esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Overview of Mamdani-Type Fuzzy Inference
This aggregated fuzzy set incorporates all the nuances and variations captured by the individual rules, ensuring a comprehensive decision-making process that considers the varying conditions of traffic density at the intersection. The aggregated fuzzy set, representing the overall system's decision on how long the green light should last based on current traffic conditions, is then ready for the final stage of the fuzzy inference process: defuzzification. The fourth component of Mamdani-type fuzzy inference is a defuzzification module--**Defuzzifier. ** Defuzzification is the final step, converting a fuzzy output set into a precise, actionable value. This is the process of producing a quantifiable result in Mamdani systems to derive a crisp output. For example, to translate this fuzzy output into a specific time duration that can be set on the traffic light timer, defuzzification is necessary. Choosing the appropriate defuzzification method affects how the system responds to varying conditions. Let us explore how different defuzzification methods would apply in this scenario:  
* **Centroid Method (Center of Area, CoA)**: This method calculates the center of gravity of the fuzzy output set, providing a balanced representation of all contributing membership values. For our traffic light example, if the fuzzy output suggests varying degrees of appropriateness for extending the green light from 30 to 90 s, the centroid method will calculate the precise duration that best represents the overall suggestion of the fuzzy set. This method provides a balanced approach, often leading to smooth transitions between different traffic light durations based on changing traffic densities. * **Mean of Maximum (MoM)**: This approach calculates the average of the output values that have the highest membership degrees in the fuzzy set. If the highest membership values are concentrated around certain durations, say 60 and 70 s, the _MoM_ method would average these to determine the green light duration. This method might be preferred in conditions where traffic patterns consistently show peaks at specific times. * **Smallest of Maximum and Largest of Maximum**: These methods would choose either the smallest or largest duration value that has the highest degree of membership, depending on whether the system aims to minimize or maximize the traffic flow. In a scenario where traffic is critical and safety must be ensured by not overextending the green phases, the smallest of the maximum methods might be used. This approach could be critical in scenarios focusing on specific safety or efficiency metrics. The centroid method is employed in our research. Its application will be explained in detail in the next subsection.
## 3 Pretreatment Methods in the Chain of Biogas Production
### Physicochemical Pretreatments
#### 3.3.2 Hydrothermal  
Of late, hydrothermal pretreatment has gained importance, as it is efficient in penetration of the biomass, cellulose hydration, and removal of hemicellulose and part of lignin. The major advantages are that there is no requirement of chemicals and corrosion-resistant material for the reactor [137]. Typically, it can remove most of hemicellulose and part of lignin in biomass by degrading them into soluble fractions and loosen the recalcitrant structure as well [138]. The temperature is the most important factor influencing the pretreatment effect [139]. Typically, hydrothermal pretreatment is conducted in range of 90-260 \({}^{\circ}\)C [130]. [PERSON] et al. [140] suggested hydrothermal pretreatment at 175 \({}^{\circ}\)C for 15 min of reaction time on Napier grass increased the methane yield by 25% than the untreated. They also reported the formation of inhibitors like 5-hydroxymethylfuran and furfural at 200 \({}^{\circ}\)C, which significantly inhibited methanogenesis. Similarly, [PERSON] et al. [141] tested 120, 140, 160, and 180 \({}^{\circ}\)C for pretreatment of wheat straw and established that the optimal temperature for hydrothermal pretreatment of wheat straw was 180 \({}^{\circ}\)C, which showed a 53% increase in the methane yield in comparison with untreated. [PERSON] et al. [142] pretreated hydrothermally Safflower straw at 120, 150, and 180 \({}^{\circ}\)C for 1, 2, and 5 h and the highest biomethane yield was obtained at the least severe pretreatment conditions (120 \({}^{\circ}\)C for 1 h), which showed 98.3% improvement in comparison to the untreated straw. On the other hand, [PERSON] et al. [143] examined hydrothermal pretreatment of rice straw at different temperatures from 90 to 130 \({}^{\circ}\)C. Both 100 \({}^{\circ}\)C and 130 \({}^{\circ}\)C pretreatments presented similar methane yield of 127.6 and 124.6 mL/g VS, respectively, which were 22.90% and 19.83% higher than untreated rice straw. [PERSON] et al. [144] evaluated hydrothermal pretreatment on cocoa pods residues at temperatures in the range of 155-220\({}^{\circ}\)C and at reaction time between 5 and 15 min and reported an optimum methane yield of 526.38 mL/g VS at 150 \({}^{\circ}\)C for 15 min and opened that higher severity conditions resulted in lower biogas yield.
