esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Partitioned Spatial-Temporal Graph Attention Convolution Network for Human Motion Recognition
## 5 Experimental Results
### Experimental Comparison and Analysis
The accuracy of NEW-STGCN-CA is 16% and 19.50% higher than that of the Deep LSTM Top-1 accuracy and Top-5 accuracy, respectively, and 12.1% higher than Top-1 accuracy and 14.80% higher than Top-5 accuracy for Temporary ConvNet. The reason for the different recognition rates on the Kinetics-Skeleton and NTU RGB+D 60 datasets is that the recognition rate on Kinetics-Skeleton is low due to the harsh data collection environment. \begin{table}
\begin{tabular}{c c c} \hline
**Algorithms** & **CS** & **CV** \\ \hline ST-GCN & 81.69\% & 88.30\% \\ ST-GCN+SE & 84.15\% & 89.30\% \\ ST-GCN+CBAM & 83.90\% & 89.30\% \\ STGCN-CA & 84.45\% & 90.10\% \\ NEW-STGCN & 83.49\% & 91.60\% \\ NEW-STGCN-CA & 84.86\% & 92.46\% \\ Two-stream & 83.20\% & 89.30\% \\ Clip+CNN+MTLN & 79.60\% & 84.80\% \\ ARRN-LSTM & 81.80\% & 88.00\% \\ BPLHM & 84.50\% & 91.10\% \\ CA-GCN & 83.50\% & 91.40\% \\ \hline \end{tabular}
\end{table} 
Table 1: Experimental results on the NTU-RGB+D 60 dataset. The NEW-STGCN-CA can accurately identify the actions in the target video in Figure 8. However, the existing two-stream has misjudgment and cross-recognition effects. For example, tai chi is recognized as baby crawling in Figure 8a, and the stretching leg action is recognized as snowboarding in Figure 8b. In practical applications, images may be contaminated by different noise sources. Therefore, we enhanced the data of the sample image, added different noise processing as shown in Figure 9, and then used NEW-STGCN-CA to identify the actions in the image. The recognition effect is shown in Figure 10. The experimental results show that the recognition effect of NEW-STGCN-CA in raising arm movements in tai chi movements is accurate under the interference of noise. \begin{table}
\begin{tabular}{c c c} \hline
**Algorithms** & **Top-1** & **Top-5** \\ \hline NEW-STGCN-CA & 32.40\% & 54.80\% \\ Deep LSTM & 16.40\% & 35.30\% \\ Temporal Conv & 20.30\% & 40.00\% \\ Feature Enc & 14.9\% & 25.80\% \\ ST-GCN & 30.7\% & 52.80\% \\ \hline \end{tabular}
\end{table} 
Table 2: Comparison of recognition accuracy of Kinetics-Skeleton dataset. Figure 8: Comparison of NEW-STGCN-CA and two-stream identification. Figures (**a,c**) show NEW-STGCN-CA and Two-Stream recognizing the stretching arm action in Tai Chi, and Figures (**b,d**) show NEW-STGCN-CA and Two-Stream recognizing the stretching leg action, respectively.
# RSB: Robust Successive Binarization for Change Detection in Bitemporal Hyperspectral Images
## 3 Numerical Experiments
### Results Evaluation  
To assess the quality and the accuracy of the final produced results, two common indicators are adopted: the overall accuracy (OA) and the Kappa coefficient (K). In particular, OA expresses the fraction of the correctly classified pixels with respect to the total number of pixels,  
\[\text{OA}:=\frac{(\text{TP}+\text{TN})}{(\text{TP}+\text{FP}+\text{FN}+\text{ TN})}.\]  
The OA indicator sometimes appears to be biased; for example, in the case of a completely black output, since the black pixels constitute the majority of the pixels in the ground truth, the computed OA could still be slightly greater than 0.50, which is obviously a faulty accuracy evaluation. Hence, to obtain a more reliable result, the Kappa coefficient is also computed as  
\[\text{K}:=\frac{\text{OA}-p}{1-p},\] \[p:=\frac{(\text{TP}+\text{FP})(\text{TP}+\text{FN})}{(\text{TP} +\text{TN}+\text{FP}+\text{FN})^{2}}+\frac{(\text{FN}+\text{TN})(\text{FP}+ \text{TN})}{(\text{TP}+\text{TN}+\text{FP}+\text{FN})^{2}}.\]  
In both the above expressions, the used symbols correspond to  
Figure 9: Binary change maps provided by different thresholding methods. From left to right: RSB, Otsu, Mean, Li, Sauvola, Triangle, Yen. From top to bottom: _Hemiston_, _USA_, _River_, and _Bay Area_ datasets.
