esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Communication for Wireless Body Area Networks: Energy Aware Link Efficient Routing Approach
## 2 Related Work
The protocol follows multi-hop topology to send data from farthest node to BNC. Relay nodes are elected at the initial stage on the basis of cost function proposed by the authors. All other BSNs send their data to their designated relay nodes using time division multiple access (TDMA). The nodes with less energy than the threshold value forward critical data only. The protocol promotes the packet delivery and decreases the packet loss in the network. However, the selected relay nodes expend their energy very fast which decreases the overall network lifetime [32]. [PERSON] et al., in [33], proposed a routing protocol aiming to optimize energy consumption in WBANs. In this protocol, the authors placed additional fixed nodes to act as forwarders for other BNSs. They proposed route selection criteria based on transmission power and energy of intermediate BSNs, velocity vector of the receiving node, and distance from the BNC. The protocol performs well in terms of network lifetime. However, the strategy of use of additional relay nodes is uncomfortable for the patients. [PERSON] [34] introduced even energy-consumption and backside routing (EEBR) for WBANs. In this work, the authors placed BSNs on both front and backside of the patient body. This protocol addressed the issues pointed out in M-ATTEMPT routing protocol and provided communication coverage at the backside of the body. A cost function based on residual energy and number of hop-counts is proposed to select the route. The path with minimum standard deviation of cost function is selected for data delivery. However, the nodes placed on backside of the body experience high path loss because of not considering link efficiency for route selection. [PERSON] et al., proposed a next-hop selection algorithm [35] for WBANs. To balance the energy consumption, multi-hop communication strategy is exploited based on hop-counts and cost function. This algorithm performed well against the benchmark protocol. However, the use of buffer size in its cost function, for selection of next-hop causes delay in data transmission. [PERSON] et al. proposed a dual sink clustering approach in BAN (DSCB) [36] which uses two sinks. Each sink maintains its own cluster to avoid contention in the network. The BSNs send data to their designated sink only. The route is chosen on the basis of the cost function which is composed of energy, distance, and transmission power. Signal to noise ratio (SNR)is used to compute the required transmission power by BSNs. The protocol uses the resources more effectively and improves the network lifetime. However, deployment of dual sink requires additional network cost [26].
## 3 Results
### Primary Assessment and Testing
#### 3.1.1 Small-Scale Test Analysis
Furthermore, prototype (d) resistance effect appeared to be better than the positive control product (\(p\) value < 0.001). Figure 4: Lab-scale water resistance test showing untreated and treated soil with candidate soil bio-stabilisers (**a**) tested on soil type i, (**b**) tested on soil type ii. Key: * \(p\) value < 0.05, ** \(p\) value < 0.01, *** \(p\) value < 0.001. Evaluation of Abrasion Resistance  
The assay was based on the ability of potential bio-stabilisers to inhibit abrasion, which is the capacity to limit the loss of material when exposed to harsh weather or vehicles. Additionally, it is well established that long-term exposure to traffic generated dust from unpaved roads can be attributed to serious health problems from the effects of exposure to high concentration of airborne particulates. The fractions that resulted in minimal loss of material in comparison to the negative and positive control were viewed as having abrasion resistant properties. As can be seen in Figure S7 (Supplementary Material), several fractions, including (b2) (i.e., 98.57 \(\pm\) 0.005%, \(p\) value! 0.0001) (i.e., statistical significance against the negative control, \(p\) value! 0.0001), displayed the highest resistance to abrasive action on both soil types. Evaluation of Erosion Resistance  
The assay was based on the ability of a potential bio-stabiliser to inhibit erosion, which is the capacity to limit loss of material caused by running water. The fractions that showed minimal loss of material in comparison to the negative and positive control exhibited erosion resistance properties. As shown in Figure S8 (Supplementary Material), several fractions, including fraction (a), showed statistical significance (98.90 \(\pm\) 0.06%, \(p\) value! 0.0001) in comparison to the positive control (83.04 \(\pm\) 0.54%, \(p\) value! 0.0001) and thus resistance to erosion on soil type ii.
