esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Method to Evaluate the Lateral Vibration of Footbridges Including the Postlock-in Behaviour
### Footbridge Response: General Formulation  
As mentioned in Section 1, the purpose of this paper is to propose an easy-to-apply but general formulation that permits the footbridge lateral response in the four stages of the response as the number of pedestrians grows to be estimated (Figure 1). The evaluation of the dynamic response is performed in the frequency domain, and thus the steady state response of the structure to a certain steady state input is determined. This consideration is equivalent to assuming that all pedestrians remain in a stationary position with respect to the footbridge, exerting an oscillatory load in their respective locations. It is considered that most pedestrians will perceive the mean acceleration response. If this is sufficient to affect people's gaits, HSI develops and the pedestrian load increases due to the auto-induced components. Another number of pedestrians is treated independently, as a different load case.  
Therefore, for each of the four stages described in Section 1, the proposed formulation is described in Figure 7 and it may be summarised in the following steps:  
Figure 6: The functions _SX(f)_ (_L_ = 144 m and \(L\) = 87.5 m) and _H(f)_\({}^{2}\) (_f\({}_{b}\)_ = 0.9 Hz, \(M\) = 165,880 kg, \(K\) = 5,304,000 Nm\({}^{-1}\) and \(C\) = 10,880 Nsm\({}^{-1}\)[18]) are shown overlapped to represent Equation (15).  
1. Prelock-in: the maximum acceleration response is calculated by using Equation (17).
2. Lock-in: the criterion for the lateral lock-in is suggested to establish that the acceleration threshold beyond which HSI develops is 0.1-0.15 m/s\({}^{2}\). When pedestrians perceive this acceleration level, they modify their gait and interact with the structure. The critical number of pedestrians is determined by comparing the mean value of the acceleration response calculated by using Equation (16), with 0.125 m/s\({}^{2}\).
3. Postlock-in: the maximum acceleration response is calculated by adding the response without bridge motion (Equation (17)) and that due to the auto-induced component of the load (Equation (10)). In Equation (10), the initial level of vibration that people perceive \(a_{0,N}\) is obtained using Equation (16).
4. Saturation: the acceleration response is limited to the value 1.2 m/s\({}^{2}\)[7; 21].  
Figure 7: Flowchart for the application of the proposed formulation.  
Consequently, the critical number of pedestrians \(N_{crit}\) and the maximum acceleration response \(a_{N}\) (m/s\({}^{2}\)) is expressed as follows:  
\[N_{crit}=\frac{0.125}{a_{0,mean}} \tag{18}\]  
\[a_{N}=a_{0,\max}N\;\;if\;\;N\leq N_{crit} \tag{19}\]  
\[a_{N}=a_{0,\max}N\;+\;G\;a_{0,mean}\;N^{2}\;\;if\;\;N_{crit}<N\leq N_{sat} \tag{20}\]  
\[a_{N}=1.2\;\;if\;\;N_{sat}<N \tag{21}\]
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 3 Methodology  
### Overview  
For the three study sites, the average Sentinel-1 SAR backscattering coefficients (\(\alpha^{0}\)) in VV polarization and the incidence angle values (\(\theta\)) were calculated at both plot scale \(\left(\alpha_{P}^{0}\right)\) and grid scale \(\left(\alpha_{G}^{0}\right)\) (10 km \(\times\) 10 km). Since rainfall and irrigation events have the same effect on SAR \(\alpha^{0}\) values (\(\sigma^{0}\) increases after rainfall or irrigation), this study proposes to remove or minimize the false detection of irrigation events due to rainfall events by using the mean SAR backscattering signal at grid scale (10 km \(\times\) 10 km). The SAR signal at grid scale obtained from bare soil areas with low vegetation cover  
Figure 2: Frequency of the Sentinel-1 images in ascending “A” (afternoon) and descending “D” (morning) modes for one month over (**a**) Montpellier, (**b**) Tarbes and (**c**) Catalonia. The number next to each line represents the incidence angle for each acquisition.  
