esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis between limbs in ACL and NI Groups Considering DL or NDL
\begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**DOMINANT LIMB (DL)**} & \multicolumn{6}{c}{**NO DOMINANT LIMB (NDL)**} \\ \cline{2-13}  & **ACL** & **NI** & & & & **Confidence** & **ACL** & **NI** & & & **Confidence** \\ \hline  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & & & **Interval** & **(95\%)** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** \\ \hline BF\_average actEMG (\%) & \(57.64\pm 57.64\) & \(63.60\pm 13.94\) & 0.315 & 0.50 & (\(-\)18.17 to 6.25) & \(49.49\pm 10.01\) & \(60.56\pm 5.28\) & \(0.023*\) & 1.38 & (\(-\)20.30 to \(-\)1.84) \\ ST\_average actEMG (\%) & \(48.57\pm 48.57\) & \(60.21\pm 15.26\) & 0.145 & 0.75 & (\(-\)27.76 to 4.49) & \(54.77\pm 10.99\) & \(53.55\pm 6.38\) & \(0.803\) & 0.14 & (\(-\)9.27 to 11.70) \\ VL\_average actEMG (\%) & \(58.95\pm 58.95\) & \(55.97\pm 12.32\) & 0.543 & 0.30 & (\(-\)7.26 to 13.23) & \(55.57\pm 8.25\) & \(57.92\pm 7.76\) & 0.615 & 0.29 & (\(-\)12.31 to 7.62) \\ VM\_average actEMG (\%) & \(54.13\pm 54.13\) & \(53.94\pm 17.31\) & 0.979 & 0.01 & (\(-\)15.79 to 16.18) & \(47.07\pm 15.42\) & \(51.83\pm 10.47\) & 0.518 & 0.36 & (\(-\)6.88 to 11.61) \\ GMax\_average actEMG (\%) & \(48.86\pm 48.86\) & \(55.83\pm 10.88\) & 0.178 & 0.71 & (\(-\)17.53 to 3.58) & \(59.82\pm 5.54\) & \(52.25\pm 5.66\) & \(0.038*\) & 1.35 & (0.52 to 14.62) \\ Gm\_average actEMG (\%) & \(56.79\pm 56.79\) & \(58.57\pm 18.71\) & 0.801 & 0.13 & (\(-\)16.68 to 13.12) & \(60.09\pm 5.37\) & \(60.02\pm 8.34\) & 0.986 & 0.01 & (\(-\)9.20 to 9.36) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of average actEMG outcomes during maximal voluntary isometric contraction between groups, ACL and NI, considering DL or NDL. sizes in average actEMG in GMax (d = 0.808) and Gm (d = 0.926). In ACL_DL players, the average actEMG in GMax was higher for NDL, the non-injured limb, while in Gm, it was higher for DL, the injured one. ACL_NDL players showed strong to excellent effect sizes in average actEMG in GMax (d = 0.912) in favor of NDL, the injured limb (Table 6). Statistical significance was obtained in ACL_NDL players in H peak torque of force (\(p\) = 0.011). H peak torque of force was higher in DL (non-injured limb). No other significant difference was obtained in ACL players. In NI players, significant differences were obtained between the limb's peak torque of force in Q (\(p\) = 0.017). They showed higher values in DL. In ACL_DL players, strong to excellent effect sizes were observed in peak torque of force in GMax (d = 0.830). Thus, they exhibited higher values for DL, the injured limb (Table 7).
## 4 Materials and Methods
### Data Source  
To ensure data availability and continuity, we employ panel data covering 285 Chinese cities from 2015 to 2023 for the empirical analysis. Owing to data constraints, the sample excludes Hong Kong Special Administrative Region (SAR), Macao SAR, Taiwan region, and other cities in Tibet. The selection of these 285 cities is determined by the consistent availability of both urban carbon emissions intensity data and city-level GPP data across all locations. The year 2015 serves as the starting point because the Ministry of Finance of China issued a notice that year on enhancing transparency in public procurement information, thus addressing issues such as incomplete or delayed disclosures on certain regional procurement platforms. This policy shift has led to more systematic and comprehensive public procurement data being reported since then.  
