esa-sceva/satcom-chunk-collection huggingface dataset:
# Two-Stage Short-Term Power Load Forecasting Based on SSA-VMD and Feature Selection
## 5 Experiment and Results
### Data Processing  
The difference in magnitude between data with different indicators affects the convergence training speed of the neural network model, and also tends to cause the problems of gradient disappearance and gradient explosion. Therefore, in this paper, the data are normalized using the Min-Max normalization process. The formula is as follows.  
\[x^{*}=\frac{x-x_{\min}}{x_{\max}-x_{\min}} \tag{30}\]  
where \(x^{*}\) is the normalized data, \(x^{*}\in[0,1]\); \(x\) is the original data; \(x_{\max}\) is the maximum value in the data; and \(x_{\min}\) is the minimum value in the data.  
In addition, for discontinuous data such as the time-type factors in this paper, one-hot coding is used.
# A Method of Detections' Fusion for GNSS Anti-Spoofing
###### Abstract
Meanwhile, most spoofing detections are equivalent to hypothesis testings, and the test statistics are quantifications of the anomaly of measurements or signal features. The quantification of the anomaly is equivalent to a soft decision that indicates the degree of anomaly, and the soft decision can be used to evaluate the belief function in binary detection. This makes it possible to implement the DST if the frame of discernment is simplified properly while the belief function is constructed on the basis of the quantification of the anomaly. This paper proposes a method to deal with this problem. There are two main points for the purpose of implementation. The first is to extract a simplified frame of discernment for spoofing detection. Compared with the existing method, some elements in the complete, but redundant frame of discernment are excluded, and only two basic and mutually exclusive elements are left. This is conducive to the valuation of belief functions. The second point and the main contribution of this work is to construct the belief function for each possible result of detections. The belief function is proposed based on the model of hypothesis testing for a spoofing detector. These two points make it feasible to fuse the information from multiple spoofing detections. The remainder of this paper is organized as follows. Section 2 describes the method of spoofing detections' fusion, including the model of detections' fusion, the valuation of the belief function and performance of fusion. Section 3 provides a detailed performance evaluation of the proposed method. Simulations based on the fusion of two spoofing detections are given. Finally, the conclusions are provided in Section 4.
## 1 Introduction
Thus, there is a large amount of macro and current data for monitoring the changes in regional vegetation ecological quality [16]. [PERSON] et al. [17] used Landsat TM and Gaogao 1 remote-sensing images from 2005 to 2016 to monitor, analyze, and evaluate the vegetation coverage dynamics of the Aksu River Basin in Xinjiang. The results show that the vegetation coverage of the Aksu River Basin has shown a benign development trend over the past 10 years. [PERSON] et al. [18] used MODIS satellite data to analyze the temporal and spatial distribution of the vegetation NDVI and its response to climate change and human activities in Xinjiang from 1982 to 2012. Recently, [PERSON] et al. [19] used AVHRR and MODIS NDVI data from 1989 to 2017 to analyze the temporal and spatial distribution changes in desert vegetation and the driving factors in Xinjiang. The results show that the total area of desert vegetation in Xinjiang increased significantly. However, scholars often use single satellite data to study the vegetation quality in desertification areas based on remote sensing, and they usually take a specific ecologically-related theme of the single vegetation NDVI, coverage, or vegetation NPP as guidance. This does not allow the comprehensive evaluation of the ecological status of the regional vegetation quality. The Kokyar Project Area is a model of large-scale, artificial tree planting in arid and water-deficient environments. Based on multisource satellite sensor data, it can break through the performance shackles of a single sensor, effectively give full play to the complementary observation advantages of multiple platforms, and provide refined and multi-platform remote-sensing data for the temporal and spatial characteristics of the vegetation ecological quality in the Kokyar Project Area, which is of great significance for the construction of the ecological civilization in Aksu, Xinjiang, and other regions of the country. Using EOS/MODIS vegetation parameter products and meteorological observation data, this study explored the temporal and spatial changes in the NDVI, the fractional vegetation cover (FVC), and the NPP in the Kokyar Project Area from 2000 to 2021 and constructed a comprehensive vegetation ecological quality index (VEQI) [20] to comprehensively analyze the vegetation quality in the study area. Then, based on the Landsat series of satellite data, the random forest algorithm built into the Google Earth Engine (GEE) [21] platform was used to construct the annual LULC and RSEI of the Kokyar Project Area in order to evaluate the temporal and spatial changes in the ecological quality in the Kokyar Project Area from 2000 to 2021. Finally, based on the Theil-Sen median [22] and TSS-RESTEND [23], this study investigated the path of mutual influence among the FVC, NPP, VEQI, and RSEI in the Kokyar Project Area over the past 22 years, as well as their responses to climate change and human activities, in order to provide technical support for ecological environment protection and regional ecological environmental planning in the study area in the future.
