esa-sceva/satcom-chunk-collection huggingface dataset:
## Methods
### Data Analysis
#### 2.5.2 Logistic Function (Sigmoid Model)  
The sigmoid model provides a good example of non-linear and quickly increasing and decreasing functions of the probability of disclosure and makes computation easier [12]. Besides, the sigmoid model is a reliable analysis technique that mimics the physiologically based prediction. In this paper, the CCF coefficient data distribution graph exhibits a sigmoidal pattern at increasing and decreasing stimulation intensities. Therefore, we proposed the sigmoid model in order to determine the transition phase of human postural control strategies. A general least square model similar to one developed in the investigation of the ascending limb of all recruitment curves and transcranial magnetic stimulation (TMS) research was used [13]. In this paper, a custom four-parameter sigmoid model was modified as shown in Equation (6) below:  
\[F_{x}\ =\ \frac{L}{1+e^{k(x-x_{0})}}+y_{0} \tag{6}\]  
where \(L\) is the vertical range of model, \(k\) is the gradient or slope of the model, and \(x\) is the CCF coefficient data distribution. Furthermore, \(x_{0}\) is determined as the transition frequency, and \(y_{0}\) is the vertical correcting position of the model.  
The sigmoid model provides a good example of a non-linear model, can predict the probability as an output, and makes computation easier. Therefore, the sigmoid model is reliable, which mimics the physiologically based data as in this study is from CCF coefficient data distribution [14]. This method utilizes curve fitting using a logistic function, which is optimized using the particle swamp optimization (PSO) technique. With this technique, the model with the lowest error was chosen as the best model providing the sigmoid model. In this paper, the lower frequency was referring to the frequencies below 0.5 Hz and high frequency was for frequencies above 0.5 Hz.  
This model clearly can divide upper and lower distributions and the slope in between the distribution, as shown in Figure 3. In order to find the transition frequency for each subject, first we determined the initial slip and convergence of the model by the velocity of the sigmoid model data. The slope between the initial slip and convergence was defined as a transition phase, indicating that transition happens from the positive phase shift to the negative phase shift and vice versa. We calculated the coefficient of determination \(\left(r^{2}\right)\), which is also defined as global goodness-of-fit of the sigmoid model, to clarify the fitness of the model towards CCF coefficient data. \(r^{2}\) was calculated in order to define the fitness of the model since the model is a linear regression. The \(r^{2}\) value of more than 0.039 with one variable was indicated as acceptable to the fitness of the model.
## 5 Case Study (Results (Step 2 to 5 in Figure 3))
### Gap of MWL in Each Factor (Step 4 in Figure 3)  
#### 5.2.1 Ship Autonomy Level  
Table 8 shows the average weighted MWL of interviewees (\(n=10\)) with the matrix of the navigation manning and level of autonomy. As described in Section 4.2, the option of a combination of MO-3 and LoA-1 (grey-coloured cell in Table 8: a responsible operator (RO) onboard navigates the ship alone without any autonomous system and support by lookout) was not applied in this study. Rough video was displayed on the screen at RCC (LoDT-2 in  
Figure 5: Relationship between the average weighted MWL and potential safety risk of navigation.  
Table 5). The ship was assumed to navigate the coastal water in clear visibility (NC-2 in Table 2). Figure 6 shows the scenarios considered to have a significant difference between the level of autonomy in the same manning options (\(p<0.05\) of \(t\)-test (two-trailed)). Cells (scenarios) at a start point and an endpoint of an arrow line in the figure are significantly different. Table 9 shows the difference of MWL in the scenarios extracted in Figure 6. Table 10 breaks down the differences into six sub-elements and describes the sub-elements considered to have a significant difference between them (\(p<0.05\) of \(t\)-test). These suggest the following findings:  
* Installation of navigation support systems (LoA-2) and autonomous navigation systems (LoA-3) do not show apparent positive effects on the MWL of responsible operators (ROs) when they are onboard a ship (MO-1, MO-2 and MO-3). The autonomous system rather negatively affected MWL in one case ((1) in Figure 6 and Table 9);
* Installation of navigation support systems (LoA-2) and autonomous navigation systems (LoA-3) show apparent positive effects on the MWL when ROs are at the RCC (MO-4, MO-5 and MO-6);
* MWL of ROs does not clearly change between navigation support (LoA-2) and autonomous navigation (LoA-3) when they are at RCC and lookouts support them (MO-4 and MO-5). Positive effects emerge when they navigate alone at the RCC (MO-6: (7) in Figure 6 and Table 9);
* Mental demands are the key sub-elements to decrease the MWL of ROs at RCC when autonomous navigation systems are installed in the case of (3), (5) and (6) in Figure 6 (Table 10). Mental demands were also the key sub-elements which increased the MWL of ROs on the bridge when autonomous navigation systems were installed in the case of (1) of Figure 6 (Table 10);
* Effort was the key sub-element to decrease the MWL of ROs at RCC when autonomous navigation systems were added to the navigation support systems in the case of (7) of Figure 6 (Table 10).
