esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Ultrasonic GW System  
The experimental setup we used for measuring the ultrasonic GW is shown in Figure 2a. It consisted of a pulser/receiver (HIS2, Krautkramer Japan Co., Ltd., Tokyo, Japan) to excite ultrasonic transducers (GAMMA series, KB-Aerotech, Lewistown, Pennsylvania, USA) and an oscilloscope (WaveRunner 62 Xi, Teledyne LeCroy GmbH, Heidelberg, Germany) to display and acquire the receiving signals. The angle of incidence for generating the guided ultrasonic waves can be obtained using [PERSON]'s law. To minimize the noise received, ultrasonic simulation was performed, so that reception of the noise signal reflected inside the wedge was avoided as much as possible. As a result, Plexiglas wedges with an incident angle of 53\({}^{\circ}\) were fabricated and attached to the front side of ultrasonic transducers, of which the nominal frequency was 2.25 MHz. The width, length, and thickness of the 304 SS plates were 1200, 1200, and 3 mm, respectively.  
Five kinds of artificial defects were fabricated on the surface of the metal specimens by the electrical discharge machining (EDM) method, as shown in Table 1. The defects were placed between the ultrasonic transmitter (Tx) and the receiver (Rx), and three directions of propagation of the ultrasound GW were selected: 0\({}^{\circ}\) (vertical), 45\({}^{\circ}\), and 90\({}^{\circ}\) (horizontal) for the longest side of the defects, as shown in Figure 2b. All the ultrasonic GW signals were analyzed by the STFT algorithm according to their propagation angles.  
Two types of data sets were acquired: one for calibrating the linear discriminant model and the other for validating the newly developed model. The number of data for each group were 200 and 100, respectively. The calibrating data set consisted of 100 data from no-defect specimens and 100 data from with-defect specimens. The validation data set consisted of 50 data from no-defect specimens and 50 from with-defect specimens.  
\begin{table}
\begin{tabular}{c c} \hline
**Defect Number** & **Length \(\times\) Width \(\times\) Depth (mm)** \\ \hline \#1 & 10 \(\times\) 0.2 \(\times\) 0.3 \\ \#2 & 10 \(\times\) 0.4 \(\times\) 0.3 \\ \#3 & 10 \(\times\) 0.6 \(\times\) 0.3 \\ \#4 & 10 \(\times\) 0.2 \(\times\) 0.05 \\ \#5 & 10 \(\times\) 0.2 \(\times\) 0.5 \\ \hline \end{tabular}
\end{table}
Table 1: Dimensions of the five types of artificial defects.
# Gradient-Based Aerodynamic Optimization of an Airfoil with Morphing Leading and Trailing Edges
## 3 Results and Discussion
### Minimization of Drag by Morphing Trailing Edge  
The second application minimizes drag coefficient using a morphing trailing edge (mTE) locating at rear 30% of the chord. Design variables are vertical position, boat tail angle of the trailing edge and two extra CST coefficients.  
The same as the mLE case, we start the optimization from baseline airfoil. An optimized solution is obtained after 21 major iterations, which shows more difficulty finding optimal design than the previous case. The drag coefficient decreases to 120.21 counts,  
Figure 8: Comparison of results for the RAE 2822 optimization using a morphing leading edge. (**a**) Convergence history. (**b**) \(C_{p}\) distribution. (**c**) Airfoil shape.  
Figure 9: Mach contours for baseline and optimized RAE 2822 by using a morphing leading edge. (**a**) Baseline airfoil. (**b**) Optimized airfoil with a morphing leading edge.  
with a 7% drag reduction. The AoA in the optimized airfoil changes to 1.65 degrees to keep a lift coefficient of 0.725.  
Figure 10 provides the comparisons of pressure coefficient distribution and the shape between baseline and optimized airfoil. The boat tail angle of the optimized mTE is 16.4 degrees, 2 times of which at baseline airfoil. Consequently, increased camber near the trailing edge is achieved. A higher pressure difference between upper and lower at the trailing edge surface is observed, as shown in Figure 10a. A smaller angle of attack results in a lower pressure difference in the leading edge region. The shock wave and the resulting pressure discontinuity slightly moves from 50.0% to 51.6% of the chord length, and a gentler pressure drop after the shock wave is achieved.
## 3 Methods
### Experimental Environment Configuration, Training Setup, and Data Augmentation
#### 3.3.2 Training Setup  
Learning Rate: The initial learning rate (lr0) was set to 0.0001. During the first 2000 iterations, it gradually decreased from 0.1 to 0.0001 to ensure training stability. After this warm-up phase, the learning rate remained constant at 0.0001, facilitating stable and effective learning of data features.  
