esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis and Discussion
### Critical Indentation Size  
A near-linear relationship between \(a\) and \(c\) was obvious and is illustrated in Figure 11. The linear fitting method was used to obtain the fitting function, expressed as \(c\) = 3.35\(a\)-5.77. When \(c\) = 0, the condition was considered to be critical, with no crack on the edge of the indentation and a critical experimental fitting result of \(a\) of 1.72 \(\mu\)m.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**c/a Ratio** & \(K_{IC}\)**-Equation (5) (MPa-m\({}^{1/2}\))** & \(K_{IC}\)**-Equation (6) (MPa-m\({}^{1/2}\))** \\ \hline
2.0 & 3.42 & 3.47 \\
2.1 & 3.28 & 3.43 \\
2.2 & 3.17 & 3.37 \\
2.3 & 3.04 & 3.35 \\
2.4 & 2.94 & 3.3 \\
2.5 & 2.83 & 3.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The fracture toughness of SiC according to different c/a ratios.  
Figure 10: The SEM morphology with a normal force of 0.2 N.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Method** & \(K_{IC}\) **(MPa-m\({}^{1/2}\))** & **Reference** \\ \hline \(\alpha\)-SiC & Chevron notched beam (CVNB) & 3.03 & [51] \\
6H-SiC (0001) & Single edge notched beam (SENB) & 3.3 & [52] \\
4H-SiC (0001) & Indentation for Berkovich indenter & 3.33 & [53] \\
4H-SiC (0001) & Indentation for Berkovich indenter & 3.42 & Present (Equation (5)) \\
4H-SiC (0001) & Indentation for Berkovich indenter & 3.47 & Present (Equation (6)) \\ \hline \hline \end{tabular}
\end{table}
Table 5: The fracture toughness of SiC measured by different methods.  
Figure 9: Relationship between \(c\)/\(\mu\) and \(K_{IC}\).  
According to [PERSON]'s research, a critical load of indentation exists for radial cracks [35]. The critical condition can be determined using the following equation:  
\[a_{c}=\Theta(K_{IC}/H)^{2} \tag{12}\]  
where \(a_{c}\) is the characteristic dimension of plastic impression and \(\Theta\) is a dimensionless factor. [PERSON] also gave the critical values of SiC: \(a_{c}=2\) um. This result was close to the experimental fitting result.
## 4 Results and Discussion
### Objective Function  
When the reduction ratio of a speed reducer is not constant, the amplitude of the oscillation of the reduction ratio affects the degree of change in the output speed. If this degree of change can be reduced so that the reduction ratio tends to a certain value, the output of the cycloidal reducer can be made closer to equal speed. In this work, the objective function is set to be the maximum reduction ratio error of \(\triangle x\) in the motion state, the variation within the highest and lowest values of the speed ratio error, as shown in Equation (18) and Figure 5.  
\[\min_{\triangle R_{p},\triangle R_{rp}\in\mathbb{R}}f_{K}=\kappa_{\text{max}}- \kappa_{\text{min}} \tag{18}\]  
Under the optimization of transmission ratio fluctuations, the three sets of transmission ratio comparison charts were obtained under different modification amounts. Among them, \(dR_{p}=0.1250\) mm, \(\text{dR}_{rp}=0.1593\) mm is the optimal amount of repair. From Figure 6, it can be seen that the blue curve (the best) has the smallest range of transmission ratio fluctuations and exhibits periodicity. The transmission ratio is around 9. It was expected that this result would produce a more stable output speed.  
Figure 5: The maximum gear ratio error chart under the motion state.  
Figure 6: The comparison diagram of gear ratios for different types of modifications in motion.  
Similarly, the transmission error fluctuation of the blue curve (the best) is also the smallest and exhibits a periodic pattern as shown in Figure 7.  
The blue curve shows the changes in bearing force under different crankshaft input angles; the maximum bearing force is significantly reduced compared with the other two groups (the black curve and the red curve) as expressed in Figure 8. Its fluctuation range is also relatively reasonable, exhibiting periodic fluctuations.  
The action range of the blue curve needle tooth force is larger than that of the black curve, indicating an increase in contact ratio. Moreover, the maximum needle tooth force on the blue curve is slightly smaller than that on the black curve as expressed in Figure 9.  
Figure 8: The comparison diagram of bearing force for different types of modifications in motion.  
Figure 7: The comparison diagram of loaded transmission errors for different types of modifications in motion.  
