esa-sceva/satcom-chunk-collection huggingface dataset:
# Adsorption of Ni2+ and Cd2+ from Water by Calcium Alginate/Spent Coffee Grounds Composite Beads
## 3 Results and Discussion
### Adsorption Experiments  
Figure 9 shows that at concentrations of 10 and 20 ppm, SCGs achieved total removal of Cd\({}^{2+}\) and 98.07% (s = 0.95%) of removal of Ni\({}^{2+}\) at 10 ppm. Nevertheless, the CA beads without SCGs had percentages of removal of 88.23% (s = 1.81%) for Cd\({}^{2+}\) and 55.66% (s = 1.25%) for Ni\({}^{2+}\) at metal concentrations of 10 ppm. The CA-SCGs beads had percentages of removal of 98.81% (s = 1.41%) for Cd\({}^{2+}\) and 80.15% (s = 1.94%) for Ni\({}^{2+}\) at 10 ppm. The CA-SCGs beads almost achieved total removal of Cd\({}^{2+}\) and showed an improvement of 24.49% in the average of removal of Ni\({}^{2+}\) at metal concentrations of 10 ppm. The adsorbent SCGs reached a level of saturation of 10.98 mg/g (s = 0.15 mg/g) for Cd\({}^{2+}\) and 5.6 mg/g (s = 0.08 mg/g) for Ni\({}^{2+}\). Below that level of saturation, SCGs reached levels of removal closer to 100%. That was not the case for CA beads; the CA beads did not reach the saturation level at metal concentrations between 10 to 100 ppm, nor did they reach average levels of adsorption above 90%. This means that in the equilibrium of metal adsorption by CA beads, part of the dissolved metal will stay in the solution, so the only way to increase the metal adsorption is to increase the concentration of  
Figure 8: Effect of adsorbent dose at a concentration of 20 ppm of metal at 24 h: (**a**) % of removal of metal and (**b**) Amount adsorbed at equilibrium (\(q_{c}\)).  
metal in the solution. Possibly, parts of the metal ions do not have enough affinity with the adsorption sites of the CA beads and are prone to stay in solution instead of being adsorbed by the CA beads. The addition of SCGs to the matrix of CA allowed for percentages of removal closer to 100% beyond of the saturation level of SCGs for metal concentrations of 10 to 100 ppm.
[PERSON] et al. [17] propose an ANN-based drive-by approach that detects track-layer stiffness loss from in-service train accelerations, and [PERSON] et al. [18] develop random forest models that reliably predict long-term permanent deformation of ballasted tracks, offering a decision-support tool for life-cycle maintenance. [PERSON] et al. [12] show that quantifying dynamic stiffness across all operating scenarios requires an extensive laboratory-testing campaign, whereas [PERSON] et al. [14] demonstrate how the mechanical properties of rail pads can be accurately estimated through machine learning models. Other recent contributions, such as by [PERSON] et al. [15], [PERSON] et al. [16], [PERSON] et al. [17], and [PERSON] et al. [18], confirm the potential of ML to capture complex relationships within railway infrastructure--from real-time monitoring of sensorized rail pads and the correlation between ballast stiffness and track geometry to drive-by detection of stiffness loss and long-term prediction of permanent settlement. Taken together, these studies reveal a clear shift toward being data-driven, ML-based approaches that can predict dynamic stiffness more accurately and thereby optimize both design and maintenance strategies for railway superstructures. This study aims to develop a machine learning (ML) model to estimate the dynamic stiffness of rail pads using minimal information, overcoming the limitations associated with the need to perform specific tests for each combination of frequency, toe load, and load range. In the case of static stiffness, it is possible to calculate its values for different conditions of toe load and load range with a single laboratory test. This work proposes using static stiffness under different test conditions as a basis for estimating dynamic stiffness values, resulting in a significant reduction in the number of laboratory tests required for dynamic stiffness characterization. To ensure a generalizable model, a wide range of case studies were evaluated, which involved performing a total of 1080 dynamic stiffness tests and only 30 static stiffness tests. Specifically, three types of rail pads were selected, covering a broad spectrum of stiffnesses, as detailed in Section 2 (Materials). The test conditions followed the values specified in the standards [19,20], and the pads were evaluated in the following different states: new, tested at high and low temperatures, and aged under mechanical and chemical conditions, as described in Section 3 (Methods). The results of the predictive models, along with their quality depending on the parameters used, are presented in Section 4 (Results). Finally, the conclusions derived from this study are summarized in Section 5 (Conclusions).
