esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Segmentation by Searching for Image Feature Density Peaks
## 3 Experiment  
### Data and Experimental Setting  
We employed the Berkeley segmentation database (BSDS300) for evaluation of our segmentation scheme. The BSDS300 database consists of 300 natural images, each with multiple ground truth segmentations provided by different individuals. This database contains a variety of content, including landscapes, animals, buildings, and portraits, makes it a challenge for any segmentation algorithm. In addition, it has found wide acceptance as a public benchmark for testing image segmentation algorithms [34; 35]. In this paper, the entire BSDS300 database is first employed to investigate the effect of several user-defined parameters and evaluate the performance of our method compared to other schemes. Then, we divided the database into 7 different subsets according to specific content in order to further evaluate the segmentation performance in preserving the salient features of the input images. The evaluation details will be discussion in Section 3.2. All of our experiments are performed on PC with Intel CoreTM i7-6700 CPU with a frequency of 3.40 GHz and 8 GB of RAM under MS Windows 10.  
Following the previous works [22; 36], we adopt the commonly used Probabilistic Rand index (PRI) [34; 37] to measure the results of image segmentation for each image within the dataset. PRI was initially introduced for measuring the similarity between two data clusterings. It is now widely used for the comparison of segmentation algorithms using multiple ground truth images. PRI operates by comparing the compatibility of assignments between pairs of elements in the clusters. Its value between test and ground truth segmentations is computed by the sum of the number of pairs of pixels that have the same label in test \(S\) and ground truth segmentations \(G\), and those that have different labels, divided by the total number of the couple of pixels [34]. Specifically, given a set of ground-truth segmentations \(\left\{G_{k}\right\}\), the PRI is defined as:  
\[PRI\left(S,\left\{G_{k}\right\}\right)=\frac{1}{T}{\sum\limits_{i<j}}\left[c_{ ij}p_{ij}+\left(1-c_{ij}\right)\left(1-p_{ij}\right)\right] \tag{8}\]  
where \(c_{ij}\) is the event that pixels \(i\) and \(j\) have the same label and \(p_{ij}\) its probability. \(T\) is the total number of pixel pairs. We employ the sample mean to estimate \(p_{ij}\), Equation (10) amounts to averaging the PRI among different ground-truth segmentations. The PRI has been reported to suffer from a small dynamic range [38; 39], and PRI values across images and algorithms are often similar. In [38], this drawback is addressed by normalization with empirical estimation of its expected value.
## 3 Methodology
### Dynamic Region Depth Estimation
#### 3.1.1 Identification of Dynamic Regions
In particular, we assume that moving objects follow rigid motion patterns, which simplifies the depth estimation problem. Since most objects in typical scenes have near-rigid motion, we can estimate the depth by assuming their motion involves translation and rotation relative to the camera. By incorporating these motion cues into the depth estimation process, we ensure that the dynamic depth predictions are more robust to variations in object motion.
# Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method
## 1 Introduction
Among the above methods, the scheme of the VMD method, combined with the Prony algorithm, can obtain relatively high identification accuracy. The VMD method can be used to decompose ambient noise signals into finite intrinsic mode functions (IMFs). Then, the potential oscillation modal information can be obtained by using the Prony algorithm. In this paper, a scheme of bandpass filtering, combined with the IA-VMD algorithm and the Prony algorithm, is proposed. The experimental results show that IA-VMD combined with the Prony algorithm has improved accuracy. The performance of the IA-VMD method for power system oscillation identification is studied in this paper. The model of ambient signals and the framework of VMD-based LFO identification are given in Section 2. The IA-VMD mode identification method and the Prony algorithm are introduced in Sections 3 and 4, respectively, and the performance of the method for analyzing LFO in a power system is studied in Section 5. Section 6 provides conclusions.
## 4 Comparative Experiments
### Deep Learning Combined with Other Algorithms
#### 4.2.2 Comparison of Deep Learning and Other Algorithm Classification Results  
Different algorithms have different advantages. Naive Bayes (NBM) and k-nearest neighbor (KNN) algorithms do not require training: when the data is simple, their reliability is high. SVM can achieve better classification results with a small number of labeled samples. However, these traditional algorithms cannot achieve better classification results for spectral data with a large number of dimensions and a small sample size, and when the data is linearly inseparable. Table 1 shows the classification of high-dimensional spectral data of the University of Pavia.  
