esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work Materials and Methods  
The literature of the field has a good number of articles featuring GNSS/DR solutions [2; 4; 5; 6; 7; 8]. Despite the fact that some other technologies may be used to support the estimates along the longitudinal axis of the vehicle, such as accelerometers [4], the use of the odometry is the most widely spread approach [1]. With respect to the estimates of the heading, some authors employ electronic compasses, but many others discard them for lack of consistency and poor performance [9]. To compensate for the errors in the estimates of travelled distance, some authors use visual odometry, like in [10; 11]. An interesting approach based on stereo vision for navigation in natural environments can be found in [12]. The robotics community is also quite active in this field, like the work presented in [13] based on cellular automata.  
Regarding positioning filters, the most popular solution for fusing GNSS and DR is the extended Kalman filter (EKF), which is a variation of the Kalman filter for non-linear systems [14]. Its success is based on the relative good performance at a low computational cost and simplicity [14]. However, the nature of the vehicle motion is clearly non-linear, and in these cases, some other approaches provide better performances [15]. In these conditions, particle filters (PF), as genetic-type Monte Carlo (MC) methods, use weighted samples to generate approximations of the probability density function. This flexibility (when compared to Kalman), serves well to solve the positioning filtering problem [16; 17].  
On another note, multiple model (MM) filters have been used with success before by the first author of this article in the problem of positioning error estimation. In [13; 18], different interactive multiple model versions of the extended Kalman filter (IMM-EKF) showed good performance for either improving the positioning solution, or maneuver prediction, respectively.  
Multiple model particle filters have been applied in the field of tracking of ground, 3D targets and [19; 20; 21; 22; 23]. However, there are not many articles with MMPF applied to road vehicle position. The authors of [24] used it for detection and estimation of multipath effects on GPS measurements.
## 3 Results
### Validation of Models  
The results of the validation of the models using the AUC curve and SCAI indicator are shown in Figure 7, and in Tables 6 and 7. The results show that the values of the AUC for the three models vary from 0.911 to 0.927, indicating very good prediction accuracy for all models, with RF resulting in the highest value. In addition, the SCAI values for the three models, RF (61.08-0.00), MARS (10.45-0.03), BRT (12.59-0.01), show that the RF model has higher SCAI values compared to the other models in the very low, low, and very high susceptibility classes (Figure 7). In spite of the high efficiency and accuracy of the RF model for GE sensitivity mapping, so far this model has not been used by the research community.  
\[\begin{array}{ll}GESP_{MARS}=&0.74+(0.659\times Lithology1)+(0.656\times Lithology7)-0.0001\\ &\times max(0,13445-Distance\ from\ road)+0.0001\\ &\times max(0,Distance\ from\ road-13445)-0.0002\\ &\times max(0,2907.97-Distance\ from\ River)-0.087\\ &\times max(0,2.377-Drainage\ density)-0.106\\ &\times max(0,Drainage\ density-2.377)+0.001\times max(0,1793\\ &-Elevation)-0.002\times max(0,Elevation-1793)-0.605\\ &\times Lithology7\times Aspect4-0.0001\times max(0,7355.32\\ &-Distance\ from\ road)\times Lithology1-0.0001\\ &\times max(0,11249.2-Distance\ from\ road)\times Lithology7\\ &-0.00002\times max(0,Distance\ from\ road-11249.2)\\ &\times Lithology7+0.0001\times max(0,13445\\ &-Distance\ from\ road)\times Lithology10-0.005\\ &\times max(0,84.853-Distance\ from\ River)\times Lithology1\\ &-0.0003\times max(0,Distance\ from\ River-84.853)\\ &\times Lithology1+0.001\times Lithology2\times max(0,Elevation\\ &-1249)-0.001\times Lithology7\times max(0,1249-Elevation)\\ &-0.019\times Lithology7\times max(0,0.772-Convergence)-23.54\\ &\times Lithology7\times max(0,NDVI-0.055)-22.23\times Lithology7\\ &\times max(0,0.055-NDVI)-0.00001\times max(0,7.65-Slope)\\ &\times max(0,Distance\ from\ road-13445)+0.00001\\ &\times max(0,Slope-7.65)\times max(0,Distance\ from\ road-13445)\\ &-0.0001\times max(0,8.186-Slope)\times max(0,\ 1793\ -Elevation)\\ &+0.00004\times max(0,Slope-8.19)\times max(0,1793-Elevation)\\ &-0.000001\times max(0,Distance\ from\ road-3877.78)\\ &\times max(0,907.97-Distance\ from\ River)+0.0000004\\ &\times max(0,8861.03-Distance\ from\ road)\times max(0,907.97\\ &-Distance\ from\ River)+0.0000001\\ &-Distance\ from\ River)+0.0000001\times max(0,Road\\ &-8861.03)\times max(0,907.97-Distance\ from\ River)-0.001\\ &\times max(0,Distance\ from\ road-13445)\\ &\times max(0,Drainage\ density-1.821)-0.000001\\ &\times max(0,Distance\ from\ road-11435.4)\times max(0,1793\\ &-Elevation)-0.00001\times max(0,Distance\ from\ road\\ &-13238.3)\times max(0,1793-Elevation)\\ \end{array} \tag{12}\]
# Security Perception of IoT Devices in Smart Homes
## 3 Data Analysis and Results
### Regression Model Analysis  
A regression analysis was built with the 5-point Likert scale survey questions presented in Section 2.3. The descriptive statistics for these variables are presented in Table 4.  
