Different Applications: Aerospace, Bridge Structures, and Wind Turbines  
SHM is currently the basis for many extensive structures and products. These systems are based on NDT sensors integrated into structures including aircraft, bridges and wind turbines, to name but a few. Furthermore, they allow periodic or continuous monitoring of the creation and growth of damage without the cost and time necessary for decommissioning disassembly and manual inspections.  
The widespread use of wireless technology and wireless sensor nodes, in fact, makes it possible to consider the sensors not individually but as a network of sensors or as a whole platform. The main motivations for using these technologies include removing heavy cables and connection ports by substituting them for a wireless connection. The lack of cable and connection ports can, on the one hand, significantly lower the structure's weight, but on the other hand, it requires a separate power source at the same time, further reducing weight. Furthermore, adding batteries to the SHM system might require extra maintenance issues. Therefore, it has been proposed by some researchers to collect energy for sensors by capturing the required electricity from solar energy or structural vibrations. Though it may seem to be a promising research area, adding this world-class technology to SHM systems without significantly increasing weight remains a challenge. For this reason, technological maturity is still rather limited and requires further research and test implementations [28; 29].  
A sensor node usually consists of three to four main elements (Figure 4): measuring part of sensor data, computing core (CPU), wireless transmitter-receiver and power supply (battery). The central part of the wireless sensor node is the microprocessor which is responsible for processing all data received from the memory, sensors and receive/transmit signals. The transmitter/receiver communicates with an access point or other sensors using a radio frequency medium. The transmitter can retrieve data from the microprocessor in order to transmit it further by radio and vice versa. The battery is designed to power all parts of the node long enough to ensure energy continuity in all parts throughout the flight. The last part is the sensors that send analog data to the microprocessor [30].  
Although traditional batteries still power most sensors, research is being carried out into battery-free sensors' production using passive radio frequency identification (RFID) systems [30].
## 6 Conclusions  
Effective and accurate extraction of the boundary of cropland is crucial to cultivated land resource monitoring and refined agricultural management. However, due to the diverse spectral features and rich texture features of cropland in very high-resolution remote sensing images, traditional semantic segmentation has limited accuracy and inaccurate boundary localization. For this purpose, we proposed a cropland extraction model based on the deep, full convolution network architecture, called HRNet-CFR, to produce agricultural fields from sub-meter remote sensing images. Considering the geographic relevance of remote sensing features, the method used the surrounding neighborhood of the pixel as the context feature to fuse the features of the backbone network. Experiments have been performed on two very high-resolution remote sensing datasets which have complex scenes and different time phases. The proposed model compares favorably against the popular models in terms of the accuracy of the IoU score and the overall accuracy. The overall accuracy of our approach was 92.3%, which is 3.4% higher than Deeplabv3+ and 5.12% higher than UPerNet. These results demonstrate that the HRNet-CFR could facilitate fast and accurate boundary extraction be that may be incorporated into an object-based cropland analysis service.  
Conceptualization, Z.L. and S.C.; Methodology, Z.L.; Software, Z.L.; Validation, X.M. and S.C.; Formal Analysis, Z.L.; Investigation, Z.L., X.M., R.Z., J.L., L.C., S.C. and P.L.; Resources, Z.L., L.C. and S.C.; Data Curation, Z.L., X.M., R.Z., J.L., L.C. and P.L.; Writing--Original Draft Preparation, Z.L.; Writing--Review and Editing, Z.L.; Visualization, Z.L., X.M., J.L. and S.C.; Supervision, S.C.; Project Administration, S.C.; Funding Acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper was supported by the National Key Research and Development Program of China, grant number 2020 YFA0714103, the Key Scientific and Technological Research and Development Project of Jilin, grant number 20210201138 GX, and the Key Scientific and Technological Research and Development Project of Jilin, grant number 20200401094 GX.  
**Data Availability Statement:** The Gaofen Image Dataset (GID) is available at [[https://x-ytong.github.io/project/GID.html](https://x-ytong.github.io/project/GID.html)]([https://x-ytong.github.io/project/GID.html](https://x-ytong.github.io/project/GID.html)) (accessed on 18 October 2020).  
