esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
In recent years, personalized learning has gained increasing attention in the field of education. This approach tailors learning environments to individual student's needs and learning states, overcoming the limitations of traditional one-size-fits-all education models [1, 2]. In particular, the integration of digital technology and artificial intelligence (AI) in learning analytics presents a significant opportunity to enhance educational effectiveness by providing personalized content and tailored learning plans [3]. Previous studies have demonstrated that personalized learning positively impacts student motivation and academic performance. However, a major challenge remains: the lack of technological tools for effectively implementing such adaptive learning systems [4]. Despite advances in learning analytics, existing analysis systems have several limitations. First, as the complexity and volume of data increase, system performance tends to degrade [5]. Traditional machine learning models, particularly those based on simple statistical methods, often fail to capture nonlinear relationships within the data, making it difficult to conduct a precise analysis of students' learning states [6]. Second, many existing systems struggle to provide detailed, individualized feedback. In many cases, their analyses result in only basic statistical summaries, failing to offer truly customized learning solutions [7, 8, 9]. As a result, such systems lack the capability to provide educators with insights tailored to each student's unique learning progress. To address these challenges, this study proposes a personalized learning feedback system based on elementary school mathematics diagnostic assessment data. This system analyzes students' learning states and error patterns while providing real-time, customized feedback. Unlike conventional approaches that rely on Logistic Regression, Support Vector Machines (SVM), LightGBM, or transformer-based models such as BERT, this study focuses on overcoming these models' limitations by developing a scalable and efficient learning analytics system. The proposed system combines Term Frequency-Inverse Document Frequency (TF-IDF) for text vectorization and eXtreme Gradient Boosting (XGBoost) for precise classification of learning errors, ensuring high accuracy and rapid processing speeds for real-world educational applications. Additionally, this study quantitatively evaluates the impact of data size on model performance. It demonstrates that the proposed system maintains stable performance even with smaller datasets while effectively scaling to handle larger data volumes. By efficiently processing complex educational data and analyzing students' individual learning patterns, the system enables educators to establish effective learning plans instantly. Moreover, this research highlights the potential for automated educational data analysis and lays the groundwork for expanding the system to other subjects and grade levels in future applications.
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 3 Methodology  
Few of the algorithms in Table 1 pay attention to fusing bilateral information of images, which is the focus of this paper. In the following paper, lowercase letters denote scalars, boldface lowercase letters denote vectors and boldface uppercase letters denote matrices. We first review related algorithms, i.e., PCA, 2 DPCA, A2 DPCA and \((2\text{D})^{2}\)PCA, in order. Then, the proposed DB2 DPCA algorithm is described and its relationships with the four related algorithms are discussed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Bibliography** & **Algorithm** \\ \hline Bilateral algorithms & [PERSON] et al. [21] & \((2\text{D})^{2}\)PCA \\  & [PERSON] et al. [22] & Generalized 2 DPCA \\  & [PERSON] et al. [23] & RC2 DPCA \\ Subimages & [PERSON] et al. [24] & RCM-2 DPCA \\  & [PERSON] et al. [25] & ESIMPCA, EFLPCA \\  & [PERSON] et al. [26] & Bi-ESIMPCA, Bi-EFLPCA \\ Robust and sparse modelling & [PERSON] et al. [27] & 2 DPCA-L1 \\  & [PERSON] et al. [28] & non-greedy 2 DPCA-L1 \\  & [PERSON] et al. [29] & 2 DPCA-\(f_{1}\) \\  & [PERSON] and [PERSON] [30] & 2 DPCA-\(f_{1}\)-S \\  & [PERSON] [31] & G2 DPCA \\ Rotational invariance & [PERSON] et al. [34] & \(R_{1}\)-2 DPCA \\  & [PERSON] et al. [35] & \(F\)-2 DPCA \\  & [PERSON] et al. [36] & Angle-2 DPCA \\  & [PERSON] and [PERSON] [37] & Area-2 DPCA \\  & [PERSON] et al. [38] & Cos-2 DPCA \\  & [PERSON] et al. [39] & ROMCA-2 DPCA \\  & [PERSON] et al. [40] & ORPCA \\  & [PERSON] et al. [41] & \(\ell_{2,p}\)-2 DPCA \\  & [PERSON] et al. [42] & GC-2 DPCA \\  & [PERSON] et al. [43] & \(F_{p}\)-2 DPCA \\  & [PERSON] et al. [44] & BA2 DPCA \\  & [PERSON] et al. [45] & \(\ell_{2,p}\)-SB-2 DPCA \\ Color image processing & [PERSON] et al. [46] & GC-2 DPCA \\  & [PERSON] et al. [47] & 2D-QPCA \\  & [PERSON] and [PERSON] [48] & QRR-2D-QPCA \\  & [PERSON] et al. [49] & \(F\)-2D-QPCA \\  & [PERSON] et al. [50] & G-2D-QPCA \\  & [PERSON] et al. [51] & R-2 DPCA \\ Nuclear norm & [PERSON] et al. [52] & N-2 DPCA, NB-2 DPCA \\ Stiefel manifold & [PERSON] et al. [53] & RPSGD-S2 DPCA \\ Human learning mechanism & [PERSON] et al. [54] & SP2 DPCA \\ Lanczos algorithm & [PERSON] et al. [55] & Lanczos-2 DPCA \\ Deep learning model & [PERSON] and [PERSON] [56] & 2 DPCA-Net \\  & [PERSON] et al. [57] & L1-2 DPCA-Net \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recent advances of 2 DPCA.
