esa-sceva/satcom-chunk-collection huggingface dataset:
# Re-Defining, Analyzing and Predicting Persistence Using Student Events in Online Learning +
## 3 Materials and Methods
### Context and Data Collection  
The analysis of students' persistence (global and local) was carried out using data from Small Private Online Courses (SPOCs) [37], offered by Universidad Carlos III de Madrid and hosted on a local instance of Open edX. These SPOCs serve as a support of face-to-face course and three possible models of use are defined: (1) SPOCs needed to pass the course or with an important weight in the final grade, (2) SPOCs that are part of the course (often used to be combined with flipped classroom) but do not count for the summative evaluation, and (3) SPOCs that are only a recommended support for the course but that are not mandatory. In total, there are data available from 38 SPOCs, which comprise all the thematic areas of the studies the university offers, mainly Social Sciences, Formal Sciences and Engineering. However, the characteristics of each SPOC (e.g., syllabus, purpose, structure, etc.) are unknown for the purpose of this analysis.  
As the SPOCs are hosted in Open edX, data format is defined by edX [38], and there are data about activity, videos and exercises (SPOCs are mainly designed with just videos and automatic correction exercises). For the analysis of local persistence, only information about exercises is considered as local persistence variable is focused on measuring whether or not students continue their exercises after getting incorrect answers. Thus, only the events produced when submitting the answers of exercises (\"problem check\") are considered. Nevertheless, other events about video-related behaviors (play, pause and seek video) are considered to gather other variables that are also used in the analyses. In total, there are data from 38 SPOCs and 3598 students. However, some students were enrolled in more than one SPOCs, which entails that there are actually 4382 pairs of course-student and 270,182 interactions with exercises.  
These exercises can be of many types, such as multiple choice, checkboxes, dropdown, numerical input ant text input problems. All these formats allow automatic grading, and grades from each exercise are continuously ranged between 0 to 100%. However, in 95% of cases, exercises are graded in a binary form (correct/incorrect), as most of the exercises can only accept binary values (e.g., multiple choice questions can only be correct or incorrect, while checkboxes can allow partial grades depending on the number of guesses). For the analysis, no information is known about the format of each exercises and the number of allowed attempts. This is a limitation because, for example, a student cannot be persistent if exercises only allow one attempt (which is typical in summative exercises), and a student is more likely to be persistent in true/false questions where the answer is known once getting feedback from the first attempt.
## 2 Conventional Channel Estimation Methods  
Irrespective of the specific channel model employed in any wireless communication system, a shared challenge arises: how to provide accurate and rapid channel estimation to facilitate precise signal demodulation, equalization, decoding, and other baseband processing tasks [21]. As delineated in Section 1, the conventional channel estimation methods already developed for wireless communications mainly include pilot-based, blind, and semi-blind methods. Figure 2 shows the classification of different conventional channel estimation methods.  
Figure 1: Organization of this paper.
## 4 The Resistivity of Solidified Soil  
### Initial Resistivity of Solidified Soil  
To measure the relationship between resistivity and contamination concentration, the control variable method was used to obtain their evolution laws under different condition factors (contamination concentration, curing agent incorporation rate). The resistivity both decreases with the increase of heavy metal ion concentration, the initial resistivity of the soil is related to the heavy metal ion content, when the red clay is contaminated, the ion concentration increases, then the resistivity decreases; at the same contamination concentration, the resistivity decreases with the increase of curing agent admixture, this is because when the curing reaction occurs, the soil pores are filled, when the soil pores become less, making the soil structure more dense, so resistivity decreases [19]. The strength of the cured soil is affected by the curing reaction and the pore structure, when some of the crystals generated by the reaction from a network structure inside the soil, form the skeleton of the cured soil, resulting in a stable soil structure, increased strength and decreased resistivity. To study the effect of different curing agent doping on the resistivity of red clay, the variation of initial resistivity with contamination concentration under different curing agent doping conditions was plotted in Figure 7.  
