esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### Image Data  
Two types of time-series images were used as input in the case study: one was Sentinel-2 time-series and the other was MODIS MCD43A4 time-series. The Sentinel-2 images of the time-series had a great number of pixels contaminated by clouds and cloud shadows. Our goal was to reconstruct a cloud-free Sentinel-2 time-series from the two type images.
## 3 Methods
### Validation  
The identified burned areas were compared against Copernicus EMS fire grading maps in order to validate the generated burned area product. Reference fire grading maps were available for 28 areas of interest covering around 9558 km\({}^{2}\) (around 3.1% of the Italian territory). In order to make the identified burned areas consistent with Copernicus EMS fire grading map products, areas falling outside the analyzed polygons, or representing burned areas corresponding to fires occurred before the pre-fire image or after the post-fire image, were removed prior to the comparison. The accuracy assessment was performed by means of omission and commission error estimation. An assessment of classification results was performed by comparing the products generated from the three spectral indices against the reference product for each of the four processing steps (A-D) of the burned area detection procedure. Accuracy assessment was calculated following a pixel-based approach, creating a single confusion matrix encompassing all 28 areas of interest.  
In addition, the identified burned areas were compared against Copernicus EMS and EFFIS products in order to evaluate the temporal consistency for the generated map products.  
Temporal consistency of post-fire dates was calculated for each burned area clump by comparing the S2 acquisition dates, corresponding to the post-fire observations, and the post-fire dates used to produce the EFFIS \"MODIS burnt areas\" products. Temporal consistency was also evaluated by comparing the post-fire dates of the generated product against the post-fire image dates of the Copernicus EMS rapid mapping activations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Unit** & **NBR** & **BAIS2** & **BAIS2\(\alpha\)** & **LAI** \\ \hline V\({}_{\mathrm{C}}\) & dimensionless & 1.08 & 0.9 & 0.67 & - \\ d\({}_{\mathrm{C}}\) & dimensionless & 0.35 & 0.25 & 0.25 & - \\ S\({}_{\mathrm{min}}\) & nectar & 1.00 & 1.00 & 1.00 & - \\ V\({}_{\mathrm{RG}}\) & dimensionless & 0.60 & 0.40 & 0.30 & - \\ d\({}_{\mathrm{RG}}\) & dimensionless & 0.20 & 0.25 & 0.20 & - \\ S\({}_{\mathrm{OBIA}}\) & nectar & 10.0 & 10.0 & 10.0 & - \\ \(\mathrm{CV}_{\mathrm{LAI\_pre}}\) & m\({}^{2}\)/m\({}^{2}\) & - & - & - & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Threshold values used for the burned area detection procedure.  
Finally, Copernicus EMS fire grading maps were used to evaluate the pixel value distribution for spectral indices and LAI under four different fire severity levels in order to compare the effectiveness of their separability.
# Effects of Cement Treatment on Mechanical Properties and Microstructure of a Granite Residual Soil
###### Abstract  
A proper treatment of granite residual soil (GRS) in geotechnical practices requires both macro and microscopic evaluations. In this study, uniaxial and oedometric compression tests were conducted to investigate the mechanical properties of the saturated untreated and cement-treated GRS. Meanwhile, XRD, SEM, and MIP tests were conducted to identify the presence and types of C-S-H and the changes in the pore structure after cement treatment. The effects of cement treatment on the uniaxial compressive strength, secant modulus, compressibility, and vertical yielding pressure were revealed and the mechanisms of the soil structure to be modified through cement treatment were clarified based on the test results. A threshold volumetric cement content of 2-3% was determined based on the mechanical properties and microstructural characteristics of the saturated cement-treated GRS. Cement contents below this threshold would produce inadequate cementation between the soil particles. In contrast, cement contents above this threshold are considered inefficient because the transformation of the soil structure from single-porosity to dual-porosity increases the total porosity and retards the strength and stiffness gains.  
g _applied_ sciences
## 2 Methods
### Data Collection and Processing  
A 20 Hz Butterworth high-pass filter was applied to the raw signal to eliminate artifacts. Additionally, the signals were rectified and smoothed by the root mean square (RMS) with a time window of 100 ms. The EMG signal is a measure of the actual muscle voltage, and because the maximum voltage that can be generated varies across individuals, it should be normalized. The most common methods are maximum voluntary isometric contraction (MVIC) and reference voluntary contraction (RVC). In both methods, the reference value is the first set, and the percentage of that value is displayed. The MVIC is based on the RMS  
Figure 3: Flow chart showing the experimental design of the study.  
