esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Effect of EF on the Quality of Rubber Plants and Marble  
The evaluation of potential phytotoxicity on rubber plants from the 4 h EF treatment was evaluated. The same rubber plants from the EF and the untreated control in the commercial-scale trials were used for quality evaluation. At the completion of the EF treatment and ventilation cycle, the potted plants were transferred to a greenhouse (\(27\pm 1\)\({}^{\circ}\)C and 50-70% relative humidity) and allowed to grow for 30 days. Then, leaf chlorophyll contents, leaf hue values, and overall damage were evaluated at the 30 th day in the greenhouse. Chlorophyll contents of 10 leaves were evaluated using a chlorophyll meter (SPAD- 502 Plus, Minolta, Tokyo, Japan) [18]. The leaf color of 10 rubber plants was measured as Hunter L, a, b values using colorimeter and expressed as hue values based on hue = [L\({}^{2}\) + a\({}^{2}\) + b\({}^{2}\)]/2 (TES 135A, Electrical & Electronic Corp., Taipei, Taiwan). Overall plant injury was scored using the following scale: 0 (no leaf damage), 1 (<5% of total leaves/plant dropped, browned, or shirveled), 2 (5-25% leaves affected), 3 (25-50% leaves affected), 4 (>50% leaves affected). The marbles from the commercial-scale trial were visually inspected to determine any potential effect of EF treatment on the visual quality of the marble.
## 3 Results and Discussion
We realize a real-time measurement and monitoring of the microwave horn field able to produce measurements of microwave fields in just seconds. Using a smaller cell would improve the spatial resolution of the measurement and so would improve the accuracy of near field measurements of more complicated fields. This technique can easily be extended to two and three dimensional field measurements, paving the way for the investigation of microwave field distributions.
# A Novel Method for Modeling the Electromagnetic Characteristics of Switched Reluctance Motors
## 4 Dynamic Simulation and Experiment
### Results Comparison  
A SRM test bench is built to compare with the simulation results, as shown in Figure 11. Two experiments that keep the same conditions (i.e., using the same sampling frequency, operation mode, speed, load and turn-on and turn-off angle) as the simulation were implemented. The experiment results are shown in Figures 9b and 10b.  
Figure 9a,b exhibits a comparison between the simulation and experiment waveforms of the phase switching signal and phase current. The speed reference and load torque in this study are 180 r/min and 4.0 Nm, respectively. At this operation point, the average switching frequency is about 1129 Hz, and the peak current is around 5.6 A. As shown in Figure 9a,b, a good agreement can be found.  
In addition, except the CCC operation, the dynamic simulation model can be also tested for voltage PWM mode. Figure 10a,b shows the simulation and experiment waveforms of the phase switching signal and phase current at the test condition of speed reference 650 r/min and load torque 1.5 Nm. The switching frequency and peak current are 10 kHz and 2.7 A, respectively. These waveforms indicate that the simulation and experiment results are in good agreement.  
Through the above comparison and analysis, the effectiveness of the proposed modeling method is demonstrated. However, in order to further quantitatively compare the simulation and experimental results, the relative error \(\sigma\) is defined as follows:  
\[\sigma=\frac{|i_{e}-i_{s}|}{|i_{e}|}\times 100\% \tag{16}\]  
Figure 11: Test bench.  
Figure 10: Switching signal and phase current with the PWM mode. (**a**) Simulation; (**b**) experiment.  
where \(i_{e}\) and \(i_{s}\) represent the experimental and simulated current values, respectively. It can be calculated that the relative error \(\sigma\) between the simulation and experiment results is less than 15% in both the current chopping and voltage PWM operations. The deviations between the results from the simulation and experiment are mainly caused by the modeling errors of the flux-linkage, phase resistance variation and measurement process. Besides, the influence of eddy current and magnetic hysteresis is ignored in the simulation, which will also lead to the difference between the simulation and experimental waveforms in some regions. In sum, the proposed modeling method has a satisfactory performance and can be used for SRM performance prediction and advanced control, but in the future, it is still necessary to further improve its accuracy.
## 2 Modeling Theory and Methods
### QSPR Model Based on the Hybrid Intelligent Method  
The back propagation artificial neural network (BP ANN) is one of the most important network models. It generally consists of an input layer, hidden layer, and output layer [44; 45; 46]. The implementation of BP ANN mainly consists of two processes: a learning process and a working process [47; 48].  
