esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Results
### Performance on Images with Added Noise  
This section considers the effect of noise on the inspection images. The test images and the corresponding template images are shown in Figure 13. The test images contain simple and complex backgrounds. The signal-to-noise ratio (SNR) for the Gaussian noise applied to the test images varies from 10 to 35 dB in increments of 5 dB. Figure 14 shows the errors in rotation at different noise levels. Table 3 lists the translation errors. The translation errors are calculated using the Euclidean distance between the real and matching positions. The rotation errors are calculated using Equation (20).  
In this experiment, the maximum rotation errors for the proposed method are 7.157\({}^{\circ}\) and 0.405\({}^{\circ}\) when the SNR ratio is 10 and 15 dB, respectively. The maximum translation errors for the proposed method are 0 when the SNR is either 10 or 15 dB. In [21], the original template image is used to identify the location of the template image in the test images by using the NCC  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Performance Index** & **Proposed Method** & **[**15**]** & **[**21**]** \\ \hline \multirow{3}{*}{Figure 12a} & \(E_{r\_m}\) (\({}^{\circ}\)) & 0.023 & 0.565 & 62.352 \\  & \(E_{r\_sd}\) (\({}^{\circ}\)) & 0.020 & 0.376 & 48.950 \\  & \(E_{r\_max}\) (\({}^{\circ}\)) & 0.086 & 1.390 & 174.422 \\ \hline \multirow{3}{*}{Figure 12b} & \(E_{r\_m}\) (\({}^{\circ}\)) & 0.010 & 1.004 & 90.306 \\  & \(E_{r\_sd}\) (\({}^{\circ}\)) & 0.007 & 0.447 & 52.024 \\  & \(E_{r\_max}\) (\({}^{\circ}\)) & 0.026 & 1.914 & 174.422 \\ \hline \multirow{3}{*}{Figure 12c} & \(E_{r\_m}\) (\({}^{\circ}\)) & 0.036 & 0.488 & 94.930 \\  & \(E_{r\_sd}\) (\({}^{\circ}\)) & 0.027 & 0.393 & 47.098 \\ \cline{1-1}  & \(E_{r\_max}\) (\({}^{\circ}\)) & 0.102 & 1.607 & 174.422 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Errors for the rotation accuracy for both the proposed and compared methods.  
Figure 12: Images for the rotation test. (**a**) IC image 1; (**b**) Metal image; (**c**) IC image 2.  
test images are not rotated in this experiment, the translation error for the test images with different Gaussian noise levels is not generated. By using the hybrid projection transformation, the features are obtained from the mean and variance values of the specific radius; therefore, it can significantly resist the noise in the inspection images. It is seen in this experiment that the overall performance of the proposed method is considerably better than that of the other methods when the SNR is greater than 10 dB.  
Figure 14: Rotation errors for the matching results with Gaussian noise. (**a**) The case of Figure 13(a); (**b**) The case of Figure 13(b).  
Figure 13: Test images. The sizes of test images are (**a**) 414 \(\times\) 525; (**b**) 800 \(\times\) 600; and the corresponding template images are (**a**) 170 \(\times\) 170; (**b**) 120 \(\times\) 120.
# Detection of Forest Tree Losses in Cote d'Ivoire Using Drone Aerial Images
## 2 Materials and Methods
### Overall Methodology
#### 2.2.9 Estimation of Tree Crown Area  
The crown is the part of the tree that is on top of the bole, including the branches, the different twigs, and the leaves [36] (Figure 7). The estimation of the ground area of the tree crown was based on photointerpretation of a sample of 124 trees based on orthomosaics from November 2018 and April 2019. First, the tree crowns were manually delineated in the QGIS 3.0 software. Then, the area (Shp) corresponding to the orthogonal projection of the tree crown to the ground was automatically calculated with QGIS 3.0 software.
## 3 Experimental Data Set and Analysis
### Single-Frequency PPP with the Constraints
In static mode, it is increased by 16.6% in the vertical component by 68% level, and the maximum increase is 88.4% in the horizontal component of 95% level; in kinematic mode, they are 2.5% and 73.9%, respectively. TIC3 has the largest boost as compared with the other two methods. In static mode, it reaches 88.8% from 27.17 to 3.04 min and the lowest is 46.5% from 39.34 to 21.05 min In the kinematic mode, the minimum increase is 5.4%, and the maximum increase is 74.5%. Three stations (FLRS, TRO1, and ZECK station) were randomly selected from 78 IGS stations to measure RMS error in the horizontal component, vertical component, and three-dimensional. It can be seen from Figures 10 and 11 that the final positioning error reaches the sub-decimetre level in the kinematic mode, and it reaches the centimetre level in the static mode. However, the final positioning errors are substantially close in the four schemes. Figure 11: _Cont_. Figure 10: (**a**) FLRS station, (**b**) TRO1 station, and (**c**) ZECK station SF PPP coordinate deviation RMS statistics in static mode.
