esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Velocity Obstacle Model for MASS
### Velocity Obstacle
#### 3.1.2 Absolute Velocity Model  
In order to determine the feasible space for \(v_{A}\), Figure 1d shows the velocity model for the MASS \(A\). By adding all vectors in \(CC_{A,B}\) with a vector \(v_{B}\), we will obtain a cone-shaped region \(VO_{B}\). The \(\oplus\) operation in (3) is called the Minkowski vector sum.  
\[VO_{B}=\text{CC}_{A,B}\oplus v_{B} \tag{3}\]  
If the velocity vector \(v_{A}\) is in \(VO_{B}\), the ship \(A\) may collide with \(B\) after some time. Therefore, the vectors outside \(VO_{B}\) can be selected for \(v_{A}\) while keeping collision avoidance with \(B\).
# Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity
## 3 Super-Resolution Imaging for Passive Radar
### Further Discussions  
#### 3.3.1 Influence of Noise  
It should be emphasized that the use of the ESPRIT algorithm for super-resolution passive radar imaging requires a high SNR (SNR \(\geq\) 30 dB). Otherwise, it will have a large reconstruction error. In contrast, the RELAX and CS methods achieve good recovery performance even in low SNR cases.  
In addition, the estimation of the noise level given in Equation (25) is still an open problem in sparse signal recovery. If \(\delta\) is too large, a part of the signal component cannot be recovered. On the other hand, if \(\delta\) is too small, some noise components may be treated as signals. In this paper, we estimate the noise level \(\delta\) using the energy-based method presented in [34].
## 3 System Description
A graphical tool will analyze, in real time, the 3D position and distance to adjacent slabs. * Provide information on structural integrity of the slabs and possible displacement (inclination and distance), vibration, etc. during their use. As for the previous objective, a tool to store and analyze information and gather real-time information will be developed. During the installation phase, sensors integrated in board will have to give the information necessary to:  
* Calculate the distance between slabs. This distance will not exceed 1 cm. with a tolerance of \(\pm\)0.1 cm. * Calculate the slope of the slab. It is necessary to achieve a precision of 0.17% (0.1 \(\mathrm{\SIUnitSymbolCelsius}\)). During the maintenance phase, the sensors will provide the information necessary to ensure the real-time knowledge of the state of the infrastructure:  
* Acceleration readings of the slab tracks when trains are passing over them. In order to detect defects or abnormal situations, the frequency of the different vibrations obtained from the acceleration data is studied. To do this the raw acceleration data are transformed to the frequency domain by means of the Fast Fourier Transform (FFT). Due to the vibrating nature of slab tracks, acceleration must be obtained with a 0.005 g resolution, accuracy of 15% and bandwidth of 1 Hz-800 Hz. * Displacement of the slab relative to its original position recorded during the installation phase. * Alarms for low battery and sensor failure. The goal of the system is to improve the safety of the infrastructure by allowing early detection of defects, even before they are visually noticeable. This will reduce maintenance interventions and costs as long as the lifespan of the system is reasonably high. In order to support this, the following systems of energy production will be studied for recharging the batteries:  
* Vibrations generated by the train captured by piezoelectric sensors. * Solar panels
* Thermal gradient  
Finally, the integration mechanism of the encapsulation in the slab should allow extraction so that maintenance tasks (replacement of faulty sensors, battery change, _etc._) can be easily carried out. The node must be accessible and will provide a plug & play attachment. In Figure 2 the high level architecture for the hardware components of the system is shown. The system is composed of a microcontroller that controls different sensors. The system is powered by a battery and optionally by an energy harvesting module. The module is installed inside selected slab tracks to monitor different parameters as described in this section.
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 4 Experiments
### Experimental Details  
Our study work was implemented in PyTorch using an NVIDIA GeForce GTX 2080. We resized all the original images of the VisA and the MPDD datasets to 256 \(\times\) 256 for both training and testing. We divided 20% of the training dataset into validation sets. For each category of these two datasets, we utilized AdamW optimizer [34] with \(\beta=(0.5,0.999)\). We set the initial learning rate to \(10^{-6}\) and used cosine annealing [35] to adjust the learning rate with \(T\_\max=100\) and \(eta\_\_\min=10^{-6}\). The maximum number of training epochs was set to 500, and the training was stopped early if the loss did not decrease within 20 consecutive epochs.  
