esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### AI-Powered Learning Management Systems (LMSs)  
#### 3.4.1 Introduction to AI-Powered Learning Management Systems  
AI-powered learning management systems (LMSs) are platforms that incorporate artificial intelligence technologies, including multimodal large language models (MLLMs), to enhance the delivery, management, and personalization of educational content [62]. Traditional LMS platforms facilitate the administration, documentation, tracking, reporting, and delivery of educational courses or training programs. The integration of AI transforms these systems into intelligent platforms capable of providing personalized learning experiences, adaptive content, and interactive engagement [63].  
MLLMs enable AI-powered LMSs to process and generate content across multiple modalities such as text, images, audio, and video. This multimodal capability allows for richer, more engaging educational experiences that cater to diverse learning styles and needs [12]. By analyzing vast amounts of learner data, AI-powered LMSs can adapt to individual learners' progress, preferences, and performance, thereby enhancing the overall effectiveness of the educational process [55].
## 3 Case Study
### Experimental Design and Statistical Analysis  
The present study employed a one-factor (participant group) between subjects design and conducted the force tracking tests in three steps (orientation, practice, and main test). In the first step, the purpose and instruction of the test were explained to the participant. In the second step, the participant was asked to practice the force tracking task to become familiarized. In the last step, the main tests were conducted four times for the participant. Breaks of about 2 min were allowed between the repetitions.  
One-factor between-subjects ANOVA and Tukey tests were conducted using MINITAB (Minitab Inc., State College, PA, USA) at \(\alpha\) = 0.05. This study excluded data that fell outside 95% confidence intervals of repeated trials [10,11]. The average proportion of excluded data was similar among the three groups of participants (normal: 4.0%, svMCI: 3.5%, and SVaD: 3.3%).
## 3 Results
### Trend Test  
In this section, the time series of observed and GPP precipitation is visualized in Figure 11 for each province and the average study area to compare the ability of the selected precipitation products to follow the observed precipitation.  
The time series plot (Figure 11) of the observed and estimated precipitation for the entire study area reveals IMERG7 and MSWEP2.8 and CRU TS closely follow the ground stations' monthly precipitation pattern. In contrast, ERA-5 frequently overestimates extreme events and normal rainfall. The considerable deviation between the ground observations and the ERA-5 precipitation pattern can be observed in the time series plot for GB province. CRU TS also exhibits an overestimation precipitation pattern throughout the study duration; however, the variations are notably less than those of ERA-5. The rest of the products capture precipitation with comparative precision. The same trend of performance is shown by all the products in KPK province, but the association with the ground observations is  
Figure 11: Time series plots for each province of Pakistan exhibit the level of fitness of the monthly scale precipitation estimates of the datasets with the ground observations.  
significantly better than in GB province. In the rest of the provinces (Punjab, Sindh, and Balochistan), the visual comparison of the time series data depicts that IMRG7, MSWEP2.8, and ERA-5 (performance order of the datasets) match well with the RGs' recorded rainfall trend, with reasonable negative and positive deviations, whereas CRU TS significantly underestimates several events and exhibits comparatively poor association with the RGs' precipitation pattern in these provinces.
## 4 Batch Simulation and Verification
### Analysis and Verification of Protection Method against Maloperation  
In this section, the maloperation prevention ability of the proposed protection method is mainly analyzed.  
For the stator single-phase grounding fault, since the neutral point of the stator winding is grounded by high resistance, the fault characteristics are weak, and only the change characteristics of fundamental frequency zero-sequence voltage and third harmonic voltage ratio will appear. The current of each branch is almost unchanged, which will not cause protection maloperation.  
For the rotor one-point grounding fault, since the neutral point of the rotor winding is not grounded, there is no current path, thus the air-gap magnetic field distribution  
Figure 14: When \(s=-0.01\), the simulation results of all possible internal short-circuit faults in rotor windings: (**a**) in-slot faults; (**b**) end faults in the same branch with different short turns; (**c**) end faults between two branches in the same phase; (**d**) end faults between phases.  
Figure 15: Diagram of the short-circuit fault in rotor windings.  
generated by rotor windings will not be changed, and the circulating current between stator branches will not be induced.  
