esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods  
### Overall Architecture  
Figure 4 illustrates the overall architecture of BPDNet, which consists of two structurally identical networks: the teacher network and the student network. The teacher network is trained on the WHU-Mix dataset for building polygon extraction. Once the teacher network is trained and reaches convergence, its model parameters are frozen. The student network is trained on the training set constructed from six images of the Wenzhou area and distilled using the trained teacher network. Therefore, the student model is capable of integrating the generalization knowledge from the large dataset and the feature distribution knowledge of the task area samples to address the issue of insufficient generalization of the building polygon extraction model caused by a small number of samples in the task area.  
The teacher network and the student network are both constructed based on HiSup [49]. HiSup serves as the baseline model for our method, consisting of the HRNet [51] feature encoder, Channel Attention module, and three building representation branches. Details of HiSup and our improvements to it are described in Section 3.2. The teacher network was trained on the WHU-Mix dataset, so during the training of the student network, the parameters of the teacher network are no longer updated to guide the training of the student network. In the training process of the student network, the remote sensing images from the task area are input to both the student network and the teacher network with frozen parameters. The forward propagation data flow of the network is shown by the solid green arrows in Figure 4. The gradients of the network parameters are backpropagated through the purple dashed arrows in Figure 4. The loss of the student network consists of two parts: distillation loss and building polygon prediction loss. The distillation loss represents the difference between the output features of the student network and the  
Figure 4: Overall Architecture of BPDNet. \(L_{d}\) denotes the distillation loss and \(L_{p}\) denotes the building polygon prediction loss. Mask Conv, Line Conv, and Vertex Conv represent the convolution operations in the Mask branch, Line branch, and Vertex branch, respectively. Mask head, Line head, and Vertex head represent the prediction head in the mask branch, line branch, and vertex branch, respectively.  
Figure 3: Sample examples from the WHU-Mix Dataset.  
teacher network in the three building representation branches, encouraging the student model to learn generalization knowledge from the teacher model and improve its ability to extract building polygons with diverse structures. The building polygon prediction loss represents the difference between the predicted building polygons by the student network and the ground truth building polygon labels, guiding the student model to learn the feature distribution knowledge of the data in the task area and enhance its accuracy in extracting building polygons in the task area.
###### Abstract
Eucalyptus was introduced into China in 1890 and has been planted for more than 130 years, making China the second largest plantation country in the world [33]. Eucalyptus plantations have greatly alleviated the shortage of timber supply from plantation forests in China, but the very short rotation cycle (about 6 years) and intensive management have led to many ecological problems [34]. Some studies show that the large-scale plantation of eucalyptus plantations has resulted in soil fertility degradation and soil erosion, limited growth of understory vegetation, and decline in biodiversity, while some studies show that eucalyptus plantations have an important role in promoting the ecological environment [33,35,36]. In the context of achieving the goal of carbon neutrality, people pay more attention to the carbon stock and carbon sequestration potential of eucalyptus, and accurate estimation of the carbon dynamics of eucalyptus has become an important issue. This study selected the eucalyptus plantations in Yuanling Forestry Farm, Zhangzhou City, Fujian Province, China as the research object. We comprehensively used SP survey data, forest inventory data, meteorological data, UAV lidar data, forest age obtained based on time-series remote sensing data, and a 3-PG model to simulate the forest structure of the study area, and assess the simulation accuracy. Specifically, the following two questions remain to be answered: (1) Can the forest age data obtained from remote sensing data support the 3-PG model to accurately simulate forest structural parameters at the SP scale and FSC scale? (2) How sensitive are the simulation results of the 3-PG model to the forest age data at the two scales?
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 3 Materials and Methods
### Work Methodology and Planning
#### 3.2.2 Development and Integration  
When designing the proposed solution, the authors considered two main phases for the development: first, a web authentication server which implements WebAuth registration and the authentication of security keys and passkeys; and second, the integration of WebAuth authentication with the captive-portal authorisation mechanism. These steps correspond to the phases PH3 and PH4, respectively.
