esa-sceva/satcom-chunk-collection huggingface dataset:
# Forward and Inverse Studies on Scattering
## 3 Inverse Problem by Modified BEM
### Numerical Examples
#### 3.2.2 Reconstruction Results for Models of Different Depth-Radius Ratio  
We established three canyon models with depth-radius ratios \(d_{\rm max}/r\) equaling 0.1, 0.2, and 0.5, respectively. For each model, we calculated the reflection coefficients of Rayleigh wave within the frequency range from 0 to \(\Omega_{\rm max}=5.0\), with a uniform interval of \({\rm d}\Omega=0.2\), as can be illustrated in Figure 8. These coefficients are substituted into the inversion Equation (27) to obtain the flaw depth function \(d(x_{1})\).  
It should be noted, Equation (27) takes an integral from minus to positive infinity. However, the reflection coefficients are only available at positive wavenumber points. Since \(d(x_{1})\) is a real function, we can define \(C^{\rm ref}(-\xi_{0})\) as the complex conjugate of \(C^{\rm ref}(\xi_{0})\) (for \(\xi_{0}>0\)), i.e., \(C^{\rm ref}(-\xi_{0})=C^{\rm ref*}(\xi_{0})\). The functions \(c_{\rm TT}(\xi_{0})\), \(c_{\rm TL}(\xi_{0})\), and \(c_{\rm LL}(\xi_{0})\) are also defined similarly. The whole integrand function is defined as 0 at \(\xi_{0}=0\). Moreover, the integration range is reduced to \(0\sim\Omega_{\rm max}\).  
After discretization, the integral of Equation (27) becomes  
\[d(p\Delta x_{1})=\sum_{k=0}^{N-1}\biggl{(}\frac{C^{\rm ref,k}\exp(-2i\pi pk/N) }{c_{\rm TT}^{k}+c_{\rm TL}^{k}+c_{\rm LL}^{k}}+\frac{C^{\rm ref,k*}\exp(+2i \pi pk/N)}{c_{\rm TT}^{k*}+c_{\rm TL}^{k}+c_{\rm LL}^{k*}}\biggr{)}\cdot 2 \Delta\xi_{0} \tag{28}\]where \(\Delta x_{1}=\pi/N\Delta\xi_{0}\) is the increment in \(x_{1}\) coordinate. Integers \(p\) and \(k\) are indices in spatial and wavenumber series, respectively. The discrete inverse Fourier transform is easily performed by FFT (Fast Fourier Transform) algorithm.  
The results are plotted in Figure 9. It is observed that when ratio \(d_{\max}/r\) is as small as 0.1 (see Figure 9a), the geometric properties (such as location, depth, and width) and shape of the flaw can be reconstructed with good precision. When \(d_{\max}/r\) value increases to 0.5 (see Figure 9c), the location of flaw can still be precisely detected, and the width and depth are slightly larger than the reality. However, the reconstructed shape is not as accurate as its shallower counterparts. The image underestimates the bottom region, and looks as if there were two local maximum depth points. The reason why the shallow and flat flaws are reconstructed better can be attributed to the application range of Born approximation, which is especially suitable for weak scatterers.  
Even the reconstructed image is not accurate for deeper and steeper canyons, the geometric characteristics (e.g., location, depth, and width) can be obtained with acceptable accuracy, so the image can be used as the \"initial solution\" in the following iterative reconstruction algorithm.  
Figure 9: Inverse reconstruction results of surface canyon-shaped flaws with different depth–radius ratios (**a**) \(d_{\max}/r=0.1\); (**b**) \(d_{\max}/r=0.2\); (**c**) \(d_{\max}/r=0.5\).
# Automatic Addition of Fault-Tolerance in Presence of Unchangeable Environment Actions +
## 4 Case Study: Stabilization of a Smart Grid
### Program Model  
We model the program of the smart grid shown in Figure 4 by program \(p\) which has five variables as follows:  
\(V_{G}:\) The value of sensor G.  
\(V_{1}:\) The value of sensor 1.  
\(V_{2}:\) The value of sensor 2.  
\(w_{1}:\) The status of switch 1.  
\(w_{2}:\) The status of switch 2.  
The value of each sensor is an integer in the range \([0,max]\). The status of each switch is a Boolean. The invariant \(S\) for this program includes all the states which are legitimate according to the conditions 1-4 mentioned above. Therefore, \(S\) is the union of state predicates \(I_{1}\) to \(I_{6}\) as follows (We need to add \(0\leq V_{1},V_{2},V_{g}\leq max\) to all conditions. For brevity, we keep these implicit.):  
Figure 4: A single generator smart grid system [9].  
