esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Standardization of Climate Change Vulnerability Index
#### 3.1.1 Exposure to Climate Change  
Seven proxy variables were selected as indices to denote climate exposure. Six of the twenty-seven ETCCDI indices presented by the World Meteorological Organization (WMO) were selected by applying detailed data for KASE-1-0-G GCM provided by the Korea Meteorological Administration based on the SSP scenario of the Coupled Model \begin{table}
\begin{tabular}{c c} \hline
**EDI Value** & **Category** \\ \hline
2.00 or more & Extremely wet \\
1.50 to 1.99 & Severely wet \\
1.00 to 1.49 & Moderately wet \\
0 to 0.99 & Mildly wet \\ \(-\)0.99 to 0 & Mild drought \\ \(-\)1.49 to \(-\)1.00 & Moderate drought \\ \(-\)1.99 to \(-\)1.50 & Severe drought \\ \(-\)2 or more & Extreme drought \\ \hline \end{tabular}
\end{table} 
Table 4: Categories associated with EDI values [37]. Intercomparison Project Phase 6 (CMIP6) by the IPCC. In addition, the drought condition was reflected by calculating the EDI. The standardized values of the proxy variables for the SSP-126, 245, 370, and 585 scenarios are shown in Tables 5-8, respectively. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Sub-Watershed**} & \multicolumn{6}{c}{**SSP-245 Scenario**} \\ \cline{2-9}  & **Rx1 Day** & **CDD** & **CD5 Day** & **R10 mm** & **R50 mm** & **CD10 Day50 mm** & **EDI** \\ \hline Andong Dam & 43.20 & 13.53 & 98.25 & 27.77 & 38.39 & 53.04 & 63.13 \\ Imha Dam & 22.73 & 35.30 & 100.00 & 6.24 & 18.45 & 41.37 & 58.93 \\ Andong Dam Estuary & 32.21 & 100.00 & 8.35 & 0.00 & 34.94 & 32.20 & 45.36 \\ Naeseongcheon & 67.27 & 69.35 & 2.59 & 37.75 & 77.50 & 81.62 & 100.00 \\ Younggang & 26.43 & 52.96 & 14.90 & 39.16 & 84.09 & 80.95 & 93.31 \\ Byeongseong-cheon & 32.76 & 0.00 & 55.22 & 44.61 & 44.53 & 50.25 & 47.38 \\ Wicheno & 20.25 & 97.21 & 20.61 & 2.31 & 18.41 & 18.83 & 37.37 \\ Nakdong Gumi & 4.91 & 68.75 & 54.58 & 25.52 & 3.63 & 24.38 & 100.00 \\ Gamcheon & 28.30 & 0.77 & 71.12 & 41.99 & 14.64 & 26.45 & 48.55 \\ Nakdong Waegwan & 5.90 & 68.85 & 49.57 & 26.07 & 4.18 & 23.00 & 89.12 \\ Nakdong Sangju & 19.02 & 57.02 & 26.11 & 37.63 & 67.17 & 70.79 & 93.84 \\ Geumhogang & 0.00 & 81.24 & 40.90 & 25.39 & 0.00 & 0.00 & 0.00 \\ Hoecheon & 46.85 & 59.13 & 28.65 & 60.69 & 57.69 & 46.88 & 33.77 \\ Nakdong Goryeong & 28.06 & 76.57 & 35.04 & 43.27 & 25.47 & 25.30 & 4.85 \\ Habcheon Dam & 40.59 & 34.99 & 42.04 & 71.18 & 70.75 & 51.72 & 93.90 \\ Hwanggang & 58.45 & 71.06 & 15.95 & 64.23 & 64.85 & 50.77 & 1.50 \\ Nakdong Changyeong & 54.61 & 71.38 & 14.80 & 62.80 & 61.09 & 53.24 & 11.96 \\ Namgang Dam & 100.00 & 50.23 & 9.60 & 100.00 & 98.57 & 83.16 & 73.39 \\ Nakgang & 63.80 & 59.77 & 20.54 & 98.72 & 100.00 & 88.54 & 5.00 \\ Nakdong Miryang & 42.34 & 68.56 & 0.00 & 58.11 & 48.66 & 64.11 & 53.50 \\ Miryanggang & 32.13 & 73.26 & 20.04 & 49.96 & 34.90 & 45.70 & 39.43 \\ Nakdong Estuary & 96.68 & 57.37 & 27.23 & 93.37 & 93.28 & 100.00 & 86.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Standardized indices of climate change exposure in scenario SSP-245.
