esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
expanded this model by incorporating destination accessibility and distance to transit, resulting in a more comprehensive \"5D\" built environment framework [31], which has become an important reference standard for quantitative research on the built environment on an international scale. In terms of analytical methods, related studies frequently utilize techniques such as ordinary least squares (OLS) [32], geographically weighted regression (GWR) [20] and spatial lag models (SLMs) [33]. For instance, [PERSON] et al. [34] utilized multi-scale geographically weighted regression (GWR) to analyze the spatiotemporal driving factors affecting 24 h urban vitality in Beijing. Moreover, the use of Geodetector in related research is on the rise. However, the traditional Geodetector model is constrained by issues related to spatial scale, data discretization methods and the number of levels. To address these limitations, [PERSON] et al. developed a new optimal parameter geographic detector (OPGD) model [35], which mitigates the challenges associated with subjective data discretization and spatial scale, allowing for a more scientifically robust evaluation of the research subject [36]. A review of existing research indicates that current studies on the built environment and urban vitality predominantly examine their linear relationships, among which the application of OLS and GWR models is relatively common. In terms of research scales, investigations primarily focus on the street, block and community levels. However, analyses of urban vitality at the block and subdistrict levels utilizing the optimal parameter geographic detector (OPGD) model and multiscale geographically weighted regression (MGWR) model remain scarce. Based on the context outlined above, this research employs exploratory spatial data analysis techniques to investigate the distribution patterns and clustering tendencies of urban vitality within Qingdao City, focusing on the subdistrict and block levels. Additionally, the OPGD and spatial regression analysis methods are employed to uncover the interactive effects and spatial heterogeneity influencing urban vitality, thus providing targeted recommendations for enhancing vitality in sustainable urban development and renewal.
## 3 Research Hypothesis and Model Construction  
### Definition of Residents' Energy-Saving Behavior  
Energy-saving behavior refers to the behavior of an individual to save energy consumption in a certain way. Residents' energy consumption is the household energy consumption in daily life, mainly including oil, natural gas, electricity, and other related energy consumables. The existing research literature generally divides energy-saving behaviors into two categories [9; 36]. One is habitual energy-saving behavior, which refers to the behavior of reducing energy consumption by adjusting daily lifestyles and energy use habits, which needs to be repeated, such as adjusting temperature control equipment and turning off lights on time, and so on. The other type is energy efficiency investment behavior, which refers to the behavior of indirectly achieving an energy-saving effect by purchasing energy-saving equipment or investing in energy-saving technology.  
With the continuous improvement of China's urbanization level, energy, and environmental pressure are increasing. Therefore, the importance of changing residents' energy use behavior should be highlighted. In the time dimension, residents' energy-saving behavior can be short-term or long-term. Generally, residents' energy-saving behaviors in the short term are very random and may show different characteristics. In the long run, individual behavior shows strong plasticity. The energy-saving behavior of residents is affected by many external factors and may also show stability in the long term. Existing research on residential energy-saving behaviors mostly focuses on the influencing factors at the initial formation stage of energy-saving behaviors, and the research on the development process of residents' energy-saving behaviors is still in the exploratory stage [7].  
Based on this, and drawing on previous studies, in this study, residents' energy-saving behaviors are divided into habit adjustment energy-saving behavior (HAB) and efficiency investment energy-saving behavior (EIB). Focusing on the sustainable development stage of residents' energy-saving behaviors, this study introduces psychological attribution theory to excavate the influencing mechanism of residents' energy-saving behaviors based on the consideration of promoting the sustainable development of energy-saving behaviors.
# Transient Numerical Modelling of the Pin-in-Paste Technology
###### Abstract
Due to these limitations, their models could not address the dynamics of hole-filling by the solder paste during stencil printing, which is the most crucial output parameter in the case of pin-in-paste technology. Similarly, the geometrical properties of plated through-holes (e.g., top-view shape) that significantly affect the hole-filling cannot be investigated with these models. [PERSON] and [PERSON] introduced an analysis method of solder paste flow into the stencil apertures [13]. Their method included a double-step 3D modelling approach due to the limitation in computational resources. First, the pressure distribution along the stencil was obtained by a large-scale model; second, the aperture filling was analyzed in a small-scale model. The limitation of their work is the usage of Newtonian fluid properties throughout their modelling. [PERSON] et al. created a 3D CFD-based (computational fluid dynamics) model to investigate the flow of solder pastes into the stencil apertures of surface mounted components during printing [14]. They also showed that the non-Newtonian paste properties significantly impact the yield of the stencil printing process; namely, an increasing printing speed will increase the shear stress within the solder paste and, consequently, increase the rate of shear. Since solder pastes are shear-thinning fluids (decreasing viscosity over an increasing rate of shear), an increase in the printing speed will aid the solder paste flow into the stencil apertures. It should be noted that with an increasing printing speed, the solder paste would have less time to fill up the plated holes belonging to through-hole components. Thereby, the increase in printing speed is not as straightforward in pin-in-paste technology as in [PERSON]'s work. [PERSON] et al. studied the flow of solder paste in stencil printing at different aperture sizes and squeeze speeds [15; 16; 17]. A transient 3D CFD model was created based on the Volume of Fluid (VOF) method, and the non-Newtonian fluid properties were addressed by [PERSON]'s viscosity model [18]. The results of their model match the experimental measurements with an acceptable error at different aperture sizes, though the error might be reduced further by using [PERSON] viscosity model [19]. Based on the literature review, none of the works dealt with the hole-filling by solder paste in pin-in-paste technology; all of them investigated the process of stencil printing with a focus on the surface-mounted components. Thus, this research aims to prepare a transient CFD model describing this technology, enabling the analyses of plated through-hole geometries and their effect on hole-filling.
