esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Based on the previous number of failures, the failure rate was obtained from a new software reliability model [25]. The reliability of Weibull-distributed components was evaluated under the small sample sizes and zero-failure data by fusing both the target and similar products [26]. By fusing the prior information of similar products, the modified Bayesian method of assessing the reliability of binomial components was proposed [27]. Expert information is also valuable especially when the field data are insufficient [28]. Integration of expert knowledge into lifetime estimation was presented [29]. An expert-judgement process for fusing multi-source prior information was developed by [PERSON] et al. [30]. Various available sources of expert knowledge and data, at both subsystem and system levels, were integrated [31], and methods for fusing lifetime data and expert information were discussed [32]. Prior information is important in Bayesian theory. After obtaining prior distributions according to different kinds of reliability information, the next step is to aggregate these prior distributions to eliminate the uncertainty of each multi-source information. Existing methods concerning the prior distribution fusion are based on confidence level [33], correlation function [34], sufficiency measure [35], expert judgement [36], maximum entropy-moment estimation [37], fuzzy logic operators [38], maximum likelihood principle [39], and the ML-II method [40]. [PERSON] [41; 42] has made special contributions to this field. These models and methodologies have formed a solid foundation for the Bayesian estimation of residual life. However, after extensive literature review, we found that (i) although work has been done on Bayesian theory and its applications, most of the existing literature concentrates mainly on certain aspects. The overall process and detailed illustration of the Bayesian method by fusing all this multi-source information simultaneously have rarely been presented or analyzed. (ii) Research on residual life estimation by fusing multi-source information is rare and needs further study. To fill the gap of the existing research, a multi-source information fusion approach based on the Bayesian theory is proposed to estimate the residual life of Weibull-distributed components of on-orbit satellites by fusing historical lifetime data, degradation data, similar data, and expert information. Both the Bayesian estimate and CI are considered. The rest of this paper is organized as follows. Multi-source information and the Bayesian model are introduced in Section 2. Section 3 contains the determination of prior distributions of multi-source information. In Section 4, we describe how posterior distributions are obtained and fused after a consistency test, and the residual life is estimated. Section 5 describes the Monte Carlo simulation study, followed by the validation using an illustrative example in Section 6. Finally, the paper is concluded in Section 7.
## 4 Impact Analysis of Cushion Blocks on Winding Axial Strength
### Impact of Cushion Block Unbalanced Height on Winding Axial Strength  
To investigate the impact of the cushion block unbalanced height on the axial strength of the transformer winding, a control group with one layer of 16 sets of cushion blocks, each with a total thickness of 10 mm, is set as the baseline. A maximum compression of 6 mm is set for the simulation analysis of cushion block height unbalances. One set of cushion blocks in the bottom layer was selected, and the total thickness of the cushion blocks is separately set to 8 mm, 6 mm, and 4 mm, corresponding to unbalanced heights with adjacent cushion blocks of 2 mm, 4 mm, and 6 mm. The resulting axial bending stress distribution maps are shown in Figure 14.  
It can be seen that, in the control group, when the cushion block unbalanced height is 0 mm, the maximum axial bending stress in the winding is 20.21 MPa. With a cushion block unbalanced height of 2 mm, 4 mm, and 6 mm, the maximum axial bending stress in the winding increases to 22.64 MPa, 24.57 MPa, and 26.51 MPa, respectively. The results  
Figure 14: Winding axial bending stress cloud for different cushion block unbalanced heights.  
Figure 13: Model of the winding and cushion block support structure.  
are plotted in the line chart shown in Figure 15. The test results under different unbalance heights are shown in Table 5.  
It can be seen from the table that, with the increase in the unbalanced height of the cushion blocks, the flexural elastic modulus of the winding decreases, which means that the winding is more prone to deformation when subjected to the same stress, and the short-circuit resistance decreases. The increase in the maximum axial bending stress on the winding, that is, the stress on the winding, will increase, and the deformation risk of the winding will also increase. The results obtained from the test and simulation are consistent, which verifies the feasibility of the structural part test.
