esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Vision  
The YOLOv8-n-seg model was trained on a custom dataset consisting of nine classes [Apple, Ball, Banana, Bottle, Box, Lemon, Orange, Strawberry, Tomato]. During training, the model achieved an overall Mean Average Precision ([EMAIL_ADDRESS]) of 0.566 and mAP for individual categories, as shown in Table 1 and the confusion matrix in Appendix A.2. The objects listed in Table 1 were carefully selected to evaluate the grasping model's adaptability across diverse geometries and material properties. Spherical objects like apples and balls test rotational stability, while elongated objects like bananas challenge force modulation due to their deformability. Rigid objects such as bottles and boxes assess grasp robustness under different stability constraints, whereas small objects like strawberries and lemons test the precision of finger grasp configurations. This selection ensures a comprehensive evaluation of the grasping model's effectiveness in real-world scenarios.  
Figure 9 shows real-time detection, classification, and segmentation results for nine classes using a three-finger grasp configuration. The model effectively classified objects and predicted appropriate grasp configurations based on their surface characteristics. As detailed in Table 1, the vision system achieved efficiencies of 66.66%, 88.88%, and 80% for two, three, and five-finger configurations, respectively. Challenges arose in classifying the Bottle, Lemon, and Tomato in the two-finger configuration, and with the Tomato and Bottle in the three and five-finger configurations. No five-finger configurations were generated for the small Lemon, Orange, Strawberry, and Tomato. Misclassification issues occurred due to similarities between classes, such as confusing a Lemon for an Orange and a Tomato for a Ball. These issues could be resolved with more data and careful labelling. Detailed object detection pictures can be found in Appendix A.2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Object Class**} & \multirow{2}{*}{**[EMAIL_ADDRESS]**} & \multicolumn{4}{c}{**Detection Based on Grasp Configurations**} \\ \cline{3-5}  & & **2 Fingers** & **3 Fingers** & **5 Fingers** \\ \hline Apple & 0.612 & & & \\ Ball & 0.476 & & & \\ Banana & 0.221 & & & \\ Bottle & 0.371 & & & \\ Box & 0.472 & & & \\ Lemon & 0.635 & & & - \\ Orange & 0.643 & & & - \\ Strawberry & 0.948 & & & - \\ Tomato & 0.716 & & & - \\ \hline
**All Classes** & **0.566** & & & \\ \hline \end{tabular}
\end{table}
Table 1: Vision model training results and real-time detections for two-, three-, and five-finger grasp configurations. (\(\bigtriangledown\)—Successfully detected and classified; \(\bigtimes\)—Unsuccessful (misclassified)).
## 4 Experimental Results and Analysis
### Experimental Findings and Analysis of the Enhanced Network Model
#### 4.3.2 Analysis of the Experimental Results of the Improved ShuffleNetv2 Network Model  
We carried out three rounds of comparative experiments to investigate whether the reconstruction of the two basic units in the ShuffleNetv2 network is effective for non-coal foreign object recognition. The reconstructed ShuffleNetv2 network model (YOLOv8n-shuffle+) was compared with the baseline YOLOv8n network model and YOLOv8n-shuffle model. As shown in Table 4 and Figure 10, by changing the stacking of basic units, the reconstructed ShuffleNetv2 network model becomes more delicate, and the depth-wise separable convolution modules within the basic units better capture the finer shape features among different non-coal foreign objects. The model learns more informative features while keeping the parameter count and network computation relatively stable, improving detection accuracy.
