esa-sceva/satcom-chunk-collection huggingface dataset:
# Bridge Displacements Monitoring Method Based on Pixel Sequence
## 2 The DoG Algorithm Applied for Edge Detection
In a normal working state, the structure responds to changes in load size or shifts in the point of application with corresponding mechanical signal feedback. This feedback is reflected in optical information as subtle displacements in the structure of the image [22], typically at the pixel level, occupying only a few or dozens of pixels in the image. In the images captured by the camera, this is specifically manifested as pixel displacement at observation points/feature points. In the Difference of Gaussians (DoG) algorithm, the Gaussian-blurred image difference can highlight the high-gradient areas of the image. Since the pixel values at the edges change drastically, and the pixel value differences at the edges of the Gaussian-blurred image are significant, calculating the difference can enhance edge information. Since displacement is a relative value, we can use the DoG algorithm to extract edges in the initial state and the loaded state. The relative deformation between the two states can be considered as the structural mechanical response. By locating the feature points, the pixel displacement of the structure in the image can be determined. Because the feature points and adjusted edge extraction proposed in this paper are obtained through differences, it is necessary to ensure that the parameters of the DoG algorithm are consistent under the same working conditions. Otherwise, different Gaussian filter bases may lead to overall deviations in the extracted results, significantly affecting the accuracy of subsequent structural mechanical performance evaluations. In the system proposed in this paper, the spatial state of the structure and the camera remains unchanged under the same time-space conditions. The structure, in reality, will be recorded on the image according to a certain scale, known as the pixel scale factor,which can be calculated based on the camera's posture and the spatial distance between the camera and the structure. The pixel scale factor (\(Z\)) is a parameter used to convert the image signal into the displacement signal in this system. The calculation formula is as follows:  
\[\
abla D(x,y)=\left(\frac{\partial D}{\partial x},\ \frac{\partial D}{\partial y}\right) \tag{4}\]1  
where \(L_{s}\) represents the actual length of the object. \(L_{p}\) represents the pixel length of the object in the image. The unit of the pixel scale factor is mm/pixel. Using the pixel scale factor, we can convert the deformation of the structure's edge in the image into the actual deformation of the structure, thereby assessing the mechanical performance of the bridge under normal working conditions and monitoring the displacement of the bridge. After determining the edge of the structure using the Gaussian difference method, the displacement of the structure is calculated and detected. In this study, a comparison between the calculated displacement obtained using our proposed method and the actual displacement allows for the evaluation and monitoring of the overall structural performance during regular operation [23].
## 3 Experimental Setup and Results & Discussion
(**a**) Considering only the primary linear frequency modulation signal in the backscattered signal. (**b**) The blue curve represents the results obtained when adding the direct current component, and the red curve the results of a conventional optical time domain reflectometry (OTDR) under the same experimental conditions.
## 4 Tracking
### Estimation
#### 4.2.3 Ukf  
To increase the nonlinear processing ability of estimation methods, [PERSON] and [PERSON] proposed a UKF based on the idea of \"approximating the probability density distribution of the system random variable to make the approximation of nonlinear function easier\" [12; 83]. According to the prior probability distribution of the state, a set of sigma points is determined and their corresponding weighted values are calculated. The sigma point is then taken as an independent variable to calculate the dependent variable of the known nonlinear function, and the mean and covariance are estimated by calculating the dependent variable. The UKF algorithm also inherits the Kalman filtering framework, and exhibits better nonlinear estimation performance than the EKF because the UKF does not need to calculate the Jacobi (Jacobian) matrix of a nonlinear system and reduces the difficulty of the calculation process.  
However, in practical applications, UKF is widely used in systems with lower state dimensions. Once the system state reaches higher than three dimensions, the covariance matrix is obtained by an unscented transform (UT) to be a non-positive definite matrix, and the filter accuracy decreases rapidly.  
Regarding the system model shown in Table 11, the UT selects \(2n+1\) sigma points as the following:  
\[\begin{split} x^{(0)}&=\bar{x}\\ x^{(  
Table 17 details the UKF estimation method. The sigma points used for the prediction of the state with the weights, and the update of the state with the filter gain, are shown in this table. Finally, the prediction and update processes for the covariance are also listed.
