esa-sceva/satcom-chunk-collection huggingface dataset:
# An Automatic Data Completeness Check Framework for Open Government Data
## 5 Discussion  
From the radar plots in Figures 7 and 8, we can obtain information about the completeness of the 93 individual standardized files of National, Incheon, Seoul, and Gyeonggi regions. Figure 8 shows that the result of DCR evaluated considering the mandatory fields seems to be more complete than that of Figure 7, in which DCR was calculated considering all fields. Moreover, in both figures, the missing DCR value for Incheon, Seoul, and Gyeonggi regions can be seen, which means that the data points in the respective files are not available for these regions. The standardized files that do not contain values for Incheon, Seoul, and Gyeonggi regions are 14, 19, and 7, respectively, which are identical for the overall and the mandatory case. This result is even more evident in Table 2, which also lists the names of the files that do not contain data points for the previously defined regions.  
In Figure 9, the 93 files of the above regions are categorized according to the different levels of completeness. A file is considered most complete if the DCR is \(\geq\)90%, whereas the lowest degree of completeness applies to the case when the DCR is \(<\)50%. Following this rule, Figure 9 shows that for the case DCR \(\geq\) 90%, the National (Mandatory) region has the highest number of files, i.e., 87, whereas Seoul (Overall) and Gyeonggi (Overall) regions have the lowest number of files, i.e., 46. For the DCR \(<\) 50% case, the National (Overall), National (Mandatory), Incheon (Mandatory), and Gyeonggi (Mandatory) regions have the highest number of files, namely 4, whereas Incheon (Overall), Seoul (Overall), and Gyeonggi (Overall) regions have the lowest number of files, namely 1. Figure 9 shows that a large proportion of files in the Seoul, Incheon, and Gyeonggi regions have a DCR of less than 90%, let alone 100%.  
Figure 10 shows the average DCR of the 93 standard files based on the regions. From the results, it can be seen that the National (Mandatory) region has the highest average DCR of 94.87%, whereas the National (Overall) region has the lowest average DCR of 86.63%. It can be inferred that in 93 standardized files, the non-mandatory fields are incomplete in most of the cases as compared to the mandatory fields.  
Figure 11 shows the daily average DCR for the National, Incheon, Seoul, and Gyeonggi regions for the overall and the mandatory case. The line graph in the figure shows the slight fluctuations in the DCR value, indicating that the government agencies are revising the dataset, but the desirable DCR level has not yet been reached.  
The DCR results shown in Figures 7-11 are visualized in real-time using the Django-based dashboard, as shown in Figure 12. The results displayed in the dashboard show the alarming state of OGD quality of South Korea. The government agencies that control and monitor the OGD data should use this platform to improve and monitor the quality of data published in their portal before releasing it to the public.
###### Abstract  
The process of heavy metals removal by soil washing using chitosan as washing agent is a multivariate problem. According to the experimental data sets obtained during experiments performed at laboratory scale, the main parameters that influenced the efficiency of the soil washing process were the stirring time of the polluted soil with the investigated washing agent, washing solution concentration, and solid/liquid ratio (S/L ratio). This study explores the statistical relationships between the removal efficiency of Cu and Pb from polluted soil by washing with chitosan and factors influencing the soil washing process by use of a non-linear regression model. The non-linear regression model contains a non-linear component and a component of interaction among the two parameters (S/L ratio \"X\({}_{1}\)\", the stirring time \"X\({}_{2}\)\") which influences the efficiency of the Cu and Pb removal from soil by soil washing with the investigated washing agent. The proposed model is useful for predicting and estimating the effectiveness of the soil decontamination process by washing with chitosan. A comparison between the data calculated using the proposed mathematical model and the experimental data was also performed in order to determine the integrity and conformity of the mathematical model obtained. The results showed a good fit of the obtained model to the experimental data.  
soil remediation; lead; copper; chitosan; prediction model +
Footnote †: journal: Applied Sciences
## 7 Conclusions  
The proposed set of methods, MSM, passive seismic interferometry, and radon surveys, made it possible to obtain a high-quality image of the studied environment. As a result, the pipe body and the controlling fault were identified. Thus, it has been shown that the proposed set of methods can be successfully applied in the search and study of kimberlite pipes in the Chernoozerskaya area of the ADP.  
