esa-sceva/satcom-chunk-collection huggingface dataset:
# Mobile Augmented Reality Application to Evaluate the River Flooding Impact in Coimbra
## 6 Discussion
### Limitations of the Mondego River Dataset  
Data limitations have a huge impact on the results. The river height data that are important for the prediction of the water level height depend on institutions whose approval requests take a long time to be obtained, and the amount and formats of the data are diverse. This limitation led to our search for open-access datasets on the internet. The historical weather API has a large sample of data points that are registered hourly and contains several features in the city of Coimbra. The CLMS dataset is the only available dataset that has been found in open access that contains several data points of the Mondego river height. As these data were taken from a space, their precision is not as accurate as it would have been if precise measurements were taken from the ground with adequate sensor monitoring. Nonetheless, the error obtained when modelling water height in the function of precipitation and month shows very good results.  
The use of the regression model can be performed by calculating the average of the month. The precipitation value obtained for February 2016 is 0.267 mm, so the water height result for the prediction is  
\[h=coeff*x+intercept=4.45*0.26+17=18.157 \tag{1}\]  
Since the closest infrastructures on the river front are at 18 m and the \(h\) value is higher at an interval of 0.157 m, these infrastructures are flooded. This approximation value approaches the real value but has a huge limitation as it predicts the flooding value afterwards or is based on estimated precipitation values. The regression model, even limited by the month output, can indicate if chances of flooding in a given month are high and which water height value is expected. The 3D model also allows us to visualise the impact in a user-friendly and intuitive way.
# Evolution of V2X Communication and Integration of Blockchain for Security Enhancements
## 2 V2X Technology
### State-Of-The-Art V2X Communications
#### 2.2.1 DSRC-Based V2X  
The 5.9 GHz DSRC-based V2X provides cooperative awareness applications such as vehicle warning, emergency brake lighting and vehicle platooning. However, such applications are only appropriate for low vehicle-density environments. In fact, hundreds of thousands of vehicles drive on the road and V2X applications need an extremely high capacity, high bandwidth, and very low latency in a densely populated environment. In the case of critical and emergency warning notifications, latency plays a very important role in the security of traffic to prevent collisions. Some of the features and requirements of future V2X are high bandwidth, low delay, high scalability, availability, reliability, and high data speeds. DSRC has limitations to satisfy all of the specifications of future V2X requirements due to weakness in the physical layer (i.e., radio technology) and the absence of collision and interference management. As a result, the realization of V2X in vehicles has been delayed. Moreover, intelligent vehicle connectivity requirements and applications are growing exponentially, while DSRC cannot get closer to the ever-increasing needs of such applications. The infrastructure of DSRC needs RSUs that take a massive amount of time and money for global implementation. DSRC is an asynchronous system that is based on the CSMA/CA protocol, so there are many physical layer inadequacies that result in reduced performance. DSRC based on the CSMA system is more vulnerable to interference, as there is no channel access as long as activity is sensed on the channel. In addition, CSMA issues are more pronounced at higher network loads and are not suitable for critical communication scenarios [10]. On the other side, a different approach based on cellular networks is investigated for V2X connectivity as a candidate technology.
