esa-sceva/satcom-chunk-collection huggingface dataset:
# Physics-Informed Surrogate Modelling in Fire Safety Engineering: A Systematic Review
###### Abstract  
Surrogate modelling is increasingly used in engineering to improve computational efficiency in complex simulations. However, traditional data-driven surrogate models often face limitations in generalizability, physical consistency, and extrapolation--issues that are especially critical in safety-sensitive fields such as fire safety engineering (FSE). To address these concerns, physics-informed surrogate modelling (PISM) integrates physical laws into machine learning models, enhancing their accuracy, robustness, and interpretability. This systematic review synthesises existing applications of PISM in FSE, classifies the strategies used to embed physical knowledge, and outlines key research challenges. A comprehensive search was conducted across Google Scholar, ResearchGate, ScienceDirect, and arXiv up to May 2025, supported by backward and forward snowballing. Studies were screened against predefined criteria, and relevant data were analysed through narrative synthesis. A total of 100 studies were included, covering five core FSE domains: fire dynamics, wildfire behaviour, structural fire engineering, material response, and heat transfer. Four main strategies for embedding physics into machine learning were identified: feature engineering techniques (FETs), loss-constrained techniques (LCTs), architecture-constrained techniques (ACTs), and offline-constrained techniques (OCTs). While LCT and ACT offer strict enforcement of physical laws, hybrid approaches combining multiple strategies often produce better results. A stepwise framework is proposed to guide the development of PISM in FSE, aiming to balance computational efficiency with physical realism. Common challenges include handling nonlinear behaviour, improving data efficiency, quantifying uncertainty, and supporting multi-physics integration. Still, PISM shows strong potential to improve the reliability and transparency of machine learning in fire safety applications.  
fire safety engineering; surrogate modelling; machine learning; physics-informed surrogate modelling
## 2 Model Description
### HMW Dynamic Modeling
#### 2.2.4 Longitudinal Dynamic Model  
In this article, the HMW longitudinal dynamic model is considered. In the case of wing deformation, we have  
\[\left\{\begin{array}{l}S_{1}=\left[S_{x1},0,S_{z1}\right]^{T}\\ S_{2}=\left[S_{x2},0,S_{z2}\right]^{T}\end{array}\right. \tag{13}\]  
Accordingly, the additional force \(F_{S}\) along the body coordinate system can be expressed as  
\[F_{S}=\left[\begin{matrix}F_{Sx}\\ F_{Sy}\\ F_{Sz}\end{matrix}\right]=F_{S1}+F_{S2}=\sum\limits_{i=1}^{2}m_{i}\left[ \begin{matrix}\ddot{S}_{xi}-S_{xi}\omega_{z}^{2}\\ S_{xi}\omega_{z}+2\hat{S}_{xi}\omega_{z}\\ 0\end{matrix}\right] \tag{14}\]  
The additional moments can be expressed as  
\[M_{S} =-\sum\limits_{i=1}^{2}m_{i}S_{xi}\Big{(}g\cos\theta+\dot{v}_{y} +v_{x}\omega_{z}+S_{xi}\omega_{z}+2\hat{S}_{xi}\omega_{z}\Big{)} \tag{15}\] \[M_{SG} =-\sum\limits_{i=1}^{2}m_{i}S_{xi}\cos\theta\] \[M_{SD} =-\sum\limits_{i=1}^{2}m_{i}S_{xi}\Big{(}\dot{v}_{y}+v_{x}\omega_{ z}+S_{xi}\omega_{z}+2\hat{S}_{xi}\omega_{z}\Big{)}\]  
where \(\theta\) is the pitch angle, \(v_{x}\) and \(v_{y}\) denote the velocity under the body coordinate system, \(m_{i}\) is the mass of the \(i\)th wing, and \(\omega_{z}\) is the pitch rate.  
Therefore, the longitudinal dynamic model of the HMW is given as  
\[\left\{\begin{array}{l}\dot{V}=\frac{-D}{m}-g\sin(\phi-\alpha)+\frac{F_{Sx}\cos \alpha-F_{Sy}\sin\alpha}{m}\\ \dot{\alpha}=\omega_{z}-\frac{L}{mV}+\frac{g}{\psi}\cos(\phi-\alpha)-\frac{F_{Sx} \sin\alpha+F_{Sy}\cos\alpha}{mV}\\ \dot{J}=\omega_{z}\\ \dot{\omega}_{z}=\frac{M_{z}+M_{SG}+M_{SD}}{J_{z}}\end{array}\right. \tag{16}\]  
where \(V\) denotes the HMW velocity, \(\alpha\) denotes the angle of attack, \(m\) denotes the mass of the HMW, \(g\) is the gravity acceleration, \(D\) denotes the drag, \(L\) denotes the lift, and \(M_{z\alpha}\) denotes the aerodynamic pitch moment.  