## 2 Theoretical Analysis
### The Third Part  
The schematic diagram of the third part is shown in Figure 7a. It is a uniform solid dielectric cylinder with a length of 2 \(\lambda\) and a diameter of 0.37 \(\lambda\). It is designed to improve the whole radiation pattern; moreover, the length of this part can be adjusted according to the gain requirement, which makes this presented antenna more universal.  
Figure 7: (**a**) Schematic of dielectric rod extension (**b**) Radiation pattern comparison with and without rod extension (**c**) Gain and length of the rod comparison.  
Figure 7b provides the radiation patterns comparison of the presented antenna with and without the rod extension. Noticeably, it is seen that this part further adds 1 dB gain along with 11 dB FBR. The relation of the gain with the length of this part is shown in Figure 7c. In increasing lengths of 2 \(\lambda\), 4 \(\lambda\), and 8 \(\lambda\), the gain increases 0.98 dB, 1.14 dB, and 0.3 dB, respectively, and the side lobe levels are suppressed.
# Advanced Air Mobility: Opportunities and Challenges Deploying eVTOLs for Air Ambulance Service
## 4 Analysis and Findings
### Revised Concept of Operations and Battery Swapping Scenarios  
The analysis finds that an eVTOL air ambulance battery range and weight (approximately 3500 pounds) reduces the ability for this aircraft to serve longer medical transports. Assuming a conservative average charging capability of 125 kilowatts (kW), the study finds that an eVTOL's time required to bring the aircraft back to service would be -3.34 h (200 min), which is significantly higher than rotorcraft (which is typically 30 min). To achieve the same total call time as rotorcraft, eVTOLs would need to reduce the time required to return back to service by 145 min. The total call time of eVTOLs is 275 min compared to 130 min for helicopters. The authors applied two different scenarios to address this challenge.  
The first scenario proposes a revised operational concept. In the original operational concept, the patient was transported from the scene to the medical facility, however the aircraft does not begin charging until the patient disembarks the aircraft. In this scenario, the authors explored the potential to start battery charging during patient disembarkation (approximately five minutes).  
The second scenario proposes swapping batteries when the eVTOL returns to the base after each mission to reduce downtime associated with recharging. Battery swapping was estimated to take approximately five minutes and the cost of an extra battery was added to the cost calculations. It was assumed that staff and equipment required to swap the batteries are included as part of IOCs. In this scenario, total call time was compared for eVTOLs, hybrid, and rotorcraft. While dispatch, chute, and scene time were estimated to remain the same for all three aircraft, without battery swapping return time increased significantly for eVTOLs. However, with the addition of battery swapping, eVTOLs and hybrid aircraft can be utilized approximately 35% more than existing rotorcraft, potentially reducing cost per transport by about 30% (Figure 8).  
Figure 7: Fixed and variable cost comparison of eVTOL, hybrid, and rotary-wing (RW) aircraft.
# Prognostic Value of Lymph Node Density in Lingual Squamous Cell Carcinoma
## 3 Results
### Disease-Free Survival (DFS)  
Disease-free survival (DFS) is used to evaluate the appearance of tumor recurrences, whether loco-regional or distant, during the study period. The analysis of DFS related to the two subgroups is described in the following figures and table (Figures 5 and 6, Table 6).  
Figure 5 shows that 77.6% of patients with LND < 0.04 did not experience recurrence throughout the study, while this rate decreased (53.9%) in the LND \(\geq\) 0.04 subgroup.  
Figure 6: Kaplan–Meier for disease-free survival depending on LND. Red line: LND \(\geq\) 0.04; Blue line: LND < 0.04.  
\begin{table}
\begin{tabular}{c c c} \hline
**LND** & **Mean \(\pm\) Standard Error of Mean (S.E.M.)** & **Log Rank (Mantel-Cox) (\(p\)-Value)** \\ \hline \(<\)0.04 & 76.97 \(\pm\) 5.27 months & \\ \hline \(\geq\)0.04 & 56.67 \(\pm\) 10.53 months & 0.147 \\ \hline \end{tabular}
\end{table}
Table 5: Estimated overall survival in months depending on LND.  
