esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Limitations and Validity Discussion  
In this section, we comment on the limitations of the designed tool and for the initial empirical evaluation.  
Concerning the tool's design, our application of the framework by [PERSON] et al. [21] is constrained to non-verbal communication. Since our overarching goal is to provide real-time feedback for many groups simultaneously, we did not consider verbal communication or content analysis due to the technical limitations of analyzing multiple voice streams in real time. That said, we think that behavioral indicators combining non-verbal and verbal communication can better inform collaboration constructs, which is the focus of our future work. Another constraint for defining behavioral indicators is that the case study presented in Section 5 was performed under the restrictions of COVID-19, so the participants were using masks. Features such as facial expressions could not be extracted to inform behavioral indicators. However, thanks to the tool's architecture, they can easily be integrated without significant changes.  
The initial empirical evaluation is limited to assessing whether the designed tool contributes to the stakeholders' goals, and further studies are required to validate the tool's effect on collaborative learning. With this aim, we explicitly decided to ask the subjects to perform two types of opposite collaborative behaviors to emphasize the differences in the visualizations for their discussion in the focus group. Alternative study designs, such as comparing the analytics and the performance of several groups performing a collaborative activity, are being considered for validating the tool.  
Finally, the design and sample size of the focus group do not allow us to generalize the results. However, since we are not validating the tool but exploring if it helps stakeholders achieve their goals, we opted for a freer focus group design, favoring deeper discussions among participants, which is appropriate to our methodological framework.
# Effect of AQM-Based RLC Buffer Management on the eNB Scheduling Algorithm in LTE Network +
## 1 Introduction
We can tune the parameter i to get different dropping functions. * We overview the effect of AQM-based buffer management on the performance of different scheduling algorithms with and without handover. We measured the performance in terms of end-to-end average Transmission Control Protocol (TCP) throughput and delay. While there is significant work on the performance of different schedulers in the absence of AQM-based buffer management [4] and handover [8], it is very difficult to capture the effect of both on the scheduling algorithms using analytical models. * Finally, we used detailed and extensive ns3 [9] realistic simulation to simulate the effect of AQM-based buffer management on the scheduling algorithms in the single cell network topology without handover and in the multi-cell network topology in the presence of handover in LTE networks across a wide range of traffic-loads. The remainder of this paper is organized as follows. Related work is presented in Section 2. A brief introduction to LTE network architecture is described in Section 3. The AQM mechanism and the proposed approach is described in Section 4. The network model and implementation of different schedulers in ns3 are described in Section 5. Section 6 describes the performance of the proposed scheme by simulation. Finally, Section 7 concludes the paper.
_Appl. Sci._**2013**, \(3\), 396-419; doi:10.3390/app3020396  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Review_  
**A Review of the Role of Amphiphiles in Biomass to Ethanol Conversion**  
**[PERSON] 1,*, [PERSON] 1 and [PERSON] 2**  
1 Department of Agricultural and Biosystems Engineering, South Dakota State University,  
1400 North Campus Drive, Brookings, SD 57007, USA; E-Mail: [EMAIL_ADDRESS]  
2 Department of Biology & Microbiology, South Dakota State University,  
1400 North Campus Drive, Brookings, SD 57007, USA; E-Mail: [PERSON]  
* Author to whom correspondence should be addressed; E-Mail: [PERSON]]; Tel.: +1-919-627-2334; Fax: +1-605-688-6764.  
_Received: 31 December 2012; in revised form: 18 February 2013 / Accepted: 25 February 2013 / Published: 2 April 2013_  
**Abstract:** One of the concerns for economical production of ethanol from biomass is the large volume and high cost of the cellulolytic enzymes used to convert biomass into fermentable sugars. The presence of acetyl groups in hemicellulose and lignin in plant cell walls reduces accessibility of biomass to the enzymes and makes conversion a slow process. In addition to low enzyme accessibility, a rapid deactivation of cellulases during biomass hydrolysis can be another factor contributing to the low sugar recovery. As of now, the economical reduction in lignin content of the biomass is considered a bottleneck, and raises issues for several reasons. The presence of lignin in biomass reduces the swelling of cellulose fibrils and accessibility of enzyme to carbohydrate polymers. It also causes an irreversible adsorption of the cellulolytic enzymes that prevents effective enzyme activity and recycling. Amphiphiles, such as surfactants and proteins have been found to improve enzyme activity by several mechanisms of action that are not yet fully understood. Reduction in irreversible adsorption of enzyme to non-specific sites, reduction in viscosity of liquid and surface tension and consequently reduced contact of enzyme with air-liquid interface, and modifications in biomass chemical structure are some of the benefits derived from surface active molecules. Application of some of these amphiphiles could potentially reduce the capital and operating costs of bioethanol production by reducing fermentation time and the amount of enzyme used for saccharification of biomass. In this review article, the benefit of applying amphiphiles at various stages of ethanol production (_i.e._,pretreatment, hydrolysis and hydrolysis-fermentation) is reviewed and the proposed mechanisms of actions are described.  
