esa-sceva/satcom-chunk-collection huggingface dataset:
The research concluded that the thermal field homogeneity inside SiC materials can be reduced by using a reduced coefficient of variance, COV\({}_{\text{T}}\) = 0.2636, which is a key indicator of temperature distribution uniformity [10]. [PERSON] et al. studied the thermal runaway of a BaCO\({}_{3}\) + Fe\({}_{2}\)O\({}_{3}\) homogenous mixture in a microwave field. They concluded that the peak temperatures during thermal runaway occurrence are directly connected to the grain size. Also, they reported that, above 700 \({}^{\circ}\)C, the temperature increases with a gradient of 350 \({}^{\circ}\)C/min [11]. Additional research in the field of microwave sintering of ceramics has been performed by [PERSON] et al. as well as [PERSON] et al. [12,13]. Other research related to microwave heating has focused mainly on process stability, rate of conversion and thermal runaway, in order to optimize the MHH process or to improve its stability [14, 15, 16, 17, 18, 19]. This research aims to study the influence of materials' internal defects as precursors for thermal runaway in Barium Ferrite W-type (based on an initial homogenous alloy of BaCO\({}_{3}\) + 9\(\alpha\)-Fe\({}_{2}\)O\({}_{3}\)) materials in a microwave field. The study consists of modeling and simulation of the temperature distribution and microwave heating mechanism and macroscopic analysis of products processed using MHH technology.
## 3 Important Indicator Screening Based on RF Algorithm
### Screening of Important Indicators  
The literature and engineering experience are summarized to provide insights into the factors affecting dust concentration. The factors that commonly influence the level of dust concentration are chosen as input variables to assess the extent of dust concentration pollution. An index system for predicting and evaluating dust concentration is initially established, as depicted in Figure 5.  
Figure 4: RF algorithm schematic diagram.  
Through the random forest (RF) model, the input index (dependent variable) of dust concentration is analyzed to obtain the degree of interpretation of each independent variable (using PM2.5 as an example) to the dependent variable. This process helps to determine the importance of each influencing factor on dust concentration, as shown in Figure 6.  
Taking PM2.5 data as an example, the importance scores of eight influencing factors, including temperature, humidity, noise, stripping amount, wind speed, wind direction, wind force, and rainfall, are 0.88, 0.67, 0.61, 1.43, 0.20, 0.54, 0.08, and 0.41, respectively. The importance is ranked from highest to lowest as follows: stripping amount > temperature > humidity > noise > wind direction > rainfall > wind speed > wind force.  
Because noise is an indicator of the amount of stripping, humidity is highly correlated with rainfall, and most of the time there is no rainfall. Combined with the quantitative calculation results of the RF algorithm, the input parameters (independent variables) for the dust concentration prediction model are finally determined as stripping amount, temperature, humidity, wind direction, and wind speed. The output parameters (dependent variables) mainly include PM2.5, with PM10 used as an auxiliary dependent variable reference. The GA-LSSVM model for predicting dust concentration based on RF index analysis is established [14], as shown in Figure 7.  
Figure 5: The initial index system diagram.  
Figure 6: The importance of RF in evaluating dust concentration index.
## 2 Problem Statement
When considering the relationship among the consecutive predicted RUL at different times, the RUL prediction combining the LSTM network and the Wiener assumption, as described in Equation (2), has two objects now: (1) the error between the predicted RUL and the real RUL can be reduced by optimizing the parameter \(\Theta\) of the LSTM network; (2) the uncertainty of the increment can be controlled by optimizing the parameter \(c\) of the Wiener process. When the historical predicted RUL is considered, the training loss function is transformed into a joint optimization with a new uncertainty control parameter \(c\)  
\[(\Theta^{*},c^{*})=\arg\min_{\Theta,c}\,Loss(\mathbf{x}_{0:t},\mathbf{r}_{0:t} ;\Theta,c), \tag{5}\]  
where the optimization processes of the parameters \(c\) and \(\Theta\) in Equation (5) are mutually dependent. How to jointly optimize \(c\) and \(\Theta\) in the function and construct the loss function of Wiener-LSTM model will be specified in Section 3.2.
# Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study
## 4 Evaluation
### Federated Learning With Varying the Number of Deep Learning Clients  
In a federated learning setup, we vary the number of nodes involved in the distributed deep learning process from 2-12 and repeat the training for 50 epochs. We expect the overall training to take less time given that the workload is shared and that the size of the neural network (i.e., the number of weights) is fairly small to cause significant network delays. However, we do anticipate that more epochs are required to reach the same loss value on the training and test sets due to the federated averaging of the weight updates of the individual local models. An overview of the experiment runtimes in minutes and the number of epochs to converge is depicted in Figure 7.  
The results appear to confirm our assumptions. Whereas the centralized learning converges after around 20 epochs, this value increases with more nodes. The overall training time for 50 epochs goes down as more nodes contribute to the distributed learning process. However, it is clear that the scalability of our solution is non-linear. We posit out that this outcome is partly due to a naive synchronous implementation of the aggregation at the parameter server, and we anticipate improved runtimes with an asynchronous implementation of the federated model averaging algorithm. A similar approach or method akin to model compression will be essential when the number of local deep learning clients increases significantly.  
Another contributing factor to the average epoch time--and hence the model updates in our synchronous implementation--is the heterogeneity in computational resources available on the deep learning clients. If we replace one of the 12 desktop nodes with either the ODROID or the NVIDIA Jetson TX2 embedded boards and their slower ARM processors, our naive synchronous aggregation algorithm must wait for the slowest node. For the Jetson TX2 board, this issue can be mitigated by leveraging the CUDA cores of the board. This accelerates the deep learning by at least a factor of four compared to the ARM processor, as long as the deep learning model fits into the memory of the graphics chip. An asynchronous version of the aggregation model may alleviate the resource heterogeneity problem as well, as long as weight updates against older deep learning models are discarded.  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Reconstruction Error** \\ \hline count & 105,984.0 \\ mean & 4.988194 \(\times\) 10\({}^{-3}\) \\ std & 9.683384 \(\times\) 10\({}^{-3}\) \\ min & 4.865089 \(\times\) 10\({}^{-7}\) \\
25\% & 4.711496 \(\times\) 10\({}^{-5}\) \\
50\% & 1.177916 \(\times\) 10\({}^{-4}\) \\
75\% & 4.335404 \(\times\) 10\({}^{-4}\) \\ max & 1.791145 \(\times\) 10\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistical breakdown of the reconstruction error.
# Evaluation of Three-Dimensional Environmental Hydraulic Modeling in Scour Hole
## 3 Results and Discussion
### Velocity  
Distribution of the time-averaged stream-wise flow is plotted in Figure 6. Flow velocity was faster in the vertically upper part (\(z>\) initial bed elevation) than in the lower part (\(z<\) initial bed elevation) in the scoured hole. In the lower part of upstream scour slope, a reverse and circulating flow was relatively much slower than the primary flow in the channel (Figure 6). The distribution of stream-wise flow velocity caused a larger value of velocity gradient in the turbulent shear layer.  
Figure 5: Numerical results of streamline by OpenFOAM and Ansys Fluent models in comparison with laboratory experimental data. The thick black lines present scoured channel beds.  
Figure 6: Distribution of mean flow velocity in the scoured hole (modified from [18]).  
Additionally, while the above streamline results are needed to show the flow patterns, the velocity profiles are important for analyzing the flow properties. In this study, although the streamline visually presents the circulation zone, the figures cannot give the exact measurement data of that zone ending point. Therefore, in this case, the velocity profile is normally used to distinguish flow separation by determining the final point of backward velocity vectors. The comparison of numerical simulation results from OpenFOAM and Ansys Fluent with the laboratory experiment data is presented in Figure 7.  
In Figure 7, the velocity profiles of the flow at \(x/h_{0}=0\) (means right at the transition point), \(x/h_{0}=2.1\), and \(x/h_{0}=4.2\) are presented. First of all, as can be seen here, the simulation results from OpenFOAM and Ansys Fluent showed a very good agreement in predicting the flow velocity in the scour hole. The difference of their averaged results is only 0.0138 calculated by RMSE value. The velocity value predicted by Ansys Fluent is a bit smaller than that from OpenFOAM. Moreover, both OpenFOAM and Ansys Fluent clearly show that the minus velocity values represent the backward velocity vectors in the region near the wall, which cause the circulation flow. However, results from both of these models show some difference compared to the laboratory experiment data, with the overall RMSE values of 0.04 and 0.07 for OpenFOAM and Ansys Fluent, respectively. For the free stream flow above the fixed bed level (\(z/h_{0}=0\)), the models and experiment data fit well. However, the numerical simulation under-predicted the flow velocity inside the scour hole. Additionally, while the results right at the transition point \(x/h_{0}=0\) (where the channel bed roughness changed abruptly) are quite similar with a logarithmic curve of velocity for all measurements, the difference increases when the flow goes downstream into the scour hole.
# Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay
## 1 Introduction
[22] evaluated the earthquake-induced deformation of geosynthetic reinforced retaining walls by an ANN-based method. [PERSON] et al. [23] predicted the stability of cantilever retaining walls based on an ANN-based approach. [PERSON] et al. [24] adopted the neural network technique to estimate the safety factors of retaining wall under different loading conditions. [PERSON] et al. [25] estimated the deflections of diaphragm walls caused by a deep braced excavation through the ANN model. More recently, [PERSON] et al. [26] reported their work on proposing a probabilistic design procedure of a retaining wall using the ANN method. It is concluded that ANNs were widely applied in the field of geotechnical engineering. However, the application of ANN in predicting the cantilever wall deflection has not yet been investigated. Moreover, the study by [PERSON] et al. [27] also indicated that an ANN-based prediction tool, which is much simpler and cost-efficient, can be developed for easy application in practical engineering. In view of the above limitations, this study is devoted to predicting the cantilever wall deflection in undrained clay based on a machine learning model, namely, an artificial neural network (ANN). First, a database of cantilever wall deflections was generated from rigorously verified finite element models. Then, six important factors were utilized as the input parameters for the proposed ANN model, while the cantilever wall deflection in undrained clay was set as the output parameter. The acceptable performance of the developed ANN model was verified, and the relative importance of various input parameters on the cantilever wall deflection was highlighted. Finally, an explicit mathematical equation as well as a graphical user interface (GUI) tool were established using the developed ANN model to facilitate the prediction of the cantilever wall deflection in undrained clay.
## 3 Proposed Methods
### Obstacle Detection Method Based on Plane Normal Vector (OD-PNV)
Compared with parameter estimation algorithms, such as least squares estimation, the RANSAC algorithm can more accurately estimate the model parameters from superpixel point clouds containing a large number of outliers. After obtaining the local plane model parameters of the superpixel, its normal vector \(\overset{\rightarrow}{\theta}\) can be obtained. Then, the angle \(\theta\) between \(\overset{\rightarrow}{\theta}\) and the positive direction of the \(y\)-axis can be expressed as:  
\[\theta=\left|\arccos\left(\frac{b}{\sqrt{a^{2}+b^{2}+c^{2}}}\right)\right|. \tag{4}\]  
Among them, \(\theta\in(0,\pi/2)\). In this paper, the Sigmoid function is used to convert \(\theta\) into probabilistic form. First, normalize \(\theta\) as follows:  
\[DV_{PNV}=\left(\frac{\theta}{\pi/2}-0.5\right)*10. \tag{5}\]  
Then, the Sigmoid function is built as follows:  
\[PR_{PNV}=\frac{1}{1+e^{-DV_{PNV}}}. \tag{6}\]  
\(PR_{PNV}\) is approximately between 0 and 1, which is used here to indicate the probability that the superpixel belongs to an obstacle. Figure 5a shows the probabilistic distribution of an image in the KITTI dataset represented by \(PR_{PNV}\). The redder the color, the greater the \(PR_{PNV}\) value, i.e., the higher the probability that the superpixel belongs to an obstacle. In order to classify the superpixel, the decision threshold is set to 0.1, i.e., superpixels with \(PR_{PNV}>0.1\) are classified as obstacles, otherwise they are classified as free spaces. Figure 5b shows the result after classification. The red area in Figure 5b represents the area classified as an obstacle. As can be seen from the figure, although only a simple threshold segmentation has been conducted, due to the obvious difference between the \(PR_{PNV}\) values of the two classes, a good classification effect has been achieved. The classification result of the OD-PNV method and the \(PR_{PNV}\) values obtained in this section will be used in Sections 3.3 and 3.4, respectively. As it is difficult for stereo matching to provide accurate depth information for each pixel, the size of the point cloud (the number of points in the point cloud) may be too small in some superpixels. To fit a reliable local plane model, the size of the point cloud must be large enough, so the superpixels with a point cloud too small in size need to be filtered out before fitting. Assuming that the point cloud size of a superpixel is \(N\), if \(N\) is less than the threshold \(TH_{N}\), it is considered that the current superpixel point cloud size is too small to provide a judgment basis for the OD-PNV method, so the probability that the superpixel belongs to an obstacle is set to 0.5.
