esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of Psychological Health on Childhood Obesity: A Cross-Developmental Stage Analysis
## 2 Materials and Methods
### Psychological Measures and BMI in Early Childhood (8-11 Years Old)
#### 2.2.2 Biometric and Demographic Data  
Please refer to Section 2.1.2 for a detailed overview of the biometric and demographic data, including BMI Category, BMI, and BMI percentile. The criteria and methodologies outlined in that section apply equally to this age group, providing a consistent basis for evaluating weight status and its potential health implications across different developmental stages.  
The dataset for children aged 11-12 years provides a detailed examination of various psychological measures, biometric data, and BMI measurements. Numerically, the dataset encompasses 15 entries, with measures such as Affect Dysregulation score and Affect Suppression score averaging at 2.27 and 2.48, respectively, indicating a range of emotional regulation capabilities among the children. Adaptive reflection scores average at 3.5, suggesting a moderate level of reflective thinking. The attachment anxiety and attachment avoidance scores hover around 1.96 and 2.04, pointing to varied levels of attachment-related anxieties and avoidance behaviors.  
The Emotional Problems Scale and Conduct Problems Scale average scores are 2.33 and 2.13, respectively. The Hyperactivity Scale averages at 3.07, with Peer Problems averaging at 0.87, indicating a relatively low level of peer-related issues. The Prosocial Scale, with an average of 8.4, suggests a high level of prosocial behavior, and the Total Difficulty score averages at 8.4. The Externalizing score and Internalizing score averages at 5.2 and 3.2, respectively, highlighting externalized behaviors and internalized emotions. The BMI data, with an average of 22.61 and a percentile average of 81.55, indicate a diverse range of physical well-being among the children.  
Categorically, the dataset reveals that most children fall into the \"Healthy\" BMI Category, with females slightly outnumbering males. This comprehensive dataset not only provides insight into the psychological well-being of children aged 11-12 but also correlates these findings with their physical health, represented by BMI and its percentile. These data underscore the intricate interplay between psychological health and physical well-being in children, serving as a valuable resource for further research into child development and health.
# Spoofing Traction Strategy Based on the Generation of Traction Code
## 2 Materials and Methods
### Proposed Strategy Spoofing traction strategy based on the generation of traction code
#### 2.3.2 Simulation and Conditions  
Considering synchronous traction with a more hidden spoofing effect as an example, simulations were performed for three traction strategies according to the theoretical model of the received signal in Section 2.3.1. Because the simulation does not require mass data processing at a high sampling rate, it can quickly test traction success under a certain strategy. For the 20-s data, the simulation of Strategy 1 took less than 1 s, and the simulation of Strategy 2 took less than 2 s.  
Spoofing was selected to enter at 1 s. Traction duration was determined by \(\mathrm{v_{drag}}\) and the tracking-loop structure. Because the carrier loop is only related to the prompt correlation value, when \(\tau_{al}\) and \(\tau_{as}\) are 1 chip, the two peaks have exited each other's prompt tracking point, whereas the code loop needs to wait for the other correlation peakto exit the early or late tracking point. If the traction duration of the carrier loop is defined as \(\mathrm{t_{f}}\) and that of the code loop is defined as \(\mathrm{t_{c}}\), then  
\[\mathrm{t_{f}}=\frac{1}{\mathrm{v_{drag}}}, \tag{25}\]  
\[\mathrm{t_{c}}=\frac{1+\mathrm{d}}{\mathrm{v_{drag}}}, \tag{26}\]  
In other words, according to the structural characteristics of the carrier and code loops, the traction duration should be at least \(\mathrm{t_{c}}\).  
The maximum spoofing power of conventional spoofing signals is equal to the genuine signal power. The correlator spacing at the receiver was set as the typical value for satellite navigation receiver applications, i.e., D = 2d = 1. The spoofing signal has the same initial carrier and code phases as the genuine signal when added.  
\[\mathrm{\theta_{sl}}=\mathrm{\theta_{al}}=\mathrm{\theta_{as}}=0 \tag{27}\]  
The first set of simulations (Nos. 1, 3, and 5) was used to directly compare the theoretical deception effects of the strategies.  
The Simulation conditions are presented in Table 1. When implementing a traction strategy to successfully implement spoofing traction, the selection of traction speed \(\mathrm{v_{drag}}\) follows certain principles. According to the code loop structure, the code phase offset within each coherence integration should not exceed 0.5 chips [12], that is the traction speed \(\mathrm{v_{drag}}\) should not be too fast. However, owing to the loose requirements, spoofing may fail even if the conditions are met.  
