esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
The content of macronutrients in plants is affected by many factors, of which the most important are the species, variety, and organs of a plant. This was confirmed in our study, in which--apart from the above factors--an important role in the shaping of the content of the two analyzed macronutrients was played by the level of soil contamination with fluorine and the soil application of substances immobilizing this xenobiotic (Tables 2-5). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Type of Neutralizing Substance**} & \multicolumn{2}{c}{**Mean**} \\ \cline{2-10} \multirow{2}{*}{**Fluorine Dose in mg kg–1**} & \multicolumn{2}{c}{**Without Neutralizing Substance**} & \multirow{2}{*}{**Lime**} & \multicolumn{2}{c}{**Charcoal**} & \multicolumn{2}{c}{**Loam**} & \\ \cline{2-10}  & & & & & & & & & \\ \cline{2-10}  & \multicolumn{2}{c}{**Aerial Parts**} & \multicolumn{2}{c}{**Roots**} & \multicolumn{2}{c}{**Aerial Parts**} & \multicolumn{2}{c}{**Roots**} & \multicolumn{2}{c}{**Aerial Parts**} & \multicolumn{2}{c}{**Roots**} & \multicolumn{2}{c}{**Aerial Parts**} & \multicolumn{2}{c}{**Roots**} \\ \hline  & & & & & Yellow lupine & & & & & \\ \cline{2-10}  & 5.12 & 1.86 & 4.23 & 1.96 & 3.65 & 1.07 & 4.97 & 1.23 & 4.49 & 1.06 \\
100 & 4.50 & 1.56 & 4.19 & 1.35 & 3.43 & 0.87 & 4.84 & 0.98 & 4.24 & 1.19 \\
200 & 4.42 & 1.34 & 4.07 & 1.24 & 3.17 & 0.77 & 4.80 & 0.90 & 4.12 & 0.98 \\
300 & 4.38 & 0.86 & 3.93 & 1.19 & 2.94 & 0.71 & 4.75 & 0.85 & 4.00 & 0.90 \\ Mean & 4.61 & 1.41 & 4.10 & 1.43 & 3.30 & 0.85 & 4.84 & 0.99 & - & - \\ \(r\) & \(-\)0.83 ** & \(-\)0.99 ** & \(-\)0.87 ** & \(-\)0.87 ** & \(-\)0.87 ** & \(-\)0.91 ** & \(-\)0.94 ** & \(-\)0.65 * & \(-\)0.93 ** & - & - \\ LSD for: & & & & & aerial parts a—0.09; b—0.09; a: b—0.18; roots a—0.02; b—0.02; a: b—0.04 & & & & & & \\ \multicolumn{10}{c}{Winter oilseed rape} \\
0 & 2.46 & 11.13 & 2.00 & 11.72 & 1.47 & 7.57 & 1.96 & 11.28 & 1.97 & 10.43 \\
100 & 1.63 & 9.79 & 1.68 & 9.79 & 1.41 & 6.23 & 1.64 & 9.94 & 1.59 & 8.94 \\
200 & 1.59 & 9.05 & 1.64 & 8.83 & 1.37 & 5.86 & 1.59 & 7.42 & 1.55 & 7.79 \\
300 & 1.60 & 8.01 & 1.60 & 8.24 & 1.30 & 5.19 & 1.55 & 7.12 & 1.51 & 7.14 \\ Mean & 1.82 & 9.50 & 1.73 & 9.64 & 1.39 & 6.21 & 1.68 & 8.94 & - & - \\ \(r\) & \(-\)0.79 ** & \(-\)0.98 ** & \(-\)0.86 ** & \(-\)0.96 ** & \(-\)0.76 ** & \(-\)0.94 ** & \(-\)0.87 ** & \(-\)0.96 ** & - & - \\ \multicolumn{10}{c}{LSD for:} & \multicolumn{4}{c}{aerial parts a—0.03; b—0.03; a: b—0.07; roots a—0.18; b—0.18; a: b—0.36} \\ \multicolumn{10}{c}{Narrow-leaf lupine} \\
0 & 1.72 & 1.63 & 2.02 & 1.34 & 2.03 & 1.86 & 1.82 & 1.42 & 1.90 & 1.56 \\
20 & 1.87 & 1.13 & 2.23 & 0.82 & 2.11 & 0.96 & 1.71 & 1.16 & 1.98 & 1.02 \\
40 & 1.96 & 0.89 & 2.08 & 0.78 & 1.84 & 0.86 & 1.47 & 1.11 & 1.84 & 0.91 \\
60 & 1.44 & 0.74 & 1.60 & 0.74 & 1.62 & 0.61 & 1.37 & 1.05 & 1.51 & 0.79 \\ Mean & 1.75 & 1.10 & 1.98 & 0.92 & 1.90 & 1.07 & 1.59 & 1.18 & - & - \\ \(r\) & \(-\)0.42 & \(-\)0.96 ** & \(-\)0.67 * & \(-\)0.84 ** & \(-\)0.83 ** & \(-\)0.91 ** & \(-\)0.97 ** & \(-\)0.91
