esa-sceva/satcom-chunk-collection huggingface dataset:
# Microstructure-Based Prediction of the Elastic Behaviour of Hydrating Cement Pastes
## 1 Introduction
The latest updated version of HYMOSTRUC3D was used in the present study to model the cement paste microstructure. HYMOSTRUC3D model uses a vector approach, with no theoretical limitation (apart from the required computational time) for considering the small size of the cement particles. These particles are assumed as spherical grains randomly distributed in the REV, which is typically considered as a cubic cell. The inward and outward growth of the spherical grains represents the hydration progress of the cement particles. This paper is an extension of the work recently carried out by the authors [12], in which the cement paste microstructure is discretised with lattices of space-filling polyhedra. The main scope is devoted to modelling the elastic performance of cement pastes, accounting for the elastic moduli of the chemical phases and the interface elements at the microscale. The lattice model in [12], constructed based on cubic polyhedra [13; 14], was not physically representative of the grains shape, the boundaries and the coordination with neighbouring grains. Furthermore, the preliminary model could not properly estimate the deformation of the microstructure due to the Poisson's effect, and thus a significant underestimation of the elastic modulus was obtained [12]. As an improvement, a lattice model based on truncated octahedrons is adopted in the present paper, showing a better performance to estimate the macroscopic elastic modulus of cement pastes, when compared to the previous model. The results obtained with the new lattice model are compared with the ones assuming a classical continuum approach but using a discretisation via the FEM, where 8-nodded solid hexahedral elements are used. Predictions from lattice and FEM models are compared to experimental results obtained through the Elasticity Modulus Measurement through Ambient Response Method (EMM-ARM) testing technique, firstly developed in 2008 for concrete [15], and further enhanced to be used for cement pastes [16]. Finally, a numerical study was carried out towards evaluating the influence of some involved microstructural parameters on the predictions of the macroscopic elastic moduli of the cement pastes.
# Gait Phase Identification and Damping Control for Knee Orthosis Using Time Series Forest Classifier
###### Abstract
The objective of this study is to develop a methodology for identifying gait phases and determining the activation level of a magnetorheological brake-mounted knee orthosis based on simulated gait knee moment. The application of a time forest tree classifier algorithm proves effective in accurately identifying gait phases, while the knee moment is obtained through an OpenSim simulation of the gait. Foot switches are utilized to collect data for labeling and to determine the individual percentage gait share. Through this approach, the study aims to provide insights into accurately identifying gait phases and quantifying the activation level of the knee orthosis based on knee moment during simulated gait.
## 3 Results and Discussion
### Analysis of Model Optimization Results
A lower failure rate of the drainage system highlights enhanced reliability and stability, thanks to better pipe design and regular maintenance. This reduces the risk of waterlogging from system malfunctions, boosting the drainage network's overall performance. The comprehensive solution package presents significant advantages for managing drainage challenges in the Wujin district. By integrating various strategies, this package reduces the frequency of internal flooding and peak water depth, enhances drainage efficiency, controls peak flow rates, increases groundwater recharge, and makes better use of rainwater resources. Though the initial implementation cost may be high, overlooking the long-term benefits would be a mistake. In densely populated and economically active areas--like central business districts and school zones--flooding can severely disrupt everyday life and regular business activities. This integrated approach helps minimize such disruptions, ensuring the safety of residents and stability for commercial activities. Moreover, this package also supports the sustainability of protected ecological areas. Measures like permeable paving and rain gardens contribute to maintaining ecological balance and reducing the impact of human activities on natural environments. From a maintenance and long-term perspective, the comprehensive package can extend the service life of the drainage system, ultimately reducing long-term costs by cutting down on the need for frequent repairs and upgrades.
## 2 Materials and Methods
### Procedures
#### 2.2.8 Range of Motion  
Passive hip range of motion (ROM) during flexion, extension and internal/external rotation was measured with a digital inclinometer (Baseline, Fabrication Enterprises Inc., White Plains, NY, USA) and abduction/adduction with a handheld goniometer (Baseline, Fabrication Enterprises Inc., White Plains, NY, USA). All of the measurements were performed by the same measurer to minimize error. For flexion and extension, the inclinometer was aligned between the tested side femur trochanter major and lateral condyle. Hip flexion ROM was measured with the subject in supine position (the non-tested leg was extended in knee and hip fixated) and the knee of the tested leg in extended position. The tested leg was then moved and kept extended during whole movement until first pelvic movement. During hip extension ROM, subject was in prone position, and tested leg was kept in knee flexion (~90\({}^{\circ}\)) during whole movement until first pelvic movement. Hip internal/external rotation ROM was measured with the inclinometer located in the center of vertically positioned (with pendulum) tibia with the subject in pronated position and knee in 90\({}^{\circ}\) flexion. Internal and external rotation were performed until the first pelvic movement, with hand-stabilization on the pelvis. Hip abduction and adduction ROM were measured with goniometer with stationary arm pointed toward the opposite anterior superior iliac  
Figure 1: Position of subjects during isometric hip strength testing. (**A**) flexion; (**B**) extension; (**C**) abduction/adduction; and (**D**) internal/external rotation.  
