esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Artificial Intelligence in Fire Safety of Agricultural Structures
## 2 Fire Safety in Agricultural Structures/Farms
### Standards, Material-Specific Limits, and Scenario Constraints to Accurate Simulations
Recent studies have confirmed significant variations in the composition of old and new concrete structures in terms of volcanic ash, lime, silicates, aluminum tobermorite, calcium, fly ash content [60], and fiber reinforcements [1]. The performance of AI networks for fire safety in old and new concrete structures is a subject that warrants further research attention. The preliminary observations made by [PERSON] and [PERSON] [1] using ANN, GA, and ANFIS systems suggested that AI had the potential to accurately determine the risk of fire spalling in reinforced concrete structures. However, the precision of the AI systems in concrete structures could be impacted by the variations in the chemical composition of fiber-reinforced high-performance and high-strength concrete, self-healing concrete [61], shape memory polymer concrete [62], and concrete with piezoelectric sensors [63]. The composition-specific effects are premised on the algorithm's training and testing phases. The input parameters in these phases include concrete strength, RC column width, applied to load, and magnitude of eccentricity. Any changes in the material properties could impact the precision and error rates of AI and IoT systems. Beyond material-specific limitations, the decision criteria for AI networks introduce additional practical constraints, an observation that is supported by [PERSON] and [PERSON]'s research on intelligent systems' behaviors and decision making. On most occasions, the merits are evaluated based on technical and environmental benefits rather than environmental and social aspects [39]. This has been especially the case for AI-based Long-Range Energy Alternatives Planning (LEAP) and HOMER, an application developed by the US National Renewable Energy Laboratory. The scenario-based autonomous decision-making approach introduces practical constraints, given there might not be a strong case for fire prevention due to the intermittent nature of fire hazards.
## 3 Results and Discussion
### Variation of Dataset in ANN  
Unlike PCA, an ANN cannot visually confirm trends in the data contained in a dataset; however, it can obtain predictive results with high accuracy. The confusion matrices present the number of correct/wrong answers for each target gas type in the visualization, as shown in Figures S3-S6. In the confusion matrices, the results are organized into the number of correct answers from the single gas, the highest concentration of the double gas, and the second highest concentration of the double gas. Table 4 lists the correct answer rates for the 14 effective components in the ANN. A correct answer rate of over 90% was obtained under all conditions, but the correct answer rate was higher for sensor array L than for sensor array C. However, even for sensor array C, the plot areas overlapped in the PCA (Figure 5), but all single gases were correct in the ANN. Therefore, an ANN is an excellent analysis method for discriminating odors in gas sensors. The correct answer rates for double gases were lower than those for single gases in all datasets. Furthermore, the correct answer rate for the second concentration component was lower than the first concentration component. The presence of coexisting components and an increase in the concentration of them could have a greater influence on the correct answer rate.  
Figure 5: PCA scores and eigenvectors: sensor array and dataset are (**a**) C and 1 min data, (**b**) L and 1 min data, (**c**) C and 12 min data, and (**d**) L and 12 min data, respectively. Numbers on plots from Lv. 4 indicate gas numbers.  