## 5 Simulation and Analysis
### Case Study 1
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Area 1**} & \multicolumn{2}{c}{**Area 2**} \\ \cline{2-5}  & **Steady-State** & **Response Time** & **Steady-State** & **Response Time** \\  & **Error (Hz)** & **(s)** & **Error (Hz)** & **(s)** \\ \hline PID & \(7.56\times 10^{-4}\) & 47.75 & \(7.34\times 10^{-4}\) & 45.62 \\ SMC & \(5.18\times 10^{-4}\) & 42.48 & \(4.96\times 10^{-4}\) & 39.57 \\ GFTSMC & \(2.47\times 10^{-4}\) & 22.81 & \(2.16\times 10^{-4}\) & 21.14 \\ AGFTSMC & \(9.52\times 10^{-5}\) & 16.48 & \(7.88\times 10^{-5}\) & 13.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of simulation results when the large frequency disturbance is set to 0.016 p.u. schemes. The response time of the system based on AGFTSMC is approximately 16.48 s and is the shortest among these four methods; it is about 66.42% shorter than that of the system based on PID. From Figure 3b, it can be observed that the maximum frequency deviation of the system based on AGFTSMC is 37.78% smaller than that of the system based on PID, which demonstrates notable superiority in terms of overshoot. It can be seen from Figure 3a,b and Table 3, the response speed of the system adopting the PID method is the slowest under disturbances. The maximum frequency deviation and steady-state error of the system adopting the PID method are the largest. Due to the fixed gains, PID controller lacks real-time adaptability to handle uncertain load frequency fluctuations, resulting in inferior performance. Adopting the SMC method can suppress the frequency fluctuation of the system, but the response time of the system is relatively long, while adopting the GFTSMC method can significantly shorten the system response time. Compared with the preceding three control schemes, adopting the AGFTSMC approach significantly reduces the overshoot and steady-state error, while the response time decreases dramatically, demonstrating the superiority of this method. As shown from Figure 3d, by adopting the AGFTSMC approach, the stabilization time dramatically decreases to roughly 14.03 s and it is about 68.87% shorter than that of the system based on PID, which demonstrates a significant advantage in terms of stabilization time. Therefore, the system adopting the AGFTSMC method demonstrates the strongest ability to restore stability when dealing with disturbances, reflecting the fastest response speed. Compared with PID, SMC, and GFTSMC, adopting the AGFTSMC approach significantly reduces the overshoot and stabilization time. Under the control of AGFTSMC, two-area power system shows excellent control performance under large frequency perturbations and continuous random disturbances. Therefore, from the above analysis, it can be concluded that the AGFTSMC method can be applied to the load frequency control of complex two-area power systems.
