esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Calculation Application and Experimental Setup
### Rotordynamics Model Setup
#### 3.4.5 Solution Setup and Damping Coefficient Selection  
Unsteady state solving was also applied in the rotordynamics finite element analysis model, whose time step and solving time keep consistent with those in the unsteady CFD analysis. Transient complete method was activated in the computation process, namely the unsteady responses of the structural to external loads were calculated by complete mass, damping, and stiffness matrixes. In this way, various nonlinear features such as elasticity, large strain, and large deformation of main shaft can be obtained more accurately.  
To assure solving stability during dynamics analysis of structures, a damping matrix is usually needed to consider influences of damping on structural movements in practical engineering. According to the viscous damping theory, the rotor damping is proportional to the vibration velocity and its acting direction is opposite to the velocity. Therefore, a proportional damping was applied onto the entire finite element analysis model. This implies that the system damping matrix [\(C\)] in Equation (6) is approximately treated as the linear combination of mass matrix [\(M\)] and the stiffness matrix [\(K\)], as expressed in the Equation (10)  
\[[C]=\alpha\cdot[M]+\beta\cdot[K] \tag{10}\]  
where \(\alpha\) and \(\beta\) denote the mass and stiffness damping coefficients respectively.  
With references to research results of [PERSON] et al. [14; 20], a stiffness damping coefficient was added into the rotordynamics model and its value was determined by trial-and-error method and engineering experiences. A trial operation was implemented under the flow rate of \(Q_{des}\), in which a relatively small stiffness damping coefficient (\(\beta=0.001\)) was set firstly. Figure 8 shows that the velocity curves of mass point of impeller was very unstable with \(\beta=0.001\), and no valid information can be obtained from them. Then, when the value of stiffness damping coefficient was increased to \(\beta=0.002\), the velocity curves began to be stable and appear periodic fluctuation feature after oscillation in the first revolution of impellers (corresponding to 0.041 s). Thirdly, when the stiffness damping coefficient \(\beta\) reached 0.003, it takes a shorter time for regulation compared with that under \(\beta=0.002\). However, it takes more system input disturbance under \(\beta=0.003\). Therefore, the stiffness damping coefficient in the computation was finally determined as \(\beta=0.002\).  
Figure 8: Impeller mass point velocity versus time with different stiffness damping coefficient: (**a**) X direction; (**b**) Y direction.
## 3 Results and Discussion
### Evaluation of Physical-Chemical and Functional Stability
The results obtained were compared with that of the raw extract (AGL-28). As reported in Figure 9a, AGL-28 exhibited in high phenolic content (104.38 ugQE/mg ex) which is extremely reduced (_p_ < 0.0001, 58.85 ug QE/mg ex) after 6 months of storage at 40 \(\pm\) 2 \({}^{\circ}\)C; 75 \(\pm\) 2% R.H. At the contrary, in MPA, the extract maintains the polyphenol content unaltered (104.65 ugQE/mg powder, after the spray-drying and 101.30 ugQE/mg powder, after six months). The results were also confirmed by the evaluation of the free radical scavenging activity examined against DPPH and ABTS radicals. As reported in Figure 9b,c, a reduction of the anti-radical activity is observed for the AGL-28 extract, which can be correlated to the reduction of the polyphenol content. Specifically, in the case of the DPPH test, an increase of about 30% in the EC50 value (from 53.618 to 70.13 ug/mL), and a reduction of about 56% in the TEAC value, (0.32 mM Trolox/mg extract to 0.18 mM Trolox/mg extract) was observed. On the contrary, in the same conditions, the antiradical efficacy of MPA remains more stable (EC\({}_{50}\) 43.79 and 52.18 ug/mL, 19% of increase, Figure 9b) or unchanged (0.33 mM Trolox/mg MPA 0.32 mM Trolox/mg MPA, Figure 9c). The obtained results showed how the spray-drying technique and the selected carriers (pectin and maltodext) can be confirmed as appropriate strategies to improve the quality and the shelf-life of the extract,  
Figure 8: Thermal behavior by DSC analysis of AGL-28 extract (blue and black lines) and MPA powder (red and green lines) before and after 6 months of harsh storage conditions. protecting it from degradation phenomena, and at the same time ensuring an adequate release of bioactive compounds, allowing its potential use in the design and development of health products.
