esa-sceva/satcom-chunk-collection huggingface dataset:
# IoT-Chain and Monitoring-Chain Using Multilevel Blockchain for IoT Security
## 1 Introduction  
The use of the Internet of Things (IoT) has been increasing, and it is applied to various services in numerous fields [1], such as healthcare, energy, and smart homes. The anticipated IoT device growth is also on the rise. According to Exploding Topics [2], there are over 700 million IoT devices installed worldwide. That number is expected to over 30 billion by 2025. As IoT devices in homes, industrial environments, transportation networks, and elsewhere continue to proliferate, so does the attack surface for malicious IoT network attackers.  
The IoT attack activity in 2020 dramatically surpassed the combined volume of IoT activity observed by IBM Security X-Force [3] in 2019. The IoT network is a centralized structure, and all IoT devices mainly use a structure that is linked to the central cloud, or data are stored in the cloud storage using fog and edge computing technologies. Owing to the centralized structure, the central server processes all data, and security vulnerabilities exist owing to the risk of data forgery and falsification, along with attacks on the central processing system, such as distributed denial of service (DDoS) [4] attacks. Depicted in Figure 1 is the centralization problem of existing IoT servers.  
**Vulnerabilities in centralized server architecture (1)**: Problems owing to IoT device performance limitations: The data measured by the sensor are transmitted to the server. Theperformance is worse than those of the existing PCs and servers, so there exists a limit [5] to applying existing security vulnerability solutions. Therefore, the Mirai botnet [6] may become a device that executes a server DDoS attack owing to malware infection through firmware updates.  
**Vulnerabilities in centralized server architecture (2)** Vulnerabilities owing to fog and edge nodes: These nodes receive real-time data from IoT devices and provide temporary data storage until the necessary data are sent to the cloud. If the fog node is subjected to a security attack [7], it is possible that IoT-generated data may be forged or tampered with. Furthermore, the edge node computes and processes data in edge computing close to the data collection source and sends the data to the cloud. It serves to transmit data. Edge nodes also exhibit a problem in that data are forged and stolen when subjected to a security attack [8].  
**Vulnerabilities in centralized server architecture (3)** Security weakness owing to centralized structure: The administrator is responsible for the performance and management of data stored in the central server. Data may be forged or falsified because of the authority of the administrator of the centralized server [9]. Moreover, owing to attacks by hackers and infected devices, the centralized server affects all systems if one server is attacked as a result of a single point of failure [10].
# Haystack Fires in Australia: Causes and Considerations for Preventative Management
## 5 Monitoring of Agricultural Parameters
### Near-Infrared Sensors
#### 5.1.3 Fire Sensors and Fire Detection Systems  
Sensors that are normally used to detect one or more of the fire products (namely, smoke, heat, infrared and/or ultraviolet light radiation, or gases) resulting from a fire event are known as fire sensors [89]. These sensors may be further categorized into the three main fire development stages (Table 7).  
Fire detection systems are basically developed as a complete package that involves both detection and action [89]. Therefore, several other devices are joined together to work as a whole. The overall system is more than just a sensor; typically, the sensor is connected to an alarm system which makes a loud sound when the sensor activates. Now, the working principle of the sensor is based on the simultaneous measurements of temperature, smoke and combustion products including oxygen (O\({}_{2}\)), carbon monoxide (CO), carbon dioxide (CO\({}_{2}\)), water vapor (H\({}_{2}\)O), hydrogen cyanide (HCN), acetylene (C\({}_{2}\)H\({}_{2}\)) and nitric oxide (NO). Fire detection algorithms utilise data from sensors to provide a potential fire alarm based on the increase in smoke and other fire products [90].
