esa-sceva/satcom-chunk-collection huggingface dataset:
# Outdoor Visible Light Communication in Intelligent Transportation Systems: Impact of Snow and Rain
## 4 Conclusions  
This work implements a mathematical model to analyze the impact of very clear weather, rain, and snow attenuation on a VLC system. The effect of this attenuation has been observed through simulation of an Infrastructure-to-Vehicle (I2V) outdoor application system. The two kinds of snow  
Figure 14: Received information (RI) vs. data rate under 16 PPM and I-16 PPM schemes for (**a**) very clear weather and 2 mm/h wet, dry snow, (**b**) 4, 8 mm/h wet, dry snow, (**c**) different rain models at 25 mm/h, (**d**) different rain models at 10 mm/h, and (**e**) different rain models at 2 mm/h.  
used are wet and dry, which are measured at different perception rates. Rain attenuation is measured for three different models: Marshall, France and Japan.  
This kind of attenuation influences light communication via light absorption or scattering and increases with the increase of communication distance. The proposed system performance is measured by its \(\mathrm{P_{rx}}\), SNR, BER, coverage area, and the amount of received information. In the case of snow, it is found that the system is more attenuated by dry snow than by wet snow. Under heavy dry snow weather, a vehicle can receive \(-23.08\) dBm power with \(30.11\) dB SNR at \(18\) m. For rain attenuation, the Japan model is found to be more accurate on high rainfall rates and shows a large impact on system compared with other models. At \(18\) m, a vehicle can receive \(-16.8\) dBm power with \(42.44\) dB SNR in heavy rain weather regarding the Japan model.  
Setting \(\theta\) to \(80^{\circ}\) and the FOV to \(10^{\circ}\) is more appropriate for a communication distance up to \(300\) m from the traffic light. The SNR for OOK, SC-BPSK, L-PPM, and I-LPPM has been determined to obtain a BER of \(10^{-6}\). The L-PPM can cover larger distances with lower SNR values as long as L increases under different weather conditions. On the other hand, I-LPPM can cover less distance with higher SNR values as long as L increases under different weather conditions. Comparing OOK with SC-BPSK, the last is found to cover a shorter distance with higher SNR values. The maximum achievable distance and the amount of received information depend on the type of modulation scheme used, which has been calculated. Since the amount of received information depends on the type of modulation scheme used that supports higher coverage distances, L-PPM is found to be the best scheme.  
Conceptualization, H.A.F. and A.A.E.A.A.; Methodology, R.W.Z., H.A.F. and A.A.E.A.A.; Software, R.W.Z., H.A.F. and A.A.E.A.; Validation, H.A.F., A.A.E.A. and M.H.A.; Formal Analysis, H.A.F. and A.A.E.A.; Investigation, R.W.Z.; Resources, R.W.Z., H.A.F. and A.A.E.A.; Data Curation, R.W.Z.; Writing--Original Draft Preparation, R.W.Z.; Writing--Review and Editing, H.A.F., A.A.E.A. and M.H.A.; Visualization, R.W.Z., H.A.F. and A.A.E.A.; Supervision, H.A.F., A.A.E.A. and M.H.A.; Project Administration, R.W.Z., H.A.F. and A.A.E.A.  
This research has no external funding.  
The authors declare no conflict of interest.
# Effect of Dynamic Loading Conditions on Maximizing Energy Dissipation of Metallic Dampers
## 4 Dynamic Optimization
### Seismic Analysis of the Optimized Results  
Cases D3 and D7 are representative of the two optimized tendencies under small and large ground acceleration, respectively. Case D7 was wider at both ends, which is analogous to the static optimization results. Likewise, the static hysteresis loop area also increased. In contrast, under low acceleration, the initial shape converged into a different form. The widths of both of the ends were smaller than that of the statically optimized shape. Consequently, the damper became slightly weaker than the initial shape, which resulted in an increase in the dissipation of the plastic energy.  
To evaluate and verify the performance of the optimized dampers, seismic analysis of the two representative dampers was conducted (Figures 16 and 17). The damper shapes of D3 and D7 were examined, with the same conditions as the dynamic optimization model. The boundary conditions, including the spring set and the concentrated mass, were maintained. However, in this case, artificial earthquake acceleration was applied instead of sine-function acceleration (Figure 16a). Artificial acceleration was created randomly with the design response spectrum. The construction of the response spectrum followed the  
Figure 15: Dynamic optimization results with respect to the frequency of applied excitation: (**a**) 0.25 Hz; (**b**) 0.5 Hz; (**c**) 1 Hz; and (**d**) 1.5 Hz.  
