esa-sceva/satcom-chunk-collection huggingface dataset:
# Fire Risk Assessment of Urban Utility Tunnels Based on Improved Cloud Model and Evidence Theory
###### Abstract  
In order to accurately assess the fire risk of urban utility tunnels, an evaluation method based on the improved cloud model and evidence theory is proposed. Firstly, an evaluation index system for the fire risk of urban utility tunnels is constructed from five aspects: fire prevention, fire control, emergency evacuation, personnel prevention and control, and safety management. Secondly, because of the randomness and fuzziness of fire risk assessment, the improved cloud model with cloud entropy optimization is used to calculate the index membership degree. The uncertainty focal elements are introduced to satisfy the basic probability assignment in evidence theory. Then, the improved evidence theory with dynamic and static weights is applied to fuse the information of the evidence and determine the final evaluation results. It avoids the possible paradoxes of the combination of strong conflict evidence in traditional evidence theory and improves the credibility of the evaluation results. Finally, the feasibility and superiority of the proposed method are verified by an example analysis, which provides a new idea for the fire risk assessment of urban utility tunnels.  
safety engineering; urban utility tunnel; fire risk; cloud entropy optimization; improved cloud model; evidence theory +
Footnote †: journal: Applied Sciences
###### Abstract  
In this paper, a low-complexity multi-cell resource allocation algorithm with a near-optimal system throughput is proposed to resolve the conflict between the high system throughput and low complexity of indoor visible light communication ultra-dense networks (VLC-UDNs). First, by establishing the optimal model of the resource allocation problem in each cell, we concluded that the problem is a convex optimization problem. After this, the analytic formula of the normalized scaling factor of each terminal for resource allocation is derived after reasonable approximate treatment. The resource allocation algorithm is subsequently proposed. Finally, the complexity analysis shows that the proposed algorithm has polynomial complexity, which is lower than the classical optimal inter-point method. The simulation results show that the proposed method achieves a improvement of 57% in performance in terms of the average system throughput and improvement of 67% in performance in terms of the quality of service (QoS) guarantee against the required data rate proportion allocation (RDR-PA) method.  
Keywords:visible light communication; ultra-dense networks; resource allocation; system throughput; quality of service guarantee +
Footnote †: journal: Applied Soft 2019
# Model-Based Virtual Components in Event-Based Controls: Linking the FMI and IEC 61499
## 4 Experimental Evaluation
### Experiment Description
#### 4.1.2 Controller Hardware  
The first OLTC controller was an embedded hardware implementation, which was interfaced via a Modbus TCP/IP connection. The communication interface enabled querying control outputs directed to the OLTC and allowed setting the low voltage readings and whether the OLTC was ready to initiate a switching operation. In order to avoid overloading the embedded controller and the resulting frequent packet loss, a minimum polling interval of 200 ms was chosen. Since the OLTC controller hardware and the reference model encoded control actions differently, a simple protocol bridge was deployed. The bridge, which was implemented in a software PLC, periodically polled the hardware controller and sent change events to the virtual component. Upon receiving events from the virtual component, data could be directly relayed without awaiting the next polling cycle.  
Figure 4: Experimental setups.
## 4 Results
### Partial Coverage
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(C_{D}\) & \(\Delta C_{D}/C_{D,0}\)\% & \(C_{D,p}\) & \(\Delta C_{D,p}/C_{D,p}\)\% & \(C_{D,f}\) & \(\Delta C_{D,f}/C_{D,f0}\)\% \\ \hline
**Clean** & **0.0508** & **-** & **0.0338** & **-** & **0.0170** & **-** \\ \hline I & 0.0493 & 3.0 & 0.0333 & 1.5 & 0.0160 & 6.1 \\ II & 0.0493 & 3.0 & 0.0333 & 1.5 & 0.0160 & 6.1 \\ III & 0.0493 & 2.9 & 0.0333 & 1.5 & 0.0160 & 5.8 \\ IV & 0.0498 & 2.0 & 0.0333 & 1.4 & 0.0165 & 3.3 \\ V & 0.0499 & 1.7 & 0.0333 & 1.5 & 0.0167 & 2.2 \\ \hline \end{tabular}
\end{table} 
Table 3: Drag breakdown for the UAV in cruise condition, for different configurations of riblets coverage, and percentage changes with the clean case. 1.4%, and friction drag reduction is seen to diminish from 6.1% to 3.3%: indeed, the area of the wing is approximately one half of the total area. Perhaps the most interesting configuration is configuration V, where only the suction side of the wing (and the entire winglet) is equipped with riblets, leading to \(\beta=0.289\). In contrast, the riblets-induced benefit remains more than one half, i.e., 1.7% instead of 3.0%.