We evaluated our approach using different metrics for comparison with other baselines. We used the area under the curve (AUC) of the receiver operating characteristic (ROC) to evaluate the performance of image-level anomaly detection and pixel-level anomaly localization.
## 4 Results and Discussions
### Hysteretic Curves
#### 4.1.1 Cast-in-Place RACSWs  
Figure 11a-c demonstrate the following:  
(1) The specimens (Figure 11a-c) are in the elastic stage at the initial loading stage, the hysteretic curves are linear, and the loops are narrow. The hysteresis loops become spindle-shaped, and the loops open gradually with the expansion and connection of cracks in the cast-in-place RACSWs. A significant \"pinch effect\" occurs at the zero point. The hysteretic curves become fully arched and have a reverse S shape after the peak load because of the large area of concrete falling off in diagonal compressive strips and the plastic deformations of the steel beam and columns. The areas enclosed by the loops increase. The bearing capacity of the specimens decreases noticeably under the same load.  
(2) Comparison of the hysteretic curves of the SPE1 infilled ordinary concrete wall (Figure 11a) and SPE2 infilled RACSWs with a 100% replacement rate of recycled coarse aggregate (Figure 11b) show that the area and shape surrounded by hysteretic curves and peak loads of the structure are close, thereby indicating that the performance of RACSWs is close to that of the ordinary concrete wall.  
(3) The hysteretic curves of specimens SPE2 (Figure 11b) and SPE3 (Figure 11c) are relatively close, thereby showing that the connecting stiffness of BCJs slightly influences the hysteretic behavior of the specimens of infilled cast-in-place RACSWs.  
Figure 10: Transfer mechanism of the lateral load.
## 1 Introduction
### Treatment
Simulations are achieved via the use of filters named head-related transfer functions (HRTFs), which simulate the free-field acoustic path of a sound source to the ear canal [33]. Each HRTF contains the directional-dependent data for each ear, including the interaural differences in time and intensity in addition to the spectral shaping of the sound caused by the pinnae, head, and torso [33]. From here, a monaural anechoic signal can be filtered with an HRTF to simulate a sound source localised at a specific point across a 360-degree sphere over headphones [34,35]. Current VR head-mounted displays (HMDs) contain accelerometers and video tracking systems to determine a player's head rotation and position within both the physical and virtual environment. With this information, the audio rendering engine will interpolate between the nearest HRTF pair [36,37]. Further to this, distance is replicated by controlling the amplitude, spectral content, and reverberant energy of a virtual sound source in response to the calculated distance between the listener and the position of the sound [33]. SoundFields, a novel virtual reality game designed to address auditory hypersensitivity in autistic children and young people, has the capability to present naturalistic representations of feared auditory stimuli to the player using binaural-based spatial audio [38; 39]. Feared auditory stimuli are incorporated into serious game mechanics established in CBT approaches. Following participation, a significant decrease in the self-reported levels of anxiety scores in the pre- and post-study measurement sessions were observed. Additionally, the results showed a significant increase in tracked interaction time with those adverse stimuli between sessions one and four. These findings indicated that SoundFields may have the potential to support young autistic people with their hypersensitivity to sound. Despite the promising results, the participants engaged with the game under experimental conditions that would evaluate how autistic children would naturally engage with the intervention within the home.
# Semi-Automated Approach for Building Event Logs for Process Mining from Relational Database
## 2 Background
### Related Work  
In [31], the relational XES (RXES) framework is proposed based on a scheme to store events in a database. This framework is derived from the UML class diagram used to define the XES standard, separates the content of the event logs, traces and events in different tables, assigns unique identifiers, and maintains the correlation between these elements. In the testing stage, it is shown that RXES generally uses less memory compared to file-based implementations of XES. In [32], the authors present a declarative process mining approach by implementing SQL query patterns for extracting the constraints of the declarative process model from the database. The authors propose using a RXES schema, assuming the data will be stored in this database type. They insert several event logs in a database based on the RXES schema to validate the approach. Defining a mapping between the attributes of the tables through a template supported by predefined SQL queries. The preceding allows for discovering constraints involving different perspectives of the business process and some control flow elements. In our approach, the event data stored in a relational database are identified and extracted to build an event log based on the XES standard.  
