esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Low Electrical Percolation Threshold Piezoresistive Sensors  
The piezoresistive effect can be used to detect different stimuli. When compression, tension, or bending is applied to these flexible sensors, the location and morphology of the fillers in the sensors change, causing a change in electrical resistance. The magnitude of the piezoresistive effect and the electrical properties vary depending on the filler's dimensions, content, and morphology [18]. The resistance change rate for the piezoresistive sensors is defined in Equation (2):  
\[\Delta\text{R}/\text{R}_{0}=(\text{R}-\text{R}_{0})/\text{R}_{0} \tag{2}\]  
where R is the measured electrical resistance and R\({}_{0}\) is its initial resistance before the application of force [25].  
All the samples were tested during 30 cycles of 3-point bending to evaluate the piezoresistive effect,. The piezoresistive effect was measured for 0.5 and 1 wt.% concentrations of MWCNTs, all the magnetic patterns, and random distribution nanocomposites. Figure 12 shows that the nanocomposites with a random distribution for 0.5 wt.% of MWCNTs did not present a piezoresistive response due to their high electrical resistance. Conversely, all the nanocomposites prepared with magnetic patterning presented a piezoresistive response. It was observed that, for an MWCNT concentration of 1 wt.%, the random distribution provides a more unstable response than the magnetically patterned nanocomposites. After analysis of the piezoelectric response obtained for all the magnetically-induced patterns, the parallel line pattern was selected for dynamic tests.  
As shown in Figure 13, both concentrations are adequate to develop piezoresistive sensors and offer good stability under the application of decreasing and increasing forces. The nanocomposites respond when the force is applied instantaneously, as well as when it is maintained for 10 s before release. The samples show good stability since the electrical resistance is similar during the application of decreasing and increasing force. In the future, a deeper analysis of the sensor performance can be carried out, including the evaluation of the use of pre-strain since the first deformation appears to have a different response compared to the deformations that follow.  
Figure 12: Piezoresistive effect for samples with (**a**) 0.5 and (**b**) 1 wt.% of MWCNTs with different magnetic patterns, from top to bottom: random, zig-zag, rectangular and parallel lines.
## 5 Results and Discussion
### Measurements of the Noise Temperature of the System
However, the noise temperature results achieved in the frequency bandwidth 84-95 GHz are considered in line with the initial goals of this project, which aimed at testing the capabilities of the SRT in the 3 mm frequency range. The receiver, if installed on the SRT, could also be suitable to conduct millimetre VLBI experiments at 86 GHz [50].
###### Abstract  
Multiuser Detection (MUD) is quite challenging in uplink grant-free non-orthogonal multiple access wireless communication networks in which users sporadically transmit data. The reason for this is that the base station (BS) must perform detection of both multiuser activity and user signals concurrently, because knowledge of user activity status is not available at the BS. In this paper, a new multiuser detector, named the Forward-Reverse Orthogonal Matching Pursuit-Union-Subspace pursuit (FROMPUS)-based MUD, is proposed. The detector takes advantage of the concept of an initial support set. This serves as initial knowledge that is then employed in the reconstruction of active users' signals. In addition, the detector uses the \"serial-include\" technique of incorporating a likely support set element candidates and a reliability testing procedure in which the most prominent elements of the support set are selected. To assess the performance of the proposed detector, computer simulations are performed. The results obtained for various parameter settings show that the FROMPUS performs better than any of the other five detectors considered in this paper. However, this excellent performance comes with a slightly higher computational complexity cost. Nonetheless, the cost is inconsequential, since the detector operates at the BS where complexity is of low priority in comparison to performance.  
