esa-sceva/satcom-chunk-collection huggingface dataset:
# Drilling Parameters Multi-Objective Optimization Method Based on PSO-Bi-LSTM
###### Abstract
[PERSON] [21] uses MSE and downhole vibration analysis for drilling optimization. The MSE method is used to identify drilling dysfunction. It also combines additional information, such as downhole drilling performance and borehole quality, to help find the source of abnormal drilling conditions. [PERSON] [22] used artificial intelligence to estimate age-mechanical parameters from mechanical-specific energy, and the results showed that intelligent models have higher accuracy and reliability than regression models in estimating geomechanical parameters. The comparison of the results of the MLP-COA and MLP-PSO models shows that COA outperforms the PSO algorithm in terms of model accuracy and reliability. The results of the three models show that the method proposed in this study has great potential in estimating CCS, UCS, and \(\varphi\) parameters. The attention mechanism [23], as a novel deep learning mechanism, has been widely used in time series prediction in recent years, and it is thus reasonable to apply it to drilling time series prediction. The paper presents a deep learning algorithm based on Bi-LSTM and the attention mechanism for predicting ROP. It introduces a particle swarm optimization algorithm (PSO) to create the PSO-Bi-LSTM method for optimizing drilling parameters. Using this method, the model optimizes multi-objective parameters to enhance drilling efficiency. By combining Bi-LSTM and attention mechanical mechanisms for intelligent ROP prediction, the PSO is integrated to quickly and accurately optimize high-dimensional spatial drilling parameters. At the same time, the excellent real-time model provides the foundation of a deep learning model for drilling efficiency analysis and optimization, expanding the application scope of deep learning in petroleum engineering.
# Development and Evaluation of Cow Dung Composting Equipment with Ventilation and Heating
## 3 Results and Analysis
### PH Value Changes in Composting  
Once the artificial heating phase starting on the 8 th day of the composting process is completed, the pH value of the composting material is sampled and tested. Figure 12 shows the changes in pH value during the composting process. From the graph, it can be seen that the pH value of compost changes from weakly alkaline to weakly acidic in the middle and later stages of composting, both before and after improvement. This observation is consistent with the results of the natural fermentation composting experiment.  
The industry standard document organic fertilizer (NY 525-2012) stipulates that the pH of composted organic fertilizer should be between 5.5 and 8.5. The pH value after composting is 6.7, which meets the requirements of national standards.  
The composting process often witnesses a decrease in pH value during the mature or stable stage, primarily due to the interplay between the production and consumption of organic acids. Initially, microorganisms decompose organic matter, generating a significant volume of organic acids and, consequently, lowering the pH value. As the process progresses, these microorganisms further break down these acids while converting ammonia nitrogen--a major component of compost--into nitrate. Both these processes consume hydrogen ions within the compost heap, facilitating an increase in pH value. However,  
Figure 11: Process of moisture change in the stack material.  
Figure 12: Test result of pH value.  
during the compost's mature stage, the generation of organic acids declines as most materials have been decomposed. Consequently, hydrogen ion concentrations rise within the compost heap, causing the pH value to decrease once more. This pH transition not only signifies compost maturity but also aids in suppressing the proliferation of harmful microorganisms, thereby enhancing the compost's safety and stability.
# Augmented Data-Driven Approach towards 3D Printed Concrete Mix Prediction
## 4 Conclusions  
The main objective of this study was to enhance the predictive performance of the models by DGAN and BR augmentation techniques on the limited available dataset. The original dataset consisted of 1679 data points with 23 features. The output features are anisotropic compressive strength (MPa), cast strength (MPa), and slump flow (mm). Using both techniques, the dataset was augmented to 67,160 (40 times the original data), and a statistical analysis was performed. SVM, XGBoost, and ANN models were trained exclusively on the augmented dataset and were tested on a real dataset and their predictive accuracy was evaluated. Predicting slump flow can be used to quantify the printable range and suitability concerning concrete printing setup, while anisotropic and cast compressive strength can indicate strength. The following conclusions can be drawn from this study.  
