esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### In Vitro Digestion  
In vitro digestion was performed based on the standardized INFOGEST protocol [22] with specific modifications introduced to better simulate the gastrointestinal conditions of the weaned pig. The digestion process was conducted in three successive stages (oral, gastric, and intestinal) while considering the entire sequence as a continuous system to reflect the physiological progression of digestion. During the oral phase, 2 g of algae powder was combined with 2 mL of simulated saliva fluid stock solution and 200 uL of \(\alpha\)-amylase solution (1500 U/mL; Sigma-Aldrich A3176-500 KU, (Merck, St. Louis, MO, USA)). The pH was then adjusted to 7 using 1 mol/L NaOH, followed by incubation in a water bath at 37 \({}^{\circ}\)C with continuous shaking for 2 min. For the gastric phase, 3.2 mL of simulated gastric fluid stock solution was added, along with 200 uL of pepsin solution (25,000 U/mL; Sigma-Aldrich P7000 (Merck, St. Louis, MO, USA)). The pH was lowered to 3 by adding 1 mol/L HCl, and the mixture was incubated at 37 \({}^{\circ}\)C for 2 h. In the final stage, representing the intestinal phase, 3.4 mL of simulated intestinal fluid stock solution, 2 mL of pancreatin solution (800 U/mL; Sigma-Aldrich P1625 (Merck, St. Louis, MO, USA)), and 1 mL of bile (Sigma-Aldrich B8631 (Merck, St. Louis, MO, USA)) were introduced. The pH was readjusted to 7 using NaOH before incubating the mixture at 37 \({}^{\circ}\)C for an additional 2 h.  
Evaluation of the Total Polyphenol Content (TPC), Total Flavonoid Content (TFC), and Total Phlorotannin Content (TPhC)  
The bioactive compounds present in the _Fucus vesicusus_ extract, as well as in the products obtained from the various stages of the simulated in vitro digestive process applied to the dried seaweed, were evaluated. Specifically, the total contents of phenols, flavonoids, and phlorotannins were quantified. Total phenol content (TPC) was measured using the Folin-Ciocalteu assay [23], with quantification based on a tannic acid calibration curve ranging from 60 ug/mL to 960 ug/mL. The TPC values were expressed as milligrams of tannic acid equivalents (TAE) per 50 mg of sample (mg TAE/50 mg). Total flavonoid content (TFC) was determined via the aluminum chloride (AlCl\({}_{3}\)) colorimetric method [24]. The calibration curve for TFC quantification was prepared through serial 1:2 dilutions of catechin standard, spanning concentrations from 250 ug/mL to 7 ug/mL. Results were reported as milligrams of catechin equivalents (CE) per 50 mg of sample (mg CE/50 mg). Lastly, total phlorotannin content (TPhc) was assessed using the 2,4-dimethoxybenzaldehyde (DMBA) colorimetric assay [25]. The quantification relied on a phloroglucinol calibration curve within the range of 0.06 to 0.1 mg/mL, and values were expressed as milligrams of phloroglucinol equivalents (PGE) per 50 mg of sample (mg PGE/50 mg). All measurements were performed in technical triplicate with three biological replicates.
# Defining the Ideal Phenological Stage for Estimating Corn Yield Using Multispectral Images
## 2 Materials and Methods
### Filtering of Maize Crop Yield Data  
For treatment and filtering of the yield data, we defined the following two sample removal criteria: (1) definition of a confidence interval to remove outliers, and (2) removal of noise from the automated harvesting process. (1) We define the confidence interval as recommended based on 151 classical statistics for removing outliers; that is, we remove any outlier measurement from a sample set given by the condition \(\mu\pm 3\times\sigma\), where \(\mu\) is the mean of the yield data, and \(\sigma\) is the standard deviation of the yield data. Thus, we established the upper and lower thresholds based on the population mean, defining a confidence interval of 99.7%. (2) We excluded the noises as recommended by the manual of the harvester manufacturers; that is, null data and those indicating negative yield were eliminated from the samples. In Equation (1), the lower values have a minimum threshold of zero because it is not possible to assign negative yield to plants to exclude outliers. Thus, values not belonging to the interval were excluded.  
Figure 2: Methodological flowchart used in research. Blue boxes: data acquisition and filtering; yellow boxes: data pre-processing; pink box: generation of prediction models; black box: validation of results.
