esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Wenchuan EQ Analysis and Discussion  
### Radon Concentration before the Wenchuan EQ  
The Wenchuan Ms8.0 EQ in 2008 in China was used as an example for our study to verify the validity of the mechanism. The distances between the epicenter of the Wenchuan EQ and the detection stations at Pixian, Wenjiang, and Guzan are 49, 52, and 202 km, respectively. As shown in Figure 5a, before the Wenchuan EQ, Guzan shows a significant increase in the daily median radon concentration from May 7 to the moment of the EQ [19]. As shown in Figure 5b, the visibility and low cloud cover at the Wenjiang and Pixian stations on the day of the EQ. As shown in Figure 5c, the wind velocity at Wenjiang and Pixian stations throughout the day on 12 May. Therefore, it can be determined that it was fair weather in the epicenter area of the Wenchuan EQ on 12 May. The atmospheric vertical electric field is disturbed only a little by meteorological effects.  
It can be seen from Figure 5a that the radon concentration before the Wenchuan EQ peaked at 8.98 Bq/L on May 10. For receiving the variation of radon concentration with height before the Wenchuan EQ, it was considered that the peak radon concentration before the Wenchuan EQ was the ground, y = 0 km, radon concentration, i.e., (8.98 Bq/L, 0 km). The vertical distribution of radon concentration varies with the changes in meteorological parameters [27]. For the simulation in this paper, we considered the constant migration and turbulent diffusion coefficients. Thus the slope of the natural logarithm of radon concentration with height was considered to be the same as the curve of Equation (1). The intercept  
Figure 5: (a) Variation of radon concentration at Guzan station before the Wenchuan EQ. (b) Visibility and low cloud cover at Wenjiang and Pixian stations before the Wenchuan EQ. (c) Wind velocity at Wenjiang and Pixian stations before the Wenchuan EQ.  
of the curve is determined from the radon concentration data before Wenchuan earthquake (8.98 Bq/L, 0 km). The radon concentration variation with height was obtained as:  
\[y=-2.351\ln(x)+31.026 \tag{5}\]  
where \(x\) (dpm/m\({}^{3}\)) is the Radon concentration and \(y\) (km) is the height.  
The concentration of alpha particles at different heights was obtained from Equation (5), and the variation of beta particles with height was obtained from the alpha to beta ratio in Section 2.1.2. Since we configured the Monte Carlo simulation alpha particle source equivalent to the number of alpha particles generated in a cylinder with a radius of 5 m and a height of 1 cm in 1 h, it was possible to obtain the number of alpha particles simulated at different heights from the alpha particle concentration at different heights.
## 2 Materials and Methods
### Determination of Antioxidant Properties
#### 2.4.1 Free Radical Neutralization Capacity Assessed Using ABTS and DPPH Methods  
The ability of the tested extracts to neutralize radicals was determined using methods using colored radicals: 2,2\({}^{\prime}\)-diphenylpicrylhydrazyl (DPPH) and 2\({}^{\prime}\)-azinobis (3-ethylbenzenethiazoline-6-sulfonate (ABTS). The latter was prepared during a 12-h oxidation reaction of ABTS with persulfate [11]. Before the measurement, the starting solutions of radicals/cation radicals were diluted with methanol to an initial absorbance of about 0.7 AU. The color change of the measuring solutions was determined at a wavelength of 516 nm (in the case of the DPPH method) [12] and 744 nm (in the case of the ABTS method). For this purpose, 2900 \(\upmu\)L of the radical or cation radical solution and 100 \(\upmu\)L of extract (prepared in methanol, water, or water-methanol (50/50, \(v/v\))) were used each time. The percentage inhibition (%I) was calculated after 60 min of reaction from the equation:  
\[(\%)=(1-A_{60}/A_{0})\times 100\% \tag{1}\]  
where \(A_{60}\) and \(A_{0}\) are the absorbance values of ABTS\({}^{\star\star}\) or DPPH\({}^{\bullet}\) at times 0 and 60, respectively.
