esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification of Tropical Cyclone Tracks in the Northwest Pacific Based on the SD-K-Means Model
## 2 Data and Methods
### Clustering Method
#### 2.3.4 SD-K-Means Clustering Algorithm
Considering factors such as time cost, the \(K\) value at the \"elbow\" position where the gain is smaller, that is, the SSE value decreases slowly, is usually selected as the optimal \(K\) value. This paper chooses to use the elbow method and determines the optimal \(K\) value by the average SSE value of 1000 clustering cycles, that is, the average value of the SSE values of 1000 clustering cycles. The specific SSE value results under different \(K\) values are shown in Figure 2.  
As can be seen from Figure 2, the change in the slowdown of the decline occurs when \(K=4\); that is, when the TC tracks are divided into 4 categories, the clustering effect of the SD-K-Means clustering model is optimal.
## 2 Materials and Methods
### Evaluation of Antioxidant Activity of Emulsions  
The antioxidant activity of the emulsions was assessed by measuring the disappearance of the purple color of the ethanolic solution of 2,2-diphenyl-1-picrylhydrazyl (DPPH). A total of 5 mg of emulsion was mixed with 10 mL of 0.04 mg/ml of DPPH ethanol solution and kept in the dark for 30 min while stirring at 200 rpm at room temperature. Subsequently, the samples were filtrated and the absorbance of the filtrate was measured at 517 nm using a UV-Vis spectrophotometer (T60, PG Instruments, Great Britain). A DPPH solution without emulsion was used as the control sample. The antioxidant activity of the emulsion was expressed as radical scavenging activity (%) and calculated using the following equation:  
\[Radical\ scavenging=\frac{A_{0}-A_{S}}{A_{0}}\cdot 100\% \tag{3}\]  
where \(A_{0}\)--the absorbance of the initial DPPH solution (control sample), and \(A_{S}\)--the absorbance of DPPH solution with a sample.  
The bacteria strains of Gram-positive cocci _Staphylococcus aureus_ (ATCC 9144) and Gram-positive sporogenic rods _Bacillus cereus_ (ATCC 6051), _Listeria monocytogenes_ (ATC35152), Gram-negative rods _Escherichia coli_ (ATCC 8739), and yeast _Candida albicans_ (ATCC 14053) were used for the study of antibacterial properties. Antibacterial properties of the aqueous emulsions consisting of 2.5, 5, and 10% (\(w/w\)) of CL, EU, or CI were evaluated according to bacteriostatic and bactericidal activities on the tested strains of bacteria employing the determination of the minimum inhibitory concentration (MIC) and the minimum bactericidal concentration (MBC). The MIC was evaluated by the broth dilution method, and the MBC was evaluated by plating [31]. The tests were carried out twice.
## 1 Introduction
### Calculation of Transverse Distribution Coefficient Based on Damage Degree Response Surface Function  
The transverse distribution coefficient is an important parameter to measure how the load is distributed between the main beams. Therefore, the relationship between the degree of hinge joints damage and the transverse distribution coefficient is established to integrate the fuzzy information of hinge joints damage into the reliability evaluation. The Latin Hypercube Sampling (LHS) method is adopted in this paper to extract the damage degree samples of each hinge joint after the corresponding probability density function is obtained, and the modified hinge plate method and response surface method are used to calculate the transverse distribution coefficient, which is also a random variable. Taking the case of \(N\) main beams and \(N\)-1 hinge joints as an example, the specific steps of this method are illustrated as follows:  
Step 1: Using the LHS method to extract samples of the damage degree of each hinge joint, each crack damage is considered as a random variable, each random variable is divided into n non-overlapping intervals within the range of [0,1], and the cumulative probability density function is obtained on the basis of the density function obtained by the equivalent randomization method of the random variable given in this paper.  
Step 2: The modified hinge plate method proposed by the author of this paper [47] was used to calculate the transverse distribution coefficient of each main beam corresponding to the hinge joint damage degree of each set of sample points.  
