esa-sceva/satcom-chunk-collection huggingface dataset:
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 4 Automated and Transparent Encryption and Decryption
### Single-User Spatial Files  
The first set of sensitive spatial files that need protection are _single-user_ files. These files are created and owned by spatial end users and should not be shared with others. They are normally stored on users' computers or in their home folder within the file server. Using the proposed security solution, these single-user spatial files are stored in their home directories on the DBFS server. Since this set of files is controlled by end users, the encryption scheme must allow users to define which files are to be encrypted for protection, without the involvement of administrators. Ideally, on user logon, these files must be automatically decrypted for use and automatically encrypted at logoff. This automation is described in Section 4.4 shortly.  
The specification of a single-user sensitive spatial file to be encrypted has been designed to be simple and can be performed using any GIS application or protocol service used to connect to ECMSDK shared folders, that is, from a desktop, web, or mobile device without the involvement of administrators. The proposed implementation system used here is to adopt the _.enc_ file type. Use of this file type is compatible with existing file formats such as \"_.slp_ and \"_.json_ and does not add additional file overhead. The _.enc_ file extension is added to the end of the filename, for example, \"_historical_places.slp_.enc_\", and the extension indicates the files are currently locked for protection and require decryption. For encryption of file-based geospatial layers, only files with valuable data would require this extension. For example, in the shapefile format, only _.slp_, _.dbf_, and _.slx_ could have the _.enc_ format appended. Figure 3 demonstrates a GIS user's single-user files decrypted for use, with the full user interaction workflow are included in Supplementary Materials online, and this YouTube video [[https://www.youtube.com/watch?v=zMJV%zV0-zs](https://www.youtube.com/watch?v=zMJV%zV0-zs)]([https://www.youtube.com/watch?v=zMJV%zV0-zs](https://www.youtube.com/watch?v=zMJV%zV0-zs)) (accessed on 18 April 2024).  
After decrypting, and using the file, if the file does not need to be encrypted, then users can simply retain the original file extension and it will not be encrypted. Since this set of files has an ECMSDK _Access Control List_ (ACL) that controls access, no other DBFS users can change the file extensions, delete the file, or view its contents while they are in decrypted form on the server. To prevent the entire encryption scheme from being dependent on one set of keys, each user has a unique set of encryption keys that are used for both encrypting and decrypting the user's files. A database table created within the database administrator schema is used to store encryption and decryption keys for each user's files in order to prevent access by other database users (see Supplementary Materials).
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 6 Conclusions and Future Work  
Dissemination of WMs is the primary method used in vehicular accident avoidance. Collisions between vehicles may result from delays or packet drops while disseminating WMs. In order to address the complex nature of bi-directional highway settings and offer effective path selection, we have developed an optimal path routing protocol (OPRP) that considers two metrics in addition to the distance metric. A node's movement direction is included in the first parameter based on Hamming distance, and the source and destination nodes' relative locations are included in the second parameter. We showed the importance of these variables in determining the next hop in bi-directional road traffic. This work has shown that the mentioned parameters can route WMs to increase throughput, decrease packet loss and delay, and accommodate topological changes in the transmission process. OPRP significantly outperforms other leading VANET routing protocols, according to simulation results. Extension of OPRP without clustering for the urban scenario with GPS-less localization will be part of our future study. Determining how channel conditions affect the transmission of WMs and secure communication can also be considered in future work. An SDN-based warning message dissemination using machine learning and deep learning can also be considered in future work.  
Conceptualization, M.A.S.; data curation, M.A.S.; formal analysis, G.A.; investigation, F.Z.K. and I.U.K.; methodology, M.A.S. and N.A.; project administration, F.Z.K. and G.A.; resources, S.S.A. and J.A.; software, Z.H.A.; supervision, F.Z.K., G.A. and Z.H.A.; writing--review and editing, M.A.S., G.A. and J.A. All authors have read and agreed to the published version of the manuscript.  
This project is funded by SAUDI ARAMCO Cybersecurity Chair.  
We would like to thank SAUDI ARAMCO Cybersecurity Chair for funding this project.  
The authors declare no conflict of interest.
# Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
###### Abstract  
Focusing on water resources assessment in ungauged or sparse gauged areas, a comparative evaluation of areal precipitation was conducted by remote sensing data, limited gauged data, and a fusion of gauged data and remote sensing data based on machine learning. The artificial neural network (ANN) model was used to fuse the remote sensing precipitation and ground gauge precipitation. The correlation coefficient, root mean square deviation, relative deviation and consistency principle were used to evaluate the reliability of the remote sensing precipitation. The case study in the Qaidam Basin, northwest of China, shows that the precision of the original remote sensing precipitation product of Tropical Precipitation Measurement Satellite (TRMM)-3B42 RT and TRMM-3B43 was 0.61, 72.25 mm, 36.51%, 27% and 0.70, 64.24 mm, 31.63%, 32%, respectively, comparing with gauged precipitation. The precision of corrected TRMM-3B42 RT and TRMM-3B43 improved to 0.89, 37.51 mm, -0.08%, 41% and 0.91, 34.22 mm, 0.11%, 42%, respectively, which indicates that the data mining considering elevation, longitude and latitude as the main influencing factors of precipitation is efficient and effective. The evaluation of areal precipitation in the Qaidam Basin shows that the mean annual precipitation is 104.34 mm, 186.01 mm and 174.76 mm based on the gauge data, corrected TRMM-3B42 RT and corrected TRMM-3B43. The results show many differences in the areal precipitation based on sparse gauge precipitation data and fusion remote sensing data.  
Qaidam Basin; remote sensing; TRMM; artificial neural network Article
## 2 Forest Fire Detection by the Satellite data in China
### The Chinese Geostationary Meteorological Satellite FY-2  
The polar orbiting satellites, including the NASA's Terra and Aqua satellites, can offer a regional coverage but provide only one daytime and one nighttime observation every 24 hours. In order to develop and implement a near real-time operational fire monitoring network for China, the China Meteorological Administration processed and analyzed imagery acquired from the China FY-2C/D, together with imagery from the GOES, MTSAT geostationary satellites. The China FY-2 series geostationary satellites are operationally similar to the GMS (Geosynchronous Meteorological  
Figure 2: The basic flow of the forest fire detection procedure based on FY1-C/D satellites data.  
Satellite) with high-resolution stretched VISSR (Visible and Infrared Spin Scan Radiometer) data [39]. The channel characteristics of VISSR onboard FY-2C/2D satellites are shown in Table 2.  
A complete 20\({}^{\circ}\)\(\times\) 20\({}^{\circ}\) scan covering the full earth disk can be accomplished each 30 minutes. Most forest fires have strong diurnal cycles, and in some regions the fires are short-lived lasting no more than a couple of hours. The geostationary satellite provides frequent and timely acquisitions to allow detection and monitoring of active fires from space. Figure 3 illustrates workflow of the forest fire hot spot detection using FY-2C/2D satellites data. Figure 4 shows the hourly progress of the forest fires hot spot obtained by using the FY-2C imagery.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Channe**} & \multirow{2}{*}{**Wavelength**} & **Resolution** & \multirow{2}{*}{**Dynamic**} & **Temperature** & \multirow{2}{*}{**S/N**} & **Primary use** \\  & & **n** & **range** & & **(K)** & \\ \hline
1 & 0.5–0.9 & 1.25 & 0–98\% & & 0.5\(p\) = 2.5\% & Burnt area \\  & & & & & 95\(p\) = 95\% & \\
2 & 3.5–4.0 & 5 & 180–330 K & 0.6–0.5 & & Hot-spot \\
3 & 6.3–7.6 & 5 & 180–280 K & 0.5–0.3 & & Water vapor \\
4 & 10.3–11.3 & 5 & 180–330 K & 0.4–0.2 & & Hot-spot \\
5 & 11.5–12.5 & 5 & 180–330 K & 0.4–0.2 & & Burnt area \\ \hline \end{tabular} \(\wp\): reflectivity; S/N: signal to noise ratio.  
\end{table}
Table 2: The channel characteristics of VISSR onboard FY-2C/2D satellites.  
Figure 3: Forest fire hot spot detecting flow chart using FY-2C/2D satellites data.  
