esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Adsorption Experiments  
The tests were conducted using 30 mL of fluoride solution (500 mg/L) and a shaking speed of 100 rpm in a shaker (SJ-808 SF, Sejong Scientific, Seoul, Republic of Korea). Kinetic experiments were performed with 3.33 g/L of WA (0.1 g WA in 30 mL of fluoride solution) for different durations (0.25, 0.5, 1, 2, 3, 6, 12, 24 h) and analyzed using kinetic models, as detailed in the Supplementary Materials (Text S2). The adsorption equilibrium was established across a range of initial fluoride concentrations (50-1000 mg/L), with a fixed WA concentration (3.33 g/L) and reaction time (24 h). The outcomes underwent analysis employing isotherm models (Text S2). To evaluate the effect of the reaction temperature, 500 mg/L or fluoride solution and 3.33 g/L of WA were reacted for 24 h at temperatures varying between 15 and 35 \({}^{\circ}\)C. For analyzing the effect of pH, 3.33 g/L of WA was reacted for 24 h with fluoride solutions (500 mg/L) having pH values ranging from 3 to 11 (pH adjustment was performed using 0.1 M HCl and NaOH). The fluoride removal efficiency of the adsorbent dose was assessed by using WA concentrations varying from 3.33 to 16.67 g/L. To investigate the competitive effect of coexisting anions, 1 to 10 mM solutions of Na\({}_{2}\)HPO\({}_{4}\) (PO\({}_{4}\)\({}^{3-}\), 94.97 mg/L and 949.7 mg/L), NaHCO\({}_{3}\) (CO\({}_{3}\)\({}^{2-}\), 61.01 mg/L and 600.09 mg/L), Na\({}_{2}\)SO\({}_{4}\) (SO\({}_{4}\)\({}^{2-}\), 96.00 mg/L and 960.00 mg/L), NaNO\({}^{3}\) (NO\({}_{3}\)\({}^{-}\), 62.00 mg/L and 620.05 mg/L), and NaCl (Cl\({}^{-}\), 35.45 mg/L and 354.53 mg/L) were prepared. After fluoride adsorption, the separated WA was washed with deionized water for 24 h at 100 rpm. The washed WA was then recovered and reacted with a 500 mg/L fluoride solution to evaluate its reusability. The fluoride removal efficiency of WA was also evaluated in groundwater and wastewater collected from actual field sites. The groundwater was obtained from irrigated agricultural fields (Anseong, Republic of Korea), while the secondary effluent was acquired a real wastewater treatment facility (Osan, Republic of Korea). The fluoride, cations, and heavy metals contents of the groundwater and wastewater are provided in Table S1. The fluoride concentrations in both the groundwater and wastewater were adjusted to model the 500 mg/L fluoride concentration used in the batch experiments. After reacting the prepared groundwater and wastewater with 3.33 g/L of WA for 24 h, the residual fluoride concentrations were analyzed. Each experiment was meticulously replicated three times under identical conditions, and the average value was used for data analysis.
## 4 Conclusions  
This paper already investigated the effect of combined two HDR dampers on1 stmodal damping ratio of stay cable under considering cable bending stiffness. The theoretical model for evaluating cable damping with two HDR dampers has been proposed. The comparative analysis of results elucidated that the parameters such as loss factor, spring factor, and installation positions of dampers have much effect on the cable damping ratio. The conclusions obtained from this study are as follow:  
* Theoretical formulation for evaluating the modal damping ratio of stay cable with two HDR dampers has been proposed and validated.
* For a stay cable with fixed values of tension (\(S\)) and cable length (\(L\)), the optimum spring factor value of HDR dampers can be obtained when the parameter \(\Phi\)i (\(K\).\(L\)/\(\pi\).\(S\)) becomes 1.
* Loss factors play a significant role in the damping efficiency of HDR dampers. There is always an optimum value for the loss factor. By varying the spring factor, the optimum loss factor value can be figured out.
* Based on the present analysis, it is found that the damping ratio is directly proportional to the installation positions of HDR dampers. The larger the value of installation distance from the cable ends, the damping ratio also becomes higher. In addition, cable damping is also directly proportional to its bending stiffness.
* Present model can be an effective tool for evaluating the stay cable damping with two HDR-dampers. Designer can choose appropriate values among various design parameters to obtain the highest damping ratio.  
