esa-sceva/satcom-chunk-collection huggingface dataset:
# Shearer-Positioning Method Based on Non-Holonomic Constraints
## 2 Positioning Algorithm of Shearer
### Definition of Coordinate System  
Due to the continuous turning and moving of the shearer, and the inevitable alignment error when the inertial measurement unit is installed [19; 20], a single coordinate system cannot directly describe the position of the shearer, nor is it conducive to position calculation [21]. Thus, the coordinate systems used in the process of solving the position information in this paper include a coordinate system of shearer body (M system), an inertial-measurement-unit coordinate system (B system), and a reference coordinate system (N system), as shown in Figure 2. They are respectively defined as follows:  
Coordinate system of shearer body (M system): The coordinate origin is selected at the contact point between the shearer traction gear and the bottom plate. The forward direction of the shearer is the x-axis direction, the y-axis is perpendicular to the z-axis and points to the hydraulic support, and the z-axis is vertical and downward.  
Inertial measurement unit coordinate system (B system): The origin of the B system coincides with the center of the inertial unit. The three coordinate axes are parallel to the three sensitive axes of the inertial measurement unit and follow the right-hand rule.  
Reference coordinate system (N system): This system is equivalent to the local horizontal coordinate system in this paper. The origin of the N system is located in the measured center of the inertial measurement unit. The x-axis direction is north, the y-axis direction is  
Figure 1: Flow chart of shearer-positioning algorithm.  
Figure 2: Definitions of the shearer body frame (M system) and IMU body frame (B system).  
east, and the z-axis direction is perpendicular to the plane formed by the other two axes and points upward.  
In these three coordinate systems, the direction of M system and B system change during the working of shearer, while the direction of N system is always the same for reference. In addition, for the purpose of introducing non-holonomic constraints and ease of calculation, the origin of M system does not coincide with the other two.
## 2 Materials and Methods  
### Specim Preparation  
Laser cladding was carried out using a fiber laser cladding system, which consisted of an IPG YLS-6000 fiber laser (IPG Photonics Corporation, Oxford, MA, USA), powder feeding system, cooling system, and control system. The laser head was integrated with an ABB-4600(Asea Brown Boveri)robot (Asea Brown Boveri Ltd., Zurich, Switzerland). A DPSF-2 powder feeding system (Beijing Institute of Aeronautical Manufacturing, Beijing China) and a coaxial nozzle (HIGHYAG Corporation, Berlin, Germany) were used to feed powders into the molten pool by argon gas. The gas flow was 15 L/min and the purity of argon gas was \(\geq\) 99.9%. The substrate material was 45 steel (Baosteel Corporation, Shanghai, China). The nominal composition of the 45 steel plate is shown in Table 1. The dimension of the specimen was 150 mm \(\times\) 150 mm \(\times\) 12 mm. The surface of the substrate was abraded on SiC grit paper (Suzhou suboli grinding material Co. Ltd, Suzhou, China) with the mesh of 200 # and 400 #, and acetone was then used to clean the surface of the substrate before the experiment. The cladding alloy material was 431 stainless steel alloy powder (Hoganas Corporation, Skane, Sweden) with a particle size of 50-105 \(\upmu\)m, and the nominal composition of 431 stainless steel alloy powder is shown in Table 1. The Molybdenum element for the experiment was provided by Mo-Fe powder (Qinghe Xinbao Alloy Material Co., Ltd, Xingtai, China). The composition of Mo-Fe powder is as follows: 50.30 wt. % Mo, 0.37 wt. % C, 0.031 wt. % P, 0.016 wt. % S, Fe: Bal. The size of the Mo-Fe powder was less than 50 \(\upmu\)m. In this paper, the Mo-Fe powder with the mass fraction of 4%, 8% and 12% was mixed with 431 alloy powder, and the Mo content in the composite powder was 2%, 4%, and 6%, respectively. In order to ensure that the powder was mixed evenly, the composite powders were mixed using the ball mill (Changsha planetary machinery and equipment factory, Changsha, China). The milling speed was 160 r/min and milling time was 11 h. The size and morphology of the 431 and Mo-Fe powders were basically not changed by the ball milling process. The powders were placed in a drying stove (Suzhou Jiangdong precision instruments Co., Ltd., Suzhou, China) and dried at 100 \({}^{\circ}\)C for about 2 h before the laser cladding experiments.  
