esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Reconstruction Results of Cloud-free Sentinel-2 Images  
Based on the cloud masks resulted from the cloud and cloud shadow detection, we reconstructed the cloud-free Sentinel-2 image time-series by replacing the cloudy, cloud shadow and haze pixels of the original cloud contaminated images with the corresponding pixels of its synthetic images. Figure 8 presents the observations, synthetic, and reconstructed images for their visual comparison.  
Figure 8: Comparison of the original cloud contaminated, synthetic, and reconstructed cloud-free Sentinel-2 images on the dates of 21 May, 25 June, 30 July, 21 August, 3 October, and 5 October 2018 (top down).  
From Figure 8, it can be found that both the synthetic and reconstructed images are cloud-free and the differences between the two images were small for all six images. The main reason was that the synthetic images were of reasonably high quality and the replacement of the clouds and cloud shadow pixels in the observations from the synthetic images overall matched well to the rest of the cloud-free pixels of the observations although some minor differences between the synthetic and reconstructed images were still visible.
## 3 Application Scenario and System Model
### System Model  
The assumed model of each of the above mentioned elements that compose the scenario is presented in the sequence.
# Interpretation of Bridge Health Monitoring Data from Satellite InSAR Technology
## 5 Conclusions
The studies presented here were carried out as part of the activities envisaged by a research agreement between the University of Trento and Autostrada del Brennero SpA and supported by Fondazione CARITRO Cassa di Risparmio di Trento e Rovereto (grant number 2021.0224), MIUR PON RI 2014-2020 Program (Project MITIGO, ARS01_00964), the Agreement between the High Council of Public Works (CSLLPP) and the ReLUIS Consortium implementing Ministerial Decree 578/2020 and Ministerial Decree 240/2022, and the Agreement between the Italian Department of Civil Protection (DPC) and the ReLUIS Consortium. The contents of this paper represent the authors' ideas and do not necessarily correspond to the official opinions and policies of CSLLPP and DPC. The data presented in this study are available on reasonable request from the corresponding author. COSMO-SkyMed products have been provided free of charge for research purposes by the Italian Space Agency, Project Card ID: 630 \"SAR SHM of bridges\". The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 7 Experimental Evaluation  
In this section, we present two experimental scenarios to evaluate the basic concept principles, which quantify the overall usability of the concept and serve as a baseline for further development. The scenarios of each of the two experiments are derived from the proposed gesture pointing method, in which the operator points to a specific location on the workspace plane and performs a command gesture, sending the robot to the desired location. Each of the experiment scenarios has two variants. In the first scenario, the operator points to the designated horizontally placed markers; in the second scenario, the operator points at vertically placed markers. In the first variant, the operator has no visual feedback about where she or he is pointing. His task is to rely solely on his best guess. In the second variant, the user receives visual feedback from the interactive monitors showing him the exact place he is pointing. The illustration of the experimental setup and the precise positions of the markers in the global coordinate system is depicted in Figures 13 and 14. The position values are in millimeters. The process of obtaining one measurement is as follows:  
1. The operator's hand is calibrated for optimal gesture recognition.
2. The operator points to the first point, and when he is satisfied with his estimation, he performs a command gesture.
3. When the operator performs a command gesture, the spatial data are logged, and the robot's TCP moves to the pointed location.
4. The operator repeats these previous two steps, iterating through each target point in order, as illustrated in Figures 13 and 14.  
In total, the operator points ten times to each of the target markers for the current experiment. During the pointing, the only requirement on the user is to keep the hand approximately 200-250 mm above the Leap Motion sensor, as this is believed to be the optimal distance according to [42] and the empirical data acquired during the method pre-testing. The user is encouraged to perform the pointing gestures as naturally as possible, allowing him to position his body and arm as he sees fit. Seven male participants of different height, physical constitution, and height executed the experiment scenarios.  
Figure 13: Scenario of experiment no. 1.  
Figure 16: The acquired position data for experiment no. 2.  