# On Thermodynamic Aspects of Oxide Crystal Growth
## 3 Discussion
### Other Examples  
Reversible mass changes during the melting and crystallization of oxide compounds were reported occasionally in the literature:  
* The rare-earth calcium oxyborates YCa\({}_{4}\)O(BO\({}_{3}\))\({}_{3}\) and GdCa\({}_{4}\)O(BO\({}_{3}\))\({}_{3}\) gain 0.009% or 0.014% mass, respectively, upon melting in air [37].
* 4 V\({}_{2}\)O\({}_{5}\) = V\({}_{9}\)Mo\({}_{6}\)O\({}_{40}\) is formed, which contains V\({}^{4+}\) and V\({}^{5+}\). If the mixtures are molten in air, the mass rises due to oxidation of V\({}^{4+}\) to V\({}^{5+}\). The reverse effect occurs during crystallization. If molten or cooled in Ar, the mass remains constant [38; 39]. It should be noted that also in some A\({}_{2}\)BO\({}_{4}\)-type materials, molybdenum plays a role in oxygen storage and transport [17; 18].
* The color of SrPrGaO\({}_{4}\) (SPG) crystals depends sensitively on the growth atmosphere. Only if the atmosphere is virtually free of O\({}_{2}\), or if grown crystals are annealed under reducing conditions (e.g., in forming gas), the crystals are green from Pr\({}^{3+}\). Already, several ppm O\({}_{2}\) result in dark red/brownish coloring. It seems that the darker color is related to partial oxidation to Pr\({}^{4+}\); however, this explanation is disputed. Nevertheless, incorporation of interstitial oxygen is proven. With simultaneous DTA/TG measurements, it was found that melting is accompanied by a positive TG step, and the magnitude of this step (\(\lesssim\)0.2%) is almost proportional to the oxygen concentration in the atmosphere during the measurement [40; 41].
* Recently [PERSON] et al. reported crystal growth experiments from the melt with delafossite-type crystals Cu\({}^{+}\)Me\({}^{3+}\)O\({}_{2}\) (Me = Al, Fe) [42; 43]. It turned out that only small millimeter-sized crystallites of CuAlO\({}_{2}\) could be obtained. The experiments with CuFeO\({}_{2}\), in contrast, were more successful, and single crystalline OFZ bars with diameters up to 10 mm and several centimeters in length were obtained. It is shown in the next section that this very different behavior of isotype crystals with similar melting temperatures around 1200 \({}^{\circ}\)C results from the different oxidation levels of the ions in the liquid and solid phases: the melts contain in both cases Cu\({}^{+}\) and Cu\({}^{2+}\), and both solids only Cu\({}^{+}\). This requires the reduction in Cu\({}^{2+}\) ions during crystallization. The valence state of Al\({}^{3+}\) does not change during the crystallization of CuAlO\({}_{2}\). For CuFeO\({}_{2}\), in contrast, the melt contains besides the incorporated Fe\({}^{3+}\) also significant amounts of Fe\({}^{2+}\) which must be oxidized.
* More recently, it was shown that also during the crystallization of Bi\({}_{2}^{3+}\)Cu\({}^{2+}\)O\({}_{4}\) and Bi\({}_{2}^{3+}\)Pd\({}^{2+}\)O\({}_{4}\), the mass rises, due to oxygen incorporation and partial oxidation of Cu\({}^{+}\) or Pd\({}^{0}\) to the two-valent state. Bi\({}_{2}\)CuO\({}_{4}\) single crystals up to 6 mm in diameter and 7 cm in length were obtained by OFZ [44].
