esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Change in Microbial Community during Yulu Fermentation  
The change in microbial community during Yulu fermentation with _B. subtilis_ is shown in Figure 2. The \(\alpha\) diversity analysis can evaluate the change in richness and evenness of microbial community [31; 32]. In this study, all coverage index in the fermentation groups was over 0.999, indicating the good coverage of high-throughput sequencing (Figure 2A). As shown in Figure 2A, higher Sobs, Chao, and ACE indexes were observed in early fermentation. They all fell to the bottom in the BS30 group, suggesting minimum richness during this period. At the end of fermentation, all these three indexes slightly increased, indicating the improvement in richness of the microbial community. The Shannon index first increased and then decreased during Yulu fermentation, and it was the maximum in the BS30 group, while the Simpson index followed the opposite trend, suggesting highest evenness in the BS30 group and low evenness in the BS5 and BS45 groups (Figure 2A). The \(\beta\) diversity analysis can evaluate the similarity in microbial community in different samples [4]. As shown in Figure 2B, the microbial community in the B45 group had more difference than the other fermentation groups, while the microbial community in the B55, BS10, and BS15 groups was much similar.  
The microbial composition in Yulu during fermentation with _B. subtilis_ is further studied. As shown in Figure 2C, there were only three phyla with a relative abundance of over 0.1%. Firmicutes and Proteobacteria were the top phyla with a total relative abundance of over 96% in the overall fermentation. As Yulu fermentation processed, the relative abundance of Firmicutes first decreased and then increased to the maximum, reaching 94.60% at the end of fermentation, while the relative abundance of Proteobacteria followed the opposite trend. The high relative abundance of Firmicutes probably resulted from the addition of _B. subtilis_, which belongs to the Firmicutes genera. As shown in Figure 2C, there were nine microbial genera (relative abundance >0.1%) in Yulu during fermentation with _B. subtilis_. As fermentation progressed, _Bacillus_ obtained the prominent position in Yulu, in that its relative abundance first decreased from 93.64% at 5 d to 73.50% at 30 d and then increased to 93.63% at 45 d. _Pseudomonas_, _Stenotrophomonas_, and _Chloroplast_ showed similar trends; their relative abundance first increased and then decreased, peaking in the  
Figure 2: Change in the microbial community of Yulu during fermentation with _B. subtilis_. (**A**) \(\alpha\) diversity, (**B**) \(\beta\) diversity, and (**C**) microbial taxonomic composition at phylum and genus levels in different fermentation groups.  
B30 group, while the abundance of _Achromobacter_, _Tetragonococcus_, _Rhodococcus_, _Cyanobium_, and _Brucella_ significantly increased and reached the maximum at 45 d.
## 5 Conclusions  
A comprehensive mathematical model for calculating the mesh stiffness of a helical gear with addendum modification is established. The results obtained by the comprehensive analytical model are consistent with that of the finite element model, but only several seconds are needed by using the analytical method in this paper. The main conclusions include the following:  
1. The mesh stiffness of a helical gear will be decreased in multiteeth regions caused by the structure coupling. The more teeth involved, the greater the influence of structure coupling is.
2. The axial component of mesh force affects the mesh stiffness of the helical gear. It should not be ignored when an accurate calculation of mesh stiffness is carried out.
3. The fluctuation value of mesh stiffness decreases when a positive addendum modification coefficient is adopted. The addendum modification also changes the phase of mesh stiffness.  
Time-varying mesh stiffness is an important internal excitation source when the dynamic analysis of a gear transmission system is executed. Therefore, the fluctuation of mesh stiffness has a significant effect on the vibration of gear systems. Based on the comprehensive analytical model established in this study, the accuracy value of mesh stiffness could be obtained quickly. The dynamic response results, such as the vibration displacement and vibration acceleration of gear transmission, could be obtained by carrying out a dynamic analysis. The detailed study would be carried out in the following research.  
Methodology, formal analysis, validation, writing--original draft preparation, S.H.; conceptualization, writing--review and editing, J.W.; software, A.Z., C.Z. and J.Y.; writing--review and editing, C.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51775058, the National Key R&D Program of China, grant number 2018 YFB2001602, and the Fundamental Research Funds for the Central Universities of China, grant number 2020 CDCGJX030.  