# AI-Driven Predictive Maintenance for Workforce and Service Optimization in the Automotive Sector
## 6 Integration of AI4 LABOUR in Workforce Analytics and Sectoral Impacts  
The dataset utilized in this study aligns with the core objectives of the AI4 LABOUR project, which seeks to understand and predict workforce transformations driven by AI technologies. By leveraging machine learning models such as XGBoost to analyze after-sales service patterns, this research provides an empirical foundation for assessing how AI-driven predictive maintenance affects workforce participation, skill demands, and job restructuring. The transition from rule-based systems to AI-enhanced decision-making reflects broader labor market trends, where automation and predictive analytics are reshaping job roles and required competencies across multiple industries.  
Furthermore, the data-driven approach adopted in this study offers valuable insights into sectoral employment multipliers. The results demonstrate that improved predictive maintenance not only enhances operational efficiency but also generates indirect employment effects in associated sectors, such as logistics, customer service, and technical support. The increased service visit rates observed--30% for individual customers and 37% for corporate clients--suggest a higher demand for skilled labor in vehicle diagnostics, repair, and AI-assisted customer engagement. These findings reinforce the AI4 LABOUR project's objectives by showcasing real-world scenarios where AI-driven optimizations influence job creation and skill shifts within industry ecosystems.
## 5 Experimental Results
### The Experimental Facility
The manipulator approaches the target and moves away from it to demonstrate the in-orbit task, which is divided into four trajectories in different directions. As shown in the results, the green vertical dotted line is the beginning time, and the black one is the stop time. The visual measures and base FTS are monitored. The visual measure accuracy of HC is (0.5 mm, 0.3 deg) to (1.5 mm, 0.5 deg), and PC is (2 mm, 0.5 deg) to (5 mm, 1 deg), which is related to the working distance. The resolution of the cameras is (0.1 mm, 0.1 deg), the threshold of sensitivity of the FTS is (2 N, 2 N, 2 Nm). A comparison results of the two cases are listed in Table 4.  
Figure 11: The setup of the ground experiment of the flexible base manipulator visual servo control. (**a**)The scenario of the visual servo without payload, (**b**) the scenario of the visual servo with payload.
## 3 BP-ADRC Based on Uncertainty Quantification  
To ensure the microgrid's stable operation, the stability of the \(DC\) bus voltage tracking must be maintained. This paper proposes a dual-loop control strategy for voltage and current in a hybrid energy storage system based on a BP-ADRC controller with uncertainty quantification, as shown in Figure 7.  
The use of dual-loop PI control has several advantages over dual-loop PID control:  
1. Lower Computational Complexity: The PI controller lacks a derivative term, resulting in lower computational requirements. This makes it suitable for real-time control systems, particularly resource-constrained embedded systems.
2. Enhanced Noise Immunity: The PI controller is less sensitive to high-frequency noise without the derivative term, enhancing system stability in noise or disturbances.
3. Simplified Parameter Tuning: The PI controller requires only the adjustment of proportional and integral parameters, simplifying the design and tuning process.
4. Suitable for Slow Dynamic Systems: The PI controller is appropriate for systems that do not require rapid response. It provides sufficient control accuracy in the lithium battery-supercapacitor hybrid energy storage system, where voltage and current change are relatively slow.  
Figure 7: Hybrid energy storage system voltage and current double-loop control.  
The proposed solution consists of two parts: Uncertainty Quantification and BP-ADRC. BP-ADRC refers to a Back Propagation Neural Network-based Active Disturbance Rejection Controller, which includes a Linear Extended State Observer (ESO) and a closed-loop controller. Additionally, \(V_{DC}\) represents the input voltage of the hybrid energy storage system, \(V_{dc}\) is the bus voltage tracked by the system, \(I_{tot}\) is the output current of the BP-ADRC, \(I_{tr}\) is the filtered current and \(I_{SC}\) and \(I_{Bat}\) are the currents of the supercapacitor and lithium battery. \(f_{0}(x_{1},x_{2})\) represents the quantified uncertainty model. The functions of the modules in the proposed solution are as follows:  
1. Uncertainty Quantification Module: Based on the measured total current and actual voltage information, this module uses uncertainty quantification methods to obtain part of the acceleration information \(f_{0}(x_{1},x_{2})\) that causes system disturbances.
2. Extended State Observer (ESO) Module: Utilizing the information \(f_{0}(x_{1},x_{2})\) obtained from the Uncertainty Quantification Module, this module constructs an ESO with partially known disturbance information to estimate the unquantified disturbance information \(f_{1}(x_{1},x_{2})\) required by the ADRC closed-loop controller.
