esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Membrane Synthesis  
Since the sulfonation of the PEEK polymer only occurs when it is dissolved, the dissolution rate is crucial for reproducibility. Using the procedure as described in Section 2.2, the polymer was fully dissolved after approximately five hours. The sulfonation reaction is an electrophilic substitution reaction in which sulfonic acid groups attach to carbon atoms showing the highest electron density [39]. The targeted sulfonation reaction is shown in Figure 3. During the washing of the polymer, the sPEEK that was sulfonated at 90 \({}^{\circ}\)C for 24 h partially dissolved in water. This is due to the increased hydrophilicity of the polymer after the sulfonation reaction. Therefore, extra attention has to be given to the washing of the polymer in order to avoid losses, e.g., by lowering the temperature of the washing water or by performing an additional filtration step after the washing step. Furthermore, flushing of the flask and solvent (DMAc) with N\({}_{2}\) before adding the AlCl\({}_{3}\) is crucial because of the reactive behavior of AlCl\({}_{3}\) with water.  
The synthesis procedure resulted in thin, semi-transparent films with a thickness of around 20 \(\upmu\)m in the dry state. The obtained membranes can be seen in the Supporting Information (Figure S1). All the membranes, except for the non-crosslinked sPEEK membrane, did not dissolve after immersion in DMAc for 72 h, indicating a crosslinked polymer structure. The crosslinking followed the Friedel-Crafts mechanism using AlCl\({}_{3}\) as the catalyst and p-xylylene dichloride (DCX) as the crosslink. Figure 3 illustrates the targeted  
Figure 3: (**a**) The sulfonation reaction of PEEK into sPEEK, (**b**) the crosslinking reaction of the sPEEK with DCX via Friedel—Crafts.  
sulfonation and crosslinking reaction. During this reaction, the carbon atoms with the highest electron density are again most favorable to bind with the crosslinker. Since the crosslinking reaction targets the same carbon atoms as the sulfonation, the crosslinking degree will be influenced by the sulfonation degree. A higher sulfonation degree lowers the availability of carbons on which the crosslinking can occur. Therefore, the crosslinking properties cannot be understood without taking the sulfonation into account.
## 1 Introduction
### Deep Learning Based Methods for Defect Detection  
There are different types of DL techniques that are used to detect defects in images [7; 8]. Some approaches such as the _Improved You Only Look Once_ (YOLO) network [9] focus on object detection, providing a short response time which is very important in the industrial manufacturing environment in order to cope with high production rates. YOLO reported a 99% detection accuracy with a speed of 83 FPS on NVIDIA GTX 1080 Ti GPU. Another example of real-time detection using these types of architectures is [10], which uses an Inception-based MobileNet-SSD architecture, reporting an accuracy of 96.1% on the DAGM 2007 [11] test dataset at 73 FPS on a NVIDIA GTX 1080 Ti, outperforming a comparable state-of-the-art fully convolutional network (FCN) model.  
Depending on the application, a lot of precision regarding the location and the size or the geometry of the defects is required. Many approaches use deep segmentation networks for this purpose. For instance, the authors in [12] propose an end-to-end UNet-shaped fully convolutional neural network for automated defect detection in surfaces of manufactured components. They report results over a publicly available 10 class dataset applying a real-time data augmentation approach during the training phase, achieving a mean intersection over union (IoU) of 68.35%. There are different works analyzing the performance of segmentation networks for superficial quality control [13; 14], where DeepLabv3+ reaches the best performance metric values in the defect segmentation.  
In general, defect detection results worsen as the number of training samples decreases because the similarities between defects are usually small so the trained models do not generalize well from small datasets. For example, the authors in [5] present two regularization techniques via incorporating abundant defect-free images into the training of a UNet-like encoder-decoder defect segmentation network. With their proposed method, they achieve a 49.19% IoU on a 1-shot setting (K = 1) and 64.45% IoU on a 5-shot setting (K = 5).
