esa-sceva/satcom-chunk-collection huggingface dataset:
# Graphs Regularized Robust Matrix Factorization and Its Application on Student Grade Prediction
## 1 Introduction
proposes to use \(L_{1}\)-norm instead of \(L_{2}\)-norm to enhance the robustness [26; 27; 28]. Besides, we often have massively available side information data in real-world applications. [PERSON] et al. proposes a method of graph regularized alternating least squares (GRALS) to integrated two graphs from the side information data of movies and viewers [29]. More specifically, in the real context of high education, the data set usually has the following properties: (1) The grade matrix is heavily lost for course selection and corrupted by some human factors. (2) The students with similar backgrounds are likely to have similar performance in a course [30]. For example, two students both have more exercises in computer programming, and then they may both obtain a perfect grade at their course of \(C\) language with a high probability. (3) The courses with similar knowledge tend to give rise to a similar grade for a student. For instance, \(C\) Language is similar with Data Structure while \(C\) Language is not similar with History, thus student who is good at \(C\) Language is likely to have good performance in Data Structure but not necessarily History. To this end, we put forth a novel MF method, called double graph regularized robust matrix factorization (GRMF), following by applying GRMF for SGP as shown in Figure 1. GRMF not only uses the robust loss function from RMF-MM but also integrates two side information graphs constructed using the background data of students and courses. The MM algorithm can effectively solve the resulting optimization problem. Two-folds contributions of our paper are summarized as follows:* We propose a new model of matrix factorization, dubbed Graph regularized Robust Matrix Factorization (GRMF), by considering both the robustness to data pollution and the side information from background descriptions. * We design a workflow by applying GRMF on the problem of SGP, shown in Figure 1, where the real-world data set is collected from our university. The rest of this paper is organized as follows--in Section 2, we formulate the problem of SGP, followed by brief reviewing the MF technique. We present GRMF in Section 3 and the GRMF algorithm in Section 4. Section 5 shows the experimental results on movie rate prediction, image recovery, and SGP. Section 6 finally concludes this paper.
## 3 Results and Discussion
### Oxide Surface Characterization
#### 3.1.5 Oxide Molecular Structure Analyses  
The representative FTIR scans for each oxide group are illustrated in Figure 5. Each oxide group revealed intensive absorption peaks for PO4\({}^{3-}\) (1050 cm\({}^{-1}\)), which agreed well with the hydroxyapatite and \(\alpha\)-tricalcium phosphate formation on the surfaces, as shown in the XRD analyses (Figure 1) [24; 61]. A weak broadband O-H representing the bending mode of adsorbed water was also present between 3000 and 3600 cm\({}^{-1}\)[24; 61; 62]. Synthetic hydroxyapatite coatings typically show a characteristic FTIR hydroxyapatite OOH\({}^{-}\) peak at 3570 cm\({}^{-1}\)[61; 62]. In contrast, the anodized hydroxyapatite coatings in the present study revealed a weak OH\({}^{-}\) peak in combination with high-intensity CO3\({}^{2-}\) absorption substitution peaks at 875, 1450, and 1570 cm\({}^{-1}\)[24; 61; 62]. These CO3\({}^{2-}\) substitution peaks confirm the presence of bone-like apatite within each anodized oxide group [61]. Biological apatite or bone-like apatite is often called carbonated hydroxyapatite because it contains carbonate substitutions that may cause changes in the lattice parameters; crystallinity; crystal symmetry; thermal stability; morphology; solubility; and physical, chemical, and biological characteristics [19; 63; 64; 65; 66]. The characteristic groups most commonly shown on the FTIR spectrum for carbonated apatite include PO4\({}^{3-}\), OH\({}^{-}\), CO3\({}^{2-}\), and HPO4\({}^{2}\)  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Elements**} & \multicolumn{4}{c}{**Titanium Alloys**} \\ \cline{2-5}  & **CPTi (At.\%)** & **TiMo \(\beta\) (At.\%)** & **TiMo \(\alpha+\beta\) (At.\%)** & **TAV (At.\%)** \\ \hline Titanium & 0.52 \(\pm\) 0.42 & 0.05 \(\pm\) 0.02 & 0.05 \(\pm\) 0.05 & 0.56 \(\pm\) 0.83 \\ Aluminum & & & & 0.03 \(\pm\) 0.04 \\ Oxygen & 53.40 \(\pm\) 0.92 & 51.58 \(\pm\) 1.35 & 52.19 \(\pm\) 2.51 & 57.00 \(\pm\) 0.99 \\ Calcium & 14.30 \(\pm\) 1.10 & 11.41 \(\pm\) 1.45 & 11.65 \(\pm\) 1.73 & 14.78 \(\pm\) 0.76 \\ Phosphorus & 6.42 \(\pm\) 0.25 & 5.25 \(\pm\) 0.50 & 5.15 \(\pm\) 0.52 & 6.96 \(\pm\) 0.54 \\ Magnesium & 0.63 \(\pm\) 0.17 & 1.13 \(\pm\) 0.22 & 0.92 \(\pm\) 0.25 & 0.75 \(\pm\) 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 2: EDS surface chemistry of each oxide group.  
