esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Modeling of Solid-Gas Reactions in Packed Beds
### Dimensional Analysis  
The modeling approaches presented above not only provide a tool for the prediction of the behavior of a packed bed sorption process, but also, they can be used for the analysis of the process which together with experimental data gives further insights that are of use beyond the scope of process modeling.  
In the following we focus on the shrinking core model that describes phenomena taking place on the pellet scale and on the bed scale (see Figure 1). Specifically, the pellet scale is the scene of reaction and intra-pellet diffusion. The characteristic times of these two processes read as \(\tau_{Rx}=R_{p}/k_{s}\) and \(\tau_{D}=R_{p}^{2}/D_{a}\) for reaction and diffusion, respectively. The ratio of these two time-scales defines a Damkohler number:  
\[\mbox{Da}=\frac{\mbox{(rate of reaction)}}{\mbox{(rate of diffusion)}}=\frac{ \tau_{D}}{\tau_{Rx}}=\frac{k_{s}R_{p}}{D_{a}} \tag{29}\]  
(the Damkohler number defined in Equation (29) can be derived rigorously by multiplying Equation (23) by \(\tau_{D}\) and substituting for \(a_{p}=6/R_{p}\) which after some re-arrangement recovers Da.) Typically, pellet sizes are of the order of \(R_{p}\sim 10^{-2}\) m, while the rate function of the surface reaction and the intra-pellet  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sorbent** & **T (\({}^{\circ}\)C)** & \(D_{\rm ash}\) **(m/s2)** & **Ref.** \\ \hline Mixed oxides & 500–600 & \(\sim\)10\({}^{-12}\) & [129; 130] \\ Zinc ferrite & 540–600 & \(\sim\)10\({}^{-12}\) & [131] \\ Zinc oxide & 400 & 10\({}^{-17}\) & [64] \\ \hline \hline \end{tabular}
\end{table}
Table 8: Grain model in the literature.  
diffusivity are of the order of \(k_{s}\sim 10^{-2}\) m/s (see Table 3) and \(D_{a}\sim 10^{-6}\) m\({}^{2}\)/s, which corresponds to a Damkohler number of Da \(\sim 10^{2}\). Such a large Damkohler number implies that diffusion is the rate limiting step on the pellet scale.  
The knowledge that diffusion is the rate-limiting step on the pellet scale also guides the analysis of the bed scale, that is the scene of convection. The characteristic time of convection is equal to the residence time \(\tau=L/u_{int}\). The ratio of the diffusion time scale \(\tau_{D}\) and the convection time scale \(\tau\) defines a Peclet number:  
\[\text{Pe}=\frac{\text{(rate of convection)}}{\text{(rate of diffusion)}}=\frac{\tau_{D}}{\tau}=\frac{R_{p}^{2}}{\tau D_{a}}. \tag{30}\]  
Clearly, effective operation of the packed bed requires Pe \(\ll 1\) to make sure that the gaseous reactant has enough residence time in the bed to fully diffuse through the pellets. Based on this, we can derive a lower limit for the fluid residence time in the packed bed sorber, as:  
\[\tau_{\text{min}}\approx\frac{R_{p}^{2}}{D_{a}}. \tag{31}\]  
This approximation can help with sizing the reactor in cases where the flow rate is fixed.
## 3 Method
### Multi-Task Loss
#### 3.3.2 Intra-Task Consistency Loss  
For intra-task consistency, the teacher model weight \(\theta_{0}\) are updated as an exponential moving average (EMA) of the weights \(\theta\) of the student: \(\theta_{0\rm t}=\alpha\theta_{0\rm t}-1+(1-\alpha)\theta_{t}\), where \(\alpha\) is the EMA decay.  
