esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Overview of Routing Protocols
### Proactive Routing Protocols
RREQ packets contains the source identifier (SId), destination identifier (DId), source sequence number (SSeq), destination sequence number (DSeq), broadcast identifier (BId) and TTL fields. The node also maintains a timer associated with every entry in order to delete an RREQ packet if the reply is not received before it expires. When an intermediate node receives an RREQ packet, it either forwards it or prepares a route reply RREP packet if it has a valid route to the destination in its cache. Figure 3: DSR route maintenance. The availability of a route at the intermediate node is determined by comparing the sequence number at the intermediate node with the destination sequence number in the \"RREQ\". Any duplicates in the \"RREQ\" are eliminated by intermediate node if the \"Sid\" and \"Bid\" of the \"RREQ\" packets are the same. Whenever an \"RREP\" packet is received by a node, it stores the information of the previous node in order to forward the packet to it as the next hop towards the destination; this represents a \"forward pointer\" to the destination node. According to these procedures, it is clearly perceived that each node maintains only the next hop information, unlike source routing, in which all the intermediate nodes on the route towards the destination are stored. The route maintenance phase diagram is simply shown in Figure 5, where all nodes that are active in the network periodically transmit hello messages that are special \"RREP\" messages. Thus, when one node does not receive a hello message from the neighbor node due to a lost connection between them, they adjust their routing table by deleting that path. Nodes also send \"RRER\" packets to the other neighbor nodes that are using that path, so that each node keeps a list of all the active surrounding nodes in each communication. However, AODV is restricted in which intermediate nodes can lead to inconsistent routes if the source sequence number is old and the intermediate nodes do not have the latest destination sequence number, thereby causing stale entries as shown in [18]. In addition, the periodic hello messages may lead to unnecessary bandwidth utilization. Figure 4: AODV route discovery. Figure 5: AODV route maintenance. Usually, reactive routing protocols outperform proactive protocols in highly congested networks because periodic routing tables updates are not required; thus the number of routing packets in the network is minimized. On the other hand, multiple route caches feature in DSR protocol, which may result in higher routing load; this may impact the protocol behavior when end-to-end delay and packet delivery fraction metrics are examined in highly loaded networks. As in [19], we can end this overview section by introducing Table 1, which briefly illustrates the major features for DSR and AODV reactive routing protocols, which can enhance the analysis of the protocols' behaviors in the network simulation.
## 2 Materials and Methods  
### Biological Material  
Fungal pathogens were previously isolated and identified by [PERSON] et al., 2020 [17]. Briefly, _Botrytis cinerea_ 62 BCV and _Fusarium brachygibbsum_ 4 BF were isolated from decaying strawberry fruits and _Alternaria alternata_ 1A was isolated from decaying blueberry fruits. Genomic DNA of the fungal strains was extracted using [PERSON]'s 2004 protocol [18] followed by PCR analysis to amplify the internal transcribed spacer (ITS) regions with the primers ITSS (5\({}^{\prime}\)-GGAAGTAAAAGTCGTAACAAGG-3\({}^{\prime}\)) and ITS4 (5\({}^{\prime}\)-TCCTCCGCTTATTGATATGC-3\({}^{\prime}\)). The amplified regions were sequenced at Macrogen, Seoul, Korea. Homology blast analysis was performed and sequences were deposited in GenBank (accession numbers: MN365049.1, MN365015.1, MK881030.1).  
The plant-growth-promoting bacteria used in this study belong to our endophytic and rhizospheric bacterial collection. UM270 strain was isolated and characterized by [PERSON] et al., 2015 [14], COPE52 by [PERSON] et al., 2019 [16], and E25 and CR71 by [PERSON] et al., 2018 [12]. DNA of all the bacterial strains was extracted, and PCR analysis was performed to amplify the RNAr _165_ gene using the primers FD1 (5\({}^{\prime}\)-CAGAGTTTTGATCCTGGCTC-3\({}^{\prime}\)) and RD1 (5\({}^{\prime}\)-AAGAGGTGATCC-3\({}^{\prime}\)). To determine the taxonomic affiliation, BLAST analysis was performed and sequences were deposited in GenBank (accession numbers: KJ801568.1, CP031292.1, CP031749.1, CP031748.1).  
