esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on a Nonlinear Dynamic Incipient Fault Detection Method for Rolling Bearings
###### Abstract  
The incipient fault detection technology of rolling bearings is the key to ensure its normal operation and is of great significance for most industrial processes. However, the vibration signals of rolling bearings are a set of time series with non-linear and timing correlation, and weak incipient fault characteristics of rolling bearings bring about obstructions for the fault detection. This paper proposes a nonlinear dynamic incipient fault detection method for rolling bearings to solve these problems. The kernel function and the moving window algorithm are used to establish a non-linear dynamic model, and the real-time characteristics of the system are obtained. At the same time, the deep decomposition method is used to extract weak fault characteristics under the strong noise, and the incipient failures of rolling bearings are detected. Finally, the validity and feasibility of the scheme are verified by two simulation experiments. Experimental results show that the fault detection rate based on the proposed method is higher than 85% for incipient fault of rolling bearings, and the detection delay is almost zero. Compared with the detection performance of traditional methods, the proposed nonlinear dynamic incipient fault detection method is of better accuracy and applicability.  
rolling bearings; incipient fault detection; feature extraction; timing correlation; nonlinear characteristics +
Footnote †: journal: Applied Sciences
## 6 Challenges and Opportunities
### Future Research Directions
#### 6.4.1 Advanced Sensor Development and Integration  
One of the most challenging paths from a research perspective is designing and improving sensor hardware robust enough to withstand various, often severe, environmental conditions. In addition to temperature and humidity sensors, other modalities such as chemical, biosensing, and acoustic sensing could introduce a new dimension to outdoor safety monitoring. For example, volatile organic compound (VOC) detection or particulate matter detection technologies could provide invaluable information about health risks related to air quality, particularly in urban and industrial areas [121]. Furthermore, exploring the integration of wearable sensors that monitor vital signs, such as heart rate variability, blood oxygen levels, or stress indicators, could ultimately lead to more personalized and preventive health interventions.  
In challenging environments, the durability of the sensors remains crucial [122; 123]. Researchers can investigate new materials or packaging methods for sensor casings to extend their lifespan in extreme climates, such as the frigid winters of Montreal or the salty air of coastal regions. Investing in power efficiency is just as vital: advances in battery technology, wireless energy transfer, or energy-harvesting solutions (solar, kinetic, etc.) could significantly reduce maintenance costs and enhance long-term reliability. Meanwhile, increased miniaturization allows sensors to seamlessly integrate into infrastructure, such as building facades, streetlights, or public transportation stations, to optimize coverage while minimizing visual impact and installation challenges.  
Another exciting frontier is the integration of more intelligence directly into the sensor nodes themselves [124; 125]. This innovation allows these sensors, through a high level of onboard signal processing or lightweight AI capabilities, to autonomously filter out noise, flag anomalies, and even perform initial activity recognition rather than simply transmitting raw data for later collection and processing. This local intelligence minimizes network congestion by reducing the volume of data sent upstream for further analysis, thus enhancing response times and facilitating faster and more context-aware decision making. For example, a smart sensor in a remote coastal watchtower could identify and categorize unusual vibration patterns indicating structural stress or wave surges, sending timely alerts about these conditions instead of simply streaming raw data on every vibration the device logs. With advances in embedded hardware power and energy efficiency, these capabilities will not only conserve bandwidth and extend battery life but will also support a more scalable and distributed approach to monitoring outdoor safety.
## 3 Experimental Results and Analysis
### Relationships between WBGT and Physiological Parameters  
To examine the correlation between the physiological parameters and the WBGT index, Pearson correlation analysis was performed using the IBM SPSS Statistics 25 software (Table 5). It can be seen from the table that there was a significant positive correlation between \(T_{sk}\), \(HR\), \(SW_{p}\), \(M\) and WBGT (\(p\leq 0.01\)), and the correlation degree \(r\) value was above 0.6, indicating that the physiological parameters were significantly affected by the thermal environment and all increased with increasing WBGT. The correlation coefficient of \(T_{sk}\) was the highest (\(r\) = 0.854 **), indicating that \(T_{sk}\) was more significantly affected by the external thermal stress.
# Scheduling Sparse LEO Satellite Transmissions for Remote Water Level Monitoring
## 4 Conclusions and Future Work
### Future Work
#### 4.1.1 Potential Simulation Model Improvements  
The most apparent way to improve the simulations is to generate a more representative RF noise distribution and to use the Swarm satellite pass information, something which is not available in the remote locations. For the latter, there are the versions of the online satellite pass library, as used in the open source SGP4 Arduino library or in Python [23; 24].
