esa-sceva/satcom-chunk-collection huggingface dataset:
# An InSAR Interference Fringe-Matching Algorithm Based on Mountain Branch Points
## 2 Methods  
### Algorithm Flow  
As analyzed in Section 1, the primary reason why traditional algorithms cannot effectively match interference fringes is that they fail to utilize the abundant terrain features present within the interference fringes. Therefore, based on this point, this paper first designs an algorithm to extract the mountain line features contained in interference fringes, and then the mountain branch points are detected based on online features, such as the keypoints, for feature matching; this matching is carried out by combining the mountain line features and curvature design feature descriptors of the area around a branch point. The algorithm for matching the interference fringes proposed in this paper is shown in Figure 2. The first step is to calculate the Hessian matrix of interference fringes and obtain the eigenvalues and eigenvectors of this matrix, set two segmentation thresholds for the eigenvalue map, one large and one small, and skeletonize on the coarse mountain range lines after threshold segmentation to obtain ridge and valley lines. In the second step, we use the branch point template to traverse the mountain ridge and obtain the mountain branch point as the key feature for image matching. To eliminate false detections caused by phase mutations, we set a threshold. In the third step, the Gaussian function is refined based on the mountain range line mask in the region around a branch point, and the descriptors are generated by assigning the direction of the descriptors from the branch point eigenvectors and obtaining a histogram of the curvature of the region around the branch point based on the eigenvalues of the pixels around the branch point. Subsequently, the branch point descriptors obtained from the two interference fringes are matched using feature descriptors at Euclidean distance according to the flow of the conventional image matching algorithm. Using the RANSAC [27] algorithm to filter out the correctly matched feature points, the navigation parameters can subsequently be output.
# Biogenic Nanoparticles: Synthesis, Characterisation and Applications
## 4 Discussion  
Recently, the area of microbial synthesis of nanoparticles has seen considerable advancement. The above-mentioned synthesis methods of nanoparticles have many benefits, including being both cost-effective and environmentally friendly [116]. In comparison to physical and chemical methods, there are no harmful chemicals present in biogenically produced nanoparticles. Another advantage of the biogenic scheme of manufacturing is that such methods need not the addition of a stabilizing compounds on the nanoparticle surface in order to achieve long-term pharmacological activity [117]. Additionally, biogenic synthesis time is much shorter than using physicochemical processes as described in the following examples. [PERSON] et al. demonstrated the synthesis of silver nanoparticles using red alga _Laurencia catarinensis_ within 2 min [118]. Their results showed various shaped nanoparticles between 39 and 77 nm in size. Gold nanoparticles have been synthesised using the fungus _Aspergillus flavus_ within 2 min [119]. Cadmium sulphide nanoparticles have been synthesised using bacteria in 10-20 min [120]. Various other studies have also shown rapid biogenic synthesis of nanoparticles [121, 122, 123, 124, 125].  
Despite these advantages, biogenic synthesis faces challenges in controlling the nanoparticles size as polydispersity is an issue. Additionally, substantial effort is required to enhance the ability to regulate particle size and morphology, and to fine tune particle size distribution. For this reason, many biogenic studies have focused on developing methods that can result in monodisperse synthesis of nanoparticles [121]. The morphology of nanoparticles can be affected either by altering the process parameters to understand the optimal synthesis parameters. Parameters such as pH, temperature, reaction time and reagent concentrations equally affect the nucleation process and hence the nanoparticles formation [123]. Another challenge is the difficulty in identifying the best microbial candidates based on their inherent properties such as the growth rate, replicability and biochemical activities. There needs to be attention given to the yield and to the overall biosynthesis rate when attempting to convert the biogenic synthesis large-scale production. The synthesis of biosynthesis nanoparticles faces challenges at the large-scale, due to yields being much lower than chemical synthesis [25]. Additionally, the scale-up or commercial processes for nanoparticle synthesis differ greatly. Nevertheless, due to the vast amount, accessibility and strong growth of microbes, the total cost would be reduced. In addition, there are no organic solvents, thermal stabilisers or expensive techniques used in these methods, further reducing the cost.
