esa-sceva/satcom-chunk-collection huggingface dataset:
# Potential of Laurel (_Laurus mobilis_ L.) Leaf Polyphenols for Modulation of Body Composition
## 4 Influence of Laurel Leaf Polypphenols on Body Composition
### Influence of Laurel Leaf Polyphenols on Bones  
Bone is a composite structure that consists of inorganic mineral (calcium, phosphorus) crystals, an organic matrix represented mainly by collagen type I, other proteins, water, and some lipids. The majority of bone cells are mesenchymal-originated chondrocytes (osteoblasts and osteocytes), which are responsible for the deposition and remodeling of the growth plate, while hematopoietic multinucleated osteoclasts are responsible for bone resorption [39]. These processes are regulated by multiple molecular pathways including systemic (e.g., growth and sex hormones, calcitriol, calcitonin, IGF-1, glucocorticoids) and local (e.g., the osteoprotegerin--receptor activator of NF-B ligand) factors [40], yielding opportunities for the action of some exogenous bioactive components. A pathway regulated by the wingless-type MMTV integration site (Wnt) genes was shown to regulate aging in all cells, as well as in bone cells, where it was first observed that decreased expression of the Wnt genes resulted in lower osteoblast formation [39]. Aside from these, physiological redox status also seems to play an important role in the maintenance of balanced osteoclast and osteoblast activity and bone turnover [41]. In addition to the imbalance in bone turnover in favor of bone resorption, aging results in changes in collagen structure and increase in non-collagenous proteins, which regulate cell-matrix and mineral-matrix interactions leading to fragile bones [42]. Moreover, the mineral content of bones increases with age (in relation to the protein part of the bone volume), leading to the exponential loss of bone toughness and increased susceptibility to breaking [39]. The potential role of polyphenols in maintaining bone health mainly originates from their aforementioned antioxidant and anti-inflammatory activity but could also be exerted by other unknown and/or unclarified mechanisms [40]. The search on the PubMed database yielded 30 studies (8 for kaempfer, 10 for hydroxycinnamic acids and 12 for apigenin) published in the last ten years. The main observed effects of the selected laurel leaf polyphenols on bones are summarized in Table 1 and discussed below.  
Figure 4: Overview of potential molecular targets for polyphenols’ antioxidant and anti-inflammatory activity. MAPK, mitogen-activated kinase; ERK, extracellular signal-regulated kinase; p38, p38 MAPK; AKT, protein kinase B; NF-\(\kappa\)B, nuclear factor-kappa \(\beta\); ROS, radical oxygen species; COX, cyclooxygenase; LOX, lipoxygenase; iNOS, inducible nitrogen oxide synthase; PPARV, peroxisome proliferatorator-activated receptor \(\gamma\); miRNA, micro ribonucleacetic acid; Nrf2, nuclear factor erythroid 2-related factor 2; GST, glutathione S-transferase; SOD, superoxide dismutase; NQO1, NADP(H) quinone oxidoreductase 1; GSH, glutathione; HO-1, heme oxygenase-1.
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## 3 Proposed SIEM Framework
### Machine Learning Module
#### 3.3.1 Problem Formulation  
The goal of the filtering process is to assign each vector with an indicator of its importance (or, more directly, assess its significance). Given the superior accuracy of supervised learning algorithms compared to other learning models, we propose to formulate the filtering problem as a supervised learning task. The supervised learning model takes the vector representation of the alert as input and predicts a class label indicating whether the sample is a critical alert.
