esa-sceva/satcom-chunk-collection huggingface dataset:
# A Solution Procedure to Improve 3D Solid Finite Element Analysis with an Enrichment Scheme
## 2 Finite Element Formulation Enriched by Cover Functions
\begin{array}{cccc}\dfrac{1-v_{ \overline{yz}}v_{\overline{zy}}}{E_{\overline{x}}Ey\Delta}&\dfrac{v_{\overline{ yx}}+v_{\overline{yz}}v_{\overline{zx}}}{E_{\overline{y}}E_{\overline{z}}\Delta}& \dfrac{v_{\overline{zx}}+v_{\overline{yz}}v_{\overline{zy}}}{E_{\overline{z}} \Delta}&0&0&0\\ \dfrac{v_{\overline{yx}}+v_{\overline{yz}}v_{\overline{zx}}}{E_{\overline{y}} E_{\overline{z}}\Delta}&\dfrac{1-v_{\overline{zx}}v_{\overline{zx}}}{E_{ \overline{x}}E_{\overline{z}}\Delta}&\dfrac{v_{\overline{zy}}-v_{\overline{zy} }v_{\overline{zx}}}{E_{\overline{x}}E_{\overline{z}}\Delta}&0&0&0\\ \dfrac{v_{\overline{zx}}+v_{\overline{yx}}v_{\overline{zy}}}{E_{\overline{y} }E_{\overline{z}}\Delta}&\dfrac{v_{\overline{zy}}-v_{\overline{zy}}v_{ \overline{zx}}}{E_{\overline{x}}E_{\overline{z}}\Delta}&\dfrac{1-v_{\overline {xy}}v_{\overline{yz}}}{E_{\overline{x}}E_{\overline{y}}\Delta}&0&0&0\\ 0&0&0&G_{\overline{xy}}&0&0\\ 0&0&0&0&G_{\overline{yz}}&0\\ 0&0&0&0&0&G_{\overline{xx}}\end{array} \right], \tag{14}\]  
in which \(E\), \(G\), and \(\
u\) are the elastic modulus, shear modulus, and Poisson's ratio, respectively, and the subscripts of each term indicate directions in the local Cartesian coordinate system. The equations of static equilibrium for a finite element analysis are expressed as  
\[\mathbf{K}\mathbf{U}=\mathbf{R}=\mathbf{R}_{B}+\mathbf{R}_{S}, \tag{16}\]  
with  
\[\mathbf{K}=\sum_{m=1}^{e}\mathbf{L}^{(m)\mathrm{T}}\mathbf{K}^{(m)}\mathbf{L} ^{(m)}=\sum_{m=1}^{e}\mathbf{L}^{(m)\mathrm{T}}\int\limits_{V^{(m)}}\mathbf{B }^{(m)\mathrm{T}}\mathbf{C}^{(m)}\mathbf{B}^{(m)}dV\mathbf{L}^{(m)}, \tag{17}\]  
\[\mathbf{R}_{B}=\sum_{m=1}^{e}\mathbf{L}^{(m)\mathrm{T}}\mathbf{R}_{B}^{(m)}= \sum_{m=1}^{e}\mathbf{L}^{(m)\mathrm{T}}\int\limits_{V^{(m)}}\mathbf{H}^{(m) \mathrm{T}}\mathbf{f}^{B}dV, \tag{18}\]  
\[\mathbf{R}_{S}=\sum_{m=1}^{e}\mathbf{L}^{(m)\mathrm{T}}\mathbf{R}_{S}^{(m)}= \sum_{m=1}^{e}\mathbf{L}^{(m)\mathrm{T}}\int\limits_{S_{f^{(m)}}}\mathbf{H}^{ (m)\mathrm{T}}\mathbf{f}^{S}dS, \tag{19}\]  
where \(\mathbf{K}\), \(\mathbf{R}_{B}\), and \(\mathbf{R}_{S}\) are the global stiffness matrix and load vectors, \(\mathbf{U}\) is the vector of the nodal DOFs, \(\mathbf{L}^{(m)}\), \(\mathbf{C}^{(m)}\), and \(\mathbf{H}^{(m)}\) are the assemblage Boolean matrix, the material law matrix, and the interpolation matrix for element \(m\), respectively. Here, the vectors \(\mathbf{f}^{B}\) and \(\mathbf{f}^{S}\) denote body force and surface traction, respectively. The summation sign indicates the procedure for the finite element assemblage and \(e\) is the number of elements [1; 2; 3].
