esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Strain-Rate-Dependent Coefficients  
By analyzing the data provided in Table 3, obviously, the coefficients of each parabola are related to the strain rate. It can be concluded that the greater the strain rate, the higher the peak of the parabola, and the normal and shear strengths increase. We assume that the coefficients of the parabolas are linearly proportional to the logarithm of strain rates, which can be written in the following form. The following relationships between the coefficients and the strain rate were derived:  
\[m\big{(}\dot{\epsilon}\big{)} = m_{0}\bigg{(}0.055\cdot lg\frac{\dot{\epsilon}}{\dot{\epsilon}_{ 0}}\bigg{)}+1 \tag{13}\] \[n\big{(}\dot{\epsilon}\big{)} = n_{0}\bigg{(}0.065\cdot lg\frac{\dot{\epsilon}}{\dot{\epsilon}_{ 0}}\bigg{)}+1\] (14) \[\alpha\big{(}\dot{\epsilon}\big{)} = \alpha_{0}\bigg{(}0.053\cdot lg\frac{\dot{\epsilon}}{\dot{\epsilon}_ {0}}\bigg{)}+1 \tag{15}\]  
The coefficient \(k\) shows minimal sensitivity to the logarithm of the strain rate and can be considered approximately constant, with a value of \(k\) = 0.001 MPa\({}^{-1}\). The values of \(m_{0},~{}n_{0},~{}\alpha_{0}\) are extracted from Table 3 under the condition of quasi-static loading with \(\dot{\epsilon}_{0}=10^{-4}\) s\({}^{-1}\). Figure 7 presents a comparison between the proposed dependencies and the data obtained from Table 3, which were derived from experimental results. Figure 7  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Loadings** & \(\dot{\epsilon}\)**\(\mathbf{s^{-1}}\)** & \(\mathbf{*}\)**\(\mathbf{R^{2}}\) (MPFE)** & \(\mathbf{R^{2}}\) (NU-Daniel)** \\ \hline Quasi-static & 0.0001 & 0.864 & 0.838 \\ Static & 1 & 0.914 & 0.900 \\ Dynamic & 400 & 0.957 & 0.951 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Determination coefficient values for the failure envelopes.  
Figure 6: Comparison of theoretical failure envelopes for three criteria and the experimental data at three different strain rates.  
clearly demonstrates a strong correspondence and agreement between the proposed dependencies and the experimental data, highlighting the accuracy and reliability of the proposed approach.  
By substituting the relationships \(m\left(\dot{e}\right)\), \(n\left(\dot{e}\right)\), \(\alpha\left(\dot{e}\right)\) into Equation (5) in place of the coefficients \(m\), \(n\), and \(\alpha\), the resulting failure envelopes \(f(\sigma_{2},\tau_{6})\) will take the form of equations that are dependent on the logarithm of the strain rate. This formulation allows for a more comprehensive representation of the failure envelopes in relation to the varying strain rates.
###### Abstract  
The present study investigated the effects of sprouted barley (SB) with different cultivation stages on fermentation characteristics and degradation kinetics in the rumen. The SB was cultivated in three different stages as follows: 0, 4, and 8 days. Dried samples from each cultivation stage of SB were incubated in the rumen buffer at 39 \({}^{\circ}\)C for 48 h in quadruplicate with three blanks. Dry matter (DM) and neutral detergent soluble carbohydrate concentrations of SB decreased linearly (\(p\) = 0.001) by increasing the cultivation stage, while crude protein, neutral detergent fiber (NDF), acid detergent fiber, and hemicellulose concentrations increased linearly (\(p\)\(\leq\) 0.001). Total volatile fatty acid and butyrate in the rumen decreased linearly (\(p\)\(\leq\) 0.020) by increasing the cultivation stage, while pH and propionate increased linearly (\(p\)\(<\) 0.001). The total degradation fraction of DM and NDF increased quadratically (\(p\)\(\leq\) 0.003). The fraction degradation rate of DM and NDF decreased linearly (\(p\)\(\leq\) 0.001) by increasing the cultivation stage, while the lag phase increased linearly (\(p\)\(\leq\) 0.010). The present study concluded that cultivated SB at 4 days was recommended for animal feed due to the highest nutrient degradation in the rumen without any adverse effects on fermentation characteristics.  
