esa-sceva/satcom-chunk-collection huggingface dataset:
# WGAN-Based Realization Process of Gravel Soil for Hydraulic Property Simulation
## 4 Generation of Gravel Soil Models and Evaluation Criteria
### Hydraulic Behaviors  
Figure 6 displays the velocity fields from various realizations and their corresponding training sets. These fields reveal distinct areas of high and low velocity, with some regions showing stagnant flow. Notably, the velocity fields across different realizations within the same group exhibit similar characteristics. This consistency suggests that within each specific group, the patterns of fluid-flow and seepage behavior remain uniform across various realizations. The observed similarity in velocity fields implies that the flow through the porous medium follows a consistent pattern irrespective of the individual realization.  
The hydraulic behaviors are characterized by the permeability, and calculations may be derived to convert intrinsic permeability \(\kappa\) to hydraulic conductivity \(k_{h}\). Generally, there is the expression for their relationship is:  
\[k_{h}(\text{in cm/s})=\kappa\Big{(}\text{in }\mu\text{m}^{2}\Big{)}\times 9.7 \times 10^{-4} \tag{8}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Fluid Dynamic Viscosity \(\mu\) [Pa-s] & \(1.0\times 10^{-3}\) \\ Fluid Density \(\rho\) [kg/m\({}^{3}\)] & 1000 \\ Porosity \(\varepsilon_{p}\) of Group 1 [\%] & 21.8 \\ Porosity \(\varepsilon_{p}\) of Group 2 [\%] & 19.1 \\ Porosity \(\varepsilon_{p}\) of Group 3 [\%] & 18.2 \\ Cross-section area of a Model 5 [mm\({}^{2}\)] & 5.0 \\ Length Along a Fluid-Flow Direction \(L\) [mm] & 2.24 \\ Pressure Difference Applied \(\Delta p\) [kPa] & 20.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters for the simulated model of all realizations.  
Figure 6: Three-dimensional fluid-flow diagrams in the pore space of the training set and its corresponding generation model. The colored flow lines represent changes in velocity in the pore space.  
Intrinsic permeability is a property of the porous medium itself, while hydraulic conductivity incorporates the properties of both the porous medium and the fluid. Intrinsic permeability quantifies the ease of fluid flow through the porous medium, while hydraulic conductivity represents the actual flow rate of the fluid under a given pressure gradient. Table 3 lists the statistical intrinsic permeability of each group of soil models based on fourteen realizations. The corresponding hydraulic conductivities are converted based on Equation (8).  
Table 3 shows the results from this research, where we present data comparing hydraulic conductivity values between the prototype's and realizations' averages (14 same scale-sized models). It indicates that there is consistency or similarity in hydraulic conductivity values between the prototype and the realizations, particularly within the standard range typical of gravel soil, at approximately \(10^{-2}\) cm/s. This agreement is an important finding as it indicates that the measured or calculated values align well with the expected values from the prototype. The exact degree of consistency and similarity is discussed in the next section.
## 3 Fusion of FL and Medical Domain
### Need of FL in Medical Domain  
With the proliferation of wearable devices and IoT-powered architecture in the medical domain, the amount of medical data is steadily increasing, which can offer many  
Figure 3: Classification of FL paradigm based on data characteristics.  
Figure 2: Illustration of the major taxonomies of the FL system.  
opportunities for innovation and knowledge extraction. However, the data owned by one hospital cannot be easily outsourced to another similar hospital/medical institute due to privacy and regulatory concerns. Moreover, each hospital can own diverse data, and training AI models over it cannot be generalized to other hospital data, leading to a data island problem. However, in some cases, the model training on diverse data owned by different hospitals is imperative when a new pandemic emerges or a rare disease occurs. However, sharing data for great good or training an AI model that can generalize to unseen data is a long-standing problem in the AI community. Thanks to the emergence of the FL, data exchange across hospitals is no longer required, and therefore, the privacy/regulatory requirements can be easily met whereas high-quality AI models can still be trained over scattered data. With the help of FL, high-quality AI models can be trained that exhibit higher generalization power and accuracy in real-world cases because models deployed in hospitals can benefit from the experience of other models. Furthermore, the population from one hospital to another is different, which can significantly contribute to a gain in the generalization power of the respective AI models. The use of FL in the medical domain can contribute to lower treatment costs, improved treatment, discovery of clinical practices, and lowering the burden of healthcare professionals.