# Using Coherent Hemodynamic Spectroscopy Model to Investigate Cardiac Arrest
## 2 Methodology
### CBF and CMRO\({}_{2}\) Calculation  
The absolute values of cerebral blood flow (_CBF_) and the cerebral metabolic rate of oxygen (_CMRO\({}_{2}\)_) were calculated using the non-linear version of the Coherent Hemodynamic Spectroscopy (CHS) model [2]. This approach integrates experimental data obtained from hNIRS and LDF, allowing for accurate assessment during significant physiological perturbations, such as cardiac arrest and CPR [3].  
_CBF_ was derived from the model's fitted capillary blood flow factor (_cBFf_) and the cerebral blood flow index (_CBFi_) measured by LDF. The velocity of capillary blood flow (\(c^{(c)}(t)\)) was calculated as  
\[c^{(c)}\ (t)\Big{(}\frac{\mathrm{mm}}{\mathrm{s}}\Big{)}=cBFf\Big{(}\frac{ \mathrm{mm}}{\mathrm{s}}\Big{)}\times CBFi(t). \tag{7}\]  
Using the capillary cross-sectional area (\(Cs^{(c)}\)) (taken from model's outputs of capillary volume fraction and capillary length), and the brain tissue density (1.081 g/mL), the absolute _CBF_ per 100 g of brain tissue was computed as in [5; 6; 7; 8; 9], as follows:  
\[CBF(t)\Big{(}\frac{\mathrm{mL\ \text{of blood}}}{\mathrm{100\ g\ tissue\ min}} \Big{)}= \tag{8}\] \[55\Big{(}\frac{\mathrm{mL\ \text{/mm}}}{\mathrm{100\ g\ tissue\ min}} \Big{)}\times C^{(c)}\big{(}\mathrm{cm^{2}}\big{)}\times c^{(c)}(t)\big{(} \frac{\mathrm{mm}}{\mathrm{s}}\big{)},\]  
so that at \(c^{(c)}\) = 5 mm/s and \(Cs^{(c)}=20\ \big{(}\mathrm{cm^{2}}\big{)}\), _CBF_ = 55 mL of blood per 100 g of the brain tissue per minute. This value falls within accepted ranges reported in similar studies [10; 11; 12], supporting its validity.  
_CMRO\({}_{2}\)_ was calculated using the equation called \"Fick's principle\" [13; 14]:  
\[CMRO_{2}\ (t)\Big{(}\frac{\mathrm{mL\ \text{O}}_{2}}{\mathrm{s\ tissue\ min}} \Big{)}= \tag{9}\] \[k\Big{(}\frac{\mathrm{mL\ \text{O}}_{2}}{\mathrm{s\ }Hb}\Big{)}\times[tHb] \big{(}\frac{\delta}{\gamma}\big{)}\times CBF(t)\Big{(}\frac{\mathrm{mL\ \text{blood}}}{\mathrm{100\ g\ tissue\ min}} \Big{)}\times\Big{(}S^{(a)}(t)-\Big{\langle}S^{(v)}\Big{\rangle}(t)\Big{)},\]  
where \(S^{(a)}(t)\) and \(\langle S^{(v)}\rangle(t)\) represent the arterial and venous oxygen saturations, respectively, and \(k\) is a factor describing the amount of \(O_{2}\) bound to hemoglobin when completely saturated (1.39 mL of \(O_{2}\) per g Hb). _CMRO\({}_{2}\)_ is expressed in the unit of ml \(O_{2}\)/100 g/min.  
Venule oxygen saturation was calculated using the CHS model [1]. To do so, the non-linear version of this model was used [2; 3]. The results were compared with the equations suggested by the authors [13], as follows:  
\[\langle S^{(v)}\rangle(t)=\frac{4}{3}StO_{2}-\frac{1}{3}S^{(a)}(t). \tag{10}\]  
where \(S_{t}O_{2}\) is the tissue oxygen saturation.
