esa-sceva/satcom-chunk-collection huggingface dataset:
# Response of Grassland Vegetation Growth to Drought in Inner Mongolia of China from 2002 to 2020
###### Abstract  
Drought poses a significant environmental risk and can deeply affect the growth of grasslands. However, there is still uncertainty regarding the precise impact of varying levels of drought on grassland growth. To address this gap, we utilized several key indicators, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Global Orbiting Carbon Observatory-2-based Solar-induced Chlorophyll Fluorescence (GOSIF), and Gross Primary Productivity (GPP), in conjunction with drought indices (the Standardized Precipitation Evaptotranspiration Index (SPEI) and soil moisture (SM). Our study aimed to comprehensively assess the consistency of spatiotemporal patterns in grassland vegetation and its responsiveness to different drought levels in the Inner Mongolia region from 2002 to 2020. The results indicated that NDVI, EVI, GOSIF, and GPP in grassland vegetation across Inner Mongolia exhibited significant increasing trends from 2002 to 2020. Specifically, NDVI, EVI, GOSIF, and GPP all displayed consistent spatial patterns, with 25.83%, 21.18%, 22.65%, and 48.13% of the grassland area showing significant increases, respectively. Drought events, as described through SPEI and SM, from June 2007 to September 2007 and June 2017 to July 2017 were selected to evaluate the response of grassland vegetation to drought. The drought events of 2007 and 2017 resulted in reductions in NDVI, EVI, GOSIF, and GPP relative to the multi-year average (2002-2020). GOSIF exhibited a more intense response to drought, suggesting that GOSIF may reflect the inhibition of water stress on grassland photosynthesis better than NDVI and EVI for the drought in 2007 and 2017. The reductions in NDVI, EVI, GOSIF, and GPP in grassland increased significantly across different drought levels, with the sharpest reductions observed during extreme drought. Under the severe and extreme drought events, the most substantial reductions in NDVI, EVI, GOSIF, and GPP were observed in the temperate steppe (TS). Moreover, the effects of different drought severity levels within the same grassland type varied, with the most significant reductions in NDVI, EVI, GOSIF, and GPP observed during extreme drought. Our results provide new perspectives for developing and implementing effective strategies to address grassland carbon cycling management and climate change in Inner Mongolia.  
vegetation indexes; GOSIF; GPP; drought events; Inner Mongolia grassland 2023
# Applications of Probabilistic Forecasting in Demand Response
## 2 Materials and Methods
### Demand Response and Physically Based Load Models  
The methodology proposed for modeling responsive loads is Physical-Based Load Models (PBLMs). These models have been used to evaluate demand, especially demand response, storage and generation for different kind of devices, from electrical transportation (electromotive units [37] and coach internal loads [38]) to storage (batteries and double-layer/super capacitors [39; 40]) and main loads from residential [41; 42] and commercial load segments [43]. They can be considered as white models because they use a set of equations based on physical laws and phenomena. Nevertheless, some authors classify PBLM approaches as grey models [44], because they are simpler than the detailed models used in thermal engineering to define energy requirements for buildings or specific dwellings. The reason is that, for many applications, PBLMs still need further aggregation, and it is necessary to balance accuracy and computing and engineering complexities (or \"costs\") due to aggregation duties. Moreover, this kind of load model is compatible with other electrical equivalent models for distributed energy resources, which are also physically based [45]. In the DR area, and for small customer segments, PBLMs usually use from one to four RC networks to build an electrical-thermal equivalent that simulates the physical processes (energy conversion, heat conduction, convection, and radiation) between the load and its environment. In our case, the equations usually are presented in a space-system format [46], and are solved using scripts from software tools such as Matlab Control (version R2024b) or Simulink Toolboxes (version R2024b) [47]. In next subsections, the characteristics of elemental and aggregated models will be revisited. A more detailed description of those models and further references can be found in [46].
## 3 Mathematical Model and Boundary Conditions
### Boundary Conditions for Fluent Calculations  
The boundary conditions in the Fluent simulation calculation were as follows:  
(1) High power VCSEL array surface: \(-k\frac{\partial T}{\partial z}=200\) W/cm\({}^{2}\)  
(2) Channel inlet, \(u=0\), \(v=0\), \(w=1.2\) m/s, \(T_{water}=293\)\(K\)  
(3) Channel outlet, \(\frac{\partial u}{\partial z}=\frac{\partial u}{\partial z}=\frac{\partial u }{\partial z}=0\), \(\frac{\partial T_{water}}{\partial z}=0\)  
(4) The contact surface between VCSEL chip and heat sink, heat sink and fluid: \(u=v=w=0\), \(-\lambda_{water}\frac{\partial T_{water}}{\partial z}=-\lambda_{solid}\frac{ \partial T_{fluid}}{\partial z}\)  
(5) Remaining walls: \(u=v=w=0\), \(\frac{\partial T}{\partial u}=0\), and the natural convection and radiation heat transfer are ignored in the next calculation.  