# Comprehensive Effects of Atmosphere and Soil Drying on
## 3 Results
### Influence of VPD and SWC on WUE and CUE of Different PFTs
The horizontal line represents the median, the point represents the average, and the ‘+’ sign represents the abnormal value. The results showed that under the influence of VPD and SWC on WUE, an increase in VPD and a decrease in SWC both led to an increase in WUE, with the dominant effect of VPD occurring at around 50 \(\upmu\)mol/mol and the ENF vegetation type showing the largest impact at 70 \(\upmu\)mol/mol. The change in WUE dominated by a decrease in SWC was around 30 \(\upmu\)mol/mol, with a median level consistent across all vegetation types, except for DBF, which showed more significant fluctuations. It should be noted that, in general, high VPD and low SWC limit the GPP and NPP of the plant. When CUE was studied, an increase in VPD led to a decrease in CUE in CRO, DBF, ENF, and GRA, with the exception of ENF (\(\Delta\)CUE(VPD 1 SWC) = \(-\)0.28), where the change in CUE for the other three vegetation types was around \(-\)0.1 \(\upmu\)mol/\(\upmu\)mol. In contrast, the change in CUE dominated by a decrease in SWC were much smaller, with the median of \(\Delta\)CUE(SWC 1 VPD) fluctuating around \(-\)0.05 \(\upmu\)mol/\(\upmu\)mol for all three vegetation types, except for the CRO and SAV vegetation types, indicating that the influence of SWC is limited compared to VPD. In contrast to the previous four vegetation types, the SAV vegetation type showed an increasing trend in CUE with both an increase in VPD and a decrease in SWC, with the median of both \(\Delta\)CUE(SWC 1 VPD) and \(\Delta\)CUE(VPD 1 SWC) being 0.1 \(\upmu\)mol/\(\upmu\)mol, indicating that an increase in VPD and a decrease in SWC have a significant promoting effect on CUE in the SAV vegetation type.
## 1 Introduction
In this context, our approach ensures the exclusion of the Zeno behavior to avoid the accumulation of triggering instants as time goes to infinity. These results guarantee the practical feasibility of the proposed method. Notation: The notations \(X\geq Y\) and \(X>Y\) mean that \(X-Y\) is positive semi-definite and positive definite, respectively. The following notations and symbols will be used throughout this paper. \(\mathbb{R}_{+}\) denotes the set of real positive numbers; \(\mathbb{R}^{n}\) and \(\mathbb{R}^{n\times m}\) stand for sets of real \(n\)-dimensional vectors and \(n\)-row \(m\)-column matrices, respectively; \(I_{n}\) is the \(n\times n\) identity matrix; and \(\lambda_{\min}\left\{W\right\}\)\((\lambda_{\max}\left\{W\right\})\) represents the minimum (maximum) eigenvalue of matrix \(W\). Next, \(\mathbf{1}_{n}\) is used for the vector having \(n\) elements equal to one, and \(\|x\|_{2}=\sqrt{x^{T}x}\) stands for the length of the vector in Euclidean space. Additionally, we have the following notations: \(c\phi=\cos(\phi(t))\), \(s\phi=\sin(\phi(t))\), \(c\theta=\cos(\theta(t))\), \(s\theta=\sin(\theta(t))\), \(c\psi=\cos(\psi(t))\), \(s\psi=\sin(\psi(t))\), and  
\[\textit{diag}\left(w_{1},\,w_{2},\,\,\ldots,w_{n}\right)=\left[ \begin{array}[ ]{cccc}w_{1}&0&\ldots&0\\ 0&w_{2}&\ldots&0\\ \vdots&\ddots&\ddots&\vdots\\ 0&0&\ldots&w_{n}\end{array} \right].\]
## 4 The Application of the ACCLN to Power Cable Fault
### Performance of the IPSO-SVM Algorithm
The performance of the power cable fault recognition of ACCLN method is better than SVM and the IPSO-SVM method. In order to test the effectiveness of the proposed method, each group of dataset add 10 test samples, 30 test samples are added on the basis of original samples. So there are 84 test samples in experiment. \begin{table}
\begin{tabular}{c c c} \hline
**Algorithm** & **Recognition Accuracy** & **Training Time** \\ \hline ACCLN & 96.2\% & 0.032 \\ IPSO-SVM & 90.7\% & 0.0523 \\ SVM & 87.0\% & 0.0575 \\ \hline \end{tabular}
\end{table} 
Table 2: Recognition accuracy of SVM, IPSO-SVM and ACCLN. Figure 11: Experimental result of ACCLN. X axis indicates amplitude entropy; Y axis indicates phase entropy.
# Deep Learning Methods in Image Matting: A Survey
## 4 Deep Learning Methods
### Trimap-Free Methods
#### 4.2.5 Real-Time Video Matting  
Real-time scenarios, such as video conferences and live streaming, commonly utilize image matting techniques. Therefore, designing a lightweight and efficient video matting model framework that operates at high resolutions without requiring auxiliary inputs is an important research direction for scholars.  
