esa-sceva/satcom-chunk-collection huggingface dataset:
# Spatial Monitoring of Wafer Map Defect Data Based on 2D Wavelet Spectrum Analysis
## 2 Theoretical Background
### Spectrum Analysis  
As mentioned before, wavelets at each resolution level are an effective tool for detecting self-similarity. 2D fractional Brownian (fBm) motion is a representative model to explain a self-similar continuous-time Gaussian process in two dimensions. For self-similar images to be non-stationary with zero mean, let us define a scaling function as \(B_{H}(\mathbf{u})\) and \(B_{H}(\mathbf{v})\), where \(\mathbf{u}\) and \(\mathbf{v}\) are the row position and the column position of the selected area, respectively. An autocorrelation function between the two scale functions is derived by a combination of the  
where \(H\in[0,1]\) denotes the Hurst exponent, which is a measure of the long range dependence of time series data. Here, \(\Gamma(\cdot)\) denotes the gamma function. The application of 2D fBm enables the increment of images to be stationary. The formula with respect to the increment \(\mathbf{h}\) is given as:  
\[E[B_{H}(\mathbf{u}+\mathbf{h})-B_{H}(\mathbf{u})]^{2}=\sigma_{H}^{2}\|\mathbf{h}\|^{2H}. \tag{4}\]  
Equation (4) shows that the oscillating patterns of \(B_{H}(\mathbf{u})\) are dependent on the distance of increments \(\|\mathbf{h}\|\). Derived from this relationship, the power spectrum of scaling functions can be simplified as [13]:  
\[S_{B_{H}}(\omega)=\|\omega\|^{-2H-2}. \tag{5}\]  
Based on Equation (5), we can estimate the appropriate Hurst exponent from the given image. According to the values of \(H\), the original signal images can be characterized; for example, with the Hurst exponent close to 0.5, the image has a random property, without any correlation or trend; whereas a large \(H\) value means that the image is manifested differently, indicating a long range persistent time series, while a small \(H\) value represents anti-persistent time series having a short length of the mean reverting property.
## 3 Predicting the Signal Attenuation Level at Free Space (Section A)
### Application of the Prediction Model
#### 3.3.1 Building the Database for the Learner  
We used the collected data related to SNR, climatic conditions, and satellite orbits to comprise a learner database to enable SNR prediction. We used data from three sources: STK simulation linking satellite orbit (time and angle) with channel orientation, weather data from a meteorological database, and SNR data measured by the satellite. Each record in the learner database represents an alignment of these three data sources. The record layout is sampling date and time, angle, temperature, frequency, humidity, bandwidths, and an indication of the passed or lost signal. Table 1 presents examples of the database used for training the deep-learning system.
## 5 Numerical Simulations
### Comparison Results with CBC and AFCFC Methods
In order to quantitatively analyze the control performance of the three controllers, we define three performance indexes including the settling time \(t_{\text{s}}\), the mean squared error in the transient stage between 0.4 s and 2.4 s, i.e.,  
\[e_{\text{1 MSE}}=\frac{1}{N}\sum_{i=1}^{N}[e(i)]^{2} \tag{54}\]  
and the maximum tracking error in the steady stage \(|e_{\text{10}\,\text{max}}|\). The details of the quantified performance indexes of the three controllers are given in Table 1.  
