esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Thermal modification (TM) improves the hydrophobicity, dimensional stability, and durability of wood, but TM commonly results in severe color change and mechanical strength loss as wood is treated at higher temperature. In this study, _Peterocarpus macrocarpus_ Kurz wood was thermally modified at moderate temperature (150 \({}^{\circ}\)C) and higher temperature (200 \({}^{\circ}\)C), and subsequently TM wood at 150 \({}^{\circ}\)C was subjected to wax impregnation (WI), the effect of a combination of TM and WI on the hygroscopicity, dimensional stability, and mechanical properties, as well as the micro-structure of wood, were investigated and compared. The results showed that the mass loss of wood was slight at 150 \({}^{\circ}\)C TM, while it became severe at 200 \({}^{\circ}\)C TM conditions. TM conditions affected the amount of the subsequent wax impregnation; the equilibrium moisture content (_EMC_), water absorption ratio, and adsorption and absorption swelling of the 150 \({}^{\circ}\)C TM + WI group were lower than that of 200 \({}^{\circ}\)C TM, and presented the lowest value. Moderate temperature TM could improve the hydrophobicity and dimensional stability of wood, but WI played a key role in the improvement. TM decreased the modulus of rupture (MOR) of wood, while WI improved the MOR. TM increased the modulus of elasticity (MOE) of wood, but WI had little effect on MOE; Scanning electron microscope (SEM) observation showed that the wax was successfully impregnated into the wood interior, and presented an even distribution on the internal surfaces of wood cells; Fourier-transform infrared spectroscopy (FTIR) spectra verified the changes of -OH and C=O after TM and TM + WI, which contributed to decreasing hygroscopicity and improving the dimensional stability of the wood. Impregnated wax improved wood mechanical strength, but decreased the lightness, and deepened the color of wood. The combination of thermal modification at moderate temperature with subsequent wax impregnation is a practical approach for improving wood properties.  
Keywords:thermal modification; wax impregnation; hygroscopicity; dimensional stability; mechanical properties +
Footnote †: journal: Applied Sciences
## 4 Discussion
For its application, it is necessary to determine the average values of the isotope parameter, as well as the concentrations of nutrients for the base waters--river runoff and incoming ocean waters. If for shelf seas, these parameters can be determined from long-term observations of the values themselves, then for the scale of the ocean, for example, the Arctic Ocean, determining the initial values for such calculations is a separate task. For incoming Atlantic and Pacific waters, the values of the isotopic parameter and the concentration of nutrients can be determined using existing databases. In the case of river runoff, it is quite difficult to determine the values of these parameters for water areas outside the shelf seas, since, due to the huge catchment area of the inflowing rivers and the dispersion of rivers along the longitude and latitude, the dispersion of these values will be very large. The presented work is based on the calculated values of the isotopic parameter \(\delta^{18}\)O according to the dependence of this value on salinity, which has a correlation coefficient close to 1. However, the data for constructing this dependence for the Barents Sea, for the most part, are confined either to the waters surrounding the Spitsbergen archipelago or to the southeastern part of the sea. It is necessary to supplement the existing salinity-\(\delta^{18}\)O series with new values. When assessing the productivity of some limited areas, to increase the accuracy of calculations, it is necessary to use unreconstructed \(\delta^{18}\)O data but measure directly with parallel determinations of salinity and nutrients. In addition, the calculations presented are based on the values of nutrient content in plankton, which were obtained and published in 1942. It is now necessary to revise and refine these values, including not only diatoms and dinoflagellates, but also other systematic groups of phytoplankton. Figure 14: Distribution of stoichiometric ratios (by mass) in the Barents Sea during the period of maximum nutrient consumption (July–September). Moreover, at small values of \(Q^{n}_{phyto}\) at the beginning of the photosynthetic activity season, in some areas, sum values of \(d\) and p were observed greater than 1 or took negative values, which cannot be according to the conditions of the presented methodology. In the case where such values (\(d>1\) or \(p>1\) or \(d<1\) or \(p<1\)) were received, the result was excluded from further calculations. This can be caused by several reasons:  
First, for small values of \(Q^{n}_{phyto}\), the magnitude of the calculation error, relative to the obtained value of \(Q^{n}_{phyto}\) is very large;  
Secondly, the sum of diatom and peridinium plankton biomass is not 100% of the total biomass. In some years, _Phaeocystis pouchetii_ (class Prymnesiophyceae) can reach high development levels at the peak of spring blooming, giving up to 95% of the total phytocenosis biomass. That can change the ratio of nutrient consumption values in such areas.