Optimizer: The Adam optimizer was used, with initial parameters set as follows: \(\beta 1=0.9\), \(\beta 2=0.999\), and \(\epsilon=1\times 10^{-8}\). Adam was chosen for its excellent performance in handling sparse gradients and large datasets.  
Batch size: A batch size of 8 was used during training. This setting ensured efficient GPU memory utilization while also aiding in the stable updating of model weights.  
Number of epochs: The model was trained on the training set for a total of 100 epochs. Early stopping was implemented by monitoring the performance on the validation set to prevent overfitting.  
Loss function: The RT DETR model's loss function consists of several key components to optimize detection performance. The main components include the box regression loss (Box Loss, weight 7.5), which uses the mean squared error (MSE) or Intersection over Union (IoU) loss to measure the difference between predicted and ground-truth bounding boxes; the classification loss (Classification Loss, weight 0.5), which uses cross-entropy loss to assess the accuracy of predicted class probabilities; and the objectness loss (Objectness Loss, weight 1.0), which employs binary cross-entropy loss to evaluate the confidence of predicted bounding boxes. Additionally, the Distribution Focal Loss (DFL, weight 1.5) was introduced to improve the accuracy of bounding box predictions.
## 3 Proposed Audio Watermarking Method  
This section first provides the following definitions:  
* The host audio signal \(x(t)\) is frequency bandpass filtered to outputs the filtered audio signal \(s(t)\) which is further modulated to be a watermark signal. There are two purposes for the frequency bandpass filtering process. One is to acquire an amount of the host signal, which will reduce disturbance to the audio signal. The other is to avoid embedding watermarks around a high frequency, as the HE-AAC will entirely remove the signals at the range of high frequency on encoding process. The lower and upper cutoff frequencies are denoted as \(F_{k}\) and \(F_{uc}\), respectively.
* Then, the filtered audio signal is divided into successive frames, each of which has length \(T_{f}\) and contains two non-overlapping sections of samples. These two sections have equal length, and we call them as the front subframe and the back subframe in this paper.  
* One piece of watermark information is represented as one binary bit of value '0' or '1,' which is embedded in one frame.
* The normalized correlation of the original signal and its delayed version (NCOD) was selected as the characteristic of each subframe. The embedding and extracting of watermark bits are decided by the difference of NCOD relations between the front subframe and back subframe. The NCOD item is calculated as follows: \[C_{i1}=\sum_{t=0}^{T}\left[s\Big{(}T_{f}\cdot i+t\Big{)}\cdot s\Big{(}T_{f}\cdot i +t+\tau\Big{)}\right]\] (1) \[N_{i1}=\sum_{t=0}^{T_{f}/2}s\Big{(}T_{f}\cdot i+t\Big{)}^{2}\] (2) \[NC_{i1}=C_{i1}/N_{i1}\] (3) \[C_{i2}=\sum_{t=T_{f}/2}^{T_{f}/2+T}\left[s\Big{(}T_{f}\cdot i+t\Big{)}\cdot s \Big{(}T_{f}\cdot i+t+\tau\Big{)}\right]\] (4) \[N_{i2}=\sum_{t=T_{f}/2}^{T_{f}}s\Big{(}T_{f}\cdot i+t\Big{)}^{2}\] (5) \[NC_{i2}=C_{i2}/N_{i2}\] (6) where \(i\) (\(i=0,1,2\ldots\)) represents the frame index. The integration time \(T\) (in samples) should be \(T\leq T_{f}-\tau\) to avoid intersymbol interference. For a frame, the \(NC_{i1}\) and \(NC_{i2}\) are the NCOD values of the front subframe and back subframe, respectively. Since the correlation value is normalized, \(NC\in[-1,1]\).
* The difference between \(NC_{i1}\) and \(NC_{i2}\) is computed to obtain: \[D_{i}=NC_{i1}-NC_{i2}\] (7)  
If the watermark bit is '1,' \(D_{i}\) should larger than 0 (\(D_{i}>0\)). If the watermark bit is '0,' \(D_{i}\) should smaller than 0 (\(D_{i}<0\)). Basically, raising \(|D_{i}|\) enhances robustness. On the basis of the value of \(D_{i}\), one bit of watermark information is embedded and extracted from each frame.
