esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### LMS Method  
The LMS algorithm is an adaptive iterative algorithm that was proposed in 1959 by [PERSON] and [PERSON] to solve system identification problems [30]. This algorithm cancels out noise from the noisy signal using the reference signal, thereby eliminating the noise in the noisy signal [31]. The objective of the LMS algorithm is to minimize the mean squared error between the known expected response and output signal of the filter, estimate the gradient vector in each iteration according to the input signal, and update the weight coefficient to adapt the algorithm iteratively and output the optimal result. The algorithm has the advantages of a simple structure, high calculation accuracy, easy implementation, and good convergence and stability. Moreover, it has been widely used in many fields to process signals [32]. The expected signal is expressed as follows:  
\[E\Big{[}e^{2}(k)\Big{]}=E\bigg{(}\Big{[}y(k)-W^{T}(k)x(k)\Big{]}^{2}\bigg{)}. \tag{8}\]  
where \(x(k)\) is the original input noisy signal, \(W(k)\) is the weighted vector of the signal at time k, and \(y(k)\) is the output signal.  
Therefore, the filter estimation method based on the LMS algorithm is as follows:  
\[w(k+1)=w(k)+2\mu e(k)x(  
\[\beta(k)=\delta\beta(k-1)+\gamma p^{2}(k). \tag{12}\]  
where \(\delta\) and \(\gamma\) are fixed constants of the adjustment function.  
Then, the step size of the improved LMS is expressed as follows:  
\[\mu(k)=\beta(k)\bigg{[}1-\frac{1}{1+\alpha(k)\times e^{2}(k)}\bigg{]}. \tag{13}\]  
By adjusting the step size in the improved LMS algorithm, the convergence speed of the weight is accelerated, the algorithm's steady-state error is reduced, and the performance of the algorithm is improved.
###### Abstract  
Preventing rebar corrosion in reinforced concrete (RC) structures has been actively researched worldwide. One of the most powerful solutions is the use of fiber-reinforced polymer (FRP) rebars. However, there are limitations in the mechanical design and construction of FRP rebars because their tensile characteristics are extremely different from those of conventional rebars and they have a different modulus of elasticity. FRP rebars are relatively cost-efficient when fabricated with glass fibers, but they are still costly compared to conventional rebars. Therefore, hybrid rebars fabricated by covering conventional rebars with glass FRP (GFRP) materials were developed in this study. GFRP hybrid rebars have increased durability in marine environments while maintaining the same mechanical properties as conventional rebars. As the difference in rebar diameter of the bonded area decreased, the tensile strength of the concrete increased. As a result, pull-out failure or tensile failure caused by the yielding of the rebars occurred in small-diameter rebars. The experimental results showed that the maximum load for the D13 deformed steel bar was 52.2 kN at a bond length of 50 mm and 76.1 kN at 100 mm, while for the D19 deformed steel bar, it was 65.3 kN at 50 mm and 103.7 kN at 100 mm. The bond properties of hybrid GFRB rebars were found to be lower than those of deformed steel bars. These properties were improved greatly by increasing the thickness of the GFRP materials on the surface of the deformed steel bars, highlighting a path toward high-performance, corrosion-resistant concrete.  
Reinforced concrete; corrosion resistance; glass-fiber-reinforced polymer; hybrid GFRP rebar; concrete polymer bonding
## 3 Results and Discussions
### Structural and Microstructural Properties
#### 3.4.3 SEM-EDX Analysis  
Figures 15 and 16 show the SEM micrographs with elemental mapping and EDX spot analyses for the formulations 0.75M-4% and 2.22M-4%, respectively. The major elements from the elemental mapping of the 0.75M-4% sample (Figure 15) were Si (from the activator) and Ca (from the BOF slag). The phases labeled A, B and C were, respectively, the RO phase, calcium silicate (from unreacted BOF slag particle) and C-S-H gel. The RO phase is an undissolved crystal phase commonly found in BOF slags and will remain like that even if hydrated for many years [27]. Figure 15 presents more unreacted BOF slag particles, which is in accordance with the lowest amount of reaction products found via TGA (Figure 12a) and the lowest compressive strength (Figure 6). An open microstructure with porous regions all over the matrix resulted from a poor hydration/activation process, which compromised the compressive strength development.  
