esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Main conclusions obtained in this study are summarized as follows.  
1. The probability density functions of the inclusion size at the crack initiation site, \(f(\rho_{n})\), was successfully derived by combining the Weibull distribution and the concept of  
Figure 14: Relationship between fatigue strength distribution and fatigue life distribution.  
extreme distribution. In addition, the probability density function of the inclusion depth, \(f_{0}(\xi)\), was also derived from the uniform distribution of the location of the inclusion in the material space. Since the inclusion size \(\rho_{n}\) and the crack depth \(\xi\) are statistically independent, the joint probability density function of these random variables, \(h(\rho_{n},\xi)\), is given by the direct multiplication of the above two probability density functions, such as \(h(\rho_{n},\xi)=f(\rho_{n})\cdot f_{0}(\xi)\).
3. For the hourglass type specimen of a bearing steel with the definite hardness, the rotating bending fatigue strength of the specimen with any size \(\rho\) and depth \(\xi\), of the inclusion at the arbitrarily given number of stress cycles \(N_{\rm fix}\) is analytically provided in the very high cycle regime.
4. Based on the above joint probability density function of \(h(\rho_{n},\xi)\), repeating the numerical calculations following \(P[\rho_{n}\in\Delta\rho_{n}\cap\bar{\zeta}\in\Delta_{\zeta}^{\zeta}]=h(\rho_{n },\xi)\Delta\rho_{n}\Delta_{\zeta}^{\zeta}=P[\sigma_{wg}^{*}\in\Delta\sigma_{wg }^{*}]\), one could obtain the fatigue strength distribution at any number of stress cycles and the fatigue life distribution at any stress level. The analytical results thus obtained were in good agreement with the statistical feature of the experimental fatigue test data.  
In the construction of the probabilistic model in this study, several assumptions were introduced for the sake of simplicity. In order to reconfirm the reasonability of such assumptions, sufficient number of experimental results should be further filed up in the future. In addition, it is still unknown whether the fatigue limit exists for the interior inclusion-induced fracture mode. This is one of the most important issues to be solved in the near future.  
T.S. has performed fatigue tests and organized whole of this work. A.N. and Y.N. have analyzed experimental data and prepared all of diagrams. N.O. has prepared fatigue specimens and performed fatigue tests. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The authors declare no conflict of interest.
## 4 Control System Design
### Model Inverse Error  
When the ideal state, the second-order system of UAV nonlinear model is described as follows:  
\[\ddot{x}=f(x,\dot{x},u), \tag{16}\]  
where \(x\) is the state quantity and \(u\) is the control input quantity. The dynamic inverse method compensates the original system by generating the inverse system of the controlled object, then it is a pseudo-linear system. The pseudo control variable \(v\) has a linear relationship with the state variable \(x\) and satisfies \(\ddot{x}=v\). The expression formula of the control variable is obtained by inverting (16).  
\[u=f^{-1}(x,\dot{x},v). \tag{17}\]  
The ideal flight environment is difficult to realize for UAV, and there are some problems such as model error and external disturbance. The uncertain part of the model and the turbulent wind are recorded as the total disturbance \(d\) of the system. The second-order system with perturbation term can be expressed as:  
\[\ddot{x}=f(x,\dot{x},\dot{u},d). \tag{18}\]where \(\hat{u}\) is the approximate control quantity, \(\hat{u}=f^{-1}(x,\dot{x},\hat{v},d)\). Compared with (16) and (18), the existence of \(d\) will lead to the generation of dynamic inverse errors.  