## 6 Detoxification Mechanisms of PPCs in Lactobacillus
### Drug Efflux and ABC-Transport Systems  
The expression of _Lactobacillus_ genes encoding several putative ABC-type and MFS drug extrusion systems are markedly modulated by different PPCs, which can contribute to directly counteract the toxic effects of these compounds.  
As shown in Table 2, _L. plantarum_ strongly induced two drug extrusion systems in presence of \(p\)-CA [25] or RSV [27]. One of these systems (_lp_0989_ to _lp_0992_) is a multidrug efflux pump of the MFS superfamily. The _L. brevis_ homolog of _lp_0991_ (the multidrug transporter), _lvis_1917_, is also strongly induced by ferulic acid [23]. The other induced extrusion system is an ABC-type multidrug resistance (MDR) system encoded by genes _lp_2393 to _lp_2395_. The permease of this system (_lp_2394_) is homologous to the LmrA MDR transporter from _Lactococcus lactis_[51], which efficiently extrudes drugs out of the cell.  
Interestingly, an ABC-type transport system from _L. plantarum_ encoded by the _lp_2739_ and _lp_2740_ genes was strongly downregulated by all PPCs compounds examined (\(p\)-CA, GA, RSV, HXT, and OLE) (Table 2). The permease of this system (_lp_2740_) displays a domain architecture that agrees with BceAB-type transporters [52], and it is homologous to the BceAB-like transporter OrABC from _L. casei_ BL23 [24]. This type of Bce-like module plays crucial roles to sense and detoxify antimicrobial peptides (AMPs) [53] via import and subsequent intracellular enzymatic inactivation. However, the strong downregulation of this Bce-like module suggests that it does not take active part in resistance to PPCs, leaving its physiological significance to be established.
## 3 Results and Discussion  
### FTIR Analysis  
The FTIR spectra of the ternary composites FMWCNTs @ CQDs @ PANI, the composite materials PANI @ FMWCNTs and PANI @ CQDs, and the monolithic material PANI are shown in Figure 1. The absorption peaks of 3851 cm\({}^{-1}\), 3728 cm\({}^{-1}\) and 3626 cm\({}^{-1}\) in the composite material PANI @ FMWCNTs spectrum correspond to free OH, while the absorption peak 1655 cm\({}^{-1}\) corresponds to C=O stretching vibration. The 1644 cm\({}^{-1}\), 1402 cm\({}^{-1}\) and 1047 cm\({}^{-1}\) of the composite material PANI @ CQDs spectrum correspond to C=O stretching vibration, O-H blending and C-O stretching, respectively [1; 6; 16; 28; 29; 30]. It can be seen from the figure that the absorption peak is broad and strong for the composite sample, but a very weak spectrum for pure PANI, which may be related to the interaction between PANI and CNS, involves \"charge transfer\" [31]. When FMWCNTs and CQDs are attached to the surface of PANI, the intensity of the band will be increased in different degrees while introducing the characteristic functional groups. For the ternary composite FMWCNTs @ CQDs @ PANI, the functional group type and peak intensity have been strengthened, and the absorption peak is also clearer. In the FTIR spectrum, the composite material's spectrum contains the characteristic peaks of PANI, indicating that the generated PANI is wrapped on FMWCNTs and CQDs after polymerization with the aniline monomer. The inhibited growth and suppressed atomic vibration resulted in some absorption peaks in the composite material spectrum being similar, and absorption peaks of hydroxyl functional groups also appeared in the PANI @ FMWCNTs spectrum. Considering all the factors, the composite material was proven to be successfully prepared.  
Figure 1: FTIR spectra of FMWCNTs @ CQDs @ PANI, PANI @ FMWCNTs, PANI @ CQDs and PANI.
