esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Electron Relaxation Model
#### 2.2.1 Non-Thermalized Carriers
Since the pulse is much longer in time than \(\tau_{\epsilon e}\), during pumping, a stationary situation is rapidly achieved for the distribution width \(\Delta_{\epsilon}(t)\), so we take its asymptotic limit: \(\Delta_{\epsilon}(t)\approx\Delta_{\epsilon}(-\infty)\);
2. For 100 fs pulses, their spectral width (\(\sim\)8 meV) should be much smaller than the thermalized distribution energy spread, so we take \(\Delta_{\epsilon}(-\infty)\) to be the Gaussian pulse's spectral width: \[n(\vec{r},t)=n_{\text{nt}}(\vec{r},t)+n_{\text{t}}(\vec{r},t)\;; \tag{3}\]0 (12) where \(\Delta t\) is the full width at half-maximum (FWHM) of the pulse in the time domain. 3. As the thermalized carriers relax toward lower energies, we assume that their distributions, \(f_{\epsilon}\) and \(f_{\text{ht}}\), for \(\epsilon\approx\hbar\omega_{b}/2\), are such that \[n(\vec{r},t)=n_{\text{nt}}(\vec{r},t)+n_{\text{t}}(\vec{r},t)\;; \tag{3}\]1 (13) \[n(\vec{r},t)=n_{\text{nt}}(\vec{r},t)+n_{\text{t}}(\vec{r},t)\;; \tag{3}\]2 (14) so they do not affect the pump photon's absorption. With these assumptions, Equation (5) is simplified to  
\[n(\vec{r},t)=n_{\text{nt}}(\vec{r},t)+n_{\text{t}}(\vec{r},t)\;; \tag{3}\]3  
The temporal evolution of \(n_{\text{nt}}\) in the center of the pump spot is illustrated in Figure 2 for different intensities of the pump beam. As expected, this concentration rises with the increase in pump beam intensity. Nonetheless, as indicated in the inset, this value starts to saturate at higher intensities, ultimately attaining its maximum value (\(n_{\text{nt}}^{\text{max}}\)), indicating saturable absorption. Furthermore, it can be seen that, at lower intensities, the peak shifts toward later times in comparison to higher intensities. This phenomenon arises because of the balance between non-thermalized electrons, excited by the pump beam, and electrons undergoing thermalization over a timescale of \(\tau_{\epsilon e}\). At lower intensities, electrons are promoted to the conduction band, but they remain non-thermalized within the timescale of \(\tau_{\epsilon e}\) until they become thermalized. This accounts for the observed shift in the peak compared to \(t=0\). In contrast, at higher pump intensities, the substantial influx of promoted electrons leads to the peak occurring at \(t=0\), yet the concentration of non-thermalized electrons remains stable for a few femtoseconds.
# Slicing on the Road: Enabling the Automotive Vertical through 5G Network Softwarization
## 2 Background, Motivation, and Contributions
### Network Slicing for V2X Communications
Lately, some tools have been proposed to analyze network slicing without having to wait for the ratification of the 3 GPP 5G standards. Among them, the open-source OpenAirInterface (OAI) platform [30] is a software implementation of the LTE protocol stack and can operate over commodity hardware for the deployment of the eNodeB and the CN. OAI has been exploited in conjunction with other platforms (e.g., FlexRAN and LL-MEC) in [31] to showcase the benefits of the coordinated programmability spanning RAN and CN domains to facilitate different V2X slices, i.e., remote diagnostics and management and vehicular infotainment slices, as defined in [6]. However, there, no details are provided about the main entities and functionalities required to manage the considered V2X slices.
# Prediction of Students' Adaptability Using Explainable AI in Educational Machine Learning Models
## 3 Methodology
### Explainable Artificial Intelligence (XAI) Used
#### 3.5.1 SHAP (Shapley Additive exPlanations)
The practical application of SHAP in our study allows us to identify the most influential factors determining student adaptability. For example, SHAP analysis revealed that \"Class Duration\" and \"Financial Condition\" are critical in predicting adaptability levels. This insight is invaluable for educational institutions seeking to understand the determinants of student success and can inform targeted interventions to support student adaptability.
# Outage Performance of Wireless Powered Decode-and-Forward Relaying Networks in Rician Fading
## 1 Introduction
### Motivations & Contributions  
However, in aforementioned works, the energy triggering threshold of the EH mode was not considered. That is, they ideally assumed that the node can normally work with any amount of available energy. In practice, when the energy collected by the EH node does not reach the energy threshold, the circuit of the node may not be successfully activated. In this case, the system cannot work normally and the outage may also occur. To fill the gap, this paper investigates the outage performance of SWIPT-enabled relay networks with the energy triggering threshold in Rician fading. The contributions of the paper are summarized as follows.  
