esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Non-Linear Finite Element Analysis
### Finite Element Modeling
#### 2.1.3 Material Properties  
For large deformation cases, the cross-sectional area undergoes a significant reduction and therefore true stress-strain indicating true material deformation must be considered. The following Equations (2) and (3) are the nominal stress-strain and true stress-strain relationships [23].  
\[A=\pi r^{2}\sigma_{true}=\sigma_{eng}\big{(}1+\varepsilon_{eng}\big{)} \tag{2}\]  
\[\varepsilon_{true}=ln\big{(}1+\varepsilon_{eng}\big{)} \tag{3}\]  
where \(\sigma_{true}\) is the true stress, \(\sigma_{eng}\) is the engineering stress, \(\varepsilon_{true}\) is the true strain and \(\varepsilon_{eng}\) is the engineering strain. It is recommended that the material properties for nonlinear analysis is based on the actual test results. However, in many cases this data is unavailable. In the absence of actual  
Figure 5: Different details of joint configurations with reinforcement. (**a**) Case 1—Beam-column connection without haunch; (**b**) Case 2—Haunch_01 Beam-column connection with haunch by [PERSON] [16] but no web stiffener plate on the beam and no continuity plates for haunch flange; (**c**) Case 3—Haunch_02 Beam-column connection with haunch by [PERSON] [16]. Web stiffener plates and haunch continuity plates were provided on the beam and column, respectively; and (**d**) Case 4—Haunch_03 Beam-column connection with haunch but the haunch configuration is modified by providing the slope angle of 45\({}^{\circ}\).  
Figure 6: Typical tie constraint at beam-column connection.  
tensile test results, Det Norske Veritas (DNV) [23] established idealized material curves according to the European Standards. The elasto-plastic material properties for steel grade S235 with isotropic hardening was selected for the non-linear properties of steel [23]. ABAQUS defines the rate-dependent behaviour in term of plastic strain rate \(\widetilde{\varepsilon}^{pl}\) expressed as:  
\[\widetilde{\varepsilon}^{pl}=D(R-1)^{q} \tag{4}\]  
where \(R\) is the ratio of the dynamic yield stress to the static yield stress, \(D\) and \(q\) are material constants. To match the Cowper-Symonds constitutive equation, the material constants for structural steel in Equation (4) were specified as \(D\) = 40.4 s\({}^{-1}\) and \(q\) = 5 [24] which were included in the power law rate dependence definition input.  
Typically, [PERSON]'s porous model is considered to model material with relative density greater than 0.9. [PERSON] et al. [25] considered [PERSON]'s porous model in the material attributes to overcome the limitation of the [PERSON] constitutive law. However, [PERSON]'s damage model on material plasticity is not considered in this study. The study is more concentrated on the structural performance based on global response without investigation of the material constitutional law, sensitivity of the local effect in the beam-column connections, and detail failure mechanism.
## 5 Model Evaluation
### Model Experiment 2: Flow Prediction  
The traffic volume results from the northbound direction (Table 4) show that BiLSTM provided the least MAPE (%) error and the best accuracy, ranging from 99% to 96% from the 15-min to 60-min prediction horizon. The second-best model was Uni-LSTM, with an accuracy above 94% for up to a 60-min prediction horizon. On the other hand, the RNN and Elman models provided an accuracy above 82% for up to 45-min prediction; then, it decreased to 75% for 60-min prediction horizons. DLBP provided a better prediction accuracy compared to RNN and Elman for a longer speed prediction horizon of 60 min into the future, with an accuracy of 78%.  
The traffic volume results from the southbound direction (Table 4) show that BiLSTM provided the least MAPE (%) error and the best accuracy, ranging from 99% to 96% from a 15-min to a 60-min prediction horizon. The green cells represent the best-performing models, while the yellow cells represent the worst-performing models. The second-best model was Uni-LSTM, with an accuracy above 93% for up to a 60-min prediction horizon. On the other hand, the RNN and Elman models provided an accuracy above 78% for up to 45-min prediction; then, it decreased to 71% for 60-min prediction horizons. DLBP  
Figure 11: Speed prediction results for 45-min horizons using the 4-BiLSTM model. Source: authors. Note: The first 500 conservative data points in the northbound direction were selected for presentation.  