## 6 Conclusions  
The advances in complexity and resolution in land-surface modeling lead to increasing demand for more detailed descriptions of canopy architecture, such as the VFP. Traditional methods for estimating LAI only focus on horizon information and do not provide vertical structure information throughout the canopy. In this work, we developed a new method to retrieve the VFP and LAI using the transmitted energy information derived from the ICESat GLAS data. The advantage of our method is that it is physically based rather than an empirical method; it follows the radiative transfer theory. We have also listed the limitations with our method, namely, the terrain factor and signal noise greatly affected our results. How these two factors affect the inversion results has been analyzed and discussed; the results can guide future efforts in this research direction. Comprehensive validations are conducted for our method from virtual and real scenes, and the results indicate that our method can be successfully used to retrieve VFP and total LAI. Our method can be used as the basis for obtaining continental- and global-scale VFP and total LAI datasets from ICESat GLAS observations, or can even be extended to new satellite lidar platforms such as GEDI and ICESat-2.  
Conceptualization, L.C., Z.J. and K.Z.; data curation, L.C., Y.D., S.Y., Y.L., Z.Z. and Y.C.; formal analysis, L.C., Z.J., J.G., S.L. and R.X.; funding acquisition, Z.J.; software, L.C. and M.S.; writing--original draft, L.C.; and writing--review and editing, Z.J., K.Z., M.S. and Y.D. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Nos. 41971288 and 41801237). The authors declare no conflicts of interest.  
The GLAS emitted and received pulse energy can be computed from the digitized pulse waveforms recorded in the GLAS product, and the mathematical formula is as follows:  
\[E=\frac{\alpha}{\eta_{e}\eta T_{Det}\bar{G}}\frac{N}{i=1}\ W(i)\Delta t \tag{1}\]  
where \(E\) is the pulse energy, \(\alpha\) is the GLAS receiver calibration constant, \(\eta_{e}\) is the electronic throughput, \(\eta\) is the optical throughput, \(T_{Det}\) is the detector responsibility, \(G\) is the variable gain amplifier (VGA) gain, _W(i)_ is the pulse waveform recorded in volts, \(N\) is the waveform sampling number, and \(\Delta t\) is the sampling time of the GLAS receiver. The above parameters are different for transmitted and received pulse energy; The corresponding parameters are given in Table 14.
## 3 Results and Discussion
### Physicochemical Properties
This degree of damage could lead to a significant increase in the permeability of the cell membrane. As a result of electroporation, various components, including phenolic compounds, could be released from the vacuole together with water during pretreatment [34]. Therefore, they could degrade more easily and faster during drying. Supplying the lowest amount of specific energy (1 kJ/kg) during the PEF pretreatment of apple could promote partial inactivation of enzymes [1], e.g., polyphenol oxidase, thus reducing the risk of oxidation of phenolic compounds [74]. Higher TPC values in apples dried at a microwave power of 100 W than in 200 W could be the result of gentler drying. It is likely that in such conditions the temperature of the material was lower, which reduced the risk of thermal degradation of phenolic compounds [51,74]. On the other hand, increasing the microwave power to 300 W was conducive to a significant reduction of the drying time (Figure 1), therefore the exposure of bioactive ingredients to negative conditions was relatively short, thanks to which an increased TPC retention was observed. This result is consistent with the literature data [51,57,75]. \[\begin{array}{l}\mathit{TPC}\ =\ 1880.65625-11.789\mathit{\text{-}Microwave Power}-72.91497\mathit{\text{-}Specific\ Energy\ Intake}-0.043157\\ \  
\(EC_{50}\) DPPH of the tested samples significantly were influenced (\(p<0.05\)) by microwave power (quadratic effect) and specific energy intake (linear effect). Based on Figure 6a,b, it can be seen that the values of \(EC_{50}\) ABTS and \(EC_{50}\) DPPH decreased with declining the amount of energy supplied during PEF treatment and with increasing the power of microwave from 180 to 300 W (between 100 and 180 W, a growth of these parameters was observed). Based on Equations (15) and (16), where the factors should be specified in actual values, it is possible to calculate the values of \(EC_{50}\) ABTS and \(EC_{50}\) DPPH of PEF-treated and microwave-convective dried apples. The highest value of TPC was observed in the sample, which was subjected to PEF treatment with a specific energy intake of 1 kJ/kg before microwave-convective drying in microwave power set at 300 W (Figure 5). As can be seen from Figure 6a,b, the above parameters also allowed to obtain the lowest \(EC_{50}\) ABTS and \(EC_{50}\) DPPH values, which indicates that this sample had the highest antioxidant activity. There are many reports of a relationship between antioxidant activity and total phenolic content [51, 76, 77, 78]. Such conditions turned out to be the most optimal for the retention of bioactive ingredients in apples subjected to pulsed electric field treatment prior to microwave-convective drying.  