We first derive the closed-form expressions of the outage probability and reliable throughput of the system theoretically. Then, we validate our theoretical analysis by simulation and also discusses the effects of the time assignment factor, the total transmit power of the source node, the Rice factor, the minimum required transmission rate of the system, the energy conversion efficiency, and the two-hop distance on the system performances. The simulation results show that the higher the minimum transmission rate required by the system, the higher the system outage probability. Besides, when the total transmit power of the source node is relatively small, the system throughput is smaller. When the total transmit power of the source node is relatively large, the system throughput is larger. In addition, increasing the system energy conversion efficiency reduces the system outage probability and increases the system throughput. Besides, the impact of the two-hop distance on the system performance is also simulated. Furthermore, the system performance with the equal two-hop distance is slightly better than that with the unequal two-hop distance.
# A State-of-the-Art Review on Robots and Medical Devices Using Smart Fluids and Shape Memory Alloys
## 2 Robots Using ERF and MRF
### ERF-Based Robots  
As far as is known, the first application of ERFs to robotic systems was reported by [PERSON] et al. [1]. In this work, the forearm of a commercial parallel robot was retrofitted by inserting ERF. The deflection of the forearm was successfully reduced during dynamic maneuvering and transient motions, owing to the increment of the field-dependent damping property of the ERF domain. [PERSON] et al. [2,3] performed a study similar to [PERSON] et al. [1], with primary focusing on the vibration control of a flexible robot arm incorporating ERF. In this work, a single-link flexible robot arm system was made by inserting ERF into the box-type aluminum arm. The simulation and experiment were both conducted to evaluate effective control of the transient vibration during a rotational maneuver. A number of other studies were conducted using the same concept for vibration control of a flexible gantry robot [4,5]. In the gantry robot system, the translational motion was controlled by a bi-directional ER clutch, and the vibration due to the flexibility of the link was controlled by a piezoelectric actuator bonded on the surface of the link. As for the controller, a robust, H-infinity controller was designed and built, using a microprocessor. Excellent positional and vibrational control responses were achieved in the presence of uncertain system parameters, such as the variation of natural frequencies. [PERSON] [6] proposed a high-resolution tactile display using ERF, applicable to the virtual reality (VR) environments encountered underwater or in space exploration. Figure 1 shows a compressive-type tactile display whose hardness can be tuned by the electric field and the changed hardness can be felt by fingers exploring the surface. The movement of the fingers over the surface allows the shape of an object to be detected by touch, with the regions that correspond to the position of the object being harder than those outside the image. In Figure 1, the white circles represent the dielectric separating mesh. The dielectric separating mesh is essential to prevent the earthed compliant surface from coming into direct contact with the uncharged tactor elements resulting in a short circuit when they become activated. More recently, a study on the feasibility and suitability of an ERF-based actuator in human-friendly manipulators was reported in [7]. In this work, the actuator criteria were checked, based on ERF properties and the application of an ER clutch. It has been shown that ERFs exhibit promising characteristics for use as robotic actuators. Specifically, they are well suited for actuation systems that interact physically with humans, because of their inherent characteristics such as intrinsic back-drivability, low-output inertia, superior performance and bandwidth, and precision controllability of output torque. The state-of-the-art robotic applications using ERF actuators are summarized in Table 1.  
Figure 1: Tactile display featuring ERF [6].
_Applied_  
_sciences_  
Methods for the Characterization of Polyetherimide Based Materials Processed by Fused Deposition Modelling  
**[PERSON]**\({}^{\circledR}\), **[PERSON], [PERSON], [PERSON]**\({}^{\circledR}\), **[PERSON] and  
**[PERSON]**\({}^{\circledR}\)  
Department of Civil Engineering and Architecture, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy; [EMAIL_ADDRESS] (C.T.); [EMAIL_ADDRESS] (L.S.); [EMAIL_ADDRESS] (E.P.); [EMAIL_ADDRESS] (I.B.); [EMAIL_ADDRESS] (G.C.)  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +39-095-738-2760  
Received: 20 April 2020; Accepted: 30 April 2020; Published: 3 May 2020  
**Featured Application: The present work aims to provide an insight into characterization techniques for Fused Deposition Modelling. The outcomes can guide the development of novel standards for FDMTM.**  
**Abstract:** Fused deposition modelling (FDMTM) is one of the most promising additive manufacturing technologies and its application in industrial practice is increasingly spreading. Among its successful applications, FDMTM is used in structural applications thanks to the mechanical performances guaranteed by the printed parts. Currently, a shared international standard specifically developed for the testing of FDMTM printed parts is not available. To overcome this limit, we have considered three different tests aimed at characterizing the mechanical properties of technological materials: tensile test (ASTM D638), flexural test (ISO 178) and short-beam shear test (ASTM D2344M). Two aerospace qualified ULTEMTM 9085 resins (i.e., tan and black grades) have been used for printing all specimens by means of an industrial printer (Fortus 400 mc). The aim of this research was to improve the understanding of the efficiency of different mechanical tests to characterize materials used for FDMTM. For each type of test, the influence on the mechanical properties of the specimen's materials and geometry was studied using experimental designs. For each test, \(2^{2}\) screening factorial designs were considered and analyzed. The obtained results demonstrated that the use of statistical analysis is recommended to ascertain the real pivotal effects and that specific test standards for FDMTM components are needed to support the development of materials in the additive manufacturing field.  