Multiple linear regression was used to test whether the four independent variables predicted one dependent variable significantly. We chose this method because it is well founded and widely used in various scientific fields [15]. We tested the hypotheses with multiple linear regression (what effect the independent variables have on the dependent variable). Although the Likert scale is often perceived as an ordinal scale, it can also be used as an interval scale in cases where scores are considered to have even spacing between them, and can therefore be used in multiple linear regression [16]. The fitted regression model was _IoT security importance_ = 1.82 + 0.44 \(\times\) _(Device vulnerability awareness)_ + 0.19 \(\times\) _(Risk of data breach)_ \(-\) 0.07 \(\times\) _(Ransomware attack)_ + 0.08 \(\times\) _(PI access breach)_.  
The overall regression was statistically significant (R\({}^{2}\) = 0.226, F(4,116) = 8.452, \(p\) = 0.000). The summary of the multiple linear regression analysis is presented in Table 5. It was found that _Device vulnerability awareness_ significantly predicted _IoT security importance_ (\(\beta\) = 0.436, \(p\) = 0.000) and _Risk of data breach_ significantly predicted _IoT security importance_ (\(\beta\) = 0.238, \(p\) = 0.069). It was found that _Ransomware attack_ did not significantly predict _IoT security importance_ (\(\beta\) = \(-\)0.083, \(p\) = 0.528), and _PI access breach_ did not significantly predict _IoT security importance_ (\(\beta\) = 0.108, \(p\) = 0.321).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable** & **Mean** & **Std. Deviation** \\ \hline IoT security importance & 4.01 & 1.004 \\ \hline Device vulnerability awareness & 3.54 & 1.000 \\ \hline Risk of data breach & 3.03 & 1.245 \\ \hline Ransomware attack & 2.79 & 1.238 \\ \hline PI access breach & 3.11 & 1.395 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Descriptive statistics of variables (\(n\) = 121).  
Figure 2: Ownership and familiarity with IoT devices.  
_Device vulnerability awareness_ and _Risk of data breach_ had a higher impact on _IoT security importance_ perception than _Ransomware attack_ and _PI access breach_, but together all variables formed a significant model. However, there was a risk of multicollinearity in the model, which means that some of the independent variables could have been correlated. Although the statements used in the model were independent, collaborators in the survey could have seen similarities in the statements, which could pose a risk to the interpretation of the results.
# A Compound Approach for Monitoring the Variation in Wind Turbine Power Performance with SCADA Data
###### Abstract  
The performance of wind turbines directly determines the profitability of wind farms. However, the complex environmental conditions and influences of various uncertain factors make it difficult to accurately assess and monitor the actual power generation performance of wind turbines. A data-driven approach is proposed to intelligently monitor the power generation performance evolution of wind turbines based on operational data. Considering the inherent nonlinearity and structural complexity of wind turbine systems, a data-derived characteristic construction and dimensionality reduction method based on KPCA is adopted as a prerequisite. Additionally, an AdaBoost-enhanced regressor is applied to wind power prediction with adequate inputs, and day-oriented deviation indicators are further constructed for quantifying performance fluctuations. The final validation phase includes two application cases: In the first case, the results show that the proposed method is sensitive enough to capture the early characteristics of blade damage faults. In the second case, an uncertainty error within \(\pm 0.5\%\) demonstrates that the proposed method has high-level accuracy in the quantitative assessment of the power performance and good practical effectiveness in real engineering applications.  