The lift, drag, and aerodynamic pitch moment are calculated by  
\[\left\{\begin{array}{l}L=QS_{ref}C_{L}\\ D=QS_{ref}C_{D}\\ M_{z}=QS_{ref}L_{ref}m_{z}\end{array}\right. \tag{17}\]  
where \(Q=0.5\rho V^{2}\) is the dynamic pressure, \(\rho\) is atmosphere density, \(S\) is the reference area, \(L\) is the reference length, \(C_{L}\) denotes the lift coefficient, \(C_{D}\) denotes the drag coefficient, and \(m_{z}\) denotes the pitch moment coefficient.
# Simulation Study on Ground Vibration Reduction Measures of the Elevated Subway Line
## 3 Numerical Results and Discussion
### Effect of Continuous Support on Ground Vibration  
The basic parameters of the continuous support vibration reduction structure are as follows: continuous support stiffness of 14 kN/mm, polymer material elastic modulus 15 MPa, loss factor 0.28, structure 0.3 m long, and total mass 21.8 kg. The parameters of the fasteners used were 10 \(\times\) 10\({}^{6}\) N/m for vertical stiffness, 13.5 \(\times\) 10\({}^{6}\) N/m for lateral stiffness, 1361.12 Ns/m for vertical damping, and 947.27 Ns/m for lateral damping.  
The time domain results and frequency domain results of the ground vibration velocity before and after the installation of the continuous support are shown in Figure 17. From  
Figure 16: Ground vibration acceleration results before and after installation of damping pad: (**a**) time domain results; (**b**) frequency domain results.  
Figure 15: Ground vibration results before and after installation of damping pad: (**a**) time domain results; (**b**) frequency domain results.  
the time domain results, it can be seen that the vibration velocity trend is the same after arranging the continuous support, and the vibration result of 0.048 mm/s at this time meets the ground vibration limit requirements. Before installing the continuous support, the vibration peak value in the frequency domain is larger, and the vibration velocity peak value is effectively suppressed after installing the continuous support.  
From Figure 18, it can be known that after installing the continuous support, the vibration trend is the same in both the time domain and frequency domain results. In the time domain results, the vibration acceleration is reduced. After installing the continuous support, the RMS value of ground vibration acceleration is reduced from 15.63 mm/s to 9.38 mm/s. In the range of 0-80 Hz, after installing the continuous support, the peak value of ground acceleration is effectively controlled. Overall, the ground vibration velocity and acceleration can be reduced after installing the continuous support.
# Coupled Dynamic Modeling and Control of Aerial Continuum Manipulation Systems
## 7 Conclusions  
This paper presents the first coupled dynamic modeling for aerial continuum manipulation systems (ACMSs), which considers both UAV and the continuum arm as a unified system. The proposed model was developed using a modified Euler-Lagrange framework to take into account damping and elasticity effects of the arm. The approach is enriched by considering a general tiltrotor UAV with fix tilt angles as a base platform, which can model both under-actuated and fully actuated ACMSs. The integrated continuum arm attached to the underneath of the UAV is a general tendon-driven type with three tendon actuators. The model can be extended to different continuum arm configurations such as series, parallel and collaborative types. Furthermore, in the future, the actuating mechanism can be replaced with other methods such as pneumatics actuators in concentric tube continuum arms. A nonlinear adaptive sliding mode control method was also formulated to assess the behavior of a fully actuated ACMS model with an octa-rotor base platform in free flight missions when there is no interaction with the environment. The stability of the proposed controller was proved using Lyapunov stability theorem. Experimental simulation results were reported to verify the proposed dynamic modeling and control approaches. Results taken from a developed ACMS simulator in MATLAB environment demonstrate the impressive performance of the controller in tracking desired helix and infinity trajectories of the UAV while the arm is fixed or moving. It was also shown that the proposed coupled model is ten times faster than the previously reported decoupled model for ACMSs. Our future work will include extending the present work to interactive aerial missions like impedance and hybrid position-force control of the system.  
Figure 12: CR tensions in the second control case.  
