esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Application and Validation Based on Machine Learning Models  
We also used a support vector machine (SVM) model to evaluate the effects of negative samples with different credibility thresholds on ML performance. Using the negative sample sets (A-E) and positive samples (landslide samples) described in Section 4.2.2, we developed FR-SVM-coupled models (models a-**e**). The validation results of the models are summarized in Table 5.  
Table 5 demonstrates that the evaluation metrics of the FR-SVM-coupled models are closely aligned with those of the FR-RF-coupled models. Both models achieve optimal performance when the credibility threshold range for negative samples is 0.7-1. This result suggests that the negative sample credibility distribution map constructed using the proposed method demonstrates a level of universality, providing a dependable foundation for selecting negative samples for landslide research in the study area [30]. In addition, as shown in Tables 3 and 5, the evaluation metrics of the FR-SVM-coupled models exhibit more substantial variations than those of the FR-RF-coupled models as the credibility threshold for negative samples changes. Furthermore, the optimal prediction values of the FR-SVM-coupled models were slightly lower than those of the FR-RF-coupled models. This result indicates that the stability and predictive capability of the FR-RF-coupled models are superior [3; 15]. In future research, the authors will explore the application of various ML models for landslide susceptibility mapping in the study area to enhance assessment precision.
## 3 Modular Modeling for BTM System
### Modeling for Inductive Coupling Module  
Depending on the working principle of on-board antenna, the inductive disturbance coupling module is established in Ansys HFSS and the S-parameters are solved by using the finite element method (FEM), as shown in Figure 10.  
The size of EMI radiation antenna and the voltage of the induction disturbance \(S_{2}\) are considered as the output of induction disturbance source module. They perform as the input of the inductive coupling module. The size of the rectangular loop antenna is \(240\times 430\) mm. In addition, the size of the on-board antenna is \(240\times 265\) mm. Port 1 on the on-board antenna is the antenna receiving port, while the Port 2 on the EMI radiation antenna is disturbance transmitting port. The vertical distance of the two antennas is 460 mm based on the height from on-board antenna to the rail.  
Set the center of the EMI radiation antenna to be at the ordinate origin. Train running orientation is the positive direction along Y-axis. The S-parameters of antennas shown in Figure 11a is the S\({}_{12}\) as different horizontal distances between two antennas' centers. The value of S\({}_{12}\) keeps stable within the scope of \(\pm 0.2\) m. Thus, S-parameter of the antenna system is used when on-board antenna is located right above the EMI radiation antenna.  
Figure 10: Numerical simulation model for radiation disturbance coupling.  
The output power \(P_{s}\) from Port 2 and the input power \(P_{r}\) at Port 1 satisfy [26],  
\[P_{r}=P_{s}\cdot|S_{12}|^{2}, \tag{7}\]  
To ensure the signal transmission performance, \(R_{s}\) and \(R_{BTM}\) are set to 50 \(\Omega\). V\({}_{2\_\textrm{eff}}\) and V\({}_{\textrm{r2\_eff}}\) are the effective values of \(V_{2}\) and \(V_{r2}\). Thus, \(P_{s}\) and \(P_{r}\) are expressed as  
\[P_{\textrm{s}}=\frac{V_{2\_\textrm{eff}}^{2}}{R_{\textrm{s}}}, \tag{8}\]  
\[P_{r}=\frac{V_{r2\_\textrm{eff}}^{2}}{R_{\textrm{BTM}}}, \tag{9}\]  
Thus, the port voltage \(V_{r2}\) of on-board antenna is,  
\[V_{r2}=V_{2}S_{12}\sqrt{\frac{R_{\textrm{BTM}}}{R_{s}}}, \tag{10}\]  
The \(R_{\textrm{BTM}}\) and \(R_{\textrm{s}}\) are the impedances of Port 1 and Port 2 respectively.  
According to Equation (2), \(V_{N}\)=\(V_{r2}\), the inductive coupling function \(C_{2}\) is expressed as,  
\[C_{2}=S_{12}\sqrt{\frac{R_{\textrm{BTM}}}{R_{s}}}, \tag{11}\]
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 4 Imaging Fusion Algorithms
### Image Fusion Indicators  
Several image fusion indicators can demonstrate fusion quality. One is the fused image assessment. The other is the fused image metrics. We can obtain the assessment results by the subjective ratings, computational metrics and objective human tasks.  
A number of image quality metrics [160] have been proposed, including mean square error (MSE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR), mean absolute error (MAE), quality index, mutual information (MI), the Petrovic and Xydeas metric and Piella's Quality Index [161].