# An Algorithm for Nutrient Mixing Optimization in Aquaponics
## 3 Results
### Simulation Studies
#### 3.1.2 Minimizing Error to Reference under Optimal Resource Usage  
The second simulation study aims to improve the resource management of the mixing tank system in comparison to the previous scenario. To achieve this, the balancing weights of the objective function are now set to their respective values given in Equation (9). Furthermore, the weights \(\omega_{\text{fertil}}\) are set to be equal to one and the weights of the water sources are set as follows:  
\[\omega_{\text{water}}=\big{[}0;\ 1;\ 1;\ -10\big{]}^{\top}, \tag{27}\]  
which strongly encourages the use of PW while the use of RW, TW, and fertilizers is discouraged. The respective weight of FW is set to zero because the consumption of this source was already high in the first scenario. The results of the optimization are shown in Figure 4 and Table 4.  
Due to the penalization of the use of fertilizers, RW, and TW, the consumption of all three resources was greatly reduced. RW consumption was reduced by 53.3%, TW usage was reduced by 100%, and fertilizer consumption was reduced by 36.4%. The main source of water remains the FW, with a consumption of 519.071 L. However, the PW now also provides a substantial share of the total water used. The consumed volume is 210.401 L and therefore 287.7% higher than in the first scenario.  
Figure 3: Resulting normalized concentrations of nutrients in the solution when only the reference error is minimized.  
The adequate consumption of PW was only possible due to a high rewarding weight in the objective function. The main reason for the limited use of this resource is the high sulfate and phosphate concentrations, which greatly exceed the reference value for both nutrients. This can also be seen in in Figure 4, where the post-mixing concentrations are presented. The use of PW resulted in high concentrations of PO4\(-\)P (32.4% higher than the reference). Generally, the new mixing procedure focused on improving resource consumption leads to a higher error--compared to the first scenario--between the nutrient concentrations in the post-mixing solution and the reference value (average relative error 7.7%).
## 2 Results and Discussion
### Antibacterial Activity  
The antibacterial activity of the synthesized compounds (**4**, **5**, **6**, **10**, **11** and **12**) was tested by the filter paper method (Figure 4). The reference solution did not show antibacterial activity for the bacteria tested. As shown in Figure 4, six compounds showed wide antibacterial activity, especially for colon bacillus, typhoid bacillus, and _Pseudomonas aeruginosa_. However, they all showed very weak sensitivity for hay bacillus and _Staphylococcus aureus_, which could be ignored. The antibacterial activity of compound **6** and **12** was especially strong in the synthesized compounds. The above results could provide reference data for further study of the antibacterial activity of acylhydrazone based on 1,10-phenanthroline-5,6-dione and 9-anthracene derivatives.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Anion** & **AcO**\({}^{-}\) & **H\({}_{2}\)PO\({}_{4}\)\({}^{-}\)** & **F**\({}^{-}\) \\ \hline \(K_{s}\) (**6**) & \((1.83\pm 0.60)\times 10^{7}\) & \((1.81\pm 0.32)\times 10^{4}\) & \((1.43\pm 0.02)\times 10^{3}\) \\ \(K_{s}\) (**7**) & ND & ND & ND \\ \(K_{s}\) (**8**) & ND & ND & ND \\ \(K_{s}\) (**9**) & \((1.26\pm 0.09)\times 10^{3}\) & \((4.45\pm 0.45)\times 10^{2}\) & ND \\ \(K_{s}\) (**10**) & ND & ND & ND \\ \(K_{s}\) (**11**) & \((4.12\pm 0.10)\times 10^{4}\) & ND & ND \\ \(K_{s}\) (**12**) & ND & ND & ND \\ \hline \end{tabular} ND: The spectrum change little and the binding constant cannot be calculated.  
