esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Virtual Reality on Student Motivation in a High School Science Course
## 3 Materials and Methods
### Implementation  
In the chemistry course, the activity was applied to 304 fourth semester students in molecular geometry using the Mel Chemistry application, which allows them to observe and manipulate compounds in three dimensions. The objective of the activity was to identify the geometry of different organic compounds. Based on their experience, the teachers participating in the experiment proposed the names of 100 other organic compounds associated with a specific geometry, which could be linear, tetrahedral, trigonal pyramidal, or trigonal planar. Each combination was written individually on paper and placed inside a plastic Easter egg in a basket. Afterwards, each student randomly took one of the Easter eggs, opened it and took note of the compound to be analyzed. Then, they placed the paper back inside the plastic container and returned it so that the basket would have many names of organic compounds available to the students.  
Mel Chemistry is an application produced by Mel Science that users can manipulate with Oculus Go devices. Among its advantages, it makes chemistry classes visual and interactive, covering all the contents of standard chemistry courses and helping to improve students' grades and averages. The app includes topics such as atomic structure, through which students who participated in this experience build molecules and explore bonds. Figure 1 shows an impression of how compounds are studied.  
The students manipulated the Mel Chemistry app, being a generation of digital natives, learning to use it required a minimum of technical indications. Then they located  
Figure 1: Contents of the courses in the Mel Chemistry application.  
the compound and used the benefits of virtual reality to rotate the molecule in different axes to get a better idea of its geometry. They made comments among themselves about the compound they were analyzing and clarified their doubts with the teacher. The exploration process for each compound took the students an average of 15 min; at the end, they returned to the basket, took another Easter egg, and repeated the process. The students reviewed a minimum of two compounds, but the exploration was finished 20 min before the end of the class to give them time to answer the questionnaire to know their point of view regarding the experience on a Likert scale and placed in the Socrative application. The present immersive experience took place in January 2020. The implementation experience is shown in Figure 2.
## 5 Discussion
### Limitations of This Study and Prospects of Future Research  
Although the proposed methods in this study improved the accuracy of landslide susceptibility assessment to a certain extent, certain limitations remain. First, grid units are most commonly used as evaluation units. However, they do not correlate well with real-world geological environments [87]. Therefore, slope units [12] and terrain units [88] have been used in landslide susceptibility assessment. The existing methods for extracting slope units are complicated, and their effect is not ideal. Thus, these methods are not widely used [29]. In addition, the size of grid units also affects the accuracy of landslide susceptibility assessment [89]. Across different study areas, environmental conditions are quite different, and there is no clear criterion for choosing an optimal grid size [56]. In this study, based on literature and expert knowledge as well as considering the computational cost and the actual conditions of the study area, a grid of 30 m \(\times\) 30 m was selected as the evaluation unit. The selection of the optimal evaluation unit is also a difficult problem that should be addressed in future research.  
Furthermore, in the process of regressing SI weights using GPR, the SI weight value of a class was assigned to the central value of this class. Although this allocation method has brought good results in this study, it still contains some subjectivity. Therefore, future research should consider more reasonable allocation methods to further improve the accuracy of landslide susceptibility assessments. Moreover, considering the second law of geography, a more reasonable screening of regional risk factors should take into account their spatial local heterogeneous (SLH) associations with landslides, and such SLH-based factor screening methods [90; 91] are also worthy of continued research in the future.
## 4 Simulations and Results Analysis
### Key Strength Probability (\(P_{f}\))  
In this section, we first present the behavior of the metric \(P_{F}\) according to Equation (6). Then, we present the accuracy achieved by \(P_{F}\) in the simulations.
# Network Performance Optimization for Low-Voltage Power Line Communications
## 2 Low-Voltage Distribution Network Topology
### Physical Topology of the Low-Voltage Distribution Network  
The LV network represents a simple tree topological structure, and the channel frequency responses between pairs of stations are obtained with a bottom-up channel generator [21], although it was generally applied to in-home networks, as depicted in Figure 1. The signal attenuation among the A, B, and C phases is very large for the secondary side of the transformer in the three-phase power distribution grid [22]. The end customers are represented as an intelligent device in each phase. Devices in the same vicinity are connected to the three different phases for load balancing. Without phase coupling, each of the A, B, and C phases of the three phases are parallel and relatively independent; therefore, this study establishes a single-phase power distribution network based on the tree-like physical topology of a low-voltage distribution network.