\(I_{1}=(V_{1}+V_{1}\leq V_{G})\wedge(w_{1}\wedge w_{2})\)  
\(I_{2}=V_{1}\leq V_{G}\wedge V_{2}>V_{G})\wedge(w_{1}\wedge\
eg w_{2})\)  
\(I_{3}=(V_{1}>V_{G}\wedge V_{2}\leq V_{G})\wedge(\
eg w_{1}\wedge w_{2})\)  
\(I_{4}=(V_{1}>V_{G}\wedge V_{2}>V_{G})\wedge(\
eg w_{1}\wedge\
eg w_{2})\)  
\(I_{5}=(V_{1}+V_{2}>V_{G}\wedge V_{1}\leq V_{G}\wedge V_{2}\leq V_{G}\wedge V_{ 1}\leq V_{2})\wedge(\
eg w_{1}\wedge w_{2})\)  
\(I_{6}=(V_{1}+V_{2}>V_{G}\wedge V_{1}\leq V_{G}\wedge V_{2}\leq V_{G}\wedge V_{ 1}>V_{2})\wedge(w_{1}\wedge\
eg w_{2})\)  
We note the following observation about states in \(S\):  
**Observation 3**.: _For any value of \(V_{1},\)\(V_{2}\) and \(V_{G}\), there exists an assignment to \(w_{1}\) and \(w_{2}\) that changes the state to a state is in \(S\)._  
The environment can change the values of sensors 1 and 2. In addition, environment can keep the current value of a sensor by self-loop environment transitions. However, environment cannot change the status of switches, change the generated load, or leave the invariant. Thus, the set of environment transitions \(\delta_{e}\) is equal to \(\{(s_{0},s_{1})|\big{(}w_{1}(s_{0})=w_{1}(s_{1})\big{)}\wedge\big{(}w_{2}(s_{0 })=w_{2}(s_{1})\big{)}\wedge\big{(}V_{G}(s_{0})=V_{G}(s_{1})\big{)}\wedge\big{(} \cap_{i=1}^{6}I_{i}(s_{0})\Rightarrow\cap_{i=1}^{6}I_{i}(s_{1})\big{)}\}\), where \(v(s_{j})\) shows the value of the variable or predicate \(v\) in state \(s_{j}\).  
Program cannot change the value of any sensor. Thus, set of program restrictions for this program is \(\delta_{r}=\{(s_{0},s_{1})|\)\(V_{G}(s_{0})\
eq V_{G}(s_{1})\lor V_{1}(s_{0})\
eq V_{1}(s_{1})\lor V_{2}(s_{0 })\
eq V_{2}(s_{1})\}\).  
For the sake of presentation, we also consider the case where that program cannot change the status of more than one switch in one transition. For this case, we add more transitions to the set of program restrictions. We call the set of program restrictions for this case \(\delta_{r_{2}}\) and it is equal to \(\{(s_{0},s_{1})|\)\(V_{G}(s_{0})\
eq V_{G}(s_{1})\lor V_{1}(s_{0})\
eq V_{1}(s_{1})\lor V_{2}(s_{0 })\
eq V_{2}(s_{1})\vee(w_{1}(s_{0})\
eq w_{1}(s_{1})\wedge w_{2}(s_{0})\
eq w _{2}(s_{1}))\}\).
# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 1 Introduction
### Survey Methodology
#### 1.2.2 Exclusion Criteria  
* Papers focused on general IoT or cloud environments without specific application to vehicular networks.
* Studies lacking experimental validation or performance metrics.
* Non-English articles or those with limited accessibility or academic rigor.  
The inclusion and exclusion criteria were designed to uphold scientific rigor and ensure the selection of relevant, technically sound studies. After applying these criteria, 58 papers were selected, each focusing on both ML/DL- and Generative AI-based IDS within the Internet of Vehicles. These studies encompass a diverse set of approaches, including traditional Machine Learning, Deep Learning, and emerging Generative AI techniques such as GANs, Transformers, LLMs, and hybrid architectures. This categorization provides valuable insight into the current landscape of IDS research in vehicular networks and highlights the growing prominence of generative approaches in this field.  
To provide a visual summary, Figure 1 presents a bar chart illustrating the distribution of techniques used across the reviewed studies. The methods are categorized as ML-based, DL-based, GAN-based, Transformer-based, Autoencoder-based (AE), and other Generative AI-driven IDS approaches.  
Figure 1: Distribution of IDS techniques reviewed in this survey.
# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
## 2 Materials and Methods
### Simulation Model for Online Learning Evaluation

Table 5: Conceptual preference models for the virtual transmitters. Each agent calculates the probabilities of selecting satellite passes from the discretized states, agents' value function approximators, and the pass midpoints. These probabilities are calculated from the policy \(\pi\) and satellite passes are chosen by these probabilities. Satellite pass and the RF noise characteristics are used by the agents' preference models for transmission success probabilities. Finally, transmission successes are determined according to the agent preference model outputs, and the process repeats.
# Challenges of Using the L-Band and S-Band for Direct-to-Cellular Satellite 5G-6G NTN Systems
## 5 Results
The C/N levels experience a degradation of at least 1 dB 90% of the time. Even more concerning is the fact that for 18% of the time, the link degrades by more than 5.5 dB. Such a substantial degradation would have severe consequences, leading to either a tremendous drop in service quality or even a complete  
Figure 12: CDF curve of interference at the Lyra receiver: (**a**) \(C/N\) and \(C/(N+I)\); (**b**) DeltaT/T. Figure 13: CDF curve of interference at the Immarsat receiver: (**a**) \(C/N\) and \(C/(N+I)\); (**b**) DeltaT/T. radio link outage. Additionally, it can be seen that the DeltaT/T threshold is exceeded by at least 5 dB and may reach 20 dB overall.
## 2 Materials and Methods
### GC\(\times\)GC-TOF/MS Condition  
The GC\(\times\)GC-TOF/MS analyses were performed using a LECO Pegasus 4D instrument (LECO Corp., St. Joseph, MI, USA) equipped with a non-moving quad-jet cryomodulator. DB-WAX (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m) and DB-17 HT (1.9 m \(\times\) 100 \(\upmu\)m \(\times\) 0.10 \(\upmu\)m) were used as the first and the second-dimensional columns, respectively. Helium (99.999%) was used as the carrier gas at a flow rate of 1 mL/min and the injection volume was 1 \(\upmu\)L. The initial temperature of the first column was held for 5 min at 60 \({}^{\circ}\)C. After that, the temperature was increased to 240 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min and then held for 5 min. The initial temperature of the second column was held for 5 min at 65 \({}^{\circ}\)C. After that, the temperature was increased to 245 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min and then held for 5 min. The modulator was offset in relation to the secondary oven, the modulation cycle was 4.0 s, and the last was 0.7 s.  
The mass spectrometry conditions were as follows. Electron ionizing source: ionizing energy, 70 eV; the mass scanning range was 41 to 415 u. The temperature of the conversion line was 250 \({}^{\circ}\)C, and the ion source temperature was 200 \({}^{\circ}\)C.  
Data were processed and consecutively visualized on 2D and 3D chromatograms using LECO ChromaTOF(tm) software. The retention index (RI) was calculated using alkane standards (C\({}_{4}\)-C\({}_{40}\); Sigma, Aldrich Trading Co., Ltd., Shanghai, China) at the same experimental conditions as mentioned above.
###### Abstract  
A wide range of defects, failures, and degradation can develop at different stages in the lifetime of photovoltaic modules. To accurately assess their effect on the module performance, these failures need to be quantified. Electroluminescence (EL) imaging is a powerful diagnostic method, providing high spatial resolution images of solar cells and modules. EL images allow the identification and quantification of different types of failures, including those in high recombination regions, as well as series resistance-related problems. In this study, almost 46,000 EL cell images are extracted from photovoltaic modules with different defects. We present a method that extracts statistical parameters from the histogram of these images and utilizes them as a feature descriptor. Machine learning algorithms are then trained using this descriptor to classify the detected defects into three categories: (i) cracks (Mode B and C), (ii) micro-cracks (Mode A) and finger failures, and (iii) no failures. By comparing the developed methods with the commonly used one, this study demonstrates that the pre-processing of images into a feature vector of statistical parameters provides a higher classification accuracy than would be obtained by raw images alone. The proposed method can autonomously detect cracks and finger failures, enabling outdoor EL inspection using a drone-mounted system for quick assessments of photovoltaic fields.  
