esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
Piloting precision data, i.e., records about error rates during individual executed manoeuvres, were used to create composite score. The score was created primarily to allow only one variable indicating piloting precision. To this end, it was necessary to standardize the data so that each dataset comprising respective monitored variable, corresponding to each subject during his or her entire training, was standardized. Data standardization for each subject was carried by the equation [47]:  
\[Z_{p}=\frac{x_{p}-\mu_{p}}{\sigma_{p}}, \tag{4}\]  
where \(Z\) are the standardized data by means of \(Z\)-score, \(x\) is observed value, \(\mu\) is average value of the monitored parameter \(p\), where \(p=1\ldots 4\) and represents flight parameters, i.e., altitude, bank,vertical speed and magnetic heading; and \(\sigma\) is standard deviation from the monitored parameter \(p\). Next, summation of all individual flight scores was done for each subject:  
\[CompositeError=\sum_{i=1}^{n}\frac{Zi,p}{2}, \tag{5}\]  
where \(Z_{i,p}\) is a matrix with \(i\) rows and \(p\) columns, with columns corresponding to mentioned flight parameters. Summation is for the sake of practicality divided by 2, because precisely two flight parameters were monitored during each manoeuvre. In other words, each standardized distribution of a monitored parameter has average zero and standard deviation (SD) equal to \(\pm 1\), i.e., division by 2 retains the SD ratio.  
Repeated measures ANOVA [48] was then applied on the adjusted data. To determine whether repeated measures ANOVA can be applied, individual datasets were subjected to normality testing by means of Shapiro-Wilk test [49]. Normality was not indicated for all datasets. Nevertheless, the repeated measures ANOVA was applied taking into consideration that datasets not indicating normality may increase type I error of the performed analysis. On the other hand, when rejecting the hypothesis about normal distribution, the testing was near to accepting the hypothesis and it is also necessary to note that this type of analysis is robust against violation of normal distribution assumption [50; 51].  
[PERSON]'s test [52] was used to test the hypothesis about sphericity and it did not confirm the assumption about meeting compound symmetry. Due to that, Greenhouse-Geisser correction [53] was used for p-value determination.  
Using Wilkinson notation, it is possible to describe the model used for applied repeated measures ANOVA as:  
\[Fl1-Fl6\sim Gr+Man+Gr\times Man, \tag{6}\]  
where \(Fl1-Fl6\) is notation for the measured flights (see Figure 2), \(Gr\) is independent categorical variable indicating a group (i.e., Gr. A a Gr. B), and \(Man\) is independent variable representing manoeuvre type. Within subject design is, therefore, based on individual measurements during the training. For the subsequent post hoc analysis, i.e., comparison of data pairs, Dunn-Sidak correction [54] was applied.  
Statistical analysis was performed in MATLAB environement (MATLAB R2017a, MathWorks, Inc., Natick, MA, USA) with the use of statistical toolbox.
## 3 Materials and Methods  
### Study Assumptions and Scenario  
To estimate interference from 5G NR to the aeronautical and maritime systems, a Monte Carlo analysis simulation with the SEAMCAT tool was used [11; 12]. Since the wanted link of aeronautical and maritime systems fluctuates depending on antenna types and the distance between wanted link transmitters and receivers, in simulations the protection criterion of the aeronautical and maritime mobile systems was interference-to-noise ratio (I/N). An increase in receiver effective noise of 1 dB would result in significant degradation in communication range. Such an increase in effective receiver noise level corresponds to an (I + N)/N ratio of 1.26 or an I/N ratio of about \(-6\) dB. When multiple potential interference sources are present, protection of the AMS and MMS systems requires that this criterion is not exceeded due to the aggregate interference from multiple sources [13].  
