esa-sceva/satcom-chunk-collection huggingface dataset:
# Arsenic Removal from Water by Adsorption onto Iron Oxide/Nano-Porous Carbon Magnetic Composite
## 3 Results and Discussion  
### Comparisons of Percentage Adsorption  
In the beginning, the removal percentage of As was evaluated by comparing the performances of the plain AC with respect to that of the Fe\({}_{3}\)O\({}_{4}\)/AC composite. Comparison of the percentage of As adsorption by plain activated carbon with the composite is presented in Figure 1.  
The composite adsorbed As around 70% while plain AC adsorbed around 29%. The result indicated that the prepared Fe\({}_{3}\)O\({}_{4}\)/AC composite was much more effective adsorbent than plain AC. It showed the synergistic effect of AC with Fe\({}_{3}\)O\({}_{4}\) for the adsorption of arsenic. Compositing AC with iron resulted in the production of adsorption sites of Fe\({}_{3}\)O\({}_{4}\) particles on the surface of AC. The adsorbed iron would coordinate with hydroxyl ions and neutral water molecules that are available in the aqueous solution. The adsorption of As (III) onto Fe\({}_{3}\)O\({}_{4}\)/AC composite surface took place through ligand exchange mechanism. The exchange process might involve ligands, such as hydroxyl ions or neutral water molecules, existing in the Fe-coordinated sphere. Hence, the adsorption of As (III) might take place by the release of a hydroxyl anion or neutral water molecules from its coordinated sphere. The mechanism that might be responsible for the adsorption of the arsenic anion is as follows [41].  
\[\equiv\rm FeOH+H_{3}AsO_{3}\to FeH_{2}AsO_{3}+H_{2}O \tag{3}\]  
\[\equiv 2[FeOH]+H_{3}AsO_{3}\to FeHAsO_{3}Fe+2H_{2}O \tag{4}\]  
\[\equiv\rm FeOH+H_{2}AsO_{3}\to FeHAsO_{3}\mbox{${}^{-}$}+H_{2}O \tag{5}\]  
\[\equiv 2[FeOH]+H_{2}AsO_{3}\mbox{${}^{-}$}\to FeHAsO_{3}\mbox{${}^{ -}$}+H_{2}O \tag{6}\]  
Figure 1: Percentage of As (III) adsorption by plain AC (activated carbon) and Fe\({}_{2}\)O\({}_{3}\)/AC composite with error bars.  
where \(\equiv\) FeOH represents the solid surface of Fe\({}_{3}\)O\({}_{4}\)/AC composite. Equations (3) and (4) represent adsorption of As (III) on the composite surface by monodentate ligand exchange, and Equations (5) and (6) represent bidentate ligand exchange with hydroxyl groups.
## 3 Network Background
### Integration of Handover and Load Balancing for Future Networks  
The integration of handover and load balancing in future wireless networks refers to the coordinated approach of managing user transitions between cells (handover) and distributing network traffic across multiple cells (load balancing) to enhance overall network performance and user experience. This integration aims to ensure seamless connectivity, reduce latency, and optimize resource utilization in complex network environments [55]. In HetNets, where multiple radio access technologies coexist, integrating handover and load balancing is crucial for maintaining the QoS. Advanced algorithms dynamically adjust handover parameters based on real-time network conditions, facilitating efficient traffic distribution and minimizing handover failures [56]. Emerging technologies, such as AI and ML, are being employed to enhance this integration. Predictive handover strategies using DL algorithms can anticipate user movements, enabling proactive load balancing and reducing unnecessary handovers. These AI-driven approaches contribute to the development of self-optimizing networks capable of adapting to dynamic environments. Overall, the integration of handover and load balancing is pivotal for the evolution of future networks, ensuring the efficient management of network resources and delivering a seamless user experience [57; 58].