## 4 Nanostructured Materials for Uranium Removal
### Uranium (U)
Membrane treatment methods, such as nano-filtration [72] or reverse osmosis, were also found to be efficient for the removal of uranium, removing the respective carbonate complexes by more than 90%, but their application requires experienced personnel and their use is quite expensive, especially when designed for the treatment of small volumes of contaminated water [8; 73; 74]. Application of Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) Composite Nanoparticles for U(VI) Removal  
[PERSON] et al. (2010) [75] studied the sorption of U(VI) on magnetite (Fe\({}_{3}\)O\({}_{4}\)) nanoparticles, but the sorption capacity was relatively small. Silica has been known as one of the most ideal coating layers for the magnetic Fe\({}_{3}\)O\({}_{4}\) nanoparticles, due to its reliable chemical stability, biocompatibility, and facile surface modification. Magnetic Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) composite particles were successfully applied by [PERSON] et al. (2012) [66] as a novel and effective adsorbent material for the removal of U(VI) from aqueous solutions. The sorption of U(VI) onto magnetic Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) composite particles was strongly dependent on pH values. With increasing pH (i.e., from 2 to 6), the adsorption capacity was also increased. When the initial pH value varied from 2 to 4, the sorption capacity of U(VI) increased from 0 to 8.5 mg g\({}^{-1}\), while when the initial pH was further increased from 4 to 6, the sorption capacity of uranium increased substantially (i.e., from 8 to 20 mg g\({}^{-1}\)). However, when increasing the pH value from 6 to 8, the sorption capacity started to decrease. The maximum sorption value for U(VI) onto Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) magnetic composites was about 20 mg g\({}^{-1}\), when the initial concentration of U was 50 mg L\({}^{-1}\). Therefore, the optimum (initial) pH value was 6, when applying this treatment technique, which is in agreement with other relevant studies regarding the sorption of U(VI) onto iron oxides or hydroxides, i.e., they present a maximum sorption capacity in the pH range between 6-7 [76; 77; 78]. The aforementioned magnetic Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) composite particles showed an excellent ability to remove uranium from aqueous solutions and the maximum U(VI) sorption capacity was about 52 mg g\({}^{-1}\) at 25 \({}^{\circ}\)C. Hence, this adsorbent material could be a potential candidate to remove the toxic U(VI) forms from aqueous solutions and the results of this study can also provide a technique for the removal or recovery of other heavy/toxic metal ions from aqueous solution [66].
## 3 The Proposed DSCA-MC-CR Technique
### Model 1  
Model 1 assumes that the proposed SUs system (i.e., DSCA-MC-CR) has the same communication application as the existing privileged PUs system and the same modulation and demodulation schemes.  
In this model topology, which is designed using OMNeT++ and shown in Figure 2(a), the proposal suggests one fixed PU transmitter tower (PU-TX) and seven fixed or moving PU receivers (PU-RX) with channel gain \(\alpha_{ui}\), where \(i\in\{1,2\ldots 7\}\). In addition, the proposal suggests one fixed SU transmitter (SU-TX) that is located within the coverage area (i.e., the region of co-access (ROC)) of the PU-TX for the RF learning process and seven fixed or moving SU receivers (SU-RX) with channel gain \(\alpha_{uj}\), where \(j\in\{1,2\ldots 7\}\).  
The representation of the model 1 spectrum is shown in Figure 2(b), which indicates that the PU-RF signals, shown by the blue color, occupy the assigned legal bands. Also, the inactive PU-RF is shown in white. On the other hand, the secondary users base-band signals (i.e., \(SU_{1}\) to \(SU_{7}\)) are shown in yellow. These signals spread in the RF mode due to the orthogonal codes within the encoder process. The SU-RF underlay signals do not affect the PU-RF signals due to the orthogonality between them.