Each simulation consisted of 10,000 events. During the simulations, the aircraft and vessels were placed at a distance of 12 nautical miles (22 km), 80 nautical miles (150 km), and 200 nautical miles (370 km). The 12- nautical-mile distance was considered as the worst-case scenario, whereas 80 and 200 are more likely distances in practice due to the EEZ areas. The 5G NR networks were located in 5 cities in the coastline area--Vladivostok, Artyom, Nakhodka, Ussuriysk, and Arsenyev. Figure 1 shows the scenario of the aeronautical mobile station (AMS) and maritime mobile station (MMS) located at a distance of 12 nautical miles from the coastline.  
In each city, a 5G NR network was simulated and according to Recommendation ITU-R M.2101 consisted of 19 tri-sector BS with 3 UEs at each sector, and thus interference was calculated from 285 sectors overall. It should be noted that many cities have much more IMT BS; however, they do not operate at the same frequency as AMS receivers simultaneously since the spectrum in this band is divided between the mobile network operators and inside each operator's deployments to avoid intercell interference, and base stations do not reuse the same spectrum as that in the neighboring cell. Additionally, in many of these areas, operators rely on the other bands as well, and therefore 57 sectors per city is sufficient to estimate the interference level [14]. The typical deployment topology of the 57 sectors networks provided in Figure 2. The red dots represent base stations and the yellow dots represent users.
## 4 Problem Formulation  
According to the above analysis, the sum rate of all users in the licensed spectrum and the unlicensed spectrum can be given by  
\[R=\sum_{l=1}^{L}(\sum_{m=1}^{M}x_{m}^{l}R_{m,l}^{c}+\sum_{n=1}^{N}y_{n}^{l}R_{ n,l}^{d})+\sum_{u=1}^{U}(\sum_{m=1}^{M}s_{m}^{u}R_{c}^{u}+\sum_{n=1}^{N}s_{n}^{ u}R_{d}^{u}+\sum_{w=1}^{W}R_{w}^{u}). \tag{17}\]  
Our goal is to maximize the sum rate of all users by jointly optimizing user access and resource allocation. The optimization problem can be formulated as follows:  
\[\underset{A,\rho}{\text{max}} R\] (18a) s.t. \[x_{m}^{l},y_{n}^{l},s_{m}^{u},s_{n}^{u}\in\{0,1\},\forall m\in \mathcal{M},n\in\mathcal{N},l\in\mathcal{L},u\in\mathcal{U}, \tag{18b}\] \[0\leq p_{m}\leq p_{m}^{max},\forall m\in\mathcal{M},\] (18c) \[0\leq p_{n}\leq p_{n}^{max},\forall n\in\mathcal{N},\] (18d) \[R_{w}^{u}\geq R_{min},\forall w\in\mathcal{W},u\in\mathcal{U},\] (18e) \[0\leq\sum_{l=1}^{L}\sum_{u=1}^{U}(x_{m}^{l}+s_{m}^{u})\leq 1, \forall m\in\mathcal{M},\] (18f) \[0\leq\sum_{l=1}^{L}\sum_{u=1}^{U}(y_{n}^{l}+s_{n}^{u})\leq 1, \forall n\in\mathcal{N}, \tag{18g}\]\[\sum_{l=1}^{L}x_{m}^{l}\leq q,\forall m\in\mathcal{M}, \tag{18h}\] \[\frac{(t-\sum_{m}x_{m}^{l}\rho_{m}-\sum_{m}y_{n}^{l}\rho_{d}^{n})}{ R_{w}^{u}}\leq R_{w}^{t},\] (18i) \[R_{m,b}^{u}\geq R_{min}^{m},\forall m\in\mathcal{M},u\in\mathcal{ U},\] (18j) \[R_{n}^{u}\geq R_{min}^{n},\forall n\in\mathcal{N},u\in\mathcal{ U}, \tag{18k}\]  
where \(p_{m}^{max}\) and \(p_{n}^{max}\) denote the maximal transmit power of the CUs and the D2D users in licensed spectrum, respectively. \(R_{min}^{n}\), \(R_{min}^{n}\) and \(R_{w}^{l}\) are the required minimal rate of the CUs and D2D users, Wi-Fi users, respectively. Constraint (18b) guarantees that the user access variables are binary. Constraints (18c) and (18d) ensure that the transmit power of the CUs and the D2D users in the licensed spectrum does not exceed the maximum value. Constraint (18e) indicates that the minimum throughput of the users on the licensed spectrum. Constraints (18f) and (18g) imply that each CU or D2D user only occupies one subchannel in the licensed or the unlicensed spectrum. Constraint (18h) ensures that one subchannel can be reused by at most \(q\) D2D users. Constraint (18i) guarantees the minimum rate of the Wi-Fi users. Constraints (18j) and (18k) represent the throughput requirements of the D2D user and the CUs in the unlicensed spectrum.