\[EC_{50}\ ABTS = -0.132461+0.005379\cdot\mathit{Microwave Power}+0.061119\cdot\mathit{Specific Energy}\ \mathit{Intake} \tag{15}\] \[-0.000015\cdot\mathit{Microwave Power}^{2}-0.004737\cdot\mathit{Specific Energy}\ \mathit{Intake}^{2}\] \[EC_{50}\ DPPH = 0.018095+0.012041\cdot\mathit{Microwave Power}+0.063221\cdot\mathit{Specific Energy}\ \mathit{Intake}\] (16) Figure 2:0
## 3 Proposed Framework
### Mechanism for Enhancing Privacy and Security
#### 3.5.3 Impact on Network Overhead  
Unlike homomorphic encryption or other complex schemes that increase the size of transmitted data due to added metadata or ciphertext expansion, Salsa20 maintains the original size of the plaintext. This is a direct consequence of the stream cipher design, which XORs the plaintext with a pseudorandom keystream without introducing additional bytes. As a result, the total data transmitted per IoT device remains identical before and after encryption:  
\[\text{Size}(c_{i})=\text{Size}(m_{i}), \tag{7}\]  
where \(\text{Size}(\cdot)\) represents the size of the data in bytes. This property ensures that the encryption process does not exacerbate communication bottlenecks, making Salsa20 highly suitable for IoT environments with limited bandwidth.
# Dynamic Stall Characteristics of the Bionic Airfoil with Different Waviness Ratios
## 4 Results and Analysis
### Iso-Surface of the Vorticity  
The flow field structures on the different airfoils are visualized in Figure 11 to analyze the effect of wavy leading edge on the aerodynamic performance. Six different angles of attack were considered in the dynamic stall cycle. The vortex structure, in terms of the iso-surfaces of the Q-criterion, can be used to explain the flow separation on the airfoil surface. Here, we clarify the influence of different waviness ratios of the leading edge on the NACA 0015 airfoil dynamic stall characteristics.  
Figure 11: Iso-surfaces of vorticity (Q = 200,000 s\({}^{-2}\)) with velocity for different airfoils.  
Figure 10: Contours of streamwise vorticity with shear stress lines. (**a**) Shear stress lines for baseline at 18\({}^{\circ}\); (**b**) Shear stress lines for R = 1.0 at 18\({}^{\circ}\); (**c**) Contours of streamwise vorticity.  
As shown in Figure 11, the airflow on the surface of the baseline airfoil had an asymmetrical distribution at various angles of attack. When a bionic wavy leading edge was added, the vortex structure on the airfoil surface changed significantly; for instance, using an 18\({}^{\circ}\) angle of attack as an example, when R = 0.2 and 0.4, the symmetrical distribution of the vortex structure on the airfoil surface is broken. However, the vortex size and strength do not decrease significantly in these cases. When R = 0.6, 0.8, and 1.0, it reduces the size and strength of the airfoil surface vortex structure and suppresses the dynamic stall effect.