## 4 Hardware Implementation  
The robot's actuators are controlled at the hardware control level. These are the drive train on the one hand and the steering on the other. The idea of this level is to control the actuators in such a way that they behave almost ideally from the point of view of the higher layers. For example, the robot should drive at a defined speed. To ensure this, the rotary encoder in the drive train must be evaluated, and the deviation of its signal from the setpoint must be included in the manipulated variable, i.e., the voltage of the drive motor. Basic control elements such as PT\({}_{1}\) delay elements and PID controllers are required for the controls. These are also defined at this level but can be used by higher levels. In the actual hardware control, only the speed of the drive train is currently controlled to a set point. For this purpose, deriving the position value of the optical incremental rotary encoder in the drive train serves as the actual value supplier. However, since it is subject to strong quantization noise, the value is first smoothed by a PT\({}_{1}\) element. Figure 4 shows the signal flow diagram of the powertrain control.  
The PID controller shown is a PID controller with anti-windup feedback. The controlled system is the complete drive train, consisting of the motor bridge, the drive motors, the gearbox and the tires, and the inertia of the entire robot. The motor bridge is connected to the controller. The actual speed \(\omega(t)\) is first integrated into a position by the incremental encoder and then quantized to integer values. This quantized position signal must be differentiated again to obtain the most original speed. Since the signal is very noisy due to the quantization, it must first be filtered. For this purpose, a PT\({}_{1}\) element is inserted in the software filter for the noisy measured values. The control error \(e(t)\) is formed from the difference between the target value \(w_{\omega(t)}\) and the filtered measurement signal \(\omega_{measurement(t)}\).  
The steering angle is not regulated because there is no steering angle sensor. Therefore, the steering is only controlled. However, it should be noted here that the relationship between the steering value (i.e., the numerical value that sets the steering position in the microcontroller program) and the deflection of the wheels is nonlinear. However, it can be assumed that this relationship is monotonic, which means that an increase in the steering value also means an increase in the wheel deflection.
## 3 Nanocomposite Properties
### Nanocomposite for optoelectronic applications  
In order to obtain innovative materials with tunable optical, electronic and dielectric properties that can be exploited for the fabrication of advanced electro optical, magnetoelectronic and photovoltaic devices, functional materials for photonic applications and for advanced information processing technologies [7, 109], semiconductor NC based nanocomposites have been recently developed. The high technological interest for such kind of nanocomposites relies in their potential application for the production of LEDs, laser and photovoltaic devices.  
In a recent work, [PERSON] _et al._ synthesized blue emitting 5-(2-methacryloylethyloxymethyl)-8-quinolinol (MQ) surface coordinated ZnS NPs, with high quantum yield and good stability by a ligand exchange process [110]. The high emitting MQ-ZnS NPs were integrated in polymer matrix obtained starting from DMAA/St/DVB monomers, resulting in transparent and blue emitting nanocomposites. The as-prepared hybrid functionalized NP-polymer materials can be therefore processed in the fabrication of multifunctional electro optical devices.  
A new type of CdTe NC based semiconductor nanocomposite was prepared and characterized by [PERSON] and co-authors [111]. The incorporation of luminescent NCs in conjugated organic polymers allows one to combine the charge injection properties of the organic molecules with the size dependent emission of the inorganic nanofillers [2, 112, 113]. In their work, the authors incorporated CdTe NCs in emissive flexible random co-polymers. The conjugated organic co-polymers were designed in order to provide a proper coordination at NC surface and convey defined optical and electronic properties to the organic molecules. The energy transfers occurring from the random co-polymers to the semiconductor NCs can be exploited for the application of these nanocomposite materials in optoelectronic field.  
CdS NCs have been covalently bounded to polyacrylonitrile (PAN) molecules to obtain nanocomposites with optical and non linear optical properties [114]. The resulting CdS-PAN nanocomposites can be processed in dispensed thin films and their non linear optical properties can be applied for optical signal processing, switching or frequency generation.  
In a recent paper, [PERSON] and co-authors reported a novel approach to prepare hybrid organic-inorganic NP based multilayers by using photocross-linkable polystyrene (PS-N\({}_{3}\)) as host matrix and/or thiole functionalized polystyrene (PS-N\({}_{3}\)-SH) as ligands of semiconductor and metal NPs (CdSe(r)ZnS, Au, and Pt) [115]. The photocross-linking layer-by-layer process is efficiently controlled turning out in layers having thickness ranging from few to hundreds of nanometers as a function of the solution concentration and spinning rate. The resulting nanocomposite multilayers exhibit enhanced and prolonged photoluminescence durability, facile color tuning in accord to the layer and number thickness and are used as structural materials for manufacturing nanoscale optical devices.
