esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mathematical Model and Optimization Framework CFD Numerical Simulation Setup  
### Conditioning Path Design  
In this study, the conditioning path is designed by integrating the State-Space Network (SSN) framework with a Genetic Algorithm (GA) [23] to explore the optimal path configuration.  
1. Each individual in the GA population encodes two sets of parameters (A, B, and C and A\({}_{1}\), B\({}_{1}\), and C\({}_{1}\)), totaling six genes per individual.  
* A, B, and C represent the velocity at each of the three tank nozzles (in m/s). The mapping rule is defined as \[\left\{\begin{array}{l}\mathrm{A+B+C=0}\\ |\mathrm{A}|+|\mathrm{B}|+|\mathrm{C}|\leq 2\mathrm{PM}\end{array}\right.\] (1) \[\bigcirc\] Positive value \[\rightarrow\] inlet with corresponding velocity; \[\bigcirc\] Negative value \[\rightarrow\] outlet with absolute value as flow rate; \[\bigcirc\] Zero \[\rightarrow\] sealed wall. PM refers to the maximum delivery velocity of the pump, constrained by nozzle diameter.
* A1, B1, and C1 define the configuration of diesel with antioxidant additives. Their constraint condition is \[\left\{\begin{array}{l}\mathrm{sign(A_{1})=sign(A)}\\ \mathrm{sign(B_{1})=sign(B)}\\ \mathrm{sign(C_{1})=sign(C)}\end{array}\right.\] (2)  
The sign() function ensures each port acts only as an inlet or an outlet, not both, ensuring the feasibility of the conditioning path.  
Each configuration parameter (e.g., A1) is further linked to a conditioning state value (A11), with the following logic:  
\[\mathrm{A_{1}}=\left\{\begin{array}{l}-1,\;\mathrm{A_{1}<0}\\ 0,\;\mathrm{A_{1}=0}\\ 1\;\mathrm{else}\;2,\;\mathrm{A_{1}>0}\end{array}\right. \tag{3}\]  
* A11 \(=-1\): diesel outlet (data recorded);
* A11 \(=0\): sealed boundary;
* A11 \(=1\): inlet with additive (conditioned diesel);
* A11 \(=2\): inlet without additive (recycled diesel with same concentration as recorded at outlet);
* B11 and C11 follow the same logic for their respective ports.
## 3 Simulation Experiment  
### Scene Configuration  
We have established a representative terminal penetration scenario involving anti-ship missiles to validate the developed algorithms and models, specifically focusing on missile engagement targeting the broadside of a ship. The missile's geometric param eters are configured with a length of 4.6 m, a diameter of 0.3 m, and a wingspan of 0.9 m, based on the RGM-84 \"Harpoon\" missile. Similarly, the ship's dimensions are defined as 172.8 m in length, 16.8 m in width, and approximately 40 m in height, following the specifications of the Navy's Ticonderoga-class cruiser. In \(Ox_{m}y_{m}z_{m}\), the missile's launch origin is set at the coordinate system's origin, with its initial state denoted as \(\mathbf{X}=[0,0,10\text{ m},0,-2\text{ Ma},0,0,0,0,0,0]\), with a maximum velocity of 2 Ma and a maximum load factor of 15 g, where \(\text{g}=9.8\text{ m/s}^{2}\). The ship maintains a constant speed, with its initial position and speed designated as \(\mathbf{p}_{s}=[0,-30,000\text{ m},0]^{\text{T}}\) and \(\dot{\mathbf{p}}_{s}=[20\text{ m/s},0,0]^{\text{T}}\), respectively. The missile maintains a collision proximity of 5 m, with a maximum mission duration of 60 s. The planning frequency is set to 100 Hz, and the total mission step count is 1000. Simulations are conducted on a computing workstation equipped with a Windows 11 operating system. The system features a 12 th Gen Intel(R) Core(TM) i5-12500 CPU and 32.0 GB of RAM. The simulation environment is implemented using pyCharm v5.3.2, with Python v3.11 as the programming language. The essential software development kits include numpy v1.2.64 and matplot v0.1.9. The study currently does not incorporate external factors such as wind and waves, as the focus is on optimizing the missile's flight performance and trajectory design in complex battlefield environments.
# Acoustic Emission Analysis of Fracture and Size Effect in Cementitious Mortars
## 4 AE Analysis: Results and Discussion
### b-Value Analysis
#### 4.4.2 Fracture Analysis Using \(b\)-Value  
This section investigates the fracture process near the peak load using the \(b\)-value analysis. For the reasons discussed in the previous section, only the AE hits captured by the sensors closer to the fracture region are used for this analysis. Hereby, the location of the fracture region is determined by 3D localization of AE sources (see Section 4.2).  