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Airplane** & **Seat Number** & **Seat Shape** & **Seat Structure** & **Manikins** & **Method** & **References** \\ \hline B767 & 49 in 7 rows & Simplified seats & Upright & Realistic body profiles & Measurement & Zhang, 2013 [9] \\ B767 & 49 in 7 rows & Simplified seats & Upright & Realistic body profiles & CFD & Zhang, 2013 [10] \\ B767 & 28 in 4 rows & Simplified seats & Upright & Rectangular boxes & CFD & Zhang, 2007 [11] \\ B767 & 49 in 7 rows & Simplified seats & Upright & Realistic body profiles & CFD & You, 2019 [12] \\ B737 & 42 in 7 rows & Simplified seats & Upright & Realistic body profiles & CFD & You, 2019 [12] \\ B737 & 42 in 7 rows & Simplified seats & Upright & Rectangular boxes and cylinders & CFD & Cao, 2022 [13] \\ B737 & 42 in 7 rows & Realistic airplane seats & Upright & Realistic body profiles & Measurement & [PERSON], 2015 [17], [PERSON], 2017 [18], [PERSON], 2017 [19] \\ B737 & 36 in 6 rows & Realistic airplane seats & Upright & Rectangular boxes and cylinders & Measurement & [PERSON], 2021 [20] \\ B737 & 42 in 7 rows & Realistic airplane seats & Upright & Rectangular boxes and cylinders & Measurement & [PERSON], 2021 [21] \\ B737 & 42 in 7 rows & Realistic airplane seats & Upright & Realistic body profiles & CFD & Yan, 2017 [4] \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of symmetric, upright seat layout and manikins in representative published studies. The asymmetric seat inclination and misalignment may lead to asymmetric airflow and the cross-transport of pollutants. In a half-occupied, twin-aisle cabin mockup, the airflow was found to be asymmetric because of the asymmetric air supply velocity profiles and also the asymmetric manikin occupation mode [14; 15]. In a twin-aisle airliner cabin, the seating patterns of passengers were reported to exert a significant impact on the localized airflow and contaminant transport [16]. The airflow in a single-aisle cabin with five passengers in each row (3 passengers + 2 passengers) was found to be asymmetric due to the asymmetric cabin geometry [22]. The asymmetric airflow would enhance the cross-transport of airborne pollutants [15]. Table 3 summarizes the asymmetric seat arrangements in the representative published studies. Different seat occupancy was addressed in these studies, while the seat misalignment had not been focused on so far. The above review revealed that an asymmetric configuration of the aircraft cabin will lead to asymmetric airflow patterns and thus enhance the cross-transport of airborne pollutants. However, the exact impacts of seat inclination and seat misalignment on airflow and airborne pollutant transport is still unclear. The objective of this investigation was to evaluate how the seat inclination and misalignment would affect airflow and airborne pollutant transport. A CFD program was used to explore the airflow and airborne pollutant transport in a single-aisle aircraft cabin mockup. Both the seat inclination and seat misalignment were considered.
## 3 Computation of the principal components
### Centralized approach  
Let us suppose that \(t\) vectors of sensor measurements \(\mathbf{x}[\tau]\in\mathbb{R}^{p}\), \(\tau\in\{1,..,t\}\) are retrieved at the base station. Let \(\mathbf{X}[t]\) denote the \(p\times t\) matrix whose \(\tau\)-th column-vector is the vector \(\mathbf{x}[\tau]\in\mathbb{R}^{p}\). An estimate of the covariance matrix \(\mathbf{C}[t]=(c_{ij}[t])_{1\leq i,j\leq p}\) is obtained at time \(t\) by computing  
\[\mathbf{C}[t] = \frac{1}{t}\sum_{\tau=1}^{t}(\mathbf{x}[\tau]-\mathbf{\bar{x}}[ t])(\mathbf{x}[\tau]-\mathbf{\bar{x}}[t])^{T} \tag{8}\] \[= \frac{1}{t}\mathbf{X}[t]\mathbf{X}[t]^{T}-\mathbf{\bar{x}}[t] \mathbf{\bar{x}}[t]^{T}\]  
where \(\mathbf{\bar{x}}[t]=\frac{1}{t}\sum_{\tau=1}^{t}\mathbf{x}[\tau]\) is the average vector.  