In Vitro Evaluation of the Antagonistic Effects of Diffusible and Volatile Compounds Produced by Bacteria  
The antagonism of compounds produced by plant-growth-promoting bacteria against the fungal pathogens was evaluated in bioassays performed in Petri dishes as previously reported [12]. In brief, the bacterial strains (_Pseudomonas fluorescens_ UM270, _Bacillus toyonensis_ COPE52, _Bacillus_ sp. E25, _B. thuringiensis_ CR71) were streaked in a cross shape on PDA plates; then, a 6 mm portion of the mycelium was deposited in the center of each formed quadrant on the plates. Subsequently, the mycelium growth was measured, and the inhibition percentage was calculated with the formula used by [PERSON] et al., 2015 [14], and described as follows: % of growth inhibition = [(Ac - Ab)/Ac] \(\times\) 100, where Ac is the control mycelial area and Ab is the mycelial area with treatment. To evaluate the antifungal effect of VOCs emitted by plant-growth-promoting bacteria, divided Petri dishes with PDA were used. A bacterial inoculum of each strain (100 uL O.D. 1) was deposited on one side of the Petri dish, and the 6 mm plug of the pathogenic fungi mycelium was inoculated on the other side of the plate. Subsequently, the mycelium growth was measured [14]. Both experiments (diffusible and volatile compounds) were independently performed three times.
## 3 Results and Discussion
### Visualisation of Bubble Characteristics
This indicated that the surface remained in contact with the bulk liquid, and liquid replenishment was sufficient, helping to maintain a lower wall superheat consistent with the boiling curve (Figure 10). Moving to 2000 kW/m\({}^{2}\) (Figure 18a-c), bubbles detaching from the Staggered surface coalesced on top of the heating surface, forming distinct vapor columns. While not entirely film boiling, these columns almost covered the surface, significantly hindering direct liquid contact and replenishment to the heated area due to the lack of separate vapor-liquid pathways provided by the interconnected channels on the heat transfer surface. In contrast, bubbles on the Large Lattice and Small Lattice surfaces were still observed coalescing after departure, forming several unstable vapor columns. This indicates that, on the heating surface, the vapor is segmented by the lattice structure, effectively suppressing the transition to film boiling. Figure 19a-c shows the boiling behavior leading to CHF and the state of the surfaces at very high heat fluxes. Figure 19a shows the Staggered surface at its CHF, with the  
entire heat transfer surface completely covered by a stable vapor film, leading to the sudden surge in wall superheat characteristic of CHF. Figure 19a,b show the Large Lattice and Small Lattice surfaces, respectively, just before the experiment was stopped due to overheating of the copper block. Bubbles coalesced into large mushroom-like clouds on these lattice surfaces that nearly covered the entire heat transfer surface. However, the visuals showed rapid bubble nucleation, growth, and expulsion cycles. This dynamic behavior, combined with adequate liquid replenishment via capillary action, kept the heat transfer surface intermittently wetted, preventing the formation of a stable vapor film and, thus, suppressing the typical CHF phenomenon. Figure 19b,c, visually confirm the absence of a vapor blanket and the sustained, vigorous nucleate boiling, even as the heat flux, supporting the concept of an extended stable boiling regime.
## 5 Conclusions, Limitations, and Outlook
There are a wide array of demand forecasting techniques and algorithms that could potentially produce different outcomes when contrasted with the ETR approach. Consequently, this study might not provide a comprehensive understanding of the optimal model for our specific use case. Thirdly, as previously mentioned, designing a deep learning model involves a greater number of hyperparameters and diverse network structures. While we made efforts to identify the most suitable parameters using random search techniques, it is possible that this research did not cover the entire hyperparameter space and network structure. Future research could compare tree-based ensembles and deep learning approaches, like LSTM, based on different time horizons, focusing on short-term, medium-term, and long-term demand predictions. In a previous publication, we conducted store clustering before training and testing demand prediction models [54]. In this context, it would be worthwhile to investigate the differences in performance between ETR and LSTM in clusters of differently behaving retail stores, which represents an interesting opportunity for future research. Furthermore, although ETR models generate important feature metrics to enhance interpretability, this aspect is not thoroughly examined in the excerpt. Subsequent studies could delve further into this matter, potentially yielding valuable insights for future analysis. Conceptualization, M.N., T.F., F.D. and P.B. ; methodology, M.N. and T.F. ; data curation, F.D. and M.N. ; data analysis M.N. and T.F. ; validation, M.N., T.F. and P.B. ; literature analysis, M.N., T.F., F.D. and P.B. ; writing--original draft preparation, T.F., M.N., P.B. and F.D. ; writing--review and editing, P.B., M.N. and T.F. ; supervision, P.B. ; project administration, P.B. ; funding acquisition, P.B. All authors have read and agreed to the published version of the manuscript. This research was funded by the Christian Doppler Research Association as part of the Josef Ressel Centre for Predictive Value Network Intelligence (PREVAIL). The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to the wish of the company providing the data. The authors declare no conflict of interest.
