esa-sceva/satcom-chunk-collection huggingface dataset:
# Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model
## 7 Conclusions  
This paper presents a novel methodology for the RUL prediction of SCIMs under a broken rotor bar. The method is based on model-based and data-driven approaches. The fault modelization, data manipulation, health indicators extraction, and RUL estimation are considered. The key aspects of the proposed methodology are as follows:  
1. A Matlab/Simulink model is developed to simulate a SCIM operating under healthy conditions and broken rotor bar fault.  
2. The proposed approach uses various condition monitoring signals and signal processing techniques to predict the RUL of the SCIM. The time- and frequency-domain  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & \multicolumn{2}{c}{**HIs**} & \multicolumn{1}{c}{**Averages**} \\  & **HI-Torque** & **HI-Current** & \\ \hline RMSE & 2.2 & 1.638 & 1.919 \\ NRMSE & 0.2371 & 0.2885 & 0.2628 \\ \(R^{2}\) & 0.8377 & 0.4782 & 0.6579 \\ MAPE & 15\% & 1.5263\% & 8.26315\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: RUL prediction performance.  
Figure 10: RUL prediction performance of the proposed approach based on HI-torque and HI-current.  
features are extracted from the raw torque, stator current, and speed signals to achieve synthetic data manipulation. The features showing the evident monotonic degradation trends are selected using suitable metric performance.  
3. The selected features are then transformed into an HI machine using the PCA technique. The first PC for both torque and stator current signals is chosen as an HI of the broken rotor bar motor, which may track the fault degradation and produce accurate prediction results.  
4. The prognostic step is based on the exponential degradation model. The degradation model fits the time evolution of both HI extracted from the torque and current signals to estimate the RUL before reaching the EOL.  
In conclusion, the results have shown that the proposed prognostics method exhibits good convergence behavior to estimate the RUL as soon as the degradation failure is detected. Thus, it can be a powerful prognostic approach with acceptable performances to predict induction machine failure modes. The application of the proposed approach can be extended to other types of components, such as stator windings and bearing failure, that are responsible for approximately 42% and 31% of the identified faults for rotating machines, respectively. As future work, we plan to develop a complete health assessment methodology composed of fault detection, and diagnostic and prognostic for condition monitoring of induction machines.  
[PERSON] proposed the idea and carried out all of the simulations; [PERSON] and [PERSON] validated the process and guided the research. 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.
# Development of a Pre-Evaluation and Health Monitoring System for FAST Cable-Net Structure
## 5 Conclusions  
As the largest radio telescope globally, health monitoring of the FAST structure is critical. In order to improve the safety of FAST observations and discover possible safety problems in advance, this paper has obtained the following conclusions through systematic research.  
1. MST is applicable to the establishment of the FAST model, which can be updated automatically and over time. The model fits in well with the on-site structure.
2. A method to update the FAST model with regularly measured data is proposed, and the field test proves that the updated model is highly reliable.  
Figure 8: Schematic diagram of PHM system.  
3. A PHM system consisting of both pre-evaluation and health monitoring components has been developed to provide a visual and automated early-warning system for the structural health monitoring of FAST, which significantly facilitates the maintenance work of on-site staff. The PHM system has provided vital support for the stable observations of FAST.  
Conceptualization, Y.S. and M.D.; data curation, Y.S.; formal analysis, Y.S.; funding acquisition, B.L. and M.D.; investigation, P.J.; methodology, B.L. and Y.W.; project administration, B.L.; resources, Q.L.; software, Q.L. and P.J.; supervision, B.L. and Y.W.; validation, B.L., Y.W. and M.D.; visualization, Y.S.; writing--original draft, Y.S., B.L., Y.W. and M.D.; writing--review and editing, B.L. and P.J. 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, Grant Numbers 11973006, 51778300 and 52108150; the Key Research and Development Project of Jiangsu Province, Grant Number BE2020703; the Natural Science Foundation of Jiangsu Province, Grant Numbers BK20190753 and BK20191390; the Six Talent Peaks Project of Jiangsu Province, Grant Number JZ-017; and the Natural Science Foundation of the Jiangsu Higher Education.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank [PERSON], [PERSON], and [PERSON] for their constructive contributions to our study and field test.  
