esa-sceva/satcom-chunk-collection huggingface dataset:
# COSMIC-2 RO Profile Ending at PBL Top with Strong Vertical Gradient of Refractivity
## 6 Conclusions  
The near-surface observations under different weather conditions are vital for the research on tropical convection and hurricane genesis and development. More than half of the COSMIC-2 RO profiles can provide high-vertical-resolution profiles with their lowest observing heights below 200 m, and only a few ROs cannot reach the lower troposphere. In order to understand what might have affected the downward penetrations of these RO profiles, the COSMIC-2 RO observations and the ERA5 reanalysis data in the SEP and SCP areas were examined and analyzed in this study. The strong vertical gradient of \(N-\overline{N}^{obs}(r)\) is confirmed to prevent the RO signals from penetrating down, and the impacts of strong horizontal gradients of refractivity and clouds were also discussed.  
First, when the strong negative vertical gradient of \(N-\overline{N}^{obs}(r)\) is less than \(-65\) N-unit \(\mathrm{km}^{-1}\), the lowest RO altitudes correspond well to the PBL heights. Secondly, the strong horizontal gradient of refractivity can also prevent the ROs from reaching the lower troposphere below it in most cases. For the two areas studied, the large negative vertical gradients of \(N-\overline{N}^{obs}(r)\) appear more frequently in the SEP area where the atmospheric ducts occur than in the SCP area. The strong horizontal gradients of refractivity happen more frequently in the SCP area. Thirdly, the absorption and scattering effects of clouds can also affect the downward detection of RO observations to some extent. There are still a few RO profiles (-0.6%) with high lowest heights which cannot be attributed to the above three reasons, and their bending angle uncertainties and confidences distribute similarly to the profiles that reached the surface.  
This study helps to understand the different penetration depths of COSMIC-2 RO profiles under different atmospheric conditions, and it will also benefit efforts to make better use of COSMIC-2 RO observations for studying weather in the tropical lower troposphere.  
Conceptualization, X.X. and X.Z.; data curation, X.X.; formal analysis, X.X. and X.Z.; funding acquisition, X.Z.; investigation, X.X. and X.Z.; methodology, X.X. and X.Z.; project administration, X.Z.; resources, X.Z.; writing--original draft, X.X.; writing--review and editing, X.Z. All authors have read and agreed to the published version of the manuscript. This research was supported by the National Natural Science Foundation of China (42192552 and 41875032). The data that support the findings of this study are available from the author upon reasonable request. We would like to acknowledge the suggestions given by the reviewers and editor. The authors declare no conflict of interest.
# An Efficient Framework for Securing the Smart City Communication Networks
## 3 Description of Our Proposed Cyber Security Framework for Smart Cities
### Authentication and Encryption Tasks  
The authentication task includes two phases, which are described as follows. In the first phase, a smart meter sends a single message with a specific size to the control  
Figure 3: Secure two-way communication by providing an authentication and key session algorithm.  
center. None of the smart meters spend any initialization time to generate this message. In order to send a response message for every individual smart meter's request, the control center spends only 0.5 s of processing time. The second phase involves confirming the authentication process in each smart meter, and it takes 0.2 s to process the control center's return message.  
During the second task, encryption, of securing two-way communication between each smart meter and the control center, two phases are applied to achieve the processing deployment. The first phase consists of encrypting messages before delivering them individually from each smart meter to the control center in order to ensure the connection between these two parties. These encrypted messages are uniformly distributed within a size of 1 to 10 kilobytes, and the process of the encrypting takes only 0.2 s. The second phase encrypts messages before sending them from the control center to each smart meter, as well as ensuring secure connection between them. Table 1 shows the authentication and encryption tasks, as well as the configurations for each phase, each of which has two phases that drive traffic from each smart meter to the control center, including two-way communication, authentication, and encryption.
