esa-sceva/satcom-chunk-collection huggingface dataset:
# Ray Tracing Method of Gradient Refractive Index Medium Based on Refractive Index Step
## 4 Conclusions  
The ray tracing method based on the refractive index step proposed in this paper has extensive adaptability to the axial gradient refractive index, especially the inhomogeneous refractive index medium with a large refractive index gradient. It can efficiently solve the problem that the traditional ray tracing method based on geometric step cannot adapt to the inhomogeneous refractive index medium with large refractive index gradient or sudden change and improve ray tracing accuracy. At the same time, the proposed method can achieve high-efficiency ray tracing in a non-uniform refractive index medium with small refractive index change rate due to the refractive index step used in the recursion process. By using the proposed method, the efficiency of ray tracing can be achieved while the accuracy of ray tracing can be improved. Thus, the method realizes the adaptive process of the gradient index medium in the ray tracing process.  
This method, however, has some problems which have to be solved. The most important one is that the constant refractive-index surfaces need to be fitted during the iterative process, so as to solve the iteration point through the fitting formula. The current method is to use high-order polynomials to fit the constant refractive-index surfaces, which might introduce a certain error. If the polynomial order is too high, it will cause Runge phenomenon. Therefore, different fitting models need to be considered in order to improve the ray tracing accuracy for different non-uniform refractive index media. Although there are some shortcomings in this method, we believe that this method will have a good application in ray tracing in gradient index media, especially in shock waves.  
Conceptualization, Q.Z.; and Y.T.; methodology, Q.Z.; software, Q.Z.; validation, Y.T., G.R. and T.T.; formal analysis, G.R.; investigation, Q.Z.; resources, Y.T.; data curation, T.T.; writing--original draft preparation, Q.Z.; writing--review and editing, T.T.; visualization, Q.Z.; supervision, T.T.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by Open Research Fund of State Key Laboratory of Pulsed Power Laser Technology (SKI2018 KF05), Excellent Youth Foundation of Sichuan Scientific Committee (2019 JDQ0012), Youth Innovation Promotion Association, CAS (2018411), CAS \"Light of West China\" Program and Young Talents of Sichuan Thousand People Program.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 6: Ray tracing error example 2.
### Water Sources Analysis and Model of High Water Production
#### 4.4.2 Model of High Water Production  
The commonly used production increase measure for coalbed methane development is coal reservoir fracturing technology, and the maximum fracture length of coal rock fracturing in the Qinshui Basin can reach 20 m [51]. Further analysis of the sand-mud ratio and stratigraphic combination relationship between high-yield water wells (SY05, SY19, and SY22) and low-yield water wells (SY04, SY23, and SY38) revealed that the lithology above and below the coal seam of the low-yield water wells for single mining of No. 15 coal is mainly composed of limestone, mudstone, and sandy mudstone. Calcite thin films often appear in limestone, which are caused by later filling of cracks and, therefore, have poor water content. Although fracture structures and fracturing cracks can lead to some sandstone layers, the sandstone layers are relatively thin and have low water production (Figure 14a). The lithology above and below the coal seam of the high-yield water well for single mining of No. 15 coal is mainly composed of limestone and sandy mudstone. However, within a thickness range of 20 m, thick layers of sandstone can be seen. When fracture or fracturing cracks lead through the sandstone layer, water production increases (Figure 14b). This is similar to the high-yield water mode of natural fracture communication and fracturing fracture communication in the southern Qinshui Basin [52]. Compared with the high-yield gas areas in the southern Qinshui Basin (such as Panzhuang, Fanzhuang, and Shizhuang), the Shouyama Block has a higher permeability, stronger reservoir fluid mobility, and is more conducive to the extraction of coalbed methane. The widely distributed sand bodies with strong water supply and storage capabilities have become the main reason for high water production and low gas production [28; 53]. Therefore, attention should be paid to the distribution and lithological combination of sandstone aquifers in coal-bearing strata in the future exploration and development process of the Shouyama Block. This will help to avoid the potential influence of fault structures and enable the identification of favorable areas for low water and high gas production.  
