esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Volatiles Present in Calypogeia suecica
Compounds belonging to the group of aliphatic compounds were also found in the cells of the liverwort samples tested: hexanal (**1**), 3-methylbutanoic acid (**2**), 2-methylbutanoic acid (**3**), 3-hexen-1-ol (**4**), 1-hexanol (**5**), hexanoic acid (**9**), 1-octen-3-ol (**11**), 3-octanone (**12**), 3-octanol (**13**), 2-ethylhexanoic acid (**16**); and aromatic compounds: benzenemethanol (**14**), benzeneacetaldehyde (**15**), benzeneethanol (**17**), phenoxyethanol (**23**), and 1-phenoxy-2-propanol (**24**). The content of aliphatic compounds in samples belonging to the first group ranged from 0.12% to 1.48%. In the second group, it ranged from 0.25% to 1.66%. Compounds classified as aromatic compounds constituted 0.88% to 5.46% of the composition in the first group, and in the second group 0.72% to 3.27% of the composition. Figures 1 and 2 show a comparison of the average content of the dominant sesquiterpenes and sesquiterpenoids in the 2021 and 2022 samples, divided based on the location of the habitat from which the material was collected, and further into groups 1 and 2. In the light of the information presented, it can be concluded that the analyzed biological material was clearly able to be divided into two groups in terms of the composition of volatile secondary metabolites. Some fluctuations in the composition of volatile compounds were also observed, resulting from the location of the sites from which the samples were collected. Based on the collected results in Tables S1a-e and S2a-e, the mean % values of compound contents were calculated, along with the standard deviation for collection place within the group and for the group in total. Table 1 presents the results for group 1, and  
Figure 1: Average contents of selected dominant compounds in samples collected in years 2021–2022, taking into account the habitat (Beskidy, Bieszczady, M. Pieniny, Pieniny, Tatry) and group 1 (CSU1) or group 2 (CSU2). Figure 2: Average contents of selected dominant compounds in samples collected in year 2021–2022, taking into account the habitat (Beskidy, Bieszczady, M. Pieniny, Pieniny, Tatry) and group 1 (CSU1) or group 2 (CSU2). Table 2 presents the results for group 2. Table 2 also provides additional \(t\)-test values for groups. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & & \multicolumn{3}{c}{**Collection Place**} & \\ \cline{4-8}
**No.
# Leading-Edge Vortex Characteristics of Low-Aspect-Ratio Sweptback Plates at Low Reynolds Number
## 1 Introduction
[PERSON] et al. [15] examined revolving wings for various ARs from 2 to 10 and observed that an LEV remained stable only up to the spanwise location of four times the chord length, i.e., at r/c \(<\) 4. [PERSON] et al. [16] examined several AR wings and reported a similar LEV behavior to that of [PERSON] et al. [15]. They further reported that AR \(\approx\) 3 showed an optimal aerodynamic performance, i.e., the maximum aerodynamic lift and reasonable efficiency. Recent studies have considered the AR and the Rossby number concurrently [17; 18]. These also showed that AR between 3.0 and 4.0 is most favorable for the lift enhancement, at least for flapping-wing flyers adept at hovering (wingroot-based revolving/flapping motion). [PERSON] and [PERSON] [18] further showed that the transition from an inboard quasi-steady region (where the robust LEV attachment exists) to an unsteady wake region appears around r/c \(\approx\) 3 at AR \(>\) 4. We noticed that a sweptback angle is also able to directly supply a spanwise flow on a suction side of LAR wings. This indicates that an LEV, interaction with TiVs and consequent aerodynamic characteristics will be affected by a sweptback angle. A jet-like LEV core flow and relevant aerodynamic performance depending on a sweptback angle of a delta-wing aircraft [19], which seems identical to an LEV on a revolving/flapping wing, support our inference. A shifted-back stroke plane of biological flyers in forward flight, which is identical to a sweptback angle, might also be related to an LEV [20; 21; 22]. The effect of a sweptback angle of LAR wings, however, has not yet been sufficiently investigated. In this study, direct measurements of the aerodynamic forces and moments on LAR flat plates at a Reynolds number of 2.85 \(\times\) 10\({}^{4}\) were conducted. Three ARs of 2.03, 3.13, and 4.50, which cover the critical AR for the LEV of a revolving/flapping wing [15; 16; 17; 18], were selected, and a sweptback angle was used to supply a spanwise flow. We found a resemblance in aerodynamic characteristics to that of a revolving/flapping wing, which is presumably due to an analogous behavior of the two LEVs. This suggests the applicability of an LEV of biological flyers for state-of-the-art aerial vehicle design.
