esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 From Asbestos to CNT: The Toxicological Aspect
### Inflammogenic and Fibrogenic Effects of CNT: Mechanisms and Adverse Outcome Pathway (AOP)  
Several decades were necessary for defining the exact pathogenesis of CNT-induced inflammation and fibrosis (Figure 1). A common scenario is now accepted and can be summarized as follows. Scavenger receptors deployed by sentinel macrophages induce CNT phagocytosis [24, 25, 26]. Interalized cytotoxic CNT destabilize phagolysosomes in macrophages and lysosomal contents released into their cytoplasm activate a sensing cytosolic complex (named inflammasome) which converts immature cytokines (pro IL-1\(\beta\)) into highly inflammatory mediators (active IL-1\(\beta\)). Our group also demonstrated that the permeabilization of lysosomes induces cell membranous and the subsequent release of danger signals (named alarmins) stored in macrophage cytoplasm [25; 27; 28]. Altogether, these inflammatory mediators orchestrate the persistent accumulation of neutrophils and macrophages. Oxidizing molecules produced by these inflammatory cells strongly damage surrounding tissues [24].  
The inflammatory cycles are then followed by a reparative stage, where damaged tissues are renewed. This regeneration involves fibroblast activation by growth factors produced by activated macrophages [29; 30]. We have also demonstrated that toxic CNT possess the unique ability to induce fibroblast proliferation, differentiation and collagen production by themselves [31]. When CNT persist into the tissue, remodeling is therefore permanent and degenerates into uncontrolled scar and fibrosis [24].  
The global interpretation of these sophisticated mechanisms resulted in the elaboration of several \"adverse outcome pathways\" (AOP), which detail the exact sequence of molecular and cellular events required for inflammation and fibrosis development after CNT exposure (e.g., AOP #173). AOP are now available to improve current in vitro assays predicting inflammatory and fibrogenic effects of CNT and assess health risks for human [29; 32; 33] (Figure 1).
# Study on Dynamic Characteristics of the Bistable Nonlinear Damper
## 4 Effects of the Structural Parameters
### Tuning Frequency Ratio f  
The present simulation parameters are set as follows: \(\beta\) = 1.0, \(\mu\) = 0.3, \(\gamma_{1}\) = 0.025 and \(\gamma_{2}\) = 0.025. Figure 8a shows the system response amplitude in relation to the tuning frequency ratio \(f\), in the case of resonance of the main system at \(\omega\) = 1.0. Overall, the main system and the vibration absorber first decreased and then remained constant as \(f\) changed. According to the optimization condition of resonance, the amplitude of the main system should be minimized, so the optimal tuning frequency ratio \(f\) was set at 1.4-2.0. Taking the condition \(f\) = 1.7 as an example, Figure 8b shows the phase diagram of the main system, which shows that the main system executed slight periodic motion while the vibration absorber was operating at a high efficiency.  
In the case of off-resonance of the main system at an off-resonance excitation frequency \(\omega\) = 0.6, the system response amplitude as \(f\) changed is shown in Figure 9a. When \(f\) = 0.2-0.6, the main system performed lower amplitude motion while the amplitude of the vibration absorber was higher; when \(f\) > 0.8, the absorber performed slight motion. According to the optimization condition of off-resonance, the vibration absorber should not only maintain a low amplitude of the main system but also maximize its own amplitude, so the optimal tuning frequency ratio \(f\) was determined at 0.2-0.6. Taking \(f\) = 0.5 as an example, the phase diagram of the vibration absorber and the main system was plotted in Figure 9b. The absorber performed a large chaotic motion, while the main system moved in a small amplitude.  
Figure 8: Response amplitude of two vibrators and phase diagram of the main system in the case of resonance \(\omega\) = 1.0. (**a**) Response amplitude; (**b**) Phase diagram (_f_ = 1.7).  