The work in [33] presents another approach to discovering multi-perspective process models from event logs that allows the user to discover declarative process models taking into account all the information that an event log can provide. Similarly, a set of SQL-based patterns is used for retrieving the event data. In [14] the authors propose a quality-based semi-automated approach for generating event logs from a relational database, developing an assessment of their quality based on common data quality dimensions. From the quality dimensions, support is assumed to decide the data's suitability for implementing a process mining approach. This approach allows the mapping of database columns to event log attributes, making it possible to generate the event log. The main difference with our experiment is the granularity level in data events extraction. Our approach proposes using the attribute values that represent an activity within the business process, focusing on the fine granularity level in extracting the event data. Additionally, the built event log is evaluated using quality metrics to guarantee the quality of the discovered business process.
# Remaining Useful Life Prediction of Broken Rotor Bar Based on Data-Driven and Degradation Model
## 6 Prognosis Results
The \(NRMSE\) facilitates the comparison between models with different scales, allowing the error to be the same magnitude as the \(RUL\), which is denoted by  
\[NRMSE=\frac{RMSE}{max(RUL_{Act})-min(RUL_{Act})} \tag{14}\]  
\(MAPE\) defines the size of the error in percentage terms, which is defined as  
\[MAPE=\frac{100}{N}\sum_{i=1}^{N}\left|\frac{RUL_{Act}-RUL_{Pre}}{RUL_{Act}}\right| \tag{15}\]  
Moreover, smaller \(RMSE\), \(NRMSE\), and \(MAPE\) values mean lower prediction errors and higher prediction accuracies. Fitness degree \(R^{2}\), within this metric better performance of RUL prediction, is achieved when \(R^{2}\) results in values are near 1. The \(R^{2}\) is computed as follows. \[R^{2}=1-\frac{\sum_{i=1}^{N}(RUL_{Act}-RUL_{Pre})^{2}}{\sum_{i=1}^{N}(RUL_{Act })^{2}} \tag{16}\]  
Figure 9: Predicted trajectories of the HI-torque and HI-current using the exponential degradation model. The \(\alpha-\lambda\) performance metric of the prediction results based on the HI-torque and HI-current is illustrated in Figure 10. The error bound \(\alpha\) for the RUL estimates is 20%. It can be observed that almost all values of the estimated RUL lie within the specified error bounds of the calculated RUL. The calculated \(RMSE\), \(NRMSE\), \(MAPE\), and \(R^{2}\) metrics are displayed in the Table 2. It is observed that the HI-current has a smaller value of \(R^{2}\) metric performance; it is more or less half of the HI-torque value. The average result of the \(R^{2}\) is equal to 0.65; it is not very close to 1 but still considered to have good performance. The average \(NRMSE\) result is equal to 0.26; this value is reduced compared with other prognostic methods, proving the proposed approach's ability to predict the RUL. Additionally, the average \(MAPE\) result is equal to 8.26%; this percentage is less than 10%, which proves the efficiency of the proposed prognostic methodology. According to the results of the RUL prediction and the calculated performance metric, it is observed that the HI-current has smaller \(RMSE\) and \(MAPE\) values during a whole life compared with the HI-torque with the possibility to anticipate the EOL of the machine better.