# Solving the Urban Transit Routing Problem Using a Cat Swarm Optimization-Based Algorithm
## 4 Computational Results
### Considering the Provider's Operational Cost
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \(d_{0}\)**(\%)** & \(d_{1}\)**(\%)** & \(d_{2}\)**(\%)** & \(d_{unsatt}\)**(\%)** & \(att\)**(mpu)** \\ \hline
**Mumford (our** & 25.61 & 49.24 & 21.28 & 3.87 & 31.66 \\
**evaluation)** & 27.46 & 50.97 & 18.76 & 2.81 & 31.44 \\
**[PERSON] (published)**([PERSON] and Gok, 2018) & 27.56 & 53.25 & 17.51 & 1.68 & 30.41 \\
**CSO** & 25.76 & 50.86 & 21.96 & **1.42** & 30.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Comparative results for the best solutions for Mumford3 network. could set \(x_{4m}\) to a value bigger than 160 and produce a route set with a cost for the provider equal to 75.45 on average. At the same time, we achieve route sets that meet the requirements of passengers with no more than two transfers. The time burden for passengers in this case reaches 10.3% on average when compared to [43]. In the second set of experiments, parameter \(K_{4}\) was set to 10, while \(x_{4m}\) varies from 80 to 160 min. In Table 18, we present the average values of all evaluation criteria. Both algorithms were executed for 100 Monte Carlo runs, the number of routes equals 4, and the maximum number of nodes is restricted to 10. As can be seen from Table 18, the approach of the CSO algorithm produces in all cases better results according to the criteria set forth above. The cost for the transmission system provider is about 65.6% on average compared to the corresponding costs generated by the approach of [43]. The volatility of \(L_{tot}\) values is much smaller than the corresponding volatility displayed at [43]. This means that we could set \(x_{4m}\) to a value equal to 160 and produce a route set with a cost for the provider equal to 75.9 on average. The time burden for passengers in this case reaches 20.9% on average when compared to [43]. In conclusion, we can adjust algorithm's parameters to fulfill passengers demands and, at the same time, succeed in decreasing operational costs. However, designing route sets with very small route lengths should be avoided, since very small route lengths demand higher frequency of bus schedules, which is not acceptable in general.
## 2 Structure Design of MRE Vibration Isolator
### Mechanical Structure Design
#### 2.2.3 Elastic Element Stiffness Calculation  
The performance of the MRE isolator varies with the formulation, hardness, temperature of the MRE material, the size of the isolator, the stress state, the size of the deformation, and many other factors. The MRE is a kind of particle-filled rubber. The stiffness calculation of the elastic element made of it can refer to the estimation method of traditional rubber vibration isolation devices. In this paper, the core intelligent elastic element of the MRE is designed according to the empirical calculation formula of the rubber spring in the mechanical design manual. The static stiffness of the isolator in this design is estimated according to the following formula [19]:  
\[k=\frac{2\pi lG}{\ln(d_{2}/d_{1})} \tag{1}\]  
Among them, \(k\) is the static stiffness of the MRE isolator, \(l\) is the sum of the heights of the two MRES, \(G\) is the shear storage modulus of the MRE, \(d_{2}\) is the outer diameter of the MRE, and \(d_{1}\) is the inner diameter of the MRE.  
In this paper, the following parameters are preliminarily selected: \(G=3\times 10^{5}\) Pa, \(l=12.0\) mm, \(d_{3}=60.0\) mm, \(d_{2}=54.0\) mm. Combined with Equation (1), the static stiffness of the MRE isolator can be calculated as \(k=214.7\) KN/m, which has good bearing capacity.
## 2 Numerical Model and Adopted FE Techniques
### Initial Imperfections  
The initial deflection and welding-induced residual stresses were considered as initial imperfections of stiffened plates in the benchmark research. The initial deflection consists of a hungry-horse mode deflection in plate and a flexural-torsional buckling mode distortion of stiffener, which are shown in Figure 3 and described as follows [11],  
Plate:  
\[w_{0p}=A_{0}\sin\frac{m\pi x}{a}\sin\frac{\pi y}{b}+B_{0}\sin\frac{\pi x}{a} \tag{1}\]  
Stiffener:  
\[w_{0s}=B_{0}\sin\frac{\pi x}{a},\quad v_{0s}=C_{0}\frac{z}{h}\sin\frac{\pi x}{a} \tag{2}\]  
where \(m\) takes the values of 3 and 5 for the plates with \(a/b\) ratios of 3.0 and 5.0, respectively, and the magnitudes of initial deflection are \(A_{0}=0.01\times t_{p}\) and \(B_{0}=C_{0}=0.001\times a\).  
