esa-sceva/satcom-chunk-collection huggingface dataset:
# Modelling Pollutant Dispersion in Urban Canyons to Enhance Air Quality and Urban Planning
## 1 Introduction  
### 1.1 Context  
Street ventilation is a highly discussed topic as it significantly affects inhabitants' health and social well-being [1, 2]. On the one hand, pollution in cities is an increasing hazard that must be addressed [3]. Several factors, such as high urbanisation and expanding cities, have led to an increase in pollutant sources, including motorised traffic and industrial or domestic chimneys [4]. These substances have dangerous consequences for people's health, creating and accelerating problems in the respiratory and cardiovascular system [5], affecting more vulnerable groups such as the elderly and children [6]. On the other hand, street ventilation is not only related to pollution but also crucial when studying climate change effects such as urban overheating [7, 8, 9]. This overheating leads to a local rise in temperatures within urban areas, particularly in streets and public spaces, with negative implications for pedestrians' health [10, 11]  
In this context, a complete understanding of street ventilation is crucial, as it provides essential information about pollutant dispersion and heat dissipation in the core of urbanlife [12]. A well-designed urban layout, including features such as wider streets and more vegetation, helps modern cities face all of these threats [13; 14].
# Machine Learning Based Color Classification by Means of Visually Evoked Potentials
## 4 Discussion
### Pre-Processing and Analysis  
In order not to lose possibly crucial discriminate attributes, we only performed standard denoising as described in Section 2. Applying more sophisticated techniques for this task, such as wavelet denoising [21] or low-dimensional waveform representation [22], might even improve the results. Additionally, the alignment of the single-trial VEPs before averaging could be investigated in more detail. Although we used a photodiode to detect the exact switching point of the monitor, the specific response might still be slightly premature or delayed, which leads to an undesired smoothing effect of the averaged evoked potentials. Investigating these factors in more detail, could help to further improve the classification performance; however, it was not the aim of this work to optimize the classification results, but to illustrate the basic possibility to classify colors.  
For that reason, we also decided to carry out the classification with a rather simple standard method of machine learning; that is an ANN with one hidden layer. Theoretically, other algorithms can also be used for this classification task, but most of these methods require a somewhat more complex selection of hyperparameters, which significantly influences their results. Since the aim of this work was to generally investigate automatic color classification using VEPs rather than comparing machine learning approaches, we limited our work to the ANN. Nevertheless, more sophisticated algorithms, for example a model driven neural network architecture [23], might even result in an improvement of classification, and should therefore be investigated in the future.
# Risk Prediction Model for Tailings Ponds Based on EEMD-DA-LSTM Model
## 4 Empirical Analysis
### EEMD-LSTM Modelling  
Tailings pond dam failure accidents are affected by a variety of risk factors, such as rainfall, infiltration line, reservoir water level, and dry beach length. The safety monitoring data of tailings pond WKB-1 were selected as the experimental data, the data were subjected to outlier detection and missing value filling to reduce the influence of noisy data, and then the displacement sequence was decomposed using the EEMD method to obtain the IMF components and the trend terms of different characteristic scales Res Figure 4.  
Figure 4: _Cont_.  
An LSTM network model was used for predictive modelling of the risk data of tailings ponds. Horizontal displacement, vertical displacement, infiltration line, and reservoir water level were taken as input features and trained on the LSTM network model. The activation function was rectified linear unit (ReLU), the loss function was MSE, and the optimisation algorithm was adaptive moment estimation (Adam). After model training, the relevant parameter settings of the EEMD-LSTM network model are shown in Table 2. Table 3 shows the partial prediction error results of the EEMD-LSTM model.  