## 3 Results
### Multiple Linear Regression Models of GPP
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Resolution** & \(\mathbf{a}\) & \(\mathbf{b}\) & **RMSE \({}^{1}\)** & **MAE \({}^{1}\)** & **R\({}^{2}\)** \\ \hline \multirow{3}{*}{**SE-Sto**} & 10 m & 0.61 & \(-\)0.23 & 0.49 & 0.39 & 0.83 \\  & 250 m & 0.60 & \(-\)0.17 & 0.45 & 0.35 & 0.86 \\  & 500 m & 0.69 & \(-\)0.13 & 0.44 & 0.35 & 0.86 \\ \hline \multirow{3}{*}{**SE-Svb**} & 10 m & 1.72 & 1.06 & 1.12 & 0.95 & 0.87 \\  & 250 m & 1.77 & 1.09 & 1.10 & 0.94 & 0.88 \\  & 500 m & 1.71 & 1.20 & 1.04 & 0.88 & 0.89 \\ \hline \multirow{3}{*}{**SE-Deg**} & 10 m & 0.54 & 0.10 & 0.51 & 0.39 & 0.71 \\  & 250 m & 0.42 & 0.13 & 0.52 & 0.40 & 0.69 \\  & 500 m & 0.42 & 0.12 & 0.51 & 0.40 & 0.70 \\ \hline \multirow{3}{*}{**FI-Hy**} & 10 m & 1.34 & 0.61 & 0.98 & 0.73 & 0.89 \\  & 250 m & 1.17 & 0.78 & 0.96 & 0.71 & 0.89 \\  & 500 m & 1.13 & 0.87 & 0.96 & 0.70 & 0.89 \\ \hline \multirow{3}{*}{**SE-Nor**} & 10 m & 1.28 & 0.63 & 1.27 & 1.02 & 0.84 \\  & 250 m & 1.29 & 0.68 & 1.36 & 1.09 & 0.81 \\  & 500 m & 1.20 & 0.81 & 1.33 & 1.07 & 0.82 \\ \hline \multirow{3}{*}{**SE-Lnn**} & 10 m & 1.25 & \(-\)0.18 & 1.60 & 1.03 & 0.88 \\  & 250 m & 1.23 & \(-\)0.68 & 1.60 & 1.19 & 0.88 \\  & 500 m & 1.23 & \(-\)0.71 & 1.96 & 1.43 & 0.82 \\ \hline \multirow{3}{*}{**SE-Htm**} & 10 m & 1.56 & 1.00 & 1.92 & 1.63 & 0.77 \\  & 250 m & 1.39 & 1.24 & 2.13 & 1.83 & 0.72 \\  & 500 m & 1.25 & 1.50 & 2.09 & 1.81 & 0.73 \\ \hline \multirow{3}{*}{**DK-Sor**} & 10 m & 1.35 & \(-\)0.36 & 1.91 & 1.44 & 0.91 \\  & 250 m & 1.52 & \(-\)0.81 & 2.19 & 1.68 & 0.88 \\ \cline{1-1}  & 500 m & 1.54 & \(-\)0.84 & 2.12 & 1.63 & 0.88 \\ \hline \hline \end{tabular} \({}^{1}\) in unit g C m\({}^{-2}\) d\({}^{-1}\).  
\end{table} 
Table 3: Linear regressions for GPP (g C m\({}^{-2}\) day\({}^{-1}\)) at each site driven by the products of T\({}_{a}\) and EVI2 in 10 m from Sentinel-2, and 250 m, 500 m from MODIS. The modelled GPP time-series were very close between Sentinel-2 and 250 m MODIS data at all sites (Figure 5). The shape and magnitude of the daily GPP time-series between satellite driven GPP and flux data-derived GPP were similar (Figure 5). Peak season GPP decreased from the south to the north. The subarctic and boreal mire sites (SE-Sto and SE-Deg, respectively) had much smaller peak GPP (less than 5 g C m\({}^{-2}\) d\({}^{-1}\)) compared to other sites. The GPP peak at the coniferous forest sites varied between 10-15 g C m\({}^{-2}\) d\({}^{-1}\), whereas the deciduous and agricultural sites had the peak around 15-20 g Cm\({}^{-2}\) d\({}^{-1}\). The agriculture site SE-Lnn had high peak GPP but a relatively short growing season with a narrow shape, mainly controlled by the activities of cultivation. The GPP time-series pattern at the deciduous and evergreen forest sites, DK-Sor and SE-Htm, was much wider than at SE-Lnn, which indicates a much longer growing season (Figure 5).
