esa-sceva/satcom-chunk-collection huggingface dataset:
# A Probable Approach to Reliability Assessment of Reinforced Plates
## 4 The Probability of Failure of the Reinforced Plates  
The distribution function (23) at fixed values \(P\) and \(\eta\) determines the failure probability of a plate reinforced with \(N\) random rigid linear inclusions  
\[P_{f}=F_{N}(P,\eta). \tag{26}\]  
We introduce dimensionless loading \(p=P\sqrt{h}/K_{0}\). Then, the probability of failure according to (19), (21), (23), and (24) is recorded for the following individual cases:  
under symmetric biaxial tension  
\[P_{f}(p,1)=1-\left(1-e^{-(\frac{p(\kappa-1)(3+\kappa)}{4\sqrt{2}\kappa})^{-2} }\right)^{N}; \tag{27}\]  
under uniaxial tension  
\[\begin{array}{l}P_{f}(p,0)=1-\left(1-\frac{2}{\pi}\left\{\int\limits_{0}^{0. 5\text{arccos}(0.5(1-\kappa))}e^{-\frac{(\varphi_{1}(0,\kappa)+\varphi_{2}(0,\kappa)\text{cos}2\kappa)^{-2}}{p^{2}}}d\alpha+\right.\right.\\ \left.+\int\limits_{0.5\text{arccos}(0.5(1-\kappa))}^{\pi/2}e^{-\frac{(\varphi _{3}(\eta,\kappa)+\varphi_{4}(\eta,\kappa)\text{cos}2\kappa)^{-2}}{p^{2}}}d \alpha\right\}\right)^{N};\end{array} \tag{28}\]under biaxial symmetric compression  
\[P_{f}(-p,1)=1-\left(1-e^{-(\frac{\mathbb{P}(\kappa-1)}{6\kappa}\sqrt{\frac{(3 \kappa-1)^{3}}{\Omega(2\kappa+1)}})^{-2}}\right)^{N}. \tag{29}\]  
According to Expressions (25) and (26), in Figures 2 and 3, diagrams of the probability of failure under different types of stress states are shown. Graphs were plotted using the Maple 7 computer mathematics package.  
According to Expression (27), Figure 4 demonstrates diagrams of the failure probability under biaxial symmetric compression at a fixed value of Poisson's ratio for different numbers of inclusions.  
Figure 3: Probability of failure under uniaxial tension (\(N=100\) for blue line, \(N=50\) for red line).  
Figure 2: Probability of failure under biaxial symmetric tension (\(N=100\) for blue line, \(N=50\) for red line).
## 4 Discussion
Such defects can be avoided by appropriately tailoring the nanostructures to have a surface that promotes conformal deposition, i.e., no sharp features or sharp corners. Alternatively, a structural buffer layer can be added on top of the nanostructures to smooth/cover any sharp features [2]. Here, as shown in Figure 4, the a-Si absorber layer thickness is approximately 500 nm, the nanostructure height is approximately 500 nm, and the nanostructure aspect ratio is approximately (500 nm)/(250 nm) = 2; as a result, \(t_{relative}\) is approximately 1, and the figure of merit is \(t_{relative}/AR\approx(1/2)=0.5\). Thus, in the folded junction solar cells considered in this study, \(t_{relative}/AR<1\) for the high aspect ratio nanostructures. Note that this discussion does not consider the extreme case of dye-sensitized solar cells, where the ultrathin absorber layer is comprised of molecules deposited in a layer of molecular thickness and thus can conformally cover the nanostructured electrode surface [10]. Here, we consider the case of enhancing the performance of traditional thin-film solar cells using a nanostructured substrate. In addition, note that it is possible to produce high-efficiency devices on nanostructured substrates with \(t_{relative}/AR<1\); however, such devices require careful design of the nanostructures to enable conformal coverage of the absorber layer (see, e.g., [5]). Alternatively, one can smooth some of the structural defects by depositing a structural buffer layer of appropriate thickness; however, this approach sacrifices some of the enhanced absorption via the reduced aspect ratio nanostructures [2].