[PERSON] et al. [28] utilized the background to assist image matting, which increases the user threshold and limits the application scenarios. Therefore, [PERSON] et al. [33] proposed MODNet, a lightweight network that employs branch networks for edge refinement and achieves real-time video matting without auxiliary inputs. MODNet also addresses the issue of flickering in the video matting process, resulting in output results that are more in line with human perception. After proposing [28], [PERSON] et al. went on to propose a real-time video matting model [34] that operates independently without requiring auxiliary inputs. This model utilizes ConvGRU [64] to leverage temporal information in videos and uses DGF [65] modules to enhance image resolution, thereby improving the robustness and mating quality of the model. The RVM [34] model highlights the importance of temporal consistency and quality in video matting models.
# Bottom-Up Model of Random Daily Electrical Load Curve for Office Building
## 2 Modeling Approach
### Personnel Model  
A non-homogeneous Markov chain is used to simulate the presence of indoor personnel. Electrical appliances related to personnel can only be used when the person is indoors, so it is assumed that the working person is in one of two states: in the room or not in the room. The specific use of electrical appliances in the room will be described in Section 2.3. The transition probability \(p_{i,j}\) defines the probability of transition from state \(i\) to state \(j\) between discrete time steps \(t-1\) and \(t\)[30]. Figure 2 below reflects the transition of indoor personnel between the two states; for example, the probability of transition from the state at time \(t\) to the state at time \(t+1\) is \(p_{0,1}\). Temporal correlation is reflected by state transitions at adjacent times.  
Figure 1: Bottom-up modeling system of daily electrical load curve for office building.  
Since either of the two possible transfers must occur, at the same moment, the sum of the probability of transition to state 1 and the probability of transition to state 2 is 1, that is, the transfer probability \(p_{i,1}+p_{i,2}=1\). The calculation process of effective number is shown in Figure 3; specific steps are as follows:  
1. Enter the total number of people \(M\), the current time \(t\), the current state \(i\), and the transition probability matrix at different times;
2. Randomly sample the number \(r\) between 0 and 1 in each time step and compare it with the transition probability to determine the status of the person in the room;
3. Determine the effective number of people in the office building by performing the entire sequence of random sampling \(M\) times.  
\(M\) represents the total number of people in the office building, that is, how many independent Markov chains are implemented, and the value of \(M\) is determined by the area index method. The following Equation (1) can be defined to reflect whether people are in the room:  
\[O_{t}=\left\{\begin{array}{c}1,r<p(O_{t}|O_{t-1})\\ 0,otherwise\end{array}\right. \tag{1}\]  
If \(O_{t}\) is 1, it means that the person is in the room, and if it is 0, the person is not in the room. The effective number of people in the office building can be obtained as Equation (2):  
\[n_{t}=\sum_{i=1}^{M}O_{i,t} \tag{2}\]  
This article is based on Vattenfal AB (a Swedish Public Utility Company, Solna, Sweden) [31] to determine the Markov chain transition probability. The agency uses sensors to collect the number of people in 47 offices in an office building, determines the number of experience transfers between states, and then calculates the transition probability as Equation (3):  
\[p_{i,j}=\frac{n_{i,j}}{\sum_{j=1}^{N}n_{i,j}} \tag{3}\]  
The sensor data is pooled into 96 data sets, one for each 15-min time step in a day, over all workdays and all office rooms. This means that the resulting transition probabilities are for an average office room on an average workday.  
Figure 2: Status transfer of personnel in the office building at time \(t\).
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 2 Data and Methods
### Analytical Methods
#### 2.3.2 Trend Analytical Methods  
In this study, the variation trends of ecosystem WUE were analyzed by the combination of Theil-Sen median trend analysis and the Mann-Kendall test. Theil-Sen median trend analysis is a widely used nonparametric estimation method for long-term data record analysis as follows:  
\[\beta=\text{Median}\bigg{(}\frac{x_{j}-x_{i}}{j-i}\bigg{)},\forall j>i, \tag{2}\]  
where \(0<i<j<n\), \(n\) is the length of the data series, \(i\) and \(j\) represent the index of the year, and \(x_{i}\) and \(x_{j}\) are the WUE values of the \(i\)th and \(j\)th years, respectively.  
