esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Conclusions  
Evaluating three different recurrent topologies implementing a similar base architecture for PM\({}_{2.5}\) and PM\({}_{10}\) particle modeling it was observed that factors such as the optimizer used affect the results obtained. The Adam optimizer showed the best results in general. At the same time, it showed greater variability in particular instances when implemented in an Elman network, in contrast with the Adamax optimizer, which maintained a lower variability in the results obtained, only having the problem of the execution time, which for a batch size of 96 was close to double that of the Adam optimizer. Mentioning the batch size, it was observed that the best results in terms of repeatability were those obtained with a batch size of 256, and in terms of the final results, comparing the CC and RMSE shows that the appropriate batch size was 128, both producing the mean values in this set of tests. The  
Figure 13: Processing time variability in modeling particle behavior PM\({}_{10}\). **(a)** Elman neural network using Adam optimizer. **(b)** Elman neural network using Adamax optimizer. **(c)** GRU neural network using Adam optimizer. **(d)** GRU neural network using Adamax optimizer. **(e)** LSTM neural network using Adam optimizer. **(f)** LSTM neural network using Adamax optimizer.  
evaluated recurrent networks present great capacity for modeling the behavior of nonlinear elements such as particulate matter. Through the CC, it was determined that there was no over-training, the evaluated records from different stations maintaining repeatability in the results demonstrates the robustness of the topologies presented. Furthermore, execution times are an important factor to consider. Producing similar results obtained with different execution times, helps one determine which RNN structure is appropriate for a defined task. Considering the RMSE and CC results together with the execution time, the best network topology implemented overall used GRU neurons, but if fast results are required, the Elman network showed a higher processing capacity in a reduced time. Its only drawback is the existence of problems in in determining chaotic patterns, which must be investigated further, but if there is a large quantity of data and the execution time is not an issue to be tackled, the LSTM network is a great option for maintaining a low variability and high repeatability in the results obtained.  
Data curation, J.C.P.-O. and A.S.-O.; Formal analysis, M.A.A.-F. and E.G.-H.; Methodology, J.A.R.-M.; Project administration, M.A.A.-F.; Software, A.S.-O.; Validation, E.G.-H.; Visualization, J.C.P.-O.; Writing--original draft, J.A.R.-M. and M.A.A.-F.; Writing--review & editing, M.A.A.-F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Raw data is publicly available at: [[http://www.aire.cdmx.gob.mx/](http://www.aire.cdmx.gob.mx/)]([http://www.aire.cdmx.gob.mx/](http://www.aire.cdmx.gob.mx/)) (accessed on 15 August 2021).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### AFM Analysis  
The mechanical properties of the gradient IPN was characterized by PeakForce QNM (quantitative nanomechanical property mapping) mode on a Bruker Multi-Mode VIII AFM, which allows quantitative nanomechanical mapping of DMT modulus simultaneously (Figure 7a-c) when imaging the topography of samples at high resolution. Due to EP and PU have disparate mechanical properties at room temperature; quantitative nanomechanical property mapping is quite suitable for detection of phase distribution in the gradient IPN [17; 46; 47]. Fracture surface of the gradient composite was prepared as the same in the contact angle measurement.  
Figure 7: Quantitative nanomechanical property mapping images for 7/8 h (**a**), 4/8 h (**b**) and 1/8 h (**c**) of the continuous gradient IPN composite. The modulus images were taken after an absolute calibration process. Distribution histogram of the Derjaguin–Muller–Toporov (DMT) modulus (**d**) and adhesive force (**e**) at the corresponding selection regions.  