# Designation of the Quality of EGNOS+SDCM Satellite Positioning in the Approach to Landing Procedure
## 2 Research Method  
This section presents the algorithm of the mathematical model for the determination of the resultant coordinates of aerial vehicles from the Multi-SBAS solution. Additionally, the modified equations for the determination of the quality parameters of SBAS positioning in aviation are presented.
# Prophet-CEEMDAN-ARBiLSTM-Based Model for Short-Term Load Forecasting
## 2 Method
### Ceemdan  
Both EMD and EEMD typically incur significant computational costs. To address this issue, [PERSON] et al. [35] introduced the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method, which mitigates these computational challenges and enhances the completeness of the resulting sequences. The steps of CEEMDAN are as follows:  
1. Incorporate the finite number of the adaptive Gaussian white noise \(n_{i}(t)\) to improve the original sequence \(x(t)\), and it is expressed as: \[X_{i}(t)=x(t)+\omega_{0}\cdot n_{i}(t),\] (4) where \(X_{i}(t)\) denotes the sequence data resulting from the \(i\)-th addition of Gaussian white noise, and \(\omega_{0}\) denotes the noise coefficient.
2. Perform EMD decomposition on \(X_{i}(t)\) with mean values for the first Intrinsic Mode Function (IMF) component \(c_{i1}(t)\), and it is expressed as: \[IMF_{1}=\frac{1}{M}{\sum_{i=1}^{M}c_{i1}},\] (5) where \(c_{i1}\) denotes the first IMF resulting from the \(i\)th addition of Gaussian white noise decomposition, and \(M\) denotes the total amount of the noise additions.  
Afterwards, CEEMDAN subtracts \(IMF_{1}\) from the input data \(x(t)\) to obtain the first residual sequence in the following: \[r_{1}=x(t)-IMF_{1},\] (6)
3. Continue the EMD decomposition for \(r_{1}(t)+\omega_{1}E_{1}[n_{i}(t)]\) to obtain the second IMF component, and it is expressed as: \[IMF_{2}=\frac{1}{M}{\sum_{i=1}^{M}E_{1}\{r_{1}(t)+\omega_{1}E_{1}[n_{i}(t)]\}},\] (7) where \(Ej(.)\) represents the \(j\)th IMF component.
4. Iterate the following steps to acquire the remaining IMF components, as follows: \[\left\{\begin{matrix}r_{k}(t)=r_{k-1}(t)-IMF_{k}\\ IMF_{k+1}=\frac{1}{M}{\sum_{i=1}^{M}E_{1}\{r_{k}(t)+\omega_{k}E_{k}[n_{i}(t)] \}}\end{matrix}\right.,\] (8) where \(k\) represents the overall quantity of IMF components. The final residual sequence is expressed as: \[\text{res}=x(t)-{\sum_{i=1}^{k}IMF_{i}}.\] (9)
## 3 Results and Discussion
### Characterization of the Prepared SPME
#### 3.1.5 Comparison between Synthesized DVB/Carbon/PDMS and Commercial DVB/CAR/PDMS Coatings  
The selection of commercial DVB/CAR/PDMS as the comparative object takes many factors into account. Our previous research found that DVB/CAR/PDMS has a competitive edge over DVB/PDMS, PDMS, CAR/PDMS and PA in that DVB/CAR/PDMS fiber has a better extraction effect in the quantification of odorants. Specifically, its extraction rate is ten times higher than that of PA [27].  
The prepared DVB/Carbon/PDMS and commercial DVB/CAR/PDMS were compared in their extraction effects on VOCs from leachate. The total ion chromatogram is  
Figure 3: SEM micrographs of (**a**) bare fiber, (**b**) prepared fiber at 400\(\times\), (**c**) prepared fiber at 2000\(\times\), and cross-section of the prepared fiber at (**d**) 400\(\times\) and (**e**) 2000\(\times\).  
shown in Figure S2. Under different experimental conditions, the extraction efficiency was evaluated by the total peak area (TA), sulfide peak area (SA) and the number of volatile organic compounds (TN) obtained from total ion chromatography. The TA and SA of the self-prepared DVB/Carbon/PDMS were 1.69 and 1.80 times higher than that of the commercial DVB/CAR/PDMS, respectively, which means that the extraction efficiency of self-made SPME coatings is higher than that of the commercial one. Furthermore, 91 VOCs were identified by GC-MS, and their name, formula, CAS number, KI value and peak area are listed in Table S1. The peak areas obtained by the self-prepared SPME fiber are larger than by the commercial ones, indicating a higher extraction efficacy of self-made DVB/Carbon/PDMS. Compounds dimethyl trisulfide, dimethyl disulfide, p-cresol, phenol and 3-methylindole are VOCs with high concentrations and are regarded as major hazardous materials in terms of malador. They are seen as typical VOCs and are further quantified in Section 3.3. Their corresponding MS spectra are provided in Figures S3-S7.