# Handover Management for Drones in Future Mobile Networks--A Survey
## 6 Related Works
### Classical Based Techniques for Handover Management
A numerical analysis was used to investigate the proposed solution based on the Monte Carlo simulation. The Monte Carlo simulation is a computational algorithm that relies on repeated random sampling. The simulation outcomes revealed that, for the Same Speed Model (SSM) case, the terrestrial scenario mirrored the static UEs and mobile drone BSs to resemble static BSs and mobile UEs. Based on the graphical representation of the results, it can be seen that the SSM and Different Speed Model (DSM) initially behaved similarly. As time passed, the handover probability of SSM became higher than that of DSM. It should be noted that this does not necessarily imply that the handover rate of SSM is higher than that of DSM.
# The Potential of _Phaeodactylum_ as a Natural Source of Antioxidants for Fish Oil Stabilization
## 2 Materials and Methods
### Experimental Set-Up
#### 2.2.2 Part II  
The second part of the experimental set-up started with obtaining a total lipid extract from the _Phaodactylum_ biomass. For this, a large-scale chloroform-methanol (1:1) extraction was used based on the method described by [PERSON] et al. [34]. The following steps were performed in eightfold: 22.5 mL of the solvent was added to 3 g _Phaodactylum_ biomass and stirred for 90 min, after centrifugation (10 min, 2000 rpm) the solvent phase was separated and the same procedure was repeated. both combined solvent phases were washed with 11.25 mL water and after vigorous shaking (2 min), centrifugation (10 min, 2000 rpm), and removal of the upper water phase, the solvent phase was dried over sodium sulfate using a Whatman 1 filter that was washed with chloroform-methanol (1:1) afterward. All eight filtered extracts were pooled to obtain one homogenous extract and the solvent was removed by rotary evaporation under reduced pressure at 40 \({}^{\circ}\)C.  
Preparation and Characterization of the Systems  
Starting from the _Phaodactylum_ total lipid extract and the fish oil, six different systems were prepared by mixing them in different proportions. They were denoted as 'Pure _Phaodactylum_ total lipid extract', 'Mixture 79/21', 'Mixture 49/51', 'Mixture 6/94', 'Mixture 1/99' and 'Pure Fish oil'. Table 1 shows an overview of the ratios used (in weight%). These ratios were chosen in such a way that different orders of magnitude of the amount of carotenoids relative to the amount of n-3 LC-PUFA could be evaluated. To ensure homogenous mixing, both phases were dissolved in chloroform-methanol (1:1) before combining them. All systems were characterized in terms of n-3 LC-PUFA content and carotenoid profile. For this, part of the mixture was taken aside, flushed dry with nitrogen, and analyzed (in triplicate) according to the methods described below.  
Oxidative Stability (Storage Experiment)  
The oxidative stability of the different systems was followed throughout a storage experiment of 10 weeks at 37 \({}^{\circ}\)C, within triplicate analysis of primary and secondary oxidation products (according to the methods described below) on the following time points: day 0, 4, 8, 12, 20, 28, 42, 56, and 70. The systems that already showed clear oxidation in the first 4 weeks were only analyzed until day 28. The systems were incubated in quantities of 10 mg in (independent) amber screw-capped 20 mL vials, obtained after distributing the different mixed systems (in solvent) and dry flushing with nitrogen. After incubation and until analysis, the vials were transferred to \(-\)80 \({}^{\circ}\)C.
# A Transparent Decision Support Tool in Screening for Laryngeal Disorders Using Voice and Query Data
## 3 Methodology
### Exploring Analysis Results
#### 3.3.3 Visualizing Data Distributions and Decisions  
Distributions of the six audio parameters and all query parameters are created and visualised for healthy and pathological cases as separate probability density functions (PDFs) using the Epanechnikov kernel smoothing, as recommended in [38]. Figure 1 presents examples of PDFs for the six audio parameters. Probability density functions allow obtaining very useful preliminary understanding based on parameters otolaryngology specialists are familiar with. These distributions evolve over time with inclusion of new subjects (patients and controls), which provides doctors with information on trends and allows comparing different groups of patients.  
To facilitate reasoning, a decision tree is also provided for the user. A fragment of such a tree is exemplified in Figure 2. The decision path (highlighted in Figure 2) helps with exploring the patient diagnosis more thoroughly.  