As shown in Figure 7a-c, there are layer structures along the gradient direction. The DMT modulus (Figure 7d) of the three selected region are 80 \(\pm\) 40 MPa (7/8 h), 160 \(\pm\) 55 MPa (4/8 h) and 255 \(\pm\) 55 MPa (1/8 h), respectively, which increases along with the gradient direction from bottom to top of the IPN structure. While, the adhesion force (Figure 7e) of the three selected region are 21 \(\pm\) 3 nN (7/8 h), 19 \(\pm\) 4 nN (4/8 h) and 12 \(\pm\) 3 nN (1/8 h), respectively, which decreases along with the gradient direction from bottom to top of the IPN structure. This results from the earlier gelled PU phase moving toward to the bottom in the solidifying process, meanwhile the EP phase was extruded to the top of this IPN. This progress would have been frozen before a complete phase separation, which leads to the generation of the gradient structure.
## 5 Analysis of Numerical Calculation Results on Support Effect for Fractured Loose Rock Slopes
### Displacement Field Analysis After Slope Support  
As shown in Figure 20, the maximum horizontal displacement of the slope in the \(Y\) direction near the excavation face after the implementation of the support measures decreases from 13.86 cm to 2.49 cm; the maximum vertical displacement in the \(Z\) direction decreases from 11.4 cm to 1.66 cm; and the maximum total displacement decreases from 18.3 cm to 2.78 cm. These findings indicate that the support effect is evident. However, in the right section of the \(\mathrm{Q^{edl}}\) sediments outside the excavation support slope surface and at the junction of the front edge of the cataclastic loose rock mass and the alteration zone SB2, the displacement value remains large because the rock mass parameter strength is low and no support treatment is carried out. Thus, special cleaning and support in the actual project are recommended.
## 4 Performance Analysis of U-Shaped Frame Optimized Structure
### Dynamic Stiffness Check  
The U-shaped frame, serving as a crucial support component of the gimbal system and comprising a substantial portion of the mass, significantly influences the system's overall dynamic characteristics. Modal analysis reveals that the effective mass attributed to the first five modal frequencies of the U-shaped frame exceeds 90% of its actual mass. Consequently, the first five modal frequencies are extracted from both design structures for a dynamic performance analysis. Finite element analysis results are detailed in Table 2.  
Finite element analysis results indicate that although the second-order frequency of the U-shaped frame with the optimized structure experiences a slight decrease, aerospace structures' dynamic performance is predominantly evaluated based on the fundamental frequency. Table 2 demonstrates that, apart from the second-order frequency, there are substantial improvements in the remaining four frequencies, notably in the U-shaped frame's  
Figure 11: Empirical design model and optimization model stress cloud diagram.  
fundamental frequency, which has risen by 11.48%, thus meeting the structural dynamic performance design requirements. Consequently, the optimized structure demonstrates superior dynamic performance compared to the empirical design structure.
## 2 Modeling and Solution Strategies
### Multi-Objective Solution Methodology
Minimum losses of active power criterion in this context represents the best solution for the DisCo viewpoint, since the utility usually seeks to improve the system efficiency. Maximum net present value criterion represents the best solution for the DG owner viewpoint, since this one intends to sell the maximum energy with the lowest investment cost. Net present value for 10 years is calculated by Equation (10), where operation and maintenance costs are fixed as 14.5, USD/kWe.year for photovoltaic, 62.5 USD/kWe.year for onshore wind generation, and 44.76 USD/kWe.year for hydro generation [26]. \[NPV=\sum_{t=1}^{10}\frac{\sum_{n=1}^{N}\left(SE.cf_{n}.P_{n}^{DG}.NDG_{n}- \left(C_{op}^{n}+C_{m}^{n}\right).P_{n}^{DG}.NDG_{n}\right)}{(1+MAR)^{t}}-C_{ instal} \tag{10}\]  
