esa-sceva/satcom-chunk-collection huggingface dataset:
# Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving
## 1 Introduction
### Research Questions  
From the related work on common take-over parameters (Section 1.1), it could be concluded that scientists have come to an agreement that measuring reaction time alone is not sufficient to assess the TO quality. Therefore, the quality aspects considered in our context also include measurements of reaction intensity, lane deviation, collision avoidance approach, and success. However, it is not yet clear which of the many driving-related and non-driving-related parameters have the greatest influence on these quality aspects and need to be additionally considered.  
The aim of this study is to determine the parameters that have the greatest impact on the quality of a TO. For this purpose, an existing dataset from a driving simulator study [87] and proven machine learning algorithms that also allow a reliable interpretation were used. By determining key parameters, researchers will be able to further improve their designs of TOR user interfaces by knowing which parameters to focus on.  
As far as the authors are aware, this is the first study to examine different ways of transition between handheld device use (specifically) and the driving task. Using the interpretable models, we hope to determine the effects of each possible action related to device handling at the moment of a TOR. Thus, the two research questions of this paper are:  
* _\"Which parameters have the greatest impact on the quality of TO?\"_ and
* _\"How does handheld device handling affect the TO process?\"_  
In Section 2, we introduce a few basic and commonly used machine learning algorithms that will be considered later in the analysis. Then we briefly introduce the methodology-the dataset, the parameters, and the procedure used to build the models. In the Results section (Section 4), we present the created models according to TO quality aspects. In the last section, we propose possible interpretations of the results and conclude with suggestions for additional steps to further improve conditionally automated vehicles.
## 2 Methodology
### Tunnel Axis Segmentation
#### 2.2.3 Segmentation of the Tunnel Axis
\begin{array}{l}y_{1}=a_{0}+a_{1}X_{11}+a_{2}X_{12}+\ldots a_{n}X_{1n}, \\ y_{2}=a_{0}+a_{1}X_{21}+a_{2}X_{22}+\ldots a_{n}X_{2n},\\ \ldots\\ y_{m}=a_{0}+a_{1}X_{m1}+a_{2}X_{m2}+\ldots a_{n}X_{mn}.\end{array} \right. \tag{4}\]  
Equation (4) can be further transformed into a matrix equation:  
\[Y=BA, \tag{5}\]  
where \(Y=[y_{1},y_{2},\ldots,y_{n}]^{T}\), \(B= \begin{bmatrix}1&X_{11}&X_{12}&\ldots&X_{1n}\\ 1&X_{21}&X_{22}&\ldots&X_{2n}\\ \vdots&\vdots&\vdots&\vdots&\vdots\\ 1&X_{m1}&X_{m2}&\ldots&X_{mn}\end{bmatrix} \), and \(A=[a_{0},a_{1},\ldots,a_{n}]^{T}\). \(Y\) and \(B\) are estimated from the observed data. Equation (5) can be solved using the linear least squares techniques:  
\[Y+e=(B+E_{B})A, \tag{6}\]  
where \(e\) and \(E_{B}\) are the error matrix of \(Y\) and \(B\), respectively. In linear algebra, the coefficients of matrix \(A\) can be solved through the singular value decomposition (SVD) [23; 24] of an augmented matrix \([BY]\).
## 5 Deep Learning
### Elements of a CNN
#### 5.1.2 Layers  
A set of interconnected neurons builds the so-called convolutional layer, another key component of these networks. CNN usually incorporate several convolutional layers, which are the most relevant elements for feature extraction. The first layers are used to extract low-level features and, as one goes deeper into the convolutional layers, high level features are obtained [50]. The trend is to move towards deeper solutions to improve the accuracy of the CNN. The features are extracted by performing the convolution operation, where a group of input pixels (the initial image or the result of the previous convolution) are multiplied (scalar product) by a small matrix called kernel. The kernel, which slides through all the input pixels, contains weight factors to store extracted features [51]. Those weights, which are at first random, are usually adjusted by applying the backpropagation algorithm, which minimises the cost function through several iterations. The resulting matrix obtained from all convolution operations is known as a feature map.  
There are different parameters that must be set up for each convolution. The three that are explained next are the most important ones.  
1. **Kernel size**: is the first parameter that needs to be established in a convolutional layer. There is a wide range of options but, commonly, the most used sizes are \(1\times 1\), \(3\times 3\) and \(5\times 5\).