In this paper, the momentum equation and energy equation were solved by using the second-order upwind scheme, the standard SIMPLE algorithm was used to solve the coupling equation of pressure and velocity. Since the chip and heat sink are regular cubic structures, the mesh was divided using hexahedral cells, as shown in Figure 5. Since the chip and heat sink are regular cubic structures, the mesh was divided using hexahedral cells, as shown in Figure 5.  
As mentioned above, [PERSON] et al. made the diamond-shaped fork row microchannel heat sink by 3D printing and compared the experimental results with the heat dissipation characteristics calculated by ANSYS FLUENT software. The results show that the experimental data are in good agreement with the simulation data, the deviation is less than 5%, and the trend of the experimental data curve is consistent with that of the simulation data curve. When the heat flow density and velocity are constant, the experimental data of the maximum temperature of the contact surface of the heat source is slightly higher than the simulation data, the cause of this phenomenon may be: (1) During the experiment, the heating plate and the heat sink are bonded by thermal grease, and there is inevitably thermal contact resistance, which is not considered in the simulation, this will introduce some error. (2) There is a certain accuracy error in the process of the micro-channel heat sink, which cannot be completely consistent with the model size in the simulation. In order to compare and verify [PERSON]'s work, the same boundary conditions are selected to simulate the temperature distribution of the bottom surface of the heat sink when the heat flux is 60 W/cm\({}^{2}\), and the flow rate is 1.2 m/s. The maximum temperature calculated is 73.12 \({}^{\circ}\)C, the temperature deviation from the original is 1.15 \({}^{\circ}\)C, and the deviation rate is 1.5%. Therefore, it can be considered that the model established in this paper is accurate and can be used to study the heat transfer performance of the microchannel heat sink.  
Figure 5: Schematic diagram of dividing the grid.
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
## 2 Trends of the _Smart Metering_ Systems
### Architecture of a Smart Metering System  
A _Smart Metering_ system implies the deployment of a heterogeneous infrastructure, including metering devices, communication networks, and data gathering and processing systems, as well as the associated management and installation duties. A _Smart Metering_ system is based on four main pillars:  
* A _Smart Metering_ device, _Smart Meter_ (_SM_);
* A data gathering device, _Data Concentrator_ (_DC_);
* A communication system used for data flow;
* A centralized management and control system, _Control Center_ (_CC_).  
_Smart Metering_ systems are heterogeneous deployments with different requirements and features since they highly depend on the intended use. In addition, different types of measurement can be  
Figure 1: Evolution of _Smart Metering_[8]. Reprinted with the permission of the Edison Electric Institute, “Smart Meters and Smart Meter Systems: A Metering Industry Perspective”, published by the Edison Electric Institute, 2011.  
found in the same _Smart Metering_ system. Three main measurement groups can be differentiated: (i) _on-demand_: measured data flows from the consuming points to the _CCs_ upon specific request of the utility when needed; (ii) _scheduled:_ measured data flows from the consuming points to the _CCs_ by pre-programmed tasks and between four and six times a day per meter; and (iii) _bulk_: the utility collects metering information from all devices several times per day [9].
## 3 Results and Discussion  
### Single-Axis Pressure Resistance and Elastic Modulus  
The uniaxial resistance strength and elasticity modulus for different degrees of early damage (0-60%) are listed in Table 2. With increasing early damage, the uniaxial compressive strength of coal-cement grout exhibited first an increasing trend (0-40%) and then a decreasing trend (40-60%) from 8.57 MPa at 0% early damage to 10.03 MPa at 20% early damage, an increase of 17.04%. A further increase of 10.66 MPa was observed at 40% early damage, an increase of 24.38% compared to 0% early damage, accompanied by a marked increase in strength. The early damage continued to increase to 60%, and its intensity dropped to 8.16 MPa, a decrease of 4.78% compared to that at 0%. Comparing the intensities of various early damaged coal-cement grout samples with the strength of the original coal, the intensities of different earlier disturbances after coal grouting were shown to be greater than the intensity of the raw coal. For early disturbed coal-cement grout samples, early damage at 40% had a significantly larger elasticity modulus than other early disturbed degrees, with 0%, 20%, 40%, and 60% early disturbed samples having elasticity moduli of 1.41, 1.55, 1.92, and 1.45 GPa, respectively.
