esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Therefore, virtual reality technology focuses on creating an \"all-inclusive, sensory illusion of being present in other environments\" [40] that integrate social and psychological aspects so that they can be perceived as real [41]. The integration of intelligent tutoring systems within augmented reality and virtual reality environments shows great potential to improve the educational domain [42]. Although there have been several review and meta-analysis studies that have explored the individual use of augmented reality [43; 44; 45; 46; 47], virtual reality [28; 48; 49; 50], and intelligent tutoring systems [11; 15; 25; 51] in education, to the best of our knowledge, there has not been any systematic literature review study that has focused on the integration of intelligent tutoringsystems in extended reality environments. As these technologies are rapidly advancing and since their combination presents several benefits and new opportunities, it is important to examine their convergence, as recent studies have shown [31]. Therefore, this study aims to explore the use of intelligent tutoring systems within augmented reality and virtual reality environments. Therefore, it carries out a systematic literature review, a content analysis, and a bibliometric and mapping analysis of the existing literature. The main research questions that drive this study are as follows: \"What are the main outcomes of integrating and using intelligent tutoring systems in extended reality environments?\", \"What are the main characteristics and distributions of studies relevant to the topic?\", \"What are the main focus areas, topics, and themes examined in the literature?\", and \"What is the current state of the art regarding the use of intelligent tutoring systems within extended reality environments?\". This study distinguishes itself from previous systematic literature review studies that have focused solely on augmented reality, virtual reality, or the metaverse in education by focusing on and examining their use in combination with intelligent tutoring systems. Additionally, this study combines the approaches of a systematic literature review and a content analysis, as well as of a bibliometric and scientific mapping study. This study contributes to the existing literature by offering an in-depth analysis of relevant to the topic documents and synthesizing their outcomes and findings. The study also offers a content analysis of the relevant studies to identify key aspects in the use of intelligent tutoring systems in extended reality environments. Additionally, the study contributes through its bibliometric analysis and scientific mapping of the documents to identifying emerging themes, limitations, and future research directions. Therefore, the study presents the methodology used and describes all steps taken to prepare the document collection (Section 2) and showcases the analysis and results (Section 3). In Section 4, the outcomes are further discussed, and conclusive remarks are offered in Section 5.
## 3 Results and Discussion
### Isothermal Analysis
#### 3.3.4. Harkins-Jura Isotherm  
The Harkins-Jura adsorption isotherm can be expressed as Equation (8), which can be solved by a plot of \(1/q_{e}\) versus \(\log C_{e}\), as shown in Figure 15. The Harkins-Jura model can be described as the presence of a distribution of heterogeneous pores and multilayer adsorption. The isotherm constants and correlation coefficients ranged from R\({}^{2}\) = 0.858 (Table 2). This may indicate that the Harkins-Jura model is less applicable.
## 2 The BEAM Toolbox and the Land Surface Temperature Processor
### Basic ENVISAT Toolbox for (A)ATSR and MERIS (BEAM)  
The BEAM Earth Observation Toolbox and Development Platform [10] is a collection of executable tools and Application Programming Interfaces (APIs) that have been developed to facilitate the utilisation, viewing and processing of a variety of remotely sensed data. The purpose of the BEAM is not to duplicate existing commercial packages, but to complement them with functions dedicated to the handling of data products of Earth observing satellites, specifically focused on ocean colour applications, but also used for other land surface processes. The main components of the BEAM are:  
* VISAT: an intuitive desktop application used for Earth Observation (EO) data visualisation, analysis and processing (Figure 1) (version 5, Brockmann Consult GmbH, Geesthacht, Germany).
* A set of scientific data processors running either from the command-line or invoked by VISAT.
* The command-line tool _gpt_ (graph processing tool) is used to execute processing graphs made up of operators nodes developed using the BEAM Graph Processing Framework (GFF, see Java API below) (Brockmann Consult GmbH, Geesthacht, Germany).
* A data product converter tool _pconvert_ (Brockmann Consult GmbH, Geesthacht, Germany) allowing a user to convert raw data products to the BEAM-DIMAP standard format, to GeoTIFF, to HDF-5 or to RGB images.
* A JavaTM API which provides ready-to-use components for remote sensing related application development and plug-in points for new BEAM extension modules. Besides a number of extension points such as product reader and writers, the BEAM API comprises the GPF, which is used to rapidly create raster data processors. The VISAT Rich Client Platform is used to develop rich Graphical User Interface (GUI) applications based on BEAM VISAT.  