**Keywords:** polyetherimide; additive manufacturing; fused filament modelling; mechanical properties; design of experiments
## 2 Briefings on the Inversion Method for Earth-Rockfill Dams during Construction Periods
### Constitutive Model and Inversion Parameters Selection  
The Duncan-Chang EB constitutive model, as one member of the Duncan-Chang models, is applied in the forward analysis. The Young's modulus and the Poisson's ratio in the Duncan-Chang EB model [18] can be calculated as follows:  
\[\text{Tangent modulus}:\ E_{t}=\left[1-R_{f}\frac{(1-\sin\varphi)(\sigma_{1} -\sigma_{3})}{2c\cdot\cos\varphi+2\sigma_{3}\cdot\sin\varphi}\right]^{2}\cdot K \cdot p_{a}\left(\frac{\sigma_{3}}{p_{a}}\right)^{n}, \tag{1}\]  
\[\text{Rebound modulus}:\ E_{ur}=K_{ur}\ p_{a}\left(\frac{\sigma_{3}}{p_{a}} \right)^{n}, \tag{2}\]\[\text{Tangent bulk modulus}:\ B_{t}=K_{b}\ p_{a}\left(\frac{\sigma_{3}}{p_{a}} \right)^{m}\,, \tag{3}\]  
\[\text{[PERSON]'s ratio}:\ v=\frac{1}{2}-\frac{E}{6B_{t}}\, \tag{4}\]  
where \(E_{t}\) and \(E_{ur}\) are the Young's modulus in loading and unloading conditions; \(c\) and \(\varphi\) are the cohesion and internal friction angle; \(\sigma_{1}\) and \(\sigma_{3}\) are the first and third principal stress; \(K\), \(K_{ur}\), and \(K_{b}\) are modulus numbers; \(m\) and \(n\) are modulus indexes; and \(R_{f}\) is the break ratio defined as  
\[R_{f}=\frac{\left(\sigma_{1}-\sigma_{3}\right)_{f}}{\left(\sigma_{1}-\sigma_{ 3}\right)_{u}}\, \tag{5}\]  
where \(\left(\ \right)_{f}\) and \(\left(\ \right)_{u}\) mean the condition when the soil is in destruction and the condition corresponding to the asymptote of hyperbolic line.  
The internal friction angle can be determined by  
\[\varphi=\varphi_{0}-\Delta\varphi\lg\left(\frac{\sigma_{3}}{p_{a}}\right)\, \tag{6}\]  
where \(\Delta\varphi\) is called the internal friction angle correction parameter and \(\varphi_{0}\) is the internal friction angle when \(\sigma_{3}\) equals to \(p_{a}\).  
There are nine material parameters in a Duncan-Chang EB constitutive model: \(c\), \(\varphi_{0}\), \(\Delta\varphi\), \(K\), \(R_{f}\), \(K_{ur}\), \(K_{b}\), \(m\), \(n\). In this paper, \(K\), \(n\), \(R_{f}\) are selected as the inversion parameters for \(c\), \(\varphi_{0}\), \(\Delta\varphi\) can be easily determined by trial and \(K_{ur}\), \(K_{b}\), \(m\) have only a limited impact during construction periods.
