esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Performance Evaluation
### Random Antenna Array
In contrast with ULA and UMA, the CPSO achieves an improvement in the maximum of the mainlobe and minimum of the sidelobes for large-scale deployment of sensors compared to conventional beamforming and convex optimization. As concluded in the previous Sections 7.1 and 7.2, the collaborative sensors are formed based on the size of the transmitted data, given the total number of deployed sensors. Figures 7(a) and 8(a) indicate how much sampling is required to optimize the beamforming which is affected by minimum \(d_{\text{max}}\) satisfying the distance constraint Equation (30a). It can be concluded that the capability of CPSO in improving the performance of CB through satisfying the connectivity constraints is revealed when designing practical random antenna arrays in various IoT applications. The PSO [27], and GA are compared against CPSO and convex optimization [21]. Figure 10 presents the objective function performance comparisons for CPSO, convex optimization, PSO, and GA. The PSO algorithm performs better than GA as shown in Figure 10(a). However, in Figure 10(b,c) GA outperforms the PSO for increased number of sensors and higher iterations. Meanwhile, CPSO proves its superiority in yielding better sidelobe reduction and higher directivity as compared to those obtained by convex optimization, PSO, and GA. This is because the CPSO efficiently selects, samples and computes the \(\kappa\) number of global optimal current excitation weights as well as the number optimal uniform interelement separation for each beampattern of the linear antenna array to arrive at the maximum mainlobe and sidelobe reduction. Table 6 summarizes the optimal values obtained using CPSO for all the antenna arrays considered where the performance is better than those of conventional linear antenna array, convex optimization, PSO, and GA. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**8 Sensors**} & \multicolumn{2}{c}{**16 Sensors**} & \multicolumn{2}{c}{**36 Sensors**} \\ \cline{2-7}  & **min** & **max** & **min** & **max** & **min** & **max** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} CPSO \\ PSO \\ GA \\ Convex \\ \end{tabular} } & \(-40\) dB & \(18\) dB & \(-18\) dB & \(18\) dB & \(-38\) dB & \(18\) dB \\ \cline{2-7}  & \(-38\) dB & \(20\) dB & \(10\) dB & \(22\) dB & \(0\) dB & \(18\) dB \\ \cline{2-7}  & \(-20\) dB & \(18\) dB & \(10\) dB & \(22\) dB & \(-24\) dB & \(10\) dB \\ \cline{2-7}  & \(-65\) dB & \(18\) dB & \(-10\) dB & \(20\) dB & \(-10\) dB & \(19\) dB \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Geometry of the linear antenna array consisting of 8, 16, and 36-elements using four different algorithms.
## 2 Materials and Methods
### Data Contraction Detection Methods
#### 2.2.1 Wavelet Energy  
The scalogram of \(x(s)\) at time \(t\) and scale \(a\)\((a>0)\), \(SC(t,a)\), is the squared modulus of the continuous wavelet transform [5]:  
\[SC(t,a)=\frac{1}{|a|}\left|\int_{-\infty}^{+\infty}x(s)\;h^{*}\left(\frac{s-t} {a}\right)ds\right|^{2} \tag{1}\]  
where \(h^{*}(s)\) is the complex conjugate of the mother wavelet function, \(t\) is the time, and \(a\)\((a>0)\) is the scale parameter. The time marginal of a time-frequency representation is given by:  
\[m_{t}(t)=\int_{-\infty}^{+\infty}SC(t,f)df \tag{2}\]  
where \(f\) is the frequency. In this context, the time marginal represents a wavelet variance estimator of the scalogram. The selected wavelet type in this work was the complex Morlet wavelet (cmor25-1). This mother wavelet has also been found to be an acceptable compromise, regarding bandwidth and length, for the contraction delineation [34].
## 4 Methodology
### Multichannel Temporal Convolutional Network  
#### 4.3.1 Temporal Convolutional Network  
The TCN adopts structures such as dilated causal convolution and residual blocks, which can obtain a larger receptive field with fewer layers and maintain the stability of the model.  
