esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Analysis
### Feasibility Analysis of Preventing Cascade Tripping  
The above simulation results show that the improved shortest path algorithm proposed in this paper can quickly and accurately search for dangerous lines greatly affected by LE after faulty line removal, quickly determining the collection range of wide-area information. In the scenario where a single node contains a wind farm and the wind power penetration is low, the APEV obtained by the conventional method still has high accuracy and can still meet the accuracy requirements in the LE identification. The error between the APEV calculated by the proposed method and the APMV is smaller than that calculated by the conventional method. In the scenario where multiple nodes contain wind farms, assuming that the value of the change in wind power active output as a proportion of total installed wind power capacity remains constant, the error between the APEV calculated by the conventional method and the APMV increases as the wind power penetration gradually increases.  
In the cases in this paper, when the wind power penetration rate exceeds 60%, the APEV calculated by the conventional method can no longer satisfy the LE criterion, which may lead to incorrect LE identification results, causing an unexpected trip of the backup protection, which may cause a cascading trip. As for the proposed method, the APEV is still close to the APMV in the scenario of high wind power penetration. The LE overload can be accurately identified based on the existing LE identification criterion.  
The time spent on each link in the simulation process of this paper is shown in Table 10. From Table 10, the entire identification process of LE can be completed within 0.1 s in different scenarios. It is worth explaining that with the increase in wind power penetration, the time for calculating the APEV increases, which is essentially due to the increase in the number of bus nodes containing wind farms, resulting in an increase in the calculation of the APEV, but the increment of time is very small, and from the point of view of the total time, the total time of the LE identification method proposed in this paper is basically the same for the different scenarios with different wind power penetration rates.  
Since the time interval of WAMS updating data is 20-50 ms, and the backup protection action delay is usually 1-5 s, this inherent delay can be used to collect wide-area information and complete relevant algorithms quickly. LE can be reliably identified, and the trip signal can be blocked before the action of the backup protection, which wins enough time for the subsequent load-shedding control and can effectively prevent the occurrence of cascade tripping accidents.  
Figure 10: Comparison of the overload branch’s LEEs calculated by the proposed and conventional methods. (**a**) The proposed method; (**b**) The conventional method.
## 3 Results
### Final Model Selection
Based on the provided information, LightGBM exhibits better performance than XGBoost in terms of training time, with LightGBM taking 2.92 s compared to XGBoost 37.26 s. The results from [PERSON] [86] support the superior prediction accuracy of the LightGBM model compared to other models. Similarly, [PERSON] [74] observed that LightGBM exhibited the highest reliability among the XGBoost and random forest models, as indicated by R\({}^{2}\), RMSE, and MAE values of 0.865, 3.56 MPa, and 1.3 MPa, respectively. Considering these findings and the evaluation of performance metrics, the LightGBM model was chosen as the optimized and final model for this study. Figure 4: _Cont._ \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{3}{*}{**Database**} & \multirow{3}{*}{**Indicators**} & \multicolumn{6}{c}{**Models**} \\ \cline{3-8}  & & \multicolumn{1}{c}{**Linear Regression**} & \multirow{2}{*}{**SVR**} & \multirow{2}{*}{**KNN**} & \multicolumn{1}{c}{**Decision Tree**} & \multicolumn{1}{c}{**Random Forest**} & \multirow{2}{*}{**XGBoost**} & \multirow{2}{*}{**LightGBM**} \\ \cline{3-3} \cline{5-8}  & \(\text{R}^{2}\) & 0.65 & 0.86 & 0.99 & 0.86 & 0.97 & 0.98 & 0.97 \\ \cline{2-8} \multirow{2}{*}{Training set} & RMSE (MPa) & 9.41 & 6.07 & 1.57 & 6.05 & 2.62 & 2.28 & 2.76 \\ \cline{2-8} \multirow{2}{*}{Training set} & MAE (MPa) & 7.41 & 3.75 & 0.38 & 4.52 & 1.8 & 1.54 & 1.98 \\ \hline \multirow{3}{*}{Test set} & \(\text{R}^{2}\) & 0.66 & 0.79 & 0.74 & 0.82 & 0.89 & 0.93 & 0.94 \\ \cline{2-8} \multirow{2}{*}{Test set} & RMSE (MPa) & 9.72 & 7.67 & 8.48 & 7.03 & 5.59 & 4.52 & 4.16 \\ \cline{2-8} \cline{2-8} \multirow{2}{*}{Training set} & MAE (MPa) & 7.67 & 5.25 & 5.91 & 5.46 & 3.98 & 3.15 & 3.03 \\ \hline \multicolumn{8}{c}{Delta RMSE (MPa)} & 0.31 & 1.6 & 6.91 & 0.98 & 2.97 & 2.24 & 1.4 \\ \hline \multicolumn{8}{c}{Training time (seconds)} & 0.03 & 5.01 & 0.07 & 0.37 & 43.37 & 37.26 & 2.92 \\ \hline \end{tabular}
\end{table} 
Table 4: Comparing the performance indicators of optimal models. Figure 4: Actual and predicted values for the test data using the optimal models of (**a**) linear regression; (**b**) SVR; (**c**) KNN; (**d**) decision tree; (**e**) random forest; (**f**) XGBoost; and (**g**) LightGBM.