In a three-layer BP ANN, each layer consists of several nodes. The input layer receives the input information of the network. Then, the input information is processed and sent to the hidden layer.  
The relationship between the input and output can be expressed as:  
\[\text{Input}:\ net=x_{1}w_{1}+x_{2}w_{2}+\ldots x_{n}w_{n} \tag{3}\]  
\[\text{Output}:\ y=f(net)=\frac{1}{1+e^{-net}} \tag{4}\]  
where \(x_{i},x_{2},\ldots x_{n}\) are the input vectors of the network; \(w_{i},w_{2},\ldots w_{n}\) are the connection weights for each input vector; and \(y\) is the output of the network.  
In the BP ANN model, the nonlinear relationship between input and output is established by determining the weight and deviation between each layer. Structurally, the nonlinear relationship between the input and output can be understood as: output \(y=f(w_{ih},w_{ho},b_{o})\), where \(w_{ih},w_{ho},b_{o}\) are, respectively, the weight vector between the input layer and the hidden layer, the weight vector between the hidden layer and the output layer, and the deviation vector of the hidden layer. The performance of the network depends on the three main parameters of the network (\(w_{ih},w_{ho},b_{o}\)).  
To improve the BP algorithm, a prediction model based on CAPSO and BP ANN, called CAPSO BP ANN, is proposed based on the optimization of BP ANN parameters with the CAPSO algorithm. The CAPSO BP ANN model makes full use of the strong global search capability of the PSO algorithm and the fast local search capability of the BP algorithm, thus improving the prediction speed and accuracy of the model. In CAPSO BP ANN, the PSO algorithm is proposed to optimize BP ANN parameters (\(w_{ih},w_{ho},b_{o}\)). Therefore, in the PSO optimization algorithm, the particle is designed as the structure with weight vector \(w_{ih}\), weight vector \(w_{ho}\), and deviation vector \(b_{o}\):  
\[particle(i)=[w_{ih},w_{ho},b_{o}] \tag{5}\]  
The implementation of the CAPSO BP ANN model can be simply described as follows:  
Step 1: To initialize the model. The connection weights, deviations, and population parameters of the model are initialized by the random method;  
Step 2: Model training. The CAPSO algorithm is used to optimize the parameters of BP ANN and the particle structure is designed.  
Step 3: Parameter adjustment. Based on the error of the output, the parameters are adjusted until the number of execution times reaches the set value or the error satisfies the setting condition.  
Step 4: Output. After training, the model outputs each parameter and then the trained model is tested.
# The Eye-Opening Arbiter-PUF FPGA Implementation with Auto Error Detection
## 5 PUF Stability Evaluation and Automatic Detection of Unstable Bits  
After having evaluated the uniqueness, the stability of the PUF responses over noise and environmental conditions is assessed. As discussed in Section 2, the distinct advantage of the EoA-PUF is the deadzone, which enables making arbiter decisions as soon as the arbiter/RO phase difference is large enough and thus stable to be decided. Moreover, as discussed with Figure 7, the PDLs used for arbiter bias tuning also allows to trade robustness in the arbiter decision vs. number of required RO oscillations by adjusting the deadzone. This is as the stability of a single PUF instance's readout depends on the phase difference \(\Phi_{\text{ab}}\) seen by the arbiter at the time of decision-making. Since small \(\Phi_{\text{ab}}\) have a high chance to be influenced by noise, a larger deadzone makes this decision more robust.  
As seen in Figure 7, the PDLs should tune the arbiter along the unbiased decision line, close to the switching region for sensitive or farther away for robust decisions. \(\text{PDL}_{\text{ac}}\) and \(\text{PDL}_{\text{bc}}\) were used to tune this sensitivity against robustness, whereas \(\text{PDL}_{\text{ad}}\) and \(\text{PDL}_{\text{bd}}\) were then used to set the deadzone along the unbiased decision line. As the PDLs for arbiter tuning are limited to 20 bits in unary code (see Section 3.2), 21 different deadzones can be adjusted and are considered in the following. For simplicity, we call this a deadzone configuration \(dz\). For example, a \(dz\) of 2 bits sets the inputs of two H-SDEs in \(\text{PDL}_{\text{ad}}\) and \(\text{PDL}_{\text{bd}}\) to 1, while the others are set to 0. Based on the average delay per H-SDE, each bit of the deadzone configuration will increase the deadzone by \(2\cdot 7\,\text{ps}{=14}\,\text{ps}\) as the deadzone covers the negative and positive phase differences (see Figure 2).
