esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### GC-MS Analysis  
GC-MS analysis was performed using an Agilent 6890N Network gas chromatography system coupled with a 5973N mass spectrometer (Agilent Technologies, CA, USA). A DB-5 MS UI column (60 m \(\times\) 0.25 mm i.d., 1.00 \(\upmu\)m film thickness; J&W Scientific, Folsom,CA, USA) was used for chromatographic separation. SPME injections were performed in splitless mode. The carrier gas (He) was fluxed at a constant flow rate of 1 mL/min with a column head pressure of 15 psi. The initial oven temperature was 40 \({}^{\circ}\)C (held for 5 min), followed by a heating ramp set at 10 \({}^{\circ}\)C/min up to 160 \({}^{\circ}\)C, and then at 8 \({}^{\circ}\)C/min to reach a final temperature of 270 \({}^{\circ}\)C, held for 5 min. The transfer line was heated to 270 \({}^{\circ}\)C. The mass spectrometer was operated in the electron impact (EI) ionization mode at 70 eV in the full scan acquisition mode, with a m/z scanning range from 25 to 300.  
Chromatograms and mass spectra were analyzed using Enhanced ChemStation software (Agilent Technologies, Santa Clara, CA, USA). The volatiles were tentatively identified by matching the mass spectra with the data system library (NIST14/NIST05/WILEY275/NBS75K) and using Web databases, such as the National Institute for Standards and Technology (NIST database [[https://webbook.nist.gov](https://webbook.nist.gov)]([https://webbook.nist.gov](https://webbook.nist.gov)), accessed on 15 November 2022) and Mass Bank of North America ([[https://mona.fiehnlab.ucdavis.edu](https://mona.fiehnlab.ucdavis.edu)]([https://mona.fiehnlab.ucdavis.edu](https://mona.fiehnlab.ucdavis.edu)), accessed on 15 November 2022).  
The Linear Retention Index (LRI) was used for an additional comparison between our data and those reported in the literature and in the NIST Standard Reference Database, considering only values referring to analyses performed under the same operating conditions (instrumental specifications and heating ramp). The LRI of the detected compounds were calculated from a standard solution of n-alkanes (C6, C9, C12, C14, and C16) analyzed following the same procedure used for the samples. It proved to be particularly useful for the distinction of E/Z isomers because these species produce mass spectra that are difficult to differentiate.  
Finally, some analytes were identified by comparing their mass spectra with those of their respective pure standards (when available) and analyzed using HS-SPME-GC-MS under the same operating conditions used for the samples. The pure standard used are as follows: 2-methyl-1-butanol; 1-pentanol; 1-hexanol; benzyl alcohol; 2-methylbutanal; 3-methylbutanal; 2-methyl-2-butrenal; methyl-hexanoate; 2-methylbutanoic acid; 3-methylbutanoic acid; and decanal.  
VPCs related to the chromatographic column or to the sorbent fiber, such as silane and siloxane derivatives, were discarded in order to collect only the information related to our samples. The amount of each identified volatile is expressed as the Total Ion Current (TIC) peak area. The data are expressed as the mean of three replicates \(\pm\) Standard Deviation (SD).
## 3 Space Matching Process Using Multi-Resolution Cube Grid
### Space Matching Condition Setting  
The space and various equipment are matched after the equalization of the unit size of space and equipment. Matching is considered in four conditions depending on the existence relating to relevant data of the space and the equipment. Matching is possible for every node except for those where both of the space and equipment data exist as shown in Table 2.  
Figure 3: The distance between points and the node size.  
Figure 2: Unit size setting and data simplification.  
The node that represents the location of the equipment in the space must be set before matching. Using Figure 2 above as an example, node 1 of the equipment is used, and this is named as the \"reference node.\"  
The matching of space and equipment depends on which node is the reference node. In the example shown in Figure 3, the nodes of the space where the node no.1 of the equipment can exist are 25 cases in the range of 5 (total horizontal length of the space-horizontal length of the space occupied by the equipment + 1: 8-4 + 1) horizontal cells, and 5 (total vertical length of the space-vertical length of the space occupied by the equipment +1: 8-4 + 1) vertical cells, and the height condition must also be considered. However, there is no additional node at which the reference node can exist by the height setting because different equipment is installed on the ground of the space except in special cases. After the length/width/height conditions of the equipment are set, the reference nodes are moved in equipment rotation. The considered equipment is rotated in four directions. Thus, 100 areas must be checked, which is four times the 25 areas that were set above.
