esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Methods
#### 2.3.2 Data Analysis, Forest Fire Protection  
For this research on implementing satellite data into the power network protection against forest fires, Sentinel 2 MSI data and Sentinel 3 LST and FRP products were used. Sentinel 2 data are used for landscape monitoring and burned area recovery monitoring. Sentinel 3 data are used for fire detection and fire-spreading danger prediction.  
One of the critical elements in forest fire protection is getting to know landscape cover very well. The Sentinel 2 mission's data, recorded in various spectral bands, enable pretty-detailed terrain monitoring in multiple ways. The Sentinel 2 True Color Image (TCI) product provides information about the actual state of the ground (when there is no cloud cover). Using data recorded in other spectral bands enables the calculation of various indices. By comparing indices values calculated at the beginning and at the end of certain time intervals (e.g., several weeks or months), it is possible to monitor vegetation growth, structure, health, etc. [3; 40]. In this research, vegetation monitoring was conducted by calculating the Normalized Burn Ratio (NBR) using (1) and the difference between pre-fire and post-fire NBR indices known as delta NBR (dNBR) [41].  
\[NBR=\frac{(NIR-SWIR)}{(NIR+SWIR)}=\frac{(Band[8A]-Band[12])}{(Band[8A]+Band[12 ])} \tag{1}\]The analysis of fire detection possibilities and the possibility of determining the spreading danger was conducted by analyzing FRP data during fire events and LST data for the whole year, regardless of whether an active fire existed.  
During the FRP analysis, it was observed that FRP values can be detected outside of the fire polygon. The area with the highest possibility of an active fire event was determined by analyzing the FRP values and FRP uncertainty in various buffers around fire polygons. The active fire area was determined by comparing the recorded FRP values with their theoretical values defined in [15] and according to the FRP values that can be recorded while various grasslands burn [42]. The LST analysis and its applicability in forest fire-spreading danger prediction have multiple steps. Firstly, data retrieval was compared from day to day before noon and in the evening. In the second step, the consistency of the data spatial distribution for each sensing time was checked. Then, the correlation between LST values and the FWI values calculated using the Canadian method [33] was conducted. The FWI calculation is conducted for the location of the meteorological measurements, and the LST data were recorded with a spatial resolution of 1 km. To calculate FWI values using satellite data, Croatia was divided into polygons according to climatological and geographical conditions. The FWI values calculated using satellite data have a different range than those calculated using the Canadian method, so danger classes had to be adapted. The adaptation was conducted according to the data dispersion between minimal and maximal values.
## 7 Conclusions  
In view of the fact that the fault signal of the bearing is weak, and the detection and diagnosis are difficult, the fiber Bragg grating generalized resonant sensor is used to realize the monitoring of the inner ring fault vibration signal. Combined with the vibration monitoring signal, the state space model is established. The FPE criterion and RLS algorithm are used to determine the model order and model parameters. After establishing a faulty bearing dynamic system, the Kalman filter is used for signal noise reduction processing, and power spectrum analysis is combined with improved autocorrelation envelope spectrum analysis to detect and identify weak bearing faults. This verifies the effectiveness and feasibility of the resonance detection method based on the Kalman filter  
Figure 20: Outer ring fault analysis results: (**a**) bearing inner ring fault signal; (**b**) power spectrum analysis; (**c**) model order; (**d**) model parameters; (**e**) signal after Kalman filter; (**f**) autocorrelation function curve; (**g**) improved autocorrelation envelope power spectrum analysis; and (**h**) autocorrelation envelope maximum entropy spectrum analysis.  
and spectrum analysis for rolling bearing fault signals. Compared with other methods, the proposed improved autocorrelation envelope spectrum analysis method can better identify the characteristic frequencies of bearing faults. The subsequent research after this article might investigate other effective methods for detection and diagnosis of bearing faults, and the problem of detection and identification of multiple simultaneous bearing faults.  
Conceptualization, S.S.; methodology, S.S.; software, X.C.; formal analysis, X.C.; writing--original draft preparation, X.C.; writing--review and editing, X.C. All authors have read and agreed to the published version of the manuscript.  
Key Project of the Natural Science Foundation in Heilongjiang Province, China (ZD2021F003).  