It can be seen from the above figure that when the heavy metal content of the soil increases, the overall trend of the initial resistivity of the soil is decreasing, and the obtained data are fitted. The fitting curves of different curing agent incorporation rates are obtained as follows: at 0% admixture, y = 2.0645x, R\({}^{-0.7432}\) = 0.9524; at 3% admixture, y = 0.9652x, R\({}^{-0.8382}\) = 0.9884; at 6% admixture, y = 1.166x, R\({}^{-0.7512}\) = 0.9767; at 12% admixture, y = 1.8724x, R\({}^{-0.6362}\) = 0.9743.
## 5 Architecture of the Proposed System
### Prosody process assessment  
Due to its numerous functions, speech prosody is a very important component of the linguistic communication process. For example, prosody organizes statements while having a similar role to punctuation in written texts; simultaneously, employing prosody enables emphasizing a word or a part of a message that the speaker considers important. Furthermore, prosody conveys information on the speaker's attitude and emotional state. The aspect of prosody is often omitted in speech or speaker recognition systems; however, it can constitute a very important factor improving the effectiveness of such a system when used in a speech-based system to identify [PERSON]'s disease.  
Evaluating the prosody process in patients with [PERSON]'s disease requires analysis of the so-called free text uttered by the subject. This is particularly important when assessing emotional prosody in people with diagnosed PD. Prosody is commonly evaluated with measures derived from the fundamental frequency, the energy contour, and duration. Changes in this aspect of speech are manifested by difficulties in expressing emotion and repeating sounds or syllables [9]. Speech signal prosody is implemented by such prosodic features as the following, among others:  
* Fundamental tone (laryngeal, \(F_{0}\)), or more specifically, an analysis of the changes in this parameter for a specific recording;
* Individual recording durations, including the duration of a sad statement or the duration of a joyful statement;
* The number of \"pa\" syllables uttered during a 5 s speech fragment;
* The duration of the intervals between \"pa\" syllables.  
First of all, it should be noted that prosody information is difficult to model, and solutions are still being sought to overcome the problem of prosody in the context of automatic recognition systems. In PD disorders, speech is generally laborious, with a need for frequent breath intake and poor breath support. This results in frequent pauses and disfluency. Silence, including its presence and duration, has a role to play, in that it is directly implicated in some prosodic effects (boundary, hesitation) and needs to be controlled for in the calculation of speech tempo and rhythm. The prosody in this research is understood as lengthening or shortening the duration of a sound. This is the reason why the rapid repetition of /pa/ and the interval between such sounds has been used in this investigation.
## 1 Introduction and Literature Survey
### Adaptive Wing
The position of the leading edge and the upper wing surface were simulated in order to generate airfoils. An optimizer (a genetic algorithm) coupled with an airfoil analysis solver (XFoil) was used to determine the aerodynamic coefficients (\(C_{L}\), \(C_{D}\) ) and then to evaluate the fitness function 'lift to drag ratio' of the generated airfoils. The validation data for the CFD model were obtained during wind tunnel tests performed at the Research Laboratory in Active Controls, Avionics and Aeroservoelasticity (LARCASE) at ETS University.
## 7 Conclusions  
The present study investigated the production of biochars from cactus cladodes (CCs) and their use as efficient, economic, and green materials for the adsorption of the industrial RB dye used in the leather industry. The findings highlight the richness of the CC biochar, with different functional groups, high porosity, and a high surface area, as well as a high content of mineral matter, in particular, Ca.  
The adsorption performance increases with the temperature of biochar preparation. The char prepared at 700 \({}^{\circ}\)C is the most efficient adsorbent for the removal of RB dye from an aqueous solution.  
According to the Langmuir isotherm, the maximum adsorption capacity of RB is 56.18 mg/g and the dye is adsorbed as a monolayer on the adsorbent. The kinetic adsorption data for RB dye follow the pseudo-second-order model.  
The pH has a significant impact on the adsorption process. The findings show that the adsorption process involves not only physical forces but also chemical and electrostatic attraction between the biochar surface and the dye molecule. The mineral content and  
Figure 6: (**a**) FTIR and (**b**) XRD spectra of CCB700 after RB dye removal (pH 2).  
the functional groups available on the surface of this biochar are also important to its performance as an adsorbent.  
