esa-sceva/satcom-chunk-collection huggingface dataset:
# An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data
## 4 Results and Discussions
### Comparisons and Analyses of the Results
From Figure 4, it can be seen that the FCM-GA contained not only a complete FCM computation procedure, but also parameter updates. Theoretically, the computation time of the FCM-GA should be higher than that of the FCM. However, as seen in Table 4, the experimental results were coherent with the theoretical analyses. As a consequence, the better imputation accuracy of the FCM-GA is also inferred from Figures 6-9. In other words, we compromised on computation time to achieve better imputation performance. Better imputation accuracy indeed has a higher priority than computation time in dealing with missing value imputation issues [49]. Figure 10: Time series comparisons with different missing ratios. Figure 9: RA comparisons under the condition of different data aggregation intervals, different missing ratios, and the \(\pm\)10% tolerance error range. In summary, when the aggregation interval and missing ratio were smaller simultaneously, the history imputation method was an alternative approach to estimate the missing values. The advantage of the history imputation method lay in its simplicity. However, when the aggregation interval and the missing ratio became larger, other complicated imputation methods with better performance should be utilized to replace the history imputation method. The conventional FCM would be another valid candidate choice for estimating missing values. However, the main drawback of the conventional FCM is that its parameters are not determined scientifically. Considering this, the proposed integrated imputation method combining the FCM and the GA could be utilized to replace missing values efficiently. From Figures 6-9, the results indicate that the integrated imputation method outperformed the conventional FCM remarkably. Moreover, based on the estimated values and existing values, an extension of how to achieve the prediction task is another interesting issue that warrants further research. The taxi GPS data utilized in this paper is also available online at [[https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page)]([https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page](https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page)). Conceptualization, J.H. and B.M. ; methodology, J.H., Y.B., and T.Z. ; software, J.H., B.M., and Y.B. ; validation, J.H. and T.Z. ; resources, Y.B. ; data curation, C.M. ; writing--original draft preparation, J.H. ; writing--review and editing, B.M. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Natural Science Foundation of China under Grant 71971021 and Grant 71621001. Thanks to [PERSON], Sichuan International Studies University, for providing the help in editing and grammar. The authors would like to thank the anonymous reviewers for their valuable comments. The authors declare no conflict of interest.
###### Abstract  
To optimize performances such as continuous curvature, safety, and satisfying curvature constraints of the initial planning path for driverless vehicles in parallel parking, a novel method is proposed to train control points of the Bezier curve using the radial basis function neural network method. Firstly, the composition and working process of an autonomous parking system are analyzed. An experiment concerning parking space detection is conducted using an Arduino intelligent minicar with ultrasonic sensor. Based on the analysis of the parallel parking process of experienced drivers and the idea of simulating a human driver, the initial path is planned via an arc-line-arc three segment composite curve and fitted by a quintic Bezier curve to make up for the discontinuity of curvature. Then, the radial basis function neural network is established, and slopes of points of the initial path are used as input to train and obtain horizontal ordinates of four control points in the middle of the Bezier curve. Finally, simulation experiments are carried out by MATLAB, whereby parallel parking of driverless vehicle is simulated, and the effects of the proposed method are verified. Results show the trained and optimized Bezier curve as a planning path meets the requirements of continuous curvature, safety, and curvature constraints, thus improving the abilities for parallel parking in small parking spaces.  
radial basis function neural network; path planning; Bezier curve; parallel parking; optimization; driverless vehicle
# Petri Net Modeling for Ising Model Formulation in Quantum Annealing
## 4 Binary Quadratic Net Construction from Problem Domain Petri Nets
### Application Example 2 (Firing-Based Construction): Job-Shop Scheduling Problems  
We implemented our method by incorporating CPNTools [24], SNAKES[26], and PyQUBO [14]. As an example, we modeled a simple job-shop scheduling problem with three jobs, four tasks per job, and three shared resources. Figure 6 shows the model drawn by the GUI software of CPNTools. Three sequential systems,  
\[p0,t0,p1,t1,p2,t2,p3,t3,p4,\] \[p5,t4,p6,t5,p7,t6,p8,t7,p9,\] \[p10,t8,p11,t9,p12,t10,p13,t11,p14,\]  
represent the jobs, and places \(m0,m1,m2\) represent the resources. Our software reads the model, represents the problem-domain Petri net model with SNAKES, and then extracts the necessary information to convert it into the target binary quadratic net.  