## 3 Results
### Monte Carlo Approaches
For this purpose, for each season, historical data from all twenty years of data for that season are considered, thus creating four databases, one for each season. Table 6a,b shows the results obtained. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & & & & & & & & & \\ \hline \multicolumn{8}{c}{**e\({}_{\text{mean}}\)(\%)**} \\ \hline  & **Win** & **Spr** & **Sum** & **Aut** & **Win** & **Spr** & **Sum** & **Aut** \\ \hline Mean & 3.1 & 4.1 & 1.2 & 3.7 & 19.2 & 11.4 & 33.3 & 17.1 \\ Median & 2.8 & 4.0 & 1.2 & 3.6 & 10.3 & 7.6 & 20.4 & 13.9 \\ STD & 1.8 & 1.1 & 0.9 & 1.7 & 23.8 & 11.9 & 32.0 & 16.4 \\ \hline \multicolumn{8}{c}{(b)} \\ \hline  & & \multicolumn{8}{c}{**e\({}_{\text{mean}}\)(\%)**} \\ \hline  & **Win** & **Spr** & **Sum** & **Aut** & **Win** & **Spr** & **Sum** & **Aut** \\ \hline Mean & 2.6 & 3.2 & 2.2 & 2.9 & 31.8 & 15.9 & 28.0 & 33.7 \\ Median & 2.4 & 3.0 & 2.2 & 3.0 & 15.2 & 13.8 & 20.6 & 19.4 \\ STD & 1.5 & 1.0 & 1.2 & 1.4 & 34.7 & 12.6 & 26.2 & 31.5 \\ \hline \end{tabular}
\end{table} 
Table 5: (a) Mean, median, and standard deviation (STD) of the errors on the mean (e\({}_{\text{mean}}\)) and the standard deviation (e\({}_{\text{STD}}\)) evaluated in the case of synthetic soil moisture daily profiles, for the Patermo case study, using data of individual seasons of individual years. (b) Mean, median, and standard deviation (STD) of the errors on the mean (e\({}_{\text{mean}}\)) and standard deviation (e\({}_{\text{STD}}\)) evaluated in the case of synthetic soil moisture daily profiles, for the Busachi case study, using data of individual seasons of individual years. Looking at the errors statistics on the mean in Table 6a, it can be seen that a slight improvement in percentage values is observed for autumn, while a slight increase in percentage values is observed for the other seasons. On the other hand, percentage values of the error statistics on the standard deviation show marked improvements for all seasons, especially for winter and spring. For the [PERSON] case study (Table 6b), there are no significant changes in the error statistics on the mean compared to when there were fewer data in the database. On the other hand, the error statistics on the standard deviation show important reductions in the percentage values for winter and spring (as in Paterno), with improvements in summer as well; autumn turns out to be the only season to show slight worsening, the only worsening outcome between the two localities, compared to Table 5a,b.
## 3 Data and Methodology
### Seismic Vulnerability Index (\(K_{g}\)) and Shear Strain (\(Y\))  
The stability of structures during an earthquake depends on the behavior of the ground, which in turn depends on the dynamic properties of the soil column. [PERSON] [36] established a relation between the shear strain deformation and the dynamic properties of the soil, by compiling several earthquake data, reports, and laboratory tests (Table 1). [PERSON] [37] introduced a method based on the vulnerability index calculation using ambient vibration data to estimate the shear strain, for the purpose of potential earthquake damage assessment (soil liquefaction, landslides).  
The vulnerability index is calculated using the following equation [37]:  
\[K_{g}=e\times\frac{\left(\frac{A_{g}^{2}}{\tau_{g}}\right)}{\left(\pi^{2}\times V _{b}\right)} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Size of Strain Y** & \(\mathbf{10^{-6}}\) & \(\mathbf{10^{-4}}\) & \(\mathbf{10^{-3}}\) & \(\mathbf{10^{-2}}\) & \(\mathbf{10^{-1}}\) \\ \hline Phenomena & Wave & Vibration & Crack & Settlement & Landslide, soil compaction, liquefaction \\ \hline Dynamic properties & Elasticity & Elasto-plasticity & Collapse \\ \hline \hline \end{tabular}
\end{table}
Table 1: Strain dependence of dynamic properties of the soil [36].  
