esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Microstructure and Mechanical Properties of Laser Welding of BR1500 HS Joints
## 4 Conclusions  
In this paper, the effects of process parameters and quenching process on the mechanical properties and microstructure of the BR1500 HS welded joints have been researched. The conclusions of this paper are as follows.  
When the BR1500 HS high strength steel is welded by laser welding and the value of heat input is 1440 J/cm, the best mechanical properties of the welded joint can be obtained. Before quenching, the dimples of the BM are small and dense, which indicates good plasticity. However, after quenching, a small plastic deformation occurred before the fracture and the fracture surface is brittle macroscopically.  
When the welding speed increases, the minimum hardness increases and the width of the softened area decreases; while the laser power increases, the width of the softened area increases, and the minimum hardness does not change significantly.  
As the welding speed increases, the front-bead width A, the back-bead width B, and the height of the smooth segment H all decrease; while the laser power increases, the back-bead width B increases, the front-bead width A and the height of the smooth segment H increase first and then decrease. The front-bead width A reaches the maximum when the laser power is 3000 W, and the height of the smooth segment H does not change much.  
Before quenching, the FZ, CGZ, and FGZ consist of M and RA, and the IRZ consists of F and M. After quenching, the microstructure of the FZ, HAZ, and BM is transformed into M and RA.  
Before quenching, in the FZ, there are coarse grains, which are larger than 39 \(\upmu\)m in diameter. There are massive high-angle misorientation boundaries in the FGZ, while the high-angle misorientation boundary of FZ, CGZ, and BM are relatively few. But after quenching, the grain size is refined and the maximum grain size is less than 32 \(\upmu\)m. The grain boundaries of the FZ are basically composed of high-angle misorientation boundaries, while more low-angle misorientation boundaries are formed in the HAZ and BM.  
Conceptualization, Y.K. and C.Z.; methodology, Y.K. and C.Z.; formal analysis, X.X. and Y.K.; investigation, Y.K. and R.W.; resources, Y.K. and R.W.; data curation, X.X. and R.W.; writing--original draft preparation, Y.K.; writing--review and editing, X.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Suizhou-WUT Industry Research Institute grant number suikefa [2019].  
Not applicable.  
The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.  
Figure 22: Distribution of kernel average misorientation: (**a**) before quenching; (**b**) after quenching.
## 3 Results  
### Total Phenolic Content  
Phenolic compounds in plants have a hydroxyl substituted benzene ring, which can have interesting biological activities such as antioxidant, anti-inflammatory, antiproliferative, and antibacterial activity [10; 11]. We measured the TPC of ISE and five different fractions--Hex, CHCl3, EtOAc, BuOH, and H2O (Table 2). The TPC of ISE and the five different fractions of ISE range from 18.21 to 286.25 mg PGE/g of dry weight. BuOH (188.44 \(\pm\) 11.77) had the highest TPC, followed by EtOAc (88.01 \(\pm\) 9.82), H2O (77.58 \(\pm\) 3.08), ISE (76.29 \(\pm\) 8.95), CHCl3 (20.38 \(\pm\) 0.99), and Hex (10.16 \(\pm\) 0.00). The BuOH fraction showed the highest TPC compared to other fractions of ISE.