BEAM is programmed in pure JavaTM to allow a maximum portability. The BEAM software (version 5, Brockmann Consult GmbH, Geesthacht, Germany) has been successfully tested under MS WindowsTMXP(r), WindowsTM Vista, WindowsTM 7 and also Mac OS X and as well as under Linux (\"Linux\" trademark is owned by [PERSON] and administered by the Linux Mark Institute)and Solaris(r) (Sun Microsystems, ORACLE, San Francisco, USA) operating systems. Since end of 2015 BEAM is not further developed. It will remain under maintenance until at least end of 2016. BEAM users are encourage using its evolutionary successor SNAP from now on (version 4, ESA). When installing SNAP along with the Sentinel-3 Toolbox (version 4, ESA) the users will have the same experience as with BEAM and they can even extend the number of features by installing other toolboxes like those for Sentinel-1 or Sentinel-2 [11].
## 3 Results
### Industrial Application and Future Prospects  
The results of the current study are promising, suggesting that almond shell biochar could be a low-cost green adsorbent for the simultaneous removal of Sr\({}^{2+}\), Mn\({}^{2+}\), Pb\({}^{2+}\) and As\({}^{5+}\) from wastewater. However, pilot-scale experiments are recommended prior to industrial application in order to evaluate the technical and economic viability of the process.  
Additional techniques, such as Synchrotron and Scanning Electron Microscopy (SEM) analysis could offer a better insight into the mechanisms of adsorption. Important issues to be investigated are optimization of the parameters involved, statistical analysis of results, application of different isotherm models, development of a low-cost modification technology of biochar to increase its adsorption efficiency, regeneration of sorbent to achieve environmental and economic advantages, prediction of the overall cost for biochar production and its application, as well as experimental trials with real wastewater containing mixtures of heavy metals and other pollutants.
## 3 Results and Discussion
### Analysis of Atmospheric-Pressure Helium Dielectric-Barrier Discharges (APHeDBDs) Gas Heating
#### 3.2.4 Spatial Distributions of Heating Sources  
Before investigating the spatial distributions of heating sources, it is essential to understand the cyclic average spatial distributions of species densities of electrons and dominant ions as shown in Figure 14 with the distribution of electron temperature included. It is noted that the sheath regions are built near the glass surfaces (i.e., at 0.7 mm and 5.7 mm) since the intensive electric fields (not shown) are built due to the distribution of charged species. The intensive electric field that is built results in energetic electrons with high electron temperature, which leads to high reaction rates in the sheath region. Similar simulated results with sheath regions were observed for atmospheric pressure helium dielectric barrier discharges excited by kHz power sources [16; 35]. It is observed that the electron temperature reaches around 3.9 eV as the maximum in the sheath region, while the density of N4+ reaches around \(5.2\times 10^{16}\) m-3. Both the species densities of electron and N4+ reach the level of \(10^{16}\) m-3 in the central bulk region.  
Figure 12: The contribution of each species to the ion Joule heating.  
Figure 13: The spatial average densities of ion species during the breakdown in the positive half period (HP).  
Figure 15 shows the cyclic average heating sources of different heating mechanisms. The simulated average total heating source is around \(5.6\times 10^{5}\) W m\({}^{-3}\) with the maximum reaching \(3.5\times 10^{6}\) W m\({}^{-3}\) in the sheath region due to the contribution of ion Joule heating. The total heating source reaches the level of \(10^{5}\) W m\({}^{-3}\) in the bulk region due to the contribution of elastic collision. It is observed that the heating source of elastic collision increases to the maximum near the sheath region since the local electron density and electron temperature are higher than those in the central region. Similarly, the heating sources of ion Joule heating and exothermic reactions reach the maxima because of the high species densities generated and the electric field built in the sheath region.
# Polar Region Integrated Navigation Method Based on Covariance Transformation
## 5 Conclusions  
The advantage of the covariance transformation method is that it establishes the transformation relationship of the integrated navigation filter between the n-frame and G-frame. It fundamentally solves the problem of filter overshoot and error discontinuity. caused by the change in navigation frame, improving navigation accuracy when crossing the polar region. Besides this, the covariance transformation method does not change the existing navigation algorithm. The results of the flight experiment and semi-physical simulation show that the covariance transformation method is effective at any latitude. As the latitude increases, the horizontal component of the earth's rotational angular velocity decreases, resulting in weaker observability of the yaw angle error. The error fluctuation caused by the frame switching will increase. In this case, the covariance transformation method still makes a smooth transformation via the integrated navigation filter.  
