esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Reflectance Indices for Remote Sensing of Plants and Revealing Actions of Stressors
## 4 Problems of Measurement and Analysis of Reflectance Indices  
Above, we showed that reflectance indices can be successfully used for revealing the plant physiological processes, biochemical composition, water content, and actions of biotic and abiotic stressors. However, the reflectance of leaves has different mechanisms and depends on the conditions of the measurements. The measured signal can be affected by numerous factors, including the leaf surface roughness, content of aerosols and molecules in the atmosphere, soil background, and changes in the intensity and spectra of light sources (e.g., sunlight), etc. These factors disturb the measurements of the reflected light and make its analysis difficult. Below, we considered some ways to minimize the influence of these factors on the measurements and analysis of RIs (see Table 2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Problem** & **Problem Solution** & **References** \\ \hline \multirow{2}{*}{Changeability of illumination parameters} & Using of white and black reflectance standard & [22; 141] \\ \cline{2-3}  & Using short pulses of measuring light & [17; 39] \\ \hline \multirow{3}{*}{Orientation of leaves and parameters of their surface} & Spectra correction using mathematical models of PROCOSIN and PROSPECT & [142] \\ \cline{2-3}  & Measuring at multitude angles. & [143] \\ \cline{2-3}  & Combination of 3D plant model and hyperspectral images & [144; 145] \\ \hline \multirow{3}{*}{The atmosphere and soil influence on plant reflectance} & Statistical methods (e.g., principal component analysis) & [10] \\ \cline{2-3}  & Using models of radiation transfer in leaves and canopy & [146; 147] \\ \cline{2-3}  & Development of corrected RIs & [64; 98] \\ \hline Saturation of measurement of reflectance indices & Development of corrected RIs & [10; 69] \\ \cline{2-3}  & Using statistical methods and methods of texture analysis & [20; 148; 149] \\ \cline{2-3}  & Prediction of changes in spectral properties using model of radiation transfer in leaves and canopy & [150; 151; 152; 153; 154] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Some problems of measurement and analysis of reflectance indices.
# Noise Reduction Method of Nanopore Based on Wavelet and Kalman Filter
## 2 The wavelet Method  
### Method Introduction  
Compared with Fourier transform [10], the wavelet transform can provide more accurate localization not only in the time domain but also in the frequency domain. Namely, it can perform multi-scale analysis of the signal in the time-frequency domain. The wavelet transform solves many difficult problems that cannot be solved by the Fourier transform, especially for non-stationary signal processing, and has \"zoom\" characteristics, which can be used in the low-frequency band with high-frequency resolution and low time resolution (wide analysis window); in the high-frequency band with low-frequency resolution and high time resolution (narrow analysis window). Given this advantage, the abrupt change points of the signal can be clearly distinguished by the wavelet transform.
## 3 Conventional Geolocation Techniques  
In geolocation techniques, several types of measurements can be employed so that the location can be estimated. In the specified techniques below, we consider the emitter source at a location \(p_{e}=[x,y]^{T}\), and there, L sensors receive the interference signal from the emitter source, where the location of the sensor is \(s_{i}=[x_{i},y_{i}]^{T}\).
