esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Testing
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{\Lambda}(\mathbf{z})\) & \(\mathbf{\Omega}(\mathbf{z})\) & Iter. & \(||\mathbf{z}-\mathbf{\kappa}||_{\infty}\) & **CCO** \\ \hline \(\mathbf{1}\) & \(\mathbf{1}\) & \(6\) & \(O\left(10^{-43}\right)\) & \(2.0\) \\ \(6+cos(\mathbf{z})/10\) & \(\mathbf{1}\) & \(6\) & \(O\left(10^{-51}\right)\) & \(2.05\) \\ \(1+\mathbf{x}^{3}/1000\) & \(\mathbf{1}\) & \(6\) & \(O\left(10^{-42}\right)\) & \(2.0\) \\ \(\mathbf{exp}(-\mathbf{z}/100)\) & \(\mathbf{1}\) & \(6\) & \(O\left(10^{-46}\right)\) & \(2.0\) \\ \(\mathbf{1}\) & \(6+cos(\mathbf{z})/10\) & \(6\) & \(O\left(10^{-38}\right)\) & \(2.0\) \\ \(\mathbf{1}\) & \(1+\mathbf{x}^{3}/1000\) & \(6\) & \(O\left(10^{-46}\right)\) & \(2.0\) \\ \(\mathbf{1}\) & \(exp(-\mathbf{z}/100)\) & \(6\) & \(O\left(10^{-39}\right)\) & \(2.0\) \\ \(6+cos(\mathbf{z})/10\) & \(6+cos(\mathbf{z})/10\) & \(6\) & \(O\left(10^{-41}\right)\) & \(2.0\) \\ Iterative method (21) & \(6+cos(\mathbf{z})/10\) & \(1+\mathbf{x}^{3}/1000\) & \(6\) & \(O\left(10^{-65}\right)\) & \(2.0\) \\ \(6+cos(\mathbf{z})/10\) & \(exp(-\mathbf{z}/100)\) & \(6\) & \(O\left(10^{-43}\right)\) & \(2.0\) \\ \(1+\mathbf{x}^{3}/1000\) & \(1+\mathbf{x}^{3}/1000\) & \(6\) & \(O\left(10^{-45}\right)\) & \(2.0\) \\ \(1+\mathbf{x}^{3}/1000\) & \(6+cos(\mathbf{z})/10\) & \(6\) & \(O\left(10^{-37}\right)\) & \(2.0\) \\ \(1+\mathbf{x}^{3}/1000\) & \(exp(-\mathbf{z}/100)\) & \(6\) & \(O\left(10^{-38}\right)\) & \(2.0\) \\ \(exp(-\mathbf{z}/100)\) & \(exp(-\mathbf{z}/100)\) & \(6\) & \(O\left(10^{-41}\right)\) & \(2.0\) \\ \(exp(-\mathbf{z}/100)\) & \(exp(\mathbf{z}/100)\) & \(6\) & \(O\left(10^{-53}\right)\) & \(2.0\) \\ \(exp(-\mathbf{z}/100)\) & \(6+cos(\mathbf{z})/10\) & \(6\) & \(O\left(10^{-40}\right)\) & \(2.0\) \\ \(exp(-\mathbf{z}/100)\) & \(1+\mathbf{x}^{3}/1000\) & \(6\) & \(O\left(10^{-53}\right)\) & \(2.0\) \\ \hline \(\mathbf{1}\) & - & \(6\) & \(O\left(10^{-30}\right)\) & \(2.0\) \\ Iterative method (11) & \(6+cos(\mathbf{z})/10\) & - & \(6\) & \(O\left(10^{-30}\right)\) & \(2.0\) \\ \(1+\mathbf{x}^{3}/1000\) & - & \(6\) & \(O\left(10^{-30}\right)\) & \(2.0\) \\ \(exp(\mathbf{z}/100)\) & - & \(6\) & \(O\left(10^{-30}\right)\) & \(2.0\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Problem 1: initial guess = \([2,1,-2]\), \(m=[4,5,6]\). CCO: computational convergence order.