Due to the fact that the change in the force acting on the needle teeth does not affect the force exerted on the output pin, the force exerted on the output pin remains basically unchanged as shown in the following Figure 10.
# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 3 Proposed DOA Estimation Method
### DOA Estimation Using TSNetIQ
This pooled feature is then passed through a linear layer, followed by a Sigmoid function defined as  
\[sigmoid(\theta)=\frac{1}{1+e^{-\theta}} \tag{10}\]  
to produce the final output. The inclusion of the Sigmoid function serves a critical role by constraining the model's prediction between 0 and 1, which corresponds to the normalized angular range from 0 to 360\({}^{\circ}\). This bounded output ensures consistency with the preprocessed training targets and prevents the model from generating physically invalid angle predictions (e.g., negative degrees or values exceeding 360\({}^{\circ}\)). Furthermore, by maintaining the output within a normalized and differentiable range, the Sigmoid function facilitates stable gradient propagation during backpropagation, thereby improving the convergence and generalization performance of the regression model. Figure 2: The designed TSNetIQ model for DOA estimation. This method not only normalizes the output range but also enhances the stability of model training and promotes better convergence. In the regression model, the Huber loss function is used as the loss function of the model, with the following expression:  
\[L_{\delta}(\Delta\theta_{i,n})=\left\{ \begin{array}{l}\frac{1}{2}(\Delta \theta_{i,n})^{2},|\Delta\theta_{i,n}|\leq\delta\\ \delta|\Delta\theta_{i,n}|-\frac{1}{2}\delta^{2},\textit{otherwise.}\end{array} \right. \tag{11}\]  
where \(\Delta\theta_{i,n}\) is a circle angle difference defined as  
\[\Delta\theta_{i,n}=\hat{\theta}_{i,n}-\theta_{i,n}-360\times\left\lfloor \frac{\hat{\theta}_{i,n}-\theta_{i,n}+180}{360}\right\rfloor \tag{12}\]  
where \(\theta_{i,n}\) and \(\hat{\theta}_{i,n}\) represent the true angle and the estimated angle of the \(i\)th signal in the \(n\)th sample, respectively. \(\delta\) denotes a positive factor. The Huber loss function combines the advantages of the mean squared error (MSE) and mean absolute error (MAE). During gradient descent, the Huber loss tends to behave like MSE, while in the presence of outliers, it tends to behave like MAE. This dynamic adjustment provides an optimization objective that balances accuracy and robustness in DOA regression tasks, making it particularly suitable for scenarios with noise or outlier angles. More detailed network code and simulation code can be accessed via the link provided in the Supplementary Materials at the end of the paper.
## 3 Results
### ALS-Based Sunlit and Shaded Forest Components  
#### 3.2.1 Landscape Scale  
Based on the VFSS algorithm described in Section 2.4, we obtained the four forest components' spatial distribution maps at landscape and plot levels in both PC and WPA sites with the solar zenith and azimuth angles of 30\({}^{\circ}\) and 90\({}^{\circ}\), respectively (Figure 6). The proportions of sunlit overstory and sunlit forest background were 63.7% and 36.4%, 77.6%, and 23.4% for the WPA and PC-1 km sites at the hotspot position. The proportions of shaded components, including both overstory and forest background, gradually increased as the observation direction changing from hotspot to dark spot where visible shadows reached to maximum positions. For example, the proportions of shaded components in WPA were 0%, 39.5%, 46.7%, and 56.3% in the hotspot (view zenith angle, VZA = 30\({}^{\circ}\), view azimuth angle, VAA = 90\({}^{\circ}\)), nadir (VZA = 0\({}^{\circ}\), VAA = 90\({}^{\circ}\)), a point in the solar perpendicular plane (VZA = 30\({}^{\circ}\), VAA = 180\({}^{\circ}\)), and dark spot (VZA = 30\({}^{\circ}\), VAA = 270\({}^{\circ}\)), respectively. The proportions of visible forest background were 36.3%, 43.5%, 41.2%, and 35.5% in WPA, and 22.3%, 27.6%, 26%, and 23.5% for PC site at the hotspot, nadir, a point in the solar perpendicular plane, and dark spot, respectively. The highest proportions of visible forest background were observed at nadir direction in both PC and WPA sites.  
Figure 5: Graphs illustrating the sampling design of six different line transects at forest plot level (**a**) and comparison between the detected gap sizes obtained from original and stratified overstory ALS data (**b**).  