We suggest that more research be conducted to determine the feasibility of using biochar to remove dyes from real wastewaters. On the other hand, Ca-rich biochar loaded with RB dye can be regenerated by desorption and reused in water pollution treatment. However, further exploration of both desorption methods and the field of regeneration and reuse is required.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15020894/s1](https://www.mdpi.com/article/10.3390/app15020894/s1)]([https://www.mdpi.com/article/10.3390/app15020894/s1](https://www.mdpi.com/article/10.3390/app15020894/s1)), Table S1: Physicochemical properties of cactus cladodes sample; Figure S1: Schematic of slow pyrolysis reactor for biochar production; Figure S2: (a) FTIR and (b) XRD spectra of cactus cladodes powder; Figure S3: FTIR spectrum of RB dye  
Writing--original draft preparation, conceptualization, methodology, software, data curation, A.M., R.C. and F.C.; writing--review and editing, validation, visualization, O.S. and B.A.; supervision, A.B.H.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results
### Calibration
#### 3.1.2 Canopy Cover (CC)  
Equation (3) was applied to convert the LAI value measured from the field to CC. The values of the extinction coefficient for rice are between 0.40 and 0.70 [32]. Therefore, an experiment to select the optimal extinction coefficient (K) value was performed. The different K values were tested for 7 values from 0.4 to 0.7. The average percentage of CC values from field observations was compared with the average percentage of CC values from the AquaCrop model simulation and the results are shown in Figures 5 and 6 and Table 6. For both groups, the best model performance was derived with a K value of 0.70.  
Figure 5: Graph and statistic display of the green canopy cover (CC) of machine transplanted rice (A); error bar represents the standard deviation of the observed CC.  
Figure 6: Graph and statistic display of the green canopy cover (CC) of manual transplanted rice (B); error bar represents the standard deviation of the observed CC.
## 1 Introduction
Considering wind turbines operate in a turbulence environment, the conclusions obtained from the previous studies might not be accurate. Therefore, to simulate wind turbines under turbulence environment, active and passive wind tunnel turbulence generation methods can be used. The active technology includes a vibrating grille and multi-fan wind tunnel; the maximum turbulence intensity can reach more than 20% [26]. The passive control structure was relatively simple, which can be divided into grille, wedge, and rough square types among the passive turbulence generators, it was more convenient to construct grilles, which have gain very popular use. In this experiment, specific turbulence levels were passively controlled by a grille with proper grid size. The investigations presented in this paper were focused on the coupled effects of Gurney flap and turbulence inflow. The Gurney flaps were experimentally investigated under various turbulence intensities and flap configurations. The desired turbulent flow passes the airfoil was achieved by changing the grille size as well as the distance between the grille and the airfoil model. The hot-wire anemometer was used to record the wind speed and turbulence intensity in a flow cross-section. The quantitative information obtained during the experiment includes: (1) turbulent field descriptions, (2) airfoil pressure coefficients, (3) lift-to-drag coefficients, (4) wake measurements. On that basis, the aerodynamic performance of Gurney flaps with different heights and thicknesses was researched. The values of lift and pitching-moment coefficients were obtained through the integration of surface pressures. The wake wake array was also used to determine the values of drag coefficient. Furthermore, the flow fields near the trailing edge of airfoil were tested which could further verify the reason for lift improvement. Finally, concluding remarks accompany the discussion of the experimental investigations.
# Automatic Modulation Recognition Using Compressive Cyclic Features
## 3 CS-based AMR
### CS-Based Cyclic Characteristic Analysis
For each frame represented by \(\mathbf{x}^{(k)}\), we acquire the compressive measurements by multiplying it by the same random matrix \(\mathbf{\
abla}\). \(\mathbf{\
abla}\) is designed as a \(M\times N\) binary matrix, \(M\ll N\), containing a single \(1\) in a random position in each row. When \(M=O(s\log N)\), where \(s\) is the sparsity level of \(\mathbf{\eta}\), \(\mathbf{\
abla}\) is proved to meet the RIP, and \(\eta\) can be reconstructed with high probability [16]. Consequently, the compressive measurements \(\mathbf{z}^{(k)}=\{z^{(k)}[m]\}_{m=0}^{M-1}\), is actually the subset of \(\mathbf{x}^{(k)}\) indexed by \(\{\lambda_{m}\}_{m=0}^{M-1}\), elements of which are randomly drawn from \(\{0,\ldots,N-1\}\). It can be easily deduced that  
\[C_{z,n,q}[m,\tau]=C_{x,n,q}[\lambda_{m},\tau], \tag{10}\]  
which implies that \(\mathbf{c}_{z}\) in (7) can be estimated from nonuniform samples by using (8) and (9). In practice, based on the strategy of non-uniform sampling (NUS) [17], compressive measurements can be directly acquired from the continuous waveform \(x(t)\). Moreover, a novel signal acquisition setup proposed in [11] was available to generate various compressive signal lag products. As a result, we just need to divide \(x(t)\) into \(W\) temporal windows, let them undergo the aforementioned setup, respectively, and then the TMs and TCs can be calculated successively.
