esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network
## 3 Measurement Process
### Thermal Environment Model Based on an RVFLNN for a Metro Station
#### 3.3.3 Build the Model  
We established three neural network models. Every neural network step was the same.  
The processes were carried out in the following steps:  
**Step 1:** Divide training set, validation set, and test set.  
The data were measured over three days--Sunday, Monday, and Tuesday. The total number of sample points is 2160. We used the first 1440 sample points as the training set, the next 360 sample points as the validation set, and the last 360 sample points as the test set.  
**Step 2:** Set the number of neural network nodes.  
From Figure 5, the input layer consists of 4, 5, and 4 neurons for \(T_{2}\), \(T_{3}\), and \(T_{4}\), respectively. The output layer has 1 neuron. The hidden layer was initially set to 20 hidden neurons. The hidden layer used sigmoid as the transfer function.  
**Step 3:** Parameter initialization.  
We set the regularization coefficient, \(\lambda\), and randomly initialized \(w\) and \(b\) so that their mean value was 0 and the variance was 1. The range was [\(-\)1, 1].  
**Step 4:** Training the RVFLNN.  
The training data were input into the RVFLNN for training to get \(\beta\). Then, the predicted data can be obtained from \(\beta\).  
**Step 5:** Training error and validation error evaluation and parameter adjustment.  
The average relative error of the training data was calculated to evaluate the model accuracy. We adjusted the number of nodes in the hidden layer by comparing the training error and the validation error. When the training error continued to decrease and the validation error no longer decreased, but increased--that is, before overfitting--the network learning was stopped and the number of hidden layer nodes at this time was determined. Then, we minimized the training error by changing the regularization coefficient and the range of \(w\) and \(b\). In the meantime, we stopped the algorithm.  
**Step 6:** Test the RVFLNN performance.  
The test data were used to evaluate the model performance based on the trained RVFLNN.
## 5 Evaluation of Material Factors and Performance Indicators by One-Way Standard Deviation and GRA
### One-Way Standard Deviation Analysis  
Performance data from single-factor tests at varying levels were used to calculate the standard deviations of the four performance indicators: setting time, flowability, bleeding rate, and compressive strength. Specifically, among the 18 ratio groups, Groups 1-6 were used for evaluating the effect of the zeolite powder-GGBFS ratio; Groups 4 and 7-10 were used for the CL dosage; Groups 8 and 11-14 were used for the NaOH dosage; and Groups 8 and 15-18 were used for the water-binder ratio. Standard deviation was used to assess the influence of different material factors on the performance indicators. The results are presented in Figure 9. The magnitude of the standard deviation provides an intuitive representation of the degree of impact, in contrast to the less intuitive nature of judging from the variance value [49; 50]. When considering a single factor, it simplifies the analysis by avoiding the complexities of multi-factor analysis, allowing the research to remain focused.  
\[S=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{\left({X_{i}-\overline{X}}\right)^{2 }}}} \tag{1}\]  
where \(X_{i}\) and \(\overline{X}\) are the sample values and sample mean values of the performance indicators obtained by testing the single factor at different levels, respectively; \(n\) is the number of different factor levels.  
Figure 9: Degree of influence of different factors on performance: (**a**) setting time; (**b**) flowability; (**c**) bleeding rate and correction rate; (**d**) compressive strength.  
As shown in Figure 9, the dosage of CL was the primary factor influencing the initial setting time of the slurry. The dosage of NaOH was a key factor in determining the final setting time. The water-binder ratio was the most significant parameter affecting flowability. The bleeding rate and concretion rate were primarily influenced by the water-binder ratio, followed by the dosage ratio of CL and zeolite powder to GGBFS. Regarding the compressive strength at 7, 14, and 28 days, the water-binder ratio was the most important factor, followed by the dosage of NaOH.