_in vitro_; rumen degradation kinetic; rumen fermentation; sprouted barley +
Footnote †: journal: Applied Life Science
# Nozzle with a Feedback Channel for Agricultural Drones
## 3 Experiment Methods  
### Spraying Performance  
The spraying performance of an agricultural drone was evaluated, using parameters such as spraying uniformity, droplet size and speed, adhesion rate of spray particles, and scattering characteristics. The selected variables depend on the objective and evaluator. The Korean government uses spraying uniformity as a criterion for agricultural drone certification [21]. Therefore, in this study, the spraying performance of the proposed and conventional drone types was compared, based on the spraying uniformity and droplet size. These parameters can evaluate the sole effect of the nozzle design on the spray performance without the influence of external factors, such as wind and temperature.  
Figure 1: Expected trajectory of spray for (**a**) commercial nozzle and (**b**) developed nozzle.  
Figure 2: Structure of the proposed nozzle.  
Actually, the spray uniformity in the agricultural drone certification means that the width has effective spraying amount within the maximum spraying width from one point. This effective width can be used to decide the distance between the cross-flights of the agricultural drone. The full width at half maximum (FWHM) is a value that represents the width of a curve between points on half of the maximum value and is used to indicate the size of the function for spraying distributions. In this study, FWHM was used to calculate the spraying width, and the spraying uniformity was compared in terms of the spraying amount and droplet size according to the radial position.
## 3 Methods
### Debris Flow Runout Simulation  
We created an initial stand-alone model of the observed debris flow that was subsequently validated in the field in order to estimate the potential of hazard modelling at local scale. Simplified numerical modelling of runout, velocity and deposition dynamics was performed using the optimized random walk-based, two-parameter friction model (PCM) integrated in the open source Gravitational Process Path (GPP) framework in SAGA GIS ([[https://saga-gis.sourceforge.io/](https://saga-gis.sourceforge.io/)]([https://saga-gis.sourceforge.io/](https://saga-gis.sourceforge.io/)), accessed on 4 January 2022 [41]). The detailed mathematical functionality of the framework can be found in [42; 43]. The random walk spreading parameters were set as suggested by [42]. To pinpoint the required initial source areas, the hillslope debris flow disposition index (HDI) approach [44] was calculated. The thickness of the mobilized unconsolidated material was derived from the UAV-generated mass balance and spatially integrated into the model. The two parameters, which primarily control the velocity and runout behaviour, the mass to drag ratio \(m/d\) (internal friction) and the sliding friction coefficient \(\mu\) (basal friction), usually have to be calibrated to model specific flow events [42; 43; 45; 46]. We approximated the values for the Biersdorf hillslope debris flow based on own field investigations by means of analysing the flow length, mass balance, significant shifts in grain size in the scar and transition track, and optimized it by soil data provided by the Geological Survey of Rheinland-Pfalz (LGB, [40]) and the approach presented by [43], to reach the best fitting model composition (Table 2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Parameters** & **Specifications/Values** & **Data Source** \\ \hline DEM & 1 \(\times\) 1 m & ALS flight mission 2018 \\ Rainfall intensity & 10.8 mm/h & Wiersdorf weather data [27] \\ Infiltration rate & 0.5 mm/h & saturation model [28], estimation \\ Manning’s & Landcover based- & Orthophotography, [40] \\ roughness coefficient & spatially distributed & \\ Iterations & 60 min & Wiersdorf weather data [27] \\ Walkers & 10,000 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Crucial model input parameters used with r.sim.water.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input Parameters** & **Specifications/Values** & **Data Source** \\ \hline DEM & 1 \(\times\) 1 m & ALS flight mission 2018 \\ Process path model & Random walk & [47] \\ Slope threshold & 40 & [42] \\ Lateral spreading exponent & 2 & [42] \\ Persistence factor & 1.5 & [42] \\ Runout model & PCM & [48] \\ Source areas & Index grid & HDI [44] \\ Initial material thickness & 3–5 m & BK 50 (Soil Data, LGB [40]) \\ source areas & 3–5 m & and Field Investigation \\ SF coefficient (a) & 0.22-0.46 & Field Investigation and [43; 46] \\ Mass to drag ratio (\(m/d\)) & 60 m & Field Investigation and [43] \\ Initial velocity & 0.5 m/s & - \\ Iterations & 1000 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Crucial model input parameters used in GPP.