This study draws on multiple data sources: (1) comprehensive financial databases (CSMAR and Wind); (2) statistical yearbooks (China City Statistical Yearbook and provincial statistical yearbooks); (3) green public procurement records from the official China Government Procurement portal ([[http://www.ccgp.gov.cn](http://www.ccgp.gov.cn)]([http://www.ccgp.gov.cn](http://www.ccgp.gov.cn))); (4) green patent data obtained from the China Research Data Service Platform (CNRDS); (5) public ecological awareness indicator constructed using city-level annual search volume data for \"haze\" and \"environmental pollution\" sourced from Baidu Index ([[https://index.baidu.com](https://index.baidu.com)]([https://index.baidu.com](https://index.baidu.com))); (6) corporate sustainability performance from Huazheng ESG ratings; and (7) city-level energy structure is proxied by provincial-level energy structure from the China Energy Statistical Yearbook. To maintain data integrity, we implemented linear interpolation for marginal missing observations. Economic indicators were standardized to 2015 constant values. We applied logarithmic transformations to absolute-value variables to normalize distributions and performed 1% Winsorization on continuous variables to minimize outlier effects. The key characteristics of the dataset are summarized in Table 1.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps Derived from a Combination of Field Biomass, LiDAR Data and Optical and/or Radar Data
#### 3.4.12 China Forest AGB Map  
[PERSON] et al. [98] derived a China forest biomass map at a spatial resolution of 1 km from more than 8000 field data points collected from published literature, GLAS data, optical imagery, climate surfaces, and topographical data using a Random Forest model. Evaluation with an independent dataset showed good map accuracy, with an R\({}^{2}\) of 0.75 and RMSE of 42.39 Mg/ha.
# Quad-Band Uniformly Spaced Array Antenna Using Diverse Patch and Fractal Antennas
## 2 Materials and Methods  
### Quad-Band Uniformly Spaced Array Antenna  
Figure 1 shows a schematic description of the quad-band array antenna. The proposed array antenna is made up of three layers: the top layer for radiating elements, the bottom layer for a feed network, and the middle layer for the ground plane of both the radiating elements and the feed network. The substrates used are two 0.8 mm thick Duroid-580 laminates with a relative dielectric constant \(\epsilon_{\mathrm{r}}\) of 2.2. The radiating elements on the top layer and the feed network on the bottom layer are depicted in Figure 2. The size of the substrate (\(w\times l\)) is 177 mm \(\times\) 252.9 mm. Each array antenna has an arrangement mixing multi-band antennas by alternating between them. The use of similarly sized radiating elements plays a role in implementing the uniformly spaced multiband array operating at different frequency bands. Four different array antennas are divided into two upper arrays operating at 2.4 GHz and 4.3 GHz and two lower arrays operating at 3.7 GHz and 5 GHz. The array arrangement can be changed to vertical extension for typical base-station applications [24]. A multi-band array antenna disposition in which the two distances between the centers of two elements are 0.6\(\lambda\)\(\sim\)0.8A and 0.8\(\lambda\)\(\sim\)1.0\(\lambda\) has been reported in [2]. Similarly, in this study, two different inter-spacings are used, namely \(d_{1}\) = 62.8 mm and \(d_{2}\) = 52 mm for the upper and lower array, respectively. The inter-element spacings in the two antenna arrays are equivalent to 0.5\(\lambda\) at 2.4 GHz, 0.9A at 4.3 GHz, 0.64A at 3.7 GHz, and 0.87\(\lambda\) at 5 GHz. Four microstrip line feeds are located at the bottom layer to provide signals for four element antennas with equivalent magnitude and phase. The microstrip feed lines are connected to each antenna element through via structures having a radius of 0.5 mm. The common ground plane presented in [25] is located between the antenna elements and the feed network.
# Failure Modelling of CP800 Using Acoustic Emission Analysis
## 2 Materials and Methods
### Failure Behaviour
The calculation of the model was performed implicitly using the LS-Dyna Solver R12.0 (parallel multifrontal sparse solver) with double precision and hourglass control. Figure 2: Measured forming force—amplitude over time curve (**A**) and used evaluation methods for determining failure of the butterfly specimen: mechanical (**B**), optical (**C**), as well as acoustical (**D**). For the evaluation of the triaxiality, normalised Lode angle, and equivalent plastic strain, the models of the mechanical and optical evaluation methods were analysed on the surface nodes of the investigation area in the middle of the unique shape, as shown in Figure 4A. The evaluation area was placed there since the butterfly specimen failed in the same region in the tests. For the acoustical evaluation method, the material failure was assumed to be in the material core and therefore analysed inside the specimen, as shown in Figure 4B. For the three evaluation methods, the equivalent plastic strain was plotted as a function of the triaxiality and as a function of the normalised Lode angle. By using the equivalent plastic strain and the characteristic values of the triaxiality, a data set was available to parametrise the widely used JC failure model [25]. The JC failure model  
\[\epsilon_{\text{JJC}}=D_{1}+D_{2}\times e^{D_{3}\times\eta} \tag{4}\]  
Describes the equivalent failure strain \(\epsilon_{\text{JJC}}\) as a function of the triaxiality \(\eta\) with the material specific parameters \(D_{1}\), \(D_{2}\), and \(D_{3}\)[8]. Finally, the JC failure models of the three evaluation methods were compared.
