esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This algorithm uses the PPO policy optimization method to allow the basic learner to learn a series of anti-jamming tasks with known jamming parameters. Then, the meta-learner in the meta-learning idea summarizes and generalizes the general rules of this series of similar anti-jamming tasks, so that when the communication system encounters new jamming similar to historical anti-jamming tasks, the communication system can make decisions more quickly using the experience provided by the meta-learner.  
Simulation results show that compared with other deep reinforcement learning algorithms that use random initial networks, Meta-PPO can still maintain good learning performance under constantly changing jamming conditions by adopting the initial strategy provided by the meta-learner. It also demonstrates more outstanding robustness and generalization characteristics, further enhancing its anti-jamming characteristics.  
In the experimental simulation of this paper, the sensitivity of communication parameters still needs further study. At the same time, the idea of combining meta-learning with the DQN algorithm itself has a lot of room for optimization, such as optimizing the computational complexity of second-order gradient descent algorithms, and measuring the specific similarity of meta-training tasks. We plan to conduct research on these issues in the next step and perform actual simulations on hardware to continuously enhance the generalization and robustness of intelligent anti-jamming decision making.  
Conceptualization, Q.C., Y.N. and B.W.; methodology, Q.C., Y.N. and B.W.; software, Q.C. and B.W.; validation, Q.C., Y.N. and B.W.; formal analysis, Q.C. and Y.N.; investigation, Q.C. and Y.N.; resources, Q.C. and Y.N.; data curation, Q.C. and Y.N.; writing--original draft preparation, Q.C., Y.N., B.W. and P.X.; writing--review and editing, Q.C., Y.N. and P.X.; visualization, Q.C. and Y.N.; supervision, Q.C. and Y.N.; project administration, Q.C. and Y.N.; funding acquisition, Q.C. and Y.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Natural Science Foundation of China] grant number [62371461], [National Natural Science Foundation of China] grant number [61971439] and [National Natural Science Foundation of China] grant number [U22B2002].  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to institutional data privacy requirements.  
The authors declare no conflict of interest.
## 6 Simulation Results and Discussions
### Different Phase Shift Sets  
Figure 3 demonstrates the SE for the multi-RIS system using different phase shift sets for one vehicle pass, which is intended to validate Table 4. Considering \(K=8\), it can be seen that using optimized phase shift set \(\phi_{in,i_{n}i_{n}}^{\text{opt}}(t)\) in (33) achieves a higher SE compared to the case without RIS. For example, the SE is from 6.2 to 6.8 bit/s/Hz when the moving distance increases from 400 m to 480 m. When the \(k_{in,i_{n}}\) in (29) is larger than \(k_{in,i_{n}}^{\text{opt}}(t)\), e.g., \(k_{in,i_{n}}^{\text{opt}}(t)+1\), then the same value of SE can also be obtained. This matches with the result in Table 4. However, when \(k_{in,i_{n}}(t)\in\mathbb{R}\) (e.g., \(k_{in,i_{n},i_{n}}^{\text{opt}}(t)+0.2\)), the SE cannot be maximized, as we expected. In addition, the highest SEs are obtained as 6.9 bit/s/Hz when the moving distance is 500 m, due to the smallest distance \(d_{0}(t)\) in (4) at this point.  
Figure 4 shows that the optimized phase shift set \(\phi_{in,i_{n}}^{\text{opt}}(t)\) results in a lower delay spread. It can be observed that the delay spread first decreases and then increases, from \(1.23\times 10^{-6}\) s to \(1.47\times 10^{-6}\) s. This is because the vehicle first approaches and then moves away from the BS. If we change the location of the BS or the RISs, the changing pattern of the delay spread will be different. However, using \(\phi_{in,i_{n}}^{\text{opt}}(t)\) in (33) can always obtain a lower delay spread. Moreover, the imperfect \(k_{in,i_{n},i_{n}}(t)\) (e.g., \(k_{in,i_{n},i_{n}}^{\text{opt}}(t)+1\)) and the RIS HWI \(\gamma_{in,i_{n}}\) will increase delay spread. It is noteworthy that the imperfect phase shift set is possible to  
Figure 2: Top view of the multi-RIS system considered in this section.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline BS height (\(h_{\text{BS}}\)) & 20 m \\ RIS height (\(h_{\text{RIS}}\)) & 15 m \\ Vehicle height (\(l_{\text{V}}\)) & 2 m \\ Speed of the vehicle (\(v\)) & 100 m/s \\ Start point of the vehicle (\(x_{\text{start}}\)) & \(-\)500 m \\ Total time for one vehicle pass (\(T\)) & 10 s \\ Transmit power (\(P_{t}\)) & 20 dBm \\ Power spectral density (\(N_{0}\)) & \(-\)80 dBm \\ Carrier frequency (\(f_{c}\)) & 24 GHz \\ Rectangular grid spacing of the RIS (\(dx\), \(dy\)) & \(\lambda/(2\sqrt{\pi})\), \(\lambda/(2\sqrt{\pi})\) \\ The number of each RIS (\(M\times N\)) & \(32^{2}\) \\ Spacing (\(sp\)) & 50 m \\ RIS HWI (\(a_{\text{HWI}}=\pi/4\)) & \(\pi/4\) \\ Transceiver HWI (\(\kappa_{t}\), \(\kappa_{t}\)) & \(0.03^{2},0.03^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameter settings [19; 43].  