When the traction strategy is determined, the range of traction speed that can make traction successful is determined. Therefore, the theoretical value range of the traction speeds of the three strategies can be determined via another set of traversal simulations (Nos. 2, 4, and 6).
# Tracking Method of GM-APD LiDAR Based on Adaptive Fusion of Intensity Image and Point Cloud
## 2 Intensity Image KCF Target Tracking Method Based on the Muti-Feature Fusion
### The Principle of KCF Tracking Algorithm
#### 2.1.2 Kernelized Correlation Detection  
In practical scenarios, there are instances where the sample is not linearly separable in the low-dimensional space, necessitating the utilization of the \"kernel trick\" to map the sample into a higher-dimensional space for achieving linear separability. Assuming the availability of training sample set \(X=[x_{1},x_{2},\cdots,x_{n}]^{T}\) and its corresponding label set \(Y=[y_{1},y_{2},\cdots,y_{n}]^{T}\), we can formulate an optimization model for \(w\) as follows:  
\[w=\underset{w}{\operatorname{argmin}}\underset{i}{\sum}(\langle w,\varphi(x_ {i})\rangle-y_{i})^{2}+\lambda\|w\|^{2} \tag{4}\]  
where \(\varphi(\cdot)\) represents the mapping from the original space to the Hilbert feature space, defined by the kernel function \(\kappa(x,x^{\prime})=\langle\varphi(x),\varphi(x^{\prime})\rangle\). \(x\) is the training sample, \(x^{\prime}\) is the  
Figure 2: Overall flow of KCF based on multi-feature fusion.  
sample to be detected; based on the idea of \"kernel technique\", the optimization model for \(w\) can be expressed as a linear combination of inputs:  
\[w=\sum_{i}\alpha_{i}\varphi(x_{i}) \tag{5}\]  
In this case, the optimal solution of \(w\) is the corresponding optimal solution \(\alpha\) in Hilbert feature space. Therefore, the response function \(f(z)\) can be expressed as:  
\[\begin{array}{ll}f(z)&=w^{T}\varphi(z)\\ &=\sum\limits_{i}\alpha_{i}\varphi^{T}(x_{i})\varphi(z_{i})\\ &=\sum\limits_{i}\alpha_{i}x(x_{i},z_{i})\end{array} \tag{6}\]  
where \(z\) represents the patch to be detected with the same size as \(x\) that is captured from the next frame during the tracking process. The closed solution of ridge regression based on \"kernel technique\" is as follows:  
\[\alpha=(K+\lambda I)^{-1}y \tag{7}\]  
where \(K\), the Kernel matrix, can be expressed as follows:  
\[K=C(k^{xx}) \tag{8}\]  
where \(k^{xx}\) represents the kernel correlation between training sample \(x\) and itself. By substituting the Equation (8) into the Equation (7), we can obtain:  
\[F(\alpha)=\frac{F(y)}{F(k^{xx})+\lambda} \tag{9}\]  
The response value of patch \(z\) can be obtained by the following equation:  
\[\hat{f}(z)=F^{-1}(F(k^{xz})\odot F(\alpha)) \tag{10}\]  
where \(k^{xz}\) represents the kernel correlation between training sample \(x\) and patch \(z\); \(\odot\) stands for element-wise product; \(F^{-1}\) is the inverse Fourier transform. Then the position, corresponding to the maximum value of \(\hat{f}(z)\) is the optimal target prediction position.
## Materials and Methods
### Membrane Characterization  
#### 2.5.1 Membrane Porosity, Pore Size and Surface Morphology  
FESEM was employed to survey the surface and cross-section morphology of the synthesized membranes (a set vacuum condition at 3 kV was used throughout). For the cross-section imaging, liquid nitrogen was used to splinter the samples. All samples were sputtered with a 3 nm thick gold coating prior to imaging.  
The gravimetric method [33] for evaluating membrane porosity was used. Briefly, this involved cutting the membranes to a set measured size before weighing the membranes wet (wiping excess DW droplets first) and then incubating them overnight at 45 \({}^{\circ}\)C. The membranes were re-weighed the next day. Multiple samples were weighed in order to obtain an average set of values. The total porosity was calculated using Equation (1) below:  
\[\varepsilon=\frac{w_{w}-w_{d}}{A\times l\times\rho}\times 100 \tag{1}\]  
where \(W_{w}\) represents wet masses and \(W_{d}\) represents the dry masses of the membranes, \(\rho\) represents the density of the DW at 25 \({}^{\circ}\)C water (998 kg/m\({}^{3}\)), \(A\) is the surface area of the membrane sample (m\({}^{2}\)) and \(l\) is the membrane thickness.  