Conceptualization, L.W.; data curation, Y.Z.; formal analysis, Y.Z.; investigation, C.G.; methodology, L.W.; supervision, G.W.; writing--original draft, Y.Z.; writing--review and editing, L.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Nature Science Foundation of China, grant number 62003360, and the Basic Research Project of College of Advanced Interdisciplinary Studies, grant number ZDJC19-07.  
Figure 4: The simulation results, based on the covariance transformation and non-covariance transformation. (**a**) The relative error of attitude; (**b**) the relative error of position; (**c**) the relative error of gyro bias estimation; (**d**) the relative error of accelerometer bias estimation.
## 4 Conclusions  
1. After the RAC undergoes high temperatures, its apparent physical changes are obvious: The color changes from blue ash to grayish white, cracking occurs when the temperature reaches 600 \({}^{\circ}\)C and bursting and spalling occurs when the temperature reaches 800 \({}^{\circ}\)C, the quality of RAC is reduced, and this phenomenon is more significant with the increase of temperature and replacement percentage.
2. The mechanical properties of RAC prism specimens and RAC members degrade significantly after subject to high temperatures, especially the bearing capacity. Among the different member, the shear performance of prism specimens and RRAC beams degrade fastest, while the flexural performance of the RRAC and SRRAC beams degrade slowest.
3. Among different member, the mass loss ratio of RRAC members is slightly larger than that of SRRAC members with the increase of temperature.
4. The change of the bearing capacity of RRAC columns with the increase of temperature is roughly the same as that of SRRAC members, but the bearing capacity degradation of RRAC beams is more serious than that of SRRAC members. The effect of the replacement percentage on the bearing capacity of RRAC members is basically the same as that of SRRAC members.
5. With the increase of temperature, the variation process of the peak deformation of RRAC columns is roughly equivalent to that of the SRRAC members. However, the increase of temperature makes the deformation capacity of RRAC beams gradually decrease, which is beneficial to the improvement of SRRAC members. The effect of the replacement percentage on the bearing capacity of RRAC members is roughly equivalent to that of SRRAC members.  
Figure 18: Influence of variation parameters on energy dissipation. (**a**) Prism; (**b**) SRRAC column; (**c**) RRAC column; (**d**) SRRAC bending beam; (**e**) SRRAC shear beam; (**f**) RRAC bending beam; (**g**) RRAC shear beam; (**h**) Temperature; (**i**) Replacement percentage.  
With the increase of temperature, the ductility of RRAC columns and RRAC bending beams is slightly better than that of SRRAC members, but the ductility of RRAC shear beams is worse than that of SRRAC members. The replacement percentage has a greater influence on the displacement ductility coefficient of RRAC shear beams than that of SRRAC members, and the displacement ductility coefficients of columns and flexural beams are approximately the same with the increase of replacement percentage.
7. The influence of temperature on energy dissipation coefficient of RRAC members is greater than that of SRRAC members, the effect of replacement percentage on the energy dissipation coefficient of RAC members is not obvious.