**Author Contributions:** [PERSON] developed formulation and simulations and authored the manuscript while [PERSON] helped with simulation. [PERSON] supervised [PERSON] and [PERSON] in problem formulation and participated in the authorship of the manuscript. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by Natural Sciences and Engineering Research Council of Canada under Discovery Grant #2017-06930, Ryerson Dean of Engineering and Architectural Science Research Fund (DRF) and Ontario Trillium Scholarship (OTS).  
**Conflicts of Interest:** The authors declare having no conflict of interests.
## 3 Results and Discussion
### The Characterization of Automotive Glass Waste
#### 3.1.5 Gas Chromatography/Mass Spectrometry Analysis  
GC/MS was applied for the identification of organic substances in both types of AGW and selected fractions (0.16-0.25 mm, 0.4-0.63 mm, 1.25-2 mm and 2-5 mm). The extraction yields were low (0.01-0.04 wt.%), corresponding to the inorganic nature of the samples. The extracts exhibited discoloration of the solutions, which varied according to the type of AGW (Figure S5 of the Supplementary Files). In all AGW1 and AGW2 samples, triethylene glycol bis(2-ethylhexanoate) was identified (formula: C\({}_{22}\)H\({}_{42}\)O\({}_{6}\), MW: 402).  
This compound serves as a plasticizer for various industrial materials, including polyvinyl butyral (PVB), which is widely used in glass lamination, especially in automotive applications [53]. In the case of windshields, the plasticizer triethylene glycol bis(2-ethylhexanoate) gives the film formability and flexibility and makes it possible to balance mechanical strength and elasticity, almost without affecting adhesion. The compound is insoluble, resistant to low temperatures and to ultraviolet radiation, has antistatic  
Figure 2: The TG curves of AGW1 and AGW2.  
properties and is not listed as toxic or environmentally dangerous [55]. However, even if the substance is not labeled as hazardous, its release should be monitored and possibly restricted. This precaution is warranted due to documented instances of this substance being identified as a contact allergen [56]. Therefore, the influences of the AGW particle size on the release of triethylene glycol bis(2-ethylhexanoate) were investigated.  
The concentrations of triethylene glycol bis(2-ethylhexanoate) in the AGW samples were quantitatively determined, ranging from 0.2 to 24.6 ng\(\cdot\)kg\({}^{-1}\) (Table 5).  
The results show that for AGW1, the particle size has no significant effect on the presence of triethylene glycol bis(2-ethylhexanoate) in the samples. The measured values are very similar except for the 0.4-0.63 mm fraction, which is noticeably lower. These values range from 8.5 ng\(\cdot\)kg\({}^{-1}\) for the 0.4-0.63 mm fraction to 13.3 ng\(\cdot\)kg\({}^{-1}\) for the 2-5 mm fraction.  
In contrast, a difference between the fractions has been found for AGW2. The contents of triethylene glycol bis(2-ethylhexanoate) in individual particle-size fractions varied. The highest concentration was found in the finest fraction, and the lowest concentration was determined in the coarsest fraction.  
Surprisingly, the 0.4-0.63 mm fraction of both types of AGW showed the presence of a small amount of triethylene glycol bis(2-ethylhexanoate).
## 2 Materials and Methods
### Data Analysis  
All details of data analysis are reported in Ref. [24]. Briefly, the measured spectra were first processed by subtracting the background signal measured in sample regions free of cells. The Resonant Mie Scattering-Extended Multiplicative Signal Correction (RMIeS-EMSC) was used to correct Mie scattering and dispersion artifacts due to scattering by cell nuclei and organelles [34; 35]. A piecewise baseline correction was also performed, and the Standard Normal Variate (SNV) method was adopted for normalizing the spectra [36]. For the different sample types, average spectra with standard deviations were evaluated. These pre-processing procedures were carried out by means of home-made algorithms. Pseudo-Voigt-shaped vibrational modes, which treat each peak as a sum of fractional contributions of a Gaussian and a Lorentzian curve, were used to analyze the spectra [37] by using a best-fit routine from the GRAMS-AI software (Version 9.3, Thermo Fischer Scientific, Waltham, MA, USA) (see Ref. [24] for further details).  
