esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Enhanced Interpolation Approach
### Complete Simulation Procedure  
Figure 2 depicts the flowchart of the suggested simulation approach, which consisted mostly of the following five steps:  
_Step 1_: Determine the interpolation point number \(N_{\tilde{\omega}}\), \(N_{\tilde{\omega}}\) and \(N_{\tilde{t}}\). Then, obtain the frequency interpolation points \(\tilde{\omega}\) with ordered frequency \(\tilde{\omega}_{1},\tilde{\omega}_{2},\cdots,\tilde{\omega}_{N_{\tilde{\omega}}}\) and the time-frequency interpolation points \((\tilde{\omega},\tilde{t})\) with ordered frequency \(\tilde{\omega}_{1},\tilde{\omega}_{2},\cdots,\tilde{\omega}_{N_{\tilde{\omega }}}\) and time \(\tilde{t}_{1},\tilde{t}_{2},\cdots,\tilde{t}_{N_{\tilde{t}}}\).  
_Step 2_: Based on the given spectral and coherence models, calculate the lagged coherence matrix \(|\mathbf{\Gamma}(\tilde{\omega})|\) and the EPSD \(S_{jj}(\tilde{\omega},\tilde{t})\) at those interpolation points.  
_Step 3_: Execute the Cholesky decomposition for \(|\mathbf{\Gamma}(\tilde{\omega})|\) to obtain \(\mathbf{B}(\tilde{\omega})\); use POD to factorize \(\sqrt{S_{jj}(\tilde{\omega},\tilde{t})}\) into the principle coordinate \(a_{q}^{jj}(\tilde{t})\) and eigenvector \(\Phi_{q}^{jj}(\tilde{\omega})\).  
_Step 4_: Obtain \(\mathbf{B}(\omega)\) by interpolating each element of \(\mathbf{B}(\tilde{\omega})\); obtain \(a_{q}^{jj}(t)\) and \(\Phi_{q}^{jj}(\omega)\) by, respectively, interpolating \(a_{q}^{jj}(\tilde{t})\) and \(\Phi_{q}^{jj}(\tilde{\omega})\).  
_Step 5_: Generate the simulated samples \(x_{j}(t)\) with Equation (36) and the FFT.  
In summary, only \(N_{\tilde{\omega}}\); Cholesky decompositions, \(n\) PODs and \(nN_{\Phi}\) FFTs were required in the whole simulation procedure. Furthermore, POD was only executed on the \(N_{\tilde{\omega}}\times N_{\tilde{t}}\) dimensional matrices. Therefore, the proposed approach was remarkably efficient for simulating spatially varying ground motions.  
Figure 1: POD interpolation procedure.
# Analysis of Floral Fragrance Components in Different Parts of _Iris typhifolia_
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**Odor Descriptions**} & \multicolumn{4}{c}{**OAV**} \\ \cline{3-6}  & & & **Stamen** & **Pistil** & **Flag Petal** & **Pendant Petal** \\ \hline
1 & caproaldehyde & apple, fat, fresh, green, oil & 18.99 & 214.73 & & & \\
2 & trans-2-hexenal & fresh, fruit & 1.94 & 0.66 & 13.73 & 64.2 \\
3 & 2-pinene & pine, resin & 1.91 & 0.64 & & \\
4 & methyl heptenone & fresh, fruit & 6.66 & 2.25 & 2.16 & 4.61 \\
5 & D-terpenediee & pine, resin & 2.44 & 0.63 & & \\
6 & 2-hexenal & sweet, fruit, apple & & 0.66 & & 17.12 \\
8 & linalool & coriander, floral, lavender, & 317.34 & 37.34 & 12.66 & 43 \\  & & & & & \\
9 & nonyl aldehyde & bitter almond, burnt & 139.86 & 16.22 & 54.16 & 25.65 \\
10 & phenylacetaldehyde & berry, geranium, honey, nut, & & 2.69 & & 1.54 \\
11 & \(\alpha\)-pinene & pine, resin & 1.64 & 0.12 & 0.49 & 0.94 \\
12 & methyl salicate & almond, caramel, & 0.79 & 1.46 & & 1.54 \\  & & peppermint, sharp & & & \\
13 & capric aldehyde & citrus, fat, green, oil, & 56.35 & 4.28 & 8.16 & 7.43 \\
14 & undecanal & rose & 2.71 & 0.65 & & 0.95 \\
15 & 2-methoxy-3-sec-butylpyrazine & fresh legumes & & 222.55 & & 316.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The OAV of volatile compounds in different parts of _Iris tyhifolia_. stimulant [23]. Linalool can be utilized in the production of basil alcohol and is appreciated for its anti-inflammatory and antimicrobial properties [21]. Nonyl aldehyde, capric aldehyde, caproaldehyde, and methyl heptone are primarily employed in the preparation of perfumes and flavorings.
