esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Cases of Different Initial Stress State  
The evolution of the shear stress \(\tau\), effective normal stress \(\sigma_{n}t\), injection pressure \(p\), slip distance \(d_{s}\), slip rate \(\upsilon_{s}\), and water injection volume under different initial stress states is shown in Figure 6. Shear stress was started at 90% (Figure 6a), 85% (Figure 6b), and 80% (Figure 6c) of their shear strength. As the initial stress state decreased, the injection pressure needed for reactivation initiation increased, from 3.63 MPa at 90%, to 6.26 MPa at 85%, to 8.97 MPa at 80% (yellow circles in Figure 6). The closer the initial stress state was to the shear strength, the easier it was to slip.  
The peak slip rate reached 10.08 \(\upmu\)m/s, 12.68 \(\upmu\)m/s, and 14.70 \(\upmu\)m/s, respectively, which meant that different initial stress states had little effect on the peak slip rate (Table 2).  
Figure 6: Time evolution of the shear stress \(\tau\), effective normal stress \(\sigma_{n}t\), injection pressure \(p\), slip distance \(d_{s}\), slip rate\(\upsilon_{s}\), and water injection volume at different initial stress states in the injection-driven shear **(a)** at \(\tau_{c}\) = 0.9 \(\tau_{s}\); **(b)** at \(\tau_{c}\) = 0.85 \(\tau_{s}\); and **(c)** at \(\tau_{c}\) = 0.8 \(\tau_{s}\). Yellow circles mark the injection pressure at fault slip initiation.  
Figure 6 shows that the sliding process in the three cases was similar. After fault slip initiation, the shear stress dropped sharply, and the effective normal stress decreased nonlinearly under all three situations along with a high slip rate (>10 \(\upmu\)m/s). Then, the slip rate dropped rapidly. Finally, faults slipped at a stable slip rate (approximately 1.5 \(\upmu\)m/s) without locking. The difference between the three initial stress states was the duration of the fault-locked phase in the early stage. Nonetheless, the total distance of shear slip varied greatly. It reached 253 \(\upmu\)m at 90% and 109 \(\upmu\)m at 85%, while it reached only 21.53 \(\upmu\)m at 80%. This had to do with the higher water injection pressure required for slip and the shorter slip time in the low initial stress state.
## 3 Results and Discussions  
The Italian 80 MW LBE-cooled reactor was chosen as the LBE-cooled ADS or equivalent critical system to investigate the safety characteristics in this work. The arrangement of assemblies and horizontal view of one-sixth of the ADS core and the CNES core are shown in Figure 7. The ADS core is divided into several regions, as shown in Figure 7a. The inner region consists of 120 hexagonal fuel assemblies, and the dummy assemblies in the outer region mainly contain LBE which is taken as the reflector. The central channel is designed to introduce proton beam into the spallation neutron target. The outside of the core region is filled with LBE coolant, which serves as axial reflector and radial reflector. The CNES core has a similar configuration without the spallation target, as shown in Figure 7b, in which 19 additional fuel assemblies are added in the central channel to bring the reactor to critical state without external neutron source. In both the ADS and CNES systems, each fuel assembly includes 90 triangle-distributed fuel rods made of MOX pellet (PuO\({}_{2}\)/UO\({}_{2}\) with 23% Pu) in a stainless steel cladding. The main technical design parameters of these two reactors are listed in Table 2. Three typical accidents are carried out to study the difference of transient behaviors between ADS and CNES, which include reactivity insertion, loss of flow, and loss of heat sink.  
Figure 7: Horizontal view of assembly arrangement for one-sixth of the ADS core and the CNES core.
## 2 Empirical Formulations
### Exit Pressure  
Various factors affect the blast propagation outside of a munition storage site, such as the storage chamber dimensions and volume, passageway dimensions and volume, mass and explosive type, exit pressure, tunnel diameter, and the angle off of the zero-degree axis. ([PERSON], 2018).  