wind turbine; wind power generation; SCADA; KPCA; AdaBoost Article
# A Load Balancing Routing Mechanism Based on SDWSN in Smart City
The fused feature data is transmitted to the controller node for quickly performing load balancing of the sensor node link. We analyze and derive the proposed traffic matrix clustering algorithm, which describes the dynamic adaptive adjustment process. The central controller performs a cluster analysis and adjustment according to the flow matrix of each controller, quickly combining the sensor node traffic data to determine the output of the improved path matrix. In addition, because the fairness of the path selection strategy is very important to load balancing, we use the principle of max-min to measure the uniformity of traffic. The max-min means that when there are multiple routing schemes, the routing scheme with the smallest bandwidth utilization in the network is selected. The rest of the paper is organized as follows: related work about routing protocol to resolve traffic load is summarized in Section 2. In Section 3, system framework and problem analysis are described. Section 4 formulates the traffic load allocation with end to end multipath, proposes the SDSNLB algorithm, and the improved solution. Section 5 shows the performance evaluation and simulation. In Section 6, a summary conclusion is derived.
# Analysis of Wind-Induced Vibration Response of Transmission Wire Under Crosswind in Canyon Terrain
## 2 Fluid-Structure Interaction Numerical Analysis Method  
When air passes over a transmission line, the wire shifts under the action of the air. This displacement of the wire changes the surrounding air flow field. It is necessary to establish two kinds of governing equations in the numerical simulation of this kind of problem to obtain the interaction between the deformed solid structure or the moving solid structure and the surrounding fluid or the internal fluid. There are three types of calculation methods for fluid-solid interactions: In the unidirectional coupling method, the fluid-solid interaction is separated, and only the effect of the flow field on the solid is considered. The flow field force is applied to the solid as the boundary or constraint condition to calculate the solid deformation. This method is simple and consumes low computing resources. Meanwhile, the weak bidirectional coupling method [23] can reflect the interaction between the fluid and elastic solid well, whereas the strong bidirectional coupling method calculates the force interaction and fluid-solid boundary deformation at each iteration step in the unsteady calculation. This method is more accurate but has a high cost in terms of computing resources.  
In this study, the strong bidirectional fluid-structure coupling calculation method was adopted. The specific process is shown in Figure 1.
## 5 Hardware Implementation Analysis
### Implementation Results and Analysis of the Key-Extraction Process
\begin{table}
\begin{tabular}{l l} \hline \hline
**NIST Statistical Test Suite (256 bits)** & \(p\)**-Value** \\ \hline Key Entropy & 0.299629 \\ \hline Monobit Test & 0.59766 \\ \hline Long Runs Test & 0.485934 \\ \hline Block Frequency Test & 0.486333 \\ \hline Maurer Universal Statistical Test & 0.156093 \\ \hline Overlapping Template Matchings Test & 0.486245 \\ \hline Discrete Fourier Transform (Spectral) Test & 0.507344 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical randomness analysis of the extracted keys. Figure 7: The scheme’s performance of the SBGR and the BMR at different SNRs and \(r=\{1,2,3\}\).
## 3 Thermal Decomposition and Flammability of Styrenic Polymers  
When ignited, PS and its copolymers can burn very fast with a visible flame, releasing volatiles into the environment including styrene monomer and the associated oligomers, benzene and lower alkylbenzenes, etc. [36; 37; 38]. During the burning process, PS can also melt flow and drip, which can lead to an increased fuel load, feeding into the enhanced flame spread [23; 37]. Generally, styrene polymers generate the minimal amounts of char residues, especially in oxygen-enriched atmospheres.  
Generally, the styrene homopolymer starts decomposing at a temperature of around 270 \({}^{\circ}\)C and continues degrading until 425 \({}^{\circ}\)C under the normal conditions in the air [39]. Through the random main-chain cleavages and associated mechanisms, PS may form the varying amounts of styrene, benzaldehyde, styrene oxide, acetophenone, \(\alpha\)-methyl styrene, and 1-phenylethanol (Figure 4) [36; 39; 40]. Styrene and benzaldehyde are found to be the prominent fractions among all the degradation products [41; 42].  
