esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Model Regression Coefficient Test  
Through regression coefficient analysis (Table 14), this study elucidates the distinct influence mechanisms of the SS substitution ratio and W/C ratio on concrete properties. The results demonstrate that the SS substitution ratio significantly negatively impacts flowability, exhibiting a regression coefficient of \(-446.773\) (\(p\) = 0.010). Comparatively, the W/C ratio shows significant positive effects, yielding a regression coefficient of +253.327 (\(p\) = 0.006), which benefits from reduced paste viscosity caused by increased water content.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dependent Variable** & **Independent Variable** & **Regression Coefficient** & **Standard Error** & \(\mathbf{t}\) & \(\boldsymbol{p}\) \\ \hline \multirow{4}{*}{Fluidity} & W/C & 253.327 & 67.420 & 3.757 & 0.006 \\  & SS & \(-446.773\) & 132.069 & \(-3.383\) & 0.010 \\  & ceramsite & 173.068 & 174.386 & 0.992 & 0.350 \\  & W/C & \(-61.940\) & 25.250 & \(-2.453\) & 0.040 \\
7d compressive & SS & \(-4.998\) & 18.125 & \(-0.276\) & 0.790 \\  & ceramsite & 3.497 & 30.916 & 0.113 & 0.913 \\  & W/C & \(-67.356\) & 27.769 & \(-2.426\) & 0.041 \\
14d compressive & SS & \(-6.048\) & 17.288 & \(-0.350\) & 0.735 \\  & ceramsite & 4.049 & 33.431 & 0.121 & 0.907 \\
28d compressive & W/C & \(-69.044\) & 28.280 & \(-2.441\) & 0.040 \\  & SS & \(-3.737\) & 17.371 & \(-0.215\) & 0.835 \\  & ceramsite & 3.163 & 33.542 & 0.094 & 0.927 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Partial least squares regression coefficient test table.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Y1** & **Y2** & **Y3** & **Y4** & **Y1** & **Y2** & **Y3** & **Y4** \\  & & & & & **(Standardized)** & **(Standardized)** & **(Standardized)** & **(Standardized)** \\ \hline Constant & & & & & & & & \\ term & 90.733 & 38.847 & 42.693 & 43.849 & 0.000 & 0.000 & 0.000 \\ X\({}_{1}\) & 253.327 & \(-61.940\) & \(-67.356\) & \(-69.044\) & 0.440 & \(-0.804\) & \(-0.801\) & \(-0.804\) \\ X\({}_{2}\) & \(-446.773\) & \(-4.998\) & \(-6.048\) & \(-3.737\) & \(-0.777\) & \(-0.065\) & \(-0.072\) & \(-0.044\) \\ X\({}_{3}\) & 173.068 & 3.497 & 4.049 & 3.163 & 0.301 & 0.045 & 0.048 & 0.037 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Regression coefficients between dependent variable Y and independent variable X.  
For compressive strength development, the W/C ratio consistently demonstrates significant negative regulation effects, showing regression coefficients of \(-61.940\) (7d), \(-67.356\) (14d), and \(-69.044\) (28d), with \(p\)-values in the range of 0.040-0.041. This strength degradation originates from elevated porosity and disorganized hydration product microstructures that are induced by excess water. Both the SS substitution ratio and ceramsite substitution ratio exhibited non-significant impacts in the strength prediction models (\(p>0.05\)).
## 3 Nanofluid Thermal Conductivity Models
### Layering Model  
One of the more significant mechanisms behind the increase in thermal conductivity of nanofluids is the formation of a layer of the base fluid around the nanoparticle. The formed layer around the nanoparticle is slightly denser than the surrounding base fluid, yet significantly less dense than the nanoparticle (see Figure 28) [73].  
When the base fluid layer forms in an ordered solid-like state, the formed layer exhibits a higher thermal conductivity than the surrounding base fluid. This interfacial layer acts as a thermal bridge  
Figure 28: An illustration showing the formed interfacial nanolayer in between the nanoparticle and the base fluid. The nanoparticle is in grey, with the liquid medium molecules being depicted as white circles. The boundaries of the nanolayer are depicted as dashed lines around the nanoparticle.  
