esa-sceva/satcom-chunk-collection huggingface dataset:
# Combining Fuzzy C-Means Clustering with Fuzzy Rough Feature Selection
## 3 Theoretical Background
### Membership Function  
American cybernetician [PERSON]. [PERSON] created fuzzy sets theory with a groundbreaking paper in 1965 [16]. Fuzzy set is the extension of classical set, which contains more general and various mathematical concepts, forming a new fuzzy mathematics discipline.  
Assuming \(X\) is the universe, \(A\) is a subset of \(X\) and can be represented by characteristic functions such as map \(A:X\rightarrow\{0,1\},\forall x\in X,A(x)=\left\{\begin{array}{c}0,x\
otin A \\ 1,x\in A\end{array}\right..\) For fuzzy subset \(A\) of \(X\), any \(x\in X\), \(x\) is not absolutely belonging to \(A\) nor absolutely not belonging to \(A\). The degree to which \(x\) belongs to \(A\) can be represented by the value in \([0,1]\).  
**Definition 5** (Membership Function): _Assuming \(X\) is the universe, the map \(A:X\rightarrow[0,1]\) is called a fuzzy subset of \(X\), \(F\) set for short. Map \(A\) is called membership function of \(F\) set about \(A\), \(A(x)\) is called degree of membership of \(x\) about \(A\)._  
The generation of the fuzzy membership function is fundamentally important. It is important to find a proper membership function. A basic method of constructing membership functions is the reference function; there are some commonly used membership functions, choosing proper parameters to get the membership functions which we need, such as triangular membership functions:  
\[f(x,a,b,c)=\left\{\begin{array}{c}0,\ x<a\\ \frac{x-a}{b-a},a\leq x\leq b\\ \frac{c-x}{c-b},b<x\leq c\\ 0,\ x>c\end{array}\right.. \tag{1}\]  
Trapezoidal membership function:  
\[f(x,a,b.c.d)=\left\{\begin{array}{c}0,\ x<a\\ \frac{x-a}{b-a},a\leq x<b\\ 1,b\leq x<c\\ \frac{d-x}{d-c},c\leq x\leq d\\ 0,\ x>d\end{array}\right.. \tag{2}\]  
Gaussian membership function:  
\[f(x,\sigma,c)=\exp{(-\frac{(x-c)^{2}}{2\sigma^{2}})}. \tag{3}\]  
Membership function can be generated from available data. Many methods can be used, such as the histogram method, the transformation of the probability distribution to the probability distribution,clustering, and neural network [32; 33; 34; 35; 36]. We need an effective membership function generation mechanism to make full use of fuzzy theory and it must have the following advantages [37]:  
1. Accuracy. Membership function should reflect the knowledge contained in the data accurately.
2. Flexibility. This method can provide a broad family of membership function.
3. Computability. The method should be feasible in calculation so that it has practical application value. The literature [38] emphasizes the importance of easy optimization and adjustment for membership functions.
4. Ease of use. Once the membership function is determined, for any given x, the corresponding A(x) can be found easily.  
In this paper, we use clustering to find membership functions. The fuzzy c-mean clustering (FCM) method is used to generate the fuzzy membership function during clustering. During the clustering process of FCM, the fuzzy membership function is generated.
## 4 Model Checking Fidelity Properties
### The Model Checking Problem  
Similarly to the other model checking problems, we are given the LQMC \(\mathfrak{M}\) and the property \(\varphi\) we want to verify. The property is usually provided as an FCTL state formula \(\varphi\) respecting the following grammar:  
\[\varphi ::=a\mid\varphi\wedge\varphi\mid\
eg\varphi\mid\mathbb{F}_{\sim \tau}[\psi]\] \[\psi ::=\mathbf{X}\varphi\mid\varphi\Uparrow\Uparrow^{\leq k}\varphi \mid\varphi\Uparrow\varphi,\]  
where \(a\in AP\), \(\sim\in\{\leq,\geq\}\), \(\tau\in\mathbb{Q}\cap[0,1]\) is a threshold, and \(k\in\mathbb{N}\). Note that all other comparisons, such as \(<\), \(>\), \(=\), and \(\
eq\), that could be used as \(\sim\) in \(\mathbb{F}_{\sim\tau}[\psi]\) can be derived from \(\leq\) and \(\geq\) with the help of conjunction and negation.  