The role of sample size in the fracture progress is investigated using the evolution of the \(b\)-value in different sample sizes. Two representative samples are shown for the large and small sample sizes in Figure 14. Generally, it is observed that the \(b\)-value indeed decreases when approaching the peak load. As discussed in Section 2.4.2, micro-cracking is related to a \(b\)-value of 1.5 or higher, whereas macro-cracking is assumed to occur when the \(b\)-value is 1 or lower. The limits of \(b\)-values are also indicated in Figure 14. Figure 14a,b show that for the larger samples, the \(b\)-value is below or close to 1 even in the pre-peak period. This would indicate the formation of macro-cracks. However, the amount of AE events in this period is too small for the large samples to reliably determine a \(b\)-value (see also Figure 6b). On the other hand, in this period the \(b\)-value for the smaller samples remains above 1 (see Figure 14c,d). This implies that for the smaller sample, micro-cracking dominates in the pre-peak period.  
To smoothen the \(b\)-value curves, a moving mean with a five data point moving window is indicated in Figure 14. In general, from the moving mean, it can be seen that the decreasing tail of the \(b\)-value curve starts at a relatively earlier time for the larger samples, and the drop almost coincides with the peak load. However, for the smaller samples, the major decrease in the \(b\)-value occurs in the post-peak period. This indicates the premature fracture that occurs in larger samples. This premature failure also explains why larger samples would fail before reaching the intrinsic material strength.  
For the larger samples, the \(b\)-value rises before it falls, which indicates the micro-cracking that develops and extends the FPZ (due to the release of fracture energy) just before the main macro-crack develops. For the smaller samples, the less gradual decrease in \(b\)-value indicates the formation of a macro-crack due to the coalescence of micro-cracks without a significant energy release during the macro-crack growth.  
Figure 14: \(b\)-value analysis of AE hits detected on sensors found close to the fracture region on a Brazilian splitting test. The plot is zoomed to the peak load region.
## 4 Datasets and Experimental Results  
### Datasets  
Our study employed two widely recognized his datasets: Indian Pines (IP) and Pavia University (PU).  
1. The AVIRIS hyperspectral sensor gathered the IP dataset used in this study. This dataset includes 220 spectral bands covering the wavelength range of 0.4 to 2.5 \(\upmu\)m, organized in 145 by 145 pixels with a spatial resolution of 20 m. A total of 200 bands remained for examination after 20 bands (104-108, 150-163, and 220) displaying water absorption and noise were eliminated to improve the data quality. A ground truth (GT) image and a color composite image created using this dataset are shown in Figure 3. According to Table 1, 10% of the labeled samples of each class are used to train the network, while the remaining data are used to evaluate the model performance.  
Figure 2: The CNN’s main layers.  
2. The PU dataset was collected by the airborne hyperspectral sensor ROSIS-03 from the University of Pavia campus in Italy. This dataset has 103 spectral bands and 610 \(\times\) 340 pixels with a spatial resolution of 1.3 m. The GT for this scene includes the nine distinct urban classes. Figure 4 shows a color composite of the HSI and the GT corresponding to the PU dataset. Following the specifications in Table 2, 1% of the labeled samples from each class were utilized for training, while the remainder samples were used to evaluate the model performance.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Class** & **Total Samples** & **Train/Validation** & **Test** \\ \hline
1 & 46 & 4 & 42 \\
2 & 1428 & 142 & 1286 \\
3 & 830 & 83 & 747 \\
4 & 237 & 23 & 214 \\
5 & 483 & 48 & 435 \\
6 & 730 & 73 & 657 \\
7 & 28 & 3 & 25 \\
8 & 478 & 47 & 431 \\
9 & 20 & 3 & 17 \\
10 & 972 & 97 & 875 \\
11 & 2455 & 245 & 2210 \\
12 & 593 & 59 & 534 \\
13 & 205 & 20 & 185 \\
14 & 1265 & 126 & 1139 \\
15 & 386 & 38 & 348 \\
16 & 93 & 9 & 84 \\ \hline \end{tabular}
\end{table}
Table 1: Sample count specification for the IP his dataset.  
Figure 3: The IP dataset.
