esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Bioaugmentation on Biogas Yields and Kinetics in Anaerobic Digestion of Sewage Sludge
## 3 Results and Discussion
### Microbial Structure of Digesters
During the methane fermentation, Methanomicrobiales are usually less numerous than Methanosarcinales [8]. In this study, the predominance of Methanomicrobiales (about 1% of all identified sequences) in the species structure of Archaea indicated hydrogenotrophic methanogenesis as the main pathway of methane generation. Although _Methanosaeta_ sp. predominated in the liquor used for bioaugmentation, _Methanoculleus_ was the most abundant genus in the reactors. The idea of the study was to bioaugment the reactor with Archaea microorganisms to support methane production because this is the most critical step in the anaerobic digestion conducted by slow-growing microorganisms and prone to changes in environmental conditions. From the microbial analysis of biomass, it can be concluded that the biodiversity of bioaugmenting mixture reflecting the potential of different species to colonize the fermentation reactors was low (only 11 species with abundance over 0.5%). Despite this, bioaugmentation was successful in terms of the most abundant group that is _Methanosaeta_ sp. belonging to Methanosarcinales that produce methane via an acetotrophic pathway. This genus was present in the experimental reactors, but its abundance was below 0.5%. _Methanosaeta_ sp. is sensitive to OLR. They were abundant in the mesophilic fermentation reactors that were operated at an OLR of 1 kg COD m\({}^{-3}\) day\({}^{-1}\) but the increase in OLR to 2 kg COD m\({}^{-3}\) day\({}^{-1}\), at the maintained process temperature, caused their disappearance from the biomass [46]. In the present study, OLR was higher than the optimal for _Methanosaeta_ sp. ; therefore, although present in the biomass, they were not able to outcompete _Methanoculleus_ sp. that predominated in the reactors. _Methanoculleus_ sp. commonly occurs in digesters operated in meso- and thermophilic temperatures, including the digesters in which the process is supported by physico-chemical treatment, e.g., microwave radiation [46]. _Methanoculleus_ sp. cope well with high OLRs, comprising over 40% of biomass during thermophilic co-digestionof manure and waste whey at an OLR of 60.4 g COD m\({}^{-3}\) day\({}^{-1}\)[40]. This fact may explain the predominance in the present study of this genera among Archaea in the experimental reactors operated at the higher OLRs.
# _remote sensing_
## 4 Satellite Architecture and System Budgets
### Mass Budget  
The spacecraft indicative mass budget is summarized in Table 10. All the components and sub-unit masses are reported with the related margins. According to [47], the margins are set to 5% for components with high technology readiness level (TRL) and flight heritage on other nano-satellite missions, to 10% for components with lower TRL or awaiting modification, to 20% for in-house manufactured fixtures or sub-units with inherently low TRL or high uncertainty. The adopted margin policy is compliant with the European Space Agency recommendations and with the European Cooperation for Space Standardization documents [54]. With respect to the maximum mass limit of 12 kg indicated by the 6U  
Figure 7: Cartesian projections of the HORUS spacecraft CAD model.  
CubeSat specification [53], the mass budget shows a margin of about 2.0 kg, corresponding to about 17% of the allowable total mass.
## 6 Conclusions and Future Directions
### Future Directions
#### 6.2.3 Application Extension Directions  
Extend to multi-modal transportation systems. Current methods primarily target motor vehicles, but urban transportation is multi-modal. Research is needed on processing trajectory data from different transportation modes, such as buses and bicycles, to extract multi-modal traffic parameters.  
Support dynamic traffic management applications. Beyond serving fixed-time signal control, support can be extended to adaptive control, dynamic route guidance, and other applications. Research is needed on real-time parameter update mechanisms and prediction methods.  
Integration with emerging technologies. With the development of connected vehicles, autonomous driving, and other technologies, both the quality and quantity of trajectory data will substantially improve. Research is needed on fully utilizing such high-quality data to provide more precise traffic parameters.  
Beyond computational advancement, this research provides practical support for traffic engineering decisions, including coordination control subarea division, offset optimization using actual travel times, and split allocation based on real turning movements. Alternative solutions include traditional fixed detectors, connected vehicle data, or smartphone-based collection, though these require substantial infrastructure investment or have coverage limitations. The trajectory-based approach offers unique advantages in cost-effectiveness for areas lacking detection infrastructure and direct applicability to signal control optimization, democratizing access to advanced traffic management capabilities without requiring extensive new infrastructure deployment.