As we can see, the only difference between QCTL and FCTL is that \(\mathbb{Q}_{\sim\mathcal{E}}[\psi]\) has been replaced with \(\mathbb{F}_{\sim\tau}[\psi]\). This means that also the semantics of FCTL differs from the one of QCTL only for this path quantifier. We give the full semantics of FCTL for completeness. Given a state \(s\in S\) and a path \(\sigma\in\mathrm{Path}^{\mathfrak{M}}\), we have  
\[s\models a \text{if }a\in L(s);\] \[s\models\varphi_{1}\wedge\varphi_{2} \text{if }s\models\varphi_{1}\text{ and }s\models\varphi_{2};\] \[s\models\
eg\varphi \text{if it is not the case that }s\models\varphi;\] \[s\models\mathbb{F}_{\sim\tau}[\psi] \text{if }\underline{\mathrm{Fid}}(Q_{s}^{\mathfrak{M}}(\{ \sigma\in\mathrm{Path}^{\mathfrak{M}}\mid\sigma\models\psi\}))\sim\tau;\] \[\sigma\models\mathbf{X}\varphi \text{if }\sigma[1]\models\varphi;\] \[\sigma\models\varphi_{1}\Uparrow^{\leq k}\varphi_{2} \text{if }\exists\mathtt{0}\leq n\leq k:\sigma[n]\models\varphi_{2} \text{ and }\forall 0\leq i<n:\sigma[i]\models\varphi_{1};\text{ and }\] \[\sigma\models\varphi_{1}\Uparrow\varphi_{2} \text{if }\exists n\in\mathbb{N}:\sigma[n]\models\varphi_{2} \text{ and }\forall 0\leq i<n:\sigma[i]\models\varphi_{1}.\]  
Given a super-operator \(\mathcal{E}\), the \(\underline{\mathrm{Fid}}\) operator is defined as  
\[\underline{\mathrm{Fid}}(\mathcal{E})=\min_{\rho\in\mathcal{D}^{1}(\mathcal{H })}\mathrm{Fid}(\mathcal{E},\rho)\]  
where  
\[\mathrm{Fid}(\mathcal{E},\rho)=\sqrt{\rho^{1/2}\mathcal{E}(\rho)\rho^{1/2}}.\]  
When \(\rho\) is a pure state \(\rho=\ket{\eta}\bra{\eta}\), then \(\mathrm{Fid}(\mathcal{E},\ket{\eta}\bra{\eta})=\sqrt{\bra{\eta}\mathcal{E}( \ket{\eta}\bra{\eta})\ket{\eta}}\); moreover, by the joint concavity property (cf. (56) (Exercise 9.19)), it holds that \(\min_{\rho\in\mathcal{D}^{1}(\mathcal{H})}\mathrm{Fid}(\mathcal{E},\rho)=\min_ {\ket{\eta}\in\mathcal{H}}\mathrm{Fid}(\mathcal{E},\ket{\eta}\bra{\eta})\).  
The model checking problem for LQMCs against an FCTL state formula \(\varphi\) is the natural counterpart of the one against QCTL (cf. Definition 3).  
Definition 4: Let \(\mathfrak{M}\) be an LQMC, \(s\in S\) be a state, and \(\varphi\) be an FCTL state formula. The model checking problem for \(\mathfrak{M}\) against \(\varphi\) asks to verify whether \(s\models\varphi\) holds.
# Heat Transfer Enhancement of Falling Film Evaporation on a Horizontal Tube by Thermal Spray Coating
## 3 Experimental Results and Discussion
### Pool Boiling Heat Transfer Characteristics  
Heat transfer coefficients of pool boiling for the smooth and thermal spray-coated surfaces are shown with those of falling film evaporation in Figures 8 and 9, respectively. For the smooth tube (Figure 8), the values for pool boiling were slightly higher than those for falling film evaporation and agreed well with [PERSON]'s correlation. Because partial dry out by liquid entrainment and evaporation did not occur in pool boiling, the increase in the gradient with heat flux was constant even at high heat flux. Other than partial dry out, the cause of deterioration in the heat transfer coefficients for falling film evaporation might be suppression of nucleate boiling in the liquid film. Evaporation from the liquid-vapor surface cooled down the superheated liquid layer. In the pool boiling experiment, no difference was observed between the heat flux increasing and decreasing processes.  