**Acknowledgments:** The authors would like to gratefully acknowledge the National Natural Science Foundation of China (No. 51775058), the National Key R&D Program of China [No. 2018 YFB2001602], and the Fundamental Research Funds for the Central Universities of China (No. 2020 CDCGJX030) for their support. Finally, the authors would like to thank the editors and reviewers for their valuable comments and constructive suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Bidirectional DC-DC Converters  
The continuous flow of power is an important concern when it comes to renewable energy systems; therefore, bidirectional _DC-DC_ converters are employed to interface storage systems with the energy resource and load by reducing or eliminating the fluctuation in the output of renewable energy systems as a result of variations in climate conditions. They are also used between the energy source and motor supplied by batteries [17]. In medium-power rank devices where familiar and efficient energy storages are supercapacitors and batteries, the energy exchange between the storage device and the other components of the system requires the presence of a _DC-DC_ converter, and such converters must have a bidirectional power flow capability and should have an adaptable control in all operation modes [31; 32; 33].  
Only one-directional power flow management can be achieved with a conventional buck-boost converter when compared with bidirectional power, which can flow in two directions (forward (_FW_) and backward (_BW_)). Bidirectional _DC_ converters (_BDCs_) are a device for either stepping up or stepping down voltage level; it can facilitate two-directional power flow (both forward and backward power flows). Bidirectional _DC-DC_ converters are mainly used to manage the flow (forward and backward) of power in _DC_ bus voltage where power flow is in both directions, as shown in Figure 1. The conversion of the conventional _DC-DC_ converter into a bidirectional converter can be achieved using a bidirectional switch with a diode, with the current flow in both paths being accepted by anti-parallel with an insulated-gate bipolar transistor (_IGBT_) or metal-oxide-semiconductor field-effect transistor (_MOSFET_) employing a controlled switching procedure. Bidirectional _DC/DC_ converters are of two kinds based on existing galvanic isolation between the input and the yield; they are isolated bidirectional _DC_ (_IBDC_) and non-isolated bidirectional _DC_ (_NIBDC_) [34; 35]. The adaptability of the energy storage system can be improved by using a high-frequency isolated _DC-DC_ converter to replace the line-frequency transformer. The circuits of most _DC-DC_ converters are arranged asymmetrically to couple the two _DC_ connections to various voltages, from tens of volts to hundreds of volts [36]. Bidirectional converters have become more popular, as opposed to traditional unidirectional converters, as they permit power flow in both directions. They are mostly used in hybrid electric vehicles (_HEVs_), electric vehicles (_EVs_), uninterruptible power supplies (_UPS_), smart grids,renewable energy systems (_RESs_), and aerospace applications; they are also used in other systems that require batteries [18].
## 3 Results and Discussions
### Photodegradation on BHA
#### 3.3.2 Effect of Calculation Temperature  
Different calcination temperature would result in different morphology of prepared materials, ultimately affecting the photodegradation activity of catalysts. As seen from Figure 8, 500 \({}^{\circ}\)C was the appropriate temperature for 0.75%La/TiO\({}_{2}\), 0.20%Ce/TiO\({}_{2}\) and 0.70%Gd/TiO\({}_{2}\) while 450 \({}^{\circ}\)C was the appropriate temperature for 0.50%Yb/TiO\({}_{2}\). As we know, the material prepared at a low temperature would have a low crystallization degree and have a large amount of recombination centers for photogenerated electrons and holes. However, the excessive sinter would cause the agglomeration of catalyst particles or transform the phase of TiO\({}_{2}\) from anatase to rutile, leading to the lower photocatalytic activity. According to the published reports, the anatase form of TiO\({}_{2}\) always had higher photocatalytic ability [14; 15]. As a result, it could be concluded that rare-earth elements and calcination temperature both affect the photodegradation of BHA.  
