esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background and Literature Review
The students were clustered and an excellent student from each cluster was selected. The model extracted features from the selected excellent students. Finally, the model used the features as training input to the neural network. An LSTM network was used to predict the learning path of the students. In another study [29], a source code bug detection technique that uses LSTM was proposed. The hyperparameters of LSTM were adjusted to determine the optimal perplexity and training time. The LSTM network produces a plausible outcome for source code bug detection. [PERSON] et al. [30] presented an attention-based RNN for source code defect prediction. F-measure score and the area under the curve (AUC) were used as model evaluation metrics. The proposed model improved the source code classification process. The F-measure score and AUC had 14% and 7% better accuracy than those of state-of-the-art models, respectively. [PERSON] et al. [31] proposed a source code classification model that uses a CNN. The model classifies source code based on the type of algorithm in the code. During CNN model training, all source code is converted into a simple structure of code without any variables, functions, keywords, etc. The obtained classification accuracy of the CNN model is very high. In summary, many promising methods have proposed. Most researchers utilized traditional supervised and unsupervised classifiers, RNNs, LSTM, or CNNs as language models for source code classification and other applications. RNNs are much better than traditional language models such as n-gram, but have limitations in terms of handling long input sequences. LSTM is a variant of RNNs that overcomes the shortcomings of RNNs. The model proposed in the present study combines the attention mechanism with LSTM (LSTM-AttM). The LSTM-AttM network is used as a language model for source code assessment and classification based on the estimated error probability. The LSTM-AttM network outperforms LSTM because the latter uses only the last hidden state outcome for prediction. In contrast, LSTM-AttM considers all previous hidden state outcomes for prediction. Most of the studies used different models for source code classification based on errors, programming language detection, archive code classification, and simple error detection. On the other hand, our proposed model specifically identifies logic, syntax and other errors in the source code. Furthermore, the model can predict the correct words in place of the error location. Overall our proposed LSTM-ATM model differs from other models in achieving unique goals.
## 7 Visible Light Communication and LiFi
### ITU G.vlc  
The International Telecommunication Union (ITU) initiated the G.9991 (G.vlc) standard as an approach to LiFi technology in 2015. As a result of this study, the standard G.9991 was introduced in 2019. This standard primarily focused on a system architecture to facilitate high-speed indoor wireless communications [71].  
The G.vlc standard provides a complete architecture for an indoor optical wireless communication system that operates in visible light and infrared ranges with the operational wavelength bands, defined as (380 nm-780 nm) and (800 nm-1675 nm), respectively [71]. The architecture reference model of the G.vlc standard presents a very generic model, as shown in Figure 8. Further, it allows five network topologies, as listed below:  
* Point-to-point (peer-to-peer);
* Point-to-multipoint;
* Multipoint-to-multipoint (mesh);
* Relayed mode;
* Centralized.  
Figure 8: Network architecture reference model of G.vlc.  
Figure 7: Network topologies supported in IEEE 802.15.13 standard.  
The networks can therefore be configured to support a variety of services for 6G applications. As shown in Figure 8, domain is a subset of the network participants and the domain master node will coordinate the resources among its members. Hence, the resource allocation happens at the domain master node, which can carefully allocate resources to meet the QoS requirements of the user applications.  
Unlike the IEEE 802.15 standards, the G.vlc standard consists of only OFDM-based physical layers. Due to the non-negative nature of optical signals, asymmetrically clipped optical OFDM (ACO-OFDM) and DC-biased optical OFDM (DCO-OFDM) are used [72]. The DCO-OFDM physical layer is intended for high-data-rate links, while the ACO-OFDM physical layer is designed for less demanding use cases. The data rates could reach around 250 Mbps in the downlink and 200 Mbps in the uplink for point-to-multipoint system configuration [72, 73]. The adaptive OFDM physical layer and other functionalities, such as robust line coding and bit loading, make G.vlc a very resilient standard for 6G indoor wireless deployments.
