esa-sceva/satcom-chunk-collection huggingface dataset:
# Train Service Design for Rail Transit Cross-Line Operation Applying Virtual Coupling
###### Abstract  
The cross-line operation (CO) of trains in urban rail transit is an effective method to efficiently satisfy transfer passenger travel demand as well as relieve the pressure of transfer stations. The primary problem of CO is designing train services to satisfy travel demand with an uneven spatial distribution of passengers. This study constructs a nonlinear integer programming model with a novel train operation scheme, i.e., virtual coupling (VC) technology, which allows the coupling/decoupling of trains on different lines at both ends of each operation zone. This scheme makes the train capacity equitably distributed in each operation zone, thereby balancing train capacity utilization over the whole CO system. Regarding the nonlinear characteristics of the proposed model, an adaptive simulated annealing genetic algorithm (ASA-GA) was designed to quickly generate high-quality solutions. Based on real-world data from the Beijing Changing Line and Line 13, the effectiveness of the proposed model and algorithm were verified. The computation results show that in comparison to a single grouping train composition scheme without CO, a VC scheme with CO would reduce operation costs by 46.8%, with 80.6% savings of train capacity equity. Furthermore, the average passenger residence time would be reduced by 25.9%.  
cross-line operation; virtual coupling; train service design; train capacity equity +
Footnote †: journal: Applied sciences
## 3 Results
### Projected Changes in Mean Monthly Streamflow  
A significantly negative change was observed in all five GCMs. NorESM1-M had exceptional results, as shown in Figure 7e, showing a positive change during monsoon.  
Figure 7: Cont.  
The effects of climate change on streamflow in the upper Xijiang River Basin at the Qianjiang outlet were estimated, as shown in Table 7. GFDL-ESM2M simulated negative change under all four scenarios, with a high chance (\(-19.5\%\)) under RCP-4.5. HadGEM2-ES showed an unlikely slightly negative change (2.8%) under the same scenario. Only NorESM1-M showed a positive change (7.6% and 6.46% under RCP-2.6 and RCP-4.5, respectively). Summarizing the results, RCP-2.6 will likely decrease the streamflow by 7%, RCP-4.5 by 8.2%, RCP-6.0 by 11.4%, and RCP-8.5 by 13.5%. The average percent change of future scenarios predicts that the future streamflow will decrease by 8.1% under RCP-2.6, 8.2% under RCP-4.5, 11.45% under RCP-6.0, and 13.5% under RCP-8.5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**GCMs Scenarios** & **GFDL-ESM2M** & **HadGEM2-ES** & **IPSL-CM5A-LR** & **MIROC-ESM-CHEM** & **NorESM1-M** \\ \hline Historical & 2433.94 (NA) & 2560.86 (NA) & 2546.93 (NA) & 2574.14 (NA) & 2546.93 (NA) \\ RCP-2.6 & 2267.93 (\(-18.3\)) & 2458.32 (\(-8.2\)) & 2172.80 (\(-12.9\)) & 2268.74 (\(-8.8\)) & 2791.36 (7.63) \\ RCP-4.5 & 2231.61 (\(-19.5\)) & 2639.80 (\(-2.8\)) & 2204.61 (\(-10.5\)) & 2250.32 (\(-14.6\)) & 2725.04 (6.46) \\ RCP-6.0 & 2306.45 (\(-14.8\)) & 2350.47 (\(-14.7\)) & 2268.96 (\(-15.3\)) & 235.68 (\(-9.9\)) & 2635.87 (\(-2.3\)) \\ RCP-8.5 & 2203.22 (\(-14.5\)) & 2516.08 (\(-8.9\)) & 1911.05 (\(-29.05\)) & 2241.57 (\(-12.2\)) & 2692.98 (\(-3.14\)) \\ \hline \end{tabular}
\end{table}
Table 7: Mean monthly historical (1971–2001) and future (2020–2050) discharge (cm\({}^{3}\)/s) and percent change of global climate models (GCMs) under all four future scenarios.  
Figure 7: Percent change of mean monthly discharge for the future period under four emission scenarios for each global climate model: (**a**) GFDL-ESM2M, (**b**) HadGEM2-ES, (**c**) IPSL-CM5A-LR, (**d**) MIROC-ESM-CHEM, and (**e**) NorESM1-M.  