Figure 7: Effects of doping amount of RE/TiO\({}_{2}\) on the photodegradation of benzohydroxamic acid (BHA). (**a**) La/TiO\({}_{2}\) (500 \({}^{\circ}\)C); (**b**) Ce/TiO\({}_{2}\) (500 \({}^{\circ}\)C); (**c**) Gd/TiO\({}_{2}\) (500 \({}^{\circ}\)C); (**d**) Yb/TiO\({}_{2}\) (450 \({}^{\circ}\)C).
# Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
## 4 Experiments
### Comparison with State-of-the-Art Methods
#### 4.3.4 Network Efficiency Analysis  
Table 4 shows the comparison results in terms of parameters, inference time, computational complexity, and restoration quality for the processing of one 1280 \(\times\) 720 resolution video frame from the GoPro dataset. The experimental results demonstrate that our method achieves the best restoration quality while maintaining moderate computational costs. In terms of model efficiency, our method employs 8.9 M parameters, which is higher than ESTRNN (2.47 M) and STFAN (5.37 M) but significantly lower than CDVD-TSP (16.2 M) and STDAN (13.8 M). Similarly, while the computational complexity of our model is higher than that of ESTRNN and STFAN, it remains more efficient than CDVD-TSP and STDAN. In terms of processing speed, our method can process one frame in 0.41 s, ranking in the middle among all compared methods, demonstrating a good balance between restoration quality and computational efficiency.
## 2 Materials and Methods
### Sensitivity Analysis  
ANN models are usually considered to be black-box prediction tools, which makes understanding their mechanism quite cumbersome [52]. To understand the features on which the model depends, quantifying the attribution of forecasting power to input features must be characterized. This issue was formerly considered in different studies [53; 54; 55].  
The attribution techniques are challenging to be analyzed empirically. Additionally, setting apart errors that are made by misbehavior of an attribution method or by misbehavior of a model is a challenging task. This gap could be addressed using an axiomatic approach, known as integrated gradients (IG) [56].  
The integrated gradients method is given as \(IG_{i}(x)\). Supposing a function \(F:R^{n}\rightarrow[0,1]\) represents a network model. Using \(x=(x^{1},\ldots,x^{n})\in R^{n}\) as an input and \(z\in R^{n}\) as a baseline relative to \(x\). Then a vector \(A_{F}(x,z)=(a^{1},\ldots,a^{n})\in R^{n}\) is the attribution of the input \(x\), where \(a^{i}\) is the attribution of \(x^{i}\) of function \(F(x)\). To extract the IG values, the gradients across the straight path between the input \(x\) and the baseline \(z\) should be integrated. Therefore, IG for \(i\)th dimension is donated by [56],  
\[IG_{i}(x)=(x_{i}-z_{i})*\int_{a=0}^{1}\frac{\partial F(z+a*(x-z))}{\partial x_ {i}}d\alpha. \tag{2}\]The integrated gradients method highlights the importance of each input parameter by giving a score to each input while approximating the integral of gradients. Thus, can lead to dimensionless diagram values. This it can be compensated and overcome by respecting units of the input values. In this study, all the input numerical values respect the SI units. This way allows the interpretation of the output diagrams of the sensitivity analysis by giving higher output values to the most significant parameters.
## 2 Methodology
### Coupling of Simulation-Optimization Models  
We used Python application libraries such as FloPy, Geatpy, and Bayes_opt to construct a simulation-optimization model for solving the practical problem of groundwater contaminant traceability in this paper. The Bayesian optimization was used to calibrate the water flow model and the SEGA algorithm was used to find the contaminant source location and model parameters related to the release history. Figure 4 shows the structure of the whole model.  
Figure 4: Model structure diagram.  
Figure 3: Example of crossover and mutation.  
The model structure is combined with several different parts (Figure 4). The simulation-optimization model includes two parts: simulation and optimization. The simulation part includes both numerical simulations of groundwater flow and solute transport simulation, while the optimization part generally only optimizes the solute transport model. However, for this paper, the optimization of parameters for the groundwater flow was added. The model coupling can be divided into two categories: firstly the coupling of the FloPy-based code and the numerical calculation software, which is the coupling of two technologies; secondly, the coupling of numerical seepage and solute transport models with the optimization algorithms, which is the intersection of two theories.  
