esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this paper, a simple theory model was developed to describe the distribution of skin friction of pile based on ESM. The measured data of an in situ test proved the applicability of this present method. FEM was also used to investigate the effect of consolidation time, friction coefficient, drainage condition, consolidation pressure, and pressure ratio on NSF and axial load of single pile.  
It was found that the position of the neutral point changed significantly with the consolidation time and friction coefficient, and the best fitting line of the neutral point and friction coefficient was given. Drainage conditions have a significant effect on consolidation time but have only a slight influence on the position of the neutral point and the maximum axial load. Consolidation pressure has a slight influence on the position of the neutral point. Meanwhile, a good linear relationship between the position of neutral point and pressure ratio was observed. The axial load of the pile is an important parameter in pile foundation design. NSF significantly enhances the axial force of piles, which is influenced mainly by the friction coefficient, consolidation pressure, and pressure ratio. Furthermore, under different consolidation pressures, the normalized maximum axial load, \(F_{max}/p\), of the pile shaft had a good linear relationship with the pressure ratio, \(n\), in which the slopes were the same and the unified fitting equation was given. The theoretical calculation by ESM and fitting equations should be combined for practice.  
Conceptualization, Y.W. and L.M.; methodology, Y.W., Y.R., J.L. and L.M.; software, Y.W., Y.R., J.L. and L.M.; validation, Y.R. and J.L.; formal analysis, Y.W., Y.R., J.L. and L.M.; investigation, Y.W., Y.R., J.L. and L.M.; resources, Y.W. and L.M.; data curation, Y.R. and J.L.; writing--original draft preparation, Y.R. and L.M.; writing--review and editing, Y.W. and J.L.; visualization, Y.R. and L.M.; supervision, Y.W.; project administration, L.M.; funding acquisition, L.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Department of Education of Anhui Province, grant number KJ2020A0080 and KJ2021A0862 and by the Ministry of Science and Technoligy of the People's Republic of China, grant number B210202034, B200204036 and B200204032.  
Not applicable.  
The data presented in this study are available in article.  
The authors would like to acknowledge the financial support of Key projects of natural science research in colleges and universities of Anhui Province (KJ2020A0080 and KJ2021A0862) and the Fundamental Research Funds for the Central University (B210202034, B200204036 and B200204032).  
The authors declare no conflict of interest.  
Figure 12: Normalized maximum axial load versus pressure ratio.
## 5 Conclusions  
The goal of this research was to look into the function of deep learning in fighting the epidemic by examining COVID-19 data. The COVID-19 pandemic has posed a major threat to humanity, with irreversible societal consequences. Research is being conducted to anticipate the development or return of the pandemic at any time, and consequently reduce the death toll. Maybe in the future context, accurate COVID-19 prediction using deep learning will gain increasing attention as deep learning approaches are more successful in dealing with non-linear situations. Time-series prediction of COVID-19, in terms of the estimated number of confirmed, death, and recovered cases, is performed in this study utilizing ARIMA, LSTM, and CNN models. Short-term, medium-term, and long-term infected cases are all predicted in the proposed methodology. We used daily data from 1 April 2020 to 31 May 2021 to train and evaluate the models for this study. The models used in this study are data-driven, and we use RMSE, nRMSE, MAE, nMAE, MAPE, and \(R^{2}\) metrics to assess all models' predictive performances. We aim to evaluate and contrast the abilities of ARIMA, LSTM, and CNN models in interpreting complex time-series trends, and ultimately to forecast new cases for the future period of 14 days, 30 days, and 60 days. Consequently, the following findings can be drawn:  
* The best model to predict the confirmed cases is LSTM, which has better RMSE and \(R^{2}\) values. Still, CNN has a similar comparative performance to LSTM.
* The best model to predict death cases is LSTM, with better RMSE, MAE, MAPE, and \(R^{2}\) values compared to the other two models.
* The best model to predict the recovered cases is CNN, with better RMSE, MAE, MAPE, and \(R^{2}\) values compared to the other two models.
