esa-sceva/satcom-chunk-collection huggingface dataset:
# A Thermomechanical Coupling Constitutive Model of Concrete Including Elastoplastic Damage
## 1 Introduction
Under the combined action of loads and high temperature, the mechanical properties of concrete deteriorate due to the growth and accumulation of inner cracks. This can be described by the damage evolution. A thermomechanical coupling constitutive model should characterize the damage evolution of concrete under the combined action of high temperature and loads. On the other hand, the present constitutive models can describe physical and mechanical behaviors of concrete such as thermal creep and crack, but they have complex mathematical expressions and include numerous parameters. It is not convenient to use these models for practical problems. In this study, a thermomechanical coupling constitutive model including elastoplastic damage was developed to characterize the mechanical behaviors of concrete under high temperature conditions. The effect of temperature on the material performance is described in a relatively simple way. The temperature variable is included directly in the expressions of the yield function and hardening parameter to characterize the effect of temperature on the elastoplastic mechanical behaviors of concrete. The constitutive model proposed in the current study has relatively simple expressions. Fewer parameters are included in this model, and they all have explicit physical meanings. The developed constitutive model is validated by the comparison of numerical simulation results of the model with the corresponding experimental results. The model was also applied to the numerical simulation of a concrete member under high temperature conditions. The structure of the paper is as follows: After the introduction, Section 2 presents the derivation process of the proposed constitutive model in detail. The expressions of the model are presented in this section. Section 3 outlines the validation of the proposed constitutive model. The model is validated by the comparison of the numerical simulation results of the model with the test results. Section 4 presents the application of the proposed model. It was applied to the numerical simulation of a concrete member. Lastly, in Section 5, the conclusions are presented.
## 4 Discussion  
\(EI\) is designed and calculated according to vibration signal samples. The average value of \(EI\) over the samples is used to judge the stability label. Samples are labeled and statistics are shown in Table 2, which gives the vibration performance of different labeled samples. Consistent with the actual situation, stable samples have lower values of average power and jerk than those of the samples with instability labels.  
Figure 4 and Tables 3 and 4 are used for feature selection in the scorecard model, where the value in Figure 4 and Table 3 means correlation strength, and the \(IV\) value in Table 4 indicates the ability to assess stability. Five features are selected for score modeling, and the logistic regression coefficients are demonstrated in Table 5, which can be further used to calculate the item scores. Table 6 gives the scorecard, and the stability score may be obtained by finding the group position of features in the scorecard for each sample. From the scorecard, we can find significant features of \(jerk\) and \(a_{p}\), which make a great difference to the score. Figures 5 and 6 prove the reasonability of the scorecard model; the score obtained by the model can sufficiently identify stable and unstable samples. Table 7 displays the statistics of features according to the score groups.  
The test of the endpoints localization method gives the examples of vibration sequence segmentation as shown in Figures 7 and 8, in which the continuous signal is divided by steps. The localization method can well identify the positions of endpoints. By applying the proposed scheme, the samples can be segmented automatically. The methods may be applied in situations where the information contained in each action segment can be analyzed and parts of the moving process can be scored.  
In summary, the results prove the reliability and objectivity of the scorecard model for the stability evaluation of robotic arms. Usually, in robot training, the teachers give the score according to the robots' working performance by their empirical data, which are subjective and random. Compared with the common artificial scoring, the proposed scheme has strict mathematical and statistical deduction as theoretical support, which is more objective.  
Future works may be investigated in the following aspects:  
1. The research of this paper is based on the SIASUN SR4B experimental platform for data acquisition and verification. The proposed method may be applied to a variety of robotic arms, but due to the influence of actual experimental conditions, no more experiments have been carried out. Therefore, in the follow-up work, the method can be verified by combining different types of robot platforms, including different degrees of freedom manipulators, parallel manipulators, etc.;
2. The method proposed in this paper is based on statistics and machine learning models. With the increase of data volume, the performance may improve.
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
## 1 Introduction
### Contribution of the Work
Section 2 presents the proposed system model for the 3D GBSM URA/CA channels for mMIMO CR-NOMA network. In Sections 3 and 4 respectively, we present the performance analysis, and simulation and numerical results of the system. We conclude the paper and provide future scope in Section 5.