Figure 6: The ALS-based four forest component results at the landscape level in PC (upper) and WPA (below) sites. View zenith angle (VZA) and View azimuth angle (VAA) represent view zenith angle and view azimuth angle, respectively.
## 4 Conclusions  
In this work, PA6/PCL blends were developed and characterized in order to explore their possible application as thermoplastic matrices for structural composites with thermally activated self-healing properties. The healing process was carried out through thermal mending, exploiting the limited melting temperature of the healing agent (PCL). The interactions between blend constituents in the prepared blends were analyzed through rheological measurements, and the blend was identified as immiscible. The morphology of the produced blends was investigated through optical microscopy and FESEM, highlighting a good dispersion of PCL domains inside the PA6 matrix, with a dimension ranging from a few micrometers to tens of micrometers, and confirming the immiscibility of the blends. The mechanical properties of the materials were evaluated through quasi-static tensile tests and fracture toughness tests, performed both in quasi-static mode and impact mode. The PCL addition caused a significant decrease in elastic modulus, passing from 3.6 GPa of neat PA6 to 1.9 GPa of PA6_30 PCL. Furthermore, the ultimate tensile strength decreased from 72.3 MPa of neat PA6 to 39.2 MPa of PA6_30 PCL. The worsening of the mechanical properties was due to the lower mechanical properties of PCL compared to PA6 and to the blend immiscibility. On the other hand, the fracture toughness of the blends was not dramatically affected by the PCL introduction, both in quasi-static and impact mode. The healing efficiency values, evaluated as the ratio of the K\({}_{\text{IC}}\) before and after the healing process, were then determined. The healing processes, performed in a temperature interval between 80 and 100 \({}^{\circ}\)C, led to a healing efficiency for the PA6_30 PCL sample up to 6% in quasi-static mode and 53% in impact conditions. Through the FESEM analysis of the fracture surfaces before and after the healing process, it was possible to highlight that during the mending processes at 80 and 100 \({}^{\circ}\)C, the PCL domains melted, enabling the healing agent to flow, and thus producing a molten PCL layer across the surface. The diffusion of the molten healing agent in the crack zone, and thus the resulting healing efficiency,  
Figure 12: SEM micrographs of the fracture surface of the PA6_30 PCL blends before and after the healing process (tested both in quasi-static and impact conditions).  
was strictly correlated to the roughness of the fracture surface. Considering the obtained results, further investigations must be performed to optimize the healing process in order to increase its efficiency, also by taking into consideration the use of compatibilizers. In the future, these matrices, coupled with reinforcing fibers, could be utilized in novel self-healing TPCs for structural applications.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Experimental Results and Discussion  
### Experimental Data Presentation and Pre-Processing  
The data used in the experiment include the whole life-cycle vibration data collected from a railway section, which reflects the state of the rail from normal to degraded up to the point of failure. Sensors with three channels were placed in different positions on the train to acquire vibration signals from horizontal, front-back, and vertical directions. Four working conditions are combined with different train speeds and loads, as shown in Table 1. Under each working condition, there are three different types of rail damage: wave abrasion, fish scale injury, and pieces stripping off the blocks. The actual state of damage to the rail is shown in Figure 6. Because the shape of the rail is different under different types of damage, the corresponding input shapes of the three types of rail damage are wave abrasion, fish scale injury, and stripping off the blocks, respectively. The corresponding input shapes are \(3*5000\), \(3*3000\), and \(3*2500\). The vibration sequence is sampled and recorded as a sample each time the detection train passes through the damaged defects. The  
Figure 5: RSA mechanism module.  
data from the three channels of the sensor are stacked under each condition, i.e., horizontal, fore-aft, and vertical vibration signals were used simultaneously. In order to ensure that each experiment is consistent for the training and the test sets, the samples collected during the forward passage of the train are used as the training set for each condition, and the reverse passage is used as the validation and test set. When testing a certain type of damage under a certain working condition, the vibration data of other damage under the same working condition were used for training.
