esa-sceva/satcom-chunk-collection huggingface dataset:
# A Model to Measure U-Learning in Virtual Higher Education: U-CLX
## 4 The U-CLX Model Proposal
### The Mathematics under the U-CLX Model
#### 4.2.3 Scaling the U-CLX Model Equation  
Bearing in mind that the maximum values of the dimensions are \(x;y;z;w=100\), the equation must be scaled to take the total value of 100 for the maximum level of ubiquitous learning or U-Learning; in order to do this, the following equations and solutions must be used.  
\[V_{4}=\frac{\pi^{2}r^{4}}{2}=\frac{\pi^{2}}{2}\Big{(}r^{2}\Big{)}^{2}\quad \text{With}\quad r^{2}=x^{2}+y^{2}+z^{2}+w^{2}\]  
\[V_{4}=\frac{\pi^{2}r^{4}}{2}=\frac{\pi^{2}}{2}\Big{(}x^{2}+y^{2}+z^{2}+w^{2} \Big{)}^{2} \tag{10}\]  
\(x,y,z,w\) has a maximum value of 100.  
Where \(V_{4_{7}}\) is the maximum value,  
When \(x=y=z=w=100\),  
Then, the maximum value of the variables in the equation.  
\[V_{4_{7}}=\frac{\pi}{2}\Big{(}100^{2}+100^{2}+100^{2}+100^{2}\Big{)}^{2} \tag{11}\]  
\[V_{4_{7}}=\frac{\pi}{2}\Big{(}4\times 100^{2}\Big{)}^{2} \tag{12}\]  
\[V_{4_{7}}=\frac{16\pi}{2}\Big{(}4\times 100^{4}\Big{)}=8\pi\Big{(}100^{4}\Big{)} \tag{13}\]  
\(V_{4_{7}}\) is the maximum value of volume.  
Figure 6: U-CLX Model Pyramid [2].  
Because the volume is the maximum value, \(V_{4_{s}}\), for the maximum values of \(x;y;z;w\), V4S must be scaled. The following relation is observed:  
\[V_{4_{T}}=\frac{16\pi}{2}\left(4\times 100^{4}\right)=8\pi\left(100^{4}\right) \tag{14}\]  
Suppose the scaled \(V_{4_{S}}\) is 100 when the maximum total volume \(V_{4}=V_{4_{T}}=8(100)^{4}\); then the ratio for the new scaled volume is:  
\[V_{4_{T}}=\frac{16\pi}{2}\left(4\times 100^{4}\right)=8\pi\left(100^{4}\right) \tag{15}\]  
\[V_{4_{S}}=\left(4\times 10^{-8}\right)\times\left(\frac{\pi}{2}r^{4}\right) \tag{16}\]  
\[V_{4_{S}}=6.28\times(10)^{-8}r^{4} \tag{17}\]  
where the equation is:  
\[r^{4}=\left(x^{2}+y^{2}+z^{2}+w^{2}\right)^{2} \tag{18}\]  
\(V_{4_{S}}\) is the volume scaled to be equivalent to 100, when  
\[x=y=z=w=100\]  
The U-CLX Model is developed in a four-dimensional hypersphere. The model has four dimensions, and each dimension is a variable of the model. The purpose of the U-CLX Model is to calculate a value with which to measure the level of U-Learning; it achieves this using the equation for the volume of a hypersphere. The volume equation allows us to find the value of U-Learning from the four dimensions or variables of the model. The minimum (0) and maximum (100) values of each dimension are defined; with these data, the minimum and maximum levels of ubiquitous learning can be obtained from the volume of the hypersphere in the equation.
