esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Validations and Applications  
### Validations  
The vertical unit displacement is defined as 0.001 m (0.0025 \(B\)), and the torsional unit displacement is defined as 0.5\({}^{\circ}\). Using the CFD numerical simulation method, the aerodynamic responses of the scaled-girder section of the GBEB, under the unit pulse excitation and twice unit pulse excitation, are calculated under the wind attack angle of 0\({}^{\circ}\) and the calculation wind speed of 5 m/s, respectively. Moreover, Equation (3) is utilized to calculate the first-order kernel. The duration of the impulse function is defined as \(T=0.01\) s, corresponding to the nondimensional time of \(T^{\star}=T\cdot U/B\approx 0.13<0.2\) ([PERSON] et al. [25]). Figure 7 shows the first-order kernel of the lift-force coefficient \(C_{L}\) and torsion-moment coefficient \(C_{M}\) of the main deck section of GBEB in the heaving and pitch motion. While the first-order kernel responds significantly to pulsed excitation, it quickly approaches zero within a few non-dimensional time steps after the excitation ends.  
Figure 6: Schematic representation of the numerical simulation based on the Volterra model.  
Figure 7: The first-order kernels of lift and moment coefficients (**a**) in the heaving motion; (**b**) in the pitch motion.  
To verify the accuracy of the established aerodynamic model of the main deck section of GBEB based on the Volterra series, the Volterra model is utilized to calculate the aerodynamic force coefficients under multiple sinusoidal displacements. The expression of the heaving and pitch motion is shown in Equation (12). The aerodynamic ROM simulation calculation is carried out at 0\({}^{\circ}\) wind attack angle. The results are compared with those of CFD numerical simulation under six operating conditions with vertical displacements of about 0.01 \(B\) and 0.10 \(B\), torsional angles of 3\({}^{\circ}\) and 6\({}^{\circ}\), and frequencies of 1.0 Hz and 4.0 Hz, respectively. Tables 1 and 2 show the parameters of each operating condition and the aerodynamic calculation results of the two methods.  
\[y =y_{h}\sin(2\pi f_{h}t) \tag{12a}\] \[\theta =\theta_{a}\sin(2\pi f_{a}t) \tag{12b}\]  
Figure 8 indicates the time histories of the aerodynamic force coefficients calculated by the two methods. It can be seen from Figure 8 that the aerodynamic system of the girder section of GBEB has good identification results from the Volterra first-order model in both the heaving and pitch motions, and the overall error is almost within 10%. When the forced vibration amplitude (the vertical amplitude is less than 0.1 \(B\) and the torsional amplitude is less than 6\({}^{\circ}\)) or frequency (less than 4 Hz) is changed in a limited range, the error of the simulation of the Volterra first-order model does not increase significantly compared with the CFD numerical simulation results. Therefore, it can be considered that the Volterra first-order model established in this section can effectively simulate the aerodynamic force of the girder section of GBEB.
# Review on Structural Health Evaluation with Acoustic Emission
## 2 Signal Attenuation
### Complex Elastic Moduli Measurements
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline
**Ref. No. 1** & **Material** & **Axis 2** & **n11** & **n22** & **n33** & **n44** & **n55** & **n66** & **H12** & **n13** & **n23** & **Freq (MHz)** \\ \hline
[60] & Epoxy & & 0.01 & 0.01 & & & & 0.011 & & & \\
[49] & PMMA & & 0.023 & 0.056 & & & & 0.026 & 0.046 & & & 0.3 \\
[49] & GFR-UD & 3 & 0.047 & 0.043 & 0.033 & & 0.050 & 0.038 & 0.033 & 0.033 & 0.036 & 0.3 \\
[61] & GFR-UD & 3 & 0.051 & 0.038 & 0.056 & 0.100 & 0.070 & 0.091 & 0.014 & 0.106 & 0.05-0.2 \\
[61] & GFR-UD & 3 & 0.027 & 0.057 & 0.06 & 0.063 & 0.11 & 0.037 & 0.1 & 0.05-0.2 \\
[62] & GFR-UD & 1 & 0.061 & 0.024 & 0.046 & 0.084 & 0.062 & 0.055 & 0.114 & 0.105 & 0.029 \\
[63] & GFR-UD & 1 & 0.086 & 0.045 & & & 0.06 & 0.037 & 0.033 & 0.005-0.2 \\
[64] & GFR-UD & 1 & 0.024 & 0.043 & 0.041 & 0.032 & 0.056 & 0.093 & 0.143 & 0.074 & 0.2–0.93 \\
[64] & GFR-QI & 1 & 0.041 & 0.067 & 0.034 & 0.045 & 0.044 & 0.068 & 0.071 & 0.016 & 0.2–0.93 \\
[65,66] & GFR-XP & 1 & 0.020 & 0.018 & 0.001 & 0.010 & 0.017 & 0.026 & 0.018 & 0.016 & 0.001 & 0.01–1 \\
[65,66] & GFR-UD & 1 & 0.015 & 0.099 & 0.099 & 0.010 & 0.045 & 0.045 & 0.015 & 0.015 & 0.261 & 0.01–1 \\ \hline \end{tabular}  
* \({}^{1}\): Ref. No. refers to reference number. 2. Ask indicates the coordinate direction along fiber direction (0° orientation in QI-quasi-isotropic or XP-cross-ply).  
