esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Enhancement of Biological Treatment Systems
### Anaerobic Methane Recovery  
In anaerobic digestion systems, particularly in systems treating high-solid or inhibitory wastes, SDB could act as a conductive platform that supports electron transfer between microbial communities, strengthens symbiotic interactions, and boosts methane generation. Specifically, SDB provides surfaces for microbial colonization and supports electron transfer between syntrophic bacteria and methanogens through direct interspecies electron transfer (DIET). For instance, [PERSON] et al. [66] used iron-modified SDB that increases functional groups such as C=O, thereby mitigating salt inhibition and significantly increasing methane yields, which in turn enhances SDB electroactivity. Mechanistically, SDB can not only adsorb inhibitory by-products (e.g., long-chain fatty acids, ammonia) but also stimulate enzymatic activities involved in hydrolysis, acidification, and methanogenesis. In another case, [PERSON] et al. [67] reported that Fe\({}_{2}\)O\({}_{3}\) nanoparticle-biochar composites improved methane production (by 42%) and reduced membrane fouling in anaerobic membrane bioreactors. Fe\({}_{2}\)O\({}_{3}\)-SDB composites facilitate enzyme activation (Cyt \(c\) and F\({}_{420}\)) during methanogenesis by acting as electron donors and acceptors. From an overview of the studies [68; 69; 70], the consensus is that SDB is a powerful accelerator for anaerobic digestion, improving gas yield, methane content, and digester stability. SDB provides a conductive substrate that facilitates electron transfer between microbial species, enhances symbiotic interactions, and promotes biogas production.
# Routing Path Assignment for Joint Load Balancing and Fast Failure Recovery in IP Network
## 4 Numerical Results and Discussions
### Network Survivability  
Network survivability refers to a network's capacity to continue providing services in the event of a failure [16]. Our optimization models (LB&LFP and LB&NFP) guarantee network survival in the event of node or link failure.
## 6 Computational Experiments
### Discussion  
The results obtained by both meta-heuristics (MO-VNSSA and MO-VNSTS) seems to be promising in terms of quality of the obtained solutions especially for the first objective \(F_{1}\) for which the optimal value was found for all the instances and near optimal and several optimal values for the second objective \(F_{2}\). The results seems to be positive also in terms of computational time even for large sized instances.  
Within the context of multi-objective optimization, using the Lexicographic Programming method for the MO-MIP model and the two meta-heuristics (MO-VNSSA and MO-VNSTS) requires a priori knowledge about the objectives which is based on the decision makers' preferences. Therefore, the two objective functions presented in Tables 3 and 4 are solved in a lexicographic arranged order (which is \(F_{1}\) and then \(F_{2}\) in this study).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{3}{c}{**Instances**} & \multicolumn{3}{c}{**MO-VNSSA**} & \multicolumn{3}{c}{**MO-VNSTS**} \\ \cline{3-10} \# & **D** & **N** & **H** & **F\({}_{1}\)** & **F\({}_{2}\)** & **Time (s)** & **F\({}_{1}\)** & **F\({}_{2}\)** & **Time (s)** \\ \hline
28 & 7 & 20 & 15 & 5001 & 772.6 & 4.322 & 5001 & 750 & 4.503 \\
29 & 10 & 20 & 15 & 7338 & 801.2 & 4.5872 & 7338 & 776 & 4.7218 \\
30 & 7 & 30 & 20 & 5081 & 1279.8 & 7.4594 & 5081 & 1216 & 7.3938 \\
31 & 10 & 30 & 20 & 6401 & 1284.8 & 7.35 & 6401 & 1201 & 7.4782 \\
32 & 15 & 30 & 20 & 9529 & 1220.4 & 8.1656 & 9529 & 1182 & 8.2032 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results for MO-VNSSA and MO-VNSTS for the large instances.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Meta-Heuristics** & **F** & \(p\)**-Value** \\ \hline MO-VNSSA & 1.623 & 0.209 \\ MO-VNSTA & 0.726 & 0.196 \\ \hline \hline \end{tabular}
\end{table}
Table 5: ANOVA measure for MO-VNSSA and MO-VNSTS.  