## 6 Results
### Modulated Voltage Constraints  
Figure 10 illustrates the VSI transient response under active modulated voltage constraints. The voltage amplitude constraint is fixed to 138 V and originally the system operates in the unconstrained region with a reference of 120 V. At \(t=2.75\) s; the voltage reference is changed to 150 V exceeding the specified restriction. In this case, the filter current and modulated voltage are constrained to ensure compliance with the imposed limits, even when the reference signal exceeds the defined limit. The experimental results show that the modulated voltage remains within the specified limits enforced by the MPC control strategy.
## 3 Results
### Chemico-Physical Characterization
#### 3.2.3 Scanning Electron Microscopy (SEM) Characterization  
The SEM analysis highlighted the presence of a porous network both on the surface area and in the cross section. In particular, the analysis of these images according to the method described (Section 2.5.2) showed that the surface area of the VitE 0.1 scaffolds was characterized by the pores having sizes between 4 and 14, while for the VitE 1 scaffolds, the pore size was within 3 and 57. In the cross section, the VitE 0.1 scaffolds showed pores in the range between 5 and 40, while the VitE 1 scaffolds, similar to the characteristics of the pores observed on the surface, displayed a bigger pore size range, which was between 10 and 57 (Figure 3).  
During scaffold preparation for SEM analysis, a visible volumetric reduction was noticed after the freeze-drying process. It was calculated that the scaffold volume, once anhydrous, was reduced to 41.7 +- 3.2, it is reasonable to assume that the pores in the network were subject to an analogue reduction. The estimated real size ranges concerning surface area were 6-20 and 4-81 and for VitE 1, respectively, while the recalculated cross section ranges were between 7 and 57 and 14 and 81 for VitE 0.1 and VitE 1, respectively. Cumulative pore size distribution is shown in Figure 4.  
From graph in Figure 4, it is clearly observable that the VitE 1 scaffolds have bigger pores compared to VitE 0.1. This result is evident both on the surface area and in the internal structure of the scaffold in the cross section.  
As seen in Figure 5, the overall morphology and topography of the porous scaffold is maintained. A three-dimensional grid of orthogonally overlapping parallel strands form a micro-patterned gauze. In particular, measurements taken for strand sizes and holes (n = at least 10/each) indicated mean values of 180.7 and 164.9 and 165. respectively. The volume correction made on the pore measurements was also applied here. An estimation of these measurements in their fully hydrated state would result in 256.1 and 15.9 for strands and 233.7 for the holes formed by the grid. Only data related to scaffolds VitE 1 are reported since the topographic parameters were exactly the same as for the VitE 0.1 set. Theoretical CAD model values were fixed at 26 for strand size and at 200 for holes, as described in the methods Section 2.3.1.  
Figure 3: SEM photographs of freeze-dried VitE 0.1 and VitE 1 scaffolds. View of surface area and cross section. VitE 0.1 Mag: 360\(\times\); VitE 1 Mag 640\(\times\).
# The Challenges of Advanced CMOS Process from 2D to 3D
## 3 Process Integration of New Transistor Architecture
### Uniform Junction Formation in Fin  
One of the critical integration challenges for FinFETs lies with doping, which in fins is significantly different than that in planar devices [31; 32]. Three issues are included: (1) the need for conformal doping in the S/D contact and extension regions so that the carrier conduction is uniform in the fin-channel; (2) the tight pitch of the fins limits the beam incidence angles, then the shadowing from neighboring fins has be avoided; (3) damage, accumulation and annealing in high-aspect ratio fins are significantly different from those in planar junctions.  
