esa-sceva/satcom-chunk-collection huggingface dataset:
# Precise Identification of Coal Thickness by Channel Wave Based on a Hybrid Algorithm
## 4 Results
### Performance Assessment  
#### 4.4.1 Accuracy and Stability Assessment  
First, the samples were selected on the basis of the coal thickness value recorded in the process of mining. In order to show the accuracy of the algorithm comprehensively, samples of 24 points were selected evenly in the survey area, and the density of samples was increased in areas of coal thickness variation. From the correlation coefficient \(r\) (Figure 7a) and the relative error (Figure 7b) between the  
Figure 6: Distribution of coal seam thickness within survey areas. (**a**) Coal seam thickness distribution with SIRT algorithms; (**b**) coal seam thickness distribution applying GA algorithms; (**c**) coal seam thickness distribution employing GA-SIRT algorithms.  
calculated coal thickness result (\(H\)) and the mining result (\(L\)), we can see that the inversion result of the GA-SIRT is closer to the real value with higher accuracy.  
\[r(H,L)=\frac{Cov(H,L)}{\sqrt{Var[H]Var[L]}} \tag{6}\]  
\[\delta=\frac{|H-L|}{L}\times 100\% \tag{7}\]  
where \(r\) is the correlation coefficient, \(\delta\) is the relative error, \(H\) is the calculated results of coal thickness, and \(L\) is the coal thickness exposed in the production process. \(Cov(H,L)\) is the covariance between \(H\) and \(L\), \(Var[H]\) is the variance of \(H\), and \(Var[L]\) is the variance of \(L\).  
Single inversion results may have a certain randomness, therefore, 15 iterative calculations were performed on the four selected coal thickness sample points, using GA, SIRT, and the hybrid algorithm (Figure 8). Comparing the results, we can see that iterative divergence occurred when the SIRT was used independently. This is because the SIRT algorithm has local convergence when the initial model is poor, and the corresponding inversion result is unstable. When the proposed hybrid algorithm was applied for the coal thickness calculation, the average relative error was less than 10%. Hence, the GA-SIRT exhibited a better accuracy rate and stability performance compared with the GA and SIRT.  
Figure 8: \(Cont.\)  
Figure 7: The correlation coefficient results (**a**) and the relative error results (**b**) of coal thickness between disclosed and calculated by GA, SIRT, and the hybrid algorithm (GA-SIRT) respectively.
## 2 Methodology
### Shield Shell-Soil Interaction Model
#### 2.3.3 Solution of Vertical and Horizontal Forces of Shield-Soil Interaction  
Through the ground reaction curve (Figure 9), considering the change of the shield attitude, the vertical and horizontal interaction forces between the shield and the soil are deduced and analyzed under the premise of small deformation.  
The ground reaction curve [24], which shows the relationship between the ground displacement and the earth pressure acting on the shield periphery as shown in Figure 9, can be represented by  
\[K_{i}(U_{i})\ =\ \left\{\begin{array}{ll}(K_{io}-K_{imin})\tanh\left[\frac{a_{i}U _{i}}{K_{io}-K_{imin}}\right]+K_{io}&(U_{i}\leq 0)\\ (K_{io}-K_{imax})\tanh\left[\frac{a_{i}U_{i}}{K_{io}-K_{imax}}\right]+K_{io}&( U_{i}\geq 0)\end{array}\right. \tag{14}\]  
Figure 8: Displacement calculation diagram.  
Figure 9: Ground reaction curve [24].  
where \(K\) is the coefficient of earth pressure, which is defined as the earth pressure \(\sigma\) divided by the initial vertical earth pressure \(\sigma_{v0}\) (\(K_{i}=\sigma_{i}/\sigma_{v0}\)); \(U\) is the ground displacement; \(a\) is the gradient of functions \(K(U)\), which represents the coefficient of subgrade reaction \(a_{i}\); subscripts \(v\) and \(h\) are the vertical horizontal directions, respectively; and subscripts \(o\), \(min\), and \(max\) are the initial, lower limit and upper limit of the coefficient of earth pressure, respectively.  
The shield is discretized, divided into \(n\) parts along the circumferential direction, and divided into \(m\) parts along the longitudinal direction, as shown in Figure 10.  
