esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### PAHs Evolution in Plants and Soils
& Sr-C & Sr-C & Sr-L & Sv.L. & Sv.A & Sv.A & Sv.A & Sv.A & Sv. & Sv.C & Sv.L & Sv.L & Sv.A & Sv.A \\ Ben Benoijs/membrane & 0.34\({}^{\circ}\) & 0.70\({}^{\circ}\) & nd & nd &  
As mentioned, adding PGPB further improves the observed levels of pyrene degradation. A direct comparison of removal percentages compared to the related initial values is shown in Figure 5.  
The results show that rhizodegradation processes promote the degradation of PAHs, leading to an effective reduction in their concentrations in soil, and this effect is amplified by the inoculation of the strains isolated as part of this experimentation. This hypothesis is supported by the low concentrations of pyrene and PAHs in plants. These complex species are not extracted from the soil but transformed by microorganisms into lighter compounds, even more effectively near the areas close to the roots. This also justifies the enhancement observed with the addition of PGPB, which, as mentioned, increases microbial activity in the rhizosphere by promoting plant development, ultimately resulting in enhanced degradation. Overall, the percentage of reduction in PAHs' concentration ranged from a minimum of about 40% to more than 70% depending on the soil and plant species, assuming pyrene as  
Figure 4: Concentration of pyrene in the soil (\(\upmu\)g kg\({}^{-1}\)d.m.) at initial conditions (Ss) and at the end of the microcosm test for non-vegetated soils (Snv) and after the growth of investigated plants (Sv) for different soils (A–D) during microcosm tests. Data reported as the mean of replicates with the related standard deviation. Figure 5: Comparison of the percentage reduction in the mean values of pyrene in vegetated soil for different soils (A–D) from microcosm tests with and without PGPB. The reduction is intended as relative to the initial values of the corresponding soil type, obtained as \((\text{Ss}-\text{Sv})/\text{Ss}\times 100\). an indicator (Figure 5), with the contribution made by the addition of PGPB resulting in an additional 5-10% removal from the respective initial concentration. Thus, contamination with organic chemicals can be effectively reduced by the combined activities of specifically selected plants and microorganisms that utilize organic compounds as a primary carbon source. In addition, relying solely on biological activity, the approach illustrated is particularly suitable to remediate land intended for agricultural use. However, an important consideration is that this great reduction in contaminant concentrations is possible due to the peculiar condition of macrocosm tests, as they allow roots to explore the polluted soil fully; such a condition is often unrealistic when working in the field.
# Efficient Multi-Identity Full Homomorphic Encryption Scheme on Lattice
## 2 Preliminaries
### Trapdoor Function and Trapdoor Generation Algorithm  
Definition 7: (**MP12 Trapdoor**[25]) For any integer \(n>1\), \(q\geq 2\), \(m=O(n\text{log}q)\), \(k=\lceil\log q\rceil\), \(\overline{m}=m-nk\), \(w=nk\), \(m\geq w\geq n\). Set matrices \(\mathbf{A}\in\mathbb{Z}^{n\times m}\) and \(\mathbf{G}\in\mathbb{Z}_{q}^{n\times n}\), and the corresponding \(\mathbf{G}\)-trapdoor matrix of \(\mathbf{A}\) is \(\mathbf{R}\in\mathbb{Z}^{\overline{m}\times w}\), which satisfies \(\mathbf{A}\begin{bmatrix}\mathbf{R}\\ \mathbf{I}_{nk}\end{bmatrix}=\mathbf{H}\mathbf{G}\), where \(\mathbf{H}\in\mathbb{Z}_{q}^{n\times n}\) is an invertible matrix, and \(\mathbf{H}\) is called label of the trapdoor. The trapdoor's quality depends on the maximum singular value \(\mathbf{s}_{1}(\mathbf{R})\).  