# Applying Deep Learning to Medical Imaging: A Review
## 2 Deep Learning Techniques in Medical Imaging  
Deep learning techniques in medical imaging can serve a wide array of functions, both in terms of the acquisition of medical images and the identification of pathologies within these images. Specifically, these techniques are leveraged not only to enhance the quality of images obtained through various modalities but also to enable effective and efficient identification of pathological markers within these images. For example, convolutional neural networks (CNNs) can be used in the reconstruction of images from MRI scanners, enhancing the resolution of the obtained images and thereby allowing for a clearer visualization of potential pathologies [53]. Moreover, CNNs are particularly adept at analyzing these images postacquisition, identifying key features within these images that could point toward specific pathologies [54]. This dual functionality--improving the acquisition of images and aiding in the identification of pathologies--is a key strength of deep learning techniques in the field of medical imaging. Throughout this section, we will discuss three major types of deep learning techniques used in medical imaging: convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). For each technique, we will detail its basic concepts, architecture and applications, role in image acquisition, and pathology detection, along with transfer learning approaches and the limitations and challenges faced.
## 3 Results
### Uncertainty Analysis under Different Water Saturation Conditions  
#### 3.1.1 \(\mathrm{FF_{new}}\)  
The sensitivity of water saturation in the new fluid factor (\(\mathrm{FF_{new}}\)) is analyzed as shown in Figure 1a. The \(\mathrm{FF_{new}}\) can distinguish the hydrocarbons accurately when the water saturation is less than 80%. The true prediction ratio (TPR) decreases with increasing water saturation. When the water saturation is 99%, the TPR is approximately 0.75. The performances of the \(\mathrm{FF_{new}}\) from 0% to 95% of water saturation do not differ significantly. The evaluation metrics of the \(\mathrm{FF_{new}}\) (the AUC, the precision, the recall, the accuracy and \(\mathrm{F_{1}}\)) are shown in Figure 1b. These metrics are approximately 0.98 when the water saturation is less than 80%. After that, they start to decrease. Their values are above 0.9, which indicates good performance when the water saturation is less than 90%. When the water saturation is higher than 90%, their values reduce rapidly. The ability of the hydrocarbon prediction is strong even for high water saturation, for example, 95%, which qualifies as good performance in the hydrocarbon prediction field. However, in another aspect, high water saturation is usually not the target in the oil and gas industry.  
The AUC, which indicates the performance of a binary classifier, is plotted in Figure 2. Its value is higher than 0.95 when the water saturation ranges from 0% to 95%; after that, the AUC decreases rapidly. The performance of the \(\mathrm{FF_{new}}\) is good when the water saturation of the reservoirs is less than 95%.  
Figure 1: (**a**) Receiver operating characteristic (ROC) of the new fluid factor (\(\mathrm{FF_{new}}\)). (**b**) Evaluation metrics of the \(\mathrm{FF_{new}}\) vary with water saturation.
# Backscatter Assisted NOMA-PLNC Based Wireless Networks
## 4 Outage Probability Analysis
### Downlink Outage Probability: A NOBC Link  
In the downlink, the near node first decodes the far node signal, and it will be removed then the near node decodes its own signal.  
Using (29) and (12), the end-to-end outage probability of the far node pair can be written as  
\[Pr_{out}^{E-to-E}(R)=Pr(min(min(\gamma_{a},\gamma_{b}),\gamma_{m}^{d})<\gamma _{q});\quad m\in(3,4), \tag{35}\]where \(\gamma_{q}=2^{3R}-1\), Let \(\gamma_{c}\ =\ min(\gamma_{a},\gamma_{b})\), then it can be further modified as  
\[Pr_{out}^{E-to-E}(R)=Pr(\gamma_{c}\ <\gamma_{q})\ +\ (1-Pr(\gamma_{c}\ <\gamma_{q}))Pr( \gamma_{m}^{d}\ <\gamma_{q}), \tag{36}\]  
where \(m\in(3,4)\). After simplifying, the end-to-end outage probability of the proposed wireless network with ABS can be determined as  
\[\begin{gathered} Pr_{out}^{E-to-E}(R)=1-\exp\frac{\gamma_{q}}{ \rho}(\eta_{a}+\eta_{b})\times\\ \left(e^{\frac{-\gamma_{q}}{\rho_{2}-\eta_{1}\gamma_{q}}}+\frac{ e^{\frac{-\gamma_{q}}{\rho_{2}-\eta_{1}\gamma_{q}}}}{(1-\beta)}\left(1-e^{ \frac{-\gamma_{q}}{\rho_{2}-\eta_{1}\gamma_{q}}(1-\beta)}\right)\right),\end{gathered} \tag{37}\]  
Proof.: See Appendix B.