Taking those points into account, the proposed methods provide only solutions that are based on the decision maker's preferences and cannot provide a set of Pareto optimal solutions to be proposed to the decision maker. Moreover, the proposed meta-heuristics are not applicable to find an alternative solution in case of any changes in the relative importance of the objectives.
## 6 Conclusions
\begin{gathered}\frac{d}{dt}V_{i}(t)\leq\xi_{i}^{\top}(t)\Big{[} P_{1,i}A_{i,1}+A_{i,1}^{\top}P_{1,i}\Big{]}\xi_{i}+\\ \\ E_{i}^{\top}(t)\Big{[}P_{2,i}(A_{2,i}+\alpha_{i}I)+(A_{2,i}+ \alpha_{i}I)^{\top}P_{2,i}\Big{]}E_{i}(t)+\\ \\ E_{i}^{\top}(t)[P_{2,i}R_{2,i}P_{2,i}+Q_{2,i}]E_{i}(t)-\alpha_{i}V _{i}(t)+\\ \\ \gamma_{i}+2\bar{k}_{1,i}(t)\bigg{\{}\pi_{1,i}\frac{d}{dt}k_{1,i} (t)+e_{i}(t)M_{a}^{\top}P_{2,i}E_{i}(t)\bigg{\}}+\\ \\ 2\bar{k}_{2,i}(t)\bigg{\{}\pi_{2,i}\frac{d}{dt}\bar{k}_{2,i}(t)+e _{i+n}(t)M_{b}^{\top}P_{2,i}E_{i}(t)\bigg{\}}\end{gathered}  \tag{77}\]  
The differential inclusion (77) appears after the application of [PERSON]'s inequality \(X^{\top}Y+Y^{\top}X\leq X^{\top}\Lambda^{-1}X+Y^{\top}\Lambda Y\), \(X\prime\in\mathbb{R}^{s\times q}\) with \(0<\Lambda=\Lambda^{\top}\in\mathbb{R}^{s\times s}\)[21]. Considering the satisfaction of the condition for the existence of positive definite solutions for the given Lyapunov equations as well as the Riccati algebraic equations given in the theorem statement and in consideration of the adjusting laws for the gains \(\bar{k}_{1,i}(t)\) and \(\bar{k}_{2,i}(t)\) in (61), inclusion (77) transforms into: \(\frac{d}{dt}V_{2,i}(t)\leq-\alpha_{i}V_{2,i}(t)+\gamma_{i}\). Considering the solution of the inclusion for \(\frac{d}{dt}V_{2,i}(t)\), and the application of the comparison Lemma, it led straightforwardly to \(V_{2,i}(t)\leq V_{2,i}(0)e^{-2\alpha_{i}t}+\frac{\gamma_{i}}{\alpha_{i}} \big{(}1-e^{-2\alpha_{i}t}\big{)}\). Considering the case when the inclusion transforms to the equivalent ordinary differential equation, by redefining \(V_{i}(t)\) as \(V_{i}^{\alpha j}(t)\), it can be proven that  
\[V(t)\leq\sum_{i=1}^{n}\sqrt{V_{1,i}(0)}-2t\sum_{i=1}^{n}\kappa_{1,i}+\sum_{i=1}^ {n}\biggl{(}V_{2,i}(0)e^{-\alpha_{i}t}+\frac{\gamma_{i}}{\alpha_{i}}\bigl{(}1-e ^{-\alpha_{i}t}\bigr{)}\biggr{)} \tag{78}\]  
Clearly, when \(t\geq T\) with \( T=\dfrac{\sum_{i=1}^{n}\sqrt{V_{1,i}(0)}}{2\sum_{i=1}^{n} \kappa_{1,i}}\), the following inequality can be justified  
\[V(t)\leq\sum_{i=1}^{n}\Bigl{(}V_{2,i}(T)e^{-\alpha_{i}(t-T)}\Bigr{)}+\sum_{i=1 }^{n}\frac{\gamma_{i}}{\alpha_{i}}\Bigl{(}1-e^{-\alpha_{i}(t-T)}\Bigr{)} \tag{79}\]  
If the upper limit is calculated, \(t\to\infty\) results in \(\varlimsup_{t\to\infty}V(t)\leq\sum_{i=1}^{n}\frac{\gamma_{i}}{\alpha_{i}}\). This result finishes the proof. Conceptualization, K.R.-M.; Formal analysis, I.C. and W.-Y.L. All authors have read and agreed to the published version of the manuscript. Ministry of Science and Higher Education of Russia within the Center \"Supersonic\" (agreement 075-15-2022-331 26 April 2022). Not applicable. Not applicable. Not applicable. Not applicable. The paper was prepared under the partial financial support of the Tecnologico de Monterrey, Institute of Advanced Materials for Sustainable Manufacturing. The authors declare no conflict of interest.