Figure 4: Representative surface Ca/P ratios for each oxide group. The EDS-derived surface Ca/P ratios for each oxide group were found to be approximately 2.  
Recently, some research groups have successfully formed hydroxyapatite on titanium surfaces directly using anodization [15; 24; 29; 30]. Most of these studies found that their surfaces had a disturbance in the characteristic OH\({}^{-}\) hydroxyapatite absorption peak in FTIR analysis due to the CO\({}_{3}\)\({}^{2-}\) substitution resulting from the formation of carbonated apatite similar to the oxides in the present study [24; 30].
# QoE Modeling on Split Features with Distributed Deep Learning
## 4 Effect of Video Content on QoE
### Training with or without Content Features  
We first train an XGBoost [34] (a robust widely used tree-based ensemble ML algorithm) both with and without the specific _TI_ and _SI_ features. Next, the model accuracy performance of each case is compared. We observe that the inclusion of content-related features, i.e., _TI_ and _SI_, makes the model a more powerful predictor with a rather high \(R^{2}\) score (an increase from 0.74 \(\pm\) 0.01 to 0.80 \(\pm\) 0.01) and low MAE (a decrease from 6.16 \(\pm\) 0.07 to 5.41 \(\pm\) 0.07), indicating that content-related features are important QoE factors.
###### Abstract  
The detection of moving objects in images is a crucial research objective; however, several challenges, such as low accuracy, background fixing or moving, 'ghost' issues, and warping, exist in its execution. The majority of approaches operate with a fixed camera. This study proposes a robust feature threshold moving object identification and segmentation method with enhanced optical flow estimation to overcome these challenges. Unlike most optical flow Otsu segmentation for fixed cameras, a background feature threshold segmentation technique based on a combination of the Horn-Schunck (HS) and Lucas-Kanade (LK) optical flow methods is presented in this paper. This approach aims to obtain the segmentation of moving objects. First, the HS and LK optical flows with the image pyramid are integrated to establish the high-precision and anti-interference optical flow estimation equation. Next, the Delaunay triangulation is used to solve the motion occlusion problem. Finally, the proposed robust feature threshold segmentation method is applied to the optical flow field to attract the moving object, which is the. extracted from the Harris feature and the image background affine transformation model. The technique uses morphological image processing to create the final moving target foreground area. Experimental results verified that this method successfully detected and segmented objects with high accuracy when the camera was either fixed or moving.  
computer vision; moving object detection; image threshold; optical flow +
Footnote †: journal: Applied Sciences
## 4 Analysis of Experimental Results
### Structural Displacement Response  
The peak value of storey drift response for each working condition is summarized in Table 4. The values in brackets are the results from numerical simulation. Figure 9 shows the time history curve for the isolation layer displacement response in the test.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Earthquake**} & \multicolumn{4}{c}{**Fortification Intensity**} & \multicolumn{4}{c}{**Rare Intensity**} \\
**Magnitude** & & & & & & & & \\ \hline
**Earthquake Wave** & **El Centro Wave** & **Taft Wave** & **Tianjin Wave** & **El Centro Wave** & **Taft Wave** \\ \hline
**Control Strategy** & **CM** & **SM** & **CM** & **SM** & **CM** & **CM** & **SM** & **CM** & **SM** \\ \hline
**Isolation Layer** & **3.80** & **2.87** & **7.04** & **5.61** & **30.52** & **15.18** & **10.4** & **14.5** & **13.0** \\
**(3.6)** & **(2.93)** & **(8.52)** & **(9.3)** & **(25.1)** & **(15.2)** & **(11.7)** & **(18.5)** & **(15.3)** \\ \hline
1 st storey & 2.14 & 1.75 & 2.23 & 3.5 & 4.59 & 3.53 & 3.83 & 3.31 & 4.21 \\
2 nd storey & 1.38 & 1.22 & 1.03 & 1.74 & 3.91 & 2.76 & 2.53 & 1.57 & 2.18 \\
3 rd storey & 1.18 & 1.09 & 1.20 & 1.62 & 3.63 & 2.16 & 2.45 & 1.90 & 2.39 \\
4 th storey & 1.01 & 0.90 & 1.10 & 1.45 & 2.37 & 1.48 & 1.95 & 1.54 & 2.03 \\
5 th storey & 0.86 & 0.75 & 0.87 & 1.41 & 1.49 & 1.88 & 1.95 & 1.47 & 1.87 \\
6 th storey & 0.72 & 0.84 & 0.55 & 0.84 & 1.46 & 1.00 & 1.42 & 0.89 & 1.35 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Peak value of story drift under different intensities and seismic waves (mm).  