The student is trained with supervised loss and unsupervised consistency loss compared to teacher outputs. We integrate consistency for both segmentation and regression tasks. Following [14], we estimate uncertainty via Monte Carlo Dropout [48] with \(T\) stochastic passes under random dropout. The segmentation uncertainty is approximated by predictive entropy.  
\[p_{i}=\frac{1}{T}\sum_{t=1}^{T}p_{i}^{t}\;\;u=-\sum_{i=1}^{C}p_{i}log(p_{i})\]  
\(P_{i}^{t}\) is prediction logits for class \(i\) at pass \(t\), \(C\) is the segmentation classes number, \(p_{i}\) is average softmax probability from \(T\) teacher passes, and \(U\) combines voxel-wise uncertainty \(u\). Using \(U\), unreliable high-uncertainty predictions are filtered out. The student only learns from confident predictions, defined by the intra-task consistency loss:  
\[L_{itc}(\theta;\theta^{\prime};D)=\beta\frac{\sum I(u<u_{th})\left\| f_{\text{{avg}}}-f^{\prime}{}_{\text{{avg}}}\right\|^{2}}{\sum I(u<u_{th})}\] \[+(1-\beta)\frac{\sum I(u<u_{th})\left\|f_{\text{{dis}}}-f^{\prime}{}_ {\text{{dis}}}\right\|^{2}}{\sum I(u<u_{th})}\]  
where \((f_{\text{{avg}}},f_{\text{{dis}}})\) and \((f_{\text{{avg}}},f_{\text{{dis}}})\) are the student and teacher outputs of segmentation and regression, respectively. \(I(\cdot)\) selects certain predictions, \(\beta\) balances tasks, and \(\tau\) thresholds uncertainty.
# Detecting Fake Reviews Using Aspect-Based Sentiment Analysis and Graph Convolutional Networks
## 6 Discussion
### OTT Dataset  
Figure 11 and Table 8 provide a comparative analysis of different models' performance on the OTT dataset. It highlights the performance metrics of FakeDetectionGCN against the traditional methods. These include the CNN-LSTM model [25] and the MLP classifier with BERT embeddings. The results focus on the 80% sample size. The reported results from [25] for this size are included.  
The CNN-LSTM model reported an exceptionally high accuracy of 95.5%. However, our attempts to replicate these results were not successful. We achieved only about 60% accuracy. This raises questions about the reproducibility and reliability of the CNN-LSTM model under varying conditions or datasets.  
FakeDetectionGCN has an accuracy of 88.12%. While this is not the highest, it is reliable. This is especially true considering the reproducibility issues with the Bathla model. FakeDetectionGCN also recorded the highest precision at 90.5% among the tested models. High precision indicates that the model effectively minimizes false positives.  
Additionally, the F1 score of the FakeDetectionGCN model is 87.6%. This score is higher than that of the MLP with BERT. It is close to the CNN-LSTM's score, indicating a good balance between precision and recall.
## 3 Results and Discussion
### Frenkel-Type Defects in Ti\({}_{4}\)N\({}_{3}\) Nanosheet
For subsurface N atom, the contribution to TDOS was extremely small. As a result, in the doped system Ti\({}_{4}\)N\({}_{3}\)-Sc system, a high spin polarization ratio was reserved, and the spin-polarization ratio was described by Formula [48]. For the Ti\({}_{4}\)N\({}_{3}\)-V system, as shown in Figure 8b, we can see that near the Fermi level, some spin-peaks appeared in the spin-up channel because of one more electron. Similar behaviors were detected in the PDOS of surface Ti atoms owing to rehybridization. However, for the doped Ti\({}_{4}\)N\({}_{3}\)-Zr system, as shown in Figure 8c, the strong Zr-Ti interaction could widen the spin split near the Fermi level owing to the short bond length of Zr-Ti. In the PDOS of surface Ti and V atoms, some peaks appeared near the Fermi level. This system presented a relatively low spin polarization but a large magnetism. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Atom** & \(a\) **(Å)** & \(L\) **(Å)** & _d\({}_{Z\text{-Ti}}\)_**11** **(Å)** & _d\({}_{Z\text{-N}2}\)_**(Å)** & _E\({}_{b}\)_ **(eV)** & \(\begin{matrix}\text{\bf{E}}_{\text{form}}\\ \text{\bf{(eV)}}\end{matrix}\) & _M\({}_{\text{Ti}}\)_**(\(\upmu_{\text{B}}\))** & \(\begin{matrix}\text{\bf{M}}_{\text{Z}}\\ \text{\bf{(\mu_{B})}}\end{matrix}\) & \(\begin{matrix}\text{\bf{M}}_{\text{tot}}\\ \text{\bf{(\mu_{B})}}\end{matrix}\) \\ \hline Ti\({}_{4}\)N\({}_{3}\) & 2.993 & 7.235 & 2.919 & 2.062 & \(-\)1.295 & – & 0.490 & 0.389 & 1.173 \\ Ti\({}_{4}\)N\({}_{3}\)-Sc & 2.999 & 7.217 & 3.029 & 2.217 & \(-\)1.280 & 0.930 & 0.594 & 0.356 & 1.649 \\ Ti\({}_{4}\)N\({}_{3}\)-Zr & 2.999 & 7.211 & 3.090 & 2.220 & \(-\)1.220 & 4.715 & 0.522 & 0.294 & 1.772 \\ Ti\({}_{4}\)N\({}_{3}\)-V & 2.991 & 7.241 & 2.802 & 1.985 & \(-\)1.335 & \(-\)2.505 & 0.668 & 1.315 & 2.365 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Calculated lattice constants \(a\), atomic layer thickness \(L\), binding energy \(E_{\text{b}}\), forming energy \(E_{\text{form}}\), total magnetic moment \(M_{\text{tot}}\), magnetic moment of \(Z\) atom or its neighbor Ti atom at the surface, bond length between the doping \(Z\) atom at surface and its neighbor Ti\({}_{1}\) or N\({}_{2}\) atom at the surface or subsurface, respectively.
# Performance Analysis of Amplitude Comparison Monopulse Direction-of-Arrival Estimation
###### Abstract  
Amplitude-comparison monopulse radar in tracking radar uses the tracking scheme of a monopulse radar to estimate the angle components of a target. The performance of the amplitude comparison monopulse radar under measurement uncertainty is analysed. Measurement noises are modelled as Gaussian random variables. Taylor series expansion is adopted to get analytic expression of the mean square error (MSE). Estimation accuracy, in terms of the MSEs for estimate the direction-of-arrival (DOA) estimation algorithm, is usually obtained from the Monte Carlo simulation, which can be computationally intensive especially for large number of repetitions in the Monte Carlo simulation. To get reliable MSE in the Monte Carlo simulation, the number of repetitions should be very large, which implies that there is a trade-off between reliability of the MSE and computational burden in the Monte Carlo simulation. This paper shows the performance of amplitude comparison monopulse radar by linear approximation of nonlinear equations to estimate the DOA. The performance of amplitude comparison monopulse radar is quantitatively analysed via the MSEs, and the derived expression is validated by comparing the analytic MSEs with the simulation based MSEs. In addition, it is shown in the numerical results that analytically derived MSE is much less computationally intensive in comparison with the Monte Carlo simulation-based MSE, which implies that the proposed scheme in this paper results in drastic reduction in computational complexity for evaluation of the MSE.  