Uniform Building Code [34] (Figure 16b). The ground coefficients \(C_{a}\) and \(C_{v}\) were identical at 0.09, which were the design spectrum accelerations for a single period and 1 s.  
Figure 16: Formation of an artificial earthquake acceleration: (**a**) Artificial earthquake accelerogram; (**b**) Design response spectrum.  
Figure 17: Relative lateral displacement between the upper and lower ends of the damper under artificial earthquake acceleration (Cases D3 and D7): (**a**) Maximum 0.1 g; (**b**) Maximum 0.2 g; (**c**) Maximum 0.3 g.  
The applied acceleration was scaled to match the intended peak ground accelerations, which were 0.1 g, 0.2 g, and 0.3 g. Comparing the plastic dissipation (ALLPD) between the two cases, case D3 surpassed the result of case D7 at the maximum 0.1 g acceleration condition. However, the amounts of dissipation became analogous to each other at the maximum 0.2 g condition, and the gap between the cases turned around at the highest acceleration condition. As illustrated in Figure 17, D3 deforms more than D7 at small accelerations. This can be interpreted as D3 having a larger applied stroke, which leads to larger energy dissipation. This apparently indicates that D3 will dissipate larger seismic energy under small-scale earthquakes. This trend of results under a given earthquake accelerogram equals that of the optimization condition, the sine-function acceleration. Thus, it can be rationalized that the previously proposed simple loading condition is sufficiently applicable for dynamic optimization.
## 4 Case Study
### Results
#### 4.2.1 Committee  
The feature space \(\overrightarrow{V}_{a}^{26}\) was used as input to the comparison between Tables 13 and 14, and it summarizes the impact of applying score balance algorithm instead of majority voting as the decision-making process used by the committee.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Situation**} & \multicolumn{5}{c}{**True Positives**} & \multirow{2}{*}{**F1 Score**} \\  & **A1** & **A2** & **A3** & **A4** & **A5** & **Total** & \\ \hline
2 & 55 & 55 & 56 & 50 & 44 & 260 & 90.98 \\
3 & 55 & 52 & 60 & 48 & 42 & 257 & 89.75 \\
4 & 54 & 54 & 53 & 47 & 37 & 245 & 85.68 \\
5 & 54 & 55 & 57 & 47 & 43 & 256 & 89.47 \\ ET & 55 & 55 & 57 & 49 & 41 & 257 & 89.82 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Majority vote decision.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Number** & **Situation** \\ \hline
1 & Application of only one kind of algorithm. \\
2 & Gathering all techniques in one ensemble and counting every inducer as one vote. \\
3 & Only parametric sets application. \\
4 & Only non-parametric sets application. \\
5 & All triads heterogeneous sets application. \\ \hline \hline \end{tabular}
\end{table}
Table 12: Situations to evaluate.  
Like many other machine learning techniques, each training pseudo-randomness process is ruled by a seed that yields different combinations for better or worse results and even different ranking positions for those situations. However, all evaluations were performed precisely with the same conditions of seed, training set, training fold, and testing set, which assures a fair analysis.  
From the FDI perspective, it was noticed that score balance decisions could easily manage the trade-off between state detection given a specific situation or among the situations. There is a clear understanding when analyzing situation 3 in both tables. Supposing that A4 is much more critical to detect than the others, even a general increase of correct detection may not be desired if A4 has its accurate detection decreased.  
After analyzing all results, the answer is that ET is worth because it is proved that in some circumstances, its classification performed better than the others; however, none of the combinations can always guarantee the best results, since it depends on the seed and feature space applied.  
Nonetheless, all nine different situations are intrinsic to this technique, and the computational effort necessary to ranking them is negligible if compared to the training one, which makes it possible to declare that there is no reason to disregard any of them. Therefore, given some sorts of algorithms divided into parametric and non-parametric sets, it can assure either the best solution overall among the nine proposed combinations or a better weighted-class solution among situations 3, 4, 5, and ET.
