esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For instance, the data retry feature is not even removable in payloads characterised by streaming of large amount of data which do not contain critical information (i.e., high-resolution imaging payloads, where it is acceptable to lose data frames in case of upsets). Moreover, for small satellites and CubeSats [16], where the size of components particularly matters, both high-speed interface and data processing unit are to be embedded in the same FPGA. An attempt to design a SpaceFibre endpoint with reduced complexity has already been presented in [17], where an architecture to reduce area occupation has been proposed. Unfortunately, the lack of details on hardware configuration (i.e., target frequency, implementation of data scrambling, VCs size, etc.) combined with the fact that the standard significantly developed since 2014 makes it inappropriate to compare analytically our proposed implementation with the one contained in [17]. The aim of the work presented in this paper is to propose a novel reduced architecture of the SpaceFibre protocol (from now R-SpaceFibre) that optimises resources utilisation at the expenses of reduced features. Furthermore, the proposed modifications to the standard are intended to be compatible with a fully compliant implementation of it. R-SpaceFibre has been realised starting from the IngeniArs SpaceFibre CODEC IP. Since the aim of this paper is to present a modified and reduced version of the SpaceFibre standard, the results presented are all referred to the same full SpaceFibre CODEC (from IngeniArs). The obtained resource reduction is then theoretically achievable also on other different implementations. In Section 2, architectural design choices for the development of R-SpaceFibre are presented focusing on the different protocol layers. In Section 3, the hardware set-up chosen to validate R-SpaceFibre and to demonstrate interoperability with a SpaceFibre CODEC is described and resources utilisation results on different space-grade FPGAs are presented. In Section 4, a possible use case of the R-SpaceFibre IP core is discussed. Finally, Section 5 summarises the presented work and its outcomes.
# A Physical Layer Security Enhancement Scheme under the Ambient Backscatter System
## 5 Conclusions  
In this paper, we proposed a physical layer security scheme (IANT) to inject artificial noise from the tag end in the context of ABC. Different from the conventional maximum power scheduling physical layer security scheme, this paper selected another tag to generate artificial noise, so as to improve the secrecy rate of the system and achieve the purpose of enhancing the security performance of the physical layer. The numerical simulation results showed that even in the case of a small number of tags, the IANT scheme proposed in this paper can also achieve better security performance from the perspective of achievable secrecy rate. The noise cancellation method adopted in the IANT scheme in this paper has reduced the influence of artificial noise on the received signal at the reader end and has significantly improved the achievable secrecy rate of the system. Therefore, we can draw the conclusion that the scheme proposed in this paper effectively enhances the physical layer security of the ABC system. In addition, we explore the influence of channel estimation error on secrecy rate. Numerical results show that the scheme proposed in this paper can achieve good secrecy rate even when there is a large channel estimation error. It is worth noting that the IANT scheme proposed in this paper still has some shortcomings, such as the BER performance is inferior to that of the contrast scheme. This points out the direction for our future work. We propose to introduce a machine learning method to decode the signal on the reader side in an attempt to improve the BER performance of the scheme. In addition, enriching security performance evaluation indicators and further reducing channel estimation errors are also issues of concern to us.  
Conceptualization, J.G. and P.H.; methodology, P.H.; software P.H.; validation, P.H.; formal analysis, J.G. and P.H.; resources, J.G.; writing--original draft preparation, P.H.; writing--review and editing, J.Z.; supervision, P.H.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The General Object of National Natural Science Foundation of China under grant number 61675025; National Key R&D Project grant number 2018 YFB200900; National Major Scientific Research Instrument Development Project grant number 6202780085.  
This work was supported by the General Object of National Natural Science Foundation of China under Grant 61675025; National Key R&D Project 2018 YFB2200900; National Major Scientific Research Instrument Development Project 6202780085.  
The authors declare no conflict of interest.
