esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Preliminaries
### Orbit Types
\(\rho_{i}\) is the inter-satellite range data when the gravity filed in Table 1 was adopted, and \(\rho_{i}^{\prime}\) is the inter-satellite range data with a small error (\(+10^{-3}\)) added to the \(C_{22}\) term. A measurement interval of 0.05(T) and an arc length of 100(T) are taken for Figure 1, where (T) is the Unit time shown in Table 2.  
From the left frame of Figure 1, we find that with the increase in the orbit inclinations, the measurement sensitivity \(\mathbf{J}\) decreases. In addition, there is a periodic patten in the contour map when different values of the longitude of the ascending node are taken. It seems that the maximum sensitivity happens when the deputy's \(\Omega\) is close to the \(\Omega\) value of the chief. Only judging from Figure 1, we should choose prograde orbits (\(i<90^{\circ}\)) for the deputy. However, due to the instability of the prograde orbit, the deputy may quickly collide with the asteroid. In such a case, we are unable to obtain measurement data long enough. Therefore, in our study, we choose the orbit of the deputy as a polar orbit. This type of orbit is relatively stable, and receives enough influence from the asteroid's non-spherical gravity. In this case, we set the deputy's longitude of ascending node as \(\Omega=0^{\circ}\), shown as the red point in Figure 1. In addition, the orbit of the chief and the deputy are shown in the left frame of Figure 1.
## 4 Design of the On-Off Resonance-Tuning Module  
### Design Parameters  
The proposed resonance-tuning system is based on MREs, which are elastomers with variable stiffness properties that can change the resonance of the BWT structure. The proposed MRE-based resonance-tuning module was designed and connected to the end of the rod, as shown in Figure 3. Two MRE blocks were aligned perpendicular to the direction of the oscillation of the rod to provide shear stiffness. When an external magnetic field was applied to the MREs, the elastomer hardened, which consequently increased the overall stiffness of the BWT structure.  
The magnetic field can be controlled by placing permanent magnets on the magnet brackets that cover the MREs, as shown in Figure 3. The direction of the magnetic field is designed to be vertical so that it does not significantly influence the deformation of the MRE shape. If a horizontal magnetic field along the vibration direction is applied, the flexible MREs bend and stick to the side of the net magnetic field, causing the deformation of the MRE.  
Figure 3: The proposed resonance-tuning module and experimental setup for the MRE property test.  
The utilized permanent magnet was a Neodymium magnet (NdFeB) with a diameter of 20 mm and a height of 10 mm. The gap between the vertical permanent magnets was 21 mm. At the center point between the magnets, the magnitude of the magnetic field was measured to be 170 mT for mode B = ON. For a state of no magnetic field (B = OFF), the permanent magnets were removed from the bracket. The magnet bracket and base pillars were manufactured with nonmagnetic aluminum materials to minimize the disturbance of the magnetic field lines that passed to the MREs.
## 2 Materials and Methods
### Adsorption and Desorption of OGN  
The adsorption of OGN was performed in two modes: ion-exchange (IE) mode and hydrophilic interactions (HI) mode.  
In the IE, the amount of 2.0 mg of coated nanoparticles (MNP-Ac, MNP-SuI, and MNP-Mal) was conditioned by mixing with 100 \(\upmu\)L MeOH in an Eppendorf tube. The sorbent was separated from the solution, using a strong magnet and the supernatant was removed. Next, the procedure was repeated with 100 \(\upmu\)L of water or 10 mM solution of NH\({}_{4}\)OAc at an appropriate pH (3.5, 4.5, 5.5, or 6.5). Then, 50 \(\upmu\)L of 5 \(\upmu\)M OGN was mixed with 50 \(\upmu\)L of water or NH\({}_{4}\)OAc at the same pH, and this mixture was added to the conditioned adsorbent and vortexed, and the phases were magnetically separated. The supernatant was removed, centrifuged (10 min, 14,462\(\times\)\(g\)), and analyzed.  