On the basis of previous experiments [33; 34], the laser cladding process parameters were chosen as follows: the laser power, the scanning speed, and the feeding powder rate were 2000 W, 240 mm/min, and 15 g/min, respectively. Specimens for metallographic examinations were cut, ground, and polished according to standard procedures and etched with a solution consisting of HCl (3 mL) (Beijing Chemical Works, Beijing, China) and HNO\({}_{3}\) (1 mL) (Beijing Chemical Works, Beijing, China) for 50 s. After the completion of etching, the residual etching solution was washed with water, and the surface was then dried with a blower (PHILIPS, Amsterdam, Netherlands).
# Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME
## 2 Datasets
### UC Merced Land-Use Dataset  
The pixel resolution of the UC Merced land-use dataset is 1 ft (=0.3048 m), and the dataset contains a total of 2100 images. The images are composed of 21 different land-use types; each class of each image has 100 RGB color images. The land-use types include agricultural, airplane, baseball diamond,beach, buildings, chaparral, dense residential, forest, freeway, golf course, harbor, intersection, medium residential, mobile home park, overpass, parking lot, river, runway, sparse residential, storage tanks, and tennis court. All the images contain different textures and colors, as shown in Figure 1. The UC Merced land-use dataset images were converted into \(224\times 224\) pixel size for transfer learning.
# Transmission Lines Insulator State Detection Method Based on Deep Learning
## 4 Discussion  
The experimental results validate the proposed enhancements to the YOLOv7 framework, including the BRA-CBS module, content-aware up-sampling operator, and Wise IOU loss. These improvements significantly enhance detection accuracy, achieving an impressive [EMAIL_ADDRESS] of 97.1% compared to the baseline YOLOv7 model. The method presented in this paper outperforms many existing approaches for insulator defect detection.  
For example, [PERSON] et al. [30] introduced a novel deep CNN cascading architecture for insulator localization and defect detection. However, their method lacks the capability to distinguish between different types of insulator defects. Similarly, other studies [31; 32; 33; 34; 35; 36] share this limitation, treating all defects as a single category, which restricts their applicability. Moreover, certain methods [37; 38] focus solely on distinguishing breakage and flashover defects, grouping self-explosion and breakage together. This oversimplification fails to address the distinct impacts of breakage and self-explosion on the insulation of transmission lines. In contrast, [PERSON] et al. [39] proposed a lightweight detection algorithm that can distinguish self-explosion, breakage, and flashover defects of insulators, IDD-YOLO, but it achieved an [EMAIL_ADDRESS] of only 66.2%, which is insufficient for practical engineering applications.  
The proposed method in this paper addresses these challenges by effectively distinguishing between self-explosion, breakage, and flashover, achieving a high recognition accuracy of 97.1%. Additionally, it excels in detecting small and obscured targets, demonstrating clear advantages over existing methods. Despite these achievements, limitationsremain. Specifically, the accuracy of detecting self-explosion defects requires further improvement to enhance the method's overall robustness and reliability.
