esa-sceva/satcom-chunk-collection huggingface dataset:
# Small-Sample Bearings Fault Diagnosis Based on ResNet18 with Pre-Trained and Fine-Tuned Method
## 5 Conclusions  
Aiming at the problem that real bearing fault data are limited, this study proposes a small-sample FD model based on pre-trained and fine-tuned VSDP-TLResNet18. The method decomposes the rolling bearing signal by VMD technology initially and selects the \(k\) value and IMF component by using the center frequency and kurtosis criteria. Then, the SDP transform is used to map the one-dimensional signal to the two-dimensional spectrum. In order to maximize the difference in two-dimensional fault images, we used Pearson correlation analysis to select the optimal SDP parameters, ensuring that the chosen parameters can maximally distinguish differences between images. Finally, the small-sample FD model is used to map the one-dimensional signal to the two-dimensional spectrum.  
Figure 21: Confusion matrix of different networks on dataset D under Case 2.  
sample FD is realized by using the pre-trained and fine-tuned method with the ResNet18 model. The experimental results indicate an average accuracy of 99.81% on the CWRU dataset and 99.11% on the PU dataset, demonstrating that the proposed method exhibits strong robustness and generalization.  
Although our model achieves good accuracy on the two datasets, compared with other methods, even in the case of small-scale training data, the improvement in accuracy is still low. This shows that under the existing data and methods, our model has limited room for improvement in practical applications. In addition, we only carried out FD under fixed working conditions. With a change in the actual fault mode and equipment, the model needs to be updated and re-trained regularly. How to effectively manage and update the model was not discussed in depth.  
During the actual fault data collection process, some fault data may lack labels due to human or other factors. In a follow-up study, we will explore the methods to solve these problems. In the future, we also plan to explore the combination of traditional signal processing technology and methods in the field of machine vision to play a potential role in small-sample FD. Through these improvements and explorations, we hope to further improve the performance and applicability of the model.  
Conceptualization, J.P. and J.N.; methodology, J.N.; software, J.N. and Z.Q.; validation, J.P., J.N. and F.H.; formal analysis, J.N. and Z.Q.; resources, J.P.; data curation, F.H., writing--original draft preparation, J.N.; writing--review and editing, J.P. and H.Q.; visualization, F.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Wuzhou Central Leading Local Science and Technology Development Fund Project under grant no. 202201001.  
Not applicable.  
The sources of data used in this article are linked in the References section. They are publicly available.  
The authors declare no conflicts of interest.
# Anisotropy and Directivity Effects on Uniaxial Compression of Carbonaceous Slate Form Jinman Mine
## 3 Results and Discussion
### Loading Effect of Mechanical Parameters
#### 3.2.3 Characteristics of Mechanical Parameters  
The above analysis shows that the uniaxial compression mechanical properties of gray slate and cinerous slate have certain discrete characteristics. The concept of variation coefficient [16] is introduced to describe the characteristics of carbonaceous slate and is defined as the ratio of the mechanical parameters of parallel bedding and vertical bedding for the same stress loading state. Based on the peak strength (\(\sigma_{c}\)), expansion strength (\(\sigma_{cd}\)),and elastic modulus (\(E\)), the variation coefficient (\(K\)) is defined by the following equation, where 'par' indicates that the loading stress is parallel to the slate structure, and 'var' indicates that the loading stress is perpendicular to the slate structure.  
\[K=\frac{\psi_{var}}{\kappa_{par}}\]  
Figure 6 shows the variation coefficients of the slate samples based on the above equation. The variation coefficient of gray slate samples is characterized by peak stress (\(\sigma_{c}\)), expansion stress (\(\sigma_{cd}\)), and elastic modulus (\(E\)) of 1.22, 1.35, and 0.69, respectively. The variation coefficient of the carbonaceous slate samples is characterized by peak stress (\(\sigma_{c}\)), expansion stress (\(\sigma_{cd}\)), and elastic modulus of 1.46, 1.38, and 0.71, respectively. The above analysis shows that the uniaxial compression mechanical properties of gray and cinerous slate under different loading conditions have significant and discrete characteristics. The variations of carbonaceous samples are greater than those of gray samples, and they show complex fracture patterns and uneven fracture patterns in macroscopic views. The discrete degree is significantly related to the direction of stress loading and different types of slate structures.  