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 6 Network Parameters Adjustment and Performance Enhancement
### Study of the Radius of the Moving Trajectory  
In our proposed schema, the mobile sink rotates in a circle and the radius of the circle needs to be optimized. We first changed the radius of the moving trajectory and kept other parameters unchanged. We set the radius to 0, 0.25R, 0.5R, 0.75R, and R, and the lifetime of the network is shown as Figure 9. From Figure 9, we found that when the mobile sink moved along the radius of 0.25R, the network achieved the longest lifetime. However, when the mobile sink ran at the outside of the circle, the network had the poorest performance.  
Figure 8: Comparison of total energy consumption between different algorithms.  
Figure 7: Comparison of the network lifetime between different algorithms.  
We compared the energy consumption of different radii for single mobile sink. Simulation results are shown in Figure 10. We can obviously see that the energy consumption rose much slower when the radius of the mobile sink was about 0.25R. Furthermore, when the radius of the mobile sink was about R, the energy consumption of the network rose rapidly. The reason for the change could be that we took residual energy of each node into consideration such that the energy consumption of the whole network was not optimal.  
Additionally, we studied the optimal radius of the moving trajectory under the different network sizes and we set the network size as 100, 200, 300, and 400 meters. With the network size R increasing, the whole energy consumption increased and the lifetime of the network decreased. As is shown in Figure 11, 0.25R was still the optimal radius for the mobile sink under the different network scales.  
Figure 10: Energy consumption of different radius of single mobile sink.  
Figure 9: Different radius of mobile sink.
# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 7 Conclusions  
In this paper, we proposed a user mobility model based on graphs for crowdsourcing applications, which we have formalized and enhanced with an advanced algorithm for identifying POIs. The advantage of the graph-based formalism lies in its ability to represent paths and relationships between POIs in a structured and dynamic manner, facilitating the analysis of user mobility patterns. This graphical representation allows for the clear visualization and efficient manipulation of mobility data, thereby improving the accuracy of movement predictions and privacy protection measures. By integrating Bayesian networks, we have introduced a model to effectively identify significant POIs. The Bayesian network can model and calculate the joint probability that a specific location is a POI, taking into account several important factors, including the day of the week, the time of day, the category of the place, the frequency of visits, and the duration of each stay. The limitation of this paper lies in the need to improve the quality of the location data with the corresponding categories to enable the model to accurately infer POIs. Our preliminary experiments have shown that similar errors occur with track points obtained outside buildings, with GPS positioning errors appearing to be inevitable. Given our focus on privacy protection and the necessity for high-quality data, these limitations highlight the importance of considering additional factors in future work. Specifically, we plan to incorporate the user's profile, time period, and the category of nearby locations to better estimate the user's location. For instance, if a user has a student profile, our system would prioritize locations such as libraries, lecture halls, and university campuses within a certain radius and during specific times of the day. By enhancing the precision and relevance of mobility predictions, our approach aims to further strengthen privacy protection against inference attacks. Future research will focus on refining the model to improve data quality, ensuring that the model can infer POIs with a higher F1 POI score. These efforts will better protect sensitive user information in crowdsourcing environments against inference attacks, taking into account the contextual information that an attacker might possess.  
Conceptualization, F.Y., S.S. and R.C.; methodology, F.Y., S.S., E.C. and R.C.; software, F.Y.; validation, F.Y., S.S., J.D. and R.C.; formal analysis, F.Y., E.C. and R.C.; investigation, F.Y., S.S., E.C. and R.C.; resources, F.Y.; data curation, F.Y., S.S., E.C. and R.C.; writing--original draft preparation, everyone; writing--review and editing, everyone; visualization, F.Y. and J.D.; supervision, S.S., J.D. and R.C.; project administration, R.C.; funding acquisition, R.C. All authors have read and agreed to the published version of the manuscript. This research was funded by OPENCMS industrial chair. The data supporting the reported results are confidential and cannot be shared due to privacy and ethical restrictions. The authors declare no conflict of interest.
# Damping of Beam Vibrations Using Tuned Particles Impact Damper
## 4 Experiments
### Spectrum Analysis  
In the literature, it is hard to find the spectral analysis for the vibrated objects dampened by the PIDs. Such a situation results from the fact, that generally PIDs are passively calibrated to attenuate vibrations in the resonant range. In this paper the authors present an innovative Tuned Particles Impact Damping strategy, therefore the spectrum analysis seems to provide important information about the dynamics of the considered system. In Figure 8 the typical spectral analysis obtained for the constant mass of particles (10% of the whole system) and various granular materials is depicted. The maximal volume of the TPID compartment corresponding to the best damping properties of the device was taken into consideration. As the reference result the spectrum for the switched \"off\" state (minimal volume of the device) is presented.  
Figure 7: RMS of displacements for various grains materials.  
