esa-sceva/satcom-chunk-collection huggingface dataset:
# Fractal Contact Mechanics Model for the Rough Surface of a Beveloid Gear with Elliptical Asperities
## 6 Conclusions  
The contact mechanics model for a rough surface with elliptical asperities was established in this paper by comprehensively considering the geometric structure and mechanical properties of elliptical asperities on rough surfaces, which are summarized as follows:  
(1) The real contact area of the rough surface is inversely proportional to the fractal dimensions \(D_{1}\) and \(D_{2}\) and the eccentricity \(e\) of the elliptical asperities, as well as the contact coefficient. \(D\) has a direct influence on the real contact area. The real contact area grows concerning the fractal dimension \(D\). Conversely, when the value of fractal dimensions is given, the real contact area declines as the eccentricity \(e\) increase, and the real contact area reduces as the contact coefficients decrease;  
(2) While the real contact area diminishes as the eccentricity \(e\) increases, the ratio of the elastic contact area to the real contact area remains almost constant. The elastic contact area ratio grows as the fractal dimension \(D\) increases and stabilizes at 1.55;  
(3) The fractal dimension, fractal roughness, eccentricity, and contact coefficient always have an effect on the real contact load and normal contact stiffness of curved surfaces. With the growing fractal dimension and contact coefficient, the real contact load and normal contact stiffness of curved surfaces increase. In comparison, the real contact load and normal contact stiffness diminish as the fractal roughness and eccentricity increase.  
Conceptualization, G.Y. and H.M.; methodology, H.M.; software, H.M.; validation, G.Y., H.M. and L.J.; formal analysis, W.L.; investigation, W.L.; resources, L.J.; data curation, H.M.; writing--original draft preparation, H.M.; writing--review and editing, G.Y. and H.M.; visualization, H.M.; supervision, T.V.; project administration, G.Y.; funding acquisition, G.Y. All authors have read and agreed to the published version of the manuscript.  
The research was supported by the Major science and technology projects of Heilongjiang Province (Grant No. GA21D004); Heilongjiang Province \"hundred million\" project science and technology major scientific projects (Grant No. 2019 ZX03A03); The Fundamental Research Funds for the Central Universities (Grant No. FRFCU5710052921); Heilongjiang Major Science and Technology Achievement Transformation Project (Grant No. CG21B010); National Key Research and Development Project of China (Grant No. 2019 YFE013200).  
Not applicable.  
The authors declare no conflict of interest.
# Iterative QR-Based SFSIC Detection and Decoder Algorithm for a Reed-Muller Space-Time Turbo System
## 5 Conclusions  
A novel, computationally-efficient IQRSFSICDD is proposed for an RM space-time turbo system. It uses a QR decomposition of the channel matrix and directs it to a simple soft feedback successive interference cancellation based on an a priori LLR of encoded bits which, in turn, economizes  
Figure 4: The BERs of three iterative detection schemes versus SNR values, with the transmitters using the 4-QAM and: (**a**) R = 121/240 outer channel RM turbo code for \(N_{t}=N_{r}=3\) (**left**); (**b**) R = 676/988 outer channel RM turbo code for \(N_{t}=N_{r}=4\) (**right**).  
computation compared to the algorithm in [13] with the same error performance. Then, the original signal is divided repeatedly into reliable/unreliable segments according to whether an a posteriori LLR of encoded bits is larger than some threshold, which assures the optimal detection sorting in case of the risk of EP. Moreover, RM-turbo codes, each dimension of which is represented by the same RM code with an optimal sectionalized trellis, in the sense of minimizing the number of multiplication operations, is utilized for suitable formation of an iterative trellis-based MAP RM turbo decoder. In this way, the proposed scheme shortens the delay of iterations between the inner decoder and the outer decoder to a certain degree which, in turn, lower the complexity of the proposed algorithm. Simulation results show that the performance of the proposed algorithm outperforms the other suboptimum detectors considered in this paper. Finally, an upper bound of the diversity acquired according to intuitional deduction can be relatively straightforwardly applied to analyze and discuss all types of iterative detectors and decoders for RM space time turbo systems.  
