esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Optical Measurement Process
### Comparative Insertion Loss Measurements at 850 nm and 1310 nm  
The parameters for the different waveguides under test are shown in Table 2.  
Table 3 shows the average insertion losses measured on the planar graded index polymer Mosquito and photo-addressed waveguides, and the planar graded index glass waveguides at wavelengths of 850 nm and 1310 nm. Unfortunately, due to damage of the photo-addressed sample, measurements of the photo-addressed waveguides at 1310 nm could not be completed, so are excluded from the results presented.  
The insertion loss per cm is shown in Figure 11 and the insertion loss results are shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Mosquito Waveguides** & **Sumitomo Waveguides** & **Glass Waveguides** \\ \hline  & & & Set 1 & Set 7 \\ Core, n\({}_{1}\) & (NP-005), 1.597 & - & 1.523 & 1.527 \\ Cladding, n\({}_{2}\) & (NP-208), 1.569 & - & 1.498 & 1.498 \\ Index difference & 0.028 & & 0.025 & 0.029 \\ Sample length & 5 cm & 17.6 cm & 19 cm & 19 cm \\ Optical layer thickness & 0.5 mm & 0.1 mm & 0.55 mm & 0.55 mm \\ Core size & \(\sim\)50 \(\mu\)m & \(\sim\)50 \(\mu\)m & \(\sim\)50 \(\mu\)m \\ Channel Pitch & 250 \(\mu\)m & 250 \(\mu\)m & 250 \(\mu\)m \\ Fab. method & Mosquito & Photo addressing & Ion diffusion & Ion diffusion \\ Index Profile & GI & GI & Elliptical GI & Elliptical GI \\ \hline \hline \end{tabular}
\end{table}
Table 2: Polymer and glass waveguide parameters.  
Figure 10: Measurement schematic of the multimode fiber launch.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Wavelength (nm)** & \multicolumn{3}{c}{**Insertion Loss (dB)**} \\ \hline  & Mosquito Waveguide & Photo-addressed Waveguide & \multicolumn{1}{c}{Glass Waveguide} \\  & & & Set 1 & Set 7 \\
850 & 2.73 \(\pm\) 0.69 & 3.32 \(\pm\) 0.31 & 4.90 \(\pm\) 0.32 & 4.23 \(\pm\) 0.20 \\
1310 & 4.32 \(\pm\) 0.67 & Not available & 2.81 \(\pm\) 0.13 & 3.00 \(\pm\) 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average insertion loss measured at 850 nm and 1310 nm on glass and polymer waveguides under test.
# Near-Surface and High-Resolution Satellite Time Series for Detecting Crop Phenology
## 3 Results
### Concordance among PlanetScope, Near-Surface and Visual Phenology
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Comparison**} & \multicolumn{3}{c}{**Crop Emergence**} & \multicolumn{3}{c}{**Crop Maturity**} \\ \cline{2-7}  & **RMSE (Days)** & **Bias (Days)** & **R\({}^{2}\)** & **RMSE (Days)** & **Bias (Days)** & **R\({}^{2}\)** \\ \hline PhenoCam vs. Visual & 6.43 & 0.50 & 0.73 & 5.31 & 0.85 & 0.70 \\ PlanetScope vs. Visual & 8.35 & 5.28 & 0.64 & 9.92 & 8.16 & 0.58 \\ PlanetScope vs. PhenoCam & 7.14 & 4.63 & 0.79 & 8.40 & 6.56 & 0.66 \\ \hline \end{tabular}
\end{table} 
Table 4: The accuracy statistics of comparisons among PlanetScope, PhenoCam, and visual phenology with all the shared site-year observations using the CUR-based Greenup for crop emergence and DER-based EOS for crop maturity. Figure 10: The comparisons between the PlanetScope- and PhenoCam-derived phenological metrics, including (**a**) CUR-based Greenup and (**b**) DER-based EOS, using all the shared site-year observations.
# SBNN: A Searched Binary Neural Network for SAR Ship Classification
## 3 SBNN
### Searched Normal Cell and Reduction Cell  
With the development of deep learning, the gap between binary networks and their real-valued counterparts is narrowing [25, 26]. We construct the binary cells for SAR ship classification under the following steps. First, efficient floating-point node convolutional cells, Normal Cell and Reduction Cell, are searched by NAS. Then, the searched cells are modified and binarized. The last step involves inserting our proposed patch shift processing. We believe this method can keep the advantages of NAS with high accuracy and small size with a much lower computational complexity benefiting from binarization.  
