esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
There is a remarkable observation that the simple matrix form (12) for the FMS filter developed by applying the Bayesian approach is equivalent to the existing batch unbiased FIR filter if the covariance \(\Pi_{M}\) of the noise term \(\Lambda_{M}W_{M}+V_{M}\) is assumed as the identity matrix. The simple matrix form (12) for the FMS filter can have several good intrinsic properties such as unbiasedness, deadbeat, and robustness according to [13; 14; 15; 16]. Thus, the simple matrix form (12) for the FMS filter might be very useful for diverse filtering problems such as high accuracy measurement processing [24] and multilayer node event processing [25]. Note that the inverse of the state transition matrix \(A\) is required to implement the simple matrix form (12) for the FMS filter, which seems to limit practical implementations. However, even though most dynamic systems are modeled in continuous time, these continuous time dynamic systems are generally discretized because they are operated in a digital system environment for practical implementations. When a continuous-time state space model \(\dot{x}(t)=A_{c}x(t)\) is discretized with the sampling time \(T\), the corresponding sampled-data system is given by \(x_{i+1}=A_{d}x_{i}\) with \(A_{d}=e^{A_{c}T}\). Therefore, the assumption for invertibility of state transition matrix \(A\) in the discrete-time state space model is not too restrictive for practical implementations. Actually, this assumption has been accepted in the IMS filter such as the information form of Kalman filter [10] as well as the FMS filter such as the batch unbiased FIR filter [13].
## 3 Results and Discussion  
### Tomato Pomace Powder Characterization  
Water activity (a\({}_{w}\)) is an important parameter for product stability and quality. Dry tomato pomace powder (TP) presented low values (0.32 \(\pm\) 0.01) indicating a good preservability. So, the TP can be kept for a long time without altering.  
The results obtained for water absorption index (WAI) (3.28 \(\pm\) 0.17%) and water retention capacity (WRC) (4.59 \(\pm\) 0.46%) showed that the TP can absorb and retain water, suggesting that the powder can be used to obtain functional solutions with the necessary industrial parameters. Similar results were reported by [PERSON] et al. [7].  
Tomato pomace powder presented a high pectin content (22.96 \(\pm\) 1.45%), the results showing that it is adequate for use in the manufacturing of products that require a specific texture. TP can be used as a thickening agent, but also as a stabilizer and water retention agent. Thus, it can be used in the formulation or development of new products with desired technological characteristics.  
The color parameters values obtained for TP showed a lightness of 51.40 \(\pm\) 0.19, while the value of \(a\)* (18.89 \(\pm\) 0.15) indicated chromaticity on a red (+) axis and the value of \(b\)* (26.22 \(\pm\) 0.11) indicated chromaticity on a yellow (+) axis. The chroma C* values (32.32 \(\pm\) 0.18) were related to raised concentration of lycopene, the carotenoid which imparts red to yellowish color to the TP.
# Dual-Band Rectifier Circuit Design for IoT Communication in 5G Systems
## 3 Design Framework
### Impedance-Matching Network Design  
To guarantee that we have power transfer with minimum losses from the receiving antenna to the rectifier, we need to design an impedance-matching network. Thus, this circuit ensures minimum signal power reflection back to the source [59].  
There are several different types of impedance-matching networks (IMNs) in the literature. These networks are made up of either distributed elements (such as microstrip lines and stubs) or lumped elements (such as inductors and capacitors). The proposed circuit works at 3.5 GHz and 5 GHz; hence, we used distributed elements because these are the best option at frequencies between 3 and 300 GHz. The lumped elements are generally used at frequencies below 3 GHz [53].  
The proposed IMN consists of two distinct branches, one for 3.5 GHz and the other for 5 GHz. Each branch consists of a T-shaped network with rectangular and radial stubs. We chose radial stubs because they require less space on the chip and produce superior results. Figure 4 illustrates the appropriate impedance-matching network.  