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Extract/Fractions**} & \multicolumn{2}{c}{**Extraction Yield**} & **TPC(mg** & **ABTS(mg** & **DPPH(mg** & **FRAP(mM** \\  & **\% (w/w)** & **5** & **PGE/g)** & **VCE/g)** & **VCE/g)** \\ \hline ISE & 15.00 & 75.00 & 76.29 \(\pm\) 8.95 b  & 119.26 \(\pm\) 9.28 b  & 44.85 \(\pm\) 5.96 b  & 0.12 \(\pm\) 0.01 b  \\ Hex & 13.87 & 6.25 & 10.16 \(\pm\) 0.00 c  & 18.21 \(\pm\) 4.43 c  & 11.63 \(\pm\) 0.38 c  & 0.04 \(\pm\) 0.00 c  \\ CHCl3 & 4.32 & 1.95 & 20.38 \(\pm\) 0.99 c  & 25.40 \(\pm\) 3.86 c  & 12.21 \(\pm\) 1.36 c  & 0.05 \(\pm\) 0.01 c  \\ EtOAc & 5.31 & 2.40 & 88.01 \(\pm\) 9.82 b  & 120.32 \(\pm\) 8.14 b  & 53.29 \(\pm\) 6.84 b  & 0.15 \(\pm\) 0.01 b  \\ BuOH & 4.26 & 1.92 & 188.44 \(\pm\) 11.77 a  & 286.25 \(\pm\) 14.61 a  & 82.14 \(\pm\) 12.13 a  & 0.40 \(\pm\) 0.05 a  \\ H2O & 29.08 & 13.11 & 77.58 \(\pm\) 3.08 b  & 109.26 \(\pm\) 8.94 b  & 36.76 \(\pm\) 3.49 b  & 0.16 \(\pm\) 0.01 b  \\ \hline \hline \end{tabular}  
* TPC, total phenolic content; ABTS, ABTS radical scavenging capacity; DPPH, DPPH radical scavenging capacity; FRAP, ferric-reducing antioxidant power. The values are expressed as the means \(\pm\) SD (\(n=3\)). Different letters in the same column indicated significant difference (\(p<0.05\)).  
\end{table}
Table 2: Extraction yield, total anti-oxidant capacity, and total phlorotannin content of the ISE and its fractions.
# A Review of Flywheel Energy Storage System Technologies and Their Applications
## 5 Recommendations for Future Research  
Although the flywheel is one of the earliest forms of energy storage, compact, reliable, low maintenance flywheels have only become available relatively recently. The numbers produced have been small, and the use of more exotic materials and their processing, such as carbon fibre composites, have kept the cost at about five times higher than steel flywheels [10,18]. New, innovative designs based on steel overcome the concern about safety for highly stressed rotors, which can now operate at much higher tip speeds than was considered safe for monolithic steel rotors [88]. Steel has the benefit that the material and processing routes are well established and understood to the supply base which is already there, for low cost manufacturing at the all critical batch scales of 10 s to 1000 s. Steel is easily recyclable in comparison to batteries, although recycling will not need to be done for decades given an effective infinite calendar life and cycle life of many 10 s of thousands.  
Another concern relates to the charge holding ability of flywheels, since the losses in currently available flywheels are high. These losses are mechanical (drag, bearing, friction), electrical (hysteresis, eddy current, copper), and power converter-related (switching and conduction) [89]. If flywheel losses can be kept to around 10%-20% run down per 24 h, given the available technologies and the lower speeds of the steel flywheel, the possibility for these applications is certainly promising. The vacuum needed for this is not unduly high and will be held by means of a hermetically sealed system, only requiring occasional re-pumping. The majority of the weight can be levitated on passive magnetic bearings, with inevitable losses in the lightly loaded ball bearing system being well within the 20% run down loss allowance. The remaining loss is the electromagnetic drag in the generator and this depends on the design. Since a flywheel for this duty is likely to have an electrical machine of kW rating similar to the kWh rating, numerically, the electromagnetic drag of a well-designed system can also be kept within the loss budget.
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 3 Review of Adaptive Data Rate Algorithms
### Scalability
Minimizing the number of hops and the delay is the objective, since higher SF values result in more airtime, leading to an increase in the end-to-end delay. According to the authors, their results showed an improved performance in contrast with the SF allocation in a single-hop LoRaWAN network. Future research could involve a faster approach in predicting connectivity that would utilise the quickest data rate for the connectivity prediction of all the SFs.