## 3 Distributed Estimation of Laplacian Spectrum
### Distributed Estimation of Non-Zero Laplacian Eigenvalues
**Step 2:** The Gradients, Jacobian matrices, and Hessian matrices are estimated for the next quadratic programming (QP) subproblems the as follows: \[\mathbf{g}_{t} = \frac{\partial}{\partial\mathbf{\alpha}_{t}}\{\frac{1}{2}\mathbf{\alpha}_ {t}^{T}\mathbf{L}\mathbf{\alpha}_{t}+\mathbf{\beta}_{t}^{T}(\mathbf{x}(h)-\bar{ \mathbf{x}})\}\] (8) \[\mathbf{C}_{t} = \frac{\partial}{\partial\mathbf{\alpha}_{t}}(\mathbf{x}(h)-\bar{ \mathbf{x}})^{T}\] (9) \[\mathbf{B}_{t} = \frac{\partial^{2}}{\partial\mathbf{\alpha}_{t}^{2}}(\frac{1}{2}\mathbf{ \alpha}_{t}^{T}\mathbf{L}\mathbf{\alpha}_{t}).\] (10)
3. **Step 3:** Analogically to inexact SQP method, the QP problem is solved to find the \(\Delta\mathbf{\alpha}_{t}[k]\) and the affine multiplier \(\lambda_{QP}[k]\) as follows: \[\mathbf{x}(h)=\tilde{\mathbf{x}}\]0 (11) subject to \[\mathbf{x}(h)=\tilde{\mathbf{x}}\]1 \[\mathbf{x}(h)=\tilde{\mathbf{x}}\]2 where \(\mathbf{s}\) is the slack variable, introduced into the QP sub-problem to attenuate the numerical reasons when the penalty parameter \(\mu\) becomes large. 4. **Step 4:** The final step is to update \(\mathbf{\lambda}[k+1],\mathbf{y}_{t}[k+1],\mathbf{B}_{t}[k+1]\): \[\mathbf{x}(h)=\tilde{\mathbf{x}}\]3 (12) \[\mathbf{x}(h)=\tilde{\mathbf{x}}\]4 (13)This update rule is relevant to the full-size step where \(a_{1}=a_{2}=a_{3}=1\) for the steplength computation, which proposed in [25]. Then, projecting \(\mathbf{\hat{y}}[k]\) on the constraint \(\mathcal{C}\) to derive \(\mathbf{y}[k+1]\). The steps are repeated until the stopping criteria is satisfied. In order to derive the distributed algorithm, let us take a closer look at each step of the proposed ALADIN-based method.
## 3 Results  
### Characterization of SSS Adsorbent  
#### 3.1.1 Fourier Transform Infrared Spectroscopy  
The FTIR spectra of the adsorbent before and after adsorption of MC are presented in Figure 1. The FTIR spectra of the SSS clearly show several absorption peaks which reflect the complex nature of the material examined. The broad absorption peak around 3331.5 cm\({}^{-1}\) was indicative of the existence of bonded hydroxyl groups on the surface of SSS. The peaks around 2912 and 2102 cm\({}^{-1}\) were assigned to the stretching vibration and bending vibration of the C-H bond, respectively [7; 16; 36]. The broadband at 1732 cm\({}^{-1}\) was assigned to the carbonyl group (C=O). The peaks associated with the stretching vibration in aromatic rings (C=O and -NH of amide groups) were observed at 1591 and 1505 cm\({}^{-1}\). The weak peaks around 1419 cm\({}^{-1}\), 1369 cm\({}^{-1}\) and 1316 cm\({}^{-1}\) were attributed to the vibration of the -CN bond [9; 16]. The peak at nearly 1234 cm\({}^{-1}\) was assigned to the bending vibration of carboxylic groups while deformation related to C-O bond was observed at 1040 cm\({}^{-1}\)[32].  