# Ultra-Reliable Link Adaptation for Downlink MISO Transmission in 5G Cellular Networks
## 3 Sources of SINR Variability
### Combined Effect
When there is one interferer, the outage probability for the joint effect of precoder, wanted signal, and interfering channel variability can thus be obtained in closed form as  
\[\mathrm{P}\left(\gamma_{\tau}\leq\gamma_{t}\right)=1-\int_{0}^{\infty}Q_{1} \left(\sqrt{\delta_{i}},\sqrt{\gamma_{t}(i+N_{0})}\right)\left[1-Q_{1}\left( \sqrt{i},\sqrt{\delta_{j}}\right)\right]di \tag{20}\]  
where \(\delta_{i}=\frac{\rho^{2}|\mathbf{h}_{0}\mathbf{v}|^{2}}{\alpha_{i}}\) and \(\delta_{j}=\frac{\rho^{2}\lambda_{j}}{\alpha_{j}}\) are the non-centrality parameters of the probability distributions of the power of the wanted signal and the interference with \(\alpha=(1-\rho^{2})I_{\mathrm{ave}}/2\). The required backoff value can be numerically evaluated. It is clear that for Rayleigh fading, the required backoff is dominated by the variability of the wanted signal power.
## 3 Results
### Dexterous Grasping
#### 3.2.1. 1.1. 3.2.1. 3.2.2.3.3.4.  
The Long Short-Term Memory (LSTM) model served as a baseline for predicting adaptive finger positions during grasping tasks. With an average Mean Squared Error (MSE) of 0.00085, the LSTM exhibited moderate accuracy in predicting grasp forces. However, it demonstrated instability when dealing with certain objects. For instance, while the grasp forces for the Ball (2.7 N in a two-finger grasp) and Bottle (2.9 N in a two-finger grasp) were reasonably precise, the model struggled to maintain consistency for items that required more complex grasp configurations, particularly the Banana and Bottle during five-finger grasps, as shown in Table 2. Figure 11 illustrates the force profiles for the three-finger grasp configuration of the Ball, Banana, Orange, and Tomato classes.  
In three-finger grasp configurations, the average forces recorded were 2.4 N, 2.1 N, 2.1 N, and 1.6 N for the Ball, Banana, Orange, and Tomato, respectively. Overall, the LSTM model achieved an efficiency of 91.3% for grasping tasks. Although the vision model misclassified the objects, it successfully detected and facilitated grasping. This limitation can be attributed to LSTM's challenges in effectively generalizing sequential dependencies for such tasks. Despite these issues, LSTM provided a robust baseline for comparison, demonstrating moderate accuracy and computational efficiency.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{**Object Class**} & \multicolumn{8}{c}{**Average Force (N) on All Flingers Based on Grasp Configuration**} \\ \cline{2-10}  & \multicolumn{2}{c}{**LSTM**} & \multicolumn{2}{c}{**GP**} & \multicolumn{2}{c}{**Transformers**} \\  & **2 Fingers** & **3 Fingers** & **5 Fingers** & **2 Fingers** & **3 Fingers** & **5 Fingers** & **2 Fingers** & **3 Fingers** & **5 Fingers** \\ \hline Apple & 4.1 & 3.1 & 2.1 & 1.6 & 2.9 & 1.9 & 3.2 & 2.4 & 2.2 \\ Ball & 2.7 & 2.4 & 1.75 & 1.3 & 1.9 & 1.63 & 2.4 & 1.8 & **1.9** \\ Banana & 3.6 & 2.1 & **1.6** & 1.8 & 2.2 & **1.7** & 3.6 & 2.9 & 2.1 \\ Bottle & 2.9 & 2.6 & **1.9** & 1.7 & 2.7 & **1.2** & 3 & 2.8 & 2.3 \\ Box & 3.8 & 3.1 & 2.3 & 2.9 & 1.9 & 2 & 2.4 & 2.7 & 2.1 \\ Lemon & 1.7 & 1.8 & - & 2.3 & 1.7 & - & 2.8 & 1.9 & **-** \\ Orange & 2.7 & 2.1 & **-** & 1.7 & 1.7 & **-** & 2 & 2.2 & **-** \\ Strawberry & 3.5 & 3.3 & **-** & 1.6 & 1.4 & **-** & 3.6 & 2.9 & **-** \\ Tomato & 2.7 & 1.6 & **-** & 1.7 & 1.7 & **-** & 3.1 & 2.7 & **-** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average force on all fingers based on grasp configuration for LSTM, GP, and Transformer models for the detected objects. (GREEN—successful grasps; RED—Unsuccessful grasps).