## Case Study and Results
### Results and Analysis  
As shown in Figure 7, the peak charging power was further reduced. Furthermore, the output power of the BESS was obviously decreased with the optimized charging load. In Figure 6a, the quantity of charging equipment was extremely reduced through the fast-charging mode during the operating hours and the normal charging at night, from seven sticks to four sticks, and the peak charging power was 912 kW. The difference of the optimization compared to the original charging load will be compared in the next section. In the case combined with the configuration of the BESS, the amount of charging sticks working synchronously was reduced to three, and the peak charging power decreased to 684 kW. The charging and discharging power of the BESS were lower with the optimized charging load. Therefore, the energy cost of the electrical energy conversion will be reduced.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Basic capacity cost of the transformer & 32 \\ (CNY/(kVA-month)) & 1500 \\ Battery cost (CNY/kWh) & 500 \\ Converter cost (CNY/kW) & 500 \\ BESS lifetime (Year) & 8 \\ Discount rate (\%) & 3 \\ SOC range of the BESS (\%) & 20–90 \\ Efficiency of charge–discharge of the BESS (\%) & 95 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Parameters of the case. SOC—state of charge.  
Figure 6: Schematic figure of the battery energy storage system (BESS) configuration.  
Figure 7: _Cont._As shown in Table 11, the total cost including the costs (converted to each single day) of the batteries and converters, was significantly decreased. In particular, the cost of the optimized charging load with the configuration of BESS was the lowest. The original charging load using BESS to shave the peak load could reduce the electricity cost, but the energy capacity of BESS was more than one megawatt. The investment may be more than 2.5 million CNY. According to the peak charging power goal, the investment of BESS may be higher.  
Figure 1 and Figure 2 separately summarize the costs of the BESS configuration with original charging load when the peak charging power is reduced to 912 kW and 684 kW.  
Figure 7: Charging load and discharging power of BESS. (**a**) The output power of BESS with the original charging load decreased to 912 kW; (**b**) the output power of BESS with the original charging load decreased to 684 kW, and (**c**) the output power of BESS with the optimized charging load decreased to 684 kW.
# Data Rate Selection Strategies for Periodic Transmission of Safety Messages in VANET
## 4 Simulation Results
### Comparison of Channel Congestion  
In this section, we compare the average CBR obtained using the proposed DRCC approach, with constant bitrates of 3, 6, 9, 18, and 24 Mbps, with two existing data rate-based congestion control algorithms (data rate-decentralized congestion control (DR-DCC) [58] and transmission data rate control (TDRC) [57]) available in the literature.  
Figure 3 shows the average CBR experienced by the vehicles using DRCC1, DRCC5, and the constant bitrates. As expected, the highest and lowest CBR values are always obtained with bitrates of 3 Mbps and 24 Mbps, respectively. For low channel loads, DRCC5 maintains the same CBR as 3 Mbps, while the CBR for DRCC1 is slightly lower, but still higher than the 6 Mbps case. This makes sense because for low loads, further lowering of CBR is not beneficial, so DRCC tries to achieve a better packet reception. As the channel load increases, the CBR, when using low bitrates, reaches unacceptable levels (over 0.8), and the performance of DRCC approaches that of the higher constant bitrates. Therefore, DRCC is able to adapt to keep the CBR within the acceptable levels.  
Figure 4 shows the average CBR for DRCC1 and DRCC5, compared to the two existing approaches, under different channel loads. With 256-byte packets sent with a beacon rate  
Figure 1: Comparison of CBR for the five DRCC variations.  
Figure 2: Comparison of received BSMs for the five DRCC variations.  
of 10 BSMs per second, TDRC has very low CBR values (less than 0.2), while the other approaches have a CBR in the 0.4-0.5 range. We can see that TDRC has the lowest CBR (less than 0.2) when channel load is very light, but as the channel load increases, TDRC yields unacceptable CBR levels, consistently above 0.8. This is likely due to the fact that TDRC uses lower bitrates as CBR increases, which may exacerbate congestion. The performances of DRCC5 and DR-DCC are rather close, and DRCC1 has the lowest CBR values for medium to high channel loads, which remains below 0.6 even for the highest loads.