esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Graph Neural Network
#### 2.1.2. GAT  
GAT can be seen as a variant of GCN. While GCN aggregates nodes using the Laplacian matrix, GAT assigns different weights to each node through an attention mechanism, aggregating nodes according to the magnitude of these weights when updating the hidden layers of nodes. Therefore, by adding attention layers in the network, GAT makes the computation more efficient and allows for the assignment of different levels of importance to different nodes without relying on previous spectral-based methods, thereby enhancing the interpretability of model. Figures 2 and 3 depict the architectural framework of GAT with self and multi-head attention.  
Figure 3: GAT with multi-head attention.  
Figure 2: GAT with self attention.  
Firstly, an attention mechanism is applied to each node to train its own weight matrix, calculated as follows:  
\[e_{ij}=a(W\vec{h_{i}},W\vec{h_{j}}) \tag{3}\]  
where the input is \(h=[\vec{h_{1}},\vec{h_{2}},\cdots,\vec{h_{N}}]\), with \(\vec{h_{i}}\in\mathbb{R}^{F}\); \(N\) is the number of nodes; \(F\) is the number of features per node; \(W\) is the weight matrix, and \(a\) represents the attention mechanism's function; \(e_{ij}\) is obtained through (3), which indicates the importance of node \(j\) to node \(i\).  
To facilitate the comparison of coefficients across different nodes, the softmax function is used to normalize them, as follows:  
\[a_{ij}=\text{softmax}_{j}(e_{ij})=\frac{\exp{(e_{ij})}}{\sum_{k\in N_{i}}\exp{ (e_{ik})}} \tag{4}\]  
In the output layer of the aforementioned feedforward neural network, add a LeakyReLU function:  
\[a_{ij}=\frac{\exp{(\text{LeakReLU}(\vec{a}^{T}[W\vec{h_{i}}||W\vec{h_{j}}]))}} {\sum_{k\in N_{i}}\exp{(\text{LeakReLU}(\vec{a}^{T}[W\vec{h_{i}}||W\vec{h_{k}}] ))}} \tag{5}\]  
where LeakyReLU is the activation function; \(T\) represents transpose; \(||\) denotes the concatenation of the vector representations of nodes \(i\) and \(j\) after transformation. Subsequently, the final output result is obtained after passing through an activation function:  
\[\vec{h_{i}^{\prime}}=\sigma(\sum_{j\in N_{i}}\alpha_{ij}W\vec{h_{j}}) \tag{6}\]  
Given that a single layer of self-attention mechanism has a limited capacity to learn from surrounding nodes, to further enhance the representational ability of each output features of node, GAT introduces the application of multi-head attention mechanisms within the network. By utilizing multiple attention mechanisms to calculate the attention coefficients of surrounding nodes, the learning effectiveness of the model becomes more stable. Executing \(k\) attention mechanisms and then concatenating the features they generate, the specific output feature representation is obtained through:  
\[h_{i}^{\prime}(K)=||_{k=1}^{K}\sigma(\sum_{j\in N_{i}}\alpha_{ij}^{k}W^{k}h_{ j}) \tag{7}\]  
where \(\alpha_{ij}^{k}\) is the normalized coefficient computed by the \(k\)th attention mechanism \(\alpha^{k}\) and \(W^{k}\) is the weight matrix corresponding to the input linear transformation.
# A Review of Blast Loading in the Urban Environment
## 2 Current Knowledge
### Surface Roughness  
[PERSON] et al. [54] compared the behaviour of shocks reflected from both real and ideal surfaces. The ground was used as a real surface, and to simulate an ideal surface, the shock was reflected off the shock from an identical explosive mass placed above the initial explosive and simultaneously detonated. It was stated that there is no energy loss from the interaction of the two shock waves, allowing this interaction plane to act as an _\"ideal non-energy-absorbing surface\"_. A key finding from this study was the change in peak overpressure measurements from the Mach stem over rough ground compared to the ideal surface. Simplified, the Mach-stem shock caused by a 1 kg explosive over rough ground would equate to that from a 0.74 kg explosive over an idealised reflecting surface. Whilst the roughness of the ground was not explicitly defined, it can be stated that any non-ideal reflecting surface will result in energy losses that may be significant, and therefore should be accounted for in numerical simulations. The relationship between the values of surface roughness and energy loss would prove useful for numerical models and improve their accuracy. Others have accounted for these energy losses by adopting a _\"local flow resistivity\"_ approach by altering model coefficients for varying local roughness [55].
