esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Modeling and Validation of Peak Ability of Biomass Units
## 1 Introduction
Reference [12] carried out simulation research on a biomass vibrating grate, and analyzes and discusses the impact of grate vibration and fuel moisture fluctuation on boiler characteristics. Reference [13] established the fuel side combustion model of a biomass fluidized bed boiler through the energy conservation relationship, which provided a model basis for the design of its coordinated control system. However, current modeling research on biomass units predominantly focuses on the combustion process in the furnace and lacks research on the unit output, especially for the dynamic modeling of biomass grate furnace units. This is unfavorable for analyzing the peak ability of biomass units. To better analyze the peak ability of the biomass grate boiler unit, according to the characteristics of the biomass grate boiler unit, the process from the biomass unit feed quantity to the unit power is divided into the feed-heat module, the heat-main steam pressure module and the main steam pressure-power module. A \"two-input and two-output\" dynamic model with the feed quantity and the turbine valve opening as the input and the main steam pressure and turbine output power as the output is established, which is verified by the actual operation data of a 30 MW biomass grate boiler unit. The dynamic model can provide a mechanism model for analyzing the impact of fuel calorific value on unit power, and provide support for a fuel management and scheduling strategy during the peak period of biomass grate furnace units. This paper first introduces the production process of the biomass power plant, and then establishes the dynamic models of feed-heat, heat-main steam pressure and main steam pressure-power based on energy conservation. Then the effectiveness of the dynamic model is validated by comparing actual operation data and analyzing the open-loop step response characteristics. Finally, the paper summarizes the work of the full text.
# A Simulation Study of FRP-PCM Reinforcement for Tunnel Liniings with Void Defects
## 3 Results and Analysis
### The Influence of the Creep Phenomenon
#### 3.2.2 Creep Calculation
FRP reinforcement  
Figure 12: The section repair rates of different void positions under the creep effect: (**a**) void at the crown; (**b**) void at the spandrel; (**c**) void at the springline. improves the situation, with CR8 showing the best performance, reducing the safety factor decline to 5.22% after 10 years, which can be compared to 6.07% for CR4. Over time, FRP effectively mitigates the reduction in safety factors, with the higher-grade FRP (CR8) being more effective. As shown in Figure 13, after 5, 8, and 10 years, the section repair rates at different positions show how effective the reinforcement is in repairing or stabilizing the tunnel lining over time. The largest section repair rate improvements can be observed at position E (180\({}^{\circ}\)), indicating that this area, typically the invert or bottom of the tunnel, experiences the greatest benefit from reinforcement measures. In contrast, positions such as A (0\({}^{\circ}\)), B (45\({}^{\circ}\)), and F (225\({}^{\circ}\)) show negative section repair rates, meaning that these areas either continue to deteriorate or did not significantly benefit from reinforcement over the time period. The results show that the CR8 FRP consistently performs the best, reducing the decline in safety factors more effectively compared to CR4 and CR6. Additionally, the spandrel position shows the most significant improvement when reinforced with FRP, while the crown and springline positions still experience moderate declines in safety factors over time, despite FRP reinforcement. When lining is built without FRP reinforcement, the safety factor declines are considerably worse in all locations, indicating the critical importance of FRP reinforcement for long-term tunnel stability. FRP grids, especially CR8, play a crucial role in maintaining tunnel stability over time. The spandrel position benefits the most from FRP reinforcement, while the crown and springline positions still experience safety factor decreases, albeit less than in cases without reinforcement. The data suggest that the higher-grade FRP (CR8) is the most effective at mitigating the reduction in safety factors, particularly over longer time frames.
