esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
The results can be extrapolated to other low voltage infrastructures to meet the voltage and electrical frequency conditions. This project aims to promote the applications of hybrid DG systems integrated into electrical microgrids. In the present work, we evaluate a control strategy that allows power transitions between the microgrid connected to the university campus and all possible operating scenarios, such as islanded or interconnected microgrids, different generation-load possibilities, and state-of-charge conditions of the battery. The converters have a low number of switches but allow adequate control of each element of the microgrid, so that the GD units operate in maximum power point tracking (MPPT) mode or promote the balance between the generated power, loads, and battery charge/discharge powers. The overall management strategy, together with the proposal of control schemes for the DG units for off-MPPT operation while ensuring proper total load sharing, are the main original contributions of this article. The rest of the paper is organized as follows. The proposed topology and modes of operation are described in Section 2. Models were constructed to enable the design of the system control loops, the PV, PMSG-wind, and ESS systems, together with the respective interface converters, and these are presented in Section 3. The control strategies for the interface converters are explained in Section 4. The operational scenarios used to verify and validate the proposed control strategies are described in Section 5, and the corresponding simulation results are presented in Section 6. The conclusions of the study are drawn in Section 7.
# Rapid Assessment of Rice Quality Traits Using Low-Cost Digital Technologies
###### Abstract  
Aroma and other physicochemical parameters are important attributes influencing consumer perception and acceptance of rice. However, current methods using multiple instruments and laboratory analysis make these assessments costly and time-consuming. Therefore, this study aimed to assess rice quality traits of 17 commercial rice types using a low-cost electronic nose and portable near-infrared spectrometer coupled with machine learning (ML). Specifically, artificial neural networks (ANN) were used to classify the type of rice and predict rice quality traits (aromas, color, texture, and pH of cooked rice) as targets. The ML models developed showed that the chemometrics obtained from both sensor technologies successfully classified the rice (Model 1: 98.7%; Model 2: 98.6%) and predicted the peak area of aromas obtained by gas chromatography-mass spectroscopy found in raw (Model 3: R = 0.95; Model 6: R = 0.95) and cooked rice (Model 4: R = 0.98; Model 7: R = 0.96). Furthermore, a high R = 0.98 was obtained for Model 5 to estimate the color, texture, and pH of cooked rice. The proposed method is rapid, low-cost, reliable, and may help the rice industry increase high-quality rice production and accelerate the adoption of digital technologies and artificial intelligence to support the rice value chain.  
rice aroma; near-infrared; electronic nose; artificial neural networks; machine learning +
Footnote †: journal: _foods_  
0000-0001-9993-3300-0000][PERSON]  
0000-0001-9999-3300-0000][PERSON]  
0000-0002-3330-0000][PERSON]
## 2 Volterra Model and FSI Method
### Computational Approach  
To verify the accuracy of the method in this study, the main deck section of GBEB is taken as an engineering background. The identification of the aerodynamic force of the main deck section without crash barriers is carried out by applying the aerodynamic force model proposed in this study. Moreover, the wind-induced vibration responses of the main deck for different wind speeds are analyzed with the FSI method proposed in this study.  
Figure 2 shows the main deck section of the GBEB. As shown in Figure 2, the width and height of the main deck section are 31 m and 4.4 m, respectively. Furthermore, the shear center is 2.354 m from the bottom of the main deck section.  
For incompressible viscous fluids, the mass conservation equation and the momentum conservation equation can be expressed as follows,  
\[\
abla\cdot\mathbf{V}=0 \tag{5}\]  
\[\frac{\partial\mathbf{V}}{\partial t}+(\mathbf{V}\cdot\
abla)\mathbf{V}=\mathbf{f}-\frac{1}{ \rho}\
abla p+\frac{\mu}{\rho}\
abla^{2}\mathbf{V} \tag{6}\]  
where \(\rho\) denotes the fluid density; \(\mathbf{V}\) denotes the velocity vector; \(p\) denotes the pressure; \(\mathbf{f}\) denotes the external force on the unit volume fluid, if gravity is considered, \(\mathbf{f}=\rho g\); \(\mu\) denotes the dynamic viscosity.  
Considering the blocking ratio of the main deck section, it is determined empirically that the calculation domain of the main deck section adopts a rectangular area of 35 \(B\times 20\)\(B\) (\(B\), the width of the main girder section), as shown in Figure 3. The boundary conditions are defined as follows: the left side of the computational domain is set as velocity inlet, the right side of the computational domain is set as pressure outlet, the main deck section is set as a non-slip wall, and the upper and lower sides of the computational domain are set as symmetry boundaries. The block meshing method was adopted to discrete the  
Figure 1: Characteristics of polynomial-basis smoothed-ramped impulse function.  
