esa-sceva/satcom-chunk-collection huggingface dataset:
# Influence of Multiple Factors on the Wettability and Surface Free Energy of Leaf Surface
## 1 Introduction
[22] found that the leaves with high villous density had strong hydrophobicity while the leaves with low villous density had good wettability. [PERSON] et al. [23] tested the wettability of 30 species plant leaves and suggested that the leaf with the highest stomatal density had the worst wettability. On the other hand, since leaf surface wettability is the result of the interaction between liquid surface tension and leaf surface properties, the wettability of plant leaves is also affected by the properties of PPP as well as some external factors such as spray application technique [24] together with sprayer adjustments [25]. [PERSON] et al. [26] suggested that the use of reasonable concentration of adjuvants could change the behavior of droplets on the leaf surface, which could improve the uniformity of spray coverage and reduce PPP use. [PERSON] et al. [27] found that adding adjuvants in spraying solution could increase the deposition of both sides of grape leaves and the optimum effect of droplet deposition can be achieved by matching the concentration of adjuvants with the spray application volume. [PERSON] et al. [28] simulated the behavior of droplet on leaf surface using a constructed virtual leaf model for chemopodium, wheat, and cotton. The results showed that the droplet characteristics, spray formulation, and leaf surface properties all affected the amount of droplet retained on the horizontal leaf. At present, most research focused on the correlation between leaf surface properties and wettability as well as the effects of external spray parameters on droplet deposition; there are few studies that concentrated on the factors directly affecting the leaf surface wettability. Therefore, the main purpose of this study is to investigate the SFE difference of rice and rape leaves as well as the effect of multiple factors on wettability. That is, to (1) investigate the effect of crop type, leaf surface, probe liquid, and leaf status on contact angles as well as the difference of SFE and its components in rice and rape leaves; (2) investigate the effect of droplet falling height and the instantaneous velocity of droplet contacting solid surface on the variation of wettability; (3) investigate the variation of wettability between different solid surfaces (rice leaves, rape leaves, water-sensitive paper, and wax); and (4) investigate the influence of PPP solution concentration on wettability based on rape leaf surface.
## 3 Results and Discussion
### The Speed of the Segmentation Algorithm  
The system used to analyze, run the segmentation algorithm and detect background objects and apples was a laptop with Intel Core i3 CFI processor, 330M at 2.13 GHz, 4 GB of RAM-4 GB and Windows 10. The results showed the processor speed was about 0.825 seconds for the segmentation of a frame. This speed is very good for this research because the background of the frames was very complex and full of different objects. After presenting the segmentation algorithm and reviewing the performance of the algorithm, it is necessary to compare the results with the results of other researchers. Due to the novelty of the proposed method as well as the different filming conditions, there is no possibility for direct comparison of the results. However, two studies, conducted by [PERSON] et al. [14] and [PERSON] et al. [15] were used to compare the proposed method. [PERSON] et al. [14] provided a method for detecting immature green citrus in citrus gardens. [PERSON] et al. [15] proposed a segmentation-based method for counting the number of grape cubes associated with a cluster in color images and in controlled light conditions. The results of this study are shown in Table 8. As Table 8 shows, the proposed method in this study with a higher number of samples than the other two studies has a higher detection rate.  
After comparing with other research, we mention advantages of the proposed method. The advantages are: 1. High process speed; 2. High accuracy; 3. Usability in natural conditions of the orchard; 4. Usability in different orchards; 5. Usability in segmentation of different fruit within trees in orchard. This algorithm can be used for different purposes such as: 1. The use in fruit picking robots with emphasis on apple fruit; 2. The use in automatic systems to estimation of fruit yields with emphasis on apple fruit; 3. The use in automatic systems to survey fruits in grow stags with emphasis on apple fruit.
