esa-sceva/satcom-chunk-collection huggingface dataset:
# An Accurate Forest Fire Recognition Method Based on Improved BPNN and IoT
## 3 Results
### Internet of Things Monitoring System Test and Analysis
#### 3.2.2 Fire-Monitoring Network Deployment of Longyandong Forest Farm  
Several sets of WSN nodes were deployed on the Maofeng Mountain forested land at Longyandong Forest Farm during the experiment, and the network deployment method was consistent with that for Shaoguan Longshan Forest Farm. The air temperature, humidity, soil moisture, light intensity, and other indicators of the forest environment were collected.  
The relative error test on the forest environmental data was conducted from 1 to 30 August 2021. The maximum temperature humidity data measured in this period and the maximum temperature humidity data measured on the platform were used for relative error statistics. The error calculation formula is as shown:  
\[\text{relative error}=\frac{|\text{Platform data }-\text{ measured data}|}{\text{measured data}}\times 100\% \tag{12}\]  
The data measurement tool recorded the maximum temperature and humidity data daily, fitting and analyzing the monthly monitoring data and generating the data curve in Figures 11 and 12.  
The results indicate that the designed forest fire monitoring/early warning system is stable. Figure 12 displays the actual and platform maximum temperature data measured in the period, and it also depicts the actual and platform maximum humidity data measured in the period. Figure 11 demonstrates that the maximum relative error of temperature and humidity is 5.75%, whereas the minimum relative error is zero.
## 3 Results and Discussion
### Antioxidant Capacity  
The second inquiry of our study addresses the contribution of the extraction temperature on antioxidant capacity of the chamomile and sage aqueous extracts. The main conclusions are provided above and presented in Table 1.  
Antioxidant activity of chamomile extracts increased when the extraction temperature increased from 80 to 100 \({}^{\circ}\)C. However, at 25 \({}^{\circ}\)C antioxidant capacity was not observed. This is similar to the current work studying water extracts of _Matricaria_ flos, where the antioxidant activity decreased at the highest temperature [19]. [PERSON] et al. also reported that chamomile infusion at 80 \({}^{\circ}\)C reached higher antioxidant capacity than at 100 \({}^{\circ}\)C [1]. The absence of antioxidant activity in the aqueous extract of chamomile at 25 \({}^{\circ}\)C was due to the fact that the intense heat was able to release cell wall phenolics or bound phenolics by breaking down cellular constituents, thus causing more polyphenols to be extracted [33].  
In the case of sage aqueous extracts, antioxidant capacity was increased from 25 to 80 \({}^{\circ}\)C, but between 80 and 100 \({}^{\circ}\)C there was a reduction. Hence the highest antioxidant capacity was obtained at 80 \({}^{\circ}\)C. The temperature of extraction procedure appears to affect total phenolic content, obtaining higher activity at 85 \({}^{\circ}\)C in comparison with room temperature at sage aqueous extracts [5]. [PERSON] et al. focused on the effectiveness of the extraction of antioxidative compounds from sage water extracts and found that higher temperatures, when water becomes significantly less polar, affect the extraction and thus antioxidant activity [32].  
For the temperature-dependent antioxidant capacity, the best extraction determined in this study was at 80 \({}^{\circ}\)C. Intense thermal treatment is responsible for a significant loss of antioxidants, as most of these compounds are relatively unstable. In accordance to this result, a research by [PERSON] et al. also indicated that the antioxidant activity of the of papaya leaf extracts increased when the extraction temperature increased to 70 \({}^{\circ}\)C, and subsequently decreased when the extraction temperature exceeded 90 \({}^{\circ}\)C [30].  
In agreement with the differentiation of antioxidant activity between chamomile and sage, in comparative studies with aqueous extracts, sage also showed higher antioxidant capacity in relation to chamomile [25], which contained a low content of antioxidants [16].  
According to the above results of aqueous herbal extracts antioxidant activity, a temperature-dependent extraction relationship was identified, which is also dependent on the species of the plant material.
