esa-sceva/satcom-chunk-collection huggingface dataset:
# Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network
## 2 Principle and Methods
### Adaptive Particle Filter Correction Method Based on DBN Reasoning  
In traditional filtering processes, state equations are often invariant. However, when there is a substantial error between the state equation and the actual state, the predictive accuracy and convergence speed of the filtering may not be ideal. This difference becomes minimal with continuous experimental data input, but in the absence of such data, predictions still exhibit a certain level of deviation. This deviation is particularly noticeable when experimental data are scarce. Additionally, traditional filtering fails to derive correction coefficients for the theoretical formulas associated with the filtering outcomes. To ensure the state equation is continuously updated during the filtering process, it is essential to adjust the parameters of the state equation using the filtering results from each time step. Moreover, to overcome the issue of particle depletion seen in traditional particle filtering, an adaptive sampling approach is introduced.  
Assuming the use of Paris' law, the specific state evolution Equation (9) and observation Equation (10) in Section 2.3.1's particle filtering formula can be respectively represented as follows:  
\[a_{t}=a_{t-1}+C(\Delta K)^{m}\cdot\Delta N+v_{a}^{t} \tag{19}\]  
\[a_{t}^{obs}=a_{t}+\varepsilon_{a}^{t} \tag{20}\]  
where \(a_{t}\) represents the calculated crack length at time \(t\), and \(a_{t}^{obs}\) is the observed crack length at number of cycles \(t\). \(v_{a}^{t}\) and \(\varepsilon_{a}^{t}\) denote the model error and measurement error at number of cycles \(t\), respectively.  
Then, through the SIS sampling and resampling described in Section 2.3.3, we can obtain the posterior estimate of the crack length at number of cycles \(t\) as \(\hat{a}_{t}=\sum\limits_{i=1}^{N}\widetilde{w}_{i}^{i}a_{t}^{i}\), where \(a_{t}^{i}\) represents the sample values from particle filtering and \(\widetilde{w}_{t}^{i}\) denotes the resample weights of the samples. Similarly, we can obtain the posterior estimate of the parameter \(\hat{\Theta}_{t}\) as \(\hat{\Theta}_{t}=\sum\limits_{i=1}^{N}\widetilde{w}_{t}^{i}\Theta_{t}^{i}\), where \(\Theta_{t}^{i}\) is the parameter corresponding to the particle filtering crack length samples \(a_{t}^{i}\).  
Utilizing the kernel density estimation approach enables us to derive the probability distributions of the posterior estimates for \(\hat{a}_{t}\) and \(\hat{\Theta}_{t}\), thus facilitating the DBN model's update. Following this, the refreshed posterior parameters \(\hat{\Theta}_{t}\) will act as the preceding parameters \(\Theta_{t+1}\) for the forthcoming DBN filtering stage in the crack propagation equation. In accordance with their probability distribution, new samples for the time \(t+1\) are recreated using the Monte Carlo method, paving the way for subsequent updates to the model.  
\(f(\cdot)\) represents the crack propagation formula. The whole process is shown in Figure 5:
# Automated Cart with VIS/NIR Hyperspectral Reflectance and Fluorescence Imaging Capabilities
## 2 Materials and Methods
### Camera and Laser Mount  
The camera and laser are mounted on a 0.25\" aluminum plate to allow alignments to be preserved when the optics are moved from the laboratory to the cart and when, during field use, the equipment is subject to normal transport vibrations. Determining the appropriate offset and angular placement of the laser relative to the camera required modeling the expected illumination field relative to the imaging field. The model also considered the height of the optics above the ground, the angle of the aluminum plate relative to fully vertical, the width of the desired imaging field, and angles relative to the optics selected for use. Calculations were done for 1\({}^{\circ}\) arc intervals using the tangent function. To align the plane of the laser to the plane of the camera, holes were drilled in the aluminum plate so that the bolts attached to the laser could be used to adjust the distance of the laser from the aluminum plate. Rotational alignment of the Powell lens and offset adjustments of the laser requires very precise movements. To facilitate such movements, cable ties were added as appropriate and a micrometer mounted on a magnetic platform is used to push against the cable ties.  