# Load Forecasting with Machine Learning and Deep Learning Methods
## 3 Methodology
### Modeling
#### 3.2.3 Extreme Gradient Boosting  
Extreme gradient boosting follows the principles presented in random forest. The main idea is improving the convergence of the RF by improving the adjustment of weak learners, or trees, whose forecasts do not have large adjustments [34; 56; 57]. To carry out this process efficiently, XGBoost uses additive training in which the previous iteration is considered as the actual forecast [58; 59] (7).  
\[\hat{y}^{[i]}=\sum_{i=1}^{t}\left(\frac{p(\hat{y}_{t}|x)^{[i-1]}+p(\hat{y}_{t} |x)^{[i]}}{t}\right) \tag{7}\]  
where the superscript \(i\) indicates the iteration considered.  
This application of the addition technique also generates a slight modification in the calculation of the cost function \(J^{[i]}\), which considers both current and previous iterations (8). The benefit of using this technique is that the loss function expands to the second order, making it possible to optimize the problem quickly [41].  
\[J^{[i]}=\sum_{k=1}^{n}\frac{2\left(\hat{y}_{k}^{[i-1]}-y_{k}\right)p(\hat{y}_{ t}|x)^{[i]}/t+\left(p(\hat{y}_{t}|x)^{[i]}/t\right)^{2}}{n}+f(\hat{y}_{k}) \tag{8}\]  
XGBoost is a popular ML technique that can handle electricity consumption forecasting. The main reasons are presented in following points:  
* Regularization and control: It offers various regularization techniques to control the complexity of the data, which prevent overfitting and improve generalization.
* Non-linearity: It captures non-linear relationships in electricity consumption to model its complex relationships.
* Scalability and efficiency: It is suitable for handling large datasets with high-dimensional feature spaces.
* Flexibility: It has a wide range of hyperparameters that can be tuned to optimize the model's performance.
# Photocatalytic Inactivation as a Method of Elimination of _E. coli_ from Drinking Water
## 3 Inactivation Process by Photocatalysts
### Inactivation of _E. coli_ by Photocatalysts Doped with Non-Metal  
Sulphur, nitrogen, phosphorus and carbon have been used to dope series of photocatalysts in order to improve their photocatalytic efficiency [89, 90]. For instance, titanium oxide doped with nitrogen was used as an active photocatalyst against _E. coli_. The performance of this photocatalyst increased when carbon, another non-metal, was used to sensitize the photocatalyst. After 60 min of visible light irradiation, approximately 80% _E. coli_ was eliminated, and this could be attributed to the lowering of band gap as a result of the introduction of non-metals [91]. [PERSON] et al. [92] doped silver vanadate and graphitic carbon nitride composite with sulphur and phosphorus. The presence of these non-metal dopants achieved 99% of _E. coli_ inactivation within 1 h in the presence of visible light radiation.
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 5 Discussion  
In this study, we explored the inner workings of Apache Spark's MLlib library, which provides a range of algorithms and tools for processing large datasets and building machine learning models. Our focus was on three key operators: K-Means Clustering, Random Forest Regressor, and Word2 Vec. We trained several models to predict the performance of each operator based on two key metrics: total execution time and memory usage. The results of the best-performing models for each operator are summarized in Table 2.  
The results indicate strong predictive accuracy for both K-Means and Random Forest operators, particularly in predicting memory usage, with \(R^{2}\) scores of 0.98 and 0.76, respectively. The Gradient Boosting Regressor (GBR) showed the highest performance for K-Means, achieving \(R^{2}\) values of 0.922 for total time and 0.98 for memory usage. For the Random Forest operator, the Random Forest Regressor (RFR) performed best with \(R^{2}\) scores of 0.942 for total time and 0.76 for memory usage.  
