esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Optical Flow Algorithm Based on Mask-R-CNN and K-Means for Velocity Calculation
## 3 Experiments and Evaluation
### The Evaluation of the Improved Optical Flow Algorithm for Velocity Calculation  
To evaluate the performance of the proposed method in moving objects recognition, the addition of similar color masks, as well as the final velocity calculation based on optical flow, three kinds of typical application circumstances are considered in our experiments, which are explained in detail as follows. As the main purpose of our algorithm is to weaken the influence of the moving objects on the velocity calculation based on optical flow, the application circumstances are cautiously chosen as different cases exist for different moving objects.  
Besides, the proposed pyramid LK algorithm based on MASK-R-CNN and K-Means (LK + MCR + KM) is investigated to evaluate the performance of velocity calculation, with three other algorithms  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Optical Flow Camera**} & \multicolumn{2}{c}{**GPS**} \\ \hline
**Sensor brand** & Sony IMX179 & **Brand** & ProPak6 \\
**Sensor category** & CMOS & **Position accuracy** & 1 cm + 1 ppm \\
**Lens size** & 1/3.2 inch & **Velocity accuracy** & 0.03 m/s \\
**Pixel size** & 1.4 \(\upmu\)m & **Time accuracy** & 20 ns \\
**Focal length** & 10 mm & & \\
**Sampling rate** & 30 Hz & & \\
**Resolution** & 1280 \(\times\) 760 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of experimental equipment.  
Figure 7: Experimental facilities.  
pyramid LK algorithm (LK), pyramid LK based on Mask-R-CNN (LK + MRC), and pyramid LK based on K-Means (LK + KM) for comparison.  
In the experiment, in order to verify the experimental results more conveniently and rationally, only one positive direction (X direction or Y direction) of the pixel movement is carried out in one experiment. Therefore, the accuracy of the results can be demonstrated by decomposing the vectors and extracting only the values of the experimental direction and comparing the decomposition results with the standard values. If the calculation results are correct, the direction of the vector should be the same as the direction of the moving pixels. That is, there are no components in other directions. Then, the velocity error can be calculated.
# Velocity-Aware Handover Self-Optimization Management for Next Generation Networks
## 4 Simulation and Performance Analysis  
### Simulation Scenario  
The performance of the proposed algorithm is evaluated in a realistic cellular communication environment using MATLAB. The simulation environment consists of a two-tier model with multiple small cells that are uniformly distributed within each macro cell coverage [1; 22; 25]. The network model enables multi-radio access technologies with multi-technology that allow the UEs to connect to one BS. The UEs are randomly distributed in the network and move in random directions within a coverage area. All UEs move with the same speed in four possible directions in each scenario. We consider a line-of-sight connection where no restrictive assumptions are drawn about any obstacles. Table 3 presents the main simulation parameters. As previously mentioned, four mobile speed scenarios are considered to represent the average vehicle speeds in the urban areas.
# Reverse Time Migration Imaging Using SH Shear Wave Data
## 2 Application Background and Method Principles
### Principle of Reverse Time Migration  
The principle of SH shear wave reverse time migration (RTM) used in this paper is briefly introduced below:  
Reverse time migration based on the solution of two-way wave equation is the most advanced pre-stack depth migration imaging. After an accurate velocity model is obtained, SH shear wave reverse time migration imaging (SH-RTM) generally includes three steps [32]:  
1. The source wave field is obtained by using the source constructed manually or extracted from actual data, and the corresponding model is numerically simulated to obtain the source wave field \(S(x,z,t)\), where \(x,z\) is the space vector.
2. Using the seismic data obtained at the receiver, the reverse continuation propagation passes through the same velocity model, and the corresponding receiver wave field \(R(x,z,t)\) is obtained, where the position of the receiver is \(x_{r},z_{r}\).
3. We can then apply appropriate imaging conditions, such as cross-correlation, we obtain the \(I(x,z)\) (reverse-time migration image results): \[I(x,z)=\int_{0}^{T}S(x,z,t)R(x,z,t)dt\] (1) where \(S(x,z,t)\) is the source wavefield obtained by forward modeling, \(R(x,z,t)\) is the receiver wavefield obtained by reverse continuation simulation at the same time under the same velocity model, and \(t\) is the total propagation time.  
