esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Based on the phase equilibrium data of H\({}_{2}\)O and CO\({}_{2}\)([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2019), under normal pressure or vacuum conditions, the solubility of CO2 is small. However, when the pressure is much higher than atmospheric conditions (such as \(P>1\) MPa), the solubility of CO\({}_{2}\) significantly increases, which may cause some obvious effects on condensation. Studies that focus on these various areas are rarely conducted. Thus, the effect of low CO\({}_{2}\) solubility on mixture condensation heat transfer is unclear and worth studying in order to serve related engineering applications ([PERSON] et al., 2016; [PERSON] et al., 2018; [PERSON] et al., 2019). Therefore, in this paper, boundary layer analysis was carried out specifically for the condensation heat transfer characteristics of steam-CO\({}_{2}\) mixtures in a larger range of parameters that fit engineering application conditions. Using the laminar boundary layer model of [PERSON] et al. (2014, 2015) and [PERSON] et al. (2019), the mixture condensation boundary layer model based on CO\({}_{2}\) solubility was further implemented for both horizontal and vertical plates. The condensation heat transfer of steam-CO\({}_{2}\) and steam-air mixtures was simulated under normal pressure conditions at the same mass and mole fraction, respectively, for data comparison purposes. Moreover, in the total pressure range of 1 atm-10 MPa, which aims to cover the highest pressure range that is needed for the engineering applications mentioned above ([PERSON] et al., 2016; [PERSON] et al., 2018; [PERSON] et al., 2019), the impact of low CO\({}_{2}\) solubility on the condensation heat transfer of steam-CO\({}_{2}\) mixtures was analyzed. Through simple laminar boundary layer analysis, the heat and mass transfer characteristics of steam-CO\({}_{2}\) mixture condensation can be preliminarily studied, which acts as the basis for the turbulent condensation heat transfer study of a steam-CO\({}_{2}\) mixture and can also offer some reference for the design of CO\({}_{2}\) separation and capture equipment.
# Numerical Analysis of Ultrasonic Multiple Scattering for Fine Dust Number Density Estimation
## 3 Number Density Estimation Approach Based on Independent Scattering Approximation  
An overview of the proposed fine dust number density estimation technique is presented in Figure 2. First, ensemble averaging is carried out over multiple realizations to obtain coherent wave responses \(\left\langle\text{s}(x,t)\right\rangle,\)  
\[\left\langle\text{s}(x_{i},t)\right\rangle=\frac{1}{M}\sum_{m=1}^{M}\text{s} _{m}(x_{i},t), \tag{9}\]  
where \(M\) is the number of realizations, \(\text{s}_{m}(x_{i},t)\) is the \(m\)th scanning measurement data, and \(x_{i}\) is the \(i\)th sensor position at each realization. The coherent wave responses are subsequently converted from the time domain to the frequency domain using the Fourier transform below  
\[\left\langle\text{S}(x_{i},f)\right\rangle=\int_{-\infty}^{\infty}\langle \text{s}(x_{i},t)\rangle\,e^{-j2\pi ft}dt, \tag{10}\]  
where \(\left\langle\text{S}(x_{i},f)\right\rangle\) is the converted frequency-domain coherent wave response data. Here, the magnitude of the frequency-domain data \(\text{A}(x_{i},f)\) can be represented as  
\[\text{A}(x_{i},f)=A_{0}(f)e^{-\alpha(f)(x_{i}-x_{0})}, \tag{11}\]  
where \(A_{0}(f)\) is the coherent wave magnitude at the first sensor position \(x_{0}\). The coherent wave attenuation \(\alpha(f)\) is then computed by seeking the slope of the least-squares fit of \(\ln(A(x_{i},\ \omega))\), where  
\[\ln(A(x_{i},\ f))=\ln(A_{0}(f))-\alpha(f)(x_{i}-x_{0}). \tag{12}\]Next, the total scattering cross-section \(\sigma_{t}\) for one fine dust particle is analytically computed. In this study, we use \(\sigma_{t}\) for the rigid cylindrical cavity seen in [24] to compute the number density of fine dust in the air, given by  
\[\sigma_{t}=4a\frac{1}{ka}\left[\left|\frac{J_{0}^{\prime}(ka)}{H_{0}^{(2)}(ka)} \right|^{2}+2\sum\left|\frac{J_{1}^{\prime}(ka)}{H_{0}^{(2)}(ka)}\right|^{2} \right], \tag{13}\]  
where \(a\) is the radius of the cylindrical cavity, and \(J_{0}\) and \(H_{0}^{(2)}\) are the Bessel function of the first kind and Henkel function of the second kind, respectively. Note that an additional elastic contribution term exists in \(\sigma_{t}\) when the scatterer is an elastic solid [25]. However, this term is negligibly small in the frequency range (\(ka\leq 1\)) that is primarily used in this study; for example, \(ka\) is approximately 0.45 at the frequency of 5 MHz, which is employed in this study. Therefore, we use the rigid contribution of \(\sigma_{t}\) displayed in Equation (13) and neglect the elastic contribution when computing \(\sigma_{t}\) of a fine dust particle. Finally, the fine dust number density \(n\) is obtained based on the ISA described in the previous section, given by  
\[n(f)=\frac{\alpha(f)}{\sigma_{t}(f)}. \tag{14}\]  
Note that the obtained \(n\) can vary for each frequency, and it is represented by taking an average value of the \(n\) values across the excitation frequency band.