**Acknowledgments:** In the experiment section, the Jilin-1 high-resolution remote sensing image data of some cities in Jilin Province were obtained with the help of Changguang Satellite Technology Co., Ltd. The authors also thank the anonymous reviewers and the editors for their insightful comments and helpful suggestions to improve our manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experimental Validations
### Inverter  
The design domain and boundary conditions are illustrated in Figure 12. The void areas at the right top and the right bottom corners have the same dimensions of 20 \(\times\) 9. Both the input point \(I1\) and the output point \(O1(O2)\) lie on the horizontal symmetry axis of the design domain. Input forces \(F_{I1}=1\) and \(F_{I2}=1\) are applied at input points \(I1\) and \(I2\), respectively. In addition, two output displacements \(u_{O1}\) and \(u_{O2}\) with the directions opposite to that of input forces are generated at the output point \(O1(O2)\) correspondingly.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \([\delta]=[\gamma]\) & \(\delta_{2,1}\) & \(\delta_{1,2}\) & \(\tau_{2,1}\) & \(\tau_{1,2}\) \\ \hline Figure 11a & 0.1 & 0.03439 & 0.09991 & 0.02718 & 0.01713 \\ Figure 11b & 0.01 & 0.00956 & 0.00999 & 0.00003 & 0.00002 \\ Figure 11c & 0.001 & 0.00100 & 0.00100 & 0.00007 & 0.00006 \\ Figure 11d & 0.0001 & 0.00010 & 0.00010 & 0.00003 & 0.00002 \\ \hline \end{tabular}
\end{table}
Table 4: Output and input coupling indices of the four design results shown in Figure 11.  
Figure 11: Path generators obtained with different \([\delta]\) and \([\gamma]\): (a) 0.1, (b) 0.01, (c) 0.001, (d) 0.0001.
# Fast Equipartition of Complex 2D Shapes with Minimal Boundaries +
###### Abstract  
In this paper, we study the 2D Shape Equipartition Problem (2D-SEP) with minimal boundaries, and we propose an efficient method that solves the problem with a low computational cost. The goal of 2D-SEP is to obtain a segmentation into N equal-area segments (regions), where the number of segments (N) is given by the user under the constraint that the length of boundaries between the segments is minimized. We define the 2D-SEP, and we study problem solutions using basic geometric shapes. We propose a 2D Shape Equipartition algorithm based on a fast balanced clustering method (SEP-FBC) that efficiently solves the 2D-SEP problem under complex 2D shapes in O(\(N\cdot|S|\cdot log(|S|)\)), where \(|S|\) denotes the number of image pixels. The proposed SEP-FBC method initializes clustering using centroids provided by the k-means algorithm, which is executed first. During each iteration of the main SEP-FBC process, a region-growing procedure is applied, starting from the smallest region and expanding until regions of equal area are achieved. Additionally, a Particle Swarm Optimization (PSO) method that uses the SEP-FBC method under different initial centroids has also been proposed to explore better 2D-SEP solutions and to show how the selection of the initial centroids affect the performance of the proposed method. Finally, we present experimental results on more than 2800 2D shapes to evaluate the performance of the proposed methods and illustrate that their solutions outperform other methods from the literature.  
Keywords:shape analysis; image segmentation; equipartition; geometric shapes +
Footnote †: This paper is an extended version of our paper published in International Conference on Pattern Recognition (ICPR), 2024.
# Key Issues on Integrating 5G into Industrial Systems
## 5 Integration of 5G with Industrial Systems
### Practical Cases
#### 5.2.3 Cloud Platform Solution  
The architecture of this platform is shown in Figure 8. As seen, the 5G base station is used as an edge server. In particular, small 5G base stations deployed in factories are integrated with servers that provide edge computing capabilities. The functions of the website can be deployed in the edge server. Specifically, the edge server reads data points from the PLC through the Modbus protocol; and then, these data are packaged and transmitted to the Internet and finally to the cloud platform. In this case, although the gateway is located inside the edge server, it is actually a fully functional gateway. This means that it supports communication protocols for both uplink and downlink. The downlink communication uses the standard Modbus protocol to connect with the PLC, while the uplink communication uses the Ethernet TCP/IP protocol on the Internet to connect with the cloud platform. In this architecture, the design of the cloud platform remains consistent with the previous architecture and the way it interfaces with the PLC remains unchanged. This architecture also retains gateway components, with the only difference being that the functionality of the gateway is now implemented in edge servers located next to 5G base stations.  
