esa-sceva/satcom-chunk-collection huggingface dataset:
# A Proof-of-Multiple-State Consensus Mechanism for Mobile Nodes in Internet of Vehicles
## 3 Proposed System Model  
### Architecture  
The mobile vehicles in the system form an in vehicle self-organizing network (VANET), which uses relays to complete message transmission. This decentralized network structure is very compatible with blockchain. Considering the dynamic characteristics of the network, communication between vehicles is achieved through the Dedicated Short Range Communication (DSRC) protocol.  
This paper models the rapid mobility of numerous vehicles within the IoV scenario, with the comprehensive structure of the proposed vehicle messaging model shown in Figure 1. The model includes an edge layer with mobile vehicles and roadside units (RSUs), a blockchain layer with selected RSUs and a trusted third party (Third Party Trusted Authority (TA).  
Mobile Vehicles: In this model, mobile vehicles act as primary data generators, continuously producing and transmitting significant amounts of data. Every vehicle has an on-board unit (OBU) with different computing power and storage capacity. Vehicles continuously collect data about their position, speed and direction of travel and send it to nearby vehicles and RSUs, creating the basic communication infrastructure of the IoV network. This information is not only fundamental to communication, but also serves as a crucial parameter in the consensus mechanism that assigns points to vehicles for leader node selection based on their travel status. Additionally, each vehicle is equipped with a Transient Protection Device (TPD) to store important key and identity information provided by a trusted third party.  
Roadside Unit (RSU): It is arranged at a certain density on both sides of the road and maintains communication with the vehicle via the DSRC protocol commonly used in the IoV. The RSU receives and checks various information sent by the vehicle. Once validated, RSUs forward the information to the leading node, package it and upload it to the blockchain ledger. Meanwhile, RSUs communicate with each other via a wired connection.  
Trusted Third Party: The Trusted Third Party (TA) is responsible for the identity and key management of the entire blockchain and is usually considered completely trustworthy and free from any malicious behavior. TA has powerful computing and storage capacities and takes over the daily work. Today's operation and maintenance of the blockchain. TA is also responsible for initializing the blockchain network, generating the system parameters, and requesting all joining vehicles to complete the registration of their identity information. Vehicles wishing to join the blockchain must first submit a registration application to TA, and only those vehicles that pass the application can join the blockchain.  
Blockchain: We use a public blockchain as the decentralized underlying architecture to instantiate VANET in our scheme. The RSU validates messages based on a consensus algorithm and registered vehicles can access the blockchain via RSUs to obtain the necessary traffic flow data [22].
# AODs-CLYOLO: An Object Detection Method Integrating Fog Removal and Detection in Haze Environments
## 2 Related Work  
Dehazing  
Traditional dehazing algorithms primarily rely on various prior knowledge [8; 10; 11; 12] to solve the parameters needed to restore haze-free images. Among these, the most representative is the Dark Channel Prior (DCP) method proposed by [PERSON] et al. [8], which assumes that in non-sky regions, there exist pixels with very low values in at least one color channel (approaching zero). With the advancement of deep learning techniques, research combining Convolutional Neural Networks (CNNs) [7; 13; 14] and Generative Adversarial Networks (GANs) [15] into dehazing algorithms has grown. AODNet [7], for instance, utilizes a CNN model to estimate atmospheric light and scattering coefficients from hazy images  
Figure 1: Step-by-step diagram of the AODs-CLYOLO model, wherein the output of the defogging module is used as an input to the detection module, i.e., the defogging model is added to the pre-processing module of the detection module.  
for dehazing. Additionally, method like MSCNN [16] use CNNs to learn image priors to enhance dehazing effects. Models such as EPDN [15], which combine a CNN's feature extraction capabilities with a GAN's generative power, can generate more realistic and clearer haze-free images. Compared to the large model sizes of the latest deep learning dehazing algorithms, AODNet offers advantages in computational efficiency and processing speed, making it the chosen baseline model for dehazing in this paper.
## 3 Verification of Theoretical Analysis and Numerical Simulation
### Inhomogeneous Whole-Space Medium  
For vehicle-mounted transient electromagnetic detection, the medium exists simultaneously in the front and back of the transmitter coil, which is a whole-space problem. When there is a water body with low resistivity directly in front of the transmitter coil, it can be simplified into a flat cylinder, as shown in Figure 9[3]. This condition is called inhomogeneous whole-space as opposed to homogeneous whole-space. In this case, the shape of the water body is simplified in order to perform fast three-dimensional forward computation.  
As for the inhomogeneous whole-space medium, it is a local medium with a finite size in the whole medium, but it is not a layered medium. Hence, the general form of the electromagnetic field analytical solution is difficult to obtain compared with the layered theory. Based on the conclusions of Section 3.1, the numerical solution based on COMSOL is consistent with the analytical solution based on MATLAB, so the numerical solution based on COMSOL is also used to describe and explain the law of the secondary magnetic field in the inhomogeneous whole-space medium.  