Detailed analysis of the data depicted in Figure 8 revealed the significant decreasing of the acceleration amplitudes of the first component. Additionally in the zoomed region the shifting phenomenon of natural frequencies is observable. Similar phenomenon is observed for Tuned Mass Damper class of devices. Moreover, the complex character of intergranular collisions and multiple contacts between the grains and walls of the TPID casing results in insignificant increases of subsequent component amplitudes. This observation seems to be the only disadvantage of the proposed damper prototype. Nevertheless, it is worth emphasizing that the proposed solution is mainly intended for systems operating close to the first resonance frequency range. In Figure 8 the authors presented the whole spectrum including the higher frequencies. The basic intention of the authors was to focus on the first harmonic as the TPID devices are developed to work effectively in devices that will be subjected to such frequencies. The presented spectrum shows that the change of the material and the filling ratio also affect the higher harmonics but it is less important from the engineering point of view.
# Multi-Step Traffic Speed Prediction Based on Ensemble Learning on an Urban Road Network
## 3 Prediction Methodology
### Performance Indices  
In this study, there are four traditional measurement of effectiveness (MOE) indices to evaluate the developed prediction method, such as mean absolute error (MAE), Mean absolute percentage error (MAPE), mean square error (MSE) and coefficient of variation (CV). CV is widely used in fields of engineering or applied statistics when doing quality assurance studies. The detailed calculation formulas of these indices are expressed as follows:  
\[MAE=\frac{1}{N}\sum\limits_{n=1}^{N}\left|v_{(d,t)}-\hat{v}_{(d,t)}\right| \tag{2}\]  
\[MAPE=\frac{1}{N}\sum\limits_{n=1}^{N}\frac{\left|\hat{v}_{(d,t)}-\hat{v}_{(d, t)}\right|}{v_{(d,t)}} \tag{3}\]  
\[MSE=\frac{1}{N}\sum\limits_{n=1}^{N}\left(v_{(d,t)}-\hat{v}_{(d,t)}\right)^{2} \tag{4}\]  
\[CV=\frac{\hat{\sigma}}{\hat{\mu}} \tag{5}\]  
where \(v_{(d,t)}\) and \(\hat{v}_{(d,t)}\) represent the actual and the predicted traffic speeds, respectively. \(N\) is the number of test samples, \(\hat{\mu}\) denotes the average value of predicted speed, and \(\hat{\sigma}\) is the standard deviation of the predicted speed.  
In this study, the stacking regression models contain three basic underlying learners of SVM, CATBOOST, and KNN, and the meta-regressor is the ridge regression method. The framework of the proposed method is showed in Figure 2.
## 4 Numerical Results
### MLR Model Developed under Op3  
The constructed MLR model is:  
\[y=-0.9079+1.295x_{1}+25.09x_{4}-0.2053x_{1}^{2}-297.0x_{4}^{2}-0.7628x_{1}x_{ 2}-1.213x_{1}x_{3}-19.28x_{2}x_{4}+e \tag{53}\]  
\((0.341)\)\((0.1755)\)\((10.3)\)\((0.0232)\)\((79.9)\)\((0.184)\)\((0.311)\)\((5.21)\)\((0.105)\)  
The performance measures are shown in the third column of Table 3. The diagnostics tests produce the following:  
(a) CODPA (see Section 3.4) was applied at the beginning in Op3. Three outliers were identified: observations 40 and 42 in the first cycle and observation 70 in the second sample. That is why the final model was constructed with a training set containing \(n\) = 123 observations.  
(b) The stepwise regression algorithm MSRA converged in 14 steps all in phase one over the purged data.  
(c) The ANOVA test for validity of the regression produced test statistics \(F_{stats}\) = 75.3 which resulted in \(p\)-\(value_{F}\) < 2.2(10\({}^{-16}\)). The conclusion of the test is that the model is valid. The same results from the ANOVA test were acquired in the previous 13 steps of MSRA.  
(d) The expected error nullity test produced test statistics \(t_{stats,null}\) = 6.68(10\({}^{-3}\)) resulting in a \(p\)-\(value_{null}\) = 0.9947. The conclusion of the test is that there is not enough statistical evidence to reject the hypothesis that the expected error in the regression model is zero. The same results for the expected error nullity test were acquired in the previous 13 steps of MSRA.  
(e) The heteroskedasticity testing algorithm MHTRA produced valid auxiliary regression with ANOVA in \(p\)-\(value_{F}\) = 4.18(10\({}^{-4}\)). However, its adjusted coefficient of determination is \(R^{2}_{adj}\)=0.154. The conclusion of MHTRA is that the constructed regression is statistically significantly heteroskedastic, but the latter is practically insignificant. Similar conclusions for the heteroskedasticity were acquired in the previous 13 steps of MSRA.  