## 3 Theoretical Framework and Hypotheses
### UTAUT Model and Universal Constructs  
Several theory models have been proposed to investigate the factors influencing user acceptance and use behavior intention, including the Technology Acceptance Model (TAM), Health Information Technology Acceptance Model (HITAM), Unified Theory of Acceptance and Use of Technology (UTAUT) model, and the Extended UTAUT model of older users' home telehealth services. UTAUT model served as the foundational theoretical framework for this investigation due to its demonstrated applicability in elucidating the factors influencing individuals' acceptance and intention to utilize specific technologies. This relevance extends to domains concerning health and aging, making it particularly suited for examining the adoption behaviors towards technology-driven interventions among older populations [12, 32, 37, 38]. The UTAUT model's robustness in capturing the multifaceted nature of technology acceptance, including aspects related to performance expectancy, effort expectancy, social influence, and facilitating conditions, renders it an ideal basis for exploring how these constructs influence the acceptance and behavioral intention (BI) of health-related technologies by the elderly. In this study, BI refers to the  
Figure 1: PP and PPS for predicting older users’ PE, EE, and BI of IEGs. Note. PP: Physical Posture, PPS: Perceived Physical Safety, PE: Performance Expectancy, EE: Effort Expectancy, FC: Facility Condition, SI: Social Influence, BI: Behavioral Intention to use.  
strength of an individual's intention to perform exercises through IEG in the future, which is a measure to indicate the acceptance and intention to use IEG. Performance expectancy (PE) is defined as the degree to which individuals believe that using IEG will help them to attain gains in their physical activity performance and enhance their quality of life. Effort expectancy (EE) presents the degree of ease associated with the use of IEG for the older population. Social influence (SI) refers to the degree to which individuals perceive those important individuals who believe they should exercise through immersive technologies. Facilitating conditions (FC) are defined as the degree to which older people believe that the organizations and technical infrastructure exist to support the use of IEG.
## 2 Methodology
### Theoretical Background
#### 2.1.1 Single-Output Gaussian Processes Modeling  
We refer to the standard GP as a single-output Gaussian process (SOGP). It can be formulated as follows. Let \(\mathfrak{D}=\{x_{i},y_{i}\}_{i=1}^{N}\), be a set of \(N\) pairs of a \(x_{i}\in\mathbb{R}^{\mathcal{B}}\) point belonging to the training dataset \(X=\{x_{i}\}_{i=1}^{N}\) assigned to a random variable \(y_{i}\). GP is a random process that uses these pairs to learn the function outputs \(f_{*}\) given a test dataset \(X_{*}\) of size \(N_{*}\times\mathfrak{D}\). Let us assume an additive noise model with a zero mean:  
\[y_{i}=f_{i}(x_{i})+\mathcal{E}_{i},\ \ \mathcal{E}_{i}\sim\mathcal{N}\Big{(}0, \sigma_{n}^{2}\Big{)} \tag{1}\]  
where \(\sigma_{n}^{2}\) is the variance of the Gaussian noise and \(f_{i}\) is the nonparametric latent function to be found. Assuming independent Gaussian noise, the joint distribution of observations and test predictions is:  
\[\begin{pmatrix}y\\ f_{*}\end{pmatrix}\sim\mathcal{N}\Big{(}\begin{bmatrix}0\\ 0\end{bmatrix}^{\prime}\Big{[}\begin{matrix}\mathbf{K}(X,X)+\sigma_{n}^{2} \mathbf{I}&\mathbf{K}(X,X_{*})\\ \mathbf{K}(X_{*},X)&\mathbf{K}(X_{*},X_{*})\end{matrix}\Big{]}\Big{)} \tag{2}\]where \(K(X,X)\) represents the self-similarities in the training set, \(K(X,X_{*})=K_{*}\) and \(K(X_{*},X)=K_{*}^{\top}\) are the similarities between training and test sets, and \(K(X_{*},X_{*})=K_{**}\) express the self-similarities in the test set. SOGP models are built through parametrizing a covariance kernel that computes the measures of similarity between two points \(x_{i}\) and \(x_{j}\). Expressive kernels allow the improved representation of complex signals. The squared exponential kernel arises as one of the most used kernels in GP modeling. It can be calculated as:  