Figure 14: Water production mode of coalbed methane wells (**a**): Low water production mode in thin aquifers; (**b**): High water production mode in thick aquifers.  
Figure 13: Diagram of ion relationships in water discharged from coalbed methane wells in Shouyang Block. (**a**) Relationship between Na\({}^{+}\) and Cl\({}^{-}\) content; (**b**) Relationship between (0.5 HCO\({}_{3}\)\({}^{-1}\) + SO\({}_{4}\)\({}^{2-}\)) and (Ca\({}^{2+}\) + Mg\({}^{2+}\)) content.
## 3 Experimental Tests and Results
### THERA(tm) Machine Tests  
From 23 October to 7 November 2023, a series of tests were conducted on the THERA(tm) Zayer gantry machine at the Zayer assembly shop floor. The objective was to validate the robustness of extending the MIIM methodology--previously applied to a single machine with a SIEMENS 840 CNC--to a new MT architecture equipped with a HEIDENHAIN TNC7 CNC control.  
Several experiments were conducted over two weeks, defining a measurement point cloud of 74 points, with each MIIM measurement requiring 48 min to complete. Figure 7 illustrates the linear axis reference system used and provides a detailed view of the laser tracker mounted on the tool holder during the measurement procedures. The measured volume and resolution for each measurement were as follows:  
* X-axis: \(-14\),\(700\div-8700\) (6000 mm) with a step size of 1000 mm; portal frame gantry movement;
* Y-axis: \(-3400\div-400\) (3000 mm) with a step size of 500 mm; transverse movement along the bridge;
* Z-axis: \(-1460\div-60\) (1400 mm) with a step size of 200 mm; ram vertical movement.  
Refer to Figure 2 for a detailed representation of the point cloud utilised in the test campaign conducted on the THERA(tm) machine tool. The figure illustrates the spatial distribution of points and the sequence of movements for each MIIM measurement.  
The machine was placed in a large environment dedicated to the assembly, setup, and factory acceptance testing of MTs before they are dispatched to the final user. The shop floor was thermally controlled during working hours, maintaining the air temperature at  
Figure 7: THERA(tm) machine tool axes definition: (**a**) general view of the machine tool and (**b**) laser tracker on the tool holder.  
20 \({}^{\circ}\)C \(\pm\) 1 \({}^{\circ}\)C. The temperature control system was switched off at 6:00 p.m. and reactivated at 5:00 a.m. daily.  
Figure 8 depicts the air temperature variations during the test campaign, illustrating how the temperature was effectively maintained during working hours on weekdays, decreased overnight, and recovered quickly when the climate control system was reactivated before the start of daily operations. Two tests are reported: the first was conducted over three weekdays with minimal temperature variations, and the second was conducted over eight days, including a weekend. The image highlights the weekends and 1 st November, when the air-conditioning system was also turned off, resulting in the lowest recorded temperatures, particularly during the second weekend, when the ambient temperature dropped to a minimum of 16.87 \({}^{\circ}\)C.  
Table 4 summarises the ambient temperature variations, across which there was an an average temperature close to 20 \({}^{\circ}\)C due to the controlled environment. A maximum difference of 4 \({}^{\circ}\)C was recorded during the second experiment, which was attributable to the three non-working days included in the test.
## 4 Discussions
### Performance Comparison of Blockchain Data Storage Methods  
Blockchain-based data storage methods have various advantages and limitations concerning key criteria such as transaction speed, scalability, cost, and security. These factors play a critical role in determining the suitability of blockchain-based solutions for sectoral applications and use cases.  
On-chain storage ensures high security and data integrity by storing all data directly on the blockchain; however, it comes with significant limitations in terms of transaction costs and scalability. In contrast, off-chain storage improves transaction speed and reduces costs by storing data outside the blockchain, but it introduces certain risks regarding data integrity and security. Hybrid solutions aim to combine the advantages of both on-chain and off-chain methods to optimize performance while maintaining security. Table 12 compares these three different storage approaches based on four key criteria, presenting their advantages and limitations as established in the literature.  