# Design and Analysis of Systematic Batched Network Codes
## 5 Inner Code for Systematic Batches
### Recoding with Message Packet Protection
\begin{array}{ccc}\mathbf{I}_{s}&\mathbf{U}_{ s,w}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{I}_{r-s}&\mathbf{U}_{r,N_{u}^{\prime}-r-w}\\ \end{array} \right];\]  
when \(w\geq N_{u}^{\prime}-r\),  
\[\mathbf{R}^{\text{sys}}=\left[ \begin{array}{ccc}\mathbf{I}_{s}&\mathbf{U}_{ s,w}&\mathbf{0}\\ \mathbf{0}&\mathbf{0}&\mathbf{J}\\ \end{array} \right],\]  
where \(\mathbf{J}\) is the first \(N_{u}^{\prime}-w-s\) columns of \(\mathbf{I}_{r-s}\). The inner code operations at node \(u\) consist of (i) the Gauss-Jordan elimination represented by the matrix \(\mathbf{V}\), (ii) the column permutation and removal of the all-zero columns represented by the matrix \(\mathbf{P}\), and (iii) (systematic) message protection recoding \(\mathbf{R}\) (\(\mathbf{R}^{\text{sys}}\)). When the overall recoding matrix at node \(u\) is \(\mathbf{VPR}\), the inner code is called the _message protection inner code (MPIC)_. When the overall recoding matrix at node \(u\) is \(\mathbf{VPR}^{\text{sys}}\), the inner code is called the _systematic message protection inner code (SMPIC)_. The value of \(w\) controls the level of protection of message packets. When \(w=0\), no additional protection is provided for message packets, and we can check that SMPIC has the same rank performance as the systematic inner code. When \(w=N_{u}^{\prime}-s\), all recoded packets generated by linear combinations are used to protect the message packets. The computation cost of the proposed message protection recoding at a network node is mainly determined by (1) the Gauss-Jordan elimination for the recovery of the message packets, and (2) the generation of the recoded packets. To simplify the discussion, we only consider the case with \(w=0\). At node \(u\), Gauss-Jordan elimination is applied on the \(N_{u}\) received packets. As the packet length \(T\) is much larger than the batch size \(M\), the computation cost of processing the transfer matrix \(\mathbf{H}^{(u)}\) is ignored. Hence, when the rank of \(\mathbf{H}^{(u)}\) is \(r\), the computation cost of recovering the message packets is about \(r(N_{u}-1)\) LCOs. If the previous node also uses message protection recoding, the cost at node \(u\) can be reduced, as the message packets received directly can help to simplify the Gauss-Jordan elimination. Let \(s_{0}\) be the message packet received at node \(u\), and we have \(s_{0}\leq s\leq r\). In this case, the computation cost for Gauss-Jordan elimination is about \((r-s_{0})(N_{u}-1)\) LCOs. For a batch of rank \(r\), the cost of generating recoded packets using \(\mathbf{R}\) or \(\mathbf{R}^{\text{sys}}\) is linear with the number of entries in uniformly random sub-matrices. Therefore, the overall recoding computation cost for SMPIC with \(w=0\) is about \(((r-s_{0})(N_{u}-1)+r(N_{u}^{\prime}-r))\) LCOs. In contrast, for RLIC, the computation cost is \(N_{u}^{\prime}N_{u}\) LCOs, and for SIC, the computation cost is \((N_{u}^{\prime}-N_{u})N_{u}\) LCOs.