# Analysis of Pressure Effect on Three-Dimensional Flame Surface Density Estimation
## 3 Flame Surface Density  
### Definition  
In premixed combustion, the surface to volume ratio of the iso\(-c^{*}\) surface, denoted as flame surface density, can be determined from the conditional gradient of the progress variable \(c\)[20] by  
\[\Sigma(c^{*})=\overline{|\
abla c|\delta(c-c^{*})} \tag{1}\]  
where \(\overline{(\cdot)}\) denotes the ensemble average. \(\delta(x)\) is the Dirac delta function and \(\delta(c-c^{*})\) measures the local probability of the isosurface \(c=c^{*}\). Equation (1) can be applied in both two or three dimensions. However, it is worth noting that \(\Sigma(c^{*})\) depends on the choice of \(c^{*}\). When the flames are not thin, a generalized flame surface density may be defined over \(c=0\sim 1\)[21] by  
\[\Sigma=\int_{0}^{1}\Sigma(c^{*})\mathrm{d}c^{*}=\overline{|\
abla c|} \tag{2}\]  
which is widely used in DNS studies [21; 22; 23]. However, within the flamelet concept of turbulent premixed flames, the flame is assumed to be thin and is identified as an interface. In this case, \(c\) and \(\Sigma(c^{*})\) do not depend on \(c^{*}\) and are physically well-defined.  
For instance, when a turbulent flame is operated in wrinkled/corrugated flamelet regime, the chemical reactions are too fast when compared with the turbulent flow, and the flame is confined within a very thin zone. In this situation, the flame is so thin that its front position can be determined by the sharp change of any progress variable, i.e., fuel or OH concentration, and the flame's surface density can be determined by Equation (1). However, in 2D experimental measurements, only the 2D flame surface is available and flame surface density is calculated based on the surface to volume definition,  
\[\Sigma=\lim_{\Delta x\to 0}\overline{\frac{A_{f}}{\Delta x^{3}}}\approx \Sigma^{2D}=\lim_{\Delta x\to 0}\overline{\frac{L_{f}}{\Delta x^{2}}} \tag{3}\]  
\(\Sigma^{2D}\) represents the two-dimensional flame surface density. The above equation assumes that there are no wrinkles present in the direction perpendicular to the measurement plane of the flame. This methodology is widely used experimentally [15; 19; 24; 25], in which the flame is extracted from the border of the unburned and burned region, denoted by OH intensity. \(\Sigma^{2D}\) in Equation (3) can be obtained as follows:  
\[\Sigma^{2D}=\frac{1}{N}\overline{\sum_{i=1}^{N}}\bigg{(}\frac{\Delta L}{\Delta A }\bigg{)} \tag{4}\]  
where \(N\) is the number of realizations, and \(\Delta L\) and \(\Delta A\) are the flame surface length and control area, respectively.
## 4 Vibration Reduction by the MRE-Based ATDVA under Variable-Frequency Excitation
### Vibration Reduction by the MRE-Based ATDVA under Variable-Frequency Excitation  
The vibration-reducing performance of the MRE-based ATDVA was evaluated under the condition of variable-frequency excitation. We applied currents of 0 A, 1 A, 2 A and 3 A and evaluated the variable performance of vibration reduction under the condition of each current application (see Figure 22).  
The vibration reduction frequency range increased with increasing magnitude of the applied current. The ATDVA reduced vibrations in frequency ranges near 110 Hz at 0 A, 122 Hz at 1 A, 128 Hz at 2 A, and 130 Hz at 3 A. These findings are consistent with the data presented in Table 5 and indicate that the ATDVA's vibration-reducing performance depended on the applied current.  
The control algorithm presented was used to evaluate the vibration-reducing performance of the MRE-based ATDVA at the time of variable-frequency excitation (see Figure 23).  
Figure 21: Reduction frequency of ATMVA due to induced current.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Current (A)** & **Stiffness (kN/m)** & **Target Frequency (Hz)** \\ \hline
0.0 & 375.2 & 110 \\
0.5 & 389.1 & 116 \\
1.0 & 439.8 & 121 \\
1.5 & 470.1 & 125 \\
2.0 & 477.8 & 129 \\
2.5 & 481.4 & 130 \\
3.0 & 485.6 & 131 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Stiffness measurement of magnetorheological elastomer due to current.  
Figure 20: Shear deformation of magnetorheological elastomer.  
Figure 23: Vibration reduction results of adaptive tuned MRE vibration absorber by frequency-variable excitation. (**a**) Frequency response function, (**b**) Time response.  
Figure 22: Vibration reduction results of adaptive tuned MRE vibration absorber by frequency-variable excitation [current: 0, 1, 2, 3A].  
The MRE-based ATDVA achieved a vibration reduction of 8.38 dB in the designed target frequency range of 110-130 Hz (Figure 23a). In the time domain, it was found to reduce vibrations in a range of 47-53 s (Figure 23b).  
