esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Network Architecture and Parameter Optimization
### The Bayesian Optimized Neural Network  
This study used the Pytorch library to implement the BP-ANN as well as the Bayesian optimization process. For this study, the hyperparameters of the training process of the neural network is optimized using Bayesian optimization. The results of the Bayesian optimization yields are shown in Table 2.  
For the fully connected neural network (FC-NN) implemented in this paper, there are eight input variables, three output variables, and seven hidden layers. The tanh () function is selected as the activation function, the mean square error (MSE) is selected as the loss function, and the ADAM optimizer is used to optimize the model parameters. The mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) are used to evaluate the predictive performance of the model. The relevant calculation formulas are:  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\Bigl{(}y_{tru}-y_{pred}\Bigr{)}^{2} \tag{5}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\Bigl{(}y_{tru}-y_{pred}\Bigr{)}^{2}} \tag{6}\]  
\[M=\frac{1}{n}\sum_{i=1}^{n}\Bigl{(}y_{true}-y_{pred}\Bigr{)} \tag{7}\]  
In the formulas, \(y_{tru}\) represents the variable value of the test set, and \(y_{pred}\) represents the corresponding predicted value.
## 3 Method for Beam Steering
### Phase Calculation for Electronic Beam Steering
In this paper, we propose implementing an Active Electronically Scanned Array (AESA) using phase differences. While conventional radar systems adjust phase and amplitude through phase shifters and power amplifiers for each channel, our experiment focuses solely on creating phase differences to verify the performance of Frequency Selective Surfaces (FSS) within the AESA. First, a power divider will be utilized to ensure uniform amplitude distribution across all channels. Subsequently, the signal will be transmitted to each antenna via coaxial cables. To adjust the phase, the simplest approach is to manipulate the electrical distance, thereby altering the phase at the desired frequency. Consequently, the length of the coaxial cable between the power distributor and the antenna is adjusted accordingly to achieve the desired phase. Through simulation and measurement, it was verified that the Frequency Selective Surface (FSS) designed in the preceding section is capable of beam steering up to 30\({}^{\circ}\). Therefore, to achieve a 30\({}^{\circ}\) beam steering angle, the incident phase on each antenna element needs to be calculated. The Array Factor of Planar Array, denoted by Equation (11), can be rearranged by isolating the common variable \(I_{0}\), leading to Equation (12). Further manipulation employing [PERSON]'s formula results in Equation (13). Subsequently, Equation (14) represents \(\Psi_{x}\), while Equation (15) represents \(\Psi_{y}\). By changing the formula in this manner, you can obtain the phase difference based on the desired azimuth and elevation angles, provided that you know the spacing between antenna elements on the x-axis and y-axis. Reversing Equations (14) and (15), you can determine that the phase difference of the coaxial cable to be used is approximately 108\({}^{\circ}\). The type of cable selected is the RMF-085 model, a multi-flexible cable. Considering the impedance characteristics of the corresponding coaxial cable, the electrical length can be calculated for each antenna line. The resulting length difference is approximately 21 mm. This indicates that by adjusting the cable length by \(\pm\)21 mm relative to the reference cable, a phase difference of 108\({}^{\circ}\) can be achieved. \[AF=\sum_{n=1}^{N}I_{1n}\left[\sum_{m=1}^{M}I_{m1}e^{i(m-1)(kd_{x}\sin\theta \cos\pi+\beta_{x})}\right]e^{i(n-1)(kd_{y}\sin\theta\cos\pi+\beta_{y})} \tag{11}\]  
Figure 10: Amplitude (dB) difference for each port: (**a**) simulation (**b**) measured. \[AF=l_{0}\sum_{m=1}^{M}e^{i(m-1)(kd_{x}\sin\theta\cos\pi+\beta_{x})}\sum_{n=1}^{N}e^ {i(n-1)(kd_{y}\sin\theta\cos\pi+\beta_{y})} \tag{12}\]  