## 3 Results
### Variation of \(T_{2}\) Spectra During Evaporation  
The variations of \(T_{2}\) spectra of montmorillonite-quartz and kaolinite-quartz mixtures during the evaporation process are shown in Figures 6 and 7, respectively. All the \(T_{2}\) spectra were single peaks, which meant that the pores in mixtures were distributed continuously. The peak signal amplitude kept decreasing during evaporation. The decay rate of the peak signal amplitude reduced gradually. All the \(T_{2}\) spectra of mixtures shifted to the left during the evaporation process. This meant that the pore sizes of the mixture decreased due to volume shrinkage during evaporation.  
The variations of the \(T_{2}\) total signal amplitude with respect to the heating time are shown in Figure 8. For the same heating time, the mixtures with a higher clay content had a bigger \(T_{2}\) total signal amplitude. With the same clay content, the \(T_{2}\) total signal amplitude of montmorillonite-quartz mixtures was bigger than that of kaolinite-quartz mixtures. The \(A_{t}\) kept decreasing during evaporation and the decay rate reduced gradually, until it reached 0.  
Figure 5: The relationship between \(T_{2 gm}\) and the clay content (CC). The lines at the top and bottom indicate the relationships between the \(T_{2 gm}\) and \(CC\) of kaolinite-quartz (K-Q) and montmorillonite-quartz (M-Q) mixtures, respectively. The Equations in the two boxes are the Equations of the two lines. \(R^{2}\) indicates the correlation coefficient of the Equation.  
Figure 6: The variation of \(T_{2}\) spectra of montmorillonite-quartz mixtures: (**a**) M40; (**b**) M60; (**c**) M80; (**d**) M100. In (**a–d**), from top to bottom, curves with different colors and symbols with different shapes indicate \(T_{2}\) spectra of montmorillonite-quartz mixtures after heating at different times. The \(T_{2}\) spectra in the box were enlarged and placed in the upper right corner of (**a–d**).  
Figure 8: Variation of the \(T_{2}\) total signal amplitude (\(A_{t}\)) with respect to the heating time. From top to bottom, curves with different colors and symbols with different shapes indicate variation of M40, M60, M80, M100, K40, K60, K80, and K100, respectively. The scales of the Y axes of the upper and lower parts of the figure are different.  
Figure 7: The variation of \(T_{2}\) spectra of kaolinite-quartz mixtures: (**a**) K40; (**b**) K60; (**c**) K80; (**d**) K100. In (**a**–**d**), from top to bottom, curves with different colors and symbols with different shapes indicate \(T_{2}\) spectra of kaolinite-quartz mixtures after heating at different times. The \(T_{2}\) spectra in the box were enlarged and placed in the upper left corner of (**a**–**d**).  
Figure 9 shows the variations of the decay rate with respect to the heating time. According to the decay rate, the evaporation process could be divided into two stages: the constant rate stage and the falling rate stage. The time when the decay rate started to fall was defined as the falling rate critical time (\(T_{f}\)). The \(T_{f}\) values of the montmorillonite-quartz mixtures and kaolinite-quartz mixtures were 6 and 20 h, respectively.
###### Abstract  
Low-Energy Adaptive Clustering Hierarchy (LEACH) is a typical routing protocol that effectively reduces transmission energy consumption by forming a hierarchical structure between nodes. LEACH on Wireless Sensor Network (WSN) has been widely studied in the recent decade as one key technique for the Internet of Things (IoT). The main aims of the autonomous things, and one of advanced of IoT, is that it creates a flexible environment that enables movement and communication between objects anytime, anywhere, by saving computing power and utilizing efficient wireless communication capability. However, the existing LEACH method is only based on the model with a static topology, but a case for a disposable sensor is included in an autonomous thing's environment. With the increase of interest in disposable sensors which constantly change their locations during the operation, dynamic topology changes should be considered in LEACH. This study suggests the probing model for randomly moving nodes, implementing a change in the position of a node depending on the environment, such as strong winds. In addition, as a method to quickly adapt to the change in node location and construct a new topology, we propose Q-learning LEACH based on Q-table reinforcement learning and Fuzzy-LEACH based on Fuzzifier method. Then, we compared the results of the dynamic and static topology model with existing LEACH on the aspects of energy loss, number of alive nodes, and throughput. By comparison, all types of LEACH showed sensitivity results on the dynamic location of each node, while Q-LEACH shows best performance of all.  
wireless sensor networks; disposable IoT; reinforcement learning; Q-learning; artificial intelligence; LEACH; fuzzy clustering Article
# A Network Analysis-Based Approach for As-Built BIM Generation and Inspection
## 3 Methodology
### Network Establishment  
The attributes extracted from IFC documents include critical details about building components, primarily focusing on their types and 3D dimensions, which are fundamental in creating a comprehensive BIM network. The types contain walls, beams, columns, windows, doors, etc., while 3D dimensions include length, width, and depth information of components. In the BIM network, the abovementioned attributes are used to create nodes and links that represent the building's structure. The network is conceptualised as follows:  
Figure 2: Four-room apartment point cloud dataset.  
