esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Sediment Export Estimation Using SDR Model
#### 4.7.3 Sediment Export Estimation of Predicted LULC under Scenario II  
The estimated total and average sediment export under predicted LULC between 2020 and 2029 and mean rainfall erosivity, according to Scenario II (Maximization of ecosystem service values), are presented in Table 22. As a result, the highest total and average sediment exports were about 48,115 tons and 53.98 tons/km\({}^{2}\), respectively, in 2026. The lowest total and average sediment exports were about 30,190 tons and 33.87 tons/km\({}^{2}\), respectively, in 2022.  
As with Scenario I, these results indicated that the primary influence of the rainfall erosivity factor and LULC types in the RUSLE model affected sediment export. The influence of rainfall erosivity on sediment export under Scenario II was relatively stronger than that in Scenario I, as shown in Figure 8. According to the R\({}^{2}\) value of 0.9589, the rainfall erosivity can explain the linear relationship with sediment export by about 96%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Sediment Export**} & \multicolumn{2}{c}{**Mean Rainfall Erosivity**} \\ \cline{2-4}  & **Total (tons)** & **Average (tons/km\({}^{2}\))** & **(MJ mm ha\({}^{-1}\) h\({}^{-1}\) y\({}^{-1}\))** \\ \hline
2020 & 41,445.02 & 46.50 & 2854.33 \\
2021 & 35,685.45 & 40.04 & 2490.34 \\
2022 & 32,373.49 & 36.32 & 2239.93 \\
2023 & 41,954.52 & 47.07 & 2711.93 \\
2024 & 39,669.68 & 44.51 & 2601.06 \\
2025 & 48,329.18 & 54.22 & 2992.66 \\
2026 & 58,797.97 & 65.97 & 3130.43 \\
2027 & 42,549.03 & 47.74 & 2456.18 \\
2028 & 46,867.05 & 52.58 & 2588.82 \\
2029 & 49,558.85 & 55.60 & 2640.18 \\ \hline
**Average** & **43,723.02** & **49.06** & **2670.59** \\ \hline \hline \end{tabular}
\end{table}
Table 21: Estimation of sediment export between 2020 and 2029 under Scenario I.  
Figure 7: Relationship between mean rainfall erosivity and average sediment export under Scenario I.  
Moreover, the contribution of the predicted LULC under Scenario II to sediment export between 2020 and 2029 revealed that miscellaneous land decreased every year under this scenario; still, it caused the highest average sediment export until 2027, with values between 1088.47 tons/km\({}^{2}\) in 2020 and 512.54 tons/km\({}^{2}\) in 2027. After that, the field crop yielded the highest average sediment export in 2028 and 2029, with values between 447.46 tons/km\({}^{2}\) and 470.16 tons/km\({}^{2}\), respectively. In contrast, forest land generated the lowest average sediment export with values between 0.64 tons/km\({}^{2}\) in 2022 and 1.02 tons/km\({}^{2}\) in 2026.
## 4 Parameter Discussion
### Effect of Scale Factor  
As previously mentioned, the continuous wavelet transform's scale factor significantly impacts the effectiveness of the proposed damage detection method. In practical applications, the optimal scale factor is usually determined through trial and error and adjustments. Taking Case 3-4 as an example, the basic wavelet scale factor corresponding to the contact point acceleration response was determined to be Scale = 84 using the Mexican hat wavelet. Then, based on this basic scale, the continuous wavelet transform signals of \(n\) times the basic scale were examined. Figure 8 shows the variational modal decomposition-continuous wavelet transform within the range of n = 3-8 times the basic scale. The figure shows that the wavelet scales of 3-7 times the basic scale can effectively identify the damage location. Moreover, as the scale increases, the influence of the wavelet boundary effect gradually increases, and the length that can be recognized will gradually decrease, so the appropriate wavelet scale should be selected for damage recognition according to the actual situation in practical use.  
Figure 7: Variational modal decomposition–continuous wavelet transform at different vehicle speeds.
## 3 Mathematical Model
### Parameters, Decision Variables, and Notations  
The model in this section determines the line stops, their frequency, and the number of carriages in a given intercity railway line such that a given fluctuating demand transportation volume, again given by an OD-matrix, can be satisfied as much as possible under the rail line, then the profit can be maximized. We predefine a line pool for each direction, in which each line starts at a terminal station and ends, respectively, at the other terminal station. The line planning problem that was studied in this paper is different from the line planning problem with equilibrium demand, and the additional constraints must be considered when studying the line planning problem with fluctuating demand. The consideration of resource availabilities and the circulating of resources comes naturally and inherently with this problem.  
