esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Building CFD Model Design Process Based on BIM
## 4 Validation through Comparison with Commercial CFD Tool
### CFD Simulation Condition for Validation  
Validation was conducted by comparing the analysis results using a commercial CFD SW with the CFD analysis results using the proposed method. A solution from ANSYS was used as the commercial CFD SW, and the CFD analysis using the proposed method utilized OpenFOAM. For each of the four targets, the modeling and simulation results using ANSYS were compared with the results of simulating the CFD model created using the method described in this study through OpenFOAM. For the simulation using ANSYS, only the geometry model was extracted and entered as an OBJ file from the BIM model to be analyzed, similar to conventional methods. The input OBJ file was used to design the CFD model and perform the simulation. Hexahedral grids were created with the same grid size for both conditions. The size of the grid was set as the optimal resolution by the characteristic length profered in a study by [PERSON] et al. (2018). The optimal grid resolution was 24 because the characteristic length of all four targets was 0.7 or higher. A grid size corresponding to grid resolution 24 was calculated and applied to create the grid model for each target.  
The simulation results were compared by evaluating the temperature and flow velocity inside the building. The operation settings for validation were the same for both conditions. Steady-state simulations were performed for incompressible gas, and thermal analysis was performed to evaluate the temperature. The buoyantSimpleFoam was employed to simulate, which is an OpenFOAM solver capable of thermal analysis. The Standard \(k\)-\(\epsilon\) model was employed as the turbulence model for both conditions. The boundary conditions were set as Inlet, Outlet, and Wall for the inlets, outlets, and other walls of each target facility, respectively. The turbulence kinetic energy (\(k\)) and turbulence dissipation rate (\(\epsilon\)) were calculated using inflow wind velocity and turbulence intensity, and the same values were applied for both conditions. The boundary condition of validation model is described in Table 4.  
Figure 3: Target geometry model for validation.
## 4 Results
### RQ6: Issues in WSN and IoT Frameworks
#### 4.6.5 Quality of Service  
Quality of Service (QoS) manages networks and resources to strengthen IoT connectivity. QoS manages delay, jitter, reliability, and bandwidth by classifying network traffic. It plays an important role in optimizing systems. Quality of Service means that energy efficiency, reliability, bit error rate, and latency should be good enough to capture data over a network. Therefore, it is classified in two ways: program-specific and network-specific. The QoS perspective of the network refers to the effective management of network resources and transmission performance, while the perspective of the program refers to mobility, time synchronization, and sampling parameters. Similarly, many algorithms have been developed to distribute heavy traffic evenly, and the energy consumption load in a network uses a cluster-head approach to achieve high performance and efficiency [91].
# Statistical Power Analysis in Reliability Demonstration Testing: The Probability of Test Success
## 1 Introduction
### Research Gaps  
Based on the preceding discussion, it can be concluded that more work remains to be carried out for the optimization of individual EoL and SR test strategies. In existing approaches, the focus is often on one target variable for a single test type. A metric and a procedure for planning reliability demonstration tests with consideration of all available test types and boundary conditions in the required frequentist manner does not yet exist. The research gaps can be thus summarizes as follows:  
* A metric for objectively assessing reliability tests based solely on their ability to demonstrate the frequentist reliability target must be established;
* A holistic approach to assessing all possible reliability tests needs to be developed;
* A procedure for efficient reliability demonstration test planning considering all possible reliability tests needs to be worked out;
* The calculation effort involved in reliability test planning needs to be reduced. These gaps are addressed and sequentially worked out in the rest of this manuscript.
## 4 Result
### RMS Delay Spread  
The RMS delay spreads for an omnidirectional and a bidirectional antenna are shown in Figure 8. For the omnidirectional antenna setup, we observed the average RMS delay spread as 177.69-212.85 ns for the rural, 198.13 ns for crossing #3, and 413.98 ns for crossing #4 with an exponential distribution. For the bidirectional antenna setup, we observed the average RMS delay spread as 93.94-137.52 ns for the rural, 126.25 ns for crossing #3, and 253.04 ns for crossing #4 with an exponential distribution. From the observations, crossing #4 is more outlying than the other crossings. We assume this behavior is due to a higher population of reflecting sources near the crossing, mainly buildings. Similar to our observation for the path loss exponent for the different antenna types, the average RMS delay spreads for the bidirectional antenna were lower than for the omnidirectional antenna setup due to the concentrated beamwidth.  