\[AF_{n}(\theta,\Psi)=(\frac{1}{M}\frac{\sin\frac{M}{2}\Psi_{x}}{\sin\frac{\Psi_{ x}}{2}})(\frac{1}{N}\frac{\sin\frac{M}{2}\Psi_{y}}{\sin\frac{\Psi_{y}}{2}}) \tag{13}\]  
\[\Psi_{x}=kd_{x}\sin\theta\cos\pi+\beta_{x} \tag{14}\]  
\[\Psi_{y}=kd_{y}\sin\theta\cos\pi+\beta_{y} \tag{15}\]
# An Efficient Precoding Algorithm for mmWave Massive MIMO Systems
## 3 Proposed Algorithm
### Computational Complexity Analysis  
The complexity of designing a hybrid precoding matrix is discussed in detail and compared with the hybrid precoding method based on the OMP algorithm in [15]. It is mainly divided into two phases: In the first phase, the initial RF precoding matrix is designed, and the complexity is \(\mathcal{O}(N_{t}N_{s})\). In the second stage, the initial precoding matrix is updated. Then, the digital precoding matrix design and the residual matrix are designed, which are the same as those in the OMP based algorithm, and the complexity is \(\mathcal{O}\Big{(}N_{t}{N_{RF}^{t}}^{2}\Big{)}\) and \(\mathcal{O}\Big{(}N_{t}{N_{RF}^{t}}^{2}N_{s}\Big{)}\), respectively. The next step is mainly to process the residual matrix to construct a new RF precoding vector \(n\). Its complexity is \(\mathcal{O}\Big{(}N_{t}{N_{RF}}N_{s}^{2}\Big{)}\). Combining these two phases, the overall complexity of the proposed BSAMIBOMP precoding method is \(\mathcal{O}\Big{(}N_{t}{N_{RF}^{t}}^{2}\Big{)}\). For the sake of analysis and comparison, it is assumed that the number of elements in the candidate vector set in the OMP algorithm is \(N_{t}\). In the OMP algorithm, the hybrid precoding method based on the OMP algorithm first uses the calculation of the correlation between the candidate vector matrix and the residual matrix to select the appropriate atom, the complexity \(\mathcal{O}\Big{(}N_{t}^{2}N_{RF}^{t}N_{s}\Big{)}\) and \(\mathcal{O}\Big{(}N_{t}^{2}N_{RF}^{t}\Big{)}\), respectively. Then, the digital precoding matrix and the residual matrix are designed, and the complexity has been given. Finally, the residual matrix is normalized and the complexity is \(\mathcal{O}\Big{(}N_{t}N_{RF}^{t}\Big{)}\). Based on the analysis of each part, the overall complexity of the OMP algorithm in [15] is mainly concentrated on the calculation of the correlation between the candidate vector set matrix and the residual matrix, which is \(\mathcal{O}\Big{(}N_{t}^{2}N_{RF}^{t}N_{s}\Big{)}\). According to the actual condition, \(N_{t}\gg N_{RF}^{t}\geq N_{s}\), the algorithm complexity of hybrid precoding design is linear with the number of antennas. Therefore, compared with the hybrid precoding method based on the OMP algorithm, the proposed BSAMIBOMP precoding complexity is lower. Table 1 summarizes the complexity of the algorithms.
## 3 Development of Thermal Condition Monitoring System
### Overview of WSNs and Communication Links Characteristics
#### 3.3.4 Deployment and Cost Implications  
Wireless communication offers low cost, high accuracy, and great flexibility solutions for wireless industrial energy monitoring platforms. The ZigBee module offers low power demand and low transmission costs, and it is proposed for systems where data exchanges between sensor nodes and the gateway frequently requires straightforward interface especially for rural and remote area application. This does not involve cloud-based interface and thus improves real-time characteristics. The cost of operation of ZigBee is very economical as its components are readily available and does not involve complicated developments skills [64; 69; 74]. The recently released NB-IoT/LTE specifications are still on rollout as a concept and hence not matured enough for industrial WSN applications in rural and remote areas [70]. Thus, additional time will be required for the network to be established. Although Sigfox and LoRa have passed the conceptual stage, they have limitations to be applied in full at rural and remote areas due to their cellular nature. Thus, they are still progressing under commercialization in limited countries and cities. LoRa has benefited from installation and operations currently in about 42 countries and Sigfox in 31 countries [69].  