Similarly, the season-wise impacts of climate change on streamflow were also evaluated. The effect will likely be largest in the winter season, due to the cold temperature. The change in streamflow in the January-April period is higher, as compared to the wet-season of May-September. The dry climate corresponded to a decrease in streamflow. The largest decrease was observed in the January-April period, likely due to the declining trend of precipitation in this period.
# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
###### Abstract  
The treatment processes from a wastewater treatment plant (WWTP) are known for their complexity and highly nonlinear behavior, which makes them challenging to analyze, model, and especially, to control. This research studies how machine learning (ML) with a focus on deep learning (DL) techniques can be applied to optimize the treatment processes of WWTPs, highlighting those case studies that propose ML and DL methods that directly address this issue. This research aims to study the ML and DL systematic applications in optimizing the wastewater treatment processes from an industrial plant, such as the modeling of complex physical-chemical processes, real-time monitoring and prediction of critical wastewater quality indicators, chemical reactants consumption reduction, minimization of plant energy consumption, plant effluent quality prediction, development of data-driven type models as support in the decision-making process, etc. To perform a detailed analysis, 87 articles were included from an initial set of 324, using criteria such as wastewater combined with ML, DL, and artificial intelligence (AI), for articles from 2010 or newer. From the initial set of 324 scientific articles, 300 were identified using Litmaps, obtained from five important scientific databases, all focusing on addressing the specific problem proposed for investigation. Thus, this paper identifies gaps in the current research, discusses ML and DL algorithms in the context of optimizing wastewater treatment processes, and identifies future directions for optimizing these processes through data-driven methods. As opposed to traditional models, IA models (ML, DL, hybrid and ensemble models, digital twin, IoT, etc.) demonstrated significant advantages in wastewater quality indicator prediction and forecasting, in energy consumption forecasting, in temporal pattern recognition, and in optimal interpretability for normative compliance. Integrating advanced ML and DL technologies into the various processes involved in wastewater treatment improves the plant systems' predictive capabilities and ensures a higher level of compliance with environmental standards.  
artificial intelligence; deep learning; nonlinear; quality; analysis; prediction; challenges; environment 2025  
3
## 2 Results and Discussion
### Principal Component Analysis (PCA)  
The differentiation of these wines regarding their aromatic profile was observed in the principal component analysis (PCA) with the first two principal components (PC1 and PC2, Figure 1). PC1 represented 68.6% of the total variance, whereas 8.5% of the variance was explained by PC2. PC1 significantly differentiated between the wines made from dried and fresh goji berries due to the negative relation with 20 esters, nine benzenes, eight aldehydes/ketones, six other volatiles, three acids, and two alcohols (volatiles with a loading value below \(-0.1\)). Meanwhile, this differentiation was also positively related with four esters, three benzenes, three alcohols, two acids, one aldehyde/ketone,and one other volatile (volatiles with a loading value above 0.1). In addition, a distinction between the fresh goji berry fermented wine made of the free-run and the pressed juice was observed in PC2 according to the relation with isopentyl alcohol (loading value, \(-0.3166\)), styrene (\(-0.2733\)), benzyl alcohol (0.26244), 1-octanol (0.24277), (\(E\))-5-decen-1-ol (0.22878), 1-hexanol (0.2413), and \(\beta\)-cyclodextrin (0.21834). The PCA analysis indicated that the goji raw material (fresh vs dried) exerted a primary role in differentiating the aromatic profile of these wines, whereas pressing treatment affected the volatiles profiles of these wines.
# A Finite Regime Analysis of Information Set Decoding Algorithms
###### Abstract  
Decoding of random linear block codes has been long exploited as a computationally hard problem on which it is possible to build secure asymmetric cryptosystems. In particular, both correcting an error-affected codeword, and deriving the error vector corresponding to a given syndrome were proven to be equally difficult tasks. Since the pioneering work of [PERSON] in the early 1960s, a significant research effort has been put into finding more efficient methods to solve the random code decoding problem through a family of algorithms known as information set decoding. The obtained improvements effectively reduce the overall complexity, which was shown to decrease asymptotically at each optimization, while remaining substantially exponential in the number of errors to be either found or corrected. In this work, we provide a comprehensive survey of the information set decoding techniques, providing finite regime temporal and spatial complexities for them. We exploit these formulas to assess the effectiveness of the asymptotic speedups obtained by the improved information set decoding techniques when working with code parameters relevant for cryptographic purposes. We also delineate computational complexities taking into account the achievable speedup via quantum computers and similarly assess such speedups in the finite regime. To provide practical grounding to the choice of cryptographically relevant parameters, we employ as our validation suite the ones chosen by cryptosystems admitted to the second round of the ongoing standardization initiative promoted by the US National Institute of Standards and Technology.  