As seen from Table 1, the proposed RBCFC exhibits better performance than the other two controllers in aspects of transient and steady tracking errors. Although the settling time of the proposed controller is a little longer than those of other controllers, the mean squared error of the proposed controller in the transient stage is reduced almost down to \(3.09\times 10^{-5}\), which is quite smaller than that (i.e., \(1.51\times 10^{-3}\)) of CBC, as well as that (i.e., \(4.84\times 10^{-4}\)) of AFCFC. In addition, the maximum tracking error of the proposed controller in the steady stage is about \(2.84\times 10^{-4}\), while those of CBC and AFCFC are \(5.79\times 10^{-2}\) and \(3.39\times 10^{-2}\), respectively. Compared with the CBC and the AFCFC, the tracking accuracy of the proposed controller is improved by 99.5% and 99.2%, respectively. From Figure 13, it is seen that the control torque \(u\) of the proposed controller in the initial stage between 0 s and 0.2 s shows large fluctuations. The reason for this may be that the proposed controller in the system adjustment stage is more susceptible to the unknown disturbances. By introducing the RESOs, however, the disturbances are quickly estimated and compensated for in the feedback control. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & \(\mathbf{t_{\text{s}}(s)}\) & \(\mathbf{e_{\text{1 MSE}}(rad)}\) & \(|\mathbf{e_{\text{10}\,\text{max}}}|(rad)\) \\ \hline RBCFC [Proposed] & 0.93 & \(3.09\times 10^{-5}\) & \(2.84\times 10^{-4}\) \\ CBC [16] & 0.68 & \(1.51\times 10^{-3}\) & \(5.79\times 10^{-2}\) \\ AFCFC [30] & 0.86 & \(4.84\times 10^{-4}\) & \(3.39\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance indexes under the three controllers. Figure 13: Control torque \(u\) under the three controllers. According to the above comparative simulation results, it can be concluded that compared with other methods, the proposed controller can estimate and compensate the unknown matched and mismatched disturbances effectively, and achieve a better transient and steady tracking performance.
# A New Hybrid ConvViT Model for Dangerous Farm Insect Detection
###### Abstract  
This study proposes a novel hybrid convolution and vision transformer model (ConvViT) designed to detect harmful insect species that adversely affect agricultural production and play a critical role in global food security. By utilizing a dataset comprising images of 15 distinct insect species, the suggested approach combines the strengths of traditional convolutional neural networks (CNNs) with vision transformer (ViT) architectures. This integration aims to capture local-level morphological features effectively while analyzing global spatial relationships more comprehensively. While the CNN structure excels at discerning fine morphological details of insects, the ViT's self-attention mechanism enables a holistic evaluation of their overall configurations. Several data preprocessing steps were implemented to enhance the model's performance, including data augmentation techniques and strategies to ensure class balance. In addition, hyperparameter optimization contributed to more stable and robust model training. Experimental results indicate that the ConvViT model outperforms commonly used benchmark architectures such as EfficientNetB0, DenseNet201, ResNet-50, VGG-16, and standalone ViT, achieving a classification accuracy of 93.61%. This hybrid approach improves accuracy and strengthens generalization capabilities, delivering steady performance during training and testing phases, thereby increasing its reliability for field applications. The findings highlight that the ConvViT model achieves high efficiency in pest detection by integrating local and global feature learning. Consequently, this scalable artificial intelligence solution can support sustainable agricultural practices by enabling the early and accurate identification of pests and reducing the need for intensive pesticide use.  
insect detection; CNN; ViT; ConvViT; deep learning Article
# Generation and Propagation of a Hermite-Gaussian Correlated Schell-Model LG\({}_{0l}\) Beam
###### Abstract  
A partially coherent beam under the combined action of a Hermite-Gaussian correlated function and vortex phase, named the HGCSMLG\({}_{0l}\) beam has been explored both theoretically and experimentally. The statistical properties, such as the intensity and distribution of the degree of coherence (DOC) on propagation are analyzed in detail, based on the deduced equations. We find that the intensity is determined dominantly by the non-conventional correlated function when the coherence length is comparatively small and by vortex phase when the coherence length is large. The modulus of the DOC is not vulnerable to coherence width, rather, it is affected by both non-conventional correlated function and vortex phase. Our results are verified well by the experiment results.  
Keywords:coherence and statistical optics; correlation function; topological charge; laser beam shaping +
Footnote †: journal: Applied sciences
# Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms
## 3 Methodologies
### Naive Bayes Classifier  
The naive Bayes classifier, as a classification method based on probability statistics [99], has demonstrated excellent performance and wide applications in many fields and has been applied to classify EOR projects. Its core principle is based on [PERSON] theorem (Equation (3)), which mathematically describes how to estimate the probability of an unknown event given known information. The advantage of the Bayesian classification method is that it can handle uncertainty and can continuously learn and adjust based on the new data to improve the accuracy of classification.  
\[p(Cm|X)=\frac{p(Cm)p(X|Cm)}{p(X)} \tag{3}\]  
where \(C_{m}\) represents the category, \(X\) stands for a sample with features (\(x_{1}\), \(x_{2}\), \(\ldots\)... \(x_{n}\)), \(p(C_{m})\) is the Priori probability, \(p(X)\) is the total probability of the occurrence of data \(X\), \(p(C_{m}\,|\,X)\) is the Posterior probability, and \(p(X|\,C_{m})\) indicates the probability of observing data \(X\) under the assumption that \(C_{m}\) is established.  