## 3 Experimental Study in Short Delay Regime
### Numerical Analysis of the Experiment  
Since integrated coupled lasers with short delay can maintain injection locking under strong coupling, which is beneficial to linewidth reduction, linewidth narrowing should be attributed to the combination of the three mechanisms analyzed in Section 2. Figure 10 shows the simulation of the device with the 4 mm coupling region which exhibits the best linewidth narrowing performance in Figure 8c. In Section 2, the mutually coupled system is treated in two perspectives. Firstly, the system is regarded as a compound cavity under strong coupling. The enhanced photon lifetime estimated in Figure 5a leads to the linewidth reduction from 8.2 to 2.9 MHz in Figure 10. Secondly, the system is regarded as two coupled oscillators governed by coupled rate equations. The linewidth is suppressed from 8.2 MHz to 640 kHz considering the mutual phase locking and increased photon number. The order of linewidth narrowing factor agrees with the experiment. In combination with these effects, the integrated device should exhibit better performance. However, the injection current at the coupling region to compensate absorption generates extra noise source. Future research by adopting passive waveguide as the coupling region may achieve better linewidth performance.  
Figure 10: Numerical simulation of linewidth performance of monolithically integrated mutually coupled laser under mutual injection locking. (\(\tau_{d}\) = 40 ps, \(\kappa_{c}\) = 2 \(\times\) 10\({}^{10}\)/s, \(\delta\) = 0 GHz).  
Figure 9: (**a**) Dependence of 1/_f_ frequency noise on coupling strength; (**b**) improvement of self-heterodyne signal from free-running to injection locked state labeled as 4.
## 4 System Model
### GPU Energy Consumption Model  
To estimate the GPU energy consumption based on frequency values, we exploit a fitting-based modeling approach, which is applicable for clusters comprising heterogeneous GPU device architectures. Let \(\delta_{i}^{F}\), \(\delta_{i}^{B}\), and \(\delta_{i}^{O}\) denote the coefficients for feed-forward, back-propagation, and other workloads, respectively, and \(\delta_{i}=\delta_{i}^{F}+\delta_{i}^{B}+\delta_{i}^{O}\). Similar to the DL job model above, these coefficients are associated with the characteristics of both the DNN model and the GPU device. The power consumption for the \(i\)-th DL job at time slot \(k\) can be defined as follows:  
\[p_{i}(k)=p_{i}^{\text{STD}}+\delta_{i}\cdot b_{i}\cdot x_{i}(k)\cdot f_{i}(k),\ \forall i\in\mathcal{I},\ \forall k\in\mathcal{K}, \tag{7}\]  
where \(p_{i}^{\text{STD}}\) represents the static power consumption of worker \(s_{i}\). We can derive the integrated coefficient \(\delta_{i}\) using an output-based estimation approach, which eliminates the need for costly offline profiling analysis. Let \(E_{i}(k)\) denote the energy consumption of the worker running the \(i\)-th DL job during the interval \([k\cdot\tau,(k+1)\cdot\tau]\). Then, \(E_{i}(k)\) can be presented as follows:  
\[E_{i}(k)=\begin{cases}p_{i}(k)\cdot\tau,&\text{if }x_{i}(k)=1\ \text{( active)}\\ p_{i}^{\text{STD}}\cdot\tau,&\text{if }x_{i}(k)=0\ \text{(idle)}\end{cases},\ \forall i\in \mathcal{I},\ \forall k\in\mathcal{K}. \tag{8}\]  
If \(x_{i}(k)=1\) (indicating that time slot \(k\) is allocated for processing the \(i\)-th DL job), then the GPU frequency significantly affects the associated energy consumption, as detailed in Equation (7). On the other hand, if \(x_{i}(k)=0\) (meaning the worker is in idle state and does not process any invoked DL job during time slot \(k\)), the energy consumption is determined solely by the static power consumption \(p_{i}^{\text{STD}}\).
# Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review
## 5 Conclusions  
In this review, several applications of WT are summarized in order to analyze non-stationary TS. All applications are divided into seven applied sciences fields, including the geo-sciences and geophysics, the remote sensing, engineering, hydrology, finance and medicine. The WT applications in other fields, such as history, ecology and chemistry, are also discussed. In addition, some challenges and future works on the use of WT for non-stationary TS are analyzed in order to provide insights to the relevant studies. In fact, we have discussed the influence of the type of the wavelet, the mother wavelet and the scale for the TS analysis. Furthermore, the combination of wavelet and machine learning have been successfully employed in recent years. In addition, the interpretation of the different components after the decomposition was discussed. In conclusion, the WT will continue to be one of the most appealing techniques that dominate the different fields.  
All of the authors contributed to the design and implementation of the research, to the analysis of the state of the art and to the writing of the manuscript.  
This study was financially supported by the National Key Research and Development Program (No. 2017 YFA0603702), the National Natural Science Foundation of China (No. 91647110), and the Youth Innovation Promotion Association CAS (No. 2017074).  
The authors would like to thank [PERSON] from the Eco-Evolutionary Mathematics, France for her valuable comments and clarifications regarding the multi-resolution analysis in the ecology field.  
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.  
The following abbreviations are used in this manuscript:  
TS Time Series WT Wavelet Transform CWT Continuous Wavelet Transform DWT Discrete Wavelet Transform MRA Multi-Resolution Analysis WPD Wavelet Packet Decomposition MODWT Maximal Overlap Discrete Wavelet Transform XWT Cross-Wavelet Transform WTC Wavelet Coherency EWT Empirical Wavelet Transform NDVI Normalized Vegetation Index EEG Electroencephalography ECG Electrocardiogram SD Standard Deviation MSE Mean Squared Error LST Land Surface Temperature MRE Mean Relative Error RE Relative Error RMSE Root Mean Square Error MAPE Mean Absolute PercentageError ACF Autocorrelation Function EVI Enhanced Vegetation Index MODIS Moderate-ResolutionImaging Spectroradiometer AGC Apparent Green Cover LAI Leaf Area Index AVHRR Advanced Very High-Resolution Radiometer MCSA Motor Current Signature Analysis SNR Signal-to-Noise Ratio CR Compression Ratio PRD Percent Root Mean Square Difference FBSE Fourier Bessel Series Expansion PV Solar Photovoltaic ANN Artificial Neural Network SVM Support Vector Machine
## 4 Numerical Simulation on Simplified Model of Preliminary Design HE and Optimized Design HE
### Boundary Conditions and Numerical Method  
The physical model of HE is established by SOLIDWORKS, and the model is divided in ICEM. The regular tube side is divided into a hexahedral grid, while the shell side is divided into a tetrahedral grid. The final generated grid is shown in Figure 8. In addition, the boundary layer grids are generated on the outer surfaces of tubes. The physical properties of liquid lead are calculated based on the physical property relationship in Table 3 and compiled in FLUENT by user-defined function (UDF). The physical properties of SCO\({}_{2}\) were obtained from the REGPROP database by activating the NIST real gas model in FLUENT.  