## 3 System Model
### Location Distribution and Sensing Radii  
We assume the participants move around in \(M\) randomly. The locations of all \(N\) participants follow a uniform distribution. To accommodate different sensing radii, the  
\begin{table}
\begin{tabular}{l l} \hline \hline \# & **Description** \\ \hline \(W\) & Total working duration \\ \(S\) & Total sleep duration \\ \(W_{k}\) & The \(k\)-th working interval \\ \(S_{k}\) & The \(k\)-th sleep interval \\ \(n\) & Number of intervals \\ \(S_{0}\) & Initial sleep interval (fixed) \\ \(s\) & Average sleep duration per duty cycle \\ \(w\) & Average working duration per duty cycle \\ \(M\) & The interested 2-D square region \\ \(M_{x}\) & Length of \(M\) \\ \(M_{y}\) & Width of \(M\) \\ \(C(t)\) & The counting process of events in \([0,t]\) \\ \(d|_{[0,t]}\) & Expected number of events processed in \([0,t]\) \\ \(\Gamma_{s}\) & Average number of missed events in sleep duration \\ \(\Gamma_{w}\) & Average number of missed events in working duration \\ \(\Gamma\) & Event space \\ \(\lambda\) & Poisson rate \\ \(d\) & Distance between sensor and event \\ \(r\) & Sensing radius \\ \(r_{min}\) & Minimum sensing radius \\ \(r_{max}\) & Maximum sensing radius \\ \(\mu\) & Detection accuracy \\ \(\xi_{1},\xi_{2}\) & Metrics of detection ability \\ \(e\) & Energy depletion for sensing \\ \(e_{S}\) & Energy depletion of sensing in sleep duration \\ \(e_{W}\) & Energy depletion of sensing in working duration \\ \(e_{S}\) & Static power loss in sleep mode \\ \(e_{W}\) & Static power loss in working mode \\ \(\gamma\) & Coefficient of energy depletion growing with distance \\ \(\alpha\) & Changeable power scaling parameter for the sensing circuit \\ \(\delta\) & Step-size increasing factor \\ \(\theta\) & Step-size diminishing factor \\ \(N\) & Number of sensors \\ \(Sgn(\cdot)\) & The sign function \\ \(\sigma(\cdot)\) & The logistic Sigmoid function \\ \hline \hline \end{tabular}
\end{table}
Table 4: List of essential notations in the model descriptions.  
sensor can adjust its transmission range \(r\) from \(r_{min}\) to \(r_{max}\), which is usually accomplished by varying the transmitting power (see Figure 1).
## 2 Methods  
### Glass Sample Preparation  
A series of four different glass compositions with varied boron content was prepared (Table 1). A 50 g batch of the calculated mass of raw chemicals for each oxide, in accordance with the compositional formula (54.5-x) SiO\({}_{2}\)\(\cdot\)xB\({}_{2}\)O\({}_{3}\)-2.5P\({}_{2}\)O\({}_{5}\)-12.7 MgO-16.1 CaO-11.6 Na\({}_{2}\)O-2.6K\({}_{2}\)O (where x = 0, 5, 10, 15 mol\(\%\)), were prepared using the melt-quench technique. The following high-purity chemicals (Sigma-Aldrich, Burlington, MA, USA) were weighed, thoroughly homogenized with a ceramic mortar and pestle, collected into a platinum crucible, and then placed in an electric oven maintained at \(\sim\)90 \({}^{\circ}\)C for 16-18 h to degas: CaCO\({}_{3}\), Na\({}_{2}\)CO\({}_{3}\), SiO\({}_{2}\), K\({}_{2}\)CO\({}_{3}\), MgO, NH\({}_{4}\)H\({}_{2}\)PO\({}_{4}\), H\({}_{3}\)BO\({}_{3}\). The mixtures were melted using a furnace (Deltech Furnace, Denver, CO, USA) at 1350 \({}^{\circ}\)C for 2 h (at a heating rate of 10 \({}^{\circ}\)C/min to 1000 \({}^{\circ}\)C and then 5 \({}^{\circ}\)C/min to 1350 \({}^{\circ}\)C) (Figure 1). The molten glass was poured onto a preheated graphite mold on a stainless-steel hot plate (350 \({}^{\circ}\)C) in order to prevent stress fractures and shattering and the crucible was quenched in water. After the melts were cooled to room temperature, they were crushed, pulverized using the TE250 Ring & Puck Mill, and separated into the following particle size groups: 20 \(\upmu\)m-32 \(\upmu\)m, 32 \(\upmu\)m-45 \(\upmu\)m, and \(<\)20 \(\upmu\)m. This was carried out using Hogentogler sieves abiding by ASTM E11 standards [16] for powder separating sieves.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Glass Name** & **SiO\({}_{2}\)** & **B\({}_{2}\)O\({}_{3}\)** & **P\({}_{2}\)O\({}_{5}\)** & **MgO** & **CaO** & **Na\({}_{2}\)O** & **K\({}_{2}\)O** & **Density (g/cm\({}^{3}\))** \\ \hline