Lemma 3: (**Trapdoor Generation Algorithm**[25]) For \(n\geq 1\),\(q\geq 2\),\(m=O(n\text{log}q)\approx 2n\text{log}q/\overline{m}=m-nk\),\(w=nkk=\lceil\log q\rceil\), modulus \(q=q(n)\), invertible matrix \(\mathbf{H}\to\mathbb{Z}_{q}^{n\times n}\), construct a gadget matrix \(\mathbf{G}=\mathbf{I}_{n}\otimes\mathbf{g}^{T}\in\mathbb{Z}^{n\times nk}\), where \(\mathbf{g}^{T}=\left[1,2^{1},2^{2},\ldots,2^{k-1}\right]\in\mathbb{Z}_{q}^{k}\). Randomly choose uniform matrix \(\overline{\mathbf{A}}\in\mathbb{Z}_{q}^{n\times\overline{m}}\). There exists a trapdoor generation algorithm \(\text{TrapGen}(1^{n},1^{m},q)\), outputs matrix \(\mathbf{A}=\left[\overline{\mathbf{A}}|\mathbf{H}\mathbf{G}-\overline{\mathbf{A}}\mathbf{R}\right]\in \mathbb{Z}_{q}^{n\times m}\) and its trapdoor matrix \(\mathbf{R}\in\mathbb{Z}^{\overline{m}\times w}\) where \(\mathbf{A}\) is statistically indistinguishable from \(\mathbb{Z}_{q}^{n\times m}\) and the size of trapdoor is \(\mathbf{s}_{1}(\mathbf{R})\leq\sqrt{m}\omega(\sqrt{\log n})\).  
**Lemma 4**: _(**Sampling Algorithm**[25]) As same as the parameter of Lemma 3, let \(\mathbf{u}\in\mathbb{Z}_{q}^{n}\) be an \(n\)-dimensional random vector, Gaussian parameter \(\sigma\geq\mathbf{s}_{1}(\mathbf{R})\cdot\omega\big{(}\sqrt{\log n}\big{)}\), and there exists a PPT (probability polynomial time) algorithm \(\text{SampleD}(\mathbf{A},\mathbf{R},\mathbf{u},\sigma)\), output a vector \(\mathbf{t}\in\mathbb{Z}_{q}^{m}\) closing to the discrete Gaussian distribution \(\mathcal{D}_{\mathfrak{F}(\mathbf{A}),\sigma\omega(\sqrt{\log n})}\), satisfying \(\mathbf{A}\cdot\mathbf{t}=\mathbf{u}\) mod \(q\), where \(\text{Pr}\Big{[}\mathbf{t}\leftarrow\mathcal{D}_{\mathfrak{F}(\mathbf{A}),\sigma\omega (\sqrt{\log n})}:\mathbf{t}>\sigma\sqrt{m}\Big{]}\leq\text{negl}(n)\)._
# Study on the Steepness of Landslide-Induced Wave and Nonlinear Motion of Container Ships
## 4 The Propagation Law of Landslide-Induced Surges
### The Wave Steepness Characteristics of Landslide-Induced Surge Waves
#### 4.3.3 The Wave Steepness Characteristics of the Curved Channel  
During the test, two working conditions were selected to study the variation rule of the landslide surge wave in the curved section of the river. Respectively, for observation points of the first group of Working Conditions #1, #6, and #7 of the wave steepness test, the wave number, maximum wave height, significant period, wave height mean, and periodic mean in Working Conditions A5, A6, A7, A8, and A9 of the second group of the wave steepness test were found and are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Point** & **Number** & **Hmax/cm** & **Tmax/s** & **1/3 H/cm** & **1/3 T/s** & **Mean Wave Height/cm** & **Mean Period/s** \\ \hline \#1 & 27 & 7.109 & 2.021 & 2.289 & 2.336 & 1.045 & 1.619 \\ \#4 & 27 & 2.064 & 1.338 & 1.417 & 1.674 & 0.88 & 1.427 \\ \#5 & 18 & 1.45 & 5.  
As can be seen in Table 5, with the same landslide volume amount, landslide dip angle, and water depth, after the landslide body enters the water, Points #1, #6, and #7 are on the same line. The maximum wave height decreases first and then increases. The same is true for A5, A6, A7, A8, and A9. This is because, during the propagation of the landslide surge in the curved channel, it is affected by a right-angle bend, which hinders the scattering propagation of the landslide surge. At the same time, during the propagation of the landslide surge, the landslide-induced surge impinges on the river bank and reflects, causing it to overlap and the wave height to increase. The steep variation curve of the landslide-induced surge wave in the straight line of the river channel under typical curved channel conditions is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Point** & **Number** & **Hmax/cm** & **Tmax/s** & **1/3 H/cm** & **1/3 T/s** & **Mean Wave Height/cm** & **Mean Period/s** \\ \hline \#1 & 27 & 7.109 & 2.021 & 2.289 & 2.336 & 1.045 & 1.619 \\ \#6 & 27 & 1.702 & 2.669 & 1.311 & 1.918 & 0.745 & 1.463 \\ \#7 & 29 & 1.880 & 1.897 & 1.319 & 1.994 & 0.817 & 1.294 \\ A5 & 32 & 1.972 & 1.924 & 1.251 & 1.661 & 0.764 & 1.313 \\ A6 & 27 & 2.029 & 2.441 & 1.373 & 2.130 & 0.79 & 1.526 \\ A7 & 31 & 1.486 & 2.476 & 1.115 & 2.454 & 0.586 & 1.303 \\ A8 & 29 & 1.604 & 2.359 & 1.041 & 1.948 & 0.578 & 1.5 \\ A9 & 30 & 1.721 & 2.262 & 0.952 & 1.819 & 0.588 & 1.309 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical table of wave height and period of monitoring points in the tortuous channel.  