# Numerical Evaluation of Structural Safety for Aged Onshore Wind Foundation to Extend Service Life
## 3 Structural Safety Evaluation for Aged Wind Foundation  
### Concrete Material Test  
In order to evaluate concrete material properties, destructive testing by core drilling as well as non-destructive testing by Schmidt hammer was carried out. However, the concrete properties obtained by destructive testing were judged to be more accurate, and this property was used in structural safety evaluation of this paper. Alternatively, if concrete core drillings are not available in the practical problem, the properties by non-destructive testing may be used.  
First, concrete cores were collected for the foundation of an in-service wind farm located in Jeju of South Korea. A concrete core was collected for the foundation where the turbine was demolished due to some recent problem. In the same wind farm, multiple turbines with the same turbine and foundation are still in service. By using these concrete cores, concrete material safety including compressive strength, modulus of elasticity, and ultrasonic measurement were evaluated, as presented in Figure 5. Three concrete cores were collected for each unit of the two foundations, having the same site, the same service life, and the same wind turbine. The material safety in terms of strength was evaluated using the average of the values measured from these cores. As a result of the material safety evaluation, it was found that the concrete compressive strength (\(\mathrm{f_{m}}\)) of the foundation was maintained at a materially sound state, such as 39.3 to 41.9 MPa, which exceeded the design strength (\(\mathrm{f_{d}}\)) of 30 MPa, as indicated in Table 2. The material safety of concrete was evaluated as follows, and it was found that the concrete strength was 1.35 times higher than the design strength.  
\[\mathrm{R_{m}}=\mathrm{f_{m}}/\mathrm{f_{d}} \tag{1}\]  
At this time, the concrete compressive strength by the destructive test (\(\mathrm{f_{m}}\)) was utilized as a concrete material property in the ultimate strength evaluation by numerical analysis in Section 3.2, so that the structural safety of the foundation reflecting the in-situ concrete condition could be evaluated [3].
## 2 Materials and Methods
### Experimental Design  
#### 2.3.1 Characterising Sentinel-2 Sub-Pixel Variability  
We quantified the average sub-pixel variability of the Sentinel-2 dataset using the temporally and spatially corresponding multispectral high-resolution UAV data. Using QGIS (v.2.18), we first generated a grid of \(20\times 20\) m cells by vectorising the 20-m resolution Sentinel-2 705 nm red-edge band data that was subset to match the extent of the UAV coverages over the field sites for each observation date. For each of the six observation dates we then calculated the red-edge chlorophyll index (CI\({}_{\text{red-edge}}\); [40]) for the UAV and Sentinel-2 datasets:  
\[\text{CI}_{\text{red-edge}}=\frac{R_{783}}{R_{705}}-1 \tag{2}\]  
where \(R_{x}\) refers to the reflectance measured by either Sentinel-2 or the UAV sensor at wavelength \(x\) nm. Using the CI\({}_{\text{red-edge}}\) in this analysis allowed us to distinguish between areas of low and high vegetation areas. Using the 20-m vector grid, we sampled the variability of UAV CI\({}_{\text{red-edge}}\) data for all of the overlapping Sentinel-2 pixels (Figure 2).  
For each farm and observation date, summary statistics quantifying the differences between the two sensors were reported. These metrics included the mean standard deviation and coefficient of variation (CV), calculated as a ratio of the standard deviation to the mean CI\({}_{\text{red-edge}}\), along with the coefficient of determination (R\({}^{2}\)) and normalised root-mean-square error (NRMSE) statistic describing the fit comparing the Sentinel-2 to UAV values, expressed as:  
\[NRMSE=\frac{\left[\sqrt{\frac{\sum_{i=1}^{n}(E_{i}-O_{i})^{2}}{n}}\right]}{ \left[\text{max}(O)-\text{min}(O)\right]} \tag{3}\]where \(E_{i}\) and \(O_{i}\) represent the Sentinel-2 and UAV CIred-edge values, respectively. \(n\) is the number of 20-m vector grid cells included in the comparison between the two sensors.