It can be seen from Equation (1) that the final result of reverse time migration is affected by the accuracy of the source wave field \(S(x,z,t)\) and the receiver wave field \(R(x,z,t)\). Therefore, in order to obtain accurate imaging of the target area, it is necessary to make good use of high-quality SH shear wave data and apply them to reverse time migration.
# Terahertz Raman Measurements Using a Spatial Heterodyne Raman Spectrometer
## 3 Materials and Methods  
### Breadboard  
The spatial heterodyne THz Raman spectrometer system consisted of an excitation system, interference spectrometer, and imaging system. The experimental platform is shown in Figure 2, and the main device parameters are shown in Table 1. A 532 nm laser (MSL-FN-532, Changchun New Industries Optoelectronics Tech. Co. Jilin, Changchun, China) was used as the excitation light source. A linear variable filter was used to adjust the laser power from 0 to 140 mW. Beam splitter 1 was used to split the beam, and then the BPFs were used to purify the beam. Then, mirror 2 reflected the beam to the collimating lens group. The sample was placed at the focal point of the objective lens, and the Raman scattering light was excited by the incident laser. Scattering light higher than 550 nm was reflected by a 550 nm short-pass filter (400-543 nm, Thorlab. Shanghai, China), and scattering light lower than 500 nm was reflected by a 500 nm long-pass filter (508-2150 nm, Thorlab). The Rayleigh scattering light was filtered by 532 nm BNFs (Opigrade). The filtered Raman scattering light entered the interference system, and was divided into two coherent beams by beam splitter 2 and incident on the respective prisms and gratings of the two interference arms. After diffraction, the light was returned to beam splitter 2 and combined. The interference fringes were imaged on the target surface of the detector (Ikon-m 934, Andor. Beijing, China) by the imaging optics (AF-S VR Micro-Nikkor 105 mm, Nikon. Beijing, China), and the detector was cooled to \(-60\)\({}^{\circ}\)C to reduce the thermal noise inside the sensor chip.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Components** & **Parameters** & **Performance Index** \\ \hline \multirow{3}{*}{Laser} & Wavelength & 532 nm, CW \\  & Beam diameter & \(\sim\)2.0 (1/e, mm) \\  & Beam divergence & \(<\)1.5 (full angle, mrad) \\ \hline \multirow{3}{*}{Grating 1} & Groove density & 150 gr/mm \\  & Littrow angle & 2.324\({}^{\circ}\) \\ \hline \multirow{3}{*}{Grating 2} & Groove density & 150 gr/mm \\  & Littrow angle & 2.324\({}^{\circ}\) \\ \hline \multirow{2}{*}{Prisms} & Apex angle & 2.665\({}^{\circ}\) \\ \hline \multirow{2}{*}{Beam splitter} & Size & 50.8 \(\times\) 50.8 \(\times\) 50.8 mm\({}^{3}\) \\ \hline \multirow{3}{*}{CCD} & Pixel numbers & 1024 \(\times\) 1024 \\  & Sensor size & 13.3 \(\times\) 13.3 nm\({}^{2}\) \\  & Pixel size & 13 \(\times\) 13 μm2 \\ \hline \multirow{3}{*}{BPF} & Design wavelength & 532 nm \\  & Diffraction efficiency & 99.8\% \\  & Transmittance & \(>\)94\% \\ \hline \multirow{3}{*}{500 nm long-pass filter} & Edge wavelength & 508-2150 nm \\  & Transition width & 15 nm \\  & Blocking region & 200-485 nm \\ \hline \multirow{3}{*}{550 nm short-pass filter} & Cut-off wavelength & 400–543 nm \\  & Transition width & 17 nm \\ \cline{1-1}  & Blocking region & 567–715 nm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key parameters of components used in the experimental breadboard.  
Figure 2: Spatial heterodyne THz Raman spectrometer.