asymmetric cryptosystems; code-based cryptosystems; information set decoding 2019 1209 10.3390/a12100209
## 5 Recent Developments in RF Energy Harvesting
### Advances in Rectenna Design and Optimization  
Rectennas, integral to RFEH systems, have seen significant advancements aimed at enhancing their efficiency, compactness, and multi-frequency capabilities. A notable development is the dual-band, dual-polarized rectenna operating efficiently at 2.4 GHz and 5.8 GHz. This design achieves a conversion efficiency exceeding 30% at 3.6 GHz for input ranges from \(-\)10 to 6.5 dBm, and one over 20% at 5.8 GHz for input ranges from \(-\)13 to 3 dBm [72]. The incorporation of dual polarization enhances energy capture from various orientations, making it suitable for powering IoT devices in environments with diverse RF signals. Similarly, a dual-band rectenna that operates efficiently at 2.4 GHz and 5.8 GHz was developed, demonstrating a high conversion efficiency of over 70% at \(-\)10 dBm input power [54]. The design incorporates a novel impedance matching network that adapts to different frequency bands, ensuring optimized power conversion efficiency across Wi-Fi and ISM bands. Another significant innovation is the development of a triple-band rectenna designed for energy harvesting at 915 MHz, 1.8 GHz, and 2.45 GHz. This rectenna integrates a high-gain antenna with a voltage doubler rectifier, enabling efficient energy scavenging from multiple RF sources. Such designs are particularly beneficial for IoT applications within smart office environments, where multiple communication standards are prevalent [54]. These recent multi-band and adaptive designs represent a notable evolution over conventional passive systems that are often limited to narrow frequency bands and fixed impedance characteristics. The added flexibility improves harvesting capability, even in environments with fluctuating signal strength. The emergence of flexible and wearable electronics has driven the development of rectennas using novel materials. Researchers have reported highly flexible graphene-film-based rectennas capable of wireless energy harvesting [73]. These rectennas maintain high performance even under significant bending, making them ideal for integration into wearable devices and flexible electronics. The integration of compact rectifier circuits using Schottky diodes and GaN-based transistors has also contributed to higher efficiency at low RF input power levels. Compared to traditional rigid structures in passive and hybrid systems (as discussed in Section 4.4), flexible rectennas offer a significant advantage in applications requiring conformability and durability under mechanical stress, such as biomedical wearables.
# Classical Optimization of Cellulase and Xylanase Production by a Marine _Streptomyces_ Species
## 3 Results
### Effect of Incubation Temperature on Cellulase and Xylanase Production  
The effect of temperature on cellulase and xylanase production by _S. albidoflavus_ strain SAMRC-UFH5 was examined and is shown in Figure 3. Although optimal xylanase production was recorded at temperature of 35 \({}^{\circ}\)C with activity of 3.47 \(\pm\) 0.1 U/mL, production was also recorded at all the incubation temperatures tested. Moreover, there was no statistical significance between xylanase production at temperatures of 25, 30, and 40 \({}^{\circ}\)C, and between 45 and 50 \({}^{\circ}\)C. On the other hand, cellulase was optimally produced at 40 \({}^{\circ}\)C, followed by 35 \({}^{\circ}\)C with respective activity of 0.011 \(\pm\) 0.001 and 0.009 \(\pm\) 0 U/mL. Other temperatures at which cellulase production was recorded in order of descent are: 30, 25, 45, and 50 \({}^{\circ}\)C with activity of 0.002 \(\pm\) 0, 0.001 \(\pm\) 0, and 0.0003 \(\pm\) 0 U/mL respectively; however, there was no significant difference between cellulase production at 25 and 30 \({}^{\circ}\)C (Figure 3b). Moreover, a sharp decline of over 50% in production of both cellulase and xylanase was observed with further increase in the temperature to 50 \({}^{\circ}\)C.
