esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
The investigation of time series often involves a search for oscillations. This is usually carried out by analyses such as the Lomb-Scargle procedure [1; 2]. Alternatively, one may use a likelihood procedure that yields the same power as the Lomb-Scargle procedure but yields, in addition, amplitude and phase estimates [3; 4]. These procedures have the convenient property that, on the assumption that the data are derived from random measurements that have a normal (i.e., Gaussian) distribution, the probability of finding a power \(S\) or more at a given frequency is given by  
\[P=e^{-S}. \tag{1}\]  
However, in practice a laboratory experiment or observational sequence may generate data that do not conform to the normality requirement. One then has the following options:  
1. Analyze the procedure that generates the data and find or derive a valid procedure for assessing the significance of an oscillation generated by such a procedure; or
2. Ignore the normality requirement and apply Equation (1) as if the normality requirement were valid.  
Option (i) can be difficult and time-consuming and is probably rarely adopted in practice. Option (ii) is simple and convenient, but has the unfortunate consequence that the validity of the result is uncertain. For these reasons, we here suggest a third option.  
If one can convert a given dataset into a \"proxy\" dataset that has a normal distribution, then one may apply the Lomb-Scargle or a similar procedure to the proxy dataset, and one would then be entitled to use Equation (1) for assessing the significance of any oscillation in the proxy dataset.  
This raises the question: what procedure can be used to convert a given dataset into a proxy form that (a) has a normal distribution, and (b) somehow retains information in the original dataset that is significant for time-series analysis, so that an oscillation in the original dataset will lead to a corresponding oscillation in the proxy dataset?  
We suggest that what we refer to as the RONO (for rank-order normalization) operation is a practical candidate for such a procedure.  
We describe this procedure in Section 2, and give an example of its application, with brief comments, in Section 3.
# Recommendation Algorithm Based on Survival Action Rules
## 4 Experiments and Results
Table 5 presents the results, showing shows the average number of actions suggested by the recommendation algorithm and the percentage of conditional attributes for which actions were defined. To conclude this part of the analysis, Table 5 presents information on the average number of elementary actions required--the values in Table 5 represent the result illustrating the operation of the recommendation algorithm that merges information from all the induced rules. As observed, the recommendation algorithm forces the execution of an average of two elementary actions for each example. This means that the algorithm suggests changes in the values of two attributes. The column \(\overline{p}_{Attr}\), shows the percentage of all attributes that are involved in the elementary actions suggested by the recommendation algorithm. On average, 33% of the attributes are involved. Specifically, for the std set, half of the attributes are involved, and for the zinc set, just under 4% of the attributes are involved. These statistics show that the induced action rules and the recommendation algorithm based on them also lead to a significant reduction in the number of features whose values need to be changed. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Dataset** & \(\overline{n}_{a}\) & \(\overline{p}_{Attr}\) \\ \hline FD001 & 2.69 & 14.62 \\ FD002 & 2.36 & 21.92 \\ FD003 & 2.92 & 21.54 \\ FD004 & 2.40 & 26.92 \\ GBSG2 & 1.76 & 46.00 \\ melanoma & 2.01 & 42.22 \\ actg320 & 1.36 & 33.08 \\ bmt-ch & 2.36 & 16.76 \\ follicle & 1.97 & 45.00 \\ hd & 1.70 & 38.75 \\ lung & 3.35 & 63.33 \\ maintenance & 1.02 & 46.00 \\ mgus & 2.21 & 35.45 \\ pbc & 1.68 & 19.47 \\ std & 3.22 & 49.57 \\ uis & 2.65 & 33.33 \\ whas1 & 1.18 & 42.22 \\ whas500 & 1.82 & 29.33 \\ zinc & 1.29 & 3.51 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Attribute statistics for the generated recommendations. The columns show the following information: the average number of actions per recommendation (\(\overline{n}_{a}\)) and the average percentage of attributes that were mutated (\(\overline{p}_{Attr}\)).