Figure 9: Response amplitude and phase diagram of two vibrators in the case of off-resonance \(\omega\) = 0.6. (**a**) Response amplitude; (**b**) Phase diagram (_f_ = 0.5).  
Figure 10a shows the system response amplitude in relation to the increasing \(f\) at an off-resonance excitation frequency \(\omega=1.6\). Apparently, the optimal value of \(f\) was determined to be 0.8-1.0. Figure 10b displays the phase diagrams of two vibrators at \(f=0.9\). It can be observed that the vibration absorber could overcome the potential barrier and performed large amplitude chaotic motion, while the main system executed a slight motion.
###### Abstract  
To explore the characteristics of and variations in aroma components across different plum varieties and maturity stages, this study employed headspace solid-phase microextraction combined with gas chromatography-mass spectrometry (HS-SPME-GC-MS). This method was used to systematically analyze the aroma components of 12 early-maturing, 15 medium-maturing, and 11 late-maturing plum varieties. The variations in volatile components among these three germplasm types were then compared using multivariate statistical methods. The examination revealed that 138 aromatic components were meticulously identified and quantified, such as 26 aldehydes, 63 esters, 13 ketones, 30 alcohols, and six other compounds. Thirteen main aroma compounds including acetic acid hexyel ester, (Z)-3-hexen-1-ol acetate, hexanol, 1-hexanol, 3-hexenal, butanoic acid butyl ester, (E)-2-hexen-1-ol, hexanoic acid butyl ester, propanoic acid butyl ester, (E)-2-hexenal, L-alpha-terpinenel, (Z)-2-hexen-1-ol acetate, and 1-butanol were considered dominant. The orthogonal partial least squares discriminant analysis (OPLS-DA) combined with variable importance projection (VIP) results showed that 24 differential aroma compounds were screened out from 38 varieties of plum fruits based on their differences in aroma components, which can be used to distinguish plum fruits at different ripening times. Twenty-four aroma-contributing compounds were identified based on their odor activity values (OAVs). Among these, 14 key aroma components with OAVs \(\geq\) 10 were highlighted. In summary, the aroma compounds of early- and late-maturing plum germplasm exhibited rich diversity, with significant differences in aroma components between plums of varying maturity and germplasm. These differences can serve as indicators for identifying different plum germplasm.  
plum; aroma components; maturity period; HS-SPME-GC-MS; aroma character 13.3515. [[https://doi.org/10.3390/foods13213515](https://doi.org/10.3390/foods13213515)]([https://doi.org/10.3390/foods13213515](https://doi.org/10.3390/foods13213515))
## 3 Results  
Figure 1b shows that the four samples, displayed in Table 1, can be considered roughly equivalent, from a geometrical point of view. The reported geometrical details are essential for the boundary conditions and may be important for modeling the heat developed through MOD cavities restored with composite materials.  
Figure 2a depicts typical temperature profiles recorded for the four bulk-fill composites. Maximum temperature rise levels are reported in Figure 2b. The time required to reach the temperature peak is reported in Figure 2c. A steep temperature profile can be observed for each composite as the light is turned on. The slope of the temperature profile (i.e., temperature rate) is shown in Figure 2d. Flowable bulk-fill RBCs show a significantly higher temperature peak (\(p<0.05\)) than packable bulk-fill RBCs. The peak temperature measured for EVF, SDR, EVC, and SFL were 49.9 \({}^{\circ}\)C \(\pm\) 1.8 \({}^{\circ}\)C, 47.5 \({}^{\circ}\)C \(\pm\) 1.7 \({}^{\circ}\)C, 44.3 \({}^{\circ}\)C \(\pm\) 1.6 \({}^{\circ}\)C, and 41.4 \({}^{\circ}\)C \(\pm\) 1.5 \({}^{\circ}\)C, respectively. Flowable bulk-fill RBCs show a significantly higher temperature rate (\(p<0.05\)) than packable bulk-fill RBC (Figure 2d), while the time required to reach the temperature peak (Figure 2c) was significantly lower (\(p<0.05\)).  