# LogEDL: Log Anomaly Detection via Evidential Deep Learning
## 1 Introduction
Through this substitution, our model can not only learn the distribution characteristics of normal log sequences but also make anomaly judgments by evaluating uncertainty when facing abnormal logs. To better reflect real-world scenarios, we only use normal log data to train the model, and then, evaluate both normal and abnormal log data separately during testing. The main contributions of this paper are as follows:  
1. Analyzing the similarity between the log anomaly detection task and open-set recognition, and introducing the relationship and differences between open-set recognition and evidential learning, clarifying the applicability of evidential learning to log anomaly detection methods. 2. Proposing an evidential learning network, LogEDL, suitable for log anomaly detection, enabling the model to identify unknown logs and quantitatively assess the uncertainty of unknown data, thereby assisting in identifying anomalous data. 3. Applying evidential learning algorithms to log anomaly detection tasks on public datasets, demonstrating the effectiveness and generalization ability of the proposed method.
# A Level-Set-Based Density Method for Buckling Optimization of Structure with Curved Grid Stiffeners
## 4 Optimization Framework and Sensitivity Analysis  
### Definition of Buckling Load Optimization Problem  
The buckling optimization problem is defined as  
\[\begin{array}{ll}\underset{\alpha_{i},\beta_{i},\eta}{\text{min}}&J=-\lambda _{1}\\ \text{s.t.}&(\mathbf{K}-\lambda_{1}\mathbf{G})\mathbf{\varphi}_{1}=0\\ &\delta_{\min}\leq\alpha_{i},\beta_{i}\leq\delta_{\max}\\ &\eta_{\min}\leq\eta\leq\eta_{\max}\\ &V\leq\overline{V}\\ &g_{\gamma},g_{\theta}\leq\xi\end{array} \tag{21}\]  
where \(\lambda_{1}\) represents the fundamental BLF; \(\mathbf{\varphi}_{1}\) denotes the eigenvector corresponding to the eigenvalue \(\lambda_{1}\); \(\mathbf{K}\) stands for the global stiffness matrix, while \(\mathbf{G}\) represents the global geometric stiffness matrix; \(\delta_{\min}\) and \(\delta_{\max}\), respectively, stand for the lower and upper limits of the design variables \(\alpha_{i}\) and \(\beta_{i}\); \(\eta_{\min}\) and \(\eta_{\max}\) indicate the minimum and maximum values of the design parameter \(\eta\); \(g_{\gamma}\) and \(g_{\theta}\) are the gradient constraint functions for the two fundamental level set functions, and \(\xi\) represents the maximum value of the gradient constraints; \(V\) denotes the total volume of stiffeners, while \(\overline{V}\) indicates the volume constraint of stiffeners.
# Dynamic Response and Damage Characteristics of Large Reinforced Concrete Slabs under Explosion
## 3 Results and Discussion
### Dynamic Response Characteristics of the RC Slab
#### 3.1.3 Acceleration  
Four acceleration sensors were used to determine the vibration characteristics under different explosion conditions. Typical acceleration-time history curves are shown in Figure 8. With the increase in the TNT charge, there was also an increase in the acceleration value measured by the acceleration sensor. The maximum accelerations under different TNT charge conditions are shown in Table 4.
