esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Analyses
### Prediction Models
#### 5.3.3 Second Scenario: Datasets with FS and without Sampling  
Table 13 presents the AUC values for prediction models across seven datasets in the second scenario. This scenario presents the prediction accuracy using ensemble FS mentioned in Section 4.1.4. Comparing the performance of the second scenario in Table 13 with that of the first scenario (without FS and sampling techniques) in Table 12, it is clear that FS improved the prediction accuracy in NB and KNN models except for one case (KNN in MapDB). In contrast, no impact was observed on SVM and RF compared to other prediction models. Additionally, FS produced either the same or an inferior performance to RF compared to the scenario without applying FS, except in antlr4 and junit. Although  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & \multicolumn{3}{c}{**Change-Proneness Dataset**} & & & & \\ \hline  & **antel** & **junit** & **MapDB** & **mcMMO** & **mct** & **oryx** & **tian** \\ \hline \multicolumn{6}{c}{**First Scenario**} & & & & \\ \hline ZeroR & 0.45 & 0.44 & 0.19 & 0.19 & 0.41 & 0.41 & 0.41 \\ \hline NB & **0.73** & 0.43 & 0.38 & 0.29 & 0.88 & 0.58 & 0.74 \\ \hline SVM & 0.51 & 0.5 & 0.5 & 0.49 & 0.5 & 0.5 & 0.5 \\ \hline KNN & 0.57 & 0.6 & 0.69 & 0.65 & 0.69 & 0.65 & 0.7 \\ \hline RF & 0.7 & **0.73** & **0.79** & **0.79** & **0.89** & **0.81** & **0.81** \\ \hline \hline \end{tabular}
\end{table}
Table 12: AUC values for performance evaluation of prediction models across seven datasets in the first scenario.  
Figure 4: Ranking of the AUC for prediction models on all scenarios.  
FS had no effect on RF, there was a clear competition between RF and NB to obtain the best prediction model in each dataset. Therefore, NB performed better than other individual models in terms of prediction accuracy, and achieved the best AUC value (0.92) in the mct dataset, which is considered outstanding according to the published criteria [52].
# A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems
## 5 Simulation Results and Analysis  
In this section, the simulation results are presented to prove the advantage of the proposed DRL-based task-offloading approach. Unless otherwise specified, the settings of the MEC system are as follows: the number of MDs is 20, the number of EDs is 5, the CPU frequency \(f_{m}\) is 5 GHz, and the deadline \(\pi_{m}\) is 10 slots. The DNN network where layers are fully connected [33; 34] is composed of four layers that contains one input layer, two hidden layers, and one output layer. The batch size is 64 and the learning rate is 0.0001.  
We also compare our algorithm with three benchmark algorithms: (1) DQN: In this algorithm, the agent makes decisions about executing a task locally or offloading it to the ED for minimizing the immediate system total delay. (2) Local computing: In this algorithm, we suppose that all MDs have a sufficient computation capability, thus all tasks are executed totally locally by their MDs. (3) Edge computing: In this algorithm, we suppose that all MDs are incapable of undertaking their computations, therefore all tasks are offloaded to the EDs for executing.  
To prove the proposed theory in this paper, we conduct simulation experiments under various settings.  
_(1) Experiment 1--Convergence performance:_ In Figure 3, we compare the mean average rewards of the four algorithms; it can be seen that, with the episodes gradually increasing, due to the efficient exploration based on the indexed function, the proposed scheme achieves better convergency performance. The local scheme achieves the lowest  
Figure 2: Network training and parameters updating process. * represents the element multiplication.
# Numerical Analysis of Flow in Building Arrangement: Computational Domain Discretization
## 2 Methods
### Numerical Analysis
#### 2.3.2 Boundary Conditions and Numerical Model Parameters  
The identical computational model (except for the mesh) was used for all cases analysed within the research. The solver was configured as pressure-based and analysis were performed for a steady state. A realizable k-epsilon viscous model according to [15; 16] was applied along with enhanced wall treatment. The viscous model constants were given the following values: C2 Epsilon = 1.9, TKE Prandtl Number = 1 and TDR Prandtl Number = 1.3.  
