esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy-Saving Routing Protocols for Smart Cities
## 2 Materials and Methods
### Related Works
#### 2.1.3 Sigfox  
Sigfox, developed by the French company Sigfox, was the first LPWAN technology proposed by the IoT industry [5]. Sigfox physical layer modulation is based on an Ultra Narrow Band (UNB) modulation. However, there is limited documentation of its operation due to commercial protection, which becomes a relevant issue in academic studies on the network and the reproduction/simulation of results. A Sigfox network operates similarly to a cellular operator for the IoT industry [16], that is, there are service costs for subscribers to use the network. The coverage or range of Sigfox networks in urban areas is between 3 km and 10 km, and in rural areas it is between 30 km and 50 km. Sigfox operates in the ISM band (868 or 915 MHz), its communication rate is around 100 bps, and it supports up to 1,000,000 nodes per gateway [5; 16].
# Settlement Prediction of Footings Using \(V_{s}\)
## 2 Development of Settlement Prediction Method Based on \(V_{s}\)
### New Settlement Prediction Method  
A new settlement prediction method based on [PERSON]'s approach is proposed combining the modulus derived from \(V_{S}\) considering soil nonlinearity and \(I_{z}\) from the elasticity theory as discussed in previous sections. The vertical stress level at each sublayer changes from the initial self-weight stress state, \(\sigma_{oxj}\), to the stress condition upon additional footing load, which is equal to \(\sigma_{oxj}+q_{i}I_{zj}\). The change of stress condition causes increases in the small strain modulus, and this stress-dependent stress-strain relationship is presented in Equation (6). Following [PERSON] & [PERSON] [20], the deformation modulus, \(E_{ij}\), considering both the stress-dependent stress-strain relationship and nonlinear stress-strain hyperbolic equation, can be expressed as:  
\[E_{ij}=E_{o,j}\left(\frac{\sigma_{oxj}+q_{i}I_{z,j}}{\sigma_{ox,j}}\right)^{n }\left(1-f\left(\frac{q_{i}I_{z,j}}{\sigma_{max}}\right)^{\delta}\right) \tag{10}\]  
where \(\sigma_{oxj}\) is the initial vertical stress at depth \(z_{j}\), \(\sigma_{max}\) is the ultimate bearing capacity, \(E_{o,j}\) is the initial small-strain Young's modulus equal to \(E_{o}=2G_{o}(1+\
u)\). The solution from [PERSON] [21] can be applied to obtain the ultimate bearing capacity with respect to the shape of the footing. By combining Equations (1) and (10) and ignoring depth or time factors, the total settlement of each sublayer corresponding to the applied stress \(q_{i}\) can be calculated using the following nonlinear stress-dependent modulus:  
\[s=q_{i}\sum\frac{I_{zi}\Delta z_{i}}{E_{o,i}\left(\frac{\sigma_{oxj}+qI_{zi}}{ \sigma_{ox,\ i}}\right)^{n}\left(1-f\left(\frac{qI_{zi}}{\sigma_{max}}\right) ^{\delta}\right)} \tag{11}\]  
Finally, the footing settlements are calculated based on [PERSON]'s framework using \(V_{S}\). For a foundation supported on granular soil, the elastic settlement is a major concern that needs consideration. In this study, the elastic load-settlement response of vertically loaded footings placed in granular soil was considered, while the plastic deformation of soil caused by the particle movement was not considered.
## 6 Conclusions  
This work has provided a thorough assessment of the performance of Sentinel-3A, Sentinel-3B and Jason-3 satellite altimeters using the Permanent Facility for Altimetry Calibration of the ESA. Performance is monitored thorough land transponder and sea surface techniques at different locations and settings, as well as crossover analysis. Cal/Val results are accompanied by their uncertainty as calculated in the context of the new ESA strategy of Fiducial Reference Measurements.  
The performance of all satellites appears to be within their design requirements, exhibiting altimetry biases of \(\pm\)1.5-2.0 cm. Figure 32 shows all results in a boxplot where all satellite altimeter biases, as presented in Table 7, are compared. It appears that transponder Cal/Val results in Sentinel-3A and Sentinel-3B are less dispersed (inter quantile range is smaller) than those in Jason-3. This may be attributed to the SAR mode of Sentinel-3 on one hand, but also to the implementation of Level-0 data in SAR calibration instead of Level-2 data carried out in Jason-3. Evaluation of the transponder and sea-surface calibration methodologies indicates that they both present an FRM uncertainty of the order of \(\pm\)3.0-3.5 cm.  