# Magnetic Nanoparticles: Synthesis, Characterization, and Their Use in Biomedical Field
## 2 Synthesis Methods
### Chemical Methods
#### 2.2.3 Hydrothermal Method  
The hydrothermal method, also known as solvothermal, is a \"bottom-up\" synthesis technique for IOMNPs, conducted in an aqueous solution under elevated pressure and temperature, typically using autoclaves or reactors [42; 43]. Primarily, it involves the rapid nucleation and growth of newly synthesized MNPs, resulting in pure particles with controlled morphologies [44].  
During the hydrothermal process, hydrolysis and oxidation reactions occur in order to generate MNPs [45]. Some authors emphasize that the morphology and crystallinity of the as-synthesized MNPs depend on the proper mixing of solvent, duration, pressure,and temperature [19]. It's crucial to note that crystal formation relies on the solubility of minerals in water [19].  
Impressive results achieved using this method include synthesis of Fe\({}_{3}\)O\({}_{4}\) NPs with a size of 15 nm and a spherical shape, successfully employed in tumor MRI applications [46]. Additionally, chitosan coated Fe\({}_{3}\)O\({}_{4}\) NPs, sized at 25 nm, were prepared and utilized for magnetic based enzyme immobilization [47].  
However, this method has drawbacks such as: the inability to synthesize particles smaller than 10 nm and slow reaction kinetics at high temperatures [48]. It requires careful execution and specialized equipment, making the protocol laborious [49]. Despite these challenges, there are noteworthy advantages, including the production of NPs with a desirable shape, size, high crystallinity, and consistent composition, offering magnetic tuning and greater control over dispersion [48; 49].
# Microcontroller Unit-Based Wireless Sensor Network Nodes: A Review
## 3 MCU-Based Hardware Platforms for Wireless Sensor Nodes
### Environmental Monitoring and Smart Farming
#### 3.2.2 Sensor Nodes for Agriculture and Rural Monitoring  
The sensing platforms proposed by [21] are targeted for rural monitoring and agriculture applications. The STM32L152 MCU with 128 kB Flash, 16 kB static random-access memory (SRAM) and 4 kB electrically erasable programmable read-only memory (EEPROM) was incorporated in the sensor modules. However, the specifications of the MCU have since changed. The ultra-low-power 32-bit MCU Arm(r)-based Cortex(r)-M3 (32 MHz) now comprises 512 kB Flash memory, 80 kB SRAM and 16 kB EEPROM. Regarding communication capabilities, the modules are equipped with IEEE Std. 802.15.4(tm)-compliant RF transceiver wireless modules (MRF24J40) capable of covering ranges above 120 m in outdoor scenarios. The transceivers operate in the unlicensed ISM Band (2.405 GHz-2.475 GHz) and can achieve up to 500 kbps data rates. The receiver sensitivity of the modules is about \(-\)94 dBm.  
The sensing modules are integrated with SHT11 (air temperature and humidity) and 5 TM (soil temperature and moisture content) sensors. The node offers the flexibility of accommodating other types of commercial sensors. The power supply required for the modules ranges from 2.7 to 3.3 V. The current consumption during the receiving mode is 20.2 mA and 19.7 mA during transmission mode at 0 dBm transmission power.
# Community Detection Based on Graph Representation Learning in Evolutionary Networks  
Dongming Chen  
Software College, Northeastern University, Shenyang 110169, China; [EMAIL_ADDRESS] (D.C.); [EMAIL_ADDRESS] (M.N.); [EMAIL_ADDRESS] (J.W.); [EMAIL_ADDRESS] (Y.K.); [EMAIL_ADDRESS] (X.H.)  
[PERSON]  
Correspondence: [EMAIL_ADDRESS]; Tel: +86-136-2403-9571  
Jie Wang  
Software College, Northeastern University, Shenyang 110169, China; chendm@mail.  
of each snapshot by using the static network community detection algorithm; however, this kind of thinking does not make full use of the evolution information of the network structure, and a fundamental drawback rooted in such schemes is that most of traditional algorithms are sensitive to tiny changes in the network structure [9].  
The DeepWalk [10] algorithm uses a random walk method to obtain the neighbors of nodes in the network to generate a fixed-length node sequence, and the node sequence is fed into the Skip-Gram model for learning representation to apply to other tasks. The SDNE [11] algorithm uses a deep sparse autoencoder [12] to simultaneously optimize the first-order and second-order similarity objective functions, conducting semi-supervised learning representation, and it is applied to visualization, multi-label classification, link prediction, etc. In machine learning research, scholars have proposed learning methods for graph embedding [13; 14; 15]; however, these methods usually only work well on certain networks.  
