esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Image Acquisition and Methodology
### Single-Channel Gravitational Kernel Density Clustering Algorithm
#### 3.3.2 Gravity Calculations between the Pixels  
It was derived from Section 3.3.1 that the number of clusters and initial cluster centers of the diseased leaves may be obtained from the feature histograms of the diseased leaves. According to the law of universal gravitation, the attraction between an object and another object is not only related to the mass of the object but also to the distance between the objects, as shown in Equation (9),  
\[F=G\frac{Mm}{r^{2}}. \tag{9}\]  
In the formula, F represents universal gravitation, m and M represent the mass, G represents the universal gravitational constant, and r is the distance between the objects. According to the formula of universal gravitation, if the mass of an object is greater, it would be more attractive to other objects, and the closer the distance between them, the greater the attraction. In the universe, if the mass of an astral is very large, many asteroids would take this astral as the center and rotate around it to form a galaxy. Based on this, this paper proposes a gravity kernel density clustering algorithm. The cluster center is similar to a large star, and the classification of the pixels is differentiated according to the gravity of the cluster center to the pixels.  
First, we defined the quality of the pixels. According to the histogram, the number of pixels n\({}_{\rm i}\) (i = 0,1, \(\ldots\), 255) with different red R-values was calculated. Therefore, for a pixel with a value of i (i = 0,1, \(\ldots\), 255), whose number is \(n_{i}\) and quality is \(m_{i}\):  
\[m_{i}=n_{i}\frac{1}{\sum\limits_{i=0}^{255}n_{i}}\ \ i=0,1\cdots 255. \tag{10}\]  
Suppose an image I has a size of M \(\times\) N, and I(x, y) represents an arbitrary pixel point in the image I, where x and y represent the coordinate values of the pixels, respectively, \(x\) = 1,2, \(\ldots\), M, \(y\) = 1,2, \(\ldots\), N. There are two-pixel points \(I(x_{s},y_{s})\) and \(I(x_{t},y_{t})\), and the corresponding values of these two pixels are \(I_{s}\) and \(I_{t}\), respectively. The kernel density distance, rst, between these two pixels is:\[r_{st}=exp\left[\frac{\left(x_{s}-y_{s}\right)^{2}+\left(x_{t}-y_{t}\right)^{2}}{h _{1}}\right]exp\left[\frac{\left(l_{s}-l_{t}\right)^{2}}{h_{2}}\right], \tag{11}\]  
where \(exp\left[\frac{\left(x_{s}-y_{s}\right)^{2}+\left(x_{t}-y_{t}\right)^{2}}{h_{1 }}\right]\) represents the spatial kernel density distance between the pixels, \(exp\left[\frac{\left(l_{s}-l_{t}\right)^{2}}{h_{2}}\right]\) represents the distance of the gray kernel density between the pixels, and \(h_{1}\) and \(h_{2}\) represent the bandwidths of the kernel density function, respectively.  
Then, according to Equation (9), the gravitational force \(F_{st}\) between the pixels \(I(x_{s},y_{s})\) and \(I(x_{t},y_{t})\) may be calculated using Equation (12). For calculation convenience, use G = 1 here.  
\[F_{st}=G\frac{m_{s}m_{t}}{r_{st}}. \tag{12}\]
## 2 Conceptual Framework of AgroTRACE
### Recording  
The carriers pertaining to GS1 system data are utilized for the sake of data management in order to address the needs of the supply chain process for a variety of products. Specifically:  
* At retail stores, the EAN/UPC barcodes are implemented for scanning.
* In the interest of identifying the product's units on the pallets and packaging for monitoring products' movement and acquiring information about them across the supply chain, the GS1-128 barcodes are employed.
* GS1 DataBar barcodes are also used, which can provide the same or more information and in less space comparing to UPC barcodes.
* UPC barcodes are used for small-sized products and for products that are difficult to track.
* RFID tags are used which are connected with the products' EPC.
* The data, which are coded in data carriers of the GS1 system, are able to identify the products and enable trading partners to share a great amount of data including the number of batch, the date of production and packaging information, to mention but a few.
# Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
## 4 Case Study
### Power Station Structure and Vulnerability
#### 4.3.6 Gas Turbines and Steam Turbines  
The station has two types of turbines: a gas turbine and a steam turbine. The gas turbine spans 62 m in height, 82 m in length, and 47 m in width. Damage to the gas turbine can result from failures of the burners, air compressor, and the turbine itself. The steam turbine stands 47 m tall, extends 71 m in length, and has a width of 33 m. The condenser of the steam turbine, measuring 82 by 44 by 62, is made of steel and is placed on a raft foundation. Damage to the steam turbine may involve the transformers, generators, the turbine itself, the air compressor, or the steam condenser.  
This analysis considers the probability of damage to the gas and steam turbines. The probability of damage to one turbine is determined to be 0.0416, based on fragility curve parameters specific to the turbine system by HAZUS (Figure 10). To ensure the station's continuous operation, 8 out of 12 gas turbines and both steam turbines are required to be functional. Following the binomial distribution analysis, the probability of damage is determined to be 0.0416 for the steam turbine and 7.68 \(\times\) 10\({}^{-5}\) for the gas turbines.  
Figure 9: Fragility curves for main subcomponents of the steam boiler system.
## 2 Materials and Methods
### A Description of GPU-Accelerated Block-Matching Algorithm in USE
#### 2.1.2 [PERSON] Sum-Table Method  
When the block matching algorithm is used, significant overlaps among adjacent tracking kernels exist, resulting in a lot of redundant computation. Thus, [PERSON] proposed to use pre-computed tables (also known as Sum-tables) to partially \"memorize\" some correlation between \(f\) and \(g\)[23]. Together with Fourier Transform (FT), [PERSON] method can conceptually reduce the computational demands. More specifically, the numerator in Equation (1) is a convolution of the tracking kernel within the reference signal \(f\) with the corresponding tracking kernel of the reversed target signal \(g(-m,-n,-k)\) and can be computed by Fourier Transform (FT). The process is defined as follows:  
\[\sum_{m,n,k}f\left(m,n,k\right)\bullet g\left(m-\tau_{x},n-\tau_{y},k-\tau_{z }\right)=\mathcal{F}^{-1}\bigg{\{}\mathcal{F}(f)\mathcal{F}^{*}(g)\bigg{\}} \tag{3}\]  
where \(\mathcal{F}\) stands for an FT operator. The complex conjugate accomplishes reversal of the template via the FT's time reversal property [23]. Equation( 3) was implemented via fast Fourier Transform (FFT) and thus, \(f\) and \(g\) were zero-padded to a common power of two.  
Now referring to the calculation of the denominator in Equation (1), two sum tables were used: one for the reference signal \(f\) and the other for the target signal \(g\). Let \(s_{f}^{2}(u,v,w)\) and \(s_{g}^{2}(u,v,w)\) denotes the created sum-tables for \(f\) and \(g\), respectively. More details about setting up these two sum-table can be found in Appendix A. Once these two tables become available, the calculation of denominator can be conducted through a very efficient manner as follows,  
\[\begin{array}{l}\sum_{m=u}^{u+W_{x}-1}\sum_{n=v}^{v+W_{y}-1}\sum_{k=w}^{w+W_ {z}-1}f^{2}\left(m  
\[\sum_{m=u}^{u+W_{x}-1}\sum_{n=v}^{v+W_{y}-1}\sum_{k=w}^{w+W_{z}-1}f \left(m,n,k\right)\bullet g\left(m+\tau_{x},n+\tau_{y},k+\tau_{z}\right) \tag{6}\] \[=\mathrm{s}_{f,g}\left(u+W_{x}-1,v+W_{y}-1,w-1+W_{z},\tau_{x},\tau _{y},\tau_{z}\right)\] \[-\mathrm{s}_{f,g}\left(u+W_{x}-1,v+W_{y}-1,w-1,\tau_{x},\tau_{y}, \tau_{z}\right)\] \[-\mathrm{s}_{f,g}\left(u+W_{x}-1,v-1,w-1+W_{z},\tau_{x},\tau_{y}, \tau_{z}\right)\] \[-\mathrm{s}_{f,g}\left(u-1,v+W_{y}-1,w-1+W_{z},\tau_{x},\tau_{y}, \tau_{z}\right)\] \[+\mathrm{s}_{f,g}\left(u-1,v-1,w-1+W_{z},\tau_{x},\tau_{y},\tau_{ z}\right)\] \[+\mathrm{s}_{f,g}\left(u-1,v+W_{y}-1,w-1,\tau_{x},\tau_{y},\tau_{ z}\right)\] \[+\mathrm{s}_{f,g}\left(u+W_{x}-1,v-1,w-1,\tau_{x},\tau_{y},\tau_{ z}\right)\] \[-\mathrm{s}_{f,g}\left(u-1,v-1,w-1,\tau_{x},\tau_{y},\tau_{z}\right)\]  
According to Equations (4)-(6), the calculation of NCC can be simplified to addition and subtraction operations. More formal analyses of algorithmic complexity and memory requirements can be found in Appendices B and C.