# Estimation of Rock Load of Multi-Arch Tunnel with Cracks Using Stress Variable Method
## 2 Estimation Methods for Rock Load
### Numerical Analysis Approach
#### 2.2.1 Criterion of Strength/Stress Ratio  
As illustrated in Figure 3, the stress in the Mohr circle can be expressed by plotting shear and normal stresses--\(\tau\) and \(\sigma\), respectively. At the point where the Mohr circle reaches the Mohr-Coulomb failure envelope, its radius, \"R,\" can be expressed as, \(\frac{\sigma_{1f}-\sigma_{3f}}{2}\) and the current shear stress \"r\" can be expressed as \(\frac{\sigma_{1}-\sigma_{3}}{2}\) as shown in Figure 3. The factor of safety can be defined as the strength to stress ratio, as presented in Equation (4). The suggested values of the factor of safety for estimating the rock load owing to excavation are 2.0 and 3.0 [8]. Hence, the value of 3.0 was used as the factor of safety in this study.  
\[F.S.=\frac{R}{r}=\frac{\sigma_{1,f}-\sigma_{3,f}}{\sigma_{1}-\sigma_{3}} \tag{4}\]  
Figure 2: Empirical method to estimate rock load acting on the multi-arch tunnel.  
where \(\sigma_{1,f}\) = major principle stress at failure, \(\sigma_{3,f}\) = minor principle stress at failure, \(\sigma_{1}\) = major principle stress, and \(\sigma_{3}\) = minor principle stress.
# XGBoost-Based Detection of DDoS Attacks in Named Data Networking
## 3 The Attack Model and Detection Method
### _Design and launch I-CIFPA attack_  
The I-CIFPA attacker exploits the content request interval to generate spoofed interest packets that are very similar to legitimate traffic patterns. First, the I-CIFPA attack is simulated and launched in the NDN topology by controlling the infected consumer node.  
**Step 2:**_Collect network traffic through routing nodes_  
In order to observe the network behavior under I-CIFPA, some routing node information is collected. These nodes are used to passively collect statistics related to interest packets and data packets, including CacheHits, TimeOutInterests and OutInterests. The data are used to provide multi-dimensional traffic attributes required for detection model training.  
**Step 3:**_Data preprocessing and training_  
The raw traffic data are preprocessed to eliminate inconsistencies and ensure compatibility with the detection model. The data are labeled for known traffic states: normal, other network states, and I-CIFPA. The data are divided into training and testing subsets to support supervised learning and facilitate performance evaluation of the detection framework.  
**Step 4:**_Develop Improved XGBoost Detection Model_  
We developed a real-time detection framework based on improved XGBoost to address the characteristics of I-CIFPA. The model incorporates optimized decision trees to capture nonlinear correlations between traffic features. In addition, we apply Bayesian optimization to fine-tune key hyperparameters (e.g., learning rate, tree depth, etc.) to maximize detection accuracy.
## 1 Introduction
(2) The magnetic forces and stiffness of all sixteen structures in four types of circular magnetic springs (CMSs) are obtained, which are used as cell units for building HCMSs. (3) HCMSs are first proposed and the '\(N\)-\(M\)'-type HCMS is testified to generate larger negative stiffness than traditional magnetic rings. The objective of this paper is to seek novel magnetic negative stiffness structures for heavy loads and provide a \"building block\" methodology for designing large-amplitude magnetic springs in engineering applications. The rest of this paper is organized as follows. Force and stiffness formulas of circular magnetic springs (CMSs) are analytically derived in Section 2. Based on them, the nonlinear stiffness of the '\(N\)-\(M\)'-type HCMS is built in Section 3. In Sections 4 and 5, the effects of geometric parameters on the nonlinear negative stiffness of basic CMSs and the 3-3-type HCMS are revealed, respectively. Then, a potential solution to HCMSs with larger-amplitude and wider-range negative stiffness is explored in Section 6. Finally, some conclusions are made in Section 7.
# An Online Multicriteria--Spatial Decision Support System for Public Services Planning
## 2 Preliminary Remarks and Basic Systematisation
### Multi-Criteria Decision Analysis and Spatial Decision Support Systems  
Multi-criteria decision analysis (MCDA) is a heading for various formal approaches to the explicit consideration of multiple criteria that help individuals or groups to make decisions. MCDA is concerned with the process of decision making in the presence of multiple, usually conflicting, criteria [5; 6; 7]. In general, the existing MCDA methods are separated into the following categories:  
* Goal, aspiration, or reference level methods;
* Outranking methods;
* Value measurement methods.  