## 6 Conclusions  
The devices in a network may have different battery power levels. When transmission power of a device also depends upon its battery power, selection of a relay for D2D communications becomes challenging. In this paper, we have designed midpoint relay selection schemes which selects socially trusted relay that is located around the midpoint of the distance between the source and the destination. Our relay selection designs incorporate battery power level of devices together with social trust information of users. A relay is selected for D2D communications only when the throughput of D2D communications via a relay is greater than that of direct D2D communications.  
When the devices in a network have different battery power levels, the performance of all of the schemes analyzed decreases. However, with the use of proposed relay selection selection scheme in this paper, the performance can be significantly improved. We compared the performance of existing relay selection schemes including HRS, MRS-ST, MRS-SD and other generic schemes, when relay devices have different power levels. The results showed that the performance of the schemes are not as good as originally claimed. Then, we compared the average throughput of our proposed schemes against state-of-the-art schemes including HRS, MRS-ST, MRS-SD and other generic schemes, when devices in the network have different battery power levels. We analyzed the performance for different social trust scenarios and device densities. The proposed relay selection scheme MRS-ST-BL significantly improved throughput of D2D communications in networks with weak social trust among users compared to others. The results show that performance is improved by minimizing number of probes and by selecting a relay with high transmission power. Additionally, we also show that the battery power threshold impacts upon the performance of proposed scheme MRS-ST-BL.  
In summary, our proposed relay selection scheme MRS-ST-BL significantly improves throughput of D2D communications compared to other schemes when relay devices have different battery power levels, particularly in networks that have weak social trust among users.  
**Author Contributions:** Conceptualization, U.S.K.; Formal analysis, U.S.K.; Investigation, U.S.K.; Methodology, U.S.K., P.S. and S.G.; Supervision, P.S. and S.G.; Writing--original draft, U.S.K.; Writing--review & editing, U.S.K., P.S. and S.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This work is supported by the Excellent Foreign Student (EFS) scholarship, Sirindhorn International Institute of Technology, Thammasat University.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Machine-Learning-Based Path Loss Prediction
###### Abstract  
Vehicle-to-vehicle (V2V) communication, which plays an important role in intelligent transportation systems, has been statistically proven to improve traffic efficiency and reduce the probability of accidents. In real-world applications, it is critical to accurately estimate the path loss parameter in communication channels due to the variable and complex propagation environments often encountered in inter-vehicle communication scenarios. This paper presents a study on various machine learning methods to improve path loss estimation in V2V communication using a dataset (192,000 observations) obtained from field measurements of highway environments in the Trabzon and Gumushane provinces in Turkiye. For this purpose, path loss estimation was carried out with different machine learning algorithms such as Artificial Neural Networks, Random Forest, Linear Regression, Gradient Boosting, Support Vector Regression, and AdaBoost by using various environmental and system features. Then, performance comparisons were conducted between machine learning methods and traditional empirical approaches such as log-distance, two-ray, and log-ray. Examining the outputs reveals that machine learning methods outperform traditional methods and yield results quickly. As a result, the Random Forest and Gradient Boosting methods demonstrated the highest prediction performances, with R' values of 0.97 and 0.96, MAE values of 0.0557 and 0.0701, and RMSE values of 0.0774 and 0.0964, respectively, outperforming both empirical methods, other machine learning techniques, and the existing studies based on V2V. Overall, our study provides significant contributions to the existing literature by providing a comprehensive parameter set for highway environments, examining the path loss prediction performance of machine learning models with different capabilities, and comparing them with traditional methods. This study not only fills a critical gap in the existing literature but also highlights the necessity, efficiency, and originality of machine learning approaches for improving reliable V2V communication systems.  
machine learning: vehicular communication; path loss; V2V; DSRC; highway Article  
Machine-Learning-Based Path Loss Prediction  
for Vehicle-to-Vehicle Communication in Highway Environments  
2024147545
# A Review of Edge Computing Technology and Its Applications in Power Systems
## 2 Review of Edge Computing Architectures
### Fog Computing
#### 2.3.4 Intelligent Traffic  
With the development of artificial intelligence technologies such as context awareness, intelligent traffic has become an important component of a smart city. Meanwhile, fog computing can be introduced into the intelligent traffic system for better performance. The authors in [55] presented a project called Laboratoire Recherche Informatique Maisonneuve (LRIMA) city, which is a fog computing-based smart city with integrated self-driving cars. By adding a fog layer between the physical layer and the network layer, traffic lights and smart cars can realize real-time interaction in this system. [PERSON] et al. [56] proposed a fog-enabled efficient car parking architecture with three tiers: physical tier, fog tier, and cloud tier. Cameras in the first tier take pictures of the parking area and send them to thefog nodes. The fog nodes receive and share the information with each other. After a certain period, the fog nodes communicate with the cloud server and send the essential data to the cloud. The experimental results show that this system reduces latency and overall network usage significantly.  
