esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Delamination Factor  
Various types of discontinuity can occur due to the drilling process in composite materials, which include delamination, cracks, spalling, and burr. Delamination is the separation of composite layers because of the failure of the interplay resin interface. Some parts of the outer layers of the composite peel off from the laminate surface to produce spalling. Burr is uncut groups of fibers protruding to the interior of the hole. These types of defects are illustrated in Figure 3a.  
In previous studies, various researchers have utilized similar parameters for quantifying and measuring the delamination regions around a drilled hole. [PERSON] [24] introduced a one-dimensional delamination factor (\(F_{d}\)) for quantifying the extent of delamination caused by the drilling of laminated composites, which is defined as the ratio of the maximum diameter of the delamination region (\(D_{max}\)) to the nominal diameter of the drilled hole (\(D_{nom}\)), as shown in Figure 3b. [PERSON] et al. [25] suggested a two-dimensional delamination factor (\(F_{a}\)), which is defined as the ratio of the nominal area of the hole (\(A_{del}\)) to the delamination area around the hole (\(A_{nom}\)). \(F_{d}\) and \(F_{a}\) are obtained using Equations (1) and (2), respectively.  
\[F_{d}=\frac{D_{max}}{D_{nom}} \tag{1}\]  
\[F_{a}=\frac{A_{del}}{A_{nom}} \tag{2}\]  
These factors provide information about the extent of the damaged area regardless of the shape of the delamination and therefore, do not indicate how the delamination is distributed around the hole area. The shape factor (\(SF\)) is another delamination factor that can be used to give further important information related to the damaged areas by considering the location associated with the maximum diameter of the delamination region (\(A_{max}\)). \(SF\) is obtained using Equation (3).  
\[SF=\frac{A_{del}}{A_{max}-A_{nom}}\times 100 \tag{3}\]  
The \(SF\) tends to be zero when the delamination is strongly directional and, conversely, 100 when it is uniformly distributed. In the next section, these factors will be used to quantify the overall quality of the experiments.  
Figure 3: (a) Types of defects in drilling area. (b) Schematic of delamination factor.
## 4 Results
### Simulation Validation
#### 4.2.2 The Parameters of the Traffic Flow Model  
In order to verify the parameters of the traffic flow model, three types of vehicles differed by their dynamics. The parameter settings of vehicles are shown in Table 2. After collecting data on average speed, flow, density, and other metrics for each segment, the data were processed to create a scatter plot. The scatter plot illustrating the average speed, flow, and density was then fitted to demonstrate the relationship among these variables. This is shown in Figure 3, which describes the relationship between speed, flow, and density in a heterogeneous MAS composed of multiple types of CAVs. The points in the figure represent traffic data generated by the simulation platform, while the curve is obtained by fitting these simulation data. It is worth noting that the parameter relationship conforms to the function of the Van Aerde model. The parameters of the heterogeneous are as follows: \(c_{1}=0.0027\), \(c_{2}=0.5726\), \(c_{3}=0.0\), \(v_{f}=97.02\) km/h.
## 2 Experimental Methodology  
### Sample Description  
Since siltstone is one of the primary rocks in many shale formations, and both siltstone and shale have similar mineral compositions and pore structures [25], siltstone sample was used in this study as an alternative to shale samples to investigate the CO\({}_{2}\) flow behavior in shale formations. The siltstone outcrop sample was collected from the Eidsvold formation, Queensland, Australia, and the total porosity of the dry siltstone measured using the mercury intrusion method was around 19.5%, which is higher than that of most shale, but still available in some shale formations [26], and the bulk density was around 2.24 g/mL. The pore size can be calculated by measuring the pressure and volume of injected mercury [27], and the pore distribution of the tested sample is plotted in Figure 1. This siltstone has very tiny pores, and the median pore diameter is around 625 nm and 17.2 nm in terms of pore volume and area, respectively. This pore structure was then validated by performing a CT scanning test on a sample with a diameter of 13 mm, as CT scanning can offer non-destructive three-dimensional imaging of the interior structure (see Figures 2 and 3). The porosity obtained through CT testing was 15.4% calculated based on the watershed algorithm [28; 29], which is slightly lower than the value obtained from mercury intrusion testing. There are many pores with varying sizes in the siltstone, although only a limited number of pores are of the order of magnitude of 100 \(\upmu\)m and most of the pores are of the order of 1 \(\upmu\)m (see Figure 2). 3-D CT images can be reconstructed by superimposing CT slices and only show the pore structure (see Figure 3), where tiny pores are evenly distributed and connected with each other through small pore throats. Regarding the mineral composition of the tested siltstone, according to XRD analysis, the tested dry siltstone is mainly composed of quartz (43%) and kaolinite (40%), and the tiny kaolinite particles (<2 \(\upmu\)m) separate quartz-quartz particle contacts and segment the space formed by quartz particle skeletons [30]. As a result, the high clay content in the tested siltstone sample contributes significantly to this pore network [31; 32]. This overall pore structure governs the low permeability of this sedimentary siltstone [33].  