Figure 6: Flow chart of ANNs and single neuron model.
## 3 Results
### Classification Accuracy Evaluation
#### 3.4.2 Experiment 2: Evaluation of Classification Capacity  
This section introduces the three conventional CNNs used for comparison, including VGG-Net, Res-Net, and Alex-Net. The performance of the original data set and the enhanced data set after adding samples generated using our method was compared, and we used this to investigate the impact of the generated data on the accuracy of the networks.  
First, we fixed the original data set and increased the number of generated samples as an enhanced data set. On five categories of the NWPU-RESISC45 public HRRS data set, we tested rivers, grasslands, forests, lakes, and islands to 60 pieces of data collected for each category, and the number of training rounds, training set ratio, etc., remained unchanged. The only variable was the size of the data set.  
First, we performed a classification experiment on the original data set, using 20%, 40%, and 60% of the original data set as a training set for the experiment. After obtaining the classification accuracy, we added the generated data, keeping the size of the training set consistent with the previous experiment, and performed the classification experiment to obtain the classification accuracy.  
In the experiment evaluating the quality of generated data quality evaluation, the training settings of the three CNNs we used are as follows. The learning rate was 0.001,  
It may be observed that all of the conventional models used for comparison exhibited decreased training accuracy when the number of training sets was 20% and 40% of the original data set, and the range of the decrease was between 0.44-3.41%, while in the training set when the number was 60%, the training accuracy of Alex-Net and ResNet also decreased, and the range of the decrease range was between 0.73% and 0.85%. In the classification experiment performed only on our own generated data, it may be observed that almost all classification accuracy results were less than the classification accuracy of the original data set.
# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
## 3 Results  
### Model Validation  
We compared the mean annual GPP estimated from flux towers with the mean annual GPP simulated by the proposed model (Figure 3). We found that the proposed model accounted for 72.3% of the spatial variability in GPP across all validation sites. The proposed model performed very well at most sites, with a statistically significant \(p\) value \(<\) 0.01. The mean relative RMSE (RRMSE) and mean bias over all the sites were 36.3% and 9.23% gC m\({}^{-2}\) year\({}^{-1}\), respectively. The relative root mean square error (RMSE) values comparing the observed data and the simulated data for each site are shown in Appendix B.  
Figure 3: Comparison of annual GPP estimated from flux towers and annual GPP simulated by the proposed model. The blue line is the regression line, and the red dashed line is the 1:1 line. The regression equation, correlation coefficients, statistical significance, relative mean square error, and bias are shown on the top.
## 5 Numerical Results
### Example 4  
In this example, we consider uniaxial tension, which is stretching along a single direction (or axis). It is a common and useful method for the mechanical testing of materials. The experiments have been conducted on hyperelastic materials, using a polynomial model for the nonlinear elastic deformations, like the one used in polymers and rubbers (e.g., [41; 42]). The stress-strain relation in tension can be written in a polynomial form for a nonlinear elastic material,  
\[\sigma(\epsilon)=\sum_{i=1}^{n}=E_{i}\epsilon^{i}, \tag{14}\]  
where \(E_{i}\) is the Young's modulus. The case \(E_{2}=E_{3}=E_{4}=0\) corresponds to [PERSON]'s law from the linear elasticity theory. Tension has been tested for \(E1=2.14663\) MPa, \(E2=-0.646588\) MPa, \(E3=-0.0697791\) MPa, \(E4=0.566989\) MPa ([PERSON] [41]).  
