esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Leaf Area Density Detection in Orchard Canopy Using LiDAR Technology
## 3 Results  
### Mathematical Model Analysis  
The results of the quadratic regression orthogonal experiment based on leaf fitting are shown in Table 4 below. The experimental results were calculated using software (MiniTab 2022, Minitab Inc., State College, PA, USA) and analyzed in conjunction with the results of the analysis of variance (ANOVA). The ANOVA results are presented in Table 5. Considering both the P-value and the F-value comprehensively, the cross-term (\(Z_{1}Z_{3}\)) between canopy thickness (\(Z_{1}\)) and leaf area density (\(Z_{3}\)) shows a strong correlation with the fitted leaf area, and both the linear (\(Z_{2}\)) and quadratic (\(Z_{2}^{2}\)) terms of detection distance (\(Z_{2}\)) and the cross-term (\(Z_{2}Z_{3}\)) also have significant effects on the results. Both canopy thickness (\(Z_{1}\)) and leaf area density (\(Z_{3}\)) will affect the degree of leaf interlacing, which directly influences the quantity of point cloud data returned by the lidar. The cross-term (\(Z_{1}Z_{3}\)) has the most significant impact on the results. The detection distance (\(Z_{2}\)) will affect the intensity of the reflected optical signal received by the lidar, and thus it will also impact the results. Therefore, the fitting equation was optimized by removing insignificant terms, leading to the optimized result shown in Equation (4). The \(R^{2}\) value of optimized fitting equation is 0.96.  
\[Y_{3}=-1.96+0.4295Z_{1}Z_{3}-1.489+3.85+0.1577Z_{2}Z_{3} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Number** & **Canopy Thickness** & **Target Distance** & **Leaf Area Density** & **Leaf Blade** \\  & **\(Z_{1}\) (m)** & **\(Z_{2}\) (m)** & **\(Z_{3}\) (m\({}^{2}\)/m\({}^{3}\))** & **Fitted Area (m\({}^{2}\))** \\ \hline
1 & 1.07 & 1.87 & 5.48 & 4.18 \\
2 & 1.07 & 1.87 &
# Centralized vs. Decentralized Cloud Computing in Healthcare
## 7 Results
#### 7.3.3 System Performance and Latency  
One of the most significant factors in managing patient records and diagnosing results in the healthcare sector is easy and fast access to the record. If latency is high, then it will take more time to deliver these services, which are very important in handling these cases and providing them appropriate treatment at the earliest. The centralization model means that data are collected, stored, and made easily retrievable from a central server [52]. These systems facilitate the optimal allocation of computational assets, encompassing processing capacity, memory allocation, data storage volumes, and network throughput. Aspects such as load disbursal, the organization of tasks, the scheduling of algorithms, and pattern analysis for the appropriate distribution of resources at the central server attenuate the response time [50]. However, the response time is contingent upon the client's location in relation to the centralized server. Thus, it would take a shorter duration, possibly because the client is situated near the central server. However, centralized structures may face problems related to overload in the network when many clients want to direct their requests to the central server simultaneously. In addition, the centralized models largely rely on the connectivity and availability of the network, which at times, slows down the response time [54]. On the other hand, in a decentralized cloud, the nodes are more dispersed to various regions compared to those in centralized clouds [61]. This is beneficial since the nodes can be closer to the healthcare organizations, and thus, the delivery of data is faster. Better data transmission latency enables faster data transmission whenever the nodes are placed nearer to the healthcare organizations since healthcare organizations need faster data access, quicker response time, and real-time interaction [66]. This is even more important in health, especially where quick access to data belonging to the patient or critical information that can be useful for diagnosis and treatment can make a difference. In this case, for decentralized models, the performance and the latency are dependent on the service of the network. Thus, a response should be given as soon as possible with low latency, which requires good connection quality, meaning high speed, better bandwidth, and low packet loss [72]. The clients ought to be well connected to the network through appropriate transmission means to the decentralized nodes in the healthcare organizations to avoid such delays.