# Simulation Model and Method for Active Torsional Vibration Control of an HEV
## 2 Materials and Methods
### Torsional Vibration Dynamics Model of Hybrid Powertrain System
#### 2.2.2 TM/ISG Dynamics Model  
The vehicle uses two permanent magnet synchronous motors, consisting of a stator and a rotor with eight pole pairs. In the transmission system, the motor shaft length is short, the material is steel, and the rigidity is relatively large, so the motor is simplified into a centralized mass model. The stator and the front/rear axles constitute the moment of inertia of the motor, and the uniform distribution of the mass of the motor system along the direction of rotation and the moment of inertia is not time-varying, so the moment of inertia of the motor is considered to be constant. (For example, the piston connecting rod system of an engine has a time-varying equivalent moment of inertia because its position changes with respect to the center of rotation of the crankshaft during operation.)  
Considering the motor torque response characteristics as first-order inertia, we can derive:  
\[\mathrm{T}_{m}=\mathrm{T}_{m\_ref}\cdot\Big{(}1-e^{-t/\tau_{m}}\Big{)}+\mathrm{ T}_{m\_fic} \tag{4}\]  
\[\mathrm{T}_{m\_fic}=\sum\mathrm{T}_{m\_a}(i)\cdot\sin(ip2\pi nt/60+\varphi) \tag{5}\]  
In the formula, \(\mathrm{T}_{m\_ref}\) is the target reference value of the motor output torque, \(\tau_{m}\) is the motor torque response time, \(\mathrm{T}_{m\_fic}\) is the total harmonic torque of the motor, \(i\) is the main harmonic of the motor torque fluctuation, \(\mathrm{T}_{m\_a}(i)\) is the \(i\) order amplitude of the torque fluctuation, \(p\) is the motor pole pair, \(n\) is the motor speed (r/min), and \(\varphi\) is the initial rotation angle. According to the literature [17], the motor torque fluctuation amplitude is usually within 10% of the target torque, and the main harmonics include the 6 th and 9 th order, and 6 th order is more significant. In this paper \(i\) is set to 6 and 9, and the amplitudes used are 10% and 4% of \(\mathrm{T}_{m\_ref}\), respectively.
## 3 Proposed Approach
### Features Selection  
We adopt a new approach-based GAN algorithm to identify the most relevant features in the generated dataset containing 40,000 malware and legitimate samples. The GAN learns the underlying data distribution and evaluates the importance of each feature by leveraging the adversarial training mechanism between the generator and discriminator. During the training process, the generator attempts to produce synthetic data samples that resemble the original data, while the discriminator tries to distinguish between real and generated samples. Features that significantly contribute to the discriminator's accuracy are deemed important, as they play an important role in differentiating real data from generated data. As explained in Algorithm 2, this process ensures that the selected features are informative and robust, leading to a refined dataset that enhances the performance of deep learning models.  
Figure 3 shows the importance of different features as evaluated by the GAN model. The hash_encoded feature stands out with a very high value, far exceeding all others, indicating its important contribution to the model. The next are n_rpte and prio with much lower but still high values, suggesting that they also have important roles to play. The other features are less significant; however, we decided to retain the top 10 most influential features to optimize the model's efficiency and relevance.  
The GAN model converges effectively, with both the generator and discriminator reaching a stable equilibrium after sufficient training epochs. Figures 4 and 5 illustrate that the discriminator loss (D-Loss) starts relatively low, increases slightly, decreases gradually, and stabilizes over the course of training. This indicates that the discriminator initially learns to distinguish real from generated data, and that its performance gets better up to the point where it stabilizes. Conversely, the generator loss (G-Loss) begins high, representing the initial difficulty of the generator in producing realistic data. However, it quickly decreases in the early epochs, showing that the generator is learning and refining its outputs. The G-Loss becomes stable with minimal variations after around 200 epochs, which shows a balance between the discriminator and generator.  
Figure 3: The top features selected.