## 2 Theoretical Approach  
### Governing Equations  
As the compression wave propagates at the local speed of sound to the tunnel exit, it induces unsteady turbulent boundary layer flow within the tunnel [39]. In the region where the initial compression wave passes through, air pressure and density change. When the tunnel length is much greater than the tunnel hydraulic diameter, and the time taken for the compression wave to reach the entire tunnel cross-section is much shorter than the propagation time along the length of the tunnel, the pressure fluctuations on the tunnel cross-section can be neglected. The pressure and airflow velocity on the tunnel cross-section can be considered uniformly distributed. Therefore, the airflow induced by the propagation of the compression wave can be reasonably simplified into a one-dimensional compressible unsteady flow model. The friction term and heat transfer term between the air and the tunnel wall are introduced into the momentum and energy equations, and the governing equations for the compression wave propagation process are derived using the laws of conservation of mass, momentum, and energy, which are specified as [4]:  
Continuity equation:  
\[\frac{\partial\rho}{\partial t}+\rho\frac{\partial u}{\partial x}+u\frac{ \partial\rho}{\partial x}=0 \tag{1}\]  
Momentum equation:  
\[\frac{\partial u}{\partial t}+u\frac{\partial u}{\partial x}+\frac{1}{\rho} \frac{\partial p}{\partial x}+G(u)=0 \tag{2}\]  
Energy equation:  
\[\frac{\partial p}{\partial t}+u\frac{\partial p}{\partial x}-a^{2}\bigg{(} \frac{\partial\rho}{\partial t}+u\frac{\partial\rho}{\partial x}\bigg{)}=\rho (\kappa-1)(q+uG(u)) \tag{3}\]  
where \(\rho\) is the air density, \(u\) is the fluid velocity, \(a\) is the sound velocity, \(\kappa\) is the ratio of principal specific heat capacities, \(p\) is the pressure, \(q\) is the heat transfer term, and \(G(u)\) is the friction term.
## 4 Proposed Frequency-Offset Estimation Method
### Algorithm
 \tag{29}\]  
where \(\mathcal{S}_{p}\subseteq\mathcal{S}_{f}\) is the possible combinatorial subset of \(\mathcal{S}_{f}\) containing \(N_{p}\leq N_{f}\) elements. In a similar way, the LLSE of the SFO is calculated by:  
\[ \begin{split}\hat{\xi}&=\frac{\sum_{k\in\mathcal{ S}_{p}}k\hat{\varphi}(k)}{\sum_{k\in\mathcal{S}_{p}}k^{2}}\\ &=\frac{1}{M_{p}}\sum_{k\in\mathcal{S}_{p}}k\hat{\varphi}(k)\end{split}  \tag{30}\]  
where:  
\[M_{p}=\sum_{k\in\mathcal{S}_{p}}k^{2}. \tag{31}\]  
Substituting (27) into (29) and (30), the proposed LLSE of CFO and SFO can be shown to be of the form:  
\[\hat{\varepsilon}=\varepsilon+\frac{\xi L_{p}}{N_{p}}+\frac{1}{\pi\beta N_{b} N_{p}}\sum_{k\in\mathcal{S}_{p}}\tilde{I}_{l}(k) \tag{32}\]and:  
\[\hat{\xi}=\xi+\frac{\varepsilon L_{p}}{M_{p}}+\frac{1}{\pi\beta N_{b}M_{p}}\sum_{ k\in\mathcal{S}_{p}}k\hat{l}_{l}(k) \tag{33}\]  
where:  
\[L_{p}=\sum_{k\in\mathcal{S}_{p}}k. \tag{34}\]  
Obviously, one can obtain the MSE of (32) and (33) as follows:  
\[E\left\{\left|\hat{\varepsilon}-\varepsilon\right|^{2}\right\}=\frac{\xi^{2}L _{p}^{2}}{N_{p}^{2}}+\frac{\sigma_{\text{I}}^{2}}{\pi^{2}\beta^{2}N_{b}^{2}N_ {p}} \tag{35}\]  
and:  
\[E\left\{\left|\hat{\xi}-\xi\right|^{2}\right\}=\frac{\varepsilon^{2}L_{p}^{2} }{M_{p}^{2}}+\frac{\sigma_{\text{I}}^{2}}{\pi^{2}\beta^{2}N_{b}^{2}M_{p}} \tag{36}\]  
where \(\sigma_{\text{I}}^{2}\) is the variance of \(\hat{l}_{l}(k)\).
## 5 Simulation and Analysis  
### Platform Setting  
#### 5.1.1 Air Combat Simulation Platform Construction  
The air combat simulation platform was built using the Python programming language and the HARFANG three-dimensional (3D) framework, which is a software framework that is used for the development of modern multimedia applications and for modeling the shapes of aircrafts. The HARFANG framework can manage and display complex 3D scenes, play sounds and music, and access virtual reality (VR) devices, such as the Oculus Rift. It is a new multimedia application development framework, which is highly suitable for the development of games; it also provides appropriate support for VR equipment. Nowadays, ground controllers use VR equipment to control the UCAVs; hence, we adopted the HARFANG 3D framework for our proposed air combat simulation platform.  
