esa-sceva/satcom-chunk-collection huggingface dataset:
# Deformation Analysis of Large Diameter Monopiles of Offshore Wind Turbines under Scour
###### Abstract  
The displacement of monopile supporting offshore wind turbines needs to be strictly controlled, and the influence of local scour can not be ignored. Using p-y curves to simulate the pile-soil interaction and the finite difference method to calculate iteratively, a numerical frame for analysis of lateral loaded pile was discussed and then verified. On the basis of the field data from Dafeng Offshore Wind Farm in Jiangsu Province, the local scour characteristics of large diameter monopile were concluded, and a new method of considering scour effect applicable to large diameter monopile was put forward. The results show that, for scour of large diameter monopiles, there was no obvious scour pit, but local erosion and deposition. Under the test conditions, the displacement errors between the proposed and traditional method were 46.4%. By the proposed method, the p-y curves of monopile considering the scour effect were obtained through ABAQUS, and the deformation of large diameter monopile under scour was analyzed by the proposed frame. The results show that, with the increase of scour depth, the horizontal displacement of the pile head increases nonlinearly, the depth of rotation point moves downward, and both of the changes are related to the load level. Under the test conditions, the horizontal displacement of the pile head after scour could reach 1.4-3.6 times of that before scour. Finally, for different pile parameters, the pile head displacement was compared, and further, the susceptibility to scour was quantified by a proposed concept of scour sensitivity. The analysis indicates that increasing pile length is a more reasonable way than pile diameter and wall thickness to limit the scour effect on the displacement of large diameter pile.  
p 27 July 2020; 7 September 2020; Published: 28 October 2020
# Effects of the Hybrid CRITIC-VIKOR Method on Product Aspect Ranking in Customer Reviews
###### Abstract  
Product aspect ranking is critical for prioritizing the most important aspects of a specific product/service to assist probable customers in selecting suitable products that can realize their needs. However, given the voluminous customer reviews published on websites, customers are hindered from manually extracting and characterizing the specific aspects of searched products. A few multicriteria decision-making methods have been implemented to rank the most relevant product aspects. As weights greatly affect the ranking results of product aspects, this study used objective methods in finding the importance degree of a criteria set to overcome the limitations of subjective weighting. The growing popularity of online shopping has led to an exponential increase in the number of customer reviews available on various e-commerce websites. The sheer volume of these reviews makes it nearly impossible for customers to manually extract and analyze the specific aspects of the products they are interested in. This challenge highlights the need for automated techniques that can efficiently rank the product aspects based on their relevance and importance. Multicriteria decision-making techniques can address the issue of product aspect ranking. These techniques seek to offer a methodical strategy for assessing and contrasting various product attributes based on various criteria. The subjective nature of determining weights for each criterion raises serious issues because it might lead to bias and inconsistent ranking outcomes. The CRITIC-VIKOR method was adopted in the product aspect ranking process. The statistical findings based on a benchmark dataset using NDCG demonstrate the superior performance of the method of using objective weighting to reasonably acquire subjective weighting results. Also, the results show that the product aspects ranked by using CRITIC-VIKOR could be considered guidelines for probable customers to make a wise purchasing decision.  
objective weight; ranking criteria; NDCG; aspect ranking; VIKOR; CRITIC; MCDM Article
###### Abstract  
Aspect ratio and crack density are essential parameters to understand the physical properties of porous-cracked rocks, although it is difficult to independently determine each parameter, as both are closely linked. The objective of this study is to propose a relationship between aspect ratio and crack density that can be used to solve for each through experimental and optimization methods. Two different constitutive equations are solved to create expressions explicitly defining aspect ratio and crack density, with all remaining variables arranged as functions of elastic wave velocity. Ten core specimens extracted from construction sites, with diameters of 46 mm, are subjected to artificial weathering to identify how their crack density and aspect ratio evolved with time. The artificial weathering process consisted of chemical and physical weathering cycles using saline solution and slake durability tests, respectively. Compressional and shear wave velocities are measured at every weathering step, and both aspect ratio and crack density are calculated. The random forest as an optimization method is selected to define the important score among input variables. The calculated aspect ratios and crack densities are converted into a crack porosity, the reliability of which is verified through percentage of crack porosity (~6%) in total porosity. This study demonstrates that the relationship between aspect ratio and crack density is robust and has wide-ranging applications in determining individual aspect ratio and crack density parameters in porous-cracked rock.  
