esa-sceva/satcom-chunk-collection huggingface dataset:
# A Voltage Multiplier Circuit Based Quadratic Boost Converter for Energy Storage Application
## 5 Experimental Verification of the Proposed Converter  
A laboratory prototype is developed by using the power circuit board technique (PCB). The main circuit and gate driver circuit with various components are soldered on the power circuit board, as shown in Figure 9. The testing parameters are shown in Table 3, and the experimental setup is presented in Figure 10. Two power supplies are used--one for the gate driver circuit and another for the main converter circuit.  
Figure 8: Normalized Voltage stress vs. Voltage Gain.  
Figure 9: Prototype of the proposed converter.  
The hardware prototype tested at a duty ratio of 0.4, and the results are presented in Figure 10.
## 1 Introduction
This circular dual polarization is essential in satellite communications, as it enables better signal reception and transmission regardless of the orientation of the satellite or the ground receiving antenna [5]. Furthermore, in the case of the Leader-S CubeSat, the antenna's size and profile must be kept within certain dimensions to adhere to the CubeSat standards. The cost and complexity of the antenna and its technique must also be considered for CubeSat integration [13]. A popular candidate for such advanced antenna systems is multilayer microstrip patch antennas. The flexibility of printed planar antennas lies in their low profile, light weight, and low fabrication cost [12; 14; 15; 16]. Regarding this, this paper is organized as follows: Section 2 presents the applications for which the S-band antenna has been developed (ANSER and ANSER-AT). Section 3 describes the geometry of the designed antenna, while Section 4 outlines a parametric study considering the effects of its notches and its parasitic element. Section 5 presents the results and compares them with the existing literature, and finally, the paper concludes with Section 6, which shows the qualification and acceptance test results.
# A Crypto Accelerator of Binary Edward Curves for Securing Low-Resource Embedded Devices
## 3 Proposed Hardware Architecture
### Arithmetic Logic Unit  
It integrates an adder, a multiplier, and a squarer unit. The adder unit leverages m bitwise exclusive OR gates. Here, m denotes the key length. For multiplication, the Montgomery Radix 4 multiplier technique is adopted, enabling the computation of the product \(X\times Y\). Moreover, it performs the reduction step \(X\times Y\) mod \(p\), where mod \(p\) denotes the reduction operation. This approach eliminates the need for costly division operations, both in hardware and software platforms.  
The squarer unit is located after the multiplier unit in Figure 1. To implement the squarer unit, each input data value is extended by appending a \"0\" as described in [33]. The squarer unit plays a crucial role in minimizing the total number of clock cycles (CCs) necessary for PM computations. Its primary objective is to enhance efficiency by enabling the efficient computation of instructions, such as \((A\times B)^{2}\). The process of polynomial multiplication and squaring often requires an inverse operation. By incorporating a dedicated squarer unit, the number of clock cycles required for the inversion process is significantly reduced. This article integrates the quad block Itoh-Tsuji method [16] for executing the inversion operation efficiently. Leveraging the multiplier and squarer units optimizes the performance of the inversion process. In the following, the details of the Montgomery Radix 4 multiplier are provided. The first Section 3.5.1 provides the necessary theoretical background on the Montgomery multiplier algorithm. During this multiplication process, the construction of a partial product is required, which is obtained using booth encoding as explained in Section 3.5.2. Finally, the corresponding hardware architecture of the multiplier is explained in Section 3.5.3.