# Wind-Induced Dynamic Performance Evaluation of Tall Buildings Considering Future Wind Climate
## 3 Fragility Analysis for Human Occupant Comfort
### Occupant Comfort Criteria  
While the evaluation of tall building vibration performance still lacks accepted vibration acceptability and occupant comfort standards [47], certain design codes have established peak vibration limits to ensure occupant comfort, even in the presence of strong winds. In 1991, the Architectural Institute of Japan (AIJ) published Guidelines for the Evaluation of Habitability for Building Vibration, which underwent revisions in 2004. This code applies the one-year-recurrence peak acceleration to evaluate the habitability of buildings to wind-induced vibrations. Based on the proportion of people who experience vibration sensations, the AIJ criterion establishes five levels of comfort performance: H-10, H-30, H-50, H-70, and H-90, corresponding to 10%, 30%, 50%, 70%, and 90% of people, respectively, experiencing vibrations without discomfort [19]. The Code of Practice on Wind Effects in Hong Kong 2019 [48] established acceptable occupant comfort levels for the peak accelerations for the return periods of 1 year and 10 years. ISO 10137 [20] offers com  
Figure 10: Peak acceleration response of the building for basic wind speed used for wind tunnel test.  
parable guidelines for assessing perception thresholds related to wind-induced vibration in tall buildings. The National Building Code of Canada, the Chinese Code, and the Hong Kong Code of Practice set peak acceleration limits of 15 and 25 milli-g for residential and commercial buildings, respectively, under strong wind conditions with a 10-year return period [34].  
The ISO 6897 [49] specifies a vibration threshold dependent on frequency, serving as a criterion for occupant comfort. This threshold is based on the standard deviation of the acceleration response over a 10-min duration, corresponding to wind conditions with a 5-year return period, as given in Equation (23).  
\[\sigma_{a}^{U}=exp(-3.65-0.41 Inf) \tag{23}\]  
where \(f\) represents the building's first modal frequency and \(\sigma_{a}^{U}\) refers to the standard derivation criteria of acceleration. The vibration criteria based on the ISO 6987 [49] are simplified in terms of the peak acceleration as follows:  
\[\hat{a}^{U}=g_{f}exp(-3.65-0.41 Inf) \tag{24}\]  
where \(g_{f}\) denotes the peak factor, which can be calculated for a Gaussian process as defined in Equation (25):  
\[g_{f}=\sqrt{2 lnf_{p}\tau}+\frac{\gamma}{\sqrt{2 lnf_{p}\tau}} \tag{25}\]  
where \(\gamma=0.5772\) is the Euler constant; \(\tau\) is the duration of wind excitation of the building.  
Moreover, [PERSON] et al. [50] proposed six levels of occupant comfort based on peak acceleration responses, providing a classification of human subjective perception to wind-induced vibrations. The comfort levels, along with their corresponding peak acceleration limits, are summarized in Table 7.
## 3 Cd-Mediated Genes Upregulation
### Master Regulatory Genes in Eye Development  
Otx 2 and Pax6  
In vertebrates, eye development starts with the specification of retinal progenitor cells in the anterior neural plate by the overlapping expression of several transcription factors. Among them, the proper expression of the two homeobox genes 0 tx2 (ortho-dentile protein homolog 2) and Pax6 (Paired Box 6) is essential for normal eye development. 0 tx2 upregulation in mice induces epithelial-mesenchymal transition and alteration of the lens cells [44]; in the same embryos, loss of Otx2 function leads to severely altered cranial phenotype [45], whereas heterozygous deletion leads to variable phenotypes [46]. Additionally, Pax6 overexpression results in optic disc malformation, progressive retinal dysplasia, and microphthalmia [47]. In Pax6-deficient mice and in heterozygous mutants, eyes develop with different severe defects [48; 49; 50].  
Incubation in Cd-contaminated soil has been observed to be highly toxic to the retina of _Podarcis_ embryos, causing significant developmental anomalies; in particular, a marked retinal folding is observed [9]. To evaluate the possible involvement of a dysregulation of Pax6 and Otx2, the levels of gene expression and localization of Pax6 and Otx2 transcripts in the developing eyes of control and Cd-treated embryos were investigated. Northern blot analysis shows an increased expression of both Pax6 and Otx2 genes in the cranial region of embryos incubated in Cd-contaminated soil. In parallel, immunocytochemical analysis shows that, in embryos exposed to cadmium, the distribution of messengers does not undergo any significant difference compared to controls, both in the intact or folded retina [9].