\[\Delta(x,\dot{x},u,\hat{u})=f(x,\dot{x},u)-f(x,\dot{x},\hat{u},d). \tag{19}\]  
The closed-loop response characteristics of the system can be expressed as:  
\[\ddot{x}=\hat{v}+\Delta(x,\dot{x},u,\hat{u}). \tag{20}\]  
The existence of the inverse error seriously reduces the performance of the controller and affects the flight quality of the aircraft. In order to improve the response speed and robustness, the command filter, linear controller and fuzzy neural network compensator are added to the control system. \(v_{d}\) is used to output the expected system response model, \(v_{pd}\) is used to improve the dynamic characteristics of tracking error and make the system stable quickly, and \(v_{fnn}\) is used to compensate the inverse error. The composition expression of the pseudo control signal is:  
\[\hat{v}=v_{pd}+v_{d}-v_{fnn}, \tag{21}\]  
where \(v_{pd}\) can be expressed as:  
\[v_{pd}=k_{p}(x_{c}-x)+k_{d}(\dot{x}_{c}-\dot{x}), \tag{22}\]  
Substitute (22) into Equations (20) and (21) to acquire:  
\[\ddot{x}=\ddot{x}_{c}+k_{p}(x_{c}-x)+k_{d}(\dot{x}_{c}-\dot{x})+\Delta(x,\dot{ x},u,\hat{u})-v_{fnn}. \tag{23}\]  
Denote the tracking error as \(\mathbf{e}=\big{[}x_{c}-x\quad\dot{x}_{c}-\dot{x}\big{]}^{T}\), so:  
\[\dot{\mathbf{e}}=\Lambda\mathbf{e}+B\big{[}v_{fnn}-\Delta(x,\dot{x},u,\hat{u})\big{]}, \ \Lambda=\begin{bmatrix}0&1\\ -k_{p}&-k_{d}\end{bmatrix},B=\begin{bmatrix}0\\ 1\end{bmatrix}. \tag{24}\]  
Make the matrix a Hurwitz matrix by choosing appropriate \(k_{p}\) and \(k_{d}\). If the compensation controller can satisfy \(v_{fnn}=\Delta(x,\dot{x},u,\hat{u})\), it means that the output of the fuzzy neural network can fully compensate the inverse error caused by \(d\). Therefore, the command tracking error of the flight control system can be eliminated.
# On the Impact of Mobility on Battery-Less RF Energy Harvesting System Performance
## 4 Simulations
### Sensor Activations  
Finally, Figure 6 shows sensor activations defined as the proportion of all (non-unique) locations where a node was able to boot and execute a task. In other words, multiple visits to the same location are counted as separate activations. This represents sensing applications, where the node needs to maximise the total amount of collected data irrespective of locations. It can be seen that a capacitor size has a drastic effect on sensor activations, reducing it by up to 90%. The hybrid configuration is not affected by a capacitor size and consistently achieves 100% activations. The number of sensor activations for hybrid configuration does not depend on the larger capacitor size, as it performs the switch only when the available energy is sufficient to charge the larger capacitor.  
Figure 4: The amount of usable harvested energy in the fixed capacitor configuration decreases with capacitor size due to self-discharge losses between intermittent charges. The hybrid configuration minimizes the losses by using a small capacitor in low energy areas.  
Figure 5: The number of unique locations served by a sensor in the fixed capacitor configuration decreases with capacitor size as the node is less likely to boot on a larger capacitor. The hybrid configuration provides nearly full coverage.
# Analysis of Blur Measure Operators for Single Image Blur Segmentation
## 2 Related Work
have discussed the problem of motion deblurring for general dynamic scenes in which object motion and camera shake both are involved [33; 34]. Now, we mention spatially non-uniform camera motion deblurring techniques. [PERSON] et al. simplified a general projective model proposed by [39] and employed a variational Bayesian framework for image deblurring [35]. [PERSON] et al. utilized motion density functions to estimate the camera motion trajectory [36]. [PERSON] et al. analyzed the rotational motion blur using a transparency map [38]. Generally, it is computationally expensive if optimization is to be applied in any of these non-uniform deblurring techniques. To tackle it, neighborhood window-based locally uniform techniques are developed which involve fast Fourier transform to provide computationally efficient results. These techniques are to counter for the camera motion [37]. In the following paragraphs, we highlight the spatially non-uniform object motion deblurring techniques. [PERSON] et al. [40] removed the motion blur by preserving the high frequency content of the imaged scene by fluttering the camera's shutter open and close during the exposure time according to a well chosen binary sequence. [PERSON] et al. [41] coupled the standard high resolution camera with an auxiliary low resolution camera to combine their data streams for deblurring the motion blur efficiently. The alpha matte solution proposed by [PERSON] et al. [42] has been utilized by [43; 44] to segment the image into two layers foreground and background for deblurring. The \(L_{0}\) intensity prior used in [45] favors the images having more pixels with zero intensity and thus can reduce light streaks and saturated regions. [46] can deblur images with large motion blur in the presence of noise. Its refinement phase helps reduce noise in blur kernels and leads to robust deblurring results. Spatially uniform defocus blur is studied in [47; 48; 49], while partially non-uniform defocus blur is addressed by [9; 15; 50]. [PERSON] et al. [51] obtained the blur amount by estimating the parameters of space-variant PSF using the local variance of first and second order derivatives. [PERSON] and [PERSON] separated a defocused background from the focused foreground in an image using a matting method, implied blind deconvolution to learn uniform blur kernel, and recovered the background using total variation minimization [52]. This produced good results for two-layer, foreground and background, defocus images but couldn't perform well for multi-depth-layer images. [PERSON] et al. proposed the maximum a posterior model based method which jointly estimates the object layer and camera shake under the guidance of soft-segmentation [33]. [PERSON] et al. have proposed an energy model consisting of weighted sum of multiple blur data models to estimate different motion blurs. Adopting non-local regularization of weights, a convex optimization approach is followed to solve the energy model [34].