Nodes: Each node represents a building element, such as a wall, beam, window, or door. The nodes are crucial points in the network where information about each component is stored and accessed.  
Links: Links are the connections between these nodes, representing the physical and functional relationships between different building elements, for instance, a wall connected to a beam, a window within a wall, or a door providing access between two rooms.  
Figure 3 shows the building elements (nodes) in the as-designed network. Taking one element as an example, it also demonstrates all elements associated with element one and the schematic diagram of its network.
# A Review of Sharp-Switching Band-Modulation Devices
## 1 Introduction
### Implementation in FD-SOI Technology  
Band modulation devices have been designed and fabricated in 14 nm [20] and 28 nm [21] FD-SOI technologies. Both advanced nodes feature promising characteristics, with a slightly different process flow. For instance, in 28 nm FDSOI, the fabrication of a Z\({}^{2}\)-FET structure (Figure 4a) starts by delimiting the device surface using STI isolation. A 25-nm-thick BOX separates the undoped ultrathin silicon film (t\({}_{\text{Si}}\) = 7 nm with -10\({}^{16}\) cm\({}^{-3}\) doping concentration) from a highly P-type doped ground plane (-10\({}^{18}\) cm\({}^{-3}\)), which  
Figure 3: \(Z^{2}\)–FET variants fabricated with FD-SOI technology: (**a**) dual ground-plane, (**b**) partially doped, and (**c**) gateless Z\({}^{3}\)–FET.  
prevents the deep depletion of the substrate and acts as a back-gate. In order to access the GP and control the well potential, the BOX is locally etched. The undoped Si film of the Z\({}^{2}\)-FET is partially covered by a single high-k/metal front-gate with a capacitance equivalent thickness (CET) of 3.7 nm, whereas the rest of the channel is ungated. Thinner dielectrics, down to CET = 1.1 nm, have been successfully tested at lower gate voltages. The gate stack formation starts with a thermally grown oxide interface (SiO\({}_{2}\)), ensuring a good interface quality, clean of fixed charges. The interface SiO\({}_{2}\) layer (IL) is covered by a high-k hafnium dioxide layer (HfO\({}_{2}\), 18 \(<\) k \(<\) 20), improving the current drive and reducing the gate leakage. It is connected via a mid-gap metal layer (TiN or AlTiN) to a low-resistance poly-Si electrode followed by gate patterning and offset spacer formation.  
After completion of the second spacer, the anode and the cathode are implanted with boron and phosphorus (10\({}^{20}\) cm\({}^{-3}\)), respectively. An epitaxial layer (t\({}_{\text{epi}}\) = 15 nm) is grown on the whole structure, to reduce series resistance. A final step, before contact formation, consists in covering the ungated region with an oxide layer (RPO), preventing silicidation.  
Unlike the 28 nm process, in 14 nm FD-SOI technology, the silicon film and the BOX are reduced down to 6 nm and 20 nm, respectively (Figure 4b). The CET of the high-k/metal gate is reduced to 3.4 nm and, for low-voltage operation, to \(\sim\)1 nm. A dual in situ doped (Si:CP/SiGeB) epitaxy exclusively covers the source and drain areas. Moreover, the anode and cathode contacts (sometimes called source and drain) take the form of trenches, permitting integration with a higher density of local interconnections between devices, without resorting to the first metal level. In order to study the impact of silicon film thickness, a variant with a thicker body was also fabricated (t\({}_{\text{Si}}\) = 12 nm, Figure 4c).  
Figure 4: Cross-section views of Z\({}^{2}\)–FETs fabricated with FD-SOI technologies: (**a**) 28 nm node, (**b**) thin 14 nm node, and (**c**) thick 14 nm node.