## 6 Conclusions  
The application of accelerometric signal analysis to monitor the torque/speed equilibrium points of HEPS is not widely documented in the existing literature. In this study, a novel equilibrium point position accelerometric-signal-based monitoring technique for an aircraft HEPS was presented. The experimental tests were conducted in two different cases:  
1. The equilibrium point position was inside the \(\pm\)5% limits of the propeller torque curve;
2. The equilibrium point position was outside the \(\pm\)5% limits of the propeller torque curve.  
The three-axis accelerometric signals were combined, while time-features were calculated and filtered to discriminate the HEPS' operating mode. Using the Holdout method, training, validation, and testing datasets were obtained, standardized, and finally transformed and reduced using PCA. An FNN was trained and optimized with only the equilibrium point position inside the \(\pm\)5% limit tests. The ML model predicted the HEPS' operating mode with a total accuracy of 97.44% in the test phase. Subsequently, the FFN is tested with inputs derived from equilibrium point positions outside the \(\pm\)5% limits, showing a decrease in total accuracy to 29.41%. Starting from the classification results, a logical vector was realized, and an RMS value was calculated. The experimental tests demonstrated the statistical index's ability to monitor equilibrium point leakage within a \(\pm\)5% limits due to air property changes.  
The approach presented in this paper could represent an innovative contribution to the field, introducing a potentially new methodology for monitoring HEPS. The proposed signal processing method has a low computational cost (statistical index and filtering) and has already been implemented in real time in other literature works. Other techniques only have higher costs in the first stage (FNN training and optimization) or in the transformation matrix calculation (PCA analysis). The proposed method can be applied to all kinds of HEPS because it is based only on accelerometric signals. The future goal is to improve the presented technique and implement it onboard to monitor the equilibrium point position in the angular speed-torque plane in real time.  
Conceptualization, P.M. and A.N.; methodology, F.M. and M.S.; software, F.M. and M.S.; validation, F.M. and M.S.; formal analysis, C.C., V.N. and S.S.; investigation, E.F.; data curation, E.F.; writing--original draft preparation, F.M. and M.S.; writing--review and editing, P.M. and A.N.; supervision, C.C., V.N. and S.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author due to privacy.  
The authors declare no conflicts of interest.
## 5 Simulation Results under Irregular Conditionsfor Various Wave Period and Height
### CAV Control Strategy  
Under the equivalent conditions of previous simulations, we determined \(\bar{P}_{t}^{*}\) and \(\bar{\eta}_{t}^{*}\) whilst using the CAV-controlled OWC-WEC system with \(k_{p}=0.2\). Figures 14-16 depict results when using the CAV controller applied OWC-WEC system under various irregular wave states with a reference angular velocity of \(\Omega_{d}=250,\;400,\;550\;\mathrm{rad/s}\), respectively. The overall analysis reveals that near optimum power extraction can be achieved through constant damping and a constant angular velocity. This, however, is dependent on an adequate reference angular velocity \(\Omega_{d}\) being maintained. Through data comparisons, we observed that an increase in wave height demands an increased reference angular velocity. In addition, since \(\bar{P}_{t}^{*}\) somewhat correlates to \(\bar{\eta}_{t}^{*}\), we observe that \(\bar{\eta}_{t}^{*}\) plays an important role in the performance of the entire OWC-WEC system in this simulation.  
Figure 14: Time averaged results of the CAV controller under the PM irregular wave model with various \(T_{p}\) and \(H_{\mathrm{s}}\) for \(T_{m}=5000\) s when \(\Omega_{d}=250\;\mathrm{rad/s}\): (**a**) relative power extraction from turbine; (**b**) relative average turbine efficiency.  
