esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country
## 2 Prerequisites for Using the UAVs in Some Spheres of Economy
### Precision Agriculture (PA)
Internationally, the expected volume of the market for agricultural drones will be USD 4.8 billion by 2024, and the USA will continue keeping the main market share [24]. The  
Figure 3: Periods of agrotechnical activities of precision farming and possible use of UAVs. Source: Generated by the authors. main sources of growth will be: increasing venture capital financing, awareness of the benefits of agricultural UAVs, and software solutions for field research and data analysis. By extrapolating this amount by the size of the population and the income level in Kazakhstan, it is possible to assume the formation of the agricultural UAV market of several tens of millions of dollars. The advantage of application of precision agriculture models for the main crops of Kazakhstan can be estimated based on the data [25; 26]. Table 1 shows generalized data on the current yield of the main crops grown in the Republic of Kazakhstan in comparison with the expected yield. According to [26], the introduction of precision agriculture leads to yield improvements from 42 to 80% for different crops. The prerequisites of employing IUAVT is low productivity in the field and growing demand for the organic products [1]. With the widespread integration of precise agriculture methods in the Republic of Kazakhstan, the expected profit increased is USD 5.4 billion. Here, 6% or USD 324 million, according to data [27], will be associated with the application of agricultural drones, without taking into account market the features of grain sales. To implement the precision agriculture systems, it is necessary to develop the models and methods of decision support systems using machine learning algorithms which are based on processing the heterogeneous data obtained with the use of UAVs. It is needed to develop models and methods of decision support using machine learning based on processing the heterogeneous data obtained with the use of UAVs, and to develop methods for UAV group control to perform plant protection and fertilization tasks. Another issue is solving the computer vision problems for processing multi-spectral images from low-flying platforms (see Appendix A Table 10).
# A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT
## 5 Privacy Implications of Intrusion Detection Systems  
Intrusion detection systems are widely used to protect devices and end-users from malicious actors. The performance of these systems often depends on the type of data and architectural setup they use while preventing the attacker from misusing personal information of victims. The usage of data for the intrusion detection introduces the challenges of security and privacy. Therefore, we discuss the privacy implications of intrusion detection systems in this section.
# Unity Power Factor Operation in Microgrid Applications Using Fuzzy Type 2 Nested Controllers
## 5 Simulations and Experimental Results  
### Simulation Results  
The MATLAB/SimPowerSystems Toolbox 2021a tests and simulates the suggested single-phase grid connected to the MG system with a near unity power factor. Figure 1 was implemented and tested to verify the robustness of the proposed control. The proposed nearly unity power factor grid connected to the MG system comprises a line filter, single-phase uncontrolled rectifier, and boost dc-dc converter. The DC-DC boost converter shown in Figure 3 was controlled using Fuzzy type 2 controllers. Two cascade control loops were designed. The inner controller is the current, and the outer control is the DC link voltage. Both controllers are PI-Fuzzy type 2. Two test cases were achieved. The gains of the autotuned PI controllers for the voltage and current loops were \(K_{\text{P}}\) = 0.361 and \(K_{\text{i}}\) = 1.452, respectively, for the voltage loop, \(K_{\text{P}}\) = 11.325 and \(K_{\text{i}}\) = 119.732 for the current loop.  
When an error occurs, the PI controller produces a dc value. A sawtooth signal is compared to the dc signal. The combined pulse width modulation of these two signals is then fed into the transistor driving circuit, which is tailored to the specifics of that circuit.  
The system was tested against nonlinear loads such as inverters, which adversely affect the system's power factor. In Case (1), the system given in Figure 2 was tested with a step change in the DC link voltage. Auto-tune PI controllers (conventional), Fuzzy type 1, and the suggested Fuzzy type 2 regulate the dc boost chopper. The source voltage and current for the three-control techniques and their respective source current spectrum are given. In Case (2), the system proposed in Figure 2 was tested against step change in the AC load (load increase and decrease). The DC link voltage, the source current, and the source current spectrums are shown. The proposed controllers were compared with Fuzzy type 1 and conventional controllers. The proposed Fuzzy type 2 controllers are advantageous compared to the other two techniques.
