esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In summary, a \(1\times 3\) power splitter based on multimode interference with a parabolic type was designed utilizing a Si-GaN slot-waveguide structure. The use of silicon and gallium nitride as materials for the slot waveguide can improve the performance of the device. The effective refractive index method and the eigenmode expansion method were employed to simulate the device. The single-mode dimension of the waveguide was calculated for the input and output. The parabolic coupling region was designed and optimized to obtain the best imaging length of 4 um. Furthermore, tapers were added to the input and output waveguides and connection sections of the coupling region to reduce excess loss and improve imbalance. In addition, a comprehensive analysis of the preparation tolerances of the structural parameters was performed to guide the fabrication process. The findings indicate that the device exhibits a total normalized transmission of 0.992, an excess loss of 0.036 dB, and an imbalance of 0.003 dB. In comparison to analogous devices, the presented power splitter boasts smaller dimensions, reduced loss, and diminished imbalance. It is expected that this study will contribute to the development of high-performance and compact devices for integrated photonic systems in the future.  
Conceptualization, H.L. and S.L.; methodology, H.L. and S.L.; software, S.L.; validation, B.L., F.L., S.L. and H.W.; formal analysis, H.L. and S.L.; investigation, S.L.; resources, H.L.; data curation, S.L.; writing--original draft preparation, S.L.; writing--review and editing, H.L. and S.L.; visualization, S.L.; supervision, X.L., F.L., B.L. and Y.C.; project administration, S.L., F.L., Y.L. and Q.G.; funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Foundation of Liaoning Province Education Administration (JYTMS20230429).  
Not applicable.  
Data availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
## Nomenclature
### Abbreviations
\begin{tabular}{l l l}
**Abbreviations** & & & \\ BRTGS & Bundled renewable energy and thermal & & \\  & generation system. & & \\ CNY & Chinese Yuan. & DPR & Deep peak regulation. \\ PDF & Probability density function. & & \\ RPR & Regular peak regulation. & & \\ VPP & Virtual power plant. & & \\ WT & Wind turbine. & & \\
**Indices** & & \\ \(h\) & Index of pollutant type. & & \\ \(t\) & Index time-step. & & \\ \(u\) & Index TPUs. & & \\
**Variables** & & \\ \(D_{u,t}^{\text{TPU,on}}\) & Continuous duration of operation for a TPU. (h) & & \\ \(F^{\text{A}}\) & Annual total revenues. (CNY) & & \\ \(F^{\text{PV,GI}}\) & Annual total generation revenues of PV (CNY) & & \\ \(F^{\text{TPU,GI}}\) & Annual total generation revenues of & & \\ \(F^{\text{TPU,OI}}\) & Annual total generation revenues of & & \\  & & \\  & & \\ \(F^{\text{TPU,OP}}\) & Annual total operation costs of TPUs. (CNY) & & \\ \(P_{t}^{\text{GO}}\) & Surplus generation of power sources. (MW) & & \\ \(p_{t}^{\text{L,P}}/p_{t}^{\text{L,F}}\) & Predicted value and real-time fluctuation of & & \\  & load demand. (MW) & & \\ \(p_{t}^{\text{PV,P}}/p_{t}^{\text{PV,F}}\) & Predicted value and real-time fluctuation of & & \\ \(p_{t}^{\text{PV,P}}/p_{t}^{\text{PV,F}}\) & PV. (MW) & & \\ \(p_{u,t}^{\text{TPU,B}}/p_{u,t}^{\text{TPU,F}}\) & Actual output of TPUs. (MW) & & \\ \(p_{t}^{\text{WT,P}}/p_{t}^{\text{WT,F}}\) & Predicted value and real-time fluctuation of & & \\  & WT. (MW) & & \\ \(\beta_{u,t}^{\text{RPR}}/\beta_{u,t}^{\text{DPR}}\) & Binary variables for the state of a TPU either & & \\  & in RPR state or DPR state. & & \\ \(a_{\text{DPR}}/b_{\text{DPR}}\) & Coefficients for TPU DPR loss derived from & & \\  & fitting. & & \\ \(C_{0}/C_{1}/C_{2}\) & Unit generation cost of TPUs in different & & \\  & compensation standards. (CNY/MW) & & \\ \(C_{h}^{\text{E}}\) & Unit cost for pollutants treatment. (CNY/kg) & & \\ \(C_{u}^{\text{GN}}\) & Cost of a single off to on transition for & & \\ \(T_{\text{PU}}\) & TUU. (CNY) & & \\ \(C_{u}^{\text{WT}}\) & Unit generation revenue of WT. (CNY/MW) & & \\ \(D_{u}^{\text{TPU,on,min}}\) & Minimum operation time. (h) & & \\ \(P_{u}^{\text{TPU,off,min}}\) & Minimum output of TPU. (MW) & & \\ \(P_{u}^{\text{TPU,min}}\) & Minimum output of TPU in RPR states. (MW) & & \\ \(P_{t}^{\text{C,max}}/P_{t}^{\text{C,min}}\) & Limits of the power grid's capability to & & \\  & accommodate RES fluctuations. (MW) & & \\ \(\tau_{u}^{\text{TPU}}\) & Flexible reservation ratio of TPUs. & & \\  & Different output rates for DPR service & & \\ \(\mu_{1}/\mu_{2}\) & compensation standards. & & \\ \(\epsilon_{h}^{\text{E}}\) & Equivalent mass of the pollutant per unit & & \\  & power generation. (kg/MW) & & \\ \end{tabular}   
\begin{tabular}{l l l}