## 3 Influence of Inherent Anisotropy
### Microscopic Results
#### 3.2.1 Fabric Anisotropy Description  
To quantify the initial fabric anisotropy of the specimens and observe the microstructure evolution in the loading process, a second-order fabric tensor [36; 37] which is from the statistics of spatial distribution of contact normal is introduced here. The fabric tensor is defined as follows:  
\[R_{ij}=\int_{\Omega}E(\Omega)n_{i}n_{j}d\Omega=\frac{1}{N}\sum_{c\in N}n_{i}n_ {j} \tag{5}\]  
where \(n_{\rm i}\) is the unit contact normal in the i-direction, \(N\) is the total number of contacts, and \(E(\Omega)\) is the distribution function on the unit sphere \(\Omega\).  
Figure 8: \(K_{0}\) value vary with initial anisotropy.  
Similarly, the distribution of normal contact force and tangential contact force in space can also be expressed by second-order tensor as follows [38; 39]:  
\[F_{ij}^{n}=\frac{1}{4\pi}{\int_{\Omega}}\overline{f}^{n}(\Omega)n_{i}n_{j}d \Omega=\sum_{c\in N}\frac{f^{n}n_{i}n_{j}}{N\big{(}1+a_{kl}^{r}n_{k}n_{l}\big{)}} \tag{6}\]  
\[F_{ij}^{t}=\frac{1}{4\pi}{\int_{\Omega}}\overline{f}^{t}(\Omega)t_{i}n_{j}d \Omega=\sum_{c\in N}\frac{f^{t}t_{i}n_{j}}{N\big{(}1+a_{kl}^{r}n_{k}n_{l}\big{)}} \tag{7}\]  
where \(\overline{f}^{n}(\Omega)\) and \(\overline{f}^{t}(\Omega)\) are the spatial distributions of normal contact force and shear contact force, respectively, and \(a_{ij}^{r}\) is the second-order anisotropy tensor of contact normal, which can be deduced from the deviation of fabric tensor (\(R_{ij}^{\prime}\)):  
\[a_{ij}^{r}=\frac{15}{2}{R^{\prime}}_{ij} \tag{8}\]  
Then, scalar anisotropy parameters are used to quantify the degree of contact normal anisotropy [36; 38; 39]:  
\[a_{r}=\sqrt{\frac{3}{2}a_{ij}^{r}a_{ij}^{r}} \tag{9}\]  
Similarly, we use two other parameters \(a_{n}\) and \(a_{t}\) to quantify normal contact force and shear contact force anisotropy:  
\[a_{ij}^{n}=\frac{15}{2}F_{ij}^{r}/\overline{f}_{0}^{n} \tag{10}\]  
\[a_{ij}^{t}=5F_{ij}^{t}/\overline{f}_{0}^{n} \tag{11}\]  
\[a_{n}=\sqrt{\frac{3}{2}a_{ij}^{n}a_{ij}^{n}} \tag{12}\]  
\[a_{t}=\sqrt{\frac{3}{2}a_{ij}^{t}a_{ij}^{t}} \tag{13}\]
## 4 Results
### Confusion Matrix Comparison  
At 18 dB SNR, the CV-CNN-TCN and CV-CNN-TCN-DCC models exhibit significant improvements over baseline models, such as CLDNN, SCRNN, ResNet, and CNN presented in Figures 8 and 9. Specifically, CV-CNN-TCN achieves higher classification accuracy for 8 PSK (88%), QPSK (75%), and QAM16 (71%), with fewer misclassifications compared to CLDNN and SCRNN. Meanwhile, CV-CNN-TCN-DCC further improves WBFM recognition accuracy to 84% and maintains robust performance across other modulation types, demonstrating its effectiveness in reducing misclassification errors.  