between the solid nanoparticle, and the surrounding base fluid, aiding the heat transfer between the two materials [74]. Looking at the different interfacial structures, [PERSON] et al. [73] developed an equation to describe the liquid molecule layering onto the nanoparticle's surface. One of the major struggles of the interfacial layer is the calculation of the layer's thermal conductivity. With smaller particle sizes the interfacial layer becomes more prominent. In their equation, the interfacial layer has a linearly distributed thermal conductivity. [PERSON] and [PERSON] [74] once again expanded the Maxwell model to include an interfacial layer. They proposed that the interfacial layer formed on the nanoparticle would act as a thermal bridge between the nanoparticle and the surrounding base fluid. The energy transport within the nanofluid was assumed to be predominantly diffusive. This assumption was made due to the mean free path of the base fluid molecules being smaller than the average interparticle distance. The interfacial layer around the nanoparticle was mathematically combined with the nanoparticle to form an equivalent nanoparticle. [PERSON] and [PERSON] focused specifically on the effects of the nanolayer and assumed that there was no particle interaction at lower nanoparticle fractions. With fewer nanoparticles in the nanofluid, the average distance between nanoparticles is greater, leading to a decrease in the probability of collisions and also decreasing the formation of clusters. The developed theoretical model does not account for the addition of surfactant. An added surfactant would change the nanofluid into a ternary system which cannot be calculated through the theoretical model. Similar models were introduced by others [75; 76].
## 4 Discussion
### Strengths and Limitations  
Although the use of GPS data in rugby and other sports codes is becoming standard practice when analysing sport performance, our novel study used GPS real-time collision data to compare the severity of each reported shoulder injury (tissue type, pathology type) in the context of momentum, intensity, and collision variables. It is also the first study to quantify the contact scenario with the resultant shoulder injury severity compared to player position. This study is the largest study of its kind, comparing the severity and time-loss of shoulder injuries in professional rugby players with real-time collision data for 4 years. Further scientific research is needed to quantify different pathologies in different rugby populations. These findings can be used in the future strategic design of shoulder injury prevention programs. This study has certain limitations. The SARS-CoV-2 (COVID-19 virus) global pandemic in 2020 negatively influenced the participation of all sports. International travel restrictions were implemented, and the South African teams were excluded from the Super Rugby competition. Therefore, the number of shoulder injuries in 2020 was lower. Momentum was calculated from variables, and intensities were based on GPS data, however it should be acknowledged that match events such as the tackle and scrum are technical. Therefore, applying these physical demands on the shoulder are dynamic and complex and future studies should consider incorporating auxiliary camera records. The actual training/match time a player was exposed to a certain contact load and the number of contact interventions by each player were not explored and future research will be of interest. The findings on male players from one sport cannot be generalised. Further scientific research is needed in different rugby populations to compare findings.
# Development of A Loss Minimization Based Operation Strategy for Embedded BTB VSC HVDC
## 4 Case Study
### SCLM Control in Transient Condition  
An example of the SCLM control for an overload condition that causes N-1 failures in a given system is shown. The transformer overload (over 100%) and the line overload (over 120%) are shown as examples. The simulation proceeds as shown in Figure 7. A fault occurred in 1 s; after six cycles (0.1 s) clear fault, trip line; six cycles later, the HVDC operating point was changed for branch overloads. In this process, fast operation is required to solve the thermal limit, so only active power control is used and the \(AC\) voltage control setpoint does not change. In addition, the operation point was changed again for loss minimization after 1 s from the failure occurrence.  
In order to verify the effect of the SCLM control, this paper compares the results of conventional control, RAS control, and the proposed control. In conventional control, regardless of the disturbance, the active power and \(AC\) voltage setpoint of the HVDC are constant [33]. Therefore, it may not respond to disturbance, and overload and overvoltage may occur. In this case, the overload condition is maintained, and the transformer is in trouble. In the RAS control, as in the literature reviews, the operating point of the HVDC changes for system stabilization [6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16]. Using the control, the system instability is solved by the active power control, but the AC voltage setpoint is constant without control. Similar to RAS control, the SCLM control solves the overload problem with active power change, and then uses the STLM control to determine the operating point after a contingency. In this case, the operation point is changed first, and in the SCLM control considering the loss minimization, it can be confirmed that the operation point is changed secondarily.
