esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Dynamic Responses of Floating OWTs
### UMaine-Hywind Spar  
Figure 17a-c show the comparisons of RAOs at surge, heave and pitch natural frequencies between hydrodynamic simulator and the experimental data from a 1:30 model implemented in Natural Ocean Engineering Laboratory (NOEL) of Reggio Calabria in Italy [32; 33]. The given wave condition adopted for dynamic response of MIT/NREL spar is JONSWAP spectrum (\(H_{s}\) = 6 m, \(T_{p}\) = 10 s, and peak-enhancement factor: \(\gamma\) = 3.3). Since the UMaine-Hywind spar is at 200 m of the water depth shallower than the OC3-Hywind spar at 320 m, the length of the catenary mooring lines resting on the seabed are shorter than that used for the OC3-Hywind spar. That means the stiffness of the catenary mooring lines used for the UMaine-Hywind spar is higher than that used for the OC3-Hywind spar. Consequently, the peak value of the surge with the mooring load computed by hydrodynamic simulator as shown in Figure 17a is smaller than the case in Figure 15a because of high stiffening of mooring lines in the surge mode. It is exhibited that the resonance peak of the heave RAO with the mooring load in Figure 17b is also smaller than the one in Figure 15b due to the effect of shallow water depth. However, decreases in the water depth would lead to more prominent resonance peaks of pitch RAOs in Figure 17c than the ones in Figure 15c.  
According to the Boundary Element Method (BEM) [34], proximity to a boundary can have the influence on the quantity of hydrodynamic coefficients. This means that added-mass and radiation damping coefficients not only depend on the floater's geometry but also its proximity to a boundary. For floating bodies, the response of the floating body is altered in finite water depth. The specific water depth at which the hydrodynamic coefficient is affected depends on the floater's geometry, location and shape of a boundary, e.g., the seabed. Therefore, the water depth may have the effects on the RAOs for OC3- and UMaine-Hywind spars.  
Figure 18a-c present time variations of wave load components at the natural frequencies of the surge, heave and pitch, respectively. Compared with the dominant force of \(F_{B}\) in Figure 16b, it is evident in Figure 18b that \(F_{B}\) is even more significant at the heave natural frequency with the increase of the significant wave height.  
Figure 16: Time histories of wave-load components at (**a**) the surge natural frequency (\(f_{p}\) = 0.008 Hz), (**b**) the heave natural frequency (\(f_{p}\) = 0.032 Hz), and (**c**) the pitch natural frequency (\(f_{p}\) = 0.037 Hz) for the OC3-Hywind spar.  
Figure 17: The performance of (**a**) surge, (**b**) heave, and (**c**) pitch RAOs as a function of wave frequency for the UMaine-Hywind spar by comparing hydrodynamic simulator with experimental data of NOEL.
# Effect of Wheel Path in Raster Grinding on Surface Accuracy of an Off-Axis Parabolic Mirror
## 5 Conclusions  
According to the geometric characteristics of off-axis parabolic double curvature, two wheel paths were conducted for raster grinding; one was designed to step along the meridian direction, and the other was designed to step along the arc vector direction. A scallop height model and surface error simulation were established to study the influence of different paths on the surface accuracy of the off-axis parabolic surface.  
1. The scallop height model error was within 5%. It was proven that the model was reasonable and accurate in predicting the scallop height of the off-axis parabolic surface.
2. Upon comparing the scallop height generated by the different raster paths, it was found that the wheel path in raster grinding stepping along the arc vector direction could obtain a smaller scallop height.  
Figure 16: Diagram of the spatial distribution of the scallop height stepping along the arc vector direction: (**a**) YOZ plane; (**b**) XOZ plane.  
3 Based on the correlation between the scallop height and surface shape error, simulation of the surface shape error distribution was carried out. The simulation results were consistent with the experimental results, which proves the correctness of the simulation.
