esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Research on hydrate dissociation mechanisms is critical to solving the issue of hydrate blockage and developing hydrate slurry transportation technology. Thus, in this paper, natural gas hydrate slurry decomposition experiments were investigated on a high-pressure hydrate experimental loop, which was equipped with two on-line particle analyzers: focused beam reflectance measurement (FBRM) and particle video microscope (PVM). First, it was observed from the PVM that different hydrate particles did not dissociate at the same time in the system, which indicated that the probability of hydrate particle dissociation depended on the particle's shape and size. Meanwhile, data from FBRM presented a periodic oscillating trend of the particle/droplet numbers and chord length during the hydrate slurry dissociation, which further demonstrated these micro hydrate particles/droplets were in a dynamic coupling process of breakage and agglomeration under the action of flow shear during the hydrate slurry dissociation. Then, the influences of flow rate, pressure, water-cut, and additive dosage on the particles chord length distribution during the hydrate decomposition were summarized. Moreover, two kinds of particle chord length treatment methods (the average un-weighted and squared-weighted) were utilized to analyze these data onto hydrate particles' chord length distribution. Finally, based on the above experimental data analysis, some important conclusions were obtained. The agglomeration of particles/droplets was easier under low flow rate during hydrate slurry dissociation, while high flow rate could restrain agglomeration effectively. The particle/droplet agglomerating trend and plug probability went up with the water-cut in the process of hydrate slurry decomposition. In addition, anti-agglomerates (AA) greatly prohibited those micro-particles/droplets from agglomeration during decomposition, resulting in relatively stable mean and square weighting chord length curves.  
natural gas hydrate; decomposition mechanism; microscopic particles agglomeration; chord length distribution +
Footnote †: journal: Applied Sciences
## 4 Experimental Simulations
### Experimental Results and Analysis
#### 4.3.7 Performance Analysis and Comparison Experiments  
Figure 10 gives a graph of the classification accuracy and loss value of the intrusion detection model CNN-GRU with the number of iteration steps. From the figure, it can be seen that the model in this paper achieves a good convergence effect.  
In order to further verify the effectiveness of the intrusion detection model proposed in this paper, a performance comparison experiment is set up in this section: under the same experimental conditions, common machine learning methods such as random forest, K-mean clustering, decision tree and other recently proposed intrusion detection models are applied to the dataset, and the performance comparison is shown in Table 13. The bold text indicates the evaluation indicators of the proposed model.  
Compared to machine learning algorithms, this model learns features through neural networks, which can combine low-level features to form a more abstract and non-linear high-level representation, and then explore the input-output relationship between data, effectively improving the accuracy of intrusion detection. Compared to the S-ResNet, CNN, CNN-GRU, CNN-LSTM, CNN-BiLSTM and CNN-GRU-attention models, the intrusion detection model CNN-GRU proposed in this paper reduces the effects of feature redundancy and class imbalance on the one hand, and can extract both spatial and temporal features of the data on the other hand, so that the extracted feature information is more comprehensive and thus achieves better results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset** **(Train/Test)**} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Evaluation Indicators**} \\ \cline{3-6}  & & & **Train/s** & **Prediction/s** & **Total Time/s** & **Accuracy** \\ \hline  & Before RFP & 739.6 & 9.51 & 749.11 & 98.71 \\  & Before ADRDB & 617.2 & 9.53 & 626.73 & 98.59 \\  & CNN & 633.2 & 7.31 & 640.51 & 98.22 \\  (125973/22544) & GRU & 272.4 & 4.89 & 277.29 & 98.67 \\  & Proposed Model & **648.4** & **8.75** & **657.15** & **99.69** \\  & Before RFP & 1553.6 & 25.03 & 1578.6 & 85.69 \\  & Before ADRDB & 1259 & 28.74 & 1287.7 & 85.56 \\ UNSW\_NB15 & CNN & 1118 & 18.11 & 1136.1 & 84.51 \\ (175341/82332) & GRU & 785.6 & 5.63 & 791.23 & 83.69 \\  & Proposed Model & **1423.6** & **30.87** & **1454.47** & **86.25** \\  & Before RFP & 23450.4 & 286.35 & 23736.75 & 98.73 \\  & Before ADRDB & 21548.4 & 268.39 & 21816.79 & 98.82 \\ CIC-IDS2017 & CNN & 16500 & 185.75 & 16685.75 & 92.94 \\ (1654737/709173) & GRU & 4146.4 & 52.8 & 4199.2 & 98.15 \\  & Proposed Model & **21,200.2** & **275.67** & **21475.9** & **99.65** \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison of run-time performance.  