tracking radar; amplitude comparison monopulse; mean square error (MSE); Taylor expansion; approximation Article
# Low-Cost Handheld Spectrometry for Detecting Flavescence Doree in Vineyards
## 2 Materials and Methods
### Machine Learning Pipeline for Classification  
The overall workflow for our research is graphically summarized in Figure 3. The spectral reflectance measurements acquired by two sensors Hamamatsu and NIRScan are exploited both separately and together (joined spectra) in order to make full use of the two wavelength ranges (340-1700 nm). To train the classifiers, spectral reflectance is fed to the classification algorithms (i.e., some opportune classification tools see Section 2.3.1) to attribute to each reflectance a binary label indicating whether it is positive or negative to FD (i.e., 1 = positive and 0 = negative to FD). The current study was conducted using a small set of samples (i.e., total plants: 60, FD positives: 33, and FD negative: 27). Thus, to evaluate the effectiveness of the proposed framework, we assess the performance of the trained models by using data that were not employed for the training phase (test samples) to obtain a non-biased estimation of its performance. In particular, we conduct the following: (1) We use the train-test split method in which the dataset is randomly divided into 70% training (FD positives: 22, and FD negative: 20) and 30% test (FD positives: 11, and FD negative: 7). However, as we have a small dataset, the train-test split method might be subject to high  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dates for Spectral** & **Sensors** & **Vineyard Rows** & **No. of Samples** & \begin{tabular}{c} **FD Positive** \\ **Samples** \\ \end{tabular} & \
\begin{tabular}{c} **FD Negative** \\ **Samples** \\ \end{tabular} \\ \hline \multirow{4}{*}{
\begin{tabular}{c} 8, 15, 21 June, 1 \\ and 6 July 2022 \\ \end{tabular} } & Hamamatsu (340–850 nm, 288 bands) and NIRScan sensor & Row 2 & 20 & 13 & 07 \\ \cline{1-1} \cline{3-5}  & (900–1700 nm, 228 bands) & Row 3 & 20 & 13 & 07 \\ \cline{1-1} \cline{3-5}  & & Total & 60 & 33 & 27 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of spectral and PCR measurements.  
Figure 2: In-field hyperspectral data acquisition setup for collecting spectral data of detached leaves of vineyard plant (**A**): Hamamatsu and NIRScan sensors data acquisition setup; (**B**): Hamamatsu and NIRScan spectra of the healthy and diseased leaves at the second survey date (15 June); (**C**) representative pictures of FD-asymptomatic and symptomatic leaves.  
variance. To address this issue, we resort also to (2) leave-one-out (LOO) cross-validation, a special case of k-folds cross-validation, where k is equal to the size of our dataset (i.e., k = 60). We repeatedly divide the dataset using just one simple test set while we use the remaining ones as training sets. Afterwards, we calculate the average of their resulting performance, which minimizes the effect of random partitioning.
## 2 Methodology
### Experimental Methodology
#### 2.1.1 Nonlinear System Identification
This leads to instabilities in the fitting algorithm, which in turn causes spurious oscillations in the terms \(A_{j}\), \(\zeta_{j}\), \(\omega_{j}\) and \(\phi_{j}\), leading to an ambiguous identification of the system parameters. This problem is solved by adding a series of ad hoc constraints imposed on the parameter search space, specifically:The amplitude of each component is limited at \(2y(0)\). 2. The frequency of each component is limited in the interval \((1\pm x)\cdot\omega_{j}(0)\), where \(\omega_{j}(0)\) is the linear estimate of the frequency component and \(x\) is the frequency deviation allowed for. 3. If the damping ratio of one component \(\zeta_{j}\) exceeds a threshold value (set here at 0.9, or 90% of critical damping), the respective component is eliminated from the signal estimate \(y_{e}\) for all subsequent bins. These constraints do not limit the parameter search space excessively and effectively regularise the search procedure when operating on the noisy data. Constraint #1 ensures that no amplitude exceeds two times the initial acceleration at the step release point; note that the likelihood of any component reaching such values under free vibrations is low. Constraint #2 ensures that the identified frequency component will be found only in the relative vicinity (\(\pm 10\%\) was used here) of the linear estimate, while still allowing for significant nonlinear effects to be manifested by the frequency shifts. Further, note that for the well-separated frequency components, this constraint can be relaxed or eliminated completely. Finally, constraint #3 is instrumental in ensuring the robustness and stability of the algorithm. Previously, it was observed that when the component \(j\) has a negligibly small presence in the signal in the bin number \(k\), the returned values for both \(A_{j}(k)\) and \(\zeta_{j}(k)\) increased steeply. Of course, this local increase did not affect the other components since the \(j\)-th amplitude was very low at this point. However, if left unchecked, this behaviour led to ever-increasing parameter values and oscillatory solutions, which ultimately affected the overall stability of the analysis. The threshold value \(\zeta=0.9\) is well above the expected damping ratios for any frequency component and, as such, it does not overconstrain the analysis.