There was no significant difference between frequency characteristics when the MRE-based ATDVA was not installed and those in ranges other than the target frequency range for vibration reduction when the ATDVA was installed (Figure 23a). This suggests that unlike SDOF or dual dynamic vibration absorbers, the MRE-based ATDVA when installed produces no resonance frequency in a target object for vibration reduction under variable-frequency excitation. The phenomenon called resonance occurs if the excitation frequency at the time of variable-frequency excitation happens to match the resonance frequency of the target object for vibration reduction. Under the condition of variable-frequency excitation, therefore, superior vibration reduction is achieved by installing an MRE-based ATDVA rather than an SDOF or dual dynamic vibration absorber.
## 3 Experimental Results and Discussion  
### Single-Pass Second-Harmonic Generation (SHG) to 780.2 nm  
First, on the basis of our previous experiment, we find the optimized phase matching by adjusting the temperature of the PPMgO:LN crystal in a single-pass SHG experiment. When the power of the fundamental wave 1560.5 nm is 5.27 W, the best phase matching of the PPMgO:LN-crystal is found at a temperature of 81.8 \({}^{\circ}\)C. The results are shown in Figure 2.  
The single-pass one PPMgO:LN crystal can produce 334 mW for the 780.2 nm laser, and the efficiency is 6.3%; single-pass two PPMgO:LN crystals can produce 634 mW for the 780.2 nm laser, and the efficiency is 12.0%. These results are shown in Figure 3. If we increase the injection, an additional 780.2 nm laser can be produced.  
The transverse beam quality of the 780.2 nm laser is evaluated by using the beam quality factor parameter (\(M^{2}\)) in the two orthogonal transverse directions \(X\) and \(Y\). Figure 4 shows the 1/e\({}^{2}\) beam radius versus the axial position \(Z\) after passing through a plano-convex lens with a focal length of 80 mm. Fitting of the experimental data gives \(M_{X}^{2}\) = 1.05 and \(M_{Y}^{2}\) = 1.11.  
Figure 1: Schematic diagram of the single-pass sum-frequency generation (SFG) system; 1560.5 nm and 1878.0 nm lasers are frequency summed in PPMgO:LN to produce 852.3 nm laser. Temperature controllers for three PPMgO:LN crystals are not shown in this figure. DFB: distributed feedback diode laser; EDFA: erbium-doped fiber amplifier; TDFA: thulium-doped fiber amplifier; OI: optical isolator; \(\uplambda\)/2: half-wave plate; \(\uplambda\)/4: quarter-wave plate; BS: beam splitter plate; PBS: polarization beam splitter cube; DM: dichromatic mirror; NDF: neutral density filters; s: polarization.  
Figure 4: Beam quality factor \(M^{2}\)of the SHG laser at 780.2 nm. Typical values for the horizontal and vertical directions are \(M_{X}^{2}=1.05\) and \(M_{Y}^{2}=1.11\). Inset shows the typical intensity profiles of the SHG laser beam spot.  
Figure 3: Experimental data for single-pass second-harmonic generation (SHG) output (**a**) and the frequency doubling efficiency (**b**). The squares represent the case using one PPMgO:LN bulk crystal, and the circles represent the case using two cascaded PPMgO:LN bulk crystals.  
Figure 2: Temperature dependence of 780.2 nm laser power. Circles represent the experimental data. The optimal quasi-phase-matched temperature is 81.1 \({}^{\circ}\)C.  
We evaluate the frequency tunability of the 780.2 nm output by measuring the absorption spectra of Rb atomic vapor cells, while the 1560.5 nm fundamental laser frequency is scanned linearly. Figure 5 shows the Doppler-broadened absorption spectra of the 55\({}_{1/2}\)-5P\({}_{3/2}\) transition (D\({}_{2}\) line) for \({}^{87}\)Rb and \({}^{85}\)Rb atoms. These spectra indicate that the continuously tunable range of the doubled laser at 780.2 nm is at least 6.8 GHz.
# Biometrics for Internet-of-Things Security: A Review
## 6 Present Challenges
### Limited Resources of IoT Devices  
Many IoT devices have limited computing resources [11]. Biometric recognition in an IoT setting, which includes complex calculations such as data processing, matching and decision making, can incur more costs and add a heavier burden to IoT devices than traditional password-based authentication. Therefore, any new biometric authentication systems should optimize the usage of limited storage and battery power of IoT devices while responding swiftly to authentication requests [11]. Moreover, the implementation of a biometric system directly relies on the hardware of an IoT device. For example, IoT devices without camera sensors cannot have face-based authentication.