# Comparison of Methods to Identify and Monitor Mold Damages in Buildings
###### Abstract  
Molds thrive in indoor environments, challenging the stability of building materials and occupants' health. Diverse sampling and analytical techniques can be applied in the microbiology of buildings, with specific benefits and drawbacks. We evaluated the use of two methods, the microscopy of visible mold growth (hereinafter \"mold\" samples) (tape lifts) and the DNA metabarcoding of mold and dust samples (swabs), for mapping mold-damage indicator fungi in residential buildings in Oslo. Overall, both methods provided consistent results for the mold samples, where nearly 80% of the microscopy-identified taxa were confirmed by DNA analyses. _Aspergillus_ was the most abundant genus colonizing all materials, while some taxa were associated with certain substrates: _Accmonium_ with gypsum board, _Chaetomium_ with chipboard, _Stachyborders_ with gypsum board and wood, and _Trichoderma_ with wood. Based on the DNA data, the community composition was clearly different between the mold and the dust, with a much higher alpha diversity in the dust. Most genera identified in the mold were also detected with a low abundance in the dust from the same apartments. Their spatial distribution indicated some local spread from the mold growth to other areas, but there was no clear correlation between the relative abundances and the distance to the damages. To study mold damages, different microbiological analyses (microscopy, cultivation, DNA, and chemistry) should be combined with a thorough inspection of buildings. The interpretation of such datasets requires the collaboration of skilled mycologists and building consultants.  
indoor microbiological quality; indoor molds; built environment molds; dust sampling; ITS DNA metabarcoding; high-throughput sequencing; building inspections; sick-building syndrome 2022
## 6 Conclusion  
In this work, we studied the convergecast problem for a general network topology with fixed transmission power in a wireless sensor network. We first proposed the scheduling algorithm (FMSTC) to find the minimum time slots for convergecast in a general topology using a line- and tree-based topology and evaluated the performance of our approach by the simulations. The results of the simulation showed that when the power is increased, the time slots decreased. The maximum time slot is 588.93 with 100 nodes when the power is three, and the minimum time slot is 23.20 with 10 nodes when the power is 15. We have compared our simulation results to the optimal time slots in the linear network topology. The highest ratio was 3.25 when the power was fixed to six with 10 nodes, and the lowest ratio was 2.28 when the power was fixed to 15 with 50 nodes. The results showed that the convergecast in the general topology has more than twice the time slots compared to the linear topology. The overall ratio was 2.27, which means that the proposed algorithm (FMSTC) for  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Power**} & \multicolumn{8}{c}{**Node**} \\ \cline{2-11}  & 10 & 20 & 30 & 40 & 50 & 60 & 70 & 80 & 90 & 100 & Average \\ \hline
3 & 3.24 & 2.91 & 2.62 & 2.31 & 2.60 & 2.52 & 2.70 & 2.75 & 2.61 & 2.60 & 2.69 \\
6 & 3.25 & 3.05 & 2.90 & 2.51 & 2.71 & 2.54 & 2.87 & 2.86 & 2.70 & 2.61 & 2.80 \\
9 & 2.80 & 3.12 & 2.81 & 2.54 & 2.54 & 2.58 & 2.96 & 2.97 & 2.81 & 2.68 & 2.78 \\
12 & 2.95 & 2.71 & 2.83 & 2.52 & 2.55 & 2.56 & 2.99 & 2.94 & 2.91 & 2.72 & 2.77 \\
15 & 2.32 & 2.25 & 2.68 & 2.36 & 2.28 & 2.55 & 2.85 & 2.87 & 2.78 & 2.72 & 2.57 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Ratio with the simulation result (The number of time slots in FMSTC/The number of time slots in optimal).  
convergecast in the general network topology has 2.27 times more time slots than that of the linear network topology.  
In this study, we showed that the linear network topology that gets the optimal scheduling time can be extended to the general network topology to get the minimum scheduling time. The result of this study is extracted from an iterative trial of the simulation. To our best knowledge, the proposed method is the first trial to apply the use of the optimal algorithm in a linear network topology to a general network topology. This study showed that the average number of required time slots for a general topology is less than 2.27 times the number of required time slots for a linear topology. This result is the best minimum scheduling time for a general topology known so far.
## 4 Results
### Experimentation on Ds2  
Importance of the Sensor Positions  
The MZS have, as it does not seek intersections between circles, a better accuracy in often case when sensor placement is not optimal. Figure 9 show a situation when the triangulation method doesn't give the best result. The RSSI value is displayed next to the sensor and the circle around them represent the value in meter of the RSSI value collected by each sensor. It is a good example of how the sensor with the greatest value of RSSI, (and thus indicate that a person is nearby) won't intersect with any circle making the triangulation method difficult.  