N +
Footnote †: journal: Electronics  
1
# A Comprehensive Survey for Deep-Learning-Based
## 4 Abnormality Detection with Images over Long Range
### Grid Anomaly Detection Based on Optical Satellite Images  
In order to deal with the potential safety hazards that high vegetation may cause to transmission lines, [PERSON] et al. [9] discussed a new method based on the analysis of multi-spectral and stereoscopic very-high-resolution satellite imagery, which contains three steps: (1) extract the tall vegetation from the multi-spectral bands; (2) use stereo images to evaluate the vegetation height; (3) map the vegetation with a potential for interference. [PERSON] et al. [93] introduced a concept for the use of multi-spectral stereo pairs of satellite images to identify dangerous trees and plants along with overhead transmission rights of way, in which the height of each pixel is determined, and the distance from the conductor is calculated with the multi-spectral stereo images. [PERSON] et al. [94] also proposed a vegetation-monitoring method with satellite stereo images. In this work, multi-spectral and panchromatic images are first merged, and then normalized difference vegetation index and stereo image matching are used to detect the vegetation and calculate the height of each tree, respectively. In recent years, some related studies have also emerged. [PERSON] et al. [95] proposed a novel method to use multi-view satellite images to detect individual trees and delineate their crowns. This method generates the DSM from multi-view high-resolution satellite images and combines it with spectral information to detect the trees. With the development of deep learning, various vegetation detection algorithms using machine learning methods have emerged. [PERSON] et al. [12] proposed an automated framework for monitoring vegetation along power lines using high-resolution satellite imagery and a semi-supervised machine learning algorithm, which effectively provided updated situational awareness to vegetation management teams in electric utilities.
## 4 Real-Time Elbow and T-Junction Detection and Estimation (RT-ETDE) Framework
### Elbow Estimation
#### 4.3.1 Elbow Direction Estimation Based on Band Centroid  
The estimation of elbow direction relies on the observation that the point cloud inside the elbow deviates toward the elbow's direction compared to the straight pipe. To evaluate this deviation reliably, we propose using \(X\)-band and its centroid. Specifically, we classify the point cloud near the elbow area into the \(X\)-band along the \(X_{S}\) at intervals of \(\delta x_{E}\), as illustrated in Figure 8. We observe the key geometric property of the point cloud that \(X\)-band farther from the camera has more deviation toward the elbow direction. To quantify this deviation, points within each \(X\)-band are aggregated into a centroid. The centroid provides a robust estimate by neutralizing random errors from individual points. As the centroid is farther away from the origin, it is more reliable for direction estimation. Therefore, we compute the elbow direction using the weighted average of the direction computed by each centroid. Given \(N\) centroids in SCS (\(x_{i,S},y_{i,S},z_{i,S}\)) (\(1\leq i\leq N\)), the elbow direction \(\theta_{E}\) is computed as follows:  
\[\theta_{E}=\frac{\sum_{i=1}^{N}\sqrt{y_{i,S}^{2}+z_{i,S}^{2}}\arctan\left( \frac{y_{i,S}}{z_{i,S}}\right)}{\sum_{i=1}^{N}\sqrt{y_{i,S}^{2}+z_{i,S}^{2}}} \tag{1}\]  
Given \(\theta_{E}\), the elbow plane can be computed. This plane contains \(X_{S}\) and creates angle \(\theta_{E}\) with the \(X_{S}Y_{S}\)-plane. The elbow plane is an important input for the elbow radius estimation in the following section.