## 3 Results
### Balanced Model Metrics  
Similarly, Figure 8 illustrates the comparison of the overall accuracy metric for the different classifiers implemented in the balanced model with SMOTE, during the training phase.  
We can see how the improvement in this metric is significant when oversampling in three of the classifiers: GAUSS\((0.948\pm 0.036)\); SVM \((0.926\pm 0.033)\); LR\((0.895\pm 0.041)\). Meanwhile, for KNN\((0.874\pm 0.057)\) and RF\((0.859\pm 0.065)\), it worsens slightly.  
Table 15 shows the metrics obtained during the testing phase for the best classifiers with oversampling. As can be seen, the best classifier is SVM, with overall accuracy of 0.92, similar to that obtained during the training phase, which, as in the case of the LR and KNN classifiers, suggests the good generalization of the model. In the case of the GAUSS classifier, a value of 0.87 \(<\) 0.94 is obtained, indicating probable overfitting.
## 3 Experimental Results and Analysis
### Complexity Analysis of the Proposed Model  
The complexity of the proposed model is mainly composed of two parts: deep spatial-temporal feature extraction and regression model establishment, where deep spatial-temporal feature extraction occupies the most parts of the complexity. The number of Float Operations (FLOPs) is usually used to evaluate the computational cost that an algorithm requires. Therefore, in this paper, we use FLOPs to evaluate the time complexity of the proposed model. The total number of parameters and the model size are adopted to the evaluated space complexity of the model. The complexity of the proposed model is shown in the Table 8.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **SROCC** \\ \hline FTW [5] & 0.507 \\ SQI [11] & 0.7707 \\ P.NATS [17] & 0.8454 \\ Liu’s [28] & 0.8039 \\
**Proposed Model** & **0.9465** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparison of QoE models on the Waterloo Streaming QoE dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **PLCC** & **SROCC** \\ \hline NAVE [29] & 0.8225 & 0.8274 \\
**Proposed Model** & **0.9124** & **0.9170** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of QoE models on LIVE-NFLX-II dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Number of Parameters (Millions)** & **Model Size (MB)** & **FLOPs (Millions)** \\ \hline
3D CNN Network & 15.62 & 62.52 & 78.08 \\ LSTM Network & 40.15 & 160.62 & 80.28 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The complexity of the proposed model.  
It can be seen that the complexity of the model is high, which means the proposed model can achieve a high performance but at the cost of high complexity. Of course, GPU-acceleration can also be used for the proposed model to improve the processing speed of the model.
## 3 Results  
### Study Selection  
In total, 145 studies were initially retrieved via the literature search, from which 47 articles were excluded as duplicates. Manual searches of the reference lists of the included studies did not identify any additional studies. After reviewing the titles and abstracts, 86 more studies were excluded for not satisfying PICO. Finally, 12 studies were included by evaluating the full text as per the eligibility criteria [40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51]. Figure 1 depicts the PRISMA flow chart of the article selection procedure.
# Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer
## 3 Weather Data
### Data from Automatic Weather Station  
This study used the data collected from 1 January 2015 to 31 May 2016 from an automatic weather station (AWS) installed in Boseong, South Korea. AWS is a device that enables automatic weather observation. Observation elements include temperature, accumulated precipitation, precipitation sensing, wind direction, wind speed, relative humidity, and sea-level air pressure. In this study, AWS data with 1 h observation interval was used; the collected information is listed in Table 3. The installation information of the AWS in Boseong is outlined in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Field** & **Value** \\ \hline Observation period & 1 January 2015–31 May 2016 \\ Observation interval & approximately 30 s (irregular) \\ data count & 1,162,077 \\ Height ranges & 10\(-\)4500 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information on the collected backscatter data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Field** & **Description or Value** \\ \hline Laser source & Indium Galium Arsenide (InGaAs) Diode Laser \\ Center wavelength & 910 \(\pm\) 10 nm at 25 \({}^{\circ}\)C \\ Operating mode & Pulsed \\ Surface diameter & 0.5 mm \\ Cloud detection range & 0–13 km \\ Measurement range & 0–15 km \\ Measurement resolution & 10 m \\ Cloud reporting & 5 m \\ resolution & 6–120 s, selectable \\ Requirement interval & 6 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specification of lidar ceilometer CL51.  
