esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effects of Operating Parameters on Continuous Cr(VI) Removal by Immobilized ChrR Biosystem
[PERSON] et al. (2012) utilized an immobilized O. intermedium batch system to remove 268-mg L\({}^{-1}\) Cr(VI) from artificial and industrial sewage waters and achieved 65% and 91.2% removal efficiencies, respectively, within 72 h [45]. [PERSON] et al. (2016) applied a microbial fuel cell to remove 300-mg L\({}^{-1}\) Cr(VI) from synthetic wastewater under anaerobic conditions and achieved a 96.5% removal efficiency [11]. [PERSON] et al. (2017) utilized a sequencing batch reactor to remove 80-mg L\({}^{-1}\) Cr(VI) from synthetic wastewater and achieved an 81.3% removal efficiency [4]. Our immobilized _Ochrobactrum_ sp. CUST210-1 and immobilized ChrR biosystems substantially reduced the Cr(VI) concentrations of \(<\)425 mg L\({}^{-1}\) and \(<\)150 mg L\({}^{-1}\), respectively, in actual Cr(VI)-containing wastewater, suggesting that these developed biosystems can be used in the bioremediation of various Cr(VI)-contaminated wastewaters.
## 3 Results and Discussion
### 2D HSQC NMR Spectra  
Finally, we also quantified the \(\beta\)-O-4 contents present in the extracted lignin samples by performing their 2D-HSQC-NMR analysis. For this, 20 mg of nonacetylated lignin was quantitatively used and was dissolved in 0.75 mL DMSO-D6 and then the analysis data was acquired with a Bruker AVANCE 600 spectrometer (Bruker, Germany). The relative quantification and the corresponding assignments of the A\({}_{\alpha}\), B\({}_{\alpha}\), and C\({}_{\alpha}\) are tabulated in Table 6 (\(\beta\)-O-4, \(\beta\)-\(\beta\), and \(\beta\)-5, respectively) and the corresponding NMR figure is shown in Figure 3. The relative quantification of the present contents in the lignin samples were calculated accordingly. As can be seen in the table values. our extracted lignin, in comparison with MWL (46.00%), was slightly lower in \(\beta\)-O-4 contents ranging from 37.80% for ODA lignin to 40.00% for DA lignin. For the values of the other linkages in the lignin such as \(\beta\)-\(\beta\) and \(\beta\)-5 a similar trend was observed but the value of \(\beta\)-5 (7.00 and 6.00 for ODA and DA, respectively) was closer to milled wood lignin value of 5.10. All these values were found to be in good comparison with the other process values. For example, in one of our previous studies this was around 28% for \(\beta\)-O-4 contents when we adapted GVL as a sole reagent for the lignin isolation at a high temperature value ranging from 120 to 160 \({}^{\circ}\)C without any acid dosing. This can be anticipated as the thermal conditions surely modify the lignin structure as well as the acid concentration. Although our values found in this study are slightly lower than the classical milled wood lignin value, they are still high enough to be compared with the previously published literature. For example, values of 33.0 and 19.3 of \(\beta\)-O-4 linkages were reported when the baggase was treated with Ethanolamonium malonate (2 He Lac), respectively. However, their lignin extraction conditions were still harsh with 150 and 160 \({}^{\circ}\)C, respectively [55]. In a similar other study, a value of 32.4 was obtained when the lignin was isolated from wheat straw with 62% (wt%) formic acid at 130 \({}^{\circ}\)C for 20 min of time [56]. However, in another study, a value of as high as 44.4 \(\beta\)-O-4 was reported when _Eucalyptus canaldulensis_ Dehnh was treated with 80% GVL/20% H\({}_{2}\)O mixture catalyzed by 20 m M H\({}_{2}\)SO\({}_{4}\) for 30 min of time [57]. Almost similar values of \(\beta\)-O-4 linkages were quantified as 45.54 units when abaca biomass was subjected to lignin isolation under alkaline conditions of 20 wt% NaOH with 10 wt.% H\({}_{2}\)O\({}_{2}\) at a temperature of 105 \({}^{\circ}\)C for a process time of 3 h [58].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\beta\)-0-4 & \(\beta\)-\(\beta\) & \(\beta\)-5 \\ \hline Pine MWL & 46.0 & 3.0 & 5.1 \\ ODA lignin & 37.8 & 6.0 & 7.0 \\ DA lignin & 40.0 & 5.0 & 6.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Relative amounts of linkages in lignin samples.