The following are the energy functions of the binary quadratic subnets corresponding to the precedence relation constraint between tasks in each job, the resource conflict constraint for each shared resource, and the firing count constraint for each task.  
\[H_{\text{\emph{precedence}}}(\mathbf{M}) =\sum_{k=0}^{MaxTime}(\sum_{(T_{i},T_{i})\in\text{\emph{P}}rec,t \leq k+FD(T_{i})}M(\hat{p}_{i}^{k})M(\hat{p}_{j}^{k})) \tag{55}\] \[H_{\text{\emph{conflict}}}(\mathbf{M}) =\sum_{(T_{i},T_{i},k,h)\in C^{\text{\emph{tim}}}}M(\hat{p}_{i}^{ k})M(\hat{p}_{j}^{h}),\] (56) \[H_{\text{\emph{f}}rings}(\mathbf{M}) =\sum_{i=1}^{|T|}(\sum_{k=0}^{MaxTime}M(\hat{p}_{i}^{k})-1)^{2}. \tag{57}\]  
The precedence set _Prec_ in Equation (55) is easily constructed by extracting the precedence relation in each sequential system (job). We can extract the timed conflict set \(\mathcal{C}^{timed}\) in Equation (56) from the connection between the transitions and resource places and the firing duration _FD_. _MaxTime_ is the only parameter we need to set before the optimization process. This value indicates the delivery time deadline. The total binary quadratic net is as follows:  
\[H_{\text{\emph{total}}}(\mathbf{M})=A\cdot H_{\text{\emph{prec}}lence}( \mathbf{M})+B\cdot H_{\text{\emph{conflict}}}(\mathbf{M})+\mathcal{C}\cdot H_{ \text{\emph{f}}rings}(\mathbf{M}) \tag{58}\]  
Note that this formulation is used to obtain a feasible solution; however, we can extend this to the optimization by incorporating it with a binary search to find the minimum _MaxTime._ This formulation is equivalent to the QUBO model in [27]. Figure 7 shows the schedule obtained through the annealing process using a Fujitsu Digital Annealer.
## 6 Discussion
### Effectiveness of Safety Mechanisms  
Our simulations have shown that the blockchain-based communication system is very effective in defending against Sybil and DoS attacks. In particular, the system achieved a 100% success rate in blocking Sybil attacks, and it neutralized 95% of malicious traffic in DoS attacks without significantly disrupting legitimate transactions. This confirms the robustness of using blockchain in vehicle networks to ensure secure, authenticated communication.  
However, these results are based on simulated environments, and it is important to consider the limitations of simulations. Real use-cases, where network conditions and vehicle mobility are unpredictable, may present challenges that were not captured in the controlled scenarios of our study.
## 3 The Proposed Method
### Double Threshold Detection  
This paper adopts the double threshold detection method named primary threshold and secondary threshold [24]. The GNSS signals are buried in thermal noise assumed to be zero mean, independent  
Figure 2: Hough transform diagram.  
and identically distributed (IID). When interference is absent, the complex random variable \(X_{a}(t)\) in Equation (8) is the zero mean and IID. Its magnitude spectrum \(\phi(k,l)\) in the TF plane can be written [25]  
\[\phi(k,l)=\sqrt{\text{Re}^{2}[X_{a}(t_{k},f_{l})]+\text{Im}^{2}[X_{a}(t_{k},f_{l })]} \tag{14}\]  
where \(k=1,\cdots,N\); \(l=1,\cdots,N\); \(N\) is the length of samples. \(X_{a}(t_{k},f_{l})\) is the result of TF transform for \(X_{a}(t)\). The variable \(\left|\phi(k,l)\right|\) follows a Rayleigh distribution. The primary threshold setting is written as [26]  
\[\eta=\overline{\phi(k)}\sqrt{\frac{4}{\pi}}\sqrt{-\text{ln}\Big{(}P_{false} \Big{)}} \tag{15}\]  
where  
\[\overline{\phi(k)}=\frac{1}{N}\underset{l=1}{\overset{N}{\underset{l=1}{ \sum}}}\left|\phi(k,l)\right| \tag{16}\]  
\(\overline{\phi(k)}\) is the average mean of \(\phi(k,l)\), \(P_{false}\) is the false alarm rate. From Equation (15), the primary threshold can be determined by a predefined \(P_{false}\). Then, the data in \(X_{a}(t_{k},f_{l})\) are separated to a new set which is higher than the primary threshold and the Hough transform is applied to these data. The secondary threshold in the Hough domain is used to detect the target peak and is based on the primary threshold. The \(P_{false}\) is determined by both the primary threshold and secondary threshold, written as  
\[P_{false}=f(\eta,\varepsilon) \tag{17}\]  
where \(\eta\) is the primary threshold and \(\varepsilon\) is the secondary threshold. When the primary threshold is determined by the initial probability of false alarm, the secondary threshold is set by Monte Carlo simulation with fixed \(P_{false}\).
