esa-sceva/satcom-chunk-collection huggingface dataset:
# A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis +
## 4 Experiments
### Experiment B: Online Dataset  
In this experiment, the objective is to assess the algorithm with data collected in a real scenario. The experiment is conducted using the GasVid dataset presented in [35]. All data were taken at the Methane Emissions Technology Evaluation Center (METEC) at Colorado State University. The dataset includes ten thermal footage of Methane gas emission recorded at a distance of 4.6-15.6 m by a FLIR GF320 thermal camera operating in the normal mode. The details of the dataset are described in Table 2 and the sample photos are shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Session 1** & **Session 2** \\ \hline Camera & FLIR GF620 cooled thermal camera \\ \hline Gas & Alcohol & Gasoline \\ \hline Container & Bottle & Metalic canister \\ \hline Duration & 384 frames & 624 frames \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of the Experiment A.  
Figure 3: Sample photos of the collected thermal footage of the containers.
## 3 Results and Discussion
### EPR Investigation
I\({}_{\mathrm{Fe(tot)}}\) can be obtained from the room temperature spectrum of the same specimen after the quantitative oxidation of the Fe\({}^{2+}\) content to Fe\({}^{3+}\). This may be achieved keeping the finely pulverized slag at 300 \({}^{\circ}\)C in the presence of air atmospheric for 6 h proved to assure the quantitative oxidation of Fe\({}^{2+}\) to Fe\({}^{3+}\). The ferrous iron quantity will be the difference of the integrated intensities (I\({}_{\mathrm{Fe(tot)}}\)-I\({}_{\mathrm{Fe(III)}}\)). The experimental EPR spectra recorded on selected samples are presented in Figure 4.  
Table 8 contains the calculated Fe\({}^{3+}\)/Fe\({}^{2+}\) ratio values from experimental results. As shown by the experimental spectra presented in Figure 4 and the data from Table 8, the EPR method can be used to characterize the redox conditions of slag formation even if, due to the pronounced  
Figure 4: EPR spectra of selected samples: 9487 (B1), 9445 (B2), 9454 (B3) from Bráncovenesti; 4137 (C1), 4264 (C3) from Calugáreni; and Vat (V1) from Vátava. Black spectra were recorded on the untreated samples; red spectra after thermal treatment applied. \begin{table}
\begin{tabular}{c c c c c} \hline
**Sample** & **10\({}^{8}\)**-I\({}_{\text{Fe(IID)}}\)** & **10\({}^{8}\)**-I\({}_{\text{Fe(tot)}}\)** & **10\({}^{8}\)**-I\({}_{\text{Fe(II)}}\)** & **Fe\({}^{3+}\)/Fe\({}^{2+}\)** \\ \hline
9487 & 3.30 & 4.13 & 0.83 & 3.976 \\ \hline
9445 & 0.23 & 1.05 & 0.82 & 0.280 \\ \hline
9454 & 0.13 & 4.92 & 4.79 & 0.027 \\ \hline
4137 & 0.61 & 5.60 & 4.98 & 0.122 \\ \hline
4264 & 0.75 & 1.79 & 1.04 & 0.721 \\ \hline Vat & 2.52 & 3.88 & 1.36 & 1.853 \\ \hline \end{tabular}
\end{table} 
Table 8: Experimental Fe\({}^{3+}\)/Fe\({}^{2+}\) ratio of the investigated samples. heterogeneity of the samples, the (obviously only local) data obtained in this way can't be extended to the whole of the sample. The Fe\({}^{3+}\)/Fe\({}^{2+}\) ratio values of Table 8 are calculated from EPR measurements realized on probes of amounts of only few milligrams. The \"unreliability of global conclusions\" results also when the data from Table 8 are compared to those from Table 6.  
Still, the differences of the Fe\({}^{3+}\)/Fe\({}^{2+}\) resulting ratios are able to suggest successfully the differing redox ambience during the slag formation caused by the supposedly different working conditions. All finds can be considered smithing slags; however, the iron-workers operant at the three different archaeological sites should use different technologies. The considerable amount of quartz present in the Brancovenesti and Calugareni samples suggests second type smithing slags, the quartz deriving from silica sand rich soils added to the smithing hearth as flux, while the findings from Vatava seem to be third type smithing slags [34]. The iron oxides could be indicators of the redox conditions at the time of the slag solidification. The appreciable amount of goethite, a typical weathering mineral, indicates mildly reducing conditions.
## 2 Basic Theory
### Permutation Entropy  
The PE quantifies uncertainty by examining the arrangement patterns of data and analyzing the complexity inherent in time series data [39]. This approach aligns with the research objectives outlined in this paper. Consequently, PE has been selected as the characteristic value following the processing of vibration signals from rolling bearings.  