The architectures of Normal Cell and Reduction Cell in SBNN are shown in Figure 2. Each cell has eight convolutional operations. The connections and types of the operations are the main difference between Normal Cell and Reduction Cell. As shown in Figure 2, Normal Cell contains three Binary Conv \(5\times 5\) Operations, three Binary Conv \(3\times 3\) Operations, two Binary Dilated Conv \(5\times 5\) Operations and Reduction Cell contains three Binary Reduction Conv \(5\times 5\) Operations, two Binary Conv \(3\times 3\) Operations, one Binary Reduction Dilated Conv \(5\times 5\) Operations, one Binary Dilated Conv \(5\times 5\) Operations and one Binary Dilated Conv \(3\times 3\) Operation.  
In each cell, linear layers reshape the inputs at first. Then, the patch shift processing enhances the presentation of feature maps. Next, the binary convolutional operations extract and output deep features. The types and connections of those binary convolutional operations are set following the searched floating-point cells.
# Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN
## 2 Materials and Methods
### Methods
#### 2.3.3 Partial Least Squares Regression  
In this study, to improve the evaluation of CLQ, GA-BPNN was proposed and compared with PLSR and SVR to predict CLQ using GPPs.  
As a linear multivariate model, PLSR relates two data matrices, \(X\) and \(Y\). Compared with traditional regression, however, PLSR can be used to analyze the predictors that have strong correlations [39; 40], which is expressed as follows:  
\[Y=X\beta+\varepsilon, \tag{6}\]  
where \(Y\) is the response variable CLQ, \(X\) is the predictor GPP at different stages, \(\beta\) is the regression coefficient, and \(\varepsilon\) is the residual.
## 1 Introduction
Furthermore, this paper delves into the application of WVD in time-frequency analysis, including its definition, specific application cases in SHM, and the advantages of high time resolution and instantaneous frequency analysis, while also pointing out the limitations of cross-terms and noise sensitivity. The second section introduces adaptive time-frequency analysis, detailing the principles, applications, and advantages and disadvantages of EMD and its optimized models (e.g., EEMD, MEEMD), emphasizing EMD's unique advantages for processing nonlinear and non-stationary signals. Additionally, it explores the concepts and applications of VMD and its variants (e.g., SVMD, AVMD) and compares VMD with traditional methods, highlighting it's flexibility and stability in signal decomposition. The final section discusses deconvolution techniques, examining the applications of deconvolution techniques such as MED and MCKD in machinery fault diagnosis and analyzing their advantages and disadvantages. This paper aims to provide researchers in the SHM field with a comprehensive understanding of signal processing techniques and to serve as a reference for future research.
## 3 360\({}^{\circ}\) Videos in Education
The results demonstrated that 360\({}^{\circ}\) videos led to better learning outcomes, higher levels of enjoyment, motivation, and immersion, with no usability issues reported. Another study by [49] targeted university students and explored the life of wolves under two conditions: immersive and non-immersive presentations of 360\({}^{\circ}\) videos. Although this study was not explicitly designed as an educational experience, the authors concluded that HMDs allowed participants to feel more present in the virtual environment and reported higher levels of interest in the nature experience involving wolves. The study suggested that HMDs can facilitate nature experiences with positive emotional and learning outcomes. Interestingly, the non-immersive screen setting was associated with more favorable attitudes toward wolves. Furthermore, ref. [50] found that immersive nature videos foster stronger feelings of spatial presence and environmental commitment compared to regular nature videos in a study involving adults. By summarizing the findings presented in the previous sections, it can be argued that 360\({}^{\circ}\) videos hold significant educational potential. However, existing research has produced mixed results regarding their impact on learning outcomes. Particularly concerning very young learners, the volume of studies is very small, rendering any conclusions about their effectiveness unreliable. The body of literature on the use of 360\({}^{\circ}\) videos for teaching subjects related to animals or the environment is also very limited. In light of these considerations and building upon the experiences gained from a previous pilot study, the study at hand aimed to determine whether 360\({}^{\circ}\) videos have a measurable impact on the knowledge acquisition of kindergarten students in subjects related to animals and to compare these outcomes with those derived from printed materials and conventional videos. Additionally, it sought to evaluate the influence of 360\({}^{\circ}\) videos on various factors cited in the literature presented in the previous sections, such as enjoyment, immersion, ease of use, and motivation, as well as how these factors, in turn, affect learning outcomes. Therefore, the following research questions were examined:  
* RQ1. Does the viewing of 360\({}^{\circ}\) videos result in a better understanding of subjects related to animals, compared to printed materials and conventional videos? To what extent do students' gender and previous knowledge influence learning outcomes? * RQ2a-d. Do 360\({}^{\circ}\) videos offer a more enjoyable (a), immersive (b), and motivational (c) experience compared to printed materials and conventional videos? Furthermore, do students find them easier to use (d) relative to the aforementioned media? Does students' gender play a role? * RQ3: To what degree, can the above factors (enjoyment, immersion, motivation, and ease of use) impact learning outcomes?