To overcome these limitations, we propose a novel community detection algorithm based on evolutionary network representation learning (Learning Community Detection Based on Evolutionary Network, LCDEN). The LCDEN algorithm uses a Laplacian matrix to obtain the node relationship information of the directly connected edges in the network structure of the previous time slice, learns the network structure under the current time slice through the deep sparse autoencoder, and uses a \(K\)-means clustering algorithm to divide the community of the obtained low-dimensional feature matrix of the network structure under the current time slice. Our contributions can be summarized as follows:  
1. We propose a novel algorithm for community detection in evolutionary networks, which solves the limitation that traditional community detection algorithms were unable to handle the temporal information of a network structure.
2. The proposed algorithm can effectively use the historical temporal information of a network structure and apply a deep learning model to the research of evolutionary network representation learning.
3. The proposed algorithm has advantages in different datasets and has higher detection performance and computational efficiency.
# Analysis and Design of a Diplexing Power Divider for Ku-Band Satellite Applications
## 2 Geometry of the Proposed Diplexing Structure and Performance Analysis
### Analysis
The vertical transition channelizes the RF signal between SIW channels 1 and 2 if port 3 is excited. Figure 2: Simulated S-parameters of the proposed structure if the vertical transition is positioned at the SIW center axis. Figure 3: _Cont._  
The simulated scattering parameters (S-parameters) of the optimized diplexing network are graphically depicted in Figure 5. Within this representation, it becomes evident that the lower channel (SIW channel 1) demonstrates an insertion loss of less than \(-1.5\) dB, spanning the frequency range of 13 to 15.8 GHz, which corresponds to approximately 19.45% of the total bandwidth (Figure 5a). Concurrently, in the frequency interval of 16.6 GHz to 18.2 GHz, encompassing about 9.3% of the bandwidth, the upper channel (SIW channel 2) exhibits an insertion loss also below \(-1.5\) dB (Figure 5b). Figure 4: _Cont._  
Figure 3: Parametric studies of the proposed structure. (**a**) Different values of thickness of copper layer, Hs from 3.2 to 5.8 mm. (**b**) Different values of Xoffset from 0.9 to 1.7 mm. (**c**) Different values of Y1 from 4.6 to 6.2 mm. (**d**) Different values of Y2 from 8.4 to 10 mm. (Solid lines, dash lines, and point-point lines represent the S31, S21, and S32 parameters, respectively, while the dash circle specifies charts for S33). Figure 4: Simulated electric field distribution for the proposed architecture and two different frequencies; (**a**) when exiting port 1, and (**b**) when exiting port 2. Figure 5: _Cont._  
Further analysis of the simulation reveals matching bandwidths for amplitudes of S\({}_{11}\) and S\({}_{22}\) that are less than \(-10\) dB. Specifically, the optimized diplexing network presents a matching bandwidth of around 20%, spanning from 12.6 to 15.4 GHz, for S\({}_{11}\). For S\({}_{22}\), the corresponding matching bandwidth covers a range of approximately 9.15%, spanning from 16.7 to 18.3 GHz. These findings underscore the effectiveness of the optimized network in achieving favorable insertion losses and suitable matching bandwidths, thereby substantiating its potential for efficient diplexing operations.
# Helicopter Safe Landing Trajectory after Main Rotor Actuator Failures
## 2 Helicopter Swashplate Actuator Geometry and Reconfiguration after Actuator Failure  
### Swashplate Actuator Geometry  
Helicopter control is achieved by the rotor blade's collective, lateral cyclic pitch and longitudinal cyclic pitch, which can be described by  
\[\theta_{\text{blade}}=\theta_{0}+\theta_{1c}\cos\psi+\theta_{1s}\sin\psi \tag{1}\]  
where \(\psi\) is the blade azimuth angle, and \(\theta_{0}\), \(\theta_{1c}\), \(\theta_{1s}\) are the collective pitch, lateral cyclic pitch and longitudinal cyclic pitch, respectively. The blade pitch is generated by the displacement of the pitch link driven by the swashplate.  