Dilated Causal Convolution: Causal convolution does not use recurrent connections, so it can achieve a faster network training speed with parallel input of time series data. However, standard causal convolution needs to stack many layers or use many large convolution kernels to increase the receptive field of neurons when dealing with large sample time series. The dilated causal convolution can improve the receptive field without significantly increasing the computational cost through the skip operation [14]. The formula is as follows:  
\[DCC(i)=\sum_{j=0}^{k-1}h(j)x(i-dj) \tag{11}\]  
where \(DCC(i)\) is the convolution result of the \(i\)th element in the sequence \((x_{0},\dots,x_{t})\); \(h(j)\) is the convolution kernel, and the size of the convolution kernel of the one-dimensional sequence is \(K=1\times k\); \(d\) is the expansion factor, when \(d=1\) is the standard causal convolution.  
The dilated causal convolution structure is shown in Figure 5, and the parameters \(K=1\times 2\), and \(d\) are 1, 2, 4, and 4, respectively. Under the same number of network layers, a very large receptive field can be achieved.  
Residual Block: The residual block can maintain the stability of model features by introducing the operation of skip connection and solving the problem of deep learning network degradation [56]. The input \(x\) of the residual block is added to the features extracted by the dilated causal convolution module, and the final output result \(o\) is obtained through the activation function. The formula is as follows:  
\[o=\text{ReLU}(x+D(x)) \tag{12}\]  
where ReLU is an activation function; \(D(\cdot)\) is the dilated causal convolution block.
## 2 Global-Local Problem Formulation  
### Reference Problem  
Considering an elastic body with a small displacement formulation, the differential eq. governing the system can be described using the following equation:\[-\
abla\cdot\sigma(u) =f\qquad\text{in}\;\Omega \tag{1}\] \[\sigma(u) =\lambda\text{tr}(\epsilon(u))I+2\mu\epsilon(u)\] (2) \[\epsilon(u) =\frac{1}{2}\Big{(}\
abla u+(\
abla u)^{T}\Big{)} \tag{3}\]  
where \(\sigma\) is the Cauchy tensor, \(f\) is the applied body force, \(\lambda\) and \(\mu\) are the Lame elasticity parameters of the material in the domain \(\Omega\), \(u\) is the displacement field of the problem, \(\epsilon\) is the strain tensor, and \(I\) is the identity tensor.  
Using the weak formulation of the problem and multiplying it by a test function \(v\), the equilibrium equation is presented as follows:  
\[\int_{\Omega}\sigma:\
abla vs.\;\text{d}x=\int_{\Omega}f\cdot vs.\;\text{d}x +\int_{\partial\Omega_{T}}T\cdot vs.\text{d}s. \tag{4}\]  
where \(T\) is the stress tensor at the boundary \(\partial\Omega_{T}\) of the problem analyzed. Applying the boundary conditions to the problem and considering that \(\Omega\) is noted as the reference domain \(\Omega_{R}\), the mechanical problem to be solved can be described by the following equations.  
\[\text{Find}\;u\in V(\Omega_{R}),\;a_{R}(u,v)=l_{R}(v),\;\forall vs.\in V_{0} (\Omega_{R}) \tag{5}\]  
The admissible space for the displacements is defined as:  
\[V(\Omega_{R})=\{V\in H_{1}(\Omega_{R})\;v=u_{d}\text{ on }\partial_{u}\Omega_{R}\} \tag{6}\]  
where \(a_{R}\) is the bilinear operator related to the equilibrium of the structure and \(l_{R}\) are the Neumann boundary conditions. For Dirichlet boundary conditions, these are within the admissible displacement space \(V(\Omega_{R})\).
# Overview of FEM-Based Resistance Models for Local Buckling of Welded Steel Box Section Columns
## 3 Finite Element Model
### Imperfections and Residual Stresses  
In a geometric and material non-linear analysis using imperfections (GMNIA), the imperfections that arise in steel members coming from the manufacturing process should be considered. The method for implementing imperfections in a computational model depends on the complexity of the buckling phenomenon under consideration. The design code gives standardized values for equivalent geometric imperfections to address all the possible imperfections [4]. However, several investigations suggest that the use of geometric imperfections and residual stresses in combination may provide a more accurate representation of the local buckling resistance of welded box section columns [43]. Both will be analyzed in the current research program.
