esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Zeta Potential
According to [23] (2020), the flocculation activity is diminished at lower doses due to reduced surface area available for adsorption. The floc exhibited a higher density than the other samples when the biofoculant was administered  
Figure 4: Microscopic images of floc formation at various pHs and biofocculant dosage (20 μm scale bar). at its maximum dose of 3.41% (\(v/v\)) under pH 6.5. However, it was not chosen as the most suitable option because its dosage was about 1.5 times higher than the optimal level. This indicated that the dosage was too high. At a dosage of 2% (\(v/v\)), the biofoculant exhibited effective flocculation activity. Consequently, using the maximum dosage would result in wastage. Generally, the biofoculant D2 exhibited favorable performance throughout a broad pH spectrum when used at an optimal dose. Moreover, its application for suspended solid elimination is supported by its beneficial influence on the aggregation of suspended particles into flocs. Assessingly, a double layer does affect the stability of flocs. By using flocculation, liquid-solid perturbation occurs, leading to the thinning of the double layer and reducing the magnitude of repulsion between the particles. This is also enhanced by the roughness of the particles. This means that bubble movement increases particle movement, resulting in particle/kaolin collisions and thinning the double-layer structure. Thus, the electrical force decreases, i.e., reduced polarizability and double-layer relaxation, as the double layer of individual particles overlaps as flocs form [40]. As zeta potential evaluates the size of the magnitude of the double layer repulsive force, increases in pH from acidic to alkaline, as observed, tend to reduce the zeta potential in kaoline-water suspensions [41], even when CaCl2 is added to the suspension [42].
# A Hybrid Adaptive Particle Swarm Optimization Algorithm for Enhanced Performance
## 4 Experiments
### Higher-Dimensional Function Analysis  
To verify the performance of the APSO algorithm for solving high-dimensional particle swarm optimization problems, we adopted the standard particle swarm algorithm and five typical improved algorithms for the performance comparisons, including PSO, AIWPSO, LDWPSO, HPSOTVAC, CPSO, and CLPSO. Two classical unimodal functions, \(F_{1}\) and \(F_{5}\), and two multimodal functions \(F_{9}\) and \(F_{10}\) are selected for the experimental valuation; the dimensions of particles are set as \(D=500\) and \(D=800\). The population size is \(N=30\), and the number of iterations is \(T=500\), respectively. To avoid the interference of randomness, each algorithm runs 30 times independently. Table 6 gives the statistical results, including the average value \(V_{avg}\), standard deviation \(V_{std}\), maximum value \(V_{max}\), and optimal value \(V_{opt}\), and the best results are bolded. Figures 6 and 7 illustrate the convergence curves of the seven algorithms for dimensions of 500 and 800, respectively. Figure 5: Convergence curves of the proposed algorithm APSO and the four other algorithms for 200 iterations on ten functions. Figure 6: Convergence curves of the seven testing algorithms for dimensions of 500. From the data in Table 6 and Figures 6 and 7, APSO performs better in high-dimensional optimization problems compared with other algorithms, especially for both unimodal and multimodal functions with accurate convergence and good stability and robustness. Therefore, the comprehensive performance of APSO algorithm is better than other comparative algorithms, and it is very suitable for solving high-dimensional optimization problems.
## 6 Challenges and Future Research Scopes
### Integration of Mobile Edge and Fixed Edge Computing  
MEC provides computing powers and service environments at the edge of cellular network architecture. Mobile networks are embracing future 5G or next generation cellular networks that could provide an environment to widely deployed edge computing. A key distinction between MEC and FEC is that MEC is primarily based on cellular networks, whereas FEC relies on the Internet. Moreover, 5G network will introduce new implementation complexity because of its requirements such as ultra-dense heterogeneous networks, high-precision location aware services and ultra-low latency [23; 82]. Deploying edge computing with 5G networks and integrating that with FEC could be a big challenge, which is another research area on edge cloud networks.
# Time-Delay Vibration Reduction Control of 3-DOF Vehicle Model with Vehicle Seat
## 1 Introduction
In general, most of the current research on shock absorbers with time-delay control focuses on the determination of feedback gain of shock absorbers, time-delay parameters, and stability analysis. However, in the research into the control of time-delay vibration reduction under complex excitation, the existing methods of time-delay control parameters do not consider the external excitation in the parameter-solving process. This means that the time-delay parameter is irrelevant to the external excitation. As a result, there are no methods available to solve the time-delay control parameters for different external excitation. The time-delay control parameters obtained by the existing methods are only targeted for specific excitation but not for complex excitation. Based on the above analysis, this paper studies a three-degree-of-freedom vehicle suspension system with a vehicle seat, which adopts a wheel-displacement feedback-control strategy. It proposes a linear function equivalent excitation method, which means that the optimization problem of random excitation is transformed into the optimization problem of the normal force in the discrete time interval. Firstly, the stability switching method is used to analyze the stability interval of the 3-DOF active suspension with time-delay control. Strum sequences are derived to ensure the stability of the control system. Secondly, the quantitative relationship between time-domain vibration response of the system, time-delay control parameters, and external excitation is designed to estimate the disturbance generated by external excitation to the body simplified model in real time. An adaptive weighted particle swarm optimization algorithm is used to obtain the optimal delay control parameters. Finally, simulation is used to analyze the control performance of the control algorithm compared with other control algorithms.