## 2 Related Works
Based on variables such the CH's residual energy, the distance between clusters, and the CH's distance from the BS, IAOAR builds the fitness function for choosing the best CHs.To determine the shortest path between CHs, the algorithm employs an ant colony optimization technique. However, it is important to remember that this approach does not handle network heterogeneity, which could be considered in future enhancements. An energy-efficient clustering technique, called EPOA-CHS (Enhanced Pelican Optimization Algorithm for Selecting Optimal CH Set), was proposed by [PERSON], [PERSON] et al. [26]. The algorithm uses POA for optimal CH selection. EPOA-CHS builds the fitness function for choosing the CH set using characteristics like node energy and inter-cluster distance. However, EPOA-CHS is designed as a single-hop clustering protocol, which means that it can only operate within single-hop communication. This characteristic may affect its applicability in specific network scenarios where multi-hop communication is more advantageous. [PERSON] et al. [27] introduced BWO-IACO, an energy-efficient clustering approach based on the combination of Ant Colony Optimization and Black Widow Optimization. Using BWO, the algorithm chooses a suitable set of CHs. The fitness function used to choose CHs is composed of several criteria, including node centrality, node degree, and intra-cluster. The algorithm uses improved ACO for inter-cluster routing. However, the technique does not account for how data aggregation affects data transmission energy. [PERSON] et al. [28] suggested a multi-hop routing protocol with Cat Swarm Optimization (CSO) and Salp Swarm Optimization (SSA) to lower the energy consumption and latency of wireless sensor networks. The algorithm uses LEACH to select CHs and C-SSA to select the optimal hops. Energy, distance, and latency are among the elements that make up the fitness function used to choose the best hops. However, the applicability of the algorithm to large-scale networks has not been fully considered, which may be a topic for development or further research. To minimize network power consumption, [PERSON], [PERSON] et al. [29] proposed a routing protocol capable of efficient cluster head election and path optimization. The protocol combines Lightning Search Algorithm and Particle Swarm Optimization and is called PSO-LSA. However, the protocol focuses too much on orphan nodes, which may lead to degradation of the performance of the protocol.
## 3 System Model
### Block Data Transmission Model
#### 3.3.1 R2V Transmission Latency  
For R2V communication, data transmission between an RSU and a vehicle occurs through wireless propagation. The communication rate between RSU \(r_{i}\) and vehicle \(v_{m}\) is given by [23]:  
\[CR_{im}=B_{im}\log_{2}\left(1+\frac{p_{i}|h_{im}|^{2}}{\sigma^{2}}\right) \tag{5}\]  
where \(B_{im}\) denotes the available spectrum bandwidth of RSU \(r_{i}\), \(p_{i}\) represents the transmit power of \(r_{i}\), \(h_{im}\) denotes the channel gain between RSU \(r_{i}\) and \(v_{m}\), and \(\sigma\) represents the noise power spectral density.  
The transmission latency incurred when RSU \(r_{j}\) transmits a single block \(b_{k}\) of size \(s_{k}\) to vehicle \(v_{m}\) can be expressed as follows:  
\[TD(r_{i},v_{m},b_{k})=\frac{s_{k}}{CR_{im}}=\frac{s_{k}}{B_{im}\log_{2}\left(1 +\frac{p_{i}|h_{im}|^{2}}{\sigma^{2}}\right)} \tag{6}\]
## 5 Results and Discussion
### Data Rate Analysis  
Figures 13 and 14 provide a comprehensive view of the calculated data rates for LoRa technology in our experimental setup. Notably, in this particular configuration, we found that data rate was not as important as reliability in assessing LoRa performance. Instead, our investigation focused on understanding the complex interplay between signal strength and data transmission.  
In our real-world example, the LoRa signal was subjected to extreme conditions, with signal diffraction occurring just 1 m from the transmitter to the moving vehicle. This proximity to a signal diffraction point was a significant challenge and provided a unique test bed for LoRa's capabilities. The results provided a compelling insight: Despite these challenging circumstances, the LoRa link demonstrated remarkable resilience, maintaining an average data rate of 1.58 kbps over the entire designated route. This performance underscores the adaptability and effectiveness of LoRa technology in maintaining communications even under adverse signal propagation conditions. In this configuration, data rate is not as critical to LoRa performance as reliability.  
Figure 12: LoRa BLER histogram.  
Figure 13: LoRa data rate results, measured while the vehicle is moving across the defined path.  
In our example, the signal is subjected to extreme conditions, where signal diffraction occurs just 1 m from the transmitter to the vehicle. According to the results, a LoRa link was able to transmit data at an average rate of 1.58 kbps while covering the entire designated route.  
