esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigating the Impact of Wildfires on Lake Water Quality Using Earth Observation Satellites
## 3 Results
### In-Lake Interactions between Chl-a and Turbidity  
The time series plots showed some cases where peaks in water turbidity concentration occurred and corresponded, or might have led, to lake Chl-a concentration decreases (Figure 11). These interactions were observed in diverse lake typologies according to depth such as Lake Togo (Africa) (a shallow lake), Lake Mtera (Tanzania, Africa) (a medium lake), and Lake Hirakud (India) (a deep lake). The succession of events is particularly evident in the shallow Lake Togo where a fire occurred at the beginning of 2019 and then eight months after a period of high precipitation occurred and was followed by a peak of turbidity (above 10 NTU) and a depression of Chl-a which then increased to maximum values when turbidity stabilized to lower values (mean 3.3 NTU). In Lake Mtera, after a major fire in 2019 was followed by a precipitation period, turbidity greatly increased (above 25 NTU) while Chl-a remained suppressed for the rest of the entire period considered (from annual mean concentration of 45.1 mg m\({}^{-3}\) during 2018 to 14.3 mg m\({}^{-3}\) during 2020). In Lake Hirakud, a time series is clearly shown every year with a correspondence between the occurrence of turbidity peaks and Chl-a concentration decline.
## 6 Use Case: Privacy Identity Management Enrollment
### Credential Issuance  
In this phase, the user typically is authenticated by the issuer, and the two parties negotiate the specific attributes to be certified for the user (e.g., name, birth date, etc.). At the end of the interaction, the user receives a digital certificate (a p-ABC credential) attesting to these attributes.  
* The user can then access their credential storage, where the certified attributes are displayed. At this point, the user has the option to review the stored information and manage what personal data they wish to disclose during future interactions with service providers.
* Before sharing any information, the user is prompted to confirm which attributes are to be revealed. A confirmation message is shown, allowing the user to review and authorize the disclosure. Once confirmed, a notification confirms the successful verification of the user's attributes.
## 2 Laser Transmitter Measurement System  
### System Composition and Measurement Principles  
A laser transmitter measurement system is primarily composed of a rotary-laser transmitter, a photoelectric receiver, and a terminal computer. As shown in Figure 1, the transmitter consists of a rotating head and a stationary base, where two laser modules capable of emitting fan-shaped laser beams are fixed on the rotating head at 90\({}^{\circ}\) angles, such that the optical axes of the laser modules are perpendicular to each other, and the tilt angles of the two planar laser beams relative to the rotating shaft are designed as \(+45^{\circ}\) and \(-45^{\circ}\), respectively. The rotating head spins at a predefined rate in the anticlockwise direction, and the lasers accordingly scan the measurement space around the transmitter. Meanwhile, a pulsed laser is mounted on the stationary base, and emits a cyclical omnidirectional laser strobe synchronously as the head rotates to a predefined position each cycle. The photoelectric receiver is fixed at the target point, where it captures the synchronous laser strobe.
## 4 Performance Analysis
### Simulation Results
#### 4.3.1 Route Setup Time  
It can be seen from Table 2 that FSACO could find the first effective route to the sink node faster than EEABR, IACO and SensorAnt under such a static network scenario. However, the difference of route setup time in Table 2 between FSACO and the other simulated routing algorithms was minuscule.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metric** & **EEABR** & **IACO** & **SensorAnt** & **FSACO** \\ \hline Route Setup Time & 9.4 & 6.5 & 7.2 & 3.4 \\ Convergence Time & 136 & 120.5 & 135.2 & 96.8 \\ Energy Consumption (J) & 134.55 & 97.65 & 127.22 & 85 \\ Energy Efficiency & 1.16 & 3.44 & 3.3 & 5.85 \\ Energy Standard Deviation & 3 & 2.3 & 1.89 & 0.89 \\ Lifetime Prediction & 0.76 & 0.79 & 0.775 & 0.86 \\ Network Throughput & 0.48 & 0.8 & 0.67 & 1.511 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overall performance comparison between Energy-Efficient Ant-Based Routing Algorithm (EEABR), IACO, SensorAnt and FSACO.  
The performance was related to the node density (number of sensor nodes) in the network. Figure 1 reports the impact of node density on the first route setup time.  