The authors thank all of the reviewers for their valuable comments, which have helped considerably in improving the overall quality of the work presented in the revised paper. This work was supported, in part, by the Key Programs of the Natural Science Research of Universities of Anhui Province under grant KJ 2015A011, the Natural Science Foundation of Anhui Province under grant 11040606M125, and the National Natural Science Foundation of China under grant 51574004.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] and [PERSON] contributed to literature search. [PERSON] wrote the paper. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Multi-State RTN  
Large V\({}_{\rm RMS}\) RTN includes both two-state and multi-state RTN [34; 53; 54; 55; 56], which is considered to be generated by multi-traps. The analysis of trap characteristics, such as time constants and amplitude, in multi-state RTN is more difficult than that of two-state. Figure 12 shows the appearance probability of RTN with two, three, four, and more than four states. A large RTN (V\({}_{\rm RMS}\) > 680 \(\mu\)V) was obtained by a frame measurement mode of the sampling period of 0.7 s/frame in I\({}_{\rm DS}\) = 1 \(\mu\)A. 131,072 MOSFETs (L/W = 0.22/0.28 \(\mu\)m) were measured, and 2575 MOSFETs with large V\({}_{\rm RMS}\) can be extracted. Then, we selected MOSFETs with large RTN and measured them by a specific measurement mode of a sampling period of 1 \(\mu\)s and a long sampling time of 10 min (sampling points = 6 \(\times\) 10\({}^{8}\)) for the same bias condition [57; 58]. Figures 13-15 show the (a) waveform, (b) time lag plot (TLP), and (c) histogram for typical three-, four-, and six-state RTN. The number of peaks and the transition of each state can be understood via TLP [53; 54; 56].  
Figure 11: Conduction band diagrams and energy distribution of the traps causing RTN for V\({}_{\rm gs}\) of 0.57, 0.53, and 0.46 V, respectively.  
Figure 12: Appearance probability of RTN with two, three, four, and more than four states. 2575 MOSFETs with large V\({}_{\rm RMS}\) can be extracted from 131,072 MOSFETs (L/W = 0.22/0.28 \(\mu\)m).  
Figures 13-15 show the (a) waveform, (b) time lag plot (TLP), and (c) histogram for typical three-, four-, and six-state RTN. The number of peaks and the transition of each state can be understood via TLP [53; 54; 56]. Figures 13, 14 and 15 show the relationship of ith and (i + 10)th Vgs for the constant IDS. As shown in Figure 13b, transitions occur not from the lowest state to the highest state, but only via the medium state. When the trapping probability of some traps is even, the number of states should be even, and the transition from one position to the next can occur. To begin with, an odd number of states implies that the trapping probability for each trap is not independent of each other. A similar transition phenomenon occurs even in a four-state case. As shown in Figure 14b, transitions did not occur from the lowest state to the highest state or from the second-lowest state to the second-highest state. This also means that there are more than two traps, and the probability of trapping for each trap is not independent of each other. The characteristics of multi-trap RTN can be understood via TLP and waveforms [56]; however, these analyses become more difficult as the number of states increases.  
Figure 14: (a) Waveform, (b) TLP, and (c) histogram for a typical four-state RTN.  
Figure 13: (a) Waveform, (b) TLP, and (c) histogram for a typical three-state RTN. The inset figure (c) shows the same data of the vertical axis of the log (histogram).  
Figure 15: (a) Waveform, (b) TLP, and (c) histogram for a typical six-state RTN. The insect figure in (c) shows the same data of the vertical axis of the log (histogram).
## 6 Performance Evaluation
### Reliability in the Network
#### 6.5.1 Data Delivery Ratio in Case of Internal Interference  
In the previous evaluations, we assumed that the interference and transmission ranges are equal and that the two hops reuse distance guarantees interference-free communication in one cell. However, in a realistic setting, the interference and transmission range of a node may not be equal. To address this issue, we evaluate the relation between packet delivery ratio and increased internal interference in the network, in the first experiment. We define five scenarios in D-MSR. In order to assess the worst-case scenario, in the first scenario (D-MSR s1), D-MSR deliberately does not attempt to release the interfered communication link and the MAC re-transmission is not used. In the second, third, and forth scenarios (D-MSR s2, s3, and s4), the re-transmission with one, two, and three retries limit is used in the MAC layer. The fifth scenario (D-MSR s5) combines the advantages of both the re-transmission scheme and the _virtual_ link method (discussed in Section 5.6.3). For WirelessHART, we have one scenario (WirelessHART s1) that does not use MAC re-transmission. Figure 24 shows the data delivery ratio for those six scenarios (D-MSR s1-5, and WirelessHART s1), in case of different interference to transmission range ratios. It is noticeable that the increase in interference range causes more internal interference in the network thereby decreasing the data delivery ration in D-MSR s1-4. On the other hand, D-MSR s5 and WirelessHART s1 provide more reliability in coping with internal interference in case of different interference ranges. This is because D-MSR s5 combines the re-transmission by _virtual_ link method, while WirelessHART s1 avoids the spatial reuse of communication resources.  