## 1 Introduction
### Related Works  
#### 1.2.1 Signal Transformation  
In the realm of direct machine learning-based AMC methods, the unprocessed signal sequence, when directly fed into the system, can be seen as a one-dimensional array housing minimal information. However, with the application of pre-processing techniques, the latent information embedded within can be more comprehensively revealed. In 1946, the Short-Time Fourier Transform (STFT) was introduced as the initial endeavor towards this goal. To tackle the aforementioned primary constraint, taking inspiration from STFT and subsequent research on two-dimensional representations of sequential signals, we have opted to address the prevailing issue by transforming the signal into a two-dimensional format.  
[PERSON] et al. suggested that encoding sequences into images allows machines to visually recognize, classify, and learn structures and patterns [18]. They utilize the Gramian Angular Field (GAF) to transform raw time-series data into a polar coordinate system. Additionally, they employ the Markov Transition Field (MTF) to compute the transition probabilities of a first-order Markov chain along the temporal axis. These pieces of information are regarded as the static and dynamic information of the time series. However, due to the inability of MTF to provide complete dynamic information and resulting in relatively lower classification accuracy compared to other newer methods, [PERSON] et al. proposed a representation method based on Recurrence Plots (RP) [19]. However, the simple CNN model they used led to imbalanced classification results on standard datasets. Reference [20] introduced a time-series image representation method called Motif Difference Field (MDF). By utilizing Gradient-weighted Class Activation Mapping (Grad-CAM) to identify important information within the sub-sequences and constructing MDF based on the differences between thisinformation, the MDF is then classified using a Fully Convolutional Network (FCN) based on VGG16. [PERSON] et al. [2019] proposed a modulation recognition model based on multi-branch CNN. By leveraging prior knowledge of the signals and network topology, the time-domain waveforms of the signals were transformed into eye diagrams and constellation diagrams. The inherent advantages of CNNs in image recognition and classification were utilized to achieve modulation recognition. In [2018], a deep learning-based method for MPSK signal modulation recognition was introduced. By extracting cyclic Poincare maps of MPSK signals, two-dimensional feature information was obtained and fed into a CNN network for training, aiming to classify the modulation modes.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## 5 Fundamentals of Nonlinear Optimization
### Duality for Nonlinear Programs  
The concept of non-linear duality is one the key things that many solution techniques for different classes of NLPs, use in the core of their operation. First, we define the _Lagrangian dual function_ which is given by:  
\[D\left(\boldsymbol{\lambda},\boldsymbol{\mu}\right)=\underset{\mathbf{x}}{min} \left(\mathcal{L}\left(\mathbf{x},\boldsymbol{\lambda},\boldsymbol{\mu}\right)\right) \tag{9}\]  
The dual problem connected to the NLP in Equation (6) is,  
\[\underset{\boldsymbol{\lambda},\boldsymbol{\mu}}{max}\ D\left(\boldsymbol{ \lambda},\boldsymbol{\mu}\right),\quad\boldsymbol{\lambda}\geq\mathbf{0} \tag{10}\]  
and finds best lower bound on the optimal objective function value (\(p^{*}\)) of Equation (6), obtained from Lagrange dual function. This is a convex non-differentiable optimization problem with optimal value (\(d^{*}\)), where \(\left(\boldsymbol{\lambda},\boldsymbol{\mu}\right)\) are dual feasible if \(\boldsymbol{\lambda}\geq\mathbf{0}\) and \(\left(\boldsymbol{\lambda},\boldsymbol{\mu}\right)\in\mathbf{dom}\ \left(D\right)=\left\{\left( \boldsymbol{\lambda},\boldsymbol{\mu}\right)\left|D\left(\boldsymbol{\lambda}, \boldsymbol{\mu}\right)>-\infty\right\}\), and \(\mathbf{dom}\left(.\right)\) represents the function's domain.  