However, the deployments of LoRa and Sigfox in the world are still in deployment and strategizing. Several cost considerations need to be taken into account, such as spectrum cost for license, network/deployment cost, and device (base station installation) [69; 70]. In general, flexibility and low-cost of scheme application abodes and facilitates the deployment of the WSNs solutions. This determines the drive to carry out a suitable design concept for the proposed system to monitor DERs grid integration conditions. In conclusion, the proposed monitoring system considers ZigBee to conform to industrial applicable standards and impacts cost effectiveness with real-time signal deployment.
# An Optimization Routing Algorithm Based on Segment Routing in Software-Defined Networks
## 4 Performance Evaluation
### Simulation Results
In the process of weight optimization, the proposed Algorithm 2 makes some improvements in optimizing the link weight for global optimal solution and thus shows better performance than the proposed Algorithm 1. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Requests** & **100** & **300** & **500** & **700** & **900** \\ \hline Proposed Algorithm 2 & 0.99 & 1.00 & 1.00 & 0.99 & 0.98 \\ Proposed Algorithm 1 & 0.68 & 0.69 & 0.70 & 0.68 & 0.75 \\ Lee algorithm & 0.45 & 0.34 & 0.38 & 0.33 & 0.36 \\ SWP & 0.28 & 0.34 & 0.30 & 0.26 & 0.29 \\ WSP & 0.23 & 0.30 & 0.28 & 0.29 & 0.23 \\ MIRA & 0.29 & 0.26 & 0.26 & 0.27 & 0.25 \\ SPF & 0.30 & 0.22 & 0.10 & 0.17 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The average value of the comprehensive evaluation path when the scheme weight \(W^{\prime}=[0.4,0.4,0.2]\). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithms** & **Proposed Algorithm 2** & **Proposed Algorithm 1** & **Lee Algorithm** & **SWP** & **WSP** & **MIRA** & **SPF** \\ \hline running time/s & 15.56 & 10.22 & 7.16 & 3.32 & 3.48 & 4.12 & 1.89 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The average running time of different algorithms to execute once. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Requests** & **100** & **300** & **500** & **700** & **900** \\ \hline Proposed Algorithm 2 & 1.00 & 1.00 & 0.99 & 0.99 & 0.98 \\ Proposed Algorithm 1 & 0.63 & 0.69 & 0.64 & 0.70 & 0.72 \\ Lee algorithm & 0.48 & 0.43 & 0.33 & 0.39 & 0.32 \\ SWP & 0.31 & 0.31 & 0.35 & 0.28 & 0.26 \\ WSP & 0.30 & 0.32 & 0.22 & 0.33 & 0.23 \\ MIRA & 0.25 & 0.21 & 0.31 & 0.27 & 0.29 \\ SPF & 0.30 & 0.25 & 0.15 & 0.11 & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The average value of the comprehensive evaluation path when the scheme weight have the same importance. Figure 8: The load balance of network under different network sizes. Figure 9: The maximum link utilization under different network sizes.
# Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank
## 2 Materials and Methods  
### Facial Video Dataset  
This study employed the Vicar-2 dataset, which consisted of 40 facial videos from ten subjects (seven men and three women) [22]. The facial videos were recorded in H264 format using an RGB Camera (Brio webcam, Logitech, Lausanne, Switzerland) in four different situations: BASE (representing the resting state), HRV (representing a naturalistic situation with conversation and movement in a Stroop task situation), RUN (representing the post-workout state), and MOV (representing a situation with various movements). Each facial video was recorded for 5 min at a frame rate of 30 frame/s and a resolution of 720 \(\times\) 1280. The dataset included both ECG (AD8232 device, Analog Devices, Wilmington, MA, USA) and cPPG (CMS50E device, CONTEC, Shanghai, China) data, which were simultaneously recorded with facial video at sampling rates of 60 and 250 Hz, respectively.