# Vibration Signal Classification Using Stochastic Configuration Networks Ensemble
## 4 Performance Evaluation
### Comparison with Deep Learning Models  
In our experimental study, the proposed method is compared with some classic deep learning models, including multi-layer perceptron (MLP) [41], CNN [42], denoising auto-encoder (DAE) [43], AlexNet [44], ResNet18 [45] and long short-term memory network (LSTM) [46]. Table 4 shows the recognition results of the comparison methods. It can be seen that all the methods can achieve 100% accuracy on the CWRU dataset because of its low data complexity. For the JNU and SEU datasets, the SCN-H and SCN-Ensemble methods obtain comparable recognition rates. In particular, the proposed SCNs-based method outperforms the compared methods on the UoC dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **Model** & **Accuracy** & **Precision** & **Recall** \\ \hline \multirow{6}{*}{JNU} & NNRW & 0.9338 & 0.8775 & 0.9887 \\  & NNRW2D & 0.8741 & 0.7673 & 0.9756 \\  & SCN & 0.9558 & 0.9182 & 0.9929 \\  & SCN2D & 0.9200 & 0.8501 & 0.9883 \\  & SCN-H & **0.9660** & **0.9372** & **0.9945** \\ \hline \multirow{6}{*}{SEU} & NNRW & 0.9490 & 0.9034 & 0.9941 \\  & NNRW2D & 0.9027 & 0.8154 & 0.9879 \\ \cline{1-1}  & SCN & 0.9786 & 0.9595 & 0.9976 \\ \cline{1-1}  & SCN2D & 0.9682 & 0.9397 & 0.9965 \\ \cline{1-1}  & SCN-H & **0.9854** & **0.9723** & **0.9984** \\ \hline \multirow{6}{*}{UoC} & NNRW & 0.9517 & 0.9143 & 0.9882 \\  & NNRW2D & 0.6740 & 0.4189 & 0.8553 \\ \cline{1-1}  & SCN & 0.9519 & 0.9148 & 0.9881 \\ \cline{1-1}  & SCN2D & 0.7147 & 0.4902 & 0.8896 \\ \cline{1-1}  & SCN-H & **0.9582** & **0.9261** & **0.9897** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison with fault dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**JNU**} & \multicolumn{3}{c}{**SEU**} & \multicolumn{3}{c}{**UoC**} \\ \cline{2-10}  & **Accuracy** & **Precision** & **Recall** & **Accuracy** & **Precision** & **Recall** & **Accuracy** & **Precision** & **Recall** \\ \hline DNNE & 0.9466 & 0.9013 & 0.9911 & 0.9571 & 0.9186 & 0.9953 & 0.9566 & 0.9233 & 0.9893 \\ SCNE & 0.9570 & 0.9203 & 0.9933 & 0.9826 & 0.9670 & 0.9981 & 0.9571 & 0.9241 & 0.9894 \\ RNNE-RVFL & 0.9573 & 0.9212 & 0.9930 & 0.9863 & 0.9739 & 0.9986 & 0.9599 & 0.9289 & 0.9903 \\ RNNE-SCN & 0.9659 & 0.9366 & 0.9948 & 0.9963 & 0.9930 & 0.9996 & **0.9638** & **0.9360** & **0.9912** \\ SCN-Ensemble (average) & 0.9722 & 0.9485 & 0.9958 & 0.9937 & 0.9881 & 0.9993 & 0.9575 & 0.9246 & 0.9897 \\ SCN-Ensemble (voting) & 0.9685 & 0.9415 & 0.9952 & 0.9834 & 0.9686 & 0.9981 & 0.9630 & 0.9345 & 0.9910 \\ SCN-Ensemble (NCL) & **0.9726** & **0.9492** & **0.9960** & **0.9975** & **0.9954** & **0.9997** & 0.9621 & 0.9327 & 0.9909 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance comparison on ensemble models.
# NaviSoC: High-Accuracy Low-Power GNSS SoC with an Integrated Application Processor
## 3 Circuit Implementation  
### Low-Noise Amplifier  
Two separate low-noise amplifiers (LNAs) for L1/E1 and L5/E5 bands were designed. LNAs were implemented as classic single-ended cascode amplifiers with inductive source degeneration. For better amplification and better matching of the output impedance, a source follower with an \"L\" matching network was used at the outputs of the LNAs. Additionally, a band calibration circuit was implemented as a bank of detachable capacitors, connected in parallel to the output LC load of the amplifiers. Figures 4 and 5 show the post-layout simulation results for the S-matrix and the voltage gain of the L1/E1 and the L5/E5 LNA, respectively. The L1/E1 and the L5/E5 LNA noise figure (NF) characteristics are presented in Figures 6 and 7, respectively. The parameters of the L1/E1 LNA are: NF = 2.2 dB, G\({}_{\rm V}\) = 21.7 dB, S\({}_{\rm 11}\) = \(-\)10.2 dB  
Figure 5: S-matrix and voltage gain characteristics of the L5/E5 low-noise amplifier.  