# Numerical Study of the Flow and Blockage Ratio of Cylindrical Pier Local Scour
## 2 Mathematical Model  
To investigate the evolution processes of local scour around the cylinder in a rectangular channel, the flow model solves the Navier-Stokes (N-S) equations using a large eddy simulation (LES). In the present study, the free surface is neglected because a rigid roof is used to isolate the effects of the wake vortices on the scouring process ([PERSON] et al., 2018 [16]). In addition, the model computes near-bed transport separately using an Exner equation, in which the bedload transport is calculated with the formula given by [PERSON] and [PERSON], 1976 [35]. The computational domain is illustrated by Figure 1.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Reference** & **Numerical Method** & **Turbulence Model** & **Sediment Load Model** & \(Re_{D}\) & \(D/W\) & **Lateral Wall B. C.** \\ \hline [PERSON] et al., & FVM & RANS (\(\bar{k}-\varepsilon\)) & Suspended load & \(5.02\times 10^{4}\) & 0.2 & Slip \\
1993 [26] & FEM & RANS (\(\bar{k}-\omega\)) & Bedload & \(3.9\times 10^{4}\) & 0.05 & Slip \\
2010 [27] & & & & ([PERSON] and [PERSON]) & & \\  & & & & and suspended load & & \\  & & & & (convection-diffusion) & & \\ [PERSON] & UFVM & URANS & Bedload & \(2.9\)–\(6.3\times 10^{4}\) & 0.14 & Slip \\ et al., 2012 [28] & & & ([PERSON]) & & & \\ [PERSON] et al., & & & & & (Lagrangian model) & \\ [PERSON] et al., & & & & & Suspended load & \(3.5\times 10^{4}\) & 0.027–0.055 & Non-slip \\
2023 [36] & & & & & & \\ Present study & UFVM & LES & Bedload & \(1.9\times 10^{3}\) & 0.1 & Non-slip \\  & & & & (Engelund and Fredsoe) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of numerical techniques used in clear-water scour simulation in the literature.
## 5 Numerical Results and Experimental Comparisons
The differences in the damage propagation rates between the two numerical models can be appreciated in Figure 13, where the evolution of the cohesive damage state is presented for the two configurations at the same values of the applied displacement. In Figure 13, the fully damaged cohesive elements are represented in red. As shown in Figure 13, during the propagation the delamination front is not straight due to the different orientation of the laminae at the interface (\(+45/-45\)). As expected, the growth rate is higher for the \"Low Bridging (LB)\" configuration indicating a lower load-carrying capability. In Figure 14, the damage extensions predicted by the numerical models are compared with the images of ultrasonic C-scans captured at an applied displacement of 4 mm. Figure 13: Damage propagation at an applied displacement of: (**a**) 3.3 mm; (**b**) 4.0 mm; (**c**) 5.0 mm; (**d**) 6.0 mm. The comparison in Figure 14 shows an excellent agreement between the numerical results and the experimental data both in terms of delamination size and delamination front shape. The effectiveness of the adopted numerical approach in simulating the R-curve effect using a trilinear traction-separation law is demonstrated in Figure 15, where the delaminated area vs. applied displacement is compared with the experimental data and with the results of analysis performed using a single bilinear traction-separation law, where only the steady-state value of the fracture toughness has been considered. As can be seen from Figure 15, the numerical results obtained using a trilinear softening law are much closer to the experimental data at the same value of the applied displacement. For both material configurations, delamination initiation occurs earlier for the models with a trilinear traction-separation law, due to the lower initial values of the fracture toughness. Figure 14: Comparison between numerical and experimental damage propagation: (**a**) Numerical; (**b**) C-Scan [23]. Figure 15: Comparison between numerical and experimental delaminated area vs. applied displacement. Then, when the fracture energies reach their steady-state values, the propagation rates obtained from the two different traction-separation laws becomes almost identical.
## 4 Future Directions
### DL for Field Estimation or Multiphysics Analysis  
DL has been successfully employed to estimate the electromagnetic field distribution and temperature distribution of transformers and PM motors, and the efficiency of PM motors. Due to the nature of high nonlinearity, more studies can be conducted to estimate other field distributions for structure analysis. The field estimation of multi-physics analysis is challenging for this aspect, especially a coupled field analysis, as for instance required for the in-wheel motors and high-speed motors. If multi-physics performance can be predicted accurately by using DL techniques, this will greatly benefit the optimization work.