Figure 3 shows the forest fire hot spot detecting flow chart using FY-2C/2D satellites data. The Ch. 1 Th, Ch. 4 Th are the bright temperature (BT) threshold of Ch. 1 and Ch. 4; CH2 PA, CH4 PA are the BT weighted average of Ch. 2 and Ch. 4; Ch. 24 bg are the blackguard BT difference of Ch. 2 and Ch. 4; Ch. 2 IJ, Ch. 4 IJ are the BT of Ch. 2 and Ch. 4 at I, J pixel location. FT is the fire hot spot threshold.  
Figure 4 shows the hourly distribution of forest fires detected (red spots) and observed land surface (yellow signs) located across the Northeast China and Russia during 02:00 to 07:00 (Beijing time) on 23 July, 2005 based on FY-2C satellite forest fire detected method (see Figure 3). The background is an image of land cover types and water bodies.
## 2 Background
### Color/Texture-Based Object Detection Methods  
The color and texture of materials are commonly used in detecting structural elements since those elements are usually composed of a single material (e.g., concrete) [11; 12; 13; 16]. Based on the finding that most color values of construction materials were in certain ranges, [PERSON] et al. [17] identified a structural element by detecting its boundary pixels whose color values satisfied the predefined color ranges. Instead of taking into account color features only, [PERSON] and [PERSON] [18] combined both color and texture features to label regions in an image. A region was considered as a structural element, if the Euclidean distance between the region label and a predefined material label was smaller than a predefined threshold. Recently, machine learning techniques are widely used in detecting concrete structural elements by recognizing their materials [11; 19; 20; 21; 22]. The input data are those quantified color or texture characteristics, such as RGB values (the color depth of red, green, and blue light, ranging from 0 to 255) [23], luminance values [24], and filter bank responses [25].  
However, color/texture-based methods are unable to cope with structural elements covered in unknown paint, tiles, or other surface treatments, which are common to be seen in school building columns and walls. Moreover, color/texture-based methods require a predefined material library, which is not only time-consuming to be built but also is unable to cover all characteristics of materials because even the same material can have different characteristics under different scenarios. For example, the color of a material can be different under various illumination intensities. Missing containing characteristics of materials can lead to the failure of detection.
## 7 Discussion
Finally, this paper discusses the effectiveness of NBR-Clust with respect to overlapping communities. There are relatively few algorithms that can account for overlap. The hypothesis motivating the application of node-based resilience for this setting was that overlap nodes would be a subset of attack set nodes because they both lie on inter-cluster boundaries. This hypothesis has been strongly supported by this paper's experiments as the attack sets contained, on average, at least 99% of the overlap nodes across all datasets with overlapping communities. On regular Girvan-Newman graphs with overlapping communities, NBR-Clust results with VAT and integrity have been promising for lower mixing factors, as both measures have clustered such graphs with at least 0.94 NMI. Amongst the Girvan-Newman graphs with overlap, the most difficult clustering situations had 32 of 256 nodes overlapping 6 out of 13 clusters, and VAT still achieved an NMI of 0.83, while integrity scored 0.81. The results are similarly notable for integrity on scale-free benchmark graphs with overlap. Integrity achieved high NMI scores while clustering every graph in one step. In the case where 100 nodes overlapped six clusters each, integrity scored an NMI of 0.84. In this case, an average of less than 2% of the nodes were misidentified (and incorrectly assigned to clusters). As far as assignment of overlap nodes is concerned, one of the problems of overlap clustering algorithms in general is that the overlaps make distinct clusters difficult to determine. Approaches such as maximizing modularity must consider many different options and do not have deterministic outcomes. One of the benefits of NBR-Clust is that the initial clusters are both disjoint (because overlaps have been removed as part of the attack set) and reflective of the natural number of clusters of the graph, so that critical node assignment can be a low time complexity, deterministic process. Due to such inherent properties of critical attack sets for node-based resilience measures, this research demonstrates that a straightforward adjacency-based approach yields a successful strategy to subsequently assign the overlapping nodes to multiple clusters after the critical attack set computation.