spine and the movable arm pointed toward the patella of the tested leg. During abduction, the subject was laying supine with both legs in neutral position (start) from which the tested leg was moved into abduction until first pelvic movement (finish). During adduction, the subject was supine with non-tested leg laying from table in \(\sim\)30\({}^{\circ}\) abduction and tested leg in neutral position (start) from which leg was moved into adduction until first pelvic movement (finish). Mean result from three attempts on each leg was taken for further analysis with all of the results expressed in degrees (\({}^{\circ}\)).  
For the trunk lateral flexion ROM, the subject was standing barefoot with feet hip width distance apart with his back and heels touching the wall. Starting position was measured with a tape as distance between the middle finger (hand on the wall) and the floor. Subjects performed lateral flexion, sliding downwards with the hands without breaking contact with the wall and lifting their heels of the ground. At the end of movement, the end position was measured, and the difference between starting and end position was calculated. Mean result from three attempts on each side was taken for further analysis with all of the results expressed in meters (m).
# Micro-Blog Sentiment Classification Method Based on the Personality and Bagging Algorithm
## 5 Micro-Blog Sentiment Classification Experiment
### Basic Sentiment Classifier Comparison  
This experiment was mainly used to verify the classification precision of each basic sentiment classifier, including five personality basic emotion classifiers and a general basic emotion classifier. The dataset contains 10,474 texts, comprising 3151 high conscientiousness datasets (HC), 3188 high agreeableness datasets (HA), 3204 low agreeableness datasets (LA), 5585 high extroversion datasets (HE), and 3154 low extroversion dataset (LE). For comparison purposes, 3600 data points were randomly extracted from the universal set to form the dataset (ALLpart) that has an equal number of samples for each category. The experimental results are shown in Table 3.  
As shown in Table 3, the F1 values of the HA classifier and HC classifier in the five personality basic emotion classifiers are higher than the ALL classifier. This indicates that the targeted sentiment analysis is effective on the different personality sets.  
HC has the highest accuracy among the five sentiment classifiers. However, the overall accuracy is not particularly high, which could be the result of having a small dataset. Another possible explanation could be that, since the personality set is divided according to user personality, some errors may exist in classifying the user's personality, which could decrease the accuracy of the trained sentiment classifier.
# Evaluation of pH and Optical Properties of Dual Ringe HEDP Irrigating Solution
## 4 Discussion
While specific overtones were not explicitly reported in our study, the presence of absorption peaks beyond those expected solely from electronic transitions suggests the possibility of overtones contributing to the observed absorption spectra. Further investigations focusing on vibrational and rotational modes within the molecules could help elucidate the presence and significance of overtones in the absorption spectra of endodontic irrigants, providing additional insights into their optical behavior in the NIR spectrum. Despite the comprehensive exploration of the optical properties of endodontic irrigants across different wavelengths in our study, it is important to acknowledge a significant limitation. Our investigation primarily focused on the UV-Visible-NIR wavelength range, due to the limitations of our spectrophotometer, which encompasses wavelengths up to approximately 1100 nm. However, the infrared spectrum, particularly the wavelengths commonly employed in erbium lasers (around 2940 nm), was not within the scope of our study. Erbium lasers have gained attention in LAI for their ability to interact with water and hydroxyapatite effectively, due to high absorption levels at that wavelength. [PERSON] et al. [31] reported high absorption coefficients in the infrared spectrum, especially at 2940 nm, for DW, NaOCl, EDTA, CA, and CHX. Numerous studies have reported the superior antimicrobial and cleaning effects of erbium lasers in LAI with NaOCl and EDTA [46; 72; 73]. However, there are a limited number of studies on HEDP + NaOCl in LAI with erbium lasers showing bactericidal [69] and sealer-removal effects [15] with promising results. Given the similar optical properties of HEDP + NaOCl to those of NaOCl in our study, it is reasonable to speculate that they would demonstrate similar optical properties in the infrared spectrum. Considering its significance in endodontic applications, future research should focus on exploring the optical properties of Dual Rinse HEDP in the infrared spectrum, particularly in the context of erbium laser activation.