# Analysis of AC DC Four-Switch Boost-Buck Battery Charger Converter for EV Applications
###### Abstract  
This paper focuses on the analysis and control strategy of a four-switch boost-buck AC/DC converter utilized in power factor correction applications with a wide output voltage range. Given the increasing importance of electric vehicles and the need for high reliability, this study addresses the internal dynamic stability problem that can arise in the converter system. The analysis begins with a thorough examination of the system's min-phase characteristic. Despite this, internal dynamic stability issues persist, requiring a solution to ensure a higher power factor and reliability. To address this challenge, this paper proposes the utilization of a damping RC circuit instead of reducing the loop gain bandwidth. To demonstrate the internal dynamic behavior of the converter, small-signal modeling is employed. This modeling highlights the importance of mitigating internal dynamic instability to achieve the desired power factor and reliability. This study emphasizes the significance of proper analysis and control strategies for boost-buck AC/DC converters in power factor correction applications. By addressing internal dynamic stability using a damping RC circuit, the converter can achieve a higher power factor and enhanced reliability, ultimately contributing to the development of more efficient and dependable EV systems. Finally, the feasibility of the proposed analysis and control strategy is confirmed through comprehensive simulations. The simulation results validate the effectiveness of using a damping RC circuit to address the internal dynamic stability problem, leading to an improved power factor and enhanced reliability.  
b 2024 14 626 10.3390/app1416262  
Deptarment d'Enginyeria Electronica, Electrica i Automatica, Universitat Rovira i Virgili, 43007 Tarrangona, Spain; [EMAIL_ADDRESS] (E.V.-I.); [EMAIL_ADDRESS] (J.C.)  
* Correspondence: [EMAIL_ADDRESS]
# Effect of _Tinospora cordifolia_-Derived Phtocomponents on Cancer: A Systematic Review
## 4 Discussion
### Dose Effect of Phytocomponents  
According to the US National Cancer Institute, an IC\({}_{50}\) value (drug concentration required for 50% inhibition in vitro) of less than 100 \(\upmu\)g/mL from a medicinal plant is sufficient to be considered as an anticancer agent [29]. Components of such plants are isolated and characterized to delineate their bioactive molecules. The methanolic extracts of _Tc_ have shown to exhibit an IC\({}_{50}\) value of less than 100 \(\upmu\)g/mL [29]. [PERSON] used 1.5 kg of dried, crushed _Tc_ soaked in 4.5 liters and found an IC\({}_{50}\) value of less than 100 \(\upmu\)g/mL [6]. [PERSON] et al. used varying dosage (200, 400 and 600 \(\upmu\)g/mL) of _Tc_, revealing dose-dependent inhibition [7]. [PERSON] et al. used 2 kg of dried stem in 80% ethanol extract. They extracted three phytocomponents and elicited an IC\({}_{50}\) value of less than 100 \(\upmu\)g/ml [9]. [PERSON] et al. used 2 kg of the dried crushed stem through alcohol extract and observed a dose which depended on cytotoxicity, with an ideal IC\({}_{50}\) value of less than 100 \(\upmu\)g/mL. They also showed that combining different phytocomponents resulted in a profound anti-carcinogenic effect [11]. [PERSON] et al. used 10 kg of 50% methanolic extract of _Tc_. The extract showed the anti-carcinogenic effect, while its rutin concentration was found to be higher than quercetin [15]. [PERSON] et al. found the phytocomponent palmitate showed anticancer effect against environmentally induced carcinogenesis. [PERSON] et al. observed that combining the various alkaloid with berberine increased the antineoplastic effect. Thus, in addition to the dose-dependent effect, the overall anti-carcinogenic effect also depended on the type, number and dosage of the used phytocomponents [7; 14; 15; 22].