Simultaneous time measurements of temperature and photo-diode signals for MOD cavities restored with the four bulk-fill RBCs according to the layering technique are reported in Figure 3. For both the bulk-fill (Figure 3a) and incremental layering (Figure 3b) techniques, a steep temperature profile is detected as the light is turned on. For flowable RBCs, the temperature decreases as the light is switched off. Instead, for packable RBCs, a delay is observed in the peak temperature for the bulk-fill technique (Figure 3a), and in the second peak temperature for the incremental layering technique. Maximum peak temperature values were recorded for the flowable EVF and SDR composites (43.9 \({}^{\circ}\)C \(\pm\) 1.8 \({}^{\circ}\)C and 42.9\({}^{\circ}\)C \(\pm\) 1.7 \({}^{\circ}\)C, respectively) as the incremental layering technique was performed to restore the MOD cavities.  
Temperature rise and temperature rate measured for the investigated composite according to the different MOD cavity layering technique are reported in Table 2.  
Figure 2: (**a**) Temperature profiles developing into bulk-fill RBC; (**b**) maximum temperature rise; (**c**) time to temperature peak; and (**d**) temperature rate. Error bars represent the standard deviation. Different letters reported beside the bars indicate a significant difference between the means (\(p<0.05\)).
## 4 Results and Discussion
Figure 10: Statistical azimuth angles of simulated sea surfaces for wind speed of (left to right) 1, 5, 10 and 15 m s\({}^{-1}\). The black line represents the spatial sea surface calculated from finite differences and the red line indicates the isotropic distribution of the statistical sea surface. Figure 9: The centroid distribution of beam power on the receiving surface (**a–d**) corresponds to wind speeds of 1, 5, 10 and 15 m s\({}^{-1}\), and the centroid offset distance (**e**). The color indicates the depth of underwater transmission. This plot uses the same legend as Figure 5.0  
These simulation results, which focus on the spatial and temporal dynamics of the sea surface, provide valuable insights into how these factors affect signal propagation. The sea surface characteristics influence important communication metrics, such as signal attenuation, scattering, and multipath fading, which directly impact signal-to-noise ratio (SNR), bit error rate (BER), and data throughput. For example, under varying transmission depths and wind speeds, the average beam energy in space fluctuates due to the time evolution of the dynamic sea surface. These fluctuations result in significant variations in the received energy, which subsequently affects the SNR and alters the BER of the communication performance. Additionally, the random drift of the laser across the dynamic sea surface impacts the receiver's ability to track and scan the collimated signal. Given the sensitive nature of laser communications, this can lead to the loss of the received signal. Therefore, developing a comprehensive reference model for evaluating optical transmissions in challenging marine environments is both essential and fundamental for improving the accuracy and reliability of WOC systems.
## 6 General Limitations of Current Anomaly Detection Models and Open Issues
### Open Issues  
This section suggests some open research issues based on the study of existing models as in the following points:_Dimensionality reduction_: there is a need for reducing the dimensionality of multivariate data in order to reduce the high energy consumption. Very few dimension reduction methods were used prior to the anomaly detection process. However, the computational complexity of these methods affects the detection efficiency as it incurs additional energy consumption. Therefore, more lightweight dimension reduction methods are needed. Some methods that share the same principle with PCA-based methods such as ICA and wavelets are potential alternatives.  
_Online detection_: to meet the demands of some real time WSN applications, it is preferable for the detection process to be online. Although many existing models were claimed to be suitable for online detection, they incur high energy consumption caused by their adopted detection methods. Therefore, more lightweight detection methods are required. The one class supervised learning methods proved to be suitable choices for detection anomalous data measurements in sensor networks such as One Class SVM, One Class PCC. These methods transfer the problem to another space and solve the problem in linear form. The problems of these types of classifiers are the need for parameter tunings and the fact that threshold determination is required.  