## 3 Results and Discussion
### Range Analysis and Analysis of Variance
#### 3.3.1 Range Analysis and ANOVA of Compressive Strength
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Performance Parameter** & **Experimental Control Factor** & **SS\({}_{\mathbf{Z}}\)** & **DOF** & **MS\({}_{\mathbf{Z}}\)** & **F\({}_{\mathbf{Z}}\)** & **P\({}_{\mathbf{Z}}\)** \\ \hline \multirow{6}{*}{Compressive strength} & Coarse aggregate size, A & 11.64 & 3 & 3.88 & 19.94 & 69.63 \\  & Fine aggregate content, B & 1.19 & 3 & 0.40 & 2.04 & 3.84 \\  & Matrix type, C & 2.46 & 3 & 0.82 & 4.21 & 26.53 \\  & Aggregate-to-binder ratio, D & 0.58 & 3 & 0.19 & 1.00 & 0.00 \\  & All other/error & 0.58 & 3 & 0.19 & & \\  & Total & 15.87 & 12 & 5.29 & & 100.00 \\ \hline \multirow{6}{*}{Elastic modulus} & Coarse aggregate size, A & 3.91 & 3 & 1.30 & 1.00 & 0.00 \\  & Fine aggregate content, B & 6.02 & 3 & 2.01 & 1.54 & 1.90 \\  & Matrix type, C & 21.87 & 3 & 7.29 & 5.60 & 30.26 \\  & Aggregate-to-binder ratio, D & 79.22 & 3 & 26.41 & 20.28 & 67.84 \\  & All other/error & 3.91 & 3 & 1.30 & & \\  & Total & 111.01 & 12 & 37.00 & & 100.00 \\ \hline \multirow{6}{*}{Flexural strength} & Coarse aggregate size, A & 1.79 & 3 & 0.60 & 1.25 & 2.38 \\  & Fine aggregate content, B & 8.31 & 3 & 2.77 & 5.81 & 46.00 \\  & Matrix type, C & 1.43 & 3 & 0.48 & 1.00 & 38.28 \\  & Aggregate-to-binder ratio, D & 3.43 & 3 & 1.14 & 2.39 & 13.35 \\  & All other/error & 1.43 & 3 & 0.48 & & \\  & Total & 14.95 & 12 & 4.98 & & 100.00 \\ \hline \multirow{6}{*}{Splitting tensile strength} & Coarse aggregate size, A & 0.70 & 3 & 0.23 & 8.91 & 8.15 \\  & Fine aggregate content, B & 6.70 & 3 & 2.23 & 85.69 & 87.20 \\  & Matrix type, C & 0.08 & 3 & 0.03 & 1.00 & 4.12 \\  & Aggregate-to-binder ratio, D & 0.12 & 3 & 0.04 & 1.52 & 0.53 \\  & All other/error & 0.08 & 3 & 0.03 & & \\  & Total & 7.60 & 12 & 2.53 & & 100.00 \\ \hline \end{tabular}  
* Notes: SS\({}_{\mathrm{Z}}\), sum of the squares; DOF, degree of freedom; MS\({}_{\mathrm{Z}}\), variance; F\({}_{\mathrm{Z}}\), F value; P\({}_{\mathrm{Z}}\), percentage of contribution.  
\end{table} 
Table 14: The analysis of variance and \(F\) test of the fiber-reinforced LPC.
## 2 The Constrained Multi-Objective Component Deployment Problem
### Problem Description  
A component deployment problem is characterised by a set of software components, \(\mathcal{C}=\{c_{1},\ldots,c_{n}\}\), a set of hardware hosts, \(\mathcal{H}=\{h_{1},\ldots,h_{m}\}\), and a set of network links, \(\mathcal{N}=\{n_{1},\ldots,n_{s}\}\). Each software component \(c_{i}\), has properties including memory size \(m_{i}\), computational requirement \(w_{i}\), and \(q_{i}\) the probability that the system starts from this component. Also, between any pair of components \((c_{i},c_{j})\), \(d_{ij}\) is the amount of data sent from \(c_{i}\) to \(c_{j}\), and \(p_{ij}\) is the probability that the execution of component \(c_{i}\) terminates with a call to \(c_{j}\).  