## 4 Simulation Results and Discussion  
In this section, the effectiveness of the proposed secondary control scheme for voltage restoration and reactive power sharing is verified by simulating an islanded microgrid using MATLAB/Simulink toolbox. As shown in Figure 5, the studied microgrid test system consists of 4 DGs, which are connected by series RL transmission lines. In the secondary control level, the communication links between the different DGs are indicated as dotted lines. The lines' parameters, loads, and related control parameters of the microgrid test system are given in Table 1. In the simulation, we consider DoS attacks that affect each of the communication links independently. When DoS is present, the communication network in the secondary control level is modeled as a random connected undirected graph.
## 2 Materials and Methods  
### Bacterial Strains and Spore Suspension  
Two strains of _A. acidoterrestris_ were used in this study: a wild-type strain isolated previously from apple juice that was kindly provided by the Laboratory of Food Microbiology and Hygiene of the Aristotle University of Thessaloniki (denoted strain A). The GenBank accession number of 16S rRNA sequence of strain A is MW142406. A reference strain of _A. acidoterrestris_ DSMZ2498 (denoted strain B) obtained from DSMZ (Deutsche Sammlung von Mikroorganismen and Zellkuturen, Braunschweig, Germany) culture collection. Both strains were maintained at \(-\)80 \({}^{\circ}\)C in Yeast Extract Starch Glucose (YSG) broth (Yeast Extract 2.0 g; Glucose 1.0 g; Soluble starch 2.0 g; 1000 mL H20) with pH adjusted to 3.7 using 1N HCl and supplemented with 20% (_v/v_) glycerol (APPLICHEM, Darmstadt, Germany). The strains were pre-cultured in the same medium at 45 \({}^{\circ}\)C for 48 h to obtain a stock culture.  
The cells from the culture were inoculated on acidified _Bacillus acidoterrestris_ medium (BAT) agar plates (pH 3.7) (BTA20500, Biolab, Budapest, Hungary) and incubated at 45 \({}^{\circ}\)C for 7 days to sporulate.  
The sporulation of the cells was confirmed by phase contrast microscopy. When at least 80% of the cells had sporulated, the spores were harvested by adding 2.5 mL of cold sterile distilled water to the plates and scraping gently the surface with a sterile glass rod. The process was repeated twice. The spores obtained were centrifuged at 5000 rpm for 20 min at 4 \({}^{\circ}\)C. The supernatant was then discarded and the spores were washed three times by centrifugation (5000 rpm for 20 min at 4 \({}^{\circ}\)C) using cold sterile distilled water. The spores of 15 plates were re-suspended in 10 mL of sterile phosphate buffer (pH 7.2) and stored at 4 \({}^{\circ}\)C until use. The concentration of the spore suspension was determined by plating and it was ca. 10\({}^{7}\) spores/mL (see Section 2.2) [18,19].
# Study on the Influence of Moisture Content and Void Ratio on the Disintegration of Red Clay
###### Abstract
Additionally, the looser the soil structure due to a larger pore ratio, the weaker the interparticle bonding force, and the easier the disintegration of red clay. When the red clay contains more water [41; 42; 43; 44], the matric suction is smaller, and pure water enters the soil slowly, the ion concentration in the diffusion layer first decreases, and the thickness of the diffusion layer increases the 'wedge splitting' effect of water molecules, leading to the ultimate disintegration of red clay. The higher the moisture content of the red clay, the larger the void ratio, the looser the contact between soil particles, the more large pores, and the faster the disintegration rate. This article conducts a disintegration test on Guilin red clay to study the influence of moisture content and void ratio on the disintegration of red clay. The disintegration characteristics of red clay with different moisture contents and void ratios were observed using a self-made disintegration apparatus. A scanning electron microscope (SEM) was employed to obtain images of structure characteristics, and nuclear magnetic resonance (NMR) was used to obtain the distribution characteristics of water under natural and saturated states. The 'right half point' method was applied to determine the content of combined water under natural and saturated states of red clay. The relationship between the disintegration rate and the combined water saturation was comprehensively analyzed to provide a scientific basis for the evaluation and prevention of soil cave geological hazards by quickly estimating the disintegration rate of red clay.