## 3 Results
### Ceramic SiC and SiC/\(\mathrm{C_{f}}\) Microspheres
Results for representative samples are shown in Figure 7. The main diffraction peak, which is observed at 26 equal 35.60\({}^{\circ}\), is attributed to the cubic \(\beta\)-SiC (111) phase. Other diffraction peaks that appear at 20: 41.4\({}^{\circ}\) (200), 60\({}^{\circ}\) (220), 71.8\({}^{\circ}\) (311) and 75.5\({}^{\circ}\) (222) also originate from the 3C \(\beta\)-SiC phase. There are no peaks characteristic for hexagonal \(\alpha\)-SiC phases at 43.3\({}^{\circ}\) and 45.3\({}^{\circ}\)[38; 47]. The absence of a broad halo at 20 between 15-30\({}^{\circ}\), characteristic of the amorphous SiC and SiO\({}_{2}\) phases, also confirms that the residual oxygen content must be low. Figure 5: \({}^{13}\)C MAS NMR spectra of ceramic SiC/C\({}_{\text{f}}\) microspheres obtained by ceramization of polysiloxane microspheres at 1600 \({}^{\circ}\)C for 5 h under argon; DVB/PHMS \(w/w\) ratio 0.97 (A-6). Raman spectroscopy study provides information on the structure of the C\({}_{\rm f}\) phase. Representative spectra for microspheres with high amounts of DVB are displayed in Figure 8 and some results are summarized in Table 3. The spectra of microspheres cross-linked with smaller amounts of DVB (A-2 and A-3) are similar to A-1, they have no free carbon in their structures. The Raman spectra show a series of bands, the most prominent of which are disorder-induced band D about 1360 cm\({}^{-1}\), a graphite-like band G around 1590 cm\({}^{-1}\) and a D-overtone peak close to 2950 cm\({}^{-1}\) which is also induced by disorder [48; 49]. Two other bands are superimposed on the G band. The resolution of these overlapping bands using Gaussian-Lorenzian approach (Figure S4 in Supporting Info) allows the evaluation of ID/IG of the integrated D and G bands intensity ratio (Table 3) used to quantify the graphite network defects, which is a measure of the disorder in the C\({}_{\rm f}\) phase. The ratio is larger than 1, indicating a high degree of disorder in the C\({}_{\rm f}\) phase which forms graphitic-like domains. The average in-plane linear size of these domains (La) was calculated using the Tunistra-Koenig (TK) approach [50]. The obtained La values were within the range in which the validity of the TK approach was previously verified [51]. Figure 6: \({}^{29}\)Si MAS NMR spectra of SiC and SiC/C microspheres obtained at different DVB/PHMS \(w\) ratio: A-1 = 0.17, A-3 = 0.48, A-5 = 0.80, A-6 = 0.97. Figure 8: Raman spectra for SiC/C\({}_{\rm f}\) microspheres. DVB/PHMS \(w/w\) ratio: A-1 = 0.17, A-5 = 0.80, A-6 = 0.97. Figure 7: X-ray diffraction traces for SiC/C\({}_{\rm f}\) microspheres obtained by cross-linking with DVB.
###### Abstract  
The work presents a structural health monitoring (SHM) electronic system with real-time acquisition and processing for the determination of impact location in laminate. The novelty of this work is the quantitative evaluation of impact location errors using the Lamb wave guided mode S\({}_{0}\), captured and processed in real-time by up to eight piezoelectric sensors. The differential time of arrival is used to minimize an error function for the position estimation. The impact energy is correlated to the amplitudes of the antisymmetric (A\({}_{0}\)) mode and the electronic design is described to avoid saturation for signal acquisition. The same electronic system is designed to acquire symmetric (S\({}_{0}\)) low level signals by adequate gain, bandwidth, and signal-to-noise ratio. Such signals propagate into a 1.4 mm thick aluminum laminate at the group velocity of 5150 m/s with frequency components above 270 kHz, and can be discriminated from the A\({}_{0}\) mode to calculate accurately the differential arrival time. The results show that the localization error stabilizes at a value comparable with the wavelength of the S\({}_{0}\) mode by increasing the number of sensors up to six, and then remains constant at up to eight sensors. This suggests that a compromise can be found between sensor density and localization error.  
Keywords:real-time electronics; structural health monitoring; Lamb wave; piezoelectric sensors; impact localization; ultrasonic guided waves +
Footnote †: This paper is an extended version of our paper published in [PERSON], [PERSON] and [PERSON], “Analysis of the accuracy in impact localization using piezoelectric sensors for Structural Health Monitoring with multichannel real-time electronics,” 2020 IEEE 7 th International Workshop on Metrology for Aerospace (MetroAerospace), Pisa, Italy, 22-24 June 2020.