# Video Temporal Grounding with Multi-Model Collaborative Learning
## 4 Experiments
### Comparison with State-of-the-Arts
#### 4.5.2 Quantitative Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Publication**} & \multicolumn{4}{c}{**Rank@1**} & \multirow{2}{*}{**mIoU**} \\ \cline{3-4}  & & **IoU = 0.3** & **IoU = 0.5** & **IoU = 0.7** & \\ \hline TGN [48] & EMNLP2018 & 43.81 & 27.93 & – & – \\ CMIN [58] & SIGIR2019 & 63.61 & 43.40 & 23.88 & – \\ QSPN [53] & AAAI2019 & 45.30 & 27.70 & 13.60 & – \\ ABLR-af [49] & AAAI2019 & 53.65 & 34.91 & – & 35.72 \\ ABLR-aw [49] & AAAI2019 & 55.67 & 36.79 & – & 36.99 \\ DEBUG [54] & EMNLP2019 & 55.91 & 39.72 & – & 39.51 \\ GDP [56] & AAAI2020 & 56.17 & 39.27 & – & 39.80 \\ DRN [44] & CVPR2020 & 58.52 & 41.51 & 23.07 & 43.13 \\ CI-MHA [57] & SIGIR2021 & 61.49 & 43.97 & 25.13 & – \\ SeqPAN [60] & ACL2021 & 61.65 & 45.50 & 29.37 & 45.11 \\ CBLN [45] & CVPR2021 & 66.34 & 48.12 & 27.60 & – \\ MMN [59] & AAAI2022 & 65.05 & 48.59 & 29.26 & – \\ PS-VTG [28] & ITM2022 & 59.71 & 39.59 & 21.98 & – \\ PFU [47] & ARXIV2023 & 59.63 & 36.35 & 16.61 & 40.15 \\ D3G [46] & ICCV2023 & 58.25 & 36.68 & 18.54 & – \\ \hline
2D-TAN [23] & AAAI2020 & 59.45 & 44.51 & 26.54 & 43.29 \\ MRNTNet (2D-TAN) [51] & ICASSP2024 & 60.71 & 45.59 & 28.07 & 44.54 \\ MMCL (2D-TAN) & – & 62.15 & 47.21 & 29.35 & 46.53 \\ \hline VSLNet [22] & ACL2020 & 63.16 & 43.22 & 26.16 & 43.19 \\ MRNTNet (VSLNet) [51] & ICASSP2024 & 64.17 & 44.09 & 27.43 & 44.82 \\ MMCL (VSLNet) & – & 65.59 & 46.82 & 28.59 & 45.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison with the state-of-the art models on ActivityNet Caption dataset. From the results in Table 2, both models achieve improved performance on the ActivityNet Caption dataset. With its semantic modeling advantage, VSLNet achieves an mIoU of 43.19 on this dataset. However, by learning richer temporal relationship modeling from 2D-TAN through the MMCL framework, its mIoU rises to 45.98, a relative improvement of 6.4%. Similarly, by sharing knowledge with VLSNet, 2D-TAN advances its mIoU from 43.29 to 46.53, achieving a 7.1% relative improvement. In sum, through cross-model knowledge sharing and joint learning, the MMCL framework allows the two baseline models to mutually address their shortcomings and amplify their respective advantages. On TACoS, VLSNet reaps the benefits of 2D-TAN's temporal relationship modeling; on Charades-STA, 2D-TAN inherits stronger boundary-modeling capabilities from VLSNet. This reciprocal learning and knowledge transfer characteristic endows the MMCL framework with robust adaptability across different task scenarios. Moreover, relative to other competing methods and MRTNet, the MMCL framework achieves superior results on all metrics in each of the three datasets, offering additional validation of its efficacy.
## 3 Methodology and Solution
### Detector Bank Based on Simplified Welch Algorithm  
As mentioned above, one of the goals of this paper is to propose a new detection method using the simplified Welch algorithm [16]. The detector uses the power spectrum samples and their indexes to detect and characterize multi interferences or unusual behaviors in the spectrum of the signal. Proposed detector can estimate the following parameters (Figure 4):  
* Number of interferences;
* Center frequency of each interference;
* Power of each interference;
* Bandwidth of each interference.  
The target of this section is to present a solid and smart algorithm that can find all the above parameters in the frequency domain while targeting practical implantation for embedded system with minimum complexity. In this system, downsampling is used before the simplified Welch's algorithm to have more precision and capability for weak signal detection. As it can be seen in Figure 4, detector structure contains three main blocks:  
* Simplified Welch algorithm: this block extracts the PSD samples from the input data stream.
* Thresholding activator: this block compares PSD samples with a threshold level and for each group of abnormality on the top of PSD, generates a pulse that activates a detector.
* Detector bank: contains \(N\) detectors that each one is activated by the enable pulse from thresholding activator. Each detector extracts and saves the characteristics of an interference.  
Figure 4: Proposed multi-narrowband detector with details.  
It should be mentioned that, any PSD estimation method, like original Welch method, can be utilized to estimate in the proposed detector. In the following sections, each part is explained with more detail.