# A Comprehensive Literature Review on Hydrogen Tanks: Storage, Safety, and Structural Integrity
## 5 Fundamental Issues
### Regulations  
Regulation of hydrogen tanks and storage is crucial to ensuring safety and promoting the adoption of hydrogen technologies. These regulations are designed to address the unique risks associated with the high flammability of hydrogen and the high pressures at which it is stored. In the United States, the Department of Transportation (DOT) [201] and the Occupational Safety and Health Administration (OSHA) [202] establish guidelines for the safe handling and transportation of hydrogen. Through its division of Energy Efficiency and Renewable Energy (EERE), the U.S. DOE has been heavily involved in renewableenergy research and energy efficiency [122]. The DOE has shown significant interest in developing FCEVs over the past two decades, collaborating with numerous companies and research institutions [63]. A key focus for the DOE is the optimization of hydrogen storage systems, which is critical for the widespread adoption of FCEVs (Table 10). These targets guide research and development efforts to improve hydrogen storage technologies, ensuring they meet stringent safety and efficiency standards. Additionally, the Society of Automotive Engineers (SAE) has established standards like SAE J2601, which provide protocols for hydrogen fueling to ensure safety and performance in hydrogen storage and fuel systems for vehicles [161, 179].  
Internationally, the International Organization for Standardization (ISO) has developed standards such as ISO 13985 and 19884, which specify requirements for the safety of hydrogen storage systems, both \(\mathrm{GH_{2}}\) and \(\mathrm{LH_{2}}\)[140, 198]. All these regulations and standards ensure that hydrogen storage systems are designed, constructed, and operated to minimize risks and protect public safety, thereby facilitating the growth of hydrogen as a clean energy source.
# AI- and Deep Learning-Powered Driver Drowsiness Detection Method Using Facial Analysis
## 4 Result and Discussion
### Evaluating All Models with Testing Dataset  
Observations revealed that the MobileNet model surpassed the other two models across all metrics when applying the trained models to the testing dataset. Its precision, recall, and F1-score were all higher than those of the other two models, and it also had the greatest accuracy score of 92.75%. The VGG16 model performed the poorest, scoring only 53.43% in accuracy. In comparison to the other two models, it had a lesser precision  
Figure 15: Loss vs. epochs of MobileNet model.  
and F1-score. With an accuracy score of 65.27%, the CNN model performed better than VGG16 but lower than MobileNet. In comparison to the MobileNet model, the CNN had a poorer precision, recall, and F1-score. Therefore, it can be asserted that the MobileNet model was the most suitable for this task. The best model's confusion matrix was obtained using the test dataset and is shown in Table 2. This confusion matrix shows the number of true positive, true negative, false positive, and false negative values for each class in a multi-class classification problem. With only a few classifications, the model correctly predicted most instances of yawning and no-yawning occurrences, i.e., three yawning instances were incorrectly labeled as no yawning. The model detected closed eyes with good accuracy, with only one false negative, i.e., one occurrence of a closed eye was incorrectly labeled as yawning. The model accurately identified open eyes; however, there were a few misclassifications. Five cases of open eyes were mistakenly classified as closed eyes, but no instances of open eyes were mistakenly classified as yawning or not yawning. With a large number of true negatives for no yawning and open eyes and a high number of true positives for yawning and closed eyes, the model appeared to detect tiredness well overall.  
The following Table 3 summarizes the different measures like the accuracy score, overall precision, recall, and F1-score of all the three models with the testing set of images collected from Kaggle.  
From Table 3, it is evident that the MobileNet model excelled in all metrics, exhibiting the highest levels in the accuracy, precision, recall, and F1-score.
## 3 Results and Discussion
### Flavor Compound Analyses  
Following the culture of WLY-B-L8T in raw wheat for a period of 3 days, our results showed that 2-pipendinone (21.95 mg/L), phenylethyl alcohol (19.08 mg/L), hydrocinamic acid (18.60 mg/L), and acetoin (7.58 mg/L) were the major volatile flavor compounds of WLY-B-L8T (Table 3, Figure 6). However, they were barely detected in the blank medium. Piperidone compounds and their derivatives possess specific bioactive properties, including anticancer activity and the ability to treat central nervous system disorders [35; 36]. In baijiu fermentation, microorganisms metabolize substrates such as proteins, fats, and carbohydrates. Carbohydrates are hydrolyzed into the key metabolic intermediate pyruvate through glycolysis and the tricarboxylic acid cycle. Pyruvate, generated via glycolysis, is then converted to ethanol by the enzymes pyruvate decarboxylase and alcohol dehydrogenase. Proteases hydrolyze soybean proteins into amino acids, including L-phenylalanine. These amino acids are further metabolized by amino acid transaminase into 3-phenylpyruvate acid, which is ultimately converted into phenethyl alcohol [8]. Phenylethyl alcohol is a derivative of aromatic hydrocarbons with a rose-like aroma and is  
Figure 5: Plate diagram of proteinase production of strain WLY-B-L8T (Created by authors).  
