esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Case Studies
#### 3.2.3 Influence of Constituent Materials on the Shear Correction Factor  
This case aims to study the effect produced using different constituent materials, specifically by considering the different ceramics (Table 3) in the constitution of the honeycomb auxetic FGM, on the shear correction factor value. The beam's length-to-thickness ratio (a/h) is 10. Regarding the unit cell geometric characteristics, one fixed \(\eta 3=0.01\). The results achieved can be observed in Table 6.  
From the results obtained, it can be said that, overall, as the relation between the lengths of the horizontal and inclined segments (\(\eta 1\)) increases, the value of the shear correction also increases. The exception is the angle \(\theta=0^{\circ}\), which corresponds to a cell rectangular configuration and where the shear coefficient remains constant for each FGM. For the minor angles (in absolute value), the influence of this ratio is small and, in some cases, is not noticeable for the number of presentation digits. Regarding the influence of the different ceramic materials and the evolution along the set of angles analyzed, a more restricted presentation is depicted in Figure 13 to provide better legibility. The curves depicted in Figure 13 represent the evolution of the shear correction factor for the four FGM auxetic cases considered along the set of angles for fixed values of the cells' ratios \(\eta 1=1\) and \(\eta 3=0.01\). From this figure, it is possible to observe that there are two trend profiles. The Al-WC and Al-ZrO\({}_{2}\) FGM auxetic materials show similar evolutions, although with the expected different values. The other trend is shared by the Al-Al\({}_{2}\)O\({}_{3}\) and Al-SiC FGM auxetic materials. This is due to the magnitudes of the Poisson's ratios of the different ceramic materials compared to the one of aluminum. Figure 12: Evolution of shear correction factor as a function of \(\theta\), \(\eta 1\), and \(\eta 3\), for the set of positive angles of the discrete domain. 2.4 Non-Dimensional Maximum Beams' Static Deflection--Influence of the Cell's Aspect Ratios \(\eta 1\), \(\eta 3\) and the Angle \(\theta\)  
This case studies the influence of the unit cell aspect ratios \(\eta 1\) and \(\eta 3\) and the angle \(\theta\) in the non-dimensional maximum static deflection of the honeycomb auxetic beam. The FGM selected for this purpose was the Al-WC, and the length-to-thickness ratio of the beam was set to 20. The results achieved are presented in Table 7 in a non-dimensional form using the multiplier \(\widehat{w}=\frac{E_{m}-\eta\eta^{3}}{10^{3}p_{z}a^{4}}\), where \(E_{m}\) is the Young's modulus of the metallic phase, \(h\) is the beam thickness, \(a\) stands for the beam length, and \(p_{z}\) the uniformly distributed load.
## 4 Integration of DL-Based NOMA with Emerging Technologies
### MIMO-Assisted NOMA
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Context** & [158] & [159] & [160] & [161] & [162] & [163] & [164] & [165] \\ \hline NOMA system & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline OMA comparison & Yes & Yes & Yes & Yes & Yes & - & - & - \\ \hline Achievable rate optimization & Yes & - & Yes & - & - & - & Yes & Yes \\ \hline Op analysis & - & Yes & - & Yes & Yes & Yes & Yes & - \\ \hline Hardware impairment & - & - & - & - & Yes & Yes & & - \\ \hline NLoS consideration & - & - & - & - & Yes & Yes & - & - \\ \hline LoS consideration & Yes & Yes & Yes & Yes & - & - & - & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of studies on IRS-assisted NOMA [157]. of freedom MIMO-NOMA provides. Most power allocation problems for MIMONOMA's sum data rate optimization are NP-hard. The performance of the current power allocation solutions is constrained since the previously presented approaches can only derive suboptimal solutions. In light of these issues, it's important to look at alternative theories that might improve the efficiency with which MIMO-NOMA systems allocate power. The deep learning approach, a subfield of machine learning, has recently been shown to be an extremely useful tool for handling large datasets and solving nonlinear issues. Although deep learning-based wireless communication is still in its infancy, its better performance has been proven by a number of ground-breaking experiments [50; 57]. Initially introduced in [57], deep learning is now an integral part of NOMA, with impressive results shown for encoding, decoding, and channel recognition using a system built on deep learning. Also, deep learning has been implemented to improve massive MIMO channel estimates [86] and orthogonal frequency division multiplexing (OFDM) channel estimations [68]. Therefore, some recent studies present a deep learning-based MIMO-NOMA framework for optimizing the combined data rate and power consumption. In particular, numerous convolutional layers and multiple hidden layers are incorporated into the architecture for a powerful communication deep neural network (CDNN) [166]. The CDNN framework solves the issue of power allocation in MIMO-NOMA, allowing for a faster data rate and greater efficiency in terms of energy consumption by employing training algorithms that take advantage of deep learning's powerful representation capabilities. Extensive simulations further demonstrate that the proposed CDNN framework achieves a higher total data rate and greater energy efficiency than conventional techniques, further supporting the claim that it is a good option to improve the performance of MIMO-NOMA in terms of power allocation. An illustration of the MIMO-NOMA system with multiple clusters is provided in Figure 11.