Not applicable.  
The authors declare no conflict of interest.
# Artificial Neural Network Assisted Error Correction for MLC NAND Flash Memory
## 4 Experiment Results
### Performance  
In Figure 9a,b, we compare RBER and frame error rate (FER) using ANN-LDPC [11], the proposed method and the original method without the neural network versus data retention time at \(s=1\). We can observe that the proposed ANNAEC significantly reduces the RBER in comparison with the ANN-LDPC and original method.  
For instance, in Figure 9a, the data retention time is about \(3\times 10^{4}\) h at \(PE=5000\) and RBER \(=2\times 10^{-2}\), using the scheme without ANNAEC. Compared to the proposed ANNAEC scheme, Figure 9b shows that for the same performance, the ANN-LDPC can make the flash memory endure up to \(3\times 10^{5}\) h and the proposed method provides a performance gain of approximately 67% of data retention, which makes the retention time  
\begin{table}
\begin{tabular}{c|c|c|c} \hline \hline
**Retention Time (h)** & **PE** & **3000** & **4000** & **5000** \\ \hline \(\approx\)6 \(\times\) 10\({}^{-3}\) & \(1\times 10^{5}\) & \(2\times 10^{4}\) & \(1\times 10^{4}\) \\ \hline \(\approx\)7 \(\times\) 10\({}^{-3}\) & \(2\times 10^{5}\) & \(4\times 10^{4}\) & \(1.5\times 10^{4}\) \\ \hline \(\approx\)8 \(\times\) 10\({}^{-3}\) & \(3\times 10^{5}\) & \(5\times 10^{4}\) & \(2\times 10^{4}\) \\ \hline \(\approx\)9 \(\times\) 10\({}^{-3}\) & \(5\times 10^{5}\) & \(1\times 10^{5}\) & \(3\times 10^{4}\) \\ \hline \(\approx\)1 \(\times\) 10\({}^{-2}\) & \(1\times 10^{6}\) & \(5\times 10^{5}\) & \(1\times 10^{5}\) \\ \hline Size of the training data & & & 336,000 \\ \hline Size of the validation data & & & 84,000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Training dataset (\(s=1\)).  
Figure 8: Performance of neural network under the different numbers of hidden layer nodes.  
of flash endure up to \(5\times 10^{5}\) h. In addition, the proposed method has a more stable error correction performance, when the memory suffers from a weak interference. Similarly, we can notice that the proposed ANNAEC improves the FER performance by up to an error rate of \(1\times 10^{-3}\) at a retention time of \(4\times 10^{6}\) h and \(PE=3000\). The ANN-LDPC has a FER performance of approximately \(5\times 10^{-3}\).
# Smart Cities in Russia: Current Situation and Insights for Future Development
## 4 Smart City Components
### Smart Transport  
A smart city should include a transport management system that can cope with the increasing load on the roads. Three main parts of a smart city transport system are dynamic traffic assignment, green energy transport, and smart public transport.  
1. **Dynamic traffic assignment:** Traffic is managed in a network by dynamic traffic assignment measurements via detection, communication, information provision, and real-time control. Several algorithms have been suggested for smart traffic management [18; 19]. They allow making individual route decisions based on the current road situation. Additionally, such algorithms can monitor the traffic and gather data for future use. However, these algorithms are still computationally demanding, as they require processing a lot of incoming data. Another approach is to dynamically regulate the traffic lights. In work [20], authors described how wireless sensor networks (WSN) and multiple fuzzy logic controllers can be used to make a system for smart traffic light management [20]. Parking space is managed via smart-parking systems [21; 22].
2. **Green energy transport:** Green energy transport should be a part of the smart city transport system. Governments the world over are taking measures to promote electric transport. An assessment of promotion measures in China can be found in the article [23]. Public transport is easier to switch to green energy than private because it is controlled by the government. However, this transition can be quite expensive.
