esa-sceva/satcom-chunk-collection huggingface dataset:
# Physics-Informed Surrogate Modelling in Fire Safety Engineering: A Systematic Review
## 3 Implementing Physics-Informed Surrogate Model
### Understanding Physics
#### 3.3.1 Identifying Sub-Models  
A complex physical system can often be decomposed into smaller sub-models, which can be categorised as series models, parallel models, and coupled models, depending on their interactions and dependencies, as elaborated below. Decomposing a system into sub-models provides several advantages. It simplifies complex problems into manageable components, enables targeted physics integration within each sub-model, improves the modularity and interpretability of the overall PISM, and allows for the application of different modelling techniques best suited to each sub-problem. By systematically identifying sub-models, it becomes easier to ensure physical consistency, enhance computational efficiency, and improve predictive accuracy.  
**Series models** consist of sequentially dependent components, where the output of one model serves as the input for the next (see Figure 8). In fire safety engineering, for instance, multiple sub-models describe the progression from ignition to structural performance, including ignition, fire development, heat transfer, material degradation, and structural response. Each of these processes directly influences the next in a chain of dependencies, and this chain of dependencies can be adopted as part of the overall model architecture when developing the surrogate model. Thus, in fire safety assessments, thermal models are often used to inform mechanical models that evaluate structural integrity under fire conditions, as in [97].  
**Parallel models** describe distinct behaviours within the same system (see Figure 8). These distinct behaviours imply that a single model may struggle to generalise across both behaviours. Both behaviours may also be distinct with regard to their influencing parameters (which is important for the feature selection discussed below). Thus, the use of separate surrogate models for each behavioural region may be beneficial. This approach has been applied, for example, by [PERSON] et al., whose model for the thermo-mechanical behaviour of functionally graded materials employs separate neural networks to represent distinct physical fields (displacement and temperature) [100] into their model.  
**Coupled models** interact dynamically rather than operating strictly in series or parallel. Recognising these interactions is crucial for integrating physics into machine learning models. One notable example is atmospheric-wildfire coupled models, where capturing the interaction between atmospheric dynamics and wildfire progression was essential for accurate modelling [85]. Similarly, coupled structural failure mechanisms may require joint modelling to account for interdependencies. Integrating such physical interactions can be done as part of the model architecture, as is explained further in Section 3.4.2.
# Design and Analysis of a Novel Floating Docking Mechanism for On-Orbit Refueling
## 4 Analysis and Discussion of Simulations and Experiments  
After the design and mechanical analyses of the mechanism, three types of numerical simulations are performed in this section to investigate the static behavior of the refueling pipe and the kinematic and dynamic behavior of the docking mechanism: (1) a static simulation of the mechanical properties of the refueling pipe, (2) a kinematic simulation of the tolerance range of the docking mechanism, and (3) a dynamic simulation of the contact force and driving force of the docking mechanism. Corresponding experiments are per-formed to validate the simulations.
## 6 Discussion  
This study provides a solution in the shape of closed-form expressions for calculating plastic limit pressure (\(P_{\text{L}}\)) and stress intensity factor (\(K_{\text{I}}\)) for elbows with part-through axial semi-elliptical part-through cracks. Generated expression, validated by symbolic regression shows high accuracy (R-squared values of 0.9985 for \(K_{\text{I}}\) and 0.1885 for \(P_{\text{L}}\)). These expressions offer a practical alternative to a time-consuming finite element analysis (FEA). Previous research, such as [PERSON]'s 'Equivalent straight pipe method' [40, 41], primarily focused on comparing elbows to straight pipes in isolation. Our study extends this by including the interactions within the entire pipeline system, pipe supports, and piping components. While we validated our FEA results using [PERSON] et al.'s analytical solution for straight  
Figure 11: Failure assessment diagram.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(a\) **(mm)** & \(c\) **(mm)** & **P\({}_{\text{F}}\) (MPa)** \\ \hline
0.84 & 0.42 & 7.1 \\
1.68 & 0.84 & 5.05 \\
2.52 & 1.26 & 4.08 \\
0.84 & 0.84 & 6.35 \\
1.68 & 1.68 & 4.45 \\
0.84 & 1.26 & 5.85 \\
1.68 & 2.52 & 4 \\
0.84 & 1.68 & 5.3 \\
1.68 & 3.36 & 3.55 \\
0.84 & 2.1 & 4.95 \\
0.84 & 2.52 & 4.75 \\
0.84 & 3.36 & 4.5 \\
0.84 & 4.2 & 4.38 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Failure pressure values determined from FAD intersections.  
pipes [8], showing consistent trends despite variations in exact values, our approach offers a wider perspective on pipeline integrity.  