Figure 1: Pore size distribution defined by incremental pore volume and pore area [24].  
Figure 3: Reconstructed 3-D CT images of pore structure with sample size of 6 mm \(\times\) 7 mm \(\times\) 7 mm (pores are shown in blue).  
Figure 2: CT images of cylindrical siltstone sample with diameter of 13.0 mm and height of 13.5 mm (black cavities represent pores).
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 1 Introduction
### Taxonomy and Requirements
#### 1.2.3 Data Rates  
Due to the great diversity of the applications in WBAN systems, data rates differ greatly, ranging from low data rate sensors focused mainly on on-body monitoring at a few kbps to high data rate systems designed for multimedia data streams of several Mbps [52]. Information may also be transmitted in bursts, though this way of transmitting information is not considered energy efficient due to the fact that burst transmission sends out very high data transmission rate with very short transmission durations. In medical applications, the reliability of the WBAN systems also depends on the employed data rates as low data rate devices are able to manage high BER environments, whereas, devices with higher data rates are most suitable to be used in lower BER conditions [53].
# Formal Algebraic Model of an Edge Data Center with a Redundant Ring Topology
## 5 Redundant Ring Topology  
In view of that there are one physical host located in each of the corners of the field, the easiest way to link them through a wired infrastructure is by deploying a physical ring passing along each of the nodes. This way, every one of the \(n\) nodes being part of the ring will have just a predecessor node and a successor node, both being only one hop away but in different directions, whilst the remaining node will be two hops away, which may be seen as its opposite node, regardless the path going through any of its two neighbors, thus accounting for a pair of redundant paths to go from node \(i\) to its opposite one.  
The more straightforward way to label those \(n\) nodes is by using modular arithmetic, where the \(n\) nodes are assigned a single value within the interval \([0\cdots n-1]\), where one of them is selected to be node \(0\) and the rest of nodes go in a sequential order along the same direction. Hence, according to modular arithmetic, a given node \(i\) will have node \((i-1)_{|n}\) as its predecessor neighbor, whereas it will have node \((i+1)_{|n}\) as its successor neighbor, whilst the opposite node will be labeled as \((i+2)_{|n}\).  
One of the advantages of implementing a ring is that there are always two redundant paths to go from a source node to a destination node, which basically avoids the existence of single points of failure. However, if one of the links go down, then the ring shape is converted into a bus shape, where its ends are just the nodes sharing the broken link. Hence, as those nodes do not share a direct link any more, such that the path between them needs to be conducted by going all around the ring, thus passing through the rest of the nodes, it might induce latency issues when dealing with critical infrastructures.  
Therefore, a common solution in production environments is to deploy redundant rings, thus having a couple of links between a particular node and its clockwise neighbor and another couple towards its counterclockwise neighbor. Figure 3 depicts a redundant ring topology with \(n\) items, where it may be appreciated its shape of a toroidal array, as the elements on both ends are linked together, which may also be compared with the shape of a bead collar where the diverse nodes may be considered as its beads.  
Regarding the port nomenclature, Figure 4 exposes its layout, where all ports are organized in a clockwise manner. This way, ports \(0\) and \(1\) are pointing to the successor node, whilst ports \(2\) and \(3\) are heading for the predecessor node.  
Figure 4: Port layout of a redundant ring node.  
Figure 3: Redundant ring topology with \(n\) elements.
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 2 Watermark System Model on 5G NR
### Basic Watermarking Scheme  
The basic watermarking scheme is discussed: the basic watermark generation scheme, embedding scheme, extraction, and authentication scheme are introduced. For the basic watermarking scheme, the watermark generation and embedding schemes were first proposed in [12], and we just apply these in the 5G NR here.