Precise alignment of the illumination field with the imaging field is a multistep process. As the 355 nm laser illumination is not within the spectral response range of the camera, fluorescence responses of glossy photo paper are used where appropriate for alignment image acquisitions. First, the spectral adapter has to be rotated so that the imaging field is parallel to the plane of the aluminum plate. Second, the Powell lens has to be rotated until the illumination field is also parallel to the plane of the aluminum plate. Rotational alignments are facilitated by a program written in-house that calculated the least squares fit of a line to an image within a selected region of interest. The program displays the angle and intercept of the calculated line. To determine the column of pixels that corresponded to the center of the imaging line of the spectral adapter, a picture with a white vertical stripe on a black background is moved across the field of view using a translation table, with a halogen lamp used for illumination. The physical location that maximizes the spectral response is noted, and the translation stage is moved to that position. The spectral adapter is then removed and to allow acquisition of full-field images. The pixel column that corresponds with the center of the white stripe in the full-fieldimages is considered to be the center of the effective imaging field of the spectral adapter. The offset of the laser from the aluminum plate is adjusted until full-field images of the illumination profile are centered on this column. To make sure that the illumination plane is parallel to the imaging plane, full-field images of the laser illumination need to be acquired with the target area at multiple distances from the camera.
# Design of Wideband Decoupling Antenna Array for 5G Smartphones at N77/N78/N79/WLAN 5 GHz Bands
## 3 Performance of the Eight-Element Antenna System
### Eight-Element MIMO Antenna System Prototype
#### 3.1.3 ECC  
In order to study the antenna's uncorrelation in radiation space, we calculated the Envelope Correlation Coefficient (ECC). ECC is a parameter used to express the degree of isolation between the communication channels of the MIMO system. The ECC can either be computed by using far-field directional maps or approximated by the S-parameter using Equation (1).  
\[EEC(m,n)=\frac{\left|s_{mm}^{*}s_{mn}+s_{nm}^{*}s_{m}\right|^{2}}{\Big{\{}1-( \left|s_{mm}\right|^{2}+\left|s_{mm}\right|^{2})\Big{\}}\Big{\{}1-(\left|s_{ nm}\right|^{2}+\left|s_{mm}\right|^{2})\Big{\}}} \tag{1}\]  
From Figure 15, we can see that the ECC between any two antenna elements is lower than 0.01 at an operating frequency of between 3.30 and 5.83 GHz, which indicates that the eight-element MIMO antenna system has good spatial independence.  
Figure 13: (**a**) Simulated and measured reflection coefficients. (**b**) Simulated and measured transmission coefficients.
## 2 Algorithms and Neural Network Definitions
### ResNet (Residual Network)  
ResNet (residual network) is a deep learning model proposed by [PERSON] et al. designed to maintain training efficiency even with a significant increase in the depth of the neural network [7]. The core idea of ResNet is to address the vanishing gradient problem through \"residual learning\" [7]. While traditional networks become increasingly difficult to train as their layers deepen, ResNet is designed to learn the difference between the input and output values at each layer, known as the residual, enabling effective training even in deeper networks [7].  
Figure 4: Comparing the performances of image analysis algorithms [15].  
Figure 3: Process of vision transformer algorithm to analyze images [14].  
ResNet is widely used in computer vision tasks such as image classification, object detection, and segmentation. Its outstanding performance, particularly demonstrated on the ImageNet dataset, has led to its adoption as a state-of-the-art model across various applications. Additionally, the residual block structure of ResNet has been applied to other deep learning architectures, extending its use beyond vision to natural language processing and speech recognition fields.  
A major advantage of ResNet is its ability to learn without performance degradation while expanding the depth of the network. This allows for the construction of deeper and more complex models compared to traditional CNN architectures, providing exceptionally high performance in image recognition and classification. It is also conducive to transfer learning and generally converges faster than other models. Nonetheless, ResNet has the drawback of consuming substantial computational resources when the model's depth becomes very high. Additionally, the basic ResNet model may exhibit limited performance on more complex datasets or highly variable images, prompting the proposal of various extended models to address these limitations. The ResNet algorithm can demonstrate higher forecast and analysis performance with the adoption of a residual block, while the previous neural network models, including CNN, demonstrate lower performance when the network depths become deeper [16] (Figure 5).
