esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Coir Fiber Addition on the Microstructure of Geopolymer Specim  
The micrographs in Figure 13a-e show the microstructure of the metakaolin-based geopolymer for different coil fiber weight percentages added. The SEM observation was performed on fractured geopolymer samples after the flexural strength tests. Observation of the MGP Control sample showed a porous geopolymer matrix with many surface cracks. However, the addition of coil fibers in varying weight percentages in the matrices of the metakaolin-based geopolymer resulted in a more compact structure with fibers bridging the propagation of cracks in the composite. This may explain the increased flexural strength values obtained in Figure 8. At 0.5 wt % coil fiber content in the geopolymer matrix (MGP--0.5% PCF), some form of fiber-matrix adhesion was noticeable, which was the onset of a homogeneous structure. At this fiber content, some pores within the structure were still noticeable with visible surface microcracks, which can be attributed to the crack bridging effect of the coil fibers [77]. At a higher coil fiber content, for instance, at 1.0 wt % of coil fibers, a more compact structure of the geopolymer composite was seen, with some form of a nonlinear crack pattern (crack deflection). Also noticeable was coin fiber-matrix debonding. Furthermore, at 1.5 wt % coil fiber content, other forms of crack bridging in the metakaolin-based geopolymer composite were observed. The micrograph of the MGP--1.5% PCF formulation showed some crack deflection patterns and microcracks, as also observed in the MGP--1.0% PCF formulation.
# Estimation of Chlorophyll Fluorescence at Different Scales: A Review
## 4 SIF Retrieval Methods in Space Scale
### The Statistical Methods  
#### 4.3.1 Singular Value Decomposition (SVD)  
Based on the concept of SVD, [PERSON] believed that radiance without SIF could be written as the linear summation of several singular vectors [34]. Therefore, the radiance received by a sensor is the composition of SIF-free radiance and SIF radiance at the top of the atmosphere. The equation can be written as [34] and parameters defination are in Table 6:  
\[F(\omega,F_{s})=\sum_{i=1}^{n_{v}}\omega_{i}\
u_{i}+SIF^{TOA}I \tag{23}\]  
In this method, it is important to obtain the singular vector. Following certain rules, the SIF-free objects were selected and trained to generate the singular vector. In Equation (23), no items showed the effects of atmospheric scattering and absorption. Therefore, the strong absorption band should be removed from the selected window to maintain the retrieved accuracy. In Equation (23), \(n_{v}\), weights \(\omega_{i}\), and \(F^{TOA}_{S}\) were unknown parameters and were fitted through linear least squares.  
When the interest window enlarges, it may include some wavelengths that are affected by atmospheric scattering, vegetation structure, and other factors. [PERSON] modified the previous SVD methods to cope with this lower frequency information, and applied this method to the GOSAT data [51]. The SVD approach had been successfully used in TanSat chlorophyll fluorescence retrival [43].
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 3 Dynamic Model of the Transmission System
### Calculation of Component Relative Displacements
#### 3.2.1 Relative Displacements between Sun gear and Planet gears  
As an illustration, the relationship between the \(i\)-th planet gear, \(p\), and the sun gear, \(s\), in the differential PGT, as shown in Figure 4. In the figure, \(\psi_{pi}\) represents the angular position of the planet gear.  
Along the meshing line, the elastic compression deformation of sun gear, \(a\), and planet gear, \(mi\), can be expressed as positive for compression and negative for tension, as follows:  
\[\delta_{amij}=(x_{a}\sin\psi_{ami}+y_{a}\cos\psi_{ami}-x_{mi}\sin\alpha_{ami}-y _{mi}\cos\alpha_{ami}+r_{ba}\theta_{za}-r_{bm}\theta_{zmi})\cos\beta_{b}\pm(- z_{a}+z_{mi})\sin\beta_{b}-e_{amij}(t), \tag{2}\]  
where \(j\) can take on the values of either \(L\) or \(R\); \(\psi_{ami}\) represents the angle between the meshing line, \(ami\), and the horizontal axis; \(\alpha_{ami}\) represents the pressure angle; \(r_{ba}\) and \(r_{bm}\) represent the base circle radius of the sun gear, \(a\), and planet gear, \(m\); \(\beta_{b}\) represents the helix angle; \(e_{amij}\) represents the transmission error between the sun gear, \(a\), and planet gear, \(m\); and the superscript sign (\(\pm\)) applies to the left-side gear, while the subscript sign (\(\pm\)) applies to the right-side gear.  