## 2 Materials and Methods
### Relationship between Phenology and the Maximum Allowable SLN  
Due to the big gap between satellite pixels (sub- to thousand-meter level) and plant leaves (up to decimeter level), it is hard to directly retrieve SLN by satellite remote sensing [54; 55]. Previous studies have demonstrated that the maximum allowable SLN is a function of crop phenology [43; 56], which is the base for simulating leaf nitrogen in several crop models, such as DSSAT-CERES [57], APSIM-Maize [58; 59], SWAP, and WOFOST [60]. This provides a way to obtain SLN information after crop phenology is quantified. For maize, the temperature plays a crucial role in controlling phenological development [61; 62]. The W-E model (Equations (1)-(4)) [63] was adopted to describe phenological development with daily mean air temperature data. To define the start of season (SOS) and the end of season (EOS) of maize, the method proposed by [PERSON] et al. [50] was used with the reconstructed NDVI time series data. Considering that photoperiod and soil conditions (e.g., water status) may change the phenological stage which completely relies onthe temperature [64; 65], a normalized maize phenological development factor of NMP (Equation (5)) was finally utilized to reflect maize's phenological development.  
\[f(T)=\frac{2(T-T_{base})^{\alpha}\big{(}T_{opt}-T_{base}\big{)}^{\alpha}-(T-T_{ base})^{2\alpha}}{\big{(}T_{opt}-T_{base}\big{)}^{2\alpha}},\ T_{base}\leq T\leq T_{up} \tag{1}\]  
\[f(T)=0,\ T>T_{up}\ or\ T<T_{base} \tag{2}\]  
\[\alpha=\frac{ln2}{\ln\big{[}\big{(}T_{up}-T_{base}\big{)}/\big{(}T_{opt}-T_{ base}\big{)}\big{]}} \tag{3}\]  
\[PD_{t}=\int_{sos}^{t}f(T)dt \tag{4}\]  
\[NMP_{t}=PD_{t}/PD_{EOS} \tag{5}\]  
where \(t\) is the growing date; \(T\) is the daily mean air temperature; \(T_{base}\), \(T_{opt}\), and \(T_{up}\) are the minimum, optimal, and maximum temperature for maize growth, respectively, and assumed as 8 \({}^{\circ}\)C, 28 \({}^{\circ}\)C, and 36 \({}^{\circ}\)C, respectively [66].  
The NMP and SLN data from the NE1, NE2, and NE3 sites were used to establish the statistical function between NMP and SLN. Subsequently, the SLN data of the RO1 site was derived by applying the established function after NMP was obtained using Equations (1)-(5).
## 5 Results and Discussions
### Comparative Analysis of Partly Visible Objects  
Figure 23 illustrates the mean depth extraction time (in milliseconds) for partially visible objects at occlusion level 1. The bounding box and the segmentation mask in CoRDE exhibit significantly higher processing times, especially for larger objects, such as buses and trucks. In contrast, the proposed CeRDE and GCRDE approaches demonstrate markedly faster performance. Notably, the bounding box in CeRDE and the segmentation mask in GCRDE stand out with the lowest computation times across all object classes, indicating their suitability for real-time applications.  
Figure 24 presents the accuracy (%) of depth extraction at occlusion level 1. The proposed GCRDE and CeRDE approaches outperform both the traditional PbyP and the CoRDE approaches. The segmentation mask in GCRDE achieves the highest accuracy across all object categories, exceeding 90% for the person class and maintaining strong performance with bus and truck. The performance gap between traditional and proposed approaches is especially wide for categories like bicycles and cars.  
Figure 23: This comparison shows mean depth extraction time (in milliseconds) at occlusion Levels 1 for the traditional approach: PbyP (Point-by-Point) and our proposed approaches: CoRDE (Complete Region Depth Extraction), CeRDE (Central Region Depth Extraction), and GCRDE (Grid Central Region Depth Extraction).  
Figure 22: This comparison shows depth extraction RMSE (in meters) at occlusion Levels 0 for the traditional approach: PbyP (Point-by-Point) and our proposed approaches: CoRDE (Complete Region Depth Extraction), CeRDE (Central Region Depth Extraction), and GCRDE (Grid Central Region Depth Extraction).  
The lowest RMSE values are provided in the CeRDE and GCRDE approaches as shown in Figure 25. The largest errors are observed in the bounding box in CoRDE and PbyP, indicating their vulnerability to noise and inaccurate estimations when objects are only partially visible.  
CeRDE and GCRDE approaches offer substantial improvements in both efficiency and accuracy over other approaches. CeRDE, in particular, consistently provides the best trade-off between speed, accuracy, and error reduction. These results validate the strength of the CeRDE approach in overcoming the challenges posed by occlusion.
