esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Amid the growing integration of AI in education, machine learning (ML) and generative AI (GenAI) have gained prominence for their ability to personalize learning, predict outcomes, and support institutional decision-making [1, 2, 3]. While higher education fosters critical thinking and innovation [4], it continues to face challenges such as dropout, inequality, and the demand for adaptive environments [5]. In response, learning analytics (LA), enhanced by ML, is increasingly used to promote equity and student success through data-driven practices [6, 7].  
Defined by Siemens [8] as the analysis of data about learners and their contexts to optimize learning, LA has embraced ML techniques--supesemi-supervised--to forecast performance, detect disengagement, and enable timely interventions [9; 10; 11; 12]. Recent advances in GenAI, especially large language models (LLMs), are expanding LA's capabilities through automated feedback, intelligent tutoring, and interaction analysis [13; 14; 15], although empirical applications remain limited and underexplored in terms of pedagogy and ethics. Some studies caution that GenAI's \"black-box\" nature and lack of pedagogical grounding may undermine trust and educational validity, contrasting with perspectives that emphasize its potential to enhance learner engagement and scalability.  
Existing reviews on ML in LA [16; 17; 18; 19] rarely address GenAI or offer an integrated synthesis of both approaches. To fill this gap, this review analyzes empirical studies from 2018 to 2025, including recent developments from major conferences.  
The study addresses two research questions:  
RQ1: How are ML and GenAI applied in LA within higher education?  
RQ2: What benefits arise from their integration in this context?  
The results indicate that, while ML remains dominant in predictive analytics, GenAI is emerging in real-time feedback and adaptive learning but still faces critical challenges around transparency and pedagogical validity. By examining methodological trends, educational applications, and broader implications, this review offers a timely perspective to guide the responsible adoption of AI in higher education.
## 3 Methodology  
Within this section, the pattern-based MAR methodology followed by the ModAR is presented. The pattern object is assumed to be a planar rectangle surface. A nadir image of the pattern object, herein after referred to as pattern image, is required for augmentation of the real-world frames via a 3D model, also required by the proposed method, along with the camera internal parameters.  
The origin of the world coordinate system is assumed to be located at the center of the pattern object. The \(X\) and \(Y\) axes lie on the pattern object plane, while the \(Z\) axis is perpendicular to it, pointing to the camera. The \(X\) and \(Y\) coordinates of the four corners of the pattern object in the world coordinate system are computed using the normalized width (\(W\)) and height (\(H\)) of the pattern image, according to Equation (1):  
\[W=\frac{cols}{\text{max}(rows,cols)},\ H=\frac{rows}{\text{max}(rows,cols)}, \tag{1}\]  
where _cols_ and _rows_ are the number of columns and rows of the pattern image, respectively. Thus, the \(X\) and \(Y\) coordinates of the four corners of the pattern object range from \(-1\) to \(1\). Their Z coordinate is set to zero (Figure 1).
# Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm
## 4 The Spatial Optimized Dynamic Path Planning Algorithm  
The optimization of the Spatial Optimized Dynamic Path Planning algorithm was mainly concentrated on two aspects:  
1. The scale of the road network was controlled. The urban road network was decomposed into different sub-nets. According to the location of the starting point, different sub-nets were chosen as the search space to avoid searching the whole road network, thus reducing the scale of network information updated in real time;
2. The search direction wwas restricted. The evaluation function was optimized, considering both the distance and search direction. More heuristic information was used to reduce the search area and search time to speed up the convergence of the algorithm.
# Bearing Capacity of Footings on Rock Masses Using Flow Laws
## 6 Conclusions  
Studying the upper and lower limits of the bearing capacity as a function of the flow law, it can be concluded that, in the cases of weightless rock mass, the variation of the flow rule conditions affected the results of the bearing capacity in the following ways:  
* The parameters that most influenced the \(\Delta P_{h}^{AW/NW}\) were the overall geotechnical quality of the rock mass (\(GSI\)) and the rock type (\(m_{i}\)).
* The cases with low values of the \(GSI\) (very poor quality rock mass) corresponded to the cases for which [PERSON] and [PERSON] [13] recommend the adoption of null dilatancy, and showed greater variation of \(\cdot P_{h}^{AW/NW}\). In these cases, the adoption of the associative flow law could overestimate the bearing capacity of the rock mass by up to 100%.
* The influence of dilatancy was more important for rock masses with low bearing capacities (in particular, for values lower than the characteristic resistance of the concrete: 25 MPa).  
In addition, taking into account the comparison described in this research in the cases considering the self-weight of rock mass, we can draw the following conclusions:  
Figure 16: Estimation of bearing capacity by FDM. (**a**) Stress–strain curve at a point of the ground; (**b**) convergence analysis.  