**Ad
## 6 Verification of Blast Hole Digit Recognition Method Applied in Tunnels  
### Results of Engineering Experiments  
The point cloud data model of each borehole in the half-hole point cloud model was extracted sequentially, the analysis and processing of the above steps were carried out. The boreholes were numbered 1-6 to represent their respective positions and shapes as identified through the analysis. The 3D point cloud processing software was then used to calculate the length of each borehole, and the results are shown in Figure 22. The fitting of the half-hole positions is shown in Figure 23.  
Figure 21: Individual borehole identification.  
Figure 23: borehole Fitting Position.  
Figure 22: borehole Length Diagram.  
Comparison and analysis of the fitted borehole data with the site construction design parameters (the design value of the site borehole length is 2 m), and finally, get the half porosity of the tunnel after blasting (Table 3).  
According to the geological exploration at the construction site, the stratum lithology of Huangtai Tunnel is mainly limestone, dolomite, and sandstone, and the groundwater has not been developed. The surrounding rocks are of Class III, IV, and V, of which Class III is 3315 m, Class IV is 2952 m, and Class V is 1714 m. The data collected from the experiment are located in Class IV and Class V, which are medium-hard rocks. The quality evaluation standard of half porosity after tunnel blasting is shown in Table 4.  
The experimental data indicate a peripheral half-porosity of 58.7%, which is classified as medium according to the evaluation criteria. To validate the accuracy of the measurements taken using this method, site construction personnel also employed the traditional half-porosity measurement method when the half-porosity approached 60%. The results obtained from both methods showed relatively small discrepancies.
## 2 Governing Equations and Numerical Methods
### Governing Equations  
For the numerical simulations of the flow through the critical nozzle, the Navier-Stokes equations are used as the governing equations. However, there are many physical and numerical issues to deal with in solving the Navier-Stokes equations numerically. The most difficult is due to the strong nonlinearity of the equations. Numerical approaches to nonlinear problems suffer from not only poor accuracy and convergence but also high numerical cost. To solve these difficulties and to apply the Navier-Stokes equations to a broader range of fields by using a numerical method, the Navier-Stokes equations are generally averaged. The most widely used averaged Navier-Stokes equations in computational fluid dynamics are the Reynold-Averaged Navier-Stokes (RANS) equations. The behavior of fluids present in nature can be thought of as the sum of deterministic and random quantities. The RANS equations average over time, which eliminates the random behavior of fluids. The compressible RANS equation is written as follows. \[\frac{\partial\rho}{\partial t}+\frac{\partial}{\partial x_{i}}(\rho u_{i})\ =\ 0 \tag{1}\]  
\[\frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x_{j}}\Big{(} \rho u_{i}u_{j}\Big{)}\ =\ -\frac{\partial p}{\partial x_{i}}+\frac{\partial}{ \partial x_{j}}\bigg{[}\mu\bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{ \partial u_{j}}{\partial x_{i}}-\frac{2}{3}\delta_{ij}\frac{\partial u_{k}}{ \partial x_{k}}\bigg{)}\bigg{]}+\frac{\partial}{\partial x_{j}}\Big{(}-\rho \overline{u_{i}u_{j}}\Big{)} \tag{2}\]  
\[\frac{\partial}{\partial t}(\rho E)+\
abla\big{(}\overline{u}\big{(}\rho E+p \big{)}\big{)}\ =\ -\
abla\big{(}\sum h_{j}\ J_{j}\big{)} \tag{3}\]  
where \(\rho\), \(u\), \(p\), and \(\mu\) are the density of the fluid, velocity, pressure, and dynamic viscosity. \(\delta\) is the Kronecker delta function. \(E\) means energy per unit mass, \(h\) means enthalpy and \(J\) diffusion flux of species \(j\). Subscripts \(i\) and \(j\) mean \(x\)-, \(y\)-, and \(z\)-direction. \(\rho\overline{u_{i}u_{j}}\) is a Reynolds stress tensor, which can be obtained from turbulent modeling based on the assumption of Boussinesq. However, the RANS equations tend to over-predict the viscosity and are known to weaken the unsteady behavior of fluids such as vortex phenomena [10; 11]. As another method for solving the Navier-Stokes equation numerically, a spatial averaging operation is performed on the basic equation. Large Eddy Simulation (LES) separates the flow field into a component that can be resolved using the given grid and the smaller ones. The former is directly calculated using the governing equations, and the latter is modeled using a subgrid-scale model. The governing equations are written as follows. \[f\ =\ \overline{f}+f^{\prime} \tag{4}\]  
Figure 1: Toroidal-throat Venturi nozzle specified by ISO 9300.
