esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Control Strategies of the HIL Equipment
### Control Logic Implementation  
The control architecture has been developed with reference to the HIL system model of Figure 4. The control system comprises of four subsystems as shown in Figure 11:  
1. control system of the impact phase;
2. control system of the desired force profile;
3. selection of operative phases;
4. model of dynamic system.  
The subsystem 3 regulates the complete process of perturbation that is composed of four phases. The four phases are:  
1. Idle: the voltage of each valve is reduced to zero. It is the starting condition of the whole system;
2. Approach: the rear chamber of the actuator is supplied while the front chamber is discharged, to move the piston rod from the initial retracted condition towards the subject's mass;  
Figure 11: Control architecture of the system.  
* Strike: a PI controller is used to modulate the valves' command signals in order to reduce the difference between the desired force (set) and the feedback signal from the load cell;
* Return: the actuator's front chamber is supplied and the rear chamber is discharged to perform the high speed retraction of piston.  
The logic of the control system is based on the concept that only one phase should be active at the same time during the process. The start signal shifts the system from the Idle phase (I) to the Approach phase (II), in which the piston is extended. If the measured contact force exceeds a pre-set threshold, the system shifts into the Strike phase (III) and performs the contact force profile tracking. The Return phase (IV) is activated when the force signal drops below a defined threshold. Following phase IV (Return), a timeout control forces the system to return to phase (I) within few seconds.  
The reference force generation block allows the user to set customizable profiles, in terms of shape, amplitude and duration. For instance, the impulse could be set as a constant signal with a predefined level and duration, or as a ramp to reduce the dynamics of the force error signal. The model is able to detect when the contact between perturbator tip and environment actually occurs; this is verified when the condition in Equation (12) is true:  
\[x_{1}+x_{3}-dx\geq x_{2} \tag{12}\]  
where \(x_{1}\), \(x_{2}\), and \(x_{3}\) are, respectively, the displacements of the cylinder frame, of the subject, and of the piston with respect to the cylinder frame (see Figures 5, 6 and 8). Since the actual control of HIL equipment was realized on a dSPACE(r) Real-Time target system, programmed directly in Simulink(r) Real-Time, the model control logic was implemented in the same environment.  
A laser sensor (component 10, in Figure 3) was used for real time detection of the \(x_{3}\) displacement; using this information, by means of a closed-loop motion control, it is possible to impose the striker speed at the impact. The difference between a desired velocity value and the actual velocity calculated from the sensor output is used by a PI controller to provide the command signal for the valves' drivers.
# Synergistic Effects of Fuel Components on Aromatics Formation in Combustion: A Review
## 4 Synergistic Effects on PAHs Formation
### Synergistic Effect on Naphthalene Formation
[129], it was observed that the mechanism proposed by [PERSON] et al. [113] highlighted the significance of reaction (14) in naphthalene formation due to the addition of toluene in the isooctane toluene blend, followed by the formation of C\({}_{10}\)H\({}_{9}\) through benzyl radical. Conversely, the mechanism proposed by [PERSON] et al. [129] emphasized that naphthalene formation during the isooctane/toluene flame study primarily occurred through the recombination reaction of a resonantly stabilized pentadienyl radical (9) and the reaction of benzene with iC\({}_{4}\)H\({}_{5}\). [PERSON] et al. [45] conducted experiments on benzene/ethylene and benzene/acetylene blend pyrolysis in a single pulse shock tube at high pressure, with product composition detected through the gas chromatography-mass spectrometry technique. Their study revealed a synergistic effect in naphthalene formation during the pyrolysis of the blend at high pressure, favoring the formation of larger PAH, as illustrated in Figure 21a. Interestingly, the ethylene/benzene blend was found to lower the temperature of naphthalene formation compared to the pure benzene and benzene/acetylene blends. Figure 21a indicates a negligible difference between the acetylene/benzene blend and pure benzene in terms of naphthalene formation, suggesting that the HACA pathway is less efficient in the presence of acetylene. The kinetic study and rate of production analysis elucidated those dominant reactions during pyrolysis, including hydrogen abstraction and benzene decomposition. Furthermore, the HACA reaction pathway through A1C\({}_{2}\)H enhanced the formation of the naphth radical, which then underwent hydrogen abstraction, while adding ethylene enhanced the formation of naphthalene via 6, 7 and 15. Additionally, [PERSON] et al. [99] investigated the addition of various fuel components, such as n-heptane, ethanol, benzene, and toluene, to ethylene in a diffusion flame. Their study concluded that adding liquid fuel components, particularly benzene and toluene, to ethylene exhibited a high synergistic effect on naphthalene compared to all mentioned fuel blends with ethylene and pure ethylene diffusion flame, as depicted in Figure 21b. \[\text{A2-}+\text{C}_{2}\text{H}_{4}=\text{A2}+\text{C}_{2}\text{H}_{3} \tag{15}\]  
Figure 20: PAH formation in the counterflow flames of (**a**) n-heptane and toluene and (**b**) iso-octane and toluene. Symbols show the PAHs measured results measured by LII (indication of larger PAH molecules such as A5) and LIF (indication of A2 and A4), and lines show the simulated results of naphthalene (A2), phenanthrene (A3), pyrene (A4) and benzo[e]pyrene (A5) (reprinted from [PERSON] et al., 2017) [97].