Figure 1: Probability (\(y\)-axis) density functions of audio parameters of _healthy_ (blue) and _pathological_ (red, dashed-dotted) cases. A dashed line and numbers in the parentheses correspond to a parameter value of a subject randomly selected from the database used to train the developed tools.
## 2 Materials and Methods
### Adaptive DBO for DPE  
#### 2.3.1 DPE-SI Framework  
Without loss of generality, we consider the feasible solution space provided a priori by A-GPS to be a Cartesian space with the same dimensions as the parameters of interest, denoted as \(\Gamma_{A}\). Therefore, by replacing the optimization function in (6) with (11), we formulate a position-time-related optimization problem as follows:  
\[\hat{\gamma}=\operatorname*{arg\,max}_{\hat{\gamma}\in\Gamma_{A}}\Lambda(\hat {\gamma}). \tag{12}\]  
The process for solving the optimization problem in (12) within the large navigation domain directly affects the accuracy and computational cost, playing a crucial role in DPE implementation. Equation (11) shows that both satellite geometry and \(C/N_{0}\) influence the objective function (12). This time-varying characteristic shifts the modality of the objective function, increasing the difficulty of solving the optimization problem. For clarity, we assume that the \(z\) coordinate and the clock bias are known, enabling two-dimensions visualization. Figure 1 shows the optimization function (12) evaluated with seven satellites under coordinate errors, \(\varepsilon_{x}\) and \(\varepsilon_{y}\). These prevalent local optima in DPE limit the implementation of traditional optimization algorithms.  
Swarm intelligence algorithms have gained popularity for their enhanced ability to effectively explore the solution space, making them well suited for addressing the optimization challenges in DPE. Based on these advantages, we propose the DPE-SI framework. In this framework, a population of \(L\) individuals is engaged to solve a four-dimensional problem related to position and time. The boundaries for each dimension are set by the feasible navigation space provided by A-GPS prior information. The fitness function in (12) evaluates the correlation between signal replicas and received signals, with each individual's fitness value indicating the signal matching degree. The position and time states of individuals are iteratively updated to explore the search space and identify the optimal solution. The schematic flowchart of the DPE-SI framework is shown in Figure 2. The swarm intelligence algorithm governs the exploration and exploitation behavior of individuals within the search space, and its selection directly influences the positioning accuracy and computational efficiency of the framework.
## 2 Materials and Methods  
### Study Area  
In this study, we collected as much publicly available data as possible from forest and grassland flux sites in China. As shown in Figure 1, China has established 53 national forest and grassland flux observation sites, of which 10 sites have publicly available data, including 5 forest sites and 5 grassland sites. (The triangles in Figure 1 indicate forest and grass flux sites with unpublished data). The forest sites are Changbai Mountain (CBS), Haibei Shrubland (HBSL), Qianyanzhou (QYZ), Dinghu Mountain (DHS), and Xishuangbanna (XSBN). The grassland sites are Changling (CL), Inner Mongolia (NMG), Duolun (DL), Haibei (HBGL), and Dangxiong (DX). These 10 sites are highly representative of China, reflecting the country's diverse vegetation and climate types. Additionally, they span a wide range of latitudes and longitudes, covering much of China's temperature and precipitation gradients. The availability of publicly accessible flux observation data at these sites made them suitable for inclusion in this study. Detailed information on the latitude, longitude, elevation, vegetation type, and socio-economic (province) context of each site is provided in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sites**} & \multirow{2}{*}{**Latitude** (\({}^{\circ}\), **N**) & **Longitude** (\({}^{\circ}\), **E**) & **Elevation** (\({}^{\circ}\), **E**) & **Plant** & **Annual** & **Precipitation** & **(mm)** & **Provines** \\ \hline CBS & 42.40 & 128.10 & 738 & DBF & 3.6 & 713 & Jilin \\ HBSL & 37.61 & 101.32 & 3190 & SHRUB & \(-\)1.2 & 535 & Qinghai \\ QYZ & 26.74 & 115.06 & 101 & ENF & 17.9 & 1542 & Jiangxi \\ DHS & 23.17 & 112.53 & 362 & EBF & 20.9 & 1956 & Guangdong \\ XSBN & 21.93 & 101.27 & 750 & EBF & 21.8 & 1493 & Yunnan \\ CL & 44.59 & 123.51 & 141 & C3 grass & 4.9 & 470 & Jilin \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of flux observation sites.  