## 3 Optimal Service Provisioning Framework
### Smart Healthcare with Emergency and Routine Services  
As illustrated in Figure 3, the ML-RASPF framework enables differentiated treatment of services. High-priority emergency alerts, such as cardiac events--are handled entirely at the mist or edge layers, ensuring sub-second responsiveness. Meanwhile, routine services like patient check-in logs or diet monitoring are processed at the cloud layer. The framework dynamically adapts delivery paths, execution points, and resource allocation based on latency requirements and real-time network states, making it ideal for robust and scalable smart healthcare IoT deployments.
## 5 Conclusions  
In this paper, a UWOC system based on a 450 nm blue laser is proposed, studied and discussed. For system performance evaluation, the blue light could be transmitted up to 6 m at a 1.25 Gbps data rate in the tap water. Overall, BER values of better than 10 \(\times\) 10\({}^{-8}\) could be obtained. Several data rates and distances were also done in experiments and comparisons.  
Next, the underwater environmental factors, which were seldom studied in previous works, such as water environmental factors of water turbulence, temperature variation and  
Figure 11: Eye diagram for transmitting 3 m (**a**) after seawater sedimentation (**b**) flow disturbance in seawater.  
Figure 10: Bit error rate results of 3 m transmission in artificial seawater.  
artificial seawater by adding salt. When a submerged motor with an output of 1200 L/h is used as an underwater flow turbulence source, the system and BER performance has little effect on the underwater communication. For the temperature variation factor study, the temperature was changed from 10 \({}^{\circ}\)C to 50 \({}^{\circ}\)C with a 5-10 \({}^{\circ}\)C step for each. It is found that the original temperature of 25 \({}^{\circ}\)C has the best performance than lower or higher temperature cases. For artificial seawater experiments by adding salt into the tap water was done to observe the seawater environment. The BER values between the precipitated seawater and the seawater combined with water turbulence were compared. It was found that the transmission distance was limited to 3 m because the salt induces a power penalty to the UWOC system. This may be due to the salt acts as particles to scatter the blue light. Moreover, due to the water flow, the seawater's impurities were raised from the bottom of the water tank so as even more degrade the BER values.  
In summary, these experimental results obtained in this paper are useful to verify the transmission quality of UWOC in practical application. However, there are still some challenges in the UWOC systems. Theoretically, the blue light system could transmit a 30 m distance if environmental factors can be appropriated solved and/or reduced.  
Conceptualization, C.-C.C. and S.-K.L.; Data curation, D.-C.L., C.-C.C. and S.-K.L.; Formal analysis, D.-C.L. and C.-C.C.; Methodology, D.-C.L., C.-C.C. and S.-K.L.; Project administration, S.-K.L.; Writing--original draft, D.-C.L. and S.-K.L.; Writing--review & editing, D.-C.L., S.-K.L., S.A., J.-Y.S. and C.-H.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study did not involve humans or animal studies.  
The study did not report any data.  
The authors declare no conflict of interest.
# Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook
## 3 PIDL for Deterministic Tse
### Real-World Data Validation
PIDL-ARZ achieves lower errors than PIDL-LWR, because the ARZ model is a second-order model which can capture more complicated traffic dynamics and inform the PIDL in a more sophisticated manner. **Effects of using FDL**: Comparing the models with FD learner (PIDL-LWR-FDL and PIDL-ARZ-FDL) to the ones without (PIDL-LWR and PIDL-ARZ), the former generally show better data efficiency. In PIDL-LWR-FDL and PIDL-ARZ-FDL, the FD equation is replaced by an internal small neural network to learn the hidden FD relation of the real traffic dynamics. A proper integration of the internal neural network may avoid directly encoding the complicated terms in PIDL and trade off between the sophistication of the model-driven aspect of PIDL and the training flexibility, making the framework a better fit to the TSE problem. **Comparison between PIDL-FDL-based models**: PIDL-LWR-FDL can achieve lower errors than PIDL-ARZ-FDL, implying that sophisticated traffic models may not always lead to a better performance, because the model may contain complicated terms that make the TSE performance sensitive to the PIDL structural design. With the NGSIM data, PIDL-LWR-FDL can balance the trade-off between the sophistication of PIDL and the training flexibility more properly. **Transition from pure physics-driven to data-driven TSE models**: The contributions of the physics-driven and data-driven components can be controlled by tuning the hyperparameters \(\alpha\) and \(\beta\) in Equation (10). Figure 10 shows how the optimal \(\beta/\alpha\) ratio changes as the data size increases. The x-axis is the number of loop detectors, which represents the training data size. The y-axis is the optimal \(\beta/\alpha\) corresponding to the minimally achievable estimation errors of the PIDL-LWR methods shown in Figure 9. The property of tuning hyperparameters enables the PIDL-based methods to make a smooth transition from a pure physics-driven to pure data-driven TSE model: in the sufficient data regime, by using a small \(\beta/\alpha\) ratio, the PIDL performs more like a pure data-driven TSE model to make ample use of the traffic data and mitigate the issue that the real dynamics cannot be easily modeled by some simple PDEs, while in the \"small\" data regime, by using a large ratio, the PIDL behaves like a pure physics-driven model to generalize better to unobserved domains.