\end{table} 
Table 1: Damping factors from complex elastic moduli measurements  
Figure 4: Damping factor of PMMA over 11 decades of frequency, peaking at 3 Hz, from the literature. Torsional damping: blue X [47], blue circle [51], green circle [52]; longitudinal damping: green + [53], red circle [54], purple square [55], purple diamond [56].
# Forest Fire Detection and Notification Method Based on AI and IoT Approaches
## 3 Fire Detection Approach
### Dataset
#### 3.4.4 Fire Department  
Our research team is not able to have a big influence on how the fire department operates. Therefore, this part of Section 3.4.4 is theoretical but possible with a simple collaboration with the fire department. Once the fire is confirmed by the YOLOv5, it then records the footage and sends it to the fire department by email using the Simple Mail Transfer Protocol (SMTP). SMTP is a very simple protocol; it uses email and password that originally come from Google account. In the code, we apply the SMTP method and write the receiver's email address and what we want to send. We can choose a file, folder, and type of image or video to be sent to the receiver. After the footage is sent, it is reviewed by the fire department, which then verifies and validates it, and acts accordingly. If the fire department confirms the video as hazardous fire, they send a notification alarm within the one-mile radius of where the footage was taken. If the fire is just a smoker or a camper trying to light a fire, the fire department sends a notification within the one-mile radius of where the footage was taken, notifying nearby hikers about the penalty of lighting a fire in  
Figure 2: YOLOv5 network structure [39].  
the forest. All the messages are sent through the Wireless Emergency Alert (WEA) that all emergency stations have in Korea [32; 33].
## 3 Test Results and Discussion
### Local Stress on the Compression Side of Anchor Hole in Fe-SMA Plates  
Figure 14 shows the strain-activation temperature curve of Fe-SMA near the outer side (strain gauge 3) of the anchor bolt hole. It can be observed that during the activation process, the strain of the adhesive-bolted hybrid connection is first positive and gradually increases. As Fe-SMA is further activated, the strain begins to decrease and gradually becomes negative. This is because in the early stage of activation, the strain of the Fe-SMA is relatively small, and in the adhesive-bolted hybrid connection, the structural adhesive mainly bears the shear force, while the Fe-SMA on the outer side of the anchor bolt is subjected to tensile force. As the total strain of the Fe-SMA further increases, the structural adhesive's load-bearing capacity becomes insufficient, and the anchor bolts begin to bear shear forces. The Fe-SMA outside the anchor bolt hole begins to receive compression from the anchor bolts, exhibiting compressive strain. The strain of the Fe-SMA at the anchor bolt hole in the bolted connection is initially compressive strain with activation of the Fe-SMA and gradually increases, with a maximum compressive strain of 0.0024, further confirming the local stress concentration problem of the Fe-SMA in the bolted connection.
# CFD Analysis of Subcooled Flow Boiling in 4 x 4 Rod Bundle
## 3 Numerical Methods
### Bubble Diameter Model  
In this three-dimensional two-phase flow analysis, the bubble size is a very important factor for accurate prediction of bubble behavior in the flow path. [PERSON] (2004) and [PERSON] (2005) developed a kinematic model based on the IAC and bubble number density transport equations for accurate bubble size prediction. The model has been developed and requires subsidiary equations [32; 33].  