## 1 Introduction
### Contribution of the Research  
* The purpose of the development of the proposed P20-ACH method is to improve the privacy and energy efficiency of the VANETs network by reducing energy consumption, delay, and routing overhead.
* Effective cluster heads are chosen using the advanced ROA-based CH selection process that results in improving network connectivity and stability that reflects in the reduction of energy consumption and delay in the network.
* To protect the vehicles from malicious activities in this paper, the privacy-preserving mobility model is introduced so that the vehicle behavior is monitored, which reflects in the increase of the trustworthiness of the network.
* Through this effective CH selection and privacy-preserving mobility model, the effectiveness of the network is highly increased, which reflects in the increase of the network lifetime.  
The rest of the paper is organized into the following sections. The related works are discussed in Section 2. The system model with network model assumptions is elaborated on in Section 3. The proposed P20-ACH method is detailed in Section 4. The simulation results are evaluated in Section 5. Finally, in Section 6, the conclusion as well as the possible future directions are given.
# Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
## 4 SPN Models
### Preliminaries
In summary, an SPN can be a useful tool for performability modeling because it allows the user to represent and analyze the behavior of a system in a graphical, intuitive way, and to evaluate the system's performance under different scenarios. The Petri Net is a modeling technique that allows for the representation of systems, using as a foundation a strong mathematical base [19]. This means of dealing with demands via statistical methods has the particularity of allowing for the modeling of parallel, concurrent, asynchronous, and non-deterministic [20]. In Figure 3, components used in SPN models are illustrated [21]. Displaying the Transitions, namely Timed Undefined, Timed Refined, and Immediate. In addition, we have the Place, where the Token may be stored, and the Arc that will carry out this Token transposition. In Figure 4, it is possible to abstract how components are used in a basic Petri net. Observing the figure from left to right, we see that the transition named \"generate\" will create tokens that correspond to service requests in the system. For each Token placed in the \"Generated\" place, a choice must be made. if there is a free position in the queue, the token can be queued to be processed by the server, and the token is placed in the \"queue\" place. However, if the queue is full, the Token is discarded. The place called \"Free\" corresponds to to the number of available resources (service nodes). More specifically, the number of available resources is given by the k marking. The \"service\" transition represents the processing queued requests by the server. Figure 3: Illustrative components for an SPN network. First, we provide a foundation model that is devoid of redundancy. Following that, we provide extended models that employ three strategies: Cold-, Warm-, and Hot-Standby [15]. The redundancy was included in the Front-End component, since, according to our study, it is the most influential aspect of the system.
# In Vitro Antioxidant Effects of Coenzyme Q10 on Cellular Metabolism in Aged Mesenchymal Stem Cells
## 3 Results
### Mitochondrial Respiration and Changes in Energy Metabolism  
Oxygen consumption following CoQ10 treatment was assessed in live non-permeabilized MSCs. In early passage cells (P5), basal respiration was relatively high (10.6643 pmol O2/min), reflecting robust metabolic activity. In contrast, basal respiration significantly declined in late passage cells (P16) to 6.79 pmol O2/min, indicating a reduction in metabolic function associated with cellular aging. CoQ10 treatment exhibited dose-dependent effects on MSC metabolism, with the most pronounced improvements observed at 50 \(\upmu\)M. This concentration notably enhanced mitochondrial function and spare respiratory capacity (SRC), particularly in older senescent cells. SRC, which represents the difference between maximal and routine respiration, reflects a cell's ability to respond to energetic stress and serves as a critical indicator of cellular fitness. A reduction in SRC may compromise a cell's ability to manage stressors, potentially leading to mitochondrial dysfunction. In early passage cells (P5), CoQ10 at 50 \(\upmu\)M significantly boosted both routine (R) and maximum respiration (M) compared with the control and the 10 \(\upmu\)M treatment, indicating enhanced mitochondrial efficiency. In late passage cells (P16), maximum respiration reached its peak at 50 \(\upmu\)M, suggesting improved mitochondrial reserve capacity in senescent cells. However, an increase in leak (L) respiration was observed in older cells treated with 50 \(\upmu\)M CoQ10, potentially indicating mild uncoupling or an adaptive mitochondrial response to stress. These findings underscore the potential of CoQ10 to support mitochondrial function and improve the metabolic fitness of both young and senescent cells. Oxygen consumption rates of early and late passage cells in response to CoQ10 treatment were measured using the Oroboros O2k system (Figure 3).
