esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermal Reflection Characteristic
#### 3.2.2 The Heat Flux Reflected by the Wall after a Coat of Paint Changes  
Figure 11 illustrates the test outcomes for the block wall, where the external conditions significantly influenced the heat flux measurements at 70\({}^{\circ}\) and 80\({}^{\circ}\), leading to the omission of related data and images. The experimental records show that when the indoor reflective heater is positioned at an angle of about 10\({}^{\circ}\), the heat flux reaching the wall is 0.959 \(\mathrm{mw/cm^{2}}\). The highest heat flux reflection from the block wall, occurring at angles between 10\({}^{\circ}\) and 20\({}^{\circ}\), is 0.211 \(\mathrm{mw/cm^{2}}\), giving a reflection ratio of 0.22. For the brick wall, the external conditions also heavily impacted the measurements at 70\({}^{\circ}\) and 80\({}^{\circ}\), and thus, these data points are excluded. When the heater is at an angle of about 10\({}^{\circ}\), the incoming heat flux is 1.395 \(\mathrm{mw/cm^{2}}\). The peak reflection, observed between 10\({}^{\circ}\) and 20\({}^{\circ}\), is 0.258 \(\mathrm{mw/cm^{2}}\), resulting in a reflection ratio of 0.18. Regarding the earth wall, the external test conditions at 70\({}^{\circ}\) and 80\({}^{\circ}\) similarly necessitated the omission of data and visuals. The heat flux reaching the wall with the heater at approximately 10\({}^{\circ}\) is 1.199 \(\mathrm{mw/cm^{2}}\). The maximum reflection, noted between 10\({}^{\circ}\) and 20\({}^{\circ}\), is 0.26 \(\mathrm{mw/cm^{2}}\), with a reflection ratio of 0.22.  
In summary, after the application of the thermal insulation coating, the heat flux reflection ratios for the three walls are relatively similar, demonstrating the pronounced effect of the coating on the heat flux reflection. The highest reflection occurs between angles of 10\({}^{\circ}\) and 20\({}^{\circ}\) for all the wall types.  
Figure 10: (**a**) Schematic diagram of the heat flux test. (**b**) The heat flux reflected by a bare wall and a coated block wall under the heat source. (**c**) The heat flux reflected by a bare wall and a coated brick wall under the heat source. (**d**) The heat flux reflected by the heat source on bare and coated earth walls (\(\theta\) is 10\({}^{\circ}\)–60\({}^{\circ}\)).
## 2 Related Work
### Multi-Scale Network Model  
A multi-scale network model is a deep learning architecture designed to capture and process features across different scales simultaneously, thereby enhancing overall model performance. Typically, the model consists of multiple branches, each handling input data at various scales. These features are then fused at specific stages, enabling the extraction of more robust and comprehensive representations. In speech emotion recognition (SER), where emotions are subjective and vary among individuals, many researchers focus on extracting emotion features from speech at multiple scales to enhance SER performance. [PERSON] et al. [13] utilized a global perceptual fusion method to extract features across various scales in speech emotion recognition. They created a special neural network that learns and combines these multi-scale features using a global perceptual fusion module. [PERSON] et al. [11] proposed a framework using an MSCNN with statistical pooling units to obtain both the audio and text hidden representations and employed an attention mechanism to improve performance further. [PERSON] et al. [24] utilized multi-head attention to extract emotional features from both the temporal and frequency domains. They employed additive attention in the frequency domain, enhancing the capability to extract nonlinear features. In modeling temporal dependence, [PERSON] et al. [15] employed an MSCNN with a bi-directional long short-term memory (BiLSTM) network to model local-aware temporal dynamics for SER. [PERSON] et al. [25; 26; 27; 28] focused on temporal features and proposed a Transformer-based model for sequential feature extraction, incorporating multi-scale temporal feature operators and attention modules. This approach effectively extracts emotion features across different time scales, thereby improving the accuracy of emotion recognition.  