Figure 2: Girder cross-section of GBEB main span (unit: mm).  
computational domain, namely the rigid domain, the dynamic grid domain and the static grid domain, respectively.  
The geometric scale ratio of the main deck section is determined as 1:80. The height of the first layer of the grid is 1.0 cm, which is about 3 \(\times\) 10\({}^{-4}\)\(B\), and the total grid number is 112,706. Figure 4 indicates the local division of the grids near the main deck section. The shear stress transport (SST) \(k\)-\(\omega\) turbulence model is adopted in the Navier-Stokes equations in the time domain (Menter [26]). Considering that the Volterra kernel identification needs to ensure a certain continuity of the smoothed-ramped function, the time step of numerical simulation needs to be small enough. Therefore, the time step is defined as \(\Delta t=0.0005\) s in this study. The wind speed ratio is 1:8, the incoming wind speed is \(U=5\) m/s, and the corresponding wind speed at the bridge is 40 m/s, which is close to the flutter critical wind speed of the GBEB for subsequent flutter analysis.
## 4 Discussion
### Discussion on the Influence of Plasma Treatment on Fiber Medullation  
Treatment using an APPJ causes an increase in the average fiber diameter (DMF) compared to untreated samples, suggesting a thickening of the fibers [14]. This result is consistent with the findings of previous research, such as that conducted by [PERSON] and [PERSON] [14; 29], which demonstrated that oxygen plasma can alter the surface of wool fibers, increase roughness, modify morphology and chemical structure, and increase crystallinity [24]. These changes indicate that plasma treatment can be an effective technique for improving the physical and chemical properties of wool fibers.  
The initial hypothesis of this study was that the APPJ would alter the medullulation properties of wool. The findings support this hypothesis, as a reduction in the total medulla percentage (TMED) was observed in samples treated for longer periods, suggesting that the APPJ reduces the amount of medulla in the fibers. Additionally, the APPJ affects the distribution of different types of medulla, increasing the percentage of fibers with fragmented medulla (FMED), continuous medulla (CMED), and strongly medullulated fibers (SMED) in treated samples [24].  
Considering that medullulation plays a crucial role in the thermal insulation and strength properties of wool, changes in medullulation could affect the quality and performance of wool textiles, thereby affecting their thermal insulation capacity, wear resistance, and texture. These results suggest that the APPJ could be used not only to dye wool sustainably but also to improve its physical and chemical properties, which would have a significant impact on the textile industry.  
Therefore, due to the limited number of studies on the effect of the APPJ on the dyeing process and its textile and medulla characteristics, further research is needed to determine the impact of the APPJ on the medullulation properties of wool. Future studies could examine the effect of the APPJ on the medullulation of other types of wool and its influence on the thermal insulation and strength of textiles. These investigations provide valuable information for optimizing the APPJ treatment process and improving the properties of wool for specific applications.
# Fabric Defect Detection Using L0 Gradient Minimization and Fuzzy C-Means
## 4 Experimental Results and Discussion
### Parameter Setting  
The parameter \(\lambda\) plays a key role in detecting defects accurately in our method. The influence of \(\lambda\) that changed the defection results is shown in Figure 7. The best filter results using LGM are marked with a red block. From the Figure 7, it can be seen that if the parameter \(\lambda\) is set too small, the noise and background texture are almost unchanged. On the contrary, if the parameter \(\lambda\) is set too large, the defect is smoothed over. If the parameter \(\lambda\) is set moderate, the defect area will be easily distinct from the surrounding background in the following segmentation, and we can obtain a better detection result, as shown in Figure 7.  
To verify the impact of value of parameter \(\lambda\) on various fabric types, the parameter \(\lambda\) is 0.008, 0.015, 0.02, 0.03, 0.04, 0.07, and 0.08 respectively, and the seven fabric types' detection accuracies are presented by a bar chart as shown in Figure 8. The experiment proved that the number of \(\lambda\) selected should be between 0.008 and 0.08 to meet the requirements of various fabric types. As the Figure 8a,c shown, when \(\lambda\) is set between 0.07 and 0.08, the plain fabric and star-patterned fabric will be excessively smooth, so the accuracy rate cannot be detected.
## 3 Methods and Data
### Strain Analysis in La Palma  
A total of 344 faults with slickensides distributed along 39 stations on the island of La Palma have been measured in different field campaigns (Figure 3). The FPA has been used to obtain the D\({}_{\text{ey}}\) directions for each station, with a differentiation for representation purposes between those faults with a reverse component, with a normal component, and those with a direction movement or strike-slip faults (Figure 4). In most of the measurement stationsanalyzed, two superimposed deformation fields can be observed, showing \(\rm D_{ey}\) directions that are perpendicular to each other, one in the NW-SE direction and the other in the NE-SW direction (Figure 4). Taking into account the opening direction of the Mid-Atlantic Ridge defined by an opening in accordance with NW-SE [16,34] and the deformation trajectories obtained by [16], the deformation NW-SE field obtained could be associated with the opening of the ridge with a NW-SE \(\rm D_{ey}\) orientation. We have called this field the Strain Atlantic Field, which is compatible with the plate-scale stress field defined by [16]. Moreover, we have called the NE-SW deformation field obtained from the population analysis of faults the Local Strain Field [1,2].  