The Volchya pipe is identified as two low-velocity, cone-shaped bodies with an anomaly in the RFD. The highest level of RFD is caused by the intersection of fault and pipe.  
The obtained results showed that the tested set of methods can increase the efficiency of verification drilling planning. In particular, it was shown that on the Volchya pipe:  
* the true dimensions are larger than was previously estimated;
* each pipe contains various blocks, which are probably different phases of intrusion;
* it was shown that during the formation of the pipe, an important role is played by the boundary inside the Padunskaya suite of Vendian deposits at a depth of 120 m;
* specific faults controlling each pipe were established;
* faults controlling each pipe have a vertical structure and high contrast in the Ust-Pinega suite of Vendian deposit and Riphean deposit
* the pipe contains blocks that have not come to the surface of Vendian deposits;
* in terms of the features of manifestation, the studied pipe is close to the pipes of the Lomonosov deposit.  
The work carried out showed that kimberlite-controlling faults are located in the Riphean deposits and the Ust-Pinezhskaya suite of the Vendian deposits. Previously, when searching for and studying pipes, the main attention was paid to the Padunskaya suite of the Vendian. The Volchya pipe requires further exploration, including drilling. Further study of the identified facts will allow us to clarify the characteristics of the pipe. In particular, to more accurately estimate the size and diamond content of the pipe.  
K.D.: conceptualization, investigation, methodology, validation, resources, supervision, project administration, funding acquisition, and writing. E.Y.: conceptualization, investigation, validation, formal analysis, and review and editing. A.P.: conceptualization, investigation, validation, methodology, formal analysis, writing, and review and editing. V.B.: investigation, validation, methodology, formal analysis, and writing. A.K.: investigation, validation, methodology, formal analysis, and review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a grant from the Russian Science Foundation, number 23-27-10022.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 3 Research Methods
### Interpretation of Satellite Image  
This data collection used the elevation data in Google Earth 2022 (7.3.3.7786) and Global Mapper 2021 (21.0.1) software to interpret the sedimentary system of the Ebinur Lake Basin. Combined with the polygon addition operation and profile elevation in Google Earth, SRTM Worldwide Elevation Date (1-arc-second Resolution, SRTM Plus V3) in Global Mapper online data, the sedimentary systems were divided up. In Google Earth, the point, line and surface attributes can be applied to measure the morphological characteristics of DFS, desert, and lake sedimentary systems. In this instance, the analysis section of Global Mapper was also used to generate contour maps.  
The data collection of this study first draws each sedimentary system according to the Global Mapper elevation data (Figure 2a) and exports the kmz file of each sedimentary system to be displayed in the Google Earth satellite image (Figure 2b). Finally, the minimum elevation of the cross-section between different sedimentary systems in Google Earth (Figure 2c) further confirms the boundaries of each sedimentary system [(29)]. After drawing each sedimentary system, the data are extracted in Google Earth 2022 (7.3.3.7786) software. The basic data such as the area (A) and radius (r) of DFS, the wavelength (L) and width (W) of dunes, and the area of lakes can be directly obtained through Google Earth's closed area. The DFS elevation change data (h\({}_{1}\)), the difference between the top and bottom elevations of the DFS radius and the gradient (G), is calculated using the DFS gradient calculation formula [(30)]. The height of the dune (h\({}_{2}\)) is the elevation difference between the highest point and the lowest point on the windward side of the dune (Figure 2d). Dune measurement sample selection divides the horizontal and vertical profiles in the desert area according to equal distance; two dunes are selected near the intersection of each horizontal and vertical profile for measurement (Figure 2e). A total of 121 DFS and 186 dunes were measured in this study, with a total of 1050 data. The measurement includes the determination of the sedimentary boundary of each sedimentary system and the statistics of the size of the morphological data. A total of 121 DFS and 186 dunes were measured, with a total of 1050 data.