## 3 Results and Discussions
### Viscosity Studies
\[log\eta_{m}=x_{1}log\eta_{1}+x_{2}log\eta_{2} \tag{6}\]  
Croenauer-Rothfus Kermore relation (\(\eta_{\rm{CRK}}\)) [30]:  
\[log\
u_{m}=\Sigma x_{i}\text{log}(v_{i}) \tag{7}\]  
Gambrill relation (\(\eta_{\rm{G}}\)) [30]:  
\[v_{m}^{1/3}=\Sigma x_{i}v_{i}^{1/3} \tag{8}\]  
where \(\
u_{\rm{m}}\) is the kinematic viscosity of mixture, whereas \(\rm{x_{i}}\) and \(\rm{v_{i}}\) are the mole fraction and kinematic viscosity of individual pure liquids. Average Percentage of \(\text{Deviation}(\text{APD})=\frac{1}{n}\Sigma\frac{\left(\text{Experimental values}-\text{ Theoretical Values}\right)}{Experimental\text{ }Values}\times 100\) (9)  
Tables 2 and 3 present the experimental and theoretical viscosities predicted by various models for binary liquid systems ethanol + 1-hexanol and ethanol + 1-octanol at 303.15 K. Irrespective of the theoretical models, the common prediction is that the relative \(\eta\) increases with the increase in the concentration of 1-hexanol/1-octanol. The \(\eta\) is the minimum at the lower 1-hexanol/1-octanol concentration, and with the concentration range (1.00) \(\eta\) becomes maximum. The increasing frictional forces are expected to arise from the presence of more carbon atoms in the linear chain of alcohol and the molecular layers formed between the lower-carbon and higher-carbon containing alcohol and to be the reason for the increase in values with concentration of the higher-carbon number containing alcohol [22]. From Tables 2 and 3, it can be observed that there are differences in the theoretical values of the viscosities of the ethanol + 1-hexanol and ethanol + 1-octanol binary mixtures at 303.15 K compared to the experimental values. The reason for the difference is ascribed to the limitations and approximation incorporated in these theories. In the ethanol + 1-hexanol binary liquid system, it is observed that the APD, as derived from the theoretical values of \(\eta\) calculated by using various theoretical models ([PERSON] relation, Kendall and Monroe relation, [PERSON] relation, Arrhenius-Eyring relation, Croenauer-Rothfus Kermore relation and Gambrill relation), follow the trend \(\Delta\eta_{\rm{IH}}=\Delta\eta_{\rm{B}}\)  
\(>\Delta\eta_{\rm AE}>\Delta\eta_{\rm CRK}>\Delta\eta_{\rm G}>\Delta\eta_{\rm ICMM}\). This study infers that the APD of \(\eta\) is more in the [PERSON] model and [PERSON] model and less in the Kendall and Monroe model. Furthermore, within Table 2, it can be noticed that the Kendall and Monroe model is the most suitable theoretical model for predicting viscosity for ethanol + 1-hexanol binary liquid systems. Regarding the results derived from the ethanol + 1-octanol binary liquid system, the comparison of the experimental \(\eta\) with the value predicted from various theoretical models is presented in Table 3. Additionally, the details of the APD are given in Table 3 which follows the following trend: \(\Delta\eta_{\rm H}=\Delta\eta_{\rm B}>\Delta\eta_{\rm AE}>\Delta\eta_{\rm CRK}> \Delta\eta_{\rm ICM}>\Delta\eta_{\rm G}\). From the results,
## Data and Methods
### Road Width Estimation and Sidewalk Segmentation  
The width of the roads in one region can be an important feature for many statistical analyses. StreetScouting supports the automatic road width estimation in each frame. Additionally, the existence of sidewalks is evaluated. A segmentation model is initially used to semantically segment the frame. Specifically, we use the _DeepLabv3_+ [48] segmentation model. This model combines a Spatial pyramid pooling module and encode-decoder structure to obtain the advantages of both methods. It also applies the depth-wise separable convolution to both atrous spatial pyramid pooling and decoder modules, resulting in a faster and stronger encoder-decoder network. This architecture is used pre-trained in the _cityscapes_[49] dataset. _Cityscapes_ is a dataset for semantic urban scene understanding and it contains several classes, such as the _BDD100k_ dataset. The effectiveness of the model is assessed on the test subsets of the PASCAL VOC 2012 [50] and Cityscapes datasets, accomplishing the performance of 0.89 and 0.821 mean IoU, respectively. An example outcome of the model is shown in Figure 2(a). Road and sidewalk classes are overlaid with purple and pink colors respectively. Using just this result we can infer whether there is a sidewalk in the frame.  
