esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Model  
### IK-MACHES: Improved K-Means with Mobility-Aware Cluster Head-Election Scored Algorithm  
The proposed protocol utilized the K-means clustering algorithm within a WSN to ensure uniform distribution of nodes across clusters, albeit at a slower rate compared to traditional hierarchical clustering methods. A recurring issue with this approach is the repeated election of the same node as the CH based primarily on its proximity to the cluster's center (CC) or its higher residual energy. This can significantly shorten the network's lifespan. In order to incorporate mobility into the improved K-means with CH election, especially for scenarios like autonomous vehicles, where node positions are dynamic, this paper adopted a dynamic cluster management and mobility-aware CH election mechanism. We named this algorithm the Improved K-means with Mobility-Aware CH-Election Scored (IK-MACHES) algorithm.  
For avoiding being a lost member node, continuous node tracking is managed by dynamic cluster management that tracks node position and recomputes the K-means clustering periodically or when significant movement is detected. Secondly, this IK-MACHES algorithm inputs mobility metrics such as predicted trajectory and speed into the CH election process, ensuring that CHs are not only energy efficient but also stable in terms of movement and connectivity. This enhanced IK-MACHES algorithm with mobility con sideration ensures that the WSN adapts to changes in node positions while optimizing energy use and maintaining effective communication paths. By integrating mobility into the scoring system, the algorithm offers a reliable solution for dynamic environments, where node positions frequently change, such as in autonomous vehicular networks. To address these issues, the protocol proposes enhancements to enhance energy efficiency and manage clustering more effectively. Re-clustering is restricted to the initial round of network activation and subsequent rounds, where additional nodes have depleted their energy, thus mitigating the time-consuming matter of frequent cluster reconfigurations.  
The proposed IK-MACHES algorithm is based on improved K-means clustering technique, designed to tackle the inherent inefficiencies in CH selection by employing traditional K-means clustering and LEACH protocol. This protocol operates in two primary phases: (1) initialization and (2) cluster-head election, each designed to optimize the distribution and management of nodes within the network.
# Increasing Ecosystem Fluxes Observed from Eddy Covariance and Solar-Induced Fluorescence Data
###### Abstract
However, most of these ML algorithms have not taken advantage of recent advances in remote sensing vegetation parameters, such as solar-induced chlorophyll fluorescence (SIF), which represents emitted electromagnetic radiation related to photosynthesis. Furthermore, only a few studies (e.g., FLUXCOM) have focused on the coupled relationships between carbon and water fluxes, such as water use efficiency (WUE), based on simultaneously derived GPP and ET. SIF is a spectral signal (650-800 nm) emitted by the photosynthetic center of plants under solar illumination, reflecting the complex physiological functions of terrestrial plants [24]. SIF has been widely regarded as a direct indicator of plant photosynthesis [25; 26; 27; 28] as it represents an energy flux emitted from plant chlorophyll molecules a few nanoseconds after light absorption [29]. Terrestrial GPP often shows strong correlations with SIF for almost all biomes in unstressed conditions [30], offering a more effective means to monitor photosynthesis compared to traditional vegetation indices like the Normalized Difference Vegetation Index and Enhanced Vegetation Index [28; 29; 31; 32; 33]. Given the physiological coupling between plant photosynthesis and transpiration, some studies have also shown that SIF could be used to estimate regional ET based on empirical SIF-ET relationships [34; 35]. [PERSON] et al. [36] demonstrated that SIF-based ET models outperformed other satellite-based methods. However, the tight relationship between SIF and ET is influenced by environmental factors such as air temperature and vapor pressure deficit (VPD), which are linked to soil and vegetation water evaporation [37; 38; 39]. Considering the limitations of simultaneous carbon and water fluxes datasets, we developed global GPP, ET, and WUE datasets (referred to as ECSIF) by combining site-level Eddy Covariance measurements with satellite-retrieval SIF products from 2001 to 2020. We first explored the relationships between GPP (or ET) and three SIF products for different plant functional types (PFTs) at the global FLUXNET sites. We then constructed and validated sixty PFT-specific machine learning models for GPP and ET, respectively, using ground measurements, two ML algorithms, and three SIF products across ten PFTs. Thereafter, we generated six ECSIF datasets of global GPP, ET, and WUE at 8-day temporal resolution and 0.05\({}^{\circ}\) spatial resolutions, based on gridded SIF, meteorological, and land cover products. The main objectives of this study are to (a) validate the effectiveness of SIF products in deriving site-level GPP and ET, (b) generate SIF-based GPP, ET, and WUE products with high spatiotemporal resolution on the global scale, and (c) explore the spatiotemporal variations in GPP, ET and their coupling WUE over the past two decades.