## 3 Results
### Wind Direction Prediction Results  
This sections shows the results of the classification of wind direction made using RF. Figure 12 is the confusion matrix of the classification done on the testing data set, while Figure 13 is the wind rose generated using the measured wind speed and the predicted wind direction from the testing data set. It is intended to be compared with the wind rose made with the measured wind speed and direction, which can be seen in Figure 6.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**ME**} & \multicolumn{2}{c}{**MAE**} & \multicolumn{2}{c}{**RMSE**} \\ \cline{2-7}  & **ERA5** & **Simple QM** & **ERA5** & **Simple QM** & **ERA5** & **Simple QM** \\ \hline grid point 1 & 1.669 & 0.013 & 1.811 & 0.832 & 2.238 & 1.108 \\ grid point 2 & 1.663 & 0.013 & 1.811 & 0.842 & 2.243 & 1.121 \\ grid point 3 & 1.274 & 0.016 & 1.476 & 0.818 & 1.842 & 1.101 \\ grid point 4 & 1.642 & 0.013 & 1.781 & 0.827 & 2.202 & 1.101 \\ grid point 5 & 1.268 & 0.016 & 1.476 & 0.826 & 1.843 & 1.111 \\ grid point 6 & 1.274 & 0.013 & 1.473 & 0.814 & 1.84 & 1.093 \\ grid point 7 & 2.238 & 0.017 & 2.337 & 0.856 & 2.847 & 1.137 \\ grid point 8 & 2.215 & 0.018 & 2.319 & 0.864 & 2.832 & 1.151 \\ grid point 9 & 1.669 & 0.012 & 1.822 & 0.853 & 2.262 & 1.136 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Error metrics of ERA5 grid points’ wind speeds and their calibrated counterparts against the measurements, expressed in m/s.  
Figure 12: Confusion matrix of the results of the wind direction classification performed on the testing data set using random forest (RF).
# A Method for the Rapid Propagation of Emergency Event Notifications in a Long Vehicle Convoy
## 3 Simulation Environment and the Convoy VANET System
### Convoy VANET System
#### 3.4.2 Receivers and Data Storage  
Every node contains a receiver for handling VS messages. The only purpose of the receiver is to save incoming VS messages into the node's internal storage for later use. Specifically, VS messages are used to determine a node's distance to every other node that it has received VS messages from. As detailed in Section 3.4.3, physical distance is used for tasks such as determining a retransmission delay for a received SM.
## 3 Survey Results
### Localization  
Another of the most addressed sensor network problems is localization, Table 17.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Nr** & **Codename** & **Localization used** &
\begin{tabular}{l} **Localization** \\ **accuracy, cm** \\ \end{tabular} & \multicolumn{2}{l}{**Advanced Localization**} & \multicolumn{2}{l}{**Self-made Localization**} \\  & & & & **Localization** \\ \hline
1 & Habitats & n & - & - & - \\
2 & Minefield & y & +/\(-\)25 & y & y \\
3 & Battlefield & y & couple feet & n & y \\
4 & Line in the sand & n & - & - & - \\
5 & Counter-sniper & y & 11 & y & y \\
6 & Electro-shepherd & y, GPS & \(>\)1 m & n & n \\
7 & Virtual fences & y, GPS & \(>\)1 m & n & n \\
8 & Oil tanker & n & - & - & - \\
9 & Enemy vehicles & y &? & n & y \\
10 & Trove game & n & - & - & - \\
11 & Elder RFID & n & - & - & - \\
12 & Murphy potatoes & n & - & - & - \\
13 & Firewxnet & n & - & - & - \\
14 & AlarmNet & y & room & n, motion sensor in rooms & y \\
15 & Ecuador Volcano & n & - & - & - \\
16 & Pet game & n & - & - & - \\
17 & Plug & n & - & - & - \\
18 & B-Live & n & - & - & - \\
19 & Biomotion & n & - & - & - \\
20 & AID-N & n & - & - & - \\
21 & Firefighting & y & \(<\)5 m? & n & y \\
22 & Rehabil & n & - & - & - \\
23 & CargoNet & n & - & - & - \\
24 & Fence monitor & n & - & - & - \\
25 & BikeNet & y, GPS & \(>\)1 m & n & n \\
26 & BriMon & n & - & - & - \\
27 & IP net & n & - & - & - \\
28 & Smart home & n & - & - & - \\
29 & SVATS & y &? & n, RSSI & y \\
30 & Hitchhiker & n & - & - & - \\
31 & Daily morning & y & room & n & y \\
32 & Heritage & n & - & - & - \\
33 & AC meter & n & - & - & - \\
34 & Coal mine & y &? & n, static & y \\
35 & ITS & y, static &? & n & n \\
36 & Underwater & y &? & n & y \\
37 & PipeProbe & y & 8 cm & y & y \\
38 & Badgers & n & - & - & - \\
39 & Helens volcano & n & - & - & - \\
40 & Tunnels & n & - & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 17: Deployments: localization.  