## Applying a Dynamic Resource Supply Model in a Smart Grid  
**[PERSON] 1,*, [PERSON] 1,2, [PERSON] 1 and [PERSON] 1**  
1 Department of Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, Suzhou 215123, China; E-Mails: [EMAIL_ADDRESS] (Y.D.); [EMAIL_ADDRESS] (Q.C.); [EMAIL_ADDRESS] (T.Q.)  
2 Department of Computer Science, University of Liverpool, Liverpool L69 3 BX, UK  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-512-8816-1507.  
_Received: 9 June 2014; in revised form: 29 August 2014 / Accepted: 5 September 2014 / Published: 22 September 2014_  
**Abstract:** Dynamic resource supply is a complex issue to resolve in a cyber-physical system (CPS). In our previous work, a resource model called the dynamic resource supply model (DRSM) has been proposed to handle resources specification, management and allocation in CPS. In this paper, we are integrating the DRSM with service-oriented architecture and applying it to a smart grid (SG), one of the most complex CPS examples. We give the detailed design of the SG for electricity charging request and electricity allocation between plug-in hybrid electric vehicles (PHEV) and DRSM through the Android system. In the design, we explain a mechanism for electricity consumption with data collection and re-allocation through ZigBee network. In this design, we verify the correctness of this resource model for expected electricity allocation.  
**Keywords:** cyber-physical system; dynamic resource supply model; service-oriented architecture; smart grid; web service; ZigBee network
## 3 Results  
### Analysis of the Main Effects and Interactions  
A preliminary analysis of the effects on the factorial design is shown in Figure 7. There is a low effect of the three factors, X1 Size of the AgNPs, X2 Concentration of AgNPs, and X3 Ultrasonic treatment on the samples, on the fungal growth based on a qualitative evaluation. The factors present a similar effect on the fungal growth, obtaining the highest inhibition on their low values. This analysis indicates a more noticeable change in the variable, indicating that the ultrasonic treatment shows a significant effect over the fungal growth inhibition.  
The graphic of the interactions, Figure 8, shows the effect of the interaction between the nanoparticle size and concentration, considering that inverted slopes can be observed on their high and low levels; therefore, both slopes change at the same time. Between the nanoparticle size and the ultrasonic treatment, the effect of the interaction can be observed as well, and it is opposite to the previous case. The nanoparticle size does not show interaction with the ultrasonic treatment due to their slopes being equal.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Number of variables & 3 \\ Population size & 50 \\ Limits & \([-1,-1,-1]\); \([1,1,1]\) \\ Selection function & Uniform stochastic \\ Initial value & \([-10,10]\) \\ Elite counting & \(0.05\times\) initial population \\ Crossover fraction & 0.8 \\ Mutation function & Dependent on restrictions \\ Migration direction & Forward \\ Generations & 100 \(\times\) Number of variables \\ Stagnant generations & 50 \\ Tolerance of the function & \(1\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters for the genetic algorithm.  
This means that a strong interaction exists between the size of the nanoparticle and the concentration used. The effect is much more significant in the case of the concentration and the nanoparticle size than on the concentration and ultrasonic treatment, i.e., if the high concentration of nanoparticles is used with a low ultrasonic treatment time, less fungal growth is observed.
# Wind Load Distribution in Float Photovoltaic System
###### Abstract  
This paper investigates wind load distribution in float PV plants. Wave and wind load are dominant environmental load factors in determining design load in float PV plants. In particular, wind load is determined based on the numerical analysis results. The literature indicates that several input parameters exist, such as inlet angle and space between PV modules. An exemplary structure with ten arrays of PV modules was generated in this study. To investigate the wind load distribution in a float PV plant, the computational fluid dynamic (CFD) analysis was conducted with variables including wind direction (inlet angles) and three wind speeds (36.2, 51.7, and 70 m/s) in PV modules in the floating structure. Based on the numerical analysis, the wind load distribution of PV modules can be characterized with respect to the inlet angle and wind speed. The numerical results show that the wind loads in the central arrays are dominant.  