The strong peak at 1029 cm\({}^{-1}\) was attributed to the Si (Si-O) valence vibrational binding. However, the weak peaks that intervene in the range 450-650 cm\({}^{-1}\) can be attributed to the Si-reflection vibration binding S-O. All these peaks indicate the massive presence of oxygenated groups on the surface of SSS [36]. However, when the two spectra are compared, it is clear that the SSS spectrum after adsorption of MC shows a slight difference, namely, a generally slight shift in all peaks as well as an increase in the intensity of the peaks around 1594, 1316 and 983 cm\({}^{-1}\). These changes are attributed to the loading of the SSS surface with the molecule of the model drug MC [22].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Symbol** & **Variable** & **Level (–1)** & **Level (0)** & **Level (+1)** \\ \hline \(\mathbf{X_{1}}\) & C\({}_{\mathrm{i}}\) (mg/l) MC initial concentration & 20 & 55 & 90 \\ \hline \(\mathbf{X_{2}}\) & m (g/L) SSS dosage & 1 & 2 & 3 \\ \hline \(\mathbf{X_{3}}\) & pH & 4 & 7 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Range of variation of operating parameters.  
Figure 1: FTIR spectra of sunflower seed shells (SSS) before and after adsorption of methylthioninium chloride (MC).
## 5 Conclusions  
The classical unscented Kalman filter does not possess the capability to monitor parameter changes due to changes in vehicle dynamics. An interacting multiple-model based method is suggested to improve the unscented Kalman filter for better navigation data fusion. The resulting IMMUKF ensures better description on the vehicle dynamics and exhibits superior navigation accuracy when compared with the classical UKF algorithm. This paper has presented a fuzzy interacting multiple model unscented Kalman filter for GPS/INS navigation processing to prevent the divergence problem in high dynamic environments.  
The fuzzy system is employed for dynamically adjusting the lower and upper bounds of the process noise covariance, which will be used in each of the parallel filters in the IMM architecture by monitoring the innovation information so as to provide further improvement in estimation accuracy. Through the proposed approach, lower order of filter model can be utilized and, therefore, less computational effort will be sufficient without compromising estimation accuracy significantly. When a designer does not have sufficient information to develop the complete filter models or when the filter parameters are slowly changing with time, the fuzzy system can be employed to enhance the IMMUKF performance. These performance comparisons of UKF, IMMUKF and FUZZY-IMMUKF have been conducted and the proposed FUZZY-IMMUKF algorithm has demonstrated very promising results in navigational accuracy. Future research can be conducted to undertake the implementation of the following issues.  
The current work essentially shows the feasibility of the approach. Evaluation of the proposed approach on a real system with better design of the fuzzy logic might be considered in the near future.  
\begin{table}
\begin{tabular}{c c c} \hline  & **East** & **North** \\ \hline
**UKF** & 7.9530 & 6.3195 \\
**IMMUKF** & 2.9967 & 2.3021 \\
**FUZZY-IMMUKF** & 1.0184 & 0.7286 \\ \hline \end{tabular}
\end{table}
Table 2: Position accuracies for the three approaches (RMS errors, in meters).  
Figure 15: Comparisons of east and north position RMS errors via three approaches: UKF, IMMUKF and FUZZY-IMMUKF.  
Other artificial intelligence such as neural work may also be incorporated for better tuning. Furthermore, the GPS RAIM (Receiver Autonomous Integrity Monitoring) in civil aviation might also be considered as one of the potential applications.