In contrast, baseline models like CLDNN and SCRNN show reduced accuracy for QAM16 and QPSK, where confusion between similar modulations remains notable. ResNet and CNN provide moderate performance, particularly excelling in QAM64 but lagging in 8 PSK accuracy. The results confirm that hybrid architectures, such as CV-CNN-TCN-DCC, outperform traditional models by effectively handling modulation diversity and minimizing errors, making them better suited for automatic modulation recognition tasks in dynamic wireless environments.  
Figure 6: Comparison of (**a**) negative predictive value, (**b**) specificity, (**c**) false discovery rate, and (**d**) sensing error of different models in the RadioML2016.10b dataset.  
Figure 8: _Cont_.  
Figure 7: Comparison of (**a**) activation functions, (**b**) batch size, (**c**) and epochs of different models in the RadioML2016.10b dataset.
# Localization in Low Power Wide Area Networks Using Wi-Fi Fingerprints
## 2 Methods and Materials
### Localization Approach
In fact, we do not know if LocationAPI knows about only one of the two access points; in this case the weight of that location is erroneously doubled, which cannot be prevented. This straightforward combination neglects the fact that the location error from the LocationAPI database is generally much better than that of the WiGLE database. Thus, another approach for combination is to choose the location provided by the LocationAPI database, unless the BSSIDs could not be found in that database; in which case the WiGLE database is consulted for a location estimate. While we will show the location error results of the first approach, this second combination approach will be analyzed further. To summarize, we apply the following localization strategy and improvements when localizing a device with Wi-Fi access points using Sigfox as communication channel:  
1. Choose any combination of two BSSIDs for localization;
2. Choose the two BSSIDs with the highest RSSI for localization;
3. Combine the two databases through averaging; and
4. Combine the two databases by preferring the LocationAPI database, using WiGLE only when the BSSIDs cannot be found in LocationAPI. A single Sigfox message allows an update period of ten minutes. Using two messages would increase the period to twenty minutes, which is why we keep the focus on a single message of 12 B. Localization with this constraint has not been researched before.
## 3 Results and Discussion
### Quantitative Analysis of Test Results
#### 3.5.2 Poisson's Ratio  
Poisson's ratio is an important indicator of the transverse deformation of concrete. Equation (7) can be obtained by a non-linear regression analysis of the measured results. Figure 16 shows the relationship of the Poisson's ratio and the fibre characteristic value. It can be seen that the Poisson's ratio specimen points had a slightly larger dispersion from the non-linear regression equation, but the Poisson's ratio of recycled concrete decreased with the fibre characteristic value increase.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Specimen** & **Slump (mm)** & \(\lambda_{\text{sf}}\) & \(\lambda_{\text{pf}}\) & \(E_{\text{c}}\) (MPa)** & \(v_{\text{r}}\) & \(f_{\text{c}}\) (MPa) & \(\varepsilon_{\text{c}}(10^{6})\) & **TI** \\ \hline NC & 201 & 0 & 0 & 40,818 & 0.229 & 49.87 & 1254 & 0.4383 \\ RC & 176 & 0 & 0 & 22,746 & 0.220 & 40.99 & 1802 & 0.2113 \\ RS-1 & 161 & 0.308 & 0 & 18,603 & 0.204 & 42.29 & 1915 & 0.2938 \\ RS-2 & 149 & 0.617 & 0 & 14,871 & 0.182 & 43.05 & 2726 & 0.4524 \\ RS-3 & 132 & 0.925 & 0 & 15,316 & 0.195 & 46.80 & 2420 & 0.6475 \\ RP-1 & 150 & 0 & 1.26 & 24,074 & 0.219 & 40.11 & 1787 & 0.2652 \\ RP-2 & 131 & 0 & 2.53 & 18,449 & 0.201 & 43.59 & 2510 & 0.3291 \\ RP-3 & 112 & 0 & 3.8 & 20,866 & 0.187 & 40.98 & 2210 & 0.5801 \\ RSP-1 & 75 & 0.308 & 3.8 & 16,979 & 0.179 & 40.76 & 2501 & 0.5662 \\ RSP-2 & 94 & 0.617 & 2.53 & 18,571 & 0.187 & 45.48 & 2391 & 0.9481 \\ RSP-3 & 110 & 0.925 & 1.26 & 15,990 & 0.177 & 43.88 & 2557 & 0.9618 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The test results of fibre recycled concrete.  