Nowadays, multi-scale feature extraction for emotion has become widely adopted. However, existing multi-scale networks often employ one-way transfers, which can compromise the temporal structure of speech. To tackle this, we have incorporated a bi-directional transfer design in our model, which better maintains the emotional features' temporal structure compared to other models.
## 5 Experiments
### Response Generator
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{2}{c}{**Embedding**} & \multicolumn{2}{c}{**Overlap**} & \multicolumn{2}{c}{**Diversity**} & \multicolumn{2}{c}{**Inform**} & \multicolumn{1}{c}{**R**} \\ \cline{2-7}  & **Emb\({}_{avg}\)** & **Emb\({}_{ex}\)** & **Bleu-2** & **Bleu-3** & **Dist-1** & **Dist-2** & **Entropy** & \(R_{a}\) \\ \hline TS2S & 0.764 & 0.845 & 1.50 & 0.44 & 1.47 & 14.17 & 8.47 & 0.87 \\ COPY & 0.868 & 0.841 & **5.43** & **2.26** & 1.73 & 8.33 & 7.87 & 1.09 \\ GenDS & **0.876** & 0.851 & 4.68 & 1.79 & 0.74 & 3.97 & 7.73 & 0.89 \\ CCM & 0.871 & 0.841 & 5.18 & 2.01 & 1.05 & 5.29 & 7.73 & 0.96 \\ ConKADI & 0.867 & 0.852 & 3.53 & 1.27 & 2.77 & 18.78 & 8.10 & 1.19 \\ RGRW & 0.794 & **0.870** & 1.70 & 0.49 & **3.24** & **25.04** & **11.42** & **1.26** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The empirical comparison of RGRW against its baselines. Scores in bold stand for the leadership among the response generators. The effectiveness of the structure with two attention layers is shown by an ablation study with the encoder-decoder attention (EDA) and the response weight (RW). Table 5 shows the results of the ablation study on the Reddit data set. Without EDA, RGRW is influenced only by RW. As a result, the Dist-2 and entropy of '- EDA' are rather higher than those of RGRW, but its Bleu-2 and \(R_{a}\) become lower than those of RGRW. This is because the information in a query is not delivered enough to the decoder. Without RW, RGRW is equivalent to a sequence-to-sequence model with a BERT encoder and a GPT-2 decoder. Since the decoder of '- RW' does not receive the response weight, Dist-2 and the entropy of the model are much lower than those of RGRW. One thing to note in this table is that RGRW shows higher \(R_{a}\) than both '- EDA' and '- RW', which implies that the response weight does not contribute only to the generation of diverse responses, but also helps generate the responses similar to the ground-truth ones.
# An Exploratory Review of Microplastic Pollution, Associated Microbiomes and Pathogens in Water
## 3 Results and Discussion
### Correlation Between Microplastic Concentration and Socio-Economic Indicators  
No significant correlation was found between the GDP per capita and the average concentration of MPs in surface waters across different environments (Figure 7). In rivers, Pearson's r was 0.10 (\(p\) = 0.66), indicating a very weak and non-significant positive relationship. Similar results were observed for lakes (r = 0.28, \(p\) = 0.40) and sea/bay areas (r = 0.20, \(p\) = 0.52).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Type**} & **Number of MPs (Particles/m3)** & **References** \\ \hline \multirow{3}{*}{River} & Median & 110.00 \\ \cline{2-3}  & Average & 222.50 \\ \cline{2-3}  & SD & 247.22 \\ \hline \multirow{3}{*}{Sea/bay} & Median & 0.07 \\ \cline{2-3}  & Average & 0.08 \\ \cline{2-3}  & SD & 0.04 \\ \hline \multirow{3}{*}{Lakes} & Median & 415.00 \\ \cline{2-3}  & Average & 401.67 \\ \cline{1-1} \cline{2-3}  & SD & 175.25 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The average level of MPs in Australia depends on water type (particles/m3).  