## 4 Conclusions  
The GP-fresh pasta samples showed, as expected, differences in the content of polyphenols, which increased proportionally with the percentage of GPP added, even if it was negatively affected by the cooking process. No differences were detected in the antioxidant activity of the raw GP-fresh pasta samples, which instead improved during the cooking process, probably due to interactions of polyphenols occurring in GPP with gluten proteins. Although, also in this case, the increase in the antioxidant activity was not proportional to the amount of GPP added.  
Gluten proteins-polyphenols interactions seemed to improve the chemical properties of newly formulated pasta samples, but also their nutritional and structural features, preventing protein loss during cooking, as happened in 100% semolina fresh pasta. Moreover, while the addition of 5% of GPP did not affect the organization of the gluten network in the raw and cooked GP-fresh pasta, a different rearrangement of the gluten structure was detectable with the addition of 10% of GGP.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Raw Fresh Pasta**} \\ \hline  & **100\% Semolina** & **5\% Grape Pomace** & **10\% Grape Pomace** \\  & **Fresh Pasta** & **Fresh Pasta** & **Fresh Pasta** \\ \hline Odor & 3.71 \(\pm\)0.12 & 3.71 \(\pm\) 0.04 & 3.43 \(\pm\) 0.28 \\ Color & 3.57 \(\pm\) 0.76 & 3.86 \(\pm\) 0.89 & 3.71 \(\pm\) 1.06 \\ Superficial aspect & 3.86 \(\pm\) 0.14 & 4.00 \(\pm\) 0.16 & 3.86 \(\pm\) 0.19 \\ General presentation & 3.71 \(\pm\) 0.12 & 3.71 \(\pm\) 0.04 & 4.00 \(\pm\) 0.09 \\ Overall acceptability & 3.86 \(\pm\) 0.09 & 4.14 \(\pm\) 0.09 & 4.00 \(\pm\) 0.04 \\ \hline \hline  & \multicolumn{2}{c}{**Cooked Fresh Pasta**} \\ \hline  & **100\% Semolina** & **5\% Grape Pomace** & **10\% Grape Pomace** \\  & **Fresh Pasta** & **Fresh Pasta** & **Fresh Pasta** \\ \hline Odor & 3.86 \(\pm\) 0.1 & 4.00 \(\pm\) 0.03 & 4.46 \(\pm\) 0.08 \\ Color & 3.71 \(\pm\) 0.04 & 4.14 \(\pm\) 0.04 & 4.61 \(\pm\) 0.07 \\ Superficial aspect & 3.71 \(\pm\) 0.06 & 4.43 \(\pm\) 0.02 & 4.61 \(\pm\) 0.07 \\ General presentation & 4.00 \(\pm\) 0.06 & 4.14 \(\pm\) 0.04 & 4.75 \(\pm\) 0.05 \\ Overall acceptability & 3.71 \(\pm\) 0.04 & 4.57 \(\pm\) 0.02 & 4.79 \(\pm\) 0.08 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Sensory analysis of raw and cooked fresh pasta samples.  
Figure 5: Spider plot with mean values for technical attributes evaluated for fresh pasta sample.  
Nevertheless, Optimal Cooking time results confirmed the positive effect of the gluten network arrangement of both GP-fresh pasta samples. Finally, sensory analysis showed the preeminence of the 5% GP-fresh pasta sample for the desirable parameters for a cooked pasta, providing important information for the production of an innovative pasta product, in line with a sustainable production cycle. Further analyses are ongoing to better assess the chemical and microbiological characteristics of the newly formulated pasta samples.
## 3 Potential Security Implications of IoT in 6G Environment
### Network- or Device-Level Vulnerabilities
#### 3.1.1 Massive Deployment of IoT Devices  
The large-scale deployment of connected IoT devices enabled by 5G greatly increases the potential attack surface. Many IoT devices suffer from inadequate security measures due to limitations in resources, including restricted processing power, memory, and battery life [45]. With 6G anticipated to support trillions of IoT devices, including ultra-low-power sensors and edge nodes, the already expansive attack surface will grow exponentially, making it harder to secure each endpoint [5]. This unprecedented growth presents new challenges for security, making it critical to develop effective security strategies.