The runtime and memory usage of the deep learning and support vector machines shown in Table 1 are based on trained networks. Of the five algorithms, only two deep learning networks and SVM have more than 90% classification accuracy; the memory usage and running time of SVM are larger than that of deep learning. As seen by repeated experimentation, deep learning for processing hyperspectral data has obvious time and memory usage advantages. In addition, deep learning is  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification Method** & **Classification Accuracy (\%)** & **Memory Usage (MB)** & **Time Spent (s)** \\ \hline SAE-LR & 93.69 & 37 & 0.79 \\ DBN-LR & 92.87 & 35 & 0.53 \\ PCA-NBM & 88.08 & 39 & 28.054 \\ KNN & 88.66 & 126 & 3.713 \\ SVM & 90.84 & 76 & 5.99 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification accuracy of different classification algorithms.  
Figure 16: Comparison of depth learning and principal component analysis.  
As seen from Figures 17 and 18, after stack automatic encoding or dimensional reduction of the data, the classification precision of the other traditional classification algorithms is significantly higher than with processing of the original data. SVM has the highest classification accuracy, followed by k-nearest neighbor and then by Naive Bayes, with the lowest classification accuracy. Naive Bayes achieves higher classification accuracy when working with compressed data (in Figure 17 a dimension 20 achieved the highest classification accuracy of 89.7%; in Figure 18, when dimension was 40, the classification accuracy increased to 88.92%), and SVM classification accuracy using compressed data with a dimension 80 achieved the highest accuracy.
## 2 Numerical Simulation Algorithm and Constitutive Model
### Chemical Model Validation  
Qu et al. [41] conducted a 9.5% concentration 100 m\({}^{3}\) volume gas explosion experiment in a large square tunnel with a cross-sectional area of 7.2 m\({}^{2}\) and a length of 900 m, and the ignition source was located at the closed end of the tunnel. According to the experiment, a 3D numerical model of the same size tunnel was established to verify the accuracy of the 18-element reaction, 53 element reaction kinetic model, and simplified detonation model of methane deflagration, as shown in Figure 2.  
The simulation results of the simplified explosion model that simplifies the complex methane combustion reaction mechanism into a one-step reaction differ greatly from the experimental data. The simulation results of the 18- and 53-element reaction kinetic models are very close, and after 120 m, they are close to the experimental data. After mesh refinement (with an average of 5.8 elements/m), the numerical simulation results using the 18-step reaction kinetic models in a gas explosion within 120 m of the front of the tunnel are closer to the experimental data. Therefore, when the density of grid cells is sufficient, using the 18-step methane reaction mechanism can reduce computational costs while ensuring the accuracy of simulation results.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Material** & \(\rho\)/kg\(\cdot\)m\({}^{-3}\) & \(E\)/GPa & \(\mu\) & \(\sigma_{y}\)/MPa & \(E_{tan}\)/GPa & \(C\)/s\({}^{-1}\) & \(P\) & \(B\) & \(F_{s}\) \\ \hline Tunnel surface & 1860 & 2.61 & 0.30 & 12.3 & 0.25 & - & - & 0.5 & 0.8 \\ Steel pipe & 7850 & 200 & 0.33 & 473 & 75.19 & 6844 & 3.91 & 1 & 0.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material parameters of coal and steel.
# Channel Pruning-Based YOLOv7 Deep Learning Algorithm for Identifying Trolley Codes
## 3 Results and Analysis
### Discussion
#### 3.3.1 Influence of Distance between Industrial Camera and Workpiece  
We employed a range of distances between the target and the camera, spanning from 15 cm to 30 cm. Figure 11 illustrates an instance where the detection of a hole is incorrect.  
When the camera-to-target distance varied between 15 cm and 25 cm, the proposed method successfully detected both large and small holes. When the distance was increased to 30 cm, erroneous decisions were made. In Figure 11a, the hole pattern located in the bottom left corner is identified as a large hole. Similarly, in Figure 11b, the pattern in the same corner is erroneously identified as both a large hole and a small hole in the workpiece is overlooked. The primary factor contributing to this issue is the significant shooting distance. At such distances, the small holes and the background exhibit similar color characteristics, while the large hole features resemble the hole pattern features of the background plate. Consequently, this similarity poses challenges in extracting the relevant features, ultimately resulting in the missed detection of certain large and small holes.  
The distance between the trolley and the industrial camera lens was consistently maintained at a fixed distance of 20 cm during regular operational circumstances. The findings indicated that the proposed method exhibited superior accuracy in detecting small holes. However, it was observed that the accuracy of the method decreased as the distance between the camera and the workpiece increased to 1.5 times the distance observed under normal working conditions. When the distance between the lens and the workpiece varied between 15 cm and 25 cm, indicating non-standard working conditions, the proposed algorithm demonstrated the capability to accurately and efficiently detect both small and large holes. This finding demonstrates the robustness of the proposed method in relation to the distance at which the image is captured.  