an important active ingredient with antioxidant properties [37]. Hydrocinnamic acid can be used as a fixative in fragrances, possesses a mildly sweet aroma, and is an important flavor compound [38]. At present, however, there are no reports of these flavor compounds being produced in the three bacteria _Bacillus rhizplane_ JJ-63 DSM 12442T, _Bacillus pseudomycoides_ DSM 12442T, and _Bacillus cytotoxic_ NVH 391-98T [27], which are the most closely related bacteria to the strain WLY-B-L8T, indicating that the microorganisms in _baobaoqu_ have the ability to produce more flavor compounds. The strain WLY-B-L8T can be widely applied in the brewing of strong-flavor baijiu, thereby enhancing the bodily quality of the baijiu.
# Conception of a System-on-Chip (SoC) Platform to Enable EMG-Guided Robotic Neurorehabilitation
###### Abstract  
Electromyography (EMG) signals are fundamental in neurorehabilitation as they provide a non-invasive means of capturing the electrical activity of muscles, enabling precise detection of motor intentions. This capability is essential for controlling assistive devices, such as therapeutic exoskeletons, that aim to restore mobility and improve motor function in patients with neuromuscular impairments. The integration of EMG into neurorehabilitation systems allows for adaptive and patient-specific interventions, addressing the variability in motor recovery needs. However, achieving the high fidelity, low latency, and robustness required for real-time control of these devices remains a significant challenge. This paper introduces a novel multi-channel electromyography (EMG) acquisition system implemented on a System-on-Chip (SoC) architecture for robotic neurorehabilitation. The system employs the Zynq-7000 SoC, which integrates an Advanced RISC Machine (ARM) processor, for high-level control and an FPGA for real-time signal processing. The architecture enables precise synchronization of up to eight EMG channels, leveraging high-speed analog-to-digital conversion and advanced filtering techniques implemented directly at the measurement site. By performing filtering and initial signal processing locally, prior to transmission to other subsystems, the system minimizes noise both through optimized processing and by reducing the distance to the muscle, thereby significantly enhancing the signal-to-noise ratio (SNR). A dedicated communication interface ensures low-latency data transfer to external controllers, crucial for adaptive control loops in exoskeletal applications. Experimental results validate the system's capability to deliver high signal fidelity and low processing delays, outperforming commercial alternatives in terms of flexibility and scalability. This implementation provides a robust foundation for real-time bio-signal processing, advancing the integration of EMG-based control in neurorehabilitation devices.  
SoC architectures; neurorehabilitation; EMG; rehabilitation robotics; human-machine interaction; sensor integration +
Footnote †: journal: Applied Sciences  
Article
# Halophyte Plants and Their Residues as Feedstock for Biogas Production--Chances and Challenges
## 5 Halophyte Composition
### Carbohydrate Content  
The content of cellulose, hemicellulose and other sugar polymers in halophyte biomass is typically analyzed by determining the content of monomeric sugars released after acid hydrolysis of the biomass material. While glucose, a C6 sugar, is the only constituent of cellulose, hemicellulose can be composed of C6 and C5 sugars: such as glucose, xylose, arabinose, galactose, mannose and rhamn. According to [PERSON] et al. [78], the stem fraction of _Salicornia bigelovii_ had a higher carbohydrate content, of 31-56%, compared to seed spikes, 16-23%, based on the dry matter content, when cultivated at different fertilizer and salinity levels. In both stem and seed spike fractions, cellulose concentration was higher than hemicellulose. Likewise, seedless _Salicornia bigelovii_ cultivated under 40 g L\({}^{-1}\) NaCl and 1-2 g N m\({}^{-2}\) fertilizer had a carbohydrate content of 19-26%, with cellulose having the highest share, while the carbohydrate concentration in the stem fraction was 32-56% ([PERSON] et al., 2018). [PERSON] et al. (2018) showed that cultivating at low salinity (10 g L\({}^{-1}\) NaCl) and medium fertilizer grade (1.5 g N m\({}^{-2}\)) contained highest carbohydrate in stem fractions. Similarly, the dry crop residue of _Chenopodium quinoa_ contained the highest content of carbohydrates when grown under 10 g L\({}^{-1}\) NaCl ([PERSON] et al., 2018) while [PERSON] et al. (2018) reported that the aerial parts of _Tripolium panonicum_ had the highest carbohydrate content when cultivated under 0 g L\({}^{-1}\) NaCl.