As shown in Figure 1, in a 500-sensor-node network, FSACO found the first effective path 53.8%, 16.7% and 42.6% faster than EEABR, IACO and SensorAnt, respectively. In FSACO, we introduced the AFSA-based initial route discovery algorithm, which achieved improved global optimisation ability in the early stage and could obtain higher quality routes quickly. Hence, we can see that FSACO could find the first feasible route more quickly than the other tested protocols.
## 7 Implementation and Evaluation
### Times Taken for Key Enrollment and Authentication  
As described in Section 5, our user authentication system consists of four components; user device \(d\), salt server \(s\), mobile device \(m\), and controller \(c\). In the following experiments, we used a MacBook Pro with Intel Core i7-7920 HQ (3.10 GHz) running on macOS Catalina for the user device, a desktop with AMD Ryzen Threadripper 1950X (3.40 GHz) running on Windows 10 Pro for the controller, a desktop with Intel Core i7-9700 (3.00 GHz) running on Ubuntu 18.04 for the salt server, and a Google Pixel 3a running on Android 9 (Pie) as the mobile device. The evaluation results are presented in the following sections.
## 4 Review of Studies Related to Sustainability in the Context of Multi-Modal Mobility
### Review of Studies
A model was developed based on an aggregate traffic representation, the Macroscopic Fundamental Diagram (MFD), to study how time-dependent travelers' choices and traffic conditions evolve from day to day. Two different congestion pricing schemes were proposed and compared to attempt to reduce traffic congestion and improve efficiency. One scheme prices all vehicles including both solo-driving and ridesharing vehicles, while the other scheme prices the solo-driving vehicles only to encourage ridesharing. The study indicates that ridesharing may not necessarily reduce traffic congestion. The results show that pricing solo-driving vehicles only might encourage ridesharing but it is less effective in reducing the overall congestion in comparison with pricing both solo-driving and ridesharing vehicles. 10. Question: How to optimally allocate and deploy designated autonomous vehicle (AV) lanes for shared autonomous vehicles (SAVs) considering flows of regular vehicles? Results: Shared autonomous vehicles (AVs) have the potential to become a popular sustainable transport mode. Deploying designated autonomous vehicle (AV) lanes for shared autonomous vehicles (SAVs) considering flows of regular vehicles is one approach to enhancing operational efficiency. Development of a solution to allocate AV lane capacity is needed to provide effective support for managers to allocate dedicated AV lane. In [79], the dedicated AV lane optimization allocation problem considering mixed SAVs and regular vehicles flows was studied. An enumeration algorithm was proposed to develop lane allocation strategies for discrete lane settings. The findings of this study was that the deployment of dedicated AV lanes should jointly consider the penetration rate of SAVs and the management objectives. In addition, the authors performed sensitivity analysis taking into account the value of travel time for SAVs and the capacity of a dedicated AV lane.
# Development and Characterization of a Flexible Soundproofing Metapanel for Noise Reduction
## 2 Materials and Methods  
### Finite Element (FE) Analysis  
Sound transmission loss values were calculated using the commercial finite element (FE) analysis program COMSOL Multiphysics 6.0. Three-dimensional Helmholtz and Navier equations were simultaneously solved to calculate the frequency domain's acoustic pressure (\(p\)) and membrane displacement (\(d\)). We employed an Acoustic-Structure Interaction model, which couples acoustic pressure (acpr) and solid mechanics (solid), to conduct a comprehensive vibro-acoustic analysis of the FSM. This approach allowed us to account for both the mechanical properties of the fluid and the vibration of the membrane through acoustic pressure while also considering the stress and displacement of the structure via solid mechanics. Figure 1 illustrates the simulation conditions for calculating the sound transmission loss of the metamaterial unit cell. In this model, we considered the frame-membrane as a single integrated structure, allowing for a comprehensive analysis of their combined acoustic behavior. The model includes two acoustic waveguides, with non-reflecting boundary conditions applied at both ends to prevent reflections. A plane wave with an amplitude of 1 Pa was set to incident from the upstream side. The incident plane wave vibrates the unit cell, radiating reflected and transmitted waves upstream and downstream. To account for the vibroacoustic characteristics of the membrane, acoustic-boundary conditions were applied at the interface between air and the membrane. The interface between air and the frame was set as a rigid boundary condition, and the edges of the frame-membrane interface were constrained for fixation. The frame material was a soft urethane rubber commonly used as a molding material, with a 1040 kg/m\({}^{3}\) density. Silicon rubber was used as the membrane material, with a density \(\rho_{\text{m}}\) = 1000 kg/m\({}^{3}\), [PERSON]'s modulus \(E_{\text{m}}\) = 9.5 MPa, and [PERSON]'s ratio \(\
u_{\text{m}}\) = 0.48.