1. DGAN and BR can be used as effective data augmentation tools for augmenting the size and extent of data to enhance the performance of ML models. The statistical, correlation, and feature frequency analysis proved that augmented data is comparable to real data and can be effectively used for model training;
2. Performance metrics indicate that XGBoost, a gradient-boosted decision tree model, is the best predictive ML model, followed by ANN and SVM. During training, DGAN-trained models showed higher R\({}^{2}\) scores for cast compressive strength (0.989 for SVM, 0.991 for ANN, 0.999 for XGBoost) compared to BR-trained models (0.983 for SVM, 0.981 for ANN, 0.997 for XGBoost). This trend holds for printed compressive strength,
# Mycoremediation as a Potentially Promising Technology: Current Status and Prospects--A Review
## 2 Classification of Fungi, Bacteria, and Microalgae Species and Their Remediation Performance  
It is crucial to understand the biotransformation and biodegradation of hazardous and toxic substances interacting with ecological diversity and its behaviour [15]. Despite the rich ecology of the 69,000 fungi found globally, only a few species have been associated with mycoremediation (Figure 1) [16]. Various principal genera of fungi, bacteria, microalgae, and fungi involved in bioremediation are highlighted in Table 1. The biotic and abiotic variables, including moisture, aeration, temperature, metal ion concentration, phosphorus availability, nitrogen, carbon, and the presence and development of fungi, are caused by interspecific microbial competition [17]. More research is required on the fungal ecology related to mycoremediation, but literature is abundant on fungal ecology.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Organism** & **Genus** & **Ref.** \\ \hline \multirow{8}{*}{Fungi} & _Cryptococcus_ & [18] \\  & _Trichoderma_ & [19] \\  & _Rhizepus_ & [20] \\  & _Penicillium_ & [21] \\  & _Mucor_ & [22] \\  & _Lasiotipidia_ & [23] \\  & _Fusarium_ & [24] \\  & _Drechslera_ & [24] \\  & _Curvularia_ & [25] \\  & _Apergillus_ & [26] \\ \hline \multirow{8}{*}{Microalgae/Cyanobacteria} & _Selenastrum_ & [27] \\  & _Nannochloropsis_ & [28] \\  & _Synechocystis_ & [29] \\  & _Chlorococcus_ & [30] \\  & _Oscillatoria_ & [31] \\  & _Senedesemus_ & [32] \\  & _Spirogyna_ & [33] \\  & _Chorella_ & [34] \\  & _Spirulina_ & [35] \\ \hline \multirow{8}{*}{Bacteria} & _Marinobacter_ & [28] \\  & _Oleispira_ & [36] \\  & _Cycloclasticus_ & [37] \\  & _Thallassolitinus_ & [38] \\  & _Alcantivorax_ & [39] \\  & _Pseudomonas_ & [40] \\  & _Flaroobacterium_ & [41] \\  & _Enterobacter_ & [42] \\  & _Bacillus_ & [43] \\  & _Acaligens_ & [44] \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of the principal genera of fungi, microalgae, and bacteria used in bioremediation.
## 5 Conclusions  
As one of the most advanced science and technology processes in the world, space gravitational wave detection is extremely challenging. In order to guarantee the smooth implementation of the subsequent in-orbit mission, semi-physical simulation tests must be carried out on the ground before the payload is launched, in order to fully mitigate the technical risks. In this paper, a comprehensive and in-depth study is conducted, involving semi-physical simulation testing of the two key technologies (the establishment of the inter-satellite laser link and the inter-satellite ranging interferometry) involved in inter-satellite laser interference in the Taiji program. The research findings related to the semi-physical simulation of the space environment are also implemented.  
Based on detailed analyses of semi-physical simulations, an experimental platform is designed. The simulation of the cosmic vacuum and cold-black environment is achieved  
Figure 13: Experimental scenario for the semi-physical simulation test: (**a**) the experimental scenario; (**b**) the dual vacuum tank system under construction.  
using a dual vacuum tank system with an ultimate vacuum level as low as 5 \(\times\) 10\({}^{-6}\) Pa, temperature less than 80 K, and vibration acceleration attenuated to 10\({}^{-5}\) g within the frequency range of 0.1-100 Hz. With the combination of DWS technology and the piezo-driven hexapod, not only can the residual error of 1 \(\upmu\)rad for the acquisition procedure based on the CCD be verified, but the inter-satellite laser link with a pointing jitter of 100 nrad/\(\surd\)Hz can also be established. The interferometry testing of the inter-satellite ranging can be conducted by measuring the system noise. A ranging noise level of better than 100 pm/\(\surd\)Hz can also be achieved. The semi-physical simulation demonstration of the in-orbit experimental processes can also be performed using the experiment platform. The technical risks of the two key technologies involved in inter-satellite laser interference can be fully mitigated, laying a solid foundation for smooth implementation of gravitational wave detection in-orbit.  