###### Abstract  
The gas adsorption characteristics in deep coal reservoirs are the focus of deep coalbed methane geology research. In order to reveal the adsorption characteristics in deep coal reservoirs and quantitatively characterize the amount of adsorbed methane in the deep coal seams, four coals were collected from the Permian Longtan Formation in southern Sichuan Province. Methane isothermal adsorption tests were carried out on the collected coal samples at 30 \({}^{\circ}\)C. The adsorption characteristic curve was established based on the data of the isothermal adsorption. The adsorption potential theory was used to predict the isothermal adsorption curves under different temperatures and the evolutionary relationship between the methane adsorption capacity and the coal seam burial depth in the C\({}_{17}\) and C\({}_{25}\) coal seams of the Permian in southern Sichuan Province, China. The results showed that the methane isothermal adsorption curve at 30 \({}^{\circ}\)C belonged to the Type I isotherm adsorption curve. The methane isothermal adsorption curves for various samples at 45 \({}^{\circ}\)C, 60 \({}^{\circ}\)C, and 75 \({}^{\circ}\)C were predicted based on the uniqueness of the methane adsorption characteristic curve. The amount of adsorbed gas in deep coal reservoirs was comprehensively controlled by pressure and temperature. The pressure showed a positive effect on the amount of methane adsorbed, while the temperature showed a negative effect on the adsorption of methane. The negative effect of temperature became more significant with the increase in pressure. The results of the study are beneficial for further promoting the exploration and development of deep coalbed methane in the southern Sichuan Province of China.  
adsorption potential; adsorption characteristics; deep coalbed methane; southern Sichuan Province +
Footnote †: journal: Applied Sciences
## 5 Discussions
### The Relationship Between Brittleness Index and Fracture Parameters  
The brittleness index is directly related to a rock's ability to generate fractures, with higher brittleness indicating a greater potential for complex fracture networks [58]. However, the effect of the brittleness index on the geometry of fracture development remains uncertain. To address this, the relationships between fracture length, aperture, dip angle, linear density, and brittleness index were statistically analyzed. The analysis reveals that as the brittleness index increases, both fracture linear density and length generally rise (Figure 15A,B). This trend is attributed to the higher Young's modulus and lower Poisson's ratio associated with greater rock brittleness, which facilitates the formation of complex fracture networks. Conversely, fracture aperture is negatively correlated with the brittleness index (Figure 15C). This inverse relationship may result from the dense fracture network in highly brittle formations, which leads to reduced spacing and smaller apertures for individual fractures. Although there is a positive correlation between fracture dip angle and brittleness index, this correlation is weak (Figure 15D). This weak correlation may be attributed to the fact that the primary factors influencing dip angles are regional tectonic activity, while the brittleness index has little effect on predicting fracture orientation.  
Figure 14: Histogram of fracture linear density distribution across different lithologies.  
Figure 13: Frequency map of fracture development in various lithologies.  
Figure 15: Correlation between fracture parameters and brittleness index. (**A**) The relationship between fracture linear density and brittleness index. (**B**) The association between fracture length and the brittleness index. (**C**) The connection between fracture aperture and the brittleness index. (**D**) The correlation between fracture dip and brittleness index.
# Parametric Optimization of a New Gear Pump Casing Based on Weight Using a Finite Element Method
## 4 Discussion  
### Assessment of the Safety Factor and Fatigue Strength of the Structure  
Strength factors were determined to evaluate the strength of the casing and the front and back covers of the gear pump.  