# Potential of Supercritical _Acrocomia aculeata_ Oil and Its Technology Trends
###### Abstract  
This study discusses the bioactive composition, supercritical extraction, and biological activity of _Acrocomia aculeata_ in publications in the last ten years. Numerous compounds have been identified in _A. aculeata_, which include fatty acids, carotenoids, phenolic compounds, and tocopherols, discussed in this article. Although there are several studies with the fruit using conventional extraction to obtain oil, there are only a few involving extraction at high pressures. Therefore, this article emphasized the potential of extraction with supercritical fluid (SFC) to obtain oil due to its good selectivity, fractions distributed in terms of mass yield, and chemical composition of the obtained extracts, which provides a solvent-free product, making it safe for application in the food industry. The biological activity of _A. aculeata_ extracts was also discussed, including antidiabetic, anti-inflammatory, diuretic, antimicrobial, antioxidant, neuroprotective, and photoprotective effects, which can produce effects on human health. This review produces important results that can act as a basis for future studies related to obtaining bioactive compounds from _A. aculeata_ with a high degree of purity and good quality in its applications.  
_Acrocomia aculeata_; bioactive compounds; supercritical extraction; biological activity +
Footnote †: journal:  
characteristics. In that regard, the application of supercritical technology becomes an excellent possibility for obtaining oil; therefore, this study aims to evaluate the nutritional composition, phytochemical composition, and human health effects of _Acrocomia aculeata_ fruit oil [13; 14; 15].
# State Residualisation and Kron Reduction for Model Order Reduction of Energy Systems
###### Abstract  
Greater numbers of power electronics (PEs) converters are being connected to energy systems due to the development of renewable energy sources, high-voltage transmission, and PE-interfaced loads. Given that power electronics-based devices and synchronous machines have very different dynamic behaviours, some modelling approximations, which may commonly be applied to run transient simulations of transmission systems, may not be optimal for future grids. Indeed, the systematic utilisation of the phasor approximation for power lines, implemented in most transient simulation programs, is increasingly not appropriate anymore. In order to avoid the requirement for full electromagnetic transient simulations, which can be resource-demanding and time-consuming, this paper proposes a combination of an event-based state residualisation approximation and the Kron reduction technique. The proposed technique has the advantage of allowing accurate transient simulations based on the optimal reduction of the number of state variables, depending on the observed variables, the considered events, and the tolerated approximation error, along with simplifying power systems equations for accelerated simulations.  
electronic transient model; phasor approximation; model order reduction; power system simulation; grid-forming converters; small-signal stability analysis +
Footnote †: journal: _Applied_  
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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Validation of Cable-Driven Experimental Setup to Assess Movements Made with Elbow Joint Assistance
###### Abstract  
This article investigates a cable-driven experimental setup to simulate elbow joint assistance in the sagittal plane provided by an exosuit. Cable-driven exosuits, particularly fabric-based designs, significantly enhance rehabilitation by enabling targeted joint exercises and promoting functional recovery. To achieve an optimal design, these devices require an analysis of the cable tension, reaction forces, and moments and their dependency on the anchor position. This study presents a cable-driven experimental setup with two rigid bars and variable anchor positions, designed to mimic the human forearm, upper arm, and elbow joint, to evaluate the performance of a potential cable-driven exosuit. Based on the experimental setup, a static model was developed to validate the measured cable tension and estimate the reaction force at the joint and the moments at the anchor positions. Furthermore, based on the observations, an optimization problem was defined to identify optimal anchor positions to improve the exosuit's design. The optimal position on the forearm and upper arm is studied between 15% and 50% distance from the elbow joint. Our findings suggest that prioritizing user comfort requires both anchor points to be as far away from the elbow joint as possible, i.e., 50% distance, whereas, for optimal exosuit performance, the forearm anchor position can be adjusted based on the joint angle while keeping the upper arm anchor position at the farthest point. The findings in the current work can be used to decide the anchor point position for designing an elbow exosuit.  