achieve in practice, since the RIS hardware can cause phases over \(2\pi\)[14]. The RIS without HWI case can be considered the same as the RIS with perfect CSI in [12; 14]. Therefore, compared to the models in [12; 14], our analysis is more practical, since HWI is considered in the svstem model.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analyses were performed using the statistical software R version 4.0.5 (R Core Team, 2021) (R Foundation, Vienna, Austria). The statistical analysis began with a Shapiro-Wilk normality test, with a significance level of \(p\leq 0.05\). Then, a nonparametric Friedman analysis of variance was performed, with a pairwise Wilcoxon rank-sum comparison test with continuity correction, using a significance level of \(p\leq 0.05\). Subsequently, to observe the correlations between indicators, a nonparametric Spearman correlation matrix [52] was developed with a Mantel test at a significance level of \(p\leq 0.05\), where a significant linear correlation was considered at values of \(r^{2}\geq\pm 0.6\). Dimensionality reduction was also performed using the principal component analysis (PCA) technique, starting with the Kaiser-Meyer-Olkin adequacy test [52]. The resulting principal components (PCs) were selected using the eigenvalue \(\geq 1\) selection criterion. Once the PCs were selected, a Spearman correlation matrix was developed, followed by a redundancy elimination process of the indicators related to the PCs under the following criteria: number of significant interactions > PC membership (PC1 > PC2 >... > PCn) > correlation with its PC. The resulting indicators were used to develop an _SQI_ based on all the indicators evaluated throughout the experiment and to analyze the effect of the MPs on the physicochemical, biological, and ecophysiological indicators of the soil.
## 8 Reduced-Order Vibroacoustic Computational Model
### Computation of the Projection Basis for the Structure  
#### 8.1.1. Case of a Weak Coupling of the Structure with the Internal Acoustic Fluid  
The undamped elastic structural modes of structure \(\Omega_{S}\) in vacuo are computed with the constitutive equation corresponding to elastic materials. Setting \(\lambda^{S}=\omega^{2}\), we have to solve the \((n_{S}\times n_{S})\) generalized symmetric real eigenvalue problem,  
\[\left[\mathbb{K}^{S}(0)\right]\mathbb{U}=\lambda^{S}\left[\mathbb{M}^{S} \right]\mathbb{U}\,. \tag{28}\]  
This generalized eigenvalue problem admits a zero eigenvalue with multiplicity 6 (corresponding to the six rigid body motions), and admits an increasing sequence of \((n_{S}-6)\) strictly positive eigenvalues (corresponding to the elastic structural modes),  
\[0<\lambda_{1}^{S}\leq\ldots\leq\lambda_{\alpha}^{S}\leq\ldots\,. \tag{29}\]  
Let \(\mathbb{U}_{1},\ldots,\mathbb{U}_{\alpha},\ldots\) be the eigenvectors (the elastic structural modes) associated with \(\lambda_{1}^{S},\ldots,\lambda_{\alpha}^{S},\ldots\). Let \(0<N_{S}\leq n_{S}-6\). We introduce the \((n_{S}\times N_{S})\) real matrix of the \(N_{S}\) elastic structural modes \(\mathbb{U}_{\alpha}\) associated with the first \(N_{S}\) strictly positive eigenvalues,  
\[\left[\mathcal{U}\,\right]=\left[\mathbb{U}_{1}\ldots\mathbb{U}_{\alpha} \ldots\mathbb{U}_{N_{S}}\,\right]. \tag{30}\]  
We have the orthogonality properties,  
\[\left[\mathcal{U}\,\right]^{T}\left[\mathbb{M}^{S}\right]\left[\mathcal{U}\, \right]=\left[M^{S}\right], \tag{31}\]  
\[\left[\mathcal{U}\,\right]^{T}\left[\mathbb{K}^{S}(0)\right]\left[\mathcal{U} \,\right]=\left[K^{S}(0)\right], \tag{32}\]  
in which \([M^{S}]\) is a diagonal matrix of positive real numbers and where \([K^{S}(0)]\) is the diagonal matrix of the eigenvalues such that \([K^{S}(0)]_{\alpha\beta}=\lambda_{\alpha}^{S}\delta_{\alpha\beta}\) (the eigenfrequencies are \(\omega_{\alpha}^{S}=\sqrt{\lambda_{\alpha}^{S}}\)).
