esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods  
* Gravimetric measurement Following ASTM NACE/ASTM169/G31-12a [33] during the measurements, each steel sample was completely immersed 2 cm below the top solution surface in a separate plastic container with 30 mL of 1 M HCl solution without and with different concentrations of the inhibitors tested (1.0 mL/L, 1.5 mL/L, and 2.0 mL/L) for 6 h at a controlled constant temperature of 298 \(\pm\) 1 K. The selected gram-to-mL ratio was equal to 0.05 h/mL. After each sample was washed with ethanol and deionized water, it was dried with hot air. The weight loss was calculated as the difference in the weight of the samples before and after immersion, determined using a digital scale Sartorius RC 210 D (Sartorius Corporate Administration GmbH, Goettingen, Germany) with a sensitivity of \(\pm\)0.0001 g.  
The corrosion rate (mg/(cm\({}^{2}\).h)) was calculated according to Equation (1) [34]:  
\[W=\frac{m_{1}-m_{2}}{A\times t} \tag{1}\]  
where \(m_{1}\) is the weight of the steel plate before immersion in the test solutions, mg; \(m_{2}\) is the weight of the steel plate after immersion in the test solutions, mg; \(A\) is the area of steel tiles, cm\({}^{2}\); and \(t\) is the residence time in working solutions in hours.  
Inhibition efficiency was calculated according to Equation (2) [11]:  
\[Ew(\%)=\frac{W_{corr}-W^{inh}_{corr}}{W_{corr}}\times 100 \tag{2}\]  
where \(W_{corr}\) is the corrosion rate of steel without an inhibitor and \(W^{inh}_{corr}\) is the corrosion rate of steel with an inhibitor (fennel fruit essential oil or anothe).  
* Electrochemical measurement  
A compartment glass electrolytic cell, a reference electrode (Ag/AgCl), an anti-electrode (platinum plate), and a working electrode (the steel plate with a working area of 1 cm\({}^{2}\)) were used. The electrodes were connected to a potentiostat Gamry Interface 1010 E Potentiostat/Galvanostat/Zero-Resistance Ammeter (Gamry Instruments, Warminster, Pennsylvania, USA). After immersing the steel samples in the HCl solution with and without an inhibitor and reaching ion equilibrium for 3300 s, the potentiodynamic examination was carried out at a scanning speed of 0.167 mV/s in the range \(-0.03\div+0.03\) V relative to open-circuit potential.  
The following polarization parameters were ascertained from the resulting Tafel polarization curve: corrosion potential, corrosion current density, and Tafel slope. The corrosion inhibition efficiency (%) was calculated according to Equation (3) [13]:  
\[Ei(\%)=\left(\frac{I_{corr}-I^{inh}_{corr}}{I_{corr}}\right)\times 100 \tag{3}\]  
where \(I_{corr}\) and \(I^{inh}_{corr}\) are the corrosion current densities without and in the presence of an inhibitor.  
* Morphological study  
The morphological characteristics of the mild steel surface were examined by taking photos of the surface before and after it was submerged in 1 M HCl solution without and containing different concentrations of the inhibitors tested. An optical microscope (Axiolab 5, Carl Zeiss, Oberkochen, Germany) was used to examine the specimens.
# Multi-Time and Multi-Band CSP Motor Imagery EEG Feature Classification Algorithm
## 3 Results  
The model was trained on the training dataset, tested on the test dataset collected from another session, and validated using the 4-fold cross-validation method. The validation accuracy is demonstrated in Tables 1 and 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**6 Windows**} & \multicolumn{4}{c}{**11 Windows**} \\ \cline{2-10}  & **1 s** & **1.5 s** & **2 s** & **2.5 s** & **1 s** & **1.5 s** & **2 s** & **2.5 s** \\ \hline Sub1 & 0.986 & 0.920 & 0.928 & 0.906 & 0.978 & 0.934 & 0.913 & 0.906 \\ Sub2 & 0.956 & 0.971 & 0.926 & 0.875 & 0.993 & 0.963 & 0.897 & 0.882 \\ Sub3 & 0.957 & 0.942 & 0.934 & 0.934 & 0.964 & 0.942 & 0.927 & 0.927 \\ Sub4 & 0.953 & 0.977 & 0.953 & 0.961 & 0.969 & 0.946 & 0.961 & 0.961 \\ Sub5 & 0.961 & 0.938 & 0.923 & 0.891 & 0.984 & 0.930 & 0.930 & 0.876 \\ Sub6 & 0.982 & 0.946 & 0.929 & 0.876 & 0.974 & 0.964 & 0.929 & 0.867 \\ Sub7 & 0.985 & 0.977 & 0.977 & 0.955 & 0.977 & 0.985 & 0.977 & 0.948 \\ Sub8 & 0.985 & 0.955 & 0.947 & 0.939 & 0.985 & 0.939 & 0.917 & 0.939 \\ Sub9 & 0.966 & 0.914 & 0.922 & 0.871 & 0.957 & 0.905 & 0.922 & 0.845 \\ \hline avg & 0.970 & 0.949 & 0.938 & 0.912 & 0.976 & 0.945 & 0.930 & 0.906 \\ std & 0.013 & 0.022 & 0.017 & 0.034 & 0.011 & 0.022 & 0.023 & 0.038 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The validation accuracy for ED algorithm.  