when the participant applies the maximum isometric contraction to the muscle, whereas RVC is based on the RMS when a specific movement is performed. In this study, the EMG signals were normalized using MVIC. To avoid confusion, the \"normalized EMG signal\" is hereafter referred to as \"muscle activity.\"  
No separate filter was applied to the torque of the gait rehabilitation system. For torque, the range of strength that can be generated by each person is different; therefore, normalization is necessary, as in the raw EMG signal. However, there is no consensus on the method of normalizing strength or torque measurements [6]. Generally, measures related to body size, such as body mass (BM) and body mass index (BMI), are indices used to normalize the strength of the lower limb. Therefore, they were used to normalize the joint torque in this study. The torque was divided by BM and BMI to obtain two types of normalized torques.  
Figure 4 shows the data collected under all experimental conditions for all participants. Therefore, the analysis method can be applied to organized data to determine the correlation.
## 4 Experimental Results and Discussion
### Hyperparameters Search
#### 4.1.2 RF Hyperparameters Search  
Following the investigation of the optimal SI parameters, were searched for the RF hyperparameters. Several RF models with different number of trees, \(N_{t}\), and maximum depth, \(N_{d}\), were trained with the optimal SI parameter combination, i.e., \(\Delta\rho\), \(\Delta\gamma\), \(N_{\rho}\), and \(N_{\gamma}\) selected as 0.6, 0.5, 10, and 10, respectively. The performances of 12 RF models are presented in Table 1. From the search results, the RF model of 1000 trees and maximum depth of 32 performed better than other models in terms of accuracy, F1 score, and recall; however, the precision was slightly lower than 0.8814. Based on these results, the number of trees and maximum depth selected for all subsequent RF models were 1000 and 32, respectively.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 2 Preliminaries  
### Multi-Tier Intra-RAT Handover  
This paper focuses on enhancing the 5G multi-tier intra-radio access technology (RAT) handover. This refers to a handover where the current and target BSs involved in the handover of user equipment (UE) are located in different tiers of the network, and the RAT of both current and target BSs is the same [6]. In 5G networks, a BS that connects the UE to the 5G core (5 GC) via next-generation (NG) interfaces is referred to as a gNodeB (gNB) [3]. The intra-RAT handovers occur in the AMF and UPF elements of the 5G architecture.  
This paper models the handover functions performed by the UE, gNB, AMF, and UPF as specified in the non-roaming architecture for 5G in 3 GPP standard TS 23.501 [7]. The AMF manages the handovers between different gNBs, while UPF supports service features such as packet routing for the UE. Both AMF and UPF communicate with the gNBs through the N2 and N3 interface, respectively.
## 4 Experimental Result
### The Influence of Image Normalization on Model  
Image normalization means scaling the values of original images within an interval to extract the features and avoid the influence of abnormal values on the training results of the model. In this paper, an Adam optimizer was used, the learning rate was set at 0.001, batch size was 16, and 100 epochs were trained. Using the same validation method, we figured out that image normalization had significantly improved the accuracy of LDCNN as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Normalization** & **Accuracy** & **Precision** & **Recall** & **F1 Score** \\ \hline Without & 89.79\% & 84.86\% & 94.00\% & 87.78\% \\ With & **96.84\%** & **97.06\%** & **96.21\%** & **96.50\%** \\ \hline \hline \end{tabular}
\end{table}
Table 2: The influence of normalization on LDCNN.  
Figure 13: Green coffee bean dataset: small optical sorter: (**a**) good, (**b**) bad, (**c**) increased brightness, (**d**) reduced brightness.
# Leaching Behavior of Cesium, Strontium, Cobalt, and Europium from Immobilized Cement Matrix
## 3 Results
### Leading of the Solidified Cement Specim
#### 3.1.3 Cumulative Fraction Leached (CFL)  
The CFL is defined as the sum of the fractions leached during all previous leaching intervals, including the fraction leached during the last leaching interval using the initial amount of the nuclide contained in the specimen [45]. The CFL of each nuclide was calculated by the following equation:  
\[\text{CFL}=\frac{\sum a_{n}}{A_{0}}. \tag{4}\]  
The CFLs of Cs, Sr, Co, and Eu mobilized from the cement specimen over 90 days are shown in Figure 6. Nuclides were rapidly released from the cement specimen within the first 5 days. After that, the leaching of nuclides was significantly reduced. Elemental leaching from the cementitious materials occurs in three steps: surface wash-off, diffusion, and dissolution [13; 61; 62]. The initial rapid release of mobile elements attached to the solid waste surface occurred due to surface wash-off. Following the first step, mass transfer occurred due to diffusion from the pore space of the solidified waste to the solution. In the last step, the leaching of cementitious materials was caused by dissolution, which can accelerate diffusion but occurs only after long periods of leaching (ANS 2017). In the leaching test performed in the present study, the leaching of nuclides may have been dominantly controlled by surface wash-off in the early period and diffusion in the later period for 90 days.  