## 6 Conclusions  
Though there are very recent publications on the reviews of mechanical alloying, basic principles to its applications [133; 134; 135], this current review is focused towards deploying ball milled materials and nanocomposite systems for energy storage applications, especially electrochemical and hydrogen storage. This review is also aimed on the recent developments on the synthesis of nanocomposites using the simplest top-down approach, mechanochemical milling, and the related aspects of the interfacial interactions. Milling constraints include time duration of milling, ball size, the ball to sample content proportion, rotation speed, and energy that took part in a vital part of the structure-property relationships and composite interactions. Milled nanocomposites are being used in different structural applications for their higher performance rate and throughput. The synthesis of different nanocomposites (magnetic and non-magnetic metallic systems, polymeric systems, and ceramic systems) and the effect of various parameters on the mill-ability of nanocomposites are discussed. A detailed review of these ball milled materials and their usage in energy storage applications are detailed at the very end of the review.  
There is an increasing interest among the materials research community in nanocomposite synthesis via the ball milling method. Simple, cost-effectiveness, and scalability make this top-down route very attractive. If the optimization parameters are properly controlled, it is possible to synthesize a wide variety of nanocomposite materials (polymeric, ceramic, metallic, etc.) with desired size and shape. Researchers integrated their investigations on the impact of different milling factors especially the energy dose and energy intensity on the morphological, physico-chemical, and mechanical properties. The nanocomposite exhibits good dispersion of nanofillers with good interfacial adhesion and also could absorb the load stress, which improves the overall mechanical properties. The ultra-high thermal conductivity should be investigated in the future for ball milled nanocomposite as it is crucial for thermal management. Dry milling at medium energy and wet milling at high energy attributes to an effective method for embedding the nanofiller into the matrix. According to the applications, the right compositions of nanofiller and the matrix should be chosen. The ball milled anode and cathode materials are applicable for large-scale production with better performance. These kinds of metal hydride nanocomposite could be utilized as a hydrogen storage material, as they possess superior hydrogen ions that could be transferred easily through the proton exchange membranes (PEM) in fuel cells. The technological challenges associated with the innovative utilization of milled nanocomposites in advanced applications still to be resolved.
# Cellular Antioxidant Effects and Bioavailability of Food Supplements Rich in Hydroxyrosol
## 3 Results
### Bioassays
#### 3.2.2 SOD and Catalase Activities  
To minimize the harmful effects of excess ROS, aerobic organisms have developed several lines of antioxidant defense, which are employed in addition to the direct action of antioxidant molecules [22]. Among these, the enzymatic defense plays a key role in oxidative damage prevention [23]. Catalase and superoxide dismutase (SOD) directly scavenge hydrogen peroxide and superoxide radicals, respectively, converting them into less reactive species.  