## 5 Conclusions  
This study considered a mixed traffic environment with a combination of AVs and HVs. We proposed a DRL-based vehicle routing control method with multiple objectives that incorporate safety and efficiency to distribute traffic flow. Therefore, the objective of this study was to maximize the long-term reward in terms of driving speed and remaining driving distance to the destination by controlling the direction at intersections to distribute traffic flow, as well as to control the routes safely and efficiently to the destination. We described and formulated a dynamic traffic environment as an MDP system. A DQN was adopted for the optimal route control method, and each AV--as an agent--learned its policy independently. We compared the proposed method with conventional methods and other methods through simulations; the simulation results showed that the proposed method minimized the driving distance, time, and waiting time of the AVs in any environment and for all MPRs and traffic densities.  
This study has limitations in that each agent learned independently like a single agent, and that the environment used was simple as it considered only four-way intersections. In the future, we will utilize driving behavior data with varying road characteristics and will apply various scenarios [26]. In addition, we will consider that each agent learns in cooperation with others, rather than learning independently, in a dynamic mixed traffic system. Furthermore, we plan to extend the vehicle routing control method based on counterfactual multi-agent policy gradient learning, which determines the contribution of each agent to the overall reward in cooperation with each other.  
Conceptualization, S.M., H.J. and Y.L.; Methodology, S.M., H.J., S.K. and Y.L.; Software, S.M.; Writing--Review and Editing, S.M., S.K. and H.J.; and Supervision, H.J. and Y.L. All authors have read and agreed to the published version of the manuscript.  
Figure 10: The average waiting time for different vehicle arrival rates: (**a**) in City1 and (**b**) in City2.  
**Funding:** This research was supported by an internal grant (code: 20230403-001) from the Korea Institute of Civil Engineering and Building Technology (KICT), Republic of Korea. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1F1A1047113).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 5 Limitations and Future Work  
Despite the promising performance of REU-Net(2 EEAM), several limitations should be acknowledged:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Parameters (M)** & **FLOPs (G)** & **Inference Time (ms)** & **FPS (Batch = 1)** \\ \hline UNet & 34.5 & 65.2 & 45.3 & 22.1 \\ DeepLabV3+ & 41.8 & 78.9 & 62.7 & 15.9 \\ UNet++ & 36.2 & 72.1 & 58.1 & 17.2 \\ NPSFF-Net & 38.4 & 81.5 & 68.9 & 14.5 \\ REU-Net & 35.1 & 68.7 & 47.5 & 21.1 \\ REU-Net(2 EEAM) & 35.6 & 69.3 & 48.9 & 20.5 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Computational cost and inference time comparison.  
The model struggles to segment buildings smaller than 10 pixels (e.g., isolated huts in rural areas), as shallow features may be lost during downsampling;  
Performance degrades when applied to scenarios with significantly different building styles (e.g., high-density urban vs. sparse villages);  
Processing 512 \(\times\) 512 images requires 4\(\times\) more FLOPs than 256 \(\times\) 256 inputs, limiting real-time deployment on edge devices.  
To address these limitations, future work will focus on:  
Enhancing small object detection via adaptive receptive fields;  
Leveraging domain adaptation techniques to reduce data dependency;  
Pruning redundant parameters and exploring knowledge distillation for edge computing.  
Conceptualization, T.Y. and B.H.; Methodology, T.Y. and B.H.; Software, T.Y. and B.H.; Validation, T.Y.; Formal analysis, T.Y.; Investigation, T.Y.; Resources, T.Y.; Writing--original draft, T.Y.; Writing--review & editing, T.Y. and B.H.; Visualization, T.Y.; Supervision, B.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
I would like to express my sincere gratitude to my supervisor, [PERSON] for his guidance in certain aspects of my experiments. His willingness to discuss methodologies with me has been invaluable in refining my approach and deepening my understanding.  
The authors declare no conflict of interest.
