esa-sceva/satcom-chunk-collection huggingface dataset:
# Aerodynamic Enhancement of Vertical-Axis Wind Turbines Using Plain and Serrated Gurney Flaps
## 2 Simulation Setup  
### Isolated Airfoil  
#### Isolated Airfoil Model and the Boundary Conditions  
Figure 1 illustrates the baseline NACA0021 airfoil with a Gurney flap of height \(H\). The flap is positioned perpendicularly to the local airfoil surface at the trailing edge. The computation is conducted at a Reynolds number based on the airfoil chord length \(c=80\) mm, resulting in a value of 1.6 \(\times\) 10\({}^{5}\) for a free stream velocity of 25.2 m/s. The flow is considered incompressible, and these flow conditions are representative of small VAWT applications.  
Three flap heights are investigated in this study, corresponding to 1.5%\(c\), 3%\(c\), and 6%. Also shown in Figure 1, the serrations with a serration angle \(\theta=60^{\circ}\) are full-depth cut-outs, with a facing area of PGF reduced by 50%. Each flap has a thickness (\(W\)) of 0.8 mm. Flaps are mounted at the trailing edge, which is proposed for optimum performance [30]. The flow field is extracted and analyzed within a rectangular region in the downstream plane and swept over the span to cover several serration pitches. The blade height \(L=3.5H\) covers three serrations.  
The computational domain contains a semi-circular domain with a radius \(r=10c\), and a rectangle of \(15c\times 20c\) from the airfoil leading edge (see Figure 2) according to [PERSON] et al. [31]. It reports that maintaining a minimum distance of 10\(c\) between the airfoil and the inlet/outlet of the region yields accurate results and mitigates the impact of uncertain boundary conditions on the isolated blade's calculations.  
Figure 1: Schematic of NACA0021 blade with a serrated Gurney flap.  
Figure 2: Geometry features and main dimensions of the computational domain.  
The free stream velocity is given at the inlet, while the ambient pressure is specified at the outlet. The surfaces of the airfoil and flap are treated as non-slip walls. Periodic boundary conditions are imposed on both spanwise boundaries without spanwise crossflows.
# Magnetic Field-Based Vehicle Positioning System in Long Tunnel Environment
###### Abstract  
Recently, long tunnels are becoming more prevalent in Korea, and exits are added at certain sections of the tunnels. Thus, a navigation system should correctly guide the user toward the exit; however, adequate guidance is not delivered because the global navigation satellite system (GNSS) signal is not received inside a tunnel. Therefore, we present an accurate position estimation system using a magnetic field for vehicles passing through a tunnel. The position can be accurately estimated using the magnetic sensor of a smartphone with an appropriate attitude estimation and magnetic sensor calibration. Position estimation was realized by attaching the smartphone on the dashboard during navigation and calibrating the sensors using position information from the GNSS and magnetic field database before entering the tunnel. This study used magnetic field sequence data to estimate vehicle positions inside a tunnel. Furthermore, subsequence dynamic time warping was applied to compare the magnetic field data stored in the buffer with the magnetic field database, and the feasibility and performance of the proposed system was reviewed through an experiment in an actual tunnel. The analysis of the position estimation results confirmed that the proposed system could appropriately deliver tunnel navigation.  
