esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Integrated Predictive Traffic Resilience Framework  
Conventional resilience metrics often fall short in predicting and quantifying the traffic resilience of a transportation infrastructure due to the dynamic and sequential properties of traffic data. Additionally, traffic flow becomes more uncertain under extreme weather conditions, which increases disaster risk and introduces other behavioral factors. To address these issues, a data-driven predictive traffic resilience framework is proposed, as shown in Figure 1.  
To implement this framework, hourly traffic volume data over a disaster period was collected. In addition, the hourly traffic volume data from the past two years at a  
Figure 1: The proposed integrated data-driven predictive traffic resilience framework.  
similar time was collected to analyze the change in traffic volume due to the disaster. It should be noted that the hourly traffic volume serves as an index or indicator of traffic resilience rather than directly representing resilience. It is a quantitative measure of traffic volume; by itself, the hourly volume index does not directly reflect the concept of traffic resilience. Traffic resilience refers to the ability of a transportation system to withstand and recover from disruptions or disturbances, such as accidents, congestion, or adverse weather conditions, while maintaining its functionality and performance of free flow or stable flow with unaffected speed.  
While the hourly volume can provide valuable information about traffic patterns and congestion levels, it does not capture the broader aspects of traffic resilience. Thus, to quantify traffic resilience, this manuscript also considers additional factors, such as travel time variations based on volume (\(\alpha\)) and capacity (\(\beta\)), road network or roadway capacity, alternate route availability (in terms of different segments/routes), and the system's ability to recover from disruptions during the post-adverse events condition. The proposed framework is modeled with a focus on traffic conditions during evacuation before and after any disaster. This proposed integrated framework consists of three main steps: (1) measurement of traffic performance under uncertainty; (2) prediction of the traffic condition with an augmented LSTM-based approach; and (3) quantification of traffic resilience during evacuation. Each of the steps will be detailed as follows.
# Coaxial Fibres Incorporated with Phase Change Materials for Thermoregulation Applications
## 2 Materials and Methods
### Fibres' Physical, Chemical, Thermal, and Mechanical Characterization
#### 2.4.6 Mechanical Performance-Dynamometer  
The mechanical properties of the fibres, such as tensile strength, were evaluated using a dynamometer test. The tensile strength and the maximum elongations of the fibres were determined using a Housefield H5 KS dynamometer (Artilab, Kerkdriel, The Netherlands), associated with the QMAT Materials Testing & Analysis software, version 3.51, following the ISO 2062-2009 standard ([PERSON] et al., 2017). Fibres of 10 cm in length were analysed at RT with a holding distance, also known as gauge length, starting at 100 mm and increasing continuously until the maximum elongation to break was reached. The extension range was 5 mm, and the crosshead velocity was set at 25 mm/min, using a load cell of 250 N applied with a preload of 0 N and a load interval of 100 N.
## 3 Results
### Output Files  
Compared to our previously published ssPINE version with a web submission form, ssPINE-POKY introduces several improvements. In addition to providing assignments and a bar graph displaying the assignment probability for each residue, ssPINE-POKY can automatically generate labels in the NMR spectra uploaded to the POKY project, significantly reducing the time required for manual assignments.  
The earlier version of ssPINE included two primary tools for providing structural and dynamic parameters: PECAN (Protein Energetic Conformational Analysis from NMR chemical shifts) for protein secondary structure prediction [39] and LACS (Linear Analysis of Chemical Shifts) for chemical shift referencing errors [40]. In contrast, ssPINE-POKY integrates several additional analysis tools. These include RCI-S\({}^{2}\) (random coil index order parameter value) for predicting protein flexibility [41], PACSY hydrophobic core prediction based on hydrophobicity scales derived from solvent-accessible surface area [42], and GetSBY secondary structure prediction, which utilizes chemical shifts of N, HN, C\(\alpha\), C\(\beta\), CO and H\(\alpha\) in the protein backbone [19]. Additionally, PSIPRED [43] predicts secondary structure based on the position-specific scoring matrices generated by PSI-BLAST [44], while TALOS-N applies a chemical shift-based method to predict secondary structure [41] and protein backbone \(\varphi/\psi\) dihedral angles, also visualized as PACSY RAMA plots [45]. For 3D structure prediction, ESMFold uses the primary protein sequence [46], and CS-Rosetta uses the chemical shifts.