On the other hand, the Word2 Vec operator exhibited different behavior, with a Linear Regressor performing best in predicting total execution time (\(R^{2}=0.972\)), but showing poor accuracy in predicting memory usage (\(R^{2}=0.06\)). This discrepancy suggests that the memory consumption patterns for Word2 Vec are more complex and may require more sophisticated modeling techniques, possibly due to the large-scale text data and the algorithm's complexity.  
These results emphasize the importance of selecting appropriate models for different operators and metrics. For structured data, such as in K-Means and Random Forest, ensemble models like Gradient Boosting and Random Forest Regressor performed well. In contrast, the simpler Linear Regressor was suitable for predicting execution time in the Word2 Vec operator, but inadequate for memory usage prediction. The poor performance in memory prediction for Word2 Vec suggests the need for additional research into more advanced techniques, possibly by incorporating more features or preprocessing steps to better capture the memory profiles of text-based algorithms.  
Overall, this study demonstrates how predictive modeling can enhance our understanding of the performance of Apache Spark's MLlib operators. By using machine learning models, we can anticipate execution time and memory usage more effectively, which is crucial for optimizing the performance of distributed systems. Future work could focus on improving the predictability of memory usage for complex operators like Word2 Vec and exploring new machine learning methods to further refine the capabilities of Spark's MLlib in large-scale data processing.
## 5 Energy-Efficient Transmission Strategies
### Path Selection  
In order to communicate in an energy-efficient manner with the core network, the terminal will analyze its selection to transmit via the direct path by a T-S link to the satellite or a relay path through \(N_{H}\) possible relay links in the HAP segment. Algorithm 1 shows the adaptive path algorithm for energy-efficient transmissions designed for our proposed model based on the above analysis. The inputs of the algorithm are the LSAs of the \(N_{H}\) HAPs and the satellite, the required BER \(\epsilon\) and the required data rate \(R_{b}\), while the outputs of the algorithm are the corresponding optimal transmission power \(P_{r}^{\mathit{opt.},i,j}(t)\), minimum energy consumption \(E_{r,i}^{j^{\mathit{opt.}}}\) and optimal path \(v_{i}^{\mathit{opt.}}\). We can observe that, for each time slot, the terminal sends the signal with the optimal power \(P_{r}^{\mathit{opt.},i,j}(t)\) via an optimal path \(v_{i}^{\mathit{opt.}}\) and consumes minimum energy \(E_{r,i}^{j^{\mathit{opt.}}}\).
# Study on Influence of Multi-Parameter Variation of Bladed Disk System on Vibration Characteristics
## 4 The Influence of Detuning Parameters on Bladed Disk System Is Analyzed
### Analysis on the Influence of Structural Changes of Bladed Disk System
#### 4.3.1 Modal Analysis of Disk and Bladed Disk System
3.3 The Effect of the Double Blades Angle Aggravation on the Modal Frequency of the Bladed Disk System  
For the blade disc system studied in this paper, the top blade of the plane was selected as the reference, and three angles of double blades were selected to weigh at the top at the same time. The three kinds of weight were compared with the typical mode shapes of the initial state of the blade disc system (without counterweight), as shown in Figure 12.  
Each column from top to bottom in turn is initial state, double blades are 60\({}^{\circ}\) to each other, and the top end adds 1.7 g counterweight; double blades are 120\({}^{\circ}\) to each other, and the top end adds 1.7 g counterweight; and double blades are 180\({}^{\circ}\) to each other, and the top end adds 1.7 g counterweight. It can be seen from Figure 12 that the frequency deviation of modes 6, 7 and 8 when the double blades are weighted at the top of the angle of mutual formation is large, so the vibration modes of the third order are extracted for comparison. It can be concluded from Figure 12 that the modes of the 6 th, 7 th and 8 th order of the bladed disk system are all impeller static and blade vibration. At this point, the amplitude of blade vibration is much greater than the amplitude of the dominant vibration of the first 5 stages of the disk. The modal vibration frequency of the double blades of the bladed disk system with counterweight is lower than the modal vibration frequency without counterweight, and there is no obvious law of amplitude change. Figure 12: Comparison of modes with different double blades’ angle of bladed disk system (**A**) 6 th mode (**B**) 7 th mode (**C**) 8 th mode.