Uniformly doping with conventional ion implantations in the narrow and long fins with a shrunk pitch is a difficult task (_I/I_) [33]. After conventional _I/I_, an amorphization of the Si fins occurs and later poor re-crystallization is obtained during junction anneal which results in poor dopant activation and defected fins [31]. The implant condition for fin doping can also impact the quality and the growth rate of the S/D epitaxy, which will have implications on the S/D and contact resistance. Hot _I/I_ with increasing the Si wafer temperature during _I/I_ up to a certain targeted value could be an option to reduce the amorphous depth and fin damage [31; 32]. Other innovative doping schemese.g., solid-source doping, conformal plasma doping, and molecular monolayer doping can be also applied to improve the doping profile. An extension less and \(I/I\)-free doping technique is preferably required, which uses rapid thermal annealing (RTA) process to drive dopants from the in situ doped epitaxial S/D regions, and to form a better controlled box-shaped profile.
## 4 Discussion
Therefore, the fractal method can provide a feasible way to describe the PSD and to convert the data or texture measurements from China (1987), Katschinski (1957), and ISSS (1929) schemes to the USDA (1993) and ISO14688 (2002) standards or vice versa. Previous studies have found more clay and silt contents associated with higher fractal dimensions of PSD [1; 35; 36]. However, subdividing the clay, silt, and sand fractions more specifically could not simply increase or decrease \(D\) values. During our research, curve of \(\mathrm{Lg}\) (\(V/V_{T}\)) and \(\mathrm{Lg}\) (\(R_{i}/R_{\mathrm{max}}\)) changes under different PSC systems indicated that refining clay and sand scales could lower slope (\(k\)) of regression equation, then \(D\)s became larger. Such a tendency is more obvious in [PERSON] (1957) and Blott & Pye (2012) PSC systems. In conclusion, \(D\)s of soil PSD could provide fully information related to desertification processes and anti-desertification methods. Thus, a general consensus is urgently needed to define the proper PSC system that can more adequately describe the PSD attributes of sediments and soils and thus estimate fractal dimensions of soil PSD. By comparing among all the PSC systems, the highest significant correlations between \(D\)s and clay, silt, and sand fractions in USDA (1993) PSC system. Blott & Pye (2012) PSC system had the most complete information regarding the subdivision of PSD, and in both of these PSC systems, \(D\)s were still sensitive to the desertification combating processes by MPPs establishment and associated forest densities. Thus, USDA (1993) and Blott & Pye (2012) PSC systems are recommended in estimating soil structure and calculating soil fractal dimensions to keep the consistency and enhance the comparability and applicability of the relevant research results. Ecological systems are complex, soil fractal dimensions vary because scientists chose different PSC systems, it remains a challenge to examine soil PSD information with fractal methods under different PSC systems from a larger field-scale are needed. Besides, further studies should focus on the range of particle size correlated with \(D\)s by using networks analysis or other methods.
# Impact of Wind on the Movement of the Load Carried by Rotary Crane
## 2 Model of the Studied System  
The model of the analyzed system (Figure 1) was developed based on a real object. The computational model may be divided into two coupled parts: kinematic--where movement parameters of the studied system were determined and dynamic--and the response of the system to kinematic forces.  
Figure 1: Model of the system: truck crane—carried load.  
In the present paper, the coupled kinematic and dynamic models were developed according to certain assumptions [1; 16]. The kinematic aspect concerns the analysis of a two-member telescopic boom and pulley system of a truck crane. The dynamic part, as well as wind influence, is related to the movement of the load. The influence of wind force on boom dynamics has not been taken into account. The main systems of the rotary crane were treated as rigid bodies, and the mobile crane was set on a rigid base. The boom of the crane was treated as a beam with infinite stiffness and variable length. The load was treated as a stable, static body attached at point \(\Omega\) on a nondeformable cable having variable length resulting from the work of the winch, boom movement, and extension. The direction of wind power impact was adopted as constant and acting following the directions of the \(X\) or \(Y\) axes of the global coordinate system.  