## 4 Results and Discussion
### Element Associations
#### 4.3.3 Abandoned Channels and Floodplain  
The lower section includes the combination of abandoned channels and floodplain (Figure 3). Abandoned channels are very common in this research area. They have various sizes, with thicknesses of 0.8-2.5 m and a width range of 7-50 m, and have an external form that is lenticular or wedge-shaped; the original channel sometimes has an irregular base. Abandoned channels, as the thinnest part of channel belt deposition, are often found in laminated and ripple fine sandstone deposits within the channel belt and are relatively well preserved. The top is covered by floodplain deposits, which consist of thin layers of approximately 40 cm and thick layers of up to 1 m but extend horizontally for several hundred meters (Figure 3). The configuration element combination of the floodplain covering the abandoned channel has no genetic relationship but represents the end of a sedimentary event [46].
# Olive Oils Classification via Laser-Induced Breakdown Spectroscopy
## 1 Introduction
Among the different advantages of the LIBS technique is its ability to analyze any state of matter (i.e., solids, gases, liquids, metals or dielectrics). Since the elemental composition of olive oil is mainly of carbon, hydrogen and oxygen, the corresponding olive oil LIBS spectra are expected to exhibit spectral features related to these elements with some intensity differences [17]. Therefore, the classification/discrimination of olive oil, based only on such LIBS spectra, is not very useful or at least extremely difficult. To overcome this difficulty, machine learning techniques can be employed to unravel any valuable information contained in these LIBS spectra. Machine learning algorithms can perform tasks suitable for categorizing/classifying data into desired and distinct classes. These algorithms have been widely applied on LIBS data [18], for classification of steel materials [19], for the classification/identification of polymeric samples [20; 21], for biomedical and agricultural applications [22; 23; 24] and for food analysis [25; 26]. LIBS analysis of olive oils for classification purposes has been reported up to very recently by few groups. Among them, [PERSON] et al. were the first using LIBS combined with artificial neural networks to study various types of olive oils and report successful classification of the oils they examined in terms of their geographical origin. Shortly after, in another work [14] using the ratio of C(I)-247.8 nm to the C\({}_{2}\) band at 516.6 nm, some vegetable oils were successfully classified by LIBS in terms of their saturated fatty acid content. More recently, in another study [15], LIBS combined with some machine learning algorithms was used for the classification of some olive oils in terms of their geographical origin and acidity content. It was the first time, to the best of our knowledge, that LIBS assisted by different machine learning algorithms was used for such classification. The reported classification accuracies were quite successful however a limited number of samples were used for the proof-of-concept of the proposed experimental approach. In the present work, a more extended study is performed, employing Laser-Induced Breakdown Spectroscopy assisted by some machine learning techniques for the classification/discrimination of a much larger number of olive oils (33 EVOOs and 3 VOOs), based on their geographical origin. The present olive oils correspond to a more geographically representative data set and they have been collected following specific protocols.
# Dynamic Node Privacy Feature Decoupling Graph Autoencoder Based on Attention Mechanism
## 6 Simulation Results
### Sensitive Study  
This section primarily evaluates the impact of the number of key nodes and the number of attention heads on data utility and privacy protection performance. Specifically, this study examines different proportions of key nodes relative to the total node count (10%,30%, 50%, 70%, 90%) and selects varying numbers of attention heads (1, 2, 4, 8). The experimental results are presented in Figures 4 and 5.  
On one hand, With the increase of key nodes, the accuracy rate of utility attribute classification generally shows an upward trend on the three datasets, while the classification accuracy rate of privacy labels remains stable. Specifically, taking the Credit defaulter dataset as an example, when the number of key nodes is only 10%, the classification accuracy rate of the utility attribute is 0.787. However, when the number of key nodes gradually increases, the change in its accuracy rate first decreases and then increases. But when the proportion of key nodes is 50%, the maximum value of 0.801 is achieved. However, during the proportion change process of key nodes, the classification accuracy rate of privacy tags changed at the maximum of only 0.021, with almost no change. The above phenomenon indicates that the privacy information of graph data is often only contained in a few important nodes, which is also in line with the sparse characteristics of social networks. Excessive decoupling will instead lose utility.  
