esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 AETA System
### Data Set Construction
This paper tried to use 0.1-0.5 as the threshold, the step size of which was set to 0.05. The amount of data was sufficient and the validation set obtained was good enough when 0.3 was chosen as the threshold. Figure 2 shows the regional sample composition process. Figure 1: Distribution of earthquakes and stations in Sichuan–Yunnan region. This paper builds the AETA feature library with a total of 95 kinds of featured data whose validity has been verified [34]. The detailed information is shown in Appendix A. In addition, due to the infrequent occurrence of earthquakes, the imbalance of positive and negative samples must be considered when constructing the prediction model. Otherwise, it will greatly affect the training of the model, resulting in the model being more likely to predict no earthquakes. For the problem of the lack of few positive samples, the synthetic minority oversampling technique (SMOTE) algorithm must be applied when constructing the sample set for the non-time series prediction model. To generate new samples by algorithm synthesis, SMOTE iterates through the sparse number of unbalanced samples, calculates the distance of each sample \(x\) from all the remaining samples, and gets the \(k\) samples closest to it. Then, according to the imbalance ratio of positive and negative samples, some samples are randomly selected from the obtained \(k\) samples to be added to the original sample. Finally, a new sample is generated based on each selected sample \(\hat{x}\) and the original sample \(x\)[35]. The equation is as follows:  
\[x_{\text{new}}=x+\text{rand}(0,1)\ \times\ (\hat{x}\ -\ x). \tag{8}\]  
In contrast, the SMOTE algorithm cannot be used directly for the time series prediction model because its samples are two-dimensional matrices. Therefore, this paper designs a two-dimensional matrix SMOTE algorithm to generate new two-dimensional samples. The aim of this is to compress two-dimensional samples into one-dimensional samples in time sequence, then process them by using the SMOTE algorithm, and finally reconstitute the two-dimensional samples in time sequence. The use of the SMOTE algorithm allows for a more productive and balanced model training with a ratio of positive and negative samples between 1:1 and 1:1.5. Figure 3 shows the two-dimensional matrix SMOTE algorithm. \[\widetilde{y}_{2}=F_{2}\big{(}x_{T_{-n}},x_{T_{-n+1}},\;\ldots,\;x_{T_{-1}} \big{)} \tag{2}\]0 The process of sample construction. For each station with different installation time and different number of total samples, 85% of the total samples of each station are taken as the training set and 15% as the validation set to build the seismic prediction model and the magnitude and epicenter prediction model respectively.
# A Review of Machine Learning and IoT in Smart Transportation
## 2 Machine Learning and IoT in Smart Transportation
### ML and IoT Applications in Smart Transportation
#### Road Anomalies Detection  
Road anomalies detection has a significant role in smart transportation since the road conditions have an immediate impact on many aspects of transportation. The main purpose of a road anomaly detection system is the detection of bumps and pot-holes on the roads and the notification of the drivers. Bad road conditions can lead to vehicle damage, road accidents, and traffic congestion. The problem of detecting road anomalies is a task very suitable for some ML techniques, so the literature reviewed here follows the ML direction too.  
Accelerometer data collected from mobile devices are processed with an FF-NN in [30], in order to detect pot-holes. Accelerometer data are also used in [33], clustered by the K-Means algorithm to distinguish normal from abnormal roads. A similar approach to detect road anomalies is used in [34], and the data in this research are also clustered using the k-Means method. Last but not least, in [35] the authors compare the methods \(k\)-NN, RF, and SVM for the task of road abnormalities detection.  
The author in [21] has conducted a detailed review of deep learning techniques applied for pavement distresses detection. Furthermore, a review of the existing and emerging deep learning frameworks used for road anomalies detection is performed. The review summarizes 12 of the most recent studies in the pavement distress detection which are based on CNN and DCNN methods. Moreover, the reviewed studies are compared in terms of objectives, datasets used, hardware, network architecture, hyper-parameters, and selected framework. Finally, the author concludes that CNNs show the best results in terms of pavement image classification in most studies. There is no clear indication that DCNNs can be more accurate than shallower CNNs and there are a lot of other parameters that affect the classification results, such as data pre-processing, augmentation, and feature selection. Unsupervised end-to-end image classification has yet to be explored in this field of study but is a promising method for the future of pavement distress detection.