## 2 Theoretical Background  
To assess the quality of the analytical model with respect to experimental data, correlation is normally the first step. It is a usual practice to correlate the analytical model results with experimental results to see if the analytical model is in reasonable agreement and can be updated in a reasonable way [42]. In dynamic modal updating, the modal data of the FE model are compared with modal data obtained from the experiment. The following are the commonly-used techniques to correlate the analytical and experimental data (such as frequencies and mode shapes) for model updating.
# Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles
## 1 Introduction
For instance, some works [27; 31] propose a scheme that enables CAVs to adapt the transmission of each tracked object based on information such as position, carrying density, and road geometry. However, to the best of our knowledge, these data selection works in the literature do not consider the presence of several regions of interest in the CP scheme. A data selection scheme based on regions reduces frequent transmissions of messages, enabling CP applications to be applied while maintaining the same perception of the environment and number of objects detected. In this article, we describe the operational impacts and benefits associated with CP for CAVs. In this sense, we review the state of the art of CP for CAVs and the critical research challenges related to CP. Based on our examination of the relevant literature, we identify four research challenges, namely, redundant information, perception fusion and dissemination, privacy, and security. In addition, we introduce a cooperatiVe perceptIon for the autonomous vechicLEs mechanism, called (VILE), to show the benefits of CP in order to reduce the amount of redundant data, while maintaining the amount of detected objects compared to the basic transmission approach. To achieve that, we divide the scenario into a set of regions and in each region, vehicles have perception data, such as the number of objects detected. In the VILE operation, it relies on a data selection algorithm to choose a given vehicle to share the perception of a specific area to reduce communication overhead. Furthermore, VILE considers three phases (i.e., knowledge, selection, and perception) in the CP process. Evaluation results demonstrate the efficiency of VILE mechanism to reduce the amount of redundant data by up to 85%, while maintaining the number of detected objects compared to the basic transmission approach defined by European Telecommunications Standards Institute (ETSI) [32]. VILE also meets the requirements for the operation of CP applications, as it maintains the same number of pedestrians and vehicles detected and shared with the other CAVs. The main contributions of this article are stated as follows: (i) Introduce the VILE mechanism to mitigate data redundancy for CAV object detection application. (ii) The evaluation of a CP to mitigate data redundancy for CAV object detection application; (iii) The identification of potential research challenges, opportunities, and requirements for CP in CAVs. The remainder of this article is structured as follows. In Section 2, we describe the current state-of-the-art about CP for CAVs. Section 3 presents the system model and operation of the proposed VILE mechanism. In Section 4, we introduce the simulation scenario, simulation methodology, and discuss the results obtained. Section 5 presents the key research challenges related to CP. Section 6 concludes this article and presents the future work direction.
# Risk-Aware Wireless Positioning in
## 2 Preliminary: Various Wireless Positioning Schemes
### Angle of Arrival (AoA)  
The AoA scheme is the wireless positioning method that estimates the signal source position from the angle of arrival of the received signal. The AoA schemes can be divided into two categories, where the antennas are fixed and rotating, respectively. It is a more typical approach to use fixed (linear or general) antenna arrays for direction of arrival estimation and beamforming [13; 14; 15]. Another approach is to use rotating directional antennas, where there are multiple BSs and each of them transmits its own positioning signal with a directional beam rotating with a constant angular velocity [16]. Then, the receiver estimates azimuths (i.e., directions) of the positioning signalsfrom the BSs according to the times at which they are received. If there are more than three base stations, the receiver can determine its own position from the directions of the BS signals using the triangulation method.  
Figure 1 illustrates the AoA scheme in a two-dimensional plane. In Figure 1, the receiver is at the position \((X_{p},\ Y_{p})\), while there are four BSs at \((L,\ M)\), \((0,\ M)\), \((0,\ 0)\), and \((L,\ 0)\). The four BSs transmit directional beams with the angular velocity \(\omega\), but with the different phases of \(\angle 0\), \(\angle\phi\), \(\angle 2\phi\) and \(\angle 3\phi\).  