# Regolith-Rich PEEK Composite Bricks: Steps Towards Space-Ready Lunar Construction Materials
## 1 Introduction  
In Situ Resource Utilization (ISRU) is critical for sustainable operations beyond Earth, as it prioritizes using local materials over transporting them, significantly reducing costs and logistical complexities [1]. For what concerns lunar missions and infrastructure manufacturing, this approach focuses on processing regolith, a layer of loose rock and dust, into resources such as construction materials, oxygen, water, and fuel [2, 3, 4].  
In [5], the authors highlighted that brick-based structures offer several advantages: standardized elements provide uniformity, simplify construction, and optimize manufacturing. Their modular nature makes them ideal for early colonization, particularly whenleveraging self-supporting designs (e.g., Nubian vaults) or interlocking shapes that eliminate the need for mortar. Building on these principles, the authors investigated aggregate bonding approaches and proposed a composite material whose manufacturing process utilizes minimal quantities of thermoplastics as binders. To demonstrate the feasibility of interlocking concepts, they presented two proof-of-concept designs: an interlocking brick (Figure 1a) [6], and an interlocking tile (Figure 1b) [7].  
Aggregate bonding solutions are particularly appealing, as they enable complex designs without the high-temperature requirements of sintering or melting. However, they depend on a defined quantity of Earth-sourced consumables, which must be minimized. For practical implementation, these processes need to be robust, avoid reliance on sophisticated control systems, and be adaptable to autonomous operations on the Moon. While polymer-based solutions are discussed extensively in the literature [8; 9; 10; 11; 12; 13; 14], significant gaps remain in developing efficient, sustainable, and scalable methods for real-sized lunar components. Furthermore, limited efforts have been made to align or verify these requirements with processes compatible with the Moon's environment.
## 5 Implementation of the Flow Rate Control System
### Control Parameters  
In the design of the flow rate control, the damping ratio in Equation (18) is given as \(\zeta=1\) for high tracking performance with suppressed vibration. In addition, we adjust the natural angular frequency \(\omega_{n}\) in Equation (18). Further, the tracking performance can be improved by increasing the natural angular frequency. However, this can also increase the signal noise in the control loop. The natural angular frequency was determined as \(\omega_{n}=2\) rad/s, in this study.
# What Do We Know about the Use of EEG Monitoring during Equine Anesthesia: A Review
## 4 Analysis of EEG and Its Usefulness in Equine General Anesthesia
On the basis of mathematical analysis (Fast-Fourier Transformation--FFT), the waves are transformed into the graph, reflecting the intensity of colors in relation as the intensity of waves at a given frequency, using generally accepted limits of: \(\delta\) (0-4 Hz), \(\theta\) (4-8 Hz), \(\alpha\) (8-13 Hz), and \(\beta\) (13-30 Hz). The spectrogram (graph) changes dynamically and allows visual interpretation that can help modulate anesthesia during its duration. It is created during measurements and does not require subsequent analysis. So far there are no published results from the use of this method on animals and particularly horses, but a few of the veterinary faculties in Europe are conducting research using this system (Figure 3). It is also worth mentioning that in horses, unlike in humans (and that is why those stages are not widely described), a big challenge is connected with the process of induction with putting the  
Figure 3: Parallel monitoring of vital signs and EEG analysis performer during inhalation anesthesia in computed tomography diagnostics in 11-month-old Arabian foal. Figure 2: Spectrogram as a real-time clinically used method of EEG analysis with combination of raw EEG during computed tomography (CT) performed under general inhalant anesthesia in a horse with use of Root with Sedline®, Masimo, Irvine, CA, USA. horse on the ground as well as with the recovery period. While the changes of the EEG activity during maintenance are rather gradual and not pronounced, which makes the EEG an additional monitoring parameter to vital sign parameters, the EEG measurement during induction and recovery can change rapidly within the loss or regaining of consciousness. As those two periods can be dangerous for a patient and the staff, it is crucial to predict when the horse is losing its consciousness and is laying down and especially when the horse is regaining consciousness and is about to move and make an attempt to stand. During that time, it is also difficult to make any other vital signs measurements and the EEG is the most sensitive to predict movement as it follows the regaining of consciousness in a short period of time. So far there was no research in that area. Furthermore, the clinical and experimental situations which were described above and in references refer to the general anesthesia performed in fully equipped clinics. In horses, the field anesthesia is still practiced to a certain extent and it has a very limited possibility of monitoring, which makes this procedure difficult to predict and to administer suitable doses of anesthetic agents. EEG might be of help in such situations; however, the practical limitations, including the cost, have to be taken under consideration.