2. **Stride**: is a parameter that defines the step size of the kernel. For example, if the stride has a value of 3, the kernel will move 3 pixels horizontally after each convolution operation. Typical values for the stride are 1, 2 and 3.
3. **Depth**: indicates the number of kernels that are used in each convolution. Each kernel generates a feature map, and the totality of the feature maps receives the name of feature mapping. The most used approach is to start with a few kernels along the first layers and continue increasing this number until the last convolutions.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 3 LIMS System: fs-Laser Ablation Ion Source and Time-of-Flight Mass Analyser
### Selected Femtosecond-LIMS Systems
#### 3.3.3 High-Resolution fs-LIMS: LMS-GT
The optical system allowed efficient sample tracking during its mass-spectrometric surface investigation and micrometre resolution while conducting chemical imaging and depth profiling studies.
# DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection
## 3 Proposed Methodology
### Segmentation Network
#### 3.1.2 Multi-Scale Fusion Mechanism  
Currently, most fully convolutional semantic segmentation networks, such as SegNet and DeepLab v3+, typically only utilize the last layer of the convolutional network as the output, which may result in the loss of shallow-level detailed information in deep features. Additionally, for defective images, there is a severe imbalance between foreground and background pixels, which can lead to gradient vanishing or exploding, making network training slow or even preventing convergence.  
Inspired by the HED network architecture [31] and U2-Net [32], we calculate the loss function in each unit (block) of the decoder and weigh them to form the final loss function (as delineated in Section 3.1.3). The advantage of this approach is the better utilization of convolutional feature information from different levels. Additionally, we fuse the feature information from all layers in the decoder to obtain the final feature representation. Deep features typically exhibit strong responses for larger targets and partial edges of targets, while shallow features provide more detailed information. Furthermore, since the receptive field of each unit is different, they are effectively integrated into the final feature map, as shown in Figure 4.  
Figure 4: Network architecture diagram of the multi-scale fusion mechanism.  
After the output of each block in the decoder is obtained, as illustrated in Figure 4, we use a \(1\times 1\) convolutional kernel to reduce the number of feature channels. Subsequently, the probability map is restored to the original size through deconvolution. Finally, these probability maps are stacked, and the final prediction result is generated through a \(1\times 1\) convolution. The advantage of this approach lies in its ability to better capture feature information at different levels in the image while addressing the imbalance between foreground and background pixels.
# Correlation between Acoustic Analysis and Psycho-Acoustic Evaluation of Violins
## 3 Results
### Results of the Psycho-Acoustic Analysis  
The results obtained after processing the answers given by the 31 listeners (experienced musicians) who were volunteer responders to a questionnaire with five criteria, are presented in Table 2.  
A detailed description of this survey was given by [27]. With an average total score between 16 and 19 (out of a maximum of 25), it is confirmed again that the differences between the seven violins are only slight. The best results according to the opinion of the listeners belong again to the violins with thickened profiles, while the thinned violins obtained lower scores.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Tested Violins**} & \\
**Criterion** & **AM6C** & **AM4C** & **AM2C** & **A00C** & **AP2C** & **AP4C** & **AP6C** \\ \hline Sound clarity & 3.452 & 3.903 & 3.484 & 3.677 & 3.903 & 3.774 & 3.968 \\ Sound warmth & 3.290 & 3.355 & 3.355 & 3.516 & 3.645 & 3.516 & 3.742 \\ Brightness & 3.419 & 3.710 & 3.258 & 3.290 & 3.774 & 3.677 & 3.774 \\ Amplitude & 3.452 & 3.581 & 3.387 & 3.355 & 3.677 & 3.645 & 3.806 \\ Equal sonority on the strings & 3.323 & 3.548 & 3.323 & 3.516 & 3.645 & 3.710 & 3.871 \\ Total & 16.936 & 18.097 & 16.807 & 17.354 & 18.644 & 18.322 & 19.161 \\ The position in the respondents’ preferences * & 6 & 4 & 7 & 5 & 2 & 3 & 1 \\ Chronological position in survey & 4 & 3 & 2 & 1 & 6 & 5 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rating of the tested violins from the viewpoint of the listeners (* 1—the highest score, 7—the lowest score).  
Figure 7: The mode frequency in the case of: (**a**) free string and Pizzicato excitation; (**b**) musical part.  