# Modeling and Test Results of an Innovative Gyroscope Wave Energy Converter
## 7 Algorithm Simulation and Experimental Verification  
### Filter Comparison  
Compared with relatively stable solar energy, wave energy changes continuously, and its energy output fluctuates greatly. The application of filters is very necessary to obtain a smooth and stable output power curve and reduce unnecessary interference. Butterworth and Chebyshev Type II filters are universal and very effective low-pass filters in signal processing, which can eliminate overshoot in the system. However, the system will have some oscillation, if rapidity is the primary goal. For signal processing, the lower the cut-off frequency of the filter and the longer the equivalent time delay, the slower the sensor response speed, which will cause phase lag. Therefore, a lead circuit needs to be added to the circuit to compensate for the phase lag, which will increase the amount of calculation and complicate the system structure. As a linear filtering method, mean filtering is usually applied in image processing, which has the advantages of high efficiency, simple structure, and less calculation. It can not only effectively filter random interference signals, but also smooth the output curve.  
The power output comparison of different filtering methods under rated conditions is shown in Figure 18. In the waveform diagram after the initial filtering, the waveform without subsequent filtering has the worst performance and the most intense oscillation. However, Butterworth and Chebyshev Type II filters perform equally, and both are better than unfiltered. Their steady-state error is slightly larger when the two provide better rapidity. The average filter with a step size of 5e\(-\)1 has the smallest steady-state error. The stability criterion of the system is fully demonstrated in this method, but the rapidity of this method is too poor to meet the system requirements. Compared with other methods, the average filter with a size of 1e\(-\)1 guarantees the excellent stability and good rapidity, especially after reaching the rated operating point, and the energy oscillation is much smaller than the other two filtering methods. The average power generation in 25 s obtained by the MPPT control is 10.9 W, which accounts for 85.96% of the optimal power generation obtained by data fitting, which directly proves the feasibility of the algorithm.  
Figure 17: The diagram of simulation in Simulink, including WEC, PTO, rectification, filtering, and MPPT algorithm by MATLAB.
# Blockchain Traceability Process for Hairy Crab Based on Cuckoo Filter
## 5 Hairy Crab Full-Chain Traceability
### Hairy Crab Traceability Platform  
As shown in Figure 10, the architecture of the hairy crab traceability platform proposed in this paper is shown. The architecture is hierarchically divided according to functional  
Figure 9: Hairy crab whole-chain traceability process.  
requirements, including the blockchain foundation layer, the core capability layer, the regulation and traceability layer, and the application and interface layer. In the blockchain foundation layer, this system adopts an open source project as the underlying architecture in order to build a traceability application platform that is both secure and efficient. This layer provides a series of basic capability services, including user authentication, data encryption, consensus mechanism, and the operating environment of smart contracts, providing a solid basic capability service for the entire traceability system. The core capability layer is responsible for providing the execution environment of smart contracts and strictly verifying the data before uploading to the chain to ensure that all information meets the preset contract standards. Based on the established smart contract, this layer automatically executes relevant algorithms and refuses to uplink data that do not meet the specifications, thus ensuring the accuracy and compliance of the data. The supervision and traceability layer assumes the key role of data management and platform configuration and realises functions such as user login, channel management, smart contract release and upgrade, and transaction and query. This layer not only ensures the flexibility and scalability of the platform operation but also provides regulators with the necessary tools to achieve the effective monitoring of the entire traceability process. Finally, the application and interface layer serves as a bridge for the interaction between the system and the outside world, providing functions such as the management of breeding bases, the querying of product traceability information, the integration and display of IoT data, and integration interfaces with external systems. This layer unifies the access points of external data and provides traceability information services externally, enabling consumers, regulators, and other relevant parties to conveniently access the whole life cycle information of hairy crabs.  