The CFLs of nuclides occurred in the order of Cs \(>\) Sr \(>\) Co \(>\) Eu throughout the leaching experiments. We observed the rapid leaching of Cs from the solidified cement specimen doped with Cs, and the CFL reached about 0.4 by the end of the test. The additional leaching of Cs may have occurred continuously even after the experiment was completed because 90 days might not have been a sufficient reaction time for Cs to fully leach from the cement specimen. The solidified cement specimen doped with Sr, which had a CFL of about 0.01 at the end of the test, had a lower CFL than that in the specimen doped with Cs due to the lower leaching rate of Sr compared to that of Cs. Co and Eu had low CFLs, indicating minor leaching from each cement specimen, although the CFL of Co was higher than that of Eu. These CFL results are comparable to the results of the LR (Figure 5).
## 2 System Model
### Mobility Energy Consumption  
The mobility energy is needed to move the UAV to the optimal position to serve the ground users. From [24], the mobility energy consumption of the UAV can be given as  
\[\begin{split} E_{\text{M}}(t_{j})=P_{\text{h}}&\frac {d(t_{j})}{v_{\text{h}}}+I(\Delta h(t_{j}))P_{\text{a}}\frac{\Delta h(t_{j})}{v _{\text{a}}}\\ &-(1-I(\Delta h(t_{j})))P_{\text{d}}\frac{\Delta h(t_{j})}{v_{ \text{d}}},\end{split} \tag{8}\]  
where \(P_{\text{h}}\) is instantaneous power consumption for mobility in the horizontal direction, \(P_{\text{a}}\) is the ascending power, \(P_{\text{d}}\) is descending power. \(d(t_{j})\) the horizontal moving distance at \(t_{j}\), while \(\Delta h(t_{j})\) is the changes in the altitude of the UAV at \(t_{j}\). \(v_{\text{h}}\), \(v_{\text{a}}\), and \(v_{\text{d}}\) are the horizontal, vertical (ascending), and vertical (descending) velocities of the UAV, respectively (For the details on the calculations of these velocities, please refer to [24]). \(I(\Delta h(t_{j}))\) is the indicator function, such that [24]  
\[I(\Delta h(t_{j}))=\begin{cases}1&\Delta h(t_{j})\geqslant 0,\\ 0&\Delta h(t_{j})<0.\end{cases} \tag{9}\]  
Lastly, the power consumption of the horizontal direction is as follows:  
\[P_{\text{h}}=P_{\text{P}}+P_{\text{I}}, \tag{10}\]  
where \(P_{\text{P}}\) is the parasitic power for overcoming the parasitic drag due to the aircraft's skin friction [24].