The primary aim of this study was to assess the performance of LoRa in NLoS scenarios for IIoT applications, and the results directly address this objective. Through careful selection of test parameters and a comprehensive analysis of key metrics, including SINR, BLER, and data rate, we have provided detailed insights into LoRa's behavior under challenging industrial conditions. Furthermore, our study goes beyond conventional assessments, demonstrating the technical sophistication and innovation embedded in our experimental design. By revealing a deep understanding of LoRa's performance across various environments, our findings contribute new insights to the existing body of knowledge. For instance, the study identifies specific parameters and configurations that optimize LoRa's reliability in industrial contexts, offering practical applications for enhancing wireless communication in IIoT scenarios. The relevance and applicability of our results are evident in their potential to influence both theoretical understanding and practical implementations in IIoT communications. Our research not only reaffirms the robustness and effectiveness of LoRa in IIoT scenarios, but also highlights its innovative features in handling high levels of interference without significant performance degradation. This has broader implications for the field, suggesting that LoRa, with its adaptive mechanisms and resilient signal strength, can be a pivotal technology in addressing communication challenges in industrial settings.
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 4 Results and Discussion
### Comparison of Comprehensive Trust Values  
Figure 4 explains the comparison of the comprehensive trust value of the proposed Efficient and Secure Fog Based Routing Mechanism (ESFRM) and Trust-aware Secure Routing Protocol (TSRP) approach by putting the intended parameter values. The results show that the ESFRM is providing 16.44% better results than TSRP.
## 2 Detailed Model of MMC-Controlled Wind Turbine Generator
### Wind Turbine Model
#### 2.1.2 Drive Train Model  
The moving parts of a direct-driven wind turbine generator are comprised of the following components: rotor blades with pitching mechanism system, a hub, and a rotor shaft. Generally, in the study of the grid connectivity of wind turbine generators, the drive train model is treated as a two-lumped mass or a one-lumped mass model [20]. Because the wind turbine generator in this study is totally seperated from the grid system by a back-to-back MMC system, the one-lumped mass model of the drive train is considered here.  
The scheme of the drive train model in the one-lumped mass model is given in Figure 4, which is represented by the following equation [20]:  
\[\frac{d\omega_{r}}{dt}=\frac{T_{e}-T_{m}}{J_{eq}}-\frac{B_{m}}{J_{eq}}\omega_{r} \tag{6}\]  
Figure 3: Power coefficient vs. tip speed ratio characteristic of wind turbine.  
Figure 2: Turbine power output vs. rotor speed characteristic.  
where \(T_{m}\) is the wind turbine mechanical torque (Nm), \(T_{e}\) is the generator electrical torque (Nm), \(I_{eq}\) is the equivalent rotational inertia of the wind turbine generator (kg.m\({}^{2}\)), and \(B_{m}\) is the damping coefficient (Nm/s), which is derived from:  
\[\frac{d\omega_{r}}{dt}=J_{S}+\frac{J_{t}}{n_{g}^{2}} \tag{7}\]  
where \(J_{S}\) and \(J_{wt}\) are, respectively, the rotational inerias of the generator rotor and the wind turbine rotor, and \(n_{g}\) is the gear ratio, which can be set at 1 in the case of a direct drive system (without gearbox).
###### Abstract  
Research and development on task offloading over the Internet of Drones (IoD) has expanded rapidly in the last few years. Task offloading in a fog IoD environment is very challenging due to the high dynamics of the IoD topology, which cause intermittent connections, as well as the stringent requirements of task offloading, such as reduced delay. To overcome these challenges, in this paper, we propose a task-offloading optimization strategy using a heuristic genetic algorithm (GA) with hybrid fog computing technology for the Internet of Drones, named GA Hybrid-Fog. The proposed solution employs a GA for task offloading from edge Unmanned Aerial Vehicles (UAVs) to both fog base stations (FBSs) and fog UAVs (FUAVs) in order to optimize offloading delays (transmission and fog computing delays) and guarantee higher storage and processing capacity. Experimental results show that GA Hybrid-Fog achieves greater improvements in task-offloading delays compared to other IoD technologies (GA BS-Fog, GA UAV-Fog, and GA UAV-Edge).  
Keywords:Internet of Drones; fog computing networks; genetic algorithm optimization; task offloading in IoD; unmanned aerial vehicles +
Footnote †: journal:
## 4 Ueap-Our Proposed Protocol
### Security Evaluation of UEAP
#### 4.1.1. Informal Security Proof  
**Resistance against de-synchronization attack:** Given that in the UEAP protocol all messages are encrypted, the adversary cannot modify the transferred messages in such a way that the protocol parties exist from synchronization. Any modification in any transferred encrypted message is identified by the tag or the reader and it will terminate the protocol.  