# Fault Detection, Diagnosis, and Prognosis of a Process Operating under Time-Varying Conditions
## 3 Methodology  
This section briefly introduces the proposed methodology. Let \(Y_{N}=\left[y_{1},y_{2},\ldots,y_{N}\right]\) be the training set with \(N\) number of observations obtained from the machine; by applying \(p_{k}=\left[y_{t-p+1},y_{t-p+2},\ldots,y_{t}\right]^{T}\) and \(f_{k}=\left[y_{t},y_{t+1},\ldots,y_{t+f-1}\right]^{T}\) at each time instance \(k\) of \(Y_{N}\), one can form the past and future matrices:  
\[Y_{p}=\left[p_{p},p_{p+1},\ldots,p_{N-f+1}\right]\]  
\[Y_{f}=\left[f_{p},f_{p+1},\ldots,f_{N-f+1}\right]\]  
Then, the sample covariance and cross-covariance of the past and future matrices can be estimated as  
\[\sum_{pp}=\frac{1}{N-p-f+1}Y_{p}Y_{p}^{T}\]  
\[\sum_{ff}=\frac{1}{N-p-f+1}Y_{f}Y_{f}^{T}\]  
\[\sum_{ff}=\frac{1}{N-p-f+1}Y_{f}Y_{f}^{T}\]  
The goal of CVA is to find a set of linear combinations between the past and future matrices such that the correlations between the two groups of linear combinations are maximized. To do so, one can perform a singular value decomposition as follows:  
\[H=\sum_{ff}^{-1/2}\sum_{fp}\sum_{pp}^{-1/2}=U\Sigma V^{T}\]  
Then, two monitoring indices \(T^{2}\) and Q can be derived as  
\[T^{2}=z_{t}z_{t}^{T},\ z_{t}=V^{T}\sum_{pp}^{-1/2}p_{t}\]  
\[Q=z_{t}z_{t}^{T},\ z_{t}=V^{T}\sum_{pp}^{-1/2}p_{t} \tag{1}\]  
When new data are available, one can calculate the monitoring indices \(T^{2}\) and \(Q\), and compare their values to the corresponding failure threshold for fault detection.  
The details of the application of the methodology are presented in Section 4, with a step by step description of the approach and related insights presented in Figure 1. For more details about CVA, see [38].  
Focusing attention for a moment on the first step of the methodology, it is not part of this article. This step has been detailed in another article [5]. As anticipated, the role of this step is twofold: on the one hand, implementing a fault detection analysis provides the ability to identify anomalies that cannot be identified with prognosis analysis. The purpose is then to support prognosis analysis with real-time data analysis, with the aim of making joint maintenance management more robust. On the other hand, the health indices calculated in this step are the input for the subsequent variable selection analyses for prognosis analyses.
## 3 Materials and Methods
### Landcover Classification  
At present, the primary open-source Land Use/Land Cover (LULC) product characterised by temporal dynamism is Google's Dynamic World product [30], which leverages the coverage, resolution, and timeliness of Sentinel-2 observations and deep learning to provide near real-time LULC products. However, the utility of such products is severely limited in tropical regions due to cloud cover and illumination constraints.  
To address these limitations, a supervised XGBoost classification model was developed, tailored to delineate mangroves, mixed forests, and areas deforested by oil contamination. The model objective was set to multi:softprobto enable multiclass classification and retain predicted class probabilities. Hyperparameters were optimised through cross-validation methods, specifically stratified 5-fold cross-validation with _sklearn's Grid Search CV_. In addition, early stopping was included in training to prevent overfitting.  
The XGBoost algorithm [31] was chosen for its regularisation techniques that prevent overfitting and consequently produce a more stable and generalisable model. Additional motivation includes the speed and scalability when working with large datasets and the ensemble learning approach, which improves performance compared to individual models.
## 4 Simulation and Analysis
### System Performance under Different Content Placement Schemes  
For content placement strategies, we compared the performance of the proposed CECP scheme to that of random scheme and Most Popular Content (MPC) scheme under different SFs (\(\gamma\)) of popularity of files. Network performance in terms of backhaul load and normalized time delay is shown in Figures 2 and 3 respectively.  