Figure 8: The weave steepness variation curve of the landslide surge in the straight line area under typical working conditions.  
Figure 9: The wave steepness variation curve of the landslide surge in the curved area under typical working conditions.
## 2 Materials and Methods
### Determination of the Enzyme Activity  
The quantitative pectinase, xylanase, and cellulase enzymatic activities of the isolates were determined by the dinitro salicylic acid reagent (DNS) enzyme assay. The method was adopted based on the reducing sugars produced for the enzymatic hydrolysis of the respective substrates, such as apple pectin, birchwood xylan, and carboxy methyl cellulose. The reducing sugar concentrations were estimated as D-galacturonic acid, D-xylose, and D-glucose equivalent standard curves by measuring the absorbable concentrations as followed by [31; 32]. The pectinase enzyme activity of the retting effluents was estimated by using the method described by [33]. About 10 mL of retting effluent was treated with an equal volume of NaOH (100 \({}^{\circ}\)C for 5 min) to remove ammonia, followed by centrifugation (10,000\(\times\)\(g\) for 15 min at 4 \({}^{\circ}\)C) and filtration with a 0.22-micron filter. A 2 mL of filtrate was added to an equal volume of 0.5% apple pectin solution maintained with a glycine-NaOH- buffer of pH 10. Then, the mixture was incubated at 45 \({}^{\circ}\)C for 30 min. About 1.5 mL of dinitro salicylic (DNS) reagents was added and heated at 100 \({}^{\circ}\)C for 15 min. Then, the mixture was diluted into a 25 mL ddH\({}_{2}\)O. The mixture's absorbance at 530 nm was measured using a spectrophotometer to determine the amount of reducing sugars; D-galacturonic acid as standard and ddH\({}_{2}\)O were used as the reagent blank. Each enzyme activity unit was defined as the amount of enzyme that liberates one micromole of galacturonic acid per minute under assay conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Treatments** & **Combinations** & **Designations** \\ \hline T1 & Uninoculated with seawater & USW \\ T2 & Uninoculated with freshwater & UFW \\ T3 & APPB1 with seawater & APPB1 + SW \\ T4 & APPB1 with freshwater & APPB1 + FW \\ T5 & APPB2 with seawater & APPB2 + SW \\ T6 & APPB2 with fresh water & APPB2 + FW \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of retting treatments used in augmented kenaf retting.
## 3 Proposed Status-Byte-Assisted RDMA Transmission Mechanism
### Design of the Status-Byte-Assisted RDMA Transmission Mechanism
#### 3.2.8 Stateless Connection at the Receiving End  
Both the synchronization module and the sending module are initiated by the sender through RDMA operations, and the receiver passively receives the data. Since the receiver does not perform sending operations, it does not need to retain connection information or maintain the sender's connection status, thereby freeing up RNIC resources on the receiver end.
# Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model
## 3 Example Study
### Comparation of the RUL Predictive Model  
Based on the estimated RUL, the mean absolute error (MAE) between the estimated RUL and the true RUL can be calculated as follows:  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}\Bigl{(}\Bigl{|}\text{RUL}_{\text{predicted}}^{ \text{i}}-\text{RUL}_{\text{actual}}^{\text{i}}\Bigr{|}\Bigr{)}. \tag{22}\]  
Consequently, the MAE from the 40 th cycle to the 120 th cycle can be used as a measure to quantify the prediction accuracy of the model. Figure 12 presents the MAE of the estimated RUL using the model with a variable degradation threshold and the model with a fixed degradation threshold. Apparently, the model with a variable degradation threshold gives a more precise RUL prediction.  
In order to verify the effect of the model, a comparison was made between the IG process model and a particle filtering method using deviation accuracy [8]. The deviation accuracy was set as 0.1, which means that the prediction distribution falls within 1 \(\pm\) 0.1 of true RUL and is regarded as the performance indicator of the two models. Table 3 shows the result of the comparison, which demonstrates the better performance of the method proposed in this paper.