###### Abstract  
The application of visible near-infrared (VIS-NIR) analysis technology to quantify the nutrients in soil has been widely recognized. It is important to improve the performance of regression models that can predict the soil-available potassium concentration. This study collected soil samples from southern Anhui, China, and concentrated on the modelling methods by using 29 pretreatment methods. The results show that a combination of three methods, Savitzky-Golay, standard normal variate, and dislodge tendency, exhibited better stability than others because it was the most capable of achieving levels A and B of the ratio of performance of deviation. The boosting algorithms that form an ensemble of multiple weak predictors exhibited better performance than partial least square (PLS) regression and support vector regression (SVR) for the prediction of soil-available potassium. These regression models could be employed to precisely predict the soil-available potassium concentration.  
visible near-infrared ray spectroscopy; soil-available potassium; pretreatment; regression model +
Footnote †: journal: Applied Sciences
# Experimental and Parametric Analysis of Pull-Out Resistance of Notched T-Perfobond Connectors
## 5 Parametric Study
### Influence of Hole Size  
Figure 18 illustrates the impact of hole height on the pull-out performance of notched T-perfobond connectors. When the hole height was increased from 10 mm to 15 mm, 20 mm, 25 mm, and 30 mm, the corresponding changes in pull-out resistance were 1.59%, \(-\)1.65%, \(-\)2.87%, and \(-\)18.13%, respectively. The data reveal that while an increase in hole height from 10 mm to 15 mm slightly enhances pull-out resistance, further increases result in a decline. This trend is primarily attributed to the reduction in the perfobond rib's thickness at the lower edge of the hole as the hole height increases. Concurrently, the moment at the notch of the perfobond rib and the inner edge of the hole at its maximum length intensifies. Despite this, the projected area of the concrete dowel and the contact area of the perfobond connectors remain constant, potentially causing earlier yielding and damage at the inner edge of the hole, which, in turn, diminishes pull-out resistance. Examination of the load-separation curve indicates that a larger hole height correlates with an extended ductile section.  
Figure 17: Influence of cut width. (**a**) Load–separation curves; (**b**) influence analysis; (**c**) failure mode.  
Figure 19 demonstrates the influence of hole length on the pull-out performance of notched T-perfobond connectors. As the hole length increased from 20 mm to 30 mm, 40 mm, 50 mm, and 60 mm, the pull-out resistance of these connectors changed by 3.8%, 2.43%, 2.43%, and \(-\)36.96%, respectively. The results indicate that increasing the hole length up to 50 mm slightly augments the tensile strength of the connectors; however, a further increase to 60 mm significantly reduces the pull-out resistance. This phenomenon can be primarily attributed to the reduction in the perfobond rib's thickness at the hole's edge as the hole length expands. Consequently, the moment at the notch of the perfobond rib within the hole and at the inner edge of the hole at its maximum length increases. Although the projected contact area between the concrete dowel and the perfobond connectors may increase, the earlier yielding and damage of the perfobond rib at the inner edge of the hole at its maximum length occur, leading to a decrease in pull-out resistance.
## 2 Feature Analysis of Existing RRSA Concepts
### Solid Surface Deployable Antenna (SSDA)  
The concept of the solid surface deployable antenna (SSDA) was proposed by [PERSON] at Cambridge University in 1996 [29], to which its detailed segmentation scheme is demonstrated in Figure 3. It can be observed from the figure that, for the SSDA, its central hub is a hexagonal panel, around which six identical wings are mounted, with each one being subdivided into three panels. When the SSDA is folded, all of its wings warp around the central hub, as shown in Figure 3b. The adjacent panels are linked by revolute joints, and all the wings are linked to the central hub by revolute joints, too. To serve the purpose of one degree-of-freedom (DOF) of the mechanism, additional connecting rods are installed between adjacent wings, as illustrated in Figure 3a. In its folded state, we can see that the gap between panels is considerably small, which precludes the possibility of introducing the supporting structures for a higher surface accuracy. To realize synchronous deployment, the number of motors shall be the same as that of the units, to which strictly synchronizing those motors is also required.  