# Removal of Methylene Blue from Aqueous Solution by Bone Char
## 2 Experimental
### Kinetic and Isotherm Models  
#### 2.3.1 Pseudo-Second-Order Kinetic Model  
Because the combination of negative bone char surface and positive methylene blue ions indicated the occurrence of electron transfer, the corresponding pseudo-second-order kinetic model was used in this work. It can be given as follows:  
\[t/q=1/{k_{q}}^{2}+t/q_{e} \tag{3}\]  
where \(k\) is the pseudo-second-order adsorption rate constant (g mg\({}^{-1}\) min), and \(q\) and \(q_{e}\) are the adsorption capacity (mg g\({}^{-1}\)) at \(t\) min and equilibrium, respectively.
# Intelligent Safety Assessment of Prestressed Steel Structures Based on Digital Twins
## 5 Case Study
#### Construction of the virtual model
(4) Analysis of the modeling method  
According to the radial cable force development process image in Figure 17, the cable force values of each construction step were extracted, as shown in Table 3. After the assessment of the intelligent method, the structural safety state of each construction step is finally guaranteed to be at level a. The validity of the modeling method can be judged by the fitting degree of data. In this study, the fitting degree was obtained from the theoretical value analyzed by the modeling method and the measured value of the cable force in the construction process. The calculation formula of the fitting degree is expressed as Equation (17):  
\[R^{2}=1-\frac{\sum_{1}^{12}(y_{MVi}-y_{TVi})^{2}}{\sum_{1}^{12}(y_{MVi}-\overline {y_{MV}})^{2}} \tag{17}\]  
In the equation, \(R^{2}\) represents the fitting degree, \(y_{MVi}\) represents the measured value of the cable force of each construction step in the construction process, and \(\overline{y_{MV}}\) means the average value of the measured value of the cable force of each construction step in the construction process. \(y_{TVi}\) is the theoretical value of the cable force in the virtual model simulation. By analyzing the changes of upper and lower radial cable forces in each construction step, the fitting degree was above 95%. On the one hand, the comparative analysis of the theoretical value and the measured value showed that the cable force analyzed by the research method can effectively reflect the real situation of the structure. On the other  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Upper Radial Cable**} & \multicolumn{2}{c}{**Lower Radial Cable**} \\ \hline  & **Theoretical Value (N)** & **Measured Value (N)** & **Theoretical Value (N)** & **Measured Value (N)** \\ \hline Construction step 1 & 0 & 0 & 0 & 0 \\ Construction step 2 & 285 & 372 & 0 & 0 \\ Construction step 3 & 914 & 1062 & 0 & 0 \\ Construction step 4 & 0 & 0 & 356 & 482 \\ Construction step 5 & 0 & 0 & 365 & 363 \\ Construction step 6 & 0 & 0 & 387 & 442 \\ Construction step 7 &  
hand, the research modeling method can intelligently evaluate the construction process of the structure. The cable force can meet the requirements of the specification in each construction step to ensure that the structure is always in the safety level a. The effectiveness of the intelligent assessment method for the construction safety of prestressed steel structures based on the DTs was verified. The DT modeling method can effectively guide the construction site and realize the intelligent assessment of structural safety performance.