## 3 Experimental Method and Results
### Results of Concentration Prediction  
The predicted concentration models were established with all spectra of the training samples and the spectra corresponding to the selected wavelengths based on our proposed method of the training samples by the PLSR to predict the concentrations of the test samples. Table 4 gives the root mean squared error of prediction concentration (RMSEP) with these two different spectra to assess the accuracy of the predicted concentrations. As can be seen, the RMSEP for Naphthalene, 1-naphthol, and 2-naphthol with the selected wavelengths are 1.35%, 1.05%, and 1.62%, respectively. However, the RMSEP for Naphthalene, 1-naphthol, and 2-naphthol with the full wavelengths are 2.89%, 1.77%, and 1.78%, respectively. The RMSEP for three compounds with the selected wavelengths are all lower than those with all wavelengths. The running time for the concentration prediction with all wavelengths and the selected wavelengths is also given in Table 4. The running time for Naphthalene, 1-naphthol, and 2-naphthol with the selected wavelengths is 127.67, 130.98, and 127.26 s, respectively. However, the running time for Naphthalene, 1-naphthol, and 2-naphthol with the full wavelengths is 356.37, 405.86, and 382.53 s, respectively. It is also obvious that the running time with the selected wavelengths is shorter than that with all wavelengths.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wavelength** & **Components** & **Naphthalene** & **1-Naphthol** & **2-Naphthol** \\ \hline \multirow{8}{*}{Full wavelength prediction} & Sample 1 & actual concentration & 0.2500 & 0.2500 & 0.2000 \\ \cline{2-5}  & (mg/L) & predication & & & \\ \cline{2-5}  & (mg/L) & concentration(mg/L) & 0.2329 & 0.2621 & 0.2150 \\ \cline{2-5}  & & actual concentration & 0.2700 & 0.4000 & 0.3000 \\ \cline{2-5}  & (mg/L) & predication & & & \\ \cline{2-5}  & & concentration(mg/L) & 0.2840 & 0.4086 & 0.3173 \\ \cline{2-5}  & & run time (s) & 356.37 & 405.86 & 382.53 \\ \cline{2-5}  & & RMSEP (\%) & 2.89 & 1.77 & 1.78 \\ \hline \multirow{8}{*}{Selected wavelength prediction} & Sample 1 & actual concentration & 0.2500 & 0.2500 & 0.2000 \\ \cline{2-5}  & (mg/L) & predication & & & \\ \cline{1-1} \cline{2-5}  & (mg/L) & predication & & & \\ \cline{1-1} \cline{2-5}  & (mg/L) & predication & & & \\ \cline{1-1} \cline{2-5}  & (mg/L) & predication & & & \\ \cline{1-1} \cline{2-5}  & & concentration(mg/L) & 0.2619 & 0.4065 & 0.2954 \\ \cline{1-1} \cline{2-5}  & & run time (s) & 127.67 & 130.98 & 127.26 \\ \cline{1-1} \cline{2-5}  & & RMSEP (\%) & 1.35 & 1.05 & 1.62 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Predicted concentrations of the three components in the test samples.  
Figure 4: Emission fluorescence spectra of naphthalene, 1-naphthol, and 2-naphthol excited by the wavelength of 220, 256, and 246 nm, respectively.