Figure 13: Time averaged results of the i-MET controller under the PM irregular wave model with various \(T_{p}\) and \(H_{\mathrm{s}}\) for \(T_{m}=5000\) s: (**a**) relative power extraction from turbine; (**b**) relative average turbine efficiency.
## 3 Model and Calculation Method
### Track Model  
The composite sleeper-ballasted track consists of a rail, fastener systems, composite sleeper, ballast bed, and subgrade. In the track model, the rail is simplified to a point-supported Euler beam. The sleepers are simulated by solid elements. Considering their viscoelastic properties, the generalized Maxwell model is used to characterize the constitutive relation. The parameters are shown in Table 1. The fasteners are simplified to spring-damper elements. The ballast bed are simplified to discrete masses and spring-damper elements; in order to account for the coupling effects of the interlocking ballast granules and the continuity, shear stiffness and shear damping coupling is introduced between adjacent ballast masses [31; 34]. The subgrades are also simplified to spring-damper  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Unit** & **Value** \\ \hline Mass of car body & kg & \(3.96\times 10^{4}\) \\ Mass of bogie & kg & \(3.5\times 10^{3}\) \\ Mass of wheelset & kg & \(2.0\times 10^{3}\) \\ Pitching moment of inertia of car & kg-m\({}^{2}\) & \(1.94\times 10^{6}\) \\ Pitching moment of inertia of bogie & kg-m\({}^{2}\) & \(1.752\times 10^{3}\) \\ Vertical stiffness of primary suspension & N/m & \(1.89\times 10^{6}\) \\ Vertical stiffness of secondary suspension & N/m & \(1.176\times 10^{6}\) \\ Vertical damping of primary suspension & N-s/m & \(1.96\times 10^{4}\) \\ Vertical damping of secondary suspension & N-s/m & \(4.0\times 10^{4}\) \\ Fixed axle spacing & m & \(2.5\times 10^{0}\) \\ Bogie center distance & m & \(1.75\times 10\) \\ Wheel radius & m & \(4.3\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The main parameters of the China Railway High-speed (CRH) 2 vehicle.  
Figure 3: Vehicle–track dynamic coupling model.  
elements. The widely used type-III pre-stressed concrete sleepers in China are also calculated in the model for comparison; two types of sleepers are shown in Figure 4. The parameters of the track structures are shown in Table 3.
###### Abstract  
This work presents a review and evaluation of studies measuring exposure to Radiofrequency Electromagnetic Fields (RF-EMF). The review meets the basic quality criteria and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines after the eligibility criteria of the PECO (Population, Exposure, Comparator, and Outcome) methodology and the instrument Critical Appraisal Skills Programme Espanol (CASPe). A total of 86 papers published between 1 January 1998 and 31 December 2023 are included: 61 studies with spot measurements and 25 studies with mixed methodologies (spot measurements, personal measurements with volunteers or with a trained researcher and prediction models) are highlighted. Forty-three percent of the studies use Spectrum Analyzers in the spot measurements, mainly the Narda SRM-3006, followed by the Narda SRM-3000, highlighting the introduction and use of Sensors for this kind of study. The minimum mean value was measured in Palestine at 0.0600 uW/m\({}^{2}\), and the maximum mean value was measured in Norway at 200,000 uW/m\({}^{2}\). The RF-EMF exposure levels measured in the different microenvironments are minimal and far from the maximum  
In 1998, the ICNIRP made the first publication on maximum reference levels, and RF-EMF exposure measurement studies began to monitor the RF-EMF exposure levels to which we are exposed (100 kHz to 300 GHz) [19; 23; 24; 25]. Systematic reviews and meta-analyses allow us to know and analyze published scientific studies based on the declaration Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [26; 27; 28; 29]. In addition, the comparison of studies allows us to know the different methodologies that these studies have used, such as RF-EMF studies (Figure 1).  
This review presents scientific works published between 1 January 1998 and 31 December 2023, in which the RF-EMF exposure has been measured by means of the spot measurements method and studies using a mixed methodology, combining spot measurements, personal measurements with volunteers or with trained researcher, spatiotemporal exposure measurements and using model predictions (Figure 1).  