## 4 Use Cases for the Automaton
### Smart Healthcare  
In smart healthcare, patient-related measurement data, such as blood pressure measurements, electrocardiogram (ECG) data, or X-ray data are recorded by smart devices and stored for later assessment. The measurements are taken according to a schedule so that they take place at defined intervals. The schedule created by the intervals can be seen as a process. In this way, a process can be defined for each patient or a group of patients.  
Patient identifiers can be used to identify related data. Our automaton can detect missing messages, duplicates, and contextual discrepancies in these processes. In the medical field, many medical treatments are customized, resulting in a process that is structured according to the type of treatment or specialty. As increasingly large amounts of data, such as the aforementioned ECG or X-ray pictures, have to be sent and managed, data retention during validation also plays a role. Appropriate timers as well as the detection of outdated data can help to reduce memory consumption. The general workflow is thus comparable to the manufacturing scenario above. Transitions for an automaton are again derived from the process flow. The timer lengths must initially be adapted to the environment.
## 4 Statistical Analysis on Computational Results  
This section presents the statistical tests which were performed on the computational results, to evaluate the performance of the three different GVNS methods. Different statistical tests are applied to different data structures. In particular, statistical analysis methods can be divided on parametric and non-parametric tests. The first category examines normal variables whereas the other methods concern non-normal variables [20].  
Initially, the application of a normality test showed that the numerical data does not follow the normal distribution. Consequently, we applied the Kruskal-Wallis test for checking the existence of a statistically significant difference between the methods. In this test receiving a \(p\)-value less than 0.05 means that there is a statistically significant difference.  
At the present point, it should be mentioned that the statistical analysis was performed on median values to eliminate potential extreme deviation on the average values based on extreme values. Also, the related to the aTSP analysis is taken from our previous work [14] and it is presented here for building a more comprehensive view.
# Multi-Body Model of Agricultural Tractor for Vibration Transmission Investigation
## 1 Introduction
RMS acceleration values at these locations were presented under different operating conditions. The research in [18] focused on predicting the vibration characteristics of a heavy machinery suspended seat using a multi-body model. The simulation results showed good agreement with the experimental test results, and the model was employed for design modifications. The vibration tests of various work conditions of the agricultural tractor were analysed in the frequency domain according to the ISO 2631 standard in [19], which found that the driver's whole body vibration was excessive. A multi-body model of the tractor was assembled to explore the tractor's dynamic behaviour riding an artificial test track; however, this model was not validated and served only for the identification of the highest-impact model parameters. This paper combines these approaches and creates a validated 3D multi-body model of the tractor, including a sprung driver seat and mass representing the driver. The tyre model should be capable of describing the non-linear characteristics of tyres. The goal is also to use several measurements to validate the model. Vibration transmission will be analysed using vertical acceleration at different tractor locations. A complex solution of vibration transmission from the road excitation to the driver will be presented.
## 3 Results
### Non-Excited Jets
#### 3.1.1 Evolution of the Amplitude Spectra  
The above-mentioned periodic behaviour of the motion of the vortices is reflected in the amplitude spectra of the axial velocity presented in Figure 4 as a function of the Strouhal number \(\mathrm{St}=fD/U_{0}\), where \(f\) stands for the frequency of the recorded signal. These spectra were computed based on the time signals of the velocity recorded over the time period of \(164D/U_{0}\). Numerical probes were placed along the axis in four points \(x/D=1.0\), \(2.0\), \(4.0\), \(6.0\), which allowed detecting the initial phase of the formation of these vortices and their evolution downstream. The results are presented only for the cases with \(Ti=0.01\%\), for which the solutions depend on the mesh topology. In the location \(x/D=1.0\), the spectra are flat, which means that the toroidal structures observed in  
Figure 3: Contours of instantaneous values of the vorticity modulus \(|\Omega|\times D/U_{0}\) in the central cross-section and isosurfaces of the Q-parameter (\(Q=1\) (\(U_{0}/D\))\({}^{2}\)) in the Cartesian and cylindrical coordinate systems obtained for \(Ti=0.01\%,0.1\%\) and \(1.0\%\).  
