esa-sceva/satcom-chunk-collection huggingface dataset:
# Continuous Recognition of Teachers' Hand Signals for Students with Attention Deficits
The study analyzes these gestures using images marked by human keypoint detection and establishes a recognition model with a ten-layer CNN. The study in [16] illustrates the effectiveness of using smart phone videos, in conjunction with OpenPose and machine learning algorithms, to estimate the direction of gaze and body orientation in elementary school classrooms. In this work, the body keypoints are provided by OpenPose for each person in the classroom. The main objective of [17] is to compare the non-verbal teaching behaviors, including arm extensions and body orientation, exhibited by both pre-service teachers and in-service teachers in asynchronous online videos. By utilizing deep learning technology, the teachers' poses are quantified while they perform instruction towards a video camera, with special attention given to the arm stretch range and body orientation related to the subject topic. In addition, hand signals or gestures have also been explored in various educational applications [18; 19; 20; 21; 22; 23]. In recent years, there has been an increasing emphasis on students' digital skills to facilitate learning, as well as on improving teachers' digital competencies to enhance teaching practices. Students necessitate a comprehensive grasp of information and communication technology, which has the potential to enrich, transform, and elevate the quality of their learning experiences. Effective utilization of technology in the classroom relies on both technological accessibility and the routine choices made by teachers who incorporate technology into their teaching practices [24]. The developments of advanced technologies in smart classrooms are expected to enhance teaching/learning experiences and interactions between teachers and students [25]. The teacher's hand signals, serving as a non-verbal form of communication, can significantly aid students in concentrating on the learning content, thereby facilitating an improvement in their learning performance. The ability of students to recognize and comprehend these hand signals is crucial as it facilitates a seamless transition of their attention, enabling them to follow the teacher's instructions, engage with the content, and actively participate in the learning process, thereby enhancing the overall educational experience more effectively. Most of the research mainly gives teachers useful feedback about their gesticulation in class and helps teachers to improve their teaching practices [12; 13; 14; 15; 16; 17; 18]. Our goal is to develop a student-centered technology and highlight the significance of recognizing teachers' hand signals. To decrease the requirement of verbal messages from the teacher and help students connect to the rhythm of teaching more quickly by themselves, we are therefore interested in exploring how to offer a dynamic and instructional reminder of simple attention cues for assisting students with attention deficits in classes of inclusive education.
# Design and Analysis of a Planar Six-Bar Crank-Driven Leg Mechanism for Walking Robots
## 4 Kinematic Analysis
### Velocity Analysis  
To obtain an expression for the angular velocities of links 3 and 4, we differentiate Equation (2) with respect to time. The equation then becomes  
\[ja\omega_{2}e^{i\theta_{2}}+jb\omega_{3}e^{i\theta_{3}}-jb\omega_{4}e^{i \theta_{4}}=0 \tag{18}\]  
Additionally, this equation leads to two scalar equations which can be solved simultaneously for \(\omega_{3}\) and \(\omega_{4}\).  
\[\omega_{3}=\frac{a\omega_{2}}{b}\frac{\sin(\theta_{4}-\theta_{2})}{\sin( \theta_{3}-\theta_{4})} \tag{19}\]  
\[\omega_{4}=\frac{a\omega_{2}}{b}\frac{\sin(\theta_{2}-\theta_{3})}{\sin( \theta_{4}-\theta_{3})} \tag{20}\]  
The pantograph (\(O_{4}BCD\)) also affects the angular velocities of the links. The angular velocities \(\omega_{4}\) and \(\omega_{6}\) are equal because the rotational speed of these links must be synchronized to maintain the pantograph's consistent movement. The skew pantograph mechanism ensures that any angular velocity imparted to one link is equally transferred to a corresponding link. Likewise, the angular velocities \(\omega_{5}\) and \(\omega_{3}\) are equal due to the pantograph's design, which translates rotational movement such that these links must have the same angular velocity to preserve its proper movement pattern.  