electroluminescence imaging; photovoltaic modules; defect classification; micro-cracks (mode A); cracks (mode B and C); finger failures; pixel intensity histogram; statistical parameters; machine learning classifiers +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion  
### Convective Heat-Transfer Characteristics of Head-Helmet Ensembles  
The convective loss from the nude manikin and the manikin with the helmets at 3 m/s and 6 m/s are shown in Figure 5. The convective heat loss increased from 16.6-22.1 W at 3 m/s to 20.8-25.1 W at 6 m/s, showing that heat transfer is strongly influenced by air velocity. Convective heat flux for the nude head-form was 192 \(\pm\) 2.5 W/m\({}^{2}\) at 3 m/s and 216 \(\pm\) 2.5 W/m\({}^{2}\) at 6 m/s. These values are approximately 25% lower than the theoretical heat flux calculated using heat-transfer coefficient values obtained from this study [53] for low (h\({}_{\rm c(3)}\) = 24.8 W/m\({}^{2}\).K) and moderate (h\({}_{\rm c(6)}\) = 28.5 W/m\({}^{2}\).K) velocities in Equation (2). Further investigation indicated that temperature-measurement sensors in the manikin were placed between the two layers, and the reported measurements are the temperature readings between the inner layer and outer layer plus fabric. The outer layer (thickness: 2 mm) acts as an insulation skin resulting in the external fabric layer temperature (measured using an IR thermometer) 2-2.5\({}^{\circ}\) C lower than the temperature measured by the sensors resulting in reduction in heat flux values recorded in the tests. The heat flux from nude head (\(216\pm 2.5\) W/m\({}^{2}\)) recorded at 6 m/s is comparable with the heat flux values recorded in these studies [31; 35].  
Dry heat losses with the tested helmet ensembles (Table 3) were 11%-25% lower than those of the nude head for all helmets except A1, indicating that most helmets hampered heat transfer from the head by serving as a thermal resistance to heat transfers. The cooling efficiency of the helmets (Table 3) ranged 75%-101% at 3 m/s and 84%-101% at 6 m/s, with Helmets A1 and A2, showing the highest and the lowest efficiency, respectively, for the two tested velocities. Total heat transfer from Helmet A1 was recorded to be higher than the nude head. To understand the reason behind this behavior a zonal heat flux investigation using two-way ANOVA was carried out on total heat flux values. The analysis of the total heat fluxes obtained with the different helmets at two tested velocities showed that all obtained heat fluxes were statistically different (\(p<0.05\)) from those obtained with the nude head except for Helmet A1 (\(p=1.0\)) thus indicating that the heat flux values of helmet A1 and nude head are comparable. Hence, analysis of zonal heat-flux values was performed using two-way ANOVA combined with post hoc (Bonferroni) analysis since helmet classification based on total heat flux may not correspond to classification based on the heat fluxes of the individual zones, as indicated in [31].
## 1 Introduction
Besides, because it is difficult to determine which cluster a job record belongs to when it falls on the border of two adjacent clusters, FCM has been used in prediction models to classify the data into several clusters in advance, an approach that has been used widely in various fields [46; 47; 48]. The FCM algorithm uses the sum of the membership value for all clusters, making it sensitive to noise and isolated data. In addition, FCM is essentially a kind of local hill-climbing algorithm, which makes it sensitive to the initial cluster center and easy to converge to a local extremum [49; 50]. To overcome these defects, an FCM-based GA has been introduced. Before applying the FCM-based GA method, all job records are divided into (i) a dataset for clustering and training and (ii) a dataset for testing. We then use an FCM-based GA classifier to classify the first dataset of job records into several clusters because (i) FCM allows for flexibility in classifying job records and reduces the sensitivity to noise and isolated data, and (ii) a GA can improve the FCM performance by preventing cluster centers from converging to local extrema. The cluster center results obtained from the FCM-based GA classifier are fed into the BPN predictor for training. We discovered that the overfitting of the BPN predictor took place when trained by the raw data containing noises. The BPN predictor predicts job cycle times and compares them with the second dataset. The remainder of this paper is organized as follows. In Section 2, we describe the proposed FCM-based GA and BPN methods for predicting job cycle times for semiconductor manufacturing. Then, Section 3 uses an actual dataset obtained from a semiconductor fabrication factory to test the proposed hybrid approach. Finally, in Section 4, we conclude by showing this study's contributions and future directions.
# Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks
## 5 Results  
In this section, we introduced the simulation results based on the tool described above. Unlike in [5], that the analysis was focused on how many UABS were needed to support the disaster; here we focused the study on the behavior of the network when there were capacity restrictions in the backhaul and different frequencies to evaluate. For this, we divided the results in the three backhaul scenarios starting with the 3.5 GHz band with no CA, then with the 3.5 GHz with CA and finishing with the 60 GHz band. Besides, we collected the results of 21 variables in groups related to the UABS, the users, the network and backhaul capacity and power consumption.