As can be seen from Equation (7) and Table 4, the Poisson's ratio was reduced when steel fibre and polyvinyl alcohol fibre were incorporated into recycled concrete. The reason for this is that the bond and friction between the steel fibre or polyvinyl alcohol fibre and the cement matrix restrained the lateral deformation of the recycled concrete specimens during the compression of the concrete, thus the Poisson's ratio was reduced.  
\[\
u_{t}=0.994\
u_{0}(1-0.162\lambda_{\text{sf}})\Big{(}1-0.033\lambda_{\text{pf} }\Big{)} \tag{7}\]  
where, \(\
u_{0}\) is the Poisson's ratio of non-fibre recycled concrete.
## 4 Implementation of ROA\({}_{3}\)
### Characteristics of Key Components in ROA\({}_{3}\)  
The measured insertion loss (IL) for the 2 \(\times\) 2 crossbar OS and the self-made four-port reversible OC of ROA\({}_{3}\) is summarized in Table 2. The IL is about 1 dB or less for all the transmission paths and switching states of the two key components. The commercial 2 \(\times\) 2 crossbar OS has a response time of <10 ms.  
Figure 5 shows the measurement setup for the response time of the four-port reversible OC. The optical circulator is controlled by a control circuit. The measured response time can be observed from Figure 6 to be 0.9 ms when the OC switches from the clockwise direction, while it is 1.8 ms for the reverse switching. The upper curves are triggered signals generated by the control circuit, while the lower curves are the output waveforms from port 3. The response time of the four-port reversible OC can meet the requirement of most network management and surveillance.  
\begin{table}
\begin{tabular}{c c c} \hline
**Components** & **States** & **Insertion Loss (Port m \(\rightarrow\) n 1)** \\ \hline \multirow{3}{*}{Switch} & Bar & 0.61 dB (Port 1 \(\rightarrow\) 2) \\  & & 0.66 dB (Port 3 \(\rightarrow\) 4) \\  & Cross & 0.72 dB (Port 1 \(\rightarrow\) 4) \\  & & 0.65 dB (Port 3 \(\rightarrow\) 2) \\ \hline \multirow{3}{*}{Circulator} & Clockwise & 1.02 dB (Port 1 \(\rightarrow\) 2) \\  & Clockwise & 0.97 dB (Port 2 \(\rightarrow\) 3) \\  & & 0.89 dB (Port 3 \(\rightarrow\) 4) \\  & & 0.97 dB (Port 2 \(\rightarrow\) 1) \\ \multirow{3}{*}{Counterclockwise} & 0.88 dB (Port 3 \(\rightarrow\) 2) \\  & & 0.92 dB (Port 4 \(\rightarrow\) 3) \\ \hline \end{tabular}  
* m and n mean the port number of crossbar optical switch or optical circulator as shown in Figure 2c.  
\end{table}
Table 2: Insertion loss of key components in ROA\({}_{3}\).  
Figure 5: Measurement setup for the response time of the self-made four-port reversible optical circulator.  