The omnidirectional antenna values are higher than any values reported in [8] or [9]; however, the Urban LoS reported in [10] is closer to our RMS delay spread for crossing #1, #2, #3, and #5. Our bidirectional antenna results for crossing #1, #2, #3, and #5 fall within the suburban street crossing category of [8] and the highway LoS category of [10].
## 4 Buried Electrode Scheme Design and Performance Analysis
### Number of Ground Electrodes  
Increasing the number of ground electrodes forms an antenna array. The working principle of the antenna array can be regarded as the superposition of electromagnetic waves (electromagnetic fields). For several columns of electromagnetic waves, when they are transmitted to the same area, according to the principle of superposition, the electromagnetic waves will produce vector superposition. The superposition result is not only related to the amplitude of each column of electromagnetic waves, but also to the phase difference between them in the encounter interval.  
For an electric dipole whose shape, size, and surrounding environment are determined, its contact resistance can be generally expressed as follows:  
\[R=\frac{1}{\sigma}\cdot f \tag{7}\]  
In Formula (7): the function is determined by the shape \(f\) and size of the electrode rod, \(1/\text{m}\); \(\sigma\) is the soil conductivity, S/m.  
Therefore, the contact impedance of the electric dipole antenna is related to its shape and size and the conductivity of the surrounding earth. The contact resistance can be reduced by increasing the conductivity of the surrounding soil and changing the shape and size of the electric dipole. However, it is difficult for humans to change the conductivity of the earth for a long time and on a large scale. Therefore, to change the size of the contact resistance, the shape function of the ground electrode needs to be changed \(f\).  
For a horizontally arranged cylindrical electrode with a length of \(L\), a diameter of \(d\), and a buried depth of \(h\), its grounding impedance can be expressed by the potential midpoint method. Its model is shown in Formula (8):  
\[R=\frac{1}{\sigma}\cdot\frac{1}{\pi L}\ln\frac{2L}{\sqrt{dh}} \tag{8}\]  
For electrodes buried on the surface, when the buried depth \(h\) is negligible, the model expression can be simplified to Equation (9):  
\[R=\frac{1}{\sigma}\cdot\frac{1}{\pi L}\ln\frac{2L}{d} \tag{9}\]  
Multiple electrodes can be used in parallel to increase the contact area between the electrodes and the ground, thereby reducing the ground impedance of the electric dipole antenna. For multiple electrodes in parallel, the grounding impedance can be expressed as:  
\[R_{d}=\frac{R_{L}}{n\eta} \tag{10}\]  
Formula (10):  
\(R_{L}\) is the grounding impedance of a single electrode, \(\Omega\);  
\(n\) is the number of grounded clicks; and  
\(\eta\) is the utilization factor of the grounding body, which is related to the shape, quantity, and position of the grounding electrode.  
It can be seen that with the increase in the number of parallel electrodes, the contact area between the electrodes and the ground increases, and the ground impedance of the electric dipole antenna decreases.
# Study on the Influence of Collision Scene on the Energy Dissipation Process for Train Collision
## 1 Introduction
After adding the energy-absorbing structure of the coupler, the acceleration of the vehicle is reduced by 12%. [PERSON] et al. [11] established a simplified one-dimensional elastic model to study the influence of elastic waves on the energy absorption and dynamic characteristics of the train. [PERSON] [12] carried out dynamic simulation calculations on 15 energy configuration schemes of a subway train (4 units) based on the collision dynamics program of train longitudinal. The energy utilization rate and the maximum average acceleration were used as evaluation indicators to determine the optimal energy configuration scheme. [PERSON] et al. [13] established a multi-body dynamics model of a high-speed train (8 units), and the influence of energy-absorbing components with various platform force configurations on train dynamic response under collision scenarios specified in EN 15227 was performed. Regarding train collision tests, Most existing train or vehicle collision tests are based on collision scenario 1 of EN 15227, such as the train collision test conducted in the United States during a train crashworthiness investigation [14]. [PERSON] et al. [15] constructed a 1/8 scale longitudinal train model and analyzed the train collision test to achieve the energy dissipation law. It's discovered that energy absorption in moving trains is greater than in stationary trains. On 4 March 2021, according to the EN 15227 and the Chinese railway standard, CRRC Changchun Railway Passenger Car Co., Ltd. carried out the collision test of a moving EMU train (8 units) with a speed of 36 km/h colliding with the same stationary train. The existing train collision accidents include collisions between moving trains and stationary trains, train frontal collisions, train rear-end collisions, etc. Nevertheless, researchers pay more attention to the scene of a moving train colliding with a stationary train and less to the collision scenes such as train rear-end collisions and train head-on collisions. Therefore, based on the analysis of collision scenarios in existing train crashworthiness standards, four typical train collision scenarios were proposed in this study, and the longitudinal collision dynamics models of trains under various collision scenarios were established; the accuracy of the model was verified by the collision test of moving train on the stationary train. The concept of collision energy dissipation rate was presented to characterize the intensity of train collision energy dissipation, and the energy dissipation process and acceleration variation law under different collision scenarios were investigated by the dynamics models. On this basis, the collision energy dissipation rate, energy absorption and maximum average acceleration were taken as the evaluation indexes, and the train collision safety under four typical collision scenarios was discussed, which provides the basis for subsequent structure parameters design of train multi-level crashworthiness.