## 2 Materials and Methods
### Mooring Data  
This study employed Teledyne RD Instruments' Workhorse II Sentinel ADCP (600 K) (Table 1) and designed a route for the towed ADCP in the Pearl River Estuary, starting at 113.66\({}^{\circ}\) E, 22.36\({}^{\circ}\) N and ending at 113.70\({}^{\circ}\) E, 22.04\({}^{\circ}\) N. The data were collected over a period of nearly three days, with a temporal resolution of 2 min and a vertical resolution of 0.5 m between the adjacent layers in the vertical direction. An ADCP was deployed at 113.66\({}^{\circ}\) E, 22.2\({}^{\circ}\) N to collect data for 21 days. The temporal resolution was 5 min, and the vertical resolution was 0.5 m between the adjacent layers. The collected data were processed as hourly data to align with the other time series.  
Figure 1: Schematic diagram of the study area. Blue indicates the coverage range of the HF radar network system, green stars mark the locations of six radar stations, red lines represent towed survey routes, and black markers indicate the deployment positions of a bottom-mounted ADCP.
# A Low-Profile Wideband Monocone Antenna Using Bent Shorting Strips
## 4 Experimental Results  
A photograph of the fabricated antenna is shown in Figure 10. Figure 11 shows the simulated and measured reflection coefficients of the proposed antenna. They agree well over the entire frequency band of interest. The measured -10 dB reflection coefficient bandwidth is 4530 MHz (810-5340 MHz) and the fractional bandwidth is about 147.3%.  
Figure 8: Simulated reflection coefficients for different height (\(H_{4}\)) values of the parasitic sleeve.  
Figure 10: Photograph of the fabricated antenna.  
Figure 9: Simulated reflection coefficients for different width (\(W_{2}\)) values of the shorting strip.  
The simulated and measured radiation patterns of the proposed antenna in the E- and H-planes at 0.91 GHz, 2.48 GHz, 4.07 GHz, and 5.03 GHz are shown in Figure 12 (the xz plane is the E-plane and the xy plane is the H-plane). The measured radiation patterns are in good agreement with the simulated ones. It can be observed that the antenna has dipole-like radiation patterns in the E-plane and omni-directional radiation patterns in the H-plane. Furthermore, the radiation patterns are relatively stable over the entire frequency band. The simulated and measured gains of the proposed antenna are shown in Figure 13. The simulated maximum gain varies from 3.60 dBi to 5.69 dBi, and measured gain varies from 3.24 dBi to 6.2 dBi within the operating frequency band.  
Figure 11: Simulated and measured reflection coefficients of the proposed antenna.  
Figure 12: _Cont._  
Table 2 provides dimensions, the -10 dB reflection coefficient bandwidth, and fractional bandwidth comparison between the broadband antennas using different methods to improve the bandwidth, along with the proposed antenna. It can be observed that the fractional bandwidth of the proposed antenna is much wider than the others, with a height comparable to the other antennas. The antennas in [18; 19] have smaller diameters, but their fractional bandwidths are narrower than the proposed antenna. Therefore, the proposed antenna has the advantages of a low profile and wide bandwidth over the other monocone antennas.  
Figure 12: Simulated and measured radiation patterns of the proposed antenna at (**a**) 0.91 GHz, (**b**) 2.48 GHz, (**c**) 4.07 GHz, and (**d**) 5.03 GHz.  
Figure 13: Simulated and measured maximum gains of the proposed antenna at the operating frequency band.