## 4 Conclusions  
This study was performed to investigate and understand the emission patterns of different automotive engines and fuel types used in Korea. Vehicles considered in this study were classified based on their type, the fuel-injection/combustion technology employed, and the emission-regulation standards complied with. Vehicular emissions were detected in real time under conditions of varying average speeds of vehicles. The conclusions drawn from this study can be summarized as follows:  
1. Emissions of all gaseous pollutants demonstrated an initial decrease with an increase in average speed. For all vehicle types, CO\({}_{2}\) accounted for more than 99% of aggregate emissions, whereas the remaining 1% was comprised of various gas species, and its composition varied depending on the type of fuel used.
2. CO and NH\({}_{3}\) were observed to be predominant non-CO\({}_{2}\) species of pollutants emitted from gasoline- and LPG-powered vehicles, whereas emissions from diesel-powered vehicles were dominated by NOx, CO, and NMHC. Additionally, SO\({}_{2}\) emissions from diesel-powered vehicles were observed to exceed those from their gasoline- and LPG-powered counterparts by up to 28 times.
3. Strengthening emission-regulation standards for diesel vehicles has somewhat reduced the effect of NOx emissions in recent years. However, the value of the NO\({}_{2}\)/NOx ratio has witnessed a significant increase, as depicted in Figure 5, owing to advancement in combustion technology and operating conditions of after-treatment devices.
4. Gaseous pollutants, such as NOx, NMHC, NH\({}_{3}\), and SO\({}_{2}\), emitted by vehicles employing different types of fuel constitute a major source of primary pollution, and these tend to ultimately react with other atmospheric species to produce secondary gases and PM. Inhibition of potential atmospheric pollution, therefore, requires the development of stringent regulations and establishment of emission-reduction policies.  
Conceptualization, G.P. and T.L.; methodology, G.P., K.K., T.L.; installation vehicles on chassis dynamometer, G.P., K.K., S.M., H.H., T.C., S.K., J.B.; formal analysis, G.P., K.K., S.K., T.P.; investigation, S.J., S.K., D.-G.Y., S.S.; validation, J.K., J.L., T.L.; resources, J.-H.W.; writing--original draft preparation, G.P. and T.L.; writing--review and editing, G.P. and T.L.; supervision, T.L.  
This work was supported by a grant from the National Institute of Environment Research (NIER), founded by the Ministry of Environment (MOE) of the Republic of Korea (NIER-2017-03-02-048). Additional data analysis was supported by the Ministry of Environment (MOE) as \"Climate Change Correspondence Program (project no.2016001300001)\".  
The authors thank the research scientists at the Transportation Pollution Research Center and the National Institute of Environmental Research (NIER) for their contributions to the success of the project.  
The authors declare no conflict of interest.
