esa-sceva/satcom-chunk-collection huggingface dataset:
# DYNASTI--Dynamic Multiple RPL Instances for Multiple IoT Applications in Smart City
## 5 Performance Evaluation and Results
### Selection of the Scheduling of Instances  
This section describes how the scheduling is selected in the simulation study. The total simulation duration is one day, with the time being divided into 24 slots (one slot per hour). A single scheduling of instances will be randomly selected for each hour using a uniform random distribution. Figure 7 shows the timeline with each scheduling instance for every hour.  
The sporadic Applications 2 and 3 will run on the network for 20 min at maximum (duration time). This time is more than enough for the generated critical information by these applications reaches the sink. The initial minute of each sporadic application inside a single hour is randomly selected, since sporadic applications occur on the network eventually. The start minute of Application 2 is chosen according to an interval randomly between 0 and 20 min, using a uniform distribution. It is randomly chosen in an interval between 10 and 30 min for Application 3. These intervals were chosen so that Applications 2 and 3 are more likely to run concurrently when started on scheduling 4. Therefore, it is possible to evaluate the traffic performance when two sporadic applications coexist concomitantly.
## 3 Communication in the 60 Ghz Band
### Beamforming  
Beamforming refers to a technique that dynamically shapes the beam pattern to focus on specific directions. It is a spatial filtering technique used in smart antennas and the main objective is to maximize the received power directed towards a certain node while minimizing the interference power towards undesired nodes [30]. A signal processor controls the excitation of antenna array elements to synthesize a desired radiation pattern [31]. In other words, beamforming works by combining elements in a phased array in such a way that signals at particular angles experience constructive interference (at the main lobe), while others experience destructive interference (at the nulls) and at the receiver by having gains in one direction and attenuation in others.  
The beamforming technique is used in smart antennas for transmitting and receiving signals in massive multiple-input multiple-output (MIMO) systems. MIMO systems combined with beamforming antenna array technologies are expected to play a key role in 5G wireless communication systems [30]. Apart from a higher directive gain, these antennas offer complex beamforming capabilities that increase the capacity of networks by improving the signal-to-interference ratio (SIR).  
Beamforming is mandatory in 802.11 ad, and both transmitter-side and receiver-side beamforming is supported. In the 802.11 ad standard, beamforming training determines the appropriate receive and transmit antenna sectors for a pair of nodes. The beamforming is split into two phases. The sector level sweep (SLS) and beam refinement phase (BRP). During SLS, an initial coarse-grained antenna sector configuration is determined. Thus, in the SLS phase each of the two nodes either trains or receives the appropriate transmit antenna sector. This information is used in a subsequent optional BRP to fine-tune the selected sectors [20]. During this stage, antenna weight vectors that vary from predefined sector patterns are evaluated to further optimize transmissions on phased antenna arrays.
## 3 Results and Discussion
no or very low concentrations of the sugars turnnose, maltulose and erlose compared to the others, and only in these samples, some sugars such as melibiose, raffinose and panose were detected. The sugars maltotriose, isomalotriose and maltottaroose were not in any sample of any group. As for invertase activity, supplementary feeding during the production led to lower invertase values in the collected samples. This may be attributed to the fact that bees, when they are fed with syrup, collect it quickly and have no time to enrich the product with enzymes, resulting in honey samples with low enzymatic content. According to the International Honey Commission, honey with invertase values greater than 10 IN or 73.43 U kg\({}^{-1}\) is characterized as fresh [30]. In the present study, the samples produced after feeding with inverted syrup and sugar syrup presented values lower than 73.43 U Kg\({}^{-1}\) (32.4 and 68.9 U Kg\({}^{-1}\), respectively) and they were characterized as adulterated (Table 3). MDA was applied for all estimated parameters to investigate the possibility of discriminating the groups based on feeding type. The analysis revealed four discriminant functions: The first of which corresponded to 96.6% of variance and it was determined by the presence of fructose and glucose, the second to 3.0% and it was related to the presence of sucrose and melezitose and the third to 0.4% of variance and it was determined by the presence of erlose and invertase (Table 4). Additionally, as shown in Figure 1, samples of the same group appear in the same quadrant grouped around the center of the corresponding group, emphasizing the large correlation within the same group. The discrimination of the group fed with the commercial inverted syrup is obvious, while the groups fed with the syrup and the control seem to stand out as they appear in a different quadrant. Indirect honey adulteration involving feeding bee colonies with commercial sugars is extremely difficult to detect [27; 29]. It seems from the present study that honey adulteration, especially with inverted syrup, can be detected through low-cost analysis. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Discriminant Function**} \\ \cline{2-4}  & **1** & **2** & **3** \\ \hline Fructose & \(-\)1.430 & 0.314 & 0.144 \\ Glucose & 1.288 & 0.722 & 0.922 \\ Sucrose & \(-\)0.232 & 0.529 & 0.116 \\ Turanose & \(-\)0.303 & \(-\)0.084 & \(-\)0.536 \\ Maltulose & 0.274 & \(-\)0.333 & 0.316 \\ Maltose & 0.944 & 0.438 & 0.193 \\ Trehalose & 0.180 & \(-\)0.235 & \(-\)0.135 \\ Isomaltose & \(-\)0.264 & 0.338 & 0.328 \\ Erlose & 0.119 & \(-\)0.438 & 0.755 \\ Melezitose & 0.089 & 0.668 & 0.597 \\ Raffinose & \(-\)0.141 & 0.000 & 0.082 \\ Panose & 0.079 & \(-\)0.257 & 0.027 \\ Invertase & 0.023 & 0.357 & \(-\)0.655 \\ Eigenvalue & 72.946 & 2.265 & 0.328 \\ \% Variance & 96.6 & 3.0 & 0.4 \\ \% Cumulative variance & 96.6 & 99.6 & 100.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Percentages of variance, cumulative and cumulative variance and discriminant functions as derived from the multi-discriminant analysis (MDA).
# University Student Dropout Prediction Using Pretrained Language Models
## 1 Introduction
Recently, in the field of natural language processing (NLP) [12], several studies [13; 14] have replaced contextual information representation with text to represent multimodal information as an integrated modality and leverage the superiority of pretrained language models (PLMs). Several researchers [15; 16; 17] began to recast various classification problems into natural language inference (NLI) tasks. The study [15] demonstrated that tabular reasoning performance could be significantly improved by recasting reasoning about tabular information as an NLI task. Another study [16] showed improved performance by training question answering and text summarization datasets by recasting them as NLI datasets. In this paper, we propose the STUdenet Dropout prediction framework (STUD), which is a novel framework that predicts which students will drop out during the semester using demographic and academic information. We first collect data from freshmen (in this study, we focused on freshmen dropout after observing that freshmen had the highest dropout rate of 45.05%) enrolled at The Catholic University of Korea from 2017 to 2021. We formulate both types of information into a natural language format using a template. In addition, we prepend the sentence (e.g., \"This student will drop out.\") or continuous vectors as \"hypothesis\" to the student information for recasting the student dropout prediction task into the NLI task. This recast NLI task is motivated by our hypothesis that the prepended hypothesis will effectively elicit the knowledge of university student dropout inherent in the language model. The contributions of this study are summarized as follows:  
* We develop a novel framework that predicts university freshmen's dropout using PLMs to capture the relationships between different types of data. * We recast the student dropout prediction task into a natural language understanding task (i.e., NLI task). To this end, we propose a novel method for modifying each type of record into a natural language form for feeding into a PLM. * The proposed model achieves superior performance in the dropout prediction of university freshmen compared to state-of-the-art methods. The remainder of this paper is organized as follows. The methods related to student dropout prediction and models are described in Section 2. Section 3 describes the university student dataset and introduces the proposed framework for university student dropout prediction. We present the performance evaluation results and perform in-depth analyses in Sections 4 and 5, respectively. Finally, we conclude the paper in Section 6.
## 2 System Model for Laser Time Transfer in Common-View  
### Ground-to-Ground Laser Time Transfer in Common-View  
The graphic depicts the fundamental concept of the two-way laser time transfer method, in which two terminals independently generate pulses at predetermined laser emission rates. For example, in a scenario involving a link between a ground receiving station and a space probe, the ground receiving station terminal records both the arrival time (\(t_{L1(R)}\)) and the emission time (\(t_{L1(E)}\)) of the pulse it transmits, and vice versa. The satellite receiving terminal records the arrival time of pulses (\(t_{S1}\)). Figure 1 illustrates the common use of asynchronous answering laser time transfer to extend operational distances. In scenarios where there is a high likelihood of detection at both ends of the link and relatively short round-trip times, basic echo transponder mechanisms are sufficient for measurement purposes [23].  