Vertical and horizontal stress of each unit are obtained as follows:  
\[\sigma_{v\mathrm{s}ij}\ =\ K_{\mathrm{P}}\big{[}U_{\mathrm{s}ij}(\eta)\big{]} \sigma_{v0 ij},\sigma_{\mathrm{h}sij}\ =\ K_{h}\big{[}U_{\mathrm{h}ij}(\eta)\big{]}\sigma_{h
# Monitoring of Cropland Non-Agriculturalization Based on Google Earth Engine and Multi-Source Data
## 5 Discussion  
### Comparison of Experimental Results of Optical Collaborating SAR with Purely Optical Imaging  
To validate the effectiveness of the proposed method, a comparative experiment was conducted using only optical imagery for change detection. In this experiment, change patches were extracted based on data exclusively from optical sources, with the change detection  
Figure 11: Number of annual change spots in Shanwei.  
Figure 10: Spatial distribution of results on DEM data.  
threshold set 50 above the reference value determined by the OTSU method--consistent with the threshold adjustment applied in the previous experiments. The experimental results are presented in Table 6, and the accuracy comparison between the two methods is illustrated in Figure 12.  
Compared to experiments using only optical imagery, the integration of SAR and optical imagery significantly improved detection accuracy, with an overall accuracy increase of 22.7%. This improvement is primarily attributed to the enhanced change signals provided by the inclusion of SAR data. The three types of change patches targeted in this study (constructed buildings, ponds, and pushed-fill soil) exhibit distinct features in SAR imagery. Constructed buildings appear as bright yellow-green regions, ponds as dark blue patches, and pushed-fill soil as light blue areas; particularly, during change vector analysis (CVA) calculations, the transitions from cropland to these types of land use exhibit substantial change magnitudes. By incorporating SAR data, this method amplifies the distinct change signals, effectively improving detection accuracy. Additionally, analysis of the number of extracted patches shows that experiments using only optical images extracted fewer patches. This is likely due to the more stringent threshold, which means the extracted patches in optical imagery have a larger scale of spectral information changes. However, the accuracy of these optical-only results is still much lower than that of the combined optical and SAR experiments, further demonstrating the effectiveness of SAR data in detecting these three types of change patches.
# Automatic Reading Algorithm of Substation Dial Gauges Based on Coordinate Positioning
## 2 Automatic Reading Algorithm of Dial Gauge Based on Coordinate Positioning  
The automatic reading process of the dial gauge on the inspection robot is shown in Figure 1. An image of the instrument is taken by the visible light camera on the inspection robot. Then, the reading is identified according to the automatic reading system, and the 3G or 4G network is used to transmit the reading result to the background monitoring center.  
In this section, the steps of the proposed method for the automatic reading of dial gauges in substations are described in detail. The procedure for the operating steps is illustrated in the flowchart shown in Figure 2. First, apply the MSRCR algorithm to enhance the image, improve the image contrast and ensure that the subsequent algorithm processing is not affected by light. Second, use circle detection based on arc-support line segments to detect the disc, and accurately locate the center and radius of the circle. Next, refine the pointer extracted using the subtraction method, making a circle with the center of the circle and an appropriate radius. Then, the coordinates of a point on the pointer can be obtained from the intersection of the circle and the pointer. Finally, calculate the reading based on the relative position of the pointer on the dial.  
Figure 1: Automatic reading based on machine vision.