* DDESB ([PERSON], 2018)  
The normalized peak overpressure data measured in the free-field beyond the tunnel portal versus the normalized distance were presented in ([PERSON], 2018). The dimensionless pressure values can be obtained by dividing the calculated effective exit pressure by the recorded peak free-field pressure.  
The exit pressure at the portal of the ammunition storage magazine can be derived from the loading density of the ammunition storage magazine, expressed as Equation (2):  
\[P_{portal}=1770.5\bigg{(}\frac{W_{C4}}{V}\bigg{)}^{0.45}, \tag{2}\]  
where  
\(P_{portal}\): blast pressure at the portal of the ammunition storage magazine (kPa)  
\(W_{C4}\): C4 explosive weight (kg)  
\(V\): volume of the storage chamber (m\({}^{3}\)).  
Figure 1: Schematic diagram of the empirical equation.  
[PERSON] [19]  
The explosive mass and storage chamber volume are essential parameters for evaluating the blast pressure. An empirical equation presented in [19] can be used to estimate the exit pressure from a tunnel:  
\[P_{portal}=24(\frac{W_{TNT}}{V})^{0.66}, \tag{3}\]  
where \(W_{TNT}\): TNT explosive weight (kg).
## 2 Materials and Methods
### Selection of Variables and Multi-Sensor Satellite Data Productions  
For the statistical analysis on the natural factors of livestock number change and _Dzud_ occurrence between 2003 and 2010, we selected a few variables from the state, control, and forcing variables from the schematic model. Because the flux variables were either implicitly reflected in the state variables (e.g., GPP, NPP, foraging), or the target variables of this study (i.e., _birth_ and _loss_), or socioeconomic variables (i.e., _otor_ and _slaughter_), they were not considered in this study. Consequently, six explanatory variables were selected that could be produced from satellite data: four summer variables (biomass, dryness, Ts, and Ps) and two winter variables (T\({}_{\rm w}\) and P\({}_{\rm w}\)). Then, the variables were used to explain changes in livestock numbers (sheep).  
Summer temperature (T\({}_{\rm s}\)) and precipitation (P\({}_{\rm s}\)) were selected because of their widespread influences on drought, vegetation, and livestock. Dryness was used in the multivariate analyses to account for the effects of drought. Dryness was defined as a ratio between summer ET and P\({}_{\rm s}\). Summer biomass was the only biotic driver selected as a state variable aggregating other carbon flux processes. The model indicated that winter temperature (T\({}_{\rm w}\)) and precipitation (P\({}_{\rm w}\)) are key climatic variables influencing the accessibility and mobility of winter foraging resources. Because winter residue is closely correlated with summer biomass, we did not include it as a potential driver.  
Various datasets were collected for study between 2003 and 2010, which include the national census data of livestock numbers from 337 _soums_ in Mongolia [22]; the 1 km 16-day Normalized Difference Vegetation Index (NDVI) from MODIS [35] as a proxy of biomass; the 25 km monthly precipitation (P) data from the Tropical Rainfall Measuring Mission (TRMM) (Goddard Distributed Active Archive Center, NASA) [36] for preparing P\({}_{\rm s}\) and P\({}_{\rm w}\); the 5 km daily mean temperature for calculating T\({}_{\rm s}\) and T\({}_{\rm w}\)[20; 37]; and the 1 km daily evapotranspiration (ET) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) [24; 38] (Table 1). Both P\({}_{\rm s}\) and ET\({}_{\rm s}\) were used for calculating dryness (ET/P). Additionally, monthly temperature and precipitation observed at 67 national weather stations in Mongolia (National Agency Meteorology and Environmental Monitoring) were used to evaluate uncertainties in satellite-derived temperature and precipitation data. Furthermore, gap-filled daily ET observed at the SKT flux tower in Mongolia [39] was used to validate our satellite-determined ET. The data streams for production, evaluation, and analysis are described in Figure 3.
# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 3 Methodology
### Flood Dominant Color Estimation
In this work, the probability for potential flood area pixels over each color component is defined in Equation (7) via the exponential component of the normal probability distribution function (Gaussian kernel) that is ranged in [0, 1]:  
\[P_{C}(p)=e^{-\frac{(\frac{(C(p)-p_{C})^{2}}{2\cdot c_{C}^{2}}}{2\cdot c_{C}^{2}}} \tag{7}\]  
The decisive probability map (\(PM\)) is then constructed, as shown in Equation (8),  
\[PM(p)=\left(P_{L}(p)\sqrt{P_{A}(p)\sqrt{P_{B}(p)}}\right)^{\frac{4}{2}} \tag{8}\]  
accounting for the greater significance of the \(L\) component with exponent term one. This is because water reflects more light than most backgrounds (excluding the sky), significantly affecting the luminance captured in the L component of the LAB color space. The component \(A\) has the second significance, with an exponent term equal to \(\frac{1}{2}\). The component \(B\) is the least significant, with an exponent term equal to \(\frac{1}{4}\). In Equation (8), the exponent term \(\frac{4}{2}\) is used for normalization purposes, giving the sum of exponent terms one. The use of different exponent terms on the three color components slightly improves the results of the proposed method, as shown in Table 3. We achieved 0.5% and 0.3% higher F1-score (\(F_{1}\)), compared to the version of the system having equal significance to all color components (see Equation (9)) and equal significance to the A and B color components (see Equation (10)), respectively. \[PM_{eqLAB}(p)=\left(P_{L}(p)P_{A}(p)P_{B}(p)\right)^{\frac{1}{3}} \tag{9}\]  
\[PM_{eqLAB}(p)=\left(P_{L}(p)\sqrt{P_{A}(p)P_{B}(p)}\right)^{\frac{1}{2}} \tag{10}\]  
Figure 7 illustrates examples of probability maps derived after the corresponding initialization masks and weight maps for examples from the Flood Area dataset. In fact, potential flood areas (cyan color) have a higher probability (red color) compared to the background. Areas classified as non-flood (dim gray color) by Equation (3) exhibit zero weights and probabilities (dark blue color) such that they will not contribute to the flood's dominant color estimation. However, when the flood's color is equivalent to the color of background areas, which were not eliminated by the masks used to initialize our method (see Sections 3.2 and 3.3), there can be the case of high probabilities turning up in non-flood areas, as depicted in the example in the last row, where parts of the road exhibit a tendency towards flood. At this point, using a single threshold value on the decisive probability map to perform the final segmentation can lead to quite satisfactory results (see Table 3, row UFS-REM). However, we opted for dual thresholds, as described in the following subsection.
## 4 Conclusions  
One key element in multi-robot systems is communicating between robots and providing coordination to achieve well-defined agricultural tasks. The aim of the wireless R2R communication system was based on a client-server model where clients send the spatio-temporal data to the server and the server sends all collected robot data to all robots in a wireless network. The designed wireless R2R communication system is suitable for providing wireless connectivity between the devices which is capable of Wi-Fi communication not only on mobile platforms but also on stationary systems for precision farming applications. This system provides a flexible, reliable, adaptable, and elegant solution to connect all mobile systems in agriculture. In conclusion, the wireless R2R communication system can be used safely in both outdoor and indoor conditions for agricultural mobile systems. In this study, a new and robust collision-avoidance algorithm was introduced for multi-robot systems. This algorithm is based on the kinematics of the robot and robot-to-robot communication system. From the test results, we found that the algorithm is collision-free and straightforward. We believe that the R2R communication system and collision-avoidance algorithm are well suited for real-time computation and sensing thanks to its low computational complexity, which allows for agricultural multi-robot systems. Future work should focus on the multi-robot spraying, fertilizing, and harvesting applications using the proposed R2R communication system and algorithm.  