## 3 Establishment of Bone Density Prediction Models and Analysis of Parameter Effects
### Results and Discussion
#### 3.2.2 Predictive Modeling and Analysis of Cancellous Bone Density
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Typology**} & **Sum of Squared** & **Degrees of** & **Mean Square** & \multirow{2}{*}{\(F\)**-Value**} & \multirow{2}{*}{\(p\)**-Value**} & \multirow{2}{*}{**Significance**} \\  & **Deviations** & **Freedom** & **Error** & & & \\ \hline Model & 0.2944 & 9 & 0.0327 & 14,799.1 & \(<\)0.0001 & significance \\ \(A\)—Filling density & 0.2819 & 1 & 0.2819 & 12,7500 & \(<\)0.0001 & significance \\ \(B\)—Material flow & 0.0097 & 1 & 0.0097 & 4403.35 & \(<\)0.0001 & significance \\ C—Layer thickness & 0.0001 & 1 & 0.0001 & 7.64 & 0.0200 & \\ \(AB\) & 0.0028 & 1 & 0.0028 & 1255.33 & \(<\)0.0001 & significance \\ \(AC\) & \(1.125\times 10^{-6}\) & 1 & \(1.125\times 10^{-6}\) & 0.5089 & 0.4919 & \\ \(BC\) & \(1.250\times 10^{-7}\) & 1 & \(1.250\times 10^{-7}\) & 0.0565 & 0.8168 & \\ \(A^{2}\) & \(5.818\times 10^{-6}\) & 1 & \(5.818\times 10^{-6}\) & 2.63 & 0.1358 & \\ \(B^{2}\) & \(2.506\times 10^{-6}\) & 1 & \(2.506\times 10^{-6}\) & 1.13 & 0.1321 & \\ \(C^{2}\) & \(6.568\times 10^{-6}\) & 1 & \(6.568\times 10^{-6}\) & 2.97 & 0.1155 & \\ Residual & 0 & 10 & \(2.211\times 10^{-6}\) & & & \\ Misfit term & \(9.273\times 10^{-6}\) & 5 & \(1.855\times 10^{-6}\) & 0.7226 & 0.6349 & insignificance \\ Pure error & 0 & 5 & \(2.567\times 10^{-6}\) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Analysis of variance for \(Y_{2}\) regression equation. Figure 7: Residual analysis of \(Y_{2}\) regression equation. (**a**) Plot of normal probability distribution of residuals for \(Y_{2}\) prediction model; (**b**) distribution of predicted and actual values of \(Y_{2}\) model. flow rate (40%, 80%), then a low filling density with a high flow rate (10%, 100%), and finally, a low filling density with a low flow rate (10%, 80%). Figure 8b shows a surface plot demonstrating the interaction effect of filling density and layer thickness on the density of the print block. The findings indicate that a high filling density combined with a thick layer (40%, 0.3 mm) yields the highest density, followed by a high filling density with a thin layer (40%, 0.1 mm). This is succeeded by a low filling density with a thick layer (10%, 0.3 mm), and lastly, a low filling density with a thin layer (10%, 0.1 mm). Figure 8c depicts a surface plot illustrating the interaction effect of the layer thickness and material flow rate on the density of cancellous bone. The data indicate that the interaction between the layer thickness and material flow rate has a negligible effect on the density, suggesting that this factor does not significantly influence the overall density.
# Integration of EMU Overall Design Model Based on Ontology-Knowledge Collaboration
## 6 Study of EMU Design with Ontology-Knowledge Collaboration
### Introduction to PIS System Case Study  
This section begins by introducing the application scenario of the case study, followed by demonstrating the application of ontology and the development of an ontology-based KG. Figure 16 illustrates the comprehensive workflow for developing the requirements, architecture, and traceability integrated ontology of the EMU PIS system, based on the application scenario, utilizing the MetaGraph2.0 tool.  
Figure 16: Semantic integration of EMU PIS system requirements, architecture, and traceability relationship model.  
Ontology-driven demonstration: In the case study of developing a PIS for an EMU, ontology plays a crucial role. Within the MBSE framework, ontology is employed to define the elements of the system and their relationships. By formalizing entities, properties, and relationships in the ontology language, the system can establish a shared, consistent understanding foundation. In this case study, ontology is applied in the description and construction process of the requirements, architecture, and traceability relationship models of the EMU PIS system. Through traceability relationships, requirements and architecture are connected, as depicted in Figure 17, where the expanded ReqIF model represents the requirements of the EMU and the expanded SysML model represents the architecture of the EMU.  