# Surround by Sound: A Review of Spatial Audio Recording and Reproduction
## 3 Soundfield Recording and Reproduction
### 3D Soundfield Recording
#### 3.2.1 3D Microphone Array  
The Ambisonic microphone was firstly designed by Dr. [PERSON] at Nimbus Records (Monmouth, England) for recording the first-order Spherical Harmonics decomposition of a soundfield, i.e., B-format. The original design, known as a native or Nimbus/Halliday microphone array, has three coincident microphones, i.e., an omnidirectional microphone, one forward-facing, and one left-facing figure of eight microphone, to record the \(W\), \(X\), and \(Y\) components separately. Since it is impossible to build a perfectly coincident microphone array, [PERSON] developed an improved version, the tetrahedral microphone, which has four cardioid or sub-cardioid microphone capsules arranged in a tetrahedron and equalized for uniform diffuse-field response [90]. The recorded signals later are converted to B-format through a matrix operation. On the market, TetraMic developed  
Figure 3: 3D soundfield recording microphones. (**a**) TetraMic; (**b**) EigenMike; (**c**) Planar Microphone Array.  
in 2007 [88] (as shown in Figure 3a) the first portable single point, stereo and surround sound Ambisonic microphone.  
Above the first-order, multiple microphones with very sophisticated digital signal processing are required to obtain the higher-order expansion components directly. An ideal higher order microphone would be comprised of a continuous spherical microphone array, which can decompose the measured 3D soundfield into its spherical harmonic coefficients. In practice, spherical microphone arrays are implemented with a discrete array that uniformly samples a spherical surface [11; 12]. To record an Nth order soundfield, the minimum sampling requirement along the spherical surface is \(\left(N+1\right)^{2}\). Note that, since the soundfield order \(N\approx\left\lceil kr_{0}\right\rceil\) is proportional to the frequency and the ROI size (or radius), the number of microphones required in the array is also proportional to those two quantities. The Eigenmike [89], as shown in Figure 3b, is an example for a commercially available 4 th order microphone array, which consists of 32 microphones uniformly spaced on a spherical baffle of radius 4 cm. Instead of using a spherical geometry, a novel array structure consisting of a set of parallel circular microphone arrays to decompose a wavefield into spherical harmonic components was proposed recently [91].
## 2 Problem Definition and Numerical Methods  
The present study extends our previous work in which we considered a highly-asymmetric 17-generation airway model (Figure 1) for aerosol transport prediction. The airway model is constructed from the airway dimensions of [PERSON] et al. [10] and the final model consists of 1453 bronchioles. The large-scale 17-generation model is one of the best available airway models in the literature. The airflow and particle transport in a whole lung model is highly computationally expensive as it needs to solve millions of computational cells. The 17-generation model consists of 7 million cells, and the multiphase flow for a single case takes approximately 55 days in high-performance computing systems. To get a precise understanding of the various shape and size-specific parameters of aerosol transport to the lower airways of the large-scale model, it needs to run a large number of simulations, and it needs a significant amount of time. Our previous study analysed particle transport numerically and developed a database for aerosol transport for various flow rates and diameter particles [11]. In the present work, the database is used to develop an ML model which can accurately predict aerosol transport to the lower airways for various aerosol sizes and flow rates.  
Figure 1: Three-dimensional 17-generation bifurcating airway model of a healthy adult.  
The numerical study solved mass and momentum equations for the primary and dispersed phase [7]. A proper grid independence and validation with the benchmark experimental, numerical and theoretical data was conducted by a numerical model. The detailed numerical methods, grid refinement and validation of the numerical data were available in the author's previous studies [7,11]. That study developed the database for the deposition efficiency at the right lung and the left lung of the airway model. Table 1 shows the deposition data at the right lung and the left lung for different flow rates. Table 2 shows the deposition data at the right (Upper, Middle and Lower) and left (Upper and Left) lungs for different flow rates.
## 3 Mathematical Formulation
### Dynamic Stiffness (DS) Matrix Assembly Procedure with Boundary Conditions  
The dynamic stiffness matrix given by Equation (21) was developed for a plate element. By obtaining the natural frequencies of a given orthotropic plate assembly, we considered four elements of the given plate geometry. Each element of the plate was connected through nodal lines. Since there were five nodal lines and the degree of freedom was two per element, a \(10\times 10\) global master stiffness matrix was formulated. The assembly procedure is similar to the finite element method, schematically shown in Figure 4. Boundary conditions can be applied the same way as we applied in the finite element method. The penalty method was applied as boundary conditions to suppress a particular degree of freedom. In this method, a considerable value of stiffness is added to the appropriate term on the leading diagonal of the dynamic stiffness matrix. The procedure for applying the boundary conditions is summarized as follows:  