The covariance matrix can be updated recursively as new vectors of observations \(\mathbf{x}[t+1]\) are made available at the base station. From Equation (8) it follows that  
\[c_{ij}[t] = \frac{1}{t}S_{ij}[t]-\frac{1}{t^{2}}S_{i}[t]S_{j}[t] \tag{9}\]  
where  
\[S_{i}[t] = \sum_{\tau=1}^{t}x_{i}[\tau]=S_{i}[t-1]+x_{i}[t]\] \[S_{ij}[t] = \sum_{\tau=1}^{t}x_{i}[\tau]x_{j}[\tau]=S_{ij}[t-1]+x_{i}[t]x_{j} [t] \tag{10}\]  
This means that the storage of the quantities \(t\), \(S_{j}\) and \(S_{ij}\), \(1\leq i,j\leq p\), in the base station makes possible the update of the covariance matrix estimation \(\mathbf{C}[t]\) at time \(t\). Once the covariance matrix is estimated, the principal components can be computed using a standard eigendecomposition method [21] and its elements communicated to the sensor nodes.
# Emerging Data-Driven Calibration Research on an Improved Link Performance Function in an Urban Area
## 4 Case Study
### Results  
#### 4.2.1 Calibration of the Link Performance Function  
In this study, we evaluated the performance of [PERSON]'s method, Bayesian optimization (BO), and differential evolution (DE) when calibrating the optimal set of parameters of a link performance function. The hyperparameters of DE were determined according to a predefined guideline. The convergence criteria for all algorithms were set to \(Z<10^{-10}\). The optimal set of parameters was \([\alpha_{1},\alpha_{2},\alpha_{3}]=[0.93,1.8799,4.8507]\).  
The results suggest that, in terms of solution efficiency, [PERSON]'s approach performed better than BO and DE, as seen in Figure 3. This was because, while DE and BO involve several calculations of the objective function values during the solution process, which can take a significant amount of time, [PERSON]'s approach is a gradient-based method with local second-order convergence. It was also shown how much of an advantage BO had over DE. The potential to achieve better solutions in fewer function evaluations was what separated BO from DE. This was because BO could take advantage of the information from each evaluation of the objective function value.  
In recent decades, the performance of computers has experienced exponential growth due to the rapid development of semiconductor technology. To improve computational efficiency, parallel computing techniques have become a major approach. In this context, the DE algorithm is suitable for designing a parallel computing framework due to its individuals being independent of each other, with no coupling between them. As a result, the update and selection of individuals can be conducted in parallel. The results in Figure 4 include a comparison of the solution efficiency of [PERSON]'s method, BO, DE, and DE_par (a parallel version of DE). The experiments were conducted using a six-core CPU to evaluate the performance of these methods.  
To measure the acceleration effect, the absolute acceleration ratio was calculated according to the following equation:  
\[r=\frac{t_{s}}{t_{p}}. \tag{23}\]  
Here, \(t_{s}\) is the serial execution time, and \(t_{p}\) is the parallel execution time.  
The DE algorithm's serial execution time was 59.173 s, while its parallel execution time was 20.398 s, resulting in a speedup ratio of 2.9 (<6). The parallelization of DE significantly improved its performance. Although it remained slightly inferior to [PERSON]'s method, it performed a little better than BO. As a global optimization algorithm, DE can always converge to the global optimum, which is its strength over [PERSON]'s method.  
Figure 3: Results of calibration.
## 1 Introduction
### Organization  
The organization of the paper is the following. To set the stage, we briefly introduce smart healthcare, IoT in smart healthcare, and IoMT architecture and applications in Section 2. The security requirements for IoMT are presented in Section 2.4. Our overall research methodology including research questions is outlined in Section 3. Section 4 presents taxonomy of authentication schemes in IoMT. Section 5 discusses some findings of literature review analysis. Some open issues and future directions for IoMT authentication are presented in Section 6. Section 7 concludes the paper.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Ref.** & **Year** & **Objective** & **IoMT Context** & **Taxonomy** & **Review** & **Open Issues** & **Attacks** & **Evaluation Techniques** & **Security Analysis Techniques** \\ \hline [PERSON] et al. [12] & 2016 & A survey of authentication for IoT & \(\times\) & \(\times\) & ✓ & \(\times\) & ✓ & ✓ & \(\times\) \\ \hline [PERSON] et al. [13] & 2017 & A review of authentication used in IoT & \(\times\) & \(\times\) & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [10] & 2017 & A review of authentication protocols in IoT & \(\times\) & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline [PERSON] et al. [8] & 2018 & A review of attacks and threats of IoT authentication & \(\times\) & \(\times\) & ✓ & \(\times\) & ✓ & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [9] & 2018 & Authentication taxonomy for IoT & \(\times\) & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [5] & 2018 & A survey of authentication, authorization & \(\times\) & \(\times\) & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [6] & 2019 & A survey of authentication for IoT & \(\times\) & \(\times\) & ✓ & \(\times\) & ✓ & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [11] & 2019 & A systematic review of IoT authentication & \(\times\) & \(\times\) & ✓ & ✓ & ✓ & ✓ & \(\times\) \\ \hline [PERSON] et al. [14] & 2019 & IoT authentication taxonomy and survey & \(\times\) & ✓ & ✓ & \(\times\) & ✓ & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [15] & 2020 & A review and current issues in IoT authentication & \(\times\) & \(\times\) & ✓ & ✓ & ✓ & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [7] & 2020 & A survey of authentication schemes in IoT & \(\times\) & \(\times\) & ✓ & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [16] & 2021 & A review of ECC and authentication in IoT & \(\times\) & \(\times\) & ✓ & ✓ & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] et al. [1] & 2021 & A comprehensive survey of authentication mechanisms in IoMT & ✓ & \(\times\) & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline Current study – & A taxonomy, review, and open issues of authentication in IoMT & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular} \(\checkmark\): indicates supported, \(\times\) : indicates not supported.  