## 3 Deep-Learning-Based Channel Estimation Methods
### Model-Driven Deep-Learning-Based Channel Estimation Methods
#### 3.4.2 Model-Driven Channel Estimation Combining OMP Algorithm  
As a greedy algorithm, the OMP algorithm offers optimal solutions to sparse recovery problems. Leveraging the sparsity of channels, it facilitates channel estimation with minimized pilot overhead. However, due to the grid mismatch issue, the OMP algorithm may not provide satisfactory estimation results [74]. To achieve better channel estimation performance, combining the OMP algorithm with deep learning is a viable approach.  
In 2022, [PERSON] et al. [75] designed a model-driven ResNet-based scheme for orthogonal time-frequency space (OTFS) systems. Considering the sparsity of the channel in the delay-Doppler domain, they first employed the OMP algorithm to obtain a coarse estimation. Then, this initial estimation was fed into the proposed ResNet for a more accurate result. During the training process, the ResNet learns from the error between the OMP rough estimation and the actual channel response. Then, this network continuously optimizes the parameters, resulting in a more realistic channel estimation result. Compared to the conventional OMP algorithm, the ResNet-based scheme has a superior NMSE performance. Furthermore, it applies to various scenarios and displays outstanding robustness to Doppler spread.  
In [76], [PERSON] et al. introduced a two-step OMP-based channel estimation algorithm. First, a composite convolution kernel function (CKF) is constructed based on the autocorrelation matrix, which then coarsely estimates the angles of arrival/departure (AoAs/AoDs) for multipath channels. Second, a squeeze-and-excitation ResNet (SE-ResNet) utilizing the Noise2 Void (N2V) algorithm is proposed to refine the AoAs/AoDs estimation. Then, the channel amplitude is estimated using the LS algorithm. Finally, the channel matrix is accurately recovered based on all the results above. In the experiments, the proposed algorithm exhibits a superior NMSE performance over the simultaneous weighted OMP (SW-OMP) algorithm, the Newtonized OMP (NOMP) algorithm, and the channel estimation neural network (CENN), while having computational complexity.  
To enhance the effectiveness of the conventional OMP algorithm in hybrid-field channel estimation, [PERSON] et al. [77] developed a novel scheme called OMP-CAE by incorporating the convolutional autoencoder (CAE) for massive MIMO channels. In this scheme, the OMP algorithm first provides a rough channel estimation. Since the channel parameters from the OMP rough estimation contain significant errors at low SNR, they are fed into the CAE network. Then, the CAE network uses its strong denoising ability to improve the accuracy of channel estimation. Through experimental verification, the OMP-CAE surpasses the MMSE algorithm and the conventional OMP algorithm in terms of NMSE and applies to different scenarios.
## 3 Preliminaries and Motivations
### RPL Fundamentals
#### 3.1.2 Routing Establishment
A downward routing of RPL can be summarized as follows:  
* DAOs are generated by a node and sent to the preferred parent;
* The preferred parent will process the DAO (as illustrated in Figure 3);
* Finally, the preferred parent will forward or generate, then send the DAO to its preferred parent;
* All those involved DAOs during a downward routing establishment form a DAOs set in logic. After the above processes, each node's downward routing table is established and maintained.
## 3 The Dataset: UWF-ZeekData22  
Since graph data models depend on the connections between data points, the Conn Log files of UWF-ZeekData22 [9; 10] were used for generating the graphs. UWF-ZeekData22 [9; 10] was generated by the Cyberrange group associated with the University of West Florida, and the full dataset is available at [10]. This dataset has 9,280,869 attack records and 9,281,599 benign records, for a total of 18,562,468 records.  
The data schema of the Conn Log files is presented in Table 1. To generate the graphs, only four fields from the Conn Log files were used in addition to count: id.orig_h (the source IP, referred to as srcIP in this paper), id.resp_h (the destination IP, referred to as dstIP in this paper), duration, and orig_bytes (referred to as bytes).