3. **Smart public transport:** Public transport in modern cities has been growing in the last few decades. It is environmentally friendly, can carry more people, and is cheaper. In a smart city, public transport must meet certain criteria. Here are some of them: * Electronic payment speeds up the passenger flow and can be more convenient for people. * Tracking and load balancing systems can track accidents on the road, detect unexpected delays, and measure the efficiency of the transport system [18]. Decisions about public transport stops and routes can be made based on this data. * Self-driving buses are expected to become a major part of the public transport system of most smart cities. According to [PERSON] and [PERSON] [24], public transport and private transport will be enhanced by the appearance of self-driving vehicles. The authors claimed that the usage of unmanned public buses may result in decreased usage of personal vehicles and a decrease in the number of drivers. However, this research was conducted in Hungary, and it is not clear how well that generalizes to other countries.
# Passive Sonar Target Detection Using Statistical Classifier and Adaptive Threshold
## 4 Experimental and Simulation Results
### Calculation of Detection Points in the Time Domain  
After applying Bayesian algorithm in the time domain, the output will be labeled with 1 (target) and 0 (noise). An example of this classification is shown in Figure 11.  
To reduce classification error of Bayesian algorithm, the labeled signals are averaged by window width \(w\) samples. The averaged values recorded at the center of the window provides \(\overset{\rightarrow}{a}\) mean vector. Figure 12 shows an example of this vector. To increase challenge complexity input signal is reconstructed by (11).  
\[\hat{I}=\left[\begin{array}{cc}noise_{1\times 5000}&[target+noise&]_{1\times 5 000}\end{array}\right]_{1\times 10000}, \tag{11}\]  
where \(\hat{I}\) is reconstructed input test signal, _target_ is propeller or ship engine sound, and _noise_ is ambient is mixed by white noise. For only noise input, _target_ is null.  
Figure 11: Bayesian classifier output, in sample of 50 thousand and 100 thousand real target is present (red ellipse).  
In this paper, the length of input test signal is 10 thousand sample (\(n\) = 10,000) and the width of the average window is selected five-thousand (\(w\) = 5000) sample, so the sample size of the vector has been reduced to 20. As shown in Figure 12, because of present target from five-thousand, detection point is increased after number 10.  
As shown in Figure 5, the clustering algorithm \(K\)-means has been used for average vector. The result is shown in Figure 13. In this figure, dots shown elements of the first cluster, crosses shown elements of the second cluster, and the hollow circles are the cluster center.  
Figure 12: Average vector \(\overrightarrow{a}\), detection point is increased after 10 samples.  
Figure 13: Results of \(K\)-means (\(K\) = 2), dots shown elements of the first cluster, crosses shown elements of the second cluster and the hollow circles are the cluster center.  
In Figure 13 the greater center of cluster (close to 1) is 0.8 and the number of its elements is 8 (from 20). According to the proposed algorithm, the detection point is calculated by the Equation (2), Table 1 shows detection point of 19 test signals in a specific sequence in the time domain.
# Trusted Composition of Internet of Medical Things over Imperfect Networks
## 11 Conclusions  
The Internet of Medical Things (IoMT) integrates medical devices, applications, and IT systems through communication networks, enabling comprehensive monitoring and control capabilities. However, this convergence poses challenges, notably security vulnerabilities and network disruptions, due to reliance on imperfect communication networks. Crucially, achieving a trusted composition in IoMT systems involves prioritizing high assurance in critical functional components while accommodating a lower-assured communication infrastructure. Integration of AADL with BLESS behavior specification is investigated to offer a systematic approach to ensure trusted composition of heterogeneous medical devices over such imperfect networks. This approach emphasizes formal verification techniques and a message-confirmation protocol to establish assume-guarantee relationships and enhance safety properties at both individual device and system levels. A case study demonstrates the application of the proposed approach in an IoMT system of a patient-controlled opioid infusion comprising a pulse oximeter, respiration monitor, PCA pump, and coordinator application. Despite the presence of an imperfect communication network, the study illustrates the feasibility of safe and correct information exchange towards a trusted composition. Enhancing trusted composition techniques can improve IoMT system scalability and dependability, ultimately leading to better healthcare delivery. Future work is directed towards applying the proposed approach for trustworthy medical device composition to other IoMT use cases.  
