esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Swelling Equilibrium  
To remove soluble radiolysis products immediately after irradiation, the prepared hydrogels were rinsed with distilled water several times per day for two weeks until reaching a constant weight for the dried samples. The gel fraction, g\({}_{t}\), was calculated as the ratio between the xerogel mass after removing soluble components and the initial polymer weight in the sample, before irradiation.  
After washing and drying, the xerogels were left to reswell in distilled water at room temperature. The swollen hydrogels were withdrawn from water, wiped with filter paper to remove excess surface water, and weighed. Equilibrium was reached when the hydrogels attained a constant mass. Equilibrium degree of swelling, SD\({}_{\text{eq}}\), was calculated as:  
\[\text{SD}_{\text{eq}}=\frac{\text{m}_{\text{s}}-\text{m}_{\text{d}}}{\text{m} _{\text{d}}} \tag{1}\]  
where m\({}_{\text{s}}\) is the weight of the hydrogel at equilibrium swelling and m\({}_{\text{d}}\) is the weight of the dry gel. The swelling procedure was performed for three samples from each xerogel and the mean values were considered.  
The swelling data were used to evaluate the average mass between crosslinks (M\({}_{\text{c}}\)) and the crosslinking degree (d\({}_{\text{c}}\)). The first structural parameter was calculated using Flory\(-\)Rehner equation [31]:  
\[\text{M}_{\text{c}}=-\frac{\rho_{\text{x}}\text{V}_{\text{w}}\Phi_{2}^{1/3}}{ \ln(1-\phi_{2})+\phi_{2}+\chi\phi_{2}^{2}} \tag{2}\]  
where \(\rho_{\text{x}}\) is the xerogel density, V\({}_{\text{w}}\)--the molar volume of water, \(\phi_{2}\)--the polymer volume fraction, and \(\chi\)--the polymer-solvent interaction parameter. The values for \(\chi\) were calculated by the following relationship [32]:  
\[\chi=\frac{\ln(1-\phi_{2})+\phi_{2}}{\phi_{2}^{2}} \tag{3}\]  
with \(\phi_{2}\) given by:  
\[\phi_{2}=\frac{1}{1+\frac{\rho_{\text{x}}}{\rho_{\text{w}}}\text{SD}_{\text{ eq}}} \tag{4}\]  
where \(\rho_{\text{w}}\) is the density of water.  
The density of the xerogels was picometrically determined using toluene as a nonsolvent (\(\rho_{\text{toluene}}\) = 0.8768 g/cm\({}^{3}\)). The measurements were done in triplicate at 20 \(\pm\) 0.1 \({}^{\circ}\)C.  
In the case of an ideal tetrafunctional network, the number of crosslinking points in 1 mL of polymer, i.e., crosslinking degree, can be evaluated using the equation below [30]:  
\[\mathrm{d_{c}}=\frac{\rho_{\mathrm{x}}}{2\mathrm{M_{c}}} \tag{5}\]
## 1 Introduction  
State-of-Health (SoH) defines the usability of the battery for an application and plays a crucial role in the accurate estimation of the remaining state-of-charge (SoC) of a battery. An erroneous SoH estimation results in overestimation of the better capability, as well as a false state-of-charge indication, eventually leading to premature system shutdowns. Furthermore, an unhealthy or aged battery has a lower charge/discharge efficiency and increased heat dissipation, which can cause thermal runaways and explosions if not treated seriously.  
The term \"State-of-Health\" is subjective, as it describes the battery's condition in general. Most studies in the literature associate the SoH of the battery with either a capacity fade, inferring mainly a decrease in the charge storage ability, or a power fade, inferring primarily the increase of impedance limiting the extractable power, or associated with both [1; 2; 3; 4]. An insight into already existing methods for evaluating the SoH using either power or capacity fade is provided in Section 2. These methods are grouped into experimental and adaptative methods. Experimental methods are very accurate [4; 5]. However, most of the methods discussed in the literature (see Section 2) address individual cell parameters and must therefore be tuned or reconfigured to fit to other cells, even if they have the same type. Other proposed methods in the literature require end-of-life parameters to calculate the current aging state.  
In this paper, we propose a SoH assessment method based on impedance spectroscopy for the online diagnosis of battery cells, which could be applied without needing pre-cycling information or being tuned by considering individual cell parameters. The aim is to realize a robust and practicable method with low complexity for SoH assessment based on measurements of the impedance spectrum.  
Typically, experimental methods for SoH assessment based on impedance spectroscopy fit equivalent circuits to the impedance spectra and correlate the most SoH sensitive equivalent circuit parameters, mainly the charge transfer's equivalent resistance, double-layer capacitance, or SEI layer, or all of them to the number of charging cycles.  