The distribution of welding residual stress is approximated as illustrated in Figure 4. By assuming the self-equilibrium of internal forces in the plate and in the stiffener independently, the compressive residual stress takes the form [1]:  
\[\sigma_{cp}=\frac{2b_{tp}\sigma_{\gamma p}}{b-2b_{tp}},\,\sigma_{cs}=\frac{h_{ ts}\sigma_{\gamma s}}{h-h_{ts}} \tag{3}\]  
where \(\sigma_{\gamma p}\) and \(\sigma_{\gamma s}\) are the yield stresses of the plate and the stiffener, respectively; \(b_{t}\) and \(h_{ts}\) are the breadths of the regions where tensile residual stress is produced in the plate and in the stiffener, respectively, which can be evaluated as follows [27]:  
\[b_{tp}=t_{w}/2+0.26\Delta Q_{\max}/\left(2t_{p}+t_{w}\right) \tag{4}\]  
\[h_{ts}=\left(t_{w}/t_{p}\right)\times 0.26\Delta Q_{\max}/\left(2t_{p}+t_{w}\right) \tag{5}\]  
Figure 3: Assumed initial deformation of stiffened plate.  
where \(\Delta Q_{\rm max}\) is the maximum welding heat input in multi-pass welding and is taken as \(\Delta Q_{\rm max}=78.8l^{2}\)[28], where \(l\) is the leg length of the fillet weld. The leg length is dependent on the thickness of the web, and can be determined as follows for ship structural members:  
\[l=\left\{\begin{array}{cc}0.7\times t_{w}\,(\rm mm)&0.7\times t_{w}\leq 7.0 \rm mm\\ 7.0\,(\rm mm)&0.7\times t_{w}>7.0\rm mm\end{array}\right. \tag{6}\]
# A Two-Stage Household Electricity Demand Estimation Approach Based on Edge Deep Sparse Coding
## 3 Proposed Methodology
### Edge Sparse Coding Architecture  
Edge computing is a concept of near computing, that is, the operation is completed in the local network which is closer to the data source [31]. However, not all data operations can be placed on the local side, and some data still require further analysis or long-term access to transfer back to the cloud computing center for processing or storage. The cloud computing center can be similar to a central coordinator or a data training center in the future.  
Evidently, the pressure exerted by communication, calculation, and storage gradually increases along the data link from local meters to the cloud computing center. Smart meters and gateways exhibit certain analysis and calculation abilities and can perform the data compression task. In such cases, the transmission pressure on the data link and cloud computing center can be substantially alleviated.  
Figure 3: Deep non-negative K-SVD algorithms with an appliance load.  
Therefore, a sparse coding architecture based on edge computing was introduced, as shown in Figure 4, where the deep K-SVD algorithm was completed in the edge nodes. Deep sparse coding requires higher computing and storage capacities than single-layer sparse coding. The edge sparse coding architecture can be three-tier or four-tier, depending on the capacity of the smart meters. The edge nodes of the three-tier sparse coding structure are smart meters only, whereas smart meters and a gateway play the role in the four-tier computing architecture to improve the data-handling ability of edge nodes. The extracted DUBPs and coefficient matrix are uploaded to the cloud computing center for load forecasting and storage.  
The advantage of the proposed edge sparse coding architecture is manifested in the following aspects: (1) The amount of data is considerably reduced after edge sparse coding, which will substantially contribute to alleviating communication and storage burden; (2) the major modifications occur at the grid side and exert minimal impact on customers; (3) this scheme can be implemented by directly installing a sparse coding module on existing smart meters or adding an intelligent gateway with data computing capability. It does not substantially change the existing electricity distribution network and considerably reduces procurement and construction costs.
## 4 Simulation Results and Analyses
### Results and Analyses
#### 4.2.1 Simulation Results and Analyses for 0:00 to 6:00  
Figure 8 shows the results of the period 0:00-6:00 taken from Figure 7. In Figure 8, there is no load shedding because all the load demand is critical load; the public grid power limitation is greater than the load demand power; thus, the excess public grid power can charge the BS. Therefore, at the beginning of the simulation, the public grid supports the load demand and charges the BS until the \(soc_{BS}\) reaches \(SOC_{BS\_MAX}\), then the public grid only supports the load demand in case 7 of sub-flowchart b; at 5:00, the SC is recharged by the BS and the public grid because the \(soc_{SC}\) reaches the \(SOC_{SC\_MIN\_MAX}\) in case 6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Period** & **Description** & **Choose of Power Strategy** & \(k_{L\_CRIT}\) & \(P_{G\_MAX}\) \\ \hline
0:00–6:00 & Low load demand & Sub-flowchart b & 100\% & 600 W \\
6:00–8:00 & Different according to the seasons & Sub-flowchart a & 80\% & 200 W \\
8:00–17:00 & Working time in building & Sub-flowchart a & 80\% & 200 W \\
17:00–22:00 & High load demand from the & Sub-flowchart a & 80\% & 200 W \\  & public grid & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Scenario parameters.  