## 2 Theoretical Background  
### Convolution Removal Principle in Computer-Controlled Optical Surfacing  
The deterministic polishing material removal process can be viewed as a two-dimensional convolution of the TIF and dwell time. The surface form error can be obtained by comparing the actual measured surface data with the ideal surface data and using filtering and other technical means to eliminate the middle-to-high-spatial frequency component and retain the low-spatial frequency component. The deterministic polishing process can precisely control the dwell time of the polishing tool at each dwelling point according to the obtained surface form error and the planned polishing path, thereby realizing different amounts of material removal at different positions, which can not only improve the accuracy of the surface, but also enhance the surface quality. In general, the above process is implemented by converting calculated dwell time to feed rate of the polishing tool, which is an iterative process. The convolution material removal and resulting residual error for each iteration can be expressed by Equation (1) as follows:  
\[\left\{\begin{array}{l}Z\left(x,y\right)=R\left(x,y\right)\otimes D\left(x,y \right)\\ E\left(x,y\right)=Z_{0}\left(x,y\right)-Z\left(x,y\right)\end{array}\right. \tag{1}\]  
where \(Z(x,y)\) represents the convolution material removal; \(R(x,y)\) represents the material removal function per unit time; \(D(x,y)\) stands for the dwell time function; \(E(x,y)\) represents the residual error after deconvolution removal; \(Z_{0}(x,y)\) represents the surface form error that is expected to be removed.
## 4 Characteristic Test and Analysis of the FBG Dynamometry Bol
### Test Results and Discussion
#### 4.3.2 Results Analysis and Discussion of Test II  
Figure 12 shows the curve graph of the strain of the sample under the long-term tensile loads measured by the resistance strain gauge sensor and the FBG dynamometry bolt in Tests II-1, II-2, and II-3.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **Resistance Strain Gauge 1** & **FBG Sensor 1** & **Resistance Strain Gauge 2** & **FBG Sensor 2** \\ \hline Range (/kN) & 166 & 166 & 167 & 167 \\ \hline Correlation coefficient & 0.99814 & 0.99617 & 0.99347 & 0.99553 \\ \hline Linearity & 10.24\% & 6.84\% & 18.68\% & 11.0\% \\ \hline Sensitivity (\(\mu\epsilon\) /kN) & 16.44 & 16.09 & 16.73 & 18.56 \\ \hline \end{tabular}
\end{table}
Table 2: A comparison of the indicators in the three groups of tests.  
Figure 11: Corresponding relationship of the strain measured by the resistance strain gauge sensors and FBG sensors.  
It can be observed in Figure 12 that, under the action of fixed tensile loads over a long period of time, the strain values of the strain gauge sensor and FBG sensor drift. From the trend change of the strain curve, the resistance strain gauge sensor is unstable and can only maintain a stable state for a short period of time. In fact, the resistance strain gauge is calibrated before use. If the axial force of the anchor bolt is still calculated according to the calibration results, it leads to an incorrect judgment. In contrast, the FBG sensor has a specific strain drift law and the stable strain can be calculated through the quasi-static equation of Equation (13), which does not misudge the axial force of the bolt rod.  
The strain curve of the FBG dynamometry bolt follows an obvious law change, which shows a nonlinear, smooth decline and gradually tends to be stable on the whole. When the tensile force reaches the predetermined value, the specimen strain reaches the maximum values of 418 \(\mu\epsilon\), 354 \(\mu\epsilon\), and 437 \(\mu\epsilon\). The strain rapidly attenuates in the first 1000 s, reaching 220 \(\mu\epsilon\), 241 \(\mu\epsilon\), and 256 \(\mu\epsilon\) at 1000 s, and then the curve becomes flat and drops to 220 \(\mu\epsilon\), 241 \(\mu\epsilon\), and 256 \(\mu\epsilon\) by 7200 s. The strain attenuation values of the FBG dynamometry bolt are 198 \(\mu\epsilon\), 113 \(\mu\epsilon\), and 184 \(\mu\epsilon\), respectively, and the attenuation rates are 47.4%, 31.9%, and 42.1%.