## 1 Introduction
### The Bending Beam Rheometer (BBR) Method  
For unmodified binders, the determination of cracking temperature is based on results from the bending beam rheometer (BBR) test in accordance with the standard test methods AASHTO TP1 [8]. Two asphalt binder parameters, i.e., the stiffness and the \(m\)-value, are determined based on the bending beam rheometer (BBR) test results at a loading time of 60 s. The cracking temperature is defined as the temperature at which the stiffness reaches the value of 300 MPa or the m-value reaches the value of 0.3, or, moreover, the temperature at which one of the specification thresholds is reached as temperature is decreased. The cracking temperatures determined using AASHTO TP1 have been found to be predictive of low-temperature cracking in asphalt pavements constructed using unmodified binders. However, AASHTO TP1 was found to grossly over predict low-temperature cracking performance for modified asphalt binders. As a result, for modified asphalt binders, additional testing using the direct tension test [3] (DTT) was proposed to address the case of binders with a stiffness higher than 300 MPa and with an m-value greater than 0.3. [PERSON] et al. [13] presented methods for predicting cracking temperatures using both the BBR and the DTT test data in the so-called dual instrument method (DIM).  
These methods involve the use of sophisticated computer software for computing thermal stress in asphalt binders using the bending beam rheometer (BBR) data and estimating binder strength using the direct tension test (DTT). The dual instrument method (DIM) has been proposed as a standard method (MP1A) [10] for evaluating both modified and unmodified asphalt binders. To be able to use the MP1A, sophisticated equipment (DTT and BBR) and analysis software are required. Concerns have already been raised about the expensive nature of the MP1A approach, because its use has become prohibitive to many practitioners. Different approaches to alleviate these issues have also been proposed by [PERSON] [15], and by [PERSON] et al. [16], by [PERSON] and [PERSON] [17], and by [PERSON] [18], with some but not enough success to evaluate the low-temperature performance of asphalt materials.  
For the practical low-temperature evaluation of binders, binder blends and mixtures for the purpose of formulation, design, control, and forensics, there is still a need for a test which is rapid, simple, compact, portable, and applicable to all modern binder types. Acoustic emission (AE) is a promising technique for evaluating embrittlement in asphalt binders, since it has been used successfully in other materials.  
In this review paper, an acoustic emission (AE) approach is used to accurately and rapidly evaluate the thermal cracking performance of asphalt concrete materials including virgin, short-term, and long-term aged asphalt binders, as well as different asphalt concrete mixtures. A review of this technique along with applications of this approach are presented and discussed. For more details regarding experimental setups, etc., the readers are encouraged to refer to the appropriate citations.
# Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy
###### Abstract  
Buildings consume a significant amount of energy throughout their lifecycle; Thus, sustainable energy management is crucial for all buildings, and controlling energy consumption has become increasingly important for achieving sustainable construction. Digital twin (DT) technology, which lies at the core of Industry 4.0, has gained widespread adoption in various fields, including building energy analysis. With the ability to monitor, optimize, and predict building energy consumption in real time. DT technology has enabled sustainable building energy management and cost reduction. This paper provides a comprehensive review of the development and application of DT technology in building energy. Specifically, it discusses the background of building information modeling (BIM) and DT technology and their application in energy optimization in buildings. Additionally, this article reviews the application of DT technology in building energy management, indoor environmental monitoring, and building energy efficiency evaluation. It also examines the benefits and challenges of implementing DT technology in building energy analysis and highlights recent case studies. Furthermore, this review emphasizes emerging trends and opportunities for future research, including integrating machine learning techniques with DT technology. The use of DT technology in the energy sector is gaining momentum as efforts to optimize energy efficiency and reduce carbon emissions continue. The advancement of building energy analysis and machine learning technologies is expected to enhance prediction accuracy, optimize energy efficiency, and improve management processes. These advancements have become the focal point of current literature and have the potential to facilitate the transition to clean energy, ultimately achieving sustainable development goals.  
digital twin; building energy management; machine learning; energy modeling +
Footnote †: journal: Applied sciences
## 5 Simulations and Comparison
### Vehicle Models and Test Conditions
#### 5.1.4 Discussion of Results  
In order to verify the effectiveness of the proposed trajectory tracking algorithm, simulations were carried out on models of two vehicles with significantly different dynamics. Two types of trajectories (linear and curvilinear) were also considered, as well as perturbation functions of constant and variable value.  