# WiGR: A Practical Wi-Fi-Based Gesture Recognition System with a Lightweight Few-Shot Network
## 2 Preliminary  
### Related Work  
#### 2.1.1 Wi-Fi-Based Gesture Recognition Methods  
With the rise of Wi-Fi sensing technology, the CSI of Wi-Fi can convey rich information and achieve precise tracking. There are many different types of methods based on CSI to achieve gesture recognition. For example, WiGeR [33] employs a multilevel wavelet decomposition algorithm and the short-time energy algorithm dynamic time warping (DTW) to recognize gestures. WiCatch [34] utilizes the support vector machine (SVM) with the MUSIC signal processing algorithm to recognize gestures. [PERSON] et al. [10] proposed SignFi, a deep learning method with a nine-layer CNN architecture, to recognize sign gestures. However, these methods have not dealt with the hard domain shift problem.  
Few-shot learning methods [18; 19; 20; 21; 22] have achieved great success in addressing the domain shift problem. [PERSON] et al. [23] proposed a new few-shot domain adaptation scheme (F-CADA). F-CADA adopts adversarial learning to construct an embedding space, which needs a large number of unlabeled target data. It then enhances the performance of the target classifier by a few labeled target data via greedy label propagation. [PERSON] et al. [24] proposed three adversarial learning process to remove the distribution discrepancy between source and target data, increasing the complexity of the system. [PERSON] et al. [25] proposed a multi-task classifier to address the domain shift problem. The basic idea of addressing domain shift is to initialize the classifier with multi-task classifier parameters so that the classifier can quickly adapt to any new sensing domain while it is difficult for the cross-tasks classifier to converge. For the deep Siamese recurrent convolutional network [26], it is a typical method of using a few-shot learning network to recognize gestures. The Siamese network relies on CNN-LSTM architecture to extract spatial-temporal features, which also increases the complexity of the model. Taken together, these methods ignore the problem of model calculation complexity, which is not beneficial for model deployment. In this paper, our proposed system adopts a different feature extraction network, i.e., a 2-D convolutional neural network, which has a better performance in feature extraction compared with the CNN-LSTM architecture. In addition, we not only focus on the domain shift issue but also introduce a lightweight block to meet the performance requirements of mobile deployment.
# Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum
## 8 Discussion  
The use of true random frequency hopping is particularly valuable in military and defense contexts, where secure and reliable communication is critical. In a drone cloud configuration, such systems can be employed for reconnaissance, surveillance, and combat support, ensuring that communications remain secure from interception and jamming. The high security provided by true random sequences helps protect sensitive information and command instructions.  
Drones equipped with secure communication systems can play a vital role in disaster response and search and rescue operations. In scenarios where the environment may be hostile or communication infrastructure is compromised, a drone cloud using true random frequency hopping can maintain secure and reliable lines of communication. This is crucial for coordinating efforts, relaying information, and ensuring the safety of personnel involved in the operations.  
Industries such as agriculture, logistics, and infrastructure inspection increasingly use drone swarms to collect data and perform tasks. True random frequency hopping can secure the communication links between drones, preventing industrial espionage and data theft. For instance, in precision agriculture, drones can securely communicate real-time data on crop health and soil conditions, enhancing the decision-making process.  
Drones are extensively used for environmental monitoring, including wildlife tracking, pollution assessment, and natural resource management. Secure communication ensures that sensitive environmental data is protected, and that the drones' operations cannot be easily disrupted. This is particularly important in protected or sensitive areas where unauthorized access or interference could have significant ecological impacts.  
In the context of smart cities, drones can be used for infrastructure monitoring, traffic management, and public safety. True random frequency hopping ensures that these drones can communicate securely, protecting data on critical infrastructure and avoiding potential security breaches that could disrupt urban services.