Figures 6 and 7 show the relationship between the true and predicted concentrations for numbers 12 and 6, respectively. The results for all the target gases are shown in Figures S7-S10. The difference between Figure 6a,c or Figure 6b,d is the interval used to obtain the sensor response values for the datasets. In both cases, the 1 min dataset, which has a narrower data range and less drift influence, had smaller plot variations, and the values listed in the panels were also better. The difference between Figure 6a,b or Figure 6c,d is the sensor array. In both cases, sensor array L had smaller plot variations, and the values listed in the panel were also better. Furthermore, in both panels, the variation in the predicted concentration plots for a single gas (black) was lower than that for double gases (blue, green, and red). These results show the same tendencies as the correct answer rates shown in Table 4. Figure 7 shows number 6, which had a larger concentration range than number 12 in Figure 6. As shown in Figure 6, the differences
## 2 Status of Smart Grids
### Standardization and Regulations
#### 2.2.4 South America  
The majority of South American countries are struggling with a wide range of power supply problems, including electricity theft, low reliability, and operational inefficiencies, due to the lack of systematic grid systems. To address these challenges, regional utilities in South America are looking to smart grid infrastructure and massive deployments are in progress. Including Brazil, Colombia, Ecuador, Chile, and Argentina, they will invest $38.1 billion in modernizing their grid infrastructure by 2025 with the guidance of Northeast Group [27]. Brazil has planned to promote South America's first nationwide smart meter deployment and deployed three million smart meters in 2014. However, in consideration of the financial burden of low income employees and distribution operators, Brazil will gradually promote the deployment and invest $25.6 billion during the upcoming decade from 2017.
## 3 Results
### Comparison of Streaking and RABBITT Delays with Classical Photoemission Times
Consequently, this imprints temporal modulation onto the wave packet that affects the delay extracted from the time-resolved photoelectron spectrogram. In the case of streaking, there is a maximum difference between streaking delay \(\tau_{\rm streaking}\) and classical photoemission time \(t_{\rm PE}\) of \(-11\) as at 7.5 eV. The same behavior can also be observed for RABBITT delays. Unfortunately, we have no knowledge about the exact potential shape of a solid at the interface. Still, we can conclude that quantum effects due to scattering at potential inhomogeneities have more impact to lower kinetic energies. Hence, we conclude that the validity of classical photoemission time deteriorates as final state energy approaches vacuum energy. The difference between delays determined from streaking spectra and RABBITT spectra is shown in Figure 5c. For kinetic energies <10 eV, there are significant differences between these two cases. We attribute this difference to the modulated energy distributions mentioned above. The impact of this effect on different electron distributions generated in RABBITT and streaking experiments results in different extracted temporal delays. Additional differences can arise due to ambiguities in streaking spectrogram analysis for low kinetic energies where asymmetries of the resulting momentum distribution are ignored. Note that IR field-driven recollision dynamics of the photoelectron with the surface were negligible at the investigated field strength for this energy range. For kinetic energies higher than 10 eV within the accuracy available here, there was no significant difference between RABBITT and streaking spectra for solid-state systems at this level of description. It is necessary to improve the method to evaluate simulated RABBITT spectra to clarify if these small differences (<2 as) have a physical origin or if it is an analysis issue.
# Directed Network Disassembly Method Based on Non-Backtracking Matrix
## 3 Preliminaries
### Non-Backtracking Matrix  
In a directed network \(\mathbf{G}\), \(i\), \(j\), \(k\) and \(l\) are all nodes in \(\mathbf{V}\), according to the definition of non-backtracking random walk, but only when \(j=k\) and \(i\
eq l\), directed edge \(i\to j\) is connected to another directed edge \(k\to l\). In a directed network, \(\mathbf{B}\) is a \(m*m\) non-backtracking matrix. This non-backtracking matrix is used to represent the adjacency relationship of edges in a directed network, defined as  
\[\mathbf{B}_{i\to j,k\to i}=\begin{cases}1,&\text{if }j=k\text{ and }i\
eq l\\ 0,&\text{other cases}\end{cases} \tag{1}\]  
The non-backtracking matrix \(\mathbf{B}\) is different from the adjacency matrix \(\mathbf{A}\), where \(\mathbf{B}\) takes each directed edge as an element, and represents the adjacency relationship between the edges in the matrix; therefore, it is also called the edge adjacency matrix. The excellent properties of the non-backtracking operator have been shown above [22], and the spectral characteristics of the non-backtracking matrix have better performance in the network than the node adjacency matrix \(\mathbf{A}\) or other matrices, especially in terms of the strong separation of its second eigenvector for the network structure division. At the same time, directed networks in the real world tend to have relatively sparse structures and large scales. The non-backtracking matrix \(\mathbf{B}\) also performs well in sparse networks compared to the node adjacency matrix \(\mathbf{A}\). The adjacency matrix of the edge, \(\mathbf{B}\), stores the relationship between the edges in the network, and is not sensitive to the information of the nodes in the network so as to avoid the tendency to remove the nodes with a relatively large degree during dismantling and cause damage to the connected subset of the network [23], thus retaining the structural information in the directed network to the greatest extent. It is also proved by experiments that applying the non-backtracking matrix to disassemble the one-way connection relationship of the edges in the directed network can reduce the disturbance of the node's topology information to the selected disassembly node set, and effectively avoid the problem of network information loss when directly using the directed network adjacency matrix as the spectral algorithm operator.
# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 3 Methods
### Classification and Accuracy Assessment
#### 3.4.3 Accuracy Assessment  
Accuracy assessment was implemented on the basis of systematic sampling through a regular square grid of points with dimensions of \(20\times 20\) m; the sampling procedure was applicable to the whole study area. After that, we selected all polygons containing the sampling points, and for each of them we compared the ground truth with the predicted vegetation class. Thus, confusion matrices for RF and SVM algorithms were generated. Finally, producer's and user's accuracy and the kappa coefficient (Khat) were calculated.
# Pose Measurement Method Based on Machine Vision and Novel Directional Target
###### Abstract  
Aiming at the shortcomings of the existing machine vision pose measurement technology, a pose measurement method based on monocular vision and a cooperative target is proposed. A planar target designed with circles and rings as the main body is dedicated to object pose measurement, and a feature point coordinate extraction and sorting algorithm is designed for this target to effectively extract image features on the target. The RANSAC algorithm and topology-based fitting for the intersection method are used to optimise data processing, further improving the accuracy of feature point coordinate extraction and ultimately achieving high-precision measurement of object poses. The experimental results show that the measurement accuracy of the roll angle perpendicular to the optical axis can reach 0.02\({}^{\circ}\), and the repeatability can reach 0.0004\({}^{\circ}\) after removing the systematic error; the measurement accuracy of the pitch angle can reach 0.03\({}^{\circ}\), and the repeatability can go to 0.002\({}^{\circ}\) after removing the systematic error. The measurement range of the pitch angle is [\(-\)30\({}^{\circ}\), \(+\)30\({}^{\circ}\)]; the measurement range of the roll angle is [\(-\)179\({}^{\circ}\), \(+\)179\({}^{\circ}\)]. The experimental results show that the system has high measurement accuracy and meets the requirements of high-precision measurement.  
machine vision; cooperative target; calibration error; camera calibration; pose measurement Article
## 3 Results and Discussion
### Power-Loss Soft Sensor
All developed soft sensors were tested for MSE and R-squared metrics of the dataset partitions; equations relating the derived and predicted outputs; error histograms of the training, validation and testing datasets; training state indicators such as gradient, mu and validation fails; validation performance curves over successive epochs and predicted versus derived plots over one lap time. Moreover, the performance of the proposed soft sensors was compared to predictions obtained by linear or nonlinear regression and the parametric models used in the system identification of complex systems. Below are the performance highlights of the proposed soft sensors. Testing dataset accuracy of the proposed FSEV SOC, SOE, PL soft sensors was 99.96%, 99.96%, and 99.99%, respectively. The MSEs of testing dataset partitions for the SOC, SOE, PL soft sensors were 8.34 \(\times\) 10\({}^{-8}\), 7.4774 and 5.55 \(\times\) 10\({}^{-3}\), respectively. The R-squared prediction metrics of the proposed ANN-based soft sensors were superior to the R-squared values of the linear or nonlinear regression models and FIT% of the system identification-based parametric models. The mean squared errors of the parametric model structures were lower than those of the ANN and linear or nonlinear regression models (except for the OE 221 PL parametric model). The best validation performance (MSE) for the SOC, SOE, PL soft sensors was 8.3017 \(\times\) 10\({}^{-8}\) at epoch 25, 7.4901 \(\times\) 10\({}^{-8}\) at epoch 32, and 5.7628 \(\times\) 10\({}^{-3}\) at epoch 205, respectively. The SOC and SOE dropped from 97% to 93.5% and 97% to 93.8%, respectively, during the FSEV running time of 118 s (one lap time). Thus, two-layer feed-forward neural-network-based soft sensors can be effectively utilised to monitor and predict the SOC, SOE, and PL profile of an FSEV. The future scope of this work includes the modelling and estimation of other EV parameters such as regenerative power. Provided the EV cell data and reviewed the manuscript, S.P., R.F., C.S., M.F. and M.-K.T. ; planned the study and wrote the manuscript, R.S. ; worked on ANN based soft sensor design, P.S. ; worked on Simulink modelling and battery pack design, K.P., S.S., V.N. and V.J. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. No data available for this work. The authors declare no conflict of interest.