# Research on Slope Stability Prediction Based on MC-BKA-MLP Mixed Model
## 3 Database Establishment and Analysis
### Data Collection and Analysis
The correlation coefficients of variables X\({}_{3}\) with X\({}_{1}\), X\({}_{2}\), and X\({}_{4}\) are 0.5516, 0.5414, and 0.4459, respectively; that is, there is a moderate correlation between volume weight and shear strength, effective stress, and slope height. The correlation between X\({}_{5}\) and X\({}_{6}\) and other variables is weak, especially the correlation coefficient between the pore ratio and the other five variables ranges from \(-\)0.0838 to \(-\)0.1299. This is because the pore ratio is a defined slope parameter, while the other variables are inherent attributes of the slope directly or indirectly, which may lead to a weak negative correlation between the pore ratio and other parameters. In general, some parameters have a strong correlation, which means that a lot of information is shared among these features, which reduces the complexity of the model to a certain extent. On the other hand, the correlation of some parameters is weak, and the features are relatively independent, which is conducive to the model capturing more information and improving the model's generalization ability. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Feature** & **X\({}_{1}\)** & **X\({}_{2}\)** & **X\({}_{3}\)** & **X\({}_{4}\)** & **X\({}_{5}\)** & **X\({}_{6}\)** \\ \hline Max & 10,563.3499 & 13,653.3961 & 60 & 565 & 59 & 1 \\ Min & 10 & 17.7907 & 4.9 & 3.6 & 1 & 0 \\ Mean & 1848.6752 & 2693.0485 & 22.05 & 108.31 & 34.55 & 0.25 \\ Median & 584.8280 & 937.3138 & 21.51 & 50 & 35 & 0.25 \\ Standard deviation & 2715.0066 & 3616.6977 & 6.38 & 132.9 & 12.51 & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset descriptive statistics. Figure 2: Distribution of six characteristic parameters of the slope.
## 5 Conclusions  
In this paper, the time-varying formation tracking problem for general MAS was addressed. A distributed adaptive formation controller based on Riccati inequalities was proposed to solve the above problem. The proposed controller could adjust the coupling weights by the information from the neighbor agents, which is different from the traditional distributed static controller. In addition, we also discussed the condition of achieving formation for MASs under fixed and switching topologies. The desired formation could be reached under a jointly connected graph with the proposed method. The stability was demonstrated by using [PERSON]'s method. Last, two numerical simulations are given to show the efficiency of our method. The comparison experiments in the simulation demonstrated that the adaptive strategy has a positive compensating effect under a switching topology situation.  
In this article, the dynamics model and formation controller were both built by MATLAB/Simulink. The experimental data which we acquired was the result of computer simulation. In the follow-up work, we would build a physical UVA platform to verify the effectiveness of the proposed algorithm.  
Future research topics will extend the results to heterogeneous MAS or with a communication time delay.  
Figure 11: The comparison results between the proposed method and the schema with the constant coupling weights.  
Figure 10: Relative position snapshots of agents at different instants: (**a**) relative position at t = 0 s, (**b**) relative position at t = 3 s, (**c**) relative position at t = 15 s, (**d**) relative position at t = 35 s.  