## 2 Materials and Methods
### Models  
#### 2.1.1 Random Forest, RF  
Random Forest is an ensemble learning technique used for both classification (categorical output) and regression (numerical output) tasks. The algorithm constructs multiple decision trees by dividing the input data into various subsets, and the final prediction is obtained by averaging the outputs of these trees. Random Forest is an advanced implementation of the bootstrap aggregation, or bagging, method, which reduces model variance by generating multiple training datasets through sampling with replacement from the original dataset. This approach ensures that each tree is trained on a slightly different subset of the data, thereby enhancing model robustness and reducing the likelihood of overfitting. Bagging operates independently and in parallel, making it particularly well-suited for time-series predictions [24] where maintaining model generalization is essential. In this study, Random Forest was employed due to its ability to effectively manage the time-series nature of the data while delivering consistent predictive performance. The following is the overall process used to train and test the RF model:  
1. In the training phase, a total of \(M\) trees are generated, given an input of training data sets \(T=t_{1},\ t_{2},\ldots,t_{m}\) with targets \(S=s_{1},\ s_{2},\ldots,s_{m}\). Every tree grown in this stage grows according to the following protocol: 1. To create a training set \((T_{m},S_{m})\), randomly sample \(m\) times with replacement. 2. The regression tree denoted as \(R_{m}\) was trained on \((T_{m},S_{m})\). At each node split, \(f\) prediction parameters are randomly sampled. The optimal split is then chosen based on information gain or the Gini impurity measure. In our regression studies, the default choice of \(f=\frac{F}{3}\), where F is the total number of variables, was used to balance the trade-off between computational efficiency and model accuracy. This value ensures that a sufficient subset of features is considered at each node split, promoting diversity among the trees in the ensemble while preventing overfitting. For our dataset, this default setting works well, as it allows the model to capture the most relevant features without overly complicating the decision-making process, which is particularly beneficial when dealing with high-dimensional time-series data like ionospheric scintillations.
2. We added an unknown data sample, denoted as \(t^{\prime}\), during testing. To make a prediction, we needed to take the majority vote, which was calculated as \(\frac{1}{M}\sum_{m=1}^{M}R_{m}(t^{\prime})\). The RF algorithm employed in this work is governed by Equation (2) \[\hat{F}(y_{i})=\frac{1}{M}\sum_{m=1}^{M}R_{m}(y_{i};\Theta_{m})\] (2) where the variable \(M\) indicates the total number of trees, each regression tree is denoted by \(R_{m}\), and the description \(\Theta_{m}\) of the \(m_{th}\) tree includes the of splitting variables, cut points at each split node, and terminal node values. The RF algorithm utilized in the study is shown in Figure 1.
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 4 Data Processing  
Data processing includes video data processing, streaming media storage, remote light control, 3 DRS base construction, GNSS, and meteorological data processing, as shown in Figure 3.
## 2 Theoretical Analysis  
In a bidirectional transceiving coaxial laser propagation system, the laser beams emitted at the same time from the two ends are transmitted in the same atmospheric channel, but in opposite directions, and are finally received by the two receiving ends at the same time. The principle diagram of the instantaneous fading correlation measurement of the bidirectional optical signal is shown in Figure 1. We will refer to the propagation from terminal A to terminal B as the \"forward propagation\", and that from terminal B to terminal A as the \"inverse propagation\". Mark the two ends of the measurement as terminal A and terminal B, respectively. \(P_{A}\) and \(P_{B}\) are the received optical signals of terminals A and B, respectively. Assuming that the entire optical signal propagation process is random and stable, then the normalized cross-correlation function definition between them is [17]  
\[\gamma_{AB}(\tau)=\frac{\langle P_{A}(t)P_{B}(t+\tau)\rangle-\langle P_{A}(t) \rangle\langle P_{B}(t)\rangle}{\left[\langle P_{A}^{2}(t)\rangle-\langle P_{ A}(t)\rangle^{2}\right]^{1/2}\!\!\left[\langle P_{B}^{2}(t)\rangle-\langle P_{B}(t) \rangle^{2}\right]^{1/2}} \tag{1}\]  
among them, \(\langle\cdot\rangle\) is the mean value of the random functions \(P_{A}(t)\) and \(P_{B}(t)\), and \(\tau\) is the instantaneous time change.  