## 1 Introduction
This study analyzed the accuracy of measured values by number and location of displacement sensors installed on a mobile device to measure rail surface conditions, such as corrugation generated by the contact of wheel and rail, as shown in Figure 1. It then verified measurement accuracy by analyzing the sensitivity for different numbers of sensors and testing the railway operating track.
# Internet of Spacecraft for Multi-Planetary Defense and Prosperity
## 4 Planetary Defense: State-of-the-Art
### Origins of Asteroids and Comets
#### 4.1.2 The Kuiper Belt
It was discovered by the Pan-STARRS telescope located at Haleakala Observatory in Hawaii on 19 October 2017 when it was 0.22 AU from Earth and already passed its closest point to the Sun approximately 40 days later [134]. It was first classified as comet C/2017 U1 but reclassified again as asteroid A/2017 U1 due to the absence of a comet coma. Eventually, it was identified as 11/2017 U1 because of its eligibility as an interstellar object. The authors conducted observation and verified a strongly hyperbolic trajectory in [135]. It has a hyperbolic excess velocity of 26.33 km/s (94,800 km/h), the speed relative to the Sun when traveling in interstellar space. In addition, there has been interesting discussion about its origin [136].
# Automatic Selective Encryption of DICOM Images
## 3 The Proposed Approach
### Approach I: Multi-Region Selective Encryption Approach for Mammography DICOM Images
#### 3.1.5 Managing Segmentation Map  
After segmenting DICOM images, the key to reconstructing the encrypted image is called a segmentation map, which is a matrix that has the same size as the encrypted image and its element represents the region/pixel membership of that element. In the proposed approach, the segmentation map will be appended with the confidential DICOM profile tags. Then, the combined data are compressed using LZ4 as an efficient compression algorithm which can also provide integrity of the compressed data, as it provides a checksum mechanism. It is believed that the combination of the segmentation map with the header will not increase the header encryption time overhead as LZ4 can significantly reduce the size of the segmentation map due to its redundant nature. Finally, the encrypted compressed data (the segmentation map and confidential DICOM profile tags) are stored within the Encrypted Attributes Sequence (0400, 0500).
## 2 Methodology
### MHA-ConvLSTM Combined Prediction Model
#### 2.2.2 Convolutional Long- and Short-Term Memory Neural Network (ConvLSTM)  
LSTM and CNN are two commonly used neural network models; each of them is good at extracting different types of features. LSTM is mainly used to extract features between adjacent sequence data, so it is effective in dealing with time series prediction problems. CNN, on the other hand, is able to extract effective bias features as feature vectors and obtain more structural information. In order to fully utilize the advantages of both models, a new model, ConvLSTM, is proposed.  