## 3 Materials and Methods
### Inertia Estimation Using RoCoF  
In this study, alongside evaluating RoCoF and Col RoCoF using the MEMD approach, an estimation of the inertia constant of the power system will also be conducted using the mentioned method based on RoCoF estimations.  
Let \(df_{i}/dt\) represent the locally estimated RoCoF at location (\(i\)). Additionally, it is proposed that \(df_{i}/dt\) represents the residual obtained by empirically decomposing the synchronized frequency measurement signals gathered from the various nodes within the system (\(i\)). The residue of the observed decomposed signal accurately describes the change of the center of inertia, making it suitable for estimating the rate of change of frequency of the center of inertia [25,38]. Assuming that the locally estimated RoCoF is an adequate approximation of the actual CoI RoCoF, the following can be written:  
\[2H_{CoI}\frac{df_{i}}{dt}|_{t=0^{+}}=\Delta p_{i} \tag{16}\]  
Given that the CoI RoCoF is directly related to the size of the disturbance, there is a concept to use this relationship to modify the estimation of inertia in response to varying conditions within the system. The MEMD approach is applied to the time series of frequency deviations following disturbances, where the residual indicates the trend of the analyzed signal. This should be effective for evaluating the actual CoI RoCoF (\(df_{col}/dt\)). The fluctuation in active power within the power system, such as a reduction or increase in load or generation, is denoted as \(\Delta p\). The MEMD estimation results of the frequency deviations represent the average gradient of the frequency on the observed time interval (\((((x_{(i+1)}-x_{i}))/\Delta t)\), \(\Delta t=0.01\) s). The sampling rate is \(100\) Hz and \(\Delta t\) represents the time interval between consecutive data points. This approach employs a single window, monitoring the frequency response immediately following a disturbance at \(t=0\).  
Equation (17) derives the average frequency gradient across windows containing \(M\) data samples. Each window spans a duration of \((M-1)\Delta t\). This approach calculates the average RoCoF, facilitating the estimation of the inertia constant:  
\[RoCoF=\frac{1}{M}\sum_{t=0}^{t_{end}}\frac{d\Delta\omega(t)}{dt}=\frac{1}{M} \sum_{t=0}^{t_{end}}\frac{dres(t)}{dt} \tag{17}\]  
Based on Equation (16), system inertia can be determined as:  
\[H=\frac{f_{0}\cdot\Delta p}{2\cdot RoCoF} \tag{18}\]  
where \(f_{0}\) denotes base system frequency. Governor control timing significantly relies on the system's inertia (\(H\)) to manage frequency response before critical levels. High inertia ensures stable systems with low RoCoF; conversely, low inertia systems pose risks due to high RoCoF, leading to rapid frequency spikes triggering protection systems. Given the time-dependent nature of inertia related to intermittent converter-interfaced generation, estimating and monitoring these values is crucial.
## 3 Materials and Methods
### UAS Surveys
Satellites from both the GPS and the GLONASS systems are used in the positioning process. 4. **Snow depth survey. ** Snow depth was probed at the GCP and samples positions, repeated five times with a 3 m long avalanche probe marked with a cm scale (Figure 3c). The probing was initially performed directly below the mark of the circular red-crossed plates and thereafter four times around them to validate the snow depth measurement. Multiple measurements were needed to assess the presence of small ground irregularities under the snow cover.
# Post-Quantum Delegated Proof of Luck for Blockchain Consensus Algorithm
## 2 Related Work  
### Blockchain Consensus Algorithms  
Consensus algorithms are essential for ensuring that blockchain networks operate securely and without a central authority. They allow nodes to agree on the validity of transactions and the addition of new blocks, maintaining the integrity and consistency of the network. Without consensus algorithms, the blockchain would be vulnerable to attacks, inconsistencies, and double-spending, undermining its reliability and trustworthiness.  
Proof of work (PoW) involves participants solving complex mathematical problems, with the first to find a solution creating a block and earning rewards. PoW prevents double-spending and network attacks by requiring participants to solve complex mathematical problems to validate transactions. However, this process is highly energy-intensive and requires substantial computational resources, which leads to centralization, as only those with powerful mining hardware can compete effectively [6].  
To address these issues, proof of stake (PoS) was introduced, which allocates block creation rights based on participants' stake percentages [7]. PoS introduces challenges like the 'Nothing at Stake' problem, where validators can vote for multiple chains without penalties. Additionally, those with larger stakes could dominate the network, undermining security and decentralization. DPoS improves scalability by allowing token holders to elect representatives to manage the consensus, but this can lead to power concentration, increasing the risk of network monopolies and vote manipulation [8].  