Figure 3: Distribution of the data compiled in this study. The orange circles correspond to data measured in [21]. The left panels correspond to the legend, the borehole data, and the array configuration, from top to bottom, respectively. Bh03 was taken from [14]. The rest was taken from [15].  
where \(F_{g}\) is the resonance frequency of the soil, and \(A_{g}\) is the corresponding amplitude obtained with HVSR method. \(V_{b}\) is the velocity at the bedrock and \(e\) is the applied dynamic force. According to [PERSON] [37], where several values of dynamic force were applied and tested with several velocity values for bedrock, the optimal results are obtained with an applied dynamic force of 60%. Thus, assuming this value and a velocity at the bedrock around 6 \(\times\) 10\({}^{4}\) cm/s [37], Equation (1) can then be simplified as follows:  
\[K_{g}=\left(\frac{A_{g}^{2}}{F_{g}}\right)\times 10^{-6} \tag{2}\]  
The shear strain can then be calculated by multiplying the vulnerability index (\(K_{g}\)) with the maximum observed acceleration, or the peak ground acceleration (\(\alpha_{g}\)) [37].  
\[Y=\ K_{g}\times\alpha_{g} \tag{3}\]  
where the \(\alpha_{g}\) value is in cm/s\({}^{2}\) (Gal). The peak ground acceleration values for El-Attaf and El-Abadia cities are 410 and 520 cm/s\({}^{2}\), respectively, for a return period of 500 years [15]. For Ain-Defla city, a value of 250 cm/s\({}^{2}\) is proposed [38].  
This technique is used in the present work mainly to assess the liquefaction potential in the study area. For a shear strain value \(Y>10^{-2}\), phenomena of liquefaction and landslides are likely to occur during earthquakes.
# A Network-Based Method to Analyze EMI Events of On-Board Signaling System in Railway
## 2 Network-Based Modeling Framework and Method
### EM Interactions between EMU and OTCS  
For the EM environment in high-speed railway, different coupling manners are connecting the EM disturbance sources of the EMU and various parts of the OTCS equipment. The relations between the source in EMU and the equipment at the dispersed position are shown in Figure 3 and various EM couplings make the interactions extremely complicated. In general, there are two types of EM coupling, namely, conductive coupling and radiation coupling. The former includes conductive direct coupling and capacitive and inductive coupling. The latter contains antenna coupling, field line coupling, and cable coupling. Different sources can affect the target devices through the aforementioned coupling methods.  
As shown in Figure 3, the EMU can be divided into the roof, interior, and bottom in space. Correspondingly, OTCS equipment is also distributed in the above three spaces, such as cables, antennas, cabinets, grounding, and sensors. Therefore, each EMI event corresponds to an interactive relationship in the figure. Thus, all events will become a complex network of connections. It will be helpful to analyze the OTCS EMC problems if all the interactions are transformed into the form of the network.  
Figure 3: The EM interactions between electric multiple units (EMU) and on-board train control system (OTCS).  
Figure 2: Different paths represent different electromagnetic interference (EMI) events in the EMC network. Path \(v_{1}\) to \(v_{11}\) with a green line and path \(v_{2}\) to \(v_{12}\) with a red line are two different EMI events.
Wake-up systems have been reported that can use active sensors as triggers to wake up the system only in case of system abnormalities [33; 34]. However, the systems still need an external power source, which limits their further applications. Using vibrational TENG simultaneously as a trigger of the wake-up system and as an energy harvester is expected to realize a fully self-powered vibrational wake-up system with ultra-low static energy consumption and autonomous vibration monitoring. Here, a self-powered and autonomous vibrational wake-up system (SAVWS) based on triboelectric nanogenerators and a micro-electromechanical system (MEMS) switch is proposed. The energy triboelectric nanogenerator (E-TENG) can harvest vibration energy to drive the wireless transmitter through a MEMS switch. The signal triboelectric nanogenerator (S-TENG) can be used as a self-powered accelerometer to control the state of the MEMS switch, which also shows good linearity in the acceleration range of 1-4.5 m/s\({}^{2}\) at 30 Hz with a sensitivity of about 14.6 V/(m/s\({}^{2}\)). When the acceleration increases, the S-TENG will turn on the MEMS switch, and the wireless transmitter transmits an alarm signal by the energy from E-TENG with only 0.64 mJ. Using TENGs as an energy source and a sensor, the SAVWS provides a self-powered vibration monitoring solution for unattended environments, and shows extensive applications and great promise in smart factories, autonomous driving, and the Internet of Things.