## 5 Spatially-Resolved Spectroscopic Technique for Optical Property Measurement
### Principle and Mathematical Theory
Later, [PERSON] and [PERSON] [108] proposed an improved solution by calculating the reflectance from the integral of the radiance over the backward hemisphere rather than from the flux alone, which is expressed as follows:  
\[\widetilde{R}(r)=c_{1}\phi(r,z_{0}=1/\mu_{t}^{\prime},z=0)+c_{2}R_{\rm flux}(r,z_{0}=1/\mu_{t}^{\prime}) \tag{15}\]  
where \(\widetilde{R}(r)\) is the improved solution for diffuse reflectance; \(\phi(r,z=0,z_{0}=1/\mu_{t}^{\prime})\) and \(R_{\rm flux}(r,z_{0}=1/\mu_{t}^{\prime})\) are given by Equations (9) and (13), respectively; \(c_{1}\) and \(c_{2}\) are constants, which are given as follows:  
\[c_{1}=\frac{1}{4\pi}\int\limits_{2\pi}\left[1-R_{\rm fres}(\theta)\right]\cos (\theta)d\Omega \tag{16}\]  
\[c_{2}=\frac{3}{4\pi}\int\limits_{2\pi}\left[1-R_{\rm fres}(\theta)\right]\cos ^{2}(\theta)d\Omega \tag{17}\]  
In Equations (16) and (17), \(R_{\rm fres}(\theta)\) is the Fresnel reflection coefficient for a photon with an incident angle \(\theta\) relative to the normal to the medium boundary and \(\Omega\) is the solid angle. A detailed expression of \(R_{\rm fres}(\theta)\) can be found in [PERSON] et al. [106]. For biological materials with a typical refractive index \(n=1.35\), \(c_{1}\) and \(c_{2}\) are equal to 0.1277 and 0.3269, respectively [109]. Spatially-resolved technique is based on the solution for diffuse reflectance, and is conceptually illustrated in Figure 19. As a narrow collimated light beam perpendicularly impinges onto a semi-infinite turbid medium, the light will scatter in the medium and a portion of the scattered light will remit from the medium. By measuring the emitted light at different distances from the incident point, a spatially-resolved profile of diffuse reflectance is obtained, which is then fitted by the diffusion model in Equation (14) or (15) to obtain the estimates of the optical parameters \(\mu_{\rm a}\) and \(\mu_{\rm s}^{\prime}\). Two types of sensing configurations for spatially-resolved technique have been developed, i.e., contact and non-contact sensing. The first uses either a single detection fiber or an array of detecting fibers, connected to a single spectrometer or an imaging spectrometer, for measuring diffuse reflectance at different positions [100; 110; 111; 112]. This configuration requires good contact between the fiber and the sample being detected, which can be problematic for measuring horticultural products due to speed and safety concerns as well as the difficulty of maintaining good contact between the probe and food sample whose surface is often not flat or of irregular shape. The second uses an imaging device such as a CCD camera to capture diffuse reflectance images [56; 113], which is better suitable for food products because no contact is needed. The spatially-resolved spectroscopic technique develop in our lab belongs to the latter, and compared to previously reported non-contact techniques, it is capable of efficiently measuring the optical properties over a broader spectral range.
## 2 Background and the Related Work
### Software-Defined Networking (SDN)
#### 2.1.1 Infrastructure Layer  
The infrastructure layer includes all physical components of a network, including a switch, router, openVswitch, a wireless access point, etc. Receiving the client's request and sending the information to the control layer are the main responsibilities of these devices'.
# Advanced Scale-Propeller Design Using a MATLAB Optimization Code
## 3 Results
### Validation of the MATLAB Program  
In order to validate the MATLAB program, its output was compared to online data for the APC 'Thin Electric' 10'' \(\times\) 7'', from the UIUC propeller database [2]. Inputs to the program, found in Table 3, were chosen to match the performance of the APC 'Thin Electric' 10'' \(\times\) 7'' propeller. For example, the propeller RPM and forward velocity were chosen to match the advance ratio at which the APC propeller has a maximum efficiency (advance ratio of 0.6). The airfoil coefficients of lift and drag are from the Clark-Y airfoil, which is a common airfoil for propeller design [30; 31]. An angle of attack of zero was used, as due to assumptions made in the method the twist angles are large, thus eliminating the requirement for additional angle of attack. The shaft power was approximated by comparing the required shaft powers of propellers of a similar diameter and pitch on the APC database [32].  
In Figures 13-15, the chord, twist and Reynolds number distribution for both the MATLAB program output and the APC 'Thin Electric' 10'' \(\times\) 7'' propeller are plotted.  
In Table 4, calculated parameters for both the MATLAB output and the UIUC database wind tunnel measurements can be found. The \(C_{T}\) and \(C_{P}\) values in Table 4 are for an advance ratio of 0.5751, which is the advance ratio at which the maximum efficiency occurs. Results from the APC propeller database [32] are also included for reference, however these are simulated and not experimental.  
As can be seen in Figures 13-15, the MATLAB program output closely resembles the geometry of the APC propeller. The APC propeller has larger chord lengths towards the propeller tip, and this is most likely due to structural issues, which the MATLAB program does not consider. Finally, there is a significant difference (16%) between the maximum propulsive efficiencies, the main source of which being the assumptions of light-loading,  
Figure 14: Plot of twist distribution for MATLAB program output and APC ’Thin Electric’ 10” \(\times\) 7” propeller [2].  