In order to enhance the model's ability to accurately and efficiently identify holes of different sizes under diverse conditions, the merging and fusion of feature maps can be employed to extract more comprehensive information. For example, various techniques can be employed to achieve this, including element-wise addition, element-wise multiplication, channel concatenation, attention mechanisms [32], and other methods.
## 3 Materials and Methods
### Ecosystem Service Assessment
#### 3.1.4 Data  
We utilized LULC data for the TRHR obtained in 2000 as part of a remote sensing (RS) project to monitor the status of Chinese land use at a 1:100,000 scale. This project was initiated by the Chinese Academy of Science and resulted in a primary dataset of Landsat TM/ETM RS images alongside data that was subsequently generated using the human-computer interactive interpretation method. Thus, for consistency, we also employed 2012 land use data that were generated using this method of interpretation from HJ-1 (Huan Jing-1: Environmental Protection & Disaster Monitoring Constellation) images (i.e., a mini-satellite constellation that is used for environmental and disaster monitoring) that cover the entire TRHR. These LULC type data were classified into 25 categories, which were themselves subsequently grouped into six classes, encompassing cultivated land, forested land, grassland, rivers/lakes, construction land and unused land. The average interpretation accuracy of this LULC type classification was 97.5% for 2000 and 85.2% for 2012.  
We obtained NPP data for the TRHR for 2000 and 2012 from [38]; these data were generated using the global production efficiency model, incorporating localized parameters extracted from large plot data obtained by cyclic sampling [47].  
We obtained climate data for the period between 2000 and 2012 (including daily mean temperature and precipitation) from the China Meteorological Administration. These data were then used to calculate annual precipitation for the water yield model, rainfall erosivity for the soil conservation model and for analysis of climate variability. Climate data from different meteorological station records were interpolated onto raster-formatted surfaces using thin plate smoothing spline methods in the software ANUSPLIN 42 [48], while raster-formatted soil data, including the proportion of sand, silt, clay and the soil depth reference, all used to measure plant available water content (PAWC) in the water yield model and K in the soil conservation model, were extracted from the Harmonized World Soil Database [49]. A 30-m resolution DEM was then used to calculate slope length and slope angle to input into our soil conservation model.
# Advances in Piezoelectric Jet and Atomization Devices
## 2 Advances in Piezoelectric Micro Jet Devices
### Shear Mode  
In 1987, [PERSON] et al. [39] proposed the concept of the \"shared wall\" shear mode for a piezoelectric nozzle brake, as presented in Figure 1(d1). When the pulse signal was applied on the piezoelectric brake wall, the volume of the brake container was changed synchronously, and then the fluid was squeezed out. By contrast, [PERSON] et al. [20] conducted a more in-depth study on the \"shared wall\" shear type piezoelectric nozzle brake, and elaborated the working principle, structural characteristics, and manufacturing process of the brake. As shown in Figure 1(d3), [PERSON] et al. [40] proposed a new type of shear type piezoelectric inkjet head in 2006. In 2010, [PERSON] et al. [41] proposed a shear type piezoelectric nozzle as shown in Figure 1(d2). The ink storage chamber of the nozzle is similar to the curved type, but it should be noted that the shear deformation was performed on the piezoelectric ceramic. At present, this type of nozzle has been used in Nova and Galaxy printers by Spectrum Company.  
The above-mentioned piezoelectric jet devices belong to shear type piezoelectric micro jet devices, and their main common feature is that the polarization direction of piezoelectric ceramics is perpendicular to the electric field direction. Under the excitation of the driving signal, the lateral deformation is produced by the role of the piezoelectric ceramic system, resulting in the fluid being squeezed in the reservoir cavity and produce jet.
## 3 Results and Discussions
### Machine Learning Classifications
#### 3.2.2 Selected Features after Correlation Analysis  
Figure 5 illustrates the classification performance of the supervised classifiers using Pearson (Figure 5a), Kendall (Figure 5b), and Spearman (Figure 5c) selected features. The inclusion of the shortlisted features with the standard statistical features significantly improves the classification performance. The best classification performance is achieved using Pearson and Spearman-based features, with a classification accuracy of 98.6% using the KNN classifier. Moreover, the other performance matrices, such as standard deviation, precision, recall, f1-score, sensitivity, and specificity, of the models have also drastically improved, achieving values well above 95%.  
Figure 4: Standard statistical features-based stress versus baseline classification using logistic regression, random forest, and K-nearest neighbours classifiers.