Step 3: The function relation between the transverse distribution coefficient and the damage degree of each hinge joint is constructed by using the response surface method according to the calculated sample point data, the selected response surface function is described in Equation (9), and the transverse distribution coefficient \(m_{i}\) of each main beam considering the fuzzy detection information of the damage degree of hinge joints is obtained by using least-squares method.  
\[m_{i}=a+\sum_{j=1}^{n-1}b_{j}x_{j}+\sum_{j=1}^{n-1}c_{j}x_{j}^{2}+\sum_{1\leq j \leq k\leq n-1}d_{jk}x_{j}x_{k}\ \ \ \ \ i=1,2,\cdots,n. \tag{9}\]  
where \(m_{i}\) represents the transverse distribution coefficient of the \(i\)-th main beam considering the fuzzy detection information of hinge joint damage degree; \(x_{i}\) represents the random variable obtained from the transformation of the fuzzy detection information of the \(i\)-th hinge joint damage; and the remaining parameters are the undetermined coefficients.
## 5 Finite Element Parametric Analysis
### Analysis of LB Series  
The LB series is a study of different combinations of steel strengths on the seismic performance of the structure. Figures 25 and 26 show the hysteresis curves and backbone curves.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Model** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Yielding** \\ **Displacement** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Yielding** \\ **Loading** \\ **(kN)** \\ \end{tabular} & \begin{tabular}{c} **Ultimate** \\ **Displacement** \\ **(mm)** \\ \end{tabular} & \begin{tabular}{c} **Ultimate** \\ **Loading** \\ **(kN)** \\ \end{tabular} & \
\begin{tabular}{c} **Initial** \\ **Stiffness** \\ **(kN/mm)** \\ \end{tabular} &
\begin{tabular}{c} **Ductility** \\ **\(\mu\)** \\ \end{tabular} \\ \hline LA-1 & 29.8 & 3560.6 & 157.6 & 4782.3 & 258.5 & 5.29 \\ LA-2 (Base) & 32.4 & 3720.4 & 170.5 & 4983.5 & 261.8 & 5.26 \\ LA-3 & 32.7 & 3796.5 & 180.5 &  
The LB series hysteresis curves in Figure 25 are all shuttle-shaped and have the same shape, of which LB-3 encloses the smallest area. LB-1 enters plasticity after completing 2 times 5\(\Delta_{\gamma}\) displacements under cyclic loading, at which time the link segment has reached  
Figure 26: Backbone curves of LB models.  
Figure 24: Shear–rotation curves of LA models.  
Figure 25: Hysteretic curves of LB models.  
the ultimate bearing capacity, the ultimate structural displacement reaches 202.5 mm, and the corresponding ultimate bearing capacity is 5863.5 kN, which is 16.8% higher than that of the LB-2 model (base model). The ultimate load capacity of the structure reaches 3177.4 kN. As can be seen from the backbone curves, different steel combinations have little effect on the initial stiffness of the high-strength eccentrically braced steel frame structure with the replaceable link. From the performance characteristics data in Table 4, it is found that changing the steel strength combination has a significant effect on the structural performance, with the loading capacity decreasing sharply as the steel strength decreases, but the ductility performance increases.  
The shear-rotation curve of the LB series under cyclic loading is shown in Figure 27. The series model hysteresis curve is full in shape, in which the LB-1 hysteresis performance is best, and the maximum plastic rotation reaches 0.19 rad, which is 17.6% higher than that of the base model structure. The plastic rotation of the three model structures all complies with the limits specified in code 1.
# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
## 3 Evaluation Metrics and Benchmark Datasets
### Benchmark Datasets
#### 3.2.2 Common Objects in Context (COCO)  
The COCO dataset [48], released by Microsoft Research in 2014, is a large-scale visual benchmark covering image recognition, instance segmentation, and image captioning. It provides annotations for 80 object categories--including the critical \"person\" class--and contains diverse visual content with fine-grained labels. To support the growing research on human pose estimation, COCO was extended in 2016 with the Keypoints subset, which was specifically curated for 2D keypoint detection. Since its release, this subset has become one of the most widely used benchmarks for evaluating pose estimation algorithms. For the keypoint task, COCO supplies annotations solely for objects labeled as \"person\". Each labeled individual is annotated with 17 anatomical keypoints, including the nose, eyes, ears, shoulders, elbows, wrists, hips, knees, and ankles. Every keypoint is specified by 2D coordinates (x, y) and a visibility flag indicating whether it is visible or occluded. The dataset offers more than 200,000 fully annotated person instances spanning a broad spectrum of real-world scenes, such as street photography, sports, and indoor environments. Owing to its diversity and complexity, COCO has become one of the most influential and widely adopted benchmarks in 2D human pose estimation. Figure 6 displays representative examples from the COCO dataset.  
Figure 5: Example images from the MPII Human Pose Dataset.  
Figure 6: Example images from the COCO Dataset.  
3.2.3. Leeds Sports Pose (LSP) and LSP Extended (LSPE)  
The LSP dataset [50], released by the University of Leeds in 2010, is one of the earliest and most representative benchmark datasets for human pose estimation. It is primarily designed for single-person 2D pose estimation and features large motion variations, complex backgrounds, and diverse body configurations. The original dataset includes 2000 images, equally split into 1000 for training and 1000 for testing. The images were primarily sourced from Flickr and depict a variety of sports activities--such as football, gymnastics, athletics, and skateboarding--spanning a wide range of dynamic and challenging poses. Each image is annotated with 14 keypoints corresponding to the major joints of the human body. Notably, the left and right joints are consistently labeled from a person-centric perspective. A subsequent extension of the dataset introduced 10,000 additional annotated training images, further enriching the dataset and enabling more robust model development. The LSP dataset has played a pivotal role in advancing early pose estimation algorithms, serving as a foundational benchmark for evaluating models under highly variable and unconstrained conditions. Figure 7 displays representative examples from the LSP dataset.
## 2 Survey of Commercial Small Platforms
### Active Microwave  
Several missions have been launched with active microwave instruments that can be grouped into three main families: Scatterometers, Synthetic Aperture Radars (SAR), and Radar Altimeters (RA). This section describes the variables of interest that can be measured with satellite-based active microwave sensors: wind speed, and direction over the sea surface using radar scatterometers, SAR and SAR altimeters; sea level, significant wave height, wave and wind speed using RA; and dominant wave direction, significant wave height and sea ice cover by SAR. Then, each variable is presented with the available active microwave technology, and the new trends of these sensors in small satellites.
# A Novel Anti-Jamming Technique for INS/GNSS Integration Based on Black Box Variational Inference
## 1 Introduction
However, in the trial data of INS/GNSS integration, it was found that the time-varying noise was more similar to the Gaussian distribution with a changeable mean value. Therefore, in order to ensure the assumed approximate distribution was closer to the real distribution and estimate the MNCM more accurately, the Gaussian distribution was proposed as the prior distribution of MNCM in this paper. However, this assumption leads to a non-conjugate problem between the prior distribution and likelihood distribution. For this reason, the VBAKF algorithm cannot be used to estimate MNCM. We learnt from [21], which used BBVI to solve the non-conjugate problem between the PNCM distribution and system state distribution. In this paper, a novel anti-jamming technique for INS/GNSS integration based on black box variational inference was proposed. We assumed the prior distribution of the MNCM was a Gaussian distribution. Then, we estimated the gradient of the Gaussian distribution parameters by using the BBVI algorithm. Lastly, we calculated the MNCM by using the parameters. The proposed algorithm and existing VBAKF were applied to the problem of INS/GNSS integration with time-varying measurement noise. Experimental results show that the proposed filter has a smaller root mean square error (RMSE) than existing VBAKF methods. The remainder of the paper is organized as follows. First, we analyze the problems of VBAKF estimates of MNCM in the INS/GNSS integration. Second, the novel anti-jamming algorithm for INS/GNSS integration is presented. Third, the experimental results are analyzed and discussed. Finally, the conclusions are presented.