## 4 Experimental Methodology  
A comprehensive and rigorous experimental framework was designed to systematically evaluate the performance, scalability, and fault tolerance of the proposed peer-to-peer (P2P) multi-site video streaming system, which integrates chain topology and intelligent self-healing capabilities. To ensure high reliability and reproducibility, the experimental procedure was structured to meticulously analyze the impact of relay-chain length, network conditions, and various fault scenarios using standardized evaluation metrics and robust statistical analysis.  
To provide a comparative baseline, this study replicates the system described in the paper by [PERSON] et al. [61]. Building on this baseline, we evaluate video quality across the relay chains using Video Multi-Resolution Fidelity (VMRF), Peak Signal-to-Noise Ratio (PSNR), and VMAF at each relay node. These tests were performed under three representative network conditions: high bandwidth with low loss (20 Mbps, 0.1%), medium bandwidth with moderate loss (10 Mbps, 1%), and low bandwidth with high loss (2 Mbps, 5%).
## 4 Pilot System and Field Measurements Setup
In both scenarios, the transmitted UDP packet had a size of 1202 bytes with a delay of 1 ms, leading to almost a 10 Mbps data rate (max.). By using the ITS-G5 and 5 GTN standards, the system's theoretical range is from 50 to 1000 m, and the 5 GTN range can be up to 1700 m (max.). In the V2I scenario, the vehicle connects to the RWS with a connection time \(t_{x}\) given by  
\[t_{x}\ =\frac{2*r_{x}}{v_{x}}\,, \tag{1}\]  
where \(v_{x}\) and \(r_{x}\) represent the vehicle speed and RWS range, respectively. During the connection time \(t_{x}\), the throughput \(T(tot)_{x}\) can be calculated as  
\[T_{x}\ =\frac{T(tot)_{x}}{t_{x}}\,, \tag{2}\]  
where \(T(tot)_{x}\) is the throughput during the connection time \(tx\) and \(Tx\) is the time period through which packets were transmitted. \(T(tot)x=Px/tx=\) total number of transmitted bits _(=no. of packets \(x\) length of packet)/temporal length duration of the transmission_. For each speed, we have taken average across a number of measurements for average throughput. Likewise, the V2V scenario used two vehicles and the connection time between two vehicles can be calculated while they are in range as  
\[t_{y}\ =\frac{r_{y}}{v_{y}}\,, \tag{3}\]  
where \(v_{y}\) is the relative velocity of a vehicle and \(r_{y}\) represents the range. The throughput \(T(tot)_{y}\) between two vehicles at connection time \(t_{y}\) is:  
\[T_{y}\ =\frac{T(tot)_{y}}{t_{y}}\,, \tag{4}\]  
Figure 3: Test-track equipped with the 5G Test Network (5 GTN) base station and Road Weather Stations (RWSs). where _T(tot)\({}_{y}\)_ is the throughput during the connection time \(t_{y}\) between vehicles and \(T_{y}\) is the time period through which packets were transmitted. _T(tot)\({}_{y}=P_{y}\)/t\({}_{y}\)_ = total number of transmitted bits (=_no. of packets x length of packet)/temporal duration of the transmission. For each speed, we have taken an average across a number of measurements for average throughput. The field measurements were tailored to calculate the pilot system capacity considering the average throughput. We also calculated the actual range of the VANET communication equipment, which was compared with the theoretical range [6,7]. For the V2I scenario, we conducted 25 measurement drives, and for the V2V scenario we conducted 30 measurements in two successive measurement sessions. The measurement conditions were the same for all field measurements. For the V2I and V2V scenarios, the vehicle passed the RWSs and the other vehicle by making a connection with RWSs and sustaining the connection within communication range with the maximum possible data rate [14, 15, 16, 17, 18, 19, 20].