# Simulation of Track-Soft Soil Interactions Using a Discrete Element Method
###### Abstract  
With the development of unmanned tracked vehicles, soil model predictions of soft terrains are becoming more essential. In order to accurately simulate the interaction characteristics between soil particles and the track, soil modeling with a discrete element method (DEM) is proposed. Volume-based scaled-up modeling of DEM soil particles and the calibration of DEM input parameters were investigated as a feasible approach to realizing many particle calculations. Calibration of DEM input parameters can solve the distortions between actual and DEM particle sizes. Cohesion and friction parameters of the scaled-up soil particle model were recalibrated by the shape accumulated through the virtual design of the experiment. Soil DEM particles were scaled up to 1 mm spherical particles, and recalibrated DEM parameter values were used to match the actual accumulated soil shape. Three calibrated scaled-up soil models were used for the shear stress-displacement DEM simulation of a track segment, and the mean absolute percentage error (_MAPE_) was less than 11% compared with the actual shear stress-displacement test. The parameter value of soil traction performance empirical model of a tracked vehicle is modified according to the soil shear stress-displacement DEM simulation. Comparative analysis was performed for travel test results of a tracked vehicle; the relative error of the soil traction prediction results to actuals was less than 16.8%. This showed that the volume-based particle scaling technique is an effective DEM for the mechanical simulation of soil.  
DEM simulation; particle scaling; parameter calibration; traction prediction; tracked vehicle
## 3 Results and Discussion  
### Results of Conventional Solvent Extraction  
Table 1 reports the TPC and antioxidant activity values of extracts corresponding to the eight temperature combinations and also the single effects of the two variables. For each time of treatment, the increase in temperature from 45 to 60 \({}^{\circ}\)C allowed to double the TPC of chicory extracts, while, in the case of fennel, the TPC doubled, tripled, or quadrupled. The highest TPCs were determined in chicory extracts treated at 60 \({}^{\circ}\)C for 180 min (6.14 \(\pm\) 0.25 mg gallic acid/g dm) and in fennel extracts treated at 60 \({}^{\circ}\)C for 90 min (7.52 \(\pm\) 0.85 mg gallic acid/g dm). Regarding the antioxidant activity of chicory, the highest values (around 0.0280 mmol Trolox/g dm) were observed in extracts obtained at 60 \({}^{\circ}\)C independent of the treatment time. The highest antioxidant activity of fennel extracts (0.0281 \(\pm\) 0.0001 mmol Trolox/g dm) was obtained with treatment performed at 60 \({}^{\circ}\)C for 30 min, and also, in this case, a slight but statistically significant decrease was registered at longer treatment times. This means that in fennel, by prolonging the treatment beyond the optimal time, a slight but significant degradation of the phenolic fraction occurred. These results are in agreement with the literature; increases in temperature may favour the diffusion and improve the solubilisation of the phenolic compounds, thus increasing the extraction yield [29]; however, the adverse effects of increasing the time of treatment observed in some vegetable matrices can be attributed to the degradation, oxidation, or  
polymerization of phenolic compounds and the generation of complexes with proteins and carbohydrates [30; 31].  