## 1 Introduction  
Mechanical, electrical and plumbing (MEP) design and coordination tools have an important role in the design process [1]. However, the use of building information modelling (BIM) in MEP disciplines has traditionally focused on coordinating specialities and not on the actual design of each of the specialities [2]. The design and capacity of pipe network systems--as well as domestic drinking-water and Sewerage systems (DDWSSs)--and their critical points/connections inside a building may affect the building components, such as architectural spaces and structural systems. Service systems, such as heating, ventilation and air conditioning (HVAC), can also be affected [1].  
Previous studies promoted the design of drinking water facilities in BIM environments in order to take into account the regulatory frameworks of corresponding countries [3].  
However, these efforts did not include the use of parametric tools that increase the efficiency of the design process. The MEP tool from one of the most common BIM software packages [4], REVIT, is based on the International Plumbing Code (IPC), but it cannot be edited and it does not take into account different country regulations, preventing its use as a drinking water design tool The use of BIM for plumbing systems has also been explored in the context of asset management in the building operation stages, even using 4.0 technologies; e.g., augmented reality and sensors [5]. However, these uses are mainly focused on visualisation-based analysis and not the design of plumbing systems.  
This research developed and validated an intuitive methodology for solving a specific problem in DDWSS design, using a design science research method (DSRM) as an applied science approach. This problem is addressed by developing a methodology as a workflow. To validate the proposed methodology in the case of the Chilean standard for DDWSS design, two case studies (one simple and one complex) were developed and compared with the traditional DDWSS design methodology. These case studies were validated in terms of the effort required, the complexity, the real automation level and the quality of the results. The obtained solution combines BIM models and parametric tools to automate the manual activities from the traditional design method.  
This work aims to bring abstract BIM concepts into practice and encourage researchers and MEP engineers to adopt BIM for DDWSSs and to automate manual tasks. This issue is relevant in light of the findings of the most recent, extensive BIM report developed in Chile [6], which reveals that the MEP speciality is the least advanced in BIM in terms of adherence to BIM standards, motivation, satisfaction and perceived value.  
The remainder of this paper is organised as follows. In Section 2, the research methodology is described. Section 3 discusses the background of this paper through a literature review. Section 4 describes the proposed workflow for DDWSS design. The case studies are developed in Section 5, and their results are provided and discussed in Section 6. Finally, the conclusions of this paper are described in Section 7.
# Development of Digital Device Using ZigBee for Environmental Monitoring in Underground Mines
## 3 Methods
### Module Installation and Data Acquisition in Underground Mine  
The vibration and dust measurement modules developed in this study were applied to the Taeyoung EMC Samdo Mine, an underground mine. The experiments were conducted in part of the main drift, where blasting was performed periodically several times a day. The vibration measurement modules were set as routers to continuously acquire data, and 29 modules were installed. To obtain dust values, 14 dust measuring modules were installed as routers, and the line of sight (LOS) was secured for smooth communication between modules. The modules were arranged at intervals of \(\sim\)20 m. The experiment was conducted for blasting work at approximately 4:40 pm, and the blasting spot is shown in Figure 5.  
Figure 4: (a) PMSA003A dust sensor, (b) schematic, and (c) product of dust measurement module.  