Figure 8: Sensors allocation scheme.  
As seen from Table 4, the maximum displacement of the isolation layer (30.52 mm) is only about 55% of displacement limit value for the rubber bearing (55 mm). Hence, the control system has the capacity to sustain greater seismic loading. In addition, the numerical calculation is shown to be in good accordance with the test measurements. It lays out a good foundation for further research on the design method associated with the control system.  
When the switch mode control is employed, the displacement response of the isolation layer is relatively small compared to continuity control mode. This is because the damper force outputs change only between the maximum and minimum values. When the isolation layer has a larger displacement response, the damper is directly applied with the maximum control force under the switch control mode, rather than continuously changing its value between the maximum and minimum damping force. The displacement of the isolation layer is small under the switch control mode, but the storey displacement corresponding to all the upper floors of the structure is relatively large due to the constrained base isolator deformations.
# A Deep Attention Model for Environmental Sound Classification from Multi-Feature Data
## 4 Experiment and Analysis
### Comparative Experiment  
#### 4.3.1 Experimental Results of Feature Map Parameters  
The two-dimensional convolution neural network calls for artificial extraction of manual features from the waveform, unlike the one-dimensional convolution neural network, which involves direct inputting of the waveform. The experimental parameters for extracting manual features have a great influence on environmental sound classification. In order to find the best feature map parameters, the accuracy of the network under different manual feature parameters was compared. The results with using different combinations of feature map are listed in Table 1.  
Figure 4: UrbanSound8K Official vs Unofficial.  
The best performance happens when the Log-Mel, phase, and time-frequency spectrogram are combined as the feature map input. Individually, as the feature input, the Log-Mel spectrogram behaves significantly higher than other types of feature maps. Comparing the results of the Log-Mel spectrogram, phase spectrogram and Log-Mel, phase, and time-frequency spectrogram, it can be seen that the phase spectrogram alone is not enough to support effective environmental sound recognition. Using the phase spectrogram in conjunction with another spectrogram can improve the network's recognition ability. Using the phase spectrogram and time-frequency spectrogram as complementary features of the Log-Mel spectrogram has the potential of achieving excellent results.  
This paper evaluates the influence of audio sampling frequency on environmental sound classification. With the Log-Mel, phase, and time-frequency spectrogram combined as the network input, comparative experiments were carried out at sampling frequencies of 8 KHz, 16 KHz, 44.1 KHz, and 48 KHz. The results are shown in Table 2. At the sampling frequency of 44.1 KHz, the proposed network gives the best results, scoring 97.25%, 89.00%, and 83.45% on the ESC10, ESC50, and UrbanSound8K datasets, respectively.  
Using the Log-Mel spectrogram, time-frequency spectrogram, and phase spectrogram as an input and at a sampling frequency of 44.1 KHz, this paper evaluates the influence of different parameters on environmental sound classification in short-time Fourier transform. The recognition differences are shown in Table 3 for processing the sound signal using STFT with different parameters. An optimal result is yielded when the frame length is 2048 points (43 ms), the frame shift is 1024 points (21 ms), and the number of transformation points N is 2048.
## 2 Assumptions of Our Target System and Challenge
### Challenge  
As described above, it is important to realize both collecting multiple periodic data from sensors within deadlines and transmitting unpredictable on-demand packets. Transmitting opportunity for downstream of IWSNs such as ISA100.11a or WirelessHART, is normally lower than that for upstream, even though industrial applications use polling-based protocols that need equal opportunities for downward packets and upward packets. Therefore, one of our challenge is to provide a communication method that controls both downward and upward network load in IWSNs. The communication scheme should also ensure sufficient responsibility of unpredictable requests and stability of periodic data collection. The second challenge of this paper is to provide a schedule which a central node normally generates uniform network load for periodic data collection by a polling-based communication protocol, in order to enable applications to transmit unpredictable packets effectively using the idle time of the IWSNs.