For the internal short-circuit faults in stator windings, the stator short-circuit current will distort the air-gap magnetic field, and then fault current with some specific frequencies will be induced in rotor windings. The above-induced current will also in turn generate a fault current with some characteristic frequencies on the stator side. However, the amplitude of this fault current is very weak after secondary induction, and the characteristic frequencies are different from that in Equation (8). Therefore, the protection action value will not reach the setting value. Still taking the above variable-speed pumped storage unit as an example, all possible internal short-circuit faults in stator windings are simulated. When the slip ratio \(s=0.1\), the calculation results of the action value of the proposed protection are as shown in Figure 16. In the figure, the abscissa is the turn difference of the short-circuit fault in stator windings, and the ordinate is the calculation result of the protection action value \(l_{op}\). It can be found that the maximum protection action value is only 0.00164 p.u. in all fault cases, which is close to 0. Therefore, the protection setting value can be flexibly selected to ensure that the proposed protection does not malfunction when other kinds of faults occur.
## 3 Results and Discussion
### Sensory Analysis
The obtained results were correlated to the ones based on microbiological quality, and it was found that the evaluation of the sensorial quality of the meat products led to a slight overestimation of the corresponding shelf life, and that the rejection of overall quality should be mainly based on the microbiological quality of the products. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Sample**} & **Storage** & **Sensorial** & **R2** & **Shelf Life** \\  & **Temperature** (\({}^{\circ}\)**C) & **Deterioration Rate** (\(\mathbf{d^{-1}}\)) & **(d)** \\ \hline \multirow{4}{*}{CNT} & 4 & 0.211 \(\pm\) 0.016 c & 0.970 & 21 \\  & 8 & 0.328 \(\pm\) 0.013 d & 0.988 & 13 \\ \cline{1-1}  & 12 & 0.608 \(\pm\) 0.082 f & 0.906 & 8 \\ \cline{1-1}  & 18 & 0.709 \(\pm\) 0.112 f & 0.861 & 6 \\ \hline \end{tabular}
\end{table} 
Table 6: Shelf life estimation based on the overall acceptance of CNT and HPP frankfurter-type sausages stored at 4, 8, 12, and 18 \({}^{\circ}\)C (limit of acceptance set to 4.5). Figure 14: Scores for the overall impression of (**a**) the reference (CNT) and (**b**) the HPP-treated frankfurter-type sausages during storage at isothermal conditions. Solid lines represent the zero-order kinetic model applied to the data based on Equation (3).
# Metachrylate-Based Polymeric Sorbents for Recovery of Metals from Aqueous Solutions
## 9 Overview of Characterization Methods
### SEM/EDS and TEM  
Scanning electron microscopy (SEM) can be applied to examine the shape, size, and morphology of the polymers. Additionally, SEM-EDX (energy-dispersive X-ray spectroscopy) analysis was used to identify the type of atoms present in the functionalized copolymers at a depth of 100-1000 nm from the surface. SEM-EDX provides information regarding the elemental distribution on the sorbent by elemental mapping of each component.  
For example, [PERSON] et al. examined the morphology of particle surface and cross-section for selected magneti10 MAG-SGE60 and 10 MAG-SGE60-data samples by SEM analysis (Figure 5) [130]. The three-dimensional porous structure of the 10 MAG-SGE60E-data, composed of a large number of globules interconnected with channels and pores was visible on SEM image, which is consistent with reported values of porosity parameters, i.e., specific pore volume (0.99 cm\({}^{3}\)/g), specific surface area (59 m\({}^{2}\)/g) and pore diameter that corresponds to half of the pore volume (104 nm) [130].  
In addition, SEM-EDS analysis confirmed the presence of N atoms at the particle surface, indicating that the reaction with diethylene triamine occurs mostly on the particle surface. The iron nanoparticles were also predominantly present at the particle surface and embedded in the bulk to a lesser extent. The distribution of d  
Figure 5: SEM micrograph of cross-section (magnification 5000\(\times\)) and TEM micrograph (inset) of 10 MAG-SGE60-data.  
throughout the gray copolymer matrix is visible from the TEM image, confirming magnetite incorporation in a macroporous polymer structure.  