## 3 Results and Discussion
### Acrylamide Content  
The principal factors influencing acrylamide formation are temperature and time, sugar and asparagine content and industrial processing [67]. It has been added to the list of food-borne toxicants since 2002 by the Swedish National Food Administration. Acrylamide typically forms at temperatures above 120 \({}^{\circ}\)C during the Maillard reactions [60; 61] and longer times or higher temperatures in commercial processing can significantly increase its levels [62; 63]. Regarding sugar and asparagine content, apples with a high quantity of reducing sugars, such as glucose and fructose and asparagine, which are precursors of acrylamide formation, are particularly susceptible. Differences in sugar content and processing conditions may explain variations between samples. Additionally, industrial processing often involves more severe conditions, such as higher temperatures and longer drying times, compared to laboratory or homemade drying methods, which contributes to elevated acrylamide levels in commercial products.  
Table 4 presents data on the presence of acrylamide in _Royal Gala_ apples processed under different methods. The values are expressed in micrograms per kilogram of dried matter (\(\mu\)g/kg d.m.) and \"n.d.\" indicates \"not detected\".  
Acrylamide was not detected (n.d.) in fresh or solar-dried (SD) apple samples, which is consistent with expectations, as acrylamide formation typically requires exposure to high temperatures, conditions not reached during solar drying or in raw, unprocessed apples. Although oven-drying at 65 \({}^{\circ}\)C occurs well below the typical threshold temperature for acrylamide formation (120 \({}^{\circ}\)C), the presence of trace amounts may be attributed to the prolonged drying time, which could promote limited formation [68]. The relatively low value observed likely reflects restricted Maillard reaction activity, due to the mild thermal conditions [69].  
In samples dried at 85 \({}^{\circ}\)C (OD85\({}^{\circ}\)), acrylamide was also not detected. This may be due to insufficient overall thermal input or other limiting factors such as shorter drying duration or higher moisture content, which can inhibit acrylamide development despite the elevated temperature compared to OD65\({}^{\circ}\)[70].  
Notably, commercial sample CC exhibited the highest acrylamide content (147.2 \(\pm\) 57.3 \(\upmu\)g/kg d.m.), accompanied by considerable variability. This discrepancy suggests inconsistencies in industrial processing conditions [71]. In contrast, commercial sample CF showed a lower but still appreciable level of acrylamide. These findings indicate that the acrylamide levels in commercial snacks are likely influenced by high-temperature industrial drying processes, which favor its formation.  
Overall, this analysis underscores the need to balance sensory attributes and food safety when developing dried apple snacks, particularly in relation to processing conditions that affect acrylamide content [72].
## 3 Analysis of Numerical Simulation Results
### Stress-Strain Curves
This is because the crack propagation in the complex pore network is disturbed and blocked many times, resulting in a tortuous stress release process. The elastic modulus of Scheme B2 (\((d_{\rm min}\), \(d_{\rm max})=(0.001\), 0.002) m) is slightly higher than that of B1, and the linearity of the curve in the elastic stage is improved. The peak strength is about 359 KPa. The stress decreases faster in the post-peak stage compared with B1 but still has a certain ductility. This is because the increase in pore size makes the stress concentration area relatively concentrated. Although the crack propagation path is affected by the larger pores, the overall failure process is still relatively slow. The elastic modulus of Scheme B3 (\((d_{\rm min}\), \(d_{\rm max})=(0.0015\), 0.003) m) further increases, and the stress-strain curve in the elastic stage is closer to linear. The peak strength is about 344 KPa. The stress decreases relatively smoothly in the post-peak stage, showing a characteristic similar to the transition from brittle to ductile. This is because the control effect of larger pores on crack propagation makes the rock failure process have a certain regularity, and the stress release is relatively uniform. The elastic modulus of Scheme B4 (\((d_{\rm min}\), \(d_{\rm max})=(0.002\), \({\rm dmax})=(0.002\), 0.004) m) is the highest in Scheme B. The stress-strain curve in the elastic stage is closest to that of an ideal elastic body. The peak strength is about 321 KPa. The stress decreases relatively steeply in the post-peak stage, but compared with the high porosity case in Scheme A, it still has a certain bearing capacity. This is because although the pore size is large and the number is small, the damage to the overall structure of the rock is relatively concentrated in the local area, and the bearing frame of the rock is not completely destroyed. By comparing different schemes, it can be seen that in Scheme A, as the pore percentage increases, the elastic modulus of the rock gradually decreases, the peak strength drops significantly, and the post-peak ductility gradually weakens. The failure mode changes from a failure with a certain ductility to a brittle failure, indicating that the influence of porosity on the mechanical properties of the rock is comprehensive and negative. In Scheme B, as the pore size increases, the elastic modulus first decreases and then increases, and the peak strength also changes similarly. The post-peak characteristics change from a complex fluctuating decrease to a relatively smooth transition, reflecting that the influence of pore size on the mechanical properties of the rock is relatively complex and not a simple linear relationship. To a certain extent, the damage of large pores to the rock structure has a relatively concentrated characteristic, which is different from the dispersed damage mode of small pores. Generally speaking, changes in either pore percentage or pore size will significantly change the stress-strain behavior and failure mechanism of the rock.