## 5 Conclusions  
This study can be regarded as a long-term pilot study and the first attempt at conducting in-house transport experiments. In addition, these comparative experimental and modeling studies provided a way to extrapolate from laboratory scale to in situ conditions. Nevertheless, this study offers some interesting results about transport in granite, and, most importantly, it emphasizes the importance of analyzing both the breakthrough and leaching processes in granite. The conclusions drawn from the results of this study are as follows:  
1. In terms of the breakthrough process, there was a better fit of the experimental data using the non-equilibrium model as compared to the equilibrium model based on the lower residuals (_SSQ or RMSE_). Three sets of dispersion coefficients (\(D\)) were found when increasing the path lengths ranging from 2 to 8 cm in the breakthrough process (up-flooding or radiotracer introduction stage), suggesting higher dispersivity (\(\alpha\)) during the HTO switching process for Se(IV) and Cs. In the physical non-equilibrium models, the increase in \(\alpha\) with increases in D may have been caused by an increase in the amount of immobile water, and hence by an increase in the diffusion length for the radionuclides from the mobile to the immobile center.
2. The Cs transport was noted by the smaller values of \(\beta\), where Cs had a higher retardation factor (\(R\)) than Se. Thus, a comparison of the micro-CT analysis images indicated that the micro-porosity in granite and dominate minerals, such as biotite, is mostly responsible for the sorption of Cs.  
Conceptualization, Y.-L.J., S.-C.T., C.-P.L. and N.-C.T.; methodology, C.-P.L., D.C. and N.-C.T.; software, C.-P.L. and N.-C.T.; validation, Y.S. (Vunfeng Shi), D.C., N.-C.T. and S.-C.T.; formal analysis, Y.S. (Vuzhen Sun), D.C., C.-P.L. and N.-C.T.; investigation, E.W., Z.W., Y.H. and Y.S. (Vunfeng Shi); resources, N.-C.T. and S.-C.T.; data curation, D.C., E.W., Z.W., Y.H. and Y.S. (Yunfeng Shi); writing--original draft preparation, C.-P.L. and N.-C.T.; writing--review and editing, C.-P.L. and N.-C.T.; visualization, Y.H. and Y.S. (Yunfeng Shi); supervision, C.-P.L. and N.-C.T.; project administration, N.-C.T. and S.-C.T.; funding acquisition, N.-C.T. and S.-C.T. All authors have read and agreed to the published version of the manuscript.  
This project was majorly supported by the Doctor Initial Financial Project (No. 1410000434), East China University of Technology, and in part by the Ministry of Science and Technology (MOST, Taiwan R.O.C) and the Atomic Energy Council (AEC, Taiwan R.O.C) through a 2-year mutual fund program project under contract numbers 109-2622-E-007-022, 109-2623-E-007-006-NU, and 110-2623-E-007-004-NU.  
The experimental and instrumental analysis of this study was supported by the Department of Nuclear Environmental Science, China Institute for Radiation Protection (CIRP), Taiyuan, China, Instrumentation Center at National Tsing Hua University, and the National Synchrotron Radiation Research Center (NSRRC) in Taiwan under contract number 2020-1-123-5.  
The authors declare no conflict of interest.
## 2 Literature Review  
Statistical methods have been widely used to analyze traffic crash severity and identify contributing factors. Common techniques include binary logit [6], ordered logit or probit [7,8], and mixed logit [9]. While effective in some cases, these methods rely on strict assumptions such as linearity and independence, which limit their applicability in complex datasets. If these assumptions are not satisfied, the reliability of the results can decrease significantly [10]. Moreover, statistical models often fail to capture complex interactions and dynamic patterns in crash data, reducing their predictive power. Many studies also assume that factors influencing crash data, like driver behavior, road conditions, and environmental variables, remain constant over time [11].  