3. BP-ADRC Module: Based on the total system disturbance estimation information obtained above, this module designs a closed-loop controller with parameter adaptive capability using a BP neural network to achieve the transient stability control objective of the bus voltage disturbance.
## 4 Discussion
### Water sorption and Solubility  
The water sorption and solubility of the dental restorative material must be minimized to increase its lifespan by preventing microleakage and expansion in the oral environment [32]. Biodentine exhibits increased solubility compared with other restorative materials but is lesser than other calcium-silicate-based cements, likely due to its controlled water-powder ratio [33]. Solubility is the loss of material; the higher solubility of biodentine is mainly due to the increased release of calcium and hydroxide ions during the hydration process, which is necessary to maintain its bioactivity [34]. Calcium ions mineralize and proliferate the pulpal cells. Moreover, it maintains the mineralization of dentin and helps in the formation of dentin bridges. However, the increased solubility of Biodentine is condemnatory when it is used as a restorative material because the hydrolysis of calcium hydroxide causes the denaturation of collagen, resulting in the crystallization of dentinal tubules. Water sorption is related to the level of the porosity of the material. The water sorption of biodentine is a result of increased hydration and a water-reducing component in its composition [32; 33; 34].  
The mechanism responsible for water sorption and solubility is the diffusion of water into the matrix, resulting in the formation of voids, weakening the material strength, resulting in its deterioration and thus reducing the lifespan of restoration [35]. The addition of nanofillers reduces water sorption and solubility because they are insoluble in water, reducing fluid uptake [18].  
In the present study, group 2, with 10% nano zirconia, showed the most negligible water sorption value among all the compared groups, with group 4 exhibiting the highest value (Table 5, and Figure 2), and the most insignificant solubility was noticed in group 1, which was the same as group 2, as there was no significant difference between them. Group 4 showed the highest solubility (Table 6 and Figure 3). This can be explained by the study by [PERSON] et al., who stated that adding nano zirconium dioxide fillers to denture liners reduces water sorption and solubility [19]. Another study, in which silver nanoparticles were added to calcium silicate cements, showed favorable results [36]. [PERSON] et al. also supported the results of the current study; they added nano titania to conventional GIC and induced a reduction in its water sorption and solubility [18], which is in harmony with the studies by [PERSON] et al. [37] and [PERSON] et al. [38]. An increase in the nanofiller percentage causes an increase in water sorption and solubility, as noted by group 4, which could be due to the formation of multiple voids and the presence of more free molecules [27; 29].
## 2 Methodologies of Developing Fragility Curves
### Probabilistic Seismic Demand Model (PSDM)  
Another way of generating fragility curves is through the probabilistic seismic demand model [40; 41; 42; 45; 46; 47; 49; 51]. A probabilistic seismic demand model is a mathematical relation between the structural response and the earthquake intensity measure (IM) [54]. The mathematical representation of the PSDM leads to a closed form solution that permits the definition of the fragility curves [31]. Therefore, Equation (1) is transformed to the following expression:  
\[\text{G}_{\text{EDP}|\text{IM}}(\text{C}|\text{IM})=\mathbb{P}[\text{EDP}|\text {IM}\geq\text{C}|\text{IM}]=\Phi\left(\frac{\ln\hat{\mathbb{E}}\hat{\mathbb{ D}}\mathbb{P}|\text{IM}-\ln\hat{\mathbb{C}}}{\beta_{\text{EDP}|\text{IM}}}\right) \tag{8}\]  
\(\Phi(.)\) denotes the standard normal cumulative function, \(\hat{\mathbb{E}}\hat{\mathbb{D}}\mathbb{P}|\text{IM}\) the median structural demand, \(\hat{\mathbb{C}}\) the median value of the capacity, and \(\beta_{\text{EDP}|\text{IM}}\) the logarithm standard deviation.  
Figure 3: 16%, 50%, 84% summarized IDAs curves and corresponding values of IM at the level of capacity \(\hat{\mathbb{C}}\).  