Figure 7: The topology for the terminal extension to the gateway.  
As shown in Figure 9, functions such as equipment management, production management, efficiency management, and downtime management can be achieved on the ProudThink cloud platform. By displaying the equipment status indicators, production line diagrams, virtual screens in factories, real-time status, efficiency, alarms, and other key business data items of the equipment, the statuses can be monitored. By utilizing the traffic management function, real-time production statistics can be performed to generate historical production reports, further facilitating in-depth analysis of production. The efficiency management function can clearly showcase the efficiency status of equipment. During the shutdown management process, PLC can collect three data items, namely \"automatic mode\", \"equipment failure\", and \"equipment warning\", so as to calculate and obtain their corresponding values and then carry out corresponding alarm management.
## 3 Results
### Transcript Analysis of Selected Candidate Genes by RT-qPCR  
To validate the RNAseq data, 6 random genes from Tables 2A and 2B, respectively, together with the crosstalk marker genes _FRKI_ and _CHS_, and several PTI-associated genes, such as structural genes for defense metabolite production (_F6H1_, _CCR2_, _HCT_, and _CCoAOM76_), signaling components (BAK1, an integral part of flg22-sensing receptor complexes, MPK3 and MPK4), as well as PDF1.2, a plant defensin commonly used as a marker gene for jasmonate/ethylene mediated defense pathways were subjected to RT-qPCR analysis. Two known TFs being implicated in this crosstalk, MYB12, a positive regulator, and MYB4, a negative regulator, were also included in the analysis. Transcript analysis by RT-qPCR generally confirmed the RNAseq data. The single flg22 treatment as well as the co-treatment F/U led to a significant induction of FRK1-group genes and a suppression of CHS-group candidates (Figure 2). However, it was not possible to obtain statistically significant data demonstrating a negative effect of UV-B on the expression of flg22 up-regulated genes, most likely because these genes are expressed at a very low level and therefore a further reduction is difficult to observe or because PTI associated signaling components are post-translationally regulated [35], suggesting this crosstalk is predominantly uni-directional.  
Figure 2: Relative expression levels of 24 selected genes from all identified clusters as analyzed byRT-qPCR. Shown is the response to different treatments with water as control (C), flg22 (F), UV-B (U), or co-treatment with flg22 and UV-B (F/U). The expression was measured after 4 h treatment of wild-type Arabidopsis Col-0 and is generally consistent with the Cluster analysis. Genes (**A**-**H**) display a reduction in response to flg22 single or co-treatment (Cluster I genes/CHS-group), while genes (**I**-**X**) are inducible by flg22 single or co-treatment (Cluster II genes/FRK1-group). Error bars represent the standard error of triplicate experiments, and statistical significance with \(p\leq 0.05\) was checked by a two-way ANOVA.  
It is noted, that even though not detected in the RNAseq data, MYB4 (Figure 2L) and MYB12 (Figure 2D) showed a similar expression pattern as reported in our previous studies, implying a limitation of the RNAseq technology applied in this study.