\end{table}
Table 1: _Cont._  
Figure 4: Antibacterial circle for compounds **4**, **5**, **6**, **10**, **11**, and **12**; a: compared with **4**, \(p<0.05\); b: compared with **4**, \(p<0.01\); c: compared with hay bacillus, \(p<0.05\); d: compared with hay bacillus, \(p<0.01\); ad: compared with **4**, \(p<0.05\), and compared with Bacillus \(p<0.01\); bd: compared with **4**, \(p<0.01\), and compared with Bacillus \(p<0.01\). Red column: hay bacillus; green column: _Staphylococcus aureus_; blue column: colon bacillus; light blue: typhoid bacillus; pink column: _Pseudomonas aeruginosa_.
## 2 Related Works  
### Automatic Text Summarization  
Automatic text summarization is a crucial task in the field of natural language processing (NLP), and it has received a significant amount of attention from researchers in recent years. Over the years, a range of methods and models have been proposed to improve the quality of automatic text summaries. In the early days of NLP research, traditional approaches to text summarization were based on sentence ranking algorithms that evaluated the importance of sentences in a given text. These methods used statistical features, such as frequency and centrality, to rank sentences and select the most important ones to form a summary [6; 7; 8].  
With the advent of machine learning techniques in the 1990s, researchers have applied these methods to NLP to improve the quality of summaries. In automatic text summarization, this is mostly considered a sequence classification problem. Models are trained to differentiate summary sentences from non-summary sentences [9; 10; 11; 12]. These methods are referred to as extractive, as they essentially extract important phrases or sentences from the text without fully understanding their meaning. Thanks to the tremendous success of deep learning techniques, many extractive summarization studies have been proposed based on techniques including the encoder-decoder classifier [13], recurrent neural network (RNN) [14], sentence embeddings [15], reinforcement learning, and long short-term memory (LSTM) network [16].  
Moreover, the development of deep learning has given rise to a method called abstract summarization. Abstract summarization has improved significantly and has become a crucial area of research in the NLP field. Researchers have made remarkable progress in this field by leveraging deep learning techniques, such as RNN [3], LSTM [17], and classic seq2 seq models [4; 5].  
With the introduction of the transformer architecture in 2017 [18], transformer-based models have significantly outperformed other models in many NLP tasks. This architecture has been naturally applied to the text summarization task, leading to the development of several models based on pre-trained language models, including BERT [19], BART [20], and T5 [21]. These models have demonstrated remarkable performance on various NLP tasks, including text summarization.
## 3 Problem Formulation
### Constraints  
#### 3.3.1 Electrical Power and Thermal Power Constraints  
As for the optimal energy routing path design, there are some constraints that should be satisfied. Firstly, the balance equation between energy users and energy suppliers should be satisfied as follows:  
\[\sum_{i=1}^{N_{CHP}}P_{CHPi}\eta_{ix}+\sum_{i=1}^{N_{PVi}}P_{PVi}\eta_{ix}+P_{ Grid}\eta_{ix}+(P_{ch}-P_{dis})\eta_{ix}-\sum C_{i-j}^{loss}=P_{load}, \tag{18}\]  
where \(P_{ch}\) and \(P_{dis}\) are charging and discharging power of battery. What is worth mentioning, as the main grid is used as a supplementary device in the proposed system, the value of \(P_{Grid}\) is zero under normal condition.  
\[\sum_{i=1}^{N_{CHP}}H_{CHPi}\eta_{ix}+\sum_{i=1}^{N_{CB}}H_{GBi}=H_{load}, \tag{19}\]  
In addition to equality constraints, considering the capacity and the upper and lower bound of the device, some inequality constraints must be satisfied at the same time.  