First, the phase space reconstruction of the time series \(\left\{X\left(i\right),i=1,2,\cdots,n\right\}\) of length \(N\) is carried out to obtain the following matrix:  
\[\begin{bmatrix}x\left(1\right)&x\left(1+\tau\right)&\cdots&x\left(1+\left(m-1 \right)\tau\right)\\ \vdots&\vdots&\vdots&\vdots\\ x\left(j\right)&x\left(j+\tau\right)&\cdots&x\left(j+\left(m-1\right)\tau \right)\\ \vdots&\vdots&\vdots&\vdots\\ x\left(K\right)&\cdots&x\left(K+\tau\right)&\cdots x\left(K+\left(m-1 \right)\tau\right)\\ &&j=1,2,\cdots,K\end{bmatrix}\]  
where \(m\) is embedding dimension; \(\tau\) is delay time.  
Then, the reconstructed components of each row are arranged in ascending order to obtain the symbol sequence of the position index:  
\[S(l)=\{j_{1},j_{2},\cdots,j_{m}\},l=1,2,\cdots,k,\text{ and }k\leq m! \tag{18}\]  
Finally, the probability \(P_{g}\) of the symbol sequence is calculated, and the PE of time series \(X\) is:  
\[H_{pe}=-\sum_{g=1}^{m!}P_{g}\text{log}\big{(}P_{g}\big{)} \tag{19}\]  
The PE value serves as an indicator of the randomness and complexity inherent in a time series, where lower values signify greater regularity, and higher values denote increased randomness. This methodology allows for the extraction of intricate features from vibration signals, which are essential inputs for the fault diagnosis model.
## 5 Scenario Description  
The site where the application is installed is the _ST Microelectronics of Catania (Italy)_ located in an area of roughly \(250,000\)\(m^{2}\).  
In industrial areas, radio frequency devices can be affected by several external and environmental factors (like electromagnetic pollution, vibration, heat, and so on) that may impair the operating conditions. These factors can reduce reliability and consistency of information exchange. For these reasons, before installing the sensors, we analysed the site of interest with an electromagnetic detector field focusing the attention on the range of frequencies used for the communication. The purpose of the analysis was the identification of possible interferences due to the presence of electromagnetic fields. The result we got was negative; _i.e._, the area was free from interference in the range of frequencies used by the sensor nodes.  
This allows the installation of the WSN by reducing the numbers of nodes required to cover the area. Also, this allows us to pre-determine and plan the ideal allocation of sensors, thus ensuring a more efficient network operation.  
Moreover, in the installation site there are several buildings with different heights and size, thus increasing the complexity of the problem. In the site there are \(12\) electric substations to be monitored. During the design phase, we decided to for the first step limit the number of the electric substations to monitor to 6. We also faced the problem of having one substation located underground. All the acquired data from each substation have to be forwarded to the control room.  
Figure 2 shows some details of the monitored area, the location of the sensor nodes (installed into the electrical substations) and the control room.
# Ethical Issues in AI-Enabled Disease Surveillance: Perspectives from Global Health
## 4 Ethical Considerations
### Non-Maleficence: Privacy, Security and \"Capability Caution\"
#### 4.2.4 Algorithm Benchmarking  
Each syndromic surveillance system also implements a unique set of outbreak detection algorithms. This requires a better understanding of the strengths and limitations of various detection techniques and their applicability, which may require capability caution in the process [71]. For instance, algorithms can perform differently in the field than in the lab, and this may create a major challenge in ensuring high performance, accurate benchmarking against any current standard care, and continuously assessing performance after deployment [63; 72]. Given the wide and powerful impacts of the decisions informed by such benchmarking, again, considerations of technical accuracy become ethical issues.
# Optical Hyperspectral Image Cryptosystem Based on Affine Transform and Fractional Fourier Transform
## 3 Numerical Simulations
### Test for Validity of Spectrum Information Encryption  
Finally, the performance of protecting the spectrum information is tested. The spectrum curves of the pixel (100, 100) before and after the encryption process are illustrated in Figure 9. As we can see from Figure 9, it is apparent that the encryption spectrum curves greatly differ from the original spectrum. Note that the original and decrypted spectrum are drawn in the same color and the two spectrum curves coincide to form one curve, which indicates that the secret spectrum information is  
Figure 8: The experiment result: (**a**) test image, (**b**) the encrypted version of (**a**), (**c**) attack result, (**d**) test image, (**e**) the encrypted version of (**d**) and (**f**) attack result.  
retrieved completely. The result shown in Figure 9 demonstrates the performance of the cryptosystem in protecting the spectrum information. Here, the encrypted spectrum curves are separated into real part and imagery part because the encryption data of the proposed algorithm is a complex function. Therefore, the proposed encryption scheme can not only protect the image information in spatial domains, but also the spectrum information in spectral domains.