\[\omega_{5}=\omega_{3} \tag{21}\]  
\[\omega_{6}=\omega_{4} \tag{22}\]The velocity components of the foot contact point P are calculated by the numerical differentiation of position Equation (14):  
\[v_{P_{x}}=-a\omega_{2}\mathrm{sin}\,\theta_{2}-l\omega_{3}\mathrm{sin}\,(\theta_{ 3}-\gamma)+c\omega_{4}\mathrm{sin}\,\theta_{4}-f\omega_{4}\mathrm{sin}(\theta_ {4}+\beta) \tag{22}\]  
\[v_{P_{y}}=a\omega_{2}\mathrm{cos}\,\theta_{2}+l\omega_{3}\mathrm{cos}\,(\theta _{3}-\gamma)-c\omega_{4}\mathrm{cos}\,\theta_{4}+f\omega_{4}\mathrm{cos}( \theta_{4}+\beta) \tag{23}\]
## 3 Results and Discussion
### L-Shape Beam  
In the analysis of the L-shape beam illustrated in Figure 11, design areas of 40 \(\times\) 40 and 120 \(\times\) 120 were utilized. A limit of 300 iterations was established for all scenarios to ensure consistency in comparison. Within this solution, the sensitivity filter was applied, aligning with the methodological framework used in previous examples. These settings facilitated the optimization process, ensuring a comprehensive assessment and alignment with the problem's constraints and goals.  
The optimization results for the L-shape beam are illustrated in Figure 12. Similarly to previous examples, the optimized structures were subsequently 3D printed. Unlike other cases, the upper right quarter square was made passive in this optimization by employing a square design area with dimensions of 40 \(\times\) 40 and 120 \(\times\) 120 specifically for the L-shape beam. During the application of the AM filter, the optimization process was conducted with particular consideration for the south (S) direction.  
Figure 11: L-shape beam boundary conditions.  
Figure 12 examines three distinct algorithms (DTO, RBTO, and AM-RBTO/S) across two varying sizes (40 \(\times\) 40 and 120 \(\times\) 120). In the case of the 120 \(\times\) 120 size, the RBTO algorithm demonstrates the greatest solution time and number of iterations, whereas the other sizes and algorithms exhibit comparable iteration counts and times. This example also reveals the inclusion of time and support structure cost when the AM filter is omitted in the 3D printing of optimal designs. Generally, variations are noticeable among the different sizes and algorithms, and the displacement values stemming from these variations are detailed in Table 1.  
Figure 12: TO design of L-shape beams using interior-point algorithm with sensitivity filter; overhang regions exceeding 45\({}^{\circ}\) are marked with red lines.  
A review of the displacement results in Table 1 reveals that the solutions derived from RBTO are less than those from deterministic solutions. Calculated displacement refers to the maximum nodal displacement magnitude obtained via finite element analysis after optimization. Introducing the additive manufacturing filter to deterministic problems has led to an increase in displacements under the influence of applied loads. Similarly, when the results are analyzed by integrating the additive manufacturing filter with reliability-based analysis, there is no substantial alteration observed in comparison to RBTO.
## 3 Verification
### Case 2: Verification of Rock Brittleness Under Varying Temperatures Using Proposed Index  
Triaxial compression tests have shown that temperature poses an obvious effect on rock brittleness. The properties of rocks shift from brittleness to ductility with the increasing temperature. To further validate Equation (15), we referred to triaxial compression test data conducted by [PERSON] et al. [31] on sandstone under a constant confining pressure of 10 MPa. For different temperature conditions (25, 400, 600, 800 and 1000 \({}^{\circ}\)C), the mechanical parameters were calculated, including peak strain, peak strength, Poisson's ratio and elastic modulus, as listed in Table 2. Using Equation (15), the sandstone brittleness index is shown in Figure 3, which decreases from 3.70 to 2.95 as the temperature grows from 25 to 1000 \({}^{\circ}\)C. This result confirms that the proposed index accurately captures the impact of temperature on rock brittleness characteristics. Our findings are consistent with an index proposed by [PERSON] et al. [4].  