## 3 Results and Discussion
### Flow Field and Particle Trajectories
To the best of our knowledge, only two previous applications of this method in particle dynamic research have been reported [20; 21]. However these studies investigated particle transport for 3-12 generations. For the efficient visualization of the content, Figure 5b,c compares one- and two-way particle deposition efficiencies for the 21 st generations in the rigid models. It can be seen that two-way coupling particles interact with the fluid pathways and have more reliable trajectories than the one-way particles. Finally the outstanding effect of moving walls in alveolar model has been investigated by Figure 5d relative to the similar two-way particle coupling in rigid one (Figure 5c). Figure 6 shows the local particle deposition patterns for 18-22 generations. The concentrated particle deposition observed there demonstrates that direct impaction is the dominant deposition mechanism for Reynolds numbers higher than \(\text{Re}_{\text{in}}\) = 0.13 (G20). Meanwhile, particle-particle interaction effects and local secondary flow effects become significant, leading to more evenly distributed particle deposition patterns, for values lower than \(\text{Re}_{\text{in}}\) = 0.13. Figure 4: _Cont._  
Figure 4: Fluid streamlines coloured by flow velocity magnitude for 18 and 21 generations of rigid and realistic models, respectively, for flow from left to right, (**a**) \(G=18\), \(t=2.5\) s, \(Re=0.55\), Rigid walls; (**b**) \(G=18\), \(t=2.5\) s, \(Re=0.55\), moving walls; (**c**) \(G=21\), \(t=2.5\) s, \(Re=0.06\), Rigid walls; (**d**) \(G=21\), \(t=2.5\) s, \(Re=0.06\), moving walls. Figure 5: Fluid streamlines which are coloured by flow velocity magnitude with particle trajectory coloured black for 21 st generation and orientation of [0, 0, \(-1\)] at different modelling conditions, (**a**) One-way coupling, rigid walls, \(d_{\rm p}=1\) μm, \(Re=0.06\); (**b**) One-way coupling, rigid walls, \(d_{\rm p}=5\) μm, \(Re=0.06\); (**c**) Two-way coupling, rigid walls, \(d_{\rm p}=5\) μm, \(Re=0.06\); (**d**) Two-way coupling, moving walls, \(d_{\rm p}=5\) μm, \(Re=0.06\). The major particle deposition mechanisms in current model lung airways from G18 to G22 can be summarized as:  
1. The direct inertial impaction causes deposition in the first rows. 2. The 3-D secondary flow effects cause wall deposition. 3. Particle-particle (elastic) contact forces cause wall deposition.
# Ensemble Voting-Based Multichannel EEG Classification in a Subject-Independent P300 Speller
## 4 Simulation Results
### Experimental Settings
The computations were performed using Python 3.7.3. The hardware used during the simulations was NVIDIA GeForce GT 650M together with the 2.6 GHz Quad-Core Intel Core i7 processor. The simulations were carried out for experimental EEG data (as described earlier) and in various settings of number of channels, viz., 8-channel EEG, 4-channel EEG, and single-channel EEG. In order to evaluate the performance of each classifier, the number of true positive (\(TP\)), true negative (\(TN\)), false positive (\(FP\)), and false negative (\(FN\)) predictions were calculated. The most commonly used metric for performance evaluation is classifier's accuracy, which is calculated as  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}. \tag{7}\]  
However, when working with unbalanced datasets, accuracy does not show the overfitting rate. If the dataset is not balanced, it would consist of 10 nontarget flashings and only 2 target flashings (as only one row and one column out of 12 rows and columns contain the chosen character). If the classifier identifies only nontarget EEG signals, but fails to classify the target flashings, there would be 10 correctly recognized nontarget components. However, in that case, there would be zero true positively recognized target class components. For this example, the accuracy would still be 85.71%, which seems quite good. However, the fact is that the classifier failed to identify all of the target peaks. In order to examine whether the target class was correctly recognized and the number of \(FN\) was low, the recall metric is calculated as  
\[\text{Recall}=\frac{TP}{TP+FN}. \tag{8}\]Precision value indicates an EEG signal labeled as positive (target response) is positive and is computed as  
\[\text{Precision}=\frac{TP}{TP+FP}. \tag{9}\]  
In our case, the data were not perfectly balanced. The number of nontarget classes exceeded the target classes, as the dataset comprised 60% of the nontarget class and 40% of the target class after the balancing. That is why the recall value was still considered. In order to see both characteristics of recall and precision metrics, the F-score was calculated as a harmonic mean  
\[\text{F-score}=\frac{2(\text{Precision}*\text{Recall})}{\text{Precision}+ \text{Recall}}. \tag{10}\]  
Thus, recall and F-score were mainly used for the performance evaluation.