The highest scores obtained from the survey are for AP2C1 and AP6C1 (Table 2). It is observed that there is a close correlation between these scores and the values of the dominant frequency that coincides with the most prominent mode in the low-frequency range, the A2 mode (Table 1). In Figure 8, a comparison ranking the violins is presented. It is worth mentioning that the chronological order in which the violins were heard in the survey was: A00C1; AM2C1; AM4C1; AM6C1; AP4C1; AP2C1; AP6C1. The rankings for all the analyzed criteria gave the following order: AM2C1; AM6C1; A00C1; AM4C1; AP4C1; AP2C1; AP6C1. In analyzing the scores obtained by the violins at the end of the questionnaire, it was found that they are higher than those in the first part of the questionnaire.
# EDC-Net: Edge Detection Capsule Network for 3D Point Clouds
## 4 Experimental Results
### Ablation Study  
We performed an ablation study based on the two hyper-parameters of \(\alpha\) and \(\gamma\) for computing the loss function during the training process (as explained in Section 3.2). The summary of this study is depicted in Figure 4 (left). All the experiments in this ablation study were performed on the validation set of the ABC dataset. This study demonstrates the importance of the weights for calculating the loss during the training process and how it accelerates the training process to converge the loss earlier.  
Furthermore, we performed another ablation study to analyze the effectiveness of the weakly-supervised transfer learning approach and the adequate number of neighbors (\(k\)) of our proposed EDC-Net. In all these experiments we sampled 1024 sample points from each point cloud, and we trained EDC-Net by different numbers of neighbors; we applied the test set of the ShapeNet dataset for evaluating the trained models. EDC-Net refers to the models that just trained on the ABC dataset and EDC-Net+WSL refers to the models that applied the weakly-supervised learning by imprecise labels of ShapeNet dataset. The results of these experiments are summarized in Figure 4 (right). This study demonstrates the adequate number of neighbors that we defined in our experiments; furthermore, it demonstrates the robustness of weakly-supervised learning for boosting the performance of EDC-Net on the ShapeNet dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Noise Level (\(\sigma\))**} \\ \cline{3-6}  & & \(\sigma\) = 0.0 & \(\sigma\) = 0.02 & \(\sigma\) = 0.05 & \(\sigma\) = 0.08 & \(\sigma\) = 0.12 \\ \hline \multirow{3}{*}{N = 1024} & P & 0.8475 & 0.8310 & 0.8179 & 0.7865 & 0.7321 \\  & R & 0.9310 & 0.8986 & 0.8849 & 0.8402 & 0.8443 \\  & F1 & 0.8873 & 0.8635 & 0.8501 & 0.8125 & 0.7842 \\ \hline \multirow{3}{*}{N = 2048} & P & 0.8458 & 0.8267 & 0.8268 & 0.8023 & 0.7684 \\  & R & 0.9561 & 0.9385 & 0.9027 & 0.8424 & 0.8306 \\ \cline{1-1}  & F1 & 0.8991 & 0.8791 & 0.8631 & 0.8219 & 0.7983 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The behavior of EDC-Net with different perturbed levels of noise on ABC dataset.  
Figure 3: Edge detection results on ABC [35] (**left**) and ShapeNet [34] (**right**) samples. The detected edge points are assigned with red and not-edge points with gray.