Conceptualization, D.T.N. and D.H.V.; methodology, D.T.N. and D.H.V.; software, D.T.N., D.H.V. and M.N.H.; validation, D.T.N., D.H.V. and M.N.H.; formal analysis, D.T.N. and D.H.V.; investigation, D.T.N. and D.H.V.; writing--original draft preparation, D.T.N., D.H.V. and M.N.H.; writing--review and editing, D.T.N., D.H.V. and M.N.H. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the University of Danang, University of Science and Technology.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Figure 12: Installation positions optimization for two HDR dampers.  
Data Availability StatementAll data, models, and codes to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# The EGNOS Augmentation in Maritime Navigation +
## 4 Results and Discussion
### Static Test  
Table 2 summarizes the figures of merit adopted for performance analysis of positioning solutions obtained by processing static data downloaded by the selected EGNOS RIMS (Section 3.1) and applying GPS SPP, GPS + SBAS SPP and DGPS setups.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Horizontal Accuracy [m]**} & \multicolumn{4}{c}{**Vertical Accuracy [m]**} & \multicolumn{2}{c}{**3D Accuracy [m]**} & \multicolumn{2}{c}{**Continuity**} & \multicolumn{2}{c}{**Signal**} \\ \cline{2-11}
**Conf.** & **Mean** & **RMS** & **Max** & **10 m (\%)** & **Mean** & **RMS** & **Max** & **Mean** & **RMS** & **(\%)** & **Signal** \\ \hline GPS SPP & 1.07 & 1.17 & 2.96 & & & 1.94 & 2.34 & 7.14 & 2.32 & 2.62 & & \\ GPS + SBAS SPP & 0.49 & 0.56 & 2.25 & 100 & 0.84 & 1.06 & 4.24 & 1.04 & 1.20 & 100 & 100 \\ DGPS & 1.32 & 1.51 & 5.94 & & 1.64 & 2.11 & 8.07 & 2.29 & 2.59 & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary results of performance analysis conducted in terms of accuracy (using as figures of merit Root Mean Square (RMS), mean and maximum errors) for both horizontal (H) and vertical (Up) components of position, tridimensional accuracy (using as figures of merit Root Mean Square (RMS) and mean errors), IMO parameter on absolute horizontal accuracy, availability and continuity (over 15 min).  
Figure 9: The altitude values obtained applying PPK mode and used for the error analysis.  
From Table 2, it can be noted that EGNOS corrections provide the best performance in terms of all considered figures of merit, with an accuracy improvement of meter order especially on vertical component. In particular, horizontal and vertical mean error are, respectively, equal to 1.1 and 1.9 m for GPS SPP and to 1.3 and 1.6 m for DGPS, while they are equal to 0.5 and 0.8 m after applying EGNOS corrections. Furthermore, comparing GPS SPP and DGPS, even if GPS SPP shows better performance on horizontal and vertical planes, DGPS performs lower values for tridimensional accuracy. The same trend can be noted both for RMS and maximum errors, as well as for the position components. Furthermore, the horizontal errors are always less than 10 m, fully satisfying the IMO requirement for horizontal accuracy. Finally, as discussed in Section 3.1, IMO requirements such as signal availability and continuity are fully satisfied as well: indeed, they are equal to 100%, as shown in Table 2.  
Horizontal and vertical position errors for the different configurations are plotted as function of time in Figure 10 where blue, magenta and orange lines are relative to GPS SPP, GPS + SBAS SPP and DGPS configurations, respectively.  
It is evident that the use of information contained in EMS file allows for having a solution more stable than the other configurations GPS + SBAS SPP, as shown in Figure 10. Furthermore, the figure shows the improvement obtained on a vertical component, with GPS + SBAS SPP vertical errors lower than others.
# Clustering for Lifetime Enhancement in Wireless Sensor Networks
## 3 Advanced Clustering Approach  
A cluster is a set of sensor nodes, one chosen periodically as the cluster head (CH), and the rest of them considered as members. The CH was almost chosen based on different criteria. In the literature, researchers have used multiple metrics, such as the distance, the number of members, and the remaining energy. At this point, we should mention that the type of routing protocol represents the main aim in the selection of these criteria. Therefore, we are obliged to choose the category of protocols this study focuses on. Mainly, clustering is used to optimize the energy consumed in the communication between nodes to fulfill the WSN application's needs. The communication process consumes a significant part of the nodes' energy. Therefore, minimizing the consumed energy in this main process leads to prolonging the WSN performance, especially its lifetime.  