The elastic compression deformation of the sun gear \(s\) and planet gear, \(pi\), along the meshing line is as follows:  
\[\delta_{spij}=(x_{s}\sin\psi_{spi}+y_{s}\cos\psi_{spi}-x_{pi}\sin\alpha_{spi}- y_{pi}\cos\alpha_{spi}+r_{bs}\theta_{zis}-r_{bp}\theta_{zpi})\cos\beta_{b}\pm(- z_{s}+z_{pi})\sin\beta_{b}-e_{spij}(t), \tag{3}\]  
where \(\psi_{spi}\) represents the angle between the meshing line, \(spi\), and the horizontal axis; \(\alpha_{spi}\) represents the pressure angle; \(r_{bs}\) and \(r_{bp}\) represent the base circle radii of the sun gear, \(s\), and planet gear, \(p\); and \(e_{spij}\) represents the transmission error between the sun gear, \(s\), and planet gear, \(p\).  
Figure 4: Relationship between the sun gear and planet gears in the differential PGT.
# Design and Performance Analysis of Hardware Realization of 3 GPP Physical Layer for 5G Cell Search
## 2 Specifications of 5G CS and Literature Review
### Review: Mapping of 5G CS PHY on Hardware  
The design, theoretical analysis, and simulation-based performance evaluation of 5G PHY have been an active research topic in the last few years [7; 8; 9; 13; 14; 29; 30]. Various works on the design and optimization of PHY sub-blocks such as channel encoders [31; 32; 33; 34], OFDM modulator [35; 36], beamforming [37; 38] and channel estimation [39; 40; 41] have been explored to improve the PHY performance compared to 4G PHY. However, only a few works have focused on 3 GPP standards and further improvements without compromising the compatibility with existing and previous standards [7; 8; 13; 14; 19; 30]. From 5G CS perspectives, works in [8; 9] provide an in-depth understanding of the SS generation while [7] offers an innovative intelligence-based CS approach. However, these works do not highlight the challenges in SS detection and applications of SS detection for timing synchronization. The work presented in this paper aims to bridge this gap.  
Another important aspect of 5G PHY deployment is an efficient implementation on the software and hardware platforms. Compared to 4G, there are numerous scenarios that have resulted in the split of PHY depending on positions of radio, distribution, and centralized units [42; 43; 44]. This demands hardware-software co-design of the PHY. In [45; 46], authors have explored the hardware-software co-design of IEEE 802.11 PHY on Zynq system-on-chip (SoC) platform. In [35; 47], authors have explored reconfigurable OFDM waveform with dynamically controlled out-of-band emission. In [48], authors have proposed reconfigurable OFDM-based PHY to support multi-standard operations. However, none of these works consider 3 GPP compatible 5G PHY. The work in [26] is limited to OFDM-based transceivers but does not consider various control and data channels in 5G while the work in [49] is limited to software-based CS implementation using USRP. In this work, we design software and hardware IP cores of 3 GPP compatible 5G PHY and demonstrate the 5G CS operations by developing the receiver PHY. We also demonstrate end-to-end 5G CS PHY using RFNoC based USRP-FPGA platform.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{6}{c}{**Root Canal Sealer**} \\ \cline{2-11}  & \multicolumn{2}{c}{**BioRoot**} & \multicolumn{2}{c}{**DiaRoot**} & \multicolumn{2}{c}{**OneFill**} & \multicolumn{2}{c}{**iRoot**} & \multicolumn{2}{c}{**CeraSeal**} \\ \cline{2-11}  & **wt.\%** & **atm.\%** & **wt.\%** & **atm.\%** & **wt.\%** & **atm.\%** & **wt.\%** & **atm.\%** & **wt.\%** & **atm.\%** \\ \hline
**O** & 37.35 & 49.34 & 26.10 & 32.81 & 38.93 & 47.97 & 30.90 & 34.21 & 32.53 & 37.54 \\
**Si** & 4.44 & 3.34 & - & - & 2.71 & 1.90 & 1.35 & 0.85 & 1.90 & 1.25 \\
**Ca** & 24.27 & 12.80 & 13.57 & 6.81 & 10.67 & 5.25 & 7.01 & 3.10 & 8.90 & 4.10 \\
**C** & 16.11 & 28.36 & 26.01 & 43.54 & 24.02 & 39.43 & 39.09 & 57.64 & 34.04 & 52.33 \\
**Zr** & 12.26 & 2.84 & - & - & 23.00 & 4.97 & 21.64 & 4.20 & 22.20 & 4.49 \\
**Al** & - & - & 4.96 & 3.70 & 0.67 & 0.49 & - & - & 0.43 & 0.29 \\
**Cl** & 5.57 & 3.32 & - & - & - & - & - & - & - & - \\ F & - & - & 10.33 & 10.94 & - & - & - & - & - & - \\
**Yb** & & & 19.02 & 2.21 & - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Percentage of mass (weight %) of different elements presented among the tested root canal sealers. Figure 3: Scanning electron micrographs of root canal sealers at 5000\(\times\), 10,000\(\times\), and 20,000\(\times\) magnification. Bands