The MAE of the EEMD-LSTM model is 0.181 mm, the MAPE is 6.330%, and the RMSE is 0.228 mm. The experimental results show that Figure 5, in response to the strong nonlinear and non-stationary characteristics of the tailings pond displacement changes, the performance of the LSTM prediction model is enhanced and the prediction accuracy is improved after smoothing the displacement time series data using the EEMD approach. In this paper, considering the complex coupling effect between the risk factors of tailings pond dam failure and the problem that LSTM is prone to losing part of the information in long time series prediction, we combined dual attention with EEMD-LSTM to further improve the prediction accuracy of the EEMD-LSTM model on the basis of EEMD-LSTM and carried out modelling experiments and comparative analyses based on the engineering examples to validate the validity of the proposed method in this paper.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Parameter Value** \\ \hline Number of iterations & 100 \\ Learning rate & 0.02 \\ Time series segmentation scale & 10 \\ Number of neurons in LSTM layer & (100, 32) \\ Number of neurons in the fully connected layer & (50, 1) \\ \hline \hline \end{tabular}
\end{table}
Table 2: EEMD-LSTM model parameter settings.  
Figure 4: EEMD decomposition results of displacement.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Phase** & **Measured** & **Predicted** & **Residual** & **Phase/Phase** & **Measured** & **Predicted** & **Residual** \\  & **Value/mm** & **Value/mm** & **Value/mm** & **Value/mm** & **Value/mm** & **Value/mm** \\ \hline
807 & 2.124 & 1.748 & 0.376 & 818 & 1.508 & 1.734 & \(-\)0.226 \\
808 & 1.435 & 1.459 & \(-\)0.024 &... &
# Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems
## 2 Related Work
 } & For fast time-varying channels, a novel network ICINet for sensing ICI is proposed & The number of carriers causing ICI cannot be found according to the actual situation \\ \hline \multirow{4}{*}{[19]} & \multirow{4}{*}{ \begin{tabular}{c} Real-time training data collection and online channel estimation for OFDM systems \\ \end{tabular}  } & Proposed a new training data generation scheme and an LMI-based online channel estimation method, which can adapt to real-time channel changes & The proposed generation scheme can provide limited data \\ \hline \multirow{4}{*}{[22]} & \multirow{4}{*}{ \begin{tabular}{c} Simplify network structure and reduce training overhead of ML-based estimators for channel estimation in large-scale MIMO systems \\ \end{tabular}  } & A KDML-based channel denoising model is designed and the network structure is simplified by using domain knowledge & Limited to a small range of noise levels \\ \hline \multirow{4}{*}{[23]} & \multirow{4}{*}{ \begin{tabular}{c} Treat the fading channel as a two-dimensional image, and simulate channel interpolation and denoising with two networks respectively \\ \end{tabular}  } & Proposed ChannelNet, which outperforms the estimated MMSE method for a specific SNR value & The network must be retrained for each SNR value \\ \hline \multirow{4}{*}{[24]} & \multirow{4}{*}{\ \begin{tabular}{c} Improved denoising performance for real noise images in mmWave Massive MIMO systems \\ \end{tabular}  } & Proposed CBDNet, the asymmetric learning can adjust the estimated noise level map and interactively reduce the noise in the channel matrix & No consideration of the actual channel model and movement speed mismatch with training \\ \hline \multirow{4}{*}{Proposed} & \multirow{4}{*}{ \begin{tabular}{c} Improving the generalizability of channel estimation algorithms used for channel estimation under unknown noise levels \\ \end{tabular}  } & Proposed NDR-Net that can be used for channel estimation under unknown noise levels & Restricted to a limited variety of channel models \\ \cline{1-1} \cline{3-3}
# Cross-Domain Transfer Learning Prediction of COVID-19 Popular Topics Based on Knowledge Graph
## 1 Introduction  
Social networks have changed the form of information dissemination about public emergencies from \"limited reporting\" to \"nationwide communication\". User-generated content (UGC) has emerged in large numbers, making every citizen both the receiver and disseminator of information. In 2020, the outbreak of COVID-19 triggered the biggest public panic in human history. As the first platform to form and disseminate information related to emergencies, social networks have become the incubator and catalyst for mass panic, which has played a driving role in promoting information dissemination. However, this pattern makes a lot of information interweave, which leads to the problem of information overload. As human attention to information is limited, attention becomes a scarce resource [1]. In the process of information transmission, the distribution of popularity is uneven; most of the information has low popularity, and only a small portion of the information can maintain high popularity. In this case, what information will attract people's attention and how it changes over time become the focus of research [2]. To solve this problem, it is necessary to make full use of network text data and construct a scientific and effective prediction model to analyze the potential evolution of popular topics.  