(f) The modified Jarque-Bera Monte-Carlo test for error normality produced test statistics \(|B_{stats,null}\) = 1.689 which resulted in \(p\)-\(value_{B}\) = 0.364. The conclusion of the test is that there is not enough statistical evidence to reject the hypothesis that the error in the regression model is normal. The same results for the modified Jarque-Bera Monte Carlo test were acquired in the previous 13 steps of MSRA.
# Monitoring of Training Load and Body Composition in Elite Male Kayakers
###### Abstract  
Background: Elite kayaking demands peak conditioning, and tracking these athletes reveals the science behind world-class performance. Physiological demands and body composition changes in elite male kayakers were tracked during the preparatory and transition periods of a kayaking competitive season. Methods: Laboratory (body composition assessment and a 4 min all-out test in a kayak ergometer) on-field tests (4 \(\times\) 1500 m incremental intermittent protocol with 30 s rest intervals in a kayaking/rowing track) were applied on separate days to follow eight elite male kayakers (23.1 \(\pm\) 5.6 y; 80 \(\pm\) 8.8 kg; 177.0 \(\pm\) 6.8 cm) at the beginning of the kayaking season (preparatory period, M1; first week of October), 22 weeks later, at the beginning of the transition period (M2; last week of February), and 5 weeks later, at the end of the transition period, i.e., beginning of the competitive period of the season (M3; first week of April). M3 corresponded to the participation in international competitions. Results: Distance at peak oxygen uptake (\(\mathrm{VO_{2 peak}}\)) on the kayak ergometer improved by 36.7 m from M1 to M3, the pace at V4 (aerobic capacity) was reduced (improved) by 25.2 \(\mathrm{s}\cdot\mathrm{km^{-1}}\) from M1 to M2, and 25.6 \(\mathrm{s}\cdot\mathrm{km^{-1}}\) by M3. Body weight decreased by 2.3 kg from M1 to M2, and fat mass percentage and kilograms decreased by 1.8% and 3.1%, respectively. Fat-free mass increased by 1.9% and 3.1%, respectively. Skinfold measurements showed a decrease in subsepular, supralize, abdominal, and geminal skinfold. Aerobic power (\(\mathrm{VO_{2 peak}}\)) in absolute values (in L. min\({}^{-1}\)) improved by 0.7 L-min\({}^{-1}\) from M1 to M2, and by 1.1 L-min\({}^{-1}\) by M3, and from M2 to M3 was \(-\)0.5 L-min\({}^{-1}\). Aerobic power in relative values improved by 15.0 from M1 to M2, and by 6.4 mL-kg\({}^{-1}\cdot\)min\({}^{-1}\) from M2 to M3. Conclusions: Elite male kayakers improved their physiological performance and body composition during the preparatory and transition phases of the competitive season. Notable gains in performance were mainly due to enhanced aerobic power, and positive body composition changes. These findings provide insights for optimizing training strategies and boosting competitive performance.  
Keywords:keyaking; aerobic power; aerobic capacity; fat mass; performance +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Two-Photon-Induced Singlet Oxygen Generation  
To gain insight in the potential of **1** as PS in 2P-PDT, the efficiency of 1O2 generation was measured by monitoring the time-dependent absorbance of ABDA, a specific probe for the detection of 1O2[42]. In particular, ABDA reacted irreversibly with an 1O2 molecule which led to the corresponding endoperoxide resulting in the decrease in its optical density at 378 nm.  
Figure 3a,b shows the absorption spectra of ABDA in aqueous solution (control) and in presence of **1** at different irradiation times (0-15 min), respectively. Although for the control sample no change in the ABDA absorbance was observed. In presence of **1** the ABDA absorption peaks decreased in intensity as the exposure time increased, highlighting the 1O2 generation ability of the Ir(III) complex on two-photon excitation. For an intuitive comparison, the values of the ABDA absorbance as a function of the irradiation time were plotted in Figure 3c, displaying a good linear relationship. Then, 1O2 generation was quantitatively estimated calculating the total number of moles produced upon photoirradiation, obtaining a value of 6.2\(\cdot\)10\({}^{-9}\) (Table 1). Considering the solubility of molecular oxygen in water at room temperature (0.27 mmol/L) [43], after 15 min of light-exposure at 800 nm, approximately one tenth of molecular oxygen present in solution was converted into 1O2.