# Performance Evaluation of Random Access Methods for IoT-over-Satellite
## 5 Conclusions  
This paper presents a comprehensive framework for the uplink performance of IoT-over-Satellite networks utilizing the LoRa modulation scheme. The framework incorporates realistic access models, practical satellite constellations, and a Satellite-to-Ground propagation channel model. Using practical synthetic LoRa traffic, the performance of a realistic IoT-over-Satellite link is evaluated, where the packet error rate, bit error rate, coverage probability, and energy consumption are obtained. The paper proposes four different access methods for IoT-over-Satellite applications, namely, (i) pure ALOHA, (ii) scheduled access, (iii) scheduled access with repetition, and (iv) scheduled round robin. Results show that the satellite constellation distribution, satellite availability, interference, random access methods, and scheduling techniques significantly contribute to the performance of LEO-based IoT-over-Satellite networks. The feasibility of using LoRa in IoT-over-Satellite communication is also investigated by using a higher spreading factor and broader bandwidth. The source code of the emulation framework is shared on GitHub. Future work will focus on the performance evaluation of different interference mitigation techniques, such as interference cancellation using machine learning, load sharing, and successive interference cancellation.  
This research was funded by the Australian government RTP Stipend Scholarship (RSS).
## 2 Materials and Methods  
### Multivariate t-Distribution  
The multivariate t-distribution is a generalized version of the univariate t-distribution for two or more variables. Let \(Y\) has a univariate t-distribution with a location parameter \(\mu\), scale parameter \(\psi\), and degrees of freedom \(v\), or written down as \(Y\sim t(\mu,\psi,v)\), then the probability density function is  
\[f(y)=\frac{\Gamma\left(\frac{\pi+1}{2}\right)}{(\pi v)^{\frac{1}{2}}\Gamma\left( \frac{y}{2}\right)\psi^{\frac{1}{2}}}\cdot\left(1+\frac{\left(y-\mu\right)^{2} }{v\psi}\right)^{-\frac{\pi+1}{2}},-\infty<y<\infty \tag{1}\]  
with \(E(Y)=\mu\), \(\text{Var}(Y)=\left(\frac{v}{v-2}\right)\psi\), \(v>2\).  