## 6 Conclusions and Limitation  
A hybrid modified MCDM model was utilized to evaluate the effectiveness of air pollution control and determine feasible improvement strategies in Kaohsiung City. The main contributions of this study are as follows. The main dimensions and criteria of the evaluation model for domestic air pollution sources were constructed according to the literature review and expert panel consensus. DEMATEL was used to verify the relationships between dimensions and criteria and to construct INRMs. DANP provided the IWs of criteria and modified VIKOR was used to acquire the gaps among dimensions and criteria. We used hybrid research methods to holistically obtain key indicators to target for continuous improvement of air pollution improvement. This empirical case study verified the effectiveness of our research structure and method model. Workplace-generated air pollution is critical for workers' and stakeholders' health [68]. Thus, based on the study result, we recommend that the Environmental Protection Bureau prioritize coal-fired power plants as a direction for improvement, which corresponds to the finding to previous studies well [69].  
There are several limitations to this study. First, the study is a case study applied specifically to Kaohsiung City, which means that results might be different due to heterogeneity in different regions and times. Second, the respondents are environmental officials of the Kaohsiung City Government and experts who long live in Kaohsiung City. There may be some bias from their answers. Additionally, we only used Kaohsiung as the sample (which might limit the generalizability of our conclusion), though it is an important and representative port city in the world. Thus, we believe that the case is still worth noting and can be implication stimulating, even every city has its own special conditions. Future studies could follow conceptual and methodological logic to create context-specific investigations of different characterized cities. Also note that there are other possible emission reduction opportunities from coal-fired power plants (not by replacement with other energy sources, but by, for example, modernizing the flue gas treatment system or by changing the coal fuel to more environmentally friendly fuel) and significant impact of mobile air pollution sources on air quality (also resulting from the analysis). Future studies could find it useful to follow such a thought and design a proper research to investigate these possible strategies.  
Conceptualization, K.-H.C., C.-H.C. and F.-S.T.; methodology, P.-C.T. and J.-M.Y.; validation, F.-S.T.; formal analysis, K.-H.C. and J.-M.Y.; investigation, P.-C.T.; writing--original draft preparation, K.-H.C., J.-M.Y., F.-S.T., and P.-C.T.; writing--review and editing, K.-H.C.; project administration, K.-H.C. All authors have read and approved the final manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# A Novel Parallel Auto-Encoder Framework for Multi-Scale Data in Civil Structural Health Monitoring
## 3 Numerical Experiments
### Infrastructure Model and Numerical Model  
A seven-storey steel frame structure has been built in the laboratory at Curtin University and its frame dimensions are illustrated in Figure 3. Each storey is 0.3 m in height, composing up to 2.1 m for the total column height of the steel frame whereas the beam length of the steel frame is 0.5 m. The cross-sections of the column and beam elements are shown with dimensions of 49.98 mm \(\times\) 4.85 mm and 49.89 mm \(\times\) 8.92 mm respectively, while the corresponding measured mass densities of the column and beam elements are 7850 kg/m\({}^{3}\) and 7734.2 kg/m\({}^{3}\). [PERSON]'s modulus of 210 GPa is applied to all members. The column and beam elements are connected continuously by welding at the top and bottom of the beam sections. The two columns at the bottom of the steel frame are welded onto a thick and solid steel plate which is fixed to the ground. In order to simulate the mass from the floor of a building structure, two pairs of mass blocks with each around 4 kg in weight are fixed at the quarter and three-quarter length of the beam in each storey.  
The finite element model of the whole frame structure is shown in Figure 4 which includes 65 nodes and 70 planar frame elements. The weights of steel blocks are loaded as concentrated masses at the corresponding nodes of the finite element model. Each node has three DOFs (two translational displacements x, y and a rotational displacement), contributing total 195 DOFs to the system. The translational (Node 1) and rotational (Node 65) restraints at the supports are expressed initially by the large stiffness of 3 \(\times\) 109 N/m and 3 \(\times\) 109 N\(\cdot\)m/rad respectively. The initial finite element model updating has been executed to minimize the discrepancies between the analytical finite element model and the experimental model in the laboratory. This updated finite element model is adopted as the baseline model for generating the training, validation and testing data. The detailed model updating process can be referred to [4].  