t +
Footnote †: journal: Applied Sciences  
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Footnote †: journal: Applied Sciences
# Nutritional Composition and Biological Properties of Sixteen Edible Mushroom Species
## 4 Sixteen Edible Mushrooms of Greece
### Boletus edulis  
_Boletus edulis_ (_B. edulis_) belongs to the family Boletaceae, is known as king bolete, and is a symbiotic ectomycorrhizal species with a tubular hypernophore and is native to Europe [35]. It is a highly appreciated mushroom species due to its nutritional value, as well as its exceptional flavor [36]. Fresh _B. edulis_ has been shown to contain 45 kcal/100 g, 88.84 g/100 g fresh weight moisture, 2.27 g/100 g proteins, 7.37 g/100 g carbohydrates, 0.87 g/100 g fat, and 0.66 g/100 g ash [36]. Furthermore, _B. edulis_ possesses antioxidant properties, most likely due to its contents in total phenols (446 mg/100 g fresh weight), total flavonoids (32 mg/100 g fresh weight), vitamin C (29.9 mg/100 g fresh weight), and \(\beta\)-carotene (1.062 mg/100 g fresh weight) [36]. When it comes to the analyses of the University of Thessaly, dry _B. edulis_ has been shown to contain 347 kcal/100 g, 21.9 g/100 g proteins, 59.2 g/100 g carbohydrates, 2.6 g/100 g fat, and 6.4 g/100 g ash. In addition, it is worth mentioning that toxic metallic elements have been found in Boletus edulis, such as Cd and Pb, but at concentrations that are considered harmless [35]. Recently, _B. edulis_ has been shown to possess anti-tumor properties. One study isolated a new protein from the dried fruit bodies of _B. edulis_, which in vitro exhibited potent anti-cancer activity on A549 cells, and this cytotoxicity was mediated by the induction of apoptosis and arrest of A549 cells in the G1 phase of the cell cycle [37]. Another study isolated a novel cold-water-soluble polysaccharide from _B. edulis_, which induced apoptosis of MDA-MB-231 and Ca761 cells through cell block in the S phase and G0/G1 phase, respectively [38]. Lastly, an issue regarding significant amounts of nicotine in dried wild mushrooms (mainly _B. edulis_ from China) was reported to the European Commission which resulted in the European Food Safety Authority (EFSA) proposing temporary maximum residue levels of 0.036 mg/kg for fresh wild mushrooms and 1.17 mg/kg for dried wild mushrooms (2.3 mg/kg for dried ceps only) [39].
# Quality of Experience (QoE)-Aware Fast Coding Unit Size Selection for HEVC Intra-Prediction
## 3 CU Split Likelihood Modelling Using SVMs
### CU Split Likelihood Modelling  
HEVC block-based encoding utilises a quadtree partitioning structure within a CTU. This enables the encoder to capture the local characteristics of the content when determining the optimum coding modes for a given content [3, 6]. During this process, each CU is recursively partitioned into four equally-sized sub-CUs. The size of a CU can range from \(64\times 64\) to \(8\times 8\), each layer of CU size representing a CU depth level (0, 1, 2, and 3). During the RD optimisation process, each CU depth level is evaluated for all corresponding PU modes and the best resultant RD cost is compared with the overall RD cost of the next available CU depth level. In this case, if the RD cost of the current CU becomes greater than the RD cost of the next CU depth level, a decision is made to _split_ the current CU into four sub-CUs. On the other hand, the current CU is considered to remain _non-split_ if the RD cost evaluates to be less than overall RD cost of its sub-CUs. This can be formulated as a binary classification problem to classify each CU into two classes (i.e., _split_ and _non-split_), during the encoding phase as  
\[\eta=\begin{cases}+1(split)&C_{2N\times 2N}^{l}>\sum \limits_{j=1}^{n=4}C_{N\times N,j}^{l+1}\\ -1(non\text{-}split)&\text{otherwise}\end{cases}. \tag{2}\]  
Here, \(\eta\in\{+1,-1\}\) is the CU split decision, \(C_{2N\times 2N}^{l}\) is the RD cost of the parent CU, \(C_{N\times N}^{l+1}\) is the RD cost of the a sub-CU where \(N\in\{32,16,8,4\}\) represents the CU size, and \(l\in\{0,1,2\}\) represents the CU depth level.  
The split likelihood of a CU can be modelled using probabilistic models [7, 35, 36] as well as machine learning methods. In the case of the latter, recent literature typically uses supervised learning algorithms such as decision trees [17, 38], neural networks [34], logistic regression, and SVMs [10, 14, 39]. However, integrating an inference engine of machine learning models into the encoding loop such that the time taken for decision making is minimal, is crucial a for fast CU size selection algorithms (such as proposed in this work). Therefore, this work utilises SVMs to model the CU split likelihood due to its ability to handle binary classification problems with significant computational advantages [40].
## 5 Simulation Experiments and Results Analysis
### Results and Analysis of the Experiment
#### 5.2.2 Delay Reduction  
The delay reduction rate refers to the ratio of the difference between the maximum tolerable delay and the sum of processing delay and transmission delay to the maximum tolerable delay. In the experiment, the average value of this metric was taken for all requests. Due to increased processing delay caused by overload, this metric may be negative. Figure 10 shows the experimental results.  