Table 1 includes the physical parameters of the transmission lines and stubs in the proposed impedance-matching network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Width/Length** & **Angle** \\ \hline TL1 & 2.7/3 mm & - \\ TL2 & 1.5/11.5 mm & - \\ TL3 & 1.5/16.6 mm & - \\ TL4 & 1/6.8 mm & - \\ TL5 & 1.5/11.5 mm & - \\ TL6 & 1.5/3 mm & - \\ TL7 & 1/15.3 mm & - \\ STUB1 & 700 mm/6.9 mm & 67 degrees \\ STUB2 & 1.3/5.9 mm & 33.6 degrees \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical parameters of the transmission lines and stubs.  
Figure 3: Greinacher topology.
## 3 Indoor Lighting Simulations  
### Lighting Project  
This section describes the detailed parameters used in several lighting simulations visualized using the 3D modelling software program Dialux-EVO. Three virtual rooms measuring 10 m wide \(\times\) 10 m deep \(\times\) 2.80 m high were designed, and luminaires with regulation and different spectral distribution curves were chosen for each room: Lambertian (beam spread 120\({}^{\circ}\)), intensive (beam spread 54\({}^{\circ}\)), and extensive (beam spread 90\({}^{\circ}\)), (Figure 4). Figure 4: Designed rooms. Left, Lambertian luminaire (beam spread 120\({}^{\circ}\)); middle, intensive luminaire (beam spread 54\({}^{\circ}\)); right, extensive luminaire (beam spread 90\({}^{\circ}\)). Figure 3: Melanopic illumininance calculated (for 100 lux photopic) using the CIE (**A**), WELL (**B**), and Rea (**C**) metrics with the LEDs divided by CRI90, CRI95, and CRI99. The MAF parameter (calculated by Equation (4) is a function of the CCT (**D**). In each room, 16 luminaires were regularly distributed in the ceiling. Their flux was 3750 lm. These luminaires can be equipped with LEDs with different SPDs and UGRs \(<\) 19, using an identical number of LEDs in the same Printed Circuit Board (PCB) format. The inner surface of the rooms was originally assumed to be ideally diffusive, with a reflectance of 80%. These parameters enable us to address the experimental design and analyze the importance of the deviations due to the spatial and spectral distribution curves of the LEDs with the materials that were employed. The effect on the luminous environment due to spectral reflectance of both furniture and surfaces (walls, ceiling, and floor) was analyzed using several specific cases. Illuminances in different planes (76 cm horizontal, 120 cm vertical) were calculated. The results exhibit varying reflectance factors (0%, 30%, 50%, and 80%), and different proportions of walls painted in black or white were analyzed in the same room (80% up and 0% down, 0% up and 80% down). These results reveal the maximum and minimum limits between which the variations in the illuminance are found. A horizontal measurement grid was positioned 76 cm above the floor to represent a working plane, and a second grid was positioned vertically at the same 120 cm height to represent the corneal plane of the eye. The nine measuring points of the calculation area were chosen to have equidistant spacing, and we established an exclusion zone of 0.5 m wide from the walls to avoid untraveled areas. The adopted calculation parameters and the simulation results are provided in Table 3. All the results are for photonic illuminance. The intensive mode shows higher variations between the maximum and minimum values (horizontal and vertical), followed by the Lambertian and, in the third position, the extensive luminaire. The melanopic illuminance can be easily calculated from these values based on Equations (7), (10) and (12).