One coordinator must be present in the ZigBee network. In this study, 29 vibration and 14 dust modules were arranged along the mine drift, and we could connect all of these modules with one coordinator. However, because the vibration module has a high sampling rate that can acquire multiple data points per second, data loss may occur in one coordinator if excessively many modules are connected. Therefore, in this study, two networks are constructed by installing two coordinators. Coordinator 1 manages the vibration modules from V1 to V15, and Coordinator 2 manages the V16 to V29 vibration modules and dust modules. The coordinator module was constructed by connecting the XBee and Arduino Uno with the XBee shield, and it was programmed to monitor all data acquired from the measurement module and connected to the laptop. The packet data acquired from the module have a unique code defining each module and analog value data, and the Arduino connected to the coordinator transmits these data to the laptop through time-to-live serial communication. Figure 6 shows a technology roadmap of the overall flow from the device hardware and software development to data acquisition and utilization. Because the acquired data are recorded in real-time in a comma-separated values format, the monitoring status can be easily checked even in the field.  
Figure 5: Drift map of Samdo Mine showing installed modules and blasting spot.
## 1 Introduction
The combination of microfluidics with SERS has tackled in a bidirectional way some of the bottlenecks of both technologies when used individually. The use of microfluidics allowed the controlled handling of NPs and NP assemblies in microfluidic channels [29; 30]. The miniaturization and flow control in microfluidics also permits the use of reduced substrate and sample volumes [1]. On the other hand, the use of a powerful sensing technique as SERS when handling microfluidics is crucial due to the low sample volumes and sensing areas. In this scenario, we present an interdisciplinary research work involving nanoparticles, SERS spectroscopy, and microfluidics. By using microfluidics for the highly controlled assembly of NPs on paper, we demonstrated the fast and low-cost fabrication of a paper-based sensing device in situ with improved efficiency and reproducibility. A hybrid and modular polydimethylsiloxane-paper (PDMS-paper) device was used for the controlled deposition of GNSs and core-shell gold silver nanorods (Au@AgNRs). These anisotropic NPs were self-assembled on a paper substrate by taking advantage of a reservoir-like architecture on the PDMS device, which allowed for the kinetic control of the wettability and evaporation during the self-assembly process. The sensitivity was tested using the Raman model molecule 1-naphthalenethiol (1-NAT), which achieved values of \(10^{-12}\) M for both GNSs and Au@AgNRs, and showed a high homogeneity over the sensing area. As a proof-of-concept, and to demonstrate the potential of these SERS substrates as biosensors, human colorectal cancer cells (SW480) and human peripheral blood mononuclear cells (PBMCs) were lysed and analyzed using the PDMS-paper substrates.
## 3 Methods
### EU-SENSE Case Study
#### 3.1.1 EU-SENSE Project Rationale and Objectives  
The International Forum to Advance First Responder Innovation (IFAFRI), as well as H2020 ENCIRCLE project, both highlighted the importance of real-time detection, monitoring, and analysis of CBRN threats and hazards. Tackling this priority requires bridging existing technological and capability gaps in the scope of first responders' work, who must safely and promptly identify hazardous agents and contaminants [30]. To improve the detection, identification, and monitoring of chemical threats, the European Sensor System for CBRN Applications--EU-SENSE project was developed [31; 32]. EU-SENSE focuses on developing a modern network of sensors for CBRN applications, both stationary and person-worn. The proposed network integrates heterogeneous sensor nodes, innovative data processing algorithms, as well as computational tools. A unified data model maintains the interoperability of the network components working with heterogeneous data. Specific EU-SENSE objectives are:  
* Contributing to better situational awareness of the CBRN practitioners through the development of a novel network of chemical sensors;* Improving the reconnaissance capabilities of the novel network of chemical sensors using machine learning algorithms (to reduce the false alarm rate) and the application of contaminant dispersion models, showcasing the usability of the EU-SENSE network to CBRN practitioners to validate the system, and maximizing its exploitation potential. The objective also involves the preparation of training sessions with CBRN practitioners [31].