## 2 Methods
### Dataset and Evaluation Metrics
#### 2.3.2 Evaluation Metrics  
Quantitative evaluation metrics are used to evaluate the effectiveness of the suggested technique, as well as other methods for comparison. These metrics include the following:  
* Overall accuracy (OA): A comprehensive assessment of the classification;
* Average accuracy (AA): The average accuracy provides a balanced representation of the model's performance by calculating the mean classification accuracy across all land cover categories;  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class No.** & **Class Name** & **Training** & **Testing** \\ \hline
1 & Buildings & 125 & 2778 \\
2 & Woods & 154 & 8969 \\
3 & Roads & 122 & 3052 \\
4 & Apples & 129 & 3905 \\
5 & Ground & 105 & 374 \\
6 & Vineyard & 184 & 10,317 \\ \hline  & Total & 819 & 29,395 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Land cover classes of the Trento dataset, with the standard training and testing sets for each class.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class No.** & **Class Name** & **Training** & **Testing** \\ \hline
1 & Trees & 1162 & 22,084 \\
2 & Grass Groundsurface & 344 & 6358 \\
3 & Road Materials & 334 & 6353 \\
4 & Buildings’ Shadow & 112 & 2121 \\
5 & Sidewalk & 69 & 1316 \\
6 & Cloth Panels & 13 & 256 \\
7 & Grass—Pure & 214 & 4056 \\
8 & Dirt and Sand & 91 & 1735 \\
9 & Water & 23 & 443 \\
10 & Buildings & 312 & 5928 \\
11 & Yellow Curb & 9 & 174 \\ \hline  & Total & 2683 & 28,920 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Land cover classes of the MUUFL dataset, with the standard training and testing sets for each class.  
* Kappa coefficient (\(\kappa\)): The kappa coefficient assesses the agreement between the anticipated classifications and the ground truth while considering any agreement that might happen by chance.
## 4 Numerical Examples
### A Four-Point Shear Beam  
[PERSON] and [PERSON] first tested and analyzed the four-point unilateral shear beam [28]. The geometry and boundary conditions of the beam are shown in Figure 10. Assuming that the structure is in the plane stress state, the Young's modulus \(E\) is 24.8 GPa, Poisson's ratio \(\upsilon\) is 0.18, tensile strength \(f_{t}\) is 3.0 MPa, mode I fracture energy \(\textbf{ }G_{f1}\) is 100 N/m, and mode II fracture energy \(\textbf{ }G_{f1}\) is 10 N/m. The crack path is also predicted by using the LEFM-based maximum circumferential stress criterion. Analyzing the linear softening curve of Figure 5b, the limit value of CODs \(w_{c}\) obtained by the mode I fracture energy \(\textbf{ }G_{f1}\) is 0.067 mm and the limit value of CSDs \(\textbf  
numerical solutions of [PERSON] et al. [12] and the effect of different steps on the calculation results is not considerable, which proves the applicability of the X-SBFEM algorithm to complex crack propagation problems. However, the FPZ energy dissipation of the crack tip makes the LEFM-based method slightly different compared to previous calculations and experimental data [15]. In this study, the iterative method used to simulate the cohesive tractions is used to simulate the energy dissipation of the FPZ. The linear softening curve in Figure 5b and the curve of Figure 5c are used to solve the cohesive tractions of the vertical crack surface and the parallel crack surface. As shown in Figure 11b, it can be seen that the Load-CMSD curve calculated herein is in good agreement with [PERSON]'s experimental data (NFM-based). Figure 12b shows that the method considered herein can describe the snap-back phenomenon of the Load-LPD curve. It can be concluded that the iterative, linear superposition method based on X-SBFEM can yield high-precision results without coupling the interface unit (CIEs) near the crack tip.  
Figure 11: Load-CMSD curves as a function of changes of the crack increment length: (**a**) LEFM-based and (**b**) NFM-based.  
Figure 10: Four-point notched shear beam for crack propagation (unit: mm).
# Study on Noise Reduction and Data Generation for sEMG Spectrogram Based User Recognition
## 3 Proposed sEMG Spectrogram-Based User Identification  
This section presents a preprocessing method for removing resting state signals and noise to solve the irregularity problem of sEMG signals, as well as a user identification system that applies a time-frequency multidimensional analysis method to 1D sEMG signals. Figure 4 illustrates the overall flowchart of the proposed sEMG spectrogram-based user identification.  