Figure 3: Stereonet of fault measuring stations (Wulff net, lower hemisphere) and representation of \(\rm D_{ey}\) obtained from the Slip Model (red arrows).  
The Atlantic deformation field on the island of La Palma is defined by both reverse fault populations (7 substations) and normal fault populations (16 substations) (Figures 3 and 4A). The areas with extensive K' are located in the NE and NW areas as well as in the most modern area of the island, the CV ridge in the S. However, the local field is fundamentally extensive (21 substations) with a strike-slip component in the NE and only two compressional stations (in the NW and SW of the island) (Figures 3 and 4B).
## 4 Performance Evaluation
### Transmission Success Ratio According to Movement Speed of Mobile Devices  
Figure 13 presents the performance evaluation results of the transmission success ratio of all devices relative to the movement speed of the mobile device. The delay in the neighbor discovery process caused by increased mobility speeds leads to more frequent disconnections in the network. In the case of [21], as previously defined in Section 2.2, the movement of mobile devices causes network changes and path disconnection. Usually, the neighbor discovery process is exploited to respond to the problems; however, it does not immediately reflect network changes caused by the movement of mobile devices. Thus, the transmission success ratio tends to decrease. However, In the case of [15], it assumes that the mobile device exists in all communicable locations and allocates all resources in advance. Therefore, it shows the highest transmission success ratio among the comparative protocols. The proposed scheme allocates resources by predicting movement paths for linear and non-linear mobility patterns for each device, while employing the same approach as [15] for random patterns, thereby achieving a high transmission success ratio.
## 4 ANN Modeling
### Evaluation of ANN Model  
The model's performance is evaluated using the validation data during training, and the trained model is tested with the test data. This evaluation process is supported by error metrics of mean squared error (MSE), mean absolute error (MAE), and \(R^{2}\) score.  
The MSE and MAE are defined as follows:  
\[\begin{split}\text{MSE}=\frac{1}{N}\sum\limits_{i=1}^{N}{(y_{i}- \hat{y}_{i})^{2}},\\ \text{MAE}=\frac{1}{N}\sum\limits_{i=1}^{N}{|y_{i}-\hat{y}_{i}|}.\end{split} \tag{27}\]  
where \(\hat{y}_{i}\) is the predicted value of the \(i\)-th sample, \(y_{i}\) is the corresponding true value, and \(N\) is the number of sample points.  
The \(R^{2}\) score reflects how closely the model's predictions match the true values, serving as a measure of the model's ability to predict outcomes for unseen samples. The \(R^{2}\) score equation is  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{N}{(y_{i}-\hat{y}_{i})^{2}}}{\sum\limits_{i=1 }^{N}{(y_{i}-\overline{y})^{2}}} \tag{28}\]  
where the mean value is represented by the upper bar.  
The model with \(R^{2}\) score close to 1.0 with the least MSE and MAE values indicates high prediction accuracy. Through a rigorous training process, the current ANN model  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Optimal Values** \\ \hline number of hidden layers & 3 \\ \hline number of neurons in each hidden layer & [5,10,20] \\ \hline learning rate & 0.001 \\ \hline epochs & 1000 \\ \hline activation function & ReLU \\ \hline solver & LBFGS \\ \hline regularization parameter & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The optimal hyperparameters of an ANN model.  
achieved the highest scores. As previously mentioned, the performance of the trained ANN model is evaluated using the test data. Table 2 presents the evaluation metrics for the output feature of the averaged reflection coefficient (\(\left|\overline{R}_{f}\right|\)) for different wave steepness. Figure 6 graphically displays the predicted values against the true values of the averaged reflection coefficient for different wave steepness. Predictions closer to the true values indicate a good prediction capability of the model.
## 5 Conclusions  
The aim of this study was to evaluate the presence and positivity level of SBPV-1 and DWV-B in different Italian regions, confirming for the first time the presence of SBPV-1 in Italy and with a rather high positivity level in the Emilia-Romagna and Lazio regions. Regarding DWV-B, the results demonstrate a high presence of this virus in all regions investigated. A good knowledge base on the dissemination of these viruses is also important to protect other pollinators, such as _Bombus_ spp., that is, the ancestral host of SBPV-1. The circulation of these viruses, in addition to that of _Varroa destructor_, is favored by the practice of nomadism, which involves the transfer of bee colonies among beekeepers, even from one territory to another or from one region to another region. For this reason, it is crucial to correctly manage Varroa infestation and apply good beekeeping practices to avoid the spread of viruses, or more broadly, pathogens.  