From Figure 10a, it can be observed that the DNS, which relies solely on network conditions for scheduling and lacks service information, led to severe task timeouts and exhibite the worst performance. In contrast, the architecture proposed in this paper showed a significant improvement in its delay reduction rates compared to the CFN, with the maximum delay reduction rate being over seven times that of the CFN. The scheduling strategy in this paper does not simply select the optimal cluster; instead, it randomly chooses from several clusters that meet the requirements. This alleviates the performance decline caused by the CFN's approach of selecting only the optimal cluster, which leads to increased processing delays. In addition, the domain division mechanism of this paper also effectively limits the transmission delay in scheduling. As the load increased, although the delay reduction rate of the three methods had decreased, the method proposed in this paper still maintained its advantage.  
From Figure 10b, it can be observed that the performance of the proposed BWM-TOPSIS algorithm under the architecture in this paper was still superior to that of the traditional EWM-TOPSIS algorithm. This is because the BWM, as a subjective weighting method, can better reflect the scheduling objectives, which mainly aim to reduce delays. The weighting method of the EWM was significantly influenced by the volume and distribution of data. Since the CFN has no domain division mechanism, each node has a large amount of global service information. Therefore, the performance of the CFN architecture was slightly better under the EWM-TOPSIS algorithm. As with the results of the scheduling success rate, even in the worst-case scenario, the performance of the algorithm presented in this paper was still better than the optimal performance of CFN.
# A Current-Mode Optoelectronic Receiver IC for Short-Range LiDAR Sensors in 180 nm CMOS
## 5 Measured Results of the Proposed C-OER  
### Experimental Methods  
Test chips of the proposed C-OER IC were implemented in the same CMOS process. Figure 16 depicts the chip photograph of the C-OER IC and a PC-board module for optical testing, where an 850 nm laser source driver (Seed LDD, Notice Korea Ltd., Anyang-si, Republic of Korea) with a laser diode (Qphotonics, Ann Arbor, MI, USA) was utilized to generate light pulses. The chip core occupies the area of 0.087 mm\({}^{2}\), and the DC measurements reveal the power dissipation of 47.8 mW from a 1.8 V supply.
# Movie Recommendation through Multiple Bias Analysis +
## 4 Experiment and Results
### Results
#### 4.3.2 Heuristics Approach  
In order to improve MBA performance, experiments were conducted using the method presented in Section 3.2. Figure 3 shows the experimental results for Case A and Figure 4 for Case B. \(bwt(RF)\) means that the weight type \(wt\) of BBP is used as RF, and \(fw(RF)\) means that the feature weight \(fw\) was initialized into RF.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **RMSE** & \(std\) & **Model Environment** \\ \hline UBCF & 1.054902 & 0.063437 & Cosine similarity \\ IBCF & 1.045365 & 0.049861 & Pearson correlation coefficient \\ \hline BBP(RF) & 1.044754 & 0.053992 & \(n=13\) \\ BBP(ARF) & 1.032625 & 0.044879 & \(n=45\) \\ BBP(LRF) & 1.034218 & 0.049582 & \(n=3\) \\ \hline MBA(RF) & 1.058177 & 0.060937 & \(n=3\), \(Iter=250,\lambda=0.001\) \\ MBA(ARF) & 1.038827 & 0.041348 & \(n=7\), \(Iter=500,\lambda=0.001\) \\ MBA(LRF) & 1.028426 & 0.048566 & \(n=11\), \(Iter=10,\lambda=0.001\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Root mean square error (RMSE) comparison of the methods used in the experiment. RMSE results of five-fold cross-validation of every method can be viewed in more detail in Table A1 of Appendix A.  
Figure 3: Comparison of MBA’s RMSE for Case A.  
It is observed that Case A performance is the best when \(\mathit{bwt}(LRF)\) was conducted with \(\mathit{fw}(RF)\) (Figure 3), and that the performance variations of the \(\mathit{bwt}(LRF)\) series is the least.  
Case B showed the results where the RMSE of \(\mathit{bwt}(LRF)\) was the best (Figure 4), and RMSE converged when \(\mathit{fw}\) was 0.7 or higher. Additionally, Cases A and B showed better RMSE when compared to the Vanilla model. Based on the foregoing, the accuracy may be enhanced by improving the Vanilla model learning policy. Table 4 summarizes the heuristic approach results (-H) and compares the RMSE with the Vanilla model.  
From Table 4, it is seen that RMSE is improved through the heuristics approach. Additionally, when considering parameter \(n\) and \(\mathit{Iter}\) used for learning, it is seen that the cost used for learning is lower.