# Passive Sampling and Analysis of Naphthalene
## 4 Results and Discussion
### Validation of TWA-SPME Method for Naphthalene Quantification in Vehicle Exhaust Gas  
The developed method was applied for measuring the concentration of naphthalene in exhaust gases of full-size pickup (Table 2, Figure S2) and tractor. TWA-SPME sampling was tested for longer (8 h) and shorter (1 h) sampling times. RSDs for TWA-SPME sampling were 16, 21 and 27%. DI analyses were below method detection limits. Table 3 presents naphthalene concentrations in exhaust gases reported in the literature. Concentrations of naphthalene found in exhaust of Perkins Prima diesel engine used in UK [44] and in Mazda E5 used in Taiwan [46] were within the range of concentrations determined with the method proposed in this study. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{5}{c}{**Experimental \(C_{g}\), ppm**} \\ \cline{2-7}
**Theoretical \(C_{g}\), ppm** & \multicolumn{3}{c}{**SPME**} & \multicolumn{3}{c}{**DI**} \\ \cline{2-7}  & **Mean** & **STDEV** & **RSD** & **Mean** & **STDEV** & **RSD** \\ \hline
0.11 & 0.052 & 0.007 & 13 & 0.15 & 0.02 & 16 \\
0.58 & 0.25 & 0.04 & 15 & 0.60 & 0.11 & 18 \\
2.3 & 1.2 & 0.3 & 21 & 2.0 & 0.3 & 14 \\
4.6 & 2.7 & 0.5 & 18 & 4.1 & 1.5 & 36 \\
12 & 3.8 & 0.6 & 15 & 13.0 & 0.2 & 1.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of mean (\(n=3\)) experimental (SPME and DI) and theoretical (gas standard) \(C_{g}\). Figure 8: Calibration plots for naphthalene with SPME fiber and direct injection (DI). PDMS 100 \(\upmu\)m retracted fiber, \(t=9\) h, \(Z=10\) mm, \(T=40\)\({}^{\circ}\)C. \(C_{g}\) for naphthalene ranged from 0.1 to 12 ppm. MDL P (0.95) = 11.5 ppb or 0.06 mg\(\cdot\)m\({}^{-3}\). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Vehicle**} & \multirow{2}{*}{\(\mathbf{Z}\), mm} & \multirow{2}{*}{\(\mathbf{t}\), h} & \multirow{2}{*}{\(\mathbf{T}\), \({}^{\circ}\)C *} & \multicolumn{3}{c}{\(C_{g}\), mg\(\cdot\)m\({}^{-3}\)} \\ \cline{5-8}  & & & & **TWA SPME Mean** & **RSD (\%)** & **DI Mean** & **RSD** \\ \hline Full-size pickup & 10 & 8 & 27–95 & (0.08 \(\pm\) 0.02) & 21 & n/d & n/d \\ Full-size pickup & 3 & 1 & 27–75 & (0.3 \(\pm\) 0.05) & 16 & n/d & n/d \\ Tractor & 10 & 1 & 27–122 & (0.2 \(\pm\) 0.05) & 27 & n/d & n/d \\ \hline \hline \end{tabular}  
* Note: Range of temperatures measured from cold start to hot engine run. DI-concentration measured by direct injection is sampled gas onto GC column. n/d: not detected.  
\end{table} 
Table 2: Measured naphthalene concentration in exhaust gases and sampling conditions.
## 3 Results
### Classification Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Training Synthetic Data Set**} \\ \cline{2-6}  & **Sensitivity (\%)** & **Specificity (\%)** & **False Positive (\%)** & **False Negative (\%)** & **Error in Classification (\%)** \\ \hline
**LSVM** & 96.65 & 100 & 0 & 3.35 & 1.67 \\ \hline
**SVM (C1)** & 96.66 & 98.33 & 1.67 & 3.34 & 2.5 \\ \hline
**SVM (C2)** & 97.65 & 99.35 & 0.65 & 2.35 & 1.5 \\ \hline \multicolumn{6}{c}{**Validation Synthetic Data Set**} \\ \hline
**LSVM** & 96.51 & 100 & 0 & 3.49 & 1.75 \\ \hline
**SVM (C1)** & 96.02 & 97.99 & 2.01 & 3.98 & 3 \\ \hline
**SVM (C2)** & 98.01 & 97.49 & 2.51 & 1.99 & 2.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of classification result of simulated (incremental delay) data set; same as Table 2 except for the variance being 0.1.
## 5 Convolutional Neural Networks  
CNNs are commonly used for processing data contained in a matrix or grid, such as images, that are represented by a 2D matrix. Their name comes from the mathematical operation called convolution, which is an operation on two functions to produce a third function that expresses how one of them is modified by the other. In computer vision and image processing, the convolution operation is used to reduce noise and enhance features in images.  