1. Displacement \((W_{i})\) is penalized for simply supported (S) boundary conditions.
2. Displacement \((W_{i})\) and rotation \((\phi_{i})\) are penalized for clamped (C) boundary conditions.
3. No penalty is implemented for the free (F) boundary condition.  
where \(i\) represents the suppressed node.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 7 Opportunities for Organizational Adoption of the Research
### Methods of Defending Organizational Systems--Business Recommendations
#### 7.1.2 Signature-Based or Anomaly-Based IDS for a Business Strategy  
Signature-based technology is highlighted to have a greater speed than anomaly-based technology, which is the most noticeable difference in capability comparison. However, despite this, a complete IDS system must offer both signature and anomaly-based operations. This is because there are advantages and constraints to using either technology as a security measure for the enterprise cybersecurity strategy, and these are highly compensated with the combination of the technologies. Therefore, we recommend that businesses deploy a hybrid solution utilizing both technologies to get the most out of their IDS system and are not constrained by the weaknesses in either technology [52].  
Three key criteria used for deciding on the most effective software to deploy in a real-world business setting have been assessed using three main criteria, which include: (1) identifying false positives, (2) staffing needs, and (3) the identification of genuine risks.
# Applications of Multi-Agent Deep Reinforcement Learning: Models and Algorithms
## 5 Attributes of Multi-Agent Deep Reinforcement Learning
### Performance Measures  
MADRL has been applied to improve the following performance measures in the literature:  
* _Lower outage (or blocking) probability_ reflects the reliability of a system to sustain its normal operation (e.g., achieving a required data rate in networking) based on: (a) internal factors (e.g., a higher amount of internal resources reduces the outage probability [31]); and (b) external factors (e.g., a communication channel with a lower interference level reduces the outage probability [35]).
* _Higher accumulated reward_ increases the total immediate rewards received by agents over time [35].
* _Higher quality of service_ reflects better system performance, such as higher throughput [35], lower end-to-end delay (or shorter packet delivery time [6]), and lower blocking probability (or the probability of an agent being denied of services due to poor system performance) [31].
* _Higher energy efficiency_ reduces energy consumption of devices, such as sensors and smart sockets embedded in wireless modules [64]. A balanced adjustment of on-time and off-time during the peak and non-peak times, respectively, increases energy efficiency [56].
* _Higher fairness_ reflects an equal distribution of network resources among distributed agents. A centralized entity may pool together network resources from distributed agents to provide shared network resources [52; 65].
* _Lower congestion levels_ reduces the congestion caused by the high traffic volume during peak hours or due to disturbances (e.g., rainfalls) in traffic networks [66; 67].
## 2 Frictional Behaviour  
An in-depth description of pertinent results concerning the frictional behaviour has been described in [2; 3]. Below, the most important results of these investigations are summarized.  
A two-step test for Coulomb friction, that suggests a constant friction coefficient between powder and die [16; 17; 18], is relied upon. Here, powder first is compacted and then removed and pushed against a moving die material from mechanical loading, yielding the relation  
\[F_{f}=\mu_{0.2}\cdot F_{N} \tag{1}\]  
where \(F_{N}\) is normal force, \(F_{f}\) is shear force, and \(\mu_{0.2}\) is the frictional coefficient. The latter quantity takes on the value 0.2, according to [16], in a standard Coulomb situation.  
The result from a continuous test method is different and believed to be more accurate. In Figure 1, the experimental setup used for the analysis of the frictional behaviour is shown schematically. In short, it is an instrumented die where eight sensors are placed on the die wall in order to determine the radial (contact) pressure. The radial stress measurements are carried out using eight pressure sensors, Kistler sensor type 6183A (Winterthur, Switzerland), that are positioned in a helical pattern around the cylindrical powder cavity. The instrumented die is mounted on a standard press. The loading and ejection speed is a maximum of 200 mm/s, and the pressing forces are measured in the press. The test setup has been described in detail in [19; 20]. In particular, the description in [19] is of direct interest for the present purposes. Experimentally determined pressure quantities relevant to the frictional analysis are shown in Figure 2.  
By using inverse modelling, it is possible [2] to determine the influence of the radial pressure \(p\) on the frictional behaviour. The details of the procedure are found in [2]. In the mentioned study, different frictional models were investigated, and the model that best describes the behaviour is used here. It was found that a (radial) pressure-dependent frictional model is best suited, and the frictional coefficient, \(\mu_{opt}\), is given via  
\[\mu_{opt}=\left\{\begin{array}{ll}a_{1}+a_{2}\cdot p&\text{ if }p<p_{1}\\ a_{3}&\text{ if }p_{1}\leq p\leq p_{2}\\ (p/a_{4})^{-a_{5}}&\text{ if }p>p_{2}\end{array}\right. \tag{2}\]  
In Table 1, the parameter values are presented. In addition, it should be mentioned that the quantities are \(p_{1}\) = 1.1 MPa and \(p_{2}\) = 2.5 MPa.  
