esa-sceva/satcom-chunk-collection huggingface dataset:
# Rockburst Hazard Evaluation Using an Extended COPRAS Method with Interval-Valued Fuzzy Information
## 4 Case Study
### Evaluation of Rockburst Hazards
#### 4.4.3 Evaluate the Hazard Level with Extended COPRAS Method  
According to Equation (27), the weighted decision-making matrix was determined, as shown in Table 11.  
Based on Equations (29) and (30), the benefit and cost scores of each sample were obtained, as shown in Table 12. The minimum value of the cost score was determined as [0, 0]. Then, the significance scores of the samples were calculated using Equation (31), as listed in Table 12. Afterwards, the complementary matrix by pairwise comparison of significance scores was constructed as Table 13. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & \(C_{1}^{*}\) & \(C_{\Pi}^{*}\) & \(C_{\Pi}^{*}\) & \(C_{\Pi}^{*}\) & \(C_{\Gamma}^{*}\) & \(C_{1}\) & \(C_{2}\) & \(C_{3}\) & \(C_{4}\) & \(C_{5}\) & \(C_{6}\) & \(C_{7}\) \\ \hline \(C_{1}^{*}\) & 0.5 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \(C_{1}^{\Pi}\) & 1.0 & 0.5 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ \(C_{\Pi}^{\Pi}\) & 1.0 & 1.0 & 0.5 & 0 & 0.3348 & 0.1263 & 0.1156 & 0.1337 & 0.1546 & 0.3347 & 0.0815 \\ \(C_{\Gamma}^{*}\) & 1.0 & 1.0 & 1.0 & 0.5 & 0.8624 & 0.5081 & 0.4148 & 0.7500 & 0.4004 & 0.7873 & 0.4010 \\ \(C_{1}\) & 1.0 & 1.0 & 0.6652 & 0.1376 & 0.5 & 0.2951 & 0.2545 & 0.3788 & 0.2664 & 0.4810 & 0.2341 \\ \(C_{2}\) & 1.0 & 1.0 & 0.8737 & 0.4919 & 0.7049 & 0.5 & 0.4342 & 0.6311 & 0.4207 & 0.6722 & 0.4252 \\ \(C_{3}\) & 1.0 & 1.0 & 0.8844 & 0.5852 & 0.7455 & 0.5658 & 0.5 & 0.6888 & 0.4806 & 0.7155 & 0.4948 \\ \(C_{4}\) & 1.0 & 1.0 & 0.8663 & 0.2500 & 0.6212 & 0.3689 & 0.3112 & 0.5 & 0.3145 & 0.5855 & 0.2927 \\ \(C_{5}\) & 1.0 & 1.0 & 0.8454 & 0.5996 & 0.7336 & 0.5793 & 0.5194 & 0.6855 & 0.5 & 0.7087 & 0.5155 \\ \(C_{6}\) & 1.0 & 1.0 & 0.6653 & 0.2127 & 0.5190 & 0.3278 & 0.2845 & 0.4145 & 0.2913 & 0.5 & 0.2671 \\ \(C_{7}\) & 1.0 & 1.0 & 0.9185 & 0.5990 & 0.7659 & 0.5748 & 0.5052 & 0.7073 & 0.4845 & 0.7329 & 0.5 \\ \hline \end{tabular}
\end{table} 
Table 11: Weighted decision-making matrix. \begin{table}
\begin{tabular}{c c c c} \hline  & \(\widehat{S}_{i}^{+}\) & \(\widehat{S}_{i}^{-}\) & \(\widehat{S}_{i}\) \\ \hline \(C_{1}^{*}\) & [0, 0] & [0.1445, 0.2066] & [0.9427, 0.9445] \\ \(C_{\Pi}^{*}\) & [0.1041, 0.1157] & [0.0548, 0.0784] & [1.1353, 1.1895] \\ \(C_{\Pi}^{*}\) & [0.1815, 0.2024] & [0.0249, 0.0356] & [1.2440, 1.3231] \\ \(C_{\Gamma}^{*}\) & [0.2909, 0.3219] & [0, 0] & [1.3802, 1.4832] \\ \(C_{1}\) & [0.1906, 0.3307] & [0.0425, 0.0641] & [1.2346, 1.4198] \\ \(C_{2}\) & [0.2305, 0.5108] & [0.0428, 0.0802] & [1.2741, 1.5826] \\ \(C_{3}\) & [0.2209, 0.5983] & [0.0422, 0.0637] & [1.2650, 1.6879] \\ \(C_{4}\) & [0.2392, 0.3383] & [0.0338, 0.0492] & [1.2922, 1.4438] \\ \(C_{5}\) & [0.1659, 0.6834] & [0.0249, 0.0742] & [1.2284, 1.7617] \\ \(C_{6}\) & [0.1568, 0.3693] & [0.0255, 0.0704] & [1.2186, 1.4517] \\ \(C_{7}\) & [0.2320, 0.6257] & [0.0341, 0.0997] & [1.2847, 1.6768] \\ \hline \end{tabular}
\end{table} 
Table 12: Scores of samples.