Figure 4: The impact of different attack model: (**a**) Yale. (**b**) Rochester. (**c**) Credit defaulter.  
Figure 5: The impact of different attention heads: (**a**) Yale. (**b**) Rochester. (**c**) Credit defaulter.  
On the other hand, With the increase in the number of attention heads, the accuracy rates of utility attribute classification and privacy attribute classification were relatively stable on the three datasets, among which the achievement on the Rochester dataset was the smallest. Specifically, when the number of attention heads increased from 1 to 8, the change rates of the accuracy rates of utility attribute classification and privacy attribute classification in Rochester were 0.05, from 0.811 to 0.861, and 0.02, from 0.572 to 0.592, respectively. It indicates that the self-attention mechanism has been able to capture relatively comprehensive relationships. Furthermore, in the Credit defaulter dataset, the influence trend of the number of attention heads on the classification accuracy of utility attributes and privacy attributes is the same. This suggests that in model optimization, we should pay attention to other potential factors (such as data processing, feature selection, etc.) to improve the privacy protection performance.
## 4 Numerical Results and Discussion
### Parametric Analysis  
#### 4.3.1 Influence of Rotational Speed  
As we all know, the rotational speed has a significant influence on the dynamic characteristics. In view of this, this section studies the influence of rotating speed. Figure 17 represents the vibration response in time domain, and Figure 18 shows the response in the frequency domain at different nodes when the rotor operates at 30,000 r/min, which is the lower limit of the working speed. The corresponding impact factors at the maximum and minimum operating speeds are both shown in Figure 19. By comparing the time-domain and frequency-domain responses at different speeds, i.e., comparing Figure 5 with Figure 10 and comparing Figure 14 with Figure 18, it can be seen that the time-domain and frequency-domain responses at different speeds have similar characteristics (the vibration amplitude increases suddenly when blade off event occurs and then decays to a stable value; in addition, the first natural frequency and the second forward and backward processional frequencies can be excited by the impact load, while the amplitude with a higher-order frequency has a faster attenuation rate), while for all the nodes, the impact factor of 50,000 r/min is greater than that of 30,000 r/min (see Figure 19), which indicates that the higher the speed, the more susceptible the rotor motion state is to the impact load, i.e., within the working speed range, the rotor system operating at higher speeds is more sensitive to impact load, which is consistent with the results reported in [9; 14].  
Figure 16: Vibration modes in different time periods: (**a**) 0.05–0.08 s; (**b**) 1–1.5 s.  
Figure 15: Response and corresponding impact factors at different locations.
## 3 Test Results and Analysis
### Analysis of Measured Curves
#### 3.3.5 Load-Deflection Curve of RRAC Beams  
Figure 12 shows the measured load-mid-span deflection curve for each specimen. From the graph, it can be seen that the load-deflection linearly develops at this stage from the start of loading to the concrete cracking. After the crack occurs, the load-deflection curve begins to shift to the deflection side, and the degree of this offset increases with the increase of the load, showing obvious nonlinear characteristics. When the peak load is reached, the specimens enter the failure stage and the curves begin to decrease. Comparing the whole process curves of two kinds of specimens with different shear span ratios, it is found that the specimens with a small shear span ratio have a higher peak load, but it has a faster drop.
## 3 Results and Discussion
### Voghurt Texture and Viscosity
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Storage Day/Variable**} & \multicolumn{4}{c}{**Treatment**} \\ \cline{2-6}  & **Control** & **UOP** & **POP** & **SEM** & **Significance** \\  & **(n = 4)** & **(n = 4)** & **(n = 4)** & & \\ \hline \hline \multicolumn{6}{c}{_Firmness (\(g\))_} \\ \hline
7 & \(310.50\) a & \(428.75\) b & \(304.00\) a & \(22.509\) & \(\ast\) \\
14 & \(310.25\) a & \(517.25\) b & \(330.00\) a & \(30.231\) & \(\ast\ast\ast\) \\
21 & \(370.5\) & \(452.25\) & \(357.75\) & \(21.069\) & NS \\ \hline \hline \multicolumn{6}{c}{_Adhesiveness (\(g\) f mm)_} \\ \hline
7 & \(-1825.75\) & \(-2578.75\) & \(-2119.25\) & \(147.132\) & NS \\
14 & \(-1935.25\) a & \(-3224.00\) b & \(-2374\) & \(219.259\) & \(\ast\) \\
21 & \(-2247\) & \(-2931\) & \(-2419.25\) & \(192.386\) & NS \\ \hline \hline \multicolumn{6}{c}{_Stickiness (\(g\))_} \\ \hline
7 & \(-86\) & \(-121.25\) & \(-96.75\) & \(10.102\) & NS \\
14 & \(-92.00\) a & \(-158.75\)b & \(-111.75\) ab & \(10.767\) & \(\ast\) \\
21 & \(-113.5\) & \(-137.75\) & \(-113\) & \(116.355\) & NS \\ \hline \hline \end{tabular} UOP = unprocessed orange peel; POP = processed orange peel; \(\ast\) = \(p<0.05\); \(\ast\ast\ast\) = \(p<0.001\); NS = non-significant; superscripts a, b differ at \(p<0.05\).  