While most previous studies only focused on information in a single network, social networks are interconnected, and the same event spread in different social networks hasrelevance and transitivity. For example, when an emergency breaks out on Weibo, it will drive the clicks of its related content on video websites, so cross-domain information association is needed. Based on this, this paper has two research questions:  
1. How can one measure the text information similarity of different social network platforms for knowledge transfer learning?
2. From the perspective of text information, how can one detect potential popular topics on social networks and predict popular topics?  
This paper adopts the transfer learning algorithm to predict the popular topics of emergencies in combination with information from other social network platforms to improve the accuracy of prediction. Based on the transfer learning results, this paper combines the knowledge graph with the emergency data to construct the public opinion topic graph, which will present the network structure of social network topics with semantic characteristics and then reach the goal of tracking the evolution of popular topics. The basic research framework of this paper is shown in Figure 1. The research results help government departments to guide practice, grasp the public opinion trends of emergencies, and realize monitoring analysis and early warning of public opinion so as to take scientific and effective measures to guide and control public opinion.
# Review of Studies on User Research Based on EEG and Eye Tracking
## 3 Indicators of User Research
### Preprocessing Methods and Device Connections  
Before analyzing EEG data, data preprocessing is required to ensure the accuracy and reliability of the analysis results. This mainly includes noise removal and artifact removal. Noise removal methods include baseline correction, high-pass filtering, low-pass filtering, and denoising filtering, which can solve the problem of data interference caused by noise such as eye movement, muscle activity, and power interference [42]. Due to the potential differences between electrodes, some artifacts may be generated, such as eye movement artifacts and ECG artifacts. Artifact removal methods include independent component analysis (ICA), average reference, and potential difference reference [16].  
The key to preprocessing eye-tracking data lies in eye movement correction and artifact removal, which effectively improve the quality and reliability of eye-tracking data, and provide strong support for the subsequent data analysis [43]. The commonly used methods for eye movement correction include linear interpolation and nonlinear interpolation, which can correct eye movement issues such as saccades and drifts [44]. The methods for artifact removal include screen brightness adjustment and glare correction. During  
Figure 6: Object perception paradigm.  
eye-tracking experiments, artifacts may be produced due to environmental light and screen glare, and these methods can effectively remove them [45].  
In EEG-ET combined experimental studies, it is necessary to connect the collection equipment, stimulus presentation equipment, behavior-recording equipment, and other hardware devices to ensure the stability and accuracy of the data. Before the experiment starts, the layout of the EEG electrodes and the position of the stimulus presentation device need to be designed to minimize signal transmission and interference between different hardware devices [46]. The electrode cap is worn on the subject's head, and there are two types of eye-tracking instruments, mobile and fixed, which are respectively worn on the eyes and fixed above the computer monitor used for target experiments. Typically, when conducting online electroencephalogram (EEG) signal acquisition, it is customary to utilize the bilateral mastoids (M1, M2) situated posteriorly to the ears as the reference electrodes for additional channels. The electrode dedicated to capturing horizontal eye movement signals (HEO) is positioned at a distance of 1.5 cm from the lateral aspect of the right eye, while the electrode for recording vertical eye movement signals (VEO) is carefully positioned on both the upper and lower eyelids of the left eye. A high-channel-count acquisition system is used for signal acquisition, and the electrode impedance is ideally maintained below 5 K\(\Omega\) throughout the experiment [47]. Task control is achieved through a synchronous platform to ultimately monitor and record data.