## 4 Conclusions  
This study optimised the preparation of NFC goji juice using sensory evaluation scores and TSS/TA ratio as evaluation criteria, employing fuzzy mathematics combined with response surface methodology. The quality and changes in volatile compounds were systematically analysed and compared between freshly pressed goji juice and processed NFC goji juice. The optimal processing parameters were determined to be a homogenisation time of 2 min, ultrasonication time of 3 min, sterilisation temperature of 102 \({}^{\circ}\)C, and sterilisation time of 85 s. Under these conditions, the NFC goji juice exhibited a rich taste, moderate astringency, and appropriate sweetness and acidity, achieving a sensory score of 85.5 points. This optimised method is accurate and feasible, and it can be applied to the optimisation of other fruit juices. Compared to fresh-pressed goji juice, processed NFC goji juice not only showed a higher TSS/TA ratio and turbidity, but also contained higher levels of nutrients such as \(\beta\)-carotene, polysaccharides, and betaine. Additionally, some volatile compounds contributing to the fruity flavour (including 2-hexenal-M, hexanal-M, 2-methylbutanal, benzaldehyde, propyl acetate, methyl acetate, 1-butanol, ethanol-D, and 2-heptanone) exhibited significant increases in relative content. The microbiological indicators of the product met national food safety standards. Processed NFC goji juice products demonstrate good nutritional quality and align with the direction of sustainable food development, suggesting broad application prospects. This study provided a theoretical basis and technical support for the production and processing of NFC goji juice. Based on these findings, further exploration of the quality variation patterns and quality control mechanisms at different processing steps is recommended to accelerate the production and development of NFC goji juice.
# Multimodal Power Management Based on Decision Tree for Internet of Wearable Things Systems
## 2 Circuit Design of the PPG-Based Wearable System
### Healthcare Sensors
#### 2.3.2 Heart Rate and SpO2-PPG Sensor  
The clinical-degree MAX30102 sensor contains a pulse oximetry and a heart rate (HR) module. The sensor comprises internal LEDs, photo-detectors, optical elements, and low-noise electronics with ambient light rejection. It has an integrated cover glass for optimal, robust performance. This sensor is shut down through software (0.7 uA Typical), allowing a low-power operation with high sample rates and high signal-to-noise-ratio (SNR).
# Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks
## 4 Experimental Results
### Performance Metrics  
The standard performance metrics used in continuous emotion recognition are the mean absolute error (MAE), the mean absolute percentage error (MAPE), Pearson correlation coefficient (PCC), and concordance correlation coefficient (CCC). PCC assesses the distance between target values and predictions, and CCC establishes the strength of a linear relationship between two variables. The range of possible values lies in the interval \([-1;1]\), where \(-1\) or \(1\) means strong relation and \(0\) means no relation at all. The MAE for a set of labels \(y\) and predictions \(y^{\prime}\) is given by:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}\left|y^{\prime}_{i}-y_{i}\right| \tag{4}\]  
The MAPE for a set of labels \(y\) and predictions \(y^{\prime}\) is given by:  
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\frac{\left|y^{\prime}_{i}-y_{i}\right|}{ y_{i}} \tag{5}\]  
The PCC is given as:  
\[\rho=\frac{\sum_{i=1}^{n}(y_{i}-\overline{y})\left(y^{\prime}_{i}-\overline{y }^{\prime}\right)}{\sqrt{\sum_{i=1}^{n}(y_{i}-\overline{y})^{2}}\sqrt{\sum_{ i=1}^{n}\left(y^{\prime}_{i}-\overline{y}^{\prime}\right)^{2}}} \tag{6}\]  
where \(n\) is the number of samples, \(y_{i}\) is the \(i\)-th label, \(y^{\prime}_{i}\) is the \(i\)-th prediction, and \(\overline{y}\) and \(\overline{y}^{\prime}\) are the mean of labels and mean of predictions, respectively. It is important to notice that these performance metrics are computed for each labeled video frame. Therefore, \(n\) represents the total number of video frames in a test dataset.  
The CCC combines the PCC with the squared difference between the mean of predictions \(\overline{y}^{\prime}\) and the mean of the labels \(\overline{y}\). CCC shows the degree of correspondence between the label and prediction distributions based on the covariance and correspondence. The CCC between a set of labels \(y\) and predictions \(y^{\prime}\) is given by:  
\[\rho_{c}=\frac{2\rho s_{y}s_{y^{\prime}}}{s_{y}^{2}+s_{y^{\prime}}^{2}+\left( \overline{y}-\overline{y}^{\prime}\right)^{2}} \tag{7}\]  
where \(s_{y}^{2}\) and \(s_{y^{\prime}}^{2}\) are the variance of \(y\) and \(y^{\prime}\) respectively.