In the HI mode, the amount of 2.0 mg of coated nanoparticles was conditioned by mixing with 100 \(\upmu\)L of appropriate water/organic solvent (ACN or ACE) mixture in an Eppendorf tube. The solid phase was separated using a magnet, and the supernatant was removed. Next, 50 \(\upmu\)L of 5 \(\upmu\)M OGN was mixed with an appropriate amount of organic solvent (ACN or ACE), and this mixture was added to the conditioned adsorbent and vortexed; then, the phases were magnetically separated. The supernatant was removed, partially evaporated by using a CentriVap vacuum concentrator (Laboconco, Kansas City, MO, USA) at 40 \({}^{\circ}\)C to 10 \(\upmu\)L, filled to 50 \(\upmu\)L, centrifuged (10 min, 14,462\(\times\)\(g\)), and then analyzed. Detailed conditions are presented in Section 3.3.2.  
The developed procedure with three different sample weights were used for determination of sorption capacity. This parameter was calculated by using Equation (1), where \(Q\) is the sorption capacity (\(\upmu\)mol g\({}^{-1}\)), \(c_{0}\) is the OGN concentration before adsorption (\(\upmu\)mol mL\({}^{-1}\)), \(c\) is the OGN concentration after adsorption, \(V\) is the volume of OGN solution used during procedure (mL), and \(m\) is the mass of the weight of the adsorbent (g).  
\[Q=\frac{(c_{0}-c)\times V_{m}}{m} \tag{1}\]  
The studies of influence of adsorption time on its effectiveness were performed for DNA-20 OGN, following the procedure for adsorption in IE mode, but for five different adsorption times (1, 5, 10, 30, and 60 min).  
The desorption of OGN was performed by vortexing sample weight of the coated nanoparticles with 50 \(\upmu\)L of elution solution (see Section 3.3) for 30 min. The nanoparticles were separated by a magnet, the liquid phase was transferred into another Eppendorf tube, centrifuged (10 min, 14,462\(\times\)\(g\)), and the supernatant was analyzed. The studies of influence of desorption time on the recovery was performed for DNA-20 OGN, following the developed procedure for desorption in IE mode, but for five different desorbtion times (1, 5, 10, 30, and 60 min).
# Image-Based Crack Detection Using Total Variation Strain DVC Regularization
## 2 Method
### Spatial Total Variation Strain Field Regularization  
The TV strain field regularization is defined as the \(l_{1}-norm\) of the strain field gradients  
\[C_{R}^{TV}=\sum_{m,n=1}^{D}\|\Delta\epsilon_{mn}(\mathbf{u}(\mathbf{x}))\|_{1 }=\sum_{x\in\Omega}\sqrt{\sum_{j,m,n=1}^{D}(\Delta_{j}\epsilon_{mn}(\mathbf{u} (\mathbf{x})))^{2}} \tag{2}\]  
where \(\Delta\) is the finite differences operator, \(\epsilon\) is the 3D Lagrangian strain computed in each point \(\mathbf{x}\) of the image domain \(\Omega\) (see Appendix A), and D is the spatial dimension. The term \(C_{R}^{TV}\) promotes non-smooth piecewise constant strain field. The regularization term must be weighed properly in order to penalize the small variations of the strain field (due to noise) and to preserve important details of the images such as discontinuities. The relative weight is introduced by the multiplicative factor \(\lambda_{TV}\). The choice of the optimal \(\lambda_{TV}\) depends on the type of motion and the image noise level. This dependency has been discussed in Section 5.  
The final cost function to be minimized, in addition to the total variation regularization term, contains the similarity metric. In this research, the selected similarity metric is the sum of squared differences (SSD). The SSD metric is based on the voxel intensity differences between two images, and it aims to minimize the average squared intensity differences between them. Mathematically, the cost function can be defined as:  
\[\underbrace{\sum_{x\in\Omega}(I_{f}(\mathbf{x})-I_{m}(\mathbf{x}+\mathbf{u}( \mathbf{x})))^{2}}_{C_{S}}+\lambda_{TV}\underbrace{\sum_{x\in\Omega}\sqrt{ \sum_{j,m,n=1}^{D}(\Delta_{j}\epsilon_{mn}(\mathbf{u}(\mathbf{x})))^{2}}}_{C _{R}^{TV}} \tag{3}\]
## 2 System Architecture
### Analog Component
#### 2.2.2 Planar Antenna Array \(4\times 4\)  
The antenna used at the interface between the free space and the RF frontend modules operates at 28 GHz and is a \(4\times 4\) series feed planar antenna array described in [26] and shown in Figure 9. The antenna consists of N = 4 individual series feed linear arrays, each using four microstrip patches. The array can provide control of the radiation pattern in the horizontal plane. In [26] is shown the simulated radiation pattern on the horizontal axis. It is possible to observe that the radiation pattern can be steered in different directions by applying a progressive phase shift in the feeding of each subarray. The gain decreases progressively when the direction of radiation moves out of the boresight.