Conceptualization, E.A. and B.L.; methodology, E.A. and B.L.; software, E.A. and B.L.; validation, E.A., B.L. and A.B.; formal analysis, E.A. and B.L.; investigation, A.B. and B.L.; resources, B.L. and A.B.; data curation, A.B.; writing--original draft preparation, E.A. and B.L.; writing--review and editing, E.A. and B.L.; visualization, B.L. and A.B.; supervision, E.A., B.L. and A.B.; project administration, A.B.; funding acquisition, E.A. and A.B. All authors have read and agreed to the published version of the manuscript.  
All authors were supported with funding granted by the Deanship of Scientific Research at Saudi Electronic University (9460).  
Complete proof of the SysIoMT system with all the subsystems, processes, and threads is available as a GitHub repository at: [[https://github.com/brlarson/IoMT](https://github.com/brlarson/IoMT)]([https://github.com/brlarson/IoMT](https://github.com/brlarson/IoMT)) (accessed on 1 May 2024).  
**Acknowledgments:** The authors extend their appreciation to the Deanship of Scientific Research at Saudi Electronic University for funding this research (9460).  
**Conflicts of Interest:** [PERSON] is the CEO of multitude Cooperation. The authors declare no conflicts of interest.
## 2 Materials and Methods  
### Soil Sample Collection  
Rhizosphere soil samples were collected from maize-cultivated fields in Eruwa, Oyo state, Nigeria (7\({}^{\circ}\)28\({}^{\prime}\)2.034\({}^{\prime}\)\" N 3\({}^{\circ}\)28\({}^{\prime}\)21.671\" E) and Mbuzini, Mpumalanga Province, South Africa (25\({}^{\circ}\)55'30.90\" S 31\({}^{\circ}\)56\({}^{\prime}\)11.70\" E). Maize has been cultivated on the two sampled fields in the two locations for more than a decade, and they both had a history of _Striga hermothical_ infestation. The rhizosphere soil samples were collected in January 2022 during the dry season in Eruwa, Nigeria, with an average temperature of 33 \({}^{\circ}\)C and cumulative rainfall of 8 mm, while in Mbuzini, Mpumalanga Province of South Africa, rhizosphere soil samples were collected in March 2022 with an average temperature of 21 \({}^{\circ}\)C and cumulative rainfall of 38 mm. The soil samples were obtained randomly in three replicates at four different points on the _Striga_-infested sites, while uninfected soil samples (bulk) were also taken at a depth and diameter of 15 cm and 8 cm, respectively (Figures 1 and 2). Each sample was divided into two parts. Before analyzing the physicochemical characteristics, one part was air-dried and homogenized, while the other part was kept at \(-\)80 \({}^{\circ}\)C for DNA extraction.  
Figure 1: Map of geographical location of sampling sites in Eruwa, Oyo State Nigeria.
## 4 Discussion
### Practical Application  
Recording the current physiological demands of world-class male football players holds scientific value and direct practical applications, as it is a rare occurrence due to limited access to such subjects. The practical significance of this observation lies in the realization that training these abilities should be developed to the highest possible level.  
Furthermore, the direct practical application of this research is that this knowledge can assist coaches in making evidence-based decisions by providing new normative physiological data that can be used for player selection and profiling. Ultimately, these data can be valuable for coaches and fitness trainers in national-level teams to highlight the requirements of higher-level competitions and to design training sessions, which can aid players in transitioning to an elite competition.
## 6 Real-Time Embedded Platform Implementation
From Figure 10, we can observe that in idle state, the mean power consumption is around 2.9W with the display connected, while during the execution of the application the power consumption increases to maximum 4.36 W. The terminal version of the application (without displaying processed frames) has a power consumption below 1.5 W in idle mode and within 2.4 W in processing mode. Such values are orders of magnitude lower than typical GPP or GPU power costs. Such an embedded device has been also considered as a final device platform by implementing a distributed antifire and surveillance and exploiting an IoT architecture with several camera nodes [12]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Platform**} & \multicolumn{5}{c}{**Frame Size**} \\ \cline{2-6}  & \(\mathbf{320\times 480}\) & \(\mathbf{640\times 480}\) & \(\mathbf{1280\times 720}\) & \(\mathbf{1920\times 1080}\) & \(\mathbf{1920\times 1440}\) \\ \hline X86 64b PC & 29.3 fps & 17.5 fps & 6.07 fps & N/A & 1.81 fps \\ RPi 3 model B & 13.4 fps & 4.47 fps & 01.38 fps & 0.87 fps & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Real-time processing in fps for different platforms, without displaying processed output frames. Figure 10: Power consumption in different configurations (each frame size is processed at the max. fps achievable in real-time from Tables 5 and 6).