cable-driven exosuit; rehabilitation robot; exoskeleton; wearable and assistive devices; medical robotics Article
# Parameter Identification Methods for Hyperelastic and Hyper-Viscoelastic Models
## 3 Parameter Identification Method for the Hyper-Viscoelastic Model
### Constraints and Initial Values of Parameters  
Before presenting results, it is necessary to clarify the constraints as well as the initial setting values for the undetermined parameters in the identified procedure. As for the weight \(u_{k}\), as well as the summation of all \(u_{k}\) except \(u_{0}\), they should be always within the range from 0 to 1. While for the relaxation time \(t_{1}\), it should be positive and a suggested initial value is the main relaxation time of materials, which can be determined by the point corresponding to the relaxed stress \(\sigma_{0.368}\) in creep tests, here the \(\sigma_{0.368}\) is defined as  
\[\sigma_{0.368}=0.368(\sigma_{0}-\sigma_{\infty})+\sigma_{\infty} \tag{19}\]  
in which the \(\sigma_{0}\) is the instantaneous stress and the \(\sigma_{\infty}\) is the equilibrium stress.  
The creep experiment of the material characterized by Equation (18) and the last group of parameters in Table 1 is conducted in ANSYS, and the main relaxation time is 12.54 s.  
As for the number of the terms considered for the Prony series, theoretically, the experiment data in Figure 5 will be more accurately fitted with a higher number, however, this may lead to uncontrolled computation cost and sometimes unreasonable estimated parameters. In this study, the number to be decided is suggested to make the largest relaxation time in Equation (16) the same order of magnitude with the largest experiment time. For example, the largest experiment time in Figure 5 is 1000 s, so, the number of the terms considered for the Prony series is 3 since the corresponding largest relaxation time now is 1254 s.  
In view of all the restrictions presented above, as well as the consideration of testing the reliability of the pattern search algorithm, four groups of initial values for the undetermined parameters are presented in Table 2. Besides, taking the third group as an example, the linear constraint equations for the parameters during iterations are shown in Equation (20).  
Figure 5: Virtual experiment data (**a**) relaxation tests of uniaxial tension (**b**) simple shear tests.
# A Review on the Performance of Linear and Mixed Integer Two-Stage Stochastic Programming Software
## Appendix C.2.1 Algorithmic Innovations in DECIS
### Computational Results for FortSP in Comparison with DECIS  
From the results in the previous subsections, it can be observed that the algorithms implemented in FortSP (Table 14) outperforms the decomposition implementations in GAMS-DECIS (Table 15) in terms of solution time. The strength of FortSP resides in the use of multiple strategies that can accelerate the convergence of the standard BD algorithm and regularization solved with MINOS. In fact, we observed that the best FortSP methodology is at least 37.3% faster than the best algorithmic implementation evaluated with DECIS for each test problem (see Figure 16). In the instances in which none of the DECIS solvers converge, the solution time is noted as 3 h of walltime.  
As expected, the performance of the BD algorithm in both FortSP and DECIS behaves similarly, having a difference of fewer than 10 iterations in each test instance. Both implementations use BD with aggregated cuts but differ in the initialization procedure. However, the BD algorithm is on average two times faster in the FortSP's implementation than DECIS's implementation.  
In this particular set of instances, the most time-consuming part of the algorithm is the cumulative solution of scenario subproblems, as can be observed in Figures 17 and 18, which is explained by the large number of scenario subproblems. This difference is especially pronounced in the SAPHIR group, where the recourse problem is larger than the first-stage program, in terms of constraints and variables. In most of the test instances, DECIS with initialization in the EV solution is the methodology that spends more time solving the master problem, as it uses BD to obtain a proper starting point. Following the general trend, FortSP is faster in the solution of both the master problem and the subproblems separately, indicating that differences in the implementation play an important role in the performance of the decomposition strategies. Warm-starting and automatic selection of the linear solver might contribute to the acceleration of the convergence of BD in FortSP.
