esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This paper has investigated the potential benefit of applying the burnup credit method for BWR spent fuel using the data obtained from the full-core calculations. The criticality analysis for the GBC-68 storage cask filled with BWR spent fuel was performed for five different refuelling scenarios developed by the authors in the last part of the research, two target burnups and two fuel assembly types: modern GE14 and the previous generation SVEA100. The BUC analysis results were based on a realistic FA model and compared with fresh fuel, and peak reactivity approaches.  
The depletion analysis for both GE14 and SVEA100 FAs, using both lattice and nodal modelling approaches, showed that while the SVEA100 assembly has lower initial enrichment and hence mostly lower criticality, its peak reactivity is higher and occurs earlier than for GE14 fuel assembly due to the lower initial gadolinium content. Further analysis showed that the refuelling scenario substantially affects the cask criticality of the loads formed from least burnt fuel assemblies. The target burnup decrease leads to the expected increase in cask criticality and reduces the benefit of the BUC except for underburned FAs, which still contain a significant amount of gadolinium in an upper FA layer leading to a reduction in cask criticality. Comparison of cask criticality filled with different FA types--GE14 and SVEA100--revealed that if FAs are burnt up to their typical target burnup, the benefit from the applying BUC is at the relatively same level in comparison with fresh fuel approach for both L68 and L5 NL loads. However, the burnup credit for the realistic model of SVEA100 assembly shows better results compared to the peak reactivity approach. This is due to the reactivity peak appearing earlier on the criticality curve and having a higher criticality value than for GE14 assembly. It was also estimated that the potential saving costs in terms of the cask manufacturing due to the burnup credit method application could potentially reach several million US dollar savings per reactor operational cycle.  
In conclusion, despite that development of the burnup credit method for BWRs is a complex problem, our research showed that it is worth investing in it due to potential savings. The burnup credit method is especially beneficial for more complex and higher enriched modern BWR fuel assembly designs.  
Conceptualisation, A.D. and B.M.; methodology, A.D. and D.L.; software, A.D.; formal analysis, A.D.; investigation, A.D.; data curation, A.D.; writing--original draft preparation, A.D.; writing--review and editing, A.P., D.L. and B.M.; visualisation, A.D.; supervision, B.M. and A.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
## 2 Basic Concepts of the Wind Power and Turbines
### ML Techniques in the Wind Power Context
Implementing ML techniques in wind farm-specific processes has reduced the level of complexity of evaluations but increased the accuracy of the results obtained [65; 66]. Numerous advantages have been gained by replacing the traditional approach with ML. The comprehensive review presented by [PERSON] et al. [65] highlighted the superiority of extreme ML and deep learning (DL) architectures in wind energy prediction. Combining convolutional neural networks (CNNs) with long short-term memory (LSTM) networks has led to forecasting capabilities [67; 68]. ML models have proven particularly valuable in predicting wind power because they can discern patterns in large datasets. In one comprehensive review, it was noted that various algorithms, including but not limited to, extreme ML and DL architectures, have been successfully employed for wind energy prediction [65]. Enhanced algorithms like the combination of CNN with LSTM networks have showcased remarkable improvements in forecasting capabilities [67]. The hybrid perspective approach allows for short-term analysis and predictions in reduced time complexity, favoring rapid decision-making [69]. The application of such models reflects the trend of ML being integrated with specific meteorological forecast data [70; 71]. [PERSON] et al. [72] offered principles indirectly applicable to wind energy, highlighting the importance of modeling renewable resources in climate uncertainty. In this way, the relationship between atmospheric conditions and wind energy production is clarified [73]. The literature also studies ML for the predictive maintenance of wind turbines. The objective of these predictions is to minimize turbine downtime. ML algorithms aim to detect failures early by analyzing specific data on turbine performance to achieve this objective. This proactive approach's direct consequence is minimized downtime, which reduces operational costs [74; 75].
# Digital Twin-Driven Virtual Network Architecture for Enhanced Extended Reality Capabilities
## 3 Architecture's Proposed Approach
### DTN Topological Architecture
To address the constraint mitigation, a variety of techniques can be leveraged including buffers and queues implementations, a _TCP_ architecture modification or even the utilization of space-tolerant protocols like those analyzed in Section 2.4. A potential solution is the the Delay/Disrupting Tolerant Networking (DTN) with the Bundle Protocol (BP) and custody transfer technique. It has to be emphasized that for a successful system deployment, the space communication issues need to be mitigated with the aim to deploy a reliable system; this analysis will be deepened in future works.