## 2 Mathematical Model  
We briefly review motion by mean curvature of the AC equation [26]. The scalar field \(\phi(x,y,z,t)\) can be represented by a local coordinate \(r(x,y,z,t)\); hence we can represent \(\phi(r)\) near \(\phi=0\) in terms of the local coordinate \(r\). Figure 2 illustrates the interface between two phases with local coordinate \(r\).  
Let \(\mathbf{m}=-\
abla\phi/|\
abla\phi|\) and \(\phi_{r}=\
abla\phi\cdot\mathbf{m}=-|\
abla\phi|\) be the directional derivative in the direction of the vector \(\mathbf{m}\); then, we can rewrite \(\Delta\phi\) as a function of \(r\):  
\[\Delta\phi = \
abla\cdot\
abla\phi=\
abla\cdot(-|\
abla\phi|\mathbf{m})= \
abla\cdot(\phi_{r}\mathbf{m})\] \[= \
abla\phi_{r}\cdot\mathbf{m}+\phi_{r}\
abla\cdot\mathbf{m}=( \
abla\phi)_{r}\cdot\mathbf{m}+\phi_{r}\
abla\cdot\mathbf{m}\]  
Figure 2: Illustration of the interface between two phases and a local coordinate \(r\).  
\[= (-|\
abla\phi|\mathbf{m})_{r}\cdot\mathbf{m}+\phi_{r}\
abla\cdot \mathbf{m}=(\phi_{r}\mathbf{m})_{r}\cdot\mathbf{m}+\phi_{r}\
abla\cdot\mathbf{m}\] \[= (\phi_{rr}\mathbf{m}+\phi_{r}\mathbf{m}_{r})\cdot\mathbf{m}+\phi_{ r}\
abla\cdot\mathbf{m}=\phi_{rr}-\kappa\phi_{r},\]  
where \(\kappa=-\
abla\cdot\mathbf{m}=\kappa_{1}+\kappa_{2}\) is the mean curvature; and \(\kappa_{1}\) and \(\kappa_{2}\) are the principal curvatures of the surface. Using this result, Equation (1) becomes  
\[\phi_{t}=-\frac{F^{\prime}(\phi)}{\epsilon^{2}}+\phi_{rr}-\kappa\phi_{r}. \tag{3}\]  
When the interfacial transition layer is in the local equilibrium state, then \(-F^{\prime}(\phi)/\epsilon^{2}+\phi_{rr}\approx 0\). Therefore, Equation (3) becomes  
\[\phi_{t}=-\kappa\phi_{r}. \tag{4}\]  
Let \(\Gamma_{t}=\{(x,y,z)|\ \phi(x,y,z,t)=0\}\) be the interface at time \(t\), then its velocity is given by  
\[\frac{d(\phi(r))}{dt}\bigg{|}_{\Gamma_{t}}=0,\]  
which implies \(\phi_{r}r_{t}+\phi_{t}=0\) and therefore \(r_{t}=-\phi_{t}/\phi_{r}=\kappa\) from Equation (4). From this result, we have \(V=\kappa\), where \(V\) is the normal velocity of the surface. Because \(\phi_{r}\leq 0\) and Equations (1) and (4), both the signs of \(-\mu\) and \(\kappa\) are the same.  
We propose the following governing equation for tissue growth under the mean curvature:  
\[\frac{\partial\phi(\mathbf{x},t)}{\partial t}=\begin{cases}-\mu(\mathbf{x},t)& \text{if }-\mu(\mathbf{x},t)>0,\\ 0&\text{otherwise},\end{cases} \tag{5}\]  
where \(\mu(\mathbf{x},t)=F^{\prime}(\phi(\mathbf{x},t))/\epsilon^{2}-\Delta\phi( \mathbf{x},t)\). That is, if a local mean curvature is positive, then a tissue grows, otherwise it does not [8; 18].