## 4 State Estimation Based on Nonlinear/Non-Gaussian Systems
### Em-Mogcpf  
The EM algorithm [28] is used on the basis of MOGCPF to calculate MOG parameters in the set of importance-sampled particles, which reduces \(G^{n}\) to \(G\), improves the problem of particle sample degradation, and reduces the computational effort. The algorithm flow is shown in Figure 4.  
The mixture weights for each model are set to \(w_{g}=\frac{1}{G}\), the mean \(u_{g}\) is set to a random number, the covariance matrix \(P_{g}\) is set to a unit matrix, and we calculate \(P_{g},w_{g}\) and take the proportion of each type of sample to the total number of samples:  
\[\rho=[(w_{1},u_{1},P_{1}),(w_{2},u_{2},P_{2}),...,(w_{i},u_{i},P_{i}),...](i=1,...,G), \tag{41}\]  
Calculate \(r_{1}(x_{k}),r_{2}(x_{k}),...,r_{i}(x_{k}),k=1,2,...,N\) by \(\rho\).  
\[r_{i}(x_{k})=\frac{\alpha_{i}N(x_{k};u_{i},P_{i})}{\sum\limits_{i=1}^{G}\alpha _{i}N(x_{k};u_{i},P_{i})}, \tag{42}\]  
Obtain the new mean \(\hat{u}_{i}\), variance \(\hat{P}_{i}\), and mixture weights \(\hat{w}_{i}\):  
\[\hat{u}_{i}=\frac{\sum\limits_{k=1}^{N}r_{i}(x^{k})x^{k}}{\sum \limits_{k=1}^{N}r_{i}(x^{k})}, \tag{43}\] \[\hat{P}_{i}=\frac{\sum\limits_{k=1}^{N}r_{i}(x^{k})(x^{k}-\hat{u }_{i})(x^{k}-\hat{u}_{i})^{\text{T}}}{\sum\limits_{k=1}^{N}r_{i}(x^{k})},\] \[\hat{w}_{i}=\frac{1}{N}\sum\limits_{k=1}^{N}r_{i}(x^{k}),\]  
Let \(\hat{\rho}=[(\hat{w}_{1},\hat{u}_{1},\hat{P}_{1}),(\hat{w}_{2},\hat{u}_{2}, \hat{P}_{2}),...,(\hat{w}_{i},\hat{u}_{i},\hat{P}_{i}),...]\); the iteration stops when \(|\rho-\hat{\rho}|<v\) (\(v\) is a minimum value); otherwise, obtain \(\rho=\hat{\rho}\) and return to Equation (42) to calculate \(r_{i}(x_{k})\).  
When \(w_{k+1}^{i}=\hat{w}_{i},u_{k+1}^{i}=\hat{u}_{i}\), \(\hat{P}_{k+1}^{i}=\hat{P}_{i}\), obtain the new posterior density:  
\[p_{G}(x_{k+1}|z_{k+1})=\sum\limits_{i=1}^{G}w_{k+1}^{i}N(x_{k+1};u_{k+1}^{i}, P_{k+1}^{i}), \tag{44}\]  
from the EM algorithm; we can observe a reduction in the number of mixture components, \(G^{n}\) to \(G\), which has the effect of reducing the posterior probability density at moment \(k+1\)  
Figure 4: EM-MOGCPF algorithm.  
State estimate at moment \(k+1\):  
\[\begin{split}\hat{x}_{k+1}&=\sum_{i=1}^{G}w_{k+1}^{i}u_ {k+1}^{i},\\ \hat{P}_{k+1}&=\sum_{i=1}^{G}w_{k+1}^{i}[P_{k}^{i}+(u_ {k+1}^{i}-\hat{x}_{k+1})(u_{k+1}^{i}-\hat{x}_{k+1})^{\mathrm{T}}],\end{split} \tag{45}\]
## 5 Conclusions  
We found limitations of MODIS and in situ NDVI in the capture of GPP seasonal dynamics for evergreen forests due to insufficient description of plant ecophysiology by the NDVI\({}_{s}\), and landscape heterogeneity estimated using the SHI of PFT is essential in upscaling GPP data.  