## 4 Implementation Strategy
### Phase II--Decision Support and Virtual Simulation  
In this phase, the digital twin evolves from a monitoring tool to an active decision-support system. Farmers define desired agronomic outcomes, such as harvest timing or target fruit size, and the system simulates candidate interventions using AI-based models. Core activities include the following:  
* Virtual simulation of robotic operations, such as seeding and harvesting, under varying crop and climate conditions--see Figures 5 and 6;
* Outcome prediction, based on models trained with historical crop data and external agronomic knowledge;
* Comparative evaluation of strategies, enabling users to analyze projected impacts on productivity, resource usage, and crop health;
* Farmer validation, using a cloud interface where proposed interventions can be reviewed, adjusted, and approved prior to physical deployment.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature/Capability** & **DigiHortiRobot** & **Conventional Monitoring Tools** \\ \hline Sensor integration & Multimodal (RGB + ToF + environmental) & Environmental sensors only \\ \hline Spatial mapping & Semantic 3D reconstruction & Absent or limited to 2D \\ \hline Plant-level identification & QR-coded tracking + vision & Manual or plot-based \\ \hline Feedback loop for task planning & Enabled via digital twin & Not supported \\ \hline Real-time bidirectional data flow & Yes (IoT + ROS2 + MQTT) & Limited or unidirectional \\ \hline Execution accuracy (seeding and harvesting) & \(>\)90\% localization and task success rate & Not applicable (monitoring only) \\ \hline Closed-loop integration with dual-arm robotic system & Full (virtual planning + physical action) & Typically not integrated \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of monitoring functionalities supported by DigiHortiRobot and those available in conventional greenhouse systems [20; 25].  
Figure 5: IsaacSim Simulation—Robotic platform.
## 3 Results and Discussion
### Effect of Irradiation
#### 3.7.1 Irradiation Stability and Thermal Stability of SC4A
The appearance of the negligible peak at 1728 cm\({}^{-1}\) in the IR spectra of samples D and E was probably due to structural changes of trace impurities caused by intense irradiation. Extractions of Cs(I) and Sr(II) with irradiated SC4A samples were examined. The results are listed in Table 5. They showed that \(D_{\mathrm{Cs}}\) remained approximately invariant when the irradiated dose was between 0 and 1.0 \(\times\) 10\({}^{5}\) Gy and then increased when the irradiated dose was higher than 1.0 \(\times\) 10\({}^{5}\) Gy. This may be due to the lone pair electrons of oxygen in the ketone group, which can enhance the extraction ability of SC4A for Cs(I). Meanwhile, no notable change in \(D_{\mathrm{Sr}}\) was observed when the irradiated dose was less than 1.1 \(\times\) 10\({}^{6}\) Gy. It can be concluded that SC4A retained good extraction ability for Cs(I) and Sr(II) after irradiation at 1.1 \(\times\) 10\({}^{6}\) Gy.
# Applications of Raman Microscopy/Spectroscopy-Based Techniques to Plant Disease Diagnosis
## 6 Raman Microscopy/Spectroscopy and Chemical Information  
Based on the above, Raman microscopy is widely used in modern analytical laboratories because it is a powerful and fast non-destructive technique that can be applied to many sample types. The chemical compounds found in a mixture can be analyzed and identified, and quantitative data can be obtained. Raman microscopy equipment is robust, reliable and easy to use. The technique is sensitive, allowing for analysis of numerous compounds in trace amounts in a single measurement [46].  