Based on the numerical solution model in Section 3.1, a water body model with low resistivity is added in front of the transmitter coil, as shown in Figures 7 and 9. The upper and lower parts of the transmitter coil are surrounding a rock medium with a resistivity of 1000 \(\Omega\cdot\)m, and the water body with low resistivity is assumed to be 5 \(\Omega\cdot\)m [3,6]. The distance between the water body with low resistivity and the coil is 5 m, and the radius and thickness of the water body are 4.0 m and 0.3 m, respectively. Meanwhile, the size of the coil is also redesigned to be combined with the characteristics of vehicle-mounted transient electromagnetic detection [19]. The radius and turn of the transmitter coil are 0.5 m and 20, respectively. As for the ideal negative step, the maximum current of the transmitter coil is 1.0 A. The area and turn of the receiver coil are 0.0001 m\({}^{2}\) and 20, respectively. Taking the secondary magnetic field at the center of the transmitting coil and the induced voltage of the receiver coil as the analysis target, the transient electromagnetic response under  
Figure 8: Calculation results and relative error of two methods.
## 4 Discussion  
### Interannual Variations in ET and WUE  
Many studies in the Amazon have addressed aspects of seasonal variations in ET; yet little information is available on ET interannual variability, especially over long time scales. In this study, annual mean ET was 2.95 kg H\({}_{2}\)O m\({}^{-2}\) d\({}^{-1}\) (95% CI (2.93, 2.98)), which is within the range of values found in other studies conducted in Amazonia (Table 3). We found a 10.4% year-to-year variation in ET within the 11-year period. This variation is low and consistent with the 5%-10% variations previously observed in an Amazonian forest [18], but is lower than in tropical forests outside the  
Figure 6: Linear relationship between radiation-normalized water use efficiency (WUE) and the soil water stress index (SWSI) during the water depletion period (WD). Gray-shaded regions are the confidence intervals and the black line in between is the linear trend. Each dot corresponds to one day. The greater the SWSI, the lower the soil water stress.  
Amazon. For example, subtropical forests in Malaysia and China showed between 10% and 25% variability [1; 17; 33; 34].  
Annual mean WUE was 2.65 g C kg H\({}_{2}\)O\({}^{-1}\) (95% CI (2.62, 2.67)) and interannual variations in WUE were low (15.8%). WUE-related literature on tropical forests is scarce (unlike for ET) [35] and this makes a comprehensive comparison of WUE values difficult. Yet, our year-to-year variations in WUE were much lower compared to the 35% variability found in a subtropical forest in China [34]. A decline in leaf WUE has been observed since the 1950s in many studies, associated with the functional response of foliage to increasing atmospheric CO\({}_{2}\) concentrations and \({}^{13}\)C enrichment of atmospheric CO\({}_{2}\) (e.g., [36]). We did not observe any such decreasing trend at the ecosystem level for our study period. As ecosystem-level WUE results from the integrative mean of leaf-level tree WUE, interspecific variability in the response of trees to these CO\({}_{2}\) changes could also be an explanation. In any case, the interannual variability in WUE we observed suggests that, although annual ET and GPP respond to the same primary climate drivers (Table 2 and [25]), the extent of their responses varies among years. Furthermore, the combined effect of radiation, soil water and temperature only explained 40% of the between-year variance in WUE; biological control mechanisms may also be playing a major role at this time-scale. Inherent rhythms of leaf phenology [37], structural changes in vegetation [38], major mortality events [39], changes in surface conductance mediated by stomata [10] or the consequences of forest disturbance [1], both individually or in combinations do affect WUE and could very well be involved in these year-to-year variations. Long-term monitoring of these biological mechanisms is needed at eddy flux tower sites to assess their potential contribution to WUE.
# Spectrum Decision-Making in Collaborative Cognitive Radio Networks
## 1 Introduction
### Contributions and Scope  
The contribution of this work is structured with three inputs. The first is the incorporation of cooperation strategies between SUs for the characterization of the decision-making process. The second is the analysis based on real spectral occupation data. The third is the analysis of the collaboration levels using failed handoff metrics. The novelty of this work is the proposal and structure of the developed collaborative model that, unlike classic systems, acts as a bidirectional source of information. Additionally, it adapts to any decision-making strategy. For this work, multi-criteria techniques were used, but the capacity also exists to adapt to probabilistic structures and artificial intelligence, among others. Finally, due to the low complexity and response times, the proposed strategy has the characteristic of being easy to implement in real systems.  
In order to achieve an efficient decision-making process, this investigation analyzes the decisions made in collaborative scenarios where SUs share different amounts of spectral occupation data.  
According to the results obtained in [23], metrics such as number of failed handoffs are used in order to measure the performance of the proposed collaborative decision-making process.  
