esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Fire Evacuation: An IoT-Aided Perspective in the 5G Era
## 3 Review of Current Developments
### Summary of Advantages and Insufficiencies  
From the state-of-the-art smart building fire evacuation systems review, some of the advantages and inefficiencies are summarized.  
First, by using various devices and sensors, the smart building fire evacuation systems can successfully detect fire situations and evacuation processes on the scene. The parameters they collected include not only environmental factors but also occupants' situations. By monitoring the environmental factors, such as flame, heat, and smoke, ICs and building managers can become more aware of the current fire situation and promptly initiate the evacuation. As for the occupants' situations, monitoring of the occupants' presence or positions during the evacuation process can help understand the bottlenecks of the evacuation. The ICs and building managers can then adjust the evacuation strategy and guide the occupants to escape smoothly. Moreover, the position information also offers help to the search of trapped occupants during the rescue mission.  
Furthermore, the development of various smart systems helping evacuation guidance also provides a valuable contribution. Lacking effective means of communication to guide occupants to escape is one of the major difficulties for building fire evacuation. Therefore, a \"smart exit sign\" can be useful when responders hardly reach the incident floor and guide the occupants to leave. In addition, the \"smart exit sign\" is a stationary device pre-installedinside buildings, so no other devices or software need to be used by occupants, which satisfies building evacuation scenarios with mass populations.  
The major insufficiency of current smart building fire evacuation systems is that most of the systems only achieve the perception of either the information of fire environment or occupants' situations. The interpretation of these data and how they will influence the total building evacuation have been less considered. Even though some previous research has enabled smart route planning based on the information collection regarding fires or occupants' situations, the focus was more on evacuees' path finding within single floors. The systematic understating and prediction of the total building evacuation considering dynamic fire development and occupants' situations was insufficient. As a result, the current smart building fire evacuation system does not seem to be able to coordinate and organize the prompt total building evacuation under fire emergencies, which is the major difficulty faced by tall building evacuations.  
With more extensive use of various wireless communications in the 5G era, the connectivity and analytical power of the IoT will increase a lot. More connectivity of various IoT sensors and fast computation of the collected data can be enabled. Therefore, a preliminary design of an IoT-aided building fire evacuation control system is proposed in view of the advantages and insufficiencies in current smart building fire evacuation systems.
# The Fusion of MRI and CT Medical Images Using Variational Mode Decomposition
## 2 Proposed Methodology
\right.\] (8) Step 4 : Obtain the enhanced decomposition modes \(\text{F}^{i}_{IMFs_{\alpha,k}}(x,y)\) \[\text{F}^{i}_{IMFs_{\alpha,k}}=X_{1}(x,y)\times IMFs_{A}^{i}(x,y)+X_{2}(x,y) \times IMFs_{\text{B}}^{i}(x,y)\] (9)
```  
**Algorithm 1**  
C. Synthesizing the Fused Image  
We linearly combine all the enhanced IMFs obtained from each LEM fusion rule to construct the fused image. The whole process of the proposed fusion framework is given in Algorithm 2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metrics**} & \multicolumn{4}{c}{**Methods**} \\ \cline{2-5}  & **VMD-AVG** & **VMD-MAX** & **VMD-MIN** & **VMD-LEM** \\ \hline EI & 48.439 & 58.322 & 36.487 & **71.751** \\ \hline MI & 4.384 & 4.376 & 3.486 & **4.391** \\ \hline VIFF & 0.335 & 0.397 & 0.063 & **0.428** \\ \hline Q\({}_{p}^{AB/F}\) & 0.307 & 0.356 & 0.198 & **0.443** \\ \hline SSIM & 0.599 & 0.232 & 0.563 & **0.621** \\ \hline AG & 4.845 & 5.714 & 3.735 & **6.973** \\ \hline RMSE & 0.0296 & **0.005** & 0.036 & 0.020 \\ \hline PSNR & 15.926 & 14.553 & 15.869 & **18.580** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average quantitative analysis of various fusion rules on 10 pairs of MRI-CT images. D. Image Fusion Evaluation Metrics  
In this paper, we used a few state-of-the-art image fusion metrics to estimate the information contribution of each source image in the fusion process. They are edge intensity (EI) [48], mutual information (MI) [49], visual information fidelity (VIF) [50; 51], edge-based similarity measure (Q\({}_{P}^{AB/F}\)) [52], structural similarity index measure (SSIM) [51; 53], average gradient (AG) [54], root mean square error (RMSE) [15], peak signal-to-noise ratio (PSNR) [13; 42]. _EI_ represents the difference of luminance along the gradient direction in images. _MI_ is used to measure the relative information between the source and the fused images. _VIF_ estimates the visual information fidelity between the fused and source images depending on the Gaussian mixture model. The edge-based similarity (\(Q_{P}^{AB/F}\)) measure will be useful to provide the edge details in the fused image. RMSE computes a difference measure between the reference image and fused image. In this work, the maximum value of RMSE of MRI-fused images and CT-fused images is considered. Similarly, PSNR is also computed. Except for RMSE, the higher values of all these metrics imply better fusion. In the case of the RMSE, the lowest value yields a better result.