## 2 Materials and Methods
### Flood Vulnerability Index Mapping  
Before collecting the criterion maps in a GIS environment, the variables need to be transformed into standard units and integrated into a spatial database. This is because they are represented by different dimensional scales (e.g., percentages and density). The criteria of the vulnerability model were standardised using a linear fuzzy set membership function. In this function, vulnerability varies linearly between values ranging from 0 (no vulnerability) to 1 (total vulnerability). The standardisation functions under consideration are presented in [PERSON] et al. [29]. For example, a decreasing function was applied to the percentage of residents aged between 14 and 65 years. These residents have a lower vulnerability than the residents who are less than 14 years old or more than 65 years old, and the higher vulnerability applies when the group has a lower percentage of people between 14 and 64 years old. For residents over 65 and over 14, who are theoretically the most vulnerable, the function increases.  
Several sub-criteria and criteria maps have been calculated according to the hierarchical structure: (i) at level 4, 21 sub-criteria (P11, P12, P13, P21, P22, P31, P32, S11, S12, S13, S21, S22, B11, B12, B21, B22, B31, B32, E11, E12, and E13), (ii) at level 3, 14 sub-criteria (P1, P2, P3, S1, S2, S3, S4, B1, B2, B3, B4, E1, E2, and E3); (iii) at level 2, 4 criteria (P, B, S, and E) and the vulnerability map at the target level.  
The flood vulnerability maps are a planning tool. They are essential for flood risk assessment and management by decision-makers. The map algebra with weighted overlay was used to flood vulnerability mapping. The standardised criteria maps were multiplied by the final weights from the AHP-GDM (Section 2.6) and successively summed bottom-up in the hierarchical structure to produce flood vulnerability maps using GIS-AHP-GDM.  
All spatial data geoprocessing tasks for map production were developed with the ArcGIS software 10.5.1 package.  
Finally, the standard deviation classification method shows how much a neighbourhood's flood vulnerability value varies from the mean. This method forms each class by adding and subtracting the standard deviation from the mean of the flood vulnerability values dataset. The resulting vulnerability values at the neighbourhood scale were classified, respectively, into five flood vulnerability categories: (i) very low (mean \(-\) 2 std dev); (ii) low (mean \(-\) 1 std dev); (iii) medium (mean); (iv) high (mean + 1 std dev); and (v) very high (mean + 2 std dev).
# Numerical Simulation of the Kelvin Wake Patterns
## 5 Discussions of Kelvin Wake Angle
### The Effect of Source Strength on Wake Angle  
In this paper, different source strengths \(e^{\prime}=1.0,~{}1.5,~{}2.5\) are utilized to generate different wave patterns for the flow past Kelvin source with \(F^{\prime}=0.8\), as shown in Figure 9. It is well known that the Kelvin angle is \(19.47^{\circ}\). Comparing the wake angles with the Kelvin angle, the wake angle is slightly smaller than the Kelvin angle in Figure 9a. However, the Kelvin angle in Figure 9c is larger than the Kelvin angle. Although the wake angle change is not significant, the wake angle is tending to slowly increase with the source strength. In other words, the wake angle is tending to slowly increase with the ship size.  
Meanwhile, Cartesian coordinates of \(\theta\) and \(\epsilon^{\prime}\) are established by keeping Froude number and source type invariant and adjusting source strength, obtaining two sets of data with \(F^{\prime}=0.8\). and \(F^{\prime}=1.2\), shown in Figure 10. The source strength \(e^{\prime}\). ranges from 1.0 to 3.0 and the wake angle increases with the source strength. However, the two sets of data show slightly different trends. With \(F^{\prime}=0.8\), the wake angle increases at an almost steady rate with source strength; with \(F^{\prime}=1.2\), the wake angle barely changes as source strength increases at first, then increases at a steady rate after source strength reaching 1.8. In a word, compared with Froude number, although the source strength cannot greatly affect the wake angle, it is clear that the wake angle increases with source strength.  
Figure 8: A plot of apparent wake angle \(\theta\) against Froude number \(F^{\prime}\). for a flow past the Rankine source, with the source strength \(\mu^{\prime}=1.0\) (the red circles) and \(\mu^{\prime}=0.5\) (the blue circles). The black solid lines are the trendlines of the wake angles with Froude numbers.  
Figure 9: Perspectives and plans view of the free surfaces past Kelvin source, computed for the cases \(F^{\prime}=0.8\): (**a**) \(\epsilon^{\prime}=1.0\), (**b**) \(\epsilon^{\prime}=1.5\), (**c**) \(\epsilon^{\prime}=2.5\). The top three free surface patterns are the perspectives and the bottom three free surface patterns are the plans. The red line marks the edge of the wake angle in each plan view.