wind load; PV plant; computational fluid dynamic analysis; array +
Footnote †: journal: Applied
# A PSO-Based Uneven Dynamic Clustering Multi-Hop Routing Protocol for Wireless Sensor Networks
###### Abstract  
Since wireless sensor networks (WSNs) are powered by energy-constrained batteries, many energy-efficient routing protocols have been proposed to extend the network lifetime. However, most of the protocols do not well balance the energy consumption of the WSNs. The hotspot problem caused by unbalanced energy consumption in the WSNs reduces the network lifetime. To solve the problem, this paper proposes a PSO (Particle Swarm Optimization)-based uneven dynamic clustering multi-hop routing protocol (PUDCRP). In the PUDCRP protocol, the distribution of the clusters will change dynamically when some nodes fail. The PSO algorithm is used to determine the area where the candidate CH (cluster head) nodes are located. The adaptive clustering method based on node distribution makes the cluster distribution more reasonable, which balances the energy consumption of the network more effectively. In order to improve the energy efficiency of multi-hop transmission between the BS (Base Station) and CH nodes, we also propose a connecting line aided route construction method to determine the most appropriate next hop. Compared with UCCGRA, multi-hop EEBCDA, EEMRP, CAMP, PSO-ECHS and PSO-SD, PUDCRP prolongs the network lifetime by between 7.36% and 74.21%. The protocol significantly balances the energy consumption of the network and has better scalability for various sizes of network.  
particle swarm optimization; clustering; routing protocol; wireless sensor networks +
Footnote †: journal: Sensors
## 4 Target State Prediction with Uncertainty Estimation  
It is necessary to consider the uncertainty of the prediction in order to improve the safety and performance of autonomous vehicles on urban roads. Therefore, this study integrates the SMPC approach with the prediction uncertainty estimator to improve the performance of SMPC by updating the probability distribution of chance constraints every moment. Before discussing SMPC, the driving mode and constraints are properly defined based on the predicted states of intersection-approaching vehicles. Therefore, the robust motion planner for intersection passing is composed of three sub-modules: The driving mode decision, the defining prediction-based constraint, and solving SMPC. The details of each module will be discussed in the following sections.
# Metallurgical Aspects in the Welding of Clad Pipelines--A Global Outlook
## 3 Microstructure Evolution
### Solidification
#### 3.1.2 Numerical Simulation of Solidification Parameters
It is important to note that the \(R\) parameter is very sensitive to the \(\alpha\) angle (based on Equation (6) and Table 4). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Cooling Rate,** & **Temperature Gradient,** & **Solidification Growth** & \multirow{2}{*}{\(F\)**-Factor = \(G\)/R**} \\  & \(\epsilon\) (\({}^{\circ}\)C/s) & **Gradient,** \(G\) (\({}^{\circ}\)C/mm) & **Rate,** \(R\) (mm/s) & \\ \hline Inconel 625/Alloy 59 & 323 & 242 & 79 & 1.336 & 181 \\
316L SS & 241 & 166 & 78 & 1.455 & 114 \\ X65 carbon steel & 373 & 149 & 69 & 2.509 & 59 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Estimated solidification parameters at the liquid/solid interface, i.e., solidification front. Cooling rate is calculated for the temperature range from the peak temperature to solidus point. Figure 7: Numerically simulated weld pool profiles from a top-down view (middle plane) and identification of the solidification angle \(\alpha\) at specific points. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Cooling Rate,** & **Temperature Gradient,** & **Solidification Growth** & \multirow{2}{*}{\(F\)**-Factor = \(G\)/R**} \\  & \(\epsilon\) (\({}^{\circ}\)C/s) & **G** (\({}^{\circ}\)C/mm) & **Rate,** \(R\) (mm/s) & \\ \hline Inconel 625/Alloy 59 & 1122 & 160 & 7.0 & 23 \\
316L SS & 925 & 132 & 7.0 & 19 \\ X65 carbon steel & 874 & 125 & 7.0 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Estimated solidification parameters at the WM centerline on top-plane. Cooling rate is calculated for temperature range from peak temperature to solidus point. The primary dendrite arm spacing (\(\lambda_{1}\)) is shown in Figure 8, and has a strong effect on mechanical properties [36]. Based on [PERSON] and [PERSON] [47], a generalized (simplified) formula for the primary dendrite spacing is:  
\[\lambda_{1}=AG^{-m}R^{-n} \tag{8}\]  
here, \(m\) (=0.5) and \(n\) (=0.25) are constants, and \(A\) is a composition-dependent (alloy) material parameter. Since \(GR\) values are obtained and \(\lambda_{1}\) can be measured from micrographs, the \(A\) parameter can be estimated based on Equation (8). The secondary dendrite arm spacing (\(\lambda_{2}\), Figure 8) may also strongly influence the mechanical properties. Its size will also depend on the cooling rate [36]:  
\[\lambda_{2}=B(GR)^{-n} \tag{9}\]  
where \(B\) is a material-dependent parameter and \(n\) (=1/3) is a constant. Tertiary dendrites (\(\lambda_{3}\), Figure 8) have minor effects, and some related studies can be found in [48]. Therefore, these are not discussed in detail here due to their lower importance.