is assumed be a descriptor of rainfall events. For the plot scale, \(\sigma^{0}_{\rm P}\) was obtained by averaging the backscattering coefficient (\(\sigma^{0}\)) in the linear unit of all the pixels within each plot. For the grid scale, \(\sigma^{0}_{\rm G}\) was obtained by averaging \(\sigma^{0}\) values in a linear unit of all agricultural bare soil pixels existing within each grid cell. The average backscattering coefficient, at plot and grid scales, helps reduce the speckle noise in the SAR data. Moreover, at each available S2 image, the average NDVI values were calculated for each plot and grid cell (10 km \(\times\) 10 km). The proposed methodology consists of two major phases. Phase 1 consists of the tree-based conditions applied over \(\sigma^{\circ}\)-values at both plot and grid scales in which, at a given SAR date, each plot is evaluated for encountering or not an irrigation event. Phase 2 is a post-processing phase where the irrigation events obtained in Phase 1 at each plot were filtered using additional criteria based on optical data. In order to ensure near real-time detection in phase 1, the absence or presence of an irrigation event at each plot for a given SAR time \(t_{i}\) is evaluated using only the SAR temporal series collected in previous dates (only before \(t_{i}\)) at each plot. However, the optical filter in phase 2 requires subsequent optical data one month after the SAR date. The significance of each proposed filter (criteria) is discussed separately in the coming sections and finally a practical overflow of the whole chain is presented.
## 2 Background Theory
### OCF Firmware Update Protocol  
As shown in Figure 1, the OCF firmware update protocol is triggered by the client's update request. For the convenient explanation of our proposed protocol, we add two notes with black circle mark (\(\bullet\)) that are called \"new firmware available\" and \"software validation\" into the original OCF firmware update protocol. The entities involved in this protocol are the client, pstat, softwareupgrade, and external server. The client is the OCF device that will initiate the update. The pstat and softwareupgrade are the OCF resource model. They reside in the OCF device. The resource model defines the concepts and mechanisms that provide consistency and core interoperability between OCF ecosystems devices [3; 4].  
The softwareupgrade resource is used to control software updates of the device [3; 18]. Then, the pstat resource stands for provisioning status resource. It maintains the device's provisioning status [19; 20]. The softwareupgrade resource has a property value called state. It will show idle, new software available (NSA), software version validation (SVV), software version available (SVA), and upgrading value. This state will affect the pstat's property value. The pstat resource has several property values, but there are two properties that will take part on this case--target mode (tm) and current mode (cm). The value of this property will be changed along with the softwareupgrade state's value. The values of tm and cm will be binary, where the tm bit indicates that the action is initiated and the cm bit indicates the result of the action [19; 20].  
From the \"observe device\" step to the \"trigger software update\" step, the OCF device will check the state of pstat and softwareupgrade resources. If these two resources are idle, the OCF device will initiate the request to check the firmware update on the external server in the \"new software availability check\" step. On receiving notification of firmware update is available from the server, the pstat and softwareupgrade resource will set their state to new software available. At the end of the \"software download\" step, it will retrieve the package from the external server. In the \"software validation\" and \"upgrade image\" steps, those two OCF resources will set the state to downloading and installing the new firmware update. A more detailed explanation is provided in Section 4.
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 3 Digital Twins for Solar Photovoltaics
### PV-DT Based on Developmental Stage  
#### 3.1.1 Solar Cell Prototyping and Manufacturing  
Recently, the use of DTs to solve critical challenges in solar cell prototyping and quality assurance has been gaining global significance [32]. Some novel applications in this area include the works by [33; 34; 35], where DTs were used to optimize the composition of solar cells. In [33], a DT of silicon heterojunction-interdigitated back contact (SHJ-IBC) was created to fast-track an in-depth study on changing the architectural design of this heterojunction solar cell. The study's DT enabled rapid estimation of the cells' theoretical (and practical) efficiency limit using both amorphous and nanocrystalline hole selective contacts. Cells of this type are already being shipped in commercial quantities due to their stability and high efficiency. It is predicted that these types of cells will comprise over 50% of the silicon solar cell market share by 2030 [36]. Similarly, the research works [34; 35; 36; 37] also  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **[29]** & **[31]** & **[30]** & **This Work** \\ \hline Framework for different PV stages & \(\times\) & \(\times\) & \(\times\) & ✓ \\ Challenges & ✓ & \(\times\) & ✓ & ✓ \\ Applications & ✓ & ✓ & ✓ & ✓ \\ Enabling technologies & ✓ & ✓ & \(\times\) & ✓ \\ Data management and use & \(\times\) & ✓ & ✓* & ✓ \\ Future applications & \(\times\) & \(\times\) & \(\times\) & ✓ \\ Autonomous platforms integration & \(\times\) & \(\times\) & \(\times\) & ✓ \\ Industry trends & \(\times\) & \(\times\) & ✓ & ✓ \\ \hline \hline \end{tabular} \({}^{\star}\) In-depth presentation of data analytics.  