## 3 Results and Discussion
### Characterization  
#### 3.2.1 SEM-EDS Analysis  
From the SEM image in Figure 2a, it can be observed that numerous spherical particles with a particle size of about 80-120 nm were gathered on the ATP. The spherical particles were supposed to be nZVI, and the nZVI was in the form of chains. By analyzing the spectra obtained by EDS in Figure 2b, the chemical element composition of the sample can be obtained as Mg, Al, Si, Ca, K, and Fe. ATP mainly contains Mg, Al, Si, Ca, and K, and the main element of nZVI is Fe. It can be seen that the EDS results show that the nZVI@ATP contains all of these elements, which can indicate that the nZVI is successfully loaded on the bumpy rods, and the relative content of Fe is 26%. nZVI@ATP has a nanoscale structure, and the larger surface area contributes to a higher reactivity and provides more active sites. nZVI@ATP loading helps to enhance the stability of nZVI and prevents particle agglomeration and oxidation to a certain extent, which prolongs the nanoparticles' lifetime and enhances their reactivity.
## 2 Description of the System
### Working Cycle  
The working cycle of TSS can be divided into two operating periods of charge and discharge. In the charge period, the molten salt coming from the CST is heated by surplus electricity generated using any renewable energy sources, as shown in Figure 2. Then, the hot molten salt is stored in the HST. The charging process continues until the HST is completely filled or the surplus electricity from renewable sources is reduced.  
Figure 1: Schematic structure of a combined cycle power plant with a storage system (discharge path).  
During the charge period, on the one side, the air coming from the air compressor enters the heat exchanger of TSS, while on the other side the hot molten salt is pumped to the same heat exchangers in order to heat the airflow, as demonstrated in Figure 3. Then, the hot air enters the expander to generate electricity. After that, the expanded hot air flows into HRSG to complete the steam cycle with three pressure circuits. Thereafter, this process continues until the HST is completely depleted. It should be mentioned here that three types of molten salt will be used in this study.
###### Abstract  
There are different missing flight data due to various reasons in the process of acquisition and storage, especially in general aviation, which cause inconvenience for flight data analysis. Effectively explaining the relationship between flight data parameters and selecting a simple and effective method for fitting and correcting flight data suitable for engineering applications are the main points of the paper. Herein, a convenient and applicable approach of missing data correction and fitting based on the least squares polynomial method is introduced in this work. Firstly, the polynomial fitting model based on the least squares method is used to establish multi-order polynomial by existing flight data since the order of the least squares polynomial has a direct impact on the fitting effect. The order is too high or too small, over-fitting or deviation will occur, resulting in improper data. Therefore, the optimization and selection of the model order are significant for flight data correction and fitting. Because the flight data of the aircraft engine exhaust gas temperature (EGT) are often lost because of the immature detection technology, a series of the multi-order polynomial are established by the relationship of aircraft engine exhaust gas temperature and Revolutions Per Minute (RPM). Case study results confirm the optimal model order is four for the fitting and correction of aircraft engine exhaust temperature, and the least squares polynomial method is applicable and effective for EGT flight data correction and fitting based on RPM data.  
flight data; exhaust gas temperature (EGT); revolutions per minute (RPM); polynomial least squares +
Footnote †: journal: Applied
###### Abstract  
Frequency response signals have been used for the non-destructive evaluation of many different structures and for the integrity evaluation of porcelain insulators. However, it is difficult to accurately estimate the integrity of porcelain insulators under various environmental conditions only by using general frequency response signals. Therefore, this study used a method that extracted several features that can be derived from the frequency response signal and reduced their dimensions to select features suitable for the evaluation of the soundness of porcelain insulators. The latest machine learning techniques were used to identify correlations and not for basic feature analyses. Two machine learning models were developed using the support vector machine and ensemble methods in MATLAB. Both models showed high reliability in distinguishing between normal and defective porcelain insulators, and they could visualize the distribution area of the data by extracting quantitative values and applying machine learning, rather than simply verifying the frequency response signal.  