Similar to the majority of main-chain carbon polymers, the thermal degradation of PS generally occurs in three steps: initiation, propagation, and termination, which follows a free-radical chain mechanism [36; 42].  
* Initiation The degradation of PS can take place via two routes, either through a random chain scission, or by a chain-end scission (Figure 5). In the case of the random chain scission, two radicals are formed, a primary radical (R\({}_{\text{P}}\)) and a secondary benzyl radical (R\({}_{\text{sb}}\)) with a strong benzylic resonance. In the chain-end scission mechanism, one secondary benzyl radical (R\({}_{\text{sb}}\)) and the resonantly stabilised allyl benzene radical (R\({}_{\text{a}}\)) are formed, as shown in Figure 5.  
Figure 4: Thermal degradation products generated by burning polystyrene (PS) [36; 38; 39; 40; 41; 42].  
Figure 5: A schematic representation of initiation during the thermal decomposition of PS [36; 42].  
* Propagation The generated free radicals are propagated through hydrogen abstraction and unzipping reactions. There are two types of hydrogen abstraction reactions: intermolecular and intramolecular abstractions.
* Termination  
Termination can occur either by recombination or by the disproportionation mechanisms of various active radical fragments.
# Nonlinear Robust Adaptive Control of Universal Manipulators Based on Desired Trajectory
## 4 Experiment and Discussion
### Multiple Joints Tracking Using Circular Trajectory  
After tuning the control parameters for each joint, the desired trajectory of a circle drawn in the vertical plane was designed and utilized to conduct trajectory tracking experiments. The proposed robust adaptive control method based on the desired trajectory was also compared with some algorithms in the literature, including traditional PID control, dynamics feedforward control, and the real-time robust adaptive control method illustrated in Section 3.5. Moreover, in order to objectively illustrate the control effect of the proposed method, we reproduced a novel, robust adaptive control method mentioned in the literature [27] for comparison. For the sake of fairness, all methods used equivalent control gains in the experiment, as shown in Table 2.  
A diagram of the desired trajectory for robot tracking is shown in Figure 7. The end-effector moved along the circular trajectory in the YZ plane. It can be seen that the fifth and sixth joints remained locked, and the control effect of the first four joints is shown below.  
Figure 8 shows the trajectory tracking performance of the above five control methods in Cartesian space; Figure 9 shows the error between the actual trajectory contour and the desired trajectory contour of the five control methods; and Figure 10 shows the joint position error of the 1-4 joints under the five control methods.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(K_{r}\) & diag[25; 35; 30; 25] * \\ \(K_{e}\) & diag[100, 150, 200, 200] \\ \(K_{c}\) & diag[50, 50, 50, 50] \\ \(\Lambda\) & diag[40, 40, 40, 40] \\ \(\epsilon\) & 0.05 \\ \(\Gamma\) & diag[5, 5, 5, 5, 5, 5, 5, 5] \\ \hline \hline \end{tabular}  
* diag[\(\star\)] represents a symmetric matrix with the elements of \(\star\) as diagonal elements.  
\end{table}
Table 2: Parameters used in the experiment.  
Figure 7: Diagram of the desired trajectory.  
Figure 8: Motion trajectories of end-effector under different control methods: (**a**) in 3D space; (**b**) in YZ plane.  
Figure 9: Trajectory contour error of end-effector under different control methods.  
The legend of each control method is illustrated as follows: PID--traditional PID control; FF--dynamics feedforward control; desired compensation--the method proposed in this article; real-time compensation--real-time robust adaptive control; and novel method--novel robust adaptive control method proposed in the literature [27].  
As for the control inputs and the adaptation results of the friction coefficients, only the experimental results of the proposed method are shown in Figures 11 and 12 for simplicity.  
Figure 10: Joint position error under different control methods: (**a**) joint 1; (**b**) joint 2; (**c**) joint 3; (**d**) joint 4.
## 2 Materials and Methods
### Propulsion and Mission Phases  
Regarding the propulsion system's performance and its relation to different mission phases, it is important to note that the proposed layout was designed to handle the specific requirements due to the UAV's spawn point being mid-air, and the primary focus during the initial phase of the simulation was on cruising rather than takeoff or landing. This configuration assumes the UAV is already at an altitude, thus bypassing the complexities of ground-based takeoff and focusing on efficient navigation and energy management once airborne.  