The velocity--inlet boundary condition was applied at the inlet of the lead and SCO\({}_{2}\), while the pressure--outlet was set as the outlet boundary condition at the outlet of lead and SCO\({}_{2}\). The coupled thermal boundary condition was applied for the inner and outer surfaces of heat transfer tubes which coupled the heat transfer between fluid and solid. The outside wall of HE was set as a zero heat flux boundary condition which assumes the outside-wall to be insulated.  
The Reynolds number (Re) of liquid lead and SCO\({}_{2}\) are more than the magnitude of \(10^{4}\) and \(10^{5}\) in this study, respectively. Previous studies [10,44] have stated that the standard \(k\)-\(\epsilon\) model is suitable for the flow of liquid metal and supercritical fluid under high Re. Therefore, the standard \(k\)-\(\epsilon\) model is chosen as the turbulent model in the present study. The turbulent Prandtl number (\(Pr_{t}\)) of liquid lead cannot directly use the constant 0.85 set in FLUENT because the molecular Prandtl number of lead is so much lower that the Reynolds analogy is no longer applicable. In the present study, the Cheng model [45] was selected to be the turbulent Prandtl number model in the flow region of liquid lead, and the model was added into FLUENT through UDF. The turbulent Prandtl number in the region of SCO\({}_{2}\) still adopts the recommended value of 0.85 in FLUENT. The expression of the Cheng model is:  
\[Pr_{t}=\left\{\begin{array}{l}4.12,Pe\leq 1000\\ \frac{0.01 Pe}{0.018 Pe^{\Delta S_{-}}(-A)}\end{array}\right\}^{-2},1000<Pe\leq 6 000 \tag{24}\]  
\[A=\left\{\begin{array}{l}5.4-0.9 Pe,1000<Pe\leq 2000\\ 3.6,2000<Pe\leq 6000\end{array}\right. \tag{25}\]
## 4 Examples of the Osmoadaptation Signaling Pathway in Other Aspergillus  
Regulatory pathways including those signaling pathways responsive to stress are relatively conserved, but not identical, in the genus Aspergillus [12]. For example, conserved SakA plays an important role in the oxidative stress response in the opportunistic human pathogen and gliotoxin-producer _Aspergillus fumigatus_, while the TcsB histidine kinase was not crucial [66]. In _A. fumigatus_, MpkC plays other roles such as in carbon source utilization, however it was not determined to be involved in osmoadaptation [67]. _Aspergillus niger_, an important citric acid, gluconic acid and hydrolytic enzyme producing fungus, does not have TcsB-type histidine kinase homologs. Other aspergillus, such as _A. nidulans_ mentioned above, present TscB but it is dispensable in the osmoadaptation response. It is likely that TcsB is dispensable or not present in other aspergillus [51, 66].  
Interestingly, the important aflatoxin-producer _Aspergillus flavus_ harbors two orthologs of the _A. nidulans_ SskA response regulator in its genome (ORFs AFL2G_06337 and AFL2G_12585), and two orthologs of the _A. nidulans_ ortholog RpdA histone deacetylase (ORFs AFL2G_08263 and AFL2G_03062). This suggests that _A. flavus_ has a more sophisticated osmotadaptation system compared to other aspergillili that may include genes with redundant functions.
## 4 Discussion  
Similar to earlier studies [4; 5; 7; 8], the headspace SPME-GC-MS sampling, sample preparation, and analysis have shown to be useful in elucidating statistically significant differences in compound profiles from'sick' and 'healthy' cattle. The same approach was used to study marking fluids of wild cats and plant-insect interactions [21; 22; 23]. Volatile organic compounds that are feasible to extract, separate and detect via relatively simple headspace SPME extraction followed by analyses using GC-MS can provide an initial glance for identifying the scope of biomarker candidates emitted from both cattle nasal swabs and serum samples. Preliminary markers in both types of samples were different except phenol which was expected considering animal metabolism and therefore expressions of compounds in different media. Future studies are warranted to improve further the extraction efficiency targeting VOCs that were preliminarily identified in this study, including identification and quantification. Research towards real-time detection of selected VOCs using portable, field-ready for animal-side capable (e.g., maximum 30 s to 5 min long cattle retention/processing in feedlot chute), and minimally invasive sampling methods is warranted.  