The Kaplan-Meier recurrence curve (Figure 6) is very significant, showing a higher DFS among patients considered to be at lower risk according to the LND. Table 6 shows that the results obtained in terms of DFS, although close, did not reach significance.  
Therefore, regarding the main objective of the study and in terms of the analysis of overall survival and disease-free survival, there are substantial, although not significant, differences between both groups depending on the LND. However, the results of the statistical analysis do not provide a basis for accepting the initial hypothesis of the study since it cannot be guaranteed that the presence of a LND \(\geq\) 0.04 implies a lower survival.
## 3 Results and Discussion
### Underwater Oil Contact Angle Behavior
In this case, the porosity of the oxide layers formed after the thermal oxidation of the samples is the ratio of the pore size (D) to the sum of the pore size and the sizes of the adjoining oxide granules that creates the pore, as represented below in Figure 8. The oxide granules are assumed to be spherical in shape. Based on the above model, the porosity of the oxide layers, \(\varphi\), is given as:  
\[\varphi=\frac{D}{D+\mathrm{k}\alpha} \tag{3}\]  
\(k\) is the statistical average of the number of oxide granules adjoining a pore. Thus, \(\varphi\) is given by Equation (4), with k equal to four. Figure 8: A simple 2D model of the spherical oxide granules of size \(\alpha\) and pore sizes, \(D\). Figure 7: Plot of the underwater oil contact angle as a function of the oxide layer thickness. \[\varphi=\frac{D}{D+4\alpha} \tag{4}\]  
The values of experimentally determined \(D\) and \(\alpha\), as well as the calculated porosities, \(\varphi\), are stated in Table 1.  
The calculated oxide layer porosities increased with the thickness of the oxide layers. This variation of the porosity, \(\varphi\), with the oxide layer thickness of the C84400 copper alloy samples that were oxidized in the N\({}_{2}\)-5 wt.% O\({}_{2}\) gas mixture is shown in Figure 9.  
The generally well-defined, small oxide granules formed in the N\({}_{2}\)-5 wt.% O\({}_{2}\) gas mixture contribute significantly to the porosity, which is stabilized at higher oxide thicknesses. This is not the case with the oxide layer formed during the thermal oxidation in the N\({}_{2}\)-0.75 wt.% O\({}_{2}\) gas mixture, where the estimated porosities were very low. This increase in porosity causes more infiltration and trapping of water in the thicker oxide layers, which is necessary to sustain a stable Cassie-Baxter state, needed for underwater superoleophobicity. The spontaneous invasion of these pores takes place through the capillary phenomenon, and the thicker oxide layers, therefore, show underwater superoleophobicity.
## 2 Feature Analysis of Existing RRSA Concepts
### Sunflower  
The basic deployment concept was initially developed by the TRW company to satisfy the requirement for large-diameter \((D/\lambda>1000)\), high-accuracy reflectors to be used in the 10 GHz to 100 GHz range or beyond, within the constraints of the shuttle [28]. In the deployed state shown in Figure 2a, the sunflower's reflective surface is split into six units in the circumferential direction, with a hub left in the center (blue). Each unit is sub-segmented into two side panels (green) and a middle panel (yellow), and all the panels and the hub are connected with revolute joints that are represented by the purple rectangles in the figure. When folded by the revolute joints and the synchronous equipment (not drawn), the overall profile can be readily stowed in a cylindrical rocket fairing, as shown in Figure 2a. In the folded configuration, all the side panels are erected, pointing into the hub center. Meanwhile, all the main panes are sandwiched by their respective side panels and are tangent to the circumferential direction in a way that looks like a regular hexagon. The number of units and the panel number of each unit can be increased to obtain a higher stowing efficiency. However, the deployment reliability may decrease accordingly due to the more complex structure.  
It can be observed that the adjacent side panels are folded in a back-to-back manner; it is therefore unfeasible to introduce the supporting structures in the gaps to provide high surface accuracy, since the interference may be induced among supporting structures. Although the above concept is often reported in various literature, the deployment details of the sunflower antenna still remain unclear. In order to further study this concept, some efforts have been made by researchers in China. [PERSON] et al. conducted relatively comprehensive research on its design and analysis [48; 49]. Impressively, two linked Bennett mechanisms in symmetric form (known as the 'Twin-Bennett mechanism') were used to fulfill the deployment [50]. [PERSON] et al. improved this concept in its segmentation scheme to achieve higher stowing efficiency [30]. Also, they implemented the deployment by means of Twin-Bennett mechanisms. In addition, [PERSON] et al. [51] reported the same deployment mechanism adopting the 'Square-Twist pattern' according to the principle of rigid origami [52]. To sum up, this concept does not draw much interest for various reasons and is not a compelling choice for high-frequency antennas.