The closed-form solutions enable quick and reliable failure assessment procedures, reducing the need for extensive FEA. Our approach considers the entire pipeline system, provides a more realistic evaluation of structural integrity, and ensures a safety threshold. While the current FAD diagram was constructed using FEA data, the proposed closed-form expressions are expected to provide similar results, giving a more efficient integrity assessment process and providing a practical tool for evaluating safe operational conditions and quick solutions.  
The applicability of our expressions to other pipeline geometries and conditions remains to be tested. Future work should study different pipe dimensions and loading conditions, as well as experimental validation to improve the practical use of the models. Including additional parameters and factors can further improve integrity assessments. Our study contributes to a more complete understanding of pipeline integrity by considering the interactions between cracked elbows and other pipeline components, providing a strong foundation for practical failure assessments.
## 2 Problem Description and Methodology
### Optimal Design of LEO Navigation Constellation
#### 2.1.2 Performance Metrics for Navigation Constellation  
The performance metrics for a navigation constellation primarily encompass the continuous coverage index, service availability index, service reliability index, and positioning accuracy index. Among these, PDOP provides a robust representation of constellation availability; the smaller the PDOP, the better the constellation's service performance and precision. In ECEF, the formula for navigation positioning error is as follows:  
\[\text{cov}(dX)=\left(H^{T}H\right)^{-1}\sigma^{2}_{\text{ \emph{UEEE}}} \tag{2}\]  
where \(\text{cov}(dX)\) represents the covariance of position and clock error, \(\sigma^{2}_{\text{\emph{UEEE}}}\) denotes the variance of pseudo distance measurements, \(\left(H^{T}H\right)^{-1}\) converts the ranging standard deviation to the position + the clock error, and \(H\) stands for the direction cosine matrix. \(\left(H^{T}H\right)^{-1}\) is defined by the following formula:  
\[\left(H^{T}H\right)^{-1}=\begin{bmatrix}D_{11}&D_{12}&D_{13}&D_{14}\\ D_{21}&D_{22}&D_{23}&D_{24}\\ D_{31}&D_{32}&D_{33}&D_{34}\\ D_{41}&D_{42}&D_{43}&D_{44}\end{bmatrix} \tag{3}\]  
The PDOP can be calculated from the elements of \(\left(H^{T}H\right)^{-1}\):  
\[PDOP=\sqrt{D_{11}+D_{22}+D_{33}} \tag{4}\]  
Visible satellites are those observed satellites with an elevation angle higher than the cut-off elevation angle when viewed from the receiver. The availability of more visible  
Figure 1: Diagram of satellite orbital plane.  
satellites provides users with a wider selection, leading to increased navigation accuracy and reliability. The formula for calculating the number of visible satellites is as follows:  
\[VisNum=\sum_{i=1}^{N}N_{d},\quad if\quad\varepsilon^{d}_{\text{min}}=\gamma \tag{5}\]  
In the given constellation, \(N_{d}\) represents the visible \(d\) satellite; \(\varepsilon^{d}_{\text{min}}=\gamma\) denotes the minimum observation elevation angle between the \(d\) satellite and the receiver. In this paper, the satellite cut-off elevation angle is uniformly set to \(5^{\circ}\).  
The constellation configuration adopts a Walker configuration, the principle of \(2\sigma\) is adopted, and the grid of observing stations at Earth's surface is \(5^{\circ}\times 5^{\circ}\). The simulation time is divided into 24 h, and the step size is 60 s to analyze the effect of different constellation configurations on the performance of navigation services.