# Handover Decision-Making Algorithm for 5G Heterogeneous Networks
## 4 Experimental Setup
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **5G** & **WLAN** & **LTE** \\ \hline Carrier frequency (GHz) & 28 & 2.4 & 1.8 \\ RSS threshold (dBm) & \(-90.6\) & \(-91\) & \(-92\) \\ Radius (m) & 200 & 55 & 2000 \\ Unit cost per Mbps (assumed) & 50 & 1 & 8 \\ Throughput (Mb/s) & 1000 & 50 & 20 \\ Delay (s) & 1 & 7 & 15 \\ Bit error rate (BER) (\%) & 0.01 & 7 & 0.08 \\ SINR & 25 & 22 & 20 \\ PLR & 1 & 20 & 10 \\ \hline Handover cost & \multicolumn{3}{c}{2 units per handover} \\ MT speed (km/h) & \multicolumn{3}{c}{40 to 100} \\ Adaptive time interval, T (s) & \multicolumn{3}{c}{\(1/v\)} \\ Dwell time threshold (s) & \multicolumn{3}{c}{2} \\ Health services U (Mb) & \multicolumn{3}{c}{0.827 Mb = ECG + vital + audio + video} \\ ECG (Mb) & \multicolumn{3}{c}{0.024} \\ Vital (Mb) & \multicolumn{3}{c}{0.010} \\ Audio (Mb) & \multicolumn{3}{c}{0.025} \\ Video (Mb) & \multicolumn{3}{c}{0.768} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters [7; 12]. Figure 5: RSS signal by taking a single RSS sample. Figure 6: RSS signal by taking the mean RSS value. \begin{table}
\begin{tabular}{c c c c} \hline
**WLAN Access Point** & **Actual Distance (m)** & **Predicted Distance (m)** & **Error (\%)** \\ \hline WLAN\#1 & 10 & 13 & 30 \\ WLAN\#2 & 30 & 33 & 10 \\ WLAN\#3 & 106 & 97 & 8.49 \\ WLAN\#4 & 110 & 101 & 8.18 \\ WLAN\#5 & 0 & 0 & 0 \\ WLAN\#6 & 90 & 86 & 4.44 \\
5G\#1 & 393 & 379 & 3.59 \\
5G\#2 & 300 & 310 & 3.33 \\
5G\#3 & 18 & 16 & 11.11 \\ \hline \end{tabular}
\end{table} 
Table 3: Trajectory in each network. In this work, the TOPSIS weight allocation is set based on two user preferences, cost, and QoS. The users who select the cost preference aim to minimize the network connection cost while maintaining the service quality, whereas the QoS preference is for those who strive for the best service quality with no care about the network connection cost. The weight allocations for the QoS preference and cost preference are shown in Table 4. For cost preference, higher weightage is assigned to the parameter cost. On the other hand, QoS preference is given higher weightage on the QoS parameters such as data rate, delay, BER, and SINR. This is assuming the MT is transmitting health data (ECG + vital + audio + video) in real time [23]. The TOPSIS scoring for each network under different user preferences is indicated in Table 5. Each network scoring value is computed using Equation (15) based on the weightage allocation shown in Table 4. The higher the score, the better the network.
## 4 Conclusions  
The current study is the first to explore the biosynthesis of ZnO-NPs using the extract of _C. chinense_ fruits and their cytotoxicity and antioxidant activities. It demonstrated a successful biosynthesis of \"green\" ZnO-NPs using _C. chinense_ fruit extract as a mediating agent and was characterized by different techniques. The XRD pattern confirmed a hexagonal wurtzite structure of ZnO-NPs of with average particle size of 19.27 nm. The external morphology showed a mixture of rice-shaped and spherical particles, with spherically shaped internal morphology. Furthermore, a UV-vis absorption peak at 302 and 481 nm confirmed the formation of ZnO-NPs with a band gap of 3.76 and 3.49 eV, respectively. The green synthesized ZnO-NPs displayed better and reasonable cytotoxic and antioxidant activities compared to conventionally synthesized ZnO-NPs. Thus, the results of these studies suggest that these NPs could be considered as a viable agent for various treatment purposes for the reduction of oxidative stress.  
Figure 8: Radical scavenging activity (%) of ZnO-NPs and ascorbic acid at various concentrations.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test Samples** & \multicolumn{5}{c}{**Sample Concentrations (\(\upmu\)g/mL)**} & \multicolumn{2}{c}{**IC\({}_{50}\) (\(\upmu\)g/mL)**} \\ \hline  & 1.56 & 3.13 & 6.25 & 12.5 & 25 & 50 & \\ \hline Ascorbic acid & 4.73 \(\pm\) 0.084 & 12.26 \(\pm\) 0.040 & 35.95 \(\pm\) 0.011 & 40.04 \(\pm\) 0.080 & 52.21 \(\pm\) 0.082 & 65.20 \(\pm\) 0.064 & 4.73 \\ \hline Green ZnO & 3.31 \(\pm\) 0.069 & 7.46 \(\pm\) 0.083 & 15.43 \(\pm\) 0.048 & 27.40 \(\pm\) 0.045 & 37.59 \(\pm\) 0.051 & 49.83 \(\pm\) 0.447 & 6.27 \\ \hline Conventional ZnO & 1.31 \(\pm\) 0.068 & 4.37 \(\pm\) 0.010 & 12.72 \(\pm\) 0.034 & 21.29 \(\pm\) 0.133 & 26.09 \(\pm\) 0.064 & 31.51 \(\pm\) 0.017 & 8.76 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Antioxidant activity (%) of green and conventional ZnO-NPs.  