Keywords:aspect ratio; crack density; elastic wave; laboratory test; porous-cracked rock; random forest +
Footnote †: journal: Applied Sciences
## 1 Introduction
### Contributions  
Our main contributions are:  
* We implement a hybrid image watermarking method that ensures high imperceptibility, improved robustness, enhanced security, and high embedding capacity to the system simultaneously.
* We embed an indistinguishable watermark image into the cover image and we also ensure the security of the watermark image by using the Arnold map.
* We use a dynamically sized watermark image that maintains a balanced visual impact by preventing the watermark from being too small or too large.  
Figure 1: Common framework for inserting and extraction of watermark image.  
The rest of this research is organized as follows: Section 2 describes the related literature with existing research gaps. Some theories about the Arnold map, DWT, and SVD are discussed in Section 3. Section 4 describes our proposed methodology by incorporating watermark embedding and extraction algorithms. The experimental results and comparison with state-of-the-art methods are described in Section 5. Finally, we have concluded our research in Section 6, with a new direction for research.
# Systematic Review of Discrete Event Simulation in Healthcare and Statistics Distributions
## 4 Discussion
### Software
#### 4.1.3 Integration and Modern Requirements  
The growing need for interoperability between simulation software and hospital information systems has made integration capabilities a key factor in software selection. [PERSON] and [PERSON] [5] identified notable differences in this regard. Arena supports structured API access, facilitating system interoperability, while Simul8 offers direct database connectivity, enabling seamless data exchange. AnyLogic provides the most comprehensive support, integrating both API-based and database-driven interactions.
## 2 Materials and Methods
### Data Processing  
In order to temporally standardize the various data products used, SIF, daily temperature, and precipitation were grouped into 16-day datasets. The 8-day LAI and FPAR datasets were compiled into 16-day datasets using the maximum value composition technique [39].  
This study explores the response of SIF in different ecosystems at both the regional and footprint scales. At the regional scale, we calculated the regional average values of SIF, EVI, FPAR, and LAI, as well as the averages of the various meteorological factors considered for each ecosystem, and scatter plots were subsequently plotted. To measure the strength of any correlations of SIF-EVI, SIF-FPAR, and SIF-LAI at the footprint scale, we randomly selected 100 SIF points in each 16-day growing period (from 22 April to 15 October) representing the cropland, forest, and grassland ecosystems. The selected 100 SIF points were buffered with a 2 km radius, and we then extracted the averaged values of EVI, FPAR, and LAI within the circle and the scatter plots were plotted accordingly. We ensured that the spatial interval between random points was greater than 10 km to eliminate the risk of spatial autocorrelation.  
In order to ensure that the relationships identified between the SIF and MODIS products in each sub-region were not biased or affected by other land cover types, the latter were selected as described in Table 1 using the 2020, 30 m resolution Global Land Cover with Fine Classification System (GLC_FCS30-2020, [[https://zenodo.org/record/4280923#](https://zenodo.org/record/4280923#)]([https://zenodo.org/record/4280923#](https://zenodo.org/record/4280923#)), accessed date: 10 August 2021).  
To de-seasonalized the time series of temperature and SIF, the method of value minus multi-year mean value was used before we calculated the relationships of them.