## Materials and Methods  
### Study Area  
South Korea the study area of this research, is located at a latitude of 33-39\({}^{\circ}\)N and a longitude of 124-131\({}^{\circ}\)E (Figure 1). According to the International Geosphere-Biosphere Programme (IGBP) land cover classification, the landmass of South Korea consists of mixed forests (40%), croplands (30%),deciduous broadleaf forests (14%), woody savannas (6%), and urban and built-up areas (5%) [7; 31]. Western South Korea is mostly composed of croplands, while the central and east parts are covered with mixed and deciduous broadleaf forests. To validate the estimated remote sensing-based CO\({}_{2}\) flux and SM, CO\({}_{2}\) flux and SM data of the Sulmacheon (hereafter SMC) and Cheongmicheon (hereafter CMC) flux tower were provided by the Korea Institute of Hydrological Survey. The SMC flux tower is in Jeokeseng-myeon, Paju-si, Gyeonggi-do, and the area is a typical mountainous terrain located at an altitude of 293 m.a.s.l. The CMC flux tower is in Janghowon-eup, Icheon-si, Gyeonggi-do, and is composed of a rice paddy terrain (Table 1). Data in the flux tower is measured by the Eddy covariance method, and reliable data is provided by quality control [30]. Both the CO\({}_{2}\) flux and SM data used in this study were extracted every 30 min, and data from 2014 and 2015 were used to analyze the relationship between the variables during a period of extreme drought status in Korea.  
Figure 1: Geographical locations of South Korea and the two flux towers ((**a**) Seolmacheon (SMC) and (**b**) Cheongmicheon (CMC) site) used in this study.
# Optomechanical Analysis and Design of Polygon Mirror-Based Laser Scanners
## 3 Multi-Parameter Optical Analysis and Design of PM Scanning Heads
### Migration Functions  
These two functions have been introduced in [28; 29; 30; 31; 32] to characterize a phenomenon that is specific to PM scanning heads, i.e., the displacement of the incidence point P on a PM facet--Figure 2b. Thus, the \"migration\" of the object point A of the lens is produced along the O.A., where point A was defined in Section 2.3 by the intersection between the reflected ray and the O.A.  
Referring to the two _migration functions_, transversal (Equation (7), Table 1) and longitudinal/axial (Equation (8), Table 1), the former appears in the scanning function (Equation (4), Table 1), but the latter is the most important for the scanner design. It allows for the design of a simpler (and lower cost) lens for a PM scanning head. The method requires one to correlate \(z(\theta)\) with the spherical aberration of the lens d\({}_{sl}\). It may allow one to obtain these two sources of errors of the scanning process to partially compensate for each other. The scope is to produce emerging beams from the lens that are more accurately parallel to the O.A. While this optical design is not within the scope of this study, it is the subject of future work in our group--including for SSs [26] and handheld probes for OCT [53; 54].  
In Figure 4, the two migration functions are studied analytically for the three characteristic intervals of the \(x\) = \(e\)/\(k\) parameter that are relevant to them: (1) \(x<\sqrt{2}-1\); (2) \(\sqrt{2}-1<x<1\); (3) \(1<x<\sqrt{2}\). A discussion on \(x\) is necessary to obtain the case (thus, the graph) that is optimal for the PM setup--Section 3.5. For the study of the \(y(\theta)\) function in Figure 4a, the marked values in these graphs are:  
\[y_{0}=R\Big{(}\sqrt{2}-1-x\Big{)} \tag{28}\]  
\[y_{max}=R\Big{[}\Big{(}\sqrt{2}-x\Big{)}-\Big{(}1+x^{2}\Big{)}^{1/2}\Big{]} \tag{29}\]  
For cases (1) and (3), the solution of the equation is  
\[y(\theta)=0=>\theta_{y}=\sqrt{2}/\Big{[}\sqrt{2}+1-x\Big{]} \tag{30}\]  
For all the cases, the tangent to the \(y(\theta)\) graph in the origin is characterized by the angle \(\xi_{0}=\text{atan}(e)\). One can see that for \(x=\sqrt{2}-1\) (i.e., the transition from the case (1) to the case (2)) the value of \(y_{0}\) passes through zero.  
In Figure 4b, the graphs of \(z(\theta)\) are obtained for three values of \(x\) included in the same three intervals of the parameter \(x\) as in the \(y(\theta)\) study in Figure 4a. One can observe the possibilities offered by correlating this function with the spherical aberration of the lens.