Figure 3 are formed only further downstream, and they are not an accidental result of the time-varying velocity profile imposed at the inlet boundary. Indeed, an evident rise of the frequency is seen starting from \(x/D=2.0\), and it manifests as a broadband peak centred at \(\text{St}=0.58\) that agrees well with a characteristic frequency of the preferred mode (\(\text{St}_{p}=0.54\)) reported in [25]. In the point \(x/D=4.0\), the amplitude of the fluctuations increases, and in the case of the solution obtained on the Cartesian mesh, the first signs of the vortex pairing process appear. They manifest in the rise of a rather broadband peak at \(\text{St}\approx 0.3\). Its amplitude grows downstream, and at \(x/D=6.0\), it reaches a level the same as the peak corresponding to the basic frequency. It is worth noticing that in the range of higher frequencies (\(\text{St}>0.8\)), the amplitude of the fluctuations are larger in the Cartesian coordinate systems. This means that in this case, the temporal variation of the small-scale phenomena is more dynamic.
# Design and Stability Analysis of Six-Degree-of-Freedom Hydro-Pneumatic Spring Wheel-Leg
## 4 Analysis of Horizontal Rotational Stability
### Analysis of Spring Correction Force
#### 4.2.1 Displacement and Tensile Force Variations of Left Tension Spring  
At the static equilibrium position, the horizontal distance between the upper and lower fixed ends of the left tension spring is  
\[a_{t0}=\sqrt{s_{t}^{2}-h_{t}^{2}}. \tag{32}\]  
The angle between the line connecting the lower fixed end of the tension spring to the center of rotation and the line connecting the upper fixed end to the center of rotation at the static equilibrium position is given as  
\[\varphi_{t}=\arccos\left(\frac{l_{c}^{2}+r_{c}^{2}-a_{t0}^{2}}{2l_{c}r_{c}} \right). \tag{33}\]  
After rotation by an angle \(\theta\), the horizontal projection length of the tension spring is given as  
\[a_{t1}=\sqrt{l_{c}^{2}+r_{c}^{2}-2l_{c}r_{c}cos(\varphi-\theta)}. \tag{34}\]  
The new length of the tension spring is given as  
\[s_{t1}=\sqrt{a_{t1}^{2}+h_{t}^{2}}. \tag{35}\]  
The rotational lever arm corresponding to the left tension spring is given as  
\[\begin{cases}\alpha_{t}=\arccos\left(\frac{l_{c}^{2}+a_{t1}^{2}-r_{c}^{2}}{2 l_{c}a_{t1}}\right)\\ L_{t1}=l_{c}\sin\alpha_{t}\end{cases} \tag{36}\]  
Figure 11: Horizontal projection force analysis diagram of the springs.  
By combining the above equations, the tensile force of the left tension spring after rotation by an angle \(\theta\) is expressed as  
\[F_{t1}=k(s_{t}-s_{t1})=k\sqrt{l_{c}^{2}+r_{c}^{2}-2l_{c}r_{c}cos(\varphi-\theta)+ h_{t}^{2}}. \tag{37}\]  
Thus, the unstable rotational torque provided by the left tension spring is given as  
\[N_{t1}=F_{t1}L_{t1}\frac{a_{t1}}{s_{t1}}. \tag{38}\]
## 3 Multi-Objective Uncertain Adjacent Only Quadratic Minimum Spanning Tree  
In the subsequent section, we focus on how the suggested mUAQMSTP is structured for a network whose parameters are expressed as uncertain variables.  