\end{table}
Table 1: Comparison of existing review papers.
# Improved Charge Injection and Transport of Light-Emitting Diodes Based on Two-Dimensional Materials
## 2 Two-Dimensional (2D) Materials in Organic Light Emitting Diodes (OLEDs)
### 2D Materials as Interlayers
#### 2.1.3 2D Materials as Electron Injection Layer (EIL)  
Guo reported OLEDs with a structure of ITO/MoS\({}_{2}\)/Bphen:Cs\({}_{2}\)CO\({}_{3}\)/Bphen/AND:DSA-ph/NPB/MoO\({}_{3}\)/Al, in which MoS\({}_{2}\) was used as EIL [46]. Maximum CE of 14.8 cd/A and maximum EQE of 7.3% were found in OLED with MoS\({}_{2}\) EIL, showing an enhancement of 45% and 40% compared with the reference device without MoS\({}_{2}\) EIL, respectively. The insertion of MoS\({}_{2}\) reduced the energy barrier at the ITO/Bphen interface and improved the electron injection from ITO to EML. Ohisa reported OLEDs with a structure of ITO/PEDOT:PSS/TFB/FSBT/Ca\({}_{2}\)Nb\({}_{3}\)O\({}_{10}\)/Al, in which two-dimensional Ca\({}_{2}\)Nb\({}_{3}\)O\({}_{10}\) (CNO) nanosheets were used as EIL [47]. Devices with CNO nanosheets showed better performance compared with those with conventional Liq EILs, in which the uniform coverage, thicknesses and work function of CNO were important factors. These results suggest that 2D materials can be good candidates for EILs in high-performance OLEDs.
## 4 RESOLVD Solution for Smart Grids
Key functionalities performed by the PED were providing flexibility to the low-voltage grid, ensuring security of supply in case of grid failure, active and reactive power control at the secondary substation level for voltage and transformer loading management, current harmonics compensation and unbalance correction among the three phases of the distribution grid. A detailed description of the PED can be found in [34; 35]. * Phasor management unit (PMU) and edge computing: A wide area monitoring solution that provides increased observability of the LV grid and has edge computing capabilities. The PMU-based solution allows for real-time monitoring, protection and control of the power grid. This allows the system to operate closer to the margin and react to network disturbances. * Smart Gateway: A gateway to measure power quality in the buildings or charging areas and communicate to third parties. The power quality monitoring capability allows for the measurement of meter information and for it to be translated to services. It allows for communication with third party services and triggers actions. * Enterprise Service Bus (ESB) and Data Management Platform: This is integration middleware software that facilitates interaction among various software applications that are relevant to a DSO. It also manages data exchange, analytics and visualization. Key functionalities include integration with legacy systems of the DSO, customization to smart grid systems, data exchange, analytics and visualization of key performance indicators. The integrated RESOLVD solution could help in achieving eight specific value propositions. These value propositions also corresponded to the use cases designed and tested in the project. The proposed and achieved value propositions of the project were:  
1. Improved capability to detect and interrupt unintentional uncontrolled islanding
2. Improved continuity of supply under intentional controlled islanding
3. Enhanced capability to prevent congestion and over/under voltage issues
4. Improved voltage control ability (through local reactive power injection)
5. Improved power quality (through power electronics)
6. Reduced technical losses (through power electronics and local energy storage/local generation)
7. Improved fault detection (observability) and critical event prevention
8. Improved cybersecurity
## 4 Proposed Method  
In this study, a combination of 2D-CNN and LSTM was proposed to classify raw eye movement data into eye movement events. The CNN part filters the input data, and the LSTM part classifies the pre-processed data into event types. A similar approach to [PERSON] et al. [21] was used. Before feeding the input to the network, velocity, acceleration, jerk, and direction were calculated from the raw positional coordinate points (x, y). The model classified each input data sample into one of four classes: fixations, saccades, PSOs, and smooth pursuits. Heuristic measures were then applied to the classifier's output, such as merging nearby fixation points and determining the duration and number of events to produce the final events.  