4 According to the simulation and experimental results of the surface shape error distribution, it was found that the surface error generated by the step along the arc vector direction was less than that of the raster processing along the meridian direction. In the actual grinding process of an off-axis parabolic surface, the wheel path along the arc vector step is preferred for raster grinding.  
Conceptualization, J.L.; methodology, J.L. and H.L.; software, J.L.; formal analysis, J.L., H.L. and X.G.; investigation, J.L. and X.G.; resources, X.G. and R.K.; data curation, J.L. and H.L.; writing--original draft, J.L.; writing--review and editing, J.L. and H.L.; visualization, J.L.; supervision, S.G.; project administration, R.K. and S.G.; funding acquisition, R.K. and S.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (52293402, 52375411, 51975091) and the Major Science and Technology Project of Henan Province of China (221100230100).  
Not applicable.  
The authors declare no conflict of interest.
## 5 Result and Discussion
### Effectiveness Comparison  
The effectiveness illustrates how well the regression model of Equation (5) fits the seismic demand. This can be described as the correlation coefficient \(R^{2}\) value ranging from 0 to 1. A larger \(R^{2}\) indicates a better correlation relationship between the demand parameter and IM. Figures 10 and 11 illustrate the linear regression analysis of PSDMs with respect to different IMs considering two cases of EDPs (max acceleration and max displacement). Figure 10 shows that peak acceleration of PSDMs with respect to EDA, A95, PGA, ASI, and SMA have significantly higher \(R^{2}\) values than others containing the values of 0.9098, 0.9049, 0.9033, 0.8732, and 0.8374, respectively. This implies that the scattering of PSDMs using the abovementioned IMs is much smaller than that of others. Figure 11 illustrates that HI, VSI, \(S_{v}\) (\(T_{1}\)), \(S_{a}\) (\(T_{1}\)), and \(S_{d}\) (\(T_{1}\)), with the \(R^{2}\) values of 0.882, 0.8633, 0.8172, 0.8063, and 0.8048, respectively, are the top five strongly correlated IMs while considering peak displacement as EDP. From Figure 10, the values of correlation coefficients are within a range of 0.9098 to 0.0522, while Figure 11 shows the range of coefficient is from 0.882 to 0.0077.  
As illustrated in Figure 12, for the case of max acceleration as EDP, EDA, with the value of 0.91, proves to be the most effective one, followed by acceleration-type IMs such as A95, PGA, ASI, and SMA.  
Furthermore, the order of the top effective IMs is HI \(>\) VSI \(>S_{v}\) (\(T_{1}\)) \(>S_{a}\) (\(T_{1}\)) \(>S_{d}\) (\(T_{1}\)) \(>\) PGV \(>\) SMV \(>\) SED since they correspond to larger values of \(R^{2}\) while max displacement is considered as EDP. Excluding \(S_{a}\) (\(T_{1}\)) and \(S_{d}\) (\(T_{1}\)), all are velocity-type IMs. Moreover, PGV/PGA, along with two displacement-type IMs, D\({}_{\text{rms}}\) and PGD prove to be the three weakest correlated IMs for both EDPs.  
A table of different IMs and four types of coefficient values is provided in Appendix A. The top five or six IMs in terms of four criteria are indicated with boldfaces.  
Figure 9: Proficiency comparison.  
Figure 10: Linear regression of PSDMs for various IMs considering maximum acceleration as EDP.  
Figure 11: Linear regression of PSDMs for various IMs considering maximum displacement as EDP.
# Analysis of Bidirectional ADR-Enabled Class B LoRaWAN Networks in Industrial Scenarios
## 5 Scenario
The parameters chosen for the simulations are shown in Table 4.  
An important part of simulating a wireless network scenario is to model the channel and interference to be as close as possible to the real environment. The channel model selected for this work is the \"_HybridBuildingsPropagationLossModel_\" [15] available for the ns-3 simulator, which integrates different path loss models, such as Okumura-Hata model, COST231, or ITU-R P.1238 (indoor communications), together to adapt to indoor-to-outdoor and different types of indoor scenarios. To do this, firstly, the Building function was used. The details concerning the configuration of this model can be seen in Table 5.  