## 4 HCR-1 Protocol Description
### Cluster Head Selection in \(\mathcal{R}(1)\)
If NEXT(j) = 1 and the CH \(j\) has already been recorded in the SERV list, which means the CH \(j\) in \(\mathcal{R}(2)\) has found its relay, the node will delete the information of CH \(j\) from the SERV list. On the other hand, when the node \(i\) receives AR message from a CH \(j\) with \(k(j)=1\), it will check the NEXT flag in the AR message and estimate its distance to the CH \(j\), denoted as \(d(i,j)\). If NEXT(j) = 1 and \(d(i,j)<d(i,0)\), the node will record the information of CH \(j\) in the RELAY list. The RELAY list saves the information of CHs that are its relay candidates. The conditions of NEXT(j) = 1 and \(d(i,j)<d(i,0)\) ensure that the relay candidate has sufficient energy and that it is closer than the sink. When the backoff timer \(T_{b}(i)\) terminates, the node firstly checks its NEXT flag. If NEXT = 1, the node will become a CH and communicate with the sink directly. If NEXT = 0, it will further check its JOIN and SERV list. If SERV is not empty or JOIN is empty, this means that the node is required to serve as a relay for the CH in \(\mathcal{R}(2)\) or it has the highest residual energy within its clustering range \(R_{c}\). In this case, the node will become a CH to ensure the network connectivity, and it will select a relay from its RELAY list to reduce its energy consumption. Otherwise, if a node does not satisfy the conditions given above, which means that this node does not impact the network connectivity and has relatively low residual energy, it will become a CM.
## 3 Results and Discussion
Splitting spectra into the same bandwidths allowed for direct comparison of the two spectra. The comparison shows that the Grms values of the recommended spectrum for Korea are lower than ISTA 3A in all bandwidths; some by as much as 68%. The greatest reduction (\(-\)68%) also occurs in the 1-4 Hz region, where the highest intensity vibrations occur in the ISTA 3A profile. Results introduced previously from the overall Grms analysis and comparisons of PSD plots consistently show that vibration levels of the last-mile delivery segment in Korea are different and lower than in previously investigated countries. Traffic conditions in Seoul, Korea, could explain this. A high travel speed was discovered to be one of the main conditions that increase vehicle vibration levels from multiple historical studies [16; 43]. However, delivery vehicles in Seoul, Korea, are often unable to drive at high speeds, even on highways, due to extreme traffic jams. Therefore, it can be inferred that the slow speed of vehicles plays a major role in keeping the vibration levels of delivery vehicles in Korea low. \begin{table}
\begin{tabular}{c} **PD Level (g\({}^{2}\)/Hz)** \\ \end{tabular} \\ \hline
1 & 0.00204 & 15 & 0.00091 \\
1.5 & 0.00434 & 19 & 0.00091 \\
2 & 0.00434 & 24 & 0.00011 \\
4 & 0.00024 & 42.5 & 0.00011 \\
5.5 & 0.00024 & 50 & 0.00018 \\
6.5 & 0.00014 & 83.5 & 0.00046 \\
9.5 & 0.00014 & 148.5 & 0.00016 \\
10 & 0.00025 & 200 & 0.00005 \\
12 & 0.00025 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Recommended composite spectrum for small truck vibration in Korea.
# Enhancing Reliability Analysis with Multisource Data:
## 1 Introduction
### Asymmetric Information and Adverse Selection Problem in Bridge Maintenance  
Asymmetric information, also known as information failure, occurs when two parties involved in a transaction do not have an equal level of knowledge about a product. Such unequal information leads to an adverse selection problem, a prevalent bias in behavioral economics, especially in the insurance industry [15]. A problem of adverse selection exists in making bridge monitoring and maintenance, rehabilitation, and repair (MRR) decisions. Bridge owners or decision makers generally do not have information about the current live loads whereas state department of transportation (DOT) have access to the real-time WIM data, which leads to an asymmetric information problem. Asymmetric information can also lead to an overconfidence problem about capacities of bridges and vice versa, as well as posting of bridges which restrict transportation of goods and services in certain industries.  