# Multi-Objective Electric Vehicles Scheduling Using Elitist Non-Dominated Sorting Genetic Algorithm
## 2 Optimization Problem and Methodology
### Constraints
The minimum and maximum active (8) and reactive (9) power generation provided by the DG units and by external suppliers (ES) are formulated as:  
\[P_{Min(DG,t)}\times X_{(DG,t)}\leq P_{(DG,t)}\leq P_{Max(DG,t)}\times X_{(DG,t )};\forall t\in\{1,\ldots,T\};\forall DG\in\{1,\ldots,N_{DG}\} \tag{8}\]  
\[Q_{Min(DG,t)}\times X_{(DG,t)}\leq Q_{(DG,t)}\leq Q_{Max(DG,t)}\times X_{(DG,t )};\forall t\in\{1,\ldots,T\};\forall DG\in\{1,\ldots,N_{DG}\} \tag{9}\]  
Energy storage systems and electric vehicles are modelled in the same way. When the electric vehicles are not connected to the network the maximum charge and discharge limits are defined as zero. The energy balance equation is defined as in Equation (10). \[ \begin{array}{l}E_{Storad(EV,t)}=E_{Storad(EV,t-1)}-E_{Trip(EV,t)}+\eta_{c( EV)}\times\left(P_{Ch(EV,t)}\right)-\frac{1}{\eta_{d(EV)}}\times\left(P_{Dch(EV,t)} \right)\\ \forall t\in\{1,\ldots,T\};\forall EV\in\{1,\ldots,N_{EV}\}\end{array}  \tag{10}\]  
In the case of energy storage systems, the energy spent on the trips (\(E_{Trip}\)) is always zero. It is also important to mention that the variable \(P_{(EV)}\) is positive when the vehicle is charging and negative when the vehicle is injecting energy to the network. The variables \(P_{Ch(EV,t)}\) and \(P_{Dch(EV,t)}\) are used instead of \(P_{(EV)}\) to facilitate the comprehension of the constraints. The energy stored in the batteries must be under a maximum and minimum energy limit for all periods as formulated in Equation (11):  
\[E_{BatMin(EV,t)}\leq E_{Storad(EV,t)}\leq E_{BatMax(EV,t)};\forall t\in\{1, \ldots,T\};\forall EV\in\{1,\ldots,N_{EV}\} \tag{11}\]  
where, \(E_{BatMax(EV,t)}\) corresponds to the battery capacity and \(E_{BatMin(EV,t)}\) corresponds to the minimum amount of energy stored that the EV user demands, in a particular period or to the minimum technical limit of the battery so that to avoid fast degradation. The power charge and discharge must be lower or equal to the power charge limit, as shown in Equations (12) and (13):  
\[P_{Ch(EV,t)}\leq P_{Ch\_max(EV,t)};\forall t\in\{1,\ldots,T\};\forall EV\in\{ 1,\ldots,N_{EV}\} \tag{12}\]  
\[P_{Dch(EV,t)}\leq P_{Dch\_max(EV,t)};\forall t\in\{1,\ldots,T\};\forall EV\in \{1,\ldots,N_{EV}\} \tag{13}\]  
Concerning demand response, the load's control can be discrete (14) or continuous (15) following the equations:  
\[P_{(DR,t)}=P_{\max(DR,t)}\times X_{(DR,t)};\forall t\in\{1,\ldots,T\};\forall DR \in\{1,\ldots,N_{DR}\} \tag{14}\]  
\[P_{(DR,t)}\leq P_{\max(DR,t)};\forall t\in\{1,\ldots,T\};\forall DR\in\{1, \ldots,N_{DR}\} \tag{15}\]
## 5 Conclusions  
The specific study focuses into the design, implementation, and evaluation of an IoT sensor monitoring system. This is achieved with the integration of an ESP32 microcontroller, the use of BLE communication protocol, the Raspberry Pi 4 as a getaway, and a 5G module for cellular connectivity.  
Our experiment has proven great benefits in terms of real time data transmission, energy utilization, and scalability. With the use of a ESP32 microcontroller and Raspberry Pi with BLE for data transmission, the system performs with high efficiency and reliability at the communication level. In terms of cloud-based data storage and visualization, the ThingSpeak platform enhances to the maximum the accessibility and usability. In parallel, we proved that its data rate limitations and storage constrain reveal scalability challenges.  
Experimental results provided key performance metrics, especially in terms of power consumption and data transmission efficiency. In terms of energy consumption, the analysis performed has shown that the system could be improved with the adoption of solar cells for further enhancement, being able to operate with minimal energy, supporting long-term deployment with photovoltaic sources.  
With the integrated 5G module, high-speed data transmission with low latency is enabled, making it ideal for smart real-time environmental monitoring solutions. Future optimization could be performed with the use of the MQTT protocol for performance improvement, scalability, and efficiency, as well as MODBUS protocols for enhancement of communication efficiency using 6G bandwidth and meeting energy efficiency challenges through novel sources of power. This work contributes to the field for understanding any practical limitations of a 5G IoT system deployment, offering a benchmark and emphasizing any trade-offs between data rates, energy consumption issues, and latency.  
Looking into the future, the transition to the 6G network will bring new challenges and opportunities, especially in terms of IoT devices power consumption interoperability and security. We can expect emerging technologies, including AI-powered threat detection, blockchain-based security, and real-time edge processing that will ensure reliable and secure IoT deployments. Future work on optimization of energy efficiency and resilience will be essential for making IoT-based sensor systems more sustainable.  