# Application of Pulsed Laser-TIG Hybrid Heat Source in Root Welding of Thick Plate Titanium Alloys
## 2 Experimental Procedures  
### Equipment Setup  
The experiments were performed at room temperature and atmospheric pressure. The heat source, which is similar to our previous report [18], was the combination of a pulsed Nd: YAG laser beam and a lateral-axial arc produced by a tungsten inert gas (TIG) welding machine, as schematically shown in Figure 1. The laser, with the wavelength of 1.064 \(\upmu\)m, was focused by a lens with a focal distance of 120 mm into a spot measuring about 0.6 mm. The average output power of the laser beam was within 0 to 1000 W by controlling the pulse energy, pulse duration and pulse frequency. In the hybrid welding process, the arc operated continuously while the laser operated in the form of pulses as shown in Figure 2. The workpiece was placed perpendicular to the laser beam. The angle of the tungsten electrode axis to the target was kept 45\({}^{\circ}\). In addition, titanium alloy is identified as a highly reactive material at ambient gases, especially at high temperature. For this reason, during welding, a trailing and back shielding gas was used to protect the both sides of the molten pool from oxidation.  
The SP-2556 spectrograph (Princeton Instruments, Princeton, NJ, USA) with 500 mm focus was used to acquire the plasma spectra. The fiber head was fixed to an X-Y controlled device, as shown in Figure 1, which acquires the accuracy of 0.01 mm. Meanwhile, according to the welding plasma spectrum and NIST (National Institute of Standards and Technology, Gaithersburg, MD, USA) database, two narrow-band optical filters were employed to observe the distribution of Ti and Ar atoms in the plasma, as shown in Figure 3a. As shown in Figure 3a, the spectrum of the hybrid welding arc plasma with the wavelength from 200 to 1000 nm is acquired. It also can be seen that the arc plasma is mainly composed of Ti atoms, Ar atoms, Ti\({}^{+}\) ions and Ar\({}^{+}\) ions. In this research, three lines of Ti atoms (Ti 1439.39 nm, Ti 1441.73 nm and Ti 1 521.04 nm) are selected to characterize the arc plasma in welding. In order to further study the plasma behavior, plasma spectrum at different locations in the
# Multi-Objective Optimization for Outer Rotor Low-Speed Permanent Magnet Motor
## 3 Comprehensive Sensitivity Analysis
### Sensitivity Analysis  
The above analysis shows that the effects of all the optimization parameters on the electromagnetic and temperature performance are basically linear. Therefore, the Pearson correlation coefficient can be used to define the sensitivity of the optimized design parameters to the multi-objective performance parameters. The Pearson correlation coefficient is a measure of vector similarity. The output ranges from \(-1\) to \(+1\), where 0 represents no correlation, negative values represent a negative correlation, and positive values represent a positive correlation. The formula for the Pearson correlation coefficient is as follows [19]  
\[\rho_{X,Y}=\frac{\text{cov}(X,Y)}{\sigma_{X}\sigma_{Y}}=\frac{E[(X-\mu_{X})(Y- \mu_{Y})]}{\sigma_{X}\sigma_{Y}} \tag{2}\]  
where \(\text{cov}(X,Y)\) is the covariance between \(X,Y\), \(\sigma_{X},\sigma_{Y}\) are the standard deviations of \(X\) and \(Y\), respectively, and \(\mu_{X},\mu_{Y}\) are the expected values of \(X,Y\), respectively.  
The sensitivity of the optimized design parameters to the motor performance parameters is shown in Figure 10. Figure 10a shows the sensitivity of the optimized design parameters to the motor performance parameters when the number of conductors per slot is N = 8; Figure 10b,c are N = 10 and N = 12, respectively.  
In Figure 10, in the process of changing the number of conductors per slot among 8, 10, and 12, when N = 8, the sensitivity of the design parameters to the electromagnetic performance is the largest, and when N = 10 and 12, the sensitivity of the design parameters to the electromagnetic performance decreases compared with N = 8. As shown, both the pole arc coefficient and the air gap length have a large influence on the six electromagnetic performance parameters. The PM thickness has the greatest impact on the core loss, followed by efficiency, copper loss and thermal load. The core length has a large influence  
Figure 10: Sensitivity values for optimization parameters. (**a**) Conductors per slot N = 8; (**b**) conductors per slot N = 10; (**c**) conductors per slot N = 12.  
on the power factor and thermal load. The stator outer diameter also affects the thermal load, efficiency and power factor.
## 3 Methodology
### Tuning Algorithms and Machine Learning Models
#### 3.3.3 Kernel Principal Component Analysis (KPCA)  
The utilization of KPCA is employed to address the issue of linear indivisibility in numerous datasets, thereby reducing their dimensionality and transforming them into linearly separable sets through nonlinear mapping. KPCA processes logarithmic data points, and transfers them into the feature space to improve the regularity of data and facilitate model processing and analysis (Figure 2). This approach significantly diminishes computational complexity while enabling seamless handling by linear classifiers or regressors. Moreover, kernel functions within KPCA can be selectively adjusted based on specific requirements, enhancing the model's flexibility and generalization capabilities through real-time adaptation to diverse data scenarios [31, 32, 33].