Differently, Figure 16 presents the 2.22M-4% formulation with an entirely different microstructure. The elemental mapping showed an even greater predominance of Si and Ca in the matrix. Phases labeled A, B, C and D were, respectively, the RO phase embedded in the BOF slag particles, the remaining steel inclusion, calcium silicate (unreacted BOF slag) and C-S-H gel. In this case, the RO phase was embedded in the BOF slag particles, blocking these particles from dissolving and forming the reaction products (gel). A more homogeneous microstructure was observed, which was characterized by a smaller number of undissolved or partially dissolved BOF slag particles and lower porosity. More hydrates were formed (Figure 12a), which filled the existing pores (voids) and bonded the remaining solid particles together to form a continuous, dense and complete matrix [32]. Therefore, the denser microstructure resulted from a better hydration/activation process, and the high presence of hydrates reduced the microstructural porosity, resulting in higher compressive strength (Figure 6).  
Figure 14: Influence of activator parameters on the relationship between the reaction products and the initial heat release (blue: 4% of Na\({}_{2}\)O; orange: 6% of Na\({}_{2}\)O; squares: reaction products; triangles: initial heat release).  
Figure 15: SEM image of 0.75M-4% (magnification of 2000\(\times\)) with elemental mapping and EDX spectra of the scanning points: (**A**) RO phase, (**B**) calcium silicate (unreacted BOF slag particle) and (**C**) C-S-H (reaction product).
## 1 Introduction
The random coordinate of each shot that satisfied 100% peening coverage was generated from a MATLAB program combined with ANSYS program Design Language. However, [PERSON] et al. 's model, the minimum distance between all the existing shots was set to be 0.75 mm, which is arbitrary. The radius of all shots was 0.5 mm and the maximum number of shots used for simulation was 96. Hence, [PERSON] et al. 's model might apply to a unit cell-based 3-D finite element analysis where an impact region to be discretized into a fine mesh is very small, e.g. 2 mm \(\times\) 2 mm \(\times\) 1 mm. [PERSON] et. al. [23] and [PERSON] et. al. [24] extended [PERSON] et. al. 's [25] work by developing a process map for random impact locations generation ensuring that no two shots occupies the same space mathematically, and applied it to simulate the Almen strip response under random multiple impacts. A slice of the Almen strip subjected to random multiple impacts was modeled. The radius of the shot used as 0.1778 mm. However, the deflected profile of the Almen strip computed using the FE model with random impact locations was not compared with the measured profile. [PERSON] and [PERSON] [26] proposed a methodology that minimizes a total number of shots, and hence makes it possible practically to simulate the multiple shot impacts, which have statistics characteristics. They showed that predetermined shot patterns (locations and sequence of the multiple shots) on a reference area of 1 mm\({}^{2}\) could predict RCS distribution, surface roughness, and cold work by comparing the model-predictions with measurements. The previously mentioned methods demonstrate that the FE model based on randomly located impacts showed a new possibility to achieve a realistic model of shot peening. The relation of impact locations and sequence of shots has been investigated in these studies, which establishes the foundation for a quantitative description of peening coverage in this paper. In all, the influence of high peening coverage on Almen intensity and RCS has not been studied in detail. The reason is that, in finite element analysis, it is difficult to describe the peening coverage quantitatively. In this study, the boundary conditions of the FE simulation of the Almen test were set up accord to reference [27]. By the definition of peening, peening coverage is the ratio of the area of the dents to the zone of the peening, a quantitative description of the peening coverage is proposed. The size of the peening zone is determined by the distance between two shots. After determining the size of the dent, the high peening coverage can be achieved by increasing the number of shots. This quantitative description peening coverages used to the simulation results of arc height and saturation curves are in accord with the Almen test results well. Therefore, the effectiveness of this quantitative description and FE simulation is proved. To analyze the influence of peening coverage on Almen intensity and residual compressive stress, further study is carried out. Through the discussion of the FE simulation and Almen test results, some new conclusions are obtained.
###### Abstract  
This paper focuses on Internet of Things (IoT) architectures and knowledge generation out of streams of events as the primary elements concerning the creation of user-centric IoT services. We provide a general, symmetrical IoT architecture, which enables two-way bidirectional communication between things and users within an application domain. We focus on two main components of the architecture (i.e., Event Engine and Process Engine) that handle event transformation by implementing parametric Complex Event Processing (CEP). More specifically, we describe and implement the transformation cycle of events starting from raw IoT data to their processing and transformation of events for calculating information that we need in an IoT-enabled application context. The implementation includes a library of composite transformations grouping the gradual and sequential steps for transforming basic IoT events into business events, which include ingestion, event splitting, and calculation of measurements' average value. The appropriateness and possibility of inclusion and integration of the implementation in an IoT environment are demonstrated by providing our implementation for a smart farming application domain with four scenarios that each reflect a user's requirements. Further, we discuss the quality properties of each scenario. Ultimately, we propose an IoT architecture and, specifically, a parametric CEP model and implementation for future researchers and practitioners who aspire to build IoT applications.  