Figure 3: Solid surface deployable antenna. (**a**) Deployed state. (**b**) Folded state. This antenna has 6 units, each of which has 3 panels.  
Figure 2: Sunflower antenna. (**a**) Deployed state. (**b**) Folded state. This antenna has 6 units, each of which has 3 panels.  
The SSDA concept is similar to the sunflower concept: its units and panels can also be increased to achieve higher stowing efficiency, under which circumstance the structure complexity, position error of each panel and number of driving motors will rise drastically. As a result, an antenna with low deployment reliability and surface accuracy is less meaningful, and this antenna concept has also been neglected for a long time, where only a few scholars have proceeded with the research. [PERSON] et al. analyzed the correlation among wing number, panel number of each wing and structural parameters [53]. Eventually, they presented the formulations of the maximum edge length of the central hub and panel number. They concluded that the configuration that remains the same as that in Figure 3 is preferable given various factors.
## 3 Results and Discussion
### Test Results
#### 3.3.2 Test 2 Results  
The evolution of the flight is represented in Figures 12 and 13  
When the distance threshold set for the activation of the ASACAS collision avoidance function was reached, the DAA system indicated the predicted collision condition to the remote pilot and automatically started to execute a suitable collision avoidance maneuver, thus allowing the ownship to avoid the collision. The remote pilot monitored the collision avoidance maneuver executed by the system, and they had the possibility to abort it and take the control of the RPAS if desired.  
The collision avoidance maneuver for this beam encounter geometry led to a 3D variation of the ownship trajectory that resulted in a modified trajectory tangent to the collision safety volume centered on the intruder. The collision safety volume size was increased with respect to the nominal one based on the closure rate between vehicles at the moment where the collision avoidance maneuver was elaborated by the system. It is worth noticing here that the collision avoidance maneuver could be automatically updated by the DAA system during its execution if needed based on the current traffic picture. The updates of the maneuver are indicated by the signal \"collision detection\" rising to 1 in Figure 13, whereas the implementation of the maneuver is indicated by the \"CA maneuver\" signal maintained at level 1 in the same figure.  
An analysis of Figure 13, then, allows one to observe that, in this real-time simulation test, the collision condition was detected by the ASACAS at about 3670 s, and the collision avoidance maneuver simultaneously started to be automatically executed by the system (after the proper, very small elaboration time).  
The minimum distance between vehicles (i.e., the distance at the CPA) was about 240 m. The collision avoidance maneuver was completed at about 3730 s, when the range rate between vehicles became positive (the vehicles started diverging). As already indicated in the previous tests, the range and range rate were calculated only during the collision avoidance functionality execution; therefore, the null values indicated outside this time interval have no meaning in the figure.  
The collision avoidance functionality of the ASACAS, therefore, was shown to perform as expected in the real-time simulation test described here.  
Figure 12: Test 2: automatic collision avoidance maneuver showing the 3D trajectory of the ownship and conflicting intruder.  
Figure 13: Test 2: automatic collision avoidance maneuver and parameters of interest.
## 4 Performance Comparison with Benchmarks
(**a**) Utility; (**b**) Offloading efficiency (**c**); (**c**) Number of copies. more effective in selecting the seed nodes that are more useful for the offloading process. Finally, Figure 19 illustrates the ratio between the optimal \(T_{s}\) and \(T_{c}\) values for the real-world mobility model. In this scenario, the optimal \(T_{s}\) can be up to 150 times greater than \(T_{c}\). Figure 19: Optimal \(T_{s}^{a}/T_{c}\) ratio vs. \(\lambda_{0}\) for OORS in real-world mobility scenario for 5% of the nodes selected as seeds.
## 4 Discussion  
### Effectiveness of the Modules Attached at the Rear Window and Doors of the Vehicle  
Global mobility companies have developed various solar cars or VIPV models. First, the next-generation model uses translucent materials to maintain a sense of openness. Second, it utilizes body-type lightweight solar lids to integrate solar cells into the bonnet, roof, and steel plates on both sides of the vehicle.  
