esa-sceva/satcom-chunk-collection huggingface dataset:
# Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network
## 3 Fatigue Crack Propagation Experiment Verification  
### Single Adaptive Variable DBN Model  
To validate the feasibility of the proposed method, three-point bending fatigue experiments were conducted using three specimens, as shown in Figure 7a. The dimensions of the specimens are 120 \(\times\) 30 \(\times\) 15 mm\({}^{3}\), 160 \(\times\) 40 \(\times\) 15 mm\({}^{3}\), and 240 \(\times\) 60 \(\times\) 15 mm\({}^{3}\), with the support span \(S\) being \(L\)-0.4\(\times W\). The material of the specimens is AL6061 aluminum alloy, with an elastic modulus, \(E\), and [PERSON]'s ratio, \(v\), of 69.6 GPa and 0.33, respectively. The bending fatigue limit of the material is around 100 MPa. The initial crack length, \(a_{0}\), is 10 mm, where the length of the prefabricated notch is 9 mm, and the crack length initiated by cyclic loadings, according to ASTM E1820, is 1 mm. According to ASTM E399 standards [33], the material's fracture toughness \(K_{IC}\) was measured to be 34 MPa. The stress ratio \(R\) is 0.2, with maximum loads of 4500 N, 6000 N, and 9000 N. The load magnitudes make the maximum stresses at the lower sides of the specimens (without considering the crack) nearly equal (around 54 MPa).  
Figure 6: The framework of the DBN for the prediction of fatigue crack propagation.  
Figure 7: **(a)** Specim dimensions; **(b)** picture of the actual specimens; **(c)** picture of three-point bending fatigue experiment.
## 4 Results and Discussions
### Surface Morphology Analysis of the Machined Microlens Array  
Figure 10 shows the scanning electron microscopy (SEM) image of the surface morphology for the microlens array machined by CS and HFUVAS. In the CS, the machined surface was filled with scratching and micro-tearing morphology, which is the explanation for the surface variation of the measured profile. It is interesting to note that even the first lenslet at the beginning of machining exhibited such surface morphology, suggesting that tool wear was not the main reason for such poor surface quality. Workpiece material adhered to the diamond tool and then rowed and plowed the workpiece surface could be the reason for this morphology due to material affinity between carbon from the diamond tool and steel from the workpiece. Besides, because of material adhesion, the diamond tool could become blunt so the edge between lenslets was not sharp and was not distinguishable, resulting in a large form deviation at this edge. In view of the HFUVAS, the machined surface was smooth with apparent sharp edges between lenslets. However, there existed small burrs on the sharp edge, which could be caused by plastic deformation of the workpiece material and it is one of the major sources of the form error for the microlens array being machined by HFUVAS.  
Figure 10: SEM of the surface morphology for the microlens array machined by: (**a**) CS, (**b**) HFUVAS.  
Figure 9: Form error between the designed profile and the measured profile: (**a**) P1, P2 for the microlens array machined by CS, (**b**) P1, P2 for the microlens array machined by HFUVAS.  
To validate the material adhesion conjecture for an explanation of the surface morphology of the machined structures, cutting tools after CS and HFUVAS of the microlens array were observed for analyzing the tool conditions. Figure 11a-d shows the SEM image and the energy dispersive spectrometer (EDS) analysis of the cutting tool after CS of the microlens array. Tool edge exhibited plenty of material adhesion of Cr and Fe and existed a considerable tool wear width. This indicated that during CS of the microlens array, material from the workpiece adhered to the cutting tool and worsen the machined surface quality. With increasing cutting distance, tool wears increased and then enhanced material adhesion, thereby further deteriorating the surface quality. For the cutting tool after HFUVAS of the microlens array as shown in Figure 11e,f, the cutting tool does not exhibit apparent material adhesion and tool wear. This leads to the conclusion that the improved surface quality of the microlens array machined by HFUVAS resulted from the reduction of tool wear and material adhesion due to the high-frequency intermittent cutting.