Compared to other Python application libraries, the numerical models coded by FloPy are not computed by underlying scripts based on the Python language. Instead, the models are compiled into input files corresponding to simulation software such as MODFLOW and MT3 DMS. The computation process was performed by this software and allows the user to upload the results from the files generated by the numerical simulation software onto FloPy. FloPy designs the models while the numerical simulation software solves the models.  
The implementation of the first coupling described above, provides the conditions for the second coupling. Only the inputs and outputs in the optimization problem are important for the optimization algorithms such as Bayesian optimization and evolutionary algorithms, and not the computational process of the results. Therefore, the seepage model and the solute transport model can be coded into two functions. Here, the inputs are the parameters to be optimized in the corresponding models and the outputs are the results of the model runs, respectively. In this way, the groundwater model can be transformed into a generalized objective function to be optimized, providing an appropriate optimization method to find the optimal solution.
# A Color Consistency Processing Method for HY-1C Images of Antarctica
## 2 Data Preprocessing and Analysis
### Data Analysis  
#### 2.2.1 Uneven Lighting  
As a result of the changes of the light incident angle, plus atmospheric absorption and refraction, there is uneven lightness and/or color in HY-1C images. Different local parts of the image are illuminated differently, which causes uneven brightness and color in the overall image. An example image with uneven lighting is shown in Figure 1.
## 1 Introduction
In 2004, JAEA implemented new measurement techniques and operational control technologies to develop and operate a 30 L/h laboratory-scale system [13]. By 2014, JAEA had constructed a pilot-scale platform using industrial materials, achieving a hydrogen production rate of 100 L/h with continuous operation for 8 h [14]. Later, JAEA addressed pump failure issues by developing an axial seal technology, which extended the pilot system's operation time to 31 h and increased the hydrogen production rate to 20 L/h [15]. Additionally, the agency introduced an improved glass-lined protective casing [16] and proposed a method for estimating the construction strength of large-scale silicon carbide (SiC) based on effective volume theory [17]. These enhancements, along with other improvements, enabled the pilot platform to achieve 150 h of continuous operation, with a hydrogen production rate ranging from 10 to 30 L/h [18]. In 2009, the Institute of Nuclear Energy Technology (INET) at Tsinghua University constructed a glass-based hydrogen production system with a hydrogen production rate of 10 L/h [19; 20]. This system was the first to employ electrodialysis (EED) technology, achieving continuous hydrogen production at 10 L/h for 7 h. Subsequently, through system optimization, the laboratory-scale system was scaled up, ultimately achieving continuous hydrogen production at 60 L/h for 60 h [21; 22]. In 2015, the State Key Laboratory of Clean Energy Utilization (CEU) of Zhejiang University constructed a laboratory-scale hydrogen production system with a hydrogen production rate of 50 L/h. The system was validated through trial runs to ensure the normal operation of each module. In 2019, CEU developed a pilot-scale system capable of producing maximum hydrogen at 5 m\({}^{3}\)/h using industrial materials. After a segmented operation to verify the normal functionality of each module, the system achieved continuous operation for 4 h in 2021, with a hydrogen production rate of 80 L/h [23]. This article primarily focuses on the updating and optimization of the CEU's system and reports the latest 24 h testing results. The redesign and manufacturing of several key reactors and components, as well as continuous operational performance, will be reported here. Compared to previous studies, this paper designs a three-stage sulfuric acid decomposition reaction module to address corrosion issues in high-temperature sulfuric acid decomposition, and develops a heat-recovery HI decomposition module to reduce system energy consumption, ultimately achieving 24 h continuous high-yield system operation. This demonstration trial achieves a 13% increase in thermal efficiency, extends hydrogen production stability from 4 h to 24 h, and improves hydrogen purity from 92% to 98%. It effectively resolves the corrosion issues and waste heat utilization problems existing in the prior system.