# The 6G Ecosystem as Support for IoE and Private Networks: Vision, Requirements, and Challenges
## 6 The 6G Network Infrastructure to Manage V2X Application Lifecycles
### Implementation Solutions  
Technical Framework: Develop a 6G-enabled IoT framework where vehicles continuously communicate operational data to a centralized management system, facilitating real-time monitoring and decision-making.  
Addressing Challenges: Implement advanced cybersecurity protocols and utilize machine learning algorithms to process and analyze the data efficiently.  
Practical Considerations: Explore aspects such as initial setup costs, adapting existing vehicles to the new system, and training personnel to manage the 6G-enabled operations management systems.
# Data Quality Analysis and Improvement: A Case Study of a Bus Transportation System
## 6 Experimental Results and Analysis
### Results of Cleaning Noisy Data  
Based on data exploration, we found a large number of anomalies in the GPS trajectory dataset. We selected a trajectory, produced by bus \"A000*\" on 1 January 2022, to illustrate anomalies and normal GPS points. Note that we replaced part of the true bus number with \"**\" for privacy reasons. Figure 5 represents a trajectory comprising a sequence of GPS points in order. Upon observing the figure, it becomes evident that numerous range anomalies exist among these points, as exemplified by the rectangular boxes, and there are instances of jump anomalies, illustrated within the oval. This observation signifies the presence of substantial noisy data points within the GPS trajectory dataset. Consequently, the upcoming focus is on designing an effective data-cleaning strategy to eliminate the noise within the GPS trajectory data.  
In this subsection, we employ 17.01 million GPS records generated by 132 different buses over the course of one month (January 2022, with 31 days) as input for the data noise-cleaning process. Each record includes a series of fields, such as \"numberPlate\", \"timestamp\", \"longitude\", and \"latitude\". Applying Algorithm 1 detected 834,393 abnormal data points, constituting 4.9% of the total data points. Subsequently, we transformed these GPS records into trajectories corresponding to each bus trip, yielding a total of 253,797 GPS trajectories. Additionally, the urban road network data comprises 65,882 nodes and 147,472 directed edges. To compare the effects before and after noise removal, we visualized both the original GPS trajectory and the trajectory after noise removal. Figure 6 shows the original GPS trajectory, revealing that GPS points are scattered around the road, with some not aligning precisely with the road segments.  
Figure 5: Noisy data sample.  
Figure 7 displays a section of the trajectory after map matching. The left side visualizes trajectory 1, while the right side visualizes trajectory 2. In the figure, the orange circles represent the trajectory before matching, whereas the blue circles represent the trajectory after matching. As depicted, the map matching process has successfully aligned the GPS trajectory.  
In this section, we performed noise elimination on the GPS trajectory dataset using a combination of heuristic-based anomaly filtering and the FMM algorithm. The experimental results demonstrate that this filtering algorithm significantly enhances the accuracy of GPS map matching. The combined cleaning method achieves an accuracy rate of 97%, a notable improvement compared to using map matching alone. Figure 8 illustrates the accuracy comparison between our proposed solution and the default solution.  
Figure 8: The accuracies of our proposed solution and default solution.  
Figure 6: The original GPS points.  
Figure 7: The original GPS trajectory (orange points) and the matched GPS trajectory (blue points). (**a**) Example trajectory 1; (**b**) Example trajectory 2.