The UAV is required to navigate efficiently once it has reached a sufficient altitude, where the propulsion system is optimized for sustaining long-duration flights at cruising altitudes. The buffer mechanism and the dynamic environment were thus calibrated to respond to the UAV's movement in this specific context, ensuring that data processing remained relevant to the cruising phase, where energy efficiency and real-time adaptability are paramount.  
This approach aligned the simulation more closely with realistic mission profiles that emphasized extended operation at altitude, where the UAV's primary tasks, such as surveillance or communication relay, take place. Future work could expand on this framework by simulating additional mission phases, such as takeoff and landing, to provide a more comprehensive evaluation of the propulsion system's performance across all mission profiles.
## 2 Collision Avoidance Method
### Avoiding Action in Encounter Situations  
#### 2.2.1 Encounter Situations between Ships  
It is important that one's maneuver makes surrounding ships understand. Otherwise, it could cause the confusion because it makes surrounding ships uncertain. Therefore, it is necessary that all taking an action for collision avoidance are clear and precise in accordance with the COLREGs.  
An evasive maneuver for collision avoidance can be divided into overtaking, head-on and crossing, and encounter situations can be judged with relative bearing according to [2; 31]. A ship shall be deemed to be overtaking when coming up with the other ship from a direction more than 22.5 degrees abaft its beam. Head-on situation shall be deemed when a ship could see the masthead lights of the other ship in a line or nearly in a line and/or both sidelights. According to the COLREGs, at least the required minimum intensity shall be maintained at all angles from 6 degrees above to 6 degrees below the horizontal. Consequently, the other ship approaching within \(\pm 6\) degrees by the standard of a ship could be considered as a head-on situation. The crossing situation could be defined that two ships are crossing involving the collision risk. At this time, the angle is between the head-on situation and overtaking situation. Figure 7 presents the different COLREGs situation by its angles.  
According to a result of the above, taking an action for collision avoidance in different situations should be as follows:  
* Overtaking: The ship being overtaken should keep the present course and speed, and overtaking ship can pass the other ship on the both sides as shown in Figure 8a.
* Head on: Both ships should avoid collision by changing the course to the starboard as shown in Figure 8b.
* Crossing: A give-way vessel having the other ship on its starboard side should change its course, and the other ship should keep the present course and speed as shown in Figure 8c.
## 2 Materials and Methods
### Satellite Data Processing  
The Landsat satellite series provides an excellent spatiotemporal resolution over recently launched satellites essential for assessing changes in plant cover over time. After the acquisition, data preprocessing improves the quality of satellite images before any interpretation or analysis can be carried out. Geometric distortion, haze reduction, cloud masking, and topographical and atmospheric corrections are among the corrections recommended for remote sensing data. Landsat data are routinely cleaned up by commercial data providers to remove systematic geometric distortion and radiometric inaccuracy. For retrieving and assessing significant information from remotely sensed data, Landsat image pre-processing is essential [37; 38]. ENVI 4.7 software was used to pre-process satellite digital images in this study. Forest land cover seems to be more sensitive in the green, red, and near-infrared band for Landsat instruments including MSS, TM, ETM+, and OLI that were imported to ENVI 4.7 software for the pre-processing. Image calibration, geometrical correction, and atmospheric correction were all performed. Pan sharpening is the process of improving the spatial resolution of multispectral imagery by combining it with the higher resolution of a panchromatic band. There are several methods that can be used to accomplish this, as described in the literature [39]. The pan sharpening processes were performed for Landsat 7 and Landsat 8 images. Pan sharpening was only conducted on data acquired from sensors mounted on Landsat ETM and OLI. This is because the pan band is unavailable in Landsat 5 and earlier sensors of Landsat satellite. The entire study extent was covered in a single scene; therefore, mosaicking was not necessary. Layer stacking for the Landsat MSS, ETM+, or OLI bands was carried out in ENVI image processing software for the years 1980, 1985, 1990, 2000, 2005, 2010, 2015, and 2020, respectively. With a spatial resolution of 30 m, the images of the research region were projected to UTM zone 41 north [40]. In preparation for the computation of several indices and random forest classifier, each satellite image was processed.