## 3 Model Construction
### Input Data Based on Spatio-Temporal Features  
Convolutional neural networks (CNNs) are characterized by local perception and weight sharing. Therefore, this paper uses a CNN to extract the location-related spatial features of passenger flow data and construct a spatio-temporal matrix containing location and time information. The rows of the matrix represent the number of entrances into a toll station over the past \(n\) moments. The columns of the matrix represent the traffic at the entrance of different toll stations at a certain moment. The temporal matrix is:  
\[x=\begin{bmatrix}x_{1}\\ x_{2}\\ x_{3}\\ \ldots\\ x_{m}\end{bmatrix}=\begin{bmatrix}x_{1}(t-n)&x_{1}(t-n-1)&\ldots&x_{1}(t-1)\\ x_{2}(t-n)&x_{2}(t-n-1)&\ldots&x_{2}(t-1)\\ x_{3}(t-n)&x_{3}(t-n-1)&\ldots&x_{3}(t-1)\\ \ldots&\ldots&&\ldots\\ x_{m}(t-n)&x_{m}(t-n-1)&\ldots&x_{m}(t-1)\end{bmatrix} \tag{5}\]  
Figure 2: 1D convolution diagram.  
where \(x_{i}\) in row \(i\) is all the flows at station \(i\) for the past \(n\) moments and \(x_{i}(t-n)\) is the passenger flow at station \(i\) until \(n\) moments.
# A Collaborative UAV-WSN Network for Monitoring Large Areas
## 3 Results
### Simulations
#### 3.1.2 Clustering  
Experiments are based on TelosB/Tmote Sky platforms compatible with Contiki OS. We took advantage of the new available virtual tools for WSN prototyping, the simulation environment Contiki COOJA. The clustering results are obtained by implementation of the deterministic clustering mechanism presented in Section 2.4, with CH assignment by the UAV system.  
Regarding the selected CHs, we imagined a navigation path over the nodes diagram and we arbitrary chosen the CHs as they would have been elected with the UAV's support using the scoring mechanism. Figure 11 illustrates a clustered WSN comprising the same deployment used as the one used for estimating the sensors' location and computing the trajectory planning. The layout of the network was obtained based on the simulation results of the proposed clustering mechanism. The network has divided in eight clusters with sizes ranging from three to seven sensory nodes. Cluster heads are circled with blue lines.  
Figure 11: Clustered WSN—Simulation result (normal and head sensors with cluster highlighting).  
Figure 10: Computed trajectories passing through or near the cluster heads: gray-filled regions—obstacles, semi-transparent red regions—communication areas.  
In addition to the measured time required for WSN clustering, we made an online estimate of the power consumption for each sensor node using the powertrace feature available for Contiki OS, which let us know the time spent in the following states: CPU (active), LPM (low power mode), Transmit and Listen. Some results of simulations made on networks of different sizes are shown in Table 1. The standard deviation value reflects a large variation in both energy consumption and time. This is a predictable result due to the random mechanism and the uneven distributed topology. Mean power increases with WSN's expansion, while time required for the clustering mechanism is not affected, in fact clustering speed is increased because network density was increased and a large number of messages were exchanged at the same time.
# Consensus Guided Multi-View Unsupervised Feature Selection with Hybrid Regularization
## 4 Experiments
### Robustness Analysis  
To evaluate our method's robustness under noisy conditions, Gaussian noise was introduced at 10%, 20%, and 30% of the feature dimensions. As depicted in Figure 6, our approach maintains performance comparable to the noise-free scenario in the Handwritten, Yale, and WebKB datasets. Meanwhile, the other three datasets exhibit marginal performance degradation.
## 5 Conclusions  
This paper puts forward a hybrid load prediction method that combines KPCA with KELM optimized by FWA. First, for the sake of forecasting the power load of DES, the KPCA method is used to select input features. In addition, the FWA method is employed to optimize the parameters of KELM. Lastly, after the optimized input subset and the best values of \(C\) and \(\sigma\) are obtained, the method is used for load prediction of DES. The following results are obtained through this study.  
1. KPCA can effectively decrease the influence of non-correlation noise and improve the prediction performance.
2. The introduction of FWA optimization algorithm can enhance the global search ability, and the KELM method optimized by FWA shows good results.