The authors declare no conflict of interest.
## 2 Background and Related Works
The techniques balance the transferred power and the consumed power, i.e., the power beacon provides more energy towards the node with low energy in its buffer, while the node reduces power consumption by lowering its duty cycle. In Ref. [41], the same authors continued to study the phase and frequency synchronization problems in a distributed wireless power transfer system. Although the distributed system comprises several technical challenges, the results show that the coverage problem related to the wireless power transfer can be improved applying distributed system. Finally, the presence of the WPT needs to be also accounted for at the network level. A good overview of the respective challenges and potential solutions is provided in Ref. [42]. To this day, the problems of optimizing data and energy transfer (within simultaneous wireless information and power transfer (SWIPT) concept) [43; 44; 45], the beam width and direction [46; 47], design of media \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Peak Efficiency (\%)** & **Input Signal for Peak Efficiency (dBm)** & **Frequency Band (GHz)** & **Source** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 1: Radio frequency to direct current conversion efficiency of selected wireless power transfer solutions (based on Refs. [11; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]). access and routing [48; 49; 50], interference management [51; 52] and many others have been addressed analytically and via network-level simulations. Despite all these immense progress in respect to the individual components, the number of the complete IoT solutions employing WPT is still very scarce today. The primary reason for this is the complexity of balancing all the critical requirements for such systems, i.e., the energy efficiency (1) both in transmission and conversions, small form-factor (2), and low-cost (3), within the power and duty cycle (4) regulations of today. Therefore, in the following sections, we investigate how one can design a real-life IoT application based on the RF-based WPT principle. We start by discussing the methodology of designing a WPT-enabled device. Then, we proceed with justifying and describing the employed technical solutions. Finally, we present the results of the evaluation and discuss the lessons learned.
# The Impact of Automated Vehicles on Road and Intersection Capacity
## 4 The Road Heterogeneous Traffic Flow Flow-Density Relationship
### Experimental Verification  
In order to verify the correctness of the derivation of the single-lane heterogeneous traffic flow theory, a single-lane road in a mixed environment was established by SUMO, and then simulated. The simulation operation was shown in Figure 7, in which the human-driven vehicle was colored green, and the autonomous vehicle was colored red.  
Figure 6: Flow-density diagram in a mixed environment with different autopilot proportions.  
The simulation step is 0.1 s, and other simulation conditions are consistent with those in Section 3. The balance speed, \(v_{b}\), of hybrid vehicles, is set successively from \(0-v_{0}\), and the vehicle flow is counted by a detector arranged at the road's starting point.  
In the case of different proportions of autonomous vehicles, the average error between the theoretical value and the actual value, is shown in Table 6.  
As can be seen from the above table, the relative error between the theoretical average and the actual average of the traffic volume of autonomous vehicles with different proportions is no more than 16%, indicating that the deduced flow-density relation of heterogeneous traffic flow on the road is correct.