# Blockchain-Enabled Secure and Interoperable Authentication Scheme for Metaverse Environments
## 2 Related Work
For this, regarding paper [2; 7], we discussed earlier the various identity-related security issues that are found in metaverse environments. We also described a need for interoperability of the identity in cross-metaverse platforms. Also, the scheme should be able to achieve this with minimum computation time and communication costs. To date, there is no authentication scheme that addresses all of these issues. Therefore, we propose a secure and interoperable authentication scheme for metaverse environments. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Publication** & **Security Challenge Addressed** & **Solution Proposed** & **What Are We Planning to Address?** \\ \hline \multirow{4}{*}{[12]} & \multirow{4}{*}{Disguise, impersonation, replay} & Secure authentication framework to & \(\bullet\) & Server-spoofing attack not addressed. \\  & & guarantee the traceability of avatars & \(\bullet\) & Authentication time and computation \\  & & in metaverse using two-factor & \(\bullet\) & costs can be reduced further. \\  & & authentication framework based on & \(\bullet\) & Cross-metaverse support to be added. \\  & & chameleon signature & \(\bullet\) & Avatar-to-device mutual authentication \\  & & and biometrics. & not considered. \\ \hline \multirow{4}{*}{[16]} & \multirow{4}{*}{Device privacy, impersonation attack, replay attack, password-guessing attack, mutual authentication} & Secure mutual authentication protocol for IoT and cloud servers based on elliptic curve cryptography. & \(\bullet\) & The solution is designed for the IoT environment and not for the metaverse. \\  & & &  based on elliptic & \(\bullet\) \\  & & curve cryptography. & \(\bullet\) & Identity interoperability is \\  & & & curve cryptography & not addressed. \\ \hline \multirow{4}{*}{[17]} & \multirow{4}{*}{Impersonation, replay, mutual authentication, centralization} & An authentication protocol based on elliptic curves and bilinear pairs. & \(\bullet\) & Further improvement in computational cost. \\  & & & elliptic curves and bilinear pairs. & \(\bullet\) \\ \hline \multirow{4}{*}{[18]} & \multirow{4}{*}{Cross-domain authentication} & \multirow{4}{*}{Mutual authentication scheme using elliptic curve cryptography (ECC) and biometric information.} & \multirow{4}{*}{\(\bullet\)} & Identity interoperability is \\  & & & & not addressed. \\ \cline{1-1}  & & & & not considered. \\ \hline \multirow{4}{*}{[20]} & \multirow{4}{*}{Cross-domain authentication} & Blockchain-Assisted Secure Device Authentication (BASA) for Cross-Domain Industrial IoT. & \multirow{4}{*}{\(\bullet\)} & \multirow{4}{*}{Extension to the metaverse environment.} \\ \cline{1-1}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of similar solutions proposed in the literature.
# Error Source Analysis and Correction of GF-3 Polarimetric Data
## 4 Polarization Correction and Verification
### Polarization Correction Method  
Through the analysis of the previous section, conclusions about GF-3 polarization system are summarized, including that: (1) imbalances of transmit/receive channel need to be corrected; (2) the consistency of the channel sample delay is quite good; (3) crosstalks and amplitude imbalances of antenna array are small, but phase imbalance of different beams is different and needs to be corrected; (4) the POA caused by satellite attitude is negligible; (5) the FR is acceptable. Then, ignoring these negligible terms, the error model of GF-3 PolSAR can be rewritten as Equation (9):  
\[\begin{array}{ll}M&=Ae^{i\Theta}R^{\mathrm{T}}ST\\ &=Ae^{i\Theta}\left[\begin{array}{cc}1&0\\ 0&f_{\mathrm{R}}e^{i\theta_{\mathrm{r}}}\end{array}\right]\left[\begin{array} []{cc}S_{HH}&S_{HV}\\ S_{VH}&S_{VV}\end{array}\right]\left[\begin{array}{cc}1&0\\ 0&f_{\mathrm{T}}e^{i\theta_{\mathrm{t}}}\end{array}\right]\end{array} \tag{9}\]  
where \(f_{T}\) and \(f_{\mathrm{R}}\) are imbalances of transmit and receive channel, including phase and amplitude imbalances, analyzed in Section 3.1, \(\theta_{t}\) and \(\theta_{r}\) are phase imbalances caused by the antenna array and analyzed in Section 3.3.  