This class allowed us to implement a propagation loss model in indoor spaces. The formula for losses is:  
\[L_{total}\ =20\log f\ +N\log d+\ L_{f}(n)-28 dB \tag{8}\]  
In this case, when choosing office type, the power loss coefficient N is 30 dB, and  
\[T_{\text{preamble}}=\left(n_{\text{preamble}}+4.25\right)\times\ T_{\text{sym}} \tag{3}\]0  
where n is the number of plants, f is the frequency (MHz), and d is the distance (m). These configurations were applied in the simulator files to analyze the performance of the network in different cases, which are presented in the following results section. \begin{table}
\begin{tabular}{c c} \hline \hline
**PARAMETERS** & **VALUE** \\ \hline Data & 8 bytes \\ LoRaWAN protocol & 13 bytes \\ Total payload length & 21 bytes (13 bytes + 8 bytes) \\ Preamble length & 8 symbols (default for frame) \\ Initial spreading factor & SF12 \\ Minimum spreading factor & SF7 \\ Code rate & 4/5 \\ Bandwidth & 125 kHz \\ Upload periodicity & 300/900/1800 s \\ SNR\({}_{\text{sc}}\) & 5, 10, 20 packets \\ Duty cycle & 0.5\% \\ Download periodicity (class B) & 450/1050/1950 s \\ Total nodes & 200 \\ Area & 2000 m \(\times\) 500 m \\ Transmission power & 14 dB \\ Current in Tx mode & 28 mA (for SX1272/76 products at 14 dB) \\ Current in Rx mode & 10 mA (typical for SX1272/76 products) \\ LoRaWAN device class & Class B \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Simulation parameters. \begin{table}
\begin{tabular}{c c} \hline \hline
**PARAMETERS** & **VALUE** \\ \hline Model & _HybridBuildingsPropagationLossModel_ \\ Area & 2000 m \(\times\) 500 m \(\times\) 10 m \\ Number of floors & 1 \\ Type of building & Office \\ Type of walls & Concrete with windows \\ Type of environment & Urban \\ Channel Band & 868 MHz \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Channel model parameters.
## 2 Materials and Methods
### Simulation Inputs  
In Section 3, comparisons between outputs from the _WEC-Sim_ and _Gazebo_ model, and the corresponding field data from the physical MBARI-WEC, are presented for multiple observations. For a meaningful comparison between simulation and field data, it is essential to define crucial simulation parameters that accurately represent the operational state of the MBARI-WEC during each specific observation. These include the following:  
1. Wave spectrum to be simulated;
2. Controller feedback scale factor;
3. Status of the heave cone doors;
4. Initial mean piston position and air spring initial pressures.  
These parameters were obtained via a Sofar Spotter buoy and data being reported from the MBARI-WEC itself.
## 3 Wireless Localization Technology
### Range-Free Techniques
#### 3.2.2 Dead Reckoning  
Dead reckoning (DR) is a localization technique that estimates a device's current position based on the distance and direction it has traveled over a certain period of time. This technique uses inertial navigation systems to measure the movement of a device and continuously updates its position in real time. It involves both movements relative to fixed axes indicated by red and green arrows, as shown in Figure 7, with reference to the device, as well as rotations represented by the blue arrow.  
DR provides stable and continuous position tracking; however, it accumulates errors over time. Moreover, there is also a technology called pedestrian dead reckoning (PDR), which incorporates the estimation of a pedestrian's direction and stride length into dead reckoning by detecting steps.  
Figure 6: An illustration of the concept of Cell-ID.  
Figure 7: An illustration of the concept of DR.