This work is to be categorised as a value measurement method. At this point, we will not go into further detail on individual aspects of the various method, since they have already been described in the literature [7; 8; 9; 10; 11].  
Spatial analyses and in particular spatial decision support systems, as a sub-area of geographic information systems (GIS), offer many synergies for implementing MCDA. GIS are systems for collecting, storing, manipulating, analysing, and presenting geographical data and can generate information for decision making. According to [PERSON] and [PERSON], the main advantage of merging MCDA into GIS is that the decision maker can introduce value judgements (i.e., preferences in terms of weighting, evaluation criteria, and/or decision alternatives) into a spatial decision support system [7]. The coupling of MCDA techniques with GIS in GIS-MCDA procedures is a key element in the implementation of a multi-criteria spatial decision support system (MC-SDSS). An MC-SDSS can be understood as an interactive, computer-based system that effectively supports one or more users (planners or decision makers) in making decisions on semi-structured problems in which multiple criteria are considered. The unstructured aspects are implemented by the user, while the structured facets can be implemented in part by computerisation [7].
# Assessment of Drought Severity and Vulnerability in the Lam Phaniang River Basin, Thailand
## 2 Materials and Methods
### Climate Models for Climate Scenarios
#### 2.3.2 Bias Correction  
The Regional Climate Models (RCMs) are generated in a high spatial resolution by nesting a secondary model with respect to the driving Global Climate Models (GCMs) over the area bounding a specified domain of interest [11]. Consequently, within this domain, more spatially detailed output, i.e., rainfall and maximum and minimum temperatures, can be produced. However, RCMs cannot be directly used in this impact assessment, as they have substantial biases [12; 13]. Therefore, a tailor-made tool for bias correction called \"Climate Model data for hydrologic modeling (CMhyd)\" developed by [PERSON] et al. [14] was used to deal with systematic model biases. In this study, as suggested by [PERSON] and [PERSON] [15], the option called \"Linear Scaling\" was applied to correct biases in the observed and historical downscaled rainfall and maximum and minimum temperature datasets from 1989 to 2005.
## 4 Results
### High-Frequency Monitoring of Methane Point Sources
#### 4.2.1 Sanyuan Nan Yao Coal Mine in Shanxi
Our research indicates that the Sentinel-2 MultiSpectral Instrument (MSI), with its high pixel resolution (20 m) and quick revisit rate (2-5 days), is capable of detecting and quantifying significant methane point sources. By employing shortwave infrared (SWIR) measurements, Sentinel-2 has successfully conducted high-frequency monitoring of the Sanyuan Nan Yao Coal Mine in Shanxi and the EOG Shale Gas Well in Eddy County, New Mexico, demonstrating its potential in methane emission surveillance. Our approach is readily adaptable to other satellite instruments with approximate SWIR spectral bands, such as Landsat-7 and Landsat-8. The high-frequency detection of anomalous methane point sources based on satellite data can enable timely corrective actions to help reduce global methane emissions. This work illustrates how spaceborne multispectral imaging instruments can facilitate global high-frequency monitoring of large methane point sources by combining a fine pixel resolution with rapid revisit rates.
## Materials
### Empirical Models of Emission and Diffusion  
The starting points of the present work are the previous empirical models of Hg emission and diffusion defined from Hg concentration measurements.  
These measurements were carried out under different temperature conditions (temperatures between 4 \({}^{\circ}\)C and 30 \({}^{\circ}\)C and without rain or wind) to evaluate the influence of temperature on the potential release of Hg in the area containing demolition debris from the metallurgical plant. In total, four research studies were conducted in the area selected. All the measurements were carried out at 1.0-1.5 m above ground level, which is the recommended height for airborne environmental values [20].  