Figure 10 show the property of areas computed by both methods. This figure is a result of the museum experimentation and is built based on three visitors wearings tags 5, 8 and 3. In this figure, we are looking at the size of areas with three metrics, the mean size of the zone computed, the median and the standard variation (SD).  
Table 5 show on how many frames both methods could have been computed. A frame is an interval of time of 5 s, where at least 2 sensors are returning an RSSI value and thus that a person is in the equipped room.  
In Table 5, we see that with tag number 5, 194 frames satisfy those conditions (Meaning that this visitor spend approximately at least 16 min within the room). Tag number 8 get 263 exploitable frames (22 min) and tag number 3 get 31 meaning that the visitor probably quickly passes this room.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Accuracy**} & **Average for** & **Standard Deviation** & \multirow{2}{*}{**Frames Computed**} \\  & & **Zone Size** & & **for Zone Size** & \\ \hline MSZ & 0.74 m & 0.49 m\({}^{2}\) & 0.85 & 25 \\ \hline Triangulation & 0.53 m & 1.21 m\({}^{2}\) & 0.95 & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy values for MZS algorithm and triangulation applied to **DS1**.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Badge** & **MZS** & **Triangulation** & **Total Number of Frames** \\ \hline
5 & 181 & 135 & 194 \\
8 & 254 & 200 & 263 \\
3 & 24 & 5 & 31 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Number of frames processed by MZS and the triangulation.
## 2 Proposed Circuits
### Voltage-Mode Quadrature Sinusoidal Oscillator  
In Figure 3, by inserting a resistance between the voltage output \(\mathrm{V_{o1}}\) and terminal X of the third CFOA, the presented CFOA-based voltage-mode multifunction biquadratic filter can be converted into a voltage-mode quadrature oscillator by making \(\mathrm{V_{i1}=V_{i2}=0}\). As shown in Figure 4, a new voltage-mode quadrature sinusoidal oscillator based on the proposed filter in Figure 3 is proposed.  
The characteristic equation of the quadrature oscillator in Figure 4 can be expressed as:  
\[\mathrm{s^{2}C_{1}C_{2}R_{2}R_{3}+s(C_{1}R_{1}-C_{2}\frac{R_{2}R_{3}}{R_{4}})+ 1=0} \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Conditions** & **Output Terminals** & **Filter Functions** & **Passband Gains** \\ \hline \multirow{3}{*}{Only \(\mathrm{V_{i1}}\) as input signal} & \(\mathrm{V_{o1}}\) & non-inverting low-pass & unity \\ \cline{2-4}  & \(\mathrm{V_{o2}}\) & non-inverting band-pass & \(\frac{R_{3}}{R_{1}}\) \\ \cline{2-4}  & \(\mathrm{V_{o3}}\) & non-inverting band-reject & unity \\ \hline \multirow{3}{*}{Only \(\mathrm{V_{i2}}\) as input signal} & \(\mathrm{V_{o1}}\) & inverting band-pass & \(-\frac{C_{2}R_{2}}{C_{1}R_{1}}\) \\ \cline{2-4}  & \(\mathrm{V_{o2}}\) & none & none \\ \cline{1-1} \cline{2-4}  & \(\mathrm{V_{o3}}\) & non-inverting high-pass & \(\frac{R_{1}}{R_{3}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Two inputs and three outputs filtering functions realized.  
Figure 4: The proposed quadrature sinusoidal oscillator using the filter in Figure 3.  