The bubble size is known to be related to the hydraulic diameter of the flow path, Laplace length scale, volume fraction, and Reynolds number of the bubble through the previously developed models of [PERSON] et al. (1994) [34], [PERSON] (1996) [35], and [PERSON] et al. (2006) [36]. Thus, bubble size model is formed by  
\[d_{sm}=\alpha^{A}N_{Reb}^{B}L_{O}^{C} \tag{19}\]  
where \(\alpha\) is the volume fraction. The bubble Reynolds number (\(N_{Reb}\)) to consider the turbulent dissipation term (\(\varepsilon\)) and the Laplace length scale (\(L_{O}\)) are given by  
\[N_{Reb}=\frac{\varepsilon^{1/3}L\alpha^{4/3}}{v_{f}} \tag{20}\]  
\[L_{O}=\sqrt{\frac{\sigma}{g\Delta\rho}} \tag{21}\]  
In Equation (20), the subscript f means liquid phase. In Equation (21), \(\sigma\) is the surface tension, \(g\) is the gravitational acceleration, and \(\Delta\rho\) is the density difference between phases.  
Based on Equation (19), [PERSON] realized a bubble size model applicable to the bubble flow as  
\[d_{sm}=4.34\alpha^{0.36}N_{Reb}^{-0.696}L_{O}^{0.665}/D_{H} \tag{22}\]A simple form of Equation (20) is derived by adding the density ratio and excluding the hydraulic diameter included in the bubble Reynolds number [37]. [PERSON] derived a bubble diameter model from [PERSON] by applying DEDALE, Hibiki, and DEBORA experimental data for both low- and high-pressure conditions. The bubble size model of [PERSON] (2015) [30] used in this study is given by  
\[d_{sm}=32.39\alpha^{0.289}N_{Reb}^{-0.529}N_{\rho}^{-0.06}L_{O} \tag{23}\]  
where the density ratio (\(N_{\rho}\)) to reflect pressure is given by  
\[N_{\rho}=\frac{\rho_{f}}{\rho_{g}} \tag{24}\]  
and the subscripts \(f\) and \(g\) are the liquid phase and gas phase, respectively.
## 5 Discussion  
By extending to the quaternion TV and using the \(L_{p}\) quasinorm constraint based on the FTV method, the proposed method fully utilizes image correlation in four directions and combines it with the \(L_{p}\) quasinorm constraint to perform image reconstruction well, even in the presence of noise interference.  
For the Bicubic and MFT methods, for both the noise-free and the added noise images, the reconstruction results are over-smoothed and the details are unclear.  
Though the TV method preserves the edge and detail information of an image because of its piece-wise smooth processing, its results often include staircase artifacts. In contrast, because of the constraints of the image with the first-order and second-order gradients, the TGV-based method effectively reduces staircase artifacts; however, it also leads to over-smoothing and image distortion.  
Without added white Gaussian noise, the TV4-based and FTV4-based methods show a \"spots\" effect in some images. Moreover, the TV4-based method has a similar \"painting\" effect under white Gaussian noisy conditions, which results in a large distortion relative to the original image. With white Gaussian noise, the visual effects of the FTV4 reconstruction are close to those of the proposed FTV4L  
Figure 11: Super-resolution x2 results of the LR figure images with added white Gaussian noise (\(\sigma=20\)); (**a–d,i-l**) ground truth and the results of the Bicubic, MFT, TV, TGV, TV4, FTV4 and FTV4 Lp methods, respectively; (**e–h,m–p**) enlarged details from the rectangles in (**a–d,i-l**), respectively.  
reconstruction; however, based on the experimental data analysis, the objective evaluation of the former is inferior to that of the FTV4L\({}_{p}\) reconstruction.  
Furthermore, after introducing a fast ADMM algorithm, the number of ADMM iterations is significantly reduced, which improves the efficiency of the algorithm.