When \(\gamma=0.2\), the backhaul load of the proposed CPCE scheme is two-times less than that of the MPC scheme and the random scheme. As the shape factor increases, the backhaul load and time delay decrease sharply for both the proposed CECP scheme and the MPC scheme. This is because as the shape factor increases, popular files tend to be prone to fewer files, thus more gain can be obtained by selecting proper caching strategies. When \(\gamma\) becomes larger and larger (\(\gamma>0.2\) in the simulation), the backhaul load and time delay of the CPCE scheme are comparable to that of the MPC, being about more than 13 times smaller than the Random scheme as shown in Figures 2 and 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Plane of Topology & \(1.5\times 1.5\) km\({}^{2}\) \\ Number of MBSs & 7 \\ Number of SBSs & 40 \\ Number of UEs & 50–200 \\ Channel Model & WINNER \\ Transmit Power of MBS & 40 W \\ Transmit Power of SBS & 2 W \\ Number of Available RB & 100 \\ Total Number of Files & 20 \\ Backhaul Capacity of MBS & 1 Gbps \\ Backhaul Capacity of SBS & 100 Mbps \\ Maximal Number of Caching Files on each BS & 10 \\ \(U_{\text{back}}\) & 10 Mbps \\ \(N\) & 3 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters setting.
## 3 Results and Discussion
### Failure Loads  
The loads at first crack \(\mathrm{P_{cr}}\), beam longitudinal reinforcement bars yield \(\mathrm{P_{y}}\), and the failure load \(\mathrm{P_{u}}\) are summarized for all the examined RC beams in Table 4. To assess the effectiveness of the different types of the adhesive material paste, the maximum loads for the tested beams were recorded and compared, as shown in Figure 9. The failure loads of beams B1-0-GPP, B2-0.60-GPP, and B3-1.2-GPP were greater than control beam B0-Control by approximately 20.80%, 25.60%, and 31.40%, respectively. The results showed that increasing the SGF ratio in the GPP had a clear effect on raising the capacity of the beam due to the enhanced interfacial bond shear strength, as illustrated in Figure 3.  
The failure loads of beams B4-0-EP, B5-0.6-EP, and B6-1.2-EP were greater than the control beam B0-Control by approximately 16.90%, 26.90%, and 26.10%, respectively. The results showed that the GPP as the adhesive material paste had a great effect on increasing the failure load capacity of the tested beams. Compared with the costs of the bonding materials EP and GPP, we found that bonding material GPP is much less expensive than EP. The GPP is a novel material to increase the bond between the GFRP laminates and the concrete surface. The results showed that the use of GFRP laminates increases the flexural capacity of RC beams [53].
## 3 Results and Discussion
### Spectral Characteristics Analysis of the Sensor Data  
Through analysis of the raw data of the left and right front wheels on the cement ground, after performing FFT transformation, power spectrum transformation, and low-pass filtering, the analysis data graphs shown in Figures 8 and 9 were obtained.  
Figure 7: Fitting circle diagram of the driving track on the subsiding paddy field.  
It can be seen from Figures 8 and 9, that there were large fluctuations in the steering angles of the left and right front wheels (close to 2\({}^{\circ}\)). This deviation is similar to the free rotation angle of the front wheels, which is mainly related to the vibration of the machinery, the movement of structural parts, and the vibration due to an uneven ground.  
When we performed FFT and power spectrum transformation, the maximum power points corresponding to the oscillation frequencies of the left and right steering wheels  
Figure 8: Analysis data of the left front wheel steering angle in mode 1.  
Figure 9: Analysis data for the right front wheel steering angle in mode 1.  
were found to be around 0.2 Hz. As such, the characteristic value was extracted, and the frequency data were filtered.  
Next, we analyzed the remaining 11 modes for the cement ground and subsiding paddy field. The obtained analysis data are provided in Tables 1 and 2.  
By analyzing the data in Tables 1 and 2, the peak frequency points of the power spectra of the left and right front wheels were found to be maintained at about 0.2 Hz under different speeds, ground environments, and steering angles. The \(z\)-axis angular velocity appeared under different environments; however, there were certain peak frequency points in the power spectrum, while the power spectra of the \(x\) and \(y\) axial angular velocity data presented no characteristic peak power frequency points. The peak frequency of the acceleration data power spectrum was generally close to 0 Hz and the fluctuation range was small, reflecting the nature of the machinery's inherent vibration. Similarly, the power spectrum frequency of the attitude sensor heading angle data was also close to 0 Hz.  
Characteristic differences in the attitude signal were determined through spectral analysis, indicating whether there was a peak frequency point in the power spectrum. Based on this, the BP neural network equivalent steering angle prediction input factors were further extracted and verified.