esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Fault Identification
### States Identification Based on Support Vector Machine
Due to copper rust on the surface of the conductive copper rod and an insufficient infrared transmission effect, some sample images show irregular dark patches or texture, resulting in similar infrared feature distances. Figure 17 shows the comparison chart of one misclassified sample and the other two samples with the correct prediction. Table 3 shows the results of the infrared feature extraction of these three samples. Further infrared feature distances are observed between sample a and b, but the infrared feature sample a is closer to that of sample c in the feature space, so the fault state of sample a can easily be misclassified as a normal operating condition. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Conductive Conditions** & **Number of Test Samples** & **Correction** & **Misclassification** & **Accuracy** & **Total Accuracy** \\ \hline Abnormal heating condition & 10 & 7 & 3 & 70\% & \\ Normal operation condition & 10 & 10 & 0 & 100\% & 90\% \\ Open circuit condition & 10 & 10 & 0 & 100\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical table of the classification results using the test sets. Figure 16: The classification results of the models using the test sets. Figure 17: The comparison chart of the misclassified sample (**a**) and other samples (**b**,**c**) with the correct prediction. Figure 15: Accuracy distribution map of the SVM model under different super-parameter combinations. At present, this method of conductive state identification can achieve a 90% accuracy on the obtained samples, so it has a certain practical significance for engineering applications. However, this method still has some defects and cannot identify individual samples well due to the close feature distance among these samples. For infrared image feature extraction, this paper mainly focuses on the global features of the sample while ignoring the local features. In the future, we can make full use of the obvious gradient change in the copper rod, and then introduce local features, such as the fluctuation amplitude, to increase the discrimination between samples. Moreover, to improve the generalization ability of the classification model, we need to expand the sample set and we can attempt to use models, such as the deep learning network, to achieve a higher accuracy.
# Study of the Error Caused by Camera Movement for the Stereo-Vision System
## 4 Experiments and Results
### Relative Rotation Experiment  
The experimental setup for the relative rotation is the same as that shown in Figure 10. First, a pair of images of the checkerboard is acquired before rotating the right camera, as shown in Figure 7d. Thereafter, the left and right cameras simultaneously take a pair of checkerboard images every time the rotation stage is rotated in an angular step of 1  
Figure 11: The 3D coordinates and distance measurement errors introduced by relative translation: (**a,b**) Impact of relative translation on 3D coordinate measurement errors when the right cameras are translated along the \(X\)-axis and \(Z\)-axis, respectively; (**c,d**) Impact of relative translation on distance measurement.  
around the X and Y-axes, respectively. Figure 7d shows the stereo image pair captured in the state of relative rotation (21\({}^{\circ}\)) around the \(X\)-axis.  
Figure 12a,b shows the 3D coordinate error introduced by the relative rotation around X and Y-axes, respectively. It can be observed that the coordinate error of any point (A or B) and the mean error of all corner points in three directions, keep the nonlinear changes of the sine-cosine curve with the increasing rotation angle. These results effectively verify the theoretical analysis given in Section 3.4. Figure 12c,d shows the comparative results of the measured distance between the two adjacent points and the real value of 25 mm. The distance error is not monotonically increasing with the rotation angle.  
Furthermore, it is evident from Figure 12 that the 3D coordinate and distance errors caused by the relative rotation are much higher than those caused by the identical translation and rotation, and the relative translation. Even for the occurrence of a rotation angle of 10\({}^{\circ}\) during images acquisition, the resultant maximum coordinate error is up to 2000 mm, and the distance error reaches 220%. Therefore, the stereo-vision system is highly sensitive to the relative rotation between the two cameras.
## 4 Experiments
### Results on MNIST Dataset
#### 4.2.3 Convergence Speed and Efficiency  
To compare the convergence speed, we further recorded the number of epochs when prediction accuracy firstly grows up than 98% (Column 3 in Table 2). The convergence speed is defined as the ratio of Epochs to \(\#_{\geq 98\%}\). Taking SGD as an example, it had 13 out of 25 times where the final prediction was greater than 98% (refer to Figure 1). Meanwhile, among the 13 successful cases, SGD took a total of 19 epochs to achieve 98%. Then, the convergence speed was \(19/13=1.46\). It is observed from Table 2 that Adacomp has the fastest convergence speed 0.92, which means Adacomp can achieve at least an accuracy of 98% using no more than one epoch on average. Subsequently, Momentum has a convergence speed of 1.09 and Adamax has the slowest speed of 3.5.  
To compare the efficiency, we performed each of the methods ten times and recorded the average training and total time in the last column of Table 2. It is also observed that Adacomp achieves the lowest time consumption (excluding SGD), while Momentum and RMSprop have a relatively large time consumption. Although the improvement is slight, it can be deduced that Adacomp performs fewer computations than other first-order adaptive methods to adjust learning rate.