The average friction over the whole powder pillar is compared to the average friction that is calculated from Equation (2) in Figure 2.  
Again, it should be stated that the experiments in [2] concluded that \(\mu=\mu(p)\) and that stick-slip phenomena can be disregarded.
## 3 Results and Discussions
### Moisture Susceptibility  
Figure 10 shows the untreated and XG-treated specimens submerged in water for over five days of the soaking test. The pure soil sample started disintegration immediately after immersing in water, and within 4 h was fully disintegrated. As a result of absorbing water, the size of all XG-treated specimens increased but kept their general shapes. Specimens with 0.5% XG and 1% XG experienced slight disintegration from the top and perimeter while still holding their cylinder shapes after two days and completely disintegrated after three days of the experiment. The general integrity of the 2% XG-treated sample was noticeable even after 5 days of submerging in the water, and its diameter was increased by almost 50%. The soaking test proves the capability of XG in maintaining soil integrity and enhancing soil resistance to water.  
Figure 10: Specimens submerged in the water (**a**) after 1 h; (**b**) after 4 h; (**c**) after 24 h; (**d**) after five days.  
Figure 9: Variations in unconfined compressive strength of the XG-treated soil with wetting and drying cycles.
# IIoT Malware Detection Using Edge Computing and Deep Learning for Cybersecurity in Smart Factories
## 3 Edge Computing and Deep Learning-Based Malware Detection Method
### Edge Computing and Deep Learning-Based Malware Detection System
The edge server is physically closer to the IIoT sensor layer than the cloud, with a lower network cost to the terminal. _Cloud layer:_ This layer consists of a server that performs the function of training a global deep learning model. The cloud server has superior computational power compared to the edge server, which enables training and detection with a global integrated deep learning model. However, because it is physically far from the IIoT sensor layer, the network cost for communication with the terminal is high, and network delays may occur. To detect malware in an environment that requires real-time service such as a smart factory, processing through remote cloud is not suitable owing to the network delay problem. Edge computing [92] has been proposed to solve this, as it works by offloading [93] the computation to an edge server with high-performance hardware that is close to the IIoT device or data source from which the data were created and processed. This processing method can increase availability and reliability as the distributed edge computing device continues to operate even when the remote cloud is unavailable owing to a DDoS attack, etc. Because the edge server that offloads deep learning operations is geographically close to the client, it provides several advantages, such as reducing data transmission latency, saving bandwidth resources for remote clouds [29], energy efficiency, and mitigating privacy and security issues [92; 94].
## 2 Overview of Positioning Principles Using UWB and Wi-Fi RTT Radio Signals
### TOF-Based Positioning Principle  
UWB and Wi-Fi RTT technologies measure the distance between a request initiator (e.g., a tag or smartphone) and a responder (e.g., a base station) using the TOF approach, which calculates the distance by measuring the signal's flight time between transmitting and receiving devices, as shown in Figure 3.  
However, Wi-Fi RTT technology differs in that it measures the round-trip time of the signal travel multiple times and then averages these values to estimate the distance between the signal's transmitting and receiving ends. The Wi-Fi RTT ranging principle is outlined as follows.  
\[\left\{\begin{array}{l}d=\frac{1}{2}\cdot T_{RTT}\cdot c-d_{trans}+d_{com}-d_{ NLOS}-d_{MPE}+a+\varepsilon\\ T_{RTT}=\frac{1}{N}(\sum_{i=1}^{N}t_{4,i}-\sum_{i=1}^{N}t_{1,j})-\frac{1}{N}( \sum_{i=1}^{N}t_{3,i}-\sum_{i=1}^{N}t_{2,i})\end{array}\right. \tag{1}\]  
Figure 3: Illustration of TOF positioning principle. (**a**) TOF-based UWB ranging protocols and (**b**) TOF-based Wi-Fi RTT ranging protocols.  