###### Abstract  
Since head-related transfer functions (HRTFs) represent the interactions between sounds and physiological structures of listeners, anthropometric parameters represent a straightforward way to customize (or predict) individualized HRTFs. This paper proposes a hybrid algorithm for predicting median-plane individualized HRTFs using anthropometric parameters. The proposed hybrid algorithm consists of three parts: decomposition of HRTFs; selection of key anthropometric parameters; and establishing a prediction formula. Firstly, an independent component analysis (ICA) is applied to median-plane HRTFs from multiple subjects to obtain independent components and subject-dependent weight coefficients. Then, a factor analysis is used to select key anthropometric parameters relevant to HRTFs. Finally, a regression formula that connects ICA weight coefficients to key anthropometric parameters is established by a multiple linear regression. Further, the effectiveness of the proposed hybrid algorithm is verified by an objective evaluation via spectral distortion and a subjective localization experiment. The results show that, when compared with generic Knowles Electronics Manikin for Acoustic Research (KEMAR) HRTFs, the spectral characteristics of the predicted HRTFs are closer to those of the individualized HRTFs. Moreover, the predicted HRTFs can alleviate front-back and up-down confusion and improve the accuracy of localization for most subjects.  
sound localization; binaural hearing; head-related transfer function; virtual auditory display Article
# Unsupervised Image Segmentation on 2D Echocardiogram
## 3 Methods
### Loss Functions  
To train the network effectively, a composite loss function is used, which combines the following components:  
* Reconstruction Loss: Ensures that the network accurately reproduces the input image by minimizing the Mean Squared Error (MSE) between the input and the reconstructed output. \[L_{rec}=\frac{1}{N}\sum_{i=1}^{N}(x_{i}-\hat{x}_{i})^{2}\] where \(x_{i}\) is the original input image and \(\hat{x}_{i}\) is the reconstructed image.
* Contour Regularization Loss: This loss penalizes sharp variations along the boundaries in the segmentation mask, encouraging smoother predictions. It computes the difference between the maximum and minimum values of neighboring pixels within a given radius to enforce smoothness [22]. In our approach, we used a radius of 3 pixels. \[L_{\text{ContReg}}=\left\|\max_{n\sqcap,n\in Wd(n)}(p_{n1}-p_{n2})\right\|^{2} =\left\|Md(p)+Md(-p)\right\|^{2}\] where \(p_{n_{1}}\) and \(p_{n_{2}}\) are the predicted probabilities at neighboring positions within a window \(W_{d}(n)\).
* Similarity Loss: To measure the similarity within the predicted segmentation mask, we use a cross-entropy-based similarity loss. This loss component enhances the similarity of features within the same cluster and differentiates features from different clusters [6], defined as: \[L_{\text{sim}}(\{\hat{r}_{n},c_{n}\})=\sum_{n=1}^{N}\sum_{i=1}^{q}-\delta(i-c_ {n})\ln\hat{r}_{n,i}\] where \(\hat{r}_{n,i}\) is the predicted probability of the \(n\)-th sample belonging to cluster \(i\). \(c_{n}\) is the true cluster label for the \(n\)-th sample. \(\delta(t)\) is the Kronecker delta function, defined as \(\delta(t)=1\) if \(t=0\) and \(\delta(t)=0\) otherwise. \(N\) is the total number of samples. \(q\) is the number of clusters.  
The overall loss function is a weighted sum of the individual components. Specifically, the weights used in the code are \(\alpha=0.15\), \(\beta=0.32\), and \(\gamma=0.53\) for the reconstruction loss, contour regularization loss, and similarity loss, respectively. These weights were selected through a grid search, where the values for each component varied from 0 to 0.9 (increments of 0.1), with the sum of the weights always constrained to 1, which reduced the number of combinations. Once the first best set of weights was identified, we further fine-tuned them (increments of 0.01) using the same grid search approach to maximize performance. The final loss is computed by:  
\[L_{\text{total}}=\alpha\cdot L_{\text{rec}}+\beta\cdot L_{\text{ContReg}}+ \gamma\cdot L_{\text{sim}}\]
# Properties of Vector Embeddings in Social Networks
## 5 Experiments
### Quantitative Results
#### 5.3.1 Inspecting Embedding Properties  
Embeddings as a low-dimensional representation of the graph are expected to preserve certain properties of the graph. Table 3 displays properties that are learned by rankSVM [42] using different embedding techniques. The following findings can be inferred from the table:  
* Overall, we can explain the ranking either by combining betweenness or eigenvector or degree centralities of the node's neighborhood. Closeness is not important in order to retain the ranking. The accuracy of SVM in all experiments is around 60%, which shows that there are some explaining network properties missing.