In the development process of the EMU PIS system, the application of a KG also plays a crucial role. Utilizing the structured descriptions provided by the ontology, a KG organizes the system's data into networks, enabling information to be more efficiently queried, analyzed, and updated. In this paper, the requirements and architecture of an EMU are presented through KGs. Through the analysis above, entities and relationships form a total of 15 CSV files that are stored in the Neo4j database to establish a knowledge base. Due to the complexity of a project's KG, this section only presents the KG of requirements. The PIS system has three levels of requirements, namely top-level requirements, first-level requirements, and second-level requirements. Top-level requirements are represented by the blue portion, first-level requirements by the yellow portion, and, finally, the pink portion represents second-level requirements, as shown in Figure 18.  
The ontology-driven presentation and knowledge-graph-driven presentation complement each other in the development process of the EMU PIS system. By utilizing the BFO upper-level ontology framework and the Neo4j graph database, the dual drive of ontology and knowledge is achieved. This approach is also applicable to other systems within the EMU. By leveraging the advantages of ontology and knowledge graphs, the development team can achieve comprehensive control and effective management of the EMU PIS system.  
Figure 17: Description and construction of the requirements, architecture, and traceability relationship models of the EMU PIS system.
## 2 Related Works  
### Evaluation of Clinical Symptoms  
Most methods of diagnosing Parkinson's disease are based on clinical symptoms, taking into account the patient's motor functions. The development of technologies and devices that allow one to record a given signal and the use of digital signal processing methods that allow one to classify such a signal have resulted in a very large increase in interest in this subject. Tremor disturbances are one of the key features in PD. The most popular systems are methods aimed at detecting tremors in a patient's limbs. MEMS sensors are used for this purpose [10], such as inertial sensors placed on the back of each hand [11], electromyograms (EMGs) [12], and electroencephalograms (EEGs) [12; 13], as well as accelerometers included in smartphones and smartwatches [14].  
The second most popular method for detecting tremors is handwriting analysis. The most common symptoms in handwriting in patients with PD include micrographia and dysgraphia, slower movements, and jerk [15]. Most studies are based on handwriting data from the on-line technique, which are enrolled from tablets and special pens [16]. It contains information related to the dynamic feature of the handwriting process (i.e., azimuth and altitude angles) [17; 18]. New research also using the deep learning method [19].  
Another method developed to support PD diagnostics is facial image analysis to recognize, for example, hypomimia in patients with Parkinson's disease based on image recordings in the visible and infrared field [20] and facial expressions [21]. Another method also uses infrared thermography to detect a significantly altered thermal skin response in PD patients compared to healthy control subjects, especially on the fingers [22]. The detection of gait using different sensors gives possibilities to build different kinds of system to detect PD. The evaluation methods include embedded wearable devices integrating a triaxial accelerometer, a triaxial gyroscope, and a triaxial magnetometer, implementing a sensor with embedded data fusion and aggregation algorithms [23]; CNN models using video datasets [24]; a 2D key point estimator that estimates the joint coordinates of people after image capture using a monocular camera [25]; and the combination of wearable sensors and machine learning (ML) to detect FOG (freezing of gait) in real time [26]. Recent work has applied data-driven machine learning techniques to analyze videos of PD patients performing motor tasks, such as finger tapping [27; 28]. This task is an essential element of the UPDRS scale, motor examination (Part III), encompassing 18 motor tasks performed by the patient.
## 3 Results and Discussion
### Methylation of ESR1 and PGR in leiomyoma and MYOMETIUM Tissues  
Methylation levels of _ESR1_ and _PGR_ in both leiomyoma tissues and myometrium tissues are shown in Figure 1. The methylation levels of _ESR1_ (18.10 \(\pm\) 4.41 vs. 28.72 \(\pm\) 4.95) and _PGR_ (2.32 \(\pm\) 0.81 vs. 3.27 \(\pm\) 0.56) were significantly lower in leiomyoma tissue compared to those in myometrium tissue.  
The methylation of _ESR1_ and _PGR_ showed no significant differences by status of parity, menstrual phases, menstrual pattern, smoking, indirect smoking, education, and family incomes. However, there was significant difference in the level of _PGR_ methylation between patients with and without dysmenorrhea (2.07 \(\pm\) 0.71 vs. 2.51 \(\pm\) 0.85) (Table S3).  
