esa-sceva/satcom-chunk-collection huggingface dataset:
# Low Current Ripple Parameter-Free MPCC of Grid-Connected Inverters for PV Systems
## 2 Model of Grid-Connected Inverters for PV Systems  
This paper concerns the typical two-stage grid-connected PV inverter system, where the front stage is a boost converter and the latter stage is a two-level voltage source inverter (2L-VSI), as shown in Figure 1.  
Specifically, the equivalent circuit topology of 2L-VSI is presented in Figure 2, where the output terminal can be, respectively, linked to the positive-bus and negative-bus. Considering all possible combinations of switching states, eight voltage vectors can be produced, as shown in Figure 3.  
Figure 1: Typical structure of two-stage grid-connected photovoltaic inverter.  
Figure 2: Main circuit of 2L-VSI.  
Undoubtedly, the controlled object is a multi-order time-varying nonlinear system so it is ill-suited to design the controller in natural coordinates. Therefore, based on the Kirchhoff voltage law, dynamic equation of grid-connected current in synchronous rotating reference frame is given by  
\[\begin{bmatrix}u_{d}\\ u_{q}\end{bmatrix}=\begin{bmatrix}R&-\omega_{e}L_{f}\\ \omega_{e}L_{f}&R\end{bmatrix}\begin{bmatrix}i_{d}\\ i_{q}\end{bmatrix}+\begin{bmatrix}L_{f}&0\\ 0&L_{f}\end{bmatrix}\frac{\mathrm{d}}{\mathrm{d}t}\begin{bmatrix}i_{d}\\ i_{q}\end{bmatrix}+\begin{bmatrix}e_{d}\\ e_{q}\end{bmatrix} \tag{1}\]  
where the subscripts \(d\) and \(q\) mean the components in the \(d\) and \(q\) axes, respectively. \(u\) represents the output voltage of the inverter; \(R\) denotes the line resistance; \(L_{f}\) represents the filter inductance; \(\omega_{e}\) denotes the grid angular frequency; \(e\) represents grid voltage; \(i\) represents the grid-connected current.
## 2 Materials and Methods
Five citrus diseases had to be distinguished using the leaf photographs (_Alternaria_, anthracose, citrus chlorotic dwarf virus (CCDV), nutrient deficiency, melanose) and four using the photographs displaying fruits (brown rot, fruit split, melanose, _Septoria_). About half of the tested photographs were captured from private citrus trees (lemon, orange, tangerine) in the Achaia region (Greece) and the rest from citrus trees in Lesvos island (Greece). These regions have a distinct climate but the appearance of the examined diseases is not significantly different. Another set of photographs displaying pear leaves with 4 diseases (fire blight, pear scab, _Mycosphaerella_, powdery mildew) have been recently tested in various color spaces using linear dynamic range expansion (DRE). These photographs have been retrieved from pear trees in the Achaia region. The specific disease displayed in each photograph was identified by a professional agronomist and this diagnosis served as ground truth for the evaluation of the developed classification system. The number of test photographs used per disease ranges between 22 and 56 and the number of these photographs that were used for training, ranges between 6 and 8. The fraction of the test set used for training, ranges between 6/56 = 10.7% and 8/22 = 36.4%. The test photographs were captured using ordinary smart phone cameras with resolution ranging from 5 MPixels (Lumia 550, Microsoft, One Microsoft Way, Redmond, Washington, U.S.) to 23 MPixels (Sony Xperia XA1, 1-7-1 Konan Minato-ku, Tokyo, 108-0075 Japan). These photographs were resized by the developed application to \(1024\times 576\) pixels for high speed operation since the employed image processing and classification methods do not require an excessively high resolution. The photographs were processed in red-green-blue (RGB) format although different formats have also been tested for color normalization as will be explained in the following sections.
# Telecom Churn Prediction System Based on Ensemble Learning Using Feature Grouping
## 1 Introduction  
Owing to fierce competition among telecom companies, customer churn is inevitable. Customer churn is the act of a customer ending a subscription to a service provider and choosing the services of another company.  