Figure 12: Speed prediction results for 60-min horizons using the 4-BiLSTM model. Source: authors. Note: The first 500 conservative data points in the northbound direction were selected for presentation.  
provided a better prediction accuracy compared to RNN and Elman for a longer speed prediction horizon of 60 min into the future, with an accuracy of 73%.  
The best traffic volume prediction performance from the BiLSTM model is illustrated in Figures 13-16. The black dashed line represents the target real volume data compared to the green trendline, which represents the results generated from the model. In the figures below, the \(x\)-axis represents the time period of data collection (in minutes), while the \(y\)-axis shows the speed and volume of observations. The figures compare the target values (black dashed line) with the prediction values from the developed model (green lines).
## 2 Problem Formulation  
### Description of the Room Geometry  
The test classroom under consideration is located at the Technical University of Sofia and during the academic year it is used for teaching purposes (up to 25 persons). Figure 1 presents the geometry model built for URANS CFD calculations (Figure 1a), alongside with the classroom interior photos (Figure 1b,c) and a close-up of the fan coil (Figure 1d). In the present study, the geometry model of the room interior includes the window sill, four radiators, four columns, two ceiling beams, four ceiling diffusers (fan coils, marked as #1-4 in Figure 1a-c) and both horizontal and vertical ventilation ducts. The form of the room is a parallelepiped with the width of \(W=5.4\) m, the length of \(L=11.8\) m, and the height of \(H=3.2\) m. The floor area of the room is equal to \(W\times L=64\) m\({}^{2}\) and the volume of the room is \(W\times L\times H=204\) m\({}^{3}\). The detailed description of all room elements could be found in [37].  
The air is supplied to the room by the four-way type MMU-AP0092H fan coils that could be used either in an isothermal regime or for cooling/heating purposes. Four fan coils are installed in the room in the staggered arrangement to provide various conditions for ventilation tests. The size of a fan coil is \(1.0\times 1.0\times 0.25\) m. It has four peripheral supply sections with the size of \(l_{\rm{in}}\times w_{\rm{in}}=0.5\times 0.07\) m equipped with turning blades (louvers) and one central suction section with the size of \(0.5\times 0.5\) m (Figure 1b-d). The supply sections are marked as cardinal points: north, east, south, and west (Figure 1b). The louver at each supply section has five preset angles of inclination, \(\theta\), to the ceiling, namely \(25^{\circ}\), \(30^{\circ}\), \(35^{\circ}\), \(40^{\circ}\), and \(45^{\circ}\). Figure 1d demonstrates the louver inclined at the extreme position of \(45^{\circ}\). In the present study, two values of \(\theta\) were considered, \(\theta=25^{\circ}\) and \(\theta=45^{\circ}\), and it is one of the varied parameters that specifies the list of cases considered, see Table 1. For each fan coil the fan speed could vary in the range up to 300 rpm, the maximum fan speed was set for the present experiments.
# Model-Driven Deep-Learning-Based Underwater Acoustic OTFS Channel Estimation
## 2 UWA OTFS System Model
### Parameter Design of UWA OTFS Communication  
In UWA communication, the multipath effect and Doppler shift are more severe than those in terrestrial radio communication. Table 1 shows a comparison of channel characteristics between terrestrial radio and UWA communications. The propagation speed of UWA waves is significantly slow: five orders of magnitude slower than the speed of radio waves. Moreover, the UWA channel experiences significant distance- and frequency-dependent attenuation, resulting in low frequency and limited bandwidth for long-range communication, typically in the KHz range. As a consequence, in the UWA channel, even minor movements can result in noticeable Doppler shifts.  
In the time-varying UWA channel, assume that the maximum multipath delay is represented by \(\tau_{\max}\), and the maximum Doppler shift is denoted by \(v_{\max}\). The setting of OTFS parameters are influenced by the UWA channel conditions, particularly when considering the characteristics in the DD domain. In the Doppler axis, the maximum supportable Doppler is restricted by the symbol duration, denoted as \(v_{\max}<1/T\). In the delay axis, the maximum supportable multipath delay is restricted by \(1/\Delta f\), as \(\tau_{\max}<1/\Delta f\). In UWA channels, the maximum multipath delay \(\tau_{\max}\) is as large as tens of milliseconds; so, the corresponding designing parameter, \(\Delta f\), needs to be set at a small value. And the corresponding \(T\) is large as \(T=1/\Delta f\).  