###### Abstract  
(1) Background: Low energy availability (LEA) has negative effects on performance, but mechanisms, timing, and other circumstances of these effects are not yet established. We aim to assess the performance-related effects of low energy availability in competitive female cyclists with a detailed approach to within-day energy availability (WDEB). (2) Methods: Female competitive cyclists (n = 8) completed the measurement of body composition, resting energy expenditure, and incremental testing; they reported diary logs and used accelerometers for energy availability (EA) calculation. Based on the Low Energy Availability in Females (LEAF) questionnaire, performance and energy parameters were compared between cyclists with risk for relative energy deficiency in sport (RED-S; the LEAF+ group) and cyclists without any risk (LEAF\(-\)). (3) Results: 87.5% had EA <30 kcal/kg. EA and energy balance in LEAF- were significantly different from LEAF+ cyclists (U = 0.00, z = -2.00, \(p\) < 0.05 and U = 0.00, z = -2.00, \(p\) < 0.05, respectively). We found significant correlations between WDEB\({}_{-300}\) and EA (\(-\)0.736, \(p\) = 0.037) and energy balance (\(-\)0.732, \(p\) = 0.039). In addition, relative power output was correlated with mRMR/pRMR (\(-\)0.763, \(p\) = 0.046). (4) Conclusions: This study is the first to show that WDEB\({}_{-300}\) in competitive female cyclists is greater in cyclists with risk for RED-S. Increased time in a catabolic state was correlated to worse anaerobic capacity in incremental testing. Measuring mRMR/pRMR might be useful as it was negatively associated with aerobic performance.  
low energy availability; relative energy deficiency in sport; road cycling; within-day energy deficiency; resting energy expenditure; energy balance; female cycling; LEAF questionnaire +
Footnote †: journal: Applied Soft. 2020, 10, 7563; doi:10.3390/app10217563
# NURBS-Enhanced Meshfree Method with an Integration Subtraction Technique for Complex Topology
## 3 NURBS-Enhanced Meshfree Method with the Integration Subtraction Technique  
### NURBS-Enhanced Meshfree Method  
In the NURBS-enhanced meshfree method, NURBS is used to describe the shape of the boundary and construct the background integration meshes as the foundation. As a result of the low requirement for integration mesh quality in the meshfree method, hybrid triangle elements can be chosen to accomplish an easy mesh for complex topology within the boundaries, as in Figure 4a. The integration elements and quadrature points are shown in Figure 4b. The integration points for the interior elements are generated with standard Gaussian quadrature rules. For the boundary elements, NURBS mapping quadrature rules for curved elements, such as the rules for high-order curved elements and NEFEM [13], are used. Even if the hybrid triangle mesh is relatively easy to implement, it is still time consuming to reconstruct the meshes and the stiffness matrix when the inner boundary is changed or a new hole is added. An alternative way to obtain the new stiffness matrix is to partially correct by refinement, but the measurement involved in deleting extra elements, repairing the gaps, reconstructing elements along the new boundary and redistributing nodes in the refinement region is relatively complex, as shown in Figure 5. Therefore, we recommend a subtraction technique in the integration process to avoid the complex measurement required for dealing with the nodes and elements in the refinement process, as illustrated in Figure 1.  
Figure 4: (**a**) Hybrid triangle meshes; (**b**) Gaussian quadrature points in boundary and inner elements.