## 4 Measurement Methods and Data Analysis
### Data Analysis
#### 4.2.2 Data Analysis of Equal Sampling Frequency at Different Measurement Distances
Figure 10: Measurement distance and dynamic display error diagram—(equal sampling frequency). Figure 9: Projection position of the sampling points with different measurement frequencies—(D = 1 m). Under the same sampling frequency, the number of measurement points decreases when the measurement spacing increases. At the same measurement distance, the frequency of sampling points has a fixed relationship with the number of measurement points. For example, when the sampling frequency is set to 20 Hz and the measurement distance is 1 m, 2 m, 3 m, 5 m, and 10 m, the number of measurement points is 27, 20, 16, 12, and 9, respectively, with a decreasing trend. When the measurement spacing is 1 m, and the frequency of the measurement points is set to 10 Hz, 20 Hz, and 50 Hz, the number of measurement points is 14, 27, and 66, respectively, indicating that the sampling frequency has a fixed relationship with the number of measurement points under the fixed measurement spacing.
## 4 Conclusions  
The present work reveals great potential of ultrasound application in waste management and food processing industries. Ultrasound-assisted extraction is not only a faster and more sustainable method of oil extraction compared to the conventional process, but it also results in a product with improved oxidative stability. The response surface methodology turns out to be useful in optimizing parameters of ultrasound-assisted extraction of oil from blackberry, chokeberry, and raspberry seeds. As a result of the process carried out in optimized conditions, oil with relatively high yield (6.02-8.10%) and long oxidation induction time (48.78-71.15 min) is extracted. All that proves that ultrasound significantly affects mass transfer from solid to liquid medium; thus, yield and bioactive compound recovery, which prevent oil from oxidation, can be maximized. Apart from that, ultrasound does not have any adverse effect on the studied parameters of oils, like kinetic parameters, melting and crystallization characteristics, fatty acid profile and distribution in triacylglycerlycerols. The studies on the topic should be continued in order to deepen the knowledge of specific mechanisms controlling the ultrasound-assisted process and to determine the way in which it influences the quality of products obtained.  
Conceptualization, I.P., A.G. and A.W.; methodology, I.P.; software, I.P. and R.B.; validation, I.P.; formal analysis, I.P.; investigation, I.P.; resources, I.P.; data curation, I.P.; writing--original draft preparation, I.P.; writing--review and editing, A.G.; visualization, I.P. and R.B.; supervision, A.G.; project administration, A.G.; funding acquisition, A.G. All authors have read and agreed to the published version of the manuscript.  
Some research equipment was purchased as part of the \"Food and Nutrition Centre--modernisation of the WULS campus to create a Food and Nutrition Research and Development Centre (CZiZ)\" co-financed by the European Union from the European Regional Development Fund under the Regional Operational Programme of the Mazowieckie Voivodeship for 2014-2020 (Project No. RPMA.01.01.00-14-8276/17).  
Not applicable.  
Data Availability Statement: Data presented in the following study are available on reasonable request from the corresponding author.  
Figure 5: The percentage share of fatty acids in the sn-2 position of triacylglycerlycerols in tested oils obtained under optimized UAE conditions (BB_US, CH_US, RB_US) and in conventional extraction (BB_C, CH_C, RB_C). The dashed line indicates the constant share of fatty acid occupying the sn-2 position assuming the equilibrium of all positions in the triacylglycerglycerol molecule.  