* LINE and DeepWalk, which are able to explore the entire graph, can learn betweenness and eigenvector centrality of nodes. Betweenness is a global centrality metric that is based on shortest-path enumeration. Therefore, it is needed to walk over the whole graph to estimate betweenness centrality of nodes. Eigenvector centrality measures the influence of a node by exploiting the idea that connections to high-scoring nodes are more influential. This means that a node is important if it is connected to important neighbors. Therefore, computing eigenvector centrality also requires exploring globally the entire graph. This is done in practice by both LINE and DeepWalk, hence they learn eigenvector and betweenness centrality of nodes around 60%.
* node2 vec with \(p<1\) and \(q>1\) walks locally around the starting node. loc also walks over a limited area of the network. Therefore, they are not able to capture the structure of the entire network to learn betweenness or eigenvector centrality. The only property that is locally available is degree of nodes, hence is it learnt by node2 vec(1) and loc.
* node2 vec with \(q<1\) and \(p>1\) is more inclined to visit nodes that are further away from the starting node. Such behavior is reflective of DFS, which encourages outward exploration. Since node2 vec(2) walks through the graph deeply, it could learn the eigenvector centrality.
## 2 Materials and Methods
### Assessment of Inhibition of Collagen, Elastase, and Tyrosine Activity
#### 2.9.2 Determination of Anti-Tyrosinase Activity  
The ability of _A. millefolium_ extract to inhibit melanin formation was assessed according to [PERSON] et al. [21]. First, 20 \(\upmu\)L of dilutions of the tested extract (1.0 and 5.0%) were added to each well on a 96-well plate. In the next step, a tyrosinase solution from a mushroom (Sigma-Aldrich, Poznan, Poland) was added at a concentration of 500 U/mL, while 120 \(\upmu\)L of phosphate buffer (100 mM, pH = 6.8) were added to the extracts and incubated in the dark (10 min, 22-24 \({}^{\circ}\)C) while shaking. Then, 40 \(\upmu\)L of L-DOPA substrate (4 mM, Merck KGaA, Darmstadt, Germany) were added. The control sample, for which 100% tyrosinase activity was assumed, was the solvent (distilled water), together with tyrosinase, substrate, and buffer. The samples prepared in this way were incubated for 20 min in the dark. The measurement of dopachrome formation was performed spectrophotometrically at a wavelength of \(\uplambda\) = 475 nm using a FilterMax F5 microplate reader (FilterMax F5 Molecular Devices, San Jose, CA, USA). Kojic acid was used as a control tyrosinase inhibitor (at a concentration of 500 \(\upmu\)g/mL). All obtained absorbance values of the tested samples were corrected for the absorbance value of the sample without fungal tyrosinase and L-DOPA. Each tested sample was analyzed three times. The inhibitory effect was expressed as a percentage of inhibition of tyrosinase activity and melanin formation according to the formula:  
\[\%\text{tyrosine inhibition}=\frac{|\text{control}|\text{sample}-|\text{tested}| \text{sample}}{|\text{control}|\text{sample}}\text{x100}\%\]
## 3 Implementation of Shehu Transform  
In this section, we have considered a time fractional (2+time fractional-order) dimensional physical model in the form  
\[u_{\tau}^{\beta}(\Im,\Re,\tau)=\kappa u_{\Im\Im}(\Im,\Re,\tau)+\varepsilon u (\Im,\Re,\tau)+\ale u(\Im,\Re,\tau),\quad\beta\in[1,2] \tag{9}\]  
with initial condition  
\[u(\Im,\Re,0)=u(\Im,\Re), \tag{10}\]  
while \(\kappa\) is a non-linear operator and \(\varepsilon\) linear operator.  