**Keywords:** lignocellulosic biomass; cellulase; enzymatic hydrolysis; pretreatment; surfactants
## 3 Case Studies: Application to Continuous and Lumped-Parameters Systems  
This section presents two case studies to demonstrate the receptance coupling and frequency-based substructuring (RCFBS) approach. The first case study involves coupling three continuous substructures with fixed-fixed boundary conditions. The second case study focuses on subsystems comprising lumped masses, springs, and dampers with varying internal degrees of freedom (DoFs). The accuracy of the RCFBS method is validated through a numerical finite element analysis (FEA), a modal analysis, and a direct method.
## 5 Experiments
### Comparison of HGA Using Different Crossover  
We performed experiments to compare the proposed crossover, RPMX, in HGA with several existing crossover operators, position-based crossover (POS) [33] and uniform crossover (UX) [34]. Note that position-based crossover, possessing the same structure as  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **DB1** & **DB2** & **DB3** & **DB4** \\ \hline Number of items & 50 & 100 & 150 & 225 \\ Layout of sites & \(10\times 5\) & \(10\times 10\) & \(10\times 15\) & \(15\times 15\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Synthetic databases (boldfaced) and the layout of sites.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**DB** & **(A)** & **(B)** & **(C)** & **(C)/(A)** & **(C)/(B)** \\ \hline DB1 & 1328.2 & 1856.8 & 2256.2 & 1.70 & 1.22 \\ \hline DB2 & 2767.8 & 4035.6 & 4967.8 & 1.79 & 1.23 \\ \hline DB3 & 4146.8 & 6204.4 & 7812.4 & 1.88 & 1.26 \\ \hline DB4 & 6253.8 & 9571.4 & 12,121.0 & 1.94 & 1.27 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of three assignments considering different factors: (A) lifts; (B) lifts and relative profits; and (C) lifts, relative profits, and relative frequencies.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Range** \\ \hline _Lift_ & 1 to 1.4 or 0 \\ _Relative profit_ & 1 to 4 \\ _Relative frequency_ & 1 to 4 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Parameters used generate synthetic data.  
RPMX, is different on how to choose exchanging genes. We compared the performance of crossover methods, RPMX, POS, and UX, using DB1, DB2, DB3, and DB4 in the item allocation situation. We present averaged fitness value and average time for each synthetic data in Table 12. We note that RPMX consistently outperforms the other crossover methods in terms of averaged fitness within comparative computational time.
## 2 Materials and Methods
### Experimental Design  
To ensure the reliability of the results, an experimental design was implemented in triplicate for two types of rice treated by biological and chemical methods for pest control, respectively; in total, four groups of samples were obtained, distributed in two plots.  
Figure 1: Graphical summary.  
* WRBC: White Rice--Biological Control.
* WRCC: White Rice--Chemical Control.
* BRBC: Brown Rice--Biological Control.
* BRCC: Brown Rice--Chemical Control.  
Each sample underwent quantification of bioactive compounds: phenolic compounds, dietary fiber, \(\gamma\)-oryzanol, phytic acid, antioxidant activity, and heavy metals (cadmium and arsenic).  
The chemical method consisted of the application of the insecticide _Lorsban_, whose active ingredient is chlorpyrifos, an organophosphate compound widely used for pest control in rice crops. This product was selected because of its proven efficacy in the management of relevant pests in the agricultural context of rice. In addition, _Diazinon_ was applied at doses of 0.50-0.75 L ha\({}^{-1}\), only when the estimated economic damage exceeded 30%, following technical recommendations to maximize control effectiveness and minimize environmental impacts.  