Figure 31: Flowchart of the way to calibrate Copernicus Sentinel-6 radar altimeter and its Advanced Microwave Radiometer-C (AMR-C) microwave radiometer at the Permanent Facility for Altimetry Calibration.  
Calibration results derived by these two calibration techniques as well as relative calibration given by the crossover analysis all agree to within \(\pm 1\) cm. Sentinel-3 results are in agreement with results given by other Cal/Val groups, as previously described.  
A major upgrade of the PFAC is currently under way. The target missions for this altimetry Cal/Val service would be primarily the Copernicus Sentinel-6 and Sentinel-3, while other international missions, such as Jason-3, HY-2, SWOT, CryoSat-2, etc., may benefit as well.  
Conceptualization, S.M., C.D. and C.M.; Methodology, S.M. and C.M.; Software, X.F., C.K., G.V.; Validation: X.F., I.N.T., and A.T.; Investigation, S.M., C.M., C.D., F.B., and P.F.; Resources, C.M., X.F., C.K., T.G. and A.T.; Data curation, X.F., C.K., and A.T.; Writing--original draft preparation, S.M. and A.T.; Writing--review and editing, S.M., C.D., A.T., R.C.; Visualization, X.F.; Supervision, S.M., P.F., and C.D.; Project administration, S.M., P.F., and C.D.; Funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by European Union and the European Space Agency grant numbers [4000122240/17/J-BG and 4000129892/20/NL/FF/ab] and the APC was funded by Technical University of Crete.  
We thank very much the three reviewers and the editor for their excellent and meticulous comments and observations made on this manuscript. We thank Dr. [PERSON] of the European Space Research and Technology Center of ESA in the Netherlands for all his help and support with the ESA radiometer. The authors declare no conflict of interest.
## 4 Numerical Simulations
### High-Earth Orbit Case
Given two PDFs, noted as \(p(x)\) and \(q(x)\), the LAM is defined as:  
\[\text{LAM}(p,q)=\int_{\infty}p(x)q(x)dx \tag{29}\]  
where Equation (29) represents the density of overlap between the two PDFs. The greater agreement for one PDF with another PDF, the larger their densities. However, the analytical distribution is intractable to obtain. In a practical problem, the true distribution is approximated using the Monte Carlo samples, and the LAM is rewritten as  
\[LAM(p,q)=\sum_{i=1}^{N}\sum_{j=1}^{O}\alpha_{i}\gamma_{j}p_{g}\big{(}x_{j}; \mathbf{m}_{i},\mathbf{P}_{i}\big{)} \tag{30}\]  
where \(Q\) is the total number of samples within the Monte Carlo simulation. The accuracy of different methods can be compared using the same set of Monte Carlo samples. The LAM of the proposed AGMM-VSG and fifth-order CR are drawn in Figure 5. Moreover, the LAM of the AGMM method, where only real samples are employed, is also tested as the standard results. To be clear, the likelihood measure of the AGMM-VSG is normalized by the value for the AGMM method. It can be observed that the LAM of AGMM-VSG is close to that of AGMM, while clearly outperforming the fifth-order CR method. Figure 4: PDF contours using fifth-order CR with Monte Carlo samples of HEO case. Figure 5: Likelihood agreement measure for the AGMM, AGMM-VSG, and fifth-order CR of HEO case.