## 4 Model Studies
### Results of the Simulated Rainfall Infiltration  
There are two reasons why slope stability is affected by rainfall infiltration. On the one hand, rainfall infiltration is considered an external load that has a direct impact on stress and deformation, affecting the slope stability. On the other hand, the weight of the sliding areas is increased because of the infiltrated rainwater. As a result, the mechanical properties of soil are softened and the slope stability is affected indirectly. Soil bulk density, pore pressure, and shear strength are considered the main factors when evaluating slope stability. The soil bulk density for every slice is the sum of the dry density for this slice and the weight of water content per unit volume. The flow of water is also affected by the airflow in the unsaturated zone triggered by water infiltration. In return, the slope stability is also affected by this interaction of water and gas. In this study, a water-air two-phase flow analysis was carried out to evaluate the effect of water-air flow on the stability of unsaturated soil slopes. Pore gas pressure, pore water pressure, and water saturation were calculated by this simulation.  
\begin{table}
\begin{tabular}{c c} \hline _Material parameter_ & _Value_ \\ \hline _Name of parameters_ & silty sand \\ \hline _Initial intrinsic permeability_ (m\({}^{2}\)) & \(5.8\times 10^{-12}\) \\ \hline _Gravitational acceleration_ \(g\) (m/s\({}^{2}\)) & 9.8 \\ \hline _Residual water saturation_\(S_{raw}\) & 0.1 \\ \hline _Residual gas saturation_\(S_{rg}\) & 0.05 \\ \hline _Water viscosity_ (kg/m-s) & \(1.0\times 10^{-13}\) \\ \hline _Air viscosity_ (kg/m-s) & \(1.8\times 10^{-5}\) \\ \hline _Initial porosity_ & 0.35 \\ \hline _Capillary pressure_ : _Equation_ (3) _with_\(p_{0}\) (kPa) & 1.5 \\ \hline _Capillary pressure_ : _Equation_ (3) _with_\(m\) & 0.445 \\ \hline _Initial water density_ (kg/m\({}^{3}\)) & 1000 \\ \hline _Initial air density_ (kg/m\({}^{3}\)) & 1.25 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters for the numerical simulation.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{_Water Content (\%)_} & _Cohesion (kPa)_ & _Internal Friction Angle (o)_ \\ \cline{2-3}  & **Silty Sand** & **Silty Sand** \\ \hline
25 & 10.94 & 40.40 \\ \hline
40 & 15.25 & 38.69 \\ \hline
55 & 12.81 & 36.67 \\ \hline
70 & 11.06 & 30.26 \\ \hline
85 & 4.45 & 25.08 \\ \hline \end{tabular}
\end{table}
Table 2: Shear strength parameters under different water content.  
Rainfall infiltration is a coupling process in which water and air interact with each other. Especially, the effect of pore gas on rainwater infiltration is obvious. The magnitude of infiltration under unsaturated conditions mainly depends on the water phase relative permeability coefficient and the pore gas pressure gradient, while the pore water pressure gradient is affected by matric suction, pore gas pressure, as well as gravity.
###### Abstract  
Recently, unmanned aerial vehicles (UAVs) have emerged as a viable solution for data collection from remote Internet of Things (IoT) applications. However, the successful implementation in this regard necessitates the development of a reliable and energy-efficient routing protocol. This paper proposes a reliable and an energy-efficient UAV-assisted clustering hierarchical (EEUCH) protocol designed for remote wireless sensor networks (WSNs) based IoT applications. The proposed EEUCH routing protocol facilitates UAVs to collect data from ground sensor nodes (SNs) that are equipped with wake-up radios (WuRs) and deployed remotely from the base station (BS) in the field of interest (FoI). During each round of the EEUCH protocol, the UAVs arrive at the predefined hovering positions at the FoI, perform clear channel assignment, and broadcast wake-up calls (WuCs) to the SNs. Upon receiving the WuCs by the SNs' wake-up receivers, the SNs perform carrier sense multiple access/collision avoidance before sending joining requests to ensure reliability and cluster-memberships with the particular UAV whose WuC is received. The cluster-member SNs turn on their main radios (MRs) for data packet transmission. The UAV assigns time division multiple access (TDMA) slots to each of its cluster-member SNs whose joining request is received. Each SN must send the data packets in its assigned TDMA slot. When data packets are successfully received by the UAV, it sends acknowledgments to the SNs, after which the SNs turn off their MRs, completing a single round of the protocol. The proposed EEUCH routing protocol with WuRel eliminates the issue of cluster overlapping, improves the overall performance, and increases network stability time by a factor of 8.7. It also improves energy efficiency by a factor of 12.55, resulting in a longer network lifespan compared to Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. Moreover, EEUCH collects 5.05 times more data from the FoI than LEACH. These results are based on simulations in which the EEUCH protocol outperformed the existing six benchmark routing protocols proposed for homogeneous, two-tier, and three-tier heterogeneous WSNs.  