The formula for calculating the light intensity scintillation factor is [19]  
\[\alpha=\frac{\left\langle I^{2}\right\rangle-\left\langle I\right\rangle^{2}} {\left\langle I\right\rangle^{2}} \tag{2}\]  
among them, \(\langle\cdot\rangle\) represents the statistical average, and \(I\) represents the sum of the image gray values.  
Equation (1) can be used to calculate the correlation coefficient of the instantaneous fading of the optical signal received by terminal A and B, and the optical intensity flicker factor of the optical signal received by terminal A and B can be calculated by Equation (2).
## 1 Introduction
Due to the spatial separation of charges occurring at the interface between a semiconductor and a metal, or between two different semiconductors, the lifetime of photogenerated electrons and holes increases significantly [35; 36]. Platinum and its compounds are most often used as such cocatalysts in photocatalytic hydrogen evolution reactions [37; 38; 39; 40; 41; 42; 43]. It is known that platinum in the metallic state is the most active cocatalyst due to the high Schottky barrier, formed in this case at the metal-semiconductor interface [44]. This phenomenon allows platinum particles to act as effective traps of electrons, significantly increasing their lifetime and, consequently, the rate of hydrogen production. However, in the case of H\({}_{2}\) evolution from solutions of organic compounds, for example, alcohols, it is important to monitor not only the rate of H\({}_{2}\) formation, but also the formation rate of oxidation products of organic substrates in the gas phase. Nevertheless, the selectivity of the H\({}_{2}\) evolution process from aqueous solutions of organic substances is rarely studied [45], and the effect of the state of platinum on the activity and selectivity of the process has not been studied at all. At the same time, the influence of the oxidation state of platinum on the rate of photocatalytic destruction of dyes, as well as the oxidation of methanol, phenol and formic acid, has been shown in the literature, which confirms the significant influence of the state of platinum on photocatalytic processes [46; 47]. In the current work, we have presented for the first time the effect of the platinum valence state on the activity and selectivity of g-C\({}_{3}\)N\({}_{4}\)-based photocatalysts in H\({}_{2}\)'s evolution from ethanol aqueous solution under light irradiation (409 nm), and, therefore, reveal the key factors that determine the activity and selectivity of photocatalysts based on g-C\({}_{3}\)N\({}_{4}\) modified with Pt and PtO\({}_{\rm x}\). The novelty of the study is provided by the establishment of correlations between the platinum oxidation state and the selectivity of the target process--hydrogen evolution from aqueous ethanol solution. The possibility of controlling the selectivity for H\({}_{2}\) evolution over g-C\({}_{3}\)N\({}_{4}\)-based photocatalysts by changing the platinum state has been shown for the first time.
# Load-Bearing Performance of a Reinforced Fill Structure with Pile Penetration
## 5 Analysis of Results
### Lateral Displacement Analysis
#### 5.1.2 Effect of Different Reinforcement Layers  
The vertical load-lateral displacement curves for the structure with varying numbers of reinforcement layers are presented in Figure 6. For Scenario 3, at the ultimate load of 35.0 kN, the corresponding lateral displacements for Scenarios 3, 7, 6, and 5 can be found in Table 8.  
Based on the results in Figure 6a, it is clear that the lateral displacement of the pile penetrating the top of the reinforced structure increases with the increase in load in the initial loading stage.  
From Figure 6b,c, it can be observed that the lateral displacement at the mid-point B, mid-point C, and bottom D of the structure increases with an increasing load. Additionally, the variation pattern across different conditions remains similar at these measurement points. In the initial and middle loading stages, the order of increase in lateral displacement is as follows: 1 layer of reinforcement > unreinforced > 3 layers of reinforcement > 2 layersof reinforcement. However, in the later loading stage, the sequence of increased lateral displacement changes to the following: 1 layer of reinforcement > unreinforced > 2 layers of reinforcement > 3 layers of reinforcement. It is clear that increasing the number of reinforcement layers has a more pronounced effect in limiting lateral displacement in the later loading stages.  
Figure 6: \(Cont\).  
