esa-sceva/satcom-chunk-collection huggingface dataset:
# Disjoint Tree Mergers for Large-Scale Maximum Likelihood Tree Estimation
## 2 Materials and Methods
### Evaluation Criteria  
Our focus is on the topological error in the estimated tree in comparison to the reference tree for the simulation study. Every tree \(t\) can be represented by its set \(\pi(t)\) of edge-induced bipartitions; hence, the set \(\pi(T)\setminus\pi(t)\), where \(t\) is the estimated tree and \(T\) is the true tree, is the set of \"missing branches\" or \"false negatives\". Conversely, the set of false positives is \(\pi(t)\setminus\pi(T)\). The false negative (FN) rate is the number of false negatives divided by the number of internal branches in the true tree, and the false positive (FP) rate is the number of false positives divided by the number of internal edges in the estimated tree. These rates are identical when the estimated and true tree are both fully resolved (i.e., binary), in which case they are also equal to the commonly used Robinson-Foulds (RF) error rate.  
1000M1-HF is the only model condition we explore that has non-binary reference trees, where the reference trees are about 0.4% unresolved (Table A1); all other model conditions have binary reference trees. In addition, all of the estimated trees are also binary. Hence, for the model conditions other than the 1000M1-HF condition, the RF error rate is exactly the same as the FN error rate. We report FN rates for all methods here, and note that the difference between FP and FN rates on the 1000M1-HF datasets is approximately 0.3%. To compute these error rates, we use a script that was coded by [PERSON] (see Appendix C).  
We also evaluate methods with respect to running time. In all of our analyses, we assume a maximum of 16 core parallelism, i.e., when running constraint tree calculations in parallel, only those analyses that will fit under 16 cores are considered to be parallel, assuming that a typical computational platform would have access to 16 cores. In cases where multiple methods are used in stages, such as the DTM pipelines, the same assumption of 16 core parallelism applies. In a DTM pipeline, the run-time calculation is the sum of running times of computing the starting tree, constraint trees, and the running time of disjoint tree merger. No given method or a set of methods running in parallel was allowed to use more than 16 cores.
## 3 Results and Discussion
### Seasonal Variation of Estimated Rain Heights Using KDE  
#### 3.3.1 Rain Height Distribution in the Summer  
The measured and estimated rain heights show that the summer season experiences the highest rain height across the nine stations. Figure 4a-i show that rain height varies between about 4500 m and 5800 m in Polokwane, 4600-5800 m in Pretoria, 3000-5500 m in Durban, 3000-5500 m in Nelspruit, 3000-5500 m in Mahikeng, 3000-5500 m in Upington, 3000-5500 m in Bloemfontein, 2500-5500 m in Port Elizabeth, and 3000-5500 m in Cape Town, as depicted in Figure 4a-i. Most of the stations do record a peak in rain height of 5000 to 5200 m, but coastal stations like Durban and Port Elizabeth settle slightly lower at 4500 m to 5000 m. Coastal stations are often characterized by smaller rain height because of the closeness of the ground to the mean sea level, accompanied by the prevailing weather conditions [37; 38].  
Figure 4: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Season**} & \multicolumn{4}{c}{**Epanechnikov**} & \multicolumn{4}{c}{**Gaussian**} & \multicolumn{4}{c}{**Triangular**} & \multicolumn{4}{c}{**Rectangular**} \\  & & **h** & **Hr** & **ISE** & **h** & **Hr** & **ISE** & **h** & **Hr** & **ISE** & **h** & **Hr** & **ISE** \\  & & **h** & **Hr** & **(E-6)** & **h** & **(E-6)** & **h** & **Hr** & **(E-6)** & **h** & **(E-6)** & **h** & **Hr** \\ \hline \multirow{4}{*}{Port Elizbe} & Autumn & 302.65 & 4564.46 & 3 & 137.09 & 4564.46 & 3 & 228.92 & 4564.46 & 2 & 258.67 & 4564.46 & 3 \\  & Winter & 380.52 & 3727.71 & 9 & 172.37 & 3727.71 & 9 & 287.83 & 3727.71 & 7 & 325.23 & 3727.71 & 10 \\  & Spring & 457.09 & 3791.51 & 6 & 207.06 & 3791.51 & 6 & 345.75 & 3791.51 & 5 & 390.68 & 3791.51 & 7 \\  & Summer & 340.15 & 4550 & 9 & 154.08 & 4550 & 8 & 257.3 & 4550 & 5 & 290.73 & 4550 & 11 \\ \hline \multirow{4}{*}{Cape Town} & Autumn & 340.55 & 4290.92 & 9 & 154.52 & 4290.92 & 8 & 257.6 & 4290.92 & 6 & 291.07 & 4290.92 & 11 \\  & Winter & 444.87 & 3503.34 & 14 & 201.52 & 3503.34 & 12 & 336.51 & 3503.34 & 8 & 380.23 & 3503.34 & 18 \\  & Spring & 453.55 & 4017.6 & 11 & 205.45 & 4017.6 & 10 & 343.07 & 4017.6 & 7 & 387.65 & 4017.6 & 13 \\  & Summer & 259.24 & 4724.63 & 4 & 117.43 & 4724.63 & 4 & 196.09 & 4724.63 & 2 & 221.58 & 4724.63 & 7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._The measured PDFs tend to have a steep slope, which suggests that the rain height is well identified and comparable in summer over these regions. The Gaussian kernel produces smooth generalized estimates of the densities, while the Epanechnikov, triangular, and rectangular kernels give sharp peaks. Yet, all kernels under consideration are close to the measured PDF.