Localization is used in 38% of deployments: 8% use GPS and 30%, other methods. In contrast to time synchronization, the localization problem is very application-specific. Required localization granularity, environment, meta-information and infrastructure vary tremendously: in one case, localization of the centimeter scale must be achieved; in another, the room of a moving object must be found; in another, GPS is used in an outdoor environment. In 73% of the cases, where localization is used, it is custom for this application. It is not possible for an operating system to provide a generic localization method for a wide application class. Neighbor discovery service could be usable--it can help to solve both, localization and routing problems.
# Analysis of Fault Classifiers to Detect the Faults and Node Failures in a Wireless Sensor Network
## 3 Challenges and Problem Statement
### Classifiers
#### 3.2.2 MLP  
MLP ([PERSON] et al.) has been implemented in WSN to identify the faults from the category of Feed-Forward Artificial Neural Networks (FFANN) methodology. This technique processes the sensor nodes in multiple layers. Every sensor node is active as a neuron that transfers the data from one point to other points by the way of nonlinear fashion. This technique has been categorized under the classification of supervised learning and it has also used the detected data for training purposes [14; 15].
## 1 Introduction
According to the budget limitation, the maintenance of coverage with at least a certain value must be provided in these unstructured types of WSNs. In this situation, the sensors can be randomly deployed in the decided range of coverage in WSNs. However, maximizing sensor coverage while simultaneously minimizing budget are conflicting objectives. A mathematical optimization model for the proposed budget-limited WSN sensing coverage problem is derived to maximize the number of coverage grids in the presence of the grid concept in our work. The sensor coverage problem and the strategy of sensor deployment in WSNs are NP-Hard, which indicates it is difficult to obtain the solution within a polynomial time. Therefore, numerous studies in these fields have adopted various heuristic algorithms to solve this difficulty, such as the harmony search method [22; 27], the greedy heuristic method [26], GA [28; 29], and PSO [30]. The swarm intelligence algorithm, which belongs to the family of heuristic algorithms, is efficient and simple as shown by countless studies solving various problems that are NP-Hard in many fields. Simplified swarm optimization (SSO), which is included in the swarm intelligence algorithm, was originally developed by [PERSON] [31] in 2009. The SSO algorithm has been indicated to be efficient, simple, and flexible by numerous studies for resolving different problems in various areas, such as intelligence microgrids [32], parameter identification for solar cells [33; 34], power generator dispatch [35], cloud computing [36], the task assignment problem [37; 38], Internet of Things (IoT) [39], supply chain networks [40], the disassembly sequencing problem [41], WSNs [42], and forecasting of the stock market [43]. In this study, a new swarm algorithm called bi-tuning SSO (bi-SSO) based on SSO is proposed. The proposed bi-SSO improves the SSO by tuning the parameter settings, which is always an important issue in all AI algorithms. The proposed Bi-SSO can also be implemented to tune the update mechanism at the same time to enhance the quality of solutions found by SSO. The proposed algorithm targets optimization of the proposed budget-limited WSN sensing coverage problem to maximize the number of coverage grids in the presence of the grid concept. The remainder of this paper is organized as follows. The related work of the sensing coverage problem is analyzed in Section 2. Section 3 introduces grids and WSNs. Section 4 presents the traditional SSO. The proposed novel bi-tuning SSO is shown in Section 5. Section 6 presents the numerical experiments. Conclusions and future research are discussed in Section 7.
# A Review of Digital Twin Technology for Electric and Autonomous Vehicles
## 4 Current Issues in Electric Vehicles
### Connectivity  
Connected cars will inevitably improve user experiences and the ability to manage the network of interconnected vehicles. Two types of connectivity can be distinguished: intra-vehicle connectivity and inter-vehicle connectivity. The bandwidth requirements in vehicles have increased dramatically because of recent innovations and changes in automotive technology. However, modern vehicles have gradually developed in terms of entertainment and networking with advanced capabilities [59]. Intra-vehicle and/or internal networks are designed to share data across the various sub-systems, ECUs, sensors, and actuators, so that a single vehicle can operate easily. Although the sensors and networks are exclusive to original vehicle companies, these technologies typically meet standards that permit vehicle diagnostics and future applications to communicate with the vehicle using external technologies or devices.  