The Amide I and Amide III bands were further investigated because modifications in protein configuration can produce relevant alterations in spectral features [38; 39; 40; 41]. To investigate protein secondary structure, pseudo-Voigt functions, obtained as described above, were adopted to deconvolute the Amide I (1740-1580 cm\({}^{-1}\)) and Amide III (1350-1200 cm\({}^{-1}\)) bands. Proportionality between the area of each subcomponent of the band and the relative amount of the structure was assumed in spectra. The Amide III (1350-1200 cm\({}^{-1}\)) band was also examined because it can provide useful information about secondary protein structures [39] without artifacts due to the presence of residual water content.  
The ratios between the area of properly chosen peaks were used to quantitatively evaluate the spectral changes related to different experimental conditions [24; 28; 29; 30; 31; 32]. The examined ratios related to protein content, rearrangement, and phosphorylation, DNA content and modification, and lipid content and saturation are listed in Table 1. The ratio values estimated from the spectra of different samples were compared by means of one-way ANOVA test with a 0.05% significance level.
# Statistical Analysis and Modeling for Optical Networks
## 4 A Brief Introduction to Optical Networks
### Link-Related Components
Advanced link technologies, such as coherent transmission and WDM, optimize spectral efficiency, enabling higher data rates and greater network scalability. The integration of these link-related components enhances the overall efficiency, resilience, and capacity of optical networks, making them indispensable for supporting modern applications, such as 5G, cloud computing, and hyper-scale data centers [2]. As optical networks continue to evolve, innovations in link technologies will further improve their performance, ensuring they meet the ever-growing demands of global data communication.
# Spatiotemporal Analysis of Urban Expansion in Beijing, China
## 2 Research Area and Research Methods
### Data Sources and Preprocessing
#### 2.3.2. Urban Expansion Intensity Index  
The urban expansion intensity index represents the growth rate of urban construction land area within a certain period of time and is generally used to measure the strength of expansion [13; 14]. The formula is as follows:  
\[R_{u}=\frac{S_{b}-S_{a}}{S_{a}\times T}\times 100\% \tag{2}\]  
where \(R_{u}\) expresses expansion intensity index.  
Research Methods on the Spatial Characteristics of Urban Expansion 2.4.1. Fractal Dimension of Urban Space  
The fractal dimension represents the logarithmic ratio of the perimeter to the area of urban construction land boundary lines and is generally used to measure the complexity of the spatial form of urban construction land [15; 16]. The fractal dimension is calculated as follows:  
\[D_{t}=2\times\frac{\ln(C_{t}\times 0.25)}{\ln S_{t}} \tag{3}\]  
In the formula, \(D_{t}\) represents the fractal dimension of the city in year t (1 \(\leq\)\(D_{t}\)\(\leq\) 2), \(C_{t}\) indicates the perimeter of the urban edge in year t, and \(s_{t}\) represents the urban area in year t. The theoretical value of the fractal dimension is 1-2, and the larger the dimension is, the more complex the boundary of urban construction land becomes.
# PAGE--Practical AES-GCM Encryption for Low-End Microcontrollers
## 1 Introduction  
Resource constrained devices for Internet of Things (IoT) applications only equip limited RAM, ROM, computation capability and battery power. Under these hard conditions, the secure and robust network connection is a fundamental building block for IoT services. General cryptography solutions, such as encryption and authentication, for high-end desktop can be straightforwardly adopted to low-end microcontrollers. However, these approaches require heavy computation overheads since it is targeting for high-end processors. For this reason, the lightweight but secure cryptography solution is an important research area for practical and real world applications. The lightweight cryptography ensures low-cost and simple computations. Many lightweight block ciphers have been investigated. Block ciphers are largely based on ARX (Addition, Rotation, and XOR) architecture, such as SIMON, SPECK, CHAM, HIGHT, and LEA block ciphers [1; 2; 3; 4; 5]. However, ARX based block cipher requires many computations to prevent the side channel attack with a masking method [6]. On the other hand, AES block cipher (i.e., SPN architecture) is efficient for secure block cipher with the masking method [7]. The AES block cipher is not lightweight block cipher. However, this is used in almost all security solutions. For this reason, the optimized implementation of AES block cipher on the low-end microcontroller is important for security and generality.  
Another concern is mode of operation. Among a number of mode of operations, the counter (CTR) mode of operation and the Galois Counter Mode (GCM) of operation are widely used for encryptionand authenticated encryption, respectively [8]. In CHES'18, the fast implementation of AES-CTR is introduced [9]. The method utilized the repeated data to pre-compute the part of AES operations. The FACE method is targeting for high-end processors, where they require high memory demands and frequent table updates.  