In (13), we have taken \(\epsilon_{ij}^{1}=-0.6\), \(h=0.05\) and \(N=50\). The neural network has been trained with a data set, \(M=90\) (45 for training and 45 for validation and test ) with four  
Figure 16: The line fitting for the partial derivatives of the components of the stress tensor \(\sigma_{xy}\) after training of the neural network and using the polynomial regression at the prediction points of the data set \(M=9261\) obtained from the representative volume element.  
hidden layers and with [15; 20; 15; 20] neurons, respectively. The number of epochs is 8000 and the batch size is 64. The stress function with respect to the strain and its derivative have been predicted from the data set \(\{\varepsilon_{i},\;\;i=1,2,...,P\}\). In the NN, the loss function incorporates the physical constraints  
\[\frac{\partial\sigma}{\partial\varepsilon}=\frac{\partial^{2}\Psi}{\partial \varepsilon^{2}}\geq 0\]  
and the Sobolev function. The physics-informed constitutive model enforces hyperelasticity principles, which ensures material stability and consistency.  
The stress-strain curve and the derivative of the stress at the predicted points \(P=40\) with the exact values of the stress from Equation (14) are shown in Figure 17.  
The loss error \(MSE=5.7\times 10^{-7}\) on the last epoch and the relative errors of the computed stress values and their derivatives with respect to the strain at the predicted points are  
\[\delta_{1}=\left\|\frac{\mathbf{\sigma}^{\mathbf{NN}}-\mathbf{\sigma}}{\mathbf{\sigma}} \right\|_{\infty}=0.000424,\;\;\;\;\delta_{2}=\left\|\frac{\mathbf{\sigma}^{\mathbf{ NN}}_{\varepsilon}-\mathbf{\sigma}_{\varepsilon}}{\mathbf{\sigma}_{\varepsilon}} \right\|_{\infty}=0.008323.\]  
Since the chain rule is used in automatic differentiation, the approximation of the derivatives can be completed with high accuracy. As the numerical results have shown, with even a small number of epochs, we can obtain a good rate of convergences of the neural network to the data values if the material tangent stiffness tensor is included in the training of the neural network.
## 2 Related Works
That is, the radio waves coverage outdoor to indoor by propagating from the transmitter outdoor to the wall of the building. Then, the radio waves penetrate the wall of the building. Last, the radio waves propagate inside the building to the receiver. Later, such methods [8; 21], aimed at using artificial neural networks (ANNs) model to predict path loss. The ANNs perform a nonlinear mapping function of a given set of input values to a set of appropriate output values. Thus, the input parameters contain propagation features, such as frequency, the distance between the transmitter and the receiver, and the desired output value, which contains the measured path loss for a better prediction. Other such models are Hata [8], COST 231 HATA [21], Walfisch [9], Stanford University Interim (SUI) [9], and the ECC-33 model [22]. These models have previously been compared [23]. Based on the related models in this field, the [PERSON] model [24] and the COST 231 HATA model [25; 26] are both used as a basis for our proposed model, given that these models cover a wide range of frequencies as well as different terrains with a high accuracy. Other models are limited to significant restrictions, such as the antenna height or covered building height, because most of these models also show low accuracy over some terrains [27; 28; 29]. Femtocells have risen as a promising solution in order to address the path loss issue and to decongest the microcell network from the increasing number of mobile users and corresponding traffic. As such, 90% of data services and 60% of phone calls take place within an indoor environment. Femtocells, which usually reside in a home/office environment and consist of an Access Point (AP) with short range (i.e., 10-50 m) are connected to a service provider's Internet network. They can provide large indoor coverage and capacity due to the short communication distance to users. Furthermore, femtocells are characterized by low cost installation over an existing microcell network. In the area of two-tier femtocell networks, the overlapping of the coverage areas of femtocells and microcell offer the ability to connect to either the Microcell or a neighboring femtocell, depending on the quality of service criteria. One of the major challenges in this area is that femtocells share the same licensed frequency with microcells [30; 31; 32]. In this paper, the proposed prediction model is based on measurement of new indicators and extensive statistical analysis, in order to compute the predicted received signal strength in a given propagation environment. We present a new path loss model that includes new parameters allowing for an accurate estimation of signal power inside of buildings, as well as the distance between transmitter and receiver. However, the Okumura and COST-Hata models do not take into consideration other factors like the buildings. Therefore, many models were proposed like Okumura, and COAST-Hata. They were the most widely used though our new model was an enhancement over them.