## 6 Simulation Test and Results Analysis
### Simulation Experiments for Trajectory Optimization  
In this section, the planning optimization methods designed above are verified via experiments. Figure 12 shows the Delta parallel robot prototype, including the frame, the robot, and its control system. This part is based on the robot as an example, and the driving parameters optimized by the IBOA algorithm are used to control the robot pickup motion.  
The driving speed parameters optimized by the IBOA algorithm and the non-optimized driving speed parameters were used as inputs, respectively, to carry out the picking motion test of the robot and to monitor the actual motor speed. The actual motor speed for the IBOA is shown in Figure 13.  
Figure 11: The motion trajectory with optimization using IBOA. (**a**) The trajectory curve in operating space after optimization. (**b**) YOZ view of trajectory curve after optimization.  
Figure 12: Delta robot.  
The vibration accelerations of the mobile platform at the end of the robot corresponding to two groups of different speed parameters are shown in Figures 14 and 15, respectively. The simulation results show that the driving parameters optimized by the IBOA can effectively reduce the vibration acceleration of the Delta parallel robot in high-speed motion. The results show that the optimization method is effective.  
Figure 13: Actual angular velocity of each drive arm. (**a**) Actual angular velocity of R1 drive arm; (**b**) Actual angular velocity of R2 drive arm; (**c**) Actual angular velocity of R3 drive arm.
# Backscatter Assisted NOMA-PLNC Based Wireless Networks
## 3 Power Optimization in Downlink NOMA
\[\
abla\mathcal{L}_{p_{1}}=\sum_{m=1}^{2}\frac{|g_{m}|^{2}}{\sigma^{2}+p_{1}|g_{m }|^{2}}(1+\lambda_{m})\] \[+\sum_{m=3}^{4}\frac{|g_{m}|^{2}+\beta|e_{m}|^{2}|b_{m}|^{2}}{|g_{ m}|^{2}(p_{1}+p_{2})+\beta|e_{m}|^{2}|b_{m}|^{2}(p_{1}+p_{2})+\sigma^{2}}\times \tag{18}\] \[(1+\lambda_{m})-\sum_{m=3}^{4}\frac{|g_{m}|^{2}+\beta|e_{m}|^{2}|b _{m}|^{2}}{|g_{m}|^{2}p_{1}+\beta|e_{m}|^{2}|b_{m}|^{2}p_{1}+\sigma^{2}}(1+ \lambda_{m})-\mu,\]  
\[\
abla\mathcal{L}_{p_{2}}=\sum_{m=3}^{4}\frac{|g_{m}|^{2}+\beta|e_{m}|^{2}|b_{ m}|^{2}}{|g_{3}|^{2}(p_{1}+p_{2})+\beta|e_{m}|^{2}|b_{m}|^{2}(p_{1}+p_{2})+ \sigma^{2}}\times \tag{19}\] \[(1+\lambda_{m})-\mu,\]  
Proof. : See Appendix A. The power updating process can be performed in iterative manner. At \(n\)th iteration,  
\[p_{i}(n)=[p_{i}(n-1)+t\
abla\mathcal{L}_{p_{i}}];\quad i\in(1,2), \tag{20}\]  
where \(t\) is the step size. In this way, the sub-gradient of the dual function can be computed as  
\[\
abla\mathrm{a}_{\lambda_{m}}=\sum_{m=1}^{2}\log\biggl{(}1+\frac{p_{1}|g_{m}| ^{2}}{\sigma^{2}}\biggr{)}-\mathrm{C}_{min}, \tag{21}\]  
\[\min_{\lambda_{1},\lambda_{2},\lambda_{3},\lambda_{4},\mu}\quad a(\lambda,\mu), \tag{16}\]0  
\[\min_{\lambda_{1},\lambda_{2},\lambda_{3},\lambda_{4},\mu}\quad a(\lambda,\mu), \tag{16}\]1  
Similarly, the Lagrangian multipliers are updated in iterative manner as given by  
\[\min_{\lambda_{1},\lambda_{2},\lambda_{3},\lambda_{4},\mu}\quad a(\lambda,\mu), \tag{16}\]2  
\[\min_{\lambda_{1},\lambda_{2},\lambda_{3},\lambda_{4},\mu}\quad a(\lambda,\mu), \tag{16}\]3  
where \(q_{m},l\) denote the step sizes. Iterative algorithm to obtain the optimal power and the optimal Lagrangian's multiplier's is given in Algorithm 1. ```
1. initialize\(\lambda_{1}(0)=0,\lambda_{2}(0)=0,\lambda_{3}(0)=0,\lambda_{4}(0)=0,\mu(0)=0\),
2. set the stopping criterion's \(\epsilon_{1}\) and \(\epsilon_{2}\)
3. Let n = 1, initialize \(p_{1}=(0)\) and \(p_{2}=(0)\)
4. updates \(p_{1}\) and \(p_{2}\) according to (20)
5. n = n + 1 repeat until convergence i.e., \(\|\
abla\mathcal{L}_{p}\|^{2}\leq\epsilon_{1}\)
6. updates \(\lambda_{1}(n),\lambda_{2}(n),\lambda_{3}(n),\lambda_{4}(n),\mu(n)\) according to (24) and (25)
7. Repeat until \(\lambda_{1}(n),\lambda_{2}(n),\lambda_{3}(n),\lambda_{4}(n),\mu(n)\) converge. 8. \(\|\lambda_{i}(n)-\lambda_{i}(n-1)\|^{2}\leq\epsilon_{2}\quad i\in(1,2,3,4)\)\(\|\mu(n)-\mu(n-1)\|^{2}\leq\epsilon_{2}\)