The calculated safety factors for the pump elements made of the various materials are given in Table 4.  
The standard safety factor of the parts in mechanical engineering is \(n\geq\) (1.5 \(\div\) 2.5) [45; 46]. In 2017, [PERSON] et al., during a project in which they replaced metal parts with polymer composite materials in motorcycle oil pumps, assumed a safety factor of at least 1.6 [47]. In 2015, [PERSON] and [PERSON] assumed a value of the safety factor of at least 2.9 for the body of a gear micropump [48].  
From Table 2, it can be seen that the safety factor is maintained for all the parts of the pump structure made of aluminum, cast iron, and polycarbonate. In this case, the safety factor is higher than the standard for a cast iron body by 14.4-32%. The safety factor of the aluminum body complies with the standard. For the back and front covers, the safety factor exceeds the standard by 1.6-2 times. The safety factor of the polycarbonate body meets the standard. For the back and front covers, the safety factor exceeds the standard of 8-24%. This indicates that these structural parts of the gear pump are not overloaded.  
The results of the fatigue strength calculations are presented in Table 5.  
Based on the results of the fatigue strength assessment, it can be said that the gear pump parts considered have a level of redundancy in terms of damage resistance (Table 6). In 2022, [PERSON] et al., during centrifugal pump studies, achieved improvements ranging from 38 to 62% [49].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Aluminum** & **Cast Iron** & **Polycarbonate** \\ \hline Casing & 2.2 & 2.8 & 1.5 \\ Back cover & 5 & 6.7 & 2.7 \\ Front cover & 4 & 4.6 & 3.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Calculated safety factors for casing, front cover, and back cover.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Fatigue Strength, \(\sigma_{\text{BK}}\), MPa** & **Fatigue Strength Factor, K\({}_{\text{BK}}\)** \\ \hline \multicolumn{3}{c}{Casing} \\ Aluminum & 37.37 & 0.81 \\ Cast iron & 128.55 & 0.54 \\ Polycarbonate (PC) & 31.32 & 0.94 \\ \multicolumn{3}{c}{Back cover} \\ Aluminum & 17.54 & 0.38 \\ Cast iron & 58.76 & 0.21 \\ Polycarbonate (PC) & 10.4 & 0.70 \\ \multicolumn{3}{c}{Front cover} \\ Aluminum & 28.16 & 0.61 \\ Cast iron & 89.14 & 0.32 \\ Polycarbonate (PC) & 23.24 & 0.72 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Calculation results for the fatigue strength of the gear pump casing.  
The results of the strength calculation of the parts of the new gear pump show that the pump design can be subjected to parametric optimization. Optimization should be carried out for the aluminum, cast iron, and polycarbonate casing because the safety factor is \(n\geq 1.5\)).
## 3 Results
### Using GNSS with Tilt Sensors
With the tilt compensation switched on, the accuracy characteristics were up to three times worse than with the compensation switched off. It was also shown that a 180\({}^{\circ}\) rotation of the GNSS instrument in the horizontal plane with compensation switched on resulted in a systematic deviation in the horizontal component of up to 61 mm. The GNSS instrument software (HIMU v.4.6) did not automatically detect the need to initiate a targeted recalibration process, which means that there is a risk of introducing unexpected systematic errors into the precision measurement. A pair of Topcon Hiper HR receivers were selected for the experiment. Both receivers were repeatedly measured five times for one minute (recording a 1 s) in both positions (0\({}^{\circ}\) and 180\({}^{\circ}\)) with IMU + eCompass on and off. Figure 4 shows the positional deviations of all measurements from the mean value with the compensation switched off. The variance of the values in the 0\({}^{\circ}\) or 180\({}^{\circ}\) position is due to the repetition of the measurements at different times with independent initialization and fixation of the GNSS solution. The results show a systematic variation of several tens of millimeters between the results of the 180\({}^{\circ}\) measurements. Figure 4: Incorrect tilt compensation of Topcon HIPER HR receivers (IMU + eCompass technology) when the GNSS sensor is rotated by 180\({}^{\circ}\) in the horizontal plane.