## 3 Experiment
### Dielectric Constant Measurement  
The imaging algorithm in this study utilizes signal time delay to achieve focused imaging. The time delay \(\gamma\) of the imaging point relative to each antenna is first calculated, then the signal is shifted using \(\gamma\), and, finally, multiple signals are focused for imaging:  
\[\gamma=\frac{d}{c/\sqrt{\varepsilon}} \tag{20}\]  
where \(d\) denotes the relative permittivity of the medium, \(\varepsilon\) denotes the propagation distance, and \(c\) denotes the speed of light. When the signal propagates the same distance in different biological tissues, the propagation delay \(\gamma\) is related only to the dielectric constant of biological tissue, and an accurate dielectric constant will improve imaging quality [27]. The dielectric constant of the phantom was measured using a Keysight coaxial probe kit (Keysight N1501A), as shown in Figure 9. Figure 10 shows the measured and target values of the dielectric constant and conductivity relative to the frequency of tumor, skin, and fat materials. The dielectric constant and conductivity of the homogeneous phantom (I) in Figure 10a,b were identical to the target curves. Figure 10c,d show the dielectric constant and conductivity of each material (skin, fat, and tumor) in the heterogeneous phantoms (II) and (III). The values of the dielectric constants of the three substances generally decreased at different slopes as the frequency increased. Tumor had the highest value of dielectric constant, followed by skin, and fat had the lowest [34]. The data of this experimental model satisfied the experimental requirements.  
Figure 9: Dielectric constant measurement.
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 3 Reliability Model
### Route Reliability  
Multiple possible routes exist between the origin and the destination node in VANETs, whereas all routes are a set of connected links. The number of connections that have been formed is shown as \(\delta:l_{1}=(s,n_{1}),l_{2}=(n_{1},n_{2}),\ldots,l_{\delta}=(n_{\delta},d)\) on any particular route by keeping the generality intact. Every single link \(l_{c}(c=1,2,\ldots,\delta)\) is represented by \(r_{t}(l_{c})\), whereas Equation (12) interprets the reliability of a link for a particular route X.  
\[R(X(s,d))=\prod_{c=1}^{\delta}r_{t}(l_{c}),\text{ {where }}l_{c}\in X(s,d) \tag{12}\]The multiple reliability product over the existing links for any route is used to classify the route's reliability. Assume that there are \(\Omega\) numbers of routes between the source and the target node. The source node will choose the best possible route, i.e., \(Q(s,d)=X_{1},X_{2},\ldots,X_{\Omega}\) amongst them, using the relation represented in Equation (13).  
\[arg\;max_{X\in Q(s,d)}R(X) \tag{13}\]
## 6 Simulation Results and Discussion
### Network Performance Parameters: Discussions
#### 6.2.2 Throughput and Packet Drop Rate  
Figures 7 and 8 show the results for the total number of packets successfully received at the sink and the number of packets dropped due to a higher BER than a certain pre-defined threshold, respectively. It is observed from Figure 7 that EInCo-CESStat has greater throughput than InCo-CESStat. This is due to the availability of more links for packet transmission, in the case of the failure of the direct link. InCo-CESStat has two cooperative nodes, whereas EInCo-CESStat has three cooperative nodes to forward the data of normal nodes. Therefore, higher throughput is achieved by increasing the diversity order in EInCo-CESStat at the cost of EE. Co-CESStat has the highest throughput due to the reception of two copies of transmitted data at the sink. In protocol; Co-CESStat, cooperative nodes always forward the data, so in the ideal scenario, the sink receives multiple copies of the original data sent by the normal nodes.  
Figure 6: Stability period and network lifetime.  
The packet drop rate of ElnCo-CESat is also less than that of InCo-CESat, as shown in Figure 8. When direct communication between the source and sink nodes fails, the other three links are available to forward the data to the sink; whereas, InCo-CESat completes its relaying in three phases and has two more redundant links after the failure of the direct link. The simulation results shown in Figures 2a and 3a reveal this fact. The packet drop rate of Co-CESat is also greater than the incremental cooperative communication protocols. A greater number of transmissions leads to a greater number of link failures and consequently more dropped packets.
# All-Integer Quantization for Low-Complexity Min-Sum Successive Cancellation Polar Decoder
## 5 Experimental Results
### Performance over Fading Channels  
Fading in wireless communication results from multipath propagation, where transmitted signals suffer from varying delays and attenuations. In our simulation, a Rayleigh fading channel is modelled using uncorrelated distributions, where independent channel coefficients are assigned to each BPSK symbol and vary randomly across transmissions. We evaluate the decoding performance of a polar code of length \(N=1024\) and \(K=512\), comparing conventional min-sum SC decoding with all-integer quantization min-sum SC decoding at 5- to 8-bit levels. The results, shown in Figure 6, highlight the impact of quantization on the decoding performance under fading conditions.  