The main assumptions are as follows. First of all, the fluctuating demand is given at an interval of 1 h. If the passengers arriving in a certain period cannot be fully satisfied, the remaining passengers have the probability of waiting for the next period to travel. This is because we have observed that in real life, some passengers may change their trip when they cannot travel at the expected time. Then, we do not assume the OD-matrix to be symmetric. Passenger flows in different directions are often asymmetric during peak periods. This also means we need to simultaneously consider the line planning problem in different directions. Next, we only consider one type of train, and the number of carriages can range from 2 to 16. Lastly, the train is only disassembled and reconnected at the terminal stations.  
The notations, input parameters, and variables for the optimization problems that are under consideration are listed in Table 1.  
Figure 2: Illustration of the operation process in this study.
# Odor Impact Assessment via Dispersion Model: Comparison of Different Input Meteorological Datasets
###### Abstract  
Dispersion modeling is a useful tool for reproducing the spatial-temporal distribution of pollutants emitted by industrial sites, particularly in the environmental odor field. One widely used tool, accepted by regulatory agencies for environmental impact assessments, is the CALPUFF model, which requires a large number of input variables, including meteorological and orographical variables. The reliability of model results depends on the accuracy of these input variables. The present research aims to discuss a comparative study of odor dispersion modeling by initializing the CALMET meteorological processor with different input data: surface and upper air observational meteorological data, 3D prognostic data, and a blend of prognostic and measured data. Two distinct sources (a point and an area source) and two different simulation domains in Cuba and Italy are considered. The analysis of results is based on odor impact criteria enforced in some Italian regions by computing the 98 th percentile of odor peak concentrations on an annual basis. For the area source, simulation results reveal that the 'OBS' and 'HYBRID' modes are largely comparable, whereas prognostic data tend to underestimate the odor concentrations, likely due to a reduced percentage of wind calms. For point sources, different input meteorological settings provide comparable results, with no significant differences.  
dispersion modeling; CALPUFF model; odor impact assessment; intercomparison; WRF; meteorological data +
Footnote †: journal: Applied Sciences
## 3 Numerical Examples
### Media Randomness
#### 3.1.3 Effect of Permeability Coefficient on Saturated Sedimentary Valleys
As can be seen in Table 2, compared with 0\({}^{\circ}\), the average coefficient of variation of displacement amplitude of 30\({}^{\circ}\) is enlarged by 1.3-4.8 times (H) and 1.1-3.0 times (V), respectively, indicating that the influence of the incident angle on seismic wave scattering cannot be ignored. From the perspective of the position of the incident wave, the coefficient of variation of the left surface (\(x/a<0\)) is higher than that of the right surface (\(x/a>0\)) as a whole. That is, the incident position has a certain influence on the coefficient of variation. Under the incident of the high frequency wave, the influence of the randomness of permeability coefficient on the seismic response at the edge of the sedimentary valley is also very prominent, and the coefficient of variation can reach 8.23 (\(\eta=2,\theta=0^{\circ}\).V). This phenomenon becomes more significant with the increase in the dimensionless frequency.
## 6 Conclusions and Future Work  
Improving the security is critical for the success of LoWPAN networks, because these types of networks are particularly vulnerable, they are used in critical services and the data collected is often sensitive. Several security solutions were proposed, most of them designed to address known attacks and implemented on particular layers. Several security attacks can be avoided if a network access control mechanism is used to restrict the network access only to authorized nodes which are compliant with the defined security requirements. This paper, we have proposed a network access control framework that can be used to accomplish these objectives. The proposed solution enables the node identification based on cryptographically generated addresses, node security compliance evaluation and node remediation with secure remote software installation. This solution is mainly based on the following open protocols: LSEND, used to secure neighbor discovery and node secure identification, RPL with ECC support, to protect the routing messages and Seluge, to enable secure remote software installation. In the current proposal, synergies between the protocols were taken in consideration. Work is still required to improve and integrate these protocols in order to create a solution. Moreover, the current proposal only enables one border router. As a consequence, when the border router becomes unreachable, all nodes must be approved and evaluated. Further research is required to circumvent this limitation since a secure mechanism is required to synchronize the ECC keys and nodes authorization state between multiple border routers. Besides the current lab implementation to validate our current proposal, this research is another direction of our future work.