Information is characterized by five levels of collaboration (10%, 20%, 50%, 80%, and 100%), where each level represents the percentage of information that is shared for training and subsequent validation of the model. The levels are chosen according to the limits of the data, where 10% and 100% correspond to the criteria of too little or too much data, respectively, 50% corresponds to the intermediate value of the limits, and 20% and 80% are the intervals for analyzing the collaboration within an information range.  
For the analysis, a collaborative algorithm is designed to segment the matrix according to the five levels of collaboration. The decision-making process is based on two multi-criteria (MCDM) techniques: Feedback Fuzzy Analytical Hierarchical Process (FFAHP) and Simple Additive Weighting (SAW) [10; 11; 15; 24; 25]. As part of the decision-making process, the connection between the collaborative module and the multi-criteria techniques is carried out with a search algorithm. The spectral occupancy data used corresponds to actual power measurements in the GSM frequency band.
## 2 Methods
### The Design of the Hardware
#### 2.1.1 Main Control Unit  
The STM32L073 RZT6 is selected as the core controller, which is an ultra-low-power 32-bit microcontroller based on the Cortex-M0 core. The chip has four UARTs and one low-power UART. In the circuit design, UART1 connects to the RDSS port of the BDM for communication between the main control chip and the BDM, and UART2 connects to the RNSS port of the BDM to receive positioning information. The low-power UART (LPUART1) connects to the MAX3232 ECUE+ chip to convert TTL-232 levels and uses an RS232 interface to connect the BTTIs low-power UART to the buoy main control board, enabling information transmission between them. The buoy main control board controls the enable pin of the power supply chip; when high, the BDM power turns on, while when low, the BDM power turns off. The buoy main control board can wake up the terminal in STOP mode through UART wake-up operation, achieving low power consumption control.
## 5 Results and Discussion
### Limitations  
The proposed hybrid approach has some limitations: First, while the hybrid model demonstrates improved accuracy compared to traditional methods, it still relies heavily on the quality and representativeness of the input data. If the dataset is biased or lacks diversity, the model's predictions may not generalize well in real-world scenarios, particularly when varying traffic conditions or demographics. Second, despite employing techniques like dropout regularization and SMOTE-ENN for data balancing, the model may still be susceptible to overfitting, especially if it encounters new types of accidents or changing patterns in driver behavior that were not present in the training dataset. Finally, the interpretability of the hybrid model, though enhanced compared to standalone DNNs, still poses challenges for practical deployment in decision-making by traffic safety practitioners. Therefore, while the hybrid approach offers advancements in predictive performance, its effectiveness is constrained by data quality, interpretability issues, and potential overfitting risks.
# Misalignment Fault Prediction of Wind Turbines Based on Combined Forecasting Model
## 4 Fault Prediction Results and Discussion
### Fault Prediction Results and Discussion of Current Signal
#### 4.2.3 The Results of the Combined Forecasting Model  
The prediction results of IMGM (1,11) and QGA_LSSVM are used as the inputs of the QGA_LSSVM combiner, and the actual kurtosis values of the current signal are used as the output. The predicted results are shown in Figure 7.  
Figure 7a indicates that, compared with IMGM (1,11), the prediction results of QGA_LSSVM and the combined forecasting model are closer to the actual values. In Figure 7b, the absolute value of relative error of QGA_LSSVM and combined forecasting model is within 6%. The absolute value of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Data Set** & **RMSE** & **MAE** & \(R^{2}\) & **Time(s)** \\ \hline Grid Search\_LSSVM & \begin{tabular}{c} Training set \\ **Testing set** \\ \end{tabular} & \begin{tabular}{c} 0.0056 \\ **0.1283** \\ \end{tabular} & \begin{tabular}{c} 0.0043 \\ **0.0978** \\ \end{tabular} & \begin{tabular}{c} 0.9994 \\ **0.6103** \\ \end{tabular} & \begin{tabular}{c} 2.6170 \\ \end{tabular} \\ \hline GA\_LSSVM & \begin{tabular}{c} Training set \\ **Testing set** \\ \end{tabular} & \begin{tabular}{c} 0.0169 \\ **0.1119** \\ \end{tabular} & \begin{tabular}{c} 0.0129 \\ **0.0854** \\ \end{tabular} & \begin{tabular}{c} 0.9940 \\ **0.7415** \\ \end{tabular} & \begin{tabular}{c} 2.6152 \\ \end{tabular} \\ \hline QGA\_LSSVM & \begin{tabular}{c} Training set \\ **Testing set** \\ \end{tabular} & \begin{tabular}{c} 0.0101 \\ **0.0777** \\ \end{tabular} & \begin{tabular}{c} 0.0073 \\ **0.0582** \\ \end{tabular} & \
\begin{tabular}{c} 0.9979 \\ **0.9054** \\ \end{tabular} &
\begin{tabular}{c} 1.8338 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 13: The forecasting evaluation indexes of the current signal.  