# Sensitivity Analysis of Multi-Temporal Sentinel-1 SAR Parameters to Crop Height and Canopy Coverage
## 3 Results  
Different Sentinel-1 dual polarimetric SAR parameters including linear polarization backscattering coefficients, H-A-\(\alpha\) decompositions, polarization intensity ratios (VH/VV, VV/VH and VV \(-\) VH/VV \(+\) VH), Radar Vegetation Index (RVI), intensity arithmetic calculations and crop variables (crop height and CC) are investigated. Mean backscattering coefficient values for each sample site for different crops are defined and the correlation between the in situ measurements are analyzed. The results show that responses of polarimetric SAR parameters to the crop variables (crop height and CC) vary for different crop types at different phenological stages of the crops. For maize, SAR backscatter coefficient is more sensitive to crop height at the early stage of crop growth compared to the later stage, while the same situation is not true for sunflower and wheat.
## 7 Discussion and Future Direction  
Additional semantic capabilities (e.g., knowledge graphs) could be explored to represent complex cyber-physical production systems. Furthermore, the DT model is used to semantically modeling the data, reflecting the DT features and their relations using object-oriented concepts. Some semantic work could be done to describe the relationships between the DTs models using model-to-model, e.g., OOP, RDF, and OWL in the complex DT systems heterogeneous DTs types.  
Task allocation issues also arises as machine or robot, and human, can achieve the task. Therefore, simulation can allocate and distribute the tasks between robot and human-based on availability and resource. Thus, the DTs, which represent human and robot, can collaborate efficiently in smart manufacturing to achieve tasks accordingly. For flexibility and productivity, DTs should be used to balance and increase processing during work. For any change in the task execution, the robot must take a new trajectory to reach the goal and achieve the tasks. Therefore, collaboration between the DTs representing human and robot in smart manufacturing can help each other accomplish the required tasks effectively and efficiently.  
Furthermore, blockchain technology could be combined with DTs collaboration to guarantee transparency, decentralized data storage, data sharing, peer-to-peer communication, secure and trusted traceability, and scalability.
## 5 Conclusions
### Nomenclature  
\begin{tabular}{l l l} \(c_{p}\) & specific heat capacity at constant pressure & (J/(kg-K)) \\ \(c_{v}\) & specific heat capacity at constant volume & (J/(kg-K)) \\ \(d_{gp}\) & gas port diameter & (m) \\ \(E_{gch}\) & gas chamber gases work & (J) \\ \(E_{p}\) & projectile energy & (J) \\ \(\mathrm{E_{ra}}\) & recoiling assembly energy & (J) \\ \(f\) & propellant force & (J/kg) \\ \(F_{cc}\) & cartridge case--chamber interaction force & (N) \\ \(F_{r}\) & projectile resistance force (with barrel bore and cartridge case) & (N) \\ \(I_{g}\) & enthalpy of gas flowing through the gas port between the barrel and & (J) \\ \(I_{m}\) & enthalpy of gas flowing out to the environment & (J) \\ \(I_{p}\) & moment of inertia of the bullet & (kg-m\({}^{2}\)) \\ \(k\) & specific heats ratio & (-) \\ \(k_{rs}\) & stiffness of recoil spring & (N/m) \\ \(l\) & travel of the bullet & (m) \\ \(l_{gp}\) & distance between the initial position of the bottom of the bullet and the & (m) \\  & gas port & (m) \\ \(l_{m}\) & length of the barrel & (m) \\ \(L\) & travel of the recoiling assembly & (m) \\ \(m\) & mass of the bullet & (kg) \\ \(M\) & mass of the recoiling assembly & (kg) \\ \(Q\) & powder combustion energy & (J) \\ \(q_{s}\) & powder