Companies must reduce customer churn because it weakens the company. A survey showed that the annual churn rate in the telecom industry ranges from 20% to 40%, and the cost of retaining existing customers is 5-10 times lower than the cost of obtaining new customers [1]. The cost of predicting churn customers is 16 times lower than that for obtaining new customers [2]. Decreasing the churn rate by 5% increases the profit from 25% to 85% [3]. This shows that customer-churn prediction is important for the telecom sector. Telecom companies consider customer relationship management (CRM) an important factor in retaining existing customers and preventing customer churn.  
To retain existing customers, CRM analyzers must predict which customers will churn and analyze the reasons for customer churn. Once the at-risk customers are identified, the company must perform marketing campaigns for churn customers to maximize the churn-customer retention. Therefore, customer-churn prediction is an important part of CRM [4].  
The accuracy of the prediction systems used by CRM analyzers is important. If analyzers are inaccurate in predicting customer churn, no campaigns can be performed. Owing to recent advancements in data science, data mining and machine learning technologies provide solutions to customer churn. However, there are several limitations in existing models. For example, logistic regression, a common churn-prediction model based on older data-mining methods, is relatively inaccurate. Furthermore, feature construction [5]is neglected during modeling development. Therefore, a good churn prediction system is necessary.  
This study proposes a new customer-churn prediction system and feature construction to improve accuracy, and the contributions of this study can be summarized as follows:  
1. A new prediction system based on ensemble learning with relatively high accuracy is proposed.
2. New features derived from equidistant grouping of customer behavior features are used to improve the system performance.  
The rest of this paper is arranged as follows: Section 2 presents literature review, Section 3 proposes the ensemble model and equidistance feature grouping, and Section 4 describes the prediction system and experimental results. Finally, Section 5 concludes the paper.
## 4 Correlation Analysis  
_Correlation between Alpha Wave Values of Each Electrode and Factor Scores_  
The article conducted a correlation analysis to determine the relationship between subjective factor scoring and EEG alpha values. Specifically, the difference values of alpha values from 18 groups of electrodes (\(\alpha\)D1-\(\alpha\)D2) and the difference values of subjective factor scoring (SD1-SD2) were chosen for this analysis (see Figure 13). The results indicated that there was a correlation between the differences in \(\alpha\) mean values of O1 and Oz electrodes and the differences in rhythmic sense scoring. Similarly, the differences in \(\alpha\) mean values of C3 electrodes were correlated with the differences in variability and color scoring. Additionally, the differences in \(\alpha\) mean values of T7 and T8 electrodes were correlated with the differences in naturalness scoring.  
From the analysis of the subjective factor scores for the two conditions, it is observed that free-form D1 received higher scores for naturalness while regular-form D2 scored higher in rhythm perception, variability, and chromaticity compared to condition D1. Analysis of the alpha values of the EEG electrodes saw that the subjects' O1, C3, and T7 electrodes had higher alpha averages with free-form landscape D1 stimulation and the T8 and O2 electrodes had higher alpha averages with regimented scheme D2 stimulation.  
Figure 14 demonstrates the trend plot of the five EEG electrode alpha averages versus the factor score averages, and it can be seen that the O1 electrode alpha value in the occipital lobe area showed a positive correlation with the rhythmic sense scoring and the O2 electrode alpha value showed a negative correlation with the rhythmic sense scoring; the C3 electrode in the left temporal lobe showed a negative correlation with the degree of landscape change and the degree of colorfulness; the T8 electrode in the right temporal lobe showed a negative correlation with the sense of naturalness and the T7 electrode in the left temporal lobe showed a positive correlation with the sense of naturalness.  
Figure 13: Subjective factor scores correlate with EEG \(\alpha\). Note: \(\bullet\)—\(p\leq 0.05\).