The biological method used the endophytic fungus _Beauveria bussiana_, recognized for its ability to significantly reduce the attack of _Spodoptera frugiperada_, one of the main rice pests. This fungus, by establishing itself asymptomatically within healthy plant tissue, strengthens the plant's natural defenses and offers sustainable and environmentally responsible control.
# Abusive Content Detection in Arabic Tweets Using Multi-Task Learning and Transformer-Based Models
## 4 Experimental Setup  
### Experiment Settings  
For the hyperparameter setting, we used different parameter settings with each model. For the ML baseline, the SVM and NB were used with default parameters, as shown in the Scikit-learn library. For the DL models with the LSTM hidden state, the dimension was 64, the dropout was 0.3, the direction was true, the number of units in the fully connected layer was 32, the dropout rate was 0.4, the learning rate was 0.001, the number of epochs was 20, the batch size was 32, and the optimizer was Adam [51]. The hyperparameters used for the CNN were filter sizes(2, 3, 4, 5), the number of filters (32), the number of units in the fully connected layer (30), the dropout rate (0.5), the learning rate (0.001), the number of epochs (20), the batch size (32), the optimizer (Adam), and the loss function (cross entropy loss). The hyperparameter settings used the same values for the pretrained Arabic LMs provided by MARBERT [11], ArabicBERT [12], CAMeLBERT [13], QARiB [14], and AraBERTv0.2 [15]. For instance, we used the same fine-tuning hyperparameter used in MARBERT, where the input sequence length was 256, the batch size was 32, the learning rate was \(2\times 10^{-6}\), the number of epochs was 25, and the optimizer was Adam. For the MTL experiments, we used the hyperparameter settings provided by [18].  
All the comparison algorithms used the split ratio train:dev:test = 8:1:1, where this split ratio was utilized in the same dataset [9], and the results provided are based on the test set. For the environment setup, all the experiments were run using Google Colab [52] with a GPU accelerator. The models were built using many libraries and tools, all of which were open-source and written in Python. The used tools were Scikit-learn and PyTorch, and all pretrained Arabic LMs were available in the HuggingFace Transformers library [53].
## 5 Results and Discussion
### Vibration Synchronization and Control Synchronization
#### 5.1.2 Experimental Results
The phase differences are stable near \(2\alpha_{1}\approx 96^{\circ}\), \(2\alpha_{2}\approx 140^{\circ}\), and \(2\alpha_{3}\approx-124^{\circ}\), and are consistent with the data in Figure 11b, which reflect the coupling characteristic of the self-adjusting of the vibration system. In order to compare Figures 7 and 13 shows the high-speed photos of three ERs with the anticlockwise rotational directions in vibration synchronization. The phase differences are stable near \(2\alpha_{1}\approx-98^{\circ}\), \(2\alpha_{2}\approx-124^{\circ}\), and \(2\alpha_{3}\approx 138^{\circ}\), which reflect another group of solutions when \(r_{\psi}<\sqrt{2}\). Using the same way, the high-speed photos of three ERs with the clockwise rotational directions in control synchronization are shown in Figure 14. From eight images of the average time, it can be seen that the phase difference approximately equals zero. Comparing control synchronization and vibration synchronization by the data in Figures 11-14, it can be concluded from these results that the vibration system has achieved the ideal motion trajectory and the max force in the state of control synchronization. Hence, it is a very practical engineering technology applying control synchronization to replace vibration synchronization in the design of the vibration machine driven by three ERs.