Our results confirm that to improve the accuracy of upscaling in situ data of site GPP seasonal responses, in situ radiation measurements should use larger field of view to sense an area or more sensors should be placed in the flux footprint area allow optimal match with satellite sensor pixel size.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2072-4292/11/14/1656/s1](http://www.mdpi.com/2072-4292/11/14/1656/s1)]([http://www.mdpi.com/2072-4292/11/14/1656/s1](http://www.mdpi.com/2072-4292/11/14/1656/s1)), Figure S1: Results of the statistical analysis between NDVI\({}_{is}\) and NDVI\({}_{MD}\) for different spatial resolutions, Table S1: Description of FLUXNET sites used in this study.  
**Author Contributions:** Conceptualization, M.B.; methodology, M.B. and F.V.; formal analysis, M.B.; writing--original draft preparation, M.B.; writing--review and editing, all co-authors.  
**Funding:** This project received funding from the European Union Horizon 2020 Research and Innovation program under the Marie Sklodowska-Curie grant (INDRO, grant no 702717).  
**Acknowledgments:** This work used radiometric and eddy covariance measurements acquired by the FLUXNET community, in particular by AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04 ER63917 and DE-FG02-04 ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeI, CarboMont, ChinaFlux, FLUXNET--Canada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OEzFlux, TCOS-Sibera, and USCCC. We acknowledge the support to data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS (the Integrated Land Ecosystem-Atmospmospence Processes Study, a core project of IGBP), Max Planck Institutefor Biogeochemistry, National Science Foundation, University of Tuscia, Universite de Laval, Environment Canada, US Department of Energy, and database development and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, University of California--Berkeley, and University of Virginia. The authors acknowledge support of the PIs of the eddy covariance sites and technicians, under- and post-graduate students and post-doctoral researchers for providing data and support to the FLUXNET network. The MODIS data used in this study are distributed by the Land Processes Distributed Active Archive Center (LP DAAC), located at the US Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center (lpdaac.usgs.gov).  
The authors declare no conflict of interest.
## 5 Conclusions
The Samsung Galaxy S21 Ultra and Xiaomi Mi 8 exhibited quality GNSS receivers, with minor performance differences, likely attributed to hardware and software variations. This study emphasizes the importance of choosing a suitable positioning method based on task-specific requirements. The impact of noise and multipath errors on the RTK accuracy is noted, with the fusion method mitigating these issues. The GNSS performance of smartphones depends on the antenna quality, hardware design, and software algorithms. Through advanced processing techniques, smartphones demonstrate high-precision GNSS capabilities in ideal conditions and reasonable performance in urban environments. Numeric performance metrics should be interpreted in the overall GNSS system performance context. While this study has demonstrated the superior performance of the fusion method in complex urban environments, several areas warrant further investigation to enhance its application and robustness. Future research could explore the integration of additional sensor data, such as LiDAR or visual odometry, to further improve the accuracy and reliability of positioning in environments with severe GNSS signal degradation. Moreover, optimizing the real-time weighting scheme through machine learning techniques could dynamically adapt the fusion method to varying environmental conditions, potentially offering even greater precision. Investigating the application of the fusion method in different vehicle platforms, such as autonomous drones or marine vessels, could also expand its usability across diverse fields. Finally, a more detailed analysis of the impact of different types of urban infrastructure on the performance of the fusion method would provide valuable insights for tailoring the system to specific operational environments. Conceptualization, M.M.A. ; Methodology, A.H.P. ; Software, A.H.P. ; Validation, A.H.P. and M.M.A. ; Formal analysis, A.H.P. ; Investigation, A.H.P. ; Resources, A.H.P. ; Data curation, A.H.P. ; Writing--original draft, A.H.P. ; Writing--review & editing, M.M.A. and H.S. ; Supervision, M.M.A. All authors have read and agreed to the published version of the manuscript. We acknowledge the Main Department Acquisitions and Collections of the TU Berlin University Library for providing funding for the open access publication of this paper. The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest.