Meanwhile, the swashplate is driven by at least three or more actuators. A general distribution is shown in Figure 1, where \(\mathrm{p_{fwd}}\), \(\mathrm{p_{aft}}\) and \(\mathrm{p_{lat}}\) represent locations of the three actuators, and \(\gamma\) and \(\psi_{0}\) are constant angles determined by the geometry of the control system. \(\left\{\mathrm{x_{sh}},\mathrm{y_{sh}}\right\}\) are aligned with the rotor shaft, and \(\mathrm{y_{sh}}\) points to the forward direction (which is denoted by 180\({}^{\circ}\) in Figure 1). \(\left\{\mathrm{x_{sw}},\mathrm{y_{sw}}\right\}\) is fixed to the swashplate, and the direction of \(\mathrm{y_{sw}}\) is determined by the positions of the actuators as shown in the figure. To determine the attitude of the swashplate, the roll angle and the pitch angle along the axis \(\mathrm{x_{sh}}\) and axis \(\mathrm{y_{sh}}\) are represented by \(\phi_{\mathrm{sw}}\) and \(\theta_{\mathrm{sw}}\), respectively.  
Let \(h_{t}\), \(h_{a}\), \(h_{1}\) denote the vertical position of \(\mathrm{p_{fwd}}\), \(\mathrm{p_{aft}}\) and \(\mathrm{p_{lat}}\), thus the attitude of the swashplate can be derived by the geometry as  
\[\left\{\begin{array}{c}\phi_{\mathrm{sw}}=\arcsin\frac{1}{r_{ \mathrm{nr}}\sin\gamma+r}\approx\frac{1}{r_{\mathrm{nr}}\cos\gamma+r}\\ \theta_{\mathrm{sw}}=\arcsin\frac{h_{a}-h_{f}}{2r_{\mathrm{nr}}\sin\gamma} \approx\frac{h_{a}-h_{f}}{2r_{\mathrm{nr}}\sin\gamma}\end{array}\right. \tag{2}\]  
where \(r_{\mathrm{nr}}\) is the radius of the non-rotating ring. Assuming \(l_{p}\) as the distance from the pitch link to the pitch hinge, \(r_{\mathrm{r}}\) as the radius of the rotating ring, and \(h_{p}\) as the vertical displacement of the pitch link from zero pitch position, the blade pitch in Equation (1) can be expressed as  
\[\left\{\begin{array}{c}\theta_{1c}=-\arcsin\left(\frac{r_{ \mathrm{r}}}{l_{p}}\sin\phi_{\mathrm{sw}}\right)\approx-\frac{r_{\mathrm{nr}} }{l_{p}}\frac{1}{r\cos\gamma+r}\\ \theta_{1s}=-\arcsin\left(\frac{r_{\mathrm{r}}}{l_{p}}\sin\theta_{ \mathrm{sw}}\right)\approx-\frac{r_{\mathrm{nr}}}{l_{p}}\frac{h_{a}-h_{f}}{2r \sin\gamma}\\ \theta_{0}\approx\frac{1}{l_{p}}\left\{\frac{1}{2}\left(h_{f}+h_{a} \right)+\left[h_{l}-\frac{1}{2}\left(h_{f}+h_{a}\right)\right]\frac{\cos \gamma}{1+\cos\gamma}\right\}\end{array}\right. \tag{3}\]  
where small angle assumptions are made.
# A Virtual Network Resource Allocation Framework Based on SR-IOV
## 1 Introduction
Comprehensive experiments are conducted to evaluate the allocation framework performance and show that the framework can effectively improve system network performance, in which the average request delay can be reduced by more than 26%, and the system bandwidth throughput rate can be improved by about 5%. The remainder of this paper is organized as follows. In the second part, we overview the related work. In addition, then we will introduce the background knowledge of KVM I/O virtualization technology and SR-IOV technology and compare the two technologies. In the third part we will present our virtual network resource scheduling framework and propose our scheduling algorithm based on the hierarchical division of VF network bandwidth. In the fifth part we will experimentally evaluate the performance of our virtual network resource scheduling algorithm and the final conclusions will be given in the sixth part.
## 5 Numerical Results
### Eavesdropping Rate and Power Consumption  
For comparison, we consider other two legitimate eavesdropping strategies: proactive eavesdropping with constant jamming power and zero jamming power. For the former scheme, we set the constant jamming power to \(10^{-8}\) W (in fact, the constant jamming power can be set to any value below \(P_{L}^{max}\), which has little effects on simulation results as observed in the performance). For the latter scheme, we set the constant jamming power to 0, which means _UAV\({}_{L}\)_ passively overhears the packets transmitted by suspicious UAVs without sending jamming signal to the suspicious link [17; 18; 21].  