Figure 17: Boxplots of euclidean distance data for experiment no. 1.  
Figure 18: Boxplots of euclidean distance data for experiment no. 2.
# Data Lake Architecture for Smart Fish Farming Data-Driven Strategy
## 5 Technical Data Lake Architecture Proposal for Fish Farming Data-Driven Strategy  
With the emergence of data-driven strategies in many fields and the massive increase in the quantity of generated data by the fish farming domain, it is imperative to design a data lake architecture able to handle data coming from different sources and offer a solid platform to perform advanced analytics. The proposed fish farming data lake architecture, represented in Figure 2, represents a flexible, scalable, and efficient solution that covers the entire process of collecting, transforming, and exposing data.  
In order to ease the implementation of this architecture and make it widely accessible, the fish farming data lake was designed according to six main guidelines:  
1. It should support handling the three types of data (structured, semi-structured, unstructured)
2. The process of collecting and ingesting data should be automated
3. Depending on the provenance and type, the data should be classified
4. It should support handling historical/backup data in raw format
5. Providing a dedicated data access layer for data analyst teams
6. Providing a data science laboratory for advanced analytics  
In the same perspective, the proposed fish farming data lake architecture is multi-zone architecture with three main zones; namely, raw zone (RZ), trusted zone (TZ), and access zone (AZ).  
The data could be gathered from different sources such as relational data base management systems (RDBMS), CRM/ERP, human-generated data such as flat files, application programming interfaces (APIs) like weather and IoT/Sensors for data like Ph, temperature, and pressure. Then, the data is directly submitted to the data lake edge node as it is considered the entry point.
## 1 Introduction  
Entry with a 5-8 m coal pillar excavated along the adjacent goat side after movement of the overlying strata in longwall mining is called goat-side entry driving with a small coal pillar (GESCP). Compared to entry driving with a pillar of 20-30 m, using GESCP can improve the coal recovery rate by 6.8-7.4%. The location of the excavated entry is normally in the lower part of the lateral abutment stress induced by nearby coal-face mining, which is beneficial for the maintenance of the goat-side entry [1, 2].  
The theory of stability control and the characteristics of the surrounding rock for GESCP in longwall mining has been studied by numerous researchers in recent decades. The reasonable size and long-term stability of the coal pillar is very important for the design of GESCP and the prevention of adjacent goat disasters [3]. When determining a reasonable coal pillar size and location, some factors, such as the pillar strength [4, 5], mining method [6, 7], geological condition [8, 9], and fracture characteristics of the main roof [10], etc., should be seriously considered. With the mining of the adjacent and present longwall coal faces, the internal stress of the surrounding rock is redistributed [11, 12, 13]. The inner stress distribution and the possibility of failure characteristics of the small coal pillar is influenced by the dynamic process of mining stress distribution [14, 15, 16, 17, 18, 19]. In order to prevent the coal pillar from failing and to control the surrounding rock deformation of GESCP, it is necessary to implement reinforcement and support measures [20, 21, 22, 23].  
However, most of the current research mainly focuses on stress distribution within a coal pillar, the design of pillar sizes, the main roof fracture structure, the reasonable location of the goat-side entry, and supporting technologies, etc. Few studies have been conducted on the whole structural stability characteristics of surrounding rocks for GESCP, and the interrelationship between the main roof fracture structure, the coal pillar, and the entry sizes still requires more research, which is of critical importance for optimizing the drive of GESCP.  
In this paper, we focus on the condition of the main roof fracture location above the integrated coal seam, considering the main factors that affect the structural stability of the surrounding rocks for GESCP. Mechanical models were established for the main roof structure and the multi-layer cracking structure of the side walls for GESCP. The limiting equilibrium equation for the structural stability of the surrounding rock for GESCP was solved. The characteristics of the main parameters affecting the structural stability of the surrounding rock were analyzed. The research results were obtained from the Dingji coal mine in China.