# Underwater Acoustic Signal LOFAR Spectrogram Denoising Based on Enhanced Simulation
## 5 Conclusions  
### Main Conclusion  
This paper presents a LOFAR spectrograms denoising method for underwater acoustic signal based on enhanced simulation. A large amount of labeled sample data is generated through the simulation algorithm to train a convolutional denoising network model, which is then applied to denoise real measured data. Extensive simulations and experimental results demonstrate that the proposed method exhibits excellent noise suppression effects in real marine environments. Furthermore, the denoising model shows good transferability and generalization, as the trained model can adapt to different marine environments, laying the foundation for subsequent target feature analysis and target type recognition.
## 4 Results
### Seizure Detection Performance
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Feature** & \begin{tabular}{c} **Seizure** \\ **Sensitivity (\%)** \\ \end{tabular} & \begin{tabular}{c} **Epoch** \\ **Sensitivity (\%)** \\ \end{tabular} & \begin{tabular}{c} **Specificity (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **FPR (\(h^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **Latency (s)** \\ \end{tabular} \\ \hline Energy & 89.58 & 46.23 & 95.86 & 37.28 & 12.50 \\ Line Length & 89.77 & 55.01 & 99.82 & 1.58 & 11.17 \\ Nonlinear Autocorr & 94.91 & 57.82 & 99.83 & 1.53 & 9.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average detection performance of the considered classification features for \(N=512\). FPR, false positive rate; Nonlinear Autocorr, nonlinear autocorrelation. Figure 2: Detection performance as a function of the epoch size (\(N\)). transmission of EEG features is the least sensitive approach to imperfect channel conditions. All of the considered performance metrics show very small degradation as we increase the BER to \(10^{-3}\). A more noticeable increase in the FPR (from 1.53 to 6.38) is observed when the BER is increased to \(10^{-2}\). The approach of transmitting raw EEG signals exhibits similar performance to the approach of transmitting EEG features, except that at higher BER values, the transmission of raw EEG signals suffers a more drastic performance degradation. Finally, the transmission of compressed EEG is the most affected approach by imperfect channel conditions. All performance metrics show significant degradation as we increase the BER value. At a BER of \(10^{-2}\), the seizure sensitivity drops to 64%, whereas the FPR increases to 66.91 per hour. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**CR**} & \multicolumn{2}{c}{**Seizure**} & \multicolumn{2}{c}{**Epoch**} & \multirow{2}{*}{**Specificity (\%)**} & \multirow{2}{*}{**FPR (\(h^{-1}\))**} & \multirow{2}{*}{**Latency (s)**} \\  & **Sensitivity (\%)** & **Sensitivity (\%)** & & & \\ \hline
1:1 & 94.91 & 57.82 & 99.83 & 1.53 & 9.75 \\
5:1 & 91.82 & 57.69 & 99.40 & 5.41 & 8.56 \\
10:1 & 88.35 & 52.66 & 99.56 & 4.00 & 9.81 \\
20:1 & 88.69 & 47.57 & 96.71 & 29.64 & 12.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average detection performance as a function of the compression ratio (CR) with \(N=512\). Figure 3: Detection performance as a function of the bit error rate (BER).
# Three-Dimensional Inversion of the Time-Lapse Resistivity Method on the MPI Parallel Algorithm
## 4 Conclusions  
In this study, a three-dimensional inversion of the time-lapse resistivity method based on time-lapse function constraint theory was implemented, and an MPI parallel design was incorporated into the program.  
Using synthetic data from the theoretical model, both separate inversion and time-lapse inversion were conducted to test the program. The MPI parallel design significantly reduces the program runtime, which is critical for geophysical monitoring. A comparison of the separate inversion results with the time-lapse inversion results reveals that the difference between the inversion results at adjacent times is smaller after time-lapse inversion, indicating that the shape of the inversion results at different times is closer to that of the true resistivity values. Especially in high-noise environments, time-lapse inversion effectively suppresses false anomalies at the boundaries and background of the model, demonstrating strong resistance to interference and the ability to reflect changes in underground resistivity. Overall, the parallel inversion program for time-lapse resistivity methods is shown to be reliable and superior.  