The receiver first measures the arrival times of the positioning signals. Then, from the differences in the arrival times, the receiver can determine the differences in the azimuths of the BSs. For example, let \((t_{1},\ t_{2},\ t_{3},\ t_{4})\) be the arrival times of the positioning signals and let \(\alpha\), \(\beta\) and \(\gamma\) be the differences of the azimuth angles of \((BS_{1},\ BS_{2})\), \((BS_{2},\ BS_{3})\) and \((BS_{3},\ BS_{4})\), respectively. Then, the receiver can find \(\alpha=\phi-\omega(t_{2}-t_{1})\), \(\beta=\phi-\omega(t_{3}-t_{2})\) and \(\gamma=\phi-\omega(t_{4}-t_{3})\). Finally, the receiver finds its own position given by \(X_{p}=Y\cos(\delta)\) and \(Y_{p}=M-Y(\delta)\), where  
\[\delta = \arctan\left[\frac{M\sin(\alpha)\cos(\beta)-L\sin(\alpha)\sin( \beta)}{L\cos(\alpha)\sin(\beta)-M\sin(\alpha)\sin(\beta)}\right], \tag{1}\] \[Y = \frac{L\sin(\delta-\alpha)}{\sin(\alpha)}, \tag{2}\]  
and transmits the position information to the BSs.
# Research on Joint Resource Allocation for Multibeam Satellite Based on Metaheuristic Algorithms
###### Abstract  
With the rapid growth of satellite communication demand and the continuous development of high-throughput satellite systems, the satellite resource allocation problem--also called the dynamic resources management (DRM) problem--has become increasingly complex in recent years. The use of metaheuristic algorithms to obtain acceptable optimal solutions has become a hot topic in research and has the potential to be explored further. In particular, the treatment of invalid solutions is the key to algorithm performance. At present, the unused bandwidth allocation (UBA) method is commonly used to address the bandwidth constraint in the DRM problem. However, this method reduces the algorithm's flexibility in the solution space, diminishes the quality of the optimized solution, and increases the computational complexity. In this paper, we propose a bandwidth constraint handling approach based on the non-dominated beam coding (NDBC) method, which can eliminate the bandwidth overlap constraint in the algorithm's population evolution and achieve complete bandwidth flexibility in order to increase the quality of the optimal solution while decreasing the computational complexity. We develop a generic application architecture for metaheuristic algorithms using the NDBC method and successfully apply it to four typical algorithms. The results indicate that NDBC can enhance the quality of the optimized solution by 9-33% while simultaneously reducing computational complexity by 9-21%.  
high-throughput satellite system; joint resource allocation; non-dominated beam coding; bandwidth constraint handling; computational complexity; generic application architecture
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 2 Advancements and Techniques Used for Improving Crop Production
### Technical Roadmap  
Figure 2 presents a systematic overall technical roadmap for our research work. Here, initially, we have gone through many research papers for review and observed that most of the models implement sensors, drones, Wi-Fi, GPS, satellite images, and automated tools for crop recommendation, cultivation, monitoring, and effective production. We thought of finding a novel way to support the naive farmers in recommending a quick and appropriate crop. For this, we explored extensively for suitable datasets. We found a dataset that contains details on different regular crops along with their season-wise productions from URL: [[https://data.world/thatzprem/agriculture-india](https://data.world/thatzprem/agriculture-india)]([https://data.world/thatzprem/agriculture-india](https://data.world/thatzprem/agriculture-india)) (accessed on 5 January 2024). Then, data reduction was applied to the collected dataset, and it was confined to only three districts. We had a continuous visit to the local experienced farmers and interacted with them to learn about their expertise in crops in the last 10 years, and we also visited officers of agriculture offices for a period of 3 months. The collected crop information has been combined into the existing dataset. This dataset is cleaned, pre-processed, and converted into CSV format and stored in Firebase cloud memory. This CSV file has been trained using 13 classifiers based on production rate and season to identify suitable crops. Here, initially, we have seen that the dataset is not balanced properly. So, we applied the SMOTE and balanced the dataset. Then, we trained for 80% of the dataset using these classifiers and tested for 20% of the data. Then, we analyzed their performance based on the accuracy rate, and it was observed that the SGDC technique had the highest accuracy compared to the others.  
Further, we have developed an Android application using Android Studio and SDK tools, which allows the farmer to input field location and season, which will be fed as an input to the model, and it identifies the suitable crop for the field.