## 3 Data and Methodology
### XGBoost  
XGBoost, or eXtreme Gradient Boosting, is a tree-based algorithm [78]. XGBoost has shown great success in terms of both performance and speed. Boosting is an ensemble strategy with the key goal of reducing bias and variance. The aim is to sequentially build weak trees in such a way that each new tree (or learner) works on the flaw (misclassified data) of the preceding tree. The data weights are re-adjusted, known as \"re-weighting\", whenever a weak learner is added. Because of this auto-correction after every new learner is introduced, the whole forms a strong model after convergence. The loss function of the model is characterized as penalizing the complexity of the model with regularization in order to decrease the possibility of overfitting. This technique performs well even when there are missing values or many zero values, demonstrating a good ability to deal with sparsity. XGBoost uses an algorithm called the \"weighted quantile sketch algorithm\" that helps the classifier to concentrate on data that are incorrectly classified. In each iteration, the aim of each new learner is to learn how to classify the incorrect data.
## 2 Materials and Methods
### Experimental Set-Up  
Lettuce (_Lactuca sativa_ L.) plantlets grown under nonsterile conditions for one week were divided into four blocks (\(n=5\)). The initial plant fresh weight (TFW\({}_{i}\)) was determined to achieve an equal plant size distribution among the treatments. The plants were then transplanted in four hydroponic systems filled with two liters of sterilized and deionized water, added with 0.1% (\(v/v\)) 0.2 \(\upmu\)m filtered nutrient solution Hydro A (B'cuzz, Atami B.V., Rosmalen, The Netherlands) containing K\({}_{2}\)O 4.7%, CaO 3.8%, MgO 1.3%, SO\({}_{3}\) 0.11%, Fe 0.04%, and N 4.9% (calcium and ammonium nitrate salts); 0.1% (\(v/v\)) filtered nutrient solution Hydro B (B'cuzz, Atami B.V., Rosmalen, The Netherlands) was also added, containing P\({}_{2}\)O5 4.1%, K\({}_{2}\)O 5.71%, B 0.01%, Mn 0.03%, Mo 0.001%, Zn 0.039%. Only one nutrient application was performed during the whole experiment. Electrical conductivity (EC), a proxy for nutrient concentration, was determined using an EC probe (EC400, Apera Instruments, Columbus, OH, USA). The EC was 1.2 \(\pm\) 0.2 dS m\({}^{-1}\) during the growth period, which is, on average, 20% less than the minimal optimum value (1.5-2.5 dS m\({}^{-1}\)[44]) and about 25-30% less compared to the recommended nutrient supply (1.6-1.7 dS m\({}^{-1}\)). The pH of the growth solution was measured daily with a portable pH meter (PH400, Apera Instruments, OH, USA). A buffering agent (pH- Terra Aquatica, Fleurance, France) was applied to adjust the pH value and keep it in the range of 5-6.5, optimal for plant and microbial growth.  
The hydroponics were placed in a grow tent (Mars Hydro, Europe, Ginsheim-Gustavsburg, Germany) equipped with the lamp Mars Hydro Smart FC 3000 Samsung LED Grow Light powered by a Samsung LM301B LED. The PPFD was set at 400 \(\upmu\)mol photon m\({}^{-2}\) s\({}^{-1}\) (14/10 h, light/dark). The temperature was set to 26 \({}^{\circ}\)C, and a ventilation system (DF150A, Inline Duct Fan, Mars Hydro, Europe) guaranteed an air exchange in the tent.