## 3 Results and Discussion
### Characterization of Samples
In a recent paper, it was shown that SUNSPACE SF contains not only macro pores but also ink bottle shaped pores in order to nm. The cement sample showed irregular needle-like structures, probably attributed to cement hydration products as ettringite. As reported in literature, its formation is caused by the reaction between calcium sulfate and calcium aluminate, available in a cementitious matrix [30,31]. The formation of ettringite prevents flash setting and improves mechanical strength [30]. Morphological characterization of _Hedera Helix_ L. was already published in a previous work [20]. SEM analysis revealed that the leaf surface has epicuticular wax in the shape of long tubules with size less than 2 \(\upmu\)m. The voids between these tubules promotes fine PM capture. As reported and discussed in literature, _Hedera Helix_ L. shows high PM densities accumulation in all size fractions due to the presence of surface wax [24,32]. Figure 3: Optical microscopy images of SUNSPACE SF (**a**), SUNSPACE BA (**b**), cement material (**c**) and _Hedera Helix_ L. leaf (**d**).
# Detection of Yunnan Pine Shoot Beetle Stress Using UAV-Based Thermal Imagery and LiDAR
## 3 Results
### The Effect of LAI on the Relationship between Temperature and SDR on Plot Scale  
The correlation analysis of LAI and \(T_{\rm P}\) revealed that LAI was also negatively correlated with temperature (r = \(-\)0.5902), which was the same as that at the canopy scale (Figure 8a). Stronger transpiration with a high number of leaves in a plot caused the \(T_{\rm P}\) to decrease with a rising LAI.  
Additionally, the plot SDRs were calculated using the average SDR of the single trees within a plot. Figure 8b shows the non-significant correlation of SDR and \(T_{\rm P}\), on the basis of the data of 50 plots, including different LAIs from 0 to 4 (R\({}^{2}\) = 0.0105).  
To further understand the relationship between SDR and \(T_{\rm P}\) on a large scale and to explore the role of the LAI, the 20 plot data were filtered using the expression LAI > 1 for analysis (Figure 9). The results revealed a significant correlation in the relationship between SDR and \(T_{\rm P}\) after the influence of a low LAI (R\({}^{2}\) = 0.5532) was weakened. Therefore, the influence of the LAI on plot temperature should be considered when using TIR data to monitor forest infestation with the SDR at the pixel scale. In TIR images, pixels with a low LAI may include an excessive surface temperature due to the background radiation of soil; this will affect the correlation between the SDR and temperature, resulting in errors when monitoring forest infestations. In particular, in sparse coniferous forests where the crown needles have a small surface area and a non-aggregated canopy structure, background thermal radiation generated below the canopy can easily be captured by sensors.  
Figure 8: The correlation analysis between plot temperature and LAI (**a**) and SDR (**b**) in 50 plots, which were demarcated from two large plots.  
Figure 7: The correlation between SDR and CST of 30 affected pines with high LAI in plots.