combustion heat & (J/kg) \\ \(p\) & barrel bore gas pressure & (Pa) \\ \(p_{0}\) & pressure of the ambient & (Pa) \\ \(p_{gch}\) & gas chamber gas pressure & (Pa) \\ \(p_{p}\) & bottom of the bullet pressure & (Pa) \\ \(R\) & individual gas constant & (J/(K-kg)) \\ \(s\) & barrel bore cross-sectional surface area & (m\({}^{2}\)) \\ \(s_{gch}\) & gas chamber cross-sectional surface area & (m\({}^{2}\)) \\ \(s_{gp}\) & gas port cross-sectional surface area & (m\({}^{2}\)) \\ \(T\) & gas temperature in the barrel bore & (K) \\ \(T_{0}\) & initial temperature & (K) \\ \(T_{1}\) & temperature of propellant combustion & (K) \\ \(T_{gch}\) & gas temperature in the gas chamber & (K) \\ \(t\) & time & (s) \\ \(U\) & internal energy of propellant gas in barrel bore & (J) \\ \(U_{gch}\) & internal energy of propellant gas in gas chamber & (J) \\ \(v\) & projectile velocity & (m/s) \\ \(V\) & recoiling assembly velocity & (m/s) \\ \(W_{0}\) & initial volume of cartridge chamber & (m\({}^{3}\)) \\ \(W_{0 gch}\) & initial volume of gas chamber & (m\({}^{3}\)) \\ \(x_{0}\) & recoil spring pre-deflection & (m) \\ \(\alpha\) & exponent in the burning law & (-) \\ \(\beta\) & first virial coefficient & (m\({}^{3}\)/g) \\ \(\gamma\) & relative mass (volume) of the propellant gas which flow out of the barrel & (-) \\  & bore through the muzzle & (-) \\ \(\Gamma\) & dynamic viscosity & ((MPa-s)\({}^{-1}\)) \\ \(\delta\) & propellant density & (kg/m\({}^{3}\)) \\  & relative mass (volume) of the propellant gas which flow out of the barrel & (-) \\ \(\eta\) & bore to the gas chamber & (-) \\ \(\eta_{b}\) & rifling twist & (m) \\ \(\theta\) & function of ratio of specific heats (\(\theta=\mathrm{k}-1\)) & (-) \\ \(\xi_{g}\) & coefficient of gas flow loss from the barrel to the gas chamber & (-) \\ \end{tabular}
# Diversity of Potential (Bio)Technological Applications of Amino Acid-Based Ionic Liquids
## 2 Bioapplications of AA ILs: An Overview
### AA IL Application in Wastewater Treatment and Separation Processes  
In recent years, ILs, including those based on AAs, have shown significant potential for the decontamination of industrial wastewater and sludges [151, 152, 153]. They can be used for the adsorption and extraction of heavy metals and organic pollutants, as well as for desalination [154]. IL-functionalized membranes further increase purification efficiency. In addition, some ILs possess inherent antimicrobial properties, making them valuable for disinfection, preventing biofouling in water systems, and inhibiting bacterial growth by disrupting microbial cell membranes. Examples of the excellent performance of AA ILs in the removal of water contaminants are summarized in Table 2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method/AA IL Used** & **Pollutant/Target** & **Achieved *** & **References** \\ \hline
**ATPE (25 \({}^{\circ}\)C; 0.1 MPa)** & & extraction efficiency & \\
[Ch][L-Gly]-K\({}_{2}\)HPO\({}_{4}\)–water (*) & extraction of & (E\%) = 97 \(\pm\) 2; & \\
[Ch][L-Ala]-K\({}_{2}\)HPO\({}_{4}\)–water & amoxicillin & partition coefficient & \\
[Ch][L-Ser]-K\({}_{2}\)HPO\({}_{4}\)–water & & (K) > (16 \(\pm\) 6)10\({}^{1}\) & \\ \hline
**ATPE (25 \({}^{\circ}\)C; 0.1 MPa)** & & & \\
[Ch][L-Gly]-K\({}_{2}\)HPO\({}_{4}\)–water & & & \\
[Ch][L-Ala]-K\({}_{2}\)HPO\({}_{4}\)–water (*) & extraction of & (*) E (\%) =97.5 & \\
[Ch][L-Ser]-K\({}_{2}\)HPO\({}_{4}\)–water & acetaminophen & (**) E (\%) =97.5 & \\
[Ch][L-Ala]-K\({}_{3}\)HPO\({}_{4}\)–water & & & \\
[Ch][L-Leu]-K\({}_{3}\)HPO\({}_{4}\)–water (**) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Applications of AA ILs in contaminant isolation, wastewater purification, and separation of target materials.