The mathematical representation of median structural demand response EDP and the IM can be approximated by a two parameters power law model [31; 55]:  
\[\text{E}\text{\text{\text{\text{D}}}}\text{P}|\text{IM}=\text{a}\ \text{IM}^{\text{b}} \tag{9}\]  
The coefficients \(\alpha\) and b are calculated through linear regression analysis of logarithm of IM and EDP, so the Equation (9) is transformed to the following expression:  
\[\text{ln}\,\text{E}\text{\text{\text{D}}}\text{P}|\text{IM}=\text{b}\text{ln} \text{IM}+\text{ln}+\epsilon|\text{IM} \tag{10}\]  
where, \(\epsilon\) i IM is the random error with mean zero and variance \(\sigma^{2}\).  
The structural response demand is assumed to follow lognormal distribution [55] with logarithm standard deviation \(\beta_{\text{EDP}|\text{IM}}\) which is calculated by the following equation:  
\[\beta_{\text{EDP}|\text{IM}}=\sqrt{\frac{\sum_{i=1}^{\text{n}}\left(\text{ ln}\text{EDP}_{i}|\text{IM}-\text{ln}\,\text{E}\text{\text{\text{D}}}\text{P}| \text{IM}\right)^{2}}{\text{n}-2}} \tag{11}\]  
Furthermore, a bilinear regression model is also considered to capture the nonlinear behavior of the structural members. The bilinear regression model can be described by the following expression:  
\[\text{ln}\,\text{EDP}|\text{IM}=(\text{a}_{1}+\text{b}_{1}\,\text{ln}\text{IM })(1-\ \text{H}_{1})+\left[\,\text{ln}\text{EDP}|\text{IM}^{*}+\text{b}_{2}\left( \text{ln}\text{M}-\text{ln}\text{M}^{*}\right)\right]\text{H}_{1}+\epsilon| \text{IM} \tag{12}\]  
The coefficients \(\alpha_{1}\), b\({}_{1}\), and b\({}_{2}\) are defined through linear regression analysis and determine the slope of the two linear branches. H\({}_{1}\) is a dummy variable which is equal to H\({}_{1}\) = 0 for IM \(\leq\) IM\({}^{*}\) and H1 = 1 for IM \(>\) IM\({}^{*}\). Finally, the parameter IM\({}^{*}\) is the intersection of two linear branches, while InEDP\(|\text{IM}^{*}\) is the value of the EDP at the intersection point.
## 3 Results  
### Batch-Fermentation Characteristics at Different Agitation Speeds  
Surfactin-production profiles varied with varying agitation speeds of 300-500 rpm, which directly affected DOT throughout the culture (Figure 1). DOT levels at 300-500 rpm decreased sharply in the exponential stage of the culture, with less than 5% of DOT at 12 h. At 500 rpm, DOT recovered to over 50% after 28 h, while a negligible DOT level was maintained throughout the culture at 300-400 rpm. However, variability in surfactin production under different agitation speeds differed from the patterns in the DOT levels. The highest concentration of surfactin of 734 mg/L was observed at 400 rpm, followed by 619 mg/L at 500 rpm, and 453 mg/L at 300 rpm. Both the concentration and volumetric productivity of surfactin were determined by agitation speed in the first half of the culture. Surfactin concentration increased with the increment of agitation speed, especially from 12 h to 24 h. Volumetric productivities were maintained above 16 mg/L-h during that time. After 24 h, the productivities sharply decreased in order of agitation speed, with rates barely being over 10 mg/L-h until 32 h at 300 rpm, 28 h at 400 rpm, and 20 h at 500 rpm, respectively.  
OUR was positively correlated with volumetric surfactin productivity (Figure 2). When the OUR value increased from 0 to 250 s\({}^{-1}\), volumetric productivity increased up to 45 mg/L\(\cdot\)h; however, no further increase was observed in proportion to volumetric productivity when the OUR value was above 255 s\({}^{-1}\). \(K_{L}a\) increased with increasing agitation speed (F = 133.5, _df_ = 2.15, \(p\) < 0.001) (Figure 3). The highest \(K_{L}a\), of 0.0235 s\({}^{-1}\), was observed at 500 rpm, followed by 0.177 s\({}^{-1}\) at 400 rpm, and 0.0138 s\({}^{-1}\) at 300 rpm.  
Figure 1: Surfactin production depends on agitation speed. Cultivations were carried out in 5 L jar fermenters for 48 h. Filled triangle, surfactin; filled square, dissolved oxygen tension (DOT).