# Superhydrophobic Polypyrrole-Coated Cigarette Filters for Effective Oil/Water Separation
###### Abstract  
To facilitate the recycling and reuse of cigarette filters and oil/water separation, a superhydrophobic cigarette filter was made by coating with dodecanethiol-modified polypyrrole (Ppy) particles by a dip-coating method. SEM, FTIR, and XPS were used to analyze the surface morphology and chemical compositions. The as-prepared superhydrophobic cigarette filter can realize wettability alteration via changing the ammonium persulfate (APS) concentration from 0.15 mol/L to 3 mol/L, and the contact angle increased from 0\({}^{\circ}\) on the original cigarette filter to 155\({}^{\circ}\) with a sliding angle of 5\({}^{\circ}\). The superhydrophobic cigarette filter could effectively separate various oils and organic solvents. The separation efficiency was 98.8% and the separation stability was good. Furthermore, the as-prepared superhydrophobic cigarette filter had a large oil absorption range and could absorb different oils and organic solvents, including petroleum ether, engine oil, vegetable oil, n-hexane, and chloroform, with maximum absorption capacities ranging from 9.4 g/g to 22.7 g/g. According to the above results, we believe that the as-prepared superhydrophobic cigarette filter should have great potential in the recovery of solid waste and high-efficiency oil/water separation.  
polypyrrole; superhydrophobicity; oil/water separation; cigarette filter +
Footnote †: journal: Applied Sciences
# Inter-Annual Variability of Peatland Vegetation Captured Using Phenocam- and UAV Imagery
## 1 Introduction
### Carbon Fluxes in Northern Peatlands and Responses to Climate Change  
Northern peatlands are particularly vulnerable to change. These ecosystems constitute a globally significant carbon sink of around 400-600 Pg C [20; 21], and have experienced favourable environmental conditions since the Holocene (e.g., a cool, wet climate and high water-table), inhibiting the decomposition of organic matter [22; 23]. Under scenarios of future climate change, however, northern latitudes are expected to experience above-average rates of warming, alongside more frequent and extreme weather events [15; 24; 25]. Such changes risk destabilising peatland carbon stocks by accelerating rates of decomposition [26]. Although an earlier start to the growing season in response to rising temperature would (in principle) increase the number of days where an ecosystem can assimilate carbon by photosynthesis [27; 28; 29], the complex feedbacks between weather, phenology, and ecosystem functioning make it difficult to predict the overall impact of phenological shifts on peatland carbon cycle dynamics [5; 19; 30; 31].  
The Net Ecosystem Exchange of CO\({}_{2}\) (NEE) in a peatland is the difference between the amount of CO\({}_{2}\) released by ecosystem respiration (R\({}_{\rm eco}\)) and that assimilated by photosynthesis (i.e., Gross Primary Production, GPP). Over short (daily, seasonal, inter-annual) timescales, peatlands display a strong variability in carbon uptake, and a number of sites have demonstrated the ability to switch from functioning as a net sink for CO\({}_{2}\), to a net source between years. This variability has often been attributed to weather events such as heatwaves and altered precipitation patterns [17; 32; 33], which can affect both plant physiology (e.g., by drought-induced stress) and phenology (e.g., if drought stress induces early senescence). Such research, however, has to date been limited and is hampered by a lack of high-quality, long-term phenology datasets.
# Analysis and Modeling of Mueller-Muller Clock and Data Recovery Circuits
## 2 Mueller-Muller Phase Detector
### Linear Gain of MMPD
By analyzing these two extreme jitter distributions of the MMPD, we perform the linearization of the MMPD and give the gain range. If the CDR system has a clean channel, then Gaussian jitter will dominate the factor and the gain of the MMPD is (6); if deterministic jitter dominates the system, the gain of the MMPD is (11). It can also be seen from (6) and (11) that the gain of the MMPD is inversely proportional to the input jitter. The higher the jitter is, the lower the gain will be. This is the key consideration when we design the MM-CDR system, because in Section 3, we will find that the performance metrics of the CDR system will vary significantly as the jitter changes. To verify the correctness of the MMPD linear gain model, we add the corresponding jitter to the sampler clock and then scan the phase difference at the input and count the number of overruns and lags to compute its average gain. Figure 4a,b show that the theoretical calculation agrees well with the simulation results. In addition, we find that for Gaussian jitter with a jitter variance of 0.1 UI and a drift bit width of 0.05 UI, the linear region of the MMPD is about \(|\phi|~{}<0.1\) UI; for uniform jitter with a jitter variance of 0.12 UI and a drift bit width of 0.05 UI, the linear region of the MMPD is about \(|\phi|~{}<0.15\) UI. When we continue to increase the phase difference at the input, it can be observed that the MMPD will no longer have linear gain, but enter the nonlinear region, as illustrated in Figure 4c,d. Figure 2:3 Smoothing of PD characteristic due to jitter. (**a**) Gaussian jitter and (**b**) uniform jitter.