## 3 Results and Discussion
### Practical Applications
Moreover, values \(-20\) kJ/mol \(>\Delta\text{G}^{0}>-80\) kJ/mol indicate the transition from the physisorption process to the chemisorption. As for the DC mechanism, the predicted equilibrium stage concentrations, qe, were higher than the initial concentration in both cases of 5.5 ppm and 10.65 ppm. Thus, the Gibbs free energies were calculated only for 20.70 ppm, 40.00 ppm, and 53.00 ppm, as the initial concentration and were found to be \(-22.2\) kJ/mol, \(-20.00\) kJ/mol and \(-18.6\) kJ/mol, respectively. According to both the PSO and DC mechanism and the calculated \(\Delta\text{G}^{0}\) values, more than one adsorption mechanism was the main mechanism during our experiments, i.e., physisorption, chemisorption, ion exchange, surface complexion and redox reaction [20; 21; 51]. To investigate if a redox reaction occurs simultaneously with the physical adsorption, diffusion and chemisorption process, a titration procedure of the liquid solution was carried out with KMnO\({}_{4}\) at the end of the adsorption process, as described in the experimental section [21]. APC eliminates 100% of the Cr(VI) from the liquid solution (5.8 ppm) after a 24 h reaction at pH = 3. Moreover, Cr(III) was raised in the liquid solution exhibiting a concentration of 23%. This fact evidences that apart from the physisorption, bonding, transient adsorption and chemisorption processes, a redox reaction is also taking place simultaneously in the pore surface. Interestingly, the sample after the adsorption process contained a low intensity Cr2p peak in the region of chromium oxides, which was deconvoluted in two components with same area percentage (Figure 13). The peak at 577.3 eV was attributed to Cr(III) oxide and the one at 579.3 eV to Cr(VI) oxide [52; 53]. This implies that chromium interacts with the carbon surface through both the ion exchange and redox reaction mechanisms. The latter was simultaneously accompanied by a roughly 10% increase of the C=O and O=C=O groups in the chromium-containing solid as a result of the carbon oxidation by the Cr(VI) species. \begin{table}
\begin{tabular}{c c c} \hline
**APC-C\({}_{\text{init}}\) (ppm)** & **qe (mg\({}_{\text{Cr(VI)}}\)/g\({}_{\text{AC}}\)) (PSO)** & **qe (mg\({}_{\text{Cr(VI)}}\)/g\({}_{\text{AC}}\)) (DC)** \\ \hline
5.50 & 31.8 & 35.8 \\
10.65 & 53.5 & 66.8 \\
20.70 & 59.8 & 74.1 \\
40.00 & 71.6 & 93.2 \\
53.00 & 69.8 & 85.3 \\ \hline \end{tabular}
\end{table} 
Table 4: Predicted Cr(VI) APC adsorption capacity for Cr(VI) at the equilibrium stage for pH = 3 and T = 23 \({}^{\circ}\)C.
# Efficient Security Scheme for Disaster Surveillance UAV Communication Networks
## 4 Preliminaries of the Security Protocol  
### The Ring-LWE Encryption Scheme  
The Ring-LWE based encryption scheme is a lattice-based crypto-system that exploits the learning with errors (LWE) problem, which is to distinguish random linear equations that have been perturbed by a small amount of noise from truly uniform ones. The LWE problem has been proven to be as hard as worst-case lattice problems and are considered to be secure against post-quantum attacks [28; 29].