## 5 Performance Analysis and Optimization
### Optimization Performance on Energy Efficiency  
In this subsection, we mainly deal with the optimization performance of DDAR scheme regarding energy efficiency. Energy efficiency is defined as the ratio of the sum of energy consumption of all the aggregators when an aggregator dies in the network to the sum of the energy of all aggregators.  
1. Overview  
Table 9 shows the average energy efficiency and the ratio of energy efficiency under various environments. DDAR significantly improves the energy efficiency. The improvement does not change with the change of \(\mathcal{Q}\).  
Figure 14: The average ratio of maximum energy consumption vs. \(\lambda\).  
Figure 13: Average maximum energy consumption vs. \(\lambda\).  
It can be seen in Figure 15 that the optimization effect increases when \(\eta\) increases.  
(b)The effect of \(P_{\alpha}\) and \(\lambda\)  
In Table 10, the energy efficiency in CDAR and DDAR is not affected by the change of \(\mathcal{Q}\). The energy efficiency decreases as \(P_{\alpha}\) increases in the networks with DDAR, but it decreases more slightly in CDAR networks. Therefore, the optimization performance decreases with the increase of \(P_{\alpha}\).  
As revealed in Figure 16, the efficiency decreases as the value of \(\lambda\) increases. The ratio approximately linearly increases with the increases of \(\lambda\).  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(\mathbf{P_{\mathrm{ff}}}=\mathbf{0.1}\) & \(\mathbf{P_{\mathrm{ff}}}=\mathbf{0.5}\) & \(\mathbf{P_{\mathrm{ff}}}=\mathbf{0.9}\) \\ \hline DDAR (\%) & & & \\ \hline \(\mathcal{Q}=3\) & 47.65 & 37.19 & 33.96 \\ \(\mathcal{Q}=5\) & 47.00 & 37.35 & 34.05 \\ \(\mathcal{Q}=7\) & 46.81 & 37.32 & 34.22 \\ \hline CDAR (\%) & & & \\ \hline \(\mathcal{Q}=3\) & 29.55 & 25.73 & 25.96 \\ \(\mathcal{Q}=5\) & 29.58 & 25.64 & 25.62 \\ \(\mathcal{Q}=7\) & 29.56 & 25.64 & 25.78 \\ \hline Ratio (\%) & & & \\ \hline \(\mathcal{Q}=3\) & 199.98 & 184.03 & 169.46 \\ \(\mathcal{Q}=5\) & 195.24 & 185.24 & 169.91 \\ \(\mathcal{Q}=7\) & 194.94 & 185.67 & 172.17 \\ \hline \end{tabular}
\end{table}
Table 10: Average energy efficiency and the ratio vs. \(P_{\alpha}\).  
Figure 15: Ratio energy efficiency vs. \(\eta\).  
\begin{table}
\begin{tabular}{c c c} \hline  & **DDAR (\%)** & **CDAR (\%)** & **Ratio (\%)** \\ \hline \(\mathcal{Q}=3\) & 39.60 & 26.94 & 184.49 \\ \(\mathcal{Q}=5\) & 39.47 & 26.97 & 183.22 \\ \(\mathcal{Q}=7\) & 39.45 & 26.92 & 184.26 \\ \hline \end{tabular}
\end{table}
Table 9: Average energy efficiency and the ratio in each environment.
## 3 A Proposed Approach
### Path Planning Approach  
In this section, the proposed path planning method is detailed in-depth. Firstly, the trajectories of OS and target ship (TS) are predicted. Then, a preliminary path is produced. Finally, an iterative calculation is performed to obtain a better path.