To sum up, this traceability process realises the efficient synergy of all links in the hairy crab supply chain through information sharing and transparent management. This not only greatly enhances the brand influence and food safety level but also significantly strengthens consumers' trust in the products and the market competitiveness of the products, providing strong technical support for the sustainable development of the hairy crab industry.
###### Abstract  
The forecasting of the evolution of natural hazards is an important and critical problem in natural sciences and engineering. Earthquake forecasting is one such example and is a difficult task due to the complexity of the occurrence of earthquakes. Since earthquake forecasting is typically based on the seismic history of a given region, the analysis of the past seismicity plays a critical role in modern statistical seismology. In this respect, the recent three significant mainshocks that occurred in Alaska (the 2002, Mw 7.9 Denali; the 2018, Mw 7.9 Kodiak; and the 2018, Mw 7.1 Anchorage earthquakes) presented an opportunity to analyze these sequences in detail. This included the modelling of the frequency-magnitude statistics of the corresponding aftershock sequences. In addition, the aftershock occurrence rates were modelled using the Omori-Utsu (OU) law and the Epidemic Type Aftershock Sequence (ETAS) model. For each sequence, the calculation of the probability to have the largest expected aftershock during a given forecasting time interval was performed using both the extreme value theory and the Bayesian predictive framework. For the Bayesian approach, the Markov Chain Monte Carlo (MCMC) sampling of the posterior distribution was performed to generate the chains of the model parameters. These MCMC chains were used to simulate the models forward in time to compute the predictive distributions. The calculation of the probabilities to have the largest expected aftershock to be above a certain magnitude after a mainshock using the Bayesian predictive framework fully takes into account the uncertainties of the model parameters. Moreover, in order to investigate the credibility of the obtained forecasts, several statistical tests were conducted to compare the performance of the earthquake rate models based on the OU formula and the ETAS model. The results indicate that the Bayesian approach combined with the ETAS model produced more robust results than the standard approach based on the extreme value distribution and the OU law.  
epidemic type aftershock sequence model; extreme value distribution; Bayesian predictive distribution +
Footnote †: journal: Applied Sciences
## 2 Numerical Methods and Theoretical Model
### Turbulence Model  
The standard \(k\)-\(\varepsilon\) model has become the workhorse of practical engineering flow calculations since it was proposed by [PERSON] and Spalding [19]. It is popular for its robustness, economy and reasonable accuracy for a wide range of turbulent flows, and is proven to be valid to simulate the turbulent flow generated during the water entry [20]. Therefore, the standard \(k\)-\(\varepsilon\) model with standard wall functions is implemented to provide turbulence closure.  
\[\frac{\partial(\rho_{m}k)}{\partial t}+\frac{\partial(\rho_{m}ku_{i})}{ \partial x_{i}}\ =\ \frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac{\mu_{t}}{ \sigma_{k}}\right)\frac{\partial k}{\partial x_{j}}\right]+G_{k}+G_{b}-\rho_{m}\varepsilon \tag{8}\]  
\[\frac{\partial(\rho_{m}\varepsilon)}{\partial t}+\frac{\partial(\rho_{m}cu_{i })}{\partial x_{i}}\ =\ \frac{\partial}{\partial x_{j}}\left[\left(\mu+\frac{\mu_{t}}{ \sigma_{\varepsilon}}\right)\frac{\partial\varepsilon}{\partial x_{j}}\right] +C_{1\varepsilon}\frac{\varepsilon}{k}(G_{k}+C_{3\varepsilon}G_{b})-C_{2 \varepsilon}\rho\frac{\varepsilon^{2}}{k} \tag{9}\]  
In these equations, \(G_{k}\) represents the generations of the turbulence kinetic energy due to the mean velocity gradients, \(G_{b}\) is the generation of turbulence kinetic energy due to buoyancy, \(C_{\varepsilon\varepsilon}\) = 1.44, \(C_{2\varepsilon}\) = 1.92 and \(C_{3\varepsilon}\) = 0.09 are constants. \(\sigma_{k}\) = 1.0 and \(\sigma_{\varepsilon}\) = 1.3 are the turbulent Prandtl numbers of \(k\) and \(\varepsilon\), respectively [19].