## 3 Preliminaries
### Definition of Input Features  
In this study, we aim to infer spatially fine-grained city air quality value based on existing air quality monitoring stations and achieve a long-term prediction on areas without monitoring stations. The input features of the proposed model are categorized into dynamic features (i.e., AQI and meteorological data) and static features (i.e., point of interest (POIs), road network, and land use). The details of input features are as follows:  
* **Grid:** We use longitude and latitude to divide the target area into disjoint square grids as the basic unit. Notably, we adjust the size of the grid to ensure that each grid contains only one air quality monitoring station. A grid region with no monitoring station is denoted as unlabeled grids \(G_{Un}\), and a grid region with a monitoring station is denoted as labeled grids \(G_{5n}\).
* **AQI:** In this study, we use the dataset collected by [22]. We choose PM\({}_{2.5}\) values observed by air-quality monitoring stations every hour as the predictive features because PM\({}_{2.5}\) is the main pollutant that influences the air quality level and also the most widely used indicators of an air quality report.
* **Meteorological Data:** The meteorological data are critical factors to air quality level. The grid-level meteorological data in [22] includes temperature, pressure, humidity, wind direction, and wind speed.
* **POIs:** The amounts of POIs represent activities and the density of people in a region, which might have a huge influence on the air quality. In this study, we collect POIs data from Gaode Map and select 14 types of POIs.
* **Road Network:** It is known that air quality is strongly affected by traffic condition. The type and accumulated length of a road might be strongly correlated to the real-time traffic condition. In this study, the road network data are collected using OpenStreetMap and four features are identified for each grid: (1) total length of highway, (2) total length of primary road, (3) total length of secondary road, and (4) total length of the pedestrian walk.
* **Land Use:** The air quality of a region is highly affected by its surrounding land. For example, \"Industrial Area\" could be the source of air pollution; \"Residential\" is the place that attracts people and traffic, which worsens the air quality. Conversely, the region with a larger area of open \"Green\" or \"Water\" land might improve the air quality. In this study, we use OpenStreetMap to collect four types of land use data including (1) total area of green land, (2) total area of industrial area, (3) total area of residential area and (4) total area of waters for each grid.
# An Effective Off-Grid DOA Estimation Algorithm Using Difference Coarrays with Limited Snapshots
## 5 Simulation Results
### Resolution Performance for NLA  
To further examine the resolution of the proposed method, the RMSE of the search-based, EPUMA, SS-MUSIC, and the proposed methods versus source separation are compared in Figures 10 and 11 at both 0 dB SNR and 10 dB SNR, respectively. The proposed algorithm demonstrated a superior spatial resolution capability, outperforming all benchmark methods in our comparative evaluation. In addition, the RMSE of the search-based, EPUMA, and SS-MUSIC methods did not approach CRB when the sources were closely spaced, whereas the proposed technique maintained a CRB-attaining RMSE performance with just \(2^{\circ}\) source separation.  
Figure 8: RMSE of the proposed method versus snapshot number with three sources for NLA.  
Figure 9: RMSE of the proposed method versus snapshot number with nine sources for NLA.
# Remote Sensing Image Dehazing via Dual-View Knowledge Transfer
## 3 Results and Discussion
### Comparison with Other Image Dehazing Methods  
This section reports comprehensive quantitative and qualitative comparisons against state-of-the-art methods on the adopted datasets. The compared methods are categorized into two primary groups: (1) those developed for natural image dehazing, encompassing techniques like DCP [11], CEP [44], SLP [45], AOD-Net [46], MOF [47], FFA-Net [23], Light-DehazeNet [48], Restormer [49], DehazeFormer [28], and Trinity-Net [19]; and (2) methods tailored specifically for dehazing remote-sensing images, such as FCTF-Net [29], DCRD-Net [50], and SCSNet [37]. To guarantee a fair assessment, the officially released code for each method was utilized, and a consistent data division strategy was applied during training.