An UWA OTFS system has a total bandwidth \(B=M\Delta f\) and a frame duration \(T_{f}=NT\) with \(NM\) subcarriers per frame. Since \(\Delta f\) is set to a small value, the setting of a large value of \(M\) will benefit broadband and high-data-rate communication. Considering the large value of \(T\) and the need of avoiding excessively long frame duration \(T_{f}=NT\) for demodulation latency, the value of \(N\) should not be set to be too large. In order to ensure effective UWA OTFS communication, it is advisable to set a small value of \(N\) and a large value of \(M\) for efficient and reliable communication.
# KQI Performance Evaluation of 3 GPP LBT Priorities for Indoor Unlicensed Coexistence Scenarios
## 3 Evaluation Framework  
To model both technologies as realistically as possible, the well-known simulator ns3 [18] was chosen. For the evaluation, a simulated setup was developed based on ns3, implementing on top a framework for the simultaneous execution of Real Time Video Streaming (RTVS) and FTP services and the multi-layer monitoring of their performance. This tool has an important acceptance within the wireless research field [19] given the fact that relevant entities in the industry have developed its WiFi and LTE modules.  
The scope of ns3 simulations is to include multi-layer interactions of commercial standard deployments. The ns3 Evalvid module [20] was used for the simulation of RTVS service. This module contains a set of tools to generate video traffic based on sender/receiver traces. In this way, it allows the encoding and comparison of the original video versus the received one, from which the differences due to its transit through the network are obtained.  
In the proposed evaluation, a 4K AVC MPEG-4 encoded file (3840 \(\times\) 2160 resolution, 120 fps with 28.3 MB size) containing 20 s of video is sent. To emulate real-time behavior, all frames with an end-to-end delay of more than 150 ms are discarded.  
Additionally, for the modeling of the 3 GPP downlink unlicensed channel, the LAA ns3-module was used [21]. This includes the indoor scenarios and FTP Model I type of traffic as proposed by 3 GPP [22] to evaluate the performance of both systems. The architecture of the proposed simulation is shown in Figure 3.  
For link performance computation, the ns3 LAA module uses the [23] model to extract the Mutual Information Effective SINR Metric (MIESM) as a Link-to-System Mapping (LSM) function, as done in [24]. Finally, MIMO was modeled as a gain of SINR over SISO, as specified in [25] where the statistical gain of several MIMO solutions with respect to SISO is argued.  
The development of the framework adapted the evalvid module code to work through the LBT protocol in the version that supports it. In addition, the LAA module was modified to support the priorities as well as the performance indicators in the following section. This setup allows a novel multi-service end-to-end simulation of a coexistence environment between 3 GPP LBT downlink technology and WiFi.  
Figure 3: Multiservice end-to-end simulation architecture.