In this paper, we follow a different approach based on a detailed model of the spectrum of a battery cell describing the positive and negative electrodes together with the SEI layer. After model simplification and adaptation to the observable spectrum behavior, we look for a metric combining model parameters in a deterministic manner, which provides information about SoH.  
The paper is structured in six sections. In Section 2, an overview of battery structure, the effect of aging, and the main reasons for power fade and capacity fade. Section 3 contains the experimental details used to conduct the cycling and the impedance spectroscopy. Section 4 includes the proposed solution to estimate the SoH. Finally, Section 5 contains the IS data results, modeling, and analysis, in addition to applying the proposed method to extract the SoH.
# A Novel Dictionary-Based Image Reconstruction for Photoacoustic Computed Tomography
## 4 Conclusions  
In this paper, we propose a novel dictionary-based image reconstruction algorithm in which the desired information of the images is shown more accurately. The proposed method uses a combination of WT, DCT, and TV in order to preserve the necessary information within the reconstruction procedure. The algorithm was evaluated experimentally (using a single-element PACT system). EPI and PSNR were used as the quantitative metrics of evaluation. The qualitative and quantitative results presented here show that the proposed algorithm can generate images with specific emphasis on a desired feature, defined by the user. Quantitative analysis of EPI showed that, for a number of angles lower than 45, the proposed algorithm preserves the edges better than other methods. In addition, the calculated PSNR at 60 angles indicated that the proposed method improves PSNR by about 27%, 9%, and 7%, compared with BP, WT, and WT+TV, respectively. At every view angle, the improvement obtained by the proposed method compared to the above-mentioned methods was statistically significant (\(p\)-value < 0.001). The proposed technique is particularly useful when a low-cost PACT system (limited number of view angles) with a fine temporal resolution is required. However, all the improvements are obtained at the expense of a higher computational burden. As a feature work, we will evaluate the proposed method in an in vivo study. Accelerating the algorithm as well as reducing its computational complexity is the next major step.  
Conceptualization, P.O. and M.M.; Methodology, P.O., M.M. and A.H.; Software, P.O., A.H., M.Z., M.M. and X.H.; Validation, P.O., M.M., A.H., M.Z. and X.H.; Formal Analysis, P.O., A.H., M.M., X.H.; Investigation, P.O., M.M.; Resources, P.O., M.M., A.H., M.Z. and X.H.; Data Curation, P.O., A.H. M.Z. and X.H.; Writing--Origiginal Draft Preparation, P.O., M.M., A.H., M.Z. and X.H.; Writing--Review & Editing, P.O., M.M., M.N., M.O.; Visualization, M.N. and M.O.; Supervision, M.N. and M.O.; Project Administration, M.N.; Funding Acquisition, M.N.  
This project has been partially supported by a WSU startup fund and the Andersen Institute fund.  
We thank [PERSON] from Buffalo University for his constructive discussion and help in image reconstruction.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm** & **BP** & **Sparse (WT)** & **Sparse (WT+TV)** & **Proposed** \\ \hline Execution Time (sec) & 18.99 & 387.28 & 369.79 & 547.30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Execution time for different reconstruction algorithms with 120 view angles.  
GLCM Gray-Level Co-occurrence Matrix
* _OPO_ (2006) Optical Parametric Oscillator
* _NI_ (2014) National Instrument
* _MSE_ (2014) Minimum Square Error
# First Assessment of GF3-02 SAR Ocean Wind Retrieval
## 3 Results
### GF3-02 SAR Wind Speed from VV Polarization
#### 3.1.2 Compared with ERA5 Wind Speed  
Figure 4 shows a comparison of the wind speeds retrieved from GF3-02 VV polarization data and the ERA5 wind speeds. Among them, the abscissa is the wind speed of ERA5, and the ordinate is the wind speed retrieved by the GF3-02 SAR. Table 2 is the wind speed retrieval accuracy of GF3-02 VV polarization data based on each GMF model compared with ERA5 wind speed. The RMSE of the retrieved wind speeds are 1.53 m/s, 1.35 m/s, 1.18 m/s, 1.50 m/s, and 1.32 m/s, respectively. It can be clearly seen that with CMOD5.N, the RMSE is also the smallest, the deviation is 0.03 m/s, and the correlation coefficient is 0.84. Furthermore, the RMSE of the CMOD_VV is 1.32 m/s and the correlation coefficient is 0.83, which are also better than those of the CSARMOD2.
