esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Effect of the Particle Size Ratio on the Macroscopic Mechanical Behavior
### Discussion  
Based on the analysis above, the decreases in the uniaxial compressive strength and elastic modulus with an increasing particle size ratio can be attributed to the changes in particle interactions and microstructural homogeneity. As the particle size ratio increases, the model exhibits a wider range of particle sizes, leading to more significant voids and irregularities in the particle arrangement. This weakens the inter-particle interactions and reduces the overall strength and stiffness. When the particle size ratio is 1.0, all particles are of the same size, resulting in a more uniform and regular arrangement. This enhances the inter-particle interactions and bonding strength, leading to a higher compressive strength and elastic modulus. As the particle size ratio increases, the presence of larger particles creates more significant voids and irregularities in the particle arrangement, weakening the inter-particle interactions and reducing the overall strength and stiffness.  
The observed decrease in the dispersion of the compressive strength and elastic modulus with an increasing particle size ratio (beyond 1.5) can be explained by the more effective stress distribution and reduced sensitivity to the particle arrangement. As the particle size ratio increases, the model becomes less sensitive to the spatial arrangement of particles. The presence of a wider range of particle sizes allows for a more effective stress distribution, reducing the impact of local variations in the particle arrangement. This leads to a more uniform stress distribution and lower dispersion of the mechanical properties.  
The decrease in shear stiffness with an increasing particle size ratio can be attributed to the reduced interlocking effect and lower microstructural stability. In models with a lower particle size ratio, the smaller particles can interlock more effectively, providing greater resistance to shear deformation. As the particle size ratio increases, the presence of larger particles reduces the interlocking effect, leading to lower shear stiffness. Additionally, a higher particle size ratio results in a more heterogeneous microstructure, with larger particles creating voids that cannot be fully filled by smaller particles. This reduces the microstructural stability of the model, leading to higher deformation under shear loading and lower shear stiffness.  
The results suggest that increasing the particle size ratio enhances the particle gradation but reduces the mechanical properties of the model. Therefore, it is crucial to optimize the particle size ratio in numerical simulations to ensure accurate and reliable results. Setting the particle size ratio greater than 1.5 can significantly reduce the dispersion of the numerical simulation results, improving the accuracy and reliability of the simulations.
# A Fiber Vibration Signal Recognition Method Based on CNN-CBAM-LSTM
## 4 Attention Mechanism CBAM  
For feedforward CNN, CBAM (convolutional block attention module) is an easy-to-use attention module [29]. Given any intermediate feature map in CNN, to complete the intricate feature adaption processing, CBAM injects the attention map along two independent dimensions of channel and space, infers the attention map in turn, and then multiplies the attention map with the input feature map [30; 31].  
CBAM can be easily implemented into CNN's architecture and trained alongside it because it is a lightweight general-purpose module. The following Figure 2 illustrates the structure of CBAM.  
Figure 1: LSTM network structure.  
The figure illustrates that CBAM's functioning can be separated into two steps when given a feature map \(\mathrm{F}\ \in\ \mathrm{R}^{\mathrm{C}\times\mathrm{H}\times\mathrm{W}}\) as the input.  
To start with, global maximum and mean pooling are performed according to the channel, and the two one-dimensional vectors obtained by pooling are sent to a fully connected layer for operation and added to obtain channel attention \(\mathrm{M}_{\mathrm{C}}\ \in\ \mathrm{R}^{\mathrm{C}\times\mathrm{1}\times \mathrm{1}}\). One must multiply it element by element with the input to obtain the adjusted feature map \(\mathrm{F}^{{}^{\prime}}\); secondly, one can perform global maximum and mean pooling on \(\mathrm{F}^{{}^{\prime}}\) according to space, and convolve the two two-dimensional vectors obtained by pooling to produce spatial attention \(\mathrm{M}_{\mathrm{S}}\ \in\ \mathrm{R}^{\mathrm{I}\times\mathrm{H}\times\mathrm{W}}\), and finally combine the attention with F according to the element-wise multiplication. The overall process of CBAM generating attention is characterized as follows:  
\[\mathrm{F}^{\prime}=\mathrm{M}_{\mathrm{C}}(\mathrm{F})\otimes\mathrm{F} \tag{9}\] \[\mathrm{F}^{\prime\prime}=\mathrm{M}_{\mathrm{S}}(\mathrm{F}) \otimes\ \mathrm{F}^{\prime}\]  
The symbol \(\otimes\) means multiplication by the corresponding elements. Until then, it is necessary to disseminate both channel and spatial attention along the dimensions.