**Author Contributions:** Conceptualization, M.S.M., D.M.N.M. and D.C.O.; methodology M.S.M., D.M.N.M., D.C.O. and M.S.; software, D.C.O. and M.S.; validation, M.S.M., D.M.N.M. and D.C.O.; formal analysis, M.S.M., D.M.N.M., D.C.O. and M.S.; investigation, M.S.M., D.M.N.M., D.C.O. and M.S.; resources, D.C.O. and M.S.; data curation, M.S.M., D.C.O. and M.S.; writing--original draft preparation, M.S.M.; writing--review and editing, D.M.N.M. and D.C.O.; visualization, M.S.M. and D.C.O.; supervision, D.M.N.M. and D.C.O. All authors have read and agreed to the published version of the manuscript.  
**Funding:** Authors gratefully acknowledge the National Research Foundation (NRF), South Africa and North-West University (NWU) for funding (141668 and 1K02799).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** There is no conflict of interest.
## 4 Discussion
### Importance of Input Parameters and Sensitivity Analysis  
A sensitivity analysis was conducted on the input parameters of the proposed ANN and SVM models to investigate their impacts on (the accuracy of) the output variable (or the damping ratio). For this purpose, each input parameter was increased and decreased individually by 100%, and the resulting error was measured to establish the sensitivity of the model to that parameter. The results of the sensitivity analysis are plotted in Figure 21, which illustrates the mean increase in error as the input parameters were increased or decreased. The higher the error, the more sensitive the model is to the input parameter.  
Figure 21: The importance of input parameters on the MAE of the best (**a**) ANN and (**b**) SVM model.  
Figure 19: Effects of cycling loading on (**a**) \(e_{\min}\) and (**b**) \(e_{\max}\) based on the sphericity \(S\) parameter.  
Table 9 presents the ranking results of variable importance for the proposed ANN and SVM models. The input parameters for each model include the number of loading cycles, \(S\), \(R\), \(\rho\), vertical stress and the CSR. Note that the ranking results are based on the total score obtained by each input parameter. From Table 9, it can be observed that the most important input parameter is vertical stress, with a total score of 2 and a ranking of 1. The significance of vertical stress in regulating the contact forces between the particles, and thus the dissipation of energy during cyclic loading, is the main reason behind this phenomenon. The second most important input parameter is \(R\), with a total score of 5 and a ranking of 2. This is followed by \(\rho\), with a total score of 6 and a ranking of 3. The fourth and fifth most important input parameters are the number of cycles and \(S\), with total scores of 7 and 10 (rankings of 4 and 5), respectively. Finally, the CSR has the lowest total score of 12 and is ranked last in terms of importance. These rankings provide valuable insight into the relative importance of the input variables in predicting the damping ratio (for the models considered in this study). This information can be useful in optimizing the models and selecting the most important input variables for particular applications.  
It is necessary to mention that the best AI model can be applied to other dry sands with similar particle shape parameters with similar particle shape parameters and minerology.