Figure 7: Falling film evaporation heat transfer coefficients on smooth and sprayed coated tubes.  
For the thermal spray coated tube (Figure 9), the trend of the thermal coefficients in relation to the heat flux for pool boiling was almost the same as that for falling film evaporation. Although the local heat transfer just on the heat transfer surface was dominant over nucleate boiling, the values were higher for falling film evaporation than pool boiling, which was contrary to the results for the smooth surface. Because the coated tube produced a large bubble site density, leading to a higher heat transfer coefficient, it was believed that a greater number of tiny bubbles were generated and detached from the liquid film. This bubble behavior enhanced evaporation on the liquid film surface. As the wall superheat at the onset of nucleate boiling was quite low for the coated surface, the deterioration effect of nucleate boiling heat transfer due to the decrease in the liquid temperature by evaporation from the liquid film surface as seen for the smooth tube was not observed. In contrast, the hysteresis  
Figure 8: Pool boiling heat transfer coefficients in comparison with those for falling film evaporation on the smooth tube.  
Figure 9: Pool boiling heat transfer coefficients in comparison with those for falling film evaporation on the thermal spray coated tube.  
effect due to the delay in bubble site activation was observed not only in pool boiling but also in falling film evaporation.
# Augmented Reality in Surgery: A Scoping Review
## 4 Results
### Orthopedics  
The application of AR in orthopedics [64; 65; 66] is relatively recent, dating back to the beginning of the 2000s [65]. The purpose of applying AR to orthopedic computer systems for computer-assisted surgery (CAS) is to increase the accuracy during surgeries, improving the possible outcomes and at the same time decreasing procedure-related complications. AR application can also contribute to the reduction of both surgery time and radiographic  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Tracking and Registration Methods** & **Percentage of Application** \\ \hline Marker based and Non-rigid Registration & 4\% \\ Markerless rigid Registration & 20\% \\ Markerless Non-rigid Registration & 8\% \\ Markerbased and rigid Registration & 68\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Augmented Reality tracking and registration methods.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Application** & **Percentage of Application** \\ \hline Telemonitoring & 4\% \\ Maxillofacial & 23\% \\ Liver Surgery & 4\% \\ Pediatric & 4\% \\ Orthopedics & 27\% \\ Oncology & 19\% \\ Training & 8\% \\ Puncture Surgery & 7\% \\ Bowel Surgery & 4\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Augmented Reality Applications.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Type of Display** & **Percentage of Application** \\ \hline Smartphone & 14\% \\ Video see through Device & 14\% \\ Generic Head Mounted Display & 17\% \\ Unspecified Display & 14\% \\ Projected Directly over the Patient & 3\% \\ HoloLens 2 & 10\% \\ HoloLens 1 & 28\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Percentage of distribution of the displays of Augmented Reality used in medical applications evaluated in our study.  
doses for both patients and surgery teams. AR avoids the use of X-rays to see through the patients, reducing their exposure time [67].
## 4 Experiments
### Evaluation Criteria  
To further evaluate the performance for CD, several widely used evaluation metrics, which are precision, recall, \(F1\), and overall accuracy (\(OA\)), are employed. Their definitions are given as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{11}\]  
\[Recall=\frac{TP}{TP+FN} \tag{12}\]  
Figure 3: Hongqi Canal dataset: (**a**) \(T_{1}\)-time image, (**b**) \(T_{2}\)-time image, (**c**) ground truth image.  
\[F1=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{13}\] \[OA=\frac{TP+TN}{TP+TN+FP+FN} \tag{14}\]  
where _TP, TN, FP_, and _FN_ are the numbers of true positive, true negative, false positive, false negative pixels, respectively. Based on these well-acknowledged evaluation metrics, the performance of different CD methods can be better revealed.