Suppose there are \(q\) random variables \(Y_{i},i=1,2,\ldots,q\), correlated with each other, so that \(\mathbf{Y}=\begin{bmatrix}Y_{1}&Y_{2}&\cdots&Y_{q}\end{bmatrix}^{\prime}\) has a \(q\)-variate (multivariate) t-distribution with a location parameter \(\mathbf{\mu}=\begin{bmatrix}\mu_{1}&\mu_{2}&\cdots&\mu_{q}\end{bmatrix}^{\prime}\), scale parameter \(\mathbf{\psi}=\begin{bmatrix}\sigma_{1}^{2}&\sigma_{12}&\cdots&\sigma_{1q}\\ \sigma_{12}&\sigma_{2}^{2}&\cdots&\sigma_{2q}\\ \vdots&\vdots&\ddots&\vdots\\ \sigma_{1q}&\sigma_{2q}&\cdots&\sigma_{q}^{2}\end{bmatrix}\), and degrees of freedom \(v\), or written down as \(\mathbf{Y}\sim t_{q}(\mathbf{\mu},\mathbf{\psi},v)\). Next, the probability density function is  
\[f(\mathbf{y})=\frac{\Gamma\left(\frac{v+q}{2}\right)}{(\pi v)^{\frac{2}{2}} \Gamma\left(\frac{y}{2}\right)|\Psi|^{\frac{1}{2}}}\cdot\left(1+\frac{\left( \mathbf{Y}-\mathbf{\mu}\right)^{\prime}\Psi^{-1}(\mathbf{\mathbf{\mathbf{y}}} -\mathbf{\mu}\right)}{v}\right)^{-\frac{v+q}{2}}, \tag{2}\]  
with \(E(\mathbf{Y})=\mathbf{\mu}\), \(\text{Var}(\mathbf{Y})=\left(\frac{v}{v-2}\right)\mathbf{\psi}\), \(v>2\).
## 1 Introduction
[19] to make blind and through holes in titanium carbide. The quality of holes was analyzed by changing the spiral number, processing time, laser fluence, and repetition rate. The concluded that ablation efficiency and quality is mainly dependent on laser fluence and pulse repetition rate. [PERSON] et al. [20; 21] developed nano-textures in Al\({}_{2}\)O\({}_{3}\)/TiC and Si\({}_{3}\)N\({}_{4}\) ceramics using a femtosecond laser. They showed that pulse energy and scanning speed are the dominant factors for the surface morphology and ablation depth and optimum settings were found to be 1.75 \(\upmu\)J with 500 \(\upmu\)m/s or 2 \(\upmu\)J with 500 \(\upmu\)m/s. Microholes in alumina ceramics were studied by [PERSON] et al. [22] using a millisecond pulsed laser. They reported that the peak power and pulse duration are the main factors in determining the crater depth for single pulse application. [PERSON] et al. [23] used two different methods with nanosecond laser to develop a micro-surface texture on alumina ceramic. For single pulse interval processing, crater depth and diameter increase with the increase in pulse energy and number of pulses. With the ring cutting processing, the micro- pit depth increases with the average power and the micro-pit diameter is mainly controlled by the scan diameter Microchannels fabrication in Al\({}_{2}\)O\({}_{3}\)/TiC ceramic by a nanosecond laser was analyzed by [PERSON] et al. [24]. The effects of overscans, frequency, scanning speed, and voltage on dimensional accuracy and surface quality were investigated. It was reported that channel width and depth increase with the increase in voltage, frequency, and number of overscans and decrease with the scanning speed. It is evident from the literature that studies related to microchannel development in alumina ceramic are rare and there is a need to assess the capabilities of Nd:YAG microsecond lasers with respect to dimensional accuracies. In this study the effects of laser fluence and pulse overlap were investigated while developing microchannels using Nd:YAG lasers having a pulse width in the microsecond range. In addition, the current study provides a relationship between laser fluence, pulse overlap, and depth of material removed per laser scan (D/S). The interpretation of relations between process parameters and microchannel quality is significant for the accurate fabrication of microchannels in alumina ceramics. The developed microchannels were analyzed in terms of variations in top width error, bottom width error, depth of material removed per laser scan, and the material removal rate.
# A Chaotic Compressive Sensing Based Data Transmission Method for Sensors within BBNs
## 6 Robustness Analysis  
To discuss the influence of noise during transmission and the robustness of the proposed method, the impact of random noise is analyzed. Here we assume that the noise in the transmission channel is white Gaussian noise, and from Equation (12) we get,  
\[y_{c}=\alpha y_{e}+\beta a_{j}+\delta_{G} \tag{23}\]  
where \(\delta_{G}\in\mathbb{R}^{M\times 1}\) is a vector conformed to white Gaussian noise.  