In summary, this section introduces three classic edge computing architectures and their corresponding applications. The primary differences among these edge computing architectures lie in the composition of the intermediate infrastructure. MAEC utilizes base stations as edge servers. Data processing usually occurs directly on the end devices or an edge node near the end devices. In Cloudlet computing, Cloudlets can be considered small cloud data centers with greater mobility at the network's edge. Fog computing employs clusters of connected devices, including routers and switches, as edge servers. These devices in the fog nodes are responsible for data transmission, storage, and computation. Table 1 lists the references in this section.
## 3 Validation of the Coupling Model
### Calculation Results and Analysis
#### 4.4.8 The Variation of the Vertical Displacement  
Figure 20 shows the contours of vertical displacement at the steady state of fluid seepage. We can see that the vertical displacement is distributed symmetrically on the central axis of the subgrade. At the same depth, the vertical displacement at the central axis of the subgrade is slightly larger than the vertical displacement at the edge of the subgrade.  
Figure 21 shows the evolution curves of vertical displacement at different observation points in saturated zone of railway subgrade. It can be seen from Figure 21 that the vertical displacement at different observation points increases first and then decreases slightly with the increase of time,  
Figure 17: Evolution curves of the volume fractions of fine particles with different initial volume fraction of fine particles at point 2.  
Figure 18: Contours of vertical effective stress at the steady state (time is 11,000 min) of fluid flow (unit kPa).  
and finally tends to be stable. The vertical displacement of the upper part of the subgrade is greater than that of the lower part of the subgrade. The final vertical displacement at observation point 1 is about 1.4 mm, and the final displacement at observation point 3 is about 0.5 mm.  
Figure 19: Evolution curves of vertical effective stress in saturated zone of railway subgrade.  
Figure 20: Contours of vertical displacement at the steady state (time is 11,000 min) of fluid flow (unit mm).  
4.9 The Relationship between Porosity Change and Accumulative Volume Fraction of Fine Particles/Displacement  
Figure 22a shows the relation curves of porosity change and accumulative volume fraction of fine particles and (b) shows the relation curves of porosity change and vertical displacement. The positive value of porosity change represents the increase of porosity, and the negative value of porosity change represents the decrease of porosity. It can be seen from Figure 22 that the accumulative volume fraction of fine particles has a significant impact on the change of porosity: with the increase of the accumulation volume fraction of fine particles, porosity change increases gradually, when the accumulation volume fraction of fine particles reaches about 10, the porosity change reaches a plateau value. The vertical displacement has little influence on the change of porosity. With the change of vertical displacement, the porosity first decreases slightly and then increases slightly, and finally remains stable.
# Towards the Unmanned Aerial Vehicles (UAVs): A Comprehensive Review
## 8 Future Research Directions
### Sensing, Navigation and Localization Algorithms  
More research towards sensing, navigation and localization is needed. These issues occur due to weak GPS systems to obtain the location which can create problems in accurate and timely parcel delivery. Among these, localization has a major role in safe operations of UAVs. Due to unpredictable and high mobility of UAVs, accurate localization tends to be problematic within a short time. The accuracy of UAV location is restricted by 3D mobility and obstacle-based and highly dynamic environment. Generally, GPS is used to track location, but it can add unnecessary costs and delay due to dense deployment of UAVs. Therefore, the incorporation of localization systems and low cost sensors must be investigated for proficient UAV location.
## 5 Methodology and Experiments  
To provide a holistic viewpoint about the methodology followed in this study, a flowchart is presented in Figure 4.  
Figure 4: Flowchart of the methodology.  
Figure 3: Time series of the asked questions with the tag value of python.  
Figure 2: Occurrences Based on Tags Groups.  
The time series data had 1464 samples for 61 days. The first 984 samples were kept for training, and this is nearly equal to 67% of all the samples. The following 240 samples were kept for validation purposes, which are very important for mitigating the overfitting effect [38; 39]. The last 240 samples were used for performance tests of all the algorithms. The test part of the dataset was used to measure the forecasting performance of the algorithms, and these data were been used before. In the scope of this work, novel deep learning models that are highly applied in time series prediction tasks were used [40]. They were: the multi-layered dense model, simple recurrent neural networks (RNN), stateful RNN, LSTM, and CNN. Among the tested models, the LSTM and CNN-based models performed especially well since they are designed to work on sequential data. All the models are implemented in Google's Colaboratory environment, which is a cloud service for machine learning and artificial intelligence computations. It utilizes python and all major python libraries, such as TensorFlow, scikit-learn, and matplotlib, among many others, which are pre-installed and ready to be imported. Another powerful feature of Colaboratory is that it can provide GPU acceleration. As a result, Colaboratory can help to train very deep networks; otherwise, it is impossible to train such networks in ordinary computers.