# A Brief Review of Multipath TCP for Vehicular Networks
## 4 Cross Layer MPTCP, MPTCP with SDN, and Energy Efficient MPTCP
### Cross Layer MPTCP Approach  
In [84], [PERSON] et al. presented a MPTCP cross-layer context aware approach called MPTCP-CA, to manage and utilize network resources. MPTCP-CA cooperates with other layers to perceive the entire network states, based on which subflows are created or destroyed. They also provide a guidance for how to utilize network interfaces. The architecture of the MPTCP-CA consists of kernel space and user space. Path manager resides in the kernel space, and triggers a communication with user space applications when a MPTCP connection starts. The core logic of the path manager is implemented at the user space in order to ensure configurability and flexibility. At the same time, it is able to have the path manager extended with an API. In [85], [PERSON] et al. proposed a cross layer MPTCP path managers, namely, MPTCP-MA. They utilize the link quality information of MAC layer to help to decide how to manage subflows. More specifically, subflows are decided either suspending or releasing according to the MAC Layer information. For example, if the number of MAC-Layer frame retransmissions becomes larger than the number of successful frame transmissions at the low PHY data rate, then it will be notified that the Wi-Fi subflow is unavailable and set to inactive.  
In [86], [PERSON] et al. proposed a MPTCP cross layer cooperation module for cloud networks, namely, augmented MPTCP (A-MPTCP). The A-MPTCP is able to fold the transmission time with the assistance of location/identifier separation protocol (LISP). In A-MPTCP, the information from the user space is used to inform the path manager about the available paths information of cloud environments.
# Research on Mechanical Performance of Porous Asphalt Mixture with High-Viscosity Modified Asphalt
## 2 Preparation and Performance Analysis of High-Viscoelastic Modified Asphalt  
### Raw Materials  
High-viscoelastic modifiers primarily consist of thermoplastic materials, resins, antioxidants, stabilizers, co-solvents, and other materials. Thermoplastic materials and resins make up 70% of the total.  
1. Thermoplastic materials Styrene-butadiene-styrene block polymers (SBS) are among the most abundant, lowest-cost, and widely used thermoplastic materials globally. SBS is primarily used in four major fields, namely rubber products, resin modifiers, adhesives, and asphalt modifiers [16]. SBS exhibits excellent tensile strength, good low-temperature performance, excellent electrical properties, and good processing performance.
2. Resin Hydrocarbon resin is one of the by-products of oil cracking. It is a resinous substance formed by the copolymerization of aldehydes, aromatic hydrocarbons, and terpenes [17]. Resin primarily acts as a binder, enhancing the viscoelasticity, acid resistance, and alkali resistance of the modified bitumen. Additionally, it can effectively reduce production costs.
3. Antioxidant  
Antioxidants are alkylated monophenolic compounds that primarily function to enhance aging resistance and improve the high-temperature stability of polymers.
4. Stabilizer  
Stabilizers are white, powdery organic substances. Their primary function is to prevent the decomposition and aging of polymer plastics, rubbers, and synthetic fibers. Stabilizers also improve the surface mechanical properties of modified bitumen.
5. Co-solvent  
The co-solvent is a product of the catalytic cracking of petroleum. It promotes the dispersion and solubility of high-viscoelastic modifiers in asphalt. The co-solvent used in this research has a relative density of 1.05 g/cm\({}^{3}\) and contains 0.1% mechanical impurities.
## 2 Materials and Methods
### Experimental Design  
The Box-Behnken design (BBD), a standard RSM design, is highly suited to fitting a quadratic surface, which is often used for process optimization. It was selected to identify the optimum extraction conditions for luteolin, caffeic acid, and apigenin from the dried parsley sample. The four independent factors were temperature (X\({}_{1}\)), time (X\({}_{2}\)), liquid to solid sample ratio (X\({}_{3}\)), and ethanol concentration (X\({}_{4}\)).  
Each factor was coded at three levels: \(-\)1, 0, and +1. The factors and their corresponding levels, both coded and actual, chosen in the four-factor-three-level BBD were based on preliminary one-factor-at-a-time experiments, literature research, and instrumental specifications, and are presented in Table 1.  