As in linear duality, non-linear duality has the _weak duality_ property where the optimal objective value of the dual problem is a lower bound on the primal (if it is a minimization problem), i.e., \(d^{*}\leq p^{*}\). If the primal problem is unbounded from below, i.e., \(p^{*}=-\infty\), we must have \(d^{*}=-\infty\) which means the dual is infeasible. If the dual problem is unbounded from above so that \(d^{*}=\infty\), then the primal is infeasible and takes the value \(\infty\). Note that the primal and dual objective functions in this discussion are considered to have infinitely large values outside their domains to be [26]. If the \(d^{*}=p^{*}\) holds, i.e., the optimal duality gap is zero, the _strong duality_ holds. This means that the best bound that can obtained from the Lagrangian dual functions is tight. This happens usually if Equation (6) is convex. Note that for a non-linear optimization problem to be convex in the presence of equality constraints, these constraints must be linear. If the constraints satisfy at least one _constraint qualification condition_, then strong duality holds.
# FEM Based Preliminary Design Optimization in Case of Large Power Transformers
## 3 Results and Discussion
### Input Parameters of the Test Transformer  
The optimization method was tested on the following case study: a cost optimization of a 31.5 MVA power transformer with 132 kV/33 kV voltage ratio. The objective of the optimization is the total cost of the ownership. The network frequency is 50 Hz, the required short circuit-impedance is 14.5 %. The parameters are selected according to the standard [4]. The TOC is calculated by the following capitalization factors: \(K_{1}=6000\,\copy\)/kW and \(K_{2}=2000\,\copy\)/kW. For the sake of simplicity, the transformer cooling was chosen to be ONAN and the ambient temperature was specified to 40 \({}^{\circ}\)C. The allowed winding oil temperature rise was defined to \(\Theta_{\rm{wo}}=65\) K, according to the IEC-60076 standard [49]. Therefore, the winding current density limit was set to 3.0 A/mm\({}^{2}\) in the main windings and 3.5 A/mm\({}^{2}\) in the regulating winding. The primary winding was modeled as a helical winding from CTC, while the secondary winding as a disc winding from twin conductors. The transformer is regulated by a diverter switch, which switch off the regulating winding at the nominal tapping stage. The applied core material in this case was a TRAN-COR H1 grade electrical steel. The maximum value of the flux density was limited to 1.7 T considering the saturation of the core material and over-voltages in the power grid. The minimal insulation distances were chosen by empirical rules [9]. These methods were based on the lightning impulse test and AC test requirements. The detailed list of the input  
Figure 5: Main dimensions and the flux density distribution of the validation example and the main parameters in Agros2D [34].  
parameters of the optimization model are presented in Table 3. The upper and the lower bounds of the searched independent parameters are presented in Table 4.
# Dual-Channel Mapping-Gas Column Concentration Inversion Method Based on Multispectral Imaging
## 5 Conclusions  
This paper is aimed at solving the problem of difficulty in calculating the gas column concentration in gas concentration detection with background radiation acquisition affected by the target gas. In this paper, a dual-channel mapping-gas column concentration inversion method based on multispectral infrared gas imaging detection technology was proposed. The infrared radiation was separated by switching the spectral channels, the infrared radiation absorption characteristics of the gas combined with the dual-channel background radiation mapping experiment were utilized to acquire background radiation, and finally, we conducted a column concentration calibration experiment to achieve the column concentration inversion of the target gas column. The mapping relationship between the temperature and gray value of two spectral channels, the absorbance of the target gas and the column concentration was established. The experimental results showed that the relative error of the dual-channel background radiation mapping method was about 1.9% in the laboratory environment. The feasibility of this method was verified by the inversion of SF\({}_{6}\) gas column concentration in different indoor and outdoor environments, and the accuracy of the method was verified in a variety of ways. The average relative error of the inversion method was 4.8%, which enables the detection of gas column concentration.  
Conceptualization, N.S. and J.L. ([PERSON]); methodology, N.S., J.L. ([PERSON]) and Y.Z.; validation, N.S., J.L. ([PERSON]) and J.L. ([PERSON]); investigation, N.S., J.L. ([PERSON]) and Y.Z.; resources, Y.Z.; writing--original draft preparation, N.S. and Y.Z.; writing--review and editing, J.L. ([PERSON]), J.L. ([PERSON]) and W.W.; supervision, Y.Q., B.Z., K.Z. and Y.C.; funding acquisition, J.L. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Jilin Scientific and Technological Development Program under grants 20230201049 GX, 20230508141 RC and 20230508137 RC; the National Natural Science Foundation of China under grants 61805239, 61627819, 61727818 and 62305339; the Youth Innovation Promotion Association Foundation of the Chinese Academy of Sciences under grant 2018254; and the National Key Research and Development Program of China under grant 2022 YFB3604702.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results.
# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
###### Abstract  
Automatic modulation recognition (AMR) plays an essential role in modern communication systems. In recent years, various modulation recognition algorithms based on deep learning have been emerging, but the problem of low recognition accuracy has not been solved well. To solve this problem, based on the existing MCLDNN algorithm, in this paper, we proposed an improved spatiotemporal multi-channel network (IQ-related features Multi-channel Convolutional Bi-LSTM with Gaussian noise, IQGMLC). Firstly, dividing the input IQ signals into three channels, time sequence feature extraction is carried out for route I, route Q, and route IQ, respectively. For route IQ convolution kernel (2,1) is first used to extract relevant features. Two layers of the small convolution kernel (1,3) are used to extract time sequence features further, and the three channels are used to extract features further. Then, a two-layer short-length memory network is used to extract features from time and space more effectively. Through comparison experiments, Bi-LSTM is introduced to replace one layer of LSTM, and a fully connected layer is removed to prevent overfitting. Finally, multiplicative Gaussian noise is introduced to naturally corrode the feature parameters, further improving the robustness and accuracy of the model. Experiments are carried out on three public datasets RML2016.10a, RML2016.10b, and RML2016.04C. The experiments show that the IQGML network has higher recognition accuracies on all datasets, especially on the RML2016.10a dataset. When the SNR is 4 dB, the recognition accuracy reaches 93.52%. When the SNR is greater than 0 dB, the average recognition accuracy reaches 92.3%, 1.31%, and 1.2% higher than the original MCLDNN network, respectively.  
modulation recognition; convolutional neural network; multiplicative Gaussian noise; IQGMLC; IQ signal Article
# Developing a Generalized Regression Forecasting Network for the Prediction of Human Body Dimensions
## 3 Methodology of the GRFN
### Construction of MMPSO Algorithm for Body Dimension Prediction
#### 3.3.6 Time Computational Complexity Analysis  
MMPSO's time complexity is analyzed according to Algorithm 1. In MMPSO, we assume that the parameters are initialized, where the swarm size of the input is denoted as \(N\), the spatial dimension is denoted by \(D\), and the fixed number of iterations is indicatedas \(T\). The time computational complexity formula for the basic PSO is \(\mathcal{O}(T\cdot N\cdot D)\). In MMPSO, we assume that the time complexity needed to initialize the population phase of the parameter is \(\mathcal{O}(N\cdot D)\). During global tuning, we determine whether the stagnant state counter, \(Stag_{s}\), is greater than threshold \(C_{t1}\) and activate state detector \(Stag_{i}>C_{t2}\). Then, the particle disturbance update and refresh compensation operations in the local tuning phase are performed, and the time computational complexity of the worst-case scenario is represented as \(\mathcal{O}(2N_{1})\). The independent learner subgroups alternately execute dynamic multi-swarm reorganization learning strategies, and their time computational complexity is \(\mathcal{O}(N_{2}\cdot D)\). The student subgroup learns under the supervision of the teacher subgroup, and the global tuning phase performs a self-cognitive or social cognitive search. The local tuning phase then performs a particle disturbance update and refreshes the compensation operations. In the event of the most unfavorable circumstances, the time computational complexity becomes \(\mathcal{O}(N_{3}\cdot D+2N_{3})\). During the fine-search phase of the global optimal solution, the time computational complexity associated with the selection of the dimensional vector for updating the global optimal solution is \(\mathcal{O}(D)\). Consequently, when the stopping criterion for the MMPSO algorithm is characterized by a fixed number of Iterations, denoted as \(T\), the total time computational complexity of the MMPSO algorithm can be expressed as \(\mathcal{O}(T\cdot N\cdot D)\).
## 2 Overview of LoRa and LoRaWAN
The value of the EH is either 0 or 1. If a low data rate is enabled, the DE value is one; otherwise, it is zero. The LoRa packet size can be up to 255 bytes [16]. LoRaWAN is the network protocol responsible for connecting LoRa nodes. LoRaWAN supports star-of-stars topologies and can be created as a single-hop network. Data are transmitted from sensors to base stations (gateways) at a rate of up to 50 kbps. LoRaWAN provides low-power communication with a range of up to 15 km. Figure 1 illustrates the LoRaWAN architecture, which consists of four basic components: LoRa end devices, LoRa gateways, network servers, and application servers [17]. The end devices (sensors) are responsible for sensing the area and transmitting the data to the GW. LoRa GWs are more capable devices that act as bridges between end devices and network servers. The network server manages the entire LoRaWAN network. Application servers are in control of securely processing, maintaining, and interpreting sensor application data [17]. In this work, we focus on studying the challenges of interconnecting the end devices and the GW.