6P55-0B & 54.5 & 0 & 2.5 & 12.7 & 16.1 & 11.6 & 2.6 & 2.674 \(\pm\) 0.004 \\
6P55-5B & 49.5 & 5 & 2.5 & 12.7 & 16.1 & 11.6 & 2.6 & 2.568 \(\pm\) 0.007 \\
6P55-10B & 44.5 & 10 & 2.5 & 12.7 & 16.1 & 11.6 & 2.6 & 2.537 \(\pm\) 0.003 \\
6P55-15B & 39.5 & 15 & 2.5 & 12.7 & 16.1 & 11.6 & 2.6 & 2.509 \(\pm\) 0.002 \\ \hline \end{tabular}
\end{table}
Table 1: Oxide composition series for glass in wt% and experimental density.
# An Oligoimide Particle as a Pickering Emulsion Stabilizer
## 1 Introduction  
A Pickering emulsion is an emulsion stabilized by a colloidal suspension of finely divided solids [1; 2; 3; 4; 5]. The stabilization by solid particles provides specific properties to such emulsions: the high resistance to coalescence is a major benefit. The control of Pickering emulsion stability and type plays an important part in various significant processes, including oil extraction and recovery, emulsion polymerization, and heterogeneous catalysis [2].  
For the adsorption of solid particles at the oil-water interface, partial wetting of the solid by both water and oil is required. Some solid particles have been used as prepared without any modification to prepare Pickering emulsions, including silica [6; 7], layered double hydroxide [8], hematite [9], Fe\({}_{2}\)O\({}_{3}\)[10], graphene oxide [11; 12], cellulose nanocrystal [13], and polymer particles [14; 15]. Generally, the unmodified particles with a specific surface wettability can only form one type of emulsion (oil-in-water or water-in-oil) [2]. To improve the partial wetting properties of the unmodified particles and consequently control the stability and type of Pickering emulsions, modification of the solid surface or pH adjustment of the dispersion medium is extensively performed [1; 2; 3; 4; 5]. There are several methods for surface modification, which include either chemical grafting of organic molecules [16; 17; 18], or the physical adsorption of different types of molecules [19; 20; 21; 22]. The surface modification often requires complex and tedious procedures. Therefore, it is desirable to develop a Pickering stabilizer that is easyto prepare without surface modification and that can stabilize both oil-in-water and water-in-oil type Pickering emulsion systems.  
Polyimides are high performance polymers with high thermal stability, good mechanical properties, and excellent chemical resistance, and are widely used in a variety of industries [23; 24]. Oligoimides have also been extensively studied, utilizing the advantages of oligomeric polyimide. For example, the relatively low melt viscosity of oligoimides can facilitate the dispersion of inorganic particles in the resulting nanocomposites [25]. Oligoimides form oriented Langmuir-Blodgett films due to their amphiphilicity and controlled short molecular length [26]. In order to improve the poor solubility and processability of polyimides, telechelic oligoimides have been developed [27; 28; 29]. In this work, oligoimide particles were used for the first time as a stabilizer for the preparation of Pickering emulsions. Oligoimide particles synthesized in aqueous medium have partial wetting properties in both water and oil phases. The stability of the prepared Pickering emulsions was evaluated, and the emulsions were used in microencapsulation applications.
## 3 Results and Discussion
At first, we resized each slice of the Radiopaedia dataset as per the standards of BVH, then we employed the proposed technique (OTRGS) with the same classifiers as shown in Table 7.  