\[k\big{(}x_{i},x_{j}\big{)}=\sigma_{f}^{2}e^{-\frac{1}{2\ell^{2}}\left\|x_{i}- x_{j}\right\|^{2}} \tag{3}\]  
where \(\sigma_{f}^{2}\) (signal variance) is a scaling factor, and parameter \(\ell\) (variance of the Gaussians) controls the length scale that modules the regression process smoothness and confidence. The model hyperparameters can be learned by maximizing the marginal likelihood of the model [45], through a cross-validation strategy [46], or applying a Bayesian learning process [47]. A SOGP establishes a prior distribution over functions. This can be converted to a posterior probability distribution of \(X_{*}\) given \(f\) and the previous \(X\) conditioning on the observations, to obtain another Gaussian:  
\[p(f_{*}|X_{*},X,y)\sim\mathcal{N}\Big{(}y_{*}|\mu_{SOGP},\sigma_{SOGP}^{2}+ \sigma_{n}^{2}\mathbf{I}\Big{)} \tag{4}\]  
According to the SOGP formulation, \(f_{*}\) is normally distributed with mean (\(\mu_{SOGP}\)) and variance (\(\sigma_{SOGP}^{2}\)) given by:  
\[\mu_{SOGP}(x_{*})=k(x_{*},X)\Big{[}\mathbf{K}(X,X)+\sigma_{n}^{2}\mathbf{I}\Big{]}^{-1}y \tag{5}\]  
\[\sigma_{SOGP}^{2}(x_{*})=k(x_{*},x_{*})-k(x_{*},X)\Big{[}\mathbf{K}(X,X)+\sigma_{n }^{2}\mathbf{I}\Big{]}^{-1}k(X,x_{*}) \tag{6}\]  
To sum up, SOGP offers a natural mechanism to construct and calibrate uncertainties automatically.
# A Review of Optical Neural Networks
## 3 Development Processes and Categories
### Silicon-Based Optical Neural Networks
However, the need for phase stabilization and many neurons with large optical components, such as fibers and lenses, has been a major obstacle to achieving this conversion. Integrated photonics solves this problem by providing a scalable solution. Photon particles are too large to be integrated at the same high level as electrons [49]. In addition, present optical neural network solutions inevitably rely on electronics. Submicron-scale etching techniques are still not available, and height differences and spacing between optics and electronics and process incompatibilities make optoelectronic integration challenging. Furthermore, nonlinear limitations and photonics integration constraints make it impossible to scale chips such as electronic neuromorphic computing chips, which are mere arrays. In addition, nonvolatile photonic storage and weighting, as well as low power consumption, are necessary to ensure neurosynaptic functions [50; 51]. Even though photonic nonlinear neural operations have been demonstrated in materials such as PCMs, there is still a large gap to electronic nonlinear neuromorphic operations, and there is still room for development in the energy efficiency and fast switching of new cumulable materials. As of yet, the goal of a large-scale, rapidly reprogrammable photonic neural network chip remains unrealized.
# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
## 1 Introduction
Gabor filters have a wide range of applications, particularly image and texture analysis, because of their exceptional feature extraction capabilities [17; 18; 19; 20]. Consequently, the Gabor filtering network (GFN) [21] technique was presented to extract unique attributes required to distinguish modulation forms. Furthermore, a genetic algorithm (GA) [22] was applied to optimize the weights of the GFN to improve classification accuracy. Based on these discussions, this research provides a strategy for integrating the GEO satellite signal with an IRS, complete with the appropriate channel modeling and system characterization. Gabor filter networks (GFNs) are proposed for extracting and categorizing different features. Hybrid optimal functions are employed to solve the approximated classification issue, and a simulation setup and appropriate channel modeling have been developed. Key contributions to the research are mentioned in the section that follows.