## 3 Advancements in Imaging and Augmented Reality (AR)
### Augmented Reality in Intraoperative Navigation
In their early experience, AR guidance enabled the accurate marking of intraprostatic lesions and suspected ECE, with high concordance between AR-identified targets and final pathology (100% lesion match; 73% positive NVB biopsies at marked ECE sites) [17]. A follow-up study validated the dimensional accuracy of these models, reporting <3 mm mismatch in 85% of prostate surfaces when compared to scanned specimens [18]. Building on this, the group introduced a three-dimensional elastic AR system to dynamically simulate prostate deformation during the nerve-sparing phase [19]. This technology achieved a 100%  
Figure 3: Augmented Reality in RAPN. Real-time projection of tumors and vessels onto the kidney during surgery. accuracy rate in identifying capsular involvement compared to only 47% with standard 2D cognitive guidance (\(p<0.05\)), offering the potential to reduce positive surgical margins while preserving functional outcomes.
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
## 3 Methodology
### Performance-Requirement-Driven Fast Satellite Selection Algorithm
#### 3.2.1 Geometric Equal Satellite Selection for the Initial Subset  
As mentioned in Sections 2.2.1 and 2.2.2, If the measurement errors are assumed to be independent and identically distributed, the ideal minimal 2 drms and HPL geometries have a similar distribution that the satellites with low elevations equally spaced at the low elevation bottom of the visible area, the subset can be constructed by geometric equally selecting the satellites so that an appropriate geometry can be formed and the relatively small accuracy and max slope values can be guaranteed. This principle has been used for \(HDOP\)-based satellite selection but is utilized for initial subset construction here. An example for initial subset selection is shown in Figure 3.  
The specific steps for initial subset selection can be summarized as:  
1. Selecting the first \(a\) satellites with the highest elevations then selecting the satellite with the lowest elevation as the starting point of the region partition process in the next step. The chosen satellites are shown as the light cyan points in Figure 3;
2. Calculating the optimal positions by dividing the visible region into equally \(m-a\) parts on the azimuth from the starting point selected in the previous step. The optimal positions are shown as the grey diamond points in Figure 3;
3. Taking out the satellites nearest to each optimal position sequentially from the rest ones, as the manganese violet points in Figure 3. Therefore, the first \(m\) satellites are selected.