Our purpose is to study the different parameters of clustering and which give the best performance. We start with the hypothesis that clustering is more efficient to prolong network lifetime. At the end of this section, we conclude with the best ways to use clustering for developing more efficient routing protocols for WSNs.  
This section is partitioned as follows: the first subsection is for selecting the real parameters that affect clustering efficiency, followed by the rest of the subsections that discuss each parameter's effect individually.
## 6 Conclusions  
The CBR may be one of the most valuable measures for triggering dynamic channel switching, as it indicates the current medium's occupancy. In addition to CBR, other parameters that affect communication can also be good inputs for the channel control algorithm, such as the number of vehicles around the ego vehicle. However, they can all ultimately be traced back to the CBR rate.  
The results showed that CPM is one of the message types capable of easily saturating channels. It can detect as many as 15-20 objects detected, and its message size is over 1000 bytes. For this reason, in a multi-channel environment, it is essential how we distribute larger message types between channels. The proposed CBR-based dynamic shifting model worked as expected, meaning that no more channels were used as long as the channel saturation was within the threshold. As more and more vehicles were on the road, the saturation increased, and more channels were activated.  
Furthermore, the existence of the service advertisement message is crucial, as it can be used to inform other ITS-Ss about the location of other services. The question is whether SAM is worth it to distribute to be able to notify other users at the expense of additional traffic.  
As a further development, object/detection prioritization would be handy, as CPM messages are large, and the information must often be sent out in several chunks. In a multi-channel environment, this could be solved by assigning data with different priorities to different channels. This would require advanced mechanisms on the CP service and the Middleware's dynamic channel assignment decision levels.  
Using our proposed dynamic MCO model, complex scenarios can be implemented to evaluate the broadest scale of advanced channel selection algorithm variants, V2X application classification schemes, and methods to optimize ITS-G5 radio resource usage.  
Conceptualization, O.V. and L.B.; methodology, O.V. and L.B.; software, O.V.; validation, O.V.; original draft preparation, O.V. and L.B.; review and editing, O.V. and L.B.; supervision, L.B.; project administration, L.B.; funding acquisition, L.B. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the European Union project RRF-2.3.1-21-2022-00004 within the framework of the Hungarian Artificial Intelligence National Laboratory.  
Not applicable.  
The authors would like to sincerely thank [PERSON] for the helpful discussions, valuable comments, and support in Artery/OMNeT++. The authors also would like to thank [PERSON] from the Budapest University of Technology and Economics for his help and support.  
The authors declare no conflict of interest.
# One-Dimensional Convolutional Neural Network for Pipe Jacking EPB TBM Cutter Wear Prediction
## 5 EPB Cutting Tool Wear and Machine Parameters
#### Machine Parameters and Tool Wear Evaluation  
Disc cutter life was predicted using each operational parameter obtained from each ring of the shield tunnelling. According to [PERSON] and [PERSON] et al. [5; 48], the number of cutter changes was mostly applied in previous studies, whereas excavated volume per disc cutter wear extent (m\({}^{3}\)/mm) was only used in [46].  
In this research, disc cutter wear was recorded at the end of four excavation lengths, namely, 205 m, 344 m, 493 m, and 623 m. The cutter tools' wear amount was measured by a disc-measuring ruler and caliper on the tunnel site. The wear measurements of the disc cutters, reamer bits, block bits, and leading bits are shown in Figure 10a-d, respectively. However, in this study, only the disc cutters mounted on the EPB TBM cutter head were considered.  
The total wear amount of the disc cutter tools is shown in Table 4. According to Equation (1), the total amount of the wear value (\(M\)) was obtained as an arithmetic average of the total amount of wear at inspection points 205, 344, 493, and 623. The (\(M\)) value was calculated using Equation (2).  
\[M=\frac{1}{n}\sum_{i=1}^{n}a_{i} \tag{2}\]  
where \(M\) is the arithmetic average of the total amount of cutter wear, \(n\) is the number of values, and \(a_{i}\) is the total wear per ring number.  
Figure 10: (**a**) Disc cutter, (**b**) reamer bit, (**c**) block bit, and (**d**) leading bit wear measurement on site.  