Internet of Things (IoT); Complex Event Processing; event-driven architecture; smart farming +
Footnote †: journal: _Future Internet_  
symmetrical architecture, which enables two-way bidirectional communication between things and users within an application domain; and focus on two main components of the architecture (i.e., Event Engine and Process Engine) that handle event transformation and implement parametric Complex Event Processing (CEP). More specifically, we describe and implement the transformation cycle of events starting from raw IoT data to their processing and transformation of events for calculating a quantity that we need in an IoT-enabled application context. We showcase our implementation considering an IoT-enabled greenhouse in a smart farming application domain.  
In Section 2, we summarize the related work. In Section 3, we detail the proposed architecture, and in Section 4, we present the Complex Event Processing model that enhances the proposed architecture. In Section 5, we present an example of implementation of the architecture based on the introduced Complex Event Processing Model to manage an Internet of Things environment. Next, in Section 6, we present a realistic example of the implementation in a smart farming domain to exemplify the suitability of the architecture and the Complex Event Processing Model. Finally, we discuss the quality properties of the implementation and conclude the paper.
## 4 Results
### Input Sensitivity and Decision Robustness  
The global sensitivity analysis revealed distinct response patterns across model outputs (Figure 10). Solar radiation emerged as the dominant driver for lint yield (\(S_{T}\) = 0.41), exhibiting non-linear interactions with temperature (\(S_{T,interaction}\) = 0.17). Soil clay content showed the strongest influence on nitrate leaching (\(S_{T}\) = 0.38), particularly through its mediation of water retention capacity. Notably, economic returns demonstrated balanced sensitivity to both agronomic (SRAD \(S_{T}\) = 0.29) and economic (FERT_$ \(S_{T}\) = 0.31) factors, confirming the need for integrated optimization. The spider diagram visualization (Figure 10) highlights three key clusters: (1) climate-driven yield determinants (SRAD, RAIN), (2) soil-mediated environmental impacts (CLAY, N_INI), and (3) economic control levers (FERT_$). Crucially, no single parameter accounted for >45% of variance in any output metric (max \(S_{T}\) = 0.41), indicating the model's inherent resilience to isolated input perturbations. These findings validate the framework's capacity to maintain decision robustness under realistic uncertainty ranges while identifying priority calibration targets for regional adaptation.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Metric** & **DSSAT-GA (Sim)** & **PPO (Sim)** & **Difference (\%)** & **95\% CI** & \(p\)**-Value** \\ \hline Nitrogen input (kg ha\({}^{-1}\)) & 358.2 \(\pm\) 12.6 & 401.5 \(\pm\) 17.9 & +12.1 & [29.3, 57.1] & \textless{}0.001 \\ Seed cotton yield (kg ha\({}^{-1}\)) & 9532.4 \(\pm\) 302 & 9214.7 \(\pm\) 327 & \(-\)3.3 & [\(-\)495.2, \(-\)141.8] & 0.003 \\ Lint yield (kg ha\({}^{-1}\)) & 4876.3 \(\pm\) 198 & 4632.8 \(\pm\) 221 & \(-\)5.0 & [\(-\)365.5, \(-\)122.3] & \textless{}0.001 \\ NUE (kg kg\({}^{-1}\)) & 13.61 \(\pm\) 0.58 & 11.54 \(\pm\) 0.66 & \(-\)15.2 & [\(-\)2.41, \(-\)1.73] & \textless{}0.001 \\ Economic return (USD ha\({}^{-1}\)) & 10873 \(\pm\) 395 & 9925 \(\pm\) 452 & \(-\)8.7 & [\(-\)1698, \(-\)602] & 0.001 \\ Fiber quality index & 0.50 \(\pm\) 0.03 & 0.46 \(\pm\) 0.04 & \(-\)8.0 & [\(-\)0.07, \(-\)0.02] & 0.009 \\ \hline \hline \end{tabular}  
* Simulated means \(\pm\) 95% CI derived from 10,000 Monte Carlo iterations. Statistical significance assessed via [PERSON]’s \(t\)-test with Sidak correction (\(\alpha_{adj}\) = 0.0083).  
\end{table}
Table 11: Simulated performance comparison between DSSAT-GA and PPO optimization strategies.