## 4 Results and Discussion
### Molecular/Mineralized Characterization
For the SP1 sample, the identified mineral phases included quartz (SiO\({}_{2}\)), calcite (CaCO\({}_{3}\)), plagioclase (albite, NaAlSi\({}_{3}\)O\({}_{8}\)), muscovite (KAl\({}_{2}\)(Si\({}_{3}\)Al)O\({}_{10}\)(OH)\({}_{3}\)), and clinochlore ((Mg, Fe)\({}_{5}\)(AlSi\({}_{3}\)O\({}_{10}\))(OH)\({}_{8}\)), while in the case of SP2, quartz (SiO\({}_{2}\)), calcite (CaCO\({}_{3}\)), muscovite (KAl\({}_{2}\)(Si\({}_{3}\)Al)O\({}_{10}\)(OH)\({}_{3}\)), and clinochlore ((Mg, Fe)\({}_{5}\)(AlSi\({}_{3}\)O\({}_{10}\))(OH)\({}_{8}\)) were identified (see Figure 9). It is worthy of note that, in the case of SP2, the presence of kaolinite (Al\({}_{2}\)Si\({}_{2}\)O\({}_{5}\)(OH)\({}_{4}\))-type phyllosilicates cannot be ruled out, with their signal being partially \"hidden\" by the even basal reflections of the clinochlore. Overall, both SP1 and SP2 exhibited similar mineralogical compositions, primarily consisting of quartz, calcite, muscovite, and clinochlore. This mineral assemblage is consistent with a common sedimentary origin, dominated by silicates, carbonates, and phyllosilicates, in agreement with the \(\mu\)-Raman results. However, a key distinction was observed in SP1, i.e., the stone collected at the Sanctuary of Apollo, where the presence of plagioclase suggested a detrital or burial-related input of feldspar, potentially linked to anthropogenic or diagenetic influences within the archaeological context. Conversely, the potential presence  
Figure 9: X-Ray Diffraction patterns from samples (**a**) SP1 (quartz (marked in red), calcite (marked in blue), plagioclase (marked in light blue), muscovite (marked in orange), and clinochlore (marked in grey); see text for details) and (**b**) SP2 (quartz (marked in blue), calcite (marked in red), muscovite (marked in grey), and clinochlore (marked in green)). of kaolinite in SP2, i.e., the \"reference\" outcrop stone taken from the natural argillite bed, would reflect a higher degree of hydrolytic weathering in the bare bedrock, consistent with prolonged surface alterations typical of an uncovered, naturally weathered lithology.
# Practical Use of Starlink Downlink Tones for Positioning
## 4 Multi-Epoch Positioning
### Results
A comparison between three values of the multi-epoch interval duration, m.  
Figure 13: (**a**) Doppler position dilution of precision as a function of time for different values of the measurement rate (1 Hz, 2 Hz, 5 Hz and 10 Hz) in multi-epoch positioning; (**b**) zoom in for data rates 1 Hz and 2 Hz. error state over the interval m is less tolerated by the measurement model, which degrades the positioning compared to m = 30 s. Thus, the duration of the multi-epoch interval, m, has to be chosen to reduce the DPDOP while satisfying the algorithm's tolerance to the assumption of a constant frequency error state over the interval. In the most favorable case of m = 30 s, the estimated user clock drift stabilizes at \(\sim\)2.65 ppm, which is close to the specification of the clock frequency stability range (2 ppm). The algorithm cannot distinguish between the user clock drift and the satellite orbit and clock errors. For this reason, the user clock drift state converges to the average value of the sum of these errors over the measurements used. For comparison, we ran the filter by removing the frequency error from the filter state vector; we obtained a position error that exceeds 10 km (3D). This result is compliant with the expectation carried out in [21] showing that Doppler-based positioning is very sensitive to orbit errors. Therefore, the frequency error state has captured most of the satellites' orbit and clock errors, preventing their total propagation into the user position state. The remaining position error is mainly due to measurement noise combined with weak satellite geometry.
## 4 Model Prediction Performance Discussion  
### Model Creation and Hyperparameter Optimization  
As shown in Figure 2, the model-building process included database creation, data processing, database partitioning, model training, result output, and evaluation metrics. The database was derived from engineering cases involving excavations above existing  
Figure 8: Correlation matrix.  
Figure 9: Categorical variable distribution.  
shield tunnels. Before being fed into the model, the data underwent a screening process and were normalized using the Z-score method to mitigate any effects. Of the database, 70% was used as the training set, while the remaining 30% served as the test set.  
In this study, the Bayesian optimization method was used to optimize the hyperparameters of the XGBoost model, taking the parameter with the minimum mean square error as the model hyperparameter. To mitigate model overfitting, a five-fold cross-validation iteration was employed. Similar procedures were followed to construct prediction models based on SVM, GBDT, XGBoost, and BO-XGBoost algorithms, and their prediction performances were evaluated using the same dataset. Table 2 provides a summary of the hyperparameters for the XGBoost model, including their descriptions, search scopes, and optimal values.  
Based on the Bayesian search results in Table 2, the optimal XGBoost model hyperparameters were determined to be n_estimators = 43, max_depth = 4, learning_rate = 0.6633, Subsample = 0.9131, Gamma = 14.6795, reg_alpha = 1.5322, and reg_lambda = 16.5579.