esa-sceva/satcom-chunk-collection huggingface dataset:
# Device Reliability Analysis of NNBI Beam Source System Based on Fault Tree
## 5 Fault Tree Analysis
### Quantitative Calculation
#### 5.6.2 Top Event Simulation Modeling  
AutoFTA 3.9 enables dynamic simulation based on the occurrence probability of top events, serving as one of the essential standards for system analysis. Through extensive random sampling, it calculates the occurrence probability of top events and generates probability curves of system reliability. The software-calculated average failure rate for the entire system is \(\lambda_{\text{T}}\) = 1.91 \(\times\) 10\({}^{-2}\) (1/h). This study conducted simulation modeling of these results, obtaining the variation in experimental failure rates after multiple simulations. The specific changes in the curve are shown in Figure 11.  
Analysis of the probability variation curve reveals that as the number of trials increases, the fluctuation amplitude of the top event occurrence probability gradually decreases and eventually converges to a stable value. This convergence characteristic aligns with the Law of Large Numbers in probability theory, thereby validating the statistical reliability of the simulation results [19]. The findings fully demonstrate the scientific rigor of fault tree analysis methodology in reliability engineering and provide dependable data support for engineering decision-making.
## 2 Materials and Methods  
### Coating Deposition Technique  
Firstly, CrN/CrCN/DLC, CrN/AlTiN, CrN/CrCN, and CrCN/TiSiCN multilayer coatings were deposited by the cathodic arc evaporation method on the cemented carbide substrates (grade TMK-45, 88% WC and 12% Co, TechMet, Hickory, NC, USA) as well as on P-type Si (100) wafers having a thickness of 520 \(\upmu\)m. This approach was selected to characterize mechanical and tribological properties of the coatings as well as surface morphology, and achievable coating thickness. The PVD-coatings were performed at Aurora Scientific Corp., Richmond, BC, Canada. Prior to deposition, the cemented carbide substrates were ground with a diamond grinding wheel having a grit size of 100, followed by ultrasonic cleaning. Finally, they were polished using 6 \(\upmu\)m and 1 \(\upmu\)m diamond suspensions. The obtained surface roughness (Ra) was approximately 0.03 \(\upmu\)m. The cleaning process of the WC-Co tips consisted of sequence of operations that included degreasing with a high-alkaline solvent-free degreaser. This was followed by wet blasting using a solution of water and 3.9 micrometer quartz particles (1:5 ratio). The last step involved degreasing in an ultrasonic bath, air blowing, drying, and heating in a furnace at 120 \({}^{\circ}\)C for 30 min. Subsequently, all substrates were heated up to about 300 \({}^{\circ}\)C. They were then exposed to an Ar\({}^{+}\) bombardment at a bias voltage of 800 V for 20 min and at a gas pressure of 0.66 Pa to remove the surface oxide layer and impurities.  
Afterwards, a chromium sub-layer with a thickness of 0.1 \(\upmu\)m was deposited to improve the adhesive strength between the substrates and coatings. The target's current was 70 A. The deposition time was 5 min. For CrN/CrCN and CrCN/TiSiCN, C\({}_{2}\)H\({}_{2}\) was added with a flow rate of 10 standard cubic centimeter per minute (sccm). For CrN/CrCN/DLC coating, the flow rate of C\({}_{2}\)H\({}_{2}\) was 160 sccm. The pressure of nitrogen was 20 mtorr for CrN/CrCN, CrN/CrCN/DLC, and CrCN/TiSiCN coatings. For CrN/AlTiN, the nitrogen pressures were 21 and 25 mtorr for CrN and AlTiN layers, respectively. The distance between the target and substrate was 150 mm in all coating recipes. The list of coatings, with the temperature deposition, bilayer periods and target materials are shown in Table 1.