## 3 Numerical Simulations
### Simulation Results
#### 3.2.2 Time Response of Certain Frequencies
The amplitude of active control force applied on \(m_{1}\) and \(m_{2}\) reduces by about 8%, which accords with the analysis of Figure 4. Therefore, under high-frequency excitation, the designed hybrid control method has a better effect on reducing the input energy. In order to investigate the effect of the friction force in the proposed hybrid control law, the time histories of the friction force under excitation at 13 Hz and 25 Hz are shown in Figure 9. The time responses of \(f_{z}\) are also drawn in Figure 9 as reference. Figure 8: Time histories of active control forces under 25 Hz excitation (**a**) Time response of \(f_{c_{1}}\) (**b**) Time response of \(f_{c_{2}}\). Figure 7: Time histories of the system under 25 Hz excitation. (**a**) Time response of \(m_{1}\) (**b**) Time response of \(m_{2}\) (**c**) Time response of \(m_{3}\). From Figure 9, it can be seen that the amplitude of friction force under 25 Hz excitation is larger than that under 13 Hz excitation. Therefore, under 25 Hz excitation, the friction force is able to dissipate more energy than under 13 Hz, which explains why under 25 Hz excitation, the proposed hybrid control law can reduce the input energy. By comparing the time histories of the system around these two resonant frequencies, the following conclusions can be drawn: under low-frequency excitation, the proposed nonlinear velocity compensation control law shows better control effect in response amplitude than the pure active friction damping control, and slightly worse effect than the pure active control; under higher-frequency excitation, the proposed hybrid control strategy shows better control effects than the other two methods and reduce the energy requirement, compared with the pure active control.
# MViDO: A High Performance Monocular Vision-Based System for Docking A Hovering AUV
## 2 Mvido: A Monocular Vision-Based System for Docking a Hovering Auv
### Resilience to Occlusions and Outliers Rejection
#### 2.3.3 Considering Constraints in the Color Space  
In the filter block (see Figure 7) we also consider constraints in the color space in order to improve the performance of the filter. The assignment of weights to the particles takes into account the color information that is sampled from the image at the inside of each particle. \(\sigma=max\left(u_{AUV}^{I}\right)\) and \(u_{AUV}^{I}=0\).  
In order to consider the color constraints, we developed and implemented the follows procedure: For each particle \(pt_{i}\) with position \(p_{i}\in\mathbb{R}^{2}\) in the image, will be evaluated \(N\) points of color \(poc_{j}\in\mathbb{R}^{3}\) (HSV color space), at positions \(o_{j}\in\mathbb{R}^{2}\) such as:  
\[poc_{j}\left(o_{j}\right),poc_{j}\in\mathbb{R}^{3},o_{j}\in\mathbb{R}^{2},j \in\{1,\cdots,N\}:\left\|p_{i}-o_{j}\right\|<rd, \tag{29}\]  
where \(rd\) is the estimated radius of the corresponding marker.  
Considering the particle \(pt_{i}^{A}\) referring to the marker \(A\) and described by the parameters \(u,v\) and \(rd\), where \(u,v\) are the localization of the particle in the image, and \(rd\) is the estimated radius for the marker \(A\). See the Figure 10  
The probability \(P\left(pt_{i}^{A}\right)\) will be the result of nine samples such as:  
\[\begin{array}{l}P\left(pt_{i}^{A}\right)=P^{A}\left(i_{(u,v)}\right)\times P ^{A}\left(i_{(u\mp q,v)}\right)\times P^{A}\left(i_{(u,v\mp a)}\right)\times \\ \times\hat{P}^{A}\left(i_{(u\mp b,v)}\right)\times\hat{P}^{A}\left(i_{(u,v\mp b )}\right).\end{array} \tag{30}\]  
\[P\left(pt_{i}^{A}\right)=\prod_{j=1}^{9}P^{A}\left(o_{j}\right) \tag{31}\]  
Figure 9: Illustration of the proposed approach to the geometric constraint in the filter.  
\[\mathbf{o_{j}}=\begin{bmatrix}u\mp rd\mp\delta\\ v\mp rd\mp\delta\end{bmatrix} \tag{32}\]  
\[\mathbf{o_{0}}=\begin{bmatrix}u\\ v\end{bmatrix}, \tag{33}\]  
where \(\delta\) is a constant adjustable parameter that serves to place the sample on the periphery of the markers edges. \(P^{A}\left(i_{(u,v)}\right)\) is a value according to the color of the pixel \(i_{(u,v)}\), positioning at position \(u,v\) of the image \(I\). The \(P^{A}\left(i_{(u,v)}\right)=F^{A}\left(H,S,V\right)\), where \(F^{A}\left(H,S,V\right)\) is a discrete function and with auto adjustable value.  