# Conserved Plastid Genomes of _Pourthiaea_ Trees: Comparative Analyses and Phylogenetic Relationship
## 3 Results
### Positive Selection Analysis  
To identify the selective pressure within the 79 PCGs of the 13 _Pourthiaea_ plastomes, the ratio dN/dS (\(\omega\)) of non-synonymous to synonymous nucleotide substitution rates was computed. In all genes, the dN/dS ranged between 0.0001 and 1.2729 (Table S7). The great mass of genes were under strong purifying selection (dN/dS values \(<\) 0.5) (Table S7). Three genes, including _rpc2_, _rps_18, and _rb_CL, showed dN/dS values \(>\) 1 (Table S7). However, only the _rb_CL gene with three sites was detected under positive selection (BEB probability \(>\) 0.95) after the likelihood ratio test (Table S8).
# A Computer-Aided Detection System for the Detection of Lung Nodules Based on 3D-ResNet
###### Abstract  
In recent years, the research into automatic aided detection systems for pulmonary nodules has been extremely active. Most of the existing studies are based on 2D convolution neural networks, which cannot make full use of computed tomography's (CT) 3D spatial information. To address this problem, a computer-aided detection (CAD) system for lung nodules based on a 3D residual network (3D-ResNet) inspired by cognitive science is proposed in this paper. In this system, we feed the slice information extracted from three different axis planes into the U-NET network set, and make the joint decision to generate a candidate nodule set, which is the input of the proposed 3D residual network after extraction. We extracted 3D samples with 40, 44, 48, 52, and 56 mm sides from each candidate nodule in the candidate set and feed them into the trained residual network to get the probability of positive nodule after re-sampling the 3D sample to \(48\times 48\times 48\) mm\({}^{3}\). Finally, a joint judgment is made based on the probabilities of five 3D samples of different sizes to obtain the final result. Random rotation and translation and data amplification technology are used to prevent overfitting during network training. The detection intensity on the largest public data set (i.e., the Lung Image Database Consortium and Image Database Resource Initiative--IIDC-IDRI) reached 86.5% and 92.3% at 1 frame per second (FPs) and 4 FPs respectively using our algorithm, which is better than most CAD systems using 2D convolutional neural networks. In addition, a 3D residual network and a multi-section 2D convolution neural network were tested on the unrelated Tianchi dataset. The results indicate that 3D-ResNet has better feature extraction ability than multi-section 2D-ConvNet and is more suitable for reduction of false positive nodules.  
Keywords:computer-aided detection; deep learning; lung nodule detection; residual network +
Footnote †: journal: Applied Sciences
## 3 Using Classifiers to Predict Relevant Change
### Classification Algorithms
#### 3.2.1 k-Nearest Neighbors  
The simplest algorithm employed in this manuscript is the k-nearest neighbors classification algorithm [21]. For a given tile to be classified, its feature vector, \(x\), is compared to those of all training tiles in the set and the class of the nearest tile in the feature space is assigned.
## 4 Discussion
### Main Findings and Clinical Relevance  
The present review provides strong evidence that shows interventions conducted in a school setting that include a mix of nutritional and physical activity interventions can have beneficial impacts on blood-derived cardiovascular and metabolic risk-associated biomarkers in children. The most reliably reported changes among the studies involved blood pressure outcomes, with six of eight studies demonstrating systolic reductions ranging from 1.41 to 6.0 mmHg [40; 47; 48]. Glucose metabolism improvements were limited, with only [PERSON] et al. [41] reporting significant inter-group glucose reduction of 0.20 mmol/L (95% CI: \(-0.24\), \(-0.16\), \(p<0.05\)), while the remaining four studies found no significant treatment effects [49; 50; 51; 45]. This result has special relevance because even small improvements in glucose metabolism in childhood can have lasting effects over metabolic development into adult life [8; 9]. However, insulin levels assessed in five studies [41; 45; 49; 50; 51] showed no significant between-group differences in any investigation, indicating limited effects on insulin sensitivity markers.  