**Acknowledgments:** Authors acknowledge [PERSON] from Cultivar Testing, Nursery and Gene Bank Resources Department, Institute of Horticulture National Research Institute in Skierniewice, Poland for providing Rubus var. Brzezina fruits cultivated from the gene bank collection in Dabrowice, Poland and [PERSON] for help in carrying out the analyses and technical support.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 1 Introduction
The temperature in the central area of partial discharge typically ranges from 700 to 1200 K, while arc discharge temperatures can reach as high as 3000 to 12,000 K. Such high temperatures lead to the decomposition of the insulating medium, which reduces dielectric insulation performance and hinders the stable operation of power equipment [15; 16]. [PERSON] et al. studied the decomposition characteristics of PMVE using density functional theory and found that the main decomposition products of PMVE molecules include C\({}_{2}\)F\({}_{2}\), C\({}_{2}\)F\({}_{4}\), and other carbon fluorides, as well as small molecules like COF\({}_{2}\) and CO [17]. However, research on PMVE/N\({}_{2}\) mixed gases is still relatively limited. In order to ensure the insulation performance of PMVE and better understand the effect of N\({}_{2}\) on PMVE, it is necessary to further study the pyrolysis characteristics of PMVE/N\({}_{2}\) mixed gases. In this paper, the pyrolysis process of PMVE/N\({}_{2}\) mixed gases was simulated by the reaction kinetic method (ReaxFF-MD), and the effect of temperature on the pyrolysis characteristics of PMVE/N\({}_{2}\) mixed gases was analyzed to better understand its pyrolysis mechanism. Finally, this study used transition state theory and thermodynamic calculations to combine thermodynamics with kinetics to explore the pyrolysis pathway of PMVE/N\({}_{2}\). The research results provide theoretical support for continuing to explore the practical engineering applications of PMVE/N\({}_{2}\) mixed gases.
## 3 Results and Discussion
### Effects on the AE and Failure Behavior
#### 3.4.1 Acoustic Behavior  
The AE and cumulative AE responses over the time of samples during the loading process were drawn in Figure 15a-d. The variation in AE counts under different F-T cycles can be divided into three regimes, including the initial period, quiet period, and active period. During loading, the AE counts became more active in the samples that experienced more F-T cycles, which was reflected in the increasing cumulative AE counts. In the initial loading period, the AE counts were a result of friction among the particles for the micro-cracks closure within the rock. In Figure 15, the AE counts showed a significant increase with the F-T cycles in the initial period, suggesting that F-T action promoted the development of microscopic defects (tensile micro-cracks) in the sample, thereby affecting the variation of AE counts [17]. After the initial crack closure, the AE activity entered the quiet period in which the elastic energy is being accumulated in the sample. This continues until the required energy or stress for crack growth is reached. However, as the number of F-T cycles increased, the density of AE counts in the quiet period also increased, with more abrupt points appearing before the failure of the sample (Figure 15d). In this process, a part of the energy was released by a stress waveform, resulting in a sharp increase in AE counts. This interesting phenomenon indicated that tested samples were about to enter the stage of unstable crack development and gradually reach the peak stress point to produce failure. Moreover, it can be observed that the duration of the active period was influenced by the F-T effects and showed an increasing trend. Therefore, the changes in AE behavior due to the F-T action need to be taken into account in the monitoring and warning of rock projects to avoid misjudgments in the cold regions.
# Retracted: W-GPCR Routing Method for Vehicular Ad Hoc Networks
###### Abstract  
The high-speed dynamics of nodes and rapid change of network topology in vehicular ad hoc networks (VANETs) pose significant challenges for the design of routing protocols. Because of the unpredictability of VANETs, selecting the appropriate next-hop relay node/which is related to the performance of the routing protocol, is a difficult task. As an effective solution for VANETs, geographic routing has received extensive attention in recent years. The Greedy Perimeter Coordinator Routing (GPCR) protocol is a widely adopted position-based routing protocol. In this paper, to improve the performance in sparse networks, the local optimum, and the routing loop in the GPCR protocol, the Weighted-GPCR (W-GPCR) protocol is proposed. Firstly, the relationship between vehicle node routing and other parameters, such as the Euclidean distance between node pairs, driving direction, and density, is analyzed. Secondly, the composite parameter weighted model is established and the calculation method is designed for the existing routing problems; the weighted parameter ratio is selected adaptively in different scenarios, so as to obtain the optimal next-hop relay node. In order to verify the performance of the W-GPCR method, the proposed method is compared with existing methods, such as the traditional Geographic Perimeter Stateless Routing (GPSR) protocol and GPCR. Results show that this method is superior in terms of the package delivery ratio, end-to-end delay, and average hop count.  