###### Abstract  
The increase in installed PV capacity worldwide and the intermittent nature of solar resources highlight the importance of power prediction for grid integration of this technology. Therefore, there is an urgent need for an effective prediction model, but the choice of model hyperparameters greatly affects the prediction performance. In this paper, a multi-strategy improved snowmelt algorithm (MISAO) is proposed for optimizing intrinsic computing-expressive empirical mode decomposition with adaptive noise (ICEEMDAN) and weighted least squares support vector machine for PV power forecasting. Firstly, a cyclic chaotic mapping initialization strategy is used to generate a uniformly distributed high-quality population, which facilitates the algorithm to enter the appropriate search domain quickly. Secondly, the Gaussian diffusion strategy enhances the local exploration ability of the intelligences and extends their search in the solution space, effectively preventing them from falling into local optima. Finally, a stochastic follower search strategy is employed to reserve better candidate solutions for the next iteration, thus achieving a robust exploration-exploitation balance. With these strategies, the optimization performance of MISAO is comprehensively improved. In order to comprehensively evaluate the optimization performance of MISAO, a series of numerical optimization experiments were conducted using IEEE CEC2017 and test sets, and the effectiveness of each improvement strategy was verified. In terms of solution accuracy, convergence speed, robustness, and scalability, MISAO was compared with the basic SAO, various state-of-the-art optimizers, and some recently developed improved algorithms. The results showed that the overall optimization performance of MISAO is excellent, with [PERSON] average rankings of 1.80 and 1.82 in the two comparison experiments. In most of the test cases, MISAO delivered more accurate and reliable solutions than its competitors. In addition, the altered algorithm was applied to the selection of hyperparameters for the ICEEMDAN-WLSSVM PV prediction model, and seven neural network models, including WLSSVM, ICEEMDAN-WLSSVM, and MISAO-ICEEMDAN-WLSSVM, were used to predict the PV power under three different weather types. The results showed that the models have high prediction accuracy and stability. The MAPE, MAE and RMSE of the proposed model were reduced by at least 25.3%, 17.8% and 13.3%, respectively. This method is useful for predicting the output power, which is conducive to the economic dispatch of the grid and the stable operation of the power system.  
solar power forecasting; snow ablation optimizer; circle chaotic mapping initialization; gaussian diffusion strategy; random follower search strategy; weighted least squares support vector machine +
Footnote †: journal: Applied Sciences
# MADDPG-Based Security Situational Awareness for Smart Grid with Intelligent Edge
## 3 System Model
### Deep Reinforcement Learning  
Traditional machine learning mainly focuses on solving the mapping relationship between inputs and outputs, describing this mapping's error as a loss function with coefficients to be determined [31]. Then, the value of the loss function with minimal error is solved by optimization ideas. However, in SSA of smart grids under edge computing, the environment contains essential situational elements. How to acquire these situational elements from the environment and train them to obtain accurate awareness ability is our research's primary concern.  
Reinforcement learning (RL) is environment-based, enabling an agent to choose actions based on the current state and thus obtain as much rewards from the environment as possible [32]. Since SSA under smart grids involves a continuous space of situational elements and perceptual interaction of multiple edge agents, MADDPG is a promising solution. As shown in Figure 3, MADDPG involves the respective actor-critic networks of \(K\) agents. Each agent has an online actor and critic network, respectively, and the same for the target network.  
First, each agent distributed executes the acquisition of current state \(\mathbf{s}\), action value \(a\), rewards \(r\), and next state \(\mathbf{s}^{{}^{\prime}}\), and then deposits the sequence \((\mathbf{s},a,r,\mathbf{s}^{{}^{\prime}})\) in the experience replay buffer \(\mathcal{D}\). When the number of caches in \(\mathcal{D}\) is greater than a threshold, the network starts learning. Each actor-network updates the policy parameters individually according to maximizing the gradient ascent. Here, \(\pi=\{\pi_{1},\pi_{2},\ldots,\pi_{K}\}\) is denoted as the policy for \(K\) agents. Then, each critic-network updates the parameters of action value by minimizing the distance between Q-function \(y^{i}\) of samples selected from \(\mathcal{X}\) and the state-action function \(Q_{k}^{y^{i}}(\mathbf{s}^{i},a_{1}^{i},\cdots,a_{K}^{i})\), respectively.  
Figure 3: Multiagent deep deterministic policy gradient (MADDPG) framework.
## 3 Background
### HSR Concepts
#### 3.1.1 Single-Ring Networks  
In a single-ring HSR network, HSR uses the two HSR ports of each DANH node to interconnect all DANH nodes to the ring network, as shown in Figure 1. There is no QuadBox used in single-rings.  
The source node sends two copies of the same frame simultaneously through each port in both directions in the ring. The two frame copies travel in opposite directions. If a DANH receives the frame, but is neither its source nor its only destination, the DANH forwards the frame to its other port, except if it already forwarded the same frame. The destination node of a unicast frame does not forward a frame for which it is the only destination. Frames forwarding in the ring carry the HSR tag inserted by the source node, which contains a sequence number. The doublet of source MAC address and sequence number uniquely identifies copies of the same frame. In the fault-free case of the network, the destination node receives two identical frames, passes the first frame to upper layers, and discards the duplicate. If a single fault on the ring occurs, only one of the two frames travelling on the two network paths through the ring will experience a communication interruption. The second frame will still reach the destination node.
