esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigating the Reliability of Nonlinear Static Procedures
## 4 Masonry Building Prototype: Modeling and Preliminary Nonlinear Analyses
### Seismic Response of Set Configurations Simulated by Nonlinear Static Analyses  
Figure 9 depicts the pushover curves obtained for the three examined configurations by referring to the uniform load pattern. It clearly emerges that the pushover curves exhibit not only a different maximum base shear but also significantly different ductility.  
Focusing on the Visso A building, which has no reinforced concrete beams, the pushover curve in the Y-direction is almost a bilinear itself (the horizontal section of the pushover is horizontal), while, in the X-direction, there is the progressive degradation of the pushover.  
The same applies to the Visso B case, with the only difference compared to case A being that, having reduced the interlocking in the Y-direction walls, the pushover curve in the Y-direction of case B is weaker than in the Y-direction of case A.  
In the X-direction, the building features a facade with spandrels, leading to the more gradual degradation of the pushover curve (see Figure 9), while, in the Y-direction (with more blind piers), the pushover curve shows less progressive degradation. In Visso B, the Y-direction is consistently more brittle due to the weak interlocking between the walls.  
Moreover, in the X-direction, there is greater ductility available.  
The difference in ductility in the positive and negative directions is linked to the change in the response of the structure in terms of damage.  
For example, examining the damage reported in Table 7, Visso C has mixed behavior in the X- and Y-directions (mainly the shear mechanism but also the flexural one is present in some panels), and this is also observable from the pushover curves of this model.  
Instead, Visso A has flexural behavior in the X- and Y-directions, and this is also observable from the pushover curves of this model, which are more flattened than in case C, with reinforced concrete beams and with greater ductility. Visso B has the same damage as Visso A, with only less strength in the Y-direction.  
All these differences in the seismic response have, firstly, an impact on the bilinear idealization, varying the different criteria adopted, and then on the final outcome consisting of the seismic verification. All these issues are investigated in detail in Section 5.
# Rice Secondary Metabolites: Structures, Roles, Biosynthesis, and Metabolic Regulation
## 4 Metabolic Regulation of Secondary Metabolites
### Metabolic Regulation by Biotic Stresses
#### 4.2.3 Metabolic Regulation by Insect Pests  
The attack by herbivorous insects is one of the major biological stresses that rice plants have to cope with. The secondary metabolites that are derived from the tryptophan pathway have been shown to play defensive roles against insects in rice plants. The biosynthesis of four tryptophan-derived metabolites was induced by the feeding on rice leaves of the rice striped stem-borer (_Chilo suppressalis_). The amounts of \(N\)-\(p\)-counaroylserotonin (**255**), \(N\)-feruolytryptamine (**257**), serotonin (**259**), and tryptamine (**260**) in the larvae-fed leaves were 12-, 3.5-, 33-, and 140-fold larger than in the control leaves 48 h after the start of feeding [118].  
The biosynthesis of serotonin (**259**) was suppressed by insect infestation in rice, which demonstrates that the regulation of serotonin biosynthesis plays an important role in the defense from insects. In rice, the cytochrome P450 gene _CYP71A1_ encodes tryptamine 5-hydroxylase, which catalyses the conversion of tryptamine to serotonin. In susceptible wild-type rice, rice brown planthopper (_N. lugens_) feeding induces the biosynthesis of serotonin, whereas, in mutants with an inactivated _CYP71A1_ gene, no serotonin is produced, and the plants are more insect-resistant [197].  
When rice leaves were infested by the white-backed planthopper (_Sogatella turicifera_), accumulation of salicylic acid, jasmonic acid, and phytoalexins such as momilactone A (**148**) and sakuranetin (**81**) was observed. It is possible that _S. turicifera_ released some elicitor compounds, which might be produced in its salivary glands, into the rice plants during feeding. Next, the defense signal systems, SA- and JA-mediated pathways, were activated by the elicitor. Finally, phytoalexins are induced in rice as antimicrobial compounds mainly through the activation of the JA-mediated pathway [132].
## 4 Results
### Crop Classification in the Yuecheng Reservoir Catchment Area  
By analyzing the growth curves of the main crops in the Yuecheng Reservoir catchment area, combined with data from the Sentinel-2 images for the year, we first identified the distribution of multiple crops. To facilitate the calculation of NSP intensity, we separated single crops into winter single crops and summer single crops. Most of the crops in the catchment area were summer single crops, and 70% of the crops in the area were summer maize. Because of regulatory policies in each county, multiple crops and winter single crops showed aggregation distributions. Combined with Google Earth high-resolution remote sensing images, we verified the accuracy of crop classification results in the Yuecheng Reservoir catchment area by analyzing 50 samples of multiple crops, summer single crops, winter single crops, and other surface feature types (including water, impervious surface, bare land, and other vegetation). The results are shown in Table 6. The decision tree and crop phenology showed good precision. The producer accuracy and user accuracy of each type of feature exceeded 70%. The overall accuracy and kappa coefficient indicated that the crop classification results were highly consistent with actual observations. The spatial distribution of crops in the Yuecheng Reservoir catchment area is shown in Figure 10.  