The platform was able to simulate the 1-vs.-1 WVR air combat in certain airspaces, use the dynamic equation with three degrees of freedom to simulate the flight characteristics of UCAV, and set the performance parameters of both the UCAVs. The platform was also able to reserve the air combat strategy interface of the two UCAVs, thereby obtaining the UCAV control signals from the external environment. The platform could also support human control, where the interface received input from a keyboard or hands-on throttle and stick system (HOTS).  
Figure 4 depicts the main interface of the air combat simulation platform. The interface presents a real-time combat situation of the UCAVs, and the five positions on the screen display the health value, altitude and speed, radar image, attitude angle, and all of the speed components.
## 1 Introduction
### Automotive Natural Exposure Test
\begin{table}
\begin{tabular}{c c c c} \hline
**Measured Parameters** & **Equipment Type** & **Measurement Range** & **Precision** \\ \hline Solar radiation intensity & TBQ-2C & 0–2000 W\(\cdot\)m\({}^{-2}\) & \textless{5\%} \\ Environmental temperature & PTS-3 & \textless{40–120} \({}^{\circ}\)C & \textless{0.1 }\({}^{\circ}\)C \\ Humidity & PTS-3 & 0–100\% & \textless{2\%} \\ Temperature in the cabin & T-type & \textless{200–150} \({}^{\circ}\)C & 0.4\% \\ Surface temperature & T-type & \textless{200–150} \({}^{\circ}\)C & 0.4\% \\ \hline \end{tabular}
\end{table} 
Table 1: Test equipment and measurement parameters. Figure 1: Schematic diagram for arrangement of temperature and humidity measuring points in automobiles. Figure 2: Radiation and temperature sensors: (**a**) solar radiation in the cabin; and (**b**) dashboard temperature. Figure 3: Annual statistical overview of the weathering external factors distribution inside and outside the automobile in non-uniform thermal environment: (**a**) annual radiation and maximum temperature of parts; (**b**) ambient air temperature and cumulative time; (**c**) solar radiation intensity and cumulative time; and (**d**) surface temperature and accumulated time of parts. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Time/h** & **Air Temp/\({}^{\circ}\)C** & \begin{tabular}{c} **Relative** \\ **Humidity/\%** \\ \end{tabular} & \
\begin{tabular}{c} **Wind** \\ **Speed/(m**\(\cdot\)**s**\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **Solar** \\ **Radiation/(W**\(\cdot\)**m**\({}^{-2}\))** \\ \end{tabular} \\ \hline
00:00 & 29.3 & 69.12 & 0.48 & 0 \\
06:00 & 28.2 & 78.5 & 1.5 & 0 \\
09:00 & 31.1 & 69.2 & 1.9 & 317 \\
12:00 & 36.6 & 56.3 & 1.5 & 659 \\
15:00 & 38.6 & 51.8 & 0.3 & 461 \\
18:00 & 31.6 & 74.5 & 1.9 & 26 \\
24:00 & 28.8 & 81.4 & 0.5 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Environmental parameters at partial time.
## 3 Results and Discussion
### Crack Pattern from Field Inspection
Compared with Series I and II, Series III and IV encountered a lower level temperature gradient before 120 days. Hence, the calculated temperature stresses at Points Edge-top and Edge-bottom are well below the tensile strength of concrete. Figure 14: \(Cont.\)  
Figure 14: Inputted meteorological data for the temperature field analysis of field base plate. (**a**) Environmental temperature; (**b**) solar radiation. Figure 15: _Cont_. The humidity-induced stress simulation considered concrete strains of swelling and shrinkage. It was assumed that during the 14-day wet-curing, the top surface of the base plate was exposed to an RH of 100%; during the sealed-curing period, no moisture exchange with the environment happened and only self-desiccation proceeded; after the  
Figure 15: Calculated temperature-induced longitudinal stresses at positions Edge-top and Edge-bottom: (**a**) Series I, (**b**) Series II, (**c**) Series III, and (**d**) Series IV. geotextile cover was removed, the top and side surfaces of the base plate were exposed to an RH of 45%. Figure 16 shows the calculated humidity-induced longitudinal stresses at Points Edge-top, Edge-middle, and Edge-bottom (mid-length). As shown, after the end of sealed curing, drying shrinkage stress develops very fast at Edge-top. It was observed that the prolonged seal-curing stage do not moderate drying shrinkage stresses. The reason lies in the fact that once exposed to external drying, a sharp drying front penetrates into the surface concrete. Steep humidity gradients arise at the exposed boundary, especially at Edge-top where two-direction drying occurs. Although the early-age creep effect has been fully considered in the stress analysis, the plate edge portion still reaches a large magnitude of self-induced shrinkage stress. This indicates that the surface layer of concrete is highly sensitive to cracking when exposed to the environment. This is in agreement with the conclusion reported by [36] that concrete cracking potential is closely related to the RH drop rate and shrinkage rate in the near-surface layer. Figure 16: \(Cont.\)  
For each curing regime, the calculated shrinkage stress at the time of plate edge cracking is much higher than the calculated temperature stress at the same time. Thus, the conclusion can be reached that drying induced stress is the leading cause of transverse cracks in the CRTS III base plate.