## 4 PV Systems--Implementation and Remote-Control Methods
### Remote-Control Methods for PV Systems
In non-uniform conditions (partial shading effects), multiple MPP are present on the power-voltage correspondence curve due to bypass diodes, which makes the global MPP estimation more difficult [41]. This is the main reason why maximum power point tracking (MPPT) is crucial for PV systems to operate at maximum efficiency. In the literature, more types of MPPT control solutions are proposed to improve systems' efficiency [42], and the MPPT techniques are classified into four main categories, as presented in [43]:  
- Classical MPPT control techniques  
- Intelligent MPPT control techniques  
- Optimization techniques  
- Hybrid techniques  
Every category contains subcategories, which are presented in [43]. It is important that the proper MPPT technique is chosen when harvesting the peak power from a PV system. Table 2 briefly describes various MPPT techniques. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Communication** & **Description** & **Comments** \\ \hline
[11] & Wi-Fi & The LabVIEW software is used to monitor and control. The current, voltage, and temperature values can be controlled. & It is a good and easy solution for systems remote monitoring and control. \\ \hline
[40] & Wi-Fi & A sliding mode control for the DC/DC converter is developed, which ensures the achievement of the maximum power point (MPP) under all environmental conditions. Additionally, a proportional-integral (PI) controller is implemented for the DC/AC converter, which guarantees the power quality. When changes have been implemented, the PV system’s signals are measured and remotely managed in order to guarantee the system’s continued operation. & The wireless communication allows the operation of the system in real-time, facilitating the detection of faults rapidly and system’s robustness. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **MPPT Method** & **Description** \\ \hline
[44] & Adaptive Neuro-Fuzzy Inference System (ANFIS) & The ANFIS MPPT technique was leveraged in this investigation. The findings demonstrated that the maximum power point (MPP) could be successfully tracked in partial shading conditions. \\ \hline
[45] & Artificial Neural Network (ANN) & A novel method for tracking MPPT was proposed. It was concluded that the proposed method could easily track the MPPT. \\ \hline
[46] & Hill Climbing Adaptive Neuro Fuzzy Inference System (HC-ANFIS) & The solution proposed shows that the irradiance and temperature values could be collected in real-time and the maximum voltage predicted \\ \hline \hline \end{tabular}
\end{table} 
Table 2: MPPT control methods.
# Fast and Improved Real-Time Vehicle Anti-Tracking System
## 1 Introduction
### Vehicle Detection
#### 2.2.2 Background Removal  
Segmentation is one of the critical steps in image analysis that determines the quality of subsequent measurements. Segmentation makes it possible to isolate in the image the objects on which the analysis must be made, thus separating the regions of interest from the background. Traditional background removal techniques have been developed based on the simple static background method [13]. This is because if the images have the same static background (such as a video taken from a CCTV camera on the road or a fixed room), the foreground and background can be separated. However, in our case, the background is constantly changing. So we have our unique approach to detect the region of interest.  
Figure 6: Example of a cascade for detecting a vehicle.  
The area of search on the image is limited by the use of the general knowledge of a street perspective. Vehicles cannot be found in the sky or a building--they are always on the street. Then, the image is analyzed through a moving window and is assessed by a detector of a different resolution, a multiple of \(32\times 32\). This is the smallest resolution allowed for a recognizable car. Vehicles are only detected and tracked as they enter a region of interest (ROI); when vehicles leave the ROI line, tracking stops. To track the region of interest, boundaries around vehicles are created, and the process continuing.  
To determine the exact location of the region of interest, we run the dummy vehicle detection software on the system while on the road. We detect vehicles and we notice that all the vehicles are located in a certain region. The heat map result shows that for an image of \(620\times 340\) pixels, the vehicles are located in the region of interest, as shown in Figure 7, inside the yellow rectangle, between y1 = 100 and y2 = 300. If this area of the frame is the only field of view, we will obtain the best output. Of course, this depends on the placement of the camera and the angle of view, which cannot be changed.