## 3 Background
### Affinity Poisson Process
** As shown in Figure 2a, the time-series citations received by paper \(i\) are modeled as a nonhomogeneous Poisson process for representing paper \(i\)'s citation intensity \(\lambda_{i}(t)\). Based on the superposition property of Poisson processes, as shown in Figure 2b, \(\lambda_{i}(t)\) can be represented as the sum of citation intensities receiving from each citing field \(f\in\mathbf{F}\), \(\lambda_{i}^{f}(t)\),  
\[\lambda_{i}(t)=\sum_{f\in\mathbf{F}}\lambda_{i}^{f}(t)\ . \tag{3}\]  
Here, three important counter-balancing factors are considered to determine \(\lambda_{i}^{f}(t)\): (1) citing field \(f\)'s _latent affinity_ to cited one \(\xi^{f}\), (2) _heterogeneous preferential attachment_ to paper \(i\) in the field \(f\), \(c_{i}^{f}(t)\), and (3) paper \(i\)'s fading attractiveness in the field \(f\) as the _time decay_ function \(p_{i}^{f}(t)\) as  
\[\lambda_{i}^{f}(t)=\xi^{f}\cdot(n_{0}+c_{i}^{f}(t))\cdot\ p_{i}^{f}(t;\theta_ {i}^{f})\ \, \tag{4}\]  
where \(n_{0}\) represents the prior citations for each time line, and \(\theta_{i}^{f}=(\
u_{i}^{f},\sigma_{i}^{f})\) denotes the parameters (mean and standard deviation) of lognormal distributions. For details, refer to [9]. Table 1 summarizes the notation used throughout the paper. The parameter values of the APP model are estimated using Bayesian inference. Based on the model formulation in this section, the likelihood of observing paper citation histories are first calculated. Then, by imposing a conjugate prior, the posterior distribution of the latent affinity \(\xi^{f}\) is computed. Accordingly, Figure 3 illustrates the corresponding graphical model of the APP model for expressing the overall conditional dependence structure between random variables. Detailed approaches of Bayesian inference and parameter learning are explained in Appendices A-D, respectively.
## 4 Comparation of the Denoising Effect  
To quantitatively evaluate the denoising effects of the methods, this study utilized four key evaluative metrics: the Root-Mean-Square Error (RMSE), Signal-to-Noise Ratio (SNR), Normalized Cross-Correlation (NCC), and Noise Reduction Ratio (NRR). The RMSE quantifies signal distortion, with lower scores indicating less distortion. The SNR assesses the denoising performance, with higher values denoting better noise suppression. The NCC measures the similarity between waveforms, with values nearing 1 implying a greater resemblance to the original, noise-free signal. Lastly, the NRR evaluates the extent of noise suppression achieved, with higher values representing more effective noise elimination [34]. The formulas for these metrics are delineated as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\lvert x(t)-y(t)\rvert^{2}} \tag{14}\]  
\[\text{SNR}=10\cdot\log_{10}\left(\frac{\sum_{i=1}^{N}s(i)^{2}}{\sum_{i=1}^{N} (y(i)-s(i))^{2}}\right) \tag{15}\]  
\[\text{NCC}=\frac{\sum_{i=1}^{N}s(i)\cdot y(i)}{\sqrt{\sum_{i=1}^{N}s(i)^{2}} \cdot\sqrt{\sum_{i=1}^{N}y(i)^{2}}} \tag{16}\]  
Figure 10: The comparison of K-SVD and DCT dictionaries.  
\[\text{NRR}=10\cdot\left(\log_{10}(\sigma_{1}^{2})-\log_{10}(\sigma_{2}^{2})\right) \tag{17}\]  
This paper compares Method A with the other three common PD denoise methods--AEEMD [10], the integration of SVD with VMD algorithm [11], and the Adaptive Wavelet Multilevel Soft Threshold algorithm [12]--on different input SNR PD signals to demonstrate the noise reduction effect. For example, when the input SNR is 7.76 dB, the signals after de-noising can be obtained as in Figure 11.  
From the denoising results, it can be seen that the method improved by this paper can remove noise efficiently, and the characteristics of the original signal remain entirely. The SVD-VMD method can remove most noise, but it can be seen that the signal is excessively reconstructed, resulting in localized distortions, maybe because it is hard for this algorithm to fully identify signal characteristics, leading to imprecise reconstructions. The denoising result of the AEEMD algorithm indicates that the method over-denotes under these conditions, filtering out some components of the useful signal. The result of the Wavelet Multilevel Soft Thresholding denoising algorithm is better; although, some noise still remains in the time domain.  
Subsequently, this paper compares the performance of these four methods under various signal-to-noise ratios and illustrates the outcomes in Figure 12.  
Figure 11: Comparison of PD signal denoising among four methods.  
In the above figure, the blue lines represent the method proposed in this paper. It can be observed that this method maintains good performance metrics across various signal-to-noise ratios. This demonstrates the effectiveness and superiority of the proposed method, capable of stable noise reduction and signal extraction in various noise environments.