## 5 Results and Discussion  
### Dataset and Experiment Environment  
In this study, we employ two methods for data collection, namely web crawling and photography of the construction site of Yunnan Power Grid Corporation workers in Yuxi City, Yunnan Province. To enhance the detection rate, confidence, and robustness of the model, a data augmentation method is utilized to optimize the dataset. The parameter settings for data augmentation are shown in Table 2. After data enhancement, the dataset comprises a total of 10,702 images, which are categorized into eight classes, namely (1) wearing a safety belt; (2) no safety belt wearing; (3) wearing a helmet; (4) no helmet wearing; (5) wearing work clothes; (6) no work clothes wearing; (7) wearing safety gloves; (8) no safety gloves wearing. The dataset has been split into a training dataset and a validation set with a ratio of 8:2, resulting in 8562 images for the training dataset and 2140 images for the validation dataset. In addition, we have also collected an additional 400 images of the power construction scene as the test set.  
To assess the real-time and effectiveness of intelligent compliance detection methods, we integrated Edge-YOLO deep learning models into Jetson Xavier NX. Additionally, a series of experiments have been conducted on the deep learning framework Pytorch to evaluate the detection performance of various inspection methods. To train our model, we utilize a computer equipped with a NVIDIA RTX-3080 GPU boasting 24 GB of memory, an Intel Core i7-10700 CPU, and 32 GB of RAM to handle the intensive computational requirements of our research. In addition, our system is deployed on the Ubuntu operating system.  
Figure 5: Segmentation and fusion process.
# The Challenges of Recognizing Offline Handwritten Chinese: A Technical Review
## 4 Handwritten Chinese Text Recognition
### Line-Level Text Recognition
#### 4.1.1 Segmentation-Based Recognition
Compared with [78], they proposed a weakly supervised learning method that trains the network with only transcript annotations, which greatly reduces the cost of manual annotation while ensuring the output of segmented characters. In addition, they devised a contextual regularization method that incorporates integrated contextual information in the training of the FCN, thus significantly improving the recognition performance. The latest research is [PERSON] et al. [80], who proposed a new retrieval-based approach that dynamically retrieves relevant content of the recognized text from the Internet to train an adaptive language model (LM) that can be integrated into the whole recognition process. Their strategy goes through a two-pass recognition process. After the initial recognition result is obtained by completing the first pass using the baseline system, the second pass recognition builds on this by retrieving relevant content on the Internet and building a domain-related language model integrated into the path search. Eventually, the method does not use much synthetic data compared to [79]. It achieves recognition results that can compete with [76], which has a large amount of character-level annotation and uses NNLM.
# Ferroelectric Transition in Sr- and W-Doped BaTiO3 Solid Solutions
## 2 Materials and Methods  
BTO and BTO doped with Sr and W were synthesized by using a solid-state reaction of raw BaCO\({}_{3}\) ([PERSON], 99.0%), TiO\({}_{2}\) (Sigma Aldrich, 99.8%), SrCO\({}_{3}\) (Sigma Aldrich, 99.9%), and WO\({}_{3}\) (Sigma Aldrich, 99.9%). The stoichiometric mixture was calcinated at 800 \({}^{\circ}\)C for 24 h and reheated at 1000 \({}^{\circ}\)C for 24-30 h. Intermediate fine grinding was performed between heating processes.  
The differential scanning calorimetry (DSC) curves and measurements were obtained by using TA DSC 250 on a powder sample. The powder sample was put into a pan, which was then covered using a lid. An empty pan that was sealed with a lid was also used as a reference sample. The powder sample and reference sample were placed in a DSC cell. The DSC experiment was conducted in a temperature range from 25 to 184 \({}^{\circ}\)C with a heating rate of 10 \({}^{\circ}\)C/min. Then, the DSC cell was cooled to 25 \({}^{\circ}\)C with a cooling rate of 10 \({}^{\circ}\)C/min. The thermodynamic data of the powder sample were recorded during the heating and cooling processes.  
The crystal structures of the BTO powders were evaluated by using X-ray diffraction (XRD, Expert pro-MPD, PANalytical) with Cu K\(\alpha\) radiation (0.15418 nm) at the Western Seoul Center, Korea Basic Science Institute and the XRD data were analyzed by using the Rietveld refinement program of Fullprof.  
The surface morphology of Sr- and W-doped BTO was studied by using a field emission scanning electron microscopy (FE-SEM) system (Hitachi SU8220 FE-SEM with a base pressure of \(\sim\)1 \(\times\) 10\({}^{-8}\) Torr) at the Western Seoul Center, Korea Basic Science Institute.