# Real-Time Optical Flow Estimation Method Based on Cross-Stage Network
## 3 Proposed CAFT Method
### Calculation of High-Resolution Images  
The correlation of all corresponding pairs of images has a scale of \(O(N^{2})\) with respect to the number of pixels \(N\), but is calculated only once and is constant with respect to the number of iterations \(M\). However, there is the same implementation method that scales on the \(O(NM)\) scale using the linearity of internal and average pooling. Considering the cost volume in level \(m\), \(C^{m}_{ijkl}\), and feature maps \(g^{(1)}=g_{\theta}(I_{1}),g^{(2)}=g_{\theta}(I_{2})\), the following Equation (6) is obtained.  
\[C^{m}_{ijkl}=\frac{1}{2^{2m}}\sum_{p}^{2^{m}}\sum_{q}^{2^{m}}\Bigl{\langle}g^{ (1)}_{ij}.g^{(2)}_{2^{m}\,k+p,\ 2^{m}l+1}\Bigr{\rangle}=\left\langle g^{(1)}_{i,i}, \frac{1}{2^{2m}}\sum_{p}^{2^{m}}g^{(2)}_{2^{m}\,k+p,\ 2^{m}l+q}\right\rangle \tag{6}\]  
The above equation is the average of the correlation responses of the \(2^{m}\times 2^{m}\) grid. It means that the value of \(C^{m}_{ijkl}\) can be calculated internally between the feature vectors of \(g_{\theta}(I_{1})_{ij}\) and \(g_{\theta}(I_{2})\). In the alternative implementation method, the correlation is not calculated in advance, but instead a pooled image feature map is calculated in advance. In each iteration, each correlation value is calculated only at the required moment, that is, when retrieved. This results in the complexity of \(O(NM)\).
# Improving Juvenile Age Estimation Based on Facial Landmark Points and Gravity Moment
###### Abstract  
Facial age estimation is of interest due to its potential to be applied in many real-life situations. However, recent age estimation efforts do not consider juveniles. Consequently, we introduce a juvenile age detection scheme called LaGMO, which focuses on the juvenile aging cues of facial shape and appearance. LaGMO is a combination of facial landmark points and Term Frequency Inverse Gravity Moment (TF-IGM). Inspired by the formation of words from morphemes, we obtained facial appearance features comprising facial shape and wrinkle texture and represented them as terms that described the age of the face. By leveraging the implicit ordinal relationship between the frequencies of the terms in the face, TF-IGM was used to compute the weights of the terms. From these weights, we built a matrix that corresponds to the possibilities of the face belonging to the age. Next, we reduced the reference matrix according to the juvenile age range (0-17 years) and avoided the exhaustive search through the entire training set. LaGMO detects the age by the projection of an unlabeled face image onto the reference matrix; the value of the projection depicts the higher probability of the image belonging to the age. With Mean Absolute Error (MAE) of 89% on the Face and Gesture Recognition Research Network (FG-NET) dataset, our proposal demonstrated superior performance in juvenile age estimation.  