## 5 Conclusions and Future Work  
In this paper, we have proposed a multilevel conceptual model that describes the user behaviour using actions, activities, intra-activity behaviour and inter-activity behaviour Using this conceptual model, we have presented a deep learning architecture based on LSTMs that models inter-activity behaviour. Our architecture offers a probabilistic model that allows us to predict the user's next actions and to identify anomalous user behaviours. We have evaluated several architectures, analysing how each one of them behaves for a different number of action predictions. The proposed behaviour model is being used in the City4 Age H2020 project to detect the risks related to the frailty and MCI in young elders.  
As future work, we plan to continue studying how the temporal and spatial information could be integrated into the architecture to improve the statistical model. We would like to explore other deep learning architectures, for example, using convolutional neural networks (CNNs) instead of LSTMs. Using CNNs, we intend to model the different action n-grams that occur in the inter-activity behaviour. This will allow us to compare different sequence modelling approaches using deep neural models. We also plan to develop architectures that will cover other aspects of the proposed multilevel conceptual model, starting with the activity recognition task. We plan to use the insights gained with the deep learning architectures proposed in this paper in order to create an activity recognition algorithm.  
This work has been supported by the European Commission under the City4 Age Project Grant Agreement (No. 689731). We gratefully acknowledge the support of the NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.  
[PERSON] and [PERSON] conceived, designed and performed the experiments; analyzed the data; and wrote the paper.  
The authors declare no conflict of interest.
## 2 Results and Discussion
### Catalytic Activity
Compared with the three resin sulfonic acids, SSA gives more _C_-alkylates because that stronger acid promotes conversion of _O_-alkylates into the thermodynamic phenol _C_-alkylates by enhancing _O_-alkylate protonation. SSA exhibited a higher water-tolerance than other catalysts based on the model systems shown in Table 3. Desulfonation of the three resin sulfonic acids catalyst occurred progressively at 120 \({}^{\circ}\)C over time, leading to partial deactivation of these catalysts. Cs\({}_{2.5}\)/K10 lost almost all catalytic activity. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Catalyst**} & \multicolumn{2}{c}{**1-Octene conversion**} & \multicolumn{2}{c}{**Phenol conversion**} & \multicolumn{2}{c}{**1-Octene isomerization**} & \multicolumn{2}{c}{**1-Butanol**} & \multicolumn{2}{c}{_O-alkylates_} \\  & **(\%)\({}^{\text{b}}\)** & **(\%)\({}^{\text{c}}\)** & **(\%)\({}^{\text{d}}\)** & **conversion (\%)\({}^{\text{e}}\)** & ** selectivity (\%)\({}^{\text{f}}\)** \\ \hline Cs\({}_{2.5}\)/K10 & 10.1 & 1.2 & 1.9 & 68.2 & 42.0 \\ A36 & 14.0 & 6.1 & 13.5 & 94.3 & 67.7 \\ A15 & 27.1 & 27.6 & 54.1 & 97.4 & 73.7 \\ DX2 & 43.1 & 37.3 & 55.5 & 90.5 & 73.9 \\ SSA & 60.1 & 64.1 & 87.9 & 97.4 & 64.1 \\ \hline \hline \end{tabular}  
* Material ratio: 1-octene: 1-butanol: phenol: water: acetic acid: acetaldehyde: hydroxyacetone: d-glucose: 2-hydroxymethylfuran (g) = 1.35: 0.15: 0.94: 0.15: 0.15: 0.12: 0.12: 0.15: 0.15, catalyst: 0.15g; \({}^{\text{b}}\) 1-Octene conversion = 100%\(\times\) (1- GC area% of unreacted octenes _versus_ the sum of the GC area% of alkylated phenols, octanols, dioctyl ethers, oligomers and octyl acetates); \({}^{\text{c}}\) Phenol conversions = 100%\(\times\) GC area% of phenol alkylates _versus_ the sum of the GC area% of unreacted phenol and phenol alkylates; \({}^{\text{d}}\) Percent of 1-octene isomerization = 100%\(\times\) (1- GC area% of 1-octene _versus_ the sum of the GC area% of 1-octene and 1-octene isomers); \({}^{\text{e}}\) 1-Butanol conversion = 100%\(\times\) (1- GC area% of unreacted 1-butanol _versus_ the sum of the GC area% of butyl acetate, dibutyl ether, 1,1-dibutoxyethane and butyl levalinate); \({}^{\text{f}}\)_O_-Alkylates selectivity = 100%\(\times\) GC area% of _O_-alkylates _versus_ the sum of the GC area% of all phenol alkylates.  