In summary, the spatial reuse of communication resources in D-MSR (without considering the re-transmission techniques and _virtual_ link method) is prone to reduced reliability due to internal interference.
## 3 Experimental Results and Analysis
### Experimental Analysis
#### 3.3.2 Effect of Activation Function  
Five common activation functions, GeLU, ReLU, Tanh, Sigmoid, and SoftMax, are selected, and the number of transformer components with N = 4 and M = 6 is used to complete the comparative experiment. The experimental results are shown in Figure 7. With the quaternion SoftMax-like activation function, the mean \(E_{R}\) and median \(E_{R}\), compared with the optimal Sigmoid, are reduced by 24.11% and 30.41%, respectively, and the pose estimation accuracy of orientation is far better than that of other activation functions.  
Figure 6: Inference speed/params heat maps for the different numbers of transformer components.  
Figure 7: Orientation accuracy between different activation functions.
## 3 Experimental Results and Discussion
### Electric Field and Temperature Field
As depicted in Figure 6b, an increase in the temperature from region A to B slightly increases within the range of 10 milli-Celsius differences for each unit. The temperature at regions A and B of the \(N_{HV5}\) is higher than that of the \(N_{HV1}\) by around 0.01%. The average liquid temperature at the outlet slightly increased for 0.02 Celsius, which could imply that the treated media would not be affected by the accumulating thermal energy in this proposed system. It should be mentioned here that an increase in the pulsed voltage and number of the treatment units could also influence the increase in the liquid temperature. Additionally, the liquid medium's electrical conductivity, the liquid media flow rate, and the insulator height would correspondingly take part in this temperature rise issue (data not shown here). Therefore, to avoid accumulating heat in the treated liquid media, the aforementioned factors should be optimized.
# Study on the Deployment of Ultra-Wideband Positioning Base Stations in Pig Farms
## 1 Introduction
In one study, the accuracy factor was calculated and simulated under different numbers of pseudo-satellites, and the optimal observation matrix was determined by eigenvalue analysis, with a four-satellite layout identified as optimal [21]. Based on various accuracy factors, the author proposes a four-dimensional model that includes the elevation angle, azimuth angle, and observation time as independent variables, with the relative accuracy factor as the dependent variable. This model aims to improve positioning accuracy and provide assurance for optimal satellite deployment [22]. The positioning trajectories and real trajectories of various factors were compared and analyzed through experiments, leading to the conclusion that UWB dynamic positioning is influenced by the position of positioning tags, travel speed, pedestrian interference, and the highest accuracy is in locating the center of the base station area [23]. Through real and simulated positioning experiments, this study analyzed the distribution of accuracy factors for different deployment schemes. It was concluded that the accuracy factor of test points is influenced by the regional coverage, number and height of deployed base stations, and that the accuracy of terminal positioning can be improved by increasing the number of base stations and the height angle within a closed area surrounded by base stations [24].
## 4 Algorithm for Carrier Aircraft Dynamic Scheduling
### Encoding and Decoding  
In the RCPSP research, the task list encoding method is more efficient to get the scheduling results, and the chromosome with a two-dimensional matrix is designed. \(X\) = [\(x_{1}\), \(x_{2}\), \(x_{3}\),..., \(x_{k}\)]; here, \(x_{k}\) is \((i,j)\), \(i\) represents the number of different carrier aircrafts \((n)\), \(j\) represents the activities \((m)\) satisfied to the procedure constraints, and \(m\times n\) represents the total activities (see Figure 4).  