Figure 3 displays these values and demonstrates that the correlation between Temperature (K) and concentration over debris area C\({}_{10}\) (ng/m\({}^{3}\)) is explained using Equation (1), [18]:  
\[\mathrm{C_{max}}=2.98\cdot 10^{-5}\ \mathrm{e}0.0704\cdot\mathrm{T} \tag{1}\]  
A similar relationship was found between the gaseous Hg concentration at point 9, on the border where the debris area ends, C\({}_{9}\) (ng/m\({}^{3}\)) and the temperature (K).  
\[\mathrm{C_{9}}\ \approx\ \frac{\mathrm{C_{max}}}{2.65} \tag{2}\]  
As previously mentioned, measurements were also taken at different distances from the center of the focus. As we move away from the source of contamination, the presence of gaseous mercury can be assimilated to a radial diffusion phenomenon, as can be seen  
Figure 2: A depiction of the sampling route (point 6 is outside of the range of the photograph).  
in Figure 4, which demonstrates how such diffusion follows a hyperbolic function that varies inversely with distance from the source. In Figure 4, dots represent Hg concentration measured under temperatures between 5 \({}^{\circ}\)C and 10 \({}^{\circ}\)C (A) and between 24 \({}^{\circ}\)C and 30 \({}^{\circ}\)C (B).  
Therefore, the concentration varies with the distance from the focus of the contamination to the measuring point according to the following empirically determined equation:  
\[\text{C}(\text{r})\approx\text{C}_{9}\ \frac{10}{\text{r}}\approx\ \frac{\text{C}_{\max}}{2.65}\frac{10}{\text{r}} \tag{3}\]  
The dashed lines in Figure 4 represent the calculated concentration according to Equation (2) for T = 283 K = 10 \({}^{\circ}\)C (A) and for T = 303 K = 30 \({}^{\circ}\)C (B).
###### Abstract  
The onus for monitoring crop growth from space is its ability to be applied anytime and anywhere, to produce crop yield estimates that are consistent at both the subfield scale for farming management strategies and the country level for national crop yield assessment. Historically, the requirements for satellites to successfully monitor crop growth and yield differed depending on the extent of the area being monitored. Diverging imaging capabilities can be reconciled by blending images from high-temporal-frequency (HTF) and high-spatial-resolution (HSR) sensors to produce images that possess both HTF and HSR characteristics across large areas. We evaluated the relative performance of Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat, and blended imagery for crop yield estimates (2009-2015) using a carbon-turnover yield model deployed across the Australian cropping area. Based on the fraction of missing Landsat observations, we further developed a parsimonious framework to inform when and where blending is beneficial for nationwide crop yield prediction at a finer scale (i.e., the 25-m pixel resolution). Landsat provided the best yield predictions when no observations were missing, which occurred in 17% of the cropping area of Australia. Blending was preferred when \(<\)42% of Landsat observations were missing, which occurred in 33% of the cropping area of Australia. MODIS produced a lower prediction error when \(\geq\)42% of the Landsat images were missing (\(\sim\)50% of the cropping area). By identifying when and where blending outperforms predictions from either Landsat or MODIS, the proposed framework enables more accurate monitoring of biophysical processes and yields, while keeping computational costs low.  
MODIS; Landsat; data blending; crop yield prediction; gap-filling Article To Blend or Not to Blend? A Framework for Nationwide Landsat-MODIS Data Selection for Crop Yield Prediction
# Improving the Performance of Vietnamese-Korean Neural Machine Translation with Contextual Embedding
## 4 Experimentations and Results
### Machine Translation Experiments  
From the bilingual corpus, we randomly extracted 2000 sentence pairs for development, 2000 sentence pairs for evaluation, and the rest for training. We built Vietnamese-to-Korean MT systems to translate Vietnamese sentences into Korean sentences, with different input forms, to compare the performance of NMT with contextual embedding using other methods, as follows:  
* Baseline: building NMT without the BERT model, using Vietnamese sentences with word segmentation and original Korean sentences;
* KrUTagger: building NMT without the BERT model, using Vietnamese sentences with word segmentation and Korean sentences with MA and WSD;
* VnPOS: building NMT without the BERT model, using Vietnamese sentences with word segmentation and POS tagging and Korean sentences with MA and WSD; and
* VietBERT+NMT: building NMT with the Vietnamese BERT model, using Vietnamese sentences with word segmentation and POS tagging and Korean sentences with MA and WSD.
## 4 RoCoMAR Algorithm
### PDR-Based Link Quality Estimator  
In RoCoMAR, each node estimates the quality of links that it shares with its neighboring nodes. The PDR based link quality estimation method [34], which is widely used due to its simplicity and accuracy, is used for this paper to estimate the quality of a link.  