# Nonlinear Optical Microscopy of Interface Layers of Epitaxial Gamet Films
## 3 Experimental Results
One can see that the intensity of the SHG response reveals a maximum with the full width at half maximum (FWHM) of about \(\approx\)2.5 um situated close to the interface. The coordinate of this SHG maximum \(z_{max}\) corresponds to the escape length of the SHG wavelength in the garnet films and thus determines the in-depth resolution of the SHG probe; its position was used as a reper when scanning along the \(z\)-direction. Similarly, we switched to the studies of the third harmonic microscopy of the garnet films. Figure 4 shows the THG intensity patterns for the hidden \((LuBi)_{3}Fe_{5}O_{12}\)/GGG interface; the experimental scheme is sketched in Figure 4a. The fundamental wavelength in the spectral range of 1200-1600 nm was used, so that the detected THG at 400-530 nm wavelength corresponds to the high absorption of the garnet film. Figure 4b shows that the effective thickness of the layer contributing to the third harmonic generation is about 2 um, which is less than for the case of the SHG scheme due to the higher-order nonlinearity of the process. Similarly, higher in-plane resolution of the THG probe is attained. Figure 2: (**a**) Scheme of the light beams propagation in the nonlinear optical microscopy measurements of the \((LuBi)_{3}Fe_{5}O_{12}\)/air interface; (**b,d**) SHG intensity patterns of the \((LuBi)_{3}Fe_{5}O_{12}\) film, the polarization plane of the fundamental radiation is oriented in the vertical direction on the figure, the polarization of the generated SHG is vertical (**b**) and horizontal (**d**). (**c**) The 3D SHG intensity pattern corresponding to that shown in panel (**d**). Contrary to the SHG case, the THG intensity pattern for the parallel polarizations of the fundamental and harmonic's beams is constant within the experimental accuracy over the whole scanning range of 10 \(\times\) 10 \(\upmu\)m. This indicates that the relative value of the magnetization-induced components of the THG polarization are negligibly small as compared to the non-magnetic (crystallographic) ones. At the same time, if the detected THG polarization is close to the orthogonal position with respect to the polarization plane of the laser beam and forms the angle of 85\({}^{\circ}\) (Figure 4c) or 90\({}^{\circ}\) (Figure 4d) with it, pronounced THG patterns appear. One can see a zigzag-type dependence similar to that attained in the SHG response and very much similar to the MFM pattern shown in Figure 1c. For crossed-pump and the THG waves' polarizations, the domain structure demonstrates nearly triangular-like features places in a chess-like order above the stripe domains. If the analyzer is rotated by 5\({}^{\circ}\) from the crossed position, an even more detailed magnetic pattern is observed, indicating the spatial resolution of the THG probe of less than 0.5 \(\upmu\)m.
# SARAC4N: Socially and Resource-Aware Caching in Clustered Content-Centric Networks
###### Abstract  
The Content-Centric Network (CCN) presents an alternative to the conventional TCP/IP network, where IP is fundamental for communication between the source and destination. Instead of relying on IP addresses, CCN emphasizes content to enable efficient data distribution through caching and delivery. The increasing demand of graphic-intensive applications requires minimal response time and optimized resource utilization. Therefore, the CCN plays a vital role due to its efficient architecture and content management approach. To reduce data retrieval delays in CCNs, traditional methods improve caching mechanisms through clustering. However, these methods do not address the optimal use of resources, including CPU, memory, storage, and available links, along with the incorporation of social awareness. This study proposes SARAC4N, a socially and resource-aware caching framework for clustered Content-Centric Networks that integrates dual-head clustering and popularity-driven content placement. It enhances caching efficiency, reduces retrieval delays, and improves resource utilization across heterogeneous network topologies. This approach will help resolve congestion issues while enhancing social awareness, lowering error rates, and ensuring efficient content delivery. The proposed Socially and Resource-Aware Caching in Clustered Content-Centric Network (SARAC4N) enhances caching effectiveness by optimally utilizing resources and positioning them with social awareness within the cluster. Furthermore, it enhances metrics such as data retrieval time, reduces computation and memory usage, minimizes data redundancy, optimizes network usage, and lowers storage requirements, all while maintaining a very low error rate.  
social awareness; content-centric networking; resource utilization; efficient data retrieval; content disbursement and socially and resource-aware caching in content-centric clustered networks (SARAC4N)
## 5 Proposal of a New Formulation for \(Y_{x}\)  
Based on the results of the present study, and on the data available in literature, a new formulation for \(Y_{X}\) is proposed. Differently from the model according to [PERSON] et al. which uses a linear interpolating function, a 2 nd order polynomial function was used to interpolate the data.  
