esa-sceva/satcom-chunk-collection huggingface dataset:
# End-to-End Performance Evaluation of MEC Deployments in 5G Scenarios
Instead, we advocate that a co-simulation approach, including models of both network and compute element, would be beneficial for the analysis. However, no simulator that we know of models both satisfactorily. For this purpose, we realize a co-simulation framework where the communication part is simulated through Simu5G, and the MEC host is simulated using CoFluent. The two tools communicate via file exchange, for maximum flexibility. The above framework is used to assess various deployment options for MEC hosts. Our results show that, as one would expect, 4G is going to be a major bottleneck, preventing MEC potential to be unleashed. On the other hand, introducing 5G will enable MEC services to scale up to a considerably larger penetration, challenging the capacity of the MEC infrastructure, and requesting it to scale accordingly. One contribution of this paper also lies in the modularity of our framework: other MEC services can be evaluated by minimally modifying a setup-namely, the application model in both Simu5G (for what concerns its communication pattern) and CoFluent (for its computation pattern). The rest of the paper is organized as follows. Section 2 describes MEC in the context of 4G and 5G networks, whereas Section 3 describes our co-simulation framework. Section 4 reports evaluation results, and Section 5 concludes the paper.
## 3 Results and Discussion
### Identification and Determination of Volatile Compounds in Forty Apple Cultivars
#### 3.2.3 Alcohols  
Alcohols are another main group of compounds contributing to apple aroma in the 40 apple cultivars. Among the alcohols, 2-hexyn-1-ol (B4) and 1-hexanol (B5) were the major components. Five types of alcohol were identified. The relative cumulative content of alcohols ranged from 0.15% in cultivar RY to 6.00% in cultivar Hauguan (HG) (Table S4). HY (941.15 \(\pm\) 80.34 \(\mathrm{\SIUnitSymbolMicro g}\)/kg FW, 3.95% total volatiles) had the highest alcohol content, followed by HG (730.52 \(\pm\) 50.46 \(\mathrm{\SIUnitSymbolMicro g}\)/kg FW, 6.00% total volatiles) and Hanfu (HF, 581.55 \(\pm\) 42.89 \(\mathrm{\SIUnitSymbolMicro g}\)/kg FW, 5.29% total volatiles) (Table 4 and Table S4). In contrast, RY (6.27 \(\pm\) 1.02 \(\mathrm{\SIUnitSymbolMicro kg}\)/\(\mathrm{kg}\) FW, 0.15% total volatiles) has the lowest alcohol content, followed by SR (18.63 \(\pm\) 2.63 \(\mathrm{\SIUnitSymbolMicro kg}\)/\(\mathrm{kg}\) FW, 0.49% total volatiles) and Starking (SI, 26.48 \(\pm\) 5.21 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{kg}\) FW, 0.49% total volatiles) (Table 4 and Table S4).  
1-hexanol and 1-butanol are the most dominant alcohols identified in apples [39; 40]. 1-Hexanol can suppress the apple-like odour due to an unpleasant and earthy odour, which contributes negatively to the apple aroma [41]. In this study, the highest content of 1-hexanol (B5) was observed in HG (393.09 \(\pm\) 21.25 \(\mathrm{\SIUnitSymbolMicro kg}\)/\(\mathrm{kg}\) FW), followed by HF (364.88 \(\pm\) 42.56 \(\mathrm{\SIUnitSymbolMicro kg}\)/\(\mathrm{kg}\) FW) and Alps Otome (AO, 343.74 \(\pm\) 40.62 \(\mathrm{\SIUnitSymbolMicro kg}\)/\(\mathrm{kg}\) FW) (Table S3). In contrast, 1-butanol is considered a positive contributor to the features of apple aroma due to its characteristic sweet aroma [39]. In this study, HY had a higher content of 1-butanol (B2) than the other apple cultivars, up to 542.07 \(\pm\) 49.62 \(\mathrm{\SIUnitSymbolMicro kg}\)/\(\mathrm{kg}\) FW (Table S3). In addition, 2-hexyn-1-ol (B4) was detected in 38 of the 40 apple cultivars (Table S1), with the highest content in QG (66.21 \(\pm\) 5.21 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{kg}\) FW), followed by JG (58.55 \(\pm\) 5.01 \(\mathrm{\SIUnitSymbolMicro g}\)/\(\mathrm{kg}\) FW) and GS (34.84 \(\pm\) 2.69 \(\mathrm{\SIUnitSymbolMicro kg}\)/\(\mathrm{kg}\) FW) (Table S3).