## 3 Results and Discussion  
### In Vitro Aroma Release  
Following one-way ANOVA, 222 mass peaks showed significant differences between the three espresso coffee samples. Higher roasting degrees are known to increase VOC contents in coffee beans [21] as well as in capsule coffee brews [14], and the results obtained here are in agreement with these findings. Coffee A showed the lowest volatile intensities for all the significantly different mass peaks. The highest concentrations were instead found in coffee B alone (171 peaks), coffee B and coffee E together (44 peaks), or coffee E alone (7 peaks). Table S1 (Supplementary Material) summarises the results of one-way ANOVA and post-hoc analysis.  
PLS-DA resulted in excellent discrimination of coffee samples. The confusion matrix and the score plot of PLS-DA are displayed in Table 1 and Figure 1, respectively. Coffee B and E were correctly predicted 100% of the time, whereas coffee A was misidentified once as coffee E. In addition to the good discrimination provided by PLS-DA, it was possible to annotate mass peaks with VIP values higher than 1.5, which were the most significant mass peaks for discrimination of the coffee samples. Table 2 gives a list of these 15 mass peaks with a sum formula, tentative identifications, and average concentrations for each coffee sample. In addition to Table 2, these mass peaks can be visualized in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & \multicolumn{3}{c}{**Headspace**} & \multicolumn{3}{c}{**Nosepace**} \\ \hline  & & \multicolumn{3}{c}{Predicted} & \multicolumn{3}{c}{Predicted} \\ \hline  & Coffee A & Coffee B & Coffee E & Coffee A & Coffee B & Coffee E \\ \hline Coffee A & 11 & 0 & 1 & 14 & 0 & 1 \\ Coffee B & 0 & 11 & 0 & 0 & 14 & 1 \\ Coffee E & 0 & 0 & 11 & 0 & 0 & 15 \\ \hline \end{tabular}
\end{table}
Table 1: Confusion matrices generated by PLS-DA (leave-group-out cross-validation). Models based on headspace and nosepace data are presented (left and right, respectively).  
The main differences between coffee A and B were observed in the intensity of most mass peaks. The most discriminating mass peaks for coffee E are \(m\)/\(\gtrsim\) 135.117, 153.130, 85.104, 97.028, 127.149, and 137.134, characterized by the highest intensities. Among them, \(m\)/\(z\) 137.134 and 153.130 can be tentatively identified as various monoterpenes and decadienal, which are responsible for fruits and citrus notes, respectively [22; 23]. Flowers, fruits, and citrus notes have been highly associated with Ethiopian coffee aroma, and the discrimination of this coffee for the related mass peaks are in accordance with our previous findings [12; 13]. Similarly, a recent study performed on coffee powders and capsule coffee brews [14] has shown that terpene profiles are important in the discrimination of mono-origin varietal coffees, with Ethiopian coffee being particularly rich in terpen.
## 3 Experimental Results
### Varying Infrastructure Density Scenario
#### 3.2.1 Packet Delivery Ratio
Surprisingly, the impact of the infrastructure density deployment seems to be minor, even for the longer communication perimeters, see Figure 15(c),d for more details. When it comes to the traffic intensity of 1500 vehicles per hour and infrastructure mode, the behavior for the less dense infrastructure deployment is more or less the same as that reported for the traffic intensity of 250 vehicles per hour. On the other hand, it seems not to be so beneficial to apply the more dense infrastructure deployment for the shorter communication perimeter (below 600 m), as it is detrimental, see Figure 16(a),b for more detail. For the D2D mode, the behavior is more or less the same as that reported for the lowest traffic intensity, with the lower values reported for the lower communication perimeters, i.e., below 600 m, see Figure 16(c) for more detail. Moreover, it can be clearly seen from Figure 16(d) that the impact of the more dense infrastructure deployment is very rarely beneficial; if so, the benefit is rather small. Figure 14: PDR simulated for the both modes of the 4G-based C-V2X technology, both infrastructure density deployments and traffic intensity of 250 vehicles per hour. Figure 16: PDR simulated for the both modes of the 5G-based C-V2X technology, both infrastructure density deployments and traffic intensity of 250 vehicles per hour. Figure 15: PDR simulated for the both modes of the 4G-based C-V2X technology, both infrastructure density deployments and traffic intensity of 1500 vehicles per hour.
