esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Comparative Analysis Results and Discussion
### Comparison of ClaimsBERT against Other Network-Based Classifier Models
#### 5.2.1 LSTM  
In Long Short-Term Memory (LSTM) neural networks learn features at the phrase-level by using a convolutional layer. Then the convolutional layers used to learn how such higher-layer representations relate to long-term features through this convolutional layer [12, 59].  
The output of this convolutional layer are higher-layer representations of the processed content, and is then provided to the LSTM in order to learn relationships regarding long-term features.  
In order to determine the optimal hyperparameters and the resulting architecture, we assessed the different numbers of LSTMs and dense layers. With the LrFinder function [53], we found that the best LR for each model architecture varied based on the different numbers of LSTM and Dense layers. Table 3 reports the highest accuracy and F1 for each architecture. The best LSTM network-based model contains a single LSTM layer with 50 hidden units and a 0.3 dropout rate, linked to two dense layers with 150 and 124 neurons, respectively. Our experiments clearly demonstrated that this configuration best adapts to the information in our dataset compared to other LSTM-based models.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Architecture** & **Accuracy** & **F1 Score** & **Precision** & **Recall** \\ \hline BERT + CNN & 12 Encoder & & & & \\ (ClaimsBERT) & 2 Convolution & 0.973 & 0.96 & 0.963 & 0.966 \\  & 2 Dense & & & & \\ \hline BERSequence & 12 Encoder & & & & \\ Classifier & 1 Dense & 0.764 & 0.751 & 0.743 & 0.741 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparing classification performance of ClaimsBERT with BertForSequenceClassification.
## 4 Design of Dynamic Torque-Coordinated Control That Considers Engine Starting Conditions
### Engine Segment Active Control
#### 4.2.1 Feedforward Control before Ignition  
As shown in Figure 8, to start the engine reliably and smoothly before ignition, the feedforward control method before ignition was adopted. Firstly, the \"S-curve\" function \(f(x,a,b,c)\) from Equation (19) was taken as the required acceleration of the engine, \(\ddot{\theta}_{\text{Eng\_des}}\). Then, the \(f(x,a,b,c)\) was brought into Equation (14) to obtain the two motor-distribution torques, which are required by the feedforward control before ignition.  
In the feedforward control, under the given cooling water temperature and different engine starting conditions, taking the absolute value of vehicle jerk as the fitness function \(F_{GA2}\), the genetic algorithm was used to optimize a, b and c to obtain the optimal \"S-curve\" function (the optimal engine demand-angular acceleration) under different engine starting conditions before engine ignition.  
The S-curve function \(f(x,a,b,c)\) is defined as follows.  
\[\ddot{\theta}_{\text{Eng\_des}}=f(x,a,b,c)=\frac{1}{1+e^{-a(x-c)}}\cdot b \tag{19}\]  
where \(a,b\) and \(c\) are the parameters of the \(\ddot{\theta}_{\text{Eng\_des}}\).  
The jerk absolute value was used as the fitness function \(F_{GA2}\) of the genetic algorithm:  
\[F_{GA2}=|j_{erk}|=\left|\ddot{V}_{x}\right| \tag{20}\]  
where \(j_{erk}\) is the vehicle jerk.
# Geographically Weighted Flow Cross K-Function for Network-Constrained Flow Data
## 2 Methods
### Statistical Inference  
To determine whether type A flows are statistically significantly associated with type B flows in the whole study area and identify the reference spatial flows at which the spatial association is statistically significant, the values of the Geographically Weighted Flow Cross K-Function of observed spatial flows need to be compared with the expected values under the null hypothesis. There are usually two benchmark null hypotheses for bivariate flow pattern analysis ([PERSON] and [PERSON], 2019): (i) independence--the two types of spatial flows are generated by a pair of independent univariate processes implemented by the complete spatial randomness (CSR) model; (ii) random labeling--the two types of spatial flows are generated by randomly assigning labels (types) to locations of observed spatial flows. In this study, we choose random labeling as the null hypothesis for bivariate flow pattern analysis for two reasons: (i) the CSR model for spatial flows is complicated to implement because spatial flows also have attributes of length and direction, in addition to origin and destination points; (ii) random labeling can maintain the joint spatial distribution of all types and avoid the impact of edge effects.  