The membrane average pore size was derived according to the Guerout-Elford-Ferry equation (Equation (2) below):  
\[r_{m}=\sqrt{\frac{(2.9-1.75\varepsilon)8\times\eta\times l\times Q}{\varepsilon \times A\times\Delta P}} \tag{2}\]  
where \(\varepsilon\) is the total porosity, \(Q\) is the volume of DW (permeate) expressed in m\({}^{3}\)/s, \(\eta\) is the viscosity of DW at 25 \({}^{\circ}\)C (8.9 \(\times\) 10\({}^{-4}\) Pa-s) and \(\Delta P\) is the operating pressure which is typically one bar.
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 3 Study Protocol
### Data Extraction  
The data extraction form was filled out using the relevant data of each selected study. To maintain ease of subsequent data synthesis, we grouped the items in the data extraction form based on definition, sustainability constructs, assessment methods, e-health solution, validation, and limitations. Table 5 presents the data extraction form. \begin{table}
\begin{tabular}{l l} \hline \hline
**No.** & **Criteria** \\ \hline IC1 & The paper was published between 2000 and 2021. \\ \hline IC2 & The paper discusses sustainability in healthcare with respect to e-health solutions. \\ \hline IC3 & The paper includes at least one of the following: sustainability model or components (dimensions or elements), sustainability measurements or assessment/evaluation \\ methods, or sustainability definition. \\ \hline IC4 & The paper must be a full research paper (not an abstract). \\ \hline IC5 & The paper is written in English. \\ \hline IC6 & The paper is available in full-text version. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Inclusion criteria. \begin{table}
\begin{tabular}{l l} \hline \hline
**No.** & **Criteria** \\ \hline EC1 & Meeting abstracts, book chapters, workshop descriptions, tutorials, posters, progress \\  & reports, masters and doctoral dissertations. \\ \hline EC2 & Papers discuss sustainability from a purely environmental perspective, such as recycling or energy conservation, etc. \\ \hline EC3 & Papers discuss sustainability of the healthcare system/organization instead of the sustainability of e-health solutions within healthcare system. \\ \hline EC4 & Papers discuss sustainability of a medical device/tool instead of e-health solution \\  & related to healthcare system and its processes. \\ \hline EC5 & Papers discuss sustainability from a general perspective without proposing or \\  & providing results related to constructs of sustainability models, definitions, or \\  & assessment methods. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Exclusion criteria. \begin{table}
\begin{tabular}{l l l} \hline
**No.** & **Quality Assessment Question** & **Score** \\ \hline QA1 & Does the paper have a clear statement & \\  & (definition) of the aims (goals, purposes, & \\  & problems, and research questions)? & \\ \hline QA2 & Does the paper explicitly & \\  & propose/discuss the sustainability & (+1) Yes/(+0) No/Partially (+0.5) \\  & aspects of e-health solutions? & \\ \hline QA3 & Does the paper evaluate/investigate the & \\  & sustainability of e-health solutions & (+1) Yes/(+0) No/Partially (+0.5) \\  & through using case studies, surveys, etc.? & \\ \hline QA4 & Are conclusions, implications for practice, & \\  & and future research, reported suitably for & (+1) Yes/(+0) No/Partially (+0.5) \\  & its audience? & \\ \hline QA5 & Were the aspects extracted from & \\  & interviews, case studies, etc.? & (+1) Yes/(+0) No/Partially (+0.5) \\ \hline \end{tabular}
\end{table} 
Table 4: Quality assessment checklist.
## 4 Results
### Analysis of the Industrial Spatial Structure of the HSR Station Area
#### 4.2.2 Evolutionary Characteristics of the Overall Industrial Spatial Structure  
ArcGIS Kernel density analysis was used to identify the characteristics of the overall industrial distribution in the Zhengzhou East railway station area in 2010 before the opening of the HSR and in 2015 and 2020 after the opening of the HSR. According to the results of the kernel density analysis (Figure 2), before the opening of the HSR, industries were mainly concentrated in the peripheral area of 1500-2000 m from the stations, specifically distributed around villages and towns such as Jicheng and Chenzhuang, and the overall distribution of industries was sparse. At this time, the stations were not open, and the spatial correlation between residential areas and traffic lines was significant. In 2015, after the opening of the HSR, the industrial clusters in the peripheral area disappeared, while the East Square of Zhanqian and the Henan Province Free Trade Zone to the northwest of the area of influence became new agglomeration areas. Additionally, weak agglomeration areas appeared in the southern portion of the area of influence. In 2020, the trend towards industrial agglomeration was further strengthened, forming a more obvious agglomeration center and multi-core spatial structure. The specific agglomeration areas are the south and north areas of the west square of Zhengzhou East railway station, the Henan Province Free Trade Zone, and the Zhengshangjian Zhengdong center, which are distributed along Xinyi Road. A large number of industries are distributed within these four agglomeration areas, and a belt has developed along Xinyi Road to the northwest and southeast.  