For the authenticated encryption, AES-GCM is widely used in practice. The core operation of GCM is a binary field multiplication. In Reference [10], fast and secure binary field multiplication for GCM was introduced. They utilized Karatsuba algorithm and Instruction Level Atomicity (ILA). However, there is a large room to enhance the performance by using Karatsuba algorithm and engineering techniques.  
In this paper, we optimized FACE technique for low-end microcontrollers (i.e., FACE-LIGHT). Afterward, the implementation of AES-GCM is efficiently constructed with the optimized GHASH function.
## 3 Methodology
### Self-Attention Based on Euclidean Distance
The figure indicates that the distribution of attention weights is overly concentrated in regions where the key vectors have longer magnitudes, thereby drowning out information from other positions and hindering the Transformer’s ability to fully comprehend the global information within the sequence. attention weights are computed based on the Euclidean distance between the query vectors and key vectors (as shown in Equations (8) and (9)). In the EDAttention mechanism, the attention score \(\alpha_{i,j}\) is defined as the negation of the Euclidean distance between vectors \(q_{i}\) and \(k_{j}\), resulting in \(\alpha_{i,j}\leq 0\). Subsequent \(softmax\) operation compresses \(\alpha_{i,j}\in(0,1]\). Moreover, EDAttention achieves that greater distances between vectors correspond to lower similarity and, consequently, smaller attention weights, which aligns with analytical logic and intuitive perception. This approach ensures that attention is more evenly distributed across all positions in the sequence, allowing the model to fully capture the global information in long video sequences.
# Evaluation of Water and Carbon Estimation Models in the Caatinga Biome Based on Remote Sensing
## 2 Material and Methods
### Data Analysis  
For the validation and analysis of the observed and estimated results in 2013 in the DC and SC areas, the mean absolute error (MAE), mean percent error (MPE), coefficient of determination (R\({}^{2}\)), correlation coefficient (r), and root mean square error (RMSE) were used and obtained as:  
\[\text{MAE}=\frac{1}{\text{N}}\sum_{\text{i=1}}^{\text{N}}\left|\mathcal{X}_{ \text{i}}-\text{X}_{\text{i}}\right| \tag{20}\]  
\[\text{MPE}=\frac{100}{\text{N}}\sum_{\text{i=1}}^{\text{N}}\left|\frac{ \mathcal{X}_{\text{i}}^{\prime}-\text{X}_{\text{i}}}{\text{X}_{\text{i}}}\right| \tag{21}\]  
\[\text{RMSE}=\sqrt{\frac{1}{\text{N}}\sum_{\text{i=1}}^{\text{N}}\left(\mathcal{ X}_{\text{i}}^{\prime}-\text{X}_{\text{i}}\right)^{2}} \tag{22}\]  
where X corresponds to the observed value, \(\mathcal{X}^{\prime}\) to the estimated value, and N the amount of data analyzed.  
For the spatial analyses, the annual cumulative, standard deviation (SD), maximum (max), and median of ET and GPP in the state of Paraiba in 2013 were calculated for the different models.
# Energy-Feedback Load Simulation Algorithm Based on Fuzzy Control
## 1 Introduction
In addition to PI control, other basic control algorithms are often applied to the power feed grid control, such as deadbeat control, PR (Proportional Resonance) control, single period control, and so on. According to the circuit equation, the duty-cycle mathematical model is established for the beat-free control. By sampling the circuit parameters of the current cycle, the duty cycle of the full-bridge driver in the next cycle is calculated. It belongs to precise control, and the principle is simple and easy to understand and easy to design [11]. PR controller can achieve high gain without static error tracking near the resonant point and can greatly suppress signals of other frequencies. However, its bandwidth depends on the stability of power grid frequency, and thus it is not suitable for the power feed [12]. None of the above control algorithms can achieve fast and efficient control. Aiming at the key technical problems such as transient performance, steady-state accuracy, power feedback quality, and overall efficiency of energy-fed DC electronic load, this paper focuses on a kind of high-efficiency energy-fed DC electronic load with medium and high voltage input and its control strategy. In this paper, the high-efficiency regenerative DC electronic load adopts the \"boost+ full bridge\" as the power circuit topology. Aiming at the transient performance and steady-state accuracy of the energy-regenerative DC electronic load, an input-current-tracking algorithm based on fuzzy control is proposed. In this paper, the control strategy of high-efficiency feed-DC electronic load is studied systematically, and the correctness of the theoretical analysis and feasibility of the scheme are verified by relevant simulation experiments.