## 3 Results
### Correlations between Tests  
A statistically significant correlation between lower back extensor strength measured in prone, sitting, and standing positions was disclosed (Figure 2). However, the correlation was strongest between values measured in sitting and standing positions (_rs_ = 0.83, \(p\) < 0.001). A strong correlation between maximum inspiratory pressure and maximum expiratory pressure (_rs_ = 0.78, \(p\) < 0.001) was also evident. [PERSON]'s correlation coefficients evaluating the relation between inspiratory and expiratory muscle strength and lower back extensor strength measured in sitting and standing positions reached 0.49-0.51 (_p_ < 0.001). A robust and significant correlation was found between outcomes obtained in side-bridge tests performed on the left and right sides (_rs_ = 0.86, \(p\) < 0.001). The results of the Biering-Sorensen test revealed a moderate correlation with the results of the prone-plank test (_rs_ = 0.41, \(p\) = 0.01). Correlations between outcomes of strength testing procedures and muscular endurance tests were weak or absent. No element of strength and endurance performance correlated significantly with either the level of physical activity or with hours spent per day in a sitting position. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variable** & **Sex** & **Total** & **18–29 Years** & **30–49 Years** & \(\geq\)**50 Years** & _p-Value Age** & _p-Value Sex_ \\ \hline Biering-Sorensen test & Male & 149.7 \(\pm\) 41.3 & 118.6 \(\pm\) 18.5 & 166.7 \(\pm\) 42.7 & 160.8 \(\pm\) 39.5 & & \\ (time (s)) & Female & 179.8 \(\pm\) 61.5 & 152.7 \(\pm\) 30.4 & 190.3 \(\pm\) 67.7 & 202.4 \(\pm\) 79.9 & \(<\)0.001 & 0.025 \\  & Post-hoc test (age) & a & b & b & & \\ \hline Prone-plank test (time & Male & 165.0 \(\pm\) 59.3 & 163.6 \(\pm\) 56.6 & 165.5 \(\pm\) 64.4 & 166.2 \(\pm\) 61.2 & \\ (s)) & Female & 141.0 \(\pm\) 50.0 & 137.4 \(\pm\) 19.4 & 142.8 \(\pm\) 61.2 & 142.6 \(\pm\) 64.0 & 0.971 & 0.101 \\ \hline Side-bridge test–right & Male & 77.7 \(\pm\) 25.4 & 79.3 \(\pm\) 28.4 & 79.5 \(\pm\) 22.2 & 69.2 \(\pm\) 32.4 & & \\ side (time (s)) & Female & 74.9 \(\pm\) 28.3 & 80.1 \(\pm\) 24.6 & 81.8 \(\pm\) 27.3 & 44.0 \(\pm\) 20.6 & 0.037 & 0.676 \\  & Post-hoc test (age) & ab & b & a & & \\ \hline Side-bridge test–left & Male & 75.3 \(\pm\) 26.5 & 79.2 \(\pm\) 27.9 & 75.3 \(\pm\) 22.7 & 67.4 \(\pm\) 37.9 & & \\ side (time (s)) & Female & 73.6 \(\pm\) 32.5 & 79.7 \(\pm\) 24.6 & 78.5 \(\pm\) 37.0 & 46.6 \(\pm\) 20.9 & 0.120 & 0.822 \\ \hline \hline \end{tabular} Data are expressed as mean \(\pm\) standard deviation. Post-hoc tests: a, b—same letters marking values of categories within given row denote mutually statistically not different groups.  
\end{table} 
Table 6: Trunk muscular endurance tests: results.
## 3 Results
### Biocompatibility of the ORHS  
To assess how the ORHS influenced cell survival and proliferation, the experiment was allowed to proceed for a total of 7 days. As a result, when the same number of cells were seeded, the rate of survival and proliferation of the cells in the experimental group ('with ORHS') was not significantly different compared to the control group ('with alginate') (Figure 6). However, comparing to the rate of HUVEC cell proliferation over the 7-day period revealed that the cell proliferation improved slightly upon exposure to oxygen release.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**H\({}_{2}\)O\({}_{2}\) \% of Control \({}^{\text{c}}\) (n = 5)** & **Weight** & **Total H\({}_{2}\)O\({}_{2}\)\(\upmu\)M/Loading Efficiency (\%) \({}^{\text{b}}\)** \\ \hline ORHS \({}^{\text{a}}\) & 10.0 mg & 5.822/0.44 \(\pm\) 0.0568 \\ \hline \({}^{\text{a:}}\) [non-treat]–[ORHS]; \({}^{\text{b:}}\) (H\({}_{2}\)O\({}_{2}\)\({}^{\text{a}}\)/4.0\% ORHS) \(\times\) 100\%; \({}^{\text{c:}}\) y = 0.0048x + 0.1344 R\({}^{2}\) = 0.9946; Control: 4.0 wt\% H\({}_{2}\)O\({}_{2}\). \\ \hline \hline \end{tabular}
\end{table}
Table 1: Loading efficiency of hydrogen peroxide in the oxygen-releasing hydrogel sponge (ORHS).  
Figure 5: Release profile of dissolved oxygen (n = 5); (A) alginate group, (B) oxygen-releasing hydrogel sponge (ORHS) group. Oxygen released from the sponges for over a period of 3 days.