## 2 Materials and Methods
### Collaborative Storage and Resolution Method with Index Neighbor Structure  
#### 2.3.1 Existence of Index Neighbor  
Latency between users and resolution nodes in different levels is the only constraint in the LNMRS structure, it is not inevitable that there is one resolution node in the lower level that satisfies Equition (6) with this loose constraint. Therefore, the existence of index neighbors needs to be validated before the index neighbor structure is constructed. The purpose of the validation process is to find a feasible construction of index neighbors for the master node. In this paper we utilize a greedy method to seek a feasible construction: check whether each child of the master node can find its own corresponding index neighbor node in turn. If all child nodes could find their index neighbor nodes, it means that there is at least one feasible solution for the index neighbor construction algorithm. In detail, we first check whether a child node could serve as an index neighbor for itself, and if not, we try to check other sibling nodes if they could serve as an index neighbor and include this child node as a sub-node in its index neighbor group. The pseudo code of the validation process is shown in Algorithm 1.  
```
Input: master node \(RN_{i}\), child nodes \(\{RN_{i+1}\}\), \(T_{i}\), \(T_{i+1}\) Output: validation result
1 for\(RN_{i+1}^{j}\) in \(\{RN_{i+1}\}\)do
2\(foundFlag\) = false
3 measure latency from \(RN_{i+1}^{j}\) to \(RN_{i}\) as \(latency(RN_{i+1}^{j},RN_{i})\)
4 if\(latency(RN_{i+1}^{j},RN_{i})>T_{i}-T_{i+1}\)then
5 for\(RN_{i+1}^{k}\) in \(\{RN_{i+1}\}\)do
6 measure latency from \(RN_{i+1}^{k}\) to \(RN_{i+1}^{j}\) as \(latency(RN_{i+1}^{k},RN_{i+1}^{j})\)
7 measure latency from \(RN_{i+1}^{k}\) to \(RN_{i}\) as \(latency(RN_{i+1}^{k},RN_{i})\)
8 if\(latency(RN_{i+1}^{k},RN_{i+1}^{j})+latency(RN_{i+1}^{k},RN_{i})<T_{i}-T_{i+1}\)then
9\(foundFlag\) = true
10 break
11 if\(foundFlag\)then
12 continue
13 else
14 return false
15 return true
```  
**Algorithm 1**Index Neighbor Existence Validation  
The validation algorithm may make every child node an index neighbor in the extreme case, and the mapping records to be co-stored by the master node need to be placed in each child node with a copy, which will inevitably cause a large amount of redundancy in the system storage. The MK-Mediod algorithm proposed in this paper firstly clusters the child nodes, and then constructs index neighbor structure with the clustered results. This algorithm might construct less index neighbor groups, which in turn reduces storage costs of the whole system.
# Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals
###### Abstract  
Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2-6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.  
automatic sleep stage classification; electroencephalogram; fast Fourier transform +
Footnote †: journal: Applied Sciences
# Biosynthetic Pathways of Hormones in Plants
## 6 Melatonin
SNAT is localized in the chloroplasts, whereas ASMT and COMT are present in the cytoplasm [91]. The biosynthetic ratio connected to the transformation of tryptophan into serotonin is considerably higher in relation to the conversion of serotonin into melatonin, leading to a low level of this phytohormone being present in plants [83]. When plants grow under normal environmental conditions and/or serotonin is pre-sent in low levels in cells, two following pathways exist in plants: (i) tryptophan \(\rightarrow\) trypta-mine \(\rightarrow\) serotonin \(\rightarrow\)\(N\)-acetylserotonin \(\rightarrow\) melatonin; and (ii) tryptophan \(\rightarrow\) 5-hydroxytyptophan \(\rightarrow\) serotonin \(\rightarrow\)\(N\)-acetylserotonin \(\rightarrow\) melatonin. However, the following reactions, which lead to the production of melatonin from tryptophan, were detected in plants that produce large amounts of serotonin during the senescence process. Furthermore, experiments performed on rice seedlings showed that exogenously applied \(N\)-acetylserotonin can be converted into serotonin by the enzyme \(N\)-acetylserotonin deacetylase, resulting in a reduction in the content of melatonin in plants. These different pathways and sites of melatonin synthesis suggest that the production of melatonin is highly diverse in plants [80; 85; 86; 88].