Let us suppose that \(s(t)\) is the output of the convolution; the operation is given by  
\[s\left(t\right)=\int l\left(a\right)h\left(t-a\right)da, \tag{14}\]  
where function \(l\) is the output of a sensor (and usually referred to as the input in CNN terminology), \(h\) is a weighting function (also known as the kernel), \(a\) is the age of the measurement. The convolution is commonly denoted with an asterisk as follows  
\[s\left(t\right)=\left(l*h\right)\left(t\right). \tag{15}\]  
Figure 4: Clouds and ground classes cannot be easily distinguished.  
Figure 3: Steps to generate the proposed descriptor for three classes (ground, cloud and sky).  
This data is usually discretized, and if time \(t\) can only take integer values then it is possible to define the convolution as a discrete operation as follows  
\[s\left(t\right)=\sum_{a=-\infty}^{\infty}l\left(a\right)h\left(t-a\right). \tag{16}\]  
The input and the output are multidimensional arrays, and every element must be explicitly stored separately. It is assumed that every element out of the set of points, for which the values are stored, is zero; therefore, the infinite summation can be implemented over a finite number of array elements, and also, it can be used over more than one axis at a time. Let \(I\) be a two-dimensional image, \(K\) a two-dimensional kernel, the convolution for images is given by  
\[S\left(i,j\right)=\left(I*K\right)\left(i,j\right)=\sum_{m}\sum_{n}I\left(m,n \right)K\left(i-m,j-n\right) \tag{17}\]  
and can graphically be described, as shown in Figure 5.  
The convolution presents two properties that can help to improve a machine learning system--sparse interactions and parameter sharing [36].  
Due to its sparse interactions, it is necessary to store fewer parameters and fewer operations; however, units in the deeper layers may indirectly interact with a more significant portion of the input and describe more complicated interactions between pixels, as described in Figure 6.  
A CNN consist of three steps. First, several convolutions in parallel produce a set of linear activations. Then, a detector step is implemented, where nonlinear activation functions take the linear activations as the argument. Finally, a pooling function is used to modify the output of the layer, making the representation invariant to small translations of the input [36].  
Figure 5: Graphical description of convolution operation.  
Figure 6: Description of sparse interactions. Even if direct connections seem to be sparse, more units at deeper layers are indirectly connected.
## 3 Results and Discussion
### Scenario Analysis
#### 3.4.3 Scenario 3 (5 Access Points)  
In this scenario, we managed to cover 47% of the total surface with a field level higher than \(-\)70 dBm and 39.6% of the surface with a level of signals between \(-\)80 dBm and \(-\)70 dBm (Figure 8c).  
The energy consumed by the five access points is equal to 600 Wh during an operating period of 10 per day. The amount of CO\({}_{2}\) resulting from the production of electricity based on natural gas is 250.8 g and 33 g if the electricity is produced from solar energy (Figure A4).
# A Spatio-Temporal Encoding Neural Network for Semantic Segmentation of Satellite Image Time Series
## 3 Methodology
### STENN Architecture  
In this section, we primarily introduce a shared dilated convolution spatial encoder for SITS and a Transformer encoder for time encoding. The input consists of SITS X, which is a four-dimensional tensor with a shape of \(T\times C\times H\times W\). Here, T represents the length of the time series, C is the number of channels in the image, and \(H\times W\) denotes the shape of the image.  
As shown in Figure 5, our STENN model encodes the sequence X in three steps:  
* First, it undergoes spatial encoding. We employ multi-level shared dilated convolutional layers, simultaneously processing each frame of the SITS, generating feature map time series with channel numbers of 16, 32, 32, and 64.
* Next is temporal encoding. We concatenate the results of spatial encoding along the channel dimension, obtaining a feature map time series with 144 channels. Then, we reshape this sequence into a time series for each pixel, and after positional encoding,  
Figure 4: Transformer encoder sequence data classification model includes an embedding layer, positional encoding, multi-head self-attention layer, feedforward fully connected layer, and linear layer.  
it is fed into the Transformer encoder. Subsequently, the encoded results are averaged along the time dimension, ultimately generating a single feature map.
* Finally, through the semantic segmentation head, we map the feature map containing spatio-temporal information into segmentation results.