## 6 Propagation Time for a Signal, Emitted and Intercepted by Satellites on a Space-Oriented Orbit
### Second Formulation of the Theorem for Four-Dimensional Uniformization
#### 12.6.3 First Way for Representation of \(m^{2}\) and of \((\rho^{\prime}(z_{1}))^{2}\)  
Further, from the second Equation (283) for \(Q_{2}\), one may express the ratio (277) \(m^{2}:=\left(\frac{\widetilde{A}_{1}-\widetilde{B}_{1}}{\widetilde{A}_{1}+ \widetilde{B}_{1}}\right)^{2}\) as  
\[m^{2}=\frac{\rho^{3}(z_{1})-\frac{1}{2}(\rho^{\prime}(z_{1}))^{2}}{\rho^{3}(z _{1})}. \tag{284}\]  
Substituting \(m^{2}\) from (284) in the first Equation (282) for \(Q_{1}\) and denoting \((\rho^{\prime}(z_{1}))^{2}:=X\), one obtains the following quadratic equation with respect to the variable \(X\):  
\[9X^{2}-18.8\rho^{3}(z_{1})X+72\rho^{6}(z_{1})=0. \tag{285}\]  
It is known that the roots of the simple quadratic equation \(x^{2}+px+q=0\) are found from the formulae [61]  
\[x=-\frac{p}{2}\pm\sqrt{\frac{p^{2}}{4}-q}. \tag{286}\]  
Consequently, the roots of the quadratic Equation (285) are  
\[(\rho^{\prime}(z_{1}))^{2}:=X=18.4\rho^{3}(z_{1})\pm\sqrt{\frac{(18.8)^{2} \rho^{6}(z_{1})}{4}-72}. \tag{287}\]  
This relation can be substituted in formulae (284) for \(m^{2}\) and for the moment investigating only the case of a positive sign in front of the square root, \(m^{2}\) can be written as  
\[m^{2}=-35-3\sqrt{12^{2}-\frac{2}{\rho^{6}(z_{1})}}. \tag{288}\]Respectively, the solution (287) for \((\rho^{\prime}(z_{1}))^{2}\) can be represented as  
\[(\rho^{\prime}(z_{1}))^{2}=72\rho^{3}(z_{1})+6\rho^{3}(z_{1})\sqrt{12^{2}-\frac{2 }{\rho^{6}(z_{1})}}. \tag{289}\]  
The last two relations are the first way for representing \(m^{2}\) and \((\rho^{\prime}(z_{1}))^{2}\). Now, it shall be proved that there is a second way for representing these expressions.
#### Research Questions  
This study examines current advances in AI-based PdM, particularly emphasizing next-generation autonomy. The following research inquiries are what this review is based on:  
1. What are the main components of AI-based PdM systems?
2. What are the state-of-the-art (SOTA) PdM methods? Regarding accuracy, cost-effectiveness, and scale, what are their advantages?
3. What are the advantages of AI-based PdM techniques over traditional techniques regarding performance and cost-effectiveness?
4. What are the challenges and limitations of AI-based PdM?
5. How can AI-based PdM systems ensure high transparency and explanation?
6. How can AI be integrated into existing PdM systems and workflows?
7. What are the ethical issues in AI-based PdM?
8. How can an efficient human-machine interaction in AI-based PdM systems be obtained?
9. How can testing and validation of AI-based PdM systems be effectively conducted in real-world scenarios?
10. What are recent advances and future trends in AI-based PdM?  
Taking in the research questions, the contributions of this review are (i) the description of the main components of AI-based PdM systems, (ii) the analysis of the SOTA methods in PdM, (iii) comparison of the AI-based PdM with traditional approaches, (iv) investigation of the challenges and limitations of AI-based PdM, (v) assessment of transparency and explainability in AI-based PdM, (vi) integration of AI into existing PdM systems and workflows, (vii) investigation of ethical issues related to AI-based PdM, (viii) enhancing human-machine interaction in the AI-based PdM systems, (ix) effective testing and validation of AI-based PdM systems, and (x) study of the AI-based PdM advances and emerging topics.  
The rest of the paper is organized as follows: Section 2 comprehensively describes the key components of AI-based PdM. The SOTA for PdM and its enabling technologies are presented in Section 3. Then, Section 4 focuses on transparency and explainability in AI-based PdM. The challenges and limitations of using AI for PdM autonomy are highlighted in Section 5. Section 6 presents recent advances and future trends in AI-based PdM. Conclusions are given in Section 7.