###### Abstract  
The article analyzes the impact of selected operational parameters of internal combustion engine vehicles on CO\({}_{2}\) emissions. The study was preceded by a detailed analysis of the issues related to CO\({}_{2}\) emissions in the EU, with a focus on Poland, where the tests were conducted. The key scientific assumption is that individual vehicle users' behaviors significantly impact global CO\({}_{2}\) emissions. Daily use of private vehicles, driving style, and attention to fuel efficiency contribute to cumulative effects that can drive the transformation toward more sustainable transport. Therefore, the study was conducted using real-time empirical data obtained from the vehicles' OBD (On-Board Diagnostics) diagnostic systems. This approach enabled the creation of a diagnostic tool allowing each vehicle user to assess CO\({}_{2}\) emissions and ultimately manage its levels, which is the biggest innovation of the work. Two levels of CO\({}_{2}\) emissions were identified as categorical variables in the model, considered either ecological or non-ecological from the perspective of sustainable transport. The CO\({}_{2}\) emission threshold of 200 g/km was adopted based on the average age of vehicles in Poland (14.5 years) and Regulation (EC) No 443/2009 of the European Parliament and of the Council. Three models of logistic regression dedicated to different driving cycle phases--starting, urban driving, and highway driving--were proposed and compared. This study demonstrated that during vehicle starting, the most significant factors influencing the probability of ecological driving are vehicle velocity, relative engine load, and relative throttle position, while for the other two types of movement, engine power and torque should also be considered. The logistic regression model for vehicle start-up obtained a value of sensitivity at about 82% and precision at about 85%. In the case of urban driving, the values of the discussed parameters reach significantly higher levels, with sensitivity at around 96% and precision at about 92%. In turn, the model related to highway driving achieved the highest values among the created models, with sensitivity at around 97% and precision at about 93%.  
emission; road transport; motor vehicles; European Union; classification methods Article
## 1 Introduction
This analysis relies on the atmospheric global reanalysis of climate by the European Centre for Medium-Range Weather Forecast (ECMWF) of fifth-generation referred as ERA5, and National Ocean and Atmospheric Administration (NOAA) datasets to assess the decadal shifts in ocean-atmospheric parameters that govern TC formation and intensification, offering insights into large-scale climate variability. The new findings from this study are expected to enhance advanced research on TC activity in the NIO region and provide a basis for a detailed investigation of possible linkages with other climate indices over the AS. The impact of global warming due to climate change and its effect on extreme weather events, such as the frequency and intensity of TCs formed over global ocean basins, is a matter of concern. High-intensity cyclones have become more frequent in the NIO, posing significant risks and vulnerabilities to coastal regions. In this study, we investigate the decadal variability of TC genesis factors over the AS during the post-monsoon season. Specifically, we examine the role of SST, vorticity, wind shear, and moisture content in influencing TC formation and intensification. By analyzing observational data from the ERA5 and NOAA datasets, we aim to identify decadal trends or shifts in these environmental factors and their potential impact on TC activity, which are discussed in subsequent sections. The main objectives of this study are as follows:  
1. Examine changes in key factors influencing tropical cyclone genesis, such as sea surface temperature (SST), vorticity, wind shear, and moisture availability. Study the effects of large-scale atmospheric shifts, including the northward movement of the Asian Jet Stream and variations in wind shear, on cyclone formation and behavior. * 3. Assess localized warming impacts, evaluating how localized warming in the Indian Ocean affects cyclone frequency and intensity, particularly in the context of global warming and recent extreme weather events. The rest of the paper is organized as follows: Section 2 discusses data and methodology, while Sections 3 and 4 present the results and concluding remarks, respectively.
# A Design of a Time Synchronization Protocol Based on Dynamic Route and Forwarding Certification
## 3 Design of a Time Synchronization Protocol Based on Dynamic Routing and Forwarding Certification
### Time Synchronization Based on Dynamic Routing  
In the time synchronization process, the root node initiates a new round of synchronization as the reference time. The others will accomplish information receiving; synchronization round recording;  
Figure 3: Flow chart of the time synchronization protocol based on dynamic routing and forwarding certification (DRFC-TSP).  
Figure 2: Clustered model.  
and reference synchronization information selecting, time synchronizing, and performance information caching. After accomplishing these processes, the node with forwarding certification should continue to forward time synchronization messages.  