_Distributed structure_: to reduce the communication overhead caused by sending all data in centralized models, the distributed design of anomaly detection models is recommended. Although some distributed models were proposed, they still have some issues that should be overcome such as the length of the reference model that is communicated in the network and the need for updating this reference model based on the changes in this structure.  
_Adaptive Detection_: due to dynamic streaming nature of sensor data, an update of the detection model is required in order to minimize false positives and improve detection accuracy. The few proposed adaptive strategies suffer from high computational complexity that affects their suitability for online detection. Therefore, modifying these strategies or proposing new lightweight ones is required.  
_Feature/Attribute correlations_: temporal and spatial correlations of sensor data should be exploited in order to improve detection effectiveness and efficiency at the same time. These correlations should be combined together with feature/attribute correlations to improve the effectiveness and efficiency together.  
_Parameter tuning_: to reduce human intervention, the design of anomaly detection model should avoid the manual setup of parameters. The design for automatic parameters tuning methods is preferred to reduce the human intervention.
## 2 Methods
### Data Analysis  
#### 2.3.1 Analysis of Vegetation Composition and Species Diversity  
For both sites, we calculated the percent cover of each PFT as a fraction of the total observations made at the plot level. The total number of plots included in our analysis was 13 across the three treatments at APEX, and 33 plots across 13 chambers at SPRUCE. At APEX, the effect of water table treatment on PFT cover was analyzed using a one-way ANOVA with a post-hoc pairwise means comparison to determine whether significant differences existed among treatments. At SPRUCE, we used a linear mixed effects modeling framework to evaluate the response of PFT cover to warming and elevated CO\({}_{2}\). Temperature was added to the model as a continuous variable, and CO\({}_{2}\) level was included as a factor. We included treatment chamber in the model as a random effect and evaluated model fit using the Akaike information criterion, as well as both marginal and conditional R\({}_{2}\), which describe the variance explained by the fixed effects and full model, respectively [69]. Linear mixed effects modeling was done using the lme4 package in R.  
We calculated species diversity using the Shannon diversity index which incorporates both species richness and evenness into a single measure of alpha diversity:  
\[H^{\prime}=\ -\sum_{i=1}^{s}p_{i}\log(p_{i}) \tag{2}\]  
In this equation, Shannon diversity (\(H^{\prime}\)) is calculated at the plot-level in which \(p_{i}\) represents the proportion of the population represented by species \(i\)[70]. Shannon diversity was calculated for each plot using the R Vegan package for community ecology [71]. We examined whether significant differences in species diversity among plots existed using a one-way ANOVA with a post-hoc pairwise means comparison at APEX, and linear mixed effects modeling at SPRUCE. We assessed significance based on an alpha level of 0.05 in the ANOVA, and evaluated model coefficients in the linear mixed effects model.