## 5 Establishment of 3D Geological Model
### Structural Model  
Structural modeling is founded on well logging data and a meticulously established fine stratigraphic framework. The modeling software employed in this research was Petrel'TM, and the single-layer three-dimensional formation structure model conforming to the principle of isochronous modeling was initially constructed. Since no faults are developed in this area, stratification data, log data as hard data, structural isocontours as trend surfaces, and formation thickness data were used to calibrate the volume. On this basis, using the method of curvature minimization and thickness superposition, combined with Kriging interpolation, the layer models of the top and bottom layers of the Chang 7\({}_{1}\)1, Chang 7\({}_{1}\)2, Chang 7\({}_{2}\)1, Chang 7\({}_{2}\)2, and Chang 7\({}_{3}\) layers were constructed. The 3D model was composed of 100 layers longitudinally, the vertical grid accuracy was 0.5 m, the horizontal geographical unit size was 50 \(\times\) 50 m, the number of I, J, K direction grids was \(120\times 120\times 100\), and the total number of grids was 144,000, ensuring a high-resolution model that captures the intricate geological features of the study area. After spatial combination, a complete three-dimensional stratigraphic structure model was built (Figure 3). Analysis of the resulting model revealed that the overall structural configuration of the area is relatively subdued, predominantly characterized by a topography that is higher in the east and lower in the west. Some regions exhibit depressions, yet there are no extensive fault zones. Within the study area's target layer, the Chang 7 member is devoid of faults, and the occurrence of fractures is exceedingly rare. The strata are gently inclined, demonstrating a strong inheritability among the various sublayers.
## 6 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**RNN Cell**} & \multirow{2}{*}{**Hidden Units**} & \multicolumn{5}{c}{_DSA_} & \multicolumn{5}{c}{_SPHAR_} \\ \cline{3-10}  & & & **Test Acc. (\%)** & **F1 Score** & **Train Time (min)** & **Model Size (KB)** & **Test Acc. (\%)** & **F1 Score** & **Train Time (min)** & **Model Size (KB)** \\ \hline BasicRNN & 8 & 0.75 & 0.73 & **4.48** & 2.86 & 0.74 & 0.72 & 2.61 & 1.02 \\ \hline  & 16 & 0.84 & 0.83 & 4.93 & 4.23 & 0.84 & 0.83 & 2.51 & 1.80 \\ \hline  & 32 & 0.90 & 0.90 & 4.75 & 6.98 & 0.86 & 0.85 & **1.71** & 3.36 \\ \hline FastGRNN & 8 & 0.92 & 0.91 & 10.98 & 2.09 & 0.88 & 0.88 & 7.15 & **0.93** \\ \hline  & 16 & 0.96 & 0.96 & 11.65 & 3.21 & 0.91 & 0.91 & 7.04 & 1.64 \\ \hline  & 32 & 0.97 & 0.97 & 8.98 & 5.46 & 0.94 & 0.93 & 5.62 & 3.08 \\ \hline FastRNN & 8 & 0.95 & 0.94 & 6.76 & **1.79** & 0.91 & 0.91 & 7.73 & 1.28 \\ \hline  & 16 & 0.97 & 0.97 & 7.95 & 2.79 & 0.94 & 0.94 & 7.97 & 2.25 \\ \hline  & 32 & 0.98 & 0.98 & 6.54 & 4.79 & 0.94 & 0.94 & 7.95 & 4.19 \\ \hline UGRNN & 8 & 0.95 & 0.94 & 7.02 & 2.72 & 0.92 & 0.92 & 11.35 & 1.23 \\ \hline  & 16 & 0.97 & 0.97 & 7.18 & 4.32 & 0.92 & 0.92 & 12.24 & 1.91 \\ \hline  & 32 & 0.98 & 0.98 & 10.49 & 7.50 & **0.94** & **0.95** & 12.66 & 3.66 \\ \hline GRU & 8 & 0.95 & 0.94 & 14.63 & 3.92 & 0.93 & 0.93 & 18.07 & 1.57 \\ \hline  & 16 & 0.98 & 0.98 & 6.19 & 6.36 & 0.93 & 0.94 & 11.19 & 2.95 \\ \hline  & 32 & **0.99** & **0.98** & 10.30 & 11.23 & 0.93 & 0.93 & 9.18 & 5.70 \\ \hline LSTM & 8 & 0.90 & 0.90 & 12.33 & 6.28 & 0.91 & 0.91 & 13.13 & 1.60 \\ \hline  & 16 & 0.98 & 0.98 & 9.52 & 10.37 & 0.94 & 0.94 & 13.60 & 3.04 \\ \hline  & 32 & 0.98 & 0.97 & 13.08 & 18.56 & 0.94 & 0.94 & 21.41 & 5.91 \\ \hline  & Best Performance & \multicolumn{5}{c}{GRU with 32 hidden units} & \multicolumn{5}{c}{UGRNN with 32 hidden units} \\ \hline Least Model Size & \multicolumn{5}{c}{FRNN, FGRNN} & \multicolumn{5}{c}{FRNN, FGRNN, BasicRNN} \\ \hline Memory savings via Fast Gates & \multicolumn{5}{c}{3.55\(\times\) with respect to GRU16, 6.27\(\times\) with respect to GRU32} & \multicolumn{5}{c}{6.35\(\times\) with respect to LSTM32, 3.93\(\times\) with respect to UGRNN2} \\ \hline Performance deviation via Fast Gates & \multicolumn{5}{c}{up to 4\%} & \multicolumn{5}{c}{up to 7\%} \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance of Recurrent Neural Networks across Daily and Sports Activity (DSA) dataset and Smart Phone Human Activity Recognition (SPHAR) dataset with 8, 16, and 32 hidden units. Numbers in bold font represent the best results under each column, and numbers in blue font represent figures that deviated from the best results in each column.