## 3 Model Validation
### Mobility Scenarios
#### 3.1.2 Synthetic (Vehicular) Mobility Model  
The inherent limitation of real-world mobility traces is that they are limited to a specific number of nodes. In contrast, synthetic mobility models can be applied to an arbitrary number of nodes and over a large scale. For this reason, in this study, we also consider a synthetic mobility scenario based on the vehicular mobility model described in Section 2.6. This mobility model depends on several parameters, such as node density and the average lane speed. In the following simulations, we focus on investigating the impact of network density on system performance by varying the number \(N\) of vehicles. Specifically, we set the lane length \(L\) to 1000 m, the average speed \(v_{m}\) to 20 km/h, the speed variability \(\Delta v\) to 8 km/h, and the transmission range to 50 m. Figure 6 shows the CCDF of aggregate inter-any-contact time for \(N=100\). The results show that the empirical distribution closely matches the exponential distribution. We highlight that using these two mobility scenarios is beneficial for model validation because it allows us to test the effectiveness of the proposed modelling approach in both cases where the mobility patterns follow or do not follow a Poisson process for the contact events.  
Figure 5: CCDF of the inter-any-contact time for the INFOCOM’06 trace compared with an exponential distribution of the same mean.
## 3 Methodology
### AIS Data Pre-Processing
\begin{vmatrix}LON_{0S}&LAT_{0S}&LON_{0E}&LAT_{0E}&L_{0}&B_{0}& st_{0}&dm_{0}&t_{00}\\ LON_{0S}&LAT_{0S}&LON_{0E}&LAT_{0E}&L_{0}&B_{0}&st_{0}&dm_{0}&t_{01}\\ \vdots&&&&&&&\vdots\\ LON_{0S}&LAT_{0S}&LON_{0E}&LAT_{0E}&L_{0}&B_{0}&st_{0}&dm_{0}&t_{0n}\\ LON_{1S}&LAT_{1S}&LON_{1E}&LAT_{1E}&L_{1}&B_{1}&st_{1}&dm_{1}&t_{10}\\ LON_{1S}&LAT_{1S}&LON_{1E}&LAT_{1E}&L_{1}&B_{1}&st_{1}&dm_{1}&t_{11}\\ \vdots&&&&&&&\vdots\\ LON_{1S}&LAT_{1S}&LON_{1E}&LAT_{1E}&L_{1}&B_{1}&st_{1}&dm_{1}&t_{1n}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ LON_{mS}&LAT_{mS}&LON_{mE}&LAT_{mE}&L_{m}&B_{m}&st_{m}&dm_{m}&t_{m0}\\ LON_{mS}&LAT_{mS}&LON_{mE}&LAT_{mE}&L_{m}&B_{m}&st_{m}&dm_{m}&t_{m1}\\ \vdots&&&&&&&\vdots\\ LON_{mS}&LAT_{mS}&LON_{mE}&LAT_{mE}&L_{m}&B_{m}&st_{m}&dm_{m}&t_{mn}\\ \end{vmatrix}  \tag{2}\]  
\[y=\begin{pmatrix}lon_{00}&lat_{00}&cgo_{00}&sg_{00}\\ lon_{01}&lat_{01}&cgo_{01}&sg_{01}\\ \vdots&&&&\vdots\\ lon_{0n}&lat_{0n}&cgo_{0n}&sg_{0n}\\ lon_{10}&lat  
creating a flowchart-like structure where each internal node represents a feature, each branch represents a feature's value, and each leaf node represents the predicted outcome for the target variable. Gradient Boosting Regression (GBR) is an ensemble learning method that iteratively trains new models to correct the prediction errors of previous models, thereby gradually improving the overall predictive accuracy of the model. When training the Decision Tree model, we adjusted the max_depth, min_samples_split, and min_samples_leaf parameters to effectively control the model's complexity and prevent overfitting, thereby enhancing the model's generalization ability on unseen data. Additionally, we employed cross-validation to evaluate various parameter configurations, ensuring that the model maintains stable performance across different subsets. In training the Random Forest model, in addition to adjusting the basic parameters of the Decision Tree, we also optimized the n_estimators parameter, which determines the number of trees in the model. Increasing the number of trees helps improve the model's stability and accuracy. We also used cross-validation techniques to find the optimal parameter combination, ensuring that the Random Forest model performs well under various data conditions. For the Gradient Boosting Regression model, we adjusted the learning_rate, n_estimators, min_samples_split, min_samples_leaf, and max_depth parameters. The learning_rate parameter controls the influence of each tree on the final prediction, with a lower learning rate potentially requiring more trees to fine-tune the model. By gradually experimenting with different numbers of trees and learning rates, we aimed to find the optimal model configuration to accurately predict vessel behavior within the channel.The framework of ship behavior model in fairway as show in Figure 6.  