Due to the adopted assumptions, the following rectangular systems were introduced: \(O_{1}x_{1}y_{1}z_{1}\)--the inertial system connected with the chassis of the crane; \(O_{2}x_{2}y_{2}z_{2}\)--the movable system connected with the point of the boom's support in the crane body; \(\Omega\xi\eta\)--the movable system connected with the boom tip (point \(\Omega\)); and \(OXYZ\)--the movable system connected with the load. Additionally, two systems of curvilinear coordinates were introduced: cylindrical coordinates \(R\), \(\alpha\), \(Z\) in the system \(O_{1}x_{1}y_{1}z_{1}\), and spherical coordinates \(r\), \(\theta\), \(\varphi\) in the system \(O_{2}x_{2}y_{2}z_{2}\).  
The kinematic problems of truck cranes were further described in Works [1; 18], where the kinematics of point \(\Omega\) were presented. The control of this type of system takes place through changes of the following coordinates: \(r\)--boom length, \(\theta\)--boom inclination angle, \(\varphi\) and \(\alpha\) (where \(\alpha=\varphi+\pi\))--boom and crane body rotation angles. The movement of the load is a composition of the boom tip movement \(\Omega\) and the center of mass of the carried load, which is dependent on the change of cable length coordinate \(\rho\), presented by the generalized coordinates as \(\rho^{2}=\xi^{2}+\eta^{2}+\zeta^{2}\).
# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 3 Results  
### Results of Benchmark Forest/Non-Forest Masking  
According to the FNF maps, at the Peru site 81.0% of the area is forest and 19.0% is non-forest, at the Gabon site 94.7% is forest and 5.3% is non-forest. These values were used for the sampling design and to calculate the respective plot weights. In total, we interpreted 5010 samples for Peru and 6000 samples for Gabon. Table 1 summarizes the main validation results. The user and producer accuracies for forest are very high, while the values for NF are considerably lower. However, the overall accuracies are also high due to the large share of forest (0.93 for Peru and 0.99 for Gabon). Tables 11 and 12 provide the individual confusion matrices including all accuracy measures.  
Figure 6: Peru validation site. Green areas show the initial optical forest mask as of 03/2016. Red polygons are the disturbances detected by the “Union” approach. The dots are the 575 validation points from stratified random sampling of which 385 are located within forest in 3/2016 (yellow) and 170 are within non-forest areas (blue) based on visual interpretation of VHR and HR imagery.
# Influence of the Microwave Heating Time on the Self-Healing Properties of Asphal Mixtures
###### Abstract  
This paper aims to evaluate the influence of the microwave heating time on the self-healing properties of fibre-reinforced asphalt mixtures. To this purpose, self-healing properties of dense asphalt mixtures with four different percentages of steel wool fibres were evaluated as the three-point bending strength before and after healing via microwave heating at four different heating times. Furthermore, the thermal behaviour of asphalt mixtures during microwave heating was also evaluated. With the aim of quantifying the efficiency of the repair process, ten damage-healing cycles were done in the test samples. In addition, self-healing results were compared with the fibre spatial distribution inside asphalt samples evaluated by CT-scans. Crack-size change on asphalt samples during healing cycles was also evaluated through optical microscopy. It was found that the heating time is the most influential variable on the healing level reached by the asphalt mixtures tested by microwave radiation. CT-Scans results proved that fibre spatial distribution into the asphalt mixtures play an important role in the asphalt healing level. Finally, it was concluded that 40 s was the optimum heating time to reach the highest healing levels with the lowest damage on the asphalt samples, and that heating times over 30 s can seal the cracks, thus achieving the self-healing of asphalt mixtures via microwave heating.  
self-healing; asphalt mixture; metallic fibres; microwave heating; heating time +
Footnote †: journal: Applied  
_Article_
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 5 Discussion
### Limitations  
This study's constraints include both the data sample and the methodological approach. Regarding the data sample range, it is essential to acknowledge that the study's findings can not be easily extrapolated to all kinds of populations or different contextual settings. This limitation arises due to the limited sample size, which may not represent the broader populace. To mitigate this constraint, prospective research endeavors should consider acquiring data from a more expansive and heterogeneous sample, facilitating a wider scope of generalizability.  