# Performance Evaluation of Laetitic Subgrade Soil Treated with Lime and Coir Fibre-Activated Carbon
## 4 Conclusions  
This study evaluates lime, AC, and ACF stabilised subgrade soils through consolidation tests, UCS tests, and microstructural analyses. According to MPWD standards, the findings showed that lime and ACF enhance the performance of this lateritic soil as a subgrade in road construction. Based on the result of the experiment work, 12% lime is the lowest content of lime that should be included in the soil to enhance the strength of soil significantly because, in this study, the soil is acidic, and a high percentage of lime is needed to reach pH value 12.4. Moreover, the UCS values confirmed that 12% lime is optimum content based on Malaysian standards. Although lime content and curing age affect UCS values of lime treatment, 3% ACF has the same UCS value as 12% lime (four weeks age). In addition, the elastic module increases with increasing additives content in lime-treated and ACF-treated soil. The elastic modulus of 3% ACF and 3% lime are almost identical when comparing lime and ACF specimens. Using lime and ACF effectively enhances the consolidation coefficient in stabilised soil due to decreasing the void ratio and hydraulic conductivity by them. However, I\({}_{\text{D}}\) decreased in lime-treated soil with rising lime content and curing time, while I\({}_{\text{D}}\) increased in AC and ACF-treated soil with growing AC content. The BET results confirmed that activated carbon improves BET value significantly compared to lime. Hence, ACF treatment can improve the compression strength of lateritic soils. Still, it needs more investigation to determine the influence of ACF on other strength factors under various loading and environmental conditions.  
The conceptualisation, design of the work to data gathering, data analysis, manuscript writing, and review have done by contributions of S.T., N.N.N.D., F.M.J., F.M.K., A.S.A.R. and M.J.R. to the preparation of this paper. All authors have read and agreed to the published version of the manuscript.  
This study was funded by Universiti Putra Malaysia under project code: GP-IPS/2021/9701400, grant type: Gearan Inisiatif Putra Siswazah (GP-IPS).  
Not applicable.  
The data used in this research can be provided by the corresponding author upon request. Because of privacy concerns, the data is not publicly available.  
The authors are thankful to Universiti Putra Malaysia for their cooperation in launching this research.  
The authors declare that this research presented contains no commercial or associative interests that would create a conflict of interest.  
Figure 13: Pore volume, surface area, and pore size.
# Megalos: A Scalable Architecture for the Virtualization of Large Network Scenarios +
## 4 The Megalos Framework
### A Reference Architecture  
In order to implement VDs, our solution leverages on containers. Containers are distributed among a cluster of physical computing nodes connected by a network. These nodes are of two types: _master nodes_ and _worker nodes_. Worker nodes are nodes where containers are deployed into and they are controlled by master nodes. Master nodes are nodes that do not execute containers but manage the worker nodes in terms of scheduling/unscheduling the containers into them and checking the state of the cluster. The master nodes also expose APIs to interact with them. The nodes can be geographically distributed, as long as full mesh L3 connectivity between them is guaranteed. The Megalos architecture is then composed by three main components:  
1. Kubernetes;
2. The _Megalos Controller_; and
3. The _Megalos CNI_.  
We use the Docker container engine to provide VDs and we use Kubernetes in order to distribute containers among the physical cluster. We remark that Kubernetes is also used by [PERSON] [21].  
The main task of the Megalos Controller is to manage the lifecycle of each VD, in particular the instantiation and termination of it. Moreover, our Controller allows to manage the _network scenario_, composed by the virtual network topology, the network devices, and their configurations. This information is transmitted in a suitable format by the Megalos Controller to Kubernetes which manages the containers accordingly.  
Since none of the available Kubernetes' networking plug-ins matches all the goals of the model described in Section 3, we implemented a custom network plug-in, called Megalos CNI, described in Section 5.
###### Abstract  
Underwater optical wireless communication (UOWC) has gained interest in recent years with the introduction of autonomous and remotely operated mobile systems in blue economic ventures such as offshore food production and energy generation. Here, we devised a model for estimating the received power distribution of diffused line-of-sight mobile optical links, accommodating irregular intensity distributions beyond the beam-spread angle of the emitter. We then used this model to conduct a spatial analysis investigating the parametric influence of the placement, orientation, and angular spread of photodiodes in array-based receivers on the mobile UOWC links in different Jerlov seawater types. It revealed that flat arrays were best for links where strict alignment could be maintained, whereas curved arrays performed better spatially but were not always optimal. Furthermore, utilizing two or more spectrally distinct wavelengths and more bandwidth-efficient modulation may be preferred for received-signal intensity-based localization and improving link range in clearer oceans, respectively. Considering the geometric implications of the array of receiver photodiodes for mobile UOWCs, we recommend the use of dynamically shape-shifting array geometries.  
photodiode array; received power distribution; mobile optical links; underwater optical wireless communication; underwater vehicles +
Footnote †: journal: Sensors