After choosing random labeling as the null hypotheses, the Monte Carlo simulation method was applied to obtain the confidence envelopes with the user-specific significance level, e.g., 0.05. Similarly to the Cross K-Function for points [29], the proposed Geographically Weighted Flow Cross K-Function analyzes spatial associations at a multiscale.
## 1 Introduction
#### Stand-Alone Bioelectrochemical Systems and Their Limitations
Due to its arrangement and treatment, the MFC's capital cost is 30 times greater (on average) than that of conventional activated sludge treatment systems for domestic wastewater. Another major hindrance to the MFC's use in wastewater treatment is fouling at the membrane, which disrupts proton transfer. Other constraints of a stand-alone MFC include low electricity generation, electric current inconsistency, sustainability of electrode materials, cost of operating conditions, and so forth. Due to the versatility of the MFC operation, it is easy to integrate this technique with other processes and operations to increase power output and sludge reduction [9]. Additionally, there is a necessity for the invention and optimization of low-cost electrodes to simplify the scale-up procedure. The progress in the configuration of the MFC, the components engaged in the electrode/separator, and the approach of the process leads to the improvement of the physical and chemical confines of the MFC. Therefore, there is a requirement to discover actual assimilation of the complementary functions in the BES and enhance them for easy scale-up [22]. An MFC could produce sufficient energy and can be a modern-day alternative for wastewater treatment, but there are still limitations, such as cost-effectiveness, biofouling, and the need for treatment of the effluent as it cannot be directly discharged. Hence, MFCs need to be integrated with other technologies for more efficient wastewater treatment that eliminates the requirement for additional effluent treatment while simultaneously gaining extra benefits [23]. MFCs were accordingly integrated with a variety of other technologies for the pretreatment or post-treatment of wastewater. These included anaerobic digesters (AD), capacitive deionization (CDI), forward osmosis (FO), and membrane bioreactors (MBRs).
# A Two-Grid Interline Dynamic Voltage Restorer Based on Two Three-Phase Input Matrix Converters
## 4 CSI Space Vector Pulse Width Modulation  
To compensate for the disturbances that occur in the feeders, the CSIs must synthesize the appropriate voltages to be injected into the faulty grid and thus mitigate them. CSIs take voltage from the virtual dc-link and transform it into an appropriate compensation voltage. To do this, the switching elements of the CSIs must turn on and off in a suitable sequence. The space vector pulse width modulation technique is used to obtain the switching on and off sequence of the commutation elements. This technique has been widely adopted to generate the trigger signals of the commutation elements of the invertersin the DVRs [34; 35; 36]. Figure 4 shows the vector representation in the complex space of the active states used by the CSI to form the desired voltage.  
Sextant 1 is delimited by the active States of Commutation (SC) SC6 and SC1. These active states correspond to the line voltages \(v_{ab}\) and \(v_{ac}\), respectively, and the angle \(\theta_{a}\) of the resultant vector in vector space has the range \(-\pi/6\leq\theta_{a}\leq\pi/6\). In this way, the line voltages are obtained from the voltage in the virtual dc-link by commuting the switches \(Sa\), \(Sb^{\prime}\) and \(Sa\), \(Sc^{\prime}\) of the inverter. The phase angle orientation of the CSI output voltages is synchronized with the difference between the phase angle of the reference voltages and the phase angle measured on the load. Three-phase output voltages are defined by:  
\[\begin{array}{c}v_{a}=V_{om}\cos\theta_{a},\ v_{b}=V_{om}\cos\theta_{b},\ v_{c}=V_{om}\cos\theta_{c}\\ \theta_{a}=\omega\ t,\ \theta_{b}=\theta_{a}-2\pi/3,\ \theta_{c}=\theta_{a}+2\pi/3\\ v_{a}+v_{b}+v_{c}=0\end{array} \tag{31}\]  
where \(V_{om}\) is the amplitude of the output voltages and \(\theta_{a}\), \(\theta_{b}\) and \(\theta_{c}\) are the phase angles.  
When the load reference voltages and the line voltages are in phase, the duty cycles \(d_{o1}\) and \(d_{o2}\) for switches \(Sb^{\prime}\) and \(Sc^{\prime}\) in sextant 1 are determined by:  
\[\begin{array}{c}-cos\theta_{b}/cos\theta_{a}-cos\theta_{c}/cos\theta_{a}=1 \\ d_{o1}=-cos\theta_{b}/cos\theta_{a},\ \ d_{o2}=-cos\theta_{c}/cos\theta_{a}\end{array} \tag{32}\]  
\(d_{o1}\) and \(d_{o2}\) can be calculated similarly for the other five sextants.  