Figure 5 reports the reconstruction results under the impact of white Gaussian noise with different values of noise power. Table 3 lists the PSNR values under the circumstance with white Gaussian noise.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Compression Rate 0.7**} \\ \hline
**Concealing** & **Channel r** & **Channel g** & **Channel b** & **Average** \\
**Section** & & & & \\ \hline
50\% & 32.59798 & 33.13968 & 33.23807 & 32.99191 \\
100\% & 36.11858 & 36.11858 & 36.11858 & 36.11858 \\ two sectors & 33.41342 & 33.73252 & 33.98569 & 33.71054 \\ \hline \multicolumn{4}{c}{**Compression Rate 0.8**} \\ \hline
**Image** & **Channel r** & **Channel g** & **Channel b** & **Average** \\ \hline
50\% & 33.30480 & 33.67279 & 33.93345 & 33.63701 \\
100\% & 33.33252 & 33.75152 & 33.86508 & 33.64971 \\ two sectors & 33.41341 & 33.73252 & 33.98569 & 33.71054 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PSNR values for various concealing sections (Unit: dB).  
Figure 4: The concealed sections in the first and second rows are 50% and 100%, respectively, and the last row shows the results of concealing two independent sections of the original image. The first column contains the original image, the second and third columns show the reconstruction results of restricted-authorized receivers and full-authorized receivers under compression rate 0.7. The fourth and fifth columns show the reconstruction results under compression rate 0.8.  
From Figure 5 and Table 3, we find that noise does have an impact on the quality of image reconstruction as well as the PSNR values. With increasing noise power, the impact gradually becomes stronger. When the power of noise is below 15 dBW, the quality of image reconstruction is almost unaffected, and the PSNR values decrease slightly, although they are still above 30. When the power of noise has reached 35 dBW, the quality of image reconstruction is still tolerable, although the values of PSNR are noticeably below 30. When the power of noise is higher than 40, the quality of image reconstruction seriously decreases, and the PSNR values also reduce significantly.  
Figure 5: Reconstruction results with the impact of white Gaussian noise. The first column shows the noise with power values of 0, 5, 15, 25, 35, 40, and 50 in dBW, respectively. The second and third columns show the reconstruction results under compression rate 0.7, and the fourth and fifth columns show the reconstruction results under compression rate 0.8.
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 5 Performance Evaluation
### Simulation Environment
#### 5.1.6 Computational Complexity  
The proposed algorithm's computational complexity is calculated as the time required by the algorithms to find the best possible path coordinates towards the target area. Finding an accurate running time of the algorithm concerning its input size is challenging. The asymptotic time complexity of the algorithm is projected based on the instructions provided in the algorithm. In the proposed algorithm, PRM and ABC are used. The time complexity of the PRM algorithm is given by the \(O\) (\(n\ log\ n\)) where \(n\) is the total number of iterations. The HPP algorithm's performance depends on the time complexity of the ABC algorithm to generate trajectory points in the dynamic environment. Hence, the proposed HPP's time complexity is O (\(nt\)), where \(t\) is the number of threats.  
The simulation parameters used in our performance study are listed in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Network simulator & MATLAB \\ UWSN deployment area & 500 m \(\times\) 500 m \\ Number of sensor nodes & 100 \\ Number of UAV & 1 \\ Sensor initial energy & 2 \\ Packet size (variable) & 100–150 bytes \\ UAV’s height & 45 m \\ Sensors & Static \\ UAV type & Quadrotor \\ UAV’s initial energy & 5 \\ UAV’s initial speed & 25 m/s \\ Antenna type & Directional \\ Antenna flare angle & 60\({}^{\circ}\) \\ Air density & 1.225 kg/m\({}^{2}\) \\ Number of transceivers & 2 \\ UAV’s transmission range & 200–250 m \\ Moments of inertia (\(l_{x}\), \(l_{y}\), \(l_{z}\)) & \(1.9757\times 10^{-4}\) kg/m\({}^{2}\); \(1.9757\times 10^{-4}\) kg/m\({}^{2}\); \(3.9757\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters.