Figure 10: Spatial distribution map of crops in the Yuecheng Reservoir catchment area.  
Remote Sensing Mapping of Spatial Distribution Difference of Crop Growth in the Yuecheng Reservoir Catchment Area  
The Yuecheng Reservoir catchment area covers 18,072 km\({}^{2}\) and spans three provinces, including 18 counties. The spatial distribution of crop growth differs from this catchment area boundary. To accurately estimate the NSP intensity, it is necessary to simulate the crop growth in the study area (i.e., spatial difference in cultivated land quality) to correct for differences in NSP intensity. To verify the cultivated land quality simulation results, the study area was divided into counties and fitted according to the output per unit area of various crops in the statistical yearbooks of each county, as well as the correction coefficient of mean cultivated land quality in the county. \(R^{2}\) was \(>\)0.7, indicating that these values had good consistency. The spatial distribution of cultivated land quality is shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types of Objects** & **Producer** & **User Accuracy** & **Overall Accuracy** & **Kappa** \\ \hline Winter single crops & 0.73 & 1.00 & & \\ Summer single crops & 0.97 & 0.73 & & \\ Multiple crops & 0.80 & 0.86 & & \\ Others & 0.90 & 0.90 & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Accuracy of remote sensing mapping of crops.  
Figure 11: Spatial difference of crop growth in the Yuecheng Reservoir catchment area. (**a**) Winter single crops GPP revision. (**b**) Summer single crops GPP revision. (**c**) Multiple crops GPP revision.
## 4 Results
### Datasets and Evaluation Metrics  
The datasets utilized in our experiments were chosen for their diversity and relevance to polyp segmentation, enabling a thorough evaluation of our method's performance.  
Our approach was evaluated on two widely recognized polyp segmentation datasets: Kvasir-SEG [65] and CVC-ClinicDB [66]. The Kvasir-SEG dataset includes 1000 images with dimensions that vary from 332 \(\times\) 487 pixels to 1920 \(\times\) 1072 pixels. The CVC-ClinicDB dataset contains 612 images, each with a resolution of 384 \(\times\) 288 pixels. Prior to training, we standardized the image sizes from both datasets to 256 \(\times\) 256 pixels to ensure uniformity in the input data. In our experiments, we adhered to the training settings outlined in the existing literature [67], which similarly divided the two datasets. We randomly selected 80% of the images for training, 10% for validation, and 10% for testing. In the training phase, a mere 15% and 30% of the images were designated as labeled data, while the rest were utilized as unlabeled data. Table 2 enumerates the image size of these datasets and specifies the number of images allocated to the training, validation, and test sets.  
Segmentation performance was evaluated using the dice similarity coefficient (DSC), which is commonly employed for the quantitative assessment of polyp segmentation. The calculation formula is given in Equation (9).  
\[\text{DSC}=\frac{2 TP}{2 TP+FP+FN} \tag{9}\]  
\begin{table}
\begin{tabular}{l c c c c c} \hline
**Datasets** & **Number of images** & **Size** & **Train** & **Valid** & **Test** \\ \hline Kvasir-SEG [65] & 1000 & Variable & 800 & 100 & 100 \\ CV-ClinicDB [66] & 612 & 384 \(\times\) 288 & 488 & 62 & 62 \\ \hline \end{tabular}
\end{table}
Table 2: The description of Kvasir and CVC-ClinicDB datasets.