# Influence of Vegetation Filter Strip on Slope Runoff, Sediment Yield and Nutrient Loss
## 2 Materials and Methods  
### Study Area  
The soil erosion experiment was carried out at a gully area test station in Xuanhua District, Zhangjiakou City, Hebei Province, China. The study area (Figure 1) located in the south of the Yanghe Basin, with an area of about 0.39 km\({}^{2}\), the landform type is typical hills. The main river channel in the study area is a non-permanent gully, with a length of nearly 1 km from north to south, and several tributaries (\(114^{\circ}48^{\prime}31^{\prime\prime}\) E-\(114^{\circ}49^{\prime}25^{\prime\prime}\) E,40\({}^{\circ}\)39\({}^{\prime}\)12\" N-40\({}^{\circ}\)39\({}^{\prime}\)34\" N). The climate features are continental, semi-arid and monsoon climate. The mean annual precipitation is 353.77 mm (mainly occurring between July and August), and the mean annual temperature is 5.8 \({}^{\circ}\)C. The soil in this area is mainly loess, which has the nature of sandy loam, loose structure, coarse soil particles, ordinary water-holding capacity and easy erosion. Sand had the highest particle-size fraction (85.0%), followed by clay (8.0%) and then silt (7.0%) in the 0-40 cm soil layer. The steady permeability is about 16.2 mm h\({}^{-1}\) and the bulk density is 1.28 g cm\({}^{-3}\). The percentage of organic matter in the topsoil (0-10 cm) is 1.41%. In addition, the total nitrogen (TN) content in the original soil is 1.10 g/kg and the total phosphorus (TP) content is 1.23 g/kg.
# Measuring Collision Risk in Mixed Traffic Flow Under the Car-Following and Lane-Changing Behavior
## 4 Evaluating Mixed Traffic Safety with CAV Degradation and Driving Behaviors
### Car-Following Behavior
#### 4.2.2 CAV  
Integrating PRT into the CACC model is pivotal for accurately simulating the behavior of CACC-equipped CAVs in real-world traffic scenarios. CACC model has been validated through empirical studies conducted by the PATH Laboratory at the University of California, Berkeley [24]. For DCAVs, \(\tau_{c}\) is used to represent their PRT, denoted as PRT_CAV. The modified CACC model considering PRT is given by the following equations:  
Spacing Error is given by Equation (16):  
\[e_{n}(t-\tau_{c})=x_{n-1}(t-\tau_{c})-x_{n}(t)-l-s_{0}-T_{c}v_{n}(t) \tag{16}\]  
Velocity Update (\(v_{n}(t)\)) is given by Equation (17):  
\[v_{n}(t)=v_{n}(t-\Delta t)+k_{cp}e_{n}(t-\tau_{c})+k_{cd}\dot{e}_{n}(t-\tau_{ c}) \tag{17}\]  
From Equations (16) and (17),  
\[a_{n}(t)=\frac{k_{cp}e_{n}(t-\tau_{c})+k_{cd}\Delta v_{n}(t-\tau_{c})}{k_{cd}T _{c}+\Delta t} \tag{18}\]  
where  
\(v_{n}(t-\Delta t)\): Velocity of the subject vehicle at the previous control time \(t-\Delta t\), where \(\Delta t\) is the control interval.  
\(k_{cp}\): Proportional gain parameter for the spacing error, determining the responsiveness of the CACC system to deviations in spacing.  
\(e_{n}(t-\tau_{c})\): Spacing error at time \(t-\tau_{c}\), representing the delayed perception of the leading vehicle's state.  
\(k_{cd}\): Derivative gain parameter for the relative speed, influencing the system's response to the rate of change of the spacing error.  
\(\dot{e}_{n}(t-\tau_{c})\): Time derivative of the spacing error at time \(t-\tau_{c}\), indicating the rate at which the spacing error is changing.  
The parameters of the CACC model, as shown in Table 5, are calibrated based on actual vehicle test data obtained from the dynamic responses of multiple test vehicles under various traffic scenarios [24]. \(T_{c}\) is calibrated from previous research [15].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Parameter** & **IDM** \\ \hline Desired speed \(v_{0}\) & 33.3 m/s \\ Maximum acceleration \(a_{m}\) & 1.0 m/s\({}^{2}\) \\ Desired deceleration \(b\) & 2.0 m/s\({}^{2}\) \\ Maximum deceleration \(b_{m}\) & 2.8 m/s\({}^{2}\) \\ Vehicle length \(l\) & 5 m \\ Minimum gap distance at standstill \(s_{0}\) & 2 m \\ Safe time headway \(T_{h}\) & 1.5 s \\ \hline \hline \end{tabular}
\end{table}
Table 4: The parameter values of IDM.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model Parameter** & **CACC** \\ \hline Proportional gain parameter for the spacing error \(k_{cp}\) & 0.45 s\({}^{-2}\) \\ Derivative gain parameter for the relative speed \(k_{cd}\) & 0.25 s\({}^{-1}\) \\ Safe time headway \(T_{c}\) & 0.6 s \\ \hline \hline \end{tabular}
\end{table}
Table 5: The parameter values of the CACC model.