\end{table}
Table 2: Illustration of contributions from similar works.  
Figure 3: Visualization of relevant themes within PV DT research: (a) Web of Science database (214 files), and (b) Scopus database (1900 files).  
applied DTs to study the mechanical properties of thin-film and perovskite solar cells using finite element analysis to understand different mechanical degradation scenarios. The research works [38; 39] developed DTs for in-line quality monitoring and carbon-dioxide estimation of polysilicon solar cells during manufacture. These are classic instances of intelligent manufacturing (IM) focused on process quality control and life cycle assessment. Furthermore, [40] implemented a human-in-the-loop DT for the quality inspection of solar cells during manufacturing. Their approach is based on a neural network analysis of purely data-driven cell images, to identify any possible defects in newly produced cells. The DT identification process is overseen by a human-in-the-loop final decision call on the defect classification.
## 4 Results and Discussion  
The results are presented in two ways. On the one hand, a representative flow station was selected to specifically analyze the correction principles of the three time series models; this is presented in Section 4.1. The Donglin Station which had noticeable runoff pattern  
Figure 4: The framework of this study.  
changes and good model modification results, was selected as an example to demonstrate the Prophet model and the other two comparison models in detail. On the other hand, an overall comparison of the correction effects of the three time series models at the four flow stations using evaluation metrics is presented in Section 4.2.
# Face Recognition and Tracking Framework for Human-Robot Interaction
## 2 Related Works
### Face Detection Algorithms  
Face detection algorithms aim to locate the main face area in input images or video frames. Furthermore, they help robots discriminate between humans and other objects in the scene.  
Before the deep learning era, the cascade-based methods and deformable part models (DPM) dominated the face detection field with limitations in unconstrained face images due to considerable variations in resolutions, illumination, expression, skin color, pose, and occlusions [9].  
In recent years, deep learning methods have shown their power in computer vision and pattern recognition. As a result, many deep convolutional neural networks (CNN or DCNN)-based face detection methods have been proposed to overcome the limitations mentioned above [3; 10; 11; 12; 13; 14]. The CNN-based face detection approaches generally have two stages: a feature extraction stage by utilizing a CNN-backbone network to generate the feature map, and a stage for predicting the bounding box locations [15]. They can be divided into two categories: (1) multi-stage; and (2) single-stage detection algorithms.  
_Two-stage algorithms:_ Most two-stage algorithms are typically based on Faster R-CNN [12] and generate several candidate boxes and then refine the candidates with a subsequent stage. The first stage utilizes a sliding window to propose the candidate bounding  
Figure 2: Influence of yaw and pitch angles variations on the head pose, showing that pitch angle have the great impact on the face features. Moving away from the front pose resultant on less distinctive features.  
boxes at a given scale, and the second stage rejects the false positives and refines the remaining boxes [16; 17; 18]. The advantage of this type of model is that they reach the highest accuracy rates, on the other hand they are typically slower.  
_Single-stage algorithms_: Most single-stage algorithms are typically based on the single shot multi-box detector (SSD) [11]. These algorithms treat object detection as a simple regression problem by performing the candidate classification and bounding box regression from the feature maps directly in only one stage, without the dependence on an extra proposal stage [3; 13]. The advantage of this type of model is that they are much faster than two-stage algorithms, but they have lower accuracy rates.  
Among the many variants using the single-stage structure, state-of-the-art face detection performance was achieved by RetinaFace [3]. RetinaFace is the latest one-stage face detection model, which is based on the structure of RetinaNet [19] and uses deformable convolution and dense regression loss. We utilized the lightweight version of RetinaFace based on the mobilenet backbone to enhance the detection speed to achieve real-time performance.