# A Survey on Shortest Unique Substring Queries
## 4 Interval-SUS Queries
### Compact Data Structures' Approaches  
In Section 3.4, we have discussed [PERSON] et al's work [8] for solving Problem 1. They also presented a compact data structure for Problem 2[14]. Their data structure has the size of \(2n+2m+o(n)\) bits and outputs an interval \(\mathsf{SUS}\) query in \(\mathcal{O}(\mathit{occ})\) time, where \(m\) is the number of minimal unique substrings (\(\mathsf{MUS}\)s) of the input string which is at most \(n\). Their technique is based on [PERSON] et al.'s work [8], which is discussed in Section 4.2. Similar to the technique for solving position-\(\mathsf{RSUS}\), their structure is based on the two bit arrays \(\mathsf{MB}\) and \(\mathsf{ME}\). In addition, they use array \(\mathsf{MUS}_{len}\) which has been used in [PERSON] et al.'s work for storing the length of the \(\mathsf{MUS}\)s [8]. On the top of \(\mathsf{MB}\) and \(\mathsf{ME}\), a successor and a predecessor data structures are maintained. \(\mathsf{MUS}_{len}\) is also endowed with RMQ data structure. Now, once an interval query \([s,t]\) comes, first we can find \(g=\mathsf{pred}_{\mathsf{ME}}(y)\) and \(r=\mathsf{succ}_{\mathsf{MB}}(s)\), then we can find the range of \(\mathsf{SUS}\) covering \([s,t]\). Consequently, it is sufficient to answer RMQ queries on the corresponding range on \(\mathsf{MUS}_{len}\). The space needed for \(\mathsf{MB}\) and \(\mathsf{ME}\) and the predecessor and successor data structure is \(2n+o(n)\) bits. The RMQ data structure on \(\mathsf{MUS}_{len}\) takes \(2m+o(m)\) bits of space. Thus, using the constant query time of the RMQ data structure (see Section 2.2), their results can be summarized as follows:  
Theorem 4.7: _There exists a data structure of size \(2n+2m+o(n)\) bits that can answer interval \(\mathsf{SUS}\) problem in \(\mathcal{O}(\mathit{occ})\) time, where \(\mathit{occ}\) is the number of the answers corresponding to the given interval._
# Feature-Selection-Based DDoS Attack Detection Using AI Algorithms
## 3 Methodology
### DDoS Dataset
#### 3.1.2 Syn  
SYN Flood (attack) is a major part of the evolving DDoS landscape. This attack exploits the widely employed TCP protocol and especially the 3-way handshake, flooding targeted end-hosts, i.e., victims, with SYN packets. These exhaust their memory and processing resources, failing to serve legitimate requests. SYN Flood attacks are difficult to counter via commonly used IP-based mitigation schemas. IP-based rules, required to block the attack traffic, increase proportionally to the number of malicious sources. This demands network devices/firewalls to store thousands/millions of filtering rules, which is unattainable due to memory resource limitations. Notably, when spoofing is employed, IP-based filtering may be ineffective. An alternative mitigation method for SYN Floods relies on the SYN cookies technique. This approach, instead of blocking malicious SYN packets, generates appropriately crafted SYN-ACK packets. Although this method protects the victim from the launched attack, it consumes significant processing resources and introduces large rates of backscatter traffic [28].
## 2 Definition and Methods of Analysis  
The TRP is the integral of the power flow out of a spherical surface surrounding the DUT. The dimensions and spherical coordinates of the DUT are defined in Figure 1. The power density \(S_{r}\) in the far field can be defined as [20]  
\[S_{r}\approx|E|^{2}/Z_{0} \tag{1}\]  
where \(Z_{0}\) is the impedance and \(E\) is the electric field of the DUT. Then, the TRP can be written as  
\[\text{TRP}=\iint_{4\pi}S_{r}(r,\theta,\phi)r^{2}\sin\theta\text{d}\theta\text {d}\phi \tag{2}\]  
where \(\theta\) and \(\phi\) are the elevation and azimuth angles, respectively. The angular ranges cover a whole sphere, e.g., \(0^{\circ}\leq\theta\leq 180^{\circ}\) and \(0^{\circ}\leq\phi\leq 360^{\circ}\). \(r^{2}\sin\theta\text{d}\theta\text{d}\phi\) denotes the infinitesimal surface area of the sphere with a radius \(r\). Usually, the EIRP of the DUT is used to calculate the TRP in practical tests. It can be calculated as  
\[\text{EIRP}(\theta,\phi)=\lim_{r\to\infty}S_{r}(r,\theta,\phi)4\pi r^{2} \tag{3}\]  
Then, the TRP can be written as  
\[\text{TRP}=\frac{1}{4\pi}\int_{\theta=0}^{\pi}\int_{\theta=0}^{2\pi}\text{EIRP }(\theta,\phi)\sin\theta\text{d}\theta\text{d}\phi \tag{4}\]
# Performance Estimate of a Spin-Stabilized Drag Sail for Spacecraft Deorbiting
## 5 Conclusions  
In this study, a spin-stabilized drag sail was proposed as a tool to perform spacecraft deorbiting from a low Earth orbit. The proposed configuration represents a sufficiently simple deorbiting device since it does not require an active attitude control system for proper operation and is well suited for satellites with a medium-to-large area-to-mass ratio. The deorbiting scenarios have been analyzed by making the assumption that the axis of rotation of the drag sail remains inertially fixed, due to gyroscopic stiffness, throughout the deorbiting trajectory. The spacecraft dynamics was simulated using the modified equinoctial elements because they ensure the absence of singularities in time responses. The model used takes into account the most relevant sources of perturbation for a spacecraft in low Earth orbit, that is, aerodynamic forces (drag and lift), Earth oblateness effect, and solar radiation pressure. The decay times obtained with a spinning drag sail were estimated assuming different initial orbits and were compared with those obtained for a three-axis stabilized sail and an uncontrolled sail. The results show that the performance of a spinning drag sail, while worse than that of a three-axis stabilized sail, yields significant time savings compared with the case of uncontrolled drag sail, especially when starting from a Sun-synchronous orbit and using an area-to-mass ratio compatible with that of a small or medium-sized satellite.  