###### Abstract  
Purpose: In this study, we aimed to compare the marginal fit of fixed dental restorations fabricated with the provisional prosthesis scanning technique versus a conventional impression technique and to determine the effect of both variables on the accuracy outcome. Materials and Methods: Twelve identical polyurethane edentulous maxillary models were equally divided into two groups: control (conventional impression group) and test (provisional prosthesis scanning group). After obtaining the impression using the above-mentioned methods and further preparing the final prosthesis, the passivity of the metal framework prosthesis was checked using a single screw test, i.e., only one screw was fixed on the terminal right abutment, and all others were empty. The marginal fit of the final prosthetic frameworks screwed onto the implants on the terminal left abutment was measured at the terminal right sight by periapical radiographs obtained immediately after metal framework placements in both groups. The medians derived from the two groups were compared using the Mann-Whitney test. In all tests, a _p_-value < 0.05 indicated statistical significance. Results: In the provisional prosthesis scanning group, the median marginal fit discrepancy was 170 \(\upmu\)m (range 120-190). In the conventional impression group, the median marginal fit discrepancy was 1080 \(\upmu\)m (range 1040-1100). There was a significant difference in the implant-framework marginal gap fit discrepancy between these two groups. Conclusion: Prostheses fabricated with the provisional prosthesis scanning technique are significantly more accurate than those fabricated with conventional impression techniques.  
accuracy of implant prosthesis; provisional prosthesis scanning; conventional implant impressions; edentulous arch +
Footnote †: journal: Applied Sciences
## 3 Key Character Detection
### CNN-Based Key Character Detection
#### 3.2.2 Model Training Procedure  
Though RetinaNet is suitably utilized for key character detection, the following problem is how to efficiently train a RetinaNet model and simultaneously prevent the issue of model overfitting. So far, RetinaNet has been used to deal with some applications such as pedestrian detection, face mask detection, and pill detection in real environments. As we know, most deeper models usually need a lot of data and corresponding labeled information for supervised learning. Unfortunately, it is usually expensive work to manually obtain the ground-truth labeling information of a large training set for model learning. Fortunately, transfer learning [33] is a machine-learning technique where a model trained on one task is re-used on a new related task even under the situation of limited annotated data. It is expected that transfer learning is an efficient way to obtain a suitable RetinaNet model for document images. Though transfer learning is an efficient way to achieve model learning, the following issues are how to efficiently obtain the training dataset with annotation information and re-train the RetinaNet.  
Figure 5 illustrates the model training for key character detection via transfer learning and synthetic images. First, one approach to reducing the cost of obtaining the ground-truth labeling information is to generate synthetic data with annotation information [34; 35]. In fact, it is easy to render a lot of synthetic images with synthesized key characters. Here, some fonts are exploited to synthesize the key characters and then render a large training dataset via synthetic image generation and data augmentation for model learning. The other is that ImageNet is one of the most widely used image datasets in many applications. It is expected that the convolutional layers of a pre-trained RetinaNet based on ImageNet should contain a lot of rich knowledge extracted from natural images. Therefore, our model learning strategy is that RetinaNet is initialized by a pre-trained model with ImageNet, and then the training set of synthetic images, including these key characters, is utilized to efficiently re-train the RetinaNet for document images. After transfer learning, the re-trained RetinaNet can be exploited to detect the key characters in unseen ROIs.  
Figure 5: Illustration of model training for key character detection via transfer learning and synthetic images.
## 2 Materials and Methods
### Instruments and Procedures
#### 2.3.7 Electromyographic Assessment  
The surface electromyographic (sEMG) signal was recorded using the Trigno Wireless System (Delsys Inc., Natick, MA, USA) integrated into the Qualisys Motion Capture System via analog board outputs. The sEMG signal was obtained through pre-amplified bipolar differential electrodes (Trigno Avanti Sensor model, Delsys Inc., Natick, MA, USA). The electrodes had a rectangular configuration of two parallel Ag bars with an interelectrode distance of 10 mm, a gain of 1000, a common mode rejection coefficient > 80 dB, and an acquisition frequency of 2000 Hz.  
Before electrode placement, the skin was shaved and wiped with 70% isopropyl alcohol to reduce skin impedance [43]. Eight electrodes were positioned on the muscles of the dominant side landmarks of each participant (Figure 2).  
Figure 2: Representation of reflective marker placements of the lateral orbital margin (LOM); the tragus of the ear (TRG); seventh cervical spine process (C7); first, third, and eleventh thoracic vertebrae (T1, T3, and T11); one and four lumbar vertebrae (L1 and L4); and the second sacral vertebra (S2) as well as the electromyography sensor placements with anatomical landmarks of the sternocleidomastoid (SCM) [34], erect spinae cervical (ESC) [34], elevator scapulae (LS) [35; 36], upper trapezius (UT) [37; 38], lower trapezius (LT) [37], pectoralis major (PM) [39], lumbar multifidus (LM) [40], and transversus abdominis/internal oblique (Tra/IO) [41].  