The improvements seen in lipid profiles between different studies are consistent with overall evidence suggesting that lifestyle changes in pediatric populations can result in beneficial impacts on cardiovascular disease risk factors [19; 20; 53]. Of the studies conducted, seven reported lipid profile assessments, with four studies showing improvements [41; 42; 43; 46]. Additionally, [PERSON] et al. [46] and [PERSON] et al. [42] demonstrated significant reductions in both total cholesterol and LDL cholesterol, highlighting that comprehensive educational interventions can produce meaningful lipid profile improvements even without formally structured exercise protocols. These findings are in agreement with the results from a recent systematic review of Mediterranean dietary interventions in children and adolescents that showed similar beneficial effects on cardiometabolic biomarkers, thus supporting the efficacy of evidence-based dietary interventions in pediatric populations [19].  
The outcomes of blood pressure were amongst the most consistently reported effects in numerous research studies and revealed significant reductions of systolic blood pressure in three investigations [40; 47; 48]. The clinical significance of these findings is extensive, given that childhood blood pressure elevation continues into adulthood and is a specific predictor of the probability of developing cardiovascular diseases [22]. Significant reductions were found in the studies of [PERSON] et al. [47], [PERSON] et al. [48] and [PERSON] et al. [50]suggest that early interventions may be particularly valuable in facilitating beneficial cardiovascular pathways prior to pathological changes establishing themselves.
# Quality Analysis of Unmanned Aerial Vehicle Images Using a Resolution Target
## 3 Materials and Methods
### Image Acquisition and Processing  
Table 1 lists the UAVs used to acquire the study data; these were: FireFly 6 pro (fixed-wing), Inspire 2, Phantom pro 4, and Matrice 600 (rotary-wing). The table also lists the specifications of the UltraCAM Eagle M2 manned aircraft sensor. The resolution of the camera in each UAV is also listed. In terms of the focal length, pixel size, and CCD sensor size, the iXM-100 sensor included in Matrice 600 had the best performance, followed by the a6000 sensor included in FireFLY 6 PRO, the FC 6520 sensor included in Inspire2, and the FC 6310 sensor included in Phantom pro 4. All unmanned aerial cameras were automatically set to capture the set shot-routed images.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**UAV Model** & **FireFLY 6 PRO** & **Inspire 2** & **Phantom Pro 4** & **Matrice 600** & **Manned Aircraft** \\ \hline Appearance & & & & & \\ \hline Camera model & a6000 & FC 6520 & FC 6310 & iXM-100 & UltraCAM Eagle M2 \\ \hline Focal length & 20 mm & 15 mm & 8.8 mm & 35 mm & 100 mm \\ \hline Pixel size & 4 \(\times\) 4 \(\mu\)m & 3.28 \(\times\) 3.28 \(\mu\)m & 2.41 \(\times\) 2.41 \(\mu\)m & 3.76 \(\times\) 3.76 \(\mu\)m & 6 \(\times\) 6 \(\mu\)m \\ \hline CCD sensor size & 6000 \(\times\) 4000 & 5280 \(\times\) 3956 & 5472 \(\times\) 3648 & 11,664 \(\times\) 8750 & 17,310 \(\times\) 11,310 \\ (24 MP) & (21 MP) & (20 MP) & (100 MP) & (193 MP) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of the UAVs, their cameras, and the manned aircraft sensor [23; 24; 25; 26; 27].  
Figure 9: Map of the location of the study area.  
To analyze the effects of flight height, camera performance, and imaging conditions on the quality of the UAV photos and outcomes, we designed the flight parameters as illustrated in Table 2. The term \"overlap\" refers to the degree of route overlap that occurred while capturing the UAV images. In overlap, P is the degree of overlap in the vertical direction of the photograph, and Q is the degree of overlap in the horizontal direction.  
Metashape (v1.8.2, Agisoft, St. Petersburg, Russia) was used to calculate the results, and all parameters within the software were set to be the same.