It should be noted that some composite indicators based on combinatorial approaches have been proposed as a means of evaluating rock brittleness [32]. Although these composite indicators can effectively characterize the brittle properties of rock-like materials, they often contain complex expressions. In contrast, the proposed index has an explicit physical background and can accurately identify the brittleness of rock-like materials.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Temperature (\({}^{\circ}\)C)** & **Peak Strength (MPa)** & **Peak Strain** & **Elastic Modulus (MPa)** & **Poisson’s Ratio** \\ \hline
25 & 139.6 & 0.011 & 18,290 & 0.28 \\
400 & 155.7 & 0.013 & 19,730 & 0.28 \\
600 & 165.79 & 0.018 & 16,480 & 0.28 \\
800 & 176.72 & 0.02 & 15,840 & 0.28 \\
1000 & 148.07 & 0.022 & 10,650 & 0.28 \\ \hline \end{tabular}
\end{table}
Table 2: Mechanical parameters of sandstone in Case 2.  
Figure 2: Brittleness indexes under different confining pressures.
## 5 Evaluations
### Training Preparation and Dataset Augmentation  
Here, the dataset preparation, training environment, and hyperparameters used to generate the _YOLOv8_ model are discussed for the _object detection function_.  
First, we prepared a custom _sign image_ dataset based on the _YOLO_ format. Each image consists of the class name and bounding boxes around the sign. We collected 213 images with separate labels from #2 and #3 Engineering Buildings at Okayama University. Since the training process of a deep learning approach requires a number of datasets [52], we performed augmentation processes using the _Albumentations_ Python package to generate a larger dataset [53]. This resulted in the generation of 2130 images from the original 213 images. It applied various augmentation methods, including lighting adjustments, image compression, and color shifts. Figure 5 shows the result of augmented methods compared to the original dataset. The image dimensions were standardized to 480 px \(\times\) 480 px. We divided 80% images for training, 10% for validation, and 10% for testing the model.  
The training processes were conducted on a device with Ubuntu 20.04 as the _Operating System (OS)_, equipped with an Intel(r) Xeon(r) Gold 5218 processor and NVIDIA QUADRO 6000 with 24 GB of VRAM to facilitate accelerated computations. _PyTorch_ version 1.12.1 was employed to train the _YOLOv8_ model. We utilized Python version 3.9 with CUDA version 11.3 to enable the GPU acceleration computation. We trained the _YOLOv8_ model for 400 epochs. The specific details of the training environment are presented in Table 2.  
In the hyperparameters, _Stochastic Gradient Descent (SGD)_ provides balance during the training process of the _YOLOv8_ model. The initial and final learning rate is defined as \(1\times 10^{-2}\). The _Weight Decay Coefficient_ of 0.937 prevents models from reaching overfitting during the training. To enable the reproducibility of the trained model, we defined the random set value as 42. The details of the hyperparameter values are described in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Component** & **CPU** & **RAM** & **GPU** & **OS** & \begin{tabular}{c} **Python** \\ **Version** \\ \end{tabular} & \
\begin{tabular}{c} **CUDA** \\ **Version** \\ \end{tabular} &
\begin{tabular}{c} **PyTorch** \\ **Version** \\ \end{tabular} \\ \hline \multirow{3}{*}{Specification} & Intel® Xeon® & \multirow{3}{*}{24 GB} & NVIDIA QUADRO & \multirow{3}{*}{3.9} & Ubuntu & \multirow{3}{*}{11.3} & \multirow{3}{*}{1.12.1} \\  & Gold 5218 & & RTX 6000 & & & & \\ \cline{1-1} \cline{6-8}  & & & & & & \\ \cline{1-1} \cline{6-8}  & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Device specification for model training.  
Figure 5: (a) Example in original dataset; (b) example in the augmented dataset.