Proof of elapsed time (PoET) [9] assigns block creation rights after a randomly assigned waiting period, which helps maintain network safety in low-power environments. However, the reliance on trusted execution environments (TEEs) introduces vulnerabilities, as TEE failures or security breaches could compromise the consensus process.  
Proof of luck (PoL) [10] randomly assigns block creation opportunities to prevent centralization and promote fairness. However, its reliance on trusted execution environments (TEEs) limits participation to those with specific hardware, reducing diversity and making the network dependent on hardware configurations.
# Overview of the Research Status of Intelligent Water Conservancy Technology System
## 3 Status Quo of Key Technologies of Smart Water Conservancy
### Digital Twin Platform Technology
#### 3.4.2 Construction of Digital Twin Scenarios Integrating BIM and GIS  
The integration of BIM and GIS plays a key role in digital twin water conservancy, achieving data integration through standardized data interfaces and metadata management, and successfully achieving integration between models through spatial reference frame alignment to support multi-level data presentation and interaction. The research shows that [59] the multi-dimensional water conservancy digital twin scene is constructed by integrating BIM and the oblique photography model into the 3D GIS platform, which provides data support for the water conservancy GIS + BIM scene. The BIM model is transformed for GIS scenarios, and GIS and BIM data are integrated to generate paging scheduling logic of LOD (Level of Detail) models at all levels, realize lightweight integration of water GIS + BIM scenarios, and provide data support for digital twin platforms [13].
## 2 Materials and Methods
### Methods
#### 2.3.2 Specific Surface Area by BET Method  
Brunauer-Emmett-Teller (BET) [25] tests were used for specific surface area (SSA) measurements, isotherm adsorption measurement and pore size distribution characterization. The BET tests were carried out with a Tristar II Plus device. The specific surface area (SSA) designates the relation of the surface of the material to its mass. It is therefore expressed in m\({}^{2}\)/g in the international system. It depends mainly on the size of the particles, their shape and their surface condition. A nitrogen (N\({}_{2}\)) layer is injected into accessible surface inside the compacted specimen. The total of surface covered by the nitrogen molecule corresponds to the specific surface area accessible inside the sample. The measurement and back calculation are carried out 88 times. The SSA results obtained for WSA, GGBS and cement as raw materials are summarized in Table 4.  
The BET specific surface area of the cement is 0.91 m\({}^{2}\)/g and can be considered a typical value for cements. The specific surface area of GGBS is less than that of the cement. Fly ash (WSA) has a significantly higher surface area than that of cement and GGBS, which should imply a higher water demand than other binders.
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 5 K-Means Clustering Results and Discussion
### Clustering Stability Analysis  
To assess the robustness of the clustering process to random initialization, a quantitative evaluation of clustering stability using the Adjusted Rand Index (ARI) [30] and Adjusted Mutual Information (AMI) [31] was used. ARI is used for evaluating the similarity between two clusters. It improves upon the Rand Index (RI) by correcting for chance--the chance that some data points can end up in the same cluster regardless of the underlying similarity. The RI uses two pieces of information, object pairs put together and object pairs assigned to different clusters in both partitions [32]. Generally, an ARI score closer to 1  
Figure 9: K-means Clustering for k = 50 (for two Principal Components).  
indicates that the clustering algorithm was successful at capturing the underlying patterns in the data, and an ARI closer to zero indicates poor clustering performance [30].  
AMI is calculated based on the mutual information between two clusters. It measures the amount of information that one cluster provides about the other. There is an adjustment for chance in AMI too. This adjustment for chance in AMI is achieved by subtracting the expected mutual information of two random clusters from the observed mutual information. AMI scores range from zero, which implies no mutual information, to one, which implies perfect mutual information. That is, higher scores imply greater similarity between clusters [31].  
For our analysis, K-means clustering with k = 325 was used, since this yielded the best overall performance in our experiments, across 10 different random seeds. Pairwise comparisons of the resulting cluster assignments were computed to measure consistency across runs. The analysis yielded an average ARI of 0.9967 and an average AMI of 0.9917, with standard deviations of 0.0041 and 0.0025, respectively. These results indicate a high degree of stability and reproducibility in our clustering results.