**Resistance against traceability attack:** The vulnerability of ULRAS protocol was due to the fact that the adversary could retrieve the value of \(K_{x}\oplus R_{t}\oplus T_{R}\)_mod_\(L\). Because of using encryption function in calculating of messages in the UEAP protocol, the adversary cannot determine \(K_{x}\oplus R_{t}\oplus T_{R}\)_mod_\(L\), and so the UEAP protocol is secure against the traceability attack presented in this manuscript.  
**Resistance against replay and impersonation attacks:** All protocols' parties participate in the randomization of the messages exchanged in the UEAP protocol, and also all the messages exchanged are encrypted. Hence, the adversary cannot use a message later or fake a message on his behalf. Therefore, the UEAP protocol resists all types of replay and impersonation attacks.
# UAV-BS Site Planning Based on Circular Coverage Strategy
## 4 Methodology
### Air-to-Ground Channel Model  
Assume that the UAV-BS is equipped with an omnidirectional antenna for communication. Considering that the communication process between the UAV-BS and ground users is subject to multipath propagation, signal fading, and other effects, the UAV-BS ground communication link includes Non-Line-of-Sight (NLoS) links. The signal propagation in free space follows the shadow fading model. The channel model between the UAV-BS and ground users can be represented as follows:  
\[PL(d,f)=PL_{FS}(f)+10\alpha lg(d)+\xi, \tag{12}\]  
where \(f\) is the carrier frequency, \(c\) is the speed of light, \(d\) is the signal transmission distance, \(\alpha\) is the attenuation exponent with \(\alpha\geq 2\), and \(\xi\) is the shadowing component, which follows a normal distribution with a mean of 0 and variance \(\sigma^{2}\). The first term of Equation (12), related to \(f\), can be calculated using the Friis transmission equation at a reference distance of 1 meter in free space, given by \(PL_{FS}(f)=20\log_{10}\left(\frac{4\pi f}{c}\right)\). The second term provides the  
Figure 1: Single stream data queuing system model.  
logarithmic relationship between \(d\) and path loss, where \(d=\sqrt{r^{2}+H^{2}}\), with \(r\) being the horizontal distance between the projection of the UAV-BS on the ground and the target user.
# Detecting Deception from Gaze and Speech Using a Multimodal Attention LSTM-Based Framework
## 2 Materials and Methods
### Deception Detection Systems
#### 2.3.2 Single-Modal Attention LSTM-Based Add System
In thecase of the gaze modality, the input \(X\) is the gaze feature vector (FPOGX, FPOGY and MPD) \(X_{G}\) and the LSTM length is \(T=T_{G}=180\), whereas in the case of speech, the network is fed with the speech features (log-mel spectrograms) \(X=X_{S}\) and the LSTM length is set to \(T=T_{S}=300\). In the same way, the components of the attention parameter vector \(u\) were initialized to \(1/T\), i.e., to \(1/180\) and \(1/300\) for gaze and speech modalities, respectively. Both, reference and LSTM-based systems work at segment level. In other words, they produce a prediction of deception/honesty for each 3-s segment. As our objective was to classify each turn as truth or lie, it was necessary to obtain the predictions at turn level. For doing that, we followed the method proposed in, among others, ref. [37] that consisted of a majority voting strategy. This way, the final label assigned to each turn was the most frequent prediction value of the segments belonging to this turn.