In this case, the minimum attractiveness rate (MAR) is adopted by the DG decision-maker. Max-Min approximation (MMA) is a metric that calculates the gravity center of a set of points aiming the best compromise among all of the objective functions, being a benchmark for equilibrium among different criteria [27; 29], since this metric is a non-preference criterion for not considering the values of the decision-maker to choose a final solution. Given a Pareto set with \(l\) non-dominated solutions, the MMA metric for the \(i\)-th alternative within the final Pareto optimal set is calculated according to Equation (11), where index \(j\) is related to the \(j\)-th objective function. \[ \begin{split} MMA_{i}=\left[\frac{y_{i}^{max}-y_{i}^{j}}{y_{1}^{ max}-y_{1}^{min}}\cdots\frac{y_{i}^{max}-y_{j}^{j}}{y_{i}^{max}-y_{j}^{min}} \cdots\frac{y_{i}^{max}-y_{i}^{j}}{y_{i}^{max}-y_{j}^{min}}\right],i\in[1,l] \\ \text{solution}=X_{k}\,;\;\text{where}\;k=index\left(max \left\{\begin{array}{cc}min&\{MMA_{i}\}\\ i=1,\ldots,l\end{array}\right\}\right)\end{split}  \tag{11}\]  
Diversity of alternatives in Pareto sets can be evaluated according to the scattering of non-dominated solutions on the Pareto frontier. Spacing metric numerically evaluates how well the solutions in the Pareto frontier are distributed, so that value zero for this metric indicates that all the solutions are equidistantly spaced [33]. Given a Pareto set with \(l\) non-dominated solutions, the Spacing metric (S) of the set is determined by Equation (12). \[ \begin{split} S=\sqrt{\frac{1}{l-1}\sum\limits_{i=1}^{l}\left( \overline{d}-d_{i}\right)}\\ d_{i}=\begin{array}{cc}min&\left\{\begin{array}{cc}\left|y_{i}^{i}-y_ {i}^{j}\right|\\ y_{i}^{max}-y_{1}^{min}\end{array}+\cdots+\frac{\left|y_{a/a^{\prime}}^{j}-y_ {a/l}^{j}\right|}{y_{a/a}^{max}-y_{a/a}^{min}}\right\},\;i\in[1,l]\\ j\
eq i\end{array}\end{split}  \tag{12}\]  
where \(d_{i}\) is the spacing factor of the \(i\)-th solution and \(\overline{d}\) is the average value of all \(d_{i}\). Figure 2: Exemplification of individual’s codification (chromosome).
## 1 Introduction
Therefore, one of the best methods is to use the experimental and logging data to obtain the dynamic-static conversion relationship. The three procedures are as follows: use present data to draw an intersection diagram (dynamic parameter as X-axis and static parameter as Y-axis); obtain relation equations between dynamic and static parameters; and predict the static parameter according to the dynamic-static conversion relation in the study area. In this regard, the method above can obtain a continuous rock static mechanics parameter profile of the well and promote the application to other wells in the study area, providing a reliable basis for oil/gas exploration and development as well as drilling engineering design. So far, several formulas have been developed to establish the correlation that exists between static and dynamic elastic moduli. The empirical equations between dynamic (\(E_{\text{d}}\)) and static (\(E_{\text{s}}\)) elastic moduli of rocks have been studied by the Institute of former Yugoslavia and the Institute of Geology and Geophysics in Belgrade. Utilizing a large number of comparative studies on experimental data, the Institute of Geology of the Chinese Academy of Sciences proposed a linear correlation for the estimate of static elastic modulus from dynamic elastic modulus. However, this equation is only suitable for microcline and granite [7; 8; 9; 24; 25; 26; 27; 28; 29]. [PERSON] [7] established a linear correlation between the dynamic and static parameters of igneous and metamorphic rocks. [PERSON] and [PERSON] [30] established linear dynamic-static relations for several types of rocks. In addition, some other conversion equations were employed for various rocks [10; 31]. [PERSON] et al. [32] conducted laboratory experiments on ten carbonate rocks extracted from Spain and established different conversion correlations. [PERSON] et al. [33] showed linear and nonlinear correlations for different rock types. However, the applicability of each of these correlations is limited to the type of rock (igneous, metamorphic, and sedimentary rocks) under particular conditions. Therefore, this paper aims to review the correlation between dynamic and static parameters of various geological formations in other regions and use acoustic tools such as acoustic logging, seismic data, and indoor ultrasonic testing to speculate on the static parameters.