Figure 7: Comparison of the combined forecasting model and the single forecasting models for current signal. (**a**) The prediction result from the 46 th points to the 60 th points; (**b**) the absolute value of relative error.  
relative error of IMGM (1,11) is much larger than those of other models. In Table 14, for the training set, the forecasting evaluation indexes of the combined forecasting model are near to those of QGA_LSSVM and better than those of IMGM (1,11), but for the testing set, the indexes of the combined forecasting model are the best. Therefore, it is proved by current signals that the combined forecasting model has a higher prediction accuracy with less running time.  
For the current signal, the optimal parameters of the combiner are \([\gamma,o^{2}]=[35.2214,\ 100]\).
# PV Tracking Design Methodology Based on an Orientation Efficiency Chart
## 2 Materials and Methods  
The goal of this investigation is to propose a methodology that maintains the PV value within an adequate range of EFO by developing a strategy for designing a low-precision, high-efficiency PV tracking system mechanism and facilitating the instrumentation and control required for this prototype. This proposed methodology is based on the effect of misalignment error with respect to PV efficiency [23] and the Norton design methodology [24]; complementary modifications of the process, directions for the developer, as well as instrumentation and control requirements that are based on the knowledge of and devices available to the developer are made using the sequential steps presented in Figure 1.  
Figure 1: Efficiency as a function of orientation (EFO) solar tracker methodology.  
The detailed description of the flow diagram shown in Figure 1 is presented below, starting with the input parameter section.
# A Robust Machine Learning Model for Detecting XSS Attacks on IoT over 5G Networks
## 3 Proposed Methodology
### Feature Selection Methods  
Feature selection is a standard preprocessing procedure for handling huge volumes of data, and it is essential for finding redundant or irrelevant characteristics within large datasets. The accuracy of classification algorithms can be greatly enhanced by reducing dataset dimensions through the application of suitable feature selection strategies [34]. There are three primary methods for selecting features: filtering, wrapping, and embedding as shown in Figure 2. In this study, we applied a wrapper approach with three widely used classification algorithms: Decision Tree, Naive Bayes, and Random Forest (RF). To pinpoint the most relevant features for classification. Each algorithm was trained on various subsets of features using the wrapper method [35].
# A Low Sampling Rate Receiver Design for Multi-Antenna Multi-User OFDM Systems
## 5 Conclusions  
This paper proposed a low sampling rate receiver design using zero-forcing precoding for multi-antenna multi-user OFDM systems. Compared to the traditional OFDM system, the proposed scheme achieved a low sampling rate at the receiver and no FFT operation was required on the receiving side. The proposed scheme significantly reduced the complexity of the receivers and thus, would contribute to the design of low cost terminals. The simulation results have shown that the proposed scheme produced an acceptable performance in terms of bit error rate, mutual information and peak-to-average power ratio.  
[PERSON], [PERSON] and [PERSON] initiated the idea; [PERSON] designed the experiments; [PERSON] contributed the simulation platform and environment; [PERSON] conducted the simulations and analyzed the data. [PERSON] wrote the manuscript; [PERSON] and [PERSON] discussed the revision for the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
## IRS Controller
### IRS Based Communication Advantages  
Finding a clear line-of-sight communication channel is a big challenge for wireless communication. In a practical scenario, many obstacles can make the transmitted signal very weak, and then interruption of the service can occur. Figure 5 shows the comparison between the energy efficiency and data rate for the IRS and decode-and-forward (DF) relay system [36]. It is evident that IRS can provide a higher data rate in low-energy consumption compared to the DF relay. Some of the striking advantages can be listed as:  
* IRS can provide an alternative path where direct communication is not possible. A virtual path is established dynamically when the communication takes place between the transmitter and receiver [29].
* IRS devices are passive in nature, which implies less power consumption compared to relay communication. The amplification and forwarding of an incoming signal are possible without employing power amplifiers [37]. Instead, the signal phase shift is controlled by the reflecting elements to direct the signal to the UE.
* Millimeter wavelength communication can address the bandwidth shortage problem for 6G communication. However, the path loss is higher than other low-frequency bands [38]. IRS can improve the communication to gain a better performance.
* IRS can be utilized to compensate for the channel rank condition in an environment suffering from a rank deficiency issue [39].
* The propagation of EM waves can be reconfigured in a software-controlled fashion, which can turn the probabilistic wireless channel model into a deterministic model [40].
* As the IRS is based on the reflection of signals with directed beamforming, Rayleigh fast fading is converted to Rician slow fading.
* IRS can provide an effective solution for both the co-channel and inter-channel interference of wireless communication [29].  
Figure 5: The comparison between energy efficiency and data rate for DF relay and IRS.