For the developed algorithm, the values of the control parameters were only slightly different for the two vehicle models and the trajectories implemented. This may suggest that in the proposed control scheme, the dynamic parameters have a significant impact on vehicle motion and the controller gains are tuned to improve performance (for the controller used for comparison, the control parameters had to be significantly changed).  
The indexes for control errors and had similar values for both vehicles but differed depending on the trajectory being tested (the effect of the disturbance function was less significant). Kinetic energy consumption depended on the vehicle and the trajectory realized. On the other hand, the figures show that the time to reach the desired trajectory is short considering the weight of the vehicles and the limitations of the thrusters (according to the technical data).  
The comparative simulations show that the control algorithm described in [42] can work for various vehicles, but its effectiveness depends on the dynamic parameters of the vehicle, the trajectory realized and the operating conditions. Tracking time is very long and velocity disturbances must have small values (about 0.1 m/s). Compared to this controller, the proposed algorithm has a slightly larger range of applicability (the interference function can be varied) and the position errors are adjusted directly (without coordinate transformation), which makes it possible to reduce their values more effectively.  
Taking into account the simulation studies conducted, the following conclusions can be made: (1) the control algorithm can be applied to different vehicles, but its effectiveness may vary due to different model parameters (mass, drag coefficients, etc.); (2) the results of trajectory tracking depend on the shape of the desired trajectory (linear, curvilinear); (3) external disturbances reduce the effectiveness of the algorithm, and this effectiveness depends on whether they have constant values or are changed during the movement of the vehicle; (4) the consumption of kinetic energy and the applied force and torque required to perform the task depend on the shape of the trajectory, and to a lesser extent on the realized trajectory (for small disturbances such as those used in this work).
## 5 Discussion
Furthermore, the deduced soil depths based on DPI, P-wave velocity and graphical bilinear method are compared through box and whisker plot as shown in Figure 11. The median values of DPI and graphical bilinear method show 0.35 m and 0.3 m, and however the median soil depth based on P-wave velocity is highly deduced to 0.5 m. The first and third quartiles of DPI and  
Figure 9: Soil depth based on dynamic cone penetration test: (**a**) 30 m of BTS; (**b**) 80 m of BTS; (**c**) 100 m of BTS; (**d**) 20 m of BS; (**e**) 30 m of MS; and (**f**) 20 m of TS. graphical bilinear method show also similar ranges of 0.15-0.55 m and 0.2-0.6 m, respectively and it shows the difference is just 0.5 m. However, the soil depth estimated by existing method shows unreasonable ranges of 1.05-1.8 m. The ranges of minimum and maximum values based on DPI (0.2-2 m) and suggested method (0.2-3 m) are also demonstrated to similar trends while those deduced by P-wave velocity exhibited huge variations (1.4-5.6 m). And thus, the Figure 11 shows that the suggested method can provide the reliable soil depth under \(\approx\)3 m with consideration of 2 m intervals of geophone. Table 1 shows the calculated error ratios (error ratio = (estimated value \(-\) measured value)/ measured value). The estimated soil depths based on reference P-wave velocity show high error ratios, ranging from 55% to 1000%, and the average value shows approximately over 100%, reflecting the estimation of soil depth using the classification of soil layers based on reference wave velocity is not reliable. In contrast, the graphical bilinear method shows relatively small average error ratios of 28.2, 6.6, 10.5, and 21.9% for BTS, BS, MS, and TS, respectively, demonstrating that the graphical bilinear method suggested in this study provides reliable estimation of soil depth. Figure 11: Comparison of soil depths through box plot. Figure 10: Comparison of soil depths estimated by seismic wave velocity and measured by dynamic cone penetration test.