* The most difficult cases to predict are the recovered cases, which have lower error metrics achieved by all algorithms.
* LSTM unexpectedly performed badly when predicting the recovered cases. It has RMSE and \(R^{2}\) values of 641.32 and 0.3134, respectively.
* There is a slight difference between ARIMA and an LSTM algorithm in predicting death cases. ARIMA has MAE and MAPE values of 2.25 and 16.27%, respectively.
* To sum up, LSTM has a better predictive performance for the confirmed and death cases, while CNN has a better performance in predicting the recovered cases.  
To sum up, these methods and predicted consequences will aid in the prevention of COVID-19 pandemic infections. Consequently, it is worth mentioning that all generated figures and tables come from our data and analysis.  
Figure 20: MAPE comparison of the three models.
# Rendering Strategy to Counter Mutual Masking Effect in Multiple Tactile Feedback
## 4 Discussion  
### Masking Function  
In general, the masking function in tactile perception has been observed in the form of a ramp function, although the slope and intercept varied according to the conditions of the stimuli presented [25; 26]. In addition, the masking effect on the AL was observed to be more prominent for in-channel masking (i.e., when the same sensory channel was stimulated) than for cross-channel masking (i.e., when different sensory channels were stimulated) [12; 27]. The general tendency of the masking functions investigated in the current study was consistent with that mentioned above. The slopes of the masking functions were approximately 0.6-0.7 and 0.9-1.1 for the EV and MV, respectively. It is remarkable that, in the case of the EV, there was a region in which there was no masking effect, i.e., the changes in the AL of the EV did not significantly differ from zero. This phenomenon has been observed when the target and masking stimuli activated different sensory channels [12]. The MV used in the current study would mainly activate rapidly adapting (RA) afferent nerve fibers, especially RA type 2 (RA II), they are known to be most sensitive at a frequency range of 120 to 270 Hz for stimuli presented perpendicularly to the skin. Thus, it is evident, based on the results, that the EV contributes to the activation of at least one of the slow adapting (SA) afferent fibers. More specifically, we expect that the SA II afferent fibers are highly related to the EV as they are more sensitive to skin stretching than SA I afferent fibers [2; 28]. In the case of the MV, the change in the AL was significantly different from zero for all positive intensities of the masker (EV). Furthermore, the slope of the masking function of the MV was steeper than that of the EV. These aspects are consistent with previously reported results for in-channel masking experiments [12; 25; 26]. That is, the EV contributes to the activation of both SA and RA (rapidly adapting) afferent fibers. [PERSON] [28] reported that dynamic skin stretching contributes to the activation of RA and SA afferent fibers, which is supported by our findings. In summary, we have confirmed that the mutual masking effect clearly appears in combinations of different tactile feedback types; MV and EV in our study. As the masking effect causes significant changes in the AL level, we should give it in-depth consideration when designing multiple tactile feedback.
## 4 Deployment Scenario and Prototype
### Logical Layer  
The logical layer has been built using ThingSpeak ([[https://thingspeak.com/](https://thingspeak.com/)]([https://thingspeak.com/](https://thingspeak.com/))) as a cloud IoT platform for storing and processing data. The middleware layer of SmartResidents consists of two channels, including the different fields in which the data generated by the sensors are allocated. Although ThingSpeak sets default public channels, SmartResidents channels are private, so a write and a read key is automatically generated on each channel by the platform and must first be used from the physical layer to update the information in each channel, and then from the application layer in order to consume the data stored there.  
The association between channels and fields can be seen in the Table 2:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Channel** & **Field** & **Sensor** \\ \hline SmartResident\_CH1 & & \\ \hline  & Field1 & Temperature \\  & Field2 & Air quality \\  & Field3 & Rain \\  & Field4 & Humidity \\  & Field5 & Movement \\  & Field6 & Water temperature \\ \hline SmartResident\_CH2 & & \\ \hline  & Field1 & Rubbish container level \\  & Field2 & Rubbish container cap \\  & Field3 & Electricity usage \\  & Field4 & Sound \\  & Field5 & Smoke \\  & Field6 & Atmospheric pressure \\ \hline \hline \end{tabular}
\end{table}
Table 2: Channels-fields association in ThingSpeak.  