## 5 Case Study and Bug Classification
### Automated Bug Classification
#### 5.2.2 Preprocessing of Bug Reports  
In the preprocessing phase, we have first removed all punctuations and several non-ASCII characters and performed lower case conversion. Then, the preprocessing activity continued with the steps of tokenization, stemming, and lemmatization.  
The tokenization step, that is, the process of breaking down text documents apart into those pieces for further processing, is the same as the conventional approach. However, the bug reports are mainly written in Turkish but also mixed with English words. Therefore, to deal with these bilingual texts, we have applied the lemmatizing step for both languages.  
In automated bug classification, classifying bilingual bug reports requires extra care. Firstly, the English and Turkish words require different stemming/lemmatizing libraries. Furthermore, some words in both languages are written in the same letters but represent different meanings. For example, the English word 'on' also exists in Turkish but means 'ten'. Suppose the word 'on' is removed in the stop-word removal process from the bug reports. In that case, there will be a loss of meaning in the available data causing extra difficulty in classification. In addition, due to the agglutinative structure of the Turkish language [19; 20], stop-word removal and lemmatizing processes are performed by considering the grammatical structure of the Turkish language.  
Figure 4: Proposed machine learning-based bug classification process for bilingual, unstructured bug reports.  
Similarly, the stop-word elimination phase has been applied for both languages. Since some of the words could be valid both in English and Turkish (for example, the word \"on\" is a valid and frequent word in English as well as it means \"ten\" in Turkish), we have applied elimination considering this type of bilingual-equivocal definitions. This process is unique to the bilingual NLP applications, which we call MUSE \"Multi-language Stop-word Elimination\".  
We have applied the same methodology for lemmatizing, Multi-language Lemmatizing (MULE) for the hybrid bug definitions. The definitions must be lemmatized in English first and then in Turkish (or vice-versa), considering that the same words might exist in both languages. In cases such as the above example, the user (the person creating the recommender system) shall check the words used in definitions and determine if the language will be lemmatized.
# Per-Pixel Manifold-Based Color Calibration Technique
## 2 Problem Definition  
Let us assume we have two images, left and right, that represent the same viewpoint of a scene. Let us assume that \(L^{p}=\left[l_{1}^{p}\;\;l_{2}^{p}\;\;\ldots\;\;l_{i}^{p}\;\;\ldots\;\;\;l_{n }^{p}\right]^{T}\) and \(R^{p}=\left[r_{1}^{p}\;\;r_{2}^{p}\;\;\ldots\;\;r_{i}^{p}\;\;\ldots\;\;r_{n}^ {p}\right]^{T}\) are \(n\)-component vectors of colors of pixel \(p\) in the left and right images. The \(n\) denotes the number of components in an image increased by 1 since the operations are performed in homogenous coordinates, which offer more flexibility over regular cartesian coordinates. Therefore, commonly \(n\) will be 4 for homogenous coordinates of RGB color space.  
We would like to estimate a manifold of per-pixel transformation \(A^{p}\) such that for all pixels \(p\)  
\[R^{p}=A^{p}.L^{p} \tag{1}\]where  
\[A^{p}=\begin{bmatrix}a_{11}^{p}&a_{12}^{p}&\cdots&a_{1}^{p}&\cdots&a_{1n}^{p}\\ a_{21}^{p}&a_{22}^{p}&\cdots&a_{2j}^{p}&\cdots&a_{2n}^{p}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ a_{i1}^{p}&a_{i2}^{p}&\cdots&a_{ij}^{p}&\cdots&a_{in}^{p}\\ \vdots&\vdots&\vdots&\vdots&\vdots&\vdots\\ a_{n1}^{p}&a_{n2}^{p}&\cdots&a_{nj}^{p}&\cdots&a_{nm}^{p}\\ \end{bmatrix} \tag{2}\]  
For an RGB image, the matrix \(A^{p}\) will be a \(4\times 4\) (since we are operating in homogenous coordinates, as explained before), and there is a \(p\) such matrix, one for every pixel in the image (\(p\) is the number of pixels in the image). Please note that at this stage, the matrix is agnostic to the way the pixels are organized in the image since \(p\) is merely an index.  