## 3 Results
### Integration Strategies for Nanosensors in Bridge Maintenance
#### 3.6.1 Sensor Placement and Distribution  
One of the key considerations in integrating nanosensors into bridge maintenance is determining the optimal sensor placement and distribution throughout the structure [76]. The placement of sensors should be based on the specific monitoring objectives and the critical areas of the bridge that require continuous monitoring.  
Strategic placement of sensors is crucial for capturing relevant data and obtaining a comprehensive understanding of the structural behavior. Sensors can be positioned in critical areas such as load-bearing components, joints, or areas prone to stress concentrations [77]. Distributed sensor networks can be designed to cover a larger area of the bridge and capture data from multiple points.  
The number and density of sensors depend on the level of detail required for monitoring [78]. While a higher number of sensors can provide more comprehensive data, it may also increase the complexity of data management and analysis. Therefore, a balance must be struck between the number of sensors, their distribution, and the monitoring objectives.
# Automatic Detection of Linear Thermal Bridges from Infrared Thermal Images Using Neural Network
## 3 Thermal Bridge Detection Method Using Neural Network
### Thermal Anomaly Area Clustering
#### 3.1.2 Thermal Anomaly Area Segmentation  
Thermal images of building envelopes contain both areas with normal temperature distributions and areas with abnormal temperature distributions. In general, areas with normal temperature distributions occupy a large proportion within the thermal image, and areas with abnormal temperature distributions cover a relatively small area. Based on this assumption, this study segmented areas with abnormal temperature distributions based on the temperature of each cluster.  
As mentioned above, GMM-based clustering allows us to obtain the temperature of the pixels included in each cluster. The average temperature in each cluster is calculated by using the temperature of the pixels in each cluster. After calculating the average temperature of each cluster, the number of pixels in each cluster is counted. The cluster with the most pixels is regarded as the area corresponding to the normal outer wall. As shown in Figure 2(a), the clusters are distinguished with an abnormal temperature  
Figure 2: Temperature area clustering based on Gaussian Mixture Model (GMM): (a) histogram of temperature and (b) clustering result.  
distribution by reflecting the season based on the average temperature \(\upmu 2\) of the cluster with the most pixels. For example, during winter, the normal outer wall shows a relatively lower temperature than areas with thermal anomalies, such as thermal bridges. Therefore, in the case of thermal images measured in winter, clusters with an average temperature higher than the average temperature of the cluster with the most pixels are classified as abnormal areas. In terms of thermal images measured in summer, clusters with an average temperature lower than the average temperature of the cluster with the most pixels are classified as abnormal areas. Since the example in Figure 3 was measured in winter, clusters with average temperatures (\(\upmu 3\) and \(\upmu 4\)) higher than \(\upmu 2\), the average temperature of the cluster with the most pixels, are classified as abnormal clusters. Figure 3b shows an example of abnormal temperature areas segmentation.
###### Abstract  
Considering the complexity of the metro pit construction environment, the existing risk early-warning methods cannot ensure high-precision early warning. A high-accuracy metro pit collapse risk fusion early-warning method is proposed in present study. The main contributions include (1) presenting a new input to the fusion model by optimizing the machine learning model through a multi-step rolling method, and then using the basic probability assignment values obtained from the cloud model as input to the fusion model and (2) developing an improved methodology to address the paradoxical results of the fusion of traditional Dempster-Shafer evidence theory when there is a high level of conflict in multi-source risk prediction data. The proposed method is successfully applied to the Guangzhou Metro station project. By analyzing the early-warning results of 240 moments in 6 monitoring points, compared with the single information source method and the traditional D-S method, the early-warning accuracy of this method is increased by 15.8% and 10.8% respectively, the false alarm rate is reduced by 6.3% and 5.5%, respectively, and the missed alarm rate is reduced by 9.5% and 5.3%, respectively. The high-accuracy fusion early-warning method proposed in this paper has good universality and effectiveness in the early warning of subway foundation pit collapse risk.  
machine learning; deformation prediction; pit collapse; multi-data fusion; risk early warning 2020  
1
## 2 Materials and Methods
### Study Areas and Datasets
#### 2.1.2 Case 2: Illinois in United States  
The second case study was conducted in the subarea of Illinois that is part of the Corn Belt in the Midwestern United States. Corn, soybean, and winter wheat are grown in the study area (Figure 2). The total area of all crop parcels in the study area is approximately 19,476 ha, and the size of the crop parcels is much larger than that of Anbandegi.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Specification** \\ \hline UAV model & eBee Classic \\ Camera & Canon IXUS/ELPH \\ Image size & 2629 by 3275 \\ Area of crop parcels & 28.7 ha \\ Spectral bands & Blue, Green, Red \\ Spatial resolution & 0.25 m \\ Acquisition date & 25 August 2017 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of UAV imagery used for crop classification in Anbandegi.  