As shown in Table 7, the results show that an appropriate increase in the number of layers of reinforcement laying can help to improve the overall stability, but the increase in the number of layers of reinforcement does not lead to a linear increase in the effect of reinforcement. It can be seen that, from the economic consideration, increasing the number of reinforcement layers will increase the structure cost of the project, so in the practical application, the needs of all parties should be taken into account, and then, the best reinforcement program should be formulated.  
A comparison analysis between Figure 6 and Table 7 indicates that the addition of one layer of reinforcement at the bottom of the structure did not result in any significant restriction on the lateral displacement. However, the capacity to restrict lateral deformation significantly increased when two or three layers of reinforcement were added, compared to having no reinforcement.
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Protocols**} & \multirow{2}{*}{**Objectives**} & **PDFH Selection Algorithm** & \multirow{2}{*}{**DFFF Parameters**} & **Data Fusion Grouping Schemes** & **DFGs** & **RDFCF Parameters** & **Impairments Handling** & **Novel Examples** \\ \hline \multirow{2}{*}{**PSO-EEC** [39] & Improve network lifespan & \multirow{2}{*}{PSO} & The ratio of SNs’ initial energy and RE, distance between DFGs and PDFH, and SN degree & \multirow{2}{*}{Distributed} & The RE of PDFH, distance between PDFH and the sink & & \multirow{2}{*}{x} & \multirow{2}{*}{x} \\  & & & SN’s RE, average distance between PDFH and its DFGMs, and distance of PDFH from the sink & & & & \\ \hline \multirow{2}{*}{**LDIWPSO** [40] & Enhance energy efficiency & \multirow{2}{*}{PSO} & SN’s RE, average distance between PDFH and its DFGMs, and distance of PDFH from the sink & & & & \\  & & & SN’s RE, average distance between PDFH and its DFGMs, and distance between PDFH from & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparative analysis table of closely related studies focusing on similar contribution aspects as in our proposed work.
# Nitrogen-Related Defects in Crystalline Silicon
## 2 Background Information of N-Related Defects in Si
### The NO\({}_{2}\) Defect  
The NO\({}_{2}\) complex has been studied theoretically, in detail, in the literature, considering two possible nearest neighbor sites for the second oxygen atom of the structure. In the first case, the two O atoms of NO\({}_{2}\) are situated on either side of the N atom, forming an O-N-O configuration (refer to Figure 7) [31] with a C\({}_{2v}\) symmetry; although, in the second case, the two O atoms are coordinated two-fold, forming a N-O-O configuration with C\({}_{1h}\) symmetry [14; 81]. Notably the energy of the O-N-O is higher than that of N-O-O by about 0.2 eV [13; 81]. Comparing these values with the experimentally [87] detected bands, it was suggested [81] that the bands at 855, 973, and 1002 cm\({}^{-1}\) correspond to modes related to the NO\({}_{2}\) complex. Additionally, two other bands at 810 and 1018 cm\({}^{-1}\) which have similar annealing behavior to the above three bands were also tentatively attributed [87] to the NO\({}_{2}\) defect. Notably, the assignment of the two latter bands has been put into question, and in particular, they have been linked [14] to the N\({}_{2}\)O\({}_{2}\) defect.  
Figure 6: A schematic representation of the NO defect. Si atoms are indicted with dark shaded circles, N with the light shaded circle, and O the empty circle. The spots represent the initial lattice positions [14].
# Prediction of Flight Delays at _Beijing_ Capital International Airport Based on Ensemble Methods
## 3 Methodology
### Ensemble Methods
#### 3.1.2 Boosting Methods  
Boosting methods employ a serial working mechanism, dependent on the training of individual learners, performed serially step by step. First, a base learner is trained by the dataset \(D_{1}\). Then, the weight of samples with low prediction accuracy is increased. The adjusted dataset \(D_{2}\) is utilized to train the next base learner. The operation is repeated \(T\) times to generate the ensemble learner, by weighting \(T\) base learners.  
The most famous and widely used boosting method is the AdaBoost algorithm. The AdaBoost algorithm uses exponential loss function, so the update of weights and sample distribution revolves around minimizing the exponential loss function.