Conceptualization, G.G. and D.J.V.; methodology, G.G. and D.J.V.; software, G.G.; validation, G.G., A.M., M.D. and D.J.V.; formal analysis, G.G.; investigation, G.G.; resources, G.G. and D.J.V.; data curation, G.G., V.K.; writing--original draft preparation, G.G. and V.K.; writing--review and editing, V.K. and D.J.V.; visualization, G.G.; supervision, A.M., M.D., and D.J.V.; project administration, D.J.V. All authors have read and agreed to the published version of the manuscript.  
**Data Availability Statement:** Data is available by request to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Data
#### 2.2.3 Crop Classification and Yield Data  
We used the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) Crop Data Layer (CDL) data to identify areas of corn and soybeans from 2015 to 2017. The CDL data are annual land cover classification products with a spatial resolution of 30 m. The data of county-level soybean and maize yields were retrieved from the USDA Quick Statistic Database.  
The CDL data in each year from 2015 to 2017 were employed to determine whether a MODIS EVI pixel and an OCO-2 footprint belong to the soybean or maize category. The area ratios of soybean and maize within all pixels and footprints were calculated. Only pixels and footprints with the area ratio of soybean or maize above 60% were selected for further analysis. A threshold of 60% was used for balancing the tradeoff between the relative purity of the pixels and the availability of the data. A similar value was employed in a previous study to identify vegetation types covered by OCO-2 footprints [62]. For a county, the three-year mean EVI of soybean and maize was calculated for each month in the growing season (from May to September) according to values of selected individual pixels, i.e.,  
\[\overline{EVI_{m}}=\frac{1}{3}\sum_{y=1}^{3}(\frac{1}{N_{y}}\sum_{p=1}^{N_{y} }EVI_{y,m,p}) \tag{1}\]  
where \(\overline{EVI_{m}}\) is the three-mean of EVI in month \(m\); EVI\({}_{y,m,p}\) is the EVI value of the p\({}^{\text{th}}\) pixel in month \(m\) of the y\({}^{\text{th}}\) year; N\({}_{y}\) is the number of pixels belong to corn or soybean in the y\({}^{\text{th}}\) year.  
OCO-2 SIF data was spatially discrete and there are gaps between nearby swaths (Figure 1). Within a county, SIF might be observed on different adjacent days for a given period. The variation of SIF caused by the difference in observing dates must be taken into account. Therefore, the county-level three-year SIF means of soybean and maize were generated in the following way. All SIF observations of soybean and maize in all three years within each county were lumped together to fit phenology curves of individual crop types (Section 2.3.1) since observations of SIF in one year were very limited for a county. Two respective phenology curves of soybean and maize were constructed for each county. With the fitted curves, daily SIF during the growing season was simulated for each county. With simulated daily SIF, monthly means and maxima of SIF were calculated.
# Optimised Twin Fluid Atomiser Design for High-Viscosity, Shear-Thinning Fluids
###### Abstract  
This study explores the optimisation of nozzle design for external twin fluid, single-stage atomisation in handling high-viscosity, shear-thinning polydimethylsiloxane (PDMS). A single PDMS grade was employed and atomised using unheated sonic air and the viscosity was varied by the fluid temperature. A systematic experimental approach was used, varying nozzle geometry--specifically apex angle, gas nozzle diameter, and number of gas nozzles--to identify the optimal nozzle configuration (ONC). The spray qualities of the nozzle configurations were evaluated via high-speed imaging at 75,000 FPS. Shadowgraphy was employed for the optical characterisation of the spray, determining the optimal volumetric air-to-liquid ratio (ALR), a key parameter influencing energy efficiency and operational cost, and for assessing droplet size distributions under varying ALR and viscosity of PDMS. The ONC yielded a Sauter mean diameter \(d_{32}\) of \(570\times 10^{-6}\) m, at an ALR of 8532 and a zero-shear viscosity of 15.9 Pa s. The results are relevant for researchers and engineers developing twin fluid atomisation systems for challenging industrial fluids with similar physical properties, such as those in wastewater treatment and coal-water slurry atomisation (CWS). This study provides design guidelines for external twin fluid atomisers to enhance atomisation efficiency under such conditions.  