## 4 Results and Discussion
### Network Planning Results and Performance
#### 4.2.2 Wireless Network Planning for 664 Mbps and a Maximal Average Received Power \(AVE_{Rx}\)  
Table 4 (\(AVE_{Rx}\) gain [dB]) shows the results of the network deployment after targeting a maximal average received power over all grid points (\(AVE_{Rx}\)), maximizing the QoS. Figure A2 shows the resulting network deployment with the same setting as the deployment of Section 4.2.1, but accounting for a maximum \(AVE_{Rx}\). It can be noticed that the AP distribution changed. In addition, to validate the approach of introducing this constraint, we evaluated the average received power of the GA-proposed deployment, according to the WHIPP IDP PL model. The results show that the average received power after applying the maximum \(AVE_{Rx}\) criterion is increased by 2.0 to 4.5 dB (see last column of Table 4). This  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{3}{*}{**Scenario**} & \multirow{3}{*}{**Possible**} & \multicolumn{2}{c}{**WHIPP Tool**} & \multicolumn{2}{c}{**ML + GA**} & & & \\ \cline{3-8}  & & **APs** & **Required** & **Optimization** & **Required** & **Pre-Processing** & **Gai Optimization** & **\% Rx** & **\% Rx** & **\% Gain [dB]** \\ \cline{1-1} \cline{3-8}  & & **APs** & **Time [min]** & **APs** & **Time [min]** & **Time [min]** & **Covered** & **Gai [dB]** \\ \hline iGent & 52 & 4 & 7.15 & 4 & 83 & 0.11 & 99.37 & 3.80 \\ Zuiderpoot & 58 & 6 & 42.80 & 6 & 95 & 0.20 & 100.00 & 4.20 \\ Vooruit & 50 & 3 & 2.00 & 3 & 78 & 0.16 & 100.00 & 4.50 \\ Koutitas & 45 & 13 & 10.00 & 10 & 38 & 0.16 & 99.56 & 2.00 \\ Library & 187 & 26 & 1358.00 & 26 & 480 & 1.56 & 100.00 & 3.00 \\ Jaume 1 & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between the WHIPP and ML + GA network planners, with respect to the required number of APs, the time to calculate the network deployment, and the coverage percentage for the setting 12/256 QAM/27 dB/664 Mbps/-56 dBm from Table 2.  
indicates that the GA-proposed solution is able to maximize and homogenize the received signal level.
## 2 Data Sources and Methods
### Meteorological Factors  
The temporal and spatial variation of surface ozone is affected not only by anthropogenic emissions, especially ozone precursors, but also by meteorological conditions. In the construction of the surface ozone estimation model, a comprehensive consideration was given to other factors impacting surface ozone variations, namely meteorological factors. The meteorological variables considered in this study encompassed temperature at a 2-m height above the surface (T), relative humidity (RH), surface pressure (SP), the height of the atmospheric boundary layer (BLH), wind speed (WS), wind direction (WD), the extent of cloud cover (TCC), total precipitation (TP), and exposure to ultraviolet (UV) radiation. The data utilized in this analysis, characterized by a spatial resolution of 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) over the period from 2019 to 2021, were sourced from the ERA5 reanalysis dataset. To enhance the accuracy of the surface ozone estimation and align with the findings of prior research, the model incorporated daily aggregated UV radiation and TP data, along with the daily mean values for RH, SP, BLH, and TCC. Additionally, the model considered the daily peak T and the hourly WS and WD measurements recorded at 14:00 PM, a timeframe commonly associated with peak MDA8 and temperature readings. All meteorological factor datasets were resampled into a uniform 0.05\({}^{\circ}\)\(\times\) 0.05\({}^{\circ}\) grid in the modeling process of surface ozone estimation. The detailed descriptions and performance metrics of these extensively utilized datasets have been thoroughly documented in prior studies [31].
# RF Energy Harvesting
## 3 Commercially Available RF Energy Harvesters
### e-Peas  
Another company was founded in 2014 after 10 years of research: e-peas has targeted the IoT (Internet of Things) market with the aim of improving the way low-energy devices are powered. In the beginning, the company prepared solar-energy-based solutions. In 2018, the company released the first AEM (Ambient Energy Manager) modules. These allow the use of RF waves to harvest energy [14]. For many years, e-peas provided neither antennas nor rectifiers, which are crucial for the harvester to operate. Only in 2022 did e-peas present the EP112 antenna [15]. This can be combined with other modules offered by the company and can be used as a full RF energy harvester; e-peas claims that their AEM30940 device can reach up to 95 % efficiency. The weakest signal for which the harvester can obtain energy from an RF wave is claimed to be \(-19\) dBm [16].  
AEM modules are supposed to harvest energy not only from electromagnetic waves but also from piezoelectric elements. It is important to underline that e-peas does not provide complete solutions but elements that can be integrated using external parts. The company Energous is such an external actor.
# Mambo-DQN: Adaptively Tunes Visual SLAM Parameters Based on Historical Observation DQN
## 3 Method
### Mamba-DQN Agent
#### 3.2.2 Computational Analysis of Mamba-DQN  
Based on the Mamba-DQN architecture described in the previous section, we analyze the computational overhead introduced by incorporating the Mamba model into the DQN framework.  
(1) Time Complexity Analysis  
The time complexity of Mamba-DQN stems from several key components:  
Sequence processing complexity:  
A significant feature of Mamba is its linear time characteristics. For an observation sequence of length \(k\), \((O_{t},O_{t+1},\cdots,O_{t+k})\), the processing time complexity is \(O(k)\).  