In this study, the PV potential of EV was evaluated by attaching modules to the roof, rear window, and side of the vehicle. The potential of module A (roof) was compared to those of modules C and D (both sides). The average PV potential of module A was 0.0125 kWh, and the sum of the PV potentials of modules C and D was 0.0034 kWh. The  
Figure 12: Comparison between normalized PV potentials predicted and observed of module B.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sections**} & \multirow{2}{*}{**Distance**} & \multicolumn{3}{c}{**PV Potential (kWh)**} & \multicolumn{3}{c}{**Shadow (Ratio)**} & \multicolumn{3}{c}{**PV Potential (kWh)**} \\  & & \multicolumn{3}{c}{**(without Shadow)**} & & \multicolumn{3}{c}{**(with Shadow)**} \\ \cline{3-11}  & & **9:00** & **12:00** & **15:00** & **9:00** & **12:00** & **15:00** & **9:00** & **12:00** & **15:00** \\ \hline  
Total & 3135 & 0.0365 & 0.0439 & 0.0236 & 67.1\% & 47.4\% & 40.9\% & 0.0117 & 0.0225 & 0.0133 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ratio of shaded area and PV potential with/without shadow effect in each section.  
potential of the modules C and D was approximately 27% of that of module A because either C or D does not face the sun and produce a low value, or the angle of solar incidence is small. Therefore, the generation potential of the two modules were inversely proportional; hence, the contribution to the increase the vehicle's generation potential was low. Similarly, by comparing the average values of the PV potential of modules A (roof) and B (rear window), the potential power generation of module B was calculated to be approximately 42% of that of module A, which is significantly greater compared to that of the module attached to the side of the vehicle. Therefore, the module attached to the vehicle's rear window of the vehicle contributes significantly to the increase in PV potential.  
Of course, in the case of the study area, east-west roads were longer than south-north roads, so this ratio value may change in areas with different road environments. For example, if most of the roads are in the north-south direction, excluding other conditions, the ratio of the potential generation capacity of module B may increase, and the bar rates of modules C and D may decrease. Therefore, the decision to attach the module to the rear window or both sides of the vehicle requires a detailed economic evaluation considering the environmental conditions of the study area.
# Dynamic Response of Outer Windshield Structure in Different Schemes under Aerodynamic Load
## 4 Results and Discussion
### Time- and Frequency-Domain Analysis of Vibration Displacement
#### 4.2.2 Frequency-Domain Analysis  
To compare the dynamic response characteristics of the rubber external windshield in the three schemes, the frequency spectrum of the two time stages of the displacement curve is analyzed. Figure 13 shows the power spectral densities (PSD) for d1, d2, d3, and d4 at the initial stage of windshield vibration for the three schemes. Figure 14 shows the power spectral densities for d1, d2, d3, and d4 in the vibration response stability stage of the rubber external windshield for the three schemes.  
The displacement response frequency for the three external windshield schemes in two different dynamic response stages is shown in Figure 14. In Case 1, the vibration frequency of d1, d2, d3, and d4 is the same at the initial dynamic response stage, and the main frequency is 8 Hz. In the vibration stability stage, the dominant frequency of vibration for each measuring point is 16 Hz. In Case 2, at the initial vibration stage, the dominant frequency of the rubber external windshield vibration is 9 Hz for W1 and 10 Hz for W2, and the dominant frequency of the vibration at each measuring point is 7 Hz in the vibration stability stage. In Case 3, at the initial vibration stage of the rubber external windshield structure, the dominant frequency of vibration at measuring points d1, d2, d3, and d4 is 14, 13, 6, and 7 Hz, respectively. In the vibration stability stage, the dominant frequency of vibration at measuring points d1, d2, and d4 is 15.7 Hz, and that of d3 is 14 Hz. By comparing the vibration frequencies for the two vibration stages of the rubber external windshield for the three schemes, it is found that from the initial vibration stage to the vibration stability stage, the vibration frequency of the rubber external windshield for Case 1 and Case 3 increases, while the vibration frequency for Case 2 decreases. With the change in the geometric shape of the windshield structure, the natural frequency and structural stiffness of the rubber external windshield are different, which leads to the change in the vibration frequency for the windshield under the aerodynamic action. Compared with Case 1 and Case 3, the structural stability of the windshield in Case 2 is better.  