\end{table} 
Table 3: 1-Octene, 1-butanol and phenol conversions, 1-octene isomerizations and _O_-alkylation selectivities in reactions of a model bio-oil with 1-octene/1-butanol over Cs\({}_{2.5}\)/K10, A36, A15, DX2 and SSA at 120 \({}^{\circ}\)C for 3 h \({}^{\text{a}}\).
## 5 Case Study
### Parameteric Study  
Both the ship design parameters and the ice properties are assumed to have significant effect on the impact load. Therefore a parametric study is performed investigating the effect of variations of the rake angle, ship speed, and the ice thickness, see Table 8.  
The critical ice force with respect to ship speed and ice thickness are plotted in Figure 20 for both ship head-on and shoulder collision. It can be seen that the critical force varies with ship speed and ice thickness and the influence is significant. For example, for thinner ice thickness, the ice critical force hardly change with speed. The influence from the speed for thin ice, \(h~{}=~{}0.1\) m, can almost be neglected. When the ice thickness increases, the force increase becomes more and more significant. The figure can be used for guidance in ship operation. For example, assume the structure load capacity is 400 kN, the ship speed can be 2 m/s when ice thickness is 0.5 m or be approximately 5 m/s when ice thickness is 0.4 m.  
The parametric study also investigates the influence of the ship stem angle, and the results are plotted in Figure 21. Usually, the stem angle for an ice-going ship is around 20\({}^{\circ}\)-25\({}^{\circ}\). The figure shows that when \(\varphi~{}=~{}25^{\circ}\) the ice critical force takes the lowest value at 1 m/s. However, the critical force increases with ship speed. For a stem angle \(\varphi~{}=~{}75^{\circ}\) the ice critical force along the whole speed range is low. One explanation can be that the projection to the _xy_-plane of the contact area and vertical force becomes small based on the ice crushing model. This results in a small ice bending force. The suitable range of the stem angle should be smaller than 15\({}^{\circ}\)-75\({}^{\circ}\), for instance, 10\({}^{\circ}\)-30\({}^{\circ}\), which causes a larger force to bend the ice sheet. It is suggested that further studies may be conducted as to verify this hypothesis.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rake Angle** & **Ship Speed** & **Ice Thickness** \\ \hline
15–75 [deg] & 1–5 [m/s] & 0.1–0.5 [m] \\ \hline \hline \end{tabular}
\end{table}
Table 8: Parameters investigated in parametric studies.  
Figure 20: Ice critical force (\(F_{\text{C}}\) ) in terms of ship speed and ice thickness for stem angle 25\({}^{\circ}\). (**a**) Ship stem collision; (**b**) Ship shoulder collision.  
Ice breaking lengths in relation to ice thickness and ship speed are plotted in Figure 22. It can be clearly seen that the reference breaking length \(l_{b2}\) is smaller than the computed one, \(l_{b1}\). However, the difference is decreasing with increasing ice thickness. It can also be deduced that the empirical value \(l_{b2}\) is very close to the computed one \(l_{b1}\) when ice thickness is approaching 0.5 m and it (\(l_{b2}\)) exceed \(l_{b1}\) at lower speed. Figure 22 indicates that the Lindqvist [1] method has a limited validity range around ice thickness of half meter.
## 2 Materials and Methods
### Thermal Recording  
A non-contact IR thermography camera (_FLIR A325; FLIR Systems Inc., Wilsonville, OR, USA_) was used to measure the OST. This thermal camera has an image resolution of 320 \(\times\) 240 pixels, a sensitivity of 50 mK, and an accuracy of \(\pm\) 2%. It can detect temperatures that range from \(-\)20 \({}^{\circ}\)C to 120 \({}^{\circ}\)C. Data acquisitions were done with a frame rate of 30 Hz and tear emissivity was set to 0.98 [28]. The camera incorporates a macro-lens to obtain a clear image of the eye and it was aligned with the geometric center of the cornea. The distance between the IR camera and the eye was established according to the best focus point in each subject. Subjects were adapted to the room environmental conditions for at least 20 min [29] before OST evaluation in order to stabilize the eye temperature and minimize the experimental error.  