# Leveraging Retrieval-Augmented Generation for Swahili Language Conversation Systems
## 4 Experimentation
### Experimental Results
#### 4.3.5 Query Performance Analysis  
The parallel categories diagram in Figure 13 compares the Query Performance across different models (mT5, mBART, GPT-2, and GPT-Neo) and techniques (fine-tuned and RAG) and Table 9 provides the Swahili queries and their corresponding English translations used in Figure 13. These queries were analyzed to evaluate model performance under different techniques, including fine-tuning and Retrieval-Augmented Generation (RAG). The table establishes the linguistic context and demonstrates the types of queries processed by the models in the RAG framework. The diagram shows how the performance changes when applying these two techniques to different types of queries and Swahili language questions. The results suggest that the RAG method generally leads to an improvement in model performance compared to the fine-tuned method. This indicates that RAG enhanced the models' ability to generate more accurate and relevant responses. The diagram highlights that RAG has a significant positive effect on the performance of all the models. For example, the performance of mT5 increased from 68.12 percent with the fine-tuned method to 84.34 percent with the RAG technique, which represents a substantial improvement. Other models such as mBART, GPT-2, and GPT-Neo also showed improvements with RAG, although the magnitude of these improvements varies across the models. For GPT-2, the improvement was particularly noteworthy. The performance of GPT-2 increased from 65.62 percent with the fine-tuned method to 76.51 percent with RAG. This demonstrates that RAG had a significant impact on the performance of GPT-2, making it more effective at handling the queries. The diagram also indicates that the type of query--whether Original or Variation--affects the performance, with Variation queries generally producing better performance across all models. The improvement in performance with RAG is likely due to its ability to retrieve and incorporate relevant external information, which allows the models to generate more accurate and contextually appropriate responses. The diagram effectively demonstrates that RAG enhances the models' ability to process Swahili language queries and produce higher-quality outputs [57].  
Figure 12: ROUGE-L score comparison between fine-tuned and RAG models. The graph compares the ROUGE-L scores for four models, mT5, mBART, GPT-2, and GPT-Neo, under both fine-tuned and Retrieval-Augmented Generation (RAG) conditions.
## 3 Results and Discussion
### Optical Properties
As mentioned earlier herein, CuO has been a top focus among transition metal oxide investigations due to its remarkable properties as a narrow-bandgap \(p\)-type semiconductor and as the basis of several high-temperature superconductors and giant magnetoresistance materials. However, increasing requests to develop energy storage systems have inspired academics and engineers to develop innovative materials with superior properties. Hence, the excellent electrochemical properties of CuO have proven to be an even more promising candidate material towards numerous uses such as supercapacitors, lithium batteries and biological sensors for specific molecules (e.g., glucose) [29; 30; 31; 32; 33; 34; 35; 36; 37; 38]. Thus, since CuO is a transition metal compound with a relatively low cost, it could be more than promising as a candidate for an electrode material in supercapac applications. Furthermore, electrochemical supercapacitors are well thought out as ultimate high density energy storage devices that will meet pressing requirements in electrical engineering and other applications, such as for the hybrid automotive transportation, due to their long life cycles and high density of power. These preliminary readings suggest that the analysis of electrochemical aspects of CuO is an area of high importance, in particular as thin films and hierarchical structures, and are therefore of paramount importance for such supercapacitor applications. It has also been reported that the materials synthesized by doping aluminium onto CuO improve its light absorption behaviour and have become a good candidate in solar cell applications [39]. Nevertheless, thin film deposition techniques need to be cost-efficient and eco-friendly. Other thin film, multilayer and nanostructure growing techniques, as well as printing, are also being envisaged for our future work, such as pulsed laser deposition (PLD) combined with colloidal lithography and/or other surface structuring techniques [40].