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
## 4 The 3D-LUNA Localization Framework
### Handling Unknown Depth
\[r_{k}(z_{i})=\sqrt{\left(x-x_{k}\right)^{2}+\left(y-y_{k}\right)^{2}},\ \ k=1,2,\ldots,N \tag{8}\]1 (17) where \(d\big{(}C_{j},C_{k}\big{)}=\sqrt{\big{(}x_{j}-x_{k}\big{)}^{2}+\big{(}y_{j}- y_{k}\big{)}^{2}}\) is the Euclidean distance between the centers of circles \(C_{j}\) and \(C_{k}\), and \(r_{j}\) and \(r_{k}\) are their radii. * The generalized formula based on the inclusion-exclusion principle for the common area of \(N\) circles is as follows: \[r_{k}(z_{i})=\sqrt{\left(x-x_{k}\right)^{2}+\left(y-y_{k}\right)^{2}},\ \ k=1,2,\ldots,N \tag{8}\]2 (18) where \(A\big{(}C_{j}\big{)}\) is the area of circle \(j\), \(A\big{(}C_{j}\cap C_{k}\big{)}\) is the pairwise intersection area between circles \(j\) and \(k\), \(A\big{(}C_{j}\cap C_{k}\cap C_{l}\big{)}\) is the intersection area of three circles, and so on. Monte Carlo approximation can be used if there is a large number of circles. If the feasible depth range is \(\mathbb{Z}\), then we optimize \[r_{k}(z_{i})=\sqrt{\left(x-x_{k}\right)^{2}+\left(y-y_{k}\right)^{2}},\ \ k=1,2,\ldots,N \tag{8}\]3 (19)  
Then, we can calculate the centroid of the overlapping area, which signifies the position of the node.
## 2 Data Sources and Methods  
### Data Sources  
Hourly ground-based ozone concentration observations from 235 ground stations of China's National Ground-Level Air Quality Observation Network were used to construct a surface ozone concentration estimation model in the NCP. The data from the National Environmental Monitoring Center of China covers the period 2019-2021. To ensure the quality of the data, the 8-h moving average ozone concentration was calculated only when at least six valid measurements had been recorded within the 8-h time period. In addition, the maximum value of the daily 8-h average surface ozone concentration was extracted as the MDA8 value. Strict quality control measures were implemented to exclude records with insufficient samples. In order to safeguard the precision of the surface ozone estimations from being compromised by variations in the data sample sizes, this study eliminated hourly records that lacked surface ozone data for a continuous ten days period within any given month and those with a sample loss exceeding 30% during the entire research timeframe. Moreover, to reduce interference from proximate monitoring stations and enhance the establishment of an accurate ozone estimation model, a threshold distance of 0.05\({}^{\circ}\) was implemented to consolidate the observational data from 235 sites in the NCP into a set of 133 sites. Figure 1 presents a visualization of the geographical arrangement of these 133 ozone monitoring locations within the NCP.  
Figure 1: Distribution of 133 ground-based observation sites in the NCP from 2019 to 2021. Different colors represent the foundation sites of different provinces, among them, red represents Shandong Province, green  
represents Hebei Province, blue represents Shanxi Province, pink represents Hebei Province, orange represents Tianjin City, cyan represents Beijing City, and yellow represents the Inner Mongolia Autonomous Region.
## 3 Results
### Volume Variation  
The median and mean of the V% variation regarding the volumes on the s-CTs and CBCTs are reported in Table 3 for each structure. [PERSON]'s signed-rank test did not show any significant difference between the volumes on the s-CTs and CBCTs for every structure, with the exception of the bladder. Box plots regarding the volume variations are presented in Figure 4 for each OAR. The cauda showed the lowest volume variation, as expected. As far as the bladder and rectum are concerned, the V% variations, calculated as the percentage variation in the daily volume with respect to V_ref, presented a random variation trend in all of the CBCTs were evaluated (see Supplementary Materials, Figures S1 and S2). The bladder mean COM displacement results were (\(-\)3.71 \(\pm\) 0.86) mm, (0.25 \(\pm\) 0.30) mm, and (1.32 \(\pm\) 0.46) mm in the inferior-superior, left-right, and anterior-posterior directions, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**OAR** & **Median DSC** & \
\begin{tabular}{c} **Median** \\ **Mean\_DA (cm)** \\ \end{tabular} & **Median V\%** &
\begin{tabular}{c} **Median Optimal** \\ **Margin (mm)** \\ \end{tabular} \\ \hline _Cauda_ & 0.72 & 0.14 & 97.47 & 3 \\ _Bladder_ & 0.70 & 0.43 & 97.02 & 8 \\ _Rectum_ & 0.68 & 0.33 & 95.39 & 8 \\ _Ileum_ & 0.50 & 0.38 & 95.86 & 10 \\ _Colon_ & 0.37 & 0.44 & 96.60 & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: From left to right: the OAR considered, median DSC, median Mean_DA, median V%, and median optimal margin for all the patients.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**OAR (Total \# of Patients)**} & \multicolumn{6}{c}{**Margin (mm)**} \\ \cline{2-9}  & \multicolumn{2}{c}{**3**} & **5** & **8** & **10** & **15** & **20** \\ \hline \multirow{3}{*}{Cauda (9)} & \# & 6 & 8 & 9 & & & \\  & \% & 66.67 & 88.89 & 100.00 & & & \\ \hline \multirow{2}{*}{Rectum (17)} & \# & 2 & 4 & 9 & 12 & 13 & 13 \\  & \% & 11.76 & 23.53 & 52.94 & 70.59 & 76.47 & 76.47 \\ \hline \multirow{2}{*}{Bladder (9)} & \# & 2 & 2 & 6 & 6 & 8 & 8 \\  & \% & 22.22 & 22.22 & 66.67 & 66.67 & 88.89 & 88.89 \\ \hline \multirow{2}{*}{Ileum (20)} & \# & 0 & 3 & 9 & 12 & 14 & 18 \\  & \% & 0.00 & 15.00 & 45.00 & 60.00 & 70.00 & 90.00 \\ \hline \multirow{2}{*}{Colon (15)} & \# & 0 & 1 & 5 & 6 & 10 & 12 \\  & \% & 0.00 & 6.67 & 33.33 & 40.00 & 66.67 & 80.00 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of patients whose V% was greater than 95% with each of the 6 considered margins.  