Conceptualization, I.U.; methodology, I.U. and O.K.; software, O.E.; validation, I.U. and O.E.; formal analysis, O.K. and G.M.; investigation, I.U., O.K. and O.E.; resources, G.M.; data curation, O.E.; writing--original draft preparation, I.U.; writing--review and editing, I.U. and G.M.; visualization, G.M. and O.K.; supervision, I.U. and O.K.; funding acquisition, G.M. All authors have read and agreed to the published version of the manuscript.  
The APC was funded by the University Politehnica of Bucharest, Romania, within the PubArt Program.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Methodology
### Features Acquisition and Forecasting  
The methodology is designed for a model to perform iterative forecasting, so it is critical to have all the features that significantly affect the model available at each time instant. For example, exterior temperature is a key feature in any HVAC system; its value can be continuously monitored or forecast using data from weather APIs. Room occupancy is another crucial feature, as it significantly impacts indoor temperature. However, predicting occupancy is challenging due to the variability in human behavior.  
Nevertheless, approximations can be made based on factors like work schedules if the room is a workspace. When schedules are unavailable but a sensor provides real-time occupancy data, modeling this occupancy over time can be achieved using techniques such as autoregressive models, moving averages, or machine learning algorithms. These  
Figure 2: Recursive model structure starting at time \(t\) until \(n-\)th iteration.  
approaches help capture occupancy trends, supporting more accurate and dynamic HVAC forecasting even when human patterns are unpredictable. Furthermore, a simple approach is to approximate the values as a Gaussian function:  
\[f(x)=Ae^{-\left(\frac{x-b}{c}\right)^{2}} \tag{1}\]  
where \(A,b,c\in\mathbb{R}\). Using the curve_fit function from the scipy.optimize library [46], this Gaussian function, along with any other appropriate function, can be fitted to the observed occupancy data to estimate the number of people in the room based on past trends. This approach offers a smooth approximation of occupancy variations over time.
## 2 Methods and Process Procedures
### Cloud and Cloud Shadow Identification
#### 2.3.5. Normalization of Reconstructed Images  
After cloudy, cloud shadow, and haze pixels of a Sentinel-2 image were identified, they were replaced with the corresponding pixels of its synthetic image to form a cloud-free image. Since the synthetic image had minor visual differences from its counterpart observation on the reconstructed cloud-free image, there was some small roughness such as salt-and-pepper noises, especially around the edges of cloudy and cloud shadow pixels. To homogenize the differences, a normalization processing for calibrating the synthetic pixel values to the observational values was required. There are two main types of normalization methods: mapping and regression [16]. The mapping method directly establishes a pixel value equation between the processed images and uses the output of the mapping equation to replace the pixel values under consideration. The regression method creates a regression model to establish the pixel value distortion relationship between the images via pseudo-invariant features.  
In this study, we took the mapping approach. The normalization process can be expressed as follows:  
\[y_{n}=ax_{b}+b \tag{11}\]where \(y_{n}\) is the pixel value after the normalization process that maps \(a\) synthetic image to the observed image; \(x_{b}\) is the replacement pixel value of the synthetic image; \(a\) and \(b\) are normalization parameters that are determined by a linear regression.  
Considering that different clusters may have different reflectance change rates, we performed the linear regression to determine the normalization parameters a and b for each cluster. We followed four steps in the normalization process:  
1. Segment the synthetic Sentinel-2 image into \(k\) clusters. The cluster number \(k\) can refer to the optimized clusters used for the image blending by PSRFM [24].
2. Identify, for each cluster, all the cloud-free pixels that are common to the synthetic and observed images and belong to the same cluster, then use the pixel pairs to estimate normalization parameters \(a\) and \(b\) in Equation (11).