Figure 1: Spatial distribution of ten flux observation sites.
## 4 Proposed Protocol
### Sending a Notification  
[PERSON] performs the notification-sending phase, during which he sends notifications to [PERSON] whenever [PERSON] is online. The steps for sending the notification are outlined in Algorithm 3. The algorithm reveals that [PERSON] initiates the process by generating public and private ephemeral keys for the session. He then utilizes the generated keys as input for the asymmetric ratchet, producing a key \(K_{2}\) and a chain key \(CK_{4}\). This step ensures that an attacker with the first ratchet key will not be able to obtain the current ratchet key.  
Subsequently, the algorithm generates a new key, encrypts it with \(K_{2}\), outputs a ciphertext along with the details, and sends the notification to [PERSON]. The process is detailed as follows in Algorithm 3.  
```
Input:\((\lambda,EPK_{[PERSON]}^{1},RK_{1}^{{}^{\prime}},T_{key}^{{}^{\prime}},NT_{key}^{{}^{ \prime}})\) Output:\((C_{3},EPK_{Bob}^{2})\)
1:\((EPK_{Bob^{\prime}}^{2},ESK_{Bob}^{2})\gets KeyGen(1^{\lambda})\)
2:\(DH_{1}^{{}^{\prime}}\gets DH(EPK_{Alicer}^{1}ESK_{Bob}^{2})\)
3:\((RK_{2}^{{}^{\prime}},CK_{3}^{{}^{\prime}})\gets HKDF(RK_{1}^{{}^{\prime}},DH)\)
4:\((K_{2}^{{}^{\prime}},CK_{4}^{{}^{\prime}})\gets HKDF(CK_{3}^{{}^{\prime}}, constant\ value^{{}^{\prime}})\)
5:if\(\mathbf{NT}_{\mathbf{key}}^{{}^{\prime}}==\bot\)
6:\(\mathbf{NT}_{key}^{{}^{\prime}}\gets RandKey(1^{\lambda})\)
7:\(\textbf{Bundle}\leftarrow T_{key}^{{}^{\prime}}||NT_{key}^{{}^{\prime}}\)
8:\(\textbf{C_{3}}\leftarrow ENC(K_{2},Bundle)\)
```  
**Algorithm 3** Sending a Notification
## 4 Results  
### The Temporal and Spatial Distribution of Ecosystem Services within the TRHR  
#### 4.1.1 Npp  
Data show that in 2000, the average NPP of ecosystems in the TRHR was 262.35 gC/m\({}^{2}\)/year, encompassing an overall total of 60.61 \(\times\) 10\({}^{6}\) tC. Breaking this down between LULC types, the NPP of forested land was largest, averaging 375 gC/m\({}^{2}\)/year, followed by cultivated land and grassland with averages of 252.73 gC/m\({}^{2}\)/year and 204.56 gC/m\({}^{2}\)/year, respectively. In contrast, the NPP of the TRHR in 2012 was 406.84 gC/m\({}^{2}\)/year, a 64.44% increase compared with 2000. Data show that the NPP of every LULC type increased over this time period; cultivated land increased the most, however, up to 118.12%, immediately followed by forested land, up to 75.83%, while NPP values for grassland changed the least, just 49.07%.  
Because it is influenced by topography, rainfall, hours of daylight and vegetation type, the NPP of the TRHR exhibits strong spatial heterogeneity, decreasing gradually along a southeast-northwest transect (Figure 6a). The NPP of the Lancang River basin averages 332.76 gC/m\({}^{2}\)/year because temperature conditions are favorable and the rate of precipitation is relatively high; this region is followed by the Yellow and Yangtze river basins where the values are 232.46 gC/m\({}^{2}\)/year and 150.80 gC/m\({}^{2}\)/year, respectively. Data show that between 2000 and 2012, the NPP of the eastern Yellow River basin changed the most, 70.12%, while values for the Lancang River basin changed the least, just 30.28% (Figure 6b).  
Figure 6: Maps to show the spatial distribution of NPP in the TRHR in 2000 (**a**) and change up to 2012 (**b**) (unit: gC m\({}^{-2}\)).