Before OST assessment, body temperature was measured using a digital thermometer (_Citizen Digital Thermometer CTA303, Citizen System, Vega Technologies.Inc., Taipei, Taiwan_) and subjects with body temperature \(\geq\) 37 \({}^{\circ}\)C were excluded. During OST measures, subjects were instructed to maintain a stable head position on the chin- and forehead rest, close their eyes for 3 s and afterwards open the eyes and look straight forward for 40 s while blinking naturally.
# Stability Analysis of the Longitudinal Slope Linear Shield Tunnel Excavation Face
###### Abstract  
In shield tunnel engineering, longitudinal slopes and other complex alignments are commonly encountered. Given the uneven stress distribution in the soil ahead of the tunnel face during excavation, studying and understanding the instability mechanisms of the excavation face is particularly crucial. In this study, a visualized transparent soil test was designed to investigate shield tunneling along a longitudinal slope. The displacement patterns of the soil in front of the excavation face were analyzed in detail. Furthermore, FLAC3D numerical simulations were employed to examine the variations in the ultimate support ratio and vertical stress of the soil under different slope conditions. The reliability of the results was also validated. The research findings reveal the stability characteristics of the excavation face and its influencing factors during shield tunneling at different slope angles. These findings provide a scientific basis for the design of shield tunnels with longitudinal slopes. The results of the study indicate that (1) the maximum destabilization width of the soil with slope \(i=15\%\) is increased by \(18.2\%\) and \(36.8\%\) compared to that with slope \(i=0\%\) and \(-15\%\), respectively; (2) the ultimate support force, as well as the horizontal and vertical displacements of the excavation face, increase significantly under an upward slope condition (\(i>0\)) compared to those in a horizontal tunnel (\(i=0\)) and a downward slope (\(i<0\)); and (3) the longitudinal slope gradient \(i\) is negatively correlated with the inflection point depth, meaning that steeper slopes result in shallower loosening zone depths.  
shield tunnel; excavation face stability; transparent soil test; longitudinal slope; ultimate support force
# Analytical Investigation of Vertical Force Control in In-Wheel Motors for Enhanced Ride Comfort
## 2 Dynamics Models and Methods
### Ride Comfort (ISO 2631)  
According to ISO 2631 [30], ride comfort is the comfort reaction evaluated by examining the frequency-weighted acceleration, \(a_{w}\). The frequency-weighted acceleration, \(a_{w}\), is computed from the Power Spectral Density (PSD) of the sprung mass vertical acceleration, \(\tilde{z}_{b}\). The equation for the frequency-weighted acceleration, \(a_{w}\), is shown in Equation (39).  
\[a_{w}=\sqrt{\sum_{i}(W_{i}a_{i})^{2}} \tag{39}\]  
where \(W_{i}\) is frequency weighting in one-third-octaves for the frequency \(i\) according to the Principal Frequency Weighting in One-Third-Octaves Table (parameter: \(W_{k}\)) [35] and \(a_{i}\) is root mean square (RMS) acceleration for the frequency, \(i\), as shown in Equation (40).  
\[a_{i}=\sqrt{P_{i}} \tag{40}\]  
where \(P_{i}\) is the area under the plot of Power Spectral Density (PSD) of the sprung mass vertical acceleration, \(\tilde{z}_{b}\), for frequency \(i\). Frequency \(i\) is the range between \(f_{i-\frac{1}{2}}\) and \(f_{i+\frac{1}{2}}\) where \(f_{i-\frac{1}{2}}=2^{(-\frac{1}{2})}f_{i}\) and \(f_{i+\frac{1}{2}}=2^{(\frac{1}{2})}f_{i}\). (Note: the frequency according to one-third-octaves is defined by \(\frac{f_{i+1}}{f_{n}}=2^{\frac{1}{3}}\).) The expanationtion of \(P_{i}\) is shown in Figure 7.