## 3 Results and Discussion
### Quantum Chemical Analysis  
The above measurements showed a good inhibition effect of TSCA on carbon steel and indicated that the inhibition effect depends on the adsorption film. To reveal the interaction between TSCA molecules and carbon steel, quantum chemical calculations were conducted with density functional theory. Figure 8 presents the density distributions of the highest and lowest occupied molecular orbitals (HOMO and LUMO) for the TSCA molecule. The highest occupied molecular orbitals are mainly distributed at O=C-N and C-O-H, and the lowest occupied molecular orbitals are mainly distributed at the thiadiazole ring. Table 4 gives the calculated HOMO energy (\(E_{\text{HOMO}}\)), LUMO energy (\(E_{\text{LUMO}}\)), and energy gap (\(\Delta E=E_{\text{LUMO}}-E_{\text{HOMO}}\)). Considering the high \(E_{\text{HOMO}}\) (\(-6.92\) eV), the O=C-N and C-O-H groups can easily donate electrons to iron atoms, which favors chemical adsorption. In addition, the low energy gap of 4.64 eV indicated a high inhibition efficiency of TSCA inhibitor [32].  
Figure 8: The frontier molecular orbital density distribution of the TSCA molecule: HOMO (**a**) and LUMO (**b**).  
Figure 7: Different adsorption isotherm plots for the adsorption of TSCA on the surface of carbon steel in 1 M HCl at 298 K from potentiodynamic polarization results: (**a**) [PERSON]’s isotherm, (**b**) [PERSON]’s isotherm, (**c**) [PERSON]’s isotherm.  
Some other parameters, such as electronegativity (\(\chi\)), global hardness (\(\gamma\)), and fraction of electrons transferred (\(\Delta N\)) can be derived from the following equations:  
\[\chi=-\frac{E_{\text{HOMO}}+E_{\text{LUMO}}}{2} \tag{8}\]  
\[\gamma=\frac{E_{\text{LUMO}}-E_{\text{HOMO}}}{2} \tag{9}\]  
\[\Delta N=\frac{\chi_{\text{Fe}}-\chi_{\text{TSCA}}}{2(\gamma_{\text{Fe}}+ \gamma_{\text{TSCA}})} \tag{10}\]  
The theoretical values of \(\chi_{\text{Fe}}\) and \(\gamma_{\text{Fe}}\) are 7 eV and 0 eV, respectively [51]. The calculated \(\Delta N\) value of 0.65 demonstrates the good chemisorption property and inhibition efficiency of TSCA for carbon steel [52].
## 4 Conclusions  
In this work, SVR models have been developed to predict the length, depth, and width of the defect images using the given GLCM features extracted from MFL images. Five different parameters that decide the performance of SVR have been considered and three various performance measures, RMSE, MAE, and \(R^{2}\) values, are taken into consideration to evaluate the performance of the SVR models. The MFO algorithm is implemented to find the best parameters of SVR models, and both DFO and PSO algorithms are used to compare the performance of the MFO algorithm. The normality test, probability, and residual plots have ensured that the results obtained using these algorithms are normally distributed and hence, accepted. The convergence, diversity, and spacing of performance measures are considered to evaluate the betterment of the functioning of the MFO with the DFO and PSO algorithms. Low convergence, a high value of diversity, and a low value of spacing reported at 34 iterations, 0.0685 and 0.0026, respectively, obtained with the MFO algorithm confirmed that it outperformed the DFO and PSO algorithms. The reported values of the three performance measures of SVR models, RMSE, MAE, and \(R^{2}\) values of responses L, D, and W using the MFO algorithm, are 0.0527, 0.0613, and 0.9999. These lower values in both RMSE and MAE and high values in \(R^{2}\) confirmed the performance of the MFO algorithm. Furthermore, in comparing the performance measures with the existing FFBP method, the proposed MFO-SVR method proved its effectiveness in responses L and W. The proposed SVR model is not suitable for a very large dataset, and all the features have been considered without finding the priority features. As a future work, this work can be extended further  
Figure 10: Comparison of performance measures (**a**) RMSE; (**b**) MAE (**c**) R\({}^{2}\).  
by adding the other additional features of GLSM extracted from the MFL image with data augmentation for different L, D, and W. Furthermore, features can be prioritized using the existing different algorithms, and based on that, the performance of the SVR models can be studied further.  