## Appendix A Analytical Solutions of the Simple Equations Used in the Paper  
### Analytical Solutions of the Simple Equations Used in Section 4  
The equation \(\left(\frac{df}{d\xi}\right)^{2}=\mu_{0}+\mu_{2}f^{2}+\mu_{4}f^{4}\) admits the following solutions [48]:  
* A hyperbolic solution \(f(\xi)=\sqrt{\frac{-\mu_{2}}{2\mu_{4}}}\tanh(\frac{\mu_{2}}{2}\xi)\) for \(\mu_{0}=\frac{\mu_{2}^{2}}{4\mu_{4}}\) and \(\mu_{2}<0,\ \mu_{4}>0\);
* A trigonometric solution \(f(\xi)=\sqrt{\frac{\mu_{2}}{2\mu_{4}}}\tan(\frac{\mu_{2}}{2}\xi)\) for \(\mu_{0}=\frac{\mu_{2}^{2}}{4\mu_{4}}\) and \(\mu_{2}>0,\ \mu_{4}<0\);
* Jacobi elliptic function solutions: \(f(\xi)=\sqrt{-\frac{\mu_{2}m^{2}}{\mu_{4}(2m^{2}-1)}}cn(\sqrt{\frac{\mu_{2}m^ {2}}{2m^{2}-1}}\xi)\) for \(\mu_{0}=\frac{\mu_{2}^{2}m^{2}(m^{2}-1)}{\mu_{4}(2m^{2}-1)^{2}},\ \mu_{2}>0,\mu_{4}<0\); \(f(\xi)=\sqrt{-\frac{\mu_{2}m^{2}}{\mu_{4}(2m^{2}-m^{2})}}dn(\sqrt{\frac{\mu_{ 2}m^{2}}{2m^{2}-1}}\xi)\) for \(\mu_{0}=\frac{\mu_{2}^{2}(1-m^{2})}{\mu_{4}(m^{2}-2)^{2}},\ \mu_{2}>0,\mu_{4}<0\); \(f(\xi)=\pm\sqrt{-\frac{\mu_{2}m^{2}}{\mu_{4}(m^{2}+1)}}sn(\sqrt{\frac{\mu_{2}} {m^{2}+1}}\xi)\) for \(\mu_{0}=\frac{\mu_{2}^{2}m^{2}}{\mu_{4}(m^{2}+1)^{2}},\ \mu_{2}<0,\mu_{4}>0\) where \(m\) is a modulus. The Jacobi elliptic functions are doubly periodical and possess properties of triangular functions. Additionally, we note that when \(m\to 1\), the Jacobi functions degenerate to the hyperbolic functions, i.e., \(sn(\xi)\rightarrow\tanh(\xi),\ \ cn(\xi)\to sech(\xi),\ \ dn(\xi)\to sech(\xi)\) When \(m\to 0\), the Jacobi functions degenerate to the triangular functions, i.e., \(sn(\xi)\rightarrow\sin(\xi),\ \ cn(\xi)\to cos(\xi),\ \ dn(\xi)\to 1\) Additional sub-variants of the elliptic solutions can be found in [54]. The equation \(\left(\frac{df}{d\xi}\right)^{2}=\
u_{2}f^{2}+\
u_{4}f^{4}\) admits the following solutions [48]:  
* A hyperbolic solution \(f(\xi)=\sqrt{-\frac{\
u_{2}}{\
u_{4}}}sech(\sqrt{\
u_{2}}\xi)\) for \(\
u_{2}>0,\ \
u_{4}<0\);
* A trigonometric solution \(f(\xi)=\sqrt{-\frac{\
u_{2}}{\
u_{4}}}\sec(\sqrt{-\
u_{2}}\xi)\) for \(\
u_{2}<0,\ \
u_{4}>0\).
# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 4 Results and Analyses
### Effect of Different Loss Functions on Model Detection Performance  
To analyze the performance of different loss functions on the cotton boll disease dataset, this experiment compares four loss functions used in the YOLOv11 model: CIoU, SIoU, Focal-EIoU [23], and Shape-IoU. The training loss curves for the four loss functions are shown in Figure 9.  
As shown in Figure 9, when using SIoU, the convergence speed is the slowest, and the loss value after convergence is the highest. Both Focal-EIoU and CIoU converge faster than SIoU, with lower final loss values. Shape-IoU performs the best, showing the fastest gradient descent speed during training and the lowest loss value after convergence. This is because Shape-IoU takes into account the shape information of the bounding box, enhancing the model's robustness to changes in object shape, allowing the loss function to more accurately guide the model's parameter updates, thereby accelerating the convergence speed and significantly reducing the final loss value.  
Figure 9: Loss curves for different loss functions.