Figure 6: Noise figure characteristics of the L1/E1 low-noise amplifier.  
Figure 7: Noise figure characteristics of the L5/E5 low-noise amplifier.
# Sentiment Classification Using Convolutional Neural Networks
## 2 Background
### Machine Learning for Sentiment Classification
[PERSON] and [PERSON] [30] defined the number opinion nouns as a feature and combined the feature with unigrams. They achieved 65.46% accuracy for ternary classification using the SVM. [PERSON] [24] used the normalized frequencies of words as features and obtained 84% accuracy for binary classification using the SVM. [PERSON] and [PERSON] [31] aimed at five-class sentiment classification, and they defined a domain-independent feature set for Twitter data. They achieved a 92.7% F1 score using the SVM. [PERSON] and [PERSON] [26] utilized the results of Named Entity (NE) extraction as features and obtained a 0.9 False Positive Rate (FPR). These studies elaborated on feature definition rather than using only n-gram features, and they accomplished better performance (e.g., 92.7% F1 score). It is not fair, of course, to compare the performance between the previous studies because they used different datasets. However, as shown in [17; 30], it is obvious that the combination of hand-crafted features and the n-grams must be better than using only n-gram features. Although there has been success using machine learning models with the hand-crafted features and the n-gram features, these studies have a common limitation that their performance varied depending how well the features were defined; for different data, much effort of domain experts will be required to achieve better performance. This limitation also exists in information fusion approaches for sentiment analysis [32] that combine other resources (e.g., ontology, lexicon), as it will cost much time and effort of domain experts. Deep learning model is one of the solutions for such a limitation, as it is known to capture arbitrary patterns (i.e., features) automatically. Furthermore, as stated in [33], using the deep learning model for sentiment analysis will provide a meta-level feature representation that generalizes well on new domains. In this paper, we take the deep learning technique to tackle the sentiment classification. In the next subsection, we review previous studies that adopted the deep learning techniques for the sentiment classification.
# On the Use of Neuro-Swarm System to Forecast the Pile Settlement
## 5 Conclusions  
To implement a hybrid ANN model namely neuro-swarm, results of 96 PDA tests together with properties of piles and earth were considered and used. In this respect, five parameters, including \(L_{s}\)/\(L_{r}\), \(L_{p}\)/\(D\), UCS, SPT N-value and \(Q_{u}\) were set as inputs to forecast \(S_{p}\). In fact, through developing neuro-swarm model, we intend to have the best relationship of \(S_{p}=f\left(L_{s}\middle/L_{r}\middle/D\right)\), _UCS_, _SPT N-value_ and \(Q_{u}\)). According to the most effective PSO parameters, several parametric studies have been conducted to first select the best one among them and then improve the prediction performance of neuro-swarm model in predicting pile settlement. It was found that the prediction performance of neuro-swarm is improved by fixing the effective PSO parameters in each stage. At the last modeling stage, 5 neuro-swarm models were constructed to see effects of various combinations of training and testing datasets. Then, these 5 models in estimating pile settlement were evaluated using five performance indices i.e., R\({}^{2}\), RMSE, VAF, MAE and _a20-index_. Eventually, a slightly higher prediction of performance was recorded by dataset number 4 among all 5 neuro-swarm models. Based on R\({}^{2}\), and system RMSE, values of (0.851 and 0.079) and (0.892 and 0.099) were observed for train and test stages, respectively, which confirm the high strength of neuro-swarm model in predicting pile settlement. It should be noted that the results of a pre-developed ANN model in predicting pile settlement based on R\({}^{2}\) were obtained as 0.809 and 0.816 for training and testing datasets, respectively, which showed a lower performance capacity of ANN model compared to neuro-swarm predictive technique. Therefore, by developing the neuro-swarm model in predicting pile settlement, the performance capacity based on R\({}^{2}\) can be significantly improved i.e., from 0.809 to 0.851 for training datasets and from 0.816 to 0.892 for testing datasets. To conclude, the neuro-swarm model can be successfully applied in order to solve ANN shortcomings. Approaching of the implement of neuro-swarm technique presented in this study can be used by other researchers for solving problems in fields of engineering.  
**Author Contributions:** Formal analysis, D.J.A.; conceptualization, M.H. and R.T.; supervision, P.G.A.; writing--review and editing, S.A.F.; data curation, A.S.A.R.; resources, V.V.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** This research paper is made possible through the support of the Universiti Teknologi Malaysia (UTM) and the authors wish to appreciate their help and support.  
**Conflicts of Interest:** The authors declare no conflict of interest.