## 2 System Model
### Propagation and Formulations
#### 2.2.2 Even Transmission Block
\begin{array}{ccc}g_{n}^{\left(1,1 \right)}&\cdots&g_{n}^{\left(1,A_{2}\right)}\\ \vdots&\ddots&\vdots\\ g_{n}^{\left(A_{1},1\right)}&\cdots&g_{n}^{\left(A_{1},A_{2}\right)}\end{array} \right] \tag{26}\]  
where the channel \(g_{n}^{\left(a_{1}a_{2}\right)}\) in the pre-coding matrix channel \(\mathbf{G}_{n}\), with \(a_{1}\in\mathcal{A}_{1}\) and \(a_{2}\in\mathcal{A}_{2}\), is a channel from transmitting antenna \(a_{1}\) at relay \(R_{2}\) to a receiving antenna \(a_{2}\) at a device \(D_{n}\), applying Rayleigh distributions for propagation. Each fading channel is represented by \(g_{n}^{\left(\cdot\right)}\) such that \(g_{n}^{\left(\cdot\right)}=v_{n}^{-\omega}\), where \(v_{n}\) is the distance from relay \(R_{2}\) to device \(D_{n}\). The SINRs obtained at devices \(D_{n}\) for \(n=\{1,\ldots,N\}\) when they decode the data symbols \(x_{i}\) for \(i=\{N,\ldots,n\}\) from the best received signal \(\max_{[A_{1}\times A_{2}]}\
# Clustered and Distributed Caching Methods for F-RAN-Based mmWave Communications
## 7 Conclusions  
This paper proposes cache distribution methods for popular network functions at fog-access radio to reduce service provisioning and fronthaul delays. This setting makes it suitable for millimeter wave communications that demand reduced fronthaul delays to alleviate the effect of path and penetration losses. Results show that the clustered caching method minimizes the total time required for service provisioning as compared to the distributed method. Meanwhile, the latter achieves a slight reduction in network cost and energy. Overall, this supports a tradeoff based on network preference, i.e., the priority metric selected for operation. Future efforts will investigate the two methods in terms of online caching for real-time traffic.  
Figure 5: Energy Consumption in the Network.  
**Author Contributions:** Conceptualization, A.A., M.B., H.E. and M.A.J.; methodology, N.S.; software, M.B.; validation, M.A.J., N.S. and R.S.; formal analysis, R.S.; investigation, M.B.; resources, H.E.; data curation, R.S.; writing--original draft preparation, A.A. and M.A.J.; writing--review and editing, H.E.; visualization, M.A.J.; supervision, A.A.; project administration, A.A.; funding acquisition, A.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is funded by Deanship of Scientific Research at King Faisal University, grant No. 1811025.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Simulation Results
### Comparison of the Different Working Pairs

Table 5: Specific energy evaluated for each working pair when the waste heat temperature is available at 80 °C, considering a GTL of 20 K, 40 K, and 50 K for all the investigated ambient temperatures. Comparison of Results for AdHT and Heat Pumping Applications  
It is interesting to compare the achieved results, in terms of optimal working pairs, with similar analyses done for adsorption air conditioning and heat pumping applications. A quite extensive comparison of different materials and refrigerants for a wide variety of operating boundaries is reported in [35]. The LiBr-silica/water and LiCl-silica/methanol working pairs showed a superiority over the other ones also for air conditioning and refrigeration applications, as already discussed for the case of AdHT, thus stressing the attractive features of these composites in real applications. At the same time, for heat pumping applications analyzed in [35], the use of methanol was disregarded since heat source temperatures of 150 \({}^{\circ}\)C were evaluated, at which decomposition and stability problems of these refrigerants can occur. This stresses that, for applications with extremely low waste heat, new possibilities open up thanks to the higher specific energy density achievable by the composite sorbents. Indeed, whereas a heat source temperature of 150 \({}^{\circ}\)C is needed to reach a maximum energy density of 1000 kJ/kg for heat pumping applications (for LiBr-silica/water and FAM Z02/water) at 45 \({}^{\circ}\)C of supply temperature to the user, the AdHT cycle with composite sorbents employing LiCl allows reaching the same energy density but at a much lower temperature.
# HPC Platform for Railway Safety-Critical Functionalities Based on Artificial Intelligence
## 3 Use Case Definition
### System Workflow  
The solution architecture contains three main logical modules. The first one captures data coming from cameras and it synchronizes them in time. If the data comes from stereo cameras, it also rectifies them. The second one performs a real time data analysis using CV and AI techniques. The third collects the results of the analysis, and for those safety functionalities the 2 oo2 (two-out-of-two) RootVoter (RV) logic is applied.  