e2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
unmanned aerial vehicles (UAVs); routing protocol; Internet of Things (IoT); wireless sensor networks (WSNs); energy efficiency; network lifetime
# Variation of the Chemical Composition of Waste Cooking Oils upon Bentonite Filtration
## 3 Results and Discussion
### Characterization of Bentonite  
Clays and in particular bentonite are commonly used as blanching agents in the vegetable oil production, as they can remove chlorophylls, phospholipids, lectures, metals, and oxidation products [47; 48]. Bentonite is a porous material characterized by a large available surface area, which is responsible for both chemical and physical interactions with the oil components that are retained (bleaching capacity) [49].  
Combining the results obtained through the above-described methodologies, we see that the treatment of WCOs with bentonite is effective mainly at removing some polar compounds, particularly those with acidic groups or with multiple-bonds. This removal of conjugated compounds, combined with the retention of metal particles and some cations, produces an enhancement of the lightness in the filtered samples. In order to relate these findings to the physical characteristics of the bentonite employed, we studied samples of bentonite prior to and after filtration by powder X-ray diffraction (XRD) and thermogravimetry (TG). The exhausted bentonite considered was that with the smaller grain size (B2), as it had been shown to retain more of the contaminants.  
Bentonite is known for its purity with a high content of montmorillonite and the presence of quartz, mostly in respirable size [50]. XRD analysis confirmed this: montmorillonite, cristobalite, and quartz were observed as major phases in all of the samples (Figure 4).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Entry**} & \multirow{2}{*}{**Sample**} & \multicolumn{4}{c}{**Color (CIE) \({}^{a}\) (S) \({}^{b}\)**} \\ \cline{3-6}  & & \(\mathbf{L}^{c}\) & \(\mathbf{a}^{d}\) & \(\mathbf{b}^{e}\) & \(\Delta\)E (\(\Delta\)S) \\ \hline
1 & **WCO** & 23.11 (0.02) & 3.25 (0.05) & 9.33 (0.04) & \\
2 & **WCO1** & 25.22 (0.02) & 2.19 (0.10) & 12.62 (0.05) & 4.04 (0.06) \\
3 & **WCO2** & 27.17 (0.01) & 0.21 (0.05) & 15.25 (0.05) & 7.79 (0.05) \\ \hline \end{tabular}
\end{table}
Table 3: Color coordinates for crude and filtered samples of WCOs. \({}^{a}\) Commission Internationale de l’Eclairage; \({}^{b}\) The values reported were calculated as results of four measurements; the standard deviation is indicated. \({}^{c}\) L, lightness; \({}^{d}\) a, green–red; \({}^{e}\) b, blue–yellow; \({}^{e}\) E: total color difference.  
From Figure 4, it is also possible to observe that no changes to the physical composition occurred during the filtration procedure: the phases present in the fresh sample, are still observed in the post-filtration sample; furthermore, each phase has a similar microstructure. However, the montmorillonite peak at 5 20 is shifted to lower angles, suggesting a swelling of the clay layers, in accordance with the presence of residual materials in the pores after filtration.
# SemABC: Semantic-Guided Adaptive Bias Calibration for Generative Zero-Shot Point Cloud Segmentation
## 4 Experiments
### Limitations  
Our semantic-guided adaptive bias calibration approach still has limitations. In the visual-semantic fusion branch, segmentation prediction is obtained by aligning visual and semantic data. However, it aligns point cloud features of the same category with a single semantic embedding, overlooking the complexity and diversity within these features. We will explore methods to achieve multi-scale visual and semantic alignment in the future, which could lead to more accurate segmentation results.