## 3 Simulation Results  
The simulation results of the proposed DL-based strategy are provided in this part for wireless MIMO communication systems with RIS assistance. The simulation work is performed with MATLAB and the Windows 10 Pro operating system. The DL layers are connected to create the DNN. The DL Toolbox(tm) creates the DL model and keeps track of the training procedure. The training performance is improved by using an NVIDIA graphics card. In the mmWave propagation environment, it is indicated that there are \(P_{1}=2\) and \(P_{2}=4\) channel paths, respectively, between the BS and RIS and between RIS and each UE. The RIS to UEs channel \(\mathbf{H}_{2,i}^{H}\) is tuned to the non-line-of-sight (LOC), while the BS to RIS channel \(\mathbf{H}_{br}\) is set to the LOC channel with 15 dB of Rician \(K\)-factor. The obstructions prevent access to the BS to UEs direct channel \(\mathbf{H}_{d}\). Additionally, this simulation takes into account a wireless system with RIS service in which transmit antennas are deployed in ULA configurations and RIS components are deployed in UPA configurations, with each transmit antenna and RIS element separated by a half-wavelength \((\lambda/2)\). The dimension of RIS elements is configured as \(N=32\times 16\). Table 1 presents the simulation settings for the proposed RIS communication system. In Sections 2.4.1 and 2.4.3, the training data generation and training and testing procedure are presented. The model training is implemented by setting the parameters listed in Table 2. The SNR value of 30 dB is configured while the training datasets _chi_ are being created. When evaluating the simulation's performance during the online testing phase, the [0:4:20] dB SNR range is taken into consideration. To calculate the channel error rate, 128 quadrature phase-shift keying (QPSK) symbols are produced using OFDM. A length of 32 cyclic prefix (CP) as a guard interval is introduced to lessen ISI. In this study, the simulation is run to assess the BER and SER analysis in comparison to the proposed DL model and earlier studies. The data received by the \(i\)-th UEs are taken into account at a time for the BER and SER calculation. The average error rate \((E_{R})\) for the \(i\)-th UEs is represented by the BER and SER. The data from the \(i\)-th UEs are first demodulated individually, then \((E_{R})=(UE1+UE2,...,+UE1)/T\) is used to determine the \((E_{R})\), where \(T\) is the number of the \(i\)-th UEs at the receiver terminal. The inaccurate demodulated bit at the receiver is referred to as the \((E_{R})\) for BER. \((E_{R})\) for  
Figure 7: The model training performance analysis: (**a**) the training accuracy; (**b**) the validation accuracy; (**c**) the training loss; (**d**) the validation loss.  
SER refers to the incorrect categorization of the received symbol. The effectiveness of the proposed DL model is compared with the LSTM model, Demod-CNN model [40], and TCN model [41], and traditional method in [40]. The effectiveness of the suggested CNN-GRU is improved by cascading with the GRU module with 1-D CNN and different parameter adjustments.
# Assessment of Earthquake Destructive Power to Structures Based on Machine Learning Methods
## 5 Convolutional Neural Network
### Results of CNN  
The training of the CNN adopts the EarlyStopping function [47]. The loss history of training and validation are shown in Figure 13. The results show that at the beginning of training, with the increase of epoch, the loss of training decreases rapidly. Subsequently, the loss of validation reaches the minimum (MAE = 0.0825) when the epoch is 75, and the program stopped training at the end of the 115 th epoch. The prediction based on the CNN model takes 0.762 s using a laptop platform (i5-4210H, RAM 8G, GTX 960M), which is over 650 times faster than that using the THA-based method (507.81 s).  
The test set is input into the optimal model, and the prediction result is shown in Figure 14 and Table 4. As evident in the result, the CNN model exhibits a good prediction accuracy, which outperforms the BPNN model (Figure 11). [PERSON] et al. [14] mentioned that conventional machine learning techniques (such as the BPNN used in this study) have limitations in processing raw forms of natural data. Therefore, build pattern recognition and machine learning system requires considerate and comprehensive engineering design of the feature extractor, which converts the raw data into a suitable internal representation or feature vector. When the feature extractor is not effective, the BPNN can learn very limited information. The CNN model, on the other hand, can extract more representative features of raw material through the training process, which can be used to solve more complex problems. Therefore, to further demonstrate the feature extraction process of the proposed CNN model, the visualization of extracted features in convolutional layers is discussed in the following section.  