Theil-Sen trend analysis can capture only the trends of a time series. However, it cannot represent the significance level of this trend. Therefore, the Mann-Kendall test is adopted to judge the significance level of a time series. For a time series \(\{\ x_{i}\ \}(i=1,\ 2,\cdots,n)\), the statistics of the variation trend can be defined as \(Z\):  
\[Z=\left\{\begin{array}{c}\frac{S-1}{\sqrt{\text{VAR}(S)}},S>0\\ 0,S=0\\ \frac{S+1}{\sqrt{\text{VAR}(S)}},S<0\end{array}\right., \tag{3}\]  
where  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}\text{sgn}\big{(}x_{j}-x_{i}\big{)}, \tag{4}\]  
\[\text{sgn}\big{(}x_{j}-x_{i}\big{)}=\left\{\begin{array}{c}1,x_{j}-x_{i}>0 \\ 0,x_{j}-x_{i}=0\\ -1,x_{j}-x_{i}<0\end{array}\right., \tag{5}\]  
\[\text{VAR}(S)=\left(n(n-1)(2n+5)-\sum_{i=1}^{m}t_{i}(t_{i}-1)(2t_{i}+5)\right) /18, \tag{6}\]  
where \(n\) is the number of elements in this time series, \(m\) is the number of repeated data in this time series, and \(t_{i}\) is the number of duplicates in group \(i\). For a given significance level of \(\alpha\) (0.01, 0.05, 0.1), if the value of \(|Z|>Z_{1-\frac{n}{2}}\), it means the increasing or decreasing trend of the time series is significant; otherwise, the variation trend is insignificant.
###### Abstract  
Green tides have increasingly become a nuisance worldwide in recent years, and especially in China. Since 2015, green tides have started recurring in Jinmeng Bay, Qinhuangdao, western Bohai Sea of China, and have severely deteriorated the tourism environment there. In order to investigate the migration process of the green tides in Jinmeng Bay, a hydrodynamic model and a particle-tracking model were applied based on the latest green tide event in August 2021. The hydrodynamic model was applied with triple-level 2 DH meshes with different refinements and scales, which provided the hydrodynamics to drive the green macroalgae into the particle-tracking model. From the model results, the semi-enclosed waters surrounded by multiple artificial structures are a low-energy hydrodynamic environment, which is not helpful for water exchange and thus the dispersal of nutrients. The green macroalgae are distributed substantially within the semi-enclosed waters, and few are transported out with low biomass. The effects of wind and artificial structures both increase the coverage of the green macroalgae trajectories; the effect of wind plays a more important role. A sensitivity analysis of the effect of wind showed that 6 m/s wind in ENE led to the maximum coverage of the green macroalgae trajectories in the cases of different magnitudes and directions of winds. This study can provide references for the pre-warning and mitigation of green tides in Jinmeng Bay and other similar places.  
green tide; Bohai Sea; particle tracking; wind effect; artificial structures +
Footnote †: journal: Applied Sciences
# A Review of Industry 4.0 Manufacturing Process Security Risks
## 4 Solutions
### CAD Model Security  
With cloud-based collaborative product development increasingly becoming the norm for many engineering design disciplines, security of proprietary technical information has become an important aspect considered by engineering design entities. In the context of CAD modeling and digital manufacturing, many engineering design entities face the challenge of securing certain aspects of their CAD models, while still making the rest of the CAD model available to other parties.  
An innovative approach to the customized encryption of CAD models has been proposed [8], where product designers can divide a CAD model into separate features that can each be separately encrypted to protect proprietary sections of the CAD model. The technique also involves an algorithm for the geometric transformation of these sections in a \"protected mode\" that still enables the visualization of the features for collaboration purposes. This proposed approach will now be briefly introduced.  
The first step of the proposed approach is to separate all the features in the CAD model into three categories: protected features, shared features and public features. Features classified as protectedfeatures contain critical parameters and proprietary information of the CAD model that cannot be shared with collaborators.  
Thus, the owner of the CAD model has exclusive rights over these features. However, collaboration is still made possible by means of the geometrical transformation algorithm that presents the protected features in such a way that sensitive information and critical parameters are not visible to any collaborators. Shared features of the CAD model are features that are encrypted by the model owner, but can be decrypted by collaborators. Such features may contain confidential information that only collaborators are authorized to access, and should thus not be made available publicly. Features that are classified as public features do not contain any proprietary or confidential information of the CAD model. Such features are not geometrically transformed, since no sensitive parameters are contained in the public features. Similar CAD model encryption approaches are also being researched and developed in [46; 53; 54].  
It has been demonstrated [13] how relatively easily a product can be compromised during its design stages. CAD model security approaches such as the aforementioned approach will play a key role in preventing 3D printed products from being compromised during the design stage.