EDS analysis of PGME-10/12-data with sorbed Cu(II), Cd(II), and Pb(II) ions performed by [PERSON] et al., showed a significantly higher amount of Cu(II) and Pb(II) on the interior surface of the particles, supporting the significance of intra-particle diffusion as the controlling step of Cu(II), Cd(II), and Pb(II) sorption by PGME-data [118].
# A Novel 1-D CCANet for ECG Classification
## 4 Experiments
### Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **OACC** & **MACC** & **SPE** & **SENS** & **PPV** & **AVG** \\ \hline DL-CCANet [20] & 95.25 & 99.40 & 99.60 & 94.60 & 96.30 & 97.03 \\  & 94.01 & 98.31 & 98.85 & 90.89 & 94.11 & 95.23 \\ \hline
1-D ResNet & 91.88 & 98.92 & 99.36 & 90.11 & 90.14 & 94.08 \\  & 86.25 & 96.07 & 97.55 & 85.05 & 80.66 & 89.12 \\ \hline
1-D CCANet-SVD & 94.75 & 99.30 & 99.57 & 93.77 & 95.81 & 96.64 \\ (w/o SVD) & 93.60 & 98.17 & 98.80 & 90.63 & 93.33 & 94.91 \\ \hline
1-D CCANet-SVD & 95.40 & 99.39 & 99.62 & 94.43 & 96.54 & 97.08 \\ (w/o time-domain feat.) & 95.35 & 98.67 & 99.11 & 93.26 & 96.22 & 96.52 \\ \hline
1-D CCANet-SVD & 95.22 & 99.36 & 99.6 & 94.03 & 96.61 & 96.96 \\ (w/o 1D-spec) & 94.77 & 98.50 & 99.02 & 92.66 & 94.59 & 95.91 \\ \hline
1-D CCANet-SVD & 95.43 & 99.39 & 99.62 & 94.53 & 96.73 & 97.14 \\ (w/o ar) & 95.12 & 98.60 & 99.09 & 93.68 & 95.13 & 96.32 \\ \hline
1-D CCANet-SVD & 94.99 & 99.33 & 99.59 & 93.85 & 96.21 & 96.79 \\ (w/o stack) & 94.83 & 98.52 & 99.03 & 92.43 & 94.95 & 95.95 \\ \hline
**1-D CCANet-SVD** & **95.52** & **99.40** & **99.63** & **94.60** & **96.65** & **97.16** \\ (proposed) & **95.70** & **98.77** & **99.19** & **93.78** & **95.89** & **96.67** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results for various methods (%). For each method, first line reports results for the MIT-BIH database while the second line reports for the INCART database. OACC stands for Overall Accuracy; MACC for Mean Accuracy; SPE for Specificity; SENS for Sensitivity; PPV for Positive Predictive Values; AVG is the Average of the first 5 columns of the table. Best results are highlighted in boldface. Our method presents a few limitations. First, the CCANet technique requires the network to be fed simultaneously with all the training data, in order to determine the filters and this may cause a growth in the computational cost as the size of the training data increases. This limitation is common to all the CCANet-based architectures. In our study, we only considered 2-lead signals as input, it could be interesting to include more leads with the hope of increasing the performance, especially for classes with fewer samples. Following the work by [PERSON] et al. [20], our approach can be quite naturally extended to 3-lead signals. The number of layers might need to be reduced to compensate for the additional cost added by the addition of a third lead. Another interesting perspective would be to include some of the techniques we have used in our study in the original two-dimensional CCANet developed by [PERSON] et al. [20]. Indeed, Table 6 shows that the use of the SVD significantly increases the performance, without adding additional computational cost compared to the original method. Therefore, we could also expect promising results when using SVD in the original 2-D CCANet. Likewise, it could be interesting to analyze how the spectrogram features influence the performance of the 2-D CCANet and allow to make significant improvement in the field of abnormal heartbeat recognition.