A time variable \(T_{ref}\) is set for choosing the reference node. During the time of \(T_{ref}\), a node without synchronization continually receives information from reference candidates. When \(T_{ref}\) is increased, the candidate node with the best performance will be selected as the reference. By choosing the best reference in each hop, an optimum synchronization route will be ensured. The parameter called \(Std\) is defined as a measurement of the node performance, which is related to the hops to root node, clock frequency performance, and value of \(Std\) in the last synchronization round. According to \(Std\), the best candidate will be selected as the reference. \(Std\) is defined as follows:  
\[Std^{i}=\lambda(H)\cdot\mu(F)\cdot\eta\big{(}Std^{i-1}\big{)} \tag{1}\]  
where \(Std^{i}\) is the performance parameter in round \(i\), \(\lambda(H)\) is a variable determined by the hop number \(H\), \(\mu(F)\) is a variable determined by the frequency skew \(F\) in the current synchronization round, and \(\eta\big{(}Std^{i-1}\big{)}\) is the performance parameter in round \(i-1\). A node with a good performance should have a smaller number of hops and a more stable clock frequency. Therefore, the node with a better node clock performance has a smaller \(Std\) parameter. Additionally, in order to achieve proper synchronization performances in different practical applications, \(Std\) should be adjusted according to the influence of hops and frequency skew in the specific network environment.
## 3 Metallic Nanoparticles Used for the Enhancement of Bio Gas Production
### Concentration of Nanoparticles
[58] carried out a study on fresh raw manure using Ni, Fe\({}_{3}\)O\({}_{4}\), Co, Fe nanoparticles and found that after 40 days, Ni NPs with a concentration of 2 mg/L and a particle size of 17 nm were the most effective NPs, with highest average methane and biogas yields of 78.53% and 116.76%, respectively. While Fe\({}_{3}\)O\({}_{4}\), with a particle size of 7 mm and a concentration of 20 mg/L, yielded a 73% increase in biogas production and 115.66% increase in methane production. Co, with a particle size of 28 nm and a concentration of 1 mg/L, exhibited a 71% increase in biogas production and a 45.92% increase in methane production. Fe NPs, with a particle size of 9 nm and a concentration of 20 mg/L, resulted in a 47.7% increase in biogas production. In another study, [PERSON] et al. [61] utilized Waste Activated Sludge (WAS) for methane production and applied nZVI (10 mg/g) and Fe\({}_{2}\)O\({}_{3}\) (100 mg/g) NPs, and found an enhancement of 120% and 117%, respectively. These results reveal that lower concentrations of said NPs stimulate the microbial populations of Archaea and bacteria.
# Beyond Static Estimates: Dynamic Simulation of Fire-Evacuation Interaction in Historical Districts
## 2 Methods
### Framework  
This study includes five parts: (1) fire simulation, (2) multi-case fire comparison, (3) evacuation simulation under fire, (4) multi-scenario evacuation comparison, and (5) assessment and comparison, as outlined in Figure 2.  
Fire simulations are commonly conducted using software like PyroSim, CFAST, and NetLogo (6.3.0), with the latter being well-suited for large-scale, multifactor cases [38; 39; 40]. The fire-spread module was implemented in NetLogo 6.3.0. Custom Logo code imports the study-area map, assigns ignition points, and simulates flame propagation, the latter being governed by the heat-transfer equations (Equations (1)-(5)). To ensure the reliability of fire simulations, an agent-based model (ABM) is established based on general fire development rules while the simulation parameters are optimized through validation against historical records for current research. Afterwards, the results under different ignition conditions are compared to identify the most severe cases.  
The evacuation module was implemented in AnyLogic Professional 8.9.0. A detailed road-network model of the study area was built in AnyLogic, and the fire-induced \"impassable\" zones exported by NetLogo were imported as dynamic obstacles. By means of AnyLogic's built-in event block, these obstacles are activated at scheduled time steps, reproducing the progressive closure of streets as the fire advances and thereby altering pedestrians' route choices. Throughout the simulation, each agent's position and time-stamp are saved to CSV files for subsequent analysis. Based on this simulation framework, different evacuation strategies are tested and their results are compared to identify scenarios with the worst evacuation outcomes [41; 42]. Finally, the evacuation results from within the historical districts to the emergency shelter are analyzed and compared with outcomes from a static analysis framework. The spatial and temporal evolution of fire incidents and their associated environmental changes are characterized. The interdependencies between environmental modifications and evacuation behaviors within the historical districts are analyzed. Comparative analyses between static and dynamic modeling approaches are then conducted.