Furthermore, the EPB TBM thrust force and torque fluctuation in the excavated tunnel length are shown in Figure 11a,b, respectively. Due to the fluctuation of advancement forces, cutter tools are exposed to unplanned wear. Moreover, an EPB TBM works in a hyperbaric pressure environment, so proper stability pressure in the cutter chamber is another critical parameter for tool life. Figure 12a-d show pressure measurement in pressure gauges 1-4, respectively. The rpm of the cutter head is important for the penetration rate, and the EPB TBM rpm change is presented in Figure 13. The machine parameters were exposed to fluctuation due to the engineering geology discussed in Section 4.
# A Data-Knowledge Hybrid Driven Method for Gas Turbine Gas Path Diagnosis
## 2 Framework of Data-Knowledge Hybrid Driven Method  
In order to support the whole gas path diagnosis process with task 1 and task 2, a data-knowledge hybrid driven method is proposed. The framework of the hybrid driven method is divided into three parts: the physical model-based GPA method, the ontology based model, and the virtual distance mapping algorithm, as shown in Figure 1.  
Figure 1: Framework of the data-knowledge hybrid driven method for gas path fault diagnosis.  
The physical model-based GPA method is used to extract the gas path fault features from the field data. The gas path fault mainly affects the aerodynamic characteristics of the compressor and turbine components. Correspondingly, the efficiency degradation and flow rate degradation in the compressor and turbine are selected as the gas path fault features. As shown in Figure 1, the physical model-based GPA method takes the field data \(\mathbf{x}\) and \(\mathbf{y}\) as the inputs, and takes the fault feature \(\mathbf{D}\) as the output. For a two-shaft gas turbine, the fault feature \(\mathbf{D}\) includes the flow degradation and efficiency degradation in the three components: compressor, high pressure turbine, and power turbine. The detailed description of the physical model-based GPA method can be seen in Section 3.1.  
The ontology based model effectively represents the heterogeneous fault knowledge based on a unified semantic expression. The gas path fault diagnosis ontologies are constructed with the knowledge from fault criteria analysis, physical structure analysis, FMECA, and the maintenance logic decision tool. The ontologies are encoded by the Web Ontology Language (OWL), which is recommended by W3C [26]. With the reasoning engine, the fault information (including fault mode, fault effect, fault criticality, etc.) is reasoned out quickly with the input of the identified feature criteria individual, and a proper maintenance strategy is finally selected. The fault information is ruled by Semantic Web Rule Language (SWRL) stored in the reasoning engine.  
The virtual distance mapping algorithm is used to connect the GPA model and the ontology model. The GPA result is mapped to the likelihood of the feature criteria individual. The reference fault features (\(\mathit{FF}_{\mathit{ref}}\)) are stored in the data property of the feature criteria individual. Firstly, the virtual distance mapping algorithm calculates the probability of each feature criteria individual according to the difference between GPA result and \(\mathit{FF}_{\mathit{ref}}\). The more similarity between the GPA result and the \(\mathit{FF}_{\mathit{ref}}\), the greater the probability. Then, the GPA result is mapped to a specific feature criteria individual, whose probability exceeds the threshold of 90%. Finally, the script of a mapping rule is generated according to the data property of the mapped feature criteria individual, and the subsequent fault reasoning is performed in the developed fault diagnosis ontology model.
## 5 Simulation Results
### DC Port External Fault  
The fourth considered event is an external fault, namely, a bolted pole-to-midpoint fault on port 6+, occurring at time 0.7 s. The corresponding voltage \(v_{6}\)+ and current \(i^{*}\)_load_6 are shown in Figure 12a, while port 6 voltage \(v_{6}\)- and current \(i^{-}\)_load_6 are shown in Figure 12b. Current \(i_{6}\)+ is shown in Figure 13; and port 5 voltages \(v_{5}\)+, \(v_{5}\)- are shown in Figure 14a and 14b, respectively. It can be seen that, even though an uncontrolled fault current is experienced from the line/load, the current supplied by the converter remains  
Figure 11: Port 5: (**a**) voltage \(v_{5}\)+ reference (black) and actual value (red), and (**b**) voltage \(v_{5}\)– reference (black) and actual value (red) during main grid out of service and reconnection.  
Figure 10: (**a**) internal bus positive pole voltage reference (black) and actual value (red); and (**b**) internal bus negative pole voltage reference (black) and actual value (red) during main grid out of service and reconnection.  