# Contrast-Invariant Edge Detection: A Methodological Advance in Medical Image Analysis
## 4 Experimental Results and Analysis  
Due to the unique characteristic of contrast invariance in the CIED, it aims to deal with different types of medical images under different contrast conditions. The existing public medical image datasets can hardly cover such diverse and targeted image samples comprehensively, and thus cannot fully validate the effect of this characteristic. Therefore, to comprehensively assess the performance of the CIED, we develop a Medical Image Edge Detection Test (MIEDT) dataset. The MIEDT includes 100 medical images randomly chosen from three publicly available datasets: Head CT-hemorrhage [36], Coronary Artery Diseases DataSet ([[https://www.kaggle.com/datasets/younesselbrag/coronary-artery-disease-dataset-normal-abnormal](https://www.kaggle.com/datasets/younesselbrag/coronary-artery-disease-dataset-normal-abnormal)]([https://www.kaggle.com/datasets/younesselbrag/coronary-artery-disease-dataset-normal-abnormal](https://www.kaggle.com/datasets/younesselbrag/coronary-artery-disease-dataset-normal-abnormal)) (accessed on 6 January 2023)), and Skin Cancer MNIST: HAM10000 [37]. In addition, we label the ground truth (GT) with the assistance of experienced physicians. We make many modifications based on the physician's recommendations. Through repeated confirmations, we finally obtain the GT that meets the actual needs. The MIEDT consists of 15 head CT images, 25 coronary artery disease images, and 60 skin lesion images. In MIEDT, the head CT images demonstrate high contrast due to the significant difference in the absorption of X-rays into tissues of different densities. The coronary artery images are acquired by CT angiography, which has low contrast, as it is characterized by subtle tissue differences. The skin images rely on optical imaging and exhibit the lowest contrast because they capture surface features and color changes. Since these medical images have different contrast and imaging modalities, the MIEDT has the capacity to comprehensively assess the performance of the edge detection algorithm. The MIEDT has been released on Kaggle ([[https://www.kaggle.com/datasets/lidang78/miedt-dataset](https://www.kaggle.com/datasets/lidang78/miedt-dataset)]([https://www.kaggle.com/datasets/lidang78/miedt-dataset](https://www.kaggle.com/datasets/lidang78/miedt-dataset)) (accessed on 12 January 2025)). The MIEDT is continuously updated and maintained.  
To objectively measure the performance of the edge detection methods, we utilize metrics like accuracy, precision, recall, and F1-score [1,38]. In this paper, we conduct a comprehensive evaluation of edge detection results, which is divided into three main parts: performance evaluation of the CIED, the contrast-invariant robustness of the CIED, and comparisons. All experiments and analysis are implemented in Python 3.8 on a computer equipped with an Intel(R) Core(TM) i5-10200H CPU, 16 GB RAM, and an NVIDIA GeForce GTX 1050 Ti graphics card.
## 2 Establishment of SDM for Large Karst Caves
### Geometric Model of SDM  
To facilitate the application of SDM, the geometric model of SDM should be established. Because the section of the tunnel and the karst cave along the direction of tunnel excavation are in a closed ring, a cylindrical coordinate system is chosen for the mathematical model, as shown in Figures 2 and 4. The central axis of the tunnel is taken as the \(Z\)-axis, and the intersection of the tunnel entrance and the karst cave is set as the origin \(Z=0\). Additionally, a cartesian coordinate system is set up. The reason for setting up two coordinate systems is that lines a, b, and g in Figure 3 are circular closed curves, which are more conveniently represented in a cylindrical coordinate system, whereas lines c and d and other straight lines are more conveniently represented in a cartesian coordinate system. The two established coordinate systems are shown in Figure 5.  
Figure 4: Schematic diagram of the cylindrical coordinate system.  
Let \(M=(x,y,z_{m})\) be any point in the surrounding rock of the karst cave, and convert it into a cylindrical coordinate system, which can be expressed as follows [22]:  
\[\left\{\begin{array}{l}x=\rho\cos\theta\\ y=\rho\sin\theta\\ z=z_{m}\end{array}\right., \tag{1}\]  
where the geometric meaning of \(\rho\) is polar diameter, which is the length of OM', but, in practice, the cave is not a regular cylinder, such that the value of \(\rho\) changes. The geometric meaning of \(\theta\) is the polar angle, which is the angle between OM' and the positive X -axis. The geometric meaning of \(Z\) is the plane parallel to plane \(XOY\).  
The SDL (line a-g) in Figure 3 is converted into a polar coordinate system, as shown in Figure 5. The following formulae are obtained:  
\[\rho_{k}=K(\theta), \tag{2}\]  
\[\rho_{r}=R(\theta), \tag{3}\]  
\[\rho_{t}=T(\theta). \tag{4}\]  
\(\rho_{k}\) is the geometric expression of the cave contour (line a), \(\rho_{r}\) is the geometric expression of the surrounding rock stability edge line (line b), \(\rho_{t}\) is the geometric expression of the tunnel contour line (line g), and \(\theta\) is the angle between the counterclockwise direction of the polar coordinate axis \(\rho\) and the \(X\)-axis.  