## 2 Materials and Methods
### Methods  
Figure 3 presents the research workflow of cotton root rot identification based on multifeature selection from Sentinel-2 image. It mainly includes four steps: (1) the airborne multispectral imagery was processed, and the cotton root rot classification and prescription maps based on the processed imagery were generated. (2) Spectral characteristics of the infested and healthy cotton in Sentinel-2A were analyzed; the spectral indices and texture features were extracted; the optimal spectral indices and texture features were identified by the RF. (3) Three BLR models were established using different  
Figure 2: Sentinel-2A color infrared (CIR) composite image with ground verification points for the study area.  
features based on spectral indices, texture features, and a mixture of spectral indices and texture features, respectively, and the corresponding cotton root rot classification and prescription maps were generated. (4) Confusion matrices, the overall accuracy (OA), producer's accuracy (PA), and user's accuracy (UA) were used to evaluate the feasibility of generating classification and prescription maps based on Sentinel-2 imagery.
# Quasi-Static Sliding Wear Analysis of 3D Rough Surface Considering Changes in the Point of Contact
## 2 Theoretical Background
### Contact Model  
Deterministic solutions to rough surface contact problems include the direct method, multi-level multi-summation, and the conjugate gradient method (CGM). In the process of the CGM, the contact problem can be solved quickly and efficiently if CC-FT is coupled based on the periodic nature of the rough surface. In the discrete convolution fast Fourier transform (DC-FFT), which deals with non-periodic problems, the domain length needs to be doubled. Periodic problems can also be solved with the DC-FFT, but it is inefficient because the domain must be extended [36].  
In the elastic contact problem, the following inequalities and constraints must be satisfied and can be solved by the CGM. For further details, refer to [37].  
\[\sum_{(k,l)\in I_{g}}K_{i-k,j-l}p_{k,l}=h_{i,j}+\alpha\qquad(i,j)\in I_{c} \tag{2}\]  
\[p_{i,j}>0\qquad(i,j)\in I_{c} \tag{3}\]  
\[\sum_{(k,l)\in I_{g}}K_{i-k,j-l}p_{k,l}\geq h_{i,j}+\alpha\qquad(i,j)\
otin I_{c} \tag{4}\]  
\[p_{i,j}=0\qquad(i,j)\
otin I_{c} \tag{5}\]  
\[\sum_{(i,j)\in I_{g}}p_{i,j}dxdy=F_{z} \tag{6}\]  
where \(K\) is the influence coefficient, \(p\) is the pressure, \(h\) is the gap between the two surfaces, \(\alpha\) is the rigid body displacement, \(I_{g}\) is the set of all nodes in the analysis domain, \(I_{c}\) is the set of nodes in contact, and \(F_{z}\) is the normal load. The convolution terms in Equations (2) and (4) are the elastic deformation \(u\) due to pressure, which can be efficiently obtained by the CC-FT.  
\[u=IDFT\Big{(}\widetilde{\widetilde{G}}\cdot\widetilde{\widetilde{p}}\Big{)} \tag{7}\]  
where \(\widetilde{\widetilde{G}}\) is the frequency response function (FRF) and \(\widetilde{\widetilde{p}}\) is the Fourier transformed pressure. According to the nature of the CC-FT, all values related to the CC-FT are repeated with the same period as the profile of the rough surface. That is, the surface height, elastic deformation, and pressure are also periodic functions with a period of \(L\), which is the total length of the analysis domain.  
Figure 2: Schematic of data movement to account for changing contact points of asperities.  