The frequency of oscillator (FO) and the condition of oscillation (CO) of Figure 4 can be obtained as:  
\[\mathrm{FO}:\ \omega_{\mathrm{o}}=\sqrt{\frac{1}{\mathrm{C_{1}C_{2}R_{2}R_{3}}}} \tag{14}\]  
\[\mathrm{CO}:\ \mathrm{C_{1}R_{1}R_{4}\leq C_{2}R_{2}R_{3}} \tag{15}\]  
For \(\mathrm{R_{1}=R_{2}=R}\), and \(\mathrm{C_{1}=C_{2}=C}\), Equations (14) and (15) simplifies to:  
\[\mathrm{FO}:\ \omega_{\mathrm{o}}=\frac{1}{C}\sqrt{\frac{1}{\mathrm{RR_{3}}}} \tag{16}\]  
\[\mathrm{CO}:\ \mathrm{R_{4}\leq R_{3}} \tag{17}\]  
From Equations (16) and (17), the FO can be controlled by adjusting the value of resistor R without affecting the CO. The CO can be controlled by adjusting the value of resistor \(\mathrm{R_{4}}\) without affecting the FO. This means that the FO and CO can be controlled independently by different two resistors. The two marked voltages, \(\mathrm{V_{o1}}\) and \(\mathrm{V_{o3}}\), in the circuit of Figure 4 are related as:  
\[\frac{\mathrm{V_{o1}}}{\mathrm{V_{o3}}}=-\frac{1}{\mathrm{sC_{1}R_{1}}} \tag{18}\]  
Under sinusoidal steady state, Equation (18) can be expressed as:  
\[\frac{\mathrm{V_{o1}}}{\mathrm{V_{o3}}}=\frac{1}{\omega_{\mathrm{o}}\mathrm{ C_{1}R_{1}}}\mathrm{e}^{\mathrm{i}\varphi} \tag{19}\]  
where the phase difference is of \(\varphi=90^{\circ}\), ensuring the voltages, \(\mathrm{V_{o1}}\) and \(\mathrm{V_{o3}}\), are in quadrature.
## 4 Experiment Verification
### Weight of Rust Removed at Different Blade Rotation Speeds  
According to the erosion rate illustrated in Equation (7), the weight of rust removed in simulation step time can be described as  
\[M_{rust}=R_{erosion}A_{face}N_{particle}/T_{step}, \tag{14}\]  
Figure 10: Impact force comparison between simulation and experiment at different blade rotation speed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Blades Rotation** & **Simulation** & **Simulation Impact** & **Experimental** \\  & **Speed (rpm)** & **Velocity (m/s)** & **Force (N)** & **Impact Force (N)** \\ \hline
1 & 600 & 13.24 & 41.29 & 33.03 \\
2 & 800 & 27.63 & 85.76 & 55.74 \\
3 & 1000 & 38.52 & 120.19 & 84.14 \\
4 & 1200 & 45.21 & 142.61 & 106.95 \\
5 & 1470 & 48.78 & 145.65 & 116.52 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Impact forces at different blade rotation speed.  
where \(N_{particle}\) is the number of particles, and \(T_{step}\) is the simulation step time. In this paper, the effective erosion area of specimen \(A_{face}\) = 450 mm \(\times\) 40 mm = 18,000 mm\({}^{2}\).  
Figure 11 shows the comparison of the specimen morphology before and after rust removal. The weight of rust removed in each test can be obtained by comparing the weight of the specimens before and after the test. The weight of rust removed by simulation and experiment under different blade rotation speeds are shown in Table 4.  
Figure 12 compares the weight of rust removal by simulation calculation and experimental measurement at different blade speeds. The weight of rust removed is proportional to the blade rotation speed, and the maximum value reaches 9.5 g/s when the rotation speed is 1470 rpm. The experimental data are consistent with the simulation trend, which is approximately 85-95% of the simulation value. The error may be due to two reasons: First, the velocity of the jet acting on the specimen surface is smaller than the theoretical velocity due to jet diffusion and air resistance. Second, although the specimens are taken from the same steel plate, the degree of the rust is inevitably different, which affects the measurement of the rust and brings errors. Generally, it is consistent with the trend of simulation data, and the error is within the acceptable range [28]. Therefore, the experimental and simulation methods are feasible for studying the performance of slurry blast rust removal.