Figure 7: The precision and recall curves on nine subjects for selecting the optimal threshold value.  
For the ED algorithm, the performance of the features with a longer time window was better than it was with a shorter time window. In contrast, for the AS algorithm, the performance of the features with a shorter time window was better than it was with a longer time window. Generally, the average accuracy of the AS algorithm is higher than that of the ED algorithm, providing the optimal average accuracy of 0.787 in the case of the six 1 s-windows.  
The threshold influences the performance of the AS algorithm. Figure 10 shows the ROC curves of the MTF-CSP feature extraction and AS algorithm-based ensemble SVM classifier with 6 1 s-windows. The x-coordinate represents the FP ratio, and the y-coordinate represents the TP ratio. The closer the ROC curve is to the upper left, the better the recognition performance is. The area under the curve is defined as the AUC value, and the closer the value is to 1, the better the performance is. As we can see from the ROC curves that most of the subjects demonstrated good performance in addition to the sub2, sub4, and sub6, which may be due to the promiscuous feature distribution in different categories, as referred to in Figure 4.  
Figure 10: Comparison of ROC curves and the corresponding AUC values on nine subjects using the AS algorithm (The blue curve represents the ROC curve, and the black dotted line is the reference auxiliary line).  
Figure 9: The comparison of the average classification accuracy and AUC value distribution using the ED and AS algorithms for the features extracted from 11 cropped windows (n = 11), each of which is 1 s/1.5 s/2 s/2.5 s in length.
# EE-UWSNs: A Joint Energy-Efficient MAC and Routing Protocol for Underwater Sensor Networks
## 2 Related Works
### The Limitations of the Existing MAC/Routing Protocols  
Based on the works presented in this section in the field of routing and MAC protocols for underwater sensor networks, we found the following limitations:  
* The existing protocols focus on either MAC or routing layer. The design of an energy-efficient protocol for UWSNs should consider both MAC and routing protocols, as they complement each other.
* Most of the recent proposed energy-aware protocols are based on only one principle to save sensor energy, which is usually putting some sensors in sleep mode. However, there are several principles that can be applied to save energy, such as providing several levels of energy according to the distances between the source sensor and the next one and narrowing the field of a sensor operation to a specific region.
* Most of the works do not consider distributing the traffic loads between sensors in UWSNs. Therefore, there is the problem of draining the energy of some sensors more than others, creating the issue of early sensor death.
## 3 Results
### Statistical Domain Classification Results  
Table 22 shows the classification accuracy ranking of all classifiers for the statistical domain features with \(p\)-values < 0.05. The J48 classifier outperformed other classifiers, with 83.89% accuracy.  
Table 23 reports more classification metrics for different BAC classes for the best-performing random forest classifier on statistical domain features with \(p\)-values < 0.05.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Classifier & \multicolumn{1}{l}{weka.classifiers.trees.RandomForest} \\ \hline Number of Trees (numTrees) & 150 \\ \hline Maximum Depth (maxDepth) & 0 (unlimited) \\ \hline Number of Features (numFeatures) & 0 (auto: \(\sqrt{\#\text{attributes}}\)) \\ \hline Seed & 1 \\ \hline Out-of-Bag Error Estimation & Enabled \\ \hline Bag Size Percent & 100 \\ \hline Batch Size & 100 \\ \hline Break Ties Randomly & False \\ \hline Print Classifier & False \\ \hline \hline \end{tabular}
\end{table}
Table 21: Random forest configuration for wavelet-domain features.  