Conceptualization, D.Z.; methodology, D.Z. and Y.Y.; software, D.Z.; validation, D.Z. and L.W.; writing--original draft preparation, D.Z.; writing--review and editing, D.Z. and L.W.; supervision, Y.Y. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Model** & **Separate Inversion** & **Time-Lapse Inversion** \\ \hline \multirow{3}{*}{\(\delta\)} & T\({}_{1}\) & 6.15 & 5.95 \\  & T\({}_{2}\) & 5.41 & 5.48 \\  & T\({}_{3}\) & 5.35 & 4.12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The \(\delta\) value of separate inversion and time-lapse inversion.  
**Funding:** This research was funded by the National Basic Geological Survey of Oil and Gas (No. DD20240052).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 1 Introduction
In general, the main contributions of this article include:  
* Combining the data-driven and the communication model-driven prior knowledge, through the introduction of the channel equalization module, it is not only more effective than the pure data-driven FTN detection algorithm. Additionally, to a certain extent, the problem caused by the data mismatch under the multipath channel is alleviated, so that the data sets under different channel states are adaptable, thereby improving the robustness of the proposed algorithm. * The LSTM network is applied to FTN signal detection in multipath channels, and a Deep-Bi-LSTM structure more suitable for multipath channel FTN detection is obtained by adjusting appropriate parameters,oss function, and network structure. Compared with traditional algorithms, it not only hasower complexity but also can obtain better ISI suppression performance for FTN signaling. * Through aarge number of simulations, the performance advantages of the proposed algorithm compared with the existing pure model-driven and pure data-driven algorithms are verified. The effects of different channel equalization algorithms and different deepearning networks on the performance of the proposed algorithm are evaluated, and the effectiveness of the proposed scheme under QPSK modulation is also verified. In addition, the network obtained by data training under a fixed channel model is used to test the data under other channel models to verify the adaptability of the proposed DMD-JD detection algorithm to actual time-varying multipath channels.
# An Image-Encryption Algorithm Based on Stage-Merging Bit Scrambling
## 3 Security Analysis
### Key Sensitivity Analysis  
Image-encryption algorithms should be highly sensitive to keys [21]. To ensure security, even if the key used is only slightly different from the correct key, it should have a completely different encryption and decryption result.  
The key used in this algorithm is a hexadecimal string, which is split into four equal-length parts to calculate the initial value and parameters of the chaotic system. In order to verify the sensitivity of the key, only the last digit of one of the segments is changed each time, and it is added by 1. The key is divided into an encryption key and decryption key. Additionally, the encryption key sensitivity test uses the key before and after the change for encryption and the difference between the two cipher images is calculated. The decryption key sensitivity test uses the changed key for decryption, and the difference between the decrypted image and the original image is calculated. The test results are shown in Table 7. Both the NPCR and UACI are close to their ideal values, indicating that the key sensitivity is excellent.
## 2 Results
### Optimization of the MSPE Parameters  
By analyzing a 10 ug mL\({}^{-1}\) standard solution of Rhodamine B, several parameters affecting the MSPE were optimized. The pH effect was first investigated in a 1-12 range; the best adsorption result was achieved at pH = 5. The contact time was studied between 1 and 20 min, proving that 7 min were enough for maximum adsorption. The initial volume was fixed at 50 mL after studying a 10-75 mL range. The amount of MMWCNT-PS-DVB was then studied between 50 and 500 mg and 400 mg were sufficient for a maximum adsorption. The desorption part was optimized afterwards; using 2 mL of pure EtOH exhibits better recoveries results than MeOH, ACN and acetone. The eluted solution was then evaporated and reconstituted in an optimal volume of 250 \(\upmu\)L of H\({}_{3}\)PO\({}_{4}\) studied in a 100-500 \(\upmu\)L range. After each extraction, MMWCNTs-PS-DVB was washed two times with 2 mL of EtOH, and it was also confirmed that the sorbent can be re-used at least four times with the same extraction efficiency. As well as the extraction efficiency, the adsorbent reusability is a crucial criterium in an extraction process. The reusability of MMWCNT-PS-DVB minimizes the total amount of the waste generation for landfills and reduces the costs of extractions. The extraction efficiencies of the MMWCNT-PS-DVB adsorbent during 15 successive extraction cycles indicated that no significant depletion could be observed after 15 extraction cycles when the MMWCNT-PS-DVB adsorbent was used.