## 4 Experiments and Analysis
### Ablation Studies  
We conducted three ablation studies to analyze the contribution of each module: (1) ACD only; (2) LinkNet; (3) ACD and GLFFNet, i.e., ColorNet; (4) ColorNet and BFWLS. The ablations are carried out on the same dataset as the main experiments. Figures 8-11 show the effect of each module on color or detail performance, respectively. The quantitative comparison results are shown in Table 2.  
Figure 8: Color performance comparison of different modules: (**a**) symmetric codec (SCD) only; (**b**) asymmetric codec (ACD) only.  
Figure 9: _Cont._  
Figure 10: _Cont._  
Figure 9: Color performance comparison of different modules: (**a**) LinkNet; (**b**) asymmetric coded (ACD) only.  
Figure 8 shows that ACD can improve the phenomenon of partial color error and texture blur of SCD. For example, the textures of vegetation and buildings are clearer and the color of the road and sky is normal. At the same time, it follows from Table 2 that ACD has the most significant improvement in PSNR, by 0.9095. Furthermore, compared to LinkNet, ACD is more accurate in local colorization, as shown in Figure 9. In terms of qualitative metrics, all metrics of ACD are better than LinkNet. The effect of GLFFNet is clearly shown in Figure 10. Without GLFFNet, the edge color spills in vegetation and the color shows unevenly in buildings. MAE and PSNR slightly improved from the qualitative analysis and LPIPS is the best. The function of BFWLS is shown in Figure 11. Comparing the results with and without this module, we can draw the conclusion that BFWLS can not only smooth the artifact of the picture, but also enhance the texture of the vegetation. The introduction of the BFWLS has a greater impact on PSNR and SSIM, achieving a 0.7978 gain in PSNR and 0.0974 gain in SSIM. Moreover, S-CIELAB lowered by 1.1651.  
Figure 10: Color performance comparison of different modules: (**a**) asymmetric codec (ACD) only; (**b**) ACD combined with GLFFNet (ColorNet).
## 3 Results
### Microbial Community Analysis of Black Spots on the Lotus Tubers  
To elucidate the blackening mechanisms relevant to microbial reactions, we performed a 16S rRNA gene sequence analysis of extracted DNA from the black spots obtained from lotus tubers in the NI cultivation field. We observed 26,416 average nonchimeric sequence reads after DADA2 treatment and 326 OTUs from two black-spot samples. Then, we removed eukaryotic 18S rRNA gene sequences derived from \(N\). _nucifera_ and red campion (_Silene dioica_) and observed 17,051 average sequence reads. At the OTU level, the predominant microorganisms were the families _Rhodocyclaceae_ OTU001 (relative abundance rate 24.7%), _Rhodoferax_ OTU002 (8.2%), _Geobacteraceae_ OTU003 (7.8%), _Dechloromonas_ OTU004 (5.1%), and _Methyldenera_ OTU005 (3.7%), which are obligate methylotrophic bacteria (Figure 4). The most predominant microorganism, _Rhodocyclaceae_ OTU001, is closely related to _Georgfichsia toluolica_ strain G5G6 (NR_115995.1) with 99% (372/376 bp) similarity, which reduces nitrate, MnO\({}_{2}\), and various Fe(III) species and degrades aromatic compounds (e.g., toluene, ethylbenzene, phenol, and benzaldehyde) [20]. In addition, _G. toluolica_ cannot grow on sugars, hydrogens, or organic acids. Although the closest strain of _Rhodoferax_ OTU002 is _Rhodoferax antarcticus_ strain DSM 24876 (CP019240.1) at 100% similarity (374/374 bp), the bacterium is also closely related to _R. ferriducucus_ strain RKAT120 (KU179848.1) of dissimilatory Fe(III)-reducing bacteria (DIRB) at 99% similarity (370/374 bp) [21]. _Geobacteraceae_ OTU003 is related to _Pelobacter propionicus_ strain DSM 2379 (NR_074975.1) at 99% similarity (375/376 bp), and the bacterium also has the capability of iron reduction with propionate and acetate production [22; 23]. The closest strain of _Dechloromonas_ OTU004 is _Dechloromonas hortensis_ strain OX0627 (MG576023.1) at 99% similarity (375/376 bp), which is a chlorate-reducing, propionate- and acetate-oxidizing bacteria [24]. Thus, this study found that several DIRB and other reducing bacteria predominated in the black spots of lotus tubers.