Inter-vehicle connectivity is a networking approach that allows data to be transferred from the vehicle to other vehicles, remote computers, and other cloud infrastructures. Remote applications can combine the vehicle's data with external sources (such as traffic or weather data) [60]. [PERSON] and [PERSON] [61] developed an authorization framework by describing data exchange scenarios on the Internet of Vehicles (IoV). The development of IoV has produced a considerable amount of real-time traffic data. These traffic data are used to generate a kind of digital twin that connects the physical vehicles and their virtual representations via 5G. Moreover, [PERSON] et al. [62] presented an effective communication framework to mitigate cyber attacks on connected and autonomous vehicles (CAVs).  
In this context, data will be collected from different networks and heterogeneous resources, including charge management software, weather sites, live EV modeling, battery modeling, telemetry devices, and route maps, such as Google Maps. Data integration in such platforms is a difficult task and needs to support data interoperability in connected vehicles to communicate effectively and efficiently. Reference [63] deals with AV collaboration as a service and proposes a DT-based scheme to facilitate collaborative and distributed autonomous driving. Specifically, a DT is designed for each AV, and a DT-enabled architecture is developed to help AVs make collaborative driving decisions in virtual networks. With this architecture, an auction game-based collaborative driving mechanism is then used to decide each group's head DT and tail DT. After that, by considering each group's computation cost and transmission cost, a coalition game-based distributed driving mechanism is developed to decide the optimal group distribution for minimizing the driving cost of each DT.
## 6 Results and Discussion
### Comparative Experiments
#### 6.3.2 Picking Station Quantity Factor Analysis  
Calculate the average value of the experimental index according to the value of WQ for the experimental data in Table 5; the results are shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**PQ**} & **MADPSO** & **TPPM** & **RMFSOM** & \multirow{2}{*}{**MADPSO/TPPM**} & \multirow{2}{*}{**MADPSO/RMFSOM**} \\ \cline{2-2} \cline{4-6}  & **AROS** & **AROS** & & & \\ \hline
4 & 347.52 & 1380.7 & 388.03 & 74.83\% & 10.44\% \\
8 & 385.76 & 1132.08 & 432.9 & 65.92\% & 10.89\% \\
12 & 435.06 & 1056 & 493.94 & 58.80\% & 11.92\% \\ \hline \end{tabular}
\end{table}
Table 6: Calculate the average value of AROS in Table 5 according to PQ.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**PF**} & \multicolumn{3}{c}{**MADPSO**} \\ \cline{2-4}  & **AROS** & **Upper Bound** & **Lower Bound** \\ \hline F & 364 & 409.17 & 321.65 \\ T & 440.35 & 503 & 412.29 \\ \hline \end{tabular}
\end{table}
Table 7: Sort and calculate data in Table 5 according to PF.  
It can be seen from the statistical data in the above table that with the increase in the number of picking stations, the AROS indicators of MADPSO and RMFSOM show a downward trend. This means that through MADPSO, the advantages of parallel picking in SACOP problems can be fully utilized, making parallel picking mode with multiple picking stations more energy efficient and operation efficient compared to single picking assembly line mode. On the contrary, the AROS indicator of TPPM will increase with the increase in WQ. This shows that under the premise of adopting scientific optimization methods, the layout of more picking stations, within limits, in the warehouse will help to reduce costs and increase efficiency. For TPPM, due to the lack of scheduling and control over AGVs during the picking process, arranging more picking stations is more likely to call unnecessary AGV resources, causing mutual interference and conflicts, thereby reducing overall efficiency and increasing energy consumption.
## 3 Results and Discussion
### Analysis of E-Nose  
PCA is a very powerful multivariate statistics method used to analyze the inherent structure of data and then express those data in such a way as to highlight their similarities and differences. In Figure 7A, the samples from different process stages are presented in PCA spatial distribution map. The contribution rates of PC1 and PC2 were 66.4% and 18.5% respectively, representing nearly 85% of the total variance. The smaller the distance between samples, the closer their quality characteristics [28]; the distance between the four samples was relatively large, indicating that the chicken odor at different stages of preparation was significantly different. The distribution of the samples in the first principal component was from L1 to L4, which showed some regular changes. The differences of samples L1 and L2 in the first principal component were obvious, as were those of samples L3 and L4 in the second principal component. The PCA loading plot of the e-nose is shown in Figure 7C. The first component (PC1) comprised 66.4%, which was positively related to the W1W, W55, W2W, W1S, W2S, W3S and W6S sensors, and negatively related to the W1C, W3C and W5C sensors. The W5S (oxynnitride) and W1W (hydrogen sulfide) sensors contributed more to sample L4, which shows that volatile compounds such as hydrocarbons and oxides were more abundant in L4 than in other samples. This result was consistent with the detection result of GC-MS, which shows that the various volatile compounds in Daokou braised chicken were more abundant after stewing.  
