esa-sceva/satcom-chunk-collection huggingface dataset:
# Geovisualization Techniques of Spatial Environmental Data Using Different Visualization Tools
## Materials and Methods
### Data Mining and Geoprocessing  
The implementation can be divided into three separate parts (Figure 2). During the data collection phase, we performed laboratory measurements on the samples collected from 40 wells in Barand settlement. This was followed by the geoinformatics processing of the data, which was performed using QGIS version 3.12.12. Since the water chemistry properties of the wells were recorded in table format with coordinates, it was therefore necessary to import them into a geoinformatics environment. After converting the data into a.shp file and defining the appropriate symbols, we exported them into a KML file.  
From the water chemistry properties, we created a regional distribution map using kriging interpolation which was saved as a raster file in GeoTiff format. In earth environmental sciences, interpolation can be considered as primary form of geostatistical analysis, which is often used to predict values in unobserved locations based on the values in observed locations. Ordinary kriging, a type of interpolation, has become a widely used interpolation method for assessment of spatial variation of groundwater quality [56; 57; 58; 59; 60; 61]. From this raster file, we created an overlapping map and placed a map legend on it using Screen Overlay. The last phase of the work was running QGIS visualization modules and the inset of Google maps and KML management functions into the website. The mash-up site was created using five different technologies. The visualization of the map and geographical data was implemented using Google Maps Avi V3 and KML tools. In order to create the website, we used the Bootstrap front-end framework created by the developer of Twitter and the interactive interface of Sufee Admin. The Bootstrap is primarily based on HTML5 and CSS3 technologies. In order to use the full version jQuery is required, jQuery is a popular framework which emphasizes the connection between the HTML code and client-side JavaScript. The aim of jQuery is to detach the JavaScript code from the HTML as much as possible and to communicate with the HTML items of the webpage through various event controllers and identifiers.
## 5 Conclusions  
This paper proposes a method for GIC detection in neutral currents of autotransformers based on CWT and CNNs. Although similar research has been performed in the past, this paper is the first to propose an analysis of data of currents in autotransformer neutrals from operating substations in northwestern Russia, based on the binary classification of current scalograms. To train the CNN model, a dataset containing 800 scalogram images of two classes, GICs and geomagnetically quiet hours, was created. As a result of comparing the performance of four CNNs with different architectures, a model that showed high GIC classification performance on the validation set (100.00% accuracy and binary cross-entropy loss of 0.0115) was chosen. The proposed CNN model, in addition to the main layers, includes pre-processing layers and a dropout layer. During further research, it is planned to analyze 12 years of data from the GIC monitoring system using the proposed CNN-based classifier to identify the line sections that are most affected by geomagnetic disturbances, as well as to develop measures to protect equipment from the GIC influence. To achieve the latter goal, it is planned to create and implement a program for detecting GICs in the neutrals of autotransformers in real time at 330 kV substations in the Murmansk region. This program will help substation personnel monitor the situation and take timely measures to protect equipment.  
Conceptualization, T.A. and V.S.; methodology, T.A. and V.S.; software, T.A.; formal analysis, T.A.; investigation, T.A. and V.S.; writing--original draft preparation, T.A.; writing--review and editing, V.S.; visualization, T.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Science Foundation, grant number 22-29-00413.  
Not applicable.  
The raw GIC data may be accessed at [[http://gic.en51.ru/](http://gic.en51.ru/)]([http://gic.en51.ru/](http://gic.en51.ru/)) (accessed on 17 January 2024). The dataset created and analyzed during the current study is available on request from the corresponding author.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Research of Security Routing Protocol for UAV Communication Network Based on AODV
## 2 Security AODV Routing Protocol
### The Process of Route Discovery
#### 2.4.2 Processing RREQ Message  
After a node receives the RREQ message, it first determines whether it has received the message in the most recent time. If it has received, the message is discarded; otherwise, the signature and hops in the message are verified. Only when the verification is correct, the reverse path corresponding to the message will be stored.  
If the RREQ message is in the double-signature extended format, the node stores the RREP signature field in the RREQ message while storing the reverse path; otherwise, the field is not stored.  