## 6 Multi-Fault Classification  
Figure 18 depicts the methodology for multi-fault classification. In the first stage, vibration signals of 13 conditions were acquired, and each condition is repeated 15 times, all the signals of 10 s length, i.e., 195 signals per class. In the pre-processing stage, each signal is subdivided into five fragments of 2 s, obtaining 975 samples for the 13 conditions. In the third stage of feature extraction, for each of fragments three different sets of features are extracted: (a) the statistical features set is composed of 12 features, (b) the SD is represented by a set of 24 features, and (c) the CCM features are composed of 50 features. For the classification stage, each feature dataset is split into a test set (16%) and a training set (84%). Two types of classifiers are trained and 10-fold cross-validated: The Ensemble Bagged Tree (EBT) [31] and the Ensemble Subspace k-NN (ESK) [37]. The classification accuracy of each trained classifier is evaluated over the test dataset. This procedure is repeated for each of the vibration signals collected from the accelerometers A1, A2 and A3, and the results are compared.  
Figure 17: CCM for a vibration signal of the healthy class (blue) and for the faulty class P2 (red). The calculation considers a variation of \(\tau\) between 1 and 50.
# Cost-Effective Satellite Ground Stations in Real-World Development for Space Classrooms
## 5 Design and Development of Satellite Ground Stations
### Directional Antenna Pattern and G/T  
To measure the performance of the four directional antennas installed at the ground station, antenna patterns and gains were measured using the open-area test site method [26] and compared with values estimated from simulations using the 4 NEC2 program. This program simulates and analyzes antenna performance based on the Numerical Electromagnetics Code (NEC) principles. The antennas were constructed in the program to match their actual development, and tests were compared both in the program and real-world usage at their respective frequencies. This included testing the crossed Yagi-Uda VHF antenna at a frequency of 145 MHz, as shown in Figure 11a; the crossed Yagi-Uda UHF antenna at 437 MHz, as shown in Figure 11b; the 60 cm \(\times\) 90 cm grid antenna at 1600 MHz, as shown in Figure 11c; and the 1.9 m dish antenna at 1700 MHz, as shown in Figure 11d.  
By comparing real measurement data with simulation data, it was found that the patterns of all four directional antennas tended to align closely with the values estimated from the simulation for the main lobe gain. However, there were some deviations in the gain in other areas, which might have been due to real-world factors not accounted for in the simulation, such as nearby metal equipment like rotators and antenna towers.  
Based on the test results in Figure 11, the Yagi-Uda antennas had a relatively wide half-power beamwidth (HPBW), with the VHF having a beamwidth of 52.5\({}^{\circ}\) and the UHF having a beamwidth of 24.4\({}^{\circ}\). In contrast, the parabolic antennas had narrower beamwidths, with the grid antenna having a beamwidth of 12.2\({}^{\circ}\) and the 1.9 m dish antenna having a beamwidth of 5.4\({}^{\circ}\). This narrower beamwidth provides better antenna gain compared to the Yagi-Uda type, but the system requires precise antenna pointing to receive signals from satellites accurately.  
The maximum gain of each antenna system was used to calculate the G/T value to assess the antenna's performance using Equation (3), and the results are shown in Table 5. It can be seen that the 1.9 m dish antenna was well suited for receiving weak signals due to its high G/T value. Conversely, the discone antenna, which had no gain amplification and a high noise temperature, had a low G/T value and was therefore unable to receive weak signals.  
Figure 11: Directional antenna patterns: (**a**) Yagi–Uda VHF test on 145 MHz, (**b**) Yagi–Uda UHF antenna tested on 437 MHz, (**c**) grid antenna of 60 cm \(\times\) 90 cm tested on 1600 MHz, and (**d**) dish antenna 1.9 m tested on 1700 MHz.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Antenna System** & **Gr (dBi)** & **Ts (dBK)** & **G/T** \\ \hline Dish antenna 1.9 m & 27 & 21.88 & 5.12 \\ Grid 60 cm \(\times\) 90 cm & 20.47 & 21.77 & –1.3 \\ Yagi–Uda UHF & 17.04 & 23.77 & –6.73 \\ Yagi–Uda VHF & 13.91 & 30.98 & –17.07 \\ Discone & 0 & 34.04 & –34.04 \\ \hline \end{tabular}
\end{table}
Table 5: The G/T value of the antenna systems.