Unlike most event detection methods, the proposed approach simultaneously classifies fixation, saccade, PSO, and SP. Identifying PSOs in the presence of fixations, saccades, and smooth pursuits is particularly challenging due to their similar movement behaviors. Because of this, PSOs are frequently misclassified when only velocity is used. For example, PSOs may resemble fixations because of the small oscillatory movements in fixations. They  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Files** & **Fixations Points** & **Saccades Points** & **PSOs Points** & **SPs Points** \\ \hline File 1 & 625 & 65 & 101 & 819 \\ File 2 & 962 & 339 & 134 & 1611 \\ File 3 & 932 & 59 & 99 & 520 \\ File 4 & 1401 & 390 & 107 & 2748 \\ File 5 & 1158 & 156 & 38 & 1468 \\ File 6 & 1100 & 147 & 50 & 1523 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The description of files used to evaluate the models.  
can also be misclassified as saccades due to their similar velocity behaviors. In order to address these issues, the model incorporates not only velocity but also higher-order derivatives of velocity as features. In addition, the impacts of different feature combinations on event detection accuracy were evaluated.
## 2 Methodology
### Artificial Neural Network (ANN)
#### 2.2.2 Error Back Propagation Training (BP)-ANN
Assuming the step length is constant, the \(\left(t+1\right)\)-th adjusted weight of the connection from neuron \(j\) to neuron \(i\) is: \[\omega_{ij}\left(t+1\right)=\omega_{ij}\left(t\right)-\eta\frac{\partial J \left(t\right)}{\partial\omega_{ij}\left(t\right)}=\omega_{ij}\left(t\right)- \eta\sum_{p}\frac{\partial E_{p}\left(t\right)}{\partial\omega_{ij}\left(t \right)}=\omega_{ij}\left(t\right)+\Delta\omega_{ij}\left(t\right),\] (14) where \(\eta\) is the step length, also called the learning operator, \[\frac{\partial E_{p}}{\partial\omega_{ij}}=\frac{\partial E_{p}}{\partial x_ {ip}}\cdot\frac{\partial x_{ip}}{\partial\omega_{ij}},\] (15) \[\delta_{ip}=\frac{\partial E_{p}}{\partial x_{ip}},\] (16) where \(\delta_{ip}\) is the sensitivity of the status of the \(i\)-th node \(x_{ip}\) to \(E_{p}\) when inputting the \(p\)-th sample. Equation (17) can be derived from Equation (15) to Equation (16): \[\frac{\partial E_{p}}{\partial\omega_{ij}}=\delta_{ip}I_{jp}.\] (17) Calculating \(\delta_{ip}\) in two different conditions: 1. If \(i\) is output node, _i.e._, \(i=k\), it can be derived from Equations (12) and (16) that \[\delta_{ip}=\delta_{kp}=\frac{\partial E_{p}}{\partial x_{kp}}=\frac{\partial E _{p}}{\partial y_{kp}}\cdot\frac{\partial y_{kp}}{\partial x_{kp}}=-e_{kp}f^{ \prime}\left(x_{kp}\right).\] (18) Substitute Equation (18) into Equation (15), then \[\frac{\partial E_{p}}{\partial\omega_{ij}}=-e_{kp}f^{\prime}\left(x_{kp} \right)I_{jp}.\] (19)If \(i\) is not an output node, _i.e.,_\(i\)\(\
eq\)\(k\), Equation (16) is: Figure 2:0 (20) in which Figure 2:1 (21) where \(m_{1}\) is the \(m_{1}\)-th node in the layer after node \(i\); \(I_{jp}\) is the \(j\)-th input for node \(i\). When \(i=j\), \(y_{jp}=I_{jp}\). Substitute Equations (20) and (21) into Equation (15), then Figure 2:2 (22)  
Equations (19) and (22) can be used to adjust the weight in Equation (14). The expression of BP-ANN mapping is a compound of a simple nonlinear function. Several such compound is able to represent complex functions, and then describe many complex processes in physical phenomena.