Keywords:age estimation; AAM; juvenile detection; information retrieval; ordinal relationship +
Footnote †: journal: Applied Sciences
###### Abstract  
The Critical Power Model (CPM) is key for assessing athletes' aerobic and anaerobic energy systems but typically involves lengthy, exhausting protocols. The 3 min all-out test (3 MT) simplifies CPM assessment, yet its duration remains demanding. Exponential decay models, specifically mono- and bi-exponential functions, offer a more efficient alternative by accurately capturing the nonlinear energy dynamics in high-intensity efforts. This study explores shortening the 3 MT using these functions to reduce athlete strain while preserving the accuracy of critical power (CP) and work capacity (W\({}^{\prime}\)) estimates. Seventy-six competitive cyclists and triathletes completed a 3 MT on a cycle ergometer, with CP and W\({}^{\prime}\) calculated at shorter intervals. Results showed that a 90 s test using the bi-exponential model yielded CP and W\({}^{\prime}\) values similar to those of the full 3 MT. Meanwhile, the mono-exponential model required at least 135 s. Bland-Altman and linear regression analyses confirmed that a 120 s test with the mono-exponential model reliably estimated CP and W\({}^{\prime}\) with minimal physical strain. These findings support a shortened, less-demanding 3 MT as a valid alternative for CPM assessment.  
exercise testing; fitness assessment; predictive algorithms; endurance performance metrics; physiological modeling +
Footnote †: journal:  
[PERSON]. [PERSON], [PERSON], M. Exponential Functions Permit Estimation of Anaerobic Work Capacity and Critical Power from Less than 2 Min All-Out Test
# Stability and Dynamics of Dark-Bright Soliton Bound States Away from the Integrable Limit
## 2 Setup and Prior Background
### Numerical Methods  
In this section, we briefly comment on the numerical methods to be used so as to obtain stationary symbiotic states consisting of two dark-bright solitons and to determine their stability and time  
Figure 1: Variational estimate of the total interaction energy \(E_{tot}\) between two OP dark-bright solitons. From left to right in panels (**a–c**), the value of the inter-species interaction coefficient is increased from \(g_{12}=0.75\) to \(g_{12}=1.4\), while \(g_{22}=0.95\) and \(\mu=2/3\) throughout. In all cases, both the full and the asymptotic (valid for large \(x_{0}\)) expressions are shown with solid green and dashed yellow lines, respectively. The equilibrium separation \(x_{0}\) of the stationary DB soliton pair as a function of \(g_{12}\) is shown in panel (**d**). In this latter panel, circles in yellow correspond to the equilibrium distance obtained by numerically solving the GPE system of Equations (2) and (3), while triangles in green denote the variational prediction of the equilibrium, i.e., the respective maximum of \(E_{tot}\).  
evolution. In the numerical computations that follow, we initially obtain stationary solutions of the system of Equations (2) and (3) in the form of \(\psi_{d}(x,t)=u_{d}(x)\) and \(\psi_{b}(x,t)=u_{b}(x)\) by means of a fixed-point numerical iteration scheme [55]. The linear stability of the latter is adressed by using the Bogolyubov-de Gennes (BdG) analysis [7; 52]. Particularly to assess the stability of the obtained fixed points, we substitute the following ansatz into Equations (2) and (3):  
\[\psi_{d}(x,t) = u_{d}(x)+\epsilon\left(a(x)e^{-i\omega t}+b^{\star}(x)e^{i\omega ^{\star}t}\right), \tag{10}\] \[\psi_{b}(x,t) = u_{b}(x)+\epsilon\left(c(x)e^{-i\omega t}+d^{\star}(x)e^{i\omega ^{\star}t}\right). \tag{11}\]  
In the above equations, the asterisk denotes complex conjugation while \(\epsilon\) is the amplitude of infinitesimal perturbations. The resulting system of equations is then linearized, by keeping only terms of order \(\mathcal{O}(\epsilon)\), and the eigenvalue problem for the eigenfrequencies \(\omega\) and eigenmodes \((a(x),b(x),c(x),d(x))^{T}\) is solved numerically. Note that an instability occurs if modes with purely imaginary or complex eigenfrequencies are identified [1]. Since the linearization spectrum is invariant under \(\omega\rightarrow-\omega\) and \(\omega\rightarrow\omega^{\star}\), only results in the first quadrant of the complex plane will be shown below. For the simulation of the time evolution based on Equations (2) and (3), a fourth order Runge-Kutta algorithm is employed and a second-order finite differences method is used for the spatial derivatives. The grid spacing is fixed to \(\Delta x=0.08\), while the time step used is \(\Delta t=0.005\). In all cases, the numerical computations are performed on a spatial grid in the presence of an almost hard-wall super-Gaussian potential [50] that is chosen wide enough for boundary effects to be negligible on the small and intermediate time scales considered herein.