## 4 Results
### PSF and Spatial Resolution
Note that we selected the frequency range 0.5-2.5 THz because for low frequencies (f < 0.5 THz) the diffractive effects caused by the pinhole were too strong and no luminous spot was clearly identifiable; for high frequencies (f > 2.5 THz) the signal-to-noise ratio (SNR) [50; 51] was too low and no detectable intensity was clearly measurable. By fitting the data with Equation (1), the NA experimental value for our broadband imaging system has been extracted and estimated (NA = 0.27 \(\pm\) 0.01).
# Durability Evaluation Study for Crumb Rubber-Asphalt Pavement
## 1 Introduction  
Crumb rubber-asphalt mixture is a new type of deicing pavement material prepared by replacing some of the aggregates in asphalt mixture with mechanically sheared particles of waste rubber tires and mixing them uniformly with high-performance modified asphalt [1; 2; 3; 4; 5; 6]. The addition of rubber crumbs changes the original structure of the mixture and the contact state between the materials so that the pavement is more easily loosened than an ordinary asphalt pavement [7]. An investigation has generally shown early damage to the domestic crumb rubber-asphalt pavement test roads for snow-melting [8]. This is mainly ascribed to the insufficient adhesive force between rubber crumbs and bitumen; therefore, the rubber crumbs detach easily from the road surface under load [9]. Water then invades the road surface, and the dynamic water pressure causes the mixture to become loose [10]. Therefore, the durability problem of the crumb rubber-asphalt mixture can be attributed to the lack of adhesion between the asphalt and various aggregates, especially crumb rubber, and the water instability [11]. Thus, related research into crumb rubber-asphalt pavement mainly aims at improving the performance and optimization of material composition [12; 13; 14; 15]. The lack of convenient and effective durability evaluation methods seriously hinders the performance research and engineering application progress of crumb rubber-asphalt pavement [16; 17]  
In terms of material composition and mechanical properties, crumb rubber-asphalt pavement is different from ordinary asphalt pavement [18]. Therefore, the durability evaluation method for conventional asphalt concrete pavement is no longer applicable to crumb rubber-asphaltpavement [19]. However, research on the effective durability evaluation method for such special bituminous mixture has rarely been carried out. Moreover, recent research tends to use more advanced scientific experimental methods, such as nuclear magnetic resonance (NMR) spectroscopy and microscopy [20; 21; 22; 23; 24]. These methods require precise and complex test instruments and are suitable for the analysis of asphalt mixtures from a microscopic perspective. These complex test methods are time-consuming and no longer convenient when evaluating the performance of crumb rubber-asphalt mixtures in engineering laboratories. More importantly, these evaluation test methods are not based on the damage mechanism and mode, which makes them unsuitable for evaluating the durability of crumb rubber-asphalt pavement.  
Rubber crumbs are materials with a certain linear elasticity [25; 26], thus their addition leads to inevitable changes in the mechanical properties of the crumb rubber-asphalt pavement [27; 28; 29], which in turn leads to a difference in the failure mechanism from that of ordinary asphalt mixture-based pavement. In order to study the durability evaluation method of crumb rubber-asphalt mixture more scientifically, it is necessary to understand the interface adhesion and failure mechanism.
# Wi-Fi Fingerprint-Based Indoor Localization Method via Standard Particle Swarm Optimization
## 2 Related Work
### Wi-Fi-Based Indoor Localization
Certain other, more deterministic, algorithms such as support vector machine [29], random forests [30] and linear discriminant analysis [31] are also used in localization. * **Probabilistic methods. ** Contrary to deterministic algorithms, probabilistic methods employ the probability density function, which characterizes changes in RSS. The key is to predict the possibility of relationships between real-time data and the coordinates of RPs. [PERSON] [32] estimated the unknown position using a probabilistic model considering the signal distribution in the site. Bayesian network [33], expectation maximization [34], Gaussian process [35] and conditional random field [36] are also effective probabilistic algorithms. In addition, some Wi-Fi fingerprint-based indoor localization frameworks have been proposed. Ref. [37] proposed a Wi-Fi localization framework via fingerprint clustering and adaptive KNN based on fusion fingerprints. They cluster offline fingerprints via the Gaussian mixture model (GMM) to divide the localization area into several subareas. In addition, a random-forest-based subarea classifier is trained by the offline data and corresponding subarea labels used for the online localization. The authors of [19] focused on indoor Wi-Fi fingerprint localization in multistory buildings and proposed a novel floor-identification module with a Wi-Fi-fingerprint-graph representation and a fingerprint graph attention mechanism, to confine the search scope to a specific floor. The two-panel fingerprint-homogeneity graph adopted is a novel mehod to gauge the similarity of Wi-Fi fingerprints robustly.