## 4 Materials and Methods
### Engineered Bacterial Strains  
The human MCPIP1 gene (1866 base pairs) was amplified from the pUC57-MCPIP1 plasmid (Tsingke Biotechnology Co., Ltd., Beijing, China) using the following primers to engineer HA-MCPIP1-expressing bacteria: forward, CATGCCATGGCCATATCC-GATGTGCCGGATATATGCGTGAGAGCGGTCGTGTGTGGAAAA; reverse, AGCTTTGTTTAAACTATTCGCTCGATGCTTTTATAGCTC. Amplified DNA was cut with _Ncol_ and _Pmel_ and used to replace Rluc8 directly at the same site in pBAD-pelB-Rluc8. The resulting plasmid was named pMCPIP1. The empty vector (control) was named pEmpty. Plasmids pEmpty and pMCPIP1 (both harboring an ampicillin resistance gene) were transferred into \(\Delta\)ppGpp _Salmonella typhimurium_ by heat-shock transformation. The new strains (named SlpEmppty or SLpMCPIP1) were maintained in an ampicillin-containing medium and kept in a deep freezer at \(-80\)\({}^{\circ}\)C as 25% glycerol stocks. The OD values of the cultured bacterial cells were measured at the absorbance of 600 nm (OD\({}_{600}\)). The number of SlpEmppty or SLpMCPIP1 was calculated by assuming that 1 OD\({}_{600}\) represents \(8\times 10^{8}\) colony-forming units (CFUs)/mL.
# An Improved Self-Born Weighted Least Square Method for Cylindicity Error Evaluation
## 3 SWLS Solution for Cylindricity Error
### Improved self-Born weighted Least Square Method
#### 3.3.1 Model Initialization  
To apply the SWLS method to a nonlinear problem, the nonlinear model must first be transformed into a linear model. The model equations are assumed to be:  
\[Z=F(X) \tag{21}\]  
In order to linearize the equation, a sufficient approximation \(X^{0}\) of \(\widetilde{X}\) is taken [19]:  
\[\widetilde{X}_{i}=X_{i}^{0}+\widetilde{x}\,\ (i=1,2,\ldots,n) \tag{22}\]Because \(\widetilde{x}\) is a tiny quantity, the Taylor formula can be used to omit the quadratic and more than quadratic terms [22], and then the functional equation is:  
\[\begin{array}{c}Z=F\left(X_{1}^{0},X_{2}^{0},\ \ldots,X_{n}^{0}\right)+\left( \frac{\partial F}{\partial X_{0}}\right)\widetilde{x}+\left(\frac{\partial F}{ \partial X_{1}}\right)\widetilde{x}\\ +\left(\frac{\partial F}{\partial X_{2}}\right)\widetilde{x}+\ldots+\left( \frac{\partial F}{\partial X_{n}}\right)\widetilde{x}+\Delta(\text{higher order terms})\end{array} \tag{23}\]  
Bringing Equations (3)-(5) into Equation (23), it is expressed in matrix form as:  
\[J=\left[b_{1}\ b_{2}\ b_{3}\ b_{4}\ b_{5}\right]\begin{pmatrix}\delta\hat{a} \\ \delta\hat{b}\\ \delta\hat{q}\\ \delta\hat{R}\end{pmatrix}-D_{i} \tag{24}\]  
Equation parameters:  
\[\begin{array}{c}\varepsilon_{1}=a-x_{i}+p\times z_{i},\ \varepsilon_{2}=b-y_{i}+q \times z_{i}\\ b_{1}=\frac{\varepsilon_{1}}{\sqrt{\varepsilon_{1}^{2}+\varepsilon_{2}^{2}}}, \ b_{2}=\frac{\varepsilon_{2}}{\sqrt{\varepsilon_{1}^{2}+\varepsilon_{2}^{2} }}\\ b_{3}=\frac{\varepsilon_{1}\times\varepsilon_{2}}{\sqrt{\varepsilon_{1}^{2}+ \varepsilon_{2}^{2}}},\ b_{4}=\frac{\varepsilon_{2}\times\varepsilon_{2}}{ \sqrt{\varepsilon_{1}^{2}+\varepsilon_{2}^{2}}}\\ b_{5}=-1;\ D_{i}=(R-\sqrt{\varepsilon_{1}^{2}+\varepsilon_{2}^{2}})^{2}\\ \delta\hat{a}=a-a_{0},\ \delta\hat{b}=b-b_{0},\ \delta\hat{p}=p-p_{0},\ \delta\hat{q}=q-q_{0}\end{array}\]  
The initialization parameters are \(a_{0},\ \ b_{0},\ \ p_{0},\ q_{0},\ \ \delta\hat{R}\), and they are determined by the coordinates of the measurement cylinder center position.