## 1 Introduction
But the reality is that each iteration in GMPSO needs to re-run the FE package, which obviously increases the cost of computation, let alone the research object is a more complex or larger scale structure. To alleviate those problems, equivalent model, called the meta-model, is usually proposed to replace the initial FE model. The most commonly used meta-model method is response surface methodology (RSM), first proposed by [PERSON] [17], which establishes explicit function between selected parameters and eigenvalues whose relationship are properly complicated, implicit, and difficult to be expressed mathematically. [PERSON] and [PERSON] [11] utilized a second order polynomial response surface (RS) representing the relationship between selected parameters and structural responses, then the parameters were updated by genetic algorithm. This method was applied to model updating of a two-way bridge located in Louisiana. [PERSON] et al. [25] presented an updating method based on polynomial RSM using uniform design and the proposed method was verified by a six-girder bridge. However, for higher order nonlinear relationship between parameters and responses, the polynomial RS cannot be applied. Instead, a more suitable meta-model--i.e., Kriging meta-model--is proposed. This meta-model, another type of RS, was originally developed by [PERSON] in 1951 [26], which is first applied in area of mining and geo-statistics. After adding a stochastic process behind the polynomial part, the accuracy of the meta-model is improved significantly. In this article, realizing the model updating of a self-anchored suspension bridge with extra-width, a practical model updating hybrid methodology by integration of GMPSO and Kriging meta-model is proposed and implemented. The architecture of the hybrid method consists of three main stages of analysis: (1) selecting reasonable samples of random parameters as input datasets using LHS and performing FE analyses with the input parameters to obtain output datasets; (2) formulating the Kriging meta-model based on input and output datasets; and (3) applying GMPSO as an optimization technique with an objective function set as errors between experimental value and Kriging model to yield the updated values of selected parameters. The accuracy of this hybrid method is first demonstrated through the analysis of model updating of a simply supported beam. Then the method is applied to model updating of Hunan Road Bridge, a self-anchored suspension bridge with extra-width in Shandong Province in China. Results show the proposed method can obtain reasonable values of updated parameters and show great efficiency and accuracy in model updating issues. This article is organized as follows. Section 2 presents the theory background of GMPSO method, Kriging meta-model method, Latin hypercube sampling, and the hybrid model updating method. Section 3 introduces the study example of model updating of a damaged simply supported beam. Section 4 describes the details model updating process of Hunan Road Bridge using the hybrid method and conclusions are made in Section 5.
# Design Optimization of the Aeronautical Sheet Hydroforming Process Using the Taguchi Method
## 1 Introduction
Nevertheless, there is a gap in the knowledge of studies that analyze the optimization of aeronautical sheet hydroforming processes. In this scenario, the need arises to make the aeronautical sheet hydroforming processes more competitive. Generally, the forging aeronautical aluminium alloy sheet is received in an annealed condition and is formed in a fresh and unstable state following the heat treatment solution. In order to avoid the loss of ductility and the increase in the resistance that occurs in  
Figure 1: Cracks due to necking in the radius of the stamping tool. natural maturation, it is necessary to keep the sheet in the refrigerator at \(-40^{\circ}\) C; this operation generates an important variability of the parameters related to the formability of the sheet. This variability occurs both within the same batch of pieces and between different batches. Being able to shape the sheet after its maturation, in a steady state T3, with lower ductility and greater resistance, could be an important competitive advantage for hydroforming; it would reduce the variability originating in the parameters related to the formability, but it would increase the risk of cracking by necking or by ductile fracture. Therefore, in this study, the robust design methodology of Taguchi has been employed [7; 8]. This was due to the good results in other industrial hydroforming processes, such as the one carried out by [PERSON], [PERSON], and [PERSON] [9]. In this work, the authors optimized the pipe hydroforming process for the construction of complex automotive parts. Also, in another work by [PERSON] and [PERSON] [10], they analyzed the use of the methodology to optimize the cutting process on 1100 aluminium alloy surfaces. Therefore, the possibilities of application of this methodology constitutes an opportunity for the optimization of aeronautical sheet hydroforming processes, so the robust design methodology was applied to make the process less sensitive to the inherent variations [11]. The aim was to make that the elastic recovery and the thinning of the sheet in the stamping area minimal and insensitive to variations in the material. Technological parameters were considered as control factors of the process, while the noise factors were the parameters related to the formability of the sheet, and process simulation tools were used by means of the Finite Elements Method (FEM), from the conceptual design phase of the pieces until the design of the forming blocks. The article is structured as follows: (i) in Section 2, a detailed description of [PERSON]'s robust design methodology and its applications in engineering is made; (ii) Section 3 describes the methodology used in this study; (iii) Section 4 discusses the results obtained; and (iv) Section 5 establishes the main conclusions of the study.