porcelain insulator; frequency response; feature extraction; non-destructive method; damage evaluation; support vector machine; ensemble Article
## 3 Convolutional Neural Networks (CNN)
### Enforcing Sparse Combinations  
The distribution of weights \(\alpha_{i}\) is imposed with sparseness constraints for a given map by incorporating a regularization penalty term \(\Omega(\alpha)\) into final error function. The regularization penalty \(\Omega(\alpha)\) can be derived as:  
\[\frac{\partial\Omega}{\partial\alpha_{i}}=\lambda sign(\alpha_{i}), \tag{20}\]  
\[\frac{\partial\Omega}{\partial c_{i}}=\sum_{k}\frac{\partial\Omega}{\partial \alpha_{k}}\frac{\partial\alpha_{k}}{\partial c_{i}}=\lambda\Biggl{(}|\alpha_ {i}|-\alpha_{i}\sum_{k}|\alpha_{k}|\Biggr{)}. \tag{21}\]  
Some examples are given to explain convolution and pooling processes, in the convolution operation, and we can see from Figure 1 that, the input data is composed of \(a\)\(7\times 7\times 3\) dataset, where \(7\times 7\) represents width and height pixels, and 3 represents R, G, B color channels.  
Filter W0 and filter W1 are two different filters. The stride is two, indicating that the window extracts \(3\times 3\) local data, and strides two steps for each time. Zero padding = 1. With the left window moving smoothly, the filters convolve by using different local data covered by the window.  
Respectively, two filters are used to calculate convolution operation and two groups of results are shown.  
In the convolutional neural network, filters (neurons with a group of fixed weights) are used to operate convolution for local input data. After calculating the data in each window, the data window moves smoothly with a specific stride, until the convolution operation is finished. There are a few parameters that need to be figured out:  
1. Depth: the number of neurons (filters), determining the depth,
2. Stride: the number of stride covering through the data,
3. Zero padding: Supplement a few zeros to make the window more from the initial location to the end of the dataset.  
Figure 2 is max pooling operation, which means taking the maximum value of the specific data window area. The other pooling method is average pooling in the CNN algorithm, i.e., take the average value of the specific data window area. Average pooling is applied in this paper.
# Enhanced IoT-Based End-To-End Emergency and Disaster Relief System
## 3 The Crow\({}^{2}\) System
### Crow\({}^{2}\) System Architecture  
The CROW\({}^{2}\) system is a set of wireless distributed devices equipped with wireless sensors intended to collect real-time data (i.e., vital signs, stress level, locations, ambient intelligence [28], etc.) from Wireless Body Area Network (WBAN) nodes towards a cloud IoT platform. Figure 1 depicts the general architecture of the next generation WBAN. A node in the proposed system could be either: (i) tactical (deployed by rescuers while moving inside the disaster area) or (ii) mobile (carried on-body by rescuers or trapped survivors). Tactical nodes establish a wireless tactical backbone, which extends the network coverage. Mobile nodes, being in proximity of the tactical backbone, could route packets through it as depicted in Figure 2b. We call these tactical devices ORACE-Net Tactical Devices (OTDs). Mobile devices carried on-body rely on both the OTDs and the other mobile devices to route data. Data collected from deployed nodes (i.e., tactical and mobile) are routed through the network towards  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Protocols and Systems**} & **Wireless** & \multirow{2}{*}{**Multi-Hop**} & **Topology** & **Infrastructure** & **Network** & **Sensing Devices** \\  & **Standard** & & **Awareness** & **Dependency** & **Extensibility** & **Integration** \\ \hline
**RRT**[14] & N/A & No & No & No & No & N/A \\
**DistressNet**[16] & ad hoc WiFi & Yes & Yes & No & No & N/A \\
**Disaster 4 G**[13] & LTE/4G & Yes & Yes & Yes & Yes & N/A \\
**Emergency Direct**[17] & WiFi Direct & No & Yes & No & No & N/A \\
**TeamPhone**[15] & ad hoc WiFi/SMS & No & No & Yes & No & N/A \\
**CROW\({}^{2}\)** (this work) & ad hoc WiFi & Yes & Yes & **No** & **Yes** & **Yes** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Recent implemented disaster management systems benchmark. RRT, Reliable Routing Technique.  