## 6 Conclusions
Future research could focus on improving the technical aspects of the method, such as developing more efficient format conversion plug-ins and data integration algorithms. It could also explore the expansion of the method to other industries or sectors that involve multi-scale information management. Additionally, research could investigate how to enhance the adaptability of contract and institutional mechanisms to a wider range of project scenarios, ensuring the method's broader applicability and effectiveness. **Author Contributions:** Conceptualization, X.Z. ; methodology, X.Z. ; software, M.L. ; validation, Y.X., Y.B. and Z.S. ; formal analysis, W.Y. ; investigation, M.L. ; resources, Y.X. ; data curation, W.Y. and Q.G. ; writing--original draft preparation, W.Y. ; writing--review and editing, Y.B. and Z.S. ; visualization, Q.G. ; supervision, Y.X. ; project administration, Z.S. and M.L. ; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was financially supported by two projects. One is the Humanities and Social Science Fund of the Ministry of Education of China, with the grant number 23 YJA630145. The other is the Education and Teaching Project of the Faculty of Urban Construction at Beijing University of Technology, with the grant number ERCJ202202. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. **Acknowledgments:** The authors would like to express their utmost gratitude to the Humanities and Social Science Fund of Ministry of Education of China, grant ref: 23 YJA630145, for funding this research, and to Beijing University of Technology for administrative and technical support. Moreover, the authors also thank all participants who shared their knowledge to validate and verify this study. **Conflicts of Interest:** Authors [PERSON] and [PERSON] were employed by the company Xiongan Urban Planning and design Research Institute Co., Ltd. Author [PERSON] was employed by the company MCC Jingcheng Engineering Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 4 Discussion
### Regulatory Role of Transcription Factors in Flavonoid Synthesis  
The WGCNA analysis led to the identification of several co-expression modules associated with the flavonoid synthesis, which contained key structural genes and transcription factors involved in the regulation of flavonoid synthesis. Specifically, 26 transcription factor family members, including bHLH, ERF, and NF-YC, were found to interact with several structural genes and participate in the regulation of the synthesis of several flavonoids, including kaempfer, pinobanksin, luteoforol, and delphinidin (Table S4). MYB and bHLH have been implicated in the biosynthesis of flavonoids in Arabidopsis thaliana, maize, tomato, Sechium edulel, and ginkgo [84; 85; 86; 87; 88; 89; 90], and can synergistically interact with each other to form an MBW complex with WD40 [85; 90; 91]. GbMYBR1 modulates flavonoid synthesis in ginkgo. However, its overexpression in Arabidopsis thaliana leads to a reduction in the levels of anthocyanins and flavonoids [88]. These previous reports indicate that transcription factors of flavonoid synthesis are modulated by mutual factors and may have different effects in different plants. In addition, the results indicate that transcription factor families such as WRKY, ERF, HD-Zip, ARF, C2H2, bZIP, and NF-YC regulate flavonoid synthesis [90; 92].  
In this study, two MYB transcription factors were identified in the yellow module which interacted with the _PALs_ to promote the accumulation of delphinidin in Flo. Eight bHLH transcription factors were detected in the black, turquoise, magenta, and yellow modules, interacting with the _FLSs_, _C3 Hs_, _PALs_, _CHSs_, _F3 Hs_, and _ANRs_ and contributing to the positive regulation of kaempfer in Sta, pinobanksin and naringenin chalcone in Pet, luteoforol in Rec, and delphinidin in Flo. Seven ERF transcription factors were identified in the turquoise, magenta, and yellow modules, all of which interacted with the _C3 Hs_, _PALs_, _CHSs_, _F3 Hs_, _ANRs_, and _CCoAOMTs_, enhancing the accumulation of pinobanksin and naringenin chalcone in Pet, luteoforol in Rec, and delphinidin in Flo. Further analysis revealed that two NF-YC transcription factors were identified in the turquoise and magenta modules, with reciprocal relationships with the _C3 Hs_, _PALs_, _CHSs_, _F3 Hs_, _ANRs_, which positively regulated pinobanksin and naringenin chalcone in Pet and luteoforol in Rec. Based on these findings, further functional validation and mechanistic studies are recommended to validate the present results and identify potential regulators which could not be identified via the single-omics approach. This will elucidate the mechanism of the regulatory network of lotus metabolism.