Given the performance of P-1 and P-2 samples in the CAA test, we evaluated the endogenous SOD and catalase defense in HepG2 human cells after treatment with both formulations, and then compared these to the untreated cells (baseline activity). Under the conditions tested, both supplements showed an increased SOD activity (\(p<0.01\)) in liver cells compared to the untreated control (Table 3). A similar effect on catalase activity was found with the P-2 sample, but not with the P-1 sample.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Treatment** & **Dilution Range \({}^{\dagger}\) (\(v\)/\(v\))** & **μmol QE/mL ** \\ \hline Sample P-1 & 1:250- 1:750 & 6.77 \(\pm\) 0.77 \({}^{\star}\) \\ Sample P-2 & 1:250- 1:750 & 9.20 \(\pm\) 1.57 \({}^{\star}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: CAA results. \({}^{\star}\) HepG2 cells’ viability \(>90\%\) in the dilution range tested; \({}^{\star\star}\) QE = quercetin equivalency; \({}^{\star}\)\(p<0.05\) according to \(t\) test.  
Figure 1: CAA of P-1 and P-2. Data are represented as box plots, showing median, 25–75% quartiles, standard error, and total range of values from 5 experiments. QE = quercetin equivalency.  
Similar results supporting the increase in catalase and SOD after treatment with phenolic-rich olive water were obtained by others through a series of cellular tests [20], and in vivo in rats' liver [24].  
In accordance with the results obtained in the CAA dosage, the P-2 formulation containing grape and olive extracts exerts a stronger antioxidant effect than the formulation without grape extract. This suggests a potent synergism of olive water polyphenols in the presence of grape extract, and could be explained by the different phenolic compositions, which include, among other things, grape-derived phenolic compounds, such as trans-resveratrol, anthocyanins and proanthocyanidins, as well as a further concentration of the olive vegetation water, which results in a higher content of hydroxytyrosol and tyrosol.
### Satellite Imagery  
In this study, PlanetScope satellite imagery was acquired from Planet Labs Inc. through submission and acceptance of the project proposal. Revisit times are almost daily worldwide with resampled spatial resolution of 3 m \(\times\) 3 m. The image products available for this study were from the third-generation sensors, Dove-R, with four bands ordered by increasing wavelength: (1) blue, (2) green, (3) red, (4) NIR (Table 3). The product scenes were approximately 25 km \(\times\) 23 km\(\cdot\) and in the case of the three study fields, all were in one image. Five satellite images were downloaded, matching the capture dates with the ground data collection dates to maintain consistency with the plant physiology and field conditions. The images were available for download as GeoTiff surface reflectance assets, orthorectified and radiometrically corrected based on the atmospheric conditions of the  
Figure 3: Flowchart of the methodology used in the study.  
specific ground locations. In ArcGIS, the large scenes were cropped to smaller images of each study field for subsequent processing.
## 5 Numerical Examples
### Model Parameter Definition  
Numerical values have been assigned to the POMDP model elements to implement the optimization process. These elements were defined for a continuous space. However, to provide a reference for comparison, a discrete problem formulation was also considered in this section. * _States_ (\(S\)): A continuous function was used to describe the level of pipeline deterioration as the percentage of the pipeline wall thickness (\(s\) = [0.0, 0.95]). A new pipeline would have a deterioration level close to 0% of the wall thickness, whereas a failed pipeline would have a value close to 95%. The initial state of the new pipeline was randomly determined using a normal distribution with a mean of 0.05 and a standard deviation of 0.003. The probability of landing at a specific state (\(s^{\prime}\)) depended on the initial state and action taken (Table 1). The specific value of the state was formulated as a function of Weibull distribution with a fixed lower bound to ensure the gradual deterioration of the pipeline (2017). For example, when the pipeline is in the deterioration range between 0.00 and 0.15 (0-15%) and a _Do-nothing_ action is taken, the succeeding state is estimated by a random function (0.15 + 0.85 \(\times\) (rand (Weibull (1.19, 0.69)))). Table 1 presents the random function that defines the probability distribution of the succeeding state for a CSPOMDP. The discrete version of the succeeding state probability distribution (for five core states) is given in Table 2.  