Analysis of the slate structural features shows that there are differences in the appearance and morphology of slate samples. Gray slate alternates with being gray and white, with a good filling and adhesive bonding in the bedding. The appearance of carbonaceous slate is uniform in color, but the slate structure is weak. The structural surface has muddy fillings, and the distribution of mineral composition and original fractures are quite different, showing complex fracture modes and uneven fracture morphology at the macro level. The superimposed structural characteristics of slate samples show that the discrete characteristics of mechanical parameters are also related to the distribution characteristics of macroscopic joints. The directionality of the cleat system restricts the anisotropy of the mechanical properties. Different stress loading directions and complex bedding superimposed structures are the external and internal reasons for the variability of slate mechanical characteristics.
# Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model
## Appendix A Derivation of SINR Coverage Probability
### Laplace Transform for \(\zeta=1\)  
For \(\zeta=1\) which corresponds to \(p=1\), (12) can be expanded in following steps,  
\[P_{c}(T) =2\pi\lambda\int\limits_{0}^{\infty}r\exp\left(\sum_{q=0}^{2} \lambda a_{q}\big{(}Ta_{\texttt{x}_{k}}\big{)}r^{q}\right)dr\] (A32) \[=2\pi\lambda\int\limits_{0}^{\infty}r\exp\left(\lambda\sum_{k=1}^ {4}\frac{2\pi}{\kappa^{2}}b_{k}Li_{2}\big{(}-Ta_{\texttt{x}_{k}}\big{)}+\lambda \sum_{k=1}^{4}\frac{2\pi}{\kappa}b_{k}Li_{1}\big{(}-Ta_{\texttt{x}_{k}}\big{)} r+\lambda(-\pi)r^{2}\right)dr\] (A33) \[=\prod_{k=1}^{4}2\pi\lambda\exp\left(\frac{2\pi\lambda}{\kappa^{2 }}b_{k}Li_{2}\big{(}-Ta_{\texttt{x}_{k}}\big{)}\right)\times\int\limits_{0}^{ \infty}\exp\left(-\frac{2\pi\lambda}{\kappa}b_{k}y\big{(}Ta_{\texttt{x}_{k}} \big{)}r\right)\exp\left(-\pi\lambda r^{2}\right)r\,dr\] (A34) \[=\prod_{k=1}^{4}\exp\left(\frac{2\pi\lambda}{\kappa^{2}}b_{k}Li_{ 2}\big{(}-Ta_{\texttt{x}_{k}}\big{)}\right)\times\left(1-\frac{\pi\sqrt{ \lambda}}{\kappa}b_{k}y\big{(}Ta_{\texttt{x}_{k}}\big{)}e^{\frac{\pi\lambda b_{k }^{2}y(Ta_{\texttt{x}_{k}})}{\kappa^{2}}}Erfc\left(\sqrt{\frac{\pi\lambda}{ \kappa}}b_{k}y\big{(}Ta_{\texttt{x}_{k}}\big{)}\right)\right)\] (A35) \[=\prod_{k=1}^{4}\exp\left(\frac{2\pi\lambda}{\kappa^{2}}b_{k}Li_{ 2}\big{(}-Ta_{\texttt{x}_{k}}\big{)}\right)\times\left(1-\frac{2\pi\sqrt{ \lambda}}{\kappa}b_{k}y\big{(}Ta_{\texttt{x}_{k}}\big{)}e^{\frac{\pi\lambda b_{k }^{2}y^{2}(Ta_{\texttt{x}_{k}})}{\kappa^{2}}}Q\left(\frac{\sqrt{2\pi\lambda}}{ \kappa}b_{k}y\big{(}Ta_{\texttt{x}_{k}}\big{)}\right)\right)\] (A36)  
In (A33) \(Li_{1}\big{(}-Ta_{\texttt{x}_{k}}\big{)}=-ln\big{(}1+Ta_{\texttt{x}_{k}}\big{)}\) and \(y\big{(}Ta_{\texttt{x}_{k}}\big{)}=ln\big{(}1+Ta_{\texttt{x}_{k}}\big{)}\). (A34) is from integral transform of exponential function \(\int_{0}^{\infty}e^{-pz}e^{\left(-ax^{2}\right)}Xdx=2b-2\pi^{1/2}b^{3/2}e^{bp^{2 }}pErfc\left(p\sqrt{b}\right)\)here, \(b=\frac{1}{4a}\). Finally, by the relation of \(Q\) and \(Erfc\), \(Q(x)=\frac{1}{2}Erfc\Big{(}\frac{x}{\sqrt{2}}\Big{)}\) the expression for coverage probability is obtained in (A36).