* _Transaction Speed_: On-chain data storage has a low transaction speed due to the requirement that all transactions be verified on the blockchain and undergo the mining process [17]. In networks such as Bitcoin and Ethereum, transaction times are inherently constrained by mining mechanisms. In contrast, off-chain solutions significantly improve transaction speed by moving data outside the blockchain [19]. This approach is particularly supported by Layer-2 solutions such as the Lightning Network [22]. Hybrid methods, on the other hand, enhance security by keeping certain data on-chain while storing large volumes of data off-chain, thereby maintaining a balance in terms of speed.
* _Scalability:_ On-chain data storage offers low scalability due to block size limitations and network congestion [18]. Off-chain solutions provide higher scalability by storing data outside the blockchain, as they are not constrained by the blockchain's transaction capacity [19]. Hybrid solutions create a balanced model by integrating both on-chain and off-chain methods to enhance scalability [97].
* _Cost_: On-chain data storage is costly due to mining expenses, transaction fees, and block size limitations [12]. In contrast, off-chain solutions are cost-effective since transactions are executed outside the blockchain [15]. Hybrid solutions reduce costs by offloading large volumes of data off-chain while preserving security by storing critical data on-chain, resulting in a moderate cost structure [22].
* _Security:_ On-chain data storage offers the highest level of security due to its distributed ledger structure, which ensures data integrity and immutability [36]. Off-chain solutions, on the other hand, provide lower security levels as data are stored outside the blockchain and often controlled by centralized entities [47]. Hybrid solutions enhance security by leveraging on-chain verification mechanisms while storing the majority of data off-chain to optimize performance [109].
## 6 Conclusions  
Crop type mapping is essential for ensuring food security and effective agricultural management. RS satellite data have emerged as a promising alternative to traditional methods, such as time-consuming field surveys, for generating crop type maps. However, accurately identifying different crops in satellite data poses challenges due to variations within and between crop classes caused by factors like crop diversity, environmental conditions, and farming practices. Consequently, there is an increasing demand for more accurate classification algorithms. Developing these algorithms in cloud processing platforms like GEE can facilitate the generation of crop type and land cover maps through online processing, eliminating the need to download large volumes of RS data. This paper proposed a novel ensemble structure of ML models, referred to as Pa-PCA-Ca, for crop type classification using GEE. The Pa structure incorporated three data sources: S1, S2, and L8/9. Within the Pa structure, PMs were generated for different target classes. These PMs demonstrated a high correlation within each target class. Consequently, PCA was employed to transform the PMs, and the resulting top components were inputted into the Ca structure. The Ca structure utilizes another ML model for the final classification decision. The proposed method demonstrated promising results, surpassing conventional crop type classification approaches. The results also indicated a significant reduction in the classification uncertainty of target classes compared to other structures. The proposed ensemble structure can be scaled up to national and global levels to generate highly accurate crop maps.  
The developed JavaScript code of the proposed ensemble structure of this paper in GEE and a portion of ground truth samples can be found at: [[https://github.com/ATDehkordi/Pa-PCA-Ca](https://github.com/ATDehkordi/Pa-PCA-Ca)]([https://github.com/ATDehkordi/Pa-PCA-Ca](https://github.com/ATDehkordi/Pa-PCA-Ca)), accessed on 20 December 2023.  
Conceptualization, E.A., A.T.D., M.J.V.Z. and E.G.; methodology, E.A. and A.T.D.; software, E.A. and A.T.D.; validation, E.A. and A.T.D.; formal analysis, E.A. and A.T.D.; data curation, E.A. and A.T.D.; writing--original draft preparation, E.A. and A.T.D.; writing--review and editing, M.J.V.Z. and E.G.; supervision, M.J.V.Z. and E.G.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
A portion of the ground truth samples is included in the Supplementary Materials. The complete dataset is available upon request.  