The voltage in the virtual dc-link in sextant 1 is determined by:  
\[v_{dclink}=d_{o1}v_{abref}+d_{o2}v_{acref} \tag{33}\]  
where \(v_{abref}\) and \(v_{acref}\) are the reference voltages for the load voltages \(v_{ab}\) and \(v_{ac}\), respectively. The voltage in the virtual dc-link in the other five sextants is calculated in a similar way.
## 2 CCFD Self-Interference Suppression Performance
### Spatial-Domain Self-Interference Suppression  
Spatial-domain self-interference suppression primarily aims to reduce the near-field coupling effect between antennas by improving the spatial isolation between transmitting and receiving antennas. It can be divided into two main categories: passive suppression and active suppression. Passive suppression methods mainly include antenna separation [35], orthogonal polarization [36], near-field cancellation [37], and decoupling structure design [38]. Active suppression techniques mainly rely on transmit-receive beamforming [39].  
Since BDS-3 ISLs utilize a Ka-band phased-array system, the focus of spatial-domain self-interference suppression is placed on array antennas operating within the central frequency range of 20 GHz to 30 GHz. As shown in Table 2, the current self-interference suppression performance of array antennas in the spatial domain can achieve a maximum level exceeding 100 dB. This significantly reduces the power of self-interference signals at the antenna, providing a strong foundation for further suppression in subsequent stages.
## 1 Introduction
### Contribution  
In IoD networks, minimizing task-offloading delays from edge UAVs to fog base stations (FBSs) and fog UAVs presents a significant challenge due to the limited available resources of UAVs. To address this challenge, in this paper, we propose a task-offloading solution called GA Hybrid-Fog that combines fog BSs, fog UAVs, and a GA to optimize both transmission and fog computing delays. Specifically, GA Hybrid-Fog allows onboard computing resources of edge UAVs to be integrated with the fog computing environment, which consists of stationary FBS and mobile fog UAV (FUAV) computational entities. Moreover, this solution reaps the benefits of the fog computing capabilities of the base stations and UAVs, including processing, storage, and sensing, to enhance traditional fog computing capabilities with UAVs.
## 5 AEDG: The Proposed Scheme
### Initialization Phase
#### 5.1.2 Member Nodes' Association  
After the selection of the GN, the member nodes join the GN through the SPT algorithm. In the SPT, nodes forward the data from the downstream nodes to the upstream nodes. In the selection of the SPT, every node shares its RSSI value with its neighbours. The member node with the highest RSSI value is selected as the next hop. The member nodes transmit their data while finding the route via the SPT algorithm. The SPT is based on the RSSI value of the hello packet that is received by each member node from the AUV. After making the SPT, the member nodes transmit their data to the GN in a multi-hop fashion. Finally, the GN forwards the aggregated data to the AUV by using a short-range acoustic link. The sub-optimal assignment of member nodes with the sub-optimal GN is describe in the flow chart of Figure 8.
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 3 Materials and Methods
### Proposed Model for Seasonal Crop Recommendation
#### 3.17.2 Phase II  
We shall apply different machine learning classifiers, analyze their performance based on different parameters, and identify the best technique.  
Implementing the SMOTE  
Here, we have applied 13 different methods such as logistic regression, decision tree, K-Neighbors, SVC, random forest, Gradient Boosting, bagging tree, XGB Classifier, Ada Boost Classifier, Cat Boost, HGB, SGDC, and MNB.  
Figure 5: Firebase account to handle the area-wise crop dataset and recommendation.  
Here, we observed that the data imbalance may sometimes provide inaccurate results. So, we applied the Synthetic Minority Oversampling Technique (SMOTE) to balance the dataset.  
Performance Analysis  
We applied these methods before the SMOTE and after the SMOTE and compared their performance extensively using 7 different measuring parameters such as accuracy, precision, recall, F1-score, ROC, sensitivity, and specificity. The outcome of the comparison leads to identifying the best technique that shall be used for predictions. This approach shall guide the new farmers in making better decisions in crop selection.