# Development of a Virtual Reality-Based Environment for Telerehabilitation
## 2 Materials and Methods
### The Telerehabilitation Robotic System  
Following a medical protocol [23], the proposed parallel robotic structure LegUp [24] was developed for spatial lower limb rehabilitation in patients with various neuro-motor disabilities, targeting major joints: hip, knee, and ankle [25; 26]. The new design targets bedridden patients through easy attachment to the bed. Additionally, the parallel robotic structure underwent comprehensive mathematical analysis and multi-criteria dimensional optimization to ensure effective operation within its workload range, free of singularities [27]. Figure 2a presents the kinematic scheme of the LegUp parallel robot. The robot is actuated using five active joints: prismatic joint q1 is used to perform the flexion and extension of the knee, prismatic joint q2 is used to perform the flexion and extension of the hip joint, while the prismatic joint q3 is used to perform the abduction and adduction of the hip. The ankle rehabilitation module is placed at the end of the L1 segment and is actuated by active joint q4, used to perform the flexion and extension of the ankle, and the prismatic joint q5, used to perform the ankle inversion/eversion. Each anatomical joint can be stimulated individually with respect to the medical protocol, or a combined motion may be created in order to simulate gait. The parallel robot also uses several passive revolute joints and prismatic joints in order to recreate the real motion of the targeted joint. Figure 2b presents the experimental model of the robot. The hardware control unit is equipped with a PLC (programmable logic controller), model X20 CP3586 [28], dedicated drivers for each servo motor, model 80 VD100 PD.C000-01 [29], a power supply unit, and various input/output modules for sensors. The PLC features an Intel Atom 1.6 GHz processor, an additional I/O-processor, Onboard Ethernet, POWERLINK with poll response chaining, USB connectivity, CompactFlash for removable application memory, and 512 MB of DDR2 SDRAM.  
Figure 2: Parallel robotic system for lower limb rehabilitation: (**a**) kinematic scheme; (**b**) experimental model.  
The driver connects two servo motors in parallel, each with EnDat encoders (ver. 2.2), and integrates a PowerLink interface with two ports (to ensure a serial connection with the PLC and other drives). For robot actuation, two types of servomotors were selected: 3 8 LVA23B1030-D000-0 motors for the linear units of the hip-knee module [30] and 2 8 LVA13B1030-D000-0 motors for the rotational units of the ankle module [31].
# Digital Twin-Driven Stability Optimization Framework for Large Underground Caverns
## 1 Introduction
It begins with geological assessments and laboratory testing, followed by BIM-based virtual modeling and numerical analysis for excavation and rock support design. Real-time sensor deformation data are continuously integrated to update the digital twin. The proposed DT framework is validated through a real case study of the Neelum Jhelum Hydropower Project powerhouse cavern construction, demonstrating its effectiveness in real-world underground construction. This research study advances data-driven decision making in underground infrastructure, contributing to the development of intelligent, resilient, and sustainable underground spaces. This approach will ultimately enhance underground cavern construction management and lifecycle stability. This research study contributes to be existing body of knowledge by developing a comprehensive DT framework tailored for the unique challenges of large underground cavern construction, particularly focusing on real-time stability optimization. The innovation can be highlighted in the following aspects:  
* This study introduces a novel five-dimensional digital twin (DT) framework specifically designed for the stability optimization of large underground caverns, addressing the limitations of traditional static assessment methods. * The proposed framework integrates real-time sensor data with virtual simulations and Building Information Modeling (BIM), enabling the continuous monitoring and adaptive optimization of rock support systems throughout the project lifecycle. * The five dimensions of the DT framework encompass physical objects, virtual objects, service systems, DT data, and their interconnections, facilitating comprehensive deformation analysis and rock support optimization. * The framework incorporates six key modules--structure, geology, material, behavior, performance, and environment--specifically chosen to enhance the understanding of the complex factors influencing underground cavern stability. * A seven-step DT-driven methodology is developed, starting from geological assessment to real-time monitoring and optimization, emphasizing a continuous feedback loop between the physical and virtual worlds. * This research study leverages Industry Foundation Classes (IFC) standards for seamless data exchange and interoperability and proposes new IFC entities and property sets tailored for underground engineering and convergence monitoring. * The framework emphasizes the real-time integration of sensor data for the deformation monitoring and adaptive optimization of rock support systems, leading to improved safety, reliability, and long-term stability compared with traditional methods.
## 2 Materials and Methods
### Optical Data  
It is vital to use multispectral data and compare it with microwave data in desert regions in order to determine if the portrayed features are on the surface or in the near-surface. Both Sentinel-2 and Landsat-8 (LS-8) data were employed in this study. Sentinel-2, acquired in 25 April 2018, provides thirteen spectral bands with a spatial resolution that ranged from 10 m to 60 m for the visible, NIR (near infrared), and SWIR (shortwave infrared) wavelengths. All spectral bands, except channels 9 and 10 (the water vapor and cirrus detection bands, respectively), were used in this research. LS8, acquired in 22 October 2017, provides eleven spectral channels with a spatial resolution of 30 m for the visible, NIR and SWIR wavelengths, 100 m for the TIR (thermal infrared), and 15 m for the panchromatic band. All spectral channels, except bands 8 and 9 (the panchromatic and cirrus detection bands, respectively), were used in the present study. The Sentinel-2 and LS-8 raw images show little contrast due to the relative homogeneity of the surface cover type in the study area. The high inter-spectral-band correlation, which commonly encountered in multispectral data, results in a reduction of the interpretability of the satellite image.