RSM was used to fit a complete second-order polynomial equation to the design points and experiment data. The following quadratic response surface model Equation (1) for four components was fitted:  
\[Y=\beta_{0}+\sum_{i=1}^{4}\beta_{i}X_{i}+\sum_{i}^{4}\beta_{ii}X_{i}^{2}+\sum_ {i=1}^{3}\sum_{j=i+1}^{4}\beta_{ij}X_{i}X_{j}+\varepsilon \tag{1}\]  
In Equation (1), \(Y\) corresponds to the response variable (ug/g DM), for each phenolic under evaluation (luteolin, caffeic acid, and apigenin) that was obtained via HPLC-DAD. \(X_{i}\) and \(X_{j}\) are the independent factors affecting the response (Table 1). The terms \(\beta_{0}\), \(\beta_{i}\), \(\beta_{ii}\), and \(\beta_{ij}\) are the regression coefficients of the model (intercept, linear, quadratic, and interaction term) and \(\varepsilon\) corresponds to the random error term.  
Analysis of variance (ANOVA) was used to estimate the fitting of the entire quadratic approximation of the BBD response surface model. The significance of each source of terms (linear, two-factor interaction, and quadratic), as well as the regression coefficients of the fitted model, were examined using the F-values and pertinent \(p\)-values. Statistically significant terms were those whose probability (\(p\)-value) at the 95% confidence level fell below 0.05.
## 3 Time Domain Problem Formulation
The spatial distribution of the magnetic-current as represented by Equations (5) and (6) is chosen such that the pertinent end conditions, \(\partial K_{x}^{\text{s}}=0\) and \(\partial K_{y}^{\text{s}}=0\) at \(x=\pm\Delta_{x}\) and \(y=\pm\Delta_{y}\), respectively, are satisfied for all \(t>0\). Along their transverse direction, the magnetic-current components are assumed to have the uniform spatial distribution. Consequently, the magnetic-current flowing in parallel to the edge does not exhibit the inverse-square-root singularity as required by the pertaining edge condition ([1], Chapter 4). It can be expected, however, that this choice will have a negligible impact on the estimation of far-field characteristics [32]. Finally, it remains to specify the testing currents on the right-hand side of the TD reciprocity relation. To this end, we may choose the \"razor-type\" testing functions  
\[\partial K_{x}^{\text{T}}(x,y,t) =\Pi(2x)\delta(y)\delta(t), \tag{9}\] \[\partial K_{y}^{\text{T}}(x,y,t) =\Pi(2y)\delta(x)\delta(t). \tag{10}\]  
It is noted that the thus-chosen testing currents satisfy the end conditions, \(\partial K_{x}^{\text{T}}=0\) and \(\partial K_{y}^{\text{T}}=0\) at \(x=\pm\Delta_{x}\) and \(y=\pm\Delta_{y}\), respectively.
# Real-Time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation
## 4 Experiments
### Dataset and Metrics  
**CDNet2014 [47].** It contains 11 categories: Baseline, Camera Shake, Severe Weather, Dynamic Background, Intermittent Object Motion, Low Frame Rate, Night Video, PTZ (Pan-Tilt-Zoom), Shadows, Heat, and Turbulence. Each category contains 4 to 6 sequences. There are 53 different video sequences in total.  
**Metrics.** The evaluation metrics are recall (\(\mathcal{R}\)), precision (\(\mathcal{P}\)), F1 score (\(\mathcal{F}\)), percentage of wrong classifications (\(\mathcal{PWC}\)) and inference frames per second (\(\mathcal{FPS}\)).
# Online Learning Based Underwater Robotic Thruster Fault Detection
## 2 General Framework of Online Learning-Based Fault Detection  
The general framework of the proposed underwater robotics thruster fault detection method is illustrated in Figure 1.  
In the online learning-based fault detection algorithm, propeller speed of the underwater robot thruster is predicted through an online learning model. Additionally, an adaptive fault detection strategy is established based on model online update mechanism. In order to achieve data alignment, the time delay between the control variable and the measured propeller speed is estimated by analyzing the trend of the correlation changes. The polynomial fitting is introduced to build a control variable-speed model based on the aligned data. To improve the accuracy of online modeling, a multi-center particle swarm optimization (PSO) algorithm with memory ability is utilized to optimize the model  
Figure 1: Structure of the online learning-based fault detection algorithm.  
order and modeling data volume. In the proposed PSO algorithm, we designed an initial optimization strategy based on prior knowledge, historical optimal solution and improved tent mapping. Eventually, a multi-center collaborative search strategy is introduced to reduce the odds of local minima.