## 4 Numerical Validations  
### Finite Element Model Description  
Numerical validations are carried out to confirm the wave-damage interaction characteristics and the efficacy of the proposed damage quantification method. The finite element model is established in COMSOL Multiphysics 5.6 as shown in Figure 4a. A 1.5 m-long steel rod with a radius of 3.5 mm is inspected. The material parameters of the steel are identical to those described in Section 2. For simplicity, axial-symmetric defects are introduced with two characteristic sizes as the width (\(w\)) and depth (\(r_{d}\)) as sketched in Figure 3. While different sizes of defects are considered in the model, their centers are fixed at 0.6 m from the left end. A shear edge load is applied at the left end for the excitation of the torsional wave. The maximum mesh size is 2 mm which ensures more than 20 elements per smallest wavelength under consideration. The shear displacement \(v\) of a receiving point on the edge is extracted to characterize the captured torsional wave.  
Figure 4: (**a**) Sketch of the finite element model; (**b**) the wavefield at 200 μs; (**c**) the wavefield at 350 μs.
# Endowed Polyphenols in Advanced Delivery Systems for Vaginal Infections
## 4 Vaginal Infections
### Trichomonal Vaginitis  
Trichomoniasis is a common global infection, with an estimated annual distribution of approximately 170 million cases of trichomonal vaginitis. In the United States, it is estimated that 2 to 3 million women contract symptomatic trichomoniasis each year [40].  
The disease is caused by the pathogenic parasite _Trichomonas vaginalis_, an anaerobic protozoa, which can cause evcriitis and urethritis [40]. The parasite is primarily transmitted through sexual activity, causing around 70% of male sexual partners of infected women to show its presence [41]. Thus, the use of non-barrier methods of contraception is significantly associated with trichomoniasis [42]. This parasite requires many essential nutrients for growth, and these are found in the vagina and vaginal secretions [43], e.g., an increase in iron salts (largely present in menstrual blood) facilitates overgrowth and clinical worsening of trichomoniasis symptoms [44]. The parasite can also obtain nutrients by digesting bacteria, such as through phagocytosis of _Neisseria gonorrhoeae_[45].  
Due to the vague character of the clinical presentation and its overlap with the clinical signs of other STIs, the diagnosis of trichomoniasis based solely on clinical features is insufficient. It ranges from asymptomatic, up to 50%, to severe acute inflammatory disease. Most common symptoms that affect up to half, and sometimes more, women that report having trichomoniasis include vaginal discharge, malodorous or not, pruritus, dyspareunia, and dysuria. Less common symptoms are lower abdominal discomfort, vulvar erythema, and \"strawberry cervix\" (punctate hemorrhages visible to the naked eye) [30].  
As previously mentioned, trichomoniasis is difficult to diagnose through clinical features, as they are not specific only to this infection. pH values greater than 4.5 are present in 90% of cases, and a fishy odor after the smell test is present in 50% of cases, but these are not specific symptoms of trichomoniasis. A better solution to differentiate this pathology from BV is through polymorphonuclear cells (PMNs). The ratio of PMNs to vaginal epithelial cells in BV is less than 1 in over 90% of cases, while for trichomoniasis, this ratio is greater than 1 in 75% of infections. Additionally, a microscopic evaluation is another valuable approach, as the direct observation of _Trichomonas_ can detect 60-80% of cases. However, the standard method for diagnosis is [PERSON]'s media culture, which can detect 95% of cases [46; 47].
## 5 Questionnaire and Quantification Method Approach  
### Overview and Purpose  
To complement the qualitative typology of technical debt types introduced in Section 4, this section presents a structured instrument for the quantitative assessment of technical debt in AI/ML competition platforms. Building upon the 18-category typology and stakeholder mapping previously established, the proposed questionnaire offers a systematic, replicable means for assessing debt visibility and severity from both organizer and participant perspectives.  
While the questionnaire quantifies technical debt as perceived by stakeholders, it is not meant to replace observational or statistical analysis, but rather to complement them. It functions as a lightweight, stakeholder-centric diagnostic tool, grounded in the literature-based typology presented in Section 4. Its purpose is twofold: (i) to foster awareness among developers and organizers of the specific types of technical debt most relevant to their role, and (ii) to provide a structured self-assessment mechanism that enables prioritization and reflection, especially in contexts where formal measurement may be difficult to implement.  