# Runway Pavement Structural Analysis Using Remote Laser Doppler Vibrometers
###### Abstract  
Structural analysis is crucial for airfield pavement evaluation and plays a critical role in ensuring airfield operation safety and efficiency. Traditionally, the evaluation has relied on the Heavy Weight Deflectometer (HWD) test. This method encounters challenges, including interruptions in airfield operations, limited coverage of inspection locations, and extensive time required for data collection and analysis. In the presented research, a remote method for the measurement and analysis of runway pavement structural deflection induced by transiting aircraft was introduced, employing a Remote Laser Doppler Vibrometer (RLDV). First, a test system was developed to acquire deflection measurements of airport pavements using RLDV. To address inaccuracies arising from minor angle measurements and fixed-end beam vibrations, vibration correction methods were developed and validated. Thereafter, a linear regression model was constructed using data from both RLDV and HWD measurements, yielding a correlation coefficient of 0.94. This correlation highlights the reliable utility of RLDV in analyzing pavement structural response. The objective of this research is to present a novel approach for the evaluation of pavement structural performance.  
structural analysis; remote laser doppler vibrometer (RLDV); runway pavement; heavy weight deflectometer (HWD) +
Footnote †: journal: Applied sciences
## 3 Artificial Neural Network
### Exploratory Data Analysis
#### 3.3.2 Detect Outliers and Anomalies  
Some data batches include outliers. An outlier is a data point that differs significantly from other observations. Some outliers may be caused by measuring, recording, or copying errors, or by errors in entering the data into the computer. When such errors occur, it is important to detect and correct them, if possible. If not, excluding the erroneous values from further analysis is another possibility. Not all outliers are erroneous. Some may merely reflect unusual circumstances or outcomes; so, having these outliers called to our attention can help to uncover valuable information [36].  
Two methods of the graphical determination of outliers are presented below. The first option is to create histograms of individual features. Histograms, in addition to visualizing outliers, allow researchers to gain insight into data, including the distribution, central tendency, spread, and modality. Figure 2 shows the histograms of the G distribution with outliers (on the left) and without outliers (on the right).  
Figure 1: Scatter matrix of the damping ratio (D), shear strain (\(\gamma\)), normalized shear modulus (G/G\({}_{\text{max}}\)), and mean effective stress (p\({}^{\prime}\)).  
Another way to determine the outliers is to create boxplots. Boxplots are interesting for representing information about the symmetry, central tendency, skew, and outliers. It is a good EDA technique since it relies on robust statistics such as the median and IQR (interquartile range). Figure 3 presents an annotated boxplot which explains how it is constructed. The central rectangle is limited by Q1 and Q3. The middle line represents the median of the data. The whiskers are drawn in each direction, to the most extreme point that is less than 1.5 IQR beyond the corresponding hinge. Values beyond 1.5 IQR are considered outliers [34]. Figure 4 shows the boxplots of the G feature with outliers (on the left) and without outliers (on the right).  
Figure 4: Boxplots of G with and without outliers.  
Figure 3: Example of boxplot with annotations.  
Figure 2: Histogram charts of G with and without outliers.  
Boxplots are an exploratory data analysis technique, and a researcher should consider designating a boxplot as an outlier as a suggestion that the points might be mistakes or otherwise unusual. Outliers should not be eliminated in the first instance, since depending on their context may provide a lot of information.