VANET; weight choice; GPSR; GPCR +
Footnote †: journal: Sensors  
02020-20, 3406; doi:10.3890/s20123406/s20123406
# An Improved Circumferential Fourier Fit (CFF) Method for Blade Tip Timing Measurements
## 5 Conclusions  
In this paper, an improved circumferential Fourier fit method named ICFF was proposed, which considers the blade vibration effect on the TOA. In this method, the ToA is not only dependent on the rotating speed and monitoring position, but also on blade vibration. Compared with TCFF, this improvement is more consistent with reality. A 12-blade assembly simulator and experimental data were used to evaluate the ICFF performance. The simulated results showed that the ICFF performance is comparable to TCFF in terms of EO identification, except the lower PSR or more number probes have a more negative effect on ICFF, which results in the poor performance of ICFF in Test 2 and Test 5. However, the accuracy of amplitude identification is higher for ICFF than TCFF on all test conditions. Meanwhile, the higher accuracy of the reconstruction of ICFF was further verified by experimental data, where the quality of reconstruction was higher for ICFF than TCFF in all measurement resonance analysis. The propagation of uncertainty in measurements, the method of sensor layout determination, and the BTT data analysis that consists of two or more modes corresponding to different EOs will be the focus of further research work.  
Conceptualization, Z.L. and F.D.; methodology, Z.L.; validation, F.D.; investigation, Z.L. and G.N.; writing--original draft preparation, Z.L.; writing--review and editing, L.M.; visualization, J.J. and X.F.; funding acquisition, F.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Equipment Pre-research Field Fund (Grant Nos. 61400040303 and 61405180505), National Science and Technology Major Project (Grant No. 2017-V-0009), National Natural Science Foundations of China (Grant Nos. 51775377, 61971307 and 61905175), National Key Research and Development Plan (Grant No. 2017 YFF0204800), Natural Science Foundations of Tianjin City (Grant No. 17 JCQNJC01100), Science and Technology on Underwater Information and Control Laboratory (Grant No. 6142218081811), and 2019 Guangdong Provincial Market Supervision Administration Quality and Safety Supervision Scientific Research Project (Grant No. 2019 ZZ02).  
The authors declare no conflict of interest.  
Figure 13: The reconstruction data of blade 7 measured by probe P0.
## 3 Preparation of Experiments
### Symmetric Mean Absolute Percentage Error  
We used SMAPE as an accuracy measure. SMAPE is an abbreviation for Symmetric Mean Absolute Percentage Error. It is an alternative to Mean Absolute Percent Error when there are zero or near-zero demand for items [25; 26; 27]. SMAPE by itself limits to error rate of 200%, reducing the influence of these low volume items. It is usually defined as Formula (2) where \(A_{t}\) is the actual value and \(F_{t}\) is the forecast value. The Formula (2) provides a result between 0% and 200%. However, Formula (3) is often used in practice since a percentage error between 0% and 100% is much easier to interpret, and we also use the formula.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Location Category** & **Count of Visit** & **Visiting Ratio** \\ \hline Foreign Institutions & 0 & 0.0000 \\ \hline Retail Business & 6 & 0.0400 \\ \hline Service Industry & 6 & 0.0400 \\ \hline Restaurant & 29 & 0.1933 \\ \hline Pub & 2 & 0.0133 \\ \hline Beverage Store & 26 & 0.1733 \\ \hline Theater and Concert Hall & 4 & 0.0267 \\ \hline Institutions of Education & 62 & 0.4133 \\ \hline Hospital & 6 & 0.0400 \\ \hline Museum, Gallery, Historical site, Tourist spots & 9 & 0.0600 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Sample Categorized Location Data: Person 16.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Learning** & **Hyperparameter** & **Meaning** & **Range of Values** \\ \hline \hline \multirow{4}{*}{Random Forest} & n\_estimators & Number of decision trees & \(50\leq n\_estimators\leq 1000\) \\ \cline{2-4}  & max\_depth & Max depth of each decision tree & \(2\leq max\_depth\leq 15\) \\ \cline{2-4}  & bootstrap & Whether to use bootstrap & [True, False] \\ \hline \multirow{4}{*}{XGBoost} & booster & Type of booster to use & \{’gbfree’, ’gblinear’, ’dart’\} \\ \cline{2-4}  & n\_estimators & Number of gradient boosted trees or boosting round & \(50\leq n\_estimators\leq 1000\) \\ \cline{2-4}  & max\_depth & Max depth of each gradient boosted tree & \(2\leq max\_depth\leq 15\) \\ \cline{2-4}  & learning\_rate & Learning rate & \(0.05\leq learning\_rate\leq 0.3\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Hyperparameter Search Space.  