More specifically, if the sender sends \(s\) probe packets during a time window, \(w\), and the receiver receives \(r\) probe packets, the PDR of link becomes \(PDR(w)=r/s\). Each node updates PDR values of its links with its neighboring nodes at every \(w\). In RoCoMAR, _Hello_ messages are used as probe packets to calculate PDR values of links.  
Note that the size of time window, \(w\), affects the accuracy of an estimated PDR value. With a larger value of \(w\), a more accurate PDR value can be obtained. However, if \(w\) is too large, the RoCoMAR may not promptly react to the network topology change.  
On the other hand, with a small value of \(w\), RoCoMAR can handle the change of a link quality more quickly. However, in this case, a short-period fluctuation of PDR value may result in unnecessary link reinforcement process, which will cause a network overhead and eventually lower the network performance.  
Therefore, in order to consider both the accuracy of PDR value and the promptness in reaction, we use a window based approach that also uses previous PDR values, that is  
\[R_{i}=\alpha*R_{i}^{w}+(1-\alpha)*R_{i-1} \tag{2}\]  
where \(R_{i}\) is the estimated PDR value of the link at the current time period, \(R_{i}^{w}\) is the sampled PDR value which is obtained at the current window (_i.e._, at the \(\vec{r}^{th}\) window), \(R_{i-1}\) is the PDR value which was estimated at \((i-1)^{th}\) time period, \(\alpha\) is a system parameter and \(0<\alpha<1\).
# Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
## 1 Introduction
A review of the current status of the remote sensing of precipitation is presented in this paper; it seeks to identify some of the most obvious gaps in view of future satellite missions. Section 2 presents an overview of observations and modeling of the water cycle. Section 3 deals with satellite observations of precipitation. Prominent applications are described in Section 4, and future directions are summarized in Section 5.
## 3 Methodology
### Data Processing: Generating Tourism Activity Chains  
The broad coverage and low acquisition cost of mobile tracking data allow for an accurate description of tourism activities from individual and group perspectives [56]. As depicted in Figure 2, a user's travel trajectory is recorded as the cell phone signal communicates with the base station. With the correspondence between the attraction area and base station location determined, the user's stay within the attractions can be detected. Our goal is to identify tourists' stays at multiple attractions within the city, thereby constituting complete tourism activity chains. First, the raw mobile tracking data needs to be preprocessed. Second, generate the stay sequences in attractions with mobile tracking data. Lastly, identify tourists with a filtering strategy based on the tourists' spatiotemporal behaviors and generate tourism activity chains. Any users who meet the following spatiotemporal behaviors criteria are detected as tourists: (1) users who access the attraction area during the opening hours and spend more than 30 min there; (2) users who stay overnight (0 A.M. to 6 A.M.) at the same attraction or visit the attraction three or more times in a single day are judged to be the residents in the attraction area, not tourists; (3) users who stay at the same attraction during the daytime (8 A.M. to 7 P.M.) with several staying days more than half of the days of interest are judged to be commuters in the attraction area, not tourists.  
Figure 2: A user’s record of points and trajectory in the process of sightseeing. \(L_{1}\)–\(L_{15}\) represent the location of recorded points from mobile tracking data. Points at attraction A (\(L_{2}\)–\(L_{6}\)) record the first attraction stay in the user’s trajectory, while points at attraction B (\(L_{10}\)–\(L_{13}\)) record the second one. These stays constitute the user’s stay sequences at attractions. Only after the user is identified as a tourist of attraction A and attraction B by the tourist filtering strategy can these detected stays be determined as tourism activities, which are used to construct tourism activity chains.  
After completing the above processes, tourism activity chains are extracted. A tourist's tourism activity chain is represented as the following list: \(C=\{\left(P_{1},T_{1}^{s},T_{1}^{e}\right),\left(P_{2},T_{2}^{s},T_{2}^{e}\right),\ldots,\)\(\left(P_{n},T_{n}^{s},T_{n}^{e}\right)\}\), where \(P_{i}\) denotes the \(i\)-th tourism attraction in the chain, while \(T_{i}^{s}\) and \(T_{i}^{e}\) denote the starting and ending time of the visit to \(P_{i}\).