As the observation interval of AWS data is different from that of backscatter data, the data of the nearest time slot based on the backscatter data were matched and used. The used elements included precipitation sensing, accumulated precipitation, relative humidity, and sea-level air pressure.
## 3 Results
### Morphology Observation  
SEM micrographs of the binary blend of PP/PS (80/20) and ternary blends with different compatibilizers are shown in Figure 4. For clarity in the image, PS dispersed domains on the fractured surface were etched by using butanone. The statistical phase diameter of the blend system from the equal area circle diameter measurement is tabulated in Table 2. The binary blend of PP/PS (80/20) showed the typical morphology of an immiscible blend system (Figure 4a) [22; 23; 24] with PS domains up to 5 \(\upmu\)m in diameter dispersed in the PP matrix. The ternary blends showed a much smaller dispersed domain size, proving that compatibilization effectiveness more or less depended on the compatibilizer used. As revealed by the statistical phase diameter data in Table 2, P1 with methylstyrene units incorporated showed better compatibilization efficiency in the blend system than that with ethylstyrene units (P2) and was much better than that with methoxylstyrene units (P3) and SEBS, which was consistent with the impact testing results. Relatively poor compatibilization efficiency of P3 resulted from the weak compatibility of its methoxylstyrene units with PS due to frustrated \(\uppi\)-\(\uppi\) interaction between the intermolecular benzene rings caused by its flexible methoxyl group. In contrast, the poor compatibility of the EB block of SEBS with PP was responsible for its poor behavior in the PP/PS blend system [25; 26; 27].  
Figure 5 shows the SEM images of the PP/PS blends with varied amounts of P1 as the compatibilizer. With the increment of P1 content, the dispersed PS domain size decreased to the minimum at 2 wt.% and then increased again. As normally seen for most polymer blends, there exists the optimal compatibilizer content, agglomerating itself to new domains instead of diffusion at the interfacial area, which is probably the reason for what occurs at higher compatibilizer content [28].  
Figure 3: Impact strength of blends with varied amounts of compatibilizers.  
Figure 4: SEM micrographs of the impact fractured surface of the PP/PS blend (**a**) and that using 2 wt.% of compatibilizers ((**b**) P1, (**c**) P2, (**d**) P3 and (**e**) SEBS).  
Figure 5: SEM images of the PP/PS blend (**a**), and that compatibilized using varied amounts of P1 (**(b)** 1 wt.%, (**c**) 2 wt.%, (**d**) 3 wt.%, (**e**) 4 wt.%).
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 6 Benefits of CR-LPWAN
### Benefits to IoT Application Areas
#### Smart Homes  
Many IoT applications are designed to automate home devices, wherein smart devices such as laptops, phones, TVs, cameras, domestic appliances, and smart meters are capable of self-organising and relaying information to users [28]. These IoT applications are presently deployed in the ISM bands, which implies that they must compete with existing ISM band devices. Competition causes further congestion of the crowed ISM bands leading to increased collision rates and poor link quality. Thus, there is need for IoT applications to switch from ISM bands to TV white spaces (TVWS) in order to improve network performance and reduce energy consumption rates in smart home applications [90]. Although intercommunication between home appliances are disposed to short range communication technologies like ZigBee, nevertheless, there are examples where CR-LPWAN may suffice. For example, smart cookers, alarm doorbells, and event switches can be remotely control by users at a distance. In this case, CR-LPWANs may be beneficial by enabling each device to migrate easily to white spaces that guarantee the best QoS.  
In the literature, the application of CR in smart homes was discussed in [21; 28; 74]. For example, [PERSON] et al., in [18; 28], discussed the use of CR to facilitate opportunistic communication in smart home devices via TVWS. In [91], CR access to TVWS was proposed supporting high definition TV (HDTV) distribution in smart homes. [PERSON] et al. [92] also discussed the use of CR in smart home environments and a prototype was developed, wherein National Instrument Universal Software Radio Peripheral (NI USRP) hardware platforms and LabView software were used to conduct experiments. These application use-cases further strengthen the potentials of deploying CR-LPWAN systems for smart home applications.