## 2 Materials and Methods
### Plant Disease Signature Definition  
A plant disease signature consists of the \(35\times 4=140\) range limits of all the features. An auxiliary tool (DSGen) has been incorporated in the developed application that automatically generates these wide/narrow ranges as well as the corresponding disease signatures. DSGen assists the end users that act as administrators, to define rules for the diagnosis of new diseases. The administrator can overwrite some of these range limits. For example, the number of spots \(N_{s}\) in the training photographs can be found between 15 and 50 (narrow range). The wide range will be arbitrarily defined as (5, 60) according to Equation (4) and Equation (5). If the administrator believes that in the corresponding disease more than 60 spots can appear on the specific plant part, he can modify the wide range (e.g., to (5,100)). The procedure for the definition of a new disease signature is simple. The administrator can analyze a few photographs of plant parts like leaves infected by the same disease. The training photographs that will be analyzed in this stage have to be representative of the test photographs that will be used in real-time, in terms of lesion color and size. The orientation of the lesions is not important. In the experiments carried out for this paper, only 6-8 photographs were used representing a set of 10-50 test photographs of a specific disease. It is obvious that more training photographs will be needed to cover a broader variety of disease symptoms in terms of progression, color and lighting conditions. In any case, using 10% or even 20% of the test photographs for training is a much smaller fraction compared to the 75% or higher that is often used in other classification methods like neural networks [4]. Currently, the developed application estimates all the feature values listed in Table 1. The administrator observes how the feature values vary in the small training set and defines the corresponding narrow and wide ranges or lets the DSGen tool perform this procedure for him. The advantage of defining broad feature ranges is that they will probably cover all the cases of photographs captured under different lighting conditions and different degrees of infection. The drawback of using excessively broad feature ranges is that they will overlap with the corresponding ranges of several other diseases and their discrimination will be difficult. The experimental results show that there is no need for a very precise definition of the wide and narrow range limits.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 7 SWOT Analysis of Quantum Money  
SWOT analysis is a strategic planning tool used to identify and assess the key factors influencing the success or viability of a project, product, or concept. Standing for **S**trengths, **W**eaknesses, **O**pportunities, and Threats, SWOT analysis provides a structured framework that allows for a comprehensive evaluation from both internal and external perspectives [51]. By categorizing these factors, SWOT analysis helps stakeholders recognize potential advantages, mitigate risks, and make informed decisions. Strengths and weaknesses focus on internal aspects, highlighting the inherent benefits and limitations, while opportunities and threats are external, encompassing broader factors like market trends, technological advancements, and competitive pressures.  
The primary goal of SWOT analysis is to support effective decision-making and strategic development by creating a balanced view of current circumstances and future possibilities. By understanding strengths, an organization or team can utilize them to enhance their offering; by acknowledging weaknesses, they can identify areas that may need improvement. Opportunities represent favorable external conditions that could support growth or success, whereas threats pose potential challenges or risks. Taken together, SWOT analysis guides organizations in aligning their resources and strategies to achieve competitive advantage, anticipate changes, and optimize outcomes. Table 2 presents the SWOT analysis for the concept of quantum money.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline
**Strengths** & **Weaknesses** \\ \hline No-cloning theorem provides unmatched security (uncounterfeitability of quantum money) & Sensitive to decoherence and noise (e.g., atmospheric turbulence in free-space transmission) \\ \hline Quantum teleportation prevents duplication during transfer & High costs of developing world-wide quantum infrastructure \\ \hline Decentralized and secure through quantum blockchain & Requires reliable and high-fidelity entanglement distribution \\ \hline Robust against classical and quantum attacks & Challenges of compatibility with existing banking infrastructure \\ \hline Encoded in specific physical quantum systems, making it a tangible form of money with the potential to restrict unrestricted money creation by commercial banks. & Quantum wallets may be costly to produce and could have limited portability compared to traditional digital banking solutions \\ \hline \end{tabular}
\end{table}
Table 2: SWOT analysis of quantum money.
## 3 Results and Discussion
### Total Particle Mass Concentration of the Smoke Particles  
Figure 5 presents the results of total particle mass concentration (_TPMC_) of smoke particles from para rubber wood combustion. In the initial combustion period, _TPMC_ values were found to be 31.25 to 77.42 mg/m\({}^{3}\). After 90 min from initial combustion, the _TPMC_ values reduced to 16.52 to 25.29 microns. In the final period (150 min after initial combustion), the _TPMC_ values reduced to 3.12 to 7.21 microns. The results showed that the increase of time periods had a prime influence on decreasing the _TPMC_ values, synonymous with the _MMAD_ values.  
Figure 4: _MMAD_ emission from the para rubber wood combustion for different wood moisture contents.