There were three parameters that strongly influenced the \(\cdot P_{h}^{AS/NS}\): the rock type (\(m_{i}\)), the uniaxial compressive strength (\(UCS\)) and the geological strength index (\(GSI\)). The \(GSI\) was the main determining factor.
* The adoption of different flow law did not influence the low results of the bearing capacity (\(\leq\)5 MPa), which were related to low values of the \(GSI\) and the \(UCS\). This differed from the cases of weightless rock mass, in which the flow law precisely conditioned low and medium bearing capacity values.
* Based on the numerical results, the \(D_{F}\) coefficients can be used in conjunction with the analytical method [18] to semi-analytically estimate the bearing capacity of rock mass considering a non-associated flow law.  
Conceptualization, A.A., R.G., C.O.M. and S.M.; Data curation, A.A. and R.G.; Funding acquisition, R.G. and C.O.M.; Investigation, A.A. and R.G.; Methodology, A.A., R.G. and S.M.; Project administration, A.A. and R.G.; Resources, A.A.; Software, A.A.; Supervision, R.G. and C.O.M.; Validation, A.A. and R.G.; Visualization, A.A.; Writing--original draft, A.A.; Writing--review & editing, R.G. and C.O.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Universidad Politecnica de Madrid.  
Not applicable.  
The research described in this paper was financially supported by the Universidad Politecnica de Madrid through the VMENTORUPM21 RAGA grant funded by the university's own program to carry out research and innovation projects.  
The authors declare no conflict of interest.
# Computer Vision-Based Approach for Reading Analog Multimeter
###### Abstract  
Multimeters are useful instruments for measuring electronic parameters. Even though the digital multimeter is commonly used in our daily life under the considerations of precision and cost, the analog multimeter is still preferable in many applications due to its easy use to monitor promptly varying values. However, the reading of analog multimeters (or A-meter) usually relies on human eyes with two obvious drawbacks of inefficiency and easy fatigue, while visual inspection onto an A-meter is needed for a long period of time. From the viewpoint of optical sensor application, computer vision, like human eyes, can also be used to sense stimuli from the real world. Therefore, in this paper, an approach of reading an A-meter based on a computer vision technique is proposed. Reading an A-meter relies on information from the arrow on the function selector and the pointer on the instrument meter; the presented method is thus mainly composed of horizontal alignment of the A-meter, detection of the instrument meter region, angle detection of the selector arrow, and angle detection of the pointer. In addition, the schemes of edge-based geometric matching (**EGM**) and pyramidal gradient matching (**PGM**) are adopted to detect the regions of interest. The mapping relationship between the function selector and the selector arrow as well as that between the instrument meter and the pointer are built and formulated to finally read the A-meter. The often used scenarios for reading AC voltage, DC voltage, and DC current as well as resistance are used for experiments and evaluations. The experimental results show that the accuracy of detecting the function selected is 100%, the mean accuracy of reading a value from the A-meter is 95% or above, except for some cases of reading resistance that are affected by the so-called _itttle-change-large-multiplier effect_. The proposed method can perform very well as long as the mean intensity is \(\geq\) 7.5. Based on a suitable modification of the proposed method, an application of monitoring a storage level meter and pressure meter installed on a 15 m\({}^{3}\) liquid nitrogen (LN2) tank is demonstrated. Our experiments and demonstrations confirm the feasibility of the proposed approach.  
analog multimeter (A-meter); angle detection; computer vision; image processing; instrument; region of interest (ROI); template matching +
Footnote †: journal: Applied sciences
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
## 4 Results and Discussion
### DD Network Solutions  
The accuracy of coordinates, the ambiguity fixing rate, and the accuracy of ZTD estimates with the DD network processing strategy were analyzed.
# Numerical Estimation of Bending in Holographic Volume Gratings by Means of RCWA and Deep Learning
## 2 Theory
### Rigorous Coupled Wave Analysis--Shooting Method Approach
In this way, we can compute the field value at the first surface using Equation (15), thus obtaining the reflection and transmission coefficients. Figure 2 summarises the sequence of the implementation of the RCWA-SM method detailed in this section. The flowchart shows how an initial boundary value problem (BVP) is transformed into an initial value problem (IVP) and finally to a linear system of equations to solve the reflection and transmission coefficients.