###### Abstract  
This study investigated the tensile and bonding properties between cement-based grouting materials (CBGM) and high-strength bolts. The associated failure mechanism, load-slip curve, ultimate pull-out load and bond stress were also studied. The effects of anchorage length and square steel tube restraint on the bonding properties were explored on the basis of 24 specimens used in central pull-out testing, and a bond stress-slip constitutive relationship model between high-strength bolts and CBGM was proposed. The results indicate that with the increase in the anchorage length of high-strength bolts, the failure modes of specimens can be divided into three types: the fracture failure of high-strength bolt that occurred when the anchorage lengths ranged from 18 d to 31 d, the specimens that experienced splitting failure with the constraint of square steel tube when the anchorage length was less than 15 d and the high-strength bolt that experienced pull-out failure without the constraint of square steel tubes. When the high-strength bolt experiences tensile failure, the ultimate pull-out load remains constant and the bond stress decreases as the anchorage length of high-strength bolts increases. Due to the lateral constrained effect of the square steel tube, the CBGM embodies a three-dimensional stress state, which can delay the generation and development of internal cracks and enhance the bond strength. A calculation formula was proposed to determine the bond strength between high-strength bolt and CBGM, and a constitutive model of the bond stress-slip constitutive relationship was modeled.  
tensile and bonding properties; bond strength; constitutive relationship; cement-based grouting material; high-strength bolt +
Footnote †: journal: Applied Sciences
# Assessing the Homogeneity of Forage Mixtures Using an RGB Camera as Exemplified by Cattle Ratios
## 3 Results
### Use of RGB Camera to Detect Mixing Quality of a Feed Mix  
The determined chlorophyll absorption capacity in green forages enables us to detect the homogeneity of forage mixes using cheaper fluorescence methods.  
The obtained statistical characteristics of the number and size of areas determine the uniformity of feed mixing. The developed system will ensure the mixing of feed components directly in a robot feeder at the final stage of cattle feeding. Quality control of the mix composition will reduce costs on feeding due to the optimized amount of mixed fodder. It will also increase the cattle's productive life through a more balanced diet.  
Figure 3: Spectral characteristics of the tested components of the feed mix.  
Production tests were carried out on an operating farm. During the tests, a camera with illumination was installed on the feeder wagon. Observations were made during the preparation of the feed mix and then after feed distribution. The machine moved over the feeding table to detect unmixed portions of the feed mixture (Figure 5).  
The physical property of light absorption and light filters enabled us to increase the contrast in the image captured by the RGB camera. The mixed feed got a pronounced green color, and the green fodder acquired duller colors. Based on this, we estimated the homogeneity of the feed mix surface layer.  
Figure 4: Fixed TMR images (from green and mixed fodder) in different mixing states: A—images at 672 nm spectral slice, B—images at 554 nm spectral slice, C—subtraction result, D—binarized images, (first column—images without mixed fodder).  
Figure 5: Process of getting images with fluorescence excitation of feed mix components on a farm.  
It was necessary to highlight the green color in the image to highlight the mixed feed. For this purpose, use was made of the point cloud available in the app program, characterizing all the colors presented in the image. We used the polygon \"Region of Interest\" tool to select the points in the point cloud representing the shades of green.  
The resulting statistical characteristics on the number and size of regions indicate how evenly the feed is mixed.  
Several signs indicated improper forage mixing. The first sign is the size of the areas. The maximum area corresponding to a single feed particle is 250 pixels. Areas with a larger area indicate that two or more particles are adjacent.  
More than two areas of up to 500 pixels or at least one area of more than 500 pixels was taken as an outlier.  
The absence of fodder particles in any part of the image can also indicate anomalies. To detect such anomalies, the image is divided into nine parts, and in each part, the area of white spots is summed up. If the difference between the spot area and the image average exceeds the permissible value, it is also considered a deviation.
# Community Structure Detection for Directed Networks through Modularity Optimisation
## 2 Iterative Mathematical Programming Model for Modularity Optimisation on Directed Networks  
We propose an iterative procedure that contains two major mathematical models to optimise modularity for directed networks. The first is a Mixed Integer Non-linear Programming Model (MINLP) that can provide a good partition of the network quickly but is likely to converge to a local optimal region, while the second model, a Mixed Integer Linear Programming Model (MIP), is harder to solve but is capable of finding a solution of higher quality. The algorithm starts by solving the MINLP a number of times, and the best partition is then selected and given as an initial point to the reduced MIP model that will iteratively improve the solution. We reduce the complexity of the MIP by allowing a few nodes to change modules while keeping all other nodes in their initial allocation and we solve each of these reduced MIPs for each module in an iteration.  
As mentioned previously, the reason for proposing two different models for solving the same problem is that although a MINLP can find an acceptable solution for a large problem, it often converges to locally optimal solutions, and thus the quality of the solution is hard to guarantee. On the other hand, an MIP model can be solved to global optimality for small problems but consumes large computational resources for larger networks. We combine the best of these two types of models by first solving the MINLP and then the reduced MIPs to improve the acquired solution. The entire computational strategy is named DiMod and the mathematical description follows.  