\[H_{i-j}^{\min}\leq H_{i-j}\leq H_{i-j}^{\max}, \tag{20}\]  
\[0\leq P_{PVi}\leq P_{PVi}^{\max}, \tag{21}\]\[0\leq P_{chi}\leq P_{chi}\leq P_{chi}^{\max}, \tag{22}\] \[0\leq P_{disi}\leq P_{disi}^{\max},\] (23) \[P_{CHPi}^{\min}\leq P_{CHPi}\leq P_{CHPi}^{\max},\] (24) \[H_{CHPi}^{\min}\leq H_{CHPi}\leq H_{CHPi}^{\max},\] (25) \[0\leq H_{GBi}\leq H_{GBi}^{\max},\] (26) \[E_{bati}^{\min}\leq E_{bati}\leq E_{bati}^{\max}, \tag{27}\]  
where \(P_{i-j}^{\max}\), \(P_{PVi}^{\max}\), \(P_{chi}^{\max}\), \(P_{disi}^{\max}\), \(P_{CHPi}^{\max}\), \(H_{i-j}^{\max}\), \(H_{CHPi}^{\max}\) and \(H_{GBi}^{\max}\) are the upper limits of the output power of devices, while those with the superscript \"\(\min\)\" are the lower limits of the output power of devices. \(E_{bati}\) stands for the capacity of the \(i\)\(th\) battery. What is worth mentioning, for the sake of security reasons and prolonging the life length of the battery, the battery is partially discharged. Therefore, the lower limit of battery is restricted to 20% of its full capacity.
## 6 Example Analysis
### Decomposition of Original Data  
The input historical data contain module temperature, radiance, and PV output data, some of which are shown in Figure 6. Due to the intermittent nature of PV output, there are many zero values in the data, and there will be an infinite number of outliers when calculating the MAPE value, so the points in the dataset where the PV output is zero are eliminated. And because there are some differences in the number of units and orders of magnitude between different feature quantities, the direct input into the model will have an impact on the model training effect, so first of all, we need to normalize the dataset, mapping to the \([0,1]\) range.  
\[X_{\text{normized}}=\frac{X_{i}-X_{\text{min}}}{X_{\text{max}}-X_{\text{min}}} \tag{37}\]  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Population Size & 3 \\ Maximum Number of Iterations \(K_{\text{max}}^{it}\) & 20 \\ Optimization Lower Bound \(lb\) & \([50,50,0.001]\) \\ Optimization Upper Bound & \([300,300,0.01]\) \\ Number of Attention Heads & 4 \\ Dropout & 0.2 \\ Number of Hidden Layer Units & 204 \\ Epoch & 300 \\ Initial Learning Rate & 0.0087 \\ Regularization Coefficient & 0.001 \\ \hline \end{tabular}
\end{table}
Table 1: Model parameter settings.  
Here, \(X_{\text{normized}}\) represents the normalized value. \(X_{\text{max}}\) and \(X_{\text{min}}\) are the maximum and minimum values of the input features, respectively. \(X_{i}\) is the \(i\)-th value of the input feature. The distribution of certain original input features and photovoltaic output data after normalization is illustrated in Figure 7.  
The photovoltaic power generation time series exhibits characteristics such as nonlinearity and volatility, which increase the complexity of predictions when directly input into the model. The data are also highly susceptible to noise, further affecting prediction  
Figure 6: Partial unprocessed original input data.  
Figure 7: Partial normalization of the original input data.  
accuracy. To address these issues, after removing zero values from the original photovoltaic output data, VMD is applied. The second-order penalty factor \(\alpha\) of the VMD optimized by GWO is 2376, and the modal number \(K\) is 5, resulting in five photovoltaic subsequence datasets. A portion of the resulting waveforms is presented in Figure 8.
# An Integration Framework for the Inpainting and Colorization of Arbitrary Masked Grayscale Images
## 3 Methodology  
### Framework Overview  
Our proposed framework for the inpainting and colorization of arbitrarily masked grayscale images is structured into three main phases: mask generation, image inpainting, and colorization. Figure 1 illustrates the end-to-end pipeline and the data flow through each component.  
In the first phase, a given test input image is processed by the segment anything model (SAM) [1] to generate an arbitrary-shaped mask. The output of this phase is a mask  
Figure 1: Proposed framework overview. The pipeline first uses the SAM to detect unknown masks from a given grayscale image. The identified masked regions are then passed into the MAT model for inpainting to reconstruct the missing areas. Finally, the DDColor model is trained (or fine-tuned) and applied to colorize the restored grayscale image, resulting in a fully colorized and artifact-free output.  
that identifies regions of the image that require restoration. Next, the masked grayscale image and the generated mask are passed into the image inpainting stage. Here, the mask-aware transformer (MAT) [2] model fills in the missing or corrupted regions defined by the mask, reconstructing plausible structural and textural content to produce an inpainted grayscale result.  