\[SMAPE=\frac{1}{n}\sum_{t=1}^{n}\frac{|F_{t}-A_{t}|}{(A_{t}+F_{t})/2} \tag{2}\]  
\[SMAPE=\frac{100\%}{n}\sum_{t=1}^{n}\frac{|F_{t}-A_{t}|}{|A_{t}|+|F_{t}|} \tag{3}\]
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 5 Simulation Results
### Position--3D Crelb  
We implemented three position estimators in our MATLAB simulation platform: ordinary least squares (OLS); iteratively reweighted least squares (IRLS); and non-linear least squares (NLLS). We distributed \(N=50\) ground antennas randomly in a 10-meter sphere centered at the origin and placed the target antenna \(h\) at (0, 0, 50) meters. We swept the distance estimator standard deviation \(\sigma_{d}\) between \(10^{-6}\) and 10 m, running 1000 Monte Carlo trials at each test point. These results are depicted in Figure 6. Each of the three estimators closely approaches the CRLB in the region of interest, with the NLLS estimator achieving slightly better performance than the IRLS and OLS estimators. This result demonstrates that the proposed CRLB is tractable for a massive number of sensing elements (50) and that it agrees with existing well-known estimators.  
We characterize the positioning performance as a function of GDoP by scaling the ground constellation to meet different integer GDoP values and plotting the CRLB at each value for the same sweep over \(\sigma_{d}\). These results are depicted in Figure 7. While this plot is not particularly interesting, it can directly inform system design decisions; if you have a fixed-ranging precision, you can determine the necessary antenna placement requirements (GDoP) to achieve a certain positioning performance. Likewise, if your antenna placements  
Figure 6: Performance comparison of the OLS, IRLS, and NLLS position estimators vs. the proposed 3D CRLB for \(N=50\) sensing elements. This demonstrates that the proposed CRLB is tractable for a massive number of elements and closely aligns with well-known results.  
are fixed, then you can determine how precise your ranging estimates need to be to achieve a certain positioning performance.
## 3 MJTAR Protocol
### E-IFTIS Overview
#### 3.3.1 Iftis  
The infrastructure-free traffic information system (IFITS) is a distributed mechanism designed to estimate vehicle traffic without relying on fixed infrastructure. Within IFTIS, each roadway, defined as a segment of street between two junctions, is subdivided into cells of a certain size. Vehicles within each cell are then grouped into a single entity. This organizational structure facilitates the efficient assessment of traffic conditions and allows for estimating vehicle density and movement patterns along the roadway segment. The following Figure 3 illustrates this grouping process:  
By employing this approach, IFTIS enables real-time vehicle traffic estimation, providing valuable information for traffic management and routing optimization without any fixed infrastructure.  
The cell size can vary depending on the transmission range of the vehicle, with the vehicle closest to the cell center (the one traveling in the direction of the J_end junction) designated as the cell group leader. As the group leader exits the road and reaches the J_end junction, it generates a new cell data packet (CDP) and forwards it in a greedy forwarding fashion toward the J_begin junction. Meanwhile, intermediate group leaders gather vehicle information from neighboring vehicles within the same cell, facilitated by periodic transmission of Hello packets. Upon nearing the J_begin junction, the CDP is disseminated to neighboring vehicles, allowing those at the J_begin junction to estimate the traffic situation up to the one-hop junction (J_end) without relying on fixed infrastructure. In our research, we introduce an E-IFITS mechanism, which extends the existing mechanism to estimate vehicle traffic to two-hop junctions. Building upon E-IFITS, we propose a more effective dynamic multiple junction selection technique.
## 4 System Model and Description of the Proposed Protocols
### Proposed Protocol 2 (BF-SPR-Three)
#### 4.2.1 Network Configuration
DPs are combined at the destined sink to generate the real DP and advanced towards control station. Why 'BF' Algorithm? In graph theory, negative weight edges can make negative weight cycles, i.e., a cycle which will diminish the path by returning to a similar point. Briefest path algorithms, i.e., 'Dijkstra' Algorithm, are not ready to distinguish such a cycle. Eventually, they give an unseemly outcome since they can experience a negative weight cycle and reduce the route length. Afterward, in this work, we chose to utilize a 'BF' algorithm for our proposed protocol. Functioning of 'BF' Algorithm? 'BF' algorithm works by overestimating the length of the route from the earliest starting vertex to all unique vertices. By then, it iteratively slackens up those evaluations by finding new routes that are shorter than the as of late overestimated ways. By doing this on and on for all vertices, we can guarantee that the final route is upgraded. The essential steps of the 'BF' algorithm are:  
1. Starts with a graph having weights,
2. Selects a starting node and allocates the infinity path value to all other nodes,
3. Visit each vertex and relax the path if it inaccurate,
4. Iterate these steps up to the number of nodes in the vertices,After all the nodes have their path lengths, the algorithm will check for the negative cycles.