# Design and Implementation of a 2D MIMO OCC System Based on Deep Learning
## 1 Introduction
The second challenge encountered in the field of photography involves the presence of noise originating from the sun, streetlights, and other forms of backlighting [17]. The presence of this cacophony further exacerbates the challenge of identifying the region of interest (RoI). The third challenge lies in the real-time detection and analysis of OCC signals, necessitating extensive computational resources for real-time execution. In this study, we propose a deep learning-based OCC system combined with an adaptive segmentation algorithm to rectify optical distortions caused by the optical flow on the image sensor. This method seeks to improve the recognition accuracy of LEDs in high-mobility conditions as well as the selection of ROI. Moreover, we optimize the OCC signal processing system so that it can operate in real time with reduced computational costs. This strategy is intended to address the previously mentioned obstacles. To the best of our knowledge, there is no article that applies to You Only Look Once version 8 (YOLOv8) in the OCC system. Our proposed method focuses on LED detection and segmentation in OCC systems based on YOLOv8. The main contributions of the paper are outlined below:  
* An effective LED array detection and segmentation based on the YOLOv8 model is proposed to improve the reliability and stability of the OCC system in an environment with high mobility. * A lightweight LED detection and tracking method that permits the OCC to operate in real time on resource-constrained embedded systems is proposed. * The proposed method is based on 2D OOK-MIMO, which is compatible with most commercial cameras such as rolling shutter and global shutter cameras. Therefore, this method can be readily implemented using closed circuit television (CCTV), which is widely used and convenient in many locations. * Finally, the performance of the proposed approach is validated by utilizing the practical implementation of OCC in indoor environments. This paper's remaining sections are structured as follows: Section 2 provides an overview of relevant studies for the OCC system and identifies a number of issues. Section 3 provides a detailed explanation of the OCC system architecture proposal. The experimental hardware, software, and evaluation metrics are described in Section 4. Section 5 discusses the results and evaluation of our system's implementation, including testing the bit error rate (BER) under high-mobility conditions, an aspect that has not been extensively investigated in prior studies. The study concludes in Section 6 with a discussion of potential avenues for future research.
# The Power System and Microgrid Protection--A Review
## 2 DG and Microgrid Challenges
### Variation of Short Circuit Level  
Short circuit current is a significant factor in determining and protecting the equipment in a power system. Short circuit current increases with DG penetration in the power system. The short circuit level depends on the type and operation mode of DGs. The fault level is different in synchronous generators and inverter-based DGs. The maximum fault current of an inverter-based DG is not more than twice the inverter-rated current [42; 60]. The synchronous machine-based DGs can generate fault currents that are 4 to 10 times greater than inverter-based DGs [61]. The variation of short circuit level is reflected in traditional protective devices, such as fuses and OC relays. Inverter-based DGs may cause trip failure in protective devices [42]. Additionally, the short circuit levels in grid-connected microgrids are greater than islanded ones [40]. In the islanded mode, some DGs have limited short circuit capabilities that are not adequate to be detected by the protective devices. Of course, the variations in the short circuit can affect the relay operating time, especially indefinite time sections, for example, in OC and distance relays. A fault current limiter (FCL) is implemented to limit the fault current values [62].  
In [63], several methods have been proposed to reduce the negative effects of DGs as follows:  
* Modifying or changing the protection scheme;
* Installing FCL;
* Restricting DG capacity;
* Isolating the DG from the power system immediately after detecting the fault;
* Using an adaptive protection.  
Figure 7: Recloser coordination in the presence of DG.