# An Approach for Pronunciation Classification of Classical Arabic Phonemes Using Deep Learning
###### Abstract  
A mispronunciation of Arabic short vowels can change the meaning of a complete sentence. For this reason, both the students and teachers of Classical Arabic (CA) are required extra practice for correcting students' pronunciation of Arabic short vowels. That makes the teaching and learning task cumbersome for both parties. An intelligent process of students' evaluation can make learning and teaching easier for both students and teachers. Given that online learning has become a norm these days, modern learning requires assessment by virtual teachers. In our case, the task is about recognizing the exact pronunciation of Arabic alphabets according to the standards. A major challenge in the recognition of precise pronunciation of Arabic alphabets is the correct identification of a large number of short vowels, which cannot be dealt with using traditional statistical audio processing techniques and machine learning models. Therefore, we developed a model that classifies Arabic short vowels using Deep Neural Networks (DNN). The model is constructed from scratch by: (i) collecting a new audio dataset, (ii) developing a neural network architecture, and (iii) optimizing and fine-tuning the developed model through several iterations to achieve high classification accuracy. Given a set of unseen audio samples of uttered short vowels, our proposed model has reached the testing accuracy of 95.77%. We can say that our results can be used by the experts and researchers for building better intelligent learning support systems in Arabic speech processing.  
deep learning; classical Arabic; short vowels; audio dataset; convolutional neural networks; optimization; regularization +
Footnote †: journal: _Applied Sciences_  
0000-0001-5000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00000-0000-0000-  
Millions of Arabic and non-Arabic speakers around the world practice CA in their daily routine in the form of recitation of the Holy book or studying in a formal educational setting.
# An Attention-Based Deep Convolution Network for Mining Airport Delay Propagation Causality
## 5 Conclusions  
This paper proposes a deep time series convolutional neural network causality discovery method, which considers the entire airport network at the same time, and correctly discovers the delay propagation causality of the airport network. This method includes two parts: a deep time series convolutional network prediction model and causal relationship analysis. The deep time series convolutional network prediction model takes the delay time series of all airports as input and the time series of a single target airport as output and introduces an attention mechanism to better discover and explain the contribution of each input airport to the target airport. The causal relationship analysis includes the potential causality analysis based on the attention mechanism, and then from the potential causality, the direct causality is verified based on the permutation importance test. According to the verified causal relationship, the network is constructed, the performance and topology properties of the network are analyzed, and the delay propagation mechanism of the airport network is studied.  
Applying this method to the research on the delay propagation mechanism of China's airport network, an airport is affected by six airports on average, and it will also affect six airports at the same time. Airports with small flight volumes and airports with moderate delays are more likely to affect other airports, and airports with small delays are more likely to be affected by other airports. The research results of this paper can provide a theoretical basis for delay control. This paper takes the Chinese airport network as a case. Further research will try to apply the method in this paper to other countries to compare whether there are differences in airport delay networks in different countries.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Metrics** & **Value** \\ \hline connection density (\(l_{d}\)) & 0.0266 \\ interaction parameter & 0.0018 \\ clustering coefficient & 0.1405 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Delay propagation causality network metric values.  
**Author Contributions:** Conceptualization, X.T., H.W. and D.L.; methodology, X.T., D.L. and W.Z.; investigation, D.Z. and Y.L.; data curation, D.L. and Y.L.; supervision, W.Z. and H.W.; validation, X.T. and D.L; writing--original draft preparation, X.T. and D.L.; writing--review and editing, D.Z. and W.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This paper is supported by State Key Laboratory of Air Traffic Management System and Technology (No: SKLATM202007) and National Natural Science Foundation of China (No. 62076126).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** No applicable.  
**Data Availability Statement:** This study did not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Development of New Spectral Amplitude Coding OCDMA Code by Using Polarization Encoding Technique
## 2 DW Family Code Construction
### Modified Double Weight (MDW) Code Construction  
Modified double weight (MDW) code is an upgrade of DW code that enables the weight to exceed more than 2. The amount of weight for MDW coding must be an even number. The coding sequence is sustained to 1,2,1. This sequence may preserve the cross correlation to not greater than 1. The link between the number of users (\(K\)) and the code length (\(N\)) is indicated in the equation below [3; 17].  
\[N=3K+\frac{3}{8}\bigg{[}\sin\bigg{(}\frac{K\pi}{3}\bigg{)}\bigg{]}^{2} \tag{1}\]  
The basic matrix for MDW weight (\(W\)) = 4 consists of a \(3\times 9\) matrix. Therefore, the code sequence for MDW code \(W\) = 4 is listed in Table 1.  