Similarly, each hardware host \(h_{i}\), has its memory capacity, \(p_{i}\), the processing capacity, \(s_{i}\), and the unit failure frequency rate of \(f_{i}\). In addition, every network link connecting a hardware host \(h_{i}\) to \(h_{j}\) has the data transmission rate of \(r_{ij}\) and the failure rate of \(f_{ij}\).  
Figure 1 represents an instance of the CDP problem. There are seven software components, four hardware units and three network links. The interaction between the software components are shown as arrows. Because of the high-level of interaction between components \(c_{1}\) and \(c_{2}\), it is preferable that they are both located on hardware unit \(h_{1}\) (note, the memory capacity of the unit cannot be violated). Components \(c_{2}\) and \(c_{4}\) require a high level of communication, but considering the limitations of memory, safety considerations, or co-localisation aspects, they are embedded on separate hardware units. These two components communicate through network link \(n_{1}\). The contents of hardware units \(h_{1}\) and \(h_{2}\) and a link between them may be duplicated in another location to maximise the reliability of the system.  
We can think of the component deployment problem as an assignment problem \(\mathcal{D}=\{\mathsf{d}|\mathsf{d}:\mathcal{C}\rightarrow\mathcal{H}\}\), where \(\mathcal{D}\) is the set of all assignments, though some of them would not be feasible. In this notation, as \(\mathsf{d}\) is a assignment, \(\mathsf{d}(c_{i})\) is the hardware host that software component \(c_{i}\) will be deployed.
## Nomenclature  
\begin{tabular}{l l l} \(C_{D}\) & Coefficient of drag \\ \(C_{L}\) & Coefficient of lift \\ \(C_{p}\) & Coefficient of pressure \\ \(C_{p_{s}}\) & Coefficient of pressure at rear of cylinder \\ \(D\) & Cylinder diameter & m \\ \(d_{o}\) & Synthetic jet orifice width & m \\ \(f_{a}\) & Synthetic jet actuation frequency & Hz \\ \(F^{+}\) & Dimensionless actuation frequency & \\ \(I\) & Turbulence intensity & \\ \(k\) & Turbulent kinetic energy & m\({}^{2}/s^{2}\) \\ \(L\) & Characteristic length scale & m \\ \(P\) & Pressure & Pa \\ \(Re\) & Reynolds number of the freestream flow & \\ \(Re_{\overline{U}_{L}}\) & Reynolds number of the synthetic jet & \\ \(Sr\) & Strouhal number of vortex shedding & \\ \(Sr_{o}\) & Strouhal number of the synthetic jet & \\ \(St_{o}\) & Stokes number of the synthetic jet & \\ \(U_{dia}\) & Pseudo diaphragm velocity & m/s \\ \(\overline{U}_{o}\) & Averaged velocity at the orifice & m/s \\ \(U_{p}\) & Peak velocity at the orifice & m/s \\ \(U_{\infty}\) & Freestream velocity & m/s \\ \(u_{o}\) & Velocity at centreline of jet orifice & m/s \\ \(x\), \(y\) & Spatial coordinates & m \\ Greek symbols and abbreviations & \\ \(\theta\) & Circumferential coordinate & (rad) \\ \(v\) & Kinematic viscosity & m\({}^{2}/s\) \\ \(\tau\) & Wall shear stress & Pa \\ \(\omega\) & Angular velocity & rad/s \\ CFL & Courant-Friedrichs-Lewy number & \\ RMS & Root-mean-squared & \\ SST & Shear stress transport & \\ URANS & Unsteady Reynolds-averaged Navier-Stokes & \\ \end{tabular}
# Ensemble-Based Correction for Anomalous Diffusion Exponent Estimation in Single-Particle Tracking
## 2 Materials and Methods
### Generalization of Ensemble-Based Correction  
The correction of exponent \(\alpha\) estimates can also be performed for cases in which the estimates are obtained not through TA-MSD, but by any other method, including both classical statistical methods and modern machine learning methods, such as neural networks. It is important to have empirical information about the dependence of the variance of \(\alpha\) estimates \(\hat{\sigma}_{method}^{2}\) on the trajectory length T, as well as the ensemble mean value; this dependence can be obtained by various methods. Such information allows the correction of predictions made using any \(\alpha\) estimation methods, based on knowledge of the error structure arising from limited trajectory length.  