The sets, parameters and variables used in the models are described below:
## 2 System Model of MC\(-\)ODBR\(-\)DCSK  
The primary disadvantage of DCSK systems is that they allocate half of the bit duration to transmit non-information-bearing reference samples, which results in a lower data rate and reduced energy efficiency. Higher data rates and energy efficiency are the core goals when designing new chaos-based communication systems. To improve energy efficiency and transmission rates, an improved MC\(-\)ODBR\(-\)DCSK system is proposed in this paper. This section introduces several key components of the system, including the chaotic signal generator, quadrature device, signal structure, and the transceiver of the MC\(-\)ODBR\(-\)DCSK system.
## 2 General System Model
In the first phase, the source broadcasts its signal to the relays and the destination (if the direct link is available). In the second phase, the selected relay forwards an amplified version of its received signal to the destination. Suppose that \(S\) communicates with \(D\) through the relay \(R_{k}\), then the total end-to-end SNR at \(D\) is given by [1]  
\[\gamma_{D}^{T}=\gamma_{SD}+\gamma_{SR_{k}D}, \tag{5}\]  
where \(\gamma_{SD}=\frac{P}{N_{0}}\left|h_{SD}\right|^{2}\) and  
\[\frac{1}{2}\min\left\{\gamma_{SR_{k}},\gamma_{R_{k}D}\right\}\leq\gamma_{SR_{ k}D}=\frac{\gamma_{SR_{k}}\gamma_{R_{k}D}}{\gamma_{SR_{k}}+\gamma_{R_{k}D}+1} \leq\min\left\{\gamma_{SR_{k}},\gamma_{R_{k}D}\right\}. \tag{6}\]  
In the BRS scheme, the relay that maximizes the bottleneck link is selected. Mathematically, the scheme is expressed as [4]  
\[b=\operatorname*{arg\,max}_{k=1,\cdots,K}\left\{\min\left\{\gamma_{SR_{k}}, \gamma_{R_{k}D}\right\}\right\}. \tag{7}\]  
On the other hand, in the PRS scheme, the relay with the strongest SNR over the source-relay (or relay-destination) link is activated, which is [3]  
\[p=\operatorname*{arg\,max}_{k=1,\cdots,K}\left[\gamma_{SR_{k}}\right], \tag{8}\]  
or  
\[p=\operatorname*{arg\,max}_{k=1,\cdots,K}\left[\gamma_{R_{k}D}\right]. \tag{9}\]  
In Equations (7)-(9), \(b\) and \(p\) are the indexes of the selected relays. It is also important to note that, sometimes, the best relay (according to either the BRS or the PRS schemes) may not be available to assist the source-destination transmission due to some scheduling schemes, loads balancing conditions, or insufficient transmit power [16]. Consequently, we have to select the \(l^{th}\) best relay instead. This relay selection criterion is referred as the \(l^{th}\)-BRS and \(l^{th}\)-PRS in the sequel. The previously presented relay selection schemes can be deployed following a centralized or a distributed approach. In a centralized approach, a central node (source, destination, or another dedicated node) maintains a table that contains all the relays with associated channel gains. Usingthe table, the best relay can be easily selected. In a distributed approach, each relay keeps a timer, whose initial value is inversely proportional to \(\min\left\{\gamma_{SR_{k}},\gamma_{R_{k}D}\right\}\) for the BRS scheme, and \(\gamma_{SR_{k}}\) (or \(\gamma_{R_{k}D}\)) for the PRS scheme. Then, the selected relay will be the one that has the smallest initial value of the timer [6].