## 4 Experimental Results and Discussion
### Ablation Experiment  
In this paper, the structure of the TCN is adjusted by the activation function, residual structure, dilation rate, and feature-matching convolution position to improve the performance of the neural network. At the same time, to prove that each of these optimizations has its contribution to the model and prove that each of these optimizations has its importance, an ablation experiment is carried out using the monitoring station 1001 dataset, and the experimental results are shown in Table 2. In Table 2, CNN-TCN denotes the application of feature-matching convolutional layers first and [1,2,4,8] indicates the dilated rate used by the model sequentially. The absence of [1,2,4,8] indicates that the model employs dilation rates of [1,2,2,2]. 'Re' indicates that the activation function used by the model is ReLu, and 'Lr' indicates that the activation function used by the model is LeakyReLu. All eight  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Learning** \\ **Rate** \\ \end{tabular} & **Epochs** & **Optimizer** & \begin{tabular}{c} **Filter** \\ **Size** \\ \end{tabular} & \
\begin{tabular}{c} **Dilation** \\ **Factor** \\ \end{tabular} & **Levels** & **Dropout** & **Loss Function** &
\begin{tabular}{c} **Batch** \\ **Size** \\ \end{tabular} \\ \hline LSTM [15] & 0.0001 & 100 & [PERSON] & None & None & 2 & None & [PERSON] & 128 \\ GRU [16] & 0.0001 & 100 & [PERSON] & None & None & 2 & None & [PERSON] & 128 \\ SRU [45] & 0.0001 & 100 & [PERSON] & None & None & 2 & None & [PERSON] & 128 \\ TCN [23] & 0.0001 & 100 & [PERSON] & 2 & [1,2,4,8] & 4 & 0.2 & [PERSON] & 128 \\ Gaussian-TCN [34] & 0.0001 & 100 & [PERSON] & 2 & [1,2,4,8] & 4 & 0.2 & [PERSON] & 128 \\ GL-TCN [33] & 0.0001 & 100 & [PERSON] & 2 & [1,2,4,8] & 4 & 0.2 & [PERSON] & 128 \\ DD-TCN [32] & 0.0001 & 100 & [PERSON] & 2 & [1,2,4,8] & 4 & 0.2 & [PERSON] & 128 \\ D-TCN [46] & 0.0001 & 100 & [PERSON] & 2 & [1,2,4,8] & 4 & 0.2 & [PERSON] & 128 \\ ST-TCN [44] & 0.0001 & 100 & [PERSON] & 4 & [1,2,4,8] & 4 & 0.2 & MSELoss & 128 \\ DMSnet [47] & 0.0001 & 100 & [PERSON] & 4 & [1,2,4,8,16] & 5 & 0.2 & MSELoss & 128 \\ LR-TCN & 0.0001 & 100 & [PERSON] & 2 & [1,2,2,2] & 2 & 0.2 & [PERSON] & 128 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter settings of each model.  
networks in the experiment, except the 'original TCN', used the new residual structure proposed in this paper.  
As shown in Table 2, in the nine ablation experiments, all the metrics of the 'original TCN' are the worst, and all metrics are improved after adjusting the residual structure. The adjustment of the activation function and the dilated rate improves each indicator, and the positional adjustment of the feature-matching convolution resulted in significant improvements in each metric. Therefore, the optimal improved TCN model can be obtained by combining all adjustment strategies.
# Improved Least Squares Phase Unwrapping Method Based on Chebyshev Filter
## 3 Results
### The Simulated Data Results
For proposed the method, compared with the other three methods, the RMSE of the unwrapping results has always been at the lowest, and there is no RMSE mutation when the phase gradient changes. Therefore, the proposed method is very little affected by the phase gradient can almost be ignored, and can perform stable phase unwrapping at steep phases. Then, with the peak to the valley constant at 10, the standard deviation of the noise is gradually increased from 1 to 1.5, and the unwrapped resulting RMSE is shown in Figure 6. Like the above phase unwrapping results, the RMSE of LS phase unwrapping is the largest and gradually increases with the increase in phase noise. The phase unwrapping effect of LS is the worst and is not suitable for phase unwrapping under high noise. PCUA, RPUA, and the proposed method all improve the effect of LS phase unwrapping under the influence of noise. From the RMSE of the phase unwrapping results in Figure 6, it can be seen that RPUA is affected by phase noise the most compared to PCUA and the proposed method. The phase unwrapping results of the proposed method have the lowest RMSE and are least affected by phase noise. Therefore, the proposed method has the best phase unwrapping effect under high noise compared with LS, PCUA, and RPUA. Figure 5: The RMSE with different phase gradients of the four methods. Figure 6: The RMSE with different phase noises of the four methods. According to the above simulation data analysis, the method proposed in this paper has the best robustness compared with LS, PCUA, and RPUA under different phase gradients and different phase noises.
## 4 Numerical Study
### Data  
Tables 3 and 4 summarize the data collected from the practical working conditions used in the simulation model. However, due to the confidentiality of the information, the cost values in Table 4 are replaced by symbols. The production rate in the model was set at 8,901 metric tons per day by taking the average value of the real production rate. In particular, the waiting times at Ports B and C are estimated as the gamma distribution, which is fitted to the limited historical data.  