Epigenetic changes are known to play an important role in the pathogenesis of uterine leiomyoma. DNA methylation profiles in leiomyoma tissue are clearly distinguished from those in myometrium tissue, while quite similar DNA methylation profiles are observed in myometrium tissues in women with and without leiomyomas [21]. Furthermore, significant methylation changes in genome-wide DNA and X chromosomes, as well as promoter regions of specific genes, have been observed in leiomyoma tissues [37; 38; 39; 40; 41]. [PERSON] et al. [20] reported that highly expressed _ESR1_, attributable to _ESR1_ hypomethyllation, is associated with pathogenesis of uterine leiomyoma. Our results--significant hypomethyllation of  
Figure 1: Methylation levels of _ESR1_ and _PGR_ in leiomyoma and myometrium tissues. Data indicate mean \(\pm\) standard deviation, and \({}^{*}p<0.05\).  
_ESR1_ and _PGR_ in leiomyoma tissues compared to adjacent myometrium tissues--are in concord with their finding.  
PR's role in pathogenesis of leiomyoma is crucial, but methylation of _PGR_ have not been extensively reported. Recently, one paper showed that PR integrates the effects of mutated _mediator complex subunit 12 (MED12)_ and altered DNA methylation to stimulate _receptor activator of nuclear factor kappa-B ligand (RANKL)_ expression and stem cell proliferation in uterine leiomyoma [41]. Progesterone action is essential for uterine leiomyoma growth and development via interaction with PR. Thus, our results could explain pathogenesis of uterine leiomyoma partially, but further investigations should be performed to validate this.
## 3 Case Study
### Implemented Methods
#### 3.2.3 Bidirectional Connection  
All measurements were displayed chronologically sorted in the graph. In the case that there is more than one measurement per one station, all values were marked onto the graph when the user makes the _hover_ to station position on the map. In case there is more than one measurement (for a given location and time range), one of the group functions is used to calculate a single value (median value in concrete example). The number of values used for aggregation is shown in brackets next to the station name. By clicking on the graph (specific column or line), the time series graph view is shown with all particular values sorted chronologically. Graph element and station marker were color matched and visual feedback was provided on hovering on either of these elements. An example is shown in Figure 8.  
Figure 8: Example of bidirectional connection from web application.
# A Novel Method for Tool Identification and Wear Condition Assessment Based on Multi-Sensor Data
## 2 System Framework and Platform Construction  
### System Framework  
The framework of tool wear evaluation system based on spindle current and multi-sensor data is shown in Figure 1. Firstly, this method analyzes the working state of the machine tool through the spindle current, and determines the current tool type, so as to prepare for the subsequent prediction of tool wear state. Secondly, the separation tool is used to properly process the collected signals and extract the mean value, variance, and other characteristics of the signals. Finally, according to the extracted feature information, the wear degree is predicted by the neural network method.
# A Semantic Segmentation Dataset and Real-Time Localization Model for Anti-UAV Applications
## 3 Anti-UAV Semantic Segmentation Dataset
### Dataset Source  
Collecting real UAV semantic segmentation images in our research environment is challenging. Therefore, we compiled our dataset by integrating images from multiple existing sources. Specifically, we combined UAV images and their corresponding bounding boxes from three different datasets:  
1. Anti-UAV dataset;
2. DUT-anti-UAV dataset;
3. Random UAV images scrape.  
From these sources, image and label pairs were randomly selected to form our proposed dataset. For folder organization, we adopted the folder structure used in the Anti-UAV dataset. Each image sequence contains RGB and infrared (IR) image pairs when available, primarily from the Anti-UAV dataset. If an IR image is not available for a given sequence, only the RGB image is included.  
Since the original datasets focus on object detection and tracking tasks, only the images and their corresponding UAV bounding boxes were utilized. Each image and bounding box pair was used to generate semantic segmentation masks in the next step. Figure 1 shows example images sampled from the various dataset sources.
# An Experimental Study of Industrial Site and Shaft Pillar Mining at Jinggezhuang Coal Mine
## 1 Introduction and Geological Conditions
### Geological and Mining Conditions  
The Jinggezhuang colliery (JGZ) is located in Tangshan, Hebei Province, East China; the location of JGZ is shown in Figure 1. Mining was started at JGZ in 1979 and the annual production is 1.2 million tons. JGZ is located in a soil deposit plain land. The field is flat, with an elevation of +38.8 m in the north and +23.85 m in the south. The average elevation is 35.6 m.  