# A Review of Modelling and Simulation Methods for Flashover Prediction in Confined Space Fires
## 2 Modelling and Simulation of Flashover Phenomenon
### Flashover Modelling
Another key factor for the occurrence of flashover is HRR/time relation. [PERSON] et al. [29] concluded that this relation largely determines the occurrence of flashover. For this work an ISO 9705 [35] sized compartment vent was used (the test room dimensions were 2.4 m \(\times\) 3.6 m \(\times\) 2.4 m and it contained a single 0.8 m \(\times\) 2.0 m high doorway), and experimental data were compared to computer simulations. Both have in common that shorter times increase the HRR at the onset of flashover for both a temperature-based and heat-flux-based definition of flashover. According to this study, for future research on room fires, apart from HRR value, the conditions and geometry of the fire plume, upper layer and compartment surfaces may be broadly defined for a deep knowledge of the pre-flashover phase. Regarding heat transfer importance in compartment fires, [PERSON] et al. [30; 36] concluded that the effect of the thermal radiation was decisive for flame temperature predictions. To reach this conclusion, different single-, two- and multi-compartment configurations were analysed and the authors highlighted the weight of wall heat transfer in enclosure fire predictions. Soot radiation was found to augment the radiation absorbed and emitted by combustion products.
## 2 Turbulence Modeling  
In the RANS framework, two-equation eddy-viscosity turbulence models have gained great popularity for industrial CFD applications. These models solve two partial differential equations to calculate two independent turbulence variables for determining the turbulent viscosity field. The basic models typically include some coefficients that can be adjusted to fit the particular flow problem, being calibrated to match experimental findings for fundamental flows (flat plate BL, free shear flows, decaying freestream turbulence). However, these coefficients must meet a number of different criteria while being linked with each other. Due to the limited flexibility of the basic models, several different versions have been developed for specific purposes. In particular, since its conception, the \(k\)-\(\omega\) model has been constantly revised and improved, becoming one of the most commonly used RANS models for highly separated flows [7; 16]. For instance, this two-equation model was recently further developed for the wavelet-based adaptive RANS approach [17]. The modeling procedure uses the turbulent kinetic energy \(k\) and the specific turbulence dissipation rate \(\omega\) as additional turbulence variables. In the current work, the SST version of the \(k\)-\(\omega\) model was used as the baseline RANS model [7]. The unknown Reynolds stresses are approximated by means of the classical Boussinesq hypothesis:  
\[\tau_{ij}=2\mu_{t}S_{ij}^{*}-\frac{2}{3}\rho k\delta_{ij}, \tag{1}\]  
where \(S_{ij}^{*}\) is the deviatoric component of the resolved strain-rate tensor. The turbulent eddy-viscosity is determined in terms of the resolved turbulence variables as follows:  
\[\mu_{t}=\frac{\rho k}{\text{max}\Big{(}\omega,\sqrt{3}\sqrt{2S_{ij}S_{ij}} \Big{)}}\, \tag{2}\]  
where a realizability constraint is imposed.
# Optimal Design and Real Time Implementation of Autonomous Microgrid Including Active Load
## 1 Introduction
Recently, different computational intelligence techniques, like particle swarm optimization (PSO), have been used for different power system problems with impressive achievement [34]. Although, complexity of the control system will be increased with these algorithms, PSO advantages--like computational efficiency, simplicity, and robustness--will enhance the microgrid transient performance [3; 35; 36]. To study and analysis the power system dynamics, offline simulation such as MATLAB is not enough especially if the power converters with high switching frequency are included. Nowadays, real time digital simulator (RTDS) with many advantages like well-established real time simulation, fast, reliable, accurate, and cost effective study of complex power systems is used for prototyping and hardware-in loop testing [37]. Microgrid test beds were implemented in the laboratory to study control strategies, unbalanced problems, and optimal structures of distributed generation microgrids [38; 39]. This paper examines microgrid dynamic stability considering active load. Current, voltage, and power controllers are used to control the voltage and current of the inverter-based DGs and to share the DGs output powers. Additionally, ac current and dc voltage of the active load are controlled using two PI controllers. This paper is a modest contribution to the ongoing discussions on the degradation of the dynamic performance of the microgrid considering active load. To best of our knowledge, this is the first study to investigate the microgrid stability including active load using heuristic techniques such as PSO. The authors' attention has focused not only on achieving the microgrid stability but also on obtaining the optimal parameters. The paper has introduced a new approach to get the all parameters that affect the microgrid dynamic stability including active load. An optimal control technique is proposed. By minimizing the error deviations in the measured active power of the DG and the dc voltage of the active load, the control problem is optimally designed. To assess the effectiveness of the proposed optimal control, the considered microgrid has been modeled in MATLAB. In both ac and dc sides, different disturbances are applied to investigate the optimal control effectiveness on the microgrid stability. Comparison between the proposed controller and literature is investigated to prove its superiority. Finally, the considered microgrid has been established and implemented in a real time digital simulator (RTDS). The experimental results validate the simulation results and approve the effectiveness of the proposed controllers to enrich the stability of the considered microgrid.