With the opening of the HSR, the overall industrial spatial structure of the Zhengzhou East railway station area has obvious agglomeration characteristics, with a number of agglomeration cores having formed. The agglomeration area has gradually expanded from the peripheral area to the station's core area and area of influence, and the locations of the agglomeration areas vary across periods. With the opening of the HSR and the development of the station area, there have been four significant agglomeration areas in the station area through 2020, and the Zhengzhou East railway station area exhibits multi-core and axial belt spatial structure characteristics.  
Figure 2: Kernel density analysis of the whole industry.
# Safety Assessment of the Cover-and-Cut Method Under Blasting Vibration Induced by Tunnel Excavation
###### Abstract  
In evaluating the construction safety of the building in the subway tunnel using the cover-and-cut method, the main objective is to analyze the diaphragm wall, the central pillar, and the roof. This article conducted a blasting vibration test based on the background of the Guiyang Metro Line 3 project and used the FLAC3D software to establish a three-dimensional numerical model. The results showed that the peak particle velocity (PPV) decreased with increasing distance from the blasting center. The PPV measured at the underground diaphragm wall was 1.424 cm/s, while at the bottom of the central pillar it was 1.482 cm/s. The predicted PPV on the roof was up to 1.537 cm/s, which met the safety standards. According to the cloud map of particle vibration velocity and the comprehensive analysis of particle vibration velocity, the degree of impact of artificial structures in the subway tunnel was the central pillar, the underground diaphragm wall, and the roof in order from high to low. After eight blasting operations per day, the vibration velocity trend at the vulnerable point of the central column increases, but it will not exceed the safety standard.  
blasting vibration; cover-and-cut method; subway tunnel construction; peak particle velocity (PPV); structural safety assessment Article
# Carbon Quota Allocation Prediction for Power Grids Using PSO-Optimized Neural Networks
## 4 Result and Discussion
### Projected Result Analysis
#### 4.1.2 Results of Initial Quota Projections  
The predicted results according to PSO-BPNN are shown in Figure 10, in 2030, the peak CO\({}_{2}\) emission from thermal power generation in China's power industry will reach 5644.54 million tons. After accounting for the neutralization effect of green energy, CO\({}_{2}\) emissions will be decreased to 5511.46 million tons, with green energy generation offsetting 133 million tons of emissions. Since this paper primarily studies the effect of green energy production on carbon emissions in the power industry, it examines only the CO\({}_{2}\) emissions of regional power grids after taking into account the influence of green energy. Among these, the North China power grid is expected to emit 1376.31 million tons, ranking first, followed by the Northwest power grid at approximately 1106.09 million tons, while the Northeast power grid will have the lowest emissions, with only 334.06 million tons.
# Chirped-Pulse Amplification in an Echo-Enabled Harmonic-Generation Free-Electron Laser
## 2 Proposed Method  
### Chirped-Pulse Amplification in EEHG  
The schematic layout of CPA-EEHG is illustrated in Figure 1a. The frequency-chirped seed laser pulses are obtained by stretching the commercial laser pulses with a pair of gratings. These chirped seed laser pulses interact with the same segment of the electron beam in two modulators to generate the desired frequency-chirped energy modulation. With two precisely tuned dispersion sections, the electron beam generates the frequency-chirped bunching at the harmonics of the seed lasers. When passing through the radiator, this bunched electron beam produces the FEL pulse, which inherits the chirped frequency of the seed lasers. Such a chirped pulse can be compressed by a double-grating compressor in the OPM geometry to generate a high-power ultrafast radiation pulse.  
The linearly chirped Gaussian pulse can be written mathematically as:  
\[E(t)=E_{0}\exp\left(-(t/\tau_{t})^{2}\right)\exp i(\omega_{0}t+\beta t^{2}). \tag{1}\]  
Hence, the instantaneous frequency is \(\omega_{\textit{ins}}=\omega_{0}+2\beta t\), as \(\beta\) determines the chirp rate. To further amplify this chirped pulse, the electron beam should have an energy chirp that matches the resonance condition:  
\[\gamma(s)=\sqrt{\frac{n\lambda_{u}}{2\lambda_{\textit{seed}}(s)}(1+a_{w}^{2})}, \tag{2}\]where \(n\) is the harmonic number, \(\lambda_{seed}(s)\) denotes the seed laser wavelength distribution along the longitudinal position \(s\), and \(\lambda_{u}\) and \(a_{w}\) denote the undulator period and strength in the radiator, respectively.  