# The Effect of Pharmaceutical Excipients for Applying to Spray-Dried Omega-3 Powder
## 3 Results
### Determination of Moisture Content, Free Oil, Encapsulated Oil, and Total Oil  
The moisture content, free oil, encapsulated oil, and total oil of formulations according to binder and surfactant were presented in Table 2. When poloxamer 188 was used as a surfactant, the moisture (%) of spray-dried oil powder (P1-P8) was from 3.50 to 8.30%. When hydrogenated lecithin was used as a surfactant, the moisture (%) of spray-dried oil powder (P11-P18) was from 2.85 to 4.00%. When PEG40 glyceryl monostearate was used as a surfactant, the moisture (%) of spray-dried oil powder (P21-P28) was from 3.80 to 6.45%. Most formulations showed about 10% of encapsulated oil. Generally, small difference between droplet size of SE and RE indicated well re-dispersibility and stability. The moisture (%) of spray-dried oil powder is 2.85-8.30% over the formulations. When hydrogenated lecithin was used as a surfactant, the encapsulated oil in spray-dried oil powder was more detected than in the group of poloxamer 188 or PEG40 glyceryl  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Formular** & **SE (nm)** & **RE (nm)** \\ \hline P1 & 488.85 \(\pm\) 30.8 & 600.85 \(\pm\) 76.6 \\ P2 & 396.85 \(\pm\) 3.0 & 483.95 \(\pm\) 65.3 \\ P3 & 252.40 \(\pm\) 11.5 & 469.55 \(\pm\) 76.2 \\ P4 & 711.70 \(\pm\) 168.1 & 660.85 \(\pm\) 171.8 \\ P5 & 214.35 \(\pm\) 3.5 & 473.50 \(\pm\) 141.8 \\ P6 & 250.80 \(\pm\) 8.9 & 360.40 \(\pm\) 50.3 \\ P7 & 268.95 \(\pm\) 13.4 & 374.75 \(\pm\) 155.6 \\ P8 & 258.95 \(\pm\) 7.3 & 293.35 \(\pm\) 19.0 \\ \hline P11 & 296.47 \(\pm\) 22.8 & 472.73 \(\pm\) 17.0 \\ P12 & 336.93 \(\pm\) 37.8 & 472.97 \(\pm\) 16.0 \\ P16 & 257.00 \(\pm\) 8.3 & 216.57 \(\pm\) 2.5 \\ P17 & 237.40 \(\pm\) 10.1 & 330.20 \(\pm\) 38.2 \\ P18 & 280.57 \(\pm\) 17.8 & 246.20 \(\pm\) 19.9 \\ \hline P21 & 284.87 \(\pm\) 5.1 & 342.13 \(\pm\) 10.9 \\ P22 & 285.97 \(\pm\) 2.0 & 405.60 \(\pm\) 9.1 \\ P26 & 540.10 \(\pm\) 39.5 & 847.70 \(\pm\) 58.3 \\ P27 & 354.87 \(\pm\) 3.3 & 695.83 \(\pm\) 108.0 \\ P28 & 339.23 \(\pm\) 24.1 & 712.20 \(\pm\) 72.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The particle size of second emulsion (SE) before spray drying and reconstituted emulsion (RE) of olive oil powder after spray drying (n = 3).
# Quaternion Codes in MIMO System of Dual-Polarized Antennas +
## 7 Conclusions  
Quaternion orthogonal designs with dual-polarized antennas have been studied to provide higher diversity gains and code rates. This paper presents the construction of QODs based on the Liang approach using the dual-polarized antennas and the quaternionic channel model. The unique method of constructing QODs provides linear and decoupled decoding at the receiving side where the computational complexity of the decoder remains independent of the number of unique transmitted symbols. Also, construction technique of quasi-QODs has been presented where the code rate is not compromised at higher number of transmit antennas. In future, these designs can be investigated for optimizing the receiver complexity further considering their suitability for future wireless communication systems.  
Conceptualization, S.A., S.S.Q., and S.A.H.; system modeling, S.A., methodology, S.A., S.S.Q. and S.A.H.; implementation, S.A. and S.S.Q.; writing--original draft preparation, S.A.; writing--review and editing, S.A.H. and S.S.Q.; supervision, S.A.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 6: BER vs. SNR performance of \(\mathbf{Q}_{quasi}\) for one receive dual-polarized antenna.