Figure 6: Logic layer update from Arduino (Rain sensor).
## 2 Problem Formulation  
A single-input single-output non-affine non-linear discrete-time system described as  
\[y(k+1)=F\big{(}y(k),\cdots,y(k-n_{y}),u(k),\cdots,u(k-n_{u})\big{)}, \tag{1}\]with unknown indices \(n_{y}\) and \(n_{u}\), it has the following affine representation:  
\[y(k+1) = f\big{(}y(k),\cdots,y(k-n_{y}),u(k-1),\cdots,u(k-n_{u})\big{)} \tag{2}\] \[+g\big{(}y(k),\cdots,y(k-n_{y}),u(k-1),\cdots,u(k-n_{u})\big{)}u(k)\] \[+\varepsilon_{h}\big{(}ky(k),\cdots,y(k-n_{y}),u(k),\cdots,u(k-n _{u})\big{)},\] \[= f(k)+g(k)u(k)+\varepsilon_{h}(k),\]  
where \(f(k)\) and \(g(k)\) are unknown functions and \(\varepsilon_{h}(\cdot)\) is a bounded residual error when the non-affine system (1) meets the following assumptions:  
**Assumption 1**.: _The non-linear function \(F(\cdot)\) in (1) needs to be continuous regarding the control law \(u(k)\). This implies that_  
\[\frac{\partial y(k+1)}{\partial u(k)}\approx g(k), \tag{3}\]  
_where \(0<|g(k)|\leq g^{M}\) and \(g^{M}\) is an unknown positive constant. Therefore, the system (1) is controllable with unknown and varying control directions._  
According to Assumption 1, the control direction is determined by utilizing the sign function of \(g(k)\) in (3) as  
\[\text{sign}\bigg{\{}\frac{\Delta y(k+1)}{\Delta u(k)}\bigg{\}}=\text{sign} \{g(k)\}, \tag{4}\]  
where \(\Delta u(k)\
eq 0\).  
Therefore, the equivalent model based on MiFREN [35] is developed in the following section.
###### Abstract  
Suspended sediment load (SLL) prediction is a significant field in hydrology and hydraulic sciences, as sedimentation processes change the soil quality. Although the adaptive neuro fuzzy system (ANFIS) and multilayer feed-forward neural network (MFNN) have been widely used to simulate hydrological variables, improving the accuracy of the above models is an important issue for hydrologists. In this article, the ANFIS and MFNN models were improved by the bat algorithm (BA) and weed algorithm (WA). Thus, the current paper introduces improved ANFIS and MFNN models: ANFIS-BA, ANFIS-WA, MFNN-BA, and MFNN-WA. The models were validated by applying river discharge, rainfall, and monthly suspended sediment load (SSL) for the Atrek basin in Iran. In addition, seven input groups were used to predict monthly SSL. The best models were identified through root-mean-square error (RMSE), Nash-Sutcliff efficiency (NSE), standard deviation ratio (RSR), percent bias (PBIAS) indices, and uncertainty analysis. For the ANFIS-BA model, RMSE and RSR varied from 1.5 to 2.5 ton/d and from 5% to 25%, respectively. In addition, a variation range of NSE was between very good and good performance (0.75 to 0.85 and 0.85 to 1). The uncertainty analysis showed that the ANFIS-BA had more reliable performance compared to other models. Thus, the ANFIS-BA model has high potential for predicting SSL.  