## 3 Results
### Seasonality of Internal Tides
#### 3.2.2 Energy Flux  
We further analyzed the seasonal variability of the energy flux intensity of internal tides in the Arabian Sea. The resulting vertically integrated energy flux is shown in Figure 7. Generally, the energy flux in winter tends to be larger than that in summer (Figure 3), with larger areas of a high-energy flux, including the northeastern Arabian Sea, the Carlsberg Ridge, and Abd al Kuri Island. One of the most significant difference is observed in the case of the northeastern Arabian Sea. During winter, the area with a high-energy flux is significantly larger than that in summer, and the stick-like pattern extends southwestward into the central basin; this pattern is not observed in summer. This winter pattern is also noticeable in the energy flux calculated using the annual data (Figure 5b), suggesting that winter internal tides significantly contribute to the annual energy flux.  
Figure 7: Long-term average vertically integrated energy flux in (**a**) summer and (**b**) winter, respectively. There are more hotspots during winter: the northeastern Arabian Sea, the Maldive Islands, the Gulf of Oman, and the southern Red Sea. The black box indicates the area of Figure 8a-c; the orange box indicates the area of Figure 8d–f; and the white box indicates the area of Figure 9.  
Similar to the SSH shown in the seasonal snapshots, the energy flux around the Carlsberg Ridge and Abd al Kuri Island is much higher in winter. To the north of Abd al Kuri Island, the maximum energy flux is estimated to be \(15\,\mathrm{kW/m}\) both in summer and winter, while the large energy flux has wider coverage in winter. Similarly, internal tides around the Carlsberg Ridge exhibit significant energy flux. In the case of the Red Sea, the seasonal difference is not pronounced. In the Arabian (or Persian) Gulf, the primary seasonal difference appears at the Strait of Hormuz, where the energy flux is more significant in winter. Apart from these generation locations, internal tides close to the coastline also exhibit significant seasonal variations. For example, along the coastline of the Gulf of Oman, the energy flux in winter is stronger than that in summer.  
Figure 8: Zoomed-in vertically integrated energy flux in summer (**a,d**) and winter (**b,e**). (**c,f**) The domain-averaged seasonal \(N^{2}\) profile in the upper 300 m. The blue and red colors represent the winter and summer, respectively. The shading area is the 95% confidence interval. (**a-c**) show the result of the black box in Figure 7, and (**d–f**) show the result of the orange box in Figure 7.
## 4 Applications of Intelligent Acoustic Monitoring  
In this section, we present the applications of intelligent acoustic monitoring in key areas of human life. We focus on public safety, including detecting shots, explosions, or screams in cities. Then, we deal with environmental monitoring and methods for analysing noise and detecting ecosystem changes. We also study industry and machine diagnostics in the context of sound-based fault detection. As part of our further considerations, we delve into health care, specifically focusing on using voice assistants and monitoring patient conditions through speech and ambient sound analysis. We also consider topics related to intelligent buildings and smart cities, considering the automatic adjustment of acoustic conditions.
# Efficient Position Change Algorithms for Prolonging Driving Range of a Truck Platoon
## 5 Discussion  
In this section, we discuss our architectural heuristic approaches in both positive and negative details. We first discuss and compare the computation complexity of the three proposed heuristic algorithms.  
In \"Fuel Amount Heuristic (FAH)\", we first need to select the optimal threshold \(c\) that makes the driving distance prolonged to the maximum, yet with a reduced position change count. Since two adjacent vehicles should switch their positions when their fuel amounts are far apart from more than or equal \(c\), the value of \(c\) should be small enough to make the position change possible, thus achieving a higher driving distance. However, it should not be too small, as it can make a large number of position change counts and waste energy for unnecessary position change operations. The computation complexity goes to the question, \"What is the optimal \(c\) and how can we obtain it?\". In order to find the optimal \(c\), we also need to look at other parameters, especially the initial fuel amount of each vehicle. We may have to conduct several rounds of trials and compare the results, which may cost high computation. The complexity goes even higher when considering the realistic scenarios of computing the actual fuel energy along traveling, not just one but all in convoy. Therefore, we recognize taking high computational cost as a negative point for FAH.  
Similar to \"Traveling Time Heuristic (TTH)\", the maximum increased driving distance with a small number of position change count can be obtained with the small value of \(\delta\). Even though our simulation results show no remarkable difference in total driving distance, still with the small number of time slots, the possibility of position change is lower likewise. Therefore, there is no influential difficulty in choosing the value \(\delta\). However, when considering the higher number of vehicles and a small amount of \(\delta\) aspects, this may lead to the complicated position arrangement in a practical environment, since vehicles should align in the decreasing order in each time slot.  