## 2 Related Work
Interference management is crucial to maintain high performance in the presence of other devices and networks that may use the same frequency bands. The techniques listed in this column include dynamic frequency selection, channel hopping, beamforming, coordinated multi-point transmission, dynamic power control, interference avoidance, MIMO (multiple-input, multiple-output) [24], cognitive radio, massive MIMO, and interference alignment. 6. Energy consumption: This metric indicates the amount of energy consumed by the IoT devices and the network. Energy consumption is an important consideration for IoT services, especially for those that operate in remote locations or rely on battery-powered devices. The values in this column range from low to high. Table 1 compares ten IoT services, including smart home automation, smart agriculture, industrial IoT, connected vehicles, healthcare monitoring, smart cities, environmental monitoring, smart grid management, augmented reality, and drones. For each IoT service, the table lists the parameters in each column relevant to the impact of 5G network interference on its performance. Note that the values in the table are just examples and should be replaced with appropriate data from relevant papers. The table provides a helpful overview of how different IoT services may be impacted by 5G network interference and what factors are essential to consider when evaluating the performance of these services in the presence of interference.
## 2 Materials and Methods
### Theory of the Frequency-Bessel Transform
With the high frequency dispersion information extracted by the frequency-Bessel method, we can obtain not only the short wavelength information of the fundamental mode, but also the long wavelength information of the higher mode of the surface wave [38]. Constrains on short and long wavelength information in the high frequency range can reduce the uncertainty of the inversion.
# Effects of Magnetic Stimulation on Dental Implant Osseointegration: A Scoping Review
## 3 Results
### Clinical Studies  
#### 3.3.1 Static Magnetic Fields from Permanent Magnets  
Only two studies have clinically evaluated the effects of SFM on dental implant osseointegration, reaching similar results with respect to its advantageous effects. [PERSON] et al. [57] reported a significant increase in stability, measured by implant stability quotient (ISQ), for implants receiving SFM (50-245 mT intensities) from circular isotropic Neodymium-Iron-Boron magnets placed in the denture vs. unexposed implants at 1 (73.25 \(\pm\) 4.53 test, 68.45 \(\pm\) 4.46 control), 2 (76.05 \(\pm\) 4.26 test, 72.05 control) and 3 months (78.95 \(\pm\) 3.50 test, 74.45 \(\pm\) 3.83 control) after positioning.  
The second clinical study showed that immediately placed maxillary implants had signifantly higher ISQ values measured by resonance frequency analysis (RFA) when exposed to SFM compared to unexposed implants only after the first month of healing (55.0 \(\pm\) 1.2 test, 51.3 \(\pm\) 4.9 control). Furthermore, the control group showed also more bone loss in the second month (0.30 \(\pm\) 0.10 test, 0.39 \(\pm\) 0.16, control), while at 3 months both groups had similar bone levels [58].
## 2 Literature Review
### Car-Following Behaviour Modelling of SPF  
As intelligent driving systems evolve, technologies such as vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), advanced driver-assistance systems (ADAS), automated emergency braking systems (AEBS), LIDAR, and GPS are enabling vehicles to acquire increasingly precise real-time data about their surrounding human-vehicle environment. This advancement facilitates early warning, assistance, and intelligent decision-making functions for driving operations [34]. [PERSON] et al. [35] developed a dynamic driving risk potential field model in a connected and automated driving environment. This model thoroughly considers the dynamic effects of vehicle acceleration and steering angle and calibrated its parameters using NGSIM data in traffic scenarios. In addition, they [36] integrated graph theory with safety potential field theory to devise a queue formation and optimisation model for CAVs under varying vehicle distributions, with simulations indicating the model's efficacy in forming a collision-free queue swiftly. [PERSON] et al. [37]introduced a risk assessment model based on potential field theory, designed to quantify driving risks. This model demonstrated strengths in risk perception and response, vehicle-tracking trajectory, and speed estimation.  
These fields collectively describe the safety risks for CAVs in motion. [PERSON] et al. [38], focusing on the autonomous decision-making capabilities of CAVs by sensing their surroundings, established a safety potential field model that includes lane-marking, a road boundary, and vehicle potential fields. [PERSON] et al. [39] addressed the microscopic state evolution mechanism of mixed traffic flow, comprising CAVs in an intelligent connected environment. They proposed a mixed traffic flow safety potential field model based on potential field theory that can adjust vehicle acceleration in real-time according to the potential field distribution and more accurately represent the actual following state. [PERSON] et al. [40] designed a risk potential field between interactive and automated vehicles, formulated a real-time risk assessment model for weaving areas, and introduced an alternative safety measure index for vehicle operation risks. However, these models have not accounted for the impact of time delays and the influence of information from multiple vehicles ahead and behind in the car-following model, indicating a potential area for further refinement in these models. Table 1 presents a summary of the car-following behaviour modelling works related to SPF found in the literature, with the final row highlighting the unique contribution of this study.