\end{table} 
Table 11: Changes in yoghurt texture during storage (days 7–21). et al. [92], the storage period did not affect the viscosity in sheep milk yoghurt throughout storage for 28 days. In relation to changes in viscosity during storage within the same group, no statistically significant (\(p>0.05\)) changes were observed for all treatment groups.
# Enhancement of PV Panel Power Production by Passive Cooling Using Heat Sinks with Perforated Fins
## 5 Results and Discussion
In terms of efficiency and power production, according to the assumption that the efficiency dropped linearly with the rise in temperature of \(-\)0.37%/\({}^{\circ}\)C [43], the following power production could be achieved--Table 7. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{\(\
u_{air}\)} & \multicolumn{3}{c}{**G = 600 W/m\({}^{2}\)**} & \multicolumn{3}{c}{**G = 800 W/m\({}^{2}\)**} & \multicolumn{3}{c}{**G = 1000 W/m\({}^{2}\)**} \\ \cline{3-8}  & & \multicolumn{4}{c}{**Drop of \(t_{PV}\) Compared to Base Case, for \(t_{air}\) = 20–35 °C**} \\ \cline{3-8}  & & \({}^{\circ}\)**C** & \% & \({}^{\circ}\)**C** & \% & \({}^{\circ}\)**C** & \% \\ \hline
0. BASE CASE & & 34.5–49.9 & - & 39.4–54.9 & - & 44.3–59.9 & - \\ \hline  
& –4.42—4.61 & –13.4—9.5 \\ \hline \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._The power production was calculated as the percentage of maximum power possible, by assuming that at 25 \({}^{\circ}\)C the ratio achieved was 100% (320 Wp in STCs). From here on, we considered that the rise of temperature determined a drop in power production for each degree over \(t_{STC}\) = 25 \({}^{\circ}\)C, according to the PV panel's data sheet [43]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multirow{2}{*}{\(\mathbf{v_{air}}\)} & \multicolumn{2}{c}{**G = 600 W/m\({}^{2}\)**} & **G = 800 W/m\({}^{2}\)** & **G = 1000 W/m\({}^{2}\)** \\ \cline{3-5}  & & \multicolumn{2}{c}{**of \(\mathbf{P_{m}}\) at 25 \({}^{\circ}\)C, for \(\mathbf{t_{air}}\)** = 20–35 \({}^{\circ}\)C} \\ \cline{3-5}  & & \% & \% & \% \\ \hline
0. BASE CASE & & 94.33–88.58 & 91.8–85.98 & 89.23–83.33 \\ \hline  
\hline \end{tabular}
\end{table} 
Table 7: Percentage of maximum power production achieved by the PV panel for each studied case. The most important effect of air cooling heat sinks was achieved in low wind conditions and high levels of solar radiation. It could be observed, Table 7, that for \(v_{air}\) = 1 m/s, \(G\) = 1000 W/m\({}^{2}\) and ambient temperature \(t_{air}\) = 35 \({}^{\circ}\)C, the percentage of maximum power production achieved was 83.33% for the base case, while the best cooling scenario (Case 3--horizontal 30 mm) could reach 88.74%. This meant a rise in power production of 6.49% compared to the base case. For the position of the PV panel and wind direction studied in the paper, the best solution of heat sink for various conditions was the following: Case 3--Horizontal 30 mm. For this type of heat sink, the enhancement of power production was also reached for higher wind velocities (2-5 m/s), but with lower impact: from 3.60-4.97%, compared to the base case. When lower ambient temperatures were involved, the effect of the heat sinks was decreased, this was due to inferior operating temperatures of the PV panel that were reached for base case. Therefore, the improvement of the power production reached values in the range 3.31-4.99%.