## 3 Results and Discussion
### Study Limitations  
Despite the numerous advantages of this type of review, it is important to state some limitations that are associated with this study. Publications related to neurotoxicity were only analyzed using the WoS database, which has wide coverage, suggesting that other articles published in journals not indexed in WoS (but available in other databases including Scopus) were excluded. Thus, the output of this study does not represent all the publications available on the subject. Moreover, the journals analyzed in this present study were limited to those written in English language, not minding the number of studies published in many other local/international languages. Therefore, it is recommended that future research in this field should consider all the listed limitations for inclusiveness.
# Ontology-Based Production Simulation with OntologySim
## 2 Foundation
### Term Ontology-Based Simulation  
Ontology-based simulation as a term is used differently throughout the literature as the degree of ontology application is not clearly defined. The proposed classification scheme, as shown in Figure 4, is based on References [30; 34] and has been adapted and integrated with respect to simulations based on an ontology. The Ontology Integration Level (OIL) for simulations continuously increases from production capacity to ontologies that store all subcategories and simulation specifications. It allows the classification of ontologies that are interwoven with an ontology-based simulation in the area of production simulations.  
Figure 3: Components of the OntologySim simulation tool (DES), based on [29].  
The definition of each level is as follows:  
* _Lv1I: Production capacity:_ The ontology contains information about the capacity and structure of the ontology, such as machines, transporters, and queues.
* _Lv12: Process specification:_ The next level adds information regarding processes, setup behavior and resource change.
* _Lv13: Product specification:_ An ontology enables products to be produced in simulation. This means that production plans are included in the ontology and the current processing step of the product is known in the ontology at any time.
* _Lv14: Event & history specification:_ Events and data from the past lay the foundation for processes and allow the determination of the next simulation step.
* _Lv15: Simulation specification:_ The highest ontology integration level contains all information of the previous levels and focuses on all further necessary information, such as current time steps or work orders, to be contained in the ontology. Fully ontology-based simulations allow saving the ontology, and, thus, the simulation core and current status, at any time without information loss. This is possible when both the data management and the simulation kernel are integrated into the ontology.  
According to this definition, OntologySim is a fully ontology-based simulation in ontology integration level 5.
# Nicotinamide Adenine Dinucleotide (NAD) Metabolism as a Relevant Target in Cancer
## 2 Redox Functions of NAD+  
Initially, NAD+ was considered to have the role of a universal electron carrier in oxidation-reduction (redox) reactions of catabolic processes for energy generation in cells. One molecule of glucose during glycolysis will give rise to two NADH molecules, two ATP molecules, and two pyruvate molecules. Depending on the availability of oxygen, pyruvate can be reduced to lactate or oxidised to acetyl-CoA. In the absence of oxygen, the enzyme lactate dehydrogenase (LDH) reduces pyruvate to lactate using NADH as a cofactor, which is oxidised to regenerate NAD+. Conversely, in the presence of oxygen, pyruvate is oxidised to acetyl-CoA with the reduction of NAD+ to NADH [2; 3; 8]. Another process that also requires the reduction of NAD+ is the oxidation of fatty acids with the generation of acetyl-CoA [1; 13]. In the mitochondria, after acetyl-CoA enters the Krebs cycle, more NAD+ is reduced to NADH by Krebs cycle enzymes. The mitochondrial NADH is oxidised by complex I (NADH:ubiquinone oxidoreductase) of the electron transport chain. Then, the electrons sequentially pass to ubiquinone, complex III, cytochrome C, and complex IV, generating a proton gradient across the inner mitochondrial membrane that ultimately leads to the production of more than 30 ATP molecules [1; 2; 3; 8]. This process generates a large number of reactive oxygen species (ROS) that can inflict damage by oxidative reactions. Efficient mitochondrial metabolism requires an optimal NAD+/NADH ratio [14; 15].  