## 3 Methodology
### Neural Network Assumptions
The effectiveness of the network is determined by the activation function of hidden and output neurons, which take the following form: linear (directly transmitting the excitation value of the neuron to the output), logistic (sigmoidal curve with values greater than 0 and less than 1), exponential (with a negative exponent), and hyperbolic (hyperbolic tangent curve with values greater than \(-1\) and less than 1). To verify the threshold value of the input signal needed to activate the neuron, the activation functions _f(x)_ are used:  
* Linear with output values in the range from \(-\infty\) to \(\infty\): \[f(x)=x\] (4)
* Logistic (sigmoidal) with output values in the range from 0 to 1: \[f(x)=\frac{1}{1-e^{-1}}\] (5)
* Exponential with output values in the range from 0 to \(\infty\): \[f(x1)=e^{-x}\] (6)
* Hyperbolic (hyperbolic tangent) with output values in the range from \(-1\) to 1: \[f(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}}\] (7)  
neurons are arranged in a unidirectional structure in which the transmission of signals takes place in a strictly defined direction--from input to output. A key task in MLP network design is to determine the appropriate number of layers and neurons, usually performed empirically. A network with radial base functions often has only one hidden layer, containing radial neurons having a Gaussian character. On the other hand, a simple linear transformation is usually applied to the output layer. The task of radial neurons is to recognize the repetitive and characteristic features of input data groups. In order to elaborate on the best model of the network, a number of constant and variable parameters were determined, tested by the multiple random sampling method, resulting in 10.000 network variants. The error of the sum of squared differences generated for each set of test parameters was established as the criterion for network effectiveness. The constant parameters of the artificial neural network are:  
1. 3 input neurons (number of tool changes, number of changes in assembly directions, and stability of the assembly unit) and 1 output neuron (assembly time);
2. Percentage of teaching (80%), testing (10%), and verification (10%) examples;
3. Regression model (determination of the quantitative and floating-point numerical values). Variable network parameters that were altered randomly during the generation of network models were:  
1. Network learning algorithms (steepest gradient, scaled conjugate gradient, Broyden-Fletcher-Goldfarb-Shanno, and RBFT radial basis function teaching);
2. Network topography (multilayer perceptron and network with radial basis functions);
3. Activation functions (linear, sigmoidal, exponential, hyperbolic, and sine);
4. Number of hidden neurons (1-12).
# The Impact of an Office Fire Combined with the Stack Effect in a Multi-Story Building
## 3 Results and Discussion
### CO and CO\({}_{2}\) Concentration Variation Inside the Building  
Next, we will analyze the concentrations of CO and CO\({}_{2}\) inside the building to assess the risk to the occupants of those spaces. Normally, the concentration of CO in the air is approximately 0.1 ppm, which corresponds to 0.00001% [51]. According to medical studies, a CO concentration in the air between 0.1 and 0.12% (1000-1200 ppm) is considered lethal with a 15 min exposure, as referenced in [52; 53; 54]. Similarly, the concentration of CO\({}_{2}\) in the air is about 400 ppm, which corresponds to 0.04% [55], while the concentration considered lethal for humans is at least 4% (4000 ppm), varying between 4 and 10% depending on exposure (4000-10,000 ppm), according to references [56; 57; 58; 59]. Therefore, monitoring the concentrations of these hazardous gasses plays a crucial role in identifying and managing associated risks, thus preventing intoxications and ensuring a safe environment for the building occupants.  
Figure 14 shows the time evolutions of CO and CO\({}_{2}\) concentrations monitored in the stairwell for the two compared scenarios. The differences read from the five control points were minimal, with the distribution of smoke being almost identical in the five critical zones. Additionally, the time evolution of these concentrations was superimposed with a continuous red line indicating the minimum lethal value of 0.12% (CO) and a blue line indicating the minimum lethal value of 4% (CO\({}_{2}\)).  
It can be observed that in the event of a fire (S2), for both types of concentrations, the critical threshold is reached, but at different time intervals, with carbon dioxide taking up to 34 s longer to accumulate to a lethal amount. Another aspect highlighted in Figure 14 is the similar trend described by both concentrations of toxic gasses.  
In conclusion, the occurrence of a fire combined with a scenario that promotes its development leads to the rapid spread of smoke throughout the building, via the stairwell. The areas where the occupants' lives are endangered include the apartment where the fire started, the entire stairwell, and the top-floor apartments. The same trend was observed in visibility, with the two parameters being strongly correlated.