# Multi-User Joint Detection Using Bi-Directional Deep Neural Network Framework in NOMA-OFDM System
## 4 Proposed Deep-Learning Model
### Model Architecture  
#### 4.2.1 Network Description  
The Bi-LSTM is comprised of the forward and backward directions of the LSTM network [29; 30]. It can use information from both sides because the input flows in both directions, as shown in Figure 2(a). The forward and backward layers are comprised of two cyclic neural networks which can connect the output layers simultaneously. The output is able to acquire the before and after sequence information of every point. In addition, it explores the relationship between them through training. The accuracy of CE can be improved by this operation. To achieve the CE and signal detection, directional LSTM, which is a special kind of recurrent neural network that consists of the cascade of LSTM cells, is exploited [31]. The LSTM network comprises 4 layers, including LSTM hidden layers, fully connected layers, softmax function layers, and classification layers. The LSTM hidden layer is implemented with 100 hidden units. In the LSTM hidden layers, the learnable weights includes input weights \(w\). The recurrent weights are \(T\), and \(b\) is the bias. The second layer is a fully connected layer that contains a 16 number of classes. The fully connected layer is implemented to sequence and time-series data for classification. The output of the LSTM layers is processed by the fully connected layer. A fully connected layer adds a bias vector \(b\) to the input after multiplying it by a weight matrix \(w\). Thus, it estimates all components of the complex modulated signal of each UE. All neurons in the fully connected layer are connected to all neurons in the previous layer. This brings together all the properties and information collected from the previous layer. The fully connected layer works individually on every time step in the LSTM network. To derive the outputs for the terminal layer, the softmax activation function is used. In the last layer, the classification layer is utilized to map the output to a vector probability and a fully connected layer with an output size equal to the number of classes is specified, and then the error between them is passed as feedback for the training. Finally, the mean-squared error (MSE) for the overall network to detect at \(UE_{i}\) is expressed as follows [32]:  
\[MSE=\frac{1}{Q}\sum_{q=1}^{Q}(S_{i}(q)-\hat{S}_{i}(q))^{2}, \tag{14}\]  
where the number of training OFDM samples is represented by \(Q\), \(S_{i}(q)\) is the target output, and \(\hat{S}_{i}(q)\) is the predicted output at the response \(q\). As a means of minimizing the loss, the well-known Adam optimization algorithm is utilized [33].
## 1 Introduction  
Aerosols are suspended particulate matter in the atmosphere and are emitted through a wide range of natural or anthropogenic processes and play an important role in the Earth's radiative balance. Elastic backscatter lidars are good candidates for range-resolved monitoring of aerosols as they provide quantitative properties of aerosols. This is important information for a wide range of applications including environments, air quality, climate change assessment, and space studies [1; 2; 3; 4; 5]. While traditional atmospheric aerosol lidars were designed for long-range measurements, they generally remain blind in the short-range, within typically the first thousand meters, due to incomplete overlap between the laser beam and the receiver field of view.  
A recent need has been raised for short-range elastic backscatter lidars with high range-resolution and temporal-resolution to characterize aerosols close to their emission sources by decreasing the minimal measurement height [6; 7; 8]. These profiles are valuable in the fields of environment and air quality for accurate modeling of aerosol dispersion during events such as industrial plumes [9; 10] or aerosol events in the atmospheric boundary layer (ABL) [11; 12; 13; 14]. Short-range micro-lidars are showing new possibilitiesfor aerosol research in aero-spatial and defense where accurate measurements of aerosol properties with high spatial-resolution are required to assess the performances of the optronic systems in adverse environmental conditions [15; 16; 17; 18]. For instance, these systems are especially useful in characterizing obscurants such as fog, or smoke plumes, which may cause undesirable effects for active optronic systems [19; 20; 21]. Yet, up to now, aerosol lidars remain scarce for probing the atmosphere in the first hundred meters with a range-resolution lower than one meter.  
ONERA, The French Aerospace Lab, has developed a short-range elastic backscatter micro-lidar dedicated to high range-resolution aerosol profiling. The system has been specifically designed to provide quantitative aerosol profiles with centimeter range resolution within the first hundred meters. The aims of this work are: (i) to demonstrate the capability of our system to characterize aerosol plumes with high range-resolution in the short-range close to their emission sources, (ii) to present a forward inverse method without boundary conditions dedicated to short-range lidar profiles when no reference molecular zone is available, and (iii) to present the different retrieved lidar products: the distribution of aerosol layers, radiative properties (i.e., backscatter profiles), and the microphysical properties (i.e., number concentration profiles). This paper is organized as follows. The system description and calibration, as well as the proposed inverse method, are reported in Section 2. Experimental results from field experiments are presented in Section 3 and are discussed in Section 4 to assess the performances of the Colibri micro-lidar. Finally, the concluding remarks and perspectives are presented in Section 5.