ConvLSTM combines the advantages of LSTM and CNN and is able to extract spatio-temporal features simultaneously instead of a single temporal feature. Unlike the traditional single-step spatio-temporal prediction, ConvLSTM uses a multi-step spatio-temporal prediction and can predict future data trends more accurately. Therefore, this paper adopts the ConvLSTM model to mine the hidden local features and spatio-temporal features of the data. The specific formula of ConvLSTM is shown in the following Equations (5)-(9):  
\[Z^{t,l}=\sigma\Bigg{(}\widetilde{W}_{XZ}^{l}*X^{t,l}+\widetilde{W}_{HZ}^{l}*H^ {t-1,l}+\widetilde{W}_{CZ}^{l}\circ C^{t-1,l}+\widetilde{b_{Z}^{l}}\Bigg{)} \tag{5}\]  
\[r^{t,l}=\sigma\Bigg{(}\widetilde{W}_{XR}^{l}*X^{t,l}+\widetilde{W}_{HR}^{l}*H^ {t-1,l}+\widetilde{W}_{CR}^{l}\circ C^{t-1,l}+\widetilde{b_{R}^{l}}\Bigg{)} \tag{6}\]  
\[C^{t,l}=z^{t,l}\circ\tanh\Bigg{(}\widetilde{W}_{XC}^{l}*X^{t,l}+\widetilde{W}_ {HC}^{l}*H^{t-1,l}+\widetilde{b_{C}^{l}}\Bigg{)}+r^{t,l}\circ C^{t-1,l} \tag{7}\]  
\[o^{t,l}=\sigma\Bigg{(}\widetilde{W}_{XO}^{l}*X^{t,l}+\widetilde{W}_{HO}^{l}*H^ {t-1,l}+\widetilde{W}_{CO}^{l}\circ C^{t,l}+\widetilde{b_{O}^{l}}\Bigg{)} \tag{8}\]  
\[H^{t,l}=o^{t,l}\circ\tanh\Big{(}C^{t,l}\Big{)} \tag{9}\]  
where \(*\) denotes the convolution operator, \(\circ\) denotes the Hadmard function, and \(\sigma\) denotes the Sigmoid function, where \(\widetilde{W}_{XZ}^{l}\), \(\widetilde{W}_{HZ}^{l}\), \(\widetilde{W}_{CZ}^{l}\), \(\widetilde{W}_{XR}^{l}\), \(\widetilde{W}_{HR}^{l}\), \(\widetilde{W}_{CR}^{l}\), \(\widetilde{W}_{XC}^{l}\), \(\widetilde{W}_{HC}^{l}\), \(\widetilde{W}_{XO}^{l}\), \(\widetilde{W}_{HO}^{l}\), and \(\widetilde{W}_{CO}^{l}\) are the bias parameters of the convolutional long- and short-term neural network in the \(l\) layer.
## 3 Methodology
### Calculation of a Long-Term Water Probability Mask  
The filling of data gaps in all monthly files is performed by using a long-term water probability mask. Therefore, all available monthly water masks between January 1984 and June 2018 are used. In the best case, 414 monthly land-water masks are available. In our study case of Lake Ray Roberts 359 of 414 (86.7%) monthly land-water masks with data gaps are available.  
The procedure for computing the long-term water probability mask is the following: First, all water pixels of all monthly masks are accumulated (Figure 9(a)). Secondly, all water and land pixels of all monthly masks are accumulated (Figure 9(b)). Then, a long-term water probability mask (Figure 9(c)) is computed by dividing the accumulated water pixels (Figure 9(a)) by the accumulated water and land pixels (Figure10(b)). The water probability mask can still contain smaller surrounding water bodies which are not connected to the water body of interest and therefore cannot be used for the step of filling the data gaps. For finding the area of interest a simple connected-component labeling algorithm [35] was applied to find all connected water pixels. Figure 9(d) shows the 10322 labeled water bodies. Finally, we use the reference location from the initialization step to find the water body of interest (Figure 9(e)) which is stored as binary mask. Then, we merge the long-term water probability mask (Figure 9(c)) and the area of interest mask (Figure 9(e)). Also, we compute a dependency function between water probability and area extent shown in Figure 9(f). This function is derived directly from the long-term water probability mask. For each existing water probability value, the surface area of all pixels that have a higher probability is computed in order to create the dependency function shown in Figure 9(f). Both are used in the next step of filling the data gaps in monthly masks. For Lake Ray Roberts, the maximum water probability is 93.9% within the period between January 1984 and June 2018. The reason is that a dam was constructed in 1987 and filled up until 1990. Therefore, no pixel has a maximum probability of 100% which is not an error in the water classification step.  
Figure 9: Cont.
## 3 System Model and Parameterization
### Operation Cycle of SCN
#### 3.3.1 Normal State  
During the normal operation of the SCN, all edges in the network perform normally. Thus, all the PMUs successfully communicate the synchrophasor data to the corresponding PDC. Consequently, the \(PDR\) at the PDC corresponding to the PMU remains equal to 1. As an example, using Equation (1), for the \(PDR\) at node \(B\) corresponding to node \(A\), we have, \(\sum\limits_{t=t_{B0}}^{t_{B0}}\alpha_{t}^{B}=\sum\limits_{t=t_{A0}}^{t_{B0}} \beta_{t}^{A}\) and thus \(PDR_{B-A}=1\).