## 5 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Control Group**} & \multicolumn{2}{c}{**Study Group**} & \multirow{2}{*}{**z**} & \multirow{2}{*}{\(p\)} \\ \cline{3-3} \cline{5-6}  & & \(\rho\) & \(p\) & \(\rho\) & \(p\) \\ \hline \multirow{3}{*}{Age} & IGF-1R [pg/mL] & 0.120 & 0.359 & 0.073 & 0.523 \\  & ERK2 [pg/mL] & 0.178 & 0.165 & 0.028 & 0.807 \\  & EGFR [pg/mL] & 0.135 & 0.294 & 0.130 & 0.267 \\ \hline \multirow{3}{*}{BMI} & IGF-1R [pg/mL] & 0.247 & 0.057 & 0.143 & 0.207 \\  & ERK2 [pg/mL] & \(-\)0.042 & 0.743 & 0.119 & 0.298 \\  & EGFR [pg/mL] & \(-\)0.289 & \(<\)0.05 & \(-\)0.673 & 0.503 \\ \hline \multirow{3}{*}{Ki-67} & IGF-1R [pg/mL] & - & - & 0.055 & 0.633 \\  & ERK2 [pg/mL] & - & - & 0.177 & 0.141 \\  & EGFR [pg/mL] & - & - & \(-\)0.013 & 0.910 \\ \hline \multirow{3}{*}{Clinical stage} & IGF-1R [pg/mL] & - & - & \(-\)0.012 & 0.921 \\  & ERK2 [pg/mL] & - & - & \(-\)0.059 & 0.606 \\ \cline{1-1}  & EGFR [pg/mL] & - & - & 0.204 & 0.079 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Correlation between age, BMI, Ki-67, clinical state, and the measured parameters for the study and control groups. Figure 1: Median concentration of EGFR in the study and control groups. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Control Group**} & \multicolumn{2}{c}{**Study Group**} & \multirow{2}{*}{**z**} & \multirow{2}{*}{\(p\)} \\ \cline{3-3} \cline{5-6}  & & **Me (Q1–Q3)** & & **Me (Q1–Q3)** & & \\ \hline
**IGF-1R [pg/mL]** & 22.9 (18.6–22.9) & 27.2 (22.9–27.2) & 1.05 & 0.289 \\ \cline{2-5}
**ERK2 [pg/mL]** & 432.9 (312.0–632.9) & 430.4 (359.2–568.9) & 0.12 & 0.901 \\ \cline{2-5}
**EGFR [pg/mL]** & 7771.1 (7296.5–8966.5) & 10,257.7 (9314.7–11,290.9) & 7.25 & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of IGF-1R, ERK2, and EGFR concentrations for the study and the control groups. duodenum, stomach, rectum) did not show any statistically significant differences for IGF-1R (\(p\) = 0.603), ERK2 (\(p\) = 0.665), and EGFR (\(p\) = 0.130). There was no relationship between any of the concentration proteins and the concentration of chromogranin A, serotonin, and 5-HIAA. However, a close-to-significant correlation was found between chromogranin A and EGFR (\(p\) < 0.05). Details are presented in Table 8. There was also no relationship between any of the concentrations of proteins and the concentrations of glucose, TCH, and TG. There were no statistically significant differences for all smokers (study group N = 9, control group N = 12) in the concentrations of IGF-1R (z = 0.174, \(p\) = 0.862), ERK2 (z = \(-\)1.176, \(p\) = 0.861), and EGFR (z = 0.931, \(p\) = 0.352). In addition, the relationships between the concentrations of individual proteins were also analyzed. There was no correlation between ERK2 and IGF-1R (\(p\) = \(-\)0.058, \(p\) = 0.625) and EGFR (\(p\) = 0.087, \(p\) = 0.476). There was also no correlation between IGF-1R and EGFR (\(p\) = 0.086, \(p\) = 0.474).
## 2 Materials and Methods
### Experimental Methods
#### 2.2.1 Slug Velocity  
In 1965 and 1977, [PERSON] [38; 39] and [PERSON] [40], respectively, experimentally measured the slug velocity by observing a particular slug movement in test sections. They both obtained a relationship between slug velocity and no-slip mixture velocity by plotting the experimentally measured slug velocity against the no-slip mixture velocity. The slug flow model of [PERSON] [39] showed better agreement at higher slug velocity. [PERSON] [39] described the relationship as:  
\[v_{s}=1.25\,v_{m} \tag{1}\]  
[PERSON] & [PERSON] [39] also predicted the actual average gas velocity as in Equation (2), which also agreed with other experimental data [41].  
\[v_{G}=1.19\,v_{m} \tag{2}\]  
Figure 1: Schematic of the experimental setup (horizontal test section) with a data acquisition system.  