## 3 Results and Discussions
### Cytotoxic Study
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Compound**} & \multicolumn{2}{c}{**IC\({}_{50}\) (\(\mu\)g/mL)**} \\ \cline{2-3}  & **HCT-116** & **MDA-MB-231** \\ \hline
**5a** & 148.8 \(\pm\) 4.7 & 172.4 \(\pm\) 14.2 \\ \hline
**5b** & 110.6 \(\pm\) 5.9 & 125.7 \(\pm\) 13.1 \\ \hline
**5c** & 63.7 \(\pm\) 14.0 & 58.2 \(\pm\) 14.7 \\ \hline
**5d** & 62.3\(\pm\)15.0 & 81.0\(\pm\)3.4 \\ \hline
**5e** & 102 \(\pm\) 14.0 & 150.1 \(\pm\) 7.2 \\ \hline
**5f** & 69.0 \(\pm\) 18.0 & 80.7 \(\pm\) 10.5 \\ \hline
**5g** & 72.7 \(\pm\) 20.0 & 88.5 \(\pm\) 10.1 \\ \hline
**5h** & 90.1 \(\pm\) 1.5 & 98.6 \(\pm\) 18 \\ \hline
**5i** & 62.3 \(\pm\) 6.0 & 79.0 \(\pm\) 7.2 \\ \hline
**5j** & 79.4 \(\pm\) 7.4 & 87.8 \(\pm\) 6.9 \\ \hline
**Standard *** & 1.2 \(\pm\) 0.3 & 0.3 \(\pm\) 0.2 \\ \hline \end{tabular}
\end{table} 
Table 4: Anticancer activity of compounds **5a-5j**. The result of the present investigation reveals that few of the tested compounds have shown significant decrease in cell viability in two test cell lines. It is evident from the results that the \(N\)-methyl-3-aryl indazoles (**5a-5j**) are found to be more potent than **3a-3j** against HCT-116 and MDA-MB-231 cell lines. The compounds **5c**, **5d**, **5f**, **5g**, and **5i** exhibit significant cytotoxic activities against the two cell lines tested. However, the test compound **5c** is found to be more potent against the cell lines HCT116 and MDA-MB-231 with IC\({}_{50}\) \(<\) 64 and IC\({}_{50}\) \(<\) 59 \(\mu\)g/mL, respectively. The test compounds **5d** and **5i** show potent activity against the cell line HCT-116 with IC\({}_{50}\) \(<\) 63 \(\mu\)g/mL. The compounds **5g**, **5f** display moderate activity against the HCT-116 cell line with IC\({}_{50}\) \(<\) 73 \(\mu\)g/mL. The differential activity among the cell lines may be due to the structure-activity relationship of the molecules. Exponentially growing cells were treated with different concentrations of indazole compounds for 24 h and cell growth inhibition is analyzed through MTT assay.
# Driver Identification and Detection of Drowsiness while Driving
## 3 Requirements and Technologies
### In-Car Camera  
One of the key hardware requirements for this work is the placement of the camera inside the car. The specific position of the camera is a highly relevant factor for accurately measuring both the facial embedding and drowsiness levels through facial features, such as eye condition.  
Figure 2 shows the three different camera angles, on the left, in the center, and on the right side of the steering wheel, that were tested during the experimentation for this work. Based on the experimental study, it was concluded that the left-side camera angle provides poor visibility of the driver's eyes, with significant interference from the driver's hand within its field of view. The center camera is positioned too closely to the steering wheel due to its central location, which necessitates placing the camera too high and could hinder road visibility. On the other hand, the right-side camera proved to be effective in detecting the driver's face and eyes. Being positioned above the air conditioning system neither obstructs the field of view of the road nor gets too close to the driver's face. Camera placements on the car's central rearview mirror and the top of the dashboard were also tested but were not considered suitable for safety reasons, as they obstructed the driver's vision. In conclusion, as shown in Figure 2, the optimal angle for detecting the driver's face and eyes is to place the camera on the right side of the steering wheel using the air conditioning vent hole.
## 5 Discussion
### The Value of our Model Comparison Exercise and Its Transferability  
It has been worthwhile to compare a number of statistical predictors, each covering a range of attributes; attributes decided upon via a focused global and local exploratory analysis. We chose models that could account for: (i) global relationships between our variable of interest (WD) and its predictors (SDRD.T and SEABED class); (ii) local relationships between the same variables; and (iii) spatial autocorrelation effects. Via a comprehensive model accuracy assessment, a standard geostatistical model (KED-LN) was found to be the best predictor of water depth. This model was one of two study models (the other being the relatively new, GWRK model) that could capture both local relationships and spatial autocorrelation.  
This study's group of five prediction models should be readily transferable to similar embayment studies with similar physical characteristics, for predicting water depth. However, it should not be expected that KED-LN would similarly provide the best predictions at all times, but only that one predictor of the group is likely to provide similar and worthwhile levels of accuracy as that found here. Thus the transferability of our approach refers to group of five models as a whole, and not one individual model in particular. That said, our experience suggests that KED will tend to provide the best results; reflecting its theoretical property as an optimal linear predictor (e.g., [39]). Observe also that our approach is suited only to situations where ground measurements exist so that the models can be calibrated; the parameters of this study's models are not transferable to ungauged areas and should never be considered so.