## 3 Results and Discussion
### Health Risks  
To assess the total risk of metal(loid)s in wheat, corn and soil to human health, the TTHQ approach was developed based on the EPA's Guidelines for Health Risk Assessment of Chemical Mixtures [46]. The hazard index is equal to the sum of the hazard quotients, as described in Equation (2-4). A significant variable in calculating health risks is the IR, which measures (kg/individual/d) the daily contact rate to an exposure medium (for soil this is 64 mg/d for adults and 104 mg/d for children) [44; 45; 47]. According to the 2018 statistical yearbook of Hebei province, for wheat IR the annual adult consumption in Hebei province is 81.74 kg/individual, which is 223.93 g/individual/d. For children, the consumption is 74.57 g/individual/d, which is approximately one-third that of adults. The statistical yearbook of Hebei province did not give statistics on corn consumption. The IR of corn in this study used consumption rates reported by [PERSON] et al. (2011a) [23], which for adults were 150 g/individual/d and for children was 100 g/individual/d. When the HQ exceeds 1, there may be concern about potential health effects (Table 2). It is important to recognize that, although no single metal exposure exceeds 1, the total risk of all metal(loid)s being consumed from crops and soil is significant for children and adults (Figure 5). The soil THQ for adults and children (0.015 and 0.024, respectively) indicates that people are unlikely to be endangered by metal(loid)s through soil consumption. However, the corn THQ for adults and children (0.47 and 1.04, respectively) indicates that corn is a likely source of exposure for children, which could result in adverse health effects. The wheat THQ for adults and children (1.29 and 1.42, respectively), as well as the TTHQ (2.48 and 1.78, respectively), will lead to health risks for both adults and children. The HQ of As, Cu, and Zn in crops is higher than that of the other metal(loid)s. In particular, the HQ of As, Cu, and Zn in wheat all exceed 0.3, with 2% of the As samples in wheat having HQ values greater than 1. Additionally, the contribution of Zn, Cu, and As in crops to total risk was the largest for different receptor populations, with the three values contributing more than 17%. A valuable finding is that the contribution to risk in wheat exceeded 25% (30.05%, 32.63%, and 26.49%), and the contribution to overall health risk in wheat was more than 80% (Figure 6). The HQ values for Hg (corn 6.79%, and wheat 2.02%), Ni (corn 5.68%, and wheat 1.44%), Cd (corn 2.25%, and wheat 3.02%), Pb (corn 6.46%, and wheat 4.34%), and Cr (corn 0.009%, and wheat 0.006%) in crops contributed to TTHQ values that were less than 10%.
# Review on Computer Aided Weld Defect Detection from Radiography Images
## 3 Image Preprocessing
### Noise Removal  
The noise pixels are usually distributed irregularly in the image. Their gray level values pixels are different from those of their surrounding pixels. Filtering methods are commonly used to remove the noise pixels based on the fact that the noise is characterized as high frequency values. [PERSON] designed a one-dimensional FFT-filter for detecting the crack flaw. This filter including a column-wise FFT high-pass Bessel operation can distinguish between undercuts and cracks [7]. The noise can be easily eliminated and the cracks can be recognized clearly when a row-oriented low-pass is used to the output of this filter. [PERSON] used a wavelet filter to transform the image in order to restrain the noise with a simple threshold operation [8]. The median filter and adaptive Wiener filter were applied successfully for removing the noise from images in many literatures [9; 10; 11; 12; 13]. Median filter is a nonlinear, low-pass filter. It usually deploys a template and replaces the pixel value with the median of the neighboring pixels. [PERSON] and [PERSON] presented that the Median filter is adequate to be applied to radiographs of continuous welds [9]. [PERSON] applied an adaptive Wiener filter and Gaussian low-pass filter for eliminating noise. The Wiener filters carried out the smoothing with different degrees. This adaptive filter retained the edges and other high-frequency information. Gaussian low-pass filter smooth the image in the frequency domain by alternating a specified range of high-frequency components [6]. The performance of the filter depends on the size of the filter. The defect may not be filtered out when the size is too small, while the background is estimated inaccurately when the size is large. Moreover, finding a filter which can be used on all radiographic images is difficult.  
Figure 2: Flowchart of the review for studies on automatic welding defect detection.  
[PERSON] used the blind image separation (BIS) instead of the filtering method. The method firstly selected the appropriate method for source extraction, then identified multiple-input multiple-output finite impulse response, lastly eliminated ambiguities of convolutive blind source separation by a correlation method. Based on the procedure, the noise is separated from the gamma radiography image [14].
# Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
###### Abstract
In this paper, we propose an image segmentation-based monocular depth estimation model that incorporates attention mechanisms. We adopted an image segmentation method (namely the cluster method) to segment foreground and background regions. Then, the depth estimation model could individually predict the depth maps of the foreground and background regions while mitigating any interference from significant differences in image depth. Moreover, we attempted to use attention mechanisms to extract meaningful features in the channel and spatial dimensions to improve depth estimation. We conducted several experiments on the KITTI dataset [37] to validate the performance of our proposed model. An ablation study showed the improvements from adding different attention mechanisms. Once we identified the most suitable attention mechanism, we used it to individually predict foreground and background depth maps and stitch together pairs of maps to create final depth maps. The results showed that our method could outperform previous methods by 27.5% on the KITTI dataset. The main contributions of our work are as follows:  
* The addition of attention mechanisms to extract meaningful features to improve depth estimation;
* A segmentation technique that can segment foreground regions via a cluster method and use the segmented regions to improve foreground depth estimation;* A new architecture that can individually predict foreground and background maps while avoiding effects from significant differences in field depth, especially in outdoor scenes. The remainder of this paper is organized as follows. Section 2 introduces some related works. Section 3 presents our proposed model. Our ablation study and comparison results are presented in Section 4. Section 5 concludes this paper.