**Author Contributions:** Software, data curation, writing--original draft preparation, Y.Y. and T.S.; methodology, validation, Y.Y. and H.L.; writing--review and editing, T.L.; supervision, project administration, T.L. and Z.C.; funding acquisition, Y.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the National Key R&D Program of China (Grant No. 2017 YFC0602000), the Science and Technology Development Project of Jilin Province (Grant No. 20190303061 SF) and the National Natural Science Foundation of China (Grant No. 51805200).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are included within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
However, although most of the aforementioned approaches have generated accurate forecasting results, the majority of them only modeled the prediction based on the historical air pollutants data and meteorological data. Only a few considered the temporal and spatial correlation of the data recorded by neighbor stations. [PERSON] et al. [PERSON] et al. (2018) developed a space-time support vector regression (STSVR) model to predict hourly PM2.5 concentrations, incorporating spatial dependence and spatial heterogeneity into the modeling process. [PERSON] et al. [PERSON] et al. (2017) proposed a long short-term memory neural network extended (LSTME) model that inherently considers spatial-temporal correlation for air pollutant concentration prediction. [PERSON] et al. [PERSON] et al. (2019) described a methodology for assigning individual estimates of long-term average air pollution concentrations that accounts for a complex spatial-temporal structure and can accommodate spatial-temporally misaligned observations. Still, these papers have a common limitation. Since the distribution of air quality monitoring stations is dense and balanced in some places but sparse and imbalanced in other places, spatial and temporal correlation of the air pollutant concentrations between two neighboring stations could be different. It might be strong when the distribution of stations is dense, and weak when the distribution is sparse. Weak correlation, on the other hand, might add more noise during the modeling process and influence the model performance. However, most of the previous studies did not well address this point. Therefore, a model that considers the strength of spatial and temporal correlation of air pollutant concentrations is required to further improve the air quality prediction accuracy. To this end, this paper proposes a correlation filtered spatial-temporal long short-term memory (CFST-LSTM) neural network for air quality prediction. A special spatial-temporal filter (STF) layer is designed into the ordinary LSTM network to optimize the various spatial-temporal time series from the input layer. In this way, highly correlated inputs are filtered, the influence of noisy data is mitigated, the complexity of the model is reduced, and therefore, the model performance can be improved. In this paper, PM2.5 concentration is selected as the prediction target. Historical PM2.5 data of California are collected.
# Deep Learning Models for Road Passability Detection during Flood Events Using Social Media Data
###### Abstract  
During natural disasters, situational awareness is needed to understand the situation and respond accordingly. A key need is assessing open roads for transporting emergency support to victims. This can be done via analysis of photos from affected areas with known location. This paper studies the problem of detecting blocked/open roads from photos during floods by applying a two-step approach based on classifiers: does the image have evidence of road? If it does, is the road passable or not? We propose a single double-ended neural network (NN) architecture which addresses both tasks simultaneously. Both problems are treated as a single class classification problem with the use of a compactness loss. The study was performed on a set of tweets, posted during flooding events, that contain (i) metadata and (ii) visual information. We studied the usefulness of each data source and the combination of both. Finally, we conducted a study of the performance gain from ensembling different networks. Through the experimental results, we prove that the proposed double-ended NN makes the model almost two times faster and the load on memory lighter while improving the results with respect to training two separate networks to solve each problem independently.  
Keywords:flood detection; road passability; image classification; emergencies; social media; deep learning +
Footnote †: journal: Applied sciences
## 6 Experimental Validation
### Overspeed Monitoring Validation  
By monitoring the displacement per unit time in the direction of the elevator Z-axis, the height difference is used to carry out the calculation of real-time elevator speed. According to GB/T 10058-2009 [29], the overspeed protection device of the elevator car should be able to monitor the speed loss control of the car, the lower limit of which is 115% of the rated speed of the elevator, so 115% of the rated speed of the elevator is set as the threshold of overspeed of the elevator, and the test is carried out by using the elevator's fast-running gear, the rated speed of the elevator is 1.5 m/s. The elevator is found to be able to effectively complete the fault determination of the elevator's overspeed, and the results of its monitoring and verification are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Actual Floor**} & **Actual Relative** & **Measured Relative** & \multirow{2}{*}{**Height Error/m**} \\  & **Height/m** & **Height/m** & \\ \hline
1 & - & 0 & 0 \\
2 & 3.5 & 3.3814 & \(-\)0.1186 \\
3 & 7 & 7.1359 & +0.1359 \\
4 & 10.5 & 10.6824 & +0.1824 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical results of data.  
Figure 20: Comparison between traditional peak-to-peak algorithm and peak-to-peak algorithm with 95% confidence interval.