Conceptualization, L.N.; methodology, L.N. and G.M.; software, L.N.; writing--original draft preparation, L.N. and G.M.; writing--review and editing, G.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University of Pisa, Progetti Ricerca di Ateneo, grant number PRA_2022_1.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.  
Figure 6: First day of the decay trajectory from a non-midnight SSO for a satellite with parameters given in Table 1 equipped with a spinning drag sail.
## 3 Measurement Campaign  
### Observation Period  
Two dedicated calibration campaigns were performed with Stripmap mode acquisitions using corner reflectors of the calibration site from the German Aerospace Center (DLR). Each campaign was covered by six repeat cycles of Sentinel-1. The S-1A campaign took place between August and October 2015, the S-1B campaign between December 2016 and February 2017 (see Table 1).  
In case of S-1A, only the Stripmap beams S1, S3, and S6 were acquired for both orbit directions, the ascending (ASC) and descending (DES) orbits. For S-1B, all six Stripmap beams were used (S1 to S6), but each one of them was used either with the ASC or DES orbit direction. The selection of the Stripmap beams ensures a maximum number of S-1 overpasses across the DLR calibration site with six per repeat cycle, i.e., in summary, 72 acquisitions (for both satellites) were evaluated for this study.
## 5 Discussion and Findings
### Predicting Injuries
#### 5.2.1 Squad  
Based on injury occurrences, the bar graph (Figure 13) depicts the injury distribution among the top ten teams in terms of the number of injury occurrences. Burnley has the most injuries on the list, totaling 12. Newcastle United is a close second with ten injuries. Manchester City, Manchester United, Crystal Palace, and Everton are tied for third position with eight injuries each. Southampton and Leicester City follow with seven injuries each, while Sheffield United is last with six. Arsenal has the fewest injuries among these top ten teams, with only five.  
Figure 13: Top 10 Squads by Injury Occurrences.  
\begin{table}
\begin{tabular}{c c c} \hline
**Player** & **Squad** & **Age** \\ \hline [PERSON] & Tottenham & 27 \\ \hline [PERSON] & Liverpool & 28 \\ \hline [PERSON] & Manchester United & 26 \\ \hline \end{tabular}
\end{table}
Table 9: Age Comparison; Top 3 Players.  
Figure 14 clearly distinguishes between the squad classifications and their average injuries. The 'Most Injury Affected' category, which includes the top five clubs with the most injuries, stands out with an average of nine injuries per team. In other words, on average, these heavily afflicted teams face a significant injury load, approaching double digits. In sharp contrast, the 'Rest' group, which includes the remaining teams not in the top five, reports an average of only four injuries per squad. This considerable gap highlights the importance of squad categorization in injury occurrences. It supports the study's conclusion that squad composition is vital in forecasting and managing injuries in football teams.  