## 3 Monitoring and Verification
### Comparative Analysis of Monitoring and Simulation
#### 3.2.1 Comparative Analysis of Earth Pressure Cell  
The stress characteristics of the steel arch support in the monitoring section are identified by examining the field monitoring test data and numerical simulation results as follows:  
1. Figure 8 demonstrates that the stress values at each monitoring point of the steel arch support exhibit a rapid increase in the first 8 m of excavation, followed by a slower and converging change over the subsequent 12 m. This phenomenon results from the stress redistribution in the surrounding rock post-tunnel excavation, transferring stress to the steel arch supports. In later stages, the stress in the surrounding rock stabilizes, and the pressure on the steel arch supports also reaches a steady state.  
Figure 7: Vertical displacement cloud of section and excavation.  
2. When comparing the field monitoring data to the numerical simulation results, both sets of results exhibit a consistent trend. However, the simulation results tend to be slightly higher than the monitoring ones. The observed stress values range from \(-5.86\) to \(-18.92\) MPa, whereas the simulated stress values fluctuate between \(-6.07\) and \(-23.33\) MPa. The maximum pressure on the steel arch supports is noted at the arch ribs on both sides, with decreased pressure at the arch shoulders. The minimum pressure is experienced at the crown of the steel arch supports. The left arch rib endures marginally more pressure than its right counterpart. This analysis infers that the surrounding rock at the crown position undergoes some relaxation and possesses a diminished load-bearing capacity. Consequently, the surrounding rock at the arch rib location exerts the maximum resistance force on the steel arch supports. This force is followed in magnitude by the force at the arch shoulder position, with the crown position experiencing the least pressure.  
A comparison of the actual monitoring results with the simulation ones revealed strong agreement, suggesting that both the numerical simulation model and the selected parameters for the surrounding rock lining are accurate. However, optimizing the existing support structures is essential due to the challenging geological conditions characterized by soft and fractured surrounding rock and resultant high-stress values. This optimization ensures the surrounding rock's stability and the project's overall safety.
## 6 Conclusions  
The following conclusions can be drawn based on this investigation:  
1. Under the same load frequency (10 Hz), the fatigue life of different SMC contents is shown as follows: under the same stress ratio/stress amplitude, the fatigue life of a mixture reaches the maximum when the effect of SMC content on fatigue performance is 10%. This also shows that the more SMC content is not better; when the content of SMC is more than 10%, the fatigue life is lower with more SMC content.
2. Taking the fatigue life of the mixture of 10% SMCSBS as an example, the fatigue performance of different asphalt aggregate ratios and different loading frequencies is shown as follows: whether under the same stress ratio/stress amplitude, the greater the loading frequency, the greater the fatigue life. The fatigue life is the largest when the asphalt aggregate ratio is 4.3 and the loading frequency is 15 Hz.
3. Four artificial intelligence models M5\({}^{\prime}\), ANN, SVM, and PSO-SVM were used to predict the fatigue life of an SMCSBS asphalt mixture, with input parameters (SMC content/%, asphalt aggregate ratio, stress ratio, and load frequency/Hz) and output parameters (fatigue life). The model was validated using standards, such as RMSE and R\({}^{2}\). The results show that M5\({}^{\prime}\) (RMSE = 4035.33 and R\({}^{2}\) = 0.779), ANN (RMSE = 3988.36 and R\({}^{2}\) = 0.814), and SVM (RMSE = 3563.26 and R\({}^{2}\) = 0.861), but the PSO-SVM (RMSE = 2253.71 and R\({}^{2}\) = 0.903) with particle swarm optimization showed the most stable algorithm compared with other methods. Particle swarm optimization (PSO) support vector machine (SVM) ha good performance in predicting the fatigue life of an SMCSBS asphalt mixture.  
Conceptualization, Y.S. and D.H.; methodology, D.H.; software, Y.S.; validation, Y.S. and J.L.; investigation, J.L.; writing--original draft preparation, Y.S.; writing--review and editing, Y.S., D.H. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing is not applicable.  
The authors declare no conflict of interest.