The node then determines whether it is the destination node. If it is, a RREP message response is generated. If it is not, but the node meets the conditions for the intermediate node to respond to the RREQ message, and has the corresponding RREP signature field, the node generates a RREP double-signature extended message to respond to the RREQ message. Otherwise, the node performs a hash operation on the hop number field of the RREQ message and broadcasts it continuously.
## 5 Performance Analysis and Simulation
### Analysis of Communication Overhead
#### 5.2.3 Comparison of the Total Communication Overhead  
The total communication overhead is the sum of the communication overheads consumed in the PN determination phase and the routing phase. The total communication overhead of each scheme is shown in Table 3. It can be seen from Table 3 that when M = 1,  
\[\text{Hop}_{\text{EPUSBRFsum}}>\text{Hop}_{\text{RPBMPsum}}>\text{Hop}_{\text{ RDPSsum}}=\text{Hop}_{\text{NRDPSsum}}>\text{Hop}_{\text{RSPSsum}}=\text{Hop}_{\text{NRSPSsum}} \tag{35}\]  
Therefore, our proposed RSPS and NRSPS schemes have the lowest total communication overhead among the seven schemes because no communication overhead is generated in the PN determination phase. The total communication overhead of our proposed NRDPS and RDPS schemes is higher than that of the RSPS and NCPSP schemes, due to the actual use of dual PNs. The EPUSBRF scheme has the largest total communication overhead because the communication overhead increases exponentially, due to the flooding in the PN determination phase. Therefore, it can be seen that the total communication overhead of our proposed schemes is lower than that of the EPUSBRF and RPBMP schemes.
# Interference Challenges and Management in B5G Network Design: A Comprehensive Review
## 4 Interference in B5G Networks  
This section reviews the multiple interference impacts in HetNet, D2D, UDNs, and UAVs. It illustrates the outcomes of the latest studies to explain different types of interferences associated with each method. Figure 3 illustrates the interference in B5G networks.  
Figure 2: The architectural advancements in B5G networks.
## 3 Results and Discussion
In Figure 8, it has been indicated that the average Nusselt number changes with the Rayleigh number for different Peclet numbers in the case that the heat source is close to down-stream, and that this change is linear. When the Peclet number is high, one sees that heat transfer decreases, and that forced convection becomes dominant. There is little in the way of change in the average Nusselt number due to the fact that conductive heat transfer is dominant when the Rayleigh number is low. The Nusselt numbers are very close to each other when the Rayleigh numbers are low, whereby the conductive heat transfer is dominant. The increase in the Rayleigh number leads to an increase in the maximum and minimum flow function values, especially by means of creating more centers of rotation around the heat source. This thus ensures the increase in the average Nusselt number by increasing the conductive heat transfer. Since the increase in Peclet number means an increase in the Reynolds number (i.e., the forced convection parameter (\(Pe=Re~{}Pr\))), the conductive heat transfer becomes dominant. Moreover, while the value of Nusselt number is high for \(Pe=5\), it is low for \(Pe=0.5\). Figure 7: Variation of local Nusselt number along the channel (\(c=0.66\)), (**a**) \(Pe=0.5\), (**b**) \(Pe=1\), (**c**) \(Pe=5\). In Figure 9, it is indicated that the average number of Nu changes with the Ra number for different Pe numbers in the case that the heat source is close to the up-stream. The average Nusselt number increases almost linearly as the Rayleigh number increases. A nearly linear increase has been observed due to the conductive heat transfer being dominant in the low \(Ra\) numbers.
# Bacterial Aerosol in Ambient Air--A Review Study
## 3 Discussion
### Interactions with Meteorological Factors
#### 3.2.3 Solar Radiation  
Solar radiation appears to have a variable effect on bacterial concentrations, although the available data on this relationship are limited (Table 1). Some studies indicate a negative correlation between solar radiation and airborne bacteria. For instance, a correlation coefficient of \(-0.489\) (\(p<0.05\)) suggests that higher levels of solar radiation may be associated with lower bacterial concentrations. However, this effect is not consistently observed across studies. One study reports a positive correlation of 0.673 (\(p<0.05\)), implying that increased solar radiation could potentially lead to higher bacterial levels [69].  