at 736 cm\({}^{-1}\) and 873 cm\({}^{-1}\) in OneFill suggested the presence of C-H bending, whereas bands at 965 cm\({}^{-1}\) showed C=C bending. Moreover, a band at 1301 cm\({}^{-1}\) revealed C-O stretching, suggesting the possible existence of CaCO\({}_{3}\). Bands at 1412 cm\({}^{-1}\) and 1639 cm\({}^{-1}\) represented S=O and C=O stretching, respectively, while bands at 2880 cm\({}^{-1}\) and 3402 cm\({}^{-1}\) represented O-H stretching, explaining the possible calcium hydroxide (CaOH\({}_{2}\)) formation. On the other hand, similar bands were observed at 937 cm\({}^{-1}\), 885 cm\({}^{-1}\), 743 cm\({}^{-1}\), and 1455 cm\({}^{-1}\) in iRoot, representing C-H stretching. The two strong bands at 1069 cm\({}^{-1}\) and 1350 cm\({}^{-1}\) appeared to be the existence of S=O bending. In addition, a band at 1786 cm\({}^{-1}\) showed C=O stretching, which could possibly be due to the monobasic found in iRoot, whereas the two strong bands at 2876 cm\({}^{-1}\) and 3403 cm\({}^{-1}\) suggested O-H stretching of CaOH\({}_{2}\). The bands that appeared in CeraSeal were similar to those of iRoot with bands at 732 cm\({}^{-1}\), 874 cm\({}^{-1}\), 945 cm\({}^{-1}\), and 1459 cm\({}^{-1}\) showing the presence of C-H bending, while the band at 1350 cm\({}^{-1}\) represented S=O stretching. The bands at 987 cm\({}^{-1}\) and 1091 cm\({}^{-1}\) showed evidence of C=C bending and C-O stretching, respectively. Furthermore, the band at 1648 cm\({}^{-1}\) represented C=O stretching, while bands at 2876 cm\({}^{-1}\) and 3405 cm\({}^{-1}\) represented O-H stretching groups, which signify the formation of CaOH\({}_{2}\) (Figure 4).
## 3 Proposed Method  
In this section, we present our Multi Similarity Metric Fusion Manifold Embedding (MSMFME) for Face Beauty Prediction. Since our knowledge of face beauty data is limited, using a single descriptor to build the similarity graph may result in an incomplete representation of the beauty information of a face image. The main goal is twofold: first, we construct different similarity graphs based on different descriptors; then, we construct the label space information graph, which we call a correlation graph, and merge both the label space graph and the data space graph into a new graph, which is used in the FME algorithm to predict the beauty value of the unknown face images.  
A graphical representation of the main idea of this approach can be seen in Figure 1. We emphasize that the unlabelled images play two roles in our proposed scheme. First, they help to capture the global and local structure of the data via the estimation of the graphs built on the facial image features/descriptors.  
Second, during the iterative process, a coarse prediction of the beauty score is produced for the unlabelled images, which will provide additional information through the score graph (correlation graph). Since this graph is merged with the data graph, a better prediction of the scores is expected.  
The graph construction technique used to construct the different graphs representing both the data space and the label space mainly uses the \(K\)-nearest neighbour (K-NNG) graph construction approach [44]. The \(K\)-NN graph of a set of vertices \(V=\{\mathbf{x}_{1},\mathbf{x}_{2},\ldots,\mathbf{x}_{n}\}\)\(\in\mathbb{R}^{d}\) is a undirected graph where \(V\) is the set of vertices and \(E\) is the set of edges from each \(\mathbf{x}_{i}\in V\) to its \(K\) most similar objects under a certain similarity measure. In this work, the Gaussian kernel function was used to encode the similarity between neighbours. Let that graph be described by the similarity matrix \(\mathbf{W}\).  
Note that the data graph quantifies the pairwise similarities based on the image descriptors, while the score graph (correlation graph) quantifies the pairwise similarities based on the image scores.  
Figure 1: The general flowchart of the multi-view graph fusion that integrates label graph for semi-supervised learning. It is used for multi-class classification problems. Given the training dataset, we construct different similarity graphs based on different descriptors. Then, from the available labelling data, we construct the label space information graph, which we call a correlation graph, and merge both the label space graph and the data space graph into a new graph that is used in the FME algorithm to predict the beauty value of the unknown face images.