## 4 Discussion  
[PERSON] et al. [21] found that the old (65-79 years) and very old age (>80 years) subgroups had the highest increase in the incidence rate of basal cell carcinoma. Several research findings indicate a positive association between aging and skin cancer risk [22; 23]. Our findings support that aging is one of the main risk factors for SPSC. Another study by [PERSON] et al. [24] found that alcohol consumption was significantly associated with an increased risk of basal cell carcinoma and cutaneous squamous cell carcinoma. The results of this study also indicated that alcohol consumption is a risk factor for SPSC. Research shows that men experience extensive exposure outside, negligible frequent sun protection, and positive tanning perception, which elevate their risk toward SPSC [1]. Several studies have shown that men are at an increased risk of SPSC [22; 25; 26; 27; 28; 29; 30; 31; 32].  
In our study, we identified results similar to those of the previous studies. We explored data mining using a machine-learning algorithm, i.e., random forest on an imbalanced dataset. Furthermore, we used SMOTE to oversample the minority to balance and prevent the model from being biased on the original majority of non- SPSC instances. With respect to metric performance, random forest showed a better prediction capability than the benchmark algorithms. The model achieved an overall accuracy of 90.2%, a recall rate of 95.2%, a precision rate of 86.6%, and an F1 of 90.7% for the SPSC category based on 10-fold cross-validation on a balanced dataset. With regard to cost-sensitive learning, if we increase the FN cost, the recall rate improves from 95.2% to 95.9%, whereas the precision rate decreases from 86.6% to 77.9%, and the accuracy decreases from 90.2% to 84.3%. Cost-sensitive learning is a quick method of conventional machine learning to assess risk, while we intend to switch between different policies. From the SHAP value interpretation analysis, we can obtain insights into how the model makes decisions based on features and the interactions between correlated features. The top four features are age, stage, gender, and regional lymph node involvement. However, the model interpretation is for the model behavior analysis instead of causal effect analysis. The model learns complicated correlations within the dataset, as shown in Figure 7, and identifies the best choice to optimize the objective function.
# Event-Triggered MFAILC Bipartite Formation Control for Multi-Agent Systems Under DoS Attacks
## 6 Conclusions  
Unlike most studies that focus on MASs composed of SISO-type single agents, this paper investigates bipartite formation trajectory tracking control MASs composed of MIMO-type single agents, considering the impact of periodic DoS attacks. To mitigate the effects of DoS attacks on bipartite formation systems, a DoS attack compensation mechanism is introduced. To reduce the frequency of controller updates, conserve network resources, and alleviate computational pressure while further enhancing the system's ability to resist DoS attacks, an event-triggered condition is designed to construct a DET mechanism. The DoS attack compensation algorithm and DET mechanism are combined with the MFAILC method for controller design. Furthermore, this algorithm is extended from fixed topology systems to time-varying topology systems, and the convergence of the system is proven.  
The simulation results show that for MIMO-type bipartite formation systems under periodic DoS attacks, the proposed algorithm ensures that the tracking errors and consensus errors of the agents converge to an ideal range close to zero and the formation shape is well maintained. The algorithm effectively suppresses the impact of DoS attacks on the formation system and reduces the frequency of controller updates, thus conserving network resources. Future research will focus on further integrating predictive control to achieve faster system convergence.  