\[Y_{X}=1-0.0176\bigg{(}\log_{10}\bigg{(}\frac{m_{n}}{m_{nT}}\bigg{)}\bigg{)}^{2 }-0.4613\cdot\log_{10}\bigg{(}\frac{m_{n}}{m_{nT}}\bigg{)} \tag{11}\]  
The coefficient of determination \(R^{2}\) results 0.9296. On the same set of data, the model of [PERSON] et al. shows a coefficient of determination of 0.9292.  
Moreover, while the 90% reliability interval according to the model proposed by [PERSON] et al. lies within \(\pm 0.0750\) around the mean value, in the present model, thanks to a lower standard deviation, the offset is \(\pm 0.0718\).  
While on the present set of data the adoption of a 2 nd order polynomial function does not introduce big benefits, for high modules the difference becomes evident (Figure 4). Only further tests or availability of data for big modules could really indicate which of the two models better describe the real (physical) trend of \(Y_{X}\) vs. \(m_{n}\).
## 5 Numerical Results
### Experimental Result  
Figure 6 shows the third type of load curve as an example, indicating the active daily load curve for the 12 th day to the 19 th day, 21 st day, and 22 nd day for the FTU measurement. The FTU measurement active daily load curve on the 14 th day is considered the active power curve to be cleaned up. Then, the average value of the remaining active daily load curves is obtained as a reference time series. Finally, the active power curve to be cleaned is filtered based on the CC-VC-UKF algorithm. As shown in Figure 11, the random disturbance of the filtered measurement data is reduced, and the data quality is improved.  
In the same way, the TTU measurement active power curve determined by the shortest Euclidean distance is filtered based on its reference time series, and the result is shown in Figure 12.  
Figure 11: Active power curve to be cleaned based on reference time series filtering.  
Figure 12: TTU measured active power curve based on reference time series filtering.  
According to the spatiotemporal joint cleaning technique described in Section 3.2, the FTU measurement data sequence is filtered based on the CC-VC-UKF algorithm with the TTU measurement data as the reference data sequence. The spatial measurement deviation before and after filtering is compared, as shown in Figure 13.  
Finally, after the joint space-time filtering and cleaning, the active power curve measured by the FTU is shown in Figure 14.  
The calculation formula of the spatial measurement deviation is \(\ v_{k}^{c}=y_{k}^{c}-x_{k}^{c}\), where \(x_{k}^{c}\) represents the TTU measurement data value at time \(k\), and \(y_{k}^{c}\) represents the FTU measurement data value at time \(k\). At the same time, the power flow relationship between FTU and TTU in Figure 7 shows that the spatial measurement deviation between FTU and TTU should theoretically be positive. The power system will be attacked by dirty data to varying degrees due to the influence of the actual site, thereby causing a large deviation in the measurement data of the measurement equipment. Thus, the spatial measurement  
Figure 14: FTU-measured data curve after space–time combined filtering cleaning.  
Figure 13: Comparison of the 29 th sky interval measurement deviation before and after filtering.  
deviation may be negative. This study selects the measurement data of FTU and TTU from 3:00 to 7:00 on the 30 th day, and the situation is that the spatial measurement deviation is negative at this time. The comparison of spatial measurement deviation before and after spatiotemporal joint cleaning using the CC-VC-UKF algorithm is shown in Figure 15.  
The figure shows that after filtering based on the CC-VC-UKF algorithm, the negative part of the spatial measurement deviation that deviates from the normal range is transformed into a more reasonable normal value, and the volatility of measurement deviation is reduced, showing that the proposed algorithm has a better filtering effect on spatial measurement deviation under a dirty data attack.