## 3 Kinetic Analysis Methodology
### Generalized Models for Kinetic Analysis of CaO Carbonation
#### 3.4.2 Diffusion Control Region: Apparent Model  
Following the kinetic control region, the reaction slows and becomes diffusion-limited [40]. A simplified generalized model (referred to as an apparent model) has been developed for the carbonation of CaO by [PERSON] [37]. This model does not involve the use of morphological parameters and is described by the following equations:  
\[X(t)=X_{u}\bigg{[}1-exp\bigg{(}-\frac{k}{X_{u}}t\bigg{)}\bigg{]} \tag{21}\]  
\[X(t)=\frac{X_{u}t}{(X_{u}/k)+t} \tag{22}\]  
where \(X_{u}\) is the ultimate conversion of CaO to CaCO\({}_{3}\) and \(k\) is the reaction rate (min\({}^{-1}\)). A constant \(b\) is introduced to represent the time taken to attain half the ultimate conversion: \(X=X_{u}/2\) at \(t=b\). \(X_{u}\) can then be expressed as:  
\[X_{u}=kb \tag{23}\]  
Substituting Equation (23) into Equation (22) leads to:  
\[X(t)=\frac{kbt}{b+t} \tag{24}\]  
To determine the constants \(k\) and \(b\) using the least squares method, the equation is written in linear form:  
\[\frac{1}{X(t)}=\frac{1}{k}\bigg{(}\frac{1}{t}\bigg{)}+\frac{1}{kb} \tag{25}\]An Arrhenius plot can then be produced using the following relationship [57]:  
\[ln(k)=-\frac{E_{a}}{R}\left(\frac{1}{T}\right)+lnA \tag{26}\]  
The kinetic parameters \(E_{a}\) (kJ mol\({}^{-1}\)) and \(A\) (min\({}^{-1}\)) can therefore be obtained, as well as the diffusion rate constant \(k\) (min\({}^{-1}\)).
## 5 Conclusions and Further Work
The time of the SfM method when applied to small objects can be reduced (more than 4 times) with careful photographing, paying attention that the object is well-framed, without unnecessary background of the surroundings. 8. The classic SfM method does not provide for placing markers next to the photographed object, so the obtained digital 3D models do not have information about their dimensions. Thus, these models are not fully suitable for the professional archiving of historical objects, but may be useful for popularising and making available tangible cultural heritage. The authors see the need to continue the work in terms of: 1. Conducting comparative studies of creating digital 3D models of museum objects using the SfM method with the use of other non-commercial and commercial programs. 2. Development of methods and algorithms for comparing the obtained 3D models, which make it possible to calculate the differences between the shape of digital artefacts obtained from both methods and to change the shape of the surface of smoothed and optimised objects in relation to the basic model obtained by the 3D SLS method. 3. Searching for better methods of post-processing 3D models from SfM to improve their visual quality while reducing their size. 4. Developing a list of good practices for the preparation of a collection of photographs of museum objects for the purposes of the SfM method for museum worker. Conceptualization, J.M. and M.B. ; methodology, J.M., M.B., M.P.-L. and A.S.; software, A.S., M.B. ; validation, M.B. and A.S.; formal analysis, J.M. ; investigation, J.M. ; resources, J.M., M.B. and M.P.-L.; data curation, J.M., M.B. and M.P.-L.; writing--original draft preparation, J.M. and M.B. ; writing--review and editing, J.M., M.B. and M.P.-L.; visualization, M.B. ; supervision, M.B. and M.P.-L.; project administration, J.M. and M.B. ; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript. This article has been supported by the Polish National Agency for Academic Exchange under Grant No. PPI/APM/2019/1/00004 titled \"3D DIGITAL SILK ROAD\". Not applicable. The authors would like to thank [PERSON]--Director of the Afrasiyab Museum in Samarkand for making it possible for us to 3D scan museum resources in 2017 and [PERSON]--the new Director of the Afrasiyab Museum, as well as the employees of the Museum for providing a collection of photos of objects from the exhibition. The authors declare no conflict of interest.