## 3 Theoretical Analysis of the Grouser Effect  
### Relationship between the Vertical Load and Subsidence of the Grouser  
At present, the more common track shoe shapes can be roughly summarized as T-shapes, \(\pi\)-shapes, V-shapes, and K-shapes, as shown in Figure 3[12]. In the L-shaped track shoe shown in Figure 4a, \(l\) is the pitch of the track shoe; d and h are the width and thickness of the grouser, respectively, where \(d=\lambda l\) and \(\lambda\) is the ratio of the thickness of the grouser to the pitch of the track shoe; \(i\) is the shear displacement of the track shoe; \(z\) is the sinking displacement of the track shoe; \(p_{1}\) is the unit of pressure of the ground acting on the track shoe; \(p_{2}\) is the unit of pressure of the soil acting on the unit of pressure of the track shoe's thickness; and \(F_{m\text{xx}}\) is the maximum tractive force produced by the soil. Figure 2: The process of switching to the walking mode of the reconfigurable wheel-crawler integrated mechanism. (**a**) The circular wheel mode; (**b**) the swing arm is recovered, and the traveling mechanism is switched from the circular wheel mode to the triangular crawler mode; (**c**) the triangular crawler mode; (**d**) the triangular crawler mode with the bracket is below; (**e**) the process of changing from the triangular crawler mode to the circular wheel mode, with the bracket is retracting, the swing arm is expanding; (**f**) the circular wheel mode after the mode switched. According to [PERSON]'s pressure-subsidence relationship formula,  
\[p=\left(\frac{k_{c}}{b}+k_{\varphi}\right)z^{n}\]  
Figure 4: Schematic diagram of the interaction force between the track shoe and the soil. (**a**) Schematic diagram of the interaction force between an L-shaped track shoe and the soil. (**b**) Schematic diagram of the interaction force between a \(\pi\)-shaped track shoe and the soil. (**c**) Schematic diagram of the interaction force between a V-shaped track shoe and the soil. Figure 3: The more common track shoe shapes are as follows: (**a**) T-shape track shoe; (**b**) \(\pi\)-shape track shoe; (**c**) V-shape track shoe; (**d**) K-shape track shoe. where \(k_{c}\) is the coefficient of soil cohesion, \(k_{\varphi}\) is the coefficient of the soil's internal friction angle, \(z\) is the amount of soil subsidence and \(n\) is the soil deformation index. It can be found that the unit of pressure \(p_{1}\) of the ground acting on the track shoe and the unit of pressure \(p_{2}\) of the soil acting on the grouser are the following, respectively. \[p_{1}=\bigg{(}\frac{k_{c}}{b}+k_{\varphi}\bigg{)}z^{n} \tag{1}\]  
\[p_{2}=\bigg{(}\frac{k_{c}}{b}+k_{\varphi}\bigg{)}(z+h)^{n} \tag{2}\]  
Since the track shoe is balanced in the vertical direction, it can be found that  
\[W^{\prime}=p_{1}(1-\lambda)lb+p_{2}\lambda lb \tag{3}\]  
\[W^{\prime}=\frac{l}{L}W\]  
where \(b\) is the width of the track, \(W\) is the load of the traveling mechanism, \(L\) is the length of the track on the ground and \(W^{\prime}\) is the force acting on a single track shoe.
## Abbreviations  
ANN--artificial neural networks; AVHRR--advanced very high resolution radiometer; CAR-- chlorophyll absorption reflectance; CCCI--canopy chlorophyll content index; PSRI--plant senescence reflectance index; CEC--cation-exchange capacity; CNNs--convolutional neural networks; DNN--deep neural networks; EC--electrical conductivity of the soil; ET--evapotranspiration; EVI--enhanced vegetation index; EVI 2--enhanced vegetation index 2; FFBN--feed-forward backpropagation neural network; FPAR--fraction of photosynthetically active radiation; GPP--gross primary production; GVI--global vegetation index; HTC--hydrothermal coefficient; LAD--leaf area duration; LAI--leaf area index; LAR--leaf area ratio; RGR--relative growth rate; MAPE--mean absolute percentage error; MLP--multilayer perceptron; MLR--multiple linear regression; MODIS--moderate resolution imaging spectroradiometer; MSE--mean squared error; MTCI--MERIS terrestrial chlorophyll index; MTVI 1--modified triangular vegetation index 1; MTVI 2--modified triangular vegetation index 2; NDRE--normalized difference red Edge; NDVI--normalized difference vegetation index; NIR--near infrared; PAR--photosynthetically active radiation; PLSR--partial least squares regression; PVI--perpendicular vegetation index; RAE--relative absolute error; RBF--radial basis function; RMSE--root mean square error; MAE--mean absolute error; RMSE--root relative squared error; RUE--radiation use efficiency; RVI--ratio vegetation index; SAVI--soil-adjusted vegetation index; SIF--solar-induced chlorophyll fluorescence; SPAD--soil plant analysis development; TMN--minimum temperature; TMX--maximum temperature; TVI--triangular vegetation index; ULR--unit leaf rate; WDVI--weighted difference vegetation index.