## 3 Results and Discussion
### Torsional Stiffness Analysis  
Table 7 presents the torsional stiffness analysis results for each torsion beam configuration. Torsional stiffness refers to the structural rigidity of a beam subjected to torsional  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Stiffener Curvature** & **Bending Stress** & **Torsional Stress** \\  & **(mm)** & **(MPa)** & **(MPa)** \\ \hline Base & 0 & 203.4 & 70.48 \\ Type 1 & +40 & 208.4 & 71.02 \\ Type 2 & +50 & 192.8 & 64.14 \\ Type 3 & +60 & 194.6 & 69.80 \\ Type 4 & \(-\)40 & 164.9 & 68.76 \\ Type 5 & \(-\)50 & 174.2 & 66.45 \\ Type 6 & \(-\)60 & 177.8 & 66.52 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of bending and torsional stress.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Property**} & **Unit** & **Value** \\ \hline Area & mm\({}^{2}\) & 1331.41 \\ I\_xx & mm\({}^{4}\) & 446,043.86 \\ I\_yy & mm\({}^{4}\) & 765,051.83 \\ I\_zz & mm\({}^{4}\) & 1,211,095.69 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Base torsion beam properties.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Parameter**} & **Unit** & **Value** \\ \hline Vehicle end & - & Rear \\ Type & - & Dependent \\ Tire static loaded radius & Mm & 310 \\ Tire vertical spring rate & N/mm & 350 \\ CG height & Mm & 795 \\ Wheel base & Mm & 2660 \\ Vehicle weight & N & 14,000 \\ Front braking ratio & - & 0.7 \\ Front drive ratio & - & 1.0 \\ Axle ratio & - & 3.0 \\ Wheel travel in roll and ride & Mm & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Vehicle parameters considered in this study.  
deformation; higher torsional stiffness implies reduced deformation under torsional loading.  
Based on the analysis results for Types 1, 2, and 3, which had convex-shaped reinforcements, the \(y\)-axis displacements were greater than those of the baseline configuration. The torsional stiffness of these configurations was lower than that of the baseline configuration, and as the curvature increased from +40 mm to +60 mm, the torsional stiffness increased.  
In contrast, for Types 4, 5, and 6, which had concave-shaped reinforcements, all the configurations exhibited smaller y-axis displacements than the baseline configuration. The torsional stiffness of these configurations was higher than that of the baseline configuration, and as the curvature decreased from \(-60\) mm, the torsional stiffness increased.  
Based on these results, we can conclude that, among all the configurations, the concave-shaped reinforcements with higher torsional stiffness are less affected by torsional forces than the convex-shaped reinforcements. Among the concave-shaped configurations, Type 4, which had the highest torsional stiffness, was the most stable when subjected to torsional loading.
# Deep Learning and Embedding Based Latent Factor Model for Collaborative Recommender Systems
## 5 Experiments
### Effect of Embedding (RQ2)  
To look into the impact of embedding, we compared the performance of our proposed method, DELCR (with embedding), with that of DCF (without embedding), which was our previously published proceeding. For a fair comparison, we made the number of hidden-layers for DCF [19] the same as DELCR. We observe that DELCR with embedding outperforms DCF on both datasets. Specifically, our proposed method outperforms DCF with a relative improvement of 3.56% and 3.49% of MAE and RMSE, respectively, for the ML-100K dataset. It also shows a relative improvement for the ML-1M dataset with a MAE of 2.87% and a RMSE of 2.85% as shown in Table 5. From this result, we can conclude that the use of an embedding technique improves the performance of recommendation quality.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{2}{c}{**ML-100K**} & \multicolumn{2}{c}{**ML-1M**} \\ \cline{2-5}  & **MAE** & **RMSE** & **MAE** & **RMSE** \\ \hline DCF & 0.698 & 0.887 & 0.662 & 0.842 \\ DELCR & 0.674 & 0.856 & 0.643 & 0.818 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Embedding effect for both datasets.
## 4 Discussion
### Tooth- vs. Bone-Borne Appliances
It was found that the bone-borne device without a banded anchorage to first molars resulted in a greater anterior than posterior expansion, while the tooth-borne device provided a more symmetrical expansion antero-posteriorly. Evidence suggests that bone-borne anchorage devices are associated with a lesser increase in both dental and alveolar inclination as a result of maxillary expansion [30; 31; 32]. [PERSON] and colleagues [30] found that tooth-borne expansion appliances resulted in 6 to 11 degrees of dental buccal tipping, while [PERSON] et al. [31] reported the same value to be between 3 and 12 degrees. In contrast, these same studies and one by [PERSON] et al. concluded that tissue-bone-borne expanders resulted in only 0 to 1.5 degrees of dental tipping, while tooth-bone-borne devices resulted in 2 to 3 degrees [30; 31; 32]. Regarding alveolar bending, devices with a bony anchorage (tissue-bone-borne and tooth-bone-borne) resulted in a change of less than 2.5 degrees, while tooth-borne devices caused up to 4 degrees of alveolar inclination [31; 32]. It is plausible that the increased buccal dental inclination associated with tooth-borne expanders may be related to the REC and decreased BBH seen with such devices.