For a BER of \(10^{-5}\) and FER of \(10^{-4}\), the performance of QMS-SC decoding is approximately 0.2 dB worse than that of high-precision MS-SC decoding, even when the quantization level is increased to 8 bits. However, this 0.2 dB gap is considered negligible  
Figure 5: FER of polar codes with the SC decoding algorithm over AWGN channels: (**a**) \(N=256\), rate = 0.5; (**b**) \(N=1024\), rate = 0.67; (**c**) \(N=2048\), rate = 0.5; (**d**) \(N=4096\), rate = 0.83.  
Figure 6: Performance of SC decoder (polar codes, \(N=1024\), rate = 0.5) with integer calculation on Rayleigh fading channels: (**a**) BER, and (**b**) FER.  
in most communication systems. Furthermore, the performance of QMS-SC decoding saturates when the quantization level exceeds 5 bits, indicating that additional bit precision does not yield further performance gains.
## 3 Governing Equations  
According to the elastodynamic theory [54], the dynamic equilibrium equation for the soil in an axisymmetric cylindrical coordinate system can be established. By disregarding the radial component of the soil displacement to simplify the equation, the dynamic governing equation for the soil with the hysteresis damping can be written as follows. It is also worth noting that the accuracy requirement can be satisfied according to [PERSON] and [PERSON] [9].  
\[\left[\left(\lambda_{ij}+2G_{ij}\right)+\mathrm{i}\left(\lambda^{\prime}_{ij}+2 G^{\prime}_{ij}\right)\right]\frac{\partial^{2}}{\partial z^{2}}u_{ij}+\left(G_{ij}+ \mathrm{i}G^{\prime}_{ij}\right)\left(\frac{1}{r}\frac{\partial}{\partial r}+ \frac{\partial^{2}}{\partial r^{2}}\right)u_{ij}=\rho_{ij}\frac{\partial^{2}}{ \partial t^{2}}u_{ij} \tag{1}\]  
where \(u_{ij}=u_{ij}(r,z,t)\) is the vertical displacement of the soil; \(\lambda_{ij}\), \(G_{ij}\), \(E_{ij}\), \(\mu_{ij}\), and \(\rho_{ij}\) represent the Lame constant, shear modulus, elastic modulus, Poisson's ratio, and density of the soil, respectively; \(\lambda^{\prime}_{ij}\) and \(G^{\prime}_{ij}\) are viscosity coefficients corresponding to \(\lambda_{ij}\) and \(G_{ij}\); \(\mathrm{i}=\sqrt{-1}\) and denotes the imaginary unit.  
In the 1D theory, the pile is treated as an Euler rod. As mentioned above, this approach is not suitable for the large-diameter-bored pile due to its 3D effect. The 3D wave equation for the pile is complex and impractical in engineering. To address this issue, [PERSON] and [PERSON] established the Rayleigh-Love rod theory by considering the transverse inertia effect of the pile in the premise of the assumptions adopted in the 1D theory. According to the Rayleigh-Love rod theory, the longitudinal vibration of the large-diameter-bored pile (including the fictitious soil pile, i.e., the sediment) can be represented by the following equation.  
\[E_{\mathrm{pi}}A_{\mathrm{pi}}\frac{\partial^{2}u_{\mathrm{pi}}}{\partial z^{2} }-\rho_{\mathrm{pi}}A_{\mathrm{pi}}\left[\frac{\partial^{2}}{\partial t^{2}}- \left(\mu_{\mathrm{pi}}r_{\mathrm{pi}}\right)^{2}\frac{\partial^{4}}{\partial z ^{2}\partial t^{2}}\right]u_{\mathrm{pi}}-2\pi r_{\mathrm{pi}}f_{\mathrm{i}}=0 \tag{2}\]  
where \(u_{\mathrm{pi}}=u_{\mathrm{pi}}(z,t)\) denotes the vertical displacement of the \(i\)th pile segment; \(E_{\mathrm{pi}}\), \(A_{\mathrm{pi}}\), \(\rho_{\mathrm{pi}}\), and \(\mu_{\mathrm{pi}}\) represent the elastic modulus, cross-sectional area, density, and Poisson's ratio of the \(i\)th pile segment, respectively; and \(f_{i}=f_{i}\big{(}r_{\mathrm{pi}},z,t\big{)}\) is the frictional resistance at the soil-pile interface.