# Dynamic UAV Inspection Boosted by Vehicle Collaboration Under Harsh Conditions in the IoT Realm
## 3 Problem Formulation
### Energy Consumption Model for Rotary-Wing UAV  
UAV energy consumption [44] generally consists of two main components: communication-related energy and propulsion energy. However, since the energy required for communication is comparatively small, and propulsion energy is essential for keeping the UAV aloft and supporting its movement, we primarily focus on propulsion energy consumption. The propulsion power consumption can be expressed using the following formula:  
\[\widetilde{P}(v_{t})=P_{\text{BLA}}\bigg{(}1+\frac{3v_{\text{taup}}^{2}}{U_{ \text{tip}}^{2}}\bigg{)}+P_{\text{IND}}\bigg{(}\sqrt{1+\frac{v_{\text{taup}}^{4} }{4v_{0}^{4}}}-\frac{v_{\text{taup}}^{2}}{2v_{0}^{2}}\bigg{)}^{\frac{1}{2}}+ \frac{1}{2}d_{0}\rho s_{0}A_{i}v_{\text{taup}}^{3} \tag{19}\]  
where \(P_{\text{BLA}}\) and \(P_{\text{IND}}\) denote the power consumed by the blade and induced power, respectively, when the UAV hovers. The UAV's speed at any given time \(t\) is represented as \(v_{\text{taup}}\). \(U_{\text{tip}}\) denotes the tip speed of the rotor blade; \(v_{0}\) is the mean rotor-induced velocity in hover; \(d_{0}\) and \(s_{0}\) are the fuselage drag ratio and rotor solidity, respectively; and \(\rho\) and \(A_{i}\) represent air density and rotor disc area, respectively.
## 4 A Hybrid-ISL System with Ranging and LoS Support
### PHY Support for Coarse and Fine Ranging
#### 4.3.2 Fine Range Measurement  
Fine range estimation, presented in Figure 6, is based on frequency diversity for phase disambiguation. Two radio waves with different wavelengths are used, which are noted wave--\(f_{1}\) and wave--\(f_{2}\) in the picture. The least common multiple of the two wavelengths gives the maximum unambiguous fine measurement range, \(R_{mx}\). Let us consider that the actual distance to be measured is \(R_{fine}\). This distance shall result in a frequency difference on a main and a secondary antenna, in \(\varphi_{1}\) and \(\varphi_{2}\) on wave--\(f_{1}\) and on wave --\(f_{2}\), respectively. The full cycles \(n_{1}\) and \(n_{2}\) on wave--\(f_{1}\) and wave--\(f_{2}\), respectively, can be computed by solving a simple equation, which yields the measurements:  
\[R_{fine}=\lambda_{1}\).\(n_{1}+\varphi_{1}\frac{\lambda_{1}}{2\pi}=\lambda_{2}\).\(n_{2}+\varphi_{2}\frac{\lambda_{2}}{2\pi} \tag{8}\]  
Figure 5: Coarse range measurement timing diagram.  
Figure 6: Fine range measurement using frequency diversity (for phase disambiguation).  
One can observe that in Equation (8) there are 2 unknowns, namely the full cycles \(n_{1}\) and \(n_{2}\), and just one equation. There are several approaches to obtain the solution, i.e., what is given in [23]. The solution in [23] discards that Equation (8) can be further constrained; the values of \(n_{1}\) and \(n_{2}\) are natural integer values. Thus, a simple search algorithm presented in Algorithm 1 suffices for a solution. A numerical example is given in Appendix C.  
By using frequency diversity to solve phase disambiguation, virtually a new wavelength is generated that is as follows [28]:  
\[\lambda_{v}=\bigg{|}\frac{\lambda_{1}\lambda_{2}}{\lambda_{1}-\lambda_{2}} \bigg{|}, \tag{9}\]  
while the distance (maximum range) covered without ambiguity is extended to the following:  
\[R_{max}=\frac{2}{\left|\frac{1}{\lambda_{1}}-\frac{1}{\lambda_{2}}\right|}. \tag{10}\]  
Let us consider two carrier frequencies \(f_{1}\) = 2050 MHz and \(f_{2}\) = 2150 MHz. Using these two frequencies, the virtual wavelength results in approximately 6.8 mm, while the maximum range that can be measured without ambiguity is R\({}_{\text{max}}\) is 6 m.  
```
Inputs:\(\varphi_{1}\) and \(\varphi_{2}\)--phase measurements; N--maximum number of cycles; \(\epsilon\)--threshold forn\({}_{2}\) = 0:N  n\({}_{1}\) = \(\left(\lambda_{2}*n_{2}+\left(\varphi_{1}-\varphi_{2}\right)/2\pi\right)/ \lambda_{1}\); if(abs(n\({}_{1}-\)round(n\({}_{1}\))) < \(\epsilon\)) break; endif; endfor; returnn\({}_{1}\) and n\({}_{2}\)
```  
**Algorithm 1** Search algorithm for solving full cycles in Equation (8)