## 3 Results
### Laboratory Testing under Controlled Conditions  
In the first case study, 10 samples of building material were scanned. We reported some examples of the point clouds acquired with 1 cm resolution at a distance of 0.5 m in Appendix A (Figure A1).  
Operating on planar samples, it was possible to observe the structure of the point cloud generated by smartphone apps. As can be seen in Figure 8, when iOS scanned with 1 cm resolution (Figure 8a), it generated an ordered point cloud; when working with \(\sim\)1.5 cm resolution, the device generated a disordered cloud (Figure 8b). Android's point cloud, shown in Figure 8c, was as ordered as the iOS one in Figure 8a, but of course had a different resolution.  
Figure 7: Functioning of the surface variation descriptor (image adapted from [PERSON] et al. [18]) (**a**); example of surface variation application in a generic case (**b**).  
We calculated the mean values of surface variation and planarity for the 60 point clouds obtained from the sample scans. The tables containing the surface variation and planarity values can be found in Appendix A (Tables A1 and A2), while below, we propose the data in graphic form for easier exploration and evaluation.  
In Figure 9, we reported the calculated surface variation values for almost all samples scanned at a distance of 0.5 m. Special cases of steel and brass will be discussed separately in Section 4. SV\({}_{\lambda}\) values obtained from the materials shown in Figure 9 can be considered excellent, as they are inferior to 0.33, the theoretical maximum value of surface variation [18]. The surface variation values of the samples scanned at 1.5 m were reported in Appendix A (Figure A2). The plaster, plastic and frosted glass data confirmed the excellent values obtained for the 0.5 m scans.  
In Figure 10, we reported the planarity values of the samples scanned at 0.5 m distance, while the values of the samples scanned at 1.5 m distance were reported in Appendix A (Figure A3). From the planarity analysis, it can be seen that they take on a value close to unity and thus the tendency of the point cloud to dispose along planar surfaces. Finally, we investigated the existence of a possible linear correlation between the albedo of the materials and their surface variation values.  
Figure 8: Point cloud structure: iOS resolution 1 cm (**a**), iOS resolution \(\sim\)1.5 cm (**b**), Android resolution 2 cm (e).  
Figure 9: Comparison of mean surface variation values for samples scanned at a distance of 0.5 m.  
In Table 3, we reported the R\({}^{2}\) values obtained. In statistics, the value of R\({}^{2}\) (called coefficient of determination) is a coefficient that indicates how much the variation of a dependent variable may depend on the variation of an independent variable in a regression model. R\({}^{2}\) values between 0.7 and 0.5 indicate a moderate relationship [21, 22], so we concluded that there was a moderate-to-weak relationship between albedo and surface variation for the LiDAR sensor of the iOS devices. In contrast, the relationship for the Android device appeared weaker.
## 2 Materials and Methods
### Preliminaries
#### 2.1.3 Graph Neural Networks  
GNNs process relational data by passing messages between nodes. For multi-agent systems modeled as graphs \(G=(V,E)\), with agents as nodes \(V\) and interactions as edges \(E\):  
* Graph Convolutional Networks (GCNs) [25]: Aggregate neighbor features \(Z\) with spectral convolution, as follows: \[Z^{(l+1)}=\sigma\left(\hat{D}^{-1/2}\hat{A}\hat{D}^{-1/2}Z^{(l)}W^{(l)}\right),\] (2) where \(\hat{A}=A+I\) is the adjacency matrix with self-loops, \(\hat{D}\) is the degree matrix, \(\sigma\) is the activation function, and \(W^{(l)}\) are learnable weights.
* GAT [16]: Computes the adaptive attention weights \(\alpha_{ij}\) between nodes \(i\) and \(j\): \[\alpha_{ij}=\text{softmax}_{j}\left(\text{LeakyReLU}\left(\mathbf{a}^{T}[ \mathbf{W}h_{i}|\mathbf{W}h_{j}]\right)\right),\] (3) where \(\mathbf{a}\) and \(\mathbf{W}\) are learnable parameters. GAT uses attention-based edge updates to handle dynamic graphs, which are common in MARL.