Figure 6: Random forest classifier confusion matrix performance on wavelet-domain features with \(p\)-values < 0.05.  
Figure 7 visualizes the confusion matrix performance of the random forest classifier on these statistical domain features. The classifier showed high classification ability, with leading values in the diagonal of the confusion matrix.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Class** & **TP Rate** & **FP Rate** & **Precision** & **Recall** & **F-Measure** & **ROC Area** \\ \hline BAC = 0 & 0.904 & 0.041 & 0.825 & 0.904 & 0.862 & 0.942 \\ BAC = 0.05 & 0.775 & 0.027 & 0.816 & 0.775 & 0.795 & 0.908 \\ BAC = 0.12 & 0.772 & 0.058 & 0.759 & 0.772 & 0.765 & 0.874 \\ BAC = 0.2 & 0.632 & 0.038 & 0.706 & 0.632 & 0.667 & 0.826 \\ BAC = 0.3 & 0.937 & 0.037 & 0.937 & 0.937 & 0.937 & 0.977 \\ \hline Weighted Avg. & 0.839 & 0.041 & 0.837 & 0.839 & 0.839 & 0.923 \\ \hline \end{tabular}
\end{table}
Table 23: J48 performance metrics for statistical features with \(p\)-values < 0.05.  
Figure 7: J48 classifier confusion matrix performance on statistical domain features with \(p\)-values < 0.05.  
Table 24 summarizes the model random forest model configuration for statistical domain features.
## 4 Conclusions  
In this study, the mechanical properties of NC and LC were analyzed at various W/B ratios after high temperature exposure. The experiment results are summarized as follows.  
1. At all levels, the residual compressive strength was reduced to approximately 0.86-0.91, which then slightly recovered at 300 \({}^{\circ}\)C. However, it continuously decreased at higher temperatures. At 700 \({}^{\circ}\)C, the residual compressive strength of LC appears to have improved compared to that of NC because of the influence of the aggregate.
2. The ultrasonic pulse velocity and modulus of elasticity continued to decrease as the temperature increased, following a similar trend to that of the residual compressive strength. It is observed that the ultrasonic pulse velocity and modulus of elasticity improved for LC compared to NC, similar to compressive strength after high temperature exposure.
3. The slope of the stress-strain curve was similar to that of the curve before heating (20 \({}^{\circ}\)C) until 300 \({}^{\circ}\)C, but tended to decrease at temperatures higher than 300 \({}^{\circ}\)C. At high temperatures, LC showed a low peak strain at a relatively low stress compared to NC. This appears to be due to the relative low stiffness of the coarse aggregate in LC compared to that in NC.
4. Equations for predicting the residual modulus of elasticity through the analysis of the ultrasonic pulse velocity after high temperature exposure were proposed, with R\({}^{2}\) values of 0.94 and 0.941 for NC and LC, respectively.  
In this study, contents on the prediction of strength through ultrasonic pulse velocity analysis after high temperature exposure in previous studies were examined. An attempt was made to propose a method for simultaneously predicting the strength and stiffness of concrete through a single ultrasonic pulse velocity measurement and analysis. As part of our future work, we intend to investigate material properties to produce various lightweight aggregates and acquire data on the mechanical properties of concrete at high temperatures.  
Conceptualization, S.P. and T.L.; methodology, S.P.; software, W.K. and K.J.; validation, S.P., T.L. and S.P.; formal analysis, W.K. and K.J.; investigation, W.K. and K.J.; resources, S.P.; data curation, W.K. and K.J.; writing--original draft preparation, W.K. and K.J.; writing--review and editing, S.P.; visualization, T.L.; supervision, S.P.; project administration, T.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 22 NANO-B156177-03).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Analyzing and Forecasting Vessel Traffic Through the Panama Canal: A Comparative Study
## 2 Materials and Methods
### Methodology  
This study employed three approaches to model and forecast vessel traffic through the Panama Canal: the Autoregressive Integrated Moving Average (ARIMA) model, the Holt-Winters (HW) method, and the Neural Network Autoregressive (NNAR) model. According to [PERSON] [29], these models can predict time series data exhibiting seasonal patterns. The methods considered in this manuscript were selected because the SARIMA, Holt-Winters, and NNAR models all generate forecasts based solely on past values of the time series, without requiring exogenous variables. This choice ensures a fair comparison among the approaches, as all models operate from the same informational basis. Additionally, given the challenge of precisely identifying linear and nonlinear patterns in the vessel traffic time series through the Panama Canal, these three methods were applied. It is worth noting that there is no consensus technique among researchers for forecasting in contexts related to navigable waterways  
To develop and fit the models, the time series was split into two subsets: 80% of the observations were allocated to the training set, and the remaining 20%, to the testing set. The training set was used for parameter estimation and model construction, while the testing set served to evaluate predictive performance.  