## 3 Methodology
### Tuning Algorithms and Machine Learning Models
#### 3.3.1 Support Vector Machine (SVM)  
The support vector machine (SVM) algorithm is utilized for both classification and regression problems. The core principle of SVM is to find a hyperplane in the dataset space that can effectively separate different types of data samples [27]. This hyperplane should ideally be positioned in the center of the various samples, maximizing the distance between them. In the face of unprocessed datasets, this method significantly improves the generalization ability of the model. Moreover, when confronted with nonlinear problems, SVM can employ kernel function techniques to address them successfully. By employing dummy variables to represent each classification attribute, SVM efficiently classifies data by mapping the original dataset into a higher-dimensional feature space where linear separability is achieved.  
Firstly, a position hypothesis of the data sample in the data space is laid out. \(T=\{(x_{1},y_{1}),(x_{2},y_{2}),\ldots(x_{i},y_{i})\}\). X represents the I-I eigenvector in this space, and y represents the class of that eigenvector. \(x_{i}\in R^{D}\), \(y_{i}\in\{-1,+1\}\).  
The main purpose of SVM is to find a hyperplane that can maximize the separation of data types, so let this plane be:  
\[\omega^{T}\mathrm{x}+b=0 \tag{1}\]  
The position of the hyperplane must be in the geometric interval of the sample, so the search for the optimal hyperplane can be turned into the search for the common line farthest from each feature point, where the distance from the point to the optimal plane can be defined as:  
\[r=\frac{|\omega^{T}\mathrm{x}+b|}{\|\omega\|} \tag{2}\]We define the distance of the function as  
\[y_{i}\Big{(}\omega^{T}x_{i}+b\Big{)},i=1,\ldots,m \tag{3}\]  
By combining Formula (2) with Formula (3), the expression of geometric distance between the dataset and the hyperplane can be obtained.  
\[min\frac{y_{i}\big{(}\omega^{T}x_{i}+b\big{)}}{\|\omega\|},i=1,\ldots,m \tag{4}\]  
To improve the fault tolerance of the model, two planes parallel to the optimal plane are added.  
\[\left\{\begin{matrix}\omega^{T}x+b\geq 1,y_{i}=+1\\ \omega^{T}x+b\leq-1,y_{i}=-1\end{matrix}\right. \tag{5}\]  
Finding the maximum interval between the two planes is beneficial to fully partitioning the eigenvalues in the dataset.  
\[\gamma=\frac{2}{\|\omega\|} \tag{6}\]  
The Lagrange formula is established by combining the constraint conditions with the objective function.  
\[L(\omega,b,\alpha)=\frac{1}{2}\cdot\|\omega\|^{2}+\sum_{i=1}^{m}\alpha_{i} \Big{[}1-y_{i}\Big{(}\omega^{T}\cdot x_{i}+b\Big{)}\Big{]} \tag{7}\]  
\[f(x)=\omega^{T}x+b=\sum_{i=1}^{m}\alpha_{i}y_{i}x_{i}{}^{T}\cdot x_{i}+b \tag{8}\]
## 4 Discussion
Other fermentation factor related to _clostridial_ fermentation the ammonia N in silage which is predominantly a product of amino acids [49]. Proteolysis occurs mostly when the pH is too high and in the presence of a large amount of soluble proteins. Our study demonstrated that LAB incubation could reduce ammonia N concentration and this might be due to their ability to compete with the undesirable microorganism activates. However, the poor silage quality occurred when LAB failsto produce sufficient lactic acid during fermentation to reduce the pH and inhibit the growth of clostridia [53]. Successful fermentation process resulted in LAB domination among other micro-organism population, which produce lactic and other acids and due to presence of lactic acid, the pH drops to 5--3.8 [37; 54] which in consist to present study. As shown in this study, the fresh grasses were rich in LAB, with a relatively high content of yeast and aerobic bacteria, this numbers of undesirable organisms was decreased in both treated and untreated silage. researchers mentioned that LAB could replace undesirable micrograms yeast and moulds, thus improving silage preservation [55]. Unlike sweet sorghum, Napier grass silage had a high number of aerobic bacteria, mould and yeasts, according to the occurrence of a number of undesirable microorganisms at low levels in fresh plant materials, leading to their probable growth during the storage of silage, heceto anaerobic or aerobic spoilage [56]. However the aerobic microbial decreases the nutritional value of the silage but also presents a hazard to animal health and the products quality and safety. As known well, the quality of silage depends on the competition between different microorganisms [56]. At the end of ensiling the strain HY1, which had been clustered into the Lactobacillus showed the best results in pH dropping in Napier grass compared to sweet sorghum. While strain HY2, which is of the _Pediococcus acidilacticis_ species, recorded excellent results in reducing pH as well as in other fermentation parameters in sweet sorghum. _Pediococcus acidilactici_ are the main species used in fermentation processes as astarter (co-culture) for avoiding contamination [21] even though the strain _Pediococcusalussenii_ was not studied as a starter intensively and in our study showed good fermentation characteristics. Previously researchers found that the homolactic fermentation such as _pediccous acidacticicii_ stable for low WSC forages [53]. It is known that, adequate WSC and LAB counts are important for the rapid establishment and growth of LAB.