## 3 Results
### Solving the Analysed Case  
The previous results presented in [27] were very promising; however, only median values were used as bodies' thresholds. Testing different thresholds even for 139 projects proved to be a complex combinatorial problem, with up to 7.5 \(\times\) 10\({}^{31}\) potential variants. However, finding the right solution could improve the support and confidence parameters, thus providing better outcomes for the clients. This is why it was decided to use a metaheuristic algorithm. Such an approach proved to be very useful and might be used even for bigger databases.  
Metaheuristic optimization of thresholds was done for three cases: two best sets of criteria established by [27] (\(C_{r}-E-J-L\) and \(A-E-K\)), and for all 12 criteria from Table 2. The best results are presented below in Tables 4-6. The presented results were obtained with the use of commercial software OptQuest(r) Engine package, OptTek Systems, Inc., based on the tabu search algorithm. However, additional tests showed that similar results can be obtained by other applications of tabu search. The decision variables were the thresholds of criteria, and the objective function (SCORE) is as follows:  
\[\text{Max}:\text{SCORE}=\text{lift}+\text{N}^{2}\times\text{sup}+\text{N}^{2} \times\text{conf} \tag{11}\]  
The results are presented in following Tables 4-6 together with the comparison to the results achieved in the previous study [27].  
Figure 8: The approximate plane of 7-Score values.  
The rule wherein all features of the body are considered is excluded from further analysis according to its low support (even if this formula is found--as in the two other rules--with the use of metaheuristic). This makes its 7-Score much lower than the 7-Score of the two other rules. The maximum informativeness is found for the following rules:  
* if (Cr and E and J and L), then D; that is, if (planned duration is lower than 1126 days and the contract is not \"design & built\" and price index in the construction industry is decreasing and the contract has the form of consortium), then the contract is delayed;
* if (A and E and K), then D; that is, if (the contract value is over 5.77 million PLN and the contract is not \"design & built\" and the total sales in Polish construction industry is decreasing), then the contract is delayed.
## 4 Discussion
### Estimating WOR AGB Using the Modified Vegetation indices NDVI (800, 1200)  
The NDVI-like indices and the PLSR VIP of each wavelength were calculated to further extract the feature wavelengths. The R\({}^{2}\) values were also calculated between NDVI-like indices and AGB at four different growth stages. Figure 5 indicated that NDVI-like indices that were based on NIR bands  
Figure 7: Regression modeling analysis between NDVI (800, 1200), Clred edge, and AGB at different stages: (**a**) model 33; (**b**) model 34; (**c**) model 35; (**d**) model 36; (**e**) model 37; and, (**f**) model 38.  
(R\({}_{\rm{i}}\): 750-1150 nm, R\({}_{\rm{j}}\): 1150-1300 nm) proved to have a relatively good correlation with the AGB. The R\({}^{2}\) values gradually increased along the development of WOR phenology, and ranged from 0.45 to 0.55 at the seeding stage, 0.47 to 0.65 at the bolting stage, 0.58 to 0.72 at the flowering stage, and 0.47 to 0.74 at the pod stage, respectively. These results demonstrated that NDVI-like indices that were calculated by simple combinations of NIR wavelengths were more sensitive to AGB, which was consistent with previous results [54].  
The two effective wavelengths (800 nm and 1200 nm) that were extracted by the PLSR VIP had clear physical significance and higher recognition; the effective wavelength of 800 nm was usually used to construct many commonly used vegetation indices for estimating crop growth parameters, such as leaf area index [55], biomass [56], leaf nitrogen content [34], and canopy chlorophyll content [57]; the canopy reflectance of 800 nm was not only relatively stable, but it was also insensitive for leaf structure. Besides, the wavelength around 1200 nm proved to be sensitive for plant moisture [58] and AGB [6].  
As Table 6 showed that NDVI (800, 1200) that was based on two effective wavelengths (800 nm and 1200 nm) could have considerably good fitting characteristics in estimating WOR AGB at different growth stages, especially at the flowering and pod stages. When comparing with the CIred edge (Table 7), NDVI (800, 1200) also retained good robustness at the vegetative stage and reproductive stage, and it could reduce the influence of flower and pod for estimating AGB. Furthermore, model 37, based on NDVI (800, 1200), significantly improved the AGB estimation accuracy (R\({}^{2}\) > 0.80, RMSE < 1530 kg/hm\({}^{2}\), RPD > 2.3) without saturation phenomenon at total for four stages. The results showed that the modified vegetation indices NDVI (800, 1200) could be used to estimate WOR AGB at different growth stages.