Figure 2: Data sets used for crop classification in Illinois: (**a**) false color composite (near-infrared (NIR)–short-wave infrared (SWIR)–Red) of Landsat-8 Operational Land Imager (OLI) imagery on 8 April 2017; (**b**) cropland data layers (CDL) data.  
A times-series Landsat-8 Operational Land Imager (OLI) image set with a spatial resolution of 30 m was used to classify the crops in a relatively large area (Table 2). Five cloud-free images from March to October 2017 were collected from the United States Geological Survey GloVis website [37] by considering the growth cycles of crops in the study area. Based on the results in a previous study where crop classification was conducted in the same area [29], three spectral bands, including Red (655 nm), near-infrared (NIR; 865 nm), and short-wave infrared (SWIR; 1600 nm) bands, were selected as inputs to provide useful spectral information for the discrimination of major crops in the study area (Table 2). The cropland data layers (CDLs) provided by the United States Department of Agriculture National Agricultural Statistics Service [38] were used as the ground truth. Similar to the case of Anbandegi, non-crop areas including urban areas and forests were masked out using the CDLs. Furthermore, minor grain classes including hay and alfalfa were excluded owing to their small occupancy in the study area.
## 3 Parameter Design of Anti Disturbance Voltage Control System  
### LADRC Basic Framework  
The block diagram of the second-order LADRC system is shown in Figure 3.  
Figure 3: Block diagram of a second-order LADRC system.  
The order selection of the LADRC is generally the same as the relative order of the controlled object [33]. From the small-signal model of the CCM flyback converter in the previous section, it can be seen that the converter can be regarded as a second-order control object. Therefore, a second-order LADRC strategy is designed to perform anti-interference control analysis on the controlled object. The model of the controlled object CCM flyback converter is:  
\[\ddot{y}=b_{0}u+f \tag{9}\]  
where \(b_{0}\) is the control gain, and \(f\) is the total unknown disturbance of the system, including the internal and external disturbances of the system.  
In Formulas (9)-(11) and (13), \(\dot{x}\) represents the first derivative of the variable, and \(\ddot{x}\) represents the second derivative of the variable.  
Introducing the state variable \(z_{1}=y\), \(z_{2}=\dot{y}\) and the extended state \(z_{3}=f\), Equation (9) has:  
\[\left\{\begin{array}{l}\dot{z}_{1}=z_{2},\\ \dot{z}_{2}=z_{3}+b_{0}u,\\ \dot{z}_{3}=\dot{f},\\ y=z_{1}\end{array}\right. \tag{10}\]  
Establish a linear extended state observer (LESO):  
\[\left\{\begin{array}{l}\dot{\hat{z}}_{1}=\beta_{1}(y-\hat{z}_{1})+\hat{x}_{ 2},\\ \dot{\hat{z}}_{2}=\beta_{2}(y-\hat{z}_{1})+\hat{x}_{3}+b_{0}u,\\ \dot{\hat{z}}_{3}=\beta_{3}(y-\hat{z}_{1})\end{array}\right. \tag{11}\]  
where \(\beta_{1}\), \(\beta_{2}\), and \(\beta_{3}\) are three adjustable LESO gains.  
To compensate for the observed values of the extended state observer and the total disturbance obtained in real-time to the control variables, the linear state error feedback control (LSEF) is set to:  
\[u=\frac{1}{b_{0}}[I_{2}(r-\hat{z}_{1})-I_{1}\hat{x}_{2}-\hat{z}_{3}] \tag{12}\]  
where \(I_{1}\) and \(I_{2}\) represent LSEF adjustable parameters.  