## 6 Conclusions  
In this study, a novel time-based multi-domain architecture is presented, and its capabilities and accuracy are verified. The method uses a multi-physics approach to embed under the same simulation platform general purpose electro-magneto-mechanical problems. Two novel Python-Matlab based bi-directional communication routines are presented, with the aim of integrating two consolidated simulation tools and languages for the electronic and magnetic domains in multi-physics models: a Spice-based analysis and a 3D Radia-based finite volume methodology, respectively. It was established that for the magnetostatic analysis the FVM principle presents significant improvements both in reliability and performance compared with the classic FEM analysis. The integration of the Spice-based electronics aims to extend this design boost to those circuits, which, due to the application complexity, require a direct interaction between different physical domains, such as in the case of mechatronic applications.  
It is important to underline how the proposed co-simulation algorithm allows the interaction of the different phenomena, while keeping the characteristic frequencies of the studied events and the properties of the called integrators fully decoupled, so as to optimize the computational load of the CPU without incurring aliasing phenomena.  
Finally, a detailed validation is faced through a complete comparison of quasi-static measurements [15] obtained from an MEMS electromagnetic actuator. The dynamic regime analysis is also considered. To enhance the capabilities and the reliability of the proposed architecture, a real case scenario is studied. The system modeled with all the equivalent constitutive components is connected to a fictitious microcontroller equipped with a firmware control logic which, through different PWM signals, allows us to study the full dynamic response. In detail, signals with exciting frequencies of [80; 90; 100] Hz and duty cycles equal to [50%; 60%; 70%] are analyzed.  
Figure 15: MOSFET drain voltage.  
**Author Contributions:** Conceptualization, F.M.R. and C.R.; methodology, F.M.R. and C.R.; software, F.M.R.; validation, M.C. and J.M.; investigation, F.M.R. and C.R.; resources, S.C.; data curation, S.C.; writing--original draft preparation, F.M.R.; writing--review and editing, F.M.R. and S.C.; visualization, M.C. and J.M.; supervision, S.C.; project administration, S.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Discussion
### Significance of the Experimental Results  
The experimental results indicated that the amplitude of ambient temperature changes is probably several times or even ten times greater than that of the stress-induced \(\Delta\)AIRT. The stress-induced infrared signal is completely submerged by the change in ambient temperature. If the stress changes periodically and there are no background radiation signals that are consistent with the period of the stress, the stress-induced TIR signal can be extracted using wavelet analysis. This problem has been investigated by some scholars. [PERSON] et al. [56] divided the TIR of the land surface into low, medium, and high frequency components when monitoring seismic TIR anomalies before earthquakes. The low and high frequency components represent the radiation due to the Earth's basic temperature field and the radiation due to rain, clouds, and cold or hot air currents, respectively. Apart from these two components, the medium-frequency component may be related to the crustal tectonic stress. Their studies on the cases of earthquakes showed that TIR anomalies often occur prior to earthquakes with different characteristic periods based on the medium-frequency data in different cases [57; 58; 59]. [PERSON] et al. [18; 19; 20] found that the TIR of land surface was closely related to strain on a drilling measured data. Based on long-term monitoring of the bedrock temperature in the Kangding area, the bedrock temperature was strongly correlated with low-level seismic activity and suggested that the drastic change in the bedrock temperature is caused by the stress adjustment. Properties that affect the temperature response of rocks to changes in stress are critical for understanding temperature anomalies in the crust [60; 61; 62].  
Based on the experimental results in this paper, it is possible to extract the stress-induced TIR information using reasonable signal processing methods in the areas with intense neotectonic movement. Additionally, the microwave brightness temperature changes with stress change synchronously [47]. Therefore, the experimental results could provide the foundation for the monitoring of stress and tectonic activity in the crust via remote sensing.