## 3 System Design and the Proposed SEAC-Based Resource Allocation Methodology
### Performance Feedback Mechanism
#### 3.3.1 Immediate Reward  
The reward \(r_{t}\) for each agent \(i\) at time \(t\) is calculated based on the following criteria:  
* The reward \(r_{t}\big{(}a_{t}^{i}/s_{t}^{i}\big{)}=S_{txs}^{i}\) if no collisions are detected and the resource allocation latency is \(L_{RA}^{i}\leq L_{PDB}\);
* The reward \(r_{t}\big{(}a_{t}^{i}/s_{t}^{i}\big{)}=S_{txs}^{i}-S_{re}^{i}\) if collisions are detected but successfully re-evaluated within \(L_{PDB}\);
* The reward \(r_{i}\big{(}a_{t}^{i}/s_{t}^{i}\big{)}=-F_{txf}^{i}\) if the agent cannot reselect resources within \(L_{PDB}\) due to multiple neighboring vehicles choosing the same resource.  
\[r_{i}\Big{(}a_{t}^{i}/s_{t}^{i}\Big{)}\!=\left\{\begin{array}{ll}&L_{RA}^{i} \leq L_{PDB}\\ &S_{txs}^{i}-S_{re}^{i}\hskip 28.452756 ptL_{RA}^{i}\leq L_{PDB}\\ &L_{RA}^{i}>L_{PDB}\end{array}\right. \tag{3}\]  
where  
* \(S_{txs}^{i}\) is the number of successful transmissions for the agent;
* \(S_{re}^{i}\) is the number of re-evaluated successful transmissions;
* \(F_{txf}^{i}\) is the number of failed transmissions;
* \(L_{RA}^{i}\) is the resource allocation latency;
* \(L_{PDB}\) is the packet delay budget.
# Assessment and Evaluation of the Intake of Sugars in European Countries
## 3 Results
### Intake of Total Sugars  
In total, twenty-six eligible surveys were found. For several European countries, data are missing due to the in- and exclusion criteria. The number of separate data on the intake of sugars is higher due to the inclusion of more than one age group in the survey and included data on total, free, and/or added sugars. In Tables S1 and S2 of the Annex the characteristics of the included surveys among children and adults with information on the intake of total, added, and/or free sugars are presented. For children, in total nineteen different surveys were identified. The number of age groups is larger because several surveys included more than one of the beforementioned age groups. Seven surveys were completed after 2015 (H  
None of the surveys presented figures for total sugars (in en%) among infants. A France survey reported a daily intake of 65.4 and 65.8 g total sugars for 0-11 months old male and female infants, respectively. Among toddlers (1-3-year-olds), the mean % contribution of total sugars to the energy intake varied from 24.9 to 31.4 with the lowest values in Ireland and Italy, and the highest value in the Netherlands. Among the 3-9-year-old children, the mean en% from total sugars ranged from 17.0 (Italy) to 28.6 (female children in The Netherlands). The mean en% in Belgium and Portugal was just above 25, whereas the proportion of energy coming from total sugars in Ireland was 18.7. For adolescents in Belgium, the en% for total sugars ranged from 21 to 25, which is comparable with Germany (24-26 en%) and The Netherlands (24-26 en%). Norway had the lowest values: 11.9 and 12.8 en% for males and females, respectively. The adolescents in Italy obtained almost 16% of their energy from total sugars; in Spain this was 17-19%, and in Ireland this was 20.4, which is about the same as for Portugal, with 20 en%. In Table S3 of the Annex the results of the intake of total sugars among adult population groups (except the elderly) are presented. The range in the intake is 13-26 energy%. Most countries had a mean intake of total sugars below 20 en%. Germany, Switzerland (women only), and the Netherlands (younger age groups) have a mean intake of total sugars of 20 en% or higher. The differences between the age groups are not consistent. Adult women tend to have a somewhat higher intake of total sugars than adult men. Among the elderly the range in the percentage of energy from total sugars is 13.3-20.6. The highest value is observed in the Netherlands and the lowest in Italy, which is in line with the findings among adults. The dietary intake of total sugars among elderly people varies between 15-20 en% for Ireland, Portugal, and Spain. The differences between the genders, women having a higher intake, is more pronounced (mostly about 3 en%) than for adults. The