Figure 9: Sources powers during main grid out of service and reconnection: main grid (black), lithium batteries (red).  
controlled during the whole fault and is correctly limited to a predetermined value. Furthermore, the healthy half of the faulted port and the other healthy port voltages are unaffected by the fault: this is again consistent with Section 4, in that there is no reason for those ports to lose the sliding condition.  
As per the presented results, the considered MC exhibits robustness properties with respect to external faults, including power supply failure/perturbations, in that (4) and (10) ensure that current controllability is maintained. The presented results reinforce the consideration that the considered MC topology is particularly suited for centralized architectures, where the possibility to have a protected internal DC bus reduces the probability of internal faults. In this case, it is possible to take advantage of the external fault insensitivity of the proposed converter, which can ensure fault clearance in cases of both pole-to-pole and pole-to-earth faults.
## 3 Underwater Routing Protocols
### Umdr  
The authors in [68] used the concept of space reuse and designed a routing protocol for ad hoc UWSNs. In space reuse, the coverage area is divided into different sectors, and nodes in each sector communicate with one another using directional antennas. This controls the redundant packet transmission, which in turn, controls energy consumption and interference. Furthermore, data packets are forwarded towards the desired destination directly without broadcasting, which reduces end-to-end delay. However, directional antennas require next-hop information and the antenna information of the next hop during every transmission. This introduces computational complexity to the routing process.
## 2 Material and Methods
### Non-Linear Regression  
Non-linear regression, a statistical technique, is employed to model intricate relationships between variables that elude capture by linear models. Despite the challenges inherent in model selection and estimation, its versatility in capturing diverse patterns in data is invaluable for understanding complex relationships.  
Non-linear regression analysis is a statistical method used to predict the value of a dependent variable based on the value of an independent variable. It assesses the association between variables by examining the coefficient values of the relevant parameters. This analysis employs a mathematical equation that characterizes the line or curve representing the best fit for the relationship between the dependent variable (Y) and the independent variable (X). The coefficient of determination, denoted as R\({}^{2}\), signifies the extent of variability in Y attributed to X.  
As a foundation for approximating the relationship between bollard pull and the other output parameters, power functions are selected. This phrase is commonly used as power function that given in Equation (1) [47]:  
\[Y=a\ X^{b}+E_{i}\, \tag{1}\]  
where \(a\) and \(b\) represent the random coefficients, and the random errors are represented by \(E_{i}\).  
R-squared, the coefficient of determination can be used as [48].  
\[R^{2}=1-\frac{\sum_{i=1}^{N}(t_{i}-o_{i})^{2}}{\sum_{i=1}^{N}(t_{i}-\overline{ o_{i}})^{2}}\, \tag{2}\]  
where \(t\) represents the goal value, \(o\) represents the output, \(\overline{\delta}\) is the average value of the samples, and \(N\) is the number of samples.
# Bioactivity and Delivery Strategies of Phytochemical Compounds in Bone Tissue Regeneration
## 4 Bioactive Phytochemicals and Bone Signaling Pathways  
Phyto-bioactive compounds (i.e., polyphenic compounds, carotenoids, tocopherols, and phytosterols) are defined as natural secondary metabolites available in fruits, vegetables, grains, and other plant-based foods which provide health benefits and reduce the risk of major chronic diseases [50; 51; 52; 53]. In addition to health benefits, bioactive molecules of natural origin are being used as prominent alternatives to chemical preservatives and additives [54; 55; 56] as well as in the green synthesis of nanomaterials (i.e., graphene nanosheets, gold nanoparticles, etc.) [57; 58; 59; 60; 61]. In bone regenerative medicine, their antioxidant and anti-inflammatory beneficial properties can regulate bone regeneration signaling pathways,offering an innovative potential therapeutic strategy [62]. Notably, phytochemicals target several critical molecular pathways involved in bone metabolism, such as estrogen signaling pathway, MAPK cascade, Wnt/\(\beta\)-catenin, sirtuin 1 (Sirt1), TGF-\(\beta\)/BMP, PI3K/Akt, and adenosine monophosphate protein kinase (AMPK) [63]. These pathways can be split into three main classes based on their activity: anti-inflammatory, antioxidants, and bone cell differentiation activity (Figure 1).