Figure 10: Accuracy and loss value curves with iteration steps: (**a**) Accuracy, loss value with the number of iteration steps curve of the NSL-KDD dataset; (**b**) Accuracy, loss value with the number of iteration steps curve of the UNSW_NB15 dataset; (**c**) Accuracy, loss value with the number of iteration steps curve of the CIC-IDS2017 dataset.
# Improved Video Anomaly Detection with Dual Generators and Channel Attention
## 3 Method
### Components
#### 3.2.1 Noise Generator  
The noise generator (Figure 3) exists only in the training phase of our method and consists of a generator, two noise modules, and a second-order channel attention module.  
The generator network in this process is constructed using an autoencoder, which consists of both an encoder and a decoder. When the training frame is input into the generator, it passes through the encoder, made up of pooling layers, convolution layers, and activation functions, which repeatedly halves the size and doubles the channels. Eventually, the encoder produces latent features that are fed into the second-order channel attention module to improve the interdependence of the features, enrich the information correlation, and produce features that reflect both global and local aspects. The decoder then reconstructs the latent features, producing a high-quality reconstructed frame that is the same size as the original input. The reconstructed frame is then compared to the real frame by the discriminator, which judges whether the reconstructed frame is similar enough to the real frame to be considered real.  
To generate pseudo-abnormal video frames, the generator network has been augmented with two noise modules, which consist of a three-layer fully-connected autoencoder. Random noise is input into the noise module, which processes the noise through cubic convolution, batch normalization, and activation functions to produce noise that can be used to generate fake video frames. The generator network and noise module are then combined, with a skip connection added between the first convolution pooling of the encoder and the last upsampling of the decoder, and the noise module A added. Additionally, noise module B is added after obtaining the latent features, and the latent features are reconstructed  
Figure 3: Illustration of the noise generator. The generator undergoes two training iterations. The first time is to train the generator without the noise module, and the second time is to train the noise generator with the noise module added. The noise generator is mainly used in the training phase to generate pseudo-anomaly frames, but not in the testing phase.  
with the added noise to produce pseudo-abnormal frames. Both noise modules have the same structure (as shown in Figure 4), but the input feature dimensions differ. Finally, the pseudo-abnormal frame and real frame are input into the discriminator, and the generator and discriminator compete to maximize the distance between the two frames.
# Metal Release and Cell Viability of 316L Stainless Steel Sputter-Coated with N-Doped a-C:H Coatings
## 2 Materials and Methods
### Metal Release in Artificial Saliva  
Static corrosion tests were performed by immersing the samples in an acidic (pH = 2.3) modified Fusayama-Meyer artificial saliva bath in sterile conditions for 30 days at, 37 \({}^{\circ}\)C, by adapting the ISO 10271 protocol [16]. A surface-to-area ratio of 1 cm\({}^{2}\)/10 mL was preserved [17; 18]. The chemical composition of the artificial saliva was the following: KCl--0.4 g/L; NaCl--0.4 g/L; CaCl\({}_{2}\) \(\upmu\)2H\({}_{2}\)O--0.906 g/L; NaH\({}_{2}\)PO\({}_{4}\) \(\upmu\)2H\({}_{2}\)O--0.69 g/L; Na\({}_{2}\)S \(\bullet\) 9H\({}_{2}\)O--0.005 g/L; and HCl (1M)--until pH = 2.3. Samples were previously sterilized by UV radiation (30 min per side), whereas the artificial saliva was autoclaved (Uniclave 77, AJC, Caem, Portugal). At the end of the immersion test, the solutions--hereby termed \"corrosion extracts\"--were retrieved for further analysis. The metallic elements (Ni, Cr, and Fe) in solution were quantified, in triplicate, by Inductively Coupled Plasma--Optical Emission Spectroscopy (ICP-OES) in a Horiba Jobin-Yvon Ultima apparatus (Edison, NJ, USA). The average metal release rates for each element, per day and per surface area, were then calculated. The ICP-OES detection limits for Cr, Fe, and Ni were 1.18, 1.48, and 3.73 \(\upmu\)g/L, respectively.