## 6 Results and Discussion  
This experiment was performed using the SARD-2022 dataset proposed in this study by us. The damage types include longitudinal and transverse cracks, alligator cracks, edge cracks, potholes, depressions, and shoving. We first classify the types of damage into six classes and then classify them according to their severity into two classes: high damage (D_high) or low damage (D_low). Table 6 demonstrates the performance of the four models in classifying six classes of damage. Multiple deep learning models were implemented to evaluate their performance and determine which best fits the dataset and is the most effective and efficient at classifying six types of road damage. These models included RoadNet, AlexNet, ResNet 34, and VGG-16. It could be seen that the RoadNet model outperforms with the highest performance on test images at 98.6% accuracy, because it has less computational complexity and less parameters than the other models. The accuracy of AlexNet is 98.5%, but it has a higher computational complexity and more parameters compared to RoadNet. Meanwhile, the lowest accuracy of 80% was achieved by the ResNet34 model.  
We further evaluate using different types of damage to detect their severity as high or low. Table 7 demonstrates the performance of the four models to classify six classes of damage according to their severity. In this experiment, there was a slight decrease in accuracy in all models due to the fact that each damage was categorized into two categories (D_high and D_low), and the total number of categories entered in the training process became 12 categories. The RoadNet achieved higher accuracy than other models. An increase in the accuracy of ResNet34 has also been observed of 81% from the previous experiment. In all these experiments, it was noticed that the VGG-16 network takes a lot of time in the training process, also followed by AlexNet, while the network of ResNet34 was fast but did not achieve good results. It has been discovered that VGG-16 has the most parameters, indicating that it has a higher computational complexity than pre-training models. The RoadNet model has fewer parameters and a shorter training time when compared to other models. It also outperformed the other pre-trained models in terms of accuracy.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Training Time** \\ \hline RoadNet (CNN) & 98.6\% & 0.99 & 0.99 & 0.99 & 9 m, 13 s \\ \hline AlexNet & 98.4\% & 0.99 & 0.98 & 0.98 & 20 m, 15 s \\ \hline VGG-16 & 95.1\% & 0.96 & 0.95 & 0.95 & 2 h, 51 m, 56 s \\ \hline ResNet-34 & 80\% & 0.82 & 0.79 & 0.76 & 14 m, 45 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison between RoadNet, AlexNet, VGG-16, and ResNet-34 to classify road damage based on six different types of surface damage using our dataset.
## 2 Related Work  
### Time Series Imputation  
The data imputation method is to solve the problem of missing data, which refers to the phenomenon where the dataset is incomplete due to a missing value for one or more attributes. Missing data will not only distort the data characteristics and increase the difficulty of analysis but also change the statistics such as expectations and high-order distances, causing serious deviations in the analysis methods based on statistical models. For the imputation of missing data in time series, many researchers have proposed solutions before. The ARIMA model proposed by [PERSON] uses the autocorrelation of the time series lag k order to construct an autoregressive model [3] for time series forecasting. To predict the time series which exhibit long-range dependence or persistence in their observations, [PERSON] compared several autoregressive fractionally integrated moving average (ARFIMA) models [5]. [PERSON] and [PERSON] [11] combined the ARFIMA model and a layer recurrent neural network (LRNN) to form a hybrid forecasting model. The ARIMA-LRNN model achieved a very robust and dependable result. [PERSON] et al. combined the online learning algorithm with the ARIMA algorithm and proposed the online ARIMA algorithm. The online ARIMA algorithm is mainly used to solve the difficult problems of parameter estimation in the traditional ARIMA algorithm, such as lag order and interference terms. [PERSON] reduced the ARIMA model to an online parameter optimization problem and verified through experiments that the online ARIMA algorithm performed better than the traditional ARIMA algorithm [12]. The network model of time series imputation based on an RNN is also a major research direction [13]. DeepAR was proposed by [PERSON] et al. by using the encoder-decoder structure combined with the autoregressive RNN model for time series prediction [10]. LSTNet is another RNN-based autoregressive model that utilizes long short-term memory (LSTM) for time series prediction [14]. However, due to the natural disadvantages of the RNN network model, parallel operations cannot be used to improve the imputation efficiency, especially when the missing rate is high, at which point it shows poor performance.