# Validation of Sentinel-2, MODIS, CGLS, SAF, GLASS and C3S Leaf Area Index Products in Maize Crops
## 2 Study Area and Data
### Leaf Area Index Satellite Products
#### 2.2.4 GEOV3 LAI  
The CGLS 300 m, here called GEOV3, LAI product is derived from PROBA-V observations with a 10-day temporal resolution and 300 m spatial resolution [10; 42]. GEOV3 algorithm is a simplification and an adaptation of GEOV2 product near real time algorithm [50]. Similar to GEOV2, GEOV3 is based on the use of NNs trained with the fusion of the MODIS C5 and CYCLOPES V3.1 products. NNs are used to provide daily LAI estimates from daily synthesis of PROBA-V reflectances in the blue, red and NIR bands and the view-sun geometry. As for GEOV2, the daily estimates of LAI issued from the NNs are also smoothed and temporally composited at 10-day step to generate the final GEOV3 products. The main difference between the GEOV2 and GEOV3 algorithms is that no climatology is used in GEOV3 methodology to fill missing data [51].
# A Two-Time-Scale Turbulence Model and Its Application in Free Shear Flows
## 4 Model Validation
### Turbulence Viscosity Relation  
Since there are now two distinct time scales, there are two ways to quantify eddy viscosity: \(\mu_{t}=C_{\mu}\rho k^{2}/\varepsilon\) or \(\mu_{t}=C_{\mu}\rho z_{0}^{-1}k\tau\), or more generally:  
\[\mu_{t}=C_{\mu}\rho\frac{k^{2}}{\varepsilon}\left[f+\frac{z}{z_{0}}(1-f)\right] \tag{29}\]  
where the weighting factor, f, can be chosen to give optimum agreement. Alternatively, partial replacement of time scale \(k/\varepsilon\) with \(\tau/z_{1}\) yields another expression for turbulent viscosity:  
\[\mu_{t}=C_{\mu}\rho z_{0}^{-1/2}k^{3/2}\left(\frac{\tau}{\varepsilon}\right)^{ 1/2} \tag{30}\]  
which can also be interpreted as the geometric average of two length scales: \(k^{3/2}/\varepsilon\) and \(k^{1/2}\tau\).  
In Figure 9a, the normalized turbulence viscosities and, in Figure 9b, two of its components arising from the length scales \"\(k^{3/2}/\varepsilon\)\" and \"\(k^{1/2}\tau\)\" (symbols in the figure), according to Equation (29), are presented for f = 0.9, f = 0.6, and f = 0.3. Inclusion of the time scale \(\tau\) into the definition of turbulence viscosity as described in Equation (29) introduces a more abrupt variation in dependent variables towards the edge of the jet, as seen in Figure 9a for normalized \(v_{t}\), and a slight increase in the spreading rate from 0.111 for f = 0.9 to 0.114 for f = 0.3, getting less stable for the latter. In Figure 9a, the turbulence viscosity distribution calculated from Equation (30) is also presented. However, this form also produces an abrupt change similar to f = 0.5 towards the edge and similar to f = 0.9 in the core region of the jet; nevertheless, it yields a spreading rate of 0.11 for plane jets.