Velocity-inlet boundary condition type was assigned to the inlet of the computational domain with the use of the user-defined function (UDF). It was described by the velocity profile according to the Equation (3) [15] whereas the turbulence kinetic energy (k) and energy dissipation rate (\(\epsilon\)) were defined on the basis of Equations (4) and (5) respectively and according to [32]. The values of the constants in Equations (3)-(5) were thoroughly investigated and validated during previous extensive research, therefore, the applied inlet profile corresponds with the inlet to the wind tunnel test section utilized within PIV research. All the measurements were carried out for the Reynolds number Re\({}_{\text{D}}\) = 9000 based on the free stream velocity U(\(\delta\)) = 5.5 m/s.  
\[U(y)=U(\delta)\cdot\left(\frac{y}{\delta}\right)^{\alpha}, \tag{3}\]  
\[k=\frac{u^{*2}}{C_{\mu}^{0.5}}, \tag{4}\]  
\[\varepsilon(y)=\frac{u^{*3}}{\text{xy}}, \tag{5}\]  
where  
\(\delta\)--the thickness of the ground layer (2D),  
\(U(\delta)\)--free stream velocity (5.5 m/s),  
\(\alpha\)--terrain type factor (0.15),  
\(u^{*}\)--friction velocity (0.04 \(U(\delta)\)),  
\(C_{\mu}\)--constant (0.09),  
\(\kappa\)--von Karman constant (0.41).  
The outlet of the computational domain was defined as pressure outlet with a gauge pressure of 0 Pa (operating pressure--101,325 Pa), backflow turbulent intensity of 5% and backflow turbulent viscosity ratio of 10. Wall no slip shear condition was assigned to the remaining surfaces. Pressure-velocity coupling by the SIMPLE algorithm was used as a solution method--this algorithm uses a relationship between velocity and pressure corrections to enforce mass conservation and to obtain the pressure field. The model convergence was defined on the basis of qualitative and quantitative monitoring of the residuals.
# Data Extraction Method for Industrial Data Matrix Codes Based on Local Adjacent Modules Structure
## 6 Experiment Results
### Test Results of CNN  
The size of the test set for this experiment is 200, and the CNN test set contains 82,636 pairs of adjacent modules sampled from the test set. The recognition results of the CNN test set are shown in Table 3. All five CNN network models are used and achieved high accuracy rates. VGG19 achieves the highest accuracy rate of 99.5%, and GoogleNet achieves the lowest accuracy. Although the difference between GoogleNet and VGG19 is 1.6%, it is enough to impact barcode reconstruction and recognition significantly.  
The performance of the five CNN models in the test set is discussed below. The classification results are used for edge images, and the number of wrong edges in an edge image compared with the standard barcode edge image can be used as an evaluation parameter for the performance. Figure 16 shows the distribution of the wrong edge number in the test set when using different CNN models. When the wrong edge number varies from 1 to 9, the five models have almost the same sample proportions. VGG19 has the largest sample proportion, of 45.5%, when the wrong edge number is 0. When the wrong edge number is over 10, GoogleNet has the largest sample proportion of 32.5%, while VGG19 has the smallest sample proportion of 4%. It shows that VGG19 has the best performance. This is because the fewer number of wrong edges in an edge image, the better it is for barcode reconstruction and recognition.
## 2 Methods
### Design of the Chatbot's Motivation Model  
The findings from the previous study conducted with 12 PwPD (mean age 70.4 years \(\pm\) 8.65; 7 male) suggest that self-efficacy, autonomy, competence, and support from others are key factors in motivating PwPD to engage in regular physical activity [32]. These factors align closely with the core principles of SDT, a well-known motivational model that has already been used to improve therapy engagement [24; 35; 36]. SDT emphasizes three essential psychological needs [27]: autonomy, competence, and relatedness, which are critical for fostering long-term behavioral change. It makes it a strong theoretical foundation for developing a CA. Participants emphasized the importance of being involved in decision-making about their exercise routines, including defining exercise goals and selecting modalities that fit their needs and preferences. This viewpoint relates to the autonomy key principle of SDT, highlighting the importance of offering users a sense of control over their exercise choices to enhance intrinsic motivation [27]. Furthermore, the study found that participants with higher self-efficacy engaged in physical activity more regularly and felt more capable of exercising at home, highlighting the importance of fostering competence, which is the confidence in one's ability to successfully achieve desired outcomes [27]. Additionally, social support, including from health professionals, caregivers, and peers, was highlighted by participants as a key motivator, which relies on the relatedness principle of SDT.  