## 3 Results and Discussion
### Correlation Analysis Between Volatile Flavors and Communities (Bacterial and Fungi)
During the CFB fermentation process, microorganisms utilize substrates like proteins, fats, and carbohydrates from soybeans. Carbohydrates were changed into the pivotal metabolic intermediate pyruvate via glycolysis and the tricarboxylic acid cycle (TCA) pathway. Pyrruvate was produced via glycolysis. Then, pyruvate was converted to ethanol by pyruvate decarboxylase and alcohol dehydrogenase (Figure 5). Furthermore, pyruvate can be changed to acetyl-CoA by 2-oxoacid oxidoreductase-take [59; 60]. Then, acetyl-CoA can be further transformed into citric acid, isocitric acid, and succinic acid, which eventually combine with ethanol to form diethyl succinate (DES). Additionally, pyruvate was converted into hexanoyl-CoA and acetoxyacetic acid [60], producing precursors for flavored ethyl esters (hexanoic and octanoic acids) through the fatty acid carbon chain elongation pathway (Figure 5). These acids were subsequently esterified with alcohols (such as ethanol) to form the corresponding ester compounds. Proteases can hydrolyze soybean proteins to yield amino acids, including L-phenylalanine. These amino acids can then be changed to 3-phenylpyruvate acid by amino acid transaminase and eventually produce phenethyl alcohol. Additionally, some amino acids (e.g., valine, L-isoleucine, and N-carbobenzoxy-DL-leucine) participate in the synthesis of higher alcohols (e.g., 2-methyl-1-propanol and 3-methyl-1-butanol) [43]. The fats in soybeans can also be hydrolyzed to produce fatty acids and glycerol. These fatty acids combine with ethanol to yield ethyl esters of higher fatty acids, including palmitic acid ethyl ester, ethyl oleate, and ethyl linoleate (Figure 5) [61]. Studies on enzymes involved in esterification reactions in baijiu microorganisms have laid a theoretical foundation for these reactions, adopting alcohols and acids as substrates [36]. This was a crucial metabolic pathway for ester formation in CFB [62]. Consequently, the metabolic activities of microorganisms significantly affect the synthesis of flavor compounds during CFB fermentation. Microorganisms metabolize carbohydrates, proteins, and fats in soybeans to produce a diverse array of flavor compounds. Therefore, further research on the metabolic pathways of these microorganisms and the management of corresponding enzyme activities is feasible. Such research could aid in regulating and controlling the flavor compounds produced during CFB fermentation. Figure 5: Metabolic networks of selected flavor chemicals during CFB fermentation.
###### Abstract  
The Internet of Things (IoT) has revolutionized connected devices, with applications in healthcare, data analytics, and smart cities. For time-sensitive applications, 5G wireless networks provide ultra-reliable low-latency communication (URLLC) and fog computing offloads IoT processing. Integrating 5G and fog computing can address cloud computing's deficiencies, but security challenges remain, especially in Authentication and Key Agreement aspects due to the distributed and dynamic nature of fog computing. This study presents an innovative mutual Authentication and Key Agreement protocol that is specifically tailored to meet the security needs of fog computing in the context of the edge-fog-cloud three-tier architecture, enhanced by the incorporation of the 5G network. This study improves security in the edge-fog-cloud context by introducing a stateless authentication mechanism and conducting a comparative analysis of the proposed protocol with well-known alternatives, such as TLS 1.3, 5G-AKA, and various handover protocols. The suggested approach has a total transmission cost of only 1280 bits in the authentication phase, which is approximately 30% lower than other protocols. In addition, the suggested handover protocol only involves two signaling expenses. The computational cost for handover authentication for the edge user is significantly low, measuring 0.243 ms, which is under 10% of the computing costs of other authentication protocols.  
fog-cloud computing; fog security; authentication protocols; key agreement protocols +
Footnote †: journal:  
0000-0001-6600-0009
## 1 Introduction
### Background of an \"Estimation Procedure\"
#### 1.1.3 Unsaturated Elastic Modulus  
The elastic modulus and shear strength are essential parameters for the design of any structure. It is commonly assumed that the value of the elastic modulus is constant below or above the water table when homogeneous soil deposits exist without considering the influence of suction on this parameter. [PERSON] et al. [35] studied the variability of the elastic modulus according to the soil's degree of saturation for three types of sand and concluded that the modulus of elasticity is related to SWCC in a manner similar to shear strength. In addition, this study proposes a semi-empirical model to predict the variation in the elastic modulus as a function of this curve and saturated elastic modulus. [PERSON] and [PERSON] [36] analyzed the unsaturated mechanical behavior of a compacted mixture of clay and sand with variations in sand content. The study concluded that the Young's modulus E\({}_{50}\) of samples composed of kaolinite alone was lower than that of samples with 25% sand but higher than that of samples with 50% sand. The study also indicated that the unconfined compressive strength and E\({}_{50}\) increased in the high-suction range for all dosages. [PERSON] [37] established a nonlinear fitting equation for the unsaturated elastic modulus that relates the water content to the elastic modulus under dry and saturated conditions. This equation satisfactorily represents the behavior of the unsaturated elastic moduli of silty and clayey soils. [PERSON] [38] established that the energy imbalance between dry soil and free water leads to energy storage as suction, which is controlled by the difference between pore water and pore air pressures within soil capillaries, along with an adsorption regime in which energy is stored as a matric potential, primarily governed by van der Waals attractive forces. Therefore, when the soil is in the capillary regime, a decrease in the water content leads to an increase in suction, which in turn enhances the elastic modulus. In the adsorption regime, suction controlled by van der Waals attractive forces induced hardening, leading to a further increase in the elastic modulus. Based on these concepts, a generalized function is proposed to predict the elastic modulus under unsaturated conditions along with an adsorptive hardening function.