Figure 6: Weather data on the 20 th June 2018 in Compiegne, France.  
Figure 9 is the zoom at the key period when the SC is recharging. Figure 9a shows the curve of the common DC bus voltage and the SOC curves of the SC and BS, and Figure 9b shows the power curves while the SC is recharging from 5:00 to 5:01.  
Figure 8: Curves of common DC bus voltage and SOC of storage (**a**) and curves of power (**b**) during the period 0:00–6:00.  
Figure 7: Curves of common DC bus voltage and state of charge (SOC) of storage (**a**) and curves of power (**b**).  
In Figure 9, the SC starts recharging at 5:00 in case 6, and the SC stops recharging at 5:01 because \(soc_{SC}\) reaches the \(SOC_{SC\_MAX\_MAX}\) and the \(\Delta p\) is negative. During the SC recharging period, the microgrid power control can keep balance, and some points are marked at the same time in Figure 9a,b; all the values marked only reserve the integer part.  
During the period from 5:00 to 5:01, in Figure 9a, the DC bus voltage value is 399 V near to the \(v_{DC}^{*}\), the \(soc_{SC}\) is 62%, and the \(soc_{BS}\) is 76%. In Figure 9b, the marked power values are listed under the legend (\(p_{PV\_MPPT}=p_{PV}=0\)W, the \(p_{G}=-600\)W, the \(p_{BS}=-1000\)W, the \(p_{L\_D}=p_{L}=493\)W, the \(p_{SC}=1106\)W, and the \(p_{DG}=0\)W) and show that the PV is not working because of low solar irradiation, the load is supplied by the BS following its demand, the SC is being charged by the BS, and the public grid and the DG are not working (case 6).
# Challenges Associated with Implementing 5G in Manufacturing
## 6 Private 5G Networks
#### Benefits of Private 5G Networks  
Non-public 5G networks turn businesses into mobile operators in limited geographic areas. A company that operates a mobile private 5G network (MPN) will become a 5G network infrastructure operator; this will probably be the case if the organisation wants to have complete control over its operating processes. As a result, parameters such as the bandwidth or real-time properties can be adapted, which is not possible when using the public network of a mobile radio provider.  
An organisation that runs a private 5G network will be able to add layered security at every level of the manufacturing process. Since the security can be higher, it will afford the owner of the network a level of control that would not be possible on a public network. These networks also offer protection against industrial espionage. Data in non-public 5G networks can be segregated and processed separately from public 5G networks which will ensure complete privacy protection of process and production-related data because the communication takes place within a separate network, enabling the data to be far better protected against unauthorised access [50,51].  
Unlike a public network, a private 5G network can be configured to the specific needs of a location, and configurations can vary by site, depending on the type of work undertaken in each venue. A private network allows companies to determine the network's deployment timetable and coverage quality. Another point of note is that the network may be installed and maintained by onsite personnel, enabling faster responses to issues, thereby reducing downtime and maintaining a level of uptime to meet the organisation's needs.