Notice that the data generated from FEM model could not totally replace the data from real structures. There are thousands of possibilities for the structural damage scenarios in real world. However, it is not practical to collect the data under such huge different damage scenarios from real civil structures. Therefore, the simulated data from FEM model is used in this paper to explore the damage identification problem.  
Figure 4: Finite element model of the steel frame structure.  
Figure 3: Laboratory steel frame model and the dimensions of the steel frame structure.
# Minimization of _n_th Order Rate Matching in Satellite Networks with One to Many Pairings
## 1 Introduction
### Our Contribution
* Simulations have also depicted that pairings originated by greater than \(n=1\) order RM lead to generally more UEs' fairness, assuming the rate matchings between the UEs. Particularly, as we observe in Figures 1 and 2, even a slight increase from \(n=1\) to \(n=2\) leads to much more fair UE pairings, and in this case, our practical approach can be fast implemented, resulting in a small relative error compared to the time-consuming exhaustive mechanism, as discussed in Section 3. The increment of fairnesswith increment in \(n\) is explained by the focus to the minimization of larger absolute differences of OCs and RCs in the minimization of rate matching as \(n\) becomes larger. This observation, based on the simulations, is of utmost importance for the satellite and generally wireless systems' operators, because \(n\) can be used as a fairness controller for the rate-matching problem that has been used widely in the literature. Figure 1: Average Relative Error between Exhaustive and Proposed Schemes and Average Iterations, Both with Constant and Random Initial Feasible Points, for 1 st and 2 nd Order Rate Matching. Figure 2: Average Fairness Indexes for Proposed and Exhaustive Schemes as \(n\)th Order Increases for Different GWsxUEs Systems. For the rest of the article, Section 2 presents the system model, the allocation problem and the proposed pairing mechanisms for the \(n\)th order RM. In Section 3, simulation results of the performance of the proposed allocation schemes are presented, while Section 4 concludes the work.
## 4 Results and Discussions
### Gas Saturationcharacteristics  
Gas saturation is an important parameter for enrichment areas selection and favorable areas prediction of CBM, and it reflects the difficulty of CBM exploitation to a certain extent [15]. Gas saturation is considered to be a more-important geological factor than gas content, affecting the high-yield of CBM wells [52]. The gas saturations of the coal seams in the study area range from 29.10% to 116.48%, with an average of 68.45%. Figure 12a shows that gas saturation is closely related to gas content and rises with the increase in gas content. When the gas contents of coal seams are greater than 20 m\({}^{3}\)/t, the gas saturations are higher than 80%. In addition, it was found that the gas saturations of coal seams are positively correlated with the reservoir pressure (Figure 12b), which suggests that the deep coal seams in the study area may have higher gas saturations.
## 2 Materials and Methods
### Literature Review
For the purpose of this research, the following models (including both models previously reported by other authors and those that have not been used in egg production forecasting) were selected:  
* Long Short-Term Memory (LSTM)--is a model based on recurrent neural network that has been used in precision agriculture [41] and, regarding poultry industry, for laying rate prediction [42] as well. * Convolutional Neural Network (CNN)--is a deep learning architecture. In poultry, it has been applied with the aim to detect and classify eggs [43]. * Random Forest (RF)--a technique that generates a single model based on a combination of multiple decision trees [44]. The usage of RF for egg weight prediction is described in [2]. * XGBoost--a \"tree boosting system\" [45] based on decision trees and the gradient boosting approach. This model was used for feature selection in order to predict the laying rate of waterfowl [42]. The comparison and evaluation between the Modified Compartmental model and the ML models is covered in Section 3.