# Reducing System Load of Effective Video Using a Network Model
###### Abstract  
Recently, as non-face-to-face work has become more common, the development of streaming services has become a significant issue. As these services are applied in increasingly diverse fields, various problems are caused by the overloading of systems when users try to transmit high-quality images. In this paper, SRGAN (Super Resolution Generative Adversarial Network) and DAIN (Depth-Aware Video Frame Interpolation) deep learning were used to reduce the overload that occurs during real-time video transmission. Images were divided into a FoV (Field of view) region and a non-FoV (Non-Field of view) region, and SRGAN was applied to the former, DAIN to the latter. Through this process, image quality was improved and system load was reduced.  
DAIN; SST; FBF; front on the backward frame; segmentation +
Footnote †: journal: Applied Sciences
## 2 Methods
### Search Strategy  
Selecting the optimal MRNR feature subset quickly and accurately poses a challenging task due to the large number of candidate MRNR feature subsets in high-dimensional datasets. In this paper, from the perspective of dimensionality reduction and classification performance, we consider that the MRNR feature subset with the minimum size is the optimal feature subset.  
Let the feature subset \(F_{min}=\{f_{k},\,k=1,\,2,\ldots,m\}\) (\(m<N\)) be the MRNR feature subset with the minimum size. When \(m=1\), the solution for \(f_{1}\) is the feature that maximizes \(MI(c;f_{i})(1\leq i\leq N)\). When \(m>1\), \(MI(c;F_{min})\) can be expressed in the form of (8):  
\[MI(c;F_{min})=MI(c;f_{1},f_{2},\ldots,f_{m}) \tag{8}\] \[=MI(c;f_{1},f_{2},\ldots,f_{m-1})+MI(c;f_{m}|f_{m-1},\ldots,f_{1})\] \[=MI(c;f_{1},f_{2},\ldots,f_{m-2})+\sum_{k=m-1}^{m}MI(c;f_{k}|f_{k -1},\ldots,f_{1})\] \[=MI(c;f_{1})+\sum_{k=2}^{m}MI(c;f_{k}|f_{k-1},\ldots,f_{1})\] \[=\sum_{k=1}^{m}MI(c;f_{k}|f_{k-1},\ldots,f_{1})\]  
the \(kth\) feature \(f_{k}\) can be determined as the one that maximizes the conditional MI \(MI(c;f_{k}|f_{k-1},\ldots,f_{1})\), where \(f_{k-1},\,\ldots,f_{1}\) are previously selected features.  
Based on the above analysis, a greedy search strategy can be used to iteratively select the feature with the maximum conditional mutual information (MCMI) until the MI between the feature subset and the target class is equal to that of the original feature set and the target class. The evaluation criterion of MCMI can be formulated as follows:  
\[\max_{f_{j}\in F-F_{s}}MI(c;f_{i}|F_{s}) \tag{9}\]  
where \(F\) is the original feature set, \(F_{s}\) is the previously selected feature subset, and feature \(f_{i}\) belongs to \(F-F_{s}\).  
Furthermore, it should be noted that in specific situations, the later selected features may cause one or more previously selected features to become redundant. Therefore, it is necessary to remove any potential redundant features from the selected feature subset after the FS process.
# Data Fusion-Based Structural Damage Identification Approach Integrating Fractal and RCPN
## 3 Structural Damage Identification Methods  
In order to improve the ability of the damage detection method in extracting structural damage features and noise processing--effectively improving the precision of structural damage detection--a structural damage detection method based on fractal dimension, data fusion and RCPN is proposed as shown in Figure 2.