## 4 Numerical Examples
### L-Shaped Al/SiC Plate  
Consider an L-shaped corner support plate made of aluminum (Al) and silicon carbide (SiC), as shown in Figure 12a. The outer edges of the plate are clamped, and the top surface is subjected to a uniform load of \(\varrho_{0}=-10^{7}\). The geometric parameters of the L-shaped plate are defined as \(h=0.2\), \(a=1\), \(b=1.3\), and \(r=\phi=0.3\). In order to achieve a more uniform mesh, \(10\times 10\) meshes are used for the 16 NURBS patches, except for the two corner patches B and C, where \(15\times 15\) meshes are applied. Fourth-order NURBS basis functions are used, and the SDF truncation numbers are taken as \(M=5\) and \(N=6\). Figure 11: Converged 3D responses of Ti/ZrO\({}_{2}\) circular plate under a uniform load. \begin{table}
\begin{tabular}{c c c c c c c c} \hline _h/r_ & **Method** & _g = 2_ & _g = 4_ & _g = 8_ & _g = 10_ & _g = 50_ & _g = 100_ \\ \hline \multirow{4}{*}{0.2} & FSDT [51] & 1.6130 & 1.4730 & 1.3620 & 1.3330 & 1.2160 & 1.1990 \\  & sFSDT-IGA [52] & 1.6051 & 1.4659 & 1.3548 & 1.3260 & 1.2097 & 1.1918 \\  & HSDT-IGA [53] & 1.5958 & 1.4557 & 1.3467 & 1.3187 & 1.2060 & 1.1884 \\  & Present & 1.5962 & 1.4556 & 1.3468 & 1.3189 & 1.2065 & 1.1891 \\  & FSDT [51] & 1.4440 & 1.3200 & 1.2170 & 1.1900 & 1.0800 & 1.0630 \\  & sFSDT-IGA [52] & 1.4428 & 1.3186 & 1.2159 & 1.1889 & 1.0785 & 1.0615 \\  & HSDT-IGA [53] & 1.4386 & 1.3143 & 1.2123 & 1.1855 & 1.0762 & 1.0592 \\  & Present & 1.4378 & 1.3131 & 1.2112 & 1.1845 & 1.0754 & 1.0620 \\  & FSDT [51] & 1.4020 & 1.2820 & 1.1810 & 1.1550 & 1.0460 & 1.0290 \\  & sFSDT-IGA [52] & 1.4023 & 1.2817 & 1.1812 & 1.1546 & 1.0458 & 1.0289 \\  & HSDT-IGA [53] & 1.3990 & 1.2786 & 1.1785 & 1.1520 & 1.0435 & 1.0267 \\  & Present & 1.3990 & 1.2785 & 1.1783 & 1.1518 & 1.0435 & 1.0266 \\ \hline \end{tabular}
\end{table} 
Table 3: Nondimensional central deflection \(\overline{w}(0,0,0)\) of Ti/ZrO\({}_{2}\) circular plates with various gradient indexes and thickness-to-radius ratios under uni-form load. Taking the gradient index as \(g=4\), Figure 13 shows the displacement and stress results obtained by the SDF-based IGA. The results exhibit good symmetry and smoothness. Reasonable stress concentrations can also be observed near the boundaries. In order to verify the accuracy of the SDF-based IGA method for complex geometries, the deflection results from the SDF-based IGA at the points P\({}_{1}\) and P\({}_{2}\) for various thicknesses and gradient indexes are compared with those obtained from the ANSYS, as listed in Table 4. The Ansys results are obtained using Shell181 elements based on the FSDT. In order to approximate the graded material properties, 100 uniform layers are used in the thickness direction. It can be seen that the SDF-based IGA results agree well with the Ansys results, and the discrepancy reduces as the thickness decreases.