We propose the following numerical approach. For a set of fixed values of \(\alpha\) and various values of trajectory length T from the range of interest, an ensemble of trajectories is generated (e.g., several thousand for each pair \(\{\alpha,T\}\)). After estimating \(\alpha\) for each trajectory from the ensemble and obtaining estimates \(\hat{\alpha}\), the sample variance of estimates \(\sigma_{total}^{2}[\hat{\alpha}]\) corresponding to the given pair of values \(\{\alpha,T\}\) is calculated. Since all generated trajectories in the ensemble have the same specified \(\alpha\) value, \(\sigma_{\alpha}^{2}=0\), and therefore \(\partial_{method}^{2}=\sigma_{total}^{2}\). This allows the obtaining of the dependence of the empirical values \(\partial_{method}^{2}\) on T.  
Then, for each fixed value of \(\alpha\), we perform linear regression to study how the estimate variance \(\hat{\sigma}_{method}^{2}\) depends on \(1/T\). This helps reveal how the variance changes as the trajectory length increases. Once we have a set of linear relationships for different values of \(\alpha\), we can build an approximating function that takes T and a given estimate \(\hat{\alpha}_{TEAMSD}\) as inputs. Using parametric interpolation over \(\alpha\), this function allows us to calculate the expected value for the variance estimate \(\hat{\sigma}_{method}^{2}\).  
Correction of estimates obtained by a specific method, for which we now have a function that provides \(\hat{\sigma}_{method}^{2}\) from T and \(\hat{\alpha}_{TEAMSD}\), is performed using the following modification of Formula (17):  
\[\hat{\alpha}_{i}^{corrected}=\hat{\alpha}_{TEAMSD}+(\hat{\alpha}_{i}-\overline{ \mu}_{a})\ \sqrt{\frac{\hat{\sigma}_{total}^{2}-\sigma_{method}^{2}}{\hat{\sigma}_{total}^ {2}}} \tag{18}\]  
Thus, we correct the estimates of anomalous exponents for each trajectory obtained by a specific method.  
Appendix B provides examples of the determinations of anomalous diffusion parameters for the TA-MSD, Whittle, and CINNAMON methods (based on machine learning and independent of trajectory length) [30], illustrating that the dependence of alpha variance on \(1/T\) can be approximated by linear regression.
## 4 Model (SIRPM)
### Proposal for a Model (SIRPM)
* Mobility and accessibility in infrastructure: The hybrid architecture of the model ensures that even without a complete migration to the cloud, organizations can enjoy the benefits of connectivity and mobility, essential for I4. For example, the automation of data collection and processing by RPA can be achieved locally but with interfaces ready for future integration into cloud-based solutions, promoting a continuous flow of information between machines, systems, and people. * Structured transition to cloud-based Industry 4.0: The model prepares the technological environment of organizations for a continuous and sustainable evolution towards the principles of I4. Each phase of SIRPM, from data collection to the analysis and integration of results, is designed to be fully compatible with cloud infrastructures, ensuring a harmonious and efficient transition. SIRPM combines flexibility and innovation to meet the immediate needs of predictive maintenance while offering a structured path to digital transformation based on Industry 4.0. The functioning of the SIRPM model will be detailed step by step in the following phases:  
The SIRPM model provides a flexible, structured approach for integrating RPA and ML into predictive maintenance systems. It emphasizes the sustainable application of these technologies while ensuring interoperability with existing systems. The flexibility embedded in the model allows it to be adapted to different organizational contexts, ensuring a responsible and efficient approach to predictive maintenance. By following the defined criteria for each phase, the integration of RPA and ML will align with best practices for sustainability and operational efficiency while allowing organizations to customize technology choices based on their specific needs.