## 5 Conclusions  
In this research, we focused on the problems arising when AUVs are required to execute a mission and autonomously home and dock to stationary or moving docking stations, and have shown how to tackle them using a common framework approach. We presented a general control strategy incorporating various vehicle behaviors into the task priority framework, targeting a system capable of staying and operating underwater, perform autonomous missions, homing, docking, obstacle avoidance, terrain following, minimum altitude, and pitch control. The paper has presented the tasks and actions with proper priorities defined to achieve the various vehicle behaviors. In particular, we have shown how to integrate the _Field Velocity_ Task for long-range parking maneuver, allowing the AUV to reach the close proximity of the docking station oriented well in advance. Furthermore, we have shown how the adoption of the task-priority framework allows to integrate tasks such as _Obstacle Avoidance_ or _Seafloor Alignment_ and exploit them both during the execution of the regular survey mission, but also while doing the homing and docking maneuvers; therefore, tackling these maneuvers in more challenging conditions w.r.t. previous works. The contributions have been shown thanks to different numerical simulations, based on the hydrodynamic model of the X300 AUV, demonstrating the successful integrated execution of surveying, homing and terminal docking maneuvers. The results very well prove that the proposed framework is flexible as additional capabilities to the system can be incorporated very easily. This architecture is ready to be expanded to manipulation tasks in the future [37; 41].  
Writing--original draft preparation, C.T.; writing--review and editing, C.T. and E.S.; supervision, E.S. and G.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflicts of interest.  
Figure 19: Constant altitude mission: Plot depicting the time behavior of desired velocities and the actual/system velocities of the vehicle.
# Vehicle Lane Change Models--A Historical Review
## 2 Vehicle Lane Change Models
### Physical Models of Lane Change
#### 2.2.2 Dynamic Models
By combining the traffic impact of the vehicle change process with low-carbon driving, the objective function is established, and a method of traffic impact and low-carbon trajectory optimization is proposed. The optimization method combines the vehicle dynamic model with the operating condition, and establishes the safety domain according to the position relationship between the vehicle itself and the fore and rear vehicles. The lane change safety zone is the area where the vehicle is not likely to collide with the vehicles behind and in front. In the process of changing lanes, if the rear vehicle does not guarantee a safe distance, the current vehicle will collide with the left rear wheel of the rear vehicle. If a safe distance is maintained between the current vehicle and the rear vehicle in the target lane, the vehicle will not interact with the rear vehicle during the lane change. In the process of changing lanes, if the speed of the current lane is relatively high, a collision between the right front of the car and the left rear of the car in front may occur. If there is sufficient safe distance between the current vehicle and the rear vehicle in the target lane, the vehicle will not interact with the rear vehicle in the process of changing lanes. Vehicles on the road are influenced by the dynamics of the surrounding vehicles. In order to ensure the vehicles involved in the process of lane changing can be realized and rationalized, the quintic polynomial model in the ideal state is improved, and the longitudinal sixtic polynomial model and the horizontal quintic polynomial model are established, as shown in Equation (7) below:  
\[\left\{ \begin{aligned} & x_{t}=a_{6}t^{6}+a_{5}t^{5}+a_{4}t^{4}+a_{3}t^{3 }+a_{2}t^{2}+a_{1}t+a_{0}\\ & y_{t}=b_{5}t^{5}+b_{4}t^{4}+b_{3}t^{3}+b_{2}t^{2}+b_{1}t+b_{0} \end{aligned} \right. \tag{7}\]  
In order to reasonably predict the lane change process, a BP neural network is established according to the acquired feature points, and the neural network is improved using the particle swarm optimization algorithm. The particle swarm optimization algorithm (PSO)-BP neural network is used to establish the lane change end time, and a relative longitudinal distance model is used to obtain the lane change end time and end position, as shown in Figure 3. By introducing the characteristics of the genetic algorithm, the evaluation results are optimized. Using car no. 689 of the NGSIM dataset as a sample, the trajectory planning model and the optimization algorithm are used to form the final lane change trajectory equation. The trajectory equations of lane change under different states are simulated, and the more suitable trajectories are highlighted to demonstrate that the method can effectively solve the problem of lane change. Table 4 gives a summary of the dynamic models.