\"Sorted Time Slot (STS)\" promises a significant reduction in position change. However, remarkably, it negatively affects processing times as it has to sort vehicle position arrangement for all time slots such that it can move duplicate columns into consecutive columns after the position change heuristic algorithm is applied. Basically, the time complexity of our algorithms without STS methodology is \(O(n^{2}\times\frac{distance}{\delta})\). With STS, the time complexity becomes \(O(n^{2}\times\frac{distance}{\delta}\times k)\). Therefore, our STS may be inefficient on a large number of vehicles in platooning with the small value of \(\delta\).  
Figure 5: Performance of STS with an unequal initial fuel (\(\delta\)).
# Electric-Field-Coupled Resonator Antenna for 5G Applications
## 2 Antenna Design and Analysis  
RT Duroid 5880 substrate of dielectric constant 2.2 and loss tangent 0.0009 with a thickness of 1.52 mm is used as the dielectric material of the antenna. The final design of the antenna is shown in Figure 1, and its dimensions are listed in Table 1. The design of the antenna is started by the analysis of the ELC (electric LC) resonator with appropriate boundary conditions. Then, the initial antenna is formed by exciting the resonator by a microstrip feed line with a finite ground plane size.  
Further, some evolutionary steps are applied to enhance the performance of the antenna. A detailed description of the entire analysis is given in the subsequent sections.
## 4 Discussion
### Limitations of this Study and Future Possible Directions  
Besides NDVI, there are many optional indexes used to describe vegetation time-series dynamic such as the Enhanced Vegetation Index ([PERSON] et al., 2016) and the plant phenology index ([PERSON] et al., 2016). Different indexes could present different time-series patterns for the same pixel, which could lead to different conclusions for different smoothing methods. Moreover, the difference between data from different satellite sensors, such as MODIS and NDVI3g, could also lead to different estimates of phenological parameters ([PERSON] et al., 2016). In addition, although the tested sites represent several different biomes, it cannot be ruled out that other vegetation types with different seasonal profiles might yield differences in relationships with ground reference data. Furthermore, although the sites used in the first and second experiments were generally spatially homogeneous, uncertainties in footprint matching may have affected the relationships. In the last experiment, it is recognized that several factors other than elevation affect the general phenology in the area. It is unknown if and to what degree these different factors affect differences in performance between the different smoothing methods. The results of this study are valid for the tested data and sites, and relationships may change with other data or under different conditions. However, the consistency of the results indicates a general validity of the conclusions also outside the tested domain.
# Privacy Policy Analysis of Banks and Mobile Money Services in the Middle East
## 5 Evaluation
### Privacy Compliance
#### 5.1.1 Purpose of Data Collection  
This principle had the highest compliance by both the banks and mobile money services compared to other principles. In addition, 100% banks and \(\sim\)86% mobile money services in our dataset clearly specified how the collected personal information will be used.
# Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System
## 3 How IoMT Works
### Challenges of IoMT  
As the medical field is sensitive due to the confidentiality and privacy of patient data, the security and accuracy of the medical equipment are necessary [14]. In IoMT, wireless technology is mostly used, and the privacy of the data is a great concern. A few of the most critical challenges are discussed here.
# Hypergraph-Clustering Method Based on an Improved Apriori Algorithm
## 3 Method Design of HDHPA  
### Basic Ideology of HDHPA  
This paper proposes a new complex data clustering method called the high-dimensional data clustering method based on hypergraph partitioning using an improved Apriori algorithm (HDHPA) based on an improved Apriori algorithm and hypergraph-partitioning model. First, the method constructs a hypergraph based on the improved Apriori algorithm for association rule mining and then uses the DSP algorithm for multi-level hypergraph partitioning to partition the hypergraph into many subclusters. Finally, the subclusters are merged to obtain the final clustering results. Although previous studies, such as those in [17, 18], have utilized hypergraphs to extract data information, there are limitations to high computational complexity. For example, the authors of [23] used the Apriori algorithm to determine the relationship between factors, but they did not improve the algorithm, which has problems with multiple iterations.  
We use t-distributed Stochastic Neighbor Embedding (t-SNE) [29] for the preprocessing and feature extraction of the original high-dimensional data. After obtaining low-dimensional data, HDHPA discovers clusters in the dataset through three stages: in the first stage, the improved Apriori algorithm is parallelized to speed up the mining of association rules and construction of the hypergraph model; in the second stage, the dense sub-hypergraph partitioning algorithm is applied to partition the hypergraph into many relatively small dense sub-hypergraphs (or subclusters); in the third stage, two evaluation functions (Conformance function C and Aggregation function A) are used to merge dense sub-hypergraphs and obtain clustering results.