The pie chart in Figure 15 provides a revealing perspective on injury occurrences, categorizing them as 'Most Injury Affected' and 'Rest'. Notably, despite accounting for only a quarter (25%) of all teams, the 'Most Injury Affected' category accounts for 42% of all injury occurrences. This notable disparity highlights the considerable impact of squad makeup on injury rates. A concentrated set of teams, albeit a minority, clearly shoulders a disproportionate injury burden. This image significantly confirms the study's fundamental finding: squad composition plays a critical role in forecasting and affecting injury occurrences, with a few teams causing a substantial amount of the league's injuries.
# On Developing Techniques for Sharing Satellite Spectrum with Indoor Small Cells in 5G +
## 3 Mathematical Analysis
### Estimation for Multiple Buildings  
Now, assume that \(L\geq 1\) (i.e., multiple buildings of small cells and CGC stations exist per macrocell of aTMS). Due to a small coverage of either a CGC station or a SBS, consider that the indoor propagation characteristics and the UEs' distances from their respective SBSs or CGC stations in each building do not deviate significantly from one another. Hence, by linear approximation, the average aggregatecapacities of aTMS for \(L\) buildings due to orthogonal allocation of the satellite spectrum is roughly given by  
\[\sigma_{\text{aTMS,orth,$L$}}^{\text{sys,all SBSs}}=\sigma_{\text{aTMS}}+\left(L \times\sigma_{\text{cl,$\text{sc,$\text{orth}$}}}^{\text{all SBSs}}\right) \tag{17}\]  
Now, the spectral efficiency for \(L\) buildings is given by  
\[\sigma_{\text{aTMS,orth,$L$}}^{\text{SE,all SBSs}}=\sigma_{\text{aTMS,orth,$L$}}^{ \text{sys,all SBSs}}/(M_{\text{aTMS}}\times Q) \tag{18}\]  
Additionally, the energy efficiency for \(L\) buildings is given by  
\[\sigma_{\text{aTMS,orth,$L$}}^{\text{EE,all SBSs}}=\left(\left.\begin{array}{l} \left(L\times S_{\text{F}}\times(1/
# Neighbor Session Solutions for Integrated Routing Protocols
## 2 Dual-Stack IP Routing Protocols
### Multiprotocol Border Gateway Protocol  
The Border Gateway Protocol (BGP) is a so-called path-vector routing protocol. The original version of the BGP protocol only supported IPv4 addresses. The current version of BGP-4 with multiprotocol extension (MP-BGP) supports the routing of the dual-stack IP protocol as well as the transfer of other information in messages. BGP transports messages using the TCP transport protocol. BGP, unlike other routing protocols, allows routing information to be transmitted independently of the network protocol used to transport messages. Therefore, it is possible to transmit IPv4 and IPv6 routing information using IPv4 adjacency only [37].
## 4 Experimental Verification  
### Overview  
In November 2020, a test was carried out at the Moganshan Lake (Figure 5d) in Huzhou City, Zhejiang Province, using a spherical target in a monostatic configuration. The device and the test scene are shown in Figure 5. The lake depth was 14 m. The environment was modeled as an isovelocity channel with a sound speed c of 1500 m/s. The target (Figure 5c), the fixed source (Figure 5a), and the fixed receiver (Figure 5b) were at the same depth of 7 m. The distance between the source and the receiver was 4 m. The spherical  
Figure 4: The simulation results in the bistatic configuration: (**a**) the interference pattern for 400\(-\)800 Hz; (**b**) the interference pattern for 4\(-\)8 kHz; (**c**) the interference pattern for 40\(-\)80 kHz; (**d**) the motion trajectory.  
Figure 3: The simulation results in the monostatic configuration: (**a**) the interference pattern for 400\(-\)800 Hz; (**b**) the interference pattern for 4\(-\)8 kHz; (**c**) the interference pattern for 40\(-\)80 kHz; (**d**) the motion trajectory.  
target with a diameter of 1.2 m moved in the direction indicated by the arrow. This test was performed at frequency bands of 20\(-\)40 kHz and 40\(-\)80 kHz. The test configuration is shown in Figure 6.  
The target echo signal received by the receiver was processed using the following steps: the signal was preprocessed to remove the interference of the noise and the clutter, the track of the target was obtained, a spectrum analysis was performed to obtain the range-frequency image of the echo signal, and the striations characteristics were analyzed.