# Deep Learning for Detecting Verticillium Fungus in Olive Trees: Using YOLO in UAV Imagery
## 3 Related Work
Despite that, YOLOv4, along with the author's proposed method of image preprocessing, achieved impressive results with 0.95 precision, 0.76 recall and a mAP of 0.94. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Reference**} & **YOLO Version** & \multirow{2}{*}{**Number of Images**} & \multirow{2}{*}{**UAV Flight Altitude**} \\  & **Tested** & & \\ \hline
[28] & YOLOv5 & - & 50 m \\
[29] & YOLOv5 (s, m, x) & 889 & - \\
[30] & YOLOv5 & 125 & 122 m \\
[31] & YOLOv3 & 221 & - \\
[32] & Improved version of & 1558 & - \\
[33] & YOLOv5 & 17,343 & 200 m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Research parameters of detecting specific trees with YOLO on UAV images. Another case study was that of the authors of [7], where the researchers applied the YOLOv5 algorithm on a vast dataset of tree UAV image data derived from 40,697 individual trees photographed during different times of the day, month and year to detect snow damage on trees. The classes were healthy, damaged and dead, while there was a great class imbalance: only 16% of the instances were damaged or dead. Nevertheless, the classification evaluation metrics proved to be high, with the small caveat that the different classes had significantly different results, with precision ranging from 0.759 to 0.546 and recall from 0.78 to 0.40 [7]--almost double. Finally, in [34], research was conducted to spot trees affected by pine wilt nematode, a fast spreading disease affecting forest areas. The disease starts from the top of the affected tree and spreads to the bottom, making the use of UAVs ideal since they provide a top view, allowing for an easy and early detection. In this research effort, 116,012 images were taken at different heights ranging from 50 m to 300 m, and the YOLOv4 algorithm was applied again with success (precision: 1, recall: 0.8969). Based on the results of the research efforts mentioned above, we can determine that the YOLO algorithm can be used effectively in damage or disease detection on trees that belong to a particular species. It was also shown that the algorithm was capable of handling class imbalance but at the same time it would be possible for one class to be more easily detectable than another, sometimes with large differences.
# Modeling of NOMA-MIMO-Based Power Domain for 5G Network under Selective Rayleigh Fading Channels
## 3 System Model
### UL Scenario
#### 3.2.2 OP of Four Users UL NOMA  
Consider that the four users have different target rates.  
\[r_{1}=1,r_{2}=2,r_{3}=3,r_{4}=4\]  
The capacity \(U4\) is calculated as follows:  
\[C_{4}=\sum_{i=1}^{N}\log_{2}\left(1+\frac{P|h_{4}|}{P|h_{1}|+P|h_{2}|+P|h_{3}|+ N_{4}}\right) \tag{14}\]  
\(U3^{\prime}\)s capacity is calculated as follows:  
\[C_{3}=\sum_{i=1}^{N}\log_{2}\left(1+\frac{P|h_{3}|}{P|h_{1}|+P|h_{2}|+N_{3}}\right) \tag{15}\]  
\(U2^{\prime}\)s capacity is calculated as follows:  
\[C_{2}=\sum_{i=1}^{N}\log_{2}\left(1+\frac{P|h_{2}|}{P|h_{1}|+N_{2}}\right) \tag{16}\]  
\(U1^{\prime}\)s capacity is calculated as follows:  
\[C_{1}=\sum_{i=1}^{N}\log_{2}\left(1+\frac{P|h_{1}|}{N_{1}}\right) \tag{17}\]For \(U1\), the OP condition is:  
\[P_{r}\ \left(C_{1}(k)\ <\ r_{1}\right)\ ||\ P_{r}\ \left(C_{2}(k)\ <r_{2}\right)\ ||\ P_{r}\ \left(C_{3}(k)\ \langle\ r_{3}||\ P_{r}\ \left(C_{4}(k)\ <\ r_{4}\right)\right)<\ r\]  
The OP of \(U1\):  
\[P_{r}(U1)=(\sum_{i=1}^{N}P_{r}(C_{1}(k))<r_{1})\|P_{r}(C_{2}(k)<r_{2})\|P_{r}(C _{3}(k)\langle r_{3}||P_{r}(C_{4}(k)<r_{4})\rangle/N \tag{18}\]  
For \(U2\), the OP condition is:  
\[P_{r}\ \left(C_{2}(k)\ <r_{2}\right)\ ||\ P_{r}\ \left(C_{3}(k)\ \langle\ r_{3}||\ P_{r}\ \left(C_{4}(k)\ <\ r_{4}\right)\right)<\ r\]  
The OP of \(U2\):  
\[Pr(U2)=(\ \sum_{i=1}^{N}\ P_{r}\ \left(C_{2}(k)\ <r_{2}\right)\ ||\ P_{r}\ \left(C_{3}(k)\ \langle\ r_{3}||\ P_{r}\ \left(C_{4}(k)\ <\ r_{4}\right)\right)/N \tag{19}\]  
For \(U3\), the OP condition is:  
\[P_{r}\ \left(C_{3}(k)\ \langle\ r_{3}||\ P_{r}\ \left(C_{4}(k)\ <\ r_{4} \right)\right)<\ r\]  
The OP of \(U3\):  
\[Pr(U3)=(\ \sum_{i=1}^{N}\ P_{r}\ \left(C_{3}(k)\ \langle\ r_{3}||\ P_{r}\ \left(C_{4}(k)\ <\ r_{4}\right)\right)/N \tag{20}\]  
For \(U4\), the OP condition is:  
\[P_{r}\ \left(C_{4}(k)\ <\ r_{4}\right)\right)<\ r_{4}\]  
The OP of \(U4\):  
\[Pr(U4)=(\ \sum_{i=1}^{N}\ P_{r}\ \left(C_{4}(k)\ <\ r_{4}\right))/N \tag{21}\]  
where \(N\) is the number of transferred samples.