# Self-Powered Fast Brazing of Ti-6 Al-4V Using Ni/Al Reactive Multilayer Films
## 1 Introduction  
In-space manufacturing covers a wide range of manufacturing and joining processes. For example, the Sealing, Seaming, Sterilization, and Separation (S\({}^{4}\)) process was developed for potential application for Mars sample return missions [1]. In addition, in-space repair is useful for a wider range of exploration mission capabilities, as well as personnel survival in human operated missions. These potential missions have requirements for an extremely high probability of success for full preservation of samples collected from space missions and for ensuring the safe handling of samples and planetary protection of samples and personnel when returned to Earth for analysis [1]. Brazing is one of the most effective methods of joining structures and supports both S\({}^{4}\) and repair procedures, however, in-space manufacturing has several stringent requirements that are not considered for terrestrial manufacturing processes. For instance, brazing, generally, requires high input energy, which is traditionally delivered by large, cumbersome equipment, which have a large energy cost. So, one of the objectives of in-space manufacturing is to minimize the necessary input energy, the weight of components, and the total operating time. Failure to consider these parameters may prohibit application and would limit exploration missions to bodies, such as ocean worlds in the solar system,including Europa, Enceladus, Ganymede, Callisto, Ceres, and Pluto, which are, increasingly, a potential target of proposed exploration.  
Reactive multilayer films (RMFs) are comprised of alternating layers of materials that can be ignited electrically, thermally, by laser pulse, or mechanical striking, producing an exothermic heat release. The process of RMF-assisted joining has been demonstrated using several types, such as Ti/Al [2; 3], Ti/Ni [4], and Ni/Al [5; 6] in air, vacuum, and underwater for joining similar dissimilar metals, ceramics, and semiconductors [6; 7]. Most RMF joining studies have been performed on solder materials (melting point \(\leq\)450 \({}^{\circ}\)C) [8] or using furnace-based joining procedures [9; 10]. However, electrically ignited RMF joining for brazing applications (>450 \({}^{\circ}\)C) has been seldom explored.  
Self-powered brazing is a novel and innovative joining process in which the heat required for joining is generated almost entirely from the energy in the brazing materials and only needs an inconsequentially small trigger to release the energy through an exothermic reaction. Using self-powered brazing via self-propagating reactions of Ni/Al RMFs offers an innovative solution that greatly reduces the significant power requirement for brazing and the weight of equipment transported into space. For this study, we will discuss, herein, the preparation of Ni/Al RMFs join Ti-6 Al-4V (Ti64) coated with BAlSi-4 as the brazing filler metal and Ni/Al RMFs as the heat source and demonstrate the potential of this new technique, in addition to discussing the phase transformations associated with the bonding procedure.
# Multi-Objective Optimisation in Multi-QoS Routing Strategy for Software-Defined Satellite Network
## 7 Conclusions  
In this paper, we propose an interval type-2 fuzzy set routing strategy to meet the multi-class QoS demand of SDSN. Firstly, the multi-QoS routing problem in SDSN is modeled. It is assumed that there are four neighbors of LEO satellite that does not pass through the polar region and there are four ISLs in total. There are four queues to store packets of four different types of applications in a LEO satellite, including time-delay sensitive application, video application, packet loss sensitive application and no special QoS requirements application. Then, the interval-type-2 fuzzy set routing algorithm is proposed to make fuzzy decisions. In the control plane, GEO satellites send control information to update routing table of LEO satellites. In the data plane, Interval Type-2 fuzzy set is used to make fuzzy decisions when selecting the next hop. When sender send a packet, the packet is placed in the corresponding queue of the sending buffer. The corresponding port is selected according to the routing table. The update of the routing table is based on Algorithm 1.  
A series of experiments are conducted in Network Simulator (Version 2.35). As a result, by simulating Iridium system, compared with ELB routing algorithm and centralized DSP routing algorithm, the proposed in-terval type-2 fuzzy set routing algorithm can reduce average delay, increase total throughput and reduce packet drop rate.  
Authors contributed as follows: thereof. Conceptualization, Y.W., G.H., F.J. and S.T.; Methodology, Y.W., G.H., F.J. and S.T.; Formal Analysis, Y.W., G.H. and F.J.; Investigation, Y.W. and S.T.; Writing Original Draft Preparation, Y.W. and G.H.; Writing Review and Editing, Y.W. and G.H.; Visualization, Y.W. and S.T.; Supervision, Y.W., G.H., F.J. and S.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No applicable.  
The authors declare no conflict of interest.