###### Abstract  
Maize cultivation performance, including the efficiency of the input and output of maize, which reflect the allocation and utilization of resources in the process of maize cultivation, is crucial for evaluating and improving maize cultivation. This paper adopts the method of quadratic regression orthogonal rotation combination experimental design to explore the effects of four main cultivation measures (planting density, nitrogen fertilizer, phosphorus fertilizer and potassium fertilizer) on maize yield at five levels (\(-2\), \(-1\), \(0\), \(1\); \(2\)). The CCR ([PERSON], [PERSON] and [PERSON]) model, which is the basic model of data envelopment analysis (DEA), was used to evaluate the 36 groups of cultivation measures. The results show that 9 groups are CCR-effective cultivation measures, but the performance of these cultivation measures cannot be further evaluated. To improve the evaluation of cultivation performance, a novel method termed as the group decision method of DEA (GDM-DEA) is proposed to detect the improvement of evaluation performance and is tested using the measurements of maize cultivation. The results suggest that the GDM-DEA method can classify and sort the performance of all the cultivation measures, which is more sensitive and accurate than the CCR method. For the effective cultivation measures that meet the requirements of GDM-DEA, the optimal cultivation measures could be determined according to the ranking of yield. This method determined the most effective cultivation measure. Further independent validation showed that the final optimal cultivation measures fall in the range of the expected cultivation measures. The GDM-DEA model is capable of more effectively evaluating cultivation performance.  
CCR model; improved DEA model; optimal cultivation measures; production efficiency of maize; quadratic regression orthogonal rotation combination experimental design +
Footnote †: journal: Applied Sciences
## 4 Avinomics Assessment
### RGB-D Cameras  
An RGB-D camera provides a perception (i.e., closely related to human visual perception) of a UAV's surroundings in the form of RedGreenBlue (RGB) images. As previously mentioned, targets can be tracked, and the UAV's localization can be acknowledged upon the processing of RGB images to computer vision algorithms through SBCs/SoM. Spatial distances of targets can also be calculated via the RGB images' depth information, thus providing better collision avoidance strategies. Overall, such a camera's quality is assessed from its frame rate, aperture, and shutter type [270]. One type of RGB-D camera is the Intel Realsense D435 [271], which was found to be ideal for fast moving applications in low light and a wide field of view. It has an RGB frame rate of 30 FPS and an RGB frame resolution of 1920 x 1080. Intel Realsense D435 was used in reference [272] to collect depth measurements in order to identify the parameters of the depth camera noise model, thus enabling more accurate SLAM algorithm execution. RGB-D cameras can also be involved in 3D mapping to describe a UAV's surroundings after SLAM fusion with a UAV [273].  
2. Thermal Cameras A thermal camera is an equipment that captures and detects Infrared Radiation (IR) emitted by surrounding objects. This type of camera allows for imaging based on heat, which can be used for aerial surveillance, environment monitoring, and search-rescue processes [274]. Thermal cameras find applications in different UAV processes, such as the bridge infrastructure assessment shown in Figure 8.  
As shown in Figure 8, a bridge's infrastructure non-destructive testing by means of a UAV saves time and gives more accurate and faster results than visual inspection. The actual captured image of the bridge (Figure 8, bottom left) is reflected in an equivalent thermal image (Figure 8, bottom right), revealing the cohesion between different parts of the bridge.
## 5 Experimental Methodology  
Our work focuses on the security aspects related to the NFs that are exposed to the outside of 5 GC, either directly or indirectly, such as the AMF (considering both the N1 interface with the UE, through the NAS, and the N2 interface) and the NRF/NEF, which acts as an API gateway between the 5 GC and third-party applications, to offer services outside the 5G network. At first, our investigation is centered on the evaluation of the reliability and security posture of the AMF component within the 5 GC. Specifically, our goals encompass the identification of vulnerabilities related to replay attacks and DoS attacks. Then we test the effectiveness of security measures implemented in the NRF/NEF, focusing on DoS attacks and API injection techniques, which are common in web application security.  
While the analysis of the NEF component is restricted due to the absence of open-source implementations at the time of this study, we leverage simulations and tests on the NRF to gain insights into the security landscape of the SBI. In more detail we aim to scrutinize the vulnerability landscape by examining the OWASP Top 10 vulnerabilities and assessing their applicability to 5 GC, considering its unique communication methodologies, particularly via REST API requests. Furthermore, we explore scenarios involving communication between different operators' NRFs, leading to considerations of potential malicious actions that might compromise competitors' 5 GC infrastructures.