Seasonal variations also influence the impact of solar radiation on bacterial concentrations. Data from the spring indicate a negative correlation of \(-0.329\) (\(p<0.05\)), while winter shows a more pronounced negative correlation of \(-0.603\) (\(p<0.05\)) [4]. These findings suggest that solar radiation may exert a stronger influence during these seasons, though the exact nature of this effect remains unclear.  
In a previous study conducted in southern Poland, we observed a significant reduction in bacterial aerosol concentrations on days with peak temperatures [94]. This decrease may be attributable to the combined effects of elevated temperatures and, more importantly, high levels of solar radiation. This result aligns with the existing literature, which suggests that increased temperatures and strong solar radiation can lead to lower levels of outdoor bacterial aerosols [95; 96].  
While some studies propose that solar radiation may affect airborne bacterial concentrations, the evidence remains limited and inconsistent. Further research is necessary to clarify the precise relationship between solar radiation and airborne bacteria, as well as to elucidate the underlying mechanisms governing this interaction.
# Robust Feature Selection-Based Speech Emotion Classification Using Deep Transfer Learning
## 6 Conclusions and Future Work  
In this work, a deep transfer learning approach with a feature selection algorithm that can select relevant features for speech emotion classification from a pool of extracted features through a deep convolutional neural network has been presented. It is obvious after a series of experiments have been carried out that the proposed method has improved accuracy and reduced the rate of misclassification as well as computational complexity using state-of-the-art emotional speech corpora. In achieving an optimum performance, the mel-spectrogram was extracted from raw speech signals through preprocessing and FFT. Thereafter, the mel-spectrogram was reshaped to match the requirement of the DCNN model for feature extraction. NCA feature selection was then applied in selecting the most salient and discriminating features that are relevant to emotion classification while reducing the workload of the classifier. With few hyperparameter tunning, our model achieved significant improvement in accuracy compared to existing methods as we carried out our experiment on the TESS, EMO-DB and combined (TESS+EMO-DB) dataset using two classifiers (MLP and SVM). Overall accuracies of 97.20%, 94.82% and 95.77% were obtained after we examined our method on TESS, EMO-DB and TESS+EMO-DB. The work has indicated that the proposed model is efficient for speech emotion classification. However, future experiments can be conducted using the cross-language speech corpus and testing with other classifiers to see their performance. We are also looking forward to adopting LSTM as an additional layer to our pretrained neural network and utilizing a multiple feature selection algorithm for an improved performance.  
Conceptualization, S.A. and S.V.; Methodology, S.A.; Software, S.A.; Validation, S.V.; Formal analysis, S.V.; Investigation, S.A.; Resources, S.V.; Data curation, S.A.; Writing--original draft preparation, S.A.; Writing--review and editing, S.V. All authors have read and agreed to the published version of the manuscript.  
This research did not receive any funding.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Reference** & **Methods** & **Reported Accuracy** \\ \hline TESS & (2018) [41] & DNN-GRU & 89.96\% \\  ” & (2019) [42] & DNN-LSTM & 70.00\% \\  ” & (2021) [2] & IMF-SVM, KNN & 93.30\% \\  ” & (2022) [30] & LSTM-FCNN & 86.02\% \\  ” & **Proposed** & **DCNN-NCA-MLP** & **96.10**\% \\ \hline EMO-DB & (2019) [43] & DCNN & 80.79\% \\  ” & (2020) [44] & CNN-BiLSTM & 85.50\% \\  ” & (2020) [22] & CNN & 89.02\% \\  ” & (2021) [31] & DCNN-LSTM-Attention & 87.86\% \\  ” & (2021) [20] & CNN-Attention & 93.00\% \\  ” & (2021) [45] & LSTM-CNN & 93.34\% \\  ” & **Proposed** & **DCNN-NCA-MLP** & **94.90**\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance Evaluation.