Applying the Shehu transform to both sides of the Equation (9) we obtain  
\[S\left\{u_{\tau}^{\beta}(\Im,\Re,\tau)\right\}=S\left\{\kappa u_{\Im\Im}( \Im,\Re,\tau)+\varepsilon u(\Im,\Re,\tau)+\ale u(\Im,\Re,\tau)+\ale u(\Im,\Re,\tau)\right\},\quad\beta\in[1,2]. \tag{11}\]  
Using the differential property of Shehu transformation we have,  
\[\frac{s^{\beta}}{\mu^{\beta}}\left\{V(s,\mu)-\frac{\mu}{s}u(0)-\frac{\mu^{2}} {s^{2}}u^{{}^{\prime}}(0)\right\}=S\left\{\kappa u_{\Im\Im}(\Im,\Re,\tau)+ \varepsilon u(\Im,\Re,\tau)+\ale u(\Im,\Re,\tau)+\ale u(\Im,\Re,\tau)\right\}. \tag{12}\]  
Simplifying Equation (12), we obtain  
\[V(s,\mu)=+\frac{\mu^{\beta}}{s^{\beta}}S\left\{\kappa u_{\Im\Im}(\Im,\Re, \tau)+\varepsilon u(\Im,\Re,\tau)+\ale u(\Im,\Re,\tau)\right\}+\frac{\mu}{s}u (0)+\frac{\mu^{2}}{s^{2}}u^{{}^{\prime}}(0). \tag{13}\]  
Applying the inverse Shehu transformation, we get  
\[u(\Im,\Re,\tau)=S^{-1}\left\{\frac{\mu^{\beta}}{s^{\beta}}S\left\{\kappa u_{ \Im\Im}(\Im,\Re,\tau)+\varepsilon u(\Im,\Re,\tau)+\ale u(\Im,\Re,\tau)\right\} +u(\Im,\Re,\tau)\right\}+u(0)+\tau u^{{}^{\prime}}(0). \tag{14}\]  
The nonlinear term \(\ale u(\Im,\Re,\tau)\) is evaluated by using the procedure of Adomian polynomial decomposition given by  
\[\ale u(\Im,\Re,\tau)=\sum_{m=0}^{\infty}A_{m}(u_{0},u_{1},\cdots),\;\;m=0,1,\cdots \tag{15}\]  
where,  
\[A_{m}(u_{0},u_{1},\cdots)=\frac{1}{m!}\left[\frac{d^{m}}{d\lambda^{m}}\ale \left(\sum_{i=0}^{\infty}\lambda^{i}u_{i}\right)\right]_{\lambda=0},\quad m>0. \tag{16}\]With the help of Equation (16), Equation (15) can be written as  
\[u(\Im,\Re,\tau)=S^{-1}\left\{\frac{\mu^{\beta}}{s^{\beta}}S\left\{\kappa u_{\Im \Im}(\Im,\Re,\tau)+\ell u(\Im,\Re,\tau)+\sum_{m=0}^{\infty}A_{m}\right\}\right\}+ u(0)+\tau u^{{}^{\prime}}(0). \tag{17}\]  
Finally, we obtain the recursive relation as  
\[\begin{split}& u_{0}(\Im,\Re,\tau)=u(0)+\tau u^{{}^{\prime}}(0), \;\;m=0\\ & u_{m}(\Im,\Re,\tau)=S^{-1}\left\{\frac{\mu^{\beta}}{s^{\beta}}S \left\{\kappa u_{(m-1)\Im}(\Im,\Re,\tau)+\ell u_{(m-1)}(\Im,\Re,\tau)+\Re u_{( m-1)}(\Im,\Re,\tau)+\sum_{m=0}^{\infty}A_{m}\right\}\right\},\;\;m\geq 1.\end{split} \tag{18}\]