###### Abstract  
Mobile Adhoc Networks (MANETs) typically employ with the aid of new technology to increase Quality-of-Service (QoS) when forwarding multiple data rates. This kind of network causes high forwarding delays and improper data transfer rates because of the changes in the node's vicinity. Although an optimized routing technique to transfer energy has been used to lessen the delay and improve the throughput by assigning a proper data rate, it does not consider the objective of minimizing the energy use, which results in less network lifetime. The goal of the proposed work is to minimize the energy depletion in a MANET, which results in an extended Lifespan of the network. In this research paper, an Extended Life span and QSSM-ML routing algorithm is proposed, which minimizes energy use and enhances the network lifetime. First, an optimization problem is formulated with the purpose of increasing the network's lifetime while limiting the energy utilization and stability of the path along with residual. Second, an adaptive policy is applied for the asymmetric distribution of energy at both origin and intermediate nodes. In order to achieve maximum network lifespan and minimal energy depletion, the optimization problem was framed when power usage is a constraint by allowing the network to make use of the leftover power. An asymmetric energy transmission strategy was also designed for the adaptive allocation of maximum transmission energy in the origin. This made the network lifespan extended with the help of reducing the node's energy use for broadcasting the data from the origin to the target. Moreover, the node's energy use during packet forwarding is reduced to recover the network lifetime. The overall benefit of the proposed work is that it can achieve both minimal energy depletion and maximizes the lifetime of the network. Finally, the simulation findings reveal that the ELQSSM-ML algorithm accomplishes a better network performance than the classical algorithms.  
energy depletion; MANET; multicast communication; network lifetime path stability; QSSM-ML algorithm +
Footnote †: journal: Information  
0000
## 2 Materials and Methods (Testing and Simulation Methodologies)
### Simulation  
Once the rooms representative of all other ETSIAE classrooms were selected (see Section 3 of this paper), their lighting situation was represented as accurately as possible using DIALux(r) simulation software (see Figure 5). Thanks to the data collected in the testing campaign, the simulations were carried out taking into account precise details from  
* the geometry of the classes;
* the layout of the luminaires;
* the arrangement of desks, chairs, blackboards, and elements that affect lighting; and the textures of the walls and floor.  
Figure 4: Illuminance measurements on the desks and blackboard in one of the classrooms studied.  
The geometry of the classes was reproduced using the data obtained in the test campaign. As explained in Section 2.1, with the aid of the distance meter DEXTER CM30, an accurate geometry of the classes was obtained. For the luminaires, a complete inventory of which luminaires were in use in  
Figure 5: Comparison between an actual image of classroom A-038 (**a**) and the simulation carried out using DIALux® software (**b**), and calculation points for the illuminance and Unified Glare Rating (UGR) in the DIALux® simulation of classroom A-016 (**c**).  
all the classrooms was performed in the test campaign. With this information, DIALux(r) ready-to-use data was obtained from the manufacturers' data catalogues. This allowed to directly implement the luminaires and their performance in the simulation, with only a need to adjust their attenuation to take into account wear and tear. In the testing campaign, together with the geometry of the classrooms, an extensive set of data regarding the position and shape of all desks, chairs, blackboards, and elements that may affect lighting was measured. Using the aforementioned data, the necessary objects were created and arranged to be taken into account in the simulation. With regard to the windows, as the lighting analysis is based on the results obtained in the most unfavorable scenario (at night), no light was set to be transmitted through them, with the blinds being lowered. Finally, taking into account the importance that surface textures have in light reflection, the colors of the walls, ceiling, blackboard, chairs, and tables were reproduced in the simulation. Furthermore and in relation to the floors, as their texture was not uniform, customized textures were created with the aid of imported photos of the present floors at the simulated classrooms.  
The different points of interest with regard to the lighting on student desks and blackboards as well as the Unified Glare Rating (UGR) were established in the simulations. The UGR calculation points were placed in the center of each chair where the eyes of an average-sized student would be sited.