Among the open databases, Ninapro DB2 is used to compose the sEMG signal dataset. Subsequently, sEMG signals are partitioned into one motion cycle signals, and a preprocessing process that includes removing noise and irregular resting state signals generated in the signal acquisition process is performed. Noise occurring in the sEMG signals is removed using filters such as band-stop and band-pass filters. Additionally, the sEMG signals of one motion cycle are partitioned into non-overlapping frames; the energy and spectrum center for each frame are calculated to set a threshold value and the irregular resting state signals are removed by extracting only signals containing activity information. The final step of the preprocessing increases the amount of sEMG data through matching pursuit. Subsequently, the preprocessed 1D sEMG signals are transformed into two-dimensional (2D) sEMG spectrogram images by applying a time-frequency multidimensional feature extraction method. Finally, the user identification performance is verified using the CNN, the most widely used technique for image classification.  
Figure 4: Flowchart of proposed sEMG spectrogram-based user identification.
## 1 Introduction
Land surface phenology (LSP) is the periodic plant life cycle, which evaluates the vegetation activity during the growing season at the ecosystem level [17]. Phenology directly or indirectly regulates carbon (e.g., photosynthesis and respiration) and water (e.g., transpiration and evaporation) fluxes between land surface and atmosphere from the plant to ecosystem scale [18]. The linkage of phenology to ecosystem SWUE remains to be investigated. Northeast China is a typical region sensitive to global climate change, with abundant forests and grassland resources [19]. The carbon cycle of the terrestrial ecosystem in northeast China has great influence on regional climate, carbon budget, and the evolution of terrestrial ecosystems in the nation. [PERSON] et al. studied the response of natural vegetation WUE to drought in northeast China [20]. [PERSON] found that WUE was mainly affected by temperature and precipitation [21]. Some existing research attempted to analyze land surface phenology variations and the effects of climate change in northeast China at different spatial and temporal scales [22; 23; 24; 25]. However, no previous studies investigated the spatio-temporal patterns of soil water use efficiency and their impact factors in northeast China. The objectives of this study were to (1) examine the spatial and temporal patterns of ecosystem soil water use efficiency in the growing season (from April to October) across northeast China during 2007-2015; (2) evaluate the effects of vegetation cover, land surface temperature, and precipitation on soil water use efficiency changes; and (3) explore the responses of ecosystem soil water use efficiency changes to land surface phenology variation. This study helps improve the understanding of variabilities of soil water use in different ecosystems and their ecological function.
## 2 Basic Theory
### Asperity Contact Model  
The load is shared by the lubricant oil film and asperities. Asperity interactions occur when the actual oil film thickness is sufficiently small. According to the Greenwood-Tripp rough contact theory [34], the asperity contact pressure and actual contact area are given by Equations (2) and (3), respectively:  
\[p_{a}=\left(\frac{16\sqrt{2}}{15}\right)\pi(\psi\beta\sigma)^{2}E^{\prime} \sqrt{\frac{\sigma}{\beta}}A_{5/2}(H) \tag{2}\]  
\[A_{c}=\pi^{2}(\psi\beta\sigma)^{2}AF_{2}(H) \tag{3}\]  
\[F_{5/2}(H)=\begin{cases}2.1399\times 10^{-4}\Big{\{}3.804\ln(4-H)+1.34[\ln(4-H) ]^{2}\Big{\}}&H\leq 3.5\\ 1.12\times 10^{-4}(4-H)^{1.9447}&3.5<H\leq 4\\ 0&H>4\end{cases} \tag{4}\]  
\[F_{2}(H)=\begin{cases}1.705\times 10^{-4}\Big{\{}4.504\ln(4-H)+1.37[\ln(4-H)]^{2} \Big{\}}&H\leq 3.5\\ 8.8123\times 10^{-5}(4-H)^{2.15}&3.5<H\leq 4\\ 0&H>4\end{cases} \tag{5}\]  
where \(\psi\) is the asperity density, \(\beta\) is the height or radius of asperity, \(E^{\prime}\) is the integrated Young's modulus, \(A\) is the nominal contact area, and \(H\) is the film thickness ratio, which is calculated using Equation (6).  