###### Abstract
If the cloud cover rate of images is high, it is difficult to produce effective image products. In addition, the area obscured by clouds is usually resubmitted as a new observation requirement. So the whole imaging task would incur additional costs, such as one day or as long as several weeks, which greatly reduces the timeliness of image data due to the constraints of satellite tracking, telemetry and control, data downlink, and other factors. Geostationary (GEO) meteorological satellites orbit the Earth above a particular location on the Equator at an altitude of about 36,000 km and provide \"full-disk\" (about 1/3 of the Earth's surface area) multi-spectral remote sensing images every 15 min or shorter. In recent decades, the spatial resolution of GEO meteorological satellite images has increased to better than 0.5 km level, and the spectral bands have extended from a few panchromatic and thermal infrared bands to cover many visible, near-infrared, mid-infrared and far-infrared bands, e.g., Chinese Fengyun-4 and Japanese Himawari-8 satellites have as many as 14 spectral bands, for example. The GEO meteorological satellite CLM products have ultra-high temporal resolution, middle spatial resolution, and high cloud identification accuracy. To address the cloud cover problem, a solution that uses cloud cover information provided by GEO meteorological satellites to guide the mission planning of LEO optical imaging satellites is proposed in this paper. The basic ideas of the solution include two parts. One is that the GEO meteorological satellite acquires multispectral 'full-disk' remote sensing images, and generates CLM products with real-time cloud detection algorithms by using a special fast processing chip on board. And the other is that the GEO meteorological satellite can judge the cloud over situation of the target area in real-time based on the CLM products, interact with the LEO optical imaging satellites through the inter-satellite communication link, complete the autonomous cooperative mission planning, and dynamically adjust the observation tasks of the LEO optical imaging satellites. The solution aims to effectively solve the cloud cover problem and improve the imaging efficiency of the optical imaging satellites. In this paper, a GEO-LEO satellites cooperation scheme is proposed and the operation flow and key elements in this scheme are analyzed. By using the Fengyun-4 CLM products, an autonomous mission planning algorithm based on real-time cloud cover information is developed. Then satellites data of Fengyun-4 and Gaofen-1/2 satellites are used as examples to perform an experimental application of the proposed GEO-LEO satellites cooperation scheme. Concluding remarks are summarized in the final section.
# Modeling of Geophysical Flows through GPUFOW
## 2 Materials and Methods  
### The Physical-Mathematical Model  
GPUFLOW is a 2D cellular automaton for the modeling of Bingham fluids with or without temperature-dependent rheological parameters. The computational domain is a rectangular grid covering the area of interest, divided into square cells. Each cell holds information about the local topography and the amount of fluid present in the cell; in the case of temperature-dependent rheology with phase transition (i.e., lava), the cell information also includes the temperature and heat for the fluid, as well as the amount of fluid that has solidified. The topographical information is taken from a digital surface model (DSM), with a resolution matching that of the cellular automaton. The initial conditions depend on whether a debris or lava flow is being simulated. For debris flow, it is possible to specify for each cell the amount of flowing material initially present in the cell. For lava flows, one or more cells can be marked as eruptive vents, with an associated mass flux rate (possibly varying in time), such that at the beginning of each time-step the amount of fluid in the cell is increased according to the current flux rate.  
GPUFLOW uses a Moore neighborhood, so that each cell has eight neighbors (four in the cardinal direction, and four in the diagonal direction). The evolution function describes the amount of fluid flowing from one cell to a neighbor according to a steady-state solution of the Navier-Stokes equation for the motion of a Bingham fluid on an inclined plane in the case of pressure- and gravity-driven flow. Given the (possibly temperature-dependent) fluid viscosity \(\eta\) and yield strength \(S_{y}\), the critical thickness \(h_{cr}\) can be approximated as  
\[h_{cr}\cong S_{y}\frac{\sqrt{\Delta z^{2}+\Delta x^{2}}}{\rho g(\Delta z+ \Delta h)} \tag{1}\]  
where \(\rho\) is the fluid density, \(g\) the gravity force per unit mass, \(\Delta x\) the cell side length, \(\Delta z\) the ground height difference (including any contribution from solidified fluid in the case of lava), and \(\Delta h\) the difference in liquid height. When the difference in fluid height between the cells is higher than \(h_{cr}\), an amount of fluid \(q\) computed as  
\[q=\frac{S_{y}h_{cr}^{2}\Delta x}{3\eta}\Big{(}a^{3}-3/2a^{2}+1/2\Big{)},\ \ \ \ \ a=h/h_{cr} \tag{2}\]  
is exchanged from the cell with a higher amount of fluid to the neighbor. The steady-state approximation is justified by the high effective viscosity of lava flows (derived from the empirical model by [PERSON] and [PERSON] [39]) and can be considered valid for debris flow under appropriate constraints for the timestep.  
In the case of temperature-dependent rheology, the heat and temperatures are updated as a weighted average between the incoming fluid and the fluid already present in the cell, and heat loss due to radiation and air convection on the surface is also computed (see Section 2.1.2). If the temperature drops below the phase transition temperature, an appropriate amount of fluid is converted into solid.