According to the field experts who manage the system studied in this research, wind speeds greater than 35 km per hour and low visibility of less than 1 km are the stopping conditions for vessel operations. We collected the wind-speed and visibility-distance hourly data from 2014 to 2019 from the world weather online database (World Weather Online) [32]. From the collected historical data, we extracted the interarrival time and duration of high-speed wind (\(>\)35 km/h) and low visibility (\(<\)1 km). Table 5 shows the distributions fitted to the extracted data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{**\#LV**} & \multirow{2}{*}{**\#SV**} & \multirow{2}{*}{**Vessel Priority**} & \multicolumn{2}{c}{**Voyage Speed Factor**} \\ \cline{3-6}  & & & & & **FS** & **BS** \\ \hline C0 & 2 & 2 & FAFU & 1 & 1 \\ \hline C1 & 2 & 2 & FAFU & 0.7\(\sim\)1.0 (*) & 0.7\(\sim\)1.0 (*) \\ \hline C2 & 0–3 (*) & 0–4 (*) & FAFU & 1 & 1 \\ \hline C3 & 0–3 (*) & 0–4 (*) & FAFU & 0.7\(\sim\)1.6 (*) & 0.7\(\sim\)1.6 (*) \\ \hline C4 & 2 & 2 & LVFU & 1 & 1 \\ \hline C5 & 2 & 2 & LVFU & 0.7\(\sim\)1.0 (*) & 0.7\(\sim\)1.0 (*) \\ \hline C6 & 0–3 (*) & 0–4 (*) & LVFU & 1 & 1 \\ \hline C7 & 0–3 (*) & 0–4 (*) & LVFU & 0.7\(\sim\)1.6 (*) & 0.7\(\sim\)1.6 (*) \\ \hline \hline \end{tabular} (*): decision factor.  
\end{table}
Table 2: Design of experiment.  
The best-fitted distribution for the interarrival times of both high-speed wind and low visibility is the Weibull distribution. The beta distribution is weakly fitted to the duration data of high-speed wind and low visibility. We employed the beta distribution for those rather than using an experimental distribution to reduce simulation runtime.  
This study also estimates CO\({}_{2}\) and SO\({}_{2}\) emissions as a performance metric of environmental effect. The total amounts of CO\({}_{2}\) and SO\({}_{2}\) emissions are calculated by multiplying the emission factors of CO\({}_{2}\) or SO\({}_{2}\) by the fuel consumption. According to [PERSON] and [PERSON] (2010), the emission factors of CO\({}_{2}\) and SO\({}_{2}\) are 3,179 g/kg marine diesel oil and 7 g/kg marine diesel oil, respectively.
## 2 Methods
As for the dynamic model of the estimated parameters in the Kalman filter, the static receiver positions and the phase ambiguities are modeled as constants, while the kinematic receiver positions and the receiver clock offsets are modeled as white noise processes (10\({}^{4}\) m\({}^{2}\)). Regarding the ISB and ZWD, they are estimated as random-walk processes. The spectral density value is set to 10\({}^{-6}\) and 10\({}^{-8}\) m\({}^{2}\)/s for the ISB and ZWD, respectively.
## 4 Collision Avoidance Algorithm
The avoidance action decision module determines the position of the helm that must be used to move to the predetermined avoidance position. To this end, Proportional-Differential (PD) or Proportional-Integral-Differential (PID) controllers are generally used in ships. While PD controllers have a higher time constant than PID controllers, they are stable and have excellent response characteristics owing to small overshoot and short correction time. Thus, a PD controller was used in this algorithm [40]. Algorithm 4 shows the pseudocode of the avoidance action decision  
Figure 6: Flowchart of the risk calculation module. module with P and D gains determined by length of all (LOA), Beam, and SOG according to the difference between COG and HDG.