Further, the experimental platform can be upgraded to form a triangle. The semi-physical simulation test for the final triangle constellation of the Taiji program can also be performed.  
Conceptualization, Y.W. and J.W.; methodology, Y.N. and K.Q.; investigation, W.B.; writing--original draft preparation, X.X.; writing--review and editing, L.M.; funding acquisition, J.J., H.L., and Q.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Strategic Priority Research Program on Space Science, Chinese Academy of Sciences (grant numbers XDA1502110102, XDA1502110103), and the National Natural Science Foundation of China (Grant No. 61905268).  
Not applicable.  
The authors thank [PERSON], a professor from the Institute of Mechanics, Chinese Academy of Sciences, for his helpful guidance on the technical solution.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Determination of Maximum Leachability for Heavy Metals by NEN 7341  
Previous studies have demonstrated that the potential risk of environmental contamination from leaching is determined not by the total content of pollutants present in the material, but by the amount of water that can dissolve into the soil and reach the surface water and/or subsurface water [29; 35]. Leaching tests are an essential tool for proving that the recycled material is safe and suitable for reuse.  
The batch leaching tests in the UNE-EN 12457-4 standard allow measurement of the concentration of the polluting elements in the leachate, obtained by the laboratory procedures that simulate the effect of rainwater, surface water and groundwater coming into contact with the material used in the infrastructure [39]. The available leaching tests in NEN 7341 detects the maximum leachability of contaminants from the recycled concrete in the worst-case scenario, compared to the real situation of the tested material in the infrastructure. The leachable available quantity, _Uavailability_, is then expressed in mg per kg dry matter, and was obtained and is represented as a logarithmic scale in Figure 4.  
Regarding the effect of the percentage of RPF in the FRC specimens, the release levels observed for the elements studied were similar between the control specimens (C-REF without fibres and FRC-REF with a commercial fibre) and specimens FRC2 and FRC4. However, a slightly higher release was observed in the FRC6 samples for Zn and Ni.  
After comparing the differences between the leachable quantity at pH 4 and pH 7, the strong influence of pH on release was confirmed, the pH in the environment being crucial in determining the release of many constituents in the monoliths and granular materials. As each material presents its own pH-dependent release curve, the leachability varies depending on the stage of the availability test and, according to Figure 4, elements such as Cr, As, Mo, and Sb present a higher release at pH 4, while heavy metals (such as Ni and Zn) showed higher release levels at pH 7. This is consistent with previous studies such as that of [PERSON] et al. [40]. [PERSON] and [PERSON] [32] presented release curves as a function of pH which were similar and systematic for different groups of elements. Cations, such as Ni, Cu, Zn, Cd, Pb, Al, or Fe, present a release curve decreasing to pH 9 (with a minimum at this value) and increasing to pH 12. Anions, such as Mo, Cr, As, Se or Sb, present a curve with a minimum at pH 3, which increases to pH 10 and then flattens to pH 12. Finally, soluble salts have a distinct leach pattern, caused by their chemical speciation, and orders of magnitude vary as a function of pH. The results obtained are also consistent with [PERSON] et al. [30], who observed that, for cations in the most acidic region (i.e., pH \(\sim\) 1-2), the leached contents for all elements are at their maxima and represent the available contents (maximum leachability) as the paste was degraded.  