# Evaluation and Characterization of Alginate Extracted from Brown Seaweed Collected in the Red Sea
## 2 Materials and Methods
### Biochemical Composition of the Extracted Sodium Alginate
[50] have determined the purity of alginic acid, which indicates the absence of tannins, flavonoids, phenols, steroids, and alkaloids; however, only saponins (due to be moiety with carbohydrates) were found, which supports the high purity of the extracted alignates. The biochemical assays demonstrated that the highest protein content in the alginate was found in _D. ciliolata_ and the lowest was in _T. triquetra_. The quantitative analysis ranged from 0.57 to 1.4%. The lipid content of the alginate ranged from 0.3 to 3.5%. The maximum content was found in _D. ciliolata_, whereas _P. boergesemii_ recorded the minimum content. Similarly, [PERSON] and [PERSON] [73] reported a maximum lipid content, which was 6.12%, in _D. ciliolate_, and the protein content was 4%. In general, the protein content fluctuates among different genera and in different species of the same genus, and this change may be spatial or temporal in nature. However, it is largely associated with the seawater quality, as stated by [PERSON] et al. [74]. The value of the ash content of the sodium alginate in this study varied between 12.3 and 20%. These results do not differ much from the ash content established by the Food Chemical Codex [67], which varied from 13 to 27%. Similarly, [PERSON] and [PERSON] [75] reported that the ash content is 23%. When compared to global standards, the ash contents of the extracted sodium alignates were in accordance with these standards. Considering the results of the alginate yield and all of the analyses done, it is supported by the spectroscopy methods (FTIR and NMR) that these seaweeds' alginate has a high manunuronic acid concentration with low content of other sulphated polysaccharide, although the viscosity of the alignates varies greatly due to the manunuronic and gluuronic acid ratio. Using the phytochemical analysis supported the clean extraction of the alginate without other seaweed compounds. In contrast, the biochemical analysis gives an overall analysis of the alginate, where proteins and lipids were detected at a low percentage. Thus, these results unveil the potential of these five seaweeds for further industrial exploitation of alginate for food industries. Although _T. triquetra_ had a higher alginate yield and also one of the highest percentages of carbohydrates, _S. aquifolium_ was the third in alginate yield, and it presented a higher percentage of carbohydrates (less impurities). _D. ciliulata_ was the second in alginate yield and in the carbohydrate yield, which demonstrates a good raw source for alginate extraction, although this species has a high protein and lipid yield, which is a problem to be surpassed. _P. boergesenii_ and _H. cuneiformis_ had the lowest alginate yield and carbohydrate yield, however, these species can be also a new source if there is a high biomass concentration of these species for alginate extraction.
## 4 Thick Ellipsoid State Estimation Algorithm
### Visualization of the Thick Ellipsoid State Estimation Procedure  
To visualize the individual steps of the thick ellipsoid state estimation procedure, the test example  
\[\begin{bmatrix}x_{1,k+1}\\ x_{2,k+1}\end{bmatrix}=\begin{bmatrix}1&0.05\\ -0.01x_{1,k}&0.95\end{bmatrix}\cdot\begin{bmatrix}x_{1,k}\\ x_{2,k}\end{bmatrix} \tag{82}\]  
is investigated. At the initial point of time \(k=0\), all possible system states are included in the set  
\[\left(\mathcal{E}\right)_{0}=\left(\mathcal{E}\right)_{0}(\mathbf{\mu}_{0},\mathbf{ \Gamma}_{0},\left[1\;;\;1\right])\quad\text{with}\quad\mathbf{\mu}_{0}=\begin{bmatrix} 1\\ 1\end{bmatrix}\quad\text{and}\quad\mathbf{\Gamma}_{0}=\left(\begin{bmatrix}1&0.9 \\ 0.9&1\end{bmatrix}\right)^{\frac{1}{2}}\enspace. \tag{83}\]  
The corresponding ellipsoid is shown as the dash-dotted line in Figure 7(a). Performing a single prediction step leads to a shift in the ellipsoid midpoint according to Equation (77). Moreover, the nonlinearity in the system model (82) causes the resulting distance between the inner and outer bounds of the predicted ellipsoid \(\left(\mathcal{E}\right)_{1}\). Now, this ellipsoid is intersectedwith the measurement information that is highlighted by the vertical strip in Figure (a)a,b. According to Figure (b)b, the correction step leads again to a shift of the midpoint of the new thick ellipsoid \((\xi)_{1}^{\prime}\) according to Equations (58) and (59). The inner bound of \((\xi)_{1}^{\prime}\) is always fully contained in the band of possible state measurements \((\xi)_{m,1}\), while its outer bound becomes tighter than the result of the preceding state prediction.  