## 1 Perspectives  
A secure smart city integrates advanced technologies to enhance efficiency, sustainability, and safety while safeguarding citizens' privacy and data. Through interconnected Internet of Things (IoT) devices, real-time monitoring, and data analytics, a city can optimize transportation, healthcare, energy usage, waste management, and public services. Robust encryption protocols and cybersecurity measures are implemented to protect against cyber threats and unauthorized access to sensitive information [1].  
In a secure smart healthcare system, cutting-edge technologies converge to optimize patient care while safeguarding sensitive data. Advanced encryption protocols ensure the confidentiality of patient information, while robust authentication mechanisms guarantee secure access to medical records. Interconnected IoT devices monitor patients remotely, enabling real-time health data collection and analysis [2]. Artificial intelligence algorithms process these data to predict health trends and provide personalized treatment recommendations [3, 4]. Additionally, blockchain technology maintains immutable records of medical transactions, enhancing transparency and accountability across the healthcare ecosystem.  
Secure smart transportation systems form the backbone of efficient urban mobility in a smart city. Integrated with advanced technologies such as IoT sensors, AI-driven traffic management systems, and encrypted communication networks, these transportation networks ensure seamless and safe journeys for citizens [5, 6]. Robust authentication protocols and encryption mechanisms safeguard user data and transactions, mitigating cybersecurity threats.  
Secure smart energy systems integrate advanced technologies to ensure the reliable, efficient, and resilient delivery of energy while addressing cybersecurity challenges. These systems leverage interconnected devices, data analytics, and automation to optimize energy generation, distribution, and consumption. Advanced authentication mechanisms, such as biometrics and multifactor authentication, mitigate unauthorized access, while continuous monitoring and response strategies enable real-time threat detection and mitigation.  
In the context of illuminating recent advancements, technologies, and case studies related to sustainability, smart cities, smart health, data-driven technology, and the IoT, this Special Issue entitled \"Secure, Sustainable Smart Cities, and the IoT\" (available at [[https://www.mdpi.com/journal/sustainability/special_issues/7](https://www.mdpi.com/journal/sustainability/special_issues/7) SKG529 PVX]([https://www.mdpi.com/journal/sustainability/special_issues/7](https://www.mdpi.com/journal/sustainability/special_issues/7) SKG529 PVX), accessed on 3 February 2024) comprehensively explores a multitude of research endeavors in this domain. We express our gratitude to all of the authors who contributed to the success of this Special Issue. In this Editorial, perspectives, challenges, and future directions for secure, sustainable smart cities and the IoT are explored.
## 3 Results
### Barrier Device Design
#### 3.1.1 Materials for Device Fabrication  
All materials were chosen to ensure biocompatibility and optical transparency throughout the device.  
The device is made of superimposed polymethyl methacrylate (PMMA) sheets. This rigid thermoplastic is biocompatible, cost-effective, suitable for mass fabrication, and has excellent optical performance of light transmission [19; 20]. A panel of different thicknesses of PMMA sheets is available. Sheets were cut by a laser process to be adapted according to our design.  
The optically transparent polyester membrane with a 0.4 \(\upmu\)m pore size was chosen as the interface to allow the exchanges between both compartments. Firstly, its transparency ensures the visualization of the cell monolayer throughout the device by phase contrast microscopy. This point is important to conveniently monitor viability, growth, and confluence over the culture time. Secondly, this material is classically used in Transwell(r) inserts. Cell culture conditions were optimized for hCMEC/D3 cell culture (including pore density and size, collagen coating, cellular adhesion, and cell growth) [18; 21].  
Figure 1: Fluidic barrier device representations. (**a**) Exploded view of the different parts of the barrier device before assembling. (**b**) Pictures and 3D representations of our home-made barrier device in microscope slide format and (**c**) in plate format (encompassing six conditions in parallel). The upper channel is represented in red, and the lower one in blue. Device modelling was carried out with PTC Creo software.