# On Classical Solutions for A Kuramoto-Sinelshchikov-Velarde-Type Equation
## 2 Proof of Theorem 1, under the Assumptions (2) and (4)
\begin{split}\partial_{t}\omega+\kappa(\partial_{x}u_{1}+\partial _{x}u_{2})\partial_{x}\omega+q[(\partial_{x}u_{1})^{2}+(\partial_{x}u_{2})^{2} +\partial_{x}u_{1}\partial_{x}u_{2}]\partial_{x}\omega\\ r[(\partial_{x}u_{1})^{2}+(\partial_{x}u_{2})^{2}](\partial_{x}u_{1 }+\partial_{x}u_{2})\partial_{x}\omega+\delta\partial_{x}^{3}\omega+\mu \partial_{x}^{2}\omega+\beta^{2}\partial_{x}^{4}\omega=0.\end{split}  \tag{38}\]  
Since  
\[ \begin{split} 2\delta\int_{\mathbb{R}}\omega\partial_{x}^{3} \omega=&-2\delta\int_{\mathbb{R}}\partial_{x}\omega\partial_{x}^{2} \omega dx=0,\\ 2\mu\int_{\mathbb{R}}\omega\partial_{x}^{2}\omega=&-2 \mu\left\|\partial_{x}\omega(t,\cdot)\right\|_{L^{2}(\mathbb{R})}^{2},\\ 2\beta^{2}\int_{\mathbb{R}}\omega\partial_{x}^{4}\omega dx=& -2\beta^{2}\int_{\mathbb{R}}\partial_{x}\omega\partial_{x}^{3}\omega dx=2 \beta^{2}\left\|\partial_{x}^{2}\omega(t,\cdot)\right\|_{L^{2}(\mathbb{R})}^{2},\end{split}  \tag{39}\]multiplying (38) by \(2\omega\), an integration on \(\mathbb{R}\) gives,  
\[ \begin{split}\frac{d}{dt}\left\|\omega(t,\cdot)\right\|_{L^{2}( \mathbb{R})}^{2}+2\beta^{2}\left\|\partial_{x}^{2}\omega(t,\cdot)\right\|_{L^{ 2}(\mathbb{R})}^{2}\\ =2\mu\left\|\partial_{x}\omega(t,\cdot)\right\|_{L^{2}(\mathbb{R })}^{2}-2\kappa\int_{\mathbb{R}}(\partial_{x}u_{1}+\partial_{x}u_{2})\omega \partial_{x}\omega dx\\ \quad-2q\int_{\mathbb{R}}[(\partial_{x}u_{1})^{2}+(\partial_{x}u _{2})^{2}+\partial_{x}u_{1}\partial_{x}u_{2}]\omega\partial_{x}\omega dx\\ \quad-2r\int_{\mathbb{R}}[(\partial_{x}u_{1})^{2}+(\partial_{x}u _{2})^{2}](\partial_{x}u_{1}+\partial_{x}u_{2})\omega\partial_{x}\omega dx. \end{split}  \tag{40}\]  
Observe that since \(u_{1},\,u_{2}\in H^{2}(\mathbb{R})\), for every \(0\leq t\leq T\), we have  
\[\left\|\partial_{x}u_{1}\right\|_{L^{\infty}((0,T)\times\mathbb{R})},\,\left\| \partial_{x}u_{2}\right\|_{L^{\infty}((0,T)\times\mathbb{R})}\leq C(T). \tag{41}\]  
Thanks to (41), we have that  
\[ \begin{split}&(\partial_{x}u_{1}+\partial_{x}u_{2})^{2}\leq \left(\left\|\partial_{x}u_{1}\right\|_{L^{\infty}((0,T)\times\mathbb{R})}+ \left\|\partial_{x}u_{2}\right\|_{L^{\infty}((0,T)\times\mathbb{R})}\right)^{ 2}\leq C(T),\\ &[(\partial_{x}u_{1})^{2}+(\partial_{x}u_{2})^{2}+\partial_{x}u_ {1}\partial_{x}u_{2}]^{2}\\ &\qquad\leq\left(\left\|\partial_{x}u_{1}\right\|_{L^{\infty}((0, T)\times\mathbb{R})}^{2}+\left\|\partial_{x}u_{2}\right\|_{L^{\infty}((0,T) \times\mathbb{R})}^{2}+\left\|\partial_{x}u_{1}\right\|_{L^{\infty}((0,T) \times\mathbb{R})}\left\|\partial_{x}u_{2}\right\|_{L^{\infty}((0,T)\times \mathbb{R})}\right)^{2}\\ &\qquad\leq C(T),\\ &[(\partial_{x}u_{1})^{2}+(\partial_{x}u_{2})^{2}]^{2}\leq\left( \left\|\partial_{x}u_{1}\right\|_{L^{\infty}((0,T)\times\mathbb{R})}^{2}+ \left\|\partial_{x}u_{2}\right\|_{L^{\infty}((0,T)\times\mathbb{R})}^{2}\right) ^{2}\leq C(T).\end{split} \]  