To address these limitations, researchers have started using Machine Learning (ML) and Deep Learning (DL) methods, which are more flexible in analyzing non-linear and complex relationships while providing better predictive accuracy. For example, [PERSON] et al. [12] estimated the injury severity caused by crashes in Malaysia using DL-based models. They compared Feedforward Neural Networks (NN), Recurrent Neural Networks (RNN), and Convolutional Neural Networks (CNN) through grid search optimization and found that RNN performed best, with 74% accuracy. Similarly, [PERSON] et al. [13] applied ML models to investigate factors influencing teen driver injury severity on rural highways the severity of teen driver injuries on rural highways. Their pipeline identified crash patterns and tested predictive models, with XGBoost and Label Encoder achieving the best balance between accuracy and efficiency. [PERSON] et al. [14] used ML techniques, including Random Forest (RF) and Support Vector Machines (SVM), to predict crash severity in light truck and van crashes in Spain, achieving over 70% accuracy. [PERSON] et al. [15] introduced a multi-task Deep Neural Network (DNN) framework for crash severity prediction, which showed a better performance compared to the best methods currently available, while also offering explainable insights using layer-wise relevance propagation.  
Other studies have explored various ML techniques for crash or injury severity prediction. [PERSON] and [PERSON] [16] evaluated RF, Conditional Inference Forest (Cforest), and Ordered Probit (OP) models using two-wheeler crash data from Chennai, India, and found that Cforest performed the best. [PERSON] et al. [17] tested methods like Decision Trees (DT), Multi-Layer Perceptron (MLP), SVM, Case-Based Reasoning (CBR), and Naive Bayes Classifier (NBC), concluding that MLP was the most accurate. [PERSON] et al. [18] combined Granger causality analysis with ML models such as DT, RF, Extreme Gradient Boosting (XGBoost), and DNN, with DNN showing the best performance for severe crash prediction. [PERSON] and [PERSON] (2015) applied RF, AdaBoost, and MLP to urban traffic crash data from Kayseri, Turkiye, with RF achieving the highest accuracy.
## 1 Introduction
During the inspection, we adopt a top-down strategy, i.e., detecting a row of solder joints as soon as it has been welded to reduce the obscured issue between adjacent rows of solder joints. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Taxonomy** & **Methods** & **Strengths** & **Weaknesses** \\ \hline One-stage & Faster R-CNN [32] & High accuracy & High computational complexity, \\ detectors & Mask R-CNN [33] & Bad real-time ability \\ \hline \multirow{4}{*}{Two-stage detectors} & SSD [34; 38] & \multirow{4}{*}{High efficiency, \(\mathrm{\SIODY}\)} \\  & YOLOv1 [35] & & \\ \cline{1-1}  & YOLOv2 [36] & High accuracy, \(\mathrm{\SIODY}\) & \\ \cline{1-1}  & YOLOv4 [37; 40] & Strong generalization & \\ \cline{1-1}  & YOLOv5 [39; 41; 42; 43] & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Strengths and weaknesses of different detection methods. Figure 2: An example of solder joints in the complex background on aviation plugs. Meanwhile, the detecting process is implemented through two phases: ROI extraction and quality assessment. Firstly, a fine-tuned YOLOv5 model is used to extract the ROI within the entire captured image, i.e., the row of solder joints to be inspected after welding. Then the extracted ROI is automatically moved to the center of the image via a sliding platform to enhance its imaging clarity. Afterward, the adjusted ROI is fed into another fine-tuned YOLOv5 model to realize the weld quality assessment. Meanwhile, a graphical user interface (GUI) has also been developed, making it easier for operators on production lines to use. In summary, the main contribution of this paper can be summarized as follows:  
1. An accurate solder joint quality detection approach for aviation plugs is proposed, which uses two fine-tuned YOLOv5 models to perform ROI extraction and quality assessment, respectively. This two-phase strategy effectively eliminates the blurring and occlusion problem of solder joints, resulting in high-quality solder joint defect detection. 2. An intelligent solder joint quality inspection system for aviation plugs is developed, which can automatically capture high-resolution images of solder joints from an oblique angle. By combining the proposed detection approach, our system can effectively recognize the weld defects of aviation plugs. 3. A concise and easy-to-use GUI has been designed and deployed in the real production lines, which enables the recognized results to be viewed and stored in real time. The application testing on real production lines also shows that our system can meet the requirements for weld defect detection of aviation plugs. The rest of this paper is structured as follows: in Section 2, the methods and materials are described in detail, including data acquisition and detection approach. The experimental results and experimental analysis are then presented in Section 4. Finally, the conclusion is summarized in Section 4.