Figure 6: Ship behavior model in fairway framework.
## 3 Results and Discussion
The results confirm that materials with different temperature readings of combustion products have a different impact on the characteristics of acoustics, which also affects the accuracy of the readings of the device. \[\Delta v=\frac{v_{in}-v_{fin}}{v_{in}}.100\% \tag{7}\]0 Graph of dependence of the temperature of the products of combustion of materials on the readings of the acoustic device. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline \multirow{2}{*}{**J\(\mathbf{\Psi}\)**} & \multirow{2}{*}{**Material**} & \multicolumn{3}{c}{**The Indicator of the Distance from the Device to the Obstacle at Different Temperatures, m**} & \multicolumn{3}{c}{**The Average Error Rate of Device Readings**} & \multicolumn{3}{c}{**Average Value of the Adjusting**} \\ \cline{3-10}  & & **20**\({}^{\circ}\)**C** & **40**\({}^{\circ}\)**C** & **60**\({}^{\circ}\)**C** & **80**\({}^{\circ}\)**C** & **100**\({}^{\circ}\)**C** & \(\Delta\)**, m** & **G**, \%** & **Parameter, m** \\ \hline
1 & Motor oil & 0.976 & 0.973 & 0.971 & 0.967 & 0.959 & 0.061 & 0.612 & 0.03 \\ \hline
2. & Plastic & 0.969 & 0.977 & 0.985 & 0.989 & 0.991 & 0.06 & 0.61 & 0.019 \\ \hline
3 & Styrofoam & 0.971 & 0.965 & 0.961 & 0.968 & 0.982 & 0.07 & 0.745 & 0.035 \\ \hline
4. & Textile & 0.992 & 0.988 & 0.976 & 0.971 & 0.968 & 0.011 & 1.15 & 0.015 \\ \hline
5. & Paper & 0.963 & 0.968 & 0.969 & 0.971 & 0.973 & 0.03 & 0.27 & 0.034 \\ \hline
6. & Gypsum & & & & & & & & \\ \cline{3-10}  & plasterboard & 0.979 & 0.981 & 0.983 & 0.985 & 0.988 & 0.172 & 1.74 & 0.017 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Dependence of the temperature of the products of combustion of materials on the readings of the acoustic device.
## 3 The Cooperative Beam Association and Power Allocation Scheme
### SCA-Based Power Allocation
The proposed SCA-based power allocation algorithm is given in detail as Algorithm 2. ```
Initialize variable \(P_{k,m}^{(0)}\) with \(P_{k,m}^{(0)}=0\); while the interference constraint (10d) and the capacity constraint (14) are not satisfied do \(P_{k,m}^{(0)}=0.5P_{k,m}^{(0)}\); endwhile  Set \(t:=1\); while\(\frac{\lambda^{(t)}-\lambda^{(t-1)}}{\lambda^{(t-1)}}\geq 10^{-3}\)do  Solve problem P4 by CVX to obtain \(P_{k,m}^{(t)}\);  Set \(t=t+1\); endwhile  Return \(P_{k,m}^{(t)}\)
```  
**Algorithm 2** Proposed SCA-bead multi-satellite multi-beam power allocation algorithm.