Conceptualization, M.V.A.W., S.R., R.C., M.S.K. and M.E.; data curation, M.V.A.W.; formal analysis, M.V.A.W.; investigation, M.V.A.W.; methodology, S.R. and M.E.; resources, R.C. and M.S.K.; software, S.R., R.C., M.S.K. and M.E.; visualization, M.V.A.W. and S.R.; writing--original draft, M.V.A.W. and S.R.; writing--review and editing, R.C., M.S.K. and M.E. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request through email to the corresponding author.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Dataset  
Given that the research objective focuses on Chinese homophone disambiguation, significant modifications were made to the structure and annotation scheme of the SIGHAN 2015 dataset to optimize speech error analysis. The original XML-based annotation framework was redesigned into JSON format to improve computational processing efficiency  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Model/Approach** & **Core Mechanism** & **Key Dataset** &
\begin{tabular}{c} **Tonal Lang.** \\ **Support** \\ \end{tabular} & **Limitations** \\ \hline Pre-training & \multirow{3}{*}{BERT-wwm} & Whole-word & Self-built & \multirow{3}{*}{\(\surd\) (Mandarin)} & Limited to \\ Enhancement & & & tokenization & & Homophone Corpus & word-level semantics \\ Frequency-Based & \multirow{3}{*}{Statistical Baseline} & High-frequency & \multirow{3}{*}{SIGHAN 2015} & \multirow{3}{*}{\(\times\)} & Fails on \\ Acoustic-Text Fusion & HMM + Phoneme & & & & homophores \\ Matrix & & & & & Sensitive to noise \\  & & & & optimization & \\  & & & Context-gloss & & \\ Cross-modal WSD & Bi-encoder WSD & & similarity (dual & WordNet, SemEval & \(\times\) (English) & \\  & & & BERT encoders) & & \\ LLM-Augmented & \multirow{3}{*}{GPT-3 + CLIP} & Zero-shot prompting & \multirow{3}{*}{WiT, WWSD} & \multirow{3}{*}{\(\times\)} & Unstable in \\  & & & & & low-resource \\ \cline{4-5}  & & & alignment & & languages \\ \cline{4-5}  & & & Context-aware sense & & Limited to \\ \cline{4-5} Dynamic Context & \multirow{3}{*}{ARES} & embeddings + & SemCor, WordNet & \(\times\) & pre-defined \\ \cline{4-5}  & & & knowledge fusion & & knowledge bases \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of homophone disambiguation approaches.  
and enhance compatibility with modern neural network architectures. Key modifications include (1) integrating error metadata into a unified \"wrong_ids\" index array to precisely locate homophone positions and eliminate redundant positional tag attributes; (2) introducing parallel \"original_text\" and \"correct_text\" fields to support contrastive learning paradigms; and (3) semantically normalizing error categories, ensuring that all error instances are genuine homophone replacement pairs (e.g., \"\"if(\"if(\")\" \(\rightarrow\) \"if(\")\") rather than generalized grammatical errors. Special attention was paid to maintaining phonetic equivalence constraints (allowing for tone variations in the same pinyin transcription) while excluding graphical similarity confusion. The modified dataset contains 280,924 annotated samples, divided into a training set (251,843 entries), a validation set (27,981 entries), and a test set (1100 entries). Data annotation follows the following rules: each sample includes the original text (containing homophone errors) and the correct text; error positions (wrong_ids) are marked using character-level offsets. Meanwhile, the percentage of corrected homophones is 70%.As shown in Table 3, we introduce the composition of the relevant dataset.
## 4 Spatial Positioning and Postural Estimation Method
### Fine Registration Process
The fine registration results for the other four components are shown in Figure 14b-e. To verify the effectiveness of the matching method, the fine registration results are obtained in different scenes. The proposed method is almost parameter-free. The only parameter to be considered is the threshold of the coincidence degree, i.e., the percentage of the matched points. Taking the magnetic system component as an example, the relationships between the threshold of the coincidence degree and the errors of the position and posture are illustrated in Figure 15. It can be observed that the errors decrease with the increase in the threshold. On the other hand, the experiment proves that a higher threshold takes a longer processing time. When the threshold is 95%, it takes more than 1.5 s per process, which is still acceptable for our application. Hence, in the following, the threshold is set to 95% to achieve the highest precision.