A very promising result was observed, that is, MLP, SVM, RF, and J48 had an overall accuracy of 98.27%, 96.72%, 96.44%, and 95.95%, respectively, as shown in Figure 11.  
Experimental results were observed to vary due to variations in the multi-institutional liver cancer image datasets. We must support the development of a dataset based on the global platform for medical patient data, in which, despite differences in patient mode, region, demography, geography, and medical history, we can address these medical health issues more accurately. A comparison diagram between the BVH and Radiopaedia fused (MRI and CT-scan) dataset is shown in Figure 12. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Classifiers** & \begin{tabular}{c} **Kappa** \\ **Statistics** \\ \end{tabular} & \begin{tabular}{c} **TP** \\ **Rate** \\ \end{tabular} & \
\begin{tabular}{c} **FP** \\ **Rate** \\ \end{tabular} & **ROC** & **Recall** & **F-Measure** & **MAE** & **RMSE** &
\begin{tabular}{c} **Time** \\ **(s)** \\ \end{tabular} & **Precision** \\ \hline MLP & 0.9793 & 0.983 & 0.003 & 0.999 & 0.983 & 0.984 & 0.0171 & 0.0725 & 0.77 & 0.983 \\ SVM & 0.9607 & 0.967 & 0.007 & 0.990 & 0.967 & 0.966 & 0.0151 & 0.1008 & 0.14 & 0.967 \\ RF & 0.9573 & 0.964 & 0.007 & 0.980 & 0.964 & 0.965 & 0.013 & 0.108 & 0.22 & 0.964 \\ J48 & 0.9513 & 0.959 & 0.008 & 0.976 & 0.959 & 0.958 & 0.0136 & 0.1165 & 0.09 & 0.959 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The overall classification accuracy results using Radiopaedia dataset using ML classifiers. Figure 11: The overall accuracy graph of employed ML classifiers on Radiopaedia fused optimized public dataset. A comparison between the proposed methodology and the current state-of-the-art techniques is shown in Table 8.
# Machine Learning in Chronic Pain Research: A Scoping Review
## 3 Results
### Machine Learning Algorithms
#### 3.2.2 Unsupervised Algorithms  
Unsupervised ML algorithms do not require labelled data for training. These algorithms are used for identifying patterns or reducing dimensionality in high-dimensional data. Four out of the 53 included publications used unsupervised method clustering (k-means) [26; 27; 28; 31; 72]. Typical problems solved by clustering algorithms were partitioning patients with chronic pain according to pain intensity/volatility, physical and mental state, and social support [31; 72]. Clustering was also used for identifying granular characteristics of FM patient subgroups [27] and grouping of symptoms reported in patient survey data [26]. Clustering results, in most cases, were used in further analysis and served as the input for task-specific data-processing pipelines.
## 3 Results
### Dentine Pulp Complex Response
#### 3.1.3 Bacterial Detection  
Only one sample from the c-GIC group of cervical restoration showed evidence of bacterial detection, as shown in Figure 5. All other samples of both groups displayed no bacterial detection when observed with Brown and Brenn staining.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study Parameters** & **Time Interval** & **c-GIC (n = 8)** & \begin{tabular}{c} **Nano-HA-SiO\({}_{2}\)-GIC** \\ **(n = 8)** \\ \end{tabular} & \begin{tabular}{c} **Two-Way ANOVA** \\ **\(p\leq 0.05\)** \\ \end{tabular} \\ \hline \begin{tabular}{c} Pulp tissue \\ disorganization \\ \end{tabular} & 1 week & 1.00 (0.53) & 1.12 (0.64) & 0.317 \\ \cline{2-5}  & 1 month & 0.25 (0.46) & 0.62 (1.06) & 0.180 \\ \hline \