## 2 Materials and Methods
### Electrochemical Impedance (EIS) Experiments  
EIS experiments were conducted using a potentiostat/galvanostat (Autolab 302N equipped with the FRA2 impedance module). Electrochemical impedance spectra were recorded at open circuit potentials in both blank solution and EEA4 NB testing solutions. The experiments were performed at room temperature in a frequency range of 0.01 Hz to 100 kHz, with a sinusoidal potential amplitude of 10 mV. The experimental data were fitted to the equivalent electrical circuit (EEC) using a complex non-linear least squares procedure with the ZView-Scribner Associated Inc. software 3.0 (Southern Pines, NC, USA). The experiments were conducted in aerated electrolyte conditions and were repeated in triplicate.
# Evaluating the Impact of the COVID-19 Pandemic on Residential Energy Use in Los Angeles
## 2 Materials and Methods
### Load Evaluation
Regression analyses were performed using the open-source Energy Charting and Metrics (ECAM) (ECAM v.6.6, Bonneville Power Administration, Portland Oregon, OR, USA) calculation engine for Microsoft Excel (Excel v.14.0 (32-bit), Microsoft Corp., Redmond, WA, USA) [39] with an 80% confidence interval (CI) used for both temperature change point determination and data boundary determination. Testing showed that an 80% CI provided a balance between valid model calculation convergence and data inclusivity for all feeders analyzed. The calculated midpoint temperature between the determined change points corresponds to the MST. As data is analyzed, CI boundaries are similarly passed through calculations to provide error estimation for multi-step calculations. Temperature-based correction was used to normalize the influence of temperature such that all data sets are corrected to a value representing MST on a daily or hourly basis (as previously discussed) and compared. This approach estimates non-temperature-sensitive load. In addition, a reporting-period basis calculation provides estimates of post-period energy difference considering pre-period basis. This calculation used ECAM's internal engine implementing modified ASHRAE Guideline 14, model guidelines [28; 40]. Analyses compared 2020 energy use to baseline data in 2018-2019 (for either the 2020 calendar year period or 2020 COVID-19 pandemic subset period) to normalize the impact of temperature between the evaluation and baseline periods in comparison. By removing this substantial factor, this provides a means to assess differences in load due to the changed factors (namely occupancy) during the COVID-19 pandemic period compared to the baseline period. A separate two-term linear regression (see Equation (2)) was performed to model the impact of temperature on load as a function of heating degree day (HDD) or cooling degree day (CDD) values on a daily basis. Raw calculated values were limited such that values with CDD or HDD values less than 1.1 \({}^{\circ}\)C (2 \({}^{\circ}\)F) were removed from the model to reduce the bias from non-temperature related load variance. A CDD or HDD value would be mutually exclusive for a given day. Analyses were performed using a multiple linear regression in Origin Pro 9.0 (Origin Lab Corp., Northampton, MA, USA). Regression results were modeled for impact across an inclusive range of HDD and CDD values for both the baseline and evaluation period and presented as a simple percent difference for change comparing the differences between evaluation and comparison period with the same change in simulated CDD and HDD values:  
\[y=m_{1}x_{1}+m_{2}x_{2}+b, \tag{2}\]  
where \(y\) is the total feeder daily energy use (kWh), \(m_{1}\) is a regressed constant (in kWh per HDD), \(x_{1}\) is the HDD (single day) value, \(m_{2}\) is a regressed constant (kWh per CDD), \(x_{2}\) is the CDD (single day) value and \(b\) is regressed energy independent of HDD or CDD change (kWh).