Evidence from previous systematic reviews [37; 38; 39] and recommendations from clinical guidelines [3] about the importance of implementing behavior-change approaches supported the research team's decision-making. By leveraging SDT in the CA's design, the app can offer personalized goal-setting, social support, autonomy in exercise choices, and feedback mechanisms that enhance users' sense of competence and intrinsic motivation, ultimately promoting sustained exercise engagement.
## 2 NB-IoT System Detailed Design and Implementation
### NPUSCH Transmitter Chain Implementation
#### 2.1.4 Channel Interleaver  
This block has a crucial function, especially for the deep-fading channels where errors often occur in bursts, and consecutive bits in the same symbol may suffer from this type of burst noise. Therefore, the channel interleaver is responsible for dividing this error into a set of discrete errors and reordering bits so that the error happens in only one bit within the same symbol [11].  
The channel interleaving process is specified as a two-step procedure as follows [10]:  
* Store bits in up-to-down rows.
* Read bits from left-to-right columns.  
Figure 3: Rate matcher block diagram.  
This mainly depends on the number of rows and columns, as the number of columns, \(Ncol\), is defined as follows:  
\[N_{col}=(N_{symbols}-1)*N_{slots} \tag{11}\]  
where \(N_{slots}\) = 2, 4, 8, or 16 and \(N_{symbols}\) = 7. Moreover, the number of rows can be defined as follows:  
\[N_{raw}=G/N_{col} \tag{12}\]  
where G is defined as follows:  
\[G=N_{col}*N_{sc}*N_{RU}*Q_{m} \tag{13}\]  
where \(N_{RU}\) is the number of resource unit repetitions and has a maximum value of 10, \(Q_{m}\) is the modulation indicator and has a maximum value of 2. \(N_{sc}\) has a maximum combination of 24 with the number of slots, \(N_{slots}\). Thus, \(G\) has a maximum value of 2880 and is always factorizable by 12 [9; 10].  
The channel interleaver's block diagram is depicted in Figure 4. To make it possible to use the RAM, bits are packed into a 12-bit long register and then stored. Furthermore, reading is carried out for 12 bits by 12 bits. The main drawbacks of using the RAM are using more logic, and one bit is read from each row (12-bit stored packet). Therefore, the number of readings is equivalent to the total number of bits. However, these drawbacks are acceptable compared to using 2880 flip-flops, which results in higher area utilization and power consumption.  
The RAM memory is controlled by the finite state machine (FSM) with three states as follows:  
* State 0: Idle state, awaits the input's arrival. It also works as a reset state.
* State 1: Writing state, works on storing incoming bits 12 by 12.
* State 2: Reading state, reads the stored bits according to the calculated number of columns and number of rows.
# Autism Screening in Toddlers and Adults Using Deep Learning and Fair AI Techniques
## 3 Experimental Analysis
### Datasets  
This study aims at detecting ASD in toddlers and adults using deep learning techniques. For the analysis, two individual datasets have been considered, i.e., Autism Screen Data for toddlers and Autism Screening in adults. Both datasets have been taken from Kaggle. The Autism Screen data for toddlers incorporates influential features for determining autistic traits. Ten behavioral features and other characteristics make up the dimensions of the dataset. The attributes range from A1 to A10, so possible answers to questions like 'Always,' 'Usually,' 'Sometimes,' and 'Never' are mapped to values 1 and 0. A user obtaining more than 3 points exhibits ASD traits. The Autism Screen data for toddlers dataset incorporates 1054 rows and 19 columns, while the Autism Screening data for adults incorporates 704 rows and 21 columns, and the features include the scores (A1-A10) followed by age, gender, ethnicity, jaundice, country of residence, etc. Some common features of both datasets are age, gender, ethnicity, jaundice, etc. The baseline and the proposed deep neural networks are applied to both datasets to analyze ASD in toddlers and adults. Following data preprocessing, the datasets have been split into 80-20 as training and test sets. For analyzing the datasets, Python 3.8 has been deployed along with Tensorflow-GPU (Graphics processing unit). Jupyter Notebook with multiple Python libraries such as Pandas, NumPy, and Scikit-Learn have been used. The processor used for the analysis is the AMD (Advanced Micro Devices) Ryzen 7 4800H with Radeon Graphics (2.90 Gigahertz). The installed RAM (Random Access Memory) has a memory of 64 gigabytes. The models have been compiled using the Adam optimizer. The learning rate was set to 0.0001, and binary cross-entropy was used as the loss function. The network has been trained in a batch size of 10. Figure 4 depicts the features associated with both datasets after the duplicates were dropped and missing values were removed.