## 6 Simulations and Discussion
### Simulations
#### 6.1.1 Simulation Description  
Simulation 1: Fault-Tolerant Performance  
In this simulation, we considered a sample WSN as shown in Figure 9. A source node sent unicast frames to the sink node. During the communications, we assumed that a node failure occurred at node 14, which belonged to a path between the source node and the sink node. In the simulation, the source node sent N unicast frames to the sink node (N = 10, 20,..., 100). The number of unicast frames received at the sink node was recorded to evaluate the fault-tolerant performance of the DSP algorithm.  
Figure 8: Network traffic under the DSP-based approach.  
Simulation 2: Network Traffic Performance  
The objective of the simulation was to validate, evaluate, and compare the traffic performance of the DSP-based approach with that of the standard HSR protocol and some state-of-the-art techniques for reducing redundant unicast traffic in HSR networks, including the quick removing (QR) technique [27], the port locking (PL) technique [28], and the dual virtual paths (DVP) [29] technique. We considered a sample HSR as shown in Figure 5. The sample network has eight DANH rings; each DANH ring included four DANH nodes and connected one QuadBox pair. We conducted the simulation for three cases, as shown in Figure 10. In the simulation, source node 1 send unicast frames to destination 6, destination 10, and destination 15 in case 1, case 2, and case 3, respectively.  
Figure 10: The sample HSR network used in Simulation 2.  
Figure 9: The sample wireless sensor network (WSN) used in Simulation 1.
# Alpha Centauri: Disc Dynamics, Planet Stability, Detectability
## 4 Detectability of Planets in Alpha Centauri  
The detection of extrasolar planets has been tremendously successful over the last thirty years, with more than 5000 confirmed exoplanets1 to date. This achievement was possible thanks to the careful study of light and radial velocity (RV) curves with reported anomalous dips or modulations. Other techniques such as direct imaging and gravitational lensing methods have also contributed to increasing the exoplanet demographics, but in lower numbers. Owing to its proximity to Earth, the Alpha Centauri system has been subjected to an intensive and scrupulous search. So far, only one planet has been confirmed around Proxima Centauri [7]--although several candidates have been proposed [9; 10; 11].
# High Power All-Fiber Supercontinuum System Based on Graded-Index Multimode Fibers
## 2 Experiment Setup  
The structure diagram of the all-fiber supercontinuum source is shown in Figure 1. The seed source is a homemade all-normal-dispersion nonlinear amplification loop mirror mode-locked fiber oscillator with a central wavelength of 1030 nm. The output pulse has a repetition rate of 8.27 MHz, a pulse width of 23 ps, and a spectral width of 25 nm. The broad spectrum and narrow pulse width of this mode-locked oscillator facilitate the broadening and flattening of the supercontinuum. The average power of the oscillator is 200 mW. Our previous work has demonstrated that this oscillator can be compressed to less than 200 fs after amplification in a Yb-doped amplification system [16]. The amplifier adopts a three-stage cascade. The length of the gain fibers in the three amplifiers is 0.5 m (PM-HI-6/125-YSF), 1 m (PM-10/130-YDF), and 1.3 m (PM-30/250-YDF), respectively. The fiber length is strictly controlled to avoid nonlinear effects. The parameters of the components are summarized in Table 1.  
Figure 1: Schematic of the supercontinuum system. WDM: wavelength-division multiplexer, LD: laser diode, ISO: isolator, and YSF: Yb-doped single-mode fiber.  
The output pigtail size of the last stage amplifier is 30/250 \(\upmu\)m, which does not match the cladding and core diameter of the GRIN fiber, while the core diameter of 30/250 \(\upmu\)m \(\gamma\)b-doped fiber is less than that of the GRIN fiber. The numerical aperture of the 30/250 \(\upmu\)m \(\gamma\)b-doped fiber is 0.07, while the numerical aperture of the GRIN fiber is 0.195. Therefore, the output laser of the amplifier can enter the GRIN fiber completely by directb splicing the GRIN fiber with the Yb-doped fiber under suitable splicing conditions. The lengths of the GRIN fiber were chosen as 5 m, 10 m, and 80 m. The GRIN fiber is the 50/125 \(\upmu\)m multimode fiber (Corning, ClearCurve OM4). The 105/125 \(\upmu\)m standard multimode fiber was spliced to GRIN fiber as the end cap and cleaved 8\({}^{\circ}\) angle to reduce return light. The splicing point of GRIN fiber can operate stably at room temperature for a long time without water cooling.