Consequently, by the Young inequality,  
\[
## 4 Results and Discussion
### Monitoring of Crack Formation and Development, Expressed Using the TBTC Method  
Monitoring of crack formation and development was expressed using the TBTC method, whose principle was explained in Section 3.7.3. Figure 14 presented below contains a respective graphical representation and an accompanying commentary. During one year of the prescribed testing, cracks formed in four of the eight tested MMs. They appeared after 75 days of exposure in the case of mortars M2 and M3, after 105 days in the case of M1, and after 150 days in the case of M4. The worst of them is again M3, because its triangle has the base in the position of 285 days. In the case of M2, this occurred after 330 days, and with M1 it was 345 days. As regards M4, full crack development did not occur until the end of the experiment (within 365 days), while in the case of M5, the first cracks were recorded only at the official end of the experiment, i.e., after 365 days. There were no cracks recorded within the 365 days of the experiment in mortars M6, M7 and M8. The test specimens from those three mortars were therefore left in the sulfate bath  
Figure 13: Dependence of relative length changes on the immersion time of samples in the sulfate bath (detail).  
even after the official end of the test, solely for the purpose of a visual inspection (length and mass were no longer determined). All three mortars exhibit the onset of cracking, i.e., the first hairline cracks, after 420 days of placement in the sulfate bath. This marked the definitive end of the test and there was no reason to monitor the samples any longer. The mortars containing metakaolin were again significantly better than M1 and all the others.  
Figure 15 shows the condition of the model mortar M6 when the first hairline cracks were detected (in the case of TBTC, the vertex of the triangle is shown at this time). Figures 16 and 17 show the condition of the model mortar M3 at the time of limit cracking damage (represented by point P2 in Figure 8).  
Figure 14: Cracking of MMs during the sulfate test, expressed with TBTC.  
Figure 15: M6 after 420 days in the sulfate bath: (**a**) most specimen edges do not exhibit any signs of cracking; (**b**) first hairline cracks can be observed on one of the four specimens; the red line indicates a length of 1 mm.  
Larger cracking of the corner parts of the small beams is caused by a more intensive supply of sulfates, which penetrate the specimen from two directions. This can be prevented, for example, by applying epoxy resin to the front and back faces [16]; however, we did not do so to ensure comparability of the results with other experiments.