# Detection Model on Fatigue Driving Behaviors Based on the Operating Parameters of Freight Vehicles
###### Abstract  
Whether in developing or developed countries, traffic accidents caused by freight vehicles are responsible for more than 10% of deaths of all traffic accidents. Fatigue driving is one of the main causes of freight vehicle accidents. Existing fatigue driving studies mostly use vehicle operating data from experiments or simulation data, exposing certain drawbacks in the validity and reliability of the models used. This study collected a large quantity of real driving data to extract sample data under different fatigue degrees. The parameters of vehicle operating data were selected based on significant driver fatigue degrees. The \(k\)-nearest neighbor algorithm was used to establish the detection model of fatigue driving behaviors, taking into account influence of the number of training samples and other parameters in the accuracy of fatigue driving behavior detection. With the collected operating data of 50 freight vehicles in the past month, the fatigue driving behavior detection models based on the \(k\)-nearest neighbor algorithm and the commonly used BP neural network proposed in this paper were tested, respectively. The analysis results showed that the accuracy of both models are 75.9%, but the fatigue driving detection model based on the \(k\)-nearest neighbor algorithm is more reliable.  
freight vehicles; fatigue driving behavior detection; \(k\)-nearest neighbor algorithm; BP neural network +
Footnote †: journal: Applied Sciences
# Applied Engineering Using Schumann Resonance for Earthquakes Monitoring
###### Abstract  
For populations that may be affected, the risks of earthquakes and tsunamis are a major concern worldwide. Therefore, early detection of an event of this type in good time is of the highest priority. The observatories that are capable of detecting Extremely Low Frequency (ELF) waves (<300 Hz) today represent a breakthrough in the early detection and study of such phenomena. In this work, all earthquakes with tsunami associated in history and all existing ELF wave observatories currently located worldwide are represented. It was also noticed how the southern hemisphere lacks coverage in this matter. In this work, the most suitable locations are proposed to cover these geographical areas. Also, ELF data processed obtained from the observatory of the University of Almeria in Calar Alto, Spain are shown. This is a contribution to help protect against natural disasters such as those caused by earthquakes and tsunamis.  
ELF wave; Schumann resonance; ELF sensor; earthquakes alerts; worldwide Article
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 3 Road Extraction Based on Different Data Sources
### Road Extraction Based on SAR Images
#### 3.3.1 Main Methods
Interferometric information has been used in road extraction by [PERSON] et al. [210], and the best results were obtained by the fusion of intensity and coherence information. Roads were considered as distributed scatterers and were separated from permanent and temporally variable scatterers. A constant false-alarm-rate line detector based on [PERSON] test statistics has been proposed by [PERSON] et al. [211] for polarimetric SAR images. Deep learning-based methods have been the topic of a few recent studies; they can be used to extract initial road information with good quality for network construction. [PERSON] [158] compared U-Net (FCN) and CNN to machine learning methods using dual-polarisation Sentinel-1 data; he reported that VV polarisation was better than VH, and dual-polarisation data was better than the single-polarisation data for road extraction. F1 score of 94% was achieved (the same area for training and testing but different shuffled samples were used) using the U-Net and dual-polarisation data. This F1 score was better than that of the best machine learning method (random forest) by 5%. [PERSON] [46] enhanced the fully CNN (FCNN) sensitivity for thin objects and compared the FCN-8s, U-Net and Deeplabv3+ methods for road segmentation. The sensitivity was increased by addressing class imbalance in training and using spatial tolerance rules. A summary of road extraction using SAR images is shown in Table 6. It can be seen that the precision in diverse situations is quite different. Appropriate methods or a combination of multiple methods should be considered according to the scene's characteristics.
# Regeneration Performance of Activated Carbon for Desulfurization
## 4 Conclusions  
The effect of SO\({}_{2}\) adsorption efficiency was experimentally studied with different carbon dosages, flue gas flow rates, and oxygen contents. Then, the characterization and desulfurization efficiency of AC was observed under the current optimum adsorption conditions for a number of 10 heated regeneration cycles. During the thermal regenerations, micropores became disrupted, and some of  
Figure 8: Sulfur capacity of the regenerated adsorbents, where 0–10 stands for number of cycles.  