## 2 Materials and Methods  
### Plant Material, Chemicals and Reagents  
Two bottle gourd (_L. siceraria_) accessions, USVL5 and USVL10, were grown in a greenhouse under standard conditions. USVL5 was derived from PI 381834, while USVL10A,USVL10B and USVL10 CYN were derived from a single line USVL10 (originally derived from PI 181948). USVL5 has elongated fruits with virus resistance, while USVL10 CYN (elongated shape), USVL10A and USVL10B accessions with segregating fruit shapes (bottle and cylinder shapes) (Figure 1) are susceptible to virus infection. The fruits were harvested during the green stage and processed for VOC determination using GC-MS. Volatile standard compounds (Table 1) and n-alkane standards for retention indices (RIs) were purchased from Sigma Aldrich (St. Louis, MO, USA). High-performance liquid-chromatography-grade dichloromethane (DCM), water and sodium chloride were purchased from Sigma Aldrich (St. Louis, MO, USA) at the Horticultural Research Laboratory, Fort Pierce, FL, USA. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**RT**} & \multirow{2}{*}{**RI**} & \multicolumn{4}{c}{**Abundance (Total Ion Current (\(\times 10^{6}\)))**} \\ \cline{3-6}  & & & **USVL10 CYN** & **USVL5** & **USVL10A** & **USVL10B** \\ \hline \multicolumn{6}{c}{**Aliphatic alcohols**} \\ \hline Ethanol & 9.304 & 480 & 3.85 & 0.88 & 1.92 & 4.98 \\
2-Methyl-2-propanol & 11.056 & 532 & 7.50 & 0.89 & 4.03 & 2.61 \\
2-Methyl-1-propanol & 14.389 & 623 & 0.80 & 0.00 & 0.00 & 0.00 \\
1-Pentren-3-ol & 16.741 & 683 & 2.18 & 5.71 & 4.50 & 3.26 \\
3-Methyl-3-butten-1-ol & 18.989 & 737 & 0.87 & 0.91 & 3.86 & 3.91 \\
2-Methyl-1-butanol & 19.196 & 742 & 0.00 & 0.61 & 0.75 & 0.76 \\ (Z)-2-Pentent-1-ol & 20.410 & 771 & 1.22 & 1.30 & 1.65 & 1.27 \\
3-Methyl-2-butten-1-ol & 20.799 & 780 & 0.00 & 0.00 & 0.53 & 0.82 \\ (E)-2-Hexen-1-ol & 25.006 & 874 & 0.00 & 0.00 & 0.84 & 0.74 \\
1-Hexanol & 25.120 & 877 & 2.15 & 3.38 & 6.47 & 5.81 \\
2-Methylbutan-2-ol & 15.071 & 641 & 3.63 & 0.75 & 3.00 & 2.35 \\
2-Ethyl-1-hexanol & 32.299 & 1038 & 0.57 & 0.52 & 1.00 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The percentage composition of volatile organic compounds in tested bottle gourd accessions, extracted via solid-phase microextraction (SPME) and abundance determined via GC-MS.  
Figure 1: Variation in fruit shapes among the tested bottle gourd accessions. (**A**) The bottle gourd accessions shown here vary in shape, with two accessions, USVL5 and USVL10 CYN, having a cylindrical shape and USVL10A and USVL10B showing bottle shapes. (**B**) Transverse section of bottle gourd fruit for USVL5, USVL10 CYN and USVL10.