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 1 Introduction
The authors of [11] investigated the realistic performance of NB-IoT in a healthcare monitoring system in terms of effective throughput and latency. However, the work did not optimize the required throughput, delay, and device density jointly. The authors mainly focused on the application-specific analysis of cell throughput, device capacity, and latency of NB-IoT, keeping aside joint optimization and radio resource allocation with prioritized scheduling. Later, in [12], an intelligent radio resource management algorithm, iRRM for NB-IoT, was developed with efficient resource allocation considering inter-cell interference (ICI), coverage extension, and the repetition factor. In a healthcare environment, due to the periodic transmission of sensory data, a higher density of User Equipment (UE) always incurs higher delays. However, the necessity of prioritizing interdependent health parameters needs to be addressed to reduce the delay, and it can be implemented through traffic classification. Moreover, achieving expected user utility under the interference-aware radio resource allocation needs further investigation [1;9]. This paper develops an interference-aware radio resource allocation for NB-IoT devices in healthcare applications that prioritizes data traffic from different sources and maximizes user utility. We define user utility as a joint metric of achievable data rate and event influenced priority, where users communicate data to the base station (eNB) minimizing interference. The proposed resource allocation is optimized according to the interdependent event under the same time slot with the intention to maximize the overall utility of all users that introduces Priority Aware Utility Maximization (PAUM). This model is projected to achieve the increased performance for healthcare applications. The key contributions of this work are summarized as follows:  
* We design a Priority Aware Utility Maximization (PAUM) system for providing high-quality medical care in such a way that is convenient for patients so that data transmission can occur according to the application's urgency level. * We develop an event-influenced prioritized access scheduling of critical and non-critical data packets exploiting Bayes Theorem, which determines the importance of events in a probabilistic distribution. * Under certain constraints, the problem of maximizing user utility has been formulated as a multi-objective non-integer linear programming (MNILP) problem that ensures high performance and fair resource allocation to the users. * The performances of the proposed system have been carried out in Network Simulator version 3, and significant performance improvements are observed for various events and prioritized users in healthcare applications. This paper is organized as follows: Section 2 presents relevant studies, Section 3 defines the system model and assumptions, Section 4 elaborates the design components of the PAUM system, and Section 5 analyzes the performance results. Finally, Section 6 contains the conclusion.
## 2 Related Work
The generation of augmented reality instructions relies on deep learning networks (i.e., Region-Based Convolutional Neural Network) trained on synthetic data to provide the appropriate information when the user engages with a specific tool to perform an assembly task. The study revealed that the system reduced the duration and also the assembly errors by approximately 33%. [PERSON] et al. [21] proposed a context-based orchestration framework composed of three layers: service registration, service discovery and selection, as well as service orchestration. Ontologies were modeled for semantic reasoning in the discovery and selection of services and were used to describe the web services provided by field devices. The service with the highest score, weighted among several matching criteria, was selected. The process was then decomposed in atomic processes and the corresponding services were invoked for the resulting composite process. In [5], a two-level contextual predictor was used to suggest the next assembly steps. The first level of such a predictor consisted in a left-shift register which contained the last assembly states. The second level was a prediction table, storing pairs of state-patterns and their associated next states. The left-shift state register selected an entry from the prediction table. Then, the state from the selected entry was provided as the predicted one. Another variant of the two-level contextual predictor extended each assembly state with an automaton which could be in stable or in unstable substate. The predictor could learn a different state for a certain pattern, only if its current state was unstable, otherwise it just switched from stable to unstable and kept the current state. Unfortunately, this scheme provided insignificant improvement, and because it used supplementary information and additional steps in the prediction process, it was considered less efficient than the scheme without automata. The Markov predictor presented in [6; 7] is another two-level predictor which can store multiple next states, together with their number of occurrencesfor each pattern. The state with the highest number of occurrences is extracted from the prediction table entry selected with the left-shift state register and is then provided as the predicted one. Such a predictor can provide multiple next assembly choices, but for time-critical decisions it can be configured to return the most probable state. Article [8] presents the use of LSTM in modeling assembly processes. The LSTM network uses the human characteristics and the current assembly state to make a prediction of the next assembly state. The implementation of the recurrent network was done through TensorFlow and Keras and is composed of several layers: an input layer, two LSTM layers and an output layer. Several tests have been performed to finetune the hyperparameters. Although the proposed network has a lower accuracy than the Markov predictor, it can better adapt to new scenarios, being able to correctly predict over twice as many assembly steps as the Markov predictor.