For example, if a group of bridges is identified as having a low reliability index, they can be further investigated for MRR decisions. Often, bridges in poor conditions are prioritized for bridge repairs. However, such bridges may not expect to carry heavy truck traffic. Thus, we opt for bridges with a lower reliability index even though they may be in fair-to-poor conditions. Conversely, there may be cases where a low reliability index may not translate to high impact to MRR decisions.
# PVDF-TrFE Film on PDMS Substrate for Energy Harvesting Applications
## 4 Conclusions  
In this work, we have demonstrated a highly flexible, biocompatible PDMS-based piezoelectric energy harvester with P(VDF-TrFE) film utilizing a simple fabrication process. The device with an active area of 4 cm\({}^{2}\) was found to have a power density of 6.62 mW/cm\({}^{3}\) and average voltage and current of 5.8 V and 3.2 uA, respectively at 30 Hz vibration frequency at 1.75 g acceleration. Using a full wave bridge rectifier, a capacitor was charged and could deliver 370 nW, which was capable of lighting 27 LEDs simultaneously. With this power output, a wide range of wearable electronics and smart monitoring devices are within the realm of possible applications. In spite of the simple design, the performance of the present device was found to be superior compared to other flexible energy harvesters on PDMS substrate.  
We thankfully acknowledge financial support for this work from the National Science Foundation through Grants# IIP-1512342, CBET-1606882 and IIP-1602006.  
This study was designed by [PERSON] and [PERSON]; [PERSON] conducted the energy harvester layer-by-layer fabrication and all measurements, and wrote a major part of the paper under the supervision of [PERSON]; [PERSON] and [PERSON] conducted design of charging circuit for the storage capacitor, and measurement of voltage and current, and analyzing output performances of the device with [PERSON]; [PERSON] and [PERSON] conducted COMSOL simulation and drawing schematic illustration of the device, and wrote simulations part of the paper; All authors contributed in revising the paper.  
The authors declare no conflict of interest.
## 2 Data and Methods  
### Sampling Site  
Since 2009, the Korean Ministry of Environment (KMOE) has been operating six intensive atmospheric monitoring sites (namely, Baengryeong Island, Seoul, Daejeon, Gwangju, Ulsan, and Jeju Island). In order to examine long-term changes in atmospheric aerosol concentrations, we selected and analyzed data from Seoul, the capital of South Korea, representing the megacity urban area, and Baengryeong Island was selected as the background area. In this study, we analyzed the data of measurements from January 2012 to December 2019 (8 years) from both stations.  
The intensive monitoring site in Seoul is located in Bulgwang-dong, Eunpyeong-gu, northwest Seoul (37\({}^{\circ}\)61\({}^{\prime}\) N, 126\({}^{\circ}\)93\({}^{\prime}\) E), adjacent to the Bukhansan National Park to the north and the suburban area to the northwest. To the south and southeast in downtown Seoul, there are residential areas and heavy traffic areas [9]. The Baengryeong Island Monitoring Station (37.97\({}^{\circ}\) N, 124.63\({}^{\circ}\) E) was built in 2007, and it is playing a role in identifying the current state of air pollution in the background area of South Korea. Baengryeong Island mostly comprises mountain areas, and many of the residents are engaged in agriculture and the fishing industry. In particular, the Baengryeong Island Monitoring station is operated for the purpose of identifying the status of air pollutants from outside of South Korea, because it does not have much of its own emission sources, and it is geographically adjacent to China and North Korea [6; 7; 10]. The most specific meteorological condition in Northeast Asia is the dominant westerlies, except in summer [11; 12], and Baengryeong Island is strongly influenced by emissions from China or North Korea [6; 7; 13]. Hourly concentrations of PM2.5, water-soluble ions, organic and elemental carbon, elemental constitutions in PM2.5 and relative humidity (RH) were measured at these sites.