# Remote Monitoring of the Impact of Oil Spills on Vegetation in the Niger Delta, Nigeria
###### Abstract  
The widespread oil extraction in the Niger Delta and the impacts on different types of vegetation are poorly understood due to lack of ground access. This study aims to determine the impact of oil spills on vegetation in the Niger Delta using a Landsat satellite-derived normalised difference vegetation index (NDVI). The impact of oil spill volume and time after an oil spill on the health of different types of vegetation were evaluated, and the time series of the changes in NDVI were analysed to determine the medium- and long-term responses of vegetation to oil spill exposure, using a combination of linear regression and paired \(t\)-tests. With regards to the relationship between spill volume and NDVI, a moderate correlation (\(R^{2}\) = 0.5018) was observed for spill volumes in the range of 401-1000 barrels for sparse vegetation, for volumes greater than 1000 barrels for dense vegetation (\(R^{2}\) = 0.4356), whilst no correlation was found for mangrove vegetation at any range of spill volume. Similarly, the results of the paired \(t\)-test confirmed a significant difference (\(p\)-value \(<\) 0.05) between the change in NDVI values for spill sites and non-spill sites for all vegetation types, with the sparse vegetation being the most affected of the three types. However, the impact of the oil spill on vegetation over a period is not statistically significant. The outcomes of this study provide insights into how different types of vegetation in the Niger Delta respond to oil spills, which could ultimately help in designing an oil spill clean-up program to reduce the impact on the environment.  
normalised difference vegetation index (NDVI); vegetation health; vegetation degradation; mangrove; sparse vegetation; dense vegetation; paired \(t\)-tests Article
## 4 Discussion
### Potential Discrepancy between GPP\({}_{\text{EC}}\) and GPP\({}_{\text{MOD}}\)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Copy** & **Climate** & **Location** & **Period** & **GPP** & **\(T\)** & \(P\)** & **Reference** \\ \hline \multirow{8}{*}{Wheat} & Sensorboard & Shoemaker, China (24.45\({}^{\circ}\)N, 12.50\({}^{\circ}\)F) & Oakborne-Mar 2002-2010 & 1071 & 10 & 351 & [53] \\  & Tempaint and semi-based & Webb, China (24.55\({}^{\circ}\)N, 12.50\({}^{\circ}\)F) & Oakborne-Mar 2002-2010 & 1174 & - & - & [53] \\  & Sub-inspiredly with sub-bund & Shoemaker, India (24.55\({}^{\circ}\)N, 12.50\({}^{\circ}\)F) & Downside-Mar 2002-2010 & 651 & 20 & 224 & [54] \\  & Temporal waiting & Schwemaker, Germany (24.55\({}^{\circ}\)N, 12.50\({}^{\circ}\)F) & Oakborne-Mar 2002-2010 & 124 & 10 & 724 & [55] \\  & Semi-based & Shoemaker, China (24.55\({}^{\circ}\)N, 12.50\({}^{\circ}\)F) & Newborne-Mar 2002-2010 & 609 & 9 & 378 & [53] \\  & Sub-projection process & Dengxia, China (24.55\({}^{\circ}\)N, 12.50\({}^{\circ}\)F) & Newborne-Mar 2002-2010 & 848 & 9 & 298 & [53] \\  & Sub-projection process & Endage (20.31\({}^{\circ}\)N, 12.50\({}^{\circ}\)F) & Newborne-Mar 2002-2010 & 704 & 9 & 300 & This study \\ \hline \multirow{8}{*}{River} & Sensorboard & Shoemaker, China (24.55\({}^{\circ}\)N, 12.50\({}^{\circ}\)F) & Oakborne-Mar 2002-2010 & 96 & 26 & 567 & [53] \\  & Sub-projection process & London, China (24.55\({}^{\circ}\)N, 12.50\({}^{\circ}\)F) & Jajr-Nov 2002-2010 & 713 & 26 & - & [53] \\  & Sub-projection process & England, China (24.55\({}^{\circ}\)N, 12.50\({}^{\circ}\)F) & Jajr-Nov 2002-2010 & 713 & 26 & - & [53] \\  & Sub-projection process & England, China (24.55\({}^{\circ}\)N, 12.50\({}^{\circ}\)F) & Jajr-Nov 2002-2010 & 173 & 23 & 776 & [53] \\  & Sub-projection process & Dengxia, China (24.55\({}^{\circ}\)N, 12.50\({}^{\circ}\)F) & Jajr-Nov 2002-2010 & 1706 & 23 & 1025 & This study \\  & Sub-projection process & Dengxia, China (24.55\({}^{\circ}\)N, 12.50\({}^{\circ}\)F) & Jajr-Nov 2002-2010 & 899 & 24 & 1025 & This study \\ \hline \end{tabular}
\end{table} 
Table 2: Review of EC-based cumulative seasonal gross primary productivity (GPP, g C m\({}^{-2}\)), mean growing-season air temperature (\(T\), \(\circ^{\circ}\)C), and cumulative growing-season precipitation (\(P\), mm) across different rice/wheat sites. model for 23 meteorological sites by using multi-source data to derive more virtual EC sites (23 sites) over the whole area, which can offer more ground-based GPP samples for calibrating MOD17 GPP. Generally, the simulations of the RF-based GPP models show a better performance with respect to other machine learning methods (e.g., Decision Tree Regression, support vector machine, artificial neural network, and deep belief network, referring to Figures S1-S4 in the Supplementary Materials), which is consistent with the results in [PERSON] et al. [23]. In our present work, thus, RF-based upscaling and calibrating methods are more suitable over large-scale agroecosystem areas if EC measurements, meteorological observations and MODIS data are available.
# A New Model for Human Running Micro-Doppler FMCW Radar Features
## 5 Comparative Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Parameter** & **Peak Simulation (Hz)** & \begin{tabular}{c} **Peak Measurement** \\ **(Hz)** \\ \end{tabular} & **Simulation Rate** & **Model Fit Rate** \\ \hline \multirow{4}{*}{Thalmann [22]} & Torso & 355 & 490 & 88\% & \multirow{4}{*}{83.2\%} \\  & Upper arm & 390 & 558 & 85\% & \\  & Lower arm & 420 & 650 & 89\% & 83.2\% \\  & \begin{tabular}{c} Thigh \\ Lower leg \\ \end{tabular} & 460 & 675 & 85\% & \\  & \begin{tabular}{c} Lower leg \\ Foot \\ \end{tabular} & 537 & 703 & 79\% & \\ \hline \multirow{4}{*}{Doppler [20]} & Torso & 330 & 500 & 90\% & \multirow{4}{*}{86.5\%} \\  & Upper arm & 352 & 510 & 87\% & \\ \cline{1-1}  & Lower arm & 385 & 603 & 87\% & \\ \cline{1-1}  & \begin{tabular}{c} Thigh \\ Lower leg \\ \end{tabular} & 365 & 653 & 88\% & \\ \cline{1-1}  & \begin{tabular}{c} Lower leg \\ Foot \\ \end{tabular} & 372 & 730 & 84\% & \\ \hline \multirow{4}{*}{Ours} & Torso & 480 & 536 & **95\%** & \multirow{4}{*}{**90.6\%**} \\  & Upper arm & 530 & 560 & **91\%** & \\ \cline{1-1}  & Lower arm & 610 & 645 & 89\% & \\ \cline{1-1}  & \
\begin{tabular}{c} Thigh \\ Lower leg \\ \end{tabular} & 706 & 743 & **93\%** & \\ \cline{1-1}  &
\begin{tabular}{c} Lower leg \\ Foot \\ \end{tabular} & 785 & 806 & **87\%** & \\ \cline{1-1}  & Foot & 1017 & 1068 & **84\%** & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The human model validates the experimental data. high-precision reconstruction. Therefore, iterative optimization and precise registration are not required. This reduces the dependence on computing power resources. The data in Table 3 show that the proposed model can achieve similar results to the advanced human skeleton model on a more general computing platform. It has high real-time performance and application potential in autonomous driving.