### Research Questions  
This study aims to address this existing knowledge gap (Section 1.3) and make a significant contribution by exploring the following research questions:  
1. **(a)** What are the components and applications of smart grids, and how do they contribute to the vulnerability of cyberattacks? **(b)** What are the different types of cyberattacks that can target smart grids, and what are their short-term and long-term technological, economic, safety, and social impacts? **(c)** How do cyberattacks on smart grids result in cascading effects and interconnected impacts on various grid components? **(d)** What are the real-world case studies of cyberattacks on operational smart grids, and what can be learned from them? **(e)** How can quantitative models and metrics be developed to assess the impacts of cyberattacks on smart grids? **(f)** What are the short-term and long-term techno-economic-safety-social implications of cybersecurity in smart grids, emphasizing the importance of confidentiality, integrity, and availability? **(g)** How do cybersecurity measures implemented in operational smart grids affect their real-world implications? **(h)** How can quantitative models and metrics be used to assess the implications of cybersecurity in smart grids?
2. **(a)** What are the main criteria for evaluating cybersecurity options in smart grids? **(b)** What are the key cybersecurity options for protecting smart-grid infrastructure? **(c)** How can AHP be employed to assess and prioritize these cybersecurity options based on multiple criteria?
3. **(a)** What are the differences between artificial intelligence (AI) and machine learning (ML), and how do they apply to cybersecurity in smart grids? **(b)** What is the potential of AI techniques for enhancing smart-grid cybersecurity? **(c)** What are the challenges associated with implementing AI techniques in smart-grid cybersecurity, and how can they be addressed? **(d)** How can MCDM-AHP be used to find the optimal cybersecurity option that incorporates AI techniques and methods? **(e)** What are the key criteria for evaluating AI-based cybersecurity options in smart grids using MCDM-AHP?  
We also discuss both the strengths and constraints of using AHP for MCDM in assessing cybersecurity options in smart grids. Additionally, we suggest improvements to its application in smart-grid cybersecurity and explore alternative MCDM methods for assessing security options in smart grids.
# Optimization of High-Performance Computing Job Scheduling Based on Offline Reinforcement Learning
## 6 Discussion  
This study proposes an offline reinforcement learning-based approach to address the complexity of job scheduling in high-performance computing systems. By focusing on learning from historical data, the impact of the online exploration phase on system efficiency can be avoided, and the stability of scheduling decisions and system performance can be improved. A hybrid dataset, combining expert-guided scheduling trajectories and early-stage trial data from online reinforcement learning, was constructed, significantly improving the robustness and generalization of the learned policies.  
The experimental results demonstrate that, compared to traditional heuristic scheduling algorithms and online reinforcement learning methods, our approach excels across various workloads and optimization objectives. It effectively reduces average job wait times and bounded slowdown. This work not only confirms the potential of offline reinforcement learning in batch job scheduling but also offers new insights for optimizing resource management in HPC systems.  
Future research could explore offline reinforcement learning for scheduling problems within directed acyclic graph (DAG) structures. In DAGs, jobs have complex dependencies  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Workload** & **FCFS** & **WFP** & **UNI** & **SJF** & **F1** & **RL** & **Ours** \\ \hline CTC-SP2 & 10613 & 6335 & 21390 & 14047 & 4796 & 7162 & 5818 \\ HPC2N & 3596 & 2435 & 4800 & 3513 & 1778 & 2417 & 1908 \\ Lublin-256 & 23461 & 15724 & 27234 & 28492 & 11355 & 11531 & 11244 \\ SDSC-BLUE & 3538 & 3292 & 4690 & 3201 & 2312 & 2673 & 2334 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scheduling mean value on AVGwt.  
that must be executed in a specific order, posing additional challenges for scheduling strategies. Offline reinforcement learning can help identify effective strategies for handling job dependencies by leveraging historical data, optimizing the scheduling sequence of DAG job graphs, and improving system performance, particularly in managing complex workflows. This would lead to better resource utilization and overall job execution efficiency.  
Conceptualization, W.D.; methodology, S.L.; software, S.L. and W.D.; resources, W.D., Y.C. and B.L.; writing--original draft, S.L. writing--review and editing, S.L., W.D., Y.C. and B.L. All authors have read and agreed to the published version of the manuscript.  
Supported by the National Natural Science Foundation of China (grant no. 12063003).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, W.D., upon reasonable request.  
The authors declare no conflicts of interest.