Figure 3: DSC (**top**) and Mean_DA (**bottom**) distributions for all the considered organs.
# VidBlock: A Web3.0-Enabled Decentralized Blockchain Architecture for Live Video Streaming
## 2 Background and Related Works
### Comparison of Traditional Centralized Approaches  
Table 1 highlights the differences between the decentralized VidBlock system and centralized streaming platforms, including Netflix [15], Amazon Prime Video [16], YouTube [17], Twitch [18], and Hulu [19].  
* **Decentralization and control:** VidBlock adopts a fully decentralized blockchain-driven architecture, ensuring distributed control and eliminating single points of failure. In contrast, centralized platforms like Netflix, Amazon Prime Video, and Hulu rely on centralized infrastructures, making them more vulnerable to outages and scalability limitations. Twitch, while partially decentralized, still relies heavily on centralized management.
* **Data sovereignty:** Centralized services maintain exclusive control over user data, limiting users' ability to manage or own their information. VidBlock, however, enables users to retain full ownership and control of their data through its blockchain framework, offering transparency and autonomy.
* **Privacy protections:** VidBlock provides greater security and privacy through data encryption and user anonymity. Centralized services, while using standard encryption and privacy policies, are more prone to breaches and privacy risks due to their control over user data [20,21].
* **Real-time streaming capabilities:** VidBlock's peer-to-peer (P2P) network enables low-latency streaming, making it highly suitable for real-time applications. While YouTube and Twitch are optimized for live streaming, platforms like Netflix and Amazon Prime Video are predominantly designed for on-demand content and lack real-time capabilities.
* **Scalability and performance:** VidBlock's scalability relies on the growth and distribution of its node network, which can dynamically expand with user participation. Centralized platforms achieve scalability through robust CDN infrastructures, offering consistently high performance but at the cost of substantial infrastructure investments.
* **Cost efficiency:** VidBlock's serverless, decentralized architecture reduces operational and maintenance expenses. In contrast, centralized services incur significant costs associated with deploying and maintaining CDN servers and data centers, impacting their overall cost efficiency [22].  
* **Security risks:** VidBlock's decentralized framework distributes security responsibilities across its nodes, reducing vulnerabilities inherent in centralized systems. However, its reliance on node participation introduces some level of security dependency. Centralized platforms, though managed from a single control point, face risks such as server outages and data breaches [23].  
In conclusion, centralized services face limitations in data sovereignty, privacy, real-time streaming, cost efficiency, and infrastructure control. VidBlock's blockchain-based architecture provides a novel approach that empowers users with data ownership and addresses these limitations.