# Compliance of Distribution System Reactive Flows with Transmission System Requirements
## 2 Problem Formulation
### Problem Definition
This constraint dictates that the average reactive power exchange via the root node is within the limits defined by Figure 1.0 This means that for a specific \(\overline{P}_{1}\), there is a corresponding lower \(\Phi^{\min}(\overline{P}_{1})\) and upper \(\Phi^{\max}(\overline{P}_{1})\) limit of \(\overline{Q}_{1}\). The model in (1)-(6) is a deterministic multi-period optimisation problem. This means that if one was 100% certain about power injections \(\mathbf{P}\) and \(\mathbf{Q}\) all over the DN over the entire control session then the problem could be solved as it is formulated. However, these injections are stochastic parameters in reality. Control actions occur more often than the one hour control session (in this particular case every 5 min). This means that the problem can be solved every time step \(\tau\) as a single period problem that considers the average power exchange until that time step only. Thus, the system has the opportunity to respond to new conditions that occurred within the duration of the control session, even without knowledge of the horizon. However, this approach is less efficient and, in some cases, might be insufficient in responding to sudden changes. Elaborating more than solving the single-period problem on every time step \(\tau\), one can use a forecast of the system parameters for the remainder of the control session and solve the problem as a multi-period problem for the rest the control session. This setup is in practice an MPC formulation [21]. We will formulate our proposed model as an MPC model where its single-period equivalent is easily implied by the equations. Some basic notation from MPC problems follows. There is an objective function, a horizon \(T\), and a dynamic model of the system that is added to the problem constraints. On every time step \(\tau\), the problem is solved for the entire horizon, \(T_{\tau}\), but we implement only the first control action \(u_{t=\tau}\). At the next time step, \(\tau+1\), we repeat the process by solving the problem for horizon \(T_{\tau+1}\) and implement only the new first control action \(u_{t=\tau+1}\). There are two options regarding the horizon \(T_{\tau}\) at step \(\tau\). If it has fixed length, it is a receding horizon, meaning that it always ends at \(\mathcal{T}\) steps away from \(\tau\). If the length is not fixed, but instead the end of the horizon is fixed, it is called shrinking horizon. In such a case, \(\tau\) moves from 0 to \(\mathcal{T}\), and at every time step the optimisation horizon is \(T_{\tau}=\mathcal{T}+1-\tau\). In other words, the horizon is getting smaller and smaller as we move towards the end of the control session. In the problem of this paper, we have a shrinking horizon problem.
## 3 State of the Art
Numerous innovative precast connection systems have been documented in the literature, such as multi-slit energy dissipators, disc spring connectors, and ultra-high-performance concrete (UHPC)-based joints; however, many of these solutions encounter constraints regarding fabrication complexity, repair logistics, or compatibility with standard construction practices. Some systems exhibit strong hysteretic activity yet need highly specialized materials or do not allow swift post-earthquake repair. Others include permanent embedding into concrete, making them difficult to access or replace after significant earthquake occurrences. ACPH, as described and experimentally examined by [PERSON] et al. [15], is a structurally efficient and modular solution that warrants more exploration at the system level. The ACPH utilizes strategically placed, interchangeable steel fuse plates to confine inelastic deformation and safeguard adjacent concrete components. The mechanical simplicity, use of standard steel grades, and bolt-on assembly provide practical benefits for separability and standardization. Nonetheless, its previous validation was confined to discrete subsemblies subjected to cyclic loads. This work broadens the assessment by integrating the ACPH into full-scale precast reinforced concrete frame systems with inferior material qualities, analyzing its relative performance under authentic ground motion inputs. This comprehensive approach seeks to elucidate the performance of the ACPH in comparison to other connection techniques when implemented in susceptible structural systems, and to assess its viability as a scalable solution within resilience-oriented retrofit and design frameworks. For example, the plug-in connector examined by [PERSON] et al. [28] and the disc spring joint presented by [PERSON] et al. [27] demonstrated promising energy dissipation but involved more complex detailing and limited accessibility after seismic events. To facilitate comparison across the diverse connection strategies reviewed, a structured summary table highlighting the key features, experimental validations, and replaceability characteristics of each system has been compiled and included in Appendix A.