The MDW code focuses on enhancing and securing the network. The increase in weight (more than 2) provides better SNR and increases the signal power of the user that is suitable for MAN. The higher the signal power, the larger the area it can cover [17; 19].
# Adoption of Multiphase and Variable Flux Motors in Automotive Applications
## 3 Analysis of Performance of VF and MP Motors
### Variable Flux Rotor  
To achieve a variable flux motor, the VFMM was selected as it permits one to obtain a VF motor without adding any extra elements such as coils. The rotor was modified as in Figure 8: the inner magnets were considered as constant flux magnets, whereas the outer magnets were considered as low-coercivity variable flux magnets. These last were considered as fully magnetised (remnant flux density of 1.31 T) in Flux-Enhanced Magnetisation State (FE-MS), and half magnetised (0.655 T) in Flux-Weakened Magnetisation State (FW-MS). Note that the magnetisation and demagnetisation processes are not covered in this paper.  
To define the magnetisation state (MS) of the VF motors in all the working range, the efficiency maps of the FE-MS and FW-MS motors were compared. In each working point, the selected magnetisation state is the one that achieves the highest efficiency. In this section, the 3-phase VF motor is taken as example.  
The efficiency maps of the 3-phase VF motor in FE-MS and FW-MS are shown in Figure 9a and 9b, respectively. By comparing both maps and choosing the most efficient MS in each working point, the MS map in Figure 9c was obtained. The FE-MS is more efficient, especially in the low-speed area (except at low loads), as with a higher magnet flux, less current is needed to achieve the required torque, this way decreasing the copper losses. At very low loads, the higher magnetic losses limit the efficiency of the FE-MS. At high speed, a flux-weakening current component needs to be applied to operate inside the voltage limit. This current is higher in the FE-MS, which makes it more efficient to operate in the FW-MS in this region. The overall efficiency map of the 3-phase VF motor is shown in Figure 9d. The same methodology to define the MS was followed in the DTP VF motor.  
Figure 8: Single pole of the VF motor with the different magnet types.  
Figure 7: Winding layout and star of slots of the DTP motor. (**a**) Winding layout. (**b**) Star of slots.
## 3 VVCM Evaluation
### Influence of VV on the Mechanical Properties of LSAM-50 Asphalt Mixture and Design Criteria
#### 4.1.3 VV Design Criteria  
To establish a relationship between the mechanical properties of the asphalt mixture and VV, the mathematical models were developed, as illustrated in Figure 6 and Table 9, where \(S_{max}\) denotes each curve's peak value.  
Figure 5: Mechanical properties of LSAM-50 asphalt mixture versus VV.  
From Figure 6 and Table 9, it can be observed that the VV range corresponding to each mechanical property \(S_{max}\) is between 3.6% and 4.2%. Considering the actual construction situation, where factors such as weather, equipment, and personnel can affect the VV values of the asphalt mixture, causing fluctuations that make it difficult to achieve the optimal value, it is essential to ensure that the asphalt mixture maintains sufficient mechanical strength. Therefore, a 95% strength assurance level is introduced to restrict the range of VV values. the VV corresponding to 0.95 \(S_{max}\) is usually adopted as the design criterion. This study, considering the VV requirements for the mechanical properties of the five aggregate  
Figure 6: Fitting curve of the mechanical properties of LSAM-50 asphalt mixtures versus VV.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Strength Indexes**} & \multicolumn{2}{c}{**Fitting Curve**} & \multicolumn{2}{c}{**VV of \(S_{max}\)**} & \multicolumn{2}{c}{**VV of 0.95-\(S_{max}\)**} \\ \cline{2-6}  & **Fitting Equation** &
\begin{tabular}{c} **Correlation** \\ **Coefficient R2** \\ \end{tabular} & **VV** & \(\mathbf{VV_{min}}\) & \(\mathbf{VV_{max}}\) \\ \hline \(R_{C}\) & \(R_{C}=-0.23394\)VV2 + \(1.94469\)VV \(-\) 0.40744 & 0.8227 & 4.2 & 3.3 & 5.0 \\ \(R_{T}\) & \(R_{T}=-0.00853\)VV2 + \(0.06197\)VV + \(0.11443\) & 0.8113 & 3.6 & \(-\) & 4.8 \\ \(DS\) & \(DS=-1435\)VV2 + \(11938\)VV \(-10197\) & 0.8225 & 4.2 & 3.5 & 4.9 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Relationship between VV and mechanical properties for LSAM-50 asphalt mixture.  
gradations, suggests an intersection of the VV value ranges. Hence, the design criteria for the VV of LSAM-50 asphalt mixtures are proposed to be between 3.5% and 4.8%.  