**Acknowledgments:** The authors sincerely appreciate ESA, NASA, and USGS for supporting the Sentinel and Landsat programs, which provide valuable earth-observed data for researchers and scientists worldwide. The authors express their gratitude to the GEE team for providing an online cloud processing platform with petabytes of remote sensing data. The authors would also like to thank the reviewers for their time and for providing constructive feedback.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Materials and Methods
### Adaptive Control Algorithm  
Considering the implementation of an SGR control algorithm in an industrial biotechnological process, it should be user-friendly and relatively simple, subject to standard control and measurement equipment, easy to tune and to develop. Adaptation properties that consider the parameter change over time during the process should also be considered in these type of control algorithms. Based on the above considerations, a PI controller with fuzzy-based parameter adaptation was developed and investigated.  
The application of an ordinary PID controller is limited due to the nonstationary and nonlinear behavior of the described biotechnological processes. An adaptation of the controller parameters may be needed because of the dynamic response variation considering the systems operation point. To fulfill the control quality and stability requirements, an adaptation of the controller parameters is needed. This can be realized by using adaptive control techniques. Therefore, a fuzzy model was designed and implemented to adapt the PI controller parameters in real time. As the SGR cannot be directly measured online, a state estimator and an auxiliary variable for the calculation of the signal were used [19]. These variables are then used for the feedback loop and fuzzy model input. In this study, it was decided that the OUR would be a suitable additional variable due to the fact it not only reflects the cultures physiological state but has a good correlation with the SGR. Nevertheless, it is possible to accurately measure it during a bioreactor-scale cultivation process. The obtained results show that the biomass concentrations calculated from the estimated SGR fit well to the measured (reference) biomass concentrations. Figure 1 represents the general structure of the developed fuzzy-based PI controller.
## 3 Results and Discussion
### Pigments
#### 3.1.4 Green  
Despite the availability of expensive pigments, for the large majority of the green parts of the wall paintings, cheap green earths were used, probably due to their chemical stability allowing for their use a fresco. More specifically, the position of the Raman bands (174, 202, 279, 393, 544 and 701 cm\({}^{-1}\)) seems to suggest the use of iron-containing phyllosilicate cladonite K(Mg,Fe\({}^{2+}\))(Fe\({}^{3+}\),Al)[Si\({}_{4}\)O\({}_{10}\)](OH)\({}_{2}\)) (Figure 5) [18].  
Copper basic carbonate malachite (Cu\({}_{2}\)(CO\({}_{3}\))(OH)\({}_{2}\)) was revealed only in sample B9. Malachite is not compatible with the fresco technique: it should be applied a secco and the presence of an organic binder is expected. To obtain the particular green hues in all the green samples, a large number of different yellow, red and blue pigments had been added. For example, in the B9 sample, together with malachite, also identified were lazurite, goethite, cinnabar, lead tin yellow type I and the organic blue dye indigo (C\({}_{16}\)H\({}_{10}\)N\({}_{2}\)O\({}_{2}\)). A second organic blue dye, still unidentified, whose Raman spectrum is slightly different from the indigo one, was detected in sample B10.  
The lead tin yellow type I (Pb\({}_{2}\)SnO\({}_{4}\)) pigment, unusual for the paintings of this church [19], was not used by [PERSON] in the yellow parts, but we found it in a blue (B1) and in a green (B9) sample; it was probably intended to be mixed with blue pigments to obtain particular green hues.
## 3 Methods
### Three-Dimensional Deformation Rate by Integrating of GNSS and InSAR  
Previous studies showed that the Weihe Fault was dominated by vertical deformation and slight strike-slip shear movement [24]. The deformation estimated from stacking-InSAR was in the one-dimensional LOS direction. GNSS observations can measure the two-dimensional horizontal displacements at discrete locations with millimeter-level accuracy. Therefore, this study calculated high-spatial-resolution three-dimensional ground deformation through the integration of InSAR LOS deformation and GNSS horizontal deformation [29].  