# Speech Emotion Recognition: Comparative Analysis of CNN-LSTM and Attention-Enhanced CNN-LSTM Models
## 3 Methodology
### Pre-Processing  
This subsection outlines the pre-processing steps to prepare RAVDESS audio data for SER models. Neural Networks yield efficient results in processing images, and CNN algorithms form the backbone of this research. While RAVDESS contains audio files, processing these audio files into images was mandatory to leverage CNN capabilities. Prior to giving the data directly to the models, the dataset is initially transformed into a form suitable for model processing. Pre-processing is a very important step because it transforms the data into a suitable format. Each audio signal is processed to create an image representation called the Mel spectrogram using mathematical transformations. To mitigate noise from multimodal data (e.g., background sounds or actor variability), audio was standardized to a 16 kHz sample rate, and silent segments were trimmed. First, the audio signal is divided into short overlapping segments using a process called windowing with a 25 ms window and 10 ms overlap. Each segment undergoes a Fourier Transform to convert it into the frequency domain, where it is represented as a spectrum of frequencies.  
Figure 1: RAVDESS dataset.  
These spectra are then transformed into a Mel scale, which approximates how humans perceive pitch. This transformation helps to emphasize perceptually relevant features in the audio. The resulting Mel spectrogram is essentially a 2D image, where time is represented on the horizontal axis and frequency on the vertical axis. Additionally, color intensities or brightness in the image correspond to the magnitude of each frequency component. Figure 2 shows a Mel spectrogram sample generated after preprocessing.  
**Converting Audio to Mel Spectrograms** The mathematics of converting audio into spectrograms is mentioned below:  
\[x(n)=\sum_{m=0}^{M-1}w(m)\cdot s(n+m) \tag{1}\]  
where \(x(n)\) is the windowed signal, \(w(m)\) is the window function, and \(s(n+m)\) is the original signal.  
Each windowed segment undergoes a Fourier Transform to convert it into the frequency domain:  
\[X(k)=\sum_{n=0}^{N-1}x(n)\cdot e^{-j\frac{2\pi}{N}kn} \tag{2}\]  
where \(X(k)\) is the frequency spectrum of the segment.  
The frequencies are then mapped onto the Mel scale using the following formula:  
\[M(f)=2595\log_{10}\left(1+\frac{f}{700}\right) \tag{3}\]  
The pre-processing phase optimizes the data for SER models by adjusting sample rates and employing noise modulation techniques (detailed in Section 3.3) to ensure the proposed models are exposed to a realistic and challenging array of audio inputs, enhancing their ability to generalize and accurately identify emotions in varied conditions, such as noisy real-world environments.
# Fluid Dynamics in a Continuous Pump-Mixer
## 3 Results
### Experimental Results
#### 3.1.3 Influences on Local Holdup  
Figure 7a depicts the local holdup, \(\Phi\), at the measurement position \(MP_{H}\) for \(\varphi_{F}\) = 20 vol.-\(\%\). It can be seen that all holdup measurements showed a linear relation for different impeller speeds. The local holdup, \(\Phi\), was between 4 and 12 and is clearly lower than the feed phase ratio \(\varphi_{F}\), indicating a significantly shorter residence time of the disperse phase in the pump-mixer than the continuous phase. This effect decreased with increasing flow rate, which was also observed by [PERSON] et al. [16]. We can assume that this is less pronounced with increasing impeller speed, but the relationship between flow rate and impeller speed must be considered. As the flow rate depends on impeller speed, this behavior cannot be clearly isolated. We can assume the effect is based on the flow rate and the mean residence time rather than on the impeller speed because identical flow rates lead to equal local holdup independent of the impeller speed. The general trend of lower local holdups with higher mean residence time was also found by [PERSON] et al. [6]. Additionally, they found a holdup larger than the feed phase ratio. This was the opposite in our work, which can be explained by the comparably small mean residence times (\(\overline{\tau}\) = 0.5 to 2 min) in the work of [PERSON] et al. [6] compared with the ones in our work (\(\overline{\tau}\approx\) 8 min to 33 min).  