3. Apply the estimated normalization parameters to map the replacement pixels of the cluster to the observed image.
4. Repeat steps 2 and 3 for all the clusters.
# Multi-Body Dynamics Modeling and Simulation of Maglev Satellites
## 2 Modeling of Three Body Dynamics of Maglev Satellite
### Payload Subsystem Dynamics Equation  
Since the PM and payload can be viewed as a whole, the payload subsystem can establish dynamics based on the Newton-Euler equation of a single rigid body as follows:  
\[M_{pl}\ddot{\mathbf{X}}_{pl}=\mathbf{A}_{b}\mathbf{C}_{r}(\mathbf{F}_{ur}+\mathbf{F}_{ar}+\mathbf{F}_ {lr}) \tag{27}\]  
\[\begin{array}{l}\mathbf{J}_{pl}\dot{\mathbf{\omega}}_{p}+\widetilde{\mathbf{\omega}}_{ p}\mathbf{J}_{pl}\mathbf{\omega}_{p}\\ =\widetilde{\mathbf{r}}_{ss}\mathbf{C}_{p}^{r}\mathbf{F}_{ur}+\widetilde{\mathbf{r}}_{ss}\mathbf{C} _{p}^{r}\mathbf{F}_{ar}+\widetilde{\mathbf{r}}_{sl}\mathbf{C}_{p}^{r}\mathbf{F}_{lr}+\mathbf{C}_{ p}^{r}\mathbf{\tau}_{q}\end{array} \tag{28}\]  
where \(M_{pl}\) refers to the mass of the payload subsystem; \(\mathbf{X}_{pl}\) is the vector from the origin of the inertial frame to the center of mass; \(\mathbf{J}_{pl}\) is the moment of inertia; \(\mathbf{\omega}_{p}\) is the payload angular velocity; \(\mathbf{r}_{ss}\), \(\mathbf{r}_{sa}\) and \(\mathbf{r}_{sl}\) are the vectors of the electromagnetic force application points of the center of mass pointing upwards and downwards OLFMBs and LLFMBs, respectively; and \(\mathbf{C}_{p}^{r}\) is the attitude transformation matrix of the relative payload subsystem of the MM.
# Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings
## 3 Nonlinear Dynamic Incipient Fault Detection Method
### The process of Deep ADKPCA  
Firstly, the EMD method was used to decompose the vibration signal to obtain \(n\) IMF components, secondly the \(n\) IMF components were composed into a matrix, and thirdly the matrix was obtained by kernel transformation to obtain a dynamic kernel matrix. Eigenvalue decomposition was performed on the dynamic kernel matrix to obtain the principal component space and residual space. Finally, the subprincipal component space and the subsresidual space were decomposed multiple times to obtain \(i\)-layer subspaces, calculate the statistics, and control limits of each subspace and compare them to determine whether a fault occurs.  
The \(T_{ij}^{2}\) and \(SPE_{ij}\) statistics for each subspace \(X_{Kij}\) as Formulas (12) and (13):  
\[T_{ij}^{2}=X_{K}^{T}P_{i+1\,\ 2j-1}\Lambda_{ij}^{-1}P_{i+1\ 2j-1}^{T}X_{K} \tag{12}\]  
Figure 2: The structure of Deep Adaptive Dynamic Kernel Principal Component Analysis (Deep ADKPCA).  
\[SPE_{ij}=\sum_{c=1}^{n}t_{ijc}^{i}-\sum_{c=1}^{a_{ij}}t_{ijc}^{i} \tag{13}\]  
where, \(a_{ij}\) is the number of principal elements of the subspace \(X_{Kij}\).  