\begin{tabular}{c} Inflammatory cell \\ infiltration \\ \end{tabular} & 1 week & 0.87 (0.83) & 1.25 (0.70) & 0.083 \\ \cline{2-5}  & 1 month & 0.00 (0.00) & 0.85 (1.06) & 0.059 \\ \hline \multirow{2}{*}{ Bacterial detection} & 1 week & 0.00 (0.00) & 0.00 (0.00) & NA \\ \cline{2-5}  & 1 month & 0.00 (0.00) & 0.00 (0.00) & NA \\ \hline
\begin{tabular}{c} Tertiary dentin \\ formation \\ \end{tabular} & 1 week & 0.62 (0.74) & 0.75 (0.46) & 0.564 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean score and SD of dentin–pulp complex reaction for occlusal c-GIC and nano-HA-SiO\({}_{2}\)-GIC at each experimental time interval.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study Parameters** & **Time Interval** & **c-GIC (n = 8)** & \begin{tabular}{c} **Nano-HA-SiO\({}_{2}\)-GIC** \\ **(n = 8)** \\ \end{tabular} & \begin{tabular}{c} **Two-Way ANOVA** \\ **\(p\leq 0.05\)** \\ \end{tabular} \\ \hline \
\begin{tabular}{c} Pulp tissue \\ disorganization \\ \end{tabular} & 1 week & 0.75 (0.88) & 0.75 (0.88) & 1.000 \\ \cline{2-5}  & 1 month & 0.75 (1.03) & 0.50 (0.75) & 0.157 \\ \hline
\begin{tabular}{c} Inflammatory cell \\ infiltration \\ \end{tabular} & 1 week & 0.50 (0.75) & 1.00 (0.53) & 0.046 * \\ \cline{2-5}  & 1 month & 0.70 (0.75) & 0.25 (0.46) & 0.170 \\ \hline \multirow{2}{*}{ Bacterial detection} & 1 week & 0.00 (0.00) & 0.00 (0.00) & NA \\ \cline{2-5}  & 1 month & 0.12 (0.35) & 0.00 (0.00) & 0.351 \\ \hline \multirow{2}{*}{ Tertiary dentin formation} & 1 week & 0.75 (0.70) & 0.87 (0.83) & 0.317 \\ \cline{2-5}  & 1 month & 2.00 (0.92) & 2.37 (0.91) & 0.083 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mean score and SD of dentin–pulp complex response for cervical c-GIC and nano-HA-SiO\({}_{2}\)-GIC at each experimental time interval.  
Figure 4: (**A–C**) (4\(\times\), 10\(\times\), and 40\(\times\)): Microscopic view of occlusal restoration with nano-HA-SiO\({}_{2}\)-GIC showing Grade 1 inflammation at 1 month of sacrifice in the area encircled as red in (**A**) ((**A**,**C**) = H&E stain, (**B**) = trichrome stain).  
Figure 3: (**A–C**) (4\(\times\), 10\(\times\) and 40\(\times\)): Microscopic view of c-GIC occlusal restoration showing Grade 1 pulp tissue disorganization after 1 week of interventional period in the area encircled as red in (**B**) (H&E).
# Enhanced Fall Detection Using YOLOv7-W6-Pose for Real-Time Elderly Monitoring
## 4 Results
### Analysis of Performance of the System in the Detection of Falls
The obtained results demonstrate that the use of the YOLO algorithm together with pose estimation brings significant improvements in the detection of falls and the quality of care for the elderly. The system has proven to be robust and effective under various conditions, providing a reliable solution for monitoring and ensuring the safety of the elderly. The continued development and adaptation of these technologies will play an important role in improving the lives and health of older people, providing them with a greater level of independence. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F1 Score** \\ \hline [PERSON] et al. (2016) & 79.30\% & 84.30\% & 73.07\% & 79.40\% & 78.28\% \\ [PERSON] et al. (2016) & 90.90\% & 94.56\% & 81.08\% & 90.84\% & 87.30\% \\ [PERSON] et al. (2015) & 86.21\% & 89.1\% & 93.18\% & 64.29\% & 91.1\% \\ Our method & **96.15\%** & **97\%** & **97.98\%** & 90.32\% & **97.48\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A comparison of our proposed method with other studies.