## 1 Introduction
Delegates to the 2019 World Radiocommunication Conference (WRC-19) in Egypt have reached a compromise on an out-of-band-emission (OOBE) limit in the 24-GHz band to protect passive weather satellites in the adjacent spectrum [11]. Theoretical studies show high sensitivity of the measured brightness temperature at 23.8 GHz to the total column of water vapor (also known total precipitable water (TPW)) over oceans, which can be clearly seen from images of the brightness temperatures. The importance of TPW is that it gives a forecaster an idea of the amount of moisture in the air. Severe weather events, such as supercell storms, lightning, and hail, are correlated with high TPW. However, the sensitivity over land is very small and even zero, typically less than 0.1 K for a change of TPW of 1 mm, which implies that the sensitivity over land is too low to acquire TPW. However, statistical results reveal that retrieved TPW over land from ATMS could be significantly degraded without the ATMS channel 1 at 23.8 GHz, the explanation of which is not straightforward to the wireless industries as well as the general public. This paper clarifies the principle of using NOAA-20 ATMS channel 1 at 23.8 GHz for acquiring water vapor in the atmosphere, particularly over land surfaces. It should be pointed out that 23.8-GHz channel observations are mainly used at numerical weather prediction centers as well as in physical retrieval systems for cloud detections and water vapor profile retrievals. The TPW is the integration of the water vapor profile. The reason we focus on TPW is because TPW was a single variable discussed multiple times for 5G technology radio frequency interference. This short paper may serve as better physical understandings and a typical case study instead of comprehensive analysis.
## 4 Separation Methodology  
The proposed methodology needs to identify the abnormal days or hours first, and then use historical data to separate abnormal data. There are five main steps, as shown in Figure 8. The details are shown in the other sections that follow.  
Figure 7: Scatter diagram of hourly electricity consumption of lighting sockets in Building A.  
For the ways to discern the abnormal hours or day and operating state of air-conditioning, mentioned in Figure 8, there are three main steps as follows:  
* Use the model of average outdoor temperature and lighting socket daily power consumption, or the k-means clustering algorithm, to obtain the eigenvalues of the method, including clustering center (c), outdoor temperature threshold (\(T_{Min}\)), and power consumption data threshold (\(\overline{E}_{Min}\)), etc.
* Use the above eigenvalues and the lighting socket power consumption with air-conditioning opened to identify the state of the air-conditioning.
* Use appropriate historical data of lighting sockets (without mixing the power consumption of the air-conditioning) to predict and interpolate the actual power consumption of the lighting socket when the air-conditioning is running.  
The OATPM method is appropriate for identifying the state of air-conditioning for daily data, because it can only identify the closed or open state. However, the KNN method is appropriate for identifying the state of air-conditioning for daily data or hourly data, because it can identify the closed, slightly open, or fully open states.
## 2 Electro-Thermal Modeling and Analysis of Composite Materials Under Lightning Strike
### Mathematical Models
#### 2.1.1 Electro-Thermal Coupling Governing Equations  
When examining the impact of lightning strikes on materials, it is crucial to consider the thermal effects induced by electrical resistance. The electro-thermal coupling governing equations introduced in this section are fundamental for understanding charge conservation and thermal effects during the transmission of electric current through materials. This system of equations integrates the principles of electro-thermal conservation, encompassing [PERSON]'s equations and [PERSON]'s law, to elucidate the distribution of electric current and the generation of thermal energy during lightning strikes.  
Charge conservation is reflected in [PERSON]'s equations, ensuring that the total charge in the system remains constant as lightning current penetrates and traverses the material. Assuming the current is a steady-state direct current, the equations simplify to  
\[\int_{\text{S}}\text{J}\cdot\text{n}\,dS=\int_{V}r_{c}\,dV \tag{1}\]  
where V represents the volume of the unit cell, S denotes the surface area of the unit cell, n signifies the outward normal direction of S, J is the current density, and \(r_{c}\) represents the internal volume current per unit volume. When charged particles move in the direction of the electric field, their potential energy increases, necessitating that the direction of the electric potential gradient be opposite to that of the electric field. Consequently, employing [PERSON]'s law, the current density is derived as follows:  
\[\text{J}=\sigma^{E}\cdot\text{E}=-\sigma^{E}\cdot\frac{\partial\varphi}{ \partial x} \tag{2}\]  
where the electric field E can be defined as the gradient of the electric potential \(\phi\). \(\sigma\) represents electrical conductivity. x represents the spatial coordinate along the x-axis. The rate of change of charge within a closed volume is equal to the difference between the current penetrating and the current traversing that volume. The relationship between the electric field and current density can be further analyzed using the chain rule and the divergence theorem as follows:  
\[\int_{V}\frac{\partial\delta\varphi}{\partial x}\cdot\sigma^{E}\cdot\frac{ \partial\varphi}{\partial x}\,dV=\int_{V}\delta\varphi r_{c}\,dV+\int_{S} \delta\varphi\,\text{J}\,dS \tag{3}\]  
where \(\delta\)\(\varphi\) denotes an arbitrary variation in the electric potential field. Equation (3) is used to describe the relationship between the electric potential field and the electric field density within a given region, incorporating contributions from both the volume and the boundaries.