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 4 Results
### Drought Propagation  
To analyze the propagation process of the extreme drought in the terrestrial water cycle, we normalized the time series of six meteorological and hydrological elements (PRE, ET, surface runoff, subsurface runoff, SM, and TWSC). In Figure 13, starting from June 2023, PRE and ET were low and high, respectively, compared to the normal years. The study region was in meteorological drought at this time. This indicates that this extreme drought was triggered by the combined effect of low PRE and high ET.  
As low PRE and high ET persisted, surface runoff was the first to be affected, with a rapid deficit emerging, marking the onset of surface water drought. The maximum correlation coefficients between surface runoff and PRE and between surface runoff and ET were 0.96 and \(-\)0.78, respectively, with lag periods of 0 months and 1 month. Meteorological drought is a combined effect of PRE and ET. Surface water is highly sensitive to precipitation deficits and responds significantly faster to ET than to PRE. Therefore, in the same month that precipitation deficits occurred, surface water deficits also emerged, indicating that meteorological drought and surface water drought occurred simultaneously. In July 2023, the continuous reduction in surface runoff led to deficits in subsurface runoff and SM, thereby triggering groundwater drought and agricultural drought. The maximum correlation coefficients between PRE and subsurface runoff and between PRE and SM were 0.89 and 0.99, respectively, with a lag period of 1 month for both. For ET, the maximum correlation coefficients with subsurface runoff and SM were \(-\)0.73 and \(-\)0.83, respectively, with a lag period of 2 months for both. Combined with the results of surface runoff, the responses of subsurface runoff and SM to PRE and ET lagged significantly by 1 month. Thus, the propagation of meteorological drought to groundwater drought and soil water  
Figure 12: The center of gravity of the extreme drought movement trajectory maps.  
drought occurred 1 month later than that to surface water drought. As surface hydrological drought, groundwater drought, and agricultural drought continued to intensify, TWSC finally showed a deficit in October 2023, triggering a comprehensive hydrological drought. It is evident that this extreme drought transitioned from meteorological drought to hydrological drought within 5 months. The maximum correlation coefficients between PRE and TWSC and between ET and TWSC were 0.94 and \(-\)0.92, respectively, with a lag period of 5 months for both. These results also confirm the timeline of the transition from meteorological drought to hydrological drought.
# Cellulases Production by a _Trichoderma_ sp. Using Food Manufacturing Wastes
###### Abstract  
The cost of cellulase enzymes is a main contributor to the operational cost of a biorefinery producing ethanol from lignocellulosic material. Therefore, onsite production of enzymes using low-value substrates might be an option to make a bio-based facility more economical, while improving environmental sustainability. Food manufacturing wastes (FMWs), such as olive mill solids, tomato pomace, and grape pomace, are some of the main wastes produced by the food industry in Chile. FMWs are mostly composed of lignocellulosic material, which is primarily made of cellulose. A fungal strain obtained from olive stones was identified as a _Trichoderma_ sp. and characterized by molecular and morphological techniques. This strain was able to grow on three FMWs in both liquid and solid cultures. In liquid cultures, cellulase and \(\beta\)-glucosidaseate activities from the culture supernatants were quantified. Identification of extracellular proteins using mass spectrometry revealed the presence of endoglucanases, exoglucanases, and \(\beta\)-glucosidaseates. Cellulase production from agroindustrial residues could be an excellent opportunity to utilize FMWs as well as decrease enzyme production costs in biorefinery processes.  
enzymes; food manufacturing wastes; olive mill solid waste; tomato pomace; grape pomace; cellulases; _Trichoderma_ sp., bioethanol; biorefinery
## 5 Data Analysis and Method Implementation
### First Stage: Data Pre-Processing  
Data pre-processing plays an essential part in machine learning as the quality of the information extracted from the data are responsible for affecting the model's learning quality and capacity. Therefore, pre-processing the data is crucial before feeding it to the algorithm for training, validation, and testing purposes.  
In general, raw datasets may contain missing feature points, disorganized data, and imbalanced input feature points over the attributes. For optimum accuracy, datasets are pre-processed, which includes handling the missing values, standardizing the data, and balancing the imbalanced distribution of feature points. An imbalanced dataset contained an uneven number of input examples for the output classes. The study utilizes Python programming language, which has inbuilt libraries such as NumPy, sklearn, matplotlib, which support ML algorithms strongly. Furthermore, the libraries offer different classes and methods responsible for performing different tasks for the provided datasets.  