## 4 Simulation Results  
In this section, simulations are conducted to evaluate the performance of the presented scheme for the cooperative driving of mixed-automated vehicles at an intersection. The proposed algorithm is coded in Python (Version 3.8.13) and run on a computer with Intel Core i5-9500 3 GHz CPU and 8 GB of DDR4-2666 RAM. The SUMO (Version 1.13.0) microscopic traffic simulator is used to construct the road network, generate traffic flows, and acquire performance measurements [36]. Vehicle information is transmitted between SUMO and our control model in Python through SUMO's TraCI interface.
# Effective Adsorption of Phenoxyacetic Herbicides by Tomato Stem-Derived Activated Carbons
## 3 Results and Discussion
### Properties of the Obtained Tomato Stem-Derived Activated Carbon (TS-AC)

Table 3: Mass loss results for tomato stem-derived activated carbon (TS-AC) samples. Another dependence exists between \(V_{\text{mi}}/V_{\text{t}}\) and \(D_{\text{h}}\). The increase in \(V_{\text{mi}}/V_{\text{t}}\) results in a decrease in \(D_{\text{h}}\). The lowest \(V_{\text{mi}}/V_{\text{t}}\) values were observed for AC KOH-750 and NaOH-750. Activated carbon (TS-AC) synthesized at 650 \({}^{\circ}\)C and 750 \({}^{\circ}\)C has significantly higher BET surface areas (\(S_{\text{BET}}\)) than those obtained by other authors using other activators. For example, the BET surface areas of the activated carbons prepared from tomato stems (TS-AC) by chemical activation using phosphoric acid were 1248 m\({}^{2}\)/g [17], 850 m\({}^{2}\)/g [19], and 813 m\({}^{2}\)/g [18]. In contrast, the BET surface area of the TS-AC prepared by activation with FeCl\({}_{2}\) was 971 m\({}^{2}\)/g [20]. In the case of chemical activation, the raw lignocellulosic precursor is usually impregnated directly with an activator. The ratio of the chemical activator to precursor mass, along with the process time and temperature, is essential, but perhaps the most critical factor is the type of activator used [10; 11]. Activators of different types (acids, alkalis, or salts) can interact differently with the lignocellulosic precursor, determining the activated carbon production process. Phosphoric acid exhibits a dual action: it degrades cellulose and hemicellulose polymers in biomass and simultaneously reacts with and dissolves lignin and cellulose. Salts, e.g., ZnCl\({}_{2}\) or FeCl\({}_{2}\), as activating agents, usually act as dehydrating agents after impregnation of the biomass, causing hydrolysis reactions. The alkaline activators react with the lignin and dissolve the ether bonds and hydroxyl groups, resulting in the delignification of the precursor [11]. An increase in activation temperature from 550 \({}^{\circ}\)C to 750 \({}^{\circ}\)C resulted in reduced process yields (Table 5). Notably, higher yields were observed when potassium hydroxide was used as the activating agent. The results suggest that the alkali interaction with the tomato stems is the most beneficial and results in activated carbons with the highest BET surface areas (\(S_{\text{BET}}\)).
## 1 Introduction  
Humanoid robots represent a groundbreaking field with immense potential to redefine human-robot interaction (HRI), particularly in personal assistance. This potential is evident in applications such as healthcare, education, and assistive robotics [1]. A key aspect of making these robots effective, especially in domains such as healthcare, education, and assistive environments, is their capacity to perceive and respond to human emotions. The humanoid robot Pepper, developed by SoftBank Robotics (Tokyo, Japan), is a prime example of how these technologies are being used to create more empathetic and intuitive systems. Despite Pepper's advanced capabilities for interaction, one of its main limitations lies in accurately interpreting and responding to human emotions.  
This research sought to overcome these challenges by enhancing Pepper's ability to recognize emotions through a multimodal approach. By combining data from visual, auditory, and textual inputs, the robot is able to gain a more nuanced understanding of human emotions, allowing for more accurate and contextually appropriate responses. Unlike previous studies, which often relied on single-modality approaches [2], our work integrates these data sources into a unified system, leveraging advanced techniques such as convolutional neural networks (CNNs), transformers, and fine-tuning methods tailored to individual users.  