## 2 RIS-Assisted System and Channel Model
### Proposed Deep Learning Model
#### 2.4.3 Overview of Training and Testing Process  
In Figure 5, an illustration of the proposed RIS-assisted MIMO wireless communication's training and inference process is presented. After the model is successfully trained, it is validated to check for optimal performance and inference accuracy. Figure 6 shows the flow chart for the testing procedure after training. The training procedure stops and the model for inference is stored when it reaches a model accuracy of 99.95%. The training and validation performance for the 50 epoch according to its loss and accuracy are shown in Figure 7. Figure 7 illustrates that the model performs consistently after 11 epochs, demonstrating that the parameters have learned from the data by the model. During model training, the Adam optimizer is used to analyze the data at a learning rate of 0.01. The training and inference process is shown in Algorithm 1.  
```
Training process:
1: Load dataset \(\chi\).
2: split \(\chi\) into a training \(\xi\) and a validation \(\tau\) set at a ratio of 80% and 20%, i.e., 4:1.
3:for e: 1 to E do:
4: Pass the processed data to build the model and configure the layers.
5: Configure the model parameters such as learning rate \(\psi\), maximum epochs \(E_{max}\), minibatch size \(mB\), gradient threshold \(gT\), and validation frequency \(f_{v}\).
6: Calculate the loss function using (19).
7: Computing the corrective parameters and obtaining the optimal performance while updating the parameters, using the Adam optimization algorithm.
8:endfor
9: Save model for inference step.
10: Output: proposed model.

Inference process:
11: Load the trained model.
12: Initialize the parameters.
13:for s: 1 to SNR do:
14:for i: 1 to Iteration do:
15: Generate data symbol to transmit.
16: Data transmit by \(H_{\textit{cus}}\) and \(H_{d}\) channel matrix.
17: Match the label classes with a trained model to classify.
18: SER and BER performance with different SNR.
19:endfor
20:endfor
21: Output: SER and BER results.
```  
**Algorithm 1** The training and inference process Figure 5: The structure of the proposed CNN-GRU network: training and testing phases.  
Figure 6: Flowchart for the testing process of the proposed CNN-GRU model.
## 1 Introduction
The instantaneous energy and time-varying centroid frequency methods offer more detailed information for identifying underground structural characteristics and potential pipe networks.
## 2 Background
### Feature Extraction Methods used for Object Detection in AVs
#### 2.1.2 LiDAR-Based  
LiDAR is one of the most widely used sensors in robotics [10]. The classical 2D LiDAR uses a rotating mirror, which sends laser beams along the x-y plane, sensing objects within range. On the other hand, a 3D LiDAR also shoots beams along the x-z plane, capturing the distance to items along this plane. Figure 1 shows an example of this setting.  
The features extracted from a LiDAR sensor are the so-called point clouds. Point clouds represent the surfaces in which the LiDAR laser bounced before reaching the LiDAR sensor back. These points follow a specific array of algorithms to be scale, position, and orientation invariant; all of this to perform Simultaneous Localization and Mapping (SLAM). LiDAR sensors can make a single point cloud swift in an outstanding short amount of time, which means that feature extraction is performed with a comparable speed to classical feature extraction. When it comes to object detection, a LiDAR sensor can also be used to aid AVs. BirdNet [11], for example, can detect 3D obstacles from the LiDAR's point cloud using three steps: first, a Bird's Eye View (BEV) image is created  
Figure 1: Diagram of the differences between the data gathered from 2D (**top**) and 3D LiDAR (**bottom**).  
from the raw sensor data. Then, using CNNs intended initially for image processing, the position and orientation of obstacles are estimated. Finally, for the post-processing phase, predictions are corrected using statistical data, and the height of the barriers is projected on a predicted plane. This method was tested using the KITTI data set to show a state-of- the-art performance. Similar approaches include the Multi-View 3D Object Detection Network for Autonomous Driving (MV3D) [12]. MV3D fuses the camera and the LiDAR features to obtain a more rigorous representation, although the results of both methods show the state-of-the-art performance. When it comes to LiDARs in AVs, 3D LiDARs [13] are used for various reasons. First, this is a more robust sensor that includes more laser scans along the z-axis so a more extensive range of obstacles can be detected, whereas a 2D LiDAR is bounded to scanning a fixed-line along this axis. Second, the 3D point cloud produced by a 3D LiDAR can easily be transformed into the camera's coordinate system [14]. Thus, the object detection task can be performed by the camera and aided by the LiDAR, as explored in the following section.