## 7 Common-Mode Voltage Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Rated power (hp) & 1.5 & No of the pole pairs & 2 \\ Phase voltage (V) & 96 & Frequency (Hz) & 50 \\ Rated speed (rpm) & 1430 & \(R_{\tau 1}\) (\(\Omega\)) & 1.06 \\ \(R_{s}\) (\(\Omega\)) & 3.4 & \(L_{\tau 1}\) (mH) & 5.8 \\ \(L_{s}\) (mH) & 5.8 & \(L_{m1}\) (mH) & 97 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Simulation Parameters. In all cases, the inverter is controlled to obtain an output peak voltage of 96 V/phase at 50 Hz, and the motor starts at no-load, then a load of 7 N-m is applied at 1.2 s. Figure 7 shows the nine-phase motor currents and phase voltage waveforms, motor torque and speed response, and finally, the CMV waveforms for all the presented PWM schemes. Moreover, Figure 8 shows the FFT analysis of the output voltages. From these results of Figures 7 and 8, it can be observed that,  
1. The output phase currents are very close in all the presented PWM schemes. Moreover, the motor currents exhibit near sinusoidal waveforms. 2. Nevertheless, eliminating the true zero and specific switching vectors to reduce the CMV magnitude in the AZS and SV-10L schemes generates more distortion in the motor voltage and currents, as shown in Figures 7 and 8. The motor terminal voltage total harmonic distortions (THDs) are higher in the CMV schemes than in the conventional SVM scheme. The THD has been evaluated considering harmonic components up to 20 kHz. 3. The reversal voltage chops in the phase-\(a\) voltage waveform, \(v_{a}\) shown in Figure 7 represents the utilization of phase-opposed vectors instead of the true zero vectors in the AZS and SV-10L schemes. 4. Moreover, motor speed and torque responses are similar. 5. However, CMV waveforms are quite different. As can be seen, the CMV schemes reduce the CMV peak more than the conventional SVM scheme, whereas the proposed SV-10L scheme gives the minimum CMV. From this point of view, it can be deduced that the SV-10L scheme presents better overall performance with additional voltage harmonics. Figure 8: FFT analysis for the output phase voltage of the conventional SVM and proposed CMV schemes. Figure 9: Experimental Setup.
## 2 Materials and Methods
### Models' Development
#### 2.4.2 Random Forest (RF)  
Random forest (RF) is a widely used ensemble learning method that combines multiple decision trees to cluster data based on similar patterns [34]. It is an extension of decision trees. The algorithm divides the feature space using hierarchical rules and builds each decision tree using a random subset of the predictor variables [35]. Each decision tree is built using bootstrap sampling, where random subsets of the training data are selected with replacements. Additionally, at each split in a tree, a random subset of features is considered, introducing further randomness that helps reduce the correlation between individual trees and enhances the overall performance of the models [36]. The final prediction is obtained by averaging the regression results of all the decision trees in the ensemble [37; 38].  