## 3 DEM-FDM Coupled Model  
### Modeling  
The CRTS II slab track was considered as a continuous domain and the subgrade was considered as a discrete domain. An FD model of a CRTS II slab track was developed by using the software of FLAC3D (Itasca, Minneapolis, MN, USA). A DE model of the surface layer of the subgrade was developed by using the software of PFC3D (Itasca, Minneapolis, MN, USA). The FLAC3D can be loaded with the program load command in PFC3D 6.0 version [26]. So the PFC3D and FLAC3D can be run at the same time. The DEM-FDM coupled model is presented in Figure 3. Because the slab track mainly experiences vertical deformation under differential subgrade settlement, the lateral deformation of the track structure can be ignored. Therefore, the horizontal size of the track structure was reduced and only a strip-shaped model was established. The FD model consisted of the precast slab, CA mortar layer, and concrete base. The rails and fasteners were not taken into account. The width of the model along the transverse direction was 0.6 m. The length of the model along the longitudinal direction was 30 m. The thicknesses of the precast slab, CA mortar layer, and concrete base in the model were 0.2, 0.03, and 0.3 m, respectively. The FD model of the CTRS II slab track has meshed with solid elements. This model contained 39,600 solid elements and 50,484 nodes. It was assumed that there is no separation or slipping between the precast slab, CA mortar layer, and concrete base. The symmetry constraints were applied to the two ends of the slab track.  
The surface layer of the subgrade is in direct contact with the slab track. The dynamic stress in the surface layer of the subgrade attenuates quickly under train load and the dynamic stress in the bottom layer of the subgrade is significantly reduced [27]. Therefore, only the surface layer of the subgrade was considered in this study. Ball units were used to model the gravel grains in the subgrade. Considering that the irregular shape of the particles will affect the mechanical properties, the contact behaviors between grains were simulated using the rolling resistance model to represent the interlocking between irregularly shaped grains [28]. The thickness of the surface layer of the subgrade was 0.4 m. The bottom and side boundaries of the surface layer of the subgrade model were simulated with wall elements. The gradation curve of the gravel used in this study is presented in Figure 4.  
The average particle size of the gravel in the model was less than 40 mm. When the container size is greater than eight times the average particle size, the boundary effects of the container can be ignored [29]. Therefore, the boundary effects in this model can be ignored. The surface of the slab track was defined as a coupled interface for data interaction. The parameters for the model are listed in Table 1.  
Figure 3: The coupled model.
## 5 Conclusions  
The control theories and structure of two polarization control structures in the homologous SHC optical communication systems are discussed, one is mainly composed of an HWP, and the other is based on an MZI. Furthermore, in terms of polarization control algorithms, an effective improvement compared with GLD and GD algorithms in tracking ability is achieved here with the proposed variable-step GLD algorithm. At the same time, we give the controllable RSOP under different update rates of HWP or phase shifter to illustrate the influence of the loop situation on tracking ability. The further improvement of tracking capability mainly depends on improving the physical performance of PC, as well as proposing new tracking algorithms. Advanced photonic integrated circuit technology will provide new possibilities for the development of polarization controllers.  
**Author Contributions:** Methodology, Q.F. and X.Z.; Software, Q.F.; Validation, R.L., Y.G., S.W. and F.L.; Formal analysis, Q.F.; Investigation, R.L., S.W. and F.L.; Resources, Y.G. and S.W.; Data curation, Y.G.; Writing--original draft, Q.F.; Writing--review & editing, X.Z.; Visualization, R.L. and K.L.; Supervision, X.Z. and K.L.; Project administration, X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (Grant No. 62171022), Beijing Natural Science Foundation (Grant No. 4222009), Guangdong Basic and Applied Basic Research Foundation (Grant No. 2021B1515120057), the Scientific and Technological Innovation Foundation of Shunde Graduate School, USTB (Grant No. BK19 AF005).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Machine Learning and Deep Learning Based Model for the Detection of Rootkits Using Memory Analysis
## 2 Background
### Machine Learning (ML) in Memory Analysis  
ML algorithms can analyze memory dumps and recognize patterns associated with malware, rootkits, and other malicious activities. it has been used for over a past decade to detect the malwares [5]. By training on labeled data, ML models can learn to identify anomalies and deviations from normal system behavior [6]. These algorithms can detect memory-resident malware, unauthorized process injection, and other stealthy attacks by detecting deviations from learned behavioral profiles [7]. For instance, ML algorithms such as Random Forest (RF) or Support Vector Machine (SVM) can be trained on features extracted from memory dumps, such as API calls, memory sections, and process relationships. These models can then classify memory contents as malicious or benign, aiding in the rapid identification of threats. A short explanation of four common ML algorithms is given below:  
* **Random Forest (RF):** Random Forest is an ensemble algorithm that combines multiple decision trees to enhance accuracy and prevent over-fitting [8]. It creates diverse decision trees and aggregates their predictions to improve model robustness. It is resistant to over-fitting, handles both classification and regression tasks, works well with high-dimensional data, and provides feature importance scores. It can be computationally intensive and may not perform well on very small datasets and usually outperformed SVM for the test dataset [7].