## 3 Development and Application Status of Intelligent LHD
They constructed an underground electronic map through the vectorization of engineering drawings using GIS software and the position display and alarm of the scraper were basically realized by combined technologies of the laser scanner system [33], track estimation and beacon correction at the same time [34]. As a result, the underground LHD autonomous navigation and control technology were initially developed in China and the model for the unmanned underground LHD was established [35]. Later on from the \"Twelfth Five-Year Plan\" period, the two institutes cooperated continuously on the program on \"Underground Intelligent LHD\" under the \"863\" theme project. The program team explored and researched deeply the autonomous driving and unloading technologies of the LHD [36] and realized the field operation in the line of sight or by remote control in the Zhangzhuang Mine, Fankou Lead-Zinc Mine, Dayingezhuang Gold Mine, etc. [4,8,10]. At present, some underground mines in China are testing and promoting fourth-generation underground fully automatically operated LHDs gradually, in order to realize unmanned mining operations [9].
# Bioreactors: Applications and Innovations for a Sustainable and Healthy Future--A Critical Review
## 6 Recent Advances in Bioreactor Applications  
### Production of Biopharmaceuticals  
Given the high variability and complexity of biological processes, the necessity to maintain a sterile environment in some cases and the availability of real-time direct measurements for controlling bioreactors present distinct challenges. These challenges have led to the development of innovative solutions and highlighted areas needing further research and development [67]. Single-use technology is being used to address these demands [68]. Single-use bioreactors may also be used in pilot-scale continuous processes, reaching 4.6 times greater productivity than a batch process, with a 15% cost reduction [69]. Regarding cost reduction, developing bioreactors and the associated equipment demands substantial investment. CFD (Computational Fluid Dynamics) may be applied to model and simulate bioreactor operations, avoiding the exclusive need for empirical tests and reducing operational costs [70]. Single-use sensors have also been widely used to avoid contamination [71]. The sensors arrive to the user already sterilized with gamma radiation and can be set up for immediate use [37].  
[PERSON] et al. [72] evaluated the use of a techno-economic model in SuperPro Designer. This model reduced costs by 4-11% compared to traditional batch operation for large-scale butylcholinesteraseerase (BChE) production in a two-stage semi-continuous bioreactor, representing a valuable simulation tool for the plant-based pharmaceutical sector and a computational system adaptable to other biotechnological processes. [PERSON] et al. [73] used Physcomitella moss as a host to produce the complement regulator MFHR1 in 5 L photobioreactors. Recombinant protein production doubled with fed-batch or batch compared to semi-continuous operation, and specific productivity increased by 35%. They also studied an unstructured kinetic model to predict protein production, nitrate uptake, and biomass growth, allowing process control and optimization. They observed that the addition of auxin increased the specific productivity of MFHR1 by up to 470% in shake flasks and 260% in bioreactors, demonstrating the potential of Physcomitrella moss as a biopharmaceutical production platform that can be applied to other plant-based expression systems.  
Figure 5: Electrical sensor developed for monitoring cellular metabolism.
## 2 Materials and Methods  
### Experimental Test Settings  
The experimental campaign is conducted on specimens manufactured of Inconel 718, which is a nickel-based alloy widely utilized in the L-PBF process for component fabrication. The mechanical-thermal properties and chemical composition of Inconel 718 are evaluated in accordance with information from prior studies [16,22,36].  
All experiments are conducted using a Renishaw AM500Q machine (Renishaw Ltd., Gloucestershire, UK) equipped with four ytterbium fiber lasers. These lasers have a minimum spot size of 82 um and a beam wavelength of 1070 nm. A maximum of 500 W of laser power is available for each independent laser.  
In every case study, the specimens utilized for the analysis are modeled according to the following specification (Figure 1). In more detail, the specimens are modeled as parallelepipeds of size 10 \(\times\) 10 \(\times\) 2 mm, which is suitable to print ten single tracks [2,4,18,40,41,42,43,44,45,46,47,48,49,50] on a stable consolidated material [36]. The distance between the tracks is established in order to prevent the adjacent melt pool from overlapping, whereas the track length is maintained at 7 mm to provide a steady-state melt pool.  