twin fluid atomisation; high-viscosity; polydimethylsiloxane; atomiser design Article
# Implementation of PMDL and DRM in OpenSees for Soil-Structure Interaction Analysis
## 1 Introduction
The implementation was verified through a comparison of results derived from the PMDL-truncated domain and those obtained from an extended domain with fixed boundaries, resulting in a notable degree of consistency. Furthermore, direct modelling of SSI problems frequently encounters difficulties associated with defining appropriate input ground motions [39; 40]. The DRM is currently regarded as one of the most effective approaches for simulating semi-infinite domains subjected to far-field excitations [41; 42; 43]. In this investigation, the DRM was initially employed to calculate the effective nodal forces, utilizing the formulations proposed by [42]. Subsequently, a comprehensive verification process was conducted, in which the DRM-generated free-field responses were compared with either analytical solutions or the results obtained from one-dimensional (1-D) site response analysis (SRA). The findings demonstrate exceptional accuracy for vertical and inclined incident SV waves in a two-dimensional domain with a homogeneous soil layer. Finally, using the combined DRM-PMDL system, we modeled tunnels (i.e., single-cell tunnel and double-cell tunnel) in a 2-D domain to investigate the effects of the incidence angle. We applied inclined SV waves at various incidence angles and plot accelerationsof the corner points, maximum bending moments, drift ratios, rocking rotations, spectral amplifications, and maximum deformations of the tunnels.
## 2 Results
### Molecular Docking Studies  
Molecular docking simulations were performed using AutoDock 4.2 to predict how compound **1** behaves in the binding site of the target protein and to clarify the fundamental biochemical processes of **1** with the \(\beta\)-glucuronidase enzyme. The results were analyzed and displayed using PyMOL and BIOVIA Discovery Studio (Figure 4). According to  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Compounds**} & \multicolumn{2}{c}{**Inhibition against \(\beta\)-Glucuronidase**} \\ \cline{2-3}  & **Inhibition\% (100 \(\mu\)M)** & **IC\({}_{50}\) (\(\mu\)M) 1** \\ \hline
**1** & 69.85 \(\pm\) 2.93 & 58.83 \(\pm\) 1.36 \\
**2** & 15.89 \(\pm\) 0.55 & >100 \\
**3** & 23.46 \(\pm\) 2.47 & >100 \\
**4** & 29.61 \(\pm\) 1.83 & >100 \\
**5** & 14.34 \(\pm\) 1.10 & >100 \\
**6** & 13.17 \(\pm\) 1.46 & >100 \\
**7** & 25.21 \(\pm\) 1.46 & >100 \\
**8** & 16.73 \(\pm\) 0.09 & >100 \\ DSA 2 & 74.70 \(\pm\) 0.95 & 24.56 \(\pm\) 1.15 \\ \hline \hline \end{tabular}  
* The values (\(\mu\)M) represent 50% inhibition of \(\beta\)-glucuronidase. Results are presented as the mean \(\pm\) standard error of triplicate experiments. 2 Positive control.  
\end{table}
Table 1: Inhibition of compounds **1**-**8** against \(\beta\)-glucuronidase  
Figure 3: Lineweaver\(-\)Burk plot (**A**) and Dixon plot (**B**) analyses using active compound **1**.  
the kinetic analysis results, compound **1** displayed a non-competitive inhibition mode, suggesting that **1** could precisely bind to a specific region of \(\beta\)-glucuronidase, named the allosteric binding site, which differed from the active site (orange spheres) that binds to the substrate PNPG (red). The allosteric binding site of \(\beta\)-glucuronidase was predicted using the AlloSite 2.10 web server (green spheres) [26] and protein allosteric sites server PASSer2.0 (rainbow spheres) [27] (Figure 4A). To optimize the docking procedure, the substrate, PNPG, was docked as a native ligand into \(\beta\)-glucuronidase (PDB ID: 6 LEL) (Figure 4B).  
The docking results revealed that compound **1** could bind to the allosteric binding site of \(\beta\)-glucuronidase with a binding energy value of \(-8.35\) kcal/mol (Figure 5 and Table 2). The carbonyl group and oxygen atom of **1** establish hydrogen bonding interactions with the amino acid residues, Thr191 and Thr438, whereas the hydroxy groups of **1** interact with Arg272, Leu435, and Asp436. Two benzene rings of **1** bind with residues Lys400 and Pro403 through \(\pi-\)alkyl interactions and Thr188 via \(\pi-\sigma\) interactions. The lactone ring displayed \(\pi-\sigma\) interactions with Val190, \(\pi-\)cation interactions with Arg439, and \(\pi-\)long pair interactions with Val189. The other residues, including Gly270, Asn401, and Pro437, from different sites of the \(\beta\)-glucuronidase enzyme, bound to compound **1** via van der Waals interactions.