## 2 Materials and Methods
### Fluorescence Lifetime Setup  
Detailed descriptions of the prototype used for FL measurements are reported in [28] and [33]. The excitation source is a picosecond pulsed laser diode emitting at 375 nm (BDL-SMN-375, Becker & Hickl GmbH, Berlin, Germany) with a repetition rate of 20 MHz. Two 200 \(\upmu\)m fibers deliver the excitation light and the aiming beam (\(\lambda\) = 660 nm, LED, M660 FP1, Thorlabs) to the sample. The fiber bundle is handheld, and the tip is moved freely over the specimen. The collection of the fluorescence emitted from the sample is selected by an emission filter (FF01-510/84-25, Semrock, Rochester, New York, NY, USA) to the band 510 \(\pm\) 42 nm. A third 200 \(\upmu\)m fiber carries the signal to the detector (HPM-100-40, Becker & Hickl GmbH), which is connected to a TCSPC acquisition card (SPC-730, Becker & Hickl GmbH). For all measurements, the average power on the sample surface was kept below 10 \(\upmu\)W.  
Real-time fluorescence lifetime maps are acquired from single-point measurements at a macroscopic scale and, remarkably, under bright illumination. To allow the visual control of the examined area during measurements, the system is equipped with a white LED that provides periodic illumination of the field of view (FOV). The LED is turned on and off asynchronously with respect to the periodic detection of the fluorescence signal, thus overcoming the limitation related to the sensitivity of TCSPC to background light. The LED intensity was kept between 100 and 200 LUX, as measured at the sample plane, located 30 cm from the source. In addition, the 660 nm LED source was superimposed on the excitation beam to provide a visual reference for measurements. The sample was imaged in white light by a color CCD camera with an image resolution of 640 \(\times\) 480 pixels so that the lifetime value obtained via phasor analysis for each acquisition point could be superimposed on the sample's surface. This enables the spatial resolution of the FL from single-point measurements, along with direct correlation with the spatial information provided by the raw white-light image.
## 3 The Method of Moments
\begin{array}{l}\frac{\partial m_{ijk}}{\partial t}+\frac{\partial u_{ i}m_{ijk}}{\partial x_{l}}+\frac{\partial\phi_{ijikl}}{\partial x_{l}}=-\frac{p}{ \mu}m_{ijk}-3 RT\frac{\partial\sigma_{cij}}{\partial x_{\mathrm{N}}}-\frac{3}{ 7}\frac{\partial R_{cij}}{\partial x_{\mathrm{N}}}+\mathfrak{M}_{ijk},\\ \frac{\partial R_{ij}}{\partial t}+\frac{\partial u_{k}R_{ij}}{\partial x_{k}} +\frac{\partial\psi_{ijk}}{\partial x_{k}}=-\frac{p}{\mu}R_{ij}-\frac{28}{5}RT \frac{\partial q_{cij}}{\partial x_{j\
u}}-2 RT\frac{\partial m_{ijk}}{\partial x _{k}}-\frac{2}{5}\frac{\partial\Omega_{cij}}{\partial x_{j\
u}}+\mathfrak{R }_{ij},\\ \frac{\partial\Delta}{\partial t}+\frac{\partial\Delta u_{i}}{\partial x_{i}} +\frac{\partial\Omega_{i}}{\partial x_{i}}=-\frac{p}{\mu}\Delta-8 RT\frac{ \partial u_{k}}{\partial x_{k}}+\mathbf{N}.\end{array} \right. \tag{12}\]  
\(f_{ijkl},\ \psi_{ijk},\ \Omega_{i}\) present the unknown high order moments, and \(\mathfrak{M}_{ijk},\ \mathfrak{R}_{ij},\ \mathbf{N}\) are the nonlinear source terms, they are all listed in Appendix A. To apply the R13 and R26 equations to non-equilibrium flows, appropriate wall boundary conditions are required. Based on the [PERSON]'s kinetic wall boundary condition, [PERSON] and [PERSON] derived a set of solid wall boundary conditions for these extended thermodynamic macroscopic equations, they are listed in ref [38; 39; 42]. With the moments available in the R13 moment equations, i.e., \(\left\{\rho,u_{i},T,\sigma_{ij},q_{i},m_{ijk},R_{ij},\Delta\right\}\), the VDF can be approximated by the third-order expansion of Hermite polynomials \(f^{(3)}\),  