# A Reinforcement Learning Based Intercell Interference Coordination in LTE Networks
## 6 Results and Discussions
### Delay  
To compare performances of proposed GA algorithm, we considered cumulative distribution of head of line delay at the end of each simulation. Figure 10 shows empirical cumulative distribution function of user's HoL at the end of a simulation for each algorithm. We plotted the figure for a maximum active users equal to 600. Recall that HoL value is between 1 and 100 ms, such that, if a packet stays longer in the buffer, the HoL increases and, when it reaches 100 ms, the last packet is discarded.  
Figure 10: Cumulative Distribution function of delay. Proposed GA showed better performances than other algorithms.  
Figure 9: Overall average packet loss with 95% confidence interval. Proposed GA showed better performances.  
It can be seen in the figure that our proposed resource allocation algorithm showed the best delay distribution compared to MT, PF, EDF and LWDF resource allocation algorithms. The Figure shows that, at the end of the simulation, HoL value of our proposed GA were between 0 ms and 20 ms, while HoL values of other algorithms were between 0 ms and 80 ms. We can see that 50% of users had HoL values between 0 ms and 40 ms and 50% of users had HoL values between 40 ms and 80 ms, which means HoL values of other algorithms tended to reach maximum HoL value leading to packets being discarded. This confirm results in shown in Figure 9.
## 5 Chemical Characterization
These results are based on the review by [PERSON] and [PERSON] [12]. \begin{table}
\begin{tabular}{l l} \hline \hline
**Compounds** & **Parts Used (Source)** \\ \hline \hline
1-Ethyl-4-methoxy-\(\beta\)-carboline & Root bark \\ \hline \hline \(\beta\)-Carboline-1-propionic acid & Root bark \\ \hline Methyl-4-methoxy-\(\beta\)-caboline carboxylate & Root bark \\ \hline
1-(1’-2’-Dihydroxyethyl)-4-methoxy-\(\beta\)-carboline & Root bark \\ \hline
1-(2’-Hydroxyethyl)-4-methoxy-\(\beta\)-carboline/Creatinate & Root bark \\ \hline
4-Methoxy-1-vinyl-\(\beta\)-carboline/Dehydrocreatine & Dried leaves \\ \hline
1-Methoxycarbonyl-4,8-dimethoxy-\(\beta\)-caboline & Leaves \\ \hline
1-Methoxycarbonyl-\(\beta\)-carboline/1-c-xomethoxy-\(\beta\)-carboline & Leaves \\ \hline
1-(1-Hydroxy-2-methoxy)-ethyl-4-methoxy-\(\beta\)-carboline & Root bark \\ \hline
1-Carbamoyl-\(\beta\)-carboline & Root bark \\ \hline
1-Acetyl-4-methoxy-\(\beta\)-carboline & Root bark \\ \hline Canthine-6-one & Root bark, leaves and wood \\ \hline
1-Hydroxycanthine-6-one & Root bark \\ \hline
1-Methoxy-canthine-6-one & Root bark, dried leaves and wood \\ \hline
5-Hydroymethylcanthine-6-one & Root bark \\ \hline Canthine-6-one-3-N-oxide & Wood \\ \hline
1-Methoxycanthine-6-one-3-N-oxide & Root bark \\ \hline Ailanthone & Stem bark \\ \hline Chapparinone & Seed \\ \hline Chapparin & Aerial part \\ \hline Shinjulactone and Shinjulactone B & Stem bark \\ \hline Shinjulactone C, D, E, F, M, N, G, H, I, J, K and L & Root bark \\ \hline A13(18) -Dehydroglaucarubinone & Stem bark \\ \hline A13(18) -Dehydroglaucarubolone & Stem bark \\ \hline Ailantinol A e B & Stem bark \\ \hline Ailantinol C, D, E, F, G and H & Aerial part \\ \hline Shinjulglycoside A, B, C and D & Seed \\ \hline Shinjulglycoside E and F & Root bark \\ \hline Shinjulidactone & Root bark \\ \hline Cycloart-25-ene-3\(\beta\)-24R-diol and Cycloart-25-ene-3\(\beta\)-24S-diol & Fruits \\ \hline
9,19-Cyclolanost-23(Z)ene-3\(\beta\),25-diol & Fruits \\ \hline Ailantholide & Seed \\ \hline
3-epi-ursolic acid & Fruits \\ \hline
12\(\beta\), 20 (S)-dihydroxy dammar-24-en-3-one & Fruits \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The main compounds identified in _A. alissima_ grouped by the major classes and which part of the plant was used for their isolation.