Figure 4: _Cont._
# Design of a CW BISOL RFQ for Three Kinds of High-Charge-State Ions Simultaneous Acceleration
## 2 Beam Dynamics Design
### Three Kinds of High-Charge-State Ions Simultaneous Acceleration
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & \({}^{132}\)**Sn\({}^{21+}\)**,\({}^{132}\)**Sn\({}^{22+}\)**, \({}^{132}\)**Sn\({}^{23+}\)** & **Unit** \\ \hline Input energy & 3 & keV/u \\ Beam current & 0.75/0.75/0.75 & emA \\ Input-normalized rms transverse emittance & 0.19/0.20 & \(\pi\)mm-mrad \\ Output energy & 0.504 & MeV/u \\ Output normalized rms transverse emittance & 0.20/0.21 & \(\pi\)mm-mrad \\ Output normalized rms longitudinal emittance & 0.38 & keV/u-ns \\ Transmission efficiency & 93.37 & \% \\ \hline \hline \end{tabular} Note: The table gives the average central energy and transmission efficiency of the three ions.  
\end{table} 
Table 3: Simulation result of three high-charge-state mixed ions. Figure 9: Output phase distribution of the high-charge-state mixed beam. (**a**) Distribution in X-X\({}^{\prime}\) direction, (**b**) Distribution in Y-Y\({}^{\prime}\) direction, (**c**) Distribution in Phase-dW/W direction. There are 48 tuners with an outer diameter of 100 mm, with 12 tuners per quadrant, as shown in Figure 12. The nominal and maximum penetration depth of each tuner is 30 mm and 60 mm, which can achieve a frequency tuning from 80.705 MHz to 81.724 MHz. Frequency changes with the insertion depth are shown in Figure 13. The frequency sensitivity per tuner is about 0.32 kHz/mm.
# From Transactions to Transformations: A Bibliometric Study on Technology Convergence in E-Payments
## 5 Discussion and Conclusions
### Practical Implications and Strategic Recommendations  
This research offers a practical roadmap for key stakeholders across the fintech ecosystem.  
For financial institutions and developers, the integration of blockchain, AI, NFC, and mobile technologies can enhance transaction security, fraud detection, and user experience. Systems emphasizing intuitive design, personalization, and decentralization are more likely to achieve widespread adoption and long-term engagement.  
For policymakers and regulators, findings emphasize the need to craft supportive yet protective regulatory frameworks that balance innovation with privacy, security, and consumer protection. Special attention should be given to interoperability standards and digital financial inclusion, especially in underserved populations.  
For consumers and educators, the convergence of advanced technologies in finance necessitates greater digital literacy, including awareness of privacy risks, secure usage practices, and the potential of new financial tools. Education and awareness campaigns are essential to bridging the digital divide.  
For global strategy planners, understanding institutional research focus (e.g., blockchain in China, e-commerce trust in the U.S.) can inform strategic alliances, regulatory alignment, or market entry planning in different regions. In this context, it is important to recognize that while blockchain and decentralized systems are gaining traction, legacy infrastructures like SWIFT (Society for Worldwide Interbank Financial Telecommunications) continue to serve as the global backbone for secure cross-border messaging. Since the 1970s, SWIFT has connected over 11,000 financial institutions across more than 200 countries, playing a foundational role in international payment interoperability. As geopolitical events and digital innovation reshape financial networks, SWIFT's coexistence with emerging blockchain-based solutions reflects the hybrid nature of the evolving global e-payment landscape. Notably, the exclusion of certain Russian banks from SWIFT following the Ukraine conflict highlights the geopolitical leverage of such centralized systems, underscoring the need for resilience, redundancy, and interoperable alternatives in future digital payment architectures. Although next-generation systems, such as RippleNet, central bank digital currencies (CBDCs), and blockchain-based settlement layers, are designed to enable decentralized cross-border transactions, most current infrastructures operate in parallel with, or as complementary frameworks to, SWIFT. This evolving hybridization represents a pragmatic transition, wherein SWIFT remains a trusted, globally adopted messaging layer, while newer technologies enhance flexibility, speed, and inclusiveness within modern e-payment ecosystems.
# Multi-Access Channel Based on Quantum Detection in Wireless Optical Communication
## 2 Multi-Access Channel and Quantum Detection
### Multi-Access Channel Based on Symmetrical Coherent States
Figure 3 shows the average inter-user interference curve when the number of users, \(K\), changes from 2 to 20. It implies that if the average photon number is large enough, the inter-code interference between users can be negligibly small even if the number of users is large. Figure 2: Multi-access channel based on symmetrical coherent-state signals.