# Time and Energy Efficient Relay Transmission for Multi-Hop Wireless Sensor Networks
## 3 Proposed Scheme
### New Superframe Structure for the Proposed Relay Scheme
The coordinator uses the beacon period, the CAP, and the CFP to communicate with the low-level coordinator and devices belonging to its own WPAN. Whereas it uses the RP to communicate with the high-level coordinator or the PAN coordinator. Namely, the RP is allocated to relay data frames between the high-level coordinator and the low-level coordinator by the high-level coordinator. In the proposed scheme, the sum of the CAP offset, the CFP offset, the RP offset, and the RP lengths cannot exceed the BI length. Figure 15 shows an example of the superframe operation between the high-level coordinator and the low-level coordinator in the proposed scheme. Figure 14: The superframe structure constructed by the coordinator. Figure 12: The format of the RP fields. Figure 13: The format of the RP Descriptor. In Figure 15, the PAN coordinator manages multiple sensor devices and a low-level coordinator, and the low-level coordinator manages multiple sensor devices belonging to its own WPAN. Devices belonging to the PAN coordinator communicate with the PAN coordinator in the SD of the PAN coordinator. As mentioned above, the BI of every coordinator is equal to the BI of the PAN coordinator, and it can be adjusted by the PAN coordinator. As shown in Figure 16, T\({}_{4}\), which is the beginning of the beacon period of the coordinator, should be the time after T\({}_{1}\), which is the beginning of the inactive period of the PAN coordinator. Also, to avoid collisions between devices belonging to the PAN coordinator and devices belonging to the coordinator, T\({}_{2}\), which is the SD of the coordinator, should be smaller than the inactive period of the PAN coordinator (T\({}_{3}\) > 0). The coordinator transmits data frames received from devices belonging to its own WPAN to the PAN coordinator in the RP. In this case, the coordinator is allocated the RP to the PAN coordinator and the RP is located in the CFP of the PAN coordinator. Also, to the contrary, the coordinator transmits the received data frame to its members in its SD after receiving the data frame from the PAN coordinator in the RP. Figure 16 shows the data flow from the PAN coordinator to the end device of the coordinator. As shown in Figure 16, the coordinator communicates with its member in its SD and receives the beacon frame from the PAN coordinator in the RP. Because it receives the beacon frame in the RP, it can synchronize with the PAN coordinator. Also, it transmits the received data frame to the PAN coordinator in the RP after receiving the data frame from its member in its SD. Figure 16: The data flow from the PAN coordinator to the end device of the coordinator. Figure 15: An example of the superframe operation between the high level coordinator and the low level coordinator in the proposed scheme.
# Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach
## 5 Using Machine Learning for Partial Replication and Selecting the Candidate Node
Sub-Topology matrix \(\Psi\) is filled as follows:  
\[\Psi(i,j)= \begin{cases}\Psi(j,i)=1&\text{if $c_{i}$ and $e_{j}$ are connected.}\\ \Psi(j,i)=0&\text{if $c_{i}$ and $e_{j}$ are not connected.}\end{cases}  \tag{14}\]  
Equation (14) describes how matrix \(\Psi\) is filled. The first case is when a cloud \(c_{i}\) and an edge server \(e_{j}\) are directly connected together and that is when the element is set to 1. The second and final case is when \(c_{i}\) and an edge server \(e_{j}\) are not connected and that is when the element is set to 0. The overall topology \(\Xi\) is formulated as follows:  
\[\Xi= \begin{bmatrix}\Phi&\Psi\\ \Psi^{T}&\Omega\end{bmatrix}  \tag{15}\]  
where \(\Psi^{T}\) is the transpose of matrix \(\Psi\), and \(\Xi\) is an \((n+m)\times(n+m)\) square matrix where \(n\times m\) matrix where \(n=\|\mathbb{C}\|=\|\Phi\|\) and \(m=\|\mathbb{E}\|=\|\Omega\|\).