As demonstrated in [31], when the seed pulse has considerable frequency dispersion, the FEL pulse bandwidth can be estimated as  
\[(\Delta\omega)_{FEL}\approx n^{1-\alpha}(\Delta\omega)_{send}, \tag{3}\]  
where \((\Delta\omega)_{send}\) denotes the bandwidth of the seed laser. The factor \(\alpha\) is closely linked to the FEL amplification process, which is about \(1/3\) when the FEL reaches saturation. This broadening of the FEL pulse bandwidth results from the competition between the frequency up-conversion process and the nonlinear interactions between the laser pulse and electron beam during the modulation. The above equation points out that the frequency up-conversion at higher harmonics will increase the FEL pulse bandwidth, leading to a smaller FEL pulse duration after compression. Since the harmonic up-conversion number of EEHG is usually larger than that of HGHG, this indicates that, in principle, the compressed FEL pulses in CPA-EEHG will be shorter compared with CPA-HGHG.
# Approximate Networking for Universal Internet Access
## 1 Introduction
### Why Adopt Approximate Networking?
#### 1.3.4 Need of Energy Efficiency  
It has been reported that information and communication technology (ICT) is one of the biggest consumer of the world's electrical energy, using up to 5% of the overall energy (2012 statistics) [23]. The urgency of delivering on the front of energy efficiency is reinforced by the impending decline of non-renewable energy resources along with the concomitant increase in ICT demand. The approximate networking trend can augment the hardware-focused \"approximate computing\" trend in managing the brewing energy crisis through the ingenuous use of approximation. In particular, approximate networking can help generalize the performance and efficiency improvements offered by approximate computing, which have largely been limited to local speedups on a single device, to broader network settings. Optimizing communication/networking cost is important since these costs can be significant (e.g., on mobile phones, the Wi-Fi and cellular radios require, on average, an order of magnitude more power than the CPU or memory [18]).
## 4 Results and Discussion
### Algorithm Design Exploration
\begin{table}
\begin{tabular}{c c c c} \hline  & **Preliminary Design** & **Optimised Design** & **Percentage Change** \\ \hline
**Efficiency (I-s)** & 73.98\% & 75.87\% & +2.55\% \\ \hline
**Power (kW)** & 20.3 & 18.79 & -7.44\% \\ \hline
**Rotor Safety Factor** & 1.78 & 2.38 & +33.71\% \\ \hline
**Stator Safety Factor** & 1.87 & 2.78 & +48.66\% \\ \hline \end{tabular}
\end{table} 
Table 4: Design optimisation final results. Figure 9e shows the maximum fitness value at each generation. As predictable, given the elitism technique application, fitness values are in steady growth. The main observation is how the sudden increments in maximum fitness values gradually fade in favour of a marginal improvement, showing how evolution is rapid at correcting parameters to a point of acceptability, and demonstrates the notion found in literature regarding the need of excessively high generation numbers to achieve absolute optimums. Figure 9f, showing fitness distribution at each generation (scatter plot) and average fitness values at each generation, complements the information found in Figure 9e. Fitness values gradually migrate towards higher values, drastically reducing the fail percentage in an extremely low number of generations. As can be seen by fitness values at generation 19, the genetic variability is essentially null,  
Figure 9: Design space exploration by (**a**) efficiency, (**b**) average safety factor, (**c**) Mach number, (**d**) power, (**e**) elite fitness vs. generation number, and (**f**) elite fitness vs. generation number. thus the algorithm would be waiting on a lucky mutation. It is important to note how generation 15 effectively marks algorithm convergence, as observable by both graphs, with mutations happening on the preceding and following generation. This data is in accordance to what is predicted in literature in a system with these parameters [18]. More statistical data would, however, be necessary in order to validate a high-convergence multi-disciplinary genetic algorithm dimension methodology using a statistical estimation. Overall, the algorithm shows convergence and no data instability. Extreme penalisation for out-of-limits designs lead to lower Mach numbers and possibly sub-optimal safety factors. The choice, however, led to time effective (\(\approx\)4 h), stable and varied design exploration. Table 5 shows the comparison between the preliminary blade parameters and the optimised.