The phenolic compounds (phenol and \(p\)-cresol) identified in the both nasal secretions and serum could simply represent the presence of the parent compound [24; 25; 26; 27; 28]. These compounds may be directly produced by certain gastrointestinal bacteria via the metabolism of tyrosine, associated with kidney failure, protein breakdown, or fatty liver disease. It is of interest to note that creatinine, which is commonly used to in cattle to evaluate muscle catabolism also is a phenolic compound. Diphenyl ether could also be included in this group as this compound may be produced from phenol ethers. It is possible that there are multiple other phenolic compounds that went undetected or the assay was not sensitive enough to differentiate between metabolites within this family.  
Other metabolites identified in this research appear to be more distinct in their identification and action. 5-Octadecenal is a fatty aldehyde lipid molecule that can be ingested with grain diets and has the potential to serve as an energy source [24; 25; 26]. It has also been utilized as an estrus biomarker in buffalo [29]. TMC is an endogenous biomarker that is produced in the biosynthesis of steroids, cell signaling, membrane stabilization, and energy metabolism [24; 25; 26]. Benzothiazole can be found in cranberries and persimmons and has multiple physiological properties [24; 25; 26]. There is evidence to support that this compound may be produced by rumen bacteria. It is also closely related structurally to the benzimidazoles which is a class of anthelmintics commonly used in cattle. However, some caution must be used when evaluating these compounds within human-based libraries such as the HMBD (human metabolome database).
# Research on Multi-Scale Point Cloud Completion Method Based on Local Neighborhood Dynamic Fusion
## 3 Method
### Attention Discriminator  
The 3D point cloud completion task was inherently a generative process. To enhance the performance of the generator, an attention discriminator \(D\) was introduced, inspired by the adversarial training concept used in GANs. The purpose of this discriminator was to encourage the generator to produce more realistic and accurate point clouds through the adversarial interaction between the generator and discriminator. During this process, the discriminator's role was to distinguish real samples from generated ones, while the generator aimed to produce samples that could deceive the discriminator, effectively  
Figure 4: A diagram of the process of gradually completing point cloud completion.  
\"passing off\" fake data as real. As a result, both the generator and discriminator improved the quality of the generated point clouds through ongoing competition.  
Thus, an attention discriminator was integrated into the point cloud generation network, as illustrated in Figure 5. The discriminator followed an autoencoder architecture. In this paper, we designed the attention discriminator by sequentially connecting the LNDFL and the biased attention layer (BAM) to enhance feature integration and the generative capabilities of the network. Specifically, the attention discriminator processed both the point clouds generated by the network and the real point cloud corresponding to the incomplete input through a feature extractor. It then output a discriminative feature vector. This vector underwent several local dynamic fusion steps before producing a binary classification value that indicated whether the generated point cloud is real or fake.
## 5 Applications and Services
### RIS Technologies
#### 5.3.2 Star-Ris  
Unlike conventional RIS, which only reflects incident signals, STAR-RIS transmits and reflects signals, enabling 360-degree coverage but requiring more complex coefficient design. In [98], a sum-rate maximization problem is addressed through joint optimization of UAV trajectory, active beamforming at the UAV, and passive beamforming at the STAR-RIS, using RL for decision-making to manage unknown obstacles. To bolster system robustness against environmental uncertainties, a distributionally robust RL (DRRL) algorithm is introduced, providing reliable worst-case performance. Results show that STAR-RIS enhances sum-rate performance compared to traditional RIS and that DRRL ensures more stable and robust outcomes than existing RL methods.