# Prediction of the Yield Strength of RC Columns Using a PSO-LSSVM Model
## 4 Model Development and Verification
### Evaluation of the PSO-LSSVM Prediction Performance  
#### 4.4.1 Performance Evaluation Indicators  
The prediction performance of PSO-LSSVM is evaluated by the coefficient of determination (\(R^{2}\)), mean absolute error (MAE) and root mean square error (RMSE). The coefficient \(R^{2}\) represents the level of explained variability between the actual values measured in the experiment and predicted values computed by the PSO-LSSVM model. The closer the coefficient \(R^{2}\) varies toward 1, the more similar the actual values and predicted values are. Likewise, the lower values for MAE and RMSE, the higher accuracy in the predicted values indicate. Equations (9)-(12) are the mathematical formulation of the adopted performance evaluation criteria RMSE, MAE and \(R^{2}\), respectively, as follows:  
\[\text{RMSE}= \sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{9}\]  
\[\text{MAE}= \frac{1}{n}\sum_{i=1}^{n}\left|y_{i}-\hat{y}_{i}\right| \tag{10}\]  
\[R^{2}=\frac{\left(n\Sigma y_{i}\times\hat{y}_{i}-\sum y_{i}\sum\hat{y}_{i} \right)^{2}}{\left(n\Sigma y_{i}^{2}-\left(\Sigma y_{i}\right)^{2}\right) \left(n\Sigma\hat{y}_{i}^{2}-\left(\Sigma\hat{y}_{i}\right)^{2}\right)} \tag{11}\]  
\[\text{Explained Variance}= \left(1-\frac{\text{Var}(y_{i}-\hat{y}_{i})}{\text{Var}(y_{i})} \right)\times 100\% \tag{12}\]  
Figure 4: Flowchart of PSO-LSSVM scheme.  
where \(y_{i}\) is the experimental value of the \(i\)th sample, \(\hat{y}_{i}\) is the predicted value of the \(i\)th sample, and \(n\) is the total number of samples, and Var() is the variance.
# Colored Petri Net Modelling and Evaluation of Drone Inspection Methods for Distribution Networks
## 4 Results
### Description of the Model  
The main elements of the model are described in Section 3.4. Figure 6 shows a scheme of the model. Eight monitoring segments have been created. The object is divided into three sections with assigned UAVs: _P0-P3_, _P3-P6_ and _P3-P8_. Each UAV is responsible for its own section. At place _P0_, there is a token with identification number 0 for the UAV which investigates section _P0-P3_; place _P3_ contains two tokens, one with identification number 1 for section _P3-P6_ and the second with identification number 2 for section _P3-P8_.  
Figure 5: (**a**) The monitoring object—an oil pipeline with 9 checkpoints. (**b**) An example of the UAV of convertible type.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Take-Off Point** & **Landing Point** &
\begin{tabular}{c} **Distance between Points** \\ **(km)** \\ \end{tabular} \\ \hline _P0_ & _P1_ & 102 \\ _P1_ & _P2_ & 122 \\ _P2_ & _P3_ & 113 \\ _P3_ & _P4_ & 112 \\ _P4_ & _P5_ & 73 \\ _P5_ & _P6_ & 141 \\ _P3_ & _P7_ & 64 \\ _P7_ & _P8_ & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The distance between checkpoints.  
The directional conditions of the tokens on the network were restricted as follows:  
* Token with ID number 0 only visits places \(P0\), \(P1\), \(P2\) and \(P3\).
* Token with ID number 1 only visits places \(P3\), \(P4\), \(P5\) and \(P6\).
* Token with ID number 2 only visits places \(P3\), \(P7\) and \(P8\).  
All transitions simulating the flight have been configured according to the expected flight time for the particular network section.  
The \"_Drone_comp_\" place contains nine tokens: three for each UAV. Based on the flight time at each section, an average flight time of 1.28 h was determined. The checklist time is 6 minutes. Thus, the periods between maintenance are 47.6 h, 476 h and 952 h for propellers, batteries and motors, respectively.  
Monitoring frequency measurements were added for each of the three sections. This frequency can be easily expressed by the mean of the throughput of the transitions (i.e., their firing frequencies) multiplied by the required time period in hours.