## 2 Materials and Methods
### System Model
Typically, GNSS receivers estimate the C/N\({}_{0}\) in units of dB \(\cdot\) Hz, and the calculation formula is as follows:  
\[\left(C/N_{0}\right)_{eq,kB}\triangleq 10 lg(C/N_{0})_{eq,k} \tag{5}\]  
According to the classic narrowband radio propagation path loss model provided in reference [26], the interference signal power \(I_{i,k}\) from interference source i at the k-th receiver can be related to the transmission power \(J_{i,T}\) of interference source i, as shown in the following equation. \[\left(J_{i,k}\right)_{dB}=\left(J_{i,T}\right)_{dB}-L_{0}-10\alpha lgd-v \tag{6}\]  
where \(\left(J_{i,T}\right)_{dB}=10 lg(J_{i,T})\), \(\left(J_{i,k}\right)_{dB}=10 lg(J_{i,k})\), \(L_{0}\) is the propagation loss at 1 m from the transmitter, \(d\) is the distance between transmitter and receiver, \(\alpha\) is the path loss index, and \(v\) is a Gaussian random variable representing noise. Considering both omnidirectional and directional interference sources, Formula (6) can be expressed as:  
\[\left(J_{i,k}\right)_{dB}=\left(P_{i,T}\right)_{dB}+\left(G_{i,k}\right)_{dB}- L_{0}-10\alpha lgd-v \tag{7}\]  
where \(\left(P_{i,T}\right)_{dB}\) represents the power of the interference source amplifier, and \(\left(G_{i,k}\right)_{dB}\) denotes the antenna gain of the interference source, which differs between omnidirectional and directional interference. Using the aforementioned model, a simulation scenario was constructed where the positions of the GNSS jammers and GNSS receivers were randomly assigned within a specified area. Using Formula (6), the signal power of each interference source at the location of each GNSS receiver can be calculated in the presence of directional interference sources. Additionally, Formula (4) can be used to calculate the C/N\({}_{0}\) of each GNSS receiver under the influence of multiple interference sources when directional interferences are present.
## 4 Field Experiment of Fire-Extinguishing Drone  
### Experiment to Test the Accuracy of Fire Extinguisher Deployment (First Experiment)  
#### 4.1.1 Accuracy Performance Test and Method of Fire Extinguisher Deployment at Different Altitudes  
The accuracy of fire extinguisher deployment was tested experimentally in an open field at 666-2 Choji-dong, Danwon-gu, Ansan-si, Gyeonggi-do, Republic of Korea. On the day of the experiment, 10 December 2021, the weather conditions were cloudy with a wind speed of 4 m/s, which was deemed suitable for the field experiment. In Experiment 1, the accuracy of the fire-extinguisher deployment was tested at different altitudes, and a wooden box measuring 1.44 m\({}^{2}\) (=1.2 m \(\times\) 1.2 m) was mounted on the drone for deploying the fire-extinguishing balls. At each altitude (10 m, 20 m, 30 m, 40 m, and 50 m), two fire-extinguishing balls were loaded into the aforementioned wooden box (1.44 m\({}^{2}\)).
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 4 Experimental Setup
### Performance Metrics  
#### 4.3.1 Packet Delivery Ratio  
We assume that forwarding a packet is successful when the packet is successfully delivered from the source node to the destination node and returned to the source node. Our assumption is that the source node knows what to send as the next packet by receiving a packet and checking whether a packet sent from the source arrives at the destination. Therefore, we express the packet delivery ratio (\(PDR\)) metric as the ratio of packets delivered and returned to the source node compared to the packets sent from the source node as follows:  
\(P_{r}\) = Number of packets delivered and returned  
\(P_{s}\) = Total number of packets sent  
\[PDR=\frac{P_{r}}{P_{s}} \tag{3}\]