## 4 Conclusions  
The modified biochar adsorbents were successfully synthesized, optimized, and characterized. FTIR analysis showed greater peak enhancement and the presence of added functional groups, which indicates that the primary material (biochar) has been modified. XRD analysis confirmed that the poorly crystalline biochar had been modified to a more crystalline structure by rGO and DES. Furthermore, SEM-EDX analyses confirmed the surface morphology and elemental composition of the modified adsorbents. The modification of the biochar nanomaterial resulting in a new composite material with well-defined porosity presents unique surface characteristics, open cavities, and pores spread out unevenly on the surface of the adsorbent. BET analysis of the modified biochar (biochar/rGO/DES-glycerol) showed a decrease in surface area and pore volume, perhaps due to the introduction of DES, resulting in surface modification and blocking of pores. Additionally, a preliminary adsorption test was conducted by assessing the dose effect. The results obtained from this preliminary adsorption test proved that biochar/rGO/DES-glycerol is a better adsorbent than biochar, biochar/rGO, and biochar/rGO/DES-CTAB for the removal of zidovudine and phenol from wastewater. Hence, biochar/rGO/DES-glycerol was used in detailed adsorption experiments. The adsorption was highly reliant on several variables, including the adsorbent dose, pH, contact time, and adsorbate concentration. The overall removal of ZDV and phenol by the DES-modified adsorbent (biochar/rGO/DES-glycerol) was satisfactory, achieving the highest percentage of removals for ZDV of 98% and 75% for phenol in an acidic medium. The adsorption of ZDV and phenol followed a pseudo-second-order kinetics model, in which adsorption assumes that pollutant molecules and the adsorbent exchange and share electrons during this process. The equilibrium data fitted well with the Langmuir isotherm revealing the monolayer adsorption process for ZDV, and phenol is primarily preferred. Regeneration showed that ion exchange is the predominant mechanism, followed by the chemisorption process. Overall, biochar/rGO/DES-glycerol has shown to be a good choice for removing ZDV and phenol from an aqueous medium.  
All authors contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by T.N.M., S.O.A. and I.A.L. Writing--original draft preparation, T.N.M. and A.L.T. Writing--review and editing, A.L.T. and M.J.K. Supervision, M.J.K., I.A.L., S.O.A. and A.L.T. Project administration, M.J.K. Funding acquisition, M.J.K. All authors have read and agreed to the published version of the manuscript.  
The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.  
Not applicable.  
All data and materials produced and analyzed in this research are included in this article.  
The authors are grateful to the Faculty of Applied and Computer Science, Vaal University of Technology South Africa for all the facilities provided and financial support.  
The authors have no relevant financial or non-financial interest to disclose.
## 4 Discussion
### Mechanical Effects  
This maximal speed during the test and speed at the physiological boundaries here reported were lower as the load increased (\(p\leq 0.003\); ES: 0.34-0.62). Moreover, based on a previous work [11], it was hypothesized that the mechanical effects of submaximal running with an extra load would be load-specific. Indeed, the response to excess load does not seem to be linear, with moderate effects observed between +0 and +5% BM (ES: 0.18-0.32) and very large effects between +5 and +10% BM conditions (ES: 0.33-0.59).  
Of note, absolute and normalized (to BM) power revealed no significant changes at VT1 and exhaustion regardless of the increase in vest load, whereas moderate-to-large effects were observed at VT2 (ES: 0.27-0.51). Pairwise comparisons revealed negligible effects (ES: 0.00-0.03) between +0 and +5% BM, while large effects were found between +5 and +10% conditions (ES: 0.42-0.51). On the one hand, current knowledge about novel running power sensors indicates that their reliability increases at aerobic paces, whereas the reported variability spikes at high intensities [23; 42]. On the other hand, the environment around the second ventilatory threshold (VT2) is characterized by the onset of metabolic instability, which could also jeopardize their running technique and affect the variables involved in the estimation of power data, such as step frequency. Despite these considerations, running power seems to be a more stable metric than speed for working at aerobic paces, regardless of the vest weight.  
Despite the findings presented herein, some limitations must be acknowledged. First, the sample size (\(n=15\)) was adequate but may need to be increased in future studies to validate the obtained results. Additional populations of trail runners (elite, females, recreational) should be tested to identify the potential differences between groups. Another limitation identified in this study is the discomfort caused by the weighted vest. Although it allowed for the extra load to be evenly distributed, the recommendation for future trials is to use appropriate trail running vests to minimize discomfort and ensure maximum specificity. Notwithstanding these limitations, the present study provides valuable insights into the acute biomechanical and physiological responses to running with an additional load. To gain a more comprehensive understanding of trail running conditions, future research should consider both positive and negative slopes, as well as in-field protocols to explore potential interactions with an additional load.