The datasets used in the study include the data collected from four different earthquakes, which occurred in Ecuador, Haiti, Nepal, and Pohang. Haiti's dataset contains few missing values, whereas all the datasets show the disproportionate distribution of feature points over all the eight input parameters. For Haiti, the missing values are replaced with the attribute column's respective mean value using the SimpleImputer class from Python's sklearn library. SimpleImputer verifies the missing values; if so, it replaces the missing value using the mean/median/most frequent/constant value from the respective column. Standardization of the data is performed by MinMaxScaler class from the sklearn library, which helps to scale the feature points between range 0 and 1. Imbalanced categorization acts as an objection for predictive modeling as most of the machine learning algorithms applied for classification are created around the idea of an equal number of examples for each category class. For balancing the feature point distribution over the different classes, the over-sampling technique of SMOTE [80] method is used. It creates synthetic samples for the minority class, hence making a balance with the majority class.  
Subsequently, the dataset splits into two subsets, a training set with 80% data and testing sets with 20% data. Splitting of data in ML is significant, where the hold-out cross-validation ensures generalization to ensure that the model is accurately interpreting the unseen data [81].
# CFD-DEM Simulation of the Transport of Manganese Nodules in a Vertical Pipe
## 4 Numerical Preparations  
### Numerical Setup  
Duplex stainless steel was selected as the material of the vertical pipe. The pipe has an inner diameter of 0.1 m and a length of 5 m. Manganese nodules, with a density of 2040 kg/m\({}^{3}\) served as solid particles. Seawater, with temperature of 24 \({}^{\circ}\)C, density of 1044 kg/m\({}^{3}\), and dynamic viscosity of 1.01 \(\times\) 10\({}^{-3}\) Pa-s, was used as the liquid phase. The reference pressure was set to 101,325 Pa. The outflow boundary condition was defined at the outlet of the computational domain. No-slip boundary conditions were set at all walls wetted by the two-phase flow. The surface roughness of the inner wall of the pipe was set to 100 \(\upmu\)m, which is equivalent to the practical situations.  
CFD-DEM simulations require appropriate time steps for both solid and liquid phases. The solid time step is limited by Rayleigh time \(\Delta t_{R}\), which is given by:  
\[\Delta t_{R}=\pi\frac{d_{s}}{2}\left(\frac{\rho_{s}}{G_{s}}\right)^{\frac{1}{ 2}}\left(0.1631\sigma_{s}+0.8766\right)^{-1} \tag{26}\]  
where \(d_{s}\) is the diameter of the particle (m), \(\rho_{s}\) is the density of the particle (kg/m\({}^{3}\)), \(G_{s}\) is the shear modulus of the particle (Pa), and \(\sigma_{s}\) is the Possion's ratio of the particle. According to the particle properties used in the study, the \(\Delta t_{R}\) is calculated as about 1.6\(\times\)10\({}^{-4}\) s. The DEM time step \(\Delta t_{s}\) is often smaller than the Rayleigh time and is set to 1.0 \(\times\) 10\({}^{-4}\) s. The CFD time step \(\Delta t_{l}\) is set to 1.0 \(\times\) 10\({}^{-3}\) s, which should be 10\(\sim\)100 times the DEM time step. The total simulation time is 3 s, in which the residual converges below 10\({}^{-3}\) in Fluent. The parameter settings of EDEM and ANSYS Fluent are listed in Table 1.  
Table 2 summarizes the cases simulated. The initial SVF (\(C_{\mathrm{p,0}}\)) indicates the ratio of the volume flow rate of particles to that of liquid at the inlet of the vertical pipe. The values of initial SVF were set to 1%, 5%, and 10%. The shape of the particles was defined as spherical. Three particle diameters, 5.0 mm, 15.0 mm, and 30.0 mm, were selected. The initial mixture velocity represents the velocity of the liquid and solid released at the inlet of the pipe, and it varies between 3 m/s, 4 m/s, and 4.5 m/s. The direction of the initial mixture velocity is comparable to the inlet section of the pipe.