## 4 Improved Algorithms
### Improved Hybrid Algorithm of the APF and RRT
#### 4.4.1 Principle of the Improved Hybrid Algorithm  
The principle of the hybrid algorithm is as follows. First, the tree node is initialized, and the distance between the nearest node and the obstacle is continuously detected. If it is detected that the minimum distance between the current node and the obstacle is greater than twice the step length, it means that there is no obstacle near the current node, and the I-APF method is used for rapid expansion. If the minimum distance between the current node and the obstacle is less than twice the step length, the I-RRT algorithm is adopted to make full use of the efficient obstacle avoidance ability of the RRT algorithm, and the above steps are repeated until the target point is reached. The hybrid algorithm can effectively improve the efficiency of path searching and resolve the following problem of the APF method: When there are obstacles at the starting point, the repulsive force causes a tortuous path, and the target is unreachable when there is an obstacle at the target point. Furthermore, the hybrid algorithm also solves the problem of the classic RRT algorithm, which has a significantly lower efficiency when there are more obstacles, and the generated path is shorter and smoother.  
Figure 6: Diagram of the I-RRT algorithm implementation to remove redundant nodes.  
Figure 7: I-RRT algorithm generation path diagram.
## 5 Machine Learning Algorithms
### Speech Emotion Recognition Models
#### 5.1.2 Convolutional Neural Networks: ResNet  
ResNet is a neural network architecture designed to solve the vanishing gradient issue in deep networks by introducing residual connections that skip layers, allowing the model to learn identity functions more effectively. These shortcut connections enable the training of very deep models with improved performance, as seen in tasks like image recognition and object detection. The number in ResNet refers to the total number of layers in the network, which include convolutional layers, pooling layers, fully connected layers, and residual blocks. The higher the number, the deeper the network, with more layers used to extract features and process information, allowing the network to capture more complex patterns. The comparison of neural networks for the problem of emotion recognition in speech was made not only based on ResNets, but also on classical convolutional networks [33]. The datasets used in the comparison are CREMA-D, IEMOCAP, RAVDESS, SAVEE, and TESS. The ResNet 50, 34, and 18 networks in different configurations with categories were used in the comparison.
###### Abstract
[30] proposed a suspension control strategy based on the adaptive fuzzy PID control method to address uncertainties in road disturbances. In summary, to mitigate the impact of unmodeled vehicle dynamic factors and driver dynamic interference during driving, some researchers employ sensors and feedback control, while others utilize fuzzy methods to address the uncertainty of road disturbances. [PERSON] and [PERSON] [31] proposed a data-driven model-free adaptive control (MFAC) method for multi-input multi-output nonlinear discrete-time systems, utilizing the concept of pseudo-partial derivatives and dynamic linearization. The primary feature of this method is that the controller design relies solely on the measured input/output (I/O) data of the controlled object. Analysis and extensive simulations demonstrate that the MFAC method ensures bounded input-bounded output (BIBO) stability and convergence of tracking errors. As of late, the MFAC method has been widely applied to complex nonlinear systems and systems that are challenging to model [32; 33; 34; 35]. Building on the author's previous work [27], this paper designs an MFAC method for the joint control of seat suspension and vehicle suspension, utilizes a genetic algorithm to optimize the controller parameters, and efficiently determines the optimal parameter combination. The efficacy of the MFAC method is investigated through comparative analysis with the original passive HVS system, an HVS system coupled with an NESI, and an active HVS system coupled with an NESI using the PID control method. In addition, the influence of the installed position of MFAC on the dynamic performance of the active HVS system coupled with NESI is examined. The structure of this paper is outlined as follows: Section 2 introduces a dynamic model of an active HVS system coupled with an NESI and establishes its nonlinear dynamic equations using [PERSON]'s second law. Section 3 presents the formulations of the MFAC method based on the genetic algorithm. Sections 4 and 5 analyze the dynamic performance of the active HVS system coupled with an NESI using the MFAC method under random pavement and shock excitations, respectively, which provides a detailed analysis of the effect of the MFAC method on the dynamic performance of the HVS system. Section 6 provides a summary of the paper.
# A Survey on Multi-User Conversational Interfaces
## 4 Capturing Multi-User Conversational Interfaces
### Multi-Modality in Multi-User Conversational Interfaces
#### 4.3.1 Virtual Agents  
For instance, in the experiments conducted by [PERSON] et al. [25] the authors collect and annotate audio and visual data, using encoding schemes to capture conversational multi-modal behaviours. The annotation scheme includes various elements, such as head movements, facial expressions, and facial gestures, which play a crucial role in managing turn-taking and appropriate feedback in multi-user scenarios. The proposed MUCI is capable to engage in conversations with students using a combination of sensors and technologies, including Kinects for gesture and head orientation recognition, lapel microphones for speech capture, and a multi-channel loudspeaker array for spatialised audio presentation. In addition, [PERSON] et al. [24] propose a learning environment framework for tutoring systems. Their aim was to achieve a more natural interaction with AI agents without the need for wake-up words. The environment can be extended to multi-party scenarios, in which several agents may interject or even compete for the users' attention.