The specimens are positioned on the building platform using Materials Magics 25.1 (Materialise NV, Leuven, Belgium). The process parameters and laser assignment are carried out using Renishaw Quantum Software Version 5.3.0.7015.  
Following the printing, the specimens are removed off the building platform using a wire EDM machine (ECUT EU MS Genesi). Then, each specimen is prepared for micro-graphic analyses following the procedure explained in [36], performing the following steps:  
* To acquire an appropriate portion for analysis, each specimen is sectioned using a Struers Secotom-20 machine at a distance of 4 mm from the border of the lateral surface (Figure 1). This operation is essential for analyzing the melt pool in its steady state, excluding edge effects.
* Using a Struers CitoPress-30 machine, the specimen segment is embedded in conductive resin.
* The specimen's surface is polished with a Struers Tegramin-30 machine.  
The specimens are prepared for the melt pool analysis following the procedure described in [16], while the analyses are carried out using an Optical Microscope Leica Leitz DMRME (Leica Microsystems GmbH, Wetzlar, Germany). The complete procedure used to perform the melt pool analysis is described in [16; 22; 36]. Basically, in order to calculate the powder melting regime, the melt pool depth and width are measured [5; 13]. In particular, for each specimen, the melt pool depth and width are measured for five random single tracks in a section obtained at a 4 mm distance from the lateral edge to analyze a melt pool in its steady-state condition. Tables 1-3 show the process parameters tested for each case study carried out in this work. Thus, the process parameters taken into account in this work to predict the melt pool shape are the scanning speed, laser power, layer thickness, and building platform temperature.
## Appendix B  
Figure 4a,c show the spectra as image sequences of DAS-f and DAS-2f, respectively. From these sequences, a feature image for the corresponding method is estimated that can be directly transferred into a concentration image. This procedure for DAS-f and DAS-2f is described in the following.  
* DAS-f method: As illustrated in Figure 4b, the one-dimensional smoothed absorbance (DAS-f) is in line with the simulation based on the spectroscopic parameters provided by HITRAN. Furthermore, the maximum absorbance (see Figure 4b) is a suitable measure for determining the concentration that can be simply and efficiently transferred to the image sequence. The pixelwise smoothed peak absorbance can be estimated by finding the maximum image along the wavenumber dimension of the DAS-f sequence in Figure 4a. This maximum image is shown in Figure A2a. The transfer function of the smoothed peak absorbance as the DAS-f feature (e.g., \(y_{p}^{f}\)) into a concentration value in ppm*m can be easily derived by further simulation, as described above. The transfer function or smoothed peak absorbance as a function of the concentration shows a linear behavior in the investigated concentration range between 0 and 50 ppm*m, as illustrated in Figure A2b. By means of this function, the feature image of DAS-f can be straightforwardly transferred into the corresponding concentration image.
* DAS-2f method: The DAS-2f concentration is derived in a similar fashion as described for DAS-f. In this case, the peak-dip-peak measure is a suitable measure for determining the concentration that can be simply and efficiently transferred to the image sequence (cf. Figure 4c,d). The peak-dip-peak feature or image will be calculated by a maximum image (first row), minimum image (second row) and maximum image (third row) of the DAS-2f sequence (e.g., \(y_{pdp}^{2f}=1/2\) (\(y_{max,row1}^{2f}-2\)\(y_{min,row2}^{2f}+y_{max,rows3}^{2f}\))). This DAS-2f feature image is shown in Figure 10(c). The corresponding transfer function based on the peak-dip-peak feature (\(y_{pdp}^{2f}\)) of the smoothed second derivative of the absorbance as a function of the concentration is documented in Figure 10(d). This DAS-2f feature also demonstrates a good linearity in the concentration between 0 and 50 ppm*m, such that a methane concentration image can be calculated straightforwardly.