## 3 Verification of the Implemented Model
### Zimmermann Method Verification
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(E_{r}\) & \(L\) & & & & & & \(I_{x}\) & \(w(x)\) \\ (N/mm\({}^{2}\)) & (mm) & \(Q\) (N) & \(x\) (mm) & \(e^{\star}\) (\(-x\)/L) & **Cos (x/ L)** & **Sin (v/L)** & \(\eta\) (x) & (mm\({}^{2}\)) & (mm) \\ \hline
200,000 & 944 & \(1\times 10^{5}\) & 0 & 1 & 1 & 0 & 1 & 23,460,433.99 & \(-\)2.2410934 \\
200,000 & 944 & \(1\times 10^{5}\) & 600 & 0.52962 & 0.80471967 & 0.59365499 & 0.74060888 & 23,460,433.99 & \(-\)1.6597737 \\
200,000 & 944 & \(1\times 10^{5}\) & 1200 & 0.280499 & 0.295147494 & 0.955451703 & 0.350791361 & 23,460,433.99 & \(-\)0.7861562 \\
200,000 & 944 & \(1\times 10^{5}\) & 1800 & 0.148585 & \(-\)0.32969768 & 0.944086563 & 0.091272398 & 23,460,433.99 & \(-\)0.20455 \\
200,000 & 944 & \(1\times 10^{5}\) & 2400 & 0.078679 & \(-\)0.82577591 & 0.56398352 & \(-\)0.02059652 & 23,460,433.99 & 0.046158793 \\
200,000 & 944 & \(1\times 10^{5}\) & 3000 & 0.04167 & \(-\)0.99933855 & \(-\)0.03636542 & \(-\)0.04315811 & 23,460,433.99 & 0.09672138 \\
200,000 & 944 & \(1\times 10^{5}\) & 3600 & 0.022069 & \(-\)0.78259887 & \(-\)0.62252630 & \(-\)0.03101039 & 23,460,433.99 & 0.06949718 \\
200,000 & 944 & \(1\times 10^{5}\) & 4200 & 0.011688 & \(-\)0.26206866 & \(-\)0.96555289 & \(-\)0.04132725 & 23,460,433.99 & 0.0210871 \\
200,000 & 944 & \(1\times 10^{5}\) & 4800 & 0.00619 & 0.368311718 & \(-\)0.93147250 & \(-\)0.00351408 & 23,460,433.99 & 0.00787539 \\
200,000 & 944 & \(1\times 10^{5}\) & 5400 & 0.003279 & 0.485739753 & \(-\)0.53395960 & 0.0010232936 & 23,460,433.99 & -0.0022935 \\
200,000 & 944 & \(1\times 10^{5}\) & 6000 & 0.001736 & 0.997355111 & 0.072682749 & 0.001858031 & 2.99 & \(-\)0.004164 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Calculation values of rail deflection according to the Zimmermann method. Figure 7: Vertical deflection values of rail. role in the defection of the deflection values between the Zimmermann method and FE model. [PERSON] et al. (2017), [PERSON] et al. (2018), and [PERSON] (2019) concluded that by decreasing the element size, the accuracy increased, but the computational time also increased. [PERSON] et al. (2018) and [PERSON] (2019) verified their numerical models, by using ABAQUS and ANSYS software programs, with the Zimmermann method. It was found that the difference between [PERSON] et al. (2018) and the Zimmermann method was 0.82%, and the difference between [PERSON] (2019) and the Zimmermann method was about 0.3%. The maximum deflection was registered at the wheel/rail contact point. Then, it decreased gradually by moving away from that point until reaching positive deflection values, after which it finally reached zero deflection. Depending on the transient system to analyze the model, an unsymmetrical deflection shape can result in the FE model results. The transient system presents the dynamic motion of the running wheel as it simulates the realistic wheel movement.
## 1 Introduction
[28] proposed an online fault diagnosis system for bearings that detects emerging fault modes and then updates the diagnostic system knowledge (DSK) to incorporate information about the newly detected fault modes. Directed at the characteristics of nonlinear and non-stationary data of rail train, a complete long-term fault diagnosis method for rolling train rolling bearings based on exponential smoothing prediction and improved integrated learning algorithm is designed and applied to the measured bearing data set designed in this paper. The Sliding Window based on Exponential Smoothing Segmentation Algorithm (SWESSA) is used in this method to segment the original data. Then, the local feature extraction is performed on the segmented data, and the improved local frequency is defined. The extreme point is replaced by the segmentation point as a marker for judging the motion limit, which can eliminate the interference from high-frequency noise and multi-excitation source to local frequency. Finally, the Improved AdaBoost Algorithm (IAA) is used to classify the sample data. Before the classification, the sample data is clustered to filter the noise data, so the anti-noise ability is enhanced, and the classification effect is improved. In this paper, the vibration data of the rolling bearing of the rail train is analyzed, and the fault is diagnosed. The content of this paper is divided into the following parts: Section 2 introduces the complete process of the proposed fault diagnosis method for the rolling bearing of the rail train and the specific work of each part. Section 3 introduces the time-series segmentation method, feature extraction algorithm based on local spectrum, and the specific algorithm flow of IAA. Section 4 presents the experimental results and analysis of the proposed method, and Section 5 concludes the paper.