###### Abstract  
Background: The blood wettability of titanium implant surfaces favors primary osseointegration and tissue healing in peri-implantitis. The current study aims to evaluate this property on the main different kinds of surfaces, brand new and after treatments. Methods: grade IV titanium disks were used, five machined, five laser-treated, five sandblasted. The experiment was on four steps. Between each step, specimens were sterilized. At the end of each step, a 4 \(\mu\)L blood drop was put on the samples, and contact angles were calculated. The steps consisted of--1: no treatment; 2: surface instrumentation with an ultrasonic titanium tip; 3: platelet-rich fibrin (PRF) coating and drying with sterile gauze; 4: etching with phosphoric acid, rinse and saline solution and air-drying. At the end of each phase, a blood drop was placed on the surfaces. Blood for PRF preparation and for calculation of contact angles was collected from a single source. Results: average wettability decreased after instrumentation, and increased after the PRF application to a superior level both to the first and second steps. The highest wettability was obtained after etching. This trend is statistically significant for machined and sandblasted surfaces. Conclusions: In the clinical environment, PRF and phosphoric acid used for conditioning exposed implant surfaces can be used for the healing of peri-implant tissues.  
ultrasonic; platelet-rich fibrin; wettability; peri-implantitis; etching; phosphoric acid; titanium; implant surface; roughness +
Footnote †: journal: Applied Sciences
## 3 Results and Discussions
### Calculation of Electricity Consumption  
In order to verify the IPMVP theory, the actual saving in daily power consumption is recorded. Readings from the west side of the building (with VGS) show that the Sun has a direct impact from noon and until 6:47 pm; during this time span, the energy saved by the VGS is 103.391 kwh/6 h. When the Sun sets, at 6:47 pm, the energy saved by the VGS is zero. The effects are shown in Figure 11.  
The results also show that monthly electricity savings can reach 7,463 kwh/month as illustrated in Figure 12. Figure 13 shows the energy savings for one year (August 2011 \(\sim\) August 2012). VGS has been proven to be effective in energy savings during summer, while in the winter days, the difference is minimal.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Time** & **PM12** & **PM13** & **PM14** & **PM15** & **PM16** & **PM17** & **PM18** & **Average** \\ \hline External wall & Before & 30.92 & 31.16 & 32.50 & 31.17 & 30.66 & 30.56 & 30.09 & 31.00 \\ temperature & Be hide & 29.00 & 29.19 & 29.82 & 28.92 & 28.42 & 28.35 & 28.07 & 28.82 \\ Cooling & & 1.92 & 1.97 & 2.68 & 2.25 & 2.24 & 2.21 & 2.02 & 2.18 \\ effectiveness (\({}^{\circ}\)C) & & 8.811 & 9.105 & 12.294 & 10.374 & 10.311 & 10.166 & 9.292 & 10.048 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Value of the temperature tested (measurement date: 25 June 2012).  
Figure 11: Daily savings in electricity consumption.  
Figure 12: Savings in electricity consumption in one month. (monitoring Date: June 2012).
## 2 System Model
### Problem Formulation  
#### 2.3.1 Scenario  
As shown in (2), the energy required to transmit data increases with distance due to path loss. For shorter distances, the energy consumption is modeled with a free space propagation model, which considers path loss to be proportional to \(d^{2}\). For longer distances, the multipath fading model is used, where the path loss is proportional to \(d^{4}\).  
The energy formulation used in the LEACH protocol has certain limitations and downsides. The model uses a fixed threshold distance (\(d_{\mathtt{th}}\)) to switch between the \(d^{2}\) and \(d^{4}\) propagation models. This creates a sharp transition, which may not accurately reflect the gradual change in path loss and signal-to-interference plus noise ratio (\(\gamma\)) experienced in real environments. The fixed threshold does not account for varying environmental conditions, such as obstacles, terrain, and interference. Additionally, the model leads to an overestimation or underestimation of the energy required for transmission, especially for distances close to \(d_{\mathtt{th}}\). Nodes located just above or below the threshold may experience significantly different energy consumption rates, leading to unbalanced energy depletion across the network. This can cause some nodes to die prematurely, reducing coverage and effectiveness.  
Path loss serves as an indicator of communication reliability due to its direct impact on \(\gamma\). The path loss _(PL)_ can be expressed as [41; 42]:  
\[PL(dB)=PL(d_{0})+10n\log(\frac{d}{d_{0}}) \tag{4}\]  
or  
\[PL(dB)=P_{\mathtt{t}}-P_{\mathtt{F}} \tag{5}\]  
where \(d_{0}\) is the reference point in 1 km, \(P_{\mathtt{t}}\) is the transmit power, \(P_{\mathtt{F}}\) is the receive power and \(n\) is the path loss exponent. Higher path loss results in weaker received signal strength and lower \(\gamma\), which indicates poor signal quality. \(\gamma\) is defined as  
\[\gamma=\frac{P_{\mathtt{F}}}{P_{\mathtt{int}}+P_{\mathtt{noise}}} \tag{6}\]  
where \(P_{\mathtt{int}}\) is the total power of all interfering signals and \(P_{\mathtt{noise}}\) is the power of the background noise present in the system, which includes thermal noise and other sources of noise.