## 3 TLE Parameter Determination
### Axial Stiffness of the Threaded Connection  
Figure 5 illustrates the threaded connection, including the nut body and bolt body. The thread is a critical component in connecting the bolt body and nut body. The interaction between the bolt body and nut body is transferred by the thread body. Among the analytical methods for the thread load distribution, the Sopwith method [22] and Yamatoto method [23] have received extensive recognition. These two methods are similar. They both regard the thread as a cantilever beam, and reflect the thread load distribution by the thread axial load distribution. On this basis, [PERSON] et al. [24] considered the load distribution of threaded connections and proposed an accurate calculation method for the axial stiffness of a threaded connection. The axial stiffness of the threaded connection can be expressed as follows:  
\[K_{a}=\frac{\sinh\lambda l}{\lambda(\cosh\lambda l-1)}(\frac{1}{E_{\text{b}}S _{\text{b}}}+\frac{1}{E_{\text{n}}S_{\text{n}}}), \tag{1}\]  
where \(E_{\text{b}}\) and \(E_{\text{n}}\) are the Young's modulus of the bolt body and nut body, respectively; \(S_{\text{b}}\) and \(S_{\text{n}}\) are the cross-sectional area of the bolt body and nut body, respectively; and \(l\) represents the thread length. \(\lambda\) can be expressed as follows:  
\[\lambda=\sqrt{\frac{\frac{1}{S_{\text{b}}E_{\text{b}}}+\frac{1}{S_{\text{n}} E_{\text{n}}}}{\frac{1}{E_{\text{b}}}+\frac{1}{E_{\text{na}}}}}, \tag{2}\]  
where \(k_{\text{ba}}\) and \(k_{\text{na}}\) represent the axial stiffness of the unit length for the threads on the bolt and nut, respectively. These can be expressed as follows:  
\[\begin{split} k_{\text{ba}}&=\frac{1}{\lambda_{ \text{ba}}\sin\beta},\\ k_{\text{na}}&=\frac{1}{\lambda_{\text{na}}\sin \beta},\end{split} \tag{3}\]  
where \(\delta_{\text{ba}}\) and \(\delta_{\text{na}}\) represent the elastic deflection caused by the unit force per unit width along the axial direction, and \(\beta\) is the lead angle. [PERSON] et al. [24] presented a calculation method for \(\delta_{\text{ba}}\) and \(\delta_{\text{na}}\) without considering the contact friction of the threaded teeth. [PERSON] et al. [25] proposed a calculation method for \(\delta_{\text{ba}}\) and \(\delta_{\text{na}}\) considering the friction force of the threaded teeth. As the consideration of friction complied with the actual situation, the method considering the friction force [25] was adopted for the present calculation.  
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **1 st (Hz)** & **2 nd (Hz)** & **3 rd (Hz)** \\ \hline
40\_3.0 & 804.58 & 2529.71 & 4197.04 \\
40\_2.0 & 815.84 & 2561.12 & 4218.47 \\
40\_1.5 & 824.86 & 2597.08 & 4214.05 \\
30\_2.0 & 787.23 & 2488.88 & 4152.55 \\
50\_2.0 & 861.48 & 2706.76 & 4438.86 \\ \hline \end{tabular}
\end{table}
Table 4: First three order frequencies of the threaded specimens in the modal tests (source elaborated by authors).
# Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot
## 2 Materials and Methods
### Angular-Domain Recurrence-Plot Analysis
#### 2.1.2 Determination of Embedding Dimension  
To determine a suitable embedding dimension in phase-space reconstruction, some methods were proposed, such as computing some invariant on the attractor, singular-value decomposition, and false nearest neighbors (FNN) [15]. In this paper, the FNN method was used to analyze angular-domain data series, which is similar to the discrete-time series in theory. In \(d\)-dimensional phase space, the square of Euclidean distance between point \(\mathbf{y}(\theta,\delta)\) and \(r\)th nearest neighbor of \(\mathbf{y}(\theta,\delta)\), \(R_{d}^{2}\) can be presented as in [14] by:  
\[R_{d}^{2}(\theta,r)=\sum_{m=0}^{d-1}[x(\theta+m\delta)-x^{(r)}(\theta+m\delta) ]^{2}. \tag{2}\]  
From the \(d\)th to (\(d+1\))th dimension, corresponding Euclidean distance \(R_{d+1}^{2}(\theta,r)\) is changed into  
\[R_{d+1}^{2}(\theta,r)=R_{d}^{2}(\theta,r)+[x(\theta+d\delta)-x^{(r)}(\theta+d \delta)]^{2}. \tag{3}\]  
In engineering applications, the observed vibration may have limited length and low signal-to-noise ratio (SNR). In this situation, by only considering the nearest neighbors [14], e.g., \(R_{d}(\theta,r)\equiv R_{d}(\theta,r=1)\), the criterion to identify the false neighbors in the angular domain can be expressed as in [14]:  
\[\frac{R_{d+1}(\theta)}{\sqrt{\frac{1}{N}\sum\limits_{\theta=1}^{N}[x(\theta)- \frac{1}{N}\sum\limits_{\theta=1}^{N}x(\theta)]^{2}}}>A_{tol} \tag{4}\]  
where \(A_{tol}\) is the threshold (without losing generally, it is set to 2). A suitable \(d\) is determined with the value of the first minimum of the FNN [9].