Figure 13: PSD in the vibration response initial stage: (**a**) d1, (**b**) d1, (**c**) d3, (**d**) d4.
## 4 Discussion
### Fatigue Crack Growth Rate Analysis  
The range of stress intensity factors further increases as the crack enters the stable propagation stage (Paris zone). The growth rate of cracks at the early stage of stable propagation is mainly controlled by the thickness of the \(\alpha\) lath [39]. For the as-built specimen, due to the thin thickness of the acicular martensite in the microstructure, when the crack tip encounters the acicular martensite, the dislocations accumulate at its boundary and eventually propagate through the martensite forward. After heat treatment, for \(\rm T_{a}<T_{\beta}\), after the crack encounters a thicker \(\alpha\) slab, the crack penetration requires more energy, so the crack propagation direction changes. The major cause of the reduction in CGR is the deflection of the crack. For \(\rm T_{a}>T_{\beta}\), the \(\alpha\) colony will complicate the crack propagation path, and differently oriented \(\alpha\) colonies increase resistance to fatigue propagation. The excellent fatigue resistance of \(\alpha\) colony microstructure is often related to the high deflection and bifurcation during crack propagation. The secondary crack caused by bifurcation will greatly share the stress intensity of the main crack tip and reduce the propagation rate.  
As the crack length increases, the inhibitory effect of the microstructure is negligible, the plastic zone at the crack tip passes directly through multiple grains, and crack propagation begins to proceed along two slip systems or multiple slip systems simultaneously or alternately. When the crack propagates in this state, the load promoting crack growth varies with time, so the CGR also varies with time and stress intensity factor. The continuous plastic deformation and the fracture process affect the crack tip geometry, which in turn affects the fracture topography characteristics caused by crack propagation. The fatigue crack growth mechanism at this stage propagates in a plastic banding mechanism, and each cyclic loading of tensile loading will produce a fatigue band. The CGR is the lowest when the fatigue crack propagates in a plastic banding mechanism, which also explains why brittleness causes an increase in the fatigue fracture growth rate of the as-built specimen.
# Time Domain Analysis of NB-IoT Signals
## 3 Italian Technical Standard CEI 211-7/E: Extrapolations Techniques  
NRS is of particular interest from the point of view of exposure measurements to electromagnetic fields since their measurement is at the basis of the extrapolation procedures of Italian CEI 211-7/E technical standard [14; 35]. In Italy, since the end of the 1990s, the adoption of more stringent exposure limits than Europe has increased the interest for measurement techniques to assess limits compliance. The techniques related to radio base stations are contained in technical standard CEI 211-7/E which was updated in 2019 with reference to the European (CENELEC) and international (IEC) standards but also introducing measurement techniques for NB-IoT signals.  
In particular, the standard provides for the measurement of the electric field level due to NRS of the used antenna ports, \(E_{NRS,u}\), by means of a vector analyzer. The value of the electric field due to each PRB assigned to NB-IoT in maximum power transmission conditions is obtained by means of the following extrapolation formula:  
\[E_{NBIoT}(V/m)=\sqrt{\frac{12}{BF_{NBIoT}}}*\sqrt{\sum_{n}E_{NRS,n}^{2}} \tag{1}\]  
where \(BF_{NBIoT}\) is the boosting factor associated with the NRS and 12 is the number of subcarriers of the PRB.  
In the presence of NB-IoT, the overall contribution of the LTE band will therefore be given by:  
\[E_{LTE+NBIoT}(V/m)=\sqrt{E_{LTE}^{2}+E_{NBIoT}^{2}} \tag{2}\]  
where \(E_{LTE}\) is the electric field contribution of the LTE signal in the same frequency band.  
In Release 13 of 3 GPP standard, NB-IoT was initially conceived to operate on a frequency division duplexing (FDD) scheme. Band 20 (LTE800) is among the bands defined by 3 GPP as available for NB-IoT transmission.