## 2 Materials and Methods
### Adsorption Studies  
#### 2.5.1 Batch Adsorption Experiments  
The adsorption equilibrium of lead ion with LPD adsorbent was carried out in 50-mL Eppendorf tubes; agitated at 150 rpm, and at the suitable conditions. Thus, the following parameters were optimized: initial lead(II) ion concentration (10-550 ppm), adsorbent dosages (0.1-2 g), pH value of the solution (3-7), and temperature (30-50 \({}^{\circ}\)C). For the kinetic adsorption study, the tubes were shaken for 0, 20, 30, 60, 90, 120, and 180 min under the following conditions (\(2\times 7\) tubes): LPD dose 0.2 g, Pb\({}^{2+}\) adsorption solution 50 mL, Pb\({}^{2+}\) concentration 55 ppm, pH 6, temperature 25 \({}^{\circ}\)C, and agitation 150 rpm. After each experiment, the adsorbent LPD was removed by filtration ([PERSON] filter paper, No. 1) and the residual ion concentration in the filtrate was measured using FAAS. All the experiments were performed in duplicate and averaged.
## 8 Rising Applications and Future Challenges  
As VSC-HVDC technologies will continue to have an important share of direct current transmission, there will be huge opportunities for further development on both its main mechanisms and alternate emerging applications, e.g., restoration mechanisms or secondary services for increasing the grid's resilience. There are bold analyses like thatof [68], which propose the implementation of DC distribution systems, further propelling DC technologies for future power grids.  
As stated in [23], the need for further inclusion of low-carbon generation will give HVDC technology an opportunity to thrive on developing Smart Grids. Some examples of huge potential with this technology include offshore renewable interconnection, unsynchronized networks links, and reinforcements to actual AC grids. Therefore, as the increasing application of VSC HVDC becomes a reality, any solution that can bring added value to this technology by enhancing the grid's performance has to be taken into account.  
Other future challenges will involve the development of economic analyses for VSC-HVDC restoration schemes, as this has not been explored widely in the literature yet. Schemes for remunerating DC links for network support for each particular case would also be an important next step towards their materialization.
## 3 Materials and Methods
### Artificial Intelligence Models  
A CNN is a deep learning model commonly used in image processing and computer vision. CNNs contain convolution, pooling, and fully connected layers to learn image features [57] automatically. One-dimensional Convolutional Neural Network (1D CNN) architecture was used in this study. This model is designed as a simple CNN architecture that works specifically on time series data or one-dimensional data.  
An RNN is a deep learning model that works with time series or sequential data. RNNs can learn correlations in sequential data by remembering information from previous steps [58].  
A CNN + RNN model is a combination of various classification and regression tasks because they can capture both local and consecutive features of the input data [58; 59]. In the model, a CNN uses convolution layers to extract local features from this data. Time series data can be organized in 2D or 3D format. An RNN learns the correlations between consecutive data by processing these features over time after the features are extracted and can make more meaningful predictions.  
An ensemble model achieves better performance by combining multiple models. In this study, performance comparisons were made with deep ensemble learning using CNN, RNN, and CNN + RNN hybrid approaches. In this study, an averaging technique from ensemble techniques was applied. This technique provides more stable and accurate predictions by taking the average of each model's prediction in regression problems [60].  
An XAI model is a field that makes the decision-making processes and outcomes of AI systems more understandable. It helps users and developers understand how AI works, what data it uses, and how it reaches certain conclusions with transparency, traceability, understandability, and bias [12].  
The study used the CNN, RNN, and CNN + RNN hybrid approaches from deep learning (DL) models. DL algorithms can process large datasets. Especially with complex data such as images, audio, and text, DL can automatically extract features and perform better than traditional machine learning methods [61]. Deep ensemble learning models used in the research combine the advantages of both deep learning models and ensemble learning, ensuring that the final model has better generalization performance [62]. In this study, the total average of the CNN, RRN, and CNN + RNN algorithms was taken with averaging, one of the ensemble techniques. Figure 3 shows the flows of the study and the deep ensemble learning methods. As seen in the figure, the weighted average of CNN, RNN, and CNN + RNN model results was taken.  
Figure 3: Deep Learning Based ensemble learning method flow charts. (**a**) Drone 92 Dataset; (**b**) Drone 129 Dataset.