Figure 6: Sampling sites for detection of MPs—Australia.  
These findings suggest that the national income level (expressed as GDP per capita) is not a strong predictor of environmental microplastic contamination based on currently available data. The large variability observed in MP levels may instead be driven by other factors such as the sampling methodology, land use, plastic waste management efficiency, or hydrological conditions.  
Despite the initial assumption that higher-income countries may have lower MP contamination due to better waste management [56], our correlation analysis showed no significant relationship between the GDP per capita and MP levels. This supports the idea that methodological heterogeneity and other environmental or policy-driven variables may have a stronger influence on the observed MP concentrations than economic status alone (Supplementary File S6--GDP, All).
## 4 Results and Discussions
### Location Results
Figure 10: The results for the leakages in position I (real angle = 79\({}^{\circ}\)) with a 1.5 mm hole and position B (real angle = 3\({}^{\circ}\)) with a 2.0 mm hole by different algorithms in polar coordinates. The polar axis value represents the normalized energy. Red and blue curves represent the normalized energy results with the FWMB algorithm and the traditional beamforming, respectively. (**a**) “I,” 1.5 mm, 100–110 kHz; (**b**) “I,” 1.5 mm, 110–120 kHz; (**c**) “I,” 1.5 mm, 120–130 kHz; (**d**) “B,” 2.0 mm, 100–110 kHz; (**e**) “B,” 2.0 mm, 110–120 kHz; and (**f**) “B,” 2.0 mm, 120–130 kHz. It can be further seen from the results that the FWMB algorithm optimized the results from two perspectives for traditional beamforming. First, due to the matrix-type multi-data superposition, the discrimination of the results was further enhanced and small errors were resolved, such as within 2\({}^{\circ}\) in Figure 10a,d,e,f. In addition, through the selection of the energy band and the calculation of the weighting coefficient, the situations where multiple peaks appeared and misjudgment occurred, such as in Figure 10b,c, were improved. It can also be seen that the FWMB algorithm results also had obvious fluctuations. For example, there is a multiple peak at about 13\({}^{\circ}\) in Figure 10a-c. This shows that the FWMB algorithm may have misjudged the location, especially when the leakage was near the edge, which was mainly related to the reflection of the boundary. However, due to the large number of FWMB overlays, this problem was compensated for by averaging, and correct results were almost obtained.
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 3 Methods
### Problem Description  
Low-light image enhancement is a significant research topic in the field of image processing and computer vision, driven by the increasing demand for high-quality images captured in various low-light environments [28]. Due to the limitations of sensors and other factors, images captured under low-light conditions often suffer from degradation. Mathematically, the degradation process of an image captured under low-light conditions can be modeled as follows:  
\[\mathbf{Y}=\mathbf{X_{0}}\odot\mathbf{S}+\mathbf{N} \tag{1}\]  
where \(\mathbf{Y}\in\mathbb{R}^{H\times W\times 3}\) denotes the observed low-light image with dimensions \(H\times W\) and three color channels (typically RGB). \(\mathbf{X_{0}}\in\mathbb{R}^{H\times W\times 3}\) represents the latent high-quality image under normal lighting conditions. \(\mathbf{S}\in\mathbb{R}^{H\times W\times 3}\) is the spatially varying illumination map that accounts for the uneven lighting effects across the scene, and \(\mathbf{N}\in\mathbb{R}^{H\times W}\) is the noise term introduced due to sensor limitations and other factors. The symbol \(\odot\) represents the element-wise multiplication.  
The main challenge in low-light image enhancement is to accurately reconstruct details in regions with extremely low light, which is difficult due to the limited amount of information available in these areas. This challenge is further complicated by the need for a solution that can adapt to varying optimal lighting conditions that depend on the content of the image.  