In agreement with the findings of [PERSON] et al. and [PERSON] et al. [32; 33], strong correlations between phenolic content and antioxidant activity were observed for both the matrices (Equation (2), chicory; Equation (3), fennel):  
\[AA=0.0056 TPC-0.004\quad\text{R}=0.904 \tag{2}\]  
\[AA=0.0043 TPC-0.001\quad\text{R}=0.981 \tag{3}\]
# A Novel SDWSN-Based Testbed for IoT Smart Applications
## 3 System Model
### Physical Network Setup for WSN-Based Clustering  
The TI CC2538 wireless module is one of the hardware targets for developing a physical network structure. The main parameters of sensor data size, sampling rate, network topology, node function, and node mobility all have an impact on physical network performance and possible cloud interaction. This is mainly critical for dynamic networks due to its critical events.  
At the Auckland University of Technology (AUT) WZ building, a testbed representing the physical network was deployed to reflect the main functionality of a given topology, e.g., star or tree network scenarios. Within the network, 33 TI CC2538 wireless sensor nodes were configured using the Contiki operating system (OS), with 30 nodes configured as leaf nodes reporting RSSI sensing values within a defined sampling rate and 3 nodes configured as coordinator nodes (cluster heads). The physical network setup is depicted in Figure 10. The physical network setup was designed to log RSSI data for each coordinator node for 24 h to allow for data analysis. Considering that both the hardware and the Cooja  
Figure 9: Raspberry Pi board.  
motes use the same Contiki firmware, the analysis of the collected data was used to fetch the minimum and maximum RSSI data for the Cooja virtual model in order to update the model based on real data.  
The modified values of the RF transmission power of the sensor nodes based on designed indoor and outdoor scenarios can reflect the mobility aspect in the network. Additionally, mobility can be reflected by using a battery attached to one of the nodes to measure RSSI values based on distance, as shown in Figure 11. In addition, the RSSI data are collected at different sampling rates with various number of node scenarios to test the delivery of packets and determine if there is any packet loss. This is mainly to evaluate the capacity of the coordinator (cluster head) node, tested virtually [8; 9].  
Figure 11: Outdoor physical node mobility testing.  
Figure 10: Indoor physical network setup.
# Cm-Level Photonic-Crystal-Like Subwavelength Waveguide Platform with High Integration Density
## 1 Introduction
### A Triangular Lattice of Circular Air-Holes  
For the triangular arrangement model, the two adjacent air holes are staggered by \(1/2\,Period\) along the longitudinal direction, and the lateral distance is \(\frac{\sqrt{3}}{2}\times Period\), so that the adjacent three air holes are arranged in an equilateral triangle, and thus expanded to have different waveguide structures because of the multiple rows of air holes.
## 3 Results and Discussion
### Transport Study of Cr(VI) through Aliquat 336/PolyHIPE Membrane
#### 3.4.4 Stability of Aliquat 336/PolyHIPE Membrane  
One major concern of using Aliquat 336/PolyHIPE membrane is the stability of impregnated extractant. The stability of the Aliquat 336/PolyHIPE was investigated by immersing the membrane in water under magnetic stirring at 450 rpm. Experimental results show that during the first 20 min period of immersion, the amount of impregnated Aliquat 336 in PolyHIPE membrane decreases about 5%. This may be due to the removal of loosely adhered Aliquat 336 from the surface of membrane, since Aliquat 336 is slightly soluble in water. After 60 min, the amount of impregnated Aliquat 336 becomes stable. It is worth to note that during the stability test, the amount of Aliquat 336 removed into the water phase is about 0.00125 g/100 mL, which is much smaller than the solubility of Aliquat 336 in aqueous phase (about 0.1 g/100 mL) [49]. This result indicates that the pore structure of PolyHIPE can effectively improve the stability of Aliquat 336 inside the membrane.  
The hydraulic stability of Aliquat 336/PolyHIPE membrane was evaluated based on the removal and recovery rate of Cr(VI), to demonstrate the reusability of PolyHIPE membrane for the separation of Cr(VI). Figure 8 shows that after ten sequential cycles of 8 h separation of Cr(VI), the removal rate drops slightly from 97.9% to 95.3%, while the recovery rate remained approximately 95% after 10 cycles.  
Figure 8: Removal and recovery rates in consecutive cycle: (\(\bullet\)) removal rate, (\(\blacktriangle\)) recovery rate. Feeding phase: [Cr(VI)] = 53 mg/L at pH 4; stripping phase: pH 12; stirring rate: 450 rpm.