It is known that the asperity of the rough surface is initially plastically deformed [38]. In order to capture a plastic behavior, it is necessary to perform an elastic-plastic contact analysis. The elastic-plastic contact analysis is divided into elastic-perfectly plastic (EPP) and elastic-plastic (EP) according to the stress-strain curve [36]. EP is a model that considers the change in contact pressure due to plastic deformation under the surface. On the other hand, EPP is a simple method of setting the pressure to be equal to the hardness if the contact pressure exceeds the hardness of the softer material [39]. In order to reduce the computational burden, the EPP method, which does not require the stress analysis of the subsurface, is adopted in the current model.
## 2 Performance Support System
### Definitions, Principles, and Theoretical Foundations
#### 2.1.5 Introducing the Most Important Key Performance Indicators
Call drop Rate  
Once the user has successfully taken over the traffic channel, due to various reasons, he or she may lose connection with the network during the call. The most important reasons that lead to the interruption of a call within a network are weak power and poor signal quality. Improper coverage of the radio path, hardware faults, and non-optimal setting of parameters affect the signal strength. Interference, issues related to coverage and handover(neighbor loss, incorrect settings in positioning algorithms), and, finally, the wrong set of parameters, can also cause deterioration of the quality of the signal received by the user.
# Research on the Deep Deterministic Policy Algorithm Based on the First-Order Inverted Pendulum
## 4 Experimental Results and Analysis
### Results and Analysis
The improved DDPG controller suppresses overestimated Q values, allowing it to explore more effectively in the action space. Therefore, after each fluctuation, it will be appropriately shifted back, and compared to the DDPG controller, it effectively reduces the range of displacement fluctuations. In the phase of relative balance after the starting vibration, under the action of white noise, the maximum speed, angular displacement, and angular velocity fluctuation range of the DDPG controller are 0.9027 m/s, 0.0892 rad, and 0.8386 rad/s, respectively, and the maximum speed, angular displacement, and angular velocity fluctuation range of the improved DDPG controller are 0.7092 m/s, 0.0759 rad, and 0.7527 rad/s, respectively. Under the influence of interference force, the maximum speed, angular displacement, and angular velocity fluctuation range of the DDPG controller are 0.3821 m/s, 0.04243 rad, and 0.2410 rad/s, respectively, and the maximum speed, angular displacement, and angular velocity fluctuation range of the DDPG controller are 0.3595 m/s, 0.02551 rad, and 0.2119 rad/s, respectively. In the longitudinal comparison, due to the high frequency and large amplitude of white noise, compared with the interference force with low frequency mechanical characteristics, it will cause greater impact on system stability, so the fluctuation of the parameter results of white noise is greater than that of the interference force. The improved DDPG controller has a small range of fluctuation in speed, especially angular displacement and angular velocity, and it has strong anti-interference ability. It is within the motion control range throughout the entire waveform, except for overshoot, which must be generated during the initiation of vibration. Under the influence of interference force, the DDPG controller regains stability after being disturbed for 1.725 s, while the improved DDPG controller regains stability after being disturbed for 1.388 s. Compared to the DDPG controller, the improved DDPG controller has a faster response speed in horizontal comparison, and in the case of sudden disturbance to the controlled system, the interference received is small, resulting in faster restoration of stability. The four evaluation indicators of displacement, velocity, angular displacement, and angular velocity are all within a reasonable and controllable range.
## 2 A Multi-Source Real Data Model for Intercity Link Communication Propagation Computing
### WRF Models Regional Duct Distribution
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Options** & **Settings** \\ \hline  & **Nested Levels** & **Double Nesting** \\ \hline \multirow{4}{*}{**Coarse grid settings**} & Longitude and latitude coordinate of grid center & (120.5\({}^{\circ}\)E, 31.035\({}^{\circ}\)N) \\  & Number of grids & 192 \(\times\) 384 \\  & Horizontal resolution & 3 km \(\times\) 3 km \\ \hline \multirow{4}{*}{**Fine grid settings**} & Position in the coarse grid & (32, 20) \\  & Number of grids & 123 \(\times\) 246 \\ \cline{1-1}  & Horizontal resolution & 1 km \(\times\) 1 km \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter settings of the WRF numerical simulation. Figure 1: Procedures of the WRF pre-processing system. Figures 4 and 5--we calculated the duct height and duct intensity distributions on 1 June and 1 December 2018 using WRF mesoscale numerical modeling software. As shown in Figure 4, the average distribution of duct strengths between cities in June is more than 60 m, the intercity link from Shanghai, Jiaxing, to Wuxi also has a hybrid duct phenomenon of up to 240 m, and its strength can reach up to 35 M-units or so, which is shown in Figure 5a. In December, the marine ducts in this area were densely distributed, and the heights and strengths of the ducts were significantly higher than those in coastal cities. In inland areas, the duct heights on the Nanjing-Wuxi link were basically distributed at about 40-80 m, while duct heights on the intercity link of Shanghai-Wuxi and Jiaxing-Wuxi could reach more than 200 m; the average duct strength was about 20 M-units. Considering the limitation of the installation height of the communication signal antenna, based on the duct distribution results simulated by the real data using WRF, this paper studied the over-the-horizon transmission of the 5G communication signal on the intercity link when the non-uniform surface-based duct with the trapping layer height was 40-80 m.