the Command Center node (CC node). The CC node is a tactical command center deployed as a gateway allowing the emergency network to be linked to wide infrastructure networks (e.g., Internet, military platforms, other emergency networks, etc.). The CC node is also the node through which the operations' commanders send their instructions to the rescuers and the rescuers send back their feedback to the CC node. It is important to note here that multiple CC nodes could be deployed and activated in the case of single CC failure.  
Figure 1: General architecture of the wireless body-area-network system. BAN: Body-Area-Network, BBN: Body-to-Body communication, Off-Body communication: all non-BAN and non-BBN communications.  
Figure 2: _Cont._
## 2 Materials and Methods
### Treatment Planning  
All 3D PET/CT, 4D PET/CT, and MF images were then input into the Eclipse Treatment Planning System (version 13.6, Varian Medical Systems, Palo Alto, CA, USA) to carry out delineation of the tumor and develop a treatment plan.  
In order to achieve the best treatment outcome of respiratory-gated radiotherapy, the respiratory-gated window is normally set as the respiratory phase of end-expiration, during which the tumor anatomical movement is the least for the longest duration. We found that the best respiratory phases to use as the respiratory-gated window in our patients were T45%, T55%, and T65%. Therefore, we used images from these three phases for target delineation [10]. To integrate the imaging information from these three respiratory phases, 4D-CT, 4D-PET, and PETMF images were processed using the maximum intensity projection (MIP) technique to generate CTMIP, PETMIP, and MF-PETMIP images. These MIP images were then used for delineation of the tumor volume (from 3D PET, PETMIP, and MF-PETMIP images) and identification of organs at risk (OARs) (from CTMIP images).  
* CTMIP: generated from 4D CT45%, 4D CT55%, and 4D CT65% images using MIP.
* PETMIP: generated from 4D PET45%, 4D PET55%, and 4D PET65% images using MIP.
* MF-PETMIP: generated from PETMF-45%, PETMF-55%, and PETMF-65% images using MIP.  
For each patient, individual three-dimensional (3D-Plan), four-dimensional (4D-Plan), and MF (MF-Plan) respiratory-gated radiotherapy plans were designed, in which the delineation of internal target volumes (ITVs) was performed as described below:  
* ITV3D: volumes of 3D PET images with a SUV over 40% of the SUVmax.
* ITV4D: volumes of PETMIP images with a SUV over 40% of the SUVmax.
* ITVMF: volumes of MF-PETMIP images with a SUV over 40% of the SUVmax.  
After ITV delineation, a 5-mm margin was added to create the PTV of each treatment plan, namely PTV3D, PTV4D, and PTVMF, respectively. OARs, such as the heart, lungs, and spinal cord, were delineated by an experienced radiation oncologist based on the CTMIP images.  
Once the extent of the PTVs and OARs had been determined, treatment plans for individual patients were custom-designed by an experienced medical physicist. For the 3D Plan, 4D Plan, and MF Plan, the target volume was determined based on the PTVs (PTV3D, PTV4D, and PTVMF, respectively). All three plans used 60 Gy in 30 fractions with 6 MV photons. In addition, the designs of the treatment plans (including the number of photon fields, number of segments, and dose constraints) were all the same. In each treatment plan, 95% of the PTV was covered by the 97% isodose curve. In addition, dose constraints for OARs were limited to radiotherapy standards: restriction of the relative volume of the lung receiving doses above 5 (V5) and 20 Gy (V20) to below 65% and 35%, respectively, and an average dose < 20 Gy; 1/3 of the volume of the heart limited to a maximum dose of 50 Gy; and the spinal cord limited to a maximum dose of 50 Gy.