## 4 Evaluation of Model Developments
### Evaluation of the Updated Surface Water  
Modelled lake surface temperatures were verified against in situ measurements recorded by the Data and Information Centre of the Finnish Environment Institute (SYKE) over 27 lakes for 2010-2014. In situ lake water surface temperatures are measured at 8 a.m. local time near the shore during the ice-free season 20 cm below the water surface [53]. The model values from offline surface experiments forced with ERA5 were sampled to correspond to the measured values (i.e., during ice-free season at 5:00 or 6:00 UTC depending on daylight saving time).  
An overall error reduction is seen in Figure 22. With a more realistic lake depth, the mean absolute error from 2010 to 2014 becomes smaller for nearly all lakes. Averaged over all 27 lakes, a 13.4% error reduction is achieved. In some cases (Kilpisjarvi, Pesiojarvi, Inarijarvi,Rehja-Nuas, Paijanne, Kuivajarvi, Pyhajarvi and Paajarvi2), the modelling errors for lake water temperature did not change much since their depth remained close to the control experiment depth. For Lappajarvi, the temperature scores showed a deterioration because its updated depth was 2.5 times bigger than the observed values due to the depth horizontal interpolation resolution.  
Figure 22: Lake temperature mean absolute error for 27 in-situ Finnish lakes over the 2010–2014 period. Results for the current operational system are in red and results with the updated lake depths are in green.  
The updated land/water maps are planned for implementation in the ECMWF IFS operational system, as it will reduce uncertainties related to such ancillaries. In this case the evaluation is performed based on data assimilation experiments with subsequent daily forecasts. The near surface temperatures are shown in Figure 23. The simulations were performed for 2020 winter and 2019 summer seasons. RMS errors of daily 24 and 36 h forecasts are computed with respect to the own analysis at the corresponding verification time. Figure 23 shows the 2 m temperature RMSE difference, normalised by rmse, between the forecast simulations based on the new land/water maps and simulations based on the current operational maps. An overall neutral signal is seen globally for both day and night ranges (t + 36 and t + 48) during the northern hemisphere Winter season (figure not shown). Summer is characterised with a neutral to positive impact (Figure 23). Locally, a slightly negative impact is seen over the Canadian Northern coast and a positive signal marked with a reduction of the 2 m temperature RMSE when using the updated land/water maps over Greenland. These impacts correspond to the differences of land/water points seen in Figure 7. The overall evaluation shows that the IFS would benefit from a more accurate surface water representation and it is therefore considered for operational implementation in the next cycle.