# Dynamic and Active THz Graphene Metamaterial Devices
## 2 Fundamentals of Graphene
### Nonlinear Optical Property
#### 2.2.1 Nonlinear Absorption  
THz nonlinear absorption is widely used in graphene-based THz modulators [35; 51]. Compared with the linear absorption, the nonlinear absorption of graphene THz is mainly reflected in the nonlinear enhancement of the transmittance of THz with the increase of the incident THz intensity or optical pump, as shown in Figure 2a. Such nonlinear enhancement is mainly due to the saturable absorption effect associated with the reduced intraband conductivity, as shown in the following equation [52]:  
\[T(w)=1/|1+Z_{0}\sigma(w)/\left(n_{\mathrm{s}}+1\right)|^{2} \tag{4}\]  
where \(Z_{0}\) is the vacuum impedance and \(n_{\mathrm{s}}\) is the substrate refractive index; \(\sigma(w)\) is the intraband conductivity of graphene. From Equation (4), we could find that the transmission of the incident THz field depends on the intraband conductivity. When the THz field is incident on the substrate, the absorption of the THz field by graphene's free carriers results in a transient increase of the electron temperature. Furthermore, according to Equation (3), an elevated electron temperature could decrease the intraband conductivity of graphene [50], which makes the absorption of the THz field decrease.  
Figure 1: Interaction of graphene with the incident field. (**a**) Interband transition when the photon energy (_two_) is more than twice the Fermi level (\(E_{F}\)); (**b**) Intraband dynamics when the incident energy (_two_) is lower than twice the Fermi level (\(E_{F}\)); (Reprinted with permission from ref. [15]. Copyright 2020, Advanced Optical Materials) (**c**) Cold – and hot–carrier distribution in graphene, before and after incident in the THz field; (**d**) Conductivity for different carriers’ temperatures and different THz frequencies. (Reprinted with permission from ref. [50]. Copyright 2015, Nature Publishing Group).
# Covert Channel Based on Quasi-Orthogonal Coding
## 5 Steganographic Analysis  
The choice of FSK modulation as the modulation for covert information is not only due to its good transmission properties and easy orthogonalization (quasi-orthogonalization) process with respect to the cover. The use of FSK modulation provides better properties in terms of low probability of detection (LPD), which is due to increased immunity to steganographic analysis compared to amplitude-phase modulations with constant constellation points.  
Figure 12: Use of 8-PSK modulation for covert transmission (**a**) Averaged FFT spectrum of unsorted cover and 8 PSK (**b**) Averaged FFT spectrum of sorted cover and 8 PSK (**c**) Averaged cross-correlation value of unsorted cover and 8 PSK (**d**) Histogram of the cross-correlation value of the unsorted cover and 8 PSK (**e**) Histogram of the cross-correlation value of the sorted cover and 8 PSK (**f**) Reception of covert information (8 PSK) for the unsorted and sorted cover (non-ideal SIC).  
By steganographic analysis we mean testing of probability density distributions and cumulative distribution function estimated by means of a histogram and cumulative histogram. Quantitatively, a measure of the difference in distributions can be calculated using the Kolmogorov-Smirnov test. To do this, the receiver must have noise information in the radio channel [30; 31; 32] and statistics formed from the signal from the SIC system output (we assume that the receiver is able to demodulate the cover information). If we denote the cumulative histogram distribution of the noise and signal after performing the SIC operation by \(F_{w}\) and \(F_{\delta_{2}}\), respectively, the Kolmogorov-Smirnov distance \(KSTEST\) is expressed by the formula [10; 33]:  
\[KSTEST=max\big{|}F_{w}-F_{\delta_{2}}\big{|} \tag{15}\]  
Results of \(KSTEST\) calculated on the basis of 200,000 IQ samples for \(SNR=45\) dB and 50 dB conditions relative to the cover in the form of 64 QAM, \(\sqrt{P_{1}}=1\), \(\sqrt{P_{2}}=0.005\) and zero channel estimation error are shown in the Table 1.  
Example histograms and cumulative histograms for 2 PSK and 2 PSK modulations are presented in Figure 13. It was assumed that \(SNR=45\) dB.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Covert Modulation**} & \multicolumn{2}{c}{**KSTEST**} \\ \cline{2-3}  & **SNR = 45 dB** & **SNR = 50 dB** \\ \hline
2 PSK & 0.153 & 0.321 \\ \hline
4 PSK & 0.087 & 0.214 \\ \hline
8 PSK & 0.082 & 0.188 \\ \hline
2 PSK & 0.081 & 0.186 \\ \hline
4 PSK & 0.081 & 0.186 \\ \hline
8 PSK & 0.080 & 0.185 \\ \hline \hline \end{tabular}
\end{table}
Table 1: KSTEST calculation.  