## 3 Results and Discussion
### Important Features
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline [PERSON] & Iran, Regional & RE, SVR, & Sentinel-1, & & & & & & \\ and [PERSON] [63] & Iran, Regional & MLE, CNN & Sentinel-2 & & VV, VH, incident angle, NDVI & & & & \\ \cline{2-8} [PERSON] & Iran, Regional & ELM & SMAP, MODIS & Precipitation, NDVI, LIST & - & 5 & - \\ et al. [149] & Iran, Regional & PSO-ANFIS, & MODIS & & & & & \\ [PERSON] et al. [150] & Iran, Regional & PSO-SVR & Landsat 8 & Evaporative fraction, TVDI, VTCI, STVDL, TVX & - & & SSM & PSO-SVR \\ \cline{2-8} [PERSON] et al. [151] & Iran, Regional & PSO-CMAC, & & & & & & \\  & PSO-GMDH & Sentinel-2 & NDVI, NDWI, all bands from Sentinel-2, slope, aspect, elevation & TDR & 5 & \ \begin{tabular}{l} PSO- \\ CMAC \\ \end{tabular}  \\ [PERSON] et al. [70] & Iran, Regional & LSTM, CNN, LSTM & Sentinel-2 & & & & & \\  & CNN-LSTM & Sentinel-2 & & & & & \\  & & & & & & \\ \cline{2-8} [PERSON] et al. [152] & Iran, Regional & MLR, CART, MSP, & Landsat & & & & \\  & GBM, RF & SWI, sand, silt, clay, BD & & & & \\ \cline{2-8} [PERSON] & Iran, Regional & RF, GBM, & SMAP, AMSR2, & Elevation, slope, sand, silt, LST, SSM, NDVI, precipitation & TDR & 5 & \begin{tabular}{l} XGBoost, \\ GBM \\ \end{tabular}  \\ \cline{2-8} [PERSON] et al. [154] & Iran, Field & M5 tree, RF, & & & & & & \\  & SMOrog, MLP & UAV & DL, RL, NDM, PI & & & & \\ \cline{2-8} [PERSON] et al. [58] & Switzerland, & SVR, MLP & Radiometer- & VV, VH, emissivity polarizations, incident angle & & & \\  & & & & & & \\ \cline{2-8} [PERSON] et al. [155] & Slovenia, Regional & FFB Neural & TerraSAR-X & Backscatter coefficients & & & & \\ \cline{2-8} Pongrac & Slovenia, & & & & & & \\  & Regional & CNN & ALOS-2 & - & & & SSM & - \\ \cline{2-8} [PERSON] et al. [157] & Italy, Regional & RF, SVM & GNSS & & & & & \\ \cline{2-8} Notarnicola & Italy, Regional & SVR & ASAR WS sensor, & & & & & \\  & & MODIS, ECBOp & & VV, VH, slope, elevation, aspect, land cover, soil type & - & SSM & - \\ \cline{2-8} [PERSON] et al. [59] & Italy, Regional & SVR & RADARSAT-2, & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \cline{2-8} [PERSON] et al. [160] & Italy, Regional & ANN & Sentinel-1, SMAP & Brightness temperature, acquisition geometry, land use, NDVI & - & SSM & - \\ \cline{2-8} [PERSON] et al. [161] & Spain, Regional & ANN & Sentinel-2, & Precipitation, reflectance, LST, elevation, slope, SM, land cover, & & & \\  & & & & & & \\  & & & & & & \\ \cline{2-8}   [PERSON] et al. [161] & Spain, Regional & ANN & MODIS, ERA5, & NDVI, NDWE, EVI, GNDVI, SAVI, NDMI, MSI, NBRI, & - & & \\  & & & & & & \\ \cline{2-8}  & & & & & & & \\ \cline{2-8}  & & & & & & \\ \cline{2-8}  & & & & & & \\ \cline{2-
# Satellite-Based Estimation of Carbon Dioxide Budget in Tropical Peatland Ecosystems
###### Abstract  
Tropical peatland ecosystems are known as large carbon (C) reservoirs and affect spatial and temporal patterns in C sinks and sources at large scales in response to climate anomalies. In this study, we developed a satellite data-based model to estimate the net biosphere exchange (NBE) in Indonesia and Malaysia by accounting for fire emissions (FE), ecosystem respiration (\(R_{e}\)), and gross primary production (GPP). All input variables originated from satellite-based datasets, e.g., the precipitation of global satellite mapping of precipitation (GSMaP), the land surface temperature (LST) of the moderate resolution imaging spectroradiometer (MODIS), the photosynthetically active radiation of MODIS, and the burned area of MODIS fire products. First, we estimated the groundwater table (_GWT_) by incorporating LST and precipitation into the Keetch-Byram Drought Index (KBDI). The _GWT_ was validated using in-situ measurements, with a root mean square error (RMSE) of 24.97 cm and an r-squared (\(R^{2}\)) of 0.61. The daily _GWT_ variations from 2002 to 2018 were used to estimate respiration (\(R_{e}\)) based on a relationship between the in situ _GWT_ and flux-tower-observed \(R_{e}\). Fire emissions are a large direct source of CO\({}_{2}\) from terrestrial ecosystems into the atmosphere and were estimated by using MODIS fire products and estimated biomass. The GPP was calculated based on the MODIS GPP product after parameter calibration at site scales. As a result, averages of long-term (17 years) \(R_{e}\), GPP, FE, and NBE from whole peatlands in the study area (6\({}^{\circ}\)N-11\({}^{\circ}\)S, 95-141\({}^{\circ}\)E) were 66.71, 39.15, 1.9, and 29.46 Mt C/month, respectively. We found that the NBE from tropical peatlands in the study area was greater than zero, acting as a C source. \(R_{e}\) and FE were influenced by El Nino, and the value of the NBE was also high in the El Nino period. In future studies, the status of peatland degradation should be clarified in detail to accurately estimate the C budget by applying appropriate algorithms of \(R_{e}\) with delineations of types of anthropogenic impacts (e.g., drainages and fires).  