## 3 Effects of Power Lines on Aerial Manipulators
### Electrostatic Discharge  
Preliminary flight tests were conducted in the context of the AERIAL-CORE project [25] in order to identify possible malfunctions of an aerial manipulation robot interacting with a live power line, taking into account that the installation procedures described in Section 2.2 involve the potential risk of crash and entrapment due to the proximity with the cables. The experiments revealed that the multirotor platform can be severely affected by the electrostatic discharge (ESD) due to the high voltage difference between the power line and the robot, whose charge increases as it approaches to the power line due to the capacitive coupling [12]. The ESD event is triggered when the electric field between the two bodies  
Figure 2: Magnetic field around the long straight conductor.  
(the power line and the aerial robot) exceeds the breakdown strength of the air, causing a charge transfer between them that equalizes the electric potential. This ESD current is introduced through the robotic manipulator and propagated through the wires and conductors of the different on-board electronic components, reaching the autopilot and electronic speed controllers (ESC). Although these components were not damaged in the realization of the tests, the ESD caused the crash of the platform, due to the perturbation introduced in the PWM (pulse width modulation) signal taken as input by the ESC of the propellers. This fault is particularly critical in platforms that integrate ESCs with active brakes, which is a common feature in many commercial multirotors, causing a sudden stop of the rotors when the PWM signal presents irregular pulses. In order to overcome this problem, two solutions were investigated in [25]: shielding the aerial manipulator to prevent the entry of the ESD, and insulating electrically the manipulator from the aerial platform to prevent its propagation.
# Reliable Positioning and mmWave Communication via Multi-Point Connectivity +
## 2 System Model
### Performance Metrics  
The system design aims at providing more robust uplink data reception while improving the localization accuracy of each active user using the coordinated multi-point reception. To evaluate that, we have considered two performance metrics: positioning accuracy and effective user-rate, i.e., \(Q_{k}\) and \(R_{k}\)\(\forall k\), respectively.
## 1 Introduction
There is no agreement/consensus as to the contribution of the magnetic core in determining the amplitude, oscillation frequency or attenuation of the resulting surge voltage at various portions of the windings. The phenomenon of lightning impulse overvoltages has been of particular interest to researchers/specialists in the field, and mathematical models, simulations and analysis have been carried out to predetermine the overvoltages occurring at the transformer level in order to avoid fault situations. It was proved that a model based on coupled circuits using leakage inductances as the concentration parameters can provide the magnitude of the magnetic flux, using the magnetomotive voltages whose magnitude/value depends on the value of the electric current that is generated in the electric circuit as sources. The derivative of the magnetic flux provides the magnitude of the electric current containing the resistances and capacitances. The disadvantage of such a model lies in using details from the transformer design in determining the leakage inductances and capacitances [24; 26; 27; 28; 29; 30; 31]. The method proposed/presented in this paper was applied on a TTUS--ONAN/ONAF 31.5/40 MVA, 110/5/6.6 kV power transformer, and the analytical/simulated results were validated by actual testing of the transformers under study, demonstrating that the relative error resulting from the comparison of the results is small. Analytical Method for Determining the Overvoltages That Are Transmitted Between Transformer Windings  
The concept of lightning impulse overvoltage in transformers defines lightning impulse voltage as an overvoltage that is generated by a sudden rise in voltage under the action of a magnetic field or sudden load. This type of overvoltage often causes damage to the insulation system and circuit components of transformers and, in severe cases, can even cause accidents such as fires and explosions. As already mentioned in the first part of this paper, the analytical method presented for the determination of the value of the overvoltages that run through the transformer windings when the transformer is subjected to a lightning impulse has two defining components: the inductive component and the capacitive component. The process of determining the analytical relationships involves describing in a step-by-step manner how the transmission/propagation of overvoltages from the winding that is subjected to the voltage impulse to the winding with one terminal free and the other earthed occurs. The steps are as follows [20; 23; 24]:  
* Determination of the component of the overvoltage that is transmitted by electromagnetic induction;
* Determination of the component of overvoltage that is transmitted by capacitive coupling;
* Determination of the oscillating component of the overvoltage that is transmitted between the transformer windings;
* Determination of the overvoltage that is transmitted between the primary and secondary windings;
* Determination of the overvoltage that is transmitted to the tertiary winding.