SSM computation complexity: for each time step, as shown in Equations (8)-(11), the computation includes the following:  
* State update: \(h_{t-i}=\tilde{A}_{t-i}\cdot h_{t-i-1}+\bar{B}_{t-i}\cdot X_{t-i}\).
* Output calculation: \(y_{t-i}=C_{t-i}\cdot h_{t-i}\).  
These matrix operations have a time complexity related to the hidden state dimension \(d\), resulting in an overall complexity of \(O(d^{2}k)\).  
(2) Memory Complexity Analysis  
The memory overhead of Mamba-DQN consists of the following components:  
Parameter storage: the parameter matrices in the Mamba module, including \(A_{i}\), \(B_{i}\), \(C_{i}\), and \(\Delta_{i}\), require storage space of approximately \(O(d^{2})\), where \(d\) is the hidden state dimension.  
Experience replay buffer: storing the historical observation sequence \((O_{t},O_{t+1},\cdots,O_{t+k})\) requires \(O(k\cdot|O|)\) memory, where \(|O|\) represents the size of a single observation.  
Embedding representations: according to Equations (4)-(7), the model needs to store observation embeddings \(E_{obs}\), action embeddings \(E_{act}\), and position encodings \(P\), with a memory requirement of approximately \(O(k\cdot d)\).  
Hidden States: the SSM computation process requires storing the hidden state \(h_{t-i}\) for each time step, with a memory requirement of \(O(k\cdot d)\).  
In summary, the time complexity of Mamba-DQN is \(O(d^{2}k)\), and the memory complexity is \(O(d^{2}+k\cdot|O|+k\cdot d)\), where \(d\) is the hidden state dimension, \(k\) is the length of the historical observation sequence, and \(|O|\) is the size of a single observation. Compared with traditional DQN, Mamba-DQN introduces additional linear time complexity but achieves superior temporal modeling capabilities.
## 7 Conclusions  
This study proposes a lightweight and efficient MEAG-YOLO model to enhance the accuracy of detecting the PPE of substation workers. Initially, the model integrates the MSCA module, optimizing feature extraction for targets of various scales in complex backgrounds. This approach improves detection performance. Secondly, inspired by the ECA mechanism, this study innovatively designed the EC2f structure, effectively enhancing the efficiency of key information extraction and detection accuracy. Furthermore, improvements were made to the model's PAN structure to further boost performance. This effectively integrates information from targets of different scales, enhancing feature fusion capabilities. Finally, the combination of GhostConv with YOLOv8n reduces the model's complexity and makes it lightweight. Experimental results demonstrate that MEAG-YOLO improves mAP by 2.4% and reduces model parameters by 6.7%. Compared to other mainstream target detection algorithms, this model demonstrates superior efficiency and accuracy. These qualities are essential for improving safety monitoring in substations and reducing accident risks among power grid workers. However, it is important to note that the dataset used in this study did not cover complex weather conditions. Future research will concentrate on enhancing both accuracy and speed of detection, as well as applying the model to devices such as inspection robots. Future efforts will aim to improve the MEAG-YOLO model's detection abilities in different weather conditions. They will also work on integrating the model more effectively into automation robots used in the power industry. Additionally, research will address the balance between model complexity and detection efficiency in environments with limited resources to ensure the model performs reliably and can be practically deployed.  
Conceptualization, H.Z. and C.M.; software, H.Z. and X.G.; validation, X.G. and C.H.; formal analysis, H.Z. and X.M.; writing--original draft preparation, H.Z.; writing--review and editing, C.M. and X.M.; funding acquisition, C.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Autonomous Region Science and Technology Innovation Leading Talent Training Project (grant number 2021 GKLRLX08), Yinchuan Science and Technology Innovation Project (grant number 2022 GX04), and Key Scientific Research Project of North MinZu University (grant number 2023 ZRLG10).  
Not applicable.  
Written informed consent has been obtained from the patient(s) to publish this paper.  
The raw data supporting the conclusions of this article will be made available by corresponding author on request.  
The authors would like to thank the editors and anonymous reviewers for their helpful suggestions. We sincerely thank [PERSON] of the University of Science and Technology of China for his suggestions. Additionally, we sincerely thank [PERSON] of North Minzu University for his guidance.  
The authors declare no conflicts of interest.