Figure 12: The architecture of the adopted the CNN model.
## 5 Test Results with a Software Receiver  
The proposed dual antenna tracking loop structure was realized in a software GPS receiver. The performance of the assisted loop was evaluated and compared with an independent one. The dual antenna IF signals were collected from a hardware simulator by a two channel collector with a complex sampling frequency 5.0625 MHz and zero central frequency. The baseline between the two antennas is set to be 265.8718 m. Hereinto, the carrier noise ratio of the master antenna signal is set to be 48 dB-Hz, while that of the slave antenna signal varies along with the time from 48 dB-Hz to 18 dB-Hz. In the assisted loop structure, the master antenna signal was tracked by a 3 rd order loop with a bandwidth of 15 Hz. A 2 nd order loop with a bandwidth of 0.5 Hz was used to track slave antenna signal. The coherent integration time in the master and slave loops are separately 1 ms and 20 ms.  
Performance of the assisted carrier tracking loop is shown in Figure 13. In the experiment, two 3 rd order loops with bandwidths of 2 Hz and 0.5 Hz were used, respectively, to track the signals from the slave antenna independently. As shown in the figure, the independent loop with a bandwidth of 2 Hz could track the slave loop with some signal attenuation, which, however, couldn't be maintained in the weak signal environment. When the bandwidth of 0.5 Hz was used, in order to enable successful frequency pull-in of the slave PLL, a bandwidth of 2 Hz was used for the first 50 s. After the pull-in stage, the bandwidth was changed to 0.5 Hz. As depicted in the figure, the loop with the bandwidth of 0.5 Hz lost lock when the signal was strong enough. This indicates that the bandwidth was too narrow for carrier tracking likely due to clock phase noise in this case. In contrast, the proposed slave tracking loop can track signal when the _C_/_N\({}_{0}\)_ values decreases to 18 dB-Hz. The enlarged details show that the Doppler frequency of the slave loop is similar to that of the master loop, even at the _C_/_N\({}_{0}\)_ 18 dB-Hz, which means that the dynamic stress and the clock phase noise of the slave antenna signal are eliminated by the assistance from the master loop.  
Figure 13: Tracking performance comparisons in a software GPS receiver.
## 5 Conclusions  
This study aimed to develop models for detecting mown or grazed parcels using easily recorded routine data, showcasing variable detection performance based on the dataset and the unsupervised method employed. Predictions from the k-means technique outperformed those derived from the IF outputs. The highest validation performance, reaching 66%, was achieved by employing a PLS-DA with a dataset utilizing k-means and incorporating week_fft features. The study's approach aims to address the challenge of acquiring reference data by leveraging all data acquired in the validation process and employing unsupervised methods for model calibration. Utilizing a larger training dataset leads to increased variability, thus enhancing the model's robustness. Moreover, incorporating a proxy like the CSH, which considers multiple data sources from S1, S2, and meteorological data, reduces the models' dependency on external factors, a limitation often encountered when exclusively using remote sensing data. We suggest further refinement of the initial PLS-DA model by implementing an assessed gap-filling method and employing a larger validation set. These aspects, challenging to explore in depth in this study, are crucial steps before considering the widespread application of this algorithm in areas such as label marking, land-use changes, or drought assessment analyses.  
Conceptualization, K.D., C.N., A.T. and H.S.; methodology, K.D., C.N. and H.S.; software, K.D. and C.N.; validation, K.D. and C.N.; formal analysis, K.D., C.N., S.F. and Y.B.; investigation, L.D., F.L. and N.G.; resources, I.D., F.L. and N.G.; data curation, K.D. and C.N.; writing--original draft preparation, K.D. and C.N.; writing--review and editing, K.D., C.N. and H.S.; visualization, K.D. and C.N.; supervision, H.S.; project administration, H.S.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Service Public de Wallonie with the projects ROAD-STEP [grant number D31-1393/S1] and WALLeSmart [grant number D65-1435] and by the Luxembourg National Research Fund with the project SIMBA [grant number T.0221.19].  
Not applicable.  
Dataset available on request from the authors. The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.