## 3 Results and Discussion
### Characterization of Dense Media
#### 3.1.2 Viscosity of Dense Media  
To characterize the viscosity of the dense media, we considered it as a Newtonian fluid. We plotted the shear stress versus the shear rate to calculate the viscosity from the slope of these curves [38]. Figure 3B shows the shear curves of CG, C40, F and FM media for a constant density of 3.00 kg/L. In all cases, the slope is less than 1, indicating that the apparent viscosity decreases with the velocity profile and that the fluid behavior can be described as pseudoplastic (shear-thinning) [6; 27; 28].  
The rheological properties of F medium show a more pseudoplastic nature and thus, a lower viscosity (0.049 Pa). Ferrosilicon media show similar rheological properties and a pseudoplastic nature (CG: 0.00928 Pa; C40: 0.0932 Pa; FM: 0.00969 Pa) [27].  
When different media are compared at equal medium density, the F (ferrosilicon) medium has a lower viscosity than the FM medium (ferrosilicon and magnetite mixture). The increased viscosity of the FM medium can be attributed to the addition of magnetite, which produces a suspension containing irregularly shaped particles and a higher volume of solids (due to its lower density) [11; 30].  
Overall, our results show that the heavy mineral suspensions or media are quite unstable and settle rapidly, making it difficult to measure their viscosity [31]. The suspension behavior is closer to a pseudoplastic fluid, and its viscosity and stability depend on the mineral composition, particle size and shape. Optimal values of stability are achieved with solutions containing finer particles and the controlled addition of magnetite, which increases the stability of the suspension.
## 2 Mathematical Setup
### Generalization to Multi-Layered Coating Systems  
The subject of the investigations is to make use of the insights outlined before and generalize Formula (17) for a multi-layered coating system; see Figure 8. Consider the following system consisting of \(n\in\mathbb{N}\) layers of coatings with material \(M_{1}\)-\(M_{n}\) having thicknesses \(L_{1}\)-\(L_{n}\) on a thermally thick substrate material \(M_{n+1}\).  
Transmitted thermal waves  
Figure 8: System of \(n\) layers of coating materials \(M_{1}\)-\(M_{n}\) and one layer of substrate \(M_{n+1}\).  
For \(n=2\), the idea was to look at the last reflection coefficient from the case \(n=1\) and, since a new layer is added, replace it by some effective reflection coefficient \(\Gamma_{1}\) including all potential wave trains that are apparent in the new system. This principle can be extended further, which leads to nested effective reflection coefficients, i.e., one defines \(\Gamma_{j}\) recursively with  
\[\begin{split}\Gamma_{n}&\coloneqq R_{n},\\ \Gamma_{j}&\coloneqq\frac{R_{j}+\Gamma_{j+1}\exp{(-2 \sigma_{j+1}L_{j+1})}}{1+R_{j}\Gamma_{j+1}\exp{(-2\sigma_{j+1}L_{j+1})}}, \ \ j=n-1,\ldots,1.\end{split} \tag{20}\]  
Although the generalized formula for the surface temperature of multi-layered coating systems looks as for \(n=2\), i.e.,  
\[T(x=0,t)=\underbrace{\left[A_{0}T_{0}\frac{1+\Gamma_{1}\exp{(-2 \sigma_{1}L_{1})}}{1-R_{0}\Gamma_{1}\exp{(-2\sigma_{1}L_{1})}}\right]\exp{ \left[i{\left(\omega t-\frac{\pi}{4}\right)}\right]}}_{=:A_{\tilde{T}}\exp{ \left(i\varphi_{\tilde{T}}\right)},\ A_{\tilde{T}}\in\mathbb{R}_{+},\varphi_ {\tilde{T}}\in\mathbb{R}}, \tag{21}\]  
the effective reflection coefficient \(\Gamma_{1}\) now contains all the significant information about the coating system, such as the layer thicknesses \((L_{1},\ldots,L_{n})\), the thermal diffusivities \((\alpha_{1},\ldots,\alpha_{n})\), and the thermal effusivities \((e_{1},\ldots,e_{n+1})\). Note that \(\alpha_{n+1}\) and \(L_{n+1}\) are not of any interest because the substrate is thermally thick, but \(e_{n+1}\) is needed for the calculation of \(\Gamma_{n}\).