3. On the basis of the error index, in comparison with ELM, KELM has achieved better prediction results, indicating that the method of improving ELM by introducing kernel function is effective (RMSE, MAPE and AAE are respectively 4.0873%, 4.0713% and 4.0649%). Therefore, the KPCA-FWA-KELM load prediction modal proposed in this paper is effective and feasible and is expected to become an effective alternative method for load prediction in power industry.  
Conceptualization, Y.L.; methodology, H.W.; software, H.W.; validation, H.W.; formal analysis, Y.F.; investigation, Y.L.; resources, H.W.; data curation, X.Z.; writing--original draft preparation, Y.F. and H.W.; writing--review and editing, Q.Y. and Y.L.; visualization, Y.F.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Science Foundation of Ministry of Education of China (Project No. 21 YJC630072), Natural Science Foundation of Hebei Province, China (Project No. G2020403008) and the Fundamental Research Funds for the Universities in Hebei Province, China (Project No. QN202210).  
The authors declare no conflict of interest.
# Combined kNN Classification and Hierarchical Similarity Hash for Fast Malware Detection
## 6 Discussion  
In this paper, we proposed two methods to improve the speed of malware detection. First, we proposed a kNN classification to increase the detection rate and reduce the detection time. Second, we provided a similarity hash using a VP tree to improve the similarity-hash search speed. By using kNN classification, we increased the detection rate by 25% and decreased the detection time by 67%. By using the VP tree with a similarity hash, we reduced the search time by 20%.  
In this study, we determined whether a file was malicious. Thus, we classified the files into a normal group, a malicious group, and an undecided group. However, there are different types of malware  
Figure 14: Search times.  
Figure 13: Number of distance comparisons.  
such as trojans, backdoors, worm, dropper, and viruses. Therefore, we need the kNN classification for specifying the type of malware. We can use the kNN classification two times. On the first use of the kNN classification, we classify files into a normal group, a malicious group, and an undecided group. On using the kNN classification the second time, we can classify malicious files into a trojan group, a backdoor group, a worm group, a dropper group, a virus group, and an undecided group.  
Moreover, we propose a method for improving the VP tree to reduce search time. Originally, we expected that when there are \(n\) training files, the search time would be \(O(\log n)\). However, we discovered that the query circle is often overlapped with the inner space and the outer space of the vantage point in the experiment. Thus, we had to search the left subtree and right subtree of the vantage point frequently. We expect that by using an \(m\)-tree [36] having \(m\) subtrees instead of a VP tree having two subtrees, we may reduce the search time.  
On the other hand, there are recent researches about evading malware detection using GAN [22; 23; 24; 25], GAN can be used to modify malware using a generator to be determined as normal by a discriminator. It is reported that GAN significantly reduces the detection rate. We need a method to prevent evading malware detection. We expect that we may solve this problem to some extent using deep learning-and similarity hash-based detection methods together.  
This work was supported by National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No.2019R1G1A11100261) and the HPC support project supported by MSIT and NIPA.  
The author declares no conflict of interest.
## 10 Comparative Analysis of Propagation Models
The investigations are carried out for all three test base stations: TestBS_1 located in an urban environment, TestBS_2 situated in a rural, flat area, and TestBS_3 located in mountainous terrain. For each of these base stations and terrain types, the previously described dataset of 30 test point results per base station is available. In addition, efforts have been made to increase the sample size. To this end, simulations were also performed along the lines connecting each base station with its corresponding test points using all wave propagation models. An example of such a line is shown in Figure 31. Based on these extended results, the relevant statistics were also computed. These are referred to as the so-called line-based results. The aim of the investigation was to assess how the individual wave propagation models behave in different environments (urban, flat rural, and mountainous) at the 3.6 GHz and 6 GHz frequency bands. For the comparative analysis, the deterministic Parabolic Equation Modeling (PEM) method was chosen as the reference model, as it was available for both frequency bands and is considered the most robust among the models. We examined the magnitude and nature of the errors with which the empirical and hybrid models estimate the field strength levels. By determining and comparing the standard deviations of terrain elevation and field strength levels, we also sought to identify which models more accurately reflect the field strength variations caused by terrain fluctuations, and which are less sensitive to the topographical conditions between the transmitting antenna and the receiving point. These findings allow us to draw conclusions regarding the reliability of the models, as well as to determine the practical tasks for which they can be applied reliably and those for which they are unsuitable.