# Synergistic Effects of CuO and ZnO Nanoadditives on Friction and Wear in Automotive Base Oil +
## 3 Results
### Scanning Electron Microscopy Results
Figure 13: EDX spectra highlighting Cu (-0.93 keV) and Zn (-1.01 keV) signal intensities in the wear track for different nanoadditive formulations. ZnO-containing samples show increased Cu incorporation, suggesting synergistic effects, while excessive Zn accumulation in the absence of CuO correlates with abrasive wear. In the 0.3 wt% CuO formulation, Cu levels ranged from 1.26% to 1.41%, with no Zn detected. This confirms consistent Cu integration and effective tribofilm formation. The 2:1 CuO-ZnO mixture exhibited Cu (0.50-1.84%) and Zn (0.82-1.38%), with some Cu values exceeding those in pure CuO. This suggests that ZnO nanoparticles promote Cu incorporation, while CuO presence controls Zn levels in the tribofilm, indicating synergy. In the 1:2 CuO-ZnO formulation, Cu content was lower and more variable, while Zn remained moderate. The reduced Cu limits tribofilm effectiveness, yet Zn is stabilized, reflecting weaker synergy. Cu was absent in the 0.3 wt% ZnO sample, and Zn levels were highest (up to 4.02%), correlating with the most severe abrasive wear and lack of protective film. These results confirm that ZnO enhances Cu deposition but requires Cu to moderate its incorporation. The best tribological performance aligns with the 2:1 CuO-ZnO ratio, where both elements are balanced within the boundary layer.
## 3 Proposed Method  
The EAR-Net is composed of an encoder to extract context information and a decoder to generate a precise segmentation map. Figure 4 shows the structure of the EAR-Net.  
The encoder consists of 16 encoding blocks with residual learning and 1 ASPP, and the decoder consists of 3 decoding blocks with 1 concatenation operation and 2 consecutive convolutions. The resolution of the feature map output from the decoder is \(\frac{1}{2}\) smaller than that of an input image. Moreover, the number of channels in the corresponding feature map exceeds the number of categories in the dataset. Therefore, after matching the number of channels in the feature map using a \(1\times 1\) convolution, the final segmentation map was generated by upsampling twice. The details of each layer in EAR-Net are introduced in Sections 3.1-3.3.  
Figure 3: Depthwise separable convolution diagram.  
Figure 2: Atrous convolution diagram. (**a**) Traditional convolution with a rate of 1 and (**b**) atrous convolution with a rate of 2.
## 4 Proposed Compression Approach
### Pruning  
The connections are pruned iteratively by defining a step sparsity level at each stage. A polynomial decay function is used to determine the step sparsity level. It is defined in Equation (4).  
\[SSL=S+(S_{r}-S)\bigg{(}1-\frac{k-r}{r-n}\bigg{)} \tag{4}\]  
where \(S\) is the targeted sparsity level; \(S_{r}\) the initial sparsity level, \(r\) the initial iteration, \(n\) the last iteration, and \(k\) the current iteration.  
\[\mathbf{W}=\mathbf{W}.\mathbf{M} \tag{6}\]  
Unlike the connections pruning, in filters pruning, the salience criteria is the value of the sum of the absolute values of the filter weights. All the values in the mask corresponding to the weight of each filter are set to 0 if the sum of the absolute values of its filter weights is less than the threshold as defined in Equation (7). Then the dot product given in Equation (6) forces the pruned weights to be set to zero.  
\[\mathbf{M}=\ 0\ \ \ \ \ if\ \ \sum_{filters}|\mathbf{W}|<\ T \tag{7}\]
### Theoretical Background  
#### 1.2.1 Work Environment  
The European Working Conditions Survey (EWCS) and Korean Working Conditions Survey (KWCS) define the working environment as one that includes physical hazards such as vibration, noise, high and low temperatures, and biochemical hazards such as exposure to fumes and vapors. Additionally, it encompasses ergonomic hazards such as handling heavy loads and performing repetitive movements [17; 26]. In the food manufacturing industry, frozen food workers are exposed to a low-temperature environment of \(-18\)\({}^{\circ}\)C [5], and rice noodle manufacturing workers are exposed to a high-temperature environment of up to 36.68 \({}^{\circ}\)C [6]. Additionally, canning workers are exposed to noise levels of up to 100 dB [7], and meat and seafood processing workers are exposed to vibration due to vibrating equipment [7; 27]. In addition, workers in poultry and seafood processing are exposed to lifting heavy objects and performing repetitive tasks [9; 10].