###### Abstract  
We focus on the optical signal-to-noise ratio (OSNR)-aware optical path (lightpath) planning problem in spectrally and spatially flexible optical networks (SS-FONs) connected using weakly coupled multi-core fibers (MCFs) in which distance-adaptive and super-channel transmission is realized using multiple modulation formats. In the SS-FON considered, the quality of transmission (QoT) of optical signals is degraded due to the inter-core crosstalk (XT) effect occurring in MCFs. To account for the XT impairment when planning lightpath connections, we make use of a reliable QoT model based on the OSNR estimation, in which the XT impairment is integrated with other physical-layer impairments. To model the lightpath planning optimization problem, we develop a novel mixed-integer programming (MIP) formulation that incorporates the OSNR model. In addition, we propose an efficient heuristic method that is capable of solving larger instances of the optimization problem considered. The results of numerical experiments indicate the low scalability of the MIP method due to the presence of XT and high effectiveness of the heuristic method. The analysis of three different network topologies and two types of MCFs shows a high impact of XT on network performance and limited performance gains from the presence of the central core in a 7-core MCF.  
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Footnote †: journal:  
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[EMAIL_ADDRESS]  
space division multiplexing; elastic optical networks; multi-core fiber; inter-core crosstalk; routing; spatial mode and spectrum allocation; offline planning; network optimization; mixed integer programming; heuristics
## 2 Automatic Identification of Analysis Methods
### Permutation Entropy  
Permutation entropy (PE) is a useful tool for assessing the illogicality and complexity of time series [20]. PE has the benefit of being computationally simple, and it possesses good anti-interference abilities as well as good nonlinear data resilience. PE has been widely used in signal identification and mechanical fault diagnosis [21; 22]. The calculation method is as follows.  
Reconstruct the phase space of the time series \(\{x(i)\), \(i=1,2,\ldots,N\}\), as follows:  
\[\begin{bmatrix}X(1)=\{x(1),x(1+\tau),\cdots,x(1+(m-1)\tau)\}\\ X(j)=\{x(j),x(j+\tau),\cdots,x(j+(m-1)\tau)\}\\ \vdots\\ X(N-(m-1)\tau)=\{x(N-(m-1)\tau),\\ x(N-(m-2)\tau),\cdots,x(N)\}\end{bmatrix} \tag{2}\]  
In Equation (2), \(m\) is the dimension, and \(\tau\) is the delay. Arrange the m \(X(j)\) vectors in ascending order to obtain \(J(k)=\{j_{1},j_{2},\ldots,j_{m}\}\), where \(k=1,2,\ldots,g,g\leq m!\). There are m different symbols \(\{j_{1},j_{2},\ldots,j_{m}\}\) in total \(m!\) (\(m\) factorial) possible symbol sequences and \(J(k)\) is merely one of them. Let the probability of each symbol sequence occurring be \(P_{1}\), \(P_{2},\ldots,P_{g}\).  
Therefore, the permutation entropy with the Shannon entropy form of time series \(\{x(i)\), \(i=1,2,\ldots,N\}\) is expressed as:  
\[H_{E_{n}}=-\sum_{i=1}^{n}P_{i}\text{lg}P_{i} \tag{3}\]  
Since PE can estimate the complexity of the signal, PE can be used to estimate the dynamic changes in the signal.
## 5 Overview of 5G-Enabling Technologies
### Key 5G-Enabling Technologies
#### 5.1.16 5G Machine Learning  
Another essential characteristic of 5G systems is the ability to deploy AI/ML (Artificial Intelligence and Machine Learning) at pre-defined network locations. Given that the SBA's UE and 5G core network functions already supply computing resources to execute ML algorithms, the significant news is that it smartly exploits the utilization of Multi-access Edge Computing (MEC) inside 5G standards to push Machine Learning to the edge of the telecom network.  
In 5G mobile communication systems, several machine learning (ML) methods were applied to solve various difficult issues, but it involves significant hand-tuning. There are three types of machine learning approaches: supervised, unsupervised, and reinforcement learning. Each of these strategies substantially influences the performance of 5G wireless networks [9].