## 2 The Application Principle of Hyperspectral Imaging in Geoscience  
### The Causes of Absorption Features of Minerals  
The bandpass of hyperspectral remote sensing is usually below 10 nm, so the continuous spectra and the spectral details can be obtained. This lays the foundation for the identification and analysis of minerals by hyperspectral remote sensing at the spectral level. It is also the main feature and biggest advantage that distinguishes hyperspectral from multispectral remote sensing.  
In the spectral range of 350-2500 nm, the spectral absorption features of minerals are the diagnostic of crystal structure and chemical composition. The spectra of different minerals are generally different, even the minerals of the same family also have subtle spectral differences due to chemical composition variations. There are two general processes that cause spectral absorption features of minerals: electronic and vibrational [15; 16; 17; 18; 19]. In the visible and near-infrared spectral range (VNIR: 350-1200 nm), the causes of spectral absorption features are electronic processes, including crystal field effects, charge transfer absorptions, conduction bands, and color centers, among which the crystal field effects of Fe\({}^{2+}\) and Fe\({}^{3+}\) and the charge transfer absorptions of Fe\({}^{2+}\)-Fe\({}^{3+}\) are most common. In the short-wave infrared spectroscopy (SWIR: 1200-2500 nm), the causes of the spectral absorption features are predominated by the vibration process, which is mainly caused by the first overtone of the hydroxyl group (OH) and the combination vibrations involving OH stretching and cation-O-H bending in the octahedron. Common vibrational absorptions include aluminum hydroxyl (Al-OH), magnesium hydroxyl (Mg-OH), water (H\({}_{2}\)O), and carbonate (CO\({}_{3}\)\({}^{2-}\)), etc.
## 4 Application in Tunnel Smooth Blasting
### Relation between Crucial Damage Zone and Practical Overbreak  
Figure 12 shows the perspective view of the final damage accumulation after the complete blasting of all the explosives. The crucial damage degree and scope distribution in the surrounding rock mass is represented by different colors--the damage degree decreases in a gradient from red to green to blue in damage contours and the damage scope tends to be degraded from the detonating position to the external free surface based on the observation of 3D damage contour. In the interest of quantifying the degree of damage for the numerical damage contour, the outermost damage contours in blue and in red are traced and presented as curves along the design contour (see Figures 13 and 14). The larger damage positions are recognized by measuring the spacing between the numerical damage contour and the design contour. Additionally, four section profiles of this blast round were also measured using a tunnel profilometer at the Zigaojian Tunnel site (see Figure 15), where the overbreak and underbreak are labeled by the data along the design contour and the main overbreak positions are circled by red ellipses. By comparing the main overbreak positions measured from the site in Figure 15 with the main damage positions from the numerical study in Figure 13, the main distributed positions for overbreak and damage are observed to match well with each other. However, the main overbreak values are smaller than the values of damage zone estimated by the damage contour in blue, while being approximately consistent with the values of damage zone estimated by the damage contour in red (See Figures 13-15). Therefore, the red damage contour can be used to estimate the overbreak/underbreak values for the excavation of smooth blasting tunnel through the numerical study. On the other hand, it also reflects the validity of the numerical study according to the analysis of the relation between the crucial damage zone and the practical overbreak above.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Explosive** & \(\rho/\mathbf{g}\cdot\mathbf{cm}^{-3}\) & \(D/\mathbf{cm}\cdot\mathbf{us}^{-1}\) & \(p_{d}/\mathbf{105}\) & **MPa** & **A/105** & **B/105** & \(R_{1}\) & \(R_{2}\) & \(\mathbf{\omega}\) & \(E_{0}/\mathbf{10^{2}GJ}\cdot\mathbf{m}^{-3}\) & \(V_{0}\) \\ \hline E. 1 & 1.60 & 0.550 & 0.096 & 5.576 & 0.0535 & 6.1 & 1.07 & 0.24 & 0.041 & 1.0 \\ E. 2 & 1.26 & 0.527 & 0.081 & 5.576 & 0.0535 & 6.1 & 1.07 & 0.24 & 0.041 & 1.0 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of emulsion explosives  
Figure 11: Process of ordered detonation in smooth blasting tunnel.  
Figure 12: Damage contour of smooth blasting tunnel in the numerical study.  
Figure 13: Damage contour in blue obtained from the numerical study.