## 4 Discussion  
### Changes in Waveform Due to the Presence of Bonding  
Changes in the ultrasonic waveform due to the absence of bonding and its strength, regarding Figure 6, are discussed in this section. The apparent difference is that only the intact specimen had sharp peaks in the low-frequency region, while all specimens had a sharp peak around 200 kHz in common, which was especially dominant in the naked steel rod and heated specimens. The reason for such a behavior may be that the energy of the ultrasonic wave leaked into the surrounding mortar, or that mode conversion of the guided wave occurred, due to the strong bonding. The results in Figure 7 also support these explanations. While the wavelet power spectrum of the naked steel rod and heated specimen agreed well with the analytical group velocity dispersion curves, that of the intact specimen had almost no overlap with the dispersion curves. Additionally, it can be observed that the sharp peak around 200 kHz found common to all the specimens was not a symmetric longitudinal mode guided wave. The corresponding wavelet power spectrum is circled with a white dashed line in Figure 7. The sharp peaks around 200 kHz correspond to a tail-like wavepacket, indicated with black arrows in Figure 6a. The waveforms of the naked steel rod and heated specimen match well, as the thermal expansion and contraction of the rod weakened the bonding, and the behavior of the wave propagating in the rod was close to that in the naked steel rod.
# A Deep-Learning-Based Secure Routing Protocol to Avoid Blackhole Attacks in VANETs +
###### Abstract  
Vehicle ad hoc networks (VANETs) are a vital part of intelligent transportation systems (ITS), offering a variety of advantages from reduced traffic to increased road safety. Despite their benefits, VANETs remain vulnerable to various security threats, including severe blackhole attacks. In this paper, we propose a deep-learning-based secure routing (DLSR) protocol using a deep-learning-based clustering (DLC) protocol to establish a secure route against blackhole attacks. The main features and contributions of this paper are as follows. First, the DLSR protocol utilizes deep learning (DL) at each node to choose secure routing or normal routing while establishing secure routes. Additionally, we can identify the behavior of malicious nodes to determine the best possible next hop based on its fitness function value. Second, the DLC protocol is considered an underlying structure to enhance connectivity between nodes and reduce control overhead. Third, we design a deep neural network (DNN) model to optimize the fitness function in both DLSR and DLC protocols. The DLSR protocol considers parameters such as remaining energy, distance, and hop count, while the DLC protocol considers cosine similarity, cosine distance, and the node's remaining energy. Finally, from the performance results, we evaluate the performance of the proposed routing and clustering protocol in the viewpoints of packet delivery ratio, routing delay, control overhead, packet loss ratio, and number of packet losses. Additionally, we also exploit the impact of the mobility model such as reference point group mobility (RPGM) and random waypoint (RWP) on the network metrics.  
deep learning; secure routing; clustering; blackhole; vehicular ad-hoc networks +
Footnote †: Copyright © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).