# Analysis of Video Transmission Capabilities in a Simulated OFDM-Based Supplementary BPL-PLC System
## 2 Related Work  
The current development of communication systems applicable to the mining industry is very dynamic. However, most advancements in this field are focused on wireless systems, such as WLAN, ZigBee and Bluetooth, and also on mobile telephony [18; 19; 20; 21; 22; 23; 24]. The authors of these works show the great development of communication systems used, i.e., in open pit as well as underground mines. They describe the use of wireless media mainly for the purpose of monitoring the mining process in a typical miner's work cycle.  
More difficult aspects to analyze are safety and support systems for mine rescuers. Once again, available literature mainly relates to wireless systems and solutions [25; 26; 27; 28; 29; 30; 31; 32; 33]. Initially, they were based on the so-called dripping cable technology, and then moved to popular wireless means of communication. Of course, the main advantages of using wireless communication interfaces in mines are the following:  
* Relatively quick way to build the telecom infrastructure;
* Low weight and portability of user terminals;
* Easy integration with sensor systems connected to the rescuer's equipment. However, wireless technologies suffer from many disadvantages, such as:
* Relatively low bandwidth; therefore, in most cases, only voice transmission is possible;
* Communication protocols are most often based on TDD;
* When transmitting a signal over long distances, it is required to utilize signal amplifiers and repeaters, which reduces the throughput of data transmission and lowers QoS for multimedia services, particularly those in real-time;
* The matter of developing efficient algorithms, network scaling issues, as well as ways of deploying network nodes, etc., is also problematic, especially whenever a malfunction or unexpected event occurs.  
Therefore, all of those factors make it necessary to look for other solutions, e.g., using the already available power lines for communication purposes as the main backbone system for the exchange of supplementary information. The literature review indicates that scenarios related to the management of rescue teams in the event of, i.e., rock falls, earthquakes and rock mass movements that cause the collapse of escape corridors for miners working underground, have been relatively rarely analyzed. As shown, this issue is therefore very poorly recognized. In our opinion, the BPL-PLC technology has a potential to serve as a transport network for multimedia services, which can effectively support the work of a mine rescuer while performing his job with the aid of, i.e., video signals. This conviction became the starting point for conducting this research.
# A Survey on Pump and Dump Detection in the Cryptocurrency Market Using Machine Learning
## 5 Discussion and Research Directions
### Discussion
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Paper** & **Strengths** & **Limitations** & \\ \hline [PERSON] et al. [45] & - & Proposing a novel approach using a combination of distance and density metrics & - & Lack of a confirmed P\&D database \\  & - & Providing the data for both social data and market data & - & Low performance of the proposed models \\  & - & Statistical analysis, pairwise \(t\)-tests & & - \\ \hline [PERSON] et al. [40] & - & A large number of data from telegram, Twitter, and market & - & Increased uncertainty of the pump detection model due to weakly supervised labeling of the data Lack of a sufficient number of ML models for proper comparison of results \\ \hline [PERSON] [35] & - & Implementing various models and employing various techniques to improve the training to achieve better results & - & Lack of a confirmed P\&D database—The analysis is exclusive to [PERSON] tweets and Dogecoin market data from a single exchange \\ \hline [PERSON] et al. [38] & - & Introducing novel DL approaches for detecting sequential anomalies with good performance & - & Limited scope: analysis just on one exchange \\ \hline [PERSON] et al. [42] & - & Introducing an attention-based approach that captures skip correlations to detect pump attempts & - & Increased uncertainty of the pump detection model due to weakly supervised labeling of the telegram data \\ \hline [PERSON] et al. [37] & - & Defining hour and minute features encoded with cosine and sine for input data & - & Limited scope: analysis just on one exchange; insufficient split size for test-set \\ \hline [PERSON] et al. [46] & - & Introducing an unsupervised LSTM-based autoencoder to detect pump events & - & Lack of a sufficient number of ML models for proper comparison of results limited scope: analysis just on one exchange \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._number of cryptocurrencies or using only data from one cryptocurrency exchange [35; 42; 43], which might result in training models that detect patterns specific to a single cryptocurrency. Our survey reveals that various articles have utilized different approaches to detect pump and dump events, incorporating factors such as candle prices, trading volume, and social media data. To defend against pump and dump, one important step would be to educate cryptocurrency traders about the taxonomy of pump and dump schemes. By increasing their awareness, traders can be more cautious and less likely to fall into these traps. Additionally, the approaches highlighted in our survey can be employed to predict the likelihood of a coin being subjected to pump and dump, providing traders with valuable insights to make more informed decisions and mitigate potential risks.