\[H=h/\sigma \tag{6}\]
## 4 Communication Channel Modeling with Spectral Efficiency and Precoder Formulation  
We apply the widely utilized 3D Saleh-Valenzuela channel model [23] to the application of mmWave propagation characteristics, where the scatterers are \(R\). Therefore, the representation of channel \(H_{1}\) between BS and IRS can be expressed as follows:  
\[\mathbf{H}_{1}=\sqrt{\frac{M\,N}{R}}\sum_{r=1}^{R}\alpha_{r}^{1}\mathbf{a}_{g} (\phi_{g,r}^{1},\theta_{g,r}^{1})\,\mathbf{a}_{t}^{H}(\phi_{t,r}^{1}), \tag{2}\]  
where \(R\) represents the number of pathways between the BS and IRS, the gain for the \(r\)th pathway is \(\alpha_{r}\), the array vectors are denoted by \(\mathbf{a}_{g}(\phi_{g,r},\theta_{g,r})\) and \(\mathbf{a}_{t}^{H}(\phi_{t,r})\), respectively, the azimuth angle of departure (AOD) as well as the AO arrival (AOA) are symbolized by \((\phi_{g,r},\theta_{g,r})\) and \((\phi_{t,r})\); finally, the factor of normalization is represented by \(\sqrt{\frac{M\,N}{R}}\). However, the array response vector (ARV) of the uniform planar array (UPA) with \(N\) IRS elements can be written as follows [24]:  
\[\mathbf{a}(\phi,\theta)=\frac{1}{\sqrt{N}}\Big{[}1,\,\ldots,\,e^{ j\frac{2\pi}{\lambda}d(\alpha\sin\phi\sin\theta+\beta\cos\theta)},\] \[\ldots,\,e^{j\frac{2\pi}{\lambda}((N_{x}-1)d\sin\phi\sin\theta+( N_{y}-1)\cos\theta)}\Big{]}^{T}, \tag{3}\]  
where \(d\) and \(\lambda\) are the spacings of the REs and the signal wavelengths of \(\alpha\) and \(\beta\) are the horizontal and perpendicular indices of the RE. In addition, at BS, the ARV of the uniform linear array (ULA) can be formulated as follows [24]:  
\[\mathbf{a}_{t}(\phi)=\frac{1}{\sqrt{M}}\Big{[}1,e^{j\frac{2\pi}{\lambda}d\sin \phi},\ldots,e^{j\frac{2\pi}{\lambda}(M-1)d\sin\phi}\Big{]}^{T}. \tag{4}\]  
Furthermore, the channel \(\mathbf{h}_{r,f}\) between the IRS and UE can be written as follows:  
\[\mathbf{h}_{r,f}^{H}=\sqrt{\frac{N}{R_{1}}}\sum_{r_{1}=1}^{R_{1}}\alpha_{r}^{ 2}\mathbf{a}_{t}^{H}(\phi_{t,r}^{2},\theta_{t,r}^{2}). \tag{5}\]  
Finally, the channel from BS to UE (i.e., the cascaded channel \(H_{cas}\)) can be expressed as follows:  
\[\mathbf{H}_{cas}=\mathbf{h}_{2,f}^{H}\phi\mathbf{H}_{1}. \tag{6}\]  
Here, the SE and precoding matrix design are described. The overall SE \(S_{se}\) of the system can be formulated as follows [22]:  
\[S_{se}=\sum_{f=1}^{F}\log\left(1+\frac{\left|\mathbf{h}_{2,f}^{H }\mathbf{\Phi}\mathbf{H}_{1}\mathbf{w}_{f}\right|^{2}}{\sum_{i\
eq f}\left| \mathbf{h}_{2,f}^{H}\mathbf{\Phi}\mathbf{H}_{1}\mathbf{w}_{i}\right|^{2}+ \sigma^{2}}\right)\] (7a) \[\sum_{f=1}^{F}\log\left(1+\frac{\left|\mathbf{H}_{cas}\mathbf{wthe \(\mathbf{E}=\text{diag}(E_{1},E_{2},E_{3},\ldots,E_{F})\) is the UE power allocation matrix, which is made up of diagonal components and is intended to meet the highest power constraint.
## 1 Introduction
It is, therefore, necessary to develop a systematic and highly reliable identification method for identifying internal debonding damage in composite blades considering the structural features and responses of large wind turbine blades. The objective of this study is to develop the debonding damage identification model for the various types of debonding damage that could occur inside composite wind turbine blades using an artificial neural network (ANN), based on the natural frequency changes due to the debonding damage. To this end, the debonding damage configurations for a wind turbine composite blade were first defined by considering the damage variables (the damaged joint, the location, and damage length), then modeled using the finite element analysis software. A debonding damage map was then established by obtaining data describing the natural frequency change due to the debonding damage configurations in this model. Then, a principal component analysis (PCA) was conducted by comparing the natural frequency change rates for each damage configuration to inform the selection of a natural frequency that exhibits a large change rate due to debonding damage. Finally, an ANN-based damage identification model for various damage configurations in composite blades was implemented and demonstrated in MATLAB.