Regarding selecting the ANFIS algorithm, two significant factors are essential for its deployments: specialized expertise and hardware resources. Both of these constraints could restrict its applicability in specific organizational settings. It also has to consider the relatively compact dataset comprising 450 records. This may elucidate why the performance of the XGBoost model does not exceed that of the GBM model. One plausible interpretation for this divergence could be overfitting, stemming from XGBoost's additional hyperparameters. Consequently, future research should consider these limitations to enhance the robustness and generalizability of the study's outcomes while considering the pragmatic constraints associated with ANFIS in real-world public-sector scenarios.
# An Evolutionary-Based MPPT Algorithm for Photovoltaic Systems under Dynamic Partial Shading
## 4 Test Case Simulations and Results
### Results: Rectangular Shape
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Efficiency**} \\ \cline{2-7}
**MPPT** & \multicolumn{2}{c}{**2 \(\times\) 1**} & \multicolumn{2}{c}{**3 \(\times\) 1**} & \multicolumn{2}{c}{**4 \(\times\) 1**} \\ \cline{2-7}  & **Sim.** & **Exp.** & **Sim.** & **Exp.** & **Sim.** & **Exp.** \\ \hline Perturb \& Observe—A & 95.34\% & 93.66\% & 96.19\% & 97.93\% & 95.37\% & 95.41\% \\ Perturb \& Observe—B & 95.85\% & 95.54\% & 93.01\% & 93.29\% & 94.23\% & 92.76\% \\ Perturb \& Observe—C & 93.40\% & 91.04\% & 94.34\% & 91.94\% & 93.48\% & 94.52\% \\ Incremental Conductance & 94.92\% & 92.74\% & 95.88\% & 94.97\% & 95.28\% & 95.68\% \\ Constant Voltage & 68.10\% & 69.37\% & 64.56\% & 63.38\% & 60.18\% & 60.09\% \\ Open Voltage & 79.33\% & 78.77\% & 73.56\% & 73.15\% & 71.38\% & 71.74\% \\ Short Current Pulse & 93.87\% & 93.17\% & 93.99\% & 94.25\% & 91.33\% & 89.30\% \\ PSO\(-3\) particles & 92.54\% & 98.40\% & 93.48\% & 91.35\% & 88.77\% & 85.99\% \\ PSO\(-4\) particles & 91.55\% & 91.65\% & 90.84\% & 88.02\% & 85.66\% & 82.86\% \\ PSO—5 particles & 90.80\% & 90.08\% & 91.76\% & 88.92\% & 89.42\% & 89.60\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Efficiency—Rectangular shapes (simulations and preliminary experimental tests). Figure 7: Rectangular shape—2 \(\times\) 1: (**a**) P&O—B; (**b**) PSO 3 particles; (**c**) PSO 4 particles; (**d**) PSO 5 particles.
# Prediction Equation for Horizontal Ground Motion Peak Values in the Loess Plateau Region
## 2 Methods and Data
### Relationship Between Magnitude and the Farthest Triggering Distance of Strong Motion Instruments  
Equation (2) expresses the relationship between magnitude (\(M_{\rm S}\)) and the farthest triggering distance (\(R\)) of the instruments capturing strong motion in the Loess Plateau region. This parabolic relationship indicates that the farthest epicentral distance where strong motion stations are triggered increases as the magnitude increases. Notably:  
\[R=-61.9-7.9M_{\rm S}+13.9M_{\rm S}^{2} \tag{2}\]  
where \(R\) (unit: km) represents the farthest triggering distance and \(M_{\rm S}\) (unitless) is the surface wave magnitude. This relationship is derived from strong motion observations in the Loess Plateau region. It applies to \(M_{\rm S}\) values ranging from 2.4 to 7.1, with corresponding epicentral distances between 0 and 400 km.
## 2 Signal and Frequency Offset Model
### CRLB of Frequency Estimation  
The Cramer-Rao Lower Bound of frequency estimation is [30]  
\[CRLB=\frac{3}{2\pi^{2}SNR\cdot N(N^{2}-1)}, \tag{2}\]  
where \(SNR\) is the signal-to-noise ratio (\(SNR\)). Its definition is \(SNR=\frac{A^{2}}{\sigma^{2}}\).