The software published by [30] was used to calculate the integrated three-dimensional deformation rates, where the north-south rate was mainly constrained by GNSS observation, the east-west rate was constrained by both InSAR and GNSS observations, and the vertical rate was constrained by the InSAR observations. The leveling observation with a more reliable vertical accuracy was not involved in the three-dimensional deformation estimation due to the nonuniform spatial distribution, which may have distorted the overall  
Figure 2: Distribution of temporal and spatial baselines. Orange dots indicate Sentinel-1 acquisitions, and blue lines represent the generated interferograms.  
deformation. The discrete GNSS deformation rates were interpolated to the same grid interval of InSAR observations by utilizing the algorithm proposed by [31], where the GNSS network density and configurations were considered during the process of data weighting. The Gaussian distance weighting function and a Voronoi cell-spatial weighting function were used in the interpolation. The offsets between InSAR and GNSS reference frames were also calculated and removed from the InSAR LOS deformation rate. Finally, the three-dimensional velocity was solved for each grid by combining the GNSS-interpolated velocities and the InSAR LOS velocities through least-squares regression.
# IoT for Smart Cities: Machine Learning Approaches in Smart Healthcare--A Review
## 4 IOT and Healthcare
### Patient Monitoring  
Advanced technologies in health care allow both inpatient and outpatient care to be monitored more closely. Remote patient monitoring (RPM) enables healthcare professionals to monitor vital signs and assess physical reactions to previous treatments without having to be in the same place as the patient. The device used depends on the health of the respective patient. For example, it could be an implanted cardiac device, an airflow monitor, or a networked blood glucose meter. The device in question collects the desired data. If the values are not as they should be, the data are simultaneously forwarded to a database for recording and to the treating doctor. The doctor can analyze the information in real time and react accordingly. Such devices are often used immediately after an operation. They help reduce the number of hospitals stays and avoid re-admissions because problems are identified more quickly. This allows doctors to react earlier and avoid potential complications. With the help of the data collected in real time, it is also possible to adjust and adapt treatment options more quickly, depending on the patient's physical reaction and condition. This allows doctors to react earlier and avoid potential complications [1].
# Risk Assessment and Source Apportionment of Heavy Metals in Soils from Handan City
## 3 Results and Discussion
### Source Apportionment of Heavy Metals
#### 3.4.2 Cluster Analysis  
Dendrograms were drawn to display the CA results (Figure 6), which can vividly reflect the distance between the elements and reveal the relationship between the elements. Three clusters were determined: Ni-Cr; Cd-Pd-Cu; Mn-Zn. Cr and Ni were correlated with each other. Cd and Pb clustered with each other and composed another cluster with Cu. Mn and Zn were isolated and joined to the Cd-Pd-Cu cluster. Metals belonging to the same cluster usually have a common source [49]. Cr and Ni were considered to derive from the parent material of soil. Cd, Pd and Cu might originate from anthropogenic sources. According to the cluster analysis, Mn and Zn were closer to the Cd-Pd-Cu group, suggesting that they were likely from anthropogenic sources.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **Cr** & **Mn** & **Ni** & **Cu** & **Zn** & **Cd** & **Pb** \\ \hline Cr & 1 & & & & & & \\ Mn & \(-\)0.054 & 1 & & & & & \\ Ni & 0.634 ** & \(-\)0.158 & 1 & & & & \\ Cu & 0.205 & 0.063 & 0.189 & 1 & & & \\ Zn & 0.022 & 0.284 * & 0.101 & 0.467 ** & 1 & & \\ Cd & \(-\)0.152 & 0.362 ** & \(-\)0.135 & 0.363 ** & 0.547 ** & 1 & \\ Pb & 0.027 & 0.284 * & 0.119 & 0.623 ** & 0.513 ** & 0.608 ** & 1 \\ \hline \end{tabular} Note: * Significant at the 0.05 level. ** Significant at the 0.01 level.  
\end{table}
Table 3: Spearman correlation coefficient analysis of heavy metal contents in soil.