Figure 2: The main structure pyramid of this article.  
where \(d\) is the actual distance between the router and the mobile device; \(T_{RTT}\) is the calculated average flight time; \(d_{trans}\) is the error caused by the signal propagation time delay in the hardware; \(d_{com}\) is the error introduced by the communication protocol; \(d_{NLOS}\) and \(d_{MPE}\) are errors caused by NLOS propagation and multipath effect, respectively, as shown in Figure 4; \(a\) is the phase distortion error; \(\varepsilon\) represents the noise effect, \(t_{1\_i}\), \(t_{2\_i}\), \(t_{3\_i}\), \(t_{4\_i}\) are the times when the signal arrives and leaves the transmitting and receiving devices, respectively; \(c\) is the speed of light in vacuum; \(N\) is the number of successful ranging measurements.  
The TOF approach directly measures the flight time of the signal traveling between a mobile carrier and the base station. Consequently, TOF only requires recording the time when the target transmits the signal and receives it back, although more accurate methods may also account for the reaction time of the base station. UWB and Wi-Fi RTT technology, both based on the TOF approach, are less susceptible to the inconsistencies in time reference between the transmitting and receiving ends. This is achieved through the calculation of the difference between the transmitting and receiving signal times at both ends, followed by the calculation of the difference in the obtained time differences and averaging them. Importantly, this processing occurs separately at each end, utilizing their respective time references. Consequently, the final calculation involves time differences rather than absolute time, eliminating the need to synchronize time references between the transmitting and receiving ends.
# Semi-Supervised Semantic Segmentation Network for Point Clouds Based on 3D Shape
## 4 Results
### Robustness Experiment  
The proposed model's robustness to different inputted point cloud resolutions and numbers was tested by sampling 512, 1024, 2048, and 2860 points. The results shown in Table 5 indicate that segmentation performance was best with 2048 input points. Segmentation accuracy was slightly decreased when the number of inputted points increased or decreased compared to the optimal number. Table 6 shows the results of chair segmentation with different inputted points, indicating that even a minimum of 256 points could be used to segment parts. The network model effectively extracted point cloud feature information and was robust to point cloud resolution.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline  & **MIQU (\%)** & **Airplane** & **Bag** & **Cap** & **Car** & **Chair** & **Earphone** & **Guitar** & **Knife** & **Lamp** & **Laptop** & **Motorbike** & **Mug** & **Pistol** & **Rocket** & **Skateboard** & **Table** \\ \hline PointNet [2] & 83.7 & 83.7 & 78.7 & 82.5 & 74.9 & 89.6 & 73.0 & 91.5 & 85.9 & 80.8 & 95.3 & 65.2 & 93.0 & 81.2 & 57.9 & 72.8 & 80.6 \\ PointNet+ [3] & 85.1 & 82.4 & 79.0 & 87.7 & 77.3 & 90.8 & 71.8 & 91.0 & 85.9 & 83.7 & 95.3 & 71.6 & 94.1 & 81.3 & 58.7 & 76.4 & 82.6 \\ DGCNN [21] & 85.1 & **84.2** & **83.7** & 84.4 & 77.1 & 90.9 & 78.5 & 91.5 & **87.3** & 82.9 & **96.0** & 67.8 & 95.3 & **82.6** & 59.7 & 75.5 & 82.0 \\ SGK-Net [25] & 85.0 & 82.9 & 80.7 & 87.6 & 77.8 & 90.5 & **79.9** & 91.0 & 88.1 & **84.0** & 95.7 & 69.9 & 94.0 & 81.1 & **60.8** & 76.4 & 81.9 \\ SHNet [26] & 73.5 & 72.6 & 74.0 & 68.0 & 48.6 & 79.4 & 61.0 & 88.0 & 78.5 & 75.4 & 75.0 & 49.6 & 82.3 & 65.4 & 46.7 & 53.0 & 72.4 \\ Pointseqseq [27] & 85.2 & 82.6 & 81.8 & 87.5 & 77.3 & 90.8 & 77.1 & 91.1 & 86.9 & 83.9 & 95.7 & 70.8 & 94.6 & 79.3 & 58.1 & 75.2 & 82.8 \\ Proposed method & **85.3** & 83.9 & 79.9 & **88.5** & **78.3** & **91.9** & 76.0 & **92.6** & 86.5 & 83.7 & 95.7 & **73.2** & **95.5** & 82.3 & 56.8 & **76.9** & **83.2** \\ \hline \end{tabular}
\end{table}
Table 3: Component segmentation mean intersection over union of different methods using the ShapeNet dataset, (Bold font represents the highest MIQU in this category).