Now, the result \((\xi)_{1}^{\prime}\) serves as the input for the subsequent state prediction. For the sake of a compact notation and for compatibility with Figures 6 and 7, the prime symbol is suppressed in Figure (c)c, where the prediction from \(k=1\) to \(k=2\) is shown. After this prediction, the next correction step can be executed according to Figure (d)d. Note, tight measurement tolerances or strongly nonlinear dependencies in the system model (re-written into the quasi-linear form) may lead to empty inner bounds after either the prediction or the correction step. Then, a slightly modified algorithm could be used. Instead of interpreting the inner bound as the state domain that is guaranteed to belong to the reachable solution set after the execution of all computations until the current instant \(k\), it can be used to determine a measure for the precision of the sequence of prediction and correction steps at a single point of time \(k\). For that purpose, it is necessary to initialize the inner bound of \((\xi)_{k}\) with the same scaling parameter as the outer bound (cf. the interval parameter with identical bounds in (83)) and to use it in analogy to the initialization of the ellipsoid in Figure (a)a.
# Real-Time Interactive Parallel Visualization of Large-Scale Flow-Field Data
## 6 Discussion  
The experimental results demonstrate that our method can reduce the data file reading time for large-scale datasets by approximately one-seventh, exhibiting excellent load-balancing performance. The proposed grid-based data-partitioning strategy accelerates the loading and processing of large-scale flow-field data, mitigating the bottleneck and latency issues associated with single-point I/O. It effectively addresses the challenge of visualizing large-scale flow fields by overcoming the limitation imposed by their immense size and thus holds significant value for large-scale flow-field visualization. By managing network communication in different cluster systems and efficiently utilizing network  
Figure 12: Visual rendering results of 500 million (a), and 800 million (b) grids.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset (Interaction Method)/Number of Processes** & **64** & **128** & **256** & **512** & **1024** \\ \hline \multirow{3}{*}{500 Million Grids} & Rotation (ms) & 4.697 & 4.832 & 4.948 & 5.547 & 4.198 \\  & Translation (ms) & 4.906 & 4.215 & 4.581 & 5.427 & 4.397 \\  & Scaling (ms) & 4.817 & 5.568 & 5.006 & 4.816 & 4.084 \\ \hline \multirow{3}{*}{800 Million Grids} & Rotation (ms) & 4.457 & 6.421 & 4.528 & 4.692 & 4.721 \\  & Translation (ms) & 5.463 & 6.197 & 5.122 & 4.371 & 5.486 \\ \cline{1-1}  & Scaling (ms) & 4.582 & 5.078 & 4.490 & 6.572 & 4.743 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Real-time interaction response times.  
resources, we minimize the interaction time, reduce operational delays, and meet the real-time human-computer interaction requirements of large-scale scientific visualization. The proposed multi-level asymmetric network communication management method enables large-scale flow-field visualization to perform complex visual feature computations and rendering using supercomputers driven by visualization terminals. This approach enhances the real-time and interactive nature of visualization, which is crucial for visualization tasks requiring rapid responses and feedback, such as exploratory analysis, parameter adjustments, and dynamic demonstrations.  
We propose a data-partitioning strategy based on grid segmentation that can effectively exploit the fine-grained parallelism within grids and improve data access efficiency. Currently, the method is only applicable to the EnSight Gold data format, but we believe it has wide applicability and scalability to other types of data formats. Our future work will explore the performance and effectiveness of the method under different data formats.
## 4 Electricity Demand Profile on Study Area
### Seasonal and Holiday Pattern  
The hourly aggregate demand profile is plotted in Figure 4b. To observe the stable variation of data over time, a rolling window of 365 samples were taken for moving average. This plot indicates that the overall demand grows with a linear trend and is influenced by seasonality. The pattern of peak demand and off-peak demand describes the significant effect of peak working hours, holidays, or special events.  
The massive Bangkok flood in the north and central Thailand from October 2011 to December 2011 caused a long period of significant demand drop. During that time many  
Figure 4: (a) Complete demand profile for the year 2012 and (b) overall demand profile with trend.  
Figure 3: Architecture of GRU showing information flow [50].  
factories, universities, and offices in Bangkok and surrounding provinces were closed. Figure 5 indicates that the measure of the demand drop compared with respect to the previous year's demand shows that the peak demand was reduced approximately by 2000 MW, and can be considered similar to the COVID-19 pandemic lockdown [22, 51].  
Each year, there is a noticeable decrease in electricity demand during holidays and special events. In particular, extended holidays such as the first week of January (New Year holiday) and second week of April (Songkran holiday) have a significant impact on demand, as is illustrated in Figure 6,b. During these periods, factories, universities, and other offices remain closed for approximately a week, while many Thai people return to their homes outside the metropolitan region. Additionally, holidays such as Mother's Day (August 12) and Father's Day (December 5) contribute to fluctuations in electricity demand, although the effect is not as substantial as that of Songkran and New Year. These variations in electricity demand due to holidays are commonly referred to as _holiday effects_; they play a crucial role in the modeling process and pose a significant challenge for researchers aiming to achieve high forecasting accuracy.