## 6 Simulation and Performance Analysis  
### Simulation Scenario and Parameter Settings  
This study simulates the node self-deployment process of an underwater wireless sensor network by using the MATLAB simulation platform to analyze the effectiveness of PSA. The target water area (length \(\times\) width \(\times\) depth) is set to 120 \(\times\) 120 \(\times\) 120 m\({}^{3}\), the node sensing radius, \(R_{s}\), is set to 10 m, and the result of each index is calculated as the average value of 30 sets of data. The other parameters are set as follows:  
1. With the method of obtaining the optimal hot spot radius in [34] as basis, the minimum communication radius in this study is set to 12.5 m. The maximum communication radius is set to thrice that of \(R_{s}\) to be consistent with the simulation conditions of TVFDA.
2. The parameter \(G\) is set to 0.2, \(V_{max}\) is set to 0.15 times that of the length of the target area, \(N_{1}\), \(N_{2}\) is respectively set to 35 and 30, and \(N_{p}\) is set to 55 after several experiments on solving the fitness function. (The process is not described in detail in this paper because the experiment is not the point of the problem studied in the paper).
3. According to [35], the distance between the node and the boundary is 0.866\(R_{s}\) when the full coverage of the network is achieved. This distance is adopted in this study; that is, \(c_{border}\) is set to 0.866\(R_{s}\).
4. The parameter of the communication energy consumption model and other parameters are set as shown in Table 2.
## 3 Results
### Results of the Importance of Tree Species Characteristics at Different Altitudes
were found to be of low importance and ranked relatively low for the classification of tree species in all three elevation study areas. Among the 36 features selected using the RFE algorithm that ranked high in importance for each study area, the numbers of features accounted for by LiDAR data were 18 for the study area at an elevation of 1600 m, 5 for the study area at an elevation of 1800 m, and 6 for the study area at an elevation of 2000 m.  
From the optimal feature variables screened, several variable features with high importance for tree species classification in each altitude study area were selected and analyzed for changes in their importance for tree species classification in the different study areas. This is shown in Figure 4.  
From the comprehensive analysis in Figure 4a, it can be seen that among the raw band features, except for the NIR band, the classification importance of the other three bands gradually decreased from the low- to high-altitude areas. All of the raw band features had the highest importance for tree species classification at 1600 m above sea level. Figure 4b shows the four vegetation index features screened, with the highest importance for tree species classification in the study area at a 1600 m altitude. However, it should be noted that at 1800 m, the importance of the NDVI among the four vegetation indices was the lowest, but at 2000 m, the importance of the NDVI was the highest, showing a trend of first decreasing and then increasing. The results in Figure 4c show that the four selected texture features were the least important for tree species classification in the study area at 1600 m above sea level. In addition, B1_Mean, B3_Mean, and B4_VA had the highest importance for tree species classification in the 1800 m altitude study area, but B2_CO had the highest importance for identifying tree species in the 2000 m altitude study area. As seen in Figure 4d, the importance of the four LiDAR features screened for tree species classification at each altitude showed a decreasing and then increasing trend with increasing altitude. Zmean, Zmax, CHM, and H90 were all of the lowest importance for tree species classification at an elevation of 1800 m.  
Due to the different altitudes, the tree species composition varies in each region. The results in Section 3.1 indicate that coniferous species gradually dominate with increasing altitude. Therefore, combined with the results in Figure 4a-d, it can be observed that the original band features and vegetation index features performed better in classifying broad-leaved tree species, while the texture features performed better in classifying coniferous tree species. Figure 4: Comparison of importance values of optimal features at different altitudes: (**a**) Original band features; (**b**) Vegetation index features; (**c**) Texture features; (**d**) LiDAR point cloud features.