When two laser ranging stations, L1 and L2, are in the common-view configuration and transmit to the satellite simultaneously, the time offset \(\tau_{L-S}\) in the ground space can be obtained by establishing the time-delay link between the ground clock and the on-board clock. By comparing the difference between \(\tau_{L1S}\) and \(\tau_{L2S}\), the time difference between the ground clocks L1 and L2 can be determined, effectively completing the synchronization of time between the ground laser stations.  
The offset \(\tau_{L1S}\) between the ground clock L1 and the space clock \(S\) is given by:  
\[\tau_{L1S}=t_{L1(E)}+\frac{t_{L1(R)}-t_{L1(E)}}{2}-t_{S1}+\Delta_{C}, \tag{1}\]  
where \(t_{L1(E)}\) denotes the moment of ground station laser pulse emission, \(t_{L1(R)}\) signifies the moment of the satellite's reflected laser pulse return, and \(t_{S1}\) indicates the on-board receiving epochs for satellite laser pulse measurements. \(\Delta_{C}\) stands for \"correction term\"  
Figure 1: Ground receiving station-space probe link.  
and includes adjustments for instrument calibration, atmospheric conditions, velocity aberration, and relativistic effects. This correction term adjusts for relativistic effects, atmospheric phenomena, instrumental calibration, and velocity aberrations. \(\tau_{L_{2}S}\) can be obtained in the same way. The clock offset \(\tau_{L_{1}-L_{2}}\) between clocks at different terrestrial laser stations and the primary clock is determined by  
\[\tau_{L_{1}-L_{2}}=\tau_{L_{1}S}-\tau_{L_{2}S}. \tag{2}\]
## 3 Solution Method for Orbital Pursuit-Evasion-Defense Game
### Hybrid Numerical Method
#### 3.2.1 Multi-Objective Genetic Algorithm  
In the multi-objective genetic algorithm preprocessing, the terminal time \(t_{\rm f}\) and the unknown initial co-state variables \(A_{\rm E}(0)\) and \(A_{\rm D}(0)\) are taken as parameters (individuals). According to the terminal conditions, the objective functions of the multi-objective genetic algorithm are set as follows:  
\[\left\{\begin{array}{l}J_{1}=\|A_{\rm E}(t_{\rm f})+\frac{\mathbf{Z}_{\rm PE}(t _{\rm f})}{\|\mathbf{Z}_{\rm PE}(t_{\rm f})\|}\|\\ J_{2}=\|A_{\rm D}(t_{\rm f})+\frac{\mathbf{Z}_{\rm DP}(t_{\rm f})}{\|\mathbf{Z}_{\rm DP}( t_{\rm f})\|}\|\end{array}\right. \tag{31}\]  
The safe distance constraint is applied to ensure that the distance between any two players is greater than the safe distance before the terminal time. The best parameters are obtained by setting the reasonable population size, the appropriate maximum generation, and the suitable operators (i.e., crossover and mutation). The multi-objective genetic algorithm improved by [PERSON] (1986) is applied to this problem. This algorithm can reduce the complexity of computation and maintain the diversity of solutions. In this paper, we used the default operators in the toolkit on multi-objective genetic algorithm which is provided by [PERSON] [PERSON] (1986). In addition, the population size and the number of generations are set as 100 and 200 respectively. Because of the use of the multi-objective genetic algorithm, the preprocessing time is relatively long. Thus, this algorithm is suitable for off-line calculation.
## 4 Validation Experiment
### Validation Experiment of Optimal Parameter Combination  
#### 4.1.1 Shear Verification Experiment  
The optimal parameter combination was acquired by analyzing the regression model developed from the Box-Behnken simulation experiments for FTB shearing, with the normal stiffness (\(x_{2}\)) per unit area of 1.438 \(\times\) 10\({}^{8}\) N\(\cdot\)m\({}^{-3}\), the tangential stiffness (\(x_{3}\)) per unit area of 0.447 \(\times\) 10\({}^{8}\) N\(\cdot\)m\({}^{-3}\), and the bonding radius (\(x_{6}\)) of 1.362 mm. The remaining non-significant influencing factors were set to constant values obtained from the single-factor test. Discrete element model simulations were conducted in EDEM software under these parameter combinations to validate the model.  