## 3 Results and Discussion
### Total Phenolics and Antioxidant Capacity  
The results of total phenolics, as well as the antioxidant capacity of the developed beverages were significantly different (Table 3). Total phenolic values were from 810.50 to 943. 38 mg GAE/100 mL. In descending order, the content of total phenolics was BC > BR > BM > BB. The highest values corresponded to the drinks containing Melissa, bougainvillea and guava while the lowest ones were observed in formulations with Melissa, apple, orange, strawberry, and, pineapple.  
The total phenolic content of the beverages developed was remarkably superior to those reported for several functional beverages prepared with a mixture of flowers, fruits, and medicinal plants. For example, a drink prepared with a combination of tigernut (_Cyperus esculent_), moringa (_Moringa oleifera_), hibiscus (_Hibiscus satfarifa_), and spiced with ginger presented total phenolic contents from 21.67 to 45.67 mg GAE/100 mL [47]. Other beverages prepared from _Aloe vera_ combinations (sweetened juice, squash and ready-to-serve) presented total phenolic contents from 8.20 to 62.65 mg/100 mL [48]. In the same way, a functional beverage containing blue pea flower (_Clitoria rentaea_), lime juice and stevia syrup presented a total phenolic value of 0.85 mg GAE/100 mL [49] and infusions prepared with some edible flowers presented contents from 375 to 433 mg GAE/100 mL [50], which are also lower than those obtained in our research.  
The total phenolics results agree with the antioxidant capacity observed, which was measured by DPPH. However, when the antioxidant capacity was measured by the FRAP method, the beverage BC presented the lowest value and BM the highest. This could be explained because of the type of antioxidant determined in each method [52; 53]. The results obtained for antioxidant capacity by FRAP were higher than those reported for 70 medicinal plant infusions including chamoline and Melissa [54].  
In our research, the main source of phenolic and antioxidant compounds are the plants used. Several studies have demonstrated high amounts of these kinds of compounds in Melissa [53; 55; 56] and lemon verbena [57; 58]. [PERSON] et al. [54] mention that consuming a cup of Melissa infusion could be a dietary source of phenolic and antioxidant compounds comparable to beverages like red wine and tea. In the case of the bougainvillea, the presence of more than 30 compounds that include betanins, their conjugates, betaxanthins and flavonoids has been confirmed [59; 60; 61; 25]. [PERSON] et al. [62] reported that some fruits peel and pulp including guava, pineapple, tangerine, orange, and strawberry have a strong antioxidant capacity and could be a rich source of antioxidants. It has also been observed that the combination of several plants and fruits for the preparation of beverages favors the antioxidant content [63; 64; 65; 66].
## 2 Materials and Methods
### Vegetation Photosynthesis Model (VPM)  
The vegetation photosynthesis model (VPM) produces daily estimates of GPP [29]. The VPM calculates the amount of solar energy absorbed by chlorophyll in the canopy (APAR\({}_{\rm chl}\)) and the light use efficiency (LUE\({}_{\rm g}\)). The EVI is used as a proxy for FPAR\({}_{\rm chl}\) (5).  
\[\rm GPP=APAR_{\rm chl}\times LUE_{\rm g} \tag{3}\]  
\[\rm APAR_{\rm chl}=PPAR_{\rm chl}\times PAR \tag{4}\]  
\[\rm FPAR_{\rm chl}=1.25\times(EVI-0.1) \tag{5}\]  
\[\rm LUE_{\rm g}=LUE_{\rm 0}\times T_{\rm scalar}\times W_{\rm scalar} \tag{6}\]  
\(\rm LUE_{\rm 0}\) is the apparent quantum yield or maximum light use efficiency (\(\rm\mu mol\) CO\({}_{2}\)/\(\rm\mu mol\) PPFD), and it has different values for C3 and C4 plants in the VPM. The LUE\({}_{\rm 0}\) value for C4 plants (e.g., sugarcane) is 0.075 mol of CO\({}_{2}\) mol\({}^{-1}\) PPFD (0.9 g C mol\({}^{-1}\) PPFD) according to earlier works on the vegetation carbon uptake and quantum yield of photosynthesis for sugarcane [35; 94]. The LUE\({}_{\rm 0}\) for C3 plants ranges between 0.42 and 0.65 g CO\({}_{2}\) mol\({}^{-1}\) PPFD (5.0-7.8 g C mol\({}^{-1}\) PPFD) [47; 95; 96]. The VPM accounts for the presence of C3 and C4 plants in areas that have both by including the fractions of C3 plants (C3F) and C4 plants (C4F).  