## 3 Results and Discussion
### Chemical Composition Analysis
#### 3.1.4 Analysis of the Three-Phase Diagram  
[PERSON] [46] conducted extensive research on SiO\({}_{2}\) + Al\({}_{2}\)O\({}_{3}\) and flux oxides in the composition of lightweight aggregate source materials. He marked the proportions that can meet the bloating requirements in the three-phase diagram with dotted lines (shown in Figure 3). For the values within the marked area, it means that the composition of the material belongs to those lightweight aggregate materials that can produce high-plasticity fluid. Accordingly, among the chemical components of the water purification sludge and textile sludge samples, the contents of SiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\), and fluxing oxide are counted in Table 5. According to the calculation results in Table 5, the three-phase diagram of the chemical composition of each sludge was drawn. As shown in Figure 7, only the results of the water purification sludge were located within the area of the three-phase diagram that indicate the appropriate melt viscosity and chemical compositions suggested by [PERSON]. This result shows that the water purification sludge should have the potential, as raw materials, to be sintered into lightweight aggregates. In contrast, the SiO\({}_{2}\) contents of the three textile sludge samples, which exceeded the range of the three-phase diagram, were too low to be presented in Figure 7.  
According to the above chemical composition analysis results, this study used water purification sludge as the main raw material. Textile sludge was used as an auxiliary raw material, and its blending amounts were 7.5%, 15%, and 22.5% (by weight). The chemical composition of each mixed sample is shown in Table 6. In addition, the calculation results of the chemical composition of each mixed sample were drawn into a three-phase diagram, as is shown in Figures 8-10. It can be seen from Figure 8 that after the water purification sludge was blended with the 7.5% and 15% textile sludge of Sample A, the compositions were within the \"bloating area\" and thus considered suitable for producing lightweight aggregates. It can be seen from Figures 9 and 10 that after the water purification sludge was blended with the 7.5% textile sludge of Sample B or Sample C, its composition was within the \"bloating area\" for sintering. However, the composition of the water purification  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**Range Proposed by [PERSON]**} & \multicolumn{4}{c}{**Code**} \\ \cline{3-6}  & & **Sample A** & **Sample B** & **Sample C** & **WPS** \\ \hline SiO\({}_{2}\) (\%) & 53–79 & 14.35 & 6.86 & 4.43 & 58.84 \\ Al\({}_{2}\)O\({}_{3}\) (\%) & 12–26 & 44.83 & 17.55 & 24.07 & 19.30 \\ Fluxing (\%) & 8–24 & 15.09 & 2.36 & 2.21 & 13.08 \\ Total (\%) & - & 100 & 100 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Composition analysis based on the three-phase diagram.  
Figure 7: Three-phase diagram of the water purification sludge.  
sludge blended with a 15% and a 22.5% textile sludge was not within the \"bloating area\" for sintering.