s 20 July 2019; Accepted: 20 August 2019; Published: 3 October 2019
# Optimization of Mechanical and Setting Properties in Acrylic Bone Cements Added with Graphene Oxide
###### Abstract
Several types of research have demonstrated that GO content in ABCs generates a reinforcement on the mechanical properties' flexion [34; 35], compression, fracture toughness [8], and fatigue performance [3]. Reduction in the maximum temperature reached during polymerization (\(T_{max}\)), increase in the setting time (\(t_{set}\)) [36; 37; 38; 38], and additionally, improvements in bioactivity [38; 39; 40] and antibacterial properties [37; 41]. Despite these improvements, it has also been reported that GO in ABCs can inhibit the polymerization reaction, which can be counteracted by increasing the radical species. However, since the BPO content influences the polymerization exotherm (\(T_{max}\)) [1], it is necessary to optimize its content. The effect of GO's sonication time has been studied in materials different from ABCs [42; 43; 44], and it has been found that this parameter is related to two key characteristics of GO: the lateral size and the thickness of the sheets [45; 46]. Increasing the sonication time decreases the thickness and particle size, as previously reported [47]. However, excessive sonication decreases hydrophilicity and functional groups in GO sheets [48] with a simultaneous increase in GO structure defects [49; 50]. Although there are several types of research where the GO content alone is studied, there are no studies on the interactive effects of the size and content of GO and the BPO content. This article shows for the first time the interactions present between the factors sonication time and GO percentage in the liquid phase, together with the percentage of benzoyl peroxide (BPO) in the solid phase, on the mechanical and setting properties established for cements in the ISO 5833-02 standard [51]. Optimization of the factors using a completely randomized experimental design with factorial structure resulted in selecting nine formulations that presented an increase in compression, flexion, and the setting time and decreased the maximum temperature reached during the polymerization. Some of these combinations have not yet been studied, which broadens the possibilities for new formulations with desirable characteristics to improve ABCs' clinical performance, which could be used in orthopedics.
## 4 Multicast Routing Approaches in DTN
### Buffer-Efficient Multicast Routing in DTN  
Resource-constrained IoT devices usually have limited storage capabilities, and they need to store message bundles until opportunistic contact with others to forward data. Enabling multicast communication in DTN causes severe buffer overhead due to the repeated replication of messages to increase delivery rate, especially in multi-copy-based routing. Without proper buffer management, obsolete messages will be removed from the message buffer when the storage is full. However, it is difficult to guarantee that all the intended recipients will receive a message if the messages are removed too early. Improper buffer management thus results in a low delivery ratio, particularly when many nodes initiate multicast sessions together. Buffer-efficient multicast routing strategies increase the overall network performance and are beneficial in terms of message delivery reliability. Buffer-efficient multicast strategies can be modeled by either using a buffer management scheme or reducing the number of message replicas in the network.
###### Abstract  
This study presents a procedure for modeling the heat transfer process in a concrete dam, taking into account the time-varying boundary conditions on the upstream and downstream sides of the dam (i.e., the water level of the reservoir, spillover, insolation, and shading) which affect the temperature conditions of the dam. The large concrete arch-gravity Moste Dam (in North West Slovenia) was analyzed, where an automated system for the measurement of concrete and water temperatures, and for the monitoring of meteorological effects, was installed. Thermal analyses (1D and 2D) for non-linear and non-stationary heat conduction through solids were performed using a finite element method (FEM) based program, TeEx, which was complemented by two specially developed programs for determining the effects of convection and insolation, considering also the effect of shading. A 15-day period was analyzed, as well as a period of one year. It was found that the results of the performed analyses fitted in well with the experimentally determined concrete temperature measurements. The results showed that at the insolated side of the dam, the temperature gradient was largest in a very narrow area along the concrete surface, but the temperature did not stabilize shallower than at a depth of about 6 m. As part of the thermal analyses, uncertainty analyses of the results of the calculations were also performed.  