# Integrative Assessment of Sediment Quality in the Sao Francisco River (Mina Gerais, Brazil)
## 3 Results
### Benthic Community Structure
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Index** & **SF1** & \multicolumn{2}{c}{**BG4**} & \multicolumn{2}{c}{**SF2**} & \multicolumn{2}{c}{**SF3**} & \multicolumn{2}{c}{**ZM2**} \\ \hline S\({}^{\prime}\) & 3.60 & \(\pm\)3.65 & 0.60 & \(\pm\)0.89 & 6.20 & \(\pm\)1.30 & 6.40 & \(\pm\)1.14 & 4.60 & \(\pm\)2.70 \\ N\({}^{\prime}\) & 185 & \(\pm\)2.45 & 94.3 & \(\pm\)206 & 896 & \(\pm\)667 & 366 & \(\pm\)144 & 1459 & \(\pm\)2588 \\ d\({}^{\prime}\) & 0.51 & \(\pm\)0.62 & 0.08 & \(\pm\)0.12 & 0.79 & \(\pm\)0.20 & 0.93 & \(\pm\)0.21 & 0.57 & \(\pm\)0.28 \\ J\({}^{\prime}\) & 0.82 & \(\pm\)0.33 & 0.88 & \(\pm\)0.00 & 0.65 & \(\pm\)0.14 & 0.72 & \(\pm\)0.12 & 0.69 & \(\pm\)0.25 \\ H\({}^{\prime}\) & 0.79 & \(\pm\)0.84 & 0.12 & \(\pm\)0.27 & 1.16 & \(\pm\)0.24 & 1.33 & \(\pm\)0.32 & 0.87 & \(\pm\)0.28 \\ BMWP & 45.7 & \(\pm\)2.5 & 23.3 \({}^{*}\) & \(\pm\)1.75 & 25.6 \({}^{*}\) & \(\pm\)2.14 & 28.13 & \(\pm\)5.02 & 27.87 & \(\pm\)3.29 \\ \hline \end{tabular} Asterisks (\({}^{*}\)) indicate significant differences (\(p<0.05\)) compared with the control site (SF1).  
\end{table} 
Table 2: Mean and standard deviation (mean \(\pm\) SD) values of different biotic indices (abundance of taxa—S\({}^{\prime}\); number of individuals—N\({}^{\prime}\); Margalef species richness—d\({}^{\prime}\); Pielou evenness—J\({}^{\prime}\); Shannon–Wiener diversity—H\({}^{\prime}\); and BMWP score) per square meter at each sampling station. Figure 2: Relative abundance of different taxonomic groups (Hirudinea, Crustacea, Insecta, Oligochaeta, Mollusca, and Nematoda) identified in sediments from each sampling station at the São Francisco River (SF1, SF2, SF3, BG4, and ZM2). The SIMPER analysis showed how the structure of the benthic community contributed to the differences among stations, indicating the main taxa and their contribution to the dissimilarity. The abundance of the macrofauna showed the highest dissimilarity (100%) between the SF3 and BG4 stations, while the least dissimilar (69.38%) stations were SF2 and SF3. Moreover, BG4 showed large differences (or dissimilarities) from the rest of the stations: BG4-ZM2 (99.52%), BG4-SF1 (99.33%), and BG4-SF2 (98.77%). The abundance of species of _Tanytarsus_ spp., _Cryptochironomus_ spp., and _Polypedilum_ spp. was responsible for the dissimilarities between the most different stations (SF3-BG4 and ZM2-BG4), showing dissimilarities of around 8.37-13.33 (_Tanytarsus_ spp. ), 6.44-11.8 (_Cryptochironomus_ spp. ), and 5.25-11.63 (_Polypedilum_ spp.). Pairwise comparisons derived from the ANOSIM test on species abundance data showed that the stations were significantly different from each other (\(p<0.01\)).