As shown in Figure 12, the curve of shear force-time for FTB was obtained through a uniaxial shear test by a TMS-Pro texture analyzer. The experimental curve was then compared with the simulation curve, which is shown in Figure 13, revealing that both exhibited a similar trend. The findings indicate that the discrete element model for FTB can effectively capture the temporal variation in force during the shearing process. The experimentally obtained maximum shear force was 36.84 N, while the simulated value was 37.6 N. A relative error of 2.06% suggests a strong agreement between the experimental and simulation results.  
Figure 11: Significant interaction factor response surface plot to maximum shear force and relative error. Note: \(x_{2}\) is the normal stiffness per unit area, \(x_{3}\) is the tangential stiffness per unit area, and \(x_{6}\) is the bonding radius.
# Safe Three-Dimensional Assembly Line Design for Robots Based on Combined Multiobjective Approach
## 3 A Combined Multiobjective Approach for Safety Layout: SE-NSGA2
### Principle of SE-NSGA2  
The core principle of SE-NSGA2 is to produce a preliminary solution set and validate the solution set. First, a DE strategy was introduced in order to cross-mutate the spaces between two devices to generate the next-generation population and complete the population consolidation process that is based on NSGA-II. The DE strategy has a large number of genetic parents, which effectively improves the global search ability of the traditional algorithm and avoids local optima. The combined approach can effectively reduce the number of repeated individuals in the Pareto set and enhance the performance of NSGA-II that is based on the corresponding convergence level and distribution. The safety indicator is determined based on the initial optimized output solution set, and the unsafe layout solutions are determined by collision detection to ensure that the optimal solution set that is output is a safe layout solution. Figure 4 shows the procedure of the proposed SE-NSGA2. The procedure of the SE-NSGA2 is shown, as follows:  
* Randomly initialize the parent population \(P_{t}\) at \(t=0\). Nondominated sorting is performed for the parent population in order to initialize the nondominated relationship for each individual and specify the virtual fitness values.
* Use the binary tournament method to select individuals, perform crossover and mutation operations, and generate a new generation of population \(Q_{t}\).
* The placement sequence of the equipment is genetically obtained by partially mapped crossover (PMX), and the spacing sequence for equipment is genetically obtained by the DE operator in order to generate a new generation of population \(Q_{t}\)'.
* Combine \(P_{t}\) and \(Q_{t}\) to generate the combined population \(R_{t}=P_{t}\cup Q_{t}\cup Q_{t}\)'.
* Perform nondominated sorting for \(R_{t}\), calculate the crowding degree of the nondominated set, and use the elite retention strategy for selecting the first \(N\) individuals to form the new generation population \(P_{t+1}\).
* Jump to step 2 through step 5, loop until the maximum number of iterations is reached, and then output the optimized solution set that meets the relevant conditions.
* Verify the safe layout thresholds, discard unsafe extreme solutions, and obtain the Pareto set.
# Stress-Dilatancy Behavior of Alluvial Sands
## 6 Stress Ratio-Plastic Dilatancy Relationship  
Figures 4a,b-7a,b show the \(\sigma_{1}^{\prime}/\sigma_{3}^{\prime}-\bar{\varepsilon}_{a}\) and \(\varepsilon_{v}-\bar{\varepsilon}_{a}\) relationships traditionally presented in the literature. These relationships are obtained using approximate values of \(q\), \(p^{\prime}\), and \(\varepsilon_{v}\). The stress ratio-dilatancy (\(\eta-D\)) and stress ratio-plastic dilatancy (\(\eta-D^{p}\)) relationships are shown in Figures 4c-7c.  
Figure 5: Relationships for sample TXC 5/22.5: (a) \(\sigma_{1}^{\prime}/\sigma_{3}^{\prime}-\bar{\tau}_{a}\); (b) \(\varepsilon_{v}-\bar{\tau}_{a}\); (c) \(\eta-D\) and \(\eta-D^{p}\).  
Figure 6: Relationships for sample TXC 7/14.5: (a) \(\sigma_{1}^{\prime}/\sigma_{3}^{\prime}-\bar{\tau}_{a}\); (b) \(\varepsilon_{v}-\bar{\tau}_{a}\); (c) \(\eta-D\) and \(\eta-D^{p}\).  