The sEMG signals of the sternocleidomastoid (SCM), erector spinae cervical (ESC), elevator scapulae (LS), upper trapezius (UT), lower trapezius (LT), pectoralis major clavicular portion (PM), lumbar multifidus, (LM) and transversus abdominis/internal obliques (Tra/IO) were positioned according to anatomic references as stated in Figure 2. Each electrode was placed in the most prominent portion of each muscle belly confirmed by palpation during an isometric contraction [37; 44].  
The quality of the sEMG signal was checked using EMG Works software version 4.7.9 (Delsys Inc., Natick, MA, USA). Only values of SNR greater than 1.2, baseline noise less than 15 microvolts root mean square (\(\mu\)Vrms), and line interference less than 2 were considered acceptable [45].
# Prototyping of Environmental Kit for Georeferenced Transient Outdoor Comfort Assessment
## 1 Introduction
[17] proposed the Open Source Building Science Sensors (OSBSS) Project, which was created to design and develop a suite of inexpensive, open-source devices based on the Arduino platform to measure and record long-term indoor environment and to build operational data. By the same token, [PERSON] [18] incorporated real-time environmental monitoring methodologies using low-power and low-cost computing platforms. This study also considered it sensible to use available open-source platforms to acquire and visualize environmental data while focusing on outdoor thermal comfort with geo-referencing techniques. Environmental data loggers are widely used assets for climate monitoring indoors; however, there is a lack of applications for climate monitoring outdoors. At the same time, branded industrial data loggers are often expensive and are not easily accessible for users. In this context, this research investigates methods to promote more constant acquisition and widely available environmental datasets using Arduino-based sensors. The prospective application of the development follows two main objectives. The first is to help decision-makers and urban planners toward transient outdoor comfort assessments to capture correlations between microclimate and the built environment. The second objective follows the concept of open-source environmental datasets, aiming for adequate accuracy with low-cost sensors. Moreover, the development can be beneficial for architects, environmental scientists, civil engineers, urban planners, and policy-makers to make data-integrated design decisions. The major contribution of this paper is to demonstrate the prototyping process of an environmental kit that can sense conditions with a portable, affordable device. We present the objectives and the prototyping methodology, including a description of all components. In the next section, we describe the data acquisition process and the validation. The following section presents the method to model the acquired georeferenced data for the primary application. We finish by presenting our conclusions and discussing future work.
## 4 Discussion
### Performance Analysis  
#### 4.2.1 Trajectory Prediction Analysis  
In order to verify the influence of the parameters on the prediction accuracy, this paper tests the influence of different values of the smoothing factor \(\varepsilon_{a}\) on the prediction accuracy, as shown in Table 2. The experiments show that, when \(\varepsilon_{a}\) varies from 0 to 1, the model's prediction accuracy tends to increase and decrease. The accuracy is highest when \(\varepsilon_{a}=0.5\). When \(\varepsilon_{a}<0.3\), the model cannot effectively alleviate the user stay problem. When \(\varepsilon_{a}>0.7\), excessive smoothing leads to the loss of trajectory dynamic features and a significant increase in the error rate. This indicates that moderate smoothing can effectively alleviate the problem of zero-state transfer probability, but excessive smoothing will blur the dynamic characteristics of the trajectory.  
This paper selects five sets of user trajectories from the dataset to simulate both the classical and the improved HMM. Figure 5 illustrates the comparison of prediction errors before and after the improvement, showing that the prediction error rate of the classical HMM is generally higher than that of the improved HMM. This is because the indoor space is limited, causing mobile objects to be constrained in their movement, resulting in many repetitive trajectories. Consequently, the classical HMM will have a transition probability of 0, leading to prediction failure. On the other hand, the improved HMM enhances the prediction accuracy by addressing the state dwell problem.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Smoothing Factor** & **Prediction Accuracy** \\ \hline
0.1 & 0.68 \\
0.3 & 0.72 \\
0.5 & 0.82 \\
0.7 & 0.71 \\
0.8 & 0.64 \\
0.9 & 0.56 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of smoothing factor on prediction accuracy.  
Figure 6 compares the prediction accuracy under different data repetition rates. It shows that the difference in prediction accuracy between the classical and improved HMM is slight when the trajectory repetition rate is low. However, as the user continues to move and the trajectory repetition rate increases, the rate of decrease in the prediction accuracy for the classical HMM becomes more extensive. In contrast, the decrease for the improved HMM is more minor.