# Analysing Land Cover Change in the Valencian Community through Landsat Imagery: From 1984 to 2022
## 2 Materials and Methods
### Validation
#### 2.5.1 Algorithm Validation Using Field Points  
To validate the model, we calculated a land cover map utilising Landsat 9 images acquired in September and October 2023. A subset of 126 points, representing approximately 5% of the training pixels for each class, was selected from this land cover map for comparison with field data. Our rationale for selecting this subset was to ensure it would adequately represent the majority of the data's variance, thereby validating the algorithm's performance (Figure 3).  
In order to achieve this objective, the validation points were selected based on their homogeneity and large spatial extent, considering that Landsat images have a resolution of 30 m. This careful selection ensures the reliability and robustness of the validation process, which is critical for the accuracy of our analysis. The comparison was conducted visually in the field on the 30 th and 31 st of October, 2023.  
Figure 3: Map of the validation points collected along the Valencian Community.
# Elliptic Curve Signcryption-Based Mutual Authentication Protocol for Smart Cards
## 8 Discussion
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & \multicolumn{8}{c}{**Security Properties**} \\ \cline{2-7}  & **M\({}_{\mathbf{UA}}\)** & **N\({}_{\mathbf{RP}}\)** & **F\({}_{\mathbf{WS}}\)** & **A\({}_{\mathbf{NY}}\)** & **CO\({}_{\mathbf{N}}\)** & **INT** & **A\({}_{\mathbf{VA}}\)** \\ \hline [PERSON] et al. [7] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ [PERSON] [11] & \(\times\) & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & ✓ \\ [PERSON] et al. [22] & ✓ & ✓ & ✓ & \(\times\) & ✓ & \(\times\) & \(\times\) & ✓ \\ [PERSON] et al. [27] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ [PERSON] et al. [29] & ✓ & ✓ & \(\times\) & ✓ & \(\times\) & \(\times\) & ✓ \\ [PERSON] et al. [47] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ [PERSON] et al. [49] & ✓ & ✓ & \(\times\) & ✓ & \(\times\) & \(\times\) & ✓ \\ [PERSON] et al. [55] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ [PERSON] et al. [61] & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\
**ECSSP-SC** & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Security attributes of different smart card security protocols. for both the _USR_ and _SVR_ is less than that of communication cost consumed in the schemes of [PERSON] et al. [7], [PERSON] et al. [27], [PERSON] et al. [55], and [PERSON] et al. [61]. The proposed ECSSP-SC consumes 34.5% less bandwidth than the protocol mentioned in [PERSON] et al. [7]. The smart card security schemes mentioned in the literature survey are based either on modular exponentiation or elliptic curves. The schemes of [PERSON] et al. [11] and [PERSON] et al. [22] listed in Table 4, Table 5, and Table 6, are based on modular exponentiation, while the other schemes mentioned in [7, 27, 29, 47, 49, 55, 61] are based on elliptic curves, although they do not use signcryption. As mentioned in Table 3, the proposed ECSSP-SC has utilized the signcryption based on an elliptic curve to reduce the computational time and to decrease the communication overhead. No scheme in the literature survey has taken the advantage of combining an elliptic curve with signcryption for smart card security. Results have shown that combining an elliptic curve with signcryption is effective for securing smart card-based systems by satisfying the required security functions, while at the same time consuming less computational and communication cost.
# An Adaptive Process Monitoring Method Based on an Improved ADALINE Neural Network
## 4 Experiment and Results Analysis
### Implementation of the Proposed Approach  
#### 4.2.1 Data Preprocessing  
First, the nonlinear wavelet transform threshold is used to denoise the historical data, and the appropriate decomposition layer is obtained by calculating the maximum value of the wavelet decomposition modulus. The wavelet base is selected as sym4, which is suitable for accurate reconstruction and a fast algorithm, and the result is shown in Figure 8. The signal-to-noise ratio (SNR), structural similarity (SSIM), and root mean square error (RMSE) are used as signal evaluation indicators for noise reduction. The results are shown in Table 1.  
Figure 8: Results of adaptive noise reduction method.  
Figure 7: The cutting experiment process.  
From Table 1, it can be found that the proposed adaptive denoising algorithm has a larger SNR and SSIM and a smaller RMSE than those of the traditional manual denoising method, indicating that better results are achieved with the proposed algorithm. It can also be observed from Figure 8 that the adaptive denoising method removes more noise and has good denoising capabilities.