## 4 The Effect of Changing the Electromagnetic Field Propagation Direction  
A regular technique was also employed to assess the effect of the changing transmission coefficient (S\({}_{21}\)) for varying field propagations. The simulation setup for the changed electric and magnetic fields is depicted in the Figure 6. The electric field (E\({}_{y}\)) was applied along the Y-axis while the magnetic field (H\({}_{x}\)) was applied along the X-axis. Two resonances that were recorded around 6 and 10 GHz.  
However, if the fields were interchanged with each other, i.e., the electric field (E\({}_{x}\)) is applied towards the X-direction and the magnetic field (H\({}_{y}\)) is applied towards the Y-direction, 3 major resonances would be observed at 2.38 GHz, 4.55 GHz and 9.42 GHz as depicted in Figure 7.
# Phenolic Compounds and Antioxidant Activity in Edible Flower Species from Oxaxa
## 3 Results
### Phenolic Compounds and Antioxidant Activity in the Infloresences of A. salmiana
\begin{table}
\begin{tabular}{l c c c c c} \hline \multirow{2}{*}{**Communities of Origin of Samples Evaluated**} & \multirow{2}{*}{
\begin{tabular}{c} **Total Polyphenols** \\ **(mg GAE g\({}^{-1}\))** \\ \end{tabular} } & \multicolumn{2}{c}{**Flavonoids**} & \multicolumn{2}{c}{**Antiox. Activity**} \\  & **Polyphenols** & \multicolumn{2}{c}{**(mg g\({}^{-1}\))**} & \multicolumn{2}{c}{**(\(\mu\)mol TE g\({}^{-1}\))**} \\ \cline{2-5}  & **CE 1** & **QE 1** & **DPPH** & **FRAP** \\ \hline [PERSON] 1 (IZ1-01, ID) & 14.6 a 2 & 4.65 a & 3.06 ab & 41.9 ab & 86.0 a \\ [PERSON] 2 (IZ1-02) & 11.6 b & 3.32 b & 3.33 a & 35.6 c & 67.5 ab \\ Santo Tomas Tamazulapan & 11.5 b & 2.58 bc & 1.68 cd & 35.3 c & 49.6 bc \\ Ejutla de Crespo & 10.1 b & 2.45 c & 1.34 d & 35.1 c & 45.5 c \\ Santa Ana Miahuatlan & 11.5 b & 2.79 bc & 1.91 cd & 37.0 bc & 51.1 bc \\ Santa Maria Sola & 11.4 b & 2.67 bc & 2.21 bc & 43.1 a & 60.8 bc \\ \hline \end{tabular}  
* CE and QE indicate catechin and quercetin equivalents, respectively; 2 within columns, means with the same letter indicate no significant difference (Tukey’s test, \(p\leq 0.05\)).  
\end{table} 
Table 3: Average total polyphenol and flavonoid contents and antioxidant activity in infloresences of _Y. filifera_ collected from different communities in Oaxaca, Mexico.
# A Channel Allocation Mechanism for Cellular Networks
## 5 Simulation Analyses
### Simulation Results and Analyses
#### 5.2.4 Discussions  
In experiments, this study designed two cases to simulate the traffic information and communication behavior. Through the CBPs derived from Case 1 and Case 2, this study could discover that the CBPs of the cellular system adopting the proposed mechanism are always less than the traditional SCA mechanism. Therefore, the simulation results indicate that the proposed mechanism can allocate the number of channels more precisely than the SCA mechanism. In Case 2, the maximum CBP in a cellular system using the proposed mechanism is 16.28%, which is less than that using the SCA mechanism.  
Figure 24: The CBPs (\(P_{6}\)) are derived by using SCA and the proposed mechanism in Cell\({}_{6}\).
# Predicting Saudi Stock Market Index by Using Multivariate Time Series Based on Deep Learning
## 4 Methodology and Implementation
### Forecast Methods
#### 4.3.1 Univariate Method Using Closing Prices  
As seen in Figure 2, the performance and price behaviours of the forecasting model were validated by directly inputting the closing prices of a stock.