concrete dam; heat transfer; solar radiation; shading; convection; thermal analysis; finite element method; temperature monitoring; temperature field; uncertainty
###### Abstract  
In order to improve the efficiency of the devices' fault diagnosis of the protection systems of intelligent substation, a fault diagnosis method based on a gradient boosting decision tree (GBDT) was proposed. Using the integrated alarm information, the device self-checking information, the link information of generic object-oriented substation event (GOOSE) and sampled value (SV) and the sampling value information generated during the fault of the protection system, the fault feature information set is constructed. According to different fault characteristics, the protection system faults are classified into simple faults and complex faults to improve the diagnosis efficiency. Using GBDT training rules, a fault diagnosis model of protection system based on GBDT is established and fault diagnosis steps are given. This study takes a 110 kV intelligent substation in southern China as an example, to verify the effectiveness and accuracy of the proposed fault diagnosis method, and compared it with the existing methods in terms of the accuracy. The diagnostic accuracy in the case of false alarms and the case of multiple faults are verified. The results show that the method can meet the practical engineering application.  
protection system; fault diagnosis; complex faults; GBDT
## 3 Controllers Design
### Static Velocity-Feedback \(H_{\infty}\) Decentralized Controllers
Obviously, the upper bound \(\widetilde{\gamma}(\mathbf{K}^{\ell})\) corresponding to the unstructured gain matrix \(\mathbf{K}^{\ell}\) must be inferior to the upper bound \(\widetilde{\gamma}(\widehat{\mathbf{K}}^{\ell})\) obtained for the diagonal gain matrix \(\widehat{\mathbf{K}}^{\ell}\), which has been computed by solving a constrained version of the same LMI optimization problem. Looking at the data in Tables 2 and 4, we can see that the inequality \(\widetilde{\gamma}(\mathbf{K}^{\ell})\leq\widetilde{\gamma}(\widehat{\mathbf{ K}}^{\ell})\) certainly holds for all the control configurations. Additionally, the actual \(\gamma\)-values \(\gamma(\mathbf{K}^{\ell})\) and \(\gamma(\widehat{\mathbf{K}}^{\ell})\) must satisfy \(\gamma(\mathbf{K}^{\ell})\leq\widetilde{\gamma}(\mathbf{K}^{\ell})\) and \(\gamma(\widehat{\mathbf{K}}^{\ell})\leq\widetilde{\gamma}(\widehat{\mathbf{K} }^{\ell})\). However, these inequalities do not exclude the observed fact that \(\gamma(\widehat{\mathbf{K}}^{\ell})<\gamma(\mathbf{K}^{\ell})\). That is, they do not exclude the unexpected possibility of obtaining a passive controller with a better performance than the corresponding active controller. **Remark 1**. : _In this paper, all the computations have been carried out using Matlab\({}^{\otimes}\) R2015b on a regular laptop with an Intel\({}^{\otimes}\) Core\({}^{\text{TM}}\) i7-2640M processor at 2.80 GHz. The LMI optimization problems corresponding to the different controller designs have been solved with the function mincx() included in the Robust Control Toolbox\({}^{\text{TM}}\). A relative accuracy of \(10^{-7}\) has been set in the solver options._
## 2 Decomposition of the Standard JRC Profile
### Standard JRC Profile Decomposition Method
#### 2.1.2 Ensemble Empirical Mode Decomposition (EEMD)  
In the application of EMD, we see the mode mixing defined as the characteristic component of different timescales decomposed into one IMF, or the same timescale component appearing in different IMFs. The mode mixing causes the IMF component to lose its original single characteristic scale, forming a mixed-scale oscillation. In order to avoid the phenomenon of mode mixing, the EEMD is proposed to decompose the signal. The EEMD takes advantage of the uniform distribution of the white noise spectrum, adding white noise to the signal so that the signals of different time scales can be automatically separated into the corresponding reference scale. As this method mainly supplements missing scales by adding white noise to the signal, it displays good performance in signal decomposition.  
The EEMD algorithm processing steps are as follows [71]:  
(1) Adding white noise to the target signal;  
Figure 1: The flowchart for Empirical Mode Decomposition [70].  
2. Decomposing the added white noise signal into the _IMF;_
3. Repeating Steps 1 and 2, but adding a different white noise sequence each time;
4. Obtaining the mean value of each _IMF_ by decomposition as the final result.