groundwater table; peat decompositions; fire emissions; gross primary production; net biosphere exchange; Southeast Asia 202021250; doi:10.3390/rs12020250
# Game Approach to HDR-TS-PV Hybrid Power System Dispatching
###### Abstract  
Hot dry rock (_HDR_) power stations have the potential to serve as an energy storage system for large-scale photovoltaic (_PV_) plants. For flexible operation, thermal storage (_TS_) power stations are required to coordinate with _HDR_ power stations. In this study, a hybrid power system is constructed by combining the _HDR_, _TS_, and _PV_ plants. Game theory is then introduced into the optimal dispatch of the hybrid power system. Considering _HDR_, _TS_, and _PV_ as players, non-cooperative and cooperative game dispatching models are established and verified by a case in the Gonghe basin of Qinghai. Finally, the stability of the coalitions and the rationality of allocation of the hybrid power system is verified, and the sensitivity of critical parameters is analyzed. The results demonstrate that the overall payoff of the hybrid power system is increased by 10.15%. The payoff of the _HDR_ power station is increased by 16.5%. The _TS_ power station has obtained 50% of the total extra profits. The _PV_ plant reduces the impact on the grid to obtain the priority of grid connection. Based on these results, a theoretical basis can be provided for developing generation systems based on the _HDR_ resources in the Gonghe Basin.  
hot dry rock; hybrid power system; game theory; coordinated dispatching; Shapley value _Acticle_  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], S. Game Approach to HDR-TS-PV Hybrid Power System Dispatching  
1 Qinghai Key Lab of Efficient Utilization of Clean Energy (TusInstitute for Renewable Energy).  
Qinghai University, Xining 810016, China; [EMAIL_ADDRESS] (Y.S.); [EMAIL_ADDRESS] (X.C.); [EMAIL_ADDRESS] (T.Z.); [EMAIL_ADDRESS] (S.M.) 1
## 4 Implementation
### Proposed Scheme
#### 4.4.2 Detect Signal  
As previously discussed, we added a preamble into each packet to detect the start frame. This is a special bit sequence, whose definition is based on the RLL code that was used, and which both the transmitter and receiver know in advance. A preamble has two tasks. Firstly, the receiver can classify the signal light source and unexpected light sources (such as background light and noise light). As shown in Figure 8, the signal noises have intensity values changing chaotically while the intensity values of the expected signal are modulated based on On-Off-Keying modulation with discriminating intensity values represented for 0 level and 1 level in On-Off-Keying modulation, which is shown in Figure 9. By using the preamble part for each data frame, payload data is inputted between two preambles. By looking, we can easily and rapidly categorize the expected signal and noise signal. At the receiver side, we just need to detect the known preamble at all signals. If the received signal stream has the preamble part, it is the desired signal and vice versa. The preamble's second task is to help the receiver identify the starting point of the transmission frame. After categorizing the expected noise, the signal is passed through the matched filter to detect the position start frame. The desired data is in the middle part, between two adjacent preambles. Then, we can decode and categorize data packets from any user, based on the LED-ID and Node ID, which is shown in Figure 7.  
Figure 8: The intensity of noise signal.  
Figure 7: The proposed data frame structure for the multiple-input multiple-output (MIMO)-OOK scheme.