# An Intelligent Ensemble-Based Detection of In-Vehicle Network Intrusion
## 4 Proposed Scheme
### Ensemble Classifiers Learning  
Ensemble learning uses techniques that allow different ML algorithms to work together on a single prediction to enhance accuracy. It has the potential to outperform any single algorithm. Ensemble learning for classification is several models that are trained to solve a classification problem. Models' predictions are combined to make a final prediction. Using ensemble learning, the performance of a single classifier can be improved by reducing overfitting, making the classifier more robust, and improving its ability to generalize. In the ensemble modeling phase, we utilized three ensemble techniques: stacking, bagging, and voting. Ensemble learning with additional cross-validation provides reliable performance estimates and can improve generalization by using varied model predictions.  
Stacking is an ensemble learning technique. The purpose of stacking is to achieve a more accurate final model by combining the strengths of multiple base models. It builds its models in two stages. These stages are called base learner and meta-learner. Stacking runs the single learners (base-learners) and combines them by training the meta-learner to make the final predictions using the predictions generated by the base-learners. The meta-classifier learns how to best combine the predictions of the base learners [63]. Any learning algorithm can be employed as a single learner or meta-learner. In our model, we used the RF, DT, and XGBoost models as base models and the XGBoost model as a meta model.  
Bagging represents a parallel ensemble method. In this method, parallel learning produces single learners. Bagging generates many dataset subsets (bags) by bootstrapping. The final prediction results are determined by averaging all predictions for subsets. To improve performance, the bagging classifier reduces the variance of the base classifiers. Its main objective is to enhance the precision and stability of supervised models [64; 65]. To reduce noise and avoid overfitting, we combined the bagging classifier with the XGBoost model.  
A voting ensemble method is a technique for combining the predictions of multiple models to enhance the system's overall performance. Different models can make different types of errors, and by combining their predictions, the ensemble can reduce errors overall [66]. The steps and evaluation of our proposed IDS scheme are depicted in Figure 7.
# Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1
## 2 Theory and Method
### Proposed Identification Approach  
As illustrated in Figure 4, the differential power values of \(\overline{\text{DDM}_{D}}(\tau,f)\) between the same reflected surfaces (Figure 4a,c) basically range from \(-0.2\) to \(0.3\), while those between different reflected surfaces (Figure 4b,d) have a much wider range and show horseshoe shape. This significant difference provides an opportunity to distinguish dDDMs of different reflected surfaces from those of the same reflected surfaces. Moreover, dDDMs of water-ice transition show a very different power range from those of ice-water transition. This characteristic can be used to distinguish water-ice from ice-water transition. The dDDMs of water-water transition also show different spreading characteristics from those of ice-ice transition. The degree of difference between two adjacent DDMs reflected from ice field is quite small as the surface is suppressed by ice, while dDDMs of water-water transition show larger spreading characteristics than those of ice-ice transition due to the effect of sea surface. The different characteristics can be used to distinguish water-water from ice-ice transition as reflected signals are more sensitive to surface of open water than that of ice covered area. Unfortunately, it seems difficult to build the relationship between the characteristics of dDDMs and the roughness of reflected surface due to the nature of the TDS-1 data (without calibration) and the characteristics of the GPS-R instrument onboard the TDS-1 satellite was not optimized for altimetry purposes. Four scenarios for one dataset which contains a certain number of consecutive DDMs are shown in Figure 5.
## 3 Experiments
### Transfer Results
#### 3.2.4 Results of Transfer Tasks at JNU  
In order to demonstrate the generalization performance of RASAN-W on different datasets, this paper simultaneously implements four transfer tasks on the Jiangnan University dataset. Similar to the transfer task on the Western Reserve University dataset, five methods were used to compare with the RASAN-W proposed in this paper, and the average accuracies of bearing fault diagnosis are listed in Table 4. On the JNU dataset, ResNet also exhibits the worst bearing fault classification accuracy, and the average accuracy of the transfer task is 92.7%; meanwhile, the domain-adaptive-based method diagnosis accuracies are all over 92.7%, and the RASAN-W proposed in this paper still maintains the highest accuracy (96.6%), which proves that the proposed method in this paper has the optimal transfer ability and strong robust performance on different datasets. Compared with DDC (92.8%) and DANN (92.7%), DSAN (92.8%), DASAN (95.2%), and RASAN-W (96.6%), which consider category information, still achieve better results, and the necessity of considering category information in the domain adaptation process was verified on different datasets.  
The visualization results of the JNU dataset transfer task are shown in Figure 7. Comparing the visualization results in Figure 7f and Figure 7a-e, the RASAN-W proposed in this paper has the best clustering effect of features of samples of the same category and  
Figure 5: The visualization of the learned features on the CWRU dataset. (**a**) ResNet; (**b**) DDC; (**c**) DANN; (**d**) DSAN; (**e**) DASAN; (**f**) RASAN-W.  
separation effect of features of samples of different categories. It enables the method to effectively migrate knowledge to the target domain through the source domain knowledge and accurately diagnose the health status of the bearing. Meanwhile, the confusion matrix of task (\(J_{0}\to J_{2}\) ) on the JNU dataset is shown in Figure 6b.  