So, Raman microscopy can identify specific molecules and their structures, including biomolecules, polymers and small molecules. It can provide information about chemical bonding patterns, including functional groups and molecular interactions, and can measure the concentration of specific molecules in a sample, allowing for quantitative analysis. Raman microscopy is non-invasive, it can detect very small concentrations of molecules, making it suitable for analyzing complex samples, and it is able to analyze multiple components in a sample simultaneously, making it suitable for analyzing complex mixtures [37; 53; 60].  
Due to these advantages, we believe that Raman microspectroscopy is a significant technique, particularly in the fields of biology and plant pathology for early disease detection, as it retains the spatial resolution inherent in microscopy and benefits from the highly specific chemical analysis provided by Raman spectroscopy.
## 6 Comparison with State of the Art  
The FHS system advances the state of the art by explicitly integrating edge computing into the autoscaling process. Unlike the RL-based methods proposed in [3; 7; 10], which primarily focus on cloud-centric environments, FHS is tailored for Kubernetes environments that simulate edge computing scenarios. This integration reduces latency and optimizes resource usage closer to the data source, providing significant advantages for innovative city applications. Moreover, RL-based autoscaling requires complex training phases and substantial computational resources, whereas FHS offers a more straightforward implementation with immediate applicability. In addition, it is independent of extensive historical data, making it more suitable for real-time edge computing environments where data are often dynamic and unpredictable.  
In [17], the authors proposed a Kubernetes scaling engine leveraging various ML forecasting methods to manage dynamic incoming requests. Although these ML-based approaches provide sophisticated scaling predictions, they require complex setups and ongoing computational power in order to maintain accuracy. Conversely, the FHS system employs fuzzy logic, offering a more straightforward implementation with immediate applicability. It also avoids reliance on extensive historical data, making it more practical for real-time edge computing scenarios where data can be highly dynamic and less predictable.  
The FHS system offers several advantages over the fuzzy logic-based autoscaling approach presented in [19], which proposed a self-adaptive cluster architecture for lightweight edge devices utilizing fuzzy logic within a serverless framework implemented on a cluster of Raspberry Pi devices. In contrast, the proposed FHS system is designed for Kubernetes environments, simulating real-time edge computing scenarios. This approach reduces latency and optimizes resource usage near the data source, which is beneficial for applications where data are frequently dynamic, such as in smart city contexts. The fuzzy logic in FHS allows for a more straightforward implementation without extensive training or dependence on large historical datasets. While the work in [19] focused on serverless functions and static configurations, FHS adapts to dynamic edge computing scenarios, handling varied workloads and offering a scalable solution for computing infrastructures.  
The FHS system also surpasses the fuzzy logic-based auto-scaling approach proposed in [18], which dynamically adjusts thresholds and cluster size for web applications in the cloud, aiming to reduce resource consumption without violating SLAs. This approach focuses on vertical scaling in cloud environments and needs to address the challenges of edge computing, where latency and data proximity are crucial. The proposed FHS system is designed for Kubernetes environments simulating real-time edge computing, focusing on horizontal scaling. This helps reduce latency and optimize resource usage near the data source.
# Nanomaterials-Based Ion-Imprinted Electrochemical Sensors for Heavy Metal Ions Detection: A Review
## 3 Application of Nanomaterials in HMIIECSs
### Metal Oxide Nanomaterials  
Metal oxide nanomaterials are suitable for analytical applications due to their excellent properties, including high selectivity and sensitivity, and are considered to be one of the most important materials in high-performance electronics and environmental monitoring [126]. Zinc oxide (ZnO) nanostructures have attracted much attention due to their attractive photoelectric properties and great structural diversity, and they have a wide range of applications in various industrial fields, such as field effect transistors, lasers, photodetectors, solar cells and batteries, chemical and biological sensors [127]. [PERSON] et al. developed an electrochemical platform for Hg\({}^{2+}\) detection based on Au/ZnO/IIP/GE [70]. The biomimetic IIP was prepared by electropolymerizing Py in the presence of L-cysteine as a chelating agent and Hg\({}^{2+}\) (template) on the GE surface modified with vertically grown ZnO nanorods. The strategy of combining diazonium salt modification and ZnO nanorod decoration of GE increased the specific surface area considerably and thus the sensor performance was improved. The LOD of the designed sensor was \(\sim\)1 pM, and Au/ZnO/IIP/GE was highly selective for Hg\({}^{2+}\) compared with Cd\({}^{2+}\), Pb\({}^{2+}\) and Cu\({}^{2+}\). This sensor design can open new horizons for monitoring toxic HMIs in water, thereby helping to improve environmental quality.