## 1 Introduction
It is generally accepted than the local ductility is larger than story ductility, which in turn is larger than global ductility. It must be noted that ductility demand is different from ductility capacity. For example, for the case of a particular story, story ductility demand is the ratio of the maximum interstory lateral displacement of a structure during the application of the seismic loading to the corresponding displacement when first yielding occurs at any member of the story, while ductility capacity is the ratio of the maximum permissible inelastic lateral displacement to the displacement when first yield occurs. Ductility capacity is usually obtained from experimental results of individual members (local ductility). Therefore, it is important to relate it to the story or to the overall structural ductility (global ductility). Theoretically, ductility capacity should be reached when a collapse mechanism develops in the structure. To obtain this, it needs to be guaranteed that plastic moments are reached at positions of maximum moments before failure due to instability, namely local buckling or lateral torsional buckling, in a member or in a connection occurs. Moreover, local ductility cannot be exceeded; otherwise, the ductility corresponding to the collapse mechanism will not be the ductility capacity. For that reason, some researchers [10] suggest using local ductility as the basis for design because there are numerous laboratory studies on ductility for members. In this regard, as stated above, some relationships need to be established between local, story and global ductility. The central objective of this paper is to evaluate the ductility parameter for steel buildings with typical welded moment-resisting frames. Different types of local ductility as well as story and global ductivity are calculated according to both, nonlinear dynamic analysis and nonlinear static analysis (pushover). The relationship between local and global ductility is calculated. Due to the advancements in the computer technology, the computational capabilities have significantly increased in the recent years allowing us to estimate the nonlinear seismic response by modeling structures as complex MDOF systems with hundred and even thousands of degrees of freedoms and applying the seismic loadings in time domain as realistically as possible. Responses obtained in this way may represent the best estimate of the seismic responses. Then the values of ductility demands for the different definitions can be properly estimated.
## 4 Discussion of Results
### Film Oven Aging Method
#### 4.1.2 Effects of Time  
Based on the film oven aging process, only X\({}_{1}\) position was selected to remove the influence of oven position and the master-curves of asphalt mastics with a filler content of 80% after aging times from 0 h to 20 h were obtained to investigate the rheological properties of the mastics after different levels of oxidation. As Figure 4a shows, the complex modulus of the mastics increase when the aging time increases. Also, it could be found that the master-curves of the complex modulus from mastics after aging time from 0 to 4 h are similar, while there is a big gap between mastics with an aging time of 4 h and an aging time of 8 h, which indicates that 8 h of aging could change the rheological properties of the mastics significantly by visualization. As illustrated in Figure 4b, the phase angle of the mastics after different aging times also presents different patterns under different reduced frequencies. The phase angle may decrease when the reduced frequency larger than 0.1 Hz with the increasing aging time. However, when the reduced frequency is less than 0.1 Hz, the phased angle of the mastics after aging times from 0 to 4 h increase slowly with the reduced frequency, which may result from the increasing influence of the fillers when the binder goes into the flow state at a high temperature or low loading frequency. Table 4 exhibits the viscous-elastic parameters obtained from the CAM models of mastics after different aging times. Results show that when aging time increases, both the glass modulus (G\({}_{\mathrm{g}}\)) and the modulus of equilibrium (G\({}_{\mathrm{e}}\)) grow at a respectable pace, which indicates that the mastics become stiffer. The crossover frequency (f\({}_{\mathrm{c}}\)), as well as the width of the relaxation spectrum R decrease, first sharply from 0 to 8 h aging time, then slowly with the increasing aging times. Lower crossover frequency (f\({}_{\mathrm{c}}\)) comes from decreasing phase angles with the increasing aging times. Less parameter R means less gradual transition from the elastic behavior to the viscous behavior, which indicates more sensitivity to frequency changes.  
Figure 4: Master-curve of the mastics with filler content of 80% after different aging times: (**a**) complex modulus; (**b**) phase angle.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Position ID**} & \multicolumn{2}{c}{**X\({}_{1}\)**} & **X\({}_{2}\)** & **X\({}_{3}\)** & **Y\({}_{1}\)** & **Y\({}_{2}\)** & **Z\({}_{1}\)** & **Z\({}_{2}\)** & **Z\({}_{3}\)** \\ \hline \multirow{2}{*}{G*(5 °C \&10 Hz, 10\({}^{7}\) Pa)} & Mean & 6.21 & 5.86 & 5.42 & 5.97 & 5.85 & 5.79 & 5.72 & 5.74 \\ \cline{2-9}  & St.d & 0.31 & 0.34 & 0.38 & 0.24 & 0.32 & 0.27 & 0.24 & 0.31 \\ \hline St.d (multiple positions) & & 0.062 & & & 0.031 & & 0.026 & \\ \hline ANOVA & P value & 0.0002\({}^{\star}\) & & 0.5288 & & 0.6931 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter G* of the mastics at different positions and sensitivity analysis of different positions to G*.