## 3 Results and Discussion
### Enzymatic Activity Variation during Desalting Process
#### 3.3.3. Lipase Activity Variation during Desalting Process  
Lipid enzymatic hydrolysis, or lipolysis by lipases hydrolyses triglycerides forming free fatty acids, which are then more susceptible to oxidation that results in the common off-flavour, is frequently referred to as rancidity [34]. The lipase activity in the cod muscle during the desalting process is presented in Figure 4B. The salted cod showed a lipase activity of 6.4 \(\pm\) 1.6 \(\times\) 102 umol free fatty acids (FFA)/min/g IS. The temperature of the cod desalting process affected the lipase activity, since this enzyme activity was considerably increased (\(p\) < 0.05) at 20 \({}^{\circ}\)C in the first 4 h to a value of 25.3 \(\pm\) 2.7 \(\times\) 102 umol FFA/min/g IS (4-fold), which was maintained (\(p\) > 0.05) with similar values occurring until 16 h of desalting. In the case of desalting at 4 \({}^{\circ}\)C, the activity was maintained at similar values (\(p\) > 0.05) during the first 8 h of the desalting process, but it increased 3-fold after 16 h (\(p\) < 0.05).  
The results indicated that the lipase activity in the desalting process at higher temperatures was higher than in the one carried out at refrigerated temperatures. It has been demonstrated that lipids in fresh fish are hydrolysed mainly by microbial enzyme activity, such as lipases and phospholipases, and non-microbial enzyme activity (i.e., natural lipase present in the fish muscle) [35]. However, in salted and hydrated cod fish, the contribution of endogenous enzymes is probably very low, with microbial growth being the main cause of higher lipase enzymes content and, consequently, a higher lipid hydrolysis rate. On the other hand, a prolonged period of desalting caused an increase in lipase activity, leading to an increase after 8 h of desalting at 4 \({}^{\circ}\)C, attributed to the increase in microbial lipases in cod muscle.  
Figure 4: Acid phosphatase ((**A**), nmol p-NP/min/g IS) and lipase ((**B**), \(\times\)102 \(\upmu\)mol FFA/min/g SI) activities in cod fish during desalting. Different letters (a–d) denote significant differences (\(p\) < 0.05) between desalting times. The symbol * denotes significant differences between desalting temperatures.
## 6 Conclusions and Future Work  
In this paper, we proposed an adaptive dimensional Gaussian mutation PSO (ADGMPSO) algorithm that incorporated three improvement strategies to enhance the performance of PSO in identifying optimal solutions. The experimental results comparing the proposed algorithm with mainstream evolutionary algorithms using ten benchmark functions demonstrated its advantages in the convergence speed, evading locally optimal solutions, and the convergence accuracy. The hyperparameter tuning experiments on the LeNet-5 and ResNet-18 models for the MNIST and CIFAR10 datasets further showed the superiority and generalization ability of the proposed algorithm.  
Despite the positive impact of our study, its limitations must be acknowledged. Firstly, since ADGMPSO aimed to improve the PSO algorithm's ability to avoid locally optimal solutions and convergence accuracy, it did not optimize the algorithm's running time and, therefore, did not reduce the algorithm's running time compared to the original algorithm. Secondly, when conducting the hyperparameters optimization experiments for more complex convolutional neural networks, this paper optimized the hyperparameters of only the first few layers, given the computational resources and time constraints, without optimizing the whole network structure.  
To overcome these limitations, our future research will first focus on considering both the algorithm's performance improvement and time complexity, propose a new multi-objective hyperparameter optimization improvement algorithm, conduct a more comprehensive hyperparameter optimization for more complex network structures to demonstrate the algorithm's superiority, and finally, carry out more extensive hyperparameter tuning of CNNs for other fields, such as speech recognition and natural language processing to demonstrate the algorithm's generalization capability.  
Figure 11: ResNet-18 average loss curve.  
**Author Contributions:** Conceptualization, C.W.; methodology, T.S.; data curation, T.S.; supervision, C.W.; writing--original draft preparation, T.S. and D.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Natural Science Foundation of China (62072363) and the Natural Science Foundation of Shaanxi Province (S2019-JC-YB-1191).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to thank [PERSON] and [PERSON] for all their support.  
**Conflicts of Interest:** The authors declare no conflict of interest.