Figure 2: Example of object position estimation pipeline. (a) Object detection. (b) Depth estimation.  
To estimate the road width additional work have to be done which is depicted in Figure 3. First, we use the outline (green line) of the road mask. Then this outline is split into two signals from the center of the camera (white line). We calculate the first differences in these two signals to detect strictly increasing intervals for the left and strictly decreasing intervals for the right signal. With this approach, we estimate the intervals that potentially represent the road boundaries. To estimate the road boundaries we perform linear regression in the intervals of these two signals. The result is two fitted lines that represent the left (yellow line) and the right (red line) road boundaries.  
For the final estimation, we calculate the number of pixels among the two boundaries for a specific horizontal line (blue line). Then we fit another linear regression model associating the pixels with ground truth measurements in meters. For fitting the model we use 15 training samples with the number of pixels and corresponding ground truth values in meters. To obtain ground truth values, we manually measured the width of various streets. The fitted linear regression model and its details are shown in Figure 4. The sum of squares error is 7.44, the slope is 0.011 and the intercept is \(-\)1.272.  
The final road width is given by Equation (3), where m denotes the width in meters and p is the number of pixels.  
\[m=0.011\times p-1.272 \tag{3}\]  
We evaluate this model in Section 5.
# Removal of Volatile Phenols From Wine Using Crosslinked Cyclodextrin Polymers
## 3 Materials and Methods
### Nuclear Magnetic Resonance Analysis  
Encapsulation of volatile phenols by CDs was investigated by two-dimensional NMR rotating frame Overhauser effect spectroscopy (\({}^{1}\)H 2D ROESY), using an Agilent DD2 600 MHz spectrometer fitted with a cryoprobe (Agilent Technologies, Santa Clara, CA, USA), operating at 600 MHz with a mixing time of 300 ms. Samples were prepared by adding guaiacol, 4-methylguaiacol or 4-ethylguaiacol to deuterated model wine (12% d\({}_{5}\)-ethanol in D\({}_{2}\)O, pD adjusted to 3.5 by dropwise addition of DCI) containing \(\beta\)-CD, as previously reported for 4-ethylphenol [35].
## Appendix A COMSOL Multiphysics(r) Simulations  
The simulations described in this work were performed in the COMSOL Multiphysics 5.4 software [43]. It utilizes the finite element method for calculation of the static potentials as well as the harmonically changing radio frequency field. The _AC/DC_ module with its _Electrostatics_ and _Electric Currents_ submodules were used in all the simulations. The 2-dimensional geometries of the different trap types were created within COMSOL.  
The ideal multipole potential is created by rods with hyperbolic shapes, however, in practice, cylindrical rods are often used instead. For the hyperbolic trap, the octupole field was modeled as [44]  
\[\psi(x,y)=\left(\frac{\psi_{0}}{2\tau_{0}^{4}}\right)(x^{4}-6x^{2}y^{2}+y^{4}). \tag{1}\]  
For the 3D simulation of the trapping potential, a 3D model of our setup including ion optics and metal and plastic holders was loaded into the software. For the different materials used in the trap assembly (Steel, PEEK, etc.), the corresponding relative permittivity constants were accounted for. Note that for both the 2D as well as the 3D simulations, COMSOL's infinite element domain feature was employed. This was used to model a grounded vacuum chamber infinitely far away, which allowed for an overall smaller geometry to be simulated.  
For extraction of ions from the trap, the _Charged Particle Tracing_ module was used. The extraction from the trap is done without including Coulomb interaction between the particles. For each temperature, an ensemble consisting of 150,000 ions was initialized into the trap and the particles were extracted simultaneously.