Mining was carried out at JGZ for 38 years and negligible coal resources remained. JGZ was closed in 2017. It was determined that the industrial site and shaft pillar at JGZ would be mined to excavate all coal resources. There are four coal seams in this pillar: 9\({}^{\lx@math@degree}\), 11\({}^{\lx@math@degree}\), 12-1\({}^{\lx@math@degree}\), and 12-2\({}^{\lx@math@degree}\) with thicknesses of 7 m, 1.3 m, 1.5 m, and 5 m, respectively. The distances from 9\({}^{\lx@math@degree}\) to 11\({}^{\lx@math@degree}\), 11\({}^{\lx@math@degree}\) to 12-1\({}^{\lx@math@degree}\), and 12-1\({}^{\lx@math@degree}\) to 12-2\({}^{\lx@math@degree}\) are 18 m, 17 m, and 4.5 m, respectively. The coal seam angles are 4\({}^{\circ}\)\(\sim\)8\({}^{\circ}\) and 15\({}^{\circ}\)\(\sim\)25\({}^{\circ}\) above and below an elevation of \(-\)260 m, respectively.  
The thickness of quaternary alluvium in this region is approximately 165 m. The stratigraphic classification of the entire region is weak and medium hard and a brief borehole histogram was outlined in the Appendix A in Figure 11. It can be seen from the attached figure that the overlying strata are mainly composed of fine sandstone, siltstone, medium sandstone, and their interbedding. In order to better explain the geological conditions of the study area, the author provided a borehole histogram and put it in the Appendix A. It can be seen from the attached figure that the overlying strata are mainly composed of fine sandstone, siltstone, medium sandstone, and their interbedding. At the beginning of the construction of JGZ, an industrial square protective coal pillar was designed, which played a great role in ensuring the safety of mine production. However, with the depletion of resources, continuing to retain the coal pillar in the original design will waste a lot of coal resources. The managers hope to extract as much coal resources as possible while ensuring the safety of the shaft. A new shaft pillar area is needed. Since 2008, JGZ has faced difficulties in terms of coal production and cost owing to the exhaustion of coal resources. To improve the plan of the subsequent production, panel 0091 was mined with surface deformation monitoring as an experiment to study the surface deformation characteristics and mine prediction parameters using the probability integral method (PIM). Furthermore, provide new parameters for the new shaft pillar design.
## 4 Materials and Methods
### Validation of Measured Reflectance and Epithelial Sensitivity with Simulated Results  
The probe was calibrated for uniform illumination by storing the intensity value of each fiber. Inverse of this intensity value was saved as a correction factor of each fiber of the probe. A homogenous intralipid solution of scattering coefficient \(\mu_{s}=100\) cm\({}^{-1}\) was irradiated using source fiber (fiber number 74 in Figure 5e) and reflectance was measured from each of the fibers. The reflectance measured from each fiber was multiplied by the calibration factor to obtain a corrected spatially resolved reflectance pattern. The measured corrected spatially resolved reflectance was compared with the modified MC (Section 3.1) simulated data and shown in Figure 7a,b for fiber numbers 1 to 58 except 24, 25, 26 and 40 (these fibers were not connected to light detection system due to lack of collection efficiency).  
Figure 6: (**a**) Schematic diagram, and (**b**) photograph of experimental set-up used to collect steady state spatially resolved reflectance; LS—Light Source, P—Probe, D—Detector, CDCS—Constant DC current supply, TC—temperature controller, P1—Proximal end of probe, P2—Distal end of probe, M1 and M2 are stepper motors connected for x-axis and y-axis translation, CPS—Chopper power supply, F74—fiber number 74 in Figure 5e used as source fiber.  
For epithelial sensitivity measurement, SRR was measured from a two-layered phantom prepared with a highly absorbing (\(\mu_{a}=100\) cm\({}^{-1}\)) and minimally scattering (\(\mu_{s}=0.001\) cm\({}^{-1}\)) bottom layer, and the optical properties of the top layer were chosen as \(\mu_{a}=2.5\) cm\({}^{-1}\), \(\mu_{s}=40\) cm\({}^{-1}\) and thickness of top layer = 300 \(\upmu\)m. The reflectance from each fiber was experimentally measured and corrected. The highly absorbing and minimally scattering bottom layer ensures that the photons which once enter the bottom layer have a negligible probability of exiting to the top layer and contribute to the SRR. Therefore, the measured SRR for such a phantom is an indicator of the epithelial sensitivity. Numerical SRR data was generated for a phantom with the same optical properties as the aforementioned two-layer phantom using the modified MC code. The experimentally measured and numerically computed epithelial sensitivity for fibers 59 to 77 are plotted in Figure 7c indicating good agreement between the experiment and the simulation. The other fibers collect photons traveling majorly in bottom layer which were absorbed and so the reflectance values on fiber numbers 1 to 58 were very low. These did not match with the simulated data primarily due to low SNR.