In conclusion, the uncontrolled spread inter- and intraspecies [22] of these pathogens, especially through _Varroa_ but not only, together with the poor management of apiaries, is responsible, in the long term, for progressive damage to Apoidea, such as _Apis mellifera_, essential for the functioning of the entire ecosystem. It is, therefore, fundamental to apply good beekeeping practices to prevent and control the spread of these pathogens and contribute to maintaining healthy and strong colonies.  
Furthermore, it is necessary to develop molecular protocols not only for a rapid diagnosis of these viruses, characterized by frequent recombination processes, but also to detect those that are still under investigated to characterize them through sequencing, while also using next generation sequencing (NGS) techniques.  
Conceptualization, E.L.M., A.C. and G.P.; methodology, A.C., E.L.M. and A.G.; validation, A.G. and A.C.; formal analysis, E.L.M.; investigation, G.F., M.P., M.M., E.L.M. and R.C.; resources, A.C. and M.T.S.; data curation, E.L.M., A.C. and A.G.; writing--original draft preparation, E.L.M., G.P. and A.C.; writing--review and editing, E.L.M., S.T., A.C. and A.G.; visualization, E.L.M. and S.T.; supervision, A.C., G.F., A.G. and M.T.S.; project administration, A.C. and M.T.S.; funding acquisition, A.C. and M.T.S. All authors have read and agreed to the published version of the manuscript.  
The Italian Ministry of Health (IZS LT 04/19 RC) supported this research.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors would like to thank [PERSON] for their support in the statistical analysis.
## Glossary  
### List of symbols  
\(V\) velocity vector  
\(V^{*}\) acceleration vector  
\(V^{*}\) Jerk vector  
\(X_{k}\) state, '\(k\)' represents the time epoch of the data sample  
\(W_{k}\) state noise  
\(Z_{k}\) measurement  
\(U_{k}\) measurement noise  
\(z_{code}\) the output of code phase discriminator  
\(z_{carrier}\) the output of carrier frequency discriminator  
\(e_{j,k}\) line-of-sight direction unit vector between the receiver and the \(j\)th satellite at time epoch \(k\)  
\(R_{k}\) measurement error covariance matrix  
\(Q_{k-1}\) represents the covariance matrix of the prediction error \(W_{k-1}\)  
\(X_{k|k-1}^{*}\) represents the weighted center matrix,  
\(S_{2z}\) square root of the innovation covariance matrix  
\(S_{Q,k}\) square root factor of \(Q_{k}\)  
\(S_{R,k}\) square root factor of the measurement error covariance matrix \(R_{k}\)  
\(\tau_{k|k-1}\) weighted center matrix  
\(K_{k}\) filter gain  
\(z_{\,k|k-1}\) measured predicted value  
\(S_{\,k|k}\) square root factor of the state estimation error covariance matrix
# Multi-Input Convolutional Neural Networks for Automatic Pollen Classification
## 2 Materials and Methods
### Datasets
#### 2.1.1 Data Acquisition  
The Rapid-E uses multiple lasers to obtain different characterizations of particles while they are in-flight, meaning that they are not deposited on slides or other capture media.  
This enables the device to work continuously in remote areas with little to no interaction required from a human operator. On the other hand, this makes creating a database of pollen quite difficult, because the features extracted by the device are very difficult for the human expert to pin to a certain species after the measurement is taken. To overcome this challenge, datasets of pollen are created in the lab by exposing the device to pollen from one species at a time. The device provides three types of features for a particle using two lasers at different wavelengths of light. Using an infrared laser, the device is able to create a scattering image that contains information about the size and the texture of the analyzed particle. After the particle is detected with the infrared sensor, a UV laser is used to excite the particle to get it to produce the fluorescence spectrum. Because of the optical nature of the setup, problems can arise from misalignment. Particles can be missed if they are not well centered on the laser beam or particles may appear bigger than they actually are if two, or more particles stick together and behave as a single object. In the following section, a more in-depth look at the data is presented.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 4 System Model Description of FBMC OFDM-OQAM and GFDM-OQAM
### OFDM-OQAM Transceiver  
The conventional stages of the OFDM-OQAM scheme consist of a transmitter, channel and a receiver structure. The OFDM-OQAM structure is made up of the SFB at the transmitter and the AFB at the receiver. Figure 15 depicts the block diagram of a filter bank OFDM-OQAM transceiver. The figure consist of a OQAM pre-processing/post processing, transformation blocks, filtering stage, CE block, and signal conversions blocks. As mentioned above, we review the performance of the filter bank OFDM-OQAM transceiver assuming two filter design conditions: NPR and Non-PR prototype filters design.