# Neural Machine Translation of Electrical Engineering with Fusion of Memory Information
## 4 Experiment
### Ablation Experiment  
To demonstrate the effectiveness of the memory information, we conducted an ablation experiment on models with memory information encoders of different depths, and the experimental results are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **BLEU/\%** & \(\sigma\) \\ \hline Baseline & 34.25 & – \\ Our methods-layer = 1 & 34.66 & 0.41\(\dagger\) \\ Our methods-layer = 2 & 34.82 & 0.57\(\dagger\) \\ Our methods-layer = 3 & 35.07 & 0.82\(\dagger\) \\ Our methods-layer = 4 & 35.48 & 1.23\(\dagger\) \\ Our methods-layer = 5 & 35.93 & 1.68\(\dagger\) \\ Our methods-layer = 6 & 36.29 & 2.04\(\dagger\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: BLEU scores for models with memory information encoders of different depths.  
Figure 3: The bar chart of training data.  
The results are shown in Figure 4 and demonstrate that the BLEU score increases with the number of layers in the memory information encoder, reaching a maximum of 36.29 when the maximum number of layers is used. This suggests that incorporating memory information into the transformer model can improve translation performance. These results also highlight the importance of memory information in the transformer model and support the effectiveness of the proposed method for incorporating memory information through integrating the memory information encoder.
## 3 Results and Discussion
### Mutagenic Activity of FFL Extracts  
The potential mutagenicity of the FFL extracts was assessed using TA98 and TA100 strains (Figure 2 and Supplementary Table S1). The test was carried out separately in the presence or absence of a metabolically active enzyme system (S9 mix). For the TA98 strain, which screens for frameshift mutagens, control groups with and without the S9 mix had 22 \(\pm\) 4 and 25 \(\pm\) 3 colonies, respectively. Positive controls showed significantly higher colonies, 735 \(\pm\) 68 without S9 and 531 \(\pm\) 31 with S9, respectively (Supplementary Table S1). All the FFL extracts showed no significant mutagenicity compared to the negative controls. Similarly, the TA100 strain, detecting base-pair mutagens, revealed control colonies of 121 \(\pm\) 11 without S9 and 118 \(\pm\) 16 with S9, and positive controls of 792 \(\pm\) 28 and 837 \(\pm\) 15, respectively (Supplementary Table S1). Colony counts from all the FFL extracts did not significantly change by a factor of two compared to the control without treatment, showing no increase or decrease in colony numbers according to the dose-response.  
The Ames test is a method used for detecting potential genetic alterations in cellular DNA [17]. Thus, the Ames test plays a crucial role in the safety assessment of chemicals, contributing to public health protection and environmental safety [17]. This test targets substances such as radiation, UV light, chemicals, and internal factors, known for their high DNA reactivity and potential to cause unrepairable DNA damage, leading to cancer [30; 31]. Considering this fact, FFL extracts showed no mutagenic activity in the presence or absence of the metabolic enzyme system, indicating that FFL has no mutation-inducing properties in both strains. Further studies are necessary to assess the extracts in various cell lines and animal models to confirm their potential mutagenic effects and safety.  
\begin{table}
\begin{tabular}{c c c} \hline
**Ethanol Solvent Percentage** & **Yields (g)** & **Yields (\%) \({}^{*}\)** \\ \hline
0 & 5.20 \(\pm\) 1.67 & 10.4 \(\pm\) 3.4 \({}^{\text{a}}\) \\
30 & 2.55 \(\pm\) 0.31 & 5.1 \(\pm\) 0.6 \({}^{\text{b}}\) \\
50 & 2.51 \(\pm\) 0.40 & 5.0 \(\pm\) 0.8 \({}^{\text{b}}\) \\
70 & 3.40 \(\pm\) 0.50 & 6.8 \(\pm\) 1.0 \({}^{\text{ab}}\) \\
100 & 1.32 \(\pm\) 0.28 & 2.6 \(\pm\) 0.5 \({}^{\text{c}}\) \\ \hline \end{tabular} \({}^{*}\) Yield (%) = solid extract (g)/raw material (dry weight) \(\times\) 100. \({}^{\text{a}+}\) The same letters are not significantly different (\(p<\) 0.05).  
\end{table}
Table 2: Extraction yields of _Fomes fomentarius_ L. (50 g).