Figure 13: Histograms and cumulative histograms for (**a**) 2 PSK and (**b**) 2 PSK signals.  
The analysis indicates that the 8 FSK signal for the additive white Gaussian noise (AWGN) channel and ideal SIC provides the highest bit rate for the covert signal, while providing the best (calculated in K-S distance) steganographic protection. In addition, simulation tests have proven (Section 4) that sorting can be successfully used, which effectively reduces the impact of channel estimation error.
## 3 Methodology
### Curve Approximation  
To simultaneously address interpolation and continuity, a curve approximation algorithm is proposed. This approach generates a smooth mathematical representation of the data, eliminating outliers and enabling evaluation at any point. Each row of the depth image is treated as an independent signal, where the \(x\)-axis represents the pixel position and the \(z\)-axis corresponds to the depth value. Figure 4 illustrates the fifth row of Figure 3 and its approximated curve.  
To approximate signals, a third-degree polynomial spline is chosen as the mathematical model, ensuring both smoothness and computational efficiency. The equations and continuity constraints for the spline are detailed in Equations (1)-(7). The coefficients of the  
Figure 3: Pixels of an image.  
Figure 2: Depth image of a sphere.  
To approximate this signal, the unknown variables are the coefficients \(a_{i,j}\) in Equation (5). Since the equations are non-linear, the LMA is chosen for curve approximation to minimize the deviation between the approximated curve and the initial input data.  
\[e=\sum_{i=0}^{w-1}(z_{i}-P_{i}(t_{i}))^{2} \tag{8}\]  
In Equation (8), \(z_{i}\) represents the discrete data, and \(P(i)\) represents the approximated curve at the same position \(i\) as \(z_{i}\).  
The deviation, denoted as \(e\), is the quantity that needs to be minimized. The LMA is an iterative method and requires an initial value. In this study, the initial values are set as a vector with value of 1, and the iteration continues until a certain threshold is reached.
## 2 Materials and Methods
### Machine Vision Algorithm
This method employs normalized gray values and implements more accurate matching when there is a dense texture. When the size of the template image is \(\text{K}\times\text{L}\) and the size of the target image is \(\text{M}\times\text{N}\), the cross correlation at \(\text{(i,j)}\) is calculated using Equation (1). Figure 3 shows the correlation between the template image and the target image when performing pattern matching [34]. \[\text{C(i,j)}=\frac{\sum_{\text{x}=0}^{\text{L-1}}\sum_{\text{y}=0}^{\text{ K-1}}(\text{w}(\text{x},\text{y})-\overline{\text{w}})(\text{f}(\text{x}+\text{i },\text{y}+\text{j})-\overline{\text{f}(\text{i},\text{j})})}}{\sqrt{\sum_{ \text{x}=0}^{\text{L-1}}\sum_{\text{y}=0}^{\text{K-1}}\left(\text{w}(\text{x}, \text{y})-\overline{\text{w}}\right)^{2}}\sqrt{\sum_{\text{x}=0}^{\text{L-1} }\sum_{\text{y}=0}^{\text{K-1}}\left(\text{f}(\text{x}+\text{i},\text{y}+ \text{j})-\text{f}(\text{i},\text{j})\right)^{2}}}} \tag{1}\]  
where i = 0, 1, 2,..., \(\text{M}-1\) and j = 0, 1, 2,... The correlation is calculated through the C(i, j) value at the highest point among the values up to \(\text{N}-1\). Figure 2: Matching result and template image of the geometric matching algorithm used in this study. The accuracy of the matching algorithm is calculated using Equation (2) [35]. In this study, National Instruments Vision Development Module, Vision Acquisition Software, Vision Builder for Automated Inspection, Vision Assistant, and LabVIEW were used to implement each matching algorithm [36]. \[\text{Match Score}=\frac{\text{Matched Pixels}}{\text{Total Pixels in ROI}}\times 1000 \tag{2}\]  
The match score indicates the matching accuracy score. It is output as a number between 0 and 1000, and the closer it is to 1000, the higher the accuracy. The region of interest (ROI) represents the area where matching is performed; in this study, it represented the entire area captured by the camera.