The system's reproducibility is a core aspect of this study. Using Pepper, a commercially available robot, and open software tools, we provide a framework that can be replicated and adapted to different contexts. These advancements offer practical applications in domains such as healthcare, education, and social robotics, paving the way for robots to engage with users empathetically and effectively.  
Such advancements in emotional recognition have significant implications for the future of assistive robotics. Through improving the human-likeness of robot interactions, robots can better fulfill roles that require emotional sensitivity, such as providing support to the elderly, educating children, or assisting individuals with disabilities.  
This paper delves into the methods and technologies used to integrate multimodal emotional recognition into Pepper. It outlines the improvements in accuracy achieved through this approach and explores the impact of these enhancements on the robot's interactions. Furthermore, the challenges encountered during development and the solutions applied are discussed, providing valuable insights for future research in the field of HRI.  
The remainder of this paper is organized as follows: Section 2 reviews related work in the field and highlights existing gaps. Section 4 describes the methodology and system components, focusing on the integration of multimodal emotion recognition. In Section 5, the experiments and results are presented, demonstrating the effectiveness of the proposed approach. Finally, Section 6 discusses the conclusions and potential directions for future research, particularly in the context of assistive and social robotics.
## 3 Results and Discussion
### Measured Signals
This lack of differentiation between distinct possibilities makes this inverse problem ill-posed, that is, more than one solution exists for the same fields on the receiver. This corroborates the use of a combined error from the three configurations, as shown in Equation (17). Figure 11: Average error between simulated and measured data, considering AIR and MUT, for different scenarios in which the complex permittivity of the floor and probe were changed. In this case, all the samples were used and the error was not normalized. For brevity, we reported only the even probes (2, 4, 8, 10, 12 and 14) and probe 7, to have an even number of subfigures.
# Control Strategy of Multiple Battery Energy Storage Stations for Power Grid Peak Shaving
## 2 Methodology
### Scalability Analysis for Multiple BESSs (N > 2)  
The core innovation of the proposed coordinated variable-power strategy lies in its scalable architecture for aggregating N discrete BESS units (N > 2) to achieve grid-level equivalent continuous-power control. While the case study in Section 3.2 employs N = 2 for clarity of demonstration, the methodology is inherently extensible to larger clusters. Key advantages of larger clusters include the following:  
1. [label=(0)]
2. Finer Power Granularity: The equivalent power resolution improves with N, enabling tighter tracking of load fluctuations.
3. Enhanced Flexibility: Combinatorial control of N units expands the solution space, extending cumulative regulation duration and reducing individual stress via dynamic power sharing.
4. Algorithmic Tractability: The phased control algorithm (Section 2.1.2) maintains complexity, ensuring real-time feasibility for practical N values.  
While validated for N = 2, the strategy's combinatorial control architecture ensures native extensibility to arbitrary N. This establishes a scalable foundation for large-scale power-grid peak-shaving applications.
# MBPPE: A Modular Batch Processing Platform for Electroencephalography
## 2 Materials and Methods
### Feature Extraction
#### 2.4.3 Wavelet Analysis  
In EEG signal analysis, frequency decomposition, and time-frequency signal transformation play pivotal roles. While the conventional Fourier transform provides a frequency-domain representation, its fixed time-frequency resolution limits its analysis capabilities for non-stationary signals [27; 28]. In contrast, wavelets offer greater adaptability for time-frequency analysis [29]. MBPPE employs the PyWavelets library in Python for wavelet analysis. Herein, we map EEG signals from the time domain to the frequency-domain using wavelet packet transform and undertake time-frequency analysis utilizing the continuous wavelet transform.  
During frequency feature extraction, we employ the Daubechies 4 as the base function for wavelet packet decomposition on each channel, enabling frequency bandwidth calculation. Subsequently, we iterate through each frequency sub-band, classify them into user-defined frequency ranges, and assign them to the corresponding frequency bands.  
The continuous wavelet transform offers a continuous representation of time and frequency, making it especially suitable for the analysis of non-stationary or transient signals. During the transformation phase, we harmonize the time-frequency resolution by modulating the wavelet scale. Subsequently, we extract frequency and frequency energy from the transformed data, establishing a mapping relationship among time, frequency, and energy to enhance visualization. To optimize the visualization impact, we assess the frequency energy within a specific timeframe while extracting time-frequency information. The peak energy in each period provides a reference for stratified coloring in the subsequent visualization phase.