The four-port reversible OC is combined with the customized bidirectional EDFA to create a reversible optical amplifier. Its dynamic switching function is characterized by inputting light sources of two different wavelengths into port 2 and port 3, as shown in Figure 7a. The power of both input light sources is \(-7\) dBm. Figure 7b shows the dynamic function measurement results, wherein the gain obtained by the clockwise amplification is \(22.46\) dB for the LD1 lightwave, and that obtained by the counterclockwise amplification is \(22.13\) dB for the LD2 lightwave. It can be clearly seen that when the light source in one of the directions is subjected to amplification, the light source in the other direction will be bypassed.
## 3 Results and Discussion
### PAH Components
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{1}{c}{**Moisture**} & \multicolumn{6}{c}{**Combustion Periods (min)**} \\ \cline{3-8} \multicolumn{1}{c}{**Content (\% db)**} & **PAHs** & **5** & **15** & **25** & **35** & **45** & **55** \\ \hline \multirow{8}{*}{60} & Phenanthrene & 1789.15 & 876.38 & 177.57 & 65.34 & 11.42 & 7.56 \\  & Acnaphthene & 15,523.64 & 8276.69 & 2246.35 & 2028.89 & 1175.47 & 785.59 \\  & Fluorene & 32,168.28 & 17,028.43 & 6578.23 & 4812.62 & 3519.94 & 1927.25 \\  & Naphthalene & 13,276.43 & 5579.85 & 2543.19 & 1875.25 & 1556.22 & 1348.28 \\  & Acnaphthylene & 19,987.12 & 9875.82 & 5129.27 & 4576.95 & 3487.77 & 1924.12 \\  & Anthracene & 1253.22 & 578.26 & 431.87 & 312.25 & 246.16 & 124.82 \\  & Indeno[1,2,3-cd]pyrene & 7986.39 & 3876.41 & 3125.85 & 1672.06 & 715.52 & 297.75 \\  & Fluoranthene & 4127.34 & 1821.37 & 997.82 & 731.29 & 562.04 & 312.05 \\  & Benz[4]anthracene & 29,871.69 & 13,089.95 & 7345.23 & 4137.72 & 1987.36 & 1198.27 \\  & Chrysene & 19,954.26 & 8765.29 & 6547.18 & 3116.75 & 2114.47 & 1375.57 \\  & Pyrene & 21,143.27 & 9425.21 & 5478.22 & 2348.83 & 1556.94 & 1127.85 \\  & Benzo[b]fluoranthene & 27,165.25 & 12,042.58 & 5672.85 & 2231.85 & 1349.08 & 985.81 \\  & Benzo[k]fluoranthene & 27,287.12 & 11,875.49 & 6221.42 & 2998.77 & 1993.81 & 1203.35 \\  & Benzo[a]pyrene & 26,549.48 & 12,079.63 & 7134.35 & 4129.52 & 1678.54 & 425.56 \\  & Dibenz[a,b]anthracene & 27,547.94 & 12,317.42 & 5869.82 & 3459.83 & 1876.65 & 921.32 \\  & Benzo[g,h,j]perylene & 41,235.29 & 19,365.61 & 10,984.89 & 4186.62 & 1731.22 & 865.29 \\  & Total & 316,865.87 & 146,874.39 & 76,484.11 & 42,684.54 & 255.626 & 1483.04 \\  & 2-3 rings & 83,997.84 & 42,215.43 & 17,106.48 & 13,671.30 & 9996.98 & 6117.62 \\  & 4-6 rings & 232,868.03 & 104,658.96 & 59,377.63 & 29,013.24 & 15,565.63 & 8712.82 \\  & _BaPTE_ & 64,060.91 & 28,825.47 & 15,443.50 & 8785.99 & 4212.56 & 1746.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The PAH concentration components for different combustion periods at the highest palm oil sewage sludge bio-char moisture content (60% d.b.) and 0.1 m/s clean air speed. The unit is in ng/m\({}^{3}\). Figure 7: Total particle mass concentration emission from the POSS bio-char combustion for different moisture contents.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 2 Principle of Signal Component Analysis for PWM and PPM
### Analysis of Components in PPM Signals
Meanwhile, the high-frequency components consist of the phase-modulated terms, which are the delayed and non-delayed versions subtracted from each other.