Beyond its use in empirical studies, the questionnaire also fills an important practical and educational gap. For example, in university-based competitions or academic settings, it enables students to engage early with software quality principles and the long-term implications of technical debt. In professional environments, it allows teams to reflect on technical decisions, structure mentoring and onboarding processes, and build debt-awareness into early development phases. This rationale underlies its presentation in this paper not merely as an auxiliary tool, but as an actionable interface between the conceptual framework and real-world practice.  
The assessment framework is structured around the 18 types of technical debt previously identified in RQ2 and assigns a corresponding set of evaluation questions to each stakeholder. The goal is to provide a practical means for AI competition platforms to detect and quantify areas where debt may be accumulating, and to enable targeted intervention. The next subsection details the structure of the questionnaire, followed by examples of its application in Section 5.3.  
The questionnaire is intended as a dynamic and iterative tool. Based on feedback received during preliminary rounds, we refined the instrument prior to this submission, reducing the number of items from 68 to 60 while preserving full typological coverage. This updated version, now reflected in Appendix A, improves clarity, reduces redundancy, and lowers cognitive load, without compromising the representativeness of the 18 (plus 1) technical debt categories. This revision reflects the instrument's adaptability and its commitment to practical usability in real-world contexts.
## 2 Materials and Methods
### Synchrotron Radiation microCT Examination  
The microCT experiments were performed at the SYRMEP beamline of ELETTRA Synchrotron Facility (Basovizza-TS, Italy) using the following settings: 1800 projections, each with 0.4 s exposure time, over a total range of 1800\({}^{\circ}\); pink beam with peak energy at \(\sim\)20 keV; sample-detector distance at \(\sim\)65 mm, resulting in (2.5 \(\upmu\)m)\({}^{3}\) isotropic voxel size in reconstructed images. Because of the coherence characteristics of the synchrotron beam, the recorded radiographs included phase contrast signals. The approach was based on discrimination between attenuation properties, related to the absorption index \(\beta\), and refractive index decrement \(\delta\) of the index of refraction \(n=1-\delta+i\beta\) in the newly-formed mineralized bone inside the defect. The reconstruction was performed using [PERSON]'s method [49], where the phase is retrieved by assuming that the \(\delta\beta\) ratio is constant. This ratio was set to 100 in the present investigation.  
The VG Studio MAX 1.2 (Volume Graphics, Heidelberg, Germany) software allowed to produce 3D images where mass density differences within samples translate into different peaks in the gray-level scale, corresponding to the different phases. The threshold of the histograms was manually set to 117 (8 bit scale: 0-255) for all the samples, separating the mineralized bone from soft tissues and scaffold phases. For each biopsy, two 3D subvolumes were analyzed: the former (test), with dimensions of \(1.25\times 1.25\times 0.25\) mm\({}^{3}\), was included in the defect bulk and the latter (ctr), with identical dimensions, in the same frontoparietal area but outside the defect.  
We exploited the reconstructed refractive index n distribution [50], which is linearly related to mass density, to study the relative bone mass density distribution (MDD\({}^{\mathrm{r}}\)). The MDD\({}^{\mathrm{r}}\) parameters were calculated within the mineralized domain inside and outside the defect. We could not retrieve the absolute values of bone mass density (proportional to calcium concentrations--Ca weight %) because \(n\) would have possibly been biased on account of the chosen constant ratio \(\delta\beta\)[51]. However, since the different subvolumes were identical in terms of size and had a similar composition, the relative difference in mass density distribution between them could be appreciated; thus, hereinafter the superscript \(r\) will denote relative values for all the parameters. Following the Roschgger approach [51], five parameters were extracted using the PeakFit software (Systat Software, San Jose, CA): the most frequent relative mass density value (MDD\({}^{\mathrm{r}}\)peak), the mean relative mass density (MDD\({}^{\mathrm{r}}\)mean), the full width at half maxima of the distribution (MDD\({}^{\mathrm{r}}\)fwhm), the 0.5 th (MDD\({}^{\mathrm{r}}\)low) and the 99.5 th (MDD\({}^{\mathrm{r}}\)high) percentiles.