## 5 Experiments and Results
### Performance of the Proposed Anomaly Forecasting Method
Similarly, the anomaly scores for the intelligent manufacturing and Industrial Internet data shown in Figures 7 and 8 highlight the same interesting phenomenon. While the overall trends in anomaly scores for both methods are very close, there are still significant differences. These inconsistencies remind us of the inherent limitations of each method--some anomalies may be misidentified. In summary, while our proposed anomaly forecasting method demonstrates a comparable performance in direct anomaly detection, it also highlights the unique advantages and shortcomings of each approach. These findings suggest the need for further research into optimizing and possibly integrating these two methodologies to maximize anomaly detection and forecasting accuracy.
# Closed-Form Performance Analysis of the Inverse Power Lomax Fading Channel Model
## 4 Simulation and Results' Analysis
### Hyper-Rayleigh Analysis  
Combining the results of the EC, AoF, and OP analysis, it is clear that there are sets of parameters \((\alpha,\beta)\) that correspond to the HRR in a specific sense. Lemma 2 presented the regions of \((\alpha,\beta)\) for full, strong, weak, and no hyper-Rayleigh regime only for the case of \(\alpha\beta=1\). Otherwise, no unique closed-form relation exists; thus, the HRR must be analyzed numerically, and it is convenient to present the results in the form of a contour map as a function of \((\alpha,\beta)\). The results for the AoF (in logarithmic scale) are presented in Figure 13, and the residual EC (the difference between the EC of the IPL channel and the Rayleigh model) are presented in Figure 14.  
Figure 12: IPL channel ergodic capacity vs. \(\bar{\gamma}\) for various channel parameters \((\alpha,\beta)\).  
Figure 13: Logarithmically scaled AoF contour map for various channel parameters \((\alpha,\beta)\); black dashed line corresponds to Rayleigh fading.  
It must be pointed out that the results for \(P_{\text{out}}\) are omitted here since they give trivial conditions (see Lemma 2) that can be expressed analytically in a closed form.  
In both plots, the black dashed line corresponds to the Rayleigh fading condition (i.e., \(\lg(\text{AoF})=0\) and \(\Delta\overline{C}=0\)), separating the regions of hyper-Rayleigh fading and lighter-than-Rayleigh fading.  
The combined analysis of Figures 13 and 14 demonstrates that the constraint implied by the ergodic capacity is stricter than that of the AoF. Recalling the result of Lemma 2, one can note that the constraint implied by the OP is stricter than by the EC. Thus, it can be finally stated that if the OP of the IPL model is worse than for Rayleigh, the channel experiences the full HRR; if the OP is better than Rayleigh's but the EC is worse, then the IPL model undergoes the strong HRR; if the EC is better than Rayleigh's but the AoF is greater than one, the channel experiences the weak HRR; otherwise, the IPL channel does not demonstrate hyper-Rayleigh behavior.
# Application of FBG Based Sensor in Pipeline Safety Monitoring
## 2 Pipeline Safety Monitoring Theory
### Leakage Monitoring  
When pipeline leakage occurs, the fluid suddenly escapes from the leakage point, causing the instantaneous pressure drop. Simultaneously, an NPW is generated and propagate toward both ends of the pipeline at a certain velocity [17]. Traditionally, several pressure sensors are mounted along the pipeline to detect the NPW. According to Equation (1), the hoop strain is proportional to the inner pressure variation, since \(t\) and \(R\) can be considered as the constants during the pipeline leakage. Thus, pipeline leakage can be detected via the hoop strain measurement.