The most demanding computer resource functionalities are concentrated in the second logical module which is fully executed in the VCU118 board and SELENE platform:  
* Platform landmark detection and identification: this detects the start/end landmarks of the platform by a pre-trained AI model (YOLOv4 [19] architecture) inference process, determining if the train is on the platform and establishing a reference point in the approximation phase for the ultimate accurate stop.
* Distance estimation: this support the precise stop process in the platform area. The distance to station stopping landmarks is calculated updating the predicted remaining distance of ATO. This calculus is based on a dense disparity map calculated by the Semi-Global Block Matching (SGBM) method [20].
* Passenger detection: using the same techniques but a different AI model, it detects passengers when they are boarding or exiting the train, managing the door opening and closing commands.
## 3 Results and Analysis of Influencing Factors  
### Reception of Helical Loop Antenna Signals  
According to Faraday's Law of Electromagnetic Induction,  
\[\
abla\times\overset{\rightarrow}{E}=-j\omega\mu\overset{\rightarrow}{H} \tag{16}\]  
Figure 3: Helical loop wireless transmission model.  
Thus, the induced electromotive force (EMF) of the helical loop is  
\[E=-NBS\omega \tag{17}\]  
From \(\overset{\rightarrow}{B}=\mu\overset{\rightarrow}{H}\) (17), we have  
\[E=-N\mu HS\omega \tag{18}\]  
\(N\): number of turns of the coil; \(S\): cross-sectional area of the coil; \(f\): transmitting frequency; \(\mu\): magnetic permeability of the core.  
The magnitude of the induced electromotive force in a helical loop depends on factors such as the number of coil turns, the magnetic permeability of the core, and the frequency. When considering a fixed receiving coil, its number of turns, core, and area are constant, so there is a direct proportional relationship between the induced EMF and the magnetic field strength.  
At the same frequency, by just calculating the amplitude of the received magnetic field at different transmission distances, one can determine the voltage at the corresponding positions. To more accurately assess the impact of various parameters, this paper employs the method of controlling variables, which is to say, ensuring all other parameters remain unchanged when discussing a particular parameter.
###### Abstract  
The investigation described in this paper aims to determine the factors in the deterioration of reinforced concrete (RC) bridge deck slab components, quantitatively evaluate them using survival analysis, and thus facilitate optimal decision-making. To consider bridge deterioration across Japan, bridge inspection data from the East Japan and Tokyo regions were selected based on their different deterioration phenomena and processes. Data cleaning and selection were conducted to increase the accuracy and reliability of the analysis. Using the Kaplan-Meier (KM) estimator and Cox multivariate regression model, the hazard risk of each variate was quantitatively estimated. For East Japan, winter precipitation and de-icing salt greatly increased the deterioration rate, indicating that high humidity and a high salinity environment were the main reasons for deterioration. However, for the Tokyo region, traffic loading resulted in high risk, indicating that fatigue failure was the main risk factor. In both areas, the slab edge showed a high deterioration rate; therefore, edge waterproofing should be improved to enhance durability. Additionally, the risk score of each bridge component was calculated and mapped using geographical coordinate information. Inspection, repairs, and rehabilitation can be more efficiently conducted using this information.  
Keywords:bridge inspection; survival analysis; Cox model; RC deck slab +
Footnote †: journal: Applied Sciences
# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 3 Materials and Methods
### Method  
In this subsection, we present the developed methodology. Taking advantage of the characteristics of the analyzed ship, as well as wind conditions in the studied region, our approach allows the segmentation of the NO\({}_{2}\) plume produced by the particular ship of interest, distinguishing it from all the other concentration peaks in the surrounding area. The results produced by the proposed approach are easily interpretable and, thus, can be used as a reliable source of information by ship inspectors.  
The method consists of the following steps: an AIS data-based interpolation of the ship tracks at the moment and just before the satellite overpass (Section 3.2.1), definition (Section 3.2.2), and enhancement (Section 3.2.3) of a ship plume image, the definition of a ship sector (Section 3.2.4) that allows the further restriction of the analyzed area, normalization of the defined ship sector, and splitting of the normalized sector into subregions (Section 3.2.5), which, finally, gives the possibility to retrieve the set of necessary features. These steps are described below.