Figure 6: Confusion matrices of the RASAN-W on the CWRU dataset (**a**) and the JNU dataset (**b**).  
Figure 7: The visualization of the learned features on the JNU dataset. (**a**) ResNet; (**b**) DDC; (**c**) DANN; (**d**) DSAN; (**e**) DASAN; (**f**) RASAN-W.
# GAN-Based Image Dehazing for Intelligent Weld Shape Classification and Tracing Using Deep Learning
## 2 Methods
### Dehazing Techniques for Weld Fume Removal  
Hazy images, in general, affect numerous tasks such as tracking [16], satellite remote sensing [17], and object detection [18; 19] due to color distortion, blurring and other visible quality degradation. Because of unequal haze distribution, many research papers have been published on deep learning-based dehazing approaches. Thus, to address these issues, discrete wavelet transform-based GAN (DW-GAN) [20] has been implemented to remove non-homogeneous weld fumes because dense welding fumes affect the path to be traced while welding. The GAN-based technique is compared with the conventional dark channel prior (DCP) method [21].
# A Nonbinary LDPC-Coded Probabilistic Shaping Scheme for a Rayleigh Fading Channel
## 4 Results  
We evaluate the performance gain of the proposed NB LDPC-coded PS scheme in Rayleigh fading channel for 16 QAM, 64 QAM and 256 QAM by Monte-Carlo simulations. The simulated software platform is Microsoft Visual Studio 2010. The frame error rate (FER) performance of the end-to-end communication is considered. The NB LDPC codes over GF(16) have an average variable node degree \(d_{v}=2.4\), while the NB LDPC codes over GF(64) and GF(256) have a regular variable node degree of \(d_{v}=2\). The LDPC-coded symbol lengths of GF(16), GF(64) and GF(256) are 3000, 2100 and 1500 symbols, respectively. For a fair comparison, the binary LDPC code lengths are 12,000, 12,600 and 12,000 bits in 16 QAM, 64 QAM and 256 QAM, respectively. The binary LDPC codes have a regular variable node degree \(d_{v}=3\). The LDPC code rate is set to 4/5 for the uniform QAM with no rotation system and the uniform-rotated QAM system, while the LDPC code rate is set to 5/6 for the PS QAM with no rotation system and the PS-rotated QAM system. The total code rate of the system is set to 4/5. The Log-FFT-BP decoding algorithm is considered for NB LDPC codes, while the Log-BP decoding algorithm is considered for binary LDPC codes. The maximum decoding iteration number of NB LDPC codes and binary LDPC codes are both set to 30. The unequal probabilistic amplitudes yield M-B distributions.  
Figure 8 shows the FER performance comparisons of 16 QAM for Rayleigh fading channel with SE at 3.2 bits/s/Hz. Compared with the uniform GF(2) LDPC-coded 16 QAM with no rotation system, the proposed PS GF(2) LDPC-coded 16 QAM with a 16.8-degree rotation system can obtain a 0.65 dB performance gain at FER\(=10^{-3}\). Compared with the uniform GF(16) LDPC-coded 16 QAM with no rotation system, the proposed PS GF(16) LDPC-coded 16 QAM with a 16.8-degree rotation system can obtain a 0.70 dB performance gain at FER\(=10^{-3}\). Compared with the uniform GF(2) LDPC-coded 16 QAM with no rotation system, the proposed PS GF(16) LDPC-coded 16 QAM with a 16.8-degree rotation system can obtain a 1.17 dB performance gain at FER\(=10^{-3}\).  
Figure 9 shows the FER performance comparisons of 64 QAM for Rayleigh fading channel with SE at 4.8 bits/s/Hz. Compared with the uniform GF(2) LDPC-coded 64 QAM with no rotation system, the proposed PS GF(2) LDPC-coded 64 QAM with a 8.6-degree rotation system and the proposed PS GF(2) LDPC-coded 64 QAM with the optimal 12.6-degree rotation system can obtain 0.60 and 0.70 dB performance gains at FER\(=10^{-3}\), respectively. Compared with the uniform GF(64) LDPC-coded 64 QAM with no rotation system, the proposed PS GF(64) LDPC-coded 64 QAM with the optimal 12.6-degree rotation system can obtain a 0.51 dB performance gain at FER\(=10^{-3}\). Compared with the uniform GF(2) LDPC-coded 64 QAM with no rotation system, the proposed PS GF(64) LDPC-coded 64 QAM with no rotation system, the proposed PS GF(64) LDPC-coded 64 QAM with the optimal 12.6-degree rotation system can obtain a 1.31 dB performance gain at FER\(=10^{-3}\).  