LESO and LSEF form second-order LADRC, substituting (13) into (12), with:  
\[\left\{\begin{array}{l}\dot{\hat{z}}_{1}=-\beta_{1}\hat{z}_{1}+\hat{z}_{2}+ \beta_{1}y\\ \dot{\hat{z}}_{2}=-(\beta_{2}+I_{2})\hat{z}_{1}-I_{1}\hat{z}_{2}+\beta_{2}y+I_ {2}r\\ \dot{\hat{z}}_{1}=-\beta_{3}\hat{z}_{1}+\beta_{3}y\end{array}\right. \tag{13}\]
## 3 Results and Discussion
### Discussion of the Results  
According to the above analysis of the aerodynamic coefficients, pressure coefficients, and the flow fields around the test models, the present bypass passive jet flow method is effective at different wind attack angles, particularly at larger wind attack angles. It is suggested that this method can achieve a better control effectiveness for the bridge flutter, which will be further investigated in an elastic test model. The model scale is set as 1:125 in the present study, which means that large-scale model tests should be conducted to validate this method in the near future.  
The present experimental study has validated the effectiveness of the bypass passive jet flow control on the bridge girder. However, the spacing of the suction/jet holes in the span-wise direction of the single box girder is fixed, and it does not indicate whether the control effect of the present configuration is the best. Therefore, a study focusing on the effects of the spacing ratio is currently being undertaken to facilitate this flow control method being used in engineering applications, and the results will be reported in the near future.  
Figure 15: Velocity profiles for the Case 0 and bypass jet control. (**a**) Case 0 at wind attack angle \(-20^{\circ}\); (**b**) Case 4 at wind attack angle \(-20^{\circ}\); (**c**) Case 0 at wind attack angle \(15^{\circ}\); (**d**) Case 4 at wind attack angle \(15^{\circ}\).
# Spatial Variation of Water Chemistry in Aries River Catchment, Western Romania
## 5 Conclusions  
According to the obtained results, the studied water system (Aries River) was characterized as vulnerable in the presence of mining activities and \"historical\" pollution.  
The mean concentrations of the major ions were in the following order: Ca \(>\) Na \(>\) Mg \(>\) K, HCO\({}_{3}\)\({}^{-}>\) SO\({}_{4}\)\({}^{2-}>\) Cl\({}^{-}>\) NO\({}_{3}\)\({}^{-}>\) F\({}^{-}\), indicating that both natural processes and anthropogenic activities conducted in the study area played an important role in the water quality.  
Neutral-alkaline and acid waters characterized the studied water according to the Durov and Piper plots, indicating low water quality. The similarities among the studied physico-chemical indicators given by the HCA indicated the sources of water pollution (natural and anthropogenic) and the low quality of water, respectively.  
Even if, in general, the heavy metals concentration was not considered to be higher than the MACs, except for Fe, Mn and Pb, the HPI scores. revealed a relatively high degree of heavy metals pollution in all the studied samples, while the HEI indicated low to high levels of heavy metal pollution in the studied water samples, due to high Fe, Mn and Pb concentrations. According to the WQI, the water quality was classified as excellent, good (A14, A15, A16), medium (A9, A10, A11, A13), poor (A7) and very poor (A12).  
Generally, the obtained results showed contamination with metals, especially with As, Fe, Mn and Pb, indicating a degree of vulnerability for the water from the Aries River catchment in front of the mining activities, with historical pollution still present. Furthermore, the Aries River needs immediate quality monitoring and proper treatment in order to prevent hazards and human healthfrom being affected. Thus, the obtained results could be very useful to the local authorities in implementing a sustainable and appropriate management strategy, indicating the clear vulnerability of the water system.  
Conceptualization, A.M., O.C. and V.M.; methodology, A.M., M.-A.H., R.B., O.C. and A.I.T.; sampling, M.R.; analysis, O.C., M.-A.H., A.M. and A.I.T.; visualization, A.M., A.I.T. and I.C.M.; writing--original draft preparation, A.M., M.-A.H., A.I.T. and I.C.M.; writing--review and editing, A.M., M.-A.H. and O.C.; funding acquisition, O.C. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the Project \"Entrepreneurial competences and excellence research in doctoral and postdoctoral programs--ANTREDOC\", a project co-funded by the European Social Fund and The Competitiveness Operational Programme of the Ministry of European Funds, code MY SMIS 107874.  
Not applicable.  
The data presented in this study is available on request from the corresponding author.  
The authors declare no conflict of interest.
## 3 Related Work
### Decentralized IAQ Monitoring Frameworks  
A considerable amount of work is dedicated to building IoT systems for IAQ monitoring, as shown in a systematic review published quite recently [4]. Some solutions rely on lightweight communication protocols such as LORAWAN technology [5] or ZigBee [6] to alleviate the processing load from the sensor and enable the use of small, less resource-consuming devices. However, most solutions send data to the cloud for further processing due to the constrained nature of those IoT devices. Although the work in [4] addresses several aspects of IAQ monitoring, the parts that lie in the scope of this paper, related to data storage, show that most solutions (65%) rely on cloud storage to store the data, the others relying on non-decentralized IoT data storage solutions.  