## 3 Results and Discussion
### Chemical Characterization of C. australasia Peel Using HPLC-QTof-MS
However, it is the first time that this flavonoid derivative compound was identified in _C. australsica_ L. peel. Four compounds were characterized among the group of other polyphenols. In this regard, coumarin (peak 2, \(\mathrm{[M-H]^{-}}\) ions at \(m/z\) 145.0274) and pyrogallol (peak 15, \(\mathrm{[M-H]^{-}}\) ion at \(m/z\) 125.0229) were identified using commercial standards as highlighted in Table 1. Peak 4, with molecular formula \(\mathrm{C_{S}H_{S}O}\), was tentatively characterized as vinylphenol. Its main fragment yielded at \(m/z\) 101 corresponds to the loss of one molecule of water. Vinylphenols, in particular 4-vinylphenol and 4-vinylguaicol, have been documented in cirtuses such as blood orange or in _Citrus aurantium_ L. bloom [34], but not in the studied matrix. Two limonoids, well-known compounds from the _Citrus_ genus, were also detected in _C. australsica_ L. peel (peaks 16, 17, 18 and 22). Peak 16, with molecular formula \(\mathrm{C_{26}H_{34}O_{9}}\), and which, according to its fragmentation pattern, included main fragments at \(m/z\) 471 \(\mathrm{[M-H-C_{2}O]}\) and 333 \(\mathrm{[M-H-C_{4}H_{4}O-2 CO_{2}]^{-}}\)[35], was tentatively identified as deacetylnomilinic acid. This limonoid derivative has been earlier described in cirtuses [35; 36], but it is the first time that this compound has been detected in _C. australsica_ L. peel. Meanwhile, peak 18 was tentatively identified as limonin. Importantly, it presents
## 4 Analysis and Discussion
### Identifying the Network Performance Changes Under Critical-Link Attacks
The above results underscore that all centrality measures demonstrate a consistent trend: increasing disruptions lead to sharper declines in CCDF values, indicating reduced connectivity, accessibility, flow-concentrated links, and link influence within the network. The severity of these declines highlights the importance of critical links, whose removal causes cascading failures that fragment the network and significantly reduce its overall network performance and resilience. During disruptive situations, the tail of the distribution is damaged as critical links of the network are attacked. Consequently, all the distribution functions under disruption depict a lower tail with reduced probability and criticality for each topological parameter, as the most impacted segments are targeted during each disruption iteration. This clearly indicates that once the critical links of the network are attacked, the network becomes fragile and cannot maintain its performance across the considered topological parameters, further emphasizing the network's growing vulnerability as disruptions escalate. However, unlike the undisruptive condition, the head of each disruptive condition distinguishes a significant increase in the probability when the significance of critical-link attacks increases. This clearly distinguishes the flow transition from the attacked road segments to the remaining road segments because the remaining road segments receive more attention when they disrupt the earlier critical link road segments from the transportation network. This increases the probability of the head of the distribution curve under disruptive conditions. It also shows that a few road segments still represented the tail of the distribution curve under disruptive conditions, but their criticalities were significantly lower than those under undisruptive conditions. This is because even during critical-link attacks, a few road segments still receive relatively high attention in each considered  
Figure 4: Statistical validation of distributional changes using Kolmogorov–Smirnov tests for undisruptive conditions and the first 10%, 20%, and 30% of critical-link attacks. topological parameter aspect. However, these trends gradually diminish with an increase in critical-link attacks on the network, as the network cannot perform any functions once it becomes isolated when critical road segments are removed from the network.
## 2 Materials and Methods
### Methodology  
#### 2.4.1 Effective Porosity and Measured Porosity  
The effective porosity and measured porosity were determined on cylinders that were dried in an oven at 50 \({}^{\circ}\)C until reaching a constant weight and then cooled to room temperature. The effective porosity, also called open porosity, was calculated according to Equation (7). This method has been described in the literature [25; 26]:  
\[P_{e}=\left[1-\frac{W_{0}-W_{1}}{V_{0}\cdot\rho_{W}}\right]\times 100\% \tag{7}\]  
Figure 1: Preparation processes for pervious concrete.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Target Porosity** & **Cement** & **Aggregate** & **Silica Fume** & **Superplasticizer** & **Water** \\
**\%** & **kg\(\cdot\)m\({}^{-3}\)** & **kg\(\cdot\)m\({}^{-3}\)** & **kg\(\cdot\)m\({}^{-3}\)** & **kg\(\cdot\)m\({}^{-3}\)** & **kg\(\cdot\)m\({}^{-3}\)** \\ \hline
8.0 & 666.8 & 1470 & 29.2 & 1.39 & 160 \\
10.0 & 631.8 & 1470 & 27.6 & 1.32 & 152 \\
12.5 & 588.0 & 1470 & 25.7 & 1.23 & 141 \\
15.0 & 544.3 & 1470 & 23.8 & 1.14 & 131 \\
17.5 & 500.5 & 1470 & 21.9 & 1.04 & 120 \\
20.0 & 456.8 & 1470 & 20.0 & 0.95 & 110 \\
22.5 & 413.0 & 1470 & 18.1 & 0.86 & 99 \\
25.0 & 369.3 & 1470 & 16.2 & 0.77 & 89 \\
27.5 & 325.5 & 1470 & 14.2 & 0.68 & 78 \\
30.0 & 281.8 & 1470 & 12.3 & 0.59 & 68 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mix ratio design of pervious concrete under different target porosities.  