## 4 Results
### Mesh and Model Selection
#### 4.2.4 Air Concentration Analysis  
Simulating water-air mixtures is critical in hydraulic studies [27; 42]. The solvers differ significantly in this regard: interFoam uses a VoF approach, while the twoPhaseEulerFoam adopts an Euler-Euler process [16]. Due to the methodological differences, interFoam air concentration results are highly sensitive to mesh resolution, as depicted in Figure 7[43]. For example, at a point located 0.20 m above the surface and 0.10 m from the jet centerline, air concentration drops from 0.50 to 0.00 as mesh resolution increases from D6 to D24 (Figure 7b). In contrast, twoPhaseEulerFoam is less affected by mesh quality, producing results more consistent with [PERSON]'s experimental findings [12].
# Improved Control Strategy of MMC-HVDC to Improve Frequency Support of AC System
## 2 Inertia of Power System  
In recent years, power electronics has significantly improved. Many types of power equipment are no longer directly connected to the power grid. However, they are connected to the power grid through various types of converters. This can greatly improve the power quality of the entire grid, but it also poses a great challenge in maintaining the stability of the power system. This article presents an investigation of the challenge of maintaining frequency stability caused by the popularity of converter technology. In this study, the offshore wind turbines considered were FSCWTs.  
The generator directly connects to the back-to-back converter in series. As its output power is regulated by the converter, it loses the inertial constant. The penetration capacity of the renewable source is significantly high. The grid can be operated in a low-inertia state. Therefore, the power system cannot maintain its frequency stability under normal conditions [4; 5]. To consider the transmission distance and system stability adjustment of offshore wind power, it is considered that the MMC-HVDC is used to connect the offshore wind power and onshore AC system, and the active power and reactive power control of MMC-HVDC can provide system stability control for AC systems.  
The virtual inertia control proposed in this article will accurately and quickly provide the required minimum inertia constant based on the strength of the AC system without the root locus analysis method or eigenvalue analysis method [12]. The detailed results are discussed in Section 5. In addition, this consideration combines the above advantages with those of the onshore AC system to provide a more suitable inertial control for the system. This also means that we are still able to clearly determine whether the system is in a stable state without confirming the eigenvalue.
# Synthesis of Biodiesel by Interestricification of Triglycerides with Methyl Formate
## 2 Intereesterification of Triglycerides with Methyl Formate
### Influence of Catalyst on the Product Yield
studied the kinetic parameters of the intereesterification process in at room temperature (25 \({}^{\circ}\)C), using of potassium tert-butoxide in tetrahydrofuran as a catalyst, and compared intereesterification with methyl formate and methyl acetate and transesterification with methanol. Based on the process kinetic parameters, it was found that, although the rate of process of transesterification with methanol is higher, the resulting by-product, glycerol, makes the process more difficult, so it is more promising to perform intereesterification with methyl formate or methyl acetate. It has been established that the process of intereesterification with methyl formate is more complicated than using methyl or ethyl acetate. Large yields of methyl are obtained over a longer period of time and by taking a higher ratio of caboxylate ester-to-oil. This can be explained by the kinetic parameters of the process. The rate of intereesterification of rapeseed oil using methyl acetate was found to be 1.6 times higher than that of intereesterification with the methyl formate. The reaction rate constants corresponded to 6.66 and 4.05 Lmol\({}^{-1}\) min\({}^{-1}\)[42]. Although the use of biocatalysts is attractive, from an environmental point of view, the biocatalytic process of oil intereesterification using carboxylate esters of low molecular weight has been little studied. [PERSON] et al. investigated the use of biocatalysts for the intereesterification of rapeseed oil with methyl formate. Fifteen different lipases were tested. Three lipases were more effective: Novozyme 435, lipase with perlite, and Lipozyme RM IM, from which, the most efficient enzyme, Lipozyme RM IM, was selected for further studies. Its optimum content was 13% (by weight of oil). The highest yield of rapeseed oil methyl esters (60.68 \(\pm\) 0.95%) was obtained at 20 \({}^{\circ}\)C, with a molar ratio of methyl formate-to-oil of 32:1 for a process duration of 42 h [43]. Other scientists used the enzymatic preparation Novozym 435 as an intereesterification catalyst [19; 21; 23; 31; 62]. It has been established that the optimal concentration of the catalyst depends on the type of carboxylate ester used for oil intereesterification. The optimum concentration of the catalyst ranged from 8% (by weight of oil) when soy oil with was intereesified with ethyl acetate [62], to 30% (by weight of oil), when with methyl acetate was used for soy oil intereesterification [21]. It is difficult to compare the most efficient amount of catalyst because different catalysts were used. It can be stated that the optimal concentration of chemical catalysts is not high (0.12% or catalyst-to-oil molar ratio of 0.08-0.1. On the other hand, biocatalysis require a higher amount of catalyst both using methyl formate and other types of carboxylate esters of low molecular weight.