According to the cartesian coordinate system:  
\[Y=y_{1}, \tag{5}\]  
\[Y=y_{2}, \tag{6}\]  
\[Y=y_{3}, \tag{7}\]  
\[Y=y_{4}, \tag{8}\]  
where \(y_{1}\) is the geometric expression of the tunnel top line (line c), \(y_{2}\) is the geometric expression of tunnel bottom line (line d), \(y_{3}\) is the geometric expression of the water level line (line f), and \(y_{4}\) is the geometric expression of the filling line (line e).  
Figure 5: Mathematical model of the SDM.
## 3 Results
### SH-SYSY Cells Do Not Express a Dominant Restriction Factor  
Continuous virus-host co-evolution has led to the emergence of host restriction factors that limit many infections, and of viral proteins that counteract these intrinsic defense mechanisms. For instance, HIV-1 has evolved proteins that target the restriction factors APOBEC3G, TRIM5, or SERINC5 (reviewed in [61]). Similarly, EBOV GP antagonizes the antiviral protein tetherin [62].  
To investigate whether the resistance of SH-SYSY cells to GP-driven transduction was due to the expression of a host restriction factor, we used doxycycline (Dox)-controlled transcriptional activation [63], and combined it with experimental cell-cell fusion assays [54]. This combination allowed us to differentiate between heterokaryons of two different cell types expressing either the transactivator or its inducible counterpart from homokaryons derived from only one cell type. In the latter case, the inducible gene would not be activated, and hence there would be no reporter gene expression even in the presence of Dox (Figure S2). We engineered Huh-7.5 and SH-SYSY cells to express transactivator proteins, and HEK293T-H6 cells to express the Tet-inducible ZsGreen1 reporter gene. We next assessed whether NoEnvpp, EBOVpp, MARVpp, or VSV-Gpp lentiviral particles expressing the mCherry reporter were able to transduce HEK293T-H6 cells, Huh-7.5 cells, SH-SYSY cells, HEK293T-H6/Huh-7.5 heterokaryons, or HEK293T-H6/SH-SYSY heterokaryons. The engineered cell lines exhibited the same susceptibility to GP-driven transduction as the respective parental cell lines (Figure 4B and Figure S3A). Moreover, heterokaryons of susceptible HEK293T and Huh-7.5 cells or of HEK293T and SH-SYSY cells were susceptible to GP-driven transduction (Figure 4A,B and Figure S3B). Importantly, reporter expression depended on viral GPs, since NoEnvpp did not transduce heterokaryons. Taken together, heterokaryons from resistant and susceptible cells were transduced by pseudoparticles bearing filovirus GPs, suggesting that SH-SYSY cells do not express a specific restriction factor but rather lack an important enabling filovirus entry factor, which is delivered to resistant cells by fusion with susceptible cells.
# A Polarity Capturing Sphere for Word to Vector Representation
## 5 Results and Discussion  
We have tested the proposed word embedding method. After a thorough design, and training using our method, we have performed the following four evaluation experiments in addition to outlining an opinion mining application for hotel reviews:  
1. Word Similarity: We present the similarity scores obtained between many selected word pairs, and compare with the scores of other published word vector representations. The comparison of the performances should be done using the judgment of the reader.
2. Human Judged Similarity: We apply our approach and some competing methods on benchmark word pair lists. These lists, published in some papers, have human-judged similarity scores. So this allows comparison with actual numbers.
3. Antonymy Detection: We evaluate our approach on answering closest-opposite questions (extracted from the GRE test), comparing our results with the published ones.
4. Antonym/Synonym Discrimination: We test the performance of our approach in discriminating between antonyms and synonyms. The performance is compared with other published word vector representations.
5. Opinion Mining: We outline the application of our method on an opinion mining task. Moreover, we use other published word vectors to compare the performance on such task.  
\begin{table}
\begin{tabular}{l l} \hline Number of distinct words (graph vertices) & 27770 \\ Number of synonymous pairs & 102260 \\ Number of antonymous pairs & 38783 \\ Number of unrelated pairs & 182287 \\ Number of graph edges & 323330 \\ Ratio of number of edges to number of vertices & 11.64 \\ Average degree of the graph & 23.29 \\ Number of connected components & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Vocabulary structure.  