## 5 Experimental Testing and Analysis
### Benchmark Test Functions
#### 5.1.1 Convergence Analysis  
Due to space limitations, Figure 4 illustrates the convergence behavior of various algorithms on selected test functions, highlighting the best results from each algorithm. The blue curve represents the convergence trajectory of the DRSTASA algorithm, with the horizontal axis indicating the number of iterations and the vertical axis representing the optimal objective function value. In the F3 and F4 tests, although DRSTASA achieved the theoretical optimal value, the convergence speed was slower compared to other algorithms, especially in unimodal test functions where the AVOA algorithm demonstrated the fastest convergence. In the F7 and F9 tests, DRSTASA also reached the minimum objective function value; however, significant oscillations were observed during the early iterations. This suggests that the algorithm likely employed techniques such as simulated annealing and perturbation to escape local optima and search for the global optimum. In the F12 and F13 tests, while DRSTASA did not surpass the FDA algorithm in finding the optimal value, it still exhibited a faster convergence speed compared to the other algorithms.  
Figure 4: \(Cont\).  
Figure 4: Convergence curves for each algorithm.
# Analysis of Hydration-Mechanical-Durability Properties of Metakaolin Blended Concrete
## 2 Modeling of Hydration for Metakaolin Blended Concrete  
### Model for Cement Hydration  
Our previous studies [16; 17] presented a Portland cement hydration model. This hydration model considered the kinetic stages involved in the hydration of cement, such as the initial dormant stage, the chemical-reaction related stage, and the diffusion related stage. The model of cement hydration considers water withdrawal due to the lack of capillary water regarding high strength concrete. The equation of cement hydration is simplified as follows [16; 17]:  
\[\frac{da}{dt}=f(B(T),C(T),D_{e}(T),k_{r}(T),r_{0})*C_{w-free}*(S_{w}/S_{0}) \tag{1}\]  
where \(\frac{da}{dt}\) is the rate of hydration; \(T\) is the curing temperature; \(B\) and \(C\) are the rate of coefficients in initial dormant process (\(B\) describes the generation of initial impermeable layer, and \(C\) describes the elimination of initial impermeable layer); \(k_{r}\) is the rate of coefficient in the reaction controlled process; \(D_{e}\) is the rate of coefficient in diffusion controlled process; \(r_{0}\) is the unreacted cement particle radius; \(S_{w}\) is the effective contact area between the surrounding capillary water and cement particles [16; 17], \(S_{0}\) is the total area if cement hydration products develop unconstrained; \(C_{w-free}\) is the capillary water content (\(C_{w-free}=\left(\frac{W_{cap}}{W_{0}}\right)^{r}\) where \(W_{cap}\) is the capillary water content in hardening concrete; \(W_{0}\) is the content of water in the proportions of concrete mix; and \(r\) is an empirical factor considering the approachability of capillary water from the outer hard shell to the inner anhydrous part of the cement particles (\(r=2.6-4\frac{W_{0}}{B}\), where \(B\) is the binder content in the mix proportions).  
The rate coefficients \(B\), \(C\), \(k_{r}\), and \(D_{e}\) can be determined based on the compound compositions of cement [16; 17]. The influence of curing temperature on the rate of cement hydration is described through [PERSON]'s law [16; 17]. Regarding high strength concrete--which has a low water to cement ratio--the hydration degree is lowered because of the withdrawal of capillary water. The water withdrawal mechanism is considered through \((S_{w}/S_{0})\) and \(C_{w-free}\) in Equation (1). \((S_{w}/S_{0})\) describes the reduction of the contact area between the hydrating cement particle and ambient capillary water, and \(C_{w-free}\) considers the decreasing capillary water concentration.  
Summarily, the reaction degree of cement is calculated using the model of cement hydration. Moreover, the thermal properties, mechanical properties, and durability aspects of concrete can be analyzed using the cement hydration degree [16; 17]. The cement hydration model is further validated through experimental results for concrete with multiple mixing proportions and cement types. However, as the cement hydration model does not take into account the effect of MK, the hydration model (Equation (1)) cannot be used to analyze the hydration of MK blended concrete.