## 4 Numerical Analysis of Active Bearing of the FSP
### Analysis of Active Bearing Effect of the FSP
After one day of application, it was found that the settlement of the vault was reduced by 17.3 mm and the cumulative difference of vault settlement within 11 days was 15.4 mm. The final vertical bearing capacity of the steel pipe increased from 137.1 kN to 158.6 kN, and the safety factor decreased from 1.236 to 1.068. It can be seen from this that the active bearing of the FSP can effectively control the settlement of the vault. Figure 16: Active bearing effect of 42 mm diameter steel pipe. Figure 17: Effect comparison of 89 \(\times\) 8 mm steel pipe preloading displacement of 50 mm. Figure 18: Stress comparison of 89 \(\times\) 8 mm steel pipe preloading displacement of 50 mm. Therefore, the active bearing of the FSP is not to permanently control the deformation of the vault as the main structure but to enable the steel pipe to provide a strong bearing capacity as soon as possible. The active bearing can slow down the deformation rate of the tunnel in a short time, reduce the disturbance degree of the surrounding rock mass caused by the tunnel construction, and reduce the release coefficient of the surrounding rock pressure to ensure the construction safety of the tunnel during the excavation process. When the primary lining is closed, the bearing effect of the steel pipe can be ignored.
## 5 Results
### Option Price for GBM Price Model
#### 5.2.2 Decision Boundary  
In Table 1, the fair price of each option has been computed using the three methods. These three methods lead to slightly different values because their decision boundaries (the decisions to continue and exercise) are slightly different.  
To delve into and understand this difference more, Figure 2 illustrates the decision boundary for the GBM price model with \(S_{0}=36\), \(\sigma=0.2\), and \(K=40\). At any point in time (during the exercise window), if the price of the underlying asset is lower than the decision boundary curve, the option should be exercised. In Figure 2, the \(x\)-axis has been normalized (Time = t/Expiration Time), and the y-axis is the price of the underlying asset. The \"locally jagged\" nature of the decision boundary in the BOMP method is noteworthy. This boundary results from the discrete, stepwise structure of underlying asset price within the binomial lattice. The \"diamond-like\" local structure of these prices forms clusters,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**S** & \(\sigma\) & \(T\) & **Closed form European** & **BOPM** & **LSM** & **RL** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 1: Option price for GBM price models, calculated using BOPM, LSM, and RL methods. Columns indicate spot price (S), volatility (\(\sigma\)), time to maturity (T).  
leading to jumps along the decision boundary curve, a phenomenon similarly discussed in [23].
## 3 Results and Discussion  
### Correlation Between Remote Sensing Data and Yield  
In general, the SAR images did not show a direct relationship with soybean yield in any of the three growth stages, analyzed filters, or SAR variables (Figure 5). The correlation results were frequently low, remaining below \(\pm\)0.35. This low association might be attributed to the SAR image parameters, which are influenced by structural factors of the crop, such as leaf area, biomass, and canopy height [14], aspects that may not have a direct relation to soybean grain yield. Although planting density was consistent across the three harvests and the cultivars had an indeterminate growth habit, factors that would affect SAR signal response, no evident pattern was found between growth stages, vegetation indices, and crop harvest in the correlations. Yield predictions using remote sensing rely on the direct relationship between vegetation indices and biomass, and the relationship between biomass and yield [3]. However, the number of pods per plant is the primary determinant of grain yield [41], and there can be significant variation in biomass and pod and grain formation, where higher biomass does not always result in higher yields. Additionally, the correlation between EVI and yield (Table 4) did not exceed 0.5, indicating that soybean shows a low correlation with optical remote sensing data, even when using other vegetation indices [35]. The negative correlations of EVI observed during growth stages 1 and 2 for harvest 3 and stage 3 for harvest 2 might indicate the presence of weeds [42]. Remote sensing data are influenced not only by the cultivated plants but also by weeds in the area. The interference from these weeds can distort the relationship between vegetative biomass and yield, as they compete for resources and can reduce the yield of the main crop while still reflecting a high EVI and distorting SAR data. Another factor to note is that filter patterns are very similar within the same harvest, indicating that there are no consistent variations in correlation with soybean yield when using different speckle noise filters.  
Figure 4: Prediction scenarios performed. Input data corresponding to each tested scenario (in red): (**a**) using all stages and SAR variables together, (**b**) using stages separately and all SAR variables together, (**c**) using the stage that previously performed best with the variables separated.