```  
**Algorithm 1**Iterative Optimal Power Allocation [5].
# Detecting Suspicious Texts Using Machine Learning Techniques
## 5 Experiments
### Evaluation Results
#### 5.2.1 Statistical Evaluation  
The proposed system experimented with five different classification algorithms for BoW and tf-idf feature extraction techniques with n-gram features. The final system evaluated with F1 = unigram, F2 = bigram, F3 = trigram, F4 = (unigram + bigram) and F5 = (unigram + bigram + trigram) features. Table 7 shows the comparison of performance between the classifiers for a different combination of features. For the BoW FE technique, random forest with an F1 feature outdoes others by acquiring 83.21% accuracy. There exists a little (0.5-1)% margin among the classifiers for F1, F2, and F5 features. All of the classifiers obtain the highest accuracy value by employing the F1 feature except DT and SGD. DT performed well with F2 feature, whereas SGD performed for F4 features. All the classifiers showed lower performance with F3 features. SGD achieved the highest precision value of 83.79%, and results showed a minimum difference between precision and recall in SGD. For the tf-idf FE technique, SGD with an F4 feature obtains the maximum accuracy of 84.57% where the maximum precision value of 83.78% achieved for F1 features. By comparing the results of two feature extraction techniques (i.e., BoW and tf-idf), impressive outcomes have observed in all the evaluation parameters. Almost all the metric values have increased (2-3)% approximately by adopting the tf-idf feature extraction technique. LR and RF obtained maximum accuracy with F1 features, MNB and SGD gained with F4 feature and DT obtained with F2 features. Thus, in summary, the tf-idf feature extraction and SGD classifier are well suited for our task as it outperforms the BoW technique with other classifiers. Figure 4 depicts the \(f_{1}\)-score comparison among the classifiers for the tf-idf feature extraction technique. We observed a tiny margin in the \(f_{1}\)-score among the classifiers with F1 and F5 features. All classifiers achieved a minimum \(f_{1}\)-score for the F3 feature except DT. The DT method obtained a minimum value of 78.74% with the F4 feature. LR and RF got the maximum value of 86.58% and 86.92%, respectively, for the F1 feature. DT obtained a maximum \(f_{1}\)-score of 82.81% while MNB got \begin{table}
\begin{tabular}{c c} \hline \hline
**Classifiers** & **Parameters** \\ \hline LR & penalty = ‘\(l_{2}\)’, C = 1.0, solver = ‘lbfgs’, max\_iter = 100 \\ \hline \multirow{2}{*}{DT} & criterion = ‘entropy’, splitter = ‘best’, \\  & max\_features = n\_features, random\_state = 0 \\ \hline \multirow{2}{*}{RF} & n\_estimators = 100, criterion = ‘gini’, \\  & min\_samples\_split = 2, max\_features = n\_features \\ \hline MNB & alpha = 1.0, fit\_prior = true, class\_prior = none \\ \hline \multirow{2}{*}{SGD} & loss = ‘log’, penalty = ‘\(l_{2}\)’, learning\_rate = ‘optimal’, \\  & max\_iter = 40, random\_state = 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of the classifier parameters. 86.57%. The results revealed that SGD with the F2 feature outperforms all other feature combinations by obtaining an 86.97% \(f_{1}\)-score.
## 2 Literature Review  
Predicting student academic performance is a central goal in educational data mining (EDM) and learning analytics, with applications in early intervention, personalized learning, and policy-making [1, 2]. The rise of machine learning (ML) has significantly advanced this field, leveraging large, multifaceted datasets--including demographic, socioeconomic, behavioral, and academic information--for predictive modeling.  