# INSPIRE-SAT 7, a Second CubeSat to Measure the Earth's Energy Budget and to Probe the Ionosphere
## 3 Description of the INSPIRE-SAT 7 Mission and Mission Operations
### The Ground Segment--The UHF/VHF Station  
For the UVSQ-SAT mission, a LATMOS ground station system has been set up [1]. It is fully operational. To have redundancy, an INSPIRE-SAT 7 ground station will be set up by LATMOS. Figures 5 and 6 show the new configuration.  
The INSPIRE-SAT 7 CubeSat will use satellite amateur radio frequency bands (i.e., VHF 145.970 MHz and UHF 437.410 MHz). An international frequency coordination is under preparation (F4 IXO and F4 IXT) with the help of the local Radio-Club (F6 KRK, France) and the AMSAT-Francophone for being submitted to IARU.  
INSPIRE-SAT 7 represents a good way to interest young people in space and radio-frequency activities. This is also an excellent support for hands-on experience to our students.
# Analysis of the Relationship between Scintillation Parameters, Multipath and ROTI
## 4 Discussion
In contrast, both S4 and \(\sigma_{\varphi}\) are analyzed and compared in this paper with reference to their relationships with ROTI. Furthermore, analysis from the stations in this study show evidence that ROTI is more similar to \(\sigma_{\varphi}\) than to S4 both visually and statistically, giving new valuable insights into the relationships between ROTI, S4 and \(\sigma_{\varphi}\). Previous research has focused principally on the linear relationship between ROTI and the scintillation parameters, this study adds to this body of research by investigating the spatial relationship. This leads to the possibility of using these data sets to generate scintillation risk maps, that may be similar in principle to tracking jitter maps generated [PERSON] et al. [34], and also similar to Figures 4-7 given by [PERSON] et al. in [35], which visualize the effect of scintillation and can be used to identify and possibly mitigate risk caused by scintillation events. Figure 2 from [PERSON] et al. in [34] shows the S4 maps as a function of time and IPP latitude where strong scintillation activity was observed between 18\({}^{\circ}\) S and 26\({}^{\circ}\) S from 8 pm to 0 am local time on March 9-11, 2011. By comparison, Figures 3c, 5c and 7c in our paper show all the parameters are intense between 18\({}^{\circ}\) S and 26\({}^{\circ}\) S from 0 am to 4 am UTC (8 pm to 0 am in local time) on March 12 2011, which is similar to the output obtained by [PERSON] et al. [34]. Additionally, ROTI is the major proxy to the scintillation parameters proposed in previous research, which has shown weaknesses in the ability of ROTI to replace S4 and \(\sigma_{\varphi}\). In this research the relationship between ROTI and the scintillation parameters S4 and \(\sigma_{\varphi}\) has been further investigated and in addition this study shows that it may be possible to add MP as additional parameter, in support of other parameters such as ROTI, computed from a standard GNSS receiver (i.e., non-scintillationmonitoring receiver) that may be indicative of scintillation. However, the increase of MP does not generally indicate the occurrence of scintillation events as the scintillation measurements can be contaminated by real multipath effects [8].