## 4 Review: Slant Links Rain Fade Models
### Empirical Model
#### 4.1.2 Singapore Model  
This model [33] uses the cumulative rainfall rate distribution modeled using the power-law relationship of 0.01% average year rain attenuation. The method of this model is shown in Algorithm 2.  
```
1 if\(0\leq\varphi<36^{\circ}\)then
2\(H_{\text{R}}\gets 3.0+0.028\varphi\)
3 else
4\(H_{\text{R}}\gets 4-0.075(\varphi-36^{\circ})\)
5 if\(\theta\geq 5^{\circ}\)then
6\(L_{S}\leftarrow(H_{\text{R}}-H_{S})/\sin\theta\)
7 else
8 Elevation angle error /* Invalid, if\(\theta\
leq 5^{\circ}\) */
9 Compute \(L_{G}\leftarrow L_{S}\cdot cos\theta\) /* \(L_{G}\) in km */
10 Compute \(RF_{0,01}\gets 1/(1+L_{G}^{0.9}/35\exp(-0.015R_{0,01})\) )/* RF: reduction factor */
11 Compute \((\text{k},\alpha)/*\) CCIC [104] */
12 Compute \(A_{0,01}\gets k(R_{0,01})^{\alpha}L_{S}\cdot RF_{0,01}\)
13 Compute \(\gamma\gets kR^{\alpha}_{0,01}\)
14 Compute \(A_{p}\gets A_{0,01}0.12p^{-(0.632+0.082\log(p))}\)
15 Return \(A_{p}\)
```  
**Algorithm 2**Singapore Model [33]  
1.3 The \(\alpha\)-ray intensity  
Advantages  
This model introduces the \"path reduction factor\" by modifying the CCIR model, analyzing the tropical climatic region in Singapore, which corrects the attenuation in the tropical region.
# Motor Vector Control Based on Speed-Torque-Current Map
## 3 Motor Vector Control Strategy Based on Speed-Torque-Current Map
### Motor Vector Control Strategy
Figure 10: External characteristics corresponding to different bus voltages. Equation (15) indicates that the bus voltage fluctuation can be corrected by the motor speed. Specifically, when the bus voltage changes, the motor speed can be appropriately increased or decreased by \(\upmu\) times to correct the voltage, as shown in the following equation:  
\[\left\{ \begin{array}{l}\upmu=\frac{\mathrm{U_{dc,lim}}}{\mathrm{U_{dc,ext}}}\\ \left(\frac{\mathrm{U_{dc,lim}}}{\mathrm{\omega_{s}u}}\right)^{2}=\left(\mathrm{ L_{d}i_{d}}\right)^{2}+\left(\mathrm{L_{d}i_{d}}+\upvarphi_{t}\right)^{2}\end{array} \right.. \tag{16}\]  
Based on Equation (16), a speed modifier considering the influence of bus voltage is added to the speed-torque-current map link to develop a vector control strategy, as shown in Figure 12. The real-time bus voltage U_cur obtained from the voltage sensor is used as the input to the speed compensator, and variable N_cps represents the speed after the compensation. To validate the control effect of the proposed vector control strategy, a working condition is designed in which the bus voltage and load torque are 650 V and 20 Nm, respectively. Besides, the motor speed first accelerates from 0 to 1000 rpm, and then again accelerates from 1000 to 5000 rpm after the speed stabilizes for a short period (around 100 ms). The simulation results are shown in Figures 13 and 14. Figure 12: Vector control strategy considering bus voltage fluctuation. Figure 13: Motor performance without considering bus voltage variation. It is seen from Figure 13 that in the process of speed regulation, torque and current fluctuate greatly due to the effect of the bus voltage changes. However, in Figure 14, the current and torque fluctuations are greatly reduced during the speed regulation period (1000-5000 rpm), when the bus voltage changes are taken into consideration. The results in these two figures indicate that the speed modifier with bus voltage regulation well reduces the torque and current fluctuation in the speed regulation process, which is beneficial to the output speed-torque characteristics of the motor.