## 1 Introduction
The aforementioned wake control techniques are also popular in floating offshore wind turbine applications [30] in which complex wind turbine dynamics and wave induced motions cannot be accounted for by simplified structural dynamic modules. Therefore, coupling aerodynamic options capable of resolving multi-rotor wake interactions in high fidelity structural dynamic modules that accommodate complicated system dynamics and non-linear flexibilities is considered essential. Furthermore, simulating asymmetric yawed flow conditions is directly linked to helicopter aerodynamics. This is the second application example addressed in the present work. More specifically, in the WT cases, the rotor of the conceptual DTU 10 MW reference wind turbine [28] is modeled. Uniform free-stream flow conditions are simulated for wind speeds of 8 and 11 m/s. Yaw misalignment conditions are considered as well, where free-stream flow angle ranges from \(-30^{\circ}\) to \(+30^{\circ}\). Constant rotational speed and blade pitch angle is assumed for all WT cases. Blade loads and elastic displacements results are depicted as azimuthal variations and radial distributions along the blade span. Comparisons are made between a standard BEM model and the classical LL approach under the framework of free vortex wake modeling [31]. WT analyses are usually based on BEM models, as they are fast and reliable in simple flow cases. Therefore, they have been widely employed in design and analysis. For this reason, BEM results are regarded as reference data in WT simulations of this work. When analyzing complex aerodynamic conditions (yaw misalignment or high shear of the atmospheric boundary layer), the LL model consists of a more accurate alternative due to the detailed vortex particle representation of the wake and the inclusion of viscous corrections on blade loads through dynamic stall models. Consequently, LL results will be used as an extra reference in complex free-stream flow conditions, where AL and BEM results are expected to deviate. As for the helicopter simulations, a scaled model of BO105 MR is simulated, for which wind tunnel measurements of loads and deflections are available. In this case, AL and LL predictions will be compared against HeliNOVI wind tunnel measured data [32]. Only forward flight conditions are tested at low (12.3 m/s), medium (50.5 & 50.9 m/s), and high (69.6 & 69.9 m/s) flight conditions. Azimuthal variation of loads and deflections and load harmonics (in terms of amplitude and phase) are compared to measured data. All the AL simulations performed in this work consider inviscid flow. The reason for that is that BEM and LL are inherently inviscid. In this way, a fair comparison can be made between the different aerodynamic models. Even for the Helicopter MR simulations, where measured data have been used as reference, viscosity is not expected to have any significant effect on the results. This is justified by the fact that the experiments have been conducted in a wind tunnel, without the introduction of any turbulence intensity in the free-stream flow.
# Subway Station Capacity Maintained by Optimizing a Maintenance Schedule of Key Equipment
## 6 Simulation Study
### Reliability Modeling of the Key Equipment
#### 6.3.2 Reliability Modeling of the Escalator  
The escalator is a large and complex form of transportation equipment with a mechanical structure as the main body. In terms of its functions, the escalator is divided into eight parts: support structure, escalator step system, guide rail system, handrail drive system, handrail device, safety protection device, electrical control system and automatic lubrication device. In light of the maintenance records of the escalator, the faults of the escalator are divided into two categories: intrinsic faults and faults caused by external environment factors, such as passenger interference and foreign objects intrusion. The intrinsic faults are divided into electrical system faults and mechanical system faults. Electrical  
Figure 12: Goodness of fit test for software faults.  
Figure 13: Goodness of fit test for hardware faults.  
system faults include control system fault, safety switch action, relay defective and etc., which need to reset control system and all kinds of switches. Mechanical system faults include handrail belt fault, motor bearing fault, drive chain fault and etc., which need to be adjusted or replaced. Based on the statistical analysis of fault data, the faults caused by external environment factors account for 34%, which are not considered since they are uncontrollable, and the intrinsic faults account for 66%, among which electrical system faults account for 37% and mechanical system faults account for 29%. These two class of faults can be repaired and are considered in the maintenance schedule optimization. The electrical system and mechanical system fault distribution fitting are done separately.  
For the electrical system faults of the escalator, 29 samples of the fault interval are used. The average fault interval is 94 days, and the standard deviation is 125. The result of goodness of fit test is shown in Figure 14. As is shown in Figure 14, Weibull distribution fits the fault data best, and the Anderson-Darling statistic of Weibull distribution is the smallest. Therefore, the optimal fault distribution of electrical system is Weibull distribution, and the shape parameter is 0.91, the scale parameter is 88.23. For the mechanical system fault of the escalator, 28 samples of the fault interval are used. The average fault interval is 161 days, and the standard deviation is 212. The result of the goodness of fit test is shown in Figure 15. As is shown in Figure 15, Weibull distribution fits the fault data best, and the Anderson-Darling statistic of Weibull distribution is the smallest. Therefore, the optimal fault distribution of the mechanical system is Weibull distribution, and the shape parameter is 0.923, the scale parameter is 153.69.