## 2 Concept of Operations  
The use of inter-satellite networks featuring radio frequency crosslinks to connect various orbital platforms within a multi-orbit, multi-waveform constellation is rapidly increasing. However, any wireless network can introduce operational latencies and bandwidth limitations. Several approaches have been proposed to address these challenges. For instance, the application of control-theoretic resource allocation has been suggested to enable multi-functional radio frequency capabilities that combine navigation and communication functions [12].  
This research effort herein aims to explore how to integrate potential networking and clock performance with ensemble algorithms to achieve GNSS timing accuracy without relying solely on GNSS. As illustrated in Figure 1, significant progress has been made by treating asynchronous interactions among ad hoc, non-dedicated pLEOT formations and primary radio communication services within a pLEO constellation as dynamic resource allocation. This approach focuses on optimizing data link and medium access control layers rather than just employing specialized methods for physical-layer optimization.  
Furthermore, by utilizing both a data regulator and an integrated communication and clock framework, an autonomous agent onboard its orbital asset can flexibly allocate radio frequency resources to meet various simultaneous service requirements with minimal interference. Insights into the limited capacities of existing orbital assets are gained at the data link layer, supporting additional ad hoc, non-dedicated clock data services, such as free-running clock states, clock difference observables, and IEM estimates during scheduled pLEOT operations.  
Additionally, situational awareness regarding achievable link capacities is beneficial for managing data regulators and for integrating communication with clock services more deeply. Lastly, principles of control engineering and local agent autonomy facilitate handling reprogrammability and flexibility.  
As shown in Figure 1, all free-running clocks onboard participating assets collectively form a clock ensemble to generate a unified time scale. During the pLEOT generation window, these member clocks and their signal outputs are measured against one another using non-dedicated inter-satellite radio links and inter-platform time transfer and synchronization protocols. The resulting clock-to-clock difference measurements are then input into a clock-ensemble Kalman filter running on a selected anchor asset. This filter estimates the time for each ensemble clock and distributes the necessary signal generation components for the onboard pLEOT time scale formation among all participating assets while remaining mindful of the underlying constraints of the crosslink network.
# Multicriteria Route Planning for In-Operation Mass Transit under Urban Data
## 4 Multicriteria Route Planning for Mass Transit
### The Strategy in BiasSpan
#### 4.2.4 Gaussian Mixture Model for Modelling Spatial Influence  
The Gaussian mixture model (GMM) is adopted for two main reasons, the countermeasure against herding effect in station deployment and the spatial influence of each station in the route-planning process. Precisely, for the first statement in the station deploying stage, if one only settles the recommended stations at locations with high potential PF, it could easily end up with a region where several stations are allocated. Therefore, the concept for applying GMM is that it enables the recomputation of PF considering the negative effects from regions (grids) where stations are already recommended. Additionally, such iterative process makes the stations suggested by BiasSpan not only based on high inferred PF of the grid itself, but also kept a distance from each other. Similarly, for the second statement in the route-planning stage, the spatial appeal for PF from each station, including must-visit ones and recommended ones, can be modelled by the two-dimensional GMM. In this way, not only the nonmonotonicity in each grid under urban space could be smoothed and thereafter the inaccuracy due to the uncertainty could be eased, but the goal-prioritization characteristic could also enhance the searching efficiency.
# Research on a Rail Defect Location Method Based on a Single Mode Extraction Algorithm
## 4 Defect Location
### Selection of Mode, Frequency, and Excitation Conditions for Defect Detection  
The following three principles are used to select the mode, frequency, and excitation conditions for railroad defect detection:  
* The mode that only vibrates in the railhead with almost no movement of rail waist and rail bottom and which has a large group velocity is selected.  
Figure 8: SMEA results: (**a**) longitudinal excitation; (**b**) horizontal excitation; and (**c**) vertical excitation.  
Figure 9: Defect location algorithm flow chart.  
* The frequency band with better non-dispersive characteristics is selected.
* The mode with the largest amplitude is selected as the excitation condition.  
The detection frequency and mode were selected in the frequency range 20-70 kHz. According to Equation (5), the mode shapes of the rail in this frequency range were calculated and drawn. The frequency of 60 kHz was taken as an example, as shown in Figure 10. Among them, the modes in which only the railroad vibrated with almost no movement in the rail waist and rail bottom were modes No. 7 and No. 14.  
According to Figure 3b, the group velocity dispersion curves of modes No. 7 and No. 14 were extracted, as shown in Figure 11.  