\[\rm LUE_{\rm 0}=LUE_{\rm 0-C3}\times C3F+LUE_{\rm 0-C4}\times C4F \tag{7}\]  
The effects of temperature and water on GPP [49; 97] are introduced by T\({}_{\rm scalar}\) and W\({}_{\rm scalar}\), respectively. The LSWI is used to calculate W\({}_{\rm scalar}\). T\({}_{\rm scalar}\) is based on the Terrestrial Ecosystem Model (TEM) [98].  
\[Wscalar=\frac{1+LSWI}{1+LSWImax} \tag{8}\]  
\[Tscalar=\frac{(^{\circ}T-Tmin)(^{\circ}T-Tmax)}{[(^{\circ}T-Tmin)(^{\circ}T-Tmax )]-(^{\circ}T-Topt)2} \tag{9}\]  
\({}^{\circ}T\) is the air temperature, and T\({}_{\rm min}\), T\({}_{\rm opt}\), and T\({}_{\rm max}\) are the minimum, optimum, and maximum temperatures for photosynthetic activity, respectively. We defined T\({}_{\rm min}\) as \(-1\)\({}^{\circ}\)C and T\({}_{\rm max}\) as 48 \({}^{\circ}\)C using the same cropland biome parameter values as in [47]. T\({}_{\rm opt}\) was set as 28 \({}^{\circ}\)C, following [35], which estimated T\({}_{\rm opt}\) from the relationship between the daily average daytime temperature (T\({}_{\rm DT}\)) and either the GPP\({}_{\rm EC}\) or vegetation index (EVI).  
For comparison, we carried out two sets of VPM simulations to estimate daily GPP using site climate data alongside (1) time-series EVI and LSWI data from Landsat and Sentinel images (GPP\({}_{\rm VPM-LS}\)) and (2) time-series EVI and LSWI data from MODIS images (GPP\({}_{\rm VPM-MOD}\)).
# A Critical Review of Flood Risk Management and the Selection of Suitable Measures
## 3 Risk-Based Flood Management  
\"Flood management\" refers to the general method involved in flood prevention and subsequent flood loss [16], whereas \"risk-based flood management\" is the combination of all of the actions that aim at the amelioration of the overall activities in a floodplain. Risk-based flood management provides a rationale to spend resources on flood management options. Resources can be spent proportional to the risk involved. Risk arises because of the combined environmental, social, and economic impacts of flooding. As stated in the preceding section, these impacts can be positive or negative. Thus, risk-based flood management facilitates the effective selection of different options.  
Human society must adopt a risk management strategy in order to coexist harmoniously with flood events. In fact, it will never be possible to eliminate the risk of floods. Nevertheless, appropriate actions and behaviors can mitigate the consequences of flooding [29]. Complete abandonment of floodplains or full flood control could be extreme reactions to flooding, but these reactions could either be practically impossible or emphatically uneconomical. The basic principle of flood risk management should be \"adjusting from both ends to achieve moderation\" [30]. Vulnerability and/or hazard parameters can be adjusted to improve the floodplains' functions. A proposed framework is explained in Figure 2 to explore all available options in a systematic way to reduce the flood risk.  
The advantage of a risk-based strategy is that it trades outcomes, which separates it from other design or decision-making methods [31]. Risk-based land-use planning in a floodplain is capable of providing a non-structural means of lowering flood losses and harmonizing floodplain activities. There is a strong need for more comprehensive studies on flood vulnerability [11]. Therefore, recent research indicates a risk-based approach to flood management [10; 32; 33; 34]. Risk-based management counts every aspect of risk (flood impacts) and also considers all options to manage it the most  
Figure 2: A proposed basic framework for risk-based flood management.  
efficiently [35]. In this way, risk-based assessment not only leads to an optimum flood management plan, but also creates an understanding of the mechanism generating the risk.