###### Abstract  
Patient compliance remains a significant challenge in orthodontic treatment with clear aligners, as adherence to prescribed wear time is often suboptimal. This study investigated the potential of colorimetric analysis as a method to assess compliance with NOXI 3D-printed night-time aligners. Specifically, it evaluated color variations in polyamide aligners due to thermo-oxidation, using the RGB (Red, Green, Blue) color model as a non-invasive indicator. In total, 10 patients participated in this prospective study, wearing aligners for either 7 or 12 h daily over a 14-day period. Colorimetric measurements were collected via a smartphone-based application, and statistical analyses examined correlations between wear duration and color changes. The results revealed a significant association between a longer wear time and increased discoloration (\(p<0.001\)), supporting the feasibility of RGB-based monitoring as a reliable compliance tool. However, individual variability in saliva composition, diet, and oral hygiene may have influenced the results, highlighting the need for further research into potential confounding variables. These findings underscore the promise of integrating digital monitoring technologies to improve adherence tracking and patient management in orthodontics. Future studies should refine the methodology and validate its efficacy in larger, more diverse populations.  
3D-printed aligners; compliance; compliance indicator; colorimetric analysis; clear aligners Article
# An Optimization Precise Model of Stroke Data to Improve Stroke Prediction
## 2 Modeling the Stroke Possibility  
### Modeling Stroke Prevention  
There are several approaches to modeling stroke prevention. Some approaches include Risk assessment models: These models can help identify individuals who are at a higher risk of stroke based on their age, sex, lifestyle factors, medical history, and other risk factors. They can also help predict the likelihood of stroke and inform preventive strategies [20].  
Pharmacological interventions: Models can be used to evaluate the effectiveness of different drugs in preventing stroke, such as anticoagulants, antiplateletlets, and blood pressure-lowering medications. These models can also help optimize dosing and identify patients who are most likely to benefit from treatment [21].  
Lifestyle interventions: Models can be used to evaluate the impact of lifestyle modifications on stroke risk, such as diet and exercise interventions. These models can help identify the most effective interventions and inform recommendations for patients [22].  
Screening programs: Models can be used to evaluate the cost-effectiveness of stroke screening programs, such as screening for atrial fibrillation, which is a common cause of stroke. These models can help determine the optimal screening strategies and inform public healthcare policy [23].  
Patient education: Models can be used to evaluate the impact of patient education programs on stroke prevention. These models can help identify the most effective educational interventions and inform recommendations for public healthcare providers [24].  
Overall, modeling can be a powerful tool for understanding stroke prevention and optimizing preventive strategies.
## 4 What Is NFV?
### Architecture of NFV
**NFV Infrastructure (NFVI):** NFVI is responsible for providing the underlying physical and virtual infrastructure for hosting the VNFs. It includes the following elements: 1. **Compute Resources:** NFVI provides general-purpose servers or cloud-based compute instances where VNFs run. 2. **Network and Storage Resources:** NFVI uses virtualized network components like connectivity, switches, routers, VLANs, and storage (including cloud storage) to handle data and configurations. These resources form the backbone for managing information within NFVI. 3. **Virtualization Layer:** The virtualization layer is responsible for providing virtualization technologies, such as hypervisors and containerization, to run VNFs on available physical hardware. 3. **Element Management System (EMS):** This manages the physical network equipment, including legacy hardware, which is used for the deployment of NFV. It ensures coordination between virtualized and traditional network elements. 4. **Operations Support System (OSS) and Business Support System (BSS):** These systems provide end-to-end network service management, billing, and provisioning. 5. **MANO:** Management and orchestration in network function virtualization, is divided into three key functional blocks: 1. **NFV Orchestrator (NFVO):** The NFVO is responsible for VNF lifecycle management, handling deployment and scaling coordination. It integrates with NFVI to provide the required resources and communicate with the VNF Manager (VNFM) for VNF-specific tasks. Figure 10: ETSI: NFV reference architectural framework [90]. * **VNF Manager (VNFM):** VNFM manages the lifecycle of VNFs, which includes instantiation, configuration, and termination (on/off) of VNFs. VNF Manager communicates with NFVO and VNF itself to manage the functionality of VNFs. * **Virtualized Infrastructure Manager (VIM):** VIM manages the virtual resources required for VNFs. It identifies, allocates, and handles faults in physical and virtual resources. These blocks work together to efficiently control and organize NFV deployments to ensure reliability and agility in network services.