## 3 Results and Discussion
### Battery Pack Model Development
#### 3.2.4 RSM Model Validation/Error Analysis  
In an attempt to validate the regression model developed in this study, equations of the developed RSM's were tested for experimental inputs to compare the output results. Figure 26 plots and compares the predicted absolute maximum temperatures against the actual maximum temperatures for the charge experiment at 0.5 C and the discharge experiment at 0.75 C.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**2 nd Degree Polynomial Fit**} \\ \hline Robustness: & \multicolumn{4}{c}{Off} \\ \hline  & Fit Type & SSE & * RMSE & R\({}^{2}\) & Adjusted R\({}^{2}\) \\ \hline Charge & ** poly21 & 0.9075 & 0.9526 & 0.9875 & 0.9373 \\ \hline Discharge & poly21 & 1.6133 & 1.2702 & 0.9824 & 0.9121 \\ \hline Robustness: & \multicolumn{4}{c}{Least Absolute Residual (LAR)} \\ \hline Charge & poly21 & 0.9075 & 0.9526 & 0.9875 & 0.9373 \\ \hline Discharge & poly21 & 1.6133 & 1.2702 & 0.9824 & 0.9121 \\ \hline Robustness: & \multicolumn{4}{c}{Bi-Square} \\ \hline Charge & poly21 & 1.3491 & 1.1615 & 0.9814 & 0.9068 \\ \hline Discharge & poly21 & 2.3984 & 1.5487 & 0.9739 & 0.8693 \\ \hline \hline \end{tabular}  
* * Root Mean Square Error. ** poly21: A second degree polynomial fit with two degrees of X and one degree of Y. SSE: sum of square error.  
\end{table}
Table 7: Comparison of Various Fitness Parameters for the Developed Regression Model.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Charge** & **Discharge** \\ \hline \(P_{0}\) & 21.92 & 16.48 \\ \hline \(P_{1}\) & \(-\)5.06 & \(-\)3.84 \\ \hline \(P_{2}\) & 37.6 & 48.75 \\ \hline \(P_{3}\) & 1.575 & 1.8 \\ \hline \(P_{4}\) & \(-\)9 & \(-\)13.2 \\ \hline \multicolumn{3}{c}{**Goodness of Fit**} \\ \hline Charge: & SSE—0.9075, R\({}^{2}\)—0.9875, Adjusted R\({}^{2}\)—0.9373 \\ \hline Discharge: & SSE—1.6133, R\({}^{2}\)—0.9824, Adjusted R\({}^{2}\)—0.9121 \\ \hline \hline \end{tabular}  
\end{table}
Table 8: Model Equation Coefficients.  
Table 9 below shows the absolute relative error calculations between the predicted model and the actual outputs for absolute maximum temperature in a battery pack for all twelve of the unique experiments performed. The results showed the developed model predicts accurately with an absolute maximum error of approximately 3.00%.
# Study on the Control of Torque Distribution of 4 WD Corn Harvester Operation Drive
## 2 Design and Modeling
### Establishment of the Mathematical Model
#### 2.2.4 The Mathematical Model of Engine  
The torque controller controls the amount of engine output torque by adjusting the throttle opening. The throttle position is controlled using a proportional-integral (PI) control algorithm, the input deviation is \(e\), and the output throttle opening increments is \(\Delta k_{d}\).  
\[e=\frac{v_{r}}{\lambda}-(\omega_{1}+\omega_{2}+\omega_{3}+\omega_{4})R/4 \tag{17}\]  
\(\Delta e\) is the increment of \(e\), corresponds to the increment of throttle opening as  
\[\Delta k_{d}=ek_{p}+\Delta ek_{i} \tag{18}\]in Equations (17) and (18), \(k_{p}\) and \(k_{i}\) are the proportional and integral factors of the harvester throttle controller, respectively; \(\lambda\) is the throttle controller adjustment parameter; \(v_{r}\) is the reference vehicle speed; \(\tau_{w}\) is the driving wheel radius; \(\omega_{1}\), \(\omega_{2}\), \(\omega_{3}\), and \(\omega_{4}\) are the left front, right front, left rear, and right rear drive wheel angular velocities, respectively.  
The engine speed variation is a dynamic process during the harvester start-up, and when the harvester is operating in the field, the engine is also in non-stationary operation all the time. Therefore, the engine was simplified to an inertial system, with the following dynamic mathematical model [36; 37; 38; 39]:  
\[T_{e} =T_{es}\frac{e^{-ST_{el}}}{1+ST_{e2}} \tag{19}\]  
where \(T_{e}\) is the output torque of the engine; \(T_{es}\) is the steady torque of the engine; \(S\) is a torque characteristic coefficient of the diesel engine; \(k_{d}\) is the throttle opening of the engine; \(T_{e1}\) and \(T_{e2}\) are the system time constant and the hysteresis constant, respectively.