# Machine Learning Techniques for Non-Terrestrial Networks
## 4 Non-Terrestrial Network Architectures
### NTN Architectures
#### 4.2.3 On-Board Distributed Unit NTN Architecture  
In the last case, the NTN can consider the gNB functional split into distributed unit (DU) and central unit (CU) as reported in Figure 8. The DU is mounted on-board, while the CU is on the ground after the NTN gateway [32]. The feeder link is based on the F1 interface protocols as highlighted in the protocol stacks for UP in Figure 9 and for CP in Figure 10. Of course, this NTN configuration is a trade-off between the transparent case, where no processing is performed on-board and the regenerative case, where all protocols of NR-Uu close.  
Figure 8: On-board distributed unit NTN architecture.  
Figure 7: Protocol stack of the control plane in the regenerative NTN architecture [27].  
Figure 9: Protocol stack of the user plane in the NTN architecture with on-board distributed unit [27].
# BeiDou Short-Message Satellite Resource Allocation Algorithm Based on Deep Reinforcement Learning
## 5 Conclusions  
In this paper we focused on the existing conditionally constrained short-message satellite resource allocation model. In order to reduce the path transmission loss of the SMSCS and maximize the satellite load balancing and terminal service quality, a multi-objective optimization mathematical model was proposed. Due to the large number of terminals and problems with the action dimension, we proposed a region division strategy and the DRL-SRA algorithm based on the feature extraction network and DDPG. This method can achieve dynamic multi-objective optimization and resource allocation with a low level of complexity. By simulating a real application scenario, DRL-SRA was shown to be more effective than traditional algorithms for optimizing the path transmission loss of the short-message terminal and maintaining the load balance and quality of service of the short-message satellite. When there are different numbers of terminals, the DRL-SRA can send better results. The simulation results also show that, compared with other algorithms, the DRL-SRA has a better effect on improving the throughput of short-message task requests.  
In future work, we will combine the short-message terminal equipment and the low-power computing chip that supports the deep learning algorithm to consider the hardware implementation flow of the DRL-SRA. We conclude that, in real application scenarios, our algorithm improves the efficiency of short-message satellite resource allocation.  
Conceptualization, K.X. and C.Y.; methodology, C.Y.; software, C.D.; validation, K.X., C.Y., and J.F.; formal analysis, C.Y.; investigation, K.X.; resources, J.F.; data curation, K.X.; writing--original draft preparation, K.X.; writing--review and editing, K.X.; visualization, C.Y.; supervision, J.F.; project administration, J.F.; funding acquisition, C.Y. All authors have read and agreed to the published version of the manuscript.  
The National Natural Science Foundation of China under contract No. 61371119 and the Blue project of Jiangsu Province.  
Not applicable.  
The authors declare that they have no conflict of interest to report regarding the present study.
# RST Resilient Watermarking Scheme Based on DWT-SVD and Scale-Invariant Feature Transform
## The Proposed Scheme
### RST Attack Correction Based on SIFT
#### Scaling Attack Correction  
Scaling attack is a type of tampering method that can change the size of one image, which can result in the image distortion. The match between scaled image and watermarked image is shown in Figure 6.  
Owing to the SIFT characteristics, the scaling coefficient is equal to the size relationship between scaled image and watermarked image. Thus, the scaling coefficient can be extracted from \(M\) pairs of matching points. \(s_{\text{w}_{k}}\) and \(s_{\text{s}_{k}}\) are the \(k\)th scale values of the matching points in watermarked image and scaled image, respectively. The corrected scaling coefficient \(\gamma\) is shown as Equation (13):  
\[\gamma=\frac{1}{M}\sum_{k=1}^{M}\frac{s_{\text{w}_{k}}}{s_{\text{s}_{k}}}. \tag{13}\]  
Figure 5: Matching between rotated image and watermarked image: (a) rotated image (\(30^{\circ}\)); (b) feature matching.  
Apparently, \(\gamma\) is the mean of ratios between \(s_{\mathrm{w}_{k}}\) and \(s_{\mathrm{g}_{\mathrm{g}}}\). After obtaining the corrected scaling coefficient, the received image should be scaled with the parameter \(\gamma\) to achieve the rotation complementary.