In [7], the authors propose a decentralized IAQ solution based on IOTA and Masked Authenticated Messaging (MAM). One limitation of this work is the relative centralization problems that come with IOTA. Another significant difference with our work is the lack of privacy management, task protection from monitoring, and optimization.  
In [8], the authors present a decentralized IoT data storage framework using IOTA Tangle and IPFS (InterPlanetary File System). The proposed framework is based on a central data handling unit (such as a local server) to collect and encrypt the data, which is subject to a single point of failure. The encrypted data are sent to the IPFS, while the corresponding hash and metadata are stored on the IOTA Tangle. However, in addition to the aforementioned limitations of IOTA, the IPFS network is immutable, which does not allow users to change the data after storing them [9].  
The authors in [10] presented a framework that integrated the blockchain with IoT-enabled air quality monitoring system. The proposed framework used the blockchain to store the air pollution data of industries in the smart city. However, the security aspects are not investigated.  
In [11], the authors provide an Azure cloud-based system to predict air quality and give a warning when the air quality status needs to change. The proposed system collects air quality data and then predicts the quality of air pollution. The air quality data are stored in the PostgreSQL database (built from the Azure cloud platform) and at the same time replicates data on the blockchain nodes to ensure data transparency. Such a combination raises several limitations, such as a lack of scalability of the blockchain for large quantities of data, lack of data privacy and fine-grained access control, and single point of failure for the PostgreSQL database.  
As far as we could see from the existing literature, a fully decentralized IAQ monitoring solution that integrates distributed data storage, fine-grained access control, integrated data mining, secure task execution, and route optimization is still lacking, thus motivating the contribution described in Section 4.
## 2 System Description and Problem Formulation  
### System Description  
#### 2.1.1 Uncertainty Model  
In this study, a group of WPGs \(\mathcal{U}=\{1,\cdots,u,\cdots,U\}\), such as a wind farm, was considered to be connected to the grid, as shown in Figure 1. To connect to the grid, the WPG operator forecasts the power generation production over \(\mathcal{T}=\{1,\cdots,t,\cdots,T\}\), e.g., \(T=24\) h for day-ahead operation. Let \(g_{t}^{u}\) and \(\tilde{g}_{t}^{u}\) be the actual WPG and its forecasting of the \(u\)-th WPG at time \(t\), respectively. The \(u\)-th WPG forecast uncertainty at time \(t\) is defined as  
\[e_{t}^{u}=\tilde{g}_{t}^{u}-g_{t}^{u}. \tag{1}\]  
An ESS is operated to manage the uncertainty by charging or discharging energy. By applying the ESS, the uncertainty is calculated as  
\[\varepsilon_{t}^{u}=e_{t}^{u}+q_{t}^{u}, \tag{2}\]  
where \(q_{t}^{u}\) is the ESS charging/discharging quantity for the \(u\) th WPG at time \(t\).
# Development and Research of a Medium Vertical Dipole Electromagnetic Pulse Simulator
## 4 Experiments and Characteristics Research
### Influence of Antenna End Conditions  
In this paper's experiment, three different treatment methods were applied to the end of the inverted conical antenna of the simulator: (1) open end, (2) connected to the ground of the laboratory far away from the antenna by a 45-m-long wire, and (3) direct connection of the end of the antenna to the metal mesh below the antenna through a piece of metal wire. The radiation field waveforms measured at different measuring points in the north direction under different antenna terminal conditions are presented in Figure 8. The main waveforms of the radiation field in the open-ended and grounded-disantly cases were consistent, but there were differences at the back ends of the waveforms. Specifically, when the end of the antenna was open, a pit appeared at the end of the waveform at 450 ns. When the antenna was grounded at the far end, a bump appeared at 450 ns at the end of the waveform. This phenomenon is related to the wave reflection caused by the different impedance at the end of the antenna. When the antenna ends were near-grounded, the pit caused by the reflection of the antenna end on the waveforms became less pronounced, and the radiation field waveform became closer to the ideal bi-exponential waveform, as shown in Figure 8a.  
Figure 7: Influence of the cable number of the wire-mesh antenna on the radiation field of the simulator. (**a**) Measuring point position: (0.5, 0, 0.02). (**b**) Measuring point position: (5, 0, 0.02).