# Numerical Simulation on Dynamic Response of Drilling Parameters in Loaded Rock Mass
## 2 In Situ Drilling Test in Underground Coal Mine  
### Downhole Test Equipment and Methods  
In order to deeply explore the changes in various parameters in the process of underground drilling and to obtain accurate data while drilling, an in situ test of measurement while drilling was carried out in the 2309 track crossheading of the Guotun Coal Mine in the Shandong mining area. The test adopts the CMS1-1200/45 (Z) (from Shandong Tianhe Technology Co., Ltd., Zoucheng City, Shandong Province, China) intelligent anti-impact drilling vehicle system. The equipment adopts the integrated structure design of a pumping station and a drilling rig and is equipped with an automatic drill pipe loading and unloading mechanism. The diameter of the drilling hole is 150 mm, and the maximum single hole continuous drilling operation can reach 25 m, which meets the needs of large diameter pressure relief drilling in the construction of the track roadway side of the working face in this test. The diameter of the rock bolt hole is 28 mm, and the depth of the hole is 2.2 m. In addition, the drilling rig is also equipped with a GPD60 (A) torque sensor (from Zhengzhou Coal Machinery Hydraulic and Electrical Control Co., Ltd. in Zhengzhou, China), KHJ16 mine speed sensor (from China's Jiangsu Province Nanjing Keyite Electric Co., Ltd. Nanjing Keyite Electric Co., Ltd., Nanjing, China), and ZGJS12.6 mine displacement sensor (from Guizhou Mining Technology Co., Ltd., Zunyi, China as shown in Figure 1b. During the drilling process, the data from each drilling are collected, including key information such as the revolution speed, drilling parameters, time, drilling depth, pressure, drilling rate, and drilling torque. The downhole drilling test and the drill truck sensor arrangement are shown in Figure 1.
# Evaluating the Functioning Quality of Data Transmission Networks in the Context of Cyberattacks
## 3 Problem Statement
It is observed in Figure 3 that the process of the data packet delivery contains a waiting subprocess, characterized by the transformation \(\omega(s)\), and a service subprocess, characterized by the transformation \(h(s)\) (Figure 4). Therefore, we will solve the problem in two stages: at the first stage we will define the equivalent function of the part of the stochastic network which corresponds to the service process (data packet transmission) \(h(s)\) and at the second stage--\(\omega(s)\). This sequence is due to the fact that the waiting time of the packet service depends not only on the characteristics of network traffic, but also on the time of the successful transmission of data packets along the advisory route.
## 1 Introduction
### Contributions  
The notion of the mean field for coexisting MTC and HTC users is explored by considering consumed power and reliability to design multi-dimensional user state dynamics. Mean field existence for an MTC ecosystem is only possible if the user devices satisfy certain conditions. Each device would have to maximize its utility by considering the channel state and available spectrum resources as well as the interference it experiences. Implemented via an MFG approach, a converged solution is established by meeting a mean field equilibrium condition. Hamilton-Jacobi-Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations are formulated for the proposed game theoretic design and solved using a finite difference scheme. The contributions of this paper are described as follows:  
* Battery lifetime modeling is performed for battery-limited MTC nodes and the notions of an individual as well as network lifetimes are introduced and proven to equate to energy efficiency and power control in the discussed coexisting network.
* The uplink power control design in a coexisting HTC/MTC network is formulated as a stochastic differential game (SDG) by selecting appropriate state dynamics.
* A mean field game (MFG) for the MTC network is formulated which can accommodate a massive number of devices. State space of remaining battery energy and interference is considered to find an optimal power control policy for MTC transmit power allocations.
* A modified utility function is proposed for the formulated MFG depending on the signal-to-interference-plus-noise-ratio (SINR) thresholding and experienced interference power. A mean field approximation (MFA) technique is utilized to modify the formulated utility function to more appropriately adapt to the converted MFG design.
* The proposed MFG is solved using finite difference scheme with Lax-Freidrichs technique being utilized to solve the coupled FPK and HJB system of equations.
* Equilibrium analysis for the SDG as well as MFG design is performed by considering the formulated cost function.  
The remainder of the paper is organized as follows: In Section 2, a detailed literature review has been presented, along with the identification of problem and related solutions with their shortcomings. In Section 3, network and system architecture is described and a problem model is formulated to be solved using game theory in later sections. In Section 4, an SDG is proposed for the presented problem along with possible limitations in the extension of this game for a massive number of user devices. In Section 5, a mean field game formulation is provided to find an optimal power control policy design for transmit power allocation of MTCDs. Section 6 presents the simulation design and results to visualize the proposed solution to the problem being discussed along with an evaluation of different performance parameters.