# 25 Years of Bluetooth Technology
## 2 Bluetooth Technologies
### BLE Beacons  
In 2013, Apple introduced a new technology, called iBeacon. iBeacon facilitates internal positioning of devices. iBeacons can be used by Android systems. A device can get push notification from nearby iBeacon devices. iBeacon works with Bluetooth Low Energy (BLE), also known as Bluetooth 4.0 or intelligent Bluetooth, and can enable us to determine our position in a store. The iBeacon system is capable of transmitting announcements regarding items that may be useful to us in the store.  
The Internet of Things (IoT) refers to uniquely identifiable objects and their virtual representations in an Internet-like structure [9]. iBeacon uses the low energy Bluetooth technology that can register the nearness of an object and send an unambiguous universal identifier token from a compatible application to transform it into a physical location. The beacons come in different formats, including small coin cell powered devices, flash drives, and software in embedded devices such smart-bulbs, and so on.  
BLE beacon uses can be classified into one of the following categories [10]:  
* Static Point of Interest (POI): Static POI refers to initiating an action when a user enters a specific physical area. For example, the location of the POI could map to a front reception desk or an elevator, etc. Once an individual is at or near a specific location, determining the position or direction of the user allows enhancing the user experience. For instance, a hotel could deploy BLE beacons and build applications for customers that offer the ability to unlock room doors by simply being in close proximity to the door.
* Indoor mapping: Many companies attempted to resolve the indoor geo-location issue and BLE beacons could be a potential solution. By creating applications and installing BLE beacons in big shopping centers, customers can steer through retail shops easily. This added value to customers will encourage them to keep the application open and the Bluetooth radio turned on.
* Two-way proximity: This allows the user device to act as the beacon. An action or event can be triggered on one device that sends the user's ID and micro-location to a peer or administrator application, enabling the capability of locating other people such as friends, etc.
* Analytics: There are many prospects for marketers to collect valuable insights from BLE beacons: getting information such as who entered a specific region, at what time and for how long. Getting this information can provide insight about a specific location. Marketers can use this data to constantly customize offerings in order to instill confidence in their customers [10].
## 4 FPGA-Based Hardware and Software Architecture Design  
This paper is based on the Vivado 2018.3 development environment on the ACX720 development board using Xilinx's XC7a35 tfgg484-2 as the FPGA master chip. The quaternion attitude data are outputs from the star sensor, which is simulated to produce a dynamic star map in the field of view of the star sensor after the attitude data are solved by the dynamic star simulator.  
The Dynamic Star Simulator chart display algorithm initializes each module, waits for the attitude quaternion data to be sent by the star sensor, and calculates the spatial right-angled coordinate direction vector of the optical axis pointing expressed as a quaternion through the attitude transformation. The optical axis pointing is converted from a spatial right-angle coordinate direction vector to a celestial sphere coordinate direction vector to confirm the target subarea within the field of view.  
After that, the FPGA reads the preprocessed star catalog data, converts the celestial coordinates of the star point into spatial Cartesian coordinates, and calculates the coordinate angle between the vector direction of the optical axis and the vector direction of the star point. If this star point is judged to be a valid star point, then the star point's star sensor coordinates to the two-dimensional plane rectangular coordinates of the transformation; otherwise, it is judged to be an invalid star point; continue to read the next star point; and finally, all the valid star points together to form a star map. The flow chart of the dynamic star chart display algorithm is shown in Figure 3.  
The hardware architecture of the dynamic star simulator is mainly composed of a communication module, a computation module, and a storage module [17]. The modular design can process the computed star map in a flow, as shown in Figure 4, which shows the FPGA data flow structure.  
The schematic diagram of using the serial computation method on a conventional computer platform is shown in Figure 5. It is necessary to carry out all the steps in the calculation to complete it, and then rerun the next calculation. The advantage of the serial calculation method is that the structure is relatively simple compared to the parallel  
In executing the computational star map algorithm, four computational modules are available in parallel on the FPGA platform. The optimized results obtained are shown in Table 1. No. 5.1 is a simulation of waveforms for optical axis pointing calculations. No. 5.3 is a simulation of waveforms for star field search range calculation. No. 5.5 is a simulation of waveforms for star point coordinate transformation calculation. No. 5.6 simulation waveform of star point coordinate transformation.