# Fault Diagnosis Method for Aircraft EHA Based on FCNN and MSPSO Hyperparameter Optimization
## 3 Multi-Strategy Hybrid Particle Swarm Optimization Algorithm (MSPSO)
### Multi-Strategy Hybrid Particle Swarm Optimization Algorithm  
#### 3.2.1 Initialization Strategy Based on Homogenization and Randomization  
Population diversity plays an important role in improving the global search ability of the PSO algorithm and preventing the algorithm from falling into local optimum. The initial populations with diversity improve the algorithm's global search capability, while the currently commonly used population random initial strategy cannot guarantee a better coverage of the entire decision space [26]. Therefore, we adopt an initialization strategy based on uniformization and randomization, which can make the particles more uniformly distributed in the decision space while ensuring random initialization. The following describes how to initialize the population:  
Step 1: Input the population size \(n\), the decision vector dimension \(d\), and the interval \(\big{[}a_{j},b_{j}\big{]}\) for each decision variable \(x_{j}(j\in\{1,2,\cdots d\})\);  
Step 2: Divide the interval of decision variable \(x_{j}\) into \(n\) equal parts, that is, \(\Delta j=\big{(}b_{j}-a_{j}\big{)}/n\);  
Step 3: Define set \(\Omega_{j}=\big{\{}\big{[}a_{j},a_{j}+\Delta j\big{]},\big{[}a_{j}+\Delta j,a _{j}+2\Delta j\big{]},\cdots,\big{[}a_{j}+(n-1)\Delta j\big{]},b_{j}\big{\}}\);  
Step 4: Select a random interval in \(\Omega_{j}\) and assign a random value to \(x_{ij}(i\in\{1,2,\cdots n\})\) within that interval;  
Step 5: Update collection \(\Omega_{j}\): Remove the selected subinterval from the set \(\Omega_{i}\) in step 4;  
Step 6: Repeat steps 4-5 to obtain all values of the decision variable \(x_{j}\): \((x_{1j},x_{2j},\cdots n_{j})\);  
Step 7: Repeat steps 2-6 for the final output of the initial population: \(\{\mathbf{x}_{1},\mathbf{x}_{2},\cdots,\mathbf{x}_{n}\}\).
## 5 Use Case
### Use-Case Participants  
The roles within each entity are defined to manage operations, financial reporting, and interactions with other entities, highlighting the comprehensive capabilities of the BFS system. The use-case participants are defined as follows:  
1. Entity A is a newly incorporated private company limited by shares, which acts as the central business entity in this use case. The roles within Entity A are designed to manage its operations, financial reporting, and interactions with other entities. Its roles are as follows: * Two director wallets--These wallets are managed by the directors of Entity A. They are responsible for overseeing the overall operations of the company, making strategic decisions, and authorising significant transactions, such as share issuance and sales. * One accountant wallet--This wallet is managed by the accountant of Entity A. The accountant is responsible for maintaining the company's financial records, executing accounting transactions, generating financial statements, and ensuring compliance with regulatory requirements. * One borrower wallet--This wallet is managed by an employee or an authorised representative and facilitates borrowing activities for Entity A. It is used to request and manage funds borrowed from other entities, such as parent companies or financial institutions.  
Figure 3: Illustrative use case for BFS implementation.  
* One employee wallet--This wallet is managed by an employee of Entity A. It is used to execute day-to-day business transactions, such as purchasing assets, paying suppliers, and other operational activities.
2. Entity B is the parent company of Entity A and holds 100% of its shares at incorporation. Entity B plays a crucial role in providing financial support and overseeing the operations of Entity A. Its roles are as follows: * One shareholder wallet--This is managed by Entity B and represents the ownership stake in Entity A. It is used to hold and manage the shares issued by Entity A, participate in shareholder decisions, and receive dividends or other shareholder benefits. * One lender wallet--This wallet is used by Entity B to lend funds or assets, such as Treasury bills, to Entity A. It facilitates inter-company lending and supports the financial needs of Entity A for its business operations.
3. Entity C is an external business entity that interacts with Entity A in business transactions, such as buying and selling financial instruments. Its role is as follows: * One counterparty wallet--This wallet is managed by Entity C and is used to engage in transactions with Entity A. In this use case, it is specifically used to sell commercial paper to Entity A in exchange for Treasury bills. The wallet ensures secure and verifiable transactions between Entity C and its counterparties.