# A Novel Benthic Phage Infecting _Shewanella_ with Strong Replication Ability
## 2 Materials and Methods
### Detection of Phage Host Range  
The host range of phage S0112 was tested by the spot test method with the ability to form plaques on bacterial lawn culture. Besides _S. indica_ KJW27, the tested bacteria included seven strains from marine sediments, i.e., _Algintiobacterium sedimentinis_ ALS81, _Woesia oceani_ SDUM189001, _Sedminicola luteus_ SDUM 701001, _Kordiniomas sedminis_ N39, _Shewanella chilikensis_ JC5, _Shewanella basaltis_ CJW-54, and _Shewanella japonica_ KCTC22435; ten strains from seawater or saline water, i.e., _Martinobacter viiiifrumus_ D7035, _Halomonas denitrificans_ D7027, _Vibrio alginolyticus_ CIP 82.01, _Vibrio neocaledonicus_ NC 470, _Shewanella algae_ JCM 21037, _Vibrio azureus_ NBRC 104587, _Vibrio harveyi_ NBRC 15634, _Vibrio parahaemolyticus_ NBRC 12711, _Vibrio campbellii_ CAIM 519, and _Roseobacter denitrificans_ OCH114. Briefly, 2 \(\upmu\)L of phage suspension (\(\sim\)10\({}^{6}\) phage particles) was spotted onto the surface of double-layer agar plate inoculated with the tested strains at 30 \({}^{\circ}\)C, for two weeks, and plaque formation was examined each day. Three replicates were tested for each bacterial strain.
###### Abstract  
Noise generated from wind turbines is a big challenge for the wind energy industry to develop further onshore wind energy. The traditional way of reducing noise is to design low noise wind turbine airfoils and blades. A wind turbine operating under wind shear and in yaw produces periodic changes of blade loading, which intensifies the amplitude modulation (AM) of the generated noise, and thus can give more annoyance to the people living nearby. In this paper, the noise emission from a wind turbine under wind shear and yaw is modelled with an advanced fluid-structure-acoustics framework, and then controlled with a pitch control strategy. The numerical tool used in this study is the coupled Navier-Stokes/Actuator Line model EllipSys3D/AL, structure model FLEX5, and noise prediction model (Brooks, Pope and Marcolini: BPM) framework. All simulations and tests were made on the NM80 wind turbine equipped with three blades made by LM Wind Power. The coupled code was first validated against field load measurements under wind shear and yaw, and a fairly good agreement was obtained. The coupled code was then used to study the noise source control of the turbine under wind shear and yaw. Results show that in the case of a moderate wind shear with a shear exponent of 0.3, the pitch control strategy can reduce the mean noise emission about 0.4 dB and reduce slightly the modulation depth that mainly occurs in the low-frequency region.  
a +
Footnote †: journal: Applied Sciences
## 3 Methodology
### Geometric Decoding  
As shown in Figure 7, \(M_{m}\) is processed by a multi-head Transformer \(T_{m}\) for decoding. The first two lines of the resulting outputs, \(CLS_{out}^{\prime}\) and \(e_{pred}\), are applied for resolving the representation of the target entity and the degree of entity reduction, respectively. To provide the model with additional fitting space, \(CLS_{out}^{\prime}\) is finally inputted into a linear feed-forward network as  
\[e_{o}=W\times CLS_{out}^{\prime}+b \tag{7}\]  
Figure 6: Architecture of quaternion space rotation module.  
where \(W\) and \(b\) correspond to the weight and bias, and the output \(e_{o}\) represents the target entity. Subsequently, we compute the similarity with each \(i\in E\) through dot product as  
\[\phi(e_{s},e_{r},e_{i})=e_{o}\cdot e_{i} \tag{8}\]  
The similarity serves as the score of the triple, which can be perceived as a confidence level when the tail entity of the triple corresponds to \(e_{i}\). The higher the score of the triple, the more likely it is that the entity is the target entity and is therefore appropriately predicted.  
During the training process, the score is established as the probability distribution of the correct entity based on the softmax activation function. This probability distribution is implemented to calculate the cross-entropy loss as  
\[Loss_{out}=-\sum_{i=1}^{E}Y_{srt}\cdot\text{softmax}\phi(e_{s},e_{r},e_{i}) \tag{9}\]  
where \(Y_{srt}\in\{0,1\}\) and \(Y_{srt}\) is 1 if \(i\) is the true target entity.  
To avoid overemphasizing neighbor information and disregarding source entity information during decoding, we use \(e_{pred}\) to reduce the impact of the source entity. This is achieved by calculating similarity through the dot product for each \(i\in E\). The degree of reduction in \(e_{pred}\) to the source entities is measured using cross-entropy loss as  
\[Loss_{pred}=-\sum_{i=1}^{E}Y_{s}\cdot\text{softmax}(e_{pred},e_{i}) \tag{10}\]  
where \(Y_{s}\in\{0,1\}\) and \(Y_{s}\) is 1 if \(i\) is the source entity.  
We obtain the final loss value by adding the two loss values with weights as  
\[Loss=\alpha Loss_{out}+\beta Loss_{pred}+\gamma\
orm{E}_{2} \tag{11}\]  
Additionally, we apply entity regularization to deter over-fitting and to generalize the embedding location of the entity.