## 2 Model Structures
### Effective Thermal Conductivity of the Foam Structure  
Similar to the definition of the solid material conductivity, a steady-state heat transfer equation of metal-foam materials can be conducted. Hence, the thermal conductivity of metal-foam materials can be defined as  
\[K_{f}=\frac{q}{\Delta T/H} \tag{3}\]  
where \(q\) is the heat flux, \(\Delta T\) represents the temperature difference of the measured surface, and \(H\) denotes the thickness of the sample.  
Figure 1: Operation in MATLAB: (**a**) Voronoi tessellation; (**b**) Spheres random packing.  
Figure 2: Operation in UG: (**a**) 3D pore solid model; (**b**) Metal-foam solid model.  
In order to predict the thermal conductivity of closed-cell metal-foams, a wide variety of theoretical modes have been developed [39; 40; 41]. For instance, the simplest models, the Series-Parallel and the Parallel-Series models were constructed with the assumption of a cubical geometry. For particles of various shapes, the Bruggemann model was presented for this particular case. Moreover, the empirical model of scaling relation was proposed for metal-foams with a fitting parameter.  
Series-Parallel model:  
\[K=K_{\mathrm{s}}(1-P^{2/3})+\frac{K_{\mathrm{s}}\cdot P^{2/3}}{K_{\mathrm{a}}+ (K_{\mathrm{s}}-K_{\mathrm{a}})P^{1/3}} \tag{4}\]  
Parallel-Series model:  
\[K=K_{\mathrm{s}}\cdot\frac{K_{\mathrm{s}}-(K_{\mathrm{s}}-K_{\mathrm{a}})P^{2 /3}}{K_{\mathrm{s}}-(K_{\mathrm{s}}-K_{\mathrm{a}})(P^{2/3}-P)} \tag{5}\]  
Bruggemann model:  
\[1-P=\left(\frac{K_{\mathrm{a}}-K}{K_{\mathrm{a}}-K_{\mathrm{s}}}\right)\cdot \left(\frac{K_{\mathrm{s}}}{K}\right)^{1/3} \tag{6}\]  
Scaling relation model:  
\[K=K_{\mathrm{s}}\cdot(1-P)^{\mathrm{n}},\quad n\in[1.65,1.85] \tag{7}\]  
where \(K\) is the overall conductivity of the metal-foam, \(K_{\mathrm{s}}\) and \(K_{\mathrm{a}}\) stand for the conductivity of the metal and air, respectively, and \(P\) denotes the porosity of the foam.  
Apart from the above theoretical prediction models, [PERSON] [40] gave the experimental data of the thermal conductivity of closed-cell AlSi7 foams with porosities between 0.5 and 0.8, which was shown in Figure 3.  
The MSC.Patran is employed to investigate the thermal conductivity of closed-cell metal-foam obtained by the methodology in Section 2.1. The conductivity of metal and air is 167 W/(m\(\cdot\)K) and 0.025 W/(m\(\cdot\)K), respectively. The Figure 3 shows the mismatch between the simulation result and the experimental data and the predicted value of models. Evidently, the simulation result showed good agreement with thermal conductivity values obtained by the literature and the prediction model. Furthermore, according to the growth of porosity, the thermal conductivity of metal-foam decreases, which further demonstrates the proposed modelling methodology of metal-foam structure can simulate the realistic pore geometry effectively, especially for the property of thermodynamic.  
Figure 3: Models’ predictions and measurement and simulation value of thermal conductivity of metal-foam with different porosity.