## 5 Data Preparation and Filtering
### Filtering of Raw Operational Data Techniques
#### 5.2.1 Control Chart Techniques  
The control chart technique (CCT) is a statistic mathematic formulated algorithm implemented in time series data to detect irregularities by sliding a predefined window along a stream of data points. This technique acts as a condition statement, i.e., accepting values within a threshold boundary and considers the data point as an outlier beyond this threshold boundary. Through this condition, the outliers could be eliminated as presented in Equation (6). The threshold boundary is taken as \(3\sigma\) exclusively for data that follows a Gaussian distribution.  
\[\chi_{j}=\left\{\begin{array}{ll}\text{outlier}&\quad\text{if }\left|\chi_{j}-\mu \right|>3\sigma\\ \text{normal}&\quad\text{otherwise}\end{array}\right. \tag{6}\]  
where \(\chi_{j}\) represents the data point for the \(j\)th number of observations, \(\mu\) refers to the average of the observation sum data and \(\sigma\) is the standard deviation.  
This technique is applied in [57, 58] to filter out the outliers in the fuel oil consumption (FOC) time series. The detected outliers would be either replaced with previous data values or removal depending on the consistency of the data's resolution to be retained. This approach is intuitive and straightforward; however, the downside of the method is that it is only applicable to normally distributed data and does not comprise noisy data captured initially in the raw data as presented in Figures 10 and 11.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Filtering Methods** & **Advantages** & **Disadvantages** \\ \hline \multirow{3}{*}{CCT} & Intuitive, straightforward, time information content is preserved & Applicable only to data that is normally distributed and without noise \\ \hline \multirow{3}{*}{HWT} & Simple, computationally efficient, suitable for signal with sudden transition (not continuous), time information content is preserved & Shift sensitivity, poor directionality, lack of phase information [54], \\ \hline \multirow{3}{*}{FFT} & Able to filter varying frequencies signal, able to convert discrete data into continuous data, maintain information on amplitudes, harmonics and phase & Time information content of signal is lost, relatively computationally expensive, sensitive to the length of Fourier transformation used \\ \hline \multirow{3}{*}{KF} & Time information content is preserved, ideal for signals that are continuously changing and uncertain, light on memory, fast & Initial state probability density function has to be known, sensitive to initial estimate of state \\ \hline \hline \end{tabular}
\end{table}
Table 2: Advantages and disadvantages of different filtering methodologies.
## 3 Results and Discussion
### Thermophysical Characteristics of PHB-Cmitosan and PLA-Cmitosan Compositions

Table 2: DSC parameters of thermal transitions observed in PLA, PHB, and their compositions with chitosan (Supplementary Materials: Calculation of thermophysical parameters obtained by DSC). Figure 4: DSC heat flow curves of PHB (1), PHB–chitosan (50:50) wt. % composition (2) and PHB–chitosan (50:50) wt. % composition containing sorbed Fe\({}^{3+}\) ions (3), obtained by heating up to 400 \({}^{\circ}\)C.  
\begin{table} \begin{tabular}{c c c c} \hline \hline
**Sample** & \(\mathbf{T_{on_{\text{\tiny{}^{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text{\text{ \text{ \text{ \text{ }}}}}}}}}}}}}}\)\({}^{\circ}\mathbf{C}\) & \(\mathbf{T_{max}}\) \({}^{\circ}\mathbf{C}\) & \(\mathbf{A}\mathbf{H_{d}}\)**J/g** \\ \hline PLA & 332 & 372 & \(-1229\) \\ PLA–chitosan (50:50) & 237 & 269 & \(-700\) \\ PLA–chitosan (50:50) Fe\({}^{3+}\) absorbed & 244 & 275 & \(-1214\) \\ PHB & 264 & 290 & \(-730\) \\ PHB–chitosan (50:50) & 255 & 268 & \(-584\) \\ PHB–chitosan (50:50) Fe\({}^{3+}\) absorbed & 246 & 278 & \(-687\) \\ \hline \hline \end{tabular}  \end{table  
Figure 3: DSC heat flow curves of PLA (1), PLA–chitosan (50:50) wt. % composition (2) and PLA–chitosan (50:50) wt. % composition containing sorbed Fe\({}^{3+}\) ions (3), obtained by heating up to 400 \({}^{\circ}\)C.  
At the same time, the analysis of the DSC (Figures 3 and 4, Table 2) leads to the conclusion about the common nature of the destabilizing effect of chitosan on the process of PLA and PHB thermal degradation. Apparently, the reason for such a significant decrease in the thermal stability of PLA and PHB in their compositions with chitosan is connected to the presence of free chitosan amino groups, which accelerate the hydrolysis and depolymerization of linear polyesters.