\[ \begin{array}{l}f^{(3)}\ \ \ \ \ =f_{M}\Big{[}1+\frac{\sigma_{ij}c\varepsilon_{ j}}{2 pRT}+\frac{c\sigma_{i}q_{i}}{pRT}\Big{(}\frac{c^{2}}{5 RT}-1\Big{)}+ \frac{m_{ijk}c\varepsilon_{j}c_{k}}{6\rho(RT)^{2}}\\ +\frac{R_{ij}c\varepsilon_{j}}{4\rho(RT)^{2}}\Big{(}\frac{c^{2}}{7 RT}-1\Big{)} +\frac{\Delta}{8 pRT}\Big{(}\frac{c^{4}}{15(RT)^{2}}-\frac{2c^{2}}{3 RT}+1\Big{)} \Big{]}.\end{array}  \tag{13}\]  
Similarly, the macroscopic quantities \(\left\{\rho,u_{i},T,\sigma_{ij},q_{i},m_{ijk},R_{ij},\Delta,\phi_{ijikl},\psi_ {ijk},\Omega_{i}\right\}\) can be obtained after solving the R26 moment equations, and the VDF can be approximated by the fifth-order expansion of Hermite polynomials \(f^{(5)}\),  
\[ \begin{array}{l}f^{(5)}\ \ \ \ =f_{M}\Big{[}1+\frac{\sigma_{ij}c\varepsilon_{ j}}{2 pRT}+\frac{c\sigma_{ii}}{pRT}\Big{(}\frac{c^{2}}{5 RT}-1\Big{)}+\frac{m_{ijk}c \varepsilon_{j}c_{k}}{6\rho(RT)^{2}}+\frac{f_{ijkl}c\varepsilon_{j}c_{k} \varepsilon_{l}c_{l}}{24\rho(RT)^{2}}\Big{(}\frac{c^{2}}{7 RT}-1\Big{)}\\ +\frac{\Delta}{8 pRT}\Big{(}\frac{c^{4}}{15(RT)^{2}}-\frac{2c^{2}}{3 RT}+1\Big{)} +\frac{\psi_{ijk}c\varepsilon_{j}c_{k}}{12p(RT)^{3}}\Big{(}\frac{c^{2}}{9 RT}-1 \Big{)}+\frac{c\Omega_{i}}{40p(RT)^{2}}\Big{(}\frac{c^{4}}{7(RT)^{2}}-\frac{2 c^{2}}{RT}+5\Big{)}\Big{]}.\end{array}  \tag{14}\]  
With Equations (10), (13) and (14), the accuracy of different macroscopic equations including the NSF equations, R13 and R26 moment equations can be evaluated and compared with the Boltzmann solutions at the microscopic level.
# The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development
## Materials and Methods
### Field Site and In Situ Crop Measurements
#### 2.1.2 In Situ Measurements  
For five different dates within the growing season (Table 1), non-destructive measurements carried out in each experimental trial plot included LAI, LCC and growth stage observations in accordance with the Zadoks decimal code [35]. Five technical replicates of LAI were taken per plot, with ten replicates of chlorophyll content on a regular grid within each plot. The replicates were then combined to give a plot average and standard deviation. The growth stage was assumed to be reached when it was observed in at least 50% of the plots. LAI was measured using a SunScan device (Delta-T Devices, Cambridge), and LCC measurements were inferred from a portable soil plant analyses development (SPAD) meter device (Konica Minolta, Japan). The CCC, expressed per unit of leaf area, was calculated as the product of the LAI and LCC [11; 24; 27]. Across the five observation dates and 50 trial plots, a total of 250 sets of LAI, LCC and CCC were derived from the ground measurements and were used in this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ground and UAV Measurement** & **Growth Stage Description** & **Weather Conditions** \\
**Date (2018)** & **Growth Stage Description** & **Weather Conditions** \\ \hline
08 May & Stem elongation—early (GS31) & Cloudy; low wind speed \\
25 May & Stem elongation—late (GS38) & Cloudy; low wind speed \\
05 June & Ear emergence (GS54) & Clear-sky; low wind speed \\
20 June & Flowering (GS68) & Clear-sky; moderate wind speed \\
04 July & Milk development (GS79) & Clear-sky; moderate wind speed \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dates, corresponding average growth stages (GS; Zadoks scale) and weather conditions during ground and Unmanned Aerial Vehicle (UAV) multispectral measurement made at the winter wheat trial plots.  
Figure 1: Winter wheat trial plot layout including: (**a**) an aerial image (acquired on 15 May 2018) and (**b**) Latin square experimental design with varying levels of nitrogen application. Destructive sample analysis was carried out at five plots highlighted within the dashed line.