# Adaptive Thermal Control of Cell Groups to Extend Cycle Life of Lithium-Ion Battery Packs
## 5 Conclusions  
This demonstration study with 21700-type cells serves as a proof-of-concept for the use of adaptive thermal control to balance the capacities of individual cell groups within a battery pack. A capacity difference between cell groups was deliberately induced by withholding cooling to specified cell groups, followed by selective group cooling to bring the capacity of all cell groups back into balance. To our knowledge, this is the first attempt of its kind where thermal control is used to balance cell capacities. For a cycling protocol with a 3C charge rate and 5C discharge rate, cooling significantly reduced the capacity degradation rate from about 8 mAh/cycle to 3 mAh/cycle. This experiment can be optimized in future work by determining the optimal cooling rates and temperatures to balance cell group capacities as soon as an imbalance is detected.  
W.D.C.: conceptualization, experimental data acquisition, and writing; S.G.A.: experimental guidance, review, and writing; A.K.P.: experimental guidance, review, and writing. All authors have read and agreed to the published version of the manuscript.  
This program is funded by the Federal Transit Administration, under grant DE-04-7004.  
Raw data were generated at the University of Delaware. Derived data supporting the findings of this study are available on request from the corresponding author, [PERSON].  
This work was conducted under the University of Delaware's Fuel Cell Bus Program to research, build, and demonstrate fuel cell-powered hybrid vehicles for transit applications.  
The authors declare no conflict of interest.
# Research Status of and Trends in 3D Geological Property Modeling Methods: A Review
## 3 Interpolation Methods for 3D Property Modeling
### Traditional Geostatistical Methods
#### 3.1.1 Kriging Method
Indicator kriging [52] was proposed to simulate this nonlinear geological model. In addition, multivariate geostatistics was introduced for the problem of correlations between multiple variables. Cokriging [53, 54] is a method for linearly estimating one variable from several other variables. For example, the value of porosity can be estimated from a linear combination of the measured values of nearby porosity and the corresponding acoustic impedance values. The applicability, advantages, and disadvantages of common Kriging methods are shown in Table 1.
## 3 Results and Discussion  
The surface morphology of the craters formed after two consecutive laser shots on a typical pig iron sample (Figure 3a) was investigated by optical microscopy. Images obtained (Figure 3b) indicated that the energy density used was sufficient to provide sample ablation and crater formation only after two laser shots. Further, the laser-impact areas in Figure 3b showed a central intensely ablated region and a rim around it where some droplets and cracks were present. Some black spots could be also observed with pinholes appearing on the surface. Further, the surface colour changed to black, and the surface reflectivity decreased significantly. The crater diameters were estimated to be about 300 \(\upmu\)m (Figure 3b).  
Figure 3: The pig iron circular disc showing the rough face with small shrinkages (**a**); laser beam craters (red circles) produced on the surface (**b**).  
Figure 2: Workflow of the analytical steps performed.  
The full broadband LIBS emission spectra (one example is shown in Figure 4) acquired directly within few seconds by a point and shoot operation without any sample preparation were used to identify the elements present in the pig iron samples. Although all spectra featured an intense background, especially in the wavelength range from 200 to 300 nm where multiple lines of the major matrix element Fe appeared, the main elemental components of the samples, i.e., C, Mn, P, Si, and Ti, could be revealed. The emission lines chosen for further analysis as a result of data pre-treatment procedure were: Si I (251.432 nm, 263.128 nm, 288.158 nm); Ti II (307.522 nm, 334.903 nm, 336.121 nm); Mn II (257.610 nm, 296.595 nm) and Mn I (404.876 nm); P I (215.294 nm, 253.560 nm); C I (193.027 nm, 247.856 nm).