## 3 Mathematical Model of Formation Operation Control
### Robot System Model  
The nonholonomic constrained robots are mainly wheeled mobile structures, including wheeled, wheel leg composite, crawler and so on. In order to facilitate the theoretical analysis of the algorithm, this article takes the mathematical model of the four-wheel mobile robot as an example, as shown in Figure 6. The front and rear wheelbase of the robot is \(\mathbf{L}\). The distance between the midpoint of the wheel and the midpoint of the robot system is \(d\), as shown in Equation (17). The motion direction angle of the robot is \(\theta\). It is assumed that the linear velocity and angular velocity of the robot motion are \(\
u\) and \(\omega\). The linear speed of the wheel is \(u\). Then  
\[\left\{\begin{array}{l}d=L\cos\varphi\\ \omega=(u\cos\varphi)/d\\ v=u\sin\varphi\end{array}\right., \tag{17}\]  
The influence of sliding can be ignored in this article, so the mobile robot belongs to a pure rolling sliding free robot. For the robot with pure rolling and no sliding, it is necessary to meet the nonholonomic constraints, as shown in Equation (18). Then  
\[\left(\begin{array}{cc}\dot{x}&\dot{y}\\ \end{array}\right)\left(\begin{array}{cc}\sin\theta&\cos\theta\\ -\cos\theta&\sin\theta\\ \end{array}\right)=\left(\begin{array}{cc}d\omega&0\\ \end{array}\right), \tag{18}\]  
By expressing all state quantities of the mobile robot, its mathematical model can be obtained, as shown in Equation (19). Then  
\[\left(\begin{array}{c}\dot{x}\\ \dot{y}\\ \dot{\theta}\\ \end{array}\right)=\left(\begin{array}{cc}\cos\theta&d\sin\theta\\ \sin\theta&-d\cos\theta\\ 0&1\\ \end{array}\right)\left(\begin{array}{c}v\\ \omega\\ \end{array}\right), \tag{19}\]  
Figure 5: The formation and communication topology of the topology matrix \(\mathbf{A}^{\prime}\).  
where \(v\) and \(w\) are the parameters that can be directly controlled, \(x\), \(y\) and \(\theta\) are the state quantities of the system. The number of parameters that can be controlled is less than the number of state qua tities of the system. Therefore, the system is an underactuated system, also known as a nonholonomic constraint system.
# Resource Management for Collaborative 5G-NR-V2X RSUs to Enhance V2I/N Link Reliability
## 5 Improved V2I/N Link Reliability with Proposed Collaborative RSU for Resource Management
### BS-Type RSU with Resource Co-Operation Management  
Quality of service (QoS) required in-vehicle communication. A resource cooperation management scheme improves the reliability of the entire communication system, by mitigating CCI in a cellular V2I network where BS-type RSUs and VUE communicate in a 5G-NR-V2X environment, and coordinated interference management is possible through dynamic ICIC and CS-CoMP technologies. For dynamic ICIC, we reallocate resources by redistributing frequencies using the fractional frequency reuse (FFR) method, which uses frequency reuse coefficients for cell edge users. Cell-centered users with multiple vehicles at a fixed full frequency are assigned a full-reuse (FR) spectrum, and users at the edge of the cell are assigned a frequency divided by a partial-reuse (PR) spectrum. The proposed dynamic ICIC allows allocating more resources to vehicles located at the edge of the cell, including intersections where we find many vehicles distributed. In this case, we set the SINR threshold by defining the criterion for dividing the PR band and FR band as 5% of the total users for allocating frequencies by distinguishing cell-center users and edge users. This approach allows us to determine whether to assign BBW. Then, using (12), we determine users assigned to the PR and FR bands, and apply dynamic ICIC technology.  
\[\begin{split} SINR_{\text{{\emph{pue}}}}&<SINR_{ \text{{\emph{threshold}}}}\text{{\emph{for}}}\text{{\emph{PR zone users}}}\\ SINR_{\text{{\emph{pue}}}}&>SINR_{\text{{\emph{threshold}}}} \text{{\emph{for}}}\text{{\emph{FR zone users}}}\end{split} \tag{12}\]In addition, applying CS-CoMP, with resource cooperation management can enhance the spectrum efficiency of users at the edge of a cell from signals received from other cell sites. It also improves received signal quality through coordination between signals transmitted from sites. To reduce unwanted CCI between BS-type RSUs, we apply a blocking-based scheduling process and each BS-type RSU receives feedback from the VUE through the central scheduler, and determines whether to use CS-CoMP. In addition, the VUE blocks interference signals from neighboring BSs with the same PRB, implements inter-sector muting, and applies CS-CoMP.