In the further part of the derivation, we will continue to use \(n\) as the size of the matrix \(A^{p}\) since the method can be applied not only to RGB images but potentially also to multispectral and hyperspectral images.  
The problem considered in this paper is to find the transformation \(A^{p}\) for a given pair of images, e.g., coming from a pair of cameras in stereoscopic applications.
## 4 Evaluation Model Based on AH-Entropy and Normal Cloud Model
### Calculation of Membership Degree for Normal Cloud Model  
Based on the normal cloud model in Section 4.4, the membership degree \(\mu(x)\) of each evaluation indicator at different performance levels can be calculated. Utilizing Equation  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Performance Rating** & **RQI** & **R\({}_{r}\)** & **C\({}_{r}\)** & **P\({}_{r}\)** & **PSSI** \\ \hline Excellent & \(\geq\)90, \(<\)100 & \(\geq\)0, \(<\)1 & \(\geq\)0, \(\leq\)0.5 & \(\geq\)0, \(\leq\)5 & \(\geq\)90, \(<\)100 \\ Good & \(\geq\)80, \(<\)90 & \(\geq\)1, \(<\)5 & \(>\)0.5, \(\leq\)3.5 & \(>\)5, \(\leq\)10 & \(\geq\)80, \(<\)90 \\ Average & \(\geq\)70, \(<\)80 & \(\geq\)5, \(<\)10 & \(>\)3.5, \(\leq\)10 & \(>\)10, \(\leq\)20 & \(\geq\)70, \(<\)80 \\ Poor & \(\geq\)60, \(<\)70 & \(\geq\)10, \(<\)25 & \(>\)10, \(\leq\)25 & \(>\)20, \(\leq\)50 & \(\geq\)60, \(<\)70 \\ Failed & \(\geq\)0, \(<\)60 & \(\geq\)25, \(<\)50 & \(>\)25, \(\leq\)50 & \(>\)50, \(\leq\)80 & \(\geq\)0, \(<\)60 \\ \hline \end{tabular}
\end{table}
Table 2: Performance ratings of evaluation indicators for the existing asphalt pavement in highway renovation and expansion project.  
(20), the comprehensive membership degree of the performance evaluation of existing asphalt pavement in highway renovation and expansion projects is determined.  
\[F=\sum_{i=1}^{n}u(x_{ij})\omega_{i},i=1,2,\ldots,n \tag{20}\]  
where \(F\) is the comprehensive membership degree of the performance ratings, \(u(x_{ij})\) is the membership degree of the \(i\)th indicator at the \(j\)th evaluation level, and \(w_{i}\) is the comprehensive weight of the \(i\)th indicator.  
The performance level \(S\) for the existing asphalt pavement in highway renovation and expansion projects is determined based on the principle of maximum membership degree.  
\[S=\max(F_{1},F_{2},\ldots,F_{n}) \tag{21}\]
## 3 Case Study and Parametric Analysis
### Factors Influencing the Sealing Capacity of the EPDM Gasket
#### 3.3.4 Roughness of Sealing Surface  
The surface roughness has a great influence on the sealing ability. The rougher the contact surface of sealing materials, the more non-contact regions will be generated, which will lead to the degradation of the waterproofing performance. Under high -contact stress, the deformation of asperity can reduce the roughness, so the rougher sealing interface requires higher contact stress. Figure 13 shows that, under different joint gaps, relationships between the leakage and surface roughness (asperity root mean square height) may all satisfy exponential functions that concave up. Specifically, as the asperity height becomes greater, the leakage grows slowly at first (asperity height is less than 15 \(\upmu\)m) and then ascends faster with an increasing slope. The reason why a larger joint gap has a more significant effect on leakage is that the flattening of asperity with a large height requires greater contact stress, as the joint gap increases, the non-contact areas around the rough body are connected to form a larger number and scale of leakage channels.  
Figure 12: Effects of the service time and joint gap coupling on leakage. In (a), the red line is the calculated leakage value for one year of service, in groundwater pressure of 0.8 MPa. The circle with half a shadow indicates the leakage monitoring data for one year of service of the second Dapu Road Tunnel in Shanghai. The dotted line is the fit to the measured data. The curves in (b) show the calculated leakage during 100 years for lining joints with gaps of 0, 2, 4, 6, and 8 mm.
## 4 Classification of MAC Protocols Based on Channel
### Cluster-Based Single-Channel MAC Protocols  
A cluster head (CH) is chosen from a group of vehicles that is responsible for channel access, allocation of time slots, and resource management. Clusters provide stable communication and extend the lifetime of communication links.