## 4 Numerical Simulation Results and Analysis
### Estimation of Turbulence Layer Position Based on TILAS Measurements  
Figure 4a shows the correlation time \(\tau_{\rm cor}\) values as functions of the relative position \(z/L\) of a single spatially localized turbulence layer, computed for different turbulence strength values varied in the interval \(10^{-16}\) m\({}^{-2/3}\leq C_{n}^{2}\leq 10^{-14}\) m\({}^{-2/3}\). These plots confirm that the correlation time is sensitive to the layer position; the farther the layer from the TILAS transceiver, the smaller the correlation time. At the same time, the correlation time appears to be weakly dependent on the turbulence layer strength \(C_{n}^{2}\), as demonstrated in Figure 4b.  
Further analysis reveals that the \(\tau_{\rm cor}\) curves stay qualitatively the same for different parameters of both the TILAS system and the moving target. Estimations of \(\tau_{\rm cor}\) performed with the increased diameter of the TILAS transceiver aperture, \(d_{\rm TILAS}=7\) cm, demonstrate comparable monotonically decreasing dependencies \(\tau_{\rm cor}\) (\(z/L\)) as in Figure 4a, although the absolute values of \(\tau_{\rm cor}\) are generally larger for larger \(d_{\rm TILAS}\). Similar observations are made for the target speed \(v_{T}\); \(\tau_{\rm cor}\) is simply inversely proportional to \(v_{T}\). Thus, assuming both the TILAS and the target parameters including the distance to the target and target speed are known, the correlation time \(\tau_{\rm cor}\), which is computed using the autocorrelation function \(B(\tau)\) of the received power signal \(P_{\rm TILAS}\) (\(t\)) measured by the TILAS system, can be used to estimate the position of the turbulence layer. The accuracy of this estimation is somewhat reduced by the dependence of \(\tau_{\rm cor}\) on the turbulence strength.  
Figure 3: **(a)** Characteristic time dependencies of the TILAS received power signal \(P_{\rm TILAS}\) (\(t\)) computed over the 10-ms interval for a single spatially localized turbulence layer with \(C_{n}^{2}~{}=~{}10^{-14}\) m\({}^{-2/3}\) (\(r_{0}=4.1\) cm) and \(L_{0}=4\) m positioned at \(z=0.1\) (blue line) and \(z=0.9\) (sky-blue line). **(b)** Autocorrelation functions \(B(\tau)\) obtained for five different locations of the turbulence layer of strength \(r_{0}=4.1\) cm: \(z=0.1\), \(0.3\), \(L_{0}\), \(0.5\), \(L_{0}\), \(0.7\), and \(0.9\), denoted by blue, red, green, purple, and sky-blue lines, respectively. The dash-dotted black line in **(b)** shows \(B(\tau)\) obtained for the homogeneously distributed turbulence (DT) model with \(C_{n}^{2}~{}=~{}2\times 10^{-15}\) m\({}^{-2/3}\) (\(r_{0}=4.1\) cm) and \(L_{0}=4\) m. Here, the TILAS outgoing beam wavelength \(\lambda=1.064\) μm, the beam radius \(W_{0}=1.5\) cm, the TILAS transceiver aperture diameter \(d_{\rm TILAS}=3.3\) cm, the propagation path length \(L=7\) km, the target size \(d_{T}=1\) cm, and the target speed \(v_{T}~{}=~{}200\) m/s.
# Predicting Rogue Content and Arabic Spammers on Twitter
## 6 Data Pre-processing  
Pre-processing steps were applied to clean up the collected textual data and increase the efficiency of the decision-making system. The steps are given below.  
1. All Twitter's separators such as '#' (hashtag symbol), 'RT' (Retweet indicator), \" (mention symbol), and double white spaces in the user's textual tweets were removed in order to work with clean text.
2. All the numbers were removed [1].
3. We removed all the stop words by deleting undervalued significant phrases, such as \"a\", \"an\", and \"the\"; this is a popular technique in the classification process.
4. All emoticons, symbols, pictographs, and flags in the text were removed.
5. All tweets that contained characters other than Arabic language characters were removed. Some users tweet or retweet in different languages (e.g., English), and our detection system concentrates only on Arabic tweets. After we applied this process, 62.09% of the tweets were removed.