The influence of the feed phase ratio \(\varphi_{F}\) at an impeller speed of \(N\) = 425 rpm on the local holdup is exemplarily depicted in Figure 7b. It shows a significant increase of the local holdup with increased feed phase ratio. It can be seen that the holdup was about 5 to 10 vol.-\(\%\) smaller than the feed phase ratio, which led to less slip between \(\varphi_{F}\) and \(\Phi\) when \(\varphi_{F}\) was increased. The effect of increased local holdup with higher flow rates can be seen in all measurement points. One exception was found for the measurements at \(N\) = 375 rpm and \(\varphi_{F}\) = 30 vol.-\(\%\), where the local holdup \(\Phi\approx\) 8-14 and the gap between feed phase ratio and local holdup was significantly larger. All measured holdup data are listed in the supplementary material in Table S2 and Figure S1.  
Figure 6: (**a**) Measured DSDs at different positions \(MP_{1-3}\) for \(N\) = 400 rpm and \(\varphi_{F}\) = 10 vol.-\(\%\); (**b**) Sauter mean diameter for the feed phase ratios \(\varphi_{F}\) = 10 and 20 vol.-\(\%\) at \(N\) = 425 rpm. A measurement at the upper location \(MP_{2}\) with a feed phase ratio \(\varphi_{F}\) = 20 vol.-\(\%\) was not possible due to droplet adhesion at the probe windows.
## 4 System Model
### Delay Models  
As we know, any network's delay is an important characteristic to keep in mind to reduce the delay. Two delay models are discussed in this research.  
1. MNs to MNs Delay.
2. MNs to GNs Delay.
# The Development of an OpenAI-Based Solution for Decision-Making
## 4 Results
### Fairness Evaluation (AI Decisions vs. Human Decisions)  
To evaluate the AI component's fairness, we conducted simulations focusing on demographic parity and equalized odds criteria. Demographic parity (DP), also known as statistical parity, requires that the AI system's decision outcomes are independent of sensitive attributes--i.e., protected and unprotected groups have equal probabilities of receiving a positive outcome [27]. Equalized odds (EO) demands that the model's true positive rate (TPR) and false positive rate (FPR) are equal across groups, ensuring the model's accuracy is consistent regardless of group membership [28].  
For example, we tested the system on a hypothetical decision scenario with a protected attribute (e.g., department size). The AI model's recommendations were compared to decisions made by a panel of human experts. We observed that the AI system's decisions matched the human experts' decisions in 85% of cases, indicating high accuracy in alignment with expert judgment. However, initial fairness metrics revealed minor disparities: the AI system's positive recommendation rate for a particular group (e.g., employees from smaller departments) was slightly lower, resulting in a DP gap of about 5%. Additionally, the AI system's true positive and false positive rates differed between groups, violating equalized odds by approximately 4 percentage points.  
We then applied debiasing techniques to mitigate these biases. Using a reweighting approach on the training data (increasing the weight of under-represented group instances) improved demographic parity, reducing differences in positive outcome rates between groups to nearly zero. We also tested adversarial debiasing, training the AI system with an adversary network to remove any implicit information about the protected attribute. This technique further improved fairness: the model's equalized odds gap shrank, as the adversarial training improved demographic parity compared to training without the adversary [29]. Finally, we incorporated human-in-the-loop monitoring: human experts reviewed and monitored AI-driven decisions in real time, especially for high-stakes out comes, to catch any biased behavior that might persist. This human oversight ensured accountability and provided an additional layer of fairness by allowing for interventions if the AI system's recommendations appeared biased.  
Through these measures, the platform achieved much more balanced outcomes. After debiasing, the AI system's decision suggestions had virtually equal acceptance rates across groups (satisfying DP within 1%), and its true/false positive rates were nearly identical for protected vs. unprotected groups (satisfying EO within 2%). This fairness testing demonstrates that the system can deliver accurate recommendations while minimizing bias, especially when coupled with active bias mitigation strategies.