# Multi-UAVs Communication-Aware Cooperative Target Tracking
## 2 Problem Description and System Model
### Target Motion Model  
The motion of the target is shown as the following linear dynamics:  
\[X_{t}[k+1]=F_{cv}X_{t}[k]+G_{cv}\rho[k] \tag{1}\]  
where \(F_{cv}\) is the state transition matrix:  
\[F_{cv}=\left[\begin{array}{cccc}1&0&\Delta T&0\\ 0&1&0&\Delta T\\ 0&0&1&0\\ 0&0&0&1\end{array}\right]\text{ and } \tag{2}\]  
\(G_{cv}\) is the noise input matrix:  
\[G_{cv}=\left[\begin{array}{ccc}\frac{1}{2}\Delta T^{2}&0\\ 0&\frac{1}{2}\Delta T^{2}\\ \Delta T&0\\ 0&\Delta T\end{array}\right] \tag{3}\]  
In Equation (1), \(X_{t}=[x_{t},y_{t},\dot{x}_{t},\dot{y}_{t}]^{T}\) represents the target state, including location and speed. \(\rho=[\rho_{vx},\rho_{vy}]^{T}\) represents the process noise. \(\rho_{vx}\) and \(\rho_{vy}\) are assumed as Gaussian and white noise with zero mean. \(\rho\sim N(0,Q)\) with \(Q\): Covariance matrix of process noise.
## 2 Materials and Methods  
### The TL-PBR Structure  
The prototype PBRs made in the context of the study are composed of 4 essential parts (Figure 1), namely, the supporting structure; \"base\" and \"cover\" for the recirculation and collection of growing media; growth chamber; and peristaltic pump (driving component).  
The supporting structure is designed to hold the base, cover, and growth chamber, and provides vertical stability ensuring a stable centre of mass (Figure 1D). The cover and base (Figure 1B,C) were 3D-designed and printed in ABS plastic polymer. The cover supports the growth panel (source layer) and has apertures for head pipes to distribute the growth medium, while the base features a funnel-shaped structure to facilitate medium recovery and recirculation. Both cover and base include a small off-centre hole for gas exchange.  
The growth chamber, made of tempered glass, ensures adequate lighting for the microalgae biofilm (Figure 1E). Inside, the macroporous source layer is securely attached to the cover, with the microporous substrate layer for microalgae growth adhering to it. For these experiments, fibreglass MAT panels with a density of 135 g/m\({}^{2}\) were used as macroporous source layer.  
The final essential component of the prototypes is the pump, which functions as the driving mechanism and is the sole element responsible for the electrical energy consumption of the TL-PBR (excluding the monitoring system). For the laboratory tests, a peristaltic pump (model: Perimax 12/6 100-240 V SM 1-80; Spectec GmbH, Erding, Germany) was used. This pump, although having a rated power of 70 W, under real operating conditions and at full load absorbs only 20 W, regardless of the flow rate. These data were measured during the experimental activities using a professional energy meter (model BX11, Trotec GmbH, Verona, Italy). In the conducted tests, the pump was set to ensure a flow rate of 0.5 L/h in each recirculation pipe.  
Figure 1: Sensor units of the monitoring system (**A**); renderings of cover and base units, respectively, (**B**,**C**); rendering of the supporting structure (**D**); photos of the TL-PBR prototype cultivation system during testing operations and cultivation tests (**E**).