# Enhancement of Metabolite Production in High-Altitude Microalgal Strains by Optimized C/N/P Ratio
## 5 Conclusions  
The optimal C/N/P ratios for the synthesis of carbohydrates, proteins, and lipids in two high-mountain algal strains, _Chlorella_ sp. UFPS019, and _Desmodesmus_ sp. UFPS021, were determined. In _Chlorella_ sp. UFPS019, the optimal conditions to enhance the synthesis of carbohydrates were high sodium carbonate content (3.53 g/L), high KH\({}_{2}\)PO\({}_{4}\) and K\({}_{2}\)HPO\({}_{4}\) content (0.06 and 0.14 g/L, respectively), and medium-high NaNO\({}_{3}\) (0.1875 g/L). In the case of lipids, a high concentration of sodium acetate (1.19 g/L) coupled with high KH\({}_{2}\)PO\({}_{4}\) and K\({}_{2}\)HPO\({}_{4}\) content (0.056 and 0.131 g/L, respectively) and a low concentration of NaNO\({}_{3}\) (0.075 g/L) drastically improved the synthesis of lipids. In the case of _Desmodesmus_ sp. UFPS021, the protein content was increased using high sodium acetate (2 g/L), high KH\({}_{2}\)PO\({}_{4}\) and K\({}_{2}\)HPO\({}_{4}\) content (0.056 and 0.131 g/L, respectively), and a high NaNO\({}_{3}\) concentration (0.25 g/L). Further studies should focus on the possible interactionsbetween other stress-inducing nutrients (Na, Mg, Ca, etc.) and LEDs that can eventually maximize the final content of these metabolites.  
Conceptualization, R.O.G.-R., A.Z. and A.F.B.-S.; methodology, W.H.S.Q., J.B.G.-M. and G.L.L.-B.; software, A.F.B.-S., and A.Z.; validation, N.A.U.-S., and G.L.L.-B.; formal analysis, W.H.S.Q., J.B.G.-M. and A.Z.; investigation, W.H.S.Q. and A.F.B.-S.; resources, A.F.B.-S. and A.Z.; data curation, A.Z.; writing--original draft preparation, W.H.S.Q. and R.O.G.-R.; writing--review and editing, A.F.B.-S., A.Z. and G.L.L.-B.; visualization, G.L.L.-B.; supervision, J.B.G.-M.; project administration, A.F.B.-S. and J.B.G.-M.; funding acquisition, A.F.B.-S. and R.O.G.-R. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by Newton-Caldas Fund Institutional Links, with the project \"Algalcolor: Bio-Platform for the Sustainable Production of Cyanobacterial-Based Colours and Fine Chemicals\" ID 527624805, and The Colombian Ministry of Science Technology and Innovation MINCIENCIAS for the support of national Ph.D. Doctorates through the [PERSON] scholarship program (753-2016).  
Not applicable.  
We would like to express our sincere gratitude to Universidad Francisco de Paula Santander (Colombia) for providing the equipment for this research and the Colombian Ministry of Science Technology and Innovation MINCIENCIAS for the support to national Ph.D. Doctorates through the [PERSON] scholarship program.  
The authors declare no conflict of interest.
# Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset
## 3 Methodology
### Classification of COVID-19, Normal and Pneumonia Using Deep Convolutional Neural Networks
#### 3.3.3 Approach 3: Deep Learning with Undersampling on an Unbalanced Dataset  
Another naive approach to address the unbalanced class problem is random resampling. One such resampling technique is undersampling. Undersampling randomly selects the samples from the majority class, in our cases, normal CXRs, and deletes them from the training dataset until a more balanced data distribution is achieved. The purpose of resampling here is to influence the fit of the CNNs; therefore, undersampling is only applied to the training dataset [44, 45]. In our cases, the normal class had the most images whereas the pneumonia class had the fewest images. We intended to resample normal and COVID-19 CXRs to balance with the pneumonia class. Therefore, we randomly discarded  
Figure 10: Augmented CXRs using rotation, flipping, shearing, and shifting.  
Figure 9: Deep learning with image augmentation on the unbalanced dataset.  
the samples from normal and COVID-19 classes until the normal class was nearly balanced with the pneumonia class. The flowchart of the undersampling approach is illustrated in Figure 11. Using the undersampled training dataset, 11 CNNs are fine-tuned using categorical cross entropy loss as described in Section 3.3.
# Evaluation of Different Drying Methods on the Quality Parameters of _Acanthopanax semicosus_ Fruits
## 3 Results
### Correlation Analysis of Physicochemical Properties, Bioactive Compounds, and Antioxidant Activity
#### 3.9.2 TPC, TFC, and Key Bioactive Compounds  
The relationships between TPC, TFC, and key bioactive compounds were comprehensively established, demonstrating strong interconnections. A highly significant positive correlation was observed between TPC and TFC (r = 0.97, \(p\) < 0.001), indicating that flavonoid-rich samples also possess higher levels of total phenolic compounds, thereby enhancing overall bioactivity. Moreover, chlorogenic acid, quercetin, and eleutheroside E exhibited robust correlations with both TPC and TFC (r > 0.90, \(p\) < 0.05), suggesting their crucial role in the retention and accumulation of bioactive compounds in AS fruit.  