Figure 11: Group velocity dispersion curves of modes No. 7 and No. 14.  
Figure 10: Rail mode shapes (60 kHz).  
In Figure 11, we can see that mode No. 7 had almost the same group velocity and excellent non-dispersive characteristics in the proximity of 60 kHz. The group velocity of mode No. 14 was greatly influenced by the frequency and the non-dispersion characteristic was poor. Therefore, mode No. 7 at 60 kHz frequency was selected to detect the defect in the railhead.  
A complete three-dimensional rail model with a length of 15 m was established. A simulation whereby the railhead was excited at 7 m along the longitudinal, transverse, and vertical directions by ANSYS was performed. The excitation signal was a sinusoidal wave modulated by Hanning window at the center frequency of 60 kHz with 10 cycles. The co-directional displacement signals between 8 m and 9 m were collected, and the amplitude of each mode under three excitation conditions were calculated using the accurate modal identification method, as shown in Figure 12.  
As shown in Figure 12, the highest amplitude for the selected mode is that of vertical excitation. Therefore, vertical excitation was selected.  
In summary, mode No. 7 was selected as the detection mode, 60 kHz was selected as the frequency, and vertical excitation was selected as the excitation condition.
## 4 Discussion
### Discussion of Major Quantitative Findings and Qualitative Feedback from the Users
#### 4.1.6 Behavioral Intention to Use  
In terms of behavioral intention to use (BIU), when task completion time was included, the research hypothesis was verified. We discovered that some users who accomplished the gestural tasks in 15 min were satisfied with the interface and wanted to use it again. This demonstrates that users support and anticipate gesture recognition, as suggested by [PERSON] et al. [48]. In contrast, when using the joystick controller for a relatively shorter period of time, users perceive a less positive experience [6]. However, when users finish the tasks within 15-20 min, their perceptions of different virtual interactive interfaces change. Users who finished the tasks within 15-20 min preferred the joystick control over the hand gesture recognition. After observing and soliciting feedback from the users, we find that users have difficulty executing gestural commands due to issues with demanded gestural postures and the detection of the helmet's cameras. Therefore, whenever users use the system for an extended period of time, they prefer the joystick control to the hand gesture recognition. This finding further confirms the issues of gesture recognition described by [PERSON] et al. [45].
## 4 Results
### Coal Permeability Anisotropy  
The permeability anisotropy along the bedding planes were estimated from the results of the microstructural analysis and cleat compressibility study. In the calculations, the average values of the cleat width and coal matrix block size were utilized (Figure 8). The initial coal permeability values were calculated from Equation (5) and were \(k_{f}=74.6\) mD and \(k_{b}=50.4\) mD. The cleat compressibility ratio \(C_{f}=0.0845\) MPa\({}^{-1}\) was utilized in the calculations. The obtained curves, which describe the stress--permeability relationships perpendicular to the butt cleat direction and perpendicular to the face cleat direction, are shown in Figure 9.  
Figure 8: Distribution histograms of the measured coal cleat parameters, S.D. Tihov mine, Leninsky area, Kuzbass: coal matrix block size between butt cleats, A\({}_{b}\) (**a**); coal matrix block size between face cleats, A\({}_{f}\) (**b**); width of the face (a\({}_{f}\), black color) and butt (a\({}_{b}\), gray color) cleats (**c**).  
The coal permeability was determined for confining hydrostatic pressures P\({}_{\rm c}\) from 1 to 8 MPa. As the confining pressure increased by eight times, the coal permeability decreased by nearly six times. The coal permeability perpendicular to the butt cleat direction varied from 59.4 mD at P\({}_{\rm c}\) = 1 MPa to 10.05 mD at P\({}_{\rm c}\) = 8 MPa. Perpendicular to the face cleat direction, the coal permeability ranged from 40.11 mD at P\({}_{\rm c}\) = 1 MPa to 6.79 mD at P\({}_{\rm c}\) = 8 MPa. A comparison of the calculated and experimental results showed that the permeability ratios determined by the cleat geometric properties were on average 2.6% more than those determined by the [PERSON] et al. (1992) model ([PERSON] et al., 1992). We estimated the anisotropy of the horizontal permeability utilizing the considered established models and measured cleat properties. A 3:2 ratio of the face cleat permeability to butt cleat permeability was observed for the coals from the Leninsky area of the Kuznetsk Basin.