## 3 The Obtained Data and Their Discussion
### Analysis of X-ray Diffraction Results  
The phases of the manufactured samples were defined using the X-ray diffractometer 4 lab model (XRD-6000) of SHIMADZU Europe with CuK\(\alpha\) radiation and wavelength (1.54056 A\({}^{\circ}\)) for each sample. Figure 3a shows the X-ray diffraction peaks for (Al/B4C) nanocomposites with different wt.% of B4C (2.5 wt.%, 5 wt.%, 7.5 wt.%, 10 wt.% and 12.5 wt.%). Figure 3b shows the X-ray diffraction peaks for (Al/TiC) nanocomposites with different wt.% of TiC (2.5 wt.%, 5 wt.%, 7.5 wt.%, 10 wt.% and 12.5 wt.%).  
As shown in Figure 3a XRD peaks for the samples (Al/B4C) nanocomposites containing (2.5 wt.%, 5 wt.%, 7.5 wt.%, 10 wt.% and 12.5 wt.%) B4C, the peaks occurring at 2\(\theta\) ranging about 20.2031\({}^{\circ}\), 22.5658\({}^{\circ}\), 24.209\({}^{\circ}\), 35.6016\({}^{\circ}\), 37.9378\({}^{\circ}\), 54.6941\({}^{\circ}\), 64.2011\({}^{\circ}\) and 66.1823\({}^{\circ}\) with an hkl of about (101), (003), (012), (104), (021), (205), (125) and (220). Also, there are four peaks for Al occurred at 2\(\theta\) ranging about 38.3011\({}^{\circ}\), 45.2120\({}^{\circ}\), 65.1901\({}^{\circ}\) and 79.0402\({}^{\circ}\) with an hkl of about (111), (200), (220) and (311), respectively. These peaks have emphasized that B4C nanoparticles were distributed homogeneously in the Al matrix.  
Figure 1: Schematic diagram of wear testing machine [26].  
While Figure 3b shows XRD peaks for the samples (Al/TiC) nanocomposites containing (2.5 wt.%, 5 wt.%, 7.5 wt.%, 10 wt.% and 12.5 wt.%) TiC, the peaks occurred at 2\(\theta\) ranging about 36.32\({}^{\circ}\), 41.83\({}^{\circ}\), 61.07\({}^{\circ}\) and 75.81\({}^{\circ}\) with an hkl of about (111), (200), (220) and (311). In addition, there are four peaks for Al appearing at 2\(\theta\) ranging about 38.3011\({}^{\circ}\), 45.2120\({}^{\circ}\), 65.1901\({}^{\circ}\) and 79. 0402\({}^{\circ}\) with an hkl of about (111), (200), (220) and (311), respectively. These peaks indicate that TiC nanoparticles were distributed homogeneously in the Al matrix.  
Each of the B4C and TiC phases is strongly with Al particles due to the good wettability between them, improving their physical and mechanical properties.  
Figure 2: FESEM images of Al/B4C and Al/TiC nanocomposites for different wt.%.
## 3 Experiments and Results  
### Dataset  
The WSJ0-2 mix dataset is commonly used in the research of speech separation [9; 10] The speech of different speakers is randomly selected in the dataset (WSJ0). The Signal-to-Noise Ratio (SNR) is randomly mixed between 0-5 dB to generate multi-speaker mixed speech data. The dataset includes a 30 h 22 min training set, a 7 h 40 min verification set and a 4 h 49 min test set.
## 3 Methodological Developments and Application to the Greater Geneva Area
### Meteorology at Large Scale  
The large extent of the region covered by the solar cadaster of the Greater Geneva area accounts for significant variation of solar irradiance and temperature throughout the region. Conditions range from typical Swiss lowland conditions (372 m to sea level) to mountain climate (1500 m to sea level) with significant differences in cloud coverage and temperature. Yet, weather observations do not cover the whole Greater Geneva area (the number of meteorological stations is limited). Therefore, comparing several interpolation models and approaches, it results that Meteonorm(r) offers reliable estimated weather data in any point of the territory based on nearby weather stations and satellite imagery of cloud coverage. On this basis, specific weather input data are calculated for each tile. More details on the comparative study are presented in Appendix A.