Protocatechuric acid and eleutheroside E displayed a strong correlation (r = 0.92, \(p\) < 0.05), implying that these compounds may share similar biosynthetic pathways during metabolic processes. Additionally, chlorogenic acid exhibited significant positive correlations with quercetin and eleutheroside B (r = 0.99 and 0.94, \(p\) < 0.05), suggesting potential similarities in their accumulation and metabolic patterns. Furthermore, rutin and hyperoside were found to be perfectly correlated (r = 1.0, \(p\) < 0.001), further reinforcing the hypothesis of a closely linked biosynthetic or regulatory mechanism governing their accumulation. Studies on freeze-dried pomegranate peel and apple pomace have also revealed a strong interdependence between flavonoid components, highlighting the stability of their biosynthetic pathways during drying processes [64].
# Common-Ground Photovoltaic Inverters for Leakage Current Mitigation: Comparative Review
## 2 Classification of Common-Ground Converters
### Current-Source CG Converters (CSCG)  
Figure 3 shows the converters that belong to this group, and Table 4 summarizes the features and limitations of each converter.  
The following remarks can be reported about these converters:  
* Current-source inverters usually use high inductance; this can increase the size, weight and decreases efficiency. Compared to other converters in this category, the converters introduced in [40; 41] need a high number of magnetic components with relatively high inductances;
* Compared to other converters in this category, the converters introduced in [38; 39] cannot offer continuous input current; thus, inefficient MPPT operation is highly expected. For the converter in [39], it also needs the highest number of semiconductors among all CSCG converters; this results in decreased efficiency and cost increase. Furthermore, this converter does not offer a symmetrical current path during positive and negative half-cycles; therefore, it may produce DC current component;
* Except for the converter presented in [38], the other converters of this category support bidirectional power flow. Therefore, they can be operated over a wide range of power factors;
* The CSCG converters introduced in [38; 39; 40] offer buck-boost operation. On the other hand, the CSCG converter introduced in [41] offers only boosting operation.  
Figure 3: Current-source common-grounded (CSCG) converters. (**a**) Converter presented in the work of [36; 37], (**b**) converter presented in the work of [38], (**c**) converter presented in the work of [39], (**d**) converter presented in the work of [40], (**e**) converter presented in the work of [41].
# An Efficient Steganographic Protocol for WebP Files
## 3 Results
### Format-Based Methods  
#### 3.1.1 Simple Injection  
The purpose of this experiment was to test the simple injection method. The tests were able to create the stego file (the carrier with embedded message), checking it in a few applications and extracting the secret from the container. Creating a stego file with an attached secret image is super easy; we just need to glue two input images together and save it as a new file.  
cat img1.webp img2.webp > img3.webp  
The newly created image is correctly recognized as RIFF (little endian) data, a Web/P image. Its size is the sum of the original files.  
The interesting part of this research was checking whether the new image would open in various programs and if it would be displayed or not. The testing suite included GIMP, Firefox, and ImageMagick. This limitation is caused by the fact that the WebP format is not implemented in every piece of software. Regardless of the hidden data, some applications just do not (yet?) support the format. During tests, ImageMagick and Firefox handled the modified file easily and displayed the cover (insignificant image denoted as img1.webp in this experiment), which was expected behavior. On the other hand, GIMP returned an error because its plugin could not read the file in a proper way.  
As we knew the offset and the length of the hidden file, we might easily recover the secret with dd:  
dd if=img3.webp count=$((179632-163802)) bs=1 skip=$((163802+8))>img4.webp  
There are multiple ways of finding the offset and the length: with the exiv2 program, from original files sizes, using any programming language, etc.  
Finally, the restored image was compared to the original img2.webp and they turned out to be identical. The experiment proved that with this technique, we may obtain lossless recovery if the stego image is not modified.  
Obviously, the secret image may be found if someone conducts an analysis of the stego file. This method is not safe, but does not require special software apart from the one available by default in the operating system. Additionally, it may be used without effort, especially on websites.