A radar graph (Figure 7B) further confirmed the conclusion from the PCA analysis. The W5S and W1W sensors had stronger responses to the odor of samples, and the odor differences in the samples were mainly detected by sensors W5S (sensitive to nitrogen oxides compound) and W1W (sensitive to organic sulfides compounds). Although few of these compounds were identified by GC-MS and GC-IMS, they were easily detected by the e-nose sensors due to their low thresholds [40]. The response values of samples L3 and L4 at sensors W5S and W1W were higher than those of the other two samples, while there was no significant difference between samples 1 and 2, indicating that the samples in the  
Figure 7: E-nose analysis. (**A**) PCA plot of the samples at different processing stages. (**B**) Radar graph of the samples at different processing stages. (**C**) PCA loading plot of the samples at different processing stages.  
braising stage might have had higher abundances of nitrogen oxides and organic sulfides. Note that the response value of sample L4 was lower than that of sample L3, i.e., the odor intensity after 2 h of braising was weaker than that after 1 h. These results were consistent with those of GC-MS and GC-IMS.
# FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism
## 3 Our Proposed Method--FDCNet
### The Loss Function
#### 3.4.3 The Fusion Loss Function  
After the SSIM-based loss penalty is added to the training process, the ultimate fusion loss function is introduced function as follows:  
\[L=L_{E}+\beta L_{s} \tag{13}\]  
where \(\beta\) is the SSIM-based weight loss to get a balance. We set \(\beta=0.001\) after experimenting for verification as well.
# An EV SRM Drive and Its Interconnected Operations Integrated into Grid, Microgrid, and Vehicle
###### Abstract  
This paper presents an electric vehicle (EV) switched reluctance motor (SRM) drive with incorporated operation capabilities integrated into the utility grid, the microgrid, and another EV. The motor drive DC-link voltage is established from the battery through an interleaved boost/buck converter with fault tolerance. The varied DC-link voltage can improve driving performance and reduce battery energy consumption over a wide speed range. Through a well-designed current control scheme, speed control scheme, and dynamic commutation tuning scheme, the established SRM drive possesses good performance in the motor driving mode. During deceleration, the regenerative braking energy can be effectively recovered to the battery. When the EV is in idle mode, the grid-to-vehicle (G2V) charging operation can be conducted through the bidirectional switch mode rectifier (SMR) and CLLC resonant converter. Satisfactory charging performance with good line drawn power quality and galvanic isolation is preserved. Conversely, the vehicle-to-grid (V2G) discharging operation can be performed. The EV can make movable energy storage device applications. Finally, the interconnected operations of the developed EV SRM drive to vehicle and microgrid are presented. Through vehicle-to-vehicle (V2V) operation, it can supply energy to the nearby EV when the battery is exhausted and needs roadside assistance. In addition, microgrid-to-vehicle (M2V) and vehicle-to-microgrid (V2M) operations can also be conductible. The EV battery can be charged from the microgrid. Conversely, it can also provide energy support to the microgrid.  
EV; SRM; battery; interface converter; resonant converter; SMR; inverter; G2V; V2G; V2V; M2V; V2M +
Footnote †: journal: Applied
## 4 Proposed Trust-Based Optimized Reporting Scheme
### Trust Calculation Mechanism  
The proposed methodology utilizes both direct and indirect trust-based assessments. The integrity of each node in the system is assessed by considering various factors, including energy, unselfishness, honesty, and dishonesty, and utilizing a forgetting curve for direct trust evaluation. In addition to these factors, similarity is also employed for indirect trust calculation to preserve the integrity of the network.