The chromosome coding example in Figure 4 shows the process of carrier aircraft scheduling. The decoding can make the chromosome into a corresponding scheduling solution in the algorithm. The start time, finish time, and resource allocation of different activities can be achieved through the decoding process. Two kinds of schedule generation schemes--the serial schedule generation scheme (SSGS) and the parallel schedule generation scheme (PSGS)--are used in the decoding process. Two allocation rules are set in this paper regarding the minimum accumulated processing time first (MAPTF) for the operational crews, and the minimum total processing time remaining (MTPTR) for the covering resource stations. In the first step, we use the SGS method to allocate the resource; two allocation rules (MAPTF and MTPTR) are used for the operational crews and resource stations. The start time and finish time of each activity can be scheduled by the designed strategies. In the second step, the PSGS is used to minimize \(\delta\) after the disruption occurs by considering the start time and finish time of each activity. In each RH window, the parallel process can modify the reschedule close to the baseline schedule without disruption. Finally, the new schedule is generated for the resource allocation after the disruption occurs.
# Correlation between Coda Wave and Stresses in Uni-Axial Compression Concrete
## 3 Experimental Investigation
### The Coda Wave Test under Uni-Axial Stress  
#### 3.4.1 CWI Analysis Method  
The CWI method is employed to measure the change of the wave velocity based on the correlation of the waveforms before and after the disturbance. Define the initial waveform as \(u(t)\). After the disturbance, the waveform transfers to \(u[t(1-\varepsilon)]\). Then, the correlation function can be expressed as [24]:  
\[C(\varepsilon)=\frac{\int u[t(1-\varepsilon)]u(t)\mathrm{d}t}{\sqrt{\int u^{2} [t(1-\varepsilon)]\mathrm{d}t\int u^{2}(t)\mathrm{d}t}} \tag{23}\]  
If \(C(\varepsilon)\) reaches the maximum value, then:  
\[\varepsilon_{k}=-\frac{\delta v}{v} \tag{24}\]  
The wave source of CWI must have high repeatability. Figure 8a depicts the receiving signal waveforms when the specimens are loaded with 0 MPa and 2 MPa. To clearly present the difference between the two waveforms, three time windows were enlarged, and they are shown in Figure 8b-d. A, B, and C represent the three windows of the waveform at different times, which enables an investigation of the development of two kinds of loading condition in different time windows. The later the time, the more complete the coda wave development. From Figure 8b, it was found that the time window A of both basically coincide with each other and no detectable changes occur, which ensures the repeatability of the experiment. As shown in Figure 8c, the time window B exhibits a certain phase deviation. In the time window C, however, there is a more obvious phase deviation, as shown in Figure 8d. This indicates that stress in concrete (<10 MPa) would make the concrete denser, resulting  
Figure 7: Received waveform of sensors at different locations: (**a**) opposite side, (**b**) top.  
in a higher elastic modulus of the concrete and a higher velocity of the acoustic wave. It can be also concluded that the coda wave is sensitive to slight changes in the medium.  
Figure 8: Comparison of the received waveforms between 0 MPa and 2 MPa. (**a,b**) Time window A, (**c**) time window B, (**d**) time window C.
## 6 Conclusions and Future Directions
### Main Conclusions
#### 6.1.5 Research Limitations and Assumptions  
Several limitations and assumptions should be acknowledged in this study. The methodology's effectiveness depends on trajectory data quality and sampling frequency, with validation conducted using predominant 3-s intervals that may not represent all data collection scenarios. The regular polygon coverage algorithm assumes vehicles follow relatively predictable paths near intersections, which may not hold during unusual traffic events or in complex network configurations. The approach assumes sufficient vehicle penetration rates across road segments, though actual penetration rates remain unknown and may affect parameter accuracy in low-traffic areas. Additionally, the intersection coordinate calibration currently requires manual adjustment, limiting fully automated deployment. While demonstrated effectiveness on a grid-like urban network, performance may vary with irregular network layouts or in different geographical contexts. These limitations suggest that careful consideration of local conditions and data characteristics is essential for successful implementation in diverse operational environments.