Figure 3: Histogram for the degree of vertices.  
All used datasets are the same for all the compared models in order to have fair comparisons. In all the conducted experiments, we used the word vectors generated using our implemented algorithm. The training vocabulary used is the one gathered as explained in the previous section. The generated word vector's number of dimensions is 50.
## 3 Materials and Methods
### Data Description  
This study defined children as those aged 0-9 years and older adults as those aged 65 years or above. Similar to the definition of a child in the previous study [29], 0 to 9 years old was defined as a child. Currently, in many countries, 65 years of age or older is regarded as the older adults [30], so older adults in this study were also defined as 65 years old or older.  
When this study was selecting illnesses for analysis based solely on the 2016 medical data, there was concern regarding a likely distortion with respect to certain illnesses that occurred that year. Accordingly, this study selected illnesses for analysis based on medical treatment data over the last five years. The illnesses in the medical data are listed only in the form of code numbers. Table 1 matches the illness code to the illness name as provided by the Korean Informative Classification of Diseases. The top 10 illnesses that are most common in older adults were found to be illnesses unrelated to seasonal factors, such as Alzheimer's and orthopedic illnesses. Thus, of the top 20 illnesses, those unrelated to seasonal factors were excluded, and the final 8 illnesses were selected as the subjects of analysis.  
The regions included in the analysis of this study were Seoul, Busan, Daegu, Incheon, Gwangju, Daejeon, Ulsan, Gyeonggi-do, Gangwon-do, Chungcheongbuk-do, Chungcheongnamdo, Jeollabuk-do, Jeollanam-do, Gyeongsangbuk-do, Gyeongsangnam-do, and Jeju. These areas are defined by the area identifier city/province code used by the public data portal and the KMA.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Illnesses for Children** & **Code** & **Illnesses for Older Adults** & **Code** \\ \hline Acute bronchitis & J20–J22 & High blood pressure, heart disease & I10–I15 \\ Common cold & J00–J06 & Common cold & J00–J06 \\ Rhinitis, tonsillitis & J30–J39 & Esophageal inflammation, gastritis & K20–K31 \\ Inner ear inflammation & H65–H75 & Acute bronchitis & J20–J22 \\ Chronic bronchitis & J40–J47 & Eczema, dermatitis & I20–L30 \\ Intestinal inflammation & A00–A09 & Chronic bronchitis & J40–J47 \\ Influenza, pneumonia & I10–I18 & Rhinitis, tonsillitis & J30–J39 \\ Eczema, dermatitis & L20–L30 & Brain disease & I60–I69 \\ Chicken pox & B00–B09 & & \\ Conjunctivitis & H10–H13 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Illnesses in children and older adults selected for analysis.
# Thiokol with Excellent Restriction on the Shuttle Effect in Lithium-Sulfur Batteries
## 3 Results and Discussion
It can be seen from Figure 4a that the color of the disperse system in (1) and (2) are almost the same, while for (3), the color gets lighter to (1) and close to (4). Considering that the color of polysulfides gradually deepens from colorless to orange as the value of \(x\) in Li\({}_{2}\)S\({}_{x}\) increases, the effect of reducing long-chain soluble polysulfides is obvious for Thiokol and invisible for PVDF. This demonstrates that Thiokol has a stronger ability in keeping long-chain soluble polysulfides from shuttling through the battery. Figure 4b compares the UV-visual spectroscopy of different solutions of soluble Li\({}_{2}\)S\({}_{x}\) in electrolyte (electrolyte as reference). The peaks at 262 nm correspond to the long-chain soluble polysulfides, while the peak at 250 nm is assigned to short-chain insoluble polysulfides [27]. It is clear that the addition of PVDF does not change the peak position of original solution, while the peak moves to lower wavelength upon the Thiokol addition. Thus, it is reasonably deduced that that Thiokol could turn the long-chain soluble polysulfides into short-chain insoluble polysulfides, thus effectively retraining the shuttle effect of soluble lithium polysulfides. Figure 4c shows the XRD pattern of the washed precipitate from the mixture in Figure 4a (2) after mixing for 2 h. Several diffraction peaks can be observed and are well matched with the crystalline data of Li\({}_{2}\)S (JCPDS: 23-0369), suggesting a generation of Li\({}_{2}\)S product in this system. The ex situ XRD result confirmed the reaction between the long-chain polysulfides and Thiokol. Figure 4c shows the schematic illustration of reactions about polysulfides in the cathode. \[\mathrm{HS}\ \sim\ \mathrm{SH}\ +\ \mathrm{LiS}_{x}\mathrm{Li}\ +\ \mathrm{Li}^{+}\leftrightarrow\mathrm{HS}\ \sim\ \mathrm{SS}_{x-y}\mathrm{Li}\ +\ \mathrm{LiS}_{y}\mathrm{Li}\ +\ \mathrm{H}^{+}\ (1\leq y<x\leq 8), \tag{1}\]  