## 5 Control Performance Evaluation
### Seismic-Induced Vibration  
According to the guideline of FEMA P-695 [46], 44 far-field, 28 near-field pulse and 28 no-pulse ground motion records are investigated here, and the peak acceleration is all set to 0.3 g. Statistical results of the structure responses are given in Figures 11 and 12. Figure 11 shows the root mean square (RMS) of the structure relative displacement \(x_{1}\) and absolute acceleration responses \(\ddot{x}_{g}+\ddot{x}_{1}\). As seen, the structural responses can be effectively attenuated by increasing the length ratio \(\alpha\) and the mass ratio \(\mu\) under all types of ground motion excitation. A comparison case for these three dampers is given in Figure 12, where the length ratio \(\alpha\) and the mass ratio \(\mu\) are set to \(\mu=0.02\) and \(\alpha=3\). As seen, the TLIMD consistently provides the minimal values.  
Here, three typical ground motions, given in Figure 13, is selected in the following discussion, and two types of structures (i.e., the long and short period structures, \(T_{s}=0.5\) and 2 s) are considered. For both the short (given in Figure 14) and long period (given in Figure 15) structures, the TLIMD exhibits a noticeably better control performance than the TID. However, for the long period structure under the adjacent-field pulse ground motion, the TLIMD is not significantly better than the TMDI, as demonstrated in Figure 15c,f.  
Figure 10: Maximum amplitude ratios of primary structure between deviations and optimization for (a) TLID, (b) TID and (c) TMDI, and (d) a 3D plot for maximum amplitude comparison between them.  
Figure 11: Root mean square (RMS) value of the TLIMD equipped structure (**a–c**) relative displacement and (**d–f**) absolute acceleration responses. \(T_{s}\) is the natural period of the host structure.  
Figure 12: Root mean square (RMS) value of the damper equipped structure (**a–c**) relative displacement and (**d–f**) absolute acceleration responses, with \(\mu=0.02\) and \(\alpha=3\).  
Figure 14: The damper equipped short period (\(T_{s}\) = 0.5) structure (**a–c**) relative displacement and (**d–f**) absolute acceleration responses.  
Figure 13: Three typical ground motion acceleration records.  
Figure 15: The damper equipped short period (\(T_{s}\) = 2) structure (**a–c**) relative displacement and (**d–f**) absolute acceleration responses.
# Spatial Assessment and Prediction of Urbanization in Maseru Using Earth Observation Data
## 3 Results
### Urban Growth Prediction for 2050  
We used the spatial variables (Figure 4) and LULC maps for 1988 and 2019 to predict changes in 2050, accounting for the 1988 and 2019 temporal differences of 31 years in the input LULC. Our results show an increase in the built-up area covering more than half of the study area (Figure 5).  
The other LULC classes, such as grassland, agricultural land, water body, and woody vegetation, were projected to decrease in their coverage area in 2050 (Table 6). The highest rate of decrease was expected for grassland (\(-\)35.8%), followed by bare soil (\(-\)33.6%), agricultural field (\(-\)22.3%), water body (\(-\)2.2%) and woody vegetation (\(-\)0.7%) as shown in Table 6. The built-up area was expected to increase by 5434.5 ha in 2050, from 11,973.2 ha in 2019 to 17,407.7 ha in 2050 (Table 6). The smallest change was expected for the water body class, with an expected decrease from 1506.1 ha in 2019 to 1495.9 ha in 2050 (Table 6). The expected LULC coverage changes for the year 2050 are presented in Table 6.  
Figure 4: Spatial variables used in the prediction of urban growth. The spatial variables are for: (**a**) Slope, (**b**) Road, (**c**) Water body and (**d**) Built-up.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value (\%)** \\ \hline Kappa (histogram) & 88 \\ Kappa (location) & 89 \\ Kappa (overall) & 84 \\ Percentage (\%) of correctness & 87 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Validation of the Predicted model using Kappa. 2019 LULC map was simulated using 2008 and 2013 classified images.