## 5 Experimental Evaluation
### Simulation Results
#### 5.2.2 DMD-ID Algorithm Performance Using Different FTN Detection Methods in Multipath Channels  
As shown in Figure 5, this is to compare the impact of different algorithms on BER performance under the TU channel compression factor \(\tau\) = 0.7. It can be seen that traditional model-driven algorithms based oninear complexity have been difficult to use to achieve better results in this situation, while data-driven CNN and LSTM structures can achieve better performance than exponential complexity. Among them, the performance of the algorithm based on the Deep-Bi-LSTM structure is the best, which is about 1.6 dB higher than the performance of the MAP algorithm. The reason why such aarge gain can be obtained is that FTN detection in multipath channels can be seen as the mutual aliasing of two nonlinear processes, which will pose a greater challenge to the receiver algorithm. Traditional model-driven methods have been difficult to fit this kind of nonlinear interference. The non-linear interference introduced by FTN is a targeted detection under the condition of correct matching with a clear compression factor when the network is trained with the prior knowledge that is controllable at the transmitting end.  
Figure 4: In the TU channel, when the compression factor \(\tau\) = 0.7, compare the performance of different channel equalization modules.  
As shown in Figures 6 and 7, the performance (blueine and blackine) of the DMD-JD detection algorithm is better than the traditional pure model-driven results in both the VA channel and the PB channel. The Deep-Bi-LSTM structure is more efficient than traditional algorithms in extracting adjacent non-linear interference from FTN. Moreover, relying on the characteristics of the network's offline training online test number, the DMD-JD FTN detection algorithm can achieve better performance than traditional algorithms while havingower complexity.
## 2 Methodology
### Color Similarity
#### 2.3.2 Representative Color Intensities and Histogram Thresholding  
Possible changes in respect of the view angle present another challenge for color comparison methods. They can lead to the same distinct areas appearing to have different sizes, thus resulting in the Lab channels' histograms for the same classes of the same object to shift dramatically. This renders bin-to-bin histogram comparison methods to be less accurate, which has inspired the development of other comparison metrics [40; 42]. However, they still may entail misleading results due to drifts in pixel intensities, which are caused by illumination and point-of-view changes. In order to address this issue, we propose a simple thresholding scheme, with the purpose of finding the most representative pixel intensities.  