Geometric analysis includes the determination of mean fracture orientation, length, aperture and volumetric density [40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50]. Fracture network parameters can be estimated manually by using semi-automatic and automatic measurements in rock samples [51; 52; 53; 54] and indirectly by using electrical conductivity, Vp and Vs velocity measurements [38]. Other approaches, involving techniques such as X-ray tomography [55; 56] or confocal scanning laser microscopy (CSLM) [57] are also used. Methods of microstructural analysis always rest on the determination of geometric parameters of all microfractures, for which its number can reach hundreds of thousands in a single-oriented thin section. Obviously, without using any special technique of automatic detection, the solution of this problem is not only technically complex but is also time consuming. Many works present results of successful automatic lineament detection at microscales [27; 58; 59; 60], mesoscales [61; 62] and macroscales [61; 62; 63]. The development of digital technologies allows taking a fresh look at microstructural analyses. The geospatial binding of thin sections allows the performance of microfracture detection by mapping objects at the area of thin section, which can be a conditional cartographic basis. GIS techniques can be used to solve spatial problems. In terms of territorial coverage, it is customary to distinguish between global GIS, subcontinental GIS, national GIS, regional GIS, subregional GIS and local GIS [64; 65; 66]. Geospatial binding of thin sections allows the performance of the transition from macro-GIS projects to micro-GIS. This paper presents a new approach for the microstructural analysis of rock samples based on an algorithm of automatically mapping microfractures and their geospatial binding using GIS tools. The advantages of STMA method are shown by analyzing the microfractures of rock samples collected across the core of the Primorsky Fault. In the context of ongoing microstructural studies, given that the studied rock samples belong directly to the damage zone of the Primorsky Fault, the rocks were characterized by the type of tectonics among other parameters. In most cases, tectonics are defined as rocks that have experienced tectonic stress [67; 68]. Rocks with pronounced schistory are presented by protomylonites, blastomylonites, nylonites and ultramylonites. Rock samples without obvious schistory are subdivided into protocatcalasites, blastocatcalasites, cataclasites and ultracatcalasites. Mylonites are fine-grained, siliceous, very densely banded or vein-like rocks resulting from the fine crushing of coarse-grained rocks without noticeable chemical transformations. Mylonites are the end product of intensive dislocation metamorphism [67; 68]. Catcalasites are rocks that formed during discontinuous deformations (brecciation and crushing) of brittle parent rocks. Catcalasites gradually transform intoylonites via a decrease in grain size and the development of banded textures.
## 3 Vitamin A, Its Vitaminoids and Carotenoids' General Profile and Health Benefits
### Vitamin A, Vitaminoids, and Carotenoids from Microorganism Sources  
Microbial-derived sources provide a sustainable, scalable platform for producing carotenoids and retinoids without relying on animal, plant, herb, or marine sources. More specifically, except for microorganisms of animal, plant, or marine origin that have been included in the previous sections, other microbial-associated sources can be cultivated in controlled bioreactors with optimized conditions, yielding high-purity carotenoids and retinoid derivatives for nutticosmetics, cosmeceuticals, and cosmetics applications. Microorganisms, including bacteria, fungi, yeast, and microalgae, represent a cutting-edge solution for meeting increasing demands in the cosmetic industry, particularly for products targeting skin photo-protection and anti-aging [18; 149]. Filamentous fungi, especially ascomycetes, generate secondary vitamin A metabolites such as \(\beta\)-carotene, while a heterotrophic zygomycetes fungus formulates lycopene with significant coloring, antimicrobial, anti-cancer, antioxidant, anti-proliferative, antiradical, and cytotoxic activity. In _Blakeslea trispora_ fungus, the biosynthesized \(\beta\)-carotene and is utilized widely in biotechnological processes for microbial stimulation and oxidative stress prevention, while other _Mucor circinelloides_ strains and _Fusarium sporotrichoides_ species yield lycopene and \(\beta\)-carotene, have the potential to produce mycotoxins, enhance health, and lower cardiovascular/circulatory disease risk [18; 106; 213; 214]. Moreover, yeast-derived microorganisms like _Rhodotorula_ spp. (e.g., _Rhodotorula_ glutinis) are prolific producers of carotenoids like \(\beta\)-carotene, torulene, and torularhodin, with several antioxidant and photo-protective properties in nutraceuticals and nutticosmetics, while others like _Phaffia rhodozyma_ generate \(\beta\)-carotene and astaxanthin, with vital skin photo-protection and anti-aging properties [48; 106; 215]. Bacteria producing microorganisms, such as _Paracoccus_ spp., _Dietzia natronolimnaea_, _Crocibacterium aestuarii_, _Microoccus luteus_, and _Gordonia jacobaea_, are capable of producing astaxanthin, canthaxanthin, and zeaxanthin and also own antioxidant, anti-inflammatory, and anti-aging properties. Additionally, non-marine microalgae (cultured in controlled systems), including _Chlorella zofingiensis_ and _Coelastrella_ spp., produce \(\beta\)-carotene, astaxanthin, and canthaxanthin under stress conditions and exhibit beneficial skin photo-protection, anti-aging, and antioxidant protection [106]. Finally, retinoid-producing microorganisms like engineered bacteria (e.g., _Escherichia coli_) that produce retinol and yeast (e.g., _Saccharomyces cerevisiae_) that convert carotenoids such as \(\beta\)-carotene to retinoids like retinol and retinoic acid in controlled fermentation systems also possess several benefits [106; 216; 217].