# Delamination Detection in Bimetallic Composite Using Laser Ultrasonic Bulk Waves
## 2 Theory and Model
### Finite Element Model  
The coupled thermo-mechanical analysis module of the commercial finite element package ABAQUS/EXPLICIT was used to simulate the propagation of laser ultrasonic in the copper-aluminum bimetallic laminates. To reduce the calculation load, the two-dimensional plane finite element (FE) shell models with two layers were established to simulate the copper-aluminum bimetallic composites with various delamination defects. The dimension of each model was 50 mm \(\times\) 3 mm, the aluminum layer thickness was 2.5 mm, and the copper layer thickness was 0.5 mm, as shown in Figure 1. The distance between the excitation point and the detection point is defined as PRD. Table 1 lists the thermo-physical parameters of copper and aluminum used in the numerical simulation. The ultrasonic wave was excited on the surface of the aluminum layer, rectangular cavities with dimensions of 6 mm \(\times\) 0.01 mm and 2 mm \(\times\) 0.01 mm, which were located on the copper layer side of the composite interface, were used to simulate delamination defects. The pulsed laser load was equivalent to a transient planar heat source (surface heat flux) and applied to the FE models to excite ultrasonic waves. The power density of surface heat flux was 5 MW/cm\({}^{2}\), which is Gaussian distributed in space. The shape of the impulse was determined by Equation (4). The diameter of laser irradiation area was 1 mm, and the duration of a single laser pulse was 8 ns. The element length of pulsed laser irradiation area was 5 \(\upmu\)m, and other areas were 10 \(\upmu\)m, connected by the gradient grid. The element type used in the simulation was thermally coupled quadrilateral element with 4 nodes (CPE4 RT). The time step was set to be 0.5 ns to guarantee the accuracy of simulation analysis. The simulation time was 5 \(\upmu\)s.
## 7 Numerical Results of NSDI
### 31-Bar Planar Truss
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Element Number** & **ESMA (PoDE) \%** & **ESMA (DMI) \%** \\ \hline
10 & 0.00 & 0.00 \\
11 & 100.00 & 25.00 \\
12 & 8.00 & 0.80 \\
14 & 2.00 & 0.50 \\
25 & 95.00 & 14.20 \\ Total time (s) & 175 & - \\ RMSE & \(1.2\times 10^{-3}\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: PoDEs and DMIs of case 2 for the 31-bar planar truss. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Element Number** & **ESMA (PoDE) \%** & **ESMA (PDE) \%** \\ \hline
1 & 100.00 & 98.00 \\
2 & 96.00 & 96.00 \\
10 & 0.00 & 7.00 \\
11 & 0.00 & 0.00 \\
12 & 0.00 & 1.00 \\
18 & 7.00 & 0.00 \\
19 & 0.00 & 5.00 \\
22 & 0.00 & 10.00 \\
26 & 0.00 & 3.00 \\ Total time (s) & 195 & 313 \\ RMSE & \(1.1\times 10^{-3}\) & \(1.2\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: PoDEs and PDEs of case 1 for the 31-bar planar truss. damage existence is typically increased by the prediction error in the statistical surrogate model (PDE) [47]. This higher standard deviation may result in lower probability values when detecting damage in structures. On other hand, in both scenarios, the PoDE is demonstrated to be a more accurate damage index and results in smaller errors. For example, in scenario 1, the suggested approach reveals a 0% PoDE value in element 10, which is undamaged, compared to a 7% PDE value. In scenario 2, the undamaged element 26 shows 0% damage, whereas the probability-based technique predicts 12% damage. It is also observed that for both scenarios, the proposed method provides higher PoDE values at the damaged elements compared to the PDE value. The main reason is that the proposed non-probabilistic method utilizes only two models to predict the upper and lower boundaries of the ESP values. Furthermore, it implemented the exact FE model. In comparison, the probabilistic method for the same example needs four surrogate models to calculate the probability density function of the damage [47], which leads to greater prediction errors. Furthermore, as can be seen in Tables 5 and 6, the proposed methodology reduces the computation time by at least 37%.