## 3 Proposed Deep PUF
### Authentication Phase  
Figure 4 shows the authentication procedure as well as the major parts of server and device. In each authentication request from the PUF device, the server sends one of the challenges (prearranged class labels) to the device to configure a particular DRAM block as a PUF. Next, the device generates the corresponding response and sends it to the trusted server. Then, the server authenticates the device in two steps:  
1. The received raw bits are classified using CNN, structured during the enrollment phase.
2. The detected label is compared with the original challenge.  
The device will be authenticated if the class label in which the response was categorized matches the original challenge. Otherwise, if the class of received response and the sent challenge are different, the authentication will be discarded, and the server will reject the device's request to exchange data. In this structure the burden of device authentication is completely moved to the server, which has almost no resource limitations. Therefore, deep PUF enables an authentication process for resource-constrained nodes without any extra implementation overheads.
# A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
## 2 Technology Overview
### Transmission Parameters
#### 2.3.4 Transmission Power  
In LoRaWAN networks, the power essential for the transmission of a data packet is adjustable as appropriate. Lowering the transmission power will save the battery but shorten the signal range and vice versa. The LoRa radio the TP is adjustable from \(-4\) dBm to 20 dBm in notches of 1 dB. However,in real life deployments, the TP range is commonly restricted to between 2 dBm and 20 dBm because of the hardware limitations. Additionally, if the TP levels greater than 17 dBm are experienced, only a duty cycle of 1% can be utilised [21].
# Bluetooth Communication Leveraging Ultra-Low Power Radio Design
## 3 BLE Radio Design Trends and Considerations
### Transmitter Design Trends
#### 3.2.1 Direct GFSK Modulation for Open-Loop LCVCO  
BLE utilizes GFSK as its modulation scheme for data transmission. Traditionally, 2-point injection in the PLL is used to modulate the output frequency while the PLL is locked [10, 37, 48, 49]. The digital baseband of the BLE transmitter generates control signals according to the data packet and injects the corresponding frequency control words to the PLL reference and the VCO at the same time. In this way, by balancing the reference phase and the VCO phase at the same time, the frequency change in the VCO output will not cause a transient signal in the loop for the PLL during transmission. However, this topology requires matched control at both of the injection points and it is easily affected by the PLL bandwidth, data rate and frequency deviation according to the BLE standards. Recently, it was discovered that typical LC-VCOs can easily satisfy the phase noise requirement of the BLE standard while running open loop [50]. More and more designs have started to utilize direct frequency modulation on an open-loop VCO [5, 6, 51, 52, 53]. This calibration and open loop modulation method has several benefits compared to the traditional modulation method: (1) it simplifies the role of the PLL in the BLE transmitter and reduce its design complexity, (2) it could reduce the average power consumption of the transmitter by duty cycling the PLL and (3) it enables new design methodologies for BLE transmitters such as crystal-less design.