## 5 Results and Discussion
### VSWT Hybrid WDPS Performance Under Normal Conditions
#### 5.1.1 Ramp Response  
To justify the performance of WDPSs and particularly their BPAC during windstorms, we consider a ramp signal (0.2634 ms\({}^{-2}\)) as the wind speed for 50 s, as shown in Figure 19. It is noted that the cut-out wind speed of the VSWT model [65] is 25 ms\({}^{-1}\), given in [100].  
Figure 18: VSWT inertial and proportional contribution response for fast frequency response and synthetic inertia.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **With K\({}_{\text{dn}}\) = 0.47, K\({}_{\text{pn}}\) = 9, K\({}_{\text{i,d}}\) = 14.89, and K\({}_{\text{p,d}}\) = 21.01** \\ \hline NADIR (p.u) & 0.975204 \\ IAE & 0.033579 \\ ISE & 0.000461 \\ C & 1 \\ Frequency (Hz) & 58.51224 \\ Z & 62,998.00405 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hybrid WDPS parameters using proposed SPBA tuning methodology.  
Because of the linear increase in wind speed, the turbine-generated power will increase until its maximum value, i.e., 1 pu. However, with the increase in \(\mathrm{P_{wind}}\), the DPP should reduce its generation to maintain the system frequency. With the further increase in wind speed after the wind turbine achieves its maximum limit, \(\mathrm{P_{max}}\), BPAC activates, as shown in Figure 20. Due to the modified BPAC (specifically, the power compensation control loop), the beta rotation is much smoother.
# A Phase Error Correction System for Bioimpedance Measurement Circuits
## 4 Simulations  
### Frequency Response  
To validate these experimental findings, simulations were conducted for both purely resistive loads and RC loads. Figure 12a,b presents the impedance magnitude percentage error and its corresponding phase errors for resistive loads of 1 k\(\Omega\), 3 k\(\Omega\), and 5 k\(\Omega\). These graphs highlight the negligible effects of erroneous phase shifts between VCCS and I &Q demodulation pulses on the impedance magnitude, unlike the phase.  
This observation is true for both compensated and uncompensated systems, with a percentage error difference of \(\leq\)1.2% across all loads. However, a phase error differential of over 12 degwas observed between the compensated and uncompensated SD system for a 5 k\(\Omega\) load, as shown in Figure 12b, which significantly impacted the impedance phase accuracy.  
Figure 13a,b also shows percentage errors for both the impedance magnitude and phase before and after compensation for RC loads of (1 k\(\Omega\), 244 pF) and (3 k\(\Omega\), 33.5 pF), respectively. Both results showed lower errors unlike the experimental data, which might be attributed to simulations' lack of consideration for realistic experimental factors, including the non-idealities of certain components, i.e., leads, connectors terminals, and printed circuit boards. Results further proved improvement made by phase compensation.  
Figure 12: Simulated Impedance magnitude and phase errors for RC loads as a function of frequency. (**a**) Simulated Impedance magnitude percentage error for phase compensated and uncompensated SD systems as a function of frequency for R = 1 k\(\Omega\), 3 k\(\Omega\), and 5 k\(\Omega\). (**b**) Simulated impedance phase error for phase compensated and uncompensated SD systems as a function of frequency for resistive loads.  
Figure 13: Simulated impedance magnitude and phase percentage error for RC loads as a function of frequency. (**a**) Simulated impedance magnitude and percentage error as a function of frequency for RC loads. (**b**) Simulated impedance and phase percentage error as a function of frequency for RC loads.