The \(D^{p}\) values were calculated using elastic parameters determined by Equations (24)-(27). The differences between \(\eta-D\) and \(\eta-D^{p}\) are small for the pre-failure shear stages and do not differ for post-failure shear stages (Figures 4c-7c). The points F representing the DFS in the \(\eta-D^{p}\) plane can be approximated by a straight line called the FSL. For the analyzed drained triaxial compression tests, the FSL is defined by Equation (1) with \(\alpha_{F}=0\) and \(\beta_{F}=\beta=1.0\); it intersects the vertical axis (\(D^{p}=0\)) at \(\eta=M_{c}^{q}=1.265\), which corresponds to \(\phi^{o}=31.5^{\circ}\) (Figure 8a).  
The \(F^{*}\) points represent conventional failure states with the maximum principal stress ratio \(\left(\sigma_{1}^{\prime}/\sigma_{3}^{\prime}\right)_{max}\). The failure states (FSs) can be approximated by the straight failure state line (F*SL) defined by Equation (1), with \(M_{c}^{q}=1.265\), \(\alpha_{F*}=0\), and \(\beta_{F*}=1.138\). For many drained triaxial compression tests presented in the literature for sand, the DFS and FS are equivalent [17]. According to the authors, the difference is influenced by the transverse anisotropy of sand in the tested samples.  
The DFS and FS are marked in Figures 4-7. These states can be easily identified only in the \(\eta-D^{p}\) plane. The DFS occurred before the FS, before the shear band developed in sheared specimens. Therefore, stresses and strains can be correctly determined for the DFS.  
It is worth noting that the FSL is equivalent to the straight line proposed by [PERSON] [18] for determining the critical state angle (\(\phi_{cs}\)) for sand sheared in conventional drained triaxial compression. Therefore, for sands sheared under low and medium stress levels, \(\phi^{o}=\phi_{cs}\). The stress ratio-plastic dilatancy relationship is very helpful in fully describing the behavior of sand (soil) during shearing.  
Figure 8: The \(\eta-D^{p}\) relationships for (**a**) DFS and (**b**) FS.
## 2 Materials and Methods
### Data Extraction and Analysis  
The publication year, characteristics of the participants, intervention methods, exercise experience, and cognitive assessment tools were recorded for each cross-sectional and intervention study examined. The number of participants in the OSE and CSE groups, as well as the mean \(\pm\) standard deviation of the relevant data in the cognitive measurement tasks, were also extracted. For the intervention studies, the mean \(\pm\) standard deviation data at baseline and post-test were extracted from the cognitive measurement tasks. If the standard deviation data were not available, we converted the standard  
Figure 1: Flowchart of study selection.  
error to a standard deviation [32], or calculated the standard deviation according to the upper and lower limits of the data (e.g., the 95% confidence interval (CI)). Comprehensive Meta-Analysis (CMA) version 3 (Bio-stat Inc., Englewood, NJ, USA) was used to summarize and analyze the data. For multi-term studies involving two or more cognitive tasks, each task was designated a type of cognitive function. Moreover, one task could include multiple subtasks. Over-quantities and inconsistencies of subtasks in evaluating different cognitive functions can lead to biased and unrepresentative results in a meta-analysis. Therefore, in each task, we screened the most representative results as a measure of cognitive function and ensured that each task had the same number of subtasks.  
The specific cognitive functions involved in all of the observational studies we screened, including inhibition, cognitive flexibility, visuospatial attention, and processing speed, were classified. Each subfunction was further analyzed using CMA.  
For the data analysis, based on the methodology used in other meta-analyses [33; 34], we used the standard mean difference as the effect size of overall cognition in each study. The explanation of the effect size estimation is consistent with [PERSON]'s guideline [35], in which 0.2 is a small effect, 0.5 is a medium effect, and 0.8 is a large effect. The significance of the \(p\)-value was set at \(p<0.05\). The statistical homogeneity (I\({}^{2}\)) of the effect size was assessed using the [PERSON]'s Q test. I\({}^{2}\) is usually used to indicate the proportion of heterogeneity; I\({}^{2}\) is considered low when it is less than 25%, 50%-75% is considered medium, and greater than or equal to 75% is considered high [36].