where:\(P_{e}\) is the effective porosity of the pervious concrete. \(W_{0}\) is the dry mass of the pervious concrete, kg. \(W_{1}\) is the mass of the pervious concrete saturated in water, kg. \(V_{o}\) is the bulk volume of the pervious concrete, m\({}^{3}\). \(\rho_{W}\) is the true density of water at 20 \({}^{\circ}\)C, taking 1000 kg/m\({}^{3}\). The measured porosity was determined by image analysis based on Image-Pro Plus software. This procedure, as shown in Figure 2, has been reported in many studies [25; 30]. Nine images of the three fracture sections (top, middle, and bottom sections) of each specimen were captured by the digital camera. Then, the black/white binarization and the measured porosity of each image were determined by the software.
## 4 Algorithm for Energy and Fuel Consumption Minimization
### Weighting Factors Calculation  
Because the driver may voluntary cease to follow the optimal speed profile or a traffic jam may force him to do so, a new speed profile should be calculated to meet the desired travel time. The computing time of the new profile should be short enough for the user of the system not to perceive a prolonged interruption of information on the proper speed.  
The idea is to provide the algorithm with the necessary tools so that the maximum travel time is met after suffering the delay caused by the traffic jam. To accomplish this, the strategy used by the optimization system is based on the input of the average speeds of every road section, depending on the time of day. To recover the time lost during the trip, the average speed is modulated based on the remaining travel time. This speed modulation is accomplished by adjusting the weighting factor \(\beta\) of Equation (4) in each distance overlap. Specifically, the influence of \(\alpha/\beta\) parameter on consumption and travel time is shown in [35]. It should be noted that there is an interval in which these parameters are influenced by the \(\alpha/\beta\) ratio, but, out of this interval, the influence of this ratio is negligible. Thus, optimization at the beginning of each overlap distance is performed in two steps:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Overlap Distance (km)** & **Fuel Consumption Difference (\%)** & **Travel Time Difference (\%)** \\ \hline
1 & \(-\)3.73 & 8.27 \\
2 & \(-\)0.50 & 0.40 \\
3 & 0 & 0.17 \\
4 & 0.04 & \(-\)0.07 \\
5 & 0 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Differences between the optimal speed profile and the approximate speed profile using overlaps.  
Figure 2: Overlap of the partial speed profiles.  
1. DP algorithm for the entire remaining distance to the destination without considering the slopes and allowing only changes in the speed on those road sections where there is a speed limit change (fixed, variable or because of traffic). This algorithm looks for the concatenation of constant speed stretches that provide minimum fuel consumption but allows the vehicle to reach the destination on time.
2. For the actual overlap stretch, the correspondence between the resulting speed of the previous step and a new value of \(\beta\) is found. This value is used in the DP algorithm for the stretch considering the vertical road profile. This second run of the DP algorithm provides the speed profile of which the driver is informed.  
It should be noted that the computational time of the first step is reduced because expansions of DP solutions are produced only on a limited number of road sections.
# Development of User-Participatory Crowdsensing System for Improved Privacy Preservation
## 3 Proposed System
### Structure of Proposed System
When the data provider requests participation of the sensing data provision, the session key-issuing module generates a session key \(K_{\textit{sses}}\big{(}p,u_{dp}\big{)}\) to be used for sending and receiving the data, encrypts the session key using the ID \(\big{(}K_{pub}\big{(}u_{dp}\big{)}\big{)}\) of the data provider, and transmits the session key to \(u_{dp}\). The data provider decrypts the encrypted session key using its own private key \(\big{(}K_{prl}\big{(}u_{dp}\big{)}\big{)}\), encrypts the sensing data using the session key \(K_{\textit{sses}}\big{(}p,u_{dp}\big{)}\), and transmits them to the data processing node. The sensing data processing module decrypts the sensing data encrypted using the session key and checks whether they satisfy the data schema defined by the data requester. If it is determined that the data are valid, they are encrypted using the ID \(K_{pub}\big{(}u_{dr}\big{)}\) of the data requester and transmitted to the data requester; furthermore, the reward payment data containing the data provider ID and the reward payment amount are transmitted to the \(SP\).