The metrics employed to assess model performance included RMSE, MPE, MAE, and MAPE. Subsequently, the models were used to forecast the number of vessels transiting the Panama Canal from January to December 2023.  
The time series analysis was conducted using the R programming language [30], supported by the integrated development environment RStudio [31], version 4.2.1.
# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
## 4 Performance Evaluation
### Simulation Results
#### 4.2.4 Effect of the Minimum Computational Capability  
In this simulation, we varied the value of the minimum computational capability of each moving object to study how the value of \(\theta\) affects the performance of the proposed method. The value of \(\theta\) indicates (i) the minimum number of query segments each moving object should process and (ii) the split threshold of the naive SSP-tree and the SSP-tree. As shown in Figure 10, both the communication cost and the server computation cost of CM are not affected by this parameter at all because CM does not utilizes the computational capabilities of moving objects. On the other hand, the performance of DM(naive SSP-tree) and DM(SSP-tree) in terms both of the communication cost and the server computation cost is improved as the value of the minimum computational capability is increased. This is because a larger value of \(\theta\) increases the average number of query segments each moving object should process, and thus the server can assign a larger vicinity region to each moving object. However, DM(SSP-tree) performs a lot better than DM(naive SSP-tree). As compared to DM(naive SSP-tree), on average, DM(SSP-tree) incurs 81.9% of the communication cost and takes 78.2% of the amount of CPU-time.
## 5 Conclusions  
In this research, a novel path planning method for ship collision avoidance has been developed using the FM method and trajectory prediction. The introduction of different speed functions for the FM method ensures that the representation of a complex scenario with static and dynamic obstacles can be modelled in a unified space. In addition, a path optimization scheme integrated trajectory prediction has been designed, ensuring that a reasonable collision-free path in complex restricted waters can always be found. Such a path planning strategy can potentially support autonomous ships to undertake missions with improved intelligence.  
In terms of future work, one possible improvement is that the navigation rules should be considered more comprehensively. Usually, a collision avoidance decision should be made by adhering to the COLREGs. Only several rules in COLREGs are considered and discussed in our research. Therefore, it is of great significance to consider a complete rule system and establish a corresponding mathematical model. In addition, a more precise and practical rule-constrained trajectory prediction algorithm will considerably improve the performance of the proposed path planning approach.  
Another potential improvement is to find an appropriate strategy to configure the parameters of the FM speed function in practical environmental constraints. As discussed previously, the static obstacle has a constant speed function, and the dynamic obstacle has an anisotropic speed function. The key distinction between dynamic and static obstacles is velocity. Hence, it is necessary to take the velocity of obstacles into consideration when selecting parameters for the FM speed function. In practice, the historical data of ship encounters can be a direct clue in the searching of parameters' values.  
Conceptualization, S.W., M.H. and C.C.; Funding acquisition, F.M. and J.S.; Methodology, S.W., F.M. and J.S.; Project administration, F.M.; Supervision, M.H., C.C. and J.S.; Validation, S.W. and C.C.; Writing--original draft, S.W.; Writing--review and editing, M.H., C.C., J.S. and F.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (2021 YFB1600404) and the National Science Foundation of China (52171352).  
Not applicable.  
The authors declare no conflict of interest.  
Figure 12: The relative distances between OS and TSs in the complex scenario.