* **Support Vector Machine (SVM):** The Support Vector Machine aims to find a hyperplane that best separates different classes. It is effective in both linear and non-linear scenarios and in high-dimensional spaces. It can be sensitive to the choice of kernel and hyper-parameters and does not scale well to large datasets and usually outperformed RF for the training dataset [7].
* **Decision Tree (DT):** Decision Trees partition the input space into segments based on feature values, enabling intuitive decision-making. Recursive splits based on feature importance create a tree structure [9]. It is easy to understand and interpret, handles non-linear data well, and requires minimal data pre-processing. However, it can be prone to over-fitting and can be sensitive to small variations in data.
* **k-Nearest Neighbors (k-NN):** k-Nearest Neighbors classifies data by comparing a data point with its neighbors. It classifies or labels a data point by considering the majority class of its k-nearest neighbors. The distance metric, often Euclidean distance [9], determines \"nearness\" as defined below: \[d(x,y)\ =\ \sqrt{\sum_{i=1}^{n}(x_{i}-y_{i})^{2}}\] (1) Intuitive and easy to implement, it adapts well to local data characteristics. However, it can be sensitive to irrelevant features and it is computationally expensive during prediction.
## 4 Experiments
### Performance Metric  
In our experiments, we use the Macro average F1-score as the evaluation metric to compare the performance of our proposed method with other methods. In particular, for the Opportunity dataset, accuracy is not a suitable measure due to its highly uneven categorization. Since the traditional F1-score measures the performance of binary classification, we use the mean F1-score, which is \(F_{m}\), weighted to categories according to their sample proportions.  
\[F_{m}=\frac{1}{C}\sum_{i=1}^{C}\frac{2\times Precision_{i}\times Recall_{i}}{ Precision_{i}+Recall_{i}} \tag{24}\]  
where \(C\) is the number of action categories. For category \(i\), \(Precision_{i}=\frac{TP_{i}}{TP_{i}+FP_{i}}\), \(Recall_{i}=\frac{TP_{i}}{TP_{i}+FN_{i}}\). \(TP_{i}\) and \(FP_{i}\) are the number of true positives and false positives, respectively, while \(FN_{i}\) refers to the number of false negatives.
# A Sensitivity Study of POD Using Dual-Frequency GPS for CubeSats Data Limitation and Resources
## 3 Orbit Determination under Different Scenarios
### Latency Applying Different GPS Products
In the case of having limited data available, i.e., with short duty-cycles, limited satellite numbers and sampling rate, the orbital accuracy using the IGR and IGC products would not be as good as shown in Figure 8 anymore. In the case of using the IGR and IGC products, Figure 9 illustrated the 3D RMSE of the difference between phase-based reduced-dynamic orbits and the best-case scenario when applying different duty-cycles, mean satellite numbers, and observation sampling rates. Note that the cases having different satellite numbers (9, 7, and 6) are given between each pair of the dashed lines. Compared to Figure 6 using the IGS final products, the 3D RMSE has generally increased due to the reduced accuracy of the GPS products and the higher sampling interval of the GPS clocks (for IGR products). As discussed for the IGS final products, when using the IGC products (Figure 9b), one can  
Figure 8: 3D orbital errors of the phase-based reduced-dynamic orbits using data of GRACE FO-1 with a 100% duty-cycle, a mean satellite number of 9, and a sampling interval of 10 s (having the IGS final as the reference). Different IGS products were applied. Figure 7: Differences in the between-satellite clocks for G02 and G32 between the IGS final and rapid clocks. similarly observe that a high observation sampling interval of, e.g., 10 s (green) or 20 s (yellow), is not helpful to reduce the RMSE due to the GPS clock sampling of 30 s. For the cases when using the IGR products (Figure 9a) with a higher GPS clock sampling interval of 300 s (Table 4), no obvious degradation can be concluded even when having an observation sampling interval of 120 s.