The \(T_{ij}^{2}\) and \(SPE_{ij}\) control the limit for each subspace \(X_{Kij}\):  
\(T_{ij}^{2}\) satisfies the F distribution, and its control limit is shown in Formula (14):  
\[{T_{ij}}^{2}\sim\frac{a_{ij}\Big{(}N^{2}-1\Big{)}}{N\Big{(}N-a_{ij}\Big{)}}F_{ a_{ij}N-a_{ij}} \tag{14}\]  
\(SPE_{ij}\) satisfies the \(\chi^{2}\) distribution, and its control limit is shown in Formula (15):  
\[{\delta_{ij}}^{2}=g_{ij}\chi^{2}\Big{(}h_{ij}\Big{)} \tag{15}\]  
where, \(g_{ij}=\frac{\rho_{ij}^{2}}{2\mu_{ij}}\), \(h_{ij}=\frac{2{\mu_{ij}}^{2}}{\rho_{ij}^{2}}\). \(\mu_{ij}\) and \(\rho_{ij}^{2}\) are the mean and variance of \(SPE_{ij}\).  
The flowchart of the fault detection method based on Deep ADKPCA is shown in Figure 3:
# Development of an Organic Culture Medium for Autotrophic Production of _Chlorella vulgaris_ Biomass
###### Abstract  
Microalgal biomass has gained increasing attention in the last decade for various biotechnological applications, including human nutrition. Certified organic products are currently a growing niche market in which the food industry has shown great interest. In this context, this work aimed at developing a certified organic culture medium for the production of autotrophic _Chlorella vulgaris_ biomass. A preliminary assay in 2 L bubble column photobioreactors was performed in order to screen different commercial organic substrates (OS) at a normalized concentration of N (2 mmol L\({}^{-1}\)). The highest growth performance was obtained using EcoMix4 and Bioscience which showed similar biomass concentrations compared to the synthetic culture medium (control). In order to meet the nutrient needs of _Chlorella_, both OS underwent elemental analyses to assess their nutrient composition. The laboratory findings allowed the development of a final organic culture medium using a proportion of Bioscape/EcoMix4 (1:1.2, m/m). This organic culture medium was later validated outdoors in 125 L flat panel and 10 m\({}^{3}\) tubular flow through photobioreactors. The results obtained revealed that the developed organic medium led to similar microalgal growth performance and biochemical composition of produced biomass, as compared to the traditional synthetic medium. Overall, the formulated organic medium was effective for the autotrophic production of organic _C. vulgaris_ biomass.  
certified organic biomass; _Chlorella vulgaris_; photobioreactors; scale-up; biochemical composition +
Footnote †: journal: Appl. Sci. 2020, 10, 2156; doi:10.3390/app10062156
## 3 Results  
### Fluorite Grade  
The optimum pulp pH for the flotation of fluorite was determined as approximately 9.5-10. Figure 1 shows how the pH affects fluorite grade in the concentrate under the use of the variousdepressants named above. Each plot compares the beneficiation process of fluorite in the conventional and modified column cells.  
On the one hand, by studying the effect of each depressant mixture separately, it can be seen that the best results were obtained for the QT + WD mixture, reaching 74% of fluorite grade for modified column flotation and 70.5% for conventional cell flotation, both at pH of 9.5. Mixtures of QT + CMC, QT + PS or even QT alone achieved results very close to QT + WD. The separation of fluorite and calcite was especially difficult since both minerals contain the same cation, Ca\({}^{2+}\).  
On the other hand, in all trials, the values obtained for the modified column flotation were higher than those obtained in the flotation cell. Another common aspect of all depressant mixtures was that they reach the maximum value of fluorite grade at a pH close to 9.5. As can be seen, there was a large difference between using a depressant and not using it. When no depressant was used, the fluorite grade obtained was 53.8% for the modified column and 51.6% for the cell, both at 8.5 pH. However,  
Figure 1: CaF\({}_{2}\) (%) on concentrate for cell and column as pH function and type of depressant.  
when QT or a mixture of depressants was added, fluorite grades rose to values close to 70% at a pH range of 8.5-9.5.  
Finally, it should be noted that the QT + SHMP mixture was not a good option as a carbonate depressant in the fluorite flotation process. Using only QT gave better results.