# Surrogate-Based Robust Design for a Non-Smooth Radiation Source Detection Problem
## 2 Radiation Transport Model and Surrogate Formulations
### Statistical Model  
To construct statistical models associated with \(N\) detectors, we consider a background with constant expected intensity \(B\). We denote by \(\mathbf{\theta}_{0}\), the true source location and intensity of a radiation source. It is well known that radioactive decay and detection are Poisson random processes. By including Poisson random effects and assuming that \(N\) detectors are available, we obtain the statistical model  
\[\mathrm{Y}_{i}\sim\mathrm{P}\left(\hat{F}_{i}(\mathbf{\theta}_{0})+B\right), \tag{6}\]  
associated with the \(ith\) detector response, \(i=1,\ldots,N\). The Poisson distribution with mean  
\[F_{i}:\mathcal{X}\rightarrow\mathbb{R},\ F_{i}(\mathbf{\theta}_{0})=\hat{F}(\mathbf{ \theta}_{0})+B \tag{7}\]  
is denoted by \(\mathrm{P}\). For large numbers (>30) of observed photons, the Poisson distribution is adequately approximated by a normal distribution, yielding the approximate statistical model  
\[\mathrm{Y}_{i}\sim\mathcal{N}\big{(}F_{i}(\mathbf{\theta}_{0}),(\sigma_{i}^{ \varrho})^{2}\big{)}, \tag{8}\]  
where \((\sigma_{i}^{\varrho})^{2}=F_{i}(\mathbf{\theta}_{0})\); i.e., with variance equal to the mean. This is equivalent to  
\[\mathrm{Y}_{i}=F_{i}(\mathbf{\theta}_{0})+\varepsilon_{i}^{\varrho},\ \varepsilon_{i}^{0}\sim\mathcal{N}(0,(\sigma_{i}^{\varrho})^{2}). \tag{9}\]  
In this manner, we model the observations associated with each detector as random variables \(\mathrm{Y}_{i},\ i=1,\ldots,N\).
# Current Status and Economic Prospects of Alternative Protein Sources for the Food Industry
## 3 Economic Potential and Market Perspectives of Alternative Protein
### Insect Protein  
Entomophagy as a possible solution for food insecurity and sustainable diets has been studied and discussed for many years, but its actual implementation in Western societies is still incipient. On the contrary, this type of protein source has been present in Asian and African markets for a long time. The global market value for insect protein represented around US$ 144 million, in 2021, and it is expected to reach USD 1.33 bn by 2026, with a growth rate of 45.7% per year (Market Data Forecast, 2021) [105]. Other market forecasts; however, have more optimistic predictions. Barclays estimates that the insect protein industry could be worth USD 8.0 bn by 2030 (Barclays, 2021) [106]. World-leading food manufacturers such as Nestle, PepsiCo and Tyson have started operations in this sector (PitchBook, 2021) [107], and restaurants, supermarkets, and even the Seattle Baseball Stadium are incorporating insect-based snacks.  
Insect protein farming is less expensive than traditional animal protein sources (Market Research Future, 2021) [108]. Besides pursuing technological advances to enable a more efficient and appealing way to incorporate insect ingredients in traditional foods, and addressing organoptic and functional issues, a lot needs to be done in the legislation arena. To take full advantage of the potential benefits of insect protein and expand markets, regulatory efforts are crucial. Extensive revisions of the food regulations are ongoing at this moment in several countries, and the future of the insect protein industry is highly dependent on this ([PERSON] et al., 2023) [109].
# Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines
## 3 Transmission Line Monitoring Using WSN in Smart Grids
### Smart Grid Architecture
For this reason, the issue of a packet loss is another crucial aspect that must be considered while deploying WSNs as a monitoring solution in a transmission power grid. The packet loss can be caused by a number of factors, including signal degradation over the network medium, congestions due to heavy traffic, collisions at link layer or buffer overflows. The transmission distance between sensor nodes and the quality of the connection affect the packet loss as well. There exists a strong correlation between packet loss and signal strength--over long distances, the signal strength fading effect leads to low signal noise ratio. Environmental interferences also contribute to packet loss. The above constraints emphasize the need for improving transmission reliability in WSNs. One of the most common approaches of dealing with the problem of packet loss is the packet retransmission. Packet retransmission requires some additional mechanisms to be implemented in order to trigger the retransmission of a lost packet. For instance, with the acknowledgment (ACK) mechanism, every successfully received packet should be directly acknowledged by the receiver with a notification message. If a sender is not able to recognize the expected notification message, then the packet should be retransmitted. The ACK mechanism offers highest reliability guarantee. It can be used by protocols providing packet reliability on hop-to-hop as well as on end-to-end levels. Generally, in order to eliminate or minimize the packet loss in the considered transmission grid environment, it is possible to introduce some retransmission mechanisms, or simply increase the signal strength by powering sensors with energy harvesting devices. However, in our case study, we perform only a single round of measurements (being triggered by the synchronization signal), where the measured data is being transmitted on quite short distances. For this reason, we assumed that in our case, the packet loss is almost equal to 0.