It is assumed that the [PERSON] & [PERSON] [39] slug flow model was verified based on one dominant presupposition that the liquid slug velocity and the maximum gas phase velocity should be similar. Therefore, theoretically, the no-slip mixture velocity should be equal to the slug velocity:  
\[\frac{v_{s}}{v_{m}}=C \tag{3}\]  
where \(C\) is a constant. Theoretically, \(C\) is assumed to be 1.0 for air-water two-phase flow. [PERSON] [38], [PERSON] et al. [42], and [PERSON] [41] considered \(C\) values of 1.25, 1.26, and 1.35, respectively for air-water flow. These \(C\) values may have varied because of different experimental setups and conditions [40]. For non-Newtonian/air two-phase flow, [PERSON] [40] compared their results with those of [PERSON] et al. [42]. The obtained results suggested that the air/Carbopol 941 concentration reported by [PERSON] [40] increased from 0.75% to 0.2% and \(C\) values increased from 1.36 to 1.41, whereas for the same concentration, the \(C\) values of [PERSON] et al. (1975) varied from 1.54 to 1.98.
# Soil Moisture Monitoring at Kilometer Scale: Assimilation of Sentinel-1 Products in ISBA
## 3 Method
### Experimental Set Up  
The numerical experiments performed in this study are summarized in Table 1. LDAS-Monde was set up in the same configuration for the background and observation error covariance matrices as in [34; 60]. The S1 SSM observations for the three regions were assimilated on a 6-day basis, while the LAI observations from PROBA-V were assimilated on a 10-day basis. The LAI observations were interpolated to the ISBA \(1/100^{\circ}\) model grid. While the LAI observations were assimilated directly, the S1 SSM was first rescaled to the model climatology before assimilation, following [32; 34]. An open-loop (OL) simulation (model run without assimilation) and the analysis were then performed for each region after initialization of the land surface variables. In this study, the LDAS-Monde system used a 24 h window starting at 18:00 UTC and ending at 18:00 UTC the following day, which corresponds to the available hours of S1 SSM products. The assimilation values from the previous window formed the initial conditions for the next 24 h window, allowing fixed estimates of background errors to be used instead of calculating them at the beginning of each cycle. In order to run ISBA-A-gs in offline mode, several surface atmospheric variables are required. In this work, for each day, we used hourly forecast data from AROME-France initiated at 00:00 UTC interpolated on a regular Cartesian grid with a spatial resolution of \(1/40^{\circ}\) (about 2.5 km at 45\({}^{\circ}\)N) made available to the public through the Meteo-France BDAP database (Base de Donnees Analysees et Prevues). The following atmospheric inputs were used: air temperature, specific humidity, wind speed, and pressure at the lowest vertical level (10 m above canopy), as well as incoming fluxes of shortwave and longwave radiation and liquid and solid (snow) precipitation. The atmospheric data obtained at \(1/40^{\circ}\) were then reinterpolated at \(1/100^{\circ}\) with a bi-linear interpolation over the spatial domains involved in this study. To achieve an equilibrium state of ISBA-A-gs and generate more realistic initial conditions, the first year of the study period (2017) was spun up 20 times.
# Lightweight Integrity Preserving Scheme for Secure Data Exchange in Cloud-Based IoT Systems
## 6 Proficiency Evaluation
### Computation Cost of the Integrity Checking Stages
#### 6.2.2 Computation Cost of the Cloud  
This study looked at the number of files sent to one smart device user (Figure 7) and the overall amount of data for smart device users (Figure 8) and compared the computation cost of the IoT-cloud system. Compared with the proposed system, it is clear that [PERSON]'s scheme [7] incurred a higher computation cost for the IoT-cloud system.  
Furthermore, in [PERSON]'s solution [7], a high computation cost time was observed on the IoT-cloud system side; this occurred due to an attempt to reduce the computation cost of the semi-trust server by moving the computing loads of the integrity-checking function to the IoT-cloud system.  
In contrast, the proposed solution led to a lower computation cost time in the IoT-cloud system side. This was due to its adoption of low-cost operations to compute the proof of data in the IoT-cloud system.  
Consequently, compared with [PERSON]'s scheme [7] which used the Bilinear Map method, the proposed solution using lightweight and secure operations offered a low computation time and cost for both the semi-trusted server and IoT-cloud side.  
Figure 5: Computation cost on the semi-trust server with a single smart device user and multi-cloud.  
Figure 6: Computation cost on semi-trust server with multi smart device user and single cloud.