## 4 Simulations and Results
### Security Analysis of the Proposed System  
In this subsection, the security analysis of the proposed system is presented. For analysis, we used Oyente software, which is compatible with Ethereum-based blockchain systems. Oyente provides flexible environment which supports Solidity, Serpent and low-level Lisp-like languages. Oyente generates a report as output, in which mostly reported vulnerabilities are shown. From critical security analysis, it is shown that our proposed system is efficient and secure against many vulnerabilities, such as Parity Multisig Bug 2, Callstack Depth Attack Vulnerability and Re-Entrancy Vulnerability. However, the proposed system still faces one vulnerability, known as Integer Overflow. The impact of this vulnerability is limited because integer overflow occurs when an input value is greater than the integer capacity. Our proposed system is designed for smart vehicles and all parameters are set accordingly, that is why it is secure against many vulnerabilities. An analysis report is shown in Figure 12, which proves that our system is secure and efficient against different vulnerabilities.
# Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning
## 3 Research Design
### Sentiment Analysis
#### 3.3.1 Vader  
To determine the polarity for each sentence, we utilized the polarity scores from the VADER tool output [34]. This approach produces four scores: negative, positive, neutral, and the compound score, which is the total of all negative, positive, and neutral lexical evaluations. Using StockTwits data, the majority of tweets were discovered to be neutral. In particular, 46.7% of tweets were neutral, 43.3% were positive, and 10.0% were negative. This observation suggests that the majority of users are neutral to positive about buying or selling Microsoft shares. However, when examining Twitter statistics, the majority of tweets were positive. In particular, 48.0% of tweets were positive, 28.0% were neutral, and 24.0% were negative (Figure 3).
# Acute Respiratory Distress Identification via Multi-Modality Using Deep Learning
## 2 Related Work
### Multi-Modality Fusion Techniques  
Deep convolutional neural networks (DCNNs) are designed to capture data features. However, their performance can be influenced by variations in skin tone [23; 24] and lighting conditions [25], particularly when trained on limited or biased RGB datasets that fail to adequately represent such diversity. In contrast, depth information remains consistent regardless of these factors, offering greater robustness, while depth data may lack the rich detail present in RGB images. It provides complementary information that can enhance overall performance when combined with RGB data. To fully leverage the strengths of both modalities, it is essential to fuse them into a comprehensive set of discriminative features.  
[PERSON] et al. [26] proposed a multi-modal three-stream fusion network, drawing inspiration from the success of two-stream fusion networks [27; 28]. This approach incorporates RGB spatial information, dense optical flow (temporal) data, and pose features to enhance model performance. Similarly, [PERSON] et al. [29] introduced a multi-modal human activity recognition method that utilizes both RGB and depth temporal information. They employed a multi-modal feature fusion approach, specifically leveraging a self-attention mechanism to improve activity recognition accuracy.  
[PERSON] et al. [30] designed an attention mechanism specifically to fuse spatial-temporal features with pose features, aiming to enhance the understanding of human actions. [PERSON] et al. [31] developed the multi-modal transfer module (MMTM), a technique designed to progressively fuse features from both RGB and depth modalities, thereby improving model performance. Additionally, [PERSON] et al. [32] utilized a bilinear pooling layer to effectively combine multi-modal features, further enhancing overall model efficacy.  
[PERSON] et al. [33] proposed a bilinear-pooling attention network to fuse RGB and skeleton features for action recognition tasks, showcasing the effectiveness of this fusion approach. [PERSON] et al. [34] adopted an ensemble modeling strategy to leverage multi-modal information, achieving first place in the ICIAP-W 2023 challenge. Numerous other fusion [35; 36; 37; 38] schemes have been proposed to effectively combine multi-modal information, highlighting the growing interest and research in this area.
# Recovery and Characterization of Orbital Angular Momentum Modes with Ghost Diffraction Holography
## 2 Principle and Methods
### Experimental Design and Recovery Scheme
The occurrence of a fork type pattern clearly indicates the encoding of the vortex mode in the retrieved correlation hologram. A Fourier domain filtering approach is implemented in the digital processing, where a Fourier transform separates the spectra and its conjugate from the direct carrier (dc) term as shown in Figure 3g. An inverse Fourier transform operation on the filtered and centrally shifted spectrum recovers the cross-correlation function with the signature of the OAM mode. The recovered amplitude and phase distributions of the OAM mode are shown in Figure 3h,i, respectively.