Random forest reduces model variance by combining multiple decision trees, which effectively lowers the risk of overfitting. It also provides feature importance, which helps in understanding the model and the data [39]. This method is particularly suitable for handling high-dimensional data, making it applicable across various fields. [PERSON] et al. [40] used the random forest model to predict heavy metal pollution in soil, and [PERSON] et al. [11] used the random forest model to predict the cadmium content in paddy rice, both achieving good predictive results. The RF prediction model in this study was built using the random forest package in R Studio.
## 4 Proposed Scheme
### Accurate Power Consumption Predictor
As a result, the K-means clustering algorithm, detailed in Section 3.1, was used to cluster consumers solely based on their electricity consumption levels, as shown in Figure 5. The careful selection of the clustering algorithm and the number of clusters is crucial for achieving good performance. In our work, we selected the K-means clustering algorithm because it is efficient for large datasets with continuous numeric attributes, such as the Irish dataset, simple to implement, and easily scalable with new data. The performance of the K-means algorithm is influenced by the number of clusters and the initial centroids. To achieve the best results, we followed these steps: First, we determined the number of clusters using the elbow method, as shown in Figure 6. This figure illustrates the relationship between the number of clusters (\(K\)) and the distortion score (i.e., the aggregate intra-cluster distance). The elbow method identifies the optimal value of \(K\) as the point where there is the greatest change in the distortion score with respect to \(K\). The vertical line indicates that using five clusters is optimal for our scenario. Next, the centroids are randomly initialized and optimized over several iterations until convergence. We also ran the K-means algorithm multiple times with different initializations and selected the solution with the minimum intra-cluster distance. The electricity consumption levels of consumers within the same cluster should be closely aligned, which is expected to enhance prediction performance. This improvement can be attributed to the fact that machine learning models trained on power consumption data exhibiting similar patterns make it easier for the models to accurately learn the patterns and thus predict accurate future consumption consumption readings. Moreover, as will be detailed in the next section, the use of LSTM and a transformer in our proposal, as shown in Figure 4, can overcome the problem of noisy data due to the use of the CAT data collection approach. Figure 4: Our proposed predictor architecture. Figure 5: Our proposed clustering approach.
# Research on the Water Entry of the Fuselage Cylindrical Structure Based on the Improved SPH Model
## 4 Numerical Simulation of Water Entry of a Cylinder Structure
### Water Entry of the Skin Cylinder  
The skin cylinder structure has a diameter of 600 mm and a thickness of 3 mm. The material of the cylinder is elastic material with a density of 2700 kg/m\({}^{3}\), an elastic modulus of 67.5 GPa, and a Poisson's ratio of 0.34. The water domain is at a length of 3 m and a height of 2 m. The initial velocity of the cylinder is 5 m/s, representing a free-falling state.  
Figure 7 shows the equivalent strain of the cylinder and the pressure of the fluid for the water entry of the cylinder at different times. From the deformation of the cylinder, it can be observed that the bottom of the cylinder undergoes deformation first when it  
Figure 5: Slamming force experienced by the cylinder obtained from the simulation, theory, and experiment.  
Figure 6: GPU parallel flow chart for the SPH method.  
contacts the water's surface. As the depth of immersion increases, bottom deformation tends to become planar, while the upper part of the cylinder becomes an elliptical shape. It can be seen that the strain originates from the bottom of the cylinder and propagates toward the upper part.  
During the deformation of the cylinder, the deformation states of the particles in the innermost and outermost layers are the opposite of each other. The particles in the outermost layer are under compression, the particles in the innermost layer are under tension, and the particles in the middle layer are in a transitional state. The deformation of the particles in the middle layer is minimal.  
To further illustrate the convergence of the improved SPH model, Figure 8 provides the variation in the slamming force acting on the cylinder for different particle spacings.  
Figure 7: Equivalent strain of the cylinder and the pressure of the fluid at different times.  
The cylinder is arranged with 2, 3, 6, and 12 layers of particles in the thickness direction. As particle spacing decreases, the impact force shows convergent behavior. It is observed that at the moment when the cylinder enters the water, the slamming force rapidly increases to its peak value, followed by a slow decrease. When the slamming force decreases to a value, it exhibits a fluctuating trend. This is due to the continuous downward deformation of the cylinder, resulting in enhanced contact with the water, and the slamming force shows a rising trend.