## 2 Materials and Methods
### Classifier Training  
The classifier was trained with three established methods: the support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) methods. For the method comparison, the samples were split with the random state of 169, the training and test set ratio was 8:2, and 9 components were used. Sensitivity, specificity, accuracy, and cross-validation were calculated for each classifier. Sensitivity = TP/(TP + FN); specificity = TN/(TN + FP); and accuracy = (TP + TN)/(TP + FN + FP + TN), where TP, TN, FP, and FN represented true positive, true negative, false-positive, and false negative, respectively. Three-fold cross-validation was used to evaluate the stability of the trained classifier, where the training set was divided into three groups; any two groups were used to train the classifier, and the other one was used to evaluate the trained classifier.
# Smart Agriculture Applications Using Deep Learning Technologies: A Survey
###### Abstract  
Agriculture is considered an important field with a significant economic impact in several countries. Due to the substantial population growth, meeting people's dietary needs has become a relevant concern. The transition to smart agriculture has become inevitable to achieve these food security goals. In recent years, deep learning techniques, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), have been intensely researched and applied in various fields, including agriculture. This study analyzed the recent research articles on deep learning techniques in agriculture over the previous five years and discussed the most important contributions and the challenges that have been solved. Furthermore, we investigated the agriculture parameters being monitored by the internet of things and used them to feed the deep learning algorithm for analysis. Additionally, we compared different studies regarding focused agriculture area, problems solved, the dataset used, the deep learning model used, the framework used, data preprocessing and augmentation method, and results with accuracy. We concluded in this survey that although CNN provides better results, it lacks in early detection of plant diseases. To cope with this issue, we proposed an intelligent agriculture system based on a hybrid model of CNN and SVM, capable of detecting and classifying plant leaves disease early.  
precision agriculture; smart farming; deep learning; CNN; RNN; SVM +
Footnote †: journal: Applied Sciences
# Nanoarchitectures in Management of Fungal Diseases: An Overview
## 4 Nanocarriers in Management of Fungal Infections
### Carbon Nanotubes
parasilisi_ and _C. albicans_ full-grown biofilms. The data revealed that AmBD-conjugated _f_-CNTs contained 85.65% AmBD and \(-\)23.8 mV zeta potential, presenting a stable performance of the nanocomposites. The MIC\({}_{50}\) of AmBD against _C. parasilisi_ and _C. albicans_ isolate was found to be 0.25 mg/L and 1 mg/L, respectively. The results exhibited that the _f_-CNTs AmBD and AmBD showed comparable antibiofilm properties for both fungal biofilms and concluded that _C. parasilisi_ showed more resistance than _C. albicans_ (at concentrations \(\geq\)1 mg/L) [87]. [PERSON] et al. showed an improved inhibition rate in spore germination and elongation using MWCNT-derived nanomaterials compared with pristine MWCNTs for the plant pathogenic fungi _F. graminearum_. Thus, after treatment with a concentration of 500 \(\upmu\)g/mL of MWCNTs containing various surface groups (COOH-, NH\({}_{2}\)-, and OH-), the spore length reduced from 54.5 \(\upmu\)m to 28.3 \(\upmu\)m, 29.5 \(\upmu\)m, and 27.4 \(\upmu\)m, respectively. Further, the germination of the spore was interestedly inhibited to about 18.2% of the germination rate by _f_-MWCNTs, which was three-fold lesser than that of the pristine MWCNTs [88]. [PERSON] et al. proposed the hydroxylated and pristine architectures of the C\({}_{240}\) fullerene and SWCNTs as nanotransporters of the existing imidazoles (e.g., econazole, ticoconazole, and sulcanazole) to enhance their pharmacokinetic characteristics, such as absorption, distribution, metabolism, and excretion. It was observed that the adsorption energies of the azoles were dependent on surface modifications and the type of nanotransporter. The CNT and fullerene surface hydroxylation gave a chance for the chemisorption of the experimented antifungal agents. The complexes of hydroxylated and pristine nanotubes displayed thermodynamic stability, while the fullerenes complexes were found to be thermodynamically unstable. Their kinetic stability was noteworthy, which allowed for the formation of such structures. The instability of energy could improve the liberation of the loaded molecule, which is advantageous for the release of the drug [89].