## 3 Uncertainty Representation of Uncertain Distribution Parameters
### Fusion of Uncertain Distribution Parameters
The probability density values \(p_{i}^{2}\) of \(Q\) discrete points \(\lambda_{i}\) for Source 2 are calculated based on the PDF \(f_{\xi}(\xi)\) in Equations (3) and (4), and the Gaussian interpolation model \(G_{2}\) for Source 2 is constructed using a similar method for insufficient data. Finally, the likelihood estimation function \(L(\mathbf{w})\) of the fusion model \(G\) considering insufficient data from Source 1 and evidence theory information from Source 2 is constructed using Equation (10):  
\[ \begin{split} L(\mathbf{w})=&\sum_{i=1}^{m_{1}} \log\Bigl{[}f_{\xi}\left(a_{i}^{1}\Big{|}\mathbf{p}\right)\Bigr{]}+\sum_{j=1} ^{n_{1}}\log\Bigl{[}f_{\xi}\left(ub_{j}^{1}\Big{|}\mathbf{p}\right)-f_{\xi} \left(lb_{j}^{1}\Big{|}\mathbf{p}\right)\Bigr{]}\\ &+\sum_{k=1}^{mx}\log\Bigl{[}f_{\xi}\left(U_{i}^{l}\Big{|}\mathbf{ p}\right)-f_{\xi}\left(L_{i}^{j}\Big{|}\mathbf{p}\right)\Bigr{]}.\end{split}  \tag{10}\]  
\(\mathbf{w}\) are the weight ratios. To find the optimal \(\mathbf{w}\), the minimization of the total deviation \(L(\mathbf{w},\mathbf{p})\) between \(G\) and \(G_{1}\), \(G_{2}\) are used in Equation (11):  
\[L(\mathbf{w},\mathbf{p})=\Bigl{[}L(\mathbf{w})-L_{\max}^{1}\Bigl{(}\mathbf{p} ^{1}\Bigr{)}\Bigr{]}^{2}+\Bigl{[}L(\mathbf{w})-L_{\max}^{2}\Bigl{(}\mathbf{p} ^{2}\Bigr{)}\Bigr{]}^{2}. \tag{11}\]  
The weight ratios \(\mathbf{w}\) are optimized through minimizing \(L(\mathbf{w},\mathbf{p})\) in Equation (11). \(L(\mathbf{w})\), \(L_{\max}^{1}\bigl{(}\mathbf{p}^{1}\bigr{)}\) and \(L_{\max}^{2}\bigl{(}\mathbf{p}^{2}\bigr{)}\) are calculated using Equation (10), based on the Gaussian model \(G\), the optimum Gaussian model \(G_{1}\) for Source 1 and the continuous probabilistic representation model \(G_{2}\) for Source 2. After the weight ratios \(\mathbf{w}\) are determined, the fusion Gaussian model is constructed through the interpolation method based on the fusion probability density values \(p_{i}\) at the determinate discrete points.
# Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery
## 1 Introduction
NTL remote sensing is widely used in macroeconomic and social parameter estimation [30; 31; 32], urban monitoring [33; 34; 35; 36], great event change [37; 38], energy consumption [39; 40], ecological environment assessment [41; 42], and other fields. Due to the low spatial resolution of NTL images, previous studies focused on macroscopic dimensions, such as national or urban scales. However, urban buildings with small-scale features are the basic unit of human activities. More sophisticated data and advanced means are needed in terms of reasonable arrangements and scientific planning for the limited land in a city, reduction of UHIs, and better ecological effects. Therefore, medium- and high-resolution NTL sensors have become an urgent requirement for urban research. On 2 June 2018, the Luojia1-01 scientific test satellite successfully launched; it carries a large-field-of-view, high-sensitivity NTL remote sensing camera with 130 m resolution and 260 km width NTL imaging capability. Compared with commonly used NTL data such as that from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) and Suomi National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), Luojia1-01 NTL imagery is a remarkable improvement in terms of spatial resolution, image saturation, and blooming effects [43]. Conducted to provide frontier data, the current research based on Luojia1-01 NTL imagery is expanding. Luojia1-01 NTL imagery has been applied to the drawing of urban range [44], extraction of impervious surfaces [45], and urban economic estimation [46; 47]. However, it is not clear whether it is feasible to estimate UBD information using Luoj1-01 NTL imagery, and there is little quantitative analysis of the single factors giving rise to UHI, especially discussion of the spatial relationship between UBD and LST. As we seek to address these problems, the purpose of this paper is to (1) test the feasibility of Luojia1-01 NTL imagery for UBD estimation; (2) combine the UBD estimation with LST data obtained from the Landsat data inversion; and (3) use the Geographically Weighted Regression (GWR) model and the spatial analysis method of the ordinary least squares (OLS) regression model to study the spatial relationship between UBD and LST in order to provide a scientific basis for the planning of urban construction layout and urban thermal environmental management.
## 2 Experimental Details
### Waveguide Fabrication and Characterizations Techniques  
Before fabrication, the geometry of the SiOC waveguides was designed to obtain a single mode operation by using a commercial electromagnetic tool based on Finite Element Method (FEM). The optical waveguides were then fabricated with UV photolithography and reactive ion etching (RIE) processes as follows. To define waveguide patterns, a photoresist AZ 5214 E was spin coated on the SiOC layer and soft baked on a hot plate; then the photoresist was exposed in hard contact mode with photo mask using I-line 365 nm mercury arc lamp emitting a power of 1000 W. The photo mask includes a set of waveguides with different widths. A controlled RIE process of CHF\({}_{3}\) and O\({}_{2}\) gases mixture was used to etch channel waveguides in SiOC layer with an etch rate of 20 nm/min. The realized channel waveguides were covered with 8 \(\upmu\)m thick SiO\({}_{2}\) (n = 1.444) deposited by a plasma enhanced chemical vapor deposition (PECVD) process (STS CVD tool) to yield symmetric optical modes.  
The optical waveguides were characterized on the optical bench through butt-coupling with small-core fibers as shown in Figure 2. The schematic diagram of the waveguide measurement setup is given in Figure 2a. The light signal was coupled from a laser source operating around telecommunication wavelength \(\lambda\) = 1550 nm. The SiOC waveguides were aligned with small core fibers having mode field diameter MFD \(\approx\) 3.6 \(\upmu\)m using index matching oil to reduce the gap between the fiber and chip facet. The light at the output facet of SiOC chip was collected and read with optical power meter. The polarization state (TE and TM) of the light wave at the input facet of the waveguide  
Figure 1: (a) Four-layer optical model employed to retrieve optical constants of SiOC film from ellipsometric data—not to scale, (b) schematic of SiOC anisotropic layer with optical axis perpendicular to surface and ordinary n\({}_{\text{o}}\) and extraordinary n\({}_{\text{e}}\) refractive indices.  
was selected with a fiber polarization controller. The real optical waveguide measurement setup is shown in Figure 2b where a SiOC chip is aligned with small core fibers.
###### Abstract  
This study investigated the relationship between grey matter (GM) volume and blood biomarkers, blood pressure, and obesity. We aimed to elucidate lifestyle factors that promote GM volume loss. A total of 1799 participants underwent the brain dock as a medical checkup. Data regarding blood pressure, obesity measurements, and standard blood biomarkers were obtained. Further, brain magnetic resonance imaging (MRI), including high-resolution T1-weighted imaging, was performed. We calculated the grey matter brain healthcare quotient (GM-BHQ), which represents GM volume as a deviation value. After adjusting for confounding variables, multiple regression analysis revealed that body mass index (BMI) (\(b=-0.28\), \(p<0.001\)), gamma-glutamyltransferase (\(\gamma\)-GTP) (\(b=-0.01\), \(p=0.16\)), and fasting blood glucose (\(b=-0.02\), \(p=0.049\)) were significantly correlated with GM-BHQ. Although the current cross-sectional study cannot determine a cause-and-effect relationship, elevated BMI, \(\gamma\)-GTP, and fasting blood glucose could promote GM volume loss.  
MRI; GM-BHQ; GM volume; BMI; blood biomarker; blood pressure Article