Fortunately, diffusion models, known for their ability to generate high-quality images using auxiliary information, such as other images or textual prompts, have emerged as a promising solution for the low-light image enhancement problem. These models are  
Figure 1: Framework of low-light image enhancement based on a diffusion model with patch-wise structure enhancement. The top part shows the diffusion model, where the low-light input \(X_{0}\) is iteratively refined using U-Net and noise prediction. The noise level \(\bar{x}_{t}\) controls the amount of noise \(c\) added to the image at each step. The U-Net model predicts the noise \(c_{\theta}(Y,X_{t},\bar{x}_{t})\) given the target image \(Y\), the current image \(X_{t}\), and the noise level \(\bar{x}_{t}\). The image is updated from \(X_{t}\) to \(X_{t-1}\) using the update factor \(\alpha_{t}\).The bottom part illustrates patch-wise structure regularization, where identical patches from input and target images are used to enforce global structure-aware regularization. This ensures the model learns a stable optimization path, preserving structural consistency during enhancement.  
particularly well suited for addressing the complexities associated with enhancing images captured under low-light conditions due to their adaptive and flexible nature.
# Energy Efficiency of a Decode-and-Forward Multiple-Relay Network with Rate Adaptive LDPC Codes +
###### Abstract  
This paper presents cooperative transmission (CT), where multiple relays are used to achieve array and diversity gains, as an enabling technology for Internet of Things (IoT) networks with hardware-limited devices. We investigate a channel coding aided decode-and-forward (DF) relaying network, considering a two-hop multiple-relay network, where the data transmission between the source and the destination is realized with the help of DF relays. Low density parity check (LDPC) codes are adopted as forward error correction (FEC) codes to encode and decode the data both at the source and relays. We consider both fixed and variable code rates depending upon the quality-of-service (QoS) provisioning such as spectral efficiency and maximum energy efficiency. Furthermore, an optimal power allocation scheme is studied for the cooperative system under the energy efficiency constraint. We present the simulation results of our proposed scheme, compared with conventional methods, which show that if decoupled code rates are used on both hops then a trade-off has to be maintained between system complexity, transmission delay, and bit error rate (BER).  
c 23 September 2019; Accepted: 1 November 2019; Published: 4 November 2019
###### Abstract  
It is critical to detect hidden, periodically impulsive signatures caused by tooth defects in a gearbox. A hybrid demodulation method for detecting tooth defects has been developed in this work based on the variational mode decomposition algorithm combined with modulation intensity distribution. An original multi-component signal is first non-recursively decomposed into a number of band-limited mono-components with specific sparsity properties in the spectral domain using variational mode decomposition. The hidden meaningful cyclostationary features can be clearly identified in the bi-frequency domain via the modulation intensity distribution (MID) technique. Moreover, the reduced frequency aliasing effect of variational mode decomposition is evaluated as well, which is very useful for separating noise and harmonic components in the original signal. The influences of the spectral coherence density and the spectral correlation density of the modulation intensity distribution on the demodulation were also investigated. The effectiveness and noise robustness of the proposed method have been well-verified using a simulated signal compared with the empirical mode decomposition algorithm associated with modulation intensity distribution. The proposed technique is then applied to detect four different defects in a multi-stage gearbox. The results demonstrated that the demodulated numerical information and pigmentation directly illustrated in the bi-frequency plot of the modulation intensity distribution can be successfully used to quantitatively differentiate the four gear defects.  