The current highway construction industry generally recommends a VV range of 3% to 5%. The rationale behind this is that when VV is too high, the weak areas in the asphalt mixture increase, leading to a larger area affected by stress concentration. This not only can trigger early-stage distresses but also accelerates the progression of these issues, reducing the service life of the asphalt pavement. Conversely, a too-low VV often reflects an excessively high asphalt-aggregate ratio, which, while increasing construction costs, also heightens the risk of pavement bleeding. Compared to the existing standards, the VV design criteria proposed in this paper are more stringent. This approach ensures that the asphalt mixture possesses adequate mechanical strength and service life.
# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 1 Introduction
### Methods and Scope of Study  
This study aims to develop a fine-tuned AI model capable of analysing EEG data to classify emotional states and applying these insights to adaptive architectural environments. Unlike conventional EEG classification models that rely on spatially structured features, this study investigates the feasibility of removing EEG channel dependencies by constructing a channel-agnostic JSONL dataset. Furthermore, it explores whether large language models (LLMs) can infer affective states from EEG signals and assesses the potential for integrating LLM inference with CNN-LSTM architectures to enhance classification performance.
## 4 Probabilistic Analyses
### Standard Based Analyses  
In a first approach to these probabilistic investigations, the focus was on the simplified procedures defined in EN1992-1 [8]. There were recognized calculation methods for these procedures which included semi-probabilistic proof at the design level. The probabilistic analysis was therefore divided into (a) preparation of the verification procedure (nominal stiffness method) for the stochastic procedure, including the removal/correction of the partial safety factors, and the associated factors, in order to be able to analyze the verification procedure without the safety elements, (b) structuring of the prepared verification procedure in the form of a limit state equation, see Equation (1), and (c) definition of the basic random variables \(X_{i}\) for the limit state equation. For the correlation between the input parameters of the EN1992-1-1 [8], provision was not explicitly discussed, since the provision formulation implements correlations implicitly. For the numerical probabilistic analysis, an explicit definition of the correlations between the input parameters was made according to [31].  
Figure 11a shows the scattering failure loads obtained from the EN1992-1 nominal stiffness calculation (pink point set) and the gradients of the tested columns (dashed lines) in relation to the \(I\)-\(D\) curves constructed according to EN1992-1-1 [8] for the design, characteristic and mean levels. These sets of \(I\)-\(D\) curves show the theoretical failure of the column in terms of stability when the maxima of the \(N\)-\(M\) gradients are on the right side of the \(I\)-\(D\) curves. The analyses were processed according to LHS procedure for 30 samples in which the values in Table 3 served as input characteristics. For a realistic modeling with respect to the interaction diagram, as is the case with EN1992-1-1 [8], non-linear modeling poses a safety/reliability problem. This fact can be seen in Figure 11b where, in the histograms of the maximum normal force associated with Figure 11a, the PDF in pink obtained according to EN1992-1-1 is clearly below the one derived from the experiments (PDF in blue), see also Table 2. For the determination of the PDFs of the test results, the probabilistic results of the optimized NLFEM model [4] have also been considered.  
However, when the experimentally obtained blue PDF of the secondary moments are considered, see Figure 11b on the right, they are clearly below the PDF obtained from the standard in pink which partly is on the left side of the \(I\)-\(D\) as defined according to EN1992-1-1 and hence shows a reliability shortcoming.  
Figure 11: \(Cont.\)
## 6 Resulting Graph Visualizations
### Visualizations of Non-Attacks by Count  
Figure 6 depicts the counts of connections that were categorized as non-attacks and shows a large cluster of different connections of IPv6 addresses. There are several areas of interest identified by the colored boxes. The IP addresses within the red boxes are routers or switches that are redirecting traffic to different subnets (ff02::fb and ff02::l:3), and these subnets are possibly redirecting it to servers or load balancers.  
As cycles were removed from the data, they appeared unidirectional. The yellow boxed area (bottom right) represents servers that were behind a load balancer. The load balancer evenly distributes traffic to the various servers.  