## 3 Results and Discussion
### Failure Modes after Exposure to High Temperatures  
#### 3.3.1 Experimental Phenomena  
The carbon fiber sheets have no obvious change when the temperature is less than 105 \({}^{\circ}\)C, but oxidation occurs when heating up to higher than 105 \({}^{\circ}\)C. The higher the temperature is, the deeper the oxidation degree is. For MPC specimens, a strong pungent smell and white smoke was emitted from the exhaust port of the furnace when the temperature was higher than 105 \({}^{\circ}\)C. The primary reason for this is considered to be the decomposition of hydration products of MPC. Explosive spalling of concrete specimens occurred at 500 \({}^{\circ}\)C for one group of MPC specimens. The epoxy resin exhibits a color of pale yellow at ambient temperature, and it gradually changes to dark yellow and ultimately black with the temperature increase. There is a burning smell when heating up to more than 105 \({}^{\circ}\)C, which is in accordance with the seriously degraded bond performance of epoxy resin specimens at higher temperature.
## 3 Proposed Method
### Time-Frequency Domain Filtering  
Filtering is a common denoising method in signal processing. In this study, a global variance consistency filtering model was introduced into the BLSTM conversion network. A low-pass filter was used to remove high-frequency recording noises from electrical devices. After the MCEPs conversion, the converted cepstrum was filtered again through the global variance consistency filter constructed using \(T_{1}\) and \(T_{2}\) in Figure 1, which is believed to be good at removing the artificial noised generated by the conversion process. The details of conducting the global variance consistency filtering are given as follows.  
1. The mean and variance of each one-dimensional MCEPs of the target sentences was calculated using Equation (10): \[\left\{\begin{array}{c}\overline{x}_{\text{\emph{tar}}}=\frac{1}{(N\times M )}\sum\limits_{n=1}^{N}\sum\limits_{m=1}^{M}x_{i}\\ \sigma^{2}{}_{i_{\text{\emph{tar}}}}=\frac{1}{(N\times M)}\sum\limits_{n=1}^{N }\sum\limits_{m=1}^{M}(x_{i}-\ \overline{x}_{\text{\emph{tar}}})^{2}\end{array}\right. (i=1,2,3,\cdots,T),\] (10) where \(N\), \(M\), and \(T\) represent the number of target sentences in the training phase, the number of frames per sentence, and the dimension of MCEPs, respectively. \(x_{i}\) denotes the \(i\)th dimensional MCEPs, \(\overline{x}_{i_{\text{\emph{tar}}}}\) and \(\sigma^{2}{}_{i_{\text{\emph{tar}}}}\) represent the mean and variance of MCEPs in the \(i\)th dimension of all the training sentences, respectively. _tar_ represents the information from the target speaker.
2. Equation (10) was also used to calculate the mean and variance of all the converted MCEPs during the training phase and the MCEPs to be converted during the conversion phase. \(\overline{x}_{i_{\text{\emph{tar}}}}\) and \(\sigma^{2}{}_{i_{\text{\emph{tar}}}}\) represent the mean and variance in \(i\)th dimension of the converted MCEPs, respectively. _con_ represents the information of the converted voice. The vector \(\overline{y}\) was constituted of each dimensional MCEP of sentences to be converted. The vector \(\sigma{y}^{2}\) was constituted of the variance of each dimensional MCEP of sentences to be converted.
3. A global variance consistency filter was constructed to obtain the primary filtered data, as shown in Equation (11): \[\hat{y}=\sqrt{\left(\frac{\overline{x}_{\text{\emph{tar}}}}{\overline{x}_{ \text{\emph{\emph{tar}}}}}\right)}\times(y\ -\ \overline{y})\ +\ \overline{y},\] (11) where \(\overline{x}_{\text{\emph{tar}}}\) denotes the vector consisting of means of each dimension of the MCEPs of target sentences. \(\overline{x}_{\text{\emph{tar}}}\) denotes the vector consisting of the means of each dimension of MCEPs of the converted sentences. \(y\) denotes the MCEP vector of the sentences to be converted. \(\hat{y}\) is the output after the initial filtering.
4. As in Equation (12), \(\alpha\) was set to adjust for the final filtering data: \[\overline{y}=\alpha\ \times\ \hat{y}\ +\ (1\ -\ \alpha)\ \times\ y,\] (12) where \(\overline{y}\) is the final MCEPs after filtering.