variational mode decomposition; modulation intensity distribution; gearbox; fault diagnosis; hybrid demodulation +
Footnote †: journal: Applied Sciences
# Thermodynamics of Liquid Uranium from Atomistic and Ab Initio Modeling
## 1 Introduction
The electronic contribution is derived from the electronic density of states, \(D(E)\), at the highest occupied energy state (Fermi level, \(E_{F}\)), \(D(E_{F})\):  
\[C_{p}^{el}(T)=\frac{\pi^{2}}{3}D(E_{F})k_{B}^{2}T=\gamma_{e}T \tag{2}\]  
where \(\gamma_{e}\) is the Sommerfeld coefficient [19]. Note that \(D(E_{F})\) depends on temperature in two ways. First, it is broadened by a Fermi-Dirac temperature distribution. Second, it is sensitive to the atomic volume that increases with the temperature due to thermal volume expansion. We account for both these effects when determining \(C_{p}^{el}(T)\). As was conducted for \(\gamma\)-U in Ref. [1], we ignore the magnetic contribution \(C_{p}^{mag}(T)\), to the constant pressure heat capacity \(Cp(T)\) while performing calculations for liquid uranium because the contribution is very small. There have numerous reports on the experimental value of the Sommerfeld constant for \(\alpha\)-U, for example the following: 10.0 mJ/(mol\(\cdot\)K\({}^{2}\)) [15], 12.2 mJ/(mol\(\cdot\)K\({}^{2}\)) [20], 9.14 mJ/(mol\(\cdot\)K\({}^{2}\)) [21]. According to Ref. [6], \(\gamma_{e}\) for \(\alpha\)-U have been shown to be microstructurally dependent with an average value of the experimental results of about 10.12 mJ/(mol\(\cdot\)K\({}^{2}\)). [PERSON] et al. [6] and [PERSON] et al. [22] calculated the electronic contribution to the specific heat using the electronic DOS derived from VASP formalism. In consistency with our paper [1], the electronic contribution to the specific heat of liquid uranium is calculated by using the electronic DOS derived from the reliable all-electron full-potential linear muffin-tin orbital (FPLMTO) method [23]. In this study, by using the LAMMPS code [24; 25] and EAM potential [4], we perform a classical MD simulation of liquid uranium in the temperature range of 1400-2000 K. The melting temperature of uranium is derived from the simulation of coexisting phases [26; 27; 28]. The ionic (EAM) heat capacity is defined by monitoring the change in the enthalpy of the system over a change in temperature with the pressure fixed. The atomic configurations generated by the LAMMPS MD simulator are used to calculate the electronic contribution to the constant pressure heat capacity of liquid uranium by using an all-electron full-potential linear muffin-tin orbitals (FPLMTO) formalism. Related details of the classical MD and FPLMTO computational methods are outlined in Sections 2 and 3, respectively, followed by a presentation of the results in Section 4 and a discussion in Section 5. Lastly, a summary and concluding remarks are outlined in Section 6.
## 2 Inter-DCI Link Modeling and OP Estimation
### OP Estimation  
In the following, the procedure used to estimate the system outage using the OP metric in IM-DD PAM4 systems impaired by ICXT is described. Firstly, the BER is calculated by the semi-analytical method known as the exhaustive Gaussian approach [44]. For a PAM4 signal, the BER is given by [38]  
\[BER=\frac{1}{2\cdot 4^{N_{org}}}\left\{\sum_{\begin{subarray}{c}j=1 \\ a_{k}=0\end{subarray}}^{4^{N_{org}}}Q\left(\frac{F_{1}-i_{0,j}}{\sigma_{0,j}} \right)+\sum_{\begin{subarray}{c}j=1\\ a_{k}=1\end{subarray}}^{4^{N_{org}}}\left[Q\left(\frac{i_{1,j}-F_{1}}{\sigma_{1,j}}\right)+Q\left(\frac{F_{2}-i_{1,j}}{\sigma_{1,j}}\right)\right]+\right.\] \[\left.\sum_{\begin{subarray}{c}j=1\\ a_{k}=2\end{subarray}}^{4^{N_{org}}}\left[Q\left(\frac{i_{2,j}-F_{2}}{\sigma_{ 2,j}}\right)+Q\left(\frac{F_{3}-i_{2,j}}{\sigma_{2,j}}\right)\right]+\sum_{ \begin{subarray}{c}j=1\\ a_{k}=3\end{subarray}}^{4^{N_{org}}}Q\left(\frac{i_{3,j}-F_{3}}{\sigma_{3,j}} \right)\right\} \tag{9}\]  