Two data points for the non-attacks by connection count are presented in Table 6. The maximum count was 6,724,017, the minimum count was 1, and the average count was 4,273,817.
## 3 Data Collection, Experimental Setup and NN Model Selection
### Dataset
#### 3.1.3 Training of the IDNN Model  
The displacement and orientation model were trained using the Keras-Tensorflow platform on the data subsets presented in Table 1, characterised by 800 min of drive time over a total travel distance of 760 km. The models are trained using a mean absolute error loss function and an adamax optimiser. In the hidden layers, 10% of units were dropped from the hidden layers in the IDNN to avoid overfitting. Furthermore, to avoid learning bias, all the features that were fed to the neural network were standardised between 0 and 1. Table 7 highlights the parameters characterising the training of the neural network approaches investigated.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**NN**} & \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Time Steps** \\ \end{tabular} } & \multirow{2}{*}{**Motorway (m)**} & \multicolumn{2}{c}{**Roundabout (m)**} & \multicolumn{2}{c}{**Quick Changes in**} & \multirow{2}{*}{\
\begin{tabular}{c} **Hard Brake (m)** \\ **Coorering (m)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Sharp** \\ **Cornering (m)** \\ \end{tabular} } \\ \cline{3-8}  & & 6 & 610.61 & 599.22 & 524.41 & 577.19 & 346.92 \\ \cline{2-8}  & & 8 & 592.27 & 595.09 & 474.55 & 557.50 & 292.78 \\ \cline{2-8}  & & 10 & 3.60 & 17.96 & 8.71 & 15.80 & 14.55 \\ \cline{2-8}  & & 12 & 3.23 & 19.52 & 8.62 & 19.36 & 14.43 \\ \cline{2-8}  & & 14 & 3.63 & 20.53 & 9.58 & 20.45 & 12.71 \\ \hline \multirow{2}{*}{vRNN} & 2 & 17.11 & 55.58 & 33.30 & 64.09 & 42.64 \\ \cline{2-8}  & & 4 & 7.87 & 47.92 & 22.45 & 51.81 & 25.95 \\ \cline{2-8}  & & 6 & 7.28 & 29.10 & 16.56 & 25.28 & 22.58 \\ \cline{2-8}  & & 8 & 3.83 & 21.08 & 10.27 & 16.39 & 14.02 \\ \cline{2-8}  & & 10 & 3.60 & 17.96 & 8.71 & 15.80 & 14.55 \\ \cline{2-8}  & & 12 & 3.23 & 19.52 & 8.62 & 19.36 & 14.43 \\ \cline{2-8}  & & 14 & 3.63 & 20.53 & 9.58 & 20.45 & 12.71 \\ \hline \multirow{2}{*}{GRU} & 2 & 21.19 & 51.54 & 36.66 & 62.04 & 45.82 \\ \cline{2-8}  & & 4 & 25.01 & 42.62 & 30.11 & 45.05 & 26.19 \\ \cline{2-8}  & & 6 & 20.24 & 33.72 & 24.64 & 23.87 & 22.20 \\ \cline{2-8}  & & 10 & 3.60 & 17.96 & 8.71 & 15.80 & 14.55 \\ \cline{2-8}  & & 8 & 11.33 & 24.14 & 15.16 & 17.59 & 15.47 \\ \cline{2-8}  & & 12 & 3.23 & 19.52 & 8.62 & 19.36 & 14.43 \\ \cline{2-8}  & & 14 & 3.63 & 20.53 & 9.58 & 20.45 & 12.71 \\ \hline \multirow{2}{*}{LSTM} & 2 & 32.97 & 54.21 & 34.74 & 60.78 & 37.53 \\ \cline{2-8}  & & 4 & 18.20 & 41.62 & 26.73 & 51.94 & 28.21 \\ \cline{2-8}  & & 6 & 6.19 & 33.82 & 16.48 & 34.68 & 20.39 \\ \cline{2-8}  & & 8 & 4.12 & 21.75 & 11.49 & 16.18 & 13.8227 \\ \cline{2-8}  & & 10 & 3.60 & 17.96 & 8.71 & 15.80 & 14.55 \\ \cline{2-8}  & & 12 & 3.23 & 19.52 & 8.62 & 19.36 & 14.43 \\ \cline{2-8}  & & 14 & 3.63 & 20.53 & 9.58 & 20.45 & 12.71 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._