# Design and Experiment of a Passive Vibration Isolator for Small Unmanned Aerial Vehicles
## 2 Design of the Vibration Isolator and Test of Rubber Stiffness
### Drone Ground Vibration Test  
A drone ground vibration test was conducted to analyze the drone's natural frequency and its operational frequency band. Through a drone ground vibration test, the drone's first natural frequency was identified. Subsequently, the effectiveness of the vibration isolators mounted on a fixed-fixed beam, which simulates the first natural frequency of the drone, was assessed in terms of their damping capacity within the drone's operational frequency band.  
Table 4 lists the specifications of the drone used in the drone ground vibration test to determine its first natural frequency.  
Figure 7 depicts the configuration of the drone ground vibration test. Single-axis accelerometers were attached to plates 1 and 2. The acquired data were analyzed using FFT to observe the resonance points of the drone.  
Figure 8 presents the FFT graphs of the accelerometer data from the drone ground vibration test. In the drone ground vibration test, the rotor rotates at 2487 rpm.  
The analysis of the natural frequency in Figure 8 revealed that the first natural frequency of the drone is 10.9 Hz. The natural frequencies in the 40-85 Hz range represent the operational frequency band of the drone's rotors. By modeling the drone's first natural frequency at 10.9 Hz as a fixed-fixed beam, the vibration characteristics of the vibration isolator rubbers at different stiffness levels were assessed within the operational frequency band.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Value** \\ \hline Frame weight & 13.9 kg \\ Type & October \\ Wheelbase & 1200 mm \\ Motor rotation [RPM] & 2487–4981 \\ Motor rotation [Hz] & 41.45–83.01 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Drone’s specification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Load (kg)**} & \multirow{2}{*}{
\begin{tabular}{c} **Experiment** \\ **Displacement (mm)** \\ \end{tabular} } & \multicolumn{2}{c}{**Static Structural**} & \multirow{2}{*}{**Error (\%)**} \\  & & **Analysis** & \\  & & **Displacement (mm)** & \\ \hline
0.5 & 0.260 & 0.265 & 1.8 \\
1 & 0.476 & 0.486 & 2.0 \\
1.5 & 0.704 & 0.727 & 3.1 \\
2 & 0.942 & 0.987 & 4.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between experiment and static structural analysis.
## 2 Methodology
### Connectivity Schemes  
There are many ways to establish satellite pairs and then assign ISLs between satellites in measurement epochs, i.e., satellite pairs choice may change in each epoch. In our research, this ranging schedule is called a \"connectivity scheme\". In our previous studies, we stated that the choice of connectivity scheme does not have a major impact on orbit determination and clock estimation unless observation conditions are not optimal (e.g., measurement accuracy and interval, or the number of ground stations) [12; 34]. However, the choice of connectivity scheme is still important to maximize efficiency of the connections and meet data-exchange requirements [39; 40]. It is also important in the case of mixed constellation configurations of geostationary orbit (GEO), inclined geostationary orbit (IGSO), and medium earth orbit (MEO) satellites (e.g., the BeiDou configuration [41]).  
The ISL observation strategy is based on the satellite pairs; satellites establish connections during predefined time slots according to the chosen connectivity scheme. Satellite pairs can change from epoch to epoch (dynamic schemes), or the connection can be kept for several measurement epochs (static schemes). Connectivity schemes might also be defined based on geometric information about satellites, the distances between them, or the pointing angles of the ISL system [42], and the selection of algorithms that maximize network connectivity and improve network performance [39; 43; 44; 45; 46; 47; 48].  
Mutual visibility is very important for establishing connections between satellites in each of the measurement epochs, as ISLs cannot be maintained permanently during the whole system period because of temporary losses of visibility [42]. Technological limitations of the measurements (e.g., distance, azimuth, and elevation) could affect the ISLs [49] but the main obstructions to establishing connections between satellites are the Earth and atmosphere, for which the height that affects the satellite links is defined as 1000 km [50; 51]. In our simulations, links crossing the atmosphere are not considered as a pair to connect the satellites.  
We applied two types of connectivity schemes: intra-plane and sequential, which were used in our previous research [12; 34]. The intra-plane scheme is created on the basis of the slot of the satellites in the orbital plane. In intra-plane closed configuration all satellites in the same orbital plane are connected, whereas in intra-plane open configuration, one connection between adjacent satellites is missing. The intra-plane schemes use one-way connection type. The sequential scheme consists of observation scenarios based on a sequence of measurements defined a priori [3; 11; 12; 34].