# Secure Trust-Based Blockchain Architecture to Prevent Attacks in VANET
## 4 Proposed Blockchain in VANET
### Design of Proposed Blockchain Architecture
#### 4.1.3 Message Rating Generation  
Once the identities of the communicating vehicles are authenticated, the vehicles can now exchange data between them. Message rating generation is the third phase, which will officially initiate communication between the nodes. When V\({}_{\rm A}\) sends a message of a particular event, M\({}_{\rm A}\), to V\({}_{\rm B}\), V\({}_{\rm B}\) calculates the credibility of M\({}_{\rm A}\) to determine the trustworthiness of the message. Messages that report similar incidents or events are recorded in the MesBC. Therefore, for every vehicle that reports similar events occurrence in the network, the trust of the events is calculated and stored in a trust set. Next, the probability of such events, known as P(e/C), occurring is calculated using the trust set. If the calculated P(e/C) is greater than the existing threshold value, then M\({}_{\rm A}\) will be reported as true. However, if P(e/C) does not exceed the threshold, then M\({}_{\rm A}\) will be reported as a false report. When M\({}_{\rm A}\) is true, V\({}_{\rm B}\) generates a positive rating on the message received from V\({}_{\rm A}\). If otherwise, V\({}_{\rm B}\) generates a negative rating on that message. Then, V\({}_{\rm B}\) stores the messages received in the MesBC blockchain. The MesBC, along with the ratings given on each message, will be then uploaded periodically to a nearby RSU by V\({}_{\rm B}\) before proceeding to the next phase, as shown in Figure 8. The message ratings  
Figure 7: System authentication of the proposed solution.  
will indirectly reflect the trustworthiness of the source vehicle, which in this case is \(\mathrm{V_{A}}\). Therefore, if a vehicle has more positive ratings, the vehicle can be identified as a trustworthy vehicle in the network. On the contrary, if a vehicle is found to have more negative ratings, the certificate and public key of the vehicle will be revoked by the LEA. Firstly, when the vehicle consistently receives negative ratings exceeding the threshold defined by the LEA, the vehicle will be temporarily blocked from sharing further information with other vehicles in VANET. Next, a direct report will be sent to the LEA for further action within 24 h. This will ensure that the revocation time is maintained at an acceptable range, which will allow the LEA to investigate the behavior of the vehicle. This algorithm, as shown in Algorithm 1, can mitigate false data injection attack, as the ratings will help users in the network to identify and differentiate between trustworthy vehicles and untrustworthy vehicles.
## 2 Materials and Methods
### Satellite Data and Processing
#### 2.3.3 Land Cover Classification
In the next step, to optimise the computational performance, RF classifier with 200 decision trees [78] was initiated and trained as several studies suggested 100-500 as the optimal number [89; 95; 96; 97]. The trained dataset then is used to classify the composite. Figure 5: Flowchart of the satellite imagery processing in Google Earth Engine (GEE) with Landsat 7, Landsat 8, Sentinel 1, and Sentinel-2. The UAV orthomosaic is used as the training and validation dataset.
# An Improved Ciphertext-Policy Attribute-Based Encryption Scheme in Power Cloud Access Control
## 6 Experiment Analysis
### Storage Consumption of CKA  
In the data obtainment stage, U needed to send his/her _Pk_, and [PERSON] was the first to verify the attribute set with his/her _Pk_, so the CKA needs to save Us' _Pk_ and Us' attribute sets. In this section, the experiment on the storage consumption of CKA has been discussed, and the results are shown in Table 3.  
As can be seen from the table, the data size saved in CKA was proportional to the number of U. When there were 30 attributes and 1 million Us, the data size CKA needed to be save was less than 3 GB. This was easy to meet with the storage technology today.
# Non-Uniform Fusion Tree Generation in a Dynamic Multi-Sensor System
## 2 Preliminaries: The Fusion Process Model
### The Local Fusion Process Model
The execution time of the update can be modeled as:  
\[T_{upt}\approx\left|FTL^{n-1}\right|\times C_{4}. \tag{4}\]  
In Equation (4), \(C_{4}\) is the execution time of the process to update a track in \(FTL^{n-1}\). The constant parameters \(C_{1},\ C_{2},\ C_{3}\), and \(C_{4}\) are dependent on the computing power of a local fusion node. Thus, we implemented and ran the fusion process on our target hardware which is configured as follows: OS (QNX 6.3), CPU (Pentium M processor @ 1.6 GHz), and RAM (2 GB). In this configuration, \(C_{1}\) is 356,972 nanoseconds; \(C_{2}\) is 74,800 nanoseconds; \(C_{3}\) is 79,398 nanoseconds; and \(C_{4}\) is 33,364 nanoseconds.