# PRG: A Distance Measurement Algorithm Based on Phase Regeneration
## Principle of PRG
### Range Signal Tracking
#### 2.4.1 Phase Detector  
The carrier tracking loop shown in Figure 7 is a Costa loop and the phase-locked loop discriminator commonly used are as follows [12; 13]:  
1. Two-quadrant arctangent function phase detector: \[\phi_{e}=\arctan 2(\frac{Q}{I})\]
2. Four-quadrant arctangent function phase detector: \[\phi_{e}=\arctan 2(Q,I)\]
3. The third method uses the following: \[\phi_{e}=\frac{Q}{I}\]
4. Classic Costas analog discriminator: \[\phi_{e}=I\cdot Q\]  
Figure 8 compares the input and output relationships of the four phase detectors above without noise.When the actual phase difference is greater than \(90^{\circ}\), all the other three phase detectors output phase discrimination results less than 0 except for the four-quadrant arctangent phase detector. This will cause the carrier phase of the tracking loop to adjust to the opposite direction, ultimately causing the loop to unlock the signal.  
Figure 7: A typical carrier tracking.
## 2 Data Collection and Preparation  
In order to develop reliable models, it was crucial to use a large number of experimental data points representing a variety of conditions. In the current work, 744 experimental data points for carbon dioxide solubility in 13 different ILs (36 to 80 points for each IL) were collected from the literature [40; 41; 42; 43; 44]. Table 1 presents the references from where the data were gathered and the observed range of experimental measurements. The dissolved mole fraction (termed \(x\)) of CO\({}_{2}\) in each IL was the parameter we wished to match and predict based on the corresponding input of temperature \(T\), pressure \(P\), and the given IL. To represent the IL, we specified the thermodynamic properties of the IL, including molecular weight (M\({}_{\rm w}\)), critical temperature (\(T_{\rm c}\)), critical pressure (\(P_{\rm c}\)), and acentric factor (\(w\)), as summarized in Table 2. These 6 input parameters were hence used for predicting the dissolved mole fraction \(x\) of CO\({}_{2}\). All the 744 experimental data points were randomly divided into a training data set (80% of the database) and a test data set (20% of the database).
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Compound** & \multicolumn{2}{c}{**S+TP**} & \multicolumn{2}{c}{**Uncooked P-ST**} & \multicolumn{2}{c}{**Cooked P-ST**} \\ \hline  & **FP** & **BP** & **FP** & **BP** & **FP** & **BP** \\ \hline _Faxonols_ & & & & & & \\ \hline Rutin-O-hesoxide & \(13.2\pm 18.8\) & n.d & \(8.3\pm 0.9\) b & n.d & \(4.2\pm 0.8\) c & n.d \\ Rutin-O-penoxide & \(13.4\pm 0.7\) a & n.d & \(1.1\pm 0.3\) b & n.d & n.d & n.d \\ Rutin & \(56.2\pm 3.6\) a & \(4.3\pm 0.6\) & \(18.7\pm 3.6\) b & n.d & \(7.4\pm 0.6\) c & n.d \\ Kaemperford-S-Orthinsoxide & \(6.4\pm 0.4\) & \(0.2\pm 0.0\) & n.d & n.d & n.d & n.d \\ Quercetin & \(9.3\pm 0.1\) a & \(3.8\pm 0.4\) A & \(5.9\pm 0.8\) b & \(2.2\pm 0.4\) B & \(1.7\pm 0.4\) c & \(1.3\pm 0.2\) C \\ \hline Total (single fraction) & \(537.5\pm 47.7\) a & \(335.6\pm 16.2\) A & \(220.7\pm 20.2\) b & \(177.3\pm 15.1\) B & \(80.3\pm 8.4\) c & \(161.9\pm 13.1\) B \\ \hline Total FP + BP & \(873.1\pm 64.0\) a & \(398.0\pm 35.3\) b & \(242.2\pm 21.5\) b & \(242.2\pm 21.5\) b \\ \hline Abbreviations: FP, free panel: BP, bound spherical, n.d. - not detected. Data are expressed as a means - standard deviation (\(\sigma\) + 0.4). Different lowercase letters in the same row mean significant differences (\(\sigma\) + 0.05) among free phenolic compounds; different capital letters in the same row mean significant differences (\(\sigma\) + 0.05) among bound periodic compounds. & & & \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline
**Compound** & \multicolumn{2}{c}{**S + SP**} & \multicolumn{2}{c}{**Uncooked P-SS**} & \multicolumn{2}{c}{**Cooked P-SS**} \\ \hline  & **FP** & **BP** & **FP** & **BP** & **FP** & **BP** \\ \hline _Phenodic acids_ & & & & & \\ \hline Vanillic acid & \(3.3\pm 0.5\) b & \(7.2\pm 0.9\) A & \(0.7\pm 0.0\) a & \(5.6\pm 0.1\) B & n.d & n.d \\ _p_-Hydroygenic acid & \(9.2\pm 0.5\) & \(4.5\pm 0.4\) A & n.d & \(1.4\pm 0.1\) B & n.d & n.d \\
4-Ferrelijquinic acid & \(1.3\pm 0.2\) & n.d & n.d & n.d & n.d & n.d \\ Vanillin & \(0.8\pm 0.0\) c & \(8.6\pm 0.9\) A & \(0.6\pm 0.0\) a & \(1.6\pm 0.2\) B & \(0.1\pm 0.0\) b & n.d \\ \(p\)-counter acid & \(0.2\pm 0.0\) & \(79.9\pm 5.4\) A & n.d & \(17.6\pm 10.8\) b & n.d & \(6.6\pm 0.9\) C \\ Ferulic acid & \(2.6\pm 0.1\) a & \(173.4\pm 16.8\) A & \(1.6\pm 0.1\) b & \(82.6\pm 5.6\) B & \(0.5\pm 0.1\) c & \(86.5\pm 7.6\) B \\ Defurici acid & \(1.4\pm 0.2\) c & \(39.5\pm 2.9\) A & \(0.8\pm 0.1\) b & \(31.3\pm 1.3\) B & \(0.4\pm 0.4\) a & \(31.7\pm 2.8\) B \\ \hline _Faxonos-C-glycosic_ & & & & & \\ \hline Apigenin-6-Carabinoside-8-C-heoside isomer 1 & \(0.7\pm 0.1\) a & n.d & \(0.4\pm 0.0\) b & n.d & \(0.8\pm 0.0\) a & n.d \\ Apigenin-6-Carabinoside-8-C-heoside isomer 2 & \(0.7\pm 0.0\) & n.d & n.d & n.d & n.d \\ \hline \end{tabular}
\end{table} 
Table 5: Free and bound phenolic compounds determined in the raw material semolinaspinch powder (97.3) (S + SP) and uncooled and cooked pans made with 97% semolina and 3% spinch powder (P-SS), expressed in mg/kg dry weight (dw).
## 2 Data and Methodology of Inversion
### Data Testing
In the horizontal direction, the anomalies were defined with a size of 40 km, and in the vertical direction, the anomaly signs changed at a 20 km depth. The section thickness was 30 km. Model reconstruction was good, especially in regions with sources and ray intersections. Figure 6: Checkerboard test to assess the spatial resolution. The anomalies are defined in cubes of 40 \(\times\) 40 \(\times\) 20 km. The recovered results for the dVp, dVs, and \(\mathrm{Vp}/\mathrm{Vs}\) ratio are shown in three horizontal sections. The shapes of the synthetic anomalies are highlighted with dotted lines (black line is the border line and the blue lines are rivers). The recovered event locations from the synthetic test are presented in Figure 8. These were compared with their input values, and the average relocation errors after the final iteration were found to be 1.18 km, which is within one grid cell dimension. This confirms that the inversion process does not significantly distort the positions of the events. Figure 8: Event locations presented in map view (**left**) and cross sections (**right**) through the checkerboard test presented in Figure 6 after one (**top**) and five (**bottom**) iteration(s). Red dots are the resulting locations; bars are the mean errors with respect to the initial locations. The configuration of the vertical cross sections is indicated in the left map. Figure 7: Vertical checkerboard test. We performed an additional set of resolution tests using realistic synthetic anomalies in Figure 9. In the P-wave model, two anomalies were introduced with amplitudes of \(-3\%\) and \(-6\%\). For the S-wave model, the anomalies shared the same shapes as those in the P-wave model, but had different amplitudes of \(-6\%\) and \(-8\%\). The inversion results showed that the damped values of the anomalies were retrieved within the initial anomaly limits P.