6. Accounts that had fewer than 100 tweets were removed because our data collection process captured fewer tweets then the targeted number of tweets, and fewer tweets for an account meant less information as a basis for building the classification learner model. There were 52,788 user accounts with fewer than 100 tweets. Hence, around 46.40% of the total tweets needed to be removed.  
After applying all the pre-processing steps, we ended up with a large-scale data set with about 2,909,455 tweets. The number of tweets was equivalent to 12,486 accounts, where 9333 accounts contained rogue and spam words, and 3153 accounts were legitimate. All the account tweets had more than 1000 clean texts to provide a reasonable amount of data to build the approach features, which affect the results of the classification algorithm.
# Parameterized Modeling and Planning of Distributed Energy Storage in Active Distribution Networks
## 3 Parameterized Modeling of Optimal DES Planning in Active Distribution Networks (ADNs)
### Objective Function
#### 3.2.2 Power Fluctuation Smoothing  
Utilizing the fast charge/discharge characteristics, DES can effectively smooth the rapid power fluctuation brought by renewable DGs comprising wind turbines (WTs) and photovoltaic generators (PVs). Considering the investment cost of DES and the ability to smooth power fluctuation of DGs, the objective function is expressed as follows:  
\[\text{min}C_{2}=\lambda_{f}C^{\text{FLU}}+C^{\text{INV}} \tag{4}\]  
where \(\lambda_{f}\) is the cost of DG power fluctuation. To demonstrate the performance of DES in the application scenario of power fluctuation smoothing, \(\lambda_{f}\) is the transformed parameter indicating the mathematical relationship between the power fluctuation and the investment cost of DES. The purpose of the transformation process is to make the objective function more clear. \(\lambda_{f}\) is designed according to [30]. \(C^{\text{FLU}}\) is the power fluctuation of DG, which is expressed as:  
\[C^{\text{FLU}}=\sum\
olimits_{s\in\Omega_{\text{S}}}\left(\sum\
olimits_{m \in\Omega_{\text{type}}}\sum\
olimits_{i\in N_{\text{N}}}\sum\
olimits_{t\in N _{\text{T}}}\left[\left(P_{s,t,i}^{\text{DG}}+P_{s,t,j,m}^{\text{DES}}\right)- \left(P_{s,t-1,i}^{\text{DG}}+P_{s,t-1,i,m}^{\text{DES}}\right)\right]^{2} \right)p_{s} \tag{5}\]  
where \(P_{s,t,i}^{\text{DG}}\) is the active power injection by DG at node \(i\) in the \(s\)th scenario, and \(P_{s,t,i,m}^{\text{DES}}\) is the active power injection by the \(m\)th DES at node \(i\) in the \(s\)th scenario.  
The power fluctuation of DG is determined by the accumulation of power difference between adjacent moments. Considering that the DES integrated with DGs can be best utilized to smooth the power fluctuation, only the nodes with DG installed are considered as candidate locations for DES planning in this application scenario.
## 1 Introduction
### Existing Gaps in Related Works  
Although there are studies on predicting solely electric and magnetic field levels using ANN, the modeling and prediction of total RF-EMF levels within the cellular frequency bands are less studied. Therefore, besides providing a realistic idea about the RF-EMF levels in the medium, this study aims to predict RF-EMF levels using proposed ANN models that are created based on experimental measurement results. To reach this goal first, extensive RF-EMF level measurements were performed within the band 100 kHz-3 GHz at Ondokuz Mays University, Kurupelit Campus, for five weeks and saved for future processing. Then, the contribution percentages of the services within the band to the total RF-EMF levels were determined, and the measurement data were analyzed in detail.  
This study provides several contributions as follows:  
* A new ANN model with Levenberg-Marquardt (LM) and Bayesian Regulation (BR) learning algorithms was proposed.
* The proposed models were used to predict RF-EMF levels and determine long-term exposure patterns without real-time measurements.
* A performance comparison was made with similar works, and it was shown that the proposed models have higher prediction accuracy.  
Therefore, the use of the proposed models for predicting RF-EMF levels leads several benefits, such as:  
* Mitigating potential health risks by implementing measures to reduce exposure to RF-EMF.
* Helping regulatory compliance by identifying areas where the maximum allowable levels are likely to be exceeded.
* Supporting the research and development of new technologies that utilize RF-EMF and helping test the effectiveness of new technologies in reducing RF-EMF exposure.  
The paper is organized as follows. Section 2 introduces the general set up of ANN. Section 3 explains the methodology of the study, and the results are given and discussed in Section 4. The paper is concluded in Section 5.