In a recent publication [30], studies were presented and carried out through personal measurements with volunteers and with a trained researcher. The present review has been motivated by this review [30] to answer the following questions: What studies have been carried out applying spot measurements and mixed methodology? Also, what were the results? Do these results comply with the reference limits established by the ICNIRP?
###### Abstract  
Road excavation-restoration work, where various construction tasks are performed, may generate large quantities of particulate matter (PM). These PM may accumulate in the surroundings or scatter into the atmosphere, thus affecting the environment and people in the surroundings. This study was conducted as part of a basic research study to reduce scattering PM generated from road excavation-restoration work. This study aimed to investigate the PM\({}_{10}\) emission trend for each activity of road excavation-restoration work, and to analyze the activity that yields the highest PM\({}_{10}\) emissions. PM were measured by using a particle spectrometer and the vacuum sweep method, by conducting field (level 1) and chamber experiments (level 2). The PM\({}_{10}\) emission trends of road-cutting, breaking, removal, excavation, and restoration activities were examined based on field experiments. It was found that the highest PM\({}_{10}\) emission was generated from road-cutting activities. The road-cutting activities were performed in an enclosed chamber, and the microstructure and the emission characteristics of PM generated by cutting were analyzed. The PM generated during the cutting activity were analyzed by dividing them into scattered and deposited PM. The results showed that as the cutting depth increased, the scattered PM decreased, while the deposited PM tended to increase. Furthermore, as a result of the microstructural analysis of PM conducted during the cutting activity, it was found that the main components were aggregates instead of the components of asphalt binder.  
excavation-restoration work; particulate matter; asphalt concrete cutting; microstructure +
Footnote †: journal: Applied Sciences
# GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest
## 2 Methodology
### Principles of MDI-Based Random Forest Retrieval
In summary, the Random Forest model is very suitable for soil moisture datasets with a large number of features, and because soil moisture yields a large number of real data,the Random Forest regression model was used in this experiment. The implementation process of the Random Forest regression model is shown in Figure 2.  
Random Forest is an ensemble comprising multiple decision trees, denoted as \(\{h(X;\theta_{m})\}\), where \(m=1,2,\ldots,M\), and \(\theta\) represent independent and identically distributed random vectors, with \(X\) being the input variables. \(Y\) represents the output variable, forming an original dataset with \(X\). The basic principle is as follows: Firstly, \(M\) sample subsets are extracted from the original dataset, where each subset's data are randomly selected from the original dataset using bootstrap sampling methods. Secondly, for the sample subset, \(M\) decision tree regression models are constructed, yielding \(M\) regression prediction results. Finally, the average of these \(M\) prediction results is used as the final prediction outcome. The use of bootstrap sampling ensures that each sample subset within the Random Forest has some degree of variability, thereby enhancing the robustness and generalization capability of the entire regression model. After \(M\) rounds of model training, a sequence of regression prediction models \(\{h(X;\theta_{1})\}\), \(h(X;\theta_{2})\), \(\ldots,h(X;\theta_{M})\}\) can be obtained, and then the final prediction results are as follows [16]:  
\[\overline{h}(X)=(1/M)\sum_{m=1}^{M}h(X;\theta_{m}) \tag{7}\]  
The process of using the Random Forest regression algorithm for soil moisture retrieval is detailed next: If the satellite dataset comprises \(N\) samples, \(N\) samples are randomly  
Figure 2: Schematic diagram of the principle of Random Forest model. selected using the bootstrap method, and \(M\) decision trees are grown. OOB data from each sample construction are used as the test sample for that decision tree. Suppose the original data contain \(p\) variables, several \(m_{\text{try}}\) variables (\(m_{\text{try}}<<p\)) are specified at each node of the regression tree as candidate branching variables. The optimal branch is then selected according to the MDI algorithm. In Random Forest regression, \(m_{\text{try}}=P/3\), each tree recursively branches from top to bottom, with the smallest scale node set as the condition to stop tree growth. A Random Forest model is established using \(M\) decision trees, and the average of the predictions from each tree provides the final prediction result.