## 4 Experimental Studies
### Simulation of Benchmark Functions  
#### 4.1.1 Parameters Setting of Algorithms  
To verify the performance of IMOPSO, we selected eight unconstrained benchmark functions; namely, ZDT1-ZDT6, Schaffer (SCH), Kursawe (KUR) and Fonseca (FON). Considering that the DWTA model established in this paper is a bi-objective optimization problem, the benchmark functions selected here are also bi-objective. These test functions are characterized with bi-objective, discontinuous, concave and convex, etc. They provide enough difficulty for testing multiobjective optimization algorithm in searching Pareto optimal solution, challenging algorithm convergence and diversity of solution set distribution. The related information of benchmark functions is shown in Table 1.  
Parameters of each algorithm are set as follows. The population size of the four algorithms is set to 100. The crossover probability of NSGA-II is 0.9, tournament selection is adopted, and the mutation probability is \(1/n\) (where \(n\) is the number of decision variables). The external archive size of IMOPSO and MOPSO is 100, the inertia weight \(w\) decreases from 0.9 to 0.4, and \(c_{1}=c_{2}=2.0\). The crossover probability and mutation probability of MOEA/D and NSGA-II are the same, which are 0.9 and \(1/n\), respectively. The neighbour size of MOEA/D is 30, and the decomposition method is tchebucheff. The number of iterations of ZDT1 and ZDT2 are 60. Since ZDT3, ZDT4 and ZDT6 functions are relatively difficult to optimize, the number of iterations is set to 100. The iteration number of SCH is 60, and that of FON and KUR is 100.
# Privacy Framework for the Development of IoT-Based Systems
## 5 Proposed Methodological Approach: Planning, Requirements, and Design (PRD)
### Phase 3: Implementation  
Once the Requirements Report is approved, the implementation phase begins. In this phase, developers evaluate a range of technologies and select the most suitable ones to meet the previously defined requirements. Following a bottom-up approach, this phase aligns with the IoT-OSRM reference model and focuses on how devices are monitored, managed, and presented to the end user. The implementation process adheres to the six levels established during the requirements gathering phase, ensuring consistency and coherence throughout the system architecture:  
* **Level 1--Sensor/Actuator Node:** Involves the selection of sensors, actuators, microcontrollers, transducers, and memory components. These hardware elements form the foundational layer of the IoT system, enabling data collection and interaction with the physical environment.
* **Level 2--Connectivity:** Establishes the networking infrastructure required to connect \"things\" to the edge components. The choice between wired, wireless, or hybrid connections is based on considerations such as bandwidth, latency, environmental factors, and scalability.
* **Level 3--Border:** Focuses on selecting the appropriate edge element, which may include a single-board computer, an inter-cloud computing system, or a dedicated cloud server. The chosen technology must comply with the requirements outlined in Phase 2, ensuring efficient data routing, pre-processing, and enhanced security.
* **Level 4--Storage:** Involves choosing the data storage solution that best meets the system's operational needs, whether through local databases, cloud storage, or a hybrid approach. This selection considers factors such as data security, redundancy, latency, and accessibility, ensuring that stored data remains both reliable and readily available.
* **Level 5--Abstraction:** Focuses on implementing data-handling techniques to process and transform raw data into actionable insights. This includes employing advanced methods such as big data analytics, ML, and DL to extract meaningful information and support informed decision-making for the end user.
* **Level 6--Display:** Involves deploying the user interface tools through which end users will interact with the system. This includes developing mobile applications, dashboards, alerts, graphs, and other forms of visual reporting to present data in an intuitive and accessible manner.  
Throughout the implementation phase, maintaining alignment with the predefined requirements is essential to ensure the system's scalability, security, and usability. The bottom-up approach facilitates iterative development, allowing for adjustments at each level to optimize overall system performance and user experience.