Conceptualization, H.L.; methodology, H.L. and L.F.; software, H.L. and W.W.; validation, H.L., L.F. and W.W.; formal analysis, H.L.; investigation, H.L.; resources, L.F.; data curation, H.L.; writing--original draft preparation, H.L.; writing--review and editing, H.L. and L.F.; visualization, H.L.; supervision, L.F. and W.W.; project administration, L.F.; funding acquisition, L.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 62263017).  
Not applicable.  
All of the data generated and analyzed during this work are included in this paper.  
The authors declare no conflict of interest.
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 2 Proposed Methods
### Real-Time Level Selection of Negotiation Granularity  
The satellite needs to select an appropriate level of granularity. Selecting a higher level brings large data transmission and complex negotiation processes which may cause negotiation failure. Selecting a lower level can improve the success rate of negotiation but may cause bad negotiations.  
Level selection is also difficult because the selections of satellites interact and unknown to each other. We collected lots of samples about selection and negotiation effectiveness. A neural network was trained to describe the relationship between the communication environment and success probabilities of negotiation in each level of granularity. Thus, the satellite can make decisions of level selection based on the probabilities in real-time onboard.
## 3 Results and Discussion
### TG/FTIR Experiments
Under the air atmosphere, the IR spectra show higher differences (Figure 5), mainly with respect to the bands corresponding to CO at 2193 cm\({}^{-1}\) and CO\({}_{2}\) at 2351 cm\({}^{-1}\). The peaks around 250 \({}^{\circ}\)C are of similar intensity for the 2351 cm\({}^{-1}\) band, and the presence of catalysts provoke their appearance at lower temperatures in both bands. During the second interval, at temperatures around 570 \({}^{\circ}\)C, a very intense signal is observed in the 2351 cm\({}^{-1}\) (CO\({}_{2}\)) band. This signal is enhanced mainly by the presence of MCM-41 and followed by the two SBA-15s, presenting less (or negligible) intensity in the experiment without a catalyst. A similar behavior is observed for 2193 cm\({}^{-1}\) band. This fact could confirm the higher generation of carbonaceous residue in the presence of MCM-41, which is oxidized at high temperatures in an oxidizing atmosphere. MCM-41 also produces a large peak of aromatic C-H band (700 cm\({}^{-1}\)) at high temperatures and presents a lower intensity of the peaks of the R-O bands at 1238 cm\({}^{-1}\), C=C at 1468 cm\({}^{-1}\) and NO at 1913 cm\({}^{-1}\). Nevertheless, the information provided by this technique is very limited and much more interesting results are obtained by EGA, where the composition of the gases evolved can be obtained. Figure 5: IR spectra of NNN and NNN with three catalysts, under oxidizing atmosphere.
# Evaluation of Remote Sensing-Based Evapotranspiration
## 2 Study Area and Data
### Data Sources
#### 2.2.2 PML  
The PML-V2 dataset is provided for the period from July 2002 to December 2017, using the Google Earth Engine [27; 28; 29]. The dataset was derived from the MODIS dataset (leaf area index, albedo, and emissivity) together with Global Land Data Assimilation System (GLDAS) meteorological forcing data. The dataset includes gross primary product (GPP), soil evaporation (Es), vegetation transpiration (Et), and vaporization of intercepted rainfall (Ei). The ET from PML-V2 is the sum of Es, Et, and Ei. The PML-V2 dataset is provided on a 500-m spatial resolution with 8-day temporal resolution. The GPP and ET estimates obtained from PML-V2 have proven to be satisfactory when they are compared with measurements across 95 globally distributed flux sites [27; 28; 29]. In this study, the 500-m and 8-day PML-V2 dataset from 2004 to 2010 is used and can be freely downloaded ([[https://data.tpdc.acn/zh-hans/data/48c16a8d-d307-4973-abab-972e9449627c/](https://data.tpdc.acn/zh-hans/data/48c16a8d-d307-4973-abab-972e9449627c/)]([https://data.tpdc.acn/zh-hans/data/48c16a8d-d307-4973-abab-972e9449627c/](https://data.tpdc.acn/zh-hans/data/48c16a8d-d307-4973-abab-972e9449627c/)), accessed on 11 December 2020).