# Terahertz Emission Enhanced by a Laser Irradiating on a T-Type Target
## 1 Introduction
Furthermore, the laser-irradiated wires have efficiently generated THz emission due to fast electrons undergoing helical motion [47; 48; 49; 50] or a current-carrying line antenna [51]. However, it is currently unclear how a flat thin target placed longitudinally affects the THz emission via the CTR. In this paper, a T-type target (TTT) including a longitudinal target and a transverse target is proposed and investigated by using two-dimensional (2D) particle-in-cell (PIC) simulations. The result shows that the TTT can significantly enhance the intensity of THz emission and affect its spatial distribution. Compared with the case of single-layer target (SLT), the maximum intensity of the THz emission passing through the spatial probe point in the \((x,y)\) plane is enhanced by more than one order of magnitude, and the THz emission along the rear surface exhibits a much weaker distribution in the center than that at both wings. We analyze the interaction processes of the laser, respectively, with the longitudinal target and transverse target in detail through a series of simulations and clarify the origin of high energy electrons. In addition, the robustness of this scheme is also verified by the target parameters scanning. The paper is organized as follows. Section 2 outlines the target configurations and laser and plasma parameters. Section 3 shows the simulation results. In addition, the reasons why THz emission was enhanced by a laser irradiating on a TTT are also discussed in detail in Section 4. Furthermore, Section 5 presents a robust analysis. Lastly, a brief summary is given in Section 6.
## 3 Results and Discussion
### Effect of Zn NPs on Disease Severity and TMV Systemic Accumulation Level  
To investigate the anti-TMV activity of the biologically prepared ZnO NPs, protective and curative assays were checked on _D. stramonium_ plants by counting local lesions that were developed on the inoculated leaves. As shown in Figure 5, the number of local lesions that appeared on the right side of the treated leaves with ZnO NPs (100 \(\upmu\)g/mL) were significantly lower than the number of lesions on the untreated left side of the leaves. The results of the protective (upper right halves of the leaves were treated with ZnO NPs, 24 h before TMV inoculation) and curative (upper right halves of the leaves were treated with ZnO NPs, 24 h after TMV inoculation) assays revealed that the biologically synthesized ZnO NPs show both protective and curative activities against TMV, with better protective activity (Figure 5).  
Under greenhouse conditions, the foliar application of the biologically synthesized ZnO NPs (100 \(\upmu\)g/mL) significantly decreased disease severity, increased plant growth, and reduced viral accumulation levels in the treated tomato plants, when compared to the infected tomato plants without treatments (T2). In the current study, at 14 dpi, the TMV-infected tomato plants (T2) showed severe mosaic symptoms similar to those reported previously [60]. On the other hand, a delayed in the development of infection symptoms for three, four, and five days was observed in ZnO NPs treated plants before viral inoculation (T3), ZnO NPs treated plants after viral inoculation (T5), and ZnO NPs treated plants before and after viral inoculation (T5), respectively (Figure 6). No symptoms were observed on the mock-treated plants (T1). Additionally, ZnO NPs-treated tomato plants showed a significant decrease in TMV accumulation levels in plant tissues. The qRT-PCR results exhibited that the higher accumulation level of TMV (136.60-fold) was in T2 plants, while significant decreases of accumulation level in T3 (37.14-fold), T4 (63.55-fold), and T5 (13.36-fold) plants were observed (Figure 5). The considerable reduction in TMV accumulation level in tomato leaves by 72.81%, 53.47%, and 90.21% in T3, T4, and T5 treatments, respectively, reflected the anti-TMV activity of the biologically prepared ZnO NPs. In this context, [PERSON] et al. [32] reported that the foliar application of the chemically prepared ZnO NPs decreased the accumulation level of TMV in tobacco plants and reduced disease severity [32]. Moreover, the treatment of tomato plants with Ag NPs reduced the disease severity and ToMV or PVY concentration levels inside plant tissues [35].  
Figure 4: SEM images of zinc oxide nanoparticles biosynthesized by _Mentha spicata_ leaf extract as reducing agents. (Bar= 1 \(\upmu\)m at \(\times\) 10,000).