Common Benchmarks and Limitations: The UCI Student Performance dataset has become a widely used benchmark [16, 17], though its limitations (e.g., two Portuguese schools, class imbalance, and a lack of ethnic diversity) have prompted caution about over-generalization [6]. Many studies focus solely on performance metrics such as accuracy or F1 score, often neglecting the fairness or interpretability of their models.  
Model Comparisons and Performance: Various ML algorithms have been benchmarked for academic prediction: logistic regression and decision trees are preferred for interpretability [6, 18], while ensemble methods like Random Forest and XGBoost typically offer superior predictive power, especially on imbalanced data [18, 19]. Recent comparative studies have reported that while tree-based models slightly outperform simpler models in accuracy, their complexity poses challenges for real-world adoption in education [4].  
Fairness and Bias in Educational ML: Recent works highlight that predictive models can exacerbate social inequalities if fairness is overlooked [16, 20]. Metrics like demographic parity (DP) and Equalized Odds (EO) have become standard for quantifying model bias [13, 14]. Techniques such as adversarial debiasing [3, 21] and pre/postprocessing adjustments are increasingly used, but few studies combine fairness and interpretability in a single pipeline.  
Explainable AI (XAI) in Education: Explainable AI tools, such as SHAP and LIME, are now commonly used to make black-box models more transparent [10, 11]. Recent case studies demonstrate that integrating XAI explanations increases educators' trust and improves intervention design [5, 22, 23]. However, empirical user studies on the impact of XAI in real educational settings are still limited.  
Reproducibility and Open Science: While open datasets have increased reproducibility, most recent reviews show only a minority of educational ML papers provide source code or fully documented pipelines, limiting the ability of others to validate or extend their findings [16, 18] (see Table 1).  
Our Contribution: Unlike most prior studies, our work simultaneously addresses performance, explainability, and fairness using an open-access dataset. We implement a unified pipeline incorporating SMOTE-NC for mixed-type balancing, SHAP and LIME for explainability, and adversarial debiasing for fairness mitigation. We further extend the fairness analysis to socioeconomic factors, systematically evaluate statistical significance, and ensure reproducibility by sharing code and processed data. This positions our study as one of the few to provide a holistic and transparent framework for trustworthy educational ML.
# An Approach of Automatic SPARQL Generation for BIM Data Extraction
## 5 Conclusions  
The SPARQL query language is widely used in different data extraction or rule compliance checking applications in AEC industry; however, SPARQL queries are generally programmed manually by experts/engineers. To automate this process, in this paper, we proposed an approach for the automatic generation of SPARQL codes without programming proficiency or full understanding of the structure of the semantic BIM model. To allow engineers without SPARQL programming experience to obtain the SPARQL queries they desire, we used the path query function provided by the Stardog RDF database to achieve path finding in the semantic BIM model. By searching the common nodes in different path files, the shortest path(s) containing all query keywords can be obtained. Because of the numerous congeneric products/elements in a BIM model, the shortest path may not be unique, but this does not affect the structure of the shortest path. By its structure information, the _WHERE_ and _SELECT_ clauses in the SPARQL query can be produced. After two case studies, we verified that the generated SPARQL queries are correct and our approach is effective. Additionally, our approach can be flexibly used in different IFC versions or semantic/information enriched BIM models.  
However, natural language processing technologies were not applied in this paper, so the query keywords written by users are restricted to some extent. For example, it is best for the query keywords to be similar to the terms in the BIM knowledge base because only string matching was adopted in our approach to find the searched terms. Additionally, data extraction of the complex relationships among multiple targets will produce a great many Stardog path query files in our approach, and it may be difficult to find the shortest path to connect all keywords manually. At this time, a common node search program may be required. The usage of the Stardog RDF database simplifies the exploration of the linked relationships (path) among different query keywords. Certainly, the linked paths among query keywords may be identified through programming or other tools. Meanwhile, our approach mainly depends on path exploration in the explicit BIM model. Implicit data extraction needs further research in the future.  
**Author Contributions:** Resources and data collection, D.G. and E.O.; methodology, D.G.; formal analysis and investigation, D.G.; writing--original draft preparation, D.G.; writing--review and editing, D.G., E.O. and A.D.L.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** NTNU Open Access publishing funds covered the article processing charges.  
**Conflicts of Interest:** The authors declare no conflict of interest.