# Efficient SMC Protocol Based on Multi-Bit Fully Homomorphic Encryption
## 3 Efficient Fully Homomorphic Encryption Scheme
### Security of the Scheme  
Supposing that parameters \(n=\text{poly}(\lambda)\) and \(q=\text{poly}(\lambda)\) are the polynomials of the security parameter \(\lambda\), if the attacker can distinguish the ciphertext of the NFHE scheme from the uniform distribution on \(\mathbb{Z}_{p}^{m\times m}\) with a non-negligible advantage, the \(DLWE_{q,n,2n+1,\chi}\) problem can also be solved. Therefore, if the problem is assumed to be difficult, then the NFHE scheme can achieve IND-CPA security.  
The detailed proof can be found in reference [18].
# Applications of Graphene-Based Nanomaterials in Environmental Analysis
## 2 Graphene-Based Nanomaterials and Nanocomposites  
One of the assets provided by graphene-based materials is selective adsorption. The rGO presents a large \(\pi\)-electron system, making it ideal for the adsorption of organic compounds that consist of aromatic rings. The GO has several functional groups that favor the adsorption of organic molecules containing oxygen- or nitrogen-functional groups.  
Even though graphene-based materials (G, GO and rGO) have an enhanced ability of absorbing many and different compounds, they show a significant drawback; the difficulty of the material with the adsorbed analytes being separated from the mother solution. To combat this problem, magnetic nanoparticles (MNPs) are impregnated into rGO or GO. Thus, the combination of high adsorption capacity from G and the convenient separation of the adsorbent from the sample, given from the MNPs, makes these nanocomposites ideal for analytical purposes [43]. Moreover, Fe\({}_{3}\)O\({}_{4}\) nanoparticles are non-toxic, have a small particle size, offer high reactivity and possess a vast specific surface area, adding to the benefits of their usage and rendering them more appealing [46; 47].
# Conjugation of Urokinase to Water-Soluble Magnetic Nanoparticles for Enhanced Thromolysis
## Materials and Methods
### In Vitro Thrombolysis Experimental System  
The thrombolysis experiment in vitro was launched in a mimic blood vascular vessel, as shown in Figure 6a. In this system, a plastic tube with an inner diameter of 2 mm is regarded as the mimic blood vessel. The blood sample is collected from a healthy white mouse provided by Tongji Hospital, Wuhan, China. The blood was collected and conserved in a plastic test tube at room temperature for 2 h, which naturally forms thrombus at the bottom of the tube. The thrombus was placed in close proximity to a semi-circular plastic fixture block glued on the vessel wall to eliminate the possibility of being flushed away by the flowing fluid. The semi-circular segmented block has the same diameter as the vessel and a thickness of 5 mm. The thrombus was surrounded by a three-axis magnetic control system. During thrombolysis experiment, the prepared magnetic NPs solution in a 50 mL syringe was gradually injected into the vessel by using a micro-injection pump with an injection rate of 250 mL/min. The final reaction solution was collected in a 500 mL retrieval beaker. Figure 6b shows a detailed magnetic control system composed of three pairs of magnetic coils for generating a coupled magnetic field. Magnetic coils are all manually wound with the same 400 turns. The coils have an iron core on the \(y\)-axis, which is affected by direct current (DC) and generates a static magnetic field that magnetizes and accumulates magnetic NPs in the thrombus region. Coils on the \(x\)-axis and \(z\)-axis are input into alternating current (AC) with a phase difference of 90 degrees to generate a rotating magnetic field (0.05 T), forcing the NPs to rotate. The rotation would lead to rapid release of urokinase from NPs, which would further improve the thrombolysis efficiency.
# Efficient Distributed Method for NLOS Cooperative Localization in WSNs
## 5 Numerical Simulations
### Performance Comparison  
In this part, we verify the superiority of our method in the aspects of localization accuracy and computational complexity, respectively. We fixed the number of source nodes and anchors as \(N=40\), \(M=10\). The mean parameter of NLOS propagation is \(\alpha_{\mathrm{NLOS}}=3\). Sensor nodes (including anchors) were randomly distributed in the square of \([-50\) m, \(50\) m\(]\times[-50\) m, \(50\) m\(]\). To show the simulation results more precisely, we compared the ADMM_P algorithm with three other state-of-art methods in the same simulation environment.