_Applied_  
_sciences_  
_Article_  
**Morphology-Controlled Versatile One-Pot Synthesis**  
**of Hydrophobic Gold Nanodots, Nanobars, Nanorods, and Nanowires and Their Applications in**  
**Surface-Enhanced Raman Spectroscopy**  
**[PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] ***  
Department of Food Science and Technology, Zhejiang University of Technology, Hangzhou 310014, China; [EMAIL_ADDRESS] (J.N.); [EMAIL_ADDRESS] (C.X.); [EMAIL_ADDRESS] (K.J.); [EMAIL_ADDRESS] (X.N.)  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-571-88320388  
Received: 14 February 2019; Accepted: 3 March 2019; Published: 6 March 2019  
**Featured Application: The prepared hydrophobic gold nanomaterials show excellent surface-enhanced Raman scattering activity and, therefore, could be used as efficient organic-soluble SERS substrates for the detection of many hydrophobic food toxicants, such as 3,4-benzopyrene, and carcinogens, such as benzidine.**  
**Abstract: Many previously reported syntheses of gold nanoparticles required lengthy reaction times, complicated operations, high temperatures, or multi-step manipulations. In this work, a morphology-controlled versatile one-pot synthesis of hydrophobic gold nanodots, nanobars, nanorods, and nanowires has been developed. A series of gold nanomaterials ranging from round nanodots, short nanobars, and long nanorods to ultrathin and ultralong nanowires (diameter <2 nm, length >2 \(\upmu\)m) have been readily prepared by simply adjusting the feeding ratio of chloroauric acid to oleylamine, oleic acid, and triphenylsilane. The silk-like ultralong and ultrathin nanowires were found to have a single crystalline structure and may have significant potential applications in microelectronics and biosensors. Large sizes of gold spherical nanoparticles were obtained from gold nanodots via a seed-mediated growth approach. These nanoparticles and ultralong nanowires showed excellent surface-enhanced Raman scattering (SERS) activity in organic solvents and, therefore, were employed as efficient organic-soluble SERS substrates for the detection of hydrophobic food toxicants, such as 3,4-benzopyrene, and carcinogens, such as benzidine.**  
**Keywords: synthesis; gold nanodots; nanobars; nanorods; nanowires; surface-enhanced Raman spectroscopy**
# EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
## 2 Model and Method
### Model Description
#### 2.2.4 EAAnet Model  
Based on the aforementioned principle analysis, when an emergency occurs, the background might be complex, and the resolution might be low. In addition, people have various gestures, which might lead to the occlusion and overlap of each other. The detection process is prone to missing information and objects. To solve this problem, the structure of the EAAnet model is shown in Figure 7.  
As shown in Figure 7a, the input scales image adaptively, and data enhancements are carried out to improve the generalization ability of the model. The model uses CSP1 and CSP2 modules in the backbone and neck, respectively. The back  
Figure 7: EAAnet model structure. (**a**) Overall structure (**b**) CBAM structure inserted in the backbone.  
extracting the features of the image and keeping the gradient smooth, so CSP1 is selected here. The neck is responsible for the further fusion of the features extracted from the backbone network, and the shortcuts of CSP2 are introduced to generate richer feature representations. Finally, the head is responsible for prediction, and the CIoU loss function is used as a part of the total loss function to better help the network's backward propagation run smoothly. Figure 7b shows the improvement of the backbone, namely the introduction of the CBAM attention mechanism in the backbone's layers 3 and 5. This improvement is selected because with the network becoming deeper, the size of the feature map decreases gradually, and the number of channels increases. Moreover, the bottom level contains more fine-grained information that may gradually get lost. Therefore, we only applied CBAM at the bottom layers of 3 and 5, which can enhance the concerns of the fine-grained information of small person objects in the COP dataset.  
Secondly, in the neck, the BiFPN structure is adopted, splicing the output of layers 6 to 19 to achieve cross-layer feature fusion and provide a richer global feature representation. After extracting information through the CBAM attention mechanism, further convolution can effectively compress the redundancy of fine-grained information. The feature map of layer 6 contains rich channel information and spatial information, which, combined with the higher-level semantic information of layer 19, helps to understand the object categories and to detect objects of different scales.  
Through the above improvement, EAAnet can better reduce the cases of missing detection and false detection when dealing with complex scenarios and crowd persons.