# GCBAM-UNet: Sun Glare Segmentation Using Convolutional Block Attention Module
###### Abstract  
Sun glare poses a significant challenge in Advanced Driver Assistance Systems (ADAS) due to its potential to obscure important visual information, reducing accuracy in detecting road signs, obstacles, and lane markings. Effective sun glare mitigation and segmentation are crucial for enhancing the reliability and safety of ADAS. In this paper, we propose a new approach called \"GCBAM-UNet\" for sun glare segmentation using deep learning. We employ a pre-trained U-Net model VGG19-UNet with weights initialized from an ImageNet. To further enhance the segmentation performance, we integrated a Convolutional Block Attention Module (CBAM), enabling the model to focus on important features in both spatial and channel dimensions. Experimental results show that GCBAM-UNet is considerably better than other state-of-the-art methods, which will undoubtedly guarantee the safety of ADAS.  
sun glare; ADAS; VGG19; UNet; ImageNet; CBAM; GCBAM-UNet Article
###### Abstract
However, when it comes to networking data, which includes both mixed discrete, continuous, and intricate temporal correlation data types, GANs perform poorly [6]. Even while GAN-based time-series generation is possible, for example, for medical time series, these methods fall short for more complicated data, showing low autocorrelation scores on lengthy sequences and being vulnerable to mode collapse [5]. Sensor readings, time-stamped log messages, stock market values, and medical records are a few examples of the potential uses for synthetic time-series data. Synthetic data face numerous difficulties because of the additional dimension of time, where trends and correlations across time are just as significant as correlations between variables [7]. To overcome this, TimeGAN (time-series generative adversarial network) [8] was introduced, but still, the multivariate factor was not properly addressed, and further increasing the number of parameters and size, it was unable to perform adequately. Unfortunately, there is no set consensus on the metrics for evaluating the generated data for time-series GANs because of the small number of research studies that have been published [9; 10; 11; 12]. Different strategies [13; 14] have been put out, but as of now, none has emerged as the leader in the metrics field. Hence, we, in this research, have aimed at improving the time-series (TS) data's multivariate factor by implementing our proposed model named MTS-TGAN, which is an extension of TimeGAN [8]. The novelty of the paper is designing, implementing, and evaluating (both qualitatively and quantitatively) the model which can generate multivariate time-series data with better evaluation scores. The further novelty of our proposed model (MTS-TGAN) includes the inception of a preprocessing layer, namely, a feature selector which outputs the important features that help in removing extra noise, which hinders the synthetic generation of data, as well as hyper-parameter tuning, such as having a number of GRU layers [15], such as six (for all four modules, namely generator (G), discriminator (D), embedding (E) and recovery (R) functions) [8] and using the activation function ReLU [16] and implementing the proposedmodel on two datasets, such as a Google stock [17] and UniMiB human activity recognition (HAR) dataset [18] to show that the model is efficient in working with the multivariate TS data. An outline of the paper is as follows: In Section 2, related research in this field is compared and contrasted. Section 3 presents the problem statement. Section 4 discusses the proposed MTS-TGAN model. By summarizing the experimentation's outcomes, Section 5 provides a summary of the main findings. Section 6 provides the conclusions and suggests potential directions for additional research.
# Cluster Head Selection Method for Edge Computing WSN Based on Improved Sparrow Search Algorithm
## 5 Simulation and Result
However, compared with other protocols, the ICSSA-CHS protocol performs well in reducing the remaining total energy of nodes, and the energy consumption of each round of the network is also less, making the node run up to 2206 rounds, which is higher than the other three methods and the limited extension of the network life cycle. In addition, we analyze the number of packets received by edge nodes, which is used to evaluate the information transmission capacity of the network. As shown in Figure 7, as the number of running rounds increases, the number of packets received by edge-end nodes increases gradually. This is because, with the operation of the network, the data exchanged by the sensor nodes gradually increase. However, the CMASSA-CHS protocol shows a higher level of data packet transmission than the LEACH, ILEACH, and WOA-RCP protocols, which shows that the CMASSA-CHS algorithm is more reasonable in selecting cluster heads and network clustering, which makes network transmission more stable and extends the network life cycle. Figure 6: The remaining energy of nodes of different algorithms after the number of running rounds increases.
## 3 Results
### Macro- and Micromutrient Removal by the Soybean  
3.4.1. The ANOVA of the Macro- and Micromutrient Rem Oval by the Plant  
The ANOVA analysis was performed to determine the effect of all parameters on soybean grain and vegetative mass yield and the removal of the macro- and micronutrients with the plant (Table 3).  