## 3 Temperature Sensor Clustering Method
### Establishment of the Distance Matrix
The correlation coefficient between the \(p\)th temperature sensor \(\mathbf{x_{p}}\) and the \(q\)th temperature sensor \(\mathbf{x_{q}}\) can be expressed as:  
\[r_{pq}=\frac{\sum_{k=1}^{m}\Big{(}x_{pk}-\overline{x_{p}}\Big{)}\Big{(}x_{qk }-\overline{x_{q}}\Big{)}}{\sqrt{\sum_{k=1}^{m}\Big{(}x_{pk}-\overline{x_{p}} \Big{)}^{2}}\sqrt{\sum_{k=1}^{m}\Big{(}x_{qk}-\overline{x_{q}}\Big{)}^{2}}}\ (\ p=1\ \sim\ n,\ q=1\ \sim n) \tag{7}\]  
in which:  
\[\overline{x_{p}}=\sum_{k=1}^{m}x_{pk}/m \tag{8}\]  
and:  
\[\overline{x_{q}}=\sum_{k=1}^{m}x_{qk}/m \tag{9}\]  
are the average value of \(\mathbf{x_{p}}\) and \(\mathbf{x_{q}}\), respectively. \(r_{pq}\) reflects the similarity between the \(p\)th temperature sensor and the \(q\)th temperature sensor, which lies in the range of \([-1,\ 1]\). When \(r_{pq}\) is close to \(0\), the similarity between temperature sensors is low. Conversely, when \(r_{pq}\) is close to \(-1\) or \(1\), the similarity between temperature sensors is high. Thus, the following expression was constructed to express the difference between \(\mathbf{x_{p}}\) and \(\mathbf{x_{q}}\) according to their similarity:  
\[d_{pq}^{r}=1-\big{|}r_{pq}\big{|} \tag{10}\]  
\(d_{pq}^{r}\) lies within the range of \([0,\ 1]\). The larger \(d_{pq}^{r}\) is, the lower the similarity between \(\mathbf{x_{p}}\) and \(\mathbf{x_{q}}\) is, so the larger the difference between \(\mathbf{x_{p}}\) and \(\mathbf{x_{q}}\) is, and vice versa. Consequently, the distance matrix representing the similarity can be obtained by assembling the \(d_{pq}^{r}\) into a \(n\times n\) symmetric matrix as follows:  
\[\mathbf{D^{r}}=\left[ \begin{array}{cccccc}0&d_{12}^{r}&\cdots&d_{1q}^{r}&\cdots&d_ {1n}^{r}\\ d_{21}^{r}&0&\cdots&d_{2q}^{r}&\cdots&d_{2n}^{r}\\ \vdots&\vdots&\ddots&\vdots&\cdots&\vdots\\ d_{p1}^{r}&d_{p2}^{r}&\cdots&\ddots&\cdots&\vdots\\ \vdots&\vdots&\vdots&\vdots&\ddots&\vdots\\ d_{n1}^{r}&d_{n2}^{r}&\cdots&\cdots&\cdots&0\end{array} \right] \tag{11}\]  
Thus, by introducing a weight coefficient, the distance matrix both representing the gap between the temperature sensors and the similarity between the temperature sensors is derived:  
\[\mathbf{D^{a}}=\alpha\mathbf{D^{e}}+(1-\alpha)\mathbf{D^{r}} \tag{12}\]  
For each element in \(\mathbf{D^{a}}\), Equation (12) can be written as Equation (13):  
\[d_{pq}^{a}=\alpha\cdot d_{pq}^{e}+(1-\alpha)\cdot d_{pq}^{r} \tag{13}\]  
in which, \(\alpha\) is the weight coefficient within the range of \([0,\ 1]\). The larger \(\alpha\) is, the more influence the gap between the value of temperature sensors has on the distance matrix, and vice versa.
## 1 Introduction
In summary, a great deal of research has been conducted on the seismo-dynamic response and stability of anti-dip bedding rock slopes using different methods, and some significant results have been achieved. However, little research has been carried out on the following two aspects: (i) Natural seismic waves occur in the form of U-P, E-W, and N-S three-directional loading: the U-P waves are primary waves (P-waves), and the E-W and N-S waves are east-west and north-south secondary waves (S-waves), respectively. The research conducted so far has been limited to the conditions appropriate to model experiments and 2D numerical simulations. Generally, therefore, only two of the above components are considered. This simplifies the actual three-dimensional (3D) space problem by approximating it as a plane-strain problem. Thus, the current approaches cannot fully and correctly model the real instability state of anti-dip bedding rock slopes in the presence of natural seismic waves. (ii) Current research focuses on ideal anti-dip bedding slopes, i.e., slopes in which the dip direction of the joints is opposite to the dip direction of the slope surface (180\({}^{\circ}\) difference) and so the joint trend is exactly the same as the slope trend. However, in reality, there is usually a small angle (5-20\({}^{\circ}\)) between the joint and slope trends. Therefore, in this paper, 3D discrete-element modeling is performed using the software package 3 DEC (ver5.20, Itasca, 2019, Minneapolis, MN, USA) in order to analyze the dynamics of anti-dip bedding rock slopes with different joint angles and different slope angles under the action of natural seismic and sinusoidal waves loaded in the E-W, U-P, and N-S directions. Our aim is to reveal the real 3D dynamic responses of anti-dip bedding rock slopes (and the mechanism responsible for their failure), and to obtain the permanent displacements of the slopes, (which we can then compare to those obtained using [PERSON]'s method). In addition, we also study the spatial effects of the structural planes to reveal the amplification effects, failure modes, and permanent displacements of anti-dip bedding rock slopes with different angles between their joint and slope trends.