Figure 15: Plot of Reynolds number distribution for MATLAB program output and APC ’Thin Electric’ 10” \(\times\) 7” propeller [2].  
incompressible flow and zero tip losses made in the MATLAB program method. On the contrary, there is only a 2% difference between the UIUC wind tunnel test data and the APC simulation database. The smaller difference is most likely due to similar assumptions made in both calculation processes. However, the fact that the MATLAB program computes an optimized geometric profile in terms of the blade twist and blade chord, as well as computes these using a baseline Re number of 100,000 may mean that this is a better propeller design in terms of propulsion efficiency. Note that the MATLAB code produces a higher pitch over the spanwise radius, \(r\) of the propeller.
# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 3 Analysis of the State of the Art
### Case-Based Applications in Structural Health Monitoring and Vibration Diagnostics
The consistent improvement in fault detection accuracy--often exceeding 15%--achieved through the fusion of diverse sensor data (vibration, acoustic, current) further exemplifies ML's ability to synthesize disparate information for a more comprehensive and precise assessment of system health. These successes demonstrate that ML is not merely a diagnostic tool but a critical enabler for overcoming the inherent challenges of real-world SHM, paving the way for more resilient and intelligent monitoring systems. The diverse range of successful applications, from infrastructure monitoring to rotating machinery diagnostics, solidifies machine learning's pivotal role in advancing condition-based maintenance and autonomous diagnostic systems. For example, SVM models have achieved over 95% accuracy in diagnosing wind turbine gearbox faults based on vibration analysis, making them highly suitable for proactive maintenance strategies. The effective identification of hydraulic brake system faults using fuzzy classifiers and histogram features from vibration signals further illustrates ML's versatility across different mechanical systems. This broad applicability, coupled with the demonstrated high performance in real-world scenarios, confirms that machine learning is transitioning from a promising research direction to a critical enabling technology. It is actively moving the field of technical diagnostics closer to a paradigm of predictive and proactive asset management, where early fault detection and degradation forecasting become standard operational practices.
## 6 Conclusions  
By utilizing 265 pairs of SPT-N values and V\({}_{\rm s}\), empirical relations between the two variables were developed for the area of Metro Manila, Philippines. Data from field surveys using standard penetration tests and refraction microtremor were obtained in 20 sites in Metro Manila, all situated along the western coast of the region and underlain by unconsolidated Quaternary Alluvium deposits. These datasets were mainly grouped into three: all soils, sandy soils, and clayey soil types. New empirical relations were determined using non-linear regression analysis. To evaluate the statistical validity of the empirical models, parameters such as coefficient of regression (R\({}^{2}\)), coefficient of correlation (r), and Spearman rank correlation (r\({}_{\rm s}\)) were determined for each soil grouping. The determined values from these coefficients (R\({}^{2}\): 0.73-0.79, r: 0.84-0.90, r\({}_{\rm s}\): 0.81-0.85) suggested a moderate to strong correlation between the two parameters. Moreover, residual histograms and probability-probability plots were also produced, to graphically analyze the datasets and evaluate the proposed empirical functions. This analysis showed the presumed normality distribution of the residuals and therefore supported the validity of the empirical models. The newly developed empirical correlations for the three categories of soil are comparable to other works on this subject by researchers around the world. This suggests that the new correlations can be applied for site-specific seismic characterizations. This was demonstrated by estimating magnitudes of V\({}_{\rm s30}\) from SPT-N values, which were consistent with pre-identified site classifications. This shows the utility of the proposed correlations for seismic microzonation efforts in the study area, as long as there are reliable SPT values.  
Conceptualization, A.S.D., O.P.C.H., A.A.T.M. and R.N.G.; methodology, A.S.D., O.P.C.H., A.A.T.M. and R.N.G.; formal analysis, O.P.C.H. and A.A.T.M.; investigation, O.P.C.H. and A.A.T.M.; writing--original draft preparation, O.P.C.H. and A.A.T.M.; writing--review and editing, A.S.D., R.N.G. and R.U.S.J. All authors have read and agreed to the published version of the manuscript. This research was funded by the Republic of the Philippines--Department of Science and Technology (DOST) and Philippine Council for Industry, Energy and Emerging Technology (DOST-PCIEERD) Grants-in-Aid (GIA).  
Not applicable.  
The authors would like to thank [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for assistance in data acquisition; [PERSON], [PERSON], [PERSON], and [PERSON] for rendering further technical support; the Specific Earthquake Project of DOST-PHIVOLCS for lending the Refraction Microtremor data processing software. We also thank the Department of Education for their participation in this research.  
The authors declare no conflict of interest.