Reaction (1) could also be used to explain the function of Thiokol. Long-chain soluble Li\({}_{2}\)S\({}_{x}\) (\(x\) = 4, 6, 8) could react with Thiokol since the existence of thiols and be divided into short-chain insoluble Li\({}_{2}\)S\({}_{y}\) (\(y\) = 1, 2). The shuttling of soluble polysulfides was restrained in that case. Figure 4: _Cont._  
Figure 3: Scanning electron microscope (SEM) image of the sulfur cathode with the Thiokol binder before (**a**) and after (**b**) cycles, and Energy Dispersive X-Ray Spectroscopy (EDX) of carbon (**c**) and sulfur (**d**) of the cathode after cycles.
## 2 Data Collection
### Static Surveying  
Four sets of static surveys were performed to validate the effect of antenna type on the final coordinates (Phase 1). In addition, the result of static surveying was used as a reference in the following analysis of RT solutions in Phase 1. All static data processing was performed using NGS Online Positioning User Service (OPUS)-Projects, a set of GPS management and processing tools for projects involving multiple sites and occupations [19]. Users can access the system easily via  
Figure 2: Study area for Phase 2 [18].  
web for data uploading and (limited) customization of the processing strategy. It uses the PAGES software suite [20] for data processing, and visualization and management aids are also provided. Although all static surveying data were collected at 1 Hz rate in this project, a 30 s resampling was performed for the purpose of this study (and is the only sample rate used by OPUS-Projects). The receiver/antenna combination changed for every session, as summarized in Table 2. The static observations for the new marks, down-sampled at 30 s, were uploaded to and processed by the OPUS-Projects server. The static solutions were used as a basis of comparison reference for the RT solutions. Only one station closest to the project site was selected as a hub station and constrained tightly, while two other CORS stations (about 1000 km away from the hub and located in different directions from it) were also included to decorrelate the tropospheric delay. The tropospheric delay was estimated every two hours in a piecewise linear mode, and the elevation cutoff angle was set to 15\({}^{\circ}\).
## 4 Experiments and Result
### System Experiment Environment
#### 4.3.1 Experimental Procedure  
The experimental procedure consisted of four steps, as shown in Figure 7. Participants were assigned to either the proposed system training group or the video training group. In addition, the training participant of the proposed system guided the operation of the VR controllers.  
First, all participants responded to the pre-education questionnaire along with the instructions for the pre-education experiment. We collected the gender and age groups of the subjects through a demographic questionnaire and an educational pre-questionnaire. The background knowledge and VR experience pertaining to aircraft maintenance for every participant was evaluated. After completing the pre-questionnaire, the participants trained in their assigned group for 18 min. In the proposed system group, the training was paused in the event that a problem occurred, or a participant was inexperienced in the use of their VR device. In these events, a researcher partially intervened to resolve the problem and resume the training, thus ensuring a smooth educational experience. After the training session, each participant responded to a post-test. This step assessed the subject's level of knowledge acquisition. The post-test consisted of three short-answer questions about the equipment and tools including the aircraft maintenance procedures, six O/X quiz questions, and three choice questions about how to use specific equipment and tools, for a total of 13 questions. The proposed system group also filled out the Igroup Presence Questionnaire (IPQ) to assess their sense of presence [50]. In addition, to evaluate whether the proposed system is suitable for use in education, a system usability scale (SUS) was conducted [51]. Both the IPQ and SUS were conducted using an online survey method. A retention-test was then performed to measure the knowledge retention of all subjects after 10 days. This test examines how well the knowledge acquired through education is maintained. To accurately judge the degree of knowledge retention, the questions on this test were different from those on the post-test.