where \(i_{0,j}\), \(i_{1,j}\), \(i_{2,j}\), and \(i_{3,j}\) correspond to the means of the currents at the input of the decision circuit for the symbols \(a_{k}\) at the time sampling instants, \(t_{j}=t_{o}+T_{s}(j-1)\), with \(j=1,\ldots,4^{N_{org}}\) and \(T_{s}\) the symbol period; \(t_{o}\) is extracted from the received eye-pattern at the decision circuit input and \(\sigma_{0,j}\), \(\sigma_{1,j}\), \(\sigma_{2,j}\) and \(\sigma_{3,j}\) are the noise standard deviations for the different time sampling instants [38], and \(Q(x)=\int_{x}^{\infty}(1/\sqrt{2\pi})e^{-\frac{i^{2}}{\pi}}d\xi\)[43]. The decision thresholds \(F_{1}\), \(F_{2}\) and \(F_{3}\) are optimized in each time-fraction by applying the bisection method to minimize the BER. The effect of ICXT and inter-symbol interference resulting from the filtering is taken into account by the waveform distortion at these \(t_{j}\) sampling time instants and, in Equation (9), this effect is included in the mean currents \(i_{k,j}\). The effect of electrical noise, signal-ASE, and ASE-ASE beat noises are taken into account semi-analytically in the standard deviations of the received symbols, \(\sigma_{k,j}\). The consideration of the impact of all noise types in a semi-analytical way allows a much faster BER computation (though accurate enough [44]) in each time fraction.  
The OP is the probability of a system becoming unavailable, i.e., the probability of the BER in the presence of ICXT exceeds a given BER limit. In the simulation, the OP is estimated by [20; 45]  
\[OP=\frac{N_{o}}{N_{r}} \tag{10}\]  
where \(N_{o}\) is the number of occurrences of BER above the BER limit, and \(N_{r}\) is the number of simulated MCF realizations necessary to reach \(N_{o}\) occurrences of BER above the BER limit. The required OP in optical communications typically does not exceed \(10^{-4}\)[45; 46].
## 2 Numerical Methods
### Subgrid Scale Models
#### 2.3.1 Functional Models  
The functional SGS models focus on modeling the action of the subgrid terms and bringing a similar effect [5]. Based on the eddy-viscosity concept, the subgrid stress tensor and the subgrid total energy flux vector of the functional SGS models are given as follows:  
\[\tau_{ij}^{gg} =-2\mu_{ggs}(\hat{S}_{ij}-\frac{1}{3}\delta_{ij}\hat{S}_{kk})\,, \tag{15}\] \[q_{j}^{ggs} =-k_{ggs}\frac{\partial\hat{T}}{\partial x_{j}}\,,\quad k_{ggs}= \frac{\mu_{ggs}C_{P}}{\mathrm{Pr}_{ggs}}\,, \tag{16}\]  
where \(\mathrm{Pr}_{ggs}=0.5\) is the subgrid Prandtl number.  
In the standard Smagorinsky model, \(\mu_{ggs}\) is given by:  
\[\mu_{ggs}^{S}=\tilde{\rho}C_{S}^{2}\Delta^{2}\sqrt{2\tilde{S}_{ij}\tilde{S}_{ ij}}\,, \tag{17}\]  
where \(\tilde{S}_{ij}\) is the resolved strain rate tensor defined in Equation (4), \(C_{S}=0.1\) is the Smagorinsky coefficient and \(\Delta\) is the filter width given by \(\Delta=s\cdot h/(P+1)\), where \(s\) is a scaling factor set to \(2\), \(h\) is the characteristic grid size and \(P\) is the order of the solution polynomial. This expression of \(\Delta\) means that \(\Delta=h\) for \(p1\) solution polynomial and \(\Delta=h/2\) for \(p3\) solution polynomial, which follows the assumption that the filter width corresponds to the cell size estimated from the DOFs.