### Related Works
To the authors' best knowledge, there are no projection-based methods employing CNNs for the segmentation of teeth in 3D mesh data represented as multiple geometry maps. The mesh segmentation framework MV-RNN [12] is methodologically closest to our work. It builds upon MV-CNN [13], where the shape is rendered and evaluated from various viewpoints, with the final joint optimization into a compact object descriptor. MV-RNN extends MV-CNN by incorporating recurrent blocks that correlate predictions across different views. While our teeth segmentation framework draws inspiration from MV-RNN, it diverges in several key aspects: (i) we apply our method to segment anatomical shapes characterized by intricate geometry and significant shape variability rather than low-frequency shapes representing human bodies; (ii) our method is specifically tailored for the tooth segmentation task, involving a localized analysis of each tooth to enhance the geometric information at the network's input; (iii) we utilize Bi-Directional ConvLSTMs [14], which preserve more spatial information as opposed to LSTM [15]; and (iv) instead of relying on shaded images, we render input maps with rich geometric information (depth and curvature) invariant to changes in lighting, shading, and reflectance. We believe in the potential of this projection-based approach, based on our previous work on the tooth detection task on digital dental casts [16] as well as on our experiments introduced in this paper. Our results show that networks can effectively learn features for the perceptual parsing of 3D medical shapes from rendered images. Our work presents a methodologically different method from the usual practices in neural tooth segmentation, which is valuable for broadening the understanding of the problem.
## 3 Results and Discussion
### EIS Analysis of Inhibition Performance
Under the blank condition, the \(R_{ct}\) was 991.6 \(\Omega\), indicating relatively high corrosion resistance. However, the \(Y_{0}\) was measured at 25.81 \(\times\) 10\({}^{-4}\)\(S\)-\(s^{n}\), suggesting the occurrence of active charge exchange at the interface between the solution and the metal surface and significant metal dissolution in the area with no protective film. And, then, \(Y_{0}\) decreased sharply in the range of 0.62\(\sim\)0.71 \(\times\) 10\({}^{-4}\)\(S\)-\(s^{n}\) with the increasing inhibitor dosage above 9000 ppm, indicating a reduction in the interfacial capacitance and an enhancement of the passive layer [36; 37]. At 7000 ppm, the \(R_{ct}\) increased to 3492 \(\Omega\), representing an enhancement compared to the blank condition. This result implies that the inhibitor contributed to the formation of a passive film on the metal surface. As the inhibitor concentration increased, the \(R_{ct}\) gradually increased as the passive film was enhanced. The IE calculated from \(R_{ct}\) (Equation (2)) increased consistently with the inhibitor dosage and reached its highest value of 97.3% at 17,000 ppm. \[IE\%=\left[\frac{R_{ct(inh)}\,-R_{ct(blank)}}{R_{ct(inh)}}\right]\,\times 100 \tag{2}\]
# Outlier Reconstruction of NDVI for Vegetation-Cover Dynamic Analyses
## 3 Results
### Analysis of Reconstructed Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Year/Ranks** & **Accuracy** & **Precision** & **Recall** & **F\({}_{1}\) Score** & **Kappa** \\ \hline \multirow{8}{*}{SWTSA} & 2019/\(1\times 3\times 2\) & 0.848 & 0.876 & 0.786 & 0.829 & 0.627 \\  & 2019/\(3\times 3\times 3\) & 0.900 & 0.931 & 0.852 & 0.890 & 0.758 \\  & 2019/\(3\times 5\times 5\) & 0.916 & 0.936 & 0.879 & 0.906 & 0.800 \\  & 2019/\(3\times 10\times 10\) & 0.918 & 0.937 & 0.881 & 0.908 & 0.805 \\  & 2020/\(1\times 3\times 2\) & 0.907 & 0.920 & 0.873 & 0.896 & 0.783 \\  & 2020/\(3\times 3\times 3\) & 0.957 & 0.960 & 0.944 & 0.952 & 0.903 \\  & 2020/\(3\times 5\times 5\) & 0.978 & 0.979 & 0.973 & 0.976 & 0.952 \\  & 2020/\(3\times 10\times 10\) & 0.980 & 0.980 & 0.975 & 0.978 & 0.955 \\ \hline \multirow{2}{*}{MLR} & 2019/- & 0.917 & 0.930 & 0.884 & 0.906 & 0.804 \\  & 2020/- & 0.981 & 0.981 & 0.977 & 0.979 & 0.957 \\ \hline \multirow{2}{*}{RFR} & 2019/- & 0.711 & 0.832 & 0.571 & 0.677 & 0.179 \\  & 2020/- & 0.751 & 0.851 & 0.636 & 0.728 & 0.330 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance evaluation of reconstruction. Figure 4: Cumulative frequency distributions of NDVI for actual and reconstructed values.