## Appendix E G-Wishart Objective Function
Case 1: for \((r,s)\in\mathcal{\bar{V}}\), \(r<s\), \(i\
eq j\), and \((r,s)\) coming after \((i,j)\), we have  
\[\frac{\partial\zeta_{rs}}{\partial\zeta_{ij}}=-\sum_{k=r}^{s-1} \xi_{ks}\frac{\partial\zeta_{rk}}{\partial\zeta_{ij}}\] \[\quad-\frac{1}{\xi_{rr}}\frac{\partial}{\partial\zeta_{ij}}\sum_ {k=1}^{r-1}\left[\zeta_{ks}\zeta_{kr}+\zeta_{km}\sum_{l=k}^{s-1}\zeta_{kl} \zeta_{ls}+\zeta_{ks}\sum_{l=k}^{r-1}\zeta_{kl}\zeta_{lr}+\left(\sum_{l=k}^{s -1}\zeta_{kl}\zeta_{kr}\right)\left(\sum_{l=k}^{s-1}\zeta_{kl}\zeta_{ks} \right)\right]. \tag{15}\]  
Case 2: for \(r=i=j\), and \(s>r\), we obtain the derivative:  
\[\frac{\partial\zeta_{rs}}{\partial\zeta_{rr}}=-\sum_{k=r}^{s-1} \xi_{ks}\frac{\partial\zeta_{rk}}{\partial\zeta_{rr}}\] \[\quad+\frac{1}{\zeta_{rr}^{2}}\sum_{k=1}^{r-1}\left[\zeta_{ks} \zeta_{kr}+\zeta_{kr}\sum_{l=k}^{s-1}\zeta_{kl}\zeta_{ls}+\zeta_{ks}\sum_{l=k} ^{r-1}\zeta_{kl}\zeta_{lr}+\left(\sum_{l=k}^{k-1}\zeta_{kl}\zeta_{kr}\right) \left(\sum_{l=k}^{s-1}\zeta_{kl}\zeta_{ks}\right)\right].\]In case 1, each term in the outer summation in Equation (A9) can be computed as:  
\[\frac{\partial}{\partial\xi_{ij}}\bigg{[}\zeta_{ks}\xi_{kr}\bigg{]} =\frac{\partial\xi_{kr}}{\partial\xi_{ij}}\zeta_{ks}+\zeta_{kr} \frac{\partial\zeta_{ks}}{\partial\xi_{ij}},\] \[\frac{\partial}{\partial\xi_{ij}}\bigg{[}\xi_{kr}\sum_{l=k}^{s-1} \xi_{kl}\xi_{ls}\bigg{]} =\frac{\partial\xi_{kr}}{\partial\xi_{ij}}\sum_{l=k}^{s-1}\xi_{kl} \xi_{ls}+\zeta_{kr}\sum_{l=k}^{s-1}\frac{\partial\xi_{kl}}{\partial\xi_{ij}} \xi_{ls},\] \[\frac{\partial}{\partial\xi_{ij}}\bigg{[}\zeta_{ks}\sum_{l=k}^{r- 1}\xi_{kl}\xi_{lr}\bigg{]} =\frac{\partial\xi_{ks}}{\partial\xi_{ij}}\sum_{l=k}^{r-1}\xi_{kl }\xi_{lr}+\zeta_{ks}\sum_{l=k}^{r-1}\frac{\partial\xi_{kl}}{\partial\xi_{ij}} \xi_{lr},\] \[\frac{\partial}{\partial\xi_{ij}}\Bigg{[}\bigg{(}\sum_{l=k}^{r- 1}\xi_{kl}\xi_{kr}\bigg{)}\bigg{(}\sum_{l=k}^{s-1}\xi_{kl}\xi_{ks}\bigg{)} \bigg{]} =\left[\sum_{l=k}^{r-1}\xi_{kr}\frac{\partial\xi_{kl}}{\zeta_{ij }}\right]\left[\sum_{l=k}^{s-1}\xi_{kl}\xi_{ks}\right]+\left[\sum_{l=k}^{r-1} \xi_{kl}\xi_{kr}\right]\left[\sum_{l=k}^{s-1}\xi_{ks}\frac{\partial\xi_{kl}}{ \partial\xi_{ij}}\right].\]  
These scalar, element-wise quantities can be substituted back into the piecewise definitions of the gradient and Hessian matrices, as given in Equations (A6) and (A8), respectively.