* _Actions (A)_: Four maintenance actions have been considered in the model, i.e., _Do-nothing, Inhibitor, Pigging_, and _Replace_. The implementation of these actions was as per the discussion of these maintenance actions in Section 3. The same application principles have been adopted for both discrete and continuous models. * _Observation (O)_: The observation represented the range of readings that an ILI reported as the succeeding state after performing an action. It is a discrete value ranging from 0% to 95%. A function was used to determine the observed value for a given succeeding state (\(s^{\prime}\)). A maximum of \(\pm 15\%\) variation in reading the state was assumed for the continuous observation (taken from [7]). \[O\big{(}s^{\prime}\big{)}=\left\{ \begin{array}{cl}0&if\;s^{\prime}=0.0\\ round(s^{\prime}*100)+random(-d:d)&if\;0<s^{\prime}<0.95\\ 95&if\;s^{\prime}=0.95\end{array} \right.\] (4) where \(s^{\prime}\) = succeeding deterioration level and d = variation value. For the discrete model observation, Equation (4) was converted into a function that creates variation to only five states reading. Equation (5) gives this formulation. \[O\big{(}s^{\prime}\big{)}=\left\{ \begin{array}{cl}1&if\;s^{\prime}+round \Big{(}\frac{random(-d:d)}{10}\Big{)}\leq 1\\ s^{\prime}+round\Big{(}\frac{random(-d:d)}{10}\Big{)}&if\;1<s^{\prime}+round \Big{(}\frac{random(-d:d)}{10}\Big{)}<5\\ 5&if\;s^{\prime}+round\Big{(}\frac{random(-d:d)}{10}\Big{)}\geq 5\end{array} \right.\] (5) where \(s^{\prime}\) = succeeding deterioration level and d = variation value.
## 3 The Control Strategy of FMSS for Maximum Consumption of DG
### The Objective Function for Consumption of DG Is Established  
#### 3.1.1 DG Maximizes Consumption Indexes  
The maximum consumption of DG refers to the maximum active power output of DG that can be absorbed by the distribution network under the constraints of system operation. Its value is mainly related to the distribution network structure, the system operation mode, the access mode, the access location and the access power factors of DG [20]. According to the theoretical analysis in the first part of this paper, the main factor limiting the consumption of DG is the maximum allowable value of voltage deviation. Therefore, for the relevant evaluation indexes of the maximum consumption of DG in the distribution network, this paper mainly considers two indexes, namely, the output of DG and voltage deviation. The calculation method of indexes is shown in (10) and (11).  
(1) The output of DG  
\[\text{min}f_{1}=\sum_{i\in N_{DG}}\left(P_{DG.\text{max}}-P_{DG.i}\right) \tag{10}\]  
where \(P_{DG.\text{max}}\) is the maximum active power output by DG; \(P_{DG.i}\) is the actual active power output of DG; and \(N_{DG}\) is the number set of DG.  
(2) Voltage deviation:  
\[\text{min}f_{2}=\sum_{l=1}^{M}\sum_{n=1}^{N_{t}}\abs{(U_{l,n})^{2}-1} \tag{11}\]In this paper, the advantages of using the above two indicators as the objective function is, in sampling time, to take all network node voltages and all of the DG output as optimization objectives in each sampling instant. Therefore, at every sampling moment, the voltage quality and DG output of the whole distribution network can be optimized to achieve a real-time operation effect. Compared with the objective function in references [15; 16] mentioned above, it has better real-time characteristics.  
Where, \(t\) is the network number, \(n\) is the number of nodes, \(U_{t,n}\) is the voltage per unit value of \(n\) nodes in network \(t\), \(U_{t}\) is the node set in each network, and \(M\) represents the number of network interconnection based on multi-terminal flexible DC devices.  
Considering the above two operating indexes, the optimization problem is transformed into a multi-objective optimization form. The multi-objective optimization problem cannot simply determine the optimal solution by comparing the value of the objective function. The linear weighted sum method is usually used to convert multiple targets into a single target, as shown in (12):  
\[\text{min}f=\lambda_{1}f_{1}+\lambda_{2}f_{2} \tag{12}\]  
where \(\lambda\) is the weight coefficient, \(\lambda_{1}+\lambda_{2}=1\).