Figure 1: NAD+ metabolism. Tumour cells depend more on glycolysis (in blue) than on OXPHOS from the mitochondria (in dashed black). The pentose phosphate pathway (in purple), serine biosynthesis (in green), and fatty acid synthesis (in red) are highly activated in cancer and depend on glycolysis, which is the axis of cancer metabolism. Glutaminolysis (in brown) is also increased as the main source of nitrogen in the cell. The NAD+/NADH and NADP+/NADPH cofactors participate in all these pathways, allowing rapid energy production, the elimination of excess ROS, and the synthesis of macromolecules to support tumour proliferation and development.  
NADP+, NAD+ phosphorylated form and NADPH, reduced NADP+, are required for anabolic processes, including the synthesis of ribose-5-phosphate, used for nucleotide synthesis, via the PPP (pentose phosphate pathway) and lipid synthesis [16; 17]. Furthermore, NADPH is essential to detoxify the high ROS levels produced in redox reactions [17]. The correct maintenance of redox homeostasis is an essential cellular process that requires a tight balance between the generation and elimination of ROS. This tight regulation is especially relevant in tumour cells, which have a higher production of ROS in general and need stricter control of this balance [18]. NADPH is also an important cofactor in the synthesis of fatty acids, which are necessary in the formation of the membrane during cell proliferation, using acetyl-CoA as a precursor [3] (Figure 1).
# Vibration Parameters Identification of Rotating Blades Based on Blade Tip-Timing Sensor Waveforms
## 4 Identification Accuracy Analysis of Waveform Mapping Method
### Identification Accuracy Analysis of Three-Sensors Waveform Mapping Method  
Figure 9 shows the influence of excitation orders on the identification average error of the three-sensor waveform matching method with different SNR. From Figure 9a-e, it can be seen that when the SNR is 10 db, 20 db, 30 db, 40 db and 50 db, and the excitation order  
Figure 8: Influence of excitation order on the average error of double-sensor waveform identification under different SNR: (**a**) SNR 20 db; (**b**) SNR 30 db; (**c**) SNR 40 db; (**d**) SNR 50 db; (**e**) SNR 60 db.  
is \(N_{e}\geq 10\), \(N_{e}\geq 6\), \(N_{e}\geq 5\), \(N_{e}\geq 4\) and \(N_{e}\geq 1\), respectively, the three-sensors waveform matching method can accurately identify the excitation order, the identified relative error of vibration initial phase, the relative error of amplitude and the relative error of constant offset meet the accuracy requirements. The identification accuracy of vibration amplitude is higher than that of vibration initial phase, while the identification accuracy of vibration initial phase is higher than that of vibration constant offset. With the SNR ratio increases, the higher the accuracy of the signal, the larger the range of accurately identified excitation orders. It can accurately identify any excitation order when the SNR is 50 db.
## 2 Proposed Methodology
### Previous Works  
#### 2.2.1 Adaptive Synthetic Sampling  
Although several techniques have been developed to address data imbalance, methods that directly augment the minority class by generating synthetic data have gained significant attention. Adaptive Synthetic Sampling (ADASYN) is an oversampling technique based on the K-Nearest Neighbor (KNN) algorithm, considering the distribution of the minority and majority classes. ADASYN generates synthetic data by interpolating between existing data points within the minority class rather than merely duplicating the original data [46]. In the minority class, the basis data point is decided as a fixed point to apply the KNN algorithm, and the nearest neighbor value is selected. After the nearest neighbors are chosen, the linear interpolation method of Equation (1) is used to generate synthetic data between the basis data point and the nearest neighbors, where \(\alpha\) is a random value from 0 to 1.  
\[\mathrm{s_{j}}=(1-\alpha)\times\mathrm{p_{i}}+\mathrm{p_{ji}},\;\mathrm{i},j=1, 2,3\ldots \tag{1}\]  
Here, data points in the majority class, which exist in the inner area of the nearest neighbor boundary, are considered. The ratio of data points within the majority class is calculated according to Equation (2), where \(\mathrm{s_{i}}\) is the number of data points in the majority class in the nearest neighbor boundary for the i-th nearest neighbor, and K is the total number of nearest neighbors.  
\[\mathrm{r_{i}}=\frac{\mathrm{\delta_{i}}}{\mathrm{K}},\;\mathrm{i}=1,2,3\ldots \tag{2}\]  
ADASYN generates more synthetic data if \(\mathrm{r_{i}}\) is larger than the others. Owing to this comparison process of data distribution, synthetic data could be distributed to maintain distinct characteristics compared to the data from other classes.