# Search of the Shortest Path in a Communication Network with Fuzzy Cost Functions
## 5 Application to a Communications Network. Fuzzy Functions and Strategies
### Strategies for the Search of Path with Minimum Cost
several paths with the same number of hops, the strategy searches for the one with the maximum residual bandwidth. Figure 4 shows the application of WS to the same network illustrated in Figure 3. The paths with the minimum number of hops are \(P_{B}\) and \(P_{D}\). Among them, the strategy WS chooses the path \(P_{B}\) because it has the maximum Residual Bandwidth used bandwidth in its worst link. _Strategy 7_: (Fuzzy Widest-Shortest, FWS). Using the same definition of fuzzy cost as in Strategy 5, the same selection pattern is followed as in Strategy 6. This strategy competes with Strategy 6. Finally, we propose a new strategy (Strategy 8), which uses a new fuzzy cost function that has no crisp equivalent since it is an empirical adaptation of the generalized fuzzy number model described above. This new strategy and its cost function are explained in detail below. _Strategy 8_. It can be verified that the smaller the bandwidth used during the interval \((n-1)\)-th time (period where the measurements were made), the higher the degree of uncertainty in the values considered for the used bandwidth at the \(n\)-th time interval. We can explain the above statement by the following two factors,  
1. The percentage of variation. When a link has a very low used bandwidth, a new connection causes a high relative variation in the value of the occupied bandwidth. On the contrary, if the used bandwidth is large, a new connection will have little impact on the relative variation. 2. The routing algorithm itself. The routing algorithm prioritizes the least used links so that the smaller the occupation of a link is, the most likely this parameter varies significantly in the next time interval. These reasons lead us to redefine the fuzzy number that describes the occupation in Strategy 3 (Equation (9)), which is multiplied by a coefficient. The _Modified Fuzzy Normalized Used Bandwidth_ in a link \((i,j)\) is defined in Equation (11). \[\widetilde{\text{Cost}_{\text{S}8}}=A*B^{-\frac{[\widetilde{\text{I}w}]_{ij}}{ [\widetilde{\text{I}w}]_{ij}}}, \tag{11}\]  
In practice, this operation produces a displacement to the right and a widening of the fuzzy number. In our experiments we set \(A=10\) and \(B=20\).
# Optical Wireless Communications Using Signal Space Diversity with Spatial Modulation
## Abbreviations  
\begin{tabular}{l l} BER & Bit-error-rate \\ CAP & Carrierless Amplitude and Phase Modulation \\ IM/DD & Intensity Modulation/Direct Detection \\ LOS & Line-of-sight \\ MFs & Matched Filters \\ MIMO & Multiple-input-multiple-output \\ MISO & Multiple-input-single-output \\ ML & Maximum Likelihood \\ OWC & Optical Wireless Communication \\ QAM & Quadrature Amplitude Modulation \\ RF & Radio Frequency \\ SM & Spatial Modulation \\ SMux & Spatial Multiplexing \\ SSD & Signal Space Diversity \\ \end{tabular}
## 4 State of Art of the Smart Energy Management Systems
### Power Grid Key Performance Indicators  
The monitoring of the electrical grid requires several types of data to be precalculated or acquired from sensors. As shown in Figure 8, these data are classified as basic readings, energy reading and power quality which are directly alimented by the power meters, and the forecasting algorithms of the energy demand response which are based on data collected and stored in the database.  
In the energy SCADA system, the most important type of visualization are charts, to supervise alarms, and critical conditions, current and voltage charts show the line main defects and failures, harmonic and instantaneous power charts shows the power quality condition in real time to help taking an action if needed, load curve, generation curve and the forecasted profile can help the supervisor to take a decision on a load or a power flow [15].  
Figure 6: Physical, software and artificial intelligence layers implementation scheme in a mining industry. Adapted with permission from Ref. [3]. Copyright 2022 IEEE.  
Figure 7: Smart energy management system general subsystems.  
Other KPIs such as generation reliability, shortage, voltage stability, total generation, and total load, give a general view of the microgrid condition, and calculate the financial KPIs, maintenance, replacement cost, capital cost, then calculating the environmental KPIs CO2 emission, and the energy deficiency.  
As discussed in the state of art section, the literature review on implementing an energy management system infrastructure in the mining industry, precisely the open-pit mine, these systems are not widely used. The lack of these infrastructures will stop the energy 4.0 transformation which will slow down the implementation of smart energy management features. The tested and proposed architecture enables real-time energy monitoring, load forecasting and grid state of health estimation using machine learning algorithms. The most used algorithms to predict the energy consumption are quantile regressions. In this paper we propose the fast forest quantile regression technique. The data are recorded into the system database which enables the system to integrate new prediction models as features. This architecture helps to add more electrical grid classification algorithms and defect detection as shown in Figure 4. So, implementing this architecture is the first step to take for digital transformation in the mining industry.