## 2 Materials and Methods  
### System Model  
The 5G positioning system with IRSs is shown in Figure 1 and comprises a base station, IRSs, and the user to be located. It was assumed that there was only one base station in the system, denoted by \(BS\), with a uniform horizontal linear arrangement of \(W\) antennas, denoted by, \(AN=\left\{AN_{1}\quad\cdots\quad AN_{w}\quad\cdots\quad AN_{W}\right\}\), where the distance between the antennas is \(d\). The user to be located in the system was denoted as \(US\) and configured with a single antenna. The system was assumed to be equipped with \(M\) (although, theoretically, an IRS can also continue to localize, in order to improve the accuracy of localization as well as to compare it with TOA/TDOA and other localization methods in subsequent studies, so here we set \(M\geq 3\)) IRSs, denoted as \(IRS=\left\{IRS_{1}\quad\cdots\quad IRS_{m}\quad\cdots\quad IRS_{M}\right\}\), where \(IRS_{m}\) denotes the \(m\)-th IRS, each of which comprised \(K\) reflective units arranged in a uniform horizontal linear pattern, denoted as \(\left\{IRS_{m,1}\quad\cdots\quad IRS_{m,k}\quad\cdots\quad IRS_{m,K}\right\}\). In addition, the distance between the reflective elements is \(d\). In Figure 1, we use \"\(\cdot\cdot\cdot\cdot\cdot\)\" to denote multiple IRSs to simplify the representation in the figure, and the brown and the blue dotted line with arrows denote the direction of signal transmission from US to IRS and IRS to BS, respectively.  
Assuming that there was no direct signal path between the user and base station, the user could communicate with the base station through an IRS. The signals received by the base station in this system from the user were divided into two stages: initially from the user to the IRS and subsequently from the IRS to the base station. The two-stage channel was considered a Rayleigh fading channel, and the channel fading coefficient \(\alpha\) was a complex Gaussian random variable with zero mean and \(\sigma_{\alpha}^{2}\) variance, that is, \(\alpha\sim\mathcal{CN}\big{(}0,\sigma_{\alpha}^{2}\big{)}\).
## 3 Results and Discussion
### DVC Analysis
#### 3.2.2 Maximum Principal Strain  
The 3D original CT images taken under different stress levels are shown in Figure 12a,b. The distribution of the maximum principal strain calculated by DVC is shown in Figure 12c,d. The strain values are all positive, indicating that the maximum principal strain inside the specimen is tensile strain. The small cracks appeared on the upper surface of the specimen under 30 MPa (marked in a red circle in Figure 12a), and no significant changes were observed in other positions. The strain map (Figure 12c) shows that the overall strain distribution is relatively uniform, but the obvious yellow concentration area of the strain appears at the edge of the upper surface (marked in a red circle in Figure 12c). When the stress increased to 50 MPa, multiple obvious cracks appeared (marked in a blue circle in Figure 12b). The localized strain area responded to low stress, continued to develop under high stress (marked in a blue circle in Figure 12d), and the strain localization area increased significantly and distributed randomly. To sum up, the specimen will eventually generate macroscopic cracks along with larger tensile deformation; the position of the cracks is consistent with strain localization.  
The distribution of the 2D maximum principal strain at different section positions along the Z-direction under 30 MPa and 50 MPa are shown in Figures 13 and 14. Figure 13a-d show that obvious strain localization zones appear inside the specimen, but the strain localization positions of different sections are not completely consistent, and the development direction and strain values of the localization zones are significantly different. From top to bottom along the Z-axis, as shown in Figure 14a-d, the strain localization zone gradually moves upward from the middle position, and the maximum principal strain value near the middle section (Figures 13 and 14b,c) is higher than the upper and lower surfaces. This phenomenon may be caused by the different response to stress, due to the heterogeneous internal structure of cement-based materials. Comparing Figure 14 with Figure 13, the existing strain localization zones in the specimen significantly  
Figure 12: DVC computing 3D image and distribution of principal strain under different stress stages.  
expand, the new strain concentration zones appear, and the maximum principal strain values all increase.