The task of GF-3 polarimetric calibration is to estimate and correct all errors throughout the complete PolSAR system for all data. Section 3 introduces the estimation of distortion parameters. Then, the correction process of GF-3 polarimetric data is as follows:  
1. The fixed phase difference in Equation (3) is calibrated by three group data with PARCs [(16)];
2. Based on the result of step 1, the phase imbalances \(\theta_{t}\) and \(\theta_{r}\) are calculated by Equation (5);
3. Channel imbalances \(f_{T}\) and \(f_{R}\) are extracted by the internal calibration corresponding to imaging data;
4. According to the results of step 2 and 3, the distortion matrixes R and T are obtained;
5. The \(M\) is corrected to obtain the true scattering matrix (\(S\)) using Equation (9).  
The whole correction process relies on several external calibration data with PARCs, and the correction of GF-3 polarimetric data is efficiently accomplished during the in-orbit testing phase. And, according to this polarization correction method, we developed the correction software and deployed it to the product generation software of the GF-3 ground segment. It performs polarization correction on the PolSAR data after imaging, which ensures the polarimetric quality of the PolSAR image products.
## 4 Discussion
### SMOTE Optimization and Real-World Reliability  
While the clinical utility scores for models with and without SMOTE optimization appear numerically comparable (0.9237 vs. 0.9163), this superficial similarity masks critical differences in their real-world reliability. Closer analysis reveals that the SMOTE-optimized model achieves its clinical advantage through superior training dynamics: learning curves demonstrate narrower generalization gaps between training and validation accuracies, a hallmark of robust models primed for deployment. Though values nearing 1 (indicating alignment with priorities like minimizing false negatives) are essential, stability outweighs marginal metric gains in clinical settings. The SMOTE-enhanced model's reduced overfitting ensures consistent performance across heterogeneous patient populations--a decisive factor where diagnostic reliability trumps isolated numerical superiority. Thus, while both models satisfy baseline clinical utility thresholds, the SMOTE approach justifies slight trade-offs in metrics by delivering generalizable robustness, a prerequisite for trustworthy deployment in variable real-world environments.
## 4 Results and Analysis
### Results of Classifiers with Hyperparameter Optimization
#### 4.2.4 Hyperparameter Optimization of AdaBoost M1  
For the AdaBoost M1 ensemble, the base classifier and the number of iterations (I) to be performed were tuned to improve its performance in predicting heart disease. The weight threshold has been maintained at 100 for all the trainings. The results of AdaBoost M1 hyperparameter optimization are given in Table 11.  
From Table 11, the AdaBoost M1 has been trained 6 times by changing the base classifier Decision Stump and J48 with the number of iterations values as 10, 20, and 30 for each case. With this optimization, it provided remarkable performance improvement, about a 10% of increase in accuracy. The best accuracy of 97.3822% has been achieved using J48 as the base classifier and I = 30 at the 6 th training.
# A Method for Extracting Acoustic Water Surface Waves Based on Phase Compensation
## 5 Conclusions  
This article analyses the possible reasons for the discontinuity of phase history under sea surface fluctuations and innovatively proposes a method for extracting acoustic water surface waves based on phase compensation. This method addresses the challenge of effectively extracting acoustic water surface waves using Wavelet-Kalman Filtering when  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Number of Erroneous Bits** & **Bit Error Rate** \\ \hline Wavelet-Kalman Filtering & 37 & 6.5\% \\ \hline Phase compensation based on a threshold and High-pass Filtering & 30 & 5.2\% \\ \hline The Proposed Method & 10 & 1.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Bit Error Rate.  
Figure 17: **Experiments:** STFT results using different methods for extracting acoustic water surface waves: (**a**) Wavelet–Kalman Filtering; (**b**) compensation with a threshold; (**c**) the proposed method.  
phase history is disrupted by sea surface interference. The effectiveness of the proposed method is verified through simulation and experiments. The results show that the phase compensation method proposed in this article can effectively reduce the impact of phase discontinuity and obtain clearer time-frequency information of underwater sound sources, which is beneficial for subsequent communication decoding, improving communication quality, and reducing communication bit error rate. Repeated experimental statistics have shown that the communication bit error rate of the proposed method is one-fourth of that of Wavelet-Kalman Filtering methods. The method for extracting acoustic water surface waves based on phase compensation provides more possibilities for the widespread application of microwave acoustic cross-medium uplink communication. In the future, it is still necessary to theoretically study the models of acoustic water surface waves under higher sea states and the influence of ocean turbulence on sound wave propagation.  
Conceptualization, M.L., X.L. and X.B.; methodology, M.L., X.L. and X.B.; software, M.L. and N.J.; validation, M.L., J.X., N.J. and Q.G.; formal analysis, M.L.; investigation, M.L.; resources, M.W., J.W. and X.B.; data curation, M.L., N.J. and X.B.; writing--original draft preparation, M.L.; writing--review and editing, M.L., Y.Z., X.L. and X.B.; visualization, M.L.; project administration, X.L. and X.B.; funding acquisition, X.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data are contained within the article.  
The authors declare no conflicts of interest.