Assume that \(G=(V,E)\) is a weighted connected undirected graph (network) (WCUG) with \(n=\mid\!\!V\!\mid\) and \(m=\mid\!\!E\!\mid\), where \(V=\{v_{1},v_{2},\ldots,v_{n}\}\) and \(E=\{e_{1},e_{2},\ldots,e_{k}\}\) are the finite sets of vertices and edges of \(G\), respectively. Any pair of vertices, \(v_{i}\) and \(v_{j}\), connected by an undirected edge, \(e_{i}\in E\), has a linear weight that stands for a construction or running cost. The quadratic weight, on the other hand, is a measure of the interaction cost that results from selecting two neighboring edges, \(e_{i}\) and \(e_{j}\), from a spanning tree at the same time. The linear weights \((\xi_{i})\) and the quadratic weights \(\left(\xi_{ij}\right)\) are regarded as zigzag uncertain variables in this context. The fuel price, the enacted toll tax, the expenses of vehicle overhauls, and the time spent in traffic congestion are just a few of the variables that might cause direct and interactive costs to be uncertain in this case. Consequently, we have presented our problem mUAQMSTP within the framework of uncertainty theory in order to address the indeterminacy, as seen in Equation (4). Within this framework, we have also included a numerical illustration in Section 7 that pertains to our problem.  
\[\left\{\begin{array}{l}Min\ Z_{1}=\sum\limits_{i=1}^{n}\xi_{i}x_{i}\\ Min\ Z_{2}=\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}\xi_{ij}x_{i}x_{j}\\ \end{array}\right. \tag{5}\] \[\left.\begin{array}{l}\sum\limits_{i=1}^{m}x_{i}=\left|V\right|-1 \\ \sum\limits_{i\in E_{k}}x_{i}\leq\left|\kappa\right|-1,\left|\kappa\right|\geq 3 \\ x_{i}\in\{0,1\},\ \forall\ e_{i}\in E.\end{array}\right.\]  
In this context, \(\xi_{i}\) represents the uncertain direct cost (linear weight) and \(\ \zeta_{ij}\) stands for the uncertain interactive cost (quadratic weight) linked to the edge, \(e_{i}\), and the pair of neighbouring edges \(\left(e_{i},e_{j}\right)\), respectively. In particular, the first objective function seeks to reduce \(G\)'s uncertain direct cost, whereas the second objective function seeks to decrease \(G\)'s uncertain interaction costs. The subgraph of \(G\) produced by the set of vertices, \(\kappa\), is denoted as \(H\) with edge set \(E_{\kappa}\). Here, \(E_{\kappa}\) is the subset of edges in \(E\), where both of the end vertices are in \(\kappa\). The first constraint in Model (5) is the cardinality constraint, which states that the selected edges must be precisely \(\left|V\right|-1\). The second constraint guarantees that the chosen edges do not form a cycle in the network. Every decision variable, \(x_{i}\), in this case can take on a value of \(0\) or \(1\). When \(x_{i}=1\), the suitable edge, \(e_{i}\), is chosen from \(G\) to build an MST. However, \(e_{i}\) is not part of the MST, when \(x_{i}=0\). Following this part, we develop the CCM of mUAQMSTP, which is necessary since Model (5) is a problem of uncertain programming.
## 3 Results
### ALEXI/DisALEXI Iterations of TSEB  
To ensure consistency between the ALEXI and DisALEXI ET estimates, the \(\mathrm{T_{a}}\) is iteratively adjusted at the ALEXI pixel scale until the DisALEXI daily ET, spatially averaged over the ALEXI pixel, converges to the ALEXI value. This iterative process requires running TSEB on each iteration. Under the TSEB system of equations (DisALEXI-PT or DisALEXI-PM), adjustments to the \(\mathrm{T_{a}}\) will change the \(\mathrm{T_{c}}\) and \(\mathrm{T_{s}}\) and subsequently, T and E. Such a process may result in unrealistic estimates of T and E partitioning even though components may sum to reasonable estimates of ET. One such case may be under conditions of strong advection over irrigated crops, where deviations between DisALEXI and ALEXI ET may be impacted more by the horizontal advection of energy rather than air temperature boundary conditions at the blending height. Given the iterative process associated with DisALEXI, we evaluate the behavior of E and T by comparing the difference between the final and initial estimates of E and T (Figure 9, y-axis) with the difference between the final and initial \(\mathrm{T_{air}}\) (Figure 9, x-axis) for DisALEXI-PT (Figure 9, top row) and DisALEXI-PM (Figure 9, bottom row).  