## 1 Introduction  
Compared with existing 4G, there are significant improvements in 5G networks in terms of coverage, management, and accounting capabilities [1]. Such improvements boost the rapid pace of innovation in cellular communication technologies and generate new use cases in the era of the Internet of Things (IoT), autonomous driving, and augmented and virtual reality services. Empowered by these emerging applications and growing number of end-users, the number of interconnected devices as well as the volume of generated data have also been growing tremendously from year to year. According to the most recent Ericsson mobility report, currently there are around 8.1 billion mobile subscriptions and it is expected that total mobile data traffic will reach around 288 EB per month in 2027, meaning leading mobile networks will carry almost 300 times more traffic than in 2011 [2]. As a result, one of the main goals of 5G systems has become solving challenges that are not effectively addressed by 4G, such as demands for higher data rates, higher capacities, lower latency, real time processing, connectivity of massive numbers of devices, higher reliability, lower cost, improved QoS, and quality of experience (QoE).  
As the conventional networks based on \"one-size-fits-all\" design are unable to address these requirements efficiently and effectively, recent efforts have sought paradigm shifts in the network architecture [3]. In this respect, the concepts of network function virtualization, software defined networking, and edge computing has appeared. These technologies are recognized as the key enablers of 5G networks as they are able to provide a scalable, flexible, and programmable network platform to manage multiple services with heterogeneous requirements within strict performance limits. All of these efforts will also enable new vertical business segments and services for consumers and enterprise customers.
## 4 Results of the Acoustic method for Mistuning Identification
### Measurement Results
In the measured Fourier Transform analyses that represent a measure of the resonance amplitude, it is clearly visible that the eigenfrequencies \(f_{i}\) of the individual blades differ. The mistuning of the neighbouring blades is reflected in the relative eigenfrequency deviations  
\[\Delta f_{i}\ =\ \frac{f_{i}-f_{0}}{f_{0}},\ \ f_{0}=\frac{1}{N}\sum_{j=1}^{N}f_ {j} \tag{6}\]  
of all blades to one another. An example of the blade eigenfrequency mistuning of the measured impeller is shown in Figure 13. Describing the mistuning based on blade eigenfrequency deviations has the advantage that not all manufacturing tolerances of the impeller, for example deviations in shape or material inhomogeneity, have to be recorded individually. It is relatively difficult to find out in more detail the cause of the size of the mistuning of the individual blades, i.e., the extent to which material inhomogeneities or geometric deviations from the blade nominal dimensions contribute to this. Figure 12: FFT analysis of measured response signal using the measuring microphone and eigenfrequencies from the FE model—measured with additional weights. In the case of the examined impeller, a scan of all the blades was subsequently carried out using an optical scanner. From the geometric model created in this way, deviations from the blade nominal dimensions according to the impeller Computer Aided Design (CAD) documentation were first determined. Subsequently, a 3D finite element model was created based on the determined actual geometry of the blades, the material parameters were considered the same throughout the model. Since the results of the computational modal analysis of this model showed good agreement with the measured values according to Figure 13, it is obvious that the main cause of this surveyed impeller mistuning are the geometric deviations of the blade profiles.
## 3 Case Study--Results
### Dynamic analysis of the HEN
#### 3.3.1 Study the Open-loop Step Responses in Case No. 1
In order to prevent that from happening, it is advisable to investigate all the three components of the tuning of each PID controller (\(K_{p}\), \(K_{i}\), \(K_{d}\)) that is, gain values in the proportional, integral and derivative components) and to check the resulting transient responses. PID controllers for loops 1 and 2 (see Figure 2) were separately tuned according to the Skogestad tuning rules [20], by assuming step (10%) increases in the crude oil mass flows (+6.11 kg/s in \(M_{At}\) and \(M_{Ct}\)) in each of the branches \(A\) and \(C\). The control variable responses for each of the selected operation periods are shown in Figure 14a,b and the resulting values of the tuning parameters for PID controllers 1 and 2 are presented in Table 3.  
Figure 12: Open-loop responses of the HEN models ((a)—the end temperature \(T_{ABend}\) and (b)—the end temperature \(T_{CDend}\), after parallel branches) to +10% step change in the shell-side flowrate of selected exchangers. Figure 13: Open-loop responses of the HEN models ((a)—the end temperature \(T_{ABend}\) and (b)—the end temperature \(T_{CDend}\), after parallel branches) to +10% step change in tube-side flowrate of branch \(A\) and \(C\), and to \(-10\%\) step change in flowrate of branch \(B\) and \(D\).