## 3 Results and Discussions
### Diameter and Roundness  
The variation in hole diameter with rotational speed during the reaming process using UVR and CR is shown in Figure 11, where the reamer diameter is 7.8 mm. In the experiment, the ultrasonic amplitude was 6 \(\upmu\)m. The hole diameter of CFRP and the titanium alloy machined by UVR is within the H7 tolerance band, and, by CR, it is within the H8 tolerance band, which is worse compared to UVR. The reliability of the UVR process was verified by comparing the average hole diameter during the reaming process. When reaming CFRP by CR, as the speed increases, more cutting heat is generated, the hole diameter increases, and the error becomes larger. At the same speed (200 rpm and 400 rpm), the increase in hole diameter of CFRP is smaller compared to that of the titanium alloy because the elastic recovery of the holes after reaming of CFRP reduces the hole diameter, which weakens the increase in hole diameter due to cutting heat.  
Figure 11: Comparison of diameter at different levels of tool rotation speed: (**a**) machined CFRP surface; (**b**) machined Ti surface.  
Figure 10: Comparison of surface roughness at different levels of tool rotation speed: (**a**) machined CFRP surface; (**b**) machined Ti surface.  
Figure 12 demonstrates the variation in roundness with speed for CFRP/Ti stacked holes. In the experiment, the ultrasonic amplitude was 6 \(\upmu\)m. The average error in roundness by UVR is 0.01 mm smaller than that of CR. The error of using UVR is on average 0.015 mm smaller than that of using CR, indicating that UVR results in a hole that is closer to round. This is because ultrasonic reaming reduces the heat generated during the reaming process, thus reducing thermal damage and deformation and making the hole closer to round.
# Wind Turbine Power Curve Modeling with a Hybrid Machine Learning Technique
## 5 Conclusions  
Wind power curves play important roles in many aspects, such as wind power forecasting, wind turbine condition monitoring, wind energy potential estimation, and wind turbine selection. In this paper, a hybrid model is proposed in order to obtain a more accurate power curve. Firstly, an FCM-based outlier detection method is employed to process the collected wind data and eliminate the adverse effects of outliers on wind power curve modeling. Secondly, different types of ELM-based regression models are trained and used to estimate the power output of wind turbines. Finally, the power forecasts generated by the above regression models on a validation set are used to train SVR, and the trained SVR is employed to forecast the power output of a wind turbine at different wind speeds. It can be concluded from the power curve modeling results that: (1) most nonparametric models perform better than parametric models; (2) in all parametric models selected in this paper, 5-PLF is superior to the other parametric models with large probability; (3) the proposed hybrid model can generate more accurate power curves than single parametric and nonparametric models for different wind turbines. In practice, even if there are numerous outliers in the collected wind data, the proposed hybrid model is also a good choice for power curve modeling.  
Conceptualization, S.P.; Data curation, S.P.; Methodology, Y.L.; Supervision, Y.L.; Writing-original draft, Y.L.; Writing-review and editing, S.P. and Y.L.  
This research was founded by the Applied Basic Research Program of Qinghai (2019-ZJ-7017).  
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions that helped improve the quality of this manuscript.  
The authors declare no conflict of interest.  
Figure 6: The estimated power curves for different wind turbines.
## 3 Metadata Design
### Fundamental Principles of Metadata Design  
Three guiding principles shape the metadata framework in FC-BENTEN:  
**Consistency:** Metadata are recorded systematically and uniformly across all stages of the experimental and analytical process. From initial sample preparation through data acquisition and analysis, carefully structured metadata ensure that the conditions and parameters influencing the results can be fully traced. This consistency underpins reproducibility, allowing independent researchers to verify the findings or reanalyze data under identical conditions.  
**Accessibility:** Essential metadata ranging from beamline settings to sample compositions and analytical parameters must be easily searched and retrieved. FC-BENTEN facilitates easy accessibility to these details, enabling researchers to streamline data interpretation and quickly identify relevant datasets for comparison, integration, and subsequent MI-driven analyses.  
**Flexibility:** The metadata design anticipates the evolving nature of research. As new techniques emerge or existing methods are refined, the metadata schema can adapt to ensure interoperability across different scientific domains. This flexibility fosters collaborative opportunities, encourages data reuse, and enhances the overall reliability and longevity of the research ecosystems.