An evaluation of Figure 9 shows distinct patterns in the DisALEXI-PT E and T response to \(\mathrm{T_{a}}\), quantified with respect to values obtained from the initial \(\mathrm{T_{a}}\) boundary conditions obtained from CFSR (Figure 9, top row). The pattern can best be observed at OLA, where differences in \(\mathrm{T_{a}}\) more directly influence the E values compared to T. In general, as \(\mathrm{T_{a}}\) increases, H will decrease as the surface-to-air temperature gradient reduces, subsequently leading to an increase in ET (E+T) to maintain energy balance. The inverse response is expected as \(\mathrm{T_{a}}\) decreases. Although the response from the individual E and T components is also dependent on specific site conditions (e.g., vegetation cover, stress, etc.), convergence on the ALEXI ET appears primarily driven by shifts in E for the DisALEXI-PT approach. Conversely, the DisALEXI-PM model results show more uniformity in E and T component responses to shifts in \(\mathrm{T_{a}}\) (Figure 9, bottom row), suggesting more proportionate adjustments to E and T in relation to ET.  
Figure 9: Scatter plot of the difference between final and initial \(\mathrm{T_{air}}\) (x-axis) and the difference between final and initial E (brown) or T (green) for DisALEXI-PT (**top**) and DisALEXI-PM (**bottom**) approaches on all Landsat overpass dates.
## 3 DD Prediction Model and Performance Evaluation Indicators
### Performance Evaluation Indicators  
In this paper, three evaluation indicators and the Taylor diagram are presented to evaluate the performance of the proposed model.  
1. Root mean square error (RMSE) RMSE is used to characterize the overall prediction precision. \[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(y_{(i)}-y_{p(i)}\right)^{2}}\] (21)where \(y_{p(i)}\) is the predicted value, \(y_{(i)}\) is the measured value, \(n\) is the length of testing set, and \(RMSE\in(0,\ +\infty)\). The prediction accuracy of the model is inversely proportional to the value of RMSE;
2. Average absolute error (MAE) The MAE visually represents the loss value of the prediction results. \[MAE=\frac{1}{n}\sum_{i=1}^{n}\left|y_{(i)}-y_{p(i)}\right|\] (22) The magnitude of the MAE value has the same relationship with prediction accuracy as the RMSE;
3. Determination coefficient (R\({}^{2}\)). The R\({}^{2}\) can be used to describe the correlation between two or more variables. If the correlation between the predictions and the original sequence is poor, the model is unreliable even if the values of RMSE and MAE are small. \[R^{2}=\sum_{i=1}^{n}\left(y_{p(i)}-\frac{1}{n}\sum_{i=1}^{n}y_{(i)}\right)^{2 }/\sum_{i=1}^{n}\left[y_{(i)}-\frac{1}{n}\sum_{i=1}^{n}y_{(i)}\right]^{2}\] (23) where \(R^{2}\in(0,\ +\infty)\), and the strength of the correlation is proportional to the value of \(R^{2}\);
4. Taylor Diagram A Taylor diagram can provide a visual framework for comparing the prediction results of a model to a reference model. It can represent the relevant information of various prediction models in a centralized way, which fully and clearly reflects the prediction capabilities of different models. Taylor diagrams have been widely adopted in recent years as an effective method for assessing the predictive ability of different models.  
Figure 4: Flow chart of dam deformation model based on VMD-SE-ER-PACF-ELM.