# Oblique Deposition of Ti/Pt/Au Electrode on Photonic Crystal for Vertical Current Injection +
## 3 Results and Discussion  
### Analysis of the Results of Oblique Deposition  
Figure 5 shows the results of the oblique electrode deposition. The left part of Figure 5a shows the electrode, and the right part is the bare PhC pattern. The lattice constant \(a\) is 360 nm and the radius of the air holes is 110 nm, which is approximately \(0.3a\). Since we deposited the metal materials on non-flat samples with the PhC lattice, there are small holes in the electrode layer arranged just like the holes in the triangular PhC lattice pattern that can be seen in Figure 5a. By cleaving the sample and observing the cross section shown in Figure 5b, we observed that these holes were slanting channels connecting the PhC air holes with the outside, which is consistent with the prediction in Figure 4b. These channels are believed to be advantageous because they connect the inside and outside of the air holes in such a way that when the sample is stored in a vacuum environment, the air hole structures do not collapse due to uneven internal and external air pressures. As shown in Figure 5b, the electrode material was stacked into the contact layer at \(\beta\approx 53^{\circ}\), a slightly smaller angle than \(\alpha=60^{\circ}\). No material enters the cladding layer or lower layers. This result indicates that the oblique electron beam deposition can perfectly prevent the metal material from entering the lower layers and influencing the optical properties of the PhC laser. We confirmed that there is no current leakage in the structure by estimating the voltage-current relationship.  
Figure 4: Schematic illustration of oblique electrode deposition. (**a**) A holder is used to make a \(60^{\circ}\) angle between the direction of the sample and the vapor flux. (**b**) Partial enlarged view of oblique electrode deposition principle. \(\alpha\) is the angle between the vapor flux and the substrate, and \(\beta\) is the direction of material deposition. When \(\alpha\) is set to \(60^{\circ}\), \(\beta\) is expected to be smaller than \(60^{\circ}\).
## 3 Results and Discussion
### Spatial Relationship Between Landslide Distribution and Conditioning Factors
Landslides were predominantly concentrated in three key geological formations: the Ghan Pokhara Formation (Gp), the Ranimatta Formation (Rm), and the Higher Himalayan Crystalline (Hm), as in Figure 11. The Gp unit, rich in carbonaceous slates and shales, showed the highest landslide number density, reflecting its weak and weathered lithology. The Rm unit, composed of phyllites and quartzites with intrusions, exhibited moderate susceptibility due to structural complexity. The Hm unit, though relatively stronger, still recorded notable landslides, likely along foliated zones within its graisses and schists. Anthropogenic influences were evident. Over 50% of the total landslide area occurred within 0-1000 m from roads, with both areal and number density showing steep declines beyond 1000 m. This reinforces the destabilizing impact of slope undercutting, improper drainage, and toe removal during road construction in steep terrains. More than 50% of mapped landslides were within 0-3000 m of active faults. The number density was especially elevated in the 0-2000 m range, underscoring the direct influence of strong ground motion amplification and tectonic fracturing near fault zones. A large proportion of landslides were observed within 0-1000 m of streams. High areal densities were evident in the 250-500 m range, suggesting that incision, groundwater accumulation, and undercutting along fluvial corridors play a crucial role in slope destabilization. Mid-elevation belts between 1000 and 2000 m recorded the highest landslide concentrations. Landslides showed higher densities in areas with NDVIs between 0.2 and 0.5. These values reflect moderately vegetated slopes where root cohesion may be insufficient to stabilize disturbed soils during strong shaking. Densely forested zones (NDVIs > 0.6) exhibited a lower landslide frequency, indicating a protective effect of vegetation. The highest number and density of landslides occurred within the 0.6-0.7 g range, particularly in areas closer to the epicenter. This aligns with zones of intense seismic shaking, confirming PGA as one of the most influential triggers in the study region. Together, these findings highlight the spatially conditioned nature of co-seismic landslides, where the interplay of seismic forces with terrain, lithology, hydrology, and human activities determines the likelihood of failure. The results are consistent with past studies [42; 43] and reinforce the need for multifactorial susceptibility modeling.