## 4 Development of ViPER+ for Construction Vehicles and Equipment Pose Estimation
### Localization Subsystem
#### 4.2.3 Pose Estimation  
Pose estimation is used to calculate the boundary of large equipment and vehicles with several tags affixed to them. This is because reporting a single location is insufficient for all entities in proximity detection applications. Figure 3 shows an illustrated image of the pose of the vehicle. Like the vehicle, the pose of other equipment can be denoted with pairing of location and orientation \(((x,y),\theta)\).  
Figure 4 shows how the pose estimation was carried out in this study. Two inputs are required for pose estimation. The first input is the equipment's physical properties, that is, the equipment's shape (dashed line) and the position of the tags on the object (numbers in circles). The position of the output from the localization subsystem is the second input. The boundary of the object is used to make the pose estimation by aligning the two inputs. The difficulty in this approach is that the locations of the tags do not perfectly align with their placements such as tags 1 and 2. To address this difficulty, we created an objective function described in Equation (6).  
\[f(x,y,\theta)=\sum_{i}^{T}\sum_{j}^{size_{i}}\sqrt{\left(X_{i}-x_{i,j}\right) ^{2}+\left(Y_{i}-y_{i,j}\right)^{2}} \tag{6}\]  
\((X_{i},\ Y_{i})\) is calculated based on Equation (7)  
\[\begin{bmatrix}X_{i}\\ Y_{i}\end{bmatrix}=\begin{bmatrix}cos(\theta)&-sin(\theta)\\ sin(\theta)&cos(\theta)\end{bmatrix}\times\begin{bmatrix}p_{x,i}\\ p_{y,i}\end{bmatrix}+\begin{bmatrix}x\\ y\end{bmatrix} \tag{7}\]  
The remaining parameters are estimated using Table 2. Using this function, the distance between the location of the tags in a given boundary and the estimated locations for that tag can be calculated. To determine the best boundary that aligns the input point, the boundary \((x^{*},y^{*},\theta^{*})\) with minimum value must be identified. The boundary can be determined by solving a non-linear optimization problem. This means that the boundary of the vehicle is the output of the optimization problem.  
Figure 3: The defined boundary of vehicles in ViPER+.
## 3 Results and Discussion
### Films Characterization
#### 3.1.3 Barrier Properties
Therefore, the developed PCL/ASE films could have potential application as packaging materials for fatty foods according to the increase observed in the solubility in water and WVP of these active films in contrast to neat PCL. Moreover, no significant differences (\(p>0.05\)) were observed between samples regarding OTR values. These results are in line with the conclusions supported by other work in which the addition of polyphenols extracted from mango peel into acetylated hemicellulose-nanocellulose-PCL films were suitable for application in packaging of oily or fatty foods [55]. In addition, a similar trend was obtained by [PERSON] et al. [15] and [PERSON] et al. [17] who reported no significant differences (\(p>0.05\)) between PCL-based films with and without the addition of \(\alpha\)-tocopherol and hydroxytyrosol, respectively, indicating the suitability of the developed active materials to be used for oxygen-sensitive products.
## 2 Model and Methods
### Lyapunov Exponents
#### 2.2.3. Window Characteristics Predicted by Lyapunov Exponents  
In Figure 3 we present 2 unique LE profiles of windows in chaos, WI and WII, that are graphed within their domains as a function of \(Y\). The 2-color scheme in each graph of WI and WII is the result of forward (black) and backward (red) scans of their domains. Note, that the backward scan is not an exact replica of the forward scan. Since these scans are executed adiabatically, the accuracy of landing on WI or WII is imperfect. That is, we believe, the backward scan follows the chaotic trajectories temporarily because the accuracy of the calculations is insufficient to acquire WI and WII at their demise.  
Figure 2: (**a**) Global view of 3 attractor domains using the first 3 LEs as a function of the injected field \(Y\), AII coexists with AIII, AV, and AVI (domains D, E, and F respectively); Chaos coexists with AVII and AVIII (domains B and C, respectively); and AI coexists with AIV (domain A). (**b**) is AIV, (**c**) is AVII, and (**d**) is AVIII.  
It is easy to see that as a function of the control parameter \(Y\), the 2 windows of Figure 3 possess asymmetric bubble patterns. The significance of these results is that windows can be identified using LEs in the same way that attractors are identified. Further, the discovery of the windows in this study was made using the power-shift method [34], as described in Appendix A. By dividing the range \(\Delta Y\) into _1001_ segments and evolving for \(T=\) 20,000\(\tau\) at \(Y=\) 1.229 we found WI and at \(Y=\) 1.2575 we found WII.