Figure 7: SO\({}_{2}\) desulfurization efficiency of the regenerated adsorbents, where 0–10 stands for number of cycles.  
them became blocked by sulfate particles on the AC surface. However, owing to sulfuric acid etching, the pore widths in the range of microporous to mesoporous increased and some new micropores were produced. Therefore, the specific surface area and pore volume of AC were slightly increased. The highest desulfurization efficiency of fresh AC was 98.7%. The highest desulfurization efficiency was 95.4% after ten cycles. The results show that AC has a high SO\({}_{2}\) adsorption efficiency and the performance of desulfurization is only slightly decreased after ten thermal regeneration cycles, so it can be concluded that AC is a suitable material for multiple adsorption-desulfurization cycles.  
Conceptualization, Z.S.; methodology, M.W.; validation, Z.S.; investigation, J.F. and Y.Z.; resources, Z.S.; writing--original draft preparation, M.W.; writing--review and editing, Z.S. and L.Z.; project administration, Z.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (No. 21806101), Natural Science Foundation of Shanghai (No.16 ZR1412600), Research Center of Resource Recycling Science and Engineering, Shanghai Polytechnic University and Gaoyuan Discipline of Shanghai-Environmental Science and Engineering (Resource Recycling Science and Engineering), Cultivate discipline fund of Shanghai Polytechnic University (No.XXKPY1601). The authors declare no conflict of interest.
## 2 Proposed System
### Sensors and Embedded System Hardware
#### 2.2.3 Thermal Camera  
In this work, the AMG8833 thermal camera, also called \"Grid-EYE\" [49], is used to measure the leaf temperature. It is a low-cost infrared array sensor with a resolution of 8 \(\times\) 8 pixels. The pixel value represents the temperature of the detected object at the point, which is measured by the infrared array sensor. Therefore, the thermal camera is not affected by the ambient lighting. It only measures the temperature of the object. This sensor is easily interfaced with the embedded system via the I\({}^{2}\)C protocol. The AMG8833 specifications are given in Table 5.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Temperature** & **Moisture** & **EC** & **pH** & **N** & **P** & **K** \\ \hline Operating range & \(-\)40 to 80 \({}^{\circ}\)C & 0–100\% & 0–10,000 \(\mu\)S/cm & 3–9 & & 0–1999 mg/kg \\ \hline Resolution & 0.1 \({}^{\circ}\)C & 0.1\% & 10 us/cm & 0.01 & & 1 mg/kg \\ \hline Accuracy & \(\pm\)0.5 \({}^{\circ}\)C &
\begin{tabular}{c} Within 0–53\% \(\pm\) 3\%; \\ within 53–100\% \(\pm\) 5\% \\ \end{tabular} & - & \(\pm\)0.3 & & \(\pm\)2\% FS \\ \hline Power supply & \multicolumn{6}{c}{12-24 V DC} \\ \hline Interface & \multicolumn{6}{c}{RS485 Modbus protocol} \\ \hline Response time & \multicolumn{6}{c}{<1 s} \\ \hline Price & \multicolumn{6}{c}{US$ 267} \\ \hline \hline \end{tabular}
\end{table}
Table 4: JXCT specifications.  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Temperature** & **Humidity** \\ \hline Operating range & \(-\)40–80 \({}^{\circ}\)C & 0–100\% RH \\ \hline Resolution & 0.1 \({}^{\circ}\)C & 0.1\% RH \\ \hline Accuracy & \(\pm\)0.5 \({}^{\circ}\)C & \(\pm\)2\% RH \\ \hline Repeatability & \(\pm\)0.2 \({}^{\circ}\)C & \(\pm\)1\% RH \\ \hline Power supply range & 3.3–5.5 V DC \\ \hline Interface & \multicolumn{6}{c}{Digital signal–single wire} \\ \hline Average sensing time & 2 s \\ \hline Price & \multicolumn{6}{c}{US$ 2} \\ \hline \hline \end{tabular}
\end{table}
Table 3: DHT22 specifications.  
\begin{table}
\begin{tabular}{l c} \hline \hline Power supply & 3.3 V DC \\ \hline Temperature range & 0–80 \({}^{\circ}\)C \\ \hline Temperature accuracy & \(\pm\)2.5 \({}^{\circ}\)C \\ \hline Viewing angle & 60\({}^{\circ}\) \\ \hline Number of pixels & 8 \(\times\) 8 pixels \\ \hline Interface & I\({}^{2}\)C \\ \hline Frame rate & 10 fps \\ \hline Price & \multicolumn{6}{c}{US$ 41} \\ \hline \hline \end{tabular}
\end{table}
Table 5: AMG8833 specifications.
# Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network
## 3 Model Establishment
### Fault Feature Extraction
#### 3.3.1 Max Probability  
It reflects the maximum probability of the simultaneous occurrence of pixel \(A\) and pixel \(B\) in the gray level co-occurrence matrix. The equation is  
\[mp=\max_{i,j}(g(i,j)) \tag{12}\]
## 2 Internet of Vehicles and Smart Cities
### Main Challenges
#### 2.4.7 Security  
The tolerance to heterogeneity and interoperability in IoV systems, which is strictly needed to allow these thousands of different vehicles, sensors, and other components to communicate in the network, brings out a bigger need for data security. [PERSON] [4] states that vehicles operate in unprotected and vulnerable environments, with vulnerabilities in the cloud, V2V, and local communications. The security vulnerabilities in communications in V2X modes could open the possibility for cyber-attacks by manipulating data streams or connection points [11]. Another issue is the possibility of hacking vehicles, which could lead to accidents and fatalities. In 2015, hackers at the Black Hat hacking conference demonstrated how they hacked vehicles through their PCs, far away from the vehicle, and it should be seen as a threat to security [11]. There's also the problem of misbehaving vehicles that interfere in the exchange and dissemination of data in the network [33]. As such, researchers have proposed various ideas on how to reduce these vulnerabilities while fulfilling the security requirements of IoV and safeguarding the network and its participants. [PERSON] et al. [34] and [PERSON] [35] have listed the security requirements for IoV, which are summarized in Table 1.  
In SloV, with an even bigger need for data availability, connectivity, and autonomy of vehicles, there is a problem in maintaining security, especially, the privacy may be compromised due to secondhand data sharing. The new applications can also emphasize security vulnerabilities due to inherent communication issues or no control in data usage.
## 2 Results
### MCPIP1, but Not Its D141N or C306R Mutant, Inhibits Cervical Cancer Tumor Growth In Vivo  
To validate the anticancer effect of MCPIP1 on cervical cancer in vivo, we first established HeLa cell lines stably expressing the control, MCPIP1, D141N, or C306R (Figure 6A). The cell proliferation test results showed that compared to the control group, Dox-induced expression of MCPIP1, but not its mutants D141N or C306R, significantly reduced the cell growth rate (Figure 6B). Then, we injected four groups of HeLa cells subcutaneously into BALB/c nude mice. We induced the expression of MCPIP1 or its mutants in xenograft tumors by intraperitoneal injection of Dox when the tumors reached an average size of approximately 200 mm\({}^{3}\). The successful inducible expression of MCPIP1 and its mutants in HeLa tumors was verified by a Western blotting assay (Supplementary Figure S8). Consistent with the results of in vitro cell experiments, compared to the control group, the growth rate of Dox-induced MCPIP1-expressing xenograft tumors, but not that of the D141N or C306R group, was significantly slowed down (Figure 6C). Similarly, on average, the Dox-induced MCPIP1-expressing mice had smaller tumor volumes and weights than in the control, D141N, and C306R groups (Figure 6D,E). We further examined the proliferation and apoptosis in tumor slices. The expression of cell proliferation marker Kief7 was lower in the MCPIP1 group than in the control, D141N, and C306R groups (Figure 6F). However, the TUNEL staining assay showed that more apoptotic cells were observed in the MCPIP1 group than in the other three groups (Figure 6G). To sum up, our findings suggest that MCPIP1, but not its mutant D141N or C306R, inhibits tumor growth but promotes the apoptosis of cervical cancer in vivo.  
Figure 6: _Cont._  
Figure 6: MCPIP1 promotes cell apoptosis but impedes tumor growth of cervical cancer in vivo. (**A**) A Western blotting assay showed MCPIP1 protein levels in HeLa cells stably transduced with control, MCPIP1, D141N, or C306R. \(\beta\)-actin was used as a loading control. (**B**) A cell counting assay was performed to examine the proliferation of HeLa cells stably expressing control, MCPIP1, D141N, or C306R. (**C**–**E**) HeLa cells were subcutaneously injected into the nude mice (n = 6 per group). Twelve days after subcutaneous injection, all xenograft tumor models were intraperitoneally injected with doxycycline daily at 5 mg/kg to induce the expression of control, MCPIP1, D141N, or C306R. (**C**) Growth curve of xenografts in vivo. (**D**) A picture of xenografts isolated from indicated groups on day 24 after subcutaneous injection. (**E**) Tumor weights were measured in the indicated groups. (**F**,**G**) Contiguous tumor sections were stained with antibodies against the proliferation biomarker Kief7 (**F**) and TUNEL kit for apoptosis detection (**G**), and the nucleus was stained with 4′,6-diamidino-2-phenylindole (DAPI, blue); scale bar = 50 \(\mu\)m. ns: no significance, ***\(p\) < 0.01, ***\(p\) < 0.001.