## 3 Results
### Composite Indicator Approach
#### 3.2.4 Standardization Methods  
Since a composite indicator can be composed of individual indicators with different measurement units, it is necessary to standardize the values by converting them into a comparable unit. The most common method of normalization applied in the analyzed studies was the min-max scaling method, which is calculated by applying a linear transformation (Equation (1)) [7]:  
\[\text{Sub}-\text{indicator score}=\frac{X-\text{Lower }X\text{ threshold}}{\text{Upper }X\text{ threshold }-\text{ Lower }X\text{ threshold}}\times 100 \tag{1}\]  
where \(X\) is the raw sub-indicator value. Suppose that it is not possible to identify theoretical recommended values. In that case, the lower and upper thresholds can be defined according to the minimums and maximums (min-max) of a set of agents, such as countries, regions, projects, or farms, among others [7; 30; 31; 35; 36].  
This method does not consider the discrepancy between the actual value of an indicator and the desired value. For this reason, some authors used the benchmark method, which defines lower and upper thresholds according to the least-desirable value and most-desirable values pointed out in the scientific literature or targets set by the government or committees [37].  
[PERSON] et al. [32] used linguistic answers to estimate the values of some key performance indicator (KPI), and the indicators were scaled to values from 0% to 100% by recording \"yes\", \"maybe\", and \"no\" answers as 0%, 50%, and 100%, for instance. Category and numerical answers were converted by applying the min-max scaling method, with a threshold definition based on the literature [32]. [PERSON] et al. [34] also compiled references and expert knowledge to establish quantitative correspondence to the qualitative classes composing their biodiversity indicators. The benchmark method confers the advantage, over other approaches, of making methods more robust and comparable across different agents and periods [37].  
Instead of using the min-max scaling method, the agroobiadiversity indicator proposed by [PERSON] and [PERSON] [39] has also been standardized considering the maximum value possible for each group of indicators. This methodology is based on the mathematical principles followed in calculating the equivalent index of land use and the general index of sustainability. The groups of indicators were standardized by applying Equation (2):  
\[\text{Specific index for each group}=\frac{\sum_{1}^{\text{Se}}\text{Vi}}{ \text{Se }\times(\text{Vi.max})}\times 100 \tag{2}\]  
where Vi stands for the value of each component, Vi.max is the maximum possible value of each component, and Se represents the total amount of components of each group of indicators.
## 5 Conclusions  
This study addresses the core issue of gravity data denoising in geophysical exploration by proposing an improved U-Net method that integrates deep learning with physical priors. By implementing multi-scale feature fusion, attention mechanisms, and Laplace constraints, the approach enhances both denoising performance and physical interpretability.  
Experimental results on the self-generated dataset--constructed using randomly simulated subsurface prism models with density perturbations (\(\pm\)20% of typical rock densities) and Gaussian noise additions (5%, 10%, 15%, and 30%)--demonstrate that this method significantly outperforms traditional filtering techniques. For gravity data denoising, the improved U-Net achieves Peak Signal-to-Noise Ratios (PSNRs) of 59.13 dB, 52.03 dB, 48.62 dB, and 48.81 dB at noise levels of 5%, 10%, 15%, and 30%, respectively, surpassing wavelet transforms, moving averages, and low-pass filtering by 10-30 dB. In multi-component gravity gradient denoising (Txx, Tyy, Tzz), the method excels in detail preservation and noise suppression, improving the Structural Similarity Index (SSIM) by 15-25% compared to wavelet transforms.  
Tests on real gravity data further validate its superiority: the denoised results effectively restore geological features obscured by noise, with enhanced performance in marineregions--where physical constraints play a more critical role--showing over 30% higher conformity to theoretical models than traditional methods.  
Overall, this improved U-Net offers a novel technical pathway for high-precision geophysical data processing, bridging deep learning with physical priors to advance gravity and gravity gradient data denoising.  
Conceptualization, B.L. and H.L.; Data curation, Y.Z.; Formal Analysis, B.J.; Funding acquisition, S.B.; Methodology, B.L.; Project Administration, Y.Z.; Software, S.B.; Validation, C.Z.; Visualization, B.J.; Writing--Original Draft, B.L.; Writing--Review and Editing, H.L. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Science Foundation for Key Program of China (grant No. 42430101), and the Natural Science Foundation of China (grant No. 42374174).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.