## 3 Empirical Analysis  
### Influencing Factors Screening for Forecasting Model Input  
According to the constructed influencing factors system for power grid investment forecasting, the data of the GDP, industrial structure (the proportion of the third industry added value), population, urbanization rate, total electricity consumption, growth rate of electricity consumption, electricity sales, peak load of power grid, power transmission line length of 220 kV and above, power transmission line length of 110 kV and below, transformer capacity of 220 kV and above, transformer capacity of 110 kV and below, total profit of power grid enterprises, power supply load of unit power grid assets, electricity sales of unit power grid assets, and electricity sales income of unit power grid assets from 1990 to 2016 in China are collected. The data sources are the China Statistical Yearbook and China Electric Power Yearbook.  
In order to determine the prediction model input, the grey relational degree is used to screen the influencing factors of power grid investment for realizing the feature reduction. The results of the calculation for grey relational degrees between the various influencing factors and the power grid investment are as follows:  
In the light of Table 1, the five factors whose grey relational degree is greater than 0.85 are selected as the power grid investment forecasting model input. They are GDP, total electricity consumption, electricity sales, power transmission line length of 220 kV and above, and transformer capacity of 220 kV and above. The model output is the power grid investment.  
In order to improve prediction accuracy and reliability, before forecasting, dimensionless processing is done for the above original data according to Equation (28).  
\[y_{i}=\frac{x_{i}-\min x_{i}}{\max x_{i}-\min x_{i}} \tag{28}\]  
The processed data are shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Influencing Factor** & **Grey Relational Degree** \\ \hline GDP & 0.8738 \\ Industrial structure & 0.6662 \\ Population & 0.6510 \\ Urbanization rate & 0.7022 \\ Total electricity consumption & 0.8679 \\ Growth rate of electricity consumption & 0.8774 \\ Electricity sales & 0.6226 \\ Peak load of power grid & 0.6128 \\ Power transmission line length of 220 kV and above & 0.8543 \\ Power transmission line length of 110 kV and below & 0.7256 \\ Transformer capacity of 220 kV and above & 0.8733 \\ Transformer capacity of 110 kV and below & 0.7672 \\ Total profit of power grid enterprises & 0.6136 \\ Power supply load of unit power grid assets & 0.8080 \\ Electricity sales of unit power grid assets & 0.6885 \\ Electricity sales income of unit power grid assets & 0.7363 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The calculation results of the grey relational degrees for influencing factors.
# Joint Radar and Communications Waveform Design Based on Complementary Sequence Sets
## 4 Results  
In this section, several simulation results are given to evaluate the performances of the designed JRC-CWG and the DR-JRC-CWG. In the following, unless otherwise mentioned, the number of groups \(G\) of the JRC-CWG or the DR-JRC-CWG is set to be 1. The peak-to-sidelobe ratio (PSLR) of SACF of the JRC-CWG and the DR-JRC-CWG is utilized to measure their sidelobe performance. The BPSK and the 16-PSK waveforms are employed as the WCIs for the designed JRC-CWG and DR-JRC-CWG. For the WCIs with BPSK modulation, the JRC-CWG is denoted as the JRC-CWG-BPSK, and the DR-JRC-CWG is denoted as DR-JRC-CWG-BPSK in the following. For the WCIs with 16-PSK modulation, the JRC-CWG and the DR-JRC-CWG are denoted as the JRC-CWG-16-PSK and the DR-JRC-CWG-16-PSK, respectively.