###### Abstract
Some industrial associations and classification societies have also prepared non-mandatory standards and guidance. For example, Maritime U.K. has developed industrial guidance for MASS less than 24 m in length [18]. DNV GL, a classification society, also sets guidelines on technical rule and acceptance criteria, etc., based on a goal-based approach [19]. These guidelines suggest the necessity of the linkage between human elements including stress and technical requirements. While these activities are expected to accelerate, the inclusion of the detailed elements of MWL in the rules is indispensable for establishing an effective regulatory framework. On the other hand, little research has focused on the relationship between MWL and MASS. [PERSON] et al. [20] measured the MWL of engineering students on two scenarios by using bridge simulators to investigate the relationship between the subjective and physiological change of remote operators. [PERSON] et al. [21] suggested factors that have effects on shore control operators' decisions, such as boredom, by using human reliability analysis. [PERSON] et al. [22] discussed the human-machine interaction of operators from a human factors perspective. Nevertheless, no research comprehensively discusses a model for identifying the factors that influence operators' MWL in the MASS related systems. Based on the above background, this paper aims to construct a scheme for identifying the relationship between MWL and MASS in the maritime domain. The scheme can be utilised for not only rulemaking, but also technology development, focusing on the navigation of a ship (Figure 1). It focuses on a normal navigational condition and excludes emergencies such as fire, collision, and search and rescue to simplify the assumption and eliminate complicated cases, considering that MWL is likely to drastically change in these cases. The remaining parts are as follows. Section 2 explains the mental workload model that is adopted in this research. This section also builds the identification scheme of the relationship between MWL and MASS based on the adopted model. Sections 3-5 carry out the case study on the typical patterns and show the results. Section 6 discusses the results, including verification of the scheme. Section 7 concludes the research.
# Fuzzy Q-Learning Agent for Online Tuning of PID Controller for DC Motor Speed Control
## 2 Preliminaries  
### DC Motor  
An electric motor converts electric energy to mechanical energy by using interacting magnetic fields. Equation (1) gives the relation between the motor torque and the field current. Equation (2) gives the relation between the armature input voltage and the armature current. Equation (3) describes the relation between back electromotive voltage and motor speed. Equation (4) shows the relation between motor torque and motor speed, Equation (5) gives the model of the DC motor speed system while Equation (6) is the transfer function of the motor speed [20].  
\[T_{m}(s)=K_{T}I_{a}(s) \tag{1}\]  
\[V_{a}(s)=(R_{a}+L_{a}s)I_{a}(s)+V_{b}(s) \tag{2}\]  
\[V_{b}(s)=K_{b}\omega(s) \tag{3}\]  
\[T_{m}(s)=J\!\!\cdot\!s\!\cdot\!\omega(s)+b\!\!\cdot\!\omega(s) \tag{4}\]  
\[(Js+b)\omega(s)=K_{T}\frac{V_{a}(s)-K_{b}\omega(s)}{(R_{a}+L_{a}s)} \tag{5}\]  
\[G(s)=\frac{K_{T}}{L_{a}Js^{2}+(R_{a}J+L_{a}b)s+(R_{a}b+K_{b}K_{T})} \tag{6}\]  
where \(T_{m}\) is the torque of the motor, \(K_{T}\) torque constant, \(V_{a}\) the input voltage, \(R_{a}\) the armature resistance, \(L_{a}\) the armature inductance, \(V_{b}\) the back electromotive force, \(K_{b}\) back electromotive force constant, \(\omega\) angular velocity of the motor, \(T_{L}\) load torque, \(J\) rotating inertial measurement of motor bearing and \(b\) fraction constant.
###### Abstract  
This study aimed to evaluate the durability and engineering performance of concrete mixed with locally produced ground granulated blast-furnace slag (GGBS) based on CaO content and ratio, and to derive the optimal CaO content range that can secure durability. Hence, tests were conducted by increasing the GGBS replacement ratio by 10% from 0% to 70%, while the unit binder weight was fixed at 330 kg/m3. The results indicated that the compressive strength exhibited a tendency to increase when the CaO content and basicity increased within 28 d of age, although similar compressive strength characteristics were observed at 56 d of age, irrespective of the CaO content and basicity. Additionally, four test items (i.e., carbonation depth, chloride penetration depth, relative dynamic elastic modulus, and weight reducing ratio) were measured to evaluate durability. The optimal CaO content satisfying all four parameters was observed as ranging between 53% and 56% (GGBS replacement ratio: 27.5%-47.1%). The results of the study can provide guidelines on the mixing proportions of GGBS concrete with excellent durability that can be applied to local construction sites and can be used as basic data to set chemical composition criteria for the development of binders to improve durability.  
d urability performance; engineering performance; CaO content and ratio; ordinary Portland cement; ground granulated blast-furnace slag +
Footnote †: journal: Applied Sciences