## 3 Results
### Setup Specifications
#### 3.1.2 Pattern Update Rate  
To demonstrate the pattern update rate, i.e., the frame rate of the modulator, we scanned a single focus over a field of view of (1.5 \(\times\) 1.5) mm\({}^{2}\). We observed the moving focus using a Phantom v1610 high speed camera. The column-wise scan pattern is illustrated in Figure 5(a). Figure 5(b) shows the time required to scan to the respective point on the sample. All of the points from (a) are indicated by color. Scanning all 961 points of the raster took 568 ms, which results a frame rate of the modulator of 1.7 kHz. Therefore, our setup achieves a temporal resolution of 0.59 ms.
## 4 Experiments and Results
### Comprehensive analysis of model performance  
According to the data table and experimental results, the improvement of ARFM is quite excellent compared with other models. It can be seen intuitively that ARFM keep a steady increase in both loss value and final prediction accuracy. In particular, we need to add an explanation about time consumption in this part. Due to equipment processing and dataset fluctuation, the time of model training and prediction has a large fluctuation. On average, however, the ARFM model performed only slightly better than the DeepCrossing model. We analyzed that this was probably due to the aging mechanism, namely, the use of the Aging Attention Layer, and the optimization of our dataset became particularly important. This is most obvious in the 10 m Movielens dataset, so we can infer that the performance advantage of the ARFM model is more obvious in the larger dataset. In the next stage, we will focus on the time complexity of the model and the sensitivity of the model to datasets of different sizes.
## 4 Discussions
### Optimization of the Fermentation Process  
Fermentation media can affect the production of antioxidants [39]. Rice was chosen as the fermentation media, based on results shown in Figure 1D, which exhibited significant differences at the probability level of \(p<0.05\) in the effectiveness of antioxidant compounds of different media. The results revealed that the antioxidant activity of _A. flavus_ grown on rice media had an efficacy rate of 74.9%, which was considerably higher than the other substrates tested, making it the most suitable media for fermentation. In contrast, the isolation revealed a significant decrease in the effectiveness of antioxidant compounds on the media of wheat bran with an efficacy rate of only 15.2%. The obtained results were in agreement with [PERSON] et al. [39], in which rice media was used to produce antioxidant compounds from molds. This also suggests the possibility of using other plant-based media such as peas for exploring the possibility of producing antioxidants, given that pea protein has increased antioxidant activity of pH-adjusted canola oil nanoemulsions [41].  
The pH value is known to have a significant effect on antioxidants produced during fermentation. Figure 1B shows significant differences (\(p<0.05\)) in the production of antioxidant compounds for all the pH values investigated. The highest effectiveness was obtained at pH 7, with an efficacy rate of 74.1%. The lowest value was observed at pH 12, with an efficacy rate of only 1.1%. This low efficacy rate was due to a lack of growth of the fungus at this high pH and thus reduced the antioxidant efficiency of the ethyl acetate extract. Since pH 7 showed the highest efficacy rate, it was used to further investigate the effect of temperature on the production of antioxidants in fermented rice koji inoculated with _A. flavus_.  
Another factor known to have a significant impact on the production of antioxidants is temperature. Figure 1C shows significant differences (\(p<0.05\)) in the production of antioxidant compounds at different temperatures. The lowest efficacy rate was observed at an incubation temperature of 20 \({}^{\circ}\)C (35.2%), while the antioxidant activity was highest at an incubation temperature of 30 \({}^{\circ}\)C (75.1%). The effectiveness of the antioxidant compounds decreased significantly at 40 \({}^{\circ}\)C, with an efficacy rate of 13.2%. These observations are in agreement with [PERSON] et al. [42]. It has been reported that molds had the ability to produce antioxidant compounds at 30 \({}^{\circ}\)C [43], while the antioxidant activity decreased at both lower and higher temperatures. Moreover, future optimization of fermentation will be performed at pH 7 at 30 \({}^{\circ}\)C.  
Furthermore, the following optimized growth conditions were chosen to further characterize antioxidants produced from rice koji inoculated with _A. flavus_: Incubation for 15 days at 30 \({}^{\circ}\)C grown on rice medium at pH 7. The overall optimized methodology is summarized in Figure 3.