In general, all the treatments and their interactions have a significant influence on the Zn and Se removal by the plant. Moreover, for all the macro- (P, K, Ca, and Mg) and microelements (Fe, Mn, Cu, Zn, and Se), there were significant differences determined among the soybean varieties.  
Figure 5: Relationship between Zn or Se foliar application rates and Zn (**a**) or Se (**b**) content in soybean grain (blue marks represent 2020 and red 2021 year). The significance is marked as \(p\leq\) 0.01 **.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{3}{*}{\
\begin{tabular}{c} **Source of** \\ **Variation** \\ \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{c} **Plant Part** \\ **Yield** \\ \end{tabular} } & \multicolumn{6}{c}{**Removal**} \\ \cline{3-11}  & & & **Rg ha\({}^{-1}\)** & & & **g ha\({}^{-1}\)** & & **mg ha\({}^{-1}\)** \\ \hline Y & * & \(<\)0.0001 & \(<\)0.0001 & ns & ** & \(<\)0.0001 & \(<\)0.0001 & ns & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 \\ V & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 \\ B & ns & ns & ns & ** & ns & \(<\)0.0001 & ns & \(<\)0.0001 & \(<\)0.0001 \\ P & ns & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & ** & \(<\)0.0001 & \(<\)0.0001 \\ Y \(\times\) V & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & ** & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 \\ Y \(\times\) B & ns & ns & ** & ns & ns & ** & \(<\) 0.0001 & \(<\)0.0001 & \(<\)0.0001 \\ Y \(\times\) P & ns & ** & \(<\)0.0001 & * & ns & \(<\)0.0001 & \(<\)0.0001 & ** & \(<\)0.0001 \\ V \(\times\) B & ns & ns & ns & \(<\)0.0001 & ns & ns & ns & \(<\)0.0001 & \(<\)0.0001 \\ V \(\times\) P & \(<\)0.0001 & \(<\)0.0001 & ns & \(<\)0.0001 & ** & ns & \(<\)0.0001 & \(<\)0.0001 & \(<\)0.0001 \\ B \(\times\) P & ns & ns & ns & ** & ns & ns & ns & \(<\)0.0001 & \(<\)0.0001 \\ Y \(\times\) V \(\times\) B & ** & ns & ** & \(<\)0.0001 & ** & ns & ns & ** & \(<\)0.0001 & \(<\)0.0001 \\ Y \(\times\) V \(\times\) P & ** & \(<\)0.0001 & ** & \(<\)0.0001 & * & ** & ** & \(<\)0.0001 & \(<\)0.0001 \\ Y \(\times\) B \(\times\) P & ns & ns & ns & * & ns & ns & ns & \(<\)0.0001 & \(<\)0.0001 \\ V \(\times\) B \(\times\) P & ns & ns & ns & \(<\)0.0001 & ns & ns & ns & ns & \(<\)0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of variance (ANOVA) for the year (Y), soybean variety (V), Zn and Se biofortification treatments (B), and grain and vegetative mass (P) yield with macro- and micronutrient removal by the plant.
## 2 Materials and Methods
### BEPS Model Simulation and Evaluation
#### 2.4.2 Evaluation of Simulation Results  
In this study, the results of the BEPS model simulation were evaluated with precision using the determination coefficient (\(\rm{R^{2}}\)), root mean square error (RMSE), and absolute bias (aBIAS). The formulas for the calculation are as follows [13]:  
\[\rm{R^{2}}=1-\frac{\sum_{i=1}^{n}(m_{i}-o_{i})^{2}}{\sum_{i=1}^{n}(o_{i}-o_{i })^{2}} \tag{11}\]  
\[\rm{RMSE}=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(m_{i}-o_{i})^{2}} \tag{12}\]\[\text{aBIAS}=\frac{1}{n}\sum_{i=1}^{n}\lvert m_{i}-o_{i}\rvert \tag{13}\]  
where m\({}_{i}\) is the simulated value; o\({}_{i}\) is the observed value; and \(\overline{o_{i}}\) is the average value of the observed value. Generally, the larger the R\({}^{2}\) value, the smaller the RMSE and aBIAS values, the higher the accuracy, and vice versa.