## 4 Discussion
Due to the in-model climatological relaxation which results in smooth data, HYCOM overall appeared to have the least noisy dataset. It also had several measurements in good agreement with the saildrone, but it failed to reproduce the fresh tongue. This is most likely due to an underestimation of the freshwater flux entering the model domain from the Amazon River system. Since the main sources of HYCOM's assimilated salinity data are not deployed close to land, freshwater discharges from rivers would be underestimated in model. River data, which accounts for freshwater runoff from the Amazon and other river inputs along the South American coastline, are required to improve model performance in the area of the fresh tongue as demonstrated by [PERSON] et al. [43]. In the [PERSON] et al. [43] study, HYCOM was initialized using surface forcing from the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-year analysis (1979-1998) and a salinity restoration condition to prevent the model from using its default seasonal salinity cycle. Climatological discharge information from 315 rivers was included as climatological mass flux input and a 17 term empirical equation calculated the near field plume dynamics. In their HYCOM output, [PERSON] et al. relied on the 35 isohaline to identify the Amazon plume. The salinity within their plume remained under 35 psu as the plume moved into the open ocean off the coasts of South America. This is not consistent with the HYCOM SSS variable as Figure 7 shows the HYCOM variable consistently above 35 psu, indicating the need for specific river input data to produce more accurate river outflow salinity estimates. SMAP SSS data, which more accurately represented the fresh tongue in this instance, could potentially be incorporated into HYCOM's data assimilation process to have a more accurate representation of freshwater fluxes in the model. Such an approach has not been described in the literature to our knowledge and represents a promising new area of research that could improve the prediction performance of HYCOM.
# Lightweight Design of Multi-Objective Topology for a Large-Aperture Space Mirror
## 4 Performance Evaluation of the New Configuration
### Optical Performance of the PMA  
We can find that the mirror surface distortion under lateral gravity is far better than that under axial gravity through our previous work, so the primary mirror is measured with an optical interferometer horizontally, as shown in Figure 11. The adhesive position of the mirror and bipod flexures affects the surface error due to the varied distance between the apex of BF and plane of the center of gravity (CG), as illustrated in Figure 12. The misalignment generates torque and the coupled reaction of bending moment and gravity will cause mirror distortion substantially.  
Figure 11: Horizontal setup for measuring the mirror surface with an optical interferometer.  
Figure 12: The non-coplanar distance \(L\) of the PMA.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Terms** & **Tri-Configuration** & **Hex-Configuration** & **New Configuration** \\ \hline \(PV_{A}\) \& \(RMS_{A}\) & 125.73 \& 28.65 nm & 150.57 \& 34.43 nm & 119.53 \& 27.58 nm \\ \(PV_{L}\) \& \(RMS_{L}\) & 8.08 \& 1.68 nm & 10.38 \& 1.92 nm & 7.03 \& 1.28 nm \\ \(PV_{P}\) \& \(RMS_{P}\) & 55.80 \& 13.56 nm & 60.74 \& 14.05 nm & 45.65 \& 10.10 nm \\ \(PV\) \& \(RMS\) (+2 \({}^{\circ}\)C) & 61.6 \& 10.48 nm & 100.5 \& 17.5 nm & 50.1 \& 8.78 nm \\ Frequency & 1400.8 Hz & 1344.5 Hz & 1555.3 Hz & 150.53 Hz \\ Mass & 13.48 kg & 11.54 kg & 11.08 kg \\ Lightweight ratio & 68.4\% & 72.9\% & 74.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons of the classic and new configuration lightweight mirror design.  
We investigate the effects of non-coplanar distance \(L\) on the surface error of the PMA under 1 g lateral gravity. When the apex is above the plane of CG, \(L\) is positive or else \(L\) is negative. Fringe Zernike polynomials are used to fit the surface deviation [24]. The primary aberration terms of the polynomials is shown in Table 6. Through comparing coefficients of the Zernike polynomials, we find that the primary astigmatism-A (Pri-Astig-A) is the dominant component. The analysis results of surface figure RMS and Pri-Astig-A coefficient due to \(L\) are shown in Figure 13a,b. From Figure 13a, we can find there is a concave curve in the plot of the surface figure RMS. When \(L\) is 3.5 mm, the RMS surface error of the mirror is minimized to 3.58 nm. As shown in Figure 13b, when \(L\) is less than 3.5 mm, the Pri-Astig-A is positive, it means the mirror will have lateral astigmatism; when \(L\) is larger than 3.5 mm, the Pri-Astig-A is negative, and it means the mirror will have lengthways astigmatism. For visualization, the residual surface error is illustrated in Figure 14. When \(L\) is 3.5 mm, there is only a small scale deformation on the mirror surface and almost has no astigmatism. Based on the analysis above, we can infer that the new configuration mirror could exhibit excellent optical performance based on given BFs.