# Research on Out-of-Plane Bending Test of PVB Laminated Glass Plate with Different Number of Layers
## 2 Materials and Methods
### Bending Experiment on PVB Laminated Glass  
#### 2.2.1 LG Sample Design  
To investigate the bending behavior of different layers of LG under out-of-plane loading, we conducted bending experiments on LG samples. All LG samples were custom-made by the manufacturer, and the glass plates used met the requirements of EN 16612 [11]. We prepared three types of LG samples: 1-layer, 2-layer, and 3-layer. Each type was represented by three identical pieces, totaling nine LG samples. The dimensions of all LG samples were 1200 mm in length and width. In the LG samples, the thickness of each glass layer was 11.8 mm, and the thickness of each PVB layer was 1.1 mm. Figure 5 illustrates the geometry and cross-section of the 3-layer LG sample. Table 4 details the parameters of all of the samples. In the table, 3L-a to 3L-c, 2L-a to 2L-c, and 1L-a to 1L-c denote the three samples of the respective 3-layer, 2-layer, and 1-layer LG.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Specimen Label** & \(n\) & \(N_{a}\) **(kN)** & \(d_{a}\) **(mm)** & \(D_{t}\) **(kN/mm)** \\ \hline
1 & 3L-a & 3 & 47.8 & 16.4 & 2.91 \\
2 & 3L-b & & 34.2 & 13.5 & 2.53 \\
3 & 3L-c & & 37.3 & 13.1 & 2.86 \\
4 & 2L-a & 2 & 31 & 16.3 & 1.9 \\
5 & 2L-b & & 35 & 21.9 & 1.6 \\
6 & 2L-c & & 42 & 25.8 & 1.63 \\
7 & 1L-a & 1 & 22.1 & 24.6 & 0.9 \\
8 & 1L-b & & 26.5 & 27.9 & 0.95 \\
9 & 1L-c & & 25.5 & 21.1 & 1.21 \\ \hline \hline \end{tabular} Note: \(N_{u}\) = ultimate load; \(d_{u}\) = ultimate deflection of plate center; \(D_{t}\) = average slope of load deflection curve; n = number of glass layers.  
\end{table}
Table 4: Sample-related parameters and experimental results.  
Figure 5: PVB laminated glass specimen.
# A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining
## 2 Background
### Privacy and Process Mining  
Privacy concerns primarily center on individual autonomy, encompassing the ability to regulate and determine the entities permitted to access personal data. This principle is regarded as a fundamental human right and is subject to regulatory frameworks across multiple jurisdictions. Privacy protection encompasses a broader scope than security, necessitating a trusted framework that fosters reliable interactions between data providers and consumers. In contemporary research and practice, process mining and privacy are addressed concurrently, highlighting their interconnected significance. The balance between the accuracy of a discovered process model and the preservation of privacy remains a critical and pressing consideration. Ensuring GDPR-compliant process mining necessitates the implementation of organizational and technical privacy safeguards across all phases of the development life-cycle.  
Privacy has become a focal point in the modern data-driven society, largely driven by the prevalence of data breaches and the enactment of regulatory frameworks such as GDPRin Europe. GDPR defines personal data as any information pertaining to an identified or identifiable natural person, referred to as the \"data subject\". An identifiable natural person is one who can be recognized, either directly or indirectly. The utilization and governance of medical data must also adhere to international legal frameworks designed to safeguard personal data privacy and security. The governance of health data entails risks that may jeopardize an individual's well-being and exerts significant influence on both their personal and professional spheres. To ensure compliance, the management of health data is regulated by multiple legal frameworks, including the Health Insurance Portability and Accountability Act (HIPAA) privacy regulations [13] and the GDPR.  
The privacy-utility trade-off in process mining pertains to the fundamental challenge of safeguarding sensitive information within event logs while preserving the analytical integrity required for deriving precise and meaningful process insights. Event logs frequently include personal or confidential information, such as user identifiers, timestamps, and case attributes. To mitigate the risk of re-identification or data leakage, privacy-preserving methodologies such as data anonymization and differential privacy are employed to enhance data privacy while maintaining analytical utility. However, these privacy-enhancing techniques often diminish the utility of the data, which may adversely affect the accuracy of process discovery, conformance checking, and model enhancement results. This trade-off is particularly significant in sectors such as healthcare and finance, where regulatory frameworks, such as the GDPR, impose stringent data protection requirements while stakeholders continue to expect insightful and meaningful process analyses. Achieving an optimal equilibrium between data utility and privacy preservation remains a fundamental challenge in the domain of PPPM.