# Experimental Validation of Peer-to-Peer Distributed Voltage Control System
## 4 Drive of the Rapid Prototyping Inverter with Voltage Support Function  
### PM15 FM30C Triphase Module  
DC/AC PM15 FM30C Triphase rapid prototyping inverter modules are used in the testbed to emulate prosumers with PV installations. A schematic diagram of the PM15 FM30C circuit is depicted in Figure 3. The PM15 FM30C module mainly consists of:  
1. A 15 kVA three phase inverter, consisting of three half-bridges with insulated-gate bipolar transistors (IGBTs).
2. A rectifier that can be connected directly to the AC voltage of the microgrid; it can be used to charge the DC bus in case one does not want to use a DC source.
3. An inductor-capacitor-inductor filter (LCL filter).
4. Three bypass resistors to limit the inrush current at the beginning of operation; these resistors are bypassed with a relay when the rapid prototyping module is running.
5. Current sensors to measure the current before and after the LCL filter.
6. Voltage sensors to measure the DC bus voltage and the AC voltage after the LCL filter at the grid side.
7. Control board to drive the IGBTs, control the switches K1-K6, and the fan of the module.  
Figure 3: Circuit diagram of Triphase PM15 FM30C rapid prototyping inverter.  
The PM15 FM30C inverter is programmed and operated through MATLAB/Simulink running on a computer. The computer communicates over Ethernet with an on-board PC-based Real-Time Target (RTT), which controls the Triphase power electronics as shown in Figure 4. Python (version 3.3, Python Software Foundation, Wilmington, DE, USA) has been used to code a software that manages the interface with the D2D communication module, fetches the Lagrangian multipliers from the D2D modules, stores the PV profiles, and solves a quadratic optimization problem with respect to local constraints as in (1). The software also manages the interface with MATLAB.  
MATLAB exposes an interface to the Python software, which allows the Python software to directly execute scripts in MATLAB. The Python software uses a MATLAB script to push updates on the PV profiles and \(\Delta P\) and \(\Delta Q\) set-points to Simulink. MATLAB also manages the interface with RTT to control the switches and fan of the PM15 FM30C.
## 4 Results
### Comparison of LEO IR and MW Sounder Data Assimilated
This is the reason why the number of assimilated data has a peak in the high-peaking CO\({}_{2}\) channels (650-680 cm\({}^{-1}\)) whose peaks of weighting function are located in the upper troposphere. The number of assimilated data tends to decrease with an increasing channel wavenumber after 650 cm\({}^{-1}\). For CrIS channels near the window region (800-1100 cm\({}^{-1}\)) that are sensitive to both the surface and cloud, only less than 10% of data are assimilated compared with the high-peaking channels in the CO\({}_{2}\) band (650-680 cm\({}^{-1}\)). This trend is reflected for all three experiments. Figure 5: _Cont._  
Similar to CrIS, the largest number of the ATMS data assimilated for both satellites (S-NPP and NOAA-20) is when data latency is reduced to 5 min (Figure 6). The limited number of observations assimilated for the surface-sensitive ATMS channels (channel 1-6; 23.8-53.6 GHz) and humidity channels (channel 16-22; 88.2-183.3 GHz) is due to the uncertain surface information (surface emissivity and surface temperature). Figure 5: The spectral distribution of mean assimilated data number of assimilated CrIS channels onboard (**a**) S-NPP and (**b**) NOAA-20 satellites for three trial runs for the period from 7 July to 22 August 2018. Black, red, and blue circles indicate the control, latency_20m, and latency_5m runs, respectively. Greyline means the reference brightness temperatures of CrIS 2211 channels in FSR mode. Figure 6: The spectral distribution of mean assimilated data number of ATMS channels onboard (**a**) S-NPP and (**b**) NOAA-20 satellites for three trial runs for the period from 7 July to 22 August 2018. Black, red, and blue circles indicate the control, latency_20m, and latency_5m runs, respectively.