Firstly, the number of bins in the color channel histograms was reduced from 256 to 64, as was the case for the \(L\) channel histograms in Section 2.3.1. This reduces the influence of the drifting effects. Secondly, for every bin \(j\) of the original histogram \(h_{o}\) of channel \(i\) (where \(i=L\),\(a\),\(b\)), where the \(L\) channel histogram is \"stretched\", we assign \"1\" in its binarized version \(h_{b}\) when the number of pixels in that bin exceeds the threshold \(k_{bi}\). Then, we assign \"0\" as otherwise shown in Figure 2d, such that:  
\[h_{bij}=\left\{\begin{array}{ll}1\;if\;h_{oij}\geq k_{bi},\\ 0\;otherwise.\end{array}\right. \tag{1}\]  
\[k_{bi}=k_{inc}\frac{\sum_{j=0}^{255}h_{bij}}{256}, \tag{2}\]  
where \(k_{inc}\) is an empirical coefficient that increases the threshold value over a simple average. In our experiments, \(k_{inc}=1.5\) was found to provide the best results. This approach also allows us to design a comparison scheme for a pair of histograms. Provided we have two binarized histograms \(h_{bi1}\) and \(h_{bi2}\) of the same class in two images, we find their sum \(h_{bi}\), which can only consist of zeroes, ones, and twos. These thus define the color channel similarity measures as:  
\[h_{ti}=h_{bi1}+h_{bi2}, \tag{3}\]  
\[S_{i}=\frac{s_{2i}(h_{ti})}{s_{1i}(h_{ti})+s_{2i}(h_{ti})}, \tag{4}\]  
Figure 3: The correlation between shadows and peaks in the \(L\) channel histograms of face, upper clothes, and pants subregions: (**a**) prominent shadows appear only on pants and (**b**) shadows appear in every subregion. Yellow arrows show correspondence between histogram regions and shadows in images.  
where \(s_{1}\) and \(s_{2}\) are the numbers of times \"1\" or \"2\", which appeared in \(h_{t}\), respectively. The intuition behind this is to find the intensities that are prominent in both histograms, as well as to obtain their fraction relative to the total number of non-zero bins, such that \(S_{i}\) lies in [0, 1] range. This operation is very similar to histogram intersection [42]. However, it does not require \(h_{bi1}\) and \(h_{bi2}\) to have the same number of non-zero bins and it is commutation-invariant. The former is very important due to the fact that the number of non-zero intensities after thresholding can vary significantly for different images.
# Estimating Eddy Dissipation Rate with QAR Flight Big Data
## 4 Conclusions  
On the one hand, AT is a typical risk in the civil aviation industry, and EDR is an ideal physical indicator for measuring its intensity. On the other hand, the collection of QAR data by CAAC provides a super data source for studying such topics, including aviation safety and flight operations quality assurance (FOQA). However, there is no straightforward strategy to calculate EDR with QAR data. Two traditional methods of EDR estimation both involve parameters that could be not provided by the QAR data. In this study, we proposed a practical step-wise procedure to bridge them up, effectively and efficiently. We compared EDR estimations with the corresponding vertical overload and DEVG, and results show that EDR provides a reasonable indicator for evaluating AT risks during a flight. Moreover, with the trillions of pieces of QAR records collected from 1 January 2016 to 31 December 2018, the EDR values for the Airbus and Boeing aircrafts are calculated within an acceptable time cost and visualized spatially. It is interesting for overviewing the spatial patterns, of which hotspots or clusters possibly indicate high risks of AT.  
Furthermore, this study also makes a good foundation for a number of further studies in the field of aviation safety and FOQA, such as: (i) Analyzing spatio-temporal patterns of AT risks across China via incorporating the temporal dimension [35]; (ii) exploring potential affecting factors on AT risks, for example meteorological, environmental, and terrain factors via spatial statistics techniques, like geographically weighted regression [36; 37]. These future studies will be meaningful for applications in flight route planning, early risk warning, and FOQA.  
Conceptualization, R.H. and B.L.; methodology, R.H. and H.S.; software, C.W. ([PERSON]); validation, R.H. and C.W. ([PERSON]); formal analysis, B.L.; data curation, H.S.; writing--original draft preparation, H.R., H.S., and B.L.; writing--review and editing, B.L.; visualization, C.W. ([PERSON]); supervision, R.H.; project administration, R.H., H.S., and B.L.; funding acquisition, R.H.  
This research was funded by Natural Science Foundation of China, grant number [NSFC: U1833201; 41725006; 41871287].  
This research was jointly supported d by National Natural Science Foundation of China grant number [NSFC: 1833201; 41725006; 41871287].  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 5: EDR estimation result of Boeing aircrafts.