## 3 Methodology of Numerical Tests of the Platform
### Functional Testing of the Platform Using the MBS Method
Therefore, a complete dynamic analysis was performed for all variants presented in Tables 1 and 2, i.e., with variable conditions of contact of the platform with the ground (described in Variants I and II together with different coefficients of friction \(\mu_{\text{min}}\) and \(\mu_{\text{max}}\)  
Figure 6: Model of the platform module—the action of force during hook lifting. platform relative to the ground was found (Figure 7). In the variants of numerical tests in which forces greater than the nominal ones by 50 N for various friction conditions (\(\mu_{\rm min}\) and \(\mu_{\rm max}\)) were applied, movement of the platform relative to the ground was found (Figure 7). Figure 7a shows the maximum displacement of the tested platform module equal to 800 mm in the 10 th second of the analysis. In Variant II of the analysis, the movement occurs both in the case of a force lower and greater than the nominal one determined analytically. The rotation of the movable rollers of the platform system based on a rigid ground reduces the resistance to movement, so even with a smaller force on the hitch of the hook mechanism, the platform with the load moves relative to the ground. These observations confirm the correctness of the assumptions made and the designation of the force components determined as loads in subsequent variants of numerical tests using FEM. Figure 8 presents changes in the values of the contact forces between the platform and the ground in Variant I and Variant II, taking into account various friction coefficients during MBS in the analysis time interval of 0-10 s. The values of the contact forces presented correspond to changes in the resistance forces generated between the movable rollers (Variant II) or fixed rollers (Variant I) of the hook mechanism of the platform and the ground. The highest values of the contact force correspond to Variant I with immobilized rollers, which occur after the complete development of friction, i.e., after approximately the 2 nd second of the analysis. Tables 1 and 2 present the numerically determined limit values of the component forces on the end carriage hitch during hook lifting of the platform with a load corresponding to the maximum weight of the log Q = 100 kN and the dead weight of the platform with two end carriages and a set of standions (Figure 6) with representation of both Variant I and Variant II of the cooperation model between the platform and the ground. The values of the limit forces determined numerically in Variant I, in which the possibility of rotation of the hook mechanism rollers was blocked, are identical to those determined analytically (Section 4.3 of the paper). In Variant II, the component forces forcing movement in the conditions of representing the maximum friction \(\mu_{\rm max}\), determined numerically in MBS, are lower by over 42%, respectively, in the case of the vertical S\({}_{\rm y}\) and horizontal S\({}_{\rm x}\) components (Tables 1 and 2).
# High-Performance Flux Tracking Controller for Reluctance Actuator
## 4 Simulation Analysis
### Parameters Identification of Bouc-Wen Model  
The TLBO algorithm [37], an intelligent patrol algorithm, is used to identify the parameters of the Bouc-Wen model. The identification principle in the reluctance actuator is shown in Figure 10.  
The steps of model identification are as follows:  
(1) Data collection: The simulation data, which stands for the reluctance motors model, that is, the hysteresis characteristic between output flux and input current, are obtained by multi-physics simulation software, namely COMSOL. Specifically, the version information is COMSOL Multiphysics 5.5 with MATLAB, and is provided by COMSOL Company in Stockholm, Sweden in 2019.  
(2) Model implementation: In Figure 11, the Bouc-Wen model, a classical hysteresis model, was established in MATLAB/Simulink;  
(3) Parameter identification: The simulation data are loaded, and then the model parameters are optimized by the TLBO.  
Figure 11: Dynamic simulation model implemented with MATLAB/Simulink.  
Figure 10: The schematic diagram of Bouc-Wen model identification.  
The identification results are listed in Table 2. Comparing the actual and the fitting data using the estimated Bouc-Wen model, the curves are depicted in Figure 12. We can conclude that the model output matches the actual output well: the RMSE is 4.7926 \(\times\) 10\({}^{-6}\), and the MSE is 3.3892 \(\times\) 10\({}^{-4}\).