## 3 Distributed Cognition-Based Data Analysis and Results
### Analysis of the Test Results of Bayesian Structural Equation Model  
The autocorrelation graph of this study shows the Markov chain Monte Carlo (MCMC) convergence characteristics of each path parameter in the Bayesian structural equation model. To evaluate the sensitivity of the posterior estimation results of the structural model parameters to the prior Settings, this paper adopts the Bayesian prior sensitivity analysis method and applies different degrees of informative priors to the regression path coefficients. Four sets of Bayesian structural equation models were constructed with the following Normal prior distributions set respectively: Non-information prior (non-informative prior): \(\beta\)-Normal (0, 1 \(\times\) 10\({}^{2}\)); weakly informative prior: \(\beta\)-Normal (0, 5000\({}^{2}\)); Moderate information prior (moderately informative prior): \(\beta\)-Normal (0, 10\({}^{2}\)); strongly informative prior: \(\beta\)-Normal (0, 0.1\({}^{2}\)). Analysis shows that the autocorrelation coefficients of core paths such as LF\(\rightarrow\)PEU, PEU\(\rightarrow\)SA, and SA\(\rightarrow\)WTCTU rapidly decay to below 0.25 within a 10-step lag, with good convergence. Among them, the SA\(\rightarrow\)WTCTU path performs the best, and the autocorrelation is close to zero when the 5-step lag occurs. However, some paths have convergence problems: The PEU\(\rightarrow\)WTCTU path shows slight persistence (7-step lag = 0.18) in chain 2, while the PEU\(\rightarrow\)PU path shows abnormal fluctuations (5-step lag = 0.42) in chain 1. It is recommended to check the prior Settings or increase the number of iterations to solve these problems. Furthermore, the autocorrelation attenuation rate of strong information prior paths (such as EF\(\rightarrow\)PF) is significantly faster than that of weak information paths. The autocorrelation patterns of non-significant paths (such as LF\(\rightarrow\)WTCTU) are significantly different from those of other paths, and their coefficients are always lower than 0.25, which may be related to the model Settings or data characteristics. Overall, the convergence results of the core path support the reliability of the model parameter estimation. Figure 2.
## 6 Discussion and Conclusions  
In this paper, we have investigated the effectiveness of using XAI in ET anomaly detection. First, we proposed a hybrid DAE+OCSVM anomaly detector that not only achieves superior detection performance but also overcomes the threshold sensitivity of DAE anomaly detection. Second, we showed that the DAE+OCSVM anomaly detector is vulnerable to benchmark evasion attacks. Third, we proposed a defense strategy to bolster this ET anomaly detector using XAI and clustering. The utilization of XAI (i.e., the proposed detector trained on the SHAP explanations of consumption readings) facilitates the distinct separation of benign and anomalous consumption patterns, leading to much easier and more robust ET detection. Moreover, the utilization of clustering enhances robustness by reducing the level of generalization. The experimental results illustrate that our detector demonstrates significant robustness against various evasion attacks, including gradient-based and optimization-based. With no defense, the \(DR\) is reduced from 97.48% to up to 46.65% under evasion attacks. However, with the proposed defense, the minimum \(DR\) values are 92.58%, 93.34%, 98.62%, 92.88%, and 92.29% for clusters C1-XAI to C5-XAI, respectively. These \(DR\) values reflects the significant detection capabilities and robustness of the proposed detector. As future work, we aim to explore different explanation (XAI) methods to enhance trust and reliability, as well as to assess the performance of our proposed detector under a white-box threat model. While the black-box threat model is considered the most realistic, it is important to evaluate the detector's effectiveness as the attacker's level of knowledge increases.  
Figure 9: PR and ROC curves of DAE+OCSVM with and without the proposed defense (XAI and clustering).  