Figure 7: 256 QAM AMI for Rayleigh fading channel.
## 7 Conclusions and Future Work  
In this paper, we analyzed the performance of different transformer models on two novel datasets, called ReDDIT and PolyHope, in terms of regret detection from Reddit posts and multi-class hope speech detection from tweets. For regret detection, we compared the averaged macro scores of the transformer models to the previous state-of-the-art results. We found that the transformer models outperformed the previous approaches. Specifically, the roberta-based model achieved the highest averaged macro F1-score of 0.83, beating the previous state-of-the-art score of 0.76. For the task of hope speech detection, the bert-based, uncased model achieved the highest averaged macro F1-score of 0.72 among the transformer models. In addition, we found that the specific performance of each model varied slightly depending on the task and dataset.  
Overall, our findings demonstrate the effectiveness of the use of context in transformer models for text classification tasks. The inclusion of the post titles in the regret detection task significantly improves the performance of the transformer models. The task's complexity, the dataset's size, and the specific transformer architecture and pre-training all influenced the performance of the models on both tasks. In future work, we would like to explore the mentioned limitations for these tasks.  
Conceptualization, G.S. and F.B.; Methodology, G.S., F.B., S.B. and A.G.; Software, A.G.; Validation, G.S. and A.G.; Formal analysis, F.B. and S.B.; Investigation, G.S., F.B., S.B. and A.G.; Resources, G.S. and F.B.; Data curation, G.S. and F.B.; Writing--original draft, F.B. and S.B.; Writing--review & editing, G.S. and A.G.; Visualization, A.G.; Supervision, G.S. and A.G.; Project administration, G.S., F.B. and S.B.; Funding acquisition, G.S. and A.G. and G.S.; All authors have read and agreed to the published version of the manuscript.  
The work was done with partial support from the Mexican Government through the grant A1-5-47854 of CONACYT, Mexico, grants 20220852 and 20220859 of the Secretaria de Investigacion y Posgrado of the Instituto Politecnico Nacional, Mexico. The authors thank the CONACYT for thecomputing resources brought to them through the Plataforma de Aprendizaje Profundo para Tecnologias del Lenguaje of the Laboratorio de Supercomputo of the INAOE, Mexico and acknowledge the support of Microsoft through the Microsoft Latin America PhD Award.  
**Institutional Review Board Statement:** Not applicable  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The dataset and the script used in this paper will be available on a request from corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Safety and Security Considerations for Online Laboratory Management Systems
## 3 Literature Review
### Computer Safety and Security Evolution
#### 3.2.2 Safety  
Safety and security have evolved into different disciplines for several years, even when they have shown a close relationship and shared numerous common traits. In 1989, [PERSON] [31], in his paper \"Safe and Secure Computer Systems\", suggested that reliability, safety, and security hold a significant meaning and importance beyond mere semantic distinctions within their respective domains. However, there are also valuable similarities between them that can be beneficial to explore and utilize. In addition, in the early 90s, [PERSON] [32] and [PERSON] [21] also shared similar concepts. However, they started to emphasize the interrelation between both concepts. [PERSON] et al., in their paper \"Cross-fertilization between safety and security engineering\" [33], discuss the methods, models, tools, and techniques developed in the field of safety engineering and their application in security engineering and vice versa. Later, [PERSON] et al. [34], in their paper \"Safety and Security in Federated Remote Labs--A Requirement Analysis\", expressed the need to look jointly at safety and security because separate research efforts can be time-consuming and very expensive. They also highlighted that the topic of safety for security in the context of cyber-physical systems is currently an area of research.  
The safety of a system is the probability that it will either perform its intended function correctly or cease its processes in a manner that does not disrupt the overall functioning of the system or put at risk the well-being of individuals associated with it [35].  
The security of a computer system is the capability to defend against unauthorized access, intentional disruptions, or attacks directed at its assets, such as data, hardware, and software. This safety concept operates based on aggressive functions initiated by individuals, also known as attackers, who want personal benefits through illegal activities. Security is typically characterized by fundamental aspects of the CIA triad, which consist of confidentiality (the computing system's capacity to safeguard system assets by blocking unauthorized access, ensuring that information remains undisclosed to unapproved parties), integrity (the computer system's capability to protect data and other assets from unauthorized tampering, deletion, or destruction), and availability (the system's ability to provide uninterrupted services to authorized users, even when faced with potential attacks) [32].