Figure 6 shows that selection policy saves 65.79%, 52.66%, 78.12%, and 13.92% more power than the constant-Jamming scheme, when D = 500 m, 1000 m, 1500 m, and 2000 m, respectively. Selection policy saves 74.73%, 39.02%, 74.35%, and 8.40% more power than the No-Jamming scheme, when D = 500 m, 1000 m, 1500 m, and 2000 m, respectively. The power consumption of selection policy increases as time goes on in each simulation. The reason is that _UAV\({}_{L}\)_ consumes power to eavesdrop suspicious UAVs either by jamming or not, thus the power consumption increases as time goes on. Power consumptions are not compared with each other under different distances, because in each simulation, UAVs fly at random speeds (e.g., random \(\Delta v\)), thus causing different power consumptions that cannot simply be compared with each other.  
Figure 7 presents the other two methods with optimal solutions in terms of the eavesdropped packets. Selection policy outperforms No-Jamming and Constant-Jamming schemes under different distances in the simulations. The reason is that selection policy purposely adapts the jamming power of _UAV\({}_{L}\)_ to change the suspicious communication (e.g., to a smaller data rate) for overhearing more packets. In each eavesdropping time slot, _UAV\({}_{L}\)_ selects proper eavesdropping case according to the selection policy, thus eavesdropping more information. When D = 500 m, selection policy outperforms the other two schemes by nearly 1.2 times. However, the divisions between the selection policy and the other two methods are narrowed when distances increase. That is because in such long-distance cases,  
Figure 6: Total power consumptions by _UAV\({}_{L}\)_ in different Ds with different jamming methods.  
channel conditions dominate the data rate rather than eavesdropping methods, so \(UAV_{L}\) can receive almost the same number of eavesdropped packets regardless which algorithm \(UAV_{L}\) has chosen.
# A Non-Fungible Token Solution for the Track and Trace of Pharmaceutical Supply Chain
## 4 Web3 Serialization with Blockchain and Non-Fungible Tokens
### Non-Fungible Token as Digital Twin of a Serialized Item
For instance, let us assume that a pharmaceutical factory characterized by its GLN and a blockchain public address (e.g., 0 xAABB.....PP34) is producing and packaging a product with a GTIN (e.g., 3353..34885); the first event of the NFT is the \"minting\", with the following properties:  
- NFTId: <next id of the smart contract>;
- SerializedItem: <3353.34885>, <serial number>, <lot number>, <date of expiration>;
- Creator Public Address: <0 xAABB.....PP34>;
- Owner Public Address: <0 xAABB.....PP34>;
- NFTEvent: [0, <0 xAABB.....PP34>, <hh:mm:ss dd/mm/yyyy>, <GPS Coordinate>, <minting>, <description of the drug packaged>]. In order to handle the hierarchical packaging, the smart contract has to contain a method for grouping the NFTs accordingly. Only the owner of the NFT can perform such types of operation, using the method of the smart contract GroupNFT(parentNFTId, childrenNFTId[]). In general, the smart contract is coded in a way that only the owner can use the method for adding events to an NFT. In this way, at each step of the distribution network, the owner responsible for the serialized item has to update the information of the packaging (for instance, the pharmaceutical factory is the owner of a pallet as long as it is not dispatched to the next actor of the distribution network). The products can be, interchangeably, a pallet, a case box, or a single unit box of product as it depends on the type of shipping and actor of the distribution network (e.g., a warehouse is likely to handle pallets, a wholesaler, several case boxes, and a pharmacy, single unit boxes). Moreover, before the dispatching of the serialized item to the next actor of the distribution network, the owner has to use a specific method that appends the event of dispatch to the next owner in the NFTEvent list. This last method modifies the property Owner Public Address with the address of the next actor of the supply chain that, from that time on, will be in charge of handling the product. Therefore, the smart contract has to reveal two more methods, which can only be used by the NFT owner:  
- AppendEvent (Event, NFTId)
- UpdateOwner (Public Address, NFTId)  
Moreover, it is important to highlight that, after the minting, the NFT is characterized by the Owner Public Address of the manufacturer. This makes it impossible for others to append information in the smart contract, although any actor can, in principle, retrieve the public information characterizing the NFT. Finally, the method BurnNFT(NFTId) is used when the last owner administers (doctor) or sells (pharmacy) the product to the patient.