Following the successful inpainting step, the third phase involves mapping the restored grayscale image into a visually coherent color space using the dual-decoder colorization model (DDColor) [3]. Prior to inference, DDColor undergoes a training process where it learns appropriate color distributions and mapping parameters from a given training dataset. After training, the inference module of DDColor transforms the inpainted grayscale image into a fully colorized output, delivering natural and contextually consistent hues.  
Overall, the integrated pipeline leverages the following strengths of each component: SAM for flexible mask generation, MAT for the robust inpainting of arbitrary regions, and DDColor for realistic colorization. By combining these modules into a unified system, our approach efficiently handles complex restoration tasks while producing high-fidelity, colorized images.
# Fault Detection in Power Transformers Using Frequency Response Analysis and Machine Learning Models
## 3 Methodology
### Theoretical Rationale for the Chosen Models  
The selection of machine learning models for fault classification in power transformers was guided by both theoretical considerations and the nature of Frequency Response Analysis (FRA) data. Decision Tree and K-Nearest Neighbors (KNN) models were specifically chosen for their ability to handle the unique challenges presented by FRA data, including its multi-dimensional nature and frequency-dependent characteristics. Each model was evaluated based on its interpretability, computational efficiency, and suitability for fault diagnosis tasks.  
Decision Trees were selected for their ability to partition the feature space into hierarchical decision regions. This model excels in handling non-linear relationships, making it ideal for analyzing FRA data, where frequency response patterns can vary significantly between different fault types. The inherent interpretability of Decision Trees also makes them valuable in diagnostics, as experts can trace classifications back to specific feature thresholds, providing insights into the physical nature of the detected faults. For instance, insulation breakdown faults may present distinct response amplitudes at specific frequency bands, which the Decision Tree model can isolate and use for accurate classification.  
KNN, an instance-based learning model, was chosen for its simplicity and effectiveness in environments where data points naturally cluster based on fault types. FRA data often exhibits such clustering, with samples from similar fault conditions forming distinct patterns in the feature space. KNN classifies new data points by comparing them to their nearest neighbors, making it highly effective in scenarios where fault signatures share similarities within localized frequency ranges. To address potential challenges, such as the high dimensionality of FRA data and class imbalance, the study employed Neighborhood Component Analysis (NCA) for feature transformation, enhancing KNN's efficiency by focusing on the most informative dimensions.
# Adaptive Cruise Control for Cut-In Scenarios Based on Model Predictive Control Algorithm
## 2 Cut-In Scenario Classification and Cut-In Possibility Quantification
### Quantification of Cut-In Possibility Based on Lateral Distance and Lateral Relative Velocity
#### 2.3.3 Quantification of Cut-In Possibilities  
For the quantification of the cut-in possibility of vehicles on both sides, lateral distance \(P_{cutin\_d_lat}\) and lateral relative velocity \(P_{cutin\_v_relt}\) are comprehensively considered. Because the lateral distance and lateral relative velocity have different influences on the cut-in possibility of a nearby vehicle under different road scenarios, the lateral distance \(P_{cutin\_d_lat}\) and the lateral relative velocity \(P_{cutin\_v_relt}\) are set with different weights to adapt to the quantification of the cut-in possibility in different road scenes, so the cut-in possibility of the cut-in vehicle is finally expressed as  
\[P_{cutin}=w_{1}P_{cutin\_d_lat}\cdot w_{2}P_{cutin\_v_relt}\;\;w_{1},w_{2}\in[ 0,1] \tag{9}\]  
where \(P_{cutin}\) is the cut-in possibility of the side car, \(w_{1}\), \(w_{2}\) is the weight.  
Figure 10 shows the quantified cut-in possibility of nearby vehicles under different weights. The maximum value of cut-in probability \(P_{cutin}\) is 1, and the extreme value changes with different weights, so that the road scenarios factor is taken into account in the quantification of the cut-in behavior of a nearby vehicle, which avoids the wrong estimation of cut-in probability when facing changing road scenarios.  
In cut-in scenarios, as a nearby vehicle gradually cuts in, the cut-in possibility also changes at any time. In the host vehicle control, the generated cut-in probability value is used as a reference variable for the MPC-ACC, so that it can control the state of the host vehicle in a coordinated way to adapt to the cut-in vehicle according to the changing cut-in possibility. Finally, under the condition that the nearby vehicle safely cuts in, the host vehicle can achieve a smooth target conversion process.  
Figure 10: The cut-in possibility of under different weights: (**a**) the cut-in possibility under the weights of a straight lane. (**b**) The cut-in possibility under the weights for a curved lane.