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
## 6 Conclusions  
This article introduces a novel deployment method that disengages a many-in-one satellite system into a constellation of smaller satellites. It presents an algorithm that employs differential drag control to determine critical mission parameters, such as disengagement velocities and timings. While real-world validation is still needed, the findings demonstrate a viable approach for managing satellite separation in multi-unit architectures, with potential applications in cost-effective small satellite constellations. It is particularly suited for the following:  
1. Constellations requiring argument of latitude phasing (as demonstrated in the case studies);
2. Clustered constellations, where clusters of satellites are phased from other clusters, with each cluster consisting of a combination of operational and redundant satellites, providing operational redundancy at each phase;
3. Short-life space probes: low-cost, lightweight, and disposable satellites for one-time use as space probes;
4. Interplanetary constellation deployment (for planets with a gaseous atmosphere), where mass and volume budgets are more stringent, and multiple launches are not a feasible option.  
Conceptualization, D.C. and M.A.A.; methodology, D.C.; formal analysis, D.C.; writing--original draft preparation, D.C.; writing--review and editing, M.A.A.; supervision, M.A.A.; project administration, M.A.A.; funding acquisition, M.A.A. and D.C. All authors have read and agreed to the published version of the manuscript.  
The research was partly funded through the projects ASTREA and QUASAR. ASTREA is financed by Xjenza Malta, through FUSION: The R&I technology Development Programme 2019 (R&I-2018-002-T). QUASAR is also funded by Xjenza Malta, through the FUSION R&I: Space Upstream Thematic Programme of 2023 (SUP-2023-02). Additionally, this research was also partly financedby the Ministry for Education, Sport, Youth, Research and Innovation of Malta through the Tertiary Education Scholarship Scheme (TESS) 2021.  
**Data Availability Statement:** The data supporting the conclusions of this article consist of simulations that are graphically reported in the figures. The numerical data from the simulations will be made available by the authors on request.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Methods
### Quality Assessment of the Studies/Risk of Bias  
The quality of each study was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS) for cohort studies [23]. This scale, recognized by the Cochrane Collaboration [24], evaluates participant selection (4 criteria), comparability (1 criterion), and study outcomes (3 criteria). Each study was independently evaluated by two reviewers according to the above scale. In studies where a different quality rating was observed, disagreements were resolved through discussion between the two reviewers.
# The Design and Implementation of a Phased Antenna Array System for LEO Satellite Communications
###### Abstract  
LEO satellite constellations can provide a viable alternative to expand connectivity to remote, isolated geographical areas and complement existing IoT terrestrial communication infrastructures. This paper aims to improve LEO satellite communications by implementing a new phased antenna array system that can significantly improve the radio communication link's performance. By adjusting the progressive phase shift to each element of the antenna array system, the direction of the main radiation lobe of the phased antenna array system can be controlled with accuracy. As far as we know, it is the first time that a four-element, three-quarter wavelength phased antenna array system has been successfully realized with the intention of being optimized for implementation in LEO IoT satellite reception systems. The proposed system's high level of performance is confirmed by the measurements, which indicate effective control of the main radiation lobe orientation. The numerical analysis shows a maximum gain close to 12 dBi for about 42\({}^{\circ}\) elevation, a Half Power Beamwidth (HPBW) of 32\({}^{\circ}\) in the vertical plane, and 80\({}^{\circ}\) in the azimuth plane. The experimental measurement results at various main lobe orientation angles revealed an HPBW ranging from 76\({}^{\circ}\) to 87\({}^{\circ}\) in the azimuth plane and a maximum Front-to-Back ratio (F/B) of 14.5 dB.  
antenna radiation pattern; beam steering; electronic phase control; LEO satellite communications; phased antenna array
# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 3 Methodology
### Workflow of the Hybrid Deep Learning Model for Time Series Prediction in Hydropower Units  
The workflow for the proposed hybrid deep learning model is illustrated in Figure 5. This model integrates advanced deep learning architectures and optimization techniques to effectively predict the operating status of hydropower units.  
Initially, the collected time series data are divided into training and testing datasets. The training dataset is utilized to train the model and optimize its parameters to achieve  
Figure 5: Flow chart of proposed work.  
the best fit. This process ensures that the model can generalize well on unseen data while accurately capturing the dynamics present in the training data.  
The parameters of the TCN and the DPMI module within the model are optimized using the TSO algorithm, as detailed in Figure 5. This optimization process is crucial for fine-tuning the model's hyperparameters, ensuring that they are set to the values that maximize predictive accuracy on the training dataset.  
Once the model is trained, the optimized weight parameters are saved. These parameters are then transferred to a validation model to assess the model's performance on the validation dataset. This step is critical for evaluating how well the model performs under different settings and on data that was not used during the training phase.  
The model's predictive performance is assessed using several statistical metrics to ensure a comprehensive evaluation. Mean squared error (MSE) calculates the average squared discrepancy between the predicted and actual values, serving as a key measure of the model's precision. Mean Absolute Error (MAE) assesses the average magnitude of prediction errors, emphasizing the extent of these errors without considering their direction. The coefficient of determination (R2) determines the percentage of variance in the dependent variable that can be explained by the independent variables, offering critical insights into the model's explanatory capabilities. Collectively, these metrics establish a solid foundation for evaluating the predictive model's effectiveness.