# Response Analysis of the Tristable Energy Harvester with an Uncertain Parameter
## 3 The Approximation of the TEH with an Uncertain Parameter
### Chebyshev Polynomial Approximation  
Uncertain parameters for engineering structures are bounded in reality. The arch-like probability density function is one of the reasonable probability density function (PDF) models for the bounded random variables, which can be described as follows  
\[p(\xi)=\left\{\begin{array}{rl}\frac{2}{\pi}\sqrt{1-\xi^{2}}&|\xi|\leq 1,\\ 0&|\xi|>1.\end{array}\right. \tag{4}\]  
As the orthogonal polynomial basis for the arch-like PDF of \(\xi\), the relevant polynomials are the second kind of Chebyshev polynomials which can be expressed as  
\[H_{n}(\xi)=\sum_{k=0}^{[n/2]}(-1)^{k}\frac{(n-k)!}{k!(n-2k)!}(2\xi)^{n-2k},n=0,1,\cdots. \tag{5}\]  
While the corresponding recurrence formula is  
\[\xi H_{n}(\xi)=\frac{1}{2}[H_{n-1}(\xi)+H_{n+1}(\xi)]. \tag{6}\]  
The orthogonality for the second kind of Chebyshev polynomials can be derived as  
\[\int_{-1}^{1}\frac{2}{\pi}\sqrt{1-\xi^{2}}H_{i}(\xi)H_{j}(\xi)\mathrm{d}\xi= \left\{\begin{array}{rl}1&i=j,\\ 0&i\
eq j.\end{array}\right. \tag{7}\]  
Figure 2: Potential functions of the TEH.  
According to the theory of functional analysis, any measurable function \(f(x)\) can be expressed into the following series form  
\[f(\xi)=\sum\limits_{i=0}^{N}f_{i}H_{i}(\xi), \tag{8}\]  
where the subscript \(i\) runs for the sequential number of Chebyshev polynomials, \(N\) represents the largest order of the polynomials we have given, \(f_{i}\) can be expanded as  
\[f_{i}=\int_{-1}^{1}p(\xi)f(\xi)H_{i}(\xi)\mathrm{d}\xi. \tag{9}\]  
This expansion is the orthogonal decomposition of measurable function \(f(\xi)\), which is the theoretical base of orthogonal decomposition methods.
# Lightweight Transformer with Adaptive Rotational Convolutions for Aerial Object Detection
## 3 Proposed Methodology
### Adaptive Rotational Convolution Module  
The ARC module dynamically adjusts convolutional kernels based on the local orientation of detected features. This ensures that the extracted features are robust to rotations, improving the accuracy of the model in detecting objects with arbitrary orientations. The degree of rotation for each kernel is determined by the orientation prediction from a preceding layer, allowing the model to adapt on the fly.
## 2 Welding Deformation Experiments of Unit Specim
Figure 7: Vertical and horizontal gridlines for welding deformation comparison. For the comparison of welding deformation, the measuring lines were set as shown in Figure 7. The maximum displacement for each case, against the measuring line of transverse (x) and longitudinal (y) directions, is shown in Tables 2 and 3. The maximum displacements along the x-axes were measured using the basis z = 0 at the welding line (x = 475 mm). Those along the y-axes were measured using the basis z = 0 at both endpoints of each y-axes. As the installed number of pieces was increased, that is, as the installation gap between piece was decreased, the welding angular deformation was reduced by a remarkable difference, as shown in Table 2. Compared to the case without pieces (specimen 1), based on the average value for each specimen, the deformation is reduced by 38% when installing three pieces and by 74% when installing five pieces. Specim 1 shows that the angular deformations of the welding start line (x1) and the end line (x5) relatively increase, due to the edge effect. However, specimens 2 and 3 show different deformation distributions than specimen 1, due to the piece restraint effect. In case of longitudinal bending deformation, presented in Table 3, specimen 2 is showing 32% reduction and specimen 3, 43%, with respect to specimen 1, which conveys that installing pieces is effective in reducing this type of deformation. These numbers are smaller than the values of the corresponding angular deformation reduction, which means that the pieces installation has the effect of reducing mainly the welding angular deformation. In addition, the difference in longitudinal bending deformation value between specimen 2 and specimen 3 is small when compared to the difference in angular deformation. The butt welding experiment, as described above, yielded qualitative as well as quantitative data for the prevention of welding deformation by installing pieces. These experimental results will be utilized for comparison and verification, to establish the reliability of the deformation analysis method and simulations.