## 2 Features of the Study Area
### Hydrological Information  
To determine the amount of rainwater that can be harvested by interception on the rooftops of buildings in the study area (residential, service, and commercial zones), daily rainfall records from the local meteorological station (Station ID 13022) were obtained. The records from 2000 to 2022 were considered, with data extracted from the CLICOM database [26]. Given the high rainfall variability in the area, it is recommended to consider a probability of occurrence more significant than 50% [27]. This study selected a probability of 75%. Figure 2 shows that average rainfall during the rainy months ranges from 13.21 to 23.50 mm (May to October, respectively), with August being the wettest month.  
Figure 1: Location of the study area and its primary land uses.  
Figure 2: Monthly rainfall distribution with a 75% probability of occurrence.  
For this study, the rainfall data for August was selected to determine the harvestable volumes from rooftops. To quantify this harvestable volume (Vrh), Equation (1) was used [28]:  
\[Vrh=\frac{R75\times A\times Rc}{1000} \tag{1}\]  
where:  
* _Vrh_ = rainwater harvesting volume (m\({}^{3}\));
* _R75_ = monthly available rainfall (75% occurrence probability, in _m_);
* \(A\) = rooftop area (m\({}^{2}\));
* _Rc_ = runoff coefficient (0.85) [29].  
Based on Equation (1), factors affecting the harvestable rainwater volume were considered to establish an adjustment factor (Vrha). Some criteria include the variety of materials that constitute rooftop surfaces (in this analysis, concrete was considered, Rc = 0.85) and different roof slopes (a minimum slope of 2% was selected); when slopes are minimal, there can be higher water absorption and increased evaporation, thus a 15% water loss was considered [29]. Additionally, the separation of the first rain was taken into account to ensure good water quality for storage (_2L_, equivalent to 8.5% of R75) [28]. Overall, the absorption and evaporation loss and the separation of the first rain volume as a pretreatment measure result in a total loss of 0.235, so the adjusted runoff coefficient equals 0.765. Based on these criteria, the adjusted equation used to determine the _Vrha_ is as follows (Equation (2)).  
\[Vrha=\frac{R75\times A\times Rc}{1000}\times(1-\begin{array}{c}\text{loss due to}\\ \text{absorption}\\ \text{and evaporation}\end{array}-\begin{array}{c}\text{loss due to}\\ \text{pretreatment}\end{array})=\frac{R75\times A\times Rc}{1000}\times 0.765 \tag{2}\]
## 2 Results
### Antimicrobial Activity Assessment
#### 2.2.2 Quantitative Assay of Antimicrobial Activity  
The broth dilution method was used to quantitatively assess the in vitro antimicrobial profile of compounds **1a-f**; **2a-f**; **3a**; **4a-f**; **5a,b**; **5d,e**; **5g,h**; **5j,k**; **5m,n**; **6g-k**; **6m**; and **6o,p** against two Gram-positive bacteria (_S. epidermidis_ 756 and _B. subtilis_ ATCC 6683), two Gram-negative bacteria (_E. coli_ ATCC 25922 and _P. aeruginosa_ ATCC 27853), and one yeast strain (_C. albicans_ 128).  
Compounds **1a-c**; **4b-f**; **5a,b**; **5d,e**; **5g,h**; **5j,k**; **5m,n**; **6g,h**; **6k**; **6m**; and **6o,p** did not inhibit the growth of any of the tested microbial strains up to a concentration of 225 ug/mL. In contrast, the MIC values of compounds **1d-f**; **2a-f**; **3a**; **4a**; and **6i,j** were much lower in the case of some microbial strains with MICs in the range of 56.2 to 14 ug/mL being recorded (Table 2). Compound **1e** exhibited a wide spectrum of antimicrobial activity, being active on both Gram-positive and -negative bacterial strains as well as against the fungal strain _C. albicans_ 128, for which MICs \(\leq\)56.2 ug/mL were recorded. Compounds **1d,e**; **3a**; **4a**; and **6i,j** exhibited very good antifungal activity with MIC values of 14 ug/mL.