If the components \(H,S,V\) of the \(i_{(u,v)}\) are within the pre-defined reference HSV range (See the Figure 11), then \(P^{A}\left(i_{(u,v)}\right)=1\). Otherwise, \(P^{A}\left(i_{(u,v)}\right)=\gamma_{min}\). Where, \(\gamma_{min}\) is a parameter of non-zero value that ensures that in case of an outlier in color, do not lead to \(P\left(pt_{i}^{A}\right)=0\).
## 3 Results and Discussion
### Key Differential Aroma Active Compounds Between FPJ0 and Frozen Samples  
To further explore the key aroma active compounds differentiating FPJ0 and frozen juices, a supervised OPLS-DA was performed. The compounds perceived via GC-O/AI were selected for OPLS-DA. In OPLS-DA model, the reliability and prediction ability of the model were assessed by \(R2\) (y) = 0.998 and \(Q2\) = 0.991. Another permutation test (\(n\) = 200) checked the reliability of the model (Figure 5A). Both the indexes indicated that the OPLS-DA model had good reliability without over-fitting. Subsequently, the variable importance in projection (VIP) values of the aroma active compounds were calculated  
Figure 4: Aroma active compounds identified in FPJ0 and 3 frozen samples (A1–C15 identified compounds as displayed in Table 2). (**A**) Aroma intensity of aroma active compounds. (**B**) Odor attributes of aroma active compounds.  
(Figure 5B). Typically, variables with VIP > 1 play important roles in distinguishing various samples. As shown, the key markers distinguishing FPJ0 from the frozen samples included \(\beta\)-myrcene, (\(Z\))-3-hexen-1-ol, hexanol, \(\alpha\)-terpineol, hexanal, nonanal, \(\beta\)-pinene, limonene, and \(\gamma\)-terpinene.  
Based on OAV > 1 and VIP > 1, five compounds (nonanal, hexanal, hexanol, (\(Z\))-3-hexen-1-ol, and \(\beta\)-myrcene (Figure 5B)) were ultimately considered as the key differential volatiles between FPJ0 and frozen samples. Among these, the last three compounds (hexanol, (\(Z\))-3-hexen-1-ol, and \(\beta\)-myrcene) were the ones that significantly decreased or even disappeared in the three frozen samples. Consequently, they were selected as the key differential aroma active compounds for further addition experiments.
## 3 System Model
### Assumptions  
Our basic assumptions for the system are:  
* A1: ECUs are able to perform cryptographic digital signature operations and key management throughout the entire lifecycle, including key establishment, storage, and usage. By using existing methods such as public key infrastructure (PKI) [21], OemR owns a private and public key pair for each EcuX. OemR uses the private key for signature generation and EcuX uses the public key for signature verification. We assume that the public keys are distributed and installed in EcuX before any software updates can be performed. The public keys shared between the OemR and EcuX are not guessable and cannot be accessed by the adversary.
* A2: The communication between OemR and ImgR is secure. We assume that they are able to connect and communicate whenever the ImgR has a new software image version to be deployed to the vehicle or whenever OemR finds out that the vehicle does not have the latest software images.
* A3: The hash function used is not breakable, meeting the requirements of preimage-resistant, second-preimage-resistant, and collision-resistant for a hash function [22]. In other words, for a hash value H(SW) calculated for a given image software SW, it is infeasible to find the software image SW (preimage-resistant feature). It is also infeasible to find another image SW' such that Hash(SW) = Hash(SW') and SW \(\
eq\) SW' (second-preimage-resistant feature), as it is also infeasible to find any two images SW and SW' such that Hash(SW) = Hash(SW') and SW \(\
eq\) SW'.
* A4: The chosen signature scheme is strongly unforgeable. Strong unforgeability assures the adversary cannot generate a new signature for a previously signed software image. In other words, assume an adversary obtains the software image and signature pair (SW, \(\delta\)); the signature is strongly unforgeable if the adversary cannot generate a new signature \(\delta^{\prime}\) for the same software image SW.
* A5: The OemR and EcuX share the same information about the Merkle tree (e.g., max number of software images, left-right bottom-up approach, static or dynamic). Otherwise, EcuX, as the verifier, cannot validate the correctness of the software image.