## 3 Overview of Hyperspectral Imaging Spectroscopy (HSI)
### Hyperspectral Imaging and Chemometrics  
#### 3.3.1 Chemometrics in NIR Imaging Processing  
A hyperspectral image has essential hidden information that requires some data mining software to explore the details. Chemometrics relates measurements obtained from a product to its physical and chemical state using mathematical algorithms. In NIR spectroscopy, chemometrics is used primarily for spectral data pre-processing, developing calibrations for quantitative analysis, and model transfer [24]. However, imaging spectroscopy still combines spatial and spectra information and requires different data processing systems to explore the information embedded in the hypercube. NIR-HSI image processing involves clustering, which aims to create subgroups of the sample's information based on their similarity and classification using explanatory variables. Then, the regression model for quantitative prediction is developed.  
Figure 2: Hyperspectral image (hypercube).  
Pre-Processing  
Preprocessing is a typical data processing routine used to obtain clean data for further processing. This usually begins with determining the area of interest (ROI). The background should be removed using hyperspectral techniques when the sample does not cover all scanned areas. In addition, pre-processing is used to remove dead pixels and spiked points. Essentially, spectral preprocessing techniques are used to avoid the effects of random and systematic variations on spectral measurement [74]. The scattered correction method and spectral derivatives are the standard spectral preprocessing techniques used in spectroscopy [71]. To reduce multiplicative interferences, scatter correction methods such as multiplicative scatter correction (MSC), and standard normal variate (SNV) can be used. Spectral derivatives remove additive and multiplicative effects depending on the order.
# IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
## 5 Discussion
### Client Interface Design  
After the KF algorithm completes the prediction of the concentration of the pollutants at the next moment, RPi will send the prediction results and monitoring data to the cloud, which stores the data in the database and feedbacks results to the client. Users can view the current air quality and the latest 24-h AQI (air quality index) trend through the browser's access, as shown in Figure 12.  
Figure 12: Client web interface.
# Effect of Water Content and Cementation on the Shear Characteristics of Remolded Fault Gouge
## 4 Discussion
### Influence of Clay Content, Water Content, and Cementation on Shear Strength Parameters
#### 4.2.1 Cohesion with Variable Clay Content  
Figure 9 presents the relationship between water content and cohesion under different clay contents and cementation degrees. Both uncomented and cemented fault gouge samples show a similar trend: cohesion increases with water content initially, reaches a peak, and then declines as water content continues to rise. This pattern is consistent with the experimental results in Figure 6a.  
The water content at which peak cohesion occurs is affected by clay content. As shown in Figure 9a, for samples with clay content below 40%, the cohesion peaks at a water content of approximately 6-8%. In contrast, for samples with higher clay content (>40%), the peak occurs at around 20-25%. Figure 9b indicates that this trend also holds true for samples with different cementation degrees, regardless of clay content. While increasing  
Figure 8: SEM images of some fault gouge specimens: (**a**) C0W20; (**b**) C1W20; (**c**) C3W10; (**d**) C3W20; (**e**) C3W30. Note: Qtz represents quartz particles; Mnt represents montmorillonite particles; Kln represents kaolinite particles; Cs represents cementing substance.  
the cementation degree enhances the overall cohesion, it does not change the trend of cohesion with water content, nor the inflection point's dependence on clay content.  
This behavior is attributed to the effect of clay content on the liquid and plastic limits of the fault gouge [47]. Higher clay content corresponds to a higher capacity for water retention before reaching saturation. For low-clay-content samples, the liquid limit is relatively low, and the cohesion begins to decrease once particle surfaces become saturated and water starts acting as a lubricant [48,49]. For high-clay-content samples, the higher liquid limit allows for greater water absorption before cohesion begins to decline. Therefore, the peak cohesion point reflects the critical water content at which the material behavior transitions from brittle to plastic.