# Low Power FPGA Implementation of a Smart Building Free Space Optical Communication System
## 5 Discussion  
Given the laser source specifications and the tested distance between the transmitter and the receiver, a quality metric of the data rate is compared via two kinds of modulation, OOK and IM/PWM technique, and a pre-defined figure of merit is declared and calculated for each scenario. The IM approach achieves a doubled data rate of 230 kbps by dividing the power into 4 separate modulated levels. As a result, instead of the OOK modulation with logic '1' as the on state and logic '0' as the off state, the constellations now have 4 potential combinations: '00', '01', '10', and '11'. Another remarkable advantage of IM modulation is its simplicity. It does not require complex interpretation or extra hardware components. It also has a huge versatility to be integrated with any other kind of modulation based on the desired platform and type of communication application. However, forward error correction methods can be applied to the transmitted signal to enhance the quality of detection of the proposed system. A qualitative approach is done in terms of the data rate and total consumed power of the system combined in the evaluated figure of merit 'G' that represents the improved performance of the system as its value increases due to either  
Figure 21: Figure of Merit against data rate at fixed total power.  
Figure 22: Figure of Merit (b/sW) change with power (W) at a fixed data rate.  
bettered data rate or lowered power consumption. This has been illustrated using a plotted graph for each parameter vs. the normalized values of G. Adaptive theory is also one of the future milestones, in which multiple modulation techniques can be embedded and the optimization algorithm can pick the most suitable modulation according to the required data type (text, audio, or video) and rate to be sent. Hence, a fully functioning monitoring communication spot is obtained between smart buildings.
## 3 Results and Discussion
### Phase 6: Principal Component Analysis (PCA)  
The scatterplots show a large overlap between seeps and spills, but their centroids are somehow distinctively independent of NLT. When all variables (16 or 19) are directly input to the PCA, the cumulative variance of the selected PCs (3 to 7) ranges between 80 to 90% for all NLTs. However, when the input is the UPGMA selected variables (4 to 7), the PC-selection (2 to 4 PCs) shows a much lower cumulative variance: from 52% to 70%; the exceptions are the Logarithm functions (NLT2: Log10; and NLT3: Ln) with 99.5% (2 PCs). Table 4 reports the number of selected PCs and their cumulative variance per NLT.
## 3 Results and Discussion
### Model Estimation for Weld Bead Shape
#### 3.2.2 Penetration Depth  
To analyze the penetration depth, the relationship between welding parameters and bead shape was quantitatively explained using RSM, identical to the analysis of leg length. Table 7 summarizes the analysis results for \(Y_{pen}\). As a result of the ANOVA the \(X_{2}\) F-value was highest among the welding parameter. So that mean \(X_{2}\) has the major influence on the \(Y_{pen}\). The \(p\)-value of all the terms were under the 0.05. The coefficient of determination (R\({}^{2}\)) was 0.9924, indicating that the estimated model can predict 99.24% of the data.  
\[\begin{array}{llll}Y_{pen}=&21.00-0.2835X_{1}-0.610X_{2}+0.059X_{3}+0.001146X _{1}^{2}+0.08315X_{2}^{2}\\ &+0.07578X_{3}^{2}-0.00312X_{1}X_{3}-0.00729X_{1}X_{3}+0.10313X_{2}X_{3}\end{array} \tag{2}\]
## 3 Monitoring System Architecture and Software Architecture
### Software Architecture of the Cloud Server
#### 3.2.2 Software Architecture of the Back-End Service Layer  
The back-end service layer can bind users with monitoring terminals, ensuring accurate retrieval of data from a specific monitoring terminal. To meet real-time data requirements, appropriate listening protocols are selected to provide uniform interface support for the front-end application layer and responsibility for requests. The back-end service layer handles the business logic processing of the functional modules shown in Figure 6 and access to the database through the corresponding business logic.  
The uniform interface of the back-end service layer serves as the communication path for \"sending requests\" and \"sending back results\" between the front-end application layer and the back-end service layer. It plays a crucial role in connecting the two layers. In this study, the Express framework based on node.js is used to provide web services specifically for building single-page, multi-page, and hybrid modes. WebSocket, a full-duplex communication protocol, is used to listen to the parameters required for monitoring core device faults and diagnosing fault situations. Furthermore, HTTP, a reliable hypertext transfer protocol, is applied to listen to the running state parameters. Additionally, the WebService interface pushes fault information to users' cellphones. The listening process of the back-end service layer parses the received data packets based on the respective protocols.  
Figure 8 shows the software architecture of the back-end service layer.