## 3 Results and Discussion
### Chemical Composition of EEP GUA-4  
The EtOH-soluble extract of sample GUA-4 was fractionated by chromatography on a VLC column and by Sephadex LH-20, giving 12 known compounds (**1**-**12**). The compounds were identified as pinochemrin (**1**, 405.4 mg) [33], chrysin (**2**, 126.3 mg) [34], galangin (**3**, 56.7 mg) [33], alpinetin (**4**, 15.6 mg) [35], 5-methyl-pinobanksin ether (**5**, 16.8 mg) [36], dillenetin (**6**, 12.3 mg) [37], isorhamtein (**7**, 24.6 mg) [38], 5-methylgalangin ether (**8**, 9.8 mg) [39], 5-methyl chrysin ether (**9**, 16.3 mg) [40], ferulic acid (**10**, 24.9 mg) [33], syringic acid (**11**, 6.7 mg), and caffe acid (**12**, 8.9 mg) [33] (Figure S1) by means of 1D (Table S1) and 2D NMR spectral analysis. The presence of pinochemrin (**1**), chrysin (**2**), galangin (**3**), ferulic acid (**10**), syringic acid (**11**), and caffe acid (**12**) suggest that its main botanical source are a species of _Populus_ typical of the country, such as _Populus mexicana_ [PERSON], _Populus guzmanantlensis_ [PERSON] and [PERSON] and _Populus simarao_ [PERSON]. Although flavonoids without B-ring substituents appear common in temperate propolis from both the northern and southern hemispheres, in this study we report for the first time the presence of dillenetin (**6**) and isorhamnetin (**7**) with B-ring substitution as constituents of Mexican poplar propolisis. To the best of our knowledge, this is the first report of dillenetin (**6**) as a propolisis constituent. 5-methylpinobanksin (**5**), alpinetin (**4**), isorhamnetin (**7**), 5-methylgalangin ether (**8**), and 5-methylchrysin ether (**9**) were previously identified by diode array detection and ESI mass spectrometry (LC-DAD-ESI-MS) as constituents of Italian, Portuguese, and Czech propolisis [41; 42].
## 4 Results and Discussion
### MICMAC Analysis--Classification of Seismic Soil Liquefaction Factors  
MICMAC is used to examine the strength of the relationship between driving power and dependence power of seismic soil liquefaction factors. The soil liquefaction factors have been categorized into four clusters based on their driving and dependence power, as shown in Figure 5 (see Table 1 in Appendix A).  
Figure 5: Cross-impact matrix multiplication applied to classification (MICMAC) analysis of soil liquefaction factors.  
The four clusters of soil liquefaction factors are:  
* Cluster I: Autonomous factors Cluster I represents autonomous factors and consists of soil liquefaction factors which have weak driving and dependence power. This cluster has ten seismic soil liquefaction factors (62.5%). Cluster I factors are relatively disconnected from the system. Autonomous factors in cluster I are earthquake magnitude F1 (M), epicentral distance F2 (S), duration of earthquake F3 (t), peak ground acceleration F4 (a\({}_{\text{max}}\)), fines content F5 (F\({}_{\text{c}}\)), mean particle size F7 (D\({}_{\text{50}}\)), effective vertical stress F12 (\(\sigma^{{}^{\prime}}_{\text{v}}\)), groundwater table F13 (D\({}_{\text{w}}\)), depth of soil deposit F14 (D\({}_{\text{s}}\)), and thickness of soil layer F15 (h).
* Cluster II: Dependence factors Dependence factors have a strong dependence power and weak driving power. This dependence cluster has five seismic soil liquefaction factors (31.25%), including relative density F8 (D\({}_{\text{r}}\)), drainage condition F9 (K), degree of consolidation F10 (OCR), standard penetration test blow counts F11 (SPT N-value), and liquefaction potential F16 (LP). In the ISM model, these factors form the top levels which need other soil liquefaction factors that collectively act to influence soil liquefaction.
* Cluster III: Linkage factors Linkage factors have a strong driving power as well as strong dependence power. No factors fell in this cluster.
* Cluster IV: Independent/Driving factors Cluster IV consists of driving factors that have strong driving power but weak dependence power. In this cluster, only one seismic soil liquefaction factor (around 6.25%), i.e., soil type F6 (C\({}_{\text{u}}\) and C\({}_{\text{c}}\)), was found, which plays a governing role in soil liquefaction susceptibility.