**Author Contributions:** Conceptualization, I.E., M.M.B., M.M., M.A. ([PERSON]), M.A. ([PERSON]) and T.A.; Methodology, I.E., M.M.B., M.M., M.A. ([PERSON]), M.A. ([PERSON]) and T.A.; Software, I.E. and M.A. ([PERSON]); Validation, I.E., M.M.B. and M.A. ([PERSON]); Investigation, I.E., M.M.B., M.M. and M.A. ([PERSON]); Writing--original draft, I.E., M.M.B. and M.M.; Writing--review & editing, I.E., M.M.B. and M.M.M.; Supervision, M.M. and M.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Researchers Supporting Project number (RSPD2024R636), King Saud University, Riyadh, Saudi Arabia and NSF grant #2349104.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Development of New Applications for DIC
#### 3.1.3 DIC Measurements  
**Strain measurement:** To measure strain build-up after AFP layup, it was assumed that, at the moment in which the tow passed under the roller and came out, there was no strain along the width and length of the tow. For DIC calculations, VIC-3D software from Correlated Solutions, Irmo, SC, USA was used, and a subset size of 35 pixels (i.e., spatial resolution \(35\times 35\) of pixels) with a step size (i.e., the spacing of the points that are analyzed during correlation) of 3 pixels was selected. A default value of 0.05 for the pixel confidence margin was set for the matching process using the covariance matrix of the correlation equation in VIC-3D software. Two 5 Megapixel cameras (monochrome CCD with 2/3-inch sensor from Point Grey) attached to Schneider 28 mm lenses were used in this experiment. Figure 18a shows this moment; three virtual strain gages were located right after the roller within a distance of 0.5 inches from one another along the length of the tow. Just after passing the roller, strains started building up, as it is shown in Figure 18b, due to heat dissipation to the environment and the substrate. As one can expect, the highest lateral strain build up was measured at Gage 1 (about 10,000 microstrains) since the temperature drop (\(\Delta\)T) would be higher than other two gages (i.e., Gage 2 and 3) after 60 s of layup. Fast strain build up can be seen in all three gages during the first 10 s after layup, which emphasizes the significance of the first few seconds just after the AFP head laid down the composite tow. It should be mentioned that in Figure 18b, photos that were taken between 5-7 s after layup were removed from the strain analysis because during this interval the AFP head moved up after finishing the layup, causing tool vibration and consequently creating errors in the photos [19].  
**Deformation measurement:** To measure lateral deformation after AFP layup, the initial condition (T = 0 s) was assumed to be at the moment the tow came out under the roller (i.e., all deformations were zero at this time). The distribution of lateral deformation across the width and along the length of the tow is shown in Figure 19a after 60 s of layup. As it can be seen, due to cooling of the composite tow, contraction happened across the width of the tow (i.e., positive deformation (red color) on the left side and negative deformation (purple color) on the right side of the tow). Along the length of the tow, the lateral deformation gradient was higher at the top side of the tow because it was closer to the AFP head, considering the layup direction, and consequently had higher temperature drop out (\(\Delta\)T). The variation of lateral deformation during the first 60 s after layup across the width of the tow in Section 1  
Figure 18: (**a**) Virtual gage locations just after layup. (**b**) Lateral strain variation after layup [19].  
(see Figure 19a) is plotted in Figure 19b. As can be seen, lateral deformation increased rapidly during the first 10 s after layup and stabilized after about 60 s [19].
# Effects and Mechanism of Plasma-Activated Medium on Angiogenesis of Vascular Endothelial Cells
## 3 Results
### Changes of RONS Concentration in HUVECs  
#### 3.7.1 Concentrations of H\({}_{2}\)O\({}_{2}\) and NO\({}_{2}\)\({}^{-}\) in PAM  
The concentration of H\({}_{2}\)O\({}_{2}\) was 1.53 \(\pm\) 0.07 uM in control group, and increased to 4.28 \(\pm\) 1.21, 17.38 \(\pm\) 2.57, 29.02 \(\pm\) 4.33, 45.68 \(\pm\) 2.14 and 60.97 \(\pm\) 3.60 uM when the treatment time prolonged.  
The concentration of NO\({}_{2}\)\({}^{-}\) in control group was (0.25 \(\pm\) 0.10) uM, and increased to 7.02 \(\pm\) 2.35, 10.91 \(\pm